kernel_id int64 24.2k 23.3M | prompt stringlengths 8 1.85M | completetion stringlengths 1 182k | comp_name stringlengths 5 57 |
|---|---|---|---|
1,511,034 | evaluate_model(y_train, lgb_model, X_train )<save_to_csv> | credit['tmp'] = credit['AMT_BALANCE']/credit['AMT_CREDIT_LIMIT_ACTUAL']
tmp = credit.groupby(["SK_ID_CURR","SK_ID_PREV"])['tmp'].max().reset_index()
tmp = tmp.groupby(['SK_ID_CURR'])['tmp'].agg({"returns": [np.min, np.max,np.mean, np.sum]})\
.reset_index()
tmp.columns = ['SK_ID_CURR','des1','des2','des3', 'des4']
for ... | Home Credit Default Risk |
1,511,034 | conversion_probs = lgb_model.predict_proba(X_test)[:, 1]
subm = pd.DataFrame({'person': y_test.index, 'label': conversion_probs})
subm.to_csv('submission.csv', index=False )<import_modules> | credit['tmp'] = credit['AMT_BALANCE']/credit['AMT_CREDIT_LIMIT_ACTUAL']
tmp = credit.groupby(["SK_ID_CURR","SK_ID_PREV"])['tmp'].min().reset_index()
tmp = credit.groupby(['SK_ID_CURR'])['tmp'].agg({"returns": [np.min, np.max,np.mean, np.sum]})\
.reset_index()
tmp.columns = ['SK_ID_CURR','des1','des2','des3', 'des4']
f... | Home Credit Default Risk |
1,511,034 | print(os.listdir(".. /input"))
<load_from_csv> | doc = [x for x in train.columns if 'FLAG_DOC' in x]
connection = ['FLAG_MOBIL', 'FLAG_EMP_PHONE', 'FLAG_WORK_PHONE',
'FLAG_CONT_MOBILE', 'FLAG_PHONE', 'FLAG_EMAIL',]
le = LabelEncoder()
categorical = ['CODE_GENDER','FLAG_OWN_CAR','FLAG_OWN_REALTY','NAME_EDUCATION_TYPE',
'NAME_FAMILY_STATUS',
'FLAG_MOBIL','FLAG_EMP_PHON... | Home Credit Default Risk |
1,511,034 | train = pd.read_csv(".. /input/train.csv")
test = pd.read_csv('.. /input/test.csv')
test.head(5 )<count_missing_values> | NFOLDS = 5
kf = StratifiedKFold(n_splits=NFOLDS, shuffle=True, random_state=2018)
pred_test_full = 0
params = {
'boosting': 'gbdt',
'objective': 'binary',
'metric': 'auc',
'learning_rate': 0.01,
'num_leaves': 25,
'max_depth': 8,
'colsample_bytree': 0.3,
'seed': 101
}
res = []
idx = 0
for dev_index, val_index in kf.spl... | Home Credit Default Risk |
1,511,034 | train.isnull().any()<count_missing_values> | tmp = install.groupby(['SK_ID_PREV','NUM_INSTALMENT_NUMBER'])['DAYS_INSTALMENT'].count().reset_index()
tmp = tmp[tmp['DAYS_INSTALMENT'] > 1]
tmp.columns = ['SK_ID_PREV','NUM_INSTALMENT_NUMBER','count_dup']
install = install.merge(tmp, on = ['SK_ID_PREV','NUM_INSTALMENT_NUMBER'], how='left')
tmp = install[install['coun... | Home Credit Default Risk |
1,511,034 | test.isnull().any()<drop_column> | install.drop(['count_dup_x','count_dup_y'], axis=1, inplace = True ) | Home Credit Default Risk |
1,511,034 | test_ids = test['Id']
test = test.drop(['Id'], 1 )<categorify> | tmp.sort_values(by=['SK_ID_PREV','NUM_INSTALMENT_NUMBER','DAYS_ENTRY_PAYMENT'] ) | Home Credit Default Risk |
1,511,034 | le = preprocessing.LabelEncoder()
le.fit(train['class'])
print(list(le.classes_))
train['class'] = le.transform(train['class'] )<drop_column> | tmp['SK_ID_CURR'].value_counts() | Home Credit Default Risk |
1,114,299 | train_ids = train['Id']
train = train.drop(['Id'], 1 )<split> | def get_combined_dataset() :
application_train = pd.read_csv('.. /input/application_train.csv')
application_test = pd.read_csv('.. /input/application_test.csv')
application=application_train.append(application_test, ignore_index=True,sort=False)
application.set_index('SK_ID_CURR')
return(application)
def get_appli... | Home Credit Default Risk |
1,114,299 | x_data = train.drop('class',axis=1)
y_labels = train['class']
X_train, X_test, y_train, y_test = train_test_split(x_data, y_labels, test_size=0.3, random_state=101 )<features_selection> | def bureau_balance() :
df = pd.read_csv('.. /input/bureau_balance.csv')
df1 = df.groupby(['SK_ID_BUREAU'] ).agg(
{'MONTHS_BALANCE': min,
})
df2 = df.groupby(['SK_ID_BUREAU'] ).agg(
{'MONTHS_BALANCE': max,
} ).reset_index()
df2 = pd.merge(df2,df,on=['SK_ID_BUREAU','MONTHS_BALANCE'],how='inner')
df2 = pd.crosstab(df... | Home Credit Default Risk |
1,114,299 | buying = tf.feature_column.categorical_column_with_vocabulary_list("buying", ['high', 'low', 'med', 'vhigh'])
