ML_Model_Building.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sigma-Thon 1.0 by Hackerearth\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Building"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "pd.options.mode.chained_assignment = None\n",
    "import numpy as np\n",
    "import warnings; \n",
    "from sklearn import metrics\n",
    "from sklearn.model_selection import cross_val_predict\n",
    "from imblearn.pipeline import Pipeline as imbPipeline\n",
    "from importlib import reload \n",
    "import ipynb.fs.full.USAID_support_functions as psf\n",
    "reload(psf)\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "import warnings; \n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['s_objects', 's_num_log', 'si', 'sc', 'sv', 'sm', 'sb', 'sml', 'trending']) \n",
      " [(8259, 29), (8259, 17), (8259, 5), (8259, 3), (8259, 6), (8259, 2), (8259, 1), (8259, 2), (8259, 6)]\n",
      "\n",
      " s_objects :\n",
      " po_new_date_mn         int64\n",
      "po_new_date_dy         int64\n",
      "sch_del_date_mn        int64\n",
      "sch_del_date_dy        int64\n",
      "po_new_date_wd         int64\n",
      "po_new_date_wk         int64\n",
      "po_new_date_qt         int64\n",
      "sch_del_date_wd        int64\n",
      "sch_del_date_wk        int64\n",
      "sch_del_date_qt        int64\n",
      "managed_by            object\n",
      "flf_via               object\n",
      "vendor                object\n",
      "ship_mode             object\n",
      "product_grp           object\n",
      "sub_class             object\n",
      "mol_test              object\n",
      "brand                 object\n",
      "dosage                object\n",
      "dos_form              object\n",
      "manu_site             object\n",
      "first_line            object\n",
      "c_ship                object\n",
      "sep_weight            object\n",
      "sep_freight           object\n",
      "cmdty_cost_freight    object\n",
      "country               object\n",
      "manu_country          object\n",
      "manu_continent        object\n",
      "dtype: object\n",
      "\n",
      " s_num_log :\n",
      " manu_logistic      float64\n",
      "manu_infrast       float64\n",
      "manu_lpi_score     float64\n",
      "manu_customs       float64\n",
      "manu_tracking      float64\n",
      "manu_shipments     float64\n",
      "manu_timeliness    float64\n",
      "manu_fsi           float64\n",
      "dp_shipments       float64\n",
      "dp_timeliness      float64\n",
      "dp_tracking        float64\n",
      "dp_customs         float64\n",
      "dp_lpi_score       float64\n",
      "dp_infrast         float64\n",
      "dp_logistic        float64\n",
      "dp_count             int64\n",
      "dp_fsi             float64\n",
      "dtype: object\n",
      "\n",
      " si :\n",
      " ln_itm_qty         int64\n",
      "ln_itm_val       float64\n",
      "pack_price       float64\n",
      "line_item_ins    float64\n",
      "real_w           float64\n",
      "dtype: object\n",
      "\n",
      " sc :\n",
      " country_qtycount      int64\n",
      "country_valmean     float64\n",
      "country_wmean       float64\n",
      "dtype: object\n",
      "\n",
      " sv :\n",
      " vendor_qtysum       int64\n",
      "vendor_qtymean    float64\n",
      "vendor_valsum     float64\n",
      "vendor_valmean    float64\n",
      "vendor_fcsum      float64\n",
      "vendor_wsum       float64\n",
      "dtype: object\n",
      "\n",
      " sm :\n",
      " manu_site_qtycount      int64\n",
      "manu_site_qtymean     float64\n",
      "dtype: object\n",
      "\n",
      " sb :\n",
      " brand_valmean    float64\n",
      "dtype: object\n",
      "\n",
      " sml :\n",
      " mol_test_qtycount      int64\n",
      "mol_test_itemmean    float64\n",
      "dtype: object\n",
      "\n",
      " trending :\n",
      " delayed_ma           float64\n",
      "delay_days_ma        float64\n",
      "cumsum_delay         float64\n",
      "cummin_delay         float64\n",
      "cumsum_del_t         float64\n",
      "cummin_delay_days    float64\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "# import train data\n",
    "files_path = '.../USAID_Data/selected features/'\n",
    "\n",
    "tr_pred_names = ['s_objects',\n",
    "                 's_num_log',\n",
    "                 'si', 'sc',\n",
    "                 'sv', 'sm',\n",
    "                 'sb', 'sml',\n",
    "                 'trending']\n",
    "\n",
    "file_dict = psf.import_sel_data(files_path + 'train/predictors/', tr_pred_names)\n",
    "\n",
    "print(file_dict.keys(), \"\\n\", [df.shape for df in file_dict.values()])\n",
    "\n",
    "for key, df in file_dict.items():\n",
    "    print(\"\\n\", key, \":\\n\", df.dtypes)\n",
    "\n",
    "s_objects, s_num_log, si, sc, sv, sm, sb, sml, trending = [file_dict[f] for f in tr_pred_names]\n",
    "\n",
    "s_objects = s_objects.apply(lambda elem: pd.Categorical(elem))\n",
    "\n",
    "# drop redundant columns\n",
    "entities = ['country',\n",
    "            'manu_site',\n",
    "            'vendor',\n",
