kernel_id int64 24.2k 23.3M | prompt stringlengths 8 1.85M | completetion stringlengths 1 182k | comp_name stringlengths 5 57 |
|---|---|---|---|
17,896,962 | classifier = Sequential()
classifier.add(Conv2D(filters = 32, kernel_size =(3,3), activation = 'relu', input_shape =(dim,dim,3)))
classifier.add(MaxPool2D(pool_size =(2,2)))
classifier.add(Conv2D(64,(3,3),activation = 'relu'))
classifier.add(Conv2D(64,(3,3),activation = 'relu'))
classifier.add(MaxPool2D(pool_size =(2... | apps, prev = get_dataset()
apps_all = get_apps_all_with_prev_agg(apps, prev)
apps_all = get_apps_all_encoded(apps_all)
apps_all_train, apps_all_test = get_apps_all_train_test(apps_all)
clf = train_apps_all(apps_all_train ) | Home Credit Default Risk |
17,896,962 | classifier.fit_generator(train_generator, epochs = 100, steps_per_epoch = 70 )<find_best_params> | preds = clf.predict_proba(apps_all_test.drop(['SK_ID_CURR'], axis=1)) [:, 1 ]
apps_all_test['TARGET'] = preds
apps_all_test[['SK_ID_CURR', 'TARGET']].to_csv('prev_baseline_03.csv', index=False ) | Home Credit Default Risk |
17,896,962 | classes = train_generator.class_indices
print(classes )<predict_on_test> | bureau = pd.read_csv('.. /input/home-credit-default-risk/bureau.csv')
bureau_bal = pd.read_csv('.. /input/home-credit-default-risk/bureau_balance.csv' ) | Home Credit Default Risk |
17,896,962 | Y_pred = []
for idx in range(test_set.shape[0]):
img = image.load_img(path=test_set['Image'][idx],target_size=(dim,dim,3))
img = image.img_to_array(img)
test_img = img.reshape(( 1,dim,dim,3))
img_class = classifier.predict_classes(test_img)
prediction = img_class[0]
Y_pred.append(prediction )<find_best_params> | bureau_app = bureau.merge(app_train[['SK_ID_CURR', 'TARGET']], left_on='SK_ID_CURR', right_on='SK_ID_CURR', how='inner')
bureau_app.shape | Home Credit Default Risk |
17,896,962 | prediction_classes = [ inverted_classes.get(item,item)for item in Y_pred ]
print(prediction_classes )<save_to_csv> | num_columns = bureau_app.dtypes[bureau_app.dtypes != 'object'].index.tolist()
num_columns = [column for column in num_columns if column not in['SK_ID_BUREAU', 'SK_ID_CURR', 'TARGET']]
num_columns | Home Credit Default Risk |
17,896,962 | predictions = []
for idx in range(test_set.shape[0]):
predictions.append([test_set['Image'][idx].split('/')[6].split('.')[0],prediction_classes[idx]])
predictions = pd.DataFrame(predictions, columns=['ID','Country'])
predictions['ID'] = predictions['ID'].astype(int)
predictions.sort_values(by=['ID'], inplace=True)
... | object_columns = bureau.dtypes[bureau.dtypes=='object'].index.tolist()
object_columns | Home Credit Default Risk |
17,896,962 | pd.read_csv?<load_from_csv> | show_category_by_target(bureau_app, object_columns ) | Home Credit Default Risk |
17,896,962 | fn = '.. /input/male-daan-schnell-mal-klassifizieren/train.csv'
df = pd.read_csv(fn)
df.head(5 )<load_from_csv> | def get_bureau_processed(bureau):
bureau['BUREAU_ENDDATE_FACT_DIFF'] = bureau['DAYS_CREDIT_ENDDATE'] - bureau['DAYS_ENDDATE_FACT']
bureau['BUREAU_CREDIT_FACT_DIFF'] = bureau['DAYS_CREDIT'] - bureau['DAYS_ENDDATE_FACT']
bureau['BUREAU_CREDIT_ENDDATE_DIFF'] = bureau['DAYS_CREDIT'] - bureau['DAYS_CREDIT_ENDDATE']
bureau['... | Home Credit Default Risk |
17,896,962 | df = pd.read_csv(fn,index_col='Id')
df.head()<create_dataframe> | def get_bureau_day_amt_agg(bureau):
bureau_agg_dict = {
'SK_ID_BUREAU':['count'],
'DAYS_CREDIT':['min', 'max', 'mean'],
'CREDIT_DAY_OVERDUE':['min', 'max', 'mean'],
'DAYS_CREDIT_ENDDATE':['min', 'max', 'mean'],
'DAYS_ENDDATE_FACT':['min', 'max', 'mean'],
'AMT_CREDIT_MAX_OVERDUE': ['max', 'mean'],
'AMT_CREDIT_SUM': ['ma... | Home Credit Default Risk |
17,896,962 | data = df.values
data<prepare_x_and_y> | def get_bureau_active_agg(bureau):
cond_active = bureau['CREDIT_ACTIVE'] == 'Active'
bureau_active_grp = bureau[cond_active].groupby(['SK_ID_CURR'])
bureau_agg_dict = {
'SK_ID_BUREAU':['count'],
'DAYS_CREDIT':['min', 'max', 'mean'],
'CREDIT_DAY_OVERDUE':['min', 'max', 'mean'],
'DAYS_CREDIT_ENDDATE':['min', 'max', 'mea... | Home Credit Default Risk |
17,896,962 | nTrain=5000
Xtrain = data[:nTrain,:-1]
ytrain = data[:nTrain,-1]
Xtest = data[nTrain:,:-1]
ytest = data[nTrain:,-1]
Xtrain.shape,ytrain.shape,Xtest.shape,ytest.shape<data_type_conversions> | def get_bureau_bal_agg(bureau, bureau_bal):
bureau_bal = bureau_bal.merge(bureau[['SK_ID_CURR', 'SK_ID_BUREAU']], on='SK_ID_BUREAU', how='left')
bureau_bal['BUREAU_BAL_IS_DPD'] = bureau_bal['STATUS'].apply(lambda x: 1 if x in['1','2','3','4','5'] else 0)
bureau_bal['BUREAU_BAL_IS_DPD_OVER120'] = bureau_bal['STATUS'].... | Home Credit Default Risk |
17,896,962 | ytrain = ytrain.astype('int')
ytest = ytest.astype('int')
ytrain, ytrain.dtype<choose_model_class> | def get_bureau_agg(bureau, bureau_bal):
bureau = get_bureau_processed(bureau)
bureau_day_amt_agg = get_bureau_day_amt_agg(bureau)
bureau_active_agg = get_bureau_active_agg(bureau)
bureau_bal_agg = get_bureau_bal_agg(bureau, bureau_bal)
