PCA_n_comps.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Initialisations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-30T23:28:25.534235Z",
     "start_time": "2020-01-30T23:27:53.281270Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd ; pd.set_option('display.max_columns', 500) # dataframes\n",
    "import numpy as np # mathsy bits\n",
    "import ipywidgets as widgets # widgets\n",
    "\n",
    "from sklearn.model_selection import train_test_split #split data into train and test sets\n",
    "\n",
    "# feature selection + gridsearch\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "# models\n",
    "from sklearn.linear_model import Lars, ElasticNet, Lasso\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor, ExtraTreesRegressor\n",
    "from sklearn.ensemble import AdaBoostRegressor, GradientBoostingRegressor\n",
    "\n",
    "import joblib # saving models\n",
    "from datetime import datetime # get time for labelling saved models\n",
    "\n",
    "# visualization\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "import mpld3 # hover-over labels for plots\n",
    "mpld3.disable_notebook()\n",
    "\n",
    "train_scores = dict() ; test_scores  = dict() #to hold scores for viewing at the end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-30T23:28:25.567241Z",
     "start_time": "2020-01-30T23:28:25.550196Z"
    }
   },
   "outputs": [],
   "source": [
    "def grid_search_return_hp_dict(model, X_train_gs, y_train_gs, param_grid):\n",
    "    \"\"\"Run GridSearchCV on given model with defined param_grid, return best params as dict.\n",
    "    Pre-assign sklearn model to variable. Pass param_grid as dict.\"\"\"\n",
    "    print(str(model).split(sep='(')[0], \"grid search.\")\n",
    "    GS = GridSearchCV(model, param_grid, n_jobs=2)\n",
    "    GS.fit(X_train_gs, y_train_gs)\n",
    "    print(GS.best_params_)\n",
    "    return(GS.best_params_)\n",
    "\n",
    "def train_model(model, hyperparameters, \n",
    "                grid_search = False):\n",
    "    \"\"\"Wrapper for training models with optional grid search and feature selection.\n",
    "    Pass model with no brackets, ie Lars not Lars()\n",
    "    If grid_search = True, hyperparameters is the hp ranges dictionary. Outputs trained model, best_hp\n",
    "    If grid_search = False, hyperparameters is the hp dictionary. Outputs trained model\"\"\"\n",
    "    \n",
    "    if grid_search == True:\n",
    "        best_hp = grid_search_return_hp_dict(model(),\n",
    "                                             X_train, y_train, hyperparameters)\n",
    "        print(\"Grid search complete!\\nHyperparameters:\\n\", best_hp)\n",
    "        training_model = model(**best_hp).fit(X_train, y_train)\n",
    "        return training_model, best_hp\n",
    "\n",
    "    if grid_search == False:\n",
    "        print(\"No grid search\\nHyperparameters:\\n\", hyperparameters)\n",
    "        training_model = model(**hyperparameters).fit(X_train, y_train)\n",
    "        return training_model\n",
    "\n",
    "def produce_exp_vs_pred_df(model, codename): # generalise!\n",
    "    \n",
    "    pred_list = model.predict(pca_features_all)\n",
    "    \n",
    "    exp_vs_calc = pd.DataFrame(constants_first)\n",
    "    exp_vs_calc['Predicted'] = pred_list\n",
    "    exp_vs_calc.rename({'Kh_first':'Experimental'}, inplace=True, axis=1)\n",
    "    exp_vs_calc.to_csv('models/PCA_DRAGON_VPAS/%s'%(\"expvcalc_\" + str(model).split(sep='(')[0] + \".csv\"))\n",
    "    now = datetime.now()\n",
    "\n",
    "    dt_string = now.strftime(\"_%d_%m_%Y_%H_%M_%S\")\n",
    "    filename = \"models/PCA_DRAGON_VPAS/\" + str(model).split(sep='(')[0] + \"_\" + dt_string + \".joblib\"\n",
    "    print(str(model).split(sep='(')[0], \"run at:\", now, \". Saving to\", filename)\n",
    "    \n",
    "    joblib.dump(model, filename)\n",
    "    return exp_vs_calc\n",
    "\n",
    "def prediction_plot_scores(model_func, pred_df):\n",
