Kaggle competition: Predict Future Sales
test_loader_gpu = DeviceDataLoader(test_loader, device )<predict_on_test>
items = pd.read_csv('.. /input/items.csv')
 shops = pd.read_csv('.. /input/shops.csv')
 cats = pd.read_csv('.. /input/item_categories.csv')
 train = pd.read_csv('.. /input/sales_train.csv')
 test = pd.read_csv('.. /input/test.csv' ).set_index('ID' )
test_preds = predict(test_loader_gpu,model )<define_variables>
train = train[train.item_price<100000]
train = train[train.item_cnt_day<1001]
passId = test[['PassengerId']].values<data_type_conversions>
median = train[(train.shop_id==32)&(train.item_id==2973)&(train.date_block_num==4)&(train.item_price>0)].item_price.median()
train.loc[train.item_price<0, 'item_price'] = median
final_pred = final_pred.data.numpy()<prepare_output>
train.loc[train.shop_id == 0, 'shop_id'] = 57
test.loc[test.shop_id == 0, 'shop_id'] = 57
train.loc[train.shop_id == 1, 'shop_id'] = 58
test.loc[test.shop_id == 1, 'shop_id'] = 58
train.loc[train.shop_id == 10, 'shop_id'] = 11
test.loc[test.shop_id == 10, 'shop_id'] = 11
sub = {'PassengerId':passId.ravel() , 'Survived':final_pred}<create_dataframe>
shops.loc[shops.shop_name == 'Сергиев Посад ТЦ "7Я"', 'shop_name'] = 'СергиевПосад ТЦ "7Я"'
shops['city'] = shops['shop_name'].str.split(' ' ).map(lambda x: x[0])
 shops.loc[shops.city == '!Якутск', 'city'] = 'Якутск'
shops['city_code'] = LabelEncoder().fit_transform(shops['city'])
 shops = shops[['shop_id','city_code']]
cats['split'] = cats['item_category_name'].str.split('-')
 cats['type'] = cats['split'].map(lambda x: x[0].strip())
 cats['type_code'] = LabelEncoder().fit_transform(cats['type'])
 cats['subtype'] = cats['split'].map(lambda x: x[1].strip() if len(x)> 1 else x[0].strip())
 cats['subtype_code'] = LabelEncoder().fit_transform(cats['subtype'])
 cats = cats[['item_category_id','type_code', 'subtype_code']]
items.drop(['item_name'], axis=1, inplace=True )
submission_csv = pd.DataFrame(sub )<save_to_csv>
len(list(set(test.item_id)- set(test.item_id ).intersection(set(train.item_id)))) , len(list(set(test.item_id))), len(test )
submission_csv.to_csv('final_sub_titanic_pth.csv',index=False )<load_from_csv>
ts = time.time()
matrix = []
cols = ['date_block_num','shop_id','item_id']
for i in range(34):
sales = train[train.date_block_num==i]
matrix.append(np.array(list(product([i], sales.shop_id.unique() , sales.item_id.unique())) , dtype='int16'))
matrix = pd.DataFrame(np.vstack(matrix), columns=cols)
 matrix['date_block_num'] = matrix['date_block_num'].astype(np.int8)
 matrix['shop_id'] = matrix['shop_id'].astype(np.int8)
 matrix['item_id'] = matrix['item_id'].astype(np.int16)
 matrix.sort_values(cols,inplace=True)
 time.time() - ts
x = pd.read_csv("./final_sub_titanic_pth.csv" )<define_variables>
train['revenue'] = train['item_price'] * train['item_cnt_day']
filename = "./titanic_pytorch.pth"<save_model>
ts = time.time()
group = train.groupby(['date_block_num','shop_id','item_id'] ).agg({'item_cnt_day': ['sum']})
 group.columns = ['item_cnt_month']
