kernel_id int64 24.2k 23.3M | prompt stringlengths 8 1.85M | completetion stringlengths 1 182k | comp_name stringlengths 5 57 |
|---|---|---|---|
9,356,808 | index = 'Occupancy'<compute_test_metric> | train_data = application_data[application_data.IS_TRAIN == 1.0] | Home Credit Default Risk |
9,356,808 | intervalo_valores(df_train[index] )<define_variables> | test_data = application_data[application_data.IS_TRAIN == 0.0] | Home Credit Default Risk |
9,356,808 | index = 'Metered Areas(Energy)'<data_type_conversions> | train_data.drop(columns='IS_TRAIN', inplace=True)
test_data.drop(columns='IS_TRAIN', inplace=True ) | Home Credit Default Risk |
9,356,808 | df_train[index] = df_train[index].astype('category' ).cat.codes
df_test[index] = df_test[index].astype('category' ).cat.codes<compute_test_metric> | del(application_data ) | Home Credit Default Risk |
9,356,808 | intervalo_valores(df_train[index] )<define_variables> | params = model.get_params() | Home Credit Default Risk |
9,356,808 | index = 'Metered Areas(Water)'<data_type_conversions> | params['objective'] = 'binary'
params['metric'] = 'auc' | Home Credit Default Risk |
9,356,808 | df_train[index] = df_train[index].astype('category' ).cat.codes
df_test[index] = df_test[index].astype('category' ).cat.codes<compute_test_metric> | skf = StratifiedKFold(n_splits=3, random_state=42, shuffle=True)
final_importance = np.zeros(len(train_data.columns))
for n_fold,(train_index, valid_index)in tqdm(enumerate(skf.split(train_data, train_target.TARGET))):
X_train = train_data.iloc[train_index]
y_train = train_target.iloc[train_index].TARGET
X_valid = tra... | Home Credit Default Risk |
9,356,808 | intervalo_valores(df_train[index] )<define_variables> | fi = pd.DataFrame()
fi['FEAT'] = train_data.columns | Home Credit Default Risk |
9,356,808 | index = 'Site EUI(kBtu/ft²)'<compute_test_metric> | fi['importance'] = final_importance | Home Credit Default Risk |
9,356,808 | intervalo_valores(df_train[index] )<define_variables> | fi = fi.sort_values(by='importance', ascending=False ) | Home Credit Default Risk |
9,356,808 | index = 'Weather Normalized Site EUI(kBtu/ft²)'<feature_engineering> | fi = fi[fi.importance != 0] | Home Credit Default Risk |
9,356,808 | df_train[index] = df_train[index].str.replace('Not Available', '0')
df_test[index] = df_test[index].str.replace('Not Available', '0' )<data_type_conversions> | cols = list(set(fi.FEAT.values ).union(set(['SK_ID_CURR'])) ) | Home Credit Default Risk |
9,356,808 | df_train[index] = df_train[index].astype('float64')
df_test[index] = df_test[index].astype('float64' )<compute_test_metric> | train_data = train_data[cols] | Home Credit Default Risk |
9,356,808 | intervalo_valores(df_train[index] )<define_variables> | test_data = test_data[cols] | Home Credit Default Risk |
9,356,808 | index = 'Weather Normalized Site Electricity Intensity(kWh/ft²)'<feature_engineering> | def get_random_params() :
params = {
'boosting_type': 'gbdt',
'metric': 'auc',
'num_leaves': random.randint(10, 60),
'max_depth': random.randint(10, 30),
'learning_rate': random.choice([0.0001, 0.0005, 0.001, 0.005, 0.01]),
'n_estimators': random.randint(1000, 20000),
'objective': 'binary',
'reg_alpha': random.choice([... | Home Credit Default Risk |
9,356,808 | df_train[index] = df_train[index].str.replace('Not Available', '0')
df_test[index] = df_test[index].str.replace('Not Available', '0' )<data_type_conversions> | best_params = {'boosting_type': 'gbdt',
'metric': 'auc',
'num_leaves': 46,
'max_depth': 18,
'learning_rate': 0.01,
'n_estimators': 6289,
'objective': 'binary',
'reg_alpha': 0.05,
'reg_lambda': 0.05,
'colsample_bytree': 0.4,
'min_child_samples': 79,
'subsample_for_bin': 113092}
best_auc = 0.787228 | Home Credit Default Risk |
9,356,808 | df_train[index] = df_train[index].astype('float64')
df_test[index] = df_test[index].astype('float64' )<compute_test_metric> | def get_best_params(hyper_rounds, n_folds, best_params=None, best_auc=0):
best_params = best_params
best_auc = best_auc
lgb_train = lgb.Dataset(data=train_data, label=train_target.TARGET)
for i in tqdm(range(hyper_rounds)) :
curr_params = get_random_params()
