kernel_id int64 24.2k 23.3M | prompt stringlengths 8 1.85M | completetion stringlengths 1 182k | comp_name stringlengths 5 57 |
|---|---|---|---|
19,576,670 | tokenizer = ppb.DistilBertTokenizer.from_pretrained("distilbert-base-uncased")
bert_model = ppb.DistilBertModel.from_pretrained("distilbert-base-uncased" )<define_variables> | sort_bureau = bureau.sort_values(by=['DAYS_CREDIT'])
gr = sort_bureau.groupby('SK_ID_CURR')['AMT_CREDIT_MAX_OVERDUE'].last().reset_index()
gr.rename({'AMT_CREDIT_MAX_OVERDUE': 'BUREAU_LAST_LOAN_MAX_OVERDUE'}, inplace=True)
agg_bureau = agg_bureau.merge(gr, on='SK_ID_CURR', how='left')
agg_bureau['BUREAU_DEBT_OVER_CR... | Home Credit Default Risk |
19,576,670 | def process_data(df_text):
tokens = df_text.apply(lambda text: tokenizer.encode(text,add_special_tokens=True))
max_len = 0;
i = 0;
for token in tokens.values:
max_len = max(max_len,len(token))
print(f"Max Length: {max_len}")
padded = np.array([i+[0]*(max_len-len(i)) for i in tokens.values])
attention_mask = np.where(... | df = pd.merge(df, agg_bureau, on='SK_ID_CURR', how='left')
del agg_bureau, bureau
gc.collect() | Home Credit Default Risk |
19,576,670 | X_train = process_data(df_train.text )<prepare_x_and_y> | prev = pd.read_csv(os.path.join(DATA_DIRECTORY, 'previous_application.csv'))
pay = pd.read_csv(os.path.join(DATA_DIRECTORY, 'installments_payments.csv')) | Home Credit Default Risk |
19,576,670 | y_train = df_train.target<split> | PREVIOUS_AGG = {
'SK_ID_PREV': ['nunique'],
'AMT_ANNUITY': ['min', 'max', 'mean'],
'AMT_DOWN_PAYMENT': ['max', 'mean'],
'HOUR_APPR_PROCESS_START': ['min', 'max', 'mean'],
'RATE_DOWN_PAYMENT': ['max', 'mean'],
'DAYS_DECISION': ['min', 'max', 'mean'],
'CNT_PAYMENT': ['max', 'mean'],
'DAYS_TERMINATION': ['max'],
'CREDIT_T... | Home Credit Default Risk |
19,576,670 | X_tr, X_val, nlp_tr, nlp_val, kw_tr, kw_val, y_tr, y_val = train_test_split(X_train,nlp_train, keyword_train, y_train, test_size=0.25, train_size=0.75,shuffle=True )<choose_model_class> | ohe_columns = [
'NAME_CONTRACT_STATUS', 'NAME_CONTRACT_TYPE', 'CHANNEL_TYPE',
'NAME_TYPE_SUITE', 'NAME_YIELD_GROUP', 'PRODUCT_COMBINATION',
'NAME_PRODUCT_TYPE', 'NAME_CLIENT_TYPE']
prev, categorical_cols = one_hot_encoder(prev, ohe_columns, nan_as_category= False ) | Home Credit Default Risk |
19,576,670 | def build_nn() :
model = tf.keras.Sequential()
model.add(layers.Input(shape=(768,)))
model.add(layers.Dense(128,activation='tanh'))
model.add(layers.Dropout(0.6))
model.add(layers.Dense(32,activation='tanh'))
model.add(layers.Dropout(0.6))
model.add(layers.Dense(8,activation='tanh'))
model.add(layers.Dense(1,activatio... | prev['APPLICATION_CREDIT_DIFF'] = prev['AMT_APPLICATION'] - prev['AMT_CREDIT']
prev['APPLICATION_CREDIT_RATIO'] = prev['AMT_APPLICATION'] / prev['AMT_CREDIT']
prev['CREDIT_TO_ANNUITY_RATIO'] = prev['AMT_CREDIT']/prev['AMT_ANNUITY']
prev['DOWN_PAYMENT_TO_CREDIT'] = prev['AMT_DOWN_PAYMENT'] / prev['AMT_CREDIT']
total_pay... | Home Credit Default Risk |
19,576,670 | kfold = KFold(n_splits=4, shuffle=True, random_state=1 )<compute_test_metric> | approved = prev[prev['NAME_CONTRACT_STATUS_Approved'] == 1]
active_df = approved[approved['DAYS_LAST_DUE'] == 365243]
active_pay = pay[pay['SK_ID_PREV'].isin(active_df['SK_ID_PREV'])]
active_pay_agg = active_pay.groupby('SK_ID_PREV')[['AMT_INSTALMENT', 'AMT_PAYMENT']].sum()
active_pay_agg.reset_index(inplace= True)
ac... | Home Credit Default Risk |
19,576,670 | def eval_f1_score(X_val, y_val, model):
pred_val =(model.predict(X_val)>0.5)
f1 = f1_score(y_val,pred_val)
return f1<define_variables> | prev['DAYS_FIRST_DRAWING'].replace(365243, np.nan, inplace= True)
prev['DAYS_FIRST_DUE'].replace(365243, np.nan, inplace= True)
prev['DAYS_LAST_DUE_1ST_VERSION'].replace(365243, np.nan, inplace= True)
prev['DAYS_LAST_DUE'].replace(365243, np.nan, inplace= True)
prev['DAYS_TERMINATION'].replace(365243, np.nan, inpla... | Home Credit Default Risk |
19,576,670 | EPOCHS = 100
BATCH_SIZE = 64<train_model> | prev['DAYS_LAST_DUE_DIFF'] = prev['DAYS_LAST_DUE_1ST_VERSION'] - prev['DAYS_LAST_DUE']
approved['DAYS_LAST_DUE_DIFF'] = approved['DAYS_LAST_DUE_1ST_VERSION'] - approved['DAYS_LAST_DUE'] | Home Credit Default Risk |
19,576,670 | fold = 0
history_by_fold = []
cv_results = []
for train,val in kfold.split(X_train,y_train):
nn_model = build_nn()
history = nn_model.fit(X_train[train],y_train[train],
validation_data=(X_train[val],y_train[val]),
epochs=EPOCHS, batch_size=BATCH_SIZE, verbose=0)
scores = nn_model.evaluate(X_train[val],y_train[val],ver... | categorical_agg = {key: ['mean'] for key in categorical_cols} | Home Credit Default Risk |
19,576,670 | nn_model = build_nn()
history = nn_model.fit(X_tr,y_tr, validation_data=(X_val,y_val),
epochs=EPOCHS, batch_size=BATCH_SIZE,verbose=0)
scores= nn_model.evaluate(X_val,y_val,verbose=0)
print(f"Accuracy: {scores[1]}")
print(f"F1 Score: {eval_f1_score(X_val,y_val,nn_model)}" )<choose_model_class> | agg_prev = group(prev, 'PREV_', {**PREVIOUS_AGG, **categorical_agg})
agg_prev = agg_prev.merge(active_agg_df, how='left', on='SK_ID_CURR')
del active_agg_df; gc.collect() | Home Credit Default Risk |
19,576,670 | def build_LSTM() :
lstm_model = tf.keras.Sequential()
lstm_model.add(layers.Input(shape=(None,300)))
lstm_model.add(layers.LSTM(16))
lstm_model.add(layers.Dense(8, activation="tanh"))
lstm_model.add(layers.Dense(8, activation="tanh"))
lstm_model.add(layers.Dense(1,activation="sigmoid"))
