kernel_id int64 24.2k 23.3M | prompt stringlengths 8 1.85M | completetion stringlengths 1 182k | comp_name stringlengths 5 57 |
|---|---|---|---|
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 n... | 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_su... | 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(... | 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_fi... | 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 =... | 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=p... | 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 )... | 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.optimiz... | 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=p... | 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['CN... | 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_m... | 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... | 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','... | 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... | 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... | 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 [tr... | 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_... | 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_... | 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=... | 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.mer... | 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.mer... | 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']]... | 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_categ... | 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']]... | 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_merg... | 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=inp... | 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... | 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.m... | 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_m... | 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[... | 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... | 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_r... | 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'] = ... | 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... | 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_C... | 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']
d... | 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... | 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... | 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']]... | 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_I... | 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'<... | 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_... | 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(t... | 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(... | 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(t... | 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... | 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['de... | 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... | 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', 'd... | 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 = ... | 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 )<co... | 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[... | 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']
... | 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_bure... | 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, ... | 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... | 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_... | 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 )<... | 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]... | 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... | 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_mer... | 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_merg... | 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 ... | 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',... | 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.col... | 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 [... | 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_CUR... | 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','de... | 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... | 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', 'de... | 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','d... | 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.me... | 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_CU... | 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'... | 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... | 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'],... | 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_PR... | 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 = cs... | 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_CUR... | 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_CUR... | 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'],... | 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, predic... | 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'] = t... | 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_CU... | 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[insta... | 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.colu... | 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 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.