kernel_id int64 24.2k 23.3M | prompt stringlengths 8 1.85M | completetion stringlengths 1 182k | comp_name stringlengths 5 57 |
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4,160,759 | class LungSliceModelGenerator(kutils.Sequence):
'Generates data for Keras'
def __init__(self, mapping_df, batch_size, shuffle=True):
'Initialization'
self.mapping_df = mapping_df
self.data_num = mapping_df.shape[0]
self.batch_size = batch_size
self.shuffle = shuffle
self.on_epoch_end()
def __len__(self):
'Denotes the number of batches per epoch'
return int(np.floor(self.data_num / self.batch_size))
def __getitem__(self, index):
'Generate one batch of data'
batch_mapping_df = \
self.mapping_df.iloc[index*self.batch_size:(index+1)*self.batch_size]
X, y = self.__data_generation(batch_mapping_df)
return X, y
def on_epoch_end(self):
'Updates indexes after each epoch'
if self.shuffle:
self.mapping_df = self.mapping_df.sample(frac=1 ).reset_index(drop=True)
def __data_generation(self, batch_mapping_df):
'Generates data containing batch_size samples'
X = np.zeros(( self.batch_size, 512, 512, 1))
y = np.zeros(( self.batch_size, 512, 512, 1))
cnt = 0
for i, row in batch_mapping_df.iterrows() :
X[cnt, :, :, 0] = np.load(row['image'])['image']
y[cnt, :, :, 0] = np.load(row['label'])['label']
cnt += 1
return X, y<choose_model_class> | df = pd.read_csv(".. /input/dfcsv/df.csv" ) | Home Credit Default Risk |
4,160,759 | batch_size = 16
slice_generator = LungSliceModelGenerator(map_df, batch_size=batch_size )<compute_test_metric> | df_model = df[df['TARGET'].notnull() ]
feats = [f for f in df_model.columns if f not in ['TARGET','SK_ID_CURR','SK_ID_BUREAU','SK_ID_PREV','index']]
train_x, test_x, train_y, test_y = train_test_split(df_model[feats], df_model['TARGET'], random_state=42)
df_submission = df.loc[df['TARGET'].isnull() , feats]
main_id_submission =df.loc[df['TARGET'].isnull() , 'SK_ID_CURR']
del df | Home Credit Default Risk |
4,160,759 | def _dice_coefficient(threshold = 0.3):
def hard_dice_coefficient(y_true, y_pred, smooth=1.0):
y_true_f = K.flatten(K.cast(y_true > threshold, dtype=float))
y_pred_f = K.flatten(K.cast(y_pred > threshold, dtype=float))
intersection = K.sum(y_true_f * y_pred_f)
return(2.* intersection + smooth)/(K.sum(y_true_f)+ K.sum(y_pred_f)+ smooth)
return hard_dice_coefficient
def dice_coefficient_loss(y_true, y_pred):
return 1 - _dice_coefficient()(y_true, y_pred )<choose_model_class> | DEBUG = False | Home Credit Default Risk |
4,160,759 | def unet(pretrained_weights=None, input_size=[512, 512, 1], depth=3, init_filter=8,
filter_size=3, padding='same', pool_size=[2, 2], strides=[2, 2]):
inputs = klayers.Input(input_size)
current_layer = inputs
encoding_layers = []
for d in range(depth + 1):
num_filters = init_filter * 2 ** d
conv = klayers.Conv2D(num_filters, filter_size, padding=padding, kernel_initializer='he_normal' )(current_layer)
conv = klayers.BatchNormalization()(conv)
conv = klayers.Activation('relu' )(conv)
conv = klayers.Conv2D(num_filters * 2, filter_size, padding=padding, kernel_initializer='he_normal' )(conv)
conv = klayers.BatchNormalization()(conv)
conv = klayers.Activation('relu' )(conv)
encoding_layers.append(conv)
pool = klayers.MaxPooling2D(pool_size=pool_size )(conv)
if d == depth:
current_layer = conv
else:
current_layer = pool
for d in range(depth, 0, -1):
num_filters = init_filter * 2 ** d
up = klayers.Deconvolution2D(num_filters * 2, pool_size, strides=strides )(current_layer)
crop_layer = encoding_layers[d - 1]
up_shape = np.array(up._keras_shape[1:-1])
conv_shape = np.array(crop_layer._keras_shape[1:-1])
crop_left =(conv_shape - up_shape)// 2
crop_right =(conv_shape - up_shape)// 2 +(conv_shape - up_shape)% 2
crop_sizes = tuple(zip(crop_left, crop_right))
crop = klayers.Cropping2D(cropping=crop_sizes )(crop_layer)
up = klayers.Concatenate(axis=-1 )([crop, up])
conv = klayers.Conv2D(num_filters, filter_size, padding=padding, kernel_initializer='he_normal' )(up)
conv = klayers.BatchNormalization()(conv)
conv = klayers.Activation('relu' )(conv)
conv = klayers.Conv2D(num_filters, filter_size, padding=padding, kernel_initializer='he_normal' )(conv)
conv = klayers.BatchNormalization()(conv)
conv = klayers.Activation('relu' )(conv)
current_layer = conv
outputs = klayers.Conv2D(1, 1, padding=padding, kernel_initializer='he_normal' )(current_layer)
outputs = klayers.Activation('sigmoid' )(outputs)
model = Model(inputs=inputs, outputs=outputs)
if(pretrained_weights):
model.load_weights(pretrained_weights)
return model<choose_model_class> | ITER = 1
SCORES = []
MINUTES = time.time()
if DEBUG == True:
init_pt = 1
n_iter_pt = 2
PT_GRAPH = 3
else:
init_pt = 10
n_iter_pt = 100
PT_GRAPH = 10
def lgb_evaluate(
numLeaves,
maxDepth,
minChildWeight,
subsample,
colsample_bytree,
learn_rate,
reg_alpha,
reg_lambda,
min_split_gain):
global ITER, SCORES, MINUTES
clf = LGBMClassifier(
nthread=4,
n_estimators=100,
verbose =-1,
silent=-1,
num_leaves= int(numLeaves),
max_depth= int(maxDepth),
min_child_weight= minChildWeight,
colsample_bytree= colsample_bytree,
subsample= subsample,
learning_rate= learn_rate,
reg_alpha = reg_alpha,
reg_lambda= reg_lambda,
min_split_gain= min_split_gain
)
scores = cross_val_score(clf, train_x, train_y, cv=5, scoring='roc_auc')
print("Mean cross validation score: {}".format(np.mean(scores)))
SCORES.append(np.mean(scores))
if ITER % PT_GRAPH == 0:
plt.figure(figsize=(11,4))
plt.plot(range(len(SCORES)) , SCORES)
plt.scatter(SCORES.index(max(SCORES)) , max(SCORES), color='red')
plt.ylabel("Score")
plt.xlabel("Attempt")
plt.title("Real time evolution of the mean score")
plt.show()
print("Minutes since beginning: {}".format(float(time.time() - MINUTES)/ 60))
ITER = ITER + 1
return np.mean(scores)
lgbBO = BayesianOptimization(lgb_evaluate, {
'numLeaves':(5, 50),
'maxDepth':(2, 63),
'minChildWeight':(0.01, 70),
'subsample':(0.4, 1),
'colsample_bytree':(0.4, 1),
'learn_rate':(0.1, 1),
'reg_alpha':(0, 1),
'reg_lambda':(0, 1),
'min_split_gain':(0, 1)
})
lgbBO.maximize(init_points=init_pt, n_iter=n_iter_pt ) | Home Credit Default Risk |
4,160,759 | model = unet(depth=3)
model.compile(optimizer=Adam(lr=1e-3), loss='binary_crossentropy', metrics=[_dice_coefficient(0.5)])
model.summary()<train_model> | best = max([lgbBO.res[i]['target'] for i in range(len(lgbBO.res)) ])
best | Home Credit Default Risk |
4,160,759 | model_folder = os.path.join('./model', 'sample-code')
if not os.path.exists(model_folder):
os.makedirs(model_folder)
callbacks = []
callbacks.append(ModelCheckpoint(os.path.join(model_folder, 'model-{epoch:03d}.h5'),
save_best_only=False,
period=5))<train_model> | best_index = [lgbBO.res[i]['target'] for i in range(len(lgbBO.res)) ].index(best)
best_index | Home Credit Default Risk |
4,160,759 | history = model.fit_generator(slice_generator,
epochs=15,
verbose=1,
callbacks=callbacks )<predict_on_test> | param_dict = lgbBO.res[best_index]["params"]
clf = LGBMClassifier(
nthread=4,
n_estimators=100,
silent=-1,
verbose=-1,
num_leaves=34,
colsample_bytree=param_dict["colsample_bytree"],
subsample=param_dict["subsample"],
max_depth=int(param_dict["maxDepth"]),
min_child_weight=param_dict["minChildWeight"],
learning_rate=param_dict["learn_rate"],
reg_alpha=param_dict["reg_alpha"],
reg_lambda=param_dict["reg_lambda"],
min_split_gain=param_dict["min_split_gain"])
clf.fit(train_x, train_y ) | Home Credit Default Risk |
4,160,759 | def retrieve_pred_str(src_dir, model, threshold=0.4):
encode_name = src_dir.split('/')[-1]
_, test_volume = load_dicom_volume(src_dir, suffix='*.dcm')
pred_label = model.predict(np.expand_dims(test_volume, axis=-1))
pred_label = np.transpose(pred_label[:, :, :, 0], axes=(2, 1, 0))
pred_label =(pred_label > threshold ).astype(np.int)
label_flatten = pred_label.flatten()
label_flatten_idx = np.where(label_flatten == 1)[0]
label_str = ''
if label_flatten_idx.size > 0:
prev_idx = label_flatten_idx[0]
idx_start = label_flatten_idx[0]
cnt = 1
for _idx in label_flatten_idx[1:]:
if _idx == prev_idx+1:
cnt += 1
else:
label_str += str(idx_start)+ ' ' + str(cnt)+ ' '
cnt = 1
idx_start = _idx
prev_idx = _idx
label_str = label_str.rstrip(' ')
return(encode_name, label_str )<load_from_csv> | print(metrics.auc(fpr, tpr)) | Home Credit Default Risk |
4,160,759 | sample_submission = np.genfromtxt('.. /input/sample_submission.csv',
delimiter=',',
dtype='str',
skip_header = 1 )<define_variables> | importance_df = pd.DataFrame()
importance_df["feature"] = feats
importance_df["importance"] = clf.feature_importances_
importance_df = importance_df.sort_values(by='importance', ascending=False)
importance_df = importance_df.reset_index(drop=True ) | Home Credit Default Risk |
4,160,759 | test_encode_list = sample_submission[:, 0]<categorify> | display_importances(importance_df ) | Home Credit Default Risk |
4,160,759 | pred_pair_list = []
for encode_name in tqdm.tqdm(test_encode_list, total=len(test_encode_list)) :
(encode, label_str)= retrieve_pred_str(os.path.join(test_image_folder, encode_name), model, threshold=0.4)
pred_pair_list.append(( encode, label_str))<save_to_csv> | best_feature = importance_df.loc[0:30, "feature"].values | Home Credit Default Risk |
4,160,759 | solution_path = './sample-code_pred.csv'
with open(solution_path, 'w')as f:
f.write('encode,pixel_value
')
for _pair in pred_pair_list:
encode = _pair[0]
label_str = _pair[1]
f.write(encode + ',' + label_str + '
' )<import_modules> | values_x = pd.concat([train_x, test_x])
values_y = pd.concat([train_y, test_y] ) | Home Credit Default Risk |
4,160,759 | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import keras
from sklearn import metrics
from sklearn.preprocessing import LabelEncoder,OneHotEncoder
from keras.models import Model
from keras.layers import LSTM, Activation, Dense, Dropout, Input, Embedding
from keras.optimizers import RMSprop
from keras.preprocessing.text import Tokenizer
from keras.preprocessing import sequence
from keras.callbacks import EarlyStopping<import_modules> | param_dict = lgbBO.res[best_index]["params"]
clf = LGBMClassifier(
nthread=4,
n_estimators=100,
silent=-1,
verbose=-1,
num_leaves=34,
colsample_bytree=param_dict["colsample_bytree"],
subsample=param_dict["subsample"],
max_depth=int(param_dict["maxDepth"]),
min_child_weight=param_dict["minChildWeight"],
learning_rate=param_dict["learn_rate"],
reg_alpha=param_dict["reg_alpha"],
reg_lambda=param_dict["reg_lambda"],
min_split_gain=param_dict["min_split_gain"])
clf.fit(values_x, values_y ) | Home Credit Default Risk |
4,160,759 | import json
from pandas.io.json import json_normalize<load_from_csv> | filename = 'clf.sav'
pickle.dump(clf, open(filename, 'wb')) | Home Credit Default Risk |
4,160,759 | raw_data=pd.read_json(".. /input/datamininglab2/tweets_DM.json",lines=True)
tweets=json_normalize(data=raw_data['_source'])
identify=pd.read_csv(".. /input/datamininglab2/data_identification.csv")
emotion=pd.read_csv(".. /input/datamininglab2/emotion.csv" )<merge> | y_pred_proba = clf.predict_proba(df_submission)[:, 1]
df_results = pd.DataFrame(columns =['SK_ID_CURR', 'TARGET'])
df_results['SK_ID_CURR'] = main_id_submission
df_results['TARGET'] = y_pred_proba | Home Credit Default Risk |
4,160,759 | <load_pretrained><EOS> | df_results.to_csv("submission.csv", index=False ) | Home Credit Default Risk |
1,511,034 | <SOS> metric: AUC Kaggle data source: home-credit-default-risk<load_pretrained> | pd.options.display.max_columns = 999
warnings.filterwarnings('ignore')
os.environ['OMP_NUM_THREADS'] = '4'
| Home Credit Default Risk |
1,511,034 | train_df = pd.read_pickle(".. /input/dm-competition-tweets-emotion/train_df.pkl")
test_df = pd.read_pickle(".. /input/dm-competition-tweets-emotion/test_df.pkl" )<feature_engineering> | train = pd.read_csv(".. /input/application_train.csv")
test = pd.read_csv(".. /input/application_test.csv")
previous = pd.read_csv(".. /input/previous_application.csv")
bureau = pd.read_csv(".. /input/bureau.csv" ) | Home Credit Default Risk |
1,511,034 | tknzr = TweetTokenizer()<string_transform> | previous['AMT_APPLICATION'].replace(0,np.nan, inplace = True)
previous['AMT_CREDIT'].replace(0,np.nan, inplace = True)
previous['AMT_GOODS_PRICE'].replace(0,np.nan,inplace =True)
previous['RATE_DOWN_PAYMENT'].replace(0, np.nan, inplace = True)
previous['AMT_ANNUITY'].replace(0, np.nan, inplace = True)
previous['CNT_PAYMENT'].replace(0, np.nan, inplace = True ) | Home Credit Default Risk |
1,511,034 | tknzr = TweetTokenizer(strip_handles=True, reduce_len=True)
s1 = '@remy: This is waaaaayyyy too much for you!!!!!!'