group.reset_index(inplace=True)
 matrix = pd.merge(matrix, group, on=cols, how='left')
 matrix['item_cnt_month'] =(matrix['item_cnt_month'] 
.fillna(0)
.clip(0,20)
.astype(np.float16))
time.time() - ts
torch.save(model.state_dict() ,filename )<load_pretrained>
test['date_block_num'] = 34
test['date_block_num'] = test['date_block_num'].astype(np.int8)
 test['shop_id'] = test['shop_id'].astype(np.int8)
 test['item_id'] = test['item_id'].astype(np.int16 )
loaded_model = Titanicnn(input_size,hidden_size1=hidden_size1, hidden_size2= hidden_size2, hidden_size3= hidden_size3,hidden_size4 = hidden_size4,num_classes = num_classes)
 to_device(model, device)
 loaded_model.load_state_dict(torch.load(filename))
loaded_model.eval()<set_options>
ts = time.time()
matrix = pd.concat([matrix, test], ignore_index=True, sort=False, keys=cols)
 matrix.fillna(0, inplace=True)
 time.time() - ts
sns.set()
%matplotlib inline<load_from_csv>
ts = time.time()
matrix = pd.merge(matrix, shops, on=['shop_id'], how='left')
 matrix = pd.merge(matrix, items, on=['item_id'], how='left')
 matrix = pd.merge(matrix, cats, on=['item_category_id'], how='left')
 matrix['city_code'] = matrix['city_code'].astype(np.int8)
 matrix['item_category_id'] = matrix['item_category_id'].astype(np.int8)
 matrix['type_code'] = matrix['type_code'].astype(np.int8)
 matrix['subtype_code'] = matrix['subtype_code'].astype(np.int8)
 time.time() - ts
train_data = pd.read_csv('train_prep.csv')
 test_data = pd.read_csv('test_prep.csv')
 train_data.columns<rename_columns>
def lag_feature(df, lags, col):
tmp = df[['date_block_num','shop_id','item_id',col]]
for i in lags:
shifted = tmp.copy()
shifted.columns = ['date_block_num','shop_id','item_id', col+'_lag_'+str(i)]
shifted['date_block_num'] += i
df = pd.merge(df, shifted, on=['date_block_num','shop_id','item_id'], how='left')
 return df
train_data.set_index('PassengerId',inplace = True)
 test_data.set_index('PassengerId',inplace = True )<concatenate>
ts = time.time()
group = matrix.groupby(['date_block_num'] ).agg({'item_cnt_month': ['mean']})
 group.columns = [ 'date_avg_item_cnt' ]
group.reset_index(inplace=True)
 matrix = pd.merge(matrix, group, on=['date_block_num'], how='left')
 matrix['date_avg_item_cnt'] = matrix['date_avg_item_cnt'].astype(np.float16)
 matrix = lag_feature(matrix, [1], 'date_avg_item_cnt')
 matrix.drop(['date_avg_item_cnt'], axis=1, inplace=True)
 time.time() - ts
all_data = pd.concat([train_data,test_data] )<categorify>
ts = time.time()
group = matrix.groupby(['date_block_num', 'item_id'] ).agg({'item_cnt_month': ['mean']})
 group.columns = [ 'date_item_avg_item_cnt' ]
group.reset_index(inplace=True)
 matrix = pd.merge(matrix, group, on=['date_block_num','item_id'], how='left')
 matrix['date_item_avg_item_cnt'] = matrix['date_item_avg_item_cnt'].astype(np.float16)
 matrix = lag_feature(matrix, [1,2,3,6,12], 'date_item_avg_item_cnt')
 matrix.drop(['date_item_avg_item_cnt'], axis=1, inplace=True)
 time.time() - ts
label = LabelEncoder()<categorify>
ts = time.time()
