kernel_id int64 24.2k 23.3M | prompt stringlengths 8 1.85M | completetion stringlengths 1 182k | comp_name stringlengths 5 57 |
|---|---|---|---|
1,289,226 | submission.to_csv('./submission.csv', index=False )<set_options> | opt_hyp['n_estimators'] = opt_hyp['n_estimators'].astype(np.int32)
opt_hyp.corr() ['score'] | Home Credit Default Risk |
1,289,226 | sns.set_style('whitegrid')
warnings.filterwarnings("ignore", category=DeprecationWarning)
%matplotlib inline
<load_from_csv> | hyp = hyp.drop(columns = ['metric', 'set', 'verbose'])
hyp['n_estimators'] = hyp['n_estimators'].astype(np.int32)
hyp['min_child_samples'] = hyp['min_child_samples'].astype(np.int32)
hyp['num_leaves'] = hyp['num_leaves'].astype(np.int32)
hyp['subsample_for_bin'] = hyp['subsample_for_bin'].astype(np.int32)
hyp = pd... | Home Credit Default Risk |
1,289,226 | train = pd.read_csv('/kaggle/input/eval-lab-2-f464/train.csv')
train.drop('id',axis = 1,inplace = True)
print('Train rows:', train.shape[0] )<normalization> | lr = LinearRegression()
lr.fit(train, train_labels ) | Home Credit Default Risk |
1,289,226 | train = shuffle(train )<count_values> | import lightgbm as lgb | Home Credit Default Risk |
1,289,226 | train['class'].value_counts()<prepare_x_and_y> | train = pd.read_csv('.. /input/home-credit-simple-featuers/simple_features_train.csv')
print('Full Training Features Shape: ', train.shape)
test = pd.read_csv('.. /input/home-credit-simple-featuers/simple_features_test.csv')
print('Full Testing Features Shape: ', test.shape ) | Home Credit Default Risk |
1,289,226 | Y_class = train['class']
train = train.drop('class', axis=1 )<split> | train_labels = np.array(train['TARGET'].astype(np.int32)).reshape(( -1,))
train = train.drop(columns = ['SK_ID_CURR', 'TARGET'])
test_ids = list(test['SK_ID_CURR'])
test = test.drop(columns = ['SK_ID_CURR'] ) | Home Credit Default Risk |
1,289,226 | Y = Y_class.to_numpy()
X = train.to_numpy()
X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.1,random_state=42)
print(X_train.shape,y_train.shape)
print(X_test.shape,y_test.shape )<train_on_grid> | random_best = ast.literal_eval(random.loc[0, 'hyperparameters'])
rmodel = lgb.LGBMClassifier(**random_best)
rmodel.fit(train, train_labels ) | Home Credit Default Risk |
1,289,226 | X_resampled, y_resampled = SMOTE().fit_resample(X, Y)
print(sorted(Counter(y_resampled ).items()))
<choose_model_class> | rpreds = rmodel.predict_proba(test)[:, 1]
rsub = pd.DataFrame({'SK_ID_CURR': test_ids, 'TARGET': rpreds})
rsub.to_csv('submission_random_search.csv', index = False ) | Home Credit Default Risk |
1,289,226 | RANDOM_SEED = 42
clf1 = KNeighborsClassifier()
clf2 = RandomForestClassifier(random_state=RANDOM_SEED)
clf3 = GaussianNB()
clf4 = ExtraTreesClassifier(random_state=RANDOM_SEED)
clf5 = XGBClassifier(random_state=RANDOM_SEED)
clf6 = LogisticRegression(random_state=RANDOM_SEED)
sclf = Classifier(classifiers=[clf2, clf... | bayes_best = ast.literal_eval(opt.loc[0, 'hyperparameters'])
bmodel = lgb.LGBMClassifier(**bayes_best)
bmodel.fit(train, train_labels ) | Home Credit Default Risk |
1,289,226 | clf1.fit(X,Y)
clf2.fit(X,Y)
clf3.fit(X,Y)
clf4.fit(X,Y)
sclf.fit(X,Y)
clf5.fit(X,Y)
clf6.fit(X,Y )<load_from_csv> | bpreds = bmodel.predict_proba(test)[:, 1]
bsub = pd.DataFrame({'SK_ID_CURR': test_ids, 'TARGET': bpreds})
bsub.to_csv('submission_bayesian_optimization.csv', index = False ) | Home Credit Default Risk |
1,289,226 | test = pd.read_csv('/kaggle/input/eval-lab-2-f464/test.csv')
print('Data columns:', test.columns.drop(['id'] ).shape[0] )<drop_column> | random_fi = pd.DataFrame({'feature': features, 'importance': rmodel.feature_importances_})
bayes_fi = pd.DataFrame({'feature': features, 'importance': bmodel.feature_importances_} ) | Home Credit Default Risk |
1,289,226 | testId = test['id']
label = test.drop('id',axis=1)
<data_type_conversions> | random.loc[0, 'hyperparameters'] | Home Credit Default Risk |
1,319,042 | label = label.to_numpy()
label.shape<predict_on_test> | import matplotlib.pyplot as plt
import lightgbm as lgb
import gc
from sklearn.model_selection import KFold, cross_val_score
from sklearn.metrics import confusion_matrix,precision_recall_curve,auc,roc_auc_score,roc_curve,recall_score,classification_report
from sklearn.preprocessing import LabelEncoder | Home Credit Default Risk |
1,319,042 | preds1 = clf1.predict(label)
preds2 = clf2.predict(label)
preds3 = clf3.predict(label)
preds4 = clf4.predict(label)
preds5 = clf5.predict(label)
preds6 = clf6.predict(label)
predss = sclf.predict(label )<save_to_csv> | app_train = pd.read_csv('.. /input/application_train.csv')
print('Training data shape: ', app_train.shape)
app_train.head() | Home Credit Default Risk |
1,319,042 | submission1 = pd.DataFrame({'id': testId, 'class': preds1})
submission1.to_csv('KNN.csv', index=False)
submission2 = pd.DataFrame({'id': testId, 'class': preds2})
