def get_installment_payments(path, num_rows=None):
"""Preprocess and extract features from installments_payments.
Arguments:
path: Path to the folder where files are saved (string).
num_rows: Number of rows to read; None reads all rows (int, default: None).
Returns:
df: DataFrame with processed data.
"""
pay = pd.read_csv(os.path.join(path, 'installments_payments.csv'), nrows=num_rows)
# Group payments and get Payment difference
pay = utils.do_sum(pay, ['SK_ID_PREV', 'NUM_INSTALMENT_NUMBER'], 'AMT_PAYMENT', 'AMT_PAYMENT_GROUPED')
pay['PAYMENT_DIFFERENCE'] = pay['AMT_INSTALMENT'] - pay['AMT_PAYMENT_GROUPED']
pay['PAYMENT_RATIO'] = pay['AMT_INSTALMENT'] / pay['AMT_PAYMENT_GROUPED']
pay['PAID_OVER_AMOUNT'] = pay['AMT_PAYMENT'] - pay['AMT_INSTALMENT']
pay['PAID_OVER'] = (pay['PAID_OVER_AMOUNT'] > 0).astype(int)
# Payment Entry: Days past due and Days before due
pay['DPD'] = pay['DAYS_ENTRY_PAYMENT'] - pay['DAYS_INSTALMENT']
pay['DPD'] = pay['DPD'].apply(lambda x: 0 if x <= 0 else x)
pay['DBD'] = pay['DAYS_INSTALMENT'] - pay['DAYS_ENTRY_PAYMENT']
pay['DBD'] = pay['DBD'].apply(lambda x: 0 if x <= 0 else x)
# Flag late payment
pay['LATE_PAYMENT'] = pay['DBD'].apply(lambda x: 1 if x > 0 else 0)
# Percentage of payments that were late
pay['INSTALMENT_PAYMENT_RATIO'] = pay['AMT_PAYMENT'] / pay['AMT_INSTALMENT']
pay['LATE_PAYMENT_RATIO'] = pay.apply(lambda x: x['INSTALMENT_PAYMENT_RATIO'] if x['LATE_PAYMENT'] == 1 else 0, axis=1)
# Flag late payments that have a significant amount
pay['SIGNIFICANT_LATE_PAYMENT'] = pay['LATE_PAYMENT_RATIO'].apply(lambda x: 1 if x > 0.05 else 0)
# Flag k threshold late payments
pay['DPD_7'] = pay['DPD'].apply(lambda x: 1 if x > 7 else 0)
pay['DPD_15'] = pay['DPD'].apply(lambda x: 1 if x > 15 else 0)
# Aggregations by SK_ID_CURR
pay_agg = utils.group(pay, 'INS_', config.INSTALLMENTS_AGG)
# Installments in the last x months
for months in [18, 36]:
recent_prev_id = pay[pay['DAYS_INSTALMENT'] >= -30*months]['SK_ID_PREV'].unique()
pay_recent = pay[pay['SK_ID_PREV'].isin(recent_prev_id)]
prefix = 'INS_{}M_'.format(months)
pay_agg = utils.group_and_merge(pay_recent, pay_agg, prefix, config.INSTALLMENTS_TIME_AGG)
# Last x periods trend features
group_features = ['SK_ID_CURR', 'SK_ID_PREV', 'DPD', 'LATE_PAYMENT',
'PAID_OVER_AMOUNT', 'PAID_OVER', 'DAYS_INSTALMENT']
group = pay[group_features].groupby('SK_ID_CURR')
func = partial(_trend_in_last_k_instalment_features, periods=[12, 24, 60, 120])
g = utils.parallel_apply(group, func, index_name='SK_ID_CURR', chunk_size=10000).reset_index()
pay_agg = pay_agg.merge(g, on='SK_ID_CURR', how='left')
# Last loan features
g = utils.parallel_apply(group, _installments_last_loan_features, index_name='SK_ID_CURR', chunk_size=10000).reset_index()
pay_agg = pay_agg.merge(g, on='SK_ID_CURR', how='left')
return pay_agg
def ensemble(args):
global dfs, ens_dets, MAX_NUM, bg_time
MAX_NUM = args.max_num
df_test = pd.read_csv(os.path.join(DATA_DIR,
'sample_empty_submission.csv'))
df_test.PredictionString = df_test.PredictionString.fillna('')
print(df_test.head())
print('loading {} ...'.format(csv_files))
dfs = [pd.read_csv(fn) for fn in csv_files]
for df in dfs:
df.PredictionString = df.PredictionString.fillna('')
#assert len(preds[0][1]) == len(classes)
for i in range(len(dfs)):
dfs[i] = dfs[i].set_index('ImageID')
dfs[i] = dfs[i].reindex(index=df_test['ImageID'])
dfs[i] = dfs[i].reset_index()
print('ensembling...')
bg_time = time.time()
counter = Value('i', 0)
with Pool(24, initializer=init, initargs=(counter, )) as p:
num_imgs = len(dfs[0])
#ens_dets = list(tqdm(iterable=p.map(get_ens_det, list(range(num_imgs))), total=num_imgs))
ens_dets = p.map(get_ens_det, range(num_imgs))
print('creating submission...')
