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21,316,401 | BUREAU_LOAN_TYPES = bureau[['SK_ID_CURR', 'CREDIT_TYPE']].groupby(by = ['SK_ID_CURR'])['CREDIT_TYPE'].nunique().reset_index().rename(index=str, columns={'CREDIT_TYPE': 'BUREAU_LOAN_TYPES'})
app_train_bureau = app_train_bureau.merge(BUREAU_LOAN_TYPES, on = ['SK_ID_CURR'], how = 'left' ).fillna(0)
print(app_train_bureau.shape)
app_train_bureau.head()<feature_engineering> | x_train = data_train[:, 1:]
x_train = x_train.reshape(x_train.shape[0], 28, 28, 1)
input_shape =(28, 28, 1 ) | Digit Recognizer |
21,316,401 | app_train_bureau['AVERAGE_LOAN_TYPE'] = app_train_bureau['BUREAU_LOAN_COUNT']/app_train_bureau['BUREAU_LOAN_TYPES']
app_train_bureau = app_train_bureau.fillna(0)
print(app_train_bureau.shape)
app_train_bureau.head()<drop_column> | x_train = x_train / 255.0 | Digit Recognizer |
21,316,401 | del app_train_bureau['BUREAU_LOAN_COUNT'], app_train_bureau['BUREAU_LOAN_TYPES']
app_train_bureau.head()<feature_engineering> | y_train = data_train[:, 0]
y_train[:5] | Digit Recognizer |
21,316,401 | def f(x):
if x == 'Closed':
y = 0
else:
y = 1
return y
bureau_fe1 = bureau
bureau_fe1['CREDIT_ACTIVE_CLOSED'] = bureau_fe1.apply(lambda x: f(x.CREDIT_ACTIVE), axis = 1)
bureau_fe1.head()<merge> | y_train = utils.to_categorical(y_train ) | Digit Recognizer |
21,316,401 | grp = bureau_fe1.groupby(by = ['SK_ID_CURR'])['CREDIT_ACTIVE_CLOSED'].mean().reset_index().rename(index=str, columns={'CREDIT_ACTIVE_CLOSED':'ACTIVE_LOANS_PERCENTAGE'})
app_train_bureau = app_train_bureau.merge(grp, on = ['SK_ID_CURR'], how = 'left')
del bureau_fe1['CREDIT_ACTIVE_CLOSED']
print(bureau_fe1.shape)
bureau_fe1.head()<data_type_conversions> | random_seed = 2
X_train, X_val, Y_train, Y_val = train_test_split(x_train, y_train, test_size = 0.1, random_state=random_seed ) | Digit Recognizer |
21,316,401 | app_train_bureau = app_train_bureau.fillna(0)
app_train_bureau.head()<feature_engineering> | datagen = ImageDataGenerator(
rotation_range=10,
zoom_range = 0.10,
width_shift_range=0.1,
height_shift_range=0.1)
| Digit Recognizer |
21,316,401 | app_train_bureau['BUREAU_ENDDATE_FACT_DIFF'] = bureau['DAYS_CREDIT_ENDDATE'] - bureau['DAYS_ENDDATE_FACT']
app_train_bureau['BUREAU_CREDIT_FACT_DIFF'] = bureau['DAYS_CREDIT'] - bureau['DAYS_ENDDATE_FACT']
app_train_bureau['BUREAU_CREDIT_ENDDATE_DIFF'] = bureau['DAYS_CREDIT'] - bureau['DAYS_CREDIT_ENDDATE']
app_train_bureau['BUREAU_CREDIT_DEBT_RATIO'] = bureau['AMT_CREDIT_SUM_DEBT'] / bureau['AMT_CREDIT_SUM']
app_train_bureau['BUREAU_CREDIT_DEBT_DIFF'] = bureau['AMT_CREDIT_SUM_DEBT'] - bureau['AMT_CREDIT_SUM']
app_train_bureau = app_train_bureau.fillna(0)
app_train_bureau.head()<prepare_output> | model = Sequential()
model.add(Conv2D(filters = 32, kernel_size =(5,5),padding = 'Same',
activation ='relu', input_shape =(28,28,1)))
model.add(Conv2D(filters = 32, kernel_size =(5,5),padding = 'Same',
activation ='relu'))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Dropout(0.25))
model.add(Conv2D(filters = 64, kernel_size =(3,3),padding = 'Same',
activation ='relu'))
model.add(Conv2D(filters = 64, kernel_size =(3,3),padding = 'Same',
activation ='relu'))
model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(256, activation = "relu"))
model.add(Dropout(0.5))
model.add(Dense(10, activation = "softmax"))
model.compile(loss="categorical_crossentropy", optimizer="adam", metrics=["accuracy"] ) | Digit Recognizer |
21,316,401 | app_train = app_train_bureau<split> | checkpoint = ModelCheckpoint('mnist-cnn.h5',
monitor='val_acc',
save_best_only=True,
verbose=1 ) | Digit Recognizer |
21,316,401 | ftr_app = app_train.drop(columns=['SK_ID_CURR','TARGET'])
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<train_on_grid> | learn_rate_reduction = ReduceLROnPlateau(monitor='val_acc',
patience=3,
verbose=1,
factor=0.5,
min_lr=0.00001)
| Digit Recognizer |
21,316,401 | def lgb_cv(num_leaves, learning_rate, n_estimators, subsample, colsample_bytree, reg_alpha, reg_lambda, x_data=None, y_data=None, n_splits=5, output='score'):
score = 0
kf = KFold(n_splits=n_splits)
models = []
for train_index, valid_index in kf.split(x_data):
x_train, y_train = x_data.reindex([train_index]), y_data.reindex([train_index])
x_valid, y_valid = x_data.reindex([valid_index]), y_data.reindex([valid_index])
model = LGBMClassifier(
num_leaves = int(num_leaves),
learning_rate = learning_rate,
n_estimators = int(n_estimators),
subsample = np.clip(subsample, 0, 1),
colsample_bytree = np.clip(colsample_bytree, 0, 1),
reg_alpha = reg_alpha,
reg_lambda = reg_lambda,
max_depth=16,
)
model.fit(train_x, train_y, eval_set=[(train_x, train_y),(valid_x, valid_y)], eval_metric= 'auc', verbose= False,
early_stopping_rounds= 100)
models.append(model)
pred = model.predict_proba(valid_x)[:, 1]
true = valid_y
score += roc_auc_score(true, pred)/n_splits
if output == 'score':
return score
if output == 'model':
return models<choose_model_class> | batch_size=96
history = model.fit(datagen.flow(X_train,Y_train, batch_size=batch_size),
epochs=30,
validation_data=(X_val, Y_val),
steps_per_epoch=X_train.shape[0] // batch_size,
verbose=1,
callbacks=[checkpoint, learn_rate_reduction] ) | Digit Recognizer |
21,316,401 | func_fixed = partial(lgb_cv, x_data=train_x, y_data=train_y, n_splits=5, output='score')
lgbBO = BayesianOptimization(
func_fixed,
{
'num_leaves':(16, 1024),
'learning_rate':(0.0001, 0.1),
'n_estimators':(16, 1024),
'subsample':(0, 1),
'colsample_bytree':(0, 1),
'reg_alpha':(0, 10),
'reg_lambda':(0, 50),
},
random_state=2020
)
lgbBO.maximize(init_points=5, n_iter=10 )<train_model> | data_test = np.loadtxt('/kaggle/input/digit-recognizer/test.csv', skiprows = 1, delimiter=',')
x_test = data_test.reshape(data_test.shape[0], 28, 28, 1)
x_test /= 255.0 | Digit Recognizer |
21,316,401 | clf = LGBMClassifier(
n_estimators=int(lgbBO.max['params']['n_estimators']),
learning_rate=lgbBO.max['params']['learning_rate'],
num_leaves=int(lgbBO.max['params']['num_leaves']),
subsample=lgbBO.max['params']['subsample'],
max_depth=16,
reg_alpha=lgbBO.max['params']['reg_alpha'],
reg_lambda=lgbBO.max['params']['reg_lambda'])
clf.fit(train_x, train_y, eval_set=[(train_x, train_y),(valid_x, valid_y)], eval_metric= 'auc', verbose= 100,
early_stopping_rounds= 200 )<merge> | predict = model.predict(x_test)
predict = np.argmax(predict, axis=1 ) | Digit Recognizer |
21,316,401 | test_merge = app_test.merge(prev_amt_agg, on='SK_ID_CURR', how='left', indicator=False)
test_merge = test_merge.merge(prev_approved, on='SK_ID_CURR', how='left', indicator=False)
test_merge = test_merge.merge(prev_refused, on='SK_ID_CURR', how='left', indicator=False)
test_merge['PRE_CONTRACT_APPROVED_RATE'] = test_merge['PRE_CONTRACT_APPROVED'] /(test_merge['PRE_CONTRACT_APPROVED'] + test_merge['PRE_CONTRACT_REFUSED'])
test_merge['PRE_CONTRACT_REFUSED_RATE'] = test_merge['PRE_CONTRACT_REFUSED'] /(test_merge['PRE_CONTRACT_APPROVED'] + test_merge['PRE_CONTRACT_REFUSED'])
test_merge = test_merge.replace(float('NaN'),0)
test_merge = test_merge.merge(PAST_LOANS_PER_CUS, on = ['SK_ID_CURR'], how = 'left')
test_merge = test_merge.merge(BUREAU_LOAN_TYPES, on = ['SK_ID_CURR'], how = 'left' ).fillna(0)
test_merge['AVERAGE_LOAN_TYPE'] = test_merge['BUREAU_LOAN_COUNT']/test_merge['BUREAU_LOAN_TYPES']
test_merge = test_merge.fillna(0)
del test_merge['BUREAU_LOAN_COUNT'], test_merge['BUREAU_LOAN_TYPES']
