def _explain(datasource,
estimator_string,
select,
feature_columns,
feature_column_names,
feature_metas={},
label_meta={},
model_params={},
save="",
pai_table="",
plot_type='bar',
result_table="",
oss_dest=None,
oss_ak=None,
oss_sk=None,
oss_endpoint=None,
oss_bucket_name=None):
estimator_cls = import_model(estimator_string)
FLAGS = tf.app.flags.FLAGS
model_params["model_dir"] = FLAGS.checkpointDir
model_params.update(feature_columns)
def _input_fn():
dataset = input_fn("",
datasource,
feature_column_names,
feature_metas,
label_meta,
is_pai=True,
pai_table=pai_table)
return dataset.batch(1).cache()
estimator = init_model_with_feature_column(estimator_cls, model_params)
driver = "pai_maxcompute"
conn = None
if estimator_cls in (tf.estimator.BoostedTreesClassifier,
tf.estimator.BoostedTreesRegressor):
explain_boosted_trees(datasource, estimator, _input_fn, plot_type,
result_table, feature_column_names, driver, conn,
"", "", "", "", oss_dest, oss_ak, oss_sk,
oss_endpoint, oss_bucket_name)
else:
shap_dataset = pd.DataFrame(columns=feature_column_names)
for i, (features, label) in enumerate(_input_fn()):
shap_dataset.loc[i] = [
item.numpy()[0][0] for item in features.values()
]
explain_dnns(datasource, estimator, shap_dataset, plot_type,
result_table, feature_column_names, driver, conn, "", "",
"", "", oss_dest, oss_ak, oss_sk, oss_endpoint,
oss_bucket_name)
def _explain(datasource,
estimator_string,
select,
feature_columns,
feature_column_names,
feature_metas={},
label_meta={},
model_params={},
save="",
pai_table="",
plot_type='bar',
result_table="",
oss_dest=None,
oss_ak=None,
oss_sk=None,
oss_endpoint=None,
oss_bucket_name=None):
estimator_cls = import_model(estimator_string)
if is_tf_estimator(estimator_cls):
model_params['model_dir'] = save
model_params.update(feature_columns)
pop_optimizer_and_loss(model_params)
is_pai = True if pai_table else False
def _input_fn():
dataset = input_fn(select,
datasource,
feature_column_names,
feature_metas,
label_meta,
is_pai=is_pai,
pai_table=pai_table)
return dataset.batch(1).cache()
estimator = init_model_with_feature_column(estimator_cls, model_params)
if not is_tf_estimator(estimator_cls):
load_keras_model_weights(estimator, save)
if result_table:
if is_pai:
conn = PaiIOConnection.from_table(result_table)
else:
conn = db.connect_with_data_source(datasource)
else:
conn = None
if estimator_cls in (tf.estimator.BoostedTreesClassifier,
tf.estimator.BoostedTreesRegressor):
explain_boosted_trees(datasource, estimator, _input_fn, plot_type,
result_table, feature_column_names, conn,
oss_dest, oss_ak, oss_sk, oss_endpoint,
oss_bucket_name)
else:
shap_dataset = pd.DataFrame(columns=feature_column_names)
for i, (features, label) in enumerate(_input_fn()):
shap_dataset.loc[i] = [
item.numpy()[0][0] for item in features.values()
]
explain_dnns(datasource, estimator, shap_dataset, plot_type,
result_table, feature_column_names, conn, oss_dest,
oss_ak, oss_sk, oss_endpoint, oss_bucket_name)
if conn is not None:
conn.close()