Python preprocessing.OrdinalEncoder方法代碼示例

# 需要導入模塊: from sklearn import preprocessing [as 別名]
# 或者: from sklearn.preprocessing import OrdinalEncoder [as 別名]
def test_gp_regressor():
    rng = np.random.RandomState(0)
    X = np.asarray([
        ["ham", "spam", "ted"],
        ["ham", "ted", "ted"],
        ["ham", "spam", "spam"]])
    y = rng.randn(3)
    hm = HammingKernel(length_scale=[1.0, 1.0, 1.0])
    if UseOrdinalEncoder:
        enc = OrdinalEncoder()
        enc.fit(X)

    gpr = GaussianProcessRegressor(hm)
    if UseOrdinalEncoder:
        gpr.fit(enc.transform(X), y)
        assert_array_almost_equal(gpr.predict(enc.transform(X)), y)
        assert_array_almost_equal(gpr.predict(enc.transform(X[:2])), y[:2])
    else:
        gpr.fit(X, y)
        assert_array_almost_equal(gpr.predict(X), y)
        assert_array_almost_equal(gpr.predict(X[:2]), y[:2]) 

# 需要導入模塊: from sklearn import preprocessing [as 別名]
# 或者: from sklearn.preprocessing import OrdinalEncoder [as 別名]
def make_xgboost_column_transformer(dtypes, missing_value_aware = True):
	"""Construct a ColumnTransformer for feeding complex data into an XGBModel.

	Parameters
	----------

	dtypes: iterable of tuples (column, dtype)

	missing_value_aware: boolean
		If true, use missing value aware transformers.

	Returns
	-------
	ColumnTransformer

	"""
	transformers = list()
	for column, dtype in dtypes.items():
		if _is_categorical(dtype):
			transformers.append((str(column), PMMLLabelBinarizer(sparse_output = True) if missing_value_aware else Pipeline([("ordinal_encoder", OrdinalEncoder()), ("one_hot_encoder", OneHotEncoder())]), [column]))
		else:
			transformers.append((str(column), "passthrough", [column]))
	return ColumnTransformer(transformers, remainder = "drop") 

# 需要導入模塊: from sklearn import preprocessing [as 別名]
# 或者: from sklearn.preprocessing import OrdinalEncoder [as 別名]
def test_model_ordinal_encoder(self):
        model = OrdinalEncoder(dtype=np.int64)
        data = np.array([[1, 2, 3], [4, 3, 0], [0, 1, 4], [0, 5, 6]],
                        dtype=np.int64)
        model.fit(data)
        model_onnx = convert_sklearn(
            model,
            "scikit-learn ordinal encoder",
            [("input", Int64TensorType([None, 3]))],
        )
        self.assertTrue(model_onnx is not None)
        dump_data_and_model(
            data,
            model,
            model_onnx,
            basename="SklearnOrdinalEncoderInt64-SkipDim1",
            allow_failure="StrictVersion("
            "onnxruntime.__version__)"
            "<= StrictVersion('0.5.0')",
        ) 

# 需要導入模塊: from sklearn import preprocessing [as 別名]
# 或者: from sklearn.preprocessing import OrdinalEncoder [as 別名]
def test_ordinal_encoder_onecat(self):
        data = [["cat"], ["cat"]]
        model = OrdinalEncoder(categories="auto")
        model.fit(data)
        inputs = [("input1", StringTensorType([None, 1]))]
        model_onnx = convert_sklearn(model, "ordinal encoder one string cat",
                                     inputs)
        self.assertTrue(model_onnx is not None)
        dump_data_and_model(
            data,
            model,
            model_onnx,
            basename="SklearnOrdinalEncoderOneStringCat",
            allow_failure="StrictVersion("
            "onnxruntime.__version__)"
            "<= StrictVersion('0.5.0')",
        ) 

# 需要導入模塊: from sklearn import preprocessing [as 別名]
# 或者: from sklearn.preprocessing import OrdinalEncoder [as 別名]
def test_ordinal_encoder_twocats(self):
        data = [["cat2"], ["cat1"]]
        model = OrdinalEncoder(categories="auto")
        model.fit(data)
        inputs = [("input1", StringTensorType([None, 1]))]
        model_onnx = convert_sklearn(model, "ordinal encoder two string cats",
                                     inputs)
        self.assertTrue(model_onnx is not None)
        dump_data_and_model(
            data,
            model,
            model_onnx,
            allow_failure="StrictVersion("
            "onnxruntime.__version__)"
            "<= StrictVersion('0.5.0')",
            basename="SklearnOrdinalEncoderTwoStringCat",
        ) 