tknzr.tokenize(s1 )<train_model> | for i in ['Revolving loans','Cash loans', 'Consumer loans']:
tmp = previous[(previous['NAME_CONTRACT_TYPE'] == i)&(previous['DAYS_LAST_DUE'] == 365243)]
for df in [train,test]:
tmp1 = tmp.groupby(['SK_ID_CURR'])['SK_ID_PREV'].count().reset_index()
tmp1.columns = ['SK_ID_CURR','des']
tmp_merge = df[['SK_ID_CURR']]
tmp_merge = tmp_merge.merge(tmp1, on=['SK_ID_CURR'], how='left')
df['count_notfinish_' + "_".join(i.lower().split())] = tmp_merge['des'].fillna(0 ) | Home Credit Default Risk |
1,511,034 | tknzr = TweetTokenizer(preserve_case=False)
tfidf = TfidfVectorizer(max_features=20000, stop_words='english',
tokenizer=tknzr.tokenize)
tfidf.fit(train_df['text'] )<categorify> | for i in ['Revolving loans','Cash loans', 'Consumer loans']:
tmp = previous[(previous['NAME_CONTRACT_TYPE'] == i)&(previous['DAYS_LAST_DUE'] == 365243)]
for df in [train,test]:
tmp1 = tmp.groupby(['SK_ID_CURR'])['AMT_CREDIT'].agg({"returns": [np.mean, np.sum]})\
.reset_index()
tmp1.columns = ['SK_ID_CURR','des1','des2']
tmp_merge = df[['SK_ID_CURR']]
tmp_merge = tmp_merge.merge(tmp1, on=['SK_ID_CURR'], how='left')
df['mean_notfinish_' + "_".join(i.lower().split())] = tmp_merge['des1'].fillna(0)
df['sum_notfinish_' + "_".join(i.lower().split())] = tmp_merge['des2'].fillna(0 ) | Home Credit Default Risk |
1,511,034 | X_train = tfidf.transform(train_df['text'])
X_train.shape<categorify> | for i in ['Revolving loans','Cash loans', 'Consumer loans']:
tmp = previous[(previous['NAME_CONTRACT_TYPE'] == i)&(previous['DAYS_TERMINATION'] == 365243)]
for df in [train,test]:
tmp1 = tmp.groupby(['SK_ID_CURR'])['AMT_ANNUITY'].agg({"returns": [np.mean, np.sum]})\
.reset_index()
tmp1.columns = ['SK_ID_CURR','des1','des2']
tmp_merge = df[['SK_ID_CURR']]
tmp_merge = tmp_merge.merge(tmp1, on=['SK_ID_CURR'], how='left')
df['mean_annuity_notfinish_' + "_".join(i.lower().split())] = tmp_merge['des1'].fillna(0)
df['sum_annuity_notfinish_' + "_".join(i.lower().split())] = tmp_merge['des2'].fillna(0 ) | Home Credit Default Risk |
1,511,034 | X_test = tfidf.transform(test_df['text'])
X_test.shape<prepare_x_and_y> | previous['SELLERPLACE_AREA'].replace(0, np.nan, inplace = True)
previous['SELLERPLACE_AREA'].replace(-1, np.nan, inplace = True)
previous['DAYS_TERMINATION'].replace(365243, np.nan, inplace = True)
previous['DAYS_LAST_DUE'].replace(365243, np.nan, inplace = True)
previous['DAYS_LAST_DUE_1ST_VERSION'].replace(365243, np.nan, inplace = True)
previous['sooner'] =(previous['DAYS_LAST_DUE_1ST_VERSION'] - previous['DAYS_LAST_DUE'])/(previous['DAYS_LAST_DUE_1ST_VERSION']-previous['DAYS_DECISION'])
previous['duration'] = previous['DAYS_TERMINATION'] - previous['DAYS_DECISION']
previous['DAYS_FIRST_DRAWING'].replace(365243, np.nan, inplace = True ) | Home Credit Default Risk |
1,511,034 | y_train = train_df['emotion']
y_test = test_df['emotion']<load_from_csv> | train['DAYS_EMPLOYED'] = train['DAYS_EMPLOYED'].replace(365243, np.nan)
test['DAYS_EMPLOYED'] = test['DAYS_EMPLOYED'].replace(365243, np.nan)
tmp = train[train['DAYS_LAST_PHONE_CHANGE'] >= 0].index
train['DAYS_LAST_PHONE_CHANGE'].iloc[tmp] = np.nan
tmp = test[test['DAYS_LAST_PHONE_CHANGE'] >= 0].index
test['DAYS_LAST_PHONE_CHANGE'].iloc[tmp] = np.nan
for df in [train, test]:
df['ORGANIZATION_TYPE_v2'] = df['ORGANIZATION_TYPE']
for i in range(1,4):
df['ORGANIZATION_TYPE_v2'].replace('Business Entity Type ' + str(i), 'Business', inplace = True)
for i in range(1,14):
df['ORGANIZATION_TYPE_v2'].replace('Industry: type ' + str(i), 'Industry', inplace = True)
for i in range(1,8):
df['ORGANIZATION_TYPE_v2'].replace('Trade: type ' + str(i), 'Trade', inplace = True)
for i in range(1,8):
df['ORGANIZATION_TYPE_v2'].replace('Transport: type ' + str(i), 'Transport', inplace = True)
df['ORGANIZATION_TYPE_v2'].replace('Other','XNA', inplace = True ) | Home Credit Default Risk |
1,511,034 | model_compare=pd.read_csv(".. /input/dm-competition-tweets-emotion/final.csv")
model_compare<train_model> | tmp = previous[(previous['NAME_CONTRACT_STATUS'] == 'Approved')&(previous['NAME_CONTRACT_TYPE'].isin(['Consumer loans','Cash loans', 'Revolving loans'])) ].groupby(['SK_ID_CURR'])['AMT_CREDIT']\
.agg({"returns": [np.min, np.max,np.mean]})\
.reset_index()
tmp.columns = ['SK_ID_CURR','des1','des2','des3']
for df in [train,test]:
tmp_merge = df[['SK_ID_CURR']]
tmp_merge = tmp_merge.merge(tmp, on=['SK_ID_CURR'], how='left')
df['min_amt_credit_master'] = tmp_merge['des1']
df['max_amt_credit_master'] = tmp_merge['des2']
df['mean_amt_credit_master'] = tmp_merge['des3'] | Home Credit Default Risk |
1,511,034 | lr = LogisticRegression(C=6,n_jobs=-1,max_iter=1000)
lr.fit(X_train,y_train )<predict_on_test> | tmp = previous[(previous['NAME_CONTRACT_STATUS'] == 'Approved')&(previous['NAME_CONTRACT_TYPE'].isin(['Cash loans'])) ].groupby(['SK_ID_CURR'])['AMT_APPLICATION']\
.agg({"returns": [np.min, np.max,np.mean]})\
.reset_index()
tmp.columns = ['SK_ID_CURR','des1','des2','des3']
for df in [train,test]:
tmp_merge = df[['SK_ID_CURR']]
tmp_merge = tmp_merge.merge(tmp, on=['SK_ID_CURR'], how='left')
df['min_amt_app'] = tmp_merge['des1']
df['max_amt_app'] = tmp_merge['des2']
df['mean_amt_app'] = tmp_merge['des3']
tmp = previous[(previous['NAME_CONTRACT_STATUS'] == 'Approved')&(previous['NAME_CONTRACT_TYPE'].isin(['Consumer loans'])) ].groupby(['SK_ID_CURR'])['AMT_APPLICATION']\
.agg({"returns": [np.min, np.max,np.mean]})\
.reset_index()
tmp.columns = ['SK_ID_CURR','des1','des2','des3']
for df in [train,test]:
tmp_merge = df[['SK_ID_CURR']]
tmp_merge = tmp_merge.merge(tmp, on=['SK_ID_CURR'], how='left')
df['min_amt_app_v1'] = tmp_merge['des1']
df['max_amt_app_v1'] = tmp_merge['des2']
df['mean_amt_app_v1'] = tmp_merge['des3']
tmp = previous[(previous['NAME_CONTRACT_STATUS'] == 'Approved')&(previous['NAME_CONTRACT_TYPE'].isin(['Revolving loans'])) ].groupby(['SK_ID_CURR'])['AMT_CREDIT']\
.agg({"returns": [np.min, np.max,np.mean]})\
.reset_index()
tmp.columns = ['SK_ID_CURR','des1','des2','des3']
for df in [train,test]:
tmp_merge = df[['SK_ID_CURR']]
tmp_merge = tmp_merge.merge(tmp, on=['SK_ID_CURR'], how='left')
df['min_amt_card'] = tmp_merge['des1']
df['max_amt_card'] = tmp_merge['des2']
df['mean_amt_card'] = tmp_merge['des3'] | Home Credit Default Risk |
1,511,034 | pred_result_lr = lr.predict(X_test)
pred_result_lr.shape<save_to_csv> | tmp = previous[(previous['NAME_CONTRACT_STATUS'].isin(['Refused','Canceled'])) &(previous['NAME_CONTRACT_TYPE'].isin(['Cash loans'])) ].groupby(['SK_ID_CURR'])['AMT_APPLICATION']\
.agg({"returns": [np.min, np.max,np.mean]})\
.reset_index()
tmp.columns = ['SK_ID_CURR','des1','des2','des3']
for df in [train,test]:
tmp_merge = df[['SK_ID_CURR']]
tmp_merge = tmp_merge.merge(tmp, on=['SK_ID_CURR'], how='left')
df['min_amt_app_fail'] = tmp_merge['des1']
df['max_amt_app_fail'] = tmp_merge['des2']
df['mean_amt_app_fail'] = tmp_merge['des3']
tmp = previous[(previous['NAME_CONTRACT_STATUS'].isin(['Refused','Canceled'])) &(previous['NAME_CONTRACT_TYPE'].isin(['Consumer loans'])) ].groupby(['SK_ID_CURR'])['AMT_APPLICATION']\
.agg({"returns": [np.min, np.max,np.mean]})\
.reset_index()
tmp.columns = ['SK_ID_CURR','des1','des2','des3']
for df in [train,test]:
tmp_merge = df[['SK_ID_CURR']]
tmp_merge = tmp_merge.merge(tmp, on=['SK_ID_CURR'], how='left')
df['min_amt_app_v1_fail'] = tmp_merge['des1']
df['max_amt_app_v1_fail'] = tmp_merge['des2']
df['mean_amt_app_v1_fail'] = tmp_merge['des3']
tmp = previous[(previous['NAME_CONTRACT_STATUS'].isin(['Refused','Canceled'])) &(previous['NAME_CONTRACT_TYPE'].isin(['Revolving loans'])) ].groupby(['SK_ID_CURR'])['AMT_CREDIT']\
.agg({"returns": [np.min, np.max,np.mean]})\
.reset_index()
tmp.columns = ['SK_ID_CURR','des1','des2','des3']
for df in [train,test]:
tmp_merge = df[['SK_ID_CURR']]
tmp_merge = tmp_merge.merge(tmp, on=['SK_ID_CURR'], how='left')
df['min_amt_card_fail'] = tmp_merge['des1']
df['max_amt_card_fail'] = tmp_merge['des2']
df['mean_amt_card_fail'] = tmp_merge['des3'] | Home Credit Default Risk |
1,511,034 | test_df['emotion']=pred_result_lr
test_df.drop(columns=['hashtags','text'],inplace=True)
test_df.index.rename('id',inplace=True)
test_df.columns=['emotion']
test_df.to_csv('lr_tfidf.csv' )<load_pretrained> | tmp = previous[(previous['NAME_CONTRACT_TYPE'].isin(['Cash loans'])) ].groupby(['SK_ID_CURR'])['RATE_DOWN_PAYMENT']\
.agg({"returns": [np.min, np.max,np.mean]})\
.reset_index()
tmp.columns = ['SK_ID_CURR','des1','des2','des3']
for df in [train,test]:
tmp_merge = df[['SK_ID_CURR']]
tmp_merge = tmp_merge.merge(tmp, on=['SK_ID_CURR'], how='left')
df['min_amt_goods'] = tmp_merge['des1']
df['max_amt_goods'] = tmp_merge['des2']
df['mean_amt_goods'] = tmp_merge['des3']
tmp = previous[(previous['NAME_CONTRACT_TYPE'].isin(['Consumer loans'])) ].groupby(['SK_ID_CURR'])['RATE_DOWN_PAYMENT']\
.agg({"returns": [np.min, np.max,np.mean]})\
.reset_index()
tmp.columns = ['SK_ID_CURR','des1','des2','des3']
for df in [train,test]:
tmp_merge = df[['SK_ID_CURR']]
tmp_merge = tmp_merge.merge(tmp, on=['SK_ID_CURR'], how='left')
df['min_amt_goods_v1'] = tmp_merge['des1']
df['max_amt_goods_v1'] = tmp_merge['des2']
df['mean_amt_goods_v1'] = tmp_merge['des3']
| Home Credit Default Risk |
1,511,034 | train_df = pd.read_pickle(".. /input/dm-competition-tweets-emotion/train_df.pkl")
test_df = pd.read_pickle(".. /input/dm-competition-tweets-emotion/test_df.pkl" )<train_model> | tmp = previous[(previous['NAME_CONTRACT_TYPE'].isin(['Cash loans','Consumer loans'])) ].groupby(['SK_ID_CURR'])['AMT_ANNUITY']\