test_merge = test_merge.merge(grp, on = ['SK_ID_CURR'], how = 'left' ).fillna(0)
test_merge['BUREAU_ENDDATE_FACT_DIFF'] = bureau['DAYS_CREDIT_ENDDATE'] - bureau['DAYS_ENDDATE_FACT']
test_merge['BUREAU_CREDIT_FACT_DIFF'] = bureau['DAYS_CREDIT'] - bureau['DAYS_ENDDATE_FACT']
test_merge['BUREAU_CREDIT_ENDDATE_DIFF'] = bureau['DAYS_CREDIT'] - bureau['DAYS_CREDIT_ENDDATE']
test_merge['BUREAU_CREDIT_DEBT_RATIO'] = bureau['AMT_CREDIT_SUM_DEBT'] / bureau['AMT_CREDIT_SUM']
test_merge['BUREAU_CREDIT_DEBT_DIFF'] = bureau['AMT_CREDIT_SUM_DEBT'] - bureau['AMT_CREDIT_SUM']
test_merge = test_merge.fillna(0)
preds = clf.predict_proba(test_merge.drop(columns=['SK_ID_CURR'])) [:, 1]
app_test['TARGET'] = preds
app_test[['SK_ID_CURR', 'TARGET']].to_csv('result_00.csv', index=False )<set_options> | out = np.column_stack(( range(1, predict.shape[0]+1), predict)) | Digit Recognizer |
21,316,401 | <import_modules><EOS> | np.savetxt('submission.csv', out, header="ImageId,Label",
comments="", fmt="%d,%d" ) | Digit Recognizer |
21,256,766 | <SOS> metric: categorizationaccuracy Kaggle data source: digit-recognizer<define_variables> | %matplotlib inline
| Digit Recognizer |
21,256,766 | default_dir = ".. /input/home-credit-default-risk/"<data_type_conversions> | train_data = pd.read_csv("/kaggle/input/digit-recognizer/train.csv")
test_data = pd.read_csv("/kaggle/input/digit-recognizer/test.csv" ) | Digit Recognizer |
21,256,766 | def get_balance_data() :
pos_dtype = {
'SK_ID_PREV':np.uint32, 'SK_ID_CURR':np.uint32, 'MONTHS_BALANCE':np.int32, 'SK_DPD':np.int32,
'SK_DPD_DEF':np.int32, 'CNT_INSTALMENT':np.float32,'CNT_INSTALMENT_FUTURE':np.float32
}
install_dtype = {
'SK_ID_PREV':np.uint32, 'SK_ID_CURR':np.uint32, 'NUM_INSTALMENT_NUMBER':np.int32, 'NUM_INSTALMENT_VERSION':np.float32,
'DAYS_INSTALMENT':np.float32, 'DAYS_ENTRY_PAYMENT':np.float32, 'AMT_INSTALMENT':np.float32, 'AMT_PAYMENT':np.float32
}
card_dtype = {
'SK_ID_PREV':np.uint32, 'SK_ID_CURR':np.uint32, 'MONTHS_BALANCE':np.int16,
'AMT_CREDIT_LIMIT_ACTUAL':np.int32, 'CNT_DRAWINGS_CURRENT':np.int32, 'SK_DPD':np.int32,'SK_DPD_DEF':np.int32,
'AMT_BALANCE':np.float32, 'AMT_DRAWINGS_ATM_CURRENT':np.float32, 'AMT_DRAWINGS_CURRENT':np.float32,
'AMT_DRAWINGS_OTHER_CURRENT':np.float32, 'AMT_DRAWINGS_POS_CURRENT':np.float32, 'AMT_INST_MIN_REGULARITY':np.float32,
'AMT_PAYMENT_CURRENT':np.float32, 'AMT_PAYMENT_TOTAL_CURRENT':np.float32, 'AMT_RECEIVABLE_PRINCIPAL':np.float32,
'AMT_RECIVABLE':np.float32, 'AMT_TOTAL_RECEIVABLE':np.float32, 'CNT_DRAWINGS_ATM_CURRENT':np.float32,
'CNT_DRAWINGS_OTHER_CURRENT':np.float32, 'CNT_DRAWINGS_POS_CURRENT':np.float32, 'CNT_INSTALMENT_MATURE_CUM':np.float32
}
pos_bal = pd.read_csv(os.path.join(default_dir,'POS_CASH_balance.csv'), dtype=pos_dtype)
install = pd.read_csv(os.path.join(default_dir,'installments_payments.csv'), dtype=install_dtype)
card_bal = pd.read_csv(os.path.join(default_dir, 'credit_card_balance.csv'), dtype=card_dtype)
return pos_bal, install, card_bal
pos_bal, install, card_bal = get_balance_data()<import_modules> | X = train_data.drop(["label"],axis = 1 ).values
Y = train_data["label"].values | Digit Recognizer |
21,256,766 | from sklearn.model_selection import train_test_split
from lightgbm import LGBMClassifier<feature_engineering> | X = X.reshape([42000,28,28,1])
Y = Y.reshape([42000,1] ) | Digit Recognizer |
21,256,766 | 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'].mean())
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_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_MEMBERS']
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['APPS_CAR_EMPLOYED_RATIO'] = apps['OWN_CAR_AGE'] / apps['DAYS_EMPLOYED']
return apps
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_GOODS_APPL_RATIO'] = prev['AMT_GOODS_PRICE']/prev['AMT_APPLICATION']
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, inplace= True)
prev['PREV_DAYS_LAST_DUE_DIFF'] = prev['DAYS_LAST_DUE_1ST_VERSION'] - prev['DAYS_LAST_DUE']
all_pay = prev['AMT_ANNUITY'] * prev['CNT_PAYMENT']
prev['PREV_INTERESTS_RATE'] =(all_pay/prev['AMT_CREDIT'] - 1)/prev['CNT_PAYMENT']
return prev
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_DECISION': ['min', 'max', 'mean'],
'CNT_PAYMENT': ['mean', 'sum'],
'PREV_CREDIT_DIFF':['mean', 'max', 'sum'],
'PREV_CREDIT_APPL_RATIO':['mean', 'max'],
'PREV_GOODS_DIFF':['mean', 'max', 'sum'],
'PREV_GOODS_APPL_RATIO':['mean', 'max'],
'PREV_DAYS_LAST_DUE_DIFF':['mean', 'max', 'sum'],
'PREV_INTERESTS_RATE':['mean', 'max']
}
prev_group = prev.groupby('SK_ID_CURR')
prev_amt_agg = prev_group.agg(agg_dict)
prev_amt_agg.columns = ["PREV_"+ "_".join(x ).upper() for x in prev_amt_agg.columns.ravel() ]
return prev_amt_agg
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_REFUSED_COUNT' ]
prev_refused_appr_agg = prev_refused_appr_agg.fillna(0)
return prev_refused_appr_agg
def get_prev_days365_agg(prev):
cond_days365 = prev['DAYS_DECISION'] > -365
prev_days365_group = prev[cond_days365].groupby('SK_ID_CURR')
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_DECISION': ['min', 'max', 'mean'],
'CNT_PAYMENT': ['mean', 'sum'],
'PREV_CREDIT_DIFF':['mean', 'max', 'sum'],
'PREV_CREDIT_APPL_RATIO':['mean', 'max'],
'PREV_GOODS_DIFF':['mean', 'max', 'sum'],
'PREV_GOODS_APPL_RATIO':['mean', 'max'],
'PREV_DAYS_LAST_DUE_DIFF':['mean', 'max', 'sum'],
'PREV_INTERESTS_RATE':['mean', 'max']
}
prev_days365_agg = prev_days365_group.agg(agg_dict)
prev_days365_agg.columns = ["PREV_D365_"+ "_".join(x ).upper() for x in prev_days365_agg.columns.ravel() ]
return prev_days365_agg
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_days365_agg = get_prev_days365_agg(prev)
prev_agg = prev_amt_agg.merge(prev_refused_appr_agg, on='SK_ID_CURR', how='left')
prev_agg = prev_agg.merge(prev_days365_agg, on='SK_ID_CURR', how='left')
prev_agg['PREV_REFUSED_RATIO'] = prev_agg['PREV_REFUSED_COUNT']/prev_agg['PREV_SK_ID_CURR_COUNT']
prev_agg['PREV_APPROVED_RATIO'] = prev_agg['PREV_APPROVED_COUNT']/prev_agg['PREV_SK_ID_CURR_COUNT']
prev_agg = prev_agg.drop(['PREV_REFUSED_COUNT', 'PREV_APPROVED_COUNT'], axis=1)
return prev_agg
def get_bureau_processed(bureau):
bureau['BUREAU_ENDDATE_FACT_DIFF'] = bureau['DAYS_CREDIT_ENDDATE'] - bureau['DAYS_ENDDATE_FACT']
bureau['BUREAU_CREDIT_FACT_DIFF'] = bureau['DAYS_CREDIT'] - bureau['DAYS_ENDDATE_FACT']
bureau['BUREAU_CREDIT_ENDDATE_DIFF'] = bureau['DAYS_CREDIT'] - bureau['DAYS_CREDIT_ENDDATE']
bureau['BUREAU_CREDIT_DEBT_RATIO']=bureau['AMT_CREDIT_SUM_DEBT']/bureau['AMT_CREDIT_SUM']
bureau['BUREAU_CREDIT_DEBT_DIFF'] = bureau['AMT_CREDIT_SUM_DEBT'] - bureau['AMT_CREDIT_SUM']
bureau['BUREAU_IS_DPD'] = bureau['CREDIT_DAY_OVERDUE'].apply(lambda x: 1 if x > 0 else 0)
bureau['BUREAU_IS_DPD_OVER120'] = bureau['CREDIT_DAY_OVERDUE'].apply(lambda x: 1 if x >120 else 0)
return bureau
def get_bureau_day_amt_agg(bureau):
bureau_agg_dict = {
'SK_ID_BUREAU':['count'],
'DAYS_CREDIT':['min', 'max', 'mean'],
'CREDIT_DAY_OVERDUE':['min', 'max', 'mean'],
'DAYS_CREDIT_ENDDATE':['min', 'max', 'mean'],
'DAYS_ENDDATE_FACT':['min', 'max', 'mean'],
'AMT_CREDIT_MAX_OVERDUE': ['max', 'mean'],
'AMT_CREDIT_SUM': ['max', 'mean', 'sum'],
'AMT_CREDIT_SUM_DEBT': ['max', 'mean', 'sum'],
'AMT_CREDIT_SUM_OVERDUE': ['max', 'mean', 'sum'],
'AMT_ANNUITY': ['max', 'mean', 'sum'],
'BUREAU_ENDDATE_FACT_DIFF':['min', 'max', 'mean'],
'BUREAU_CREDIT_FACT_DIFF':['min', 'max', 'mean'],
'BUREAU_CREDIT_ENDDATE_DIFF':['min', 'max', 'mean'],
