kernel_id int64 24.2k 23.3M | prompt stringlengths 8 1.85M | completetion stringlengths 1 182k | comp_name stringlengths 5 57 |
|---|---|---|---|
1,072,962 | %matplotlib inline
plt.rcParams['figure.figsize'] = [9, 12]
warnings.simplefilter('ignore' )<load_from_csv> | data = data.merge(right=avg_buro.reset_index() , how='left', on='SK_ID_CURR')
test = test.merge(right=avg_buro.reset_index() , how='left', on='SK_ID_CURR')
data = data.merge(right=avg_prev.reset_index() , how='left', on='SK_ID_CURR')
test = test.merge(right=avg_prev.reset_index() , how='left', on='SK_ID_CURR')
data... | Home Credit Default Risk |
1,072,962 | train = pd.read_csv("/kaggle/input/whoisafriend/train.csv")
test = pd.read_csv("/kaggle/input/whoisafriend/test.csv")
sub = pd.read_csv("/kaggle/input/whoisafriend/sample_submission.csv")
train.shape, test.shape, sub.shape<groupby> | gc.enable()
folds = KFold(n_splits=6, shuffle=True, random_state=546789)
oof_preds = np.zeros(data.shape[0])
sub_preds = np.zeros(test.shape[0])
feature_importance_df = pd.DataFrame()
feats = [f for f in data.columns if f not in ['SK_ID_CURR']] | Home Credit Default Risk |
1,072,962 | agg_train = train.groupby(['Person A', 'Person B'])['Years of Knowing'].count().reset_index()
agg_train.rename({
"Years of Knowing": "Interaction Count"
}, axis=1, inplace=True)
agg_test = test.groupby(['Person A', 'Person B'])['Years of Knowing'].count().reset_index()
agg_test.rename({
"Years of Knowing": "Interactio... | for n_fold,(trn_idx, val_idx)in enumerate(folds.split(data)) :
trn_x, trn_y = data[feats].iloc[trn_idx], y.iloc[trn_idx]
val_x, val_y = data[feats].iloc[val_idx], y.iloc[val_idx]
clf = LGBMClassifier(
n_estimators=10000,
learning_rate=0.03,
num_leaves = 22,
colsample_bytree=0.8,
subsample=0.8,
max_depth=6,
reg_alpha=0... | Home Credit Default Risk |
1,072,962 | <feature_engineering><EOS> | print('Full AUC score %.6f' % roc_auc_score(y, oof_preds))
test['TARGET'] = sub_preds
test[['SK_ID_CURR', 'TARGET']].to_csv('first_submission.csv', index=False ) | Home Credit Default Risk |
1,056,158 | <SOS> metric: AUC Kaggle data source: home-credit-default-risk<save_to_csv> | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.metrics import confusion_matrix,f1_score
import gc
| Home Credit Default Risk |
1,056,158 | test[['ID', 'Friends']].to_csv("1.0_sub.csv", index=False )<load_from_csv> | application_test=pd.read_csv('.. /input/application_test.csv')
application_train=pd.read_csv('.. /input/application_train.csv')
bureau=pd.read_csv('.. /input/bureau.csv')
bureau_balance=pd.read_csv('.. /input/bureau_balance.csv')
credit_card_balance=pd.read_csv('.. /input/credit_card_balance.csv')
installments_pay... | Home Credit Default Risk |
1,056,158 | !sed 's/\+AF8-//g' /kaggle/input/chh-ola/train.csv > train.csv
!sed 's/_//g' /kaggle/input/chh-ola/test.csv > test.csv<data_type_conversions> | def check_missing_data(df):
total = df.isnull().sum().sort_values(ascending = False)
percent =(( df.isnull().sum() /df.isnull().count())*100 ).sort_values(ascending = False)
return pd.concat([total, percent], axis=1, keys=['Total', 'Percent'] ) | Home Credit Default Risk |
1,056,158 | class Ut:
@staticmethod
def to_timestamp(dt):
return dt_parse(dt, dayfirst=False ).timestamp()
@staticmethod
def flag_to_num(vl):
if vl == 'N':
return 0
else:
return 1
@staticmethod
def to_float(vl):
try:
if type(vl)== type('str'):
idx = vl.find('-')
if idx != -1:
txt = vl.split('-')
return float(txt[1])
return floa... | def categorical_features(df):
cat_features=df.columns[df.dtypes=='object']
return list(cat_features ) | Home Credit Default Risk |