    "    \"\"\"Print train+test scores, then plot scatter of predicted vs actual HLCs. Uses {X/y}_{train/test},\n",
    "    redefining these variables will change the output.\"\"\"\n",
    "    fig = plt.figure(figsize=(10, 10))\n",
    "    scatter = plt.scatter(pred_df['Experimental'], pred_df['Predicted'])\n",
    "    plt.xlabel('Experimental')\n",
    "    plt.ylabel('Predicted')\n",
    "    plt.title('%s predictions of HLCs' %str(model_func).split(sep='(')[0])\n",
    "    labels = ['{}'.format(i) for i in species_names]\n",
    "    tooltip = mpld3.plugins.PointLabelTooltip(scatter, labels=labels)\n",
    "    mpld3.plugins.connect(fig, tooltip)\n",
    "    plt.plot([-30, 10], [-30, 10], c='red')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DRAGON descriptors\n",
    "## Bringing in data and splitting it into parts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-30T23:39:31.447582Z",
     "start_time": "2020-01-30T23:39:30.803870Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Input Shape (2075, 1480)\n",
      "Removed NaN, new shape (2068, 1480)\n"
     ]
    }
   ],
   "source": [
    "csv = pd.read_csv('filtered_organics_desc_kh.csv') # contains VP/AS HLCs\n",
    "\n",
    "print(\"Input Shape\", csv.shape) #input shape\n",
    "\n",
    "csv.dropna(axis=0, inplace=True)\n",
    "\n",
    "print(\"Removed NaN, new shape\", csv.shape) #removed NaN shape\n",
    "\n",
    "smiles_strings = csv.pop('Unnamed: 0')\n",
    "species_names = csv.pop('0')\n",
    "constants_mean = csv.pop('Kh_mean')\n",
    "constants_first = csv.pop('Kh_first')\n",
    "\n",
    "varying_columns = csv[['Varying_1', 'Varying_2', 'Varying_3', 'Varying_4',\n",
    " 'Varying_5', 'Varying_6', 'Varying_7', 'Varying_8', 'Varying_9']] # popping one-hot encoding columns\n",
    "\n",
    "dragon_features = csv.drop(['Varying_1', 'Varying_2', 'Varying_3', 'Varying_4',\n",
    " 'Varying_5', 'Varying_6', 'Varying_7', 'Varying_8', 'Varying_9'], axis=1) #seperating features\n",
    "\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(dragon_features, constants_first, test_size=0.3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-30T23:44:15.450054Z",
     "start_time": "2020-01-30T23:44:14.917639Z"
    }
   },
   "outputs": [],
   "source": [
    "pca_standard_x_train = StandardScaler().fit_transform(X_train)\n",
    "pca = PCA(n_components=250)\n",
    "pca_fit = pca.fit(pca_standard_x_train)\n",
    "pca_components_train = pca_fit.transform(pca_standard_x_train)\n",
    "pca_features_train = pd.DataFrame(data = pca_components_train)\n",
    "X_train = pca_features_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-30T23:44:17.906949Z",
     "start_time": "2020-01-30T23:44:17.675137Z"
    }
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(np.cumsum(pca_fit.explained_variance_ratio_))\n",
    "plt.xlabel('Number of principal components')\n",
    "plt.ylabel('Total explained variance ratio')\n",
    "plt.title('Variance explained by components, n=100')\n",
    "\n",
    "plt.savefig('scree_plot100.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-30T23:39:38.818090Z",
     "start_time": "2020-01-30T23:39:35.361201Z"
    },
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "None"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "None"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "None"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "None"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "None"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for n_comp in [50, 100, 150, 200, 250]:\n",
    "    pca_standard_x_train = StandardScaler().fit_transform(X_train)\n",
    "    pca = PCA(n_components=n_comp)\n",
    "    pca_fit = pca.fit(pca_standard_x_train)\n",
    "    plt.plot(np.cumsum(pca_fit.explained_variance_ratio_))\n",
    "    plt.xlabel('Number of principal components')\n",
    "    plt.ylabel('Total explained variance ratio')\n",
    "    plt.title('Variance explained by components, n=%s'%str(n_comp))\n",
    "    \n",
    "    plt.savefig('scree_plot_%s.png'%str(n_comp))\n",