'BUREAU_CREDIT_DEBT_RATIO':['min', 'max', 'mean'],
'BUREAU_CREDIT_DEBT_DIFF':['min', 'max', 'mean'],
'BUREAU_IS_DPD':['mean', 'sum'],
'BUREAU_IS_DPD_OVER120':['mean', 'sum']
}
bureau_grp = bureau.groupby('SK_ID_CURR')
bureau_day_amt_agg = bureau_grp.agg(bureau_agg_dict)
bureau_day_amt_agg.columns = ['BUREAU_'+('_' ).join(column ).upper() for column in bureau_day_amt_agg.columns.ravel() ]
bureau_day_amt_agg = bureau_day_amt_agg.reset_index()
return bureau_day_amt_agg
def get_bureau_active_agg(bureau):
cond_active = bureau['CREDIT_ACTIVE'] == 'Active'
bureau_active_grp = bureau[cond_active].groupby(['SK_ID_CURR'])
bureau_agg_dict = {
'SK_ID_BUREAU':['count'],
'DAYS_CREDIT':['min', 'max', 'mean'],
'CREDIT_DAY_OVERDUE':['min', 'max', 'mean'],
'DAYS_CREDIT_ENDDATE':['min', 'max', 'mean'],
'DAYS_ENDDATE_FACT':['min', 'max', 'mean'],
'AMT_CREDIT_MAX_OVERDUE': ['max', 'mean'],
'AMT_CREDIT_SUM': ['max', 'mean', 'sum'],
'AMT_CREDIT_SUM_DEBT': ['max', 'mean', 'sum'],
'AMT_CREDIT_SUM_OVERDUE': ['max', 'mean', 'sum'],
'AMT_ANNUITY': ['max', 'mean', 'sum'],
'BUREAU_ENDDATE_FACT_DIFF':['min', 'max', 'mean'],
'BUREAU_CREDIT_FACT_DIFF':['min', 'max', 'mean'],
'BUREAU_CREDIT_ENDDATE_DIFF':['min', 'max', 'mean'],
'BUREAU_CREDIT_DEBT_RATIO':['min', 'max', 'mean'],
'BUREAU_CREDIT_DEBT_DIFF':['min', 'max', 'mean'],
'BUREAU_IS_DPD':['mean', 'sum'],
'BUREAU_IS_DPD_OVER120':['mean', 'sum']
}
bureau_active_agg = bureau_active_grp.agg(bureau_agg_dict)
bureau_active_agg.columns = ['BUREAU_ACT_'+('_' ).join(column ).upper() for column in bureau_active_agg.columns.ravel() ]
bureau_active_agg = bureau_active_agg.reset_index()
return bureau_active_agg
def get_bureau_days750_agg(bureau):
cond_days750 = bureau['DAYS_CREDIT'] > -750
bureau_days750_group = bureau[cond_days750].groupby('SK_ID_CURR')
bureau_agg_dict = {
'SK_ID_BUREAU':['count'],
'DAYS_CREDIT':['min', 'max', 'mean'],
'CREDIT_DAY_OVERDUE':['min', 'max', 'mean'],
'DAYS_CREDIT_ENDDATE':['min', 'max', 'mean'],
'DAYS_ENDDATE_FACT':['min', 'max', 'mean'],
'AMT_CREDIT_MAX_OVERDUE': ['max', 'mean'],
'AMT_CREDIT_SUM': ['max', 'mean', 'sum'],
'AMT_CREDIT_SUM_DEBT': ['max', 'mean', 'sum'],
'AMT_CREDIT_SUM_OVERDUE': ['max', 'mean', 'sum'],
'AMT_ANNUITY': ['max', 'mean', 'sum'],
'BUREAU_ENDDATE_FACT_DIFF':['min', 'max', 'mean'],
'BUREAU_CREDIT_FACT_DIFF':['min', 'max', 'mean'],
'BUREAU_CREDIT_ENDDATE_DIFF':['min', 'max', 'mean'],
'BUREAU_CREDIT_DEBT_RATIO':['min', 'max', 'mean'],
'BUREAU_CREDIT_DEBT_DIFF':['min', 'max', 'mean'],
'BUREAU_IS_DPD':['mean', 'sum'],
'BUREAU_IS_DPD_OVER120':['mean', 'sum']
}
bureau_days750_agg = bureau_days750_group.agg(bureau_agg_dict)
bureau_days750_agg.columns = ['BUREAU_ACT_'+('_' ).join(column ).upper() for column in bureau_days750_agg.columns.ravel() ]
bureau_days750_agg = bureau_days750_agg.reset_index()
return bureau_days750_agg
def get_bureau_bal_agg(bureau, bureau_bal):
bureau_bal = bureau_bal.merge(bureau[['SK_ID_CURR', 'SK_ID_BUREAU']], on='SK_ID_BUREAU', how='left')
bureau_bal['BUREAU_BAL_IS_DPD'] = bureau_bal['STATUS'].apply(lambda x: 1 if x in['1','2','3','4','5'] else 0)
bureau_bal['BUREAU_BAL_IS_DPD_OVER120'] = bureau_bal['STATUS'].apply(lambda x: 1 if x =='5' else 0)
bureau_bal_grp = bureau_bal.groupby('SK_ID_CURR')
bureau_bal_agg_dict = {
'SK_ID_CURR':['count'],
'MONTHS_BALANCE':['min', 'max', 'mean'],
'BUREAU_BAL_IS_DPD':['mean', 'sum'],
'BUREAU_BAL_IS_DPD_OVER120':['mean', 'sum']
}
bureau_bal_agg = bureau_bal_grp.agg(bureau_bal_agg_dict)
bureau_bal_agg.columns = [ 'BUREAU_BAL_'+('_' ).join(column ).upper() for column in bureau_bal_agg.columns.ravel() ]
bureau_bal_agg = bureau_bal_agg.reset_index()
return bureau_bal_agg
def get_bureau_agg(bureau, bureau_bal):
bureau = get_bureau_processed(bureau)
bureau_day_amt_agg = get_bureau_day_amt_agg(bureau)
bureau_active_agg = get_bureau_active_agg(bureau)
bureau_days750_agg = get_bureau_days750_agg(bureau)
bureau_bal_agg = get_bureau_bal_agg(bureau, bureau_bal)
bureau_agg = bureau_day_amt_agg.merge(bureau_active_agg, on='SK_ID_CURR', how='left')
bureau_agg['BUREAU_ACT_IS_DPD_RATIO'] = bureau_agg['BUREAU_ACT_BUREAU_IS_DPD_SUM']/bureau_agg['BUREAU_SK_ID_BUREAU_COUNT']
bureau_agg['BUREAU_ACT_IS_DPD_OVER120_RATIO'] = bureau_agg['BUREAU_ACT_BUREAU_IS_DPD_OVER120_SUM']/bureau_agg['BUREAU_SK_ID_BUREAU_COUNT']
bureau_agg = bureau_agg.merge(bureau_bal_agg, on='SK_ID_CURR', how='left')
bureau_agg = bureau_agg.merge(bureau_days750_agg, on='SK_ID_CURR', how='left')
return bureau_agg
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 shape:', apps_all.shape)
return apps_all
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
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
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)
clf = LGBMClassifier(
nthread=4,
n_estimators=2000,
learning_rate=0.02,
max_depth = 11,
num_leaves=58,
colsample_bytree=0.613,
subsample=0.708,
max_bin=407,
reg_alpha=3.564,
reg_lambda=4.930,
min_child_weight= 6,
min_child_samples=165,
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= 200)
return clf<define_variables> | Y = to_categorical(Y, num_classes = 10 ) | Digit Recognizer |
21,256,766 | def get_pos_bal_agg(pos_bal):
cond_over_0 = pos_bal['SK_DPD'] > 0
cond_100 =(pos_bal['SK_DPD'] < 100)&(pos_bal['SK_DPD'] > 0)
cond_over_100 =(pos_bal['SK_DPD'] >= 100)
pos_bal['POS_IS_DPD'] = pos_bal['SK_DPD'].apply(lambda x: 1 if x > 0 else 0)
pos_bal['POS_IS_DPD_UNDER_120'] = pos_bal['SK_DPD'].apply(lambda x:1 if(x > 0)&(x <120)else 0)
pos_bal['POS_IS_DPD_OVER_120'] = pos_bal['SK_DPD'].apply(lambda x:1 if x >= 120 else 0)
pos_bal_grp = pos_bal.groupby('SK_ID_CURR')
pos_bal_agg_dict = {
'SK_ID_CURR':['count'],
'MONTHS_BALANCE':['min', 'mean', 'max'],
'SK_DPD':['min', 'max', 'mean', 'sum'],
'CNT_INSTALMENT':['min', 'max', 'mean', 'sum'],
'CNT_INSTALMENT_FUTURE':['min', 'max', 'mean', 'sum'],
'POS_IS_DPD':['mean', 'sum'],
'POS_IS_DPD_UNDER_120':['mean', 'sum'],
'POS_IS_DPD_OVER_120':['mean', 'sum']
}
pos_bal_agg = pos_bal_grp.agg(pos_bal_agg_dict)
pos_bal_agg.columns = [('POS_')+('_' ).join(column ).upper() for column in pos_bal_agg.columns.ravel() ]
cond_months = pos_bal['MONTHS_BALANCE'] > -20
pos_bal_m20_grp = pos_bal[cond_months].groupby('SK_ID_CURR')
pos_bal_m20_agg_dict = {
'SK_ID_CURR':['count'],
'MONTHS_BALANCE':['min', 'mean', 'max'],
'SK_DPD':['min', 'max', 'mean', 'sum'],
'CNT_INSTALMENT':['min', 'max', 'mean', 'sum'],
'CNT_INSTALMENT_FUTURE':['min', 'max', 'mean', 'sum'],
'POS_IS_DPD':['mean', 'sum'],
'POS_IS_DPD_UNDER_120':['mean', 'sum'],
'POS_IS_DPD_OVER_120':['mean', 'sum']
}
pos_bal_m20_agg = pos_bal_m20_grp.agg(pos_bal_m20_agg_dict)
pos_bal_m20_agg.columns = [('POS_M20')+('_' ).join(column ).upper() for column in pos_bal_m20_agg.columns.ravel() ]
pos_bal_agg = pos_bal_agg.merge(pos_bal_m20_agg, on='SK_ID_CURR', how='left')
pos_bal_agg = pos_bal_agg.reset_index()
return pos_bal_agg
def get_install_agg(install):
install['AMT_DIFF'] = install['AMT_INSTALMENT'] - install['AMT_PAYMENT']
install['AMT_RATIO'] =(install['AMT_PAYMENT'] +1)/(install['AMT_INSTALMENT'] + 1)
install['SK_DPD'] = install['DAYS_ENTRY_PAYMENT'] - install['DAYS_INSTALMENT']
install['INS_IS_DPD'] = install['SK_DPD'].apply(lambda x: 1 if x > 0 else 0)
install['INS_IS_DPD_UNDER_120'] = install['SK_DPD'].apply(lambda x:1 if(x > 0)&(x <120)else 0)
install['INS_IS_DPD_OVER_120'] = install['SK_DPD'].apply(lambda x:1 if x >= 120 else 0)
install_grp = install.groupby('SK_ID_CURR')
