def test_numeric_column(self):
nc = NumericColumn(FieldDesc(name='c1', shape=(2, 3)))
for model_type in [TENSORFLOW, XGBOOST]:
compiled_nc = self.compile_fc(nc, model_type)
self.assertEqual(compiled_nc.key, 'c1')
self.assertEqual(compiled_nc.shape, (2, 3))
def derive_label(label, fd_map):
"""
Derive the feature column of the label.
Args:
label (FeatureColumn): the FeatureColumn object of the label.
fd_map: (dict[str -> FieldDesc]): a FieldDesc map, where the key is the
field name.
Returns:
A derived NumericColumn of the label.
"""
label_name = label.get_field_desc()[0].name if label is not None else None
if not label_name:
return # NOTE: clustering model may not specify Label
label_field_desc = fd_map[label_name]
assert label_field_desc is not None, \
"deriveLabel: LABEL COLUMN '%s' not found" % label_name
# use shape [] if label shape is [1] for TensorFlow scalar label
# shape should be [].
shape = label_field_desc.shape
if shape is None or (len(shape) == 1 and shape[0] == 1):
label_field_desc.shape = []
return NumericColumn(label_field_desc)
def test_train(self):
ds = testing.get_datasource()
original_sql = """SELECT * FROM iris.train
TO TRAIN xgboost.gbtree
WITH
objective="multi:softmax",
num_boost_round=20,
num_class=3,
validation.select="SELECT * FROM iris.test"
INTO iris.xgboost_train_model_test;
"""
select = "SELECT * FROM iris.train"
val_select = "SELECT * FROM iris.test"
train_params = {
"num_boost_round": 20,
"original_sql": original_sql,
"feature_column_map": None,
"label_column": NumericColumn(FieldDesc(name="class")),
"model_image": "sqlflow:step"
}
model_params = {"num_class": 3, "objective": "multi:softmax"}
eval_result = train(ds, "xgboost.gbtree", select, val_select,
model_params, "iris.xgboost_train_model_test",
None, train_params)
self.assertLess(eval_result['train']['merror'][-1], 0.01)
self.assertLess(eval_result['validate']['merror'][-1], 0.01)
def test_cross_column(self):
cc = CrossColumn(['c1', NumericColumn(FieldDesc(name='c2'))], 4096)
compiled_cc = self.compile_fc(cc, TENSORFLOW)
self.assertEqual(list(compiled_cc.keys), ['c1', 'c2'])
self.assertEqual(compiled_cc.hash_bucket_size, 4096)
with self.assertRaises(AssertionError):
self.compile_fc(cc, XGBOOST)
def test_bucket_column(self):
nc = NumericColumn(FieldDesc(name='c1', shape=(1, )))
bc = BucketColumn(nc, (-10, -5, 3, 7))
for model_type in [TENSORFLOW, XGBOOST]:
compiled_bc = self.compile_fc(bc, model_type)
self.assertEqual(compiled_bc.source_column.key, 'c1')
self.assertEqual(compiled_bc.boundaries, (-10, -5, 3, 7))
def test_no_column_clause(self):
columns = [
"sepal_length",
"sepal_width",
"petal_length",
"petal_width",
]
select = "select %s, class from iris.train" % ",".join(columns)
conn = testing.get_singleton_db_connection()
features = None
label = NumericColumn(
FieldDesc(name='class', dtype=DataType.INT64, shape=[1]))
features, label = fd.infer_feature_columns(conn, select, features,
label)
self.check_json_dump(features)
self.check_json_dump(label)
self.assertEqual(len(features), 1)
self.assertTrue("feature_columns" in features)