group = matrix.groupby(['date_block_num', 'shop_id'] ).agg({'item_cnt_month': ['mean']})
 group.columns = [ 'date_shop_avg_item_cnt' ]
group.reset_index(inplace=True)
 matrix = pd.merge(matrix, group, on=['date_block_num','shop_id'], how='left')
 matrix['date_shop_avg_item_cnt'] = matrix['date_shop_avg_item_cnt'].astype(np.float16)
 matrix = lag_feature(matrix, [1,2,3,6,12], 'date_shop_avg_item_cnt')
 matrix.drop(['date_shop_avg_item_cnt'], axis=1, inplace=True)
 time.time() - ts
all_data['FareBin_Code'] = label.fit_transform(all_data['FareBin'])
 all_data['AgeBin_Code'] = label.fit_transform(all_data['AgeBin'] )<drop_column>
ts = time.time()
group = matrix.groupby(['date_block_num', 'item_category_id'] ).agg({'item_cnt_month': ['mean']})
 group.columns = [ 'date_cat_avg_item_cnt' ]
group.reset_index(inplace=True)
 matrix = pd.merge(matrix, group, on=['date_block_num','item_category_id'], how='left')
 matrix['date_cat_avg_item_cnt'] = matrix['date_cat_avg_item_cnt'].astype(np.float16)
 matrix = lag_feature(matrix, [1], 'date_cat_avg_item_cnt')
 matrix.drop(['date_cat_avg_item_cnt'], axis=1, inplace=True)
 time.time() - ts
all_data.drop(columns=['AgeBin'],inplace=True)
 all_data.drop(columns=['FareBin'],inplace=True )<feature_engineering>
ts = time.time()
group = matrix.groupby(['date_block_num', 'shop_id', 'item_category_id'] ).agg({'item_cnt_month': ['mean']})
 group.columns = ['date_shop_cat_avg_item_cnt']
group.reset_index(inplace=True)
 matrix = pd.merge(matrix, group, on=['date_block_num', 'shop_id', 'item_category_id'], how='left')
 matrix['date_shop_cat_avg_item_cnt'] = matrix['date_shop_cat_avg_item_cnt'].astype(np.float16)
 matrix = lag_feature(matrix, [1], 'date_shop_cat_avg_item_cnt')
 matrix.drop(['date_shop_cat_avg_item_cnt'], axis=1, inplace=True)
 time.time() - ts
for i in range(1,len(all_data)+1):
all_data.loc[i,'Srate']=0.5<feature_engineering>
ts = time.time()
group = matrix.groupby(['date_block_num', 'shop_id', 'type_code'] ).agg({'item_cnt_month': ['mean']})
 group.columns = ['date_shop_type_avg_item_cnt']
group.reset_index(inplace=True)
 matrix = pd.merge(matrix, group, on=['date_block_num', 'shop_id', 'type_code'], how='left')
 matrix['date_shop_type_avg_item_cnt'] = matrix['date_shop_type_avg_item_cnt'].astype(np.float16)
 matrix = lag_feature(matrix, [1], 'date_shop_type_avg_item_cnt')
 matrix.drop(['date_shop_type_avg_item_cnt'], axis=1, inplace=True)
 time.time() - ts
for i in range(1,len(all_data)+1):
number = all_data.loc[i,'TicketNumber']
group = all_data.loc[i,'TicketGroup']
if group>1:
max = all_data.drop(i)[all_data.drop(i)['TicketNumber']==number]['Survived'].max()
if not pd.isna(max):
all_data.loc[i,'Srate']=all_data.drop(i)[all_data.drop(i)['TicketNumber']==number]['Survived'].max()<split>
ts = time.time()
group = matrix.groupby(['date_block_num', 'shop_id', 'subtype_code'] ).agg({'item_cnt_month': ['mean']})
 group.columns = ['date_shop_subtype_avg_item_cnt']
group.reset_index(inplace=True)
 matrix = pd.merge(matrix, group, on=['date_block_num', 'shop_id', 'subtype_code'], how='left')
