step3.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tutorial how to use [xgboost](https://github.com/dmlc/xgboost)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import xgboost as xgb\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.dummy import DummyRegressor\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor, ExtraTreesRegressor\n",
    "from sklearn.ensemble import GradientBoostingRegressor, AdaBoostRegressor, BaggingRegressor\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "train = pd.read_csv('bike.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10886 entries, 0 to 10885\n",
      "Data columns (total 12 columns):\n",
      "datetime      10886 non-null object\n",
      "season        10886 non-null int64\n",
      "holiday       10886 non-null int64\n",
      "workingday    10886 non-null int64\n",
      "weather       10886 non-null int64\n",
      "temp          10886 non-null float64\n",
      "atemp         10886 non-null float64\n",
      "humidity      10886 non-null int64\n",
      "windspeed     10886 non-null float64\n",
      "casual        10886 non-null int64\n",
      "registered    10886 non-null int64\n",
      "count         10886 non-null int64\n",
      "dtypes: float64(3), int64(8), object(1)\n",
      "memory usage: 1020.6+ KB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There're **10 886** observations and **12** features.\n",
    "\n",
    "### High level\n",
    "1. There are no missing values.\n",
    "2. Most values are integers, few of them floats and only one an object (should be a date).\n",
    "3. season, weather are categorical variables (contains for possible values - 1, 2, 3, 4)\n",
    "\n",
    "\n",
    "### More detailed\n",
    "1. **datetime** - hourly date + timestamp\n",
    "2. **season** -  \n",
    "    1 = spring  \n",
    "    2 = summer  \n",
    "    3 = fall  \n",
    "    4 = winter  \n",
    "3. **holiday** - whether the day is considered a holiday\n",
    "4. **workingday** - whether the day is neither a weekend nor holiday\n",
    "5. **weather** -   \n",
    "    1: Clear, Few clouds, Partly cloudy, Partly cloudy   \n",
    "    2: Mist + Cloudy, Mist + Broken clouds, Mist + Few clouds, Mist  \n",
    "    3: Light Snow, Light Rain + Thunderstorm + Scattered clouds, Light Rain + Scattered clouds  \n",
    "    4: Heavy Rain + Ice Pallets + Thunderstorm + Mist, Snow + Fog  \n",
    "6. **temp** - temperature in Celsius\n",
    "7. **atemp** - \"feels like\" temperature in Celsius\n",
    "8. **humidity** - relative humidity\n",
    "9. **windspeed** - wind speed\n",
    "10. **casual** - number of non-registered user rentals initiated\n",
    "11. **registered** - number of registered user rentals initiated\n",
    "12. **count** - number of total rentals\n",
    "\n",
    "\n",
    "\n",
    "## Target variable¶\n",
    "\n",
    "The goal is predict - **count**\n",
    "Note: **count** = **registered** + **casual**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Quality function\n",
    "\n",
    "$$ \\sqrt{\\frac{1}{n} \\sum_{i=1}^n (\\ln(p_i + 1) - \\ln(a_i+1))^2 }$$\n",
    "\n",
    "where  \n",
    "**n** is the number of hours in the test set  \n",
    "**pi** is your predicted count  \n",
    "**ai** is the actual count  \n",
    "**ln(x)** is the natural logarithm  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simple Feature Engineering\n",
    "Let's extract day in separeted column (is needed for validation as well)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train['datetime'] = pd.to_datetime( train['datetime'] )\n",
    "train['day'] = train['datetime'].map(lambda x: x.day)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def assing_test_samples(data, last_training_day=0.3, seed=1):\n",
    "    days = data.day.unique()\n",
    "    np.random.seed(seed)\n",
    "    np.random.shuffle(days)\n",
    "    test_days = days[: int(len(days) * 0.3)]\n",
    "    \n",
    "    data['is_test'] = data.day.isin(test_days)\n",
    "\n",
    "\n",
    "def select_features(data):\n",
    "    columns = data.columns[ (data.dtypes == np.int64) | (data.dtypes == np.float64) | (data.dtypes == np.bool) ].values    \n",