start = time.time()
print(curr_params)
eval_hist = lgb.cv(c... | Home Credit Default Risk |
9,356,808 | intervalo_valores(df_train[index] )<define_variables> | N_FOLDS = 10 | Home Credit Default Risk |
9,356,808 | index = 'Weather Normalized Site Natural Gas Intensity(therms/ft²)'<feature_engineering> | skf = StratifiedKFold(n_splits=N_FOLDS, random_state=42, shuffle=True)
sub_preds = np.zeros(len(test_data))
avg_valid_auc = 0
for n_fold,(train_index, valid_index)in tqdm(enumerate(skf.split(train_data, train_target.TARGET))):
print("FOLD N:", n_fold)
X_train = train_data.iloc[train_index]
y_train = train_target.iloc... | Home Credit Default Risk |
9,356,808 | df_train[index] = df_train[index].str.replace('Not Available', '0')
df_test[index] = df_test[index].str.replace('Not Available', '0' )<data_type_conversions> | submit['TARGET'] = sub_preds | Home Credit Default Risk |
9,356,808 | df_train[index] = df_train[index].astype('float64')
df_test[index] = df_test[index].astype('float64' )<compute_test_metric> | submit.to_csv('submission.csv', index = False ) | Home Credit Default Risk |
3,337,146 | intervalo_valores(df_train[index] )<define_variables> | file_path = ".. /input/" | Home Credit Default Risk |
3,337,146 | index = 'Weather Normalized Source EUI(kBtu/ft²)'<feature_engineering> | @contextmanager
def timer(title):
t0 = time.time()
yield
print("{} - done in {:.0f}s".format(title, time.time() - t0))
def one_hot_encoder(df, categorical_columns=None, nan_as_category=True):
original_columns = list(df.columns)
if not categorical_columns:
categorical_columns = [col for col in df.columns if df[col].dty... | Home Credit Default Risk |
3,337,146 | df_train[index] = df_train[index].str.replace('Not Available', '0')
df_test[index] = df_test[index].str.replace('Not Available', '0' )<data_type_conversions> | def application_train_test(file_path , nan_as_category = True,num_rows= None):
df_train = pd.read_csv(file_path + 'application_train.csv',nrows = num_rows)
df_test = pd.read_csv(file_path + 'application_test.csv',nrows = num_rows)
df = df_train.append(df_test ).reset_index()
del df_train, df_test
gc.collect()
df.drop... | Home Credit Default Risk |
3,337,146 | df_train[index] = df_train[index].astype('float64')
df_test[index] = df_test[index].astype('float64' )<compute_test_metric> | def bureau_and_balance(file_path,num_rows = None, nan_as_category = True):
bureau = pd.read_csv(file_path+'bureau.csv', nrows = num_rows)
bb = pd.read_csv(file_path+'bureau_balance.csv', nrows = num_rows)
bb, bb_cat = one_hot_encoder(bb, nan_as_category=False)
bureau, bureau_cat = one_hot_encoder(bureau, nan_as_cate... | Home Credit Default Risk |
3,337,146 | intervalo_valores(df_train[index] )<define_variables> | def previous_application(file_path ,num_rows = None, nan_as_category = True):
df_prev = pd.read_csv(file_path + 'previous_application.csv',nrows = num_rows)
df_prev.loc[df_prev['AMT_CREDIT'] > 6000000, 'AMT_CREDIT'] = np.nan
df_prev.loc[df_prev['SELLERPLACE_AREA'] > 3500000, 'SELLERPLACE_AREA'] = np.nan
df_prev[['DAYS... | Home Credit Default Risk |
3,337,146 | index = 'Fuel Oil<feature_engineering> | def pos_cash(file_path,num_rows = None, nan_as_category = True):
pos = pd.read_csv(file_path+'POS_CASH_balance.csv', nrows = num_rows)
pos, cat_cols = one_hot_encoder(pos, nan_as_category= False)
aggregations = {
'MONTHS_BALANCE': ['max', 'mean', 'size'],
'SK_DPD': ['max', 'mean'],
'SK_DPD_DEF': ['max', 'mean']
}
for... | Home Credit Default Risk |
3,337,146 | df_train[index] = df_train[index].str.replace('Not Available', '0')
df_test[index] = df_test[index].str.replace('Not Available', '0' )<data_type_conversions> | def bayes_parameter_opt_lgb(X, y, init_round=15, opt_round=25, n_folds=5, random_seed=6, n_estimators=10000, learning_rate=0.05, output_process=False):
train_data = lgb.Dataset(data=X, label=y, categorical_feature = categorical_feats, free_raw_data=False)
def lgb_eval(num_leaves, feature_fraction, bagging_fraction, ma... | Home Credit Default Risk |
3,337,146 | <compute_test_metric><EOS> | def main(debug = False):
num_rows = 30000 if debug else None