lstm_model.compile(loss=tf.kera... | agg_prev = group_and_merge(approved, agg_prev, 'APPROVED_', PREVIOUS_APPROVED_AGG)
refused = prev[prev['NAME_CONTRACT_STATUS_Refused'] == 1]
agg_prev = group_and_merge(refused, agg_prev, 'REFUSED_', PREVIOUS_REFUSED_AGG)
del approved, refused; gc.collect() | Home Credit Default Risk |
19,576,670 | EPOCHS = 30;
BATCH_SIZE = 64;<choose_model_class> | for loan_type in ['Consumer loans', 'Cash loans']:
type_df = prev[prev['NAME_CONTRACT_TYPE_{}'.format(loan_type)] == 1]
prefix = 'PREV_' + loan_type.split(" ")[0] + '_'
agg_prev = group_and_merge(type_df, agg_prev, prefix, PREVIOUS_LOAN_TYPE_AGG)
del type_df; gc.collect() | Home Credit Default Risk |
19,576,670 | kfold = KFold(n_splits=4, shuffle=True, random_state=1 )<train_model> | pay['LATE_PAYMENT'] = pay['DAYS_ENTRY_PAYMENT'] - pay['DAYS_INSTALMENT']
pay['LATE_PAYMENT'] = pay['LATE_PAYMENT'].apply(lambda x: 1 if x > 0 else 0)
dpd_id = pay[pay['LATE_PAYMENT'] > 0]['SK_ID_PREV'].unique() | Home Credit Default Risk |
19,576,670 | fold = 0
history_by_fold = []
cv_results = []
for train, val in kfold.split(nlp_train,y_train):
lstm_model = build_LSTM()
history = lstm_model.fit(nlp_train[train],y_train[train],
validation_data=(nlp_train[val],y_train[val]),
epochs=EPOCHS,batch_size=BATCH_SIZE,verbose=0)
scores = lstm_model.evaluate(nlp_train[val],y... | agg_dpd = group_and_merge(prev[prev['SK_ID_PREV'].isin(dpd_id)], agg_prev,
'PREV_LATE_', PREVIOUS_LATE_PAYMENTS_AGG)
del agg_dpd, dpd_id; gc.collect() | Home Credit Default Risk |
19,576,670 | lstm_model = build_LSTM()
history = lstm_model.fit(nlp_tr,y_tr,validation_data=(nlp_val,y_val), epochs=EPOCHS, batch_size=BATCH_SIZE )<predict_on_test> | df = pd.merge(df, agg_prev, on='SK_ID_CURR', how='left' ) | Home Credit Default Risk |
19,576,670 | valid_predict =(lstm_model.predict(nlp_val)> 0.5)
f1 = f1_score(y_val, valid_predict)
print(f" F1 Score: {f1}" )<compute_test_metric> | train = df[df['TARGET'].notnull() ]
test = df[df['TARGET'].isnull() ]
del df
del agg_prev
gc.collect() | Home Credit Default Risk |
19,576,670 | lr_keywords = LogisticRegression(max_iter=500)
lr_keywords.fit(kw_tr,y_tr)
val_pred = lr_keywords.predict(kw_val)
print(f"Accurcay: {accuracy_score(y_val, val_pred)}")
print(f"F1 score: {f1_score(y_val,val_pred)}" )<predict_on_test> | labels = train['TARGET']
test_lebels=test['TARGET']
train = train.drop(columns=['TARGET'])
test = test.drop(columns=['TARGET'] ) | Home Credit Default Risk |
19,576,670 | nn_tr_predict = nn_model.predict(X_tr)
kw_tr_predict = lr_keywords.predict_proba(kw_tr)[:,1]
lstm_tr_predict = lstm_model.predict(nlp_tr)
nn_val_predict = nn_model.predict(X_val)
kw_val_predict = lr_keywords.predict_proba(kw_val)[:,1]
lstm_val_predict = lstm_model.predict(nlp_val)
kw_tr_predict = kw_tr_predict.resh... | feature = list(train.columns)
train.replace([np.inf, -np.inf], np.nan, inplace=True)
test.replace([np.inf, -np.inf], np.nan, inplace=True)
test_df = test.copy()
train_df = train.copy()
train_df['TARGET'] = labels
test_df['TARGET'] = test_lebels | Home Credit Default Risk |
19,576,670 | lr = LogisticRegression()
lr.fit(concat_tr,y_tr)
val_pred = lr.predict(concat_val)
print(f"Accurcay: {accuracy_score(y_val, val_pred)}")
print(f"F1 score: {f1_score(y_val,val_pred)}" )<prepare_x_and_y> | imputer = SimpleImputer(strategy = 'median')
imputer.fit(train)
imputer.fit(test)
train1 = imputer.transform(train)
test1 = imputer.transform(test)
del train
del test
gc.collect() | Home Credit Default Risk |
19,576,670 | X_test = process_data(df_test.text )<feature_engineering> | scaler = MinMaxScaler(feature_range =(0, 1))
scaler.fit(train1)
scaler.fit(test1)
train = scaler.transform(train1)
test = scaler.transform(test1)
del train1
del test1
gc.collect() | Home Credit Default Risk |
19,576,670 | nlp_test = build_nlp_vectors(df_test.text )<predict_on_test> | from keras.models import Sequential
from keras.layers import Dense | Home Credit Default Risk |
19,576,670 | df_test["nn_predict"]= nn_model.predict(X_test)
df_test["lstm_predict"]= lstm_model.predict(nlp_test)
df_test["keyword_predict"] = lr_keywords.predict_proba(keyword_test)[:,1]
features = ["nn_predict","keyword_predict","lstm_predict"]
test_features = df_test[features]
predict = lr.predict(test_features )<save_to_csv> | model_2 = Sequential([
Dense(1000, activation='relu', input_shape=(461,)) ,
Dense(1000, activation='relu'),
Dense(1000, activation='relu'),
Dense(1000, activation='relu'),
Dense(1, activation='sigmoid'),
])
model_2.compile(optimizer='adam',
loss='binary_crossentropy',
metrics=['accuracy'])
hist_2 = model_2.fit(train,... | Home Credit Default Risk |
19,576,670 | output = pd.DataFrame({"id":df_test.id, "target":predict})
output.to_csv("submission.csv",index=False)
output<set_options> | pred = model_2.predict_proba(test ) | Home Credit Default Risk |
19,576,670 | %matplotlib inline
InteractiveShell.ast_node_interactivity = 'all'
!pip install chart_studio
plotly.offline.init_notebook_mode(connected=True)
cufflinks.go_offline()
cufflinks.set_config_file(world_readable=True, theme='pearl')
warnings.filterwarnings('ignore' )<load_from_csv> | submit = test_df[['SK_ID_CURR']]
submit['TARGET'] = pred
submit.to_csv('NN.csv', index = False ) | Home Credit Default Risk |
17,857,459 | data = pd.read_csv('.. /input/nlp-getting-started/train.csv' )<string_transform> | %matplotlib inline
warnings.filterwarnings('ignore')