    "            'brand',\n",
    "            'mol_test']\n",
    "\n",
    "for s in [sc, sv, sm, sb, sml]:\n",
    "    for e in entities:\n",
    "        try:\n",
    "            s.drop(e, axis=1, inplace=True)\n",
    "        except:\n",
    "            pass\n",
    "        \n",
    "# import responses\n",
    "delayed_del = pd.read_csv(files_path + 'train/responses/' + 'delayed_del.csv')\n",
    "delayed_del = delayed_del.apply(lambda elem: pd.Categorical(elem))\n",
    "\n",
    "datetime64 = pd.read_csv(files_path + 'train/responses/' + 'datetime64.csv')\n",
    "datetime64['delay_t'] = datetime64['delay_t'].apply(lambda elem: pd.to_timedelta(elem))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X shape:  (8259, 695) \n",
      "y shape: (8259, 1)\n"
     ]
    }
   ],
   "source": [
    "# balance dummy values for train/test categorical data\n",
    "s_ts_objects = pd.read_csv(files_path + 'test/predictors/' + 's_ts_objects.csv')\n",
    "s_ts_objects = s_ts_objects.apply(lambda elem: pd.Categorical(elem))\n",
    "\n",
    "# transform data\n",
    "s_objects, s_ts_objects = psf.cat_pipeline(s_objects, s_ts_objects)\n",
    "\n",
    "train_data, delayed_del_tr = psf.transform_pipepline(s_objects,\n",
    "                                                     s_num_log,\n",
    "                                                     si, sc, sv,\n",
    "                                                     sm, sb, sml,\n",
    "                                                     trending,\n",
    "                                                     delayed_del)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import classifiers\n",
    "from sklearn.neighbors import KNeighborsClassifier \n",
    "from sklearn.ensemble import RandomForestClassifier, BaggingClassifier, ExtraTreesClassifier\n",
    "from sklearn.svm import SVC, LinearSVC\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neural_network import MLPClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of X_train: (6607, 695) X_val: (1652, 695) y_train: (6607, 1) y_val: (1652, 1)\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Benchmark: RandomForestClassifier \n",
    "psf.fix_nulls(train_data) # fix nan values\n",
    "\n",
    "X, y = train_data, delayed_del_tr\n",
    "\n",
    "# train-validation split\n",
    "X_train_c, X_val_c, y_train_c, y_val_c = psf.split_set(X, y, 0.2, use_smote=False)\n",
    "\n",
    "# fit\n",
    "random_forest = RandomForestClassifier(random_state=109)\n",
    "random_forest.fit(X_train_c, y_train_c)\n",
    "\n",
    "# visualize\n",
    "psf.visualize_importance(random_forest, X_train_c, 0.08)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# evaluation\n",
    "plt.subplots(figsize=(6,5))\n",
    "psf.class_report(X_train_c, X_val_c, y_train_c, y_val_c, random_forest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True positives:\n",
      " delayed_del    74\n",
      "prediction     74\n",
      "dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Blue: Predicted | Green: Actual')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# predict\n",
    "y_pred = random_forest.predict(X_val_c)\n",
    "pred_df = y_val_c.copy()\n",
    "pred_df['prediction']= y_pred\n",
    "#print(pred_df.describe())\n",
    "\n",
    "# create df of true positives\n",
    "true_pred_df = pred_df[(pred_df.delayed_del==1) & (pred_df.delayed_del==pred_df.prediction)]\n",
    "#print(true_pred_df.sum())\n",
    "\n",
    "print(\"True positives:\\n\", true_pred_df.sum())\n",
    "\n",
    "# visualize prediction\n",
    "plt.subplots(figsize=(15,2))\n",
    "plt.scatter(pred_df.index, pred_df.iloc[:,0], color='g')\n",
    "plt.scatter(pred_df.index, pred_df.iloc[:,1], color='b')\n",
    "plt.title(\"Blue: Predicted | Green: Actual\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import regressors\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "from sklearn.svm import LinearSVR, SVR\n",
    "from sklearn.linear_model import LinearRegression, SGDRegressor\n",
    "from sklearn.ensemble import BaggingRegressor, RandomForestRegressor, ExtraTreesRegressor\n",
    "from sklearn.neural_network import MLPRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of X_train: (876, 695) X_val: (74, 695) y_train: (876,) y_val: (74,)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Benchmark: RandomForestRegressor\n",
    "X_train_r, X_val_r, y_train_r, y_val_r = psf.split_set_reg(X, true_pred_df, datetime64, y)\n",
    "\n",
    "reg = RandomForestRegressor(random_state=110)\n",
    "reg.fit(X_train_r, y_train_r)\n",
    "\n",
    "psf.visualize_importance(reg, X_train_r, 0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R-Squared:  0.7829504310182616 RMSE : 18.501318433662604 Target Variable Mean:  31.7972972972973\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x19d067b1ba8>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x2160 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# reg predict\n",
    "y_pred_reg = reg.predict(X_val_r)\n",
    "\n",
    "# accuracy and error calcs\n",
    "r2, rmse = metrics.r2_score(y_val_r, y_pred_reg), np.sqrt(metrics.mean_squared_error(y_val_r, y_pred_reg))\n",
