Kaggle competition: Google Analytics Customer Revenue Prediction
warnings.filterwarnings('ignore' )<install_modules>
warnings.simplefilter('error', SettingWithCopyWarning)
 gc.enable()
%matplotlib inline
!cp -rp /kaggle/input/pyspellchecker-and-ekpharasis-libs-for-python /kaggle/working/
!cp -rp /kaggle/input/transformers280 /kaggle/working/
!pip install /kaggle/working/pyspellchecker-and-ekpharasis-libs-for-python/pyspellchecker-0.5.4-py2.py3-none-any.whl >> /dev/null
!pip install /kaggle/working/pyspellchecker-and-ekpharasis-libs-for-python/ekphrasis-0.5.1/ekphrasis-0.5.1/ >> /dev/null
!python /kaggle/working/pyspellchecker-and-ekpharasis-libs-for-python/ekphrasis-0.5.1/ekphrasis-0.5.1/setup.py install >> /dev/null
!mkdir -p /root/.ekphrasis/stats
!cp -r /kaggle/working/pyspellchecker-and-ekpharasis-libs-for-python/stats/* /root/.ekphrasis/stats >> /dev/null
!pip install /kaggle/working/transformers280/transformers-2.8.0 >> /dev/null
!rm -rf /kaggle/working/*<define_variables>
train = pd.read_csv('.. /input/create-extracted-json-fields-dataset/extracted_fields_train.gz',
dtype={'date': str, 'fullVisitorId': str, 'sessionId':str}, nrows=None)
 test = pd.read_csv('.. /input/create-extracted-json-fields-dataset/extracted_fields_test.gz',
dtype={'date': str, 'fullVisitorId': str, 'sessionId':str}, nrows=None)
 train.shape, test.shape
in_testing_mode = True<import_modules>
y_reg = train['totals.transactionRevenue'].fillna(0)
 del train['totals.transactionRevenue']
if 'totals.transactionRevenue' in test.columns:
del test['totals.transactionRevenue']
import numpy as np
import pandas as pd
import random
import copy
import os
import re
from nltk.tokenize import SpaceTokenizer
from ekphrasis.classes.preprocessor import TextPreProcessor
from ekphrasis.classes.tokenizer import SocialTokenizer
from ekphrasis.dicts.emoticons import emoticons
from spellchecker import SpellChecker
import torch
import torch.nn as nn
from torch.utils.data import TensorDataset, DataLoader, RandomSampler, SequentialSampler
from transformers import RobertaConfig, RobertaModel, BertPreTrainedModel, RobertaTokenizer, AdamW, get_linear_schedule_with_warmup
import datetime, time
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import train_test_split<define_variables>
for df in [train, test]:
df['date'] = pd.to_datetime(df['visitStartTime'], unit='s')
 df['sess_date_dow'] = df['date'].dt.dayofweek
df['sess_date_hours'] = df['date'].dt.hour
df['sess_date_dom'] = df['date'].dt.day
input_dir = '/kaggle/input'
working_dir = '/kaggle/working'
data_dir = os.path.join(input_dir, "tweet-sentiment-extraction/")
 model_dir = os.path.join(input_dir, "robertabase/")
 train_file = os.path.join(data_dir, "train.csv")
 test_file = os.path.join(data_dir, "test.csv")
 sample_submission_file = os.path.join(data_dir, "sample_submission.csv")
 submission_file = os.path.join(working_dir, "submission.csv" )<string_transform>
excluded_features = [
'date', 'fullVisitorId', 'sessionId', 'totals.transactionRevenue',
'visitId', 'visitStartTime'
]
categorical_features = [
_f for _f in train.columns
if(_f not in excluded_features)&(train[_f].dtype == 'object')
 ]
def jaccard(str1, str2):
a = set(str1.lower().split())
 b = set(str2.lower().split())
 c = a.intersection(b)
 return float(len(c)) /(len(a)+ len(b)- len(c))<feature_engineering>
for f in categorical_features:
train[f], indexer = pd.factorize(train[f])
 test[f] = indexer.get_indexer(test[f] )
dfJaccards = copy.deepcopy(dfTrain)
 dfJaccards["jaccard"] = dfJaccards.apply(lambda df: jaccard(df.text, df.selected_text), axis=1)
 print("Ratios of selected text in text")
 dfJaccards = dfJaccards[['sentiment','jaccard']].groupby(['sentiment'] ).mean()
dfJaccards<string_transform>
folds = get_folds(df=train, n_splits=5)
 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=50,
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)
 mean_squared_error(np.log1p(y_reg), oof_reg_preds)**.5
def stringtowords(text, selected_text):
text_words = str(text ).lower().split()
selected_text_words = str(selected_text ).lower().split()
selected_length = len(selected_text_words)
 start_char = "".join(text_words ).find("".join(selected_text_words))
if(start_char >= 0 and selected_length > 0):
remaining_char = start_char
for i,word in enumerate(text_words):
remaining_char-=len(word)
 if remaining_char < 0:
selected_text_words = text_words[i:i+selected_length]
break
text_word_numbers = [i for i in range(len(text_words)) ]
return text_words, text_word_numbers ,selected_text_words<define_variables>
train['predictions'] = np.expm1(oof_reg_preds)
 test['predictions'] = sub_reg_preds
test_string = "is back home now gonna miss every one"
test_selected_string = "onna mis"
print("Sample text:\t", test_string)
 print("Sample selected text:\t", test_selected_string)
 output_tweet_list, output_tweet_word_numbers, output_selected_list = stringtowords(test_string,test_selected_string)
 print("
stringtowords outputs")
 print("output tweet list: ",output_tweet_list)
 print("output tweet word numbers:",output_tweet_word_numbers)
 print("output selected text list: ",output_selected_list )<feature_engineering>
trn_data = train[train_features + ['fullVisitorId']].groupby('fullVisitorId' ).mean()
dfTrain['text_w'], dfTrain['text_w_n'], dfTrain['selected_w'] = zip(*dfTrain.apply(lambda df: stringtowords(df.text,df.selected_text), axis=1))
dfTrain.head()<drop_column>
%%time
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)} )
def fix_difference(old_word, new_word, oldtext_w_n, newtext_w, newtext_w_n, word_ind, expand_number):
new_word = [word for word in new_word if word != ""]
if len(new_word)== 0:
newtext_w.pop(word_ind+expand_number)
 if newtext_w_n is not None:
newtext_w_n.pop(word_ind+expand_number)
 expand_number -= 1
elif(len(new_word)> 1)|(new_word[0] != old_word):
for i, word in enumerate(new_word):
if i == 0:
newtext_w[word_ind+expand_number] = word
else:
expand_number += 1
newtext_w.insert(word_ind+expand_number,word)
 if newtext_w_n is not None:
newtext_w_n.insert(word_ind+expand_number,oldtext_w_n[word_ind])
 return newtext_w, newtext_w_n, expand_number<drop_column>
trn_all_predictions = pd.DataFrame(list(trn_pred_list.values), index=trn_data.index)
 trn_feats = trn_all_predictions.columns
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()
full_data.shape
def cleanstring(oldtext_w, oldtext_w_n = None):
dictionary = {'w/':'with',
'dont':'do not',
'doesnt':'does not',
'didnt':'did not',
'cant':'can not',
'couldnt':'could not',
'wouldnt':'would not',
'shouldnt':'should not'
}
expand = 0
newtext_w = copy.deepcopy(oldtext_w)
 newtext_w_n = copy.deepcopy(oldtext_w_n)
 for word_ind ,current_word in enumerate(oldtext_w):
new_word = current_word
if current_word in dictionary:
new_word = dictionary[current_word]
new_word = new_word.split()
newtext_w, newtext_w_n, expand = fix_difference(current_word, new_word, oldtext_w_n, newtext_w, newtext_w_n, word_ind, expand)
 return newtext_w, newtext_w_n<categorify>
%%time
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)} )
test_string = ["u","cant","seperate","it","w/","this"]
print("Before cleanstring")
 print(test_string)
 print("After cleanstring")
 print(cleanstring(test_string)[0] )<feature_engineering>
sub_data = test[train_features + ['fullVisitorId']].groupby('fullVisitorId' ).mean()
sub_all_predictions = pd.DataFrame(list(sub_pred_list.values), index=sub_data.index)
 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()
sub_full_data.shape
dfTrain['text_w'], dfTrain['text_w_n'] = zip(*dfTrain.apply(lambda df: cleanstring(df.text_w,df.text_w_n), axis=1))
dfTrain['selected_w'], _ = zip(*dfTrain.apply(lambda df: cleanstring(df.selected_w), axis=1))
print("===== E> Replaced unpunctuated texts with punctuated ones <3 =====" )<choose_model_class>
train['target'] = y_reg
trn_user_target = train[['fullVisitorId', 'target']].groupby('fullVisitorId' ).sum()
text_processor = TextPreProcessor(
 normalize=['url'],
annotate={},
fix_html=True,
segmenter="twitter",
corrector="twitter",
unpack_hashtags=False,
unpack_contractions=True,
spell_correct_elong=True,
tokenizer=SpaceTokenizer().tokenize,
dicts=[emoticons] 
)
 def tweetCleaner(oldtext_w, oldtext_w_n = None):
expand = 0
newtext_w = copy.deepcopy(oldtext_w)
 newtext_w_n = copy.deepcopy(oldtext_w_n)
 for word_ind ,current_word in enumerate(oldtext_w):
new_word = current_word
new_word = text_processor.pre_process_doc(current_word)
 for i,word in enumerate(new_word):
if "<" in word and ">" in word:
new_word[i] = re.sub("[<>]","",word)
 newtext_w, newtext_w_n, expand = fix_difference(current_word, new_word, oldtext_w_n, newtext_w, newtext_w_n, word_ind, expand)
 return newtext_w, newtext_w_n<feature_engineering>
df = pd.concat([full_data,sub_full_data],sort=False)
 trainsize = full_data.shape[0]
del full_data, sub_full_data
gc.collect()
df = df.reset_index(drop=False)
 for c in df.columns[1:]:
if(( df[c].min() >=0)&(df[c].max() >=10)) :
df[c] = np.log1p(df[c])
 elif(( df[c].min() <0)&(( df[c].max() -df[c].min())>=10)) :
df.loc[df[c]!=0,c] = np.sign(df.loc[df[c]!=0,c])*np.log(np.abs(df.loc[df[c]!=0,c]))
for c in df.columns[1:]:
ss = StandardScaler()
df.loc[~np.isfinite(df[c]),c] = np.nan
df.loc[~df[c].isnull() ,c] = ss.fit_transform(df.loc[~df[c].isnull() ,c].values.reshape(-1,1))
df.fillna(-99999,inplace=True)
 gp_trn_users = df[:trainsize].copy().set_index('fullVisitorId')
 maxval = np.log1p(trn_user_target['target'].values ).max()
gp_trn_users['target'] = np.log1p(trn_user_target['target'].values)
 gp_trn_users.target /= maxval
gp_sub_users = df[trainsize:].copy().set_index('fullVisitorId')
 newcols = [x.replace('.','_')for x in gp_trn_users.columns]
gp_trn_users.columns = newcols
newcols = [x.replace('.','_')for x in gp_sub_users.columns]
gp_sub_users.columns = newcols
del df
gc.collect()
dfTrain['text_w'], dfTrain['text_w_n'] = zip(*dfTrain.apply(lambda df: tweetCleaner(df.text_w,df.text_w_n), axis=1))
dfTrain['selected_w'], _ = zip(*dfTrain.apply(lambda df: tweetCleaner(df.selected_w), axis=1))
print("===== E> Applied ekphrasis text preprocessor <3 =====" )<drop_column>
def Output(p):
return 1.0/(1+np.exp(-p))
def GP1(data):
v = pd.DataFrame()
v["i0"] = 0.100000*np.tanh(((((( np.tanh(((( data["pred_4"])+(data["pred_6"])) /2.0)))) *(( 12.07693195343017578)))) -(( 10.0))))
v["i1"] = 0.100000*np.tanh(((np.where(( np.tanh(((((((((( 10.0)) -(data["pred_5"])))+(2.0)))-(data["pred_0"])))*(3.0)))) <0, 3.0,(( -3.0)-(( 10.0)))))*(((( 9.80441665649414062)) * 2.0))))
v["i2"] = 0.100000*np.tanh(((((( np.tanh(((((-1.0)/ 2.0)) +(data["pred_8"])))))+(np.maximum(((( np.tanh(((((-1.0)/ 2.0)) +(data["pred_8"])))))) ,(( -1.0)))))) *(( 6.0))))
v["i3"] = 0.100000*np.tanh(((( np.tanh(((-2.0)+(data["pred_3"])))))*(((( -1.0*(((((3.0)+(((((-3.0)-(( 9.0)))) * 2.0)))/2.0)))))* 2.0))))
v["i4"] = 0.100000*np.tanh(((( np.tanh(((data["pred_3"])-(( 2.44455146789550781)))))) *(( 10.55293273925781250))))
v["i5"] = 0.100000*np.tanh(((np.where(( np.tanh(((( data["pred_2"])+(-3.0)) /2.0)))>0, -3.0,(9.0)))*(((((((-3.0)-(( 9.0)))) * 2.0)) * 2.0))))
v["i6"] = 0.100000*np.tanh(((((((( np.tanh(((((((2.48090791702270508)) +(data["pred_4"])) /2.0)) +(-2.0)))))*(( 10.93176364898681641)))) * 2.0)) * 2.0))
v["i7"] = 0.100000*np.tanh(((((((( np.tanh(((data["pred_1"])-(2.0)))))* 2.0)) / 2.0)) *(( 9.0))))
v["i8"] = 0.100000*np.tanh(((((( np.where(((-3.0)-(( 9.55589389801025391)))< -99998, -1.0, 1.0)) >(( np.tanh(((data["pred_0"])-(-1.0)))))) *1.))*(((-3.0)* 2.0))))
v["i9"] = 0.100000*np.tanh(((( 12.06137466430664062)) *(( np.tanh(((( data["t_mean"])+(((-3.0)+(-2.0)))) /2.0))))))
v["i10"] = 0.100000*np.tanh(((-3.0)*(((( 10.0)) *(( np.tanh(((( 8.0)) -(data["t_mean"])))))))))
 v["i11"] = 0.100000*np.tanh(((((( -1.0*(((((5.0)) * 2.0)))))/ 2.0)) +(((np.where(( np.tanh(((data["pred_2"])-(3.0)))) <0,(np.tanh(((data["pred_2"])-(3.0)))) ,(6.70732259750366211)))* 2.0))))
v["i12"] = 0.100000*np.tanh(((( np.tanh(((((((-1.0*(( 3.0)))) * 2.0)) +(np.maximum(((-3.0)) ,(( data["t_mean"])))))/2.0)))) *(( 13.89055156707763672))))
v["i13"] = 0.100000*np.tanh(((( 7.0)) *(np.where(np.minimum(((( 5.90891504287719727))),(((((np.tanh(np.minimum(((((data["t_mean"])-(( 4.69426012039184570))))),(((((((-3.0)+(data["t_mean"])))+(np.maximum(((data["pred_5"])) ,(( -3.0)))))/2.0)))))))*(( np.tanh(np.minimum(((((data["t_mean"])-(( 4.69426012039184570))))),(((((((-3.0)+(data["t_mean"])))+(np.maximum(((data["pred_5"])) ,(( -3.0)))))/2.0)))))))))))>0,(np.tanh(np.minimum(((((data["t_mean"])-(( 4.69426012039184570))))),(((((((-3.0)+(data["t_mean"])))+(np.maximum(((data["pred_5"])) ,(( -3.0)))))/2.0)))))) ,(np.tanh(np.minimum(((((data["t_mean"])-(( 4.69426012039184570))))),(((((((-3.0)+(data["t_mean"])))+(np.maximum(((data["pred_5"])) ,(( -3.0)))))/2.0))))))))))
v["i14"] = 0.100000*np.tanh(((( 11.24391651153564453)) *(((( np.tanh(((((((data["t_sum_log"])+(((( -2.0)+(((( -1.0)+(-2.0)) /2.0)))/2.0)))/2.0)) +(((np.tanh(( -2.0)))* 2.0)))/2.0)))) * 2.0))))
v["i15"] = 0.100000*np.tanh(((np.where(( 7.0)>0,(np.tanh(((((((((data["t_sum_log"])/ 2.0)) <(data["pred_0"])) *1.))+(((data["t_sum_log"])-(( 6.0)))))/2.0))),(((((np.tanh(((((((((data["t_sum_log"])/ 2.0)) <(data["pred_0"])) *1.))+(((data["t_sum_log"])-(( 6.0)))))/2.0)))) *(((( 9.0)) *(( np.tanh(((((((((data["t_sum_log"])/ 2.0)) <(data["pred_0"])) *1.))+(((data["t_sum_log"])-(( 6.0)))))/2.0)))))))) * 2.0)))*(( 6.0))))
v["i16"] = 0.100000*np.tanh(((((( np.tanh(np.tanh(((( 1.0)-(( -1.0*(( 0.0)))))))))) -(( 9.39388847351074219)))) *(((((((( np.tanh(( -1.0*(( np.maximum(((-3.0)) ,(((( data["t_sum_log"])/ 2.0)))))))))) -(((-2.0)/ 2.0)))) * 2.0)) * 2.0))))
v["i17"] = 0.100000*np.tanh(((((np.where(( np.tanh(((( -3.0)+(((data["t_sum_act"])/ 2.0)))/2.0)))>0,(np.tanh(((( -3.0)+(((data["t_sum_act"])/ 2.0)))/2.0))),(((((((np.tanh(((( -3.0)+(((data["t_sum_act"])/ 2.0)))/2.0)))) +(( np.tanh(((( -3.0)+(((data["t_sum_act"])/ 2.0)))/2.0)))))) * 2.0)) +(( 1.0)))))* 2.0)) * 2.0))
v["i18"] = 0.100000*np.tanh(((((((( 12.23419475555419922)) *(( np.tanh(((np.maximum(((((-3.0)+(data["pred_3"])))) ,(( -3.0)))) +(((data["t_sum_log"])+(-3.0)))))))))* 2.0)) -(((((( 9.04281616210937500)) +(( 9.0)))) *(( np.tanh(((np.maximum(((((-3.0)+(data["pred_3"])))) ,(( -3.0)))) +(((data["t_sum_log"])+(-3.0)))))))))))
 v["i19"] = 0.100000*np.tanh(((((( np.tanh(((data["t_sum_log"])-(((np.maximum(((( 7.0))),(( data["t_sum_log"])))) / 2.0)))))) * 2.0)) * 2.0))
v["i20"] = 0.100000*np.tanh(((( np.tanh(np.minimum(((data["pred_5"])) ,(( data["totals_pageviews"])))))) *(( 13.97667598724365234))))
v["i21"] = 0.100000*np.tanh(((((np.tanh(np.maximum(((data["pred_4"])) ,(((( data["t_sum_log"])+(-3.0)))))))) +(((((( 6.0)) *(( np.tanh(np.maximum(((data["pred_4"])) ,(((( data["t_sum_log"])+(-3.0)))))))))) -(( 6.0)))))/2.0))
v["i22"] = 0.100000*np.tanh(((np.where(( np.tanh(((( data["pred_1"])+(-1.0)) /2.0)))<0, 2.0,(np.tanh(((( data["pred_1"])<(-1.0)) *1.))))) *(((-3.0)*(( 14.82431602478027344))))))
v["i23"] = 0.100000*np.tanh(((( 13.51132678985595703)) *(( np.tanh(((( -3.0)+(data["t_sum_log"])) /2.0))))))
v["i24"] = 0.100000*np.tanh(( -1.0*(((( np.minimum(((3.0)) ,(((np.tanh(((( data["t_sum_log"])<(( 9.0)))*1.))))))) *(( 11.02354240417480469)))))))
 v["i25"] = 0.100000*np.tanh(((((( 14.53642463684082031)) *(((np.where(( 14.62061119079589844)<0,(np.tanh(np.minimum(((data["t_sum_log"])) ,(( 3.0))))),(np.tanh(np.minimum(((data["t_sum_log"])) ,(( 3.0)))))))-(( np.tanh(((( 3.0)<(data["pred_2"])) *1.))))))))-(( 14.53642177581787109))))
v["i26"] = 0.100000*np.tanh(((( np.tanh(((data["t_sum_log"])+(-3.0)))))*(( 10.0))))
v["i27"] = 0.100000*np.tanh(((( 10.0)) *(( np.tanh(((( -1.0)+(((( data["t_median"])>(( 5.0)))*1.))) /2.0))))))
v["i28"] = 0.100000*np.tanh(((np.where(( np.tanh(((-2.0)+(data["t_sum_log"])))) >0, 0.0,(-1.0*(((7.0)))))) * 2.0))
v["i29"] = 0.100000*np.tanh(((((np.where(-3.0 < -99998,(np.tanh(((data["t_sum_act"])+(( -1.0*(((((((data["t_median"])+(-2.0)) /2.0)) *(np.maximum(((data["pred_2"])) ,(( -2.0)))))))))))) ,(((( -2.0)+(((( np.tanh(((data["t_sum_act"])+(( -1.0*(((((((data["t_median"])+(-2.0)) /2.0)) *(np.maximum(((data["pred_2"])) ,(( -2.0)))))))))))))* 2.0)))) * 2.0)))* 2.0)) * 2.0))
v["i30"] = 0.100000*np.tanh(((np.where(( 8.40624523162841797)>0,(((np.tanh(np.minimum(((data["t_mean"])) ,(((((((( data["t_sum_act"])+(-2.0)))*(data["t_sum_act"])))+(-1.0)))))))) *(( 8.40624523162841797))),(((np.tanh(np.minimum(((data["t_mean"])) ,(((((((( data["t_sum_act"])+(-2.0)))*(data["t_sum_act"])))+(-1.0)))))))) *(( 8.40624141693115234)))))-(( 8.31408786773681641))))
