kernel_id int64 24.2k 23.3M | prompt stringlengths 8 1.85M | completetion stringlengths 1 182k | comp_name stringlengths 5 57 |
|---|---|---|---|
1,189,554 | dtc = DecisionTreeClassifier(random_state=0 ).fit(x_Train, y_Train[:,1])
score(dtc, x_Test, y_Test[:,1] )<train_model> | print("Start Bureau................ " ) | Home Credit Default Risk |
1,189,554 | etc = ExtraTreesClassifier(n_estimators=10, max_depth=None,
min_samples_split=2, random_state=0 ).fit(x_Train, y_Train[:,1])
score(etc, x_Test, y_Test[:,1] )<train_model> | bureau = pd.read_csv('.. /input/bureau.csv', nrows = num_rows ) | Home Credit Default Risk |
1,189,554 | sgd = SGDClassifier(loss="log", penalty="elasticnet", max_iter=5 ).fit(x_Train, y_Train[:,1])
score(sgd, x_Test, y_Test[:,1] )<prepare_output> | bb = pd.read_csv('.. /input/bureau_balance.csv', nrows = num_rows)
bb, bb_cat = one_hot_encoder(bb, nan_as_category)
bureau, bureau_cat = one_hot_encoder(bureau, nan_as_category ) | Home Credit Default Risk |
1,189,554 | submission = pd.DataFrame({
"Id": ids.Id,
"Expected": probs[:,1]
})
submission.head()<save_to_csv> | bb_aggregations = {'MONTHS_BALANCE': ['min', 'max', 'size']}
for col in bb_cat:
bb_aggregations[col] = ['mean']
bb_agg = bb.groupby('SK_ID_BUREAU' ).agg(bb_aggregations)
bb_agg.columns = pd.Index([e[0] + "_" + e[1].upper() for e in bb_agg.columns.tolist() ])
bureau = bureau.join(bb_agg, how='left', on='SK_ID_BUREAU')... | Home Credit Default Risk |
1,189,554 | submission.to_csv('sampleSubmission.csv', index=False )<set_options> | num_aggregations = {
'DAYS_CREDIT': ['min', 'max', 'mean', 'var'],
'CREDIT_DAY_OVERDUE': ['max', 'mean'],
'DAYS_CREDIT_ENDDATE': ['min', 'max', 'mean'],
'AMT_CREDIT_MAX_OVERDUE': ['mean'],
'CNT_CREDIT_PROLONG': ['sum'],
'AMT_CREDIT_SUM': ['max', 'mean', 'sum'],
'AMT_CREDIT_SUM_DEBT': ['max', 'mean', 'sum'],
'AMT_CREDIT... | Home Credit Default Risk |
1,189,554 | plt.style.use('ggplot')
%matplotlib inline
<choose_model_class> | cat_aggregations = {}
for cat in bureau_cat: cat_aggregations[cat] = ['mean']
for cat in bb_cat: cat_aggregations[cat + "_MEAN"] = ['mean']
bureau_agg = bureau.groupby('SK_ID_CURR' ).agg({**num_aggregations, **cat_aggregations})
bureau_agg.columns = pd.Index(['BURO_' + e[0] + "_" + e[1].upper() for e in bureau_agg.col... | Home Credit Default Risk |
1,189,554 | xgb.XGBClassifier()<load_from_csv> | active = bureau[bureau['CREDIT_ACTIVE_Active'] == 1]
active_agg = active.groupby('SK_ID_CURR' ).agg(num_aggregations)
active_agg.columns = pd.Index(['ACT_' + e[0] + "_" + e[1].upper() for e in active_agg.columns.tolist() ])
bureau_agg = bureau_agg.join(active_agg, how='left')
del active, active_agg
gc.collect()
clos... | Home Credit Default Risk |
1,189,554 | df_train = pd.read_csv('.. /input/homework-for-students3/train.csv', index_col=0)
df_test = pd.read_csv('.. /input/homework-for-students3/test.csv', index_col=0)
print(len(df_test))
print(len(df_train))<load_from_csv> | print("End Bureau................ " ) | Home Credit Default Risk |
1,189,554 | gdp=pd.read_csv('.. /input/homework-for-students3/US_GDP_by_State.csv')
zipdata=pd.read_csv('.. /input/homework-for-students3/free-zipcode-database.csv')
drop_col = ['WorldRegion',
'Country', 'LocationText', 'Location', 'Decommisioned',
'TaxReturnsFiled', 'EstimatedPopulation', 'TotalWages', 'Notes']
zipdata=zipdata.... | print("Start previous_application................ " ) | Home Credit Default Risk |
1,189,554 | df_train["issue_d"]=pd.to_datetime(df_train["issue_d"])
df_test["issue_d"]=pd.to_datetime(df_test["issue_d"])
df_train = df_train[df_train.issue_d.dt.year >= 2015]
df_train = df_train[df_train['annual_inc'] < df_train['annual_inc'].quantile(0.999)]
df_train['IDdami']=df_train.index
df_test['IDdami']=df_test.index<dat... | prev = pd.read_csv('.. /input/previous_application.csv', nrows = num_rows ) | Home Credit Default Risk |
1,189,554 | df_train["earliest_cr_line"]=pd.to_datetime(df_train["earliest_cr_line"])
df_test["earliest_cr_line"]=pd.to_datetime(df_test["earliest_cr_line"])
df_train["issue_d_unix"] = df_train["issue_d"].view('int64')// 10**9
df_test["issue_d_unix"] = df_test["issue_d"].view('int64')// 10**9
df_train["earliest_cr_line_unix"] = ... | prev, cat_cols = one_hot_encoder(prev, nan_as_category= True)
prev['DAYS_FIRST_DRAWING'].replace(365243, np.nan, inplace= True)
prev['DAYS_FIRST_DUE'].replace(365243, np.nan, inplace= True)
prev['DAYS_LAST_DUE_1ST_VERSION'].replace(365243, np.nan, inplace= True)
prev['DAYS_LAST_DUE'].replace(365243, np.nan, inplace... | Home Credit Default Risk |
1,189,554 | df_train = df_train.reset_index()
df_test = df_test.reset_index()
kari_df_train=pd.merge(df_train, state, how='left',left_on='addr_state',right_on='State')
kari_df_test=pd.merge(df_test, state, how='left',left_on='addr_state',right_on='State')
df_train = kari_df_train.set_index("ID")