    "    display(plt.show())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-30T23:44:21.833737Z",
     "start_time": "2020-01-30T23:44:21.756532Z"
    }
   },
   "outputs": [],
   "source": [
    "pca_standard_x_test = StandardScaler().fit_transform(X_test)\n",
    "pca_components_test = pca_fit.transform(pca_standard_x_test)\n",
    "\n",
    "pca_features_test = pd.DataFrame(data = pca_components_test)\n",
    "X_test = pca_features_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-30T23:29:33.510278Z",
     "start_time": "2020-01-30T23:29:33.329800Z"
    }
   },
   "outputs": [],
   "source": [
    "pca_standard_x_all = StandardScaler().fit_transform(dragon_features)\n",
    "pca_components_all = pca_fit.transform(pca_standard_x_all)\n",
    "\n",
    "pca_features_all = pd.DataFrame(data = pca_components_all)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## GradientBoosting\n",
    "### Model-chosen features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-30T23:45:09.532942Z",
     "start_time": "2020-01-30T23:44:55.221005Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No grid search\n",
      "Hyperparameters:\n",
      " {'learning_rate': 0.05, 'loss': 'huber', 'max_depth': 6, 'max_features': 0.3, 'min_samples_leaf': 3, 'n_estimators': 500}\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "too many values to unpack (expected 2)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-21-e2ff3242b84d>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      7\u001b[0m gbr_model, gbr_hp = train_model(model = GradientBoostingRegressor,\n\u001b[0;32m      8\u001b[0m             \u001b[0mgrid_search\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 9\u001b[1;33m            hyperparameters=hp_dict)\n\u001b[0m\u001b[0;32m     10\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     11\u001b[0m exp_vs_calc_gbr_model = produce_exp_vs_pred_df(gbr_model,\n",
      "\u001b[1;31mValueError\u001b[0m: too many values to unpack (expected 2)"
     ]
    }
   ],
   "source": [
    "hp_dict = {'learning_rate': 0.05, \n",
    "                            'loss': 'huber', \n",
    "                            'max_depth': 6, \n",
    "                            'max_features': 0.3, \n",
    "                            'min_samples_leaf': 3, \n",
    "                            'n_estimators': 500}\n",
    "gbr_model, gbr_hp = train_model(model = GradientBoostingRegressor,\n",
    "            grid_search=False,\n",
    "           hyperparameters=hp_dict)\n",
    "\n",
    "exp_vs_calc_gbr_model = produce_exp_vs_pred_df(gbr_model,\n",
    "                                         \"GradBoost\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Decision Tree\n",
    "### Model-Chosen Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-27T10:38:26.588891Z",
     "start_time": "2020-01-27T10:38:26.578864Z"
    }
   },
   "outputs": [],
   "source": [
    "dtr_param_grid = {\n",
    "    'criterion': ['mse', 'friedman_mse', 'mae'], #function measuring quality of a split\n",
    "    'max_depth': [5, 10, 20] , # max depth of tree\n",
    "    'max_features': [5, 10, 15], # N features to be considered when looking for split\n",
    "    'max_leaf_nodes': [None, 10, 15, 20, 30], #grows tree with N nodes in best-first fashion\n",
    "    'min_impurity_decrease': [0.0, 0.1],\n",
    "    'min_samples_leaf': [1, 2, 3], \n",
    "    'min_samples_split': [1.0, 2, 3],\n",
    "    'min_weight_fraction_leaf': [0.0, 0.1, 0.5] , \n",
    "    'splitter': ['best', 'random']\n",
    "} #hyperparameters for dtr gridsearch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-27T10:44:12.557691Z",
     "start_time": "2020-01-27T10:38:26.591899Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DecisionTreeRegressor grid search.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\jtd1g16\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:1978: FutureWarning: The default value of cv will change from 3 to 5 in version 0.22. Specify it explicitly to silence this warning.\n",