v["i31"] = 0.100000*np.tanh(((((( 3.0)) *(((( np.where(3.0 < -99998,(9.90981674194335938),(np.tanh(((((data["t_sum_log"])-(((( np.maximum(((data["t_mean"])) ,(((((data["t_mean"])>(data["t_mean"])) *1.))))) +(data["visitNumber"])) /2.0)))) * 2.0)))))+(-1.0)) /2.0)))) *(( 9.90981674194335938))))
v["i32"] = 0.100000*np.tanh(((((((( np.tanh(np.minimum(((1.0)) ,(( np.minimum(((((((((data["t_sum_log"])*(( 1.95805239677429199)))) +(-2.0)))-(( 2.0))))),(( data["geoNetwork_continent"])))))))))* 2.0)) -(( 2.54177641868591309)))) +(((( np.tanh(np.minimum(((1.0)) ,(( np.minimum(((((((((data["t_sum_log"])*(( 1.95805239677429199)))) +(-2.0)))-(( 2.0))))),(( data["geoNetwork_continent"])))))))))*(( 2.54177641868591309))))))
v["i33"] = 0.100000*np.tanh(((((( 13.79329872131347656)) *(((( np.tanh(((( data["pred_0"])+(((((((data["t_sum_log"])-(((data["t_median"])*(data["visitNumber"])))))+(data["t_sum_log"])))-(data["visitNumber"])))) /2.0)))) +(np.where(( np.tanh(((( data["pred_0"])+(((((((data["t_sum_log"])-(((data["t_median"])*(data["visitNumber"])))))+(data["t_sum_log"])))-(data["visitNumber"])))) /2.0)))< -99998,(np.tanh(np.minimum(((data["pred_3"])) ,(((((( data["totals_pageviews"])+(((( np.maximum(((data["totals_pageviews"])) ,(( -2.0)))) >(-2.0)) *1.))))+(data["t_mean"]))))))), -1.0)))))) * 2.0))
v["i34"] = 0.100000*np.tanh(( -1.0*(((((( -1.0)+(( np.tanh(((data["t_sum_act"])+(-3.0)))))))*(( -1.0*(((((((np.tanh(((data["t_sum_act"])+(-3.0)))))+(( np.tanh(((data["t_sum_act"])+(-3.0)))))))*(-3.0)))))))))))
 v["i35"] = 0.100000*np.tanh(((np.minimum(((((( np.tanh(((((data["pred_0"])* 2.0)) -(data["pred_3"])))))* 2.0))),(((((((np.tanh(((((-1.0)+(((((data["t_sum_log"])+(-2.0)))+(data["t_sum_log"])))))* 2.0)))) * 2.0)) -(( 3.0)))))))+(( np.tanh(((((-1.0)+(((((data["t_sum_log"])+(-2.0)))+(data["t_sum_log"])))))* 2.0))))))
v["i36"] = 0.100000*np.tanh(((np.where(-1.0 < -99998,(np.tanh(((((((((((data["t_sum_act"])*(data["t_sum_act"])))+(-2.0)))* 2.0)) +(-1.0)))* 2.0))),(((((np.tanh(((((((((((data["t_sum_act"])*(data["t_sum_act"])))+(-2.0)))* 2.0)) +(-1.0)))* 2.0)))) * 2.0)) +(-2.0)))) * 2.0))
v["i37"] = 0.100000*np.tanh(( -1.0*(( np.where(( np.tanh(((((data["t_mean"])*(np.where(data["pred_4"]<0, data["t_sum_log"], data["t_sum_log"])))) +(-3.0)))) >0,(((((np.tanh(np.maximum(((((( data["t_nb_sess"])+(data["t_sum_act"])) /2.0))),(( data["t_nb_sess"])))))) -(1.0)))*(( 7.0))),(8.39564132690429688))))))
v["i38"] = 0.100000*np.tanh(((((((np.where(( np.tanh(((data["t_sum_log"])*(np.where(((data["t_sum_log"])-(np.minimum(((data["visitNumber"])) ,(( data["t_sum_log"])))))>0, data["t_sum_log"], -1.0)))))< -99998,(((np.tanh(((data["t_sum_log"])*(np.where(((data["t_sum_log"])-(np.minimum(((data["visitNumber"])) ,(( data["t_sum_log"])))))>0, data["t_sum_log"], -1.0)))))) * 2.0),(((np.tanh(((data["t_sum_log"])*(np.where(((data["t_sum_log"])-(np.minimum(((data["visitNumber"])) ,(( data["t_sum_log"])))))>0, data["t_sum_log"], -1.0)))))) * 2.0)))-(2.0)))* 2.0)) * 2.0))
v["i39"] = 0.100000*np.tanh(((np.minimum(((((((((((-1.0)+(( np.tanh(((np.where(((data["pred_2"])+(data["t_sum_act"])) < -99998, data["pred_5"], data["t_sum_act"])) +(((data["t_sum_act"])+(np.tanh(( -3.0)))))))))))+(( np.tanh(((( data["pred_6"])<(((((data["pred_6"])* 2.0)) +(data["t_mean"])))) *1.))))))* 2.0)) * 2.0))),(((np.tanh(((np.where(((data["pred_2"])+(data["t_sum_act"])) < -99998, data["pred_5"], data["t_sum_act"])) +(((data["t_sum_act"])+(np.tanh(( -3.0)))))))))))) * 2.0))
v["i40"] = 0.100000*np.tanh(((((-2.0)+(((np.where(-2.0<0, np.where(( np.tanh(((((((data["t_sum_act"])-(data["visitNumber"])))* 2.0)) +(-1.0)))) >0,(np.tanh(((((((data["t_sum_act"])-(data["visitNumber"])))* 2.0)) +(-1.0)))) , -2.0), -2.0)) * 2.0)))) * 2.0))
v["i41"] = 0.100000*np.tanh(np.minimum(((((((((np.tanh(((((data["t_sum_log"])*(( -1.0*(( data["totals_hits"])))))) +(((((data["t_mean"])+(data["t_sum_log"])))+(data["t_sum_log"])))))))-(( 3.08904719352722168)))) +(2.0)) /2.0))),(((((( 3.0)*(( np.tanh(np.where(data["t_sum_act"] < -99998, np.minimum(((((data["t_mean"])* 2.0))),(( np.maximum(((0.0)) ,(( data["t_sum_log"])))))) , data["t_sum_log"])))))) -(3.0))))))
v["i42"] = 0.100000*np.tanh(((((( -3.0)+(((( -2.0)+(( np.tanh(((((((((((data["t_sum_log"])*(data["t_sum_act"])))*(data["geoNetwork_continent"])))*(data["t_sum_act"])))*(data["t_sum_act"])))-(1.0)))))) /2.0)))/2.0)) +(((( np.tanh(((((((((((data["t_sum_log"])*(data["t_sum_act"])))*(data["geoNetwork_continent"])))*(data["t_sum_act"])))*(data["t_sum_act"])))-(1.0)))))* 2.0))))
v["i43"] = 0.100000*np.tanh(((((((( np.tanh(((((data["t_sum_log"])*(((data["t_sum_log"])*(((np.minimum(((data["t_sum_log"])) ,(( data["t_sum_log"])))) -(data["visitNumber"])))))))+(data["t_sum_log"])))))+(-1.0)))* 2.0)) * 2.0))
v["i44"] = 0.100000*np.tanh(np.where(( np.tanh(np.where(np.where(data["pred_1"]>0, data["pred_2"], data["t_sum_log"])< -99998, data["pred_2"],(( -1.0)+(data["t_sum_act"])))))<0,(( -2.0)*(( 8.40580558776855469))),(((np.tanh(((((np.tanh(np.where(np.where(data["pred_1"]>0, data["pred_2"], data["t_sum_log"])< -99998, data["pred_2"],(( -1.0)+(data["t_sum_act"])))))) *(( 4.0)))))) >(( np.tanh(np.where(np.where(data["pred_1"]>0, data["pred_2"], data["t_sum_log"])< -99998, data["pred_2"],(( -1.0)+(data["t_sum_act"])))))))*1.)))
v["i45"] = 0.100000*np.tanh(((((((((-1.0)+(( np.tanh(np.where(((((((data["t_sum_log"])* 2.0)) -(data["visitNumber"])))-(data["visitNumber"])) >0,(( data["t_sum_log"])* 2.0), data["pred_3"])))))) * 2.0)) * 2.0)) * 2.0))
v["i46"] = 0.100000*np.tanh(np.where(((( np.tanh(np.where(data["visitNumber"]<0, np.where(data["t_mean"]>0,(((data["geoNetwork_continent"])+(data["visitNumber"])) /2.0), data["pred_1"]),(((data["pred_1"])+(data["t_sum_log"])) /2.0)))))/ 2.0)>0,(((((np.tanh(np.where(data["visitNumber"]<0, np.where(data["t_mean"]>0,(((data["geoNetwork_continent"])+(data["visitNumber"])) /2.0), data["pred_1"]),(((data["pred_1"])+(data["t_sum_log"])) /2.0)))))/ 2.0)) / 2.0), np.minimum(((((((-3.0)* 2.0)) * 2.0))),(((np.tanh(np.where(data["visitNumber"]<0, np.where(data["t_mean"]>0,(((data["geoNetwork_continent"])+(data["visitNumber"])) /2.0), data["pred_1"]),(((data["pred_1"])+(data["t_sum_log"])) /2.0)))))))))
 v["i47"] = 0.100000*np.tanh(((( 7.32015085220336914)) *(((np.tanh(( 1.0)))*(((( np.tanh(((((((np.minimum(((data["t_sum_log"])) ,(( data["geoNetwork_metro"])))) * 2.0)) +(np.where(data["pred_0"]>0, data["pred_0"], data["t_sum_log"])))) * 2.0)))) +(-1.0)))))))
 v["i48"] = 0.100000*np.tanh(((((((( np.tanh(((np.tanh(( data["t_sum_log"])))+(((( data["t_sum_log"])+(data["t_sum_log"])) /2.0)))))) -(((((((0.0)<(( np.tanh(((np.tanh(( data["t_sum_log"])))+(((( data["t_sum_log"])+(data["t_sum_log"])) /2.0)))))))*1.))>(((((np.tanh(((np.tanh(( data["t_sum_log"])))+(((( data["t_sum_log"])+(data["t_sum_log"])) /2.0)))))) +(( np.tanh(((np.tanh(( data["t_sum_log"])))+(((( data["t_sum_log"])+(data["t_sum_log"])) /2.0)))))))/2.0)))*1.))))* 2.0)) * 2.0))
v["i49"] = 0.100000*np.tanh(((((( np.where(( np.tanh(np.where(np.where(((data["t_sum_act"])+(data["t_sum_log"])) <0, data["pred_4"],(((data["totals_pageviews"])>(data["totals_hits"])) *1.))>0, data["totals_hits"], data["pred_4"])))>0, np.where(( np.tanh(np.where(np.where(((data["t_sum_act"])+(data["t_sum_log"])) <0, data["pred_4"],(((data["totals_pageviews"])>(data["totals_hits"])) *1.))>0, data["totals_hits"], data["pred_4"])))>0,(np.tanh(np.where(np.where(((data["t_sum_act"])+(data["t_sum_log"])) <0, data["pred_4"],(((data["totals_pageviews"])>(data["totals_hits"])) *1.))>0, data["totals_hits"], data["pred_4"]))), -3.0), -3.0)) +(np.tanh(( -1.0)))) /2.0)) * 2.0))
v["i50"] = 0.100000*np.tanh(np.minimum(((((( np.tanh(((((data["t_sum_act"])/ 2.0)) +(((data["t_mean"])-(((1.0)* 2.0)))))))) *(((( 11.44591140747070312)) +(1.0)))))) ,(((((1.0)>(((( 1.0)>(( np.tanh(((((data["t_sum_act"])/ 2.0)) +(((data["t_mean"])-(((1.0)* 2.0)))))))))*1.))) *1.)))))
v["i51"] = 0.100000*np.tanh(np.where(( np.tanh(((( data["t_mean"])+(np.where(((( data["t_sum_log"])+(data["pred_1"])) /2.0)<0, data["t_sum_log"], np.tanh(( data["t_mean"])))))/2.0)))>0,(((( np.tanh(((( data["t_mean"])+(np.where(((( data["t_sum_log"])+(data["pred_1"])) /2.0)<0, data["t_sum_log"], np.tanh(( data["t_mean"])))))/2.0)))) <(((((np.tanh(((( data["t_mean"])+(np.where(((( data["t_sum_log"])+(data["pred_1"])) /2.0)<0, data["t_sum_log"], np.tanh(( data["t_mean"])))))/2.0)))) <(np.tanh(((3.0)))))*1.))) *1.) , -3.0))
v["i52"] = 0.099756*np.tanh(np.where(( np.tanh(np.where(np.minimum(((np.where(data["t_sum_act"] < -99998, data["geoNetwork_subContinent"], data["t_mean"]))),(((( data["geoNetwork_subContinent"])* 2.0)))) <0, data["pred_3"], np.tanh(( data["t_sum_act"])))))>0,(( np.maximum(((( np.tanh(np.where(np.minimum(((np.where(data["t_sum_act"] < -99998, data["geoNetwork_subContinent"], data["t_mean"]))),(((( data["geoNetwork_subContinent"])* 2.0)))) <0, data["pred_3"], np.tanh(( data["t_sum_act"]))))))),(((np.tanh(np.where(np.minimum(((np.where(data["t_sum_act"] < -99998, data["geoNetwork_subContinent"], data["t_mean"]))),(((( data["geoNetwork_subContinent"])* 2.0)))) <0, data["pred_3"], np.tanh(( data["t_sum_act"])))))))))-(np.tanh(( 2.0)))) ,(( -3.0)-(2.0))))
v["i53"] = 0.100000*np.tanh(((((((((((((( np.tanh(((((((-1.0)+(((((((data["t_sum_log"])*(( 9.82299137115478516)))) -(3.0)))* 2.0)))) -(1.0)))* 2.0)))) +(((( np.tanh(((((((-1.0)+(((((((data["t_sum_log"])*(( 9.82299137115478516)))) -(3.0)))* 2.0)))) -(1.0)))* 2.0)))) -(( np.tanh(((( data["totals_hits"])+(((((( data["t_mean"])+(data["t_sum_log"])) /2.0)) +(data["geoNetwork_metro"])))) /2.0)))))))) -(( np.tanh(((((((-1.0)+(((((((data["t_sum_log"])*(( 9.82299137115478516)))) -(3.0)))* 2.0)))) -(1.0)))* 2.0)))))) * 2.0)) * 2.0)) * 2.0)) * 2.0))
v["i54"] = 0.100000*np.tanh(((((((( np.tanh(np.where(data["pred_4"]<0,(( data["t_median"])+(np.where(data["pred_1"]<0,(( data["t_sum_act"])* 2.0), data["totals_pageviews"]))), data["totals_pageviews"])))) +(-1.0)))* 2.0)) * 2.0))
v["i55"] = 0.100000*np.tanh(((((((((np.tanh(np.minimum(((np.minimum(((((data["geoNetwork_metro"])+(np.minimum(((((data["geoNetwork_metro"])+(1.0)))) ,(( data["t_sum_act"]))))))),(( data["t_sum_act"]))))),(( data["t_mean"])))))) <(0.0)) *1.))*(-3.0)))* 2.0))
v["i56"] = 0.099951*np.tanh(np.where(( np.tanh(((np.tanh(( data["pred_1"])))+(((((( data["t_sum_log"])+(((((data["t_median"])* 2.0)) *(((data["pred_0"])* 2.0)))))/2.0)) * 2.0)))))<0, np.minimum(((np.minimum(((-3.0)) ,(( np.maximum(((0.0)) ,(( 0.0)))))))) ,(( -3.0))), 0.0))
v["i57"] = 0.098095*np.tanh(((np.minimum(((((( 14.66910171508789062)) *(((((( 14.66910171508789062)) *(( np.tanh(((( -1.0)+(np.where(((data["totals_hits"])+(-3.0)) >0, data["geoNetwork_metro"],(( data["t_mean"])+(((data["t_sum_act"])* 2.0)))))) /2.0)))))) * 2.0))))),(( np.minimum(((0.0)) ,(( 0.0)))))))* 2.0))
v["i58"] = 0.100000*np.tanh(np.where(( np.tanh(((((((np.minimum(((((data["totals_hits"])-(data["totals_pageviews"])))) ,(( data["t_mean"])))) * 2.0)) * 2.0)) +(np.tanh(( data["totals_hits"])))))) >0, np.where(( 0.24389153718948364)>0, np.where(( np.tanh(((((((np.minimum(((((data["totals_hits"])-(data["totals_pageviews"])))) ,(( data["t_mean"])))) * 2.0)) * 2.0)) +(np.tanh(( data["totals_hits"])))))) >0,(0.24389153718948364), -3.0),(0.24389153718948364)) , -3.0))
v["i59"] = 0.051295*np.tanh(np.minimum(((( np.tanh(np.where(np.where(((data["t_mean"])* 2.0)>0, data["t_mean"], data["t_mean"])>0,(((data["t_mean"])>(data["t_sum_log"])) *1.) , -3.0))))),(((( 3.0)*(np.where(( np.tanh(np.where(np.where(((data["t_mean"])* 2.0)>0, data["t_mean"], data["t_mean"])>0,(((data["t_mean"])>(data["t_sum_log"])) *1.) , -3.0)))<0, -3.0,(np.tanh(np.where(np.where(((data["t_mean"])* 2.0)>0, data["t_mean"], data["t_mean"])>0,(((data["t_mean"])>(data["t_sum_log"])) *1.) , -3.0))))))))))
v["i60"] = 0.099853*np.tanh(((((-1.0)+(( np.tanh(((np.where(data["t_sum_log"]>0,(((np.where(data["pred_1"]<0, data["t_sum_act"], 2.0)) >(data["pred_3"])) *1.) , data["pred_3"])) * 2.0)))))) * 2.0))
v["i61"] = 0.099805*np.tanh(((( 3.0)) *(np.minimum(((( np.tanh(((data["t_sum_log"])+(np.minimum(((np.minimum(((((data["t_sum_log"])-(data["visitNumber"])))) ,(((( data["t_mean"])+(data["t_median"]))))))),(( data["t_median"]))))))))),(((( 3.0)*(np.minimum(((0.0)) ,(((np.tanh(((data["t_sum_log"])+(np.minimum(((np.minimum(((((data["t_sum_log"])-(data["visitNumber"])))) ,(((( data["t_mean"])+(data["t_median"]))))))),(( data["t_median"]))))))))))))))))))
v["i62"] = 0.083781*np.tanh(((((( np.tanh(((data["totals_hits"])+(((data["t_median"])*(np.maximum(((np.minimum(((data["t_sum_act"])) ,(( data["t_sum_log"]))))),(((( data["t_sum_act"])+(data["t_median"])))))))))))) -(np.tanh(((( 2.0)+(2.0)))))))*(2.0)))
 v["i63"] = 0.099951*np.tanh(np.where(( np.tanh(np.minimum(((((( data["t_mean"])<(data["t_sum_act"])) *1.))) ,(((((( data["t_mean"])+(data["t_sum_act"])))+(data["t_sum_act"])))))))<0,(( -3.0)* 2.0), np.minimum(((((( np.tanh(np.minimum(((((( data["t_mean"])<(data["t_sum_act"])) *1.))) ,(((((( data["t_mean"])+(data["t_sum_act"])))+(data["t_sum_act"])))))))) * 2.0))),(( np.tanh(( np.minimum(((( np.tanh(np.minimum(((((( data["t_mean"])<(data["t_sum_act"])) *1.))) ,(((((( data["t_mean"])+(data["t_sum_act"])))+(data["t_sum_act"]))))))))),(((np.tanh(np.minimum(((((( data["t_mean"])<(data["t_sum_act"])) *1.))) ,(((((( data["t_mean"])+(data["t_sum_act"])))+(data["t_sum_act"])))))))))))))))))
 v["i64"] = 0.076014*np.tanh(np.minimum(((np.minimum(((( np.tanh(((((-2.0)+(((data["t_sum_log"])/ 2.0)))) *(( 12.65624237060546875))))))),(((((np.tanh(((((-2.0)+(((data["t_sum_log"])/ 2.0)))) *(( 12.65624237060546875)))))) +(-1.0))))))),(((np.tanh(((((-2.0)+(((data["t_sum_log"])/ 2.0)))) *(( 12.65624237060546875)))))))))
 v["i65"] = 0.046165*np.tanh(np.minimum(((np.minimum(((( np.tanh(((data["t_sum_act"])-(data["t_sum_log"])))))) ,(((((0.0)>(( np.tanh(((data["t_sum_act"])-(data["t_sum_log"])))))) *1.)))))),(( 0.0))))
v["i66"] = 0.100000*np.tanh(np.minimum(((((((( np.tanh(((((data["t_sum_log"])* 2.0)) +(((( np.where(np.minimum(((data["t_sum_log"])) ,(( data["visitNumber"])))<0, data["totals_hits"], data["pred_1"])) +(-2.0)) /2.0)))))) * 2.0)) * 2.0))),(((( np.minimum(((( 0.06700874865055084))),(((np.tanh(((((data["t_sum_log"])* 2.0)) +(((( np.where(np.minimum(((data["t_sum_log"])) ,(( data["visitNumber"])))<0, data["totals_hits"], data["pred_1"])) +(-2.0)) /2.0)))))))))* 2.0)))))
 v["i67"] = 0.098828*np.tanh(np.minimum(((((( 0.0)>(((( 0.0)>(0.0)) *1.))) *1.))) ,(((np.tanh(((data["t_sum_log"])*(2.0))))))))
v["i68"] = 0.100000*np.tanh(((np.minimum(((((( np.tanh(np.minimum(((((data["totals_pageviews"])-(((((data["totals_pageviews"])-(data["totals_hits"])))*(( 10.0))))))),(((( data["totals_pageviews"])-(data["totals_hits"])))))))) * 2.0))),(((((np.tanh(np.minimum(((((data["totals_pageviews"])-(((((data["totals_pageviews"])-(data["totals_hits"])))*(( 10.0))))))),(((( data["totals_pageviews"])-(data["totals_hits"])))))))) * 2.0)))))* 2.0))
v["i69"] = 0.049731*np.tanh(np.minimum(((( 0.04890204593539238))),(((( -2.0)*(np.maximum(((( np.tanh(((((( data["t_mean"])+(-1.0)))>(data["t_sum_log"])) *1.))))) ,(((0.04890204593539238))))))))))
v["i70"] = 0.098925*np.tanh(np.minimum(((0.0)) ,(((np.tanh(((data["t_sum_log"])-(1.0))))))))
v["i71"] = 0.068100*np.tanh(((np.tanh(((( np.tanh(( np.tanh(((( -3.0)+(-2.0)))))))+(( np.tanh(np.minimum(((((((( data["t_sum_act"])-(((2.0)*(np.tanh(( -1.0)))))))<(( 7.0)))*1.))) ,(( data["pred_2"])))))))))) +(1.0)))
 v["i72"] = 0.100000*np.tanh(np.minimum(((((np.minimum(((( np.tanh(((( 7.90549468994140625)) +(((((((data["t_sum_log"])*(( -1.0*(( data["pred_2"])))))) +(data["t_mean"])))+(data["t_mean"])))))))) ,(((np.tanh(((( 7.90549468994140625)) +(((((((data["t_sum_log"])*(( -1.0*(( data["pred_2"])))))) +(data["t_mean"])))+(data["t_mean"])))))))))) +(( np.tanh(((( 7.90549468994140625)) +(((((((data["t_sum_log"])*(( -1.0*(( data["pred_2"])))))) +(data["t_mean"])))+(data["t_mean"])))))))))) ,(((((( np.tanh(((( 7.90549468994140625)) +(((((((data["t_sum_log"])*(( -1.0*(( data["pred_2"])))))) +(data["t_mean"])))+(data["t_mean"])))))))<(((((np.tanh(((( 7.90549468994140625)) +(((((((data["t_sum_log"])*(( -1.0*(( data["pred_2"])))))) +(data["t_mean"])))+(data["t_mean"])))))))>(( 0.0)))*1.))) *1.)))))