pd.set_option('display.max_rows', 100)
pd.set_option('display.max_columns', 200 ) | Home Credit Default Risk |
17,857,459 | def create_corpus(target):
corpus = []
for i in data[data['target']==target]['text'].str.split() :
for x in i:
corpus.append(x)
return corpus<categorify> | app_train = pd.read_csv('.. /input/home-credit-default-risk/application_train.csv')
app_test = pd.read_csv('.. /input/home-credit-default-risk/application_test.csv' ) | Home Credit Default Risk |
17,857,459 | lemmatizer = WordNetLemmatizer()
def preprocess_data(data):
text = re.sub(r'https?://\S+|www\.\S+|http?://\S+',' ',data)
text = re.sub(r"won't", " will not", text)
text = re.sub(r"won't've", " will not have", text)
text = re.sub(r"can't", " can not", text)
text = re.sub(r"don't", " do not", text)
text = re.sub(r"c... | print('Training data shape: ', app_train.shape)
app_train.head()
| Home Credit Default Risk |
17,857,459 | common_words = ['via','like','build','get','would','one','two','feel',
'lol','fuck','take','way','may','first','latest','want',
'make','back','see','know','let','look','come','got',
'still','say','think','great','pleas','amp']
def text_cleaning(data):
return ' '.join(i for i in data.split() if i not in common_words)
d... | app_train['TARGET'].value_counts() | Home Credit Default Risk |
17,857,459 | def top_ngrams(data,n,grams):
count_vec = CountVectorizer(ngram_range=(grams,grams)).fit(data)
bow = count_vec.transform(data)
add_words = bow.sum(axis=0)
word_freq = [(word, add_words[0, idx])for word, idx in count_vec.vocabulary_.items() ]
word_freq = sorted(word_freq, key = lambda x: x[1], reverse=True)
return w... | app_train.dtypes.value_counts()
| Home Credit Default Risk |
17,857,459 | common_uni = top_ngrams(data["Cleaned_text"],10,1)
common_bi = top_ngrams(data["Cleaned_text"],10,2)
common_tri = top_ngrams(data["Cleaned_text"],10,3)
common_uni_df = pd.DataFrame(common_uni,columns=['word','freq'])
common_bi_df = pd.DataFrame(common_bi,columns=['word','freq'])
common_tri_df = pd.DataFrame(common... | np.linspace(20,70,num=11 ) | Home Credit Default Risk |
17,857,459 | X_inp_clean = data['Cleaned_text']
X_inp_original = data['text']
y_inp = data['target']<train_model> | age_data=app_train[['TARGET','DAYS_BIRTH']]
age_data['DAYS_BIRTH']=-age_data['DAYS_BIRTH']
age_data['YEARS_BIRTH']=age_data['DAYS_BIRTH']/365
age_data['YEARS_BINNED']=pd.cut(age_data['YEARS_BIRTH'],bins=np.linspace(20,70,num=11))
age_data.head(10 ) | Home Credit Default Risk |
17,857,459 | word_tokenizer = Tokenizer()
word_tokenizer.fit_on_texts(X_inp_clean.values)
vocab_length = len(word_tokenizer.word_index)+ 1<string_transform> | age_groups = age_data.groupby('YEARS_BINNED' ).mean()
age_groups | Home Credit Default Risk |
17,857,459 | def embed(corpus):
return word_tokenizer.texts_to_sequences(corpus)
longest_train = max(X_inp_clean.values, key=lambda sentence: len(word_tokenize(sentence)))
length_long_sentence = len(word_tokenize(longest_train))
padded_sentences = pad_sequences(embed(X_inp_clean.values),
length_long_sentence, padding='post' )<fea... | anom = app_train[app_train['DAYS_EMPLOYED'] == 365243]
non_anom = app_train[app_train['DAYS_EMPLOYED'] != 365243]
print('이상값이 아닌 data의 target 평균: %0.2f%%' %(100 * non_anom['TARGET'].mean()))
print('이상값인 data의 target 평균: %0.2f%%' %(100 * anom['TARGET'].mean()))
| Home Credit Default Risk |
17,857,459 | embeddings_dictionary = dict()
embedding_dim = 100
glove_file = open('.. /input/glove6b100dtxt/glove.6B.100d.txt')
for line in glove_file:
records = line.split()
word = records[0]
vector_dimensions = np.asarray(records[1:], dtype='float32')
embeddings_dictionary [word] = vector_dimensions
glove_file.close()<feature_e... | app_test['DAYS_EMPLOYED_ANOM']=app_test['DAYS_EMPLOYED']==365243
app_test['DAYS_EMPLOYED'].replace({365243:np.nan}, inplace=True)
print('%d 개의 data 중에 testing data에서 %d 개의 이상값이 있다.'%(len(app_test),app_test['DAYS_EMPLOYED_ANOM'].sum())) | Home Credit Default Risk |
17,857,459 | embedding_matrix = np.zeros(( vocab_length, embedding_dim))
for word, index in word_tokenizer.word_index.items() :
embedding_vector = embeddings_dictionary.get(word)
if embedding_vector is not None:
embedding_matrix[index] = embedding_vector<split> | app_train['DAYS_BIRTH'] = app_train['DAYS_BIRTH'] / -365
app_test['DAYS_BIRTH'] = app_test['DAYS_BIRTH'] / -365
ext=app_train[['TARGET','EXT_SOURCE_1','EXT_SOURCE_2','EXT_SOURCE_3','DAYS_BIRTH']]
extcorr = ext.corr()
extcorr | Home Credit Default Risk |
17,857,459 | X_train, X_val, y_train, y_val = train_test_split(padded_sentences,
y_inp.values,test_size=0.2,random_state=1 )<choose_model_class> | app_train.dtypes.value_counts()
| Home Credit Default Risk |
17,857,459 | def CNN(hp):
model = keras.Sequential()
hp_learning_rate = hp.Choice('learning_rate', values=[3e-2, 3e-3, 3e-4, 3e-5])
model.add(Embedding(vocab_length, 100, weights=[embedding_matrix],
input_length=length_long_sentence,trainable=False))
model.add(Conv1D(filters=hp.Int('conv_1_filter',min_value=21,max_value=200,step=1... | object_columns = app_train.dtypes[app_train.dtypes == 'object'].index.tolist()
object_columns | Home Credit Default Risk |
17,857,459 | tuner_CNN = kt.Hyperband(CNN,objective='val_accuracy',
max_epochs=15,factor=5,
directory='my_dir',
project_name='DisasterTweets_kt',
overwrite=True )<train_on_grid> | cond_1 =(app_train['TARGET'] == 1)
cond_0 =(app_train['TARGET'] == 0)
for a in obj:
print(a)
print('
연체인 경우
