Kaggle competition: Google Analytics Customer Revenue Prediction
<SOS> metric: RMSE Kaggle data source: google-analytics-customer-revenue-prediction<train_model>
warnings.simplefilter("ignore")
 pd.set_option("display.max_rows", 1000, "display.max_columns", 1000)
 pd.options.mode.chained_assignment = None
def load_df(csv_path='.. /input/ga-customer-revenue-prediction/train.csv', nrows=None):
JSON_COLUMNS = ['device', 'geoNetwork', 'totals', 'trafficSource']
df = pd.read_csv(csv_path,
converters={column: json.loads for column in JSON_COLUMNS},
dtype={'fullVisitorId': 'str'},
nrows=nrows)
 for column in JSON_COLUMNS:
column_as_df = json_normalize(df[column])
 column_as_df.columns = [f"{column}.{subcolumn}" for subcolumn in column_as_df.columns]
df = df.drop(column, axis=1 ).merge(column_as_df, right_index=True, left_index=True)
 print(f"Loaded {os.path.basename(csv_path)}.Shape: {df.shape}")
 return df
train = load_df()
test = load_df(".. /input/ga-customer-revenue-prediction/test.csv")
 train_store_1 = pd.read_csv('.. /input/exported-google-analytics-data/Train_external_data.csv', low_memory=False, skiprows=6, dtype={"Client Id":'str'})
 train_store_2 = pd.read_csv('.. /input/exported-google-analytics-data/Train_external_data_2.csv', low_memory=False, skiprows=6, dtype={"Client Id":'str'})
 test_store_1 = pd.read_csv('.. /input/exported-google-analytics-data/Test_external_data.csv', low_memory=False, skiprows=6, dtype={"Client Id":'str'})
 test_store_2 = pd.read_csv('.. /input/exported-google-analytics-data/Test_external_data_2.csv', low_memory=False, skiprows=6, dtype={"Client Id":'str'})
 def get_visitid(id):
bef_, af_ = str(x ).split('.')
 return int(bef_),(int(af_)*10 if len(af_)==1 else int(af_))
for df in [train_store_1, train_store_2, test_store_1, test_store_2]:
df["visitId"] = df["Client Id"].apply(lambda x: x.split('.', 1)[1] ).astype(str)
 train_exdata = pd.concat([train_store_1, train_store_2], sort=False)
 test_exdata = pd.concat([test_store_1, test_store_2], sort=False)
 for df in [train, test]:
df["visitId"] = df["visitId"].astype(str)
 train_new = train.merge(train_exdata, how="left", on="visitId")
 test_new = test.merge(test_exdata, how="left", on="visitId")
 for df in [train_new, test_new]:
df.drop("Client Id", 1, inplace=True)
 for df in [train_new, test_new]:
df["Revenue"].fillna('$', inplace=True)
 df["Revenue"] = df["Revenue"].apply(lambda x: x.replace('$', '' ).replace(',', ''))
df["Revenue"] = pd.to_numeric(df["Revenue"], errors="coerce")
 df["Revenue"].fillna(0.0, inplace=True)
 for df in [train_new, test_new]:
df["Sessions"] = df["Sessions"].fillna(0)
 df["Avg.Session Duration"] = df["Avg.Session Duration"].fillna(0)
 df["Bounce Rate"] = df["Bounce Rate"].fillna(0)
 df["Revenue"] = df["Revenue"].fillna(0)
 df["Transactions"] = df["Transactions"].fillna(0)
 df["Goal Conversion Rate"] = df["Goal Conversion Rate"].fillna(0)
 df['trafficSource.adContent'].fillna('N/A', inplace=True)
 df['trafficSource.adwordsClickInfo.slot'].fillna('N/A', inplace=True)
 df['trafficSource.adwordsClickInfo.page'].fillna(0.0, inplace=True)
 df['trafficSource.adwordsClickInfo.isVideoAd'].fillna('N/A', inplace=True)
 df['trafficSource.adwordsClickInfo.adNetworkType'].fillna('N/A', inplace=True)
 df['trafficSource.adwordsClickInfo.gclId'].fillna('N/A', inplace=True)
 df['trafficSource.isTrueDirect'].fillna('N/A', inplace=True)
 df['trafficSource.referralPath'].fillna('N/A', inplace=True)
 df['trafficSource.keyword'].fillna('N/A', inplace=True)
 df['totals.bounces'].fillna(0.0, inplace=True)