submission2.to_csv('Random.csv', index=False)
submission3 = pd.DataFrame({'id': testId, 'class': preds3})
submission3.to_csv('Gauss.csv', index=False)
... | app_test = pd.read_csv('.. /input/application_test.csv')
print('Testing data shape: ', app_test.shape)
app_test.head() | Home Credit Default Risk |
1,319,042 | df = pd.read_csv('/kaggle/input/eval-lab-2-f464/train.csv')
t = pd.read_csv('/kaggle/input/eval-lab-2-f464/test.csv' )<prepare_x_and_y> | app_train['TARGET'].value_counts()
print('The proportion of label 1 is %.2f' %(sum(app_train['TARGET']==1)/app_train.shape[0]*100), '%' ) | Home Credit Default Risk |
1,319,042 | y = df['class']
params = [ 'chem_1','chem_2', 'chem_4', 'chem_6', 'attribute']
x = df[params]
df
<import_modules> | def missing_values_table(df):
mis_val = df.isnull().sum()
mis_val_percent = df.isnull().sum() * 100 / df.shape[0]
mis_val_table = pd.concat([mis_val, mis_val_percent], axis = 1)
mis_val_table_rename_columns = mis_val_table.rename(columns = {0: 'Missing Values', 1: 'Percentage'})
mis_val_table_rename_columns = mis_val... | Home Credit Default Risk |
1,319,042 | import xgboost as xgb
import matplotlib.pyplot as plt
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import ExtraTreesClassifier
from sklearn.ensemble import AdaBoostClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.linear_model import LogisticRegression
from xgboost imp... | missing_values = missing_values_table(app_train)
missing_values.head(20 ) | Home Credit Default Risk |
1,319,042 | from sklearn.model_selection import GridSearchCV
from sklearn.metrics import make_scorer
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
<train_model> | app_train.dtypes.value_counts() | Home Credit Default Risk |
1,319,042 | model = RandomForestClassifier(n_estimators = 1000)
model.fit(x,y )<save_to_csv> | app_train.select_dtypes('object' ).apply(pd.Series.nunique, axis=0 ) | Home Credit Default Risk |
1,319,042 | y_pred = model.predict(x_test[params])
y_pred = pd.DataFrame(data = y_pred)
answer = pd.concat([x_test['id'], y_pred], axis = 1)
answer.columns = ['id', 'class']
answer.to_csv('lab2_9.csv', index = False )<load_from_csv> | app_test.dtypes.value_counts() | Home Credit Default Risk |
1,319,042 | df_train = pd.read_csv('/kaggle/input/1056lab-diabetes-diagnosis/train.csv', index_col=0)
df_test = pd.read_csv('/kaggle/input/1056lab-diabetes-diagnosis/test.csv', index_col=0 )<set_options> | app_train = pd.get_dummies(app_train)
app_test = pd.get_dummies(app_test)
print('Training Features shape: ', app_train.shape)
print('Testing Features shape: ', app_test.shape ) | Home Credit Default Risk |
1,319,042 | %matplotlib inline
sns.countplot(x='Diabetes', data=df_train)
plt.show()<categorify> | train_labels = app_train['TARGET']
app_train, app_test = app_train.align(app_test, join = 'inner', axis = 1)
app_train['TARGET'] = train_labels
print('Training Features shape: ', app_train.shape)
print('Testing Features shape: ', app_test.shape ) | Home Credit Default Risk |
1,319,042 | df_train_dummies = pd.get_dummies(df_train, columns=['Gender'], drop_first=True)
df_train_dummies<categorify> | app_train['DAYS_EMPLOYED_ANOM'] = app_train['DAYS_EMPLOYED'] == 365243
app_train['DAYS_EMPLOYED'].replace({365243: np.nan}, inplace = True)
app_test['DAYS_EMPLOYED_ANOM'] = app_test['DAYS_EMPLOYED'] == 365243
app_test['DAYS_EMPLOYED'].replace({365243: np.nan}, inplace = True)
print('Training Features shape: ', app_tr... | Home Credit Default Risk |
1,319,042 | df_test_dummies = pd.get_dummies(df_test, columns=['Gender'], drop_first=True)
df_test_dummies<train_model> | app_train_domain = app_train.copy()
app_test_domain = app_test.copy()
app_train_domain['CREDIT_INCOME_PERCENT'] = app_train_domain['AMT_CREDIT'] / app_train_domain['AMT_INCOME_TOTAL']
app_train_domain['ANNUITY_INCOME_PERCENT'] = app_train_domain['AMT_ANNUITY'] / app_train_domain['AMT_INCOME_TOTAL']
app_train_domain['CR... | Home Credit Default Risk |
1,319,042 | X_train_dummies = df_train_dummies.drop(columns='Diabetes' ).values
y_train_dummies = df_train_dummies['Diabetes'].values
X_train, X_valid, y_train, y_valid = train_test_split(X_train_dummies, y_train_dummies, test_size=0.2, random_state=0)
dtc = RandomForestClassifier()
dtc.fit(X_train, y_train )<train_on_grid> | bureau = pd.read_csv('.. /input/bureau.csv')
bureau.head() | Home Credit Default Risk |
1,319,042 | clf = RandomForestClassifier()
params = {'criterion':('gini', 'entropy'), 'max_depth':[1, 2, 3, 4, 5], 'n_estimators':list(range(20,100,10)) }
gscv = GridSearchCV(clf, params, cv=5)
gscv.fit(X_train_dummies, y_train_dummies )<find_best_score> | previous_loan_counts = bureau.groupby('SK_ID_CURR', as_index=False)['SK_ID_BUREAU'].count().rename(columns = {'SK_ID_BUREAU': 'previous_loan_counts'})
previous_loan_counts.head()
| Home Credit Default Risk |
