def main():
feature_name = 'other_features1'
train_features = pd.read_csv('other_feature1/feature_train.csv')
test_features = pd.read_csv('other_feature1/feature_test.csv')
feature_score = pd.read_csv(
'other_feature1/action_process_features_importances.csv')
feature_score = feature_score.sort_values(
by='importance', ascending=False).reset_index(drop=True)
used_features = feature_score['feature'].values.tolist()[:80]
if 'userid' not in used_features:
used_features.append('userid')
train = train_features[used_features]
test = test_features[used_features]
train_features = pd.read_csv('other_feature2/type_typevalue_train.csv')
test_features = pd.read_csv('other_feature2/type_typevalue_test.csv')
train = pd.merge(train, train_features, on='userid', how='left')
test = pd.merge(test, test_features, on='userid', how='left')
# train_features = pd.read_csv('other_feature2/type_type_train.csv')
# test_features = pd.read_csv('other_feature2/type_type_test.csv')
# train = pd.merge(train, train_features, on='userid', how='left')
# test = pd.merge(test, test_features, on='userid', how='left')
# train_features = pd.read_csv('other_feature2/actiontype_cloest_train.csv')
# test_features = pd.read_csv('other_feature2/actiontype_cloest_test.csv')
# train = pd.merge(train, train_features, on='userid', how='left')
# test = pd.merge(test, test_features, on='userid', how='left')
data_utils.save_features(train, test, feature_name)
def main():
feature_name = 'basic_user_action_features'
if data_utils.is_feature_created(feature_name):
return
train_action = pd.read_csv(Configure.base_path + 'train/action_train.csv')
test_action = pd.read_csv(Configure.base_path + 'test/action_test.csv')
train_action = build_time_features(train_action)
test_action = build_time_features(test_action)
print('save cleaned datasets')
train_action.to_csv(Configure.cleaned_path + 'cleaned_action_train.csv',
index=False,
columns=train_action.columns)
test_action.to_csv(Configure.cleaned_path + 'cleaned_action_test.csv',
index=False,
columns=test_action.columns)
train_action_features = basic_action_info(train_action)
test_action_features = basic_action_info(test_action)
print('save ', feature_name)
data_utils.save_features(train_action_features,
test_action_features,
features_name=feature_name)
def main():
# 待预测订单的数据 （原始训练集和测试集）
train = pd.read_csv(Configure.base_path + 'train/orderFuture_train.csv', encoding='utf8')
test = pd.read_csv(Configure.base_path + 'test/orderFuture_test.csv', encoding='utf8')
orderHistory_train = pd.read_csv(Configure.base_path + 'train/orderHistory_train.csv', encoding='utf8')
orderHistory_test = pd.read_csv(Configure.base_path + 'test/orderHistory_test.csv', encoding='utf8')
orderHistory_train = build_time_category_encode(orderHistory_train)
orderHistory_test = build_time_category_encode(orderHistory_test)
orderHistory_train.to_csv(Configure.cleaned_path + 'cleaned_orderHistory_train.csv', index=False,
columns=orderHistory_train.columns)
orderHistory_test.to_csv(Configure.cleaned_path + 'cleaned_orderHistory_test.csv', index=False,
columns=orderHistory_test.columns)
feature_name = 'user_order_history_features'
if not data_utils.is_feature_created(feature_name):
print('build train user_order_history_features')
train_features = build_order_history_features(train, orderHistory_train)
print('build test user_order_history_features')
test_features = build_order_history_features(test, orderHistory_test)
print('save ', feature_name)
data_utils.save_features(train_features, test_features, feature_name)
feature_name = 'user_order_history_features2'
if not data_utils.is_feature_created(feature_name):
print('build train user_order_history_features2')
train_features = build_order_history_features2(train, orderHistory_train)
print('build test user_order_history_features2')
test_features = build_order_history_features2(test, orderHistory_test)
print('save ', feature_name)
data_utils.save_features(train_features, test_features, feature_name)
feature_name = 'user_order_history_features3'
if not data_utils.is_feature_created(feature_name):
orderHistory = pd.concat([orderHistory_train, orderHistory_test])
print('build train user_order_history_features3')
train_features = build_order_history_features3(train, orderHistory, orderHistory_train)