bureau_agg = bureau_day_amt_agg.merge(bureau_active_agg, on='SK_ID_CURR', how='l... | Home Credit Default Risk |
17,896,962 | k=7<define_variables> | def get_apps_all_with_prev_bureau_agg(apps, prev, bureau, bureau_bal):
apps_all = get_apps_processed(apps)
prev_agg = get_prev_agg(prev)
bureau_agg = get_bureau_agg(bureau, bureau_bal)
print('prev_agg shape:', prev_agg.shape)
print('bueau_agg shape:', bureau_agg.shape)
print('apps_all before merge shape:', apps_al... | Home Credit Default Risk |
17,896,962 | zufälliger_Index = np.random.randint(low=0,high=len(ytest))
zufälliger_Index<split> | apps_all = get_apps_all_with_prev_bureau_agg(apps, prev, bureau, bureau_bal ) | Home Credit Default Risk |
17,896,962 | Testzeile = Xtest[zufälliger_Index,:]
Testlabel = ytest[zufälliger_Index]
Testzeile,Testlabel<compute_test_metric> | apps_all = get_apps_all_encoded(apps_all)
apps_all_train, apps_all_test = get_apps_all_train_test(apps_all ) | Home Credit Default Risk |
17,896,962 | distanz = np.sqrt(((Xtrain - Testzeile)**2 ).sum(axis=1))<sort_values> | clf = train_apps_all(apps_all_train ) | Home Credit Default Risk |
17,896,962 | <sort_values><EOS> | preds = clf.predict_proba(apps_all_test.drop(['SK_ID_CURR'], axis=1)) [:, 1 ]
apps_all_test['TARGET'] = preds
apps_all_test[['SK_ID_CURR', 'TARGET']].to_csv('bureau_baseline_04.csv', index=False ) | Home Credit Default Risk |
6,039,066 | <SOS> metric: AUC Kaggle data source: home-credit-default-risk<define_search_space> | import numpy as np
import pandas as pd
import os | Home Credit Default Risk |
6,039,066 | np.argsort([1,7,4,9] )<sort_values> | app_train = pd.read_csv('.. /input/home-credit-simple-featuers/simple_features_train.csv')
app_test = pd.read_csv('.. /input/home-credit-simple-featuers/simple_features_test.csv' ) | Home Credit Default Risk |
6,039,066 | distanz[np.argsort(distanz)[:k]]
np.min(distanz )<sort_values> | sk_id = pd.read_csv('.. /input/home-credit-default-risk/application_test.csv' ) | Home Credit Default Risk |
6,039,066 | ytrain[np.argsort(distanz)[:k]]<define_search_space> | train = app_train.drop(columns = ['TARGET'])
train_labels = app_train['TARGET'] | Home Credit Default Risk |
6,039,066 | [1,0,1,0,1,1,0]<define_search_space> | X_train, X_test, y_train, y_test = train_test_split(
train, train_labels, test_size=0.2 ) | Home Credit Default Risk |
6,039,066 | np.around(np.mean(np.array([1,0,1,0,1,1,0])) )<define_search_space> | clf = LGBMClassifier(boosting_type = 'goss', n_estimators = 10000, learning_rate= 0.005134, num_leaves= 54, max_depth= 10, subsample_for_bin= 240000, reg_alpha= 0.436193, reg_lambda= 0.479169, colsample_bytree=0.508716, min_split_gain= 0.024766, subsample= 1, is_unbalance= False, silent=-1, verbose=-1)
clf.fit(X_train... | Home Credit Default Risk |
6,039,066 | np.median([1,0,1,0,1,1,0] )<sort_values> | Home Credit Default Risk | |
6,039,066 | <sort_values><EOS> | y_pred = clf.predict_proba(app_test, num_iteration=clf.best_iteration_)[:, 1]
submit = sk_id[['SK_ID_CURR']]
submit['TARGET'] = y_pred
submit.to_csv('sub1.csv', index = False)
| Home Credit Default Risk |
1,052,311 | <SOS> metric: AUC Kaggle data source: home-credit-default-risk<split> | train = pd.read_csv(".. /input/automation-of-feature-creation/train.csv")
test = pd.read_csv(".. /input/automation-of-feature-creation/test.csv")
tmp = pd.read_csv(".. /input/home-credit-default-risk/application_test.csv")
tmp_train = pd.read_csv(".. /input/home-credit-default-risk/application_train.csv" ) | Home Credit Default Risk |
1,052,311 | distanz[sorted_indices[:k]], sorted_indices[:k], ytrain[sorted_indices[:k]]<define_variables> | train['SK_ID_CURR'] = tmp_train['SK_ID_CURR']
test['SK_ID_CURR'] = tmp['SK_ID_CURR'] | Home Credit Default Risk |
1,052,311 | Auftretende_Trainingslabels = ytrain[sorted_indices[:k]]<define_variables> | probss = test['ProbTARGET1']
del test['ProbTARGET1']
del train['ProbTARGET1']
y = train['TARGET']
del train['TARGET'] | Home Credit Default Risk |
1,052,311 | if np.mean(Auftretende_Trainingslabels)>=0.5:
yhat = 1
else:
yhat = 0
yhat<define_search_space> | def mean_(x):
if '{' in x:
return x
if x=='Missing':
return -1
if '+inf' in x:
return float(x.replace(']','' ).replace('[','' ).split(';')[0])
if '-inf' in x:
return float(x.replace(']','' ).replace('[','' ).split(';')[1])
l = x.replace(']','' ).replace('[','' ).split(';')
return(float(l[0])+float(l[1])) /2
dictionn... | Home Credit Default Risk |
1,052,311 | print('Häufigstes Label in [1,0,1,0,0]:',np.argmax(np.bincount([1,0,1,0,0])))
print('Häufigstes Label in [1,0,1,0,1]:',np.argmax(np.bincount([1,0,1,0,1])) )<define_search_space> | for i in test.columns:
if test[i].dtype!='object':
continue
try :
for j in test[i].unique() :
dictionnary[j] = mean(j)
except :
continue
for i in test.columns:
if test[i].dtype!='object':
continue
test[i] = test[i].map(dictionnary ) | Home Credit Default Risk |
1,052,311 | np.bincount([0,3,3,2,1,1] )<define_search_space> | to_dummy =[]
for i in test.columns:
if test[i].dtype==object:
try :