features = features["feature_columns"]
self.assertEqual(len(features), 4)
for i, f in enumerate(features):
self.assertTrue(isinstance(f, NumericColumn))
self.assertEqual(len(f.get_field_desc()), 1)
field_desc = f.get_field_desc()[0]
self.assertEqual(field_desc.name, columns[i])
self.assertEqual(field_desc.dtype, DataType.FLOAT32)
self.assertEqual(field_desc.format, DataFormat.PLAIN)
self.assertFalse(field_desc.is_sparse)
self.assertEqual(field_desc.shape, [1])
self.assertTrue(isinstance(label, NumericColumn))
self.assertEqual(len(label.get_field_desc()), 1)
field_desc = label.get_field_desc()[0]
self.assertEqual(field_desc.name, "class")
self.assertEqual(field_desc.dtype, DataType.INT64)
self.assertEqual(field_desc.format, DataFormat.PLAIN)
self.assertFalse(field_desc.is_sparse)
self.assertEqual(field_desc.shape, [])
def test_train(self):
ds = testing.get_datasource()
original_sql = """SELECT * FROM iris.train
TO TRAIN xgboost.gbtree
WITH
objective="multi:softmax",
num_boost_round=20,
num_class=3,
validation.select="SELECT * FROM iris.test"
INTO iris.xgboost_train_model_test;
"""
select = "SELECT * FROM iris.train"
val_select = "SELECT * FROM iris.test"
train_params = {
"num_boost_round": 20,
}
model_params = {"num_class": 3, "objective": "multi:softmax"}
with temp_file.TemporaryDirectory(as_cwd=True):
eval_result = train(ds, original_sql, select, val_select,
"xgboost.gbtree", "", None,
NumericColumn(FieldDesc(name="class")),
model_params, train_params, None,
"iris.xgboost_train_model_test", None)
self.assertLess(eval_result['train']['merror'][-1], 0.01)
self.assertLess(eval_result['validate']['merror'][-1], 0.01)
with temp_file.TemporaryDirectory(as_cwd=True):
pred_original_sql = """SELECT * FROM iris.test
TO PREDICT iris.xgboost_pred_result.pred_val
USING iris.xgboost_train_model_test;"""
pred(ds, pred_original_sql, "SELECT * FROM iris.test",
"iris.xgboost_train_model_test", "pred_val", model_params,
"iris.xgboost_pred_result")
with temp_file.TemporaryDirectory(as_cwd=True):
explain_original_sql = """SELECT * FROM iris.test
TO EXPLAIN iris.xgboost_train_model_test
INTO iris.xgboost_explain_result;"""
explain(ds, explain_original_sql, "SELECT * FROM iris.test",
"iris.xgboost_train_model_test", model_params,
"iris.xgboost_explain_result")
with temp_file.TemporaryDirectory(as_cwd=True):
evaluate_original_sql = """SELECT * FROM iris.test
TO EVALUATE iris.xgboost_train_model_test
WITH label_col=class
INTO iris.xgboost_evaluate_result;"""
evaluate(ds, evaluate_original_sql, "SELECT * FROM iris.test",
"class", "iris.xgboost_train_model_test", model_params,
"iris.xgboost_evaluate_result")
def new_feature_column(field_desc):
"""
Create a new FeatureColumn object by the given FieldDesc object.
Args:
field_desc (FieldDesc): a given FieldDesc object.
Returns:
If field_desc.dtype is STRING, return an EmbeddingColumn object.
Otherwise, return a NumericColumn object.
"""
if field_desc.dtype != DataType.STRING:
return NumericColumn(field_desc)
else:
category_column = CategoryIDColumn(field_desc,
len(field_desc.vocabulary))
