def _predict(datasource,
estimator_string,
select,
result_table,
feature_columns,
feature_column_names,
feature_column_names_map,
train_label_name,
result_col_name,
feature_metas={},
model_params={},
save="",
batch_size=1,
pai_table=""):
estimator = import_model(estimator_string)
model_params.update(feature_columns)
is_estimator = is_tf_estimator(estimator)
conn = PaiIOConnection.from_table(pai_table)
selected_cols = db.selected_cols(conn, None)
predict_generator = db.db_generator(conn, None)
pop_optimizer_and_loss(model_params)
if not is_estimator:
if not issubclass(estimator, tf.keras.Model):
# functional model need field_metas parameter
model_params["field_metas"] = feature_metas
print("Start predicting using keras model...")
keras_predict(estimator, model_params, save, result_table,
feature_column_names, feature_metas, train_label_name,
result_col_name, conn, predict_generator, selected_cols)
else:
model_params['model_dir'] = save
print("Start predicting using estimator model...")
estimator_predict(result_table, feature_column_names, feature_metas,
train_label_name, result_col_name, conn,
predict_generator, selected_cols)
print("Done predicting. Predict table : %s" % result_table)
def predict_step(datasource,
select,
result_table,
label_name,
model,
pai_table=None):
if isinstance(model, six.string_types):
model = Model.load_from_db(datasource, model)
else:
assert isinstance(model,
Model), "not supported model type %s" % type(model)
model_params = model.get_meta("attributes")
train_fc_map = model.get_meta("features")
label_meta = model.get_meta("label")
train_label_desc = label_meta.get_field_desc()[0] if label_meta else None
train_label_name = train_label_desc.name if train_label_desc else None
estimator_string = model.get_meta("class_name")
save = "model_save"
field_descs = get_ordered_field_descs(train_fc_map)
feature_column_names = [fd.name for fd in field_descs]
feature_metas = dict([(fd.name, fd.to_dict(dtype_to_string=True))
for fd in field_descs])
feature_columns = compile_ir_feature_columns(train_fc_map,
model.get_type())
is_pai = True if pai_table else False
if is_pai:
select = "SELECT * FROM %s" % pai_table
conn = db.connect_with_data_source(datasource)
result_column_names, train_label_idx = create_predict_table(
conn, select, result_table, train_label_desc, label_name)
if is_pai:
conn.close()
conn = PaiIOConnection.from_table(pai_table)
select = None
selected_cols = result_column_names[0:-1]
if train_label_idx >= 0:
selected_cols = selected_cols[0:train_label_idx] + [
train_label_name
] + selected_cols[train_label_idx:]
estimator = import_model(estimator_string)
model_params.update(feature_columns)
is_estimator = is_tf_estimator(estimator)
predict_generator = db.db_generator(conn, select)
pop_optimizer_and_loss(model_params)
if not is_estimator:
if not issubclass(estimator, tf.keras.Model):
# functional model need field_metas parameter
model_params["field_metas"] = feature_metas
print("Start predicting using keras model...")
keras_predict(estimator, model_params, save, result_table,
feature_column_names, feature_metas, train_label_name,
label_name, conn, predict_generator, selected_cols)
else:
model_params['model_dir'] = save
print("Start predicting using estimator model...")
estimator_predict(result_table, feature_column_names, feature_metas,
train_label_name, label_name, conn,
predict_generator, selected_cols)
print("Done predicting. Predict table : %s" % result_table)
conn.close()
def _predict(datasource,
estimator_string,
select,
result_table,
feature_columns,
feature_column_names,
feature_column_names_map,
train_label_name,
result_col_name,
feature_metas={},
model_params={},
save="",
batch_size=1,
pai_table=""):
estimator = import_model(estimator_string)
model_params.update(feature_columns)
is_estimator = is_tf_estimator(estimator)
conn = None
driver = "pai_maxcompute"
pai_table_parts = pai_table.split(".")
formatted_pai_table = "odps://%s/tables/%s" % (pai_table_parts[0],
pai_table_parts[1])
selected_cols = db.pai_selected_cols(formatted_pai_table)
predict_generator = db.pai_maxcompute_db_generator(formatted_pai_table)
if not is_estimator:
if not issubclass(estimator, tf.keras.Model):
# functional model need field_metas parameter
model_params["field_metas"] = feature_metas
print("Start predicting using keras model...")
keras_predict(estimator,
model_params,
save,
result_table,
feature_column_names,
feature_metas,
train_label_name,
result_col_name,
driver,
conn,
predict_generator,
selected_cols,
hdfs_namenode_addr="",
hive_location="",
hdfs_user="",
hdfs_pass="")
else:
model_params['model_dir'] = save
print("Start predicting using estimator model...")
estimator_predict(estimator,
model_params,
save,
result_table,
feature_column_names,
feature_column_names_map,
feature_columns,
feature_metas,
train_label_name,
result_col_name,
driver,
conn,
predict_generator,
selected_cols,
hdfs_namenode_addr="",
hive_location="",
hdfs_user="",
hdfs_pass="")
print("Done predicting. Predict table : %s" % result_table)