1,056,158 | train_set = pd.read_csv('train.csv', low_memory=False, dtype=str)
train_set.dropna(inplace=True)
train_set.reset_index(drop=True, inplace=True)
test_set = pd.read_csv('test.csv', low_memory=False, dtype=str)
test_set['totalamount'] = 0
train_set['PROPOSITO'] = 1
test_set['PROPOSITO'] = 0
all_set = pd.concat([train_... | def onehot_encoding(df,cat_features_name):
df=pd.get_dummies(df,columns=cat_features_name)
return df | Home Credit Default Risk |
1,056,158 | all_set['totaltime'] = all_set['droptime'] - all_set['pickuptime']
all_set['taxes'] = all_set['drivertip'] + all_set['mtatax'] + all_set['tollamount'] + all_set['extracharges'] + all_set['improvementcharge']<data_type_conversions> | categorical_features(bureau ) | Home Credit Default Risk |
1,056,158 | features_cat = ['vendorid', 'paymentmethod', 'ratecode', 'storedflag']
features_num = ['drivertip', 'pickuploc', 'droploc', 'mtatax', 'distance', 'pickuptime', 'droptime', 'numpassengers',
'tollamount', 'extracharges', 'improvementcharge', 'totalamount', 'totaltime', 'taxes']
target = 'totalamount'
for col in features_... | bureau.CREDIT_ACTIVE.value_counts() | Home Credit Default Risk |
1,056,158 | all_dum = pd.get_dummies(all_set)
train_df = all_dum[all_dum['PROPOSITO'] == 1].copy()
test_df = all_dum[all_dum['PROPOSITO'] == 0].copy()
del(all_dum)
train_df.drop(columns=['PROPOSITO'], inplace=True)
test_df.drop(columns=['PROPOSITO'], inplace=True)
train_df = train_df[train_df['pickuploc'] != train_df['droploc'... | bureau.CREDIT_CURRENCY.value_counts() | Home Credit Default Risk |
1,056,158 | features_to_keep = [
'taxes',
'pickuploc',
'ratecode_2.0',
'ratecode_1.0',
'ratecode_5.0',
'storedflag_0.0',
'ratecode_4.0',
'totaltime',
'ratecode_3.0',
'droploc',
'numpassengers',
'distance',
'storedflag_1.0',
'vendorid_2.0',
'paymentmethod_1.0',
'vendorid_1.0',
'paymentmethod_2.0'
]
X = train_df[features_to_keep].co... | bureau.CREDIT_TYPE.value_counts() | Home Credit Default Risk |
1,056,158 | if False:
params = {
'colsample_bytree':[0.9],
'gamma':[0.3],
'max_depth': [9],
'min_child_weight':[2],
'subsample':[0.9],
'n_estimators': [50],
'objective': ['reg:squarederror'],
'n_jobs': [8],
}
eval_model = xgb.XGBRegressor(nthread=-1)
grid = GridSearchCV(eval_model, params, cv=2)
grid.fit(train_X, train_y)
pred_... | bureau.AMT_CREDIT_SUM.fillna(value=bureau.AMT_CREDIT_SUM.median() ,inplace=True ) | Home Credit Default Risk |
1,056,158 | if False:
params = {
'min_child_weight': st.randint(2, 9),
'gamma': st.uniform(0.1, 0.9),
'subsample': st.uniform(0.1, 0.9),
'colsample_bytree': st.uniform(0.1, 0.9),
'max_depth': st.randint(3, 9),
'n_estimators': [50],
'objective': ['reg:squarederror'],
}
eval_model = xgb.XGBRegressor(nthread=-1)
grid = RandomizedSea... | bureau['DAYS_CREDIT_ENDDATE']=np.where(bureau.DAYS_CREDIT_ENDDATE.isnull() ,bureau.DAYS_ENDDATE_FACT,bureau.DAYS_CREDIT_ENDDATE ) | Home Credit Default Risk |
1,056,158 | train_X, test_X, train_y, test_y = train_test_split(norm_X, y, test_size=0.2, random_state=42)
params = {
'objective': 'reg:squarederror',
'n_estimators': 1000,
'subsample': 0.9,
'min_child_weight': 1,
'max_depth': 9,
'gamma': 0.3,
'colsample_bytree': 0.9,
'n_jobs': 8,
'verbose_eval':'False',
}
model = xgb.XGBRegresso... | bureau.DAYS_CREDIT_ENDDATE.fillna(value=0.0,inplace=True ) | Home Credit Default Risk |
1,056,158 | real_X = normalizer.transform(test_df[features_to_keep].copy())
model = xgb.XGBRegressor(**params)
model.fit(norm_X, y)
predictions = np.exp(model.predict(real_X))<save_to_csv> | bureau.drop('DAYS_ENDDATE_FACT',axis=1,inplace=True ) | Home Credit Default Risk |
1,056,158 | result = []