test[i]=test[i].astype(float)
except :
continue
if len(test[i].unique())<5:
to_dummy.append(i)
else :
del test[i]
test = pd.get_dummies(test,columns=to_dummy)
to_dummy =[]
for i in train.columns:
if train[i].dtype==object:
try :
train[i]=train[i].as... | Home Credit Default Risk |
1,052,311 | np.bincount([0,3,3,3,2,2,2,2,2,2,2,1,1,4,4,4,4,5,6] )<define_search_space> | train.fillna(0,inplace=True)
test.fillna(0,inplace=True ) | Home Credit Default Risk |
1,052,311 | np.argmax(np.bincount([0,3,3,3,2,2,2,2,2,2,2,1,1,4,4,4,4,5,6]))<statistical_test> | col_dict = {}
for i in train.columns:
if '>' in i:
col_dict[i] = i.replace('>','' ).replace('<','')
train.rename(columns=col_dict,inplace = True)
test.rename(columns=col_dict, inplace =True ) | Home Credit Default Risk |
1,052,311 | def kNN_Vorhersage(Xtrain,Testzeile,k):
distanz =(((Xtrain - Testzeile)**2 ).sum(axis=1)) **0.5
sorted_indices = np.argsort(distanz)
Auftretende_Trainingslabels = ytrain[sorted_indices[:k]]
return np.argmax(np.bincount(Auftretende_Trainingslabels))
zufälliger_Index = np.random.randint(low=0,high=len(ytest))
Testzeile ... | for i in train.columns:
if train[i].dtype==object:
del train[i]
for i in test.columns:
if test[i].dtype==object:
del test[i]
for i in train.columns:
if i not in test.columns:
del train[i]
for j in test.columns:
if j not in train.columns:
del test[j] | Home Credit Default Risk |
1,052,311 | clf = DecisionTreeClassifier(max_depth=5)
<load_from_csv> | def train_model(data_, test_, y_, folds_):
oof_preds = np.zeros(data_.shape[0])
sub_preds = np.zeros(test_.shape[0])
feature_importance_df = pd.DataFrame()
feats = [f for f in data_.columns if f not in ['SK_ID_CURR']]
for n_fold,(trn_idx, val_idx)in enumerate(folds_.split(data_)) :
trn_x, trn_y = data_[feats].iloc[tr... | Home Credit Default Risk |
1,052,311 | <prepare_x_and_y><EOS> | if __name__ == '__main__':
gc.enable()
folds = KFold(n_splits=5, shuffle=True, random_state=123)
oof_preds, test_preds, importances = train_model(train, test, y, folds)
test_preds.to_csv('first_automated_submission.csv', index=False)
folds_idx = [(trn_idx, val_idx)for trn_idx, val_idx in folds.split(train)]
display_... | Home Credit Default Risk |
1,446,148 | <SOS> metric: AUC Kaggle data source: home-credit-default-risk<train_model> | warnings.simplefilter(action='ignore', category=FutureWarning ) | Home Credit Default Risk |
1,446,148 | clf.fit(Xtrain,ytrain )<predict_on_test> | 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 |
1,446,148 | yhat = clf.predict(Xtest )<save_to_csv> | def application_train_test(num_rows = None, nan_as_category = False):
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... | Home Credit Default Risk |
1,446,148 | ser = pd.Series(yhat,name='y' ).astype('int')
ser.index.name='Id'
ser.to_csv('Submission.csv',header=True)
!head Submission.csv<import_modules> | def bureau_and_balance(num_rows = None, nan_as_category = True):
bureau = pd.read_csv('.. /input/bureau.csv', nrows = num_rows)
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)
bb_aggreg... | Home Credit Default Risk |
1,446,148 | import numpy as np
import pandas as pd
import keras as ks
import cv2
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import tqdm
from keras.models import Sequential, Model, Input
from keras.layers import Activation, Flatten, Dense, Dropout, ZeroPadding2D, Conv2D, MaxPool2D, BatchNormalization, GlobalAv... | def previous_applications(num_rows = None, nan_as_category = True):
prev = pd.read_csv('.. /input/previous_application.csv', nrows = num_rows)
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,... | Home Credit Default Risk |
1,446,148 | CATEGORIES = ['airplane','car','cat','dog','flower','fruit','motorbike','person']
IMG_WIDTH = 100
IMG_HEIGHT = 100
TRAIN_PATH = '.. /input/natural_images/natural_images/'
TEST_PATH = '.. /input/evaluate/evaluate/'<define_variables> | def pos_cash(num_rows = None, nan_as_category = True):
pos = pd.read_csv('.. /input/POS_CASH_balance.csv', nrows = num_rows)
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']
}
for cat in cat... | Home Credit Default Risk |
1,446,148 | folders = os.listdir(TRAIN_PATH)
images = []
for folder in folders:
files = os.listdir(TRAIN_PATH + folder)
images += [(folder, file, folder + '/' + file)for file in files]
image_locs = pd.DataFrame(images, columns=('class','filename','file_loc'))
display(image_locs.head(10))
display(image_locs.shape )<define_variabl... | def installments_payments(num_rows = None, nan_as_category = True):
ins = pd.read_csv('.. /input/installments_payments.csv', nrows = num_rows)
ins, cat_cols = one_hot_encoder(ins, nan_as_category= True)
ins['PAYMENT_PERC'] = ins['AMT_PAYMENT'] / ins['AMT_INSTALMENT']
ins['PAYMENT_DIFF'] = ins['AMT_INSTALMENT'] - ins[... | Home Credit Default Risk |
1,446,148 | row_count = len(image_locs_shuffled.index)
val_split = 0.1
train_split = 1 - val_split
train_image_locs = image_locs_shuffled[:math.floor(train_split * row_count)]
