2021-05-28_model_step.ipynb

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  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np \n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from collections import Counter\n",
    "plt.style.use(\"dark_background\")\n",
    "import pickle\n",
    "import lightgbm as lgb\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from mlxtend.classifier import StackingClassifier\n",
    "\n",
    "from sklearn.ensemble import RandomForestClassifier, VotingClassifier\n",
    "from sklearn import svm\n",
    "from sklearn.model_selection import train_test_split, cross_val_score\n",
    "from sklearn.metrics import precision_score, confusion_matrix, f1_score, accuracy_score, recall_score, classification_report, roc_auc_score, auc, roc_curve\n",
    "from sklearn.preprocessing import label_binarize, normalize\n",
    "\n",
    "from utils import DataLoader, DataProcesser, label_plot, TimeKeeper\n",
    "\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "label_dic = {1: 'Still', 2: 'Walking', 3: 'Run', 4: 'Bike', 5: 'Car', 6: 'Bus', 7: 'Train', 8: 'Subway'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = DataLoader.SHLDataLoader('data/train/', ratio = None)\n",
    "val = DataLoader.SHLDataLoader('data/validate/', ratio = None)\n",
    "data.df = pd.read_csv('data/mid_res/20210526_data_df.csv')\n",
    "val.df = pd.read_csv('data/mid_res/20210526_val_df.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.df['label_1'] = data.df.apply(lambda x: int(x['label'] > 4), axis = 1)\n",
    "val.df['label_1'] = val.df.apply(lambda x: int(x['label'] > 4), axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "col_drop_x = ['time', 'label', 'label_1']\n",
    "col_drop = ['latitude',\n",
    " 'longitude',\n",
    " 'altitude',\n",
    " 'time_dlt',\n",
    " 'valid_dlt',\n",
    " 'east',\n",
    " 'north',\n",
    " 'east_dlt',\n",
    " 'north_dlt',\n",
    " 'east_speed',\n",
    " 'north_speed',\n",
    " 'cells_ctype_mode',\n",
    " 'speed_dif',\n",
    " 'speed_dlt',\n",
    " 'speed'\n",
    " ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate(y_true, y_pred, class_names = list(label_dic.values())):\n",
    "    conf = confusion_matrix(y_true , y_pred)\n",
    "    print(conf)\n",
    "    sns.heatmap(conf)\n",
    "    print(classification_report(y_true, y_pred, target_names = class_names))"
   ]
  },
  {
   "source": [
    "# Step1\n",
    "\n",
    "Class 1: {1: 'Still', 2: 'Walking', 3: 'Run', 4: 'Bike'}  \n",
    "Class 2: {5: 'Car', 6: 'Bus', 7: 'Train', 8: 'Subway'}"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_ratio = 0.2\n",
    "col_label = 'label_1'\n",
    "X = data.df.drop(col_drop + col_drop_x, axis = 1).fillna(0)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, data.df[col_label], test_size = test_ratio)\n",
    "X_val, y_val = val.df.drop(col_drop + col_drop_x, axis = 1).fillna(0), val.df[col_label]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "RandomForestClassifier(max_depth=8, n_estimators=20, random_state=0)"
      ]
     },
     "metadata": {},
     "execution_count": 8
    }
   ],
   "source": [
    "model_step1 = RandomForestClassifier(n_estimators = 20, random_state = 0, max_depth = 8)\n",
    "model_step1.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "[[51789 19001]\n",
      " [ 5851 67317]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "        foot       0.90      0.73      0.81     70790\n",
      "      viecle       0.78      0.92      0.84     73168\n",
      "\n",
      "    accuracy                           0.83    143958\n",
      "   macro avg       0.84      0.83      0.83    143958\n",
      "weighted avg       0.84      0.83      0.83    143958\n",
      "\n"
     ]
    },
    {
     "output_type": "display_data",
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     },
     "metadata": {}
    }
   ],
   "source": [
    "pred_step1 = model_step1.predict(X_val) # label predict\n",
    "# pred_prob_step1 = model_step1.predict_proba(X_test) # prob predict\n",
    "evaluate(y_val, pred_step1, ['foot', 'viecle'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "32"
      ]
     },
     "metadata": {},
     "execution_count": 25
    }
   ],
   "source": [
    "len(list(X_val))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Random Forest Feature Importance')"
      ]
     },
     "metadata": {},
     "execution_count": 26
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 864x576 with 1 Axes>",
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\n"
     },
     "metadata": {}
    }
   ],
   "source": [
    "plt.figure(figsize = [12, 8])\n",
    "sorted_idx = model_step1.feature_importances_.argsort()\n",
    "plt.barh(list(X_val), model_step1.feature_importances_[sorted_idx])\n",
    "plt.xlabel(\"Random Forest Feature Importance\")"