for idx in range(test_df.shape[0]):
result.append([idx, predictions[idx]])
result = pd.DataFrame(result, columns=['ID', 'total_amount'])
result.to_csv('result.csv', index=False )<drop_column> | bureau.AMT_CREDIT_MAX_OVERDUE.fillna(0.0,inplace=True ) | Home Credit Default Risk |
1,056,158 | !rm train.csv
!rm test.csv<import_modules> | bureau.AMT_CREDIT_SUM_LIMIT.fillna(0.0,inplace=True ) | Home Credit Default Risk |
1,056,158 | import numpy as np
import pandas as pd
import os
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error
import statsmodels... | bureau.AMT_CREDIT_SUM_DEBT.fillna(0.0,inplace=True ) | Home Credit Default Risk |
1,056,158 | train_df = pd.read_csv('.. /input/train.csv', index_col=0)
test_df = pd.read_csv('.. /input/test.csv', index_col=0)
train_df.head()<count_missing_values> | bureau.drop('AMT_ANNUITY',axis=1,inplace=True ) | Home Credit Default Risk |
1,056,158 | train_df.isna().sum()<drop_column> | bureau_onehot=onehot_encoding(bureau,categorical_features(bureau))
bureau_onehot.head() | Home Credit Default Risk |
1,056,158 | cleaned_train_df = train_df.drop(['Critic_Score', 'Critic_Count', 'User_Score', 'User_Count', 'Developer', 'Rating'], axis=1 )<data_type_conversions> | del bureau
gc.collect() | Home Credit Default Risk |
1,056,158 | cleaned_train_df.Year_of_Release.fillna(cleaned_train_df.Year_of_Release.median() , inplace=True )<count_values> | month_count=bureau_balance.groupby('SK_ID_BUREAU' ).size() | Home Credit Default Risk |
1,056,158 | cleaned_train_df.Genre.value_counts()<count_values> | bureau_balance.STATUS.value_counts() | Home Credit Default Risk |
1,056,158 | cleaned_train_df.Publisher.value_counts()<drop_column> | bureau_balance_unstack=bureau_balance.groupby('SK_ID_BUREAU')['STATUS'].value_counts(normalize = False ).unstack('STATUS')
bureau_balance_unstack.columns=['status_DPD0','status_DPD1','status_DPD2','status_DPD3','status_DPD4','status_DPD5','status_closed','status_X']
bureau_balance_unstack['month_count']=month_count
bu... | Home Credit Default Risk |
1,056,158 | cleaned_train_df.dropna(subset=['Genre', 'Publisher'], inplace=True )<count_missing_values> | del bureau_balance
gc.collect() | Home Credit Default Risk |
1,056,158 | cleaned_train_df.isna().sum()<data_type_conversions> | bureau_merge=bureau_onehot.merge(bureau_balance_unstack,how='left',on='SK_ID_BUREAU' ) | Home Credit Default Risk |
1,056,158 | cleaned_train_df.Year_of_Release = cleaned_train_df.Year_of_Release.astype('int64' )<feature_engineering> | cnt_id_bureau=bureau_merge[['SK_ID_CURR','SK_ID_BUREAU']].groupby('SK_ID_CURR' ).size() | Home Credit Default Risk |
1,056,158 | cleaned_train_df['JP_Sales_sqrt'] = np.sqrt(cleaned_train_df.JP_Sales)
cleaned_train_df['NA_Sales_sqrt'] = np.sqrt(cleaned_train_df.NA_Sales )<drop_column> | del bureau_merge,bureau_onehot,bureau_balance_unstack
gc.collect() | Home Credit Default Risk |
1,056,158 | cleaned_train_df.drop(['JP_Sales_sqrt', 'NA_Sales_sqrt'], axis=1, inplace=True )<drop_column> | categorical_features(previous_application ) | Home Credit Default Risk |
1,056,158 | cleaned_train_df.JP_Sales.replace({0: 0.001}, inplace=True)
cleaned_train_df.NA_Sales.replace({0: 0.001}, inplace=True )<feature_engineering> | previous_application.drop(['RATE_INTEREST_PRIVILEGED','RATE_INTEREST_PRIMARY'],axis=1,inplace=True ) | Home Credit Default Risk |
1,056,158 | cleaned_train_df.JP_Sales = np.log(cleaned_train_df.JP_Sales)
cleaned_train_df.NA_Sales = np.log(cleaned_train_df.NA_Sales )<drop_column> | previous_application.AMT_CREDIT.fillna(previous_application.AMT_CREDIT.median() ,inplace=True ) | Home Credit Default Risk |