print('build test user_order_history_features3')
test_features = build_order_history_features3(test, orderHistory, orderHistory_test)
print('save ', feature_name)
data_utils.save_features(train_features, test_features, feature_name)
feature_name = 'user_order_history_features4'
if not data_utils.is_feature_created(feature_name):
print('build train user_order_history_features4')
train_features = build_order_history_features4(train, orderHistory_train)
print('build test user_order_history_features4')
test_features = build_order_history_features4(test, orderHistory_test)
print('save ', feature_name)
data_utils.save_features(train_features, test_features, feature_name)
feature_name = 'user_order_history_features_wxr'
if not data_utils.is_feature_created(feature_name):
orderHistory = pd.concat([orderHistory_train, orderHistory_test])
print('build train user_order_history_features3')
train_features = build_order_history_features_wxr(train, orderHistory, orderHistory_train)
print('build test user_order_history_features3')
test_features = build_order_history_features_wxr(test, orderHistory, orderHistory_test)
print('save ', feature_name)
data_utils.save_features(train_features, test_features, feature_name)
def main():
feature_name = 'basic_user_info'
if data_utils.is_feature_created(feature_name):
return
# 用户个人基本信息
train_user = pd.read_csv(Configure.base_path +
'train/userProfile_train.csv',
encoding='utf8')
test_user = pd.read_csv(Configure.base_path + 'test/userProfile_test.csv',
encoding='utf8')
# 1. 性别 dummy code
train_user['gender'] = train_user['gender'].map(gender_convert)
test_user['gender'] = test_user['gender'].map(gender_convert)
dummies = pd.get_dummies(train_user['gender'], prefix='gender')
train_user[dummies.columns] = dummies
dummies = pd.get_dummies(test_user['gender'], prefix='gender')
test_user[dummies.columns] = dummies
# province = pd.read_csv('province_economic.csv', encoding='utf8')
# train_user = train_user.merge(province, on='province', how='left')
# test_user = test_user.merge(province, on='province', how='left')
# 2. 省份进行 LabelEncoder
train_user['province'] = train_user['province'].map(province_convert)
test_user['province'] = test_user['province'].map(province_convert)
le = LabelEncoder()
le.fit(train_user['province'].values)
train_user['province_code'] = le.transform(train_user['province'])
test_user['province_code'] = le.transform(test_user['province'])
# 3. 年龄段进行 dummy code
train_user['age'] = train_user['age'].map(lambda age: 'lg' + age[:2]
if age == age else 'None')
test_user['age'] = test_user['age'].map(lambda age: 'lg' + age[:2]
if age == age else 'None')
print('save cleaned datasets')
train_user.to_csv(Configure.cleaned_path + 'cleaned_userProfile_train.csv',
index=False,
columns=train_user.columns)
test_user.to_csv(Configure.cleaned_path + 'cleaned_userProfile_test.csv',
index=False,
columns=test_user.columns)
dummies = pd.get_dummies(train_user['age'], prefix='age')
train_user[dummies.columns] = dummies
dummies = pd.get_dummies(test_user['age'], prefix='age')
test_user[dummies.columns] = dummies
print('save ', feature_name)
data_utils.save_features(train_user, test_user, features_name=feature_name)
def main():
# 待预测订单的数据 （原始训练集和测试集）
train = pd.read_csv(Configure.base_path + 'train/orderFuture_train.csv',
encoding='utf8')
test = pd.read_csv(Configure.base_path + 'test/orderFuture_test.csv',
encoding='utf8')
orderHistory_train = pd.read_csv(Configure.cleaned_path +
'cleaned_orderHistory_train.csv',
encoding='utf8')
orderHistory_test = pd.read_csv(Configure.cleaned_path +
'cleaned_orderHistory_test.csv',
encoding='utf8')
action_train = pd.read_csv(Configure.base_path + 'train/action_train.csv')
action_test = pd.read_csv(Configure.base_path + 'test/action_test.csv')
action_train = build_time_features(action_train)
action_test = build_time_features(action_test)
orderHistory_train['city'] = orderHistory_train['city'].astype(str)
orderHistory_test['city'] = orderHistory_test['city'].astype(str)
orderHistory_train['orderTime'] = pd.to_datetime(
orderHistory_train['orderTime'])
orderHistory_test['orderTime'] = pd.to_datetime(
orderHistory_test['orderTime'])
feature_name = 'advance_order_history_features'