v["i73"] = 0.100000*np.tanh(((( 0.0)<(( np.tanh(((( np.maximum(((data["t_mean"])) ,(( 0.0)))) <(data["totals_pageviews"])) *1.))))) *1.))
 v["i74"] = 0.099707*np.tanh(np.minimum(((((np.minimum(((( np.tanh(np.minimum(((((((((((((data["geoNetwork_continent"])+(data["geoNetwork_metro"])))*(( 6.0)))) +(data["geoNetwork_metro"])))* 2.0)) * 2.0))),(( data["t_median"]))))))),(( 0.0)))) * 2.0))),(((( np.minimum(((( np.tanh(np.minimum(((((((((((((data["geoNetwork_continent"])+(data["geoNetwork_metro"])))*(( 6.0)))) +(data["geoNetwork_metro"])))* 2.0)) * 2.0))),(( data["t_median"]))))))),(( 0.0)))) * 2.0)))))
 v["i75"] = 0.099951*np.tanh(((((((np.tanh(((( data["pred_2"])>(np.maximum(((( 5.28380632400512695))),(( data["pred_0"])))))*1.))))* 2.0)) >(0.0)) *1.))
 v["i76"] = 0.099951*np.tanh(np.minimum(((( np.tanh(((data["trafficSource_referralPath"])*(-2.0)))))) ,(( 0.0))))
v["i77"] = 0.100000*np.tanh(((((np.minimum(((np.minimum(((( 0.04203201457858086))),(( 2.0))))),(((np.tanh(np.minimum(((data["totals_pageviews"])) ,(((((((data["t_mean"])*(( -1.0*(( data["trafficSource_referralPath"])))))) +(data["visitNumber"])) /2.0)))))))))) *(2.0)))* 2.0))
v["i78"] = 0.099951*np.tanh(((( np.minimum(((( np.tanh(((data["t_nb_sess"])+(( 4.0))))))),(((np.tanh(np.maximum(((2.0)) ,(( data["totals_hits"])))))))))<(0.0)) *1.))
 v["i79"] = 0.099902*np.tanh(((((np.tanh(((((( 0.0)+(data["geoNetwork_continent"])) /2.0)) *(( 4.63319158554077148)))))) <(0.0)) *1.))
 v["i80"] = 0.099756*np.tanh(((((((np.tanh(((data["pred_2"])*(((( data["pred_2"])<(3.0)) *1.))))))+(((((((((((((( -1.0)+(( np.tanh(((data["pred_2"])*(((( data["pred_2"])<(3.0)) *1.))))))) /2.0)) / 2.0)) / 2.0)) / 2.0)) / 2.0)) / 2.0)))/2.0)) / 2.0))
v["i81"] = 0.099463*np.tanh(((((((0.0)<(( np.tanh(((( data["totals_hits"])>(( 4.0)))*1.))))) *1.))>(( np.tanh(((( data["totals_hits"])>(( 4.0)))*1.))))) *1.))
 v["i82"] = 0.099756*np.tanh(((( 0.0)<(( np.tanh(((( 3.0)>(data["t_sum_log"])) *1.))))) *1.))
 v["i83"] = 0.099951*np.tanh(np.minimum(((((((np.tanh(np.minimum(((((((data["t_sum_act"])-(((( data["totals_hits"])+(data["totals_hits"])) /2.0)))) +(data["visitNumber"])))) ,(((( data["visitNumber"])-(data["trafficSource_referralPath"])))))))) <(np.minimum(((( -1.0*(((((-1.0)<(((2.0)/ 2.0)))*1.)))))),(((np.tanh(np.minimum(((((((data["t_sum_act"])-(((( data["totals_hits"])+(data["totals_hits"])) /2.0)))) +(data["visitNumber"])))) ,(((( data["visitNumber"])-(data["trafficSource_referralPath"])))))))))))) *1.))) ,(((np.tanh(np.minimum(((((((data["t_sum_act"])-(((( data["totals_hits"])+(data["totals_hits"])) /2.0)))) +(data["visitNumber"])))) ,(((( data["visitNumber"])-(data["trafficSource_referralPath"])))))))))))
 v["i84"] = 0.100000*np.tanh(((np.minimum(((((((( np.minimum(((( 9.0))),(((np.tanh(((((data["channelGrouping"])+(((data["geoNetwork_continent"])+(data["geoNetwork_metro"])))))*(data["geoNetwork_metro"])))))))) >(2.0)) *1.))*(( np.tanh(((((data["channelGrouping"])+(((data["geoNetwork_continent"])+(data["geoNetwork_metro"])))))*(data["geoNetwork_metro"])))))))) ,(((np.tanh(((((data["channelGrouping"])+(((data["geoNetwork_continent"])+(data["geoNetwork_metro"])))))*(data["geoNetwork_metro"])))))))) *(( 11.39656257629394531))))
v["i85"] = 0.099951*np.tanh(((((((np.minimum(((( np.tanh(np.minimum(((((((((1.0)<(data["t_sum_log"])) *1.))>(data["geoNetwork_metro"])) *1.))) ,(( np.minimum(((data["t_sum_log"])) ,(((((data["t_mean"])+(data["totals_hits"])) /2.0))))))))))),(((0.03772259503602982)))))* 2.0)) * 2.0)) * 2.0))
v["i86"] = 0.099365*np.tanh(((((np.tanh(((( -2.0)+(data["t_mean"])) /2.0)))) <(0.0)) *1.))
 v["i87"] = 0.099853*np.tanh(np.minimum(((np.minimum(((0.0)) ,(( np.minimum(((0.0)) ,(( 0.0)))))))) ,(( np.minimum(((( np.tanh(((((-2.0)+(np.minimum(((((((( 13.26135158538818359)) *(data["geoNetwork_country"])))-(data["geoNetwork_country"])))) ,(( data["totals_hits"])))))) *(data["trafficSource_source"])))))) ,(( 0.0)))))))
 v["i88"] = 0.099951*np.tanh(np.minimum(((((( np.tanh(np.where(data["t_sum_act"]<0, data["t_sum_log"], np.tanh(((((np.maximum(((data["pred_1"])) ,(((5.75660467147827148)))))>(data["t_sum_log"])) *1.))))))) -(( 0.17407540977001190))))),(((np.tanh(np.where(data["t_sum_act"]<0, data["t_sum_log"], np.tanh(((((np.maximum(((data["pred_1"])) ,(((5.75660467147827148)))))>(data["t_sum_log"])) *1.))))))))))
 v["i89"] = 0.099902*np.tanh(((np.tanh(((4.0)))) +(np.tanh(((( np.where(( np.tanh(((data["t_sum_log"])-(((np.maximum(((-2.0)) ,(( data["trafficSource_source"])))) +(data["pred_2"])))))) <0,(13.98869228363037109), -1.0)) +(-2.0)))))))
 v["i90"] = 0.100000*np.tanh(( -1.0*(((((((np.minimum(((0.0)) ,(((np.tanh(((np.maximum(((-1.0)) ,(((( np.minimum(((data["t_sum_log"])) ,(( data["t_sum_act"])))) *(data["channelGrouping"])))))) -(((data["t_sum_log"])*(data["channelGrouping"])))))))))) *(2.0)))<(( 0.0)))*1.)))))
v["i91"] = 0.066097*np.tanh(((((0.05042554065585136)) +(((((((( np.tanh(((((((data["t_median"])-(((( data["geoNetwork_metro"])+(((( data["t_mean"])+(data["pred_2"])) /2.0)))/2.0)))) *(data["pred_1"])))* 2.0)))) +(((( np.tanh(((((((data["t_median"])-(((( data["geoNetwork_metro"])+(((( data["t_mean"])+(data["pred_2"])) /2.0)))/2.0)))) *(data["pred_1"])))* 2.0)))) / 2.0)))/2.0)) +(( np.tanh(((((((data["t_median"])-(((( data["geoNetwork_metro"])+(((( data["t_mean"])+(data["pred_2"])) /2.0)))/2.0)))) *(data["pred_1"])))* 2.0)))))/2.0)))/2.0))
v["i92"] = 0.100000*np.tanh(((((( np.tanh(((((12.78093624114990234)) <(((data["pred_0"])+(np.maximum(((data["channelGrouping"])) ,(( np.maximum(((data["totals_hits"])) ,(( data["channelGrouping"])))))))))) *1.))))+(((( np.tanh(((((12.78093624114990234)) <(((data["pred_0"])+(np.maximum(((data["channelGrouping"])) ,(( np.maximum(((data["totals_hits"])) ,(( data["channelGrouping"])))))))))) *1.))))/ 2.0)))) / 2.0))
v["i93"] = 0.100000*np.tanh(((( 6.0)) *(((( 6.0)) *(np.minimum(((((((6.0)) <(np.minimum(((0.0)) ,(( 0.0)))))*1.))) ,(((np.tanh(np.where(np.maximum(((((data["geoNetwork_continent"])+(data["geoNetwork_metro"])))) ,(( -1.0)))>0, np.minimum(((data["t_sum_act"])) ,(( data["t_sum_log"]))), data["pred_2"])))))))))))
 v["i94"] = 0.077088*np.tanh(((( np.where(( np.tanh(np.where(data["pred_4"] < -99998,(((( 1.0)+(data["pred_4"])))/ 2.0),(( data["pred_2"])-(data["trafficSource_source"])))))<0,(0.01666546240448952),(12.52340126037597656)))+(( 0.01666546240448952)))/2.0))
v["i95"] = 0.082413*np.tanh(((((( np.tanh(((((data["channelGrouping"])-(data["visitNumber"])))* 2.0)))) / 2.0)) / 2.0))
v["i96"] = 0.100000*np.tanh(np.maximum(((( np.tanh(np.tanh(( np.tanh(((((((( data["geoNetwork_continent"])+(((data["t_nb_sess"])/ 2.0)))/2.0)) <(-1.0)) *1.))))))))) ,(( -3.0))))
v["i97"] = 0.099902*np.tanh(((((np.tanh(((( 4.26091861724853516)) -(data["totals_pageviews"])))))<(0.0)) *1.))
 v["i98"] = 0.100000*np.tanh(((( 0.0)>(( np.tanh(((data["t_nb_sess"])+(( 7.0)))))))*1.))
 v["i99"] = 0.046654*np.tanh(((np.minimum(((0.0)) ,(((np.tanh(((data["t_sum_act"])+(((np.minimum(((((( 3.0)>(data["geoNetwork_metro"])) *1.))) ,(( data["totals_pageviews"])))) -(((data["t_mean"])+(data["geoNetwork_subContinent"])))))))))))) * 2.0))
return Output(v.sum(axis=1))
def GP2(data):
v = pd.DataFrame()
v["i0"] = 0.100000*np.tanh(((np.tanh(( data["pred_4"])))+(((((((((data["t_sum_act"])-(((3.0)* 2.0)))) * 2.0)) +(-2.0)))* 2.0))))
v["i1"] = 0.100000*np.tanh(((((((data["pred_4"])+(((((np.maximum(((data["t_median"])) ,(( data["pred_3"])))) -(( 4.92847061157226562)))) +(data["pred_2"])))))* 2.0)) * 2.0))
v["i2"] = 0.100000*np.tanh(((((-1.0)-(data["t_sum_log"])))+(((((((data["t_sum_log"])-(( 13.55201435089111328)))) +(data["t_mean"])))*(( 13.55201435089111328))))))
v["i3"] = 0.100000*np.tanh(((3.0)*(((data["t_sum_act"])-(np.where(np.where(data["pred_2"] < -99998, data["pred_1"], 3.0)>0,(7.0),(9.0)))))))
 v["i4"] = 0.100000*np.tanh(((data["t_mean"])+(((((((( 9.21076107025146484)) / 2.0)) -(((( 9.21076107025146484)) * 2.0)))) +(np.maximum(((( 3.85717129707336426))),(( data["t_sum_log"]))))))))
v["i5"] = 0.100000*np.tanh(((((np.maximum(((((((data["pred_4"])* 2.0)) +(data["pred_0"])))) ,(( data["pred_0"])))) -(((( 12.73920822143554688)) -(data["geoNetwork_metro"])))))* 2.0))
v["i6"] = 0.100000*np.tanh(((((np.minimum(((((((data["t_median"])*(((data["visitNumber"])* 2.0)))) +(data["visitNumber"])))) ,(( data["pred_1"])))) * 2.0)) -(( 9.0))))
v["i7"] = 0.100000*np.tanh(((((( 6.33722066879272461)) *(((((((data["t_sum_log"])-(( 6.33722066879272461)))) +(((-3.0)* 2.0)))) +(data["t_mean"])))))* 2.0))
v["i8"] = 0.100000*np.tanh(((((np.maximum(((data["pred_4"])) ,(( data["pred_2"])))) +(data["pred_6"])))-(((3.0)-(np.maximum(((data["pred_4"])) ,(( data["pred_1"]))))))))
v["i9"] = 0.100000*np.tanh(((((((((-2.0)+(data["t_sum_act"])))-(( 3.93038845062255859)))) * 2.0)) * 2.0))
v["i10"] = 0.100000*np.tanh(((((((np.maximum(((data["pred_10"])) ,(( np.maximum(((data["pred_13"])) ,(( np.maximum(((data["pred_5"])) ,(( data["pred_3"])))))))))) -(2.0)))* 2.0)) * 2.0))
v["i11"] = 0.100000*np.tanh(((((((((data["t_sum_log"])-(((( 9.0)) -(((data["t_mean"])+(-2.0)))))))* 2.0)) * 2.0)) * 2.0))
v["i12"] = 0.100000*np.tanh(((((data["t_sum_log"])-(((3.0)* 2.0)))) -(((((((((data["visitNumber"])>(2.0)) *1.))* 2.0)) +(data["visitNumber"])) /2.0))))
v["i13"] = 0.100000*np.tanh(((((((-2.0)+(np.where(data["pred_2"]>0, np.where(data["pred_3"]>0, data["pred_5"], data["pred_2"]), data["pred_3"])))) * 2.0)) * 2.0))
v["i14"] = 0.100000*np.tanh(np.where(((data["t_sum_log"])+(((data["t_mean"])-(( 9.50519275665283203)))))<0,(( -3.0)-(( 9.50519275665283203))), 1.0))
v["i15"] = 0.100000*np.tanh(((((((((((data["pred_6"])* 2.0)) * 2.0)) +(data["pred_1"])))+(np.minimum(((data["pred_7"])) ,(( -3.0)))))) * 2.0))
v["i16"] = 0.100000*np.tanh(((data["t_sum_act"])+(((((((data["t_sum_act"])*(2.0)))-(np.maximum(((3.0)) ,(( data["visitNumber"])))))) -(( 11.07681083679199219))))))
v["i17"] = 0.100000*np.tanh(((-3.0)+(((((-2.0)+(data["t_mean"])))-(np.maximum(((0.0)) ,(((( data["visitNumber"])*(data["visitNumber"]))))))))))
v["i18"] = 0.100000*np.tanh(((np.minimum(((((( 3.0)) -(((data["t_mean"])-(( 4.0))))))),(((( data["t_sum_log"])-(( 4.0)))))))* 2.0))
v["i19"] = 0.100000*np.tanh(((((data["t_sum_act"])-(np.maximum(((data["visitNumber"])) ,(( np.maximum(((data["t_mean"])) ,(((((7.68621540069580078)) -(data["t_mean"])))))))))))* 2.0))
v["i20"] = 0.100000*np.tanh(((-3.0)+(((np.where(((data["t_sum_log"])-(data["visitNumber"])) >0, data["t_sum_log"],(( data["t_sum_log"])/ 2.0)))+(-2.0)))))
 v["i21"] = 0.100000*np.tanh(( -1.0*(( np.maximum(((((( data["t_sum_act"])>(( 7.0)))*1.))) ,(( np.where(((( data["t_sum_log"])+(-3.0)) /2.0)<0,(7.0), data["pred_6"]))))))))
v["i22"] = 0.100000*np.tanh(np.minimum(((((((((((data["t_sum_log"])-(( 4.0)))) *(( 13.82894325256347656)))) *(3.0)))*(( 4.0))))),(( 0.0))))
v["i23"] = 0.100000*np.tanh(((((((np.minimum(((0.0)) ,(((((((( data["t_sum_act"])* 2.0)) +(( -1.0*(((7.07149600982666016)))))))* 2.0)))))* 2.0)) * 2.0)) * 2.0))
v["i24"] = 0.100000*np.tanh(np.minimum(((((((data["t_sum_log"])+(np.minimum(((data["t_sum_log"])) ,(( data["t_mean"])))))) -(( 6.0))))),(((((data["t_sum_act"])<(( 6.0)))*1.)))))
v["i25"] = 0.100000*np.tanh(np.where(((( -3.0)+(data["t_sum_act"])) /2.0)>0,(((((data["t_sum_log"])-(3.0)))>(data["t_mean"])) *1.) ,(-1.0*(((9.0))))))
v["i26"] = 0.100000*np.tanh(((((((( 3.0)>(data["t_sum_act"])) *1.))*(((-3.0)* 2.0)))) -(((( data["t_sum_act"])<(((data["totals_hits"])* 2.0)))*1.))))