# NOTE(typhoonzero): a default embedding size of 128 is enough
# for most cases.
embedding = EmbeddingColumn(category_column=category_column,
dimension=128,
combiner="sum")
embedding.name = field_desc.name
return embedding
def test_with_cross(self):
c1 = NumericColumn(
FieldDesc(name='c1', dtype=DataType.INT64, shape=[1]))
c2 = NumericColumn(
FieldDesc(name='c2', dtype=DataType.INT64, shape=[1]))
c4 = NumericColumn(
FieldDesc(name='c4', dtype=DataType.INT64, shape=[1]))
c5 = NumericColumn(
FieldDesc(name='c5',
dtype=DataType.INT64,
shape=[1],
is_sparse=True))
features = {
'feature_columns': [
c1,
c2,
CrossColumn([c4, c5], 128),
CrossColumn([c1, c2], 256),
]
}
label = NumericColumn(
FieldDesc(name='class', dtype=DataType.INT64, shape=[1]))
select = "select c1, c2, c3, c4, c5, class " \
"from feature_derivation_case.train"
conn = testing.get_singleton_db_connection()
features, label = fd.infer_feature_columns(conn, select, features,
label)
self.check_json_dump(features)
self.check_json_dump(label)
self.assertEqual(len(features), 1)
self.assertTrue("feature_columns" in features)
features = features["feature_columns"]
self.assertEqual(len(features), 5)
fc1 = features[0]
self.assertTrue(isinstance(fc1, NumericColumn))
self.assertEqual(len(fc1.get_field_desc()), 1)
field_desc = fc1.get_field_desc()[0]
self.assertEqual(field_desc.name, "c1")
self.assertEqual(field_desc.dtype, DataType.FLOAT32)
self.assertEqual(field_desc.format, DataFormat.PLAIN)
self.assertFalse(field_desc.is_sparse)
self.assertEqual(field_desc.shape, [1])
fc2 = features[1]
self.assertTrue(isinstance(fc2, NumericColumn))
self.assertEqual(len(fc2.get_field_desc()), 1)
field_desc = fc2.get_field_desc()[0]
self.assertEqual(field_desc.name, "c2")
self.assertEqual(field_desc.dtype, DataType.FLOAT32)
self.assertEqual(field_desc.format, DataFormat.PLAIN)
self.assertFalse(field_desc.is_sparse)
self.assertEqual(field_desc.shape, [1])
fc3 = features[2]
self.assertTrue(isinstance(fc3, NumericColumn))
self.assertEqual(len(fc3.get_field_desc()), 1)
field_desc = fc3.get_field_desc()[0]
self.assertEqual(field_desc.name, "c3")
self.assertEqual(field_desc.dtype, DataType.INT64)
self.assertEqual(field_desc.format, DataFormat.CSV)
self.assertFalse(field_desc.is_sparse)
self.assertEqual(field_desc.shape, [4])
fc4 = features[3]
self.assertTrue(isinstance(fc4, CrossColumn))
self.assertEqual(len(fc4.get_field_desc()), 2)
field_desc1 = fc4.get_field_desc()[0]
self.assertEqual(field_desc1.name, "c4")
self.assertEqual(field_desc1.dtype, DataType.FLOAT32)
self.assertEqual(field_desc1.format, DataFormat.CSV)
self.assertEqual(field_desc1.shape, [4])
self.assertFalse(field_desc1.is_sparse)
field_desc2 = fc4.get_field_desc()[1]
self.assertEqual(field_desc2.name, "c5")
self.assertEqual(field_desc2.dtype, DataType.INT64)
self.assertEqual(field_desc2.format, DataFormat.CSV)
self.assertTrue(field_desc2.is_sparse)
fc5 = features[4]
self.assertTrue(isinstance(fc5, CrossColumn))
self.assertEqual(len(fc4.get_field_desc()), 2)
field_desc1 = fc5.get_field_desc()[0]
self.assertEqual(field_desc1.name, "c1")
self.assertEqual(field_desc1.dtype, DataType.FLOAT32)
self.assertEqual(field_desc1.format, DataFormat.PLAIN)
self.assertEqual(field_desc1.shape, [1])
self.assertFalse(field_desc1.is_sparse)
field_desc2 = fc5.get_field_desc()[1]
self.assertEqual(field_desc2.name, "c2")
self.assertEqual(field_desc2.dtype, DataType.FLOAT32)
self.assertEqual(field_desc2.format, DataFormat.PLAIN)
self.assertEqual(field_desc2.shape, [1])
self.assertFalse(field_desc2.is_sparse)
self.assertTrue(isinstance(label, NumericColumn))
self.assertEqual(len(label.get_field_desc()), 1)
