Upload P2 - Secom Notebook - Mercury.ipynb

P2 - Secom Notebook - Mercury.ipynb

@@ -26,7 +26,7 @@
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    "cell_type": "code",
-   "execution_count": 117,
+   "execution_count": 139,
    "metadata": {
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@@ -53,7 +53,7 @@
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   {
    "cell_type": "code",
-   "execution_count": 118,
+   "execution_count": 140,
    "metadata": {
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@@ -64,7 +64,7 @@
      "data": {
       "application/mercury+json": {
        "allow_download": true,
-       "code_uid": "App.0.40.24.1-rand92992328",
+       "code_uid": "App.0.40.24.1-rand99a3439b",
        "continuous_update": false,
        "description": "Recumpute everything dynamically",
        "full_screen": true,
@@ -96,7 +96,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 119,
+   "execution_count": 141,
    "metadata": {
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@@ -138,7 +138,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 120,
+   "execution_count": 142,
    "metadata": {
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@@ -195,7 +195,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 121,
+   "execution_count": 143,
    "metadata": {
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@@ -290,7 +290,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 122,
+   "execution_count": 144,
    "metadata": {
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@@ -341,7 +341,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 123,
+   "execution_count": 145,
    "metadata": {
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@@ -419,7 +419,7 @@
   },
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    "cell_type": "code",
-   "execution_count": 124,
+   "execution_count": 146,
    "metadata": {
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@@ -499,7 +499,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 125,
+   "execution_count": 147,
    "metadata": {
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@@ -585,7 +585,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 126,
+   "execution_count": 148,
    "metadata": {
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@@ -648,7 +648,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 127,
+   "execution_count": 149,
    "metadata": {
     "slideshow": {
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@@ -737,7 +737,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 128,
+   "execution_count": 150,
    "metadata": {
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@@ -750,13 +750,6 @@
     "from sklearn.metrics import confusion_matrix, accuracy_score, precision_score, recall_score, f1_score\n",
     "\n",
     "def evaluate_models(model='random_forest'):\n",
-    "    print('Have the duplicates been removed?', drop_duplicates_var)\n",
-    "    print('Missing values threshold is:', missing_values_threshold_var,' - Variance threshold is:,', variance_threshold_var,' - Correlation threshold is:', correlation_threshold_var)\n",
-    "    print('Outlier removal threshold is:', outlier_var)\n",
-    "    print('Scaling method is:', scale_model_var)\n",
-    "    print('Imputation method is:', imputation_var)\n",
-    "    print('Feature selection method is:', feature_selection_var)\n",
-    "    print('Imbalance treatment method is:', imbalance_var)\n",
     "\n",
     "    all_models = ['random_forest', 'logistic_regression', 'knn', 'svm', 'naive_bayes', 'decision_tree', 'xgboost']\n",
     "    evaluation_score_append = []\n",
@@ -825,7 +818,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 129,
+   "execution_count": 151,
    "metadata": {
     "slideshow": {
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@@ -839,17 +832,17 @@
         "yes",
         "no"
        ],
-       "code_uid": "Select.0.40.16.25-rand7e848899",
+       "code_uid": "Select.0.40.16.25-rand28de2701",
        "disabled": false,
        "hidden": false,
        "label": "Drop Duplicates",
-       "model_id": "78db72d25e074b869614de47137d0448",
+       "model_id": "1b5513f0c74f4b789b06e528c0702927",
        "url_key": "",
        "value": "yes",
        "widget": "Select"
       },
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "78db72d25e074b869614de47137d0448",
+       "model_id": "1b5513f0c74f4b789b06e528c0702927",
        "version_major": 2,
        "version_minor": 0
       },
@@ -863,18 +856,18 @@
     {
      "data": {
       "application/mercury+json": {
-       "code_uid": "Text.0.40.15.28-rand8e5732e8",
+       "code_uid": "Text.0.40.15.28-rand47e76187",
        "disabled": false,
        "hidden": false,
        "label": "Missing Value Threeshold",
-       "model_id": "f78ef6cc053648c19f15aa01597b534a",