.agg({"returns": [np.min, np.max,np.mean]})\
.reset_index()
tmp.columns = ['SK_ID_CURR','des1','des2','des3']
for df in [train,test]:
tmp_merge = df[['SK_ID_CURR']]
tmp_merge = tmp_merge.merge(tmp, on=['SK_ID_CURR'], how='left')
df['min_amt_annuity'] = tmp_merge['des1']
df['max_amt_annuity'] = tmp_merge['des2']
df['mean_amt_annuity'] = tmp_merge['des3']
tmp = previous[(previous['NAME_CONTRACT_TYPE'].isin(['Revolving loans'])) ].groupby(['SK_ID_CURR'])['AMT_ANNUITY']\
.agg({"returns": [np.min, np.max,np.mean]})\
.reset_index()
tmp.columns = ['SK_ID_CURR','des1','des2','des3']
for df in [train,test]:
tmp_merge = df[['SK_ID_CURR']]
tmp_merge = tmp_merge.merge(tmp, on=['SK_ID_CURR'], how='left')
df['min_amt_card_annuity'] = tmp_merge['des1']
df['max_amt_card_annuity'] = tmp_merge['des2']
df['mean_amt_card_annuity'] = tmp_merge['des3'] | Home Credit Default Risk |
1,511,034 | max_words = 20000
max_len = 300
tok = Tokenizer(num_words=max_words)
tok.fit_on_texts(train_df['text'] )<string_transform> | tmp = previous[(previous['NAME_CONTRACT_TYPE'].isin(['Cash loans','Consumer loans'])) ].groupby(['SK_ID_CURR'])['CNT_PAYMENT']\
.agg({"returns": [np.min, np.max,np.mean]})\
.reset_index()
tmp.columns = ['SK_ID_CURR','des1','des2','des3']
for df in [train,test]:
tmp_merge = df[['SK_ID_CURR']]
tmp_merge = tmp_merge.merge(tmp, on=['SK_ID_CURR'], how='left')
df['min_cntpay'] = tmp_merge['des1']
df['max_cntpay'] = tmp_merge['des2']
df['mean_cntpay'] = tmp_merge['des3']
tmp = previous[(previous['NAME_CONTRACT_TYPE'].isin(['Consumer loans'])) ].groupby(['SK_ID_CURR'])['CNT_PAYMENT']\
.agg({"returns": [np.min, np.max,np.mean]})\
.reset_index()
tmp.columns = ['SK_ID_CURR','des1','des2','des3']
for df in [train,test]:
tmp_merge = df[['SK_ID_CURR']]
tmp_merge = tmp_merge.merge(tmp, on=['SK_ID_CURR'], how='left')
df['min_cntpay_v1'] = tmp_merge['des1']
df['max_cntpay_v1'] = tmp_merge['des2']
df['mean_cntpay_v1'] = tmp_merge['des3']
| Home Credit Default Risk |
1,511,034 | train_seq = tok.texts_to_sequences(train_df['text'])
test_seq = tok.texts_to_sequences(test_df['text'])
train_seq_mat = sequence.pad_sequences(train_seq,maxlen=max_len)
test_seq_mat = sequence.pad_sequences(test_seq,maxlen=max_len)
print(train_seq_mat.shape)
print(test_seq_mat.shape )<categorify> | tmp = previous[(previous['NAME_CONTRACT_STATUS'] == 'Approved')&(previous['AMT_APPLICATION'] > 0)&(previous['NAME_CONTRACT_TYPE'].isin(['Cash loans','Consumer loans'])) ].sort_values(by=['SK_ID_CURR','DAYS_DECISION'])
tmp = tmp.groupby(['SK_ID_CURR'] ).nth(-1 ).reset_index()
tmp = tmp[['SK_ID_CURR','AMT_APPLICATION']]
tmp.columns = ['SK_ID_CURR','des']
for df in [train,test]:
tmp_merge = df[['SK_ID_CURR']]
tmp_merge = tmp_merge.merge(tmp, on=['SK_ID_CURR'], how='left')
df['1st_recent_app'] = tmp_merge['des']
tmp = previous[(previous['NAME_CONTRACT_STATUS'] == 'Approved')&(previous['AMT_APPLICATION'] > 0)&(previous['NAME_CONTRACT_TYPE'].isin(['Cash loans','Consumer loans'])) ].sort_values(by=['SK_ID_CURR','DAYS_DECISION'])
tmp = tmp.groupby(['SK_ID_CURR'] ).nth(-1 ).reset_index()
tmp = tmp[['SK_ID_CURR','AMT_CREDIT']]
tmp.columns = ['SK_ID_CURR','des']
for df in [train,test]:
tmp_merge = df[['SK_ID_CURR']]
tmp_merge = tmp_merge.merge(tmp, on=['SK_ID_CURR'], how='left')
df['1st_recent_credit'] = tmp_merge['des']
tmp = previous[(previous['NAME_CONTRACT_STATUS'] == 'Approved')&(previous['AMT_CREDIT'] > 0)&(previous['NAME_CONTRACT_TYPE'].isin(['Revolving loans'])) ].sort_values(by=['SK_ID_CURR','DAYS_DECISION'])
tmp = tmp.groupby(['SK_ID_CURR'] ).nth(-1 ).reset_index()
tmp = tmp[['SK_ID_CURR','AMT_CREDIT']]
tmp.columns = ['SK_ID_CURR','des']
for df in [train,test]:
tmp_merge = df[['SK_ID_CURR']]
tmp_merge = tmp_merge.merge(tmp, on=['SK_ID_CURR'], how='left')
df['1st_recent_card'] = tmp_merge['des'] | Home Credit Default Risk |
1,511,034 | label_encoder = LabelEncoder()
label_encoder.fit(y_train)
print('check label: ', label_encoder.classes_)
print('
print('y_train[0:4]:
', y_train[0:4])
print('
y_train.shape: ', y_train.shape)
print('y_test.shape: ', y_test.shape)
def label_encode(le, labels):
enc = le.transform(labels)
return keras.utils.to_categorical(enc)
def label_decode(le, one_hot_label):
dec = np.argmax(one_hot_label, axis=1)
return le.inverse_transform(dec)
y_train = label_encode(label_encoder, y_train)
y_test = label_encode(label_encoder, y_test)
print('
print('y_train[0:4]:
', y_train[0:4])
print('
y_train.shape: ', y_train.shape)
print('y_test.shape: ', y_test.shape )<categorify> | tmp = previous[(previous['NAME_CONTRACT_STATUS'] != 'Approved')&(previous['AMT_APPLICATION'] > 0)&(previous['NAME_CONTRACT_TYPE'].isin(['Cash loans','Consumer loans'])) ].sort_values(by=['SK_ID_CURR','DAYS_DECISION'])
tmp = tmp.groupby(['SK_ID_CURR'] ).nth(-1 ).reset_index()
tmp = tmp[['SK_ID_CURR','AMT_APPLICATION']]
tmp.columns = ['SK_ID_CURR','des']
for df in [train,test]:
tmp_merge = df[['SK_ID_CURR']]
tmp_merge = tmp_merge.merge(tmp, on=['SK_ID_CURR'], how='left')
df['1st_recent_app_fail'] = tmp_merge['des']
tmp = previous[(previous['NAME_CONTRACT_STATUS'] != 'Approved')&(previous['AMT_APPLICATION'] > 0)&(previous['NAME_CONTRACT_TYPE'].isin(['Cash loans','Consumer loans'])) ].sort_values(by=['SK_ID_CURR','DAYS_DECISION'])
tmp = tmp.groupby(['SK_ID_CURR'] ).nth(-1 ).reset_index()
tmp = tmp[['SK_ID_CURR','AMT_CREDIT']]
tmp.columns = ['SK_ID_CURR','des']
for df in [train,test]:
tmp_merge = df[['SK_ID_CURR']]
tmp_merge = tmp_merge.merge(tmp, on=['SK_ID_CURR'], how='left')
df['1st_recent_credit_fail'] = tmp_merge['des']
tmp = previous[(previous['NAME_CONTRACT_STATUS'] != 'Approved')&(previous['AMT_CREDIT'] > 0)&(previous['NAME_CONTRACT_TYPE'].isin(['Revolving loans'])) ].sort_values(by=['SK_ID_CURR','DAYS_DECISION'])
tmp = tmp.groupby(['SK_ID_CURR'] ).nth(-1 ).reset_index()
tmp = tmp[['SK_ID_CURR','AMT_CREDIT']]
tmp.columns = ['SK_ID_CURR','des']
for df in [train,test]:
tmp_merge = df[['SK_ID_CURR']]
tmp_merge = tmp_merge.merge(tmp, on=['SK_ID_CURR'], how='left')
df['1st_recent_card_fail'] = tmp_merge['des'] | Home Credit Default Risk |
1,511,034 | input_shape = X_train.shape[1]
print('input_shape: ', input_shape)
output_shape = len(label_encoder.classes_)
print('output_shape: ', output_shape )<choose_model_class> | tmp = previous[(previous['NAME_CONTRACT_STATUS'] == 'Approved')&(previous['RATE_DOWN_PAYMENT'] > 0)].sort_values(by=['SK_ID_CURR','DAYS_DECISION'])
tmp = tmp.groupby(['SK_ID_CURR'] ).nth(-1 ).reset_index()
tmp = tmp[['SK_ID_CURR','RATE_DOWN_PAYMENT']]
tmp.columns = ['SK_ID_CURR','des']
for df in [train,test]:
tmp_merge = df[['SK_ID_CURR']]
tmp_merge = tmp_merge.merge(tmp, on=['SK_ID_CURR'], how='left')
df['1st_recent_ratedown'] = tmp_merge['des']
tmp = previous[(previous['NAME_CONTRACT_STATUS'] != 'Approved')&(previous['RATE_DOWN_PAYMENT'] > 0)].sort_values(by=['SK_ID_CURR','DAYS_DECISION'])
tmp = tmp.groupby(['SK_ID_CURR'] ).nth(-1 ).reset_index()
tmp = tmp[['SK_ID_CURR','RATE_DOWN_PAYMENT']]
tmp.columns = ['SK_ID_CURR','des']
for df in [train,test]:
tmp_merge = df[['SK_ID_CURR']]
tmp_merge = tmp_merge.merge(tmp, on=['SK_ID_CURR'], how='left')
df['1st_recent_ratedown_fail'] = tmp_merge['des'] | Home Credit Default Risk |
1,511,034 | inputs = Input(name='inputs',shape=[max_len])
layer = Embedding(max_words+1,128,input_length=max_len )(inputs)
layer = LSTM(128 )(layer)
layer = Dense(128,activation="relu",name="FC1" )(layer)
layer = Dropout(0.5 )(layer)
layer = Dense(output_shape,activation="softmax",name="FC2" )(layer)
model = Model(inputs=inputs,outputs=layer)
model.summary()
model.compile(loss="categorical_crossentropy",optimizer=RMSprop() ,metrics=["accuracy"] )<train_model> | tmp = previous[(previous['NAME_CONTRACT_TYPE'].isin(['Consumer loans','Cash loans'])) &(previous['CNT_PAYMENT'] > 0)].sort_values(by=['SK_ID_CURR','DAYS_DECISION'])
tmp = tmp.groupby(['SK_ID_CURR'] ).nth(-1 ).reset_index()
tmp = tmp[['SK_ID_CURR','CNT_PAYMENT']]
tmp.columns = ['SK_ID_CURR','des']
for df in [train,test]:
tmp_merge = df[['SK_ID_CURR']]
tmp_merge = tmp_merge.merge(tmp, on=['SK_ID_CURR'], how='left')
df['1st_recent_cntpay'] = tmp_merge['des']
| Home Credit Default Risk |
1,511,034 | model_fit = model.fit(train_seq_mat,y_train,batch_size=128,epochs=3,
callbacks=[EarlyStopping(monitor='val_loss',min_delta=0.0001)])
<predict_on_test> | tmp = previous[previous['AMT_CREDIT'] > 0]
for i in ['Cash loans','Consumer loans','Revolving loans']:
for df in [train,test]:
tmp1 = tmp[tmp['NAME_CONTRACT_TYPE'] == i].groupby(['SK_ID_CURR'])['AMT_CREDIT'].count().reset_index()
tmp1.columns = ['SK_ID_CURR','des']
tmp_merge = df[['SK_ID_CURR']]
tmp_merge = tmp_merge.merge(tmp1, on=['SK_ID_CURR'], how='left')
df['count_' + "_".join(i.lower().split())] = tmp_merge['des'] | Home Credit Default Risk |
1,511,034 | pred_result_lstm = label_decode(label_encoder, model.predict(test_seq_mat, batch_size=128))
pred_result_lstm[:5]<save_to_csv> | tmp = previous[previous['AMT_CREDIT'].isnull() ]
for i in ['Cash loans','Consumer loans','Revolving loans']:
for df in [train,test]:
tmp1 = tmp[tmp['NAME_CONTRACT_TYPE'] == i].groupby(['SK_ID_CURR'])['SK_ID_PREV'].count().reset_index()
tmp1.columns = ['SK_ID_CURR','des']
tmp_merge = df[['SK_ID_CURR']]
tmp_merge = tmp_merge.merge(tmp1, on=['SK_ID_CURR'], how='left')
df['count_null_' + "_".join(i.lower().split())] = tmp_merge['des'] | Home Credit Default Risk |