 matrix['date_shop_subtype_avg_item_cnt'] = matrix['date_shop_subtype_avg_item_cnt'].astype(np.float16)
 matrix = lag_feature(matrix, [1], 'date_shop_subtype_avg_item_cnt')
 matrix.drop(['date_shop_subtype_avg_item_cnt'], axis=1, inplace=True)
 time.time() - ts
train_data = all_data[:891]
test_data = all_data[891:]<define_variables>
ts = time.time()
group = matrix.groupby(['date_block_num', 'city_code'] ).agg({'item_cnt_month': ['mean']})
 group.columns = [ 'date_city_avg_item_cnt' ]
group.reset_index(inplace=True)
 matrix = pd.merge(matrix, group, on=['date_block_num', 'city_code'], how='left')
 matrix['date_city_avg_item_cnt'] = matrix['date_city_avg_item_cnt'].astype(np.float16)
 matrix = lag_feature(matrix, [1], 'date_city_avg_item_cnt')
 matrix.drop(['date_city_avg_item_cnt'], axis=1, inplace=True)
 time.time() - ts
numerical = ["Pclass","Age","SibSp","Parch","Fare","TicketGroup","FamilySize","FareBin_Code","AgeBin_Code","Srate","TicketNumber"]
categorical = ["Embarked","Title","Sex"]
all_features = numerical + categorical<import_modules>
ts = time.time()
group = matrix.groupby(['date_block_num', 'item_id', 'city_code'] ).agg({'item_cnt_month': ['mean']})
 group.columns = [ 'date_item_city_avg_item_cnt' ]
group.reset_index(inplace=True)
 matrix = pd.merge(matrix, group, on=['date_block_num', 'item_id', 'city_code'], how='left')
 matrix['date_item_city_avg_item_cnt'] = matrix['date_item_city_avg_item_cnt'].astype(np.float16)
 matrix = lag_feature(matrix, [1], 'date_item_city_avg_item_cnt')
 matrix.drop(['date_item_city_avg_item_cnt'], axis=1, inplace=True)
 time.time() - ts
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline
from sklearn.model_selection import cross_val_score
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import OneHotEncoder
from sklearn.preprocessing import OrdinalEncoder
from sklearn.model_selection import GridSearchCV
from sklearn.ensemble import VotingClassifier<prepare_x_and_y>
ts = time.time()
group = matrix.groupby(['date_block_num', 'type_code'] ).agg({'item_cnt_month': ['mean']})
 group.columns = [ 'date_type_avg_item_cnt' ]
group.reset_index(inplace=True)
 matrix = pd.merge(matrix, group, on=['date_block_num', 'type_code'], how='left')
 matrix['date_type_avg_item_cnt'] = matrix['date_type_avg_item_cnt'].astype(np.float16)
 matrix = lag_feature(matrix, [1], 'date_type_avg_item_cnt')
 matrix.drop(['date_type_avg_item_cnt'], axis=1, inplace=True)
 time.time() - ts
y = train_data['Survived']
X = train_data.drop(columns = ['Survived'] )<choose_model_class>
ts = time.time()
group = matrix.groupby(['date_block_num', 'subtype_code'] ).agg({'item_cnt_month': ['mean']})
 group.columns = [ 'date_subtype_avg_item_cnt' ]
group.reset_index(inplace=True)
 matrix = pd.merge(matrix, group, on=['date_block_num', 'subtype_code'], how='left')
 matrix['date_subtype_avg_item_cnt'] = matrix['date_subtype_avg_item_cnt'].astype(np.float16)
 matrix = lag_feature(matrix, [1], 'date_subtype_avg_item_cnt')
 matrix.drop(['date_subtype_avg_item_cnt'], axis=1, inplace=True)