 v["i27"] = 0.100000*np.tanh(((np.where(((data["t_sum_log"])+(-3.0)) >0, np.where(data["pred_8"]<0,(0.0), data["t_sum_log"]),(( -3.0)* 2.0)))* 2.0))
v["i28"] = 0.100000*np.tanh(((((np.minimum(((data["t_median"])) ,(((((( data["t_sum_act"])-(3.0)))*(((( 3.0)>(data["t_median"])) *1.))))))) * 2.0)) * 2.0))
v["i29"] = 0.100000*np.tanh(np.minimum(((((((((( -3.0)+(data["t_mean"])))* 2.0)) <(3.0)) *1.))) ,(((((( data["t_sum_log"])-(3.0)))* 2.0)))))
 v["i30"] = 0.100000*np.tanh(np.where(((((data["t_sum_log"])* 2.0)) -(( 3.95256853103637695)))>0,(((data["t_nb_sess"])<(((-3.0)* 2.0)))*1.) ,(( -3.0)* 2.0)))
 v["i31"] = 0.100000*np.tanh(np.minimum(((np.minimum(((0.0)) ,(( np.minimum(((data["pred_0"])) ,(( data["t_median"])))))))) ,(((( data["t_sum_log"])-(((( 5.0)) -(data["t_sum_log"]))))))))
v["i32"] = 0.100000*np.tanh(((((data["t_mean"])-(( 10.0)))) *(((((((data["t_median"])>(data["pred_5"])) *1.))>(((((data["t_mean"])/ 2.0)) / 2.0)))*1.))))
 v["i33"] = 0.100000*np.tanh(((((np.minimum(((((( 2.0)>(((data["t_sum_act"])+(-2.0)))) *1.))) ,(((( data["t_sum_log"])+(-2.0)))))) * 2.0)) * 2.0))
v["i34"] = 0.100000*np.tanh(((((((((((data["t_sum_log"])*(data["t_sum_log"])))+(-3.0)))*(((( 3.0)>(data["t_sum_log"])) *1.))))* 2.0)) * 2.0))
v["i35"] = 0.100000*np.tanh(np.where(((( -3.0)+(data["t_sum_log"])) /2.0)>0, 0.0,(((( data["t_sum_log"])-(3.0)))* 2.0)))
 v["i36"] = 0.100000*np.tanh(((( -1.0*(((8.26050090789794922)))))*(((( data["t_sum_act"])<(np.maximum(((((data["t_median"])-(data["totals_hits"])))) ,(((2.0)))))) *1.))))
 v["i37"] = 0.100000*np.tanh(np.minimum(((((((((data["t_sum_log"])-(data["visitNumber"])))* 2.0)) -(((2.0)* 2.0))))),(((((data["totals_pageviews"])>(( 4.61927986145019531)))*1.)))))
v["i38"] = 0.100000*np.tanh(((np.where(((( data["t_sum_log"])<(1.0)) *1.) >0, -3.0,(((-3.0)<(( -1.0*(( data["t_sum_log"])))))*1.))) * 2.0))
v["i39"] = 0.100000*np.tanh(((( -1.0*(((6.07970380783081055)))))*(((( data["t_sum_log"])<(((((3.29153609275817871)) +(np.maximum(((data["visitNumber"])) ,(( data["totals_hits"])))))/2.0)))*1.))))
 v["i40"] = 0.100000*np.tanh(np.where(((((data["t_sum_act"])/ 2.0)) -(np.tanh(( 1.0)))) <0,(( -3.0)* 2.0),(((data["t_median"])<(0.0)) *1.)))
v["i41"] = 0.100000*np.tanh(((np.where(((((((data["pred_0"])+(-3.0)) /2.0)) +(data["t_sum_act"])) /2.0)<0, -3.0, np.minimum(((0.0)) ,(( data["t_median"])))))* 2.0))
v["i42"] = 0.100000*np.tanh(np.where(((((( data["t_sum_log"])<(1.0)) *1.))*(data["pred_2"])) <0, data["pred_5"],(-1.0*(((((data["pred_2"])>(data["totals_pageviews"])) *1.))))))
 v["i43"] = 0.100000*np.tanh(((((((((((data["geoNetwork_continent"])+(data["t_sum_act"])) /2.0)) <(np.maximum(((data["pred_5"])) ,(( 1.0)))))*1.))* 2.0)) *(((data["pred_3"])* 2.0))))
v["i44"] = 0.100000*np.tanh(((( -1.0*(((7.0)))))*(np.where(data["t_mean"]<0, 2.0,(((((( 1.0)>(data["t_sum_log"])) *1.))>(data["t_sum_act"])) *1.)))))
v["i45"] = 0.100000*np.tanh(np.where(data["t_mean"]<0,(( data["t_mean"])-(( 6.90487623214721680))),(((((data["totals_pageviews"])>(data["totals_hits"])) *1.))/ 2.0)))
 v["i46"] = 0.100000*np.tanh(((data["pred_4"])*(((((((data["pred_4"])<(data["geoNetwork_continent"])) *1.))>(((data["geoNetwork_continent"])*(((( data["totals_hits"])+(1.0)) /2.0)))))*1.))))
 v["i47"] = 0.100000*np.tanh(((((np.where(((data["t_sum_act"])+(np.tanh(( -3.0)))) >0,(((( 2.22855496406555176)) >(data["t_mean"])) *1.) , -2.0)) * 2.0)) * 2.0))
v["i48"] = 0.100000*np.tanh(np.where(data["t_mean"]>0, np.minimum(((0.0)) ,(( np.where(data["geoNetwork_subContinent"]>0,(((data["geoNetwork_subContinent"])+(data["geoNetwork_metro"])) /2.0), -3.0)))) , -3.0))
v["i49"] = 0.099805*np.tanh(np.where(((np.minimum(((data["geoNetwork_continent"])) ,(((( data["t_mean"])-(1.0)))))) * 2.0)>0,(( data["t_sum_act"])-(data["t_mean"])) , -3.0))
v["i50"] = 0.099756*np.tanh(np.minimum(((( -1.0*(( data["pred_3"]))))),(((((( -3.0)* 2.0)) *(((( 1.0)>(((data["t_sum_act"])* 2.0)))*1.)))))))
v["i51"] = 0.099951*np.tanh(((np.where(np.minimum(((data["geoNetwork_subContinent"])) ,(( np.minimum(((data["geoNetwork_continent"])) ,(( data["t_sum_act"])))))) <0, -3.0, np.maximum(((data["t_nb_sess"])) ,(( 0.0)))))* 2.0))
v["i52"] = 0.098339*np.tanh(np.where(data["t_mean"]<0, -3.0, np.minimum(((((( np.where(data["geoNetwork_metro"]<0,(3.0), data["totals_pageviews"])) >(data["totals_pageviews"])) *1.))) ,(( data["t_mean"])))))
 v["i53"] = 0.100000*np.tanh(np.minimum(((np.where(data["pred_3"]<0, data["t_mean"], data["geoNetwork_metro"]))),(( np.where(data["t_mean"]>0,(((data["visitNumber"])<(data["pred_3"])) *1.) , data["pred_3"])))))
 v["i54"] = 0.100000*np.tanh(np.where(np.minimum(((np.where(data["trafficSource_referralPath"]>0, data["pred_5"], data["t_mean"]))),(( data["geoNetwork_country"])))>0, 0.0,(( -3.0)+(-3.0))))
v["i55"] = 0.099951*np.tanh(( -1.0*(((((((data["t_sum_log"])<(((np.maximum(((data["t_sum_act"])) ,(( data["geoNetwork_subContinent"])))) *(data["geoNetwork_subContinent"])))) *1.))*(( 7.03404140472412109)))))))
 v["i56"] = 0.100000*np.tanh(np.where(data["t_sum_log"]<0, data["pred_3"], np.where(data["t_nb_sess"]<0, 0.0,(-1.0*(((((data["totals_pageviews"])<(data["totals_hits"])) *1.)))))))
v["i57"] = 0.100000*np.tanh(((((np.where(data["t_mean"]<0, data["pred_3"],(((data["pred_2"])>(((data["t_nb_sess"])+(data["t_sum_log"])))) *1.))) * 2.0)) * 2.0))
v["i58"] = 0.100000*np.tanh(((((np.where(data["t_sum_log"]<0, -2.0,(-1.0*(((((((data["t_sum_log"])+(data["totals_pageviews"])))<(data["visitNumber"])) *1.))))))* 2.0)) * 2.0))
v["i59"] = 0.099805*np.tanh(np.where(data["t_mean"]<0, -3.0,(((data["t_sum_act"])<(((( data["t_sum_log"])+(((( data["t_mean"])+(( 4.0)))/2.0)))/2.0)))*1.)))
v["i60"] = 0.099707*np.tanh(((np.where(data["t_sum_log"]>0,(-1.0*(((((data["geoNetwork_continent"])<(np.tanh(( 1.0)))) *1.)))), -3.0)) * 2.0))
v["i61"] = 0.100000*np.tanh(np.where(data["t_sum_log"]>0,(((( data["trafficSource_referralPath"])* 2.0)) *(((data["device_browser"])-(data["trafficSource_referralPath"])))) ,(( data["trafficSource_referralPath"])-(( 7.0)))))
 v["i62"] = 0.099756*np.tanh(np.where(data["t_sum_log"]<0, -3.0,(( np.where(data["t_median"]<0, data["geoNetwork_metro"],(((-3.0)>(data["geoNetwork_metro"])) *1.))) / 2.0)))
 v["i63"] = 0.100000*np.tanh(( -1.0*(((((((( np.maximum(((data["totals_pageviews"])) ,(((( data["t_median"])/ 2.0)))))<(data["totals_hits"])) *1.))>(np.tanh(( data["t_mean"])))) *1.)))))
v["i64"] = 0.089253*np.tanh(np.where(data["t_sum_act"]<0, -3.0,(( data["totals_hits"])*(((( data["t_median"])<(np.where(data["pred_0"]>0, data["pred_2"], 2.0)))*1.)))))
v["i65"] = 0.100000*np.tanh(np.where(data["pred_0"]<0,(( data["pred_0"])* 2.0),(-1.0*(((((3.0)>(((data["pred_0"])+(((data["geoNetwork_metro"])* 2.0)))))*1.))))))
 v["i66"] = 0.100000*np.tanh(( -1.0*(( np.where(data["channelGrouping"]>0, np.where(data["geoNetwork_metro"]>0, 0.0,(( data["visitNumber"])/ 2.0)) , np.minimum(((0.0)) ,(( data["geoNetwork_metro"]))))))))
v["i67"] = 0.100000*np.tanh(np.where(data["t_mean"]>0, np.where(data["geoNetwork_continent"]>0,(((-1.0*(((((data["geoNetwork_subContinent"])>(data["geoNetwork_continent"])) *1.))))) * 2.0), data["pred_1"]), -3.0))
v["i68"] = 0.083537*np.tanh(np.where(data["t_mean"]<0, -3.0,(((((((data["geoNetwork_continent"])*(data["pred_1"])))>(((data["t_mean"])/ 2.0)))*1.))*(data["pred_2"]))))
v["i69"] = 0.071373*np.tanh(np.where(data["t_sum_act"]<0,(( -3.0)* 2.0),(((-3.0)>(((0.0)-(((data["geoNetwork_metro"])* 2.0)))))*1.)))
v["i70"] = 0.099853*np.tanh(np.where(data["geoNetwork_continent"]<0, data["totals_pageviews"],(-1.0*(((((data["pred_2"])>(((np.where(data["geoNetwork_metro"]<0, data["pred_0"], data["t_median"])) / 2.0)))*1.))))))
 v["i71"] = 0.099951*np.tanh(np.minimum(((data["totals_pageviews"])) ,(((( data["t_sum_act"])*(((( data["t_mean"])<(np.where(data["pred_3"]<0, data["totals_pageviews"], data["visitNumber"])))*1.)))))))
v["i72"] = 0.099902*np.tanh(np.where(np.minimum(((data["t_mean"])) ,(( data["t_sum_log"])))<0, data["pred_4"],(((-1.0*(((((data["geoNetwork_metro"])>(((data["t_mean"])/ 2.0)))*1.))))) * 2.0)))
 v["i73"] = 0.100000*np.tanh(((((np.minimum(((np.minimum(((data["t_sum_act"])) ,(((((data["pred_0"])<(data["t_median"])) *1.)))))),(((((data["totals_hits"])<(data["trafficSource_referralPath"])) *1.))))) * 2.0)) * 2.0))
v["i74"] = 0.100000*np.tanh(np.where(np.where(data["trafficSource_referralPath"]<0, data["geoNetwork_country"],(((data["trafficSource_referralPath"])>(data["geoNetwork_country"])) *1.))>0, np.minimum(((0.0)) ,(( data["geoNetwork_country"]))), data["pred_2"]))
return Output(v.sum(axis=1))
def GP3(data):
v = pd.DataFrame()
v["i0"] = 0.100000*np.tanh(((((((((((np.maximum(((data["t_sum_log"])) ,(( data["t_mean"])))) -(( 8.52710533142089844)))) * 2.0)) +(np.tanh(( data["t_mean"])))))* 2.0)) * 2.0))
v["i1"] = 0.100000*np.tanh(((((((((data["t_sum_act"])+(((np.minimum(((data["pred_1"])) ,(( -2.0)))) -(( 5.0)))))) * 2.0)) * 2.0)) * 2.0))
v["i2"] = 0.100000*np.tanh(((( 9.12465190887451172)) *(((( 9.12465190887451172)) *(((((data["totals_hits"])+(((((data["t_sum_log"])-(( 9.12465190887451172)))) * 2.0)))) * 2.0))))))
v["i3"] = 0.100000*np.tanh(np.minimum(((((((data["t_sum_log"])-(((( 5.99947023391723633)) -(((data["t_mean"])-(( 8.0)))))))) * 2.0))),(( data["visitNumber"]))))
v["i4"] = 0.100000*np.tanh(((((np.minimum(((((data["t_sum_log"])-(np.maximum(((( 7.0))),(( data["visitNumber"]))))))),(( data["visitNumber"])))) * 2.0)) * 2.0))
v["i5"] = 0.100000*np.tanh(((((data["t_sum_act"])* 2.0)) -(np.maximum(((data["t_sum_act"])) ,(( np.maximum(((((((( data["t_sum_act"])* 2.0)) +(( 13.88008403778076172)))/2.0))),(((13.80931282043457031))))))))))
v["i6"] = 0.100000*np.tanh(((np.where(((-3.0)+(data["pred_3"])) >0, data["totals_hits"],(( data["pred_0"])+(((-3.0)-(( 8.0)))))))* 2.0))
v["i7"] = 0.100000*np.tanh(((((data["pred_2"])+(((((data["pred_4"])+(-2.0)))-(((( data["pred_9"])<(data["pred_2"])) *1.))))))* 2.0))
v["i8"] = 0.100000*np.tanh(((np.where(((((data["t_mean"])+(-3.0)))+(-3.0)) >0,(9.18304538726806641), data["pred_3"])) +(( -1.0*(((7.48107624053955078)))))))
 v["i9"] = 0.100000*np.tanh(((data["pred_2"])-(((((((( 3.65108466148376465)) -(data["pred_1"])))*(((( 3.65108466148376465)) -(data["pred_2"])))))-(data["pred_1"])))))
 v["i10"] = 0.100000*np.tanh(((((( 11.89422702789306641)) *(((((data["t_sum_log"])+(2.0)))-(( 8.0)))))) +(np.maximum(((data["pred_0"])) ,(( 2.0))))))
v["i11"] = 0.100000*np.tanh(((np.where(data["pred_8"]>0, data["pred_5"],(( data["pred_6"])+(np.minimum(((data["pred_7"])) ,(((( -3.0)+(data["pred_6"])))))))))* 2.0))
v["i12"] = 0.100000*np.tanh(((data["t_sum_log"])-(np.maximum(((np.maximum(((( 6.0))),(((4.0)))))) ,(((((( data["t_sum_log"])-(( 6.0)))) *(( 6.0)))))))))
 v["i13"] = 0.100000*np.tanh(((data["t_sum_log"])-(np.where(((data["t_sum_log"])+(((data["t_sum_log"])-(( 9.73407459259033203)))))<0,(11.21657562255859375), data["visitNumber"]))))
v["i14"] = 0.100000*np.tanh(((((((((((((((data["pred_4"])* 2.0)) -(3.0)))+(data["pred_5"])))+(data["pred_5"])))* 2.0)) * 2.0)) * 2.0))
v["i15"] = 0.100000*np.tanh(((((data["t_sum_act"])-(( 5.0)))) *(((( 4.0)) -(((((data["t_sum_act"])-(( 5.0)))) * 2.0))))))
v["i16"] = 0.100000*np.tanh(((((((((np.where(((-3.0)+(data["visitNumber"])) <0, data["t_sum_act"], data["pred_7"])) -(( 4.87859916687011719)))) * 2.0)) * 2.0)) * 2.0))
v["i17"] = 0.100000*np.tanh(((((((((((((((((data["t_mean"])* 2.0)) -(( 8.0)))) * 2.0)) * 2.0)) * 2.0)) * 2.0)) +(data["pred_4"])))* 2.0))
v["i18"] = 0.100000*np.tanh(((((((data["t_mean"])+(((((data["t_sum_log"])+(((((data["t_sum_log"])-(( 7.0)))) * 2.0)))) * 2.0)))) * 2.0)) * 2.0))
v["i19"] = 0.100000*np.tanh(((((3.0)-(data["t_median"])))+(((((data["pred_6"])-(np.where(data["t_median"]<0, 3.0,(5.0)))))* 2.0))))
v["i20"] = 0.100000*np.tanh(((((np.where(((data["t_mean"])-(( 6.0)))>0,(4.0), data["t_sum_log"])) -(( 4.0)))) *(( 8.0))))
v["i21"] = 0.100000*np.tanh(((-3.0)+(np.where(((data["t_median"])-(( 4.61104393005371094)))<0,(( data["t_mean"])-(3.0)) , 3.0))))
v["i22"] = 0.100000*np.tanh(((np.where(((( -3.0)+(data["t_sum_log"])) /2.0)>0,(((-3.0)+(( 7.15739059448242188)))/2.0),(-1.0*(((7.15739059448242188)))))) -(data["visitNumber"])))
 v["i23"] = 0.100000*np.tanh(((data["t_sum_act"])-(((np.maximum(((data["totals_pageviews"])) ,(( np.maximum(((np.maximum(((data["pred_2"])) ,(((( data["visitNumber"])/ 2.0)))))) ,(( 2.0)))))))* 2.0))))
v["i24"] = 0.100000*np.tanh(np.where(((-3.0)+(data["t_sum_act"])) <0,(( data["t_sum_act"])-(( 9.0))),(((data["t_nb_sess"])<(-3.0)) *1.)))
v["i25"] = 0.100000*np.tanh(((data["t_sum_act"])-(np.maximum(((np.where(((( -3.0)+(data["t_sum_act"])) /2.0)<0,(7.0), data["pred_3"]))),(( data["t_sum_act"]))))))
v["i26"] = 0.100000*np.tanh(((((((data["t_sum_log"])-(3.0)))*(((((((( data["t_mean"])-(3.0)))* 2.0)) <(3.0)) *1.))))* 2.0))
v["i27"] = 0.100000*np.tanh(np.minimum(((((( data["t_sum_act"])<(( 5.0)))*1.))) ,(( np.minimum(((((data["t_sum_act"])-(( 4.0))))),(((((4.0)) -(data["pred_1"])))))))))
 v["i28"] = 0.100000*np.tanh(((((((data["t_sum_log"])-(( 13.52637577056884766)))) *(((( data["t_mean"])<(3.0)) *1.))))*(((( data["t_sum_log"])<(3.0)) *1.))))
 v["i29"] = 0.100000*np.tanh(((((( 1.0)>(((-3.0)+(data["t_sum_act"])))) *1.))*(((2.0)*(((-3.0)+(data["t_sum_log"])))))))
 v["i30"] = 0.100000*np.tanh(((((( data["t_sum_log"])<(((np.tanh(( np.maximum(((data["totals_hits"])) ,(( 3.0)))))) * 2.0)))*1.))*(((data["t_sum_act"])-(( 7.80285549163818359))))))
v["i31"] = 0.100000*np.tanh(np.where(((-2.0)+(data["t_sum_log"])) >0,(((((-2.0)+(data["t_sum_log"])))>(data["t_sum_act"])) *1.) ,(-1.0*(((8.0))))))
v["i32"] = 0.100000*np.tanh(((np.where(((((data["t_sum_act"])-(2.0)))*(data["pred_6"])) >0, -3.0,(((data["pred_6"])>(-3.0)) *1.))) * 2.0))
v["i33"] = 0.100000*np.tanh(((((( 2.0)>(data["t_sum_log"])) *1.))*(((((( 2.0)>(data["t_sum_act"])) *1.))*(((((-3.0)* 2.0)) * 2.0))))))
v["i34"] = 0.100000*np.tanh(np.minimum(((np.minimum(((((-3.0)+(np.where(data["t_mean"]>0, data["t_mean"], -3.0))))),(( 0.0))))),(( data["t_median"]))))
v["i35"] = 0.100000*np.tanh(((np.minimum(((((( data["t_sum_log"])<(3.0)) *1.))) ,(( np.minimum(((((((data["t_sum_act"])+(-2.0)))* 2.0))),(( data["t_median"])))))))* 2.0))
v["i36"] = 0.100000*np.tanh(np.minimum(((0.0)) ,(((((data["geoNetwork_metro"])+(((3.0)*(((((((( -2.0)+(data["t_sum_log"])) /2.0)) * 2.0)) * 2.0)))))/2.0)))))
 v["i37"] = 0.100000*np.tanh(np.where(data["t_mean"]<0,(-1.0*(((14.43007755279541016)))) ,(((data["pred_3"])>(np.where(data["pred_2"]<0, data["visitNumber"], data["t_mean"])))*1.)))
