Final Bakeoff.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "UC_Sampled = pd.read_csv('datasets/uncontrollable_sample.csv', index_col='id')\n",
    "df_controllable_sampled = pd.read_csv('datasets/controllable_sampled.csv', index_col = 'id')\n",
    "df_controllable_all = pd.read_csv('Faizan_scratch/Controllables/controllable_cleaned_features.csv', index_col='id')\n",
    "df_uncontrollable_all = pd.read_csv('Faizan_scratch/uncontrollable_cleaned_features.csv', index_col='id')\n",
    "\n",
    "df_all_features = df_controllable_all.merge(df_uncontrollable_all, left_index=True, right_index=True)\n",
    "df_all_features = df_all_features.drop(columns=['host_is_superhost', 'Label_y'])\n",
    "df_all_features = df_all_features.rename(columns={'Label_x':'Label'})\n",
    "\n",
    "uncontrollable_columns = UC_Sampled.drop(columns='Label').columns\n",
    "controllable_columns = df_controllable_sampled.drop(columns='Label').columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split, PredefinedSplit\n",
    "\n",
    "\n",
    "X_trainval, X_test, Y_trainval, Y_test = train_test_split(df_all_features.drop('Label',1), \n",
    "                                                          df_all_features['Label'],\n",
    "                                                         test_size = 0.2,\n",
    "                                                         random_state = 42)\n",
    "\n",
    "\n",
    "X_train, X_val, Y_train, Y_val = train_test_split(X_trainval, \n",
    "                                                  Y_trainval, \n",
    "                                                  test_size = 0.25, # Corresponds to 20% of total data\n",
    "                                                  random_state = 42)\n",
    "\n",
    "# Use 'pds' as the cv parameter in GridSearchCV/RandomizedSearchCV:\n",
    "\n",
    "split_index = [-1 if x in X_train.index else 0 for x in X_trainval.index]\n",
    "pds = PredefinedSplit(test_fold = split_index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split training/validation and test sets by feature categories\n",
    "\n",
    "X_trainval_uc, X_trainval_c = X_trainval[uncontrollable_columns], X_trainval[controllable_columns]\n",
    "X_test_uc, X_test_c = X_test[uncontrollable_columns], X_test[controllable_columns]\n",
    "\n",
    "# For baseline models\n",
    "X_train_uc, X_train_c = X_train[uncontrollable_columns], X_train[controllable_columns]\n",
    "X_val_uc, X_val_c = X_val[uncontrollable_columns], X_val[controllable_columns]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# Scale features for LR\n",
    "# Note these will be used again for hyperparameter tuning, so need to scale:\n",
    "\n",
    "scaler = StandardScaler()\n",
    "\n",
    "X_train_uc = pd.DataFrame(scaler.fit_transform(X_train_uc), columns = uncontrollable_columns)\n",
    "X_train_c = pd.DataFrame(scaler.fit_transform(X_train_c), columns = controllable_columns)\n",
    "\n",
    "X_val_uc = pd.DataFrame(scaler.fit_transform(X_val_uc), columns = uncontrollable_columns)\n",
    "X_val_c = pd.DataFrame(scaler.fit_transform(X_val_c), columns = controllable_columns)\n",
    "\n",
    "X_trainval_uc = pd.DataFrame(scaler.fit_transform(X_trainval_uc), columns = uncontrollable_columns)\n",
    "X_trainval_c = pd.DataFrame(scaler.fit_transform(X_trainval_c), columns = controllable_columns)\n",
    "\n",
    "X_test_uc = pd.DataFrame(scaler.fit_transform(X_test_uc), columns = uncontrollable_columns)\n",
    "X_test_c = pd.DataFrame(scaler.fit_transform(X_test_c), columns = controllable_columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Final Bakeoff"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Uncontrollable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n",
    "from sklearn import svm, linear_model, metrics\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.model_selection import RandomizedSearchCV, GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Best models:\n",
    "\n",
    "best_rf_uc = rf_uc_best = RandomForestClassifier(criterion='entropy',n_estimators=101\n",
    "                                    ,min_samples_split=300, min_samples_leaf=1\n",