1,319,042 | print('%.3f %r' %(gscv.best_score_, gscv.best_params_))<train_model> | def agg_numeric(df, group_var, df_name):
for col in df:
if col != group_var and 'SK_ID' in col:
df = df.drop(columns = col)
group_ids = df[group_var]
numeric_df = df.select_dtypes('number')
numeric_df[group_var] = group_ids
agg = numeric_df.groupby(group_var ).agg(['count', 'mean', 'max', 'min', 'sum'] ).reset_inde... | Home Credit Default Risk |
1,319,042 | dtc = RandomForestClassifier(criterion='entropy', max_depth=5, n_estimators=60)
dtc.fit(X_train_dummies, y_train_dummies )<compute_train_metric> | def count_categorical(df, group_var, df_name):
categorical = pd.get_dummies(df.select_dtypes('object'))
categorical[group_var] = df[group_var]
categorical = categorical.groupby(group_var ).agg(['sum', 'mean'])
column_names = []
for var in categorical.columns.levels[0]:
for stat in ['count', 'count_norm']:
column_nam... | Home Credit Default Risk |
1,319,042 | y_pred = dtc.predict_proba(X_valid)[:, 1]
fpr, tpr, thresholds = roc_curve(y_valid, y_pred)
auc(fpr, tpr )<predict_on_test> | train = app_train_domain.merge(previous_loan_counts, on = 'SK_ID_CURR', how = 'left')
train['previous_loan_counts'] = train['previous_loan_counts'].fillna(0)
test = app_test_domain.merge(previous_loan_counts, on = 'SK_ID_CURR', how = 'left')
test['previous_loan_counts'] = test['previous_loan_counts'].fillna(0 ) | Home Credit Default Risk |
1,319,042 | X_test = df_test_dummies.values
y_pred = dtc.predict_proba(X_test)[:, 1]<save_to_csv> | train = train.merge(bureau_agg, on = 'SK_ID_CURR', how = 'left')
test = test.merge(bureau_agg, on = 'SK_ID_CURR', how = 'left' ) | Home Credit Default Risk |
1,319,042 | submit = pd.read_csv('/kaggle/input/1056lab-diabetes-diagnosis/sampleSubmission.csv')
submit['Diabetes'] = y_pred
submit.to_csv('submission.csv', index=False )<load_from_csv> | train = train.merge(bureau_counts, on = 'SK_ID_CURR', how = 'left')
test = test.merge(bureau_counts, on = 'SK_ID_CURR', how = 'left' ) | Home Credit Default Risk |
1,319,042 | df_train = pd.read_csv('.. /input/1056lab-diabetes-diagnosis/train.csv',index_col=0)
df_test = pd.read_csv('.. /input/1056lab-diabetes-diagnosis/test.csv',index_col=0)
df_train<count_values> | print('Before align train.shape: ', train.shape)
print('Before align test.shape: ', test.shape)
train_labels = train['TARGET']
train, test = train.align(test, join = 'inner', axis = 1)
train['TARGET'] = train_labels
print('After align train.shape: ', train.shape)
print('After align test.shape: ', test.shape ) | Home Credit Default Risk |
1,319,042 | df_train['Diabetes'].value_counts()<categorify> | bureau_balance = pd.read_csv('.. /input/bureau_balance.csv')
bureau_balance.head() | Home Credit Default Risk |
1,319,042 | df_train_dummies = pd.get_dummies(df_train, columns=['Gender'], drop_first=True)
df_train_dummies<categorify> | bureau_balance_agg = agg_numeric(bureau_balance, group_var = 'SK_ID_BUREAU', df_name = 'bureau_balance')
bureau_balance_agg.head() | Home Credit Default Risk |
1,319,042 | df_test_dummies = pd.get_dummies(df_test, columns=['Gender'], drop_first=True)
df_test_dummies<set_options> | bureau_balance_counts = count_categorical(bureau_balance, group_var = 'SK_ID_BUREAU', df_name = 'bureau_balance')
bureau_balance_counts.head() | Home Credit Default Risk |
1,319,042 | %matplotlib inline
sns.pairplot(df_train_dummies, hue='Diabetes')
plt.show()<categorify> | bureau_by_loan = bureau_balance_agg.merge(bureau_balance_counts, right_index = True, left_on = 'SK_ID_BUREAU', how = 'outer' ) | Home Credit Default Risk |
1,319,042 | df_train_dummies2 = df_train_dummies[['HDL Chol', 'Chol/HDL ratio', 'Weight', 'Systolic BP', 'Diastolic BP','Waist','Hip','Diabetes']]
df_test_dummies2 = df_test_dummies[['HDL Chol', 'Chol/HDL ratio', 'Weight', 'Systolic BP', 'Diastolic BP','Waist','Hip']]<split> | bureau_by_loan = bureau_by_loan.merge(bureau[['SK_ID_BUREAU', 'SK_ID_CURR']], on = 'SK_ID_BUREAU', how = 'left' ) | Home Credit Default Risk |
1,319,042 | X_train_dummies = df_train_dummies2.drop('Diabetes', axis=1 ).values
y_train_dummies = df_train_dummies2['Diabetes'].values
X_train, X_valid, y_train, y_valid = train_test_split(X_train_dummies, y_train_dummies, test_size=0.2, random_state=0 )<train_on_grid> | bureau_balance_by_client = agg_numeric(bureau_by_loan.drop(columns = ['SK_ID_BUREAU']), group_var = 'SK_ID_CURR', df_name = 'client')
bureau_balance_by_client.head() | Home Credit Default Risk |
1,319,042 | clf = RandomForestClassifier()
params = {'criterion':('gini', 'entropy'),
'n_estimators':[240,260,280,300],
'max_depth':[1, 2, 3, 4, 5],
'random_state':[5]
}
gscv = GridSearchCV(clf, params, cv=5,scoring='roc_auc')
gscv.fit(X_train, y_train )<find_best_params> | train = train.merge(bureau_balance_by_client, on = 'SK_ID_CURR', how = 'left')
test = test.merge(bureau_balance_by_client, on = 'SK_ID_CURR', how = 'left' ) | Home Credit Default Risk |