df_test =kari_df_test.set_index(... | prev['APP_CREDIT_PERC'] = prev['AMT_APPLICATION'] / prev['AMT_CREDIT'] | Home Credit Default Risk |
1,189,554 | df_train['dami_year']=df_train.issue_d.dt.year
df_test['dami_year']=int(2015)
df_train = df_train.reset_index()
df_test = df_test.reset_index()
kari_df_train=pd.merge(df_train, gdp, how='left',left_on=['City','dami_year'],right_on=['State','year'])
kari_df_test=pd.merge(df_test, gdp, how='left',left_on=['City','dami_... | num_aggregations = {
'AMT_ANNUITY': ['min', 'max', 'mean'],
'AMT_APPLICATION': ['min', 'max', 'mean'],
'AMT_CREDIT': ['min', 'max', 'mean'],
'APP_CREDIT_PERC': ['min', 'max', 'mean', 'var'],
'AMT_DOWN_PAYMENT': ['min', 'max', 'mean'],
'AMT_GOODS_PRICE': ['min', 'max', 'mean'],
'HOUR_APPR_PROCESS_START': ['min', 'max', ... | Home Credit Default Risk |
1,189,554 | df_train = df_train.reset_index()
df_test = df_test.reset_index()
kari_df_train=pd.merge(df_train, spi, how='left',left_on=['issue_d'],right_on=['date'])
kari_df_test=pd.merge(df_test, spi, how='left',left_on=['issue_d'],right_on=['date'])
df_train = kari_df_train.set_index("ID")
df_test =kari_df_test.set_index("ID"... | cat_aggregations = {}
for cat in cat_cols:
cat_aggregations[cat] = ['mean']
prev_agg = prev.groupby('SK_ID_CURR' ).agg({**num_aggregations, **cat_aggregations})
prev_agg.columns = pd.Index(['PREV_' + e[0] + "_" + e[1].upper() for e in prev_agg.columns.tolist() ] ) | Home Credit Default Risk |
1,189,554 | zipdata["Zipcode"]=zipdata["Zipcode"].astype(str)
zipdata["Zipcode"]=zipdata["Zipcode"].str[:3]
zipdata=zipdata[['Zipcode','State','Xaxis', 'Yaxis', 'Zaxis']]
zipdata=zipdata.groupby(['Zipcode','State'],as_index=False ).mean()
<data_type_conversions> | approved = prev[prev['NAME_CONTRACT_STATUS_Approved'] == 1]
approved_agg = approved.groupby('SK_ID_CURR' ).agg(num_aggregations)
approved_agg.columns = pd.Index(['APR_' + e[0] + "_" + e[1].upper() for e in approved_agg.columns.tolist() ])
prev_agg = prev_agg.join(approved_agg, how='left')
refused = prev[prev['NAME_C... | Home Credit Default Risk |
1,189,554 | df_train['zip_code']=df_train['zip_code'].str[:3]
df_test['zip_code']=df_test['zip_code'].str[:3]
df_train["zip_code"]=df_train["zip_code"].astype(str)
df_test["zip_code"]=df_test["zip_code"].astype(str)
<count_duplicates> | print("End previous_application................ " ) | Home Credit Default Risk |
1,189,554 | zipdata[zipdata.duplicated() ]<merge> | print("Start POS_CASH_balance................ " ) | Home Credit Default Risk |
1,189,554 | df_train = df_train.reset_index()
df_test = df_test.reset_index()
kari_df_train=pd.merge(df_train, zipdata, how='left',left_on=['zip_code','addr_state'],right_on=['Zipcode','State'])
kari_df_test=pd.merge(df_test, zipdata, how='left',left_on=['zip_code','addr_state'],right_on=['Zipcode','State'])
df_train = kari_df_t... | pos = pd.read_csv('.. /input/POS_CASH_balance.csv', nrows = num_rows ) | Home Credit Default Risk |
1,189,554 | encoder = OrdinalEncoder()
enc_train = encoder.fit_transform(df_train['zip_code'].values)
enc_test = encoder.transform(df_test['zip_code'].values)
df_train = df_train.reset_index()
df_test = df_test.reset_index()
df_train['zip_code_la']=enc_train.iloc[:,0]
df_test['zip_code_la']=enc_test.iloc[:,0]
df_train = df_train... | pos, cat_cols = one_hot_encoder(pos, nan_as_category= True)
aggregations = {
'MONTHS_BALANCE': ['max', 'mean', 'size'],
'SK_DPD': ['max', 'mean'],
'SK_DPD_DEF': ['max', 'mean']
} | Home Credit Default Risk |
1,189,554 | zi_cal1='zip_code'
zi_summary1 = df_train[zi_cal1].value_counts()
df_train['zip_code_co'] = df_train[zi_cal1].map(zi_summary1)
df_test['zip_code_co'] = df_test[zi_cal1].map(zi_summary1 )<categorify> | for cat in cat_cols:
aggregations[cat] = ['mean']
pos_agg = pos.groupby('SK_ID_CURR' ).agg(aggregations)
pos_agg.columns = pd.Index(['POS_' + e[0] + "_" + e[1].upper() for e in pos_agg.columns.tolist() ])
pos_agg['POS_COUNT'] = pos.groupby('SK_ID_CURR' ).size()
del pos
gc.collect() | Home Credit Default Risk |
1,189,554 | encoder = OrdinalEncoder()
enc_train = encoder.fit_transform(df_train['addr_state'].values)
enc_test = encoder.transform(df_test['addr_state'].values)
df_train = df_train.reset_index()
df_test = df_test.reset_index()
df_train['addr_state_la']=enc_train.iloc[:,0]
df_test['addr_state_la']=enc_test.iloc[:,0]
df_train = ... | print("Start POS_CASH_balance................ " ) | Home Credit Default Risk |
1,189,554 | zi_cal2='addr_state'
zi_summary2 = df_train[zi_cal2].value_counts()
df_train['addr_state_co'] = df_train[zi_cal2].map(zi_summary2)
df_test['addr_state_co'] = df_test[zi_cal2].map(zi_summary2 )<data_type_conversions> | ins = pd.read_csv('.. /input/installments_payments.csv', nrows = num_rows)
ins, cat_cols = one_hot_encoder(ins, nan_as_category= True ) | Home Credit Default Risk |
1,189,554 |
<data_type_conversions> | ins['PAYMENT_PERC'] = ins['AMT_PAYMENT'] / ins['AMT_INSTALMENT']