v["i38"] = 0.100000*np.tanh(np.minimum(((((((( data["t_sum_log"])+(-3.0)))<(data["pred_2"])) *1.))) ,(((( data["t_sum_log"])+(((data["t_sum_log"])+(-3.0))))))))
v["i39"] = 0.100000*np.tanh(((np.minimum(((data["t_median"])) ,(((( data["t_sum_act"])-(np.maximum(((np.maximum(((2.0)) ,(( data["totals_hits"]))))),(( data["t_sum_act"])))))))))* 2.0))
v["i40"] = 0.100000*np.tanh(((np.where(((data["t_sum_act"])-(1.0)) <0, -3.0,(((((( 8.0)) -(data["t_sum_log"])))<(-3.0)) *1.))) * 2.0))
v["i41"] = 0.100000*np.tanh(np.where(np.where(data["geoNetwork_continent"]<0, data["pred_4"],(( data["t_sum_log"])+(((data["t_sum_log"])+(-3.0)))))>0, 0.0, -3.0))
v["i42"] = 0.100000*np.tanh(((((data["pred_5"])* 2.0)) *(np.where(((( 3.0)>(data["pred_4"])) *1.) >0,(((data["t_sum_log"])<(1.0)) *1.) , data["pred_4"]))))
v["i43"] = 0.100000*np.tanh(((((((( np.tanh(((( data["t_sum_log"])*(data["t_sum_log"])))))*(data["geoNetwork_continent"])))<(((1.0)/ 2.0)))*1.))*(data["pred_5"])))
 v["i44"] = 0.100000*np.tanh(((np.where(((( -1.0)+(data["t_mean"])) /2.0)>0, np.where(data["pred_5"]<0, 0.0, data["geoNetwork_metro"]), data["pred_1"])) *(( 10.0))))
v["i45"] = 0.100000*np.tanh(np.where(data["t_sum_log"]<0, -3.0,(((-2.0)+(np.where(((data["totals_hits"])-(data["totals_pageviews"])) <0, 3.0, -3.0)))/2.0)))
 v["i46"] = 0.100000*np.tanh(((np.where(((data["t_sum_act"])+(np.tanh(( -3.0)))) >0,(((data["totals_hits"])>(( 4.0)))*1.) ,(-1.0*(((4.0)))))) * 2.0))
v["i47"] = 0.100000*np.tanh(((-3.0)+(np.where(data["geoNetwork_subContinent"]>0, np.where(data["t_mean"]>0, 3.0, -3.0), -3.0))))
v["i48"] = 0.100000*np.tanh(np.where(np.where(data["t_mean"]<0, data["pred_2"], data["t_sum_log"])<0, -3.0,(((np.maximum(((data["pred_3"])) ,(( data["pred_2"])))) >(data["t_median"])) *1.)))
v["i49"] = 0.100000*np.tanh(((-3.0)*(((( data["t_sum_log"])<(((( data["visitNumber"])+(np.maximum(((data["t_mean"])) ,(((( 3.0)/ 2.0)))))) /2.0)))*1.))))
 v["i50"] = 0.100000*np.tanh(np.where(((((data["t_sum_act"])* 2.0)) +(-1.0)) <0, -3.0,(((3.0)>(((data["t_sum_act"])+(-1.0)))) *1.)))
v["i51"] = 0.100000*np.tanh(np.where(((((( data["t_sum_act"])* 2.0)) +(-1.0)) /2.0)<0,(( -1.0)-(( 10.0))),(((data["geoNetwork_continent"])+(-1.0)) /2.0)))
 v["i52"] = 0.100000*np.tanh(((np.where(((( data["t_sum_act"])+(np.tanh(( data["pred_1"])))) /2.0)<0, -3.0,(((data["t_sum_act"])<(data["pred_1"])) *1.))) * 2.0))
v["i53"] = 0.100000*np.tanh(((np.minimum(((np.where(data["t_mean"]<0, -3.0,(((data["t_mean"])<(data["t_sum_log"])) *1.)))),(((((data["t_mean"])<(data["totals_pageviews"])) *1.))))) * 2.0))
v["i54"] = 0.100000*np.tanh(((np.minimum(((((((data["t_sum_act"])* 2.0)) +(-3.0)))) ,(((((data["t_sum_act"])<(((data["t_sum_log"])+(-3.0)))) *1.))))) * 2.0))
v["i55"] = 0.100000*np.tanh(((((( -1.0*(( np.maximum(((data["trafficSource_referralPath"])) ,(((((data["t_sum_log"])<(( -1.0*(((( data["visitNumber"])*(data["trafficSource_referralPath"])))))))*1.))))))))* 2.0)) * 2.0))
v["i56"] = 0.100000*np.tanh(((( 9.97595596313476562)) *(( -1.0*(((((0.0)>(((data["t_sum_act"])+(np.where(data["visitNumber"]>0, data["pred_1"], data["visitNumber"])))))*1.)))))))
v["i57"] = 0.100000*np.tanh(np.where(data["t_mean"]<0, data["pred_3"],(((((np.where(data["pred_2"]<0, data["t_mean"],(( data["pred_2"])* 2.0)))<(data["t_sum_act"])) *1.))/ 2.0)))
 v["i58"] = 0.099951*np.tanh(( -1.0*(((((((( -1.0*(( data["pred_4"])))) +(data["t_sum_act"])) /2.0)) *(((( data["t_sum_act"])<(((data["t_mean"])*(data["pred_5"])))) *1.)))))))
v["i59"] = 0.099853*np.tanh(((np.minimum(((np.where(data["t_mean"]>0,(((data["t_sum_log"])<(2.0)) *1.) , -3.0))),(((-1.0*(( data["trafficSource_referralPath"])))))))* 2.0))
v["i60"] = 0.099511*np.tanh(((((((((( data["visitNumber"])-(-3.0)))/ 2.0)) >(data["t_sum_log"])) *1.))*(-3.0)))
 v["i61"] = 0.100000*np.tanh(((np.where(data["t_sum_act"]<0, -3.0,(((((data["geoNetwork_subContinent"])>(np.tanh(((( data["geoNetwork_subContinent"])* 2.0)))))*1.))*(-3.0)))) * 2.0))
v["i62"] = 0.099951*np.tanh(np.where(data["t_mean"]<0, -3.0,(((-1.0*(((((( -1.0*(( np.tanh(( np.tanh(( data["t_sum_act"])))))))) >(data["geoNetwork_metro"])) *1.))))) * 2.0)))
 v["i63"] = 0.100000*np.tanh(np.where(((((data["totals_hits"])* 2.0)) -(((data["t_median"])/ 2.0)))<0,(( data["pred_2"])* 2.0),(((data["t_sum_log"])>(( 9.24615192413330078)))*1.)))
v["i64"] = 0.100000*np.tanh(((np.where(data["t_sum_log"]<0, -3.0, np.minimum(((((data["totals_pageviews"])-(data["totals_hits"])))) ,(((( data["totals_hits"])-(data["totals_pageviews"])))))))* 2.0))
v["i65"] = 0.099756*np.tanh(np.minimum(((data["totals_pageviews"])) ,(( np.minimum(((np.tanh(( np.tanh(( data["geoNetwork_subContinent"])))))) ,(((((data["trafficSource_referralPath"])+(((( data["geoNetwork_continent"])>(data["geoNetwork_subContinent"])) *1.))) /2.0))))))))
v["i66"] = 0.099951*np.tanh(((np.where(data["t_sum_act"]<0, data["pred_1"],(((-1.0*(((((data["geoNetwork_metro"])<(( -1.0*(( np.tanh(( data["geoNetwork_continent"])))))))*1.))))) * 2.0)))* 2.0))
v["i67"] = 0.099756*np.tanh(((data["visitNumber"])*(( -1.0*(((((data["channelGrouping"])>(np.minimum(((np.minimum(((data["trafficSource_referralPath"])) ,(( 0.0))))),(((( data["visitNumber"])* 2.0)))))) *1.)))))))
v["i68"] = 0.100000*np.tanh(np.where(data["t_sum_act"]<0,(((( -1.0)* 2.0)) * 2.0),(((data["pred_4"])>(((((( data["geoNetwork_metro"])* 2.0)) +(data["pred_1"])) /2.0)))*1.)))
v["i69"] = 0.099902*np.tanh(np.where(data["t_sum_log"]<0, data["pred_3"],(((((((data["t_mean"])<(data["totals_hits"])) *1.))*(data["totals_hits"])))*(data["visitNumber"]))))
v["i70"] = 0.100000*np.tanh(((((( data["t_nb_sess"])<(( -1.0*(((((((( data["geoNetwork_metro"])>(data["geoNetwork_continent"])) *1.))+(data["t_sum_log"])) /2.0)))))) *1.))*(data["t_sum_log"])))
 v["i71"] = 0.099902*np.tanh(np.minimum(((((((( data["totals_pageviews"])<(( 2.0)))*1.))*(((data["t_median"])* 2.0))))),(((((( 2.0)) <(((data["t_median"])* 2.0)))*1.)))))
v["i72"] = 0.099805*np.tanh(np.minimum(((data["t_sum_act"])) ,(((((((data["t_mean"])<(( 2.48734521865844727)))*1.))*(np.where(data["t_mean"]<0, data["pred_3"],(( data["totals_pageviews"])/ 2.0))))))))
v["i73"] = 0.099902*np.tanh(( -1.0*(( np.where(data["pred_3"]<0, 0.0, np.maximum(((data["pred_2"])) ,(((((((data["totals_pageviews"])>(data["pred_3"])) *1.))*(data["pred_3"]))))))))))
v["i74"] = 0.100000*np.tanh(((((((np.where(data["t_mean"]>0,(((data["geoNetwork_continent"])<(((( data["totals_hits"])+(-3.0)) /2.0)))*1.) , -3.0)) * 2.0)) * 2.0)) * 2.0))
v["i75"] = 0.100000*np.tanh(np.where(data["geoNetwork_subContinent"]>0,(((((data["pred_0"])>(((data["totals_hits"])-(data["pred_3"])))) *1.))*(( -1.0*(( data["trafficSource_referralPath"]))))), data["pred_1"]))
return Output(v.sum(axis=1))
def GP4(data):
v = pd.DataFrame()
v["i0"] = 0.100000*np.tanh(((((((((((( data["t_sum_act"])+(data["t_sum_log"])) /2.0)) -(( 7.28741741180419922)))) * 2.0)) * 2.0)) * 2.0))
v["i1"] = 0.100000*np.tanh(((np.maximum(((-2.0)) ,(( data["pred_4"])))) +(((((((((data["t_mean"])-(3.0)))-(( 4.06806802749633789)))) * 2.0)) * 2.0))))
v["i2"] = 0.100000*np.tanh(((((((((((((((((((data["t_sum_log"])-(( 8.19811630249023438)))) * 2.0)) * 2.0)) * 2.0)) * 2.0)) * 2.0)) * 2.0)) * 2.0)) * 2.0))
v["i3"] = 0.100000*np.tanh(((np.minimum(((((data["t_sum_log"])-(data["visitNumber"])))) ,(((( data["t_sum_log"])-(( 7.0)))))))+(((data["t_sum_act"])-(( 7.0))))))
v["i4"] = 0.100000*np.tanh(((((((((((((((( data["t_sum_act"])+(( -1.0*(((7.0)))))) /2.0)) * 2.0)) * 2.0)) * 2.0)) * 2.0)) * 2.0)) -(-3.0)))
 v["i5"] = 0.100000*np.tanh(np.minimum(((((data["t_mean"])+(((data["t_sum_log"])-(( 12.75276947021484375))))))),(( np.minimum(((data["t_median"])) ,(((( data["t_mean"])+(data["pred_1"])))))))))
 v["i6"] = 0.100000*np.tanh(((((((((((-1.0)+(((((((data["t_sum_log"])-(( 9.0)))) * 2.0)) * 2.0)))) * 2.0)) * 2.0)) * 2.0)) * 2.0))
v["i7"] = 0.100000*np.tanh(((((((((data["t_sum_log"])-(( 7.04534244537353516)))) -(np.tanh(((((data["t_sum_log"])+(data["pred_9"])) /2.0)))))) * 2.0)) * 2.0))
v["i8"] = 0.100000*np.tanh(((((((((((data["pred_1"])+(((data["pred_2"])+(np.minimum(((-3.0)) ,(( data["pred_6"])))))))) * 2.0)) * 2.0)) * 2.0)) * 2.0))
v["i9"] = 0.100000*np.tanh(((((((((( 4.59890556335449219)) +(((((((((data["t_median"])-(( 8.0)))) * 2.0)) * 2.0)) * 2.0)))) * 2.0)) * 2.0)) * 2.0))
v["i10"] = 0.100000*np.tanh(((((((np.maximum(((data["pred_3"])) ,(( np.maximum(((data["pred_4"])) ,(((( data["pred_1"])+(data["pred_2"])))))))))+(-3.0)))* 2.0)) * 2.0))
v["i11"] = 0.100000*np.tanh(((((((((((((((data["t_sum_log"])-(((( 4.0)) * 2.0)))) * 2.0)) +(data["t_mean"])))* 2.0)) * 2.0)) * 2.0)) * 2.0))
v["i12"] = 0.100000*np.tanh(((((((data["pred_10"])+(((((data["t_mean"])-(((data["visitNumber"])/ 2.0)))) * 2.0)))) +(data["pred_3"])))* 2.0))
v["i13"] = 0.100000*np.tanh(((((data["t_mean"])+(np.minimum(((((((((data["t_sum_log"])-(( 6.0)))) * 2.0)) +(-3.0)))) ,(( -3.0)))))) * 2.0))
v["i14"] = 0.100000*np.tanh(((((data["t_sum_act"])+(((np.minimum(((-3.0)) ,(((((-1.0*(( data["visitNumber"])))) *(data["visitNumber"])))))) +(-2.0)))))* 2.0))
v["i15"] = 0.100000*np.tanh(np.minimum(((((data["pred_3"])+(((data["t_mean"])-(( 4.77416133880615234))))))),(((((5.0)) +(( -1.0*(( data["visitNumber"])))))))))
 v["i16"] = 0.100000*np.tanh(np.minimum(((((((data["t_sum_act"])-(( 4.75877285003662109)))) * 2.0))),(((((((((data["t_sum_act"])-(( 4.75877285003662109)))) * 2.0)) <(( 4.75877285003662109)))*1.)))))
v["i17"] = 0.100000*np.tanh(np.minimum(((( -1.0*(((((data["pred_2"])>(((( 6.0)) / 2.0)))*1.)))))),(((( data["t_mean"])-(( 6.0)))))))
 v["i18"] = 0.100000*np.tanh(np.where(((data["t_sum_log"])-(( 4.0)))>0,(((( 5.0)) >(data["t_mean"])) *1.) ,(( data["pred_8"])-(( 5.0)))))
 v["i19"] = 0.100000*np.tanh(( -1.0*(((((((((((data["t_mean"])<(( 4.0)))*1.))* 2.0)) * 2.0)) * 2.0)))))
 v["i20"] = 0.100000*np.tanh(np.minimum(((((( 4.56055736541748047)) -(data["pred_1"])))) ,(( np.minimum(((((data["t_sum_log"])-(( 4.56055355072021484))))),(((((data["pred_4"])>(data["pred_1"])) *1.))))))))
 v["i21"] = 0.100000*np.tanh(np.minimum(((((((((( data["t_mean"])* 2.0)) -(( 9.0)))) <(3.0)) *1.))) ,(((((( data["t_mean"])* 2.0)) -(( 8.0)))))))
 v["i22"] = 0.100000*np.tanh(((np.minimum(((( 6.0))),(( np.where(( 6.0)>0,(( data["t_sum_log"])*(((data["t_sum_log"])/ 2.0))), data["t_sum_log"])))))-(( 6.0))))
v["i23"] = 0.100000*np.tanh(np.minimum(((((( data["t_sum_log"])>(( 10.95762443542480469)))*1.))) ,(((( -3.0)-(((( 10.95762443542480469)) *(((( 3.0)) -(data["t_sum_log"]))))))))))
v["i24"] = 0.100000*np.tanh(((((np.minimum(((0.0)) ,(((((( data["t_sum_act"])+(((data["t_sum_act"])-(( 6.0)))))) -(data["visitNumber"])))))) * 2.0)) * 2.0))
v["i25"] = 0.100000*np.tanh(((np.minimum(((data["t_mean"])) ,(((( np.minimum(((((( data["t_mean"])<(( 4.22032451629638672)))*1.))) ,(((( -3.0)+(data["t_sum_log"])))))) * 2.0)))))* 2.0))
v["i26"] = 0.100000*np.tanh(((np.where(((-3.0)+(data["t_sum_log"])) <0,(((( data["pred_2"])+(-2.0)))+(-2.0)) , 0.0)) * 2.0))
v["i27"] = 0.100000*np.tanh(np.minimum(((((((data["t_sum_log"])-(( 4.0)))) -(((data["visitNumber"])/ 2.0))))),(((((data["t_sum_act"])<(data["visitNumber"])) *1.)))))
v["i28"] = 0.100000*np.tanh(np.minimum(((((((((data["t_sum_act"])-(3.0)))* 2.0)) * 2.0))),(( np.minimum(((data["t_mean"])) ,(((( data["t_sum_act"])-(data["t_mean"])))))))))
 v["i29"] = 0.100000*np.tanh(np.where(((-3.0)+(data["t_median"])) <0,(( data["t_sum_log"])+(((((data["t_sum_log"])* 2.0)) -(( 8.0))))), 0.0))
v["i30"] = 0.100000*np.tanh(np.where(data["pred_1"]>0, data["t_nb_sess"], np.minimum(((((((data["pred_0"])-(( 8.80866241455078125)))) * 2.0))),(( data["geoNetwork_metro"])))))
 v["i31"] = 0.100000*np.tanh(np.where(((( 2.0)<(data["t_sum_log"])) *1.) >0,(((((5.0)) -(data["totals_hits"])))-(data["visitNumber"])) , data["pred_5"]))
v["i32"] = 0.100000*np.tanh(np.where(((( 2.0)<(((data["t_sum_act"])/ 2.0)))*1.) >0, 0.0,(((( -3.0)+(data["t_sum_act"])))* 2.0)))
 v["i33"] = 0.100000*np.tanh(((np.where(np.where(((data["t_sum_act"])-(2.0)) >0, data["geoNetwork_continent"], data["pred_5"])>0,(6.0), data["pred_1"])) -(( 6.0))))
v["i34"] = 0.100000*np.tanh(np.minimum(((((( data["pred_4"])>(-3.0)) *1.))) ,(((((((((( data["t_sum_act"])* 2.0)) -(3.0)))* 2.0)) -(data["t_sum_act"]))))))
v["i35"] = 0.100000*np.tanh(np.minimum(((((((((data["t_sum_act"])-(3.0)))* 2.0)) +(data["t_nb_sess"])))) ,(((((data["pred_5"])>(( 0.0)))*1.)))))
v["i36"] = 0.100000*np.tanh(((np.minimum(((((((-3.0)+(((data["t_sum_act"])* 2.0)))) *(((( 2.0)>(data["t_sum_act"])) *1.))))) ,(( data["t_median"])))) * 2.0))
v["i37"] = 0.100000*np.tanh(((((((((( data["t_sum_log"])<(3.0)) *1.))* 2.0)) *(((((( data["t_sum_log"])* 2.0)) +(-3.0)) /2.0)))) * 2.0))
v["i38"] = 0.100000*np.tanh(np.minimum(((data["totals_pageviews"])) ,(((((((((((1.0)>(data["t_median"])) *1.))*(data["pred_2"])))*(data["pred_3"])))*(data["pred_5"]))))))
v["i39"] = 0.100000*np.tanh(((((((data["pred_5"])*(((( 2.0)>(data["t_sum_act"])) *1.))))*(( 11.94209194183349609)))) *(((( 2.0)>(data["t_sum_log"])) *1.))))
 v["i40"] = 0.100000*np.tanh(np.minimum(((((((((data["pred_6"])* 2.0)) * 2.0)) *(((((( data["t_sum_act"])<(((1.0)* 2.0)))*1.))* 2.0))))),(( data["t_median"]))))
v["i41"] = 0.100000*np.tanh(np.where(((((data["t_nb_sess"])+(((data["t_sum_log"])* 2.0)))) +(-3.0)) <0, -3.0,(((-2.0)>(data["t_nb_sess"])) *1.)))