',app_train[cond_1][a].value_counts() /app_train[cond_1].shape[0])
print('
연체가 아닌 경우
',app_train[cond_0][a].value_counts() /app_train[cond_0].shape[0])
print('----------------------------' ) | Home Credit Default Risk |
17,857,459 | stop_early = EarlyStopping(monitor='val_loss', mode='min',
verbose=1, patience=10)
tuner_CNN.search(X_train, y_train, epochs=15,
validation_data=(X_val,y_val),callbacks=[stop_early])
best_hps_CNN=tuner_CNN.get_best_hyperparameters(num_trials=1)[0]<train_model> | def missing_values_table(df):
miss = df.isnull().sum()
miss_percent = 100 * miss / len(df)
mis_table = pd.concat([miss, miss_percent], axis=1)
mis_val_table = mis_table.rename(
columns = {0 : 'Missing Values', 1 : '% of Total Values'})
mis_val_table = mis_val_table[
mis_val_table.iloc[:,1] != 0].sort_values(
'% of... | Home Credit Default Risk |
17,857,459 | model_CNN = tuner_CNN.hypermodel.build(best_hps_CNN)
checkpoint = ModelCheckpoint(
'model_CNN.h5',
monitor = 'val_loss',
verbose = 1,
save_best_only = True
)
history_CNN = model_CNN.fit(X_train, y_train,epochs=50,
validation_data=(X_val,y_val),
callbacks=[checkpoint,stop_early] )<choose_model_class> | missing_values = missing_values_table(app_train)
missing_values.head(20 ) | Home Credit Default Risk |
17,857,459 | def MultichannelCNN(hp):
inputs1 = Input(shape=(length_long_sentence,))
embedding1 = Embedding(vocab_length, 100, weights=[embedding_matrix],
input_length=length_long_sentence, trainable=False )(inputs1)
conv1 = Conv1D(filters=hp.Int('conv_1_filter',min_value=21,max_value=150,step=14),
kernel_size=hp.Choice('conv_1_ke... | apps = pd.concat([app_train,app_test])
print(apps.shape ) | Home Credit Default Risk |
17,857,459 | tuner_MCNN = kt.Hyperband(MultichannelCNN,objective='val_accuracy',
max_epochs=15,factor=5,
directory='my_dir',
project_name='DisasterTweetsMCNN_kt',
overwrite=True)
stop_early = EarlyStopping(monitor='val_loss', mode='min',
verbose=1, patience=10)
tuner_MCNN.search([X_train,X_train], y_train, epochs=15,
validation_d... | apps['TARGET'].value_counts(dropna=False ) | Home Credit Default Risk |
17,857,459 | model_MCNN = tuner_MCNN.hypermodel.build(best_hps_MCNN)
checkpoint = ModelCheckpoint(
'model_MCNN.h5',
monitor = 'val_loss',
verbose = 1,
save_best_only = True
)
history_MCNN = model_MCNN.fit([X_train,X_train], y_train,epochs=50,
validation_data=([X_val,X_val], y_val),
callbacks=[checkpoint,stop_early] )<choose_mod... | object_col = apps.dtypes[apps.dtypes == 'object'].index.tolist()
for column in object_col:
apps[column] = pd.factorize(apps[column])[0] | Home Credit Default Risk |
17,857,459 | def BiLSTM(hp):
model = Sequential()
model.add(Embedding(input_dim=embedding_matrix.shape[0],
output_dim=embedding_matrix.shape[1],
weights = [embedding_matrix],
input_length=length_long_sentence,trainable = False))
model.add(Bidirectional(CuDNNLSTM(units = hp.Int('dense_1',
min_value=21,max_value=120,step=14)
,return... | apps['CREDIT_INCOME_PERCENT'] = apps['AMT_CREDIT'] / apps['AMT_INCOME_TOTAL']
apps['ANNUITY_INCOME_PERCENT'] = apps['AMT_ANNUITY'] / apps['AMT_INCOME_TOTAL']
apps['CREDIT_TERM'] = apps['AMT_ANNUITY'] / apps['AMT_CREDIT']
apps['GOODS_CREDIT_RATIO'] = apps['AMT_GOODS_PRICE']/apps['AMT_CREDIT']
apps['CREDIT_GOODS_DIFF'] =... | Home Credit Default Risk |
17,857,459 | tuner_BiLSTM = kt.Hyperband(BiLSTM,objective='val_accuracy',
max_epochs=15,factor=5,
directory='my_dir',
project_name='DisasterTweetsBiLSTM_kt',
overwrite=True)
stop_early = EarlyStopping(monitor='val_loss', mode='min',
verbose=1, patience=12)
tuner_BiLSTM.search(X_train, y_train, epochs=15,
validation_data=(X_val, y... | apps['APPS_EXT_SOURCE_MEAN'] = apps[['EXT_SOURCE_1', 'EXT_SOURCE_2', 'EXT_SOURCE_3']].mean(axis=1)
apps['APPS_EXT_SOURCE_STD'] = apps[['EXT_SOURCE_1', 'EXT_SOURCE_2', 'EXT_SOURCE_3']].std(axis=1 ) | Home Credit Default Risk |
17,857,459 | model_BiLSTM = tuner_BiLSTM.hypermodel.build(best_hps_BiLSTM)
checkpoint = ModelCheckpoint(
'model_BiLSTM.h5',
monitor = 'val_loss',
verbose = 1,
save_best_only = True
)
history_BiLSTM = model_BiLSTM.fit(X_train, y_train, epochs=50,
validation_data=(X_val, y_val),
callbacks=[checkpoint,stop_early] )<categorify> | apps['APPS_EXT_SOURCE_STD'] = apps['APPS_EXT_SOURCE_STD'].fillna(apps['APPS_EXT_SOURCE_STD'].mean() ) | Home Credit Default Risk |
17,857,459 | onehot_encoder = OneHotEncoder(sparse=False)
y =(np.asarray(y_inp)).reshape(-1,1)
Y = onehot_encoder.fit_transform(y)
X_train, X_val, y_train, y_val = train_test_split(X_inp_clean,Y,
test_size=0.2, random_state=1 )<load_pretrained> | apps['EMPLOYED_BIRTH_RATIO'] = apps['DAYS_EMPLOYED']/apps['DAYS_BIRTH']
apps['INCOME_EMPLOYED_RATIO'] = apps['AMT_INCOME_TOTAL']/apps['DAYS_EMPLOYED']
apps['INCOME_BIRTH_RATIO'] = apps['AMT_INCOME_TOTAL']/apps['DAYS_BIRTH']
apps['CAR_BIRTH_RATIO'] = apps['OWN_CAR_AGE'] / apps['DAYS_BIRTH']
apps['CAR_EMPLOYED_RATIO'] = ... | Home Credit Default Risk |
17,857,459 | model_checkpoint = "distilbert-base-uncased-finetuned-sst-2-english"
tokenizer = AutoTokenizer.from_pretrained(model_checkpoint, use_fast=True )<define_variables> | ccb = pd.read_csv('.. /input/home-credit-default-risk/credit_card_balance.csv' ) | Home Credit Default Risk |
17,857,459 | tokenizer("Hello, this one sentence!", "And this sentence goes with it." )<categorify> | app_ccb = ccb.merge(app_train, left_on='SK_ID_CURR', right_on='SK_ID_CURR', how='outer')
app_ccb.shape | Home Credit Default Risk |