ins['PAYMENT_DIFF'] = ins['AMT_INSTALMENT'] - ins['AMT_PAYMENT']
ins['DPD'] = ins['DAYS_ENTRY_PAYMENT'] - ins['DAYS_INSTALMENT']
ins['DBD'] = ins['DAYS_INSTALMENT'] - ins['DAYS_ENTRY_PAYMENT']
ins['DPD'] = ins['DPD'].apply(lambda x: x if x > 0 else 0)
in... | Home Credit Default Risk |
1,189,554 |
<data_type_conversions> | aggregations = {
'NUM_INSTALMENT_VERSION': ['nunique'],
'DPD': ['max', 'mean', 'sum'],
'DBD': ['max', 'mean', 'sum'],
'PAYMENT_PERC': ['max', 'mean', 'sum', 'var'],
'PAYMENT_DIFF': ['max', 'mean', 'sum', 'var'],
'AMT_INSTALMENT': ['max', 'mean', 'sum'],
'AMT_PAYMENT': ['min', 'max', 'mean', 'sum'],
'DAYS_ENTRY_PAYMENT'... | Home Credit Default Risk |
1,189,554 |
<drop_column> | print("End POS_CASH_balance................ " ) | Home Credit Default Risk |
1,189,554 | df_train=df_train.drop(['issue_d','earliest_cr_line'],axis=1)
df_test=df_test.drop(['issue_d','earliest_cr_line'],axis=1)
drop_col=['City','acc_now_delinq']
df_train=df_train.drop(drop_col,axis=1)
df_test=df_test.drop(drop_col,axis=1 )<data_type_conversions> | print("Start credit_card_balance................ " ) | Home Credit Default Risk |
1,189,554 |
<data_type_conversions> | cc = pd.read_csv('.. /input/credit_card_balance.csv', nrows = num_rows ) | Home Credit Default Risk |
1,189,554 |
<categorify> | cc, cat_cols = one_hot_encoder(cc, nan_as_category= True)
cc.drop(columns = ['SK_ID_PREV'], inplace = True)
cc_agg = cc.groupby('SK_ID_CURR' ).agg(['min', 'max', 'mean', 'sum', 'var'])
cc_agg.columns = pd.Index(['CC_' + e[0] + "_" + e[1].upper() for e in cc_agg.columns.tolist() ])
cc_agg['CC_COUNT'] = cc.groupby('S... | Home Credit Default Risk |
1,189,554 | ce_cal2='initial_list_status'
ce_summary2 = df_train[ce_cal2].value_counts()
df_train['initial_list_status'] = df_train[ce_cal2].map(ce_summary2)
df_test['initial_list_status'] = df_test[ce_cal2].map(ce_summary2 )<categorify> | print("End credit_card_balance................ " ) | Home Credit Default Risk |
1,189,554 | ce_cal2='application_type'
ce_summary2 = df_train[ce_cal2].value_counts()
df_train['application_type'] = df_train[ce_cal2].map(ce_summary2)
df_test['application_type'] = df_test[ce_cal2].map(ce_summary2 )<categorify> | with timer("Process bureau and bureau_balance"):
print("Bureau df shape:", bureau_agg.shape)
df = df.join(bureau_agg, how='left',on='SK_ID_CURR')
gc.collect()
with timer("Process previous_applications"):
print("Previous applications df shape:", prev_agg.shape)
df = df.join(prev_agg, how='left', on='SK_ID_CURR')
gc.... | Home Credit Default Risk |
1,189,554 | df_train['grade'].unique()
df_train=df_train.replace({'grade':{'A':1,'B':2,'C':3,'D':4,'E':5,'F':6,'G':7}})
df_test=df_test.replace({'grade':{'A':1,'B':2,'C':3,'D':4,'E':5,'F':6,'G':7}})
df_train["grade"]=df_train["grade"].astype(int)
df_test["grade"]=df_test["grade"].astype(int )<categorify> | print("Done.;.............. ")
| Home Credit Default Risk |
1,189,554 | df_train=df_train.replace({'sub_grade':{'A1':1,'A2':2,'A3':3,'A4':4,'A5':5,
'B1':6,'B2':7,'B3':8,'B4':9,'B5':10,
'C1':11,'C2':12,'C3':13,'C4':14,'C5':15,
'D1':16,'D2':17,'D3':18,'D4':19,'D5':20,
'E1':21,'E2':22,'E3':23,'E4':24,'E5':25,
'F1':26,'F2':27,'F3':28,'F4':29,'F5':30,
'G1':31,'G2':32,'G3':33,'G4':34,'G5':35}})
... | train_df = df[df['TARGET'].notnull() ]
test_df = df[df['TARGET'].isnull() ] | Home Credit Default Risk |
1,189,554 | in_0=df_train[df_train.loan_condition==0].installment.median()
df_train['in_0_sa'] =df_train['installment']-in_0
df_test['in_0_sa'] =df_test['installment']-in_0
lo_0=df_train[df_train.loan_condition==0].loan_amnt.median()
df_train['lo_0_sa'] =df_train['loan_amnt']-lo_0
df_test['lo_0_sa'] =df_test['loan_amnt']-lo_0
dti_... | train_df = train_df.drop(['index'],axis=1)
test_df = test_df.drop(['index','TARGET'],axis=1)
train_df = train_df.fillna(0)
test_df = test_df.fillna(0 ) | Home Credit Default Risk |
1,189,554 | df_train['home_ownership'].unique()
df_train=df_train.replace({'home_ownership':{'MORTGAGE':3,'RENT':2,'OWN':4,'ANY':1}})
df_test=df_test.replace({'home_ownership':{'MORTGAGE':3,'RENT':2,'OWN':4,'ANY':1}})
df_train["home_ownership"]=df_train["home_ownership"].astype(int)
df_test["home_ownership"]=df_test["home_owner... | label = u'TARGET'
a = list(train_df.columns)
a.remove(label)
labels = train_df[label]
data_only = train_df[list(a)]
col_name = data_only.columns
X_train, X_test, y_train, y_test = train_test_split(data_only, labels, test_size=0.1,random_state = 42 ) | Home Credit Default Risk |
1,189,554 | summary = df_train['purpose'].value_counts()
summary
df_train['purpose_co'] = df_train['purpose'].map(summary)
df_test['purpose_co'] = df_test['purpose'].map(summary )<categorify> | clf_xgBoost = xgb.XGBClassifier(
learning_rate =0.01, n_estimators=1000, max_depth=4, min_child_weight=4, subsample=0.8, colsample_bytree=0.8, objective= 'binary:logistic',
