kernel_id int64 24.2k 23.3M | prompt stringlengths 8 1.85M | completetion stringlengths 1 182k | comp_name stringlengths 5 57 |
|---|---|---|---|
1,276,329 | def conv3x3(in_planes, out_planes, stride=1, padding=1):
return nn.Conv2d(in_planes, out_planes, kernel_size=3,
stride=stride, padding=padding, bias=False)
def conv1x1(in_planes, out_planes, stride=1):
return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)
class SSELayer(nn.Module):
def __i... | if run_mode == 'LGBM_KFold':
folds = KFold(n_splits=10, shuffle=True, random_state=1024)
oof_preds = np.zeros(train.shape[0])
sub_preds = np.zeros(test.shape[0])
feature_importance = pd.DataFrame()
feats = train.columns
start = time()
for n_fold,(train_index, valid_index)in enumerate(folds.split(train, target)) :
tr... | Home Credit Default Risk |
1,276,329 | class AverageMeter:
def __init__(self):
self.reset()
def reset(self):
self.val = 0.0
self.avg = 0.0
self.sum = 0.0
self.count = 0
def update(self, val, n=1):
self.val = val
self.sum += val * n
self.count += n
self.avg = self.sum / self.count
def accuracy_score_torch(y_pred, y):
y_pred = torch.argmax(y_pred, axis=1 ).cp... | if run_mode == 'LGBM_KFold':
submission = pd.DataFrame()
submission['SK_ID_CURR'] = test_id
submission['TARGET'] = sub_preds
submission.to_csv('LGBM_SKFold.csv', index=False ) | Home Credit Default Risk |
1,276,329 | def train(
params,
model,
optimizer,
criterion,
dataloader,
parent_bar=None,
):
model.train()
losses = AverageMeter()
metrics = AverageMeter()
for x, y in progress_bar(dataloader, parent=parent_bar):
x = x.to(dtype=torch.float32, device=DEVICE)
y = y.to(dtype=torch.long, device=DEVICE)
optimizer.zero_grad()
y_pred ... | if run_mode == 'train_estimator_RFR':
estimator = RandomForestRegressor(n_estimators=125,
max_features=0.2,
min_samples_split=2,
min_samples_leaf=75,
n_jobs=-1,
random_state=42,
verbose=0)
print("
Preparing to train the following estimator:
{}".format(estimator))
start = time()
estimator.fit(X_train, y_train)
end = t... | Home Credit Default Risk |
1,276,329 | <split><EOS> | if 'train_estimator_' in run_mode:
pred_test = estimator.predict(test)
print("
Preparing prediction for submission.")
submission = pd.DataFrame()
submission['SK_ID_CURR'] = test_id
submission['TARGET'] = pred_test
submission.head()
file_name = run_mode.split('train_estimator_')[1] + '.csv'
submission.to_csv(file_name... | Home Credit Default Risk |
1,185,243 | <SOS> metric: AUC Kaggle data source: home-credit-default-risk<compute_test_metric> | warnings.simplefilter(action='ignore', category=FutureWarning)
py.init_notebook_mode(connected=True)
%matplotlib inline
cf.go_offline()
| Home Credit Default Risk |
1,185,243 | cv_score = accuracy_score(train_df[TARGET], np.argmax(oof, axis=1))
logger.info(f'CV: {cv_score:.5f}' )<categorify> | def one_hot_encoder(df, nan_as_category = True):
original_columns = list(df.columns)
categorical_columns = [col for col in df.columns if df[col].dtype == 'object']
df = pd.get_dummies(df, columns= categorical_columns, dummy_na= nan_as_category)
new_columns = [c for c in df.columns if c not in original_columns]
return... | Home Credit Default Risk |
1,185,243 | test_transform = A.Compose([
A.Resize(IMSIZE, IMSIZE, p=1),
A.Normalize(( 0.485, 0.456, 0.406),(0.229, 0.224, 0.225)) ,
ToTensorV2() ,
])
test_dataloader = get_dataloader(
submission_df[ID].apply(lambda x: os.path.join(PATH['test_image_dir'], x)) ,
submission_df[TARGET],
transform=test_transform,
with_memory_cache=Tr... | num_rows = None
nan_as_category = True | Home Credit Default Risk |
1,185,243 | predictions = np.zeros(( len(submission_df), 10))
for state_dict in best_state_dicts:
model = Model().to(DEVICE)
