def aggregate():
df = utils.get_dummies(cre)
li = []
for c1 in df.columns:
for c2 in col_cat:
if c1.startswith(c2 + '_'):
li.append(c1)
break
cat_aggregations = {}
for cat in li:
cat_aggregations[cat] = ['mean', 'sum']
df_agg = df.groupby(KEY).agg({
**utils_agg.cre_num_aggregations,
**cat_aggregations
})
df_agg.columns = pd.Index(
[e[0] + "_" + e[1] for e in df_agg.columns.tolist()])
df_agg['CRE_COUNT'] = df.groupby(KEY).size()
df_agg.reset_index(inplace=True)
tmp = pd.merge(train, df_agg, on=KEY, how='left').drop(KEY, axis=1)
utils.to_feature(tmp.add_prefix(PREF), '../feature/train')
tmp = pd.merge(test, df_agg, on=KEY, how='left').drop(KEY, axis=1)
utils.to_feature(tmp.add_prefix(PREF), '../feature/test')
return
def aggregate():
df = utils.get_dummies(pos)
li = []
for c1 in df.columns:
for c2 in col_cat:
if c1.startswith(c2 + '_'):
li.append(c1)
break
cat_aggregations = {}
for cat in li:
cat_aggregations[cat] = ['mean', 'sum']
df_agg = df.groupby(KEY).agg({
**utils_agg.pos_num_aggregations,
**cat_aggregations
})
df_agg.columns = pd.Index(
[e[0] + "_" + e[1] for e in df_agg.columns.tolist()])
# std / mean
col_std = [c for c in df_agg.columns if c.endswith('_std')]
for c in col_std:
df_agg[f'{c}-d-mean'] = df_agg[c] / df_agg[c.replace('_std', '_mean')]
# max / min
col_max = [c for c in df_agg.columns if c.endswith('_max')]
for c in col_max:
try:
df_agg[f'{c}-d-min'] = df_agg[c] / df_agg[c.replace(
'_max', '_min')]
except:
pass
df_agg['POS_COUNT'] = df.groupby(KEY).size()
df_agg.reset_index(inplace=True)
utils.remove_feature(df_agg,
var_limit=0,
corr_limit=0.98,
sample_size=19999)
tmp = pd.merge(train, df_agg, on=KEY, how='left').drop(KEY, axis=1)
utils.to_feature(tmp.add_prefix(PREF), '../feature/train')
tmp = pd.merge(test, df_agg, on=KEY, how='left').drop(KEY, axis=1)
utils.to_feature(tmp.add_prefix(PREF), '../feature/test')
return
def aggregate(args):
print(args)
k, v, prefix = args
df = utils.get_dummies(bure[bure[k] == v])
li = []
for c1 in df.columns:
for c2 in col_cat:
if c1.startswith(c2 + '_'):
li.append(c1)
break
cat_aggregations = {}
for cat in li:
cat_aggregations[cat] = ['mean', 'sum']
df_agg = df.groupby(KEY).agg({
**utils_agg.bure_num_aggregations,
**cat_aggregations
})
df_agg.columns = pd.Index(
[prefix + e[0] + "_" + e[1] for e in df_agg.columns.tolist()])
# std / mean
col_std = [c for c in df_agg.columns if c.endswith('_std')]
for c in col_std:
df_agg[f'{c}-d-mean'] = df_agg[c] / df_agg[c.replace('_std', '_mean')]
# max / min
col_max = [c for c in df_agg.columns if c.endswith('_max')]
for c in col_max:
try:
df_agg[f'{c}-d-min'] = df_agg[c] / df_agg[c.replace(
'_max', '_min')]
df_agg[f'{c}-m-min'] = df_agg[c] - df_agg[c.replace(
'_max', '_min')]
except:
pass
df_agg[f'{prefix}BURE_COUNT'] = df.groupby(KEY).size()
df_agg.reset_index(inplace=True)
tmp = pd.merge(train, df_agg, on=KEY, how='left').drop(KEY, axis=1)
utils.to_feature(tmp.add_prefix(PREF), '../feature/train')
tmp = pd.merge(test, df_agg, on=KEY, how='left').drop(KEY, axis=1)
utils.to_feature(tmp.add_prefix(PREF), '../feature/test')
return
def aggregate():
df = utils.get_dummies(ins)
df_agg = df.groupby(KEY).agg({**num_aggregations})
df_agg.columns = pd.Index(
[e[0] + "_" + e[1] for e in df_agg.columns.tolist()])
df_agg.reset_index(inplace=True)
# utils.remove_feature(df_agg, var_limit=0, corr_limit=0.98, sample_size=19999)
tmp = pd.merge(train, df_agg, on=KEY, how='left').drop(KEY, axis=1)
utils.to_feature(tmp.add_prefix(PREF), '../feature/train')
tmp = pd.merge(test, df_agg, on=KEY, how='left').drop(KEY, axis=1)
utils.to_feature(tmp.add_prefix(PREF), '../feature/test')
return
'DAYS_ENDDATE_FACT', 'AMT_CREDIT_MAX_OVERDUE', 'CNT_CREDIT_PROLONG',
'AMT_CREDIT_SUM', 'AMT_CREDIT_SUM_DEBT', 'AMT_CREDIT_SUM_LIMIT',
'AMT_CREDIT_SUM_OVERDUE', 'DAYS_CREDIT_UPDATE', 'AMT_ANNUITY'
]
col_cat = ['CREDIT_ACTIVE', 'CREDIT_CURRENCY', 'CREDIT_TYPE']