nthread=4, scale_pos_weight=2, seed=27)
clf_xgBoost.fit(data_only,labels ) | Home Credit Default Risk |
1,189,554 | df_train=df_train.replace({'emp_length':{'< 1 year':0.5,'1 year':1,'2 years':2,'3 years':3,
'4 years':4,'5 years':5,'6 years':6,'7 years':7,
'8 years':8,'9 years':9,'10+ years':10}})
df_test=df_test.replace({'emp_length':{'< 1 year':0.5,'1 year':1,'2 years':2,'3 years':3,
'4 years':4,'5 years':5,'6 years':6,'7 years':... | pred = clf_xgBoost.predict_proba(test_df)
test_df['TARGET'] = pred[:, 0] | Home Credit Default Risk |
1,189,554 | <feature_engineering><EOS> | test_df[['SK_ID_CURR', 'TARGET']].to_csv('submission_clf_xgBoost.csv', index= False ) | Home Credit Default Risk |
1,087,344 | <SOS> metric: AUC Kaggle data source: home-credit-default-risk<feature_engineering> | plt.style.use('fivethirtyeight')
%matplotlib inline
init_notebook_mode(connected=True)
print(os.listdir(".. /input"))
PATH = ".. /input"
| Home Credit Default Risk |
1,087,344 | df_train['ggg']=round(df_train['loan_amnt']*df_train['sub_grade'],5)
df_test['ggg']=round(df_test['loan_amnt']*df_test['sub_grade'],5)
df_train['hhh']=round(df_train['installment']*df_train['sub_grade'],5)
df_test['hhh']=round(df_test['installment']*df_test['sub_grade'],5)
df_train['iii']=round(df_train['annual_inc... | data = pd.read_csv(PATH+"/application_train.csv")
test = pd.read_csv(PATH+"/application_test.csv")
bureau = pd.read_csv(PATH+"/bureau.csv")
bureau_balance = pd.read_csv(PATH+"/bureau_balance.csv")
credit_card_balance = pd.read_csv(PATH+"/credit_card_balance.csv")
installments_payments = pd.read_csv(PATH+"/installm... | Home Credit Default Risk |
1,087,344 | df_train[df_train.loan_condition==1].loan_amnt.mean()<normalization> | data = pd.read_csv(PATH+"/application_train.csv", nrows=10000)
test = pd.read_csv(PATH+"/application_test.csv", nrows=10000)
bureau = pd.read_csv(PATH+"/bureau.csv", nrows=10000)
bureau_balance = pd.read_csv(PATH+"/bureau_balance.csv", nrows=10000)
credit_card_balance = pd.read_csv(PATH+"/credit_card_balance.csv", ... | Home Credit Default Risk |
1,087,344 | df_train[df_train.loan_condition==0].loan_amnt.mean()<count_values> | data['DAYS_EMPLOYED'].replace(365243, np.nan, inplace= True)
data['CODE_GENDER'].replace({'XNA': 'F'}, inplace=True)
data['DAYS_EMPLOYED'].replace(365243, np.nan, inplace= True)
data['YEARS_BUILD_CREDIT'] = data['AMT_CREDIT']/data['YEARS_BUILD_AVG']
data['Annuity_Income'] = data['AMT_ANNUITY']/data['AMT_INCOME_TOTAL... | Home Credit Default Risk |
1,087,344 | f = 'purpose'
df_train[f].value_counts() / len(df_train )<count_values> | bureau_new = bureau
group = bureau_new[['SK_ID_CURR', 'DAYS_CREDIT']].groupby('SK_ID_CURR')['DAYS_CREDIT'].count().reset_index().rename(index=str, columns={'DAYS_CREDIT': 'BUREAU_LOAN_COUNT'})
bureau_new = bureau_new.merge(group, how = 'left', on = 'SK_ID_CURR')
bureau_new.head()
del group | Home Credit Default Risk |
1,087,344 | df_test[f].value_counts() / len(df_test )<count_unique_values> | group = bureau_new[['SK_ID_CURR', 'CREDIT_TYPE']].groupby('SK_ID_CURR')['CREDIT_TYPE'].nunique().reset_index().rename(index=str, columns = {'CREDIT_TYPE': 'LOAN_TYPES_PER_CUST'})
bureau_new = bureau_new.merge(group,on = ['SK_ID_CURR'], how = 'left')
bureau_new.head()
del group | Home Credit Default Risk |
1,087,344 | cats = []
for col in df_train.columns:
if df_train[col].dtype == 'object':
cats.append(col)
print(col, df_train[col].nunique())
print(cats )<count_values> | bureau_new["AVERAGE_LOAN_TYPE"] = bureau_new['BUREAU_LOAN_COUNT']/bureau_new['LOAN_TYPES_PER_CUST'] | Home Credit Default Risk |
1,087,344 | print(df_train['title'].unique())
print(len(df_test.columns))
print(len(df_train.columns))<categorify> | replace = {'Active': 1, 'Closed':0, 'Sold': 1, 'Bad debt': 1}
bureau_new['CREDIT_ACTIVE'] = bureau_new['CREDIT_ACTIVE'].replace(replace)
gp = bureau_new.groupby('SK_ID_CURR')['CREDIT_ACTIVE'].mean().reset_index().rename(index=str, columns={'CREDIT_ACTIVE': 'ACTIVE_LOANS_PERCENTAGE'})
bureau_new = bureau_new.merge(gp,... | Home Credit Default Risk |
1,087,344 | encoder = OrdinalEncoder()
enc_train = encoder.fit_transform(df_train['emp_title'].values)
enc_test = encoder.transform(df_test['emp_title'].values)
df_train = df_train.reset_index()
df_test = df_test.reset_index()
df_train['emp_title_lab']=enc_train.iloc[:,0]
df_test['emp_title_lab']=enc_test.iloc[:,0]
df_train = df... | def repl(x):
if x < 0:
y = 0
else:
y= 1
return y
bureau_new['CREDIT_ENDDATE_BINARY'] = bureau_new['DAYS_CREDIT_ENDDATE'].apply(lambda x: repl(x))
grp = bureau_new.groupby('SK_ID_CURR')['CREDIT_ENDDATE_BINARY'].mean().reset_index().rename(index=str, columns={'CREDIT_ENDDATE_BINARY': 'CREDIT_ENDDATE_PERCENTAGE'})
bureau... | Home Credit Default Risk |
1,087,344 | ce_cal1='emp_title'
ce_summary1 = df_train[ce_cal1].value_counts()
df_train['emp_title_co'] = df_train[ce_cal1].map(ce_summary1)
