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The attributes describe the status of the aeroplane, while the goal is to predict the control action on the ailerons of the aircraft.", + "task_type": "regression", + "openml_id": 296, + "n_classes": 1, + "num_feature_intro": { + "climbRate": "climbRate", + "Sgz": "Sgz", + "p": "p", + "q": "q", + "curPitch": "curPitch", + "curRoll": "curRoll", + "absRoll": "absRoll", + "diffClb": "diffClb", + "diffRollRate": "diffRollRate", + "diffDiffClb": "diffDiffClb", + "SeTime1": "SeTime1", + "SeTime2": "SeTime2", + "SeTime3": "SeTime3", + "SeTime4": "SeTime4", + "SeTime5": "SeTime5", + "SeTime6": "SeTime6", + "SeTime7": "SeTime7", + "SeTime8": "SeTime8", + "SeTime9": "SeTime9", + "SeTime10": "SeTime10", + "SeTime11": "SeTime11", + "SeTime12": "SeTime12", + "SeTime13": "SeTime13", + "SeTime14": "SeTime14", + "diffSeTime1": "diffSeTime1", + "diffSeTime2": "diffSeTime2", + "diffSeTime3": "diffSeTime3", + "diffSeTime4": "diffSeTime4", + "diffSeTime5": "diffSeTime5", + "diffSeTime6": "diffSeTime6", + "diffSeTime7": "diffSeTime7", + "diffSeTime8": "diffSeTime8", + "diffSeTime9": "diffSeTime9", + "diffSeTime10": "diffSeTime10", + "diffSeTime11": "diffSeTime11", + "diffSeTime12": "diffSeTime12", + "diffSeTime13": "diffSeTime13", + "diffSeTime14": "diffSeTime14", + "alpha": "alpha", + "Se": "Se" + }, + "cat_feature_intro": {} +} \ No newline at end of file diff --git a/Ailerons/y_test.npy b/Ailerons/y_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..17c3d13ebc5210d8e44a9d263ea9c693d8a47c58 --- /dev/null +++ b/Ailerons/y_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:52a9c8144582380957c5b007d7926b14b44636687d2631311422e5bab6a1a8e9 +size 22128 diff --git a/Ailerons/y_train.npy b/Ailerons/y_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..f91f4183d69fd4f7cfdbb839f4333f15bf70972a --- /dev/null +++ b/Ailerons/y_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:1429d0b71952e3ac922a08bfd5f3c7b7a7703c3189fbb28bb7c4e30169b0b713 +size 70528 diff --git a/Ailerons/y_val.npy b/Ailerons/y_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..5665d2a6f215eaea12fb20ffa0b1cee443d7e0ef --- /dev/null +++ b/Ailerons/y_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4c0f6b3ec2aeb7a5fc0626edcfc05644f47e53db1517ac60328c1ad0198d8ba0 +size 17728 diff --git a/Amazon_employee_access/C_test.npy b/Amazon_employee_access/C_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..996c9a7e1dc07c2cf4221a553ff75c1c45783056 --- /dev/null +++ b/Amazon_employee_access/C_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:89ffc55a1a8192fa621dbcdd545658a7e833d89bd2db9536edd91fdc69cb047e +size 172109 diff --git a/Amazon_employee_access/C_train.npy b/Amazon_employee_access/C_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..c75c54ce24fa39eac8b788a75e18168ec2bfd515 --- /dev/null +++ b/Amazon_employee_access/C_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:39096b4cdae77f4c48dfe1613692e0346ddd4b4991c8b0077efc051eec49b8db +size 500644 diff --git a/Amazon_employee_access/C_val.npy b/Amazon_employee_access/C_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..d46e953a914f9e1ae6b96fd087bc2184866be57f --- /dev/null +++ b/Amazon_employee_access/C_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a106d03219f36340af48dc28f095cf69ad8dfa223464fc34da9a45304bddfbc0 +size 141079 diff --git a/Amazon_employee_access/info.json b/Amazon_employee_access/info.json new file mode 100644 index 0000000000000000000000000000000000000000..20c24529fe9e721a9237bf1f34b348a1dea7c9cc --- /dev/null +++ b/Amazon_employee_access/info.json @@ -0,0 +1,24 @@ +{ + "name": "Amazon_employee_access", + "n_num_features": 0, + "n_cat_features": 7, + "train_size": 20972, + "val_size": 5243, + "test_size": 6554, + "source": "https://www.openml.org/search?type=data&status=active&id=4135&sort=runs", + "task_intro": "**Author**: \n**Source**: [Kaggle Amazon Employee Access Challenge](https://www.kaggle.com/c/amazon-employee-access-challenge) \n**Please cite**: \n\n### Description\n\nThe data consists of real historical data collected from 2010 & 2011. Employees are manually allowed or denied access to resources over time. \nThe data is used to create an algorithm capable of learning from this historical data to predict approval/denial for an unseen set of employees.\n\n### Dataset Information\n\nWhen an employee at any company starts work, they first need to obtain the computer access necessary to fulfill their role. This access may allow an employee to read/manipulate resources through various applications or web portals. It is assumed that employees fulfilling the functions of a given role will access the same or similar resources. It is often the case that employees figure out the access they need as they encounter roadblocks during their daily work (e.g. not able to log into a reporting portal). A knowledgeable supervisor then takes time to manually grant the needed access in order to overcome access obstacles. As employees move throughout a company, this access discovery/recovery cycle wastes a non-trivial amount of time and money.\n\nThere is a considerable amount of data regarding an employee’s role within an organization and the resources to which they have access. Given the data related to current employees and their provisioned access, models can be built that automatically determine access privileges as employees enter and leave roles within a company. These auto-access models seek to minimize the human involvement required to grant or revoke employee access.\n\nThe original training and test set were merged.\n\n### Attributes Information\n\n* ACTION [target]: ACTION is 1 if the resource was approved, 0 if the resource was not \n* RESOURCE: An ID for each resource \n* MGR_ID: The EMPLOYEE ID of the manager of the current EMPLOYEE ID record; an employee may have only one manager at a time \n* ROLE_ROLLUP_1: Company role grouping category id 1 (e.g. US Engineering) \n* ROLE_ROLLUP_2: Company role grouping category id 2 (e.g. US Retail) \n* ROLE_DEPTNAME: Company role department description (e.g. Retail) \n* ROLE_TITLE: Company role business title description (e.g. Senior Engineering Retail Manager) \n* ROLE_FAMILY_DESC: Company role family extended description (e.g. Retail Manager, Software Engineering) \n* ROLE_FAMILY: Company role family description (e.g. Retail Manager) \n* ROLE_CODE: Company role code; this code is unique to each role (e.g. Manager)", + "task_type": "binclass", + "openml_id": 4135, + "imbalance_ratio": 16.274117026884554, + "n_classes": 2, + "num_feature_intro": {}, + "cat_feature_intro": { + "ROLE_ROLLUP_1": "ROLE_ROLLUP_1", + "ROLE_ROLLUP_2": "ROLE_ROLLUP_2", + "ROLE_DEPTNAME": "ROLE_DEPTNAME", + "ROLE_TITLE": "ROLE_TITLE", + "ROLE_FAMILY_DESC": "ROLE_FAMILY_DESC", + "ROLE_FAMILY": "ROLE_FAMILY", + "ROLE_CODE": "ROLE_CODE" + } +} \ No newline at end of file diff --git a/Amazon_employee_access/y_test.npy b/Amazon_employee_access/y_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..57d5caf561f20b71d7445bc4005f894570a69a50 --- /dev/null +++ b/Amazon_employee_access/y_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:5be3cc69c16dd8e9fe591a3ce58ec2dfd226d0a75d2be94d714fcdf025799fcf +size 13415 diff --git a/Amazon_employee_access/y_train.npy b/Amazon_employee_access/y_train.npy new file mode 100644 index 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dataset has been created)\nkm: kilometers of the car\nprevious_owners: number of previous owners\nlat: latitude of the seller (the price of cars in Italy varies from North to South of the country)\nlon: longitude of the seller (the price of cars in Italy varies from North to South of the country)\nprice: selling price (the target)\nI collected this dataset to train myself and test regression algorithms. 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This dataset belongs to the \"regression on numerical features\" benchmark. Original description: \n \n**Author**: Kaggle \n**Source**: [original](https://www.kaggle.com/rubenssjr/brasilian-houses-to-rent) - 20-03-2020 \n**Please cite**: \n\nThis dataset contains 10962 houses to rent with 13 diferent features.\n\n**Outliers **\nSome values in the dataset can be considered as outliers for further analyses. Bear in mind that the Web Crawler was used only to get the data, so it's possible that errors in the original data exist.\n\n**Changes in data between versions of dataset **\nSince the WebCrawler was ran in different days for each version of dataset, there may be differences like added or deleted houses (as well as added cities).\n\nNotes: \n\n1) This dataset corresponds to the 2nd version of the original dataset (\"houses_to_rent_v2.csv\").\n\n2) The value '-' in the attribute floor was replaced by '0' as the data contributor stated that this refers to houses with just one floor (see https://www.kaggle.com/rubenssjr/brasilian-houses-to-rent/discussion).\n\n Reproduced from OpenML: https://www.openml.org/d/44141", + "task_type": "regression", + "openml_id": 44152, + "n_classes": 1, + "num_feature_intro": { + "area": "area", + "rooms": "rooms", + "bathroom": "bathroom", + "parking_spaces": "parking_spaces", + "hoa_(BRL)": "hoa_(BRL)", + "rent_amount_(BRL)": "rent_amount_(BRL)", + 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Tierney and Y. Malitsky (features) / K. Tierney and D. Pacino and S. Voss (algorithms)\ntranslator in coseal format: K. Tierney\n\nThis is an extension of the 2013 premarshalling dataset that includes more features and a set of test instances. \n\nThere are three sets of features:\n\nfeature_values.arff contains the full set of features from iteration 2 of our latent feature analysis (LFA) process (see paper)\nfeature_values_itr1.arff contains only the features after iteration 1 of LFA\nfeature_values_orig.arff containers the features used in PREMARHSALLING-ASTAR-2013\n\nWe also provide test data with an identical naming scheme (see _test). \n\nThe features for the pre-marshalling problem are all extremely easy and fast to\ncompute, thus the feature_costs.arff file has been omitted, as it would be time\n0 for every feature (regardless of using original, iteration 1 or iteration 2\nfeatures).\n\nThe feature computation code is available at https://bitbucket.org/eusorpb/cpmp-as\n\nNote: previously the scenario was called PREMARSHALLING-ASTAR-2015. To save same space, we renamed the scenario.", + "task_type": "regression", + "openml_id": 41700, + "n_classes": 1, + "num_feature_intro": { + "repetition": "repetition", + "stacks": "stacks", + "tiers": "tiers", + "stack.tier.ratio": "stack.tier.ratio", + "container.density": "container.density", + "empty.stack.pct": "empty.stack.pct", + "overstowing.stack.pct": "overstowing.stack.pct", + "overstowing.2cont.stack.pct": "overstowing.2cont.stack.pct", + "group.same.min": "group.same.min", + "group.same.max": "group.same.max", + "group.same.mean": "group.same.mean", + "group.same.stdev": "group.same.stdev", + "top.good.min": "top.good.min", + "top.good.max": "top.good.max", + "top.good.mean": "top.good.mean", + "top.good.stdev": "top.good.stdev", + "overstowage.pct": "overstowage.pct", + "bflb": "bflb", + "left.density": "left.density", + "tier.weighted.groups": "tier.weighted.groups", + "avg.l1.top.left.lg.group": "avg.l1.top.left.lg.group", + "cont.empty.grt.estack": 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**AER** R package (see citation request below).\n\n**Data description**\n\nCross-section data originating from the March 1988 Current Population Survey by the US Census Bureau.\nThe data is a sample of men aged 18 to 70 with positive annual income greater than USD 50 in 1992, who\nare not self-employed nor working without pay. Wages are deflated by the deflator of Personal\nConsumption Expenditure for 1992.\nA problem with CPS data is that it does not provide actual work experience. It is therefore customary\nto compute experience as age - education - 6 (as was done by Bierens and Ginther, 2001), this\nmay be considered potential experience. As a result, some respondents have negative experience\n\n\n**Attribute Information**\n\n* wage Wage (in dollars per week).\n* education Number of years of education.\n* experience Number of years of potential work experience.\n* ethnicity Factor with levels \"cauc\" and \"afam\" (African-American).\n* smsa Factor. Does the individual reside in a Standard Metropolitan Statistical Area (SMSA)?\n* region Factor with levels \"northeast\", \"midwest\", \"south\", \"west\".\n* parttime Factor. Does the individual work part-time?\n\n\n**Citation Request**\n\nChristian Kleiber and Achim Zeileis (2008). Applied Econometrics with\nR. New York: Springer-Verlag. ISBN 978-0-387-77316-2. URL\nhttps://CRAN.R-project.org/package=AER\n\n*Bibtex*\n\n@book{kleiber2008applied,\n title={Applied econometrics with R},\n author={Kleiber, Christian and Zeileis, Achim},\n year={2008},\n publisher={Springer Science \\& Business Media}\n}", + "task_type": "regression", + "openml_id": 43963, + "n_classes": 1, + "num_feature_intro": { + "education": "education", + "experience": "experience" + }, + "cat_feature_intro": { + "ethnicity": "ethnicity", + "smsa": "smsa", + "region": "region", + "parttime": "parttime" + } +} \ No newline at end of file diff --git a/CPS1988/y_test.npy b/CPS1988/y_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..ee5df379aba96bf593eddcc2a5d4a9901bb5e280 --- /dev/null +++ b/CPS1988/y_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:cf2efb4f4cb0111271b5413a69f7171e618f22e02edfe2b09781634bc9e6d098 +size 45176 diff --git a/CPS1988/y_train.npy b/CPS1988/y_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..fa24abe4c65227c2040a040fd8802dd0ea988641 --- /dev/null +++ b/CPS1988/y_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2882748902d986b191a423d845fef224bcc2d059e5190f5c4a56d83b05cc0705 +size 144280 diff --git a/CPS1988/y_val.npy b/CPS1988/y_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..3c4dbcb915838dfe5787be221d28373a8497c75b --- /dev/null +++ b/CPS1988/y_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8f16fb3f9e55d2ea46e797475ea36a92e4db5ef037e0debca4c5c212091de648 +size 36168 diff --git a/California-Housing-Classification/N_test.npy b/California-Housing-Classification/N_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..4dcbf070f3cf1a126494a812f57ca488c89d60cb --- /dev/null +++ b/California-Housing-Classification/N_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:75745e257745fa73694d4e20f1250f40e1775725953426c57fbc971200cebc5a +size 264320 diff --git a/California-Housing-Classification/N_train.npy b/California-Housing-Classification/N_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..14fd7acdf7af60b39e0f6356d2219bcce905b282 --- /dev/null +++ b/California-Housing-Classification/N_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3ec31e3a27a2c1dafcc4c352f96c639f5efe457638aa74464e4f7c6ffbc9ea99 +size 845504 diff --git a/California-Housing-Classification/N_val.npy b/California-Housing-Classification/N_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..254cc022880f948f47444c9846d4f8f7420d6ebc --- /dev/null +++ b/California-Housing-Classification/N_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d0343903cdc9a0627bd5f1da315f44dc9c5196a0ab9ee215a9c3dbeed1e43754 +size 211520 diff --git a/California-Housing-Classification/info.json b/California-Housing-Classification/info.json new file mode 100644 index 0000000000000000000000000000000000000000..0fc4e10d807828be6bfed8aac4b97162a7881d01 --- /dev/null +++ b/California-Housing-Classification/info.json @@ -0,0 +1,25 @@ +{ + "name": "California-Housing-Classification", + "n_num_features": 8, + "n_cat_features": 0, + "train_size": 13209, + "val_size": 3303, + "test_size": 4128, + "source": "https://www.openml.org/search?type=data&status=active&id=45578&sort=runs", + "task_intro": "Binarized version of the California Housing Dataset\n\nThis dataset was obtained from Luis Torgo's collection of regression datasets. It was binarized to serve as the original, unprocessed date for the California Housing dataset used by:\n\n> Grinsztajn, Leo and Oyallon, Edouard and Varoquaux, Gael\n> Why do tree-based models still outperform deep learning on typical tabular data?\n> In: Advances in Neural Information Processing Systems (2022)\n\n## Description:\n\nThis is a dataset obtained from the StatLib repository. Here is the included description:\n\nS&P Letters Data\nWe collected information on the variables using all the block groups in California from the 1990 Cens us. In this sample a block group on average includes 1425.5 individuals living in a geographically co mpact area. Naturally, the geographical area included varies inversely with the population density. W e computed distances among the centroids of each block group as measured in latitude and longitude. W e excluded all the block groups reporting zero entries for the independent and dependent variables. T he final data contained 20,640 observations on 9 variables. The dependent variable is ln(median house value).\n\n| | Bols | tols |\n|-----------------------------|----------|----------|\n| INTERCEPT | 11.4939 | 275.7518 |\n| MEDIAN INCOME | 0.4790 | 45.7768 |\n| MEDIAN INCOME2 | -0.0166 | -9.4841 |\n| MEDIAN INCOME3 | -0.0002 | -1.9157 |\n| ln(MEDIAN AGE) | 0.1570 | 33.6123 |\n| ln(TOTAL ROOMS/ POPULATION) | -0.8582 | -56.1280 |\n| ln(BEDROOMS/ POPULATION) | 0.8043 | 38.0685 |\n| ln(POPULATION/ HOUSEHOLDS) | -0.4077 | -20.8762 |\n| ln(HOUSEHOLDS) | 0.0477 | 13.0792 |\n\nThe file contains all the the variables. Specifically, it contains median house value, med ian income, housing median age, total rooms, total bedrooms, population, households, latitude, and lo ngitude in that order.\n\nReference\nPace, R. Kelley and Ronald Barry, Sparse Spatial Autoregressions, Statistics and Probability Letters, 33 (1997) 291-297.\n\nThe manuscript describing the data can be found at www.spatial-statistics.com. The data are also available as Matlab files.\n\nContact kelley@spatial-statistics.com or kelley@pace.am if you have any further questions. Thanks.", + "task_type": "binclass", + "openml_id": 45578, + "imbalance_ratio": 1.0005815644082583, + "n_classes": 2, + "num_feature_intro": { + "longitude": "longitude", + "latitude": "latitude", + "housingMedianAge": "housingMedianAge", + "totalRooms": "totalRooms", + "totalBedrooms": "totalBedrooms", + "population": "population", + "households": "households", + "medianIncome": "medianIncome" + }, + "cat_feature_intro": {} +} \ No newline at end of file diff --git a/California-Housing-Classification/y_test.npy b/California-Housing-Classification/y_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..0e0936459f17c78f2693ce94993b9b117ce33c00 --- /dev/null +++ b/California-Housing-Classification/y_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c43c51849ca206df515ef7da39533e49e2b54ed777543329fa3537b9497b9127 +size 4256 diff --git a/California-Housing-Classification/y_train.npy b/California-Housing-Classification/y_train.npy new 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information;\n* Examination: results of medical examination;\n* Subjective: information given by the patient.\n\nFeatures:\n\n1. Age | Objective Feature | age | int (days)\n2. Height | Objective Feature | height | int (cm) |\n3. Weight | Objective Feature | weight | float (kg) |\n4. Gender | Objective Feature | gender | categorical code |\n5. Systolic blood pressure | Examination Feature | ap_hi | int |\n6. Diastolic blood pressure | Examination Feature | ap_lo | int |\n7. Cholesterol | Examination Feature | cholesterol | 1: normal, 2: above normal, 3: well above normal |\n8. Glucose | Examination Feature | gluc | 1: normal, 2: above normal, 3: well above normal |\n9. Smoking | Subjective Feature | smoke | binary |\n10. Alcohol intake | Subjective Feature | alco | binary |\n11. Physical activity | Subjective Feature | active | binary |\n12. Presence or absence of cardiovascular disease | Target Variable | cardio | binary |\n\nAll of the dataset values were collected at the moment of medical examination.\n\n**Notes by Uploader to OpenML**\n\n* Gender: 1 - women, 2 - men\n* There is no information available on Kaggle where this data was collected.", + "task_type": "binclass", + "openml_id": 45547, + "imbalance_ratio": 1.0012007204322593, + "n_classes": 2, + "num_feature_intro": { + "age": "age", + "height": "height", + "weight": "weight", + "ap_hi": "ap_hi", + "ap_lo": "ap_lo" + }, + "cat_feature_intro": { + "gender": "gender", + "cholesterol": "cholesterol", + "gluc": "gluc", + "smoke": "smoke", + "alco": "alco", + "active": "active" + } +} \ No newline at end of file diff --git a/Cardiovascular-Disease-dataset/y_test.npy b/Cardiovascular-Disease-dataset/y_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..5d3f7cec32246db647704d86942f3ebe6f53b818 --- /dev/null +++ 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"n_num_features": 30, + "n_cat_features": 0, + "train_size": 1536, + "val_size": 384, + "test_size": 480, + "source": "https://www.openml.org/search?type=data&status=active&id=45538&sort=runs", + "task_intro": "Contaminant-detection-in-packaged-cocoa-hazelnut-spread-jars-using-Microwaves-Sensing-and-Machine-Learning-10.0GHz(Urbinati)\n----------------\n\nThis dataset is part of a series of five different datasets\neach one measured with a different microwave frequency: 9.0, 9.5, 10.0, 10.5, 11.0 GHz.\nPAY ATTENTION: THE DATASET PRESENTED IN THIS PAGE HAS BEEN ACQUIRED AT 10.0 GHz!\nIF YOU ARE LOOKING FOR THE OTHER FOUR DATASETS, VISIT THE OPENML PROFILE\nOF THE AUTHOR OF THIS DATASET.\n\nThe following description is valid for all the five datasets.\n\nDataset description.\nTo detect contaminants accidentally included in industrial food,\nMicrowave Sensing (MWS) can be used as a contactless detection method,\nin particular when the food is already packaged.\n\nMWS uses microwaves to illuminate the target object through a set of antennas,\nrecords the scattered waves, and uses Machine Learning to predict\nthe presence of contaminants inside the target object.\n\nIn this application the target object is a cocoa-hazelnut spread jar\nand each instance (sample) of this dataset consists in 30 scattering parameters\nof the network composed by: antennas, target object (a jar\nw/ or w/o a contaminant inside) and medium (i.e. the air) in between.\nThe types of contaminants vary from metal to glass and plastic.\nEach sample has been measured at five different microwave frequencies\nthat are: 9.0, 9.5, 10.0, 10.5, 11.0 GHz.\nPAY ATTENTION: THE DATASET PRESENTED IN THIS PAGE HAS BEEN ACQUIRED AT 10.0 GHz!\nIF YOU ARE LOOKING FOR THE OTHER FOUR DATASETS, VISIT THE OPENML PROFILE\nOF THE AUTHOR OF THIS DATASET.\n\n**Data Set Characteristics:**\n\n :Microwave frequency used for acquisition: 10.0 GHz\n\n :Total Number of Instances: 2400\n\n :Total Number of Uncontaminated Instances: 1200\n\n :Total Number of Contaminated Instances: 1200\n\n :Total Number of Classes: 11\n\n :Target: The last column, column 31, contains the class label as integer value\n\n :Number of Contaminated Instances Divided Per Class (full explanation and\n pictures in [2]):\n\n - \"air_surface\" (i.e. cap-shape plastic with the same dielectric constant of the air): 200\n\n - \"big_pink_plastic_shere_middle\": 100\n\n - \"big_pink_plastic_shere_surface\": 100\n\n - \"glass_fragment_middle\": 100\n\n - \"glass_fragment_surface\": 100\n\n - \"small_metal_sphere_middle\": 100\n\n - \"small_metal_sphere_surface\": 100\n\n - \"small_plastic_sphere_middle\": 100\n\n - \"small_plastic_sphere_surface\": 100\n\n - \"small_triangular_plastic_fragment_surface\": 200\n\n \"surface\" means that the instance was placed on top of the cocoa-hazelnut spread,\n at the spread-air interface\n\n \"middle\" means that the instance was placed in the middle of the jar filled with\n cocoa-hazelnut spread\n\n\n :Number of Attributes in a generic Instance: 30\n\n\n :Attribute Information: This is the 6x6 Scattering Matrix (S):\n\n S = [ s12, s13, s14, s15, s16, \n s21, s23, s24, s25, s26, \n s31, s32, s34, s35, s36, \n s41, s42, s43, s45, s46, \n s51, s52, s53, s54, s56, \n s61, s62, s63, s64, s65, ]\n\n The first 30 attributes (columns) of an instance are the 15 elements of the triangular\n upper part of S. Since each of these elements is a complex number with real and\n imaginary parts, each instance is a vector of 15x2=30 attributes. The real (real) and\n imaginary (img) parts of each element are placed one after the other.\n The scattering parameters are ordered row by row from left to right, e.g.:\n s12_real, s12_img, s13_real, s13_img, ..., s16_real, s16_img, s21_real, s21_img, ... .\n The self-scattering parameters, i.e. those placed on the main diagional of S,\n are not part of the dataset, as exaplined in [1].\n\n\nNote: This dataset is realeased without pre-processing.\n\nFor more information read the reference papers:\n\n[1] L. Urbinati, M. Ricci, G. Turvani, J. A. T. Vasquez, F. Vipiana and M. R. Casu, \"A Machine-Learning Based Microwave Sensing Approach to Food Contaminant Detection,\" 2020 IEEE International Symposium on Circuits and Systems (ISCAS), Seville, Spain, 2020, pp. 1-5, doi: 10.1109/ISCAS45731.2020.9181293\nLink to the paper: https://ieeexplore.ieee.org/abstract/document/9181293\n\n[2] M. Ricci, B. Stitic, L. Urbinati, G. D. Guglielmo, J. A. T. Vasquez, L. P. Carloni, F. Vipiana, and M. R. Casu, \"Machine-learning-based microwave sensing: A case study for the food industry,\" IEEE Journal on Emerging and Selected Topics in Circuits and Systems, vol. 11, no. 3, pp. 503-514, 2021\nLink to the paper: https://ieeexplore.ieee.org/abstract/document/9489295", + "task_type": "binclass", + "openml_id": 45538, + "n_classes": 1, + "num_feature_intro": { + "s12": "s12", + "s13": "s13", + "s14": "s14", + "s15": "s15", + "s16": "s16", + "s21": "s21", + "s23": "s23", + "s24": "s24", + "s25": "s25", + "s26": "s26", + "s31": "s31", + "s32": "s32", + "s34": "s34", + "s35": "s35", + "s36": "s36", + "s41": "s41", + "s42": "s42", + "s43": "s43", + "s45": "s45", + "s46": "s46", + "s51": "s51", + "s52": "s52", + "s53": "s53", + "s54": "s54", + "s56": "s56", + "s61": "s61", + "s62": "s62", + "s63": "s63", + "s64": "s64", + "s65": "s65" + }, + "cat_feature_intro": {} +} \ No newline at end of file diff --git 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"n_num_features": 30, + "n_cat_features": 0, + "train_size": 1536, + "val_size": 384, + "test_size": 480, + "source": "https://www.openml.org/search?type=data&status=active&id=45539&sort=runs", + "task_intro": "Contaminant-detection-in-packaged-cocoa-hazelnut-spread-jars-using-Microwaves-Sensing-and-Machine-Learning-10.5GHz(Urbinati)\n----------------\n\nThis dataset is part of a series of five different datasets\neach one measured with a different microwave frequency: 9.0, 9.5, 10.0, 10.5, 11.0 GHz.\nPAY ATTENTION: THE DATASET PRESENTED IN THIS PAGE HAS BEEN ACQUIRED AT 10.5 GHz!\nIF YOU ARE LOOKING FOR THE OTHER FOUR DATASETS, VISIT THE OPENML PROFILE\nOF THE AUTHOR OF THIS DATASET.\n\nThe following description is valid for all the five datasets.\n\nDataset description.\nTo detect contaminants accidentally included in industrial food,\nMicrowave Sensing (MWS) can be used as a contactless detection method,\nin particular when the food is already packaged.\n\nMWS uses microwaves to illuminate the target object through a set of antennas,\nrecords the scattered waves, and uses Machine Learning to predict\nthe presence of contaminants inside the target object.\n\nIn this application the target object is a cocoa-hazelnut spread jar\nand each instance (sample) of this dataset consists in 30 scattering parameters\nof the network composed by: antennas, target object (a jar\nw/ or w/o a contaminant inside) and medium (i.e. the air) in between.\nThe types of contaminants vary from metal to glass and plastic.\nEach sample has been measured at five different microwave frequencies\nthat are: 9.0, 9.5, 10.0, 10.5, 11.0 GHz.\nPAY ATTENTION: THE DATASET PRESENTED IN THIS PAGE HAS BEEN ACQUIRED AT 10.5 GHz!\nIF YOU ARE LOOKING FOR THE OTHER FOUR DATASETS, VISIT THE OPENML PROFILE\nOF THE AUTHOR OF THIS DATASET.\n\n**Data Set Characteristics:**\n\n :Microwave frequency used for acquisition: 10.5 GHz\n\n :Total Number of Instances: 2400\n\n :Total Number of Uncontaminated Instances: 1200\n\n :Total Number of Contaminated Instances: 1200\n\n :Total Number of Classes: 11\n\n :Target: The last column, column 31, contains the class label as integer value\n\n :Number of Contaminated Instances Divided Per Class (full explanation and\n pictures in [2]):\n\n - \"air_surface\" (i.e. cap-shape plastic with the same dielectric constant of the air): 200\n\n - \"big_pink_plastic_shere_middle\": 100\n\n - \"big_pink_plastic_shere_surface\": 100\n\n - \"glass_fragment_middle\": 100\n\n - \"glass_fragment_surface\": 100\n\n - \"small_metal_sphere_middle\": 100\n\n - \"small_metal_sphere_surface\": 100\n\n - \"small_plastic_sphere_middle\": 100\n\n - \"small_plastic_sphere_surface\": 100\n\n - \"small_triangular_plastic_fragment_surface\": 200\n\n \"surface\" means that the instance was placed on top of the cocoa-hazelnut spread,\n at the spread-air interface\n\n \"middle\" means that the instance was placed in the middle of the jar filled with\n cocoa-hazelnut spread\n\n\n :Number of Attributes in a generic Instance: 30\n\n\n :Attribute Information: This is the 6x6 Scattering Matrix (S):\n\n S = [ s12, s13, s14, s15, s16, \n s21, s23, s24, s25, s26, \n s31, s32, s34, s35, s36, \n s41, s42, s43, s45, s46, \n s51, s52, s53, s54, s56, \n s61, s62, s63, s64, s65, ]\n\n The first 30 attributes (columns) of an instance are the 15 elements of the triangular\n upper part of S. Since each of these elements is a complex number with real and\n imaginary parts, each instance is a vector of 15x2=30 attributes. The real (real) and\n imaginary (img) parts of each element are placed one after the other.\n The scattering parameters are ordered row by row from left to right, e.g.:\n s12_real, s12_img, s13_real, s13_img, ..., s16_real, s16_img, s21_real, s21_img, ... .\n The self-scattering parameters, i.e. those placed on the main diagional of S,\n are not part of the dataset, as exaplined in [1].\n\n\nNote: This dataset is realeased without pre-processing.\n\nFor more information read the reference papers:\n\n[1] L. Urbinati, M. Ricci, G. Turvani, J. A. T. Vasquez, F. Vipiana and M. R. Casu, \"A Machine-Learning Based Microwave Sensing Approach to Food Contaminant Detection,\" 2020 IEEE International Symposium on Circuits and Systems (ISCAS), Seville, Spain, 2020, pp. 1-5, doi: 10.1109/ISCAS45731.2020.9181293\nLink to the paper: https://ieeexplore.ieee.org/abstract/document/9181293\n\n[2] M. Ricci, B. Stitic, L. Urbinati, G. D. Guglielmo, J. A. T. Vasquez, L. P. Carloni, F. Vipiana, and M. R. Casu, \"Machine-learning-based microwave sensing: A case study for the food industry,\" IEEE Journal on Emerging and Selected Topics in Circuits and Systems, vol. 11, no. 3, pp. 503-514, 2021\nLink to the paper: https://ieeexplore.ieee.org/abstract/document/9489295", + "task_type": "binclass", + "openml_id": 45539, + "n_classes": 1, + "num_feature_intro": { + "s12": "s12", + "s13": "s13", + "s14": "s14", + "s15": "s15", + "s16": "s16", + "s21": "s21", + "s23": "s23", + "s24": "s24", + "s25": "s25", + "s26": "s26", + "s31": "s31", + "s32": "s32", + "s34": "s34", + "s35": "s35", + "s36": "s36", + "s41": "s41", + "s42": "s42", + "s43": "s43", + "s45": "s45", + "s46": "s46", + "s51": "s51", + "s52": "s52", + "s53": "s53", + "s54": "s54", + "s56": "s56", + "s61": "s61", + "s62": "s62", + "s63": "s63", + "s64": "s64", + "s65": "s65" + }, + "cat_feature_intro": {} +} \ No newline at end of file diff --git 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b/Contaminant-detection-in-packaged-cocoa-hazelnut-spread-jars-using-Microwaves-Sensing-and-Machine-Learning-11.0GHz(Urbinati)/N_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:eaeea6f6b4ef7f663b84fa58ec5013e91bd15734c81ea1aa2ff75cfe57f77789 +size 92288 diff --git a/Contaminant-detection-in-packaged-cocoa-hazelnut-spread-jars-using-Microwaves-Sensing-and-Machine-Learning-11.0GHz(Urbinati)/info.json b/Contaminant-detection-in-packaged-cocoa-hazelnut-spread-jars-using-Microwaves-Sensing-and-Machine-Learning-11.0GHz(Urbinati)/info.json new file mode 100644 index 0000000000000000000000000000000000000000..b364486a1d2497a94b03f9fdbe5c1836ac175ab5 --- /dev/null +++ b/Contaminant-detection-in-packaged-cocoa-hazelnut-spread-jars-using-Microwaves-Sensing-and-Machine-Learning-11.0GHz(Urbinati)/info.json @@ -0,0 +1,46 @@ +{ + "name": "Contaminant-detection-in-packaged-cocoa-hazelnut-spread-jars-using-Microwaves-Sensing-and-Machine-Learning-11.0GHz(Urbinati)", + "n_num_features": 30, + "n_cat_features": 0, + "train_size": 1536, + "val_size": 384, + "test_size": 480, + "source": "https://www.openml.org/search?type=data&status=active&id=45540&sort=runs", + "task_intro": "Contaminant-detection-in-packaged-cocoa-hazelnut-spread-jars-using-Microwaves-Sensing-and-Machine-Learning-11.0GHz(Urbinati)\n----------------\n\nThis dataset is part of a series of five different datasets\neach one measured with a different microwave frequency: 9.0, 9.5, 10.0, 10.5, 11.0 GHz.\nPAY ATTENTION: THE DATASET PRESENTED IN THIS PAGE HAS BEEN ACQUIRED AT 11.0 GHz!\nIF YOU ARE LOOKING FOR THE OTHER FOUR DATASETS, VISIT THE OPENML PROFILE\nOF THE AUTHOR OF THIS DATASET.\n\nThe following description is valid for all the five datasets.\n\nDataset description.\nTo detect contaminants accidentally included in industrial food,\nMicrowave Sensing (MWS) can be used as a contactless detection method,\nin particular when the food is already packaged.\n\nMWS uses microwaves to illuminate the target object through a set of antennas,\nrecords the scattered waves, and uses Machine Learning to predict\nthe presence of contaminants inside the target object.\n\nIn this application the target object is a cocoa-hazelnut spread jar\nand each instance (sample) of this dataset consists in 30 scattering parameters\nof the network composed by: antennas, target object (a jar\nw/ or w/o a contaminant inside) and medium (i.e. the air) in between.\nThe types of contaminants vary from metal to glass and plastic.\nEach sample has been measured at five different microwave frequencies\nthat are: 9.0, 9.5, 10.0, 10.5, 11.0 GHz.\nPAY ATTENTION: THE DATASET PRESENTED IN THIS PAGE HAS BEEN ACQUIRED AT 11.0 GHz!\nIF YOU ARE LOOKING FOR THE OTHER FOUR DATASETS, VISIT THE OPENML PROFILE\nOF THE AUTHOR OF THIS DATASET.\n\n**Data Set Characteristics:**\n\n :Microwave frequency used for acquisition: 11.0 GHz\n\n :Total Number of Instances: 2400\n\n :Total Number of Uncontaminated Instances: 1200\n\n :Total Number of Contaminated Instances: 1200\n\n :Total Number of Classes: 11\n\n :Target: The last column, column 31, contains the class label as integer value\n\n :Number of Contaminated Instances Divided Per Class (full explanation and\n pictures in [2]):\n\n - \"air_surface\" (i.e. cap-shape plastic with the same dielectric constant of the air): 200\n\n - \"big_pink_plastic_shere_middle\": 100\n\n - \"big_pink_plastic_shere_surface\": 100\n\n - \"glass_fragment_middle\": 100\n\n - \"glass_fragment_surface\": 100\n\n - \"small_metal_sphere_middle\": 100\n\n - \"small_metal_sphere_surface\": 100\n\n - \"small_plastic_sphere_middle\": 100\n\n - \"small_plastic_sphere_surface\": 100\n\n - \"small_triangular_plastic_fragment_surface\": 200\n\n \"surface\" means that the instance was placed on top of the cocoa-hazelnut spread,\n at the spread-air interface\n\n \"middle\" means that the instance was placed in the middle of the jar filled with\n cocoa-hazelnut spread\n\n\n :Number of Attributes in a generic Instance: 30\n\n\n :Attribute Information: This is the 6x6 Scattering Matrix (S):\n\n S = [ s12, s13, s14, s15, s16, \n s21, s23, s24, s25, s26, \n s31, s32, s34, s35, s36, \n s41, s42, s43, s45, s46, \n s51, s52, s53, s54, s56, \n s61, s62, s63, s64, s65, ]\n\n The first 30 attributes (columns) of an instance are the 15 elements of the triangular\n upper part of S. Since each of these elements is a complex number with real and\n imaginary parts, each instance is a vector of 15x2=30 attributes. The real (real) and\n imaginary (img) parts of each element are placed one after the other.\n The scattering parameters are ordered row by row from left to right, e.g.:\n s12_real, s12_img, s13_real, s13_img, ..., s16_real, s16_img, s21_real, s21_img, ... .\n The self-scattering parameters, i.e. those placed on the main diagional of S,\n are not part of the dataset, as exaplined in [1].\n\n\nNote: This dataset is realeased without pre-processing.\n\nFor more information read the reference papers:\n\n[1] L. Urbinati, M. Ricci, G. Turvani, J. A. T. Vasquez, F. Vipiana and M. R. Casu, \"A Machine-Learning Based Microwave Sensing Approach to Food Contaminant Detection,\" 2020 IEEE International Symposium on Circuits and Systems (ISCAS), Seville, Spain, 2020, pp. 1-5, doi: 10.1109/ISCAS45731.2020.9181293\nLink to the paper: https://ieeexplore.ieee.org/abstract/document/9181293\n\n[2] M. Ricci, B. Stitic, L. Urbinati, G. D. Guglielmo, J. A. T. Vasquez, L. P. Carloni, F. Vipiana, and M. R. Casu, \"Machine-learning-based microwave sensing: A case study for the food industry,\" IEEE Journal on Emerging and Selected Topics in Circuits and Systems, vol. 11, no. 3, pp. 503-514, 2021\nLink to the paper: https://ieeexplore.ieee.org/abstract/document/9489295", + "task_type": "binclass", + "openml_id": 45540, + "n_classes": 1, + "num_feature_intro": { + "s12": "s12", + "s13": "s13", + "s14": "s14", + "s15": "s15", + "s16": "s16", + "s21": "s21", + "s23": "s23", + "s24": "s24", + "s25": "s25", + "s26": "s26", + "s31": "s31", + "s32": "s32", + "s34": "s34", + "s35": "s35", + "s36": "s36", + "s41": "s41", + "s42": "s42", + "s43": "s43", + "s45": "s45", + "s46": "s46", + "s51": "s51", + "s52": "s52", + "s53": "s53", + "s54": "s54", + "s56": "s56", + "s61": "s61", + "s62": "s62", + "s63": "s63", + "s64": "s64", + "s65": "s65" + }, + "cat_feature_intro": {} +} \ No newline at end of file diff --git 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"n_num_features": 30, + "n_cat_features": 0, + "train_size": 1536, + "val_size": 384, + "test_size": 480, + "source": "https://www.openml.org/search?type=data&status=active&id=45536&sort=runs", + "task_intro": "Contaminant-detection-in-packaged-cocoa-hazelnut-spread-jars-using-Microwaves-Sensing-and-Machine-Learning-9.0GHz(Urbinati)\n----------------\n\nThis dataset is part of a series of five different datasets\neach one measured with a different microwave frequency: 9.0, 9.5, 10.0, 10.5, 11.0 GHz.\nPAY ATTENTION: THE DATASET PRESENTED IN THIS PAGE HAS BEEN ACQUIRED AT 9.0 GHz!\nIF YOU ARE LOOKING FOR THE OTHER FOUR DATASETS, VISIT THE OPENML PROFILE\nOF THE AUTHOR OF THIS DATASET.\n\nThe following description is valid for all the five datasets.\n\nDataset description.\nTo detect contaminants accidentally included in industrial food,\nMicrowave Sensing (MWS) can be used as a contactless detection method,\nin particular when the food is already packaged.\n\nMWS uses microwaves to illuminate the target object through a set of antennas,\nrecords the scattered waves, and uses Machine Learning to predict\nthe presence of contaminants inside the target object.\n\nIn this application the target object is a cocoa-hazelnut spread jar\nand each instance (sample) of this dataset consists in 30 scattering parameters\nof the network composed by: antennas, target object (a jar\nw/ or w/o a contaminant inside) and medium (i.e. the air) in between.\nThe types of contaminants vary from metal to glass and plastic.\nEach sample has been measured at five different microwave frequencies\nthat are: 9.0, 9.5, 10.0, 10.5, 11.0 GHz.\nPAY ATTENTION: THE DATASET PRESENTED IN THIS PAGE HAS BEEN ACQUIRED AT 9.0 GHz!\nIF YOU ARE LOOKING FOR THE OTHER FOUR DATASETS, VISIT THE OPENML PROFILE\nOF THE AUTHOR OF THIS DATASET.\n\n**Data Set Characteristics:**\n\n :Microwave frequency used for acquisition: 9.0 GHz\n\n :Total Number of Instances: 2400\n\n :Total Number of Uncontaminated Instances: 1200\n\n :Total Number of Contaminated Instances: 1200\n\n :Total Number of Classes: 11\n\n :Target: The last column, column 31, contains the class label as integer value\n\n :Number of Contaminated Instances Divided Per Class (full explanation and\n pictures in [2]):\n\n - \"air_surface\" (i.e. cap-shape plastic with the same dielectric constant of the air): 200\n\n - \"big_pink_plastic_shere_middle\": 100\n\n - \"big_pink_plastic_shere_surface\": 100\n\n - \"glass_fragment_middle\": 100\n\n - \"glass_fragment_surface\": 100\n\n - \"small_metal_sphere_middle\": 100\n\n - \"small_metal_sphere_surface\": 100\n\n - \"small_plastic_sphere_middle\": 100\n\n - \"small_plastic_sphere_surface\": 100\n\n - \"small_triangular_plastic_fragment_surface\": 200\n\n \"surface\" means that the instance was placed on top of the cocoa-hazelnut spread,\n at the spread-air interface\n\n \"middle\" means that the instance was placed in the middle of the jar filled with\n cocoa-hazelnut spread\n\n\n :Number of Attributes in a generic Instance: 30\n\n\n :Attribute Information: This is the 6x6 Scattering Matrix (S):\n\n S = [ s12, s13, s14, s15, s16, \n s21, s23, s24, s25, s26, \n s31, s32, s34, s35, s36, \n s41, s42, s43, s45, s46, \n s51, s52, s53, s54, s56, \n s61, s62, s63, s64, s65, ]\n\n The first 30 attributes (columns) of an instance are the 15 elements of the triangular\n upper part of S. Since each of these elements is a complex number with real and\n imaginary parts, each instance is a vector of 15x2=30 attributes. The real (real) and\n imaginary (img) parts of each element are placed one after the other.\n The scattering parameters are ordered row by row from left to right, e.g.:\n s12_real, s12_img, s13_real, s13_img, ..., s16_real, s16_img, s21_real, s21_img, ... .\n The self-scattering parameters, i.e. those placed on the main diagional of S,\n are not part of the dataset, as exaplined in [1].\n\n\nNote: This dataset is realeased without pre-processing.\n\nFor more information read the reference papers:\n\n[1] L. Urbinati, M. Ricci, G. Turvani, J. A. T. Vasquez, F. Vipiana and M. R. Casu, \"A Machine-Learning Based Microwave Sensing Approach to Food Contaminant Detection,\" 2020 IEEE International Symposium on Circuits and Systems (ISCAS), Seville, Spain, 2020, pp. 1-5, doi: 10.1109/ISCAS45731.2020.9181293\nLink to the paper: https://ieeexplore.ieee.org/abstract/document/9181293\n\n[2] M. Ricci, B. Stitic, L. Urbinati, G. D. Guglielmo, J. A. T. Vasquez, L. P. Carloni, F. Vipiana, and M. R. Casu, \"Machine-learning-based microwave sensing: A case study for the food industry,\" IEEE Journal on Emerging and Selected Topics in Circuits and Systems, vol. 11, no. 3, pp. 503-514, 2021\nLink to the paper: https://ieeexplore.ieee.org/abstract/document/9489295", + "task_type": "binclass", + "openml_id": 45536, + "n_classes": 1, + "num_feature_intro": { + "s12": "s12", + "s13": "s13", + "s14": "s14", + "s15": "s15", + "s16": "s16", + "s21": "s21", + "s23": "s23", + "s24": "s24", + "s25": "s25", + "s26": "s26", + "s31": "s31", + "s32": "s32", + "s34": "s34", + "s35": "s35", + "s36": "s36", + "s41": "s41", + "s42": "s42", + "s43": "s43", + "s45": "s45", + "s46": "s46", + "s51": "s51", + "s52": "s52", + "s53": "s53", + "s54": "s54", + "s56": "s56", + "s61": "s61", + "s62": "s62", + "s63": "s63", + "s64": "s64", + "s65": "s65" + }, + "cat_feature_intro": {} +} \ No newline at end of file diff --git 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"n_num_features": 30, + "n_cat_features": 0, + "train_size": 1536, + "val_size": 384, + "test_size": 480, + "source": "https://www.openml.org/search?type=data&status=active&id=45537&sort=runs", + "task_intro": "Contaminant-detection-in-packaged-cocoa-hazelnut-spread-jars-using-Microwaves-Sensing-and-Machine-Learning-9.5GHz(Urbinati)\n----------------\n\nThis dataset is part of a series of five different datasets\neach one measured with a different microwave frequency: 9.0, 9.5, 10.0, 10.5, 11.0 GHz.\nPAY ATTENTION: THE DATASET PRESENTED IN THIS PAGE HAS BEEN ACQUIRED AT 9.5 GHz!\nIF YOU ARE LOOKING FOR THE OTHER FOUR DATASETS, VISIT THE OPENML PROFILE\nOF THE AUTHOR OF THIS DATASET.\n\nThe following description is valid for all the five datasets.\n\nDataset description.\nTo detect contaminants accidentally included in industrial food,\nMicrowave Sensing (MWS) can be used as a contactless detection method,\nin particular when the food is already packaged.\n\nMWS uses microwaves to illuminate the target object through a set of antennas,\nrecords the scattered waves, and uses Machine Learning to predict\nthe presence of contaminants inside the target object.\n\nIn this application the target object is a cocoa-hazelnut spread jar\nand each instance (sample) of this dataset consists in 30 scattering parameters\nof the network composed by: antennas, target object (a jar\nw/ or w/o a contaminant inside) and medium (i.e. the air) in between.\nThe types of contaminants vary from metal to glass and plastic.\nEach sample has been measured at five different microwave frequencies\nthat are: 9.0, 9.5, 10.0, 10.5, 11.0 GHz.\nPAY ATTENTION: THE DATASET PRESENTED IN THIS PAGE HAS BEEN ACQUIRED AT 9.5 GHz!\nIF YOU ARE LOOKING FOR THE OTHER FOUR DATASETS, VISIT THE OPENML PROFILE\nOF THE AUTHOR OF THIS DATASET.\n\n**Data Set Characteristics:**\n\n :Microwave frequency used for acquisition: 9.5 GHz\n\n :Total Number of Instances: 2400\n\n :Total Number of Uncontaminated Instances: 1200\n\n :Total Number of Contaminated Instances: 1200\n\n :Total Number of Classes: 11\n\n :Target: The last column, column 31, contains the class label as integer value\n\n :Number of Contaminated Instances Divided Per Class (full explanation and\n pictures in [2]):\n\n - \"air_surface\" (i.e. cap-shape plastic with the same dielectric constant of the air): 200\n\n - \"big_pink_plastic_shere_middle\": 100\n\n - \"big_pink_plastic_shere_surface\": 100\n\n - \"glass_fragment_middle\": 100\n\n - \"glass_fragment_surface\": 100\n\n - \"small_metal_sphere_middle\": 100\n\n - \"small_metal_sphere_surface\": 100\n\n - \"small_plastic_sphere_middle\": 100\n\n - \"small_plastic_sphere_surface\": 100\n\n - \"small_triangular_plastic_fragment_surface\": 200\n\n \"surface\" means that the instance was placed on top of the cocoa-hazelnut spread,\n at the spread-air interface\n\n \"middle\" means that the instance was placed in the middle of the jar filled with\n cocoa-hazelnut spread\n\n\n :Number of Attributes in a generic Instance: 30\n\n\n :Attribute Information: This is the 6x6 Scattering Matrix (S):\n\n S = [ s12, s13, s14, s15, s16, \n s21, s23, s24, s25, s26, \n s31, s32, s34, s35, s36, \n s41, s42, s43, s45, s46, \n s51, s52, s53, s54, s56, \n s61, s62, s63, s64, s65, ]\n\n The first 30 attributes (columns) of an instance are the 15 elements of the triangular\n upper part of S. Since each of these elements is a complex number with real and\n imaginary parts, each instance is a vector of 15x2=30 attributes. The real (real) and\n imaginary (img) parts of each element are placed one after the other.\n The scattering parameters are ordered row by row from left to right, e.g.:\n s12_real, s12_img, s13_real, s13_img, ..., s16_real, s16_img, s21_real, s21_img, ... .\n The self-scattering parameters, i.e. those placed on the main diagional of S,\n are not part of the dataset, as exaplined in [1].\n\n\nNote: This dataset is realeased without pre-processing.\n\nFor more information read the reference papers:\n\n[1] L. Urbinati, M. Ricci, G. Turvani, J. A. T. Vasquez, F. Vipiana and M. R. Casu, \"A Machine-Learning Based Microwave Sensing Approach to Food Contaminant Detection,\" 2020 IEEE International Symposium on Circuits and Systems (ISCAS), Seville, Spain, 2020, pp. 1-5, doi: 10.1109/ISCAS45731.2020.9181293\nLink to the paper: https://ieeexplore.ieee.org/abstract/document/9181293\n\n[2] M. Ricci, B. Stitic, L. Urbinati, G. D. Guglielmo, J. A. T. Vasquez, L. P. Carloni, F. Vipiana, and M. R. Casu, \"Machine-learning-based microwave sensing: A case study for the food industry,\" IEEE Journal on Emerging and Selected Topics in Circuits and Systems, vol. 11, no. 3, pp. 503-514, 2021\nLink to the paper: https://ieeexplore.ieee.org/abstract/document/9489295", + "task_type": "binclass", + "openml_id": 45537, + "n_classes": 1, + "num_feature_intro": { + "s12": "s12", + "s13": "s13", + "s14": "s14", + "s15": "s15", + "s16": "s16", + "s21": "s21", + "s23": "s23", + "s24": "s24", + "s25": "s25", + "s26": "s26", + "s31": "s31", + "s32": "s32", + "s34": "s34", + "s35": "s35", + "s36": "s36", + "s41": "s41", + "s42": "s42", + "s43": "s43", + "s45": "s45", + "s46": "s46", + "s51": "s51", + "s52": "s52", + "s53": "s53", + "s54": "s54", + "s56": "s56", + "s61": "s61", + "s62": "s62", + "s63": "s63", + "s64": "s64", + "s65": "s65" + }, + "cat_feature_intro": {} +} \ No newline at end of file diff --git a/Contaminant-detection-in-packaged-cocoa-hazelnut-spread-jars-using-Microwaves-Sensing-and-Machine-Learning-9.5GHz(Urbinati)/y_test.npy b/Contaminant-detection-in-packaged-cocoa-hazelnut-spread-jars-using-Microwaves-Sensing-and-Machine-Learning-9.5GHz(Urbinati)/y_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..ec4325a9b2c1058ef4fdb10beaedeefc627f4d29 --- /dev/null +++ b/Contaminant-detection-in-packaged-cocoa-hazelnut-spread-jars-using-Microwaves-Sensing-and-Machine-Learning-9.5GHz(Urbinati)/y_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:115915eeb1efe6af72a8b1d88d7d4310c23738e6b34fa7d06f6685e8d304496a +size 608 diff --git a/Contaminant-detection-in-packaged-cocoa-hazelnut-spread-jars-using-Microwaves-Sensing-and-Machine-Learning-9.5GHz(Urbinati)/y_train.npy b/Contaminant-detection-in-packaged-cocoa-hazelnut-spread-jars-using-Microwaves-Sensing-and-Machine-Learning-9.5GHz(Urbinati)/y_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..71ead5a5e78f5bd96eeaf39d7e67e76eb26848de --- /dev/null +++ b/Contaminant-detection-in-packaged-cocoa-hazelnut-spread-jars-using-Microwaves-Sensing-and-Machine-Learning-9.5GHz(Urbinati)/y_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a3c629dab24ca3c11a62ae9871556fcc1ffcdfde41120b5ffbdc9b4f3a546fcc +size 1664 diff --git a/Contaminant-detection-in-packaged-cocoa-hazelnut-spread-jars-using-Microwaves-Sensing-and-Machine-Learning-9.5GHz(Urbinati)/y_val.npy b/Contaminant-detection-in-packaged-cocoa-hazelnut-spread-jars-using-Microwaves-Sensing-and-Machine-Learning-9.5GHz(Urbinati)/y_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..b4f96fb595800c4a2cbfc0ce1f196fddb0b0b713 --- /dev/null +++ b/Contaminant-detection-in-packaged-cocoa-hazelnut-spread-jars-using-Microwaves-Sensing-and-Machine-Learning-9.5GHz(Urbinati)/y_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:75d6e489c0b0a84a150c2a7b25781c7a1fa08153d3f16daaba9fdea34b0b7a94 +size 512 diff --git a/CookbookReviews/N_test.npy b/CookbookReviews/N_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..d6402f188b77a61cc46347d23ec9707e1acd3b68 --- /dev/null +++ b/CookbookReviews/N_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8cd834f17ff02be6ef595a0da9d124d6015917c9cf38f85defcd85fac3af26ee +size 203800 diff --git a/CookbookReviews/N_train.npy b/CookbookReviews/N_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..38f5427247d453c8eff25e66fe860e351c685f77 --- /dev/null +++ b/CookbookReviews/N_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ce342b0b42207b3a954b69b5f960cc28158621f37e730d73b88820b19de3ba6b +size 651744 diff --git a/CookbookReviews/N_val.npy b/CookbookReviews/N_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..843340ef31f74e1f54afce880e34398764d01c81 --- /dev/null +++ b/CookbookReviews/N_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:539ad08a44272c48463e49e4794490f45c3616eceb78837412a4cd054119288f +size 163032 diff --git a/CookbookReviews/info.json b/CookbookReviews/info.json new file mode 100644 index 0000000000000000000000000000000000000000..022ef24c9e43273812a18d18bd9058dd935ea381 --- /dev/null +++ b/CookbookReviews/info.json @@ -0,0 +1,23 @@ +{ + "name": "CookbookReviews", + "n_num_features": 7, + "n_cat_features": 0, + "train_size": 11636, + "val_size": 2909, + "test_size": 3637, + "source": "https://www.openml.org/search?type=data&status=active&id=45744&sort=runs", + "task_intro": "The \"Cookbook Reviews\" is an extensive data set that includes a range of information about user interactions and recipe reviews. 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The marketing campaigns were based on phone calls. Often, more than one contact to the same client was required, in order to access if the product (bank term deposit) would be (or not) subscribed. The classification goal is to predict if the client will subscribe (1/0) a term deposit (variable y)", + "feature_intro": { + "cat":{ + "Education": "Education Qualification of customer", + "Marital_Status": "Marital Status of customer" + }, + "num":{ + "Year_Birth": "Customer's age", + "Income": "Yearly Income of customer", + "Kidhome": "Number of children in household", + "Teenhome": "Number of teenagers in household", + "Recency": "Number of days since the last purchase", + "MntWines": "Amount spent on wine in last 2 years", + "MntFruits": "Amount spent on fruits in last 2 years", + "MntMeatProducts": "Amount spent on meat in last 2 years", + "MntFishProducts": "Amount spent on fish in last 2 years", + "MntSweetProducts": "Amount spent on sweets in last 2 years", + "MntGoldProds": "Amount spent on gold in last 2 years", + "NumDealsPurchases": "Number of purchases made with discount", + "NumWebPurchases": "Number of purchases made through the company’s web site", + "NumCatalogPurchases": "Number of purchases made using a catalogue", + "NumStorePurchases": "Number of purchases made directly in stores", + "NumWebVisitsMonth": "Number of visits to company’s web site in the last month", + "AcceptedCmp3": "1 if customer accepted the offer in the 3rd campaign, 0 otherwise", + "AcceptedCmp4": "1 if customer accepted the offer in the 4th campaign, 0 otherwise", + "AcceptedCmp5": "1 if customer accepted the offer in the 5th campaign, 0 otherwise", + "AcceptedCmp1": "1 if customer accepted the offer in the 1st campaign, 0 otherwise", + "AcceptedCmp2": "1 if customer accepted the offer in the 2nd campaign, 0 otherwise", + "Complain": "1 if customer complained in the last 2 years, 0 otherwise" + } + }, + "target_intro": { + "0": "customer didn't accept the offer in the last campaign", + "1": "customer accepted the offer in the last campaign" + }, + "source":"https://www.kaggle.com/datasets/imakash3011/customer-personality-analysis" +} \ No newline at end of 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Teams will be challenged to create machine learning models with both high accuracy and explainability; they will use a real-world financial dataset provided by FICO. Designers and end users of machine learning algorithms will both benefit from more interpretable and explainable algorithms. Machine learning model designers will benefit from Model explanations, written explanations describing the functioning of a trained model. These might include information about which variables or examples are particularly important, they might explain the logic used by an algorithm, and/or characterize input/output relationships between variables and predictions. We expect teams to tell the story of their model such that these explanations will be qualitatively evaluated by data scientists at FICO.\n\nFurther information can be retrieved from the [FICO website](https://community.fico.com/s/explainable-machine-learning-challenge).\n\n**Notes**\n* We have obtained the dataset from [Kaggle](https://www.kaggle.com/datasets/averkiyoliabev/home-equity-line-of-creditheloc)\n* This is a cleaned version of the Kaggle dataset, in which we have removed all rows that only contained `-9`, a special value according to the FAQ.\n* Please request access to the data on the FICO website to obtain the full description of the features.\n* In this version we have encoded the special values (-9, -8, -7) as missing values to make the data more amenable to non-tree models.", + "task_type": "binclass", + "openml_id": 45553, + "imbalance_ratio": 1.0846884899683211, + "n_classes": 2, + "num_feature_intro": { + "ExternalRiskEstimate": "ExternalRiskEstimate", + "MSinceOldestTradeOpen": "MSinceOldestTradeOpen", + "MSinceMostRecentTradeOpen": "MSinceMostRecentTradeOpen", + "AverageMInFile": "AverageMInFile", + "NumSatisfactoryTrades": "NumSatisfactoryTrades", + "NumTrades60Ever2DerogPubRec": "NumTrades60Ever2DerogPubRec", + "NumTrades90Ever2DerogPubRec": "NumTrades90Ever2DerogPubRec", + "PercentTradesNeverDelq": "PercentTradesNeverDelq", + "MSinceMostRecentDelq": "MSinceMostRecentDelq", + "NumTotalTrades": "NumTotalTrades", + "NumTradesOpeninLast12M": "NumTradesOpeninLast12M", + "PercentInstallTrades": "PercentInstallTrades", + "MSinceMostRecentInqexcl7days": "MSinceMostRecentInqexcl7days", + "NumInqLast6M": "NumInqLast6M", + "NumInqLast6Mexcl7days": "NumInqLast6Mexcl7days", + "NetFractionRevolvingBurden": "NetFractionRevolvingBurden", + "NetFractionInstallBurden": "NetFractionInstallBurden", + "NumRevolvingTradesWBalance": "NumRevolvingTradesWBalance", + "NumInstallTradesWBalance": "NumInstallTradesWBalance", + "NumBank2NatlTradesWHighUtilization": "NumBank2NatlTradesWHighUtilization", + "PercentTradesWBalance": "PercentTradesWBalance" + }, + "cat_feature_intro": { + "MaxDelq2PublicRecLast12M": "MaxDelq2PublicRecLast12M", + "MaxDelqEver": "MaxDelqEver" + } +} \ No newline at end of file diff --git a/FICO-HELOC-cleaned/y_test.npy b/FICO-HELOC-cleaned/y_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..7f283071458cf154247dfbb00e67f89fb7d88a1e --- /dev/null +++ b/FICO-HELOC-cleaned/y_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2716fc831e86d4530452c95540eddb33364f0541b16a524181bbca581bad4c36 +size 4252 diff --git a/FICO-HELOC-cleaned/y_train.npy b/FICO-HELOC-cleaned/y_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..f9cdf8e69a048b4672374b259d230f7034dde087 --- /dev/null +++ b/FICO-HELOC-cleaned/y_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid 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price is going down (or stays the same) the following day.\n**Note that this is a hypothetical task, meant for scientific purposes only. Realistic trade strategies can only be applied to predictions on 'Close'-attributes (also available).\n# Attributes \n`Timestamp`: The time of the current data point (Europe/Amsterdam)\n`Bid_Open`: The bid price at the start of this time interval\n`Bid_High`: The highest bid price during this time interval\n`Bid_Low`: The lowest bid price during this time interval\n`Bid_Close`: The bid price at the end of this time interval\n`Bid_Volume`: The number of times the Bid Price changed within this time interval\n`Ask_Open`: The ask price at the start of this time interval\n`Ask_High`: The highest ask price during this time interval\n`Ask_Low`: The lowest ask price during this time interval\n`Ask_Close`: The ask price at the end of this time interval\n`Ask_Volume`: The number of times the Ask Price changed within this time interval\n`Class`: Whether the average price will go up during the next interval", + "task_type": "binclass", + "openml_id": 41721, + "imbalance_ratio": 1.0310077519379846, 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attribute is True when the mean High price is going up the following hour,\nand the class attribute is False when the mean High price is going down (or stays the same) the following hour.\n**Note that this is a hypothetical task, meant for scientific purposes only. Realistic trade strategies can only be applied to predictions on 'Close'-attributes (also available).