col_group = ['CREDIT_ACTIVE', 'CREDIT_CURRENCY', 'CREDIT_TYPE']
# =============================================================================
# feature
# =============================================================================
bureau = utils.read_pickles('../data/bureau')
bureau = bureau[bureau['DAYS_CREDIT_ENDDATE'].between(-365, 0)]
bureau = utils.get_dummies(bureau)
bureau.drop('SK_ID_BUREAU', axis=1, inplace=True)
gr = bureau.groupby(KEY)
train = utils.load_train([KEY])
test = utils.load_test([KEY])
def nunique(x):
return len(set(x))
# =============================================================================
# gr1
from multiprocessing import Pool
import utils
utils.start(__file__)
#==============================================================================
KEY = 'SK_ID_CURR'
PREF = 'prev_102'
# =============================================================================
# feature
# =============================================================================
prev = utils.read_pickles('../data/previous_application')
prev = prev[prev['NAME_CONTRACT_STATUS'] == 'Refused']
prev = utils.get_dummies(prev)
prev.columns = [c.replace('/', '') for c in prev.columns]
prev.drop('SK_ID_PREV', axis=1, inplace=True)
base = prev[[KEY]].drop_duplicates().set_index(KEY)
gr = prev.groupby(KEY)
train = utils.load_train([KEY])
test = utils.load_test([KEY])
def nunique(x):
return len(set(x))
'MONTHS_BALANCE', 'CNT_INSTALMENT', 'CNT_INSTALMENT_FUTURE', 'SK_DPD',
'SK_DPD_DEF'
]
col_cat = ['NAME_CONTRACT_STATUS']
col_group = ['SK_ID_PREV', 'NAME_CONTRACT_STATUS']
# =============================================================================
# feature
# =============================================================================
pos = utils.read_pickles('../data/POS_CASH_balance')
pos = pos[pos['MONTHS_BALANCE'] > -12]
pos = utils.get_dummies(pos)
pos.drop('SK_ID_PREV', axis=1, inplace=True)
base = pos[[KEY]].drop_duplicates().set_index(KEY)
gr = pos.groupby(KEY)
train = utils.load_train([KEY])
test = utils.load_test([KEY])
def nunique(x):
return len(set(x))
PREF = f'ins_{No}'
col_num = [
'NUM_INSTALMENT_VERSION', 'NUM_INSTALMENT_NUMBER', 'DAYS_INSTALMENT',
'DAYS_ENTRY_PAYMENT', 'AMT_INSTALMENT', 'AMT_PAYMENT'
]
col_group = ['SK_ID_PREV', 'NUM_INSTALMENT_VERSION', 'NUM_INSTALMENT_NUMBER']
# =============================================================================
# feature
# =============================================================================
ins = utils.read_pickles('../data/installments_payments')
ins = ins[ins['DAYS_INSTALMENT'].between(-365, 0)]
ins = utils.get_dummies(ins)
ins.drop('SK_ID_PREV', axis=1, inplace=True)
base = ins[[KEY]].drop_duplicates().set_index(KEY)
gr = ins.groupby(KEY)
train = utils.load_train([KEY])
test = utils.load_test([KEY])
def nunique(x):
return len(set(x))
KEY = 'SK_ID_CURR'
No = '301'
PREF = f'ins_{No}'
NTHREAD = 3
col_num = [
'NUM_INSTALMENT_VERSION', 'NUM_INSTALMENT_NUMBER', 'DAYS_INSTALMENT',
'DAYS_ENTRY_PAYMENT', 'AMT_INSTALMENT', 'AMT_PAYMENT'
]
col_group = ['SK_ID_PREV', 'NUM_INSTALMENT_VERSION', 'NUM_INSTALMENT_NUMBER']
# =============================================================================
# feature
# =============================================================================
ins = utils.get_dummies(utils.read_pickles('../data/installments_payments'))
ins.drop('SK_ID_PREV', axis=1, inplace=True)
base = ins[[KEY]].drop_duplicates().set_index(KEY)
gr = ins.groupby(KEY)
train = utils.load_train([KEY])
test = utils.load_test([KEY])
def nunique(x):
return len(set(x))
# 'AMT_DRAWINGS_CURRENT', 'AMT_DRAWINGS_OTHER_CURRENT',
# 'AMT_DRAWINGS_POS_CURRENT', 'AMT_INST_MIN_REGULARITY',
# 'AMT_PAYMENT_CURRENT', 'AMT_PAYMENT_TOTAL_CURRENT',
# 'AMT_RECEIVABLE_PRINCIPAL', 'AMT_RECIVABLE', 'AMT_TOTAL_RECEIVABLE',
# 'CNT_DRAWINGS_ATM_CURRENT', 'CNT_DRAWINGS_CURRENT',
# 'CNT_DRAWINGS_OTHER_CURRENT', 'CNT_DRAWINGS_POS_CURRENT',
# 'CNT_INSTALMENT_MATURE_CUM', 'SK_DPD', 'SK_DPD_DEF']
#
#col_cat = ['CNT_DRAWINGS_OTHER_CURRENT', 'NAME_CONTRACT_STATUS']