df = application_train_test(file_path,num_rows)
with timer("Process bureau and bureau_balance"):
bureau = bureau_and_balance(file_path,num_rows)
print("Bureau df shape:", bureau.shape)
df = df.join(bureau, how='left', on='SK_ID_CURR')
del bureau
gc.collec... | Home Credit Default Risk |
4,870,466 | <SOS> metric: AUC Kaggle data source: home-credit-default-risk<define_variables> | print(f"Pandas version: {pd.__version__}" ) | Home Credit Default Risk |
4,870,466 | index = 'Fuel Oil<feature_engineering> | INPUT_DIR = ".. /input/"
get_full_path = partial(os.path.join, INPUT_DIR)
PATH_APP_TRAIN = get_full_path("application_train.csv")
PATH_APP_TEST = get_full_path("application_test.csv")
PATH_PRE_APP = get_full_path("previous_application.csv")
PATH_INST_PAY = get_full_path("installments_payments.csv")
PATH_INST_PAY_P... | Home Credit Default Risk |
4,870,466 | df_train[index] = df_train[index].str.replace('Not Available', '0')
df_test[index] = df_test[index].str.replace('Not Available', '0' )<data_type_conversions> | LoanType = CategoricalDtype(["Cash loans", "Revolving loans", "Consumer loans"], False)
HouseType = CategoricalDtype(["block of flats", "terraced house", "specific housing"])
WeekDayType = CategoricalDtype(['SUNDAY', 'MONDAY', 'TUESDAY', 'WEDNESDAY', 'THURSDAY', 'FRIDAY', 'SATURDAY'], True)
YesNoType = CategoricalDt... | Home Credit Default Risk |
4,870,466 | df_train[index] = df_train[index].astype('float64')
df_test[index] = df_test[index].astype('float64' )<compute_test_metric> | def load_app_data(train_only=False):
gender_type = CategoricalDtype(["M", "F"], False)
yes_no_type2 = CategoricalDtype(["No", "Yes"], True)
wall_material_type = CategoricalDtype(["Stone, brick", "Wooden", "Block", "Panel",
"Monolithic", "Mixed", "Others"], False)
fondkapremont_type = CategoricalDtype(["reg oper acco... | Home Credit Default Risk |
4,870,466 | intervalo_valores(df_train[index] )<define_variables> | def flatten_agg_df_columns(df_agg, prefix=None):
if prefix is None:
df_agg.columns = ['_'.join([c0, c1.upper() ])for c0, c1 in df_agg.columns]
else:
df_agg.columns = ['_'.join([prefix, c0, c1.upper() ])for c0, c1 in df_agg.columns]
return df_agg
def clean_inst_pay(df_inst_pay):
print("Cleaning installment payments data... | Home Credit Default Risk |
4,870,466 | index = 'Fuel Oil<feature_engineering> | def group_values(col_orig, new_col_values):
return pd.DataFrame({col: col_orig.isin(values)for col, values in new_col_values})
def get_preprocessed_bureau_data() :
df_bureau = load_bureau()
df_bureau_balance = load_bureau_balance()
df_bureau_balance["STATUS"] = df_bureau_balance["STATUS"].cat.codes - 1
df_bureau_balan... | Home Credit Default Risk |
4,870,466 | df_train[index] = df_train[index].str.replace('Not Available', '0')
df_test[index] = df_test[index].str.replace('Not Available', '0' )<data_type_conversions> | def get_preprocessed_data(impute=True, random_seed=None):
print("Reading data")
df_app_train, df_app_test = load_app_data()
print("Finish reading data")
print("Preprocess training data")
df_bureau_agg = None
df_prev_app_agg = None
df_bureau_agg = get_preprocessed_bureau_data()
print("Finish preprocessing bureau data... | Home Credit Default Risk |
4,870,466 | df_train[index] = df_train[index].astype('float64')
df_test[index] = df_test[index].astype('float64' )<compute_test_metric> | def build_model_and_classify(X_train, y_train, X_test=None, classifier="xgb", early_stopping=True,
cv=CROSS_VALIDATION_FOLD, tune_param=False, random_seed=None,
output_path="submission.csv"):
print("Initializing classifier")
if classifier == "xgb":
if tune_param:
clf = XGBClassifier(seed=random_seed, tree_method="gpu_... | Home Credit Default Risk |
4,870,466 | intervalo_valores(df_train[index] )<define_variables> | build_model_and_classify(X_train, y_train, X_test, "lgbm", cv=CROSS_VALIDATION_FOLD, tune_param=True,
random_seed=RANDOM_SEED, output_path="submission_lgbm.csv" ) | Home Credit Default Risk |
4,870,466 | index = 'Fuel Oil<feature_engineering> | build_model_and_classify(X_train, y_train, X_test, tune_param=True, random_seed=RANDOM_SEED,
output_path="submission_xgb.csv" ) | Home Credit Default Risk |
