train.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Income Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import time\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn.compose import TransformedTargetRegressor\n",
    "\n",
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "from xgboost import XGBRegressor\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.linear_model import RidgeCV\n",
    "from catboost import CatBoostRegressor\n",
    "from lightgbm import LGBMRegressor\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "\n",
    "TARGET_VARIABLE = 'Total Yearly Income [EUR]'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data preprocessing & Feature extraction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\fitze\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\IPython\\core\\interactiveshell.py:3058: DtypeWarning: Columns (2,4) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    }
   ],
   "source": [
    "DATA_DIR = os.path.abspath('data')\n",
    "training_data_filepath = os.path.join(DATA_DIR, 'tcd-ml-1920-group-income-train.csv')\n",
    "df = pd.read_csv(training_data_filepath)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fix and Format data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def format_data(df):\n",
    "    # Years are in order so can replace missing years with the last valid year before\n",
    "    df['Year of Record'] = df['Year of Record'].fillna(method='ffill')\n",
    "\n",
    "    # Replace invalid Housing situation with other\n",
    "    df['Housing Situation'] = df['Housing Situation'].replace([0, '0', 'nA',np.nan], 'Unknown')\n",
    "\n",
    "    # Convert years Work Experience to float and replace '#NUM!' entries with mean\n",
    "    df['Work Experience in Current Job [years]'] = pd.to_numeric(df['Work Experience in Current Job [years]'], errors='coerce')\n",
    "    df['Work Experience in Current Job [years]'].fillna(df['Work Experience in Current Job [years]'].mean(), inplace=True)\n",
    "\n",
    "    # Replace Nan Satisfation with employer with unknown\n",
    "    df['Satisfation with employer'].fillna('Unknown', inplace=True)\n",
    "\n",
    "    # Remove ' EUR' from addition to salary and convert to float\n",
    "    df['Yearly Income in addition to Salary (e.g. Rental Income)'].replace(' EUR$', '', regex=True, inplace=True)\n",
    "    df['Yearly Income in addition to Salary (e.g. Rental Income)'] = pd.to_numeric(\n",
    "        df['Yearly Income in addition to Salary (e.g. Rental Income)'], errors='raise')\n",
    "\n",
    "    # Fix age values\n",
    "    df['Age'] = df['Age'].fillna(df['Age'].median())\n",
    "    df['Age'] = df['Age'].astype(int)\n",
    "\n",
    "    # Fix gender values\n",
    "    df['Gender'] = df['Gender'].replace(['0', 'unknown', np.NaN], 'other')\n",
    "    df['Gender'] = df['Gender'].replace('f', 'female')\n",
    "\n",
    "    # Fix university values\n",
    "    df['University Degree'] = df['University Degree'].replace(['0', np.NaN], 'No')\n",
    "\n",
    "    # Fix hair color values\n",
    "    df['Hair Color'] = df['Hair Color'].replace(['0', np.NaN], 'Unknown')\n",
    "    \n",
    "    # Remove unwanted features\n",
    "    del df['Instance']\n",
    "    \n",
    "    return df\n",
    "\n",
    "df = format_data(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Encode Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "'''\n",
    "def one_hot_encode_data(df, one_hot_encode):\n",
    "    for feature in one_hot_encode:\n",
    "        df = pd.concat([df,pd.get_dummies(df[feature], prefix=feature, drop_first=True, sparse=True)],axis=1)\n",
    "        df.drop([feature],axis=1, inplace=True)\n",
    "        \n",
    "    return df\n",
    "\n",
    "one_hot_encode = ['Housing Situation', 'Satisfation with employer', \n",
    "                  'Gender', 'University Degree', 'Hair Color']\n",
    "df = one_hot_encode_data(df, one_hot_encode)\n",
    "'''\n",
    "\n",
    "def frequency_encode_data(df, target_encode, use_mappings={}):\n",
    "    calculated_mappings = {}\n",
    "    \n",
    "    for feature in target_encode:\n",
    "        feature_mappings = None  \n",
    "        \n",
    "        if use_mappings != {}:\n",
    "            # Use previous mappings\n",
    "            if feature not in use_mappings:\n",
    "                ValueError('{} mappings is not in provided use_mappings'.format(feature))\n",
    "            else:\n",
    "                feature_mappings = use_mappings[feature]\n",
    "        else:\n",
    "            # Calculate new feature mappings\n",
    "            feature_mappings = df.groupby(feature).size()\n",
    "            \n",
    "        # Store calculated mappings\n",
    "        calculated_mappings[feature] = feature_mappings\n",
    "                    \n",
    "        df[f'{feature}_freq'] = df[feature].map(calculated_mappings[feature])\n",
    "    return df, calculated_mappings\n",
    "\n",
    "\n",
    "frequency_encode = ['Gender', 'University Degree', 'Country', 'Profession', \n",
    "                       'Housing Situation', 'Satisfation with employer']\n",
    "df, frequency_encode_mappings = frequency_encode_data(df, frequency_encode)\n",
    "\n",
    "\n",
    "def target_encode_data(df, target_encode, use_mappings={}):\n",
    "    mean_target = df[TARGET_VARIABLE].mean()\n",
    "    calculated_mappings = {}\n",
    "    \n",
    "    for feature in target_encode:\n",