1,319,042 | scores = gscv.cv_results_['mean_test_score']
params = gscv.cv_results_['params']
for score, param in zip(scores, params):
print('%.3f %r' %(score, param))<find_best_score> | print('Before align train.shape: ', train.shape)
print('Before align test.shape: ', test.shape)
train_labels = train['TARGET']
train, test = train.align(test, join = 'inner', axis = 1)
train['TARGET'] = train_labels
print('After align train.shape: ', train.shape)
print('After align test.shape: ', test.shape ) | Home Credit Default Risk |
1,319,042 | print('%.3f %r' %(gscv.best_score_, gscv.best_params_))<train_model> | gc.enable()
del app_train, app_test, app_train_domain, app_test_domain, bureau, bureau_balance, bureau_agg, bureau_counts, bureau_balance_agg, bureau_balance_counts, bureau_by_loan, bureau_balance_by_client
gc.collect() | Home Credit Default Risk |
1,319,042 | clf = RandomForestClassifier(criterion='entropy', max_depth= 4, n_estimators= 280,random_state= 5)
clf.fit(X_train_dummies,y_train_dummies )<predict_on_test> | missing_train = missing_values_table(train)
missing_train.head(10 ) | Home Credit Default Risk |
1,319,042 | X_test = df_test_dummies2.values
y_pred = clf.predict_proba(X_test)[:, 1]<save_to_csv> | missing_test = missing_values_table(test)
missing_test.head(10 ) | Home Credit Default Risk |
1,319,042 | submit = pd.read_csv('/kaggle/input/1056lab-diabetes-diagnosis/sampleSubmission.csv')
submit['Diabetes'] = y_pred
submit.to_csv('submission2.csv', index=False )<define_variables> | missing_train_vars = list(missing_train.index[missing_train['Percentage'] > 90])
print('There are', len(missing_train_vars), 'columns having missing percent larger than 90%% in the train')
missing_test_vars = list(missing_test.index[missing_test['Percentage'] > 90])
print('There are', len(missing_test_vars), 'column... | Home Credit Default Risk |
1,319,042 | sys.path.insert(0,'.. /input/efficientnet/EfficientNet-b5')
<import_modules> | train = train.drop(columns = missing_columns)
test = test.drop(columns = missing_columns)
| Home Credit Default Risk |
1,319,042 | from __future__ import print_function, division
import argparse
import torch
import torch.nn as nn
import torch.optim as optim
from torch.autograd import Variable
from torchvision import datasets, models, transforms
import time
import os
import pandas as pd
from efficientnet.model import EfficientNet
import torch.nn.fu... | corrs = corrs.sort_values('TARGET', ascending = False)
pd.DataFrame(corrs['TARGET'].head(10)) | Home Credit Default Risk |
1,319,042 | use_gpu = torch.cuda.is_available()
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
data_dir = '.. /input/issm2020-ai-challenge'
batch_size = 4
lr = 0.1
momentum = 0.9
num_epochs = 100
input_size = 480
class_num = 10
num_workers = 4<compute_train_metric> | threshold = 0.8
above_threshold_vars = {}
for col in corrs:
above_threshold_vars[col] = list(corrs.index[corrs[col] > threshold] ) | Home Credit Default Risk |
1,319,042 | def linear_combination(x, y, epsilon):
return epsilon*x +(1-epsilon)*y
def reduce_loss(loss, reduction='mean'):
return loss.mean() if reduction=='mean'else loss.sum() if reduction=='sum' else loss
class LabelSmoothingCrossEntropy(nn.Module):
def __init__(self, epsilon:float=0.1, reduction='mean'):
super().__init__()
se... | cols_to_remove = []
cols_seen = []
cols_to_remove_pair = []
for key, value in above_threshold_vars.items() :
cols_seen.append(key)
for x in value:
if x == key:
next
else:
if x not in cols_seen:
cols_to_remove.append(x)
cols_to_remove_pair.append(key)
cols_to_remove = list(set(cols_to_remove))
print('Number of column... | Home Credit Default Risk |
1,319,042 | def exp_lr_scheduler(optimizer, epoch, init_lr=0.01, lr_decay_epoch=10):
lr = init_lr *(0.8**(epoch // lr_decay_epoch))
print('LR is set to {}'.format(lr))
for param_group in optimizer.param_groups:
param_group['lr'] = lr
return optimizer<choose_model_class> | train_corrs_removed = train.drop(columns = cols_to_remove)
test_corrs_removed = test.drop(columns = cols_to_remove)
print('Training Corrs Removed Shape: ', train_corrs_removed.shape)
print('Testing Corrs Removed Shape: ', test_corrs_removed.shape)
| Home Credit Default Risk |
1,319,042 | def train_model(model_ft, criterion, optimizer, lr_scheduler, num_epochs=50):
train_loss = []
since = time.time()
best_model_wts = model_ft.state_dict()
best_acc = 0.0
best_train_acc = 0.0
for epoch in range(num_epochs):
model_ft.train(True)
dset_loaders, dset_sizes = loaddata(data_dir=data_dir, batch_size=batch_size,... | gc.enable()
del train, test
gc.collect() | Home Credit Default Risk |
1,319,042 | model_ft = EfficientNet.from_name('efficientnet-b2')
net_weight = '.. /input/efficientnet-pytorch-b0-b7/efficientnet-b2-8bb594d6.pth'
state_dict = torch.load(net_weight)
model_ft.load_state_dict(state_dict)