\n# Attributes \n`Timestamp`: The time of the current data point (Europe/Amsterdam)\n`Bid_Open`: The bid price at the start of this time interval\n`Bid_High`: The highest bid price during this time interval\n`Bid_Low`: The lowest bid price during this time interval\n`Bid_Close`: The bid price at the end of this time interval\n`Bid_Volume`: The number of times the Bid Price changed within this time interval\n`Ask_Open`: The ask price at the start of this time interval\n`Ask_High`: The highest ask price during this time interval\n`Ask_Low`: The lowest ask price during this time interval\n`Ask_Close`: The ask price at the end of this time interval\n`Ask_Volume`: The number of times the Ask Price changed within this time interval\n`Class`: Whether the average price will go up during the next interval", + "task_type": "binclass", + "openml_id": 41763, + "imbalance_ratio": 1.0605106022850157, + "n_classes": 2, + "num_feature_intro": { + "Bid_Open": "Bid_Open", + "Bid_High": "Bid_High", + "Bid_Low": "Bid_Low", + "Bid_Close": "Bid_Close", + "Bid_Volume": "Bid_Volume", + "Ask_Open": "Ask_Open", + "Ask_High": "Ask_High", + "Ask_Low": "Ask_Low", + "Ask_Close": "Ask_Close", + "Ask_Volume": "Ask_Volume" + }, + "cat_feature_intro": {} +} \ No newline at end of file diff --git a/FOREX_audcad-hour-High/y_test.npy b/FOREX_audcad-hour-High/y_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..6330a43dc2ac67810c28d9df95607c44c018e31e --- /dev/null +++ b/FOREX_audcad-hour-High/y_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7d918aec8b5561fa9be280506ebb057b67f739e36707b00bc4ba31ca5a31c2c0 +size 8893 diff --git a/FOREX_audcad-hour-High/y_train.npy b/FOREX_audcad-hour-High/y_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..12ce361717e75ade30460184a53b205da8afd820 --- /dev/null +++ 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b/FOREX_audchf-day-High/info.json @@ -0,0 +1,27 @@ +{ + "name": "FOREX_audchf-day-High", + "n_num_features": 10, + "n_cat_features": 0, + "train_size": 1172, + "val_size": 294, + "test_size": 367, + "source": "https://www.openml.org/search?type=data&status=active&id=41875&sort=runs", + "task_intro": "**Source**: Dukascopy Historical Data Feed https://www.dukascopy.com/swiss/english/marketwatch/historical/\n**Edited by**: Fabian Schut\n \n# Data Description\nThis is the historical price data of the FOREX AUD/CHF from Dukascopy.\nOne instance (row) is one candlestick of one day.\nThe whole dataset has the data range from 1-1-2018 to 13-12-2018 and does not include the weekends, since the FOREX is not traded in the weekend.\nThe timezone of the feature Timestamp is Europe/Amsterdam.\nThe class attribute is the direction of the mean of the High_Bid and the High_Ask of the following day,\nrelative to the High_Bid and High_Ask mean of the current minute.\nThis means the class attribute is True when the mean High price is going up the following day,\nand the class attribute is False when the mean High price is going down (or stays the same) the following day.\n**Note that this is a hypothetical task, meant for scientific purposes only. Realistic trade strategies can only be applied to predictions on 'Close'-attributes (also available).\n# Attributes \n`Timestamp`: The time of the current data point (Europe/Amsterdam)\n`Bid_Open`: The bid price at the start of this time interval\n`Bid_High`: The highest bid price during this time interval\n`Bid_Low`: The lowest bid price during this time interval\n`Bid_Close`: The bid price at the end of this time interval\n`Bid_Volume`: The number of times the Bid Price changed within this time interval\n`Ask_Open`: The ask price at the start of this time interval\n`Ask_High`: The highest ask price during this time interval\n`Ask_Low`: The lowest ask price during this time interval\n`Ask_Close`: The ask price at the end of this time interval\n`Ask_Volume`: The number of times the Ask Price changed within this time interval\n`Class`: Whether the average price will go up during the next interval", + "task_type": "binclass", + "openml_id": 41875, + "imbalance_ratio": 1.0276548672566372, 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b/FOREX_audjpy-day-High/info.json @@ -0,0 +1,27 @@ +{ + "name": "FOREX_audjpy-day-High", + "n_num_features": 10, + "n_cat_features": 0, + "train_size": 1172, + "val_size": 293, + "test_size": 367, + "source": "https://www.openml.org/search?type=data&status=active&id=41882&sort=runs", + "task_intro": "**Source**: Dukascopy Historical Data Feed https://www.dukascopy.com/swiss/english/marketwatch/historical/\n**Edited by**: Fabian Schut\n \n# Data Description\nThis is the historical price data of the FOREX AUD/JPY from Dukascopy.\nOne instance (row) is one candlestick of one day.\nThe whole dataset has the data range from 1-1-2018 to 13-12-2018 and does not include the weekends, since the FOREX is not traded in the weekend.\nThe timezone of the feature Timestamp is Europe/Amsterdam.\nThe class attribute is the direction of the mean of the High_Bid and the High_Ask of the following day,\nrelative to the High_Bid and High_Ask mean of the current minute.\nThis means the class attribute is True when the mean High price is going up the following day,\nand the class attribute is False when the mean High price is going down (or stays the same) the following day.\n**Note that this is a hypothetical task, meant for scientific purposes only. Realistic trade strategies can only be applied to predictions on 'Close'-attributes (also available).\n# Attributes \n`Timestamp`: The time of the current data point (Europe/Amsterdam)\n`Bid_Open`: The bid price at the start of this time interval\n`Bid_High`: The highest bid price during this time interval\n`Bid_Low`: The lowest bid price during this time interval\n`Bid_Close`: The bid price at the end of this time interval\n`Bid_Volume`: The number of times the Bid Price changed within this time interval\n`Ask_Open`: The ask price at the start of this time interval\n`Ask_High`: The highest ask price during this time interval\n`Ask_Low`: The lowest ask price during this time interval\n`Ask_Close`: The ask price at the end of this time interval\n`Ask_Volume`: The number of times the Ask Price changed within this time interval\n`Class`: Whether the average price will go up during the next interval", + "task_type": "binclass", + "openml_id": 41882, + "imbalance_ratio": 1.0561167227833894, 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b/FOREX_audjpy-hour-High/info.json @@ -0,0 +1,27 @@ +{ + "name": "FOREX_audjpy-hour-High", + "n_num_features": 10, + "n_cat_features": 0, + "train_size": 28048, + "val_size": 7012, + "test_size": 8765, + "source": "https://www.openml.org/search?type=data&status=active&id=41718&sort=runs", + "task_intro": "**Source**: Dukascopy Historical Data Feed https://www.dukascopy.com/swiss/english/marketwatch/historical/\n**Edited by**: Fabian Schut\n \n# Data Description\nThis is the historical price data of the FOREX AUD/JPY from Dukascopy.\nOne instance (row) is one candlestick of one hour.\nThe whole dataset has the data range from 1-1-2018 to 13-12-2018 and does not include the weekends, since the FOREX is not traded in the weekend.\nThe timezone of the feature Timestamp is Europe/Amsterdam.\nThe class attribute is the direction of the mean of the High_Bid and the High_Ask of the following hour,\nrelative to the High_Bid and High_Ask mean of the current minute.\nThis means the class attribute is True when the mean High price is going up the following hour,\nand the class attribute is False when the mean High price is going down (or stays the same) the following hour.\n**Note that this is a hypothetical task, meant for scientific purposes only. Realistic trade strategies can only be applied to predictions on 'Close'-attributes (also available).\n# Attributes \n`Timestamp`: The time of the current data point (Europe/Amsterdam)\n`Bid_Open`: The bid price at the start of this time interval\n`Bid_High`: The highest bid price during this time interval\n`Bid_Low`: The lowest bid price during this time interval\n`Bid_Close`: The bid price at the end of this time interval\n`Bid_Volume`: The number of times the Bid Price changed within this time interval\n`Ask_Open`: The ask price at the start of this time interval\n`Ask_High`: The highest ask price during this time interval\n`Ask_Low`: The lowest ask price during this time interval\n`Ask_Close`: The ask price at the end of this time interval\n`Ask_Volume`: The number of times the Ask Price changed within this time interval\n`Class`: Whether the average price will go up during the next interval", + "task_type": "binclass", + "openml_id": 41718, + "imbalance_ratio": 1.0619648066246354, + "n_classes": 2, + "num_feature_intro": { + "Bid_Open": "Bid_Open", + "Bid_High": "Bid_High", + "Bid_Low": "Bid_Low", + "Bid_Close": "Bid_Close", + "Bid_Volume": "Bid_Volume", + "Ask_Open": "Ask_Open", + "Ask_High": "Ask_High", + "Ask_Low": "Ask_Low", + "Ask_Close": "Ask_Close", + "Ask_Volume": "Ask_Volume" + }, + "cat_feature_intro": {} +} \ No newline at end of file diff --git a/FOREX_audjpy-hour-High/y_test.npy b/FOREX_audjpy-hour-High/y_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..1a45212239f10e343753c10d57511091b9b06c4d --- /dev/null +++ b/FOREX_audjpy-hour-High/y_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:58b9e450d9f8e55f30804a6ac2a2e9ce0649bf5bd3a96f9312f506ac4db2fe14 +size 8893 diff --git a/FOREX_audjpy-hour-High/y_train.npy b/FOREX_audjpy-hour-High/y_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..ae0091ef1bd1f0ae1cacefbed9c51ff620d8380b --- /dev/null +++ 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b/FOREX_audsgd-hour-High/info.json @@ -0,0 +1,27 @@ +{ + "name": "FOREX_audsgd-hour-High", + "n_num_features": 10, + "n_cat_features": 0, + "train_size": 28048, + "val_size": 7012, + "test_size": 8765, + "source": "https://www.openml.org/search?type=data&status=active&id=41865&sort=runs", + "task_intro": "**Source**: Dukascopy Historical Data Feed https://www.dukascopy.com/swiss/english/marketwatch/historical/\n**Edited by**: Fabian Schut\n \n# Data Description\nThis is the historical price data of the FOREX AUD/SGD from Dukascopy.\nOne instance (row) is one candlestick of one hour.\nThe whole dataset has the data range from 1-1-2018 to 13-12-2018 and does not include the weekends, since the FOREX is not traded in the weekend.\nThe timezone of the feature Timestamp is Europe/Amsterdam.\nThe class attribute is the direction of the mean of the High_Bid and the High_Ask of the following hour,\nrelative to the High_Bid and High_Ask mean of the current minute.\nThis means the class attribute is True when the mean High price is going up the following hour,\nand the class attribute is False when the mean High price is going down (or stays the same) the following hour.\n**Note that this is a hypothetical task, meant for scientific purposes only. Realistic trade strategies can only be applied to predictions on 'Close'-attributes (also available).\n# Attributes \n`Timestamp`: The time of the current data point (Europe/Amsterdam)\n`Bid_Open`: The bid price at the start of this time interval\n`Bid_High`: The highest bid price during this time interval\n`Bid_Low`: The lowest bid price during this time interval\n`Bid_Close`: The bid price at the end of this time interval\n`Bid_Volume`: The number of times the Bid Price changed within this time interval\n`Ask_Open`: The ask price at the start of this time interval\n`Ask_High`: The highest ask price during this time interval\n`Ask_Low`: The lowest ask price during this time interval\n`Ask_Close`: The ask price at the end of this time interval\n`Ask_Volume`: The number of times the Ask Price changed within this time interval\n`Class`: Whether the average price will go up during the next interval", + "task_type": "binclass", + "openml_id": 41865, + "imbalance_ratio": 1.0607043776743312, 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b/FOREX_audusd-hour-High/info.json @@ -0,0 +1,27 @@ +{ + "name": "FOREX_audusd-hour-High", + "n_num_features": 10, + "n_cat_features": 0, + "train_size": 28048, + "val_size": 7012, + "test_size": 8765, + "source": "https://www.openml.org/search?type=data&status=active&id=41843&sort=runs", + "task_intro": "**Source**: Dukascopy Historical Data Feed https://www.dukascopy.com/swiss/english/marketwatch/historical/\n**Edited by**: Fabian Schut\n \n# Data Description\nThis is the historical price data of the FOREX AUD/USD from Dukascopy.\nOne instance (row) is one candlestick of one hour.\nThe whole dataset has the data range from 1-1-2018 to 13-12-2018 and does not include the weekends, since the FOREX is not traded in the weekend.\nThe timezone of the feature Timestamp is Europe/Amsterdam.\nThe class attribute is the direction of the mean of the High_Bid and the High_Ask of the following hour,\nrelative to the High_Bid and High_Ask mean of the current minute.\nThis means the class attribute is True when the mean High price is going up the following hour,\nand the class attribute is False when the mean High price is going down (or stays the same) the following hour.\n**Note that this is a hypothetical task, meant for scientific purposes only. Realistic trade strategies can only be applied to predictions on 'Close'-attributes (also available).\n# Attributes \n`Timestamp`: The time of the current data point (Europe/Amsterdam)\n`Bid_Open`: The bid price at the start of this time interval\n`Bid_High`: The highest bid price during this time interval\n`Bid_Low`: The lowest bid price during this time interval\n`Bid_Close`: The bid price at the end of this time interval\n`Bid_Volume`: The number of times the Bid Price changed within this time interval\n`Ask_Open`: The ask price at the start of this time interval\n`Ask_High`: The highest ask price during this time interval\n`Ask_Low`: The lowest ask price during this time interval\n`Ask_Close`: The ask price at the end of this time interval\n`Ask_Volume`: The number of times the Ask Price changed within this time interval\n`Class`: Whether the average price will go up during the next interval", + "task_type": "binclass", + "openml_id": 41843, + "imbalance_ratio": 1.1258792141644434, 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b/FOREX_cadjpy-day-High/info.json @@ -0,0 +1,27 @@ +{ + "name": "FOREX_cadjpy-day-High", + "n_num_features": 10, + "n_cat_features": 0, + "train_size": 1173, + "val_size": 294, + "test_size": 367, + "source": "https://www.openml.org/search?type=data&status=active&id=41839&sort=runs", + "task_intro": "**Source**: Dukascopy Historical Data Feed https://www.dukascopy.com/swiss/english/marketwatch/historical/\n**Edited by**: Fabian Schut\n \n# Data Description\nThis is the historical price data of the FOREX CAD/JPY from Dukascopy.\nOne instance (row) is one candlestick of one day.\nThe whole dataset has the data range from 1-1-2018 to 13-12-2018 and does not include the weekends, since the FOREX is not traded in the weekend.\nThe timezone of the feature Timestamp is Europe/Amsterdam.\nThe class attribute is the direction of the mean of the High_Bid and the High_Ask of the following day,\nrelative to the High_Bid and High_Ask mean of the current minute.\nThis means the class attribute is True when the mean High price is going up the following day,\nand the class attribute is False when the mean High price is going down (or stays the same) the following day.\n**Note that this is a hypothetical task, meant for scientific purposes only. Realistic trade strategies can only be applied to predictions on 'Close'-attributes (also available).\n# Attributes \n`Timestamp`: The time of the current data point (Europe/Amsterdam)\n`Bid_Open`: The bid price at the start of this time interval\n`Bid_High`: The highest bid price during this time interval\n`Bid_Low`: The lowest bid price during this time interval\n`Bid_Close`: The bid price at the end of this time interval\n`Bid_Volume`: The number of times the Bid Price changed within this time interval\n`Ask_Open`: The ask price at the start of this time interval\n`Ask_High`: The highest ask price during this time interval\n`Ask_Low`: The lowest ask price during this time interval\n`Ask_Close`: The ask price at the end of this time interval\n`Ask_Volume`: The number of times the Ask Price changed within this time interval\n`Class`: Whether the average price will go up during the next interval", + "task_type": "binclass", + "openml_id": 41839, + "imbalance_ratio": 1.0065645514223194, 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b/FOREX_cadjpy-hour-High/info.json @@ -0,0 +1,27 @@ +{ + "name": "FOREX_cadjpy-hour-High", + "n_num_features": 10, + "n_cat_features": 0, + "train_size": 28048, + "val_size": 7012, + "test_size": 8765, + "source": "https://www.openml.org/search?type=data&status=active&id=41833&sort=runs", + "task_intro": "**Source**: Dukascopy Historical Data Feed https://www.dukascopy.com/swiss/english/marketwatch/historical/\n**Edited by**: Fabian Schut\n \n# Data Description\nThis is the historical price data of the FOREX CAD/JPY from Dukascopy.\nOne instance (row) is one candlestick of one hour.\nThe whole dataset has the data range from 1-1-2018 to 13-12-2018 and does not include the weekends, since the FOREX is not traded in the weekend.\nThe timezone of the feature Timestamp is Europe/Amsterdam.\nThe class attribute is the direction of the mean of the High_Bid and the High_Ask of the following hour,\nrelative to the High_Bid and High_Ask mean of the current minute.\nThis means the class attribute is True when the mean High price is going up the following hour,\nand the class attribute is False when the mean High price is going down (or stays the same) the following hour.\n**Note that this is a hypothetical task, meant for scientific purposes only. Realistic trade strategies can only be applied to predictions on 'Close'-attributes (also available).\n# Attributes \n`Timestamp`: The time of the current data point (Europe/Amsterdam)\n`Bid_Open`: The bid price at the start of this time interval\n`Bid_High`: The highest bid price during this time interval\n`Bid_Low`: The lowest bid price during this time interval\n`Bid_Close`: The bid price at the end of this time interval\n`Bid_Volume`: The number of times the Bid Price changed within this time interval\n`Ask_Open`: The ask price at the start of this time interval\n`Ask_High`: The highest ask price during this time interval\n`Ask_Low`: The lowest ask price during this time interval\n`Ask_Close`: The ask price at the end of this time interval\n`Ask_Volume`: The number of times the Ask Price changed within this time interval\n`Class`: Whether the average price will go up during the next interval", + "task_type": "binclass", + "openml_id": 41833, + "imbalance_ratio": 1.0738690138179066, 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"val_size": 1580, + "test_size": 1975, + "source": "https://www.openml.org/search?type=data&status=active&id=4538&sort=runs", + "task_intro": "**Author**: Renata Cristina Barros Madeo (Madeo\",\"R. C. B.) Priscilla Koch Wagner (Wagner\",\"P. K.) Sarajane Marques Peres (Peres\",\"S. M.) {renata.si\",\"priscilla.wagner\",\"sarajane} at usp.br http://each.uspnet.usp.br/sarajane/ \n**Source**: [UCI](https://archive.ics.uci.edu/ml/datasets/gesture+phase+segmentation) \n**Please cite**: Please refer to the [Machine Learning Repository's citation policy](https://archive.ics.uci.edu/ml/citation_policy.html). Additionally, the authors require a citation to one or more publications from those cited as relevant papers. \n\nCreators: \nRenata Cristina Barros Madeo (Madeo, R. C. B.) \nPriscilla Koch Wagner (Wagner, P. K.) \nSarajane Marques Peres (Peres, S. M.) \n{renata.si, priscilla.wagner, sarajane} at usp.br \nhttp://each.uspnet.usp.br/sarajane/ \n\nDonor: \nUniversity of Sao Paulo - Brazil\n\nData Set Information:\n\nThe dataset is composed by features extracted from 7 videos with people gesticulating, aiming at studying Gesture Phase Segmentation. \nEach video is represented by two files: a raw file, which contains the position of hands, wrists, head and spine of the user in each frame; and a processed file, which contains velocity and acceleration of hands and wrists. See the data set description for more information on the dataset.\n\nAttribute Information:\n\nRaw files: 18 numeric attributes (double), a timestamp and a class attribute (nominal). \nProcessed files: 32 numeric attributes (double) and a class attribute (nominal). \nA feature vector with up to 50 numeric attributes can be generated with the two files mentioned above.\n\nThis is the processed data set with the following feature description:\n\n Processed files:\n\n 1. Vectorial velocity of left hand (x coordinate)\n 2. Vectorial velocity of left hand (y coordinate)\n 3. Vectorial velocity of left hand (z coordinate)\n 4. Vectorial velocity of right hand (x coordinate)\n 5. Vectorial velocity of right hand (y coordinate)\n 6. Vectorial velocity of right hand (z coordinate)\n 7. Vectorial velocity of left wrist (x coordinate)\n 8. Vectorial velocity of left wrist (y coordinate)\n 9. Vectorial velocity of left wrist (z coordinate)\n 10. Vectorial velocity of right wrist (x coordinate)\n 11. Vectorial velocity of right wrist (y coordinate)\n 12. Vectorial velocity of right wrist (z coordinate)\n 13. Vectorial acceleration of left hand (x coordinate)\n 14. Vectorial acceleration of left hand (y coordinate)\n 15. Vectorial acceleration of left hand (z coordinate)\n 16. Vectorial acceleration of right hand (x coordinate)\n 17. Vectorial acceleration of right hand (y coordinate)\n 18. Vectorial acceleration of right hand (z coordinate)\n 19. Vectorial acceleration of left wrist (x coordinate)\n 20. Vectorial acceleration of left wrist (y coordinate)\n 21. Vectorial acceleration of left wrist (z coordinate)\n 22. Vectorial acceleration of right wrist (x coordinate)\n 23. Vectorial acceleration of right wrist (y coordinate)\n 24. Vectorial acceleration of right wrist (z coordinate)\n 25. Scalar velocity of left hand\n 26. Scalar velocity of right hand\n 27. Scalar velocity of left wrist\n 28. Scalar velocity of right wrist\n 29. Scalar velocity of left hand\n 30. Scalar velocity of right hand\n 31. Scalar velocity of left wrist\n 32. Scalar velocity of right wrist\n 33. phase:\n - D (rest position, from portuguese \"descanso\")\n - P (preparation)\n - S (stroke)\n - H (hold)\n - R (retraction)\n\nRelevant Papers:\n\n1. Madeo, R. C. B. ; Lima, C. A. M. ; PERES, S. M. . Gesture Unit Segmentation using Support Vector Machines: Segmenting \nGestures from Rest Positions. In: Symposium on Applied Computing (SAC), 2013, Coimbra. Proceedings of the 28th Annual \nACM Symposium on Applied Computing (SAC), 2013. p. 46-52. \n* In this paper, the videos A1 and A2 were studied. \n\n2. Wagner, P. K. ; PERES, S. M. ; Madeo, R. C. B. ; Lima, C. A. M. ; Freitas, F. A. . Gesture Unit Segmentation Using \nSpatial-Temporal Information and Machine Learning. In: 27th Florida Artificial Intelligence Research Society Conference \n(FLAIRS), 2014, Pensacola Beach. Proceedings of the 27th Florida Artificial Intelligence Research Society Conference \n(FLAIRS). Palo Alto : The AAAI Press, 2014. p. 101-106. \n* In this paper, the videos A1, A2, A3, B1, B3, C1 and C3 were studied. \n\n3. Madeo, R. C. B.. Support Vector Machines and Gesture Analysis: incorporating temporal aspects (in Portuguese). Master \nThesis - Universidade de Sao Paulo, Sao Paulo Researcher Foundation. 2013. \n* In this document, the videos named B1 and B3 in the document correspond to videos C1 and C3 in this dataset. Only \nfive videos were explored in this document: A1, A2, A3, C1 and C3. \n\n4. Wagner, P. K. ; Madeo, R. C. B. ; PERES, S. M. ; Lima, C. A. M. . Segmentaçao de Unidades Gestuais com Multilayer \nPerceptrons (in Portuguese). In: Encontro Nacional de Inteligencia Artificial e Computacional (ENIAC), 2013, Fortaleza. \nAnais do X Encontro Nacional de Inteligencia Artificial e Computacional (ENIAC), 2013. \n* In this paper, the videos A1, A2 and A3 were studied.\n\n\n\nCitation Request:\n\nPlease refer to the Machine Learning Repository's citation policy. \nAdditionally, the authors require a citation to one or more publications from those cited as relevant papers.", + "task_type": "multiclass", + "openml_id": 4538, + "imbalance_ratio": 2.9559118236472948, + "n_classes": 5, + "num_feature_intro": { + "X1": "X1", + "X2": "X2", + "X3": "X3", + "X4": "X4", + "X5": "X5", + "X6": "X6", + "X7": "X7", + "X8": "X8", + "X9": "X9", + "X10": "X10", + "X11": "X11", + "X12": "X12", + "X13": "X13", + "X14": "X14", + "X15": "X15", + "X16": "X16", + "X17": "X17", + "X18": "X18", + "X19": "X19", + "X20": "X20", + "X21": "X21", + "X22": "X22", + "X23": "X23", + "X24": "X24", + "X25": "X25", + "X26": "X26", + "X27": "X27", + "X28": "X28", + "X29": "X29", + "X30": "X30", + "X31": "X31", + "X32": "X32" + }, + "cat_feature_intro": {} +} \ No newline at end of file diff --git a/GesturePhaseSegmentationProcessed/y_test.npy b/GesturePhaseSegmentationProcessed/y_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..3d8c18502db0d77d94390f2e54658db5742f29b4 --- /dev/null +++ b/GesturePhaseSegmentationProcessed/y_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2e68086d260c63cad82824bcb047d3583e7de5dc45155360e1bc66ae3603f04b +size 4265 diff --git a/GesturePhaseSegmentationProcessed/y_train.npy b/GesturePhaseSegmentationProcessed/y_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..05a9f9522d8c997c07ae1ea7734c6545dcaed658 --- /dev/null +++ b/GesturePhaseSegmentationProcessed/y_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:66ac23e95a2824bb2fd71eaa29a12f7fa01048a8cfca4cacaa52ce34cb257dde +size 12961 diff --git a/GesturePhaseSegmentationProcessed/y_val.npy b/GesturePhaseSegmentationProcessed/y_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..a9a5db0403f051dc5ccde5743f6b8198dbc9bd09 --- /dev/null +++ b/GesturePhaseSegmentationProcessed/y_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7fcf9682d0c58f973f65fa607b1a580384a7e391f0327f6205b45a1437b38bb2 +size 3475 diff --git a/Goodreads-Computer-Books/N_test.npy b/Goodreads-Computer-Books/N_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..7db027d4e4e48c9140360168f4d180e1ecc9d3be --- /dev/null +++ b/Goodreads-Computer-Books/N_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:eba609051a670efc428a23ba0ce28130999692bfda8a98d83c37158eba841c99 +size 10008 diff --git a/Goodreads-Computer-Books/N_train.npy b/Goodreads-Computer-Books/N_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..3b250ca262aeaf846a537428d851361428f2d306 --- /dev/null +++ b/Goodreads-Computer-Books/N_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d73d7af1a987b917b58c6144d3df4e0c4859ce5304ea831959cfd50179115401 +size 31688 diff --git a/Goodreads-Computer-Books/N_val.npy b/Goodreads-Computer-Books/N_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..78d7a9adbe59438cff647a0b5f772a710a2c1bdb --- /dev/null +++ b/Goodreads-Computer-Books/N_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2e73d8bed0c67f3103d4f9e8e90c87bd4c5cb7ad39ae3195d8fc9de257263ef6 +size 8048 diff --git a/Goodreads-Computer-Books/info.json b/Goodreads-Computer-Books/info.json new file mode 100644 index 0000000000000000000000000000000000000000..29d8aed06b423761e4bbd3bcf71b82558ca657b9 --- /dev/null +++ b/Goodreads-Computer-Books/info.json @@ -0,0 +1,21 @@ +{ + "name": "Goodreads-Computer-Books", + "n_num_features": 5, + "n_cat_features": 0, + "train_size": 789, + "val_size": 198, + "test_size": 247, + "source": "https://www.openml.org/search?type=data&status=active&id=43785&sort=runs", + "task_intro": "Context\nThe reason for creating this dataset is the requirement of a good clean dataset of computer books. I had searched for datasets on books in Kaggle and I found out that while most of the datasets had a good amount of books listed, there were either major columns missing or grossly unclean data. I mean, you can't determine how good a book is just from a few text reviews. So I collected this data from the Goodreads website from the \"Computer\" category to help people who are like this type of book.\nAcknowledgements\nThis data was entirely scraped via the Webdriver\nInspiration\nThe reason behind creating this dataset is pretty straightforward, I'm listing the books for all who need computer books, irrespective of the language and publication and all of that. So go ahead and use it to your liking, find out what book you should be reading next, all possible approaches to exploring this dataset are welcome.\nI started creating this dataset on Jan 18, 2021, and intend to update it frequently.\nP.S. 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CVDs are a group of disorders of the heart and blood vessels and include coronary heart disease, cerebrovascular disease, rheumatic heart disease, and other conditions. Four out of 5CVD deaths are due to heart attacks and strokes, and one-third of these deaths occur prematurely in people under 70 years of age.\nWe have curated this dataset by combining different datasets already available independently but not combined before. W have combined them over 11 common features which makes it the largest heart disease dataset available for research purposes. The five datasets used for its curation are:\nDatabase: of instances:\n\nCleveland: 303\nHungarian: 294\nSwitzerland: 123\nLong Beach VA: 200\nStalog (Heart) Data Set: 270\n\nTotal 1190\nAcknowledgements\nThe dataset is taken from three other research datasets used in different research papers. 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This is a surprisingly common problem in machine learning (specifically in classification), occurring in datasets with a disproportionate ratio of observations in each class.\nContent\nStandard accuracy no longer reliably measures performance, which makes model training much trickier.\nImbalanced classes appear in many domains, including:\n\nAntifraud\nAntispam\n\n\nInspiration\n5 tactics for handling imbalanced classes in machine learning:\n\nUp-sample the minority class\nDown-sample the majority class\nChange your performance metric\nPenalize algorithms (cost-sensitive training)\nUse tree-based algorithms", + "task_type": "binclass", + "openml_id": 43442, + "n_classes": 2, + "num_feature_intro": { + "month": "month", + "credit_amount": "credit_amount", + "credit_term": "credit_term", + "age": "age", + "having_children_flg": "having_children_flg", + "region": "region", + "income": "income", + "phone_operator": "phone_operator", + "is_client": "is_client" + }, + "cat_feature_intro": { + "sex": "sex", + "education": "education", + "product_type": "product_type", + "family_status": "family_status" + } +} \ No newline at end of file diff --git a/Is-this-a-good-customer/y_test.npy b/Is-this-a-good-customer/y_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..625c735cfb8338c3f15d3e6cb6d90b56e0b3ed8c --- /dev/null +++ b/Is-this-a-good-customer/y_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:eb218c60ded221e1ea1fcc327fb79df7f041e48a006d4dacfe73b56065bcc0b8 +size 473 diff --git a/Is-this-a-good-customer/y_train.npy b/Is-this-a-good-customer/y_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..44681a648489dbcbba2836c7590b6501598ef238 --- /dev/null +++ b/Is-this-a-good-customer/y_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8aef9700bc3feba08667ea069b86951293fea2e8d70ed926bc14b1e92528f3f8 +size 1230 diff --git a/Is-this-a-good-customer/y_val.npy b/Is-this-a-good-customer/y_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..76e5f781a928afa1006b77e1f2449c1fb7669be1 --- /dev/null +++ b/Is-this-a-good-customer/y_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b24fd14b624073e9383dd283ef171c5f3f761d6e68a331a0c54fb8dd065dafe4 +size 404 diff --git a/JapaneseVowels/N_test.npy b/JapaneseVowels/N_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..7599850c9db6d88595f997929eff9ba30ab836fa --- /dev/null +++ b/JapaneseVowels/N_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:49d3c66d463541027cf553215540bccc1a5d42ea93544a5de19d8dd188318b91 +size 223344 diff --git a/JapaneseVowels/N_train.npy b/JapaneseVowels/N_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..9c2daf74e263db2d3b2976535f00f876c9a77efe --- /dev/null +++ b/JapaneseVowels/N_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b7254c3fcabeb1378ee3cbd912777e504830b9551386dea660933292a7556a2d +size 714016 diff --git a/JapaneseVowels/N_val.npy b/JapaneseVowels/N_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..c7f3b87118fac9a4b24a51ccd4c68df30d271b5f --- /dev/null +++ b/JapaneseVowels/N_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b916c3fe6d3662d2c37e66dc6068594f42de260b4a294341f152a6fe6dbad26d +size 178656 diff --git a/JapaneseVowels/info.json b/JapaneseVowels/info.json new file mode 100644 index 0000000000000000000000000000000000000000..54753efc128d70680f1c87977030b76b39840f7c --- /dev/null +++ b/JapaneseVowels/info.json @@ -0,0 +1,31 @@ +{ + "name": "JapaneseVowels", + "n_num_features": 14, + "n_cat_features": 0, + "train_size": 6374, + "val_size": 1594, + "test_size": 1993, + "source": "https://www.openml.org/search?type=data&status=active&id=375&sort=runs", + "task_intro": "**Author**: Mineichi Kudo, Jun Toyama, Masaru Shimbo \n**Source**: [UCI](https://archive.ics.uci.edu/ml/datasets/Japanese+Vowels) \n**Please cite**: \n\n**Japanese vowels** \nThis dataset records 640 time series of 12 LPC cepstrum coefficients taken from nine male speakers.\n\nThe data was collected for examining our newly developed classifier for multidimensional curves (multidimensional time series). Nine male speakers uttered two Japanese vowels /ae/ successively. For each utterance, with the analysis parameters described below, we applied 12-degree linear prediction analysis to it to obtain a discrete-time series with 12 LPC cepstrum coefficients. This means that one utterance by a speaker forms a time series whose length is in the range 7-29 and each point of a time series is of 12 features (12 coefficients).\n\nSimilar data are available for different utterances /ei/, /iu/, /uo/, /oa/ in addition to /ae/. Please contact the donor if you are interested in using this data.\n\nThe number of the time series is 640 in total. We used one set of 270 time series for training and the other set of 370 time series for testing.\n\nAnalysis parameters: \n* Sampling rate : 10kHz\n* Frame length : 25.6 ms\n* Shift length : 6.4ms\n* Degree of LPC coefficients : 12\n\nEach line represents 12 LPC coefficients in the increasing order separated by spaces. This corresponds to one analysis\nframe. Lines are organized into blocks, which are a set of 7-29 lines separated by blank lines and corresponds to a single speech utterance of /ae/ with 7-29 frames.\n\nEach speaker is a set of consecutive blocks. In ae.train there are 30 blocks for each speaker. Blocks 1-30 represent speaker 1, blocks 31-60 represent speaker 2, and so on up to speaker 9. In ae.test, speakers 1 to 9 have the corresponding number of blocks: 31 35 88 44 29 24 40 50 29. Thus, blocks 1-31 represent speaker 1 (31 utterances of /ae/), blocks 32-66 represent speaker 2 (35 utterances of /ae/), and so on.\n\n**Past Usage**\n\nM. Kudo, J. Toyama and M. Shimbo. (1999). \"Multidimensional Curve Classification Using Passing-Through Regions\". Pattern Recognition Letters, Vol. 20, No. 11--13, pages 1103--1111.\n\nIf you publish any work using the dataset, please inform the donor. Use for commercial purposes requires donor permission.\n\nReferences \n\n1. http://ips9.main.eng.hokudai.ac.jp/index_e.html\n2. mailto:mine@main.eng.hokudai.ac.jp\n3. mailto:jun@main.eng.hokudai.ac.jp\n4. mailto:shimbo@main.eng.hokudai.ac.jp\n5. http://kdd.ics.uci.edu/\n6. http://www.ics.uci.edu/\n7. http://www.uci.edu/", + "task_type": "multiclass", + "openml_id": 375, + "imbalance_ratio": 2.0639386189258313, + "n_classes": 9, + "num_feature_intro": { + "utterance": "utterance", + "frame": "frame", + "coefficient1": "coefficient1", + "coefficient2": "coefficient2", + "coefficient3": "coefficient3", + "coefficient4": "coefficient4", + "coefficient5": "coefficient5", + "coefficient6": "coefficient6", + "coefficient7": "coefficient7", + "coefficient8": "coefficient8", + "coefficient9": "coefficient9", + "coefficient10": "coefficient10", + "coefficient11": "coefficient11", + "coefficient12": "coefficient12" + }, + "cat_feature_intro": {} +} \ No newline at end of file diff --git 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important to job profitability", + "task_type": "regression", + "openml_id": 44311, + "n_classes": 1, + "num_feature_intro": { + "Jobs_Gross_Margin": "Jobs_Gross_Margin", + "Labor_Pay": "Labor_Pay", + "Labor_Burden": "Labor_Burden", + "Material_Costs": "Material_Costs", + "PO_Costs": "PO_Costs", + "Labor": "Labor", + "Equipment_Costs": "Equipment_Costs", + "Jobs_Total": "Jobs_Total", + "Materials__Equip__POs_As_percent_of_Sales": "Materials__Equip__POs_As_percent_of_Sales", + "Labor_Burden_as_percent_of_Sales": "Labor_Burden_as_percent_of_Sales", + "Labor_Pay_as_percent_of_Sales": "Labor_Pay_as_percent_of_Sales", + "Sold_Hours": "Sold_Hours", + "Total_Hours_Worked": "Total_Hours_Worked", + "NonBillable_Hours": "NonBillable_Hours", + "Jobs_Total_Costs": "Jobs_Total_Costs", + "Is_Lead": "Is_Lead", + "Opportunity": "Opportunity", + "Warranty": "Warranty", + "Recall": "Recall", + "Converted": "Converted", + "Jobs_Subtotal": "Jobs_Subtotal", + "Pricebook_Price": "Pricebook_Price", + 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This dataset belongs to the \"classification on categorical and\n numerical features\" benchmark. Original description: \n \n**Author**: \n**Source**: Unknown - Date unknown \n**Please cite**: \n\nDatasets from ACM KDD Cup (http://www.sigkdd.org/kddcup/index.php)\n\nKDD Cup 2009\nhttp://www.kddcup-orange.com\n\nConverted to ARFF format by TunedIT\nCustomer Relationship Management (CRM) is a key element of modern marketing strategies. The KDD Cup 2009 offers the opportunity to work on large marketing databases from the French Telecom company Orange to predict the propensity of customers to switch provider (churn), buy new products or services (appetency), or buy upgrades or add-ons proposed to them to make the sale more profitable (up-selling).\nThe most practical way, in a CRM system, to build knowledge on customer is to produce scores. A score (the output of a model) is an evaluation for all instances of a target variable to explain (i.e. churn, appetency or up-selling). Tools which produce scores allow to project, on a given population, quantifiable information. The score is computed using input variables which describe instances. Scores are then used by the information system (IS), for example, to personalize the customer relationship. An industrial customer analysis platform able to build prediction models with a very large number of input variables has been developed by Orange Labs. This platform implements several processing methods for instances and variables selection, prediction and indexation based on an efficient model combined with variable selection regularization and model averaging method. The main characteristic of this platform is its ability to scale on very large datasets with hundreds of thousands of instances and thousands of variables. The rapid and robust detection of the variables that have most contributed to the output prediction can be a key factor in a marketing application.\nUp-selling (wikipedia definition): Up-selling is a sales technique whereby a salesman attempts to have the customer purchase more expensive\nitems, upgrades, or other add-ons in an attempt to make a more profitable sale.\nUp-selling usually involves marketing more profitable services or products, but up-selling can also be simply exposing the customer\nto other options he or she may not have considered previously.\nUp-selling can imply selling something additional, or selling something that is more profitable or otherwise preferable for the seller instead of the original sale.\nThe training set contains 50,000 examples.\nThe first predictive 190 variables are numerical and the last 40 predictive variables are categorical.\nThe last target variable is binary {-1,1}.", + "task_type": "binclass", + "openml_id": 44186, + "imbalance_ratio": 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3044, + "test_size": 3804, + "source": "https://www.openml.org/search?type=data&status=active&id=1120&sort=runs", + "task_intro": "**Author**: R. K. Bock. Major Atmospheric Gamma Imaging Cherenkov Telescope project (MAGIC) \nDonated by P. Savicky, Institute of Computer Science, AS of CR, Czech Republic \n**Source**: [UCI](https://archive.ics.uci.edu/ml/datasets/magic+gamma+telescope) \n**Please cite**: Bock, R.K., Chilingarian, A., Gaug, M., Hakl, F., Hengstebeck, T., Jirina, M., Klaschka, J., Kotrc, E., Savicky, P., Towers, S., Vaicilius, A., Wittek W. (2004). \nMethods for multidimensional event classification: a case study using images from a Cherenkov gamma-ray telescope. Nucl.Instr.Meth. A, 516, pp. 511-528. \n\nThe data are MC generated (see below) to simulate registration of high energy gamma particles in a ground-based atmospheric Cherenkov gamma telescope using the imaging technique. Cherenkov gamma telescope observes high energy gamma rays, taking advantage of the radiation emitted by charged particles produced inside the electromagnetic showers initiated by the gammas, and developing in the atmosphere. This Cherenkov radiation (of visible to UV wavelengths) leaks through the atmosphere and gets recorded in the detector, allowing reconstruction of the shower parameters. The available information consists of pulses left by the incoming Cherenkov photons on the photomultiplier tubes, arranged in a plane, the camera. Depending on the energy of the primary gamma, a total of few hundreds to some 10000 Cherenkov photons get collected, in patterns (called the shower image), allowing to discriminate statistically those caused by primary gammas (signal) from the images of hadronic showers initiated by cosmic rays in the upper atmosphere (background). \n\nTypically, the image of a shower after some pre-processing is an elongated cluster. Its long axis is oriented towards the camera center if the shower axis is parallel to the telescope's optical axis, i.e. if the telescope axis is directed towards a point source. A principal component analysis is performed in the camera plane, which results in a correlation axis and defines an ellipse. If the depositions were distributed as a bivariate Gaussian, this would be an equidensity ellipse. The characteristic parameters of this ellipse (often called Hillas parameters) are among the image parameters that can be used for discrimination. The energy depositions are typically asymmetric along the major axis, and this asymmetry can also be used in discrimination. There are, in addition, further discriminating characteristics, like the extent of the cluster in the image plane, or the total sum of depositions. \n\nThe data set was generated by a Monte Carlo program, Corsika, described in: \nD. Heck et al., CORSIKA, A Monte Carlo code to simulate extensive air showers, Forschungszentrum Karlsruhe FZKA 6019 (1998).\nThe program was run with parameters allowing to observe events with energies down to below 50 GeV.\n\nAttribute Information:\n\n1. fLength: continuous # major axis of ellipse [mm] \n2. fWidth: continuous # minor axis of ellipse [mm] \n3. fSize: continuous # 10-log of sum of content of all pixels [in #phot] \n4. fConc: continuous # ratio of sum of two highest pixels over fSize [ratio] \n5. fConc1: continuous # ratio of highest pixel over fSize [ratio] \n6. fAsym: continuous # distance from highest pixel to center, projected onto major axis [mm] \n7. fM3Long: continuous # 3rd root of third moment along major axis [mm] \n8. fM3Trans: continuous # 3rd root of third moment along minor axis [mm] \n9. fAlpha: continuous # angle of major axis with vector to origin [deg] \n10. fDist: continuous # distance from origin to center of ellipse [mm] \n11. class: g,h # gamma (signal), hadron (background) \n\ng = gamma (signal): 12332 \nh = hadron (background): 6688 \n\nFor technical reasons, the number of h events is underestimated. 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The relevant points on this curve are those, where the probability of accepting a background event as signal is below one of the following thresholds: 0.01, 0.02, 0.05, 0.1, 0.2 depending on the required quality of the sample of the accepted events for different experiments.", + "task_type": "binclass", + "openml_id": 1120, + "imbalance_ratio": 1.8438995215311005, + "n_classes": 2, + "num_feature_intro": { + "fLength:": "fLength:", + "fSize:": "fSize:", + "fConc:": "fConc:", + "fConc1:": "fConc1:", + "fAsym:": "fAsym:", + "fM3Long:": "fM3Long:", + "fM3Trans:": "fM3Trans:", + "fAlpha:": "fAlpha:", + "fDist:": "fDist:" + }, + "cat_feature_intro": {} +} \ No newline at end of file diff --git a/MagicTelescope/y_test.npy b/MagicTelescope/y_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..137f715d0a63bfa9c0088acdfcc56a6154e8e5f8 --- /dev/null +++ b/MagicTelescope/y_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid 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Besides publicly available information, the dataset creator Steven C. Bourassa has added distance variables, aviation noise as well as latitude and longitude.\n\nThe dataset containts the following columns:\n\n - PARCELNO: unique identifier for each property. About 1% appear multiple times.\n - SALE_PRC: sale price ($)\n - LND_SQFOOT: land area (square feet) \n - TOT_LVG_AREA: floor area (square feet) \n - SPEC_FEAT_VAL: value of special features (e.g., swimming pools) ($)\n - RAIL_DIST: distance to the nearest rail line (an indicator of noise) (feet)\n - OCEAN_DIST: distance to the ocean (feet) \n - WATER_DIST: distance to the nearest body of water (feet) \n - CNTR_DIST: distance to the Miami central business district (feet)\n - SUBCNTR_DI: distance to the nearest subcenter (feet)\n - HWY_DIST: distance to the nearest highway (an indicator of noise) (feet) \n - age: age of the structure\n - avno60plus: dummy variable for airplane noise exceeding an acceptable level \n - structure_quality: quality of the structure \n - month_sold: sale month in 2016 (1 = jan)\n - LATITUDE\n - LONGITUDE\n \nA typical model would try to predict log(SALE_PRC) as a function of all variables except the PARCELNO.", + "task_type": "regression", + "openml_id": 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whether a player has a career of 5 years or more.\n0-Signifies the career of the player is shorter than 5 years.\nSome features other than target variable:\nGamesPlayed\nPointsPerGame\n3PointMade etc.\nInspiration\n-This dataset is helpful in finding the features which are helpful in predicting that how long a player can play.\n-Use EDA to get the insight information about the features.\n-Create a classification model to predict the target variable.", + "task_type": "binclass", + "openml_id": 43812, + "n_classes": 2, + "num_feature_intro": { + "GamesPlayed": "GamesPlayed", + "MinutesPlayed": "MinutesPlayed", + "PointsPerGame": "PointsPerGame", + "FieldGoalsMade": "FieldGoalsMade", + "FieldGoalsAttempt": "FieldGoalsAttempt", + "FieldGoalPercent": "FieldGoalPercent", + "3PointMade": "3PointMade", + "3PointAttempt": "3PointAttempt", + "3PointPercent": "3PointPercent", + "FreeThrowMade": "FreeThrowMade", + "FreeThrowAttempt": "FreeThrowAttempt", + "FreeThrowPercent": "FreeThrowPercent", + "OffensiveRebounds": "OffensiveRebounds", + "DefensiveRebounds": "DefensiveRebounds", + "Rebounds": "Rebounds", + "Assists": "Assists", + "Steals": "Steals", + "Blocks": "Blocks", + "Turnovers": "Turnovers" + }, + "cat_feature_intro": {} +} \ No newline at end of file diff --git a/Performance-Prediction/y_test.npy b/Performance-Prediction/y_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..d1ccba5bee967e95c32cd42197dd90de30388551 --- /dev/null +++ b/Performance-Prediction/y_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:1e3cfeb70233ac5ff72494f779cc6871ce52e02549eea772b03a97a8264a74bc +size 396 diff --git a/Performance-Prediction/y_train.npy b/Performance-Prediction/y_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..82766ea4f8d171fc2e196216eb78f6c8e33e59cb --- /dev/null +++ b/Performance-Prediction/y_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid 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0000000000000000000000000000000000000000..6df908b7efd5ad2e918c7d5e436777717a2edab6 --- /dev/null +++ b/PhishingWebsites/C_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4e502f25372f4a0c3773b5773a3c141ebf3cdecd72bf7e928880f5d42a29f1c1 +size 425424 diff --git a/PhishingWebsites/C_val.npy b/PhishingWebsites/C_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..fd922d146d5c7447181d5360056bc91fd8758093 --- /dev/null +++ b/PhishingWebsites/C_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:29cbb9c082235da88e3c67ce242d9bee7f001b6fcbca3557de5af77d2d8f3f0c +size 106746 diff --git a/PhishingWebsites/info.json b/PhishingWebsites/info.json new file mode 100644 index 0000000000000000000000000000000000000000..90143d331c9d73f642d3f7f1d72163ad6f5dac81 --- /dev/null +++ b/PhishingWebsites/info.json @@ -0,0 +1,47 @@ +{ + "name": "PhishingWebsites", + "n_num_features": 0, + "n_cat_features": 30, + "train_size": 7075, + "val_size": 1769, + "test_size": 2211, + "source": "https://www.openml.org/search?type=data&status=active&id=4534&sort=runs", + "task_intro": "**Author**: Rami Mustafa A Mohammad ( University of Huddersfield\",\"rami.mohammad '@' hud.ac.uk\",\"rami.mustafa.a '@' gmail.com) Lee McCluskey (University of Huddersfield\",\"t.l.mccluskey '@' hud.ac.uk ) Fadi Thabtah (Canadian University of Dubai\",\"fadi '@' cud.ac.ae) \n**Source**: [UCI](https://archive.ics.uci.edu/ml/datasets/phishing+websites) \n**Please cite**: Please refer to the [Machine Learning Repository's citation policy](https://archive.ics.uci.edu/ml/citation_policy.html) \n\nSource:\n\nRami Mustafa A Mohammad ( University of Huddersfield, rami.mohammad '@' hud.ac.uk, rami.mustafa.a '@' gmail.com)\nLee McCluskey (University of Huddersfield,t.l.mccluskey '@' hud.ac.uk )\nFadi Thabtah (Canadian University of Dubai,fadi '@' cud.ac.ae)\n\n\nData Set Information:\n\nOne of the challenges faced by our research was the unavailability of reliable training datasets. In fact this challenge faces any researcher in the field. However, although plenty of articles about predicting phishing websites have been disseminated these days, no reliable training dataset has been published publically, may be because there is no agreement in literature on the definitive features that characterize phishing webpages, hence it is difficult to shape a dataset that covers all possible features. \nIn this dataset, we shed light on the important features that have proved to be sound and effective in predicting phishing websites. In addition, we propose some new features.\n\n\nAttribute Information:\n\nFor Further information about the features see the features file in the [data folder](https://archive.ics.uci.edu/ml/machine-learning-databases/00327/Phishing Websites Features.docx) of UCI.\n\nRelevant Papers:\n\nMohammad, Rami, McCluskey, T.L. and Thabtah, Fadi (2012) An Assessment of Features Related to Phishing Websites using an Automated Technique. In: International Conferece For Internet Technology And Secured Transactions. ICITST 2012 . IEEE, London, UK, pp. 492-497. ISBN 978-1-4673-5325-0\n\nMohammad, Rami, Thabtah, Fadi Abdeljaber and McCluskey, T.L. (2014) Predicting phishing websites based on self-structuring neural network. Neural Computing and Applications, 25 (2). pp. 443-458. ISSN 0941-0643\n\nMohammad, Rami, McCluskey, T.L. and Thabtah, Fadi Abdeljaber (2014) Intelligent Rule based Phishing Websites Classification. IET Information Security, 8 (3). pp. 153-160. ISSN 1751-8709\n\n \n\nCitation Request:\n\nPlease refer to the Machine Learning Repository's citation policy", + "task_type": "binclass", + "openml_id": 4534, + "imbalance_ratio": 1.2570436913025724, + "n_classes": 2, + "num_feature_intro": {}, + "cat_feature_intro": { + "having_IP_Address": "having_IP_Address", + "URL_Length": "URL_Length", + "Shortining_Service": "Shortining_Service", + "having_At_Symbol": "having_At_Symbol", + "double_slash_redirecting": "double_slash_redirecting", + "Prefix_Suffix": "Prefix_Suffix", + "having_Sub_Domain": "having_Sub_Domain", + "SSLfinal_State": "SSLfinal_State", + "Domain_registeration_length": "Domain_registeration_length", + "Favicon": "Favicon", + "port": "port", + "HTTPS_token": "HTTPS_token", + "Request_URL": "Request_URL", + "URL_of_Anchor": "URL_of_Anchor", + "Links_in_tags": "Links_in_tags", + "SFH": "SFH", + "Submitting_to_email": "Submitting_to_email", + "Abnormal_URL": "Abnormal_URL", + "Redirect": "Redirect", + "on_mouseover": 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There are twenty-four chlorophyll fluorescence images captured every alternative day (Control and Drought) that have been captured for a period of sixty days. A total of (594 x 2) images are used for this research work.\nThis dataset comprises of images of wheat crop using Chlorophyll Fluorescence modality. 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All datasets are formatted in a uniform way, though the type of data might differ. The data are provided as preprocessed matrices, so that participants can focus on classification, although participants are welcome to use additional feature extraction procedures (as long as they do not violate any rule of the challenge). All problems are binary classification problems and are assessed with the normalized Area Under the ROC Curve (AUC) metric (i.e. 2*AUC-1).\n The identity of the datasets and the type of data is concealed, though its structure is revealed. The final score in phase 2 will be the average of rankings on all testing datasets, a ranking will be generated from such results, and winners will be determined according to such ranking.\n The tasks are constrained by a time budget. The Codalab platform provides computational resources shared by all participants. 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This is a two-class classification problem. The raw data from the census bureau is known as the Adult database in the UCI machine-learning repository. The 14 original attributes (features) include age, workclass, education, education,\nmarital status, occupation, native country, etc. It contains continuous, binary and categorical features. This dataset is from \"prior knowledge track\", i.e. has access to the original features and their identity.\n\n\nNumber of examples:\nPos_ex Neg_ex Tot_ex\nTrain 1029 3118 4147\nValid 103 312 415\n\nThis dataset contains samples from both training and validation datasets.\n\n### Attribute information \n1. age Instance\u2019s age (numeric)\n2. workclass Instance\u2019s work class (nominal)\n3. fnlwgt Instance\u2019s sampling weight (numeric)\n4. education Instance\u2019s education level (nominal)\n5. educationNum Instance\u2019s education level (numeric version)\n6. maritalStatus Instance\u2019s marital status (nominal)\n7. occupation Instance\u2019s occupation (nominal)\n8. relationship Instance\u2019s type of relationship (nominal)\n9. race Instance\u2019s race (nominal)\n10. sex Instance\u2019s sex (nominal)\n11. capitalGain Instance\u2019s capital gain (numeric)\n12. capitalLoss Instance\u2019s capital loss (numeric)\n13. hoursPerWeek Instance\u2019s number of working hours 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"train_size": 961, + "val_size": 241, + "test_size": 301, + "source": "https://www.openml.org/search?type=data&status=active&id=43919&sort=runs", + "task_intro": "**Citation**\n\nPlease refer to UCI's [citation policy](https://archive.ics.uci.edu/ml/citation_policy.html).\n\n**Source**\n\nDonor: Dr Roberto Lopez robertolopez '@' intelnics.com Intelnics\n\nCreators:\nThomas F. Brooks, D. Stuart Pope and Michael A. Marcolini NASA\n\nNote that the names of the columns have been altered to be more descriptive.\n\n**Data Description**\n\nThe NASA data set comprises different size NACA 0012 airfoils at various wind tunnel speeds and angles of attack. The span of the airfoil and the observer position were the same in all of the experiments.\n\n**Attribute description**\n\nThis problem has the following inputs:\n\n* frequency - Frequency, in Hertzs.\n* angle - Angle of attack, in degrees.\n* length - Chord length, in meters.\n* velocity - Free-stream velocity, in meters per second.\n* thickness - Suction side displacement thickness, in meters.\n\nThe only output is:\n\n* pressure - Scaled sound pressure level, in decibels.\n\nRelevant Papers:\n\nT.F. Brooks, D.S. Pope, and A.M. Marcolini.\nAirfoil self-noise and prediction.\nTechnical report, NASA RP-1218, July 1989.\n\nK. Lau.\nA neural networks approach for aerofoil noise prediction.\nMaster thesis, Department of Aeronautics.\nImperial College of Science, Technology and Medicine (London, United Kingdom), 2006.\n\nR. 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0000000000000000000000000000000000000000..1d6c8b73e402468c59968bd3f6c643b3ee1a9a3e --- /dev/null +++ b/airlines_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True/info.json @@ -0,0 +1,25 @@ +{ + "name": "airlines_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True", + "n_num_features": 3, + "n_cat_features": 4, + "train_size": 1280, + "val_size": 320, + "test_size": 400, + "source": "https://www.openml.org/search?type=data&status=active&id=44528&sort=runs", + "task_intro": "Subsampling of the dataset airlines (1169) with\n\nseed=0\nargs.nrows=2000\nargs.ncols=100\nargs.nclasses=10\nargs.no_stratify=True\nGenerated with the following source code:\n\n\n```python\n def subsample(\n self,\n seed: int,\n nrows_max: int = 2_000,\n ncols_max: int = 100,\n nclasses_max: int = 10,\n stratified: bool = True,\n ) -> Dataset:\n rng = np.random.default_rng(seed)\n\n x = self.x\n y = self.y\n\n # Uniformly sample\n classes = y.unique()\n if len(classes) > nclasses_max:\n vcs = y.value_counts()\n selected_classes = rng.choice(\n classes,\n size=nclasses_max,\n replace=False,\n p=vcs / sum(vcs),\n )\n\n # Select the indices where one of these classes is present\n idxs = y.index[y.isin(classes)]\n x = x.iloc[idxs]\n y = y.iloc[idxs]\n\n # Uniformly sample columns if required\n if len(x.columns) > ncols_max:\n columns_idxs = rng.choice(\n list(range(len(x.columns))), size=ncols_max, replace=False\n )\n sorted_column_idxs = sorted(columns_idxs)\n selected_columns = list(x.columns[sorted_column_idxs])\n x = x[selected_columns]\n else:\n sorted_column_idxs = list(range(len(x.columns)))\n\n if len(x) > nrows_max:\n # Stratify accordingly\n target_name = y.name\n data = pd.concat((x, y), axis=\"columns\")\n _, subset = train_test_split(\n data,\n test_size=nrows_max,\n stratify=data[target_name],\n shuffle=True,\n random_state=seed,\n )\n x = subset.drop(target_name, axis=\"columns\")\n y = subset[target_name]\n\n # We need to convert categorical columns to string 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"on_antithyroid_medication", + "sick": "sick", + "pregnant": "pregnant", + "thyroid_surgery": "thyroid_surgery", + "I131_treatment": "I131_treatment", + "query_hypothyroid": "query_hypothyroid", + "query_hyperthyroid": "query_hyperthyroid", + "lithium": "lithium", + "goitre": "goitre", + "tumor": "tumor", + "hypopituitary": "hypopituitary", + "psych": "psych", + "TSH_measured": "TSH_measured", + "T3_measured": "T3_measured", + "TT4_measured": "TT4_measured", + "T4U_measured": "T4U_measured", + "FTI_measured": "FTI_measured", + "TBG_measured": "TBG_measured", + "TBG": "TBG", + "referral_source": "referral_source" + } +} \ No newline at end of file diff --git a/allrep/y_test.npy b/allrep/y_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..b8b909fd2c4036754a98b68cd66b3fab5d4c9217 --- /dev/null +++ b/allrep/y_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a07111e1028234582e05fa9589868447c94f45adf2e628775722bd26ef23ebe9 +size 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file mode 100644 index 0000000000000000000000000000000000000000..56c86f22527bc274c4baee0493f4c5ef3f9dfdc3 --- /dev/null +++ b/analcatdata_authorship/info.json @@ -0,0 +1,86 @@ +{ + "name": "analcatdata_authorship", + "n_num_features": 69, + "n_cat_features": 0, + "train_size": 537, + "val_size": 135, + "test_size": 169, + "source": "https://www.openml.org/search?type=data&status=active&id=458&sort=runs", + "task_intro": "**Author**: \n**Source**: Unknown - Date unknown \n**Please cite**: \n\nanalcatdata A collection of data sets used in the book \"Analyzing Categorical Data,\"\nby Jeffrey S. Simonoff, Springer-Verlag, New York, 2003. The submission\nconsists of a zip file containing two versions of each of 84 data sets,\nplus this README file. Each data set is given in comma-delimited ASCII\n(.csv) form, and Microsoft Excel (.xls) form.