    "                                    ,max_features=161)\n",
    "best_gb_uc = GradientBoostingClassifier(learning_rate=0.025, max_depth=15,\n",
    "                            max_features='sqrt', min_samples_leaf=0.034,\n",
    "                            min_samples_split=0.08916666666666667,\n",
    "                            n_estimators=2500)\n",
    "best_dt_uc = DecisionTreeClassifier(criterion='entropy',\n",
    "                       min_samples_leaf=0.0030204081632653063,\n",
    "                       min_samples_split=0.03326530612244898)\n",
    "best_svm_uc = svm.SVC(kernel = 'rbf', C = 25.402764157911143)\n",
    "best_lr_uc = linear_model.LogisticRegression(C = 0.2520004993764092, penalty = 'l1', max_iter = 4000, solver = 'liblinear')\n",
    "\n",
    "\n",
    "models = {'RF': best_rf_uc, 'GB' : best_gb_uc, 'DT' : best_dt_uc, 'SVM': best_svm_uc, 'LR': best_lr_uc}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "for model in models:\n",
    "    models[model].fit(X_train_uc, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions_rf = best_rf_uc.predict_proba(X_val_uc)[:,1]\n",
    "predictions_gb = best_gb_uc.predict_proba(X_val_uc)[:,1]\n",
    "predictions_dt = best_dt_uc.predict_proba(X_val_uc)[:,1]\n",
    "predictions_svm = best_svm_uc.decision_function(X_val_uc)\n",
    "predictions_lr = best_lr_uc.predict_proba(X_val_uc)[:,1]\n",
    "\n",
    "predictions = {'RF': predictions_rf, 'GB' : predictions_gb, 'DT' : predictions_dt, 'SVM': predictions_svm, 'LR': predictions_lr}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot ROC curves of the two models\n",
    "\n",
    "plt.figure(figsize=(12,8))\n",
    "\n",
    "aucs = []\n",
    "labels = []\n",
    "\n",
    "for prediction in predictions:\n",
    "    fpr, tpr, thresholds = metrics.roc_curve(Y_val, predictions[prediction])\n",
    "    auc = metrics.auc(fpr,tpr)\n",
    "    aucs.append(auc)\n",
    "    labels.append(prediction)\n",
    "    label = prediction + \" AUC = %.3f\"%auc\n",
    "    plt.plot(fpr,tpr,label = label)\n",
    "    \n",
    "\n",
    "#Plot 45 degree line\n",
    "plt.plot([0,1],[0,1])\n",
    "\n",
    "plt.title('ROC')\n",
    "plt.xlabel('FPR')\n",
    "plt.ylabel('TPR')\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "results = np.array(aucs)\n",
    "labs = np.array(labels)[results.argsort()]\n",
    "results.sort()\n",
    "\n",
    "fig = plt.figure(facecolor = 'w', figsize = (12, 6))\n",
    "ax = plt.subplot(111)\n",
    "\n",
    "width = 0.25\n",
    "ind = np.arange(5)\n",
    "rec = ax.bar(ind + width, results, width, color='r')\n",
    "\n",
    "ax.set_xticks(ind + width)\n",
    "ax.set_xticklabels(labs, size = 14)\n",
    "ax.set_ylim([0.5, max(results)*1.1])\n",
    "\n",
    "plt.plot(np.arange(6), max(results) * np.ones(6))\n",
    "plt.ylabel('Validation AUC')\n",
    "plt.title('Uncontrollable Features Model: Validation AUC by Model Type')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Controllable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_rf_c = RandomForestClassifier(criterion='entropy',max_features=10,oob_score=True)\n",
    "\n",
    "best_gb_c = GradientBoostingClassifier(learning_rate=0.025, max_depth=30,\n",
    "                            max_features='sqrt',\n",
    "                            min_samples_leaf=0.021625000000000002,\n",
    "                            min_samples_split=0.12083333333333333,\n",
    "                            n_estimators=2500)\n",
    "best_dt_c = DecisionTreeClassifier(criterion='entropy',\n",
    "                       min_samples_leaf=0.0030204081632653063,\n",
    "                       min_samples_split=0.017755102040816328)\n",
    "best_svm_c = svm.SVC(kernel = 'linear',  C = 1.9510035139852595)\n",
    "best_lr_c = linear_model.LogisticRegression(solver = 'liblinear', penalty = 'l1', max_iter = 4000, C = 50.89870193519685)\n",
    "\n",
    "models = {'RF': best_rf_c, 'GB' : best_gb_c, 'DT' : best_dt_c, 'SVM': best_svm_c, 'LR': best_lr_c}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "for model in models:\n",
    "    models[model].fit(X_train_c, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions_rf = best_rf_c.predict_proba(X_val_c)[:,1]\n",