1,056,158 | cleaned_train_df.drop('Publisher', axis=1, inplace=True )<count_values> | previous_application.CHANNEL_TYPE.value_counts() | Home Credit Default Risk |
1,056,158 | platform_counts = cleaned_train_df.Platform.value_counts()
platform_counts<feature_engineering> | previous_application.drop(['PRODUCT_COMBINATION','NAME_TYPE_SUITE',],axis=1,inplace=True ) | Home Credit Default Risk |
1,056,158 | uncommon_platforms = cleaned_train_df.Platform.isin(platform_counts.index[platform_counts<200])
cleaned_train_df.loc[uncommon_platforms, 'Platform'] = 'Other'<count_unique_values> | previous_application.RATE_DOWN_PAYMENT.fillna(previous_application.RATE_DOWN_PAYMENT.median() ,inplace=True ) | Home Credit Default Risk |
1,056,158 | platform_cats = list(cleaned_train_df.Platform.unique())
print(cleaned_train_df.Platform.nunique())
cleaned_train_df.Platform.value_counts()<categorify> | previous_application.AMT_DOWN_PAYMENT.fillna(0.0,inplace=True ) | Home Credit Default Risk |
1,056,158 | cleaned_train_df = pd.get_dummies(cleaned_train_df )<prepare_x_and_y> | previous_application.AMT_GOODS_PRICE.fillna(previous_application.AMT_GOODS_PRICE.mean() ,inplace=True ) | Home Credit Default Risk |
1,056,158 | X = cleaned_train_df.drop('NA_Sales', axis=1)
y = cleaned_train_df.NA_Sales<normalization> | previous_application.AMT_ANNUITY.fillna(previous_application.AMT_ANNUITY.mean() ,inplace=True ) | Home Credit Default Risk |
1,056,158 |
<split> | previous_application.CNT_PAYMENT.fillna(previous_application.CNT_PAYMENT.median() ,inplace=True ) | Home Credit Default Risk |
1,056,158 | X_train, X_test, y_train, y_test = train_test_split(X,y )<train_on_grid> | previous_application_onehot=onehot_encoding(previous_application,categorical_features(previous_application)) | Home Credit Default Risk |
1,056,158 | def stepwise_selection(X, y,
initial_list=[],
threshold_in=0.01,
threshold_out = 0.05,
verbose=True):
included = list(initial_list)
while True:
changed=False
excluded = list(set(X.columns)-set(included))
new_pval = pd.Series(index=excluded)
for new_column in excluded:
model = sm.OLS(y, sm.add_constant(pd.DataFrame(... | cnt_id_prev1=previous_application_onehot[['SK_ID_CURR','SK_ID_PREV']].groupby('SK_ID_CURR' ).size() | Home Credit Default Risk |
1,056,158 | final_features = stepwise_selection(X_train, y_train )<train_model> | previous_application_min=previous_application_onehot.groupby('SK_ID_CURR' ).min().drop('SK_ID_PREV',axis=1)
previous_application_max=previous_application_onehot.groupby('SK_ID_CURR' ).max().drop('SK_ID_PREV',axis=1)
previous_application_median=previous_application_onehot.groupby('SK_ID_CURR' ).median().drop('SK_ID_PR... | Home Credit Default Risk |
1,056,158 | predictors = sm.add_constant(X_train[final_features])
final_model = sm.OLS(y_train,predictors ).fit()
final_model.summary()<compute_train_metric> | previous_application_merge=previous_application_mean.merge(previous_application_min,on='SK_ID_CURR' ).merge(previous_application_max,on='SK_ID_CURR' ).merge(previous_application_median,on='SK_ID_CURR')
previous_application_merge['cnt_id_prev1']=cnt_id_prev1
previous_application_merge.fillna(0,inplace=True)
previous_a... | Home Credit Default Risk |
1,056,158 | linreg = LinearRegression()
linreg.fit(X_train[final_features], y_train)
y_hat_train = linreg.predict(X_train[final_features])
y_hat_test = linreg.predict(X_test[final_features])
train_mse = mean_squared_error(y_train, y_hat_train)
test_mse = mean_squared_error(y_test, y_hat_test)
print("Train MSE:", train_mse)
p... | del previous_application,previous_application_max,previous_application_mean,previous_application_min,previous_application_onehot