model.load_state_dict(state_dict)
model.eval()
_predictions = []
for x, _ in test_dataloader:
x = x.to(dtype=torch.float32, device=DEVICE)
with torch.no_grad() :
y_pred = model(x)
_predictions.append(y_p... | print("Start Train Test................." ) | Home Credit Default Risk |
1,185,243 | np.save('oof', oof)
np.save('predictions', predictions)
saved_best_state_dicts = {}
for i, bsd in enumerate(best_state_dicts):
saved_best_state_dicts[f'f{i}'] = bsd
torch.save(saved_best_state_dicts, 'best_state_dicts.pth')
submission_df[TARGET] = np.argmax(predictions, axis=1 ).tolist()<save_to_csv> | df = pd.read_csv('.. /input/application_train.csv', nrows= num_rows)
test_df = pd.read_csv('.. /input/application_test.csv', nrows= num_rows)
print("Train samples: {}, test samples: {}".format(len(df), len(test_df)))
df = df.append(test_df ).reset_index()
del test_df
gc.collect() | Home Credit Default Risk |
1,185,243 | submission_df.to_csv('submission.csv', index=False)
FileLink('submission.csv' )<load_from_csv> | for bin_feature in ['CODE_GENDER', 'FLAG_OWN_CAR', 'FLAG_OWN_REALTY']:
df[bin_feature], uniques = pd.factorize(df[bin_feature])
df, cat_cols = one_hot_encoder(df, nan_as_category)
df['DAYS_EMPLOYED'].replace(365243, np.nan, inplace= True ) | Home Credit Default Risk |
1,185,243 | train_users_game1 = pd.read_csv('/kaggle/input/ds2019uec-task2/train_users_game1.csv')
train_users_game2 = pd.read_csv('/kaggle/input/ds2019uec-task2/train_users_game2.csv')
test_users_game1 = pd.read_csv('/kaggle/input/ds2019uec-task2/test_users_game1.csv')
test_user_ids = pd.read_csv('/kaggle/input/ds2019uec-task2... | df['DAYS_EMPLOYED_PERC'] = df['DAYS_EMPLOYED'] / df['DAYS_BIRTH']
df['INCOME_CREDIT_PERC'] = df['AMT_INCOME_TOTAL'] / df['AMT_CREDIT']
df['INCOME_PER_PERSON'] = df['AMT_INCOME_TOTAL'] / df['CNT_FAM_MEMBERS']
df['ANNUITY_INCOME_PERC'] = df['AMT_ANNUITY'] / df['AMT_INCOME_TOTAL'] | Home Credit Default Risk |
1,185,243 | users_game1 = pd.concat([train_users_game1, test_users_game1] ).drop(['play_hour'], axis=1 ).drop_duplicates()
users_game1['label'] = 1<concatenate> | a = df['DAYS_EMPLOYED_PERC'].tolist()
a = [x for x in a if str(x)!= 'nan']
b = df['INCOME_CREDIT_PERC'].tolist()
b = [x for x in b if str(x)!= 'nan']
c = df['INCOME_PER_PERSON'].tolist()
c = [x for x in c if str(x)!= 'nan']
d = df['ANNUITY_INCOME_PERC'].tolist()
d = [x for x in d if str(x)!= 'nan'] | Home Credit Default Risk |
1,185,243 | user_ids = pd.concat([train_users_game1['user_id'], train_users_game2['user_id'], test_user_ids['user_id']] ).unique()<drop_column> | print("End Train Test.................. " ) | Home Credit Default Risk |
1,185,243 | users_game1_matrix = users_game1.set_index(['user_id', 'game_title'])['label'].unstack().reindex(user_ids ).fillna(0 )<concatenate> | print("Start Bureau................ " ) | Home Credit Default Risk |
1,185,243 | train_user_ids = pd.concat([train_users_game1['user_id'], train_users_game2['user_id']] ).unique()
test_user_ids = test_user_ids['user_id'].values<compute_test_metric> | bureau = pd.read_csv('.. /input/bureau.csv', nrows = num_rows ) | Home Credit Default Risk |
1,185,243 | users_similarity = 1 - cosine_distances(users_game1_matrix.loc[test_user_ids], users_game1_matrix.loc[train_user_ids] )<remove_duplicates> | 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 |
1,185,243 | users_game2 = train_users_game2.drop(['play_hour', 'predict_game_id'], axis=1 ).drop_duplicates()
users_game2['label'] = 1<filter> | 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 |
1,185,243 | test_users_game2_matrix =(users_similarity @ users_game2_matrix.loc[train_user_ids] )<data_type_conversions> | 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 |