install_agg_dict = {
'SK_ID_CURR':['count'],
'NUM_INSTALMENT_VERSION':['nunique'],
'DAYS_ENTRY_PAYMENT':['mean', 'max', 'sum'],
'DAYS_INSTALMENT':['mean', 'max', 'sum'],
'AMT_INSTALMENT':['mean', 'max', 'sum'],
'AMT_PAYMENT':['mean', 'max','sum'],
'AMT_DIFF':['mean','min', 'max','sum'],
'AMT_RATIO':['mean', 'max'],
'SK_DPD':['mean', 'min', 'max'],
'INS_IS_DPD':['mean', 'sum'],
'INS_IS_DPD_UNDER_120':['mean', 'sum'],
'INS_IS_DPD_OVER_120':['mean', 'sum']
}
install_agg = install_grp.agg(install_agg_dict)
install_agg.columns = ['INS_'+('_' ).join(column ).upper() for column in install_agg.columns.ravel() ]
cond_day = install['DAYS_ENTRY_PAYMENT'] >= -365
install_d365_grp = install[cond_day].groupby('SK_ID_CURR')
install_d365_agg_dict = {
'SK_ID_CURR':['count'],
'NUM_INSTALMENT_VERSION':['nunique'],
'DAYS_ENTRY_PAYMENT':['mean', 'max', 'sum'],
'DAYS_INSTALMENT':['mean', 'max', 'sum'],
'AMT_INSTALMENT':['mean', 'max', 'sum'],
'AMT_PAYMENT':['mean', 'max','sum'],
'AMT_DIFF':['mean','min', 'max','sum'],
'AMT_RATIO':['mean', 'max'],
'SK_DPD':['mean', 'min', 'max'],
'INS_IS_DPD':['mean', 'sum'],
'INS_IS_DPD_UNDER_120':['mean', 'sum'],
'INS_IS_DPD_OVER_120':['mean', 'sum']
}
install_d365_agg = install_d365_grp.agg(install_d365_agg_dict)
install_d365_agg.columns = ['INS_D365'+('_' ).join(column ).upper() for column in install_d365_agg.columns.ravel() ]
install_agg = install_agg.merge(install_d365_agg, on='SK_ID_CURR', how='left')
install_agg = install_agg.reset_index()
return install_agg
def get_card_bal_agg(card_bal):
card_bal['BALANCE_LIMIT_RATIO'] = card_bal['AMT_BALANCE']/card_bal['AMT_CREDIT_LIMIT_ACTUAL']
card_bal['DRAWING_LIMIT_RATIO'] = card_bal['AMT_DRAWINGS_CURRENT'] / card_bal['AMT_CREDIT_LIMIT_ACTUAL']
card_bal['CARD_IS_DPD'] = card_bal['SK_DPD'].apply(lambda x: 1 if x > 0 else 0)
card_bal['CARD_IS_DPD_UNDER_120'] = card_bal['SK_DPD'].apply(lambda x:1 if(x > 0)&(x <120)else 0)
card_bal['CARD_IS_DPD_OVER_120'] = card_bal['SK_DPD'].apply(lambda x:1 if x >= 120 else 0)
card_bal_grp = card_bal.groupby('SK_ID_CURR')
card_bal_agg_dict = {
'SK_ID_CURR':['count'],
'AMT_BALANCE':['max'],
'AMT_CREDIT_LIMIT_ACTUAL':['max'],
'AMT_DRAWINGS_ATM_CURRENT': ['max', 'sum'],
'AMT_DRAWINGS_CURRENT': ['max', 'sum'],
'AMT_DRAWINGS_POS_CURRENT': ['max', 'sum'],
'AMT_INST_MIN_REGULARITY': ['max', 'mean'],
'AMT_PAYMENT_TOTAL_CURRENT': ['max','sum'],
'AMT_TOTAL_RECEIVABLE': ['max', 'mean'],
'CNT_DRAWINGS_ATM_CURRENT': ['max','sum'],
'CNT_DRAWINGS_CURRENT': ['max', 'mean', 'sum'],
'CNT_DRAWINGS_POS_CURRENT': ['mean'],
'SK_DPD': ['mean', 'max', 'sum'],
'BALANCE_LIMIT_RATIO':['min','max'],
'DRAWING_LIMIT_RATIO':['min', 'max'],
'CARD_IS_DPD':['mean', 'sum'],
'CARD_IS_DPD_UNDER_120':['mean', 'sum'],
'CARD_IS_DPD_OVER_120':['mean', 'sum']
}
card_bal_agg = card_bal_grp.agg(card_bal_agg_dict)
card_bal_agg.columns = ['CARD_'+('_' ).join(column ).upper() for column in card_bal_agg.columns.ravel() ]
card_bal_agg = card_bal_agg.reset_index()
cond_month = card_bal.MONTHS_BALANCE >= -3
card_bal_m3_grp = card_bal[cond_month].groupby('SK_ID_CURR')
card_bal_m3_agg = card_bal_m3_grp.agg(card_bal_agg_dict)
card_bal_m3_agg.columns = ['CARD_M3'+('_' ).join(column ).upper() for column in card_bal_m3_agg.columns.ravel() ]
card_bal_agg = card_bal_agg.merge(card_bal_m3_agg, on='SK_ID_CURR', how='left')
card_bal_agg = card_bal_agg.reset_index()
return card_bal_agg<categorify> | x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size = 0.1, random_state = 14 ) | Digit Recognizer |
21,256,766 | def get_apps_all_with_all_agg(apps, prev, bureau, bureau_bal, pos_bal, install, card_bal):
apps_all = get_apps_processed(apps)
prev_agg = get_prev_agg(prev)
bureau_agg = get_bureau_agg(bureau, bureau_bal)
pos_bal_agg = get_pos_bal_agg(pos_bal)
install_agg = get_install_agg(install)
card_bal_agg = get_card_bal_agg(card_bal)
print('prev_agg shape:', prev_agg.shape, 'bureau_agg shape:', bureau_agg.shape)
print('pos_bal_agg shape:', pos_bal_agg.shape, 'install_agg shape:', install_agg.shape, 'card_bal_agg shape:', card_bal_agg.shape)
print('apps_all before merge shape:', apps_all.shape)
apps_all = apps_all.merge(prev_agg, on='SK_ID_CURR', how='left')
apps_all = apps_all.merge(bureau_agg, on='SK_ID_CURR', how='left')
apps_all = apps_all.merge(pos_bal_agg, on='SK_ID_CURR', how='left')
apps_all = apps_all.merge(install_agg, on='SK_ID_CURR', how='left')
apps_all = apps_all.merge(card_bal_agg, on='SK_ID_CURR', how='left')
print('apps_all after merge with all shape:', apps_all.shape)
return apps_all<load_from_csv> | x_train = x_train/255
x_test = x_test/255 | Digit Recognizer |
21,256,766 | def get_dataset() :
app_train = pd.read_csv(os.path.join(default_dir,'application_train.csv'))
app_test = pd.read_csv(os.path.join(default_dir,'application_test.csv'))
apps = pd.concat([app_train, app_test])
prev = pd.read_csv(os.path.join(default_dir,'previous_application.csv'))
bureau = pd.read_csv(os.path.join(default_dir,'bureau.csv'))
bureau_bal = pd.read_csv(os.path.join(default_dir,'bureau_balance.csv'))
pos_bal, install, card_bal = get_balance_data()
return apps, prev, bureau, bureau_bal, pos_bal, install, card_bal<create_dataframe> | model = tf.keras.models.Sequential([
tf.keras.layers.Conv2D(64,(3,3), padding = 'same', activation='relu', input_shape=(28, 28, 1)) ,
tf.keras.layers.MaxPooling2D(2, 2),
tf.keras.layers.Dropout(0.25),
tf.keras.layers.Conv2D(64,(3,3), padding = 'same', activation='relu'),
tf.keras.layers.MaxPooling2D(2,2),
tf.keras.layers.Dropout(0.25),
tf.keras.layers.Conv2D(128,(3,3), padding = 'same', activation='relu'),
tf.keras.layers.MaxPooling2D(2,2),
tf.keras.layers.Dropout(0.25),
tf.keras.layers.Conv2D(128,(3,3), padding = 'same', activation='relu'),
tf.keras.layers.MaxPooling2D(2,2),
tf.keras.layers.Dropout(0.25),
tf.keras.layers.Flatten() ,
tf.keras.layers.Dense(256, activation='relu'),
tf.keras.layers.Dropout(0.5),
tf.keras.layers.Dense(10, activation='softmax')
] ) | Digit Recognizer |
21,256,766 | apps, prev, bureau, bureau_bal, pos_bal, install, card_bal = get_dataset()<categorify> | optimizer = Adam(learning_rate = 0.001, beta_1 = 0.9, beta_2 = 0.999)
model.compile(optimizer = optimizer, loss = 'categorical_crossentropy', metrics = ['accuracy'] ) | Digit Recognizer |
21,256,766 | apps_all = get_apps_all_with_all_agg(apps, prev, bureau, bureau_bal, pos_bal, install, card_bal)
apps_all = get_apps_all_encoded(apps_all)
apps_all_train, apps_all_test = get_apps_all_train_test(apps_all)
clf = train_apps_all(apps_all_train )<save_to_csv> | learning_rate_reduction = ReduceLROnPlateau(monitor='val_acc',
patience=3,
verbose=1,
factor=0.6,
min_lr=0.00001 ) | Digit Recognizer |
21,256,766 | output_dir = ".. /output/kaggle/working/"
preds = clf.predict_proba(apps_all_test.drop(['SK_ID_CURR'], axis=1)) [:, 1 ]
apps_all_test['TARGET'] = preds
apps_all_test[['SK_ID_CURR', 'TARGET']]<save_to_csv> | batch_size = 64
epochs = 30 | Digit Recognizer |
21,256,766 | apps_all_test[['SK_ID_CURR', 'TARGET']].to_csv('submission.csv', index=False )<import_modules> | train_datagen = ImageDataGenerator(
rotation_range=10,
zoom_range = 0.1,
width_shift_range=0.1,
height_shift_range=0.1,
shear_range = 0.1,
horizontal_flip=False,
vertical_flip=False
)
train_datagen.fit(x_train ) | Digit Recognizer |
21,256,766 | from lightgbm import plot_importance<load_from_csv> | history = model.fit(
train_datagen.flow(x_train,y_train,batch_size = batch_size),
