feature_imp_plot_rando_for.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.dummy import DummyClassifier\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import precision_score\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.ensemble import VotingClassifier\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import plot_confusion_matrix\n",
    "from imblearn.over_sampling import SMOTE\n",
    "import warnings\n",
    "warnings.simplefilter(action='ignore', category=FutureWarning)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('Datasets/final_data.csv', index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Date', 'Location', 'TempLow', 'TempHigh', 'Rainfall', 'Evaporation',\n",
       "       'Sunshine', 'WindGustSpeed', 'WindSpeed9am', 'WindSpeed3pm',\n",
       "       'Humidity9am', 'Humidity3pm', 'Pressure9am', 'Pressure3pm', 'Cloud9am',\n",
       "       'Cloud3pm', 'Temp9am', 'Temp3pm', 'RainToday', 'RainTomorrow',\n",
       "       'Lat_Lon', 'Mean_Rainfall', 'WDGust_N', 'WDGust_NE', 'WDGust_NW',\n",
       "       'WDGust_S', 'WDGust_SE', 'WDGust_SW', 'WDGust_W', 'WD9am_N', 'WD9am_NE',\n",
       "       'WD9am_NW', 'WD9am_S', 'WD9am_SE', 'WD9am_SW', 'WD9am_W', 'WD3pm_N',\n",
       "       'WD3pm_NE', 'WD3pm_NW', 'WD3pm_S', 'WD3pm_SE', 'WD3pm_SW', 'WD3pm_W'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((112629, 39), (28158, 39))"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fucntion to split data into train and test data\n",
    "def tt_split(df):\n",
    "    X = df.drop(['RainTomorrow'], axis=1)\n",
    "    y = df['RainTomorrow']\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1)\n",
    "    return X_train, X_test, y_train, y_test\n",
    "# Split training data into train and test data\n",
    "model_df = df.drop(['Date', 'Location', 'Lat_Lon'], axis=1)\n",
    "X_train, X_test, y_train, y_test = tt_split(model_df)\n",
    "X_train.shape, X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(175218, 39)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Function to perform SMOTE balancing\n",
    "def smt(X_train, y_train):\n",
    "    smt = SMOTE(n_jobs=-1, random_state=1)\n",
    "    X_smt, y_smt = smt.fit_sample(X_train, y_train)\n",
    "    return X_smt, y_smt\n",
    "# Perform SMOTE balancing on train and scaled train data\n",
    "X_train_smt, y_train_smt = smt(X_train, y_train)\n",
    "X_train_smt.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.8604304282974643, 0.5892250444911826, 0.6495452113429642)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Function to fit a random forest model\n",
    "def rf(X_train, y_train):\n",
    "    rf = RandomForestClassifier(n_jobs=-1, random_state=1, n_estimators=1000, oob_score=True)\n",
    "    rf_fit = rf.fit(X_train, y_train)\n",
    "    return rf_fit\n",
    "# Fit random forest model on SMOTE train data, predict on SMOTE test data\n",
    "rf_fit_smt = rf(X_train_smt, y_train_smt)\n",
    "rf_pred_smt = rf_fit_smt.predict(X_test)\n",
    "# F1 score of scaled random forest predictions\n",
    "accuracy_score(rf_pred_smt, y_test), precision_score(rf_pred_smt, y_test), f1_score(\n",
    "    rf_pred_smt, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf_feat_imp = pd.Series(rf_fit_smt.feature_importances_, index=X_train.columns).sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 2304x1800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(32,25))\n",
    "ax = rf_feat_imp.plot(kind='barh')\n",
    "ax.set()\n",
    "# ax = sns.barplot(x='Mean_Rainfall', y='Location', data=new_df)\n",
    "sns.set(font_scale=1.9)\n",
    "plt.title('Feature Importance')\n",
    "plt.ylabel('Features')\n",
    "plt.xlabel('Feature Rating')\n",
    "plt.savefig('rf_feat_imp.png', dpi = 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "43"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "pcafeat = list(df.drop(['Date', 'Location', 'Lat_Lon', 'RainToday', 'RainTomorrow', 'WDGust_N', 'WDGust_NE',\n",
    "                        'WDGust_NW','WDGust_S', 'WDGust_SE', 'WDGust_SW', 'WDGust_W', 'WD9am_N', 'WD9am_NE',\n",
    "                        'WD9am_NW', 'WD9am_S', 'WD9am_SE', 'WD9am_SW', 'WD9am_W', 'WD3pm_N','WD3pm_NE',\n",
    "                        'WD3pm_NW', 'WD3pm_S', 'WD3pm_SE', 'WD3pm_SW', 'WD3pm_W'], axis=1).columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(pcafeat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "# Separating out the features\n",
    "x = df.loc[:, pcafeat].values\n",
    "# Separating out the target\n",
    "y = df.loc[:,['RainTomorrow']].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Standardizing the features\n",
    "x = StandardScaler().fit_transform(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create principal components \n",
    "from sklearn.decomposition import PCA\n",
    "pca = PCA()\n",
    "principalComponents = pca.fit_transform(x)\n",
