def pred(datasource,
estimator_string,
select,
result_table,
feature_columns,
feature_column_names,
feature_column_names_map,
result_col_name,
feature_metas={},
model_params={},
save="",
batch_size=1,
hdfs_namenode_addr="",
hive_location="",
hdfs_user="",
hdfs_pass="",
is_pai=False,
pai_table=""):
# import custom model package
sqlflow_submitter.import_model_def(estimator_string, globals())
estimator = eval(estimator_string)
if not is_pai:
conn = db.connect_with_data_source(datasource)
model_params.update(feature_columns)
is_estimator = issubclass(
estimator,
(tf.estimator.Estimator, tf.estimator.BoostedTreesClassifier,
tf.estimator.BoostedTreesRegressor))
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, is_pai,
pai_table, feature_column_names, feature_metas,
result_col_name, datasource, select, 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, result_col_name,
datasource, select, hdfs_namenode_addr,
hive_location, hdfs_user, hdfs_pass, is_pai,
pai_table)
print("Done predicting. Predict table : %s" % result_table)
def evaluate(datasource,
estimator_string,
select,
result_table,
feature_columns,
feature_column_names,
feature_metas={},
label_meta={},
model_params={},
validation_metrics=["Accuracy"],
save="",
batch_size=1,
validation_steps=None,
verbose=0,
hdfs_namenode_addr="",
hive_location="",
hdfs_user="",
hdfs_pass="",
is_pai=False,
pai_table=""):
# import custom model package
sqlflow_submitter.import_model_def(estimator_string, globals())
estimator_cls = eval(estimator_string)
is_estimator = is_tf_estimator(estimator_cls)
set_log_level(verbose, is_estimator)
eval_dataset, _ = get_dataset_fn(select,
"",
datasource,
feature_column_names,
feature_metas,
label_meta,
is_pai,
pai_table,
"",
1,
batch_size,
1,
is_estimator=is_estimator)
model_params.update(feature_columns)
if is_estimator:
if is_pai:
FLAGS = tf.app.flags.FLAGS
model_params["model_dir"] = FLAGS.checkpointDir
else:
model_params["model_dir"] = save
# tf estimator always have feature_column argument
estimator = estimator_cls(**model_params)
result_metrics = estimator_evaluate(estimator, eval_dataset,
validation_metrics)
else:
keras_model = init_model_with_feature_column(estimator, model_params)
keras_model_pkg = sys.modules[estimator_cls.__module__]
result_metrics = keras_evaluate(keras_model, eval_dataset, save,
keras_model_pkg, validation_metrics)
# write result metrics to a table
if is_pai:
driver = "pai_maxcompute"
conn = None
else:
conn = connect_with_data_source(datasource)
driver = conn.driver
if result_table:
metric_name_list = ["loss"] + validation_metrics
write_result_metrics(result_metrics,
metric_name_list,
result_table,
driver,
conn,
hdfs_namenode_addr=hdfs_namenode_addr,
hive_location=hive_location,
hdfs_user=hdfs_user,
hdfs_pass=hdfs_pass)
def train(datasource,
estimator_string,
select,
validation_select,
feature_columns,
feature_column_names,
feature_metas={},
label_meta={},
model_params={},
validation_metrics=["Accuracy"],
save="",
batch_size=1,
epoch=1,
validation_steps=1,
verbose=0,
max_steps=None,
validation_start_delay_secs=0,
validation_throttle_secs=0,
save_checkpoints_steps=100,
log_every_n_iter=10,
load_pretrained_model=False,
is_pai=False,
pai_table="",
pai_val_table="",
feature_columns_code="",
model_repo_image="",
original_sql=""):
model_meta = collect_model_metadata(original_sql, select,
validation_select, estimator_string,
model_params, feature_columns_code,
feature_metas, label_meta, None,
model_repo_image)
# import custom model package
sqlflow_submitter.import_model_def(estimator_string, globals())
estimator = eval(estimator_string)
is_estimator = is_tf_estimator(estimator)
if is_pai and verbose < 1: # always use verbose == 1 when using PAI to get more logs
verbose = 1
set_log_level(verbose, is_estimator)
# fill in feature columns parameters
model_params.update(feature_columns)
FLAGS = None
num_workers = 1
worker_id = 0
# only support distributed training on PAI (TF version 1.x)
if is_pai:
FLAGS = define_tf_flags()
set_oss_environs(FLAGS)
num_workers = len(FLAGS.worker_hosts.split(","))
worker_id = FLAGS.task_index
# TODO(typhoonzero): remove this after update the keras models.
# copy feature_name to name field for Keras functional models:
# https://github.com/sql-machine-learning/models/blob/develop/sqlflow_models/dnnclassifier_functional_api_example.py
for k in feature_metas:
feature_metas[k]["name"] = feature_metas[k]["feature_name"]
train_dataset_fn, val_dataset_fn = get_dataset_fn(
select,
validation_select,
datasource,
feature_column_names,
feature_metas,
label_meta,
is_pai,
pai_table,
pai_val_table,
epoch,
batch_size,
1000,
num_workers=num_workers,
worker_id=worker_id,
is_estimator=is_estimator)
if not is_estimator: # keras
if isinstance(estimator, types.FunctionType):
# functional model need field_metas parameter
model_params["field_metas"] = feature_metas
keras_train_and_save(estimator, model_params, save, is_pai, FLAGS,
train_dataset_fn, val_dataset_fn, label_meta,
epoch, verbose, validation_metrics,
validation_steps, load_pretrained_model,
model_meta)
else:
estimator_train_and_save(estimator, model_params, save, is_pai, FLAGS,
train_dataset_fn, val_dataset_fn,
log_every_n_iter, max_steps,
validation_start_delay_secs,
validation_throttle_secs,
save_checkpoints_steps, validation_metrics,
load_pretrained_model, model_meta)
# remove cache files
any(map(os.remove, glob.glob('cache_train.*')))
any(map(os.remove, glob.glob('cache_validation.*')))
print("Done training")
def explain(datasource,
estimator_string,
select,
feature_columns,
feature_column_names,
feature_metas={},
label_meta={},
model_params={},
save="",
is_pai=False,
pai_table="",
plot_type='bar',
result_table="",
hdfs_namenode_addr="",
hive_location="",
hdfs_user="",
hdfs_pass="",
oss_dest=None,
oss_ak=None,
oss_sk=None,
oss_endpoint=None,
oss_bucket_name=None):
# import custom model package
sqlflow_submitter.import_model_def(estimator_string, globals())
estimator_cls = eval(estimator_string)
if is_pai:
FLAGS = tf.app.flags.FLAGS
model_params["model_dir"] = FLAGS.checkpointDir
else:
model_params['model_dir'] = save
def _input_fn():
if is_pai:
dataset = input_fn("",
datasource,
feature_column_names,
feature_metas,
label_meta,
is_pai=True,
pai_table=pai_table)
else:
dataset = input_fn(select, datasource, feature_column_names,
feature_metas, label_meta)
return dataset.batch(1).cache()
model_params.update(feature_columns)
estimator = init_model_with_feature_column(estimator_cls, model_params)
if estimator_cls in (tf.estimator.BoostedTreesClassifier,
tf.estimator.BoostedTreesRegressor):
explain_boosted_trees(datasource, estimator, _input_fn, plot_type,
result_table, feature_column_names, is_pai,
pai_table, hdfs_namenode_addr, hive_location,
hdfs_user, hdfs_pass, 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, is_pai, pai_table,
hdfs_namenode_addr, hive_location, hdfs_user, hdfs_pass,
oss_dest, oss_ak, oss_sk, oss_endpoint, oss_bucket_name)