df_test['emp_title_co'] = df_test[ce_cal1].map(ce_summary1 )<categorify> | num_aggregations = {
'DAYS_CREDIT': ['min', 'max', 'mean', 'var'],
'DAYS_CREDIT_ENDDATE': ['min', 'max', 'mean'],
'DAYS_CREDIT_UPDATE': ['mean'],
'CREDIT_DAY_OVERDUE': ['max', 'mean'],
'AMT_CREDIT_MAX_OVERDUE': ['mean'],
'AMT_CREDIT_SUM': ['max', 'mean', 'sum'],
'AMT_CREDIT_SUM_DEBT': ['max', 'mean', 'sum'],
'AMT_CREDI... | Home Credit Default Risk |
1,087,344 | encoder = OrdinalEncoder()
enc_train = encoder.fit_transform(df_train['title'].values)
enc_test = encoder.transform(df_test['title'].values)
df_train = df_train.reset_index()
df_test = df_test.reset_index()
df_train['title_la']=enc_train.iloc[:,0]
df_test['title_la']=enc_test.iloc[:,0]
df_train = df_train.set_index("... | buro_cat_features = [bcol for bcol in bureau_merge.columns if bureau_merge[bcol].dtype == 'object']
buro = pd.get_dummies(bureau_merge, columns=buro_cat_features)
cat_columns = [col for col in bureau_balance.columns if bureau_balance[col].dtype == 'object']
bureau_balance = pd.get_dummies(bureau_balance,cat_columns, d... | Home Credit Default Risk |
1,087,344 | ce_cal2='title'
ce_summary2 = df_train[ce_cal2].value_counts()
df_train['title_co'] = df_train[ce_cal2].map(ce_summary2)
df_test['title_co'] = df_test[ce_cal2].map(ce_summary2 )<feature_engineering> | cat_columns = [col for col in installments_payments.columns if installments_payments[col].dtype == 'object']
installments_payments = pd.get_dummies(installments_payments,cat_columns, dummy_na = True)
installments_payments['AMOUNT_DIFF'] = installments_payments['AMT_INSTALMENT'] - installments_payments['AMT_PAYMENT']
i... | Home Credit Default Risk |
1,087,344 | df_train['NaN']=df_train.isnull().sum(axis=1)
df_test['NaN']=df_test.isnull().sum(axis=1)
df_train['NaN']=df_train["NaN"].fillna(0)
df_test['NaN']=df_test["NaN"].fillna(0)
<drop_column> | previous_application['DAYS_FIRST_DRAWING'].replace(365243, np.nan, inplace= True)
previous_application['DAYS_FIRST_DUE'].replace(365243, np.nan, inplace= True)
previous_application['DAYS_LAST_DUE_1ST_VERSION'].replace(365243, np.nan, inplace= True)
previous_application['DAYS_LAST_DUE'].replace(365243, np.nan, inplac... | Home Credit Default Risk |
1,087,344 |
df_train=df_train.drop("pub_rec",axis=1)
df_test=df_test.drop("pub_rec",axis=1)
df_train=df_train.drop("annual_inc",axis=1)
df_test=df_test.drop("annual_inc",axis=1)
<prepare_x_and_y> | approved = previous_application[previous_application['NAME_CONTRACT_STATUS'] == 'Approved']
approved_agg = approved.groupby('SK_ID_CURR' ).agg(num_aggregations)
approved_agg.columns = pd.Index(['APPROVED_' + e[0] + "_" + e[1].upper() for e in approved_agg.columns.tolist() ])
previous_application = previous_applicatio... | Home Credit Default Risk |
1,087,344 | y_train = df_train.loan_condition
X_train = df_train.drop(['loan_condition'], axis=1)
X_test = df_test
<split> | aggregations = {
'MONTHS_BALANCE': ['max', 'mean', 'size'],
'SK_DPD': ['max', 'mean'],
'SK_DPD_DEF': ['max', 'mean']
}
POS_CASH_AGG = POS_CASH_balance.groupby('SK_ID_CURR' ).agg(aggregations)
POS_CASH_AGG.columns = pd.Index(['POS_CASH_' + e[0] + "_" + e[1].upper() for e in POS_CASH_AGG.columns.tolist() ])
POS_CASH_AG... | Home Credit Default Risk |
1,087,344 | col='title'
target = 'loan_condition'
X_temp = pd.concat([X_train, y_train], axis=1)
summary = X_temp.groupby([col])[target].mean()
enc_test = X_test[col].map(summary)
skf = StratifiedKFold(n_splits=5, random_state=71, shuffle=True)
enc_train = Series(np.zeros(len(X_train)) , index=X_train.index)
for i,(train_ix, v... | y = data['TARGET']
del data['TARGET']
categorical_features = [col for col in data.columns if data[col].dtype == 'object']
one_hot_df = pd.concat([data,test])
one_hot_df = pd.get_dummies(one_hot_df, columns=categorical_features)
data = one_hot_df.iloc[:data.shape[0],:]
test = one_hot_df.iloc[data.shape[0]:,]
print(dat... | Home Credit Default Risk |
1,087,344 | col='emp_title'
target = 'loan_condition'
X_temp = pd.concat([X_train, y_train], axis=1)
summary = X_temp.groupby([col])[target].mean()
enc_test = X_test[col].map(summary)
skf = StratifiedKFold(n_splits=5, random_state=71, shuffle=True)
enc_train = Series(np.zeros(len(X_train)) , index=X_train.index)
for i,(train_i... | 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')
print(data.shape, test.shape)
data = data.merge(right=previous_application.reset_index() , how='left', on='SK_ID_CURR')
test = test.merge(right=previous_applica... | Home Credit Default Risk |
1,087,344 | col='zip_code'
target = 'loan_condition'
X_temp = pd.concat([X_train, y_train], axis=1)
summary = X_temp.groupby([col])[target].mean()
enc_test = X_test[col].map(summary)
skf = StratifiedKFold(n_splits=5, random_state=71, shuffle=True)
enc_train = Series(np.zeros(len(X_train)) , index=X_train.index)
for i,(train_ix... | print('Removing features with more than 80% missing...')
