coverage 1

rdt/transformers/categorical.py

@@ -9,36 +9,11 @@
 
 from rdt.errors import TransformerInputError
 from rdt.transformers.base import BaseTransformer
-from rdt.transformers.utils import check_nan_in_transform, fill_nan_with_none
+from rdt.transformers.utils import check_nan_in_transform, fill_nan_with_none, try_convert_to_dtype
 
 LOGGER = logging.getLogger(__name__)
 
 
-def try_convert_to_dtype(data, dtype):
-    """Try to convert data to a given dtype.
-
-    Args:
-        data (pd.Series or numpy.ndarray):
-            Data to convert.
-        dtype (str):
-            Data type to convert to.
-
-    Returns:
-        data:
-            Data converted to the given dtype.
-    """
-    try:
-        data = data.astype(dtype)
-    except ValueError as error:
-        is_integer = pd.api.types.is_integer_dtype(dtype)
-        if is_integer:
-            data = data.astype(float)
-        else:
-            raise error
-
-    return data
-
-
 class UniformEncoder(BaseTransformer):
     """Transformer for categorical data.
 

rdt/transformers/utils.py

@@ -209,3 +209,28 @@ def check_nan_in_transform(data, dtype):
             message += '.'
 
         warnings.warn(message)
+
+
+def try_convert_to_dtype(data, dtype):
+    """Try to convert data to a given dtype.
+
+    Args:
+        data (pd.Series or numpy.ndarray):
+            Data to convert.
+        dtype (str):
+            Data type to convert to.
+
+    Returns:
+        data:
+            Data converted to the given dtype.
+    """
+    try:
+        data = data.astype(dtype)
+    except ValueError as error:
+        is_integer = pd.api.types.is_integer_dtype(dtype)
+        if is_integer:
+            data = data.astype(float)
+        else:
+            raise error
+
+    return data

tests/unit/transformers/test_categorical.py

@@ -9,32 +9,11 @@
 from rdt.errors import TransformerInputError
 from rdt.transformers.categorical import (
     CustomLabelEncoder, FrequencyEncoder, LabelEncoder, OneHotEncoder, OrderedLabelEncoder,
-    OrderedUniformEncoder, UniformEncoder, try_convert_to_dtype)
+    OrderedUniformEncoder, UniformEncoder)
 
 RE_SSN = re.compile(r'\d\d\d-\d\d-\d\d\d\d')
 
 
-def test_try_convert_to_dtype():
-    """Test ``try_convert_to_dtype`` method.
-
-    If the data can be converted to the specified dtype, it should be converted.
-    If the data cannot be converted, a ValueError should be raised.
-    Should allow to convert integer with NaNs to float.
-    """
-    # Setup
-    data_int_with_nan = pd.Series([1.0, 2.0, np.nan, 4.0, 5.0])
-    data_not_convetible = pd.Series(['a', 'b', 'c', 'd', 'e'])
-
-    # Run
-    output_int_with_nan = try_convert_to_dtype(data_int_with_nan, 'int')
-    with pytest.raises(ValueError, match="could not convert string to float: 'a'"):
-        try_convert_to_dtype(data_not_convetible, 'int')
-
-    # Assert
-    expected_data_with_nan = pd.Series([1, 2, np.nan, 4, 5])
-    pd.testing.assert_series_equal(output_int_with_nan, expected_data_with_nan)
-
-
 class TestUniformEncoder:
     """Test class for the UniformEncoder."""
 
@@ -2359,6 +2338,7 @@ def test__fit(self):
         transformer._fit(data)
 
         # Assert
+        assert transformer.dtype == 'float'
         expected_values_to_categories = {0: 2, 1: 3, 2: np.nan, 3: 1}
         expected_categories_to_values = {2: 0, 3: 1, 1: 3, np.nan: 2}
         for key, value in transformer.values_to_categories.items():

tests/unit/transformers/test_utils.py

@@ -6,7 +6,8 @@
 import pytest
 
 from rdt.transformers.utils import (
-    _any, _max_repeat, check_nan_in_transform, flatten_column_list, strings_from_regex)
+    _any, _max_repeat, check_nan_in_transform, flatten_column_list, strings_from_regex,
+    try_convert_to_dtype)
 
 
 def test_strings_from_regex_literal():
@@ -81,8 +82,10 @@ def test_check_nan_in_transform():
     """
     # Setup
     transformed = pd.Series([0.1026, 0.1651, np.nan, 0.3116, 0.6546, 0.8541, 0.7041])
+    data_without_nans = pd.Series([0.1026, 0.1651, 0.3116, 0.6546, 0.8541, 0.7041])
 
     # Run and Assert
+    check_nan_in_transform(data_without_nans, 'float')
     expected_message = (
         'There are null values in the transformed data. The reversed '
         'transformed data will contain null values'
@@ -94,3 +97,27 @@ def test_check_nan_in_transform():
 
     with pytest.warns(UserWarning, match=expected_message_integer):
         check_nan_in_transform(transformed, 'int')
+
+
+def test_try_convert_to_dtype():
+    """Test ``try_convert_to_dtype`` method.
+
+    If the data can be converted to the specified dtype, it should be converted.
+    If the data cannot be converted, a ValueError should be raised.
+    Should allow to convert integer with NaNs to float.
+    """
+    # Setup
+    data_int_with_nan = pd.Series([1.0, 2.0, np.nan, 4.0, 5.0])
+    data_not_convertible = pd.Series(['a', 'b', 'c', 'd', 'e'])
+
+    # Run
+    output_convertibe = try_convert_to_dtype(data_int_with_nan, 'str')
+    output_int_with_nan = try_convert_to_dtype(data_int_with_nan, 'int')
+    with pytest.raises(ValueError, match="could not convert string to float: 'a'"):
+        try_convert_to_dtype(data_not_convertible, 'int')
+
+    # Assert
+    expected_data_with_nan = pd.Series([1, 2, np.nan, 4, 5])
+    expected_data_covertibe = pd.Series(['1.0', '2.0', 'nan', '4.0', '5.0'])
+    pd.testing.assert_series_equal(output_int_with_nan, expected_data_with_nan)
+    pd.testing.assert_series_equal(output_convertibe, expected_data_covertibe)