def predict(datasource, select, data_table, result_table, label_column,
oss_model_path):
"""PAI TensorFlow prediction wrapper
This function do some preparation for the local prediction, say,
download the model from OSS, extract metadata and so on.
Args:
datasource: the datasource from which to get data
select: data selection SQL statement
data_table: tmp table which holds the data from select
result_table: table to save prediction result
label_column: prediction label column
oss_model_path: the model path on OSS
"""
try:
tf.enable_eager_execution()
except: # noqa: E722
pass
(estimator, feature_column_names, feature_column_names_map, feature_metas,
label_meta, model_params,
feature_columns_code) = oss.load_metas(oss_model_path,
"tensorflow_model_desc")
feature_columns = eval(feature_columns_code)
# NOTE(typhoonzero): No need to eval model_params["optimizer"] and
# model_params["loss"] because predicting do not need these parameters.
is_estimator = is_tf_estimator(import_model(estimator))
# Keras single node is using h5 format to save the model, no need to deal
# with export model format. Keras distributed mode will use estimator, so
# this is also needed.
if is_estimator:
oss.load_file(oss_model_path, "exported_path")
# NOTE(typhoonzero): directory "model_save" is hardcoded in
# codegen/tensorflow/codegen.go
oss.load_dir("%s/model_save" % oss_model_path)
else:
oss.load_file(oss_model_path, "model_save")
_predict(datasource=datasource,
estimator_string=estimator,
select=select,
result_table=result_table,
feature_columns=feature_columns,
feature_column_names=feature_column_names,
feature_column_names_map=feature_column_names_map,
train_label_name=label_meta["feature_name"],
result_col_name=label_column,
feature_metas=feature_metas,
model_params=model_params,
save="model_save",
batch_size=1,
pai_table=data_table)
def evaluate(datasource, select, data_table, result_table, oss_model_path,
metrics):
"""PAI Tensorflow evaluate wrapper
This function do some preparation for the local evaluation, say,
download the model from OSS, extract metadata and so on.
Args:
datasource: the datasource from which to get data
select: data selection SQL statement
data_table: tmp table which holds the data from select
result_table: table to save prediction result
oss_model_path: the model path on OSS
metrics: metrics to evaluate
"""
(estimator, feature_column_names, feature_column_names_map, feature_metas,
label_meta, model_params,
feature_columns_code) = oss.load_metas(oss_model_path,
"tensorflow_model_desc")
feature_columns = eval(feature_columns_code)
# NOTE(typhoonzero): No need to eval model_params["optimizer"] and
# model_params["loss"] because predicting do not need these parameters.
is_estimator = is_tf_estimator(import_model(estimator))
# Keras single node is using h5 format to save the model, no need to deal
# with export model format. Keras distributed mode will use estimator, so
# this is also needed.
if is_estimator:
oss.load_file(oss_model_path, "exported_path")
# NOTE(typhoonzero): directory "model_save" is hardcoded in
# codegen/tensorflow/codegen.go
oss.load_dir("%s/model_save" % oss_model_path)
else:
oss.load_file(oss_model_path, "model_save")
_evaluate(datasource=datasource,
estimator_string=estimator,
select=select,
result_table=result_table,
feature_columns=feature_columns,
feature_column_names=feature_column_names,
feature_metas=feature_metas,
label_meta=label_meta,
model_params=model_params,
validation_metrics=metrics,
save="model_save",
batch_size=1,
validation_steps=None,
verbose=0,
is_pai=True,
pai_table=data_table)
def explain(datasource, select, data_table, result_table, label_column,
oss_model_path):
"""Do XGBoost model explanation, this function use selected data to
explain the model stored at oss_model_path
Args:
datasource: The datasource to load explain data
select: SQL statement to get the data set
data_table: tmp table to save the explain data
result_table: table to store the explanation result
label_column: name of the label column
oss_model_path: path to the model to be explained