1,511,034 | test_df['emotion']=pred_result_lstm
test_df.drop(columns=['hashtags','text'],inplace=True)
test_df.index.rename('id',inplace=True)
test_df.columns=['emotion']
test_df.to_csv('keras_tfidf.csv' )<load_from_csv> | tmp = previous[previous['AMT_CREDIT'] > 0].groupby(['SK_ID_CURR'])['DAYS_DECISION']\
.agg({"returns": [np.min, np.max,np.mean]})\
.reset_index()
tmp.columns = ['SK_ID_CURR','des1','des2','des3']
for df in [train,test]:
tmp_merge = df[['SK_ID_CURR']]
tmp_merge = tmp_merge.merge(tmp, on=['SK_ID_CURR'], how='left')
df['min_day_decision'] = tmp_merge['des1']
df['max_day_decision'] = tmp_merge['des2']
df['mean_day_decision'] = tmp_merge['des3'] | Home Credit Default Risk |
1,511,034 | model_compare=pd.read_csv(".. /input/dm-competition-tweets-emotion/final.csv")
model_compare<import_modules> | tmp = previous[previous['NAME_CONTRACT_STATUS'] != 'Approved'].groupby(['SK_ID_CURR'])['DAYS_DECISION']\
.agg({"returns": [np.min, np.max,np.mean]})\
.reset_index()
tmp.columns = ['SK_ID_CURR','des1','des2','des3']
for df in [train,test]:
tmp_merge = df[['SK_ID_CURR']]
tmp_merge = tmp_merge.merge(tmp, on=['SK_ID_CURR'], how='left')
df['min_day_decision_fail'] = tmp_merge['des1']
df['max_day_decision_fail'] = tmp_merge['des2']
df['mean_day_decision_fail'] = tmp_merge['des3'] | Home Credit Default Risk |
1,511,034 | import pandas as pd
import numpy as np
import sklearn
import matplotlib.pyplot as plt
from sklearn.neighbors import KNeighborsClassifier
from sklearn.model_selection import cross_val_score
from sklearn.metrics import accuracy_score
from sklearn import preprocessing<load_from_csv> | tmp = previous[(~previous['NAME_CASH_LOAN_PURPOSE'].isin(['XAP','XNA'])) ]
for df in [train,test]:
tmp1 = tmp.groupby(['SK_ID_CURR'])['SK_ID_PREV'].count().reset_index()
tmp1.columns = ['SK_ID_CURR','des']
tmp_merge = df[['SK_ID_CURR']]
tmp_merge = tmp_merge.merge(tmp1, on=['SK_ID_CURR'], how='left')
df['count_clear_reason'] = tmp_merge['des'] | Home Credit Default Risk |
1,511,034 | adult = pd.read_csv(".. /input/adult-data/train_data.csv",
sep=r'\s*,\s*',
engine='python',
na_values="?")
adult.head()<correct_missing_values> | tmp = previous.groupby(['SK_ID_CURR'])['DAYS_TERMINATION']\
.agg({"returns": [np.min, np.max,np.mean]})\
.reset_index()
tmp.columns = ['SK_ID_CURR','des1','des2','des3']
for df in [train,test]:
tmp_merge = df[['SK_ID_CURR']]
tmp_merge = tmp_merge.merge(tmp, on=['SK_ID_CURR'], how='left')
df['min_day_termination'] = tmp_merge['des1']
df['max_day_termination'] = tmp_merge['des2']
df['mean_day_termination'] = tmp_merge['des3'] | Home Credit Default Risk |
1,511,034 | adult = adult.dropna()<count_values> | tmp = previous.groupby(['SK_ID_CURR'])['DAYS_LAST_DUE_1ST_VERSION']\
.agg({"returns": [np.min, np.max,np.mean]})\
.reset_index()
tmp.columns = ['SK_ID_CURR','des1','des2','des3']
for df in [train,test]:
tmp_merge = df[['SK_ID_CURR']]
tmp_merge = tmp_merge.merge(tmp, on=['SK_ID_CURR'], how='left')
df['min_day_lastdue'] = tmp_merge['des1']
df['max_day_lastdue'] = tmp_merge['des2']
df['mean_day_lastdue'] = tmp_merge['des3'] | Home Credit Default Risk |
1,511,034 | adult["native.country"].value_counts()<load_from_csv> | tmp = previous[~previous['DAYS_LAST_DUE_1ST_VERSION'].isnull() ].sort_values(by=['SK_ID_CURR','DAYS_LAST_DUE_1ST_VERSION'])
tmp = tmp.groupby(['SK_ID_CURR'] ).nth(-2 ).reset_index()
tmp = tmp[['SK_ID_CURR','DAYS_LAST_DUE_1ST_VERSION']]
tmp.columns = ['SK_ID_CURR','des']
for df in [train,test]:
tmp_merge = df[['SK_ID_CURR']]
tmp_merge = tmp_merge.merge(tmp, on=['SK_ID_CURR'], how='left')
df['2nd_day_lastdue'] = tmp_merge['des'] | Home Credit Default Risk |
1,511,034 | testadult = pd.read_csv(".. /input/adult-data/test_data.csv",
sep=r'\s*,\s*',
engine='python',
na_values="?")
testadult.head()<feature_engineering> | tmp = previous.groupby(['SK_ID_CURR'])['sooner']\
.agg({"returns": [np.min, np.max,np.mean]})\
.reset_index()
tmp.columns = ['SK_ID_CURR','des1','des2','des3']
for df in [train,test]:
tmp_merge = df[['SK_ID_CURR']]
tmp_merge = tmp_merge.merge(tmp, on=['SK_ID_CURR'], how='left')
df['min_sooner'] = tmp_merge['des1']
df['max_sooner'] = tmp_merge['des2']
df['mean_sooner'] = tmp_merge['des3'] | Home Credit Default Risk |
1,511,034 | adult.loc[adult['sex']!="Male", 'sex'] = '0'
adult.loc[adult['sex']=="Male", 'sex'] = '1'
testadult.loc[testadult['sex']!="Male", 'sex'] = '0'
testadult.loc[testadult['sex']=="Male", 'sex'] = '1'<count_values> | tmp = previous.groupby(['SK_ID_CURR'])['SELLERPLACE_AREA']\
.agg({"returns": [np.min, np.max,np.mean]})\
.reset_index()
tmp.columns = ['SK_ID_CURR','des1','des2','des3']
for df in [train,test]:
tmp_merge = df[['SK_ID_CURR']]
tmp_merge = tmp_merge.merge(tmp, on=['SK_ID_CURR'], how='left')
df['min_seller'] = tmp_merge['des1']
df['max_seller'] = tmp_merge['des2']
df['mean_seller'] = tmp_merge['des3'] | Home Credit Default Risk |
1,511,034 | adult['sex'].value_counts()<count_values> | for i in ['middle','low_normal','high','low_action']:
tmp = previous[(previous['NAME_CONTRACT_TYPE'] == 'Cash loans')&(previous['NAME_YIELD_GROUP'] == i)]
for df in [train,test]:
tmp1 = tmp.groupby(['SK_ID_CURR'])['SK_ID_PREV'].count().reset_index()
tmp1.columns = ['SK_ID_CURR','des']
tmp_merge = df[['SK_ID_CURR']]
tmp_merge = tmp_merge.merge(tmp1, on=['SK_ID_CURR'], how='left')
df['count_' + str(i)] = tmp_merge['des'].fillna(0 ) | Home Credit Default Risk |
1,511,034 | testadult['sex'].value_counts()<feature_engineering> | for df in [train,test]:
df['tmp'] = df[['count_middle','count_low_normal','count_high','count_low_action']].sum(axis=1)
for i in ['middle','low_normal','high','low_action']:
df['ratio_' + i] = df['count_' + i]/df['tmp']
| Home Credit Default Risk |
1,511,034 | adult.loc[adult['race']!="White", 'race'] = '0'
adult.loc[adult['race']=="White", 'race'] = '1'
testadult.loc[testadult['race']!="White", 'race'] = '0'
testadult.loc[testadult['race']=="White", 'race'] = '1'<count_values> | for i in ['middle','low_normal','high','low_action']:
tmp = previous[(previous['NAME_CONTRACT_TYPE'] == 'Consumer loans')&(previous['NAME_YIELD_GROUP'] == i)]
for df in [train,test]:
tmp1 = tmp.groupby(['SK_ID_CURR'])['SK_ID_PREV'].count().reset_index()
tmp1.columns = ['SK_ID_CURR','des']
tmp_merge = df[['SK_ID_CURR']]
tmp_merge = tmp_merge.merge(tmp1, on=['SK_ID_CURR'], how='left')
df['count_' + str(i)+ '_v1'] = tmp_merge['des'].fillna(0 ) | Home Credit Default Risk |
1,511,034 | adult['race'].value_counts()<count_values> | for df in [train,test]:
df['tmp'] = df[['count_middle_v1','count_low_normal_v1','count_high_v1','count_low_action_v1']].sum(axis=1)
for i in ['middle','low_normal','high','low_action']:
df['ratio_' + i +"_v1"] = df['count_' + i + "_v1"]/df['tmp']
| Home Credit Default Risk |
1,511,034 | testadult['race'].value_counts()<feature_engineering> | previous['tmp'] =(previous['AMT_ANNUITY'] * previous['CNT_PAYMENT'])/previous['AMT_CREDIT']
tmp = previous[(previous['NAME_CONTRACT_TYPE'] == 'Cash loans')&(previous['NAME_CONTRACT_STATUS'] != 'Approved')].groupby(['SK_ID_CURR'])['tmp']\
.agg({"returns": [np.min, np.max,np.mean]})\
.reset_index()
tmp.columns = ['SK_ID_CURR','des1','des2','des3']
for df in [train,test]:
tmp_merge = df[['SK_ID_CURR']]
tmp_merge = tmp_merge.merge(tmp, on=['SK_ID_CURR'], how='left')
df['min_interest'] = tmp_merge['des1']
df['max_interest'] = tmp_merge['des2']
df['mean_interest'] = tmp_merge['des3']
tmp = previous[(previous['NAME_CONTRACT_TYPE'] == 'Consumer loans')&(previous['NAME_CONTRACT_STATUS'] != 'Approved')].groupby(['SK_ID_CURR'])['tmp']\
.agg({"returns": [np.min, np.max,np.mean]})\
.reset_index()
tmp.columns = ['SK_ID_CURR','des1','des2','des3']
for df in [train,test]:
tmp_merge = df[['SK_ID_CURR']]
tmp_merge = tmp_merge.merge(tmp, on=['SK_ID_CURR'], how='left')
df['min_interest_v1'] = tmp_merge['des1']
df['max_interest_v1'] = tmp_merge['des2']
df['mean_interest_v1'] = tmp_merge['des3'] | Home Credit Default Risk |
1,511,034 | adult.loc[adult['native.country']!="United-States", 'native.country'] = '0'
adult.loc[adult['native.country']=="United-States", 'native.country'] = '1'
testadult.loc[testadult['native.country']!="United-States", 'native.country'] = '0'
testadult.loc[testadult['native.country']=="United-States", 'native.country'] = '1'<count_values> | tmp = previous.groupby(['SK_ID_CURR'])['DAYS_FIRST_DRAWING']\
.agg({"returns": [np.min, np.max,np.mean]})\
.reset_index()
tmp.columns = ['SK_ID_CURR','des1','des2','des3']
for df in [train,test]:
tmp_merge = df[['SK_ID_CURR']]
tmp_merge = tmp_merge.merge(tmp, on=['SK_ID_CURR'], how='left')
df['min_firstdraw'] = tmp_merge['des1']
df['max_firstdraw'] = tmp_merge['des2']
df['mean_firstdraw'] = tmp_merge['des3'] | Home Credit Default Risk |
1,511,034 | adult['native.country'].value_counts()<count_values> | previous['tmp'] = previous['DAYS_FIRST_DRAWING'] - previous['DAYS_DECISION']
tmp = previous.groupby(['SK_ID_CURR'])['tmp']\
.agg({"returns": [np.min, np.max,np.mean]})\
.reset_index()
tmp.columns = ['SK_ID_CURR','des1','des2','des3']
for df in [train,test]:
tmp_merge = df[['SK_ID_CURR']]
tmp_merge = tmp_merge.merge(tmp, on=['SK_ID_CURR'], how='left')
df['min_firstdraw_decision'] = tmp_merge['des1']
df['max_firstdraw_decision'] = tmp_merge['des2']
df['mean_firstdraw_decision'] = tmp_merge['des3'] | Home Credit Default Risk |
1,511,034 | testadult['native.country'].value_counts()<train_model> | previous['tmp'] = previous['DAYS_FIRST_DUE'] - previous['DAYS_FIRST_DRAWING']