   ]
  },
  {
   "source": [
    "# Step2-1: Within Class 1\n",
    "\n",
    "- differentiate {1: 'Still', 2: 'Walking', 3: 'Run', 4: 'Bike'}"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_ratio = 0.2\n",
    "col_label = 'label'\n",
    "X = data.df[data.df['label'] <= 4]\n",
    "X_train, X_test, y_train, y_test = train_test_split(X.drop(col_drop + col_drop_x, axis = 1).fillna(0), X[col_label], test_size = test_ratio)\n",
    "X_val, y_val = val.df[val.df['label'] <= 4].drop(col_drop + col_drop_x, axis = 1).fillna(0), val.df[val.df['label'] <= 4][col_label]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "RandomForestClassifier(max_depth=8, n_estimators=20, random_state=0)"
      ]
     },
     "metadata": {},
     "execution_count": 11
    }
   ],
   "source": [
    "# train_data = lgb.Dataset(X_train, label = y_train - 1)\n",
    "# test_data = lgb.Dataset(X_test, label = y_test - 1)\n",
    "# params={\n",
    "#     'learning_rate':0.1,\n",
    "#     'lambda_l1':0.1,\n",
    "#     'lambda_l2':0.2,\n",
    "#     'max_depth':6,\n",
    "#     'objective':'multiclass',\n",
    "#     'num_class':8,  \n",
    "# }\n",
    "# model_step21 = lgb.train(params, train_data, valid_sets = [test_data])\n",
    "\n",
    "model_step21 = RandomForestClassifier(n_estimators = 20, random_state = 0, max_depth = 8)\n",
    "model_step21.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pred_step21 = model_step21.predict(X_val).argmax(axis = 1)\n",
    "pred_step21 = model_step21.predict(X_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "[[24737  4937     2   160]\n [ 5343 19043   330  1433]\n [   27  1325   919   503]\n [  219  2094   920  8798]]\n              precision    recall  f1-score   support\n\n       Still       0.82      0.83      0.82     29836\n     Walking       0.70      0.73      0.71     26149\n         Run       0.42      0.33      0.37      2774\n        Bike       0.81      0.73      0.77     12031\n\n    accuracy                           0.76     70790\n   macro avg       0.69      0.65      0.67     70790\nweighted avg       0.75      0.76      0.75     70790\n\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 2 Axes>",
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     },
     "metadata": {}
    }
   ],
   "source": [
    "evaluate(y_val, pred_step21, list(label_dic.values())[:4])"
   ]
  },
  {
   "source": [
    "# Step2-2: Within Class 1\n",
    "\n",
    "- differentiate {5: 'Car', 6: 'Bus', 7: 'Train', 8: 'Subway'}"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_ratio = 0.2\n",
    "col_label = 'label'\n",
    "X = data.df[data.df['label'] > 4]\n",
    "X_train, X_test, y_train, y_test = train_test_split(X.drop(col_drop + col_drop_x, axis = 1).fillna(0), X[col_label], test_size = test_ratio)\n",
    "X_val, y_val = val.df[val.df['label'] > 4].drop(col_drop + col_drop_x, axis = 1).fillna(0), val.df[val.df['label'] > 4][col_label]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "RandomForestClassifier(max_depth=8, n_estimators=20, random_state=0)"
      ]
     },
     "metadata": {},
     "execution_count": 92
    }
   ],
   "source": [
    "# train_data = lgb.Dataset(X_train, label = y_train - 5)\n",
    "# test_data = lgb.Dataset(X_test, label = y_test - 5)\n",
    "# params={\n",
    "#     'learning_rate':0.1,\n",
    "#     'lambda_l1':0.1,\n",
    "#     'lambda_l2':0.2,\n",
    "#     'max_depth':6,\n",
    "#     'objective':'multiclass',\n",
    "#     'num_class':8,  \n",
    "# }\n",
    "# model_step22 = lgb.train(params, train_data, valid_sets = [test_data])\n",
    "\n",
    "\n",
    "model_step22 = RandomForestClassifier(n_estimators = 20, random_state = 0, max_depth = 8)\n",
    "model_step22.fit(X_train, y_train-5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pred_step22 = model_step22.predict(X_val).argmax(axis = 1)\n",
    "pred_step22 = model_step22.predict(X_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "[[11089  4741  4231   412]\n [   87  6483   863  1745]\n [ 4442  1564 14395  1407]\n [ 2191   673  2878 15967]]\n              precision    recall  f1-score   support\n\n         Car       0.62      0.54      0.58     20473\n         Bus       0.48      0.71      0.57      9178\n       Train       0.64      0.66      0.65     21808\n      Subway       0.82      0.74      0.77     21709\n\n    accuracy                           0.66     73168\n   macro avg       0.64      0.66      0.64     73168\nweighted avg       0.67      0.66      0.66     73168\n\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 2 Axes>",
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\n"
     },
     "metadata": {}
    }
   ],
   "source": [
    "evaluate(y_val-5, pred_step22, list(label_dic.values())[4:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}