val_image_locs = image_locs_shuffled[-math.ceil(val_split * row_count):]
display(train_image_locs.shape)
display(val_image_locs.shape )<choose_model_class... | def credit_card_balance(num_rows = None, nan_as_category = True):
cc = pd.read_csv('.. /input/credit_card_balance.csv', nrows = num_rows)
cc, cat_cols = one_hot_encoder(cc, nan_as_category= True)
cc.drop(['SK_ID_PREV'], axis= 1, inplace = True)
cc_agg = cc.groupby('SK_ID_CURR' ).agg([ 'max', 'mean', 'sum', 'var'])
... | Home Credit Default Risk |
1,446,148 | train_datagen = ImageDataGenerator(
rescale=1./255,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True
)
test_datagen = ImageDataGenerator(
rescale=1./255
)<define_variables> | 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 |
1,446,148 | train_generator = train_datagen.flow_from_dataframe(
train_image_locs,
directory=TRAIN_PATH,
x_col='file_loc',
target_size=(IMG_WIDTH, IMG_HEIGHT)
)
val_generator = test_datagen.flow_from_dataframe(
val_image_locs,
directory=TRAIN_PATH,
x_col='file_loc',
target_size=(IMG_WIDTH, IMG_HEIGHT),
shuffle=False
)
test_ge... | def main(debug = False):
num_rows = 10000 if debug else None
df = application_train_test(num_rows)
with timer("Process bureau and bureau_balance"):
bureau = bureau_and_balance(num_rows)
print("Bureau df shape:", bureau.shape)
df = df.join(bureau, how='left', on='SK_ID_CURR')
del bureau
gc.collect()
with timer("Proc... | Home Credit Default Risk |
1,446,148 | def build_model(weights_path=None):
model = Sequential()
model.add(Conv2D(32,(3,3), activation='relu', padding='same', input_shape=(IMG_WIDTH, IMG_HEIGHT, 3)))
model.add(Conv2D(32,(3,3), activation ='relu', padding='same'))
model.add(BatchNormalization())
model.add(MaxPool2D(pool_size=(2,2), strides=2))
model.add(Con... | if __name__ == "__main__":
submission_file_name = "lightgbm.csv"
with timer("Full model run"):
main() | Home Credit Default Risk |
1,471,481 | def build_model_2(weights_path=None):
model = Sequential()
model.add(Conv2D(16,(3,3), activation='relu', padding='same', input_shape=(IMG_WIDTH, IMG_HEIGHT, 3)))
model.add(Conv2D(16,(3,3), activation='relu', padding='same'))
model.add(BatchNormalization())
model.add(MaxPool2D(pool_size=(2,2), strides=2))
model.add(Dr... | df = pd.read_pickle('.. /input/save-dromosys-features/df.pkl.gz')
print("Raw shape: ", df.shape)
y = df['TARGET']
feats = [f for f in df.columns if f not in ['TARGET','SK_ID_CURR','SK_ID_BUREAU','SK_ID_PREV','index']]
X = df[feats]
print("X shape: ", X.shape, " y shape:", y.shape)
print("
Preparing data...")
X = X.... | Home Credit Default Risk |
1,471,481 | def build_model_3(weights_path=None):
model = Sequential()
model.add(Conv2D(8,(3,3), activation='relu', padding='same', input_shape=(IMG_WIDTH, IMG_HEIGHT, 3)))
model.add(BatchNormalization())
model.add(MaxPool2D(pool_size=(2,2), strides=2))
model.add(Dropout(0.1))
model.add(Conv2D(16,(3,3), activation='relu', paddin... | def rank_gauss(x):
N = x.shape[0]
temp = x.argsort()
rank_x = temp.argsort() / N
rank_x -= rank_x.mean()
rank_x *= 2
efi_x = erfinv(rank_x)
efi_x -= efi_x.mean()
return efi_x | Home Credit Default Risk |
1,471,481 | for(i, final_model)in ensemble_final_models:
display(final_model.evaluate_generator(generator=val_generator, steps=val_steps))<predict_on_test> | for i in X.columns:
X[i] = rank_gauss(X[i].values ) | Home Credit Default Risk |
1,471,481 | for(i, final_model)in ensemble_final_models:
val_generator.reset()
val_predictions = final_model.predict_generator(
val_generator,
steps=val_steps,
verbose=1
)
display(val_predictions.shape)
val_predictions_labels = np.argmax(val_predictions, axis=1)
val_true_labels = val_generator.classes[:val_predictions.shape[0... | training = y.notnull()
testing = y.isnull()
X_train = X[training].values
X_test = X[testing].values
y_train = np.array(y[training])
print(X_train.shape, X_test.shape, y_train.shape)
gc.collect() | Home Credit Default Risk |
1,471,481 | model_input = Input(shape=(IMG_WIDTH, IMG_HEIGHT, 3))
yModels=[m(model_input)for i, m in ensemble_final_models]
overall_model = Model(
model_input,
Average()(yModels),
name='ensemble'
)<predict_on_test> | class roc_callback(Callback):
def __init__(self,training_data,validation_data):
self.x = training_data[0]
self.y = training_data[1]
self.x_val = validation_data[0]
self.y_val = validation_data[1]
def on_train_begin(self, logs={}):
return
def on_train_end(self, logs={}):
return
def on_epoch_begin(self, epoch, logs={}):
... | Home Credit Default Risk |
1,471,481 | test_generator.reset()
predictions = overall_model.predict_generator(
test_generator,
steps=test_generator.n,
verbose=1
)
predicted_class_indices = np.argmax(predictions, axis=1)
display(predicted_class_indices )<define_variables> | folds = KFold(n_splits=10, shuffle=True, random_state=42)
sub_preds = np.zeros(X_test.shape[0])
for n_fold,(trn_idx, val_idx)in enumerate(folds.split(X_train)) :
trn_x, trn_y = X_train[trn_idx], y_train[trn_idx]
val_x, val_y = X_train[val_idx], y_train[val_idx]
print('Setting up neural network...')