+       "model_id": "d938d6a0b2744021b8a2869fc3ed8d56",
        "rows": 1,
        "url_key": "",
        "value": "80",
        "widget": "Text"
       },
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "f78ef6cc053648c19f15aa01597b534a",
+       "model_id": "d938d6a0b2744021b8a2869fc3ed8d56",
        "version_major": 2,
        "version_minor": 0
       },
@@ -888,18 +881,18 @@
     {
      "data": {
       "application/mercury+json": {
-       "code_uid": "Text.0.40.15.31-rand6f7ca014",
+       "code_uid": "Text.0.40.15.31-randbeb3d20d",
        "disabled": false,
        "hidden": false,
        "label": "Variance Threshold",
-       "model_id": "5261497c6c9d48ff98150666a710b79f",
+       "model_id": "7628bd791a434d4881994be8f0e7e104",
        "rows": 1,
        "url_key": "",
        "value": "0",
        "widget": "Text"
       },
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "5261497c6c9d48ff98150666a710b79f",
+       "model_id": "7628bd791a434d4881994be8f0e7e104",
        "version_major": 2,
        "version_minor": 0
       },
@@ -913,18 +906,18 @@
     {
      "data": {
       "application/mercury+json": {
-       "code_uid": "Text.0.40.15.34-rand08bf9f01",
+       "code_uid": "Text.0.40.15.34-rand7204e09b",
        "disabled": false,
        "hidden": false,
        "label": "Correlation Threshold",
-       "model_id": "4368fac8a54944ec8869b93c28f79673",
+       "model_id": "9c36001207a9406290a44dfbd27296e2",
        "rows": 1,
        "url_key": "",
        "value": "1",
        "widget": "Text"
       },
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "4368fac8a54944ec8869b93c28f79673",
+       "model_id": "9c36001207a9406290a44dfbd27296e2",
        "version_major": 2,
        "version_minor": 0
       },
@@ -944,17 +937,17 @@
         4,
         5
        ],
-       "code_uid": "Select.0.40.16.38-rand8c9dc1e9",
+       "code_uid": "Select.0.40.16.38-rand6c036095",
        "disabled": false,
        "hidden": false,
        "label": "Outlier Removal Threshold",
-       "model_id": "7a670fc3850143b39f8d41bb867b09c2",
+       "model_id": "deea9036e1dd45bdaf729893fb2c03ad",
        "url_key": "",
        "value": "none",
        "widget": "Select"
       },
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "7a670fc3850143b39f8d41bb867b09c2",
+       "model_id": "deea9036e1dd45bdaf729893fb2c03ad",
        "version_major": 2,
        "version_minor": 0
       },
@@ -975,17 +968,17 @@
         "minmax",
         "robust"
        ],
-       "code_uid": "Select.0.40.16.46-rand3225540c",
+       "code_uid": "Select.0.40.16.46-rand6e19100d",
        "disabled": false,
        "hidden": false,
        "label": "Scaling Variables",
-       "model_id": "63bb246f2aef4cdb818b9db80076ad6b",
+       "model_id": "74c9a2bf7d774007a6e0aaee3c77b47a",
        "url_key": "",
        "value": "none",
        "widget": "Select"
       },
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "63bb246f2aef4cdb818b9db80076ad6b",
+       "model_id": "74c9a2bf7d774007a6e0aaee3c77b47a",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1005,17 +998,17 @@
         "knn",
         "most_frequent"
        ],
-       "code_uid": "Select.0.40.16.50-rand6b935ac8",
+       "code_uid": "Select.0.40.16.50-rand44961a40",
        "disabled": false,
        "hidden": false,
        "label": "Imputation Methods",
-       "model_id": "343d094ce57041bea6fc249e1e6b3fc0",
+       "model_id": "e7d32db61422400db77aed46104991be",
        "url_key": "",
        "value": "mean",
        "widget": "Select"
       },
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "343d094ce57041bea6fc249e1e6b3fc0",
+       "model_id": "e7d32db61422400db77aed46104991be",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1036,17 +1029,17 @@
         "pca",
         "boruta"
        ],
-       "code_uid": "Select.0.40.16.55-rand0bacb10c",
+       "code_uid": "Select.0.40.16.55-rand17be4326",
        "disabled": false,
        "hidden": false,
        "label": "Feature Selection",
-       "model_id": "6cb844c4413442c7af4907d9f0af5a79",
+       "model_id": "fecc32733d914aff9b0ad61cd4b7b6b5",
        "url_key": "",
        "value": "none",
        "widget": "Select"
       },
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "6cb844c4413442c7af4907d9f0af5a79",
+       "model_id": "fecc32733d914aff9b0ad61cd4b7b6b5",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1066,17 +1059,17 @@
         "undersampling",
         "rose"
        ],
-       "code_uid": "Select.0.40.16.59-randb88939bd",
+       "code_uid": "Select.0.40.16.59-rand8b476756",
        "disabled": false,
        "hidden": false,
        "label": "Imbalance Treatment",
-       "model_id": "23f135fd27ca4174b4f80b53f9e2878b",
+       "model_id": "e9479d12145f46009daeac5020fcea48",
        "url_key": "",
