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dingdong.py
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dingdong.py
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"""
Ref: https://www.kaggle.com/the1owl/surprise-me
with inspiration from other kernels
"""
import glob, re
import numpy as np
import pandas as pd
from sklearn import preprocessing
from sklearn.model_selection import GridSearchCV
import xgboost as xgb
from datetime import datetime
data = {
'tra':
pd.read_csv('../input/air_visit_data.csv'),
'as':
pd.read_csv('../input/air_store_info.csv'),
'hs':
pd.read_csv('../input/hpg_store_info.csv'),
'ar':
pd.read_csv('../input/air_reserve.csv'),
'hr':
pd.read_csv('../input/hpg_reserve.csv'),
'id':
pd.read_csv('../input/store_id_relation.csv'),
'tes':
pd.read_csv('../input/sample_submission.csv'),
'hol':
pd.read_csv('../input/date_info.csv').rename(columns={
'calendar_date': 'visit_date'
})
}
data['hr'] = pd.merge(data['hr'], data['id'], how='inner', on=['hpg_store_id'])
for df in ['ar', 'hr']:
data[df]['visit_datetime'] = pd.to_datetime(data[df]['visit_datetime'])
data[df]['visit_datetime'] = data[df]['visit_datetime'].dt.date
data[df]['reserve_datetime'] = pd.to_datetime(data[df]['reserve_datetime'])
data[df]['reserve_datetime'] = data[df]['reserve_datetime'].dt.date
data[df]['reserve_datetime_diff'] = data[df].apply(
lambda r: (r['visit_datetime'] - r['reserve_datetime']).days, axis=1)
data[df] = data[df].groupby(
['air_store_id', 'visit_datetime'], as_index=False)[[
'reserve_datetime_diff', 'reserve_visitors'
]].sum().rename(columns={
'visit_datetime': 'visit_date'
})
print(data[df].head())
data['tra']['visit_date'] = pd.to_datetime(data['tra']['visit_date'])
data['tra']['dow'] = data['tra']['visit_date'].dt.dayofweek
data['tra']['year'] = data['tra']['visit_date'].dt.year
data['tra']['month'] = data['tra']['visit_date'].dt.month
data['tra']['visit_date'] = data['tra']['visit_date'].dt.date
data['tes']['visit_date'] = data['tes']['id'].map(
lambda x: str(x).split('_')[2])
data['tes']['air_store_id'] = data['tes']['id'].map(
lambda x: '_'.join(x.split('_')[:2]))
data['tes']['visit_date'] = pd.to_datetime(data['tes']['visit_date'])
data['tes']['dow'] = data['tes']['visit_date'].dt.dayofweek
data['tes']['year'] = data['tes']['visit_date'].dt.year
data['tes']['month'] = data['tes']['visit_date'].dt.month
data['tes']['visit_date'] = data['tes']['visit_date'].dt.date
unique_stores = data['tes']['air_store_id'].unique()
stores = pd.concat(
[
pd.DataFrame({
'air_store_id': unique_stores,
'dow': [i] * len(unique_stores)
}) for i in range(7)
],
axis=0,
ignore_index=True).reset_index(drop=True)
#sure it can be compressed...
tmp = data['tra'].groupby(
['air_store_id', 'dow'],
as_index=False)['visitors'].min().rename(columns={
'visitors': 'min_visitors'
})
stores = pd.merge(stores, tmp, how='left', on=['air_store_id', 'dow'])
tmp = data['tra'].groupby(
['air_store_id', 'dow'],
as_index=False)['visitors'].mean().rename(columns={
'visitors': 'mean_visitors'
})
stores = pd.merge(stores, tmp, how='left', on=['air_store_id', 'dow'])
tmp = data['tra'].groupby(
['air_store_id', 'dow'],
as_index=False)['visitors'].median().rename(columns={
'visitors': 'median_visitors'
})
stores = pd.merge(stores, tmp, how='left', on=['air_store_id', 'dow'])
tmp = data['tra'].groupby(
['air_store_id', 'dow'],
as_index=False)['visitors'].max().rename(columns={
'visitors': 'max_visitors'
})
stores = pd.merge(stores, tmp, how='left', on=['air_store_id', 'dow'])
tmp = data['tra'].groupby(
['air_store_id', 'dow'],
as_index=False)['visitors'].count().rename(columns={
'visitors': 'count_observations'
})
stores = pd.merge(stores, tmp, how='left', on=['air_store_id', 'dow'])
stores = pd.merge(stores, data['as'], how='left', on=['air_store_id'])
lbl = preprocessing.LabelEncoder()
stores['air_genre_name'] = lbl.fit_transform(stores['air_genre_name'])
stores['air_area_name'] = lbl.fit_transform(stores['air_area_name'])
data['hol']['visit_date'] = pd.to_datetime(data['hol']['visit_date'])
data['hol']['day_of_week'] = lbl.fit_transform(data['hol']['day_of_week'])
data['hol']['visit_date'] = data['hol']['visit_date'].dt.date
