def db_generator(driver,
conn,
statement,
feature_column_names,
label_column_name,
feature_specs,
fetch_size=128):
def read_feature(raw_val, feature_spec, feature_name):
# FIXME(typhoonzero): Should use correct dtype here.
if feature_spec["is_sparse"]:
indices = np.fromstring(raw_val,
dtype=int,
sep=feature_spec["delimiter"])
indices = indices.reshape(indices.size, 1)
values = np.ones([indices.size], dtype=np.int32)
dense_shape = np.array(feature_spec["shape"], dtype=np.int64)
return (indices, values, dense_shape)
else:
# Dense string vector
if feature_spec["delimiter"] != "":
if feature_spec["dtype"] == "float32":
return np.fromstring(raw_val,
dtype=float,
sep=feature_spec["delimiter"])
elif feature_spec["dtype"] == "int64":
return np.fromstring(raw_val,
dtype=int,
sep=feature_spec["delimiter"])
else:
raise ValueError('unrecognize dtype {}'.format(
feature_spec[feature_name]["dtype"]))
else:
return (raw_val, )
def reader():
if driver == "hive":
cursor = conn.cursor(configuration=conn.session_cfg)
else:
cursor = conn.cursor()
cursor.execute(statement)
if driver == "hive":
field_names = None if cursor.description is None \
else [i[0][i[0].find('.') + 1:] for i in cursor.description]
else:
field_names = None if cursor.description is None \
else [i[0] for i in cursor.description]
if label_column_name:
try:
label_idx = field_names.index(label_column_name)
except ValueError:
# NOTE(typhoonzero): For clustering model, label_column_name may not in field_names when predicting.
label_idx = None
else:
label_idx = None
while True:
rows = cursor.fetchmany(size=fetch_size)
if not rows:
break
# NOTE: keep the connection while training or connection will lost if no activities appear.
if driver == "mysql":
conn.ping(True)
for row in rows:
# NOTE: If there is no label clause in the extened SQL, the default label value would
# be -1, the Model implementation can determine use it or not.
label = row[label_idx] if label_idx is not None else -1
features = []
for name in feature_column_names:
feature = read_feature(row[field_names.index(name)],
feature_specs[name], name)
features.append(feature)
if label_idx is None:
yield (tuple(features), )
else:
yield tuple(features), label
if len(rows) < fetch_size:
break
cursor.close()
if driver == "maxcompute":
from sqlflow_submitter.maxcompute import MaxCompute
return MaxCompute.db_generator(conn, statement, feature_column_names,
label_column_name, feature_specs,
fetch_size)
if driver == "hive":
# trip the suffix ';' to avoid the ParseException in hive
statement = statement.rstrip(';')
return reader
def db_generator(driver,
conn,
statement,
feature_column_names,
label_spec,
feature_specs,
fetch_size=128):
def reader():
if driver == "hive":
cursor = conn.cursor(configuration=conn.session_cfg)
else:
cursor = conn.cursor()
cursor.execute(statement)
if driver == "hive":
field_names = None if cursor.description is None \
else [i[0][i[0].find('.') + 1:] for i in cursor.description]
else:
field_names = None if cursor.description is None \
else [i[0] for i in cursor.description]
if label_spec:
try:
label_idx = field_names.index(label_spec["feature_name"])
except ValueError:
# NOTE(typhoonzero): For clustering model, label_column_name may not in field_names when predicting.
label_idx = None
else:
label_idx = None
while True:
rows = cursor.fetchmany(size=fetch_size)
if not rows:
break
# NOTE: keep the connection while training or connection will lost if no activities appear.
if driver == "mysql":
conn.ping(True)
for row in rows:
# NOTE: If there is no label clause in the extended SQL, the default label value would
# be -1, the Model implementation can determine use it or not.
label = row[label_idx] if label_idx is not None else -1
if label_spec and label_spec["delimiter"] != "":
if label_spec["dtype"] == "float32":
label = np.fromstring(label,
dtype=float,
sep=label_spec["delimiter"])
elif label_spec["dtype"] == "int64":
label = np.fromstring(label,
dtype=int,
sep=label_spec["delimiter"])
if label_idx is None:
yield list(row), None
else:
yield list(row), label
if len(rows) < fetch_size:
break
cursor.close()
if driver == "maxcompute":
from sqlflow_submitter.maxcompute import MaxCompute
return MaxCompute.db_generator(conn, statement, feature_column_names,
label_spec, feature_specs, fetch_size)
if driver == "hive":
# trip the suffix ';' to avoid the ParseException in hive
statement = statement.rstrip(';')
return reader