test = test[test.columns[data.isnull().mean() < 0.80]]
data = data[data.columns[data.isnull().mean() < 0.80]]
print(data.shape, test.shape ) | Home Credit Default Risk |
1,087,344 | <split><EOS> | gc.enable()
folds = KFold(n_splits=4, 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']]
for n_fold,(trn_idx, val_idx)in enumerate(folds.split(data)) :
trn_x, tr... | Home Credit Default Risk |
1,154,375 | <SOS> metric: AUC Kaggle data source: home-credit-default-risk<categorify> | from fastai.imports import *
from fastai.structured import *
from fastai.column_data import *
from torch.nn import functional as F
from sklearn.metrics import roc_auc_score
from sklearn.model_selection import train_test_split | Home Credit Default Risk |
1,154,375 | col='application_type'
target = 'loan_condition'
X_temp = pd.concat([X_train, y_train], axis=1)
summary = X_temp.groupby([col])[target].mean()
enc_test = X_test[col].map(summary)
skf = StratifiedKFold(n_splits=5, random_state=71, shuffle=True)
enc_train = Series(np.zeros(len(X_train)) , index=X_train.index)
for i,(... | df_train = pd.read_feather('.. /input/home-credit-data-processing-for-neural-networks/tables_merged_train')
df_test = pd.read_feather('.. /input/home-credit-data-processing-for-neural-networks/tables_merged_test' ) | Home Credit Default Risk |
1,154,375 | X_train['Yaxis'].isnull().sum()<categorify> | df_train.dtypes.value_counts() | Home Credit Default Risk |
1,154,375 | X_train['Yaxis']=X_train['Yaxis'].replace([np.inf, -np.inf,np.nan], -9999)
X_test['Yaxis']=X_test['Yaxis'].replace([np.inf, -np.inf,np.nan], -9999)
X_train['Yaxis'].astype(str)
X_test['Yaxis'].astype(str)
col='Yaxis'
target = 'loan_condition'
X_temp = pd.concat([X_train, y_train], axis=1)
summary = X_temp.groupby(... | cat_vars = [col for col in df_train if df_train[col].dtype.name != 'float64' and df_train[col].dtype.name != 'float32' and len(df_train[col].unique())< 150]
cat_vars.remove('TARGET' ) | Home Credit Default Risk |
1,154,375 | X_train=X_train.replace([np.inf, -np.inf,np.nan], -9999)
X_test=X_test.replace([np.inf, -np.inf,np.nan], -9999)
<split> | cat_sz = [(c, len(df_train[c].unique())+1)for c in cat_vars] | Home Credit Default Risk |
1,154,375 | scores = []
y_pred_test=np.zeros(len(X_test))
df = pd.DataFrame(index=[], columns=[])
df['feature']=X_train.columns
n=10
for i in range(n):
X_train_,X_val,y_train_,y_val=train_test_split(X_train,y_train,test_size=0.3,random_state=i*10)
clf = LGBMClassifier(boosting_type='gbdt', class_weight=None, colsample_bytree=1,
... | y = np.array(df_train['TARGET'])
df_train.drop('TARGET', axis = 1, inplace=True)
df_to_nn_train, df_to_nn_valid, y_train, y_valid = train_test_split(df_train, y, test_size=0.33, random_state=23, stratify = y ) | Home Credit Default Risk |
1,154,375 | scores_xg=[]
y_pred_test_xg=np.zeros(len(X_test))
df_xg = pd.DataFrame(index=[], columns=[])
df_xg['feature']=X_train.columns
n=10
for i in range(n):
X_train_,X_val,y_train_,y_val=train_test_split(X_train,y_train,test_size=0.3,random_state=i*10)
xg=xgb.XGBClassifier()
xg.fit(X_train_, y_train_,early_stopping_rounds=1... | def preprocess_fast_ai(df_to_nn_train, df_to_nn_valid, cat_vars):
for v in cat_vars: df_to_nn_train[v] = df_to_nn_train[v].astype('category' ).cat.as_ordered()
apply_cats(df_to_nn_valid, df_to_nn_train)
df, _, nas, mapper = proc_df(df_to_nn_train, do_scale=True, skip_flds=['SK_ID_CURR'])
df_valid, _, nas, mapper = pr... | Home Credit Default Risk |
1,154,375 | scores_cb = []
y_pred_test_cb=np.zeros(len(X_test))
df_cb = pd.DataFrame(index=[], columns=[])
df_cb['feature']=X_train.columns
n=10
for i in range(n):
X_train_,X_val,y_train_,y_val=train_test_split(X_train,y_train,test_size=0.3,random_state=i*10)
cb = catboost.CatBoostClassifier(eval_metric='AUC')
cb.fit(X_train_, ... | %time df, df_valid = preprocess_fast_ai(df_to_nn_train, df_to_nn_valid, cat_vars ) | Home Credit Default Risk |
1,154,375 |
<define_variables> | emb_szs = [(c, min(50,(c+1)//2)) for _,c in cat_sz] | Home Credit Default Risk |
1,154,375 | y_pred=(ykai+ykai_xg+ykai_cb)/3
<save_to_csv> | md = ColumnarModelData.from_data_frames('', trn_df = df, val_df = df_valid,
trn_y = y_train.astype('int'), val_y = y_valid.astype('int'),
cat_flds=cat_vars, bs=512, is_reg= False ) | Home Credit Default Risk |
1,154,375 | submission = pd.read_csv('.. /input/homework-for-students3/sample_submission.csv', index_col=0)
submission.loan_condition = y_pred
submission.to_csv('submission.csv' )<load_from_csv> | class MixedInputModel(nn.Module):
def __init__(self, emb_szs, n_cont, emb_drop, out_sz, szs, drops,
y_range=None, use_bn=False, is_reg=True, is_multi=False):
super().__init__()
self.embs = nn.ModuleList([nn.Embedding(c, s)for c,s in emb_szs])
for emb in self.embs: emb_init(emb)
n_emb = sum(e.embedding_dim for e in se... | Home Credit Default Risk |
1,154,375 | df_train = pd.read_csv('.. /input/train.csv')
df_valid = pd.read_csv('.. /input/valid.csv')
df_sample_submission = pd.read_csv('.. /input/sample_submission.csv' )<drop_column> | m = MixedInputModel(emb_szs, n_cont = len(df.columns)-len(cat_vars),
emb_drop = 0.05, out_sz = 2, szs = [500, 250, 250], drops = [0.1, 0.1, 0.1],
y_range = None, use_bn = False, is_reg = False, is_multi = False)
bm = BasicModel(m.cuda() , 'binary_classifier' ) | Home Credit Default Risk |
1,154,375 | df_train.drop(['article_link'], axis=1)
df_valid.drop(['article_link'], axis=1 )<import_modules> | class StructuredLearner(Learner):
def __init__(self, data, models, **kwargs):
super().__init__(data, models, **kwargs)