<feature_engineering> | def train_with_cv(train_data, test_data, n_folds, seed_varying):
train_ids = train_data['SK_ID_CURR']
test_ids = test_data['SK_ID_CURR']
train_labels = train_data['TARGET']
train_features = train_data.drop(columns = ['SK_ID_CURR', 'TARGET'])
test_features = test_data.drop(columns = ['SK_ID_CURR'])
feature_names = lis... | Home Credit Default Risk |
1,319,042 | num_ftrs = model_ft._fc.in_features
model_ft._fc = nn.Linear(num_ftrs, class_num)
criterion = LabelSmoothingCrossEntropy()<choose_model_class> | train_times = 5
n_folds = 5
i = 0
metrics_all = np.zeros(( train_times, 2))
for seed_varying in range(train_times):
print('
=======================================================')
print('The ', seed_varying, ' time of train')
print('
=======================================================')
sub, fi, metrics = trai... | Home Credit Default Risk |
1,316,372 | if use_gpu:
model_ft = model_ft.cuda()
criterion = criterion.cuda()
optimizer = optim.SGD(( model_ft.parameters()), lr=lr,
momentum=momentum, weight_decay=0.0004)
<train_model> | import matplotlib.pyplot as plt
import lightgbm as lgb
import gc
from sklearn.model_selection import KFold, cross_val_score
from sklearn.metrics import confusion_matrix,precision_recall_curve,auc,roc_auc_score,roc_curve,recall_score,classification_report
from sklearn.preprocessing import LabelEncoder | Home Credit Default Risk |
1,316,372 |
<find_best_params> | app_train = pd.read_csv('.. /input/application_train.csv')
print('Training data shape: ', app_train.shape)
app_train.head() | Home Credit Default Risk |
1,316,372 | model_ft_test = EfficientNet.from_name('efficientnet-b2')
num_ftrs = model_ft_test._fc.in_features
model_ft_test._fc = nn.Linear(num_ftrs, class_num)
net_weight = '.. /input/weight/best_val_efficientnet-b2.pth'
state_dict = torch.load(net_weight)
model_ft_test.load_state_dict(state_dict)
model_ft_test.cuda()
model_... | app_test = pd.read_csv('.. /input/application_test.csv')
print('Testing data shape: ', app_test.shape)
app_test.head() | Home Credit Default Risk |
1,316,372 | res = []
hash1 = {}
for imagepath in os.listdir('.. /input/issm2020-ai-challenge/semTest/semTest'):
p = []
image = Image.open('.. /input/issm2020-ai-challenge/semTest/semTest/'+imagepath)
imgprob = data_transforms(image ).unsqueeze(0)
imgprob = Variable(imgprob ).cuda()
torch.no_grad()
logit = model_ft_test(imgprob)
... | app_train['TARGET'].value_counts()
print('The proportion of label 1 is %.2f' %(sum(app_train['TARGET']==1)/app_train.shape[0]*100), '%' ) | Home Credit Default Risk |
1,316,372 | for i in sorted(hash1):
res.append(hash1[i]+1)
data = {'Id':list(range(1,351)) ,'LABEL':res}
df = pd.DataFrame(data)
df.to_csv('submission.csv',index=None )<import_modules> | def missing_values_table(df):
mis_val = df.isnull().sum()
mis_val_percent = df.isnull().sum() * 100 / df.shape[0]
mis_val_table = pd.concat([mis_val, mis_val_percent], axis = 1)
mis_val_table_rename_columns = mis_val_table.rename(columns = {0: 'Missing Values', 1: 'Percentage'})
mis_val_table_rename_columns = mis_val... | Home Credit Default Risk |
1,316,372 | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import os<define_variables> | missing_values = missing_values_table(app_train)
missing_values.head(20 ) | Home Credit Default Risk |
1,316,372 | wd = "/kaggle/input/jamp-hackathon-drive-1/train_set/"<define_variables> | app_train.dtypes.value_counts() | Home Credit Default Risk |
1,316,372 | images_dir=os.listdir(wd )<feature_engineering> | app_train.select_dtypes('object' ).apply(pd.Series.nunique, axis=0 ) | Home Credit Default Risk |
1,316,372 | df=pd.DataFrame()
df['Images']=data
classes=df["Images"].str.split("/", n = 6, expand = True)[5]
df['Label']=classes
df = df.sample(frac=1 ).reset_index(drop=True )<count_values> | app_test.dtypes.value_counts() | Home Credit Default Risk |
1,316,372 | df['Label'].value_counts()<split> | app_train = pd.get_dummies(app_train)
app_test = pd.get_dummies(app_test)
print('Training Features shape: ', app_train.shape)
print('Testing Features shape: ', app_test.shape ) | Home Credit Default Risk |
1,316,372 | train, test = train_test_split(df, test_size=0.2,stratify=df['Label'] )<set_options> | train_labels = app_train['TARGET']
app_train, app_test = app_train.align(app_test, join = 'inner', axis = 1)
app_train['TARGET'] = train_labels
print('Training Features shape: ', app_train.shape)
print('Testing Features shape: ', app_test.shape ) | Home Credit Default Risk |
1,316,372 | get_ipython().magic('matplotlib inline')
<import_modules> | app_train['DAYS_EMPLOYED_ANOM'] = app_train['DAYS_EMPLOYED'] == 365243
app_train['DAYS_EMPLOYED'].replace({365243: np.nan}, inplace = True)
app_test['DAYS_EMPLOYED_ANOM'] = app_test['DAYS_EMPLOYED'] == 365243
app_test['DAYS_EMPLOYED'].replace({365243: np.nan}, inplace = True)
