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lozhn/grandmother.ipynb

Created July 24, 2017 21:11
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grandmother.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import seaborn as sb\n",
"import matplotlib.pyplot as plt\n",
"\n",
"from catboost import CatBoostRegressor, Pool\n",
"\n",
"from sklearn.model_selection import train_test_split, GridSearchCV\n",
"from sklearn.metrics import log_loss\n",
"from sklearn.preprocessing import StandardScaler\n",
"from sklearn.feature_extraction import DictVectorizer as DV\n",
"\n",
"import datetime as dt"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Exploration"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>CampaignId</th>\n",
" <th>AdGroupId</th>\n",
" <th>KeywordId</th>\n",
" <th>Query</th>\n",
" <th>QueryMatchTypeWithVariant</th>\n",
" <th>Device</th>\n",
" <th>Date</th>\n",
" <th>AveragePosition</th>\n",
" <th>Clicks</th>\n",
" <th>Impressions</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>150950690.0</td>\n",
" <td>7.953835e+09</td>\n",
" <td>116881275.0</td>\n",
" <td>may loan</td>\n",
" <td>NEAR_EXACT</td>\n",
" <td>HIGH_END_MOBILE</td>\n",
" <td>2016-08-26</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>150950690.0</td>\n",
" <td>7.953835e+09</td>\n",
" <td>116881275.0</td>\n",
" <td>my lloan</td>\n",
" <td>NEAR_EXACT</td>\n",
" <td>DESKTOP</td>\n",
" <td>2016-09-16</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>150950690.0</td>\n",
" <td>7.953835e+09</td>\n",
" <td>116881275.0</td>\n",
" <td>my loan</td>\n",
" <td>EXACT</td>\n",
" <td>DESKTOP</td>\n",
" <td>2016-04-05</td>\n",
" <td>2.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>150950690.0</td>\n",
" <td>7.953835e+09</td>\n",
" <td>116881275.0</td>\n",
" <td>my loan</td>\n",
" <td>EXACT</td>\n",
" <td>DESKTOP</td>\n",
" <td>2016-04-12</td>\n",
" <td>2.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>150950690.0</td>\n",
" <td>7.953835e+09</td>\n",
" <td>116881275.0</td>\n",
" <td>my loan</td>\n",
" <td>EXACT</td>\n",
" <td>DESKTOP</td>\n",
" <td>2016-04-18</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" CampaignId AdGroupId KeywordId Query QueryMatchTypeWithVariant \\\n",
"0 150950690.0 7.953835e+09 116881275.0 may loan NEAR_EXACT \n",
"1 150950690.0 7.953835e+09 116881275.0 my lloan NEAR_EXACT \n",
"2 150950690.0 7.953835e+09 116881275.0 my loan EXACT \n",
"3 150950690.0 7.953835e+09 116881275.0 my loan EXACT \n",
"4 150950690.0 7.953835e+09 116881275.0 my loan EXACT \n",
"\n",
" Device Date AveragePosition Clicks Impressions \n",
"0 HIGH_END_MOBILE 2016-08-26 1.0 1.0 1.0 \n",
"1 DESKTOP 2016-09-16 1.0 1.0 1.0 \n",
"2 DESKTOP 2016-04-05 2.0 1.0 1.0 \n",
"3 DESKTOP 2016-04-12 2.0 1.0 1.0 \n",
"4 DESKTOP 2016-04-18 1.0 0.0 1.0 "
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train = pd.read_csv('train.csv')\n",
"test = pd.read_csv('test.csv')\n",
"train.head()"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"X_train = train.drop(['Clicks', 'Impressions'], axis=1)\n",
"Y_train = train['Clicks']/train['Impressions']"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>CampaignId</th>\n",
" <th>AdGroupId</th>\n",
" <th>KeywordId</th>\n",
" <th>Query</th>\n",
" <th>QueryMatchTypeWithVariant</th>\n",
" <th>Device</th>\n",
" <th>Date</th>\n",
" <th>AveragePosition</th>\n",
