def latent_sim_features(base_path,
log,
examples,
latent_path=None,
keys=KEYS,
sizes=SIZES,
redo=False):
name = 'latent_sim_features'
if latent_path is None:
latent_path = base_path
path = Path(base_path + 'features/' + name + '.fthr')
if path.is_file() and not redo:
features = load_feather(path)
features = features[features.session_id.isin(
examples.session_id.unique())]
examples = copy_features(examples, features)
else:
examples, cols = create_features(log,
examples,
latent_path=latent_path,
keys=keys,
sizes=sizes)
examples = reduce_mem_usage(examples, cols=cols)
write_feather(examples[['session_id', 'impressions'] + list(cols)],
path)
#examples[['session_id','impressions','label','step'] + list(cols)].to_csv( base_path + 'features/' + name + '.csv' )
print_col_list(cols)
return examples
def time_features(base_path,
log,
examples,
preprocessed_path=PREPROCESSED_FOLDER,
redo=False):
name = 'time_features'
path = Path(base_path + 'features/' + name + '.fthr')
if path.is_file() and not redo:
features = load_feather(path)
features = features[features.session_id.isin(
examples.session_id.unique())]
examples = copy_features(examples, features)
else:
examples, cols = create_features(log,
examples,
preprocessed_path=preprocessed_path)
examples = reduce_mem_usage(examples, cols=cols)
write_feather(examples[['session_id', 'impressions'] + list(cols)],
path)
#examples[['session_id','impressions','label','step'] + list(cols)].to_csv( base_path + 'features/' + name + '.csv' )
print_col_list(cols)
return examples
def pop_features(base_path,
log,
examples,
hidden=False,
min_pop=None,
train_only=False,
redo=False):
name = 'pop_features'
if hidden:
name += '_hidden'
if min_pop is not None:
name += '_mp' + str(min_pop)
if train_only:
name += '_trainonly'
path = Path(base_path + 'features/' + name + '.fthr')
if path.is_file() and not redo:
features = load_feather(path)
features = features[features.session_id.isin(
examples.session_id.unique())]
examples = copy_features(examples, features)
else:
examples, cols = create_features(log,
examples,
hidden=hidden,
min_pop=min_pop,
train_only=train_only)
examples = reduce_mem_usage(examples)
write_feather(examples[['session_id', 'impressions'] + list(cols)],
path)
#examples[['session_id','impressions','prices','label'] + list(cols)].to_csv( base_path + 'features/' + name + '.csv' )
print_col_list(cols)
return examples
def meta_features(base_path,
meta_path,
log,
examples,
latent='d2v',
latent_size=16,
redo=False):
name = 'meta_features'
if latent == None:
name += '_all'
else:
name += '_' + str(latent_size)
path = Path(base_path + 'features/' + name + '.fthr')
if path.is_file() and not redo:
features = load_feather(path)
features = features[features.session_id.isin(
examples.session_id.unique())]
examples = copy_features(examples, features)
else:
examples, cols = create_features(meta_path,
log,
examples,
latent_prefix=latent,
latent_size=latent_size)
examples = reduce_mem_usage(examples)
write_feather(examples[['session_id', 'impressions'] + list(cols)],
path)
#examples[['session_id','impressions','prices','label'] + list(cols)].to_csv( base_path + 'features/' + name + '.csv' )
print_col_list(cols)
return examples
def crawl_features(base_path, crawl_path, log, examples, redo=False):
name = 'crawl_features'
path = Path( base_path + 'features/' + name + '.fthr' )
if path.is_file() and not redo:
features = load_feather( path )
features = features[features.session_id.isin( examples.session_id.unique() )]