\n\nNOTICE: These data sets may be used freely for scientific, educational and/or\nnoncommercial purposes, provided suitable acknowledgment is given (by citing\nthe above-named reference).\n\nFurther details concerning the book, including information on statistical software\n(including sample S-PLUS/R and SAS code), are available at the web site\n\nhttp://www.stern.nyu.edu/~jsimonof/AnalCatData\n\n\nInformation about the dataset\nCLASSTYPE: nominal\nCLASSINDEX: last\n\n\nNote: Quotes, Single-Quotes and Backslashes were removed, Blanks replaced\nwith Underscores", + "task_type": "multiclass", + "openml_id": 458, + "imbalance_ratio": 5.763636363636364, + "n_classes": 4, + "num_feature_intro": { + "a": "a", + "all": "all", + "also": "also", + "an": "an", + "and": "and", + "any": "any", + "are": "are", + "as": "as", + "at": "at", + "be": "be", + "been": "been", + "but": "but", + "by": "by", + "can": "can", + "do": "do", + "down": "down", + "even": "even", + "every": "every", + "for": "for", + "from": "from", + "had": "had", + "has": "has", + "have": "have", + "her": "her", + "his": "his", + "if": "if", + "in": "in", + "into": "into", + "is": "is", + "it": "it", + "its": "its", + "may": "may", + "more": "more", + "must": "must", + "my": "my", + "no": "no", + "not": "not", + "now": "now", + "of": "of", + "on": "on", + "one": "one", + "only": "only", + "or": "or", + "our": "our", + "should": "should", + "so": "so", + "some": "some", + "such": "such", + "than": "than", + "that": "that", + "the": "the", + "their": "their", + "then": "then", + "there": "there", + "things": "things", + "this": "this", + "to": "to", + "up": "up", + "upon": "upon", + "was": "was", + "were": "were", + "what": "what", + "when": "when", + "which": "which", + "who": "who", + "will": "will", + "with": "with", + "would": "would", + "your": "your" + }, + "cat_feature_intro": {} +} \ No newline at end of file diff --git a/analcatdata_authorship/y_test.npy b/analcatdata_authorship/y_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..af256031e402cf73f614d54c30e5509aa552eaa6 --- /dev/null +++ b/analcatdata_authorship/y_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:464f26902b870323933e3e8cfe52e9ad73ac51e86aa81e0b41eadfd257cfd9c7 +size 669 diff --git a/analcatdata_authorship/y_train.npy b/analcatdata_authorship/y_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..c0a1839ba45fd0f8a38cb17c08cbf7b0a18f7ee5 --- /dev/null +++ b/analcatdata_authorship/y_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:1f313fddb5a8bfc62de5dd5d66fcebee519b0d1503d0ec4e7be8f43dcd697a74 +size 1406 diff --git a/analcatdata_authorship/y_val.npy b/analcatdata_authorship/y_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..98373b86335e61437fc32e8e56c9ab7b9d857d24 --- /dev/null +++ b/analcatdata_authorship/y_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4e74ef56229c32dd1deb476d8c2fdf160ca5959179f92200c1a37ec1b1168a96 +size 601 diff --git a/analcatdata_supreme/N_test.npy b/analcatdata_supreme/N_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..ac3aaf847869835285960e4d1c7a073f76ffa57b --- /dev/null +++ b/analcatdata_supreme/N_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9c41e71577c493d92453638413293f29fc4e028e5e40e3f07791ce336268020e +size 45544 diff --git a/analcatdata_supreme/N_train.npy b/analcatdata_supreme/N_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..d62ebfaa2cd0d9a947288704053bc626891dd460 --- /dev/null +++ b/analcatdata_supreme/N_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:578f4362044b1a1a2935ea6e8f43e3558925819f2c1786e01d223e8a8ab235f9 +size 145280 diff --git a/analcatdata_supreme/N_val.npy b/analcatdata_supreme/N_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..4c5e0b8c54acabfe44753876f35e5b61563057bd --- /dev/null +++ b/analcatdata_supreme/N_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:aab5cb3f43aa4d86c6244dd53b49e059489713828d805bc8fab5ea77a8085548 +size 36472 diff --git a/analcatdata_supreme/info.json b/analcatdata_supreme/info.json new file mode 100644 index 0000000000000000000000000000000000000000..b9035f153774be3ed7b2f7d68c993951027faa79 --- /dev/null +++ b/analcatdata_supreme/info.json @@ -0,0 +1,23 @@ +{ + "name": "analcatdata_supreme", + "n_num_features": 7, + "n_cat_features": 0, + "train_size": 2592, + "val_size": 649, + "test_size": 811, + "source": "https://www.openml.org/search?type=data&status=active&id=504&sort=runs", + "task_intro": "**Author**: \n**Source**: Unknown - 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Simonoff, Springer-Verlag, New York, 2003. The submission\nconsists of a zip file containing two versions of each of 84 data sets,\nplus this README file. 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0000000000000000000000000000000000000000..9dec612e70c378bb0022868a019019b920da259c --- /dev/null +++ b/artificial-characters/N_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d68bbaeab5e09a7817eb27379b098d544e8253674cc2da8a317191ade79818cb +size 91688 diff --git a/artificial-characters/info.json b/artificial-characters/info.json new file mode 100644 index 0000000000000000000000000000000000000000..2717e8fffb83fe65993b32b86263b39350dbbda6 --- /dev/null +++ b/artificial-characters/info.json @@ -0,0 +1,24 @@ +{ + "name": "artificial-characters", + "n_num_features": 7, + "n_cat_features": 0, + "train_size": 6539, + "val_size": 1635, + "test_size": 2044, + "source": "https://www.openml.org/search?type=data&status=active&id=1459&sort=runs", + "task_intro": "**Author**: H. Altay Guvenir, Burak Acar, Haldun Muderrisoglu \n**Source**: [UCI](https://archive.ics.uci.edu/ml/datasets/Artificial+Characters) - 1992 \n**Please cite**: [UCI](https://archive.ics.uci.edu/ml/citation_policy.html) \n\nThis database has been artificially generated. It describes the structure of the capital letters A, C, D, E, F, G, H, L, P, R, indicated by a number 1-10, in that order (A=1,C=2,...). Each letter's structure is described by a set of segments (lines) which resemble the way an automatic program would segment an image. The dataset consists of 600 such descriptions per letter. \n\nOriginally, each 'instance' (letter) was stored in a separate file, each consisting of between 1 and 7 segments, numbered 0,1,2,3,... Here they are merged. That means that the first 5 instances describe the first 5 segments of the first segmentation of the first letter (A). Also, the training set (100 examples) and test set (the rest) are merged. The next 7 instances describe another segmentation (also of the letter A) and so on.\n\n### Attribute Information \n\n* V1: object number, the number of the segment (0,1,2,..,7) \n* V2-V5: the initial and final coordinates of a segment in a cartesian plane (XX1,YY1,XX2,YY2). \n* V6: size, this is the length of a segment computed by using the geometric distance between two points A(X1,Y1) and B(X2,Y2).\n* V7: diagonal, this is the length of the diagonal of the smallest rectangle which includes the picture of the character. The value of this attribute is the same in each object.\n\n### Relevant Papers \n\nM. Botta, A. Giordana, L. Saitta: \"Learning Fuzzy Concept Definitions\", IEEE-Fuzzy Conference, 1993. \nM. Botta, A. 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J. Hickey \n**Source**: UCI \n**Please cite**: \n\n* Dataset Title: \n\nAutoUniv Dataset \ndata problem: autoUniv-au4-2500 \n\n* Abstract: \n\nAutoUniv is an advanced data generator for classifications tasks. The aim is to reflect the nuances and heterogeneity of real data. Data can be generated in .csv, ARFF or C4.5 formats.\n\n* Source: \n\nAutoUniv was developed by Ray. J. Hickey. Email: ray.j.hickey '@' gmail.com \nAutoUniv web-site: http://sites.google.com/site/autouniv/.\n\n\n* Data Set Information:\n\nThe user first creates a classification model and then generates classified examples from it. To create a model, the following are specified: the number of attributes (up to 1000) and their type (discrete or continuous), the number of classes (up to 10), the complexity of the underlying rules and the noise level. AutoUniv then produces a model through a process of constrained randomised search to satisfy the user's requirements. A model can have up to 3000 rules. Rare class models can be designed. A sequence of models can be designed to reflect concept and/or population drift. \n\nAutoUniv creates three text files for a model: a Prolog specification of the model used to generate examples (.aupl); a user-friendly statement of the classification rules in an 'if ... then' format (.aurules); a statistical summary of the main properties of the model, including its Bayes rate (.auprops).\n\n\n* Attribute Information: \n\nAttributes may be discrete with up to 10 values or continuous. A discrete attribute can be nominal with values v1, v2, v3 ... or integer with values 0, 1, 2 , ... .\n\n\n* Relevant Papers:\n\nMarrs, G, Hickey, RJ and Black, MM (2010) Modeling the example life-cycle in an online classification learner. In Proceedings of HaCDAIS 2010: International Workshop on Handling Concept Drift in Adaptive Information Systems. \n[Web Link]#proc . \n\nMarrs, G, Hickey, RJ and Black, MM (2010) The Impact of Latency on Online Classification Learning with Concept Drift. In Y. Bi and M.A. Williams (Eds.): KSEM 2010, LNAI 6291, Springer-Verlag, Berlin, pp. 459\u00e2\u20ac\u201c469. \n\nHickey, RJ (2007) Structure and Majority Classes in Decision Tree Learning. Journal of Machine Learning Research, 8, pp. 1747-1768.", + "task_type": "multiclass", + "openml_id": 1548, + "imbalance_ratio": 5.98469387755102, + "n_classes": 3, + "num_feature_intro": { + "V1": "V1", + "V2": "V2", + "V5": "V5", + "V7": "V7", + "V8": "V8", + "V10": "V10", + "V11": "V11", + "V16": "V16", + "V17": "V17", + "V18": "V18", + "V20": "V20", + "V21": "V21", + "V22": "V22", + "V23": "V23", + "V24": "V24", + "V26": "V26", + "V27": "V27", + "V28": "V28", + "V29": "V29", + "V30": "V30", + "V32": "V32", + "V33": "V33", + "V35": "V35", + "V36": "V36", + "V41": "V41", + "V42": "V42", + "V43": "V43", + "V45": "V45", + "V46": "V46", + "V50": "V50", + "V52": "V52", + "V53": "V53", + "V54": "V54", + "V59": "V59", + "V60": "V60", + "V61": "V61", + "V62": "V62", + "V63": "V63", + "V65": "V65", + "V67": "V67", + "V70": "V70", + "V71": "V71", + "V72": "V72", + "V73": "V73", + "V74": "V74", + "V75": "V75", + "V76": "V76", + "V77": "V77", + "V78": "V78", + "V80": "V80", + "V82": "V82", + 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J. Hickey \n**Source**: UCI \n**Please cite**: \n\n* Dataset Title: \n\nAutoUniv Dataset \ndata problem: autoUniv-au7-300-drift-au7-cpd1-800 \n\n* Abstract: \n\nAutoUniv is an advanced data generator for classifications tasks. The aim is to reflect the nuances and heterogeneity of real data. Data can be generated in .csv, ARFF or C4.5 formats.\n\n* Source: \n\nAutoUniv was developed by Ray. J. Hickey. Email: ray.j.hickey '@' gmail.com \nAutoUniv web-site: http://sites.google.com/site/autouniv/.\n\n\n* Data Set Information:\n\nThe user first creates a classification model and then generates classified examples from it. To create a model, the following are specified: the number of attributes (up to 1000) and their type (discrete or continuous), the number of classes (up to 10), the complexity of the underlying rules and the noise level. AutoUniv then produces a model through a process of constrained randomised search to satisfy the user's requirements. A model can have up to 3000 rules. Rare class models can be designed. A sequence of models can be designed to reflect concept and/or population drift. \n\nAutoUniv creates three text files for a model: a Prolog specification of the model used to generate examples (.aupl); a user-friendly statement of the classification rules in an 'if ... then' format (.aurules); a statistical summary of the main properties of the model, including its Bayes rate (.auprops).\n\n\n* Attribute Information: \n\nAttributes may be discrete with up to 10 values or continuous. A discrete attribute can be nominal with values v1, v2, v3 ... or integer with values 0, 1, 2 , ... .\n\n\n* Relevant Papers:\n\nMarrs, G, Hickey, RJ and Black, MM (2010) Modeling the example life-cycle in an online classification learner. In Proceedings of HaCDAIS 2010: International Workshop on Handling Concept Drift in Adaptive Information Systems. \n[Web Link]#proc . \n\nMarrs, G, Hickey, RJ and Black, MM (2010) The Impact of Latency on Online Classification Learning with Concept Drift. In Y. Bi and M.A. Williams (Eds.): KSEM 2010, LNAI 6291, Springer-Verlag, Berlin, pp. 459\u00e2\u20ac\u201c469. \n\nHickey, RJ (2007) Structure and Majority Classes in Decision Tree Learning. 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The marketing campaigns were based on phone calls. Often, more than one contact to the same client was required, in order to access if the product (bank term deposit) would be (or not) subscribed.The classification goal is to predict if the client will subscribe a term deposit (variable y).", + "feature_intro": { + "num":{ + "age": "age", + "balance": "balance", + "day":"day", + "duration": "duration", + "campaign": "campaign", + "pdays": "pdays", + "previous":"previous" + }, + "cat":{ + "job":"job", + "marital":"marital", + "education":"education", + "default":"default", + "housing":"housing", + "loan":"loan", + "contact":"contact", + "month":"month", + "poutcome":"poutcome" + } + }, + "target_intro":{ + "the product (bank term deposit) would be subscribed":"the product (bank term deposit) would not be subscribed", + "the product (bank term deposit) would not be subscribed":"the product (bank term deposit) would be subscribed" + }, + "source": "https://archive.ics.uci.edu/ml/datasets/Bank+Marketing" +} \ No newline at end of file diff --git a/bank/y_test.npy b/bank/y_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..fb04e1707aed67778c11e8b8f775c80de0782661 --- /dev/null +++ b/bank/y_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:14bf6bc0af8c76081bdbd297e2c999adcaf076657b8f94eaac9dfcc5ce8d5b2d +size 72472 diff --git a/bank/y_train.npy b/bank/y_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..0876fa9fbe3dd4c17fedca86d49eb44dff420324 --- /dev/null +++ b/bank/y_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:06f1d2b7dcaffd16ad945bcfb86c09afd4401a47de200176223e3d0ad348ba24 +size 231600 diff --git a/bank/y_val.npy b/bank/y_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..8ef5e5402e09a0f6727238c32b775373607f85c5 --- /dev/null +++ b/bank/y_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:cc059d5137ee666b1b4ee235cdb9f61e3ee3e5e2dab3007ac29291feef4e6516 +size 58000 diff --git a/bank32nh/N_test.npy b/bank32nh/N_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..4df28950ca1692b9d8a92e97fd0dac74027acf8e --- /dev/null +++ b/bank32nh/N_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:1b73e2463baab56cce5388413fe5385114c24dc3d92e6768217a762488de54fd +size 419712 diff --git a/bank32nh/N_train.npy b/bank32nh/N_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..f1e486b5a4f4a2c74169e95e084e836a0b69dcfe --- /dev/null +++ b/bank32nh/N_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b48c5f17fdd41bc73a32f927c56a9a89352a404b36e2dfb3ca0ee51b3a169efb +size 1342080 diff --git a/bank32nh/N_val.npy b/bank32nh/N_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..b859c75a45ecfb5808edc5335985d45063f35576 --- /dev/null +++ b/bank32nh/N_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:dc62d4107bcba44b117f235a0920f5086fd86905efd815bc03f3f42a3b8a13dd +size 335744 diff --git a/bank32nh/info.json b/bank32nh/info.json new file mode 100644 index 0000000000000000000000000000000000000000..cef5779bad8218bb7fd01f3e8e32de7f4ae7c4ce --- /dev/null +++ b/bank32nh/info.json @@ -0,0 +1,48 @@ +{ + "name": "bank32nh", + "n_num_features": 32, + "n_cat_features": 0, + "train_size": 5242, + "val_size": 1311, + "test_size": 1639, + "source": "https://www.openml.org/search?type=data&status=active&id=558&sort=runs", + "task_intro": "**Author**: \n**Source**: Unknown - Date unknown \n**Please cite**: \n\nA family of datasets synthetically generated from a simulation of how bank-customers choose their banks. Tasks are\nbased on predicting the fraction of bank customers who leave the bank because of full queues. The bank family of\ndatasets are generated from a simplistic simulator, which simulates the queues in a series of banks. The simulator was\nconstructed with the explicit purpose of generating a family of datasets for DELVE. Customers come from several\nresidential areas, choose their preferred bank depending on distances and have tasks of varying complexity, and various\nlevels of patience. Each bank has several queues, that open and close according to demand. The tellers have various\neffectivities, and customers may change queue, if their patience expires. In the rej prototasks, the object is to predict the\nrate of rejections, ie the fraction of customers that are turned away from the bank because all the open tellers have full\nqueues.\nSource: collection of regression datasets by Luis Torgo (ltorgo@ncc.up.pt) at\nhttp://www.ncc.up.pt/~ltorgo/Regression/DataSets.html\nOrginal source: DELVE repository of data.\nCharacteristics: Data set contains 8192 (4500+3692) cases. and 33 continuous\nattributes", + "task_type": "regression", + "openml_id": 558, + "n_classes": 1, + "num_feature_intro": { + "a1cx": "a1cx", + "a1cy": "a1cy", + "a1sx": "a1sx", + "a1sy": "a1sy", + "a1rho": "a1rho", + "a1pop": "a1pop", + "a2cx": "a2cx", + "a2cy": "a2cy", + "a2sx": "a2sx", + "a2sy": "a2sy", + "a2rho": "a2rho", + "a2pop": "a2pop", + "a3cx": "a3cx", + "a3cy": "a3cy", + "a3sx": "a3sx", + "a3sy": "a3sy", + "a3rho": "a3rho", + "a3pop": "a3pop", + "temp": "temp", + "b1x": "b1x", + "b1y": "b1y", + "b1call": "b1call", + "b1eff": "b1eff", + "b2x": "b2x", + "b2y": "b2y", + "b2call": "b2call", + "b2eff": "b2eff", + "b3x": "b3x", + "b3y": "b3y", + "b3call": "b3call", + "b3eff": "b3eff", + "mxql": "mxql" + }, + "cat_feature_intro": {} +} \ No newline at end of file diff --git a/bank32nh/y_test.npy b/bank32nh/y_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..c216055cf2d7bc248a2cfb50d660ea166531fa28 --- /dev/null +++ b/bank32nh/y_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8c416c9454d38d7ff97b1ef21c0b55afdba9a49d7a288108cab6edca1f4fd152 +size 13240 diff --git a/bank32nh/y_train.npy b/bank32nh/y_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..d88ad799a59147b48208a0236fc97918ebc5050b --- /dev/null +++ b/bank32nh/y_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ad4bd2f175bd6281e932497402f585cc8a7311403842704bd29ebbd37b043c69 +size 42064 diff --git a/bank32nh/y_val.npy b/bank32nh/y_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..4b9595aa03d37e9dc9d48f7dbe982b53dfe08794 --- /dev/null +++ b/bank32nh/y_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b8bdbd75258abb5c0c89ec33a36f45163b06c6b0313b31c06df2493bba08d83f +size 10616 diff --git a/bank8FM/N_test.npy b/bank8FM/N_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..ecc558aee395587b83c6273a50bb858a0fb0f6f0 --- /dev/null +++ b/bank8FM/N_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7bb8a532974a7d284dd1150e6c95c25ddf457826c95112cde39bfc50aef029d3 +size 105024 diff --git a/bank8FM/N_train.npy b/bank8FM/N_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..b6312b109b641d0b189dc0ac8bf3fc0979ded29b --- /dev/null +++ b/bank8FM/N_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6e35c26fbfc04cb0c5d454c8c433cd79890125f4c15c20356e45e9890c14538a +size 335616 diff --git a/bank8FM/N_val.npy b/bank8FM/N_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..2652bb774458c150cc6c0c2fe01d86a5fcd925c5 --- /dev/null +++ b/bank8FM/N_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:499eb60f2b344c34af8e5993c513c79419122e75e82a68f317d9f6c083602232 +size 84032 diff --git a/bank8FM/info.json b/bank8FM/info.json new file mode 100644 index 0000000000000000000000000000000000000000..5f1989d6666c87b6d41cc391e5fb9cbe353e1ac2 --- /dev/null +++ b/bank8FM/info.json @@ -0,0 +1,24 @@ +{ + "name": "bank8FM", + "n_num_features": 8, + "n_cat_features": 0, + "train_size": 5242, + "val_size": 1311, + "test_size": 1639, + "source": "https://www.openml.org/search?type=data&status=active&id=572&sort=runs", + "task_intro": "**Author**: \n**Source**: Unknown - Date unknown \n**Please cite**: \n\nA family of datasets synthetically generated from a simulation of how bank-customers choose their banks. Tasks are\nbased on predicting the fraction of bank customers who leave the bank because of full queues. The bank family of\ndatasets are generated from a simplistic simulator, which simulates the queues in a series of banks. The simulator was\nconstructed with the explicit purpose of generating a family of datasets for DELVE. Customers come from several\nresidential areas, choose their preferred bank depending on distances and have tasks of varying complexity, and various\nlevels of patience. Each bank has several queues, that open and close according to demand. The tellers have various\neffectivities, and customers may change queue, if their patience expires. 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Originally used in [Discovering Knowledge in Data: An Introduction to Data Mining](http://secs.ac.in/wp-content/CSE_PORTAL/DataMining_Daniel.pdf).", + "task_type": "binclass", + "openml_id": 40701, + "imbalance_ratio": 6.072135785007072, + "n_classes": 2, + "num_feature_intro": { + "state": "state", + "account_length": "account_length", + "phone_number": "phone_number", + "number_vmail_messages": "number_vmail_messages", + "total_day_minutes": "total_day_minutes", + "total_day_calls": "total_day_calls", + "total_day_charge": "total_day_charge", + "total_eve_minutes": "total_eve_minutes", + "total_eve_calls": "total_eve_calls", + "total_eve_charge": "total_eve_charge", + "total_night_minutes": "total_night_minutes", + "total_night_calls": "total_night_calls", + "total_night_charge": "total_night_charge", + "total_intl_minutes": "total_intl_minutes", + "total_intl_calls": "total_intl_calls", + "total_intl_charge": "total_intl_charge" + }, + "cat_feature_intro": { + "area_code": "area_code", + "international_plan": "international_plan", + "voice_mail_plan": "voice_mail_plan", + "number_customer_service_calls": "number_customer_service_calls" + } +} \ No newline at end of file diff --git a/churn/y_test.npy b/churn/y_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..e01aab24b9595d5d7571529c2756edfcf4f7765e --- /dev/null +++ b/churn/y_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d096b289c10becc07dcbae2c331262aa46d8f89caaebdb4e52f525d56cedf2dc +size 2295 diff --git a/churn/y_train.npy b/churn/y_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..8a6869d7c7ea2accdd66d554940c30a1dce8fe0f --- /dev/null +++ b/churn/y_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:5299f91aa28f8605faba0d9e54e2f79b2cddcf670a6d9b5c6cc7eb7a024c1424 +size 6701 diff --git a/churn/y_val.npy b/churn/y_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..287862841ea122153a79abdb104b899e4b5a15e6 --- /dev/null +++ b/churn/y_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:373134d8a99a1af3266c7fc3dffbab5170e8b7448fa765ed89c205a870f6fd31 +size 1895 diff --git a/cmc/C_test.npy b/cmc/C_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..e22486088220e864d5b5af59988180914790312d --- /dev/null +++ b/cmc/C_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0e6d51b31937eb742a69ec16211614278fc91ce0220054799ab219347f91d084 +size 4449 diff --git a/cmc/C_train.npy b/cmc/C_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..76e3fd554bf75bd9f419c28836232b528e1bed32 --- /dev/null +++ b/cmc/C_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:53733a9d9d2874e4a18bf50b26c47345119affced608d600990b7d75f6aa63db +size 13515 diff --git a/cmc/C_val.npy b/cmc/C_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..851be24f07db38fa5b5d92b6255f82f9c656a10a --- /dev/null +++ b/cmc/C_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9df8be4f3952d7d3b6b649a5221a52426d67d04d45b3d0fdcc8eaa9e545a312c +size 3620 diff --git a/cmc/N_test.npy b/cmc/N_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..1fd7198b4718614a72725638749762d1c40206c9 --- /dev/null +++ b/cmc/N_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0579c7461051e63e01de87e4fb94e261ebd67c69c383af2fce7cd269a792de55 +size 718 diff --git a/cmc/N_train.npy b/cmc/N_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..f6614e77596bf33d835e57ddf1526d6858335b50 --- /dev/null +++ b/cmc/N_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:cb1ed69e22c78dc5c174b936ca95be889162ad96f602d0f64867ab99220c66e1 +size 2012 diff --git a/cmc/N_val.npy b/cmc/N_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..ed5d0fd8dbbb07b44606a1c18543916ca5113f78 --- /dev/null +++ b/cmc/N_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c907c080d08c3828a0ccd854fba1e65e2b2b3dc4d3cc09db8b3f51ab6329ac84 +size 600 diff --git a/cmc/info.json b/cmc/info.json new file mode 100644 index 0000000000000000000000000000000000000000..0536351f24ca9ad4c4389dcb5e9392e505898e69 --- /dev/null +++ b/cmc/info.json @@ -0,0 +1,27 @@ +{ + "name": "cmc", + "n_num_features": 2, + "n_cat_features": 7, + "train_size": 942, + "val_size": 236, + "test_size": 295, + "source": "https://www.openml.org/search?type=data&status=active&id=23&sort=runs", + "task_intro": "**Author**: [Tjen-Sien Lim](limt@stat.wisc.edu) \n**Source**: [As obtained from UCI](https://archive.ics.uci.edu/ml/datasets/Contraceptive+Method+Choice)\n**Please cite**: [UCI citation](https://archive.ics.uci.edu/ml/citation_policy.html)\n\n1. Title: Contraceptive Method Choice\n \n 2. Sources:\n (a) Origin: This dataset is a subset of the 1987 National Indonesia\n Contraceptive Prevalence Survey\n (b) Creator: Tjen-Sien Lim (limt@stat.wisc.edu)\n (c) Donor: Tjen-Sien Lim (limt@stat.wisc.edu)\n (c) Date: June 7, 1997\n \n 3. Past Usage:\n Lim, T.-S., Loh, W.-Y. & Shih, Y.-S. (1999). A Comparison of\n Prediction Accuracy, Complexity, and Training Time of Thirty-three\n Old and New Classification Algorithms. Machine Learning. Forthcoming.\n (ftp://ftp.stat.wisc.edu/pub/loh/treeprogs/quest1.7/mach1317.pdf or\n (http://www.stat.wisc.edu/~limt/mach1317.pdf)\n \n 4. Relevant Information:\n This dataset is a subset of the 1987 National Indonesia Contraceptive\n Prevalence Survey. The samples are married women who were either not \n pregnant or do not know if they were at the time of interview. The \n problem is to predict the current contraceptive method choice \n (no use, long-term methods, or short-term methods) of a woman based \n on her demographic and socio-economic characteristics.\n \n 5. Number of Instances: 1473\n \n 6. Number of Attributes: 10 (including the class attribute)\n \n 7. Attribute Information:\n \n 1. Wife's age (numerical)\n 2. Wife's education (categorical) 1=low, 2, 3, 4=high\n 3. Husband's education (categorical) 1=low, 2, 3, 4=high\n 4. Number of children ever born (numerical)\n 5. Wife's religion (binary) 0=Non-Islam, 1=Islam\n 6. Wife's now working? (binary) 0=Yes, 1=No\n 7. Husband's occupation (categorical) 1, 2, 3, 4\n 8. Standard-of-living index (categorical) 1=low, 2, 3, 4=high\n 9. Media exposure (binary) 0=Good, 1=Not good\n 10. Contraceptive method used (class attribute) 1=No-use \n 2=Long-term\n 3=Short-term\n \n 8. 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The data was collected from a Sun Sparcstation\n 20/712 with 128 Mbytes of memory running in a multi-user university\n department. Users would typically be doing a large variety of tasks\n ranging from accessing the internet, editing files or running very\n cpu-bound programs. The data was collected continuously on two\n separate occasions. On both occassions, system activity was gathered\n every 5 seconds. The final dataset is taken from both occasions with\n equal numbers of observations coming from each collection epoch.\n \n System measures used:\n 1. lread - Reads (transfers per second ) between system memory and user memory.\n 2. lwrite - writes (transfers per second) between system memory and user memory.\n 3. scall - Number of system calls of all types per second.\n 4. sread - Number of system read calls per second.\n 5. swrite - Number of system write calls per second . \n 6. fork - Number of system fork calls per second. \n 7. exec - Number of system exec calls per second. \n 8. rchar - Number of characters transferred per second by system read calls.\n 9. wchar - Number of characters transfreed per second by system write calls. \n 10. pgout - Number of page out requests per second.\n 11. ppgout - Number of pages, paged out per second. \n 12. pgfree - Number of pages per second placed on the free list. \n 13. pgscan - Number of pages checked if they can be freed per second.\n 14. atch - Number of page attaches (satisfying a page fault by reclaiming a page in memory) per second.\n 15. pgin - Number of page-in requests per second.\n 16. ppgin - Number of pages paged in per second.\n 17. pflt - Number of page faults caused by protection errors (copy-on-writes). \n 18. vflt - Number of page faults caused by address translation. \n 19. runqsz - Process run queue size.\n 20. freemem - Number of memory pages available to user processes.\n 21. freeswap - Number of disk blocks available for page swapping. \n 22. usr - Portion of time (%) that cpus run in user mode.\n 23. sys - Portion of time (%) that cpus run in system mode.\n 24. wio - Portion of time (%) that cpus are idle waiting for block IO.\n 25. idle - Portion of time (%) that cpus are otherwise idle.\n \n The two different regression tasks obtained from these databases are:\n \n CompAct \n Predict usr, the portion of time that cpus run in user mode from all attributes 1-21.\n \n CompAct(s) \n Predict usr using a restricted number (excluding the paging information (10-18)\n \n Source: collection of regression datasets by Luis Torgo (ltorgo@ncc.up.pt) at\n http://www.ncc.up.pt/~ltorgo/Regression/DataSets.html\n Original source: DELVE repository of data. \n Characteristics: 8192 cases, 22 continuous attributes", + "task_type": "regression", + "openml_id": 197, + "n_classes": 1, + "num_feature_intro": { + "lread": "lread", + "lwrite": "lwrite", + "scall": "scall", + "sread": "sread", + "swrite": "swrite", + "fork": "fork", + "exec": "exec", + "rchar": "rchar", + "wchar": "wchar", + "pgout": "pgout", + "ppgout": "ppgout", + "pgfree": "pgfree", + "pgscan": "pgscan", + "atch": "atch", + "pgin": "pgin", + "ppgin": "ppgin", + "pflt": "pflt", + "vflt": "vflt", + "runqsz": "runqsz", + "freemem": "freemem", + "freeswap": "freeswap" + }, + "cat_feature_intro": {} +} \ No newline at end of file diff --git a/cpu_act/y_test.npy b/cpu_act/y_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..db28b8be4f3041b0bfc11afb4192f494f1a0119c --- /dev/null +++ b/cpu_act/y_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:bf5aebf0c6183da3acd08f0f18dc70cfa3c4ae859de9e8c6e46fca1dc35dfe55 +size 1767 diff --git a/cpu_act/y_train.npy b/cpu_act/y_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..43e85207f7383755ca6d73934cdfd5e5329c7aa1 --- /dev/null +++ b/cpu_act/y_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d0242aa4159931960f960ec97e9ca7fa3f85a01caa3d2d71045a457432ab982f +size 5370 diff --git a/cpu_act/y_val.npy b/cpu_act/y_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..bbe2c04597ef3f64bb4563e720abc0d1470b919d --- /dev/null +++ b/cpu_act/y_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b1f309b04e147ac6f0fb84c7f6eb91df8366c700e3aeaeef72595bbba6c3cc50 +size 1439 diff --git a/cpu_small/N_test.npy b/cpu_small/N_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..aed6be269bc9a5a37afbc26de75027c4a83b7b14 --- /dev/null +++ b/cpu_small/N_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a64e36af49c2256a589906a954e053283abe40830ca5a12a461ac4372443f149 +size 157472 diff --git a/cpu_small/N_train.npy b/cpu_small/N_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..9432350904641110cbe07c7c0f1eec2f3d9bad0b --- /dev/null +++ b/cpu_small/N_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:99212eee449a7cbd84b95dae146940b2fbdbadf2f107cb38182ac25053080ef5 +size 503360 diff --git a/cpu_small/N_val.npy b/cpu_small/N_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..7425183f455a7973a74a17f351c4aa1d4db86de1 --- /dev/null +++ b/cpu_small/N_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:68c9f541d717dfafe91f7759a432318b2b06e9e31ce73567ad3a74c5a0a06441 +size 125984 diff --git a/cpu_small/info.json b/cpu_small/info.json new file mode 100644 index 0000000000000000000000000000000000000000..8dc5bb00444b7f09ada2aee72843d19c70276c8a --- /dev/null +++ b/cpu_small/info.json @@ -0,0 +1,28 @@ +{ + "name": "cpu_small", + "n_num_features": 12, + "n_cat_features": 0, + "train_size": 5242, + "val_size": 1311, + "test_size": 1639, + "source": "https://www.openml.org/search?type=data&status=active&id=562&sort=runs", + "task_intro": "**Author**: \n**Source**: Unknown - Date unknown \n**Please cite**: \n\nThe Computer Activity databases are a collection of computer systems\nactivity measures. The data was collected from a Sun Sparcstation\n20/712 with 128 Mbytes of memory running in a multi-user university\ndepartment. Users would typically be doing a large variety of tasks\nranging from accessing the internet, editing files or running very\ncpu-bound programs. The data was collected continuously on two\nseparate occasions. On both occassions, system activity was gathered\nevery 5 seconds. The final dataset is taken from both occasions with\nequal numbers of observations coming from each collection epoch.\n\nSystem measures used:\n1. lread - Reads (transfers per second ) between system memory and user memory.\n2. lwrite - writes (transfers per second) between system memory and user memory.\n3. scall - Number of system calls of all types per second.\n4. sread - Number of system read calls per second.\n5. swrite - Number of system write calls per second .\n6. fork - Number of system fork calls per second.\n7. exec - Number of system exec calls per second.\n8. rchar - Number of characters transferred per second by system read calls.\n9. wchar - Number of characters transfreed per second by system write calls.\n10. pgout - Number of page out requests per second.\n11. ppgout - Number of pages, paged out per second.\n12. pgfree - Number of pages per second placed on the free list.\n13. pgscan - Number of pages checked if they can be freed per second.\n14. atch - Number of page attaches (satisfying a page fault by reclaiming a page in memory) per second.\n15. pgin - Number of page-in requests per second.\n16. ppgin - Number of pages paged in per second.\n17. pflt - Number of page faults caused by protection errors (copy-on-writes).\n18. vflt - Number of page faults caused by address translation.\n19. runqsz - Process run queue size.\n20. freemem - Number of memory pages available to user processes.\n21. freeswap - Number of disk blocks available for page swapping.\n22. usr - Portion of time (%) that cpus run in user mode.\n23. sys - Portion of time (%) that cpus run in system mode.\n24. wio - Portion of time (%) that cpus are idle waiting for block IO.\n25. idle - Portion of time (%) that cpus are otherwise idle.\n\nThe two different regression tasks obtained from these databases are:\n\nCompAct\nPredict usr, the portion of time that cpus run in user mode from all attributes 1-21.\n\nCompAct(s)\nPredict usr using a restricted number (excluding the paging 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Xibilia, M.G. "Fortuna, L., Graziani, S., Rizzo, A., Xibilia, M.G." 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Originally from the StatLog project. \n**Please Cite**: \n\n**Primate Splice-Junction Gene Sequences (DNA)** \nOriginally from the StatLog project. The raw data is still available on [UCI](https://archive.ics.uci.edu/ml/datasets/Molecular+Biology+(Splice-junction+Gene+Sequences)).\n\nThe data consists of 3,186 data points (splice junctions). The data points are described by 180 indicator binary variables and the problem is to recognize the 3 classes (ei, ie, neither), i.e., the boundaries between exons (the parts of the DNA sequence retained after splicing) and introns (the parts of the DNA sequence that are spliced out). The StatLog DNA dataset is a processed version of the [Irvine database]((https://archive.ics.uci.edu/ml/datasets/Molecular+Biology+(Splice-junction+Gene+Sequences))). The main difference is that the symbolic variables representing the nucleotides (only A,G,T,C) were replaced by 3 binary indicator variables. Thus the original 60 symbolic attributes were changed into 180 binary attributes. The names of the examples were removed. The examples with ambiguities were removed (there was very few of them, 4). The StatLog version of this dataset was produced by Ross King at Strathclyde University. For original details see the Irvine database documentation.\n\nThe nucleotides A,C,G,T were given indicator values as follows:\nA -> 1 0 0\nC -> 0 1 0\nG -> 0 0 1\nT -> 0 0 0\n\nHint: Much better performance is generally observed if attributes closest to the junction are used. 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sha256:b503f5183e1feb4cc39602bdc0a3d88a25dae9f7ccbfc54c3aeb74694c2010bd +size 268592 diff --git a/eeg-eye-state/info.json b/eeg-eye-state/info.json new file mode 100644 index 0000000000000000000000000000000000000000..1934ea8ba452f1c8fa489fb578c855ae038eef2b --- /dev/null +++ b/eeg-eye-state/info.json @@ -0,0 +1,31 @@ +{ + "name": "eeg-eye-state", + "n_num_features": 14, + "n_cat_features": 0, + "train_size": 9587, + "val_size": 2397, + "test_size": 2996, + "source": "https://www.openml.org/search?type=data&status=active&id=1471&sort=runs", + "task_intro": "**Author**: Oliver Roesler \n**Source**: [UCI](https://archive.ics.uci.edu/ml/datasets/EEG+Eye+State), Baden-Wuerttemberg, Cooperative State University (DHBW), Stuttgart, Germany \n**Please cite**: [UCI](https://archive.ics.uci.edu/ml/citation_policy.html) \n\nAll data is from one continuous EEG measurement with the Emotiv EEG Neuroheadset. The duration of the measurement was 117 seconds. The eye state was detected via a camera during the EEG measurement and added later manually to the file after analyzing the video frames. '1' indicates the eye-closed and '0' the eye-open state. All values are in chronological order with the first measured value at the top of the data.\n\nThe features correspond to 14 EEG measurements from the headset, originally labeled AF3, F7, F3, FC5, T7, P, O1, O2, P8, T8, FC6, F4, F8, AF4, in that order.", + "task_type": "binclass", + "openml_id": 1471, + "imbalance_ratio": 1.228171947047449, + "n_classes": 2, + "num_feature_intro": { + "V1": "V1", + "V2": "V2", + "V3": "V3", + "V4": "V4", + "V5": "V5", + "V6": "V6", + "V7": "V7", + "V8": "V8", + "V9": "V9", + "V10": "V10", + "V11": "V11", + "V12": "V12", + "V13": "V13", + "V14": "V14" + }, + "cat_feature_intro": {} +} \ No newline at end of file diff --git a/eeg-eye-state/y_test.npy b/eeg-eye-state/y_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..7c39624f0a6fc4f74797126973c0ab1f1e464831 --- /dev/null +++ b/eeg-eye-state/y_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c914940504b1f8d446e264c31bcceefba9cadd73973facb8f05602cc64c8124c +size 6291 diff --git a/eeg-eye-state/y_train.npy b/eeg-eye-state/y_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..d38fbc4afede6116a47452fb7f3e22126d487e54 --- /dev/null +++ b/eeg-eye-state/y_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b7fa989f20442fa52e8a094996f5a478f308ade98516d1e622e835e84c08ab27 +size 19487 diff --git a/eeg-eye-state/y_val.npy b/eeg-eye-state/y_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..3d50d69396e38614d7129bfe90234b5b7b6870d5 --- /dev/null +++ b/eeg-eye-state/y_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:edc64a7fbccbb2a81b61c266a6de7d23fcb0aaa67b78321389c225a691521948 +size 5093 diff --git a/electricity/C_test.npy b/electricity/C_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..f042882ae6080ed3920b9549a83bdb996b10d148 --- /dev/null +++ b/electricity/C_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:89cda7093ca99735e58fcf886e7b2777c138d161ec03b463c16207f15e721b75 +size 18471 diff --git a/electricity/C_train.npy b/electricity/C_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..3faf07c09c6a71a697a31cab20279d10d58cda21 --- /dev/null +++ b/electricity/C_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f5393f5110ac951c318001a269447a060d410981af5ecbd5d5dd415dd38f7070 +size 58381 diff --git a/electricity/C_val.npy b/electricity/C_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..2c18e135db26f0079b84833703fd5fd37d62781f --- /dev/null +++ b/electricity/C_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:da81766fc03600f4da102bfcd24064d45238cad9ad98850779ad30b0689db9a9 +size 14841 diff --git a/electricity/N_test.npy b/electricity/N_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..0ba7ab7a767086f75401a32bc1e3668dfa1f5c15 --- /dev/null +++ b/electricity/N_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3b8d62793838d54961f0363cc444f3c7fd1b2543c52634a1a16729e684d434b2 +size 507656 diff --git a/electricity/N_train.npy b/electricity/N_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..0e8e39f6003c6bb858ef4c8727abf98c2ef78525 --- /dev/null +++ b/electricity/N_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6313a54ed0793eaccc01bd4025b2e18c61cde666b2e6aa492b37def713165485 +size 1624072 diff --git a/electricity/N_val.npy b/electricity/N_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..6f6860fe8b1395142aeef9819341a297af2a3938 --- /dev/null +++ b/electricity/N_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:219e687ec28a030671b3a2e9ec1e4b1950be952ca9fbee4244aa31bc0ff45f35 +size 406128 diff --git a/electricity/info.json b/electricity/info.json new file mode 100644 index 0000000000000000000000000000000000000000..0f120d33d75c5357c2f2e722a300463326a5746d --- /dev/null +++ b/electricity/info.json @@ -0,0 +1,26 @@ +{ + "name": "electricity", + "n_num_features": 7, + "n_cat_features": 1, + "train_size": 28999, + "val_size": 7250, + "test_size": 9063, + "source": "https://www.openml.org/search?type=data&status=active&id=151&sort=runs", + "task_intro": "**Author**: M. Harries, J. Gama, A. Bifet \n**Source**: [Joao Gama](http://www.inescporto.pt/~jgama/ales/ales_5.html) - 2009 \n**Please cite**: None \n\n**Electricity** is a widely used dataset described by M. Harries and analyzed by J. Gama (see papers below). This data was collected from the Australian New South Wales Electricity Market. In this market, prices are not fixed and are affected by demand and supply of the market. They are set every five minutes. Electricity transfers to/from the neighboring state of Victoria were done to alleviate fluctuations.\n\nThe dataset (originally named ELEC2) contains 45,312 instances dated from 7 May 1996 to 5 December 1998. Each example of the dataset refers to a period of 30 minutes, i.e. there are 48 instances for each time period of one day. Each example on the dataset has 5 fields, the day of week, the time stamp, the New South Wales electricity demand, the Victoria electricity demand, the scheduled electricity transfer between states and the class label. The class label identifies the change of the price (UP or DOWN) in New South Wales relative to a moving average of the last 24 hours (and removes the impact of longer term price trends). \n\nThe data was normalized by A. Bifet.\n\n### Attribute information \n* Date: date between 7 May 1996 to 5 December 1998. Here normalized between 0 and 1\n* Day: day of the week (1-7)\n* Period: time of the measurement (1-48) in half hour intervals over 24 hours. Here normalized between 0 and 1\n* NSWprice: New South Wales electricity price, normalized between 0 and 1\n* NSWdemand: New South Wales electricity demand, normalized between 0 and 1\n* VICprice: Victoria electricity price, normalized between 0 and 1\n* VICdemand: Victoria electricity demand, normalized between 0 and 1\n* transfer: scheduled electricity transfer between both states, normalized between 0 and 1\n\n### Relevant papers \nM. Harries. Splice-2 comparative evaluation: Electricity pricing. Technical report, The University of South Wales, 1999. \nJ. Gama, P. Medas, G. Castillo, and P. Rodrigues. Learning with drift detection. In SBIA Brazilian Symposium on Artificial Intelligence, pages 286\u2013295, 2004.", + "task_type": "binclass", + "openml_id": 151, + "imbalance_ratio": 1.3554608306908562, + "n_classes": 2, + "num_feature_intro": { + "date": "date", + "period": "period", + "nswprice": "nswprice", + "nswdemand": "nswdemand", + "vicprice": "vicprice", + "vicdemand": "vicdemand", + "transfer": "transfer" + }, + "cat_feature_intro": { + "day": "day" + } +} \ No newline at end of file diff --git a/electricity/y_test.npy b/electricity/y_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..8d171e1d053e3209b63a5a9b01fce1b2bd7306e3 --- /dev/null +++ b/electricity/y_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:430a5c725c4b3f390335b4cb8a7f24c10452154b9423c1687a5010391ecb03f1 +size 18443 diff --git a/electricity/y_train.npy b/electricity/y_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..d9a98d1c70fdbb1dce16415b297d6397490f288b --- /dev/null +++ b/electricity/y_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:736c21aac987ce5e141e5aec5ea680bb959e83a6328de6c3d11588c64d1bce63 +size 58353 diff --git a/electricity/y_val.npy b/electricity/y_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..6cb170a68d38f87d225067f3f37643ca15febe42 --- /dev/null +++ b/electricity/y_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c4cc943ac844802b3dece58233faec369d6b60a2a034b6675c355cc552498c9b +size 14813 diff --git a/elevators/N_test.npy b/elevators/N_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..0cd4cc05a21ed4e826a21c94c55ecf156a683076 --- /dev/null +++ b/elevators/N_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8184e3b2c046b71d57d8be1c4de1dd349a3c96d0e3b6d2650aacbec1f2e549eb +size 478208 diff --git a/elevators/N_train.npy b/elevators/N_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..4dd4e955a55b70817bebe788f9e0312c3eb55c04 --- /dev/null +++ b/elevators/N_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:94862ad45403e5292880dd1e3bb3a883961d53fd8292cc16520127fcd0b3019b +size 1529840 diff --git a/elevators/N_val.npy b/elevators/N_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..072c8ddbbfb6175e89c046ea88a7a999b99ce13e --- /dev/null +++ b/elevators/N_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3fee3fba8c5896df5246532e7b593a57c39db1d599cd1d48df24fec2d4810025 +size 382592 diff --git a/elevators/info.json b/elevators/info.json new file mode 100644 index 0000000000000000000000000000000000000000..305277d2a03e86a4de132c67064afcc483d9690a --- /dev/null +++ b/elevators/info.json @@ -0,0 +1,34 @@ +{ + "name": "elevators", + "n_num_features": 18, + "n_cat_features": 0, + "train_size": 10623, + "val_size": 2656, + "test_size": 3320, + "source": "https://www.openml.org/search?type=data&status=active&id=216&sort=runs", + "task_intro": "This data set is also obtained from the task of controlling a F16\naircraft, although the target variable and attributes are different\nfrom the ailerons domain. 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Inferring Relevance from Eye Movements: Feature Extraction. Helsinki University of Technology, Publications in Computer and Information Science, Report A82. 3 March 2005. Data set at http://www.cis.hut.fi/eyechallenge2005/\n\nCompetition 1 (preprocessed data)\nA straight-forward classification task. We provide pre-computed feature vectors for each word in the eye movement trajectory, with class labels.\n\nThe dataset consist of several assignments. Each assignment consists of a question followed by ten sentences (titles of news articles). One of the sentences is the correct answer to the question (C) and five of the sentences are irrelevant to the question (I). Four of the sentences are relevant to the question (R), but they do not answer it.\n\n\n* Features are in columns, feature vectors in rows.\n* Each assignment is a time sequence of 22-dimensional feature vectors.\n* The first column is the line number, second the assignment number and the next 22 columns (3 to 24) are the different features. Columns 25 to 27 contain extra information about the example. The training data set contains the classification label in the 28th column: \"0\" for irrelevant, \"1\" for relevant and \"2\" for the correct answer.\n* Each example (row) represents a single word. You are asked to return the classification of each read sentence.\n* The 22 features provided are commonly used in psychological studies on eye movement. All of them are not necessarily relevant in this context.\n\nThe objective of the Challenge is to predict the classification labels (I, R, C).\n\n\n\nPlease see the technical report for information of eye movements, experimental setup, baseline methods and references:\n\nJarkko Salojarvi, Kai Puolamaki, Jaana Simola, Lauri Kovanen, Ilpo Kojo, Samuel Kaski. Inferring Relevance from Eye Movements: Feature Extraction. Helsinki University of Technology, Publications in Computer and Information Science, Report A82. 3 March 2005. [PDF]\n\n\n\nModified by TunedIT (converted to ARFF format)\n\n\nFEATURES\n\nThe values in columns marked with an asterisk (*) are same for all occurances of the word.\n\nCOL\tNAME\t\tDESCRIPTION\n1\t#line\t\tLine number\n2\t#assg\t\tAssignment Number\n3\tfixcount\tNumber of fixations to the word\n4*\tfirstPassCnt\tNumber of fixations to the word when it is first encountered\n5*\tP1stFixation\t'1' if fixation occured when the sentence the word was in was encountered the first time\n6*\tP2stFixation\t'1' if fixation occured when the sentence the word was in was encountered the second time\n7*\tprevFixDur\tDuration of previous fixation\n8*\tfirstfixDur\tDuration of the first fixation when the word is first encountered\n9*\tfirstPassFixDur\tSum of durations of fixations when the word is first encountered\n10*\tnextFixDur\tDuration of the next fixation when gaze initially moves from the word\n11\tfirstSaccLen\tLength of the first saccade\n12\tlastSaccLen\tDistance between fixation on the word and the next fixation\n13\tprevFixPos\tDistance between the first fixation preceding the word and the beginning ot the word\n14\tlandingPos\tDistance between the first fixation on the word and the beginning of the word\n15\tleavingPos\tDistance between the last fixation on the word and the beginning of the word\n16\ttotalFixDur\tSum of all durations of fixations to the word\n17\tmeanFixDur\tMean duration of the fixations to the word\n18*\tnRegressFrom\tNumber of regressions leaving from the word\n19*\tregressLen\tSum of durations of regressions initiating from this word\n20*\tnextWordRegress\t'1' if a regression initiated from the following word\n21*\tregressDur\tSum of durations of the fixations on the word during regression\n22\tpupilDiamMax\tMaximum pupil diameter\n23\tpupilDiamLag\tMaximum pupil diameter 0.5 - 1.5 seconds after the beginning of fixation\n24\ttimePrtctg\tFirst fixation duration divided by the total number of fixations\n25\tnWordsInTitle\tNumber of word in the sentence (title) this word is in\n26\ttitleNo\t\tTitle number\n27\twordNo\t\tWord number (ordinal) in this title\n28\tlabel\t\tClassification for training data ('0'=irrelevant, '1'=relevant, '2'=correct)", + "task_type": "multiclass", + "openml_id": 1044, + "imbalance_ratio": 1.4850174216027874, + "n_classes": 3, + "num_feature_intro": { + "lineNo": "lineNo", + "assgNo": "assgNo", + "fixcount": "fixcount", + "firstPassCnt": "firstPassCnt", + "prevFixDur": "prevFixDur", + "firstfixDur": "firstfixDur", + "firstPassFixDur": "firstPassFixDur", + "nextFixDur": "nextFixDur", + "firstSaccLen": "firstSaccLen", + "lastSaccLen": "lastSaccLen", + "prevFixPos": "prevFixPos", + "landingPos": "landingPos", + "leavingPos": "leavingPos", + "totalFixDur": "totalFixDur", + "meanFixDur": "meanFixDur", + "nRegressFrom": "nRegressFrom", + "regressLen": "regressLen", + "regressDur": "regressDur", + "pupilDiamMax": "pupilDiamMax", + "pupilDiamLag": "pupilDiamLag", + "timePrtctg": "timePrtctg", + "nWordsInTitle": "nWordsInTitle", + "titleNo": "titleNo", + "wordNo": "wordNo" + }, + "cat_feature_intro": { + "P1stFixation": "P1stFixation", + "P2stFixation": "P2stFixation", + "nextWordRegress": "nextWordRegress" + } +} \ No newline at end of file diff --git a/eye_movements/y_test.npy b/eye_movements/y_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..d26583780eeb4eb1d4ad95e639e30e5d4e25dbbf --- /dev/null +++ b/eye_movements/y_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:26807720f6278d7673e055c1feea0a0166290a01402b269e709282714873dae2 +size 4681 diff --git a/eye_movements/y_train.npy b/eye_movements/y_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..3cbee310c77fb656663e6848846510363bde92c5 --- /dev/null +++ b/eye_movements/y_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:06595a75577c6980503f48d6933423d1599af458968032debc99b1d8608863c5 +size 14309 diff --git a/eye_movements/y_val.npy b/eye_movements/y_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..158293b9673cf9ed8aed5f2f36295ebf0430246e --- /dev/null +++ b/eye_movements/y_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:cfae950c8599d42977698faa33e81d5551747c4155b0678b2c2236e664ead141 +size 3803 diff --git a/eye_movements_bin/N_test.npy b/eye_movements_bin/N_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..25d6afc8d1a7494f7a12dea707e019bd3d3fe86d --- /dev/null +++ b/eye_movements_bin/N_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b78108f38c2e2d25a2e2714eadbb3cf6c551df51c39171beae19f784eed7fbb4 +size 243648 diff --git a/eye_movements_bin/N_train.npy b/eye_movements_bin/N_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..d118673086391609ceef09f4f87aab04c69a8df4 --- /dev/null +++ b/eye_movements_bin/N_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4be0971468d757b9583eb7abb16f17426150d86de42b49f697efcd0eb67a3cbe +size 779008 diff --git a/eye_movements_bin/N_val.npy b/eye_movements_bin/N_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..13ac125a7c37889abf30bed526bc47cdcfbdc48a --- /dev/null +++ b/eye_movements_bin/N_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:114f3eba825ba97451b547fa77442bbba6fdd440d152797a6145644d499302d8 +size 195008 diff --git a/eye_movements_bin/info.json b/eye_movements_bin/info.json new file mode 100644 index 0000000000000000000000000000000000000000..ac019a5549a8e5f36ed7097b2bce5b68e5127f79 --- /dev/null +++ b/eye_movements_bin/info.json @@ -0,0 +1,37 @@ +{ + "name": "eye_movements", + "n_num_features": 20, + "n_cat_features": 0, + "train_size": 4868, + "val_size": 1218, + "test_size": 1522, + "source": "https://www.openml.org/search?type=data&status=active&id=44130&sort=runs", + "task_intro": "Dataset used in the tabular data benchmark https://github.com/LeoGrin/tabular-benchmark, transformed in the same way. This dataset belongs to the \"classification on numerical features\" benchmark. Original description: \n \n**Author**: \n**Source**: Unknown - Date unknown \n**Please cite**: \n\nJarkko Salojarvi, Kai Puolamaki, Jaana Simola, Lauri Kovanen, Ilpo Kojo, Samuel Kaski. Inferring Relevance from Eye Movements: Feature Extraction. Helsinki University of Technology, Publications in Computer and Information Science, Report A82. 3 March 2005. Data set at http://www.cis.hut.fi/eyechallenge2005/\n\nCompetition 1 (preprocessed data)\nA straight-forward classification task. We provide pre-computed feature vectors for each word in the eye movement trajectory, with class labels.\n\nThe dataset consist of several assignments. Each assignment consists of a question followed by ten sentences (titles of news articles). One of the sentences is the correct answer to the question (C) and five of the sentences are irrelevant to the question (I). Four of the sentences are relevant to the question (R), but they do not answer it.\n\n\n* Features are in columns, feature vectors in rows.\n* Each assignment is a time sequence of 22-dimensional feature vectors.\n* The first column is the line number, second the assignment number and the next 22 columns (3 to 24) are the different features. Columns 25 to 27 contain extra information about the example. The training data set contains the classification label in the 28th column: \"0\" for irrelevant, \"1\" for relevant and \"2\" for the correct answer.\n* Each example (row) represents a single word. You are asked to return the classification of each read sentence.\n* The 22 features provided are commonly used in psychological studies on eye movement. All of them are not necessarily relevant in this context.\n\nThe objective of the Challenge is to predict the classification labels (I, R, C).\n\n\n\nPlease see the technical report for information of eye movements, experimental setup, baseline methods and references:\n\nJarkko Salojarvi, Kai Puolamaki, Jaana Simola, Lauri Kovanen, Ilpo Kojo, Samuel Kaski. Inferring Relevance from Eye Movements: Feature Extraction. Helsinki University of Technology, Publications in Computer and Information Science, Report A82. 3 March 2005. [PDF]\n\n\n\nModified by TunedIT (converted to ARFF format)\n\n\nFEATURES\n\nThe values in columns marked with an asterisk (*) are same for all occurances of the word.