1,185,243 | sample_submission['purchased_games'] = test_users_game2_matrix.apply(lambda x: ' '.join(x.sort_values(ascending=False)[:10].index.astype('str')) , axis=1 )<data_type_conversions> | 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 |
1,185,243 | sample_submission.loc[test_users_game2_matrix.sum(axis=1)== 0, 'purchased_games'] = ' '.join(train_users_game2['predict_game_id'].value_counts(normalize=True ).index.astype('str')[:10] )<save_to_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 |
1,185,243 | sample_submission.to_csv('submission.csv', index=None )<import_modules> | print("End Bureau................ " ) | Home Credit Default Risk |
1,185,243 | import pandas as pd
import numpy as np
from sklearn import tree
import graphviz
from sklearn.model_selection import cross_val_score<load_from_csv> | print("Start previous_application................ " ) | Home Credit Default Risk |
1,185,243 | df = pd.read_csv('/kaggle/input/predict-the-income-bi-hack/train.csv')
dft = pd.read_csv('/kaggle/input/predict-the-income-bi-hack/test.csv')
print('train dataset length',len(df),'
test dataset length',len(dft))
<drop_column> | prev = pd.read_csv('.. /input/previous_application.csv', nrows = num_rows ) | Home Credit Default Risk |
1,185,243 | ids=dft['ID']
df.drop('ID', axis=1,inplace =True)
dft.drop('ID', axis=1,inplace =True )<categorify> | 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 |
1,185,243 | df = pd.get_dummies(df, columns=['Work', 'Education', 'Marital_Status', 'Occupation', 'Relationship', 'Race', 'Gender','Nationality'])
dft = pd.get_dummies(dft, columns=['Work', 'Education', 'Marital_Status', 'Occupation', 'Relationship', 'Race', 'Gender','Nationality'] )<drop_column> | prev['APP_CREDIT_PERC'] = prev['AMT_APPLICATION'] / prev['AMT_CREDIT'] | Home Credit Default Risk |
1,185,243 | df.drop('Nationality_Holand-Netherlands',axis=1, inplace=True )<count_values> | 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 |
1,185,243 | df['Income'].value_counts()<feature_engineering> | 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 |
1,185,243 | df["Income"] = df["Income"].replace({'<=50K':1,'>50K':0} )<create_dataframe> | 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 |
1,185,243 | d_train = df.copy()
d_test = dft.copy()
df1 = df<prepare_x_and_y> | print("End previous_application................ " ) | Home Credit Default Risk |
1,185,243 | X = d_train.drop('Income', axis=1)
y = d_train['Income']<import_modules> | print("Start POS_CASH_balance................ " ) | Home Credit Default Risk |
1,185,243 | from sklearn.model_selection import train_test_split<import_modules> | pos = pd.read_csv('.. /input/POS_CASH_balance.csv', nrows = num_rows ) | Home Credit Default Risk |
1,185,243 | from sklearn.model_selection import train_test_split<split> | 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 |
1,185,243 | X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.30,random_state=42 )<choose_model_class> | 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 |
1,185,243 | t = tree.DecisionTreeClassifier(criterion='entropy', max_depth=7 )<train_model> | print("Start POS_CASH_balance................ " ) | Home Credit Default Risk |
1,185,243 | t = t.fit(X_train,y_train )<compute_test_metric> | 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 |
1,185,243 | t.score(X_test,y_test )<predict_on_test> | 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 |
1,185,243 | sol=t.predict(dft)
print(sol )<save_to_csv> | 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 |
1,185,243 | with open('income-predicted.csv','w')as fw:
fw.write('ID,Income
')
ct=24001
for i in sol:
s=""
if i==1:
s="<=50K"
else:
s=">50K"