4,870,466 | df_train[index] = df_train[index].str.replace('Not Available', '0')
df_test[index] = df_test[index].str.replace('Not Available', '0' )<data_type_conversions> | df_result = pd.DataFrame.from_records(results.cv_results_["params"])
df_result["rank"] = results.cv_results_["rank_test_score"]
df_result["score"] = results.cv_results_["mean_test_score"]
df_result.sort_values(by="rank", inplace=True)
df_result | Home Credit Default Risk |
4,870,466 | <compute_test_metric><EOS> | for c in df_result.columns[:-2]:
print(df_result.groupby(c)["score"].mean().sort_values() ) | Home Credit Default Risk |
1,245,855 | <SOS> metric: AUC Kaggle data source: home-credit-default-risk<define_variables> | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import gc
import xgboost as xgb
from sklearn.metrics import roc_auc_score
from sklearn.model_selection import train_test_split | Home Credit Default Risk |
1,245,855 | index = 'Diesel<feature_engineering> | def bureau_balance_preprocess() :
bureau = pd.read_csv(".. /input/bureau.csv")
balance = pd.read_csv(".. /input/bureau_balance.csv")
cat_cols = []
for i in balance.columns.values:
if balance[i].dtype == 'object':
cat_cols.append(i)
num_cols = [col for col in balance.columns if col not in cat_cols][1:]
balance = pd.g... | Home Credit Default Risk |
1,245,855 | df_train[index] = df_train[index].str.replace('Not Available', '0')
df_test[index] = df_test[index].str.replace('Not Available', '0' )<data_type_conversions> | def prev_application() :
df = pd.read_csv(".. /input/previous_application.csv")
df.loc[df['AMT_CREDIT'] > 6000000, 'AMT_CREDIT'] = np.nan
df.loc[df['SELLERPLACE_AREA'] > 3500000, 'SELLERPLACE_AREA'] = np.nan
df['DAYS_FIRST_DRAWING'].replace(365243, np.nan, inplace= True)
df['DAYS_FIRST_DUE'].replace(365243, np.nan, i... | Home Credit Default Risk |
1,245,855 | df_train[index] = df_train[index].astype('float64')
df_test[index] = df_test[index].astype('float64' )<compute_test_metric> | def posh_cash_balance() :
df = pd.read_csv(".. /input/POS_CASH_balance.csv")
df.loc[df['CNT_INSTALMENT_FUTURE'] > 60, 'CNT_INSTALMENT_FUTURE'] = np.nan
df.drop(['SK_ID_PREV'], axis = 1, inplace = True)
df['pos CNT_INSTALMENT more CNT_INSTALMENT_FUTURE'] = \
(df['CNT_INSTALMENT'] > df['CNT_INSTALMENT_FUTURE'] ).astyp... | Home Credit Default Risk |
1,245,855 | intervalo_valores(df_train[index] )<define_variables> | def cc_balance() :
df = pd.read_csv(".. /input/credit_card_balance.csv")
df.loc[df['AMT_PAYMENT_CURRENT'] > 4000000, 'AMT_PAYMENT_CURRENT'] = np.nan
df.loc[df['AMT_CREDIT_LIMIT_ACTUAL'] > 1000000, 'AMT_CREDIT_LIMIT_ACTUAL'] = np.nan
df['card missing'] = df.isnull().sum(axis = 1 ).values
df['card SK_DPD - MONTHS_BALANC... | Home Credit Default Risk |
1,245,855 | index = 'District Steam Use(kBtu)'<feature_engineering> | def installments() :
df = pd.read_csv(".. /input/installments_payments.csv")
df.loc[df['NUM_INSTALMENT_VERSION'] > 70, 'NUM_INSTALMENT_VERSION'] = np.nan
df.loc[df['DAYS_ENTRY_PAYMENT'] < -4000, 'DAYS_ENTRY_PAYMENT'] = np.nan
df['ins DAYS_ENTRY_PAYMENT - DAYS_INSTALMENT'] = df['DAYS_ENTRY_PAYMENT'] - df['DAYS_INSTALME... | Home Credit Default Risk |
1,245,855 | df_train[index] = df_train[index].str.replace('Not Available', '0')
df_test[index] = df_test[index].str.replace('Not Available', '0' )<data_type_conversions> | def get_beautiful_data() :
print('Getting train data...')
train = pd.read_csv(".. /input/application_train.csv")
print('Getting test data...')
test = pd.read_csv(".. /input/application_test.csv")
y = train.pop('TARGET')
index_end = y.shape[0]
test_index = test.pop('SK_ID_CURR')
cat_cols = []
for i in train.column... | Home Credit Default Risk |
1,245,855 | df_train[index] = df_train[index].astype('float64')
df_test[index] = df_test[index].astype('float64' )<compute_test_metric> | X, X_test, y, scale_pos_weight, test_index = get_beautiful_data() | Home Credit Default Risk |
1,245,855 | intervalo_valores(df_train[index] )<define_variables> | upper = corr_matrix.where(np.triu(np.ones(corr_matrix.shape), k=1 ).astype(np.bool))
upper.head() | Home Credit Default Risk |