\n\nCOL\tNAME\t\tDESCRIPTION\n1\t#line\t\tLine number\n2\t#assg\t\tAssignment Number\n3\tfixcount\tNumber of fixations to the word\n4*\tfirstPassCnt\tNumber of fixations to the word when it is first encountered\n5*\tP1stFixation\t'1' if fixation occured when the sentence the word was in was encountered the first time\n6*\tP2stFixation\t'1' if fixation occured when the sentence the word was in was encountered the second time\n7*\tprevFixDur\tDuration of previous fixation\n8*\tfirstfixDur\tDuration of the first fixation when the word is first encountered\n9*\tfirstPassFixDur\tSum of durations of fixations when the word is first encountered\n10*\tnextFixDur\tDuration of the next fixation when gaze initially moves from the word\n11\tfirstSaccLen\tLength of the first saccade\n12\tlastSaccLen\tDistance between fixation on the word and the next fixation\n13\tprevFixPos\tDistance between the first fixation preceding the word and the beginning ot the word\n14\tlandingPos\tDistance between the first fixation on the word and the beginning of the word\n15\tleavingPos\tDistance between the last fixation on the word and the beginning of the word\n16\ttotalFixDur\tSum of all durations of fixations to the word\n17\tmeanFixDur\tMean duration of the fixations to the word\n18*\tnRegressFrom\tNumber of regressions leaving from the word\n19*\tregressLen\tSum of durations of regressions initiating from this word\n20*\tnextWordRegress\t'1' if a regression initiated from the following word\n21*\tregressDur\tSum of durations of the fixations on the word during regression\n22\tpupilDiamMax\tMaximum pupil diameter\n23\tpupilDiamLag\tMaximum pupil diameter 0.5 - 1.5 seconds after the beginning of fixation\n24\ttimePrtctg\tFirst fixation duration divided by the total number of fixations\n25\tnWordsInTitle\tNumber of word in the sentence (title) this word is in\n26\ttitleNo\t\tTitle number\n27\twordNo\t\tWord number (ordinal) in this title\n28\tlabel\t\tClassification for training data ('0'=irrelevant, '1'=relevant, '2'=correct)", + "task_type": "binclass", + "openml_id": 44130, + "imbalance_ratio": 1.0, + "n_classes": 2, + "num_feature_intro": { + "lineNo": "lineNo", + "assgNo": "assgNo", + "prevFixDur": "prevFixDur", + "firstfixDur": "firstfixDur", + "firstPassFixDur": "firstPassFixDur", + "nextFixDur": "nextFixDur", + "firstSaccLen": "firstSaccLen", + "lastSaccLen": "lastSaccLen", + "prevFixPos": "prevFixPos", + "landingPos": "landingPos", + "leavingPos": "leavingPos", + "totalFixDur": "totalFixDur", + "meanFixDur": "meanFixDur", + "regressLen": "regressLen", + "regressDur": "regressDur", + "pupilDiamMax": "pupilDiamMax", + "pupilDiamLag": "pupilDiamLag", + "timePrtctg": "timePrtctg", + "titleNo": "titleNo", + "wordNo": "wordNo" + }, + "cat_feature_intro": {} +} \ No newline at end of file diff --git a/eye_movements_bin/y_test.npy b/eye_movements_bin/y_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..9a5d79981a39fd37fb603774f99a1598c1ff8fc3 --- /dev/null +++ b/eye_movements_bin/y_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c236fb9b0eebaf4d81035453a96d1e89f6bca4cc896879a3ff47a5e4969ebd30 +size 3341 diff --git a/eye_movements_bin/y_train.npy b/eye_movements_bin/y_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..f9198086112ad92f356efc20845f5ee52be06d5c --- /dev/null +++ b/eye_movements_bin/y_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7dbbad5845e9ff686d72be015170558013201d66cf5f82f8c527a7985e796ed1 +size 10039 diff --git a/eye_movements_bin/y_val.npy b/eye_movements_bin/y_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..e8a7e096bad7cd0a62faf5024195ce212946d67c --- /dev/null +++ b/eye_movements_bin/y_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:14546e55115cfa953cdb6a226fc965e274bdcfcb2d1b18e070bc51e9c035e9a5 +size 2733 diff --git a/fifa/N_test.npy b/fifa/N_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..921dbf86841b8ca8e2e85c97368248580940c65b --- /dev/null +++ b/fifa/N_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ff91b9618994e313d22823422adb54574e8c2833274de526718798a750f3b95f +size 144648 diff --git a/fifa/N_train.npy b/fifa/N_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..443d765cb03379ce976e9cb820499c6099e23b08 --- /dev/null +++ b/fifa/N_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d0586c7bdddd4ab11a576d7ca8b39e920bc453fdfb1f10dcac3a470ec016254b +size 462528 diff --git a/fifa/N_val.npy b/fifa/N_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..05be9ac4daf3f1fa2f3b005678533e8a95db6ad3 --- /dev/null +++ b/fifa/N_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8476b4326cc8d4d792197b35d99140c5816a6f51180d08646ba7e8a1f4b37075 +size 115728 diff --git a/fifa/info.json b/fifa/info.json new file mode 100644 index 0000000000000000000000000000000000000000..caa1a7512d301640bb39352b2ac2676a925d5bdb --- /dev/null +++ b/fifa/info.json @@ -0,0 +1,21 @@ +{ + "name": "fifa", + "n_num_features": 5, + "n_cat_features": 0, + "train_size": 11560, + "val_size": 2890, + "test_size": 3613, + "source": "https://www.openml.org/search?type=data&status=active&id=44026&sort=runs", + "task_intro": "Dataset used in the tabular data benchmark https://github.com/LeoGrin/tabular-benchmark, transformed in the same way. This dataset belongs to the \"regression on numerical features\" benchmark.\n Original source: https://www.kaggle.com/datasets/stefanoleone992/fifa-22-complete-player-dataset?select=players_22.csv Please give credit to the original source if you use this dataset.", + "task_type": "regression", + "openml_id": 44026, + "n_classes": 1, + "num_feature_intro": { + "age": "age", + "height_cm": "height_cm", + "weight_kg": "weight_kg", + "release_clause_eur": "release_clause_eur", + "club_joined": "club_joined" + }, + "cat_feature_intro": {} +} \ No newline at end of file diff --git a/fifa/y_test.npy b/fifa/y_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..5a95c4f857973f4b03215aae262959168034c620 --- /dev/null +++ b/fifa/y_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:76697024456a5e3c6c8abca529a3f096d586e396540601beca4836d8617b5b1e +size 29032 diff --git a/fifa/y_train.npy b/fifa/y_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..52d0aeeaab576127bc59afceef29c4c11dc9b0bd --- /dev/null +++ b/fifa/y_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:df215e128d72fd4f0f66c5cab82a6d220b696594047234df98d1d7ce602e6f94 +size 92608 diff --git a/fifa/y_val.npy b/fifa/y_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..642d3c90e5f177d11535c296b1ab0c33494c802f --- /dev/null +++ b/fifa/y_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:64b51e94502a1a568d763eec2481596cebc8cf9bea78d64c3a86acb38228e471 +size 23248 diff --git a/first-order-theorem-proving/N_test.npy b/first-order-theorem-proving/N_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..b4a9d45cbd369c9116090afc4aaaa0eab916a6db --- /dev/null +++ b/first-order-theorem-proving/N_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e96da69b679af52cf13777d636e380dfc747d307d3210a2b62efb6eff84ef85c +size 499520 diff --git a/first-order-theorem-proving/N_train.npy b/first-order-theorem-proving/N_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..5e6e4eb12bf2ef2e8358b6fc276dd2f53b0c1f92 --- /dev/null +++ b/first-order-theorem-proving/N_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b2701164751c68cf5ebd1a3872ac7c7336965e083a6555238a9360980eb813e5 +size 1597448 diff --git a/first-order-theorem-proving/N_val.npy b/first-order-theorem-proving/N_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..23306447941d02ae37d58c70184e4f9ede0789c4 --- /dev/null +++ b/first-order-theorem-proving/N_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:39cfd5fa646aaa4a4c44537a1ded79409dfcb88621f5973e8251bd2a6fe96b10 +size 399560 diff --git a/first-order-theorem-proving/info.json b/first-order-theorem-proving/info.json new file mode 100644 index 0000000000000000000000000000000000000000..89b8531513d52f76d0cd2d2d63388716f33da925 --- /dev/null +++ b/first-order-theorem-proving/info.json @@ -0,0 +1,68 @@ +{ + "name": "first-order-theorem-proving", + "n_num_features": 51, + "n_cat_features": 0, + "train_size": 3915, + "val_size": 979, + "test_size": 1224, + "source": "https://www.openml.org/search?type=data&status=active&id=1475&sort=runs", + "task_intro": "**Author**: James P Bridge, Sean B Holden and Lawrence C Paulson \n \n**Source**: [UCI](https://archive.ics.uci.edu/ml/datasets/First-order+theorem+proving) \n\n**Please cite**: James P Bridge, Sean B Holden and Lawrence C Paulson . Machine learning for first-order theorem proving: learning to select a good heuristic. Journal of Automated Reasoning, Springer 2012/13. \n\nSource:\n\nJames P Bridge, Sean B Holden and Lawrence C Paulson \n\nUniversity of Cambridge \nComputer Laboratory \nWilliam Gates Building \n15 JJ Thomson Avenue \nCambridge CB3 0FD \nUK \n\n+44 (0)1223 763500 \nforename.surname '@' cl.cam.ac.uk\n\n\nData Set Information:\n\nSee the file dataset file.\n\n\nAttribute Information:\n\nThe attributes are a mixture of static and dynamic features derived from theorems to be proved. See the paper for full details.", + "task_type": "multiclass", + "openml_id": 1475, + "imbalance_ratio": 5.255144032921811, + "n_classes": 6, + "num_feature_intro": { + "V1": "V1", + "V2": "V2", + "V3": "V3", + "V4": "V4", + "V5": "V5", + "V6": "V6", + "V7": "V7", + "V8": "V8", + "V9": "V9", + "V10": "V10", + "V11": "V11", + "V12": "V12", + "V13": "V13", + "V14": "V14", + "V15": "V15", + "V16": "V16", + "V17": "V17", + "V18": "V18", + "V19": "V19", + "V20": "V20", + "V21": "V21", + "V22": "V22", + "V23": "V23", + "V24": "V24", + "V25": "V25", + "V26": "V26", + "V27": "V27", + "V28": "V28", + "V29": "V29", + "V30": "V30", + "V31": "V31", + "V32": "V32", + "V33": "V33", + "V34": "V34", + "V35": "V35", + "V36": "V36", + "V37": "V37", + "V38": "V38", + "V39": "V39", + "V40": "V40", + "V41": "V41", + "V42": "V42", + "V43": "V43", + "V44": "V44", + "V45": "V45", + "V46": "V46", + "V47": "V47", + "V48": "V48", + "V49": "V49", + "V50": "V50", + "V51": "V51" + }, + "cat_feature_intro": {} +} \ No newline at end of file diff --git a/first-order-theorem-proving/y_test.npy b/first-order-theorem-proving/y_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..a85c5e318110d53b7537f54edd8a3b6b0f910319 --- /dev/null +++ b/first-order-theorem-proving/y_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:867803bf8883bbf2582d6ec74ebfc0faa710389a0b05aaf7b54db6997d58f117 +size 2769 diff --git a/first-order-theorem-proving/y_train.npy b/first-order-theorem-proving/y_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..df5d911cd2d98fbb4151f72774184bcbcf278f04 --- /dev/null +++ b/first-order-theorem-proving/y_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:702df4061df40b0c5ee71eb232dfe3fef08ac9d45aaa997b862b7f471ea44c52 +size 8155 diff --git a/first-order-theorem-proving/y_val.npy b/first-order-theorem-proving/y_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..95ff62512fcee50cac76f34482245bba346894a9 --- /dev/null +++ b/first-order-theorem-proving/y_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:177b5f1face52b77915f4f355a0b3b4b424a78679efce031d30c459384f74da2 +size 2277 diff --git a/fried/N_test.npy b/fried/N_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..691e6181d4b5d6985ee64ce853060dbad6a494d3 --- /dev/null +++ b/fried/N_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:066861258598d182942ebfbc580cbc3d1735c57beeda11854e7159ade6fd4550 +size 652448 diff --git a/fried/N_train.npy b/fried/N_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..9465758624543075fde616021c06fc54b9f22c19 --- /dev/null +++ b/fried/N_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:5dec96ed015e975fb7e1db0ee0beb06c8f40d13151fd327b5e15de7dd312118a +size 2087408 diff --git a/fried/N_val.npy b/fried/N_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..b54af43a3a463f84c8e5023ce0df20b51633ad5c --- /dev/null +++ b/fried/N_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0137b51d9879597235b34a9b3d2e0798a9b3a0363fc9f134bcd7bf9fdbec6611 +size 521968 diff --git a/fried/info.json b/fried/info.json new file mode 100644 index 0000000000000000000000000000000000000000..0a1bd92cd6a7cc4109c7331e0933687a35c4aaea --- /dev/null +++ b/fried/info.json @@ -0,0 +1,26 @@ +{ + "name": "fried", + "n_num_features": 10, + "n_cat_features": 0, + "train_size": 26091, + "val_size": 6523, + "test_size": 8154, + "source": "https://www.openml.org/search?type=data&status=active&id=564&sort=runs", + "task_intro": "**Author**: \n**Source**: Unknown - Date unknown \n**Please cite**: \n\nThis is an artificial data set used in Friedman (1991) and also\ndescribed in Breiman (1996,p.139). The cases are generated using the\nfollowing method: Generate the values of 10 attributes, X1, ..., X10\nindependently each of which uniformly distributed over [0,1]. Obtain\nthe value of the target variable Y using the equation:\n\nY = 10 * sin(pi * X1 * X2) + 20 * (X3 - 0.5)^2 + 10 * X4 + 5 * X5 + sigma(0,1)\n\nSource: collection of regression datasets by Luis Torgo (ltorgo@ncc.up.pt) at\nhttp://www.ncc.up.pt/~ltorgo/Regression/DataSets.html\nOriginal source: Breiman (1996, p.139).\nCharacteristics: 40768 cases, 11 continuous attributes\n\nReferences\n\nBREIMAN, L. (1996): Bagging Predictors. Machine Learning, 24(3), 123--140. Kluwer Academic Publishers.\nFRIEDMAN, J. (1991): Multivariate Adaptative Regression Splines. Annals of Statistics, 19:1, 1--141.", + "task_type": "regression", + "openml_id": 564, + "n_classes": 1, + "num_feature_intro": { + "X1": "X1", + "X2": "X2", + "X3": "X3", + "X4": "X4", + "X5": "X5", + "X6": "X6", + "X7": "X7", + "X8": "X8", + "X9": "X9", + "X10": "X10" + }, + "cat_feature_intro": {} +} \ No newline at end of file diff --git a/fried/y_test.npy b/fried/y_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..aa57edceed130f1426b337b1225569f92a718c55 --- /dev/null +++ b/fried/y_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6050d0e13f51fadd3145944c47fd6b22fc4e3193efd78e9587bdd749e384cc18 +size 65360 diff --git a/fried/y_train.npy b/fried/y_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..2fc2894d7053c08b5fd1850c7d0f9b48bb967c57 --- /dev/null +++ b/fried/y_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid 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0000000000000000000000000000000000000000..5bd11eed946d2e7f4f52b4c758d63d5f1e748e8c --- /dev/null +++ b/gas-drift/info.json @@ -0,0 +1,145 @@ +{ + "name": "gas-drift", + "n_num_features": 128, + "n_cat_features": 0, + "train_size": 8902, + "val_size": 2226, + "test_size": 2782, + "source": "https://www.openml.org/search?type=data&status=active&id=1476&sort=runs", + "task_intro": "**Author**: Alexander Vergara \n**Source**: [UCI](https://archive.ics.uci.edu/ml/datasets/gas+sensor+array+drift+dataset) - 2012 \n**Please cite**: Alexander Vergara, Shankar Vembu, Tuba Ayhan, Margaret A. Ryan, Margie L. Homer, Ram\u00f3n Huerta. Chemical gas sensor drift compensation using classifier ensembles, Sensors and Actuators B: Chemical (2012) doi: 10.1016/j.snb.2012.01.074. \n\n### Description\nGas Sensor Array Drift Dataset Data Set\n\n### Sources\n```\n(a) Creators: Alexander Vergara (vergara '@' ucsd.edu) \nBioCircutis Institute \nUniversity of California San Diego \nSan Diego, California, USA \n\n(b) Donors: Alexander Vergara (vergara '@' ucsd.edu) \nRamon Huerta (rhuerta '@' ucsd.edu) \n```\n\n### Dataset Information\n\nThis archive contains 13910 measurements from 16 chemical sensors utilized in simulations for drift compensation in a discrimination task of 6 gases at various levels of concentrations. \nThe goal is to achieve good performance (or as low degradation as possible) over time, as reported in the paper mentioned below in Section 2: Data collection. \n\nThe primary purpose of providing this dataset is to make it freely accessible online to the chemo-sensor research community and artificial intelligence to develop strategies to cope with sensor/concept drift. The dataset can be used exclusively for research purposes. Commercial purposes are fully excluded.\n\nThe dataset was gathered within January 2007 to February 2011 (36 months) in a gas delivery platform facility situated at the ChemoSignals Laboratory in the BioCircuits Institute, University of California San Diego. \n\nBeing completely operated by a fully computerized environment controlled by a LabVIEW's National Instruments software on a PC fitted with the appropriate serial data acquisition boards. The measurement system platform provides versatility for obtaining the desired concentrations of the chemical substances of interest with high accuracy and in a highly reproducible manner, minimizing thereby the common mistakes caused by human intervention and making it possible to exclusively concentrate on the chemical sensors for compensating real drift.\n\nThe resulting dataset comprises recordings from six distinct pure gaseous substances, namely Ammonia, Acetaldehyde, Acetone, Ethylene, Ethanol, and Toluene, each dosed at a wide variety of concentration values ranging from 5 to 1000 ppmv. An extension of this dataset with the concentration values is available at [Gas Sensor Array Drift Dataset at Different Concentrations Data Set](http://archive.ics.uci.edu/ml/datasets/Gas+Sensor+Array+Drift+Dataset+at+Different+Concentrations).\n\n\n### Attribute Information\n\nThe response of the said sensors is read-out in the form of the resistance across the active layer of each sensor. Hence each measurement produced a 16-channel time series, each of which represented by an aggregate of features reflecting all the dynamic processes occurring at the sensor surface in reaction to the chemical substance being evaluated. \n\nIn particular, two distinct types of features were considered in the creation of this dataset: \n(i) The so-called steady-state feature (\u00ce\u201dR), defined as the difference of the maximal resistance change and the baseline and its normalized version expressed by the ratio of the maximal resistance and the baseline values when the chemical vapor is present in the test chamber; and \n(ii) an aggregate of features reflecting the sensor dynamics of the increasing/decaying transient portion of the sensor response during the entire measurement procedure under controlled conditions, namely the exponential moving average (ema\u00ce\u00b1). These aggregate of features is a transform, borrowed from the field of econometrics originally introduced to the chemo-sensing community by Muezzinoglu et al. (2009), that converts the said transient portion into a real scalar, by estimating the maximum value \u00e2\u20ac\u201dminimum for the decaying portion of the sensor response\u00e2\u20ac\u201d of its exponential moving average (ema\u00ce\u00b1), with an initial condition set to zero and a scalar smoothing parameter of the operator, \u00ce\u00b1, that defines both the quality of the feature and the time of its occurrence along the time series the scalar, set to range between 0 and 1. In particular, three different values for \u00ce\u00b1 were set to obtain three different feature values from the pre-recorded rising portion of the sensor response and three additional features with the same \u00ce\u00b1 values but for the decaying portion of the sensor response, covering thus the entire sensor response dynamics. \n\nFor a more detailed analysis and discussion on these features as well as a graphical illustration of them please refer to Section 2.3 and Figure 2, respectively of the annotated manuscript.\n\nOnce the abovementioned features are calculated, one is to form a feature vector containing the 8 features extracted from each particular sensor multiplied by the 16 sensors considered here. In the end, there is a resulting 128-dimensional feature vector containing all the features indicated above.\n\nThere are six possible classes: \n```\n1: Ethanol \n2: Ethylene \n3: Ammonia \n4: Acetaldehyde \n5: Acetone \n6: Toluene\n```\n### Relevant Papers\n\nAlexander Vergara, Shankar Vembu, Tuba Ayhan, Margaret A. Ryan, Margie L. 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This dataset belongs to the \"classification on numerical features\" benchmark.\n Original source: https://www.kaggle.com/datasets/averkiyoliabev/home-equity-line-of-creditheloc?select=heloc_dataset_v1+%281%29.csv Please give credit to the original source if you use this dataset.", + "task_type": "binclass", + "openml_id": 45023, + "imbalance_ratio": 1.0, + "n_classes": 2, + "num_feature_intro": { + "ExternalRiskEstimate": "ExternalRiskEstimate", + "MSinceOldestTradeOpen": "MSinceOldestTradeOpen", + "MSinceMostRecentTradeOpen": "MSinceMostRecentTradeOpen", + "AverageMInFile": "AverageMInFile", + "NumSatisfactoryTrades": "NumSatisfactoryTrades", + "NumTrades60Ever2DerogPubRec": "NumTrades60Ever2DerogPubRec", + "NumTrades90Ever2DerogPubRec": "NumTrades90Ever2DerogPubRec", + "PercentTradesNeverDelq": "PercentTradesNeverDelq", + "MSinceMostRecentDelq": "MSinceMostRecentDelq", + "MaxDelq2PublicRecLast12M": "MaxDelq2PublicRecLast12M", + "NumTotalTrades": "NumTotalTrades", + "NumTradesOpeninLast12M": "NumTradesOpeninLast12M", + "PercentInstallTrades": "PercentInstallTrades", + "MSinceMostRecentInqexcl7days": "MSinceMostRecentInqexcl7days", + "NumInqLast6M": "NumInqLast6M", + "NumInqLast6Mexcl7days": "NumInqLast6Mexcl7days", + "NetFractionRevolvingBurden": "NetFractionRevolvingBurden", + "NetFractionInstallBurden": "NetFractionInstallBurden", + "NumRevolvingTradesWBalance": "NumRevolvingTradesWBalance", + "NumInstallTradesWBalance": "NumInstallTradesWBalance", + "NumBank2NatlTradesWHighUtilization": "NumBank2NatlTradesWHighUtilization", + "PercentTradesWBalance": "PercentTradesWBalance" + }, + "cat_feature_intro": {} +} \ No newline at end of file diff --git a/heloc/y_test.npy b/heloc/y_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..fb3a8e94355133b888a5ad0ddd861863e136ee19 --- /dev/null +++ b/heloc/y_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:111cb8ac90b226fdfd1b733854266dea0fd0620e2d3599fb436f8c974d5daa97 +size 4297 diff --git a/heloc/y_train.npy b/heloc/y_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..ebfc5257f5c9e783b50be765211ff1c35b7f76ec --- /dev/null +++ b/heloc/y_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:dc8dc344fd8a3308497a3abf853c9a0e5c6115b909ad26cd71f168af182a4dfd +size 13107 diff --git a/heloc/y_val.npy b/heloc/y_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..a59114050e2aa81bf874a8a1de9fe5975e25bb15 --- /dev/null +++ b/heloc/y_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3458d7e19bd08543a7a921cd19f9d332a519d0d145d3114b4e95e900f6f4c2ca +size 3497 diff --git a/hill-valley/N_test.npy b/hill-valley/N_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..19a43ea0502ca85a63649475be67bdc70d072a60 --- /dev/null +++ b/hill-valley/N_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:56f13a09a005afbd92d2cd837b25edcd39e6b8ebd56d429c48367952f90a100c +size 194528 diff --git a/hill-valley/N_train.npy b/hill-valley/N_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..15c450ae97f6e76277aac1a62f434eefce688bdf --- /dev/null +++ b/hill-valley/N_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:73f7b5d23aa57b78568f9128f7825f64b88215acd6243fde4a2583a9ce55f445 +size 620128 diff --git a/hill-valley/N_val.npy b/hill-valley/N_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..8f2a53660c48dc086ca1cbf52b923b62e9f9ebc8 --- /dev/null +++ b/hill-valley/N_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:828a01fad81316e3255cfb46d00da14146d8271bf7318ae15a05165e99ab0c30 +size 155328 diff --git a/hill-valley/info.json b/hill-valley/info.json new file mode 100644 index 0000000000000000000000000000000000000000..813aa03cfed65bd747dfed668038a6ae54d7bf1b --- /dev/null +++ b/hill-valley/info.json @@ -0,0 +1,117 @@ +{ + "name": "hill-valley", + "n_num_features": 100, + "n_cat_features": 0, + "train_size": 775, + "val_size": 194, + "test_size": 243, + "source": "https://www.openml.org/search?type=data&status=active&id=1479&sort=runs", + "task_intro": "**Author**: Lee Graham, Franz Oppacher \n**Source**: [UCI](http://archive.ics.uci.edu/ml/datasets/hill-valley) \n**Please cite**: \n\nEach record represents 100 points on a two-dimensional graph. When plotted in order (from 1 through 100) as the Y coordinate, the points will create either a Hill (a \u201cbump\u201d in the terrain) or a Valley (a \u201cdip\u201d in the terrain). \nSee the original source for some examples of these graphs. \n\nIn the original form, there are six files. This is the non-noisy version, with training and test sets merged. \n\n### Attribute Information:\n\n1-100: Labeled \u201cX##\u201d. Floating point values (numeric), the Y-values of the graphs. \n101: Labeled \u201cclass\u201d. Binary {0, 1} representing {valley, hill}", + "task_type": "binclass", + "openml_id": 1479, + "imbalance_ratio": 1.0, + "n_classes": 2, + "num_feature_intro": { + "V1": "V1", + "V2": "V2", + "V3": "V3", + "V4": "V4", + "V5": "V5", + "V6": "V6", + "V7": "V7", + "V8": "V8", + "V9": "V9", + "V10": "V10", + "V11": "V11", + "V12": "V12", + "V13": "V13", + "V14": "V14", + "V15": "V15", + "V16": "V16", + "V17": "V17", + "V18": "V18", + "V19": "V19", + "V20": "V20", + "V21": "V21", + "V22": "V22", + "V23": "V23", + "V24": "V24", + "V25": "V25", + "V26": "V26", + "V27": "V27", + "V28": "V28", + "V29": "V29", + "V30": "V30", + "V31": "V31", + "V32": "V32", + "V33": "V33", + "V34": "V34", + "V35": "V35", + "V36": "V36", + "V37": "V37", + "V38": "V38", + "V39": "V39", + "V40": "V40", + "V41": "V41", + "V42": "V42", + "V43": "V43", + "V44": "V44", + "V45": "V45", + "V46": "V46", + "V47": "V47", + "V48": "V48", + "V49": "V49", + "V50": "V50", + "V51": "V51", + "V52": "V52", + "V53": "V53", + "V54": "V54", + "V55": "V55", + "V56": "V56", + "V57": "V57", + "V58": "V58", + "V59": "V59", + "V60": "V60", + "V61": "V61", + "V62": "V62", + "V63": "V63", + "V64": "V64", + "V65": "V65", + "V66": "V66", + "V67": "V67", + "V68": "V68", + "V69": "V69", + "V70": "V70", + "V71": "V71", + "V72": "V72", + "V73": "V73", + "V74": "V74", + "V75": "V75", + "V76": "V76", + "V77": "V77", + "V78": "V78", + "V79": "V79", + "V80": "V80", + "V81": "V81", + "V82": "V82", + "V83": "V83", + "V84": "V84", + "V85": "V85", + "V86": "V86", + "V87": "V87", + "V88": "V88", + "V89": "V89", + "V90": "V90", + "V91": "V91", + "V92": "V92", + "V93": "V93", + "V94": "V94", + "V95": "V95", + "V96": "V96", + "V97": "V97", + "V98": "V98", + "V99": "V99", + "V100": "V100" + }, + "cat_feature_intro": {} +} \ No newline at end of file diff --git a/hill-valley/y_test.npy b/hill-valley/y_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..7acbc1a1662508b0c6bfcf2ccc1be62b71fbabb4 --- /dev/null +++ b/hill-valley/y_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:85d021e45b64b78f46f66d978724d1bda651236fab14168a62114ebfa5248735 +size 780 diff --git a/hill-valley/y_train.npy b/hill-valley/y_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..f5ecc4b1fb24dc04cfcd9f0f79ba1333b4abfb4e --- /dev/null +++ b/hill-valley/y_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:383412403c37062bd7bc6a549122e174b956645757b1cc461b3a7066090288a9 +size 1845 diff --git a/hill-valley/y_val.npy b/hill-valley/y_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..cdb5b1375be691770eb570c30397631947372c78 --- /dev/null +++ b/hill-valley/y_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7baa6cc65605273bfafbcd154afeb1e3703382fd17b693407b3820c4a991848e +size 682 diff --git a/house_16H/N_test.npy b/house_16H/N_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..6437e5edaa3fef1ad3a446b86735c0e86cb56dc8 --- /dev/null +++ b/house_16H/N_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4b3217b4b706161a192ee0b29a583b52f85daf01e563385112e6cd3947e7ad81 +size 345472 diff --git a/house_16H/N_train.npy b/house_16H/N_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..7d13d9d300167406990b419db3ce785c177ddcf8 --- /dev/null +++ b/house_16H/N_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b9a31bffe0c0fd97c7f3ca7de50a0da359646301965118c93d7b5100066d5553 +size 1105024 diff --git a/house_16H/N_val.npy b/house_16H/N_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..a9f702ff0863ecdb7636c6240e4698eaa91e2753 --- /dev/null +++ b/house_16H/N_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9610effdfa0485df00ef90cc71506f63f327262cd47bd81601668ed2e4e3fb34 +size 276352 diff --git a/house_16H/info.json b/house_16H/info.json new file mode 100644 index 0000000000000000000000000000000000000000..6a1cf0c1bd0ee98be631cb9aa7b19080fb60ff24 --- /dev/null +++ b/house_16H/info.json @@ -0,0 +1,33 @@ +{ + "name": "house_16H", + "n_num_features": 16, + "n_cat_features": 0, + "train_size": 8632, + "val_size": 2158, + "test_size": 2698, + "source": "https://www.openml.org/search?type=data&status=active&id=44123&sort=runs", + "task_intro": "Dataset used in the tabular data benchmark https://github.com/LeoGrin/tabular-benchmark, transformed in the same way. This dataset belongs to the \"classification on numerical features\" benchmark. Original description: \n \n**Author**: \n**Source**: Unknown - Date unknown \n**Please cite**: \n\nBinarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others as negative ('N').", + "task_type": "binclass", + "openml_id": 44123, + "imbalance_ratio": 1.0, + "n_classes": 2, + "num_feature_intro": { + "P1": "P1", + "P5p1": "P5p1", + "P6p2": "P6p2", + "P11p4": "P11p4", + "P14p9": "P14p9", + "P15p1": "P15p1", + "P15p3": "P15p3", + "P16p2": "P16p2", + "P18p2": "P18p2", + "P27p4": "P27p4", + "H2p2": "H2p2", + "H8p2": "H8p2", + "H10p1": "H10p1", + "H13p1": "H13p1", + "H18pA": "H18pA", + "H40p4": "H40p4" + }, + "cat_feature_intro": {} +} \ No newline at end of file diff --git a/house_16H/y_test.npy b/house_16H/y_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..44fd97728a95f549538fdec5a1997350b7606e87 --- /dev/null +++ b/house_16H/y_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7aefece0b129d0d6ebc9467a4e44ebffe1da82a379047f2a41da26ecef6ad346 +size 5695 diff --git a/house_16H/y_train.npy b/house_16H/y_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..fb529a49e463528fdebb60e033d3b254f4101e4f --- /dev/null +++ b/house_16H/y_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2b022a9940bcb7627361a016c84ee4e14afc768e1a207c39c9789494a28dadae +size 17575 diff --git a/house_16H/y_val.npy b/house_16H/y_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..c2c1d13b44bba8a660099fe058fff2ccd3ea7e01 --- /dev/null +++ b/house_16H/y_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6e29324d13a1e32553f9925c95c911712b14226f6a9cb4dfb4d946091827e61a +size 4615 diff --git a/house_16H_reg/N_test.npy b/house_16H_reg/N_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..bea6ba08e43bcd7f174a4d1ade28b9b44221ef13 --- /dev/null +++ b/house_16H_reg/N_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8efb76b0e943de0c001b38c73eb60963ded0f435b0ff9711953b9cedb2c0ea4e +size 583424 diff --git a/house_16H_reg/N_train.npy b/house_16H_reg/N_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..99afa1ebdf756e13e6c881037c765b44b929b256 --- /dev/null +++ b/house_16H_reg/N_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6847b7ea190a1255b3ae07b4cc9a971a7dfc7e2009512a574eb06b615d7da855 +size 1866496 diff --git a/house_16H_reg/N_val.npy b/house_16H_reg/N_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..4230113a1db6c2be67ae1689eafbdab8d952e9c6 --- /dev/null +++ b/house_16H_reg/N_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9a4f8caae41ebf31ecfe97a3a70efadaff3b45a42f489df3726d76cedf21667c +size 466816 diff --git a/house_16H_reg/info.json b/house_16H_reg/info.json new file mode 100644 index 0000000000000000000000000000000000000000..d25b401b2b3e496a885130afc03fba5c0ee57e34 --- /dev/null +++ b/house_16H_reg/info.json @@ -0,0 +1,32 @@ +{ + "name": "house_16H", + "n_num_features": 16, + "n_cat_features": 0, + "train_size": 14581, + "val_size": 3646, + "test_size": 4557, + "source": "https://www.openml.org/search?type=data&status=active&id=574&sort=runs", + "task_intro": "**Author**: \n**Source**: Unknown - Date unknown \n**Please cite**: \n\nThis database was designed on the basis of data provided by US Census\nBureau [http://www.census.gov] (under Lookup Access\n[http://www.census.gov/cdrom/lookup]: Summary Tape File 1). The data\nwere collected as part of the 1990 US census. These are mostly counts\ncumulated at different survey levels. For the purpose of this data set\na level State-Place was used. Data from all states was obtained. Most\nof the counts were changed into appropriate proportions. There are 4\ndifferent data sets obtained from this database: House(8H) House(8L)\nHouse(16H) House(16L) These are all concerned with predicting the\nmedian price of the house in the region based on demographic\ncomposition and a state of housing market in the region. A number in\nthe name signifies the number of attributes of the data set. A\nfollowing letter denotes a very rough approximation to the difficulty\nof the task. For Low task difficulty, more correlated attributes were\nchosen as signified by univariate smooth fit of that input on the\ntarget. Tasks with High difficulty have had their attributes chosen to\nmake the modelling more difficult due to higher variance or lower\ncorrelation of the inputs to the target.\n\nOriginal source: DELVE repository of data.\nSource: collection of regression datasets by Luis Torgo (ltorgo@ncc.up.pt) at\nhttp://www.ncc.up.pt/~ltorgo/Regression/DataSets.html\nCharacteristics: 22784 cases, 17 continuous attributes.", + "task_type": "regression", + "openml_id": 574, + "n_classes": 1, + "num_feature_intro": { + "P1": "P1", + "P5p1": "P5p1", + "P6p2": "P6p2", + "P11p4": "P11p4", + "P14p9": "P14p9", + "P15p1": "P15p1", + "P15p3": "P15p3", + "P16p2": "P16p2", + "P18p2": "P18p2", + "P27p4": "P27p4", + "H2p2": "H2p2", + "H8p2": "H8p2", + "H10p1": "H10p1", + "H13p1": "H13p1", + "H18pA": "H18pA", + "H40p4": "H40p4" + }, + "cat_feature_intro": {} +} \ No newline at end of file diff --git a/house_16H_reg/y_test.npy b/house_16H_reg/y_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..213ce12001cf0016a045c76dcb766abf28c9cd86 --- /dev/null +++ b/house_16H_reg/y_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ad7925c4475b21675b92f0aaab5e3d391df838e9aab65f2b97e6faf09ff6b5ac +size 36584 diff --git a/house_16H_reg/y_train.npy b/house_16H_reg/y_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..7cb6377f3641dd7cfb844dc3ac6a7c46fc7f0692 --- /dev/null +++ b/house_16H_reg/y_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:26359cfa6c4cfb633503e10ad72418001bae309ff24402334bfdd81f40e2e265 +size 116776 diff --git a/house_16H_reg/y_val.npy b/house_16H_reg/y_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..c83f5f31b2c6fe84e3c67daf9c23e9c0fcb9c7b7 --- /dev/null +++ b/house_16H_reg/y_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:22b0b69474151d4afe5fd4c4b4e7bdf9d3d4b2e651fa47c53b0f6462b842222c +size 29296 diff --git a/house_8L/N_test.npy b/house_8L/N_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..643112249f4e94c86fcf4d33516fefb10222e450 --- /dev/null +++ b/house_8L/N_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:469cf425796af4ed0af3c16d87bea660fe66d9f8b71e3ba1058f7503b7972ea2 +size 291776 diff --git a/house_8L/N_train.npy b/house_8L/N_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..7cfa6d367aa0cebdc234c246a93b71bf0184e66d --- /dev/null +++ b/house_8L/N_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0af31c4f913c368871683d727afc8585dd57da0c401ac2145a14d696a605eb48 +size 933312 diff --git a/house_8L/N_val.npy b/house_8L/N_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..83bb7c510108c4ec1c79c638a0621e7855c87f14 --- /dev/null +++ b/house_8L/N_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d9e0d8d371368a00f593adcde8612284250b9e2042330ccbe858536553be6dc7 +size 233472 diff --git a/house_8L/info.json b/house_8L/info.json new file mode 100644 index 0000000000000000000000000000000000000000..c43fbd1e46d7e1bc537ca9334d4470ff3d2fab19 --- /dev/null +++ b/house_8L/info.json @@ -0,0 +1,24 @@ +{ + "name": "house_8L", + "n_num_features": 8, + "n_cat_features": 0, + "train_size": 14581, + "val_size": 3646, + "test_size": 4557, + "source": "https://www.openml.org/search?type=data&status=active&id=218&sort=runs", + "task_intro": "**Author**: \n**Source**: Unknown - \n**Please cite**: \n\nThis database was designed on the basis of data provided by US Census\n Bureau [http://www.census.gov] (under Lookup Access\n [http://www.census.gov/cdrom/lookup]: Summary Tape File 1). 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sha256:d54ce9ce5bff799ef1f45858935ea83b0fc6e1ab796e08942c877af988a9c3e5 +size 498080 diff --git a/house_sales_reduced/info.json b/house_sales_reduced/info.json new file mode 100644 index 0000000000000000000000000000000000000000..13ca7462d25f78677f70f89860e89bba547c4db8 --- /dev/null +++ b/house_sales_reduced/info.json @@ -0,0 +1,34 @@ +{ + "name": "house_sales_reduced", + "n_num_features": 18, + "n_cat_features": 0, + "train_size": 13832, + "val_size": 3458, + "test_size": 4323, + "source": "https://www.openml.org/search?type=data&status=active&id=42635&sort=runs", + "task_intro": "**Author**: https://www.kaggle.com/harlfoxem/ \nhttps://www.kaggle.com/harlfoxem/ \n**Source**: [original](https://www.kaggle.com/harlfoxem/housesalesprediction) - 2016-08-25 \n**Please cite**: \n\nThis dataset contains house sale prices for King County, which includes Seattle. It includes homes sold between May 2014 and May 2015.\n\nIt contains 19 house features plus the price and the id columns, along with 21613 observations.\nIt's a great dataset for evaluating simple regression models.", + "task_type": "regression", + "openml_id": 42635, + "n_classes": 1, + "num_feature_intro": { + "attribute_0": "attribute_0", + "bedrooms": "bedrooms", + "bathrooms": "bathrooms", + "sqft_living": "sqft_living", + "sqft_lot": "sqft_lot", + "floors": "floors", + "waterfront": "waterfront", + "view": "view", + "condition": "condition", + "grade": "grade", + "sqft_above": "sqft_above", + "sqft_basement": "sqft_basement", + "yr_built": "yr_built", + "yr_renovated": "yr_renovated", + "zipcode": "zipcode", + "lat": "lat", + "long": "long", + "sqft_living15": "sqft_living15" + }, + "cat_feature_intro": {} +} \ No newline at end of file diff --git a/house_sales_reduced/y_test.npy b/house_sales_reduced/y_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..2d1545b9008309967e3bb7b320cb6ae295333731 --- /dev/null +++ b/house_sales_reduced/y_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f635b5eabe76577fe9dd3c8e2e214ba8dc393ad8d47baecd9b46a0f4e4fad85a +size 34712 diff --git a/house_sales_reduced/y_train.npy b/house_sales_reduced/y_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..6abf71904d53e95145eca3855ede3a5ca34ce49b --- /dev/null +++ b/house_sales_reduced/y_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9ef2ce345720ffd8fbfc3f9b6ac63cc3431fd809480fbe8fce53160927b1b3ca +size 110784 diff --git a/house_sales_reduced/y_val.npy b/house_sales_reduced/y_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..3a624d4b0bea29c8240ef44cfd050f253d09f106 --- /dev/null +++ b/house_sales_reduced/y_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8137309c351ae0c6c8031ba1e245fe2c6975808b1d0dd021cfd6ad5d22898cd3 +size 27792 diff --git a/houses/N_test.npy b/houses/N_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..9707af975483b4642ed06b3702de49e63661a0da --- /dev/null +++ b/houses/N_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8719d6d506ab8ebd02b6ac72b3de66ebfc42ad66f8c684201d178adee49f01a7 +size 264320 diff --git a/houses/N_train.npy b/houses/N_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..9ee0b47af0bc0d7b3819d14e296890f421d13f6a --- /dev/null +++ b/houses/N_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:fd1013ad935535f4ae769ada77e99cbf76ba8dbf06fb3e4d002295ac26dbb0fa +size 845504 diff --git a/houses/N_val.npy b/houses/N_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..b93db5e86e5def190ef21360536a618f847b276d --- /dev/null +++ b/houses/N_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e2827880a2cbd97964725ee1c5f76a8a89958e4195cd954be671a4e6e56bf4be +size 211520 diff --git a/houses/info.json b/houses/info.json new file mode 100644 index 0000000000000000000000000000000000000000..f1362dc7347e9bf84ee4f48fa4d5ebfbc6b29b4a --- /dev/null +++ b/houses/info.json @@ -0,0 +1,24 @@ +{ + "name": "houses", + "n_num_features": 8, + "n_cat_features": 0, + "train_size": 13209, + "val_size": 3303, + "test_size": 4128, + "source": "https://www.openml.org/search?type=data&status=active&id=537&sort=runs", + "task_intro": "**Author**: Kelley Pace\n\n**Source**: http://lib.stat.cmu.edu/datasets/\n\n**Please cite**: \n\nIf you use an algorithm, dataset, or other information from StatLib, please acknowledge both StatLib and the original contributor of the material. \n\nPace and Barry (1997), \"Sparse Spatial Autoregressions\", Statistics and Probability Letters.\n\n@article{pace1997sparse,\n title={Sparse spatial autoregressions},\n author={Pace, R Kelley and Barry, Ronald},\n journal={Statistics \\& Probability Letters},\n volume={33},\n number={3},\n pages={291--297},\n year={1997},\n publisher={Elsevier}\n}\n\n**Data description**\n\nS&P Letters Data\nWe collected information on the variables using all the block groups in California from the 1990 Census. In this sample a block group on average includes 1425.5 individuals living in a geographically compact area. Naturally, the geographical area included varies inversely with the population density. We computed distances among the centroids of each block group as measured in latitude and longitude. We excluded all the block groups reporting zero entries for the independent and dependent variables. The final data contained 20,640 observations on 9 variables. 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(2012). 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Statistics\n Iowa State University\n \n Date Donated: June 30, 1999\n \n Data Characteristics\n \n This data comes from a survey conducted by the Graphics and\n Visualization Unit at Georgia Tech October 10 to November 16, 1997.\n The full details of the survey are available [3]here.\n \n The particular subset of the survey provided here is the \"general\n demographics\" of internet users. The data have been recoded as\n entirely numeric, with an index to the codes described in the \"Coding\"\n file.\n \n The full survey is available from the web site above, along with\n summaries, tables and graphs of their analyses. In addition there is\n information on other parts of the survey, including technology\n demographics and web commerce.\n \n Data Format\n \n The data is stored in an ASCII files with one observation per line.\n Spaces separate fields.\n \n Past Usage\n \n This data was used in the American Statistical Association Statistical\n Graphics and Computing Sections 1999 Data Exposition.\n _________________________________________________________________\n \n \n [4]The UCI KDD Archive\n [5]Information and Computer Science\n [6]University of California, Irvine\n Irvine, CA 92697-3425\n \n Last modified: June 30, 1999\n \n References\n \n 1. http://www.gvu.gatech.edu/gvu/user_surveys/survey-1997-10/\n 2. http://www.public.iastate.edu/~dicook/\n 3. http://www.cc.gatech.edu/gvu/user_surveys/survey-1997-10/\n 4. http://kdd.ics.uci.edu/\n 5. http://www.ics.uci.edu/\n 6. http://www.uci.edu/\n\n\n Information about the dataset\n CLASSTYPE: nominal\n CLASSINDEX: none specific", + "task_type": "multiclass", + "openml_id": 372, + "n_classes": 46, + "num_feature_intro": {}, + "cat_feature_intro": { + "Age": "Age", + "Community_Building": "Community_Building", + "Community_Membership_Family": "Community_Membership_Family", + "Community_Membership_Hobbies": "Community_Membership_Hobbies", + "Community_Membership_None": "Community_Membership_None", + "Community_Membership_Other": "Community_Membership_Other", + "Community_Membership_Political": "Community_Membership_Political", + "Community_Membership_Professional": "Community_Membership_Professional", + "Community_Membership_Religious": "Community_Membership_Religious", + "Community_Membership_Support": "Community_Membership_Support", + "Country": "Country", + "Disability_Cognitive": "Disability_Cognitive", + "Disability_Hearing": "Disability_Hearing", + "Disability_Motor": "Disability_Motor", + "Disability_Not_Impaired": "Disability_Not_Impaired", + "Disability_Not_Say": "Disability_Not_Say", + "Disability_Vision": "Disability_Vision", + "Education_Attainment": "Education_Attainment", + "Falsification_of_Information": "Falsification_of_Information", + "Gender": "Gender", + "Household_Income": "Household_Income", + "How_You_Heard_About_Survey_Banner": "How_You_Heard_About_Survey_Banner", + "How_You_Heard_About_Survey_Friend": "How_You_Heard_About_Survey_Friend", + "How_You_Heard_About_Survey_Mailing_List": "How_You_Heard_About_Survey_Mailing_List", + "How_You_Heard_About_Survey_Others": "How_You_Heard_About_Survey_Others", + "How_You_Heard_About_Survey_Printed_Media": "How_You_Heard_About_Survey_Printed_Media", + "How_You_Heard_About_Survey_Remebered": "How_You_Heard_About_Survey_Remebered", + "How_You_Heard_About_Survey_Search_Engine": "How_You_Heard_About_Survey_Search_Engine", + "How_You_Heard_About_Survey_Usenet_News": "How_You_Heard_About_Survey_Usenet_News", + "How_You_Heard_About_Survey_WWW_Page": "How_You_Heard_About_Survey_WWW_Page", + "Major_Geographical_Location": "Major_Geographical_Location", + "Major_Occupation": "Major_Occupation", + "Marital_Status": "Marital_Status", + "Most_Import_Issue_Facing_the_Internet": "Most_Import_Issue_Facing_the_Internet", + "Opinions_on_Censorship": "Opinions_on_Censorship", + "Primary_Computing_Platform": "Primary_Computing_Platform", + "Primary_Language": "Primary_Language", + "Primary_Place_of_WWW_Access": "Primary_Place_of_WWW_Access", + "Race": "Race", + "Not_Purchasing_Bad_experience": "Not_Purchasing_Bad_experience", + "Not_Purchasing_Bad_press": "Not_Purchasing_Bad_press", + "Not_Purchasing_Cant_find": "Not_Purchasing_Cant_find", + "Not_Purchasing_Company_policy": "Not_Purchasing_Company_policy", + "Not_Purchasing_Easier_locally": "Not_Purchasing_Easier_locally", + "Not_Purchasing_Enough_info": "Not_Purchasing_Enough_info", + "Not_Purchasing_Judge_quality": "Not_Purchasing_Judge_quality", + "Not_Purchasing_Never_tried": "Not_Purchasing_Never_tried", + "Not_Purchasing_No_credit": "Not_Purchasing_No_credit", + "Not_Purchasing_Not_applicable": "Not_Purchasing_Not_applicable", + "Not_Purchasing_Not_option": "Not_Purchasing_Not_option", + "Not_Purchasing_Other": "Not_Purchasing_Other", + "Not_Purchasing_Prefer_people": "Not_Purchasing_Prefer_people", + "Not_Purchasing_Privacy": "Not_Purchasing_Privacy", + "Not_Purchasing_Receipt": "Not_Purchasing_Receipt", + "Not_Purchasing_Security": "Not_Purchasing_Security", + "Not_Purchasing_Too_complicated": "Not_Purchasing_Too_complicated", + "Not_Purchasing_Uncomfortable": "Not_Purchasing_Uncomfortable", + "Not_Purchasing_Unfamiliar_vendor": "Not_Purchasing_Unfamiliar_vendor", + "Registered_to_Vote": "Registered_to_Vote", + "Sexual_Preference": "Sexual_Preference", + "Web_Ordering": "Web_Ordering", + "Web_Page_Creation": "Web_Page_Creation", + "Who_Pays_for_Access_Dont_Know": "Who_Pays_for_Access_Dont_Know", + "Who_Pays_for_Access_Other": "Who_Pays_for_Access_Other", + "Who_Pays_for_Access_Parents": 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repeatable, verifiable, refutable, and/or improvable predictive models of software engineering.\n\nIf you publish material based on PROMISE data sets then, please follow the acknowledgment guidelines posted on [the PROMISE repository web page](http://promise.site.uottawa.ca/SERepository).\n\n## Title/Topic\nJM1/software defect prediction\n\n\n## Sources\n* **Creators:** NASA, then the NASA Metrics Data Program, http://mdp.ivv.nasa.gov. \n* **Contacts:** \n * Mike Chapman, Galaxy Global Corporation (Robert.Chapman@ivv.nasa.gov) +1-304-367-8341\n * Pat Callis, NASA, NASA project manager for MDP (Patrick.E.Callis@ivv.nasa.gov) +1-304-367-8309\n\n* **Donor:** Tim Menzies (tim@barmag.net)\n\n* **Date:** December 2nd, 2004\n\n## Past usage\n**_How Good is Your Blind Spot Sampling Policy?_; 2003; Tim Menzies and Justin S. Di Stefano; 2004 IEEE Conference on High Assurance Software Engineering (http://menzies.us/pdf/03blind.pdf).**\n\n### Results:\n* Very simple learners (ROCKY) perform as well in this domain as more sophisticated methods (e.g. J48, model trees, model trees) for predicting detects\n* Many learners have very low false alarm rates.\n* Probability of detection (PD) rises with effort and rarely rises above it.\n* High PDs are associated with high PFs (probability of failure)\n* PD, PF, effort can change significantly while accuracy remains essentially stable\n* With two notable exceptions, detectors learned from one data set (e.g. KC2) have nearly they same properties when applied to another (e.g. PC2, KC2). Exceptions:\n* LinesOfCode measures generate wider inter-data-set variances;\n* Precision's inter-data-set variances vary wildly\n\n**_\"Assessing Predictors of Software Defects\"_, T. Menzies and J. DiStefano and A. Orrego and R. Chapman, 2004,**\n**Proceedings, workshop on Predictive Software Models, Chicago, Available from http://menzies.us/pdf/04psm.pdf.**\n\n### Results:\n* From JM1, Naive Bayes generated PDs of 25% with PF of 20%\n* Naive Bayes out-performs J48 for defect detection\n* When learning on more and more data, little improvement is seen after processing 300 examples.\n* PDs are much higher from data collected below the sub-sub-system level.\n* Accuracy is a surprisingly uninformative measure of success for a defect detector. Two detectors with the same accuracy can have widely varying PDs and PFs.\n\n## Relevant information\n* JM1 is written in \"C\" and is a real-time predictive ground system: Uses simulations to generate predictions\n* Data comes from McCabe and Halstead features extractors of source code. These features were defined in the 70s in an attempt to objectively characterize code features that are associated with software quality. The nature of association is under dispute.\n\nNotes on McCabe and Halstead follow.\n\n* The McCabe and Halstead measures are \"module\"-based where a \"module\" is the smallest unit of functionality. In C or Smalltalk, \"modules\" would be called \"function\" or \"method\" respectively.\n\n* Defect detectors can be assessed according to the following measures:\n\n module actually has defects\n +-------------+------------+\n | no | yes |\n +-----+-------------+------------+\n classifier predicts no defects | no | a | b |\n +-----+-------------+------------+\n classifier predicts some defects | yes | c | d |\n +-----+-------------+------------+\n\n\n accuracy = acc = (a+d)/(a+b+c+d\n probability of detection = pd = recall = d/(b+d)\n probability of false alarm = pf = c/(a+c)\n precision = prec = d/(c+d)\n effort = amount of code selected by detector = (c.LOC + d.LOC)/(Total LOC)\n\n\nIdeally, detectors have high PDs, low PFs, and low effort. This ideal state rarely happens:\n\n* PD and effort are linked. The more modules that trigger the detector, the higher the PD. However, effort also gets increases\n* High PD or low PF comes at the cost of high PF or low PD (respectively). This linkage can be seen in a standard receiver operator curve (ROC). Suppose, for example, LOC> x is used as the detector (i.e. we assume large modules have more errors). LOC > x represents a family of detectors. At x=0, EVERY module is predicted to have errors. This detector has a high PD but also a high false alarm rate. At x=0, NO module is predicted to have errors. This detector has a low false alarm rate but won't detect anything at all. At 0 but does not reach it.\n* The line pf=pd on the above graph represents the \"no information\" line. If pf=pd then the detector is pretty useless. The better the detector, the more it rises above PF=PD towards the \"sweet spot\".\n\nNOTES ON MCCABE/HALSTEAD\n========================\nMcCabe argued that code with complicated pathways are more error-prone. His metrics therefore reflect the pathways within a code module.\n\n @Article{mccabe76,\n title = \"A Complexity Measure\",\n author = \"T.J. McCabe\",\n pages = \"308--320\",\n journal = \"IEEE Transactions on Software Engineering\",\n year = \"1976\",\n volume = \"2\",\n month = \"December\",\n number = \"4\"}\n\nHalstead argued that code that is hard to read is more likely to be fault prone. Halstead estimates reading complexity by counting the number of concepts in a module; e.g. number of unique operators.\n\n @Book{halstead77,\n Author = \"M.H. Halstead\",\n Title = \"Elements of Software Science\",\n Publisher = \"Elsevier \",\n Year = 1977}\n\nWe study these static code measures since they are useful, easy to use, and widely used:\n\n* USEFUL: E.g. this data set can generate highly accurate predictors for defects\n* EASY TO USE: Static code measures (e.g. lines of code, the McCabe/Halstead measures) can be automatically and cheaply collected.\n* WIDELY USED: Many researchers use static measures to guide software quality predictions (see the reference list in the above \"blind spot\" paper. Verification and validation (V\\&V) textbooks advise using static code complexity measures to decide which modules are worthy of manual inspections. Further, we know of several large government software contractors that won't review software modules _unless_ tools like McCabe predict that they are fault prone. Hence, defect detectors have a major economic impact when they may force programmers to rewrite code.\n\nNevertheless, the merits of these metrics has been widely criticized. Static code measures are hardly a complete characterization of the internals of a function. Fenton offers an insightful example where the same functionality is achieved using different programming language constructs resulting in different static measurements for that module. Fenton uses this example to argue the uselessness of static code measures.\n\n @Book{fenton97,\n author = \"N.E. Fenton and S.L. Pfleeger\",\n title = {Software metrics: a Rigorous \\& Practical Approach},\n publisher = {International Thompson Press},\n year = {1997}}\n\nAn alternative interpretation of Fenton's example is that static measures can never be a definite and certain indicator of the presence of a fault. Rather, defect detectors based on static measures are best viewed as probabilistic statements that the frequency of faults tends to increase in code modules that trigger the detector. By definition, such probabilistic statements will are not categorical claims with some a non-zero false alarm rate. The research challenge for data miners is to ensure that these false alarms do not cripple their learned theories.\n\nThe McCabe metrics are a collection of four software metrics: essential complexity, cyclomatic complexity, design complexity and LOC, Lines of Code.\n\n* Cyclomatic Complexity, or \"v(G)\", measures the number of \"linearly independent paths\". A set of paths is said to be linearly independent if no path in the set is a linear combination of any other paths in the set through a program's \"flowgraph\". A flowgraph is a directed graph where each node corresponds to a program statement, and each arc indicates the flow of control from one statement to another. \"v(G)\" is calculated by \"v(G) = e - n + 2\" where \"G\" is a program's flowgraph, \"e\" is the number of arcs in the flowgraph, and \"n\" is the number of nodes in the flowgraph. The standard McCabes rules (\"v(G)\">10), are used to identify fault-prone module.\n* Essential Complexity, or \"ev(G)$\" is the extent to which a flowgraph can be \"reduced\" by decomposing all the subflowgraphs of $G$ that are \"D-structured primes\". Such \"D-structured primes\" are also sometimes referred to as \"proper one-entry one-exit subflowgraphs\" (for a more thorough discussion of D-primes, see the Fenton text referenced above). \"ev(G)\" is calculated using \"ev(G)= v(G) - m\" where $m$ is the number of subflowgraphs of \"G\" that are D-structured primes.\n* Design Complexity, or \"iv(G)\", is the cyclomatic complexity of a module's reduced flowgraph. The flowgraph, \"G\", of a module is reduced to eliminate any complexity which does not influence the interrelationship between design modules. According to McCabe, this complexity measurement reflects the modules calling patterns to its immediate subordinate modules.\n* Lines of code is measured according to McCabe's line counting conventions.\n\nThe Halstead falls into three groups: the base measures, the derived measures, and lines of code measures.\n\n* Base measures:\n * mu1 = number of unique operators\n * mu2 = number of unique operands\n * N1 = total occurrences of operators\n * N2 = total occurrences of operands\n * length = N = N1 + N2\n * vocabulary = mu = mu1 + mu2\n * Constants set for each function:\n * mu1' = 2 = potential operator count (just the function name and the \"return\" operator)\n * mu2' = potential operand count. (the number of arguments to the module)\n\nFor example, the expression \"return max(w+x,x+y)\" has \"N1=4\" operators \"return, max, +,+)\", \"N2=4\" operands (w,x,x,y), \"mu1=3\" unique operators (return, max,+), and \"mu2=3\" unique operands (w,x,y).\n\n* Derived measures:\n * P = volume = V = N * log2(mu) (the number of mental comparisons needed to write\na program of length N)\n * V* = volume on minimal implementation = (2 + mu2')*log2(2 + mu2')\n * L = program length = V*/N\n * D = difficulty = 1/L\n * L' = 1/D\n * I = intelligence = L'*V'\n * E = effort to write program = V/L\n * T = time to write program = E/18 seconds\n\n## Number of instances\n10885\n\n## Number of attributes\n22 (5 different lines of code measure, 3 McCabe metrics, 4 base Halstead measures, 8 derived Halstead measures, a branch-count, and 1 goal field)\n\n## Attribute Information\n1. loc : numeric % McCabe's line count of code\n2. v(g) : numeric % McCabe \"cyclomatic complexity\"\n3. ev(g) : numeric % McCabe \"essential complexity\"\n4. iv(g) : numeric % McCabe \"design complexity\"\n5. n : numeric % Halstead total operators + operands\n6. v : numeric % Halstead \"volume\"\n7. l : numeric % Halstead \"program length\"\n8. d : numeric % Halstead \"difficulty\"\n9. i : numeric % Halstead \"intelligence\"\n10. e : numeric % Halstead \"effort\"\n11. b : numeric % Halstead\n12. t : numeric % Halstead's time estimator\n13. lOCode : numeric % Halstead's line count\n14. lOComment : numeric % Halstead's count of lines of comments\n15. lOBlank : numeric % Halstead's count of blank lines\n16. lOCodeAndComment: numeric\n17. uniq_Op : numeric % unique operators\n18. uniq_Opnd : numeric % unique operands\n19. total_Op : numeric % total operators\n20. total_Opnd : numeric % total operands\n21: branchCount : numeric % of the flow graph\n22. defects : {false,true} % module has/has not one or more reported defects\n\n## Missing attributes\nNone\n\n## Class Distribution\nThe class value (defects) is discrete\nfalse: 2106 = 19.35%\ntrue: 8779 = 80.65%", + "task_type": "binclass", + "openml_id": 1053, + "imbalance_ratio": 4.168566001899335, + "n_classes": 2, + "num_feature_intro": { + "loc": "loc", + "v(g)": "v(g)", + "ev(g)": "ev(g)", + "iv(g)": "iv(g)", + "n": "n", + "v": "v", + "l": "l", + "d": "d", + "i": "i", + "e": "e", + "b": "b", + "t": "t", + "lOCode": "lOCode", + "lOComment": "lOComment", + "lOBlank": "lOBlank", + "locCodeAndComment": "locCodeAndComment", + "uniq_Op": "uniq_Op", + "uniq_Opnd": "uniq_Opnd", + "total_Op": "total_Op", + "total_Opnd": "total_Opnd", + "branchCount": "branchCount" + }, + "cat_feature_intro": {} +} \ No newline at end of file diff --git a/jm1/y_test.npy b/jm1/y_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..636a246014d51793f45747fc80fc59c76f1424c1 --- /dev/null +++ b/jm1/y_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8ecd395f78bd063c6d3cce16fca98a626845a64c9bd85a63d13a646b3d78da11 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sha256:8ced60ad30e4319e1542c0035ac13afce8fdd8b8a4544cda81fa91642ef14e92 +size 43154 diff --git a/jungle_chess_2pcs_raw_endgame_complete/info.json b/jungle_chess_2pcs_raw_endgame_complete/info.json new file mode 100644 index 0000000000000000000000000000000000000000..8bf7a82b1813b7a2b0f786a9fe91978174dfdaad --- /dev/null +++ b/jungle_chess_2pcs_raw_endgame_complete/info.json @@ -0,0 +1,23 @@ +{ + "name": "jungle_chess_2pcs_raw_endgame_complete", + "n_num_features": 6, + "n_cat_features": 0, + "train_size": 28684, + "val_size": 7171, + "test_size": 8964, + "source": "https://www.openml.org/search?type=data&status=active&id=41027&sort=runs", + "task_intro": "### Description ###\n\nThis dataset is part of a collection datasets based on the game \"Jungle Chess\" (a.k.a. Dou Shou Qi). For a description of the rules, please refer to the paper (link attached). The paper also contains a description of various constructed features. As the tablebases are a disjoint set of several tablebases based on which (two) pieces are on the board, we have uploaded all tablebases that have explicit different content:\n\n* Rat vs Rat\n* Rat vs Panther\n* Rat vs. Lion\n* Rat vs. Elephant\n* Panther vs. Lion\n* Panther vs. Elephant\n* Tiger vs. Lion\n* Lion vs. Lion\n* Lion vs. Elephant\n* Elephant vs. Elephant\n* Complete (Combination of the above)\n* RAW Complete (Combination of the above, containing for both pieces just the rank, file and strength information). This dataset contains a similar classification problem as, e.g., the King and Rook vs. King problem and is suitable for classification tasks. \n\n(Note that this dataset is one of the above mentioned datasets). Additionally, note that several subproblems are very similar. Having seen a given positions from one of the tablebases arguably gives a lot of information about the outcome of the same position in the other tablebases. \n\n### Please cite ###\nJ. N. van Rijn and J. K. Vis, Endgame Analysis of Dou Shou Qi. ICGA Journal 37:2, 120--124, 2014. ArXiv link: https://arxiv.org/abs/1604.07312", + "task_type": "multiclass", + "openml_id": 41027, + "imbalance_ratio": 5.3199538638985, + "n_classes": 3, + "num_feature_intro": { + "white_piece0_strength": "white_piece0_strength", + "white_piece0_file": "white_piece0_file", + "white_piece0_rank": "white_piece0_rank", + "black_piece0_strength": "black_piece0_strength", + "black_piece0_file": "black_piece0_file", + "black_piece0_rank": "black_piece0_rank" + }, + "cat_feature_intro": {} +} \ No newline at end of file diff --git a/jungle_chess_2pcs_raw_endgame_complete/y_test.npy b/jungle_chess_2pcs_raw_endgame_complete/y_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..209cf0bfac11d5bb0dfcbb58421933fa91085960 --- /dev/null +++ b/jungle_chess_2pcs_raw_endgame_complete/y_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:33c277b0ce49a7d1196e5722600e80dc3877bbcea15ce705bf40f096ff5bbafc +size 18245 diff --git a/jungle_chess_2pcs_raw_endgame_complete/y_train.npy b/jungle_chess_2pcs_raw_endgame_complete/y_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..082b96c6c0cb51b036f41e7a26742dd88a584598 --- /dev/null +++ b/jungle_chess_2pcs_raw_endgame_complete/y_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6174e6aa44f20e6e491953ded39fd9de58ace970bc6e1ffdf73fc82b378e5257 +size 57725 diff --git a/jungle_chess_2pcs_raw_endgame_complete/y_val.npy b/jungle_chess_2pcs_raw_endgame_complete/y_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..14119600c5737afc8b0a145b32eb616d8940c4e2 --- /dev/null +++ b/jungle_chess_2pcs_raw_endgame_complete/y_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:45768f9ad08cfac77d08efbf97a8a272eb82acbef6c8fdf817808500c55aca26 +size 14657 diff --git a/kc1/N_test.npy b/kc1/N_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..acbbab806754ca0f250453834e573bd7ae5c7dc8 --- /dev/null +++ b/kc1/N_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:651d088fd941de9f32965778b8d00bb1ffd8b3695609421626e59cc865908562 +size 71024 diff --git a/kc1/N_train.npy b/kc1/N_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..beb0e7cbe50cd7ac3cf007323140c7e965199981 --- /dev/null +++ b/kc1/N_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:eed69008966be49d68a85de836bc601fe96edcfc9f383952f2e840ad338097c2 +size 226760 diff --git a/kc1/N_val.npy b/kc1/N_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..3a82459c6d575635d338a690d4c617771302d7d2 --- /dev/null +++ b/kc1/N_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4cb2b1d18b657682f65ccb488b41c08e6e60079960b7b8e6a7aef90124cb5726 +size 56912 diff --git a/kc1/info.json b/kc1/info.json new file mode 100644 index 0000000000000000000000000000000000000000..a6d6d8c05da3069e27992cb865c1a7fb1840e484 --- /dev/null +++ b/kc1/info.json @@ -0,0 +1,38 @@ +{ + "name": "kc1", + "n_num_features": 21, + "n_cat_features": 0, + "train_size": 1349, + "val_size": 338, + "test_size": 422, + "source": "https://www.openml.org/search?type=data&status=active&id=1067&sort=runs", + "task_intro": "**Author**: Mike Chapman, NASA \n**Source**: [tera-PROMISE](http://openscience.us/repo/defect/mccabehalsted/kc1.html) - 2004 \n**Please cite**: Sayyad Shirabad, J. and Menzies, T.J. (2005) The PROMISE Repository of Software Engineering Databases. School of Information Technology and Engineering, University of Ottawa, Canada. \n \n**KC1 Software defect prediction** \nOne of the NASA Metrics Data Program defect data sets. Data from software for storage management for receiving and processing ground data. Data comes from McCabe and Halstead features extractors of source code. These features were defined in the 70s in an attempt to objectively characterize code features that are associated with software quality.