      "  warnings.warn(CV_WARNING, FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'criterion': 'mse', 'max_depth': 10, 'max_features': 15, 'max_leaf_nodes': 30, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 3, 'min_samples_split': 3, 'min_weight_fraction_leaf': 0.0, 'splitter': 'best'}\n",
      "Grid search complete!\n",
      "Hyperparameters:\n",
      " {'criterion': 'mse', 'max_depth': 10, 'max_features': 15, 'max_leaf_nodes': 30, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 3, 'min_samples_split': 3, 'min_weight_fraction_leaf': 0.0, 'splitter': 'best'}\n",
      "DecisionTreeRegressor run at: 2020-01-27 10:44:12.548657 . Saving to models/PCA_DRAGON_VPAS/DecisionTreeRegressor__27_01_2020_10_44_12.joblib\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\jtd1g16\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py:814: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
      "  DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "dtr_model, dtr_hp = train_model(model = DecisionTreeRegressor,\n",
    "            grid_search=True,\n",
    "           hyperparameters=dtr_param_grid)\n",
    "\n",
    "\n",
    "exp_vs_calc_dtr_model = produce_exp_vs_pred_df(dtr_model,\n",
    "                                         \"Decision Tree\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## AdaBoost Regressor\n",
    "### Model Chosen Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-27T10:44:12.569713Z",
     "start_time": "2020-01-27T10:44:12.561692Z"
    }
   },
   "outputs": [],
   "source": [
    "base_models = [ExtraTreesRegressor(n_estimators= 5,\n",
    "                                   criterion= 'mse',\n",
    "                                   max_features = 'log2'),\n",
    "               RandomForestRegressor(n_estimators= 5,\n",
    "                                     criterion= 'mse',\n",
    "                                     max_features = 'sqrt',\n",
    "                                     min_samples_split = 3),\n",
    "              GradientBoostingRegressor(),\n",
    "              DecisionTreeRegressor(),\n",
    "              Lars(),\n",
    "              ElasticNet()]\n",
    "\n",
    "ada_param_grid = {\n",
    "    'base_estimator' : base_models, \n",
    "    'learning_rate' : [0.3, 0.5, 0.8, 1], \n",
    "    'loss' : ['linear', 'square', 'exponential'], \n",
    "    'n_estimators' : [50, 100]\n",
    "} #hyperparameters for adaboost gridsearch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-27T11:09:47.468825Z",
     "start_time": "2020-01-27T10:44:12.573724Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AdaBoostRegressor grid search.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\jtd1g16\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:1978: FutureWarning: The default value of cv will change from 3 to 5 in version 0.22. Specify it explicitly to silence this warning.\n",
      "  warnings.warn(CV_WARNING, FutureWarning)\n",
      "C:\\Users\\jtd1g16\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py:814: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
      "  DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'base_estimator': GradientBoostingRegressor(alpha=0.9, criterion='friedman_mse', init=None,\n",
      "                          learning_rate=0.1, loss='ls', max_depth=3,\n",
      "                          max_features=None, max_leaf_nodes=None,\n",
      "                          min_impurity_decrease=0.0, min_impurity_split=None,\n",
      "                          min_samples_leaf=1, min_samples_split=2,\n",
      "                          min_weight_fraction_leaf=0.0, n_estimators=100,\n",
      "                          n_iter_no_change=None, presort='auto',\n",
      "                          random_state=None, subsample=1.0, tol=0.0001,\n",
      "                          validation_fraction=0.1, verbose=0, warm_start=False), 'learning_rate': 0.5, 'loss': 'exponential', 'n_estimators': 50}\n",
      "Grid search complete!\n",
      "Hyperparameters:\n",