maintainence = tf.feature_column.categorical_column_with_vocabulary_list("maintainence", ['high', 'low', 'med', 'vhigh'])
doors = tf.feature_column.categorical_column_with_vocabulary_list("doors", ['3', '4', ... | def get_previous_application() :
df = pd.read_csv('.. /input/previous_application.csv')
df.loc[df.DAYS_FIRST_DRAWING >0,'DAYS_FIRST_DRAWING'] = np.nan
df.loc[df.DAYS_FIRST_DUE >0,'DAYS_FIRST_DUE'] = np.nan
df.loc[df.DAYS_LAST_DUE_1ST_VERSION >2000,'DAYS_LAST_DUE_1ST_VERSION'] = np.nan
df.loc[df.DAYS_LAST_DUE >3000,'DA... | Home Credit Default Risk |
1,114,299 | feat_cols = [buying, maintainence, doors, persons, lug_boot, safety]<train_model> | def get_previous_application() :
df = pd.read_csv('.. /input/previous_application.csv')
df.loc[df.DAYS_FIRST_DRAWING >0,'DAYS_FIRST_DRAWING'] = np.nan
df.loc[df.DAYS_FIRST_DUE >0,'DAYS_FIRST_DUE'] = np.nan
df.loc[df.DAYS_LAST_DUE_1ST_VERSION >2000,'DAYS_LAST_DUE_1ST_VERSION'] = np.nan
df.loc[df.DAYS_LAST_DUE >3000,'DA... | Home Credit Default Risk |
1,114,299 | input_func = tf.estimator.inputs.pandas_input_fn(x=X_train,
y=y_train,
batch_size=500,
num_epochs=10000,
shuffle=True )<train_model> | def get_POS_CASH_balance() :
POS_CASH_balance = pd.read_csv('.. /input/POS_CASH_balance.csv')
Closed_Loans = POS_CASH_balance[POS_CASH_balance['SK_ID_PREV'].isin(POS_CASH_balance.query('NAME_CONTRACT_STATUS == "Completed"' ).SK_ID_PREV)]
Active_Loans = POS_CASH_balance[~POS_CASH_balance['SK_ID_PREV'].isin(POS_CASH_bal... | Home Credit Default Risk |
1,114,299 | input_func_test = tf.estimator.inputs.pandas_input_fn(x=test,
num_epochs=500,
shuffle=False )<choose_model_class> | def get_installment_payments() :
instalment_payments = pd.read_csv('.. /input/installments_payments.csv')
instalment_payments['MONTH']=(instalment_payments['DAYS_INSTALMENT']/30 ).astype(int)
Active = instalment_payments.query('MONTH == -1' ).groupby('SK_ID_CURR' ).agg({
'NUM_INSTALMENT_VERSION':max,
'NUM_INSTALMENT_... | Home Credit Default Risk |
1,114,299 | model = tf.estimator.LinearClassifier(feature_columns=feat_cols, n_classes=4 )<train_model> | def get_credit_card_balance() :
df = pd.read_csv('.. /input/credit_card_balance.csv')
dfa = df.query('NAME_CONTRACT_STATUS == "Active"' ).groupby(['SK_ID_CURR','MONTHS_BALANCE'] ).agg({
'AMT_BALANCE':sum,
'AMT_CREDIT_LIMIT_ACTUAL':sum,
'AMT_DRAWINGS_ATM_CURRENT':sum,
'AMT_DRAWINGS_CURRENT':sum,
'AMT_DRAWINGS_OTHER_CUR... | Home Credit Default Risk |
1,114,299 | model.train(input_fn=input_func,steps=5000 )<train_model> | def getFinalDataSet() :
application = get_application_dataset()
application = transform_application(get_application_dataset())
bureau = transform_bureau(get_bureau_dataset())
previous_application = get_previous_application()
POS_CASH_balance = transform_POS_CASH_balance(get_POS_CASH_balance())
installment_payments =... | Home Credit Default Risk |
1,114,299 | pred_fn = tf.estimator.inputs.pandas_input_fn(x=X_test, batch_size=len(X_test), shuffle=False )<predict_on_test> | def scaleNfillna(df):
df.replace([np.inf, -np.inf], np.nan,inplace=True)
df.fillna(0,inplace=True)
scaler = MinMaxScaler()
df = scaler.fit_transform(df)
return(df ) | Home Credit Default Risk |
1,114,299 | predictions = list(model.predict(input_fn=pred_fn))
probs = pd.Series([pred['class_ids'][0] for pred in predictions] )<compute_test_metric> | from keras.models import Sequential, Model
from keras.layers import Input, Dense, Dropout, BatchNormalization
from sklearn.model_selection import train_test_split
from sklearn import metrics | Home Credit Default Risk |
1,114,299 | final_preds = []
for pred in predictions:
final_preds.append(pred['class_ids'][0])
print(classification_report(y_test,final_preds))<train_model> | def kfold_lightgbm(df, num_folds, stratified = False, debug= False):
train_df = df[df['TARGET'].notnull() ]
test_df = df[df['TARGET'].isnull() ]
print("Starting LightGBM.Train shape: {}, test shape: {}".format(train_df.shape, test_df.shape))
del df
gc.collect()
if stratified:
folds = StratifiedKFold(n_splits= num_folds... | Home Credit Default Risk |