field_desc = label.get_field_desc()[0]
self.assertEqual(field_desc.name, "class")
self.assertEqual(field_desc.dtype, DataType.INT64)
self.assertEqual(field_desc.format, DataFormat.PLAIN)
self.assertFalse(field_desc.is_sparse)
self.assertEqual(field_desc.shape, [])
def test_without_cross(self):
features = {
'feature_columns': [
EmbeddingColumn(dimension=256, combiner="mean", name="c3"),
EmbeddingColumn(category_column=CategoryIDColumn(
FieldDesc(name="c5",
dtype=DataType.INT64,
shape=[10000],
delimiter=",",
is_sparse=True),
bucket_size=5000),
dimension=64,
combiner="sqrtn",
name="c5"),
]
}
label = NumericColumn(
FieldDesc(name="class", dtype=DataType.INT64, shape=[1]))
select = "select c1, c2, c3, c4, c5, c6, class " \
"from feature_derivation_case.train"
conn = testing.get_singleton_db_connection()
features, label = fd.infer_feature_columns(conn, select, features,
label)
self.check_json_dump(features)
self.check_json_dump(label)
self.assertEqual(len(features), 1)
self.assertTrue("feature_columns" in features)
features = features["feature_columns"]
self.assertEqual(len(features), 6)
fc1 = features[0]
self.assertTrue(isinstance(fc1, NumericColumn))
self.assertEqual(len(fc1.get_field_desc()), 1)
field_desc = fc1.get_field_desc()[0]
self.assertEqual(field_desc.name, "c1")
self.assertEqual(field_desc.dtype, DataType.FLOAT32)
self.assertEqual(field_desc.format, DataFormat.PLAIN)
self.assertFalse(field_desc.is_sparse)
self.assertEqual(field_desc.shape, [1])
fc2 = features[1]
self.assertTrue(isinstance(fc2, NumericColumn))
self.assertEqual(len(fc2.get_field_desc()), 1)
field_desc = fc2.get_field_desc()[0]
self.assertEqual(field_desc.name, "c2")
self.assertEqual(field_desc.dtype, DataType.FLOAT32)
self.assertEqual(field_desc.format, DataFormat.PLAIN)
self.assertFalse(field_desc.is_sparse)
self.assertEqual(field_desc.shape, [1])
fc3 = features[2]
self.assertTrue(isinstance(fc3, EmbeddingColumn))
self.assertEqual(len(fc3.get_field_desc()), 1)
field_desc = fc3.get_field_desc()[0]
self.assertEqual(field_desc.name, "c3")
self.assertEqual(field_desc.dtype, DataType.INT64)
self.assertEqual(field_desc.format, DataFormat.CSV)
self.assertFalse(field_desc.is_sparse)
self.assertEqual(field_desc.shape, [4])
self.assertEqual(fc3.dimension, 256)
self.assertEqual(fc3.combiner, "mean")
self.assertEqual(fc3.name, "c3")
self.assertTrue(isinstance(fc3.category_column, CategoryIDColumn))
self.assertEqual(fc3.category_column.bucket_size, 10)
fc4 = features[3]
self.assertTrue(isinstance(fc4, NumericColumn))
self.assertEqual(len(fc4.get_field_desc()), 1)
field_desc = fc4.get_field_desc()[0]
self.assertEqual(field_desc.name, "c4")
self.assertEqual(field_desc.dtype, DataType.FLOAT32)
self.assertEqual(field_desc.format, DataFormat.CSV)
self.assertFalse(field_desc.is_sparse)
self.assertEqual(field_desc.shape, [4])
fc5 = features[4]
self.assertTrue(isinstance(fc5, EmbeddingColumn))
self.assertEqual(len(fc5.get_field_desc()), 1)
field_desc = fc5.get_field_desc()[0]
self.assertEqual(field_desc.name, "c5")
self.assertEqual(field_desc.dtype, DataType.INT64)
self.assertEqual(field_desc.format, DataFormat.CSV)
self.assertTrue(field_desc.is_sparse)
self.assertEqual(field_desc.shape, [10000])
self.assertEqual(fc5.dimension, 64)
self.assertEqual(fc5.combiner, "sqrtn")
self.assertEqual(fc5.name, "c5")
self.assertTrue(isinstance(fc5.category_column, CategoryIDColumn))
self.assertEqual(fc5.category_column.bucket_size, 5000)
fc6 = features[5]
self.assertTrue(isinstance(fc6, EmbeddingColumn))
self.assertEqual(len(fc6.get_field_desc()), 1)
field_desc = fc6.get_field_desc()[0]
self.assertEqual(field_desc.name, "c6")