    "print(\"R-Squared: \", r2, \"RMSE :\", rmse, \"Target Variable Mean: \", y_val_r.mean())\n",
    "\n",
    "# pred vs. delay_t\n",
    "df_pred_reg =pd.DataFrame(y_val_r)\n",
    "df_pred_reg['pred']= y_pred_reg\n",
    "df_pred_reg.plot(kind=\"barh\", figsize=(20,30), cmap=\"Spectral\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Select classifier ##\n",
    "classifiers = [KNeighborsClassifier,\n",
    "               RandomForestClassifier,\n",
    "               ExtraTreesClassifier,\n",
    "               BaggingClassifier,\n",
    "               MLPClassifier,\n",
    "               LogisticRegression,\n",
    "               LinearSVC,\n",
    "               SVC]\n",
    "\n",
    "clfs_names = ['KNeighborsClassifier',\n",
    "              'RandomForestClassifier',\n",
    "              'ExtraTreesClassifier',\n",
    "              'BaggingClassifier',\n",
    "              'MLPClassifier',\n",
    "              'LogisticRegression',\n",
    "              'LinearSVC',\n",
    "              'SVC']   \n",
    "\n",
    "# append scores for each clf\n",
    "f1s = []\n",
    "for c in classifiers:\n",
    "    try:\n",
    "        f1s.append(psf.select_classifier(X_train_c, X_val_c, y_train_c, y_val_c, c()))\n",
    "    except:\n",
    "        f1s.append(0)\n",
    "\n",
    "f1_df = pd.DataFrame(f1s, clfs_names)\n",
    "f1_df.columns = [\"f1_score\"]\n",
    "f1_df.sort_values(by ='f1_score',ascending=False).plot(kind=\"barh\",\n",
    "                                                       title=\"Classifiers Comparison\",\n",
    "                                                       figsize=(9,6),\n",
    "                                                       cmap=\"tab10\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# cross-validation reports\n",
    "for c in classifiers:\n",
    "    i=classifiers.index(c)\n",
    "    psf.cross_val_report(classifiers[i](), X, y, clfs_names[i]+' CV Report')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True positives:\n",
      " delayed_del    93\n",
      "prediction     93\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# collect true positives from MLPClassifier\n",
    "sel_clf = MLPClassifier(random_state=102)\n",
    "sel_tr_pred = psf.collect_tr_pred(sel_clf, X_train_c, X_val_c, y_train_c, y_val_c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of X_train: (857, 695) X_val: (93, 695) y_train: (857,) y_val: (93,)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x204a3af9588>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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WkIOTmZmZWUEOTmZmZmYFOTiZmZmZFeTgZGZmZlaQg5OZmZlZQX0rXYCZ2eJm9zEzYcztlS6je7l/1a2D/k0/a1gPFtL7eMTJzMzMrCAHJzMzM7OCHJzMzMzMCnJwMjMzMyuoZheHS2oAxgF7RsR1ZdufBB4DGoB1I+L9sn0jgFOB54FmoD9wXkRcL2k8sAzwLilwrggcGxF39kB3zMzMrBeo2eCUTQP2Aq4DkLQB8KlOzrk6Io7Lx68EPClpTN63X0RMy/sE3Ag4OJmZmS0maj04/Q1YR9KnI+JfwHeAq4DPFzz/08B7EdGcctLHrAG8Cf8JZKOBPsAbwIHA28BvgU2BmcBg4OvAKcCA/GcYcCywDWkU69yIGCPpcGB/YD4wMSJ+LOnbwCjgA2A6sB+wPHBl/toXODEi7pXUCDwDNEXEXgX7amZmZp2o9eAEcBPwLUmXAZsDZ9NxcNpb0hak0PIusG/ZviskzcvnPwQckLdfDBwYEU9LGkkKQ5OBARGxuaRVgGfL2rk3Is6TtAswOCK+Iqk/MEnS3bndIyNikqTDJPUljZydFxHXSmoJTScCd0fE+ZJWByZKWhNYFjgtIh7v7MlpbGzs7JCqViqVKl1Ct3L/zKynLe735eIQnK4GLiStW5pQ5PiWqbo27BcR0yQdAuwNvJS3rwdckEelliKN9qxHCldExCxJ08raifx1A6A+r59qOXcNUnA6RtLZuY0+wNHATyQdBkwFbsnXuCpf42VJbwOrtLpGh4YMGUJdXV2RQ6tOqVSivr6+0mV0G/evitX6hydaTavm+7KpqWmRBwxq/l11EfE8aV3T90nTWl3R5u9JoemMlk2kUNVAGm26HWgEtgSQtCKwTlkT8/PXacC4fN72wPWkgHcQcGhEbAtsDGwFHAyckrf1Ab5FClBD8zVWJy1Yf6PVNczMzKyLLA4jTpAWh+8bEc9I+kLZ9gckNefvrwb+uQBtHkVaOH4lcBhpGm/JvG8kaWpuF0kPktY4vUtan1TuVqBB0gTS9NrNETFH0lPAI5JmAS8DD5Om5u6W9AYwB7gtn3+ppD2ApYGDI2JeG+uxzMzMrAv0aW5u7vwoW2CS1gU2ymuSBgBTgDUioqnCpQFQKpUGAS94qq56uX/Va9Bxnqqz6lXNv6uubKpucH19/fSFaaPmp+oqaAawl6RJwFhgVG8JTWZmZrZwFpepuh4XEf8Gdqt0HWZmZtZ1POJkZmZmVpBHnMzMetiNwwfW7PotqO31aeD+Le484mRmZmZWkIOTmZmZWUEOTmZmZmYFOTiZmZmZFeTgZGZmZlaQg5OZmZlZQQ5OZmZmZgU5OJmZmZkV5OBkZmZmVpCDk5mZmVlBDk5mZmZmBTk4mZmZmRXk4GRmZmZWkIOTmZmZWUF9K12AmdniZvcxM2HM7ZUuo3u5f9WtVf+mnzWsQoX0Ph5xMjMzMyvIwcnMzMysIAcnMzMzs4IcnMzMzMwKqprF4ZIagEMjYs+ybdcC+0XE3C6+1gjgVOD5vOnTwAMRcURXXmcBaxoInBQRh1eqBjMzs8Vd1QSntpSHqG5wdUQcByBpCWCCpE0j4tFuvGa7ImIm4NBkZmZWQVUdnCRNB9YFfgc0AYOA1YAREfGYpOHA0cCHwMSIOE7SZ4ELgf7AAODUiLhFUiPwTG7nL60utRxp1OktSUvl661Nmuo8MSLGS/oaaZTqLeBN4ElgPHA2MBe4CHgJOCPX83fgEGAwcBnwATAP2C8ff11ufyngUGAOcG1EbCFpR+B04H3gDeBAYCNgVD53MHBdRJyxsM+tmZmZfVJVB6dWXoyIQyQdBBws6XjgZ8CmEfGupD/mwNEMnJPDzlb5mFuAZYHTIuLxPFW3t6QtSUFsDnBGRDwr6TBgdkSMlDQAuF/ShsBoYMuIeE3SVWV19Y+IL0vqAwSwdUS8Luk0YATQDyiRAt5QYEVgDVIA2xtYH1g+10Bu56LczsuSjgJOBG7L520I1AGvkEJahxobGxfwaa4upVKp0iV0K/fPzHqC78WP1FJwejx/nQF8BVgLWAW4QxKkUaMvABOBEyWNJIWopcraiLLvr84jVIOBsaTRKIANgKGSvpwf9wUGAm9HxGt524S8rbzNVUgh7Ppcz9LAXaRwMypf4y3geOBO0ojWn0gjUaeX1bVyvtbL+fH9wM9JwempiJgHzJP0XifPFwBDhgyhrq6uyKFVp1QqUV9fX+kyuo37V8Vq/cMTrebUyr3Y1NS0yAMGtfSuuuZWj18ghagdI6IB+DXwMHAacEVE7AuMA/qUnTO/daMR8QJwBDBG0jLANOCa3OYuwBjgVWA5Savk07Zoo83ZwD+A3fK5Z+Tr7wZMiIiv5rZGAQ3AqxGxEyk0/bysvdnA8pJWy4+35aNQ1/o5MDMzsy5UbSNOO0kqX5zdr70DI2KWpHOB+yQtCUwHrieFk9GSZpKC1cqdXTQi7pF0D2la70TgYkn3kabQLoiI+ZK+RxrdeosUSJ9t1cb8PK12e15s/jZpPdNywJWS5pFC1g+BF4HrJP2AtB7q1LJ2