fw.write(str(ct)+','+str(s)+'
')
ct+=1<load_from_csv> | print("End POS_CASH_balance................ " ) | Home Credit Default Risk |
1,185,243 | sdf = pd.read_csv('income-predicted.csv', sep=',')
sdf.head()<count_values> | print("Start credit_card_balance................ " ) | Home Credit Default Risk |
1,185,243 | sdf.Income.value_counts()<load_from_csv> | cc = pd.read_csv('.. /input/credit_card_balance.csv', nrows = num_rows ) | Home Credit Default Risk |
1,185,243 | %%capture
train = pd.read_csv('.. /input/fakenewsvortexbsb/train_df.csv', sep=';', error_bad_lines=False, quoting=3);<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 |
1,185,243 | example = train["manchete"][1]
print(unidecode(example))<categorify> | print("End credit_card_balance................ " ) | Home Credit Default Risk |
1,185,243 | letters_only=re.sub("[^a-zA-Z]"," ",unidecode(example))
print(letters_only )<string_transform> | 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 |
1,185,243 | lower_case=letters_only.lower()
words=lower_case.split()<string_transform> | print("Done.;.............. ")
| Home Credit Default Risk |
1,185,243 | print(stopwords.words("portuguese"))<string_transform> | train_df = df[df['TARGET'].notnull() ]
test_df = df[df['TARGET'].isnull() ] | Home Credit Default Risk |
1,185,243 | stop = stopwords.words("portuguese" )<categorify> | 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 |
1,185,243 | lista_stop = [unidecode(x)for x in stop]
print(lista_stop )<define_variables> | 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 |
1,185,243 | words=[w for w in words if not w in lista_stop]
print(words )<string_transform> | clf_catboost = CatBoostClassifier(iterations=1200,
learning_rate=0.1,
depth=7,
l2_leaf_reg=40,
bootstrap_type='Bernoulli',
subsample=0.7,
scale_pos_weight=5,
eval_metric='AUC',
metric_period=50,
od_type='Iter',
od_wait=45,
random_seed=15,
allow_writing_files=False)
clf_catboost.fit(data_only,labels,verbose=True ) | Home Credit Default Risk |
1,185,243 | def review_to_words(raw_review):
raw_review = unidecode(raw_review)
raw_review.lstrip('Jovem Pan')
letters_only=re.sub("[^a-zA-Z]"," ",raw_review)
words=letters_only.lower().split()
meaningful_words=[w for w in words if not w in lista_stop]
return(' '.join(meaningful_words))<string_transform> | pred = clf_catboost.predict_proba(test_df)
test_df['TARGET'] = pred[:, 0] | Home Credit Default Risk |
1,185,243 | <find_best_params><EOS> | test_df[['SK_ID_CURR', 'TARGET']].to_csv('submission_catboost1.csv', index= False ) | Home Credit Default Risk |
10,259,780 | <SOS> metric: AUC Kaggle data source: home-credit-default-risk<categorify> | color = sns.color_palette()
py.init_notebook_mode(connected=True)
init_notebook_mode(connected=True)
offline.init_notebook_mode()
cf.go_offline()
| Home Credit Default Risk |
10,259,780 | clean_train_review=[]
for i in range(0,num_reviews):
clean_train_review.append(review_to_words(train['manchete'][i]))<normalization> | application_train = pd.read_csv('/kaggle/input/home-credit-default-risk/application_train.csv')
POS_CASH_balance = pd.read_csv('/kaggle/input/home-credit-default-risk/POS_CASH_balance.csv')
bureau_balance = pd.read_csv('/kaggle/input/home-credit-default-risk/bureau_balance.csv')
previous_application = pd.read_csv('/... | Home Credit Default Risk |
10,259,780 | vectorizer=CountVectorizer(analyzer='word',tokenizer=None,preprocessor = None, stop_words = None,max_features = 7000)
train_data_features=vectorizer.fit_transform(clean_train_review)
train_data_features=train_data_features.toarray()<feature_engineering> | application_train.isnull().mean().sort_values(ascending = False ) | Home Credit Default Risk |