# 需要導入模塊: from sklearn import preprocessing [as 別名]
# 或者: from sklearn.preprocessing import OrdinalEncoder [as 別名]
def test_model_ordinal_encoder_cat_list(self):
        model = OrdinalEncoder(categories=[[0, 1, 4, 5],
                                           [1, 2, 3, 5],
                                           [0, 3, 4, 6]])
        data = np.array([[1, 2, 3], [4, 3, 0], [0, 1, 4], [0, 5, 6]],
                        dtype=np.int64)
        model.fit(data)
        model_onnx = convert_sklearn(
            model,
            "scikit-learn ordinal encoder",
            [("input", Int64TensorType([None, 3]))],
        )
        self.assertTrue(model_onnx is not None)
        dump_data_and_model(
            data,
            model,
            model_onnx,
            basename="SklearnOrdinalEncoderCatList",
            allow_failure="StrictVersion("
            "onnxruntime.__version__)"
            "<= StrictVersion('0.5.0')",
        ) 

# 需要導入模塊: from sklearn import preprocessing [as 別名]
# 或者: from sklearn.preprocessing import OrdinalEncoder [as 別名]
def encode_variables(data):
  """ Encodes variables using simple ordinal encoding."""
  data2 = np.copy(data)
  encoder = OrdinalEncoder()
  categorical_indices = kBytesIndices
  data2[:,
        categorical_indices] = encoder.fit_transform(data2[:,
                                                           categorical_indices])
  return data2 

# 需要導入模塊: from sklearn import preprocessing [as 別名]
# 或者: from sklearn.preprocessing import OrdinalEncoder [as 別名]
def test_ordinal_encoder(X):
    enc = OrdinalEncoder()
    exp = np.array([[0, 1, 0],
                    [1, 0, 0]], dtype='int64')
    assert_array_equal(enc.fit_transform(X), exp.astype('float64'))
    enc = OrdinalEncoder(dtype='int64')
    assert_array_equal(enc.fit_transform(X), exp) 

# 需要導入模塊: from sklearn import preprocessing [as 別名]
# 或者: from sklearn.preprocessing import OrdinalEncoder [as 別名]
def test_ordinal_encoder_specified_categories(X, X2, cats, cat_dtype):
    enc = OrdinalEncoder(categories=cats)
    exp = np.array([[0.], [1.]])
    assert_array_equal(enc.fit_transform(X), exp)
    assert list(enc.categories[0]) == list(cats[0])
    assert enc.categories_[0].tolist() == list(cats[0])
    # manually specified categories should have same dtype as
    # the data when coerced from lists
    assert enc.categories_[0].dtype == cat_dtype

    # when specifying categories manually, unknown categories should already
    # raise when fitting
    enc = OrdinalEncoder(categories=cats)
    with pytest.raises(ValueError, match="Found unknown categories"):
        enc.fit(X2) 

# 需要導入模塊: from sklearn import preprocessing [as 別名]
# 或者: from sklearn.preprocessing import OrdinalEncoder [as 別名]
def test_ordinal_encoder_inverse():
    X = [['abc', 2, 55], ['def', 1, 55]]
    enc = OrdinalEncoder()
    X_tr = enc.fit_transform(X)
    exp = np.array(X, dtype=object)
    assert_array_equal(enc.inverse_transform(X_tr), exp)

    # incorrect shape raises
    X_tr = np.array([[0, 1, 1, 2], [1, 0, 1, 0]])
    msg = re.escape('Shape of the passed X data is not correct')
    assert_raises_regex(ValueError, msg, enc.inverse_transform, X_tr) 

# 需要導入模塊: from sklearn import preprocessing [as 別名]
# 或者: from sklearn.preprocessing import OrdinalEncoder [as 別名]
def test_ordinal_encoder_raise_missing(X):
    ohe = OrdinalEncoder()

    with pytest.raises(ValueError, match="Input contains NaN"):
        ohe.fit(X)

    with pytest.raises(ValueError, match="Input contains NaN"):
        ohe.fit_transform(X)

    ohe.fit(X[:1, :])

    with pytest.raises(ValueError, match="Input contains NaN"):
        ohe.transform(X) 

# 需要導入模塊: from sklearn import preprocessing [as 別名]
# 或者: from sklearn.preprocessing import OrdinalEncoder [as 別名]
def test_ordinal_encoder_raise_categories_shape():

    X = np.array([['Low', 'Medium', 'High', 'Medium', 'Low']], dtype=object).T
    cats = ['Low', 'Medium', 'High']
    enc = OrdinalEncoder(categories=cats)
    msg = ("Shape mismatch: if categories is an array,")

    with pytest.raises(ValueError, match=msg):
        enc.fit(X) 

# 需要導入模塊: from sklearn import preprocessing [as 別名]
# 或者: from sklearn.preprocessing import OrdinalEncoder [as 別名]
def make_lightgbm_column_transformer(dtypes, missing_value_aware = True):
	"""Construct a ColumnTransformer for feeding complex data into a LGBMModel.