gc.collect() | Home Credit Default Risk |
1,056,158 | cleaned_test_df = test_df.drop(['Critic_Score', 'Critic_Count', 'User_Score', 'User_Count', 'Developer', 'Rating', 'Publisher'], axis=1)
cleaned_test_df.JP_Sales.replace({0: 0.001}, inplace=True)
cleaned_test_df.JP_Sales = np.log(cleaned_test_df.JP_Sales )<drop_column> | POS_CASH_balance.NAME_CONTRACT_STATUS.value_counts() | Home Credit Default Risk |
1,056,158 | plts = platform_cats
plts.remove('Other' )<feature_engineering> | check_missing_data(POS_CASH_balance ) | Home Credit Default Risk |
1,056,158 | cleaned_test_df.loc[~cleaned_test_df['Platform'].isin(plts), 'Platform'] = 'Other'<categorify> | POS_CASH_balance.CNT_INSTALMENT_FUTURE.fillna(POS_CASH_balance.CNT_INSTALMENT_FUTURE.median() ,inplace=True ) | Home Credit Default Risk |
1,056,158 | cleaned_test_df = pd.get_dummies(cleaned_test_df )<drop_column> | POS_CASH_balance.drop('CNT_INSTALMENT',axis=1,inplace=True ) | Home Credit Default Risk |
1,056,158 | cleaned_test_df = cleaned_test_df[final_features]<filter> | POS_CASH_balance_onehot=onehot_encoding(POS_CASH_balance,categorical_features(POS_CASH_balance))
POS_CASH_balance_onehot.head() | Home Credit Default Risk |
1,056,158 | test_data_notnull = cleaned_test_df[cleaned_test_df.Year_of_Release.notnull() ]<filter> | cnt_id_prev2=POS_CASH_balance_onehot[['SK_ID_CURR','SK_ID_PREV']].groupby('SK_ID_CURR' ).size() | Home Credit Default Risk |
1,056,158 | test_data_null = cleaned_test_df[cleaned_test_df.Year_of_Release.isna() ]<predict_on_test> | del POS_CASH_balance,POS_CASH_balance_onehot
gc.collect() | Home Credit Default Risk |
1,056,158 | test_data_notnull['Prediction'] = linreg.predict(test_data_notnull )<prepare_output> | categorical_features(credit_card_balance ) | Home Credit Default Risk |
1,056,158 | predictions = test_data_notnull['Prediction']<create_dataframe> | credit_card_balance.NAME_CONTRACT_STATUS.value_counts() | Home Credit Default Risk |
1,056,158 | predictions = pd.DataFrame(predictions )<feature_engineering> | credit_card_balance_onehot=onehot_encoding(credit_card_balance,categorical_features(credit_card_balance)) | Home Credit Default Risk |
1,056,158 | test_data_null['Prediction'] = cleaned_train_df.NA_Sales.median()<prepare_output> | credit_card_balance_onehot.fillna(credit_card_balance_onehot.median() ,inplace=True)
credit_card_balance.head() | Home Credit Default Risk |
1,056,158 | null_predictions = test_data_null['Prediction']<create_dataframe> | cnt_id_prev3=credit_card_balance_onehot[['SK_ID_CURR','SK_ID_PREV']].groupby('SK_ID_CURR' ).size() | Home Credit Default Risk |
1,056,158 | null_predictions = pd.DataFrame(null_predictions )<concatenate> | del credit_card_balance,credit_card_balance_onehot
gc.collect() | Home Credit Default Risk |
1,056,158 | predictions = predictions.append(null_predictions )<prepare_output> | check_missing_data(installments_payments ) | Home Credit Default Risk |
1,056,158 | predictions.Prediction = np.exp(predictions.Prediction )<feature_engineering> | categorical_features(installments_payments ) | Home Credit Default Risk |
1,056,158 | predictions['Id'] = predictions.index<prepare_output> | installments_payments.dropna(inplace=True ) | Home Credit Default Risk |
1,056,158 | predictions = predictions[['Id', 'Prediction']]<save_to_csv> | cnt_id_prev4=installments_payments[['SK_ID_CURR','SK_ID_PREV']].groupby('SK_ID_CURR' ).size() | Home Credit Default Risk |
1,056,158 | predictions.to_csv('submission.csv', index=False )<import_modules> | installments_payments_min=installments_payments.groupby('SK_ID_CURR' ).min().drop('SK_ID_PREV',axis=1)