if not data_utils.is_feature_created(feature_name):
print('build train advance_order_history_features')
train_features = gen_history_features(train, orderHistory_train)
print('build test advance_order_history_features')
test_features = gen_history_features(test, orderHistory_test)
print('save ', feature_name)
data_utils.save_features(train_features, test_features, feature_name)
feature_name = 'advance_action_features'
if not data_utils.is_feature_created(feature_name):
print('build train advance_action_features')
train_features = gen_action_features(train, action_train)
print('build test advance_action_features')
test_features = gen_action_features(test, action_test)
print('save ', feature_name)
data_utils.save_features(train_features, test_features, feature_name)
feature_name = 'advance_action_features1'
if not data_utils.is_feature_created(feature_name):
print('build train advance_action_features1')
train_features = gen_action_features1(train, action_train)
print('build test advance_action_features1')
test_features = gen_action_features1(test, action_test)
print('save ', feature_name)
data_utils.save_features(train_features, test_features, feature_name)
feature_name = 'advance_action_features2'
if not data_utils.is_feature_created(feature_name):
print('build train advance_action_features2')
train_features = gen_action_features2(train, action_train)
print('build test advance_action_features2')
test_features = gen_action_features2(test, action_test)
print('save ', feature_name)
data_utils.save_features(train_features, test_features, feature_name)
def save_all_features(train, test, s):
funcs = {
'speed_variance_mean': speed_variance_mean,
'trip_id_count': trip_id_count,
'trip_id_interval_mean': trip_id_interval_mean,
'speed_final_mean': speed_final_mean,
'time_gap_direction_change_feat': time_gap_direction_change_feat,
# 通话时间(在总行程时间中的占比)
"calling_time": calling_time,
'callstate_feat': callstate_feat,
"build_time_features": build_time_features,
"height": height_feet,
}
for feat_name in Configure.features:
save_features(funcs[feat_name](train), 'train', feat_name, s)
save_features(funcs[feat_name](test), 'test', feat_name, s)
def main():
feature_name = 'user_order_comment_features'
if data_utils.is_feature_created(feature_name):
return
# 待预测订单的数据 （原始训练集和测试集）
train = pd.read_csv(Configure.base_path + 'train/orderFuture_train.csv',
encoding='utf8')
test = pd.read_csv(Configure.base_path + 'test/orderFuture_test.csv',
encoding='utf8')
userComment_train = pd.read_csv(Configure.base_path +
'train/userComment_train.csv',
encoding='utf8')
userComment_test = pd.read_csv(Configure.base_path +
'test/userComment_test.csv',
encoding='utf8')
userComment_train.loc[userComment_train['rating'] == 4.33, 'rating'] = 4
userComment_train.loc[userComment_train['rating'] == 3.67, 'rating'] = 4
userComment_test.loc[userComment_train['rating'] == 2.33, 'rating'] = 2
userComment_train['rating'] = userComment_train['rating'].astype(int)
userComment_test['rating'] = userComment_test['rating'].astype(int)
print('save cleaned datasets')
userComment_train.to_csv(Configure.cleaned_path +
'cleaned_userComment_train.csv',
index=False,
columns=userComment_train.columns,
encoding='utf8')
userComment_test.to_csv(Configure.cleaned_path +
'cleaned_userComment_test.csv',
index=False,
columns=userComment_test.columns,
encoding='utf8')
print('build train features')
train_features = built_comment_features(train, userComment_train)
print('build test features')
test_features = built_comment_features(test, userComment_test)
print('save ', feature_name)
data_utils.save_features(train_features, test_features, feature_name)
def main():
feature_name = 'sqg_features'
# if data_utils.is_feature_created(feature_name):
# return
print('add stage_one_features')