self.assertEqual(field_desc.dtype, DataType.STRING)
self.assertEqual(field_desc.format, DataFormat.PLAIN)
self.assertFalse(field_desc.is_sparse)
self.assertEqual(field_desc.shape, [1])
self.assertEqual(field_desc.vocabulary, set(['FEMALE', 'MALE',
'NULL']))
self.assertEqual(fc6.dimension, 128)
self.assertEqual(fc6.combiner, "sum")
self.assertEqual(fc6.name, "c6")
self.assertTrue(isinstance(fc6.category_column, CategoryIDColumn))
self.assertEqual(fc6.category_column.bucket_size, 3)
self.assertTrue(isinstance(label, NumericColumn))
self.assertEqual(len(label.get_field_desc()), 1)
field_desc = label.get_field_desc()[0]
self.assertEqual(field_desc.name, "class")
self.assertEqual(field_desc.dtype, DataType.INT64)
self.assertEqual(field_desc.format, DataFormat.PLAIN)
self.assertFalse(field_desc.is_sparse)
self.assertEqual(field_desc.shape, [])
def test_main(self):
ds = testing.get_datasource()
original_sql = """SELECT * FROM iris.train
TO TRAIN xgboost.gbtree
WITH
objective="multi:softprob",
num_boost_round=20,
num_class=3,
validation.select="SELECT * FROM iris.test"
LABEL class
INTO iris.xgboost_train_model_test;
"""
select = "SELECT * FROM iris.train"
val_select = "SELECT * FROM iris.test"
train_params = {"num_boost_round": 20}
model_params = {"num_class": 3, "objective": "multi:softprob"}
save_name = "iris.xgboost_train_model_test"
class_name = "class"
with temp_file.TemporaryDirectory(as_cwd=True):
eval_result = train(datasource=ds,
original_sql=original_sql,
select=select,
validation_select=val_select,
estimator_string="xgboost.gbtree",
model_image="sqlflow:step",
feature_column_map=None,
label_column=NumericColumn(
FieldDesc(name=class_name)),
model_params=model_params,
train_params=train_params,
validation_params=None,
save=save_name,
load=None)
self.assertLess(eval_result['train']['merror'][-1], 0.01)
self.assertLess(eval_result['validate']['merror'][-1], 0.01)
conn = db.connect_with_data_source(ds)
pred_select = "SELECT * FROM iris.test"
with temp_file.TemporaryDirectory(as_cwd=True):
result_column_names, train_label_idx = create_predict_table(
conn, select, "iris.predict_result_table",
FieldDesc(name=class_name), "class")
predict(ds, pred_select, "iris.predict_result_table",
result_column_names, train_label_idx, save_name)
self.assertEqual(
self.get_table_row_count(conn, "iris.test"),
self.get_table_row_count(conn, "iris.predict_result_table"))
schema1 = self.get_table_schema(conn, "iris.test")
schema2 = self.get_table_schema(conn, "iris.predict_result_table")
self.assertEqual(len(schema1), len(schema2))
for name in schema1:
if name == 'class':
self.assertEqual(schema2[name], "BIGINT")
continue
self.assertTrue(name in schema2)
self.assertEqual(schema1[name], schema2[name])
diff_schema = schema2.keys() - schema1.keys()
self.assertEqual(len(diff_schema), 0)
with temp_file.TemporaryDirectory(as_cwd=True):
result_column_names = create_evaluate_table(
conn, "iris.evaluate_result_table", ["accuracy_score"])
evaluate(ds,
pred_select,
"iris.evaluate_result_table",
save_name,
label_name='class',
model_params={'validation.metrics': 'accuracy_score'},
result_column_names=result_column_names)
eval_schema = self.get_table_schema(conn, "iris.evaluate_result_table")
self.assertEqual(eval_schema.keys(), set(['loss', 'accuracy_score']))
with temp_file.TemporaryDirectory(as_cwd=True):
feature_column_names = [
"petal_width", "petal_length", "sepal_width", "sepal_length"
]
create_explain_table(conn, EstimatorType.XGBOOST, "TreeExplainer",
"xgboost.gbtree", "iris.explain_result_table",
feature_column_names)
explain(ds, select, "TreeExplainer", {"plot_type": "decision"},