    "    return [feat for feat in columns if feat not in ['count', 'casual', 'registered'] and 'log' not in feat ] \n",
    "\n",
    "def get_X_y(data, target_variable):\n",
    "    features = select_features(data)\n",
    "        \n",
    "    X = data[features].values\n",
    "    y = data[target_variable].values\n",
    "    \n",
    "    return X,y\n",
    "\n",
    "def train_test_split(train, target_variable):\n",
    "    df_train = train[train.is_test == False]\n",
    "    df_test  = train[train.is_test == True]\n",
    "    \n",
    "    X_train, y_train = get_X_y(df_train, target_variable)\n",
    "    X_test, y_test = get_X_y(df_test, target_variable)\n",
    "    \n",
    "    return X_train, X_test, y_train, y_test\n",
    "\n",
    "\n",
    "\n",
    "def fit_and_predict(train, model, target_variable):\n",
    "    X_train, X_test, y_train, y_test = train_test_split(train, target_variable)\n",
    "    \n",
    "    model.fit(X_train, y_train)\n",
    "    y_pred = model.predict(X_test)\n",
    "    \n",
    "    return (y_test, y_pred)\n",
    "\n",
    "def post_pred(y_pred):\n",
    "    y_pred[y_pred < 0] = 0\n",
    "    return y_pred\n",
    "\n",
    "def rmsle(y_true, y_pred, y_pred_only_positive=True):\n",
    "    if y_pred_only_positive: y_pred = post_pred(y_pred)\n",
    "        \n",
    "    diff = np.log(y_pred+1) - np.log(y_true+1)\n",
    "    mean_error = np.square(diff).mean()\n",
    "    return np.sqrt(mean_error)\n",
    "\n",
    "##########\n",
    "\n",
    "def count_prediction(train, model, target_variable='count'):\n",
    "    (y_test, y_pred) = fit_and_predict(train, model, target_variable)\n",
    "\n",
    "    if target_variable == 'count_log': \n",
    "        y_test = train[train.is_test == True]['count']\n",
    "        y_pred = np.exp2(y_pred)\n",
    "        \n",
    "    return rmsle(y_test, y_pred)\n",
    "\n",
    "    \n",
    "def registered_casual_prediction(train, model):\n",
    "    (_, registered_pred) = fit_and_predict(train, model, 'registered')\n",
    "    (_, casual_pred) = fit_and_predict(train, model, 'casual')\n",
    "\n",
    "    y_test = train[train.is_test == True]['count']\n",
    "    y_pred = registered_pred + casual_pred\n",
    "    \n",
    "    return rmsle(y_test, y_pred)\n",
    "\n",
    "def log_registered_casual_prediction(train, model):\n",
    "    (_, registered_pred) = fit_and_predict(train, model, 'registered_log')\n",
    "    (_, casual_pred) = fit_and_predict(train, model, 'casual_log')\n",
    "   \n",
    "    y_test = train[train.is_test == True]['count']\n",
    "    y_pred = (np.exp2(registered_pred) - 1) + (np.exp2(casual_pred) -1)\n",
    "    \n",
    "    return rmsle(y_test, y_pred)\n",
    "    \n",
    "##########\n",
    "\n",
    "def importance_features(model, data):\n",
    "    impdf = []\n",
    "    fscore = model.booster().get_fscore()\n",
    "    maps_name = dict([ (\"f{0}\".format(i), col) for i, col in enumerate(data.columns)])\n",
    "\n",
    "    for ft, score in fscore.iteritems():\n",
    "        impdf.append({'feature': maps_name[ft], 'importance': score})\n",
    "    impdf = pd.DataFrame(impdf)\n",
    "    impdf = impdf.sort_values(by='importance', ascending=False).reset_index(drop=True)\n",
    "    impdf['importance'] /= impdf['importance'].sum()\n",
    "    impdf.index = impdf['feature']\n",
    "        \n",
    "    return impdf  \n",
    "\n",
    "def draw_importance_features(model, train):\n",
    "    impdf = importance_features(model, train)\n",
    "    return impdf.plot(kind='bar', title='Importance Features', figsize=(20, 8))\n",
    "    \n",
    "    \n",
    "assing_test_samples(train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dummy Regressor\n",
    "The most simple one :)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('dummy', 1.5758678110942455)\n",
      "('xgboost', 1.3222047419868168)\n"
     ]
    }
   ],
   "source": [
    "print('dummy', count_prediction(train, DummyRegressor()))\n",
    "print('xgboost', count_prediction(train, xgb.XGBRegressor()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature engineering\n",