# train_features = pd.read_csv('train_sqg_stage_one_features.csv')
# test_features = pd.read_csv('test_sqg_stage_one_features.csv')
#
# used_features = ['userid', 'rate_all_good',
# 'big_than_mean', 'rate_user_click',
# 'click_1_rate', 'click_2_rate', 'click_3_rate', 'click_4_rate',
# 'click_5_rate', 'click_6_rate', 'click_7_rate', 'click_8_rate',
# 'click_9_rate', 'less_than_4_rate', 'more_than_5_rate',
# 'click_1_num', 'click_2_num', 'click_3_num', 'click_4_num',
# 'click_5_num', 'click_6_num', 'click_7_num', 'click_8_num',
# 'click_9_num', 'less_than_4_num', 'more_than_6_num',
# 'action_time_min', 'action_time_max', 'diff_time_num_click',
# 'max_diff_days', 'rate_diff_num_time_in_max',
# 'diff_max_x', 'diff_median_x', 'diff_min_x', 'diff_max_y', 'diff_median_y', 'diff_min_y',
# 'rate_orderNum_in_clickNum', 'rate_goodNum_in_clickNum', 'lessthan4_Num_minus_more_than_6_num',
# 'order_time_max', 'order_time_min', 'order_time_median']
#
# train_features = train_features[used_features]
# test_features = test_features[used_features]
print('add stage_two_features')
# u'userid',:用户ID
# u'action_last_week_count':用户最后一周点击的次数
# u'action_last_month_count':用户最后一个月点击的总次数
# u'rate_last_weekcount_vs_last_monthcount':用户最后一周点击的次数除以用户最后一个月的总次数
# u'rate_last_week_count_in_all_usercount':用户最后一周点击次数占所有用户最后一周点击总次数比值(可能不加这个好些, 测试使用)
# u'rate_last_month_count_in_all_usercount':用户最后一个月点击次数占所有用户最后一月点击总次数比值(可能不加这个好些, 测试使用)
# u'action_last_momnth_to_now_days':用户最后一个月点击总频次除以距离现在的时间间隔(现在使用的间隔是天)
train_features = pd.read_csv('train_sqg_stage_two_features.csv')[[
'userid', 'action_last_week_count', 'action_last_month_count'
]]
test_features = pd.read_csv('test_sqg_stage_two_features.csv')[[
'userid', 'action_last_week_count', 'action_last_month_count'
]]
print('save ', feature_name, train_features.shape, test_features.shape)
data_utils.save_features(train_features, test_features, feature_name)
def main():
train = pd.read_csv(Configure.base_path + 'train/orderFuture_train.csv',
encoding='utf8')
test = pd.read_csv(Configure.base_path + 'test/orderFuture_test.csv',
encoding='utf8')
orderHistory_train = pd.read_csv(Configure.base_path +
'train/orderHistory_train.csv',
encoding='utf8')
orderHistory_test = pd.read_csv(Configure.base_path +
'test/orderHistory_test.csv',
encoding='utf8')
action_train = pd.read_csv(Configure.base_path + 'train/action_train.csv')
action_test = pd.read_csv(Configure.base_path + 'test/action_test.csv')
action_train = generate_new_action(action_train, orderHistory_train)
action_test = generate_new_action(action_test, orderHistory_test)
train_action_grouped = dict(list(action_train.groupby('userid')))
test_action_grouped = dict(list(action_test.groupby('userid')))
feature_name = 'action_order_features1'
if not data_utils.is_feature_created(feature_name):
print('build train action_order_features1')
train_features = build_action_order_features1(train,
train_action_grouped)
print('build test action_order_features1')
test_features = build_action_order_features1(test, test_action_grouped)
print('save ', feature_name)
data_utils.save_features(train_features, test_features, feature_name)
feature_name = 'action_order_features2'
if not data_utils.is_feature_created(feature_name):
print('build train action_order_features2')
train_features = build_action_order_features2(train,
train_action_grouped)
print('build test action_order_features2')
test_features = build_action_order_features2(test, test_action_grouped)
print('save ', feature_name)
data_utils.save_features(train_features, test_features, feature_name)
feature_name = 'action_order_features3'
if not data_utils.is_feature_created(feature_name):
print('build train action_order_features3')
train_features = build_action_order_features3(train,
train_action_grouped)
print('build test action_order_features3')
test_features = build_action_order_features3(test, test_action_grouped)
print('save ', feature_name)
data_utils.save_features(train_features, test_features, feature_name)
def main():
feature_name = 'wxr_features'
if data_utils.is_feature_created(feature_name):
return
print('add comment score features')
with open('wxr_train_comment_features.pkl', "rb") as f:
user_comment_train = cPickle.load(f)
with open('wxr_test_comment_features.pkl', "rb") as f:
user_comment_test = cPickle.load(f)
user_comment_train.fillna(-1, inplace=True)
user_comment_test.fillna(-1, inplace=True)
train_features = user_comment_train
test_features = user_comment_test
# print('add user_info features')
# with open('wxr_train_user_info_features.pkl', "rb") as f:
# train_user_info = cPickle.load(f)
# with open('wxr_test_user_info_features.pkl', "rb") as f:
# test_user_info = cPickle.load(f)
# train_user_info.drop(['gender', 'province', 'age'], axis=1, inplace=True)
# test_user_info.drop(['gender', 'province', 'age'], axis=1, inplace=True)
#
# train_features = train_features.merge(train_user_info, on='userid', how='left')
# test_features = test_features.merge(test_user_info, on='userid', how='left')
print('add history features')