    "predictions_gb = best_gb_c.predict_proba(X_val_c)[:,1]\n",
    "predictions_dt = best_dt_c.predict_proba(X_val_c)[:,1]\n",
    "predictions_svm = best_svm_c.decision_function(X_val_c)\n",
    "predictions_lr = best_lr_c.predict_proba(X_val_c)[:,1]\n",
    "\n",
    "predictions = {'RF': predictions_rf, 'GB' : predictions_gb, 'DT' : predictions_dt, 'SVM': predictions_svm, 'LR': predictions_lr}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot ROC curves of the two models\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "from sklearn import linear_model, svm, metrics\n",
    "\n",
    "\n",
    "plt.figure(figsize=(12,8))\n",
    "aucs = []\n",
    "labels = []\n",
    "\n",
    "for prediction in predictions:\n",
    "    fpr, tpr, thresholds = metrics.roc_curve(Y_val, predictions[prediction])\n",
    "    auc = metrics.auc(fpr,tpr)\n",
    "    aucs.append(auc)\n",
    "    labels.append(prediction)\n",
    "    label = prediction + \" AUC = %.3f\"%auc\n",
    "    plt.plot(fpr,tpr,label = label)\n",
    "    \n",
    "\n",
    "#Plot 45 degree line\n",
    "plt.plot([0,1],[0,1])\n",
    "\n",
    "plt.title('ROC')\n",
    "plt.xlabel('FPR')\n",
    "plt.ylabel('TPR')\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "results = np.array(aucs)\n",
    "labs = np.array(labels)[results.argsort()]\n",
    "results.sort()\n",
    "\n",
    "fig = plt.figure(facecolor = 'w', figsize = (12, 6))\n",
    "ax = plt.subplot(111)\n",
    "\n",
    "width = 0.25\n",
    "ind = np.arange(5)\n",
    "rec = ax.bar(ind + width, results, width, color='r')\n",
    "\n",
    "ax.set_xticks(ind + width)\n",
    "ax.set_xticklabels(labs, size = 14)\n",
    "ax.set_ylim([0.5, max(results)*1.1])\n",
    "\n",
    "plt.plot(np.arange(6), max(results) * np.ones(6))\n",
    "plt.ylabel('Validation AUC')\n",
    "plt.title('Controllable Features Model: Validation AUC by Model Type')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Winners\n",
    "\n",
    "best_model_uc = rf_uc_best = RandomForestClassifier(criterion='entropy',n_estimators=101\n",
    "                                    ,min_samples_split=300, min_samples_leaf=1\n",
    "                                    ,max_features=161)\n",
    "\n",
    "best_model_c = RandomForestClassifier(criterion='entropy',max_features=10,oob_score=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(criterion='entropy', max_features=161,\n",
       "                       min_samples_split=300, n_estimators=101)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_model_uc.fit(X_trainval_uc, Y_trainval)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(criterion='entropy', max_features=10, oob_score=True)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_model_c.fit(X_trainval_c, Y_trainval)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions_uc = best_model_uc.predict_proba(X_test_uc)[:,1]\n",
    "predictions_c = best_model_c.predict_proba(X_test_c)[:,1]\n",
    "\n",
    "predictions = {'UC Model': predictions_uc, 'C Model': predictions_c}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot ROC curves of the two models\n",
    "\n",
    "plt.figure(figsize=(12,8))\n",
    "\n",
    "for prediction in predictions:\n",
    "    fpr, tpr, thresholds = metrics.roc_curve(Y_test, predictions[prediction])\n",
    "    auc = metrics.auc(fpr,tpr)\n",
    "    label = prediction + \" AUC = %.3f\"%auc\n",
    "    plt.plot(fpr,tpr,label = label)\n",
    "    \n",
    "\n",
    "#Plot 45 degree line\n",
    "plt.plot([0,1],[0,1])\n",
    "\n",
    "plt.title('ROC')\n",
    "plt.xlabel('FPR')\n",
    "plt.ylabel('TPR')\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_test_with_predictions = pd.DataFrame(Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_test_with_predictions['Location_predictions_score'] = predictions_uc\n",
    "df_test_with_predictions['Property_predictions_score'] = predictions_c\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_test_with_predictions['Loc_bucket'] = pd.qcut(df_test_with_predictions['Location_predictions_score'], 3, labels=[\"low\", \"medium\", \"high\"])\n",
    "\n",