tmp = previous.groupby(['SK_ID_CURR'])['tmp']\
.agg({"returns": [np.min, np.max,np.mean]})\
.reset_index()
tmp.columns = ['SK_ID_CURR','des1','des2','des3']
for df in [train,test]:
tmp_merge = df[['SK_ID_CURR']]
tmp_merge = tmp_merge.merge(tmp, on=['SK_ID_CURR'], how='left')
df['min_firstdraw_firstdue'] = tmp_merge['des1']
df['max_firstdraw_firstdue'] = tmp_merge['des2']
df['mean_firstdraw_firstdue'] = tmp_merge['des3'] | Home Credit Default Risk |
1,511,034 | Xadult = adult[['education.num','age','race','sex','capital.gain','capital.loss','hours.per.week']]
Yadult = adult.income
Xtestadult = testadult[['education.num','age','race','sex','capital.gain','capital.loss','hours.per.week']]
knn = KNeighborsClassifier(n_neighbors=25)
knn.fit(Xadult,Yadult )<compute_train_metric> | previous['tmp'] = previous['DAYS_LAST_DUE'] - previous['DAYS_FIRST_DRAWING']
tmp = previous.groupby(['SK_ID_CURR'])['tmp']\
.agg({"returns": [np.min, np.max,np.mean]})\
.reset_index()
tmp.columns = ['SK_ID_CURR','des1','des2','des3']
for df in [train,test]:
tmp_merge = df[['SK_ID_CURR']]
tmp_merge = tmp_merge.merge(tmp, on=['SK_ID_CURR'], how='left')
df['min_firstdraw_lastdue'] = tmp_merge['des1']
df['max_firstdraw_lastdue'] = tmp_merge['des2']
df['mean_firstdraw_lastdue'] = tmp_merge['des3'] | Home Credit Default Risk |
1,511,034 | cval = 10
scores = cross_val_score(knn, Xadult, Yadult, cv=cval)
scores<define_variables> | tmp = bureau[(bureau['CREDIT_ACTIVE'] == "Active")].groupby(['SK_ID_CURR'])['SK_ID_BUREAU'].count().reset_index()
tmp.columns = ['SK_ID_CURR','des']
for df in [train,test]:
tmp_merge = df[['SK_ID_CURR']]
tmp_merge = tmp_merge.merge(tmp, on=['SK_ID_CURR'], how='left')
df['count_active_bureau'] = tmp_merge['des'].fillna(0)
tmp = bureau[bureau['CREDIT_ACTIVE'] != "Active"].groupby(['SK_ID_CURR'])['SK_ID_BUREAU'].count().reset_index()
tmp.columns = ['SK_ID_CURR','des']
for df in [train,test]:
tmp_merge = df[['SK_ID_CURR']]
tmp_merge = tmp_merge.merge(tmp, on=['SK_ID_CURR'], how='left')
df['count_closed_bureau'] = tmp_merge['des'].fillna(0 ) | Home Credit Default Risk |
1,511,034 | total = 0
for i in scores:
total += i
acuracia_esperada = total/cval
acuracia_esperada<predict_on_test> | tmp = bureau[(bureau['CREDIT_TYPE'] == "Consumer credit")].groupby(['SK_ID_CURR'])['SK_ID_BUREAU'].count().reset_index()
tmp.columns = ['SK_ID_CURR','des']
for df in [train,test]:
tmp_merge = df[['SK_ID_CURR']]
tmp_merge = tmp_merge.merge(tmp, on=['SK_ID_CURR'], how='left')
df['count_active_bureau_v2'] = tmp_merge['des'].fillna(0)
tmp = bureau[(bureau['CREDIT_ACTIVE'] != "Active")&(bureau['CREDIT_TYPE'] == "Credit card")].groupby(['SK_ID_CURR'])['SK_ID_BUREAU'].count().reset_index()
tmp.columns = ['SK_ID_CURR','des']
for df in [train,test]:
tmp_merge = df[['SK_ID_CURR']]
tmp_merge = tmp_merge.merge(tmp, on=['SK_ID_CURR'], how='left')
df['count_closed_bureau_v2'] = tmp_merge['des'].fillna(0 ) | Home Credit Default Risk |
1,511,034 | YtestPred = knn.predict(Xtestadult)
YtestPred<define_variables> | tmp = bureau[(~bureau['CREDIT_TYPE'].isin(["Credit card","Consumer credit"])) ].groupby(['SK_ID_CURR'])['SK_ID_BUREAU'].count().reset_index()
tmp.columns = ['SK_ID_CURR','des']
for df in [train,test]:
tmp_merge = df[['SK_ID_CURR']]
tmp_merge = tmp_merge.merge(tmp, on=['SK_ID_CURR'], how='left')
df['count_active_bureau_v3'] = tmp_merge['des'].fillna(0)
tmp = bureau[(bureau['CREDIT_ACTIVE'] != "Active")&(~bureau['CREDIT_TYPE'].isin(["Credit card","Consumer credit"])) ].groupby(['SK_ID_CURR'])['SK_ID_BUREAU'].count().reset_index()
tmp.columns = ['SK_ID_CURR','des']
for df in [train,test]:
tmp_merge = df[['SK_ID_CURR']]
tmp_merge = tmp_merge.merge(tmp, on=['SK_ID_CURR'], how='left')
df['count_closed_bureau_v3'] = tmp_merge['des'].fillna(0 ) | Home Credit Default Risk |
1,511,034 | maior_50 = 0
menor_50 = 0
for i in YtestPred:
if i == '<=50K':
menor_50 += 1
else:
maior_50 += 1
dicio = {'<=50K':menor_50, '>50K':maior_50}<save_to_csv> | bureau['AMT_CREDIT_SUM'].replace(0, np.nan, inplace = True)
tmp = bureau[(bureau['CREDIT_ACTIVE'] == "Active")&(bureau['CREDIT_TYPE'] == "Consumer credit")].groupby(['SK_ID_CURR'])['AMT_CREDIT_SUM'].agg({"returns": [np.min, np.max,np.mean, np.sum]})\
.reset_index()
tmp.columns = ['SK_ID_CURR','des1','des2','des3', 'des4']
for df in [train,test]:
tmp_merge = df[['SK_ID_CURR']]
tmp_merge = tmp_merge.merge(tmp, on=['SK_ID_CURR'], how='left')
df['min_active_credit_bureau'] = tmp_merge['des1']
df['max_active_credit_bureau'] = tmp_merge['des2']
df['mean_active_credit_bureau'] = tmp_merge['des3']
df['sum_active_credit_bureau'] = tmp_merge['des4']
tmp = bureau[(bureau['CREDIT_ACTIVE'] != "Active")&(bureau['CREDIT_TYPE'] == "Consumer credit")].groupby(['SK_ID_CURR'])['AMT_CREDIT_SUM'].agg({"returns": [np.min, np.max,np.mean, np.sum]})\
.reset_index()
tmp.columns = ['SK_ID_CURR','des1','des2','des3', 'des4']
for df in [train,test]:
tmp_merge = df[['SK_ID_CURR']]
tmp_merge = tmp_merge.merge(tmp, on=['SK_ID_CURR'], how='left')
df['min_closed_credit_bureau'] = tmp_merge['des1']
df['max_closed_credit_bureau'] = tmp_merge['des2']
df['mean_closed_credit_bureau'] = tmp_merge['des3']
df['sum_closed_credit_bureau'] = tmp_merge['des4'] | Home Credit Default Risk |
1,511,034 | result = np.vstack(( testadult["Id"], YtestPred)).T
x = ["Id","income"]
Resultado = pd.DataFrame(columns = x, data = result)
Resultado.to_csv("Resultado1.csv", index = False )<train_model> | bureau['AMT_CREDIT_SUM'].replace(0, np.nan, inplace = True)
tmp = bureau[(bureau['CREDIT_TYPE'] == "Credit card")].groupby(['SK_ID_CURR'])['AMT_CREDIT_SUM'].agg({"returns": [np.min, np.max,np.mean, np.sum]})\
.reset_index()
tmp.columns = ['SK_ID_CURR','des1','des2','des3', 'des4']
for df in [train,test]:
tmp_merge = df[['SK_ID_CURR']]
tmp_merge = tmp_merge.merge(tmp, on=['SK_ID_CURR'], how='left')
df['min_active_credit_bureau_v1'] = tmp_merge['des1']
df['max_active_credit_bureau_v1'] = tmp_merge['des2']
df['mean_active_credit_bureau_v1'] = tmp_merge['des3']
df['sum_active_credit_bureau_v1'] = tmp_merge['des4']
| Home Credit Default Risk |
1,511,034 | Xadult = adult[['education.num','age','sex','capital.gain','capital.loss','hours.per.week','native.country']]
Yadult = adult.income
Xtestadult = testadult[['education.num','age','sex','capital.gain','capital.loss','hours.per.week','native.country']]
knn = KNeighborsClassifier(n_neighbors=25)
knn.fit(Xadult,Yadult )<compute_train_metric> | bureau['AMT_CREDIT_SUM'].replace(0, np.nan, inplace = True)
tmp = bureau[(bureau['CREDIT_TYPE'] == "Car loan")].groupby(['SK_ID_CURR'])['AMT_CREDIT_SUM'].agg({"returns": [np.min, np.max,np.mean, np.sum]})\
.reset_index()
tmp.columns = ['SK_ID_CURR','des1','des2','des3', 'des4']
for df in [train,test]:
tmp_merge = df[['SK_ID_CURR']]
tmp_merge = tmp_merge.merge(tmp, on=['SK_ID_CURR'], how='left')
df['min_active_credit_bureau_v2'] = tmp_merge['des1']
df['max_active_credit_bureau_v2'] = tmp_merge['des2']
df['mean_active_credit_bureau_v2'] = tmp_merge['des3']
df['sum_active_credit_bureau_v2'] = tmp_merge['des4']
| Home Credit Default Risk |
1,511,034 | cval = 10
scores = cross_val_score(knn, Xadult, Yadult, cv=cval)
scores<define_variables> | bureau['AMT_CREDIT_SUM'].replace(0, np.nan, inplace = True)
tmp = bureau[(~bureau['CREDIT_TYPE'].isin(["Credit card","Consumer credit","Car loan"])) ].groupby(['SK_ID_CURR'])['AMT_CREDIT_SUM'].agg({"returns": [np.min, np.max,np.mean, np.sum]})\
.reset_index()
tmp.columns = ['SK_ID_CURR','des1','des2','des3', 'des4']
for df in [train,test]:
tmp_merge = df[['SK_ID_CURR']]
tmp_merge = tmp_merge.merge(tmp, on=['SK_ID_CURR'], how='left')
df['min_active_credit_bureau_v3'] = tmp_merge['des1']
df['max_active_credit_bureau_v3'] = tmp_merge['des2']
df['mean_active_credit_bureau_v3'] = tmp_merge['des3']
df['sum_active_credit_bureau_v3'] = tmp_merge['des4']
| Home Credit Default Risk |
1,511,034 | total = 0
for i in scores:
total += i
acuracia_esperada = total/cval
acuracia_esperada<predict_on_test> | tmp = bureau.groupby(['SK_ID_CURR'])['AMT_CREDIT_SUM'].agg({"returns": [np.min, np.max,np.mean, np.sum]})\
.reset_index()
tmp.columns = ['SK_ID_CURR','des1','des2','des3', 'des4']
for df in [train,test]:
tmp_merge = df[['SK_ID_CURR']]
tmp_merge = tmp_merge.merge(tmp, on=['SK_ID_CURR'], how='left')
df['min_credit_bureau'] = tmp_merge['des1']
df['max_credit_bureau'] = tmp_merge['des2']
df['mean_credit_bureau'] = tmp_merge['des3']
df['sum_credit_bureau'] = tmp_merge['des4'] | Home Credit Default Risk |
1,511,034 | YtestPred = knn.predict(Xtestadult)
YtestPred<define_variables> | tmp = bureau[(bureau['CREDIT_TYPE'] == "Consumer credit")].groupby(['SK_ID_CURR'])['DAYS_CREDIT_ENDDATE'].agg({"returns": [np.min, np.max,np.mean, np.sum]})\
.reset_index()
tmp.columns = ['SK_ID_CURR','des1','des2','des3', 'des4']
for df in [train,test]:
tmp_merge = df[['SK_ID_CURR']]
tmp_merge = tmp_merge.merge(tmp, on=['SK_ID_CURR'], how='left')
df['min_endate_bureau'] = tmp_merge['des1']
df['max_endate_bureau'] = tmp_merge['des2']
df['mean_endate_bureau'] = tmp_merge['des3']
df['sum_endate_bureau'] = tmp_merge['des4']
tmp = bureau[(~bureau['DAYS_CREDIT_ENDDATE'].isnull())&(( bureau['CREDIT_TYPE'] == "Consumer credit")) ].sort_values(by=['SK_ID_CURR','DAYS_CREDIT_ENDDATE'])
tmp = tmp.groupby(['SK_ID_CURR'] ).nth(-2 ).reset_index()
tmp = tmp[['SK_ID_CURR','DAYS_CREDIT_ENDDATE']]
tmp.columns = ['SK_ID_CURR','des']
for df in [train,test]:
tmp_merge = df[['SK_ID_CURR']]