17,857,459 | def regular_encode(texts, tokenizer, maxlen=512):
enc_di = tokenizer.batch_encode_plus(
texts,
return_token_type_ids=False,
pad_to_max_length=True,
max_length=maxlen,
add_special_tokens = True,
truncation=True
)
return np.array(enc_di['input_ids'])
X_train_t = regular_encode(list(X_train), tokenizer, maxlen=512)
X... | missing_values = missing_values_table(ccb)
missing_values.head(20 ) | Home Credit Default Risk |
17,857,459 | AUTO = tf.data.experimental.AUTOTUNE
batch_size = 16
train_dataset =(
tf.data.Dataset
.from_tensor_slices(( X_train_t, y_train))
.repeat()
.shuffle(1995)
.batch(batch_size)
.prefetch(AUTO)
)
valid_dataset =(
tf.data.Dataset
.from_tensor_slices(( X_val_t, y_val))
.batch(batch_size)
.cache()
.prefetch(AUTO)
)<c... | app_ccb.groupby('SK_ID_CURR' ).count() | Home Credit Default Risk |
17,857,459 | def build_model(transformer, max_len=512):
input_word_ids = Input(shape=(max_len,), dtype=tf.int32,
name="input_word_ids")
sequence_output = transformer(input_word_ids)[0]
cls_token = sequence_output[:, 0, :]
out = Dense(2, activation='softmax' )(cls_token)
model = Model(inputs=input_word_ids, outputs=out)
model.com... | app_ccb.groupby('SK_ID_CURR')['SK_ID_CURR'].count() | Home Credit Default Risk |
17,857,459 | transformer_layer = TFAutoModel.from_pretrained(model_checkpoint)
model_DistilBert = build_model(transformer_layer )<train_model> | app_ccb_target = ccb.merge(app_train[['SK_ID_CURR', 'TARGET']], on='SK_ID_CURR', how='left')
app_ccb_target.shape | Home Credit Default Risk |
17,857,459 | n_steps = X_train.shape[0] // batch_size
history_DistilBert = model_DistilBert.fit(train_dataset,
steps_per_epoch=n_steps,
validation_data=valid_dataset,
epochs=3 )<feature_engineering> | num_columns = app_ccb_target.dtypes[app_ccb_target.dtypes != 'object'].index.tolist() | Home Credit Default Risk |
17,857,459 | test = pd.read_csv('.. /input/nlp-getting-started/test.csv')
test["Cleaned_text"] = test["text"].apply(preprocess_data)
test["Cleaned_text"] = test["Cleaned_text"].apply(text_cleaning)
test_sentences = pad_sequences(embed(test.Cleaned_text.values),
length_long_sentence, padding='post' )<save_to_csv> | num_columns = [column for column in num_columns if column not in ['SK_ID_PREV', 'SK_ID_CURR', 'TARGET']]
num_columns | Home Credit Default Risk |
17,857,459 | predsCNN = model_CNN.predict_classes(test_sentences)
predictions_test = pd.DataFrame(predsCNN)
test_id = pd.DataFrame(test["id"])
submissionCNN = pd.concat([test_id,predictions_test],axis=1)
submissionCNN.columns = ["id","target"]
submissionCNN.to_csv("submissionCNN.csv",index=False )<save_to_csv> | print(app_ccb_target.groupby('TARGET' ).agg({'AMT_BALANCE': ['mean', 'median', 'count','sum','max']}))
print(app_ccb_target.groupby('TARGET' ).agg({'AMT_CREDIT_LIMIT_ACTUAL': ['mean', 'median', 'count','sum','max']}))
print(app_ccb_target.groupby('TARGET' ).agg({'AMT_INST_MIN_REGULARITY': ['mean', 'median', 'count','su... | Home Credit Default Risk |
17,857,459 | predsMCNN = model_MCNN.predict([test_sentences,test_sentences])
predsMCNN =(predsMCNN[:,0] > 0.5 ).astype(np.int)
predictions_test = pd.DataFrame(predsMCNN)
submissionMCNN = pd.concat([test_id,predictions_test],axis=1)
submissionMCNN.columns = ["id","target"]
submissionMCNN.to_csv("submissionMCNN.csv",index=False )... | ccb_amt_agg=ccb_amt_agg.reset_index()
ccb_amt_agg | Home Credit Default Risk |
17,857,459 | predsBiLSTM = model_BiLSTM.predict(test_sentences)
predsBiLSTM =(predsBiLSTM[:,0] > 0.5 ).astype(np.int)
predictions_test = pd.DataFrame(predsBiLSTM)
submissionBiLSTM = pd.concat([test_id,predictions_test],axis=1)
submissionBiLSTM.columns = ["id","target"]
submissionBiLSTM.to_csv("submissionBiLSTM.csv",index=False ... | ccb_amt_agg=ccb_amt_agg.drop(['CCB_SK_ID_CURR_COUNT'],axis=1)
ccb_amt_agg | Home Credit Default Risk |
17,857,459 | X_test = regular_encode(list(test.Cleaned_text), tokenizer, maxlen=512)
test1 =(tf.data.Dataset.from_tensor_slices(X_test ).batch(batch_size))
pred = model_DistilBert.predict(test1,verbose = 0)
pred = np.argmax(pred,axis=-1)
pred = pred.astype('int32')
res=pd.read_csv('.. /input/nlp-getting-started/sample_submissio... | apps = apps.merge(ccb_amt_agg, left_on='SK_ID_CURR', right_on='SK_ID_CURR', how='left')
app_ccb.shape | Home Credit Default Risk |
17,857,459 | !pip install -U lightautoml<import_modules> | object_col = apps.dtypes[apps.dtypes == 'object'].index.tolist()
for column in object_col:
apps[column] = pd.factorize(apps[column])[0] | Home Credit Default Risk |
17,857,459 | from lightautoml.automl.presets.tabular_presets import TabularAutoML, TabularUtilizedAutoML
from lightautoml.dataset.roles import DatetimeRole, CategoryRole
from lightautoml.tasks import Task
from sklearn.metrics import classification_report, roc_auc_score<load_from_csv> | apps_train = apps[~apps['TARGET'].isnull() ]
apps_test = apps[apps['TARGET'].isnull() ]
apps.shape, apps_train.shape, apps_test.shape | Home Credit Default Risk |
17,857,459 | train_df = pd.read_csv('.. /input/cat-in-the-dat/train.csv')
test_df = pd.read_csv('.. /input/cat-in-the-dat/test.csv')
submission_df = pd.read_csv('.. /input/cat-in-the-dat/sample_submission.csv' )<define_variables> | ftr_app = apps_train.drop(['SK_ID_CURR', 'TARGET'], axis=1)
target_app = apps_train['TARGET']
train_x, valid_x, train_y, valid_y = train_test_split(ftr_app, target_app, test_size=0.3, random_state=2020)
train_x.shape, valid_x.shape | Home Credit Default Risk |