    "\n",
    "Let's a bit improve quality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def etl_datetime(df):\n",
    "    df['year'] = df['datetime'].map(lambda x: x.year)\n",
    "    df['month'] = df['datetime'].map(lambda x: x.month)\n",
    "\n",
    "    df['hour'] = df['datetime'].map(lambda x: x.hour)\n",
    "    df['minute'] = df['datetime'].map(lambda x: x.minute)\n",
    "    df['dayofweek'] = df['datetime'].map(lambda x: x.dayofweek)\n",
    "    df['weekend'] = df['datetime'].map(lambda x: x.dayofweek in [5,6])\n",
    "\n",
    "    \n",
    "etl_datetime(train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('xgboost', 0.71226527946687523)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x112084c90>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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/O/679PRJF9SBra2197bW/r219r5JF9OD1trnWmu/mOQJSb47yWeq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\nREQPpSgUEREREREREdFD/we7OlW91lXxfwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x109d92810>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = xgb.XGBRegressor()\n",
    "print('xgboost', count_prediction(train, model))\n",
    "draw_importance_features(model, train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predict count = register + casual"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('xgboost', 0.73669841949326831)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x112be34d0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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hr+Ohr6Onp+Ohr+Ohr+MxDX1dTCh0R5L57/Sg4c49vWZ2duciSlm8bdt2jHS8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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11270be10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = xgb.XGBRegressor()\n",
    "print('xgboost', registered_casual_prediction(train, model))\n",
    "draw_importance_features(model, train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Logarithm combination - count_log, registered_log, casual_log"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train['{0}_log'.format('count')] = train['count'].map(lambda x: np.log2(x) )\n",
    "\n",
    "for name in ['registered', 'casual']:\n",
    "    train['{0}_log'.format(name)] = train[name].map(lambda x: np.log2(x+1) )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predict count = exp(count_log)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('xgboost', 0.41322977213972051)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x112bafc90>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Tk6yqqg9W1YfHYRIAAAAAB4n5hEJH5FvLwJJke1V9c1xr7VOttS8lWTbrnG1J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\nRPRQikIRERERERERET30P/v8XWELzjW2AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1124d3c10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = xgb.XGBRegressor()\n",
    "print('xgboost', count_prediction(train, model, 'count_log'))\n",
    "draw_importance_features(model, train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predict count = exp(registered_log) + exp(casual_log)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('xgboost', 0.39339177031871342)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11285b950>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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fx0Nfx0Nfx0Nfx0NfR09Px0Nfx0Nfx2Ma+rqQUOiWJHPf6UHDnbt6zczMrgWU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\nIiL6UIpCERERERERERF96P8A3NvqxkH5qSEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113915bd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = xgb.XGBRegressor()\n",
    "print('xgboost', log_registered_casual_prediction(train, model))\n",
    "draw_importance_features(model, train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What about a bit magic feature engineering? :)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('xgboost', 0.34919888465050125)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x107f8b710>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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1S7hNx7yv+6z3uPw+7Ky73/N4Ocel4/Z6qc92Fz1+LPMx3e+4tJzX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\nSZIkSZK0gAwKSZIkSZIkLaD/B/3cRgBsm27XAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1120e6d10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import itertools\n",