mvN05E2S5pPWU40AhnTWDzMzM1s0fZqbPUjRFST9BDg3IpokXQncFRFXVLqu9pRKpUHAC56qq17uX/UadJyn6qy61MrvqiubqhtcX18/fWHaqLYRp95sDjBJ0ruk0a3rKluOmZmZdTUHpy4SEb8BflPpOszMzKz71NLicDMzM7Nu5REnM7MeduPwgTW7fgtqe30auH+LO484mZmZmRXk4GRmZmZWkIOTmZmZWUEOTmZmZmYFOTiZmZmZFeTgZGZmZlaQg5OZmZlZQQ5OZmZmZgU5OJmZmZkV5OBkZmZmVpCDk5mZmVlBDk5mZmZmBTk4mZmZmRXk4GRmZmZWUN9KF2BmtrjZfcxMGHN7pcvoXu5f1bpx+MBKl9CrecTJzMzMrCAHJzMzM7OCHJzMzMzMCnJwMjMzMyuo08XhkhqA64GngWZgeeB5YJ+ImLswF5V0LfC7iBi/kOcPAp4EHivbfG9EnLow7XVwnc8DX4qIWyVdBmwC/BPoAwwAzomI/+vKa5qZmVnvVfRddfdGxJ4tDyRdDXwDuKFbqirm6Yho6OZrbA+sC9yaHx8bEWMBJK0ETJF0WUQ0d3MdZmZm1gss8McRSOoHrAa8KekS4HOk0Zc7I+KneWSmCRiUjxsREY9JOgL4LvAqsGpuayngUmBNYEng3Ii4TtJ44G/AEOAdYAKwM/BpYKdO6jsH2Do/vDoizs81Dch/hgHHAtuQpirPjYgxkg4H9gfmAxOB4/KfZSQ92MalBgLvR0SzpM8BFwH9gfeBgyNihqSfAt8CZgHLAD8FGoCtgGWBkcAOwN6k0bxrI2K0pG8Do4APgOnAfsCWwDl525vAPvla7T1/s4AVgZ0j4sOOnjMzMzMrpmhw2j7/Y7wqKVhcBPwdmBQR35XUH/gHKRgAvBgRh0g6CDhY0ijgKGCDfH4pH3cIMDsi9pW0HPCYpL/mfZMj4ihJY4F3I2JHSZcD2wJPAOvnmlrsA2wMDAa2yH2bKOnevP/eiDhP0i7A4Ij4Sq57kqS7gQOAIyNikqTDSNNxZwHrRsSfc5j5haQTgDVIU5fDc9u/AkZHxJ2SvgqcJekXwC7AZkA/4KmyWqfmvq0P/A8p6DUD90j6C7AXcF5EXCtpP9L06DeBm/K1vkEKRd/o4Pm7OiJubvcVzRobGzs7pKqVSqXOD6pi7p+ZdQffe+1boKk6SQOAu4EXSGt9NpO0HfA2UFd2/OP56wzgK6TprikR0QQgaXLevx5wD0BEzJH0NGn0BD5av/QvUkiBNNLSP3//iak6SXsDE/LU2QeSJgHr592Rv24A1JeFrqVIQegA4BhJZwMPkYJTa8dGxFhJuwJnk8JjS5vH54DYB5ib+zY5j/a8J+nRsnZaahmSr90SdlYE1gKOBn6SA9xU4Bbg58AJ+diXgYc7ef5artGhIUOGUFdX1/mBVahUKlFfX1/pMrqN+1fFavjDE6021Oq919TUtMgDBgv0rrqIeAP4DnAJ8EPgXxGxD2kKaRlJLWGj9Zqf50kjREtLWpI0MgQpFAwFyCMmG5BCWVttFDGVPE2XpwG3Ap7N++bnr9OAcTl0bU9a+P48cBBwaERsm+vbKp/ziecoIu4ghZmLytoclds8hLT2awopWC4hqa6sz+W1RD5uu3zuZaSRqYOBU3ItfUjTffsAl0XEdvmcg+n4+Wu5hpmZmXWRBf44goh4GhhNGi3ZNa//uZAUUD7TzjmzgJOAB4E7gX/nXRcBAyRNBMYDP4uI1xe0prLr3Aa8IOkhYBJwQ0Q81uqwW4F3JE0gTRk2R8QcUmB5JE/tvU4a0XkK2E3SnnzSacB6koYBxwAnS7oPuAJ4MiKeAu7IddxMWpv0Qat6/0YaQZqYR6TWJo0mTQbuzrUMBG4DHgEuz9fYPl+nS58/MzMz61if5ma/Iaw7SFoV2CMiLsgjTlOA7SPipQqXBkCpVBoEvOCpuurl/lWvQcd5qs56rxuHD6zZe69sqm5wfX399IVpw7/kt/vMJk3VPUKadrykt4QmMzMzWzgOTt0kIuaTFpybmZlZjfCvXDEzMzMryCNOZmY9rJbXkEBtr0+DxaN/1j6POJmZmZkV5OBkZmZmVpCDk5mZmVlBDk5mZmZmBTk4mZmZmRXk4GRmZmZWkIOTmZmZWUEOTmZmZmYFOTiZmZmZFeTgZGZmZlaQg5OZmZlZQQ5OZmZmZgU5OJmZmZkV5OBkZmZmVlDfShdgZra42X3MTBhze6XL6F7uX4+bftawSpewWPCIk5mZmVlBDk5mZmZmBTk4mZmZmRXk4GRmZmZWkIOTmZmZWUE1+646SccBOwDzgWbg+IgoSdoTOCIf9iHwBHBsRMyVNB14KZ/THygBP4qI9yWNB5YB3iUFzhXzeXf2WKfMzMysompyxEnS+sA3gB0jYidgFHCppF2Bg4CvR8RQYDtSqNq/7PSdIqIhIrYAXgHOKNu3X963DbAH8Mse6I6ZmZn1ErU64vQ68HngQEljI+IJSZsDtwA/joh/AUREs6SjI6K5nXbOBaYCP2pj3xrAmwCSNgBGA32AN4ADgbeB3wKbAjOBwcDXgVOAAfnPMOBYYBtSiD03IsZIOpwU5uYDEyPix5K+TQqAHwDTgf2A5YEr89e+wIkRca+kRuAZoCki9uroiWpsbOxod9UrlUqVLqFbuX9m1qIr7xffe+2ryeAUEbMlfQP4HnCypHeBE0jh5TkASVsCZwJLSZoREXu20c57kvqXbbpC0jxSKHsIOCBvvxg4MCKeljSSFIYmAwMiYnNJqwDPlrVzb0ScJ2kXYHBEfCVfZ5Kku3O7R0bEJEmHSeoL7AWcFxHXSmoJTScCd0fE+ZJWByZKWhNYFjgtIh7v7LkaMmQIdXV1nT+pVahUKlFfX1/pMrqN+1fFeuGHJ1r166r7pZbvvaampkUeMKjVqbq1gLcj4sCI+DzwHeBCYAYpPBERD0VEAzASGNhOO8sDc8o27RcRWwGnAauS1kMBrAdckNdBHQh8Jm97KF9rFjCtrJ3IXzcA6vN5Y4GlSCNZBwCHSrovP+4DHA1sk7dtRRqNWg+4P1/jZdIo1yqtrmFmZmZdpCaDE7AhcGHZaNEzwFvAb4BfSlqh7NgG0jqnthwLXNd6Y0T8nhSaWtY/BXn9Uz7ndqAR2BJA0orAOmVNzM9fpwHj8nnbA9cDz5PWYR0aEdsCG5OC0sHAKXlbH+BbpGnEofkaq5MWrL/R6hpmZmbWRWp1qu4mSesBD0t6hxQQfxwRf8rTXrdIgjTd9QQfXxx+l6QPgSXzvmPaucxRwJOSrgQOI03jLZn3jSRNze0i6UHSGqd3SeuTyt0KNEiaQJpeuzki5kh6CnhE0izgZeDhXOvdkt4gjYLdls+/VNIewNLAwRExL/fNzMzMulhNBieAiDiDj78jrmX7jcCN7ZwzqIP2Glo9/ifw2bJNH9svaV1gQkQcIWkAMAWYHREjytpoJk3Btb7WJcAlrTbfmv+09s0F6YeZmZktvFqdqusNZgB7SZpEWr80KiKaKlyTmZmZLYKaHXGqtIj4N7BbpeswMzOzruPgZGbWw24cPrBm3+4Ntf12dqjH7t0XAAAMIElEQVT9/lnHPFVnZmZmVpCDk5mZmVlBDk5mZmZmBTk4mZmZmRXk4GRmZmZWkIOTmZmZWUEOTmZmZmYFOTiZmZmZFeTgZGZmZlaQg5OZmZlZQQ5OZmZmZgU5OJmZmZkV5OBkZmZmVpCDk5mZmVlBDk5mZmZmBfWtdAFmZoub3cfMhDG3V7qM7uX+FTb9rGFd1pZ1P484mZmZmRXk4GRmZmZWkIOTmZmZWUEOTmZmZmYFddvicEkNwKERsWd+vAdwCvA68K+I+HbZsTMjYmAHbd1UfnyrfYOAayNii1bbL8vbxy5aT/7T3nTgJWA+sCSwLHBQRDzaFe2bmZlZ79cjI06S9gR+AnyVFD62lrRv0fPbC00VsFNENETEUOA4UhA0MzOzxUS3fxxBDkhHAjtExJuSIIWOn0kaFxH/KDt2BeAPwIC86fsR8VTLiJSkzYHfAnNII1fvk8LLKpJuAVYDnoyIg/L5h0v6ce7nyIh4TtKPgD2BecD9ETFK0inAVqRRpJHA2cAKwNLAsRExvo2urQG8meveFjgD+BD4O3BIvuYVwGeAGcA2EfEZSeOBWcCKwDDgAmBtUog9MSLGSzoD2D5vuyYi/lfS4cD+pBGviRHx4zza9gdgKaA5P19/k/QiMA2YGhE/6Oj1aWxs7Gh31SuVSpUuoVu5f2bVrzf+Pe+NNfUW3R2chgKrAyu1utYrwE9J/+jvXLb9eOCvEXGhpLWB/wO2Ltv/O2DfiJiSw8XqefvywAHAW8BzklbN2x+MiLMk7Qr8QtLJwH+TQtI84EZJX8vHTo2IoyR9ERgI7ACsCqxTdv27JPUnhaGxwDGS+gAXA1tHxOuSTgNGAMsAL0TEcEnrAlPK2rk6Im6WdBgwOyJGShoA3A98EdgP2CY/TyPyOQcAR0bEJEmHSeoL/AoYHRF/krRRfj43BT4HbBIRb3ziFWllyJAh1NXVdXZYVSqVStTX11e6jG7j/lWxWv+MI1sgve3veS3fe01NTYs8YNDdU3WvAjsC/wtcKek/14uIq4A5OTy02AA4MI/KXEwalSn3mYhoCSATyrY/HxFvRsR80kjUMnn7/fnrg4CAdYFJEfFBRDTnNr7YUlKuawppVOsa0mhQ+XO0U0RsThpJ+lS+1iqkka7rc907AZ8H1svXJSKmkUaZ/tP9sv7ums+7EeibA9SewJnAX4BP52MPAA6VdB9ptKtPvsb9+RpPkAITpDDWaWgyMzOzBdPdwem5iHg/In4DzAVOaLX/UOAYYLn8eBpwXkQ0kEaGrmp1/AxJ6+fvyxeDN7dz/c3z16FAY27/y5L65pGibYBn8jHzASRtACwXEcNIU2O/bqPdE0mjTocDs4F/ALvlus8AxuXrbZnbXBNYuez8+WX9vSaftwswBngHGA7sRZquGyFpDeAg0mL7bYGNSaNmU3PfyCNOM1u1b2ZmZl2oJz+O4EDS2p/tWjZExGzgaD4aIToD+O88AjOWFD7KHQ5cKukeUij6oJNrbiHpXuAHpLVKTwHXAw8Ak4HpwC2tznkWaJA0mRRkTmrdaB7ZGkkKUAOBo4DbJT2Ya2wkTZsNknQ/aR3W+23U93tg3TyK9CDwYkQ0Af8EngDuBe