1,245,855 | index = 'Natural Gas Use(kBtu)'<feature_engineering> | to_drop = [column for column in upper.columns if any(upper[column] > threshold)]
print('There are %d columns to remove.' %(len(to_drop)) ) | Home Credit Default Risk |
1,245,855 | df_train[index] = df_train[index].str.replace('Not Available', '0')
df_test[index] = df_test[index].str.replace('Not Available', '0' )<data_type_conversions> | X = X.drop(columns = to_drop)
X_test = X_test.drop(columns = to_drop)
print('Training shape: ', X.shape)
print('Testing shape: ', X_test.shape ) | Home Credit Default Risk |
1,245,855 | df_train[index] = df_train[index].astype('float64')
df_test[index] = df_test[index].astype('float64' )<compute_test_metric> | train_missing =(X.isnull().sum() / len(X)).sort_values(ascending = False)
train_missing.head() | Home Credit Default Risk |
1,245,855 | intervalo_valores(df_train[index] )<define_variables> | test_missing =(X_test.isnull().sum() / len(X_test)).sort_values(ascending = False)
test_missing.head() | Home Credit Default Risk |
1,245,855 | index = 'Weather Normalized Site Natural Gas Use(therms)'<feature_engineering> | train_missing = train_missing.index[train_missing > 0.75]
test_missing = test_missing.index[test_missing > 0.75]
all_missing = list(set(set(train_missing)| set(test_missing)))
print('There are %d columns with more than 75%% missing values' % len(all_missing)) | Home Credit Default Risk |
1,245,855 | df_train[index] = df_train[index].str.replace('Not Available', '0')
df_test[index] = df_test[index].str.replace('Not Available', '0' )<data_type_conversions> | X = X.drop(columns = all_missing)
X_test = X_test.drop(columns = all_missing)
print('Training shape: ', X.shape)
print('Testing shape: ', X_test.shape ) | Home Credit Default Risk |
1,245,855 | df_train[index] = df_train[index].astype('float64')
df_test[index] = df_test[index].astype('float64' )<compute_test_metric> | clf = xgb.XGBClassifier(learning_rate = 0.025,
n_estimators = 10000,
max_depth = 8,
min_child_weight = 1,
subsample = 0.8,
colsample_bytree = 0.7,
colsample_bylevel = 0.7,
objective = 'binary:logistic',
n_jobs = -1,
scale_pos_weight = scale_pos_weight,
silent = True ) | Home Credit Default Risk |
1,245,855 | intervalo_valores(df_train[index] )<define_variables> | X_train, X_val, y_train, y_val = train_test_split(X, y, test_size = 0.2, random_state = 42 ) | Home Credit Default Risk |
1,245,855 | index = 'Electricity Use - Grid Purchase(kBtu)'<feature_engineering> | clf.fit(X_train, y_train, eval_set = [(X_train, y_train),(X_val, y_val)], eval_metric= 'auc', verbose= 100, early_stopping_rounds= 400 ) | Home Credit Default Risk |
1,245,855 | <data_type_conversions><EOS> | result = clf.predict_proba(X_test)
submit = pd.DataFrame({'SK_ID_CURR': test_index, 'TARGET': result[:, 1]})
submit.to_csv('solution.csv', index = False ) | Home Credit Default Risk |
1,537,787 | <SOS> metric: AUC Kaggle data source: home-credit-default-risk<compute_test_metric> | %matplotlib inline
plt.style.use('ggplot')
seed =420
pd.options.display.max_rows = 100 | Home Credit Default Risk |
1,537,787 | intervalo_valores(df_train[index] )<define_variables> | nrows = 100000
path = '.. /input/'
app_train = pd.read_csv(path+'application_train.csv', nrows= nrows)
app_test = pd.read_csv(path+'application_test.csv', nrows= None)
bureau_balance = pd.read_csv(path+'bureau_balance.csv', nrows=nrows)
bureau = pd.read_csv(path+'bureau.csv', nrows=nrows)
installments_payments = pd... | Home Credit Default Risk |
1,537,787 | index = 'Weather Normalized Site Electricity(kWh)'<feature_engineering> | def basic_details(df):
k = pd.DataFrame()
k['missing_value'] = df.isnull().sum()
k['%missing_value'] = round(df.isnull().sum() *100/df.shape[0],2)
k['dtypes'] = df.dtypes
k['N unique'] = df.nunique()
return k | Home Credit Default Risk |
1,537,787 | df_train[index] = df_train[index].str.replace('Not Available', '0')
df_test[index] = df_test[index].str.replace('Not Available', '0' )<data_type_conversions> | def missing_value_fill(df ,columns, mean_or_mode='mode'):
for i in columns:
if(df[i].isnull().sum() >0)and(mean_or_mode =='mode'):
df[i].fillna(df[i].mode() [0], inplace=True)