      " {'base_estimator': GradientBoostingRegressor(alpha=0.9, criterion='friedman_mse', init=None,\n",
      "                          learning_rate=0.1, loss='ls', max_depth=3,\n",
      "                          max_features=None, max_leaf_nodes=None,\n",
      "                          min_impurity_decrease=0.0, min_impurity_split=None,\n",
      "                          min_samples_leaf=1, min_samples_split=2,\n",
      "                          min_weight_fraction_leaf=0.0, n_estimators=100,\n",
      "                          n_iter_no_change=None, presort='auto',\n",
      "                          random_state=None, subsample=1.0, tol=0.0001,\n",
      "                          validation_fraction=0.1, verbose=0, warm_start=False), 'learning_rate': 0.5, 'loss': 'exponential', 'n_estimators': 50}\n",
      "AdaBoostRegressor run at: 2020-01-27 11:09:47.234199 . Saving to models/PCA_DRAGON_VPAS/AdaBoostRegressor__27_01_2020_11_09_47.joblib\n"
     ]
    }
   ],
   "source": [
    "ada_model, ada_hp = train_model(model = AdaBoostRegressor,\n",
    "            grid_search=True,\n",
    "           hyperparameters=ada_param_grid)\n",
    "\n",
    "exp_vs_calc_ada_model = produce_exp_vs_pred_df(ada_model,\n",
    "                                               \"AdaBoost\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## LASSO\n",
    "### Model Chosen Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-27T11:09:47.479855Z",
     "start_time": "2020-01-27T11:09:47.471833Z"
    }
   },
   "outputs": [],
   "source": [
    "lasso_param_grid = {\n",
    "    'alpha':[0.2, 0.4, 0.6, 0.8],\n",
    "    'max_iter':[1000, 5000, 10000, 50000],\n",
    "    'selection':['cyclic', 'random']\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-27T11:09:51.448274Z",
     "start_time": "2020-01-27T11:09:47.484868Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lasso grid search.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\jtd1g16\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:1978: FutureWarning: The default value of cv will change from 3 to 5 in version 0.22. Specify it explicitly to silence this warning.\n",
      "  warnings.warn(CV_WARNING, FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'alpha': 0.6, 'max_iter': 5000, 'selection': 'random'}\n",
      "Grid search complete!\n",
      "Hyperparameters:\n",
      " {'alpha': 0.6, 'max_iter': 5000, 'selection': 'random'}\n",
      "Lasso run at: 2020-01-27 11:09:51.441253 . Saving to models/PCA_DRAGON_VPAS/Lasso__27_01_2020_11_09_51.joblib\n"
     ]
    }
   ],
   "source": [
    "lasso_model, lasso_hp = train_model(model = Lasso,\n",
    "            grid_search=True,\n",
    "           hyperparameters=lasso_param_grid)\n",
    "\n",
    "\n",
    "exp_vs_calc_lasso_model = produce_exp_vs_pred_df(lasso_model,\n",
    "                                                 \"LASSO\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Forest Regressor\n",
    "### Model Chosen Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-27T11:09:51.457297Z",
     "start_time": "2020-01-27T11:09:51.451282Z"
    }
   },
   "outputs": [],
   "source": [
    "random_forest_param_grid = {\n",
    "    'max_depth': [10, 20, 30, 40, 50, 60, 70, 80, 90, None],\n",
    "    'max_features': ['auto', 'sqrt'],\n",
    "    'min_samples_leaf': [1, 2, 4],\n",
    "    'min_samples_split': [2, 5, 10],\n",
    "    'n_estimators': [200, 400, 600, 800, 1000, 1200, 1400, 1600]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-27T19:18:47.085499Z",
     "start_time": "2020-01-27T11:09:51.460305Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RandomForestRegressor grid search.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\jtd1g16\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:1978: FutureWarning: The default value of cv will change from 3 to 5 in version 0.22. Specify it explicitly to silence this warning.\n",
      "  warnings.warn(CV_WARNING, FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'max_depth': 20, 'max_features': 'auto', 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 800}\n",
