model-Copy1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "from time import time\n",
    "\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.model_selection import StratifiedShuffleSplit\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import plot_confusion_matrix, f1_score\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def SplitLabels(data, label):\n",
    "    '''\n",
    "    Split the dataset into inputs and labels.\n",
    "    \n",
    "    @param data The pandas dataframe which we need to split.\n",
    "    @param label String value which is the name of output label.\n",
    "    \n",
    "    @return X Inputs\n",
    "    @return y Outputs\n",
    "    '''\n",
    "    \n",
    "    X = data.drop(label, axis=1)\n",
    "    y = data[label]\n",
    "    return X,y\n",
    "\n",
    "def DropFeatures(features, data):\n",
    "    '''\n",
    "    Drops the list of features from data.\n",
    "    \n",
    "    @param features List of features to drop.\n",
    "    @param data Dataframe from which we want to drop features.\n",
    "    \n",
    "    @return dropped_data The dataset with dropped features.\n",
    "    '''\n",
    "    \n",
    "    dropped_data = data.drop(features, axis = 1)\n",
    "    return dropped_data\n",
    "    \n",
    "def Score(score_func, y_true, y_pred, **args):\n",
    "    '''\n",
    "    Calculates the score of predictions.\n",
    "    \n",
    "    @param score_func The function which we need as a measure of score.\n",
    "    @param y_true True labels.\n",
    "    @param y_pred Predicted labels.\n",
    "    @param args Additional arguments fed to the score_func.\n",
    "    \n",
    "    @return calc_score The score value calculated by the score_func.\n",
    "    '''\n",
    "    \n",
    "    calc_score = score_func(y_true, y_pred, **args)\n",
    "    return calc_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def PlotConfusionMatrix(classifier, X, y, normalize='true', axes=None):\n",
    "    '''\n",
    "    Plots the confusion matrix of X based on predictions\n",
    "    from classifier.\n",
    "    \n",
    "    @param classifier Classifier for doing predictions.\n",
    "    @param X Input data.\n",
    "    @param y Output labels.\n",
    "    @param normalize Specifies the mode of normalization.\n",
    "        It takes values 'true', 'pred' and 'all'.\n",
    "    '''\n",
    "    plot = plot_confusion_matrix(classifier, X, y, normalize = normalize,\n",
    "                                 display_labels=CLASS_NAMES, cmap=plt.cm.Blues, ax=axes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Splits the dataset\n",
    "def split(X, y, test_size=0.2):\n",
    "    X = X.to_numpy()\n",
    "    y = y.to_numpy()\n",
    "    sss = StratifiedShuffleSplit(test_size = test_size, random_state=0)\n",
    "    for train_index, test_index in sss.split(X, y):\n",
    "        X_train, X_test = X[train_index], X[test_index]\n",
    "        y_train, y_test = y[train_index], y[test_index]\n",
    "        \n",
    "    return (X_train, y_train, X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def TrainingPipeline(model, X_train, y_train, X_val, y_val):\n",
    "    \n",
    "    # A dictionary to store the history of the trained model.\n",
    "    history = {}\n",
    "    \n",
    "    # Training and Predicting.\n",
    "    model.fit(X_train, y_train)\n",
    "    y_train_hat = model.predict(X_train)\n",
    "    y_val_hat = model.predict(X_val)\n",
    "    history[\"model\"] = model\n",
    "    history[\"y_train_hat\"] = y_train_hat\n",
    "    history[\"y_val_hat\"] = y_val_hat\n",
    "    \n",
    "    # Plotting confusion matrices.\n",
    "    fig, axes = plt.subplots(1, 2, figsize=(13,5))\n",
    "    PlotConfusionMatrix(model, X_train, y_train, axes=axes[0])\n",
    "    PlotConfusionMatrix(model, X_val, y_val, axes=axes[1])\n",
    "    axes[0].set_title(\"Confustion matrix of Train Data\")\n",
    "    axes[1].set_title(\"Confustion matrix of Test Data\")\n",
    "    \n",
    "    # Calculating the scores.\n",
    "    train_score = Score(f1_score, y_train, y_train_hat, average=\"weighted\").round(4)\n",
    "    val_score = Score(f1_score, y_val, y_val_hat, average=\"weighted\").round(4)\n",
    "    history[\"train_score\"] = train_score\n",
    "    history[\"val_score\"] = val_score\n",
    "    \n",
    "    print(\"Training Score is\", train_score)\n",
    "    print(\"Validation Score is\", val_score)\n",
    "    return history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_csv(\"data/train_age_dataset.csv\")\n",
    "test = pd.read_csv(\"data/test_age_dataset.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Names of classes\n",
    "CLASS_NAMES = list(train[\"age_group\"].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, y_train = SplitLabels(train, \"age_group\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = MinMaxScaler().fit(X_train)\n",