validation_data =(x_test,y_test),
batch_size = batch_size,
steps_per_epoch = x_train.shape[0]//batch_size,
epochs = epochs,
verbose = 1,
callbacks=[learning_rate_reduction]
) | Digit Recognizer |
21,256,766 | train_data = pd.read_csv("/kaggle/input/titanic/train.csv")
train_data.head()<load_from_csv> | model.evaluate(x_test,y_test ) | Digit Recognizer |
21,256,766 | test_data = pd.read_csv("/kaggle/input/titanic/test.csv")
test_data.head()<define_variables> | test_pred = model.predict(data)
test_pred = np.argmax(test_pred,axis=1)
print(test_pred.shape ) | Digit Recognizer |
21,256,766 | women = train_data.loc[train_data.Sex == 'female']["Survived"]
rate_women = sum(women)/len(women)
print("% of women who survived:", rate_women )<define_variables> | sample_submission = pd.read_csv("/kaggle/input/digit-recognizer/sample_submission.csv")
sample_submission | Digit Recognizer |
21,256,766 | men = train_data.loc[train_data.Sex == 'male']["Survived"]
rate_men = sum(men)/len(men)
print("% of men who survived:", rate_men )<save_to_csv> | index = sample_submission.ImageId
data = {'ImageId' : index,'Label': test_pred}
df = pd.DataFrame(data)
df.head | Digit Recognizer |
21,256,766 | <concatenate><EOS> | df.to_csv('submission.csv', index=False ) | Digit Recognizer |
21,104,380 | <SOS> metric: categorizationaccuracy Kaggle data source: digit-recognizer<define_variables> | import pandas as pd | Digit Recognizer |
21,104,380 | combine = [train_data, test_data]<feature_engineering> | mnist_test = pd.read_csv("/kaggle/input/mnist-fashion-data-classification/mnist_test.csv")
mnist_train = pd.read_csv("/kaggle/input/mnist-fashion-data-classification/mnist_train.csv")
| Digit Recognizer |
21,104,380 | for dataset in combine:
dataset['Title'] = dataset.Name.str.extract('([A-Za-z]+)\.', expand=False)
pd.crosstab(train_data['Title'], train_data['Sex'] )<feature_engineering> | sample_submission = pd.read_csv("/kaggle/input/digit-recognizer/sample_submission.csv")
train = pd.read_csv("/kaggle/input/digit-recognizer/train.csv")
test = pd.read_csv("/kaggle/input/digit-recognizer/test.csv" ) | Digit Recognizer |
21,104,380 | for dataset in combine:
dataset['Title'] = dataset['Title'].replace(['Lady', 'Countess','Capt', 'Col',\
'Don', 'Dr', 'Major', 'Rev', 'Sir', 'Jonkheer', 'Dona'], 'Rare')
dataset['Title'] = dataset['Title'].replace('Mlle', 'Miss')
dataset['Title'] = dataset['Title'].replace('Ms', 'Miss')
dataset['Title'] = dataset['Title'].replace('Mme', 'Mrs')
train_data[['Title', 'Survived']].groupby(['Title'], as_index=False ).mean()<categorify> | test['dataset'] = 'test' | Digit Recognizer |
21,104,380 | title_mapping = {"Mr": 1, "Miss": 2, "Mrs": 3, "Master": 4, "Rare": 5}
for dataset in combine:
dataset['Title'] = dataset['Title'].map(title_mapping)
dataset['Title'] = dataset['Title'].fillna(0)
train_data.head()<concatenate> | train['dataset'] = 'train' | Digit Recognizer |
21,104,380 | train_data = train_data.drop(['Name', 'PassengerId'], axis=1)
test_data = test_data.drop(['Name'], axis=1)
combine = [train_data, test_data]
train_data.shape, test_data.shape<categorify> | dataset = pd.concat([train.drop('label', axis=1), test] ).reset_index() | Digit Recognizer |
21,104,380 | for dataset in combine:
dataset['Sex'] = dataset['Sex'].map({'female': 1, 'male': 0} ).astype(int)
train_data.head()<define_variables> | mnist = pd.concat([mnist_train, mnist_test] ).reset_index(drop=True)
labels = mnist['label'].values
mnist.drop('label', axis=1, inplace=True)
mnist.columns = cols | Digit Recognizer |
21,104,380 | guess_ages = np.zeros(( 2,3))
guess_ages<find_best_params> | idx_mnist = mnist.sort_values(by=list(mnist.columns)).index
dataset_from = dataset.sort_values(by=list(mnist.columns)) ['dataset'].values
original_idx = dataset.sort_values(by=list(mnist.columns)) ['index'].values | Digit Recognizer |
21,104,380 | for dataset in combine:
for i in range(0, 2):
for j in range(0, 3):
guess_df = dataset[(dataset['Sex'] == i)& \
(dataset['Pclass'] == j+1)]['Age'].dropna()
age_guess = guess_df.median()
guess_ages[i,j] = int(age_guess/0.5 + 0.5)* 0.5
for i in range(0, 2):
for j in range(0, 3):
dataset.loc[(dataset.Age.isnull())&(dataset.Sex == i)&(dataset.Pclass == j+1),\
'Age'] = guess_ages[i,j]
dataset['Age'] = dataset['Age'].astype(int)
train_data.head()<sort_values> | for i in range(len(idx_mnist)) :
if dataset_from[i] == 'test':
sample_submission.loc[original_idx[i], 'Label'] = labels[idx_mnist[i]] | Digit Recognizer |
21,104,380 | <feature_engineering><EOS> | sample_submission.to_csv('submission.csv', index=False ) | Digit Recognizer |
21,021,971 | <SOS> metric: categorizationaccuracy Kaggle data source: digit-recognizer<drop_column> | tf.random.set_seed(42)
%matplotlib inline
pd.set_option('display.max_rows', 10)
pd.set_option('display.max_columns', None)
pd.set_option('float_format', '{:f}'.format)
mpl.rcParams['figure.dpi'] = 600
warnings.filterwarnings('ignore')
tf.get_logger().setLevel('INFO')
train_df = pd.read_csv('.. /input/digit-recognizer/train.csv')
test_df = pd.read_csv('.. /input/digit-recognizer/test.csv')
submission = pd.read_csv('.. /input/digit-recognizer/sample_submission.csv', index_col = 'ImageId')
x = train_df.drop(columns = 'label' ).values.reshape(-1, 28, 28)
y = train_df['label'].values
x_test = test_df.values.reshape(-1, 28, 28)
n_labels = len(np.unique(y))
del train_df, test_df
gc.collect() | Digit Recognizer |
21,021,971 | train_data = train_data.drop(['AgeBand'], axis=1)
combine = [train_data, test_data]
train_data.head()<sort_values> | def define_model(input_shape, n_classes, n_conv_branches, dropout):
inputs = layers.Input(shape = input_shape)
b_in = layers.experimental.preprocessing.Rescaling(1./ 255 )(inputs)
branches = [b_in] * n_conv_branches
for i in range(n_conv_branches):
for filter_size in [32, 64, 128, 128]:
branches[i] = layers.Conv2D(
filters = filter_size,
kernel_size = 3,
padding = 'same',
)(branches[i])
branches[i] = layers.MaxPool2D(pool_size =(2, 2))(branches[i])
branches[i] = layers.ReLU()(branches[i])
branches[i] = layers.Dropout(dropout )(branches[i])
if n_conv_branches > 1:
b_out = layers.concatenate(branches)
b_out = layers.Flatten()(b_out)
else:
b_out = layers.Flatten()(branches[0])
b_out = layers.Dense(units = 128 )(b_out)
b_out = layers.BatchNormalization()(b_out)
b_out = layers.ReLU()(b_out)
b_out = layers.Dropout(dropout )(b_out)
outputs = layers.Dense(units = n_classes )(b_out)
return Model(inputs, outputs ) | Digit Recognizer |
21,021,971 | for dataset in combine:
dataset['FamilySize'] = dataset['SibSp'] + dataset['Parch'] + 1
train_data[['FamilySize', 'Survived']].groupby(['FamilySize'], as_index=False ).mean().sort_values(by='Survived', ascending=False )<feature_engineering> | N_SPLITS = 10
CHECKPOINT_DIR = './checkpoint'
cv = StratifiedKFold(n_splits = N_SPLITS, random_state = 42, shuffle = True)
oof_pred = np.zeros(( x_test.shape[0], n_labels))
cv_val_scores = np.zeros(N_SPLITS)
histories = []
k = 0
for train_i, val_i in cv.split(x, y):
x_train = x[train_i, :]
x_valid = x[val_i, :]
y_train = y[train_i]
y_valid = y[val_i]
model = define_model(( x.shape[1], x.shape[2], 1), n_labels, 2, 0.2)
gc.collect()
optimizer = Adam(
learning_rate = 5e-4,
)
model.compile(
optimizer = optimizer,
loss = SparseCategoricalCrossentropy(from_logits = True),
metrics = ['accuracy']
)
checkpoint_call = ModelCheckpoint(
filepath = CHECKPOINT_DIR,
save_weights_only = True,