v["i42"] = 0.100000*np.tanh(np.where(((data["t_sum_log"])-(1.0)) <0,(( data["t_sum_log"])-(( 12.49324989318847656))),(( data["geoNetwork_continent"])-(np.tanh(( data["totals_hits"]))))))
v["i43"] = 0.100000*np.tanh(((np.minimum(((0.0)) ,(((((((((( data["t_sum_act"])* 2.0)) -(2.0)))* 2.0)) * 2.0)))))* 2.0))
v["i44"] = 0.100000*np.tanh(np.where(((data["t_sum_act"])-(1.0)) <0,(( -3.0)* 2.0),(( np.tanh(((-1.0*(( data["trafficSource_referralPath"])))))) / 2.0)))
 v["i45"] = 0.100000*np.tanh(((-3.0)*(np.where(data["t_mean"]>0,(((((( data["t_sum_log"])<(( 9.68791198730468750)))*1.))<(data["pred_5"])) *1.) ,(9.68791198730468750)))))
 v["i46"] = 0.099951*np.tanh(((((((((-1.0)+(np.minimum(((np.tanh(((( data["totals_pageviews"])* 2.0))))),(( data["t_sum_act"])))))) * 2.0)) * 2.0)) * 2.0))
v["i47"] = 0.100000*np.tanh(((((((( -1.0*(((((((((data["t_sum_act"])* 2.0)) +(-1.0)))<(data["pred_0"])) *1.))))) * 2.0)) * 2.0)) * 2.0))
v["i48"] = 0.099902*np.tanh(((data["pred_4"])*(((((((((data["t_sum_act"])-(data["visitNumber"])))>(data["pred_4"])) *1.))>(((data["t_mean"])-(data["visitNumber"])))) *1.))))
 v["i49"] = 0.099756*np.tanh(((((-3.0)* 2.0)) *(((( np.tanh(( data["t_sum_act"])))<(((( np.minimum(((data["totals_pageviews"])) ,(( data["totals_hits"])))) <(data["totals_hits"])) *1.))) *1.))))
 v["i50"] = 0.099951*np.tanh(np.minimum(((np.where(data["t_mean"]>0,(((-3.0)<(data["pred_1"])) *1.) , -3.0))),(( np.where(data["geoNetwork_subContinent"]>0, data["geoNetwork_subContinent"], data["pred_1"])))))
 v["i51"] = 0.100000*np.tanh(np.where(data["t_mean"]<0, -3.0,(((np.maximum(((( 3.0))),(((( data["t_sum_log"])-(data["totals_pageviews"])))))) >(data["t_mean"])) *1.)))
v["i52"] = 0.100000*np.tanh(((((( -1.0*(((((data["t_sum_log"])<(((np.maximum(((((data["visitNumber"])* 2.0))),(( data["t_sum_act"])))) / 2.0)))*1.))))) * 2.0)) * 2.0))
v["i53"] = 0.100000*np.tanh(((np.where(np.minimum(((data["geoNetwork_subContinent"])) ,(( data["geoNetwork_subContinent"])))<0, data["pred_3"],(((-1.0*(((((data["geoNetwork_subContinent"])>(data["t_sum_log"])) *1.))))) * 2.0)))* 2.0))
v["i54"] = 0.099658*np.tanh(( -1.0*(( np.where(data["t_sum_log"]<0,(7.95942497253417969), np.where(data["pred_3"]<0,(((data["t_sum_log"])>(( 7.95942497253417969)))*1.) , data["pred_2"]))))))
v["i55"] = 0.099951*np.tanh(( -1.0*(( np.where(data["t_mean"]<0,(6.22654199600219727),(((((( data["geoNetwork_metro"])>(0.0)) *1.))>(((data["geoNetwork_continent"])+(data["geoNetwork_metro"])))) *1.))))))
 v["i56"] = 0.099951*np.tanh(((((((np.where(data["t_mean"]>0,(( data["visitNumber"])*(((( data["totals_hits"])>(data["t_mean"])) *1.))) , -3.0)) * 2.0)) * 2.0)) * 2.0))
v["i57"] = 0.099951*np.tanh(((((( -1.0*(( np.where(data["t_mean"]>0,(((((( data["geoNetwork_continent"])>(data["geoNetwork_subContinent"])) *1.))<(data["geoNetwork_continent"])) *1.) ,(9.77862071990966797)))))) * 2.0)) * 2.0))
v["i58"] = 0.099609*np.tanh(((((( -1.0*(((((np.tanh(( data["geoNetwork_subContinent"])))<(((( data["t_mean"])<(((data["t_mean"])*(data["geoNetwork_subContinent"])))) *1.))) *1.))))) * 2.0)) * 2.0))
v["i59"] = 0.100000*np.tanh(((np.where(data["geoNetwork_country"]>0,(( np.where(data["t_sum_act"]>0,(((data["pred_3"])<(data["pred_5"])) *1.) , data["pred_5"])) * 2.0), data["pred_3"])) * 2.0))
v["i60"] = 0.099951*np.tanh(np.where(((( data["pred_0"])<(((data["t_nb_sess"])/ 2.0)))*1.) >0, data["pred_4"],(((((data["pred_0"])<(data["pred_4"])) *1.))*(-3.0))))
v["i61"] = 0.100000*np.tanh(np.where(data["t_sum_log"]<0, -3.0,(((np.maximum(((data["pred_3"])) ,(( 3.0)))) <(((data["totals_hits"])+(np.tanh(( -2.0)))))) *1.)))
v["i62"] = 0.100000*np.tanh(((((np.where(data["t_sum_log"]<0, data["pred_3"],(-1.0*(((((((data["trafficSource_referralPath"])*(data["t_sum_log"])))>(data["visitNumber"])) *1.))))))* 2.0)) * 2.0))
v["i63"] = 0.100000*np.tanh(np.where(data["t_sum_act"]<0,(( data["totals_hits"])+(-3.0)) ,(((data["t_median"])<(((((data["totals_pageviews"])+(-2.0)))* 2.0)))*1.)))
v["i64"] = 0.095457*np.tanh(np.where(data["t_mean"]<0,(-1.0*(((9.0)))) ,(((data["t_mean"])<(np.where(data["t_sum_act"]>0,(((9.0)) / 2.0), data["t_mean"])))*1.)))
v["i65"] = 0.100000*np.tanh(np.minimum(((data["t_median"])) ,(((-1.0*(( np.where(data["t_sum_log"]<0, 3.0,(((data["geoNetwork_metro"])>(((data["t_mean"])/ 2.0)))*1.)))))))))
v["i66"] = 0.100000*np.tanh(np.where(((data["geoNetwork_continent"])-(data["geoNetwork_subContinent"])) <0, -3.0, np.where(data["t_sum_act"]<0, -3.0,(((data["totals_pageviews"])>(data["t_mean"])) *1.))))
 v["i67"] = 0.100000*np.tanh(((-3.0)*(((((((np.tanh(((((( data["totals_pageviews"])-(data["totals_hits"])))* 2.0)))) >(data["geoNetwork_country"])) *1.))>(data["geoNetwork_country"])) *1.))))
 v["i68"] = 0.099951*np.tanh(np.where(((( data["pred_0"])+(data["t_sum_act"])) /2.0)<0, -3.0,(( np.tanh(((((((data["pred_0"])<(data["t_median"])) *1.))/ 2.0)))) / 2.0)))
 v["i69"] = 0.100000*np.tanh(((((( data["t_sum_log"])<(((np.maximum(((data["t_nb_sess"])) ,(( data["totals_hits"])))) * 2.0)))*1.))*(( -1.0*(((((data["totals_pageviews"])<(data["totals_hits"])) *1.)))))))
v["i70"] = 0.100000*np.tanh(((((np.where(data["geoNetwork_continent"]<0, data["visitNumber"],(((np.tanh(( data["t_sum_act"])))>(((( data["totals_hits"])+(data["visitNumber"])) /2.0)))*1.))) * 2.0)) * 2.0))
v["i71"] = 0.099902*np.tanh(((-3.0)*(((((( data["geoNetwork_metro"])/ 2.0)) >(np.where(data["t_mean"]>0,(( np.tanh(( data["totals_hits"])))* 2.0), data["t_mean"])))*1.))))
 v["i72"] = 0.100000*np.tanh(((((data["t_mean"])*(((( data["t_nb_sess"])<(((np.maximum(((((data["t_sum_log"])/ 2.0))),(( data["pred_2"])))) -(data["t_sum_log"])))) *1.))))* 2.0))
v["i73"] = 0.100000*np.tanh(np.where(data["pred_0"]<0,(((-1.0*(((((data["pred_3"])>(( 5.0)))*1.))))) * 2.0),(-1.0*(((((data["pred_0"])<(data["visitNumber"])) *1.))))))
 v["i74"] = 0.083586*np.tanh(np.minimum(((data["t_sum_act"])) ,(((((data["geoNetwork_metro"])>(np.maximum(((((data["pred_1"])/ 2.0))),(( np.maximum(((( 1.40993750095367432))),(( data["visitNumber"])))))))) *1.)))))
return Output(v.sum(axis=1))
def GP5(data):
v = pd.DataFrame()
v["i0"] = 0.100000*np.tanh(((((((data["t_sum_act"])+(((data["t_sum_act"])-(( 9.0)))))) +(((((data["t_sum_log"])-(( 10.0)))) * 2.0)))) * 2.0))
v["i1"] = 0.100000*np.tanh(((data["t_sum_act"])-(((( 6.94758796691894531)) -(((data["t_sum_log"])+(((((-2.0)* 2.0)) * 2.0))))))))
v["i2"] = 0.100000*np.tanh(((((((((data["t_sum_act"])+(((3.0)-(( 10.0)))))) * 2.0)) * 2.0)) * 2.0))
v["i3"] = 0.100000*np.tanh(((((np.tanh(( np.tanh(( data["pred_3"])))))+(((((data["t_sum_act"])* 2.0)) -(( 13.52748107910156250)))))) * 2.0))
v["i4"] = 0.100000*np.tanh(((((((data["t_sum_log"])-(np.maximum(((( 10.0))),(( data["t_median"])))))) +(((data["t_sum_log"])/ 2.0)))) +(-3.0)))
 v["i5"] = 0.100000*np.tanh(((((((((((data["t_mean"])-(( 10.0)))) * 2.0)) +(data["t_sum_log"])))* 2.0)) * 2.0))
v["i6"] = 0.100000*np.tanh(((np.where(np.where(data["pred_0"]>0, data["pred_2"],(( data["sess_date_dow"])-(data["pred_0"])))>0, data["pred_0"], data["t_sum_log"])) -(( 9.53606986999511719))))
v["i7"] = 0.100000*np.tanh(np.minimum(((np.tanh(((( data["t_median"])* 2.0))))),(((((( 3.0)* 2.0)) *(((data["t_sum_act"])-(( 6.01647520065307617)))))))))
 v["i8"] = 0.100000*np.tanh(((((((((((np.minimum(((data["t_sum_log"])) ,(((11.64070796966552734)))))* 2.0)) -(( 11.64070796966552734)))) * 2.0)) -(-1.0)))-(data["visitNumber"])))
 v["i9"] = 0.100000*np.tanh(((((np.where(data["t_median"]>0, data["t_sum_log"],(((((((( data["pred_2"])/ 2.0)) * 2.0)) * 2.0)) * 2.0)))-(( 10.57200813293457031)))) * 2.0))
v["i10"] = 0.100000*np.tanh(((((( 7.02941036224365234)) -(data["t_sum_act"])))*(((data["t_sum_act"])-(((((((( 7.02941036224365234)) -(data["t_sum_act"])))* 2.0)) * 2.0))))))
v["i11"] = 0.100000*np.tanh(np.minimum(((data["pred_1"])) ,(((((((6.0)) / 2.0)) *(((data["t_sum_log"])-(( 6.0)))))))))
 v["i12"] = 0.100000*np.tanh(((((data["t_sum_act"])-(np.maximum(((data["t_sum_act"])) ,(((((( -3.0)*(np.tanh(((1.58075487613677979)))))) *(-2.0)))))))) * 2.0))
v["i13"] = 0.100000*np.tanh(((((data["t_sum_act"])* 2.0)) -(np.maximum(((((data["visitNumber"])* 2.0))),(( np.maximum(((((data["t_sum_act"])* 2.0))),(((10.78281593322753906))))))))))
v["i14"] = 0.100000*np.tanh(np.minimum(((((( data["t_sum_act"])<(( 6.0)))*1.))) ,(((((( np.minimum(((data["sess_date_hours"])) ,(((( data["t_sum_act"])-(( 5.0)))))))* 2.0)) * 2.0)))))
 v["i15"] = 0.100000*np.tanh(np.minimum(((((( data["pred_1"])<(((( 10.0)) -(data["t_sum_log"])))) *1.))) ,(((( data["t_sum_log"])-(((( 10.0)) -(data["t_sum_log"]))))))))
v["i16"] = 0.100000*np.tanh(np.minimum(((0.0)) ,(((( data["t_sum_log"])+(np.minimum(((((-1.0)-(data["visitNumber"])))) ,(((( data["t_mean"])-(( 9.99547958374023438))))))))))))
v["i17"] = 0.100000*np.tanh(((((-3.0)* 2.0)) +(np.minimum(((np.maximum(((data["pred_0"])) ,(( data["t_sum_log"]))))),(((( -3.0)*(-2.0))))))))
v["i18"] = 0.100000*np.tanh(((((((np.minimum(((((((5.0)) >(data["t_sum_act"])) *1.))) ,(((( data["t_sum_act"])-(( 4.49360561370849609)))))))* 2.0)) * 2.0)) * 2.0))
v["i19"] = 0.100000*np.tanh(((np.minimum(((((((((((data["t_mean"])/ 2.0)) +(data["t_sum_log"])) /2.0)) +(((data["t_sum_log"])* 2.0)))/2.0))),(((6.0)))))-(( 6.0))))
v["i20"] = 0.100000*np.tanh(np.minimum(((0.0)) ,(((( np.where(((( data["geoNetwork_networkDomain"])+(( 1.0)))/2.0)<0, 0.0,(( data["t_sum_act"])* 2.0)))-(( 9.07516670227050781)))))))
 v["i21"] = 0.100000*np.tanh(np.minimum(((((((data["t_mean"])-(( 3.69601702690124512)))) *(( 7.17828702926635742))))),(((((3.0)>(((data["t_sum_act"])-(3.0)))) *1.)))))
v["i22"] = 0.100000*np.tanh(((np.minimum(((0.0)) ,(((( data["t_sum_log"])+(((((data["t_sum_log"])+(((-2.0)+(-3.0)))))* 2.0)))))))* 2.0))
v["i23"] = 0.100000*np.tanh(np.minimum(((0.0)) ,(( np.minimum(((((data["geoNetwork_networkDomain"])/ 2.0))),(((((7.0)) *(((data["t_sum_act"])-(((( 7.0)) / 2.0))))))))))))
v["i24"] = 0.100000*np.tanh(np.minimum(((0.0)) ,(((((( data["t_sum_log"])-(( 7.0)))) +(((( data["t_sum_log"])+(data["t_sum_act"])) /2.0)))))))
 v["i25"] = 0.100000*np.tanh(((np.minimum(((data["t_median"])) ,(((((((( np.minimum(((0.0)) ,(((( data["t_sum_log"])+(-3.0)))))) * 2.0)) * 2.0)) * 2.0)))))* 2.0))
v["i26"] = 0.100000*np.tanh(((np.minimum(((((((((((data["t_sum_act"])+(-3.0)))* 2.0)) * 2.0)) * 2.0))),(( 0.0)))) *(( 9.0))))
v["i27"] = 0.100000*np.tanh(((((data["pred_2"])-(( 5.99040746688842773)))) *(((((( np.maximum(((3.0)) ,(((( data["pred_2"])* 2.0)))))>(data["t_sum_log"])) *1.))* 2.0))))
v["i28"] = 0.100000*np.tanh(np.minimum(((((data["sess_date_dow"])/(-2.0)))) ,(((((( data["t_sum_log"])*(data["t_sum_log"])))-(( 7.08554697036743164)))))))
 v["i29"] = 0.100000*np.tanh(np.minimum(((((((((data["t_sum_log"])* 2.0)) * 2.0)) -(( 9.0))))),(((((((0.0)-(data["pred_0"])))>(data["geoNetwork_networkDomain"])) *1.)))))
v["i30"] = 0.100000*np.tanh(((((((data["t_sum_log"])+(-3.0)))*(((( 2.0)>(((data["t_sum_act"])/ 2.0)))*1.))))* 2.0))
v["i31"] = 0.100000*np.tanh(np.minimum(((0.0)) ,(((( data["totals_pageviews"])-(np.where(((( data["t_sum_act"])+(((-1.0)* 2.0)))/2.0)<0,(9.12072467803955078), data["totals_hits"])))))))
 v["i32"] = 0.100000*np.tanh(np.minimum(((((((((((((((-2.0)+(data["t_sum_log"])))* 2.0)) * 2.0)) * 2.0)) * 2.0)) * 2.0))),(( 0.0))))
v["i33"] = 0.100000*np.tanh(np.where(((( -2.0)+(data["t_sum_log"])) /2.0)<0,(( data["t_sum_log"])-(( 10.0))),(( -1.0)/(( 9.0)))))
 v["i34"] = 0.100000*np.tanh(((np.minimum(((data["t_median"])) ,(((( np.minimum(((((( data["pred_2"])>(data["t_median"])) *1.))) ,(((( data["t_sum_act"])+(-2.0)))))) * 2.0)))))* 2.0))
v["i35"] = 0.100000*np.tanh(((((np.where(((( -3.0)+(data["t_sum_log"])) /2.0)<0,(((( data["t_sum_log"])* 2.0)) +(-3.0)) , 0.0)) * 2.0)) * 2.0))
v["i36"] = 0.100000*np.tanh(((np.where(((-3.0)+(data["t_sum_act"])) <0, -3.0, 0.0)) * 2.0))
v["i37"] = 0.100000*np.tanh(np.minimum(((((( data["t_mean"])<(3.0)) *1.))) ,(((((((((( -3.0)+(((data["t_sum_act"])* 2.0)))) * 2.0)) * 2.0)) * 2.0)))))
 v["i38"] = 0.100000*np.tanh(((((np.minimum(((data["pred_1"])) ,(( np.where(data["pred_2"]>0, data["t_mean"], -3.0)))))* 2.0)) *(((( data["t_mean"])<(2.0)) *1.))))
 v["i39"] = 0.100000*np.tanh(np.minimum(((( 0.0))),(((( data["t_median"])-(((( 5.18295526504516602)) -(((data["t_sum_log"])*(((data["t_sum_act"])+(data["t_sum_log"]))))))))))))
v["i40"] = 0.100000*np.tanh(np.minimum(((((data["totals_pageviews"])-(((((2.0)/(data["t_mean"])))/(data["t_mean"])))))) ,(( 0.0))))
v["i41"] = 0.100000*np.tanh(((((((-3.0)*(((( 1.0)>(data["t_sum_act"])) *1.))))* 2.0)) * 2.0))
v["i42"] = 0.100000*np.tanh(((-3.0)*(((( 6.85303115844726562)) *(((( data["t_sum_log"])<(np.maximum(((data["visitNumber"])) ,(( np.tanh(( 2.0)))))))*1.))))))
 v["i43"] = 0.100000*np.tanh(np.minimum(((((data["t_sum_log"])-(2.0)))) ,(( np.minimum(((((2.0)/(data["t_sum_log"])))) ,(((( data["t_sum_act"])-(data["t_mean"])))))))))
 v["i44"] = 0.100000*np.tanh(np.minimum(((((((2.0)-(data["visitNumber"])))/(data["t_sum_log"])))) ,(((((( data["t_sum_log"])-(data["visitNumber"])))-(2.0))))))
v["i45"] = 0.099951*np.tanh(np.minimum(((np.minimum(((data["t_sum_act"])) ,(((( 1.0)/(data["t_sum_act"]))))))),(((( data["sess_date_dom"])+(np.tanh(( np.tanh(( data["t_sum_log"]))))))))))
v["i46"] = 0.100000*np.tanh(np.minimum(((np.minimum(((data["t_mean"])) ,(((((((3.0)/(( 5.0)))) >(data["t_median"])) *1.)))))),(((((3.95915246009826660)) /(data["t_mean"]))))))
v["i47"] = 0.100000*np.tanh(np.minimum(((np.minimum(((((data["visitNumber"])/(data["t_sum_act"])))) ,(((( data["pred_1"])/(((data["t_sum_act"])/(data["visitNumber"]))))))))),(( 0.0))))
v["i48"] = 0.100000*np.tanh(np.where(data["t_mean"]>0,(( -1.0)*(np.minimum(((((( data["totals_hits"])>(data["totals_pageviews"])) *1.))) ,(( np.tanh(( data["totals_hits"]))))))), -3.0))
v["i49"] = 0.100000*np.tanh(np.minimum(((((3.0)/(((data["t_mean"])* 2.0))))),(((( data["t_sum_act"])+(((((data["t_sum_act"])* 2.0)) -(3.0))))))))
v["i50"] = 0.100000*np.tanh(np.where(((((data["t_nb_sess"])+(data["t_sum_log"])))+(data["t_sum_log"])) >0,(((-3.0)>(data["t_nb_sess"])) *1.) , -3.0))
v["i51"] = 0.100000*np.tanh(((np.minimum(((((((( data["pred_0"])>(((data["t_sum_log"])* 2.0)))*1.))/(data["trafficSource_source"])))) ,(((( -1.0)+(data["t_sum_act"])))))) * 2.0))
v["i52"] = 0.099902*np.tanh(((((((((( data["totals_hits"])*(data["t_sum_act"])))<(1.0)) *1.))*(-3.0)))* 2.0))
v["i53"] = 0.100000*np.tanh(np.minimum(((((((( 2.0)<(data["totals_pageviews"])) *1.))+(data["t_median"])))) ,(((((((data["t_median"])<(2.0)) *1.))/(data["t_mean"]))))))
v["i54"] = 0.099805*np.tanh(((((((( data["t_sum_act"])* 2.0)) <(data["pred_0"])) *1.))*(((((-2.0)* 2.0)) * 2.0))))
v["i55"] = 0.099902*np.tanh(np.minimum(((((( data["pred_1"])>(data["geoNetwork_city"])) *1.))) ,(((((((( 5.19124889373779297)) >(data["t_mean"])) *1.))/(np.tanh(( data["t_mean"]))))))))
v["i56"] = 0.100000*np.tanh(np.where(((( -1.0)+(((data["t_sum_log"])* 2.0)))/2.0)>0,(((( data["geoNetwork_networkDomain"])*(data["geoNetwork_networkDomain"])))/(-2.0)) , -3.0))
v["i57"] = 0.100000*np.tanh(((np.where(data["t_mean"]>0,(((data["trafficSource_source"])>(np.tanh(( np.tanh(( np.tanh(( data["totals_pageviews"])))))))) *1.) ,(7.56566238403320312)))/(-2.0)))
 v["i58"] = 0.099951*np.tanh(np.minimum(((((((3.0)* 2.0)) /(data["t_mean"])))) ,(( np.minimum(((data["t_median"])) ,(((((-1.0)>(data["sess_date_dom"])) *1.))))))))