10,259,780 | vcab=vectorizer.get_feature_names()
<prepare_x_and_y> | POS_CASH_balance.isnull().mean().sort_values(ascending = False ) | Home Credit Default Risk |
10,259,780 | train_y = train["Class"]<split> | bureau_balance.isnull().mean().sort_values(ascending = False ) | Home Credit Default Risk |
10,259,780 | X_train, X_test, y_train, y_test = train_test_split(train_data_features, train_y, test_size=0.25, random_state=42 )<train_model> | previous_application.isnull().mean().sort_values(ascending = False ) | Home Credit Default Risk |
10,259,780 | model = KNeighborsClassifier(n_neighbors=3)
%time model = model.fit(X_train, y_train )<predict_on_test> | installments_payments.isnull().mean().sort_values(ascending = False ) | Home Credit Default Risk |
10,259,780 | result = model.predict(X_test )<compute_test_metric> | credit_card_balance.isnull().mean().sort_values(ascending = False ) | Home Credit Default Risk |
10,259,780 | accuracy_score(y_test, result )<compute_test_metric> | bureau.isnull().mean().sort_values(ascending = False ) | Home Credit Default Risk |
10,259,780 | print(classification_report(y_test, result))<choose_model_class> | for c in application_train.columns:
if(c!='SK_ID_CURR')&(application_train[c].dtypes==object):
LE = LabelEncoder()
LE.fit(list(application_train[c].values.astype('str')) + list(application_test[c].values.astype('str')))
application_train[c] = LE.transform(list(application_train[c].values.astype('str')))
application_t... | Home Credit Default Risk |
10,259,780 | nb = MultinomialNB()
%time nb = nb.fit(X_train, y_train)
result = nb.predict(X_test )<compute_test_metric> | application_train.fillna(-1, inplace = True ) | Home Credit Default Risk |
10,259,780 | accuracy_score(y_test, result )<compute_test_metric> | X = application_train.drop(['SK_ID_CURR', 'TARGET'],axis=1)
Y = application_train.TARGET
xgb = XGBClassifier(n_estimators=500, max_depth=8, random_state=2018)
xgb.fit(X, Y ) | Home Credit Default Risk |
10,259,780 | print(classification_report(y_test, result))<train_model> | df = application_train.append(application_test ).reset_index()
df['DAYS_EMPLOYED'].replace(365243, -1, inplace= True)
df['DAYS_EMPLOYED_PERC'] = df['DAYS_EMPLOYED'] / df['DAYS_BIRTH']
df['INCOME_CREDIT_PERC'] = df['AMT_INCOME_TOTAL'] / df['AMT_CREDIT']
df['INCOME_PER_PERSON'] = df['AMT_INCOME_TOTAL'] / df['CNT_FAM_MEM... | Home Credit Default Risk |
10,259,780 | clf2 = DecisionTreeClassifier(random_state=42)
%time clf2 = clf2.fit(X_train, y_train)
result = clf2.predict(X_test )<compute_test_metric> | bb_aggregations = {'MONTHS_BALANCE': ['min', 'max', 'size']}
bb_agg = bureau_balance.groupby('SK_ID_BUREAU' ).agg(bb_aggregations)
bb_agg.columns = pd.Index([col[0] + "_" + col[1].upper() for col in bb_agg.columns.tolist() ])
bureau = bureau.join(bb_agg, how='left', on='SK_ID_BUREAU')
bureau.drop(['SK_ID_BUREAU'], a... | Home Credit Default Risk |
10,259,780 | accuracy_score(y_test, result )<compute_test_metric> | for c in previous_application.columns:
if previous_application[c].dtypes==object:
LE = LabelEncoder()
previous_application[c] = LE.fit_transform(list(previous_application[c].values.astype('str')))
aggregations = {
'AMT_ANNUITY': ['min', 'max', 'mean'],
'AMT_APPLICATION': ['min', 'max', 'mean'],
'AMT_CREDIT': ['min', '... | Home Credit Default Risk |
10,259,780 | print(classification_report(y_test, result))<predict_on_test> | for c in POS_CASH_balance.columns:
if POS_CASH_balance[c].dtypes==object:
LE = LabelEncoder()
POS_CASH_balance[c] = LE.fit_transform(list(POS_CASH_balance[c].values.astype('str')))
aggregations = {
'MONTHS_BALANCE': ['max', 'mean', 'size'],
'SK_DPD': ['max', 'mean', 'var'],
'SK_DPD_DEF': ['max', 'mean', 'var']