elif(df[i].isnull().sum() >0)and(mean_or_mode =='mean'):
df[i].fillna(df[i].mean() , inplace=True ) | Home Credit Default Risk |
1,537,787 | df_train[index] = df_train[index].astype('float64')
df_test[index] = df_test[index].astype('float64' )<compute_test_metric> | def replace_XNA_XAP(df):
"Replace XNA,XAP"
df.replace(to_replace = {'XNA':np.nan,'XAP':np.nan},inplace=True,value= None)
return df | Home Credit Default Risk |
1,537,787 | intervalo_valores(df_train[index] )<define_variables> | def one_hot_encoding(df,columns,nan_as_category = True):
print('*'*5,'One hot encoding of categorical variable','*'*5)
print('Original shape',df.shape)
original_columns = df.columns
df = pd.get_dummies(df, columns= columns,drop_first=True,dummy_na=nan_as_category)
new_columns = [i for i in df.columns if i not in o... | Home Credit Default Risk |
1,537,787 | index = 'Total GHG Emissions(Metric Tons CO2e)'<feature_engineering> | def descriptive_stat_feat(df,columns):
print('*'*5,'Descriptive statistics feature','*'*5)
print('Before',df.shape)
mean = df[columns].mean()
median = df[columns].median()
Q1 = np.percentile(df[columns], 25, axis=0)
Q3 = np.percentile(df[columns], 75, axis=0)
for i,j in enumerate(columns):
df['mean_'+j] =(df[j] <... | Home Credit Default Risk |
1,537,787 | df_train[index] = df_train[index].str.replace('Not Available', '0')
df_test[index] = df_test[index].str.replace('Not Available', '0' )<data_type_conversions> | def binary_encoding(df,columns):
print('*'*5,'Binary encoding','*'*5)
lb = LabelBinarizer()
print('Original shape:',df.shape)
original_col = df.columns
for i in columns:
if df[i].nunique() >2:
result = lb.fit_transform(df[i].fillna(df[i].mode() [0],axis=0))
col = ['BIN_'+ str(i)+'_'+str(c)for c in lb.classes_]
resu... | Home Credit Default Risk |
1,537,787 | df_train[index] = df_train[index].astype('float64')
df_test[index] = df_test[index].astype('float64' )<compute_test_metric> | test_index = app_test['SK_ID_CURR']
app_train_col_drop = [] | Home Credit Default Risk |
1,537,787 | intervalo_valores(df_train[index] )<define_variables> | print('Count
',app_train['TARGET'].value_counts())
print('%
',app_train['TARGET'].value_counts() *100/app_train.shape[0] ) | Home Credit Default Risk |
1,537,787 | index = 'Direct GHG Emissions(Metric Tons CO2e)'<feature_engineering> | app_train[['NAME_CONTRACT_TYPE', 'CODE_GENDER','FLAG_OWN_CAR', 'FLAG_OWN_REALTY']] = app_train[
['NAME_CONTRACT_TYPE', 'CODE_GENDER','FLAG_OWN_CAR', 'FLAG_OWN_REALTY']].astype('object')
app_test[['NAME_CONTRACT_TYPE', 'CODE_GENDER','FLAG_OWN_CAR', 'FLAG_OWN_REALTY']] = app_test[
['NAME_CONTRACT_TYPE', 'CODE_GENDER','F... | Home Credit Default Risk |
1,537,787 | df_train[index] = df_train[index].str.replace('Not Available', '0')
df_test[index] = df_test[index].str.replace('Not Available', '0' )<data_type_conversions> | tmp = app_train['CNT_CHILDREN'].value_counts().to_frame()
tmp['%'] =(app_train['CNT_CHILDREN'].value_counts() *100 / app_train.shape[0])
tmp | Home Credit Default Risk |
1,537,787 | df_train[index] = df_train[index].astype('float64')
df_test[index] = df_test[index].astype('float64' )<compute_test_metric> | app_train['CNT_CHILDREN'] = app_train['CNT_CHILDREN'].astype('object')
app_test['CNT_CHILDREN'] = app_test['CNT_CHILDREN'].astype('object' ) | Home Credit Default Risk |
1,537,787 | intervalo_valores(df_train[index] )<define_variables> | print('Default',app_train[app_train['AMT_INCOME_TOTAL'] >0.2e8]['AMT_INCOME_TOTAL'])
app_train['AMT_INCOME_TOTAL'] = np.log(app_train['AMT_INCOME_TOTAL'])
app_test['AMT_INCOME_TOTAL'] = np.log(app_test['AMT_INCOME_TOTAL'] ) | Home Credit Default Risk |
1,537,787 | index = 'Indirect GHG Emissions(Metric Tons CO2e)'<feature_engineering> | app_train['AMT_CREDIT'] = np.sqrt(app_train['AMT_CREDIT'])
app_test['AMT_CREDIT'] = np.sqrt(app_test['AMT_CREDIT'] ) | Home Credit Default Risk |
1,537,787 | df_train[index] = df_train[index].str.replace('Not Available', '0')
df_test[index] = df_test[index].str.replace('Not Available', '0' )<data_type_conversions> | app_train['AMT_ANNUITY'] = np.log(app_train['AMT_ANNUITY'])
app_test['AMT_ANNUITY'] = np.log(app_test['AMT_ANNUITY'] ) | Home Credit Default Risk |