1,114,299 | eval_input_func = tf.estimator.inputs.pandas_input_fn(x=X_test,y=y_test,batch_size=len(X_test),shuffle=False )<prepare_output> | def ANN(X_train,y_train,X_test,y_test,L_dim,num_epochs = 2):
ann = Sequential()
ann.add(Dense(L_dim[0], input_dim=X_train.shape[1], activation='relu'))
ann.add(BatchNormalization())
ann.add(Dropout(0.2))
ann.add(Dense(L_dim[1], activation='relu'))
ann.add(BatchNormalization())
ann.add(Dropout(0.2))
ann.add(Dense(L_di... | Home Credit Default Risk |
1,114,299 | results = model.evaluate(eval_input_func)
results<train_model> | def AE(X):
input_data = Input(shape=(X.shape[1],))
encoded = Dense(128, activation='relu' )(input_data)
encoded = BatchNormalization()(encoded)
encoded = Dense(32, activation='relu' )(encoded)
encoded = BatchNormalization()(encoded)
encoded = Dense(16, activation='relu' )(encoded)
encoded = BatchNormalization(name... | Home Credit Default Risk |
1,114,299 | pred_fn_test = tf.estimator.inputs.pandas_input_fn(x=test, batch_size=len(test), shuffle=False )<predict_on_test> | def submitLGBM(debug=True):
df = getFinalDataSet()
submission = kfold_lightgbm(df, 4, stratified = True)
if not debug:
print("writing the submission file")
submission.to_csv('submission_1.csv', index=False ) | Home Credit Default Risk |
1,114,299 | predictions_test = list(model.predict(input_fn=pred_fn_test))
probs_test = pd.Series([pred['class_ids'][0] for pred in predictions_test] )<define_variables> | def ANN_prediction(df):
feats =[x for x in list(df)if x not in ['SK_ID_CURR','TARGET']]
df[feats] = scaleNfillna(df[feats])
X = df.loc[df['TARGET'].notnull() ,feats].values
y = df[df['TARGET'].notnull() ].TARGET.values
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.10, random_state=42)
ros = Ra... | Home Credit Default Risk |
1,114,299 | preds_test_1 = []
for pred in predictions_test:
preds_test_1.append(pred['class_ids'][0])
print(len(preds_test_1))<categorify> | def ilo() :
df = getFinalDataSet()
feats =[x for x in list(df)if x not in ['SK_ID_CURR','TARGET']]
df[feats] = scaleNfillna(df[feats])
X = df[feats].values
ae = AE(X)
intermediate_layer_model = Model(inputs=ae.input, outputs=ae.get_layer('encoded_layer' ).output)
X = intermediate_layer_model.predict(X[df["TARGET"].n... | Home Credit Default Risk |
1,414,913 | preds_test = le.inverse_transform(preds_test_1)
print(type(preds_test))<create_dataframe> | data = pd.read_csv(PATH+"/application_train.csv")
test = pd.read_csv(PATH+"/application_test.csv")
bureau = pd.read_csv(PATH+"/bureau.csv", nrows=50000)
bureau_balance = pd.read_csv(PATH+"/bureau_balance.csv", nrows=50000)
credit_card_balance = pd.read_csv(PATH+"/credit_card_balance.csv", nrows=50000)
installments... | Home Credit Default Risk |
1,414,913 | df = pd.DataFrame(columns=['Id', 'Class_vgood', 'Class_good', 'Class_acc', 'Class_unacc'])
for i, ids, preds in zip(range(len(test_ids)) , test_ids, preds_test):
if(preds == 'vgood'):
submission = pd.DataFrame({
"Id": ids,
"Class_vgood": 1,
"Class_good": 0,
"Class_acc": 0,
"Class_unacc": 0,
}, index=[i])
df = df.appe... | import numpy as np | Home Credit Default Risk |
1,414,913 | df.to_csv('sampleSubmission.csv', index=False )<load_from_csv> | import pandas as pd | Home Credit Default Risk |
1,414,913 | train_df = pd.read_csv(".. /input/tmu-inclass-competition/train.csv")
test_df = pd.read_csv(".. /input/tmu-inclass-competition/test.csv")
sub_df = pd.read_csv(".. /input/tmu-inclass-competition/sample_submission.csv" )<count_values> | import pandas as pd | Home Credit Default Risk |
1,414,913 | cat_list = ["jurisdiction_names", "country_code", "smart_location", "property_type", "host_id", "host_response_time", "room_type"]
def preprocess(train_df, test_df):
new_df = pd.concat([train_df, test_df] ).reset_index(drop=True)
d = {}
for s in new_df["calendar_updated"].value_counts().index:
if s == "today":
d[s] = ... | import matplotlib
import matplotlib.pyplot as plt
import seaborn as sns | Home Credit Default Risk |
1,414,913 | def rmsle(y_true, y_pred):
assert len(y_true)== len(y_pred)
return np.sqrt(np.mean(np.power(np.log1p(y_true + 1)- np.log1p(y_pred + 1), 2)))
def rmsle_lgb(preds, data):