    "df_test_with_predictions['Prop_bucket'] = pd.qcut(df_test_with_predictions['Property_predictions_score'], 3, labels=[\"low\", \"medium\", \"high\"])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "statistics = df_test_with_predictions.groupby(['Loc_bucket', 'Prop_bucket']).agg(['mean', 'count'])['Label']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "statistics['percent'] = statistics['count'] / len(df_test_with_predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>count</th>\n",
       "      <th>percent</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Loc_bucket</th>\n",
       "      <th>Prop_bucket</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">low</th>\n",
       "      <th>low</th>\n",
       "      <td>0.206897</td>\n",
       "      <td>1189</td>\n",
       "      <td>0.149353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>medium</th>\n",
       "      <td>0.428030</td>\n",
       "      <td>792</td>\n",
       "      <td>0.099485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>high</th>\n",
       "      <td>0.632987</td>\n",
       "      <td>673</td>\n",
       "      <td>0.084537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">medium</th>\n",
       "      <th>low</th>\n",
       "      <td>0.275782</td>\n",
       "      <td>863</td>\n",
       "      <td>0.108403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>medium</th>\n",
       "      <td>0.511270</td>\n",
       "      <td>976</td>\n",
       "      <td>0.122598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>high</th>\n",
       "      <td>0.675676</td>\n",
       "      <td>814</td>\n",
       "      <td>0.102248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">high</th>\n",
       "      <th>low</th>\n",
       "      <td>0.342062</td>\n",
       "      <td>611</td>\n",
       "      <td>0.076749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>medium</th>\n",
       "      <td>0.551373</td>\n",
       "      <td>983</td>\n",
       "      <td>0.123477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>high</th>\n",
       "      <td>0.706604</td>\n",
       "      <td>1060</td>\n",
       "      <td>0.133149</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            mean  count   percent\n",
       "Loc_bucket Prop_bucket                           \n",
       "low        low          0.206897   1189  0.149353\n",
       "           medium       0.428030    792  0.099485\n",
       "           high         0.632987    673  0.084537\n",
       "medium     low          0.275782    863  0.108403\n",
       "           medium       0.511270    976  0.122598\n",
       "           high         0.675676    814  0.102248\n",
       "high       low          0.342062    611  0.076749\n",
       "           medium       0.551373    983  0.123477\n",
       "           high         0.706604   1060  0.133149"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "statistics_new = statistics.reset_index()\n",
    "statistic_new = statistics_new.sort_values(by='Prop_bucket', ascending=False).sort_values(by='Loc_bucket', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Loc_bucket</th>\n",
       "      <th>Prop_bucket</th>\n",
       "      <th>mean</th>\n",
       "      <th>count</th>\n",
       "      <th>percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>high</td>\n",
       "      <td>high</td>\n",
       "      <td>0.706604</td>\n",
       "      <td>1060</td>\n",
       "      <td>0.133149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>high</td>\n",
       "      <td>medium</td>\n",
       "      <td>0.551373</td>\n",
       "      <td>983</td>\n",
       "      <td>0.123477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>high</td>\n",
       "      <td>low</td>\n",
       "      <td>0.342062</td>\n",
       "      <td>611</td>\n",
       "      <td>0.076749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>medium</td>\n",
       "      <td>high</td>\n",
       "      <td>0.675676</td>\n",
       "      <td>814</td>\n",
       "      <td>0.102248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>medium</td>\n",
       "      <td>medium</td>\n",
       "      <td>0.511270</td>\n",
       "      <td>976</td>\n",