    "        feature_mappings = None\n",
    "        \n",
    "        if use_mappings != {}:\n",
    "            # Use previous mappings\n",
    "            if feature not in use_mappings:\n",
    "                ValueError('{} mappings is not in provided use_mappings'.format(feature))\n",
    "            else:\n",
    "                feature_mappings = use_mappings[feature]\n",
    "        else:\n",
    "            # Calculate new feature mappings\n",
    "            feature_mappings = df.groupby(feature)[TARGET_VARIABLE].mean()\n",
    "            \n",
    "        # Store calculated mappings\n",
    "        calculated_mappings[feature] = feature_mappings\n",
    "                    \n",
    "        df[feature] = df[feature].map(calculated_mappings[feature])\n",
    "        df[feature].fillna(mean_target, inplace=True)\n",
    "        \n",
    "    return df, calculated_mappings\n",
    "\n",
    "target_encode = ['Country', 'Profession', 'Housing Situation', 'Satisfation with employer', \n",
    "                  'Gender', 'University Degree', 'Hair Color']\n",
    "df, target_encode_mappings = target_encode_data(df, target_encode)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"\\n# Log age and exp\\nimport matplotlib.pyplot as plt\\nimport seaborn as sns\\n\\ndf['Age_sq'] = np.square(df['Age'])\\ndf['Age_log'] = np.log(df['Age'])\\n\\ndf['exp_sq'] = np.square(df['Work Experience in Current Job [years]'])\\ndf['exp_log'] = np.log(df['Work Experience in Current Job [years]'].clip(lower=1))\\n\""
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "# Log age and exp\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "df['Age_sq'] = np.square(df['Age'])\n",
    "df['Age_log'] = np.log(df['Age'])\n",
    "\n",
    "df['exp_sq'] = np.square(df['Work Experience in Current Job [years]'])\n",
    "df['exp_log'] = np.log(df['Work Experience in Current Job [years]'].clip(lower=1))\n",
    "'''\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Standardize and Split data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# Standardize data\n",
    "scaler = StandardScaler()\n",
    "scale_columns = df.columns.to_list()\n",
    "scale_columns.remove(TARGET_VARIABLE)\n",
    "df[scale_columns] = scaler.fit_transform(df[scale_columns])\n",
    "\n",
    "# Split data\n",
    "X = df.drop(columns=TARGET_VARIABLE).values\n",
    "y = df[TARGET_VARIABLE].values\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Train Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5986:\tlearn: 20061.2250297\ttotal: 7m 52s\tremaining: 1.02s\n",
      "5987:\tlearn: 20060.9370671\ttotal: 7m 52s\tremaining: 947ms\n",
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      "5999:\tlearn: 20058.8600439\ttotal: 7m 53s\tremaining: 0us\n",
      "--- Training took 478.67406392097473 sec ---\n"
     ]
    }
   ],
   "source": [
    "start_time = time.time()\n",
    "\n",
    "model = CatBoostRegressor(iterations=6000)\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "training_time = time.time() - start_time\n",
    "print('--- Training took {} sec ---'.format(training_time))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE Train: 8,522.566355403891, MAE Test: 8,978.68004769769\n"
     ]
    }
   ],
   "source": [
    "y_train_pred = model.predict(X_train)\n",
    "y_test_pred = model.predict(X_test)\n",
    "\n",
    "train_accuracy = mean_absolute_error(y_train, y_train_pred)\n",
    "test_accuracy = mean_absolute_error(y_test, y_test_pred)\n",
    "\n",
    "print('MAE Train: {:,}, MAE Test: {:,}'.format(\n",
    "        train_accuracy, test_accuracy))\n",
    "\n",
    "# MAE Train: 8,522.566355403891, MAE Test: 8,978.68004769769"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "CatBoostRegressor(iterations=6000) target encode everything\n",
    "- MAE Train: 8,522.566355403891, MAE Test: 8,978.68004769769 (~8min)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create Submission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\fitze\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\IPython\\core\\interactiveshell.py:3058: DtypeWarning: Columns (4) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    }
   ],
   "source": [
    "submission_data_filepath = os.path.join(DATA_DIR, 'tcd-ml-1920-group-income-test.csv')\n",
    "df_submission_data = pd.read_csv(submission_data_filepath)\n",
    "\n",
    "# Clean and encode data\n",
    "df_submission_data = format_data(df_submission_data)\n",
    "df_submission_data, _ = frequency_encode_data(df_submission_data, frequency_encode, frequency_encode_mappings)\n",
    "df_submission_data, _ = target_encode_data(df_submission_data, target_encode, target_encode_mappings)\n",
    "\n",
    "# TODO make func\n",
    "# Standardize data\n",
    "df_submission_data[scale_columns] = scaler.fit_transform(df_submission_data[scale_columns])\n",
    "\n",
    "# Make predictions\n",
    "X = df_submission_data.drop(columns=TARGET_VARIABLE).values\n",
    "y_pred = model.predict(X)\n",
    "\n",
    "# Create submission\n",
    "df_submission_filepath = os.path.join(DATA_DIR, 'tcd-ml-1920-group-income-submission.csv')\n",
    "df_submission = pd.read_csv(df_submission_filepath)\n",
    "df_submission['Total Yearly Income [EUR]'] = pd.Series(y_pred)\n",
    "submission_filepath = os.path.join(DATA_DIR, 'submission', 'submission.csv')\n",
    "df_submission.to_csv(submission_filepath, index=False)\n",
    "\n"
   ]
  }
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