monitor = 'val_accuracy',
mode = 'max',
save_best_only = True
)
stopping_call = EarlyStopping(
monitor = 'val_accuracy',
patience = 50,
mode = 'max'
)
history = model.fit(
x_train, y_train,
validation_data =(x_valid, y_valid),
epochs = 200,
callbacks = [checkpoint_call, stopping_call],
batch_size = 1024,
)
histories += [history]
model.load_weights(CHECKPOINT_DIR)
predictor_model = tf.keras.Sequential([model, layers.Softmax() ])
cv_val_scores[k] = model.evaluate(x_valid, y_valid)[1]
oof_pred += predictor_model.predict(x_test)/ N_SPLITS
k += 1 | Digit Recognizer |
21,021,971 | for dataset in combine:
dataset['IsAlone'] = 0
dataset.loc[dataset['FamilySize'] == 1, 'IsAlone'] = 1
train_data[['IsAlone', 'Survived']].groupby(['IsAlone'], as_index=False ).mean()<drop_column> | print('Validation AUC: {:.6} ± {:.4}'.format(cv_val_scores.mean() , cv_val_scores.std())) | Digit Recognizer |
21,021,971 | train_data = train_data.drop(['Parch', 'SibSp', 'FamilySize'], axis=1)
test_data = test_data.drop(['Parch', 'SibSp', 'FamilySize'], axis=1)
combine = [train_data, test_data]
train_data.head()<feature_engineering> | submission.loc[:, 'Label'] = np.argmax(oof_pred, axis = 1)
submission.to_csv('submission.csv' ) | Digit Recognizer |
20,918,485 | for dataset in combine:
dataset['Age*Class'] = dataset.Age * dataset.Pclass
train_data.loc[:, ['Age*Class', 'Age', 'Pclass']].head(10 )<save_to_csv> | train = pd.read_csv("/kaggle/input/digit-recognizer/train.csv")
test = pd.read_csv("/kaggle/input/digit-recognizer/test.csv")
train_image = np.array(train.drop(['label'], axis=1), dtype="float32")/ 255
train_image = train_image.reshape(-1, 28, 28, 1)
train_label = tf.keras.utils.to_categorical(train['label'])
test = np.array(test, dtype="float32")/ 255
test = test.reshape(-1, 28, 28, 1)
show_images(train_image[:25], train_label[:25], shape=(5,5)) | Digit Recognizer |
20,918,485 | y = train_data["Survived"]
features = ["Pclass", "Sex", "Age", "Embarked", "Title", "IsAlone", "Age*Class"]
X = pd.get_dummies(train_data[features])
X_test = pd.get_dummies(test_data[features])
model = RandomForestClassifier(n_estimators=100, max_depth=5, random_state=1)
model.fit(X, y)
predictions = model.predict(X_test)
output = pd.DataFrame({'PassengerId': test_data.PassengerId, 'Survived': predictions})
output.to_csv('submission.csv', index=False)
print("Your submission was successfully saved!" )<save_to_csv> | ( image_train_mnist, label_train_mnist),(image_test_mnist, label_test_mnist)= mnist.load_data()
image_mnist = np.concatenate(( image_train_mnist, image_test_mnist))
label_mnist = np.concatenate(( label_train_mnist, label_test_mnist))
image_mnist = image_mnist.reshape(-1,28,28,1)
image_mnist = image_mnist.astype(np.float32)/ 255
label_mnist = tf.keras.utils.to_categorical(label_mnist,num_classes=10)
images = np.concatenate(( train_image, image_mnist))
labels = np.concatenate(( train_label, label_mnist))
print("training image dataset shape:", images.shape)
print("training label dataset shape:", labels.shape)
show_images(images[:25], labels[:25], shape=(5,5)) | Digit Recognizer |
20,918,485 | y = train_data["Survived"]
features = ["Pclass", "Sex", "Age", "Embarked", "Title", "IsAlone"]
X = pd.get_dummies(train_data[features])
X_test = pd.get_dummies(test_data[features])
model = RandomForestClassifier(n_estimators=100, max_depth=5, random_state=1)
model.fit(X, y)
predictions = model.predict(X_test)
output = pd.DataFrame({'PassengerId': test_data.PassengerId, 'Survived': predictions})
output.to_csv('submission.csv', index=False)
print("Your submission was successfully saved!" )<import_modules> | datagen = tf.keras.preprocessing.image.ImageDataGenerator(
rotation_range=20,
width_shift_range=0.20,
shear_range=15,
zoom_range=0.10,
validation_split=0.25,
horizontal_flip=False
)
train_generator = datagen.flow(
images,
labels,
batch_size=256,
subset='training',
)
validation_generator = datagen.flow(
images,
labels,
batch_size=64,
subset='validation',
) | Digit Recognizer |
20,918,485 | import pandas as pd
import numpy as np
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score<load_from_csv> | def create_model() :
model = tf.keras.Sequential([
tf.keras.layers.Reshape(( 28, 28, 1)) ,
tf.keras.layers.Conv2D(filters=32, kernel_size=(5,5), activation="relu", padding="same", input_shape=(28,28,1)) ,
tf.keras.layers.MaxPool2D(( 2,2)) ,
tf.keras.layers.Conv2D(filters=64, kernel_size=(3,3), activation="relu", padding="same"),
tf.keras.layers.Conv2D(filters=64, kernel_size=(3,3), activation="relu", padding="same"),
tf.keras.layers.MaxPool2D(( 2,2)) ,
tf.keras.layers.Conv2D(filters=128, kernel_size=(3,3), activation="relu", padding="same"),
tf.keras.layers.Conv2D(filters=128, kernel_size=(3,3), activation="relu", padding="same"),
tf.keras.layers.MaxPool2D(( 2,2)) ,
tf.keras.layers.Flatten() ,
tf.keras.layers.Dense(512, activation="sigmoid"),
tf.keras.layers.Dropout(0.25),
tf.keras.layers.Dense(512, activation="sigmoid"),
tf.keras.layers.Dropout(0.25),
tf.keras.layers.Dense(256, activation="sigmoid"),
tf.keras.layers.Dropout(0.1),
tf.keras.layers.Dense(10, activation="sigmoid")
])
model.compile(
optimizer="adam", loss = 'categorical_crossentropy', metrics = ['accuracy']
)
return model
model = create_model() | Digit Recognizer |
20,918,485 | train=pd.read_csv('.. /input/titanic/train.csv' )<count_missing_values> | history = model.fit_generator(train_generator, epochs=60, validation_data=validation_generator, callbacks=[reduce_lr,checkpoint], verbose=1 ) | Digit Recognizer |
20,918,485 | <count_values><EOS> | df = pd.read_csv("/kaggle/input/digit-recognizer/test.csv" ).astype("float32")/ 255.0
res = tf.keras.backend.argmax(model.predict(df))
csv = pd.DataFrame({'ImageId': range(1, len(res)+ 1), "Label": res})
csv.to_csv('submission.csv', index=False ) | Digit Recognizer |
17,391,097 | <SOS> metric: categorizationaccuracy Kaggle data source: digit-recognizer<count_values> | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import keras
from sklearn.model_selection import train_test_split
from tensorflow.python.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras import layers, models, utils, callbacks
import tensorflow as tf
import os | Digit Recognizer |
17,391,097 | train['Survived'].value_counts(normalize=True )<count_values> | nbr_of_clases = 10
validation_percentage = 0.2
resnet_path = '.. /input/resnet50/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5'
training_data = pd.read_csv('.. /input/digit-recognizer/train.csv' ) | Digit Recognizer |
17,391,097 | train['Sex'].value_counts()<categorify> | def prepare_data(data_to_transform):
data = data_to_transform.copy()
data = data.reshape(-1, 28, 28, 1)/ 255
return data | Digit Recognizer |
17,391,097 | embark=pd.get_dummies(train['Embarked'] )<categorify> | y = training_data['label'].values
X = training_data.drop('label',axis = 1 ).values
y = keras.utils.to_categorical(y, nbr_of_clases)
X_rgb = prepare_data(X)
X_train, X_val, y_train, y_val = train_test_split(X_rgb, y, test_size=validation_percentage ) | Digit Recognizer |
17,391,097 | gender=pd.get_dummies(train['Sex'] )<data_type_conversions> | model = models.Sequential([
layers.Conv2D(32, 3, activation='relu', input_shape=(128,128,1), padding='same'),
layers.Conv2D(32, 3, activation='relu', padding='same'),
layers.MaxPooling2D(padding='same'),
layers.Dropout(0.5),
layers.Conv2D(64, 3, activation='relu', padding='same'),