" <th>RowId</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>150950810.0</td>\n",
" <td>1.540162e+10</td>\n",
" <td>6.252174e+10</td>\n",
" <td>consida</td>\n",
" <td>EXACT</td>\n",
" <td>DESKTOP</td>\n",
" <td>2017-02-13</td>\n",
" <td>1.0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>150951290.0</td>\n",
" <td>7.953845e+09</td>\n",
" <td>1.910697e+08</td>\n",
" <td>billån</td>\n",
" <td>EXACT</td>\n",
" <td>DESKTOP</td>\n",
" <td>2017-02-10</td>\n",
" <td>1.5</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>150950810.0</td>\n",
" <td>2.457336e+10</td>\n",
" <td>5.701888e+08</td>\n",
" <td>wasa kredit</td>\n",
" <td>EXACT</td>\n",
" <td>HIGH_END_MOBILE</td>\n",
" <td>2017-01-22</td>\n",
" <td>1.6</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>150950810.0</td>\n",
" <td>2.457336e+10</td>\n",
" <td>5.701888e+08</td>\n",
" <td>wasa kredit</td>\n",
" <td>EXACT</td>\n",
" <td>DESKTOP</td>\n",
" <td>2017-01-18</td>\n",
" <td>1.2</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>150950810.0</td>\n",
" <td>1.540162e+10</td>\n",
" <td>6.252174e+10</td>\n",
" <td>consida</td>\n",
" <td>EXACT</td>\n",
" <td>HIGH_END_MOBILE</td>\n",
" <td>2017-02-14</td>\n",
" <td>1.0</td>\n",
" <td>4</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" CampaignId AdGroupId KeywordId Query \\\n",
"0 150950810.0 1.540162e+10 6.252174e+10 consida \n",
"1 150951290.0 7.953845e+09 1.910697e+08 billån \n",
"2 150950810.0 2.457336e+10 5.701888e+08 wasa kredit \n",
"3 150950810.0 2.457336e+10 5.701888e+08 wasa kredit \n",
"4 150950810.0 1.540162e+10 6.252174e+10 consida \n",
"\n",
" QueryMatchTypeWithVariant Device Date AveragePosition \\\n",
"0 EXACT DESKTOP 2017-02-13 1.0 \n",
"1 EXACT DESKTOP 2017-02-10 1.5 \n",
"2 EXACT HIGH_END_MOBILE 2017-01-22 1.6 \n",
"3 EXACT DESKTOP 2017-01-18 1.2 \n",
"4 EXACT HIGH_END_MOBILE 2017-02-14 1.0 \n",
"\n",
" RowId \n",
"0 0 \n",
"1 1 \n",
"2 2 \n",
"3 3 \n",
"4 4 "
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test.head()"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"CampaignId False\n",
"AdGroupId False\n",
"KeywordId False\n",
"Query False\n",
"QueryMatchTypeWithVariant False\n",
"Device False\n",
"Date False\n",
"AveragePosition False\n",
"Clicks False\n",
"Impressions False\n",
"dtype: bool"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train.isnull().any()"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>CampaignId</th>\n",
" <th>AdGroupId</th>\n",
" <th>KeywordId</th>\n",
" <th>AveragePosition</th>\n",
" <th>Clicks</th>\n",
" <th>Impressions</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>5.622200e+04</td>\n",
" <td>5.622200e+04</td>\n",
" <td>5.622200e+04</td>\n",
" <td>56222.000000</td>\n",
" <td>56222.000000</td>\n",
" <td>56222.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>1.509510e+08</td>\n",
" <td>1.233973e+10</td>\n",
" <td>2.121920e+10</td>\n",
" <td>2.963296</td>\n",
" <td>0.509374</td>\n",
" <td>10.130056</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>2.384959e+02</td>\n",
" <td>5.637272e+09</td>\n",
" <td>2.513130e+10</td>\n",
" <td>1.942774</td>\n",
" <td>1.455485</td>\n",
" <td>36.591271</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>1.509507e+08</td>\n",
" <td>7.953835e+09</td>\n",
" <td>3.764936e+07</td>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>1.509508e+08</td>\n",