 v["i59"] = 0.099951*np.tanh(np.minimum(((np.where(data["t_sum_log"]<0, -3.0,(( data["channelGrouping"])*(np.tanh(( data["t_nb_sess"]))))))),(((( data["channelGrouping"])*(data["channelGrouping"]))))))
v["i60"] = 0.093258*np.tanh(np.where(data["t_mean"]>0, np.minimum(((np.tanh(( np.tanh(( data["pred_0"])))))) ,(((( data["geoNetwork_networkDomain"])/(data["visitNumber"]))))), -3.0))
v["i61"] = 0.099951*np.tanh(np.minimum(((((((((data["t_sum_log"])* 2.0)) * 2.0)) -(( 4.0))))),(((((((( data["visitNumber"])+(data["pred_0"])) /2.0)) >(( 9.99041843414306641)))*1.)))))
v["i62"] = 0.097460*np.tanh(((((( data["t_mean"])<(((-1.0)/(data["pred_1"])))) *1.))/(((np.tanh(( data["t_mean"])))/ 2.0))))
v["i63"] = 0.099951*np.tanh(np.where(((data["geoNetwork_networkDomain"])+(np.tanh(( np.tanh(( data["t_sum_log"])))))) >0,(( -1.0)+(np.tanh(( data["totals_pageviews"])))) , -3.0))
v["i64"] = 0.093747*np.tanh(((((((data["t_sum_act"])+(((-3.0)* 2.0)))) *(((((((data["t_sum_log"])>(( 10.0)))*1.))>(data["sess_date_hours"])) *1.))))* 2.0))
v["i65"] = 0.100000*np.tanh(np.minimum(((((( data["sess_date_hours"])<(((-2.0)*(data["visitNumber"])))) *1.))) ,(((((( data["t_sum_log"])-(data["visitNumber"])))/(data["geoNetwork_country"]))))))
v["i66"] = 0.100000*np.tanh(((((( data["geoNetwork_continent"])<(((((((((( data["trafficSource_referralPath"])+(data["geoNetwork_subContinent"])) /2.0)) +(data["geoNetwork_subContinent"])) /2.0)) +(1.0)) /2.0)))*1.))*(-3.0)))
 v["i67"] = 0.100000*np.tanh(np.where(data["t_mean"]>0, np.where(data["geoNetwork_country"]>0,(((((-2.0)/(data["geoNetwork_city"])))>(data["totals_pageviews"])) *1.) , -3.0), -3.0))
v["i68"] = 0.099853*np.tanh(np.minimum(((np.where(data["geoNetwork_country"]>0, np.where(data["t_median"]>0,(((data["t_sum_log"])<(data["totals_pageviews"])) *1.) , data["sess_date_hours"]), -3.0))),(( data["geoNetwork_subContinent"]))))
v["i69"] = 0.100000*np.tanh(np.minimum(((0.0)) ,(( np.minimum(((data["totals_pageviews"])) ,(((((((((data["totals_pageviews"])>(data["totals_hits"])) *1.))+(data["trafficSource_source"])))* 2.0))))))))
v["i70"] = 0.100000*np.tanh(((np.minimum(((((data["geoNetwork_continent"])-(data["geoNetwork_country"])))) ,(((( data["geoNetwork_country"])-(np.where(data["t_median"]>0, data["geoNetwork_country"], data["trafficSource_source"])))))))* 2.0))
v["i71"] = 0.100000*np.tanh(np.where(data["t_sum_log"]>0, np.where(data["geoNetwork_subContinent"]>0,(((data["t_sum_log"])>(( 11.69831085205078125)))*1.) ,(((( data["trafficSource_source"])* 2.0)) * 2.0)) , -3.0))
v["i72"] = 0.091060*np.tanh(np.minimum(((((((( data["sess_date_hours"])<(((data["geoNetwork_networkDomain"])-(((( -3.0)+(data["t_mean"])) /2.0)))))*1.))/(data["geoNetwork_networkDomain"])))) ,(( data["totals_hits"]))))
v["i73"] = 0.099951*np.tanh(np.minimum(((((2.0)/(data["geoNetwork_subContinent"])))) ,(((((((((data["geoNetwork_country"])>(data["geoNetwork_subContinent"])) *1.))*(data["t_median"])))*(data["geoNetwork_city"]))))))
v["i74"] = 0.099951*np.tanh(np.minimum(((((((( data["t_sum_log"])-(data["totals_pageviews"])))>(data["t_mean"])) *1.))) ,(((( data["t_sum_log"])*(( 10.0)))))))
 v["i75"] = 0.097557*np.tanh(np.minimum(((((data["t_sum_log"])* 2.0))),(((((((data["t_median"])+(((data["trafficSource_source"])* 2.0)))) <(((( data["t_median"])>(data["totals_pageviews"])) *1.))) *1.)))))
v["i76"] = 0.100000*np.tanh(((((((((((((( 4.0)) <(data["t_mean"])) *1.))/ 2.0)) /(data["trafficSource_source"])))+(-1.0)) /2.0)) / 2.0))
v["i77"] = 0.099902*np.tanh(((np.minimum(((data["t_median"])) ,(((( np.minimum(((data["totals_pageviews"])) ,(((((data["totals_pageviews"])>(((( 8.65321922302246094)) / 2.0)))*1.))))) *(( 8.65321922302246094)))))))* 2.0))
v["i78"] = 0.099218*np.tanh(((np.where(data["t_median"]>0, data["channelGrouping"], np.where(data["channelGrouping"]>0, data["t_median"], data["pred_0"])))*(np.minimum(((data["geoNetwork_country"])) ,(( data["geoNetwork_region"]))))))
v["i79"] = 0.099853*np.tanh(((( np.where(data["t_median"]<0, 2.0, data["t_mean"])) <(np.minimum(((data["t_sum_log"])) ,(( np.minimum(((data["t_sum_log"])) ,(( data["totals_hits"])))))))) *1.))
 v["i80"] = 0.099951*np.tanh(((np.where(data["t_sum_act"]>0,(((2.0)<(((((-2.0)/(data["trafficSource_source"])))/ 2.0)))*1.) , 2.0)) *(-3.0)))
 v["i81"] = 0.058964*np.tanh(((( data["geoNetwork_region"])>(((( data["geoNetwork_networkDomain"])<(((((( data["geoNetwork_country"])*(( 11.84922599792480469)))) <(data["t_sum_log"])) *1.))) *1.))) *1.))
 v["i82"] = 0.097655*np.tanh(np.minimum(((((data["geoNetwork_country"])/(((-3.0)+(data["geoNetwork_networkDomain"])))))) ,(((((( -1.0)/ 2.0)) /(data["pred_0"]))))))
v["i83"] = 0.080459*np.tanh(np.minimum(((((( data["geoNetwork_city"])<(data["t_median"])) *1.))) ,(( np.minimum(((data["t_median"])) ,(( np.minimum(((data["t_sum_act"])) ,(((((data["t_sum_act"])>(data["t_mean"])) *1.)))))))))))
v["i84"] = 0.099951*np.tanh(((( np.where(data["geoNetwork_city"]<0, data["sess_date_dom"], np.where(data["sess_date_dom"]<0,(( data["sess_date_dom"])/(data["totals_hits"])) , data["sess_date_hours"])))<(-1.0)) *1.))
 v["i85"] = 0.099316*np.tanh(((((( data["totals_pageviews"])<(data["totals_hits"])) *1.))/(np.minimum(((np.where(data["channelGrouping"]>0, data["totals_hits"], -2.0))),(( -1.0))))))
v["i86"] = 0.100000*np.tanh(((((((((((((data["geoNetwork_networkDomain"])<(0.0)) *1.))+(data["geoNetwork_city"])))>(( 2.0)))*1.))/(data["geoNetwork_city"])))/(data["geoNetwork_city"])))
 v["i87"] = 0.067904*np.tanh(np.minimum(((((data["t_sum_act"])-(data["t_mean"])))) ,(((((1.0)) -(data["geoNetwork_city"]))))))
v["i88"] = 0.099902*np.tanh(((((( -1.0)+(((((( data["sess_date_hours"])>(data["geoNetwork_country"])) *1.))/(data["sess_date_hours"])))) /2.0)) / 2.0))
v["i89"] = 0.099951*np.tanh(((((((( np.minimum(((data["pred_0"])) ,(((((data["pred_0"])>(data["t_median"])) *1.))))) <(((data["t_median"])/(data["pred_0"])))) *1.))/ 2.0)) / 2.0))
v["i90"] = 0.099951*np.tanh(((np.where(data["t_sum_act"]>0,(((data["geoNetwork_region"])>(((np.minimum(((data["sess_date_dom"])) ,(( data["sess_date_hours"])))) +(data["t_sum_log"])))) *1.) , -3.0)) * 2.0))
v["i91"] = 0.099902*np.tanh(((((np.minimum(((np.minimum(((data["totals_hits"])) ,(((((data["t_mean"])<(data["totals_pageviews"])) *1.)))))),(((( data["totals_pageviews"])-(data["totals_hits"])))))) * 2.0)) * 2.0))
v["i92"] = 0.099951*np.tanh(((((( data["t_median"])>(np.where(data["geoNetwork_networkDomain"]<0, data["geoNetwork_networkDomain"], data["t_sum_act"])))*1.))/(((((-3.0)+(data["t_mean"])))* 2.0))))
v["i93"] = 0.099951*np.tanh(((((((((((((( data["geoNetwork_networkDomain"])/(data["pred_0"])))+(data["sess_date_dom"])))+(-1.0)))>(3.0)) *1.))* 2.0)) * 2.0))
v["i94"] = 0.099853*np.tanh(((((((np.minimum(((data["sess_date_dow"])) ,(((( 1.0)/(data["trafficSource_source"])))))) / 2.0)) / 2.0)) / 2.0))
v["i95"] = 0.082169*np.tanh(np.tanh(((((( data["sess_date_dow"])-(data["geoNetwork_country"])))/(((((data["sess_date_hours"])*(data["sess_date_hours"])))-(data["geoNetwork_country"])))))))
 v["i96"] = 0.099951*np.tanh(((((( data["totals_hits"])<(((3.0)/ 2.0)))*1.))*(-1.0)))
 v["i97"] = 0.100000*np.tanh(np.where(data["t_sum_act"]<0, -1.0,(((((( np.minimum(((-1.0)) ,(( data["sess_date_dow"])))) / 2.0)) +(data["geoNetwork_country"])))/(data["geoNetwork_networkDomain"]))))
v["i98"] = 0.099951*np.tanh(np.where(data["t_sum_act"]<0, -3.0,(((((( 6.35631465911865234)) -(( 8.0)))) >(np.where(data["sess_date_dow"]<0, 0.0, data["sess_date_hours"])))*1.)))
v["i99"] = 0.099951*np.tanh(((np.minimum(((((((((((data["trafficSource_referralPath"])+(2.0)) /2.0)) >(data["totals_hits"])) *1.))*(data["trafficSource_referralPath"])))) ,(( data["totals_hits"])))) * 2.0))
return Output(v.sum(axis=1))
def GP6(data):
v = pd.DataFrame()
v["i0"] = 0.100000*np.tanh(((((data["t_mean"])-(( 9.82170104980468750)))) +(((data["t_mean"])-(((((data["t_sum_log"])-(( 9.04127788543701172)))) *(-3.0)))))))
 v["i1"] = 0.100000*np.tanh(((((( 14.27411556243896484)) *(((data["t_mean"])+(((((data["t_sum_log"])+(((((-3.0)* 2.0)) * 2.0)))) * 2.0)))))) * 2.0))
v["i2"] = 0.100000*np.tanh(((((((((np.where(data["pred_1"] < -99998, data["pred_1"],(( data["t_mean"])-(( 7.67527198791503906)))))* 2.0)) * 2.0)) * 2.0)) +(3.0)))
 v["i3"] = 0.100000*np.tanh(((-3.0)+(((((((data["t_sum_log"])+(((data["t_mean"])-(((( 7.0)) * 2.0)))))) *(( 7.0)))) * 2.0))))
v["i4"] = 0.100000*np.tanh(((((data["t_sum_log"])-(np.maximum(((np.maximum(((( 6.96472883224487305))),(((((( data["visitNumber"])* 2.0)) -(data["t_sum_log"]))))))),(( data["t_median"])))))) * 2.0))
v["i5"] = 0.100000*np.tanh(((((((1.0)+(((((1.0)+(((((data["t_mean"])-(( 7.40952205657958984)))) * 2.0)))) * 2.0)))) * 2.0)) * 2.0))
v["i6"] = 0.100000*np.tanh(((np.where(((data["t_sum_log"])-(( 9.06132984161376953)))>0,(( data["t_sum_log"])-(( 9.06132698059082031))),(( data["t_median"])-(( 9.06132698059082031)))))* 2.0))
v["i7"] = 0.100000*np.tanh(((np.minimum(((((((((data["t_sum_act"])-(((3.0)* 2.0)))) * 2.0)) * 2.0))),(((((data["t_sum_act"])>(data["t_mean"])) *1.))))) * 2.0))
v["i8"] = 0.100000*np.tanh(np.minimum(((((((data["t_sum_act"])-(((( 12.94684314727783203)) / 2.0)))) * 2.0))),(((((data["pred_3"])>(((((data["t_sum_act"])/ 2.0)) / 2.0)))*1.)))))
v["i9"] = 0.100000*np.tanh(((data["t_sum_log"])+(((((((((((data["t_sum_log"])+(((-3.0)* 2.0)))) * 2.0)) * 2.0)) -(data["visitNumber"])))* 2.0))))
v["i10"] = 0.100000*np.tanh(((((((((((((((-3.0)* 2.0)) +(data["sess_date_dom"])))+(data["t_sum_log"])))-(( 5.83093786239624023)))) * 2.0)) * 2.0)) * 2.0))
v["i11"] = 0.100000*np.tanh(((((-3.0)+(((data["t_sum_log"])+(((((((data["t_sum_act"])+(((-3.0)* 2.0)))) * 2.0)) * 2.0)))))) * 2.0))
v["i12"] = 0.100000*np.tanh(((((( 10.66844654083251953)) *(((((( 10.66844272613525391)) *(((data["t_sum_log"])-(( 10.66844272613525391)))))) -(data["t_sum_log"])))))-(( 10.66844272613525391))))
v["i13"] = 0.100000*np.tanh(((((((data["t_sum_log"])+(((data["t_sum_act"])-(np.maximum(((np.maximum(((( 10.0))),(( data["pred_0"]))))),(((10.0)))))))))* 2.0)) * 2.0))
v["i14"] = 0.100000*np.tanh(((-3.0)+(np.minimum(((np.minimum(((((data["t_mean"])+(((data["t_sum_act"])-(( 9.0))))))),(((2.37444567680358887)))))) ,(((2.37444567680358887)))))))
 v["i15"] = 0.100000*np.tanh(((((( 14.27473449707031250)) *(((np.where(data["sess_date_hours"]>0,(( data["t_sum_act"])-(( 3.87348985671997070))), -1.0)) +(-1.0)))))* 2.0))
v["i16"] = 0.100000*np.tanh(np.minimum(((data["geoNetwork_networkDomain"])) ,(((( np.maximum(((data["t_median"])) ,(( data["pred_0"])))) -(np.maximum(((((( 12.08827495574951172)) -(data["pred_0"])))) ,(( data["pred_0"])))))))))
 v["i17"] = 0.100000*np.tanh(np.minimum(((((data["t_sum_log"])-(((( 10.0)) / 2.0))))),(((( 2.0)-(((data["t_sum_act"])-(((( 10.0)) / 2.0)))))))))
 v["i18"] = 0.100000*np.tanh(((((((((np.minimum(((((( data["t_sum_log"])<(( 5.0)))*1.))) ,(((( data["t_sum_log"])-(( 4.0)))))))* 2.0)) * 2.0)) * 2.0)) * 2.0))
v["i19"] = 0.100000*np.tanh(np.minimum(((((( 6.83367300033569336)) -(data["t_mean"])))) ,(( np.minimum(((data["pred_1"])) ,(((( data["t_mean"])-(((( 6.93451642990112305)) -(data["t_mean"])))))))))))
 v["i20"] = 0.100000*np.tanh(np.minimum(((((( -1.0)+(2.0)) /2.0))),(((((((((((( data["t_mean"])-(( 4.01557540893554688)))) * 2.0)) * 2.0)) * 2.0)) * 2.0)))))
 v["i21"] = 0.100000*np.tanh(np.minimum(((0.0)) ,(((((((data["t_sum_log"])* 2.0)) +(((data["t_sum_log"])-(((( 12.81262588500976562)) +(data["visitNumber"])))))) /2.0)))))
 v["i22"] = 0.100000*np.tanh(((np.minimum(((((data["t_sum_act"])-(((( 2.0)) * 2.0))))),(((((((data["t_sum_act"])-(3.0)))<(( 2.0)))*1.))))) * 2.0))
v["i23"] = 0.100000*np.tanh(np.minimum(((0.0)) ,(((((((( np.where(data["pred_0"]>0, data["pred_0"], data["t_mean"])) -(3.0)))* 2.0)) * 2.0)))))
 v["i24"] = 0.100000*np.tanh(((( 10.90211677551269531)) *(((( 8.0)) *(((-3.0)+(np.minimum(((3.0)) ,(( np.minimum(((data["t_sum_act"])) ,(( data["t_sum_log"])))))))))))))
 v["i25"] = 0.100000*np.tanh(((data["t_sum_log"])-(np.where(((-3.0)+(((( data["t_sum_log"])+(data["t_mean"])) /2.0)))<0,(7.14634370803833008), data["t_sum_log"]))))
v["i26"] = 0.100000*np.tanh(((((((np.where(((data["t_mean"])-(3.0)) >0, 3.0, data["t_sum_log"])) -(3.0)))* 2.0)) * 2.0))
v["i27"] = 0.100000*np.tanh(((((((( np.minimum(((((((data["t_mean"])* 2.0)) * 2.0))),(( data["t_sum_act"])))) <(3.0)) *1.))*(-3.0)))*(( 6.24691629409790039))))
v["i28"] = 0.100000*np.tanh(((((((((((( data["t_sum_act"])/ 2.0)) <(2.0)) *1.))*(((((data["t_sum_act"])* 2.0)) -(( 5.52667522430419922)))))) * 2.0)) * 2.0))
v["i29"] = 0.100000*np.tanh(np.minimum(((((np.minimum(((0.0)) ,(((((((((data["t_sum_log"])-(( 6.83591127395629883)))) +(data["t_sum_act"])) /2.0)) * 2.0)))))* 2.0))),(( data["t_median"]))))
v["i30"] = 0.100000*np.tanh(((np.where(np.where(((-2.0)+(data["t_sum_act"])) >0, data["t_mean"], -3.0)>0, 0.0, -3.0)) * 2.0))
v["i31"] = 0.100000*np.tanh(((((( data["t_sum_log"])<(2.0)) *1.))*(((((-3.0)* 2.0)) * 2.0))))
v["i32"] = 0.100000*np.tanh(((( 3.0)+(np.where(((( data["t_sum_act"])+(-2.0)) /2.0)<0,(( -2.0)*(( 9.0))), -3.0)))/2.0))
v["i33"] = 0.100000*np.tanh(np.minimum(((np.tanh(((((((( data["trafficSource_source"])<(data["pred_2"])) *1.))>(data["sess_date_hours"])) *1.))))) ,(((((( -2.0)+(data["t_sum_log"])))* 2.0)))))
 v["i34"] = 0.100000*np.tanh(((np.minimum(((((( data["t_median"])>(( 10.0)))*1.))) ,(((( np.minimum(((data["t_median"])) ,(((( data["t_sum_act"])+(-2.0)))))) * 2.0)))))* 2.0))
v["i35"] = 0.100000*np.tanh(((np.minimum(((((np.minimum(((((data["t_sum_act"])-(( 2.0))))),(((((( data["geoNetwork_country"])* 2.0)) * 2.0)))))* 2.0))),(( 0.0)))) * 2.0))
v["i36"] = 0.100000*np.tanh(((((np.minimum(((((( np.maximum(((data["t_median"])) ,(( data["t_median"])))) <(data["pred_2"])) *1.))) ,(((( data["t_sum_log"])+(-2.0)))))) * 2.0)) * 2.0))
v["i37"] = 0.100000*np.tanh(np.minimum(((0.0)) ,(((((((-3.0)-(((data["pred_2"])*(((-3.0)+(data["t_median"])))))))+(data["t_sum_log"])) /2.0)))))
 v["i38"] = 0.100000*np.tanh(((np.where(((-3.0)+(data["t_sum_log"])) <0,(((( -3.0)+(data["t_sum_log"])))+(data["t_mean"])) , 0.0)) * 2.0))
v["i39"] = 0.100000*np.tanh(np.minimum(((((-3.0)+(data["t_mean"])))) ,(( np.where(data["trafficSource_source"]<0, -1.0,(( 3.0)-(data["visitNumber"])))))))
 v["i40"] = 0.100000*np.tanh(((((-3.0)* 2.0)) *(((( np.tanh(((( 1.0)* 2.0)))) >(data["t_sum_log"])) *1.))))
