def test_to_and_from_json_convert_integers(self):
# training data
d = {
"col1": [1, 2, 3],
"col2": ["a", "a", "c"],
"col3": [1, 1, 3],
"col4": ["a", "b", "c"],
}
df = pd.DataFrame(data=d)
cat1 = PreprocessingCategorical(
df.columns, PreprocessingCategorical.CONVERT_INTEGER)
cat1.fit(df)
cat2 = PreprocessingCategorical(
df.columns, PreprocessingCategorical.CONVERT_INTEGER)
cat2.from_json(cat1.to_json())
df = cat2.transform(df)
for col in ["col1", "col2", "col3", "col4"]:
self.assertTrue(col in df.columns)
self.assertEqual(df["col2"][0], 0)
self.assertEqual(df["col2"][1], 0)
self.assertEqual(df["col2"][2], 1)
self.assertEqual(df["col4"][0], 0)
self.assertEqual(df["col4"][1], 1)
self.assertEqual(df["col4"][2], 2)
def from_json(self, data_json):
if "remove_columns" in data_json:
self._remove_columns = data_json.get("remove_columns", [])
if "missing_values" in data_json:
self._missing_values = []
for mv_data in data_json["missing_values"]:
mv = PreprocessingMissingValues()
mv.from_json(mv_data)
self._missing_values += [mv]
if "categorical" in data_json:
self._categorical = []
for cat_data in data_json["categorical"]:
cat = PreprocessingCategorical()
cat.from_json(cat_data)
self._categorical += [cat]
if "scale" in data_json:
self._scale = []
for scale_data in data_json["scale"]:
sc = Scale()
sc.from_json(scale_data)
self._scale += [sc]
if "categorical_y" in data_json:
if "new_columns" in data_json["categorical_y"]:
self._categorical_y = LabelBinarizer()
else:
self._categorical_y = LabelEncoder()
self._categorical_y.from_json(data_json["categorical_y"])
if "scale_y" in data_json:
self._scale_y = Scale()
self._scale_y.from_json(data_json["scale_y"])
if "ml_task" in data_json:
self._params["ml_task"] = data_json["ml_task"]
def from_json(self, data_json):
self._params = data_json.get("params", self._params)
if "remove_columns" in data_json:
self._remove_columns = data_json.get("remove_columns", [])
if "missing_values" in data_json:
self._missing_values = []
for mv_data in data_json["missing_values"]:
mv = PreprocessingMissingValues()
mv.from_json(mv_data)
self._missing_values += [mv]
if "categorical" in data_json:
self._categorical = []
for cat_data in data_json["categorical"]:
cat = PreprocessingCategorical()
cat.from_json(cat_data)
self._categorical += [cat]
if "datetime_transforms" in data_json:
self._datetime_transforms = []
for dtt_params in data_json["datetime_transforms"]:
dtt = DateTimeTransformer()
dtt.from_json(dtt_params)
self._datetime_transforms += [dtt]
if "text_transforms" in data_json:
self._text_transforms = []
for tt_params in data_json["text_transforms"]:
tt = TextTransformer()
tt.from_json(tt_params)
self._text_transforms += [tt]
if "golden_features" in data_json:
self._golden_features = GoldenFeaturesTransformer()
self._golden_features.from_json(data_json["golden_features"])
if "scale" in data_json:
self._scale = []
for scale_data in data_json["scale"]:
sc = Scale()
sc.from_json(scale_data)
self._scale += [sc]
if "categorical_y" in data_json:
if "new_columns" in data_json["categorical_y"]:
self._categorical_y = LabelBinarizer()
else:
self._categorical_y = LabelEncoder()
self._categorical_y.from_json(data_json["categorical_y"])
if "scale_y" in data_json:
self._scale_y = Scale()
self._scale_y.from_json(data_json["scale_y"])
if "ml_task" in data_json:
self._params["ml_task"] = data_json["ml_task"]
self._add_random_feature = data_json.get("add_random_feature", False)
self._drop_features = data_json.get("drop_features", [])