print('Training Features shape: ', app_tr... | Home Credit Default Risk |
1,316,372 | from tqdm import tqdm
import pickle<categorify> | app_train_domain = app_train.copy()
app_test_domain = app_test.copy()
app_train_domain['CREDIT_INCOME_PERCENT'] = app_train_domain['AMT_CREDIT'] / app_train_domain['AMT_INCOME_TOTAL']
app_train_domain['ANNUITY_INCOME_PERCENT'] = app_train_domain['AMT_ANNUITY'] / app_train_domain['AMT_INCOME_TOTAL']
app_train_domain['CR... | Home Credit Default Risk |
1,316,372 | train_img=[]
for i in tqdm(df['Images']):
temp_img=image.load_img(i,target_size=(224,224))
temp_img=image.img_to_array(temp_img)
train_img.append(temp_img)
train_img=np.array(train_img)
train_img=preprocess_input(train_img)
<feature_engineering> | bureau = pd.read_csv('.. /input/bureau.csv')
bureau.head() | Home Credit Default Risk |
1,316,372 | test_wd = "/kaggle/input/jamp-hackathon-drive-1/test_set/"
test_dir=os.listdir(test_wd)
test_data=[]
for i in test_dir:
test_data.append(os.path.join(test_wd,i))
test_df=pd.DataFrame()
test_df['Images']=test_data
test_df.head()<normalization> | previous_loan_counts = bureau.groupby('SK_ID_CURR', as_index=False)['SK_ID_BUREAU'].count().rename(columns = {'SK_ID_BUREAU': 'previous_loan_counts'})
previous_loan_counts.head()
| Home Credit Default Risk |
1,316,372 | test_img=[]
for i in tqdm(test_df['Images']):
temp_img=image.load_img(i,target_size=(224,224))
temp_img=image.img_to_array(temp_img)
test_img.append(temp_img)
test_img=np.array(test_img)
test_img=preprocess_input(test_img )<choose_model_class> | def agg_numeric(df, group_var, df_name):
for col in df:
if col != group_var and 'SK_ID' in col:
df = df.drop(columns = col)
group_ids = df[group_var]
numeric_df = df.select_dtypes('number')
numeric_df[group_var] = group_ids
agg = numeric_df.groupby(group_var ).agg(['count', 'mean', 'max', 'min', 'sum'] ).reset_inde... | Home Credit Default Risk |
1,316,372 | model = VGG16(weights='imagenet', include_top=False )<predict_on_test> | def count_categorical(df, group_var, df_name):
categorical = pd.get_dummies(df.select_dtypes('object'))
categorical[group_var] = df[group_var]
categorical = categorical.groupby(group_var ).agg(['sum', 'mean'])
column_names = []
for var in categorical.columns.levels[0]:
for stat in ['count', 'count_norm']:
column_nam... | Home Credit Default Risk |
1,316,372 | features_train=model.predict(train_img )<predict_on_test> | train = app_train_domain.merge(previous_loan_counts, on = 'SK_ID_CURR', how = 'left')
train['previous_loan_counts'] = train['previous_loan_counts'].fillna(0)
test = app_test_domain.merge(previous_loan_counts, on = 'SK_ID_CURR', how = 'left')
test['previous_loan_counts'] = test['previous_loan_counts'].fillna(0 ) | Home Credit Default Risk |
1,316,372 | features_test=model.predict(test_img )<prepare_x_and_y> | train = train.merge(bureau_agg, on = 'SK_ID_CURR', how = 'left')
test = test.merge(bureau_agg, on = 'SK_ID_CURR', how = 'left' ) | Home Credit Default Risk |
1,316,372 | train_x=features_train.reshape(1027,-1 )<prepare_x_and_y> | train = train.merge(bureau_counts, on = 'SK_ID_CURR', how = 'left')
test = test.merge(bureau_counts, on = 'SK_ID_CURR', how = 'left' ) | Home Credit Default Risk |
1,316,372 | test_x=features_test.reshape(256,-1 )<split> | print('Before align train.shape: ', train.shape)
print('Before align test.shape: ', test.shape)
train_labels = train['TARGET']
train, test = train.align(test, join = 'inner', axis = 1)
train['TARGET'] = train_labels
print('After align train.shape: ', train.shape)
print('After align test.shape: ', test.shape ) | Home Credit Default Risk |
1,316,372 | train_y=np.asarray(df['Label'])
train_y=pd.get_dummies(train_y)
train_y=np.array(train_y)
X_train, X_valid, Y_train, Y_valid=train_test_split(train_x,train_y,test_size=0.3, random_state=42)
<categorify> | def train_with_cv(train_data, test_data, n_folds, seed_varying):
train_ids = train_data['SK_ID_CURR']
test_ids = test_data['SK_ID_CURR']
train_labels = train_data['TARGET']
train_features = train_data.drop(columns = ['SK_ID_CURR', 'TARGET'])
test_features = test_data.drop(columns = ['SK_ID_CURR'])
feature_names = lis... | Home Credit Default Risk |
1,316,372 | <choose_model_class><EOS> | train_times = 3
n_folds = 5
i = 0
metrics_all = np.zeros(( train_times, 2))
for seed_varying in range(train_times):
print('
=======================================================')
print('The ', seed_varying, ' time of train')
print('
=======================================================')
sub, fi, metrics = trai... | Home Credit Default Risk |
1,276,329 | <SOS> metric: AUC Kaggle data source: home-credit-default-risk<train_model> | init_notebook_mode(connected=True ) | Home Credit Default Risk |
1,276,329 | model.fit(X_train, Y_train, epochs=20, batch_size=128,validation_data=(X_valid,Y_valid))<predict_on_test> | print("Loading data files...")