y_true = np.array(data.get_label())
result = rmsle(preds, y_true)
return 'RMSLE', result, False<init_hyperparams> | data['DAYS_EMPLOYED'] | Home Credit Default Risk |
1,414,913 | params = {
'boosting_type': 'gbdt',
'objective': 'regression',
'metric': 'rmsle',
'max_depth': 20,
'max_bin': 200,
'num_leaves': 97,
'min_data_in_leaf': 10,
'learning_rate': 0.0022,
'feature_fraction': 0.8,
'bagging_fraction': 0.8,
'bagging_freq': 10,
'min_sum_hessian_in_leaf': 10,
'lambda_l1': 0.01,
'lambda_l2': 0.01,... | data['DAYS_EMPLOYED'].replace(365243, np.nan, inplace= True ) | Home Credit Default Risk |
1,414,913 | y = processed_train_df["price"].values
X = processed_train_df.drop("price", axis=1 ).values
features = processed_train_df.drop("price", axis=1 ).columns
X_test = processed_test_df.values
cols = processed_train_df.drop("price", axis=1 ).columns.values
categorical_cols = cat_list[:]
feature_importance_df = pd.DataFrame()... | data['CODE_GENDER'].loc[data['CODE_GENDER']=='XNA'] | Home Credit Default Risk |
1,414,913 | sub_df["price"] = test_preds
sub_df.to_csv(f"submission{sum(cv_score)/len(cv_score)}.csv", index=False )<compute_test_metric> | data['CODE_GENDER'].replace({'XNA': 'F'}, inplace=True ) | Home Credit Default Risk |
1,414,913 | rmsle(y, oof )<prepare_x_and_y> | data['CODE_GENDER'].loc[data['CODE_GENDER']=='F'] | Home Credit Default Risk |
1,414,913 | l = []
for idx,(true, pred)in enumerate(zip(y, oof)) :
l.append([np.power(np.log1p(true + 1)- np.log1p(pred + 1), 2), idx])
l.sort(reverse=True)
l_idx = [x[1] for x in l[:len(l)//20]]
l_idx.sort()
idx = []
j = 0
for i in range(len(l)) :
if i == l_idx[j]:
if j < len(l_idx)- 1:
j += 1
else:
idx.append(i)
y = processed... | data['DAYS_EMPLOYED'].replace(365243, np.nan, inplace= True ) | Home Credit Default Risk |
1,414,913 | N = 5
oof = np.zeros(len(X))
test_preds = np.zeros(len(test_df))
kf = KFold(n_splits=N, shuffle=True, random_state=1)
cv_score = []
for fold_,(train_idx, val_idx)in enumerate(kf.split(X), start=1):
X_train, X_val = X[train_idx], X[val_idx]
y_train, y_val = y[train_idx], y[val_idx]
lgb_train = lgb.Dataset(X_train, y_tr... | data['YEARS_BUILD_CREDIT'] = data['AMT_CREDIT']/data['YEARS_BUILD_AVG'] | Home Credit Default Risk |
1,414,913 | sub_df["price"] = test_preds
sub_df.to_csv(f"v2_submission{sum(cv_score)/len(cv_score)}.csv", index=False )<prepare_x_and_y> | data['Annuity_Income'] = data['AMT_ANNUITY']/data['AMT_INCOME_TOTAL'] | Home Credit Default Risk |
1,414,913 | ")
<load_from_csv> | data['Income_Cred'] = data['AMT_CREDIT']/data['AMT_INCOME_TOTAL'] | Home Credit Default Risk |
1,414,913 | train = pd.read_csv('.. /input/train.csv')
test = pd.read_csv('.. /input/test.csv')
<categorify> | data['EMP_AGE'] = data['DAYS_EMPLOYED']/data['DAYS_BIRTH'] | Home Credit Default Risk |
1,414,913 | class_names = ['toxic', 'severe_toxic', 'obscene', 'threat', 'insult', 'identity_hate']
train_text = train['comment_text']
test_text = test['comment_text']
all_text = pd.concat([train_text, test_text])
word_vectorizer = TfidfVectorizer(
sublinear_tf=True,
strip_accents='unicode',
analyzer='word',
token_pattern=r'\w{1... | data['Income_PP'] = data['AMT_INCOME_TOTAL']/data['CNT_FAM_MEMBERS'] | Home Credit Default Risk |
1,414,913 | from sklearn.svm import LinearSVC
from sklearn.ensemble import AdaBoostClassifier
from sklearn.calibration import CalibratedClassifierCV
<create_dataframe> | data['CHILDREN_RATIO'] =(1 + data['CNT_CHILDREN'])/ data['CNT_FAM_MEMBERS'] | Home Credit Default Risk |
1,414,913 | scores = []
submission = pd.DataFrame.from_dict({'id': test['id']})
for class_name in class_names:
train_target = train[class_name]
classifier = LogisticRegression(solver='sag')
cv_score = np.mean(cross_val_score(classifier, train_features, train_target, cv=3, scoring='roc_auc'))
scores.append(cv_score)
print('CV sc... | data['PAYMENTS'] = data['AMT_ANNUITY']/ data['AMT_CREDIT'] | Home Credit Default Risk |
1,414,913 | ")
<define_variables> | data['NEW_CREDIT_TO_GOODS_RATIO'] = data['AMT_CREDIT'] / data['AMT_GOODS_PRICE']