tmp_merge = tmp_merge.merge(tmp, on=['SK_ID_CURR'], how='left')
df['1st_endate_bureau'] = tmp_merge['des'] | Home Credit Default Risk |
1,511,034 | maior_50 = 0
menor_50 = 0
for i in YtestPred:
if i == '<=50K':
menor_50 += 1
else:
maior_50 += 1
dicio = {'<=50K':menor_50, '>50K':maior_50}<save_to_csv> | tmp = bureau[(bureau['CREDIT_TYPE'] == "Car loan")].groupby(['SK_ID_CURR'])['DAYS_CREDIT_ENDDATE'].agg({"returns": [np.min, np.max,np.mean, np.sum]})\
.reset_index()
tmp.columns = ['SK_ID_CURR','des1','des2','des3', 'des4']
for df in [train,test]:
tmp_merge = df[['SK_ID_CURR']]
tmp_merge = tmp_merge.merge(tmp, on=['SK_ID_CURR'], how='left')
df['min_endate_bureau_v1'] = tmp_merge['des1']
df['max_endate_bureau_v1'] = tmp_merge['des2']
df['mean_endate_bureau_v1'] = tmp_merge['des3']
df['sum_endate_bureau_v1'] = tmp_merge['des4']
tmp = bureau[(~bureau['DAYS_CREDIT_ENDDATE'].isnull())&(( bureau['CREDIT_TYPE'] == "Car loan")) ].sort_values(by=['SK_ID_CURR','DAYS_CREDIT_ENDDATE'])
tmp = tmp.groupby(['SK_ID_CURR'] ).nth(-2 ).reset_index()
tmp = tmp[['SK_ID_CURR','DAYS_CREDIT_ENDDATE']]
tmp.columns = ['SK_ID_CURR','des']
for df in [train,test]:
tmp_merge = df[['SK_ID_CURR']]
tmp_merge = tmp_merge.merge(tmp, on=['SK_ID_CURR'], how='left')
df['1st_endate_bureau_v1'] = tmp_merge['des'] | Home Credit Default Risk |
1,511,034 | result = np.vstack(( testadult["Id"], YtestPred)).T
x = ["Id","income"]
Resultado = pd.DataFrame(columns = x, data = result)
Resultado.to_csv("Resultado2.csv", index = False )<train_model> | tmp = bureau[(bureau['CREDIT_TYPE'] == "Consumer credit")].groupby(['SK_ID_CURR'])['DAYS_CREDIT'].agg({"returns": [np.min, np.max,np.mean, np.sum]})\
.reset_index()
tmp.columns = ['SK_ID_CURR','des1','des2','des3', 'des4']
for df in [train,test]:
tmp_merge = df[['SK_ID_CURR']]
tmp_merge = tmp_merge.merge(tmp, on=['SK_ID_CURR'], how='left')
df['min_startdate_bureau'] = tmp_merge['des1']
df['max_startdate_bureau'] = tmp_merge['des2']
df['mean_startdate_bureau'] = tmp_merge['des3']
df['sum_startdate_bureau'] = tmp_merge['des4'] | Home Credit Default Risk |
1,511,034 | Xadult = adult[['education.num','race','sex','capital.gain','capital.loss','hours.per.week','native.country']]
Yadult = adult.income
Xtestadult = testadult[['education.num','race','sex','capital.gain','capital.loss','hours.per.week','native.country']]
knn = KNeighborsClassifier(n_neighbors=25)
knn.fit(Xadult,Yadult )<compute_train_metric> | tmp = bureau[(~bureau['DAYS_CREDIT_ENDDATE'].isnull())&(( bureau['CREDIT_TYPE'] == "Consumer credit")) ].sort_values(by=['SK_ID_CURR','DAYS_CREDIT_ENDDATE'])
tmp = tmp.groupby(['SK_ID_CURR'] ).nth(-2 ).reset_index()
tmp = tmp[['SK_ID_CURR','DAYS_ENDDATE_FACT']]
tmp.columns = ['SK_ID_CURR','des']
for df in [train,test]:
tmp_merge = df[['SK_ID_CURR']]
tmp_merge = tmp_merge.merge(tmp, on=['SK_ID_CURR'], how='left')
df['1st_endatefact_bureau'] = tmp_merge['des'] | Home Credit Default Risk |
1,511,034 | cval = 10
scores = cross_val_score(knn, Xadult, Yadult, cv=cval)
scores<define_variables> | tmp = bureau[(bureau['CREDIT_TYPE'] == "Consumer credit")].groupby(['SK_ID_CURR'])['DAYS_ENDDATE_FACT'].agg({"returns": [np.min, np.max,np.mean, np.sum]})\
.reset_index()
tmp.columns = ['SK_ID_CURR','des1','des2','des3', 'des4']
for df in [train,test]:
tmp_merge = df[['SK_ID_CURR']]
tmp_merge = tmp_merge.merge(tmp, on=['SK_ID_CURR'], how='left')
df['min_endatefact_bureau'] = tmp_merge['des1']
df['max_endatefact_bureau'] = tmp_merge['des2']
df['mean_endatefact_bureau'] = tmp_merge['des3']
df['sum_endatefact_bureau'] = tmp_merge['des4'] | Home Credit Default Risk |
1,511,034 | total = 0
for i in scores:
total += i
acuracia_esperada = total/cval
acuracia_esperada<predict_on_test> | bureau['tmp'] = bureau['DAYS_CREDIT_ENDDATE'] - bureau['DAYS_ENDDATE_FACT']
tmp = bureau.groupby(['SK_ID_CURR'])['tmp'].agg({"returns": [np.min, np.max,np.mean, np.sum]})\
.reset_index()
tmp.columns = ['SK_ID_CURR','des1','des2','des3', 'des4']
for df in [train,test]:
tmp_merge = df[['SK_ID_CURR']]
tmp_merge = tmp_merge.merge(tmp, on=['SK_ID_CURR'], how='left')
df['min_deltaendate_bureau'] = tmp_merge['des1']
df['max_deltaendate_bureau'] = tmp_merge['des2']
df['mean_deltaendate_bureau'] = tmp_merge['des3']
df['sum_deltaendate_bureau'] = tmp_merge['des4'] | Home Credit Default Risk |
1,511,034 | YtestPred = knn.predict(Xtestadult)
YtestPred<define_variables> | bureau['tmp'] =(bureau['DAYS_CREDIT_ENDDATE'] - bureau['DAYS_CREDIT'])
tmp = bureau[(bureau['CREDIT_TYPE'] == "Consumer credit")].groupby(['SK_ID_CURR'])['tmp'].agg({"returns": [np.min, np.max,np.mean, np.sum]})\
.reset_index()
tmp.columns = ['SK_ID_CURR','des1','des2','des3', 'des4']
for df in [train,test]:
tmp_merge = df[['SK_ID_CURR']]
tmp_merge = tmp_merge.merge(tmp, on=['SK_ID_CURR'], how='left')
df['min_duration_bureau'] = tmp_merge['des1']
df['max_duration_bureau'] = tmp_merge['des2']
df['mean_duration_bureau'] = tmp_merge['des3']
df['sum_duration_bureau'] = tmp_merge['des4']
bureau['tmp'] =(bureau['DAYS_CREDIT_ENDDATE'] - bureau['DAYS_CREDIT'])
tmp = bureau[(bureau['CREDIT_TYPE'] != "Consumer credit")].groupby(['SK_ID_CURR'])['tmp'].agg({"returns": [np.min, np.max,np.mean, np.sum]})\
.reset_index()
tmp.columns = ['SK_ID_CURR','des1','des2','des3', 'des4']
for df in [train,test]:
tmp_merge = df[['SK_ID_CURR']]
tmp_merge = tmp_merge.merge(tmp, on=['SK_ID_CURR'], how='left')
df['min_duration_bureau_v1'] = tmp_merge['des1']
df['max_duration_bureau_v1'] = tmp_merge['des2']
df['mean_duration_bureau_v1'] = tmp_merge['des3']
df['sum_duration_bureau_v1'] = tmp_merge['des4'] | Home Credit Default Risk |
1,511,034 | maior_50 = 0
menor_50 = 0
for i in YtestPred:
if i == '<=50K':
menor_50 += 1
else:
maior_50 += 1
dicio = {'<=50K':menor_50, '>50K':maior_50}<save_to_csv> | bureau['tmp'] =(bureau['DAYS_ENDDATE_FACT'] - bureau['DAYS_CREDIT'])
tmp = bureau[(bureau['CREDIT_TYPE'] == "Consumer credit")].groupby(['SK_ID_CURR'])['tmp'].agg({"returns": [np.min, np.max,np.mean, np.sum]})\
.reset_index()
tmp.columns = ['SK_ID_CURR','des1','des2','des3', 'des4']
for df in [train,test]:
tmp_merge = df[['SK_ID_CURR']]
tmp_merge = tmp_merge.merge(tmp, on=['SK_ID_CURR'], how='left')
df['min_durationfact_bureau'] = tmp_merge['des1']
df['max_durationfact_bureau'] = tmp_merge['des2']
df['mean_durationfact_bureau'] = tmp_merge['des3']
df['sum_durationfact_bureau'] = tmp_merge['des4'] | Home Credit Default Risk |
1,511,034 | result = np.vstack(( testadult["Id"], YtestPred)).T
x = ["Id","income"]
Resultado = pd.DataFrame(columns = x, data = result)
Resultado.to_csv("Resultado3.csv", index = False )<load_from_csv> | bureau['tmp'] =(bureau['DAYS_ENDDATE_FACT'] - bureau['DAYS_CREDIT_ENDDATE'])/(bureau['DAYS_CREDIT'] - bureau['DAYS_CREDIT_ENDDATE'])
tmp = bureau[(bureau['CREDIT_TYPE'] == "Consumer credit")].groupby(['SK_ID_CURR'])['tmp'].agg({"returns": [np.min, np.max,np.mean, np.sum]})\
.reset_index()
tmp.columns = ['SK_ID_CURR','des1','des2','des3', 'des4']
for df in [train,test]:
tmp_merge = df[['SK_ID_CURR']]
tmp_merge = tmp_merge.merge(tmp, on=['SK_ID_CURR'], how='left')
df['min_sooner_bureau'] = tmp_merge['des1']
df['max_sooner_bureau'] = tmp_merge['des2']
df['mean_sooner_bureau'] = tmp_merge['des3']
df['sum_sooner_bureau'] = tmp_merge['des4'] | Home Credit Default Risk |
1,511,034 | raw = pd.read_csv(".. /input/badult/train_data.csv",
names= None,
engine='python',
na_values = '?' )<set_options> | bureau['tmp'] =(bureau['DAYS_ENDDATE_FACT'] - bureau['DAYS_CREDIT_ENDDATE'])/(bureau['DAYS_CREDIT'] - bureau['DAYS_CREDIT_ENDDATE'])
tmp = bureau[(~bureau['CREDIT_TYPE'].isin(['Credit card','Consumer credit'])) ].groupby(['SK_ID_CURR'])['tmp'].agg({"returns": [np.min, np.max,np.mean, np.sum]})\
.reset_index()
tmp.columns = ['SK_ID_CURR','des1','des2','des3', 'des4']
for df in [train,test]:
tmp_merge = df[['SK_ID_CURR']]
tmp_merge = tmp_merge.merge(tmp, on=['SK_ID_CURR'], how='left')
df['min_sooner_bureau_v1'] = tmp_merge['des1']
df['max_sooner_bureau_v1'] = tmp_merge['des2']
df['mean_sooner_bureau_v1'] = tmp_merge['des3']
df['sum_sooner_bureau_v1'] = tmp_merge['des4'] | Home Credit Default Risk |
1,511,034 | clean = raw.dropna()
clean.info()<count_unique_values> | bureau['tmp'] =(bureau['AMT_CREDIT_SUM'])/(bureau['DAYS_CREDIT'] - bureau['DAYS_CREDIT_ENDDATE'])
tmp = bureau[bureau['CREDIT_TYPE'] == "Credit card"].groupby(['SK_ID_CURR'])['tmp'].agg({"returns": [np.min, np.max,np.mean, np.sum]})\
.reset_index()
tmp.columns = ['SK_ID_CURR','des1','des2','des3', 'des4']
for df in [train,test]:
tmp_merge = df[['SK_ID_CURR']]
tmp_merge = tmp_merge.merge(tmp, on=['SK_ID_CURR'], how='left')
df['min_annuity_bureau'] = tmp_merge['des1']
df['max_annuity_bureau'] = tmp_merge['des2']
df['mean_annuity_bureau'] = tmp_merge['des3']
df['sum_annuity_bureau'] = tmp_merge['des4'] | Home Credit Default Risk |
1,511,034 | obg = raw[['workclass','education','marital.status','occupation','relationship','race','sex','native.country','income']]
obg.nunique()<categorify> | bureau['tmp'] =(bureau['AMT_CREDIT_SUM'])/(bureau['DAYS_CREDIT'] - bureau['DAYS_CREDIT_ENDDATE'])
tmp = bureau[(bureau['CREDIT_ACTIVE'] == "Active")&(bureau['CREDIT_TYPE'] == "Consumer credit")].groupby(['SK_ID_CURR'])['tmp'].agg({"returns": [np.min, np.max,np.mean, np.sum]})\
.reset_index()
tmp.columns = ['SK_ID_CURR','des1','des2','des3', 'des4']
for df in [train,test]:
tmp_merge = df[['SK_ID_CURR']]
tmp_merge = tmp_merge.merge(tmp, on=['SK_ID_CURR'], how='left')