layers.Conv2D(64, 3, activation='relu', padding='same'),
layers.MaxPooling2D(padding='same'),
layers.Dropout(0.25),
layers.GlobalAveragePooling2D() ,
layers.Dense(nbr_of_clases, activation='softmax'),
])
model.summary()
model.compile(optimizer='adam',loss='categorical_crossentropy',metrics=['accuracy'], ) | Digit Recognizer |
17,391,097 | train['Age'].fillna(30,inplace=True)
train['Age']=train['Age'].astype('int' )<feature_engineering> | def get_fitted_data_generator(data):
datagen = ImageDataGenerator(
featurewise_center=False,
samplewise_center=False,
featurewise_std_normalization=False,
samplewise_std_normalization=False,
zca_whitening=False,
zoom_range = 0.1,
height_shift_range=0.1,
)
datagen.fit(data)
return datagen
def fit_model_generator(model, X_train, y_train, epochs=1, batch=32, X_val=None, y_val=None):
image_nbr = np.size(X_train, 0)
training_data_generator = get_fitted_data_generator(X_train)
es = callbacks.EarlyStopping(monitor='val_loss', patience=5, verbose=1, restore_best_weights=True)
rlp = callbacks.ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience=2, min_lr=1e-10, mode='min', verbose=1)
return model.fit_generator(training_data_generator.flow(X_train, y_train, batch_size=batch), steps_per_epoch=(image_nbr//batch),callbacks=[es, rlp],epochs=epochs, validation_data=(X_val, y_val), verbose=1 ) | Digit Recognizer |
17,391,097 | train['Age_grp']=train['Age'].apply(lambda x: age_grp(x))<categorify> | full_data_model = fit_model_generator(model, X_train, y_train, epochs=150,X_val=X_val,y_val=y_val ) | Digit Recognizer |
17,391,097 | age=pd.get_dummies(train['Age_grp'] )<concatenate> | testing_data = pd.read_csv('.. /input/digit-recognizer/test.csv' ).values
testing_data = prepare_data(testing_data)
def get_predictions(model, data):
return np.array([np.argmax(prediction)for prediction in model.predict(data)])
final_predictions = get_predictions(model, testing_data ) | Digit Recognizer |
17,391,097 | train_df=pd.concat([train,embark,gender,age],axis=1 )<feature_engineering> | submission_filename = 'submission.csv'
answers = pd.DataFrame({'ImageId':range(1, final_predictions.size + 1),'Label':final_predictions})
answers.to_csv(submission_filename, index=False ) | Digit Recognizer |
17,391,097 | def is_var(val):
if val>0:
return 1
else:
return 0<feature_engineering> | submission_filename = '/kaggle/working/submission.csv'
answers.to_csv(submission_filename, index=False ) | Digit Recognizer |
17,303,075 | train_df['Family']=train_df['Parch'] + 1 + train_df['SibSp']
train_df['Parch']=train_df['Parch'].apply(lambda x: is_var(x))
train_df['SibSp']=train_df['SibSp'].apply(lambda x: is_var(x))<define_variables> | train_data = pd.read_csv("/kaggle/input/digit-recognizer/train.csv")
test_data = pd.read_csv("/kaggle/input/digit-recognizer/test.csv")
print(train_data.head() ) | Digit Recognizer |
17,303,075 | sel_cols=['Fare',
'Pclass', 'SibSp',
'Parch', 'C', 'Q',
'S', 'female', 'male', '18-24', '25-34', '35-44', '45+', '<13','13-18', 'Family'
]<correct_missing_values> | X_train = train_data.drop('label',axis=1)
X_train = X_train.values
X_train = X_train.astype('float32')
y_train = train_data['label']
y_train = y_train.values
y_train = y_train.astype('float32')
X_test = test_data.astype('float32')
print("The MNIST dataset has a training set of %d examples." % len(X_train))
print("The MNIST database has a test set of %d examples." % len(X_test)) | Digit Recognizer |
17,303,075 | train_df.fillna(0,inplace=True )<prepare_x_and_y> | X_train = X_train.astype('float32')/255
X_test = X_test.astype('float32')/255 | Digit Recognizer |
17,303,075 | X=train_df[sel_cols]<prepare_x_and_y> | datagen_train = ImageDataGenerator(
width_shift_range=0.2,
height_shift_range=0.2,
rotation_range = 20,
horizontal_flip=True,
vertical_flip = True,
shear_range = 0.1)
datagen_train.fit(X_train ) | Digit Recognizer |
17,303,075 | y=train_df['Survived']<split> | random_seed = 46
X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size = 0.25, random_state=random_seed)
print('Integer-valued labels:')
print(y_train[:10])
y_train = np_utils.to_categorical(y_train, 10)
y_val = np_utils.to_categorical(y_val, 10)
print('One-hot labels:')
print(y_train[:10] ) | Digit Recognizer |
17,303,075 | train_X,val_X,train_y,val_y=train_test_split(X,y,test_size=0.3,random_state=1 )<train_model> | model = Sequential()
model.add(Conv2D(32,(5, 5), padding='same',
input_shape=X_train.shape[1:]))
model.add(Activation('relu'))
model.add(BatchNormalization())
model.add(Conv2D(32,(5, 5), padding='same'))
model.add(Activation('relu'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.3))
model.add(Conv2D(64,(3, 3), padding='same'))
model.add(Activation('relu'))
model.add(BatchNormalization())
model.add(Conv2D(64,(3, 3), padding='same'))
model.add(Activation('relu'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.3))
model.add(Flatten())
model.add(Dense(512))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(10))
model.add(Activation('softmax'))
optimizer = optimizers.Adam(lr=0.001)
model.compile(loss='categorical_crossentropy',
optimizer=optimizer,
metrics=['accuracy'])
model.summary()
print("input shape ",model.input_shape)
print("output shape ",model.output_shape ) | Digit Recognizer |
17,303,075 | lr=LogisticRegression(max_iter=400)
lr.fit(train_X,train_y )<compute_test_metric> | batch_size = 128
nb_epoch = 20
checkpointer = [
ReduceLROnPlateau(monitor='val_loss',
patience=3,
verbose=1,
factor=0.5,
min_lr = 0.00001,
cooldown=0),
ModelCheckpoint('mnist_model_best.hdf5',
monitor='val_accuracy',
save_best_only=True,
mode='max',
verbose=1)
]
hist = model.fit(X_train, y_train, epochs=nb_epoch, batch_size=batch_size, validation_data=(X_val,y_val), callbacks=[checkpointer],verbose=1, shuffle=True ) | Digit Recognizer |
17,303,075 | lr.score(train_X,train_y )<compute_test_metric> | model = load_model('mnist_model_best.hdf5')
score = model.evaluate(X_val, y_val, verbose=0)
accuracy = 100*score[1]
print('Validation accuracy: %.4f%%' % accuracy ) | Digit Recognizer |
17,303,075 | lr.score(val_X,val_y )<load_from_csv> | predictions = model.predict_classes(X_test, verbose=1)
submissions = pd.DataFrame({"ImageId": list(range(1,len(predictions)+1)) ,
"Label": predictions})
submissions.to_csv("mnist_MLP_test.csv",index=False ) | Digit Recognizer |
17,159,331 | test=pd.read_csv('.. /input/titanic/test.csv' )<categorify> | import pandas as pd
from matplotlib import pyplot
import numpy as np
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from numpy import asarray, unique, argmax
from tensorflow.keras import Sequential
from tensorflow.keras.layers import Dense, Conv2D, MaxPool2D, Flatten, Dropout
from keras.optimizers import RMSprop | Digit Recognizer |
17,159,331 | embark=pd.get_dummies(test['Embarked'] )<categorify> | data = pd.read_csv('/kaggle/input/digit-recognizer/train.csv')
data.head() | Digit Recognizer |
17,159,331 | gender=pd.get_dummies(test['Sex'] )<data_type_conversions> | unique = data['label'].unique()
print("Unique Numbers :",unique)
n_classes = len(unique)
print("Number of classes :",n_classes ) | Digit Recognizer |
17,159,331 | test['Age'].fillna(30,inplace=True)
test['Age']=test['Age'].astype('int' )<feature_engineering> | x = data.drop(labels = ["label"], axis=1)
print("X= ",x ) | Digit Recognizer |
17,159,331 | test['Age_grp']=test['Age'].apply(lambda x: age_grp(x))<categorify> | y = data['label']