\n\n### Attribute Information \n\n1. loc : numeric % McCabe's line count of code\n2. v(g) : numeric % McCabe \"cyclomatic complexity\"\n3. ev(g) : numeric % McCabe \"essential complexity\"\n4. iv(g) : numeric % McCabe \"design complexity\"\n5. n : numeric % Halstead total operators + operands\n6. v : numeric % Halstead \"volume\"\n7. l : numeric % Halstead \"program length\"\n8. d : numeric % Halstead \"difficulty\"\n9. i : numeric % Halstead \"intelligence\"\n10. e : numeric % Halstead \"effort\"\n11. b : numeric % Halstead \n12. t : numeric % Halstead's time estimator\n13. lOCode : numeric % Halstead's line count\n14. lOComment : numeric % Halstead's count of lines of comments\n15. lOBlank : numeric % Halstead's count of blank lines\n16. lOCodeAndComment: numeric\n17. uniq_Op : numeric % unique operators\n18. uniq_Opnd : numeric % unique operands\n19. total_Op : numeric % total operators\n20. total_Opnd : numeric % total operands\n21. branchCount : numeric % of the flow graph\n22. problems : {false,true} % module has/has not one or more reported defects\n\n### Relevant papers \n\n- Shepperd, M. and Qinbao Song and Zhongbin Sun and Mair, C. (2013)\nData Quality: Some Comments on the NASA Software Defect Datasets, IEEE Transactions on Software Engineering, 39.\n\n- Tim Menzies and Justin S. Di Stefano (2004) How Good is Your Blind Spot Sampling Policy? 2004 IEEE Conference on High Assurance\nSoftware Engineering.\n\n- T. Menzies and J. DiStefano and A. Orrego and R. Chapman (2004) Assessing Predictors of Software Defects\", Workshop on Predictive Software Models, Chicago", + "task_type": "binclass", + "openml_id": 1067, + "imbalance_ratio": 5.469325153374233, + "n_classes": 2, + "num_feature_intro": { + "loc": "loc", + "v(g)": "v(g)", + "ev(g)": "ev(g)", + "iv(g)": "iv(g)", + "n": "n", + "v": "v", + "l": "l", + "d": "d", + "i": "i", + "e": "e", + "b": "b", + "t": "t", + "lOCode": "lOCode", + "lOComment": "lOComment", + "lOBlank": "lOBlank", + "locCodeAndComment": "locCodeAndComment", + "uniq_Op": "uniq_Op", + "uniq_Opnd": "uniq_Opnd", + "total_Op": "total_Op", + "total_Opnd": "total_Opnd", + "branchCount": "branchCount" + }, + "cat_feature_intro": {} +} \ No newline at end of file diff --git a/kc1/y_test.npy b/kc1/y_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..c3cc887d04fa334d4083cb1537c7a55492db5e03 --- /dev/null +++ b/kc1/y_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:43f86611302e631fbfc84d2e1eb976e2a2ea329da8a5854aee6d34c4e8990c06 +size 550 diff --git a/kc1/y_train.npy b/kc1/y_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..032597234a235908029aed6f6228c00daec8c5ab --- /dev/null +++ b/kc1/y_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:fca96e6601c57b8acaef6f45a466ff855fc4182b7224d165bf24c201b78b3482 +size 1477 diff --git a/kc1/y_val.npy b/kc1/y_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..5b753b13f50572938000fb72875ed5e62941e12e --- /dev/null +++ b/kc1/y_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:85f10feee73d2ffcea198a7015bb15f1b7670ac21a428dfa9985768a7e8194fc +size 466 diff --git a/kdd_ipums_la_97-small/N_test.npy b/kdd_ipums_la_97-small/N_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..bc60faeadb661cffeb6e3111ff355e8a7484677b --- /dev/null +++ b/kdd_ipums_la_97-small/N_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:abedbb5420c625f046698229a492a5e14ae864f25f003885da11b619d5973067 +size 166208 diff --git a/kdd_ipums_la_97-small/N_train.npy b/kdd_ipums_la_97-small/N_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..d2e4e103a43e183fcd10c8dad8c7cd3f986411f8 --- /dev/null +++ b/kdd_ipums_la_97-small/N_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:bb6c889639775664b512514d95b38e1d4204584fb6ecd89cefc14e3de93d4105 +size 531328 diff --git a/kdd_ipums_la_97-small/N_val.npy b/kdd_ipums_la_97-small/N_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..fec876cf52d6955ca5d85a9679398380d4fbdb7c --- /dev/null +++ b/kdd_ipums_la_97-small/N_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ddd83e2bf27d1a778a306d50f2616488b35e5ea646b5866a87e150d6737a9a3f +size 132928 diff --git a/kdd_ipums_la_97-small/info.json b/kdd_ipums_la_97-small/info.json new file mode 100644 index 0000000000000000000000000000000000000000..921c23c43e1c9efc206477eecf32b8f3ba9525b0 --- /dev/null +++ b/kdd_ipums_la_97-small/info.json @@ -0,0 +1,37 @@ +{ + "name": "kdd_ipums_la_97-small", + "n_num_features": 20, + "n_cat_features": 0, + "train_size": 3320, + "val_size": 830, + "test_size": 1038, + "source": "https://www.openml.org/search?type=data&status=active&id=44124&sort=runs", + "task_intro": "Dataset used in the tabular data benchmark https://github.com/LeoGrin/tabular-benchmark, transformed in the same way. This dataset belongs to the \"classification on numerical features\" benchmark. Original description: \n \n**Author**: \n**Source**: Unknown - Date unknown \n**Please cite**: \n\nBinarized version of the original data set (see version 1). The multi-class target feature is converted to a two-class nominal target feature by re-labeling the majority class as positive ('P') and all others as negative ('N'). Originally converted by Quan Sun.", + "task_type": "binclass", + "openml_id": 44124, + "imbalance_ratio": 1.0, + "n_classes": 2, + "num_feature_intro": { + "value": "value", + "rent": "rent", + "ftotinc": "ftotinc", + "momloc": "momloc", + "famsize": "famsize", + "nchild": "nchild", + "eldch": "eldch", + "yngch": "yngch", + "nsibs": "nsibs", + "age": "age", + "occscore": "occscore", + "sei": "sei", + "inctot": "inctot", + "incwage": "incwage", + "incbus": "incbus", + "incfarm": "incfarm", + "incss": "incss", + "incwelfr": "incwelfr", + "incother": "incother", + "poverty": "poverty" + }, + "cat_feature_intro": {} +} \ No newline at end of file diff --git a/kdd_ipums_la_97-small/y_test.npy b/kdd_ipums_la_97-small/y_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..10bdac70821945cadf38362f5aba537653733548 --- /dev/null +++ b/kdd_ipums_la_97-small/y_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a2c43b0fdfcfb7b5738d4141af0979beea637008e6ebf548d78ff39e7b512d43 +size 2373 diff --git a/kdd_ipums_la_97-small/y_train.npy b/kdd_ipums_la_97-small/y_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..441718d6b4c0d54cc514fb48e9a4f4d9c4b074f5 --- /dev/null +++ b/kdd_ipums_la_97-small/y_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:119110a7ea32914c2052d5a2cd851f4f5609f9b1af0b4f85b7b9b7af65b6ecba +size 6941 diff --git a/kdd_ipums_la_97-small/y_val.npy b/kdd_ipums_la_97-small/y_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..e8a90746b219a73e69ac223b1491f84d9008f351 --- /dev/null +++ b/kdd_ipums_la_97-small/y_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f2bfa36b325607fb3406761e5b46223ed98c8d6b71921df78cc4b7d1d18ecc70 +size 1955 diff --git a/kin8nm/N_test.npy b/kin8nm/N_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..77adb4197c0bceeb6d134da00efe09f875dca618 --- /dev/null +++ b/kin8nm/N_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b64a8d588f99b6c2eda37227cf95f3d594030360b4e8d9177dfddd54e8c9c799 +size 105024 diff --git a/kin8nm/N_train.npy b/kin8nm/N_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..759dfe49ef7bd0dd2f77377cc6a29681eeb0bd7e --- /dev/null +++ b/kin8nm/N_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0b8c0f7e1618d309fa44c3ec3eded0707fcc595bf1d23fca44f67afa514f19e8 +size 335616 diff --git a/kin8nm/N_val.npy b/kin8nm/N_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..7a59262895f670d674c7c19f55c3e6f8f2291242 --- /dev/null +++ b/kin8nm/N_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:bb6ec10723684c9a1b7ccf591b1d6b2fbd630aaba11d31bcbb8ad5a1fa582113 +size 84032 diff --git a/kin8nm/info.json b/kin8nm/info.json new file mode 100644 index 0000000000000000000000000000000000000000..3eb252f490d529dfe5b88105a6b11689182c6290 --- /dev/null +++ b/kin8nm/info.json @@ -0,0 +1,24 @@ +{ + "name": "kin8nm", + "n_num_features": 8, + "n_cat_features": 0, + "train_size": 5242, + "val_size": 1311, + "test_size": 1639, + "source": "https://www.openml.org/search?type=data&status=active&id=189&sort=runs", + "task_intro": "**Author**: \n**Source**: Unknown - \n**Please cite**: \n\nThis is data set is concerned with the forward kinematics of an 8 link\n robot arm. Among the existing variants of this data set we have used\n the variant 8nm, which is known to be highly non-linear and medium\n noisy.\n\n Original source: DELVE repository of data. \n Source: collection of regression datasets by Luis Torgo (ltorgo@ncc.up.pt) at\n http://www.ncc.up.pt/~ltorgo/Regression/DataSets.html\n Characteristics: 8192 cases, 9 attributes (0 nominal, 9 continuous).", + "task_type": "regression", + "openml_id": 189, + "n_classes": 1, + "num_feature_intro": { + "theta1": "theta1", + "theta2": "theta2", + "theta3": "theta3", + "theta4": "theta4", + "theta5": "theta5", + "theta6": "theta6", + "theta7": "theta7", + "theta8": "theta8" + }, + "cat_feature_intro": {} +} \ No newline at end of file diff --git a/kin8nm/y_test.npy b/kin8nm/y_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..e535f18f9abe0fb976fbfdf06960d52efcbff6be --- /dev/null +++ b/kin8nm/y_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:24e4289d428574286ebea3344fc61d05835e44c46fa424825da3df1b20850f70 +size 13240 diff --git a/kin8nm/y_train.npy b/kin8nm/y_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..8c0badd6bb140dde18747d0ea941ed9caa5eb7c3 --- /dev/null +++ b/kin8nm/y_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d9c90a8dee6b65b992012e35f60b83765ecc153d983d77a4d755f68acd575ef8 +size 42064 diff --git a/kin8nm/y_val.npy b/kin8nm/y_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..4db4d4733e543d086c75175abc535cd05bf95ff6 --- /dev/null +++ b/kin8nm/y_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:62214ab4307c4ca8cb6acd65e7ea78da5b63a5116e751083c72497ab00f358c5 +size 10616 diff --git a/kr-vs-k/C_test.npy b/kr-vs-k/C_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..ff72b53257c2e67767a06026262ec172a1671bdd --- /dev/null +++ b/kr-vs-k/C_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:513dd04aa80eb762fcaba75cc7da3408d3f68fe3177e09abe36c74bd03bbe1f0 +size 34037 diff --git a/kr-vs-k/C_train.npy b/kr-vs-k/C_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..a198c56895cfa1ca788c837570f91e4b78807d34 --- /dev/null +++ b/kr-vs-k/C_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c78352a64ea0264c64f437fb4f988a68f3b6c143cdf0670063d72af15637faa5 +size 108169 diff --git a/kr-vs-k/C_val.npy b/kr-vs-k/C_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..8a892e8bef29c6d4c4b48413ae775bbb88816b8f --- /dev/null +++ b/kr-vs-k/C_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2f0d9c596a3b4f18bf3fb926856e8b685fb40964b2c0863b37b885f2178f2e2b +size 27293 diff --git a/kr-vs-k/N_test.npy b/kr-vs-k/N_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..6637045341885866df3ae5140ee893eb75666775 --- /dev/null +++ b/kr-vs-k/N_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6611f21a8b2833b7ef5f3fa45b3f6b739dfda524550d6300bc97a2b0df5898fd +size 16964 diff --git a/kr-vs-k/N_train.npy b/kr-vs-k/N_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..ca195b3bafcc8dcb2371bd838a5d31fef9c69f3f --- /dev/null +++ b/kr-vs-k/N_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c498fa6ec6d1938268dc5cec3fd8013a950fd1b2abbabc302ef483f721c05f34 +size 53993 diff --git a/kr-vs-k/N_val.npy b/kr-vs-k/N_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..4ebeff26e1cadf4285953c939841e734ac30cffc --- /dev/null +++ b/kr-vs-k/N_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b6129deeacf307e4620bf61b62447510b67251f993be459d7b08f86211206d0b +size 13595 diff --git a/kr-vs-k/info.json b/kr-vs-k/info.json new file mode 100644 index 0000000000000000000000000000000000000000..ad5125b568cd57b436defe193cdce4773850bf8a --- /dev/null +++ b/kr-vs-k/info.json @@ -0,0 +1,24 @@ +{ + "name": "kr-vs-k", + "n_num_features": 3, + "n_cat_features": 3, + "train_size": 17955, + "val_size": 4489, + "test_size": 5612, + "source": "https://www.openml.org/search?type=data&status=active&id=1481&sort=runs", + "task_intro": "**Author**: \n**Source**: KEEL \n**Please cite**: \n\nAbstract:\n\nA chess endgame data set representing the positions on the board of the white king, the white rook, and the black king. The task is to determine the optimum number of turn required for white to win the game, which can be a draw if it takes more than sixteen turns.\n\nAttributes Details:\n\n\n1. White_king_col {a, b, c, d, e, f, g, h}\n2. White_king_row {1, 2, 3, 4, 5, 6, 7, 8}\n3. White_rook_col {a, b, c, d, e, f, g, h}\n4. White_rook_row {1, 2, 3, 4, 5, 6, 7, 8}\n5. Black_king_col {a, b, c, d, e, f, g, h}\n6. Black_king_row {1, 2, 3, 4, 5, 6, 7, 8}\n7. Class - Game {draw, zero, one, two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen, fifteen, sixteen}", + "task_type": "multiclass", + "openml_id": 1481, + "imbalance_ratio": 168.62962962962962, + "n_classes": 18, + "num_feature_intro": { + "V2": "V2", + "V4": "V4", + "V6": "V6" + }, + "cat_feature_intro": { + "V1": "V1", + "V3": "V3", + "V5": "V5" + } +} \ No newline at end of file diff --git a/kr-vs-k/y_test.npy b/kr-vs-k/y_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..588645c2c39e2e4167651d00a5fd12eac126f95f --- /dev/null +++ b/kr-vs-k/y_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4b6b8bd2b7de813b12609ae54e8dc65f3603ccf388e769972b28d884ab790e4f +size 11634 diff --git a/kr-vs-k/y_train.npy b/kr-vs-k/y_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..0ed547b08cd87276b7b08cb385b5cc946745f3f0 --- /dev/null +++ b/kr-vs-k/y_train.npy @@ -0,0 +1,3 @@ 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Slate \n**Source**: [UCI](https://archive.ics.uci.edu/ml/datasets/Letter+Recognition) - 01-01-1991 \n**Please cite**: P. W. Frey and D. J. Slate. \"Letter Recognition Using Holland-style Adaptive Classifiers\". Machine Learning 6(2), 1991 \n\n1. TITLE: \n Letter Image Recognition Data \n \n The objective is to identify each of a large number of black-and-white\n rectangular pixel displays as one of the 26 capital letters in the English\n alphabet. The character images were based on 20 different fonts and each\n letter within these 20 fonts was randomly distorted to produce a file of\n 20,000 unique stimuli. Each stimulus was converted into 16 primitive\n numerical attributes (statistical moments and edge counts) which were then\n scaled to fit into a range of integer values from 0 through 15. We\n typically train on the first 16000 items and then use the resulting model\n to predict the letter category for the remaining 4000. See the article\n cited above for more details.", + "task_type": "multiclass", + "openml_id": 6, + "imbalance_ratio": 1.1076294277929155, + "n_classes": 26, + "num_feature_intro": { + "x-box": "x-box", + "y-box": "y-box", + "high": "high", + "onpix": "onpix", + "x-bar": "x-bar", + "y-bar": "y-bar", + "x2bar": "x2bar", + "y2bar": "y2bar", + "xybar": "xybar", + "x2ybr": "x2ybr", + "xy2br": "xy2br", + "x-ege": "x-ege", + "xegvy": "xegvy", + "y-ege": "y-ege", + "yegvx": "yegvx" + }, + "cat_feature_intro": {} +} \ No newline at end of file diff --git a/letter/y_test.npy b/letter/y_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..28f4b7121e9fe8d495a4c4d470c7f0f250fb5a70 --- /dev/null +++ b/letter/y_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ba29c4311d46fa268c3ce6026f00b73dd13c8d7e1155f12b98db1d68478162f8 +size 8445 diff --git a/letter/y_train.npy b/letter/y_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..1f407fd3e6565143cef68c9874a6f5cc2bc1b7cc --- /dev/null +++ b/letter/y_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:792d6f3dfe74f784e58415a2df2c731904b4257a194e327e1902f7851b115710 +size 26063 diff --git a/letter/y_val.npy b/letter/y_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..b50a38ae674c60743efbf24e63e6f9fbcaf04a99 --- /dev/null +++ b/letter/y_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e3e5c5fb9e178153025633a873a6de11399725f2df3149dc462a438262ddb0d3 +size 6845 diff --git a/madeline/N_test.npy b/madeline/N_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..fdaf8d19de1d53fbe0b6c8016d47c730e2653cb9 --- /dev/null +++ b/madeline/N_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:5c3c6c8fb9fda823220b6c890b7c2a092b116c296ff82a8dba5677510e46fc8e +size 1301344 diff --git a/madeline/N_train.npy b/madeline/N_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..2a175af885356943f801f70a4b402021e9a3b76e --- /dev/null +++ b/madeline/N_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3525461ccd0fb60eb83e8f4fa49a4767003f51ff7e80cf39f0399c9e0f5a517f +size 4162776 diff --git a/madeline/N_val.npy b/madeline/N_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..7604b00cbe5ebfd7085b6a4f8ba05e3236381c9e --- /dev/null +++ b/madeline/N_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d47f2f8000b76dd792c023853876e5d1aa7ff6cc296fdb2985f4e380ce57287a +size 1042344 diff --git a/madeline/info.json b/madeline/info.json new file mode 100644 index 0000000000000000000000000000000000000000..457822f0d4113ac878d0809b5afd890cc12aad32 --- /dev/null +++ b/madeline/info.json @@ -0,0 +1,276 @@ +{ + "name": "madeline", + "n_num_features": 259, + "n_cat_features": 0, + "train_size": 2009, + "val_size": 503, + "test_size": 628, + "source": "https://www.openml.org/search?type=data&status=active&id=41144&sort=runs", + "task_intro": "The goal of this challenge is to expose the research community to real world datasets of interest to 4Paradigm. All datasets are formatted in a uniform way, though the type of data might differ. The data are provided as preprocessed matrices, so that participants can focus on classification, although participants are welcome to use additional feature extraction procedures (as long as they do not violate any rule of the challenge). All problems are binary classification problems and are assessed with the normalized Area Under the ROC Curve (AUC) metric (i.e. 2*AUC-1).\n The identity of the datasets and the type of data is concealed, though its structure is revealed. The final score in phase 2 will be the average of rankings on all testing datasets, a ranking will be generated from such results, and winners will be determined according to such ranking.\n The tasks are constrained by a time budget. The Codalab platform provides computational resources shared by all participants. Each code submission will be exceuted in a compute worker with the following characteristics: 2Cores / 8G Memory / 40G SSD with Ubuntu OS. To ensure the fairness of the evaluation, when a code submission is evaluated, its execution time is limited in time.\n http://automl.chalearn.org/data", + "task_type": "binclass", + "openml_id": 41144, + "imbalance_ratio": 1.011531069827034, + "n_classes": 2, + "num_feature_intro": { + "V1": "V1", + "V2": "V2", + "V3": "V3", + "V4": "V4", + "V5": "V5", + "V6": "V6", + "V7": "V7", + "V8": "V8", + "V9": "V9", + "V10": "V10", + "V11": "V11", + "V12": "V12", + "V13": "V13", + "V14": "V14", + "V15": "V15", + "V16": "V16", + "V17": "V17", + "V18": "V18", + "V19": "V19", + "V20": "V20", + "V21": "V21", + "V22": "V22", + "V23": "V23", + "V24": "V24", + "V25": "V25", + "V26": "V26", + "V27": "V27", + "V28": "V28", + "V29": "V29", + "V30": "V30", + "V31": "V31", + "V32": "V32", + "V33": "V33", + "V34": "V34", + "V35": "V35", + "V36": "V36", + "V37": "V37", + "V38": "V38", + "V39": "V39", + "V40": "V40", + "V41": "V41", + "V42": "V42", + "V43": "V43", + "V44": "V44", + "V45": "V45", + "V46": 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\nThese weekly averages are ultimately based on measurements of 4 air samples per hour taken atop intake lines on several towers during steady periods of CO2 concentration of not less than 6 hours per day; if no such periods are available on a given day, then no data are used for that day. The _Weight_ column gives the number of days used in each weekly average. _Flag_ codes are explained in the NDP writeup, available electronically from the [home page](http://cdiac.ess-dive.lbl.gov/ftp/trends/co2/sio-keel-flask/maunaloa_c.dat) of this data set. CO2 concentrations are in terms of the 1999 calibration scale (Keeling et al., 2002) available electronically from the references in the NDP writeup which can be accessed from the home page of this data set.\n

\n### Feature Descriptions\n_co2_: average co2 concentration in ppvm
\n_year_: year of concentration measurement
\n_month_: month of concentration measurement
\n_day_: day of month of concentration measurement
\n_weight_: number of days used in each weekly average
\n_flag_: flag code
\n_station_: station code
\n
\n**Author**: Carbon Dioxide Research Group, Scripps Institution of Oceanography, University of California-San Diego, La Jolla, California, USA 92023-0444
\n**Source**: [original](http://cdiac.ess-dive.lbl.gov/ftp/trends/co2/sio-keel-flask/maunaloa_c.dat) - September 2004", + "task_type": "regression", + "openml_id": 41187, + "n_classes": 1, + "num_feature_intro": { + "year": "year", + "month": "month", + "day": "day", + "weight": "weight", + "flag": "flag" + }, + "cat_feature_intro": { + "station": "station" + } +} \ No newline at end of file diff --git a/mauna-loa-atmospheric-co2/y_test.npy b/mauna-loa-atmospheric-co2/y_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..a76299df64c80b7d5211494e82a0eebdb613f937 --- /dev/null +++ b/mauna-loa-atmospheric-co2/y_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2cd26f179ec49bf494a15a8a27b926c8a21c8838bacfd88ba14dbdcf96c0e66d +size 3688 diff --git a/mauna-loa-atmospheric-co2/y_train.npy b/mauna-loa-atmospheric-co2/y_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..85f07b6acf867d87b9de520677e7206832779671 --- /dev/null +++ b/mauna-loa-atmospheric-co2/y_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:712c19953395970881d6baa1649d8097e8f4d4b56fc7afbc7ea93e7c6ebf9004 +size 11520 diff --git a/mauna-loa-atmospheric-co2/y_val.npy b/mauna-loa-atmospheric-co2/y_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..157fb78dde9f02d28640ea9e3afc86c76401ca83 --- /dev/null +++ b/mauna-loa-atmospheric-co2/y_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6025e3ada052e644c76c596b4838d81aad0f38f313601af8881db673ab3e0f46 +size 2976 diff --git a/mfeat-factors/N_test.npy b/mfeat-factors/N_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..4181752dacd9e79863cac02a7473d87e70e726c8 --- /dev/null +++ b/mfeat-factors/N_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2aca21e4bd286eaed8034ca3416b09849da9d7a3c0554fdac3e65311d8402e9e +size 691328 diff --git a/mfeat-factors/N_train.npy b/mfeat-factors/N_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..93744f9421d2d335c6922c8684334a9b8561fb7a --- /dev/null +++ b/mfeat-factors/N_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:977faa5d7fdfef8ac170a5913e1136ffcf8997bc9832e801ab3a0e4abce81846 +size 2211968 diff --git a/mfeat-factors/N_val.npy b/mfeat-factors/N_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..3e51ceb3f389d61177993b4b9acc4be696511631 --- /dev/null +++ b/mfeat-factors/N_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:364d31bbfa4b7afc67631c5b607ae5b2ae0526b2779ccac8406aa95531d847eb +size 553088 diff --git a/mfeat-factors/info.json b/mfeat-factors/info.json new file mode 100644 index 0000000000000000000000000000000000000000..8110907ec40b1ea0dd81e74b0bf491b6ec0eb5ba --- /dev/null +++ b/mfeat-factors/info.json @@ -0,0 +1,233 @@ +{ + "name": "mfeat-factors", + "n_num_features": 216, + "n_cat_features": 0, + "train_size": 1280, + "val_size": 320, + "test_size": 400, + "source": "https://www.openml.org/search?type=data&status=active&id=12&sort=runs", + "task_intro": "**Author**: Robert P.W. Duin, Department of Applied Physics, Delft University of Technology \n**Source**: [UCI](https://archive.ics.uci.edu/ml/datasets/Multiple+Features) - 1998 \n**Please cite**: [UCI](https://archive.ics.uci.edu/ml/citation_policy.html) \n\n**Multiple Features Dataset: Factors** \nOne of a set of 6 datasets describing features of handwritten numerals (0 - 9) extracted from a collection of Dutch utility maps. Corresponding patterns in different datasets correspond to the same original character. 200 instances per class (for a total of 2,000 instances) have been digitized in binary images. \n\n### Attribute Information \nThe attributes represent 216 profile correlations. No more information is known.\n\n### Relevant Papers \nA slightly different version of the database is used in \nM. van Breukelen, R.P.W. Duin, D.M.J. Tax, and J.E. den Hartog, Handwritten digit recognition by combined classifiers, Kybernetika, vol. 34, no. 4, 1998, 381-386.\n \nThe database as is is used in: \nA.K. Jain, R.P.W. Duin, J. Mao, Statistical Pattern Recognition: A Review, IEEE Transactions on Pattern Analysis and Machine Intelligence archive, Volume 22 Issue 1, January 2000", + "task_type": "multiclass", + "openml_id": 12, + "imbalance_ratio": 1.0, + "n_classes": 10, + "num_feature_intro": { + "att1": "att1", + "att2": "att2", + "att3": "att3", + "att4": "att4", + "att5": "att5", + "att6": "att6", + "att7": "att7", + "att8": "att8", + "att9": "att9", + "att10": "att10", + "att11": "att11", + "att12": "att12", + "att13": "att13", + "att14": "att14", + "att15": "att15", + "att16": "att16", + "att17": "att17", + "att18": "att18", + "att19": "att19", + "att20": "att20", + "att21": "att21", + "att22": "att22", + "att23": "att23", + "att24": "att24", + "att25": "att25", + "att26": "att26", + "att27": "att27", + "att28": "att28", + "att29": "att29", + "att30": "att30", + "att31": "att31", + "att32": "att32", + "att33": "att33", + "att34": "att34", + "att35": "att35", + "att36": "att36", + "att37": "att37", + 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Duin, Department of Applied Physics, Delft University of Technology \n**Source**: [UCI](https://archive.ics.uci.edu/ml/datasets/Multiple+Features) - 1998 \n**Please cite**: [UCI](https://archive.ics.uci.edu/ml/citation_policy.html) \n\n**Multiple Features Dataset: Fourier** \nOne of a set of 6 datasets describing features of handwritten numerals (0 - 9) extracted from a collection of Dutch utility maps. Corresponding patterns in different datasets correspond to the same original character. 200 instances per class (for a total of 2,000 instances) have been digitized in binary images.\n\n### Attribute Information \nThe attributes represent 76 Fourier coefficients of the character shapes.\n\n### Relevant Papers \nA slightly different version of the database is used in \nM. van Breukelen, R.P.W. Duin, D.M.J. Tax, and J.E. den Hartog, Handwritten digit recognition by combined classifiers, Kybernetika, vol. 34, no. 4, 1998, 381-386.\n \nThe database as is is used in: \nA.K. Jain, R.P.W. Duin, J. 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Duin, Department of Applied Physics, Delft University of Technology \n**Source**: [UCI](https://archive.ics.uci.edu/ml/datasets/Multiple+Features) - 1998 \n**Please cite**: [UCI](https://archive.ics.uci.edu/ml/citation_policy.html) \n\n**Multiple Features Dataset: Karhunen** \nOne of a set of 6 datasets describing features of handwritten numerals (0 - 9) extracted from a collection of Dutch utility maps. Corresponding patterns in different datasets correspond to the same original character. 200 instances per class (for a total of 2,000 instances) have been digitized in binary images. \n\n### Attribute Information \nThe attributes represent 64 descriptors from the Karhunen-Loeve Transform, a linear transform that corresponds to the projection of the images on the eigenvectors of a covariant matrix. Also see: \nM.D. Garris et al: NIST Form-Based Handprint Recognition System. Internal Report. National Institute of Standards and Technology, NISTIR 5469, 1994.\n\n### Relevant Papers \nA slightly different version of the database is used in \nM. van Breukelen, R.P.W. Duin, D.M.J. Tax, and J.E. den Hartog, Handwritten digit recognition by combined classifiers, Kybernetika, vol. 34, no. 4, 1998, 381-386.\n \nThe database as is is used in: \nA.K. Jain, R.P.W. Duin, J. Mao, Statistical Pattern Recognition: A Review, IEEE Transactions on Pattern Analysis and Machine Intelligence archive, Volume 22 Issue 1, January 2000", + "task_type": "multiclass", + "openml_id": 16, + "imbalance_ratio": 1.0, + "n_classes": 10, + "num_feature_intro": { + "att1": "att1", + "att2": "att2", + "att3": "att3", + "att4": "att4", + "att5": "att5", + "att6": "att6", + "att7": "att7", + "att8": "att8", + "att9": "att9", + "att10": "att10", + "att11": "att11", + "att12": "att12", + "att13": "att13", + "att14": "att14", + "att15": "att15", + "att16": "att16", + "att17": "att17", + "att18": "att18", + "att19": "att19", + "att20": "att20", + "att21": "att21", + "att22": "att22", + "att23": "att23", + "att24": "att24", + "att25": "att25", + "att26": "att26", + "att27": "att27", + "att28": "att28", + "att29": "att29", + "att30": "att30", + "att31": "att31", + "att32": "att32", + "att33": "att33", + "att34": "att34", + "att35": "att35", + "att36": "att36", + "att37": "att37", + "att38": "att38", + "att39": "att39", + "att40": "att40", + "att41": "att41", + "att42": "att42", + "att43": "att43", + "att44": "att44", + "att45": "att45", + "att46": "att46", + "att47": "att47", + "att48": "att48", + "att49": "att49", + "att50": "att50", + "att51": "att51", + "att52": "att52", + "att53": "att53", + "att54": "att54", + "att55": "att55", + "att56": "att56", + "att57": "att57", + "att58": "att58", + "att59": "att59", + "att60": "att60", + "att61": "att61", + "att62": "att62", + "att63": "att63", + "att64": "att64" + }, + "cat_feature_intro": {} +} \ No newline at end of file diff --git a/mfeat-karhunen/y_test.npy b/mfeat-karhunen/y_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..cd642a0bfc774ed785be688623830c74ac9fa46a --- /dev/null +++ b/mfeat-karhunen/y_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:582ba4f0aa20d6c014e723370f421f40dc2bf82645b407e851c8a3196ea3b38a +size 1144 diff --git a/mfeat-karhunen/y_train.npy b/mfeat-karhunen/y_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..dbf5e897639ea27200e21ecc6b4cc0f416c4c520 --- /dev/null +++ b/mfeat-karhunen/y_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3dcd3f7e866efedea58143f551f8ced2d5225e99f5ea933fb477d64318649a45 +size 2906 diff --git a/mfeat-karhunen/y_val.npy b/mfeat-karhunen/y_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..4edd319454e016b7ba645ae906374367547ca920 --- /dev/null +++ b/mfeat-karhunen/y_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a4ea3719f72490b60f25c2e711358235e1071e5bafced3d0635c4f59ef4d4382 +size 984 diff --git a/mfeat-morphological/N_test.npy b/mfeat-morphological/N_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..d7ca14654e182c10069d187a5da18fb02fc3b139 --- /dev/null +++ b/mfeat-morphological/N_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:cf96bb12ba71175fb2eff210a7b52c75ef40c30d9af383970c7807e5491c3adc +size 19328 diff --git a/mfeat-morphological/N_train.npy b/mfeat-morphological/N_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..ca90d44f8ce8fd1115ae0f3b72b22ddb12632255 --- /dev/null +++ b/mfeat-morphological/N_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c2d2b2b5c97ec1808b175910106be787782dd50a933103ab4628e97c90e6a6a1 +size 61568 diff --git a/mfeat-morphological/N_val.npy b/mfeat-morphological/N_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..e72c575148b2483fb4946498e4612f72cbab3220 --- /dev/null +++ b/mfeat-morphological/N_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:784dc290a2878a9f1f6b453659a12a11d7b7f794b1ee8d617ea077dbe132684a +size 15488 diff --git a/mfeat-morphological/info.json b/mfeat-morphological/info.json new file mode 100644 index 0000000000000000000000000000000000000000..383be68678fb9fa10b2428c9991a60b206a050ce --- /dev/null +++ b/mfeat-morphological/info.json @@ -0,0 +1,23 @@ +{ + "name": "mfeat-morphological", + "n_num_features": 6, + "n_cat_features": 0, + "train_size": 1280, + "val_size": 320, + "test_size": 400, + "source": "https://www.openml.org/search?type=data&status=active&id=18&sort=runs", + "task_intro": "**Author**: Robert P.W. Duin, Department of Applied Physics, Delft University of Technology \n**Source**: [UCI](https://archive.ics.uci.edu/ml/datasets/Multiple+Features) - 1998 \n**Please cite**: [UCI](https://archive.ics.uci.edu/ml/citation_policy.html) \n\n**Multiple Features Dataset: Morphological** \nOne of a set of 6 datasets describing features of handwritten numerals (0 - 9) extracted from a collection of Dutch utility maps. Corresponding patterns in different datasets correspond to the same original character. 200 instances per class (for a total of 2,000 instances) have been digitized in binary images. \n\nIn this dataset, these digits are represented in terms of 6 morphological features. \n\n### Attribute Information \nThe meaning of the features is mostly unknown. They are never named in the original files, and the paper only talks about 'morphological features, such as the number of endpoints'.\n\n### Relevant Papers \nA slightly different version of the database is used in \nM. van Breukelen, R.P.W. Duin, D.M.J. Tax, and J.E. den Hartog, Handwritten digit recognition by combined classifiers, Kybernetika, vol. 34, no. 4, 1998, 381-386.\n \nThe database as is is used in: \nA.K. Jain, R.P.W. Duin, J. Mao, Statistical Pattern Recognition: A Review, IEEE Transactions on Pattern Analysis and Machine Intelligence archive, Volume 22 Issue 1, January 2000", + "task_type": "multiclass", + "openml_id": 18, + "imbalance_ratio": 1.0, + "n_classes": 10, + "num_feature_intro": { + "att1": "att1", + "att2": "att2", + "att3": "att3", + "att4": "att4", + "att5": "att5", + "att6": "att6" + }, + "cat_feature_intro": {} +} \ No newline at end of file diff --git a/mfeat-morphological/y_test.npy b/mfeat-morphological/y_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..cd642a0bfc774ed785be688623830c74ac9fa46a --- /dev/null +++ b/mfeat-morphological/y_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:582ba4f0aa20d6c014e723370f421f40dc2bf82645b407e851c8a3196ea3b38a +size 1144 diff --git a/mfeat-morphological/y_train.npy b/mfeat-morphological/y_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..dbf5e897639ea27200e21ecc6b4cc0f416c4c520 --- /dev/null +++ b/mfeat-morphological/y_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3dcd3f7e866efedea58143f551f8ced2d5225e99f5ea933fb477d64318649a45 +size 2906 diff --git a/mfeat-morphological/y_val.npy b/mfeat-morphological/y_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..4edd319454e016b7ba645ae906374367547ca920 --- /dev/null +++ b/mfeat-morphological/y_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a4ea3719f72490b60f25c2e711358235e1071e5bafced3d0635c4f59ef4d4382 +size 984 diff --git a/mfeat-pixel/C_test.npy b/mfeat-pixel/C_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..b46ee6efaa1a0cb4fd42517672b4c0b68fa78afd --- /dev/null +++ b/mfeat-pixel/C_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d13d80b0a465825303aa388886cfa3e4437ea4e515d4656a605f6d7da3e25e85 +size 192517 diff --git a/mfeat-pixel/C_train.npy b/mfeat-pixel/C_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..e807fa2cf44d6ea3c24c641cbc92ae03fd193e71 --- /dev/null +++ b/mfeat-pixel/C_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:356bd3905fafa570e06a8850fc893730b518f29f406bcda39039a6519af747b8 +size 615341 diff --git a/mfeat-pixel/C_val.npy b/mfeat-pixel/C_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..a292b49ff9d09da8bc998da60ba7401427eaf2cf --- /dev/null +++ b/mfeat-pixel/C_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:01bfeaf8fce9dd3a6ef19be2ed468f1abc87e2d4994452198a754da063bce69d +size 154079 diff --git a/mfeat-pixel/info.json b/mfeat-pixel/info.json new file mode 100644 index 0000000000000000000000000000000000000000..ea636bf8ce2c051b70d590f187db23c4e64fc1ba --- /dev/null +++ b/mfeat-pixel/info.json @@ -0,0 +1,257 @@ +{ + "name": "mfeat-pixel", + "n_num_features": 0, + "n_cat_features": 240, + "train_size": 1280, + "val_size": 320, + "test_size": 400, + "source": "https://www.openml.org/search?type=data&status=active&id=20&sort=runs", + "task_intro": "**Author**: Robert P.W. Duin, Department of Applied Physics, Delft University of Technology \n**Source**: [UCI](https://archive.ics.uci.edu/ml/datasets/Multiple+Features) - 1998 \n**Please cite**: [UCI](https://archive.ics.uci.edu/ml/citation_policy.html) \n\n**Multiple Features Dataset: Pixel** \nOne of a set of 6 datasets describing features of handwritten numerals (0 - 9) extracted from a collection of Dutch utility maps. The maps were scanned in 8 bit grey value at density of 400dpi, scanned, sharpened, and thresholded. Corresponding patterns in different datasets correspond to the same original character. 200 instances per class (for a total of 2,000 instances) have been digitized in binary images. \n\nUsing this dataset, sampled versions of the original images may be obtained (15 x 16 pixels). \n\n### Attribute Information \nThe mfeatures represent 240 (15 x 16) pixel averages in 2 x 3 windows. \n\n### Relevant Papers \nA slightly different version of the database is used in \nM. van Breukelen, R.P.W. Duin, D.M.J. Tax, and J.E. den Hartog, Handwritten digit recognition by combined classifiers, Kybernetika, vol. 34, no. 4, 1998, 381-386.\n \nThe database as is is used in: \nA.K. Jain, R.P.W. Duin, J. Mao, Statistical Pattern Recognition: A Review, IEEE Transactions on Pattern Analysis and Machine Intelligence archive, Volume 22 Issue 1, January 2000", + "task_type": "multiclass", + "openml_id": 20, + "imbalance_ratio": 1.0, + "n_classes": 10, + "num_feature_intro": {}, + "cat_feature_intro": { + "att1": "att1", + "att2": "att2", + "att3": "att3", + "att4": "att4", + "att5": "att5", + "att6": "att6", + "att7": "att7", + "att8": "att8", + "att9": "att9", + "att10": "att10", + "att11": "att11", + "att12": "att12", + "att13": "att13", + "att14": "att14", + "att15": "att15", + "att16": "att16", + "att17": "att17", + "att18": "att18", + "att19": "att19", + "att20": "att20", + "att21": "att21", + "att22": "att22", + "att23": "att23", + "att24": "att24", + "att25": "att25", + "att26": "att26", + "att27": "att27", + "att28": "att28", + "att29": "att29", + "att30": "att30", + "att31": "att31", + "att32": "att32", + "att33": "att33", + "att34": "att34", + "att35": "att35", + "att36": "att36", 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"test_size": 400, + "source": "https://www.openml.org/search?type=data&status=active&id=22&sort=runs", + "task_intro": "**Author**: Robert P.W. Duin, Department of Applied Physics, Delft University of Technology \n**Source**: [UCI](https://archive.ics.uci.edu/ml/datasets/Multiple+Features) - 1998 \n**Please cite**: [UCI](https://archive.ics.uci.edu/ml/citation_policy.html) \n\n**Multiple Features Dataset: Zernike** \nOne of a set of 6 datasets describing features of handwritten numerals (0 - 9) extracted from a collection of Dutch utility maps. Corresponding patterns in different datasets correspond to the same original character. 200 instances per class (for a total of 2,000 instances) have been digitized in binary images. \n\nIn this dataset, these digits are represented in terms of 47 Zernike moments. \n\n### Attribute Information \nThe attributes represent 47 rotation invariant Zernike moments. They can't distinguish samples of class '6' from those of class '9'. More information on Zernike moments can be found in: \nA. Khotanzad and Y.H. Hong: Rotation invariant pattern recognition using Zernike moments. Int. 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-0,0 +1,21 @@ +{ + "name": "mozilla4", + "n_num_features": 4, + "n_cat_features": 0, + "train_size": 9948, + "val_size": 2488, + "test_size": 3109, + "source": "https://www.openml.org/search?type=data&status=active&id=1046&sort=runs", + "task_intro": "**Author**: \n**Source**: Unknown - Date unknown \n**Please cite**: \n\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\nThis is a PROMISE Software Engineering Repository data set made publicly\navailable in order to encourage repeatable, verifiable, refutable, and/or\nimprovable predictive models of software engineering.\n\nIf you publish material based on PROMISE data sets then, please\nfollow the acknowledgment guidelines posted on the PROMISE repository\nweb page http://promisedata.org/repository .\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n(c) 2007 A. Gunes Koru\nContact: gkoru AT umbc DOT edu Phone: +1 (410) 455 8843\nThis data set is distributed under the\nCreative Commons Attribution-Share Alike 3.0 License\nhttp://creativecommons.org/licenses/by-sa/3.0/\n\nYou are free:\n\n* to Share -- copy, distribute and transmit the work\n* to Remix -- to adapt the work\n\nUnder the following conditions:\n\nAttribution. You must attribute the work in the manner specified by\nthe author or licensor (but not in any way that suggests that they endorse\nyou or your use of the work).\n\nShare Alike. If you alter, transform, or build upon this work, you\nmay distribute the resulting work only under the same, similar or a\ncompatible license.\n\n* For any reuse or distribution, you must make clear to others the\nlicense terms of this work.\n* Any of the above conditions can be waived if you get permission from\nthe copyright holder.\n* Apart from the remix rights granted under this license, nothing in\nthis license impairs or restricts the author's moral rights.\n\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n\n\n1. Title: Recurrent event (defect fix) and size data for Mozilla Classes\nThis one includes a binary attribute (event) to show defect fix.\nThe data is at the \"observation\" level. Each modification made to\na C++ class was entered as an observation. A newly added class\ncreated an observation. The observation period was between\nMay 29, 2002 and Feb 22, 2006.\n\n2. Sources\n(a) Creator: A. Gunes Koru\n(b) Date: February 23, 2007\n(c) Contact: gkoru AT umbc DOT edu Phone: +1 (410) 455 8843\n\n3. Donor: A. Gunes Koru\n\n4. Past Usage: This data set was used for:\n\nA. Gunes Koru, Dongsong Zhang, and Hongfang Liu, \"Modeling the\nEffect of Size on Defect Proneness for Open-Source Software\",\nPredictive Models in Software Engineering Workshop, PROMISE 2007,\nMay 20th 2007, Minneapolis, Minnesota, US.\n\nAbstract:\nQuality is becoming increasingly important with the continuous\nadoption of open-source software. Previous research has found that\nthere is generally a positive relationship between module size and\ndefect proneness. Therefore, in open-source software development, it\nis important to monitor module size and understand its impact on\ndefect proneness. However, traditional approaches to quality\nmodeling, which measure specific system snapshots and obtain future\ndefect counts, are not well suited because open-source modules\nusually evolve and their size changes over time. In this study, we\nused Cox proportional hazards modeling with recurrent events to\nstudy the effect of class size on defect-proneness in the Mozilla\nproduct. We found that the effect of size was significant, and we\nquantified this effect on defect proneness.\n\nThe full paper can be downloaded from A. Gunes Koru's Website\nhttp://umbc.edu/~gkoru\nby following the Publications link or from the Web site of PROMISE 2007.\n\n5. Features:\n\nThis data set is used to create a conditional Cox Proportional\nHazards Model\n\nid: A numeric identification assigned to each separate C++ class\n(Note that the id's do not increment from the first to the last\ndata row)\n\nstart: A time infinitesimally greater than the time of the modification\nthat created this observation (practically, modification time). When a\nclass is introduced to a system, a new observation is entered with start=0\n\nend: Either the time of the next modification, or the end of the\nobservation period, or the time of deletion, whichever comes first.\n\nevent: event is set to 1 if a defect fix takes place\nat the time represented by 'end', or 0 otherwise. A class deletion\nis handled easily by entering a final observation whose event is set\nto 1 if the class is deleted for corrective maintenance, or 0 otherwise.\n\nsize: It is a time-dependent covariate and its column carries the\nnumber of source Lines of Code of the C++ classes\nat time 'start'. Blank and comment lines are not counted.\n\nstate: Initially set to 0, and it becomes 1 after the class\nexperiences an event, and remains at 1 thereafter.", + "task_type": "binclass", + "openml_id": 1046, + "imbalance_ratio": 2.043265465935787, + "n_classes": 2, + "num_feature_intro": { + "start": "start", + "end": "end", + "event": "event", + "size": "size" + }, + "cat_feature_intro": {} +} \ No newline at end of file diff --git a/mozilla4/y_test.npy b/mozilla4/y_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..298b7660665ff48de5ddd9f80210bf5c8200aa77 --- /dev/null +++ b/mozilla4/y_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:117034beec5802949e402f1e83695fcefffcdd755cedfcfec66144d89827c8f9 +size 6519 diff --git a/mozilla4/y_train.npy b/mozilla4/y_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..bdf472195c4865bc6f5686fbc2f48ee4ae55246f --- /dev/null +++ b/mozilla4/y_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:95adcb8caf059450152a72ebd06fe0187b0001074d0505a702c61d9d002cb971 +size 20209 diff --git a/mozilla4/y_val.npy b/mozilla4/y_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..d9057626d9fadcf80ae34423a9d721ce8b5197e3 --- /dev/null +++ b/mozilla4/y_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:157fd0f74e50c059df67494d01a272861d12a8d359f4329db6c6ce449666c559 +size 5275 diff --git a/mv/C_test.npy b/mv/C_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..4fa48f57103634a1cb6920468ee007cda8e126d5 --- /dev/null +++ b/mv/C_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:99e42386d37e36af55a9c75288cdf932f93e1f11f9e406a5c5bb91eea4e0dee4 +size 49321 diff --git a/mv/C_train.npy b/mv/C_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..2be202750e2f71d46a44dd052b57f2612ccfebfc --- /dev/null +++ b/mv/C_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a4f0b6f0922d60d603982e63d477759b97fc0f3675cb21c1d52054db3ebe1cc8 +size 157051 diff --git a/mv/C_val.npy b/mv/C_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..c8ccee2d162e6755400856b9515ab680e705e5d2 --- /dev/null +++ b/mv/C_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:57756b71f65eb7db8fece17d0248169e8716de56ac205e8919c37b96e6a91796 +size 39525 diff --git a/mv/N_test.npy b/mv/N_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..dd064d7f94aa8b5ae1a12f12b0458f961861d45b --- /dev/null +++ b/mv/N_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c2d6196321caf46e48a712511360cdae3d4cb9fedc34588c7d441cefa8433dea +size 456752 diff --git a/mv/N_train.npy b/mv/N_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..9146a0e7898ae3f99465ba8e2cc977a0ef9850a8 --- /dev/null +++ b/mv/N_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:dfcc29f762fae5bb94f5db4c185685cf861f780ec4e737ed715b45c352fadd86 +size 1461224 diff --git a/mv/N_val.npy b/mv/N_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..8154e187bdd1ad40a31a01361b408ea80b1713fe --- /dev/null +++ b/mv/N_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8fd3454d9340bedb1b9d4d7f77f8c5cdac33bf86a04054e587366c928e17df52 +size 365416 diff --git a/mv/info.json b/mv/info.json new file mode 100644 index 0000000000000000000000000000000000000000..7f3d7e833213681d00d27ab582e30b70034244b2 --- /dev/null +++ b/mv/info.json @@ -0,0 +1,27 @@ +{ + "name": "mv", + "n_num_features": 7, + "n_cat_features": 3, + "train_size": 26091, + "val_size": 6523, + "test_size": 8154, + "source": "https://www.openml.org/search?type=data&status=active&id=344&sort=runs", + "task_intro": "**Author**: Luis Torgo \n**Source**: [original](http://www.dcc.fc.up.pt/~ltorgo/Regression/DataSets.html) - \n**Please cite**: \n\nThis is an artificial data set with dependencies between the attribute values. The cases are generated using the following method:\n\nX1 : uniformly distributed over [-5,5]\nX2 : uniformly distributed over [-15,-10]\nX3 : IF (X1 > 0) THEN X3 = green\n ELSE X3 = red with probability 0.4 and X4=brown with prob. 0.6\nX4 : IF (X3=green) THEN X4=X1+2X2\n ELSE X4=X1/2 with prob. 0.3, and X4=X2/2 with prob. 0.7\nX5 : uniformly distributed over [-1,1]\nX6 : X6=X4*[epsilon], where [epsilon] is uniformly distribute over [0,5]\nX7 : X7=yes with prob. 0.3 and X7=no with prob. 0.7\nX8 : IF (X5 < 0.5) THEN X8 = normal ELSE X8 = large\nX9 : uniformly distributed over [100,500]\nX10 : uniformly distributed integer over the interval [1000,1200]\n \nObtain the value of the target variable Y using the rules:\nIF (X2 > 2 ) THEN Y = 35 - 0.5 X4\n ELSE IF (-2 <= X4 <= 2) THEN Y = 10 - 2 X1\n ELSE IF (X7 = yes) THEN Y = 3 -X1/X4\n ELSE IF (X8 = normal) THEN Y = X6 + X1\n ELSE Y = X1/2\n\nSource: collection of regression datasets by Luis Torgo (ltorgo@ncc.up.pt) at http://www.dcc.fc.up.pt/~ltorgo/Regression/DataSets.html", + "task_type": "regression", + "openml_id": 344, + "n_classes": 1, + "num_feature_intro": { + "x1": "x1", + "x2": "x2", + "x4": "x4", + "x5": "x5", + "x6": "x6", + "x9": "x9", + "x10": "x10" + }, + "cat_feature_intro": { + "x3": "x3", + "x7": "x7", + "x8": "x8" + } +} \ No newline at end of file diff --git a/mv/y_test.npy b/mv/y_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..26417731e1a6c00986f7c8308553352a583ebf19 --- /dev/null +++ b/mv/y_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ff6843743cf6e734671ddc767b8b17d73cab470f35d2a962d3c62c11e1cefc49 +size 65360 diff --git a/mv/y_train.npy b/mv/y_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..1cd4fc218b4d9365a91548b6fb32593ab23f28e7 --- /dev/null +++ b/mv/y_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid 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"https://www.openml.org/search?type=data&status=active&id=43892&sort=runs", + "task_intro": "National Longitudinal Survey (Binarized) \n Survey results from the U.S. Bureau of Labor Statistics to gather information on the labour market activities and other life events of several groups. \n The dataset was obtained from the R-package fairml. \n The response variable has been changed to a binary version: Whether income90 is greater than 1.7", + "task_type": "binclass", + "openml_id": 43892, + "imbalance_ratio": 1.648677819751754, + "n_classes": 2, + "num_feature_intro": { + "age": "age", + "income96": "income96", + "height": "height", + "weight": "weight", + "famsize": "famsize", + "illegalact": "illegalact", + "charged": "charged", + "jobsnum90": "jobsnum90", + "afqt89": "afqt89" + }, + "cat_feature_intro": { + "race": "race", + "gender": "gender", + "grade90": "grade90", + "partner": "partner", + "genhealth": "genhealth", + "typejob90": "typejob90", + "jobtrain90": "jobtrain90" + } +} 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0000000000000000000000000000000000000000..9c97df812176f87ddb2cce2bdc7fdbb535f6ab25 --- /dev/null +++ b/okcupid_stem/N_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:aafb94816fae9dbe55326ab9526c4904821f1402d3f296f57ed1daedb287c40b +size 34272 diff --git a/okcupid_stem/N_val.npy b/okcupid_stem/N_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..039e8a0bb5170442cbf930872b200498eedd5ba3 --- /dev/null +++ b/okcupid_stem/N_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d9a5ea252531a39482fefad8d5a40f7d52419eeeca0cc4b903b307a10a8b3ae6 +size 8666 diff --git a/okcupid_stem/info.json b/okcupid_stem/info.json new file mode 100644 index 0000000000000000000000000000000000000000..67afe5745452ffb4a9df58b5488510acf12883ad --- /dev/null +++ b/okcupid_stem/info.json @@ -0,0 +1,31 @@ +{ + "name": "okcupid_stem", + "n_num_features": 2, + "n_cat_features": 11, + "train_size": 17072, + "val_size": 4269, + "test_size": 5336, + "source": "https://www.openml.org/search?type=data&status=active&id=45067&sort=runs", + "task_intro": "User profile data for San Francisco OkCupid users published in [Kim, A. Y., & Escobedo-Land, A. (2015). OKCupid data for introductory statistics and data science courses. Journal of Statistics Education, 23(2).]. The curated dataset was downloaded from [https://github.com/rudeboybert/JSE_OkCupid]. The original dataset was created with the use of a python script that pulled the data from public profiles on www.okcupid.com on 06/30/2012. It includes people (n = 59946) within a 25 mile radius of San Francisco, who were online in the last year (06/30/2011), with at least one profile picture. Permission to use this data was obtained by the author of the original paper from OkCupid president and co-founder Christian Rudder under the condition that the dataset remains public. As target, the variable 'job' was collapsed into three categories: 'stem', 'non_stem', and 'student'. STEM jobs were defined as 'job' %in% c('computer / hardware / software', 'science / tech / engineering'). Observations with 'job' %in% c('unemployed', 'retired', 'rather not say') or missing values in 'job' were removed. The factor labels of the variable 'speaks' had to be changed to integers to prevent a bug which would not allow the upload of the variable as a nominal feature. The original dataset also included ten open text variables 'essay0' to 'essay9', which were removed from the dataset uploaded here. The dataset further includes the date/time variable 'last_online' (ignored by default) which could be used to construct additional features. Using OkCupid data for predicting STEM jobs was inspired by Max Kuhns book 'Feature Engineering and Selection: A Practical Approach for Predictive Models' [https://bookdown.org/max/FES/].", + "task_type": "multiclass", + "openml_id": 45067, + "imbalance_ratio": 6.826226012793177, + "n_classes": 3, + "num_feature_intro": { + "age": "age", + "height": "height" + }, + "cat_feature_intro": { + "body_type": "body_type", + "drinks": "drinks", + "drugs": "drugs", + "education": "education", + "ethnicity": "ethnicity", + "location": "location", + "orientation": "orientation", + "sex": "sex", + "sign": "sign", + "smokes": "smokes", + "status": "status" + } +} \ No newline at end of file diff --git a/okcupid_stem/y_test.npy b/okcupid_stem/y_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..809a462e2d0feb1b34a8811565edd0d5230e88ab --- /dev/null +++ b/okcupid_stem/y_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:cd4d646a69000b4cd14ee39519826361bdbb903807589c8711477d994913b700 +size 10999 diff --git a/okcupid_stem/y_train.npy b/okcupid_stem/y_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..2e4c3b1bff743dd48772bfe0e0f3e536aa202724 --- /dev/null +++ b/okcupid_stem/y_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:064d5a3bc3aed4967c5fa62301bff027957edf3b322f136775ed4c66f558483f +size 34495 diff --git a/okcupid_stem/y_val.npy b/okcupid_stem/y_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..8fcaf57a98097b82232f83917c75f38a97ced42c --- /dev/null +++ b/okcupid_stem/y_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:802d94b08604f3b3de935b108b8938759e3290f78b8f88b1550ccf211fa76016 +size 8863 diff --git a/one-hundred-plants-margin/N_test.npy b/one-hundred-plants-margin/N_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..71561fe90275c3f9eb0b8d80c894a583bd752f3f --- /dev/null +++ b/one-hundred-plants-margin/N_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c111b268a5ea2ca722ef7086b11ff6ab06ff94455f206f3ad4a475cb0c9aa7e0 +size 163968 diff --git a/one-hundred-plants-margin/N_train.npy b/one-hundred-plants-margin/N_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..959753b86610129e7ce70b819a15c5f0cc109685 --- /dev/null +++ b/one-hundred-plants-margin/N_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:93f65ab91d13180cb0867f4a94aeca233b53ab8861d24062e8fbb8fcda320504 +size 524416 diff --git a/one-hundred-plants-margin/N_val.npy b/one-hundred-plants-margin/N_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..73ec935a59b539713f22ab1b1cdae1fff67607c0 --- /dev/null +++ b/one-hundred-plants-margin/N_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:60714595ccfe5ba59efe002b391053b1c01476f8f168b60a109b1d3d6dc47fcf +size 131200 diff --git a/one-hundred-plants-margin/info.json b/one-hundred-plants-margin/info.json new file mode 100644 index 0000000000000000000000000000000000000000..ffee7a9d6de1217f8044fac3bc2df9e698b4d9e0 --- /dev/null +++ b/one-hundred-plants-margin/info.json @@ -0,0 +1,81 @@ +{ + "name": "one-hundred-plants-margin", + "n_num_features": 64, + "n_cat_features": 0, + "train_size": 1024, + "val_size": 256, + "test_size": 320, + "source": "https://www.openml.org/search?type=data&status=active&id=1491&sort=runs", + "task_intro": "**Author**: James Cope, Thibaut Beghin, Paolo Remagnino, Sarah Barman. \n**Source**: [UCI](https://archive.ics.uci.edu/ml/datasets/One-hundred+plant+species+leaves+data+set) - 2010 \n**Please cite**: Charles Mallah, James Cope, James Orwell. Plant Leaf Classification Using Probabilistic Integration of Shape, Texture and Margin Features. Signal Processing, Pattern Recognition and Applications, in press. 