       "      <td>0.122598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>medium</td>\n",
       "      <td>low</td>\n",
       "      <td>0.275782</td>\n",
       "      <td>863</td>\n",
       "      <td>0.108403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>low</td>\n",
       "      <td>high</td>\n",
       "      <td>0.632987</td>\n",
       "      <td>673</td>\n",
       "      <td>0.084537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>low</td>\n",
       "      <td>medium</td>\n",
       "      <td>0.428030</td>\n",
       "      <td>792</td>\n",
       "      <td>0.099485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>low</td>\n",
       "      <td>low</td>\n",
       "      <td>0.206897</td>\n",
       "      <td>1189</td>\n",
       "      <td>0.149353</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Loc_bucket Prop_bucket      mean  count   percent\n",
       "8       high        high  0.706604   1060  0.133149\n",
       "7       high      medium  0.551373    983  0.123477\n",
       "6       high         low  0.342062    611  0.076749\n",
       "5     medium        high  0.675676    814  0.102248\n",
       "4     medium      medium  0.511270    976  0.122598\n",
       "3     medium         low  0.275782    863  0.108403\n",
       "2        low        high  0.632987    673  0.084537\n",
       "1        low      medium  0.428030    792  0.099485\n",
       "0        low         low  0.206897   1189  0.149353"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "statistic_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "actuals_9_box = statistics_new.pivot(index = 'Loc_bucket', columns='Prop_bucket', values ='mean').sort_index(ascending=False).sort_index(axis=1, ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "count_9_box = statistics_new.pivot(index = 'Loc_bucket', columns='Prop_bucket', values ='percent').sort_index(ascending=False).sort_index(axis=1, ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Prop_bucket</th>\n",
       "      <th>high</th>\n",
       "      <th>medium</th>\n",
       "      <th>low</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Loc_bucket</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>high</th>\n",
       "      <td>0.706604</td>\n",
       "      <td>0.551373</td>\n",
       "      <td>0.342062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>medium</th>\n",
       "      <td>0.675676</td>\n",
       "      <td>0.511270</td>\n",
       "      <td>0.275782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>low</th>\n",
       "      <td>0.632987</td>\n",
       "      <td>0.428030</td>\n",
       "      <td>0.206897</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Prop_bucket      high    medium       low\n",
       "Loc_bucket                               \n",
       "high         0.706604  0.551373  0.342062\n",
       "medium       0.675676  0.511270  0.275782\n",
       "low          0.632987  0.428030  0.206897"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "actuals_9_box"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Prop_bucket</th>\n",
       "      <th>high</th>\n",
       "      <th>medium</th>\n",
       "      <th>low</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Loc_bucket</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>high</th>\n",
       "      <td>0.133149</td>\n",
       "      <td>0.123477</td>\n",
       "      <td>0.076749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>medium</th>\n",
       "      <td>0.102248</td>\n",
       "      <td>0.122598</td>\n",
       "      <td>0.108403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>low</th>\n",
       "      <td>0.084537</td>\n",
       "      <td>0.099485</td>\n",
       "      <td>0.149353</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Prop_bucket      high    medium       low\n",
       "Loc_bucket                               \n",
       "high         0.133149  0.123477  0.076749\n",
       "medium       0.102248  0.122598  0.108403\n",
       "low          0.084537  0.099485  0.149353"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_9_box"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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