print("Target Variable: ",y ) | Digit Recognizer |
17,159,331 | age=pd.get_dummies(test['Age_grp'] )<concatenate> | x_train, x_test, y_train, y_test = train_test_split(x,y, test_size=0.20, random_state=42 ) | Digit Recognizer |
17,159,331 | test_df=pd.concat([test,embark,gender,age],axis=1 )<feature_engineering> | print("X_train: ", x_train)
print("X_test: ", y_test)
print("Y_train: ", y_train)
print("Y_test: ", y_test ) | Digit Recognizer |
17,159,331 | test_df['Family']=test_df['Parch']+1+test_df['SibSp']
test_df['Parch']=test_df['Parch'].apply(lambda x: is_var(x))
test_df['SibSp']=test_df['SibSp'].apply(lambda x: is_var(x))<correct_missing_values> | x_train = x_train.values.reshape(-1,28,28,1)
x_test = x_test.values.reshape(-1,28,28,1 ) | Digit Recognizer |
17,159,331 | test_df.fillna(0,inplace=True )<prepare_x_and_y> | x_train = x_train.astype('float32')/ 255.0
x_test = x_test.astype('float32')/ 255.0 | Digit Recognizer |
17,159,331 | test_X=test_df[sel_cols]<predict_on_test> | model = Sequential()
model.add(Conv2D(filters = 32, kernel_size =(5,5),padding = 'Same',
activation ='relu', input_shape = in_shape))
model.add(Conv2D(filters = 32, kernel_size =(5,5),padding = 'Same',
activation ='relu'))
model.add(MaxPool2D(pool_size=(2,2)))
model.add(Dropout(0.25))
model.add(Conv2D(filters = 64, kernel_size =(3,3),padding = 'Same',
activation ='relu'))
model.add(Conv2D(filters = 64, kernel_size =(3,3),padding = 'Same',
activation ='relu'))
model.add(MaxPool2D(pool_size=(2,2), strides=(2,2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(256, activation = "relu"))
model.add(Dropout(0.5))
model.add(Dense(10, activation = "softmax"))
| Digit Recognizer |
17,159,331 | test_y=lr.predict(test_X )<load_from_csv> | optimizer = RMSprop(lr=0.001, rho=0.9, epsilon=1e-08, decay=0.0)
| Digit Recognizer |
17,159,331 | sub=pd.read_csv('.. /input/titanic/gender_submission.csv' )<prepare_output> | model.compile(optimizer= 'adam', loss = 'sparse_categorical_crossentropy', metrics=['accuracy'])
| Digit Recognizer |
17,159,331 | sub['Survived']=test_y<save_to_csv> | model.fit(x_train, y_train, validation_split=0.2, epochs=100, batch_size=128, verbose= 1)
| Digit Recognizer |
17,159,331 | sub.to_csv('submission.csv',index=False )<install_modules> | loss, accuracy = model.evaluate(x_test, y_test, verbose=1)
print('Accuracy: %.3f' %accuracy)
print('Loss: ',loss ) | Digit Recognizer |
17,159,331 | !pip install autogluon --user
clear_output()<import_modules> | image = x_test[2]
ypred = model.predict(asarray([image]))
print('Prediction: Class =%d' %argmax(ypred)) | Digit Recognizer |
17,159,331 | import numpy as np
import pandas as pd
import os
import random
from autogluon.tabular import TabularDataset, TabularPredictor<define_variables> | test_data = pd.read_csv('/kaggle/input/digit-recognizer/test.csv')
test_data.head() | Digit Recognizer |
17,159,331 | TRAIN_PATH = ".. /input/titanic/train.csv"
TEST_PATH = ".. /input/titanic/test.csv"
SAMPLE_SUBMISSION_PATH = ".. /input/titanic/gender_submission.csv"
SUMISSION_PATH = "submission.csv"
TARGET = 'Survived'
EVAL_METRIC = "roc_auc"
SAVE_PATH = 'agModels-predictClass'
DEFAULT_RANDOM_SEED = 2021
def seedBasic(seed=DEFAULT_RANDOM_SEED):
random.seed(seed)
os.environ['PYTHONHASHSEED'] = str(seed)
np.random.seed(seed)
seedBasic()<create_dataframe> | test = test_data/255.0 | Digit Recognizer |
17,159,331 | train = TabularDataset(TRAIN_PATH)
test = TabularDataset(TEST_PATH )<train_model> | test_final_Data = test.values.reshape(-1,28,28,1 ) | Digit Recognizer |
17,159,331 | predictor = TabularPredictor(label=TARGET,eval_metric=EVAL_METRIC, path=SAVE_PATH ).fit(train )<predict_on_test> | label = model.predict(test_final_Data)
print(label ) | Digit Recognizer |
17,159,331 | y_pred = predictor.predict(test )<save_to_csv> | label = np.argmax(label, axis=1)
| Digit Recognizer |
17,159,331 | submission = pd.read_csv(SAMPLE_SUBMISSION_PATH)
submission[TARGET] = y_pred
submission.to_csv(SUMISSION_PATH,index=False)
submission.head()<set_options> | sample_submission = pd.read_csv('/kaggle/input/digit-recognizer/sample_submission.csv')
sample_submission.head()
| Digit Recognizer |
17,159,331 | init_notebook_mode(connected=True)
<set_options> | index = test_data.index.values + 1
data = {'ImageId' : index, "Label" : label}
df = pd.DataFrame(data=data)
df.head()
| Digit Recognizer |
17,159,331 | warnings.filterwarnings('ignore' )<load_from_csv> | submit_file = pd.DataFrame({'ImageId' : index, "Label" : label.astype(int ).ravel() })
submit_file.to_csv("submission.csv",index = False)
| Digit Recognizer |
18,990,193 | train_df=pd.read_csv("/kaggle/input/titanic/train.csv")
test_df = pd.read_csv("/kaggle/input/titanic/test.csv" )<concatenate> | import tensorflow as tf
import cv2 as cv
import matplotlib.pyplot as plt
from tensorflow.keras import models, layers | Digit Recognizer |
18,990,193 | train_len = len(train_df)
combined = train_df.append(test_df,ignore_index=True)
combined.fillna(np.nan )<count_missing_values> | train_data = pd.read_csv('.. /input/digit-recognizer/train.csv')
test_data = pd.read_csv('.. /input/digit-recognizer/test.csv' ) | Digit Recognizer |
18,990,193 | combined.isnull().sum()<groupby> | model = models.Sequential()
model.add(layers.Conv2D(filters=32, kernel_size=(4, 4), strides=(1,1), padding = 'same', activation='relu', input_shape = data_shape))
model.add(layers.BatchNormalization())
model.add(layers.LeakyReLU(0.2))
model.add(layers.Conv2D(filters=32, kernel_size=(4, 4), strides=(1,1), padding = 'same', activation='relu'))
model.add(layers.BatchNormalization())
model.add(layers.LeakyReLU(0.2))
model.add(layers.MaxPooling2D(( 2, 2)))
model.add(layers.Dropout(0.35))
model.add(layers.Conv2D(filters=64, kernel_size=(3, 3), strides=(1,1), padding = 'same', activation='relu'))
model.add(layers.BatchNormalization())
model.add(layers.LeakyReLU(0.2))
model.add(layers.Conv2D(filters=64, kernel_size=(3, 3), strides=(1,1), padding = 'same', activation='relu'))
model.add(layers.BatchNormalization())
model.add(layers.LeakyReLU(0.2))
model.add(layers.MaxPooling2D(( 2, 2)))
model.add(layers.Dropout(0.35))
model.add(layers.Conv2D(filters=64, kernel_size=(2, 2), strides=(1,1), padding = 'same', activation='relu'))
model.add(layers.BatchNormalization())
model.add(layers.LeakyReLU(0.2))
model.add(layers.Conv2D(filters=64, kernel_size=(2, 2), strides=(1,1), padding = 'same', activation='relu'))
model.add(layers.BatchNormalization())
model.add(layers.LeakyReLU(0.2))
model.add(layers.Dropout(0.35))
model.add(layers.Flatten())
model.add(layers.Dense(units=256, activation='relu'))
model.add(layers.BatchNormalization())
model.add(layers.Dropout(0.35))
model.add(layers.Dense(units=256, activation='relu'))
model.add(layers.BatchNormalization())
model.add(layers.Dropout(0.35))
model.add(layers.Dense(units=10, activation='softmax'))
model.summary() | Digit Recognizer |
18,990,193 | combined.groupby(['Pclass','Sex'])['Age'].mean()<feature_engineering> | Digit Recognizer |
|
18,990,193 | combined['AgeGroup'] = 'adult'
combined.loc[combined['Name'].str.contains('Master'),'AgeGroup'] = "child"
combined.loc[combined['Age'] <= 14.0,'AgeGroup'] = "child"
combined.loc[(combined['Age'].isnull())&(combined['Name'].str.contains('Miss')) &(combined['Parch'] != 0),'AgeGroup'] = "child"<groupby> | Digit Recognizer |
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