 time.time() - ts
log_reg = LogisticRegression(C=1.9 )<categorify>
ts = time.time()
group = train.groupby(['item_id'] ).agg({'item_price': ['mean']})
 group.columns = ['item_avg_item_price']
group.reset_index(inplace=True)
 matrix = pd.merge(matrix, group, on=['item_id'], how='left')
 matrix['item_avg_item_price'] = matrix['item_avg_item_price'].astype(np.float16)
 group = train.groupby(['date_block_num','item_id'] ).agg({'item_price': ['mean']})
 group.columns = ['date_item_avg_item_price']
group.reset_index(inplace=True)
 matrix = pd.merge(matrix, group, on=['date_block_num','item_id'], how='left')
 matrix['date_item_avg_item_price'] = matrix['date_item_avg_item_price'].astype(np.float16)
 lags = [1,2,3,4,5,6]
matrix = lag_feature(matrix, lags, 'date_item_avg_item_price')
 for i in lags:
matrix['delta_price_lag_'+str(i)] = \ 
(matrix['date_item_avg_item_price_lag_'+str(i)] - matrix['item_avg_item_price'])/ matrix['item_avg_item_price']
def select_trend(row):
for i in lags:
if row['delta_price_lag_'+str(i)]:
return row['delta_price_lag_'+str(i)]
return 0
matrix['delta_price_lag'] = matrix.apply(select_trend, axis=1)
 matrix['delta_price_lag'] = matrix['delta_price_lag'].astype(np.float16)
 matrix['delta_price_lag'].fillna(0, inplace=True)
 fetures_to_drop = ['item_avg_item_price', 'date_item_avg_item_price']
for i in lags:
fetures_to_drop += ['date_item_avg_item_price_lag_'+str(i)]
fetures_to_drop += ['delta_price_lag_'+str(i)]
matrix.drop(fetures_to_drop, axis=1, inplace=True)
 time.time() - ts
num_att_LR = ["Pclass","Age","TicketNumber","SibSp","Parch","Srate","Fare"]
cat_att1_LR = ["Sex"]
cat_att2_LR = ["Title"]
drop_att_LR = list(set(all_features)- set(num_att_LR+cat_att1_LR+cat_att2_LR))
full_pipline_LR = ColumnTransformer([ 
("toDrop",'drop',drop_att_LR), 
("numerical",StandardScaler() ,num_att_LR), 
("categorical1",OrdinalEncoder() ,cat_att1_LR), 
("categorical2",OneHotEncoder() ,cat_att2_LR)
 ] )<compute_train_metric>
ts = time.time()
group = train.groupby(['date_block_num','shop_id'] ).agg({'revenue': ['sum']})
 group.columns = ['date_shop_revenue']
group.reset_index(inplace=True)
 matrix = pd.merge(matrix, group, on=['date_block_num','shop_id'], how='left')
 matrix['date_shop_revenue'] = matrix['date_shop_revenue'].astype(np.float32)
 group = group.groupby(['shop_id'] ).agg({'date_shop_revenue': ['mean']})
 group.columns = ['shop_avg_revenue']
group.reset_index(inplace=True)
 matrix = pd.merge(matrix, group, on=['shop_id'], how='left')
 matrix['shop_avg_revenue'] = matrix['shop_avg_revenue'].astype(np.float32)
 matrix['delta_revenue'] =(matrix['date_shop_revenue'] - matrix['shop_avg_revenue'])/ matrix['shop_avg_revenue']
matrix['delta_revenue'] = matrix['delta_revenue'].astype(np.float16)
 matrix = lag_feature(matrix, [1], 'delta_revenue')
 matrix.drop(['date_shop_revenue','shop_avg_revenue','delta_revenue'], axis=1, inplace=True)
 time.time() - ts
X_prep = full_pipline_LR.fit_transform(X)
 cross_val_score(log_reg,X_prep,y,cv=10,scoring='roc_auc' ).mean()<choose_model_class>