installments_payments_max=installments_payments.groupby('SK_ID_CURR' ).max().drop('SK_ID_PREV',axis=1)
installments_payments_median=installments_payments.groupby('SK_ID_CURR' ).median().drop('SK_ID_PREV',axis=1 ) | Home Credit Default Risk |
1,056,158 | import numpy as np
import pandas as pd
from sklearn.ensemble import RandomForestClassifier as RFC
from sklearn.linear_model import LinearRegression as LR
from sklearn.neural_network import MLPRegressor as MLPR<load_from_csv> | installments_payments_merge=installments_payments_min.merge(installments_payments_max,on='SK_ID_CURR' ).merge(installments_payments_median,on='SK_ID_CURR' ) | Home Credit Default Risk |
1,056,158 | data_dir = '.. /input/ieee-pes-bdc-datathon-year-2020'
df = pd.read_csv(f'{data_dir}/train.csv')
test_df = pd.read_csv(f'{data_dir}/test.csv' )<split> | installments_payments_merge['cnt_id_prev4']=cnt_id_prev4
installments_payments_merge.fillna(0,inplace=True)
installments_payments_merge.head() | Home Credit Default Risk |
1,056,158 | data_len = len(df)
pct = 1.0
train_len = int(1.0*data_len)
train_df = df[:train_len]
val_df = df[train_len:]<prepare_x_and_y> | del installments_payments,installments_payments_max,installments_payments_min
gc.collect() | Home Credit Default Risk |
1,056,158 | X_train = train_df.drop(['ID', 'global_horizontal_irradiance'], axis=1 ).values.reshape(-1, 6)
y_train = train_df['global_horizontal_irradiance'].values.reshape(len(train_df))<prepare_x_and_y> | target=application_train['TARGET'] | Home Credit Default Risk |
1,056,158 | X_val = val_df.drop(['ID', 'global_horizontal_irradiance'], axis=1 ).values.reshape(-1, 6)
y_val = val_df['global_horizontal_irradiance'].values.reshape(len(val_df))<prepare_x_and_y> | application_train.drop('TARGET',axis=1,inplace=True ) | Home Credit Default Risk |
1,056,158 | X_test = test_df.drop(['ID'], axis=1 ).values.reshape(-1, 6)
test_ID = test_df['ID'].values.reshape(len(test_df))<train_model> | application_train['TARGET']=target
application_train.head() | Home Credit Default Risk |
1,056,158 | reg = LR(normalize=True)
reg.fit(X_train, y_train )<predict_on_test> | application_test['TARGET']=-999 | Home Credit Default Risk |
1,056,158 | preds = reg.predict(X_test )<train_model> | df=pd.concat([application_train,application_test] ) | Home Credit Default Risk |
1,056,158 | regr = MLPR(random_state=1, hidden_layer_sizes =(32, 8, 2), max_iter=5, validation_fraction=0.1, learning_rate_init=0.02, verbose=True)
regr.fit(X_train, y_train )<predict_on_test> | categorical_features(df ) | Home Credit Default Risk |
1,056,158 | preds = regr.predict(X_test)
preds = [0 if p<0 else p for p in preds]<save_to_csv> | df_onehot=onehot_encoding(df,categorical_features(df))
df_onehot.shape | Home Credit Default Risk |
1,056,158 | zippedList = list(zip(test_ID, preds))
submission = pd.DataFrame(zippedList, columns = ['ID','global_horizontal_irradiance'])
submission.to_csv('submission.csv', index=False )<set_options> | df_onehot.fillna(0,inplace=True ) | Home Credit Default Risk |
1,056,158 | pd.options.display.max_columns = 999
warnings.simplefilter(action='ignore')
<load_from_csv> | del application_test,application_train,df
gc.collect() | Home Credit Default Risk |
1,056,158 | test = pd.read_csv(".. /input/seleksidukungaib/test.csv")
train = pd.read_csv(".. /input/seleksidukungaib/train.csv")