1,537,787 | df_train[index] = df_train[index].astype('float64')
df_test[index] = df_test[index].astype('float64' )<compute_test_metric> | app_train[['NAME_TYPE_SUITE', 'NAME_INCOME_TYPE', 'NAME_EDUCATION_TYPE', 'NAME_FAMILY_STATUS',
'NAME_HOUSING_TYPE']] = app_train[['NAME_TYPE_SUITE', 'NAME_INCOME_TYPE', 'NAME_EDUCATION_TYPE',
'NAME_FAMILY_STATUS', 'NAME_HOUSING_TYPE']].astype('object')
app_test[['NAME_TYPE_SUITE', 'NAME_INCOME_TYPE', 'NAME_EDUCATION_T... | Home Credit Default Risk |
1,537,787 | intervalo_valores(df_train[index] )<define_variables> | ((app_train['DAYS_EMPLOYED']/-365)[(app_train['DAYS_EMPLOYED']/-365)<0][:5],
app_train['DAYS_EMPLOYED'][app_train['DAYS_EMPLOYED']>0][:5],
app_test['DAYS_EMPLOYED'][app_test['DAYS_EMPLOYED']>0][:5] ) | Home Credit Default Risk |
1,537,787 | index = 'Property GFA - Self-Reported(ft²)'<compute_test_metric> | app_train['DAYS_EMPLOYED'].replace({365243:np.nan},inplace=True)
app_test['DAYS_EMPLOYED'].replace({365243:np.nan},inplace=True ) | Home Credit Default Risk |
1,537,787 | intervalo_valores(df_train[index] )<define_variables> | app_train['OWN_CAR_AGE'] = app_train['OWN_CAR_AGE'].astype('object')
app_test['OWN_CAR_AGE'] = app_test['OWN_CAR_AGE'].astype('object' ) | Home Credit Default Risk |
1,537,787 | index = 'Water Use(All Water Sources )(kgal)'<feature_engineering> | app_train_col_drop.append('FLAG_MOBIL')
app_train_col_drop.append('FLAG_CONT_MOBILE')
app_train_col_drop.append('FLAG_EMAIL')
app_train[['FLAG_EMP_PHONE','FLAG_WORK_PHONE','FLAG_PHONE']] = app_train[[
'FLAG_EMP_PHONE','FLAG_WORK_PHONE','FLAG_PHONE']].astype('object')
app_test[['FLAG_EMP_PHONE','FLAG_WORK_PHONE','FL... | Home Credit Default Risk |
1,537,787 | df_train[index] = df_train[index].str.replace('Not Available', '0')
df_test[index] = df_test[index].str.replace('Not Available', '0' )<data_type_conversions> | app_train['CNT_FAM_MEMBERS'] = app_train['CNT_FAM_MEMBERS'].astype('object')
app_test['CNT_FAM_MEMBERS'] = app_test['CNT_FAM_MEMBERS'].astype('object' ) | Home Credit Default Risk |
1,537,787 | df_train[index] = df_train[index].astype('float64')
df_test[index] = df_test[index].astype('float64' )<compute_test_metric> | app_train[['REGION_RATING_CLIENT','REGION_RATING_CLIENT_W_CITY']] = app_train[[
'REGION_RATING_CLIENT','REGION_RATING_CLIENT_W_CITY']].astype('object')
app_test[['REGION_RATING_CLIENT','REGION_RATING_CLIENT_W_CITY']] = app_test[[
'REGION_RATING_CLIENT','REGION_RATING_CLIENT_W_CITY']].astype('object' ) | Home Credit Default Risk |
1,537,787 | intervalo_valores(df_train[index] )<define_variables> | app_train['WEEKDAY_APPR_PROCESS_START'] = app_train['WEEKDAY_APPR_PROCESS_START'].astype('object')
app_test['WEEKDAY_APPR_PROCESS_START'] = app_test['WEEKDAY_APPR_PROCESS_START'].astype('object' ) | Home Credit Default Risk |
1,537,787 | index = 'Water Intensity(All Water Sources )(gal/ft²)'<feature_engineering> | app_train['HOUR_APPR_PROCESS_START'] = app_train['HOUR_APPR_PROCESS_START'].astype('object')
app_test['HOUR_APPR_PROCESS_START'] = app_test['HOUR_APPR_PROCESS_START'].astype('object' ) | Home Credit Default Risk |
1,537,787 | df_train[index] = df_train[index].str.replace('Not Available', '0')
df_test[index] = df_test[index].str.replace('Not Available', '0' )<data_type_conversions> | app_train_col_drop.append('REG_REGION_NOT_LIVE_REGION')
app_train[['REG_REGION_NOT_WORK_REGION','LIVE_REGION_NOT_WORK_REGION', 'REG_CITY_NOT_LIVE_CITY',
'REG_CITY_NOT_WORK_CITY', 'LIVE_CITY_NOT_WORK_CITY']] = app_train[['REG_REGION_NOT_WORK_REGION','LIVE_REGION_NOT_WORK_REGION', 'REG_CITY_NOT_LIVE_CITY',
'REG_CITY_NOT... | Home Credit Default Risk |
1,537,787 | df_train[index] = df_train[index].astype('float64')
df_test[index] = df_test[index].astype('float64' )<compute_test_metric> | app_train['ORGANIZATION_TYPE'].replace('XNA',np.nan,inplace=True)
app_test['ORGANIZATION_TYPE'].replace('XNA',np.nan,inplace=True)
app_train['ORGANIZATION_TYPE'] = app_train['ORGANIZATION_TYPE'].astype('object')
app_test['ORGANIZATION_TYPE'] = app_test['ORGANIZATION_TYPE'].astype('object' ) | Home Credit Default Risk |