" <td>7.953837e+09</td>\n",
" <td>1.766531e+09</td>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>1.509510e+08</td>\n",
" <td>7.953852e+09</td>\n",
" <td>1.080321e+10</td>\n",
" <td>2.300000</td>\n",
" <td>0.000000</td>\n",
" <td>2.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>1.509513e+08</td>\n",
" <td>1.539922e+10</td>\n",
" <td>2.988553e+10</td>\n",
" <td>4.000000</td>\n",
" <td>1.000000</td>\n",
" <td>4.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>1.509516e+08</td>\n",
" <td>2.457348e+10</td>\n",
" <td>2.563378e+11</td>\n",
" <td>11.000000</td>\n",
" <td>52.000000</td>\n",
" <td>991.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" CampaignId AdGroupId KeywordId AveragePosition \\\n",
"count 5.622200e+04 5.622200e+04 5.622200e+04 56222.000000 \n",
"mean 1.509510e+08 1.233973e+10 2.121920e+10 2.963296 \n",
"std 2.384959e+02 5.637272e+09 2.513130e+10 1.942774 \n",
"min 1.509507e+08 7.953835e+09 3.764936e+07 1.000000 \n",
"25% 1.509508e+08 7.953837e+09 1.766531e+09 1.000000 \n",
"50% 1.509510e+08 7.953852e+09 1.080321e+10 2.300000 \n",
"75% 1.509513e+08 1.539922e+10 2.988553e+10 4.000000 \n",
"max 1.509516e+08 2.457348e+10 2.563378e+11 11.000000 \n",
"\n",
" Clicks Impressions \n",
"count 56222.000000 56222.000000 \n",
"mean 0.509374 10.130056 \n",
"std 1.455485 36.591271 \n",
"min 0.000000 1.000000 \n",
"25% 0.000000 1.000000 \n",
"50% 0.000000 2.000000 \n",
"75% 1.000000 4.000000 \n",
"max 52.000000 991.000000 "
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train.describe()"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"DESKTOP 32934\n",
"TABLET 14509\n",
"HIGH_END_MOBILE 8779\n",
"Name: Device, dtype: int64"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train.Device.value_counts()\n"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"150950810.0 19058\n",
"150951290.0 18870\n",
"150951050.0 9304\n",
"150950690.0 4195\n",
"150950930.0 2139\n",
"150951410.0 1788\n",
"150951650.0 465\n",
"150951530.0 205\n",
"150951170.0 198\n",
"Name: CampaignId, dtype: int64"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train.CampaignId.value_counts()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Preprocessing"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Extract / Split feature labels by type."
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
"numeric_cols = ['AveragePosition']\n",
"categorial_cols = list(set(train.columns) - set(numeric_cols))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Scale numeric features."
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [],
"source": [
"SCALER = StandardScaler()\n",
"X_train_num = pd.DataFrame(SCALER.fit_transform(train[numeric_cols]), columns=numeric_cols)\n",
"X_test_num = pd.DataFrame(SCALER.transform(test[numeric_cols]), columns=numeric_cols)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Map *ids* to strings. *IDs* are not real values but class values."
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"categorial_float_cols = ['CampaignId', 'AdGroupId', 'KeywordId']\n",
"X_train_cat = train[categorial_float_cols].applymap(int).applymap(str)\n",
"X_test_cat = test[categorial_float_cols].applymap(int).applymap(str)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"*Date* is not a great feature but day of a year might be reasonable."