sample_submission = pd.read_csv(".. /input/seleksidukungaib/sample_submission.csv" )<define_variables> | total=df_onehot.merge(right=bureau_final_median,on='SK_ID_CURR',how='left' ).merge(right=previous_application_median,on='SK_ID_CURR',how='left' ).merge(right=POS_CASH_balance_median,on='SK_ID_CURR',how='left' ).merge(right=credit_card_balance_median,on='SK_ID_CURR',how='left' ).merge(right=installments_payments_merge,o... | Home Credit Default Risk |
1,056,158 | dropped_column = ['idx', 'userId', 'num_transfer_trx', 'max_transfer_trx',
'min_transfer_trx', 'date', 'date_collected', 'isUpgradedUser']<concatenate> | del total,df_onehot,bureau_final_median,previous_application_merge,previous_application_median
del POS_CASH_balance_median,credit_card_balance_median,installments_payments_median,installments_payments_merge
gc.collect() | Home Credit Default Risk |
1,056,158 | data = pd.concat([train,test],ignore_index=True)
data = data.drop(dropped_column, axis = 1 )<drop_column> | df_train=df_total[df_total.TARGET!=-999]
| Home Credit Default Risk |
1,056,158 | data = data.drop(['average_transfer_trx'], axis = 1 )<drop_column> | df_test=df_total[df_total.TARGET==-999]
| Home Credit Default Risk |
1,056,158 | data.loc[data.isActive.isnull() == True]
data = data.dropna(subset=["isActive"] )<feature_engineering> | test=df_test.drop(columns=["SK_ID_CURR",'TARGET'],axis=1)
test.shape | Home Credit Default Risk |
1,056,158 | data['premium'] = data['premium'].fillna(data['premium'].mode() )<feature_engineering> | y=df_train['TARGET'].values
y | Home Credit Default Risk |
1,056,158 | for column in data.columns:
if(column != "isChurned"):
data[column] = data[column].fillna(data[column].median() )<categorify> | train=df_train.drop(columns=["SK_ID_CURR",'TARGET'],axis=1 ).values
train.shape | Home Credit Default Risk |
1,056,158 | categorical_features = ['premium', 'super', 'pinEnabled']
le = LabelEncoder()
for col in categorical_features:
data[col] = le.fit_transform(list(data[col].values))<set_options> | del df_train,df_test,df_total
gc.collect() | Home Credit Default Risk |
1,056,158 | Q3 = data.quantile(0.85 )<feature_engineering> | gc.collect() | Home Credit Default Risk |
1,056,158 | numerik_col = ['average_recharge_trx','average_topup_trx','max_recharge_trx','max_topup_trx',
'min_recharge_trx','min_topup_trx','num_recharge_trx','num_topup_trx','num_transaction',
'random_number','total_transaction']
<drop_column> | from sklearn.model_selection import train_test_split | Home Credit Default Risk |
1,056,158 | data['num_transaction_plus_num_recharge'] = data['num_transaction'] + data['num_recharge_trx']
data.drop(['num_transaction', 'num_recharge_trx'], axis=1, inplace = True)
<split> | from sklearn.model_selection import train_test_split | Home Credit Default Risk |
1,056,158 | train = data[~data.isChurned.isnull() ]
test = data[data.isChurned.isnull() ]
numerik_col = ['max_recharge_trx','average_recharge_trx',
'average_topup_trx', 'max_topup_trx',
'min_recharge_trx','min_topup_trx','num_topup_trx',
'random_number','total_transaction',
]
for col in(numerik_col):
train[col]=(( train[col]-train... | X_train,X_test,y_train,y_test=train_test_split(train,y,test_size=0.2 ) | Home Credit Default Risk |
1,056,158 | train.duplicated().value_counts()
<remove_duplicates> | del train
gc.collect() | Home Credit Default Risk |
1,056,158 | train.drop_duplicates(keep = 'first', inplace = True)
<set_options> | import lightgbm | Home Credit Default Risk |
1,056,158 | train.corr().style.background_gradient(cmap='coolwarm' )<count_values> | train_data=lightgbm.Dataset(X_train,label=y_train)
valid_data=lightgbm.Dataset(X_test,label=y_test ) | Home Credit Default Risk |
1,056,158 | min_cor = ['isActive', 'isVerifiedPhone', 'blocked', 'super', 'random_number']
for col in min_cor:
print('====== ', col, " ======")