}
pos_a... | Home Credit Default Risk |
10,259,780 | forest = RandomForestClassifier(random_state=42)
%time forest = forest.fit(X_train, y_train)
result = forest.predict(X_test )<compute_test_metric> | for c in installments_payments.columns:
if installments_payments[c].dtypes==object:
LE = LabelEncoder()
installments_payments[c] = LE.fit_transform(list(installments_payments[c].values.astype('str')))
aggregations = {
'NUM_INSTALMENT_VERSION': ['nunique'],
'DPD': ['max', 'mean', 'sum'],
'DBD': ['max', 'mean', 'sum'],
... | Home Credit Default Risk |
10,259,780 | accuracy_score(y_test, result )<compute_test_metric> | for c in credit_card_balance.columns:
if credit_card_balance[c].dtypes==object:
LE = LabelEncoder()
credit_card_balance[c] = LE.fit_transform(list(credit_card_balance[c].values.astype('str')))
credit_card_balance.drop(['SK_ID_PREV'], axis= 1, inplace = True)
cc_agg = credit_card_balance.groupby('SK_ID_CURR' ).agg(['m... | Home Credit Default Risk |
10,259,780 | print(classification_report(y_test, result))<train_model> | df_train, df_test = df.iloc[:len(application_train)], df.iloc[len(application_train):]
del application_train, application_test, df
gc.collect() | Home Credit Default Risk |
10,259,780 | clf3 = GradientBoostingClassifier(random_state=42)
%time clf3 = clf3.fit(X_train, y_train)
result = clf3.predict(X_test )<compute_test_metric> | folds = StratifiedKFold(n_splits= 10, shuffle=True, random_state=2020)
sub_preds = np.zeros(df_test.shape[0])
feats = [f for f in df_train.columns if f not in ['TARGET','SK_ID_CURR','SK_ID_BUREAU','SK_ID_PREV','index']]
for n_fold,(train_idx, valid_idx)in enumerate(folds.split(df_train[feats], df_train['TARGET'])) :
... | Home Credit Default Risk |
10,259,780 | <compute_test_metric><EOS> | df_test[['SK_ID_CURR', 'TARGET']].to_csv('submission', index= False ) | Home Credit Default Risk |
11,198,944 | <SOS> metric: AUC Kaggle data source: home-credit-default-risk<predict_on_test> | pd.set_option('display.max_columns', None)
%matplotlib inline
bureau_balance = pd.read_csv('.. /input/bureau_balance.csv')
bureau_balance['STATUS_mod'] = bureau_balance.STATUS.map({'0':0, '1':1, '2':2, '3':3, '4':4, '5':5, 'X':np.nan, 'C':0} ).map(lambda x: 0 if x=='C' else x ).interpolate(method = 'linear')
bureau_... | Home Credit Default Risk |
11,198,944 | clf = LogisticRegression(random_state=42, solver='lbfgs')
%time clf = clf.fit(X_train, y_train)
result = clf.predict(X_test )<compute_test_metric> | pd.set_option('display.max_columns', None)
%matplotlib inline
warnings.filterwarnings("ignore")
application_train = pd.read_csv('.. /input/application_train.csv')
credit_card_balance = pd.read_csv('.. /input/credit_card_balance.csv')
credit_card_balance = credit_card_balance.sort_values(['SK_ID_CURR', 'SK_ID_PREV',... | Home Credit Default Risk |
11,198,944 | accuracy_score(y_test, result )<compute_test_metric> | pd.set_option('display.max_columns', None)
%matplotlib inline
warnings.filterwarnings("ignore")
installments_payments = pd.read_csv('.. /input/installments_payments.csv')
installments_payments = installments_payments.sort_values(['SK_ID_CURR', 'SK_ID_PREV', 'NUM_INSTALMENT_NUMBER'])
previous_application = pd.read_c... | Home Credit Default Risk |
11,198,944 | print(classification_report(y_test, result))<predict_on_test> | pd.set_option('display.max_columns', None)
%matplotlib inline
warnings.filterwarnings("ignore")
application_train = pd.read_csv('.. /input/application_train.csv')
POS_CASH_balance = pd.read_csv('.. /input/POS_CASH_balance.csv')
POS_CASH_balance = POS_CASH_balance.sort_values(['SK_ID_CURR', 'SK_ID_PREV', 'MONTHS_BAL... | Home Credit Default Risk |
11,198,944 | clf4 = SVC(random_state=42)