"iris.explain_result_table", save_name)
explain_schema = self.get_table_schema(conn,
"iris.explain_result_table")
self.assertEqual(explain_schema.keys(), set(feature_column_names))
with temp_file.TemporaryDirectory(as_cwd=True):
create_explain_table(conn, EstimatorType.XGBOOST,
"XGBoostExplainer", "xgboost.gbtree",
"iris.explain_result_table_2",
feature_column_names)
explain(ds, select, "XGBoostExplainer", {},
"iris.explain_result_table_2", save_name)
explain_schema = self.get_table_schema(conn,
"iris.explain_result_table_2")
self.assertEqual(explain_schema.keys(),
set(['feature', 'fscore', 'gain']))
conn.close()
def check_main_impl(self, estimator):
if testing.get_driver() != "mysql":
return
ds = testing.get_datasource()
original_sql = """SELECT * FROM iris.train
TO TRAIN %s
WITH
model.hidden_units=[32,64],
model.n_classes=3,
validation.select="SELECT * FROM iris.test"
LABEL class
INTO iris.tensorflow_train_model_test;
""" % estimator
select = "SELECT * FROM iris.train"
val_select = "SELECT * FROM iris.test"
train_params = {"batch_size": 10}
model_params = {"n_classes": 3, "hidden_units": [32, 64]}
save_name = "iris.tensorflow_train_model_test"
class_name = "class"
with temp_file.TemporaryDirectory(as_cwd=True):
train(original_sql=original_sql,
model_image="sqlflow:step",
estimator_string=estimator,
datasource=ds,
select=select,
validation_select=val_select,
model_params=model_params,
train_params=train_params,
validation_params=None,
feature_column_map=None,
label_column=NumericColumn(
FieldDesc(name=class_name, shape=[])),
save=save_name,
load=None)
conn = db.connect_with_data_source(ds)
pred_select = "SELECT * FROM iris.test"
with temp_file.TemporaryDirectory(as_cwd=True):
pred(ds, pred_select, "iris.predict_result_table", class_name,
save_name)
self.assertEqual(
self.get_table_row_count(conn, "iris.test"),
self.get_table_row_count(conn, "iris.predict_result_table"))
schema1 = self.get_table_schema(conn, "iris.test")
schema2 = self.get_table_schema(conn, "iris.predict_result_table")
self.assertEqual(len(schema1), len(schema2))
for name in schema1:
if name == 'class':
self.assertEqual(schema2[name], "BIGINT")
continue
self.assertTrue(name in schema2)
self.assertEqual(schema1[name], schema2[name])
diff_schema = schema2.keys() - schema1.keys()
self.assertEqual(len(diff_schema), 0)
with temp_file.TemporaryDirectory(as_cwd=True):
evaluate(ds, select, "iris.evaluate_result_table", save_name,
class_name, {'validation.metrics': 'Accuracy'})
eval_schema = self.get_table_schema(conn, "iris.evaluate_result_table")
eval_schema = set([k.lower() for k in eval_schema.keys()])
self.assertEqual(eval_schema, set(['loss', 'accuracy']))
with temp_file.TemporaryDirectory(as_cwd=True):
explain(ds, select, None, {"plot_type": "bar"},
"iris.explain_result_table", save_name)
explain_schema = self.get_table_schema(conn,
"iris.explain_result_table")
self.assertEqual(
explain_schema.keys(),
set(['petal_length', 'petal_width', 'sepal_length',
'sepal_width']))
conn.close()
def test_metadata(self):
original_sql = '''
SELECT c1, c2, class FROM my_db.train_table
TO TRAIN my_docker_image:latest/DNNClassifier
WITH
model.n_classes = 3,
model.hidden_units = [16, 32],
validation.select="SELECT c1, c2, class FROM my_db.val_table"
INTO my_db.my_dnn_model;
'''
select = "SELECT c1, c2, class FROM my_db.train_table"
validation_select = "SELECT c1, c2, class FROM my_db.val_table"
model_repo_image = "my_docker_image:latest"
estimator = "DNNClassifier"
attributes = {
'n_classes': 3,
'hidden_units': [16, 32],
}
features = {
'feature_columns': [
NumericColumn(FieldDesc(name='c1', shape=[3], delimiter=",")),