nn = Sequential()... | Home Credit Default Risk |
1,471,481 | <define_variables><EOS> | print('Saving results...')
sub = pd.DataFrame()
sub['SK_ID_CURR'] = df[testing]['SK_ID_CURR']
sub['TARGET'] = sub_preds
sub[['SK_ID_CURR', 'TARGET']].to_csv('sub_nn.csv', index= False)
print(sub.head() ) | Home Credit Default Risk |
1,462,214 | <SOS> metric: AUC Kaggle data source: home-credit-default-risk<save_to_csv> | import gc
import time
import numpy as np
import pandas as pd
from contextlib import contextmanager
from sklearn.metrics import roc_auc_score, roc_curve
from sklearn.preprocessing import StandardScaler | Home Credit Default Risk |
1,462,214 | df = pd.DataFrame()
df['filename'] = filenames
df['label'] = predictions
df = df.sort_values(by='filename')
df.to_csv('results.csv', header=True, index=False )<import_modules> | @contextmanager
def timer(title):
t0 = time.time()
yield
print("{} - done in {:.0f}s".format(title, time.time() - t0))
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= cat... | Home Credit Default Risk |
1,462,214 | import pandas as pd
import numpy as np
import cv2
import matplotlib.pyplot as plt
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
from sys import getsizeof<load_from_csv> | warnings.simplefilter(action='ignore', category=FutureWarning)
debug = None
num_rows = 10000 if debug else None
df = application_train_test(num_rows)
with timer("Process bureau and bureau_balance"):
bureau = bureau_and_balance(num_rows)
print("Bureau df shape:", bureau.shape)
df = df.join(bureau, how='left', on='SK... | Home Credit Default Risk |
1,462,214 | train = pd.read_csv('.. /input/ava/train.csv', index_col=0)
test = pd.read_csv('.. /input/ava/test.csv', index_col=0)
train<prepare_x_and_y> | feats = [f for f in df.columns if f not in ['TARGET','SK_ID_CURR','SK_ID_BUREAU','SK_ID_PREV','index']]
for c in feats:
ss = StandardScaler()
df.loc[~np.isfinite(df[c]),c] = np.nan
df.loc[~df[c].isnull() ,c] = ss.fit_transform(df.loc[~df[c].isnull() ,c].values.reshape(-1,1))
df[c].fillna(-99999.,inplace=True ) | Home Credit Default Risk |
1,462,214 | X_train = train['image'].values
y_train = train['label'].values
X_test = test['image'].values<normalization> | def Output(p):
return 1./(1.+np.exp(-p))
def GP1(data):
v = pd.DataFrame()
v["i0"] = 0.005976*np.tanh(((((np.minimum(((((((((np.tanh(( np.minimum(((data["DAYS_EMPLOYED"])) ,(( data["REGION_RATING_CLIENT_W_CITY"])))))) -(data["EXT_SOURCE_3"])))* 2.0)) +(data["NEW_CREDIT_TO_GOODS_RATIO"])))) ,(((-1.0*(( data["NEW_SOURCES... | Home Credit Default Risk |
1,462,214 | shapes = []
for i in range(len(X_train)) :
path = '.. /input/ava/dataset/dataset/'+str(X_train[i])+'.jpg'
img = cv2.imread(path)
shapes.append(img.shape)
shapes = np.array(shapes[:])
print(np.mean(shapes[:,0]), np.mean(shapes[:,1]), np.mean(shapes[:,2]))<normalization> | roc_auc_score(train_df.TARGET,GP1(train_df)) | Home Credit Default Risk |
1,462,214 | def get_feature(img):
img = cv2.resize(img,(32, 32), interpolation = cv2.INTER_AREA)
return np.array(img ).flatten()<feature_engineering> | roc_auc_score(train_df.TARGET,GP2(train_df)) | Home Credit Default Risk |
1,462,214 | <train_model><EOS> | x = test_df[['SK_ID_CURR']].copy()
x['TARGET'] =.5*GP1(test_df)+.5*GP2(test_df)
x.to_csv('pure_submission.csv', index = False ) | Home Credit Default Risk |
1,443,616 | <SOS> metric: AUC Kaggle data source: home-credit-default-risk<prepare_output> | import gc
import time
import numpy as np
import pandas as pd
from contextlib import contextmanager
from sklearn.metrics import roc_auc_score, roc_curve
from sklearn.preprocessing import StandardScaler | Home Credit Default Risk |
1,443,616 | prediction = pd.DataFrame()
prediction['labels'] = lr.predict(features_test )<save_to_csv> | @contextmanager
def timer(title):
t0 = time.time()
yield
print("{} - done in {:.0f}s".format(title, time.time() - t0))
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= cat... | Home Credit Default Risk |
1,443,616 | prediction.to_csv("submittion.csv", index_label='id' )<categorify> | warnings.simplefilter(action='ignore', category=FutureWarning)
debug = None
num_rows = 10000 if debug else None
df = application_train_test(num_rows)
with timer("Process bureau and bureau_balance"):
bureau = bureau_and_balance(num_rows)
print("Bureau df shape:", bureau.shape)