data['GOODS_INCOME'] = data['AMT_GOODS_PRICE']/data['AMT_INCOME_TOTAL'] | Home Credit Default Risk |
1,414,913 | ship_dir = '.. /input/almaz-antey-hackathon-l1/'
train_image_dir = os.path.join(ship_dir, 'train/train')
test_image_dir = os.path.join(ship_dir, 'test/test' )<load_from_csv> | data['Ext_source_mult'] = data['EXT_SOURCE_1'] * data['EXT_SOURCE_2'] * data['EXT_SOURCE_3']
data['Ext_SOURCE_MEAN'] = data[['EXT_SOURCE_1', 'EXT_SOURCE_2', 'EXT_SOURCE_3']].mean(axis = 1)
data['Ext_SOURCE_SD'] = data[['EXT_SOURCE_1', 'EXT_SOURCE_2', 'EXT_SOURCE_3']].std(axis = 1 ) | Home Credit Default Risk |
1,414,913 | train_df = pd.read_csv(os.path.join(ship_dir, 'train_segmentation.csv'))
sample_sub = pd.read_csv(os.path.join(ship_dir, 'sample_submission.csv'))<categorify> | columns = ['Annuity_Income', 'Income_Cred', 'EMP_AGE', 'Income_PP']
| Home Credit Default Risk |
1,414,913 | montage_rgb = lambda x: np.stack([montage(x[:, :, :, i])for i in range(x.shape[3])], -1)
def multi_rle_encode(img, **kwargs):
labels = label(img)
if img.ndim > 2:
return [rle_encode(np.sum(labels==k, axis=2), **kwargs)for k in np.unique(labels[labels>0])]
else:
return [rle_encode(labels==k, **kwargs)for k in np.uni... | test['CODE_GENDER'].replace({'XNA': 'F'}, inplace=True)
test['YEARS_BUILD_CREDIT'] = test['AMT_CREDIT']/test['YEARS_BUILD_AVG']
test['DAYS_EMPLOYED'].replace(365243, np.nan, inplace= True)
test['Annuity_Income'] = test['AMT_ANNUITY']/test['AMT_INCOME_TOTAL']
test['Income_Cred'] = test['AMT_CREDIT']/test['AMT_INCOME_T... | Home Credit Default Risk |
1,414,913 | generator = ship_generator(train_df, train_image_dir, batch_size=4 )<compute_test_metric> | bureau_new = bureau | Home Credit Default Risk |
1,414,913 | def dice_coef2(y_true, y_pred):
y_true_f = y_true.flatten()
y_pred_f = y_pred.flatten()
union = np.sum(y_true_f)+ np.sum(y_pred_f)
if union==0: return 1
intersection = np.sum(y_true_f * y_pred_f)
return 2.* intersection / union<categorify> | 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'} ) | Home Credit Default Risk |
1,414,913 | val_data = train_df.sample(200, replace=False ).reset_index()
val_data.EncodedPixels = val_data.EncodedPixels.map(lambda x: rle_decode(x))<categorify> | bureau_new = bureau_new.merge(group, how = 'left', on = 'SK_ID_CURR' ) | Home Credit Default Risk |
1,414,913 | pbar = tqdm(val_data.index)
predicts = []
for idx in pbar:
fpath = os.path.join(train_image_dir, val_data.iloc[idx].ImageId)
image, mask = find_mask(fpath)
predicts.append(mask )<compute_test_metric> | del group | Home Credit Default Risk |
1,414,913 | dice = np.mean([dice_coef2(y, predict)for y, predict in zip(val_data.EncodedPixels.to_numpy() , predicts)] )<train_model> | 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'} ) | Home Credit Default Risk |
1,414,913 | print(f'DICE = {dice}' )<categorify> | bureau_new = bureau_new.merge(group,on = ['SK_ID_CURR'], how = 'left')
del group | Home Credit Default Risk |
1,414,913 | pbar = tqdm(sample_sub.index[:])
for idx in pbar:
fpath = os.path.join(test_image_dir, sample_sub.iloc[idx].ImageId)
image, mask = find_mask(fpath)
encode_mask = rle_encode(mask)
sample_sub.iloc[idx].EncodedPixels = encode_mask<save_to_csv> | bureau_new["AVERAGE_LOAN_TYPE"] = bureau_new['BUREAU_LOAN_COUNT']/bureau_new['LOAN_TYPES_PER_CUST'] | Home Credit Default Risk |
1,414,913 | sample_sub.to_csv('submission.csv', index=False)
sample_sub.head()<load_from_csv> | replace = {'Active': 1, 'Closed':0, 'Sold': 1, 'Bad debt': 1}
bureau_new['CREDIT_ACTIVE'] = bureau_new['CREDIT_ACTIVE'].replace(replace ) | Home Credit Default Risk |
1,414,913 | test = pd.read_csv('.. /input/test.csv')
train = df = pd.read_csv('.. /input/train.csv' )<concatenate> | gp = bureau_new.groupby('SK_ID_CURR')['CREDIT_ACTIVE'].mean().reset_index().rename(index=str, columns={'CREDIT_ACTIVE': 'ACTIVE_LOANS_PERCENTAGE'} ) | Home Credit Default Risk |
1,414,913 | df = pd.concat([train, test] )<define_variables> | bureau_new = bureau_new.merge(gp, on = 'SK_ID_CURR', how = 'left' ) | Home Credit Default Risk |
1,414,913 | numero = [c for c in df.columns if c not in texto]