self.crit = F.nll_loss
learn = StructuredLearner(md, bm ) | Home Credit Default Risk |
1,154,375 | from sklearn.feature_extraction.text import TfidfVectorizer<feature_engineering> | learn.lr_find(1e-4, 1)
learn.sched.plot(100 ) | Home Credit Default Risk |
1,154,375 | TfidfVec = TfidfVectorizer()<create_dataframe> | lr = 1e-1
learn.fit(lr, 3, metrics=[roc_auc_own] ) | Home Credit Default Risk |
1,154,375 | all_headlines = pd.DataFrame()
all_headlines = pd.concat([df_train, df_valid] )<feature_engineering> | logpreds = learn.predict()
preds = np.exp(logpreds[:,1] ) | Home Credit Default Risk |
1,154,375 | Tfidf_vectorized_data = TfidfVec.fit_transform(all_headlines.headline )<split> | print(classification_report(y_valid,
preds_binary,
target_names= ['0', '1'])) | Home Credit Default Risk |
1,154,375 | df_train_vec = Tfidf_vectorized_data[:18696]
df_valid_vec = Tfidf_vectorized_data[18696:]<prepare_x_and_y> | class ColumnarDataset(Dataset):
def __init__(self, cats, conts, y, is_reg, is_multi):
n = len(cats[0])if cats else len(conts[0])
self.cats = np.stack(cats, 1 ).astype(np.int64)if cats else np.zeros(( n,1))
self.conts = np.stack(conts, 1 ).astype(np.float32)if conts else np.zeros(( n,1))
self.y = np.zeros(( n,1)) if y ... | Home Credit Default Risk |
1,154,375 | y_train = df_train.is_sarcastic<import_modules> | train_ids = df_train['SK_ID_CURR']
test_ids = df_test['SK_ID_CURR']
%time train_df, test_df = preprocess_fast_ai(df_train, df_test, cat_vars ) | Home Credit Default Risk |
1,154,375 | from sklearn.linear_model import SGDClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import roc_auc_score<split> | ros = RandomOverSampler()
df_resampled, y_resampled = ros.fit_sample(df, y_train)
df_resampled = pd.DataFrame(df_resampled, columns = df.columns)
y_valid.mean() , y_resampled.mean() | Home Credit Default Risk |
1,154,375 | df_train_1, df_train_2 = train_test_split(df_train_vec, test_size=0.1)
df_y_1, df_y_2 = train_test_split(y_train, test_size=0.1 )<choose_model_class> | md = ColumnarModelData.from_data_frames('', trn_df = df_resampled,
val_df = df_valid, trn_y = y_resampled.astype('int'),
val_y = y_valid.astype('int'), cat_flds=cat_vars, bs=1024, is_reg = False,
test_df = test_df)
class StructuredLearner(Learner):
def __init__(self, data, models, **kwargs):
super().__init__(data, mod... | Home Credit Default Risk |
1,154,375 | model = SGDClassifier(n_jobs=-1, loss='hinge', random_state=42 )<predict_on_test> | learn.lr_find(1e-2, 2)
learn.sched.plot(100 ) | Home Credit Default Risk |
1,154,375 | pred = model.predict(df_valid_vec )<save_to_csv> | lr = 0.1
learn.fit(lr, 3, metrics=[roc_auc_own] ) | Home Credit Default Risk |
1,154,375 | my_submission=pd.DataFrame({'ID': df_valid['ID'], 'is_sarcastic': pred})
my_submission.to_csv('fepas_submission_3.csv',index=False )<set_options> | learn.fit(lr, 2, metrics=[roc_auc_own], cycle_len=1, cycle_mult=2 ) | Home Credit Default Risk |
1,154,375 | %matplotlib inline
print(tf.config.experimental.list_physical_devices('CPU'))
print(tf.config.experimental.list_physical_devices('GPU'))
print(tf.__version__ )<load_from_csv> | print(classification_report(y_valid,
preds_binary,
target_names= ['0', '1']))
false_positive_rate, true_positive_rate, threshold = roc_curve(y_valid,
preds_valid ) | Home Credit Default Risk |
1,154,375 | train_data = pd.read_csv('.. /input/bird-or-aircraft-dafe-open/train_x.csv', index_col=0, header=None)
train_labels = pd.read_csv('.. /input/bird-or-aircraft-dafe-open/train_y.csv', index_col=0)
test_data = pd.read_csv('.. /input/bird-or-aircraft-dafe-open/test_x.csv', index_col=0, header=None )<count_values> | logpreds = learn.predict(True)
preds = np.exp(logpreds[:,1])
submission = pd.DataFrame({'SK_ID_CURR': df_test['SK_ID_CURR'],
'TARGET': preds})
submission.to_csv('submission.csv', index=False, float_format='%.8f' ) | Home Credit Default Risk |
1,154,375 | train_labels['target'].value_counts()<data_type_conversions> | m=learn.model
m.cuda() | Home Credit Default Risk |
1,154,375 | train_data = train_data.to_numpy()
test_data = test_data.to_numpy()
train_labels = train_labels.to_numpy()<train_model> | def get_embeddings(embs, dataframe, ids, cat_vars):
embeddings = np.concatenate([to_np(embs[i](V(dataframe[cat_vars[i]])))for i in range(len(embs)) ], axis = 1)
embedding_columns = ["embedding_"+str(i)for i in range(embeddings.shape[1])]
embedding_df = pd.DataFrame(embeddings, columns=embedding_columns)
embedding_df ... | Home Credit Default Risk |
1,154,375 | <choose_model_class><EOS> | train_embeddings = get_embeddings(m.embs, train_df, train_ids, cat_vars)
test_embeddings = get_embeddings(m.embs, test_df, test_ids, cat_vars)
train_embeddings.to_csv('train_embeddings.csv', index=False)
test_embeddings.to_csv('test_embeddings.csv', index=False ) | Home Credit Default Risk |
9,067,638 | <SOS> metric: AUC Kaggle data source: home-credit-default-risk<train_model> | warnings.filterwarnings("ignore", category=DeprecationWarning)
warnings.filterwarnings("ignore", category=FutureWarning)
warnings.filterwarnings("ignore", category=UserWarning ) | Home Credit Default Risk |
9,067,638 | kf = KFold(n_splits=5, shuffle=True, random_state=42)
epochs_num = 150
all_loss = []
all_accuracy = []
i = 0
for train_index, val_index in kf.split(train_data):
i += 1
print('Processing fold
X_train = train_data[train_index]
y_train = train_labels[train_index]
X_val = train_data[val_index]
y_val = train_labels[val_ind... | print('