df = df.join(bureau, how='left', on='SK... | Home Credit Default Risk |
1,443,616 | def createSubmission(filename,coords,classes,test_directory = '.. /input/test/test/'):
if coords.shape !=(225,2):
raise ValueError('coords must have shape(225,2)')
if classes.shape !=(225,31):
raise ValueError('classes must have shape(225,31)')
files = os.listdir(test_directory)
with open(filename + '.csv','w')as ... | feats = [f for f in df.columns if f not in ['TARGET','SK_ID_CURR','SK_ID_BUREAU','SK_ID_PREV','index']]
for c in feats:
ss = StandardScaler()
df.loc[~np.isfinite(df[c]),c] = np.nan
df.loc[~df[c].isnull() ,c] = ss.fit_transform(df.loc[~df[c].isnull() ,c].values.reshape(-1,1))
df[c].fillna(-99999.,inplace=True ) | Home Credit Default Risk |
1,443,616 | classes = np.random.rand(225,31)
classes[classes >= 0.5] = 1.0
classes[classes != 1.0] = 0
createSubmission("submission.csv",np.random.rand(225,2),classes )<compute_test_metric> | def Output(p):
return 1./(1.+np.exp(-p))
def GP1(data):
v = pd.DataFrame()
v["i0"] = 0.010000*np.tanh(((((((( -1.0*(((((((((( data["CODE_GENDER"])>(data["NEW_EXT_SOURCES_MEAN"])) *1.))>(( -1.0*(( data["CLOSED_AMT_CREDIT_SUM_SUM"])))))*1.))+(((data["NEW_EXT_SOURCES_MEAN"])*(3.141593)))))))) * 2.0)) * 2.0)) * 2.0))
v["i1... | Home Credit Default Risk |
1,443,616 | class SigmoidNeuron:
def __init__(self):
self.w = None
self.b = None
def perceptron(self, x):
return np.dot(x, self.w.T)+ self.b
def sigmoid(self, x):
return 1.0/(1.0 + np.exp(-x))
def grad_w_mse(self, x, y):
y_pred = self.sigmoid(self.perceptron(x))
return(y_pred - y)* y_pred *(1 - y_pred)* x
def grad_b_mse(self, x, y... | roc_auc_score(train_df.TARGET,GP1(train_df)) | Home Credit Default Risk |
1,443,616 | languages = ['ta', 'hi', 'en']
images_train = read_all(".. /input/level_4b_train/"+LEVEL+"/"+"background", key_prefix='bgr_')
for language in languages:
images_train.update(read_all(".. /input/level_4b_train/"+LEVEL+"/"+language, key_prefix=language+"_"))
print(len(images_train))
images_test = read_all(".. /input/leve... | roc_auc_score(train_df.TARGET,GP2(train_df)) | Home Credit Default Risk |
1,443,616 | <compute_test_metric><EOS> | x = test_df[['SK_ID_CURR']].copy()
x['TARGET'] =.5*GP1(test_df)+.5*GP2(test_df)
x.to_csv('pure_submission.csv', index = False ) | Home Credit Default Risk |
1,136,016 | <SOS> metric: AUC Kaggle data source: home-credit-default-risk<train_model> | pd.set_option("display.max_columns",100)
%matplotlib inline
py.init_notebook_mode(connected=True)
print(os.listdir(".. /input"))
| Home Credit Default Risk |
1,136,016 |
<train_model> | df_train = pd.read_csv('.. /input/application_train.csv')
df_train.head() | Home Credit Default Risk |
1,136,016 | sn_ce = SigmoidNeuronMy()
sn_ce.fit(X_scaled_train, Y_train, epochs=800, learning_rate=0.00005, loss_fn="ce", display_loss=True)
<predict_on_test> | df_test = pd.read_csv('.. /input/application_test.csv')
| Home Credit Default Risk |
1,136,016 | def print_accuracy(sn):
Y_pred_train = sn.predict(X_scaled_train)
Y_pred_binarised_train =(Y_pred_train >= 0.5 ).astype("int" ).ravel()
accuracy_train = accuracy_score(Y_pred_binarised_train, Y_train)
print("Train Accuracy : ", accuracy_train)
print("-"*50 )<compute_test_metric> | y = df_train['TARGET']
X = df_train.drop(['TARGET'], axis=1)
X.head() | Home Credit Default Risk |
1,136,016 | print_accuracy(sn_ce )<save_to_csv> | print(X.shape)
X = pd.concat([X, df_test], axis=0)
X.shape | Home Credit Default Risk |
1,136,016 | Y_pred_test = sn_ce.predict(X_scaled_test)
Y_pred_binarised_test =(Y_pred_test >= 0.5 ).astype("int" ).ravel()
submission = {}
submission['ImageId'] = ID_test
submission['Class'] = Y_pred_binarised_test
submission = pd.DataFrame(submission)
submission = submission[['ImageId', 'Class']]
submission = submission.sort_va... | cat_X = X.drop(['SK_ID_CURR'], axis=1)
cat_X = [col for col in X.columns if X[col].dtype == 'object']
SK_ID = X['SK_ID_CURR']
cat_X = X[cat_X]
cat_X.head() | Home Credit Default Risk |
1,136,016 | np.random.seed(100)
LEVEL = 'level_4b'
warnings.filterwarnings("ignore" )<compute_test_metric> | ncat_X = ncat_X.fillna(0)
| Home Credit Default Risk |
1,136,016 | class SigmoidNeuron:
def __init__(self):
self.w = None
self.b = None
def perceptron(self, x):
return np.dot(x, self.w.T)+ self.b
def sigmoid(self, x):