df['min_annuity_bureau_v1'] = tmp_merge['des1']
df['max_annuity_bureau_v1'] = tmp_merge['des2']
df['mean_annuity_bureau_v1'] = tmp_merge['des3']
df['sum_annuity_bureau_v1'] = tmp_merge['des4']
tmp = bureau[(bureau['CREDIT_ACTIVE'] == "Active")&(bureau['CREDIT_TYPE'] == "Consumer credit")].groupby(['SK_ID_CURR'])['tmp'].agg({"returns": [np.min, np.max,np.mean, np.sum]})\
.reset_index()
tmp.columns = ['SK_ID_CURR','des1','des2','des3', 'des4']
for df in [train,test]:
tmp_merge = df[['SK_ID_CURR']]
tmp_merge = tmp_merge.merge(tmp, on=['SK_ID_CURR'], how='left')
df['min_annuity_bureau_v2'] = tmp_merge['des1']
df['max_annuity_bureau_v2'] = tmp_merge['des2']
df['mean_annuity_bureau_v2'] = tmp_merge['des3']
df['sum_annuity_bureau_v2'] = tmp_merge['des4'] | Home Credit Default Risk |
1,511,034 | analysis = clean
analysis = analysis.apply(preprocessing.LabelEncoder().fit_transform)
plt.matshow(analysis.corr() )<sort_values> | bureau['tmp'] =(bureau['AMT_CREDIT_SUM'])/(bureau['DAYS_CREDIT'] - bureau['DAYS_CREDIT_ENDDATE'])
tmp = bureau[~bureau['CREDIT_TYPE'].isin(["Credit card","Consumer credit"])].groupby(['SK_ID_CURR'])['tmp'].agg({"returns": [np.min, np.max,np.mean, np.sum]})\
.reset_index()
tmp.columns = ['SK_ID_CURR','des1','des2','des3', 'des4']
for df in [train,test]:
tmp_merge = df[['SK_ID_CURR']]
tmp_merge = tmp_merge.merge(tmp, on=['SK_ID_CURR'], how='left')
df['min_annuity_bureau_v3'] = tmp_merge['des1']
df['max_annuity_bureau_v3'] = tmp_merge['des2']
df['mean_annuity_bureau_v3'] = tmp_merge['des3']
df['sum_annuity_bureau_v3'] = tmp_merge['des4'] | Home Credit Default Risk |
1,511,034 | anl0 = analysis.corr().income.sort_values(ascending=True)
anl0<categorify> | bureau['AMT_CREDIT_SUM_DEBT_v1'] = bureau['AMT_CREDIT_SUM_DEBT'].replace(0, np.nan)
tmp = bureau[(bureau['CREDIT_ACTIVE'] == "Active")&(bureau['CREDIT_TYPE'].isin(["Credit card"])) ].groupby(['SK_ID_CURR'])['AMT_CREDIT_SUM_DEBT_v1'].agg({"returns": [np.min, np.max,np.mean, np.sum]})\
.reset_index()
tmp.columns = ['SK_ID_CURR','des1','des2','des3', 'des4']
for df in [train,test]:
tmp_merge = df[['SK_ID_CURR']]
tmp_merge = tmp_merge.merge(tmp, on=['SK_ID_CURR'], how='left')
df['min_debt_bureau'] = tmp_merge['des1']
df['max_debt_bureau'] = tmp_merge['des2']
df['mean_debt_bureau'] = tmp_merge['des3']
df['sum_debt_bureau'] = tmp_merge['des4']
tmp = bureau[(bureau['CREDIT_ACTIVE'] == "Active")&(bureau['CREDIT_TYPE'].isin(["Consumer credit"])) ].groupby(['SK_ID_CURR'])['AMT_CREDIT_SUM_DEBT_v1'].agg({"returns": [np.min, np.max,np.mean, np.sum]})\
.reset_index()
tmp.columns = ['SK_ID_CURR','des1','des2','des3', 'des4']
for df in [train,test]:
tmp_merge = df[['SK_ID_CURR']]
tmp_merge = tmp_merge.merge(tmp, on=['SK_ID_CURR'], how='left')
df['min_debt_bureau_v1'] = tmp_merge['des1']
df['max_debt_bureau_v1'] = tmp_merge['des2']
df['mean_debt_bureau_v1'] = tmp_merge['des3']
df['sum_debt_bureau_v1'] = tmp_merge['des4'] | Home Credit Default Risk |
1,511,034 | anl1 = pd.get_dummies(clean[['relationship','marital.status','capital.loss', 'sex', 'hours.per.week', 'age', 'education.num', 'capital.gain', 'income']])
anl1 = anl1.corr().loc[:,'income_>50K'].sort_values(ascending=True)
anl1<categorify> | bureau['AMT_CREDIT_SUM_LIMIT_v1'] = bureau['AMT_CREDIT_SUM_LIMIT'].replace(0, np.nan)
tmp = bureau[(bureau['CREDIT_TYPE'].isin(["Credit card"])) ].groupby(['SK_ID_CURR'])['AMT_CREDIT_SUM_LIMIT_v1'].agg({"returns": [np.min, np.max,np.mean, np.sum]})\
.reset_index()
tmp.columns = ['SK_ID_CURR','des1','des2','des3', 'des4']
for df in [train,test]:
tmp_merge = df[['SK_ID_CURR']]
tmp_merge = tmp_merge.merge(tmp, on=['SK_ID_CURR'], how='left')
df['min_limit_bureau'] = tmp_merge['des1']
df['max_limit_bureau'] = tmp_merge['des2']
df['mean_limit_bureau'] = tmp_merge['des3']
df['sum_limit_bureau'] = tmp_merge['des4']
| Home Credit Default Risk |
1,511,034 | anl1_5 = pd.get_dummies(clean)
anl1_5 = anl1_5.corr().loc[:,'income_>50K'].sort_values(ascending=True ).where(lambda x : abs(x)> 0.15 ).dropna()
anl1_5<categorify> | bureau['AMT_CREDIT_MAX_OVERDUE_v1'] = bureau['AMT_CREDIT_MAX_OVERDUE'].replace(0,np.nan)
tmp = bureau[(bureau['CREDIT_TYPE'].isin(["Consumer credit"])) ].groupby(['SK_ID_CURR'])['AMT_CREDIT_MAX_OVERDUE_v1'].agg({"returns": [np.min, np.max,np.mean, np.sum]})\
.reset_index()
tmp.columns = ['SK_ID_CURR','des1','des2','des3', 'des4']
for df in [train,test]:
tmp_merge = df[['SK_ID_CURR']]
tmp_merge = tmp_merge.merge(tmp, on=['SK_ID_CURR'], how='left')
df['min_overdue_bureau'] = tmp_merge['des1'].fillna(0)
df['max_overdue_bureau'] = tmp_merge['des2'].fillna(0)
df['mean_overdue_bureau'] = tmp_merge['des3'].fillna(0)
df['sum_overdue_bureau'] = tmp_merge['des4'].fillna(0 ) | Home Credit Default Risk |
1,511,034 | anl2 = clean[['occupation','income','race']]
anl2 = pd.get_dummies(anl2 ).drop(columns = 'income_<=50K')
anl2 = anl2.corr().loc[:,'income_>50K'].sort_values(ascending=True ).where(lambda x : abs(x)> 0.088 ).dropna()
anl2<categorify> | bureau['tmp'] = bureau['AMT_CREDIT_SUM_DEBT']/bureau['AMT_CREDIT_SUM']
tmp = bureau.groupby(['SK_ID_CURR'])['tmp'].agg({"returns": [np.min, np.max,np.mean, np.sum]})\
.reset_index()
tmp.columns = ['SK_ID_CURR','des1','des2','des3', 'des4']
for df in [train,test]:
tmp_merge = df[['SK_ID_CURR']]
tmp_merge = tmp_merge.merge(tmp, on=['SK_ID_CURR'], how='left')
df['min_ratio_debt_credit_bureau'] = tmp_merge['des1']
df['max_ratio_debt_credit_bureau'] = tmp_merge['des2']
df['mean_ratio_debt_credit_bureau'] = tmp_merge['des3']
df['sum_ratio_debt_credit_bureau'] = tmp_merge['des4'] | Home Credit Default Risk |
1,511,034 | train_clean = pd.get_dummies(clean)
index = anl1.where(lambda x : abs(x)> 0.07 ).dropna().index[1:-1].append(anl2.index[:-1] )<load_from_csv> | bureau['tmp'] = bureau['AMT_ANNUITY'].fillna(0)
tmp = bureau.groupby(['SK_ID_CURR'])['tmp'].agg({"returns": [np.min, np.max,np.mean, np.sum]})\
.reset_index()
tmp.columns = ['SK_ID_CURR','des1','des2','des3', 'des4']
for df in [train,test]:
tmp_merge = df[['SK_ID_CURR']]
tmp_merge = tmp_merge.merge(tmp, on=['SK_ID_CURR'], how='left')
df['min_annuity_bureau_v2'] = tmp_merge['des1']
df['max_annuity_bureau_v2'] = tmp_merge['des2']
df['mean_annuity_bureau_v2'] = tmp_merge['des3']
df['sum_annuity_bureau_v2'] = tmp_merge['des4'] | Home Credit Default Risk |
1,511,034 | test_raw = pd.read_csv(".. /input/badult/test_data.csv",
names= None,
engine='python' )<prepare_x_and_y> | install = pd.read_csv(".. /input/installments_payments.csv" ) | Home Credit Default Risk |
1,511,034 | X_train = train_clean[index].drop(columns='sex_Female')
Y_train = train_clean.loc[:,'income_>50K']<categorify> | tmp = install.groupby(['SK_ID_PREV','SK_ID_CURR'] ).agg({'NUM_INSTALMENT_NUMBER':["count","max"]} ).reset_index()
tmp.columns = ['SK_ID_PREV','SK_ID_CURR','count','max']
tmp['delta'] = tmp['count'] - tmp['max']
tmp = tmp.merge(previous[['SK_ID_PREV','CNT_PAYMENT','DAYS_LAST_DUE','NAME_CONTRACT_TYPE']], on=['SK_ID_PREV'], how='left')
tmp_1 = tmp.groupby(['SK_ID_CURR'])['delta'].agg({"returns": [np.min, np.max,np.mean, np.sum]})\
.reset_index()
tmp_1.columns = ['SK_ID_CURR','des1','des2','des3', 'des4']
for df in [train,test]:
tmp_merge = df[['SK_ID_CURR']]
tmp_merge = tmp_merge.merge(tmp_1, on=['SK_ID_CURR'], how='left')
df['min_delta_num_install'] = tmp_merge['des1']
df['max_delta_num_install'] = tmp_merge['des2']
df['mean_delta_num_install'] = tmp_merge['des3']
df['sum_delta_num_install'] = tmp_merge['des4'] | Home Credit Default Risk |
1,511,034 | test_clean = pd.get_dummies(test_raw)
test_clean = test_clean.dropna()
X_test = test_clean[index].drop(columns='sex_Female' )<import_modules> | tmp = install.groupby(['SK_ID_PREV','SK_ID_CURR'] ).agg({'NUM_INSTALMENT_VERSION':["count","max"]} ).reset_index()
tmp.columns = ['SK_ID_PREV','SK_ID_CURR','count','max']
tmp = tmp.merge(previous[['SK_ID_PREV','CNT_PAYMENT','DAYS_LAST_DUE','NAME_CONTRACT_TYPE']], on=['SK_ID_PREV'], how='left')
tmp_1 = tmp.groupby(['SK_ID_CURR'])['max'].agg({"returns": [np.min, np.max,np.mean, np.sum]})\
.reset_index()
tmp_1.columns = ['SK_ID_CURR','des1','des2','des3', 'des4']
for df in [train,test]:
tmp_merge = df[['SK_ID_CURR']]
tmp_merge = tmp_merge.merge(tmp_1, on=['SK_ID_CURR'], how='left')
df['min_max_version_install'] = tmp_merge['des1']
df['max_max_version_install'] = tmp_merge['des2']
df['mean_max_version_install'] = tmp_merge['des3']
df['sum_max_version_install'] = tmp_merge['des4'] | Home Credit Default Risk |
1,511,034 | from sklearn.neighbors import KNeighborsClassifier
from sklearn.model_selection import GridSearchCV<define_search_space> | tmp = install.groupby(['SK_ID_PREV','SK_ID_CURR'] ).agg({'NUM_INSTALMENT_NUMBER':["count","max"]} ).reset_index()
tmp.columns = ['SK_ID_PREV','SK_ID_CURR','count','max']
tmp['delta'] = tmp['count']/tmp['max']
tmp = tmp.merge(previous[['SK_ID_PREV','CNT_PAYMENT','DAYS_LAST_DUE','NAME_CONTRACT_TYPE']], on=['SK_ID_PREV'], how='left')
tmp_1 = tmp.groupby(['SK_ID_CURR'])['delta'].agg({"returns": [np.min, np.max,np.mean, np.sum]})\
.reset_index()
tmp_1.columns = ['SK_ID_CURR','des1','des2','des3', 'des4']
for df in [train,test]:
tmp_merge = df[['SK_ID_CURR']]
tmp_merge = tmp_merge.merge(tmp_1, on=['SK_ID_CURR'], how='left')
df['min_ratio_num_install'] = tmp_merge['des1']
df['max_ratio_num_install'] = tmp_merge['des2']
df['mean_ratio_num_install'] = tmp_merge['des3']
df['sum_ratio_num_install'] = tmp_merge['des4'] | Home Credit Default Risk |