2013. \n\n### Description\n\nOne-hundred plant species leaves dataset (Class = Margin).\n \n### Sources\n```\n (a) Original owners of colour Leaves Samples:\n\n James Cope, Thibaut Beghin, Paolo Remagnino, Sarah Barman. \n The colour images are not included. \n The Leaves were collected in the Royal Botanic Gardens, Kew, UK. \n email: james.cope@kingston.ac.uk \n \n (b) This dataset consists of work carried out by James Cope, Charles Mallah, and James Orwell. \n Donor of database Charles Mallah: charles.mallah@kingston.ac.uk; James Cope: james.cope@kingston.ac.uk \n```\n\n### Dataset Information\n\nThe original data directory contains the binary images (masks) of the leaf samples (colour images not included).\nThere are three features for each image: Shape, Margin and Texture.\nFor each feature, a 64 element vector is given per leaf sample.\nThese vectors are taken as a contiguous descriptor (for shape) or histograms (for texture and margin).\nSo, there are three different files, one for each feature problem: \n * 'data_Sha_64.txt' -> prediction based on shape\n * 'data_Tex_64.txt' -> prediction based on texture\n * 'data_Mar_64.txt' -> prediction based on margin [**dataset provided here**] \n\nEach row has a 64-element feature vector followed by the Class label.\nThere is a total of 1600 samples with 16 samples per leaf class (100 classes), and no missing values.\n\n### Attributes Information\n\nThree 64 element feature vectors per sample.\n\n### Relevant Papers\n\nCharles Mallah, James Cope, James Orwell. \nPlant Leaf Classification Using Probabilistic Integration of Shape, Texture and Margin Features. \nSignal Processing, Pattern Recognition and Applications, in press.\n\nJ. Cope, P. Remagnino, S. Barman, and P. Wilkin.\nPlant texture classification using gabor co-occurrences.\nAdvances in Visual Computing,\npages 699-677, 2010.\n\nT. Beghin, J. Cope, P. Remagnino, and S. Barman.\nShape and texture based plant leaf classification. \nIn: Advanced Concepts for Intelligent Vision Systems,\npages 345-353. Springer, 2010.", + "task_type": "multiclass", + "openml_id": 1491, + "imbalance_ratio": 1.0, + "n_classes": 100, + "num_feature_intro": { + "V1": "V1", + "V2": "V2", + "V3": "V3", + "V4": "V4", + "V5": "V5", + "V6": "V6", + "V7": "V7", + "V8": "V8", + "V9": "V9", + "V10": "V10", + "V11": "V11", + "V12": "V12", + "V13": "V13", + "V14": "V14", + "V15": "V15", + "V16": "V16", + "V17": "V17", + "V18": "V18", + "V19": "V19", + "V20": "V20", + "V21": "V21", + "V22": "V22", + "V23": "V23", + "V24": "V24", + "V25": "V25", + "V26": "V26", + "V27": "V27", + "V28": "V28", + "V29": "V29", + "V30": "V30", + "V31": "V31", + "V32": "V32", + "V33": "V33", + "V34": "V34", + "V35": "V35", + "V36": "V36", + "V37": "V37", + "V38": "V38", + "V39": "V39", + "V40": "V40", + "V41": "V41", + "V42": "V42", + "V43": "V43", + "V44": "V44", + "V45": "V45", + "V46": "V46", + "V47": "V47", + "V48": "V48", + "V49": "V49", + "V50": "V50", + "V51": "V51", + "V52": "V52", + "V53": "V53", + "V54": "V54", + "V55": "V55", + "V56": "V56", + "V57": "V57", + "V58": "V58", + "V59": "V59", + "V60": "V60", + "V61": "V61", + "V62": "V62", + "V63": "V63", + "V64": "V64" + }, + "cat_feature_intro": {} +} \ No newline at end of file diff --git a/one-hundred-plants-margin/y_test.npy b/one-hundred-plants-margin/y_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..a469719b4ee8c6254c001148b75328ac44fe5ec6 --- /dev/null +++ b/one-hundred-plants-margin/y_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:947c58a54bb48d58b322a68729cc0cf424c69c483708cfc36514a28cb11a34c4 +size 1615 diff --git a/one-hundred-plants-margin/y_train.npy b/one-hundred-plants-margin/y_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..bb5379d270349b1ea936b2b636067cc938be85e3 --- /dev/null +++ b/one-hundred-plants-margin/y_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:38cb13e6740098de71f779826a2031432c3aaaece25573e44ddd6b741739d30b +size 3025 diff --git a/one-hundred-plants-margin/y_val.npy b/one-hundred-plants-margin/y_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..bc9e4f948d039002099ee3869b569f03ccc3c916 --- /dev/null +++ b/one-hundred-plants-margin/y_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:1cf825597fa7a9c1e9e9fcccf3a356f114f3a7a4a0ccabc71954aa772a8f71c6 +size 1487 diff --git a/one-hundred-plants-shape/N_test.npy b/one-hundred-plants-shape/N_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..7b8db1f6011a8fbd1db5a3944fc1fe7399aa3480 --- /dev/null +++ b/one-hundred-plants-shape/N_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8a4abdab2827cc790ca76d731e8d8e15e9f0f01f8b9d7c8b33b31b5d2cb6ab58 +size 163968 diff --git a/one-hundred-plants-shape/N_train.npy b/one-hundred-plants-shape/N_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..34ff290b6dc05223c17e177eca91652796b3e753 --- /dev/null +++ b/one-hundred-plants-shape/N_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:500eb426d6e9de1a1b62318254f0e7b49b8f30f4076ddeb90a29460562f4e8db +size 524416 diff --git a/one-hundred-plants-shape/N_val.npy b/one-hundred-plants-shape/N_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..d58cddcb9cf123a7340ec48d19bd0a2a0987edb9 --- /dev/null +++ b/one-hundred-plants-shape/N_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:fbfb456a3b444caf7854d458cee71977f7039b24ab0c07c7fbd3872d87902f35 +size 131200 diff --git a/one-hundred-plants-shape/info.json b/one-hundred-plants-shape/info.json new file mode 100644 index 0000000000000000000000000000000000000000..00bfa518a805a54dff3d9a94a646643703a93b57 --- /dev/null +++ b/one-hundred-plants-shape/info.json @@ -0,0 +1,81 @@ +{ + "name": "one-hundred-plants-shape", + "n_num_features": 64, + "n_cat_features": 0, + "train_size": 1024, + "val_size": 256, + "test_size": 320, + "source": "https://www.openml.org/search?type=data&status=active&id=1492&sort=runs", + "task_intro": "**Author**: James Cope, Thibaut Beghin, Paolo Remagnino, Sarah Barman. \n**Source**: [UCI](https://archive.ics.uci.edu/ml/datasets/One-hundred+plant+species+leaves+data+set) - 2010 \n**Please cite**: Charles Mallah, James Cope, James Orwell. Plant Leaf Classification Using Probabilistic Integration of Shape, Texture and Margin Features. Signal Processing, Pattern Recognition and Applications, in press. 2013. \n\n### Description\n\nOne-hundred plant species leaves dataset (Class = Shape).\n \n### Sources\n```\n (a) Original owners of colour Leaves Samples:\n\n James Cope, Thibaut Beghin, Paolo Remagnino, Sarah Barman. \n The colour images are not included. \n The Leaves were collected in the Royal Botanic Gardens, Kew, UK. \n email: james.cope@kingston.ac.uk \n \n (b) This dataset consists of work carried out by James Cope, Charles Mallah, and James Orwell. \n Donor of database Charles Mallah: charles.mallah@kingston.ac.uk; James Cope: james.cope@kingston.ac.uk \n```\n\n### Dataset Information\n\nThe original data directory contains the binary images (masks) of the leaf samples (colour images not included).\nThere are three features for each image: Shape, Margin and Texture.\nFor each feature, a 64 element vector is given per leaf sample.\nThese vectors are taken as a contiguous descriptor (for shape) or histograms (for texture and margin).\nSo, there are three different files, one for each feature problem: \n * 'data_Sha_64.txt' -> prediction based on shape [**dataset provided here**]\n * 'data_Tex_64.txt' -> prediction based on texture\n * 'data_Mar_64.txt' -> prediction based on margin \n\nEach row has a 64-element feature vector followed by the Class label.\nThere is a total of 1600 samples with 16 samples per leaf class (100 classes), and no missing values.\n\n### Attributes Information\n\nThree 64 element feature vectors per sample.\n\n### Relevant Papers\n\nCharles Mallah, James Cope, James Orwell. \nPlant Leaf Classification Using Probabilistic Integration of Shape, Texture and Margin Features. \nSignal Processing, Pattern Recognition and Applications, in press.\n\nJ. Cope, P. Remagnino, S. Barman, and P. Wilkin.\nPlant texture classification using gabor co-occurrences.\nAdvances in Visual Computing,\npages 699-677, 2010.\n\nT. Beghin, J. Cope, P. Remagnino, and S. Barman.\nShape and texture based plant leaf classification. \nIn: Advanced Concepts for Intelligent Vision Systems,\npages 345-353. 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Plant Leaf Classification Using Probabilistic Integration of Shape, Texture and Margin Features. Signal Processing, Pattern Recognition and Applications, in press. 2013. \n\n### Description\n\nOne-hundred plant species leaves dataset (Class = Texture).\n \n### Sources\n```\n (a) Original owners of colour Leaves Samples:\n\n James Cope, Thibaut Beghin, Paolo Remagnino, Sarah Barman. \n The colour images are not included. \n The Leaves were collected in the Royal Botanic Gardens, Kew, UK. \n email: james.cope@kingston.ac.uk \n \n (b) This dataset consists of work carried out by James Cope, Charles Mallah, and James Orwell. \n Donor of database Charles Mallah: charles.mallah@kingston.ac.uk; James Cope: james.cope@kingston.ac.uk \n```\n\n### Dataset Information\n\nThe original data directory contains the binary images (masks) of the leaf samples (colour images not included).\nThere are three features for each image: Shape, Margin and Texture.\nFor each feature, a 64 element vector is given per leaf sample.\nThese vectors are taken as a contiguous descriptor (for shape) or histograms (for texture and margin).\nSo, there are three different files, one for each feature problem: \n * 'data_Sha_64.txt' -> prediction based on shape\n * 'data_Tex_64.txt' -> prediction based on texture [**dataset provided here**] \n * 'data_Mar_64.txt' -> prediction based on margin \n\nEach row has a 64-element feature vector followed by the Class label.\nThere is a total of 1600 samples with 16 samples per leaf class (100 classes), and no missing values.\n\n### Attributes Information\n\nThree 64 element feature vectors per sample.\n\n### Relevant Papers\n\nCharles Mallah, James Cope, James Orwell. \nPlant Leaf Classification Using Probabilistic Integration of Shape, Texture and Margin Features. \nSignal Processing, Pattern Recognition and Applications, in press.\n\nJ. Cope, P. Remagnino, S. Barman, and P. Wilkin.\nPlant texture classification using gabor co-occurrences.\nAdvances in Visual Computing,\npages 699-677, 2010.\n\nT. Beghin, J. Cope, P. Remagnino, and S. Barman.\nShape and texture based plant leaf classification. \nIn: Advanced Concepts for Intelligent Vision Systems,\npages 345-353. 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"Weekend" + }, + "cat_feature_intro": { + "Browser": "Browser", + "OperatingSystems": "OperatingSystems", + "VisitorType": "VisitorType", + "Month": "Month", + "Region": "Region", + "ProductRelated": "ProductRelated", + "Informational": "Informational", + "TrafficType": "TrafficType", + "Administrative": "Administrative" + } +} \ No newline at end of file diff --git a/online_shoppers/y_test.npy b/online_shoppers/y_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..a2f2e15f7c1ed9378f58d6422c8dc52a4efd71ee --- /dev/null +++ b/online_shoppers/y_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ed13eb60539b1719d336076b446d1efefcef6ccccb08201f4c271fd8d48e135c +size 2594 diff --git a/online_shoppers/y_train.npy b/online_shoppers/y_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..601c988a4169481b5d26e28f433a876b34107c3c --- /dev/null +++ b/online_shoppers/y_train.npy @@ -0,0 +1,3 @@ +version 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"https://www.openml.org/search?type=data&status=active&id=28&sort=runs", + "task_intro": "**Author**: E. Alpaydin, C. Kaynak \n**Source**: [UCI](http://archive.ics.uci.edu/ml/datasets/optical+recognition+of+handwritten+digits) \n**Please cite**: [UCI citation policy](https://archive.ics.uci.edu/ml/citation_policy.html) \n\n1. Title of Database: Optical Recognition of Handwritten Digits\n \n 2. Source:\n E. Alpaydin, C. Kaynak\n Department of Computer Engineering\n Bogazici University, 80815 Istanbul Turkey\n alpaydin@boun.edu.tr\n July 1998\n \n 3. Past Usage:\n C. Kaynak (1995) Methods of Combining Multiple Classifiers and Their\n Applications to Handwritten Digit Recognition, \n MSc Thesis, Institute of Graduate Studies in Science and \n Engineering, Bogazici University.\n \n E. Alpaydin, C. Kaynak (1998) Cascading Classifiers, Kybernetika,\n to appear. ftp://ftp.icsi.berkeley.edu/pub/ai/ethem/kyb.ps.Z\n \n 4. Relevant Information:\n We used preprocessing programs made available by NIST to extract\n normalized bitmaps of handwritten digits from a preprinted form. From\n a total of 43 people, 30 contributed to the training set and different\n 13 to the test set. 32x32 bitmaps are divided into nonoverlapping \n blocks of 4x4 and the number of on pixels are counted in each block.\n This generates an input matrix of 8x8 where each element is an \n integer in the range 0..16. This reduces dimensionality and gives \n invariance to small distortions.\n \n For info on NIST preprocessing routines, see \n M. D. Garris, J. L. Blue, G. T. Candela, D. L. Dimmick, J. Geist, \n P. J. Grother, S. A. Janet, and C. L. Wilson, NIST Form-Based \n Handprint Recognition System, NISTIR 5469, 1994.\n \n 5. Number of Instances\n optdigits.tra Training 3823\n optdigits.tes Testing 1797\n \n The way we used the dataset was to use half of training for \n actual training, one-fourth for validation and one-fourth\n for writer-dependent testing. The test set was used for \n writer-independent testing and is the actual quality measure.\n \n 6. Number of Attributes\n 64 input+1 class attribute\n \n 7. For Each Attribute:\n All input attributes are integers in the range 0..16.\n The last attribute is the class code 0..9\n \n 8. Missing Attribute Values\n None\n \n 9. Class Distribution\n Class: No of examples in training set\n 0: 376\n 1: 389\n 2: 380\n 3: 389\n 4: 387\n 5: 376\n 6: 377\n 7: 387\n 8: 380\n 9: 382\n \n Class: No of examples in testing set\n 0: 178\n 1: 182\n 2: 177\n 3: 183\n 4: 181\n 5: 182\n 6: 181\n 7: 179\n 8: 174\n 9: 180\n \n Accuracy on the testing set with k-nn \n using Euclidean distance as the metric\n \n k = 1 : 98.00\n k = 2 : 97.38\n k = 3 : 97.83\n k = 4 : 97.61\n k = 5 : 97.89\n k = 6 : 97.77\n k = 7 : 97.66\n k = 8 : 97.66\n k = 9 : 97.72\n k = 10 : 97.55\n k = 11 : 97.89", + "task_type": "multiclass", + "openml_id": 28, + "imbalance_ratio": 1.032490974729242, + "n_classes": 10, + "num_feature_intro": { + "input1": "input1", + "input2": "input2", + "input3": "input3", + "input4": "input4", + "input5": "input5", + "input6": "input6", + "input7": "input7", + "input8": "input8", + "input9": "input9", + "input10": "input10", + "input11": "input11", + "input12": "input12", + "input13": "input13", + "input14": "input14", + 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a/optdigits/y_val.npy b/optdigits/y_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..777ca0231f007896aef823859fe48483f52de7ad --- /dev/null +++ b/optdigits/y_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:df581665fe3b0e14400eb580c3872de04acc7e5a1c7a6ce1ed16a6fd7a01fe46 +size 2143 diff --git a/ozone-level-8hr/N_test.npy b/ozone-level-8hr/N_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..8d0156415cad8ea23d2bc1b79abc161e10ef841a --- /dev/null +++ b/ozone-level-8hr/N_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d7b247c95b1d8376d55ed4e7e17ef06439a54fa8d4e01f6910b6a0020146185c +size 292160 diff --git a/ozone-level-8hr/N_train.npy b/ozone-level-8hr/N_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..19fc2b7eb226cd065e889e69033749d5e242a8f8 --- /dev/null +++ b/ozone-level-8hr/N_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:23bb98d512d11f07634910d7b9c0707539ae349551942ae5c020cbf42c2602c9 +size 933824 diff --git a/ozone-level-8hr/N_val.npy b/ozone-level-8hr/N_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..cc3dfc42d0bc207352821670bfc947b32e742773 --- /dev/null +++ b/ozone-level-8hr/N_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b0162939add5e04985344e93b13abbbbee729a758c24e45ef4e2690878b857b1 +size 233984 diff --git a/ozone-level-8hr/info.json b/ozone-level-8hr/info.json new file mode 100644 index 0000000000000000000000000000000000000000..6e80dbc3d1a31245120f04d8c23bafed0a390bde --- /dev/null +++ b/ozone-level-8hr/info.json @@ -0,0 +1,89 @@ +{ + "name": "ozone-level-8hr", + "n_num_features": 72, + "n_cat_features": 0, + "train_size": 1621, + "val_size": 406, + "test_size": 507, + "source": "https://www.openml.org/search?type=data&status=active&id=1487&sort=runs", + "task_intro": "**Author**: Kun Zhang, Wei Fan, XiaoJing Yuan\n\n**Source**: [UCI](https://archive.ics.uci.edu/ml/datasets/ozone+level+detection)\n\n**Please cite**: \n\nForecasting skewed biased stochastic ozone days: analyses, solutions and beyond, Knowledge and Information Systems, Vol. 14, No. 3, 2008. \n\n\n1 . Abstract: \nTwo ground ozone level data sets are included in this collection. One is the eight hour peak set (eighthr.data), the other is the one hour peak set (onehr.data). Those data were collected from 1998 to 2004 at the Houston, Galveston and Brazoria area.\n\n2. Source:\n\nKun Zhang, zhang.kun05 '@' gmail.com, Department of Computer Science, Xavier University of Lousiana \nWei Fan, wei.fan '@' gmail.com, IBM T.J.Watson Research \nXiaoJing Yuan, xyuan '@' uh.edu, Engineering Technology Department, College of Technology, University of Houston \n\n\n3. Data Set Information:\n\nAll the attribute start with T means the temperature measured at different time throughout the day; and those starts with WS indicate the wind speed at various time. \n\nWSR_PK: continuous. peek wind speed -- resultant (meaning average of wind vector) \nWSR_AV: continuous. average wind speed \nT_PK: continuous. Peak T \nT_AV: continuous. Average T \nT85: continuous. T at 850 hpa level (or about 1500 m height) \nRH85: continuous. Relative Humidity at 850 hpa \nU85: continuous. (U wind - east-west direction wind at 850 hpa) \nV85: continuous. V wind - N-S direction wind at 850 \nHT85: continuous. Geopotential height at 850 hpa, it is about the same as height at low altitude \nT70: continuous. T at 700 hpa level (roughly 3100 m height) \n\nRH70: continuous. \nU70: continuous. \nV70: continuous. \nHT70: continuous. \n\nT50: continuous. T at 500 hpa level (roughly at 5500 m height) \n\nRH50: continuous. \nU50: continuous. \nV50: continuous. \nHT50: continuous. \n\nKI: continuous. K-Index [Web Link] \nTT: continuous. T-Totals [Web Link] \nSLP: continuous. Sea level pressure \nSLP_: continuous. SLP change from previous day \n\nPrecp: continuous. -- precipitation\n\n\n4. Attribute Information:\n\nThe following are specifications for several most important attributes that are highly valued by Texas Commission on Environmental Quality (TCEQ). More details can be found in the two relevant papers. \n\nO 3 - Local ozone peak prediction \nUpwind - Upwind ozone background level \nEmFactor - Precursor emissions related factor \nTmax - Maximum temperature in degrees F \nTb - Base temperature where net ozone production begins (50 F) \nSRd - Solar radiation total for the day \nWSa - Wind speed near sunrise (using 09-12 UTC forecast mode) \nWSp - Wind speed mid-day (using 15-21 UTC forecast mode) \n\n\n5. Relevant Papers:\n\nForecasting skewed biased stochastic ozone days: analyses, solutions and beyond, Knowledge and Information Systems, Vol. 14, No. 3, 2008. \n\nIt Discusses details about the dataset, its use as well as various experiments (both cross-validation and streaming) using many state-of-the-art methods. \nA shorter version of the paper (does not contain some detailed experiments as the journal paper above) is in: \nForecasting Skewed Biased Stochastic Ozone Days: Analyses and Solutions. ICDM 2006: 753-764", + "task_type": "binclass", + "openml_id": 1487, + "imbalance_ratio": 14.8375, + "n_classes": 2, + "num_feature_intro": { + "V1": "V1", + "V2": "V2", + "V3": "V3", + "V4": "V4", + "V5": "V5", + "V6": "V6", + "V7": "V7", + "V8": "V8", + "V9": "V9", + "V10": "V10", + "V11": "V11", + "V12": "V12", + "V13": "V13", + "V14": "V14", + "V15": "V15", + "V16": "V16", + "V17": "V17", + "V18": "V18", + "V19": "V19", + "V20": "V20", + "V21": "V21", + "V22": "V22", + "V23": "V23", + "V24": "V24", + "V25": "V25", + "V26": "V26", + "V27": "V27", + "V28": "V28", + "V29": "V29", + "V30": "V30", + "V31": "V31", + "V32": "V32", + "V33": "V33", + "V34": "V34", + "V35": "V35", + "V36": "V36", + "V37": "V37", + "V38": "V38", + "V39": "V39", + "V40": "V40", + "V41": "V41", + "V42": "V42", + "V43": "V43", + "V44": "V44", + "V45": "V45", + "V46": "V46", + "V47": "V47", + "V48": "V48", + "V49": "V49", + "V50": "V50", + "V51": "V51", + "V52": "V52", + "V53": "V53", + "V54": "V54", + "V55": "V55", + "V56": "V56", + "V57": "V57", + "V58": "V58", + "V59": "V59", + "V60": "V60", + "V61": "V61", + "V62": "V62", + "V63": "V63", + "V64": "V64", + "V65": "V65", + "V66": "V66", + "V67": "V67", + "V68": "V68", + "V69": "V69", + "V70": "V70", + "V71": "V71", + "V72": "V72" + }, + "cat_feature_intro": {} +} \ No newline at end of file diff --git a/ozone-level-8hr/y_test.npy b/ozone-level-8hr/y_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..958af580788c8ada489a440166b7c99f76a7fe10 --- /dev/null +++ b/ozone-level-8hr/y_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:abdb821216034733accdbf4132c202b15cc8fd480b6dec07e8e321c13461fc3e +size 1309 diff --git a/ozone-level-8hr/y_train.npy b/ozone-level-8hr/y_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..bc4550a6f578002d15bd97e7cd5f7551f87348e4 --- /dev/null +++ b/ozone-level-8hr/y_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:bd288b163d38198a9311be0342002eb00ae14d9e93a9976e2318f9d545e5023e +size 3539 diff --git a/ozone-level-8hr/y_val.npy b/ozone-level-8hr/y_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..085ec5cfaa99b46bf9a70776f72ea3fad1240b16 --- /dev/null +++ b/ozone-level-8hr/y_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:303b4870d5b36a5dde6bf13c13758fe7f38139e34896553985fd0069f5810da9 +size 1107 diff --git a/ozone_level/C_test.npy b/ozone_level/C_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..ce1d2d654a11b622e3786ce175010511c903938d --- /dev/null +++ b/ozone_level/C_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2c3f6786de33cdb5a9c4e4779105458168625ad08f0bb071904e8a98c1c437f1 +size 98522 diff --git a/ozone_level/C_train.npy b/ozone_level/C_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..6036637437e2459450c30bc0c66d614342a1fd3b --- /dev/null +++ b/ozone_level/C_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b6a8611dd817095113472aac9527c35d9892da3266ab28ca15cd23df3201547c +size 258883 diff --git a/ozone_level/C_val.npy b/ozone_level/C_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..5206ed30ee5d8e1b01db0d98786b54ffb1ee2e5a --- /dev/null +++ b/ozone_level/C_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b2bd4887cc9a2134e68c4e88eac53b8f51f11a0230b54cafcf20b1db7812bbb0 +size 77621 diff --git a/ozone_level/info.json b/ozone_level/info.json new file mode 100644 index 0000000000000000000000000000000000000000..6c3dbf5c8a0b827ced889a9792a10ae73db91b7c --- /dev/null +++ b/ozone_level/info.json @@ -0,0 +1,52 @@ +{ + "name": "ozone_level", + "n_num_features": 0, + "n_cat_features": 36, + "train_size": 1622, + "val_size": 406, + "test_size": 508, + "source": "https://www.openml.org/search?type=data&status=active&id=301&sort=runs", + "task_intro": "**Author**: \n**Source**: Unknown - \n**Please cite**: \n\n1. Title: Ozone Level Detection\n2. Source:\nKun Zhang\nzhang.kun05 '@' gmail.com\nDepartment of Computer Science, \nXavier University of Lousiana\n\nWei Fan\nwei.fan '@' gmail.com\nIBM T.J.Watson Research\n\nXiaoJing Yuan\nxyuan '@' uh.edu\nEngineering Technology Department, \nCollege of Technology, University of Houston \n\n3. Past Usage:\nForecasting skewed biased stochastic ozone days: analyses, solutions and beyond, Knowledge and Information Systems, Vol. 14, No. 3, 2008.\nDiscusses details about the dataset, its use as well as various experiments (both cross-validation and streaming) using many state-of-the-art methods.\n\nA shorter version of the paper (does not contain some detailed experiments as the journal paper above) is in:\nForecasting Skewed Biased Stochastic Ozone Days: Analyses and Solutions. ICDM 2006: 753-764 \n\n4. Relevant Information:\nThe following are specifications for several most important attributes \nthat are highly valued by Texas Commission on Environmental Quality (TCEQ). \nMore details can be found in the two relevant papers.\n \n-- O 3 - Local ozone peak prediction\n-- Upwind - Upwind ozone background level\n-- EmFactor - Precursor emissions related factor\n-- Tmax - Maximum temperature in degrees F\n-- Tb - Base temperature where net ozone production begins (50 F)\n-- SRd - Solar radiation total for the day\n-- WSa - Wind speed near sunrise (using 09-12 UTC forecast mode)\n-- WSp - Wind speed mid-day (using 15-21 UTC forecast mode) \n\n5. Number of Instances: 2536\n\n6. Number of Attributes: 73\n\n7. Attribute Information:\n1,0 | two classes 1: ozone day, 0: normal day", + "task_type": "binclass", + "openml_id": 301, + "n_classes": 2, + "num_feature_intro": {}, + "cat_feature_intro": { + "WSR0": "WSR0", + "WSR1": "WSR1", + "WSR2": "WSR2", + "WSR3": "WSR3", + "WSR4": "WSR4", + "WSR5": "WSR5", + "WSR6": "WSR6", + "WSR7": "WSR7", + "WSR8": "WSR8", + "WSR9": "WSR9", + "WSR10": "WSR10", + "WSR11": "WSR11", + "WSR12": "WSR12", + "WSR13": "WSR13", + "WSR14": "WSR14", + "WSR15": "WSR15", + "WSR16": "WSR16", + "WSR17": "WSR17", + "WSR18": "WSR18", + "WSR19": "WSR19", + "WSR20": "WSR20", + "WSR21": "WSR21", + "WSR22": "WSR22", + "WSR23": "WSR23", + "WSR_PK": "WSR_PK", + "WSR_AV": "WSR_AV", + "T85": "T85", + "RH85": "RH85", + "T70": "T70", + "RH70": "RH70", + "T50": "T50", + "RH50": "RH50", + "HT50": "HT50", + "SLP": "SLP", + "SLP_": "SLP_", + "Precp": "Precp" + } +} \ No newline at end of file diff --git a/ozone_level/y_test.npy b/ozone_level/y_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..50e186a75245b194f8f913d7fe3cf02fadce1aa8 --- /dev/null +++ b/ozone_level/y_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e9506cb3726d73965ffee6426f4cf860935d8a90918cc97740384e5721d473c7 +size 636 diff --git a/ozone_level/y_train.npy b/ozone_level/y_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..9c772b2bd354ec8191736c45e7e5556ae0599dc2 --- /dev/null +++ b/ozone_level/y_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d04a32966d6a89b6fe6d17e0ef03693f526d5ba067d0ed3e9db2b3bff626e394 +size 1750 diff --git a/ozone_level/y_val.npy b/ozone_level/y_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..36d02a248c058701d12565e21e9b20777ede67c3 --- /dev/null +++ b/ozone_level/y_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ec87feba04f47efb00654d9981a0259f934099c61bf0839946d85532eccca7df +size 534 diff --git a/page-blocks/N_test.npy b/page-blocks/N_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..53e77ea779a5739127856f2b1cf4a7edf0a9c10f --- /dev/null +++ b/page-blocks/N_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:989ab6b5e72c33d5f5c30288f5a07dd16489e9d723c29fb1fcf5272ab3e2d656 +size 87728 diff --git a/page-blocks/N_train.npy b/page-blocks/N_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..90a9ff6e2724bc589fb9619e40a325e79eb8e397 --- /dev/null +++ b/page-blocks/N_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:871e5c05279b70bbcc67f3b45a3e5451dfa6ccd42b5a7ad29c38175aadc4eb9c +size 280288 diff --git a/page-blocks/N_val.npy b/page-blocks/N_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..3bf8cb382cd238d310eaddcafd122465ee71f023 --- /dev/null +++ b/page-blocks/N_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:52d1fa5f0836d7ec68b08f1bbe1482066deb955c202aa68235c881e421ee072a +size 70208 diff --git a/page-blocks/info.json b/page-blocks/info.json new file mode 100644 index 0000000000000000000000000000000000000000..38593227ccd392082e352ee3eb5a6763f33e683d --- /dev/null +++ b/page-blocks/info.json @@ -0,0 +1,27 @@ +{ + "name": "page-blocks", + "n_num_features": 10, + "n_cat_features": 0, + "train_size": 3502, + "val_size": 876, + "test_size": 1095, + "source": "https://www.openml.org/search?type=data&status=active&id=30&sort=runs", + "task_intro": "**Author**: \n**Source**: Unknown - \n**Please cite**: \n\n1. Title of Database: Blocks Classification\n 2. Sources:\n (a) Donato Malerba\n Dipartimento di Informatica\n University of Bari\n via Orabona 4\n 70126 Bari - Italy\n phone: +39 - 80 - 5443269\n fax: +39 - 80 - 5443196\n malerbad@vm.csata.it\n (b) Donor: Donato Malerba\n (c) Date: July 1995\n 3. Past Usage:\n This data set have been used to try different simplification methods\n for decision trees. A summary of the results can be found in:\n \n Malerba, D., Esposito, F., and Semeraro, G.\n \"A Further Comparison of Simplification Methods for Decision-Tree Induction.\"\n In D. Fisher and H. Lenz (Eds.), \"Learning from Data: \n Artificial Intelligence and Statistics V\", Lecture Notes in Statistics,\n Springer Verlag, Berlin, 1995.\n \n The problem consists in classifying all the blocks of the page\n layout of a document that has been detected by a segmentation\n process. This is an essential step in document analysis\n in order to separate text from graphic areas. Indeed, \n the five classes are: text (1), horizontal line (2),\n picture (3), vertical line (4) and graphic (5).\n For a detailed presentation of the problem see:\n \n Esposito F., Malerba D., & Semeraro G.\n Multistrategy Learning for Document Recognition\n Applied Artificial Intelligence, 8, pp. 33-84, 1994\n \n All instances have been personally checked so that\n low noise is present in the data.\n \n 4. Relevant Information Paragraph:\n \n The 5473 examples comes from 54 distinct documents. \n Each observation concerns one block. \n All attributes are numeric.\n Data are in a format readable by C4.5.\n \n 5. Number of Instances: 5473.\n \n 6. Number of Attributes \n \n height: integer. | Height of the block.\n lenght: integer. | Length of the block. \n area: integer. | Area of the block (height * lenght);\n eccen: continuous. | Eccentricity of the block (lenght / height);\n p_black: continuous. | Percentage of black pixels within the block (blackpix / area);\n p_and: continuous. | Percentage of black pixels after the application of the Run Length Smoothing Algorithm (RLSA) (blackand / area);\n mean_tr: continuous. | Mean number of white-black transitions (blackpix / wb_trans);\n blackpix: integer. | Total number of black pixels in the original bitmap of the block.\n blackand: integer. | Total number of black pixels in the bitmap of the block after the RLSA.\n wb_trans: integer. | Number of white-black transitions in the original bitmap of the block.\n \n \n \n 7. Missing Attribute Values: No missing value.\n \n 8. Class Distribution: \n \n Valid Cum\n Class Frequency Percent Percent Percent\n \n text 4913 89.8 89.8 89.8\n horiz. line 329 6.0 6.0 95.8\n graphic 28 .5 .5 96.3\n vert. line 88 1.6 1.6 97.9\n picture 115 2.1 2.1 100.0\n ------- ------- -------\n TOTAL 5473 100.0 100.0\n \n Summary Statistics:\n \n Variable Mean Std Dev Minimum Maximum Correlation \n \n HEIGHT 10.47 18.96 1 804 .3510\n LENGTH 89.57 114.72 1 553 -.0045\n AREA 1198.41 4849.38 7 143993 .2343\n ECCEN 13.75 30.70 .007 537.00 .0992\n P_BLACK .37 .18 .052 1.00 .2130\n P_AND .79 .17 .062 1.00 -.1771\n MEAN_TR 6.22 69.08 1.00 4955.00 .0723\n BLACKPIX 365.93 1270.33 7 33017 .1656\n BLACKAND 741.11 1881.50 7 46133 .1565\n WB_TRANS 106.66 167.31 1 3212 .0337\n \n\n Information about the dataset\n CLASSTYPE: nominal\n CLASSINDEX: last", + "task_type": "multiclass", + "openml_id": 30, + "imbalance_ratio": 175.46428571428572, + "n_classes": 5, + "num_feature_intro": { + "height": "height", + "lenght": "lenght", + "area": "area", + "eccen": "eccen", + "p_black": "p_black", + "p_and": "p_and", + "mean_tr": "mean_tr", + "blackpix": "blackpix", + "blackand": "blackand", + "wb_trans": "wb_trans" + }, + "cat_feature_intro": {} +} \ No newline at end of file diff --git a/page-blocks/y_test.npy b/page-blocks/y_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..223d008c1c07020ded30b89f5d8f28c0f702ce19 --- /dev/null +++ b/page-blocks/y_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d4b7b361c5fdad46f6c864585acf9446a41b3902dc78524a4ad8eefc22c36cf0 +size 2505 diff --git a/page-blocks/y_train.npy b/page-blocks/y_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..519edb360b0a339735c52584c6dd52f1d45e2590 --- /dev/null +++ b/page-blocks/y_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9032ee6623f898c820bc95b1ffda490811c841fc83d2e06d89117fb2d7503f31 +size 7323 diff --git a/page-blocks/y_val.npy b/page-blocks/y_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..e4a41fce1bf498941e3e2f747352b79b60abf9b2 --- /dev/null +++ b/page-blocks/y_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:439e57e7759d6759e1874f3309f53a8d2ddc65534f9121023303026466ac2cc2 +size 2065 diff --git a/pc1/N_test.npy b/pc1/N_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..47699eac58c1a6a942da083234c824881399b1b3 --- /dev/null +++ b/pc1/N_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c0030c9d93a960962d89b7d0c733c096e8b39729aa9b5bb1a0394b76067fc4bd +size 37424 diff --git a/pc1/N_train.npy b/pc1/N_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..30217de67fbb4c41d8e94122eb733e708a3be2ef --- /dev/null +++ b/pc1/N_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b620938b6b6d871e76e80ddeb5360915f5b5dc7d66e2274cef03aaa3ffbbfd16 +size 119240 diff --git a/pc1/N_val.npy b/pc1/N_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..468861246f6572bdeb36d25127a03ca57ea5737d --- /dev/null +++ b/pc1/N_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:901f2e31a2da332b86590fa38295a1f4d6ee2c5c16233a47af239bd2667cb2c4 +size 30032 diff --git a/pc1/info.json b/pc1/info.json new file mode 100644 index 0000000000000000000000000000000000000000..d30d8c41f5e291afd6954fccea313689da486113 --- /dev/null +++ b/pc1/info.json @@ -0,0 +1,38 @@ +{ + "name": "pc1", + "n_num_features": 21, + "n_cat_features": 0, + "train_size": 709, + "val_size": 178, + "test_size": 222, + "source": "https://www.openml.org/search?type=data&status=active&id=1068&sort=runs", + "task_intro": "**Author**: Mike Chapman, NASA \n**Source**: [tera-PROMISE](http://openscience.us/repo/defect/mccabehalsted/pc1.html) - 2004 \n**Please cite**: Sayyad Shirabad, J. and Menzies, T.J. (2005) The PROMISE Repository of Software Engineering Databases. School of Information Technology and Engineering, University of Ottawa, Canada. \n \n\n**PC1 Software defect prediction** \nOne of the NASA Metrics Data Program defect data sets. Data from flight software for earth orbiting satellite. Data comes from McCabe and Halstead features extractors of source code. These features were defined in the 70s in an attempt to objectively characterize code features that are associated with software quality.\n\n### Attribute Information \n\n1. loc : numeric % McCabe's line count of code\n2. v(g) : numeric % McCabe \"cyclomatic complexity\"\n3. ev(g) : numeric % McCabe \"essential complexity\"\n4. iv(g) : numeric % McCabe \"design complexity\"\n5. n : numeric % Halstead total operators + operands\n6. v : numeric % Halstead \"volume\"\n7. l : numeric % Halstead \"program length\"\n8. d : numeric % Halstead \"difficulty\"\n9. i : numeric % Halstead \"intelligence\"\n10. e : numeric % Halstead \"effort\"\n11. b : numeric % Halstead \n12. t : numeric % Halstead's time estimator\n13. lOCode : numeric % Halstead's line count\n14. lOComment : numeric % Halstead's count of lines of comments\n15. lOBlank : numeric % Halstead's count of blank lines\n16. lOCodeAndComment: numeric\n17. uniq_Op : numeric % unique operators\n18. uniq_Opnd : numeric % unique operands\n19. total_Op : numeric % total operators\n20. total_Opnd : numeric % total operands\n21. branchCount : numeric % of the flow graph\n22. branchCount : numeric % of the flow graph\n23. defects : {false,true} % module has/has not one or more reported defects\n\n### Relevant papers \n\n- Shepperd, M. and Qinbao Song and Zhongbin Sun and Mair, C. (2013)\nData Quality: Some Comments on the NASA Software Defect Datasets, IEEE Transactions on Software Engineering, 39.\n\n- Tim Menzies and Justin S. Di Stefano (2004) How Good is Your Blind Spot Sampling Policy? 2004 IEEE Conference on High Assurance\nSoftware Engineering.\n\n- T. Menzies and J. DiStefano and A. Orrego and R. Chapman (2004) Assessing Predictors of Software Defects\", Workshop on Predictive Software Models, Chicago", + "task_type": "binclass", + "openml_id": 1068, + "imbalance_ratio": 13.402597402597403, + "n_classes": 2, + "num_feature_intro": { + "loc": "loc", + "v(g)": "v(g)", + "ev(g)": "ev(g)", + "iv(G)": "iv(G)", + "N": "N", + "V": "V", + "L": "L", + "D": "D", + "I": "I", + "E": "E", + "B": "B", + "T": "T", + "lOCode": "lOCode", + "lOComment": "lOComment", + "locCodeAndComment": "locCodeAndComment", + "lOBlank": "lOBlank", + "uniq_Op": "uniq_Op", + "uniq_Opnd": "uniq_Opnd", + "total_Op": "total_Op", + "total_Opnd": "total_Opnd", + "branchCount": "branchCount" + }, + "cat_feature_intro": {} +} \ No newline at end of file diff --git a/pc1/y_test.npy b/pc1/y_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..271a4a66be2803a11d9ea498557258f2eb3d7f88 --- /dev/null +++ b/pc1/y_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:75cd928b99d01cbe27bccab6360120f0d83f7790071274f286502a1d6c982c36 +size 350 diff --git a/pc1/y_train.npy b/pc1/y_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..7c56ea3d4937bd122c1277336cdf241365961147 --- /dev/null +++ b/pc1/y_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4f0ef86d2138141746c089db0377d7428019ee4448fbf81d193265daab5516bb +size 837 diff --git a/pc1/y_val.npy b/pc1/y_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..9abf62b0091288a3caf0cb932160615c21256fb6 --- /dev/null +++ b/pc1/y_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a37619399f44ae8d752e7c3fd9a851423adb6c67ca951870479ddc89f3395604 +size 306 diff --git a/pc3/N_test.npy b/pc3/N_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..42a7fd912119be2992dc257e2ca2ef738e4f6f08 --- /dev/null +++ b/pc3/N_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:619dc1637583d4946a775d40b0d106eff425b70a5e476e4ba793d1473d9e4dba +size 92776 diff --git a/pc3/N_train.npy b/pc3/N_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..d11e465eb03d00c42d1d076e29dcadfda31f4255 --- /dev/null +++ b/pc3/N_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8c8e1bea5ede29c9f5f30ec9957696cb48bf9a99d542d54011962f4af6484887 +size 296128 diff --git a/pc3/N_val.npy b/pc3/N_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..b37e05bfee1685cba454f52168af1f78bd8527df --- /dev/null +++ b/pc3/N_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:310d2450ad2b6cc7004c337f69b3eb68a342bfc8427c20f1e3cd49c0c28c089e +size 74128 diff --git a/pc3/info.json b/pc3/info.json new file mode 100644 index 0000000000000000000000000000000000000000..bc39e7f144f9d5579353f60fb48f3160cd48182e --- /dev/null +++ b/pc3/info.json @@ -0,0 +1,54 @@ +{ + "name": "pc3", + "n_num_features": 37, + "n_cat_features": 0, + "train_size": 1000, + "val_size": 250, + "test_size": 313, + "source": "https://www.openml.org/search?type=data&status=active&id=1050&sort=runs", + "task_intro": "**Author**: Mike Chapman, NASA \n**Source**: [tera-PROMISE](http://openscience.us/repo/defect/mccabehalsted/pc3.html) - 2004 \n**Please cite**: Sayyad Shirabad, J. and Menzies, T.J. (2005) The PROMISE Repository of Software Engineering Databases. School of Information Technology and Engineering, University of Ottawa, Canada. \n \n**PC3 Software defect prediction** \nOne of the NASA Metrics Data Program defect data sets. Data from flight software for earth orbiting satellite. Data comes from McCabe and Halstead features extractors of source code. These features were defined in the 70s in an attempt to objectively characterize code features that are associated with software quality.\n\n### Relevant papers \n\n- Shepperd, M. and Qinbao Song and Zhongbin Sun and Mair, C. (2013)\nData Quality: Some Comments on the NASA Software Defect Datasets, IEEE Transactions on Software Engineering, 39.\n\n- Tim Menzies and Justin S. Di Stefano (2004) How Good is Your Blind Spot Sampling Policy? 2004 IEEE Conference on High Assurance\nSoftware Engineering.\n\n- T. Menzies and J. DiStefano and A. Orrego and R. Chapman (2004) Assessing Predictors of Software Defects\", Workshop on Predictive Software Models, Chicago", + "task_type": "binclass", + "openml_id": 1050, + "imbalance_ratio": 8.76875, + "n_classes": 2, + "num_feature_intro": { + "LOC_BLANK": "LOC_BLANK", + "BRANCH_COUNT": "BRANCH_COUNT", + "CALL_PAIRS": "CALL_PAIRS", + "LOC_CODE_AND_COMMENT": "LOC_CODE_AND_COMMENT", + "LOC_COMMENTS": "LOC_COMMENTS", + "CONDITION_COUNT": "CONDITION_COUNT", + "CYCLOMATIC_COMPLEXITY": "CYCLOMATIC_COMPLEXITY", + "CYCLOMATIC_DENSITY": "CYCLOMATIC_DENSITY", + "DECISION_COUNT": "DECISION_COUNT", + "DECISION_DENSITY": "DECISION_DENSITY", + "DESIGN_COMPLEXITY": "DESIGN_COMPLEXITY", + "DESIGN_DENSITY": "DESIGN_DENSITY", + "EDGE_COUNT": "EDGE_COUNT", + "ESSENTIAL_COMPLEXITY": "ESSENTIAL_COMPLEXITY", + "ESSENTIAL_DENSITY": "ESSENTIAL_DENSITY", + "LOC_EXECUTABLE": "LOC_EXECUTABLE", + "PARAMETER_COUNT": "PARAMETER_COUNT", + "HALSTEAD_CONTENT": "HALSTEAD_CONTENT", + "HALSTEAD_DIFFICULTY": "HALSTEAD_DIFFICULTY", + "HALSTEAD_EFFORT": "HALSTEAD_EFFORT", + "HALSTEAD_ERROR_EST": "HALSTEAD_ERROR_EST", + "HALSTEAD_LENGTH": "HALSTEAD_LENGTH", + "HALSTEAD_LEVEL": "HALSTEAD_LEVEL", + "HALSTEAD_PROG_TIME": "HALSTEAD_PROG_TIME", + "HALSTEAD_VOLUME": "HALSTEAD_VOLUME", + "MAINTENANCE_SEVERITY": "MAINTENANCE_SEVERITY", + "MODIFIED_CONDITION_COUNT": "MODIFIED_CONDITION_COUNT", + "MULTIPLE_CONDITION_COUNT": "MULTIPLE_CONDITION_COUNT", + "NODE_COUNT": "NODE_COUNT", + "NORMALIZED_CYLOMATIC_COMPLEXITY": "NORMALIZED_CYLOMATIC_COMPLEXITY", + "NUM_OPERANDS": "NUM_OPERANDS", + "NUM_OPERATORS": "NUM_OPERATORS", + "NUM_UNIQUE_OPERANDS": "NUM_UNIQUE_OPERANDS", + "NUM_UNIQUE_OPERATORS": "NUM_UNIQUE_OPERATORS", + "NUMBER_OF_LINES": "NUMBER_OF_LINES", + "PERCENT_COMMENTS": "PERCENT_COMMENTS", + "LOC_TOTAL": "LOC_TOTAL" + }, + "cat_feature_intro": {} +} \ No newline at end of file diff --git a/pc3/y_test.npy b/pc3/y_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..cf57afd60af7aedddea4d86f5601cbdd43f834ac --- /dev/null +++ b/pc3/y_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2628ec3756bd3400014750177ea385c93bafbfc7342dc1bfb35af103ee35ea90 +size 441 diff --git a/pc3/y_train.npy b/pc3/y_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..367c91202a28bfd51458fa6a3e997bf57cf7d7b4 --- /dev/null +++ b/pc3/y_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:331245b9558ef2cb7e2acb047e82eb12071e3ad6cd383d4ad8b766847ba22f71 +size 1128 diff --git a/pc3/y_val.npy b/pc3/y_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..d82f2f43bd6113f68046ce61bb260af8304734c6 --- /dev/null +++ b/pc3/y_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7a7c9e5ae2e5dca61e8f480bcc8c42ef1ba2aa6d279c1619d3561ab972634ea9 +size 378 diff --git a/pc4/N_test.npy b/pc4/N_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..1db3a27625e6a87d700fb08da09d826c6eb3d806 --- /dev/null +++ b/pc4/N_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6084152549ac25d5949ab9060b77dd6ddf318a276941424007e74d6b1bff1afc +size 86560 diff --git a/pc4/N_train.npy b/pc4/N_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..71421193cc3ff35b27c0a9e8fe8fe38b5dcdc6b9 --- /dev/null +++ b/pc4/N_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:831060ec9e4de5a7287aa1805ca1e3cfe75147aa189f0dc9fc651e5929fb752f +size 276000 diff --git a/pc4/N_val.npy b/pc4/N_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..bf7b4e00f633b4376e45c243fdb80c6e21e907be --- /dev/null +++ b/pc4/N_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ab156165f54d9a0b115e5105e7b330438fb2cfc6439915b9a369b958cc489b64 +size 69392 diff --git a/pc4/info.json b/pc4/info.json new file mode 100644 index 0000000000000000000000000000000000000000..c23493d1ea9934c7e5d824e6ea7aabeb5250a153 --- /dev/null +++ b/pc4/info.json @@ -0,0 +1,54 @@ +{ + "name": "pc4", + "n_num_features": 37, + "n_cat_features": 0, + "train_size": 932, + "val_size": 234, + "test_size": 292, + "source": "https://www.openml.org/search?type=data&status=active&id=1049&sort=runs", + "task_intro": "**Author**: Mike Chapman, NASA \n**Source**: [tera-PROMISE](http://openscience.us/repo/defect/mccabehalsted/pc1.html) - 2004 \n**Please cite**: Sayyad Shirabad, J. and Menzies, T.J. (2005) The PROMISE Repository of Software Engineering Databases. School of Information Technology and Engineering, University of Ottawa, Canada. \n \n**PC4 Software defect prediction** \nOne of the NASA Metrics Data Program defect data sets. Data from flight software for earth orbiting satellite. Data comes from McCabe and Halstead features extractors of source code. These features were defined in the 70s in an attempt to objectively characterize code features that are associated with software quality.\n\n### Relevant papers \n\n- Shepperd, M. and Qinbao Song and Zhongbin Sun and Mair, C. (2013)\nData Quality: Some Comments on the NASA Software Defect Datasets, IEEE Transactions on Software Engineering, 39.\n\n- Tim Menzies and Justin S. Di Stefano (2004) How Good is Your Blind Spot Sampling Policy? 2004 IEEE Conference on High Assurance\nSoftware Engineering.\n\n- T. Menzies and J. DiStefano and A. Orrego and R. Chapman (2004) Assessing Predictors of Software Defects\", Workshop on Predictive Software Models, Chicago", + "task_type": "binclass", + "openml_id": 1049, + "imbalance_ratio": 7.191011235955056, + "n_classes": 2, + "num_feature_intro": { + "LOC_BLANK": "LOC_BLANK", + "BRANCH_COUNT": "BRANCH_COUNT", + "CALL_PAIRS": "CALL_PAIRS", + "LOC_CODE_AND_COMMENT": "LOC_CODE_AND_COMMENT", + "LOC_COMMENTS": "LOC_COMMENTS", + "CONDITION_COUNT": "CONDITION_COUNT", + "CYCLOMATIC_COMPLEXITY": "CYCLOMATIC_COMPLEXITY", + "CYCLOMATIC_DENSITY": "CYCLOMATIC_DENSITY", + "DECISION_COUNT": "DECISION_COUNT", + "DECISION_DENSITY": "DECISION_DENSITY", + "DESIGN_COMPLEXITY": "DESIGN_COMPLEXITY", + "DESIGN_DENSITY": "DESIGN_DENSITY", + "EDGE_COUNT": "EDGE_COUNT", + "ESSENTIAL_COMPLEXITY": "ESSENTIAL_COMPLEXITY", + "ESSENTIAL_DENSITY": "ESSENTIAL_DENSITY", + "LOC_EXECUTABLE": "LOC_EXECUTABLE", + "PARAMETER_COUNT": "PARAMETER_COUNT", + "HALSTEAD_CONTENT": "HALSTEAD_CONTENT", + "HALSTEAD_DIFFICULTY": "HALSTEAD_DIFFICULTY", + "HALSTEAD_EFFORT": "HALSTEAD_EFFORT", + "HALSTEAD_ERROR_EST": "HALSTEAD_ERROR_EST", + "HALSTEAD_LENGTH": "HALSTEAD_LENGTH", + "HALSTEAD_LEVEL": "HALSTEAD_LEVEL", + "HALSTEAD_PROG_TIME": "HALSTEAD_PROG_TIME", + "HALSTEAD_VOLUME": "HALSTEAD_VOLUME", + "MAINTENANCE_SEVERITY": "MAINTENANCE_SEVERITY", + "MODIFIED_CONDITION_COUNT": "MODIFIED_CONDITION_COUNT", + "MULTIPLE_CONDITION_COUNT": "MULTIPLE_CONDITION_COUNT", + "NODE_COUNT": "NODE_COUNT", + "NORMALIZED_CYLOMATIC_COMPLEXITY": "NORMALIZED_CYLOMATIC_COMPLEXITY", + "NUM_OPERANDS": "NUM_OPERANDS", + "NUM_OPERATORS": "NUM_OPERATORS", + "NUM_UNIQUE_OPERANDS": "NUM_UNIQUE_OPERANDS", + "NUM_UNIQUE_OPERATORS": "NUM_UNIQUE_OPERATORS", + "NUMBER_OF_LINES": "NUMBER_OF_LINES", + "PERCENT_COMMENTS": "PERCENT_COMMENTS", + "LOC_TOTAL": "LOC_TOTAL" + }, + "cat_feature_intro": {} +} \ No newline at end of file diff --git a/pc4/y_test.npy b/pc4/y_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..7aad868f14841696d949b51f52d4bac7b46e171f --- /dev/null +++ b/pc4/y_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ae9ae95a7d0e9e783ce8e533499b38548ea6eba8e423627f97b67ae895b4df09 +size 420 diff --git a/pc4/y_train.npy b/pc4/y_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..46578c23f8bd8892bb2a208b40f79184c6c371c0 --- /dev/null +++ b/pc4/y_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8792c2217a801bc3db89c3b5a3a06e8cd432cbdfcb7fb32a7bbbffc5e3d1ecdd +size 1060 diff --git a/pc4/y_val.npy b/pc4/y_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..5d4c9fbd6575eb5993f4edba6bc74fb4c4a426f8 --- /dev/null +++ b/pc4/y_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6e62526593b5d21b8cfc125fabb4398d86570a90314d7eeb05cc090854303c28 +size 362 diff --git a/pendigits/N_test.npy b/pendigits/N_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..07a00755397b3afe828eb175d71894029daa7d63 --- /dev/null +++ b/pendigits/N_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:cdb3682eeccec30deb4888b10b61777e773bb405eb4006c20335f4285c321f9c +size 281600 diff --git a/pendigits/N_train.npy b/pendigits/N_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..97fbe61631d877aaf9b32acf5b7f92dfc60e8b82 --- /dev/null +++ b/pendigits/N_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6503e6d584085baa02c31f09b2c5f65400bd40a6c882710b70043a27040f6c7e +size 900480 diff --git a/pendigits/N_val.npy b/pendigits/N_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..c06d72092699f4f4c3d369c835d4abac4fc16674 --- /dev/null +++ b/pendigits/N_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4ab64b3d1a4fe45254a3a9d4fe50ba445e9a875cfaaa34bb1ea55d4b3024317c +size 225280 diff --git a/pendigits/info.json b/pendigits/info.json new file mode 100644 index 0000000000000000000000000000000000000000..909c4c40cd3c0fde7697209cae591aef0b6a2329 --- /dev/null +++ b/pendigits/info.json @@ -0,0 +1,33 @@ +{ + "name": "pendigits", + "n_num_features": 16, + "n_cat_features": 0, + "train_size": 7034, + "val_size": 1759, + "test_size": 2199, + "source": "https://www.openml.org/search?type=data&status=active&id=32&sort=runs", + "task_intro": "**Author**: E. Alpaydin, Fevzi. Alimoglu \n**Source**: [UCI Machine Learning Repository](https://archive.ics.uci.edu/ml/datasets/Pen-Based+Recognition+of+Handwritten+Digits) \n**Please cite**: [UCI citation policy](https://archive.ics.uci.edu/ml/citation_policy.html)\n\n**Pen-Based Recognition of Handwritten Digits** \nWe create a digit database by collecting 250 samples from 44 writers. The samples, written by 30 writers, are used for training, cross-validation and writer dependent testing, and the digits written by the other 14 are used for writer independent testing. This database is also available in the UNIPEN format.\n\nWe use a WACOM PL-100V pressure sensitive tablet with an integrated LCD display and a cordless stylus. The input and display areas are located in the same place. Attached to the serial port of an Intel 486 based PC, it allows us to collect handwriting samples. The tablet\nsends $x$ and $y$ tablet coordinates and pressure level values of the pen at fixed time intervals (sampling rate) of 100 miliseconds. \n \nThese writers are asked to write 250 digits in random order inside boxes of 500 by 500 tablet pixel resolution. Subject are monitored only during the first entry screens. Each screen contains five boxes with the digits to be written displayed above. Subjects are told to write only inside these boxes. If they make a mistake or are unhappy with their writing, they are instructed to clear the content of a box by using an on-screen button. The first ten digits are ignored because most writers are not familiar with this type of input devices, but subjects are not aware of this. \n \nIn our study, we use only ($x, y$) coordinate information. The stylus pressure level values are ignored. First we apply normalization to make our representation invariant to translations and scale distortions. The raw data that we capture from the tablet consist of integer values between 0 and 500 (tablet input box resolution). The new coordinates are such that the coordinate which has the maximum range varies between 0 and 100. Usually $x$ stays in this range, since most characters are taller than they are wide. \n\n### Attribute information \n\nIn order to train and test our classifiers, we need to represent digits as constant length feature vectors. A commonly used technique leading to good results is resampling the ( x_t, y_t) points. Temporal resampling (points regularly spaced in time) or spatial resampling (points regularly spaced in arc length) can be used here. Raw point data are already regularly spaced in time but the distance between them is variable. Previous research showed that spatial resampling to obtain a constant number of regularly spaced points on the trajectory yields much better performance, because it provides a better alignment between points. Our resampling algorithm uses simple linear interpolation between pairs of points. The resampled digits are represented as a sequence of T points ( x_t, y_t )_{t=1}^T, regularly spaced in arc length, as opposed to the input sequence, which is regularly spaced in time.\n \nSo, the input vector size is 2*T, two times the number of points resampled. We considered spatial resampling to T=8,12,16 points in our experiments and found that T=8 gave the best trade-off between accuracy and complexity.\n \nThe way we used the dataset was to use first half of training for actual training, one-fourth for validation and one-fourth for writer-dependent testing. The test set was used for writer-independent testing and is the actual quality measure.", + "task_type": "multiclass", + "openml_id": 32, + "imbalance_ratio": 1.0843601895734598, + "n_classes": 10, + "num_feature_intro": { + "input1": "input1", + "input2": "input2", + "input3": "input3", + "input4": "input4", + "input5": "input5", + "input6": "input6", + "input7": "input7", + "input8": "input8", + "input9": "input9", + "input10": "input10", + "input11": "input11", + "input12": "input12", + "input13": "input13", + "input14": "input14", + "input15": "input15", + "input16": "input16" + }, + "cat_feature_intro": {} +} \ No newline at end of file diff --git a/pendigits/y_test.npy b/pendigits/y_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..c7409631051327f3b20d88f27c9878657b17bca1 --- /dev/null +++ b/pendigits/y_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:75122e14a4f31560d535fc37a7df9750af2a420e9d03eca2a702e57ef5f04f5d +size 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sha256:2df848abd5bf60d77396027133183f485e02490b0588b3e0dcd87c522c2737d9 +size 138448 diff --git a/phoneme/N_val.npy b/phoneme/N_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..19749bfe869fe416cc67c384a5eddf72e46e2e36 --- /dev/null +++ b/phoneme/N_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:47bbee8356c4eb65a5a62ca2ea78d3317c0ef5e73b9a1362768532d572f62db7 +size 34728 diff --git a/phoneme/info.json b/phoneme/info.json new file mode 100644 index 0000000000000000000000000000000000000000..1f66e7206e2ceb9009448131d11457176effa267 --- /dev/null +++ b/phoneme/info.json @@ -0,0 +1,22 @@ +{ + "name": "phoneme", + "n_num_features": 5, + "n_cat_features": 0, + "train_size": 3458, + "val_size": 865, + "test_size": 1081, + "source": "https://www.openml.org/search?type=data&status=active&id=1489&sort=runs", + "task_intro": "**Author**: Dominique Van Cappel, THOMSON-SINTRA \n**Source**: [KEEL](http://sci2s.ugr.es/keel/dataset.php?cod=105#sub2), [ELENA](https://www.elen.ucl.ac.be/neural-nets/Research/Projects/ELENA/databases/REAL/phoneme/) - 1993 \n**Please cite**: None \n\nThe aim of this dataset is to distinguish between nasal (class 0) and oral sounds (class 1). Five different attributes were chosen to characterize each vowel: they are the amplitudes of the five first harmonics AHi, normalised by the total energy Ene (integrated on all the frequencies): AHi/Ene. The phonemes are transcribed as follows: sh as in she, dcl as in dark, iy as the vowel in she, aa as the vowel in dark, and ao as the first vowel in water. \n\n### Source\n\nThe current dataset was formatted by the KEEL repository, but originally hosted by the [ELENA Project](https://www.elen.ucl.ac.be/neural-nets/Research/Projects/ELENA/elena.htm#stuff). The dataset originates from the European ESPRIT 5516 project: ROARS. The aim of this project was the development and the implementation of a real time analytical system for French and Spanish speech recognition. \n\n### Relevant information\n\nMost of the already existing speech recognition systems are global systems (typically Hidden Markov Models and Time Delay Neural Networks) which recognizes signals and do not really use the speech\nspecificities. On the contrary, analytical systems take into account the articulatory process leading to the different phonemes of a given language, the idea being to deduce the presence of each of the\nphonetic features from the acoustic observation.\n\nThe main difficulty of analytical systems is to obtain acoustical parameters sufficiantly reliable. These acoustical measurements must :\n\n - contain all the information relative to the concerned phonetic feature.\n - being speaker independent.\n - being context independent.\n - being more or less robust to noise.\n\nThe primary acoustical observation is always voluminous (spectrum x N different observation moments) and classification cannot been processed directly.\n\nIn ROARS, the initial database is provided by cochlear spectra, which may be seen as the output of a filters bank having a constant DeltaF/F0, where the central frequencies are distributed on a\nlogarithmic scale (MEL type) to simulate the frequency answer of the auditory nerves. The filters outputs are taken every 2 or 8 msec (integration on 4 or 16 msec) depending on the type of phoneme\nobserved (stationary or transitory). \n\nThe aim of the present database is to distinguish between nasal and\noral vowels. There are thus two different classes:\n\n- Class 0 : Nasals \n- Class 1 : Orals \n\nThis database contains vowels coming from 1809 isolated syllables (for example: pa, ta, pan,...). Five different attributes were chosen to characterize each vowel: they are the amplitudes of the five first harmonics AHi, normalised by the total energy Ene (integrated on all the frequencies): AHi/Ene. Each harmonic is signed: positive when it corresponds to a local maximum of the spectrum and negative otherwise.\n\nThree observation moments have been kept for each vowel to obtain 5427 different instances: \n\n - the observation corresponding to the maximum total energy Ene. \n \n - the observations taken 8 msec before and 8 msec after the observation corresponding to this maximum total energy.\n\nFrom these 5427 initial values, 23 instances for which the amplitude of the 5 first harmonics was zero were removed, leading to the 5404 instances of the present database. The patterns are presented in a random order.