return 1.0/(1.0 + np.exp(-x))
def grad_w_mse(self, x, y):
y_pred = self.sigmoid(self.perceptron(x))
return(y_pred - y)* y_pred *(1 - y_pred)* x
def grad_b_mse(self, x, y... | ncat_X = pd.DataFrame(ncat_X, columns=cols_ncat_X)
ncat_X['SK_ID_CURR'] = SK_ID.values
ncat_X.head(10 ) | Home Credit Default Risk |
1,136,016 |
<load_pretrained> | ncat_X['PERC_INCOME'] = ncat_X.AMT_CREDIT/ncat_X.AMT_INCOME_TOTAL
ncat_X.head() | Home Credit Default Risk |
1,136,016 | languages = ['ta', 'hi', 'en']
images_train = read_all(".. /input/level_4b_train/"+LEVEL+"/"+"background", key_prefix='bgr_')
for language in languages:
images_train.update(read_all(".. /input/level_4b_train/"+LEVEL+"/"+language, key_prefix=language+"_"))
print(len(images_train))
images_test = read_all(".. /input/leve... | ncat_X['GOODS_BIGGER_CREDIT'] = np.where(ncat_X.AMT_GOODS_PRICE > ncat_X.AMT_CREDIT, 1, 0)
ncat_X['AMOUNT_NOT_CREDIT'] = ncat_X.AMT_CREDIT - ncat_X.AMT_GOODS_PRICE
ncat_X.head(20 ) | Home Credit Default Risk |
1,136,016 | scaler = StandardScaler()
X_scaled_train = scaler.fit_transform(X_train)
X_scaled_test = scaler.transform(X_test )<choose_model_class> | ncat_X['AMT_INCOME_TOTAL'] = np.log(ncat_X.AMT_INCOME_TOTAL ) | Home Credit Default Risk |
1,136,016 | kfold = KFold(5, True, 10)
<find_best_model_class> | ncat_X['AMT_CREDIT'] = np.log(ncat_X.AMT_CREDIT ) | Home Credit Default Risk |
1,136,016 | sn_ce = SigmoidNeuron()
for train, test in kfold.split(X_scaled_train,Y_train):
sn_ce.fit(X_scaled_train[train], Y_train[train], epochs=500, learning_rate=0.00002, loss_fn="ce", display_loss=True,initialise=False)
Y_pred_train = sn_ce.predict(X_scaled_train[test])
Y_pred_binarised_train =(Y_pred_train >= 0.5 ).astype... | ncat_X['AMT_ANNUITY'] = np.log(ncat_X.AMT_ANNUITY ) | Home Credit Default Risk |
1,136,016 | def print_accuracy(sn):
Y_pred_train = sn.predict(X_scaled_train)
Y_pred_binarised_train =(Y_pred_train >= 0.5 ).astype("int" ).ravel()
accuracy_train = accuracy_score(Y_pred_binarised_train, Y_train)
print("Train Accuracy : ", accuracy_train)
print("-"*50 )<compute_test_metric> | ncat_X['AMT_GOODS_PRICE'] = np.log(ncat_X.AMT_GOODS_PRICE ) | Home Credit Default Risk |
1,136,016 | print_accuracy(sn_ce )<save_to_csv> | ncat_X['DAYS_BIRTH'] = ncat_X.DAYS_BIRTH.apply(lambda x: x/-365)
ncat_X['DAYS_EMPLOYED'] = ncat_X.DAYS_EMPLOYED.apply(lambda x: x/-365)
ncat_X['DAYS_REGISTRATION'] = ncat_X.DAYS_REGISTRATION.apply(lambda x: x/-365)
ncat_X['DAYS_ID_PUBLISH'] = ncat_X.DAYS_ID_PUBLISH.apply(lambda x: x/-365)
ncat_X['DAYS_LAST_PHONE_CH... | Home Credit Default Risk |
1,136,016 | Y_pred_test = sn_ce.predict(X_scaled_test)
Y_pred_binarised_test =(Y_pred_test >= 0.5 ).astype("int" ).ravel()
submission = {}
submission['ImageId'] = ID_test
submission['Class'] = Y_pred_binarised_test
submission = pd.DataFrame(submission)
submission = submission[['ImageId', 'Class']]
submission = submission.sort_va... |
for x in cat_X.columns.values:
print(x)
keys = cat_X[x].unique()
dicts = dict(zip(keys, range(len(keys))))
cat_X[x] = cat_X[x].map(dicts ).astype(int)
cat_X.head() | Home Credit Default Risk |
1,136,016 | warnings.simplefilter("ignore")
np.random.seed(100)
LEVEL = 'level_4b'<compute_test_metric> | cat_X = pd.concat([SK_ID, cat_X], axis=1 ) | Home Credit Default Risk |
1,136,016 | class SigmoidNeuron:
def __init__(self):
self.w = None
self.b = None
def perceptron(self, x):
return np.dot(x, self.w.T)+ self.b
def sigmoid(self, x):
return 1.0/(1.0 + np.exp(-x))
def grad_w_mse(self, x, y):
y_pred = self.sigmoid(self.perceptron(x))
return(y_pred - y)* y_pred *(1 - y_pred)* x
def grad_b_mse(self, x, y... | X = ncat_X.merge(cat_X, how='left', on='SK_ID_CURR' ) | Home Credit Default Risk |
1,136,016 | languages = ['en','ta', 'hi']
images_train = read_all(".. /input/"+LEVEL+"_train/"+LEVEL+"/"+"background", key_prefix='bgr_')
for language in languages:
images_train.update(read_all(".. /input/"+LEVEL+"_train/"+LEVEL+"/"+language, key_prefix=language+"_"))
print(len(images_train))
images_test = read_all(".. /input/"+L... | gc.enable()
del ncat_X
del cat_X
gc.collect() | Home Credit Default Risk |
1,136,016 | X_train = []
Y_train = []
ID_train = []
for key, value in images_train.items() :
X_train.append(value)
ID_train.append(key)
if key[:4] == "bgr_":
Y_train.append(0)
else:
Y_train.append(1)
ID_test = []