start = time()
posc_bal = reduce_mem_usage(pd.read_csv(".. /input/POS_CASH_balance.csv"))
bureau_bal = reduce_mem_usage(pd.read_csv(".. /input/bureau_balance.csv"))
app_train = reduce_mem_usage(pd.read_csv(".. /input/application_train.csv"))
prev_app = reduce_mem_usage(pd.read_csv(".. /i... | Home Credit Default Risk |
1,276,329 | model.predict(X_valid )<compute_test_metric> | eda = pd.DataFrame(
[
['Point of Sale Cash Balance', posc_bal.shape[0], posc_bal.shape[1] - 2, np.sum(posc_bal.dtypes=='category'),
np.sum(posc_bal.isnull().sum() > 0), posc_bal.isnull().sum().sum() ],
['Bureau Balance', bureau_bal.shape[0], bureau_bal.shape[1] - 1, np.sum(bureau_bal.dtypes=='category'),
np.sum(bureau... | Home Credit Default Risk |
1,276,329 | model.evaluate(X_valid,Y_valid )<compute_test_metric> | app_train['is_train'] = 1
app_train['is_test'] = 0
app_test['is_train'] = 0
app_test['is_test'] = 1
print("
Joining the training(app_train)and testing(app_test)dataset for pre-processing into pandas DataFrame 'data'.")
data = pd.concat([app_train, app_test], axis=0)
print("app_train has {0:,} samples and {1} features... | Home Credit Default Risk |
1,276,329 | scores=model.evaluate(test_x,test_y )<compute_test_metric> | def _one_hot_encoding(data):
return pd.get_dummies(data)
print("
Performing one-hot encoding on {} dataset.".format('data'))
data = _one_hot_encoding(data)
print("app has {0:,} samples and {1} features AFTER one-hot encoding.".format(data.shape[0], data.shape[1]-4))
posc_bal = _one_hot_encoding(posc_bal)
prev_app = ... | Home Credit Default Risk |
1,276,329 | print(f"Accuracy is {scores[1]*100} %" )<predict_on_test> | data_train_test = data.copy() | Home Credit Default Risk |
1,276,329 | output=np.argmax(model.predict(test_x),axis=1 )<create_dataframe> | print("Merge 'Point of Sale Cash Balance' dataset.")
posc_bal_count = posc_bal[['SK_ID_CURR', 'SK_ID_PREV']].groupby('SK_ID_CURR' ).count()
posc_bal['POSC_BAL_COUNT'] = posc_bal['SK_ID_CURR'].map(posc_bal_count['SK_ID_PREV'])
posc_bal = posc_bal.drop(['SK_ID_PREV'], axis=1)
posc_bal_avg = posc_bal.groupby('SK_ID_CUR... | Home Credit Default Risk |
1,276,329 | submission=pd.DataFrame()
submission['name']=test_df['Images'].str.split("/", n = 6, expand = True)[5].str.split(".", n = 3, expand = True)[0]
submission['class']=output<categorify> | Home Credit Default Risk | |
1,276,329 | submission['class'] = submission['class'].replace({0:1, 1:0} )<save_to_csv> | print("Merge 'Previous Applications' dataset.")
prev_app_count = prev_app[['SK_ID_CURR', 'SK_ID_PREV']].groupby('SK_ID_CURR' ).count()
prev_app['PREV_COUNT'] = prev_app['SK_ID_CURR'].map(prev_app_count['SK_ID_PREV'])
prev_app = prev_app.drop(['SK_ID_PREV'], axis=1)
prev_app_avg = prev_app.groupby('SK_ID_CURR' ).mean... | Home Credit Default Risk |
1,276,329 | submission.to_csv("submission.csv", index=False )<save_to_csv> | print("Merge 'Installments Payments' dataset.")
inst_pay_count = inst_pay[['SK_ID_CURR', 'SK_ID_PREV']].groupby('SK_ID_CURR' ).count()
inst_pay['INST_PAY_COUNT'] = inst_pay['SK_ID_CURR'].map(inst_pay_count['SK_ID_PREV'])
inst_pay = inst_pay.drop(['SK_ID_PREV'], axis=1)
inst_pay_avg = inst_pay.groupby('SK_ID_CURR' ).... | Home Credit Default Risk |
1,276,329 | submission.to_csv("submission.csv", index=False )<install_modules> | print("Merge 'Credit Card Balance' dataset.")
cc_bal_count = cc_bal[['SK_ID_CURR', 'SK_ID_PREV']].groupby('SK_ID_CURR' ).count()
cc_bal['CC_BAL_COUNT'] = cc_bal['SK_ID_CURR'].map(cc_bal_count['SK_ID_PREV'])
cc_bal = cc_bal.drop(['SK_ID_PREV'], axis=1)
cc_bal_avg = cc_bal.groupby('SK_ID_CURR' ).mean()
cc_bal_avg.colu... | Home Credit Default Risk |
1,276,329 | !pip install torchsummary<set_options> | print("Merge 'Bureau' dataset.")