#
#col_group = ['SK_ID_PREV', 'CNT_DRAWINGS_OTHER_CURRENT', 'NAME_CONTRACT_STATUS']
# =============================================================================
# feature
# =============================================================================
cre = utils.get_dummies(utils.read_pickles('../data/credit_card_balance'))
cre.drop('SK_ID_PREV', axis=1, inplace=True)
base = cre[[KEY]].drop_duplicates().set_index(KEY)
gr = cre.groupby(KEY)
train = utils.load_train([KEY])
test = utils.load_test([KEY])
def nunique(x):
return len(set(x))
# =============================================================================
KEY = 'SK_ID_CURR'
PREF = 'pos_201'
NTHREAD = 2
col_num = ['MONTHS_BALANCE', 'CNT_INSTALMENT', 'CNT_INSTALMENT_FUTURE',
'SK_DPD', 'SK_DPD_DEF']
col_cat = ['NAME_CONTRACT_STATUS']
col_group = ['SK_ID_PREV', 'NAME_CONTRACT_STATUS']
# =============================================================================
# feature
# =============================================================================
pos = utils.get_dummies(utils.read_pickles('../data/POS_CASH_balance'))
pos.drop('SK_ID_PREV', axis=1, inplace=True)
base = pos[[KEY]].drop_duplicates().set_index(KEY)
gr = pos.groupby(KEY)
train = utils.load_train([KEY])
test = utils.load_test([KEY])
def nunique(x):
return len(set(x))
col_num = [
'DAYS_CREDIT', 'CREDIT_DAY_OVERDUE', 'DAYS_CREDIT_ENDDATE',
'DAYS_ENDDATE_FACT', 'AMT_CREDIT_MAX_OVERDUE', 'CNT_CREDIT_PROLONG',
'AMT_CREDIT_SUM', 'AMT_CREDIT_SUM_DEBT', 'AMT_CREDIT_SUM_LIMIT',
'AMT_CREDIT_SUM_OVERDUE', 'DAYS_CREDIT_UPDATE', 'AMT_ANNUITY'
]
col_cat = ['CREDIT_ACTIVE', 'CREDIT_CURRENCY', 'CREDIT_TYPE']
col_group = ['CREDIT_ACTIVE', 'CREDIT_CURRENCY', 'CREDIT_TYPE']
# =============================================================================
# feature
# =============================================================================
bureau = utils.get_dummies(utils.read_pickles('../data/bureau'))
bureau.drop('SK_ID_BUREAU', axis=1, inplace=True)
gr = bureau.groupby(KEY)
train = utils.load_train([KEY])
test = utils.load_test([KEY])
def nunique(x):
return len(set(x))
# =============================================================================
# gr1
def run_training():
if torch.cuda.is_available():
DEVICE = 'cuda'
else:
DEVICE = 'cpu'
df_train = pd.read_csv(PATH + 'train_features.csv')
targets = pd.read_csv(PATH + 'train_targets_scored.csv')
utils.get_dummies(df_train, ['cp_type', 'cp_dose', 'cp_time'])
sig_ids = df_train['sig_id']
df_train.drop('sig_id', axis=1, inplace=True)
targets.drop('sig_id', axis=1, inplace=True)
# TODO use unscored data for training as well
X_train, X_val, y_train, y_val = train_test_split(df_train.values,
targets.values,
test_size=0.3,
random_state=42)
train_dataset = utils.ModelDataset(X_train, y_train)
train_loader = DataLoader(train_dataset,
batch_size=BATCH_SIZE,
shuffle=True,
num_workers=4)
val_dataset = utils.ModelDataset(X_val, y_val)
val_loader = DataLoader(val_dataset, batch_size=1)
model = utils.Model(X_train.shape[1], y_train.shape[1], num_layers,
hidden_size)
model.to(DEVICE)
optimizer = torch.optim.Adam(model.parameters(), lr=1e-4)
scheduler = torch.optim.lr_scheduler.StepLR(optimizer,
step_size=30,
gamma=0.1)
engine = utils.Engine(model, optimizer, device=DEVICE)
best_loss = np.inf
early_stopping = 10
early_stopping_counter = 0
# TODO use optuns for trails
for epoch in range(EPOCHS):
train_loss = engine.train(train_loader)
val_loss = engine.validate(val_loader)
scheduler.step(val_loss)
print(f'Epoch {epoch}, train_loss {train_loss}, val_loss {val_loss}')
if val_loss < best_loss:
best_loss = val_loss
torch.save(model.state_dict(), '/models')
else:
early_stopping_counter += 1
if early_stopping_counter > early_stopping:
break
print(f'best loss {best_loss}')
return best_loss