 df['totals.newVisits'].fillna(0.0, inplace=True)
 df['totals.pageviews'].fillna(0.0, inplace=True)
 del train
del test
train = train_new
test = test_new
del train_new
del test_new
for df in [train, test]:
df['date_new'] = pd.to_datetime(df['visitStartTime'], unit='s')
 df['date'] = df['date_new'].dt.date
df['sess_date_dow'] = df['date_new'].dt.dayofweek
df['sess_date_hours'] = df['date_new'].dt.hour
df['sess_month'] = df['date_new'].dt.month
const_cols = [col for col in train.columns if len(train[col].unique())==1]
for df in [train, test]:
df.drop(const_cols, 1, inplace=True)
 train.drop('trafficSource.campaignCode', 1, inplace=True)
 def get_folds(df=None, n_splits=5):

 unique_vis = np.array(sorted(df['fullVisitorId'].unique()))
folds = GroupKFold(n_splits=n_splits)
 fold_ids = []
ids = np.arange(df.shape[0])
 for trn_vis, val_vis in folds.split(X=unique_vis, y=unique_vis, groups=unique_vis):
fold_ids.append(
 [
ids[df['fullVisitorId'].isin(unique_vis[trn_vis])],
ids[df['fullVisitorId'].isin(unique_vis[val_vis])]
] 
)
 return fold_ids
y_reg = train['totals.transactionRevenue'].fillna(0)
 del train['totals.transactionRevenue']
if 'totals.transactionRevenue' in test.columns:
del test['totals.transactionRevenue']
excluded_features = [
'date', 'date_new', 'fullVisitorId', 'sessionId', 'totals.transactionRevenue',
'visitId', 'visitStartTime'
]
categorical_features = [
_f for _f in train.columns
if(_f not in excluded_features)&(train[_f].dtype == 'object')
 ]
for f in categorical_features:
train[f], indexer = pd.factorize(train[f])
 test[f] = indexer.get_indexer(test[f])
 for df in [train, test]:
df['hits/pageviews'] =(df["totals.pageviews"]/(df["totals.hits"])).apply(lambda x: 0 if np.isinf(x)else x)
 df['is_high_hits'] = np.logical_or(df["totals.hits"]>4,df["totals.pageviews"]>4 ).astype(np.int32)
 df["Revenue"] = np.log1p(df["Revenue"])
 warnings.simplefilter(action='ignore', category=FutureWarning)
 folds = get_folds(df=train, n_splits=2)
 y_reg = y_reg.astype(float)
 train_features = [_f for _f in train.columns if _f not in excluded_features]
print(train_features)
 importances = pd.DataFrame()
oof_reg_preds = np.zeros(train.shape[0])
 sub_reg_preds = np.zeros(test.shape[0])
 for fold_,(trn_, val_)in enumerate(folds):
trn_x, trn_y = train[train_features].iloc[trn_], y_reg.iloc[trn_]
val_x, val_y = train[train_features].iloc[val_], y_reg.iloc[val_]
reg = lgb.LGBMRegressor(
 num_leaves=31,
learning_rate=0.03,
n_estimators=1000,
subsample=.9,
colsample_bytree=.9,
random_state=1 
)
 reg.fit(
 trn_x, np.log1p(trn_y),
eval_set=[(val_x, np.log1p(val_y)) ],
early_stopping_rounds=100,
verbose=100,
eval_metric='rmse' 
)
 imp_df = pd.DataFrame()
imp_df['feature'] = train_features
imp_df['gain'] = reg.booster_.feature_importance(importance_type='gain')
 imp_df['fold'] = fold_ + 1
importances = pd.concat([importances, imp_df], axis=0, sort=False)
 oof_reg_preds[val_] = reg.predict(val_x, num_iteration=reg.best_iteration_)
 oof_reg_preds[oof_reg_preds < 0] = 0
_preds = reg.predict(test[train_features], num_iteration=reg.best_iteration_)
 _preds[_preds < 0] = 0
sub_reg_preds += np.expm1(_preds)/ len(folds)
 print(mean_squared_error(np.log1p(y_reg), oof_reg_preds)**.5)
 train['predictions'] = np.expm1(oof_reg_preds)
 test['predictions'] = sub_reg_preds
trn_data = train[train_features + ['fullVisitorId']].groupby('fullVisitorId' ).mean()
trn_pred_list = train[['fullVisitorId', 'predictions']].groupby('fullVisitorId')\ 
.apply(lambda df: list(df.predictions)) \ 