NumericColumn(FieldDesc(name='c2', shape=[1])),
],
}
label = NumericColumn(FieldDesc(name='class', shape=[5],
delimiter=','))
def check_metadata(meta):
self.assertEqual(meta['original_sql'], original_sql)
self.assertEqual(meta['select'], select)
self.assertEqual(meta['validation_select'], validation_select)
self.assertEqual(meta['model_repo_image'], model_repo_image)
self.assertEqual(meta['class_name'], estimator)
self.assertEqual(meta['attributes'], attributes)
meta_features = meta['features']
meta_label = meta['label']
self.assertEqual(len(meta_features), 1)
self.assertEqual(len(meta_features['feature_columns']), 2)
meta_features = meta_features['feature_columns']
self.assertEqual(type(meta_features[0]), NumericColumn)
self.assertEqual(type(meta_features[1]), NumericColumn)
field_desc = meta_features[0].get_field_desc()[0]
self.assertEqual(field_desc.name, 'c1')
self.assertEqual(field_desc.shape, [3])
self.assertEqual(field_desc.delimiter, ',')
field_desc = meta_features[1].get_field_desc()[0]
self.assertEqual(field_desc.name, 'c2')
self.assertEqual(field_desc.shape, [1])
self.assertEqual(type(meta_label), NumericColumn)
field_desc = meta_label.get_field_desc()[0]
self.assertEqual(field_desc.name, 'class')
self.assertEqual(field_desc.shape, [5])
self.assertEqual(field_desc.delimiter, ',')
self.assertEqual(meta['evaluation'], {'accuracy': 0.5})
self.assertEqual(meta['my_data'], 0.25)
meta = collect_metadata(original_sql,
select,
validation_select,
model_repo_image,
estimator,
attributes,
features,
label, {'accuracy': 0.5},
my_data=0.25)
check_metadata(meta)
save_metadata(self.file_name, meta)
meta = load_metadata(self.file_name)
check_metadata(meta)
def test_main(self):
ds = testing.get_datasource()
original_sql = """SELECT * FROM iris.train
TO TRAIN xgboost.gbtree
WITH
objective="multi:softmax",
num_boost_round=20,
num_class=3,
validation.select="SELECT * FROM iris.test"
INTO iris.xgboost_train_model_test;
"""
select = "SELECT * FROM iris.train"
val_select = "SELECT * FROM iris.test"
train_params = {"num_boost_round": 20}
model_params = {"num_class": 3, "objective": "multi:softmax"}
save_name = "iris.xgboost_train_model_test"
class_name = "class"
with temp_file.TemporaryDirectory(as_cwd=True):
eval_result = train(original_sql=original_sql,
model_image="sqlflow:step",
estimator_string="xgboost.gbtree",
datasource=ds,
select=select,
validation_select=val_select,
model_params=model_params,
train_params=train_params,
feature_column_map=None,
label_column=NumericColumn(
FieldDesc(name=class_name)),
save=save_name)
self.assertLess(eval_result['train']['merror'][-1], 0.01)
self.assertLess(eval_result['validate']['merror'][-1], 0.01)
conn = db.connect_with_data_source(ds)
pred_select = "SELECT * FROM iris.test"
pred(ds, pred_select, "iris.predict_result_table", class_name,
save_name)
self.assertEqual(
self.get_table_row_count(conn, "iris.test"),
self.get_table_row_count(conn, "iris.predict_result_table"))
schema1 = self.get_table_schema(conn, "iris.test")
schema2 = self.get_table_schema(conn, "iris.predict_result_table")
self.assertEqual(len(schema1), len(schema2))
for name in schema1:
if name == 'class':
self.assertEqual(schema2[name], "BIGINT")
continue
self.assertTrue(name in schema2)
self.assertEqual(schema1[name], schema2[name])
diff_schema = schema2.keys() - schema1.keys()
self.assertEqual(len(diff_schema), 0)
evaluate(ds, pred_select, "iris.evaluate_result_table", save_name,
'class', ['accuracy_score'])
eval_schema = self.get_table_schema(conn,
"iris.evaluate_result_table")
self.assertEqual(eval_schema.keys(),
set(['loss', 'accuracy_score']))