1,511,034 | k_range = list(range(1, 31))
weight_options = ['uniform', 'distance']
p_options = list(range(1,3))
param_grid = dict(n_neighbors=k_range, p=p_options )<choose_model_class> | tmp = install[['SK_ID_PREV','SK_ID_CURR','NUM_INSTALMENT_NUMBER','AMT_INSTALMENT']].drop_duplicates() | Home Credit Default Risk |
1,511,034 | knn = KNeighborsClassifier(n_neighbors=5)
grid = GridSearchCV(knn, param_grid, cv=10, scoring='accuracy', n_jobs = -2 )<train_model> | tmp = tmp.groupby(['SK_ID_PREV','SK_ID_CURR'])['AMT_INSTALMENT'].sum().reset_index()
tmp.columns = ['SK_ID_PREV','SK_ID_CURR','need_to_pay'] | Home Credit Default Risk |
1,511,034 | grid.fit(X_train, Y_train)
print(grid.best_estimator_)
print(grid.best_score_ )<train_model> | tmp_1 = install.groupby(['SK_ID_PREV'])['AMT_PAYMENT'].sum().reset_index()
tmp_1.columns = ['SK_ID_PREV','paid'] | Home Credit Default Risk |
1,511,034 | f_kNN.fit(X_train,Y_train )<predict_on_test> | tmp = tmp.merge(tmp_1, on=['SK_ID_PREV'], how='left' ) | Home Credit Default Risk |
1,511,034 | Y_test = f_kNN.predict(X_test )<save_to_csv> | payment_history = tmp
payment_history['ratio'] = payment_history['paid']/payment_history['need_to_pay']
payment_history['delta'] = payment_history['need_to_pay'] - payment_history['paid']
payment_history = payment_history.merge(previous[['SK_ID_PREV','AMT_ANNUITY','CNT_PAYMENT','NAME_CONTRACT_TYPE']], \
on = ['SK_ID_PREV'], how='left')
payment_history['all_credit'] = payment_history['AMT_ANNUITY'] * payment_history['CNT_PAYMENT']
payment_history['ratio'] = payment_history['paid']/payment_history['all_credit']
payment_history['delta'] = payment_history['all_credit'] - payment_history['paid']
tmp = install.groupby(['SK_ID_PREV'])['NUM_INSTALMENT_VERSION'].mean().reset_index()
tmp.columns = ['SK_ID_PREV','mean_version']
payment_history = payment_history.merge(tmp, on=['SK_ID_PREV'], how='left' ) | Home Credit Default Risk |
1,511,034 | Y_test_copy = Y_test
Y_test_copy = Y_test_copy.tolist()
answer = [["Id","income"]]
for output in range(len(Y_test_copy)) :
if Y_test_copy[output] == 0:
Y_test_copy[output] = '<=50K'
else:
Y_test_copy[output] = '>50K'
answer.append([output,Y_test_copy[output]])
myFile = open("submit.csv", 'w')
with myFile:
writer = csv.writer(myFile)
writer.writerows(answer )<set_options> | tmp = payment_history[payment_history['mean_version'] > 0].groupby(['SK_ID_CURR'])['ratio'].agg({"returns": [np.min, np.max,np.mean, np.sum]})\
.reset_index()
tmp.columns = ['SK_ID_CURR','des1','des2','des3', 'des4']
for df in [train,test]:
tmp_merge = df[['SK_ID_CURR']]
tmp_merge = tmp_merge.merge(tmp, on=['SK_ID_CURR'], how='left')
df['min_ratio_paid_install'] = tmp_merge['des1']
df['max_ratio_paid_install'] = tmp_merge['des2']
df['mean_ratio_paid_install'] = tmp_merge['des3']
df['sum_ratio_paid_install'] = tmp_merge['des4'] | Home Credit Default Risk |
1,511,034 | pd.set_option('display.max_columns', 999)
warnings.filterwarnings(action='ignore', category=DataConversionWarning )<load_from_csv> | tmp = payment_history[payment_history['mean_version'] > 0].groupby(['SK_ID_CURR'])['delta'].agg({"returns": [np.min, np.max,np.mean, np.sum]})\
.reset_index()
tmp.columns = ['SK_ID_CURR','des1','des2','des3', 'des4']
for df in [train,test]:
tmp_merge = df[['SK_ID_CURR']]
tmp_merge = tmp_merge.merge(tmp, on=['SK_ID_CURR'], how='left')
df['min_delta_paid_install'] = tmp_merge['des1']
df['max_delta_paid_install'] = tmp_merge['des2']
df['mean_delta_paid_install'] = tmp_merge['des3']
df['sum_delta_paid_install'] = tmp_merge['des4'] | Home Credit Default Risk |
1,511,034 | events = pd.read_csv('.. /input/events_up_to_01062018.csv', low_memory=False)
labels = pd.read_csv('.. /input/labels_training_set.csv')
test = pd.read_csv('.. /input/trocafone_kaggle_test.csv' )<compute_train_metric> | tmp = install.groupby(['SK_ID_PREV','SK_ID_CURR'] ).agg({'NUM_INSTALMENT_NUMBER':["count","max"]} ).reset_index()
tmp.columns = ['SK_ID_PREV','SK_ID_CURR','count','max']
tmp['delta'] = tmp['count']/tmp['max']
tmp = tmp.merge(previous[['SK_ID_PREV','CNT_PAYMENT','DAYS_LAST_DUE','NAME_CONTRACT_TYPE']], on=['SK_ID_PREV'], how='left')
tmp_1 = tmp.groupby(['SK_ID_CURR'])['max'].agg({"returns": [np.min, np.max,np.mean, np.sum]})\
.reset_index()
tmp_1.columns = ['SK_ID_CURR','des1','des2','des3', 'des4']
for df in [train,test]:
tmp_merge = df[['SK_ID_CURR']]
tmp_merge = tmp_merge.merge(tmp_1, on=['SK_ID_CURR'], how='left')
df['min_max_num_install'] = tmp_merge['des1']
df['max_max_num_install'] = tmp_merge['des2']
df['mean_max_num_install'] = tmp_merge['des3']
df['sum_max_num_install'] = tmp_merge['des4'] | Home Credit Default Risk |
1,511,034 | def evaluate_model(y_true, model=None, X_test=None, prediction=None, probabilites=None):
if model is not None:
if prediction is None:
prediction = model.predict(X_test)
if probabilites is None:
probabilites = model.predict_proba(X_test)[:, 1]
if prediction is not None:
print('Accuracy: ', accuracy_score(y_true, prediction))
print('ROC AUC Predict: ', roc_auc_score(y_true, prediction))
if probabilites is not None:
print('Avg Log loss: ', log_loss(y_true, probabilites))
print('Sum Log loss: ', log_loss(y_true, probabilites, normalize=False))
print('ROC AUC Proba: ', roc_auc_score(y_true, probabilites))<compute_train_metric> | tmp = install.groupby(['SK_ID_PREV'])['AMT_PAYMENT'].max().reset_index()
tmp.columns = ['SK_ID_PREV','max_install']
payment_history = payment_history.merge(tmp, on=['SK_ID_PREV'], how='left' ) | Home Credit Default Risk |
1,511,034 | all_false = np.zeros(len(labels))
evaluate_model(labels.label, prediction=all_false, probabilites=all_false )<define_variables> | payment_history['tmp'] = payment_history['max_install']/payment_history['AMT_ANNUITY']
tmp = payment_history[payment_history['mean_version'] > 0].groupby(['SK_ID_CURR'])['tmp'] | Home Credit Default Risk |
1,511,034 | full_train = get_train_set(events )<filter> | tmp = install.groupby(['SK_ID_PREV'])['NUM_INSTALMENT_NUMBER'].max().reset_index()
tmp.columns = ['SK_ID_PREV','max_num_install']
payment_history = payment_history.merge(tmp, on=['SK_ID_PREV'], how='left' ) | Home Credit Default Risk |
1,511,034 | train = full_train.loc[labels.person]
assert all(train.index == labels.person )<normalization> | tmp = install[install['AMT_INSTALMENT'] > install['AMT_PAYMENT']]
tmp = tmp.groupby(['SK_ID_CURR'])['SK_ID_PREV'].count().reset_index()
tmp.columns = ['SK_ID_CURR','des']
for df in [train,test]:
tmp_merge = df[['SK_ID_CURR']]
tmp_merge = tmp_merge.merge(tmp, on=['SK_ID_CURR'], how='left')
df['count_small_payment'] = tmp_merge['des'].fillna(0)
for i in [0, 5, 10, 15, 20, 25, 30, 40, 50, 60]:
print(i)
tmp = install[(install['DAYS_ENTRY_PAYMENT'] - install['DAYS_INSTALMENT'])> i]
tmp = tmp.groupby(['SK_ID_CURR'])['SK_ID_PREV'].count().reset_index()
tmp.columns = ['SK_ID_CURR','des']
for df in [train,test]:
tmp_merge = df[['SK_ID_CURR']]
tmp_merge = tmp_merge.merge(tmp, on=['SK_ID_CURR'], how='left')
df['count_late_payment_' + str(i)] = tmp_merge['des'].fillna(0)
| Home Credit Default Risk |
1,511,034 | scaler = StandardScaler()
features = scaler.fit_transform(train)
features.shape<prepare_x_and_y> | install['tmp'] = install['AMT_PAYMENT']/install['AMT_INSTALMENT']
for i in range(10):
print(i)
tmp = install[(install['tmp'] > i/10)&(install['tmp'] <(( i+1)/10)) ]
tmp = tmp.groupby(['SK_ID_CURR'])['SK_ID_PREV'].count().reset_index()
tmp.columns = ['SK_ID_CURR','des']
for df in [train,test]:
tmp_merge = df[['SK_ID_CURR']]
tmp_merge = tmp_merge.merge(tmp, on=['SK_ID_CURR'], how='left')
df['count_ratio_payment_' + str(i)] = tmp_merge['des'].fillna(0)
| Home Credit Default Risk |
1,511,034 | X_train, y_train = features, labels.label<normalization> | tmp = install.groupby(['SK_ID_PREV','NUM_INSTALMENT_NUMBER'])['DAYS_INSTALMENT'].count().reset_index()
tmp = tmp[tmp['DAYS_INSTALMENT'] > 1]
tmp.columns = ['SK_ID_PREV','NUM_INSTALMENT_NUMBER','count_dup']
install = install.merge(tmp, on = ['SK_ID_PREV','NUM_INSTALMENT_NUMBER'], how='left')
dup_install = install[install['count_dup'] > 1]
dup_install.reset_index(drop=True, inplace = True)
| Home Credit Default Risk |
1,511,034 | y_test = test.set_index('person')
X_test = scaler.transform(full_train.loc[y_test.index] )<train_model> | tmp = install[(install['AMT_PAYMENT'] < install['AMT_INSTALMENT'])&(install['DAYS_ENTRY_PAYMENT'] < install['DAYS_INSTALMENT'])]
tmp['ratio'] = tmp['AMT_PAYMENT']/tmp['AMT_INSTALMENT']
tmp = dup_install.groupby(['SK_ID_CURR'])['AMT_PAYMENT'].agg({"returns": [np.min, np.max,np.mean, np.sum]})\
.reset_index()
tmp_1.columns = ['SK_ID_CURR','des1','des2','des3', 'des4']
for df in [train,test]:
tmp_merge = df[['SK_ID_CURR']]
tmp_merge = tmp_merge.merge(tmp_1, on=['SK_ID_CURR'], how='left')
df['min_special_install'] = tmp_merge['des1']
df['max_special_install'] = tmp_merge['des2']
df['mean_special_install'] = tmp_merge['des3']
df['sum_special_install'] = tmp_merge['des4'] | Home Credit Default Risk |
1,511,034 |
<train_model> | dup_install.sort_values(by=['SK_ID_PREV','NUM_INSTALMENT_NUMBER','DAYS_ENTRY_PAYMENT'] ) | Home Credit Default Risk |
1,511,034 | lgb_model = lgb.LGBMClassifier(boosting_type='dart', num_leaves=5, n_estimators=1000, metric='AUC',
learning_rate=0.05, colsample_bytree=0.9)
lgb_model.fit(X_train, y_train )<compute_test_metric> | credit = pd.read_csv(".. /input/credit_card_balance.csv" ) | Home Credit Default Risk |
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