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 add_last_poi( poi_path, log ):
def _add_last_poi(row, save=None):
session = row[0]
action = row[1]
ref = row[2]
city = row[3]
if 'session' in save and save['session'] != session or not 'session' in save:
#new session
save['session'] = session
save['last_poi'] = -1
if 'city' in save and save['city'] != city or not 'city' in save:
#new session
save['city'] = city
save['last_poi'] = -1
if action == POI and not np.isnan( ref ):
save['last_poi'] = ref
return save['last_poi']
file = poi_path + 'last_poi.fthr'
if not Path( file ).is_file():
log_full = load_hdfs( poi_path + 'data_log.hd5' )
log_full['last_poi'] = apply(log_full, ['session_id','action_type','reference','city'], _add_last_poi, verbose=100000)
write_feather( log_full[['session_id','last_poi']], file )
last_poi = load_feather( file )
print( len(last_poi) )
last_poi = last_poi[last_poi.session_id.isin( log.session_id.unique() )]
print( len(last_poi) )
print( len(log) )
log['last_poi'] = last_poi['last_poi'].values
del last_poi
return log
def create_set(base_path=SET, key='dataset', conf={}, redo=False):
name = key
path = Path(base_path + 'sets/' + name + '.fthr')
if path.is_file() and not redo:
print('loaded')
examples = load_feather(path)
gc.collect()
else:
print('create')
log = load_hdfs(base_path + 'data_log.hd5')
examples = load_hdfs(base_path + 'data_examples.hd5')
if 'current_filters' in set(examples.columns):
print('current_filters')
del examples['current_filters']
if 'session_id_pre' in set(examples.columns):
print('session_id_pre')
del examples['session_id_pre']
examples = pop_features(conf['path_pop'],
log,
examples,
hidden=conf['pop_hidden'],
min_pop=conf['min_pop'],
train_only=conf['train_only'],
redo=redo)
examples = price_features(conf['path_price'],
log,
examples,
min_occurences=conf['min_occurences'],
hidden=conf['price_hidden'],
train_only=conf['train_only'],
fillna_mean=conf['fillna_mean'],
redo=redo)
examples = session_features(conf['path_session'],
log,
examples,
crawl_path=conf['path_crawl'],
redo=redo)
examples = crawl_features(base_path,
conf['path_crawl'],
log,
examples,
redo=redo)
examples = geo_features(base_path,
conf['path_crawl'],
log,
examples,
redo=redo)
examples = meta_features(base_path,
conf['path_meta'],
log,
examples,
latent=conf['meta_latent'],
redo=redo)
examples = user_features(conf['path_session'],
log,
examples,
crawl_path=conf['path_crawl'],
poi_path=conf['path_poi'],
redo=redo)
examples = position_features(base_path, log, examples, redo=redo)
examples = properties_features(base_path,
conf['path_meta'],
log,
examples,
redo=redo)
#examples = latent_features(base_path, log, examples, latent_path=conf['path_latent'], redo=redo)
examples = latent_sim_features(base_path,
log,
examples,
latent_path=conf['path_latent'],
redo=redo)
examples = combine_features(base_path, log, examples, redo=redo)
examples = rank_features(base_path, log, examples, redo=redo)
examples = time_features(base_path, log, examples, redo=redo)
examples = list_context_features(base_path, log, examples, redo=redo)
examples = stars_features(base_path,
conf['path_meta'],
log,
examples,
redo=redo)
#examples = prediction_features(base_path, log, examples, redo=redo)
#examples.to_csv( base_path + 'sets/' + name + '.csv' )
write_feather(examples, path)
del log
gc.collect()
#print_col_list( examples.columns )
#examples = reduce_mem_usage(examples)
return examples