bureau_count = bureau[['SK_ID_CURR', 'SK_ID_BUREAU']].groupby('SK_ID_CURR' ).count()
bureau['BUREAU_COUNT'] = bureau['SK_ID_CURR'].map(bureau_count['SK_ID_BUREAU'])
bureau = bureau.drop(['SK_ID_BUREAU'], axis=1)
bureau_avg = bureau.groupby('SK_ID_CURR' ).mean()
bureau_avg.columns = [... | Home Credit Default Risk |
1,276,329 | %matplotlib inline<set_options> | Home Credit Default Risk | |
1,276,329 | def seed_everything(seed=42):
os.environ['PYTHONHASHSEED'] = str(seed)
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.backends.cudnn.deterministic = True
def get_logger(
filename='log',
disable_stream_handler=False,
disable_file_handler=False
):
logger = getLogg... | scaler = MinMaxScaler()
numerical = ['DAYS_BIRTH', 'AMT_CREDIT', 'DAYS_ID_PUBLISH', 'DAYS_REGISTRATION', 'DAYS_EMPLOYED']
data[numerical] = scaler.fit_transform(data[numerical] ) | Home Credit Default Risk |
1,276,329 | logger = get_logger(
filename='running',
disable_stream_handler=False,
disable_file_handler=False,
)<define_variables> | data_to_use = 'ALL'
if data_to_use == 'data_train_test':
data = data_train_test.copy() | Home Credit Default Risk |
1,276,329 | INPUT_DIR = '.. /input/ailab-ml-training-1/'
ARTIFACT_DIR = '.. /input/v1-ailab1-cv/'
PATH = {
'train': os.path.join(INPUT_DIR, 'train.csv'),
'submission': os.path.join(INPUT_DIR, 'sample_submission.csv'),
'train_image_dir': os.path.join(INPUT_DIR, 'train_images/train_images'),
'test_image_dir': os.path.join(INPUT_DIR,... | print("
Filling NaN values in the dataset using pandas.fillna() using the column mean() value.")
print("Number of NaN values in the dataset BEFORE running pandas.fillna() : {:,}".format(data.isnull().sum().sum()))
data = data.fillna(data.mean())
nan_after = data.isnull().sum().sum()
print("Number of NaN values in the... | Home Credit Default Risk |
1,276,329 | train_df = pd.read_csv(PATH['train'])
submission_df = pd.read_csv(PATH['submission'] )<feature_engineering> | del posc_bal, posc_bal_count, posc_bal_avg, bureau_bal, app_train, app_test
del prev_app, prev_app_count, prev_app_avg, inst_pay, inst_pay_count, inst_pay_avg, cc_bal, cc_bal_count, cc_bal_avg
del bureau, bureau_count, bureau_avg, data_train_test
gc.collect() | Home Credit Default Risk |
1,276,329 | train_df[ID] = train_df[ID].apply(lambda x: os.path.join(PATH['train_image_dir'], x))<prepare_output> | print("
Separating the training and testing dataset after completing pre-processing.")
train = data[data['is_train'] == 1]
target = train['TARGET']
train = train.drop(['TARGET', 'SK_ID_CURR', 'is_test', 'is_train'], axis=1)
test = data[data['is_test'] == 1]
test_id = test['SK_ID_CURR']
test = test.drop(['TARGET', 'SK... | Home Credit Default Risk |
1,276,329 | def softmax(logits):
return np.exp(logits)/ np.sum(np.exp(logits), axis=1, keepdims=True )<feature_engineering> | print("
Splitting the training dataset into actual training and validation datasets")
X_train, X_val, y_train, y_val = train_test_split(train, target, test_size=0.2, random_state=42)
assert(train.shape[0] == X_train.shape[0] + X_val.shape[0])
assert(X_train.shape[1] == train.shape[1])
assert(X_val.shape[1] == train... | Home Credit Default Risk |
1,276,329 | predictions = np.load(PATH['predictions'])
predictions = softmax(predictions)
predictions_label, predictions_proba = np.argmax(predictions, axis=1), np.max(predictions, axis=1)
pseudo_df = submission_df.copy()
pseudo_df[TARGET] = predictions_label
pseudo_df['proba'] = predictions_proba
pseudo_df = pseudo_df.loc[pseu... | run_mode = 'LGBM_KFold' | Home Credit Default Risk |
1,276,329 |
<train_model> | if run_mode == 'grid_search_LGBM':
perc_samples = 0.15
print("
Preparing to run Hyperparameters tunning with GridSearchCV using {0:.2f}% of the training samples".format(perc_samples * 100))
X_train_small = X_train[:int(perc_samples * X_train.shape[0])]
y_train_small = y_train[:int(perc_samples * y_train.shape[0])]
X_va... | Home Credit Default Risk |
1,276,329 | class KmnistDataset(Dataset):
def __init__(
self,
paths,
labels,
transform=None,
with_memory_cache=False,
):
super().__init__()
self.paths = paths
self.labels = labels
self.transform = transform
self.with_memory_cache = with_memory_cache
if with_memory_cache:
self.images = [None,] * len(paths)
def load_image(self, p... | if run_mode == 'grid_search_RFR':
perc_samples = 0.15
print("
Preparing to run Hyperparameters tunning with GridSearchCV using {0:.2f}% of the training samples".format(perc_samples * 100))
features_train_small = X_train[:int(perc_samples * X_train.shape[0])]
target_train_small = y_train[:int(perc_samples * y_train.shap... | Home Credit Default Risk |
1,276,329 | def get_dataloader(
X,
Y,
transform=None,
with_memory_cache=False,
batch_size=32,
shuffle=False,
num_workers=0,
pin_memory=True,
):
dataset = KmnistDataset(
X,
Y,
transform=transform,
with_memory_cache=with_memory_cache,
)
loader = DataLoader(
dataset=dataset,
batch_size=batch_size,
shuffle=shuffle,
num_workers=n... | if run_mode == 'train_estimator_LGBM':
params = {
'boosting_type': 'dart',
'objective': 'binary',
'learning_rate': 0.1,
'min_data_in_leaf': 30,
'num_leaves': 31,
'max_depth': -1,
'feature_fraction': 0.5,
'scale_pos_weight': 2,
'drop_rate': 0.02,
'metric': 'auc',
'num_boost_round': 200,
}
data_split = 'kfold'
if data_sp... | Home Credit Default Risk |
1,276,329 | num_samples = 20
sample = train_df.groupby(TARGET ).apply(lambda df: df.sample(num_samples))
fnames = sample[ID].to_list()
labels = sample[TARGET].to_list()<concatenate> | print(estimator ) | Home Credit Default Risk |
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