%time clf4 = clf4.fit(X_train, y_train)
result = clf4.predict(X_test )<compute_test_metric> | pd.set_option('display.max_columns', None)
%matplotlib inline
warnings.filterwarnings("ignore")
previous_application = pd.read_csv(".. /input/previous_application.csv")
previous_application = previous_application.sort_values(['SK_ID_CURR', 'DAYS_DECISION'])
cat_col = []
for i in range(len(previous_application.colum... | Home Credit Default Risk |
11,198,944 | accuracy_score(y_test, result )<compute_test_metric> | bureau = pd.read_csv('.. /input/bureau.csv')
bureau_balance = pd.read_csv('.. /input/bureau_balance.csv')
med = bureau.AMT_CREDIT_SUM.median()
bureau.AMT_CREDIT_SUM = bureau.AMT_CREDIT_SUM.fillna(med)
med = bureau.AMT_CREDIT_SUM_DEBT.median()
bureau.AMT_CREDIT_SUM_DEBT = bureau.AMT_CREDIT_SUM_DEBT.fillna(med)
burea... | Home Credit Default Risk |
11,198,944 | print(classification_report(y_test, result))<predict_on_test> | a = time.time()
row1=None
row2=None
row3=None
app_train = pd.read_csv('.. /input/application_train.csv', nrows=row1 ).sort_values('SK_ID_CURR')
app_test = pd.read_csv('.. /input/application_test.csv', nrows=row1 ).sort_values('SK_ID_CURR')
bureau = pd.read_csv('.. /input/bureau.csv', nrows=row2 ).sort_values(['SK_ID_... | Home Credit Default Risk |
11,198,944 | def prevendo_noticias(string, model):
to_array=[]
to_array.append(review_to_words(string))
sample_final=vectorizer.transform(to_array)
sample_final=sample_final.toarray()
result = model.predict(sample_final)
if result[0] == 1:
label = 'Fake News'
else:
label = 'Verdadeira'
return label, string<compute_test_metric> | full_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")
test_df
| Home Credit Default Risk |
11,198,944 | prevendo_noticias('Bolsonaro pessoalmente incendêia a amazonia e mata as girafas', forest )<drop_column> | full_df = pd.get_dummies(full_df)
test_df = pd.get_dummies(test_df)
full_df, test_df = full_df.align(test_df, join = 'inner', axis = 1)
test_df.head(10)
test_df.count() | Home Credit Default Risk |
11,198,944 | prevendo_noticias('Jornalista joga água benta em Temer e ele admite que impeachment foi golpe', forest )<train_model> | Home Credit Default Risk | |
11,198,944 | model_final = RandomForestClassifier(random_state=42)
<define_search_space> | def model(features, test_features, encoding = 'ohe', n_folds = 5):
train_ids = features['SK_ID_CURR']
test_ids = test_features['SK_ID_CURR']
labels = features['TARGET']
features = features.drop(columns = ['SK_ID_CURR', 'TARGET'])
test_features = test_features.drop(columns = ['SK_ID_CURR'])
if encoding == 'ohe':
fea... | Home Credit Default Risk |
11,198,944 | <train_on_grid><EOS> | submission, fi, metrics = model(full_df, test_df)
print('Baseline metrics')
print(metrics)
submission.to_csv('baseline_lgb.csv', index = False)
| Home Credit Default Risk |
9,356,808 | <SOS> metric: AUC Kaggle data source: home-credit-default-risk<find_best_params> | import numpy as np
import pandas as pd | Home Credit Default Risk |
9,356,808 | CV_rf.best_params_<train_model> | from tqdm.notebook import tqdm
import random
import gc
import time | Home Credit Default Risk |
9,356,808 | model_fit = RandomForestClassifier(random_state=42, bootstrap= True, criterion= 'entropy', n_estimators= 800 )<train_model> | from sklearn.preprocessing import LabelEncoder, StandardScaler
from sklearn.metrics import roc_auc_score
from sklearn.model_selection import StratifiedKFold
from sklearn.ensemble import ExtraTreesClassifier | Home Credit Default Risk |
9,356,808 | %time model_fit = model_fit.fit(X_train, y_train)
result = model_fit.predict(X_test )<compute_test_metric> | import lightgbm as lgb | Home Credit Default Risk |