matrix['month'] = matrix['date_block_num'] % 12
cat_bayes = CategoricalNB(alpha=0.05 )<categorify>
days = pd.Series([31,28,31,30,31,30,31,31,30,31,30,31])
 matrix['days'] = matrix['month'].map(days ).astype(np.int8 )
num_att_NB = ["FareBin_Code","AgeBin_Code"]
cat_att_NB = ["Title"]
drop_att_NB = list(set(all_features)- set(num_att_NB+cat_att_NB))
full_pipline_NB = ColumnTransformer([ 
("cat",OrdinalEncoder() ,cat_att_NB), 
("num",'passthrough',num_att_NB), 
("drop",'drop',drop_att_NB)
 ] )<compute_train_metric>
ts = time.time()
cache = {}
matrix['item_shop_last_sale'] = -1
matrix['item_shop_last_sale'] = matrix['item_shop_last_sale'].astype(np.int8)
 for idx, row in matrix.iterrows() :
key = str(row.item_id)+' '+str(row.shop_id)
 if key not in cache:
if row.item_cnt_month!=0:
cache[key] = row.date_block_num
else:
last_date_block_num = cache[key]
matrix.at[idx, 'item_shop_last_sale'] = row.date_block_num - last_date_block_num
cache[key] = row.date_block_num
time.time() - ts
X_prep = full_pipline_NB.fit_transform(X)
 cross_val_score(cat_bayes,X_prep,y,cv=10,scoring='roc_auc' ).mean()<import_modules>
ts = time.time()
cache = {}
matrix['item_last_sale'] = -1
matrix['item_last_sale'] = matrix['item_last_sale'].astype(np.int8)
 for idx, row in matrix.iterrows() :
key = row.item_id
if key not in cache:
if row.item_cnt_month!=0:
cache[key] = row.date_block_num
else:
last_date_block_num = cache[key]
if row.date_block_num>last_date_block_num:
matrix.at[idx, 'item_last_sale'] = row.date_block_num - last_date_block_num
cache[key] = row.date_block_num
time.time() - ts
from sklearn import tree<categorify>
ts = time.time()
matrix['item_shop_first_sale'] = matrix['date_block_num'] - matrix.groupby(['item_id','shop_id'])['date_block_num'].transform('min')
 matrix['item_first_sale'] = matrix['date_block_num'] - matrix.groupby('item_id')['date_block_num'].transform('min')
 time.time() - ts
num_att_DT =["Pclass","FamilySize","Srate","AgeBin_Code","FareBin_Code"]
cat_att_DT = ["Sex"]
drop_att_DT = list(set(all_features)- set(num_att_DT + cat_att_DT))
full_pipline_DT = ColumnTransformer([ 
("droping",'drop',drop_att_DT), 
("num",'passthrough',num_att_DT), 
("cat",OrdinalEncoder() ,cat_att_DT),
] )<normalization>
ts = time.time()
matrix = matrix[matrix.date_block_num > 11]
time.time() - ts
X_prep = full_pipline_DT.fit_transform(X )<choose_model_class>
ts = time.time()
def fill_na(df):
for col in df.columns:
if('_lag_' in col)&(df[col].isnull().any()):
if('item_cnt' in col):
df[col].fillna(0, inplace=True)
 return df
matrix = fill_na(matrix)
 time.time() - ts
criterion = ["gini", "entropy"]
max_depth = list(range(1,12,1))
max_features = [1,2,3,4,5,6]
splitter = ["best","random"]
min_samples_split = [2,3]
hyperparams = {'criterion': criterion, 'max_depth': max_depth, 'max_features': max_features,
'splitter': splitter, 'min_samples_split': min_samples_split}
gd = GridSearchCV(estimator = tree.DecisionTreeClassifier(random_state=1), param_grid = hyperparams, verbose=0,