print(train[col].value_counts())
<drop_column> | params = {'boosting_type': 'gbdt',
'max_depth' : 10,
'objective': 'binary',
'nthread': 5,
'num_leaves': 64,
'learning_rate': 0.1,
'max_bin': 512,
'subsample_for_bin': 200,
'subsample': 1,
'subsample_freq': 1,
'colsample_bytree': 0.8,
'reg_alpha': 5,
'reg_lambda': 10,
'min_split_gain': 0.005,
'min_child_weight': 1,
'min... | Home Credit Default Risk |
1,056,158 | drop_from_cor = ['isActive', 'isVerifiedPhone', 'blocked', 'super', 'random_number']
train.drop(drop_from_cor, axis = 1, inplace = True)
test.drop(drop_from_cor, axis = 1, inplace = True )<import_modules> | lgbm = lightgbm.train(params,
train_data,
25000,
valid_sets=valid_data,
early_stopping_rounds= 80,
verbose_eval= 10
) | Home Credit Default Risk |
1,056,158 | from sklearn.model_selection import KFold
from sklearn.model_selection import cross_val_score
from sklearn.tree import ExtraTreeClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.svm.classes import OneClassSVM
from sklearn.neural_network.multilayer_perceptron import MLPClassifier
from sklearn.neigh... | predictions_lgbm_prob = lgbm.predict(test.values ) | Home Credit Default Risk |
1,056,158 | Y = train["isChurned"]
X = train.drop(["isChurned"], axis = 1 )<split> | sub=pd.read_csv('.. /input/sample_submission.csv' ) | Home Credit Default Risk |
1,056,158 | random_state = 1
X_train, X_valid, y_train, y_valid = train_test_split(X, Y, test_size = 0.2, random_state = random_state )<choose_model_class> | sub.TARGET=predictions_lgbm_prob | Home Credit Default Risk |
1,056,158 | def get_kfold() :
return KFold(n_splits=5, shuffle=True, random_state=1 )<choose_model_class> | sub.to_csv('sub.csv',index=False ) | Home Credit Default Risk |
1,046,068 | all_model = [RandomForestClassifier() ,ExtraTreeClassifier() , LogisticRegression() ,RidgeClassifier() ,
DecisionTreeClassifier() , KNeighborsClassifier() , PassiveAggressiveClassifier() ,
]<choose_model_class> | df_application = pd.read_csv('.. /input/application_train.csv')
df_application_test = pd.read_csv('.. /input/application_test.csv')
df_application.head() | Home Credit Default Risk |
1,046,068 | params = {'loss_function':'Logloss',
'eval_metric':'F1',
'iterations' : 1000,
'learning_rate': 0.01,
'verbose': 1000,
'random_seed': random_state
}
cbc = CatBoostClassifier(**params )<prepare_x_and_y> | df_application['Source'] = 'Train'
df_application_test['Source'] = 'Test'
df = pd.concat(( df_application,df_application_test),axis = 0,sort = False)
cat_cols = [col for col in df.columns if(df[col].dtype == object)&(col != 'Source')]
le = preprocessing.LabelEncoder()
for col in cat_cols:
df[col] = le.fit_transform(df... | Home Credit Default Risk |
1,046,068 | data_dmatrix = xgb.DMatrix(data=X,label=Y )<train_on_grid> | df_bureau = pd.read_csv(".. /input/bureau.csv")
df_bureau_balance = pd.read_csv(".. /input/bureau_balance.csv")
df_bureau_balance["MONTHS_BALANCE"]= np.abs(df_bureau_balance["MONTHS_BALANCE"])
df_bureau_balance["Period"] = np.where(( df_bureau_balance["MONTHS_BALANCE"] < 7),"short",np.where(( df_bureau_balance["MONT... | Home Credit Default Risk |
1,046,068 | params = {"objective":"binary:logistic",'colsample_bytree': 0.3,'learning_rate': 0.1,
'max_depth': 10, 'alpha': 10}
<compute_train_metric> | df_bureau_balance = df_bureau_balance.groupby(["SK_ID_BUREAU","Period_status"])\
.agg({"MONTHS_BALANCE" : ["count","min","max","mean"]})\
.reset_index()
df_bureau_balance.columns = [''.join(col ).strip() for col in df_bureau_balance.columns.values]
df_bureau_balance.head() | Home Credit Default Risk |
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