      "Grid search complete!\n",
      "Hyperparameters:\n",
      " {'max_depth': 20, 'max_features': 'auto', 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 800}\n",
      "RandomForestRegressor run at: 2020-01-27 19:18:46.645327 . Saving to models/PCA_DRAGON_VPAS/RandomForestRegressor__27_01_2020_19_18_46.joblib\n"
     ]
    }
   ],
   "source": [
    "random_forest_model, rfr_hp = train_model(model = RandomForestRegressor,\n",
    "            grid_search=True,\n",
    "           hyperparameters=random_forest_param_grid)\n",
    "exp_vs_calc_random_forest_model = produce_exp_vs_pred_df(random_forest_model,\n",
    "                                                         \"RandomForest\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## LARS - Least angle regression\n",
    "### Model chosen features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-27T19:18:47.097532Z",
     "start_time": "2020-01-27T19:18:47.089511Z"
    }
   },
   "outputs": [],
   "source": [
    "lars_param_grid = {'n_nonzero_coefs': [0, 5, 10, 15]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-27T19:19:11.981037Z",
     "start_time": "2020-01-27T19:18:47.100540Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lars grid search.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\jtd1g16\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:1978: FutureWarning: The default value of cv will change from 3 to 5 in version 0.22. Specify it explicitly to silence this warning.\n",
      "  warnings.warn(CV_WARNING, FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'n_nonzero_coefs': 15}\n",
      "Grid search complete!\n",
      "Hyperparameters:\n",
      " {'n_nonzero_coefs': 15}\n",
      "Lars run at: 2020-01-27 19:19:11.974017 . Saving to models/PCA_DRAGON_VPAS/Lars__27_01_2020_19_19_11.joblib\n"
     ]
    }
   ],
   "source": [
    "lars_model, lars_hp = train_model(model = Lars,\n",
    "            grid_search=True,\n",
    "           hyperparameters= lars_param_grid)\n",
    "exp_vs_calc_lars_model = produce_exp_vs_pred_df(lars_model, \"LARS\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Elastic Net (linear regression l1 l2 norm regularization)\n",
    "### Model chosen features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-27T19:19:11.989058Z",
     "start_time": "2020-01-27T19:19:11.983042Z"
    }
   },
   "outputs": [],
   "source": [
    "net_param_grid = {\n",
    "    'alpha':[0.1, 0.3, 0.5, 0.8, 1.0],\n",
    "    'l1_ratio':[0.0, 0.2, 0.4, 0.6, 0.8, 1.0],\n",
    "    'fit_intercept':[True, False],\n",
    "    'max_iter': [1000, 3000, 5000],\n",
    "    'tol':[0.0001, 0.001, 0.01],\n",
    "    'selection':['cyclic', 'random']\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-27T19:20:09.942360Z",
     "start_time": "2020-01-27T19:19:11.991063Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ElasticNet grid search.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\jtd1g16\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:1978: FutureWarning: The default value of cv will change from 3 to 5 in version 0.22. Specify it explicitly to silence this warning.\n",
      "  warnings.warn(CV_WARNING, FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'alpha': 0.8, 'fit_intercept': True, 'l1_ratio': 0.6, 'max_iter': 1000, 'selection': 'random', 'tol': 0.01}\n",
      "Grid search complete!\n",
      "Hyperparameters:\n",
      " {'alpha': 0.8, 'fit_intercept': True, 'l1_ratio': 0.6, 'max_iter': 1000, 'selection': 'random', 'tol': 0.01}\n",
      "ElasticNet run at: 2020-01-27 19:20:09.935340 . Saving to models/PCA_DRAGON_VPAS/ElasticNet__27_01_2020_19_20_09.joblib\n"
     ]
    }
   ],
   "source": [
    "net_model, net_hp = train_model(model = ElasticNet,\n",
    "            grid_search=True,\n",
    "           hyperparameters= net_param_grid)\n",
    "exp_vs_calc_net_model = produce_exp_vs_pred_df(net_model, \"ElasticNet\")"
   ]
  }
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