X_test = []
for key, value in images_test.items() :
ID_test.append(int(key))
X_test.append(value)
X_train = np.arr... | credit_card_balance = pd.read_csv('.. /input/credit_card_balance.csv')
credit_card_balance.head() | Home Credit Default Risk |
1,136,016 | scaler = StandardScaler()
X_scaled_train = scaler.fit_transform(X_train)
X_scaled_test = scaler.transform(X_test )<train_model> | credit_card_balance['NAME_CONTRACT_STATUS'] = pd.get_dummies(credit_card_balance['NAME_CONTRACT_STATUS'])
credit_card_balance = credit_card_balance.fillna(0)
credit_card_balance.head(10 ) | Home Credit Default Risk |
1,136,016 | sn_ce = SigmoidNeuron()
sn_ce.fit(X_scaled_train, Y_train, epochs=100, learning_rate=1,initialise=True,loss_fn="ce",display_loss=True)
sn_ce.fit(X_scaled_train, Y_train, epochs=200, learning_rate=0.1,initialise=False,loss_fn="ce",display_loss=True)
sn_ce.fit(X_scaled_train, Y_train, epochs=200, learning_rate=0.01,ini... | ID_PREV = credit_card_balance[['SK_ID_CURR', 'SK_ID_PREV']].groupby('SK_ID_CURR' ).count()
credit_card_balance['SK_ID_PREV'] = credit_card_balance['SK_ID_CURR'].map(ID_PREV['SK_ID_PREV'] ) | Home Credit Default Risk |
1,136,016 | def print_accuracy(sn):
Y_pred_train = sn.predict(X_scaled_train)
Y_pred_binarised_train =(Y_pred_train >= 0.5 ).astype("int" ).ravel()
accuracy_train = accuracy_score(Y_pred_binarised_train, Y_train)
print("Train Accuracy : ", accuracy_train)
print("-"*50)
mismatch_name = []
for x,y,z in zip(Y_pred_binarised_train... | credit_card_mean = credit_card_balance.groupby('SK_ID_CURR' ).mean()
credit_card_mean.columns = ['cc_' + col for col in credit_card_mean.columns]
credit_card_mean = credit_card_mean.reset_index()
credit_card_mean.head() | Home Credit Default Risk |
1,136,016 | print_accuracy(sn_ce )<save_to_csv> | X = X.merge(right=credit_card_mean, how='left', on='SK_ID_CURR')
X.head(10 ) | Home Credit Default Risk |
1,136,016 | Y_pred_test = sn_ce.predict(X_scaled_test)
Y_pred_binarised_test =(Y_pred_test >= 0.5 ).astype("int" ).ravel()
submission = {}
submission['ImageId'] = ID_test
submission['Class'] = Y_pred_binarised_test
submission = pd.DataFrame(submission)
submission = submission[['ImageId', 'Class']]
submission = submission.sort_va... | gc.enable()
del credit_card_balance
del credit_card_mean
gc.collect() | Home Credit Default Risk |
1,136,016 | class SigmoidNeuron:
def __init__(self):
self.w = None
self.b = None
self.best_epoch = 0
def perceptron(self, x):
return np.dot(x, self.w.T)+ self.b
def sigmoid(self, x):
return 1.0/(1.0 + np.exp(-x))
def grad_w_mse(self, x, y):
y_pred = self.sigmoid(self.perceptron(x))
return(y_pred - y)* y_pred *(1 - y_pred)* x
def g... | POS_CASH_balance = pd.read_csv('.. /input/POS_CASH_balance.csv')
POS_CASH_balance.head() | Home Credit Default Risk |
1,136,016 | a = np.array([1,2,3])
c = list(a)
print(a)
print(c)
b = [(lambda x: 0 if x < 2 else 255 )(x)for x in c]
print(b )<load_pretrained> | POS_CASH_balance['NAME_CONTRACT_STATUS'] = pd.get_dummies(POS_CASH_balance['NAME_CONTRACT_STATUS'])
POS_CASH_balance = POS_CASH_balance.fillna(0 ) | Home Credit Default Risk |
1,136,016 | languages = ['ta', 'hi', 'en']
images_train = read_all(".. /input/level_4b_train/level_4b/" + "background/", key_prefix='bgr_')
for language in languages:
images_train.update(read_all(".. /input/level_4b_train/level_4b/"+language, key_prefix=language+"_"))
print(len(images_train))
images_test = read_all(".. /input/lev... | pcb_count = POS_CASH_balance[['SK_ID_CURR', 'SK_ID_PREV']].groupby('SK_ID_CURR' ).count()
POS_CASH_balance['SK_ID_PREV'] = POS_CASH_balance['SK_ID_CURR'].map(pcb_count['SK_ID_PREV'] ) | Home Credit Default Risk |
1,136,016 | a = np.array(( [1,2],[3,4]), dtype = int)
print(a)
b = a.flatten()
print(b)
c = b.reshape(2,-1)
print(c)
<normalization> | pos_cash_mean = POS_CASH_balance.groupby('SK_ID_CURR' ).mean()
pos_cash_mean.columns = ['pc_' + col for col in pos_cash_mean.columns]
pos_cash_mean = pos_cash_mean.reset_index()
pos_cash_mean.head() | Home Credit Default Risk |
1,136,016 | scaler = StandardScaler()
X_scaled_train = scaler.fit_transform(X_train)
X_scaled_test = scaler.transform(X_test)
<train_model> | X = X.merge(right=pos_cash_mean, how='left', on='SK_ID_CURR')
X.shape | Home Credit Default Risk |
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