17,857,459 | N_THREADS = 4
N_FOLDS = 5
RANDOM_STATE = 42
TEST_SIZE = 0.2
TIMEOUT = 1800
TARGET_NAME = 'target'
np.random.seed(RANDOM_STATE )<data_type_conversions> | clf = LGBMClassifier(
n_jobs=-1,
n_estimators=1000,
learning_rate=0.02,
num_leaves=32,
subsample=0.8,
max_depth=12,
silent=-1,
verbose=-1
)
clf.fit(train_x, train_y, eval_set=[(train_x, train_y),(valid_x, valid_y)], eval_metric= 'auc', verbose= 100,
early_stopping_rounds= 100 ) | Home Credit Default Risk |
17,857,459 | def preprocess(df):
df['time'] =(np.datetime64('2018-01-01')+ df['day'].astype(np.dtype('timedelta64[D]')) + df['month'].astype(np.dtype('timedelta64[M]')) ).astype(str)
return df.drop(columns=['id', 'day', 'month'])
train = preprocess(train_df)
test = preprocess(test_df )<drop_column> | preds = clf.predict_proba(apps_test.drop(['SK_ID_CURR', 'TARGET'], axis=1)) [:, 1 ] | Home Credit Default Risk |
17,857,459 | task = Task('binary',)
roles = {'target': TARGET_NAME,
DatetimeRole(base_date=True, seasonality=('m', 'd', 'wd', 'hour'), base_feats=True): 'time',
CategoryRole(ordinal=False): ['bin_0', 'bin_1', 'bin_2', 'bin_3', 'bin_4', 'nom_0', 'nom_1', 'nom_2', 'nom_3', 'nom_4', 'nom_5', 'nom_6', 'nom_7', 'nom_8', 'nom_9',],
Cate... | apps_test['TARGET'] = preds
apps_test[['SK_ID_CURR', 'TARGET']].to_csv('apps_baseline05.csv', index=False ) | Home Credit Default Risk |
17,896,962 | automl = TabularUtilizedAutoML(task = task,
verbose=2,
timeout = TIMEOUT,
general_params = {'nested_cv': False, 'use_algos': [['linear_l2', 'lgb', 'lgb_tuned', 'cb', 'cb_tuned']]},
reader_params = {'cv': N_FOLDS, 'random_state': RANDOM_STATE},
tuning_params = {'max_tuning_iter': 20},
lgb_params = {'default_params': {'n... | %matplotlib inline
warnings.filterwarnings('ignore')
pd.set_option('display.max_rows', 100)
pd.set_option('display.max_columns', 200 ) | Home Credit Default Risk |
17,896,962 | print(roc_auc_score(train[TARGET_NAME].values.ravel() , oof_pred.data.ravel()))<compute_test_metric> | app_train = pd.read_csv('.. /input/home-credit-default-risk/application_train.csv')
app_test = pd.read_csv('.. /input/home-credit-default-risk/application_test.csv' ) | Home Credit Default Risk |
17,896,962 | thres =.5
print(classification_report(train[TARGET_NAME].values.ravel() ,(oof_pred.data.ravel() > thres ).astype(int), digits=6))<predict_on_test> | print(app_train.isnull().sum())
print("결측치 있는 컴럼 개수: ",sum(app_train.isnull().sum() !=0)) | Home Credit Default Risk |
17,896,962 | test_pred = automl.predict(test )<feature_engineering> | app_train['TARGET'].value_counts() | Home Credit Default Risk |
17,896,962 | submission_df['target'] = test_pred.data.ravel()
submission_df.head()<save_to_csv> | columns = ['AMT_INCOME_TOTAL','AMT_CREDIT', 'AMT_ANNUITY', 'AMT_GOODS_PRICE', 'DAYS_BIRTH', 'DAYS_EMPLOYED', 'DAYS_ID_PUBLISH',
'DAYS_REGISTRATION', 'DAYS_LAST_PHONE_CHANGE', 'CNT_FAM_MEMBERS', 'REGION_RATING_CLIENT', 'EXT_SOURCE_1',
'EXT_SOURCE_2', 'EXT_SOURCE_3', 'AMT_REQ_CREDIT_BUREAU_HOUR', 'AMT_REQ_CREDIT_BUREAU_D... | Home Credit Default Risk |
17,896,962 | submission_df.to_csv('submission.csv', index=False )<import_modules> | cond_1 =(app_train['TARGET'] == 1)
cond_0 =(app_train['TARGET'] == 0)
print('CODE_GENDER
')
print(app_train['CODE_GENDER'].value_counts() /app_train.shape[0])
print('
연체인 경우
',app_train[cond_1]['CODE_GENDER'].value_counts() /app_train[cond_1].shape[0])
print('
연체가 아닌 경우
',app_train[cond_0]['CODE_GENDER'].value_cou... | Home Credit Default Risk |
17,896,962 | import numpy as np
import pandas as pd
import lightgbm as lgb
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.metrics import roc_auc_score
from multiprocessing import cpu_count
from tqdm.notebook import tqdm
from cairosvg import svg2png
from PIL import Image
from io import BytesIO
import gc
import os... | app_train['DAYS_EMPLOYED'].value_counts() | Home Credit Default Risk |
17,896,962 | VERSION = 'V1E'
NUM_BOOST_ROUND = 5000
VERBOSE_EVAL = 10
METRICS = ['auc']
N_ROWS = 99271300
def get_index_np() :
return np.arange(N_ROWS )<load_pretrained> | app_train['DAYS_EMPLOYED'] = app_train['DAYS_EMPLOYED'].replace(365243, np.nan)
app_train['DAYS_EMPLOYED'].value_counts(dropna=False ) | Home Credit Default Risk |
17,896,962 | FEATURES = np.load(f'/kaggle/input/riiid-training-and-prediction-using-a-state-data/train_features_{VERSION}.npz', allow_pickle=True )<define_variables> | app_train['CODE_GENDER'].value_counts() | Home Credit Default Risk |
17,896,962 | given_features = [
'prior_question_elapsed_time',
]
deduced_features = [
'mean_user_accuracy',
'answered_correctly_user',
'answered_user',
'mean_content_accuracy',
'part',
'hmean_user_content_accuracy',
'attempt',
]
features = given_features + deduced_features
target = 'answered_correctly'
categorical_feature = ['part'... | apps = pd.concat([app_train, app_test])
print(apps.shape ) | Home Credit Default Risk |
17,896,962 | def get_train_val_idxs(TRAIN_SIZE, VAL_SIZE):
train_idxs = []
val_idxs = []
NEW_USER_FRAC = 1/4
np.random.seed(42)
df = pd.DataFrame(index=get_index_np())
for col in ['user_id']:
df[col] = FEATURES[col]
df['index'] = df.index.values.astype(np.uint32)
user_id_index = df.groupby('user_id')['index'].apply(np.array)
fo... | apps['APPS_EXT_SOURCE_MEAN'] = apps[['EXT_SOURCE_1', 'EXT_SOURCE_2', 'EXT_SOURCE_3']].mean(axis=1)
apps['APPS_EXT_SOURCE_STD'] = apps[['EXT_SOURCE_1', 'EXT_SOURCE_2', 'EXT_SOURCE_3']].std(axis=1 ) | Home Credit Default Risk |