\n\n### Past Usage \n\nAlinat, P., Periodic Progress Report 4, ROARS Project ESPRIT II- Number 5516, February 1993, Thomson report TS. ASM 93/S/EGS/NC/079 \n \nGuerin-Dugue, A. and others, Deliverable R3-B4-P - Task B4: Benchmarks, Technical report, Elena-NervesII \"Enhanced Learning for Evolutive Neural Architecture\", ESPRIT-Basic Research Project Number 6891, June 1995 \n\nVerleysen, M. and Voz, J.L. and Thissen, P. and Legat, J.D., A statistical Neural Network for high-dimensional vector classification, ICNN'95 - IEEE International Conference on Neural Networks, November 1995, Perth, Western Australia. \n \nVoz J.L., Verleysen M., Thissen P. and Legat J.D., Suboptimal Bayesian classification by vector quantization with small clusters. ESANN95-European Symposium on Artificial Neural Networks, April 1995, M. Verleysen editor, D facto publications, Brussels, Belgium. \n \nVoz J.L., Verleysen M., Thissen P. and Legat J.D., A practical view of suboptimal Bayesian classification, IWANN95-Proceedings of the International Workshop on Artificial Neural Networks, June 1995, Mira, Cabestany, Prieto editors, Springer-Verlag Lecture Notes in Computer Sciences, Malaga, Spain", + "task_type": "binclass", + "openml_id": 1489, + "imbalance_ratio": 2.407313997477932, + "n_classes": 2, + "num_feature_intro": { + "V1": "V1", + "V2": "V2", + "V3": "V3", + "V4": "V4", + "V5": "V5" + }, + "cat_feature_intro": {} +} \ No newline at end of file diff --git a/phoneme/y_test.npy b/phoneme/y_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..8fa17b4a03b5e0f3568da2d8a91beb86e068273d --- /dev/null +++ b/phoneme/y_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d72f25f629ee0c9e614df79ae24297accb4e97f0b98ce6e77a175aabd4c927d7 +size 2459 diff --git a/phoneme/y_train.npy b/phoneme/y_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..031dbf6ae7461a2d88ea219717bf3f64df6f6e01 --- /dev/null +++ b/phoneme/y_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:815b935c1d7090360011f25215a267a5b3929d4be38502097bd9f5a57f200e88 +size 7217 diff --git a/phoneme/y_val.npy b/phoneme/y_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..728dcd70c37ca54dd68790f5337365c85610f4ca --- /dev/null +++ b/phoneme/y_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2588166026c8d0ca948e0d372fc67639c35139c8e0dd136ecafc8def8828f5b5 +size 2025 diff --git a/pol/N_test.npy b/pol/N_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..57c4707f20e096e2865de532b76c4000eb06c2cb --- /dev/null +++ b/pol/N_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b359201900567bced04c116e0a0a9cf431b2bddfb1a42b43306a713fa919fc95 +size 52570 diff --git a/pol/N_train.npy b/pol/N_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..bd9080090d21893b4108f7a33d67f13c656bb234 --- /dev/null +++ b/pol/N_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:83438d6eab54f58ceb50814b14050c8637c2d239a68148bcd628b2f92da0f03b +size 167880 diff --git a/pol/N_val.npy b/pol/N_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..54de82bd372cb685c40ccd62deeea2bbf47d301e --- /dev/null +++ b/pol/N_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b7f1e3705ef1bbde80ee1c06e177e0b67f1b64b4d9823bbc958f369f985d7d44 +size 42066 diff --git a/pol/info.json b/pol/info.json new file mode 100644 index 0000000000000000000000000000000000000000..62de1c3aa39a10b5f3f614d7adb0a2a2b40bcc5a --- /dev/null +++ b/pol/info.json @@ -0,0 +1,43 @@ +{ + "name": "pol", + "n_num_features": 26, + "n_cat_features": 0, + "train_size": 6452, + "val_size": 1613, + "test_size": 2017, + "source": "https://www.openml.org/search?type=data&status=active&id=44122&sort=runs", + "task_intro": "Dataset used in the tabular data benchmark https://github.com/LeoGrin/tabular-benchmark, transformed in the same way. 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Each class is drawn from a multivariate normal distribution. Class 1 has mean zero and covariance 4 times the identity. Class 2 has mean (a,a,..a) and unit covariance. a = 2/sqrt(20). \n\n2: Data set description.\n\nThis is an implementation of Leo Breiman's ringnorm example[1]. It is a 20 dimensional, 2 class classification example. Each class is drawn from a multivariate normal distribution. Class 1 has mean zero and covariance 4 times the identity. Class 2 has mean (a,a,..a) and unit covariance. a = 2/sqrt(20). Breiman reports the theoretical expected misclassification rate as 1.3%. He used 300 training examples with CART and found an error of 21.4%.\n\n\n- Type. Classification \n- Origin. Laboratory\n- Instances. 7400\n- Features. 20\n- Classes. 2 \n- Missing values. No\n\n3: Attributes information\n\n@relation ring\n@attribute A1 real [-6879.0, 6285.0]\n@attribute A2 real [-7141.0, 6921.0]\n@attribute A3 real [-7734.0, 7611.0]\n@attribute A4 real [-6627.0, 7149.0]\n@attribute A5 real [-7184.0, 6383.0]\n@attribute A6 real [-6946.0, 6743.0]\n@attribute A7 real [-7781.0, 6285.0]\n@attribute A8 real [-6882.0, 6357.0]\n@attribute A9 real [-7184.0, 7487.0]\n@attribute A10 real [-7232.0, 6757.0]\n@attribute A11 real [-7803.0, 7208.0]\n@attribute A12 real [-7395.0, 6791.0]\n@attribute A13 real [-7096.0, 6403.0]\n@attribute A14 real [-7472.0, 7261.0]\n@attribute A15 real [-7342.0, 7372.0]\n@attribute A16 real [-7121.0, 6905.0]\n@attribute A17 real [-7163.0, 7175.0]\n@attribute A18 real [-8778.0, 6896.0]\n@attribute A19 real [-7554.0, 5726.0]\n@attribute A20 real [-6722.0, 7627.0]\n@attribute Class {0, 1}\n@inputs A1, A2, A3, A4, A5, A6, A7, A8, A9, A10, A11, A12, A13, A14, A15, A16, A17, A18, A19, A20\n@outputs Class", + "task_type": "binclass", + 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"https://www.openml.org/search?type=data&status=active&id=44160&sort=runs", + "task_intro": "Dataset used in the tabular data benchmark https://github.com/LeoGrin/tabular-benchmark, \n transformed in the same way. This dataset belongs to the \"classification on categorical and\n numerical features\" benchmark. Original description: \n \nThe goal of this challenge is to expose the research community to real world datasets of interest to 4Paradigm. All datasets are formatted in a uniform way, though the type of data might differ. The data are provided as preprocessed matrices, so that participants can focus on classification, although participants are welcome to use additional feature extraction procedures (as long as they do not violate any rule of the challenge). All problems are binary classification problems and are assessed with the normalized Area Under the ROC Curve (AUC) metric (i.e. 2*AUC-1).\n The identity of the datasets and the type of data is concealed, though its structure is revealed. The final score in phase 2 will be the average of rankings on all testing datasets, a ranking will be generated from such results, and winners will be determined according to such ranking.\n The tasks are constrained by a time budget. The Codalab platform provides computational resources shared by all participants. Each code submission will be exceuted in a compute worker with the following characteristics: 2Cores / 8G Memory / 40G SSD with Ubuntu OS. To ensure the fairness of the evaluation, when a code submission is evaluated, its execution time is limited in time.\n http://automl.chalearn.org/data", + "task_type": "binclass", + "openml_id": 44160, + "imbalance_ratio": 1.0, + "n_classes": 2, + "num_feature_intro": { + "V1": "V1", + "V5": "V5", + "V6": "V6", + "V20": "V20", + "V21": "V21" + }, + "cat_feature_intro": { + "V8": "V8", + "V14": "V14", + "V15": "V15", + "V17": "V17", + "V18": "V18", + "V19": "V19", + "V22": "V22" + } +} \ No newline at end of file diff --git a/rl/y_test.npy b/rl/y_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..16579f3daf1e806a376e52e7ba900097cb6726c1 --- /dev/null +++ b/rl/y_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:43c7d0613cf5cbd338ed333b98cd5d70a0640ca4fe2eb6d2b89be69d45cfe795 +size 2283 diff --git a/rl/y_train.npy b/rl/y_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..23e52eecc09224a84339d17a4967684c31d3c2f7 --- /dev/null +++ b/rl/y_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d279b93b191cf7758d3b4c36164243f4ad375e171a1a72dbb98aa331de1278c7 +size 6661 diff --git a/rl/y_val.npy b/rl/y_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..a6277e3f1c93c9abd9339f2b41fd90e0fb1da2fd --- /dev/null +++ b/rl/y_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e3ecfa1c5dbeae350a14f406237da6ca2c0b5aa6c3631cd400855f366d91a372 +size 1887 diff --git a/satellite_image/N_test.npy b/satellite_image/N_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..114813c8fac6f60bf4713789b5b60ba8d3ced5c6 --- /dev/null +++ b/satellite_image/N_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d07b0ac8a65fb6fcfc884079a122812f56488042c00b3b7be7ef7634f03491ef +size 46460 diff --git a/satellite_image/N_train.npy b/satellite_image/N_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..97a7f62d0a383cc69186193c6ec6b5707dc24f7f --- /dev/null +++ b/satellite_image/N_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:50102115bd01c2c55d86eab4c1eff095778e6f1e6d9f5c1c0539399dc55b45f3 +size 148376 diff --git a/satellite_image/N_val.npy b/satellite_image/N_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..875d57ec1cf0302cfedb32545045c49dfea8009d --- /dev/null +++ b/satellite_image/N_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:1856a4d61c92fe40957df07ff70647f62253d409aeb1a218dad4c978955bba20 +size 37208 diff --git a/satellite_image/info.json b/satellite_image/info.json new file mode 100644 index 0000000000000000000000000000000000000000..52e326c33ccc8c12293db0193913dd10f34011b3 --- /dev/null +++ b/satellite_image/info.json @@ -0,0 +1,52 @@ +{ + "name": "satellite_image", + "n_num_features": 36, + "n_cat_features": 0, + "train_size": 4118, + "val_size": 1030, + "test_size": 1287, + "source": "https://www.openml.org/search?type=data&status=active&id=294&sort=runs", + "task_intro": "**Author**: \n**Source**: Unknown - 1993 \n**Please cite**: \n\nSource:\nAshwin Srinivasan\nDepartment of Statistics and Data Modeling\nUniversity of Strathclyde\nGlasgow\nScotland\nUK\nross '@' uk.ac.turing\n\nThe original Landsat data for this database was generated from data purchased from NASA by the Australian Centre for Remote Sensing, and used for research at: \nThe Centre for Remote Sensing\nUniversity of New South Wales\nKensington, PO Box 1\nNSW 2033\nAustralia.\n\nThe sample database was generated taking a small section (82 rows and 100 columns) from the original data. The binary values were converted to their present ASCII form by Ashwin Srinivasan. The classification for each pixel was performed on the basis of an actual site visit by Ms. Karen Hall, when working for Professor John A. Richards, at the Centre for Remote Sensing at the University of New South Wales, Australia. Conversion to 3x3 neighbourhoods and splitting into test and training sets was done by Alistair Sutherland.\n\nData Set Information:\nThe database consists of the multi-spectral values of pixels in 3x3 neighbourhoods in a satellite image, and the classification associated with the central pixel in each neighbourhood. The aim is to predict this classification, given the multi-spectral values. In the sample database, the class of a pixel is coded as a number. The Landsat satellite data is one of the many sources of information available for a scene. The interpretation of a scene by integrating spatial data of diverse types and resolutions including multispectral and radar data, maps indicating topography, land use etc. is expected to assume significant importance with the onset of an era characterised by integrative approaches to remote sensing (for example, NASA's Earth Observing System commencing this decade). Existing statistical methods are ill-equipped for handling such diverse data types. Note that this is not true for Landsat MSS data considered in isolation (as in this sample database). This data satisfies the important requirements of being numerical and at a single resolution, and standard maximum-likelihood classification performs very well. Consequently, for this data, it should be interesting to compare the performance of other methods against the statistical approach. One frame of Landsat MSS imagery consists of four digital images of the same scene in different spectral bands. Two of these are in the visible region (corresponding approximately to green and red regions of the visible spectrum) and two are in the (near) infra-red. Each pixel is a 8-bit binary word, with 0 corresponding to black and 255 to white. The spatial resolution of a pixel is about 80m x 80m. Each image contains 2340 x 3380 such pixels. The database is a (tiny) sub-area of a scene, consisting of 82 x 100 pixels. Each line of data corresponds to a 3x3 square neighbourhood of pixels completely contained within the 82x100 sub-area. Each line contains the pixel values in the four spectral bands (converted to ASCII) of each of the 9 pixels in the 3x3 neighbourhood and a number indicating the classification label of the central pixel. The number is a code for the following classes:\n\nNumber Class\n1 red soil\n2 cotton crop\n3 grey soil\n4 damp grey soil\n5 soil with vegetation stubble\n6 mixture class (all types present)\n7 very damp grey soil\nNB. There are no examples with class 6 in this dataset.\n \nThe data is given in random order and certain lines of data have been removed so you cannot reconstruct the original image from this dataset. In each line of data the four spectral values for the top-left pixel are given first followed by the four spectral values for the top-middle pixel and then those for the top-right pixel, and so on with the pixels read out in sequence left-to-right and top-to-bottom. Thus, the four spectral values for the central pixel are given by attributes 17,18,19 and 20. If you like you can use only these four attributes, while ignoring the others. This avoids the problem which arises when a 3x3 neighbourhood straddles a boundary.\n\nAttribute Information:\nThe attributes are numerical, in the range 0 to 255.\n\nUCI: http://archive.ics.uci.edu/ml/datasets/Statlog+(Landsat+Satellite)", + "task_type": "regression", + "openml_id": 294, + "n_classes": 1, + "num_feature_intro": { + "attr1": "attr1", + "attr2": "attr2", + "attr3": "attr3", + "attr4": "attr4", + "attr5": "attr5", + "attr6": "attr6", + "attr7": "attr7", + "attr8": "attr8", + "attr9": "attr9", + "attr10": "attr10", + "attr11": "attr11", + "attr12": "attr12", + "attr13": "attr13", + "attr14": "attr14", + "attr15": "attr15", + "attr16": "attr16", + "attr17": "attr17", + "attr18": "attr18", + "attr19": "attr19", + "attr20": "attr20", + "attr21": "attr21", + "attr22": "attr22", + "attr23": "attr23", + "attr24": "attr24", + "attr25": "attr25", + "attr26": "attr26", + "attr27": "attr27", + "attr28": "attr28", + "attr29": "attr29", + "attr30": "attr30", + "attr31": "attr31", + "attr32": "attr32", + "attr33": "attr33", + "attr34": "attr34", + "attr35": "attr35", + "attr36": "attr36" + }, + "cat_feature_intro": {} +} \ No newline at end of file diff --git a/satellite_image/y_test.npy b/satellite_image/y_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..665fa77d72dcb1e2c43ea23f689082cabf3765f6 --- /dev/null +++ b/satellite_image/y_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:be7bcbb840d06ede0f921f9e3c68c8008fc3e2fa4972d1ce7eece11bc4003079 +size 1415 diff --git a/satellite_image/y_train.npy b/satellite_image/y_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..55a09332f0a4c9fcb50d21ce74912052f24685ba --- /dev/null +++ b/satellite_image/y_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6282c1d87c15e4719d1be4ba8024154601d8535a2580a51071a5a491470b70c8 +size 4246 diff --git a/satellite_image/y_val.npy b/satellite_image/y_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..16b979935044ddfff5f49b39f53a7bc791160913 --- /dev/null +++ b/satellite_image/y_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ff9392b99384cc2a38d9c0270c19b9bbe8b2fe55171884994df23eea75210716 +size 1158 diff --git a/satimage/N_test.npy b/satimage/N_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..e46ef7238afd47d1a33eb247822773b6d6c4aaa7 --- /dev/null +++ b/satimage/N_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:13dd091d8455602ce47be2944f15006634e273d17f84e206cc9abee5164dd961 +size 370496 diff --git a/satimage/N_train.npy b/satimage/N_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..7e23aa0f4c1810bea865602e67ba8aeba7a9e741 --- /dev/null +++ b/satimage/N_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2e7b50e2c5f0b8fcdf87c024eace1153af32ca440c6fa0a23e97dfbda9ee7902 +size 1185248 diff --git a/satimage/N_val.npy b/satimage/N_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..ceb790ea9480b2c943ef03ec454b652ea7ace6fc --- /dev/null +++ b/satimage/N_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9cf73e88e89dbec3771b6d97d41e5ce7ab651484a5bc35613c31fbca312f502e +size 296480 diff --git a/satimage/info.json b/satimage/info.json new file mode 100644 index 0000000000000000000000000000000000000000..621cec1764d8210427f6952fcc1b30d53098ef72 --- /dev/null +++ b/satimage/info.json @@ -0,0 +1,53 @@ +{ + "name": "satimage", + "n_num_features": 36, + "n_cat_features": 0, + "train_size": 4115, + "val_size": 1029, + "test_size": 1286, + "source": "https://www.openml.org/search?type=data&status=active&id=182&sort=runs", + "task_intro": "**Author**: Ashwin Srinivasan, Department of Statistics and Data Modeling, University of Strathclyde \n**Source**: [UCI](https://archive.ics.uci.edu/ml/datasets/Statlog+(Landsat+Satellite)) - 1993 \n**Please cite**: [UCI](https://archive.ics.uci.edu/ml/citation_policy.html) \n\nThe database consists of the multi-spectral values of pixels in 3x3 neighbourhoods in a satellite image, and the classification associated with the central pixel in each neighbourhood. The aim is to predict this classification, given the multi-spectral values. In the sample database, the class of a pixel is coded as a number. \n\nOne frame of Landsat MSS imagery consists of four digital images of the same scene in different spectral bands. Two of these are in the visible region (corresponding approximately to green and red regions of the visible spectrum) and two are in the (near) infra-red. Each pixel is a 8-bit binary word, with 0 corresponding to black and 255 to white. The spatial resolution of a pixel is about 80m x 80m. Each image contains 2340 x 3380 such pixels. \n\nThe database is a (tiny) sub-area of a scene, consisting of 82 x 100 pixels. Each line of data corresponds to a 3x3 square neighbourhood of pixels completely contained within the 82x100 sub-area. Each line contains the pixel values in the four spectral bands (converted to ASCII) of each of the 9 pixels in the 3x3 neighbourhood and a number indicating the classification label of the central pixel. \n\nEach pixel is categorized as one of the following classes: \n1 red soil \n2 cotton crop \n3 grey soil \n4 damp grey soil \n5 soil with vegetation stubble \n6 mixture class (all types present) \n7 very damp grey soil \n\nNB. There are no examples with class 6 in this dataset. \n\nThe data is given in random order and certain lines of data have been removed so you cannot reconstruct the original image from this dataset. \n\n### Attribute information \nThere are 36 predictive attributes (= 4 spectral bands x 9 pixels in neighborhood).\nIn each line of data the four spectral values for the top-left pixel are given first followed by the four spectral values for the top-middle pixel and then those for the top-right pixel, and so on with the pixels read out in sequence left-to-right and top-to-bottom. Thus, the four spectral values for the central pixel are given by attributes 17,18,19 and 20. If you like you can use only these four attributes, while ignoring the others. This avoids the problem which arises when a 3x3 neighbourhood straddles a boundary. \n\nIn this version, the pixel values 0..255 are normalized around 0.\n\n**Note: it is unclear why the attributes are named Aattr - Fattr in this version, since there are only 4 bands and 9 pixels, naming them A1, B1, C1, D1, A2, B2, C2, D2, ... would have made more sense.**", + "task_type": "multiclass", + "openml_id": 182, + "imbalance_ratio": 2.4496, + "n_classes": 6, + "num_feature_intro": { + "Aattr": "Aattr", + "Battr": "Battr", + "Cattr": "Cattr", + "Dattr": "Dattr", + "Eattr": "Eattr", + "Fattr": "Fattr", + "A1attr": "A1attr", + "B2attr": "B2attr", + "C3attr": "C3attr", + "D4attr": "D4attr", + "E5attr": "E5attr", + "F6attr": "F6attr", + "A7attr": "A7attr", + "B8attr": "B8attr", + "C9attr": "C9attr", + "D10attr": "D10attr", + "E11attr": "E11attr", + "F12attr": "F12attr", + "A13attr": "A13attr", + "B14attr": "B14attr", + "C15attr": "C15attr", + "D16attr": "D16attr", + "E17attr": "E17attr", + "F18attr": "F18attr", + "A19attr": "A19attr", + "B20attr": "B20attr", + "C21attr": "C21attr", + "D22attr": "D22attr", + "E23attr": "E23attr", + "F24attr": "F24attr", + "A25attr": "A25attr", + "B26attr": "B26attr", + "C27attr": "C27attr", + "D28attr": "D28attr", + "E29attr": "E29attr", + "F30attr": "F30attr" + }, + "cat_feature_intro": {} +} \ No newline at end of file diff --git a/satimage/y_test.npy b/satimage/y_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..50d6fc1d0402eb1f250dfb909c898427e21f5b7b --- /dev/null +++ b/satimage/y_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d9cba08417aee818ceb056e82ce30767bb80b110ec15b23265835cec865857c2 +size 2899 diff --git a/satimage/y_train.npy b/satimage/y_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..31d8e9b8c65437b45caec9eb8f492326acdf6a96 --- /dev/null +++ b/satimage/y_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:fb5b7d3c091e1e7ccd9b7d5b90542d05dbbda2a5a893810a0dd69cb6dd36488b +size 8563 diff --git a/satimage/y_val.npy b/satimage/y_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..d08c353b42d06b776ed94b5ac183d5ee53b80fc1 --- /dev/null +++ b/satimage/y_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:259faf9b834ce548632c6ac8f05d24eda90855471dea3c5de228f623de23d230 +size 2385 diff --git a/segment/N_test.npy b/segment/N_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..a4350c56c65f6d1f99516cee7d44d4caa74193a2 --- /dev/null +++ b/segment/N_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:09560474ad4ee70ce6720f4b173b18b55e4fc4fd89214a6dbaafbe315711755f +size 62960 diff --git a/segment/N_train.npy b/segment/N_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..171e0c96607ddf64e581fa3bd33daf83129da972 --- /dev/null +++ b/segment/N_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:14879b98965b8c0aca33aa2453ddb41961dc8dc05fd63328c51f7c0a1d115b3c +size 201136 diff --git a/segment/N_val.npy b/segment/N_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..d66c28a53ac212fd1f87433b56c7e12fd6e62cb8 --- /dev/null +++ b/segment/N_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a5cafe03c5ab3be75d305cb88b659c46e7cbd39ae8ddd7100c520f7c0a0877d8 +size 50448 diff --git a/segment/info.json b/segment/info.json new file mode 100644 index 0000000000000000000000000000000000000000..ce3c2a6520ea180d8da0b45d8cf88bae7367c7b8 --- /dev/null +++ b/segment/info.json @@ -0,0 +1,34 @@ +{ + "name": "segment", + "n_num_features": 17, + "n_cat_features": 0, + "train_size": 1478, + "val_size": 370, + "test_size": 462, + "source": "https://www.openml.org/search?type=data&status=active&id=36&sort=runs", + "task_intro": "**Author**: University of Massachusetts Vision Group, Carla Brodley \n**Source**: [UCI](http://archive.ics.uci.edu/ml/datasets/image+segmentation) - 1990 \n**Please cite**: [UCI](http://archive.ics.uci.edu/ml/citation_policy.html) \n\n**Image Segmentation Data Set**\nThe instances were drawn randomly from a database of 7 outdoor images. The images were hand-segmented to create a classification for every pixel. Each instance is a 3x3 region.\n \n### Attribute Information \n\n1. region-centroid-col: the column of the center pixel of the region.\n2. region-centroid-row: the row of the center pixel of the region.\n3. region-pixel-count: the number of pixels in a region = 9.\n4. short-line-density-5: the results of a line extractoin algorithm that \n counts how many lines of length 5 (any orientation) with\n low contrast, less than or equal to 5, go through the region.\n5. short-line-density-2: same as short-line-density-5 but counts lines\n of high contrast, greater than 5.\n6. vedge-mean: measure the contrast of horizontally\n adjacent pixels in the region. There are 6, the mean and \n standard deviation are given. This attribute is used as\n a vertical edge detector.\n7. vegde-sd: (see 6)\n8. hedge-mean: measures the contrast of vertically adjacent\n pixels. Used for horizontal line detection. \n9. hedge-sd: (see 8).\n10. intensity-mean: the average over the region of (R + G + B)/3\n11. rawred-mean: the average over the region of the R value.\n12. rawblue-mean: the average over the region of the B value.\n13. rawgreen-mean: the average over the region of the G value.\n14. exred-mean: measure the excess red: (2R - (G + B))\n15. exblue-mean: measure the excess blue: (2B - (G + R))\n16. exgreen-mean: measure the excess green: (2G - (R + B))\n17. value-mean: 3-d nonlinear transformation\n of RGB. (Algorithm can be found in Foley and VanDam, Fundamentals\n of Interactive Computer Graphics)\n18. saturatoin-mean: (see 17)\n19. hue-mean: (see 17)", + "task_type": "multiclass", + "openml_id": 36, + "imbalance_ratio": 1.0, + "n_classes": 7, + "num_feature_intro": { + "region-pixel-count": "region-pixel-count", + "short-line-density-5": "short-line-density-5", + "short-line-density-2": "short-line-density-2", + "vedge-mean": "vedge-mean", + "vegde-sd": "vegde-sd", + "hedge-mean": "hedge-mean", + "hedge-sd": "hedge-sd", + "intensity-mean": "intensity-mean", + "rawred-mean": "rawred-mean", + "rawblue-mean": "rawblue-mean", + "rawgreen-mean": "rawgreen-mean", + "exred-mean": "exred-mean", + "exblue-mean": "exblue-mean", + "exgreen-mean": "exgreen-mean", + "value-mean": "value-mean", + "saturation-mean": "saturation-mean", + "hue-mean": "hue-mean" + }, + "cat_feature_intro": {} +} \ No newline at end of file diff --git a/segment/y_test.npy b/segment/y_test.npy new file mode 100644 index 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a/semeion/info.json b/semeion/info.json new file mode 100644 index 0000000000000000000000000000000000000000..fe907dfdd54724fa86f068e843b0c8f60c30bce8 --- /dev/null +++ b/semeion/info.json @@ -0,0 +1,273 @@ +{ + "name": "semeion", + "n_num_features": 256, + "n_cat_features": 0, + "train_size": 1019, + "val_size": 255, + "test_size": 319, + "source": "https://www.openml.org/search?type=data&status=active&id=1501&sort=runs", + "task_intro": "**Author**: Semeion Research Center of Sciences of Communication \n**Source**: [UCI](http://archive.ics.uci.edu/ml/datasets/semeion+handwritten+digit) \n**Please cite**: Semeion Research Center of Sciences of Communication, via Sersale 117, 00128 Rome, Italy \nTattile Via Gaetano Donizetti, 1-3-5,25030 Mairano (Brescia), Italy. \n\n### Dataset Description\n\nSemeion Handwritten Digit Data Set, where 1593 handwritten digits from around 80 persons were scanned and documented. The each of the 256 variables V1 - V256 describe one of the pixels and their corresponding values. \n\n### Sources\n\nThe dataset was created by Tactile Srl, Brescia, Italy (http://www.tattile.it) and donated in 1994 to Semeion Research Center of Sciences of Communication, Rome, Italy (http://www.semeion.it), for machine learning research. \n\nFor any questions, e-mail Massimo Buscema (m.buscema '@' semeion.it) or Stefano Terzi (s.terzi '@' semeion.it)\n\n### DataSet Information\n\nA total of 1593 handwritten digits from around 80 persons were scanned, stretched in a rectangular box 16x16 in a gray scale of 256 values. Then each pixel of each image was scaled into a boolean (1/0) value using a fixed threshold. \n\nEach person wrote in a paper all the digits from 0 to 9, twice. The commitment was to write the digit the first time in the normal way (trying to write each digit accurately) and the second time in a fast way (with no accuracy). \n\nThe best validation protocol for this dataset seems to be a 5x2CV, 50% Tune (Train +Test) and completely blind 50% Validation\n\n### Attribute Information\n\nThis dataset consists of 1593 records (rows) and 256 attributes (columns). \nEach record represents a handwritten digit, originally scanned with a resolution of 256 grays scale (28). \nEach pixel of the each original scanned image was first stretched, and after scaled between 0 and 1 (setting to 0 every pixel whose value was under the value 127 of the grey scale (127 included) and setting to 1 each pixel whose original value in the grey scale was over 127). \nFinally, each binary image was scaled again into a 16x16 square box (the final 256 binary attributes).\n\n### Relevant Papers\n\nM Buscema, MetaNet: The Theory of Independent Judges, in Substance Use & Misuse 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sha256:fdc647c3cc4f3a1788a23c72ffd111e468fc6748f61561519e70ec62bf70ef99 +size 668288 diff --git a/shuttle/info.json b/shuttle/info.json new file mode 100644 index 0000000000000000000000000000000000000000..76ef24ce86e926a8b3112e2c39b3d6a59e19ef80 --- /dev/null +++ b/shuttle/info.json @@ -0,0 +1,26 @@ +{ + "name": "shuttle", + "n_num_features": 9, + "n_cat_features": 0, + "train_size": 37120, + "val_size": 9280, + "test_size": 11600, + "source": "https://www.openml.org/search?type=data&status=active&id=40685&sort=runs", + "task_intro": "Source: [UCI](https://archive.ics.uci.edu/ml/datasets/Statlog+(Shuttle))\n\nDonor:\n\nJason Catlett\nBasser Department of Computer Science,\nUniversity of Sydney, N.S.W., Australia\n\n\n\nData Set Information:\n\nApproximately 80% of the data belongs to class 1. Therefore the default accuracy is about 80%. The aim here is to obtain an accuracy of 99 - 99.9%.\n\nThe examples in the original dataset were in time order, and this time order could presumably be relevant in classification. However, this was not deemed relevant for StatLog purposes, so the order of the examples in the original dataset was randomised, and a portion of the original dataset removed for validation purposes.\n\n\nAttribute Information:\n\nThe shuttle dataset contains 9 attributes all of which are numerical. The first one being time. The last column is the class which has been coded as follows :\n1 Rad Flow\n2 Fpv Close\n3 Fpv Open\n4 High\n5 Bypass\n6 Bpv Close\n7 Bpv Open\n\n\nRelevant Papers:\n\nN/A", + "task_type": "multiclass", + "openml_id": 40685, + "imbalance_ratio": 4558.6, + "n_classes": 7, + "num_feature_intro": { + "A1": "A1", + "A2": "A2", + "A3": "A3", + "A4": "A4", + "A5": "A5", + "A6": "A6", + "A7": "A7", + "A8": "A8", + "A9": "A9" + }, + "cat_feature_intro": {} +} \ No newline at end of file diff --git a/shuttle/y_test.npy b/shuttle/y_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..8caf2223d7c23d54653bb85909f9e2a8d0fef5c4 --- /dev/null +++ b/shuttle/y_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2048fa75cde3b13688e2eb6d6e035ac5b223bfa0024007d6843e33a6cd28b1d7 +size 23547 diff --git a/shuttle/y_train.npy b/shuttle/y_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..f0dbb71e45b8b699e172f83be91b58a1d3a01c4e --- /dev/null +++ b/shuttle/y_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:086bdeba2d836f20380e234400efba578e01cb2c3682e3e34da238848508a92e +size 74639 diff --git a/shuttle/y_val.npy b/shuttle/y_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..16228253bfb6bcf5a9bcaa8cbbbe51773bfbdb5e --- /dev/null +++ b/shuttle/y_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0df13e4ad4aa180dce81d4c494546c9140e9ebce503ee504e483b257e0a512b1 +size 18903 diff --git a/socmob/C_test.npy b/socmob/C_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..61dc6cd4c901c2abcfa01c202e2917f819231711 --- /dev/null +++ b/socmob/C_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a3dd01ae2c005069eda340d73ebd839d580261ca95e62a351147e6e74c0f9fda +size 2957 diff --git a/socmob/C_train.npy b/socmob/C_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..12648406a055e2633c55b9e10bd2a2e48c901afb --- /dev/null +++ b/socmob/C_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b71646e05302298dc7d550d4df5b994ed5ad2251776ba80902fdcfdab9881343 +size 7018 diff --git a/socmob/C_val.npy b/socmob/C_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..7ef1605e40fbb41eb89968e29d02a16530c3377c --- /dev/null +++ b/socmob/C_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:08d2cda29e8b972d2f2cdc5c260e9e5e9ce20f150c88118fe31f7cefdee26bcd +size 2581 diff --git a/socmob/N_test.npy b/socmob/N_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..17ea04472184099c594b63bcf5df5a2615ef152e --- /dev/null +++ b/socmob/N_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6b971976fd0be736c03036430a803add9673b284430752f2a6bb7643b2665473 +size 1984 diff --git a/socmob/N_train.npy b/socmob/N_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..720dbffd5f889caeeff2338581efe9276dccdbfe --- /dev/null +++ b/socmob/N_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ee337f5290cd897c1f9990ce46e91d199f893fec59c38ad67aea28cd05a2d15c +size 6040 diff --git a/socmob/N_val.npy b/socmob/N_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..b46dde37d9f6a1009f3ecf97460965c121217fcb --- /dev/null +++ b/socmob/N_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4b54385b9098adee37f46cb3c1b614fdb4c7bfbeb8a0ebaa84cc0bbce3531289 +size 1608 diff --git a/socmob/info.json b/socmob/info.json new file mode 100644 index 0000000000000000000000000000000000000000..b6694395895d0cb90a91ef15fdba77eee1af46f9 --- /dev/null +++ b/socmob/info.json @@ -0,0 +1,22 @@ +{ + "name": "socmob", + "n_num_features": 1, + "n_cat_features": 4, + "train_size": 739, + "val_size": 185, + "test_size": 232, + "source": "https://www.openml.org/search?type=data&status=active&id=541&sort=runs", + "task_intro": "**Author**: \n**Source**: Unknown - Date unknown \n**Please cite**: \n\n17x17x2x2 tables of counts in GLIM-ready format used for the analyses\nin Biblarz, Timothy J., and Adrian E. Raftery. 1993. \"The Effects of\nFamily Disruption on Social Mobility.\" American Sociological Review\n(In press). For further details of the data, see this reference.\nColumn 1 is father's occupation, coded as follows:\n17. Professional, Self-Employed\n16. Professional-Salaried\n15. Manager\n14. Salesman-Nonretail\n13. Proprietor\n12. Clerk\n11. Salesman-Retail\n10. Craftsman-Manufacturing\n9. Craftsmen-Other\n8. Craftsman-Construction\n7. Service Worker\n6. Operative-Nonmanufacturing\n5. Operative-Manufacturing\n4. Laborer-Manufacturing\n3. Laborer-Nonmanufacturing\n2. Farmer/Farm Manager\n1. Farm Laborer\nColumn 2 is son's occupation, coded in the same way as father's.\nColumn 3 is family structure, coded 1=intact family background and\n2=nonintact family background.\nColumn 4 is race, coded 1=white and 2=black.\nColumn 5 is counts for son's first occupation.\nColumn 6 is counts for son's current occupation.\nThe counts have been weighted to take account of the survey\ndesign, which is why they are not integers.\n************************************************************\n***********************************************************\nThis file was constructed from publicly available data collected\nby David Featherman and Robert Hauser in 1973: the \"Occupational\nChange in a Generation II\" (OCG II) Survey. Permission is hereby given to\nuse the above data for non-commercial scholarly and teaching purposes.\nIf these data are used in a published article or book,\nthe authors, the original data (in the form given in\nBiblarz and Raftery (1993), cited above), and StatLib should\nall be acknowledged.\n\n\nInformation about the dataset\nCLASSTYPE: numeric\nCLASSINDEX: none specific", + "task_type": "regression", + "openml_id": 541, + "n_classes": 1, + "num_feature_intro": { + "counts_for_sons_first_occupation": "counts_for_sons_first_occupation" + }, + "cat_feature_intro": { + "fathers_occupation": "fathers_occupation", + "sons_occupation": "sons_occupation", + "family_structure": "family_structure", + "race": "race" + } +} \ No newline at end of file diff --git a/socmob/y_test.npy b/socmob/y_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..5ea3cfec6b20ea7d3235b3870e495cc8ddb1e49c --- /dev/null +++ b/socmob/y_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:72a39383068bcbf547d133a8fed353b4cbe84cb81a93518cf15920d9f08de837 +size 1984 diff --git a/socmob/y_train.npy b/socmob/y_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..23d2ec59f3f5234725bcf816ab4a194197acd363 --- /dev/null +++ b/socmob/y_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7d309d155149abaa8081f33bb1f618fccd48d8e4c3e9adecb8d865e0165f28da +size 6040 diff --git a/socmob/y_val.npy b/socmob/y_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..621b62a05f6b6e27b381b6415ad72de0eb049a5a --- /dev/null +++ b/socmob/y_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7df1de5634ba2483607f7c6181628b36a69a02f6ae64017f18173eed558c33ec +size 1608 diff --git a/space_ga/N_test.npy b/space_ga/N_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..38b193e5f0b1b7a8889a43bbbdc195779b60f920 --- /dev/null +++ b/space_ga/N_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:21100cf1eaffe385ce15467ac095be4bed44c0179e0d10922c5eb11a20de512a +size 29984 diff --git a/space_ga/N_train.npy b/space_ga/N_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..77161e229d24a2584d4f8c6aea6668a35118301b --- /dev/null +++ b/space_ga/N_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c32357381007a6bd92300ee51f664142bb83e665310b87651d40403a52caaeaa +size 95552 diff --git a/space_ga/N_val.npy b/space_ga/N_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..a8d700d3be0865aa962851c83f95cb37404a7991 --- /dev/null +++ b/space_ga/N_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:af21e27d16d862b56409a32f303e04c823887cb830fa2ffaf190b2fe1028a51b +size 23984 diff --git a/space_ga/info.json b/space_ga/info.json new file mode 100644 index 0000000000000000000000000000000000000000..7187110f26e9ac0848a6190b3de05c24e0f0f013 --- /dev/null +++ b/space_ga/info.json @@ -0,0 +1,22 @@ +{ + "name": "space_ga", + "n_num_features": 6, + "n_cat_features": 0, + "train_size": 1988, + "val_size": 497, + "test_size": 622, + "source": "https://www.openml.org/search?type=data&status=active&id=507&sort=runs", + "task_intro": "**Author**: \n**Source**: Unknown - Date unknown \n**Please cite**: \n\nGeographical Analysis Spatial Data\n\nThis georeferenced data set was used in:\n\nPace, R. Kelley, and Ronald Barry, Quick Computation of Regressions with a Spatially\nAutoregressive Dependent Variable, Geographical Analysis, Volume 29, Number 3, July\n1997, p. 232-247.\n\nIt contains 3,107 observations on U.S. county votes cast in the 1980 presidential election.\nSpecifically, it contains the total number of votes cast in the 1980 presidential election per\ncounty (VOTES), the population in each county of 18 years of age or older (POP), the\npopulation in each county with a 12th grade or higher education (EDUCATION), the\nnumber of owner-occupied housing units (HOUSES), the aggregate income (INCOME), the X\nspatial coordinate of the county (XCOORD), and the Y spatial coordinate of the county\n(YCOORD).\n\nThe dependent variable is the log of the proportion of votes cast for both candidates in the\n1980 presidential election. Hence, we can express our dependent variable as ln(VOTES/\nPOP) = ln(VOTES)-ln(POP).\n\nThe overall data set has the following structure\n\n[ln(VOTES/POP) POP EDUCATION HOUSES INCOME XCOORD YCOORD]\n\nAdditional details can be found, along with other data, manuscripts, free spatial software, and\nso forth, at www.spatial-statistics.com or www.finance.lsu.edu/re (follow the spatial statistics\nlink). In particular, the above mentioned manuscript which used the data is available for\ndownload. If you have any questions, send an email to kelley@spatial-statistics.com.\n\n\n\n\n\n\nInformation about the dataset\nCLASSTYPE: numeric\nCLASSINDEX: 1", + "task_type": "regression", + "openml_id": 507, + "n_classes": 1, + "num_feature_intro": { + "POP": "POP", + "EDUCATION": "EDUCATION", + "HOUSES": "HOUSES", + "INCOME": "INCOME", + "XCOORD": "XCOORD", + "YCOORD": "YCOORD" + }, + "cat_feature_intro": {} +} \ No newline at end of file diff --git a/space_ga/y_test.npy b/space_ga/y_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..a8d570f563c565c14bcea87a269b055fa68723d3 --- /dev/null +++ b/space_ga/y_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:80d98b8390daa8c77a63ad202d10493577a0d2d58364e81c4e09e7ce3fa37118 +size 5104 diff --git a/space_ga/y_train.npy b/space_ga/y_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..839ec46a26a510770689947c8279c2e5acb900ae --- /dev/null +++ b/space_ga/y_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ae9e999f3c7ad01d71c305bde45df1b87f3ab71d53daed99a24ae713586674b4 +size 16032 diff --git a/space_ga/y_val.npy b/space_ga/y_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..42480d85aef47087a297233768f0bd90f754995f --- /dev/null +++ b/space_ga/y_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d447f36ae773ae64e2433ecc2b0fe2d42eac2d600fb8ea06ff6b9a1a6c60b8cc +size 4104 diff --git a/spambase/N_test.npy b/spambase/N_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..4c8302ea4180d6a77a1f63fbe3b399c0c09c0dc8 --- /dev/null +++ b/spambase/N_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0ed035d3e4129064a980f126cd330d958b14f7421add76c1ea459604366f5d0c +size 420104 diff --git a/spambase/N_train.npy b/spambase/N_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..1d6b500b410b03c120485706e86f71ab7f78d38c --- /dev/null +++ b/spambase/N_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:69bb16f8309fb6b5191b3d9b62f39827069ec246b86b7839abb5615ef7fa3a6f +size 1342592 diff --git a/spambase/N_val.npy b/spambase/N_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..1afdfd839651719c97f09c686f0c9d0000edf186 --- /dev/null +++ b/spambase/N_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:77e86640d541c99b5ec680b10724c984e639e81728a51ab941ffbe72c64ef76b +size 335744 diff --git a/spambase/info.json b/spambase/info.json new file mode 100644 index 0000000000000000000000000000000000000000..9e18e3cdda6578e67061954abb60d5b567584f6c --- /dev/null +++ b/spambase/info.json @@ -0,0 +1,74 @@ +{ + "name": "spambase", + "n_num_features": 57, + "n_cat_features": 0, + "train_size": 2944, + "val_size": 736, + "test_size": 921, + "source": "https://www.openml.org/search?type=data&status=active&id=44&sort=runs", + "task_intro": "**Author**: Mark Hopkins, Erik Reeber, George Forman, Jaap Suermondt \n**Source**: [UCI](https://archive.ics.uci.edu/ml/datasets/spambase) \n**Please cite**: [UCI](https://archive.ics.uci.edu/ml/citation_policy.html)\n\nSPAM E-mail Database \nThe \"spam\" concept is diverse: advertisements for products/websites, make money fast schemes, chain letters, pornography... Our collection of spam e-mails came from our postmaster and individuals who had filed spam. Our collection of non-spam e-mails came from filed work and personal e-mails, and hence the word 'george' and the area code '650' are indicators of non-spam. These are useful when constructing a personalized spam filter. One would either have to blind such non-spam indicators or get a very wide collection of non-spam to generate a general purpose spam filter.\n \nFor background on spam: \nCranor, Lorrie F., LaMacchia, Brian A. Spam! Communications of the ACM, 41(8):74-83, 1998. \n\n### Attribute Information: \nThe last column denotes whether the e-mail was considered spam (1) or not (0), i.e. unsolicited commercial e-mail. Most of the attributes indicate whether a particular word or character was frequently occurring in the e-mail. The run-length attributes (55-57) measure the length of sequences of consecutive capital letters. \n\nFor the statistical measures of each attribute, see the end of this file. Here are the definitions of the attributes: \n\n48 continuous real [0,100] attributes of type \nword_freq_WORD = percentage of words in the e-mail that match WORD, i.e. 100 * (number of times the WORD appears in the e-mail) / total number of words in e-mail. A \"word\" in this case is any string of alphanumeric characters bounded by non-alphanumeric characters or end-of-string.\n \n6 continuous real [0,100] attributes of type char_freq_CHAR = percentage of characters in the e-mail that match CHAR, i.e. 100 * (number of CHAR occurences) / total characters in e-mail\n \n1 continuous real [1,...] attribute of type capital_run_length_average\n = average length of uninterrupted sequences of capital letters\n \n1 continuous integer [1,...] attribute of type capital_run_length_longest\n = length of longest uninterrupted sequence of capital letters\n \n1 continuous integer [1,...] attribute of type capital_run_length_total\n = sum of length of uninterrupted sequences of capital letters\n = total number of capital letters in the e-mail\n \n1 nominal {0,1} class attribute of type spam\n = denotes whether the e-mail was considered spam (1) or not (0), \n i.e. unsolicited commercial e-mail.", + "task_type": "binclass", + "openml_id": 44, + "imbalance_ratio": 1.5377826806398236, + "n_classes": 2, + "num_feature_intro": { + "word_freq_make": "word_freq_make", + "word_freq_address": "word_freq_address", + "word_freq_all": "word_freq_all", + "word_freq_3d": "word_freq_3d", + "word_freq_our": "word_freq_our", + "word_freq_over": "word_freq_over", + "word_freq_remove": "word_freq_remove", + "word_freq_internet": "word_freq_internet", + "word_freq_order": "word_freq_order", + "word_freq_mail": "word_freq_mail", + "word_freq_receive": "word_freq_receive", + "word_freq_will": "word_freq_will", + "word_freq_people": "word_freq_people", + "word_freq_report": "word_freq_report", + "word_freq_addresses": "word_freq_addresses", + "word_freq_free": "word_freq_free", + "word_freq_business": "word_freq_business", + "word_freq_email": "word_freq_email", + "word_freq_you": "word_freq_you", + "word_freq_credit": "word_freq_credit", + "word_freq_your": "word_freq_your", + "word_freq_font": "word_freq_font", + "word_freq_000": "word_freq_000", + "word_freq_money": "word_freq_money", + "word_freq_hp": "word_freq_hp", + "word_freq_hpl": "word_freq_hpl", + "word_freq_george": "word_freq_george", + "word_freq_650": "word_freq_650", + "word_freq_lab": "word_freq_lab", + "word_freq_labs": "word_freq_labs", + "word_freq_telnet": "word_freq_telnet", + "word_freq_857": "word_freq_857", + "word_freq_data": "word_freq_data", + "word_freq_415": "word_freq_415", + "word_freq_85": "word_freq_85", + "word_freq_technology": "word_freq_technology", + "word_freq_1999": "word_freq_1999", + "word_freq_parts": "word_freq_parts", + "word_freq_pm": "word_freq_pm", + "word_freq_direct": "word_freq_direct", + "word_freq_cs": "word_freq_cs", + "word_freq_meeting": "word_freq_meeting", + "word_freq_original": "word_freq_original", + "word_freq_project": "word_freq_project", + "word_freq_re": "word_freq_re", + "word_freq_edu": "word_freq_edu", + "word_freq_table": "word_freq_table", + "word_freq_conference": "word_freq_conference", + "char_freq_%3B": "char_freq_%3B", + "char_freq_%28": "char_freq_%28", + "char_freq_%5B": "char_freq_%5B", + "char_freq_%21": "char_freq_%21", + "char_freq_%24": "char_freq_%24", + "char_freq_%23": "char_freq_%23", + "capital_run_length_average": "capital_run_length_average", + "capital_run_length_longest": "capital_run_length_longest", + "capital_run_length_total": "capital_run_length_total" + }, + "cat_feature_intro": {} +} \ No newline at end of file diff --git a/spambase/y_test.npy b/spambase/y_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..afa52b195820e0a3d281a197b31461953d639a76 --- /dev/null +++ b/spambase/y_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0341ba230952a31b1e9c786cb5fe9326b5a2521979ec03b262f435dc68a979d4 +size 2137 diff --git a/spambase/y_train.npy b/spambase/y_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..2b094db6e430f27f513eeb04e849481c68f31083 --- /dev/null +++ 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"https://www.openml.org/search?type=data&status=active&id=46&sort=runs", + "task_intro": "**Author**: Genbank. Donated by G. Towell, M. Noordewier, and J. Shavlik \n**Source**: [UCI](https://archive.ics.uci.edu/ml/datasets/Molecular+Biology+(Splice-junction+Gene+Sequences)) \n**Please cite**: None \n\nPrimate splice-junction gene sequences (DNA) with associated imperfect domain theory.\nSplice junctions are points on a DNA sequence at which 'superfluous' DNA is removed during the process of protein creation in higher organisms. The problem posed in this dataset is to recognize, given a sequence of DNA, the boundaries between exons (the parts of the DNA sequence retained after splicing) and introns (the parts of the DNA sequence that are spliced out). This problem consists of two subtasks: recognizing exon/intron boundaries (referred to as EI sites), and recognizing intron/exon boundaries (IE sites). (In the biological community, IE borders are referred to a ''acceptors'' while EI borders are referred to as ''donors''.)\n\nAll examples taken from Genbank 64.1. Categories \"ei\" and \"ie\" include every \"split-gene\" for primates in Genbank 64.1. Non-splice examples taken from sequences known not to include a splicing site.\n \n### Attribute Information \n>\n 1 One of {n ei ie}, indicating the class.\n 2 The instance name.\n 3-62 The remaining 60 fields are the sequence, starting at \n position -30 and ending at position +30. Each of\n these fields is almost always filled by one of \n {a, g, t, c}. 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Date unknown \n**Please cite**: \n\n#Dataset from the LIBSVM data repository.\n\nPreprocessing: Original data: someone from Germany working with the car industry.", + "task_type": "binclass", + "openml_id": 1589, + "n_classes": 2, + "num_feature_intro": { + "att_1": "att_1", + "att_2": "att_2", + "att_3": "att_3", + "att_4": "att_4", + "att_5": "att_5", + "att_6": "att_6", + "att_7": "att_7", + "att_8": "att_8", + "att_9": "att_9", + "att_10": "att_10", + "att_11": "att_11", + "att_12": "att_12", + "att_13": "att_13", + "att_14": "att_14", + "att_15": "att_15", + "att_16": "att_16", + "att_17": "att_17", + "att_18": "att_18", + "att_19": "att_19", + "att_20": "att_20", + "att_21": "att_21", + "att_22": "att_22" + }, + "cat_feature_intro": {} +} \ No newline at end of file diff --git a/svmguide3/y_test.npy b/svmguide3/y_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..b928db4783527b2bd2431ea224b82dd9e3e14571 --- /dev/null +++ b/svmguide3/y_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:63552b3709d7fbb664b94c5e2502ce09983a51634a6304c86e64ee7e21ec6977 +size 2120 diff --git a/svmguide3/y_train.npy b/svmguide3/y_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..548853a71f0b3b03cff89d975abe2517adafcdee --- /dev/null +++ b/svmguide3/y_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:92b1ccb7b5d6622e9e8d0d858dafd2bca763d083a72ee88484bf43ebbdf867e5 +size 6488 diff --git a/svmguide3/y_val.npy b/svmguide3/y_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..e7b5bc325e3cefdd2abf148d91b9b9c60e746cca --- /dev/null +++ b/svmguide3/y_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:383a1d9cbc6e381d00dad5df189718bc1863da329190ba0c2ce91b2196164cb7 +size 1720 diff --git a/sylvine/N_test.npy b/sylvine/N_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..a6c7adcb46764a706619c0f782ad02b00fc04f82 --- /dev/null +++ b/sylvine/N_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:fc5185f872831b2fe29ce1ebb19a946b027c7cd979e880ba5d94c90c52e28943 +size 164128 diff --git a/sylvine/N_train.npy b/sylvine/N_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..1fc3d997b127506a8f2eaa521f8ea000574328fe --- /dev/null +++ b/sylvine/N_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9f6ec89e53c16508dac4516ab8a05dc91fb2d7131c32e9d113d694063b130e93 +size 524768 diff --git a/sylvine/N_val.npy b/sylvine/N_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..a4fb37eded06d913a5ffdba93e25d8d4ae333566 --- /dev/null +++ b/sylvine/N_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:57df673d1ad4a6b58910dca7741a8912aa584292a7a4523007a0af5d9a77cbe4 +size 131328 diff --git a/sylvine/info.json b/sylvine/info.json new file mode 100644 index 0000000000000000000000000000000000000000..49b57f63c737b9c861afe4a5eb66bda2a7397f61 --- /dev/null +++ b/sylvine/info.json @@ -0,0 +1,37 @@ +{ + "name": "sylvine", + "n_num_features": 20, + "n_cat_features": 0, + "train_size": 3279, + "val_size": 820, + "test_size": 1025, + "source": "https://www.openml.org/search?type=data&status=active&id=41146&sort=runs", + "task_intro": "SOURCE: [ChaLearn Automatic Machine Learning Challenge (AutoML)](https://competitions.codalab.org/competitions/2321), [ChaLearn](https://automl.chalearn.org/data) \n\nThis is a \"supervised learning\" challenge in machine learning. We are making available 30 datasets, all pre-formatted in given feature representations (this means that each example consists of a fixed number of numerical coefficients). The challenge is to solve classification and regression problems, without any further human intervention.\n\nThe difficulty is that there is a broad diversity of data types and distributions (including balanced or unbalanced classes, sparse or dense feature representations, with or without missing values or categorical variables, various metrics of evaluation, various proportions of number of features and number of examples). The problems are drawn from a wide variety of domains and include medical diagnosis from laboratory analyses, speech recognition, credit rating, prediction or drug toxicity or efficacy, classification of text, prediction of customer satisfaction, object recognition, protein structure prediction, action recognition in video data, etc. While there exist machine learning toolkits including methods that can solve all these problems, it is still considerable human effort to find, for a given combination of dataset, task, metric of evaluation, and available computational time, the combination of methods and hyper-parameter setting that is best suited. Your challenge is to create the \"perfect black box\" eliminating the human in the loop.\n\nThis is a challenge with code submission: your code will be executed automatically on our servers to train and test your learning machines with unknown datasets. However, there is NO OBLIGATION TO SUBMIT CODE. Half of the prizes can be won by just submitting prediction results. There are six rounds (Prep, Novice, Intermediate, Advanced, Expert, and Master) in which datasets of progressive difficulty are introduced (5 per round). There is NO PREREQUISITE TO PARTICIPATE IN PREVIOUS ROUNDS to enter a new round. The rounds alternate AutoML phases in which submitted code is \"blind tested\" in limited time on our platform, using datasets you have never seen before, and Tweakathon phases giving you time to improve your methods by tweaking them on those datasets and running them on your own systems (without computational resource limitation).\n\nNOTE: This dataset corresponds to one of the datasets of the challenge.", + "task_type": "binclass", + "openml_id": 41146, + "imbalance_ratio": 1.0, + "n_classes": 2, + "num_feature_intro": { + "V1": "V1", + "V2": "V2", + "V3": "V3", + "V4": "V4", + "V5": "V5", + "V6": "V6", + "V7": "V7", + "V8": "V8", + "V9": "V9", + "V10": "V10", + "V11": "V11", + "V12": "V12", + "V13": "V13", + "V14": "V14", + "V15": "V15", + "V16": "V16", + "V17": "V17", + "V18": "V18", + "V19": "V19", + "V20": "V20" + }, + "cat_feature_intro": {} +} \ No newline at end of file diff --git a/sylvine/y_test.npy b/sylvine/y_test.npy new file mode 100644 index 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"https://www.openml.org/search?type=data&status=active&id=42178&sort=runs", + "task_intro": "Context\n\"Predict behavior to retain customers. You can analyze all relevant customer data and develop focused customer retention programs.\" [IBM Sample Data Sets]\n\nContent\nEach row represents a customer, each column contains customer's attributes described on the column Metadata.\n\nThe data set includes information about:\n\nCustomers who left within the last month - the column is called Churn\nServices that each customer has signed up for - phone, multiple lines, internet, online security, online backup, device protection, tech support, and streaming TV and movies\nCustomer account information - how long they've been a customer, contract, payment method, paperless billing, monthly charges, and total charges\nDemographic info about customers - gender, age range, and if they have partners and dependents\nInspiration\nTo explore this type of models and learn more about the subject.\n\nTaken from Kaggle: https://www.kaggle.com/blastchar/telco-customer-churn/download", + "task_type": "binclass", + "openml_id": 42178, + "imbalance_ratio": 2.7683253076511503, + "n_classes": 2, + "num_feature_intro": { + "SeniorCitizen": "SeniorCitizen", + "tenure": "tenure", + "MonthlyCharges": "MonthlyCharges" + }, + "cat_feature_intro": { + "gender": "gender", + "Partner": "Partner", + "Dependents": "Dependents", + "PhoneService": "PhoneService", + "MultipleLines": "MultipleLines", + "InternetService": "InternetService", + "OnlineSecurity": "OnlineSecurity", + "OnlineBackup": "OnlineBackup", + "DeviceProtection": "DeviceProtection", + "TechSupport": "TechSupport", + "StreamingTV": "StreamingTV", + "StreamingMovies": "StreamingMovies", + "Contract": "Contract", + "PaperlessBilling": "PaperlessBilling", + "PaymentMethod": "PaymentMethod" + } +} \ No newline at end of file diff --git a/telco-customer-churn/y_test.npy b/telco-customer-churn/y_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..8c81750f6a21dc65f0b9358c7886998327d559d0 --- /dev/null +++ b/telco-customer-churn/y_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:220f09e2d3722542f881342c73c6a5fb83035437f96c5cad2dad0e31c68b2fc9 +size 3118 diff --git a/telco-customer-churn/y_train.npy b/telco-customer-churn/y_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..665fbb81dce811f50e2ddc07e189fb032e94d055 --- /dev/null +++ b/telco-customer-churn/y_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4e397d2014063ad6b95725028de54a713e4d83d6d7b0456d56f9955674f13405 +size 9320 diff --git a/telco-customer-churn/y_val.npy b/telco-customer-churn/y_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..f206db922b51c6dffb89f4f66788033a4587478d --- /dev/null +++ b/telco-customer-churn/y_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ebbcdb08c3c2744275c366a2cceaec871aaf0737324997fc10e48cdbc5b9d25b +size 2554 diff --git a/texture/N_test.npy b/texture/N_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..cc90dc2ab306516c52ae0692987aff5177b50db3 --- /dev/null +++ b/texture/N_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2b70f6743928f9c6389da2833c6e77e5f92d732fae26f083a6e2fff5a0a487bd +size 352128 diff --git a/texture/N_train.npy b/texture/N_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..6191bc3e9da94ee45e3287eb42585483cec13c17 --- /dev/null +++ b/texture/N_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:bb7375207992efab9143b7cf43a200dfe833106a12dd4c70a99e7c1e9fff294b +size 1126528 diff --git a/texture/N_val.npy b/texture/N_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..cd6d89ed8075697d893fc2b2b6eeaf1da7112672 --- /dev/null +++ b/texture/N_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:78149819e48560dd64f305d9419accf6d341785d8f32435ba292720db7a1ede7 +size 281728 diff --git a/texture/info.json b/texture/info.json new file mode 100644 index 0000000000000000000000000000000000000000..a5d4e1b38d02bdcb990bd170bc2c3393afe1571a --- /dev/null +++ b/texture/info.json @@ -0,0 +1,57 @@ +{ + "name": "texture", + "n_num_features": 40, + "n_cat_features": 0, + "train_size": 3520, + "val_size": 880, + "test_size": 1100, + "source": "https://www.openml.org/search?type=data&status=active&id=40499&sort=runs", + "task_intro": "**Author**: Laboratory of Image Processing and Pattern Recognition (INPG-LTIRF), Grenoble - France. \n**Source**: [ELENA project](https://www.elen.ucl.ac.be/neural-nets/Research/Projects/ELENA/databases/REAL/texture/) \n**Please cite**: None\n\n####1. Summary\n\nThis database was generated by the Laboratory of Image Processing and Pattern Recognition (INPG-LTIRF) in the development of the Esprit project ELENA No. 6891 and the Esprit working group ATHOS No. 6620.\n```\n (a) Original source:\n\n P. Brodatz \"Textures: A Photographic Album for Artists and Designers\",\n Dover Publications,Inc.,New York, 1966.\n\n (b) Creation: Laboratory of Image Processing and Pattern Recognition\n\n Institut National Polytechnique de Grenoble INPG\n Laboratoire de Traitement d'Image et de Reconnaissance de Formes LTIRF\n Av. Felix Viallet, 46\n F-38031 Grenoble Cedex\n France\n\n (c) Contact: Dr. A. Guerin-Dugue, INPG-LTIRF, guerin@tirf.inpg.fr\n```\n\n####2. Past Usage:\n\nThis database has a private usage at the TIRF laboratory. It has been created in order to study the textures discrimination with high order statistics.