1,537,787 | intervalo_valores(df_train[index] )<define_variables> | app_train[['FONDKAPREMONT_MODE', 'HOUSETYPE_MODE', 'WALLSMATERIAL_MODE', 'EMERGENCYSTATE_MODE']] = app_train[['FONDKAPREMONT_MODE', 'HOUSETYPE_MODE',
'WALLSMATERIAL_MODE', 'EMERGENCYSTATE_MODE']].astype('object')
app_test[['FONDKAPREMONT_MODE', 'HOUSETYPE_MODE', 'WALLSMATERIAL_MODE', 'EMERGENCYSTATE_MODE']] = app_test... | Home Credit Default Risk |
1,537,787 | index = 'Source EUI(kBtu/ft²)'<compute_test_metric> | app_train[['OBS_30_CNT_SOCIAL_CIRCLE', 'DEF_30_CNT_SOCIAL_CIRCLE',
'OBS_60_CNT_SOCIAL_CIRCLE', 'DEF_60_CNT_SOCIAL_CIRCLE',]] = app_train[['OBS_30_CNT_SOCIAL_CIRCLE', 'DEF_30_CNT_SOCIAL_CIRCLE',
'OBS_60_CNT_SOCIAL_CIRCLE', 'DEF_60_CNT_SOCIAL_CIRCLE',]].astype('object')
app_test[['OBS_30_CNT_SOCIAL_CIRCLE', 'DEF_30_CNT_... | Home Credit Default Risk |
1,537,787 | intervalo_valores(df_train[index] )<define_variables> | app_train[['FLAG_DOCUMENT_2', 'FLAG_DOCUMENT_3','FLAG_DOCUMENT_4', 'FLAG_DOCUMENT_5', 'FLAG_DOCUMENT_6',
'FLAG_DOCUMENT_7', 'FLAG_DOCUMENT_8', 'FLAG_DOCUMENT_9', 'FLAG_DOCUMENT_10', 'FLAG_DOCUMENT_11',
'FLAG_DOCUMENT_12','FLAG_DOCUMENT_13', 'FLAG_DOCUMENT_14', 'FLAG_DOCUMENT_15','FLAG_DOCUMENT_16',
'FLAG_DOCUMENT_17', ... | Home Credit Default Risk |
1,537,787 | index = 'Release Date'<drop_column> | app_train[['AMT_REQ_CREDIT_BUREAU_HOUR', 'AMT_REQ_CREDIT_BUREAU_DAY','AMT_REQ_CREDIT_BUREAU_WEEK',
'AMT_REQ_CREDIT_BUREAU_MON','AMT_REQ_CREDIT_BUREAU_QRT', 'AMT_REQ_CREDIT_BUREAU_YEAR',]] = app_train[['AMT_REQ_CREDIT_BUREAU_HOUR', 'AMT_REQ_CREDIT_BUREAU_DAY','AMT_REQ_CREDIT_BUREAU_WEEK',
'AMT_REQ_CREDIT_BUREAU_MON','AM... | Home Credit Default Risk |
1,537,787 | del df_train[index]
del df_test[index]<define_variables> | gc.collect() | Home Credit Default Risk |
1,537,787 | index = 'Water Required?'<data_type_conversions> | train_test = train_test.drop(list(set(app_train_col_drop)) , axis=1)
categorical_col = train_test.select_dtypes('object' ).columns
numeric_col = train_test.select_dtypes('number' ).columns
numeric_col = numeric_col.drop('TARGET')
gc.collect() | Home Credit Default Risk |
1,537,787 | df_train[index] = df_train[index].astype('category' ).cat.codes
df_test[index] = df_test[index].astype('category' ).cat.codes<filter> | train_test = replace_XNA_XAP(train_test)
train_test,_ = binary_encoding(train_test, categorical_col)
train_test,_ = one_hot_encoding(train_test,categorical_col,nan_as_category=True)
descriptive_stat_feat(train_test,numeric_col)
del app_train,app_test
reduce_memory_usage(train_test)
gc.collect() | Home Credit Default Risk |
1,537,787 | df_train = df_train[df_train[index] > -1]<compute_test_metric> | bureau_balance = replace_XNA_XAP(bureau_balance)
bb_bin =[]
bureau_balance, bb_cat = one_hot_encoding(bureau_balance,['STATUS'],nan_as_category=False)
bb_aggregations = {'MONTHS_BALANCE':['min','max','mean']}
for col in bb_cat+bb_bin:
bb_aggregations[col] =['sum','mean']
bb_agg = bureau_balance.groupby('SK_ID_BUREAU'... | Home Credit Default Risk |
1,537,787 | intervalo_valores(df_train[index] )<define_variables> | bureau[['CREDIT_TYPE','CNT_CREDIT_PROLONG']] = bureau[['CREDIT_TYPE','CNT_CREDIT_PROLONG']].astype('object')
| Home Credit Default Risk |
1,537,787 | index = 'DOF Benchmarking Submission Status'<drop_column> | bureau['AMT_CREDIT_SUM'] = bureau['AMT_CREDIT_SUM'].apply(np.log1p ) | Home Credit Default Risk |
1,537,787 | del df_train[index]
del df_test[index]<define_variables> | bureau['AMT_CREDIT_SUM_DEBT'] = np.log1p(bureau['AMT_CREDIT_SUM_DEBT'] ) | Home Credit Default Risk |
1,537,787 | index = 'Latitude'<drop_column> | bureau['AMT_ANNUITY'] = np.log1p(bureau['AMT_ANNUITY'] ) | Home Credit Default Risk |
1,537,787 | del df_train[index]
del df_test[index]<define_variables> | bureau = bureau.drop(bureau_drop_col, axis=1)
bureau = replace_XNA_XAP(bureau)
bureau_cat_col = bureau.select_dtypes('object' ).columns
bureau_numeric_col = bureau.select_dtypes('number' ).columns
bureau_numeric_col = bureau_numeric_col.drop(['SK_ID_CURR', 'SK_ID_BUREAU'])
bureau_bin =[]
bureau, bureau_bin = binary_... | Home Credit Default Risk |
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