17,896,962 | def make_x_y(FEATURES, train_idxs, val_idxs):
X_train = np.ndarray(shape=(len(train_idxs), len(features)) , dtype=np.float32)
X_val = np.ndarray(shape=(len(val_idxs), len(features)) , dtype=np.float32)
for idx, feature in enumerate(tqdm(features)) :
X_train[:,idx] = FEATURES[feature][train_idxs].astype(np.float32)
X... | apps['APPS_EXT_SOURCE_STD'] = apps['APPS_EXT_SOURCE_STD'].fillna(apps['APPS_EXT_SOURCE_STD'].mean() ) | Home Credit Default Risk |
17,896,962 | pd.DataFrame(X_train[:10], columns=features )<create_dataframe> | apps['APPS_ANNUITY_CREDIT_RATIO'] = apps['AMT_ANNUITY']/apps['AMT_CREDIT']
apps['APPS_GOODS_CREDIT_RATIO'] = apps['AMT_GOODS_PRICE']/apps['AMT_CREDIT']
apps['APPS_CREDIT_GOODS_DIFF'] = apps['AMT_CREDIT'] - apps['AMT_GOODS_PRICE'] | Home Credit Default Risk |
17,896,962 | train_data = lgb.Dataset(
data = X_train,
label = y_train,
categorical_feature = None,
)
val_data = lgb.Dataset(
data = X_val,
label = y_val,
categorical_feature = None,
)<drop_column> | apps['APPS_ANNUITY_INCOME_RATIO'] = apps['AMT_ANNUITY']/apps['AMT_INCOME_TOTAL']
apps['APPS_CREDIT_INCOME_RATIO'] = apps['AMT_CREDIT']/apps['AMT_INCOME_TOTAL']
apps['APPS_GOODS_INCOME_RATIO'] = apps['AMT_GOODS_PRICE']/apps['AMT_INCOME_TOTAL']
apps['APPS_CNT_FAM_INCOME_RATIO'] = apps['AMT_INCOME_TOTAL']/apps['CNT_FAM_ME... | Home Credit Default Risk |
17,896,962 | del X_train, y_train, X_val, y_val
gc.collect()<init_hyperparams> | apps['APPS_EMPLOYED_BIRTH_RATIO'] = apps['DAYS_EMPLOYED']/apps['DAYS_BIRTH']
apps['APPS_INCOME_EMPLOYED_RATIO'] = apps['AMT_INCOME_TOTAL']/apps['DAYS_EMPLOYED']
apps['APPS_INCOME_BIRTH_RATIO'] = apps['AMT_INCOME_TOTAL']/apps['DAYS_BIRTH']
apps['APPS_CAR_BIRTH_RATIO'] = apps['OWN_CAR_AGE'] / apps['DAYS_BIRTH']
apps['APP... | Home Credit Default Risk |
17,896,962 | lgbm_params = {
'objective': 'binary',
'metric': METRICS,
}<train_model> | object_columns = apps.dtypes[apps.dtypes=='object'].index.tolist()
for column in object_columns:
apps[column] = pd.factorize(apps[column])[0] | Home Credit Default Risk |
17,896,962 | %%time
def train() :
evals_result = {}
model = lgb.train(
params = lgbm_params,
train_set = train_data,
valid_sets = [val_data],
num_boost_round = NUM_BOOST_ROUND,
verbose_eval = VERBOSE_EVAL,
evals_result = evals_result,
early_stopping_rounds = 10,
categorical_feature = categorical_feature_idxs,
feature_name = featur... | ftr_app = apps_train.drop(['SK_ID_CURR', 'TARGET'], axis=1)
target_app = app_train['TARGET']
train_x, valid_x, train_y, valid_y = train_test_split(ftr_app, target_app, test_size=0.3, random_state=2020)
train_x.shape, valid_x.shape
| Home Credit Default Risk |
17,896,962 | def show_feature_importances(model, importance_type, max_num_features=10**10):
feature_importances = pd.DataFrame()
feature_importances['feature'] = features
feature_importances['value'] = pd.DataFrame(model.feature_importance(importance_type))
feature_importances = feature_importances.sort_values(by='value', ascending... | clf = LGBMClassifier(
n_jobs=-1,
n_estimators=1000,
learning_rate=0.02,
num_leaves=32,
subsample=0.8,
max_depth=12,
silent=-1,
verbose=-1
) | Home Credit Default Risk |
17,896,962 | del train_data
gc.collect()<create_dataframe> | clf.fit(train_x, train_y, eval_set=[(train_x, train_y),(valid_x, valid_y)], eval_metric= 'auc', verbose= 100,
early_stopping_rounds= 100 ) | Home Credit Default Risk |
17,896,962 | def get_features_questions_df() :
features_df = pd.DataFrame(index=get_index_np())
for col in tqdm(['content_id', 'part', 'tags', 'tags_label', 'mean_content_accuracy']):
features_df[col] = FEATURES[col]
features_questions_df = features_df.groupby('content_id')[[
'content_id',
'part',
'tags',
'tags_label',
'mean_conte... | preds = clf.predict_proba(apps_test.drop(['SK_ID_CURR', 'TARGET'], axis=1)) [:, 1 ] | Home Credit Default Risk |
17,896,962 | def get_state() :
features_df = pd.DataFrame(index=get_index_np())
for col in tqdm(['user_id', 'content_id', 'answered_correctly']):
features_df[col] = FEATURES[col]
mean_user_accuracy = features_df.groupby('user_id')['answered_correctly'].mean().values
answered_correctly_user = features_df.groupby('user_id')['answere... | app_test['TARGET'] = preds
app_test[['SK_ID_CURR', 'TARGET']].to_csv('apps_baseline_02.csv', index=False ) | Home Credit Default Risk |
17,896,962 | def get_user_data(state, test_df):
attempt, mean_user_accuracy, answered_correctly_user, answered_user = [], [], [], []
for idx,(user_id, content_id)in test_df[['user_id', 'content_id']].iterrows() :
if user_id in state:
if content_id in state[user_id]['user_content_attempts']:
state[user_id]['user_content_attempts'][c... | def get_apps_processed(apps):
apps['APPS_EXT_SOURCE_MEAN'] = apps[['EXT_SOURCE_1', 'EXT_SOURCE_2', 'EXT_SOURCE_3']].mean(axis=1)
apps['APPS_EXT_SOURCE_STD'] = apps[['EXT_SOURCE_1', 'EXT_SOURCE_2', 'EXT_SOURCE_3']].std(axis=1)
apps['APPS_EXT_SOURCE_STD'] = apps['APPS_EXT_SOURCE_STD'].fillna(apps['APPS_EXT_SOURCE_STD']... | Home Credit Default Risk |
17,896,962 | def update_user_data(state, features_questions_df, prev_test_df):
for user_id, content_id, answered_correctly in prev_test_df[['user_id', 'content_id', 'answered_correctly']].values:
state[user_id]['answered_correctly_user'] += answered_correctly
state[user_id]['answered_user'] += 1
state[user_id]['mean_user_accuracy']... | prev = pd.read_csv('.. /input/home-credit-default-risk/previous_application.csv')