4iLah/Cngk9+d14GFS6DwyX+PCXJOZmZl1kz7Nze0N1vQ+ko4Aro+IWZJOB+ZGxKmVrqstkrYClo2Iu/J6rbERsWal62pRKpUGAS94jVP1cv+q16DjvMbJPtLbflddLd97ZWucBtfX109fmDaq7Zf8vkZaoP0OaSH4/hWupyPPA9fkBelLAUdUuB4zMzNbRFUVnCLiBuCGStdRRETMpGxa0szMzKpfVQUnM7NacOPwgTU7FQK1PdUDtd8/65h/V52ZmZlZQQ5OZmZmZgU5OJmZmZkV5OBkZmZmVpCDk5mZmVlBDk5mZmZmBfnjCBZfSwLMnTu30nV0q6ampkqX0K3cv+pVy30D96/a1Wr/yv7NW3Jh26iqX7liXadUKm0NTKh0HWZmZhUwtL6+fuLCnOgRp8XXI8BQ4FXgwwrXYmZm1hOWBFYj/Ru4UDziZGZmZlaQF4ebmZmZFeTgZGZmZlaQg5OZmZlZQQ5OZmZmZgU5OJmZmZkV5I8jWExI+hYwPCL2bmPfQcAhwDzg9Ii4TdLKwNXA0sArwAER8W5P1lyEpKWBK4FVgTnA/hExq2z/fwHH5Yd9gK2BIaR+3Qo8m/ddGBHX9VTdRXXWv3zMn4EBwAfAexGxi6S1gMuAZqAROCIi5vdk7UUU7N8vSa9bX+CiiLhY0krAM6S+AdwcEef3XOXtk7QEcAHwJaAJ+G5EPFe2v2rvNyjUvx8Ce+aHd0TEzyT1Af7BR/fbQxHxkx4su7AC/RsNfIX09xVgN2ApauD1k7QR8L9lh28BfBOYTC+939oi6cvA2RHR0Gr714GTSPfepflnSac/g1rziNNiQNL5wJm08XpLGgh8n/SDYGfgTEl1pL9cV0fEUOBx0g/63ugw4Klc5xXAieU7I2JsRDTkG+g20s00FdgEOLdlX28MTVmH/cvWArbO/dglbzsXODGf14f0w7036rB/krYD1oqILUnhaZSkFUmv3zVlr19v+iH+TaB/rvk44JyWHTVwv0HH/fsCsA+wFbAlsJOkDYE1gcfKXq9eGZqydvuXbQLsXNaXt6iR1y8inij7eflb4KaIGEvvvt8+RtKxwCVA/1bblwLOA3YCtgUOzvdjkZ+xH+PgtHh4kPSXoy2bAw9ERFP+AfAcsCHpH6mx+Zg7gR26vcqFU6hOSZ8F9gV+ljfVA8Mk3S/pD5KW6/ZKF06H/ZP0/4BPA7dKmijpa3lXPXBfe+f1Ip29fg8BB+bvm0kfXvcBqX+bSLpP0hhJq/VEsQX9p08RMQnYtGxftd9v0HH/ZgD/FREf5hHOpYD3Sa/X6pLGSbpDknq66AXQbv/yaM3awEWSHpB0YOtzqO7XDwBJnyL9rPx+3tSb77fW/g58u43t6wHPRcSbETEXmEj6EOgFfu08VVdDJI0Efthq8wERcZ2khnZOWx54q+zxHGCFVttbtlVUO/17jWJ1Hg2cFxEtv4BpMnBJRJQknQCcDBzTxSUvkIXsXz/S/xjPB1YCHpA0GegTEc0dnNfjFqZ/EfE+8H7+3+LlpKm6dyRNA0oRcY+kfYBfA3t0aweKa31PfSipb0TMa2Nfr73fOtBu/yLiA2B2npr7JfB4RDyT/2d/ZkSMkbQ1aWpks54vvZCOXr9Pkf6unUsK8eMkPUqNvH5l20YCYyJidn7cm++3j4mIGyUNamNXl917Dk41JCL+APxhAU97GygfbVkO+FfZ9vfKtlVUW/2TdBMf1d9mnfl/iV8DTijbfHNEtBx7M+kHQUUtZP9mAr/LP/Rel/Q4IKB8PVO1v34rAjcA4yPizLz5XqBlDcnNwKndUfNCan1PLVH2j1LV3G8d6Kh/SOoPXEr6R+jwvPlR0roSImKipNUllYf73qSj/r0LnN+yfknSvaS1QjXz+mX78PFg1Jvvt6I6u/fKt3XIU3U2GRgqqb+kFUjDmY3AA8Cu+Zhd6L2/ELhInUOAaRHxXtm2v0jaPH//VaDUfSUuks76twNwPYCkZUl9nQo8XjbKWLWvX164+VfSQs7TynZdAuyev+9tr99/+iRpC+Cpsn3Vfr9BB/3LI01/Av4WEYdERMvvwTwZ+EE+5kvAS700NEHHr986wERJS+ZR0K2Bx6iR1y9vWwGoi4gZZZt78/1W1FRgbUkrSeoHbENaCrDAr51HnBZTko4mzff+Ob9LZAIpSJ8QEe9LOh24PL8DaDbwiXfj9RIXkuqcCMwl1ynpF8ANETGZNALzfKvzDgN+I2kuadTm4J4reYF01r87Je0saRJplOn4iJgt6UfAxfkHxFTSiE1v1GH/SIuovwAclP8uAhxAWtR6qaTDgX8D3+3pwjtwM7CjpAdJC/MPqKH7DTroH2n6alugTlLLGxV+ApwFXClpGGnkaUSPV11cZ6/fVcAk0lq7KyJiSq28fhHxZ1I4nN7qnN58v3VI0t7AshFxUe7nX0j33qUR8bKkNn8GdcS/5NfMzMysIE/VmZmZmRXk4GRmZmZWkIOTmZmZWUEOTmZmZmYFOTiZmZmZFeTgZGZmZlaQg5OZmZlZQf8fKI2D+cA0mtQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Select regressor\n",
    "X_train_r, X_val_r, y_train_r, y_val_r = psf.split_set_reg(X, sel_tr_pred, datetime64, y)\n",
    "\n",
    "regressors = [KNeighborsRegressor,\n",
    "              SGDRegressor,\n",
    "              BaggingRegressor,\n",
    "              RandomForestRegressor,\n",
    "              ExtraTreesRegressor,\n",
    "              MLPRegressor,\n",
    "              LinearRegression]\n",
    "\n",
    "reg_names = ['KNeighborsRegressor',\n",
    "             'SGDRegressor',\n",
    "             'BaggingRegressor',\n",
    "             'RandomForestRegressor',\n",
    "             'ExtraTreesRegressor',\n",
    "             'MLPRegressor',\n",
    "             'LinearRegression'] \n",
    "\n",
    "# collect r2, rmse scores\n",
    "r_scores = []\n",
    "for reg in regressors:\n",
    "    try:\n",
    "        r_scores.append(psf.select_regressor(X_train_r, X_val_r, y_train_r, y_val_r, reg()))\n",
    "    except:\n",
    "        r_scores.append((0,0))\n",
    "\n",
    "# plot regression scores\n",
    "r2_rmse_df = pd.DataFrame(r_scores, reg_names, columns=['r2', 'rmse'])\n",
    "r2_rmse_df['r2'].sort_values().plot(kind=\"barh\", xlim=(-1,1), title=\"R-Squared\", figsize=(8,4), cmap=\"tab20\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x204a4f31c18>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "r2_rmse_df['rmse'].sort_values().plot(\n",
    "                    kind=\"barh\", xlim=(0,80)\n",
    "                    , title=\"RMSE\"\n",
    "                    , figsize=(8,4), color=\"#f5564e\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>r2</th>\n",
       "      <th>rmse</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ExtraTreesRegressor</th>\n",
       "      <td>0.844223</td>\n",
       "      <td>14.806937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BaggingRegressor</th>\n",
       "      <td>0.787653</td>\n",
       "      <td>17.287636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MLPRegressor</th>\n",
       "      <td>0.785857</td>\n",
       "      <td>17.360611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LinearRegression</th>\n",
       "      <td>0.773821</td>\n",
       "      <td>17.841822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RandomForestRegressor</th>\n",
       "      <td>0.749958</td>\n",
       "      <td>18.759433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SGDRegressor</th>\n",
       "      <td>0.669920</td>\n",
       "      <td>21.553715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KNeighborsRegressor</th>\n",