    "\n",
    "def get_num_features(data):\n",
    "    columns = data.loc[ :, (data.dtypes == np.float) |  (data.dtypes == np.int64) ].columns.values\n",
    "    \n",
    "    return [c for c in columns if 'count' not in c and 'registered' not in c and 'casual' not in c]\n",
    "\n",
    "def generate_new_features(data):\n",
    "    num_cols = get_num_features(data)\n",
    "    for feat_x, feat_y in itertools.product(num_cols, num_cols):\n",
    "        name_times = '{0}_x_{1}'.format(feat_x, feat_y)\n",
    "        name_plus  = '{0}_+_{1}'.format(feat_x, feat_y)\n",
    "        name_diff  = '{0}_-_{1}'.format(feat_x, feat_y)\n",
    "\n",
    "        data[name_times] =  data[feat_x] * data[feat_y]\n",
    "        data[name_plus]  =  data[feat_x] + data[feat_y]\n",
    "        data[name_diff]  =  data[feat_x] - data[feat_y]\n",
    "        \n",
    "\n",
    "train_magic = train.copy()\n",
    "generate_new_features(train_magic)\n",
    "\n",
    "model = xgb.XGBRegressor()\n",
    "print('xgboost', log_registered_casual_prediction(train_magic, model))\n",
    "draw_importance_features(model, train_magic)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compare with other models\n",
    "- DecisionTreeRegressor\n",
    "- RandomForestRegressor\n",
    "- ExtraTreesRegressor\n",
    "- GradientBoostingRegressor\n",
    "- AdaBoostRegressor\n",
    "- BaggingRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('decision_tree', 0.44508655314844409)\n"
     ]
    }
   ],
   "source": [
    "models = [\n",
    "    ('decision_tree', DecisionTreeRegressor()),\n",
    "##put here other algorithms (mentioned above)\n",
    "    \n",
    "]\n",
    "\n",
    "for model in models:\n",
    "    print(model[0], log_registered_casual_prediction(train, model[1]))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tuning hyperparameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "({'n_estimators': 100, 'max_depth': 2}, 0.46000367817350141)\n",
      "({'n_estimators': 200, 'max_depth': 2}, 0.383063126639346)\n",
      "({'n_estimators': 300, 'max_depth': 2}, 0.36217291062931611)\n",
      "({'n_estimators': 100, 'max_depth': 5}, 0.33877146331666108)\n",
      "({'n_estimators': 200, 'max_depth': 5}, 0.3329926658794557)\n",
      "({'n_estimators': 300, 'max_depth': 5}, 0.33179067955100161)\n",
      "({'n_estimators': 100, 'max_depth': 10}, 0.33338905009262376)\n",
      "({'n_estimators': 200, 'max_depth': 10}, 0.33411251538787728)\n",
      "({'n_estimators': 300, 'max_depth': 10}, 0.33430611210836408)\n"
     ]
    }
   ],
   "source": [
    "for max_depth in [2, 5, 10]:\n",
    "    for n_estimators in [100, 200, 300]:\n",
    "        params = {'max_depth': max_depth, 'n_estimators': n_estimators}\n",
    "        model = xgb.XGBRegressor(**params)\n",
    "        print(params, log_registered_casual_prediction(train, model))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "let's try play around with subsample, learning_rate and ohers..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What about tuning training_magic dataset? One example possible values..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "({'reg_alpha': 0.1, 'colsample_bytree': 0.3, 'learning_rate': 0.05, 'n_estimators': 600, 'subsample': 0.95, 'max_depth': 6}, 0.32089853387438022)\n"
     ]
    }
   ],
   "source": [
    "params = {\n",
    "    'max_depth': 6, \n",
    "    'n_estimators': 600, \n",
    "    'subsample': 0.95, \n",
    "    'colsample_bytree': 0.3, \n",
    "    'learning_rate': 0.05, \n",
    "    'reg_alpha': 0.1\n",
    "}\n",
    "model = xgb.XGBRegressor(**params)\n",
    "print(params, log_registered_casual_prediction(train_magic, model))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "What about feature selection (remove all useless features)? Tips: `sklearn.feature_selection.*`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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