train = pd.merge(data['tra'], data['hol'], how='left', on=['visit_date'])
test = pd.merge(data['tes'], data['hol'], how='left', on=['visit_date'])
train = pd.merge(data['tra'], stores, how='left', on=['air_store_id', 'dow'])
test = pd.merge(data['tes'], stores, how='left', on=['air_store_id', 'dow'])
stores['air_genre_name'] = stores['air_genre_name'].map(lambda x: str(str(x).replace('/',' ')))
stores['air_area_name'] = stores['air_area_name'].map(lambda x: str(str(x).replace('-',' ')))
lbl = preprocessing.LabelEncoder()
for i in range(10):
stores['air_genre_name'+str(i)] = lbl.fit_transform(stores['air_genre_name'].map(lambda x: str(str(x).split(' ')[i]) if len(str(x).split(' '))>i else ''))
stores['air_area_name'+str(i)] = lbl.fit_transform(stores['air_area_name'].map(lambda x: str(str(x).split(' ')[i]) if len(str(x).split(' '))>i else ''))
stores['air_genre_name'] = lbl.fit_transform(stores['air_genre_name'])
stores['air_area_name'] = lbl.fit_transform(stores['air_area_name'])
stores['area']=stores['air_area_name'].map(lambda x: str(str(x).split(' ')[:2]))
stores['area']=lbl.fit_transform(stores['area'])
stores['area']=stores['air_area_name'].map(lambda x: str(str(x).split(' ')[:2]))
stores['area']=lbl.fit_transform(stores['area'])
data['hol']['visit_date'] = pd.to_datetime(data['hol']['visit_date'])
data['hol']['day_of_week'] = lbl.fit_transform(data['hol']['day_of_week'])
data['hol']['visit_date'] = data['hol']['visit_date'].dt.date
train = pd.merge(data['tra'], data['hol'], how='left', on=['visit_date'])
test = pd.merge(data['tes'], data['hol'], how='left', on=['visit_date'])
train = pd.merge(train, stores, how='left', on=['air_store_id','dow'])
test = pd.merge(test, stores, how='left', on=['air_store_id','dow'])
for df in ['ar','hr']:
train = pd.merge(train, data[df], how='left', on=['air_store_id','visit_date'])
test = pd.merge(test, data[df], how='left', on=['air_store_id','visit_date'])
train['id'] = train.apply(lambda r: '_'.join([str(r['air_store_id']), str(r['visit_date'])]), axis=1)
train['total_reserv_sum'] = train['rv1_x'] + train['rv1_y']
train['total_reserv_mean'] = (train['rv2_x'] + train['rv2_y']) / 2
train['total_reserv_dt_diff_mean'] = (train['rs2_x'] + train['rs2_y']) / 2
test['total_reserv_sum'] = test['rv1_x'] + test['rv1_y']
test['total_reserv_mean'] = (test['rv2_x'] + test['rv2_y']) / 2
test['total_reserv_dt_diff_mean'] = (test['rs2_x'] + test['rs2_y']) / 2
train['date_int'] = train['visit_date'].apply(lambda x: x.strftime('%Y%m%d')).astype(int)
test['date_int'] = test['visit_date'].apply(lambda x: x.strftime('%Y%m%d')).astype(int)
train['var_max_lat'] = train['latitude'].max() - train['latitude']
train['var_max_long'] = train['longitude'].max() - train['longitude']
test['var_max_lat'] = test['latitude'].max() - test['latitude']
test['var_max_long'] = test['longitude'].max() - test['longitude']
train['lon_plus_lat'] = train['longitude'] + train['latitude']
test['lon_plus_lat'] = test['longitude'] + test['latitude']
lbl = preprocessing.LabelEncoder()
train['air_store_id2'] = lbl.fit_transform(train['air_store_id'])
test['air_store_id2'] = lbl.transform(test['air_store_id'])
col = [c for c in train if c not in ['id', 'air_store_id', 'visit_date','visitors']]
train = train.fillna(-1)
test = test.fillna(-1)
for c, dtype in zip(train.columns, train.dtypes):
if dtype == np.float64:
train[c] = train[c].astype(np.float32)
for c, dtype in zip(test.columns, test.dtypes):
if dtype == np.float64:
test[c] = test[c].astype(np.float32)
train_x = train.drop(['air_store_id', 'visit_date', 'visitors'], axis=1)
train_y = np.log1p(train['visitors'].values)
#print(train_x.shape, train_y.shape)
test_x = test.drop(['id', 'air_store_id', 'visit_date', 'visitors'], axis=1)
boost_params = {'eval_metric': 'rmse'}
x = xgb.XGBRegressor(
max_depth=8,
learning_rate=0.01,
n_estimators=10000,
objective='reg:linear',
gamma=0,
min_child_weight=1,
subsample=1,
colsample_bytree=1,
scale_pos_weight=1,
seed=27,
**boost_params)
x.fit(train_x, train_y)
predict_y = x.predict(test_x)
#print(predict_y)
test['visitors'] = np.expm1(predict_y)
test[['id', 'visitors']].to_csv('submission.csv', index=False, float_format='%.3f')