examples = copy_features( examples, features )
else:
examples, cols = create_features( crawl_path, log, examples )
examples = reduce_mem_usage(examples)
write_feather( examples[['session_id','impressions'] + list(cols)], path )
#examples[['session_id','impressions','prices','label'] + list(cols)].to_csv( base_path + 'features/' + name + '.csv' )
print_col_list( cols )
return examples
def session_features(base_path, log, examples, price_path=None, crawl_path=CRAWL_FOLDER, poi_path=POI_FOLDER, redo=False):
name = 'session_features'
if price_path is None:
price_path = base_path
path = Path( base_path + 'features/' + name + '.fthr' )
if path.is_file() and not redo:
features = load_feather( path )
features = features[features.session_id.isin( examples.session_id.unique() )]
examples = copy_features( examples, features )
else:
examples, cols = create_features( log, examples, price_path=price_path, crawl_path=crawl_path, poi_path=poi_path )
examples = reduce_mem_usage(examples, cols=cols)
write_feather( examples[['session_id','impressions'] + list(cols)], path )
#examples[['session_id','impressions','label','step'] + list(cols)].to_csv( base_path + 'features/' + name + '.csv' )
print_col_list( cols )
return examples
def list_context_features(base_path, log, examples, shifts=SHIFTS, redo=False):
name = 'list_context_features_' + str(shifts)
path = Path(base_path + 'features/' + name + '.fthr')
if path.is_file() and not redo:
features = load_feather(path)
features = features[features.session_id.isin(
examples.session_id.unique())]
examples = copy_features(examples, features)
else:
examples, cols = create_features(log, examples, shifts=shifts)
examples = reduce_mem_usage(examples)
write_feather(examples[['session_id', 'impressions'] + list(cols)],
path)
#examples[['session_id','impressions','prices','label','position'] + list(cols)].to_csv( base_path + 'features/' + name + '.csv' )
print_col_list(cols)
return examples
def split_competition(data):
if DAYS_TRAIN_COMPETITION is not None:
maxtr = datetime.fromtimestamp(data[data.train == 1].timestamp.max())
mintr = datetime.fromtimestamp(data[data.train == 1].timestamp.min())
start = maxtr - timedelta(days=DAYS_TRAIN_COMPETITION)
data['mintimestamp'] = data.groupby('user_id').timestamp.transform(min)
if RANDOM_TRAIN:
keep = ((data['mintimestamp'] >= start.timestamp()) &
(data.train == 1))
num_sess = data[keep].session_id.nunique()
sess = list(data[data.train == 1].session_id.unique())
shuffle(sess)
keep = sess[:num_sess]
keep = (data.train == 0) | data.session_id.isin(keep)
data = data[keep]
else:
keep = ((data['mintimestamp'] >= start.timestamp()) &
(data.train == 1))
keep = keep | (data.train == 0)
data = data[keep]
mintr = data[data.train == 1].timestamp.min()
minva = data[data.train == 0].timestamp.min()
maxva = data[data.train == 0].timestamp.max()
print(datetime.fromtimestamp(mintr))
print(datetime.fromtimestamp(minva))
print(datetime.fromtimestamp(maxva))
del data['mintimestamp']
data['hidden'] = 0
hide_test = data.reference.isnull() & (data.action_type == CLICK)
data.ix[hide_test, 'reference'] = np.nan
data.ix[hide_test, 'hidden'] = 1
hide_train = data[(data.train == 1)
& (data.action_type == CLICK)].copy() # filter clickout
hide_train = hide_train.drop_duplicates('user_id', keep='last')
data.ix[hide_train.index, 'reference'] = np.nan
data.ix[hide_train.index, 'hidden'] = 1
tmp = pd.DataFrame()
tmp['maxstamp'] = data[data.hidden == 1].groupby(
'session_id').timestamp.max()