9,356,808 | accuracy_score(y_test, result )<train_model> | gc.enable() | Home Credit Default Risk |
9,356,808 | %time model_final = model_fit.fit(train_data_features, train_y )<load_from_csv> | train_data = pd.read_csv('/kaggle/input/home-credit-default-risk/application_train.csv',
na_values=['XNA', 'XAP'], na_filter=True)
test_data = pd.read_csv('/kaggle/input/home-credit-default-risk/application_test.csv',
na_values=['XNA', 'XAP'], na_filter=True ) | Home Credit Default Risk |
9,356,808 | test = pd.read_csv('.. /input/fakenewsvortexbsb/sample_submission.csv', sep=';', error_bad_lines=False, quoting=3);<categorify> | train_counts = train_data.count().sort_values() /len(train_data)
test_counts = test_data.count().sort_values() /len(test_data ) | Home Credit Default Risk |
9,356,808 | clean_test_review=[]
for i in range(0,num_reviews):
clean_test_review.append(review_to_words(test['Manchete'][i]))<categorify> | cols = set(train_counts[(train_counts < 1)&(train_counts > 0.99)].index)- set(test_counts[(test_counts < 1)&(test_counts > 0.9)].index ) | Home Credit Default Risk |
9,356,808 | test_data_features = vectorizer.transform(clean_test_review)
test_data_features=test_data_features.toarray()<predict_on_test> | train_data.dropna(subset=cols, inplace=True ) | Home Credit Default Risk |
9,356,808 | result_test = model_final.predict(test_data_features )<save_to_csv> | train_target = train_data[['SK_ID_CURR', 'TARGET']] | Home Credit Default Risk |
9,356,808 | minha_sub = pd.DataFrame({'index': test.index, 'Category': result_test})
minha_sub.to_csv('submission.csv', index=False )<import_modules> | submit = test_data[['SK_ID_CURR']] | Home Credit Default Risk |
9,356,808 | import pandas as pd
import numpy as np
import sklearn
import matplotlib.pyplot as plt
from sklearn import ensemble, neighbors
from sklearn.model_selection import cross_val_score
from sklearn.metrics import mean_squared_log_error, make_scorer
from sklearn.feature_selection import f_regression
import os<load_from_csv> | train_data.drop(columns=['TARGET'], inplace=True ) | Home Credit Default Risk |
9,356,808 | test_d = pd.read_csv('.. /input/test.csv',index_col='Id')
train_d = pd.read_csv('.. /input/train.csv', index_col='Id' )<concatenate> | test_data['IS_TRAIN'] = 0
train_data['IS_TRAIN'] = 1 | Home Credit Default Risk |
9,356,808 | test_d['median_house_value'] = np.nan
data = train_d.append(test_d, sort=False )<feature_engineering> | application_data = train_data.append(test_data ) | Home Credit Default Risk |
9,356,808 | train_d['median_age_sum'] = train_d.groupby('median_age')['median_house_value'].transform('sum')
ages = train_d['median_age'].value_counts()
for i, row in train_d.iterrows() :
train_d.loc[i,'median_age_freq'] = ages[train_d.loc[i,'median_age']]<feature_engineering> | del(train_data)
del(test_data ) | Home Credit Default Risk |
9,356,808 | data['rooms_not_bedrooms'] = np.subtract(data['total_rooms'],data['total_bedrooms'])
data['bedrooms_per_house'] = np.divide(data['total_bedrooms'],data['households'])
data['rooms_not_bedrooms_per_house'] = np.divide(data['total_bedrooms'],data['households'])
data['pop_per_house'] = np.divide(data['population'],data[... | appl_counts = application_data.count().sort_values() /len(application_data ) | Home Credit Default Risk |
9,356,808 | ages_disc=[]
for i,row in data.iterrows() :
if row['median_age'] < 16:
ages_disc.append(0)
elif row['median_age'] < 36:
ages_disc.append(1)
elif row['median_age'] < 52:
ages_disc.append(2)
else:
ages_disc.append(3)
data['age_class'] = ages_disc<data_type_conversions> | appl_counts[(appl_counts < 0.6)] | Home Credit Default Risk |
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