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+    "source": "https://www.openml.org/search?type=data&status=active&id=43714&sort=runs",
+    "task_intro": "Context\nSome camera enthusiast went and described 1,000 cameras based on 13 properties! \nContent\nRow one describes the datatype for each column and can probably be removed.\nThe 13 properties of each camera:\n\nModel\nRelease date\nMax resolution\nLow resolution\nEffective pixels\nZoom wide (W)\nZoom tele (T)\nNormal focus range\nMacro focus range\nStorage included\nWeight (inc. batteries)\nDimensions\nPrice\n\nAcknowledgements\nThese datasets have been gathered and cleaned up by Petra Isenberg, Pierre Dragicevic and Yvonne Jansen.  The original source can be found here.\nThis dataset has been converted to CSV.",
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+    "openml_id": 43714,
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+        "Macro_focus_range": "Macro_focus_range",
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+    "task_intro": "**Author**:   \n**Source**: Unknown -   \n**Please cite**:   \n\nThis is an artificial data set described in Breiman et al. (1984,p.238) \n (with variance 1 instead of 2).  \n \n Generate the values of the 10 attributes independently\n using the following probabilities:\n\n P(X_1 = -1) = P(X_1 = 1) = 1/2\n P(X_m = -1) = P(X_m = 0) = P(X_m = 1) = 1/3, m=2,...,10\n\n Obtain the value of the target variable Y using the rule:\n\n if X_1 = 1 set Y = 3 + 3X_2 + 2X_3 + X_4 + sigma(0,1)\n if X_1 = -1 set Y = -3 + 3X_5 + 2X_6 + X_7 + sigma(0,1)\n\n Characteristics: 40768 cases, 11 continuous attributes\n Source: collection of regression datasets by Luis Torgo ([email protected]) at\n http://www.ncc.up.pt/~ltorgo/Regression/DataSets.html\n Original source: Breiman et al. (1984, p.238).",
+    "task_type": "regression",
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+        "UnixTime": "UnixTime",
+        "Latitude": "Latitude",
+        "Longitude": "Longitude",
+        "Frequency": "Frequency",
+        "Channel": "Channel",
+        "RSSI": "RSSI"
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+        "ap_latitude": "ap_latitude",
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+    "val_size": 207,
+    "test_size": 259,
+    "source": "https://www.openml.org/search?type=data&status=active&id=41705&sort=runs",
+    "task_intro": "Author: Marius Lindauer\nDate: 27.02.2014\n\nThese data set was generated for a publication about claspfolio 2.0,\ni.e., an algorithm selector for ASP.\nThe algorithm portfolio of clasp (2.1.4) configuration is generated by the hydra method (see http://www.cs.ubc.ca/labs/beta/Projects/Hydra/)\nin combination with SMAC.\nTo generate the features, I used claspre, a light-weight version of the ASP solver clasp,\nwith static and dynamic features (4 restarts each after 32 conflicts).",
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+        "Frac_Integrity_Rules": "Frac_Integrity_Rules",
+        "Tight": "Tight",
+        "Problem_Variables": "Problem_Variables",
+        "Free_Problem_Variables": "Free_Problem_Variables",
+        "Assigned_Problem_Variables": "Assigned_Problem_Variables",
+        "Constraints": "Constraints",
+        "Constraints.Vars": "Constraints.Vars",
+        "Created_Bodies": "Created_Bodies",
+        "Program_Atoms": "Program_Atoms",
+        "SCCS": "SCCS",
+        "Nodes_in_Positive_BADG": "Nodes_in_Positive_BADG",
+        "Rules": "Rules",
+        "Normal_Rules": "Normal_Rules",
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+        "Frac_Weight_Rules": "Frac_Weight_Rules",
+        "Equivalences": "Equivalences",
+        "Atom.Atom_Equivalences": "Atom.Atom_Equivalences",
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+        "Other_Constraints": "Other_Constraints",
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+        "Frac_Ternary_Constraints": "Frac_Ternary_Constraints",
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+        "Learnt_Ternary.1": "Learnt_Ternary.1",
+        "Learnt_Others.1": "Learnt_Others.1",
+        "Frac_Removed_Nogood.1": "Frac_Removed_Nogood.1",
+        "Frac_Learnt_Binary.1": "Frac_Learnt_Binary.1",
+        "Frac_Learnt_Ternary.1": "Frac_Learnt_Ternary.1",