 v["i41"] = 0.099951*np.tanh(np.where(((data["t_mean"])+(data["pred_2"])) >0,(((data["totals_hits"])<(data["totals_pageviews"])) *1.) ,(( data["totals_pageviews"])+(((-3.0)* 2.0)))))
 v["i42"] = 0.100000*np.tanh(((((((((data["t_sum_log"])+(((data["t_sum_log"])+(-2.0)))))*(((( data["t_mean"])<(2.0)) *1.))))* 2.0)) * 2.0))
v["i43"] = 0.100000*np.tanh(np.minimum(((((2.0)-(np.where(data["t_mean"]<0,(8.0), data["visitNumber"]))))),(((((data["totals_pageviews"])>(data["totals_hits"])) *1.)))))
v["i44"] = 0.100000*np.tanh(np.minimum(((((data["t_sum_act"])+(((-3.0)+(data["t_sum_act"])))))) ,(((((((data["t_mean"])<(data["t_sum_act"])) *1.))-(data["trafficSource_source"]))))))
v["i45"] = 0.100000*np.tanh(np.where(data["t_mean"]<0, -3.0, np.where(data["geoNetwork_networkDomain"]>0,(( np.tanh(( data["geoNetwork_networkDomain"])))-(data["geoNetwork_networkDomain"])) ,(0.20732049643993378))))
v["i46"] = 0.100000*np.tanh(((((((((( 1.0)) +(data["t_sum_log"])) /2.0)) >(data["t_sum_act"])) *1.))*(((-3.0)* 2.0))))
v["i47"] = 0.100000*np.tanh(((-3.0)*(((( np.tanh(((( -1.0)+(data["t_sum_log"])))))<(((( data["geoNetwork_networkDomain"])<(np.tanh(( -1.0)))) *1.))) *1.))))
 v["i48"] = 0.100000*np.tanh(np.minimum(((((((data["t_sum_act"])-(3.0)))+(data["t_sum_act"])))) ,(((((((( data["pred_0"])+(data["sess_date_hours"])) /2.0)) >(data["t_sum_log"])) *1.)))))
v["i49"] = 0.100000*np.tanh(np.where(((data["t_median"])+(data["pred_1"])) < -99998, data["pred_1"], np.where(data["t_mean"]<0, -3.0,(((data["pred_1"])>(data["pred_1"])) *1.))))
 v["i50"] = 0.100000*np.tanh(((((-3.0)*(((( np.maximum(((data["visitNumber"])) ,(( np.tanh(( np.tanh(( np.tanh(( data["t_mean"])))))))))) >(data["t_sum_log"])) *1.))))* 2.0))
v["i51"] = 0.099951*np.tanh(np.where(data["t_sum_log"]<0,(( -3.0)* 2.0),(((((( data["t_sum_log"])<(data["pred_0"])) *1.))<(((( -3.0)+(data["visitNumber"])) /2.0)))*1.)))
v["i52"] = 0.099902*np.tanh(np.where(np.where(data["t_mean"]>0,(((data["geoNetwork_networkDomain"])+(( 1.0)))/2.0), -3.0)>0,(((data["t_mean"])<(data["totals_pageviews"])) *1.) , -3.0))
v["i53"] = 0.100000*np.tanh(np.where(data["t_sum_log"]<0, -3.0, np.minimum(((data["pred_0"])) ,(( np.minimum(((data["t_median"])) ,(((((( 9.0)) <(((data["pred_0"])/ 2.0)))*1.)))))))))
v["i54"] = 0.100000*np.tanh(np.where(((((data["t_nb_sess"])/ 2.0)) +(data["t_sum_log"])) <0,(( -2.0)* 2.0),(( data["sess_date_dow"])*(((data["t_nb_sess"])/ 2.0)))))
 v["i55"] = 0.100000*np.tanh(((-1.0)+(((np.tanh(((( data["t_sum_act"])* 2.0)))) -(((( data["totals_hits"])>(data["totals_pageviews"])) *1.))))))
 v["i56"] = 0.099609*np.tanh(np.where(data["t_mean"]>0,(((data["t_mean"])<(((((((3.0)+(data["t_sum_log"])) /2.0)) +(data["t_sum_log"])) /2.0)))*1.) , -2.0))
v["i57"] = 0.100000*np.tanh(((((((((np.tanh(( np.tanh(((( data["totals_hits"])/ 2.0)))))) <(data["trafficSource_source"])) *1.))>(np.tanh(( data["t_sum_act"])))) *1.))*(-3.0)))
 v["i58"] = 0.099951*np.tanh(np.minimum(((((((((((-1.0)+(data["t_sum_log"])))+(data["t_sum_log"])))* 2.0)) * 2.0))),(((((-2.0)>(data["sess_date_hours"])) *1.)))))
v["i59"] = 0.100000*np.tanh(((np.minimum(((0.0)) ,(( np.minimum(((((data["t_sum_log"])-(data["visitNumber"])))) ,(((((( data["t_sum_log"])-(1.0)))* 2.0)))))))) * 2.0))
v["i60"] = 0.100000*np.tanh(np.minimum(((np.where(data["t_sum_log"]<0, -3.0,(((((((( 2.0)) >(data["totals_pageviews"])) *1.))/ 2.0)) / 2.0)))) ,(( data["totals_pageviews"]))))
v["i61"] = 0.099951*np.tanh(np.where(data["t_sum_act"]<0, -2.0,(( np.minimum(((data["sess_date_dom"])) ,(((((data["pred_0"])<(data["t_median"])) *1.))))) / 2.0)))
 v["i62"] = 0.099951*np.tanh(np.where(data["t_sum_act"]>0, np.minimum(((((( data["visitNumber"])<(np.tanh(( data["sess_date_dom"])))) *1.))) ,(((((data["visitNumber"])<(data["trafficSource_source"])) *1.)))), -3.0))
v["i63"] = 0.100000*np.tanh(np.minimum(((((((( data["pred_1"])*(((data["totals_hits"])-(data["totals_pageviews"])))))+(data["totals_pageviews"])) /2.0))),(((((data["sess_date_dom"])<(-1.0)) *1.)))))
v["i64"] = 0.099951*np.tanh(np.minimum(((np.where(data["t_sum_act"]<0, -3.0, 0.0))),(((( data["totals_hits"])-(np.maximum(((data["totals_pageviews"])) ,(( data["sess_date_hours"])))))))))
 v["i65"] = 0.100000*np.tanh(np.minimum(((((-1.0)+(data["t_sum_log"])))) ,(( np.minimum(((data["totals_pageviews"])) ,(((((2.0)>(((data["geoNetwork_city"])+(data["totals_pageviews"])))) *1.))))))))
 v["i66"] = 0.100000*np.tanh(((np.minimum(((data["totals_hits"])) ,(( np.minimum(((((data["totals_pageviews"])-(data["totals_hits"])))) ,(((( data["totals_hits"])-(data["totals_pageviews"])))))))))* 2.0))
v["i67"] = 0.099951*np.tanh(((((((((4.0)) <(data["totals_pageviews"])) *1.))*(((data["sess_date_hours"])+(((data["pred_0"])-(data["totals_pageviews"])))))))*(data["t_mean"])))
 v["i68"] = 0.099707*np.tanh(np.where(data["t_mean"]<0,(( -3.0)* 2.0),(( np.minimum(((((( 1.0)<(data["geoNetwork_region"])) *1.))) ,(( data["totals_hits"])))) / 2.0)))
 v["i69"] = 0.082218*np.tanh(((((( np.where(data["sess_date_hours"]>0, data["sess_date_hours"], data["pred_1"])) >(2.0)) *1.))-(((( data["sess_date_hours"])<(((data["geoNetwork_city"])/ 2.0)))*1.))))
 v["i70"] = 0.078065*np.tanh(np.where(data["t_sum_log"]>0, np.tanh(((( np.where(data["sess_date_hours"]>0,(( 3.0)* 2.0), data["t_mean"])) -(data["t_sum_act"])))) , -3.0))
v["i71"] = 0.100000*np.tanh(np.where(data["geoNetwork_country"]>0,(((((1.0)<(((data["geoNetwork_city"])*(((( data["geoNetwork_city"])+(data["geoNetwork_region"])) /2.0)))))*1.))/ 2.0), -3.0))
v["i72"] = 0.100000*np.tanh(((np.where(data["geoNetwork_subContinent"]<0, -3.0, np.where(((( data["geoNetwork_subContinent"])>(data["geoNetwork_country"])) *1.) >0, 0.0, -3.0)))* 2.0))
v["i73"] = 0.099951*np.tanh(((( 11.99727344512939453)) *(np.minimum(((data["geoNetwork_subContinent"])) ,(( np.minimum(((data["geoNetwork_country"])) ,(( np.minimum(((data["t_sum_log"])) ,(((((data["pred_1"])>(data["t_sum_log"])) *1.)))))))))))))
v["i74"] = 0.099951*np.tanh(np.minimum(((((data["geoNetwork_networkDomain"])*(np.tanh(((((( data["pred_1"])+(data["sess_date_dom"])))+(((data["geoNetwork_networkDomain"])* 2.0))))))))),(( 0.0))))
v["i75"] = 0.099707*np.tanh(((data["totals_pageviews"])*(np.maximum(((((( data["totals_pageviews"])>(data["t_mean"])) *1.))) ,(((((((data["t_sum_log"])-(data["totals_pageviews"])))>(data["t_mean"])) *1.)))))))
v["i76"] = 0.099902*np.tanh(np.minimum(((((( data["geoNetwork_country"])<(data["sess_date_hours"])) *1.))) ,(((( data["geoNetwork_country"])-(((( data["geoNetwork_country"])<(((( data["geoNetwork_subContinent"])<(data["sess_date_hours"])) *1.))) *1.)))))))
v["i77"] = 0.099902*np.tanh(np.where(data["sess_date_dom"]>0,(((( 0.05049348995089531)) >(data["sess_date_dom"])) *1.) ,(((((data["sess_date_hours"])>(data["geoNetwork_country"])) *1.))/ 2.0)))
 v["i78"] = 0.100000*np.tanh(np.minimum(((np.where(data["geoNetwork_subContinent"]<0, -1.0, data["t_median"]))),(((((data["sess_date_hours"])<(((data["geoNetwork_networkDomain"])-(2.0)))) *1.)))))
v["i79"] = 0.100000*np.tanh(((np.minimum(((((data["t_mean"])-(((((data["geoNetwork_region"])+(data["geoNetwork_city"])))+(((data["geoNetwork_region"])* 2.0))))))),(( 0.0)))) * 2.0))
v["i80"] = 0.100000*np.tanh(np.minimum(((((((( data["trafficSource_referralPath"])* 2.0)) >(-1.0)) *1.))) ,(((((( -2.0)*(((data["trafficSource_referralPath"])* 2.0)))) +(data["geoNetwork_continent"]))))))
v["i81"] = 0.099951*np.tanh(((((((((( data["trafficSource_source"])>(((data["totals_hits"])-(((data["trafficSource_referralPath"])/ 2.0)))))*1.))*(data["sess_date_hours"])))*(data["t_mean"])))* 2.0))
v["i82"] = 0.099853*np.tanh(((((((np.where(data["geoNetwork_subContinent"]<0, data["geoNetwork_city"],(((3.0)<(((( data["geoNetwork_region"])+(data["totals_pageviews"])) /2.0)))*1.))) * 2.0)) * 2.0)) * 2.0))
v["i83"] = 0.099951*np.tanh(((np.minimum(((data["totals_hits"])) ,(((((data["t_median"])<(((( data["totals_hits"])+(((-2.0)-(np.tanh(( data["geoNetwork_country"])))))) /2.0)))*1.))))) * 2.0))
v["i84"] = 0.099902*np.tanh(((((((((((data["trafficSource_referralPath"])<(data["totals_pageviews"])) *1.))-(data["totals_pageviews"])))>(data["trafficSource_source"])) *1.))-(((( 1.0)<(data["geoNetwork_country"])) *1.))))
 v["i85"] = 0.081974*np.tanh(((((( data["totals_pageviews"])>(data["t_sum_log"])) *1.))-(np.where(data["trafficSource_referralPath"]<0,(((((3.0)/ 2.0)) >(data["totals_pageviews"])) *1.) , data["geoNetwork_city"]))))
v["i86"] = 0.070982*np.tanh(((np.minimum(((((data["geoNetwork_city"])*(data["trafficSource_medium"])))) ,(((((((( data["trafficSource_source"])<(((data["trafficSource_medium"])* 2.0)))*1.))<(data["trafficSource_source"])) *1.))))) * 2.0))
v["i87"] = 0.099951*np.tanh(((((( np.maximum(((((data["totals_pageviews"])* 2.0))),(( data["t_mean"])))) >(np.maximum(((((data["totals_pageviews"])+(data["totals_hits"])))) ,(( data["pred_0"])))))*1.))/ 2.0))
v["i88"] = 0.076160*np.tanh(((((( data["pred_0"])<(np.minimum(((data["t_sum_log"])) ,(( 2.0)))))*1.))*(((data["t_median"])*(np.minimum(((data["pred_0"])) ,(( data["geoNetwork_region"]))))))))
v["i89"] = 0.099951*np.tanh(np.where(((data["channelGrouping"])+(data["t_sum_act"])) >0,(((( np.minimum(((data["geoNetwork_city"])) ,(( data["sess_date_dow"])))) / 2.0)) *(data["channelGrouping"])) , -3.0))
v["i90"] = 0.085344*np.tanh(((np.minimum(((data["trafficSource_source"])) ,(( np.where(data["trafficSource_source"]<0, -1.0,(((((( data["geoNetwork_city"])>(data["geoNetwork_region"])) *1.))>(data["trafficSource_source"])) *1.))))))/ 2.0))
v["i91"] = 0.099951*np.tanh(((((((((((data["sess_date_dom"])>(data["geoNetwork_country"])) *1.))>(((( -1.0)+(data["t_mean"])) /2.0)))*1.))* 2.0)) *(data["t_sum_log"])))
 v["i92"] = 0.099853*np.tanh(np.where(data["t_mean"]<0, -3.0, np.minimum(((data["totals_hits"])) ,(((((data["trafficSource_referralPath"])>(data["totals_hits"])) *1.))))))
 v["i93"] = 0.099511*np.tanh(((((((data["sess_date_hours"])<(data["sess_date_dow"])) *1.))<(np.tanh(( np.where(data["sess_date_dom"]<0,(((data["sess_date_dow"])<(-1.0)) *1.) , data["sess_date_dow"])))))*1.))
 v["i94"] = 0.099853*np.tanh(np.where(np.minimum(((data["geoNetwork_country"])) ,(( data["channelGrouping"])))>0,(( data["channelGrouping"])-(1.0)) ,(((( -1.0)-(data["channelGrouping"])))/ 2.0)))
 v["i95"] = 0.067220*np.tanh(((( np.minimum(((data["t_median"])) ,(( np.where(data["sess_date_hours"]>0, np.where(data["geoNetwork_city"]>0, data["pred_0"], data["t_median"]), 0.0)))))>(data["pred_0"])) *1.))
 v["i96"] = 0.100000*np.tanh(((((data["t_median"])*(((((((( data["geoNetwork_subContinent"])<(((( data["geoNetwork_subContinent"])<(data["geoNetwork_country"])) *1.))) *1.))*(data["geoNetwork_region"])))* 2.0)))) * 2.0))
v["i97"] = 0.099951*np.tanh(np.minimum(((((( data["sess_date_dow"])>(np.tanh(( data["geoNetwork_networkDomain"])))) *1.))) ,(( np.where(data["geoNetwork_subContinent"]<0, -3.0,(((data["totals_hits"])<(data["geoNetwork_city"])) *1.))))))
 v["i98"] = 0.099951*np.tanh(np.minimum(((data["t_mean"])) ,(((((((((2.0)-(((( data["t_mean"])<(data["totals_hits"])) *1.))))<(data["geoNetwork_city"])) *1.))*(-3.0))))))
v["i99"] = 0.075574*np.tanh(np.where(((( data["geoNetwork_networkDomain"])>(data["geoNetwork_subContinent"])) *1.) >0, data["geoNetwork_city"],(((((data["geoNetwork_country"])*(data["geoNetwork_city"])))>(data["geoNetwork_subContinent"])) *1.)))
return Output(v.sum(axis=1))
def cleanstring2(oldtext_w, oldtext_w_n = None):
dictionary = {'ahah':'laugh',
'haha':'laugh',
'ahhah':'laugh',
'haaha':'laugh',
'hahha':'laugh',
'ahaah':'laugh',
'oxox':'kiss',
'xoxo':'kiss',
'xxox':'kiss',
'ooxo':'kiss'
}
expand = 0
newtext_w = copy.deepcopy(oldtext_w)
 newtext_w_n = copy.deepcopy(oldtext_w_n)
 for word_ind ,current_word in enumerate(oldtext_w):
new_word = current_word
for repeated in dictionary.keys() :
if repeated in current_word:
new_word = dictionary[repeated]
new_word = new_word.split()
newtext_w, newtext_w_n, expand = fix_difference(current_word, new_word, oldtext_w_n, newtext_w, newtext_w_n, word_ind, expand)
 return newtext_w, newtext_w_n<categorify>
a = maxval*GP1(gp_trn_users)
 b = maxval*GP2(gp_trn_users)
 c = maxval*GP3(gp_trn_users)
 d = maxval*GP4(gp_trn_users)
 e = maxval*GP5(gp_trn_users)
 f = maxval*GP6(gp_trn_users)
 print(mean_squared_error(( maxval*gp_trn_users['target']), a)**.5)
 print(mean_squared_error(( maxval*gp_trn_users['target']), b)**.5)
 print(mean_squared_error(( maxval*gp_trn_users['target']), c)**.5)
 print(mean_squared_error(( maxval*gp_trn_users['target']), d)**.5)
 print(mean_squared_error(( maxval*gp_trn_users['target']), e)**.5)
 print(mean_squared_error(( maxval*gp_trn_users['target']), f)**.5)
 print(mean_squared_error(( maxval*gp_trn_users['target']),(a+b+c+d+e+f)/6)**.5 )
test_string = ["ahhahahahaha","it","is","a","dummy","string","xoxoxooxoxoxo"]
print("Before cleanstring2")
 print(test_string)
 print("After cleanstring2")
 print(cleanstring2(test_string)[0] )<feature_engineering>
a = maxval*GP1(gp_sub_users)
 b = maxval*GP2(gp_sub_users)
 c = maxval*GP3(gp_sub_users)
 d = maxval*GP4(gp_sub_users)
 e = maxval*GP5(gp_sub_users)
 f = maxval*GP6(gp_sub_users )
dfTrain['text_w'], dfTrain['text_w_n'] = zip(*dfTrain.apply(lambda df: cleanstring2(df.text_w,df.text_w_n), axis=1))
dfTrain['selected_w'], _ = zip(*dfTrain.apply(lambda df: cleanstring2(df.selected_w), axis=1))
print("===== E> Some repeated structures replaced with their meanings <3 =====" )<string_transform>
gp_sub_users['PredictedLogRevenue'] = a.values
gp_sub_users[['PredictedLogRevenue']].to_csv('gp1_test.csv', index=True)
 gp_sub_users['PredictedLogRevenue'] = b.values
gp_sub_users[['PredictedLogRevenue']].to_csv('gp2_test.csv', index=True)
 gp_sub_users['PredictedLogRevenue'] = c.values
gp_sub_users[['PredictedLogRevenue']].to_csv('gp3_test.csv', index=True)
 gp_sub_users['PredictedLogRevenue'] = d.values
gp_sub_users[['PredictedLogRevenue']].to_csv('gp4_test.csv', index=True)
 gp_sub_users['PredictedLogRevenue'] = e.values
gp_sub_users[['PredictedLogRevenue']].to_csv('gp5_test.csv', index=True)
 gp_sub_users['PredictedLogRevenue'] = f.values
gp_sub_users[['PredictedLogRevenue']].to_csv('gp6_test.csv', index=True)
 gp_sub_users['PredictedLogRevenue'] =(a+b+c+d+e+f ).values/6
gp_sub_users[['PredictedLogRevenue']].to_csv('gpari_test.csv', index=True )