numero = [c for c in numero if c not in ['nota_mat', 'codigo_mun', 'Unnamed: 0']]<statistical_test> | del gp | Home Credit Default Risk |
1,414,913 | def outliers_iqr(ys):
quartile_1, quartile_3 = np.percentile(ys, [25, 75])
iqr = quartile_3 - quartile_1
lower_bound = quartile_1 -(iqr * 1.5)
upper_bound = quartile_3 +(iqr * 1.5)
return np.where(( ys > upper_bound)|(ys < lower_bound))<define_variables> | def rep(x):
if x<0:
y=0
else:
y=1
return y | Home Credit Default Risk |
1,414,913 | for a in numero:
quartile_1, quartile_3 = np.percentile(df[a], [25, 75])
iqr = quartile_3 - quartile_1
lower_bound = quartile_1 -(iqr * 1.5)
upper_bound = quartile_3 +(iqr * 1.5)
df[a][df[a] < lower_bound] = None
df[a][df[a] > upper_bound] = None<define_variables> | bureau_new['CREDIT_ENDDATE_BINARY'] = bureau_new['DAYS_CREDIT_ENDDATE'].apply(lambda x: rep(x)) | Home Credit Default Risk |
1,414,913 | texto = [c for c in texto if c not in ['codigo_mun']]<data_type_conversions> | grp = bureau_new.groupby('SK_ID_CURR')['CREDIT_ENDDATE_BINARY'].mean().reset_index().rename(index=str, columns={'CREDIT_ENDDATE_BINARY': 'CREDIT_ENDDATE_PERCENTAGE'} ) | Home Credit Default Risk |
1,414,913 | for c in texto:
df[c]=df[c].astype('category' ).cat.codes<define_variables> | bureau_new = bureau_new.merge(grp, on = 'SK_ID_CURR', how = 'left')
del grp | Home Credit Default Risk |
1,414,913 | feats = [c for c in df.columns if c not in ['nota_mat','codigo_mun', 'Unnamed: 0']]<groupby> | 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 |
1,414,913 | nme = df.groupby(['estado'], as_index=False ).mean()
for c in nme.columns:
nme[c].fillna(nme[c].mean() , inplace=True )<merge> | bureau_agg = bureau_new.groupby('SK_ID_CURR' ).agg({**num_aggregations} ) | Home Credit Default Risk |
1,414,913 | df2 = pd.merge(df, nme, left_on='estado', right_on='estado', how='left', suffixes=('', '_mean'))
df2['estado_mean']=df2['estado']<define_variables> | bureau_agg.columns = pd.Index(['BURO_' + e[0] + "_" + e[1].upper() for e in bureau_agg.columns.tolist() ])
bureau_agg.reset_index(inplace=True ) | Home Credit Default Risk |
1,414,913 | feats2 = [c for c in feats if c not in ['codigo_mun']]<data_type_conversions> | bureau_merge = bureau_new.merge(bureau_agg, on = 'SK_ID_CURR', how = 'left')
del bureau_agg | Home Credit Default Risk |
1,414,913 | for c in feats2:
df[c].fillna(df2[c+'_mean'], inplace=True )<feature_engineering> | buro_cat_features = [bcol for bcol in bureau_merge.columns if bureau_merge[bcol].dtype == 'object'] | Home Credit Default Risk |
1,414,913 | df['nota_mat'] = np.log(df['nota_mat'] )<import_modules> | buro = pd.get_dummies(bureau_merge, columns=buro_cat_features ) | Home Credit Default Risk |
1,414,913 | import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
<set_options> | cat_columns = [col for col in bureau_balance.columns if bureau_balance[col].dtype == 'object'] | Home Credit Default Risk |
1,414,913 | plt.rcParams['figure.figsize'] =(20,30 )<prepare_x_and_y> | bureau_balance = pd.get_dummies(bureau_balance,cat_columns, dummy_na = True ) | Home Credit Default Risk |
1,414,913 | test = df[df['nota_mat'].isnull() ]
train = df[df['nota_mat'].notnull() ]<import_modules> | bb_group = bureau_balance.groupby('SK_ID_BUREAU' ).agg(['min', 'max', 'mean'] ) | Home Credit Default Risk |
1,414,913 | from sklearn.model_selection import train_test_split<import_modules> | bb_group.columns = pd.Index([e[0] + "_" + e[1].upper() for e in bb_group.columns.tolist() ])
bb_group.reset_index(inplace=True)
| Home Credit Default Risk |
1,414,913 | from sklearn.model_selection import train_test_split<split> | buro = buro.merge(bb_group, on = 'SK_ID_BUREAU', how = 'left' ) | Home Credit Default Risk |
1,414,913 | train, valid = train_test_split(train, random_state = 42 )<find_best_params> | avg_buro = buro.groupby('SK_ID_CURR' ).mean() | Home Credit Default Risk |
1,414,913 | rf = RandomForestRegressor(random_state = 42)
print('Parameters currently in use:
')
pprint(rf.get_params() )<find_best_params> | avg_buro['buro_count'] = buro[['SK_ID_BUREAU', 'SK_ID_CURR']].groupby('SK_ID_CURR' ).count() ['SK_ID_BUREAU']