+        "Frac_Learnt_Others.1": "Frac_Learnt_Others.1",
+        "Skipped_Levels_while_Backjumping.1": "Skipped_Levels_while_Backjumping.1",
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+        "Longest_Backjumping.1": "Longest_Backjumping.1",
+        "Running_Avg_Conflictlevel.1": "Running_Avg_Conflictlevel.1",
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+        "Learnt_Ternary.2": "Learnt_Ternary.2",
+        "Learnt_Others.2": "Learnt_Others.2",
+        "Frac_Removed_Nogood.2": "Frac_Removed_Nogood.2",
+        "Frac_Learnt_Binary.2": "Frac_Learnt_Binary.2",
+        "Frac_Learnt_Ternary.2": "Frac_Learnt_Ternary.2",
+        "Frac_Learnt_Others.2": "Frac_Learnt_Others.2",
+        "Skipped_Levels_while_Backjumping.2": "Skipped_Levels_while_Backjumping.2",
+        "Avg_Skipped_Levels_while_Backjumping.2": "Avg_Skipped_Levels_while_Backjumping.2",
+        "Longest_Backjumping.2": "Longest_Backjumping.2",
+        "Running_Avg_Conflictlevel.2": "Running_Avg_Conflictlevel.2",
+        "Running_Avg_LBD.2": "Running_Avg_LBD.2",
+        "Choices.3": "Choices.3",
+        "Conflicts.Choices.3": "Conflicts.Choices.3",
+        "Avg_Conflict_Levels.3": "Avg_Conflict_Levels.3",
+        "Avg_LBD_Levels.3": "Avg_LBD_Levels.3",
+        "Learnt_from_Conflict.3": "Learnt_from_Conflict.3",
+        "Learnt_from_Loop.3": "Learnt_from_Loop.3",
+        "Frac_Learnt_from_Conflict.3": "Frac_Learnt_from_Conflict.3",
+        "Frac_Learnt_from_Loop.3": "Frac_Learnt_from_Loop.3",
+        "Literals_in_Conflict_Nogoods.3": "Literals_in_Conflict_Nogoods.3",
+        "Literals_in_Loop_Nogoods.3": "Literals_in_Loop_Nogoods.3",
+        "Frac_Literals_in_Conflict_Nogoods.3": "Frac_Literals_in_Conflict_Nogoods.3",
+        "Frac_Literals_in_Loop_Nogoods.3": "Frac_Literals_in_Loop_Nogoods.3",
+        "Removed_Nogoods.3": "Removed_Nogoods.3",
+        "Learnt_Binary.3": "Learnt_Binary.3",
+        "Learnt_Ternary.3": "Learnt_Ternary.3",
+        "Learnt_Others.3": "Learnt_Others.3",
+        "Frac_Removed_Nogood.3": "Frac_Removed_Nogood.3",
+        "Frac_Learnt_Binary.3": "Frac_Learnt_Binary.3",
+        "Frac_Learnt_Ternary.3": "Frac_Learnt_Ternary.3",
+        "Frac_Learnt_Others.3": "Frac_Learnt_Others.3",
+        "Skipped_Levels_while_Backjumping.3": "Skipped_Levels_while_Backjumping.3",
+        "Avg_Skipped_Levels_while_Backjumping.3": "Avg_Skipped_Levels_while_Backjumping.3",
+        "Longest_Backjumping.3": "Longest_Backjumping.3",
+        "Running_Avg_Conflictlevel.3": "Running_Avg_Conflictlevel.3",
+        "Running_Avg_LBD.3": "Running_Avg_LBD.3",
+        "Choices.4": "Choices.4",
+        "Conflicts.Choices.4": "Conflicts.Choices.4",
+        "Avg_Conflict_Levels.4": "Avg_Conflict_Levels.4",
+        "Avg_LBD_Levels.4": "Avg_LBD_Levels.4",
+        "Learnt_from_Conflict.4": "Learnt_from_Conflict.4",
+        "Learnt_from_Loop.4": "Learnt_from_Loop.4",
+        "Frac_Learnt_from_Conflict.4": "Frac_Learnt_from_Conflict.4",
+        "Frac_Learnt_from_Loop.4": "Frac_Learnt_from_Loop.4",
+        "Literals_in_Conflict_Nogoods.4": "Literals_in_Conflict_Nogoods.4",
+        "Literals_in_Loop_Nogoods.4": "Literals_in_Loop_Nogoods.4",
+        "Frac_Literals_in_Conflict_Nogoods.4": "Frac_Literals_in_Conflict_Nogoods.4",
+        "Frac_Literals_in_Loop_Nogoods.4": "Frac_Literals_in_Loop_Nogoods.4",
+        "Removed_Nogoods.4": "Removed_Nogoods.4",
+        "Learnt_Binary.4": "Learnt_Binary.4",
+        "Learnt_Ternary.4": "Learnt_Ternary.4",
+        "Learnt_Others.4": "Learnt_Others.4",
+        "Frac_Removed_Nogood.4": "Frac_Removed_Nogood.4",
+        "Frac_Learnt_Binary.4": "Frac_Learnt_Binary.4",
+        "Frac_Learnt_Ternary.4": "Frac_Learnt_Ternary.4",
+        "Frac_Learnt_Others.4": "Frac_Learnt_Others.4",
+        "Skipped_Levels_while_Backjumping.4": "Skipped_Levels_while_Backjumping.4",
+        "Avg_Skipped_Levels_while_Backjumping.4": "Avg_Skipped_Levels_while_Backjumping.4",
+        "Longest_Backjumping.4": "Longest_Backjumping.4",
+        "Running_Avg_Conflictlevel.4": "Running_Avg_Conflictlevel.4",
+        "Running_Avg_LBD.4": "Running_Avg_LBD.4",
+        "runtime": "runtime"
+    },
+    "cat_feature_intro": {
+        "runstatus": "runstatus"
+    }
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