print(prev.shape, apps.shape ) | Home Credit Default Risk |
17,896,962 | env = riiideducation.make_env()
iter_test = env.iter_test()<feature_engineering> | prev.groupby('SK_ID_CURR')['SK_ID_CURR'].count().mean() | Home Credit Default Risk |
17,896,962 | prev_test_df = None
mean_attempt_acc_factor = FEATURES['mean_attempt_acc_factor']
for idx,(test_df, _)in tqdm(enumerate(iter_test)) :
if prev_test_df is not None:
prev_test_df['answered_correctly'] = eval(test_df['prior_group_answers_correct'].iloc[0])
update_user_data(state, features_questions_df, prev_test_df.loc[pr... | app_prev_target = prev.merge(app_train[['SK_ID_CURR', 'TARGET']], on='SK_ID_CURR', how='left')
app_prev_target.shape | Home Credit Default Risk |
17,896,962 | submission = pd.read_csv('./submission.csv' )<set_options> | def get_prev_processed(prev):
prev['PREV_CREDIT_DIFF'] = prev['AMT_APPLICATION'] - prev['AMT_CREDIT']
prev['PREV_GOODS_DIFF'] = prev['AMT_APPLICATION'] - prev['AMT_GOODS_PRICE']
prev['PREV_CREDIT_APPL_RATIO'] = prev['AMT_CREDIT']/prev['AMT_APPLICATION']
prev['PREV_ANNUITY_APPL_RATIO'] = prev['AMT_ANNUITY']/prev['AMT_AP... | Home Credit Default Risk |
17,896,962 | %reload_ext autoreload
%autoreload 2
%matplotlib inline<import_modules> | def get_prev_amt_agg(prev):
agg_dict = {
'SK_ID_CURR':['count'],
'AMT_CREDIT':['mean', 'max', 'sum'],
'AMT_ANNUITY':['mean', 'max', 'sum'],
'AMT_APPLICATION':['mean', 'max', 'sum'],
'AMT_DOWN_PAYMENT':['mean', 'max', 'sum'],
'AMT_GOODS_PRICE':['mean', 'max', 'sum'],
'RATE_DOWN_PAYMENT': ['min', 'max', 'mean'],
'DAYS_DE... | Home Credit Default Risk |
17,896,962 | import numpy as np
import pandas as pd
from datetime import datetime
from collections import Counter
import json
import os
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Conv2D, MaxPool2D, Flatten, Dropout
from tensorflow.keras.preprocessing.image import ImageDataGenerator<def... | def get_prev_refused_appr_agg(prev):
prev_refused_appr_group = prev[prev['NAME_CONTRACT_STATUS'].isin(['Approved', 'Refused'])].groupby([ 'SK_ID_CURR', 'NAME_CONTRACT_STATUS'])
prev_refused_appr_agg = prev_refused_appr_group['SK_ID_CURR'].count().unstack()
prev_refused_appr_agg.columns = ['PREV_APPROVED_COUNT', 'PREV_... | Home Credit Default Risk |
17,896,962 | dim = 256
train_files = []
test_files = []
country_file = ''
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
path = os.path.join(dirname, filename)
if 'train' in path:
train_files.append(path)
elif 'test' in path:
test_files.append(path)
elif 'json' in path:
country_file = path<feat... | def get_prev_agg(prev):
prev = get_prev_processed(prev)
prev_amt_agg = get_prev_amt_agg(prev)
prev_refused_appr_agg = get_prev_refused_appr_agg(prev)
prev_agg = prev_amt_agg.merge(prev_refused_appr_agg, on='SK_ID_CURR', how='left')
prev_agg['PREV_REFUSED_RATIO'] = prev_agg['PREV_REFUSED_COUNT']/prev_agg['PREV_SK_ID... | Home Credit Default Risk |
17,896,962 | with open(country_file)as json_file:
tmp = json.load(json_file)
country = {}
for key in tmp.keys() :
country[int(key)] = tmp[key].split(',')[-1]<create_dataframe> | def get_apps_all_with_prev_agg(apps, prev):
apps_all = get_apps_processed(apps)
prev_agg = get_prev_agg(prev)
print('prev_agg shape:', prev_agg.shape)
print('apps_all before merge shape:', apps_all.shape)
apps_all = apps_all.merge(prev_agg, on='SK_ID_CURR', how='left')
print('apps_all after merge with prev_agg sha... | Home Credit Default Risk |
17,896,962 | train_set = []
for f in train_files:
idx = int(f.split('/')[6])
train_set.append([f, country[idx]])
train_set = pd.DataFrame(train_set, columns=['Image','Country'])
test_set = []
for f in test_files:
test_set.append(f)
test_set = pd.DataFrame(test_set, columns=['Image'] )<choose_model_class> | def get_apps_all_encoded(apps_all):
object_columns = apps_all.dtypes[apps_all.dtypes == 'object'].index.tolist()
for column in object_columns:
apps_all[column] = pd.factorize(apps_all[column])[0]
return apps_all | Home Credit Default Risk |
17,896,962 | train_data_gen = ImageDataGenerator(rescale = 1./255,
shear_range = 0.2,
zoom_range = 0.2,
horizontal_flip = True )<create_dataframe> | def get_apps_all_train_test(apps_all):
apps_all_train = apps_all[~apps_all['TARGET'].isnull() ]
apps_all_test = apps_all[apps_all['TARGET'].isnull() ]
apps_all_test = apps_all_test.drop('TARGET', axis=1)
return apps_all_train, apps_all_test | Home Credit Default Risk |
17,896,962 | train_generator = train_data_gen.flow_from_dataframe(
dataframe = train_set,
directory="",
x_col="Image",
y_col="Country",
class_mode="categorical",
target_size=(dim,dim),
batch_size=32 )<count_values> | def train_apps_all(apps_all_train):
ftr_app = apps_all_train.drop(['SK_ID_CURR', 'TARGET'], axis=1)
target_app = apps_all_train['TARGET']
train_x, valid_x, train_y, valid_y = train_test_split(ftr_app, target_app, test_size=0.3, random_state=2020)
print('train shape:', train_x.shape, 'valid shape:', valid_x.shape)
cl... | Home Credit Default Risk |
17,896,962 | num_classes = len(Counter(train_generator.classes ).keys() )<choose_model_class> | def get_dataset() :
app_train = pd.read_csv('.. /input/home-credit-default-risk/application_train.csv')
app_test = pd.read_csv('.. /input/home-credit-default-risk/application_test.csv')
apps = pd.concat([app_train, app_test])
prev = pd.read_csv('.. /input/home-credit-default-risk/previous_application.csv')
return a... | Home Credit Default Risk |
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