.apply(lambda x: {'pred_'+str(i): pred for i, pred in enumerate(x)})
 trn_all_predictions = pd.DataFrame(list(trn_pred_list.values), index=trn_data.index)
 trn_feats = trn_all_predictions.columns
with warnings.catch_warnings() :
warnings.simplefilter("ignore")
 trn_all_predictions['t_mean'] = np.log1p(trn_all_predictions[trn_feats].mean(axis=1))
trn_all_predictions['t_median'] = np.log1p(trn_all_predictions[trn_feats].median(axis=1))
trn_all_predictions['t_sum_log'] = np.log1p(trn_all_predictions[trn_feats] ).sum(axis=1)
 trn_all_predictions['t_sum_act'] = np.log1p(trn_all_predictions[trn_feats].fillna(0 ).sum(axis=1))
trn_all_predictions['t_nb_sess'] = trn_all_predictions[trn_feats].isnull().sum(axis=1)
 full_data = pd.concat([trn_data, trn_all_predictions], axis=1)
 del trn_data, trn_all_predictions
gc.collect()
print(full_data.shape)
 sub_pred_list = test[['fullVisitorId', 'predictions']].groupby('fullVisitorId')\ 
.apply(lambda df: list(df.predictions)) \ 
.apply(lambda x: {'pred_'+str(i): pred for i, pred in enumerate(x)})
 sub_data = test[train_features + ['fullVisitorId']].groupby('fullVisitorId' ).mean()
sub_all_predictions = pd.DataFrame(list(sub_pred_list.values), index=sub_data.index)
 with warnings.catch_warnings() :
warnings.simplefilter("ignore")
 for f in trn_feats:
if f not in sub_all_predictions.columns:
sub_all_predictions[f] = np.nan
sub_all_predictions['t_mean'] = np.log1p(sub_all_predictions[trn_feats].mean(axis=1))
sub_all_predictions['t_median'] = np.log1p(sub_all_predictions[trn_feats].median(axis=1))
sub_all_predictions['t_sum_log'] = np.log1p(sub_all_predictions[trn_feats] ).sum(axis=1)
 sub_all_predictions['t_sum_act'] = np.log1p(sub_all_predictions[trn_feats].fillna(0 ).sum(axis=1))
sub_all_predictions['t_nb_sess'] = sub_all_predictions[trn_feats].isnull().sum(axis=1)
 sub_full_data = pd.concat([sub_data, sub_all_predictions], axis=1)
 del sub_data, sub_all_predictions
gc.collect()
print(sub_full_data.shape)
 train['target'] = y_reg
trn_user_target = train[['fullVisitorId', 'target']].groupby('fullVisitorId' ).sum()
warnings.simplefilter(action='ignore', category=FutureWarning)
 folds = get_folds(df=full_data[['totals.pageviews']].reset_index() , n_splits=2)
 oof_preds = np.zeros(full_data.shape[0])
 sub_preds = np.zeros(sub_full_data.shape[0])
 vis_importances = pd.DataFrame()
for fold_,(trn_, val_)in enumerate(folds):
trn_x, trn_y = full_data.iloc[trn_], trn_user_target['target'].iloc[trn_]
val_x, val_y = full_data.iloc[val_], trn_user_target['target'].iloc[val_]
reg = lgb.LGBMRegressor(
 num_leaves=31,
learning_rate=0.03,
n_estimators=1000,
subsample=.9,
colsample_bytree=.9,
random_state=1 
)
 reg.fit(
 trn_x, np.log1p(trn_y),
eval_set=[(trn_x, np.log1p(trn_y)) ,(val_x, np.log1p(val_y)) ],
eval_names=['TRAIN', 'VALID'],
early_stopping_rounds=100,
eval_metric='rmse',
verbose=100 
)
 imp_df = pd.DataFrame()
imp_df['feature'] = trn_x.columns
imp_df['gain'] = reg.booster_.feature_importance(importance_type='gain')
 imp_df['fold'] = fold_ + 1
vis_importances = pd.concat([vis_importances, imp_df], axis=0, sort=False)
 oof_preds[val_] = reg.predict(val_x, num_iteration=reg.best_iteration_)
 oof_preds[oof_preds < 0] = 0
_preds = reg.predict(sub_full_data[full_data.columns], num_iteration=reg.best_iteration_)
 _preds[_preds < 0] = 0
sub_preds += _preds / len(folds)
 print(mean_squared_error(np.log1p(trn_user_target['target']), oof_preds)**.5)
 sub_full_data['PredictedLogRevenue'] = sub_preds
sub_full_data[['PredictedLogRevenue']].to_csv('submission_new.csv', index=True )