with open('wxr_operate_4_train_order_history_features.pkl', "rb") as f:
history_features_train = cPickle.load(f)
with open('wxr_operate_4_test_order_history_features.pkl', "rb") as f:
history_features_test = cPickle.load(f)
use_features = [
'userid', 'avg_days_between_order', 'days_ratio_since_last_order',
'city_num', 'country_num', 'continent_num', 'city_rich',
'city_avg_rich', 'country_rich', 'country_avg_rich',
'histord_time_last_1_year', 'histord_time_last_1_month',
'histord_sum_cont1', 'histord_sum_cont2', 'histord_sum_cont3',
'histord_sum_cont4', 'histord_sum_cont5', 'timespan_lastord_1_2',
'timespan_lastord_2_3'
]
history_features_train = history_features_train[use_features]
history_features_test = history_features_test[use_features]
train_features = train_features.merge(history_features_train,
on='userid',
how='left')
test_features = test_features.merge(history_features_test,
on='userid',
how='left')
print('add action features')
with open('wxr_operate_3_train_action_features.pkl', "rb") as f:
action_features_train = cPickle.load(f)
with open('wxr_operate_3_test_action_features.pkl', "rb") as f:
action_features_test = cPickle.load(f)
use_features = [
'userid', 'avg_browse_num_after_last_order',
'browse_num_after_last_order', 'operate_num_after_last_order',
'avg_operate_num_after_last_order', 'open_num_after_last_order',
'action_1_num_after_last_order', 'action_2_num_after_last_order',
'action_3_num_after_last_order', 'action_4_num_after_last_order',
'action_5_num_after_last_order', 'action_6_num_after_last_order',
'action_7_num_after_last_order', 'action_8_num_after_last_order',
'action_9_num_after_last_order'
]
action_features_train = action_features_train[use_features]
action_features_test = action_features_test[use_features]
train_features = train_features.merge(action_features_train,
on='userid',
how='left')
test_features = test_features.merge(action_features_test,
on='userid',
how='left')
print('add someother features')
some_other_train = pd.read_csv('some_other_train_features.csv')
some_other_test = pd.read_csv('some_other_test_features.csv')
train_features = train_features.merge(some_other_train,
on='userid',
how='left')
test_features = test_features.merge(some_other_test,
on='userid',
how='left')
print('save ', feature_name)
data_utils.save_features(train_features, test_features, feature_name)
def save_all_features(train, test):
funcs = {
}
for name in Configure.features:
save_features(*funcs[name](train, test), name)
def main():
print('load datasets')
questions = pd.read_csv(Configure.question_file)
train = pd.read_csv(Configure.train_data_file).sample(n=1000)
test = pd.read_csv(Configure.test_data_file).sample(n=1000)
train['id'] = np.arange(train.shape[0])
train = pd.merge(train,
questions,
left_on=['q1'],
right_on=['qid'],
how='left')
train = train.rename(columns={'words': 'q1_words', 'chars': 'q1_chars'})
del train['qid']
train = pd.merge(train,
questions,
left_on=['q2'],
right_on=['qid'],
how='left')
train = train.rename(columns={'words': 'q2_words', 'chars': 'q2_chars'})
train.drop(['q1', 'q2', 'qid'], axis=1, inplace=True)
test['id'] = np.arange(test.shape[0])
test = pd.merge(test,
questions,
left_on=['q1'],
right_on=['qid'],
how='left')
test = test.rename(columns={'words': 'q1_words', 'chars': 'q1_chars'})
del test['qid']
test = pd.merge(test,
questions,
left_on=['q2'],
right_on=['qid'],
how='left')
test = test.rename(columns={'words': 'q2_words', 'chars': 'q2_chars'})
test.drop(['q1', 'q2', 'qid'], axis=1, inplace=True)
feature_name = 'basic_features'
if not data_utils.is_feature_created(feature_name):
train_words = pd.Series(
train['q1_words'].map(lambda x: x.split(' ')).tolist() +
train['q2_words'].map(lambda x: x.split(' ')).tolist())
words = [x for y in train_words for x in y]
counts = Counter(words)
words_weights = {
word: get_weight(count)
for word, count in counts.items()
}
train_chars = pd.Series(
train['q1_chars'].map(lambda x: x.split(' ')).tolist() +