       "      <td>0.588828</td>\n",
       "      <td>24.056072</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             r2       rmse\n",
       "ExtraTreesRegressor    0.844223  14.806937\n",
       "BaggingRegressor       0.787653  17.287636\n",
       "MLPRegressor           0.785857  17.360611\n",
       "LinearRegression       0.773821  17.841822\n",
       "RandomForestRegressor  0.749958  18.759433\n",
       "SGDRegressor           0.669920  21.553715\n",
       "KNeighborsRegressor    0.588828  24.056072"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_rmse_df.sort_values('r2', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R-squared: \n",
      "\n",
      "('KNeighborsRegressor', 0.588827825768071)\n",
      "('SGDRegressor', 0.6699204942842644)\n",
      "('BaggingRegressor', 0.7876533086138048)\n",
      "('RandomForestRegressor', 0.7499575727324586)\n",
      "('ExtraTreesRegressor', 0.8442225063157036)\n",
      "('MLPRegressor', 0.7858567994819221)\n",
      "('LinearRegression', 0.773820795958221)\n",
      "\n",
      "\n",
      "RMSE: \n",
      "\n",
      "('KNeighborsRegressor', 24.056072490244826)\n",
      "('SGDRegressor', 21.55371548344396)\n",
      "('BaggingRegressor', 17.28763620601145)\n",
      "('RandomForestRegressor', 18.759433469218134)\n",
      "('ExtraTreesRegressor', 14.806936764371153)\n",
      "('MLPRegressor', 17.36061116991437)\n",
      "('LinearRegression', 17.841821958160967)\n"
     ]
    }
   ],
   "source": [
    "print(\"R-squared: \\n\")\n",
    "for row in r2_rmse_df['r2'].items():\n",
    "    print(row)\n",
    "\n",
    "print(\"\\n\\nRMSE: \\n\")\n",
    "for row in r2_rmse_df['rmse'].items():\n",
    "    print(row)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of X_train: (11690, 695) X_val: (1652, 695) y_train: (11690, 1) y_val: (1652, 1)\n",
      "\n",
      "Can't visualize feature importance for: MLPClassifier(activation='relu', alpha=0.0001, batch_size='auto', beta_1=0.9,\n",
      "       beta_2=0.999, early_stopping=True, epsilon=1e-08,\n",
      "       hidden_layer_sizes=(100, 30, 100), learning_rate='constant',\n",
      "       learning_rate_init=0.001, max_iter=700, momentum=0.9,\n",
      "       n_iter_no_change=10, nesterovs_momentum=True, power_t=0.5,\n",
      "       random_state=1, shuffle=True, solver='adam', tol=0.0001,\n",
      "       validation_fraction=0.1, verbose=False, warm_start=False)\n",
      "Shape of X_train: (862, 695) X_val: (88, 695) y_train: (862,) y_val: (88,)\n",
      "\n",
      "Confusion Matrix:\n",
      " [[1406   58]\n",
      " [ 100   88]] \n",
      " \n",
      "RSquared: 0.9065572911595611 \n",
      "RMSE: 11.678326153954297\n"
     ]
    }
   ],
   "source": [
    "# run variations of classifiers and regressors\n",
    "\n",
    "'''Classifiers'''\n",
    "# temp_clf = ExtraTreesClassifier(n_estimators=1500,\n",
    "#                                 criterion='gini',\n",
    "#                                 max_depth=100,\n",
    "#                                 min_samples_split=2,\n",
    "#                                 min_samples_leaf=1,\n",
    "#                                 min_weight_fraction_leaf=0.0,\n",
    "#                                 max_features=100,\n",
    "#                                 min_impurity_decrease=0.0,\n",
    "#                                 bootstrap=True,\n",
    "#                                 n_jobs=None,\n",
    "#                                 random_state=998)\n",
    "\n",
    "# temp_clf = BaggingClassifier(base_estimator=None,\n",
    "#                              n_estimators=50,\n",
    "#                              max_samples=1.0,\n",
    "#                              max_features=1.0,\n",
    "#                              bootstrap=True,\n",
    "#                              bootstrap_features=True, \n",
    "#                              oob_score=False,\n",
    "#                              warm_start=True,\n",
    "#                              random_state=997)\n",
    "\n",
    "temp_clf = MLPClassifier(hidden_layer_sizes=(100),\n",
    "                         activation='relu',\n",
    "                         solver='adam',\n",
    "                         alpha=0.0001,\n",
    "                         batch_size='auto',\n",
    "                         learning_rate='constant',\n",
    "                         learning_rate_init=0.001,\n",
    "                         max_iter=700,\n",
    "                         shuffle=True,\n",
    "                         random_state=1,\n",
    "                         tol=0.0001)\n",
    "\n",
    "'''Regressors'''\n",
    "temp_reg = ExtraTreesRegressor(n_estimators=900,\n",
    "                               max_features=50,\n",
    "                               max_depth=30,\n",
    "                               random_state=2)\n",
    "\n",
    "# temp_reg = MLPRegressor(hidden_layer_sizes=(100,10,30),\n",
    "#                          activation='relu',\n",
    "#                          solver='adam',\n",
    "#                          alpha=0.0001,\n",
    "#                          batch_size='auto',\n",