"""
# NOTE(typhoonzero): the xgboost model file "my_model" is hard coded
# in xgboost/train.py
oss.load_file(oss_model_path, "my_model")
(estimator, model_params, train_params, feature_field_meta,
feature_column_names, label_field_meta,
feature_column_code) = oss.load_metas(oss_model_path,
"xgboost_model_desc")
feature_column_transformers = eval('[{}]'.format(feature_column_code))
transform_fn = xgboost_extended.feature_column.ComposedColumnTransformer(
feature_column_names, *feature_column_transformers)
explain_xgb(
datasource=datasource,
select=select,
feature_field_meta=feature_field_meta,
feature_column_names=feature_column_names,
label_meta=label_field_meta,
summary_params={},
result_table=result_table,
is_pai=True,
pai_explain_table=data_table,
hdfs_namenode_addr="",
hive_location="",
hdfs_user="",
hdfs_pass="",
# (TODO:lhw) save/load explain result storage info into/from FLAGS
oss_dest="",
oss_ak="",
oss_sk="",
oss_endpoint="",
oss_bucket_name="",
transform_fn=transform_fn,
feature_column_code=feature_column_code)
def explain(datasource, select, data_table, result_table, label_column,
oss_model_path):
try:
tf.enable_eager_execution()
except Exception as e:
sys.stderr.write("warning: failed to enable_eager_execution: %s" % e)
pass
(estimator, feature_column_names, feature_column_names_map, feature_metas,
label_meta, model_params,
feature_columns_code) = oss.load_metas(oss_model_path,
"tensorflow_model_desc")
feature_columns = eval(feature_columns_code)
# NOTE(typhoonzero): No need to eval model_params["optimizer"] and
# model_params["loss"] because predicting do not need these parameters.
is_estimator = is_tf_estimator(import_model(estimator))
# Keras single node is using h5 format to save the model, no need to deal
# with export model format. Keras distributed mode will use estimator, so
# this is also needed.
if is_estimator:
oss.load_file(oss_model_path, "exported_path")
# NOTE(typhoonzero): directory "model_save" is hardcoded in
# codegen/tensorflow/codegen.go
oss.load_dir("%s/model_save" % oss_model_path)
else:
oss.load_file(oss_model_path, "model_save")
# (TODO: lhw) use oss to store result image
_explain(datasource=datasource,
estimator_string=estimator,
select=select,
feature_columns=feature_columns,
feature_column_names=feature_column_names,
feature_metas=feature_metas,
label_meta=label_meta,
model_params=model_params,
save="model_save",
result_table=result_table,
pai_table=data_table,
oss_dest=None,
oss_ak=None,
oss_sk=None,
oss_endpoint=None,
oss_bucket_name=None)
def predict(datasource, select, data_table, result_table, label_column,
oss_model_path):
"""PAI XGBoost prediction wrapper
This function do some preparation for the local prediction, say,
download the model from OSS, extract metadata and so on.
Args:
datasource: the datasource from which to get data
select: data selection SQL statement
data_table: tmp table which holds the data from select
result_table: table to save prediction result
label_column: prediction label column
oss_model_path: the model path on OSS
"""
# NOTE(typhoonzero): the xgboost model file "my_model" is hard coded
# in xgboost/train.py
oss.load_file(oss_model_path, "my_model")
(estimator, model_params, train_params, feature_metas,
feature_column_names, label_meta,
feature_column_code) = oss.load_metas(oss_model_path,
"xgboost_model_desc")
pred_label_meta = copy.copy(label_meta)
pred_label_meta["feature_name"] = label_column
feature_column_transformers = eval('[{}]'.format(feature_column_code))
transform_fn = xgboost_extended.feature_column.ComposedColumnTransformer(
feature_column_names, *feature_column_transformers)
pred(datasource=datasource,
select=select,
feature_metas=feature_metas,
feature_column_names=feature_column_names,
train_label_meta=label_meta,
pred_label_meta=label_meta,
result_table=result_table,
is_pai=True,
hdfs_namenode_addr="",
hive_location="",
hdfs_user="",
hdfs_pass="",
pai_table=data_table,
model_params=model_params,
train_params=train_params,
transform_fn=transform_fn,
feature_column_code=feature_column_code)