df_test['img_index'] = df_test.index
df_test = parallel_apply(df_test, set_pred_str)
df_test = df_test.drop(columns=['img_index'], axis=1)
df_test.to_csv(args.out, index=False)
print('done')
def submit(args):
global preds, classes
classes, _ = get_top_classes(args.start_index, args.end_index,
args.class_file)
print('loading {}...'.format(args.pred_file))
with open(args.pred_file, 'rb') as f:
preds = pickle.load(f)
print('len(preds):', len(preds))
print('num classes of preds:', len(preds[0][1]))
print('specified num classes:', len(classes))
#assert len(preds[0][1]) == len(classes)
print('creating submission...')
df_test = pd.read_csv(osp.join(DATA_DIR, 'sample_empty_submission.csv'))
df_test.ImageWidth = df_test.ImageID.map(
lambda x: get_image_size(get_fn(x))[0])
df_test.ImageHeight = df_test.ImageID.map(
lambda x: get_image_size(get_fn(x))[1])
df_test['img_index'] = df_test.index
df_test = parallel_apply(df_test, set_pred_str)
df_test = df_test.drop(columns=['img_index'], axis=1)
df_test.to_csv(args.out, index=False)
print('done')
def _update(self, update_helper_func):
self._current_resolution_level = parallel_apply(
self._current_resolution_level,
update_helper_func,
self._n_threads, 1,
**{"current_vector":
self._current_resolution.values}
)
def ensemble(args):
global all_preds, classes, ens_dets, MAX_NUM, bg_time
MAX_NUM = args.max_num
#print('getting img size...')
df_test = pd.read_csv(osp.join(DATA_DIR, 'sample_empty_submission.csv'))
print(df_test.head())
#df_test.ImageWidth = df_test.ImageID.map(lambda x: get_image_size(get_fn(x))[0])
#df_test.ImageHeight = df_test.ImageID.map(lambda x: get_image_size(get_fn(x))[1])
classes, _ = get_top_classes(args.start_index, args.end_index,
args.class_file)
for fn in pred_files:
print('loading {} ...'.format(fn))
with open(fn, 'rb') as f:
all_preds.append(pickle.load(f))
print('len(preds):', len(all_preds[0]))
print('num classes of preds:', len(all_preds[0][0][1]))
print('specified num classes:', len(classes))
#assert len(preds[0][1]) == len(classes)
print('ensembling...')
bg_time = time.time()
counter = Value('i', 0)
with Pool(24, initializer=init, initargs=(counter, )) as p:
num_imgs = len(all_preds[0])
#ens_dets = list(tqdm(iterable=p.map(get_ens_det, list(range(num_imgs))), total=num_imgs))
ens_dets = p.map(get_ens_det, range(num_imgs))
#num_imgs = len(preds1)
#for idx in tqdm(range(num_imgs), total=num_imgs):
# ens_dets.append(get_ens_det(idx))
print('creating submission...')
df_test['img_index'] = df_test.index
df_test = parallel_apply(df_test, set_pred_str)
df_test = df_test.drop(columns=['img_index'], axis=1)
df_test.to_csv(args.out, index=False)
print('done')
historical_transactions = pd.read_csv(
os.path.join(PATH, 'historical_transactions.csv'))
historical_transactions['purchase_date'] = pd.to_datetime(
historical_transactions['purchase_date'])
historical_transactions['days'] = (
datetime.date(2018, 2, 28) -
historical_transactions['purchase_date'].dt.date).dt.days
historical_transactions = historical_transactions.query(
'0 <= installments and installments <= 12')
# =============================================================================
#
# =============================================================================
groupby = historical_transactions.groupby('card_id')
func = partial(last_k_instalment_features_with_fractions,
periods=[60, 180, 360, 540],
fraction_periods=[(60, 180), (60, 360), (180, 540), (360, 540)])
g = utils.parallel_apply(groupby,
func,
index_name='card_id',
num_workers=4,
chunk_size=10000).reset_index()
g.to_pickle(f'../feature/{PREF}.pkl')
#==============================================================================
utils.end(__file__)
print("flag:", 7)
data_link_split = data_link_split.reset_index(drop=True)
data_link_split[[
'link_id', 'link_time', 'link_ratio', 'link_current_status',
'link_arrival_status'
]] = data_link_split['link_info'].str.split(':|,', 5, expand=True)
print("flag:", 8)
data_link_split = data_link_split[['order_id', 'link_id']]
data_link_split['link_id'] = data_link_split['link_id'].astype(int)
features = pd.DataFrame(
{'order_id': data_link_split['order_id'].unique()})
groupby = data_link_split.groupby(['order_id'])
func = partial(link_id_find)
g = parallel_apply(groupby,
func,
index_name='order_id',
num_workers=5,
chunk_size=10000)
g = pd.DataFrame(g, columns=['from_id', 'to_id'])
g = g.drop_duplicates()
nextlinks_new.append(g)
nextlinks_new = pd.concat(nextlinks_new, axis=0)
nextlinks_new = nextlinks_new.drop_duplicates()
nextlinks_new = nextlinks_new.sort_values(by='from_id').reset_index(
drop=True)
nextlinks = pd.concat([nextlinks, nextlinks_new], axis=0)
nextlinks = nextlinks.drop_duplicates()
nextlinks = nextlinks.sort_values(by='from_id').reset_index(drop=True)
print('save all csv')
nextlinks.to_csv(root_path + 'nextlinks_allday.csv', index=False)
print('calcute weight')
def _score(self, score_helper_func):
score = parallel_apply(
self._current_resolution_level,
score_helper_func,
self._n_threads, 1)
return score