    "#                          learning_rate='constant',\n",
    "#                          learning_rate_init=0.001,\n",
    "#                          random_state=151)                    \n",
    "\n",
    "pred_df_cr, tr_pred_cr,  clf_importance, clf_report \\\n",
    "          , conf_mtrx, r2_reg, rmse_reg = psf.classify_regress(X, y,\n",
    "                                                               datetime64,\n",
    "                                                               temp_clf,\n",
    "                                                               temp_reg,\n",
    "                                                               test_size=0.2,\n",
    "                                                               use_smote=False)\n",
    "\n",
    "print(\"\\nConfusion Matrix:\\n {} \\n \\nRSquared: {} \\nRMSE: {}\".format(\n",
    "                                                            conf_mtrx,\n",
    "                                                            r2_reg,\n",
    "                                                            rmse_reg))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2873f3a6cf8>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Classifier Selection ##\n",
    "fin_clf = MLPClassifier(hidden_layer_sizes=(100),\n",
    "                        activation='relu',\n",
    "                        solver='adam',\n",
    "                        alpha=0.001,\n",
    "                        batch_size='auto',\n",
    "                        learning_rate='adaptive',\n",
    "                        learning_rate_init=0.001,\n",
    "                        power_t=0.5,\n",
    "                        max_iter=700,\n",
    "                        shuffle=True,\n",
    "                        random_state=17,\n",
    "                        tol=0.000001,\n",
    "                        verbose=False,\n",
    "                        warm_start=False,\n",
    "                        momentum=0.9,\n",
    "                        nesterovs_momentum=True,\n",
    "                        early_stopping=True,\n",
    "                        validation_fraction=0.1,\n",
    "                        beta_1=0.9,\n",
    "                        beta_2=0.999,\n",
    "                        epsilon=1e-08)\n",
    "\n",
    "# oversample\n",
    "from imblearn.over_sampling import SMOTE\n",
    "smote = SMOTE(ratio = 1.0, random_state=145)\n",
    "\n",
    "pipe = imbPipeline([\n",
    "    ('oversample', smote),\n",
    "    ('clf', fin_clf)])\n",
    "\n",
    "# cross-validate\n",
    "psf.cross_val_report(pipe, X, y, \"MLP Classifier with SMOTE\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of X_train: (11690, 695) X_val: (1652, 695) y_train: (11690, 1) y_val: (1652, 1)\n",
      "True positives:\n",
      " delayed_del    88\n",
      "prediction     88\n",
      "dtype: int64\n",
      "Shape of X_train: (862, 695) X_val: (88, 695) y_train: (862,) y_val: (88,)\n",
      "\n",
      "-R-Squared:  0.9065572911595611  \n",
      "-RMSE: 11.678326153954297\n"
     ]
    }
   ],
   "source": [
    "## Regressor Selection ##\n",
    "X_train_cf, X_val_cf, y_train_cf, y_val_cf = psf.split_set(X, y, 0.2, use_smote=True)\n",
    "\n",
    "# true predictions\n",
    "clf_true_pred = psf.collect_tr_pred(temp_clf, X_train_cf, X_val_cf, y_train_cf, y_val_cf)\n",
    "\n",
    "X_train_rf, X_val_rf, y_train_rf, y_val_rf = psf.split_set_reg(X, clf_true_pred, datetime64, y)\n",
    "\n",
    "fin_reg = ExtraTreesRegressor(n_estimators=900,\n",
    "                               max_features=100,\n",
    "                               max_depth=50,\n",
    "                               random_state=21)\n",
    "\n",
    "reg_score = []\n",
    "reg_score.append(psf.select_regressor(X_train_rf, X_val_rf, y_train_rf, y_val_rf, fin_reg))\n",
    "print(\"\\n-R-Squared: \", reg_score[0][0],\" \\n-RMSE:\", reg_score[0][1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['s_ts_num_log', 'sti', 'stc', 'stv', 'stm', 'stb', 'stml', 'trending_ts']) \n",
      " [(2065, 17), (2065, 5), (2065, 3), (2065, 6), (2065, 2), (2065, 1), (2065, 2), (2065, 6)]\n",
      "\n",
      " s_ts_num_log :\n",
      " manu_logistic      float64\n",
      "manu_infrast       float64\n",
      "manu_lpi_score     float64\n",
      "manu_customs       float64\n",
      "manu_tracking      float64\n",
      "manu_shipments     float64\n",
      "manu_timeliness    float64\n",
      "manu_fsi           float64\n",
      "dp_shipments       float64\n",
      "dp_timeliness      float64\n",
      "dp_tracking        float64\n",
      "dp_customs         float64\n",
      "dp_lpi_score       float64\n",
      "dp_infrast         float64\n",
      "dp_logistic        float64\n",
      "dp_count             int64\n",
      "dp_fsi             float64\n",
      "dtype: object\n",
      "\n",
      " sti :\n",
      " ln_itm_qty         int64\n",
      "ln_itm_val       float64\n",
      "pack_price       float64\n",
      "line_item_ins    float64\n",
      "real_w           float64\n",
      "dtype: object\n",
      "\n",
      " stc :\n",