	Parameters
	----------

	dtypes: iterable of tuples (column, dtype)

	missing_value_aware: boolean
		If true, use missing value aware transformers.

	Returns:
	Tuple (ColumnTransformer, list of categorical column indices)

	"""
	transformers = list()
	categorical_features = list()
	i = 0
	for column, dtype in dtypes.items():
		if _is_categorical(dtype):
			transformers.append((str(column), PMMLLabelEncoder(missing_values = -1) if missing_value_aware else OrdinalEncoder(), [column]))
			categorical_features.append(i)
		else:
			transformers.append((str(column), "passthrough", [column]))
		i += 1
	return (ColumnTransformer(transformers, remainder = "drop"), categorical_features) 

# 需要導入模塊: from sklearn import preprocessing [as 別名]
# 或者: from sklearn.preprocessing import OrdinalEncoder [as 別名]
def test_ordinal_encoder_mixed_string_int_drop(self):
        data = [
            ["c0.4", "c0.2", 3],
            ["c1.4", "c1.2", 0],
            ["c0.2", "c2.2", 1],
            ["c0.2", "c2.2", 1],
            ["c0.2", "c2.2", 1],
            ["c0.2", "c2.2", 1],
        ]
        test = [["c0.2", "c2.2", 1]]
        model = OrdinalEncoder(categories="auto")
        model.fit(data)
        inputs = [
            ("input1", StringTensorType([None, 2])),
            ("input2", Int64TensorType([None, 1])),
        ]
        model_onnx = convert_sklearn(
            model, "ordinal encoder", inputs)
        self.assertTrue(model_onnx is not None)
        dump_data_and_model(
            test,
            model,
            model_onnx,
            basename="SklearnOrdinalEncoderMixedStringIntDrop",
            allow_failure="StrictVersion("
            "onnxruntime.__version__)"
            "<= StrictVersion('0.5.0')",
        ) 

# 需要導入模塊: from sklearn import preprocessing [as 別名]
# 或者: from sklearn.preprocessing import OrdinalEncoder [as 別名]
def create_data(X: dt.Frame = None):
        if X is None:
            return []

        data = X.to_pandas().copy()

        # identify categorical colmns and trasform them
        cats = [x for x in data.select_dtypes(exclude=np.number).columns if x not in [target] + cols2ignore]

        for c in cats:
            data[c] = OrdinalEncoder().fit_transform(data[c].astype(str).values.reshape(-1, 1))

        # Get the actual importance, i.e. without shuffling
        actual_imp_df = get_feature_importances(data=data, cats=cats, shuffle=False, seed=42)

        # Seed the unexpected randomness of this world
        np.random.seed(123)

        seeds = np.random.randint(0, 2 ** 30, size=number_of_iterations)
        null_imp_df = pd.DataFrame()

        for i, s in enumerate(seeds):
            # Get current run importances
            imp_df = get_feature_importances(data=data, cats=cats, shuffle=True, seed=s)
            imp_df['run'] = i + 1
            # Concat the latest importances with the old ones
            null_imp_df = pd.concat([null_imp_df, imp_df], axis=0)

        feature_scores = []
        for _f in actual_imp_df['feature'].unique():
            f_null_imps_gain = null_imp_df.loc[null_imp_df['feature'] == _f, 'importance'].values
            f_act_imps_gain = actual_imp_df.loc[actual_imp_df['feature'] == _f, 'importance'].mean()
            _score = np.log(
                1e-10 + f_act_imps_gain / (1 + np.percentile(f_null_imps_gain, max(75, min(99, threshold)))))

            feature_scores.append((_f, _score))

        scores_df = pd.DataFrame(feature_scores, columns=['feature', 'score'])
        # final feature selection
        selected_features = scores_df[scores_df['score'] > 0]['feature'].values.tolist()
        selected_features = np.unique(selected_features).tolist()

        data = X.to_pandas().copy()
        return data[cols2ignore + selected_features + [target]]