        "value": "none",
        "widget": "Select"
       },
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "23f135fd27ca4174b4f80b53f9e2878b",
+       "model_id": "e9479d12145f46009daeac5020fcea48",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1099,17 +1092,17 @@
         "decision_tree",
         "xgboost"
        ],
-       "code_uid": "Select.0.40.16.64-rand2cb8e572",
+       "code_uid": "Select.0.40.16.64-randaa2cafdf",
        "disabled": false,
        "hidden": false,
        "label": "Model Selection",
-       "model_id": "ac627c0a6ae64f34a97ce1b2f803d50a",
+       "model_id": "a17088a739d847fcad51c1efc4aae6ff",
        "url_key": "",
        "value": "random_forest",
        "widget": "Select"
       },
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ac627c0a6ae64f34a97ce1b2f803d50a",
+       "model_id": "a17088a739d847fcad51c1efc4aae6ff",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1217,7 +1210,7 @@
   },
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    "cell_type": "code",
-   "execution_count": 130,
+   "execution_count": 152,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -1298,7 +1291,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 131,
+   "execution_count": 153,
    "metadata": {
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@@ -1336,26 +1329,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 132,
+   "execution_count": 154,
    "metadata": {
     "slideshow": {
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     }
    },
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Have the duplicates been removed? yes\n",
-      "Missing values threshold is: 80  - Variance threshold is:, 0.0  - Correlation threshold is: 1.0\n",
-      "Outlier removal threshold is: none\n",
-      "Scaling method is: none\n",
-      "Imputation method is: mean\n",
-      "Feature selection method is: none\n",
-      "Imbalance treatment method is: none\n"
-     ]
-    },
     {
      "data": {
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@@ -1459,9 +1439,9 @@
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",
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",
       "text/plain": [
-       "<Figure size 350x350 with 1 Axes>"
+       "<Figure size 500x500 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -1494,7 +1474,7 @@
     "\n",
     "#change the size of the graph\n",
     "\n",
-    "plt.rcParams['figure.figsize'] = [3.5, 3.5]\n",
+    "plt.rcParams['figure.figsize'] = [5, 5]\n",
     "\n",
     "fig, ax = plot_confusion_matrix(\n",
     "    conf_mat=conf_matrix,\n",
@@ -1504,11 +1484,58 @@
    ]
   },
   {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
+   "cell_type": "code",
+   "execution_count": 155,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Have the duplicates been removed? yes\n",
+      "What is the missing values threshold? 80\n",
+      "What is the variance threshold? 0.0\n",
+      "How many features have been removed? 145\n",
+      "---------------------\n",
+      "What is the outlier removal threshold? none\n",
+      "How many outliers have been removed? 0\n",
+      "---------------------\n",
+      "What is the scaling method? none\n",
+      "---------------------\n",
+      "What is the imputation method? mean\n",
+      "---------------------\n",
+      "What is the feature selection method? none\n",
+      "What is the number of features selected? 445\n",
+      "---------------------\n",
+      "What is the imbalance treatment method? none\n",
+      "---------------------\n",
+      "What is the model? random_forest\n"
+     ]
+    }
+   ],
    "source": [
-    "#### **Plot Evaluation**"
+    "print('Have the duplicates been removed?', drop_duplicates_var)\n",
+    "print('What is the missing values threshold?', missing_values_threshold_var)\n",
+    "print('What is the variance threshold?', variance_threshold_var)\n",
+    "print('How many features have been removed?', len(dropped))\n",
+    "print('---------------------')\n",
+    "print('What is the outlier removal threshold?', outlier_var)\n",
+    "print('How many outliers have been removed?', len(X_train2) - len(X_train_dropped_outliers))\n",
+    "print('---------------------')\n",
+    "print('What is the scaling method?', scale_model_var)\n",
+    "print('---------------------')\n",
+    "print('What is the imputation method?', imputation_var)\n",
+    "print('---------------------')\n",
+    "print('What is the feature selection method?', feature_selection_var)\n",
+    "print('What is the number of features selected?', len(selected_features))\n",
+    "print('---------------------')\n",
+    "print('What is the imbalance treatment method?', imbalance_var)\n",
+    "print('---------------------')\n",
+    "print('What is the model?', input_model)"
    ]
   }
  ],