    "principalDf = pd.DataFrame(data = principalComponents)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.30857134, 0.18267124, 0.12218826, 0.07421616, 0.06439738,\n",
       "       0.05106842, 0.04114733, 0.03236833, 0.02879131, 0.02546516,\n",
       "       0.02471678, 0.0176661 , 0.01633187, 0.00617627, 0.00202557,\n",
       "       0.00147102, 0.00072748])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.explained_variance_ratio_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5.24575004, 3.1054331 , 2.07721519, 1.26168361, 1.09476316,\n",
       "       0.86816927, 0.69950954, 0.55026547, 0.48945575, 0.43291079,\n",
       "       0.42018822, 0.30032589, 0.27764372, 0.10499734, 0.03443493,\n",
       "       0.02500744, 0.01236728])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.explained_variance_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'proportion of explained variance')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "dark"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "index = np.arange(17)\n",
    "plt.bar(index, pca.explained_variance_ratio_)#[:11])\n",
    "plt.title('Scree plot for PCA')\n",
    "plt.xlabel('Num of components')\n",
    "plt.ylabel('proportion of explained variance')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TempHigh         0.903235\n",
       "Temp3pm          0.890137\n",
       "Temp9am          0.879220\n",
       "TempLow          0.740695\n",
       "Evaporation      0.593640\n",
       "Sunshine         0.456622\n",
       "WindGustSpeed    0.308373\n",
       "WindSpeed3pm     0.266630\n",
       "WindSpeed9am     0.251287\n",
       "Mean_Rainfall    0.132185\n",
       "Rainfall        -0.068153\n",
       "Cloud3pm        -0.314849\n",
       "Cloud9am        -0.324165\n",
       "Pressure9am     -0.502581\n",
       "Humidity3pm     -0.535712\n",
       "Pressure3pm     -0.563135\n",
       "Humidity9am     -0.665316\n",
       "dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "structure_loading_1 = pca.components_[0]* np.sqrt(pca.explained_variance_[0])\n",
    "str_loading_1 = pd.Series(structure_loading_1, index=pcafeat)\n",
    "str_loading_1.sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Cloud3pm         0.594543\n",
       "Cloud9am         0.592189\n",
       "Humidity3pm      0.573689\n",
       "WindGustSpeed    0.527030\n",
       "WindSpeed3pm     0.459770\n",
       "Rainfall         0.420825\n",
       "WindSpeed9am     0.383194\n",
       "TempLow          0.343855\n",
       "Humidity9am      0.305060\n",
       "Mean_Rainfall    0.291215\n",
       "Temp9am          0.144595\n",
       "Evaporation     -0.075315\n",
       "TempHigh        -0.103767\n",
       "Temp3pm         -0.153012\n",
       "Sunshine        -0.484238\n",
       "Pressure3pm     -0.538752\n",
       "Pressure9am     -0.614378\n",
       "dtype: float64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "structure_loading_2 = pca.components_[1]* np.sqrt(pca.explained_variance_[1])\n",
    "str_loading_2 = pd.Series(structure_loading_2, index=pcafeat)\n",
    "str_loading_2.sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "WindGustSpeed    0.642837\n",
       "WindSpeed9am     0.613688\n",
       "WindSpeed3pm     0.569836\n",
       "Sunshine         0.212254\n",
       "Evaporation      0.062278\n",
       "Pressure3pm     -0.023289\n",
       "Pressure9am     -0.066459\n",
       "Rainfall        -0.115463\n",
       "Cloud3pm        -0.165104\n",
       "Cloud9am        -0.230513\n",
       "Humidity3pm     -0.288619\n",
       "TempHigh        -0.293456\n",
       "Temp3pm         -0.295303\n",
       "Humidity9am     -0.325115\n",
       "Temp9am         -0.345097\n",
       "Mean_Rainfall   -0.404359\n",
       "TempLow         -0.412447\n",
       "dtype: float64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "structure_loading_3 = pca.components_[2]* np.sqrt(pca.explained_variance_[2])\n",
    "str_loading_3 = pd.Series(structure_loading_3, index=pcafeat)\n",
    "str_loading_3.sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Mean_Rainfall    0.693265\n",
       "Pressure3pm      0.346626\n",
       "Pressure9am      0.331869\n",
       "WindSpeed3pm     0.308800\n",
       "Humidity3pm      0.303398\n",
       "Sunshine         0.249286\n",
       "WindSpeed9am     0.242114\n",
       "TempLow          0.173577\n",
       "Rainfall         0.148922\n",
       "Temp9am          0.115165\n",
       "Humidity9am      0.101733\n",
       "WindGustSpeed    0.057606\n",
       "Evaporation     -0.064450\n",
       "TempHigh        -0.071886\n",
       "Temp3pm         -0.072484\n",
       "Cloud9am        -0.255961\n",
       "Cloud3pm        -0.288585\n",
       "dtype: float64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "structure_loading_4 = pca.components_[3]* np.sqrt(pca.explained_variance_[3])\n",
    "str_loading_4 = pd.Series(structure_loading_4, index=pcafeat)\n",
    "str_loading_4.sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}