    "X_train = scaler.transform(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
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      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-10-a56155cefa85>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mensemble\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mGradientBoostingClassifier\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mGradientBoostingClassifier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_estimators\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mmax_depth\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m12\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrandom_state\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/.local/lib/python3.8/site-packages/sklearn/ensemble/_gb.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight, monitor)\u001b[0m\n\u001b[1;32m    496\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    497\u001b[0m         \u001b[0;31m# fit the boosting stages\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 498\u001b[0;31m         n_stages = self._fit_stages(\n\u001b[0m\u001b[1;32m    499\u001b[0m             \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mraw_predictions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_rng\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_val\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_val\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    500\u001b[0m             sample_weight_val, begin_at_stage, monitor, X_idx_sorted)\n",
      "\u001b[0;32m~/.local/lib/python3.8/site-packages/sklearn/ensemble/_gb.py\u001b[0m in \u001b[0;36m_fit_stages\u001b[0;34m(self, X, y, raw_predictions, sample_weight, random_state, X_val, y_val, sample_weight_val, begin_at_stage, monitor, X_idx_sorted)\u001b[0m\n\u001b[1;32m    553\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    554\u001b[0m             \u001b[0;31m# fit next stage of trees\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 555\u001b[0;31m             raw_predictions = self._fit_stage(\n\u001b[0m\u001b[1;32m    556\u001b[0m                 \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mraw_predictions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_mask\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    557\u001b[0m                 random_state, X_idx_sorted, X_csc, X_csr)\n",
      "\u001b[0;32m~/.local/lib/python3.8/site-packages/sklearn/ensemble/_gb.py\u001b[0m in \u001b[0;36m_fit_stage\u001b[0;34m(self, i, X, y, raw_predictions, sample_weight, sample_mask, random_state, X_idx_sorted, X_csc, X_csr)\u001b[0m\n\u001b[1;32m    209\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    210\u001b[0m             \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mX_csr\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mX_csr\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 211\u001b[0;31m             tree.fit(X, residual, sample_weight=sample_weight,\n\u001b[0m\u001b[1;32m    212\u001b[0m                      check_input=False, X_idx_sorted=X_idx_sorted)\n\u001b[1;32m    213\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.8/site-packages/sklearn/tree/_classes.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight, check_input, X_idx_sorted)\u001b[0m\n\u001b[1;32m   1240\u001b[0m         \"\"\"\n\u001b[1;32m   1241\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1242\u001b[0;31m         super().fit(\n\u001b[0m\u001b[1;32m   1243\u001b[0m             \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1244\u001b[0m             \u001b[0msample_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.8/site-packages/sklearn/tree/_classes.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight, check_input, X_idx_sorted)\u001b[0m\n\u001b[1;32m    373\u001b[0m                                            min_impurity_split)\n\u001b[1;32m    374\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 375\u001b[0;31m         \u001b[0mbuilder\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuild\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtree_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_idx_sorted\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    376\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    377\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_outputs_\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mis_classifier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "model = GradientBoostingClassifier(n_estimators=100,max_depth=12, random_state=0)\n",
    "model.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Making predictions.\n",
    "test_pred = model.predict(test)\n",
    "test_pred = pd.Series(test_pred, name=\"prediction\")\n",
    "test_pred.to_csv(\"final_model.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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