del avg_buro['SK_ID_BUREAU'], bb_group | Home Credit Default Risk |
1,414,913 | best = rf_random.best_params_
best2 = {'n_estimators': 800,
'min_samples_split': 2,
'min_samples_leaf': 2,
'max_features': 'sqrt',
'max_depth': 50,
'bootstrap': False}
best<choose_model_class> | cat_columns = [col for col in installments_payments.columns if installments_payments[col].dtype == 'object'] | Home Credit Default Risk |
1,414,913 | best_random = RandomForestRegressor(n_estimators= 800, min_samples_split = 2, min_samples_leaf= 2, max_features = 'sqrt', max_depth =50, bootstrap= False )<compute_test_metric> | installments_payments = pd.get_dummies(installments_payments,cat_columns, dummy_na = True ) | Home Credit Default Risk |
1,414,913 | def evaluate(model, test_features, test_labels):
predictions = model.predict(test_features)
errors = abs(predictions - test_labels)
mape = 100 * np.mean(errors / test_labels)
accuracy = 100 - mape
print('Model Performance')
print('Average Error: {:0.4f} degrees.'.format(np.mean(errors)))
print('Accuracy = {:0.2f}%... | installments_payments['AMOUNT_DIFF'] = installments_payments['AMT_INSTALMENT'] - installments_payments['AMT_PAYMENT']
| Home Credit Default Risk |
1,414,913 | base_model = RandomForestRegressor(n_estimators = 10, random_state = 42)
base_model.fit(train[feats], train['nota_mat'])
base_accuracy = evaluate(base_model, valid[feats], valid['nota_mat'] )<train_model> | installments_payments['AMOUNT_PERC'] = installments_payments['AMT_PAYMENT']/installments_payments['AMT_INSTALMENT'] | Home Credit Default Risk |
1,414,913 | best_random.fit(train[feats], train['nota_mat'])
random_accuracy = evaluate(best_random, valid[feats], valid['nota_mat'] )<compute_test_metric> | installments_payments['DAYS_P'] = installments_payments['DAYS_ENTRY_PAYMENT']-installments_payments['DAYS_INSTALMENT']
installments_payments['DAYS_I'] = installments_payments['DAYS_INSTALMENT']-installments_payments['DAYS_ENTRY_PAYMENT'] | Home Credit Default Risk |
1,414,913 | print('Improvement of {:0.2f}%.'.format(100 *(random_accuracy - base_accuracy)/ base_accuracy))<predict_on_test> | aggregations = {
'NUM_INSTALMENT_VERSION': ['nunique'],
'DAYS_P': ['max', 'mean', 'sum'],
'DAYS_I': ['max', 'mean', 'sum'],
'AMOUNT_DIFF': ['max', 'mean', 'sum', 'var'],
'AMOUNT_PERC': ['max', 'mean', 'sum', 'var'],
'AMT_INSTALMENT': ['max', 'mean', 'sum'],
'AMT_PAYMENT': ['min', 'max', 'mean', 'sum'],
'DAYS_ENTRY_PAYM... | Home Credit Default Risk |
1,414,913 | test['nota_mat'] = best_random.predict(test[feats] )<data_type_conversions> | installments_payments_agg = installments_payments.groupby('SK_ID_CURR' ).agg(aggregations ) | Home Credit Default Risk |
1,414,913 | test['nota_mat'] = np.exp(test['nota_mat'])
test['codigo_mun'] = test['codigo_mun'].apply(lambda x: x.replace('ID_ID_', ''))
<save_to_csv> | installments_payments_agg.columns = pd.Index(['INSTALL_' + e[0] + "_" + e[1].upper() for e in installments_payments_agg.columns.tolist() ])
installments_payments_agg.reset_index(inplace=True)
| Home Credit Default Risk |
1,414,913 | test[['codigo_mun','nota_mat']].to_csv('rf.csv', index=False )<load_from_csv> | installments_payments = installments_payments.merge(installments_payments_agg, on = 'SK_ID_CURR',how = 'left' ) | Home Credit Default Risk |
1,414,913 | df = pd.read_csv(".. /input/train.csv")
dfTeste = pd.read_csv(".. /input/test.csv" )<prepare_x_and_y> | 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 |
1,414,913 | y = 'nota_mat'<split> | previous_application['APPLICATION_ACTUAL_CREDIT'] = previous_application['AMT_APPLICATION']/previous_application['AMT_CREDIT'] | Home Credit Default Risk |
1,414,913 | train, test = train_test_split(df, random_state=42)
train.shape, test.shape<feature_engineering> | num_aggregations = {
'AMT_ANNUITY': ['min', 'max', 'mean'],
'AMT_APPLICATION': ['min', 'max', 'mean'],
'AMT_CREDIT': ['min', 'max', 'mean'],
'INTEREST_CREDIT_PERC': ['min', 'max', 'mean', 'var'],
'AMT_DOWN_PAYMENT': ['min', 'max', 'mean'],
'AMT_GOODS_PRICE': ['min', 'max', 'mean'],
'HOUR_APPR_PROCESS_START': ['min', 'm... | Home Credit Default Risk |
