def test_submit_xgb_train_task(self):
model_params = {
"booster": "gbtree",
"eta": 0.4,
"num_class": 3,
"objective": "multi:softprob"
}
train_params = {"num_boost_round": 10}
feature_columns_code = """
xgboost_extended.feature_column.numeric_column(
"sepal_length", shape=[1]),
xgboost_extended.feature_column.numeric_column(
"sepal_width", shape=[1]),
xgboost_extended.feature_column.numeric_column(
"petal_length", shape=[1]),
xgboost_extended.feature_column.numeric_column(
"petal_width", shape=[1])
"""
submitter.submit_pai_train(
testing.get_datasource(),
"XGBoost",
"SELECT * FROM alifin_jtest_dev.sqlflow_iris_train",
"select * from alifin_jtest_dev.sqlflow_iris_train",
model_params,
"e2etest_xgb_classify_model",
None,
train_params=train_params,
feature_columns=eval("[%s]" % feature_columns_code),
feature_metas=iris_feature_metas,
label_meta=iris_label_meta,
feature_column_names=iris_feature_column_names,
feature_columns_code=feature_columns_code)
def test_submit_pai_random_forest_train_task(self):
submitter.submit_pai_train(
testing.get_datasource(),
"RandomForests",
"SELECT * FROM alifin_jtest_dev.sqlflow_iris_train",
"", {
"tree_num": 3,
},
"e2e_test_random_forest",
"",
feature_column_names=iris_feature_column_names,
label_meta=iris_label_meta)
def test_submit_pai_kmeans_train_task(self):
submitter.submit_pai_train(
testing.get_datasource(),
"KMeans",
"SELECT * FROM alifin_jtest_dev.sqlflow_iris_train",
"", {
"excluded_columns": "class",
"idx_table_name": "alifin_jtest_dev.e2e_test_kmeans_output_idx"
},
"e2e_test_kmeans",
"",
feature_column_names=[*iris_feature_column_names, "class"])
def test_submit_pai_train_task(self):
model_params = dict()
model_params["hidden_units"] = [10, 20]
model_params["n_classes"] = 3
# feature_columns_code will be used to save the training information
# together with the saved model.
feature_columns_code = """{"feature_columns": [
tf.feature_column.numeric_column("sepal_length", shape=[1]),
tf.feature_column.numeric_column("sepal_width", shape=[1]),
tf.feature_column.numeric_column("petal_length", shape=[1]),
tf.feature_column.numeric_column("petal_width", shape=[1]),
]}"""
feature_columns = eval(feature_columns_code)
submitter.submit_pai_train(
testing.get_datasource(),
"DNNClassifier",
"SELECT * FROM alifin_jtest_dev.sqlflow_iris_train",
"",
model_params,
"e2etest_pai_dnn",
None,
feature_columns=feature_columns,
feature_column_names=iris_feature_column_names,
feature_column_names_map=iris_feature_column_names_map,
feature_metas=iris_feature_metas,
label_meta=iris_label_meta,
validation_metrics="Accuracy".split(","),
save="model_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=True,
feature_columns_code=feature_columns_code,
model_repo_image="",
original_sql='''
SELECT * FROM alifin_jtest_dev.sqlflow_test_iris_train
TO TRAIN DNNClassifier
WITH model.n_classes = 3, model.hidden_units = [10, 20]
LABEL class
INTO e2etest_pai_dnn;''')