\n\n```\nA.Guerin-Dugue, C. Aviles-Cruz, \"High Order Statistics from Natural Textured Images\",\nIn ATHOS workshop on System Identification and High Order Statistics, Sophia-Antipolis, France, September 1993.\n\nGuerin-Dugue, A. and others, Deliverable R3-B4-P - Task B4: Benchmarks, Technical report,\nElena-NervesII \"Enhanced Learning for Evolutive Neural Architecture\", ESPRIT-Basic Research Project Number 6891,\nJune 1995.\n```\n\n####3. Relevant Information:\n\nThe aim is to distinguish between 11 different textures (Grass lawn, Pressed calf leather, Handmade paper, Raffia looped to a high pile, Cotton canvas, ...), each pattern (pixel) being characterised by 40 attributes built by the estimation of fourth order modified moments in four orientations: 0, 45, 90 and 135 degrees.\n\nA statistical method based on the extraction of fourth order moments for the characterization of natural micro-textures was developed called \"fourth order modified moments\" (mm4) [Guerin93], this method measures the deviation from first-order Gauss-Markov process, for each texture. The features were estimated in four directions to take into account the possible orientations of the textures (0, 45, 90 and 135 degrees). Only correlation between the current pixel, the first neighbourhood and the second neighbourhood are taken into account. This small neighbourhood is adapted to the fine grain property of the textures.\n\nThe data set contains 11 classes of 500 instances and each class refers to a type of texture in the Brodatz album.\n\nThe database dimension is 40 plus one for the class label. The 40 attributes were build respectively by the estimation of the following fourth order modified moments in four orientations: 0, 45, 90 and 135 degrees: mm4(000), mm4(001), mm4(002), mm4(011), mm4(012), mm4(022), mm4(111), mm4(112), mm4(122) and mm4(222).\n\n!! Patterns are always sorted by class and are presented in the increasing order of their class label in each dataset relative to the texture database (texture.dat, texture_CR.dat, texture_PCA.dat, texture_DFA.dat)\n\n####4. Class:\n\nThe class label is a code for the following classes:\n```\n Class Class label\n 2 Grass lawn (D09) \n 3 Pressed calf leather (D24) \n 4 Handmade paper (D57) \n 6 Raffia looped to a high pile: (D84) \n 7 Cotton canvas (D77) \n 8 Pigskin (D92) \n 9 Beach sand: (D28) \n 10 Beach sand (D29) \n 12 Oriental straw cloth (D53) \n 13 Oriental straw cloth (D78) \n 14 Oriental grass fiber cloth (D79) \n```\n\n####5. Summary Statistics:\n\nTable here below provides for each attribute of the database the dynamic (Min and Max values), the mean value and the standard deviation.\n\n```\nAttribute Min Max Mean Standard \n deviation \n\n 1 -1.4495 0.7741 -1.0983 0.2034\n 2 -1.2004 0.3297 -0.5867 0.2055\n 3 -1.3099 0.3441 -0.5838 0.3135\n 4 -1.1104 0.5878 -0.4046 0.2302\n 5 -1.0534 0.4387 -0.3307 0.2360\n 6 -1.0029 0.4515 -0.2422 0.2225\n 7 -1.2076 0.5246 -0.6026 0.2003\n 8 -1.0799 0.3980 -0.4322 0.2210\n 9 -1.0570 0.4369 -0.3317 0.2361\n 10 -1.2580 0.3546 -0.5978 0.3268\n 11 -1.4495 0.7741 -1.0983 0.2034\n 12 -1.0831 0.3715 -0.5929 0.2056\n 13 -1.1194 0.6347 -0.4019 0.3368\n 14 -1.0182 0.1573 -0.6270 0.1390\n 15 -0.9435 0.1642 -0.4482 0.1952\n 16 -0.9944 0.0357 -0.5763 0.1587\n 17 -1.1722 0.0201 -0.7331 0.1955\n 18 -1.0174 0.1155 -0.4919 0.2335\n 19 -1.0044 0.0833 -0.4727 0.2257\n 20 -1.1800 0.4392 -0.4831 0.3484\n 21 -1.4495 0.7741 -1.0983 0.2034\n 22 -1.2275 0.5963 -0.7363 0.2220\n 23 -1.3412 0.4464 -0.7771 0.3290\n 24 -1.1774 0.6882 -0.5770 0.2646\n 25 -1.1369 0.4098 -0.5085 0.2538\n 26 -1.1099 0.3725 -0.4038 0.2515\n 27 -1.2393 0.6120 -0.7279 0.2278\n 28 -1.1540 0.4221 -0.5863 0.2446\n 29 -1.1323 0.3916 -0.5090 0.2526\n 30 -1.4224 0.4718 -0.7708 0.3264\n 31 -1.4495 0.7741 -1.0983 0.2034\n 32 -1.1789 0.5647 -0.6463 0.1890\n 33 -1.1473 0.6755 -0.4919 0.3304\n 34 -1.1228 0.3132 -0.6435 0.1441\n 35 -1.0145 0.3396 -0.4918 0.1922\n 36 -1.0298 0.1560 -0.5934 0.1704\n 37 -1.2534 0.0899 -0.7795 0.1641\n 38 -1.0966 0.1944 -0.5541 0.2111\n 39 -1.0765 0.2019 -0.5230 0.2015\n 40 -1.2155 0.4647 -0.5677 0.3091\n```\n\nThe dynamic of the attributes is in [-1.45 - 0.775]. The database resulting from the centering and reduction by attribute of the Texture database is on the ftp server in the `REAL/texture/texture_CR.dat.Z' file.\n\n####6. Confusion matrix.\n\nThe following confusion matrix of the k_NN classifier was obtained with a Leave_One_Out error counting method on the texture_CR.dat database. k was set to 1 in order to reach the minimum mean error rate : 1.0 +/- 0.8%.\n\n```\n Class 2 3 4 6 7 8 9 10 12 13 14 \n 2 97.0 1.0 0.4 0.0 0.0 0.0 1.6 0.0 0.0 0.0 0.0 \n 3 0.2 99.0 0.0 0.0 0.0 0.0 0.4 0.0 0.0 0.0 0.4 \n 4 1.0 0.0 98.8 0.0 0.0 0.0 0.2 0.0 0.0 0.0 0.0 \n 6 0.0 0.0 0.0 99.4 0.0 0.0 0.0 0.6 0.0 0.0 0.0 \n 7 0.0 0.0 0.0 0.0 100.0 0.0 0.0 0.0 0.0 0.0 0.0 \n 8 0.0 0.0 0.0 0.0 0.0 98.6 0.0 1.4 0.0 0.0 0.0 \n 9 0.4 0.0 0.2 0.0 0.0 0.2 98.8 0.4 0.0 0.0 0.0 \n 10 0.0 0.0 0.0 0.0 0.0 1.4 0.0 98.6 0.0 0.0 0.0 \n 12 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 100.0 0.0 0.0 \n 13 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 99.8 0.2 \n 14 0.0 0.4 0.0 0.0 0.0 0.4 0.0 0.0 0.2 0.0 99.0 \n```\n\n7. Result of the Principal Component Analysis:\n\nThe Principal Components Analysis is a very classical method in pattern recognition [Duda73]. PCA reduces the sample dimension in a linear way for the best representation in lower dimensions keeping the maximum of inertia. The best axe for the representation is however not necessary the best axe for the discrimination. After PCA, features are selected according to the percentage of initial inertia which is covered by the different axes and the number of features is determined according to the percentage of initial inertia to keep for the classification process.\n\nThis selection method has been applied on the texture_CR database. When quasi-linear correlations exists between some initial features, these redundant dimensions are removed by PCA and this preprocessing is then recommended. In this case, before a PCA, the determinant of the data covariance matrix is near zero; this database is thus badly conditioned for all process which use this information (the quadratic classifier for example).\n\nThe following file is available for the texture database: ''texture_PCA.dat.Z'', it is the projection of the ''texture_CR'' database on its principal components (sorted in a decreasing order of the related inertia percentage; so, if you desire to work on the database projected on its x first principal components you only have to keep the x first attributes of the texture_PCA.dat database and the class labels (last attribute)).\n\nTable here below provides the inertia percentages associated to the eigenvalues corresponding to the principal component axis sorted in the decreasing order of the associated inertia percentage. 99.85 percent of the total database inertia will remain if the 20 first principal components are kept.\n\n```\n Eigen Value Inertia Cumulated\n value percentage inertia\n\n 1 30.267500000 75.6687000000 75.6687000000 \n 2 3.6512500000 9.1281300000 84.7969000000 \n 3 2.2937000000 5.7342400000 90.5311000000 \n 4 1.7039700000 4.2599300000 94.7910000000 \n 5 0.6716540000 1.6791300000 96.4702000000 \n 6 0.5015290000 1.2538200000 97.7240000000 \n 7 0.1922830000 0.4807070000 98.2047000000 \n 8 0.1561070000 0.3902670000 98.5950000000 \n 9 0.1099570000 0.2748920000 98.8699000000 \n 10 0.0890891000 0.2227230000 99.0926000000 \n 11 0.0656016000 0.1640040000 99.2566000000 \n 12 0.0489988000 0.1224970000 99.3791000000 \n 13 0.0433819000 0.1084550000 99.4875000000 \n 14 0.0345022000 0.0862554000 99.5738000000 \n 15 0.0299203000 0.0748007000 99.6486000000 \n 16 0.0248857000 0.0622141000 99.7108000000 \n 17 0.0167901000 0.0419752000 99.7528000000 \n 18 0.0161633000 0.0404083000 99.7932000000 \n 19 0.0128898000 0.0322246000 99.8254000000 \n 20 0.0113884000 0.0284710000 99.8539000000 \n 21 0.0078481400 0.0196204000 99.8735000000 \n 22 0.0071527800 0.0178820000 99.8914000000 \n 23 0.0067661400 0.0169153000 99.9083000000 \n 24 0.0053149500 0.0132874000 99.9216000000 \n 25 0.0051102600 0.0127757000 99.9344000000 \n 26 0.0047116600 0.0117792000 99.9461000000 \n 27 0.0036193700 0.0090484300 99.9552000000 \n 28 0.0033222000 0.0083054900 99.9635000000 \n 29 0.0030722400 0.0076806100 99.9712000000 \n 30 0.0026373300 0.0065933300 99.9778000000 \n 31 0.0020996800 0.0052492000 99.9830000000 \n 32 0.0019376500 0.0048441200 99.9879000000 \n 33 0.0015642300 0.0039105700 99.9918000000 \n 34 0.0009679080 0.0024197700 99.9942000000 \n 35 0.0009578000 0.0023945000 99.9966000000 \n 36 0.0007379780 0.0018449400 99.9984000000 \n 37 0.0006280250 0.0015700600 100.000000000\n 38 0.0000000040 0.0000000099 100.000000000 \n 39 0.0000000001 0.0000000003 100.000000000 \n 40 0.0000000008 0.0000000019 100.000000000 \n\n```\n\nThis matrix can be found in the texture_EV.dat file.\n\nThe Discriminant Factorial Analysis (DFA) can be applied to a learning database where each learning sample belongs to a particular class [Duda73]. The number of discriminant features selected by DFA is fixed in function of the number of classes (c) and of the number of input dimensions (d); this number is equal to the minimum between d and c-1. In the usual case where d is greater than c, the output dimension is fixed equal to the number of classes minus one and the discriminant axes are selected in order to maximize the between-variance and to minimize the within-variance of the classes.\n\nThe discrimination power (ratio of the projected between-variance over the projected within-variance) is not the same for each discriminant axis: this ratio decreases for each axis. So for a problem with many classes, this preprocessing will not be always efficient as the last output features will not be so discriminant. This analysis uses the information of the inverse of the global covariance matrix, so the covariance matrix must be well conditioned (for example, a preliminary PCA must be applied to remove the linearly correlated dimensions).\n\nThe Discriminant Factorial Analysis (DFA) has been applied on the 18 first principal components of the texture_PCA database (thus by keeping only the 18 first attributes of these databases before to apply the DFA preprocessing) in order to build the texture_DFA.dat.Z database file, having 10 dimensions (the texture database having 11 classes). In the case of the texture database, experiments shown that a DFA preprocessing is very useful and most of the time improved the classifiers performances.\n\n[Duda73] Duda, R.O. and Hart, P.E.,Pattern Classification and Scene Analysis, John Wiley & Sons, 1973.", + "task_type": "multiclass", + "openml_id": 40499, + "imbalance_ratio": 1.0, + "n_classes": 11, + "num_feature_intro": { + "V1": "V1", + "V2": "V2", + "V3": "V3", + "V4": "V4", + "V5": "V5", + "V6": "V6", + "V7": "V7", + "V8": "V8", + "V9": "V9", + "V10": "V10", + "V11": "V11", + "V12": "V12", + "V13": "V13", + "V14": "V14", + "V15": "V15", + "V16": "V16", + "V17": "V17", + "V18": "V18", + "V19": "V19", + "V20": "V20", + "V21": "V21", + "V22": "V22", + "V23": "V23", + "V24": "V24", + "V25": "V25", + "V26": "V26", + "V27": "V27", + "V28": "V28", + "V29": "V29", + "V30": "V30", + "V31": "V31", + "V32": "V32", + "V33": "V33", + "V34": "V34", + "V35": "V35", + "V36": "V36", + "V37": "V37", + "V38": "V38", + "V39": "V39", + "V40": "V40" + }, + "cat_feature_intro": {} +} \ No newline at end of file diff --git a/texture/y_test.npy b/texture/y_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..a39b6a49dd132e81fd294d2d12be98bf96a46d71 --- /dev/null +++ b/texture/y_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e62c8beefb5f0602f66dea30ac000298884ce226da96b9fde40dcba73dbad72b +size 2553 diff --git a/texture/y_train.npy b/texture/y_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..7c387bbc3a0b50ffe2fffac442c4b537276246d7 --- /dev/null +++ b/texture/y_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7bc95b94c7c748d2ed57cc942d81e0fe1b33c11a17553a784a2cb1044390297e +size 7397 diff --git a/texture/y_val.npy b/texture/y_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..a39f88f3b864582470b37ae9dda5637061ca2c0a --- /dev/null +++ b/texture/y_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8a491ea9b4cf3e4b7d05a9761bd6dbc0e50436477f32806c5178605a91244f92 +size 2111 diff --git a/thyroid-ann/N_test.npy b/thyroid-ann/N_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..293caf184ae5361ec15513efc8faed4eb16a3671 --- /dev/null +++ b/thyroid-ann/N_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2e4956cae168b8a0a86f3ab0c18c186cdc593ecccc3770e736709461841e3945 +size 126968 diff --git a/thyroid-ann/N_train.npy b/thyroid-ann/N_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..1ff4bba177afe26444d57e2890e7a0fe35146809 --- /dev/null +++ b/thyroid-ann/N_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9e1af86d9f4fffc7a4a34d75816141b09f3ab0f9601ede012c3bfb0274cfe97c +size 405512 diff --git a/thyroid-ann/N_val.npy b/thyroid-ann/N_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..c5e7b2193f6a4932cc7fc031d169dad37f891b75 --- /dev/null +++ b/thyroid-ann/N_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:5e857ac6b34e83a9650533c2775a366e299b90c64df1a654ba6e332f0a6e7d1f +size 101600 diff --git a/thyroid-ann/info.json b/thyroid-ann/info.json new file mode 100644 index 0000000000000000000000000000000000000000..32456dc2e6d11203697adc0fd113ea4da381ee09 --- /dev/null +++ b/thyroid-ann/info.json @@ -0,0 +1,38 @@ +{ + "name": "thyroid-ann", + "n_num_features": 21, + "n_cat_features": 0, + "train_size": 2413, + "val_size": 604, + "test_size": 755, + "source": "https://www.openml.org/search?type=data&status=active&id=40497&sort=runs", + "task_intro": "This directory contains Thyroid datasets. "ann-train.data" contains 3772 \nlearning examples and "ann-test.data" contains 3428 testing examples. I have \nobtained this data from Daimler-Benz. This are the informations I have got \ntogether with the dataset:\n\n-------------------------------------------------------------------------------\n1. Data setp summary\n\nNumber of attributes: 21 (15 attributes are binary,\n 6 attributes are continuous)\nNumber of classes: 3\nNumber of learning examples: 3772\nNumber of testing examples: 3428\nData set is availbale on ASCII-file\n\n2. Description\n\nThe problem is to determine whether a patient referred to the clinic is\nhypothyroid. Therefore three classes are built: normal (not hypothyroid),\nhyperfunction and subnormal functioning. Because 92 percent of the patients\nare not hyperthyroid a good classifier must be significant better than 92%.\n\nNote\n\nThese are the datas Quinlans used in the case study of his article\n"Simplifying Decision Trees" (International Journal of Man-Machine Studies \n(1987) 221-234)\n-------------------------------------------------------------------------------\n\n\nUnfortunately this data differ from the one Ross Quinlan placed in\n"pub/machine-learning-databases/thyroid-disease" on "ics.uci.edu".\nI don't know any more details about the dataset. But it's hard to\ntrain Backpropagation ANNs with it. The dataset is used in two technical\nreports:\n\n-------------------------------------------------------------------------------\n"Optimization of the Backpropagation Algorithm for Training Multilayer\nPerceptrons":\n\n ftp archive.cis.ohio-state.edu or ftp 128.146.8.52\n cd pub/neuroprose\n binary\n get schiff.bp_speedup.ps.Z\n quit\n\nThe report is an overview of many different backprop speedup techniques.\n15 different algorithms are described in detail and compared by using\na big, very hard to solve, practical data set. Learning speed and network\nclassification performance with respect to the training data set and also\nwith respect to a testing data set are discussed.\nThese are the tested algorithms:\n\nbackprop\nbackprop (batch mode)\nbackprop + Learning rate calculated by Eaton and Oliver's formula\nbackprop + decreasing learning rate (Darken and Moody)\nbackprop + Learning rate adaptation for each training pattern (J. Schmidhuber)\nbackprop + evolutionarily learning rate adaptation (R. Salomon)\nbackprop + angle driven learning rate adaptation(Chan and Fallside)\nPolak-Ribiere + line search (Kramer and Vincentelli)\nConj. gradient + line search (Leonard and Kramer)\nbackprop + learning rate adaptation by sign changes (Silva and Almeida)\nSuperSAB (T. Tollenaere)\nDelta-Bar-Delta (Jacobs)\nRPROP (Riedmiller and Braun)\nQuickprop (Fahlman)\nCascade correlation (Fahlman)\n\n-------------------------------------------------------------------------------\n"Synthesis and Performance Analysis of Multilayer eural Network Architectures":\n\n\n ftp archive.cis.ohio-state.edu or ftp 128.146.8.52\n cd pub/neuroprose\n binary\n get schiff.gann.ps.Z\n quit\n\nIn this paper we present various approaches for automatic topology-optimization\nof backpropagation networks. First of all, we review the basics of genetic\nalgorithms which are our essential tool for a topology search. Then we give a\nsurvey of backprop and the topological properties of feedforward networks. We\nreport on pioneer work in the filed of topology--optimization. Our first\napproach was based on evolutions strategies which used only mutation to change\nthe parent's topologies. Now, we found a way to extend this approach by an\ncrossover operator which is essential to all genetic search methods.\nIn contrast to competing approaches it allows that two parent networks with\ndifferent number of units can mate and produce a (valid) child network, which\ninherits genes from both of the parents. We applied our genetic algorithm to a\nmedical classification problem which is extremly difficult to solve. The\nperformance with respect to the training set and a test set of pattern samples\nwas compared to fixed network topologies. Our results confirm that the topology\noptimization makes sense, because the generated networks outperform the fixed\ntopologies and reach classification performances near optimum.\n\n-------------------------------------------------------------------------------\n\nRandolf Werner (evol@infko.uni-koblenz.de)", + "task_type": "multiclass", + "openml_id": 40497, + "imbalance_ratio": 37.505376344086024, + "n_classes": 3, + "num_feature_intro": { + "V1": "V1", + "V2": "V2", + "V3": "V3", + "V4": "V4", + "V5": "V5", + "V6": "V6", + "V7": "V7", + "V8": "V8", + "V9": "V9", + "V10": "V10", + "V11": "V11", + "V12": "V12", + "V13": "V13", + "V14": "V14", + "V15": "V15", + "V16": "V16", + "V17": "V17", + "V18": "V18", + "V19": "V19", + "V20": "V20", + "V21": "V21" + }, + "cat_feature_intro": {} +} \ No newline at end of file diff --git a/thyroid-ann/y_test.npy b/thyroid-ann/y_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..ac9788feffdb5236e2908bfd23505155dfa4dd2d --- /dev/null +++ b/thyroid-ann/y_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a93f9c0c8572c8f98b67d2ab6521bb1dc0b7f8d8f9a8e5f7f4d1e14299a50967 +size 1811 diff --git a/thyroid-ann/y_train.npy b/thyroid-ann/y_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..a890ff8637daff755e0e61128cf34770175c0bf8 --- /dev/null +++ b/thyroid-ann/y_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e0dbc423818024852d879624146c7e2b10190cfec0a5504d78c0c35067e981d0 +size 5131 diff --git a/thyroid-ann/y_val.npy b/thyroid-ann/y_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..40fc0a5af8fb17c640aac7d4957757e65f6a405d --- /dev/null +++ b/thyroid-ann/y_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4a976e632d154cb641e5d8fcdcab4b89cde2e6d3fffe6272c464de267845d791 +size 1509 diff --git a/thyroid-dis/C_test.npy b/thyroid-dis/C_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..4a1974c1447bb3fdb576e894ac6cd6f12d76aaa5 --- /dev/null +++ b/thyroid-dis/C_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:deb3362a44ecaa6a5ee0cc44779e491eeaf696d831521e223ae2d27cdb082345 +size 22719 diff --git a/thyroid-dis/C_train.npy b/thyroid-dis/C_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..13c828ba4bdc6897c5f78ef43a2a8eae7494c80a --- /dev/null +++ b/thyroid-dis/C_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ac69068c7a38116f5ff904cb5d678d19e0edd011fcbf3d52e1e636a37f314cdf +size 72047 diff --git a/thyroid-dis/C_val.npy b/thyroid-dis/C_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..6e45a3f1316ad9b1d7d86d0d3cec0506a9639e00 --- /dev/null +++ b/thyroid-dis/C_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7a8e856bdfa844c74658574a78994c7071a1fabcd515546bba8bddc4e8cc1e04 +size 18233 diff --git a/thyroid-dis/N_test.npy b/thyroid-dis/N_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..f185dd2ffb43e5a4d4f9554c1a1ade06940c6b86 --- /dev/null +++ b/thyroid-dis/N_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a157ff2d7d5d0d859e38c27c93bdbd958bf0ae504b61017e178b19d274863bff +size 27008 diff --git a/thyroid-dis/N_train.npy b/thyroid-dis/N_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..fe9bff0b56e6401a4e607e7b4a853588c28badba --- /dev/null +++ b/thyroid-dis/N_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0d1d5dd7d2a7213b91c9e3fc889c5bb22edca3aa43c88f3e0f952f564feb9009 +size 86144 diff --git a/thyroid-dis/N_val.npy b/thyroid-dis/N_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..43e47735e5156d81fa057410139d2bc0db5d17db --- /dev/null +++ b/thyroid-dis/N_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ba06ec75f094c287aa099059d228c816e9ba0b17ffe134a9ae584cf7ef054275 +size 21632 diff --git a/thyroid-dis/info.json b/thyroid-dis/info.json new file mode 100644 index 0000000000000000000000000000000000000000..8dfb2d0d4edaba1544e717a282b6392ead8c3e26 --- /dev/null +++ b/thyroid-dis/info.json @@ -0,0 +1,44 @@ +{ + "name": "thyroid-dis", + "n_num_features": 6, + "n_cat_features": 20, + "train_size": 1792, + "val_size": 448, + "test_size": 560, + "source": "https://www.openml.org/search?type=data&status=active&id=40478&sort=runs", + "task_intro": "General Description of Thyroid Disease Databases \n and Related Files\n\nThis directory contains 6 databases, corresponding test set, and \ncorresponding documentation. They were left at the University of\nCalifornia at Irvine by Ross Quinlan during his visit in 1987 for\nthe 1987 Machine Learning Workshop. \n\nThe documentation files (with file extension "names") are formatted to\nbe read by Quinlan's C4 decision tree program. Though briefer than\nthe other documentation files found in this database repository, they\nshould suffice to describe the database, specifically:\n\n 1. Source\n 2. Number and names of attributes (including class names)\n 3. Types of values that each attribute takes\n\nIn general, these databases are quite similar and can be characterized\nsomewhat as follows:\n\n 1. Many attributes (29 or so, mostly the same set over all the databases)\n 2. mostly numeric or Boolean valued attributes\n 3. thyroid disease domains (records provided by the Garavan Institute\n of Sydney, Australia)\n 4. several missing attribute values (signified by "?")\n 5. small number of classes (under 10, changes with each database)\n 7. 2800 instances in each data set\n 8. 972 instances in each test set (It seems that the test sets' instances\n are disjoint with respect to the corresponding data sets, but this has \n not been verified)\n\nSee the following for a discussion of relevant experiments and related work:\n Quinlan,J.R., Compton,P.J., Horn,K.A., & Lazurus,L. (1986).\n Inductive knowledge acquisition: A case study.\n In Proceedings of the Second Australian Conference on Applications\n of Expert Systems. Sydney, Australia.\n\n Quinlan,J.R. (1986). Induction of decision trees. Machine Learning,\n 1, 81--106.\n\nNote that the instances in these databases are followed by a vertical\nbar and a number. These appear to be a patient id number. The vertical\nbar is interpreted by Quinlan's algorithms as "ignore the remainder of\nthis line". \n\n======================================================================\n\nThis database now also contains an additional two data files, named \nhypothyroid.data and sick-euthyroid.data. They have approximately the\nsame data format and set of attributes as the other 6 databases, but\ntheir integrity is questionable. Ross Quinlan is concerned that they\nmay have been corrupted since they first arrived at UCI, but we have not\nyet established the validity of this possibility. These 2 databases differ\nin terms of their number of instances (3163) and lack of corresponding \ntest files. They each have 2 concepts (negative/hypothyroid and \nsick-euthyroid/negative respectively). Their source also appears to\nbe the Garavan institute. Each contains several missing values.\n\nAnother relatively recent file thyroid0387.data has been added that \ncontains the latest version of an archive of thyroid diagnoses obtained \nfrom the Garvan Institute, consisting of 9172 records from 1984 to early 1987.\n\nA domain theory related to thyroid disease has also been added recently \n(thyroid.theory).\n\nThe files new-thyroid.[names,data] were donated by Stefan Aberhard.", + "task_type": "multiclass", + "openml_id": 40478, + "imbalance_ratio": 52.645161290322584, + "n_classes": 5, + "num_feature_intro": { + "V1": "V1", + "V18": "V18", + "V20": "V20", + "V22": "V22", + "V24": "V24", + "V26": "V26" + }, + "cat_feature_intro": { + "V2": "V2", + "V3": "V3", + "V4": "V4", + "V5": "V5", + "V6": "V6", + "V7": "V7", + "V8": "V8", + "V9": "V9", + "V10": "V10", + "V11": "V11", + "V12": "V12", + "V13": "V13", + "V14": "V14", + "V15": "V15", + "V16": "V16", + "V17": "V17", + "V19": "V19", + "V21": "V21", + "V23": "V23", + "V25": "V25" + } +} 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[original](https://sci2s.ugr.es/keel/dataset.php?cod=42) - Date unknown \n**Please cite**: \n\n**Treasury Dataset**\n\nThis file contains the Economic data information of USA from 01/04/1980 to 02/04/2000 on a weekly basis. From given features, the goal is to predict 1 Month CD Rate.\n\n**Attributes Information**\n 1. 1Y-CMaturityRate real [77.055, 142.645]\n 2. 30Y-CMortgageRate real [3.02, 17.15]\n 3. 3M-Rate-AuctionAverage real [6.49, 18.63]\n 4. 3M-Rate-SecondaryMarket real [2.67, 16.75]\n 5. 3Y-CMaturityRate real [2.69, 16.76]\n 6. 5Y-CMaturityRate real [4.09, 16.47]\n 7. BankCredit real [4.17, 16.13]\n 8. Currency real [1130.9, 4809.2]\n 9. DemandDeposits real [105.6, 533.0]\n 10. FederalFunds real [225.8, 412.1]\n 11. MoneyStock real [2.86, 20.06]\n 12. CheckableDeposits real [381.1, 1154.1]\n 13. LoansLeases real [269.9, 803.4]\n 14. SavingsDeposits real [868.1, 3550.3]\n 15. TradeCurrencies real [175.6, 1758.1]\n 16. 1MonthCDRate real [3.02, 20.76]", + "task_type": "regression", + "openml_id": 42367, + "n_classes": 1, + "num_feature_intro": { + "1Y-CMaturityRate": "1Y-CMaturityRate", + "30Y-CMortgageRate": "30Y-CMortgageRate", + "3M-Rate-AuctionAverage": "3M-Rate-AuctionAverage", + "3M-Rate-SecondaryMarket": "3M-Rate-SecondaryMarket", + "3Y-CMaturityRate": "3Y-CMaturityRate", + "5Y-CMaturityRate": "5Y-CMaturityRate", + "bankCredit": "bankCredit", + "currency": "currency", + "demandDeposits": "demandDeposits", + "federalFunds": "federalFunds", + "moneyStock": "moneyStock", + "checkableDeposits": "checkableDeposits", + "loansLeases": "loansLeases", + "savingsDeposits": "savingsDeposits", + "tradeCurrencies": "tradeCurrencies" + }, + "cat_feature_intro": {} +} \ No newline at end of file diff --git a/treasury/y_test.npy b/treasury/y_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..ee5749fb3254da03b5482b22137ab9b8daf68173 --- 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It is a 20 dimensional, 2 class classification example. Each class is drawn from a multivariate normal distribution with unit variance. Class 1 has mean (a,a,..a) while Class 2 has mean (-a,-a,..-a). Where a = 2/sqrt(20). Breiman reports the theoretical expected misclassification rate as 2.3%. 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a/vehicle/N_val.npy b/vehicle/N_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..cb6693c2b6e72e9d30c21c0b6962a6d1a41d9e0f --- /dev/null +++ b/vehicle/N_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:47d73b5757119812d2f781639eea222a0757136d55aee591048d072e59cbf829 +size 19712 diff --git a/vehicle/info.json b/vehicle/info.json new file mode 100644 index 0000000000000000000000000000000000000000..d020cd3f5f69748ff206d0d17b280619c9821e91 --- /dev/null +++ b/vehicle/info.json @@ -0,0 +1,35 @@ +{ + "name": "vehicle", + "n_num_features": 18, + "n_cat_features": 0, + "train_size": 540, + "val_size": 136, + "test_size": 170, + "source": "https://www.openml.org/search?type=data&status=active&id=54&sort=runs", + "task_intro": "**Author**: Dr. Pete Mowforth and Dr. Barry Shepherd \n**Source**: [UCI](https://archive.ics.uci.edu/ml/datasets/Statlog+(Vehicle+Silhouettes))\n**Please cite**: Siebert,JP. Turing Institute Research Memorandum TIRM-87-018 \"Vehicle Recognition Using Rule Based Methods\" (March 1987) \n\n NAME\n vehicle silhouettes\n \n PURPOSE\n to classify a given silhouette as one of four types of vehicle,\n using a set of features extracted from the silhouette. The\n vehicle may be viewed from one of many different angles. \n \n PROBLEM TYPE\n classification\n \n SOURCE\n Drs.Pete Mowforth and Barry Shepherd\n Turing Institute\n George House\n 36 North Hanover St.\n Glasgow\n G1 2AD\n \n CONTACT\n Alistair Sutherland\n Statistics Dept.\n Strathclyde University\n Livingstone Tower\n 26 Richmond St.\n GLASGOW G1 1XH\n Great Britain\n \n Tel: 041 552 4400 x3033\n \n Fax: 041 552 4711 \n \n e-mail: alistair@uk.ac.strathclyde.stams\n \n HISTORY\n This data was originally gathered at the TI in 1986-87 by\n JP Siebert. It was partially financed by Barr and Stroud Ltd.\n The original purpose was to find a method of distinguishing\n 3D objects within a 2D image by application of an ensemble of\n shape feature extractors to the 2D silhouettes of the objects.\n Measures of shape features extracted from example silhouettes\n of objects to be discriminated were used to generate a class-\n ification rule tree by means of computer induction.\n This object recognition strategy was successfully used to \n discriminate between silhouettes of model cars, vans and buses\n viewed from constrained elevation but all angles of rotation.\n The rule tree classification performance compared favourably\n to MDC (Minimum Distance Classifier) and k-NN (k-Nearest Neigh-\n bour) statistical classifiers in terms of both error rate and\n computational efficiency. An investigation of these rule trees\n generated by example indicated that the tree structure was \n heavily influenced by the orientation of the objects, and grouped\n similar object views into single decisions.\n \n DESCRIPTION\n The features were extracted from the silhouettes by the HIPS\n (Hierarchical Image Processing System) extension BINATTS, which \n extracts a combination of scale independent features utilising\n both classical moments based measures such as scaled variance,\n skewness and kurtosis about the major/minor axes and heuristic\n measures such as hollows, circularity, rectangularity and\n compactness.\n Four \"Corgie\" model vehicles were used for the experiment:\n a double decker bus, Cheverolet van, Saab 9000 and an Opel Manta 400.\n This particular combination of vehicles was chosen with the \n expectation that the bus, van and either one of the cars would\n be readily distinguishable, but it would be more difficult to\n distinguish between the cars.\n The images were acquired by a camera looking downwards at the\n model vehicle from a fixed angle of elevation (34.2 degrees\n to the horizontal). The vehicles were placed on a diffuse\n backlit surface (lightbox). The vehicles were painted matte black\n to minimise highlights. The images were captured using a CRS4000\n framestore connected to a vax 750. All images were captured with\n a spatial resolution of 128x128 pixels quantised to 64 greylevels.\n These images were thresholded to produce binary vehicle silhouettes,\n negated (to comply with the processing requirements of BINATTS) and\n thereafter subjected to shrink-expand-expand-shrink HIPS modules to\n remove \"salt and pepper\" image noise.\n The vehicles were rotated and their angle of orientation was measured\n using a radial graticule beneath the vehicle. 0 and 180 degrees\n corresponded to \"head on\" and \"rear\" views respectively while 90 and\n 270 corresponded to profiles in opposite directions. Two sets of\n 60 images, each set covering a full 360 degree rotation, were captured\n for each vehicle. The vehicle was rotated by a fixed angle between \n images. These datasets are known as e2 and e3 respectively.\n A further two sets of images, e4 and e5, were captured with the camera \n at elevations of 37.5 degs and 30.8 degs respectively. These sets\n also contain 60 images per vehicle apart from e4.van which contains\n only 46 owing to the difficulty of containing the van in the image\n at some orientations.\n \n ATTRIBUTES\n \n COMPACTNESS (average perim)**2/area\n \n CIRCULARITY (average radius)**2/area\n \n DISTANCE CIRCULARITY area/(av.distance from border)**2\n \n RADIUS RATIO (max.rad-min.rad)/av.radius\n \n PR.AXIS ASPECT RATIO (minor axis)/(major axis)\n \n MAX.LENGTH ASPECT RATIO (length perp. max length)/(max length)\n \n SCATTER RATIO (inertia about minor axis)/(inertia about major axis)\n \n ELONGATEDNESS area/(shrink width)**2\n \n PR.AXIS RECTANGULARITY area/(pr.axis length*pr.axis width)\n \n MAX.LENGTH RECTANGULARITY area/(max.length*length perp. to this)\n \n SCALED VARIANCE (2nd order moment about minor axis)/area\n ALONG MAJOR AXIS\n \n SCALED VARIANCE (2nd order moment about major axis)/area\n ALONG MINOR AXIS \n \n SCALED RADIUS OF GYRATION (mavar+mivar)/area\n \n SKEWNESS ABOUT (3rd order moment about major axis)/sigma_min**3\n MAJOR AXIS\n \n SKEWNESS ABOUT (3rd order moment about minor axis)/sigma_maj**3\n MINOR AXIS\n \n KURTOSIS ABOUT (4th order moment about major axis)/sigma_min**4\n MINOR AXIS \n \n KURTOSIS ABOUT (4th order moment about minor axis)/sigma_maj**4\n MAJOR AXIS\n \n HOLLOWS RATIO (area of hollows)/(area of bounding polygon)\n \n Where sigma_maj**2 is the variance along the major axis and\n sigma_min**2 is the variance along the minor axis, and\n \n area of hollows= area of bounding poly-area of object \n \n The area of the bounding polygon is found as a side result of\n the computation to find the maximum length. Each individual\n length computation yields a pair of calipers to the object\n orientated at every 5 degrees. The object is propagated into\n an image containing the union of these calipers to obtain an\n image of the bounding polygon. \n \n NUMBER OF CLASSES\n \n 4 OPEL, SAAB, BUS, VAN\n \n NUMBER OF EXAMPLES\n \n Total no. = 946\n \n No. in each class\n \n opel 240\n saab 240\n bus 240\n van 226\n \n \n 100 examples are being kept by Strathclyde for validation.\n So StatLog partners will receive 846 examples.\n \n NUMBER OF ATTRIBUTES\n \n No. of atts. = 18", + "task_type": "multiclass", + "openml_id": 54, + "imbalance_ratio": 1.0954773869346734, + "n_classes": 4, + "num_feature_intro": { + "COMPACTNESS": "COMPACTNESS", + "CIRCULARITY": "CIRCULARITY", + "DISTANCE_CIRCULARITY": "DISTANCE_CIRCULARITY", + "RADIUS_RATIO": "RADIUS_RATIO", + "PR.AXIS_ASPECT_RATIO": "PR.AXIS_ASPECT_RATIO", + "MAX.LENGTH_ASPECT_RATIO": "MAX.LENGTH_ASPECT_RATIO", + "SCATTER_RATIO": "SCATTER_RATIO", + "ELONGATEDNESS": "ELONGATEDNESS", + "PR.AXIS_RECTANGULARITY": "PR.AXIS_RECTANGULARITY", + "MAX.LENGTH_RECTANGULARITY": "MAX.LENGTH_RECTANGULARITY", + "SCALED_VARIANCE_MAJOR": "SCALED_VARIANCE_MAJOR", + "SCALED_VARIANCE_MINOR": "SCALED_VARIANCE_MINOR", + "SCALED_RADIUS_OF_GYRATION": "SCALED_RADIUS_OF_GYRATION", + "SKEWNESS_ABOUT_MAJOR": "SKEWNESS_ABOUT_MAJOR", + "SKEWNESS_ABOUT_MINOR": "SKEWNESS_ABOUT_MINOR", + "KURTOSIS_ABOUT_MAJOR": "KURTOSIS_ABOUT_MAJOR", + "KURTOSIS_ABOUT_MINOR": "KURTOSIS_ABOUT_MINOR", + "HOLLOWS_RATIO": "HOLLOWS_RATIO" + }, + "cat_feature_intro": {} +} \ No newline at end of file diff --git a/vehicle/y_test.npy b/vehicle/y_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..a44820ec8869910b8fecf0a8d54d860198a09d7c --- /dev/null +++ b/vehicle/y_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a5293b5c89213d0fe7dd8b6698cb7a3310d2f29884451872030eba0ecd9a7bbb +size 656 diff --git a/vehicle/y_train.npy b/vehicle/y_train.npy new file mode 100644 index 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8159, + "test_size": 10199, + "source":"https://archive.ics.uci.edu/dataset/363/facebook+comment+volume+dataset" +} \ No newline at end of file diff --git a/volume/y_test.npy b/volume/y_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..a9b9289936fea23241c5e343e35075a24fcd8566 --- /dev/null +++ b/volume/y_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:1984fc51300ffdf946a0abaa69cc18563d97bddb7e56a90e66189fe51bbc78d1 +size 40924 diff --git a/volume/y_train.npy b/volume/y_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..29be3b43273126b9a6c6f12e9d77c5575eaee99f --- /dev/null +++ b/volume/y_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:cd8b1eb395a17d138f31661279e46552cb11893395d2a81394d3c04706fe7a04 +size 130668 diff --git a/volume/y_val.npy b/volume/y_val.npy new file mode 100644 index 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Casale, O. Pujol, P. Radeva. \n**Source**: [UCI](https://archive.ics.uci.edu/ml/datasets/User+Identification+From+Walking+Activity) \n**Please cite**: Casale, P. Pujol, O. and Radeva, P. 'Personalization and user verification in wearable systems using biometric walking patterns' Personal and Ubiquitous Computing, 16(5), 563-580, 2012\n\n**User Identification From Walking Activity Data Set** \nThe dataset collects data from an Android smartphone positioned in the chest pocket. Accelerometer Data are collected from 22 participants walking in the wild over a predefined path. The dataset is intended for Activity Recognition research purposes. It provides challenges for identification and authentication of people using motion patterns. \n\n**Note: the original per-user datasets were joined into one dataset**\n\n### Attribute Information \nTime-step, x acceleration, y acceleration, z acceleration \nTarget: User ID.", + "task_type": "multiclass", + "openml_id": 1509, + "n_classes": 22, + "num_feature_intro": { + "V1": "V1", + "V2": "V2", + "V3": "V3", + "V4": "V4" + }, + "cat_feature_intro": {} +} \ No newline at end of file diff --git a/walking-activity/y_test.npy b/walking-activity/y_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..7e16756a411b45016a856e47071843029dd8a60f --- /dev/null +++ b/walking-activity/y_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:fbc39e97663d1ef74b5e2151f978d2954b2b60be5b624e66eada2073d0ff9e05 +size 60220 diff --git a/walking-activity/y_train.npy b/walking-activity/y_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..8de176f23287fba8897ec632b25303561f81e31f --- /dev/null +++ b/walking-activity/y_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e647f8dad54b76f17f2acf6e349dc83d8e42c3feab30d1d66435ea0a851430cb +size 191764 diff --git a/walking-activity/y_val.npy b/walking-activity/y_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..897802f90675fb06af99de4435bdd9c7f7b5c9b5 --- /dev/null +++ b/walking-activity/y_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:da1e7c895baaf556324021bf36cc7fc1ac61bf8ad78847fda1efb36116cecbb7 +size 48260 diff --git a/wall-robot-navigation/N_test.npy b/wall-robot-navigation/N_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..6c14624127677544581395a22b1349cf8ef65252 --- /dev/null +++ b/wall-robot-navigation/N_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d444d26ec932e4aec4acb62e3ef41abeb298bc864f154046b767d6e0b2e0e72a +size 209792 diff --git a/wall-robot-navigation/N_train.npy b/wall-robot-navigation/N_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..59e7e7137a70af45052a97975da36abb11811c6b --- /dev/null +++ b/wall-robot-navigation/N_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3e377709e85f6a6507ef77ef3c5e135b69eb97992f9ef4c2d77e6ae2334abc39 +size 670400 diff --git a/wall-robot-navigation/N_val.npy b/wall-robot-navigation/N_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..5c2150ec92d57b4fe2c8930ddf9a65230a18ef67 --- /dev/null +++ b/wall-robot-navigation/N_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:82e3be696484d1733aea684072ae6779a3168ae9c5dc917b810633fa8a8ce4d4 +size 167744 diff --git a/wall-robot-navigation/info.json b/wall-robot-navigation/info.json new file mode 100644 index 0000000000000000000000000000000000000000..c6b97cec06b5139135a1a9ffde18430e72df4967 --- /dev/null +++ b/wall-robot-navigation/info.json @@ -0,0 +1,41 @@ +{ + "name": "wall-robot-navigation", + "n_num_features": 24, + "n_cat_features": 0, + "train_size": 3491, + "val_size": 873, + "test_size": 1092, + "source": "https://www.openml.org/search?type=data&status=active&id=1497&sort=runs", + "task_intro": "**Author**: Ananda Freire, Marcus Veloso and Guilherme Barreto \n**Source**: [UCI](https://archive.ics.uci.edu/ml/datasets/Wall-Following+Robot+Navigation+Data) - 2010 \n**Please cite**: [UCI](https://archive.ics.uci.edu/ml/citation_policy.html)\n\n**Wall-Following Robot Navigation Data Data Set** \nThe data were collected as the SCITOS G5 robot navigates through the room following the wall in a clockwise direction, for 4 rounds, using 24 ultrasound sensors arranged circularly around its 'waist'.\n\nThe data consists of raw values of the measurements of all 24 ultrasound sensors and the corresponding class label. Sensor readings are sampled at a rate of 9 samples per second.\n\nThe class labels are: \n1. Move-Forward, \n2. Slight-Right-Turn, \n3. Sharp-Right-Turn, \n4. Slight-Left-Turn \n\nIt is worth mentioning that the 24 ultrasound readings and the simplified distances were collected at the same time step, so each file has the same number of rows (one for each sampling time step). \n\nThe wall-following task and data gathering were designed to test the hypothesis that this apparently simple navigation task is indeed a non-linearly separable classification task. Thus, linear classifiers, such as the Perceptron network, are not able to learn the task and command the robot around the room without collisions. Nonlinear neural classifiers, such as the MLP network, are able to learn the task and command the robot successfully without collisions. \n\n### Attribute Information:\n\n1. US1: ultrasound sensor at the front of the robot (reference angle: 180\u00b0) \n2. US2: ultrasound reading (reference angle: -165\u00b0)\n3. US3: ultrasound reading (reference angle: -150\u00b0)\n4. US4: ultrasound reading (reference angle: -135\u00b0)\n5. US5: ultrasound reading (reference angle: -120\u00b0)\n6. US6: ultrasound reading (reference angle: -105\u00b0)\n7. US7: ultrasound reading (reference angle: -90\u00b0)\n8. US8: ultrasound reading (reference angle: -75\u00b0) \n9. US9: ultrasound reading (reference angle: -60\u00b0) \n10. US10: ultrasound reading (reference angle: -45\u00b0)\n11. US11: ultrasound reading (reference angle: -30\u00b0) \n12. US12: ultrasound reading (reference angle: -15\u00b0)\n13. US13: reading of ultrasound sensor situated at the back of the robot (reference angle: 0\u00b0) \n14. US14: ultrasound reading (reference angle: 15\u00b0)\n15. US15: ultrasound reading (reference angle: 30\u00b0)\n16. US16: ultrasound reading (reference angle: 45\u00b0)\n17. US17: ultrasound reading (reference angle: 60\u00b0)\n18. US18: ultrasound reading (reference angle: 75\u00b0)\n19. US19: ultrasound reading (reference angle: 90\u00b0)\n20. US20: ultrasound reading (reference angle: 105\u00b0)\n21. US21: ultrasound reading (reference angle: 120\u00b0)\n22. US22: ultrasound reading (reference angle: 135\u00b0)\n23. US23: ultrasound reading (reference angle: 150\u00b0)\n24. US24: ultrasound reading (reference angle: 165\u00b0)\n\n\n### Relevant Papers\n\nAnanda L. Freire, Guilherme A. Barreto, Marcus Veloso and Antonio T. Varela (2009), 'Short-Term Memory Mechanisms in Neural Network Learning of Robot Navigation Tasks: A Case Study'. Proceedings of the 6th Latin American Robotics Symposium (LARS'2009), pages 1-6", + "task_type": "multiclass", + "openml_id": 1497, + "imbalance_ratio": 6.722560975609756, + "n_classes": 4, + "num_feature_intro": { + "V1": "V1", + "V2": "V2", + "V3": "V3", + "V4": "V4", + "V5": "V5", + "V6": "V6", + "V7": "V7", + "V8": "V8", + "V9": "V9", + "V10": "V10", + "V11": "V11", + "V12": "V12", + "V13": "V13", + "V14": "V14", + "V15": "V15", + "V16": "V16", + "V17": "V17", + "V18": "V18", + "V19": "V19", + "V20": "V20", + "V21": "V21", + "V22": "V22", + "V23": "V23", + "V24": "V24" + }, + "cat_feature_intro": {} +} \ No newline at end of file diff --git a/wall-robot-navigation/y_test.npy b/wall-robot-navigation/y_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..ec243bad952527dbf7744ebcd924b3901b5e1efb --- /dev/null +++ b/wall-robot-navigation/y_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid 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--- /dev/null +++ b/waveform-5000/info.json @@ -0,0 +1,57 @@ +{ + "name": "waveform-5000", + "n_num_features": 40, + "n_cat_features": 0, + "train_size": 3200, + "val_size": 800, + "test_size": 1000, + "source": "https://www.openml.org/search?type=data&status=active&id=60&sort=runs", + "task_intro": "**Author**: Breiman,L., Friedman,J.H., Olshen,R.A., & Stone,C.J. \n**Source**: [UCI](http://archive.ics.uci.edu/ml/datasets/waveform+database+generator+(version+2)) - 1988 \n**Please cite**: [UCI](http://archive.ics.uci.edu/ml/citation_policy.html) \n\n**Waveform Database Generator** \nGenerator generating 3 classes of waves. Each class is generated from a combination of 2 of 3 \"base\" waves. \n\nFor details, see Breiman,L., Friedman,J.H., Olshen,R.A., and Stone,C.J. (1984). \nClassification and Regression Trees. Wadsworth International, pp 49-55, 169. \n\nNote: There is [an earlier version](http://archive.ics.uci.edu/ml/datasets/Waveform+Database+Generator+(Version+1)) of this dataset that only has 21 attributes (it does not add the 19 noise features).\n\n### Attribute Information\n\n40 attributes describing the waveform, all of which include noise. The latter 19 attributes are all noise attributes with mean 0 and variance 1.", + "task_type": "multiclass", + "openml_id": 60, + "imbalance_ratio": 1.0235934664246824, + "n_classes": 3, + "num_feature_intro": { + "x1": "x1", + "x2": "x2", + "x3": "x3", + "x4": "x4", + "x5": "x5", + "x6": "x6", + "x7": "x7", + "x8": "x8", + "x9": "x9", + "x10": "x10", + "x11": "x11", + "x12": "x12", + "x13": "x13", + "x14": "x14", + "x15": "x15", + "x16": "x16", + "x17": "x17", + "x18": "x18", + "x19": "x19", + "x20": "x20", + "x21": "x21", + "x22": "x22", + "x23": "x23", + "x24": "x24", + "x25": "x25", + "x26": "x26", + "x27": "x27", + "x28": "x28", + "x29": "x29", + "x30": "x30", + "x31": "x31", + "x32": "x32", + "x33": "x33", + "x34": "x34", + "x35": "x35", + "x36": "x36", + "x37": "x37", + "x38": "x38", + "x39": "x39", + "x40": "x40" + }, + "cat_feature_intro": {} +} \ No newline at end of file diff --git a/waveform-5000/y_test.npy b/waveform-5000/y_test.npy new file mode 100644 index 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sha256:093017be0be466f2339b7d7437c37a13befa88f61db718e3594c48f98202da52 +size 67376 diff --git a/weather_izmir/N_val.npy b/weather_izmir/N_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..6d8d3b0acc2e24c37ea8c0cc274c85819d3bac0d --- /dev/null +++ b/weather_izmir/N_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:eea0301a3f02723bd9475469eedb72dc2b7e2c026d724ebb1a76caebc6bb8daa +size 16976 diff --git a/weather_izmir/info.json b/weather_izmir/info.json new file mode 100644 index 0000000000000000000000000000000000000000..eff2f1609ec9edf8ecc1aeb2e7c9183c5d608689 --- /dev/null +++ b/weather_izmir/info.json @@ -0,0 +1,25 @@ +{ + "name": "weather_izmir", + "n_num_features": 9, + "n_cat_features": 0, + "train_size": 934, + "val_size": 234, + "test_size": 293, + "source": "https://www.openml.org/search?type=data&status=active&id=42369&sort=runs", + "task_intro": "**Author**: \n**Source**: KEEL - [original](https://sci2s.ugr.es/keel/dataset.php?cod=78) - Date unknown \n**Please cite**: \n\n**Weather Izmir dataset**\n\nThis file contains the weather information of Izmir from 01/01/1994 to 31/12/1997. From given features, the goal is to predict the mean temperature.\n\n**Attributes Information**\n\n 1. Max_temperature real[36.7,105.0]\n 2. Min_temperature real[15.8,78.6]\n 3. Dewpoint real[13.6,64.4]\n 4. Precipitation real[0.0,7.6]\n 5. Sea_level_pressure real[29.26,30.48]\n 6. Standard_pressure real[2.3,10.1]\n 7. Visibility real[0.92,29.1]\n 8. Wind_speed real[4.72,68.8]\n 9. Max_wind_speed real[16.11,55.24]\n 10. Mean_temperature real[29.4,89.9] [target]", + "task_type": "regression", + "openml_id": 42369, + "n_classes": 1, + "num_feature_intro": { + "Max_temperature": "Max_temperature", + "Min_temperature": "Min_temperature", + "Dewpoint": "Dewpoint", + "Precipitation": "Precipitation", + "Sea_level_pressure": "Sea_level_pressure", + "Standard_pressure": "Standard_pressure", + "Visibility": "Visibility", + "Wind_speed": "Wind_speed", + "Max_wind_speed": "Max_wind_speed" + }, + "cat_feature_intro": {} +} \ No newline at end of file diff --git a/weather_izmir/y_test.npy b/weather_izmir/y_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..1f8eebd01ff3a4a9f085a8c8fe347c5e78c5ae92 --- /dev/null +++ b/weather_izmir/y_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:edc3fa0b985cc7cf2724040ba1c8e5b7fd3cc9054d716e60c018d4b4f34cdddd +size 2472 diff --git a/weather_izmir/y_train.npy b/weather_izmir/y_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..a979a4afa12b8c41541de4cf68cffc60f7499378 --- /dev/null +++ b/weather_izmir/y_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:11aee52383b34bb6d82703d6cd2a6d66af81dc67683a1bf1cdc3cb9d3b108ed6 +size 7600 diff --git a/weather_izmir/y_val.npy b/weather_izmir/y_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..5c3984fd4e0cfb0c82f8a4c8b87f69761fb28091 --- /dev/null +++ b/weather_izmir/y_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:eded1fdea1408d78e43df66fbc3c88af7e45cf1c5d2cd75ee778119632e7aa38 +size 2000 diff --git a/website_phishing/C_test.npy b/website_phishing/C_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..1633a2bbe805c9ea9056444959b70899ab287832 --- /dev/null +++ b/website_phishing/C_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:599ffa1b28ef21a699263a44722d00a6d1a0dd602d38e54b5c63fcabbe74224d +size 19640 diff --git a/website_phishing/C_train.npy b/website_phishing/C_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..00e92be2f5dd52afea489916f04c16940f57c333 --- /dev/null +++ b/website_phishing/C_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:37bdbe3840d343551bff7ac175242ee1e0ad254d4c2ec2207dd32d40b46e2c90 +size 62408 diff --git a/website_phishing/C_val.npy b/website_phishing/C_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..024ce67e0df1cdfccb42fc0180690d4d4a4f3337 --- /dev/null +++ b/website_phishing/C_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4e7a116abb5caeeaa8ab557957b25a8bc1aa9888c947e5cc666d03b4d5ada961 +size 15752 diff --git a/website_phishing/info.json b/website_phishing/info.json new file mode 100644 index 0000000000000000000000000000000000000000..1aa0e224a50842c86b311bd4d5087f36c907c244 --- /dev/null +++ b/website_phishing/info.json @@ -0,0 +1,25 @@ +{ + "task_type": "multiclass", + "num_classes": 3, + "n_num_features": 0, + "n_cat_features": 9, + "train_size": 865, + "val_size": 217, + "test_size": 271, + "task_intro": "The phishing problem is considered a vital issue in .COM industry especially e-banking and e-commerce taking the number of online transactions involving payments. We have identified different features related to legitimate and phishy websites and collected 1353 different websites from difference sources. We collected 548 legitimate websites out of 1353 websites. There is 702 phishing URLs, and 103 suspicious URLs.", + "feature_intro": { + "SFH": "if having server-side request forgery.", + "popUpWidnow": "if popping up widnow.", + "SSLfinal_State": "SSL final state.", + "Request_URL": "request URL class.", + "URL_of_Anchor": "URL of anchor class.", + "web_traffic": "web traffic.", + "URL_Length": "URL length.", + "age_of_domain": "age of domain.", + "having_IP_Address": "if having IP address." + }, + "target_intro": { + "Result": "website class (1=Legitimate; 0=Suspicious; -1=Phishy) ." + }, + "source": "https://archive.ics.uci.edu/dataset/379/website+phishing" +} \ No newline at end of file diff --git a/website_phishing/y_test.npy b/website_phishing/y_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..c6ac478c3c289b46e5ec8bb6eb80963f2df9841d --- /dev/null +++ b/website_phishing/y_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:05f64ee0bf6cef9f4d0a5c9333bf42d392a85a639b549822818ba8dbb640d738 +size 2296 diff --git a/website_phishing/y_train.npy b/website_phishing/y_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..9b155f2ef7a72f9020c4a9e6fea9ce328aea6cd1 --- /dev/null +++ b/website_phishing/y_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f33220cbc237717782cae8473a22b6f25b0e8ee967ec97042802f49ff22a0726 +size 7048 diff --git a/website_phishing/y_val.npy b/website_phishing/y_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..243b7e14ce5a6af671ad321df9be8975252a6c99 --- /dev/null +++ b/website_phishing/y_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c1e6a48f0338ece5d9bdf1e977e84196fe109bf4ed821daeb96e5b9c3626777c +size 1864 diff --git a/wind/N_test.npy b/wind/N_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..5693d76cee2aacde2d5c30a4aeadeb3698280cff --- /dev/null +++ b/wind/N_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:90761803c2aecf40b30d7007f0fe135db8c5c37cb20c8d144695220ceee9353e +size 147408 diff --git a/wind/N_train.npy b/wind/N_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..632f81ce2fea79444927556ef92c973a3283f3e9 --- /dev/null +++ b/wind/N_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:84726d1bef9942912a6db31d5abfe75fffea8eccf7b99c002fed0479198f8087 +size 471312 diff --git a/wind/N_val.npy b/wind/N_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..7e25e123d178449d6bfc8e809944a2305d37006f --- /dev/null +++ b/wind/N_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:34e50248fa06cbc15f04e648bbda924d9abbf004c6ee4291d7402a4bf98096ec +size 117952 diff --git a/wind/info.json b/wind/info.json new file mode 100644 index 0000000000000000000000000000000000000000..adf994487e5db59de2bb173418bfeb9e8057b2a8 --- /dev/null +++ b/wind/info.json @@ -0,0 +1,30 @@ +{ + "name": "wind", + "n_num_features": 14, + "n_cat_features": 0, + "train_size": 4207, + "val_size": 1052, + "test_size": 1315, + "source": "https://www.openml.org/search?type=data&status=active&id=503&sort=runs", + "task_intro": "**Author**: \n**Source**: Unknown - Date unknown \n**Please cite**: \n\nwind daily average wind speeds for 1961-1978 at 12 synoptic meteorological\nstations in the Republic of Ireland (Haslett and raftery 1989).\n\nThese data were analyzed in detail in the following article:\nHaslett, J. and Raftery, A. E. (1989). Space-time Modelling with\nLong-memory Dependence: Assessing Ireland's Wind Power Resource\n(with Discussion). Applied Statistics 38, 1-50.\n\nEach line corresponds to one day of data in the following format:\nyear, month, day, average wind speed at each of the stations in the order given\nin Fig.4 of Haslett and Raftery :\nRPT, VAL, ROS, KIL, SHA, BIR, DUB, CLA, MUL, CLO, BEL, MAL\n\nFortan format : ( i2, 2i3, 12f6.2)\n\nThe data are in knots, not in m/s.\n\nPermission granted for unlimited distribution.\n\nPlease report all anomalies to fraley@stat.washington.edu\n\nBe aware that the dataset is 532494 bytes long (thats over half a\nMegabyte). Please be sure you want the data before you request it.\n\n\nInformation about the dataset\nCLASSTYPE: numeric\nCLASSINDEX: none specific", + "task_type": "regression", + "openml_id": 503, + "n_classes": 1, + "num_feature_intro": { + "year": "year", + "month": "month", + "day": "day", + "RPT": "RPT", + "VAL": "VAL", + "ROS": "ROS", + "KIL": "KIL", + "SHA": "SHA", + "BIR": "BIR", + "DUB": "DUB", + "CLA": "CLA", + "MUL": "MUL", + "CLO": "CLO", + "BEL": "BEL" + }, + "cat_feature_intro": {} +} \ No newline at end of file diff --git a/wind/y_test.npy b/wind/y_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..0960c7c9b46356a3daecb650beef70b754a05a3c --- /dev/null +++ b/wind/y_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:00d0c03c182ec69fde818bf368a57ab206edf9672d909c7da844ae59024e4c78 +size 10648 diff --git a/wind/y_train.npy b/wind/y_train.npy new file mode 100644 index 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"https://www.openml.org/search?type=data&status=active&id=40498&sort=runs", + "task_intro": "Citation Request:\n This dataset is public available for research. The details are described in [Cortez et al., 2009]. \n Please include this citation if you plan to use this database:\n\n P. Cortez, A. Cerdeira, F. Almeida, T. Matos and J. Reis. \n Modeling wine preferences by data mining from physicochemical properties.\n In Decision Support Systems, Elsevier, 47(4):547-553. ISSN: 0167-9236.\n\n Available at: [@Elsevier] http://dx.doi.org/10.1016/j.dss.2009.05.016\n [Pre-press (pdf)] http://www3.dsi.uminho.pt/pcortez/winequality09.pdf\n [bib] http://www3.dsi.uminho.pt/pcortez/dss09.bib\n\n1. Title: Wine Quality \n\n2. Sources\n Created by: Paulo Cortez (Univ. Minho), Antonio Cerdeira, Fernando Almeida, Telmo Matos and Jose Reis (CVRVV) @ 2009\n \n3. Past Usage:\n\n P. Cortez, A. Cerdeira, F. Almeida, T. Matos and J. Reis. \n Modeling wine preferences by data mining from physicochemical properties.\n In Decision Support Systems, Elsevier, 47(4):547-553. ISSN: 0167-9236.\n\n In the above reference, two datasets were created, using red and white wine samples.\n The inputs include objective tests (e.g. PH values) and the output is based on sensory data\n (median of at least 3 evaluations made by wine experts). Each expert graded the wine quality \n between 0 (very bad) and 10 (very excellent). Several data mining methods were applied to model\n these datasets under a regression approach. The support vector machine model achieved the\n best results. Several metrics were computed: MAD, confusion matrix for a fixed error tolerance (T),\n etc. Also, we plot the relative importances of the input variables (as measured by a sensitivity\n analysis procedure).\n \n4. Relevant Information:\n\n The two datasets are related to red and white variants of the Portuguese "Vinho Verde" wine.\n For more details, consult: http://www.vinhoverde.pt/en/ or the reference [Cortez et al., 2009].\n Due to privacy and logistic issues, only physicochemical (inputs) and sensory (the output) variables \n are available (e.g. there is no data about grape types, wine brand, wine selling price, etc.).\n\n These datasets can be viewed as classification or regression tasks.\n The classes are ordered and not balanced (e.g. there are munch more normal wines than\n excellent or poor ones). Outlier detection algorithms could be used to detect the few excellent\n or poor wines. Also, we are not sure if all input variables are relevant. So\n it could be interesting to test feature selection methods. \n\n5. Number of Instances: red wine - 1599; white wine - 4898. \n\n6. Number of Attributes: 11 + output attribute\n \n Note: several of the attributes may be correlated, thus it makes sense to apply some sort of\n feature selection.\n\n7. Attribute information:\n\n For more information, read [Cortez et al., 2009].\n\n Input variables (based on physicochemical tests):\n 1 - fixed acidity\n 2 - volatile acidity\n 3 - citric acid\n 4 - residual sugar\n 5 - chlorides\n 6 - free sulfur dioxide\n 7 - total sulfur dioxide\n 8 - density\n 9 - pH\n 10 - sulphates\n 11 - alcohol\n Output variable (based on sensory data): \n 12 - quality (score between 0 and 10)\n\n8. 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