'.join([''.join([(' I_Love_Data_Science_'[(x-y)% len('I_Love_Data_Science_')] if(( x*0.05)**2+(y*0.1)**2-1)**3-(x*0.05)**2*(y*0.1)**3 <= 0 else ' ')for x in range(-30, 30)])for y in range(15, -15, -1)])) | Home Credit Default Risk |
9,067,638 | epochs_num = 75
X_train = train_data
y_train = train_labels
model = build_model()
model.fit(X_train, y_train, epochs=epochs_num, batch_size=128, verbose=0);<train_model> | def application_train() :
df = pd.read_csv('.. /input/home-credit-default-risk/application_train.csv')
test_df = pd.read_csv('.. /input/home-credit-default-risk/application_test.csv')
df = df.append(test_df ).reset_index()
df = df[df['CODE_GENDER'] != 'XNA']
lbe = LabelEncoder()
for col in ['CODE_GENDER', 'FLAG_OWN_C... | Home Credit Default Risk |
9,067,638 | X_test = test_data / 255
X_test = X_test.reshape(test_data.shape[0], 32, 32, 3 )<predict_on_test> | def bureau_bb() :
bb = pd.read_csv('.. /input/home-credit-default-risk/bureau_balance.csv')
bb = pd.get_dummies(bb, dummy_na = True)
agg_list = {"MONTHS_BALANCE":"count",
"STATUS_0":["sum","mean"],
"STATUS_1":["sum"],
"STATUS_2":["sum"],
"STATUS_3":["sum"],
"STATUS_4":["sum"],
"STATUS_5":["sum"],
"STATUS_C":["sum","m... | Home Credit Default Risk |
9,067,638 | y_pred = model.predict(X_test)
y_pred[:10]<create_dataframe> | def installments_payments() :
ins = pd.read_csv('.. /input/home-credit-default-risk/installments_payments.csv')
ins['NEW_DAYS_PAID_EARLIER'] = ins['DAYS_INSTALMENT']-ins['DAYS_ENTRY_PAYMENT']
ins['NEW_NUM_PAID_LATER'] = ins['NEW_DAYS_PAID_EARLIER'].map(lambda x: 1 if x<0 else 0)
agg_list = {'NUM_INSTALMENT_VERSION':[... | Home Credit Default Risk |
9,067,638 | submission = pd.DataFrame({'id': range(test_data.shape[0]),
'target':(y_pred >= 0.5 ).astype('int' ).flatten()
} )<save_to_csv> | def pos_cash_balance(agg_list_previous_application):
pos = pd.read_csv('.. /input/home-credit-default-risk/POS_CASH_balance.csv')
pos = pd.get_dummies(pos, columns=['NAME_CONTRACT_STATUS'], dummy_na = True)
agg_list = {'MONTHS_BALANCE':['min','max'],
'CNT_INSTALMENT':['min','max'],
'CNT_INSTALMENT_FUTURE':['min','max... | Home Credit Default Risk |
9,067,638 | submission.to_csv('submission.csv', index=False )<save_to_csv> | def credit_card_balance() :
CCB = pd.read_csv('.. /input/home-credit-default-risk/credit_card_balance.csv')
CCB = pd.get_dummies(CCB, columns= ['NAME_CONTRACT_STATUS'])
dropthis = ['NAME_CONTRACT_STATUS_Approved', 'NAME_CONTRACT_STATUS_Demand',
'NAME_CONTRACT_STATUS_Refused', 'NAME_CONTRACT_STATUS_Sent proposal',
'NA... | Home Credit Default Risk |
9,067,638 | submission.to_csv('submission.csv', index=False )<set_options> | def previous_application(agg_list_previous_application):
df_prev = pd.read_csv('.. /input/home-credit-default-risk/previous_application.csv')
df_prev["WEEKDAY_APPR_PROCESS_START"] = df_prev["WEEKDAY_APPR_PROCESS_START"].replace(['MONDAY','TUESDAY', 'WEDNESDAY','THURSDAY','FRIDAY'], 'WEEK_DAY')
df_prev["WEEKDAY_APPR_P... | Home Credit Default Risk |
9,067,638 | %matplotlib inline
<train_model> | def pre_processing_and_combine() :
with timer("Process application train"):
df = application_train()
print("application train & test shape:", df.shape)
with timer("Bureau and Bureau Balance"):
bureau_and_bb_agg = bureau_bb()
print("Bureau and Bureau Balance:", bureau_and_bb_agg.shape)
with timer("Installment Payments... | Home Credit Default Risk |
9,067,638 | train_image_folder = ".. /input/train-images/image/"
train_label_folder = ".. /input/train-labels/label/"
test_image_folder = ".. /input/test-images/image/"
train_list = os.listdir(train_image_folder)
if 'hmvsa0loxh3ek2y8rzmcyb6zrrh9mwyp' in train_list:
train_list.remove('hmvsa0loxh3ek2y8rzmcyb6zrrh9mwyp')
print('Tra... | Home Credit Default Risk | |
9,067,638 | def load_dicom_volume(src_dir, suffix='*.dcm'):
encode_name = src_dir.split('/')[-1]
dicom_scans = [dicom.read_file(sp)\
for sp in glob.glob(os.path.join(src_dir, suffix)) ]
dicom_scans.sort(key=lambda s: float(s[(0x0020, 0x0032)][2]))
volume_image = np.stack([ds.pixel_array \
for ds in dicom_scans] ).astype(np.int16... | def modeling(all_data):
all_data.columns = ["".join(c if c.isalnum() else "_" for c in str(x)) for x in all_data.columns]
train_df = all_data[all_data['TARGET'].notnull() ]
test_df = all_data[all_data['TARGET'].isnull() ]
folds = KFold(n_splits = 10, shuffle = True, random_state = 1001)
oof_preds = np.zeros(train_df.s... | Home Credit Default Risk |
9,067,638 | for encode in tqdm.tqdm(train_list):
_, volume_image = load_dicom_volume(os.path.join(train_image_folder, encode))
npz_folder = os.path.join(train_image_npz_folder, encode)
if not os.path.exists(npz_folder):
os.mkdir(npz_folder)
num_slice = volume_image.shape[0]
for _z in range(0, num_slice):
npz_path = os.path.join(... | def main() :
with timer("Preprocessing Time"):
all_data = pre_processing_and_combine()
with timer("Modeling"):
feat_importance = modeling(all_data)
| Home Credit Default Risk |
9,067,638 | <set_options><EOS> | if __name__ == "__main__":
with timer("Full model run"):
main() | Home Credit Default Risk |
5,966,704 | <prepare_output><EOS> | warnings.simplefilter(action='ignore', category=FutureWarning)
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename))
@contextmanager
def timer(title):
t0 = time.time()
yield
print("{} - done in {:.0f}s".format(title, time.time() - t0))
def one_hot_enco... | Home Credit Default Risk |
4,160,759 | <SOS> metric: AUC Kaggle data source: home-credit-default-risk<define_variables> | warnings.simplefilter(action='ignore', category=FutureWarning ) | Home Credit Default Risk |
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