cv=10, scoring = "roc_auc" )<train_model>
matrix.to_pickle('data.pkl')
 del matrix
del cache
del group
del items
del shops
del cats
del train
gc.collect() ;
gd.fit(X_prep, y)
 print(gd.best_score_)
 print(gd.best_estimator_ )<choose_model_class>
data = pd.read_pickle('data.pkl' )
DT_clf = tree.DecisionTreeClassifier(criterion='entropy', max_depth=5, max_features=6,
random_state=1, splitter='random' )<choose_model_class>
data = data[[
'date_block_num',
'shop_id',
'item_id',
'item_cnt_month',
'city_code',
'item_category_id',
'type_code',
'subtype_code',
'item_cnt_month_lag_1',
'item_cnt_month_lag_2',
'item_cnt_month_lag_3',
'item_cnt_month_lag_6',
'item_cnt_month_lag_12',
'date_avg_item_cnt_lag_1',
'date_item_avg_item_cnt_lag_1',
'date_item_avg_item_cnt_lag_2',
'date_item_avg_item_cnt_lag_3',
'date_item_avg_item_cnt_lag_6',
'date_item_avg_item_cnt_lag_12',
'date_shop_avg_item_cnt_lag_1',
'date_shop_avg_item_cnt_lag_2',
'date_shop_avg_item_cnt_lag_3',
'date_shop_avg_item_cnt_lag_6',
'date_shop_avg_item_cnt_lag_12',
'date_cat_avg_item_cnt_lag_1',
'date_shop_cat_avg_item_cnt_lag_1',
'date_city_avg_item_cnt_lag_1',
'date_item_city_avg_item_cnt_lag_1',
'delta_price_lag',
'month',
'days',
'item_shop_last_sale',
'item_last_sale',
'item_shop_first_sale',
'item_first_sale',
]]
svm_clf = LinearSVC(C=0.1, loss = 'hinge', max_iter=100000, random_state=1, )<categorify>
X_train = data[data.date_block_num < 33].drop(['item_cnt_month'], axis=1)
 Y_train = data[data.date_block_num < 33]['item_cnt_month']
X_valid = data[data.date_block_num == 33].drop(['item_cnt_month'], axis=1)
 Y_valid = data[data.date_block_num == 33]['item_cnt_month']
X_test = data[data.date_block_num == 34].drop(['item_cnt_month'], axis=1 )
num_att_SVM = numerical
cat_att1_SVM = ["Sex"]
cat_att2_SVM = ["Title"]
drop_att_SVM = list(set(all_features)- set(num_att_SVM + cat_att1_SVM+cat_att2_SVM))
full_pipline_SVM = ColumnTransformer([ 
("drop",'drop',drop_att_SVM), 
("cat",OrdinalEncoder() ,cat_att1_SVM), 
("num",StandardScaler() ,num_att_SVM), 
("cat2",OneHotEncoder() ,cat_att2_SVM),
] )<compute_train_metric>
del data
gc.collect() ;
X_prep = full_pipline_SVM.fit_transform(X)
 cross_val_score(svm_clf,X_prep,y,cv=10,scoring='roc_auc' ).mean()<compute_train_metric>
ts = time.time()
model = XGBRegressor(
 max_depth=8,
n_estimators=1000,
min_child_weight=300,
colsample_bytree=0.8,
subsample=0.8,
eta=0.3,
seed=42)
 model.fit(
 X_train,
Y_train,
eval_metric="rmse",
eval_set=[(X_train, Y_train),(X_valid, Y_valid)],
verbose=True,
early_stopping_rounds = 10)
 time.time() - ts
LR_pipe = Pipeline([('logReg pipline', full_pipline_LR), 
('log_reg', log_reg)])
 cross_val_score(LR_pipe,X,y,cv=10,scoring='accuracy' ).mean()<compute_train_metric>
Y_pred = model.predict(X_valid ).clip(0, 20)
 Y_test = model.predict(X_test ).clip(0, 20)
 submission = pd.DataFrame({
"ID": test.index,
"item_cnt_month": Y_test
})
 submission.to_csv('xgb_submission.csv', index=False)
 pickle.dump(Y_pred, open('xgb_train.pickle', 'wb'))
pickle.dump(Y_test, open('xgb_test.pickle', 'wb'))