1,414,913 | df['codigo_mun'] = df['codigo_mun'].str.replace('ID_ID_','')
df['comissionados_por_servidor'] = df['comissionados_por_servidor'].str.replace('%','')
df['area']=df['area'].str.replace(',','')
df['ranking_igm']=df['ranking_igm'].str.replace('º','')
df['densidade_dem']=df['densidade_dem'].str.replace(',','')
dfTeste[... | approved = previous_application[previous_application['NAME_CONTRACT_STATUS'] == 'Approved'] | Home Credit Default Risk |
1,414,913 | df['codigo_mun']=df['codigo_mun'].values.astype('int64')
df['area']=df['area'].values.astype('float64')
df['densidade_dem']=df['densidade_dem'].values.astype('float64')
dfTeste['codigo_mun']=dfTeste['codigo_mun'].values.astype('int64')
dfTeste['area']=dfTeste['area'].values.astype('float64')
dfTeste['densidade_dem... | approved_agg = approved.groupby('SK_ID_CURR' ).agg(num_aggregations ) | Home Credit Default Risk |
1,414,913 | for c in['regiao', 'estado', 'porte']:
df[c] = df[c].astype('category' ).cat.codes
for c in['regiao', 'estado', 'porte']:
dfTeste[c] = dfTeste[c].astype('category' ).cat.codes<data_type_conversions> | approved_agg.columns = pd.Index(['APPROVED_' + e[0] + "_" + e[1].upper() for e in approved_agg.columns.tolist() ] ) | Home Credit Default Risk |
1,414,913 | for c in['densidade_dem', 'perc_pop_econ_ativa', 'exp_vida','exp_anos_estudo','gasto_pc_saude','hab_p_medico','gasto_pc_educacao','exp_anos_estudo','idhm']:
df[c] = df[c].fillna(( df[c].mean()))
for c in['densidade_dem', 'perc_pop_econ_ativa', 'exp_vida','exp_anos_estudo','gasto_pc_saude','hab_p_medico','gasto_pc_educa... | previous_application = previous_application.merge(approved_agg, how='left', on='SK_ID_CURR' ) | Home Credit Default Risk |
1,414,913 | feats = ['exp_anos_estudo']<split> | refused = previous_application[previous_application['NAME_CONTRACT_STATUS'] == 'Refused']
refused_agg = refused.groupby('SK_ID_CURR' ).agg(num_aggregations)
refused_agg.columns = pd.Index(['REFUSED_' + e[0] + "_" + e[1].upper() for e in refused_agg.columns.tolist() ])
refused_agg.reset_index(inplace=True)
previous_a... | Home Credit Default Risk |
1,414,913 | train, test = train_test_split(df, random_state=42)
train.shape, test.shape<choose_model_class> | aggregations = {
'MONTHS_BALANCE': ['max', 'mean', 'size'],
'SK_DPD': ['max', 'mean'],
'SK_DPD_DEF': ['max', 'mean']
}
| Home Credit Default Risk |
1,414,913 | rf = RandomForestRegressor(random_state=42, n_jobs=-1,n_estimators=30000,min_samples_leaf=650)
y='nota_mat'
<train_model> | POS_CASH_AGG = POS_CASH_balance.groupby('SK_ID_CURR' ).agg(aggregations ) | Home Credit Default Risk |
1,414,913 | rf.fit(df[feats], df[y] )<import_modules> | POS_CASH_AGG.columns = pd.Index(['POS_CASH_' + e[0] + "_" + e[1].upper() for e in POS_CASH_AGG.columns.tolist() ] ) | Home Credit Default Risk |
1,414,913 | from sklearn.metrics import mean_squared_error
<predict_on_test> | POS_CASH_AGG['COUNT'] = POS_CASH_AGG.groupby('SK_ID_CURR' ).size() | Home Credit Default Risk |
1,414,913 | valid_preds = rf.predict(test[feats])
mean_squared_error(test[y], valid_preds)**(1/2)
<predict_on_test> | cat_columns = [col for col in POS_CASH_balance.columns if POS_CASH_balance[col].dtype == 'object']
POS_CASH_balance = pd.get_dummies(POS_CASH_balance,cat_columns, dummy_na = True)
POS_CASH_balance = POS_CASH_balance.merge(POS_CASH_AGG, how = 'left', on = 'SK_ID_CURR')
POS_CASH_balance.head() | Home Credit Default Risk |
1,414,913 | dfTeste[y]=rf.predict(dfTeste[feats])
<save_to_csv> | POS_CASH_balance = POS_CASH_balance.groupby('SK_ID_CURR' ).mean().reset_index() | Home Credit Default Risk |
1,414,913 | dfTeste[['codigo_mun', y]].to_csv('rf3.csv', index=False )<load_from_csv> | del POS_CASH_AGG, POS_CASH_balance['SK_ID_PREV'] | Home Credit Default Risk |
1,414,913 | df = pd.read_csv('.. /input/train.csv' )<load_from_csv> | 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 |
1,414,913 | test = pd.read_csv('.. /input/test.csv' )<feature_engineering> | 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 |
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