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [],
"source": [
"X_train_date = train.Date.apply(lambda rec: str(dt.datetime.strptime(rec, \"%Y-%m-%d\").date().timetuple().tm_yday))\n",
"X_train_cat = pd.concat([X_train_cat, X_train_date], axis=1)\n",
"\n",
"X_test_date = test.Date.apply(lambda rec: str(dt.datetime.strptime(rec, \"%Y-%m-%d\").date().timetuple().tm_yday))\n",
"X_test_cat = pd.concat([X_test_cat, X_test_date], axis=1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Vectorize categorials to binaries"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [],
"source": [
"cat_encoder = DV(sparse=False)\n",
"\n",
"encoded_train_cat = cat_encoder.fit_transform(X_train_cat.T.to_dict().values())\n",
"encoded_test_cat = cat_encoder.transform(X_test_cat.T.to_dict().values())`"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"###### TODO: balance (sample) classes"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Concat numeric and categorial"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [],
"source": [
"X_train = pd.concat([X_train_num, pd.DataFrame(encoded_train_cat)], axis=1)\n",
"X_test = pd.concat([X_test_num, pd.DataFrame(encoded_test_cat)], axis=1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Train regressor"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [],
"source": [
"estimator = CatBoostRegressor(verbose=True)\n"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"collapsed": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Borders generated\n",
"0:\tlearn 0.3956714289passed: 1.53 sec\ttotal: 4.55s\tremaining: 37m 50s\n",
"1:\tlearn 0.3921500625passed: 1.54 sec\ttotal: 6.09s\tremaining: 25m 17s\n",
"2:\tlearn 0.3887630263passed: 1.47 sec\ttotal: 7.57s\tremaining: 20m 53s\n",
"3:\tlearn 0.3856912952passed: 1.5 sec\ttotal: 9.07s\tremaining: 18m 44s\n",
"4:\tlearn 0.3825765155passed: 1.68 sec\ttotal: 10.8s\tremaining: 17m 44s\n",
"5:\tlearn 0.3797674968passed: 1.71 sec\ttotal: 12.5s\tremaining: 17m 6s\n",
"6:\tlearn 0.3769521238passed: 1.58 sec\ttotal: 14s\tremaining: 16m 29s\n",
"7:\tlearn 0.374355343passed: 1.5 sec\ttotal: 15.5s\tremaining: 15m 56s\n",
"8:\tlearn 0.3717064351passed: 1.52 sec\ttotal: 17.1s\tremaining: 15m 31s\n",
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]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
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]
}
],
"source": [
"estimator.fit(X_train, Y_train)\n",
"\n",
"answer = pd.DataFrame(estimator.predict(X_test))"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {
"collapsed": true
},
"outputs": [
{
"data": {
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"</table>\n",
"<p>87804 rows × 2 columns</p>\n",
"</div>"
],
"text/plain": [
" ClickProbability RowId\n",
"0 0.150815 0\n",
"1 0.166842 1\n",
"2 0.048369 2\n",
"3 0.060394 3\n",
"4 0.150815 4\n",
"5 0.127208 5\n",
"6 0.028498 6\n",
"7 0.162666 7\n",
"8 0.071013 8\n",
"9 0.048369 9\n",
"10 0.028498 10\n",
"11 0.048369 11\n",
"12 0.028896 12\n",
"13 0.332460 13\n",
"14 0.160068 14\n",
"15 0.071013 15\n",
"16 0.186185 16\n",
"17 0.060394 17\n",
"18 0.186185 18\n",
"19 0.044052 19\n",
"20 0.061326 20\n",
"21 0.168530 21\n",
"22 0.150815 22\n",
"23 0.095391 23\n",
"24 0.034365 24\n",
"25 0.320750 25\n",
"26 0.071013 26\n",
"27 0.071013 27\n",
"28 0.061326 28\n",
"29 0.247529 29\n",
"... ... ...\n",
"87774 0.082573 87774\n",
"87775 0.157355 87775\n",
"87776 0.150815 87776\n",
"87777 0.048369 87777\n",
"87778 0.162666 87778\n",
"87779 0.080127 87779\n",
"87780 0.052029 87780\n",
"87781 0.160068 87781\n",
"87782 0.056679 87782\n",
"87783 0.028896 87783\n",
"87784 0.186185 87784\n",
"87785 0.028498 87785\n",
"87786 0.132345 87786\n",
"87787 0.064769 87787\n",
"87788 0.166842 87788\n",
"87789 0.052029 87789\n",
"87790 0.096100 87790\n",
"87791 0.186185 87791\n",
"87792 0.160068 87792\n",
"87793 0.028498 87793\n",
"87794 0.028498 87794\n",
"87795 0.028896 87795\n",
"87796 0.247529 87796\n",
"87797 0.032356 87797\n",
"87798 0.071013 87798\n",
"87799 0.071013 87799\n",
"87800 0.028896 87800\n",
"87801 0.100854 87801\n",
"87802 0.028498 87802\n",
"87803 0.089960 87803\n",
"\n",
"[87804 rows x 2 columns]"
]
},
"execution_count": 49,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"answer.columns = ['ClickProbability']\n",
"answer['RowId'] = np.arange(0, answer.shape[0])\n",
"answer.to_csv('answer.csv',index=False)\n",
"answer"
]
}
],
"metadata": {
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"language": "python",
"name": "python3"
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"file_extension": ".py",
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"name": "python",
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"pygments_lexer": "ipython3",
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