      " country_qtycount      int64\n",
      "country_valmean     float64\n",
      "country_wmean       float64\n",
      "dtype: object\n",
      "\n",
      " stv :\n",
      " vendor_qtysum       int64\n",
      "vendor_qtymean    float64\n",
      "vendor_valsum     float64\n",
      "vendor_valmean    float64\n",
      "vendor_fcsum      float64\n",
      "vendor_wsum       float64\n",
      "dtype: object\n",
      "\n",
      " stm :\n",
      " manu_site_qtycount      int64\n",
      "manu_site_qtymean     float64\n",
      "dtype: object\n",
      "\n",
      " stb :\n",
      " brand_valmean    float64\n",
      "dtype: object\n",
      "\n",
      " stml :\n",
      " mol_test_qtycount      int64\n",
      "mol_test_itemmean    float64\n",
      "dtype: object\n",
      "\n",
      " trending_ts :\n",
      " delayed_ma           float64\n",
      "delay_days_ma        float64\n",
      "cumsum_delay         float64\n",
      "cummin_delay         float64\n",
      "cumsum_del_t         float64\n",
      "cummin_delay_days    float64\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "## Import Test Data ##\n",
    "ts_pred_names = ['s_ts_num_log',\n",
    "                 'sti', 'stc',\n",
    "                 'stv', 'stm',\n",
    "                 'stb', 'stml',\n",
    "                 'trending_ts']\n",
    "\n",
    "ts_file_dict = psf.import_sel_data(files_path + 'test/predictors/', ts_pred_names)\n",
    "\n",
    "print(ts_file_dict.keys(), \"\\n\", [df.shape for df in ts_file_dict.values()])\n",
    "\n",
    "for key, df in ts_file_dict.items():\n",
    "    print(\"\\n\", key, \":\\n\", df.dtypes)\n",
    "\n",
    "s_ts_num_log, sti, stc, stv, stm, stb, stml, trending_ts = [ts_file_dict[f] for f in ts_pred_names]\n",
    "\n",
    "# drop redundant columns\n",
    "entities =['country',\n",
    "           'manu_site',\n",
    "           'vendor',\n",
    "           'brand',\n",
    "           'mol_test']\n",
    "\n",
    "for s in [stc, stv, stm, stb, stml]:\n",
    "    for e in entities:\n",
    "        try:\n",
    "            s.drop(e, axis=1, inplace=True)\n",
    "        except:\n",
    "            pass\n",
    "        \n",
    "# import responses\n",
    "delayed_del_ts = pd.read_csv(files_path + 'test/responses/' + 'delayed_del_ts.csv')\n",
    "delayed_del_ts = delayed_del_ts.apply(lambda elem: pd.Categorical(elem))\n",
    "\n",
    "datetime64_ts = pd.read_csv(files_path + 'test/responses/' + 'datetime64_ts.csv')\n",
    "datetime64_ts['delay_t'] = datetime64_ts['delay_t'].apply(lambda elem: pd.to_timedelta(elem))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X shape:  (2065, 695) \n",
      "y shape: (2065, 1)\n"
     ]
    }
   ],
   "source": [
    "# transform test data\n",
    "test_data, delayed_del_ts = psf.transform_pipepline(s_ts_objects,\n",
    "                                                    s_ts_num_log,\n",
    "                                                    sti, stc, stv,\n",
    "                                                    stm, stb,stml,\n",
    "                                                    trending_ts,\n",
    "                                                    delayed_del_ts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Test classifier on unseen data ##\n",
    "\n",
    "psf.fix_nulls(test_data)\n",
    "X_test, y_test = test_data, delayed_del_ts\n",
    "\n",
    "clf_smote_pipe = imbPipeline([\n",
    "                            ('oversample', smote),\n",
    "                            ('clf', fin_clf)])\n",
    "\n",
    "plt.subplots(figsize=(6,5))\n",
    "psf.class_report(X, X_test, y, y_test, clf_smote_pipe)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "-R-Squared:  0.9026142397834055  \n",
      "-RMSE: 11.720131596094419\n"
     ]
    }
   ],
   "source": [
    "## Test regressor on unseen data ##\n",
    "\n",
    "test_pred = clf_smote_pipe.predict(X_test)\n",
    "ts_pred_df = y_test.copy()\n",
    "ts_pred_df['prediction'] = test_pred\n",
    "true_ts_pred = ts_pred_df[(ts_pred_df.delayed_del==1) & (ts_pred_df.delayed_del==ts_pred_df.prediction)]\n",
    "\n",
    "# construct data for reg test\n",
    "X_fr = X.loc[y[y==1].dropna().index.tolist(),:]\n",
    "X_test_fr = X_test.loc[true_ts_pred.index.tolist(),:]\n",
    "y_r_total = datetime64.loc[y[y==1].dropna().index.tolist(),['delay_t']]\n",
    "y_fr = y_r_total.delay_t.dt.days\n",
    "y_test_fr = datetime64_ts.loc[true_ts_pred.index.tolist(),['delay_t']]['delay_t'].dt.days\n",
    "\n",
    "# score\n",
    "test_reg_score = []\n",
    "test_reg_score.append(psf.select_regressor(X_fr, X_test_fr, y_fr, y_test_fr, fin_reg))\n",
    "print(\"\\n-R-Squared: \", test_reg_score[0][0],\" \\n-RMSE:\", test_reg_score[0][1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# export final model\n",
    "import pickle\n",
    "model_path = '.../USAID_Data/final model/'\n",
    "clf_file = 'MLP_Classifier.sav'\n",
    "pickle.dump(clf_smote_pipe, open(model_path + clf_file, 'wb'))\n",
    "reg_file = 'ExtraTrees_Regressor.sav'\n",
    "pickle.dump(fin_reg, open(model_path + reg_file, 'wb'))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}