train['q2_chars'].map(lambda x: x.split(' ')).tolist())
chars = [x for y in train_chars for x in y]
counts = Counter(chars)
chars_weights = {
word: get_weight(count)
for word, count in counts.items()
}
ques = pd.concat(
[train[['q1_words', 'q2_words']], test[['q1_words', 'q2_words']]],
axis=0).reset_index(drop='index')
q_dict = defaultdict(set)
for i in range(ques.shape[0]):
q_dict[ques.q1_words[i]].add(ques.q2_words[i])
q_dict[ques.q2_words[i]].add(ques.q1_words[i])
print('train build_basic_features')
train_features = build_features1(train, words_weights, chars_weights,
q_dict)
print('test build_basic_features')
test_features = build_features1(test, words_weights, chars_weights,
q_dict)
data_utils.save_features(train_features, test_features, feature_name)
feature_name = 'basic_features2'
if data_utils.is_feature_created(feature_name):
print('create gensim model')
word_model = gensim.models.KeyedVectors.load_word2vec_format(
Configure.word_embed_path, binary=False)
char_model = gensim.models.KeyedVectors.load_word2vec_format(
Configure.char_embed_path, binary=False)
norm_word_model = gensim.models.KeyedVectors.load_word2vec_format(
Configure.word_embed_path, binary=False)
norm_word_model.init_sims(replace=True)
norm_char_model = gensim.models.KeyedVectors.load_word2vec_format(
Configure.char_embed_path, binary=False)
norm_char_model.init_sims(replace=True)
print('train build_features2')
train_features = build_features2(train, word_model, char_model)
print('test build_features2')
test_features = build_features2(test, word_model, char_model)
data_utils.save_features(train_features, test_features, feature_name)
def main():
feature_name = 'user_order_comment_features'
if data_utils.is_feature_created(feature_name):
return
# 待预测订单的数据 （原始训练集和测试集）
train = pd.read_csv(Configure.base_path + 'train/orderFuture_train.csv',
encoding='utf8')
test = pd.read_csv(Configure.base_path + 'test/orderFuture_test.csv',
encoding='utf8')
userComment_train = pd.read_csv(Configure.base_path +
'train/userComment_train.csv',
encoding='utf8')
userComment_test = pd.read_csv(Configure.base_path +
'test/userComment_test.csv',
encoding='utf8')
userComment_train.loc[userComment_train['rating'] == 4.33, 'rating'] = 4
userComment_train.loc[userComment_train['rating'] == 3.67, 'rating'] = 4
userComment_test.loc[userComment_train['rating'] == 2.33, 'rating'] = 2
userComment_train['rating'] = userComment_train['rating'].astype(int)
userComment_test['rating'] = userComment_test['rating'].astype(int)
orderHistory_train = pd.read_csv(Configure.cleaned_path +
'cleaned_orderHistory_train.csv',
encoding='utf8')
orderHistory_test = pd.read_csv(Configure.cleaned_path +
'cleaned_orderHistory_test.csv',
encoding='utf8')
userComment_train = pd.merge(userComment_train,
orderHistory_train[['orderid', 'orderType']],
on='orderid',
how='left')
userComment_test = pd.merge(userComment_test,
orderHistory_test[['orderid', 'orderType']],
on='orderid',
how='left')
userComment_train = commentKey_score(userComment_train)
userComment_test = commentKey_score(userComment_test)
userComment_train = tag_score(userComment_train)
userComment_test = tag_score(userComment_test)
print('save cleaned datasets')
userComment_train.to_csv(Configure.cleaned_path +
'cleaned_userComment_train.csv',
index=False,
columns=userComment_train.columns,
encoding='utf8')
userComment_test.to_csv(Configure.cleaned_path +
'cleaned_userComment_test.csv',
index=False,
columns=userComment_test.columns,
encoding='utf8')
print('build train features')
train_features = built_comment_features(train, userComment_train)
print('build test features')
test_features = built_comment_features(test, userComment_test)
print('save ', feature_name)
data_utils.save_features(train_features, test_features, feature_name)
print('build wxr features')
feature_name = 'user_order_comment_features_wxr'
if not data_utils.is_feature_created(feature_name):
print('build train action history features11')
train_features = built_comment_features_wxr(train, userComment_train,
orderHistory_train)
print('build test action history features11')
test_features = built_comment_features_wxr(test, userComment_test,
orderHistory_test)
print('save ', feature_name)
data_utils.save_features(train_features, test_features, feature_name)