data = data.merge(tmp, right_index=True, left_on='session_id', how='left')
data['maxstamp'] = data['maxstamp'].fillna(data.timestamp.max())
data['exclude'] = 0
data.ix[data.timestamp > data.maxstamp, 'exclude'] = 1
del data['maxstamp'], tmp
data = reduce_mem_usage(data)
write_hdfs(data, TARGET + 'data_log.hd5')
#data.to_csv( TARGET + 'data_log.csv', index=False )
data[data.train == 0].to_csv(TARGET + 'data_log_test.csv')
examples = expand_and_label(data)
write_hdfs(examples, TARGET + 'data_examples.hd5')
def split_sample(data):
data = data[data.train == 1].copy()
data = reduce_mem_usage(data)
maxtr = datetime.fromtimestamp(data.timestamp.max())
mintr = datetime.fromtimestamp(data.timestamp.min())
minva = maxtr - timedelta(days=DAYS_TEST)
if DAYS_TRAIN is not None:
mintr = maxtr - timedelta(days=DAYS_TEST + DAYS_TRAIN)
print(mintr)
print(maxtr)
print(minva)
data['mintimestamp'] = data.groupby('user_id').timestamp.transform(min)
data['train'] = (data['mintimestamp'] >= mintr.timestamp()).astype(int)
if RANDOM_TRAIN:
data.ix[data['mintimestamp'] >= minva.timestamp(), 'train'] = 0
num_sess = data[data.train == 1].session_id.nunique()
data['train'] = 1
data.ix[data['mintimestamp'] >= minva.timestamp(), 'train'] = 0
sess = list(data[data.train == 1].session_id.unique())
shuffle(sess)
keep = sess[:num_sess]
keep = (data.train == 0) | data.session_id.isin(keep)
data = data[keep]
else:
data = data[data.train == 1]
data.loc[data['mintimestamp'] >= minva.timestamp(), 'train'] = 0
print(data[['session_id', 'timestamp', 'mintimestamp', 'train']])
mintr = data[data.train == 1].timestamp.min()
minva = data[data.train == 0].timestamp.min()
maxva = data[data.train == 0].timestamp.max()
print(datetime.fromtimestamp(mintr))
print(datetime.fromtimestamp(minva))
print(datetime.fromtimestamp(maxva))
print(len(data[data.train == 1]))
print(len(data[data.train == 0]))
data = data.reset_index(drop=True)
del data['mintimestamp']
#print( len( set(test.session_id.unique()) & set(train.session_id.unique()) ) )
data['hidden'] = 0
data['exclude'] = 0
examples_log = data[data.action_type == CLICK].copy() # filter clickout
examples_log = examples_log.drop_duplicates('user_id', keep='last')
truth = examples_log[examples_log.train == 0]
#hide all
data.loc[examples_log.index.values, 'reference'] = np.nan
data.loc[examples_log.index.values, 'hidden'] = 1
print('hidden test sum ',
data[(data.hidden == 1) & (data.train == 0)].hidden.sum())
tmp = pd.DataFrame()
tmp['maxstamp'] = data[data.hidden == 1].groupby(
'session_id').timestamp.max()
data = data.merge(tmp, right_index=True, left_on='session_id', how='left')
data['maxstamp'] = data['maxstamp'].fillna(data.timestamp.max())
data.loc[data.timestamp > data.maxstamp, 'exclude'] = 1
del data['maxstamp'], tmp
print('hidden test sum ',
data[(data.hidden == 1) & (data.train == 0)].hidden.sum())
examples = expand_and_label(data)
#hide test completely
data.loc[examples_log[examples_log.train == 0].index.values,
'item_id'] = np.nan
data.loc[examples_log[examples_log.train == 0].index.values,
'price_session'] = np.nan
data = reduce_mem_usage(data)
write_hdfs(data, TARGET + 'data_log.hd5')
#data.to_csv( TARGET + 'data_log.csv' )
data[data.train == 0].to_csv(TARGET + 'data_log_test.csv')
write_hdfs(examples, TARGET + 'data_examples.hd5')
#examples.to_csv( TARGET + 'data_examples.csv', index=False )
truth.to_csv(TARGET + 'truth.csv', index=False)
with open(TARGET + 'size.txt', 'w') as out:
out.write('train_size: {}, test_size: {}'.format(
DAYS_TRAIN, DAYS_TEST))
