Source: sklearn-pandas
Version: 2.2.0-1
Severity: serious
Justification: FTBFS
Tags: bookworm sid ftbfs
User: lu...@debian.org
Usertags: ftbfs-20220624 ftbfs-bookworm
Hi,
During a rebuild of all packages in sid, your package failed to build
on amd64.
Relevant part (hopefully):
> debian/rules binary
> dh binary --with python3 --buildsystem=pybuild
> dh_update_autotools_config -O--buildsystem=pybuild
> dh_autoreconf -O--buildsystem=pybuild
> dh_auto_configure -O--buildsystem=pybuild
> install -d /<<PKGBUILDDIR>>/debian/.debhelper/generated/_source/home
> pybuild --configure -i python{version} -p "3.9 3.10"
> I: pybuild base:239: python3.9 setup.py config
> running config
> I: pybuild base:239: python3.10 setup.py config
> running config
> dh_auto_build -O--buildsystem=pybuild
> pybuild --build -i python{version} -p "3.9 3.10"
> I: pybuild base:239: /usr/bin/python3.9 setup.py build
> running build
> running build_py
> creating
> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.9_sklearn-pandas/build/sklearn_pandas
> copying sklearn_pandas/__init__.py ->
> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.9_sklearn-pandas/build/sklearn_pandas
> copying sklearn_pandas/pipeline.py ->
> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.9_sklearn-pandas/build/sklearn_pandas
> copying sklearn_pandas/features_generator.py ->
> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.9_sklearn-pandas/build/sklearn_pandas
> copying sklearn_pandas/cross_validation.py ->
> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.9_sklearn-pandas/build/sklearn_pandas
> copying sklearn_pandas/transformers.py ->
> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.9_sklearn-pandas/build/sklearn_pandas
> copying sklearn_pandas/dataframe_mapper.py ->
> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.9_sklearn-pandas/build/sklearn_pandas
> I: pybuild base:239: /usr/bin/python3 setup.py build
> running build
> running build_py
> creating
> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.10_sklearn-pandas/build/sklearn_pandas
> copying sklearn_pandas/__init__.py ->
> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.10_sklearn-pandas/build/sklearn_pandas
> copying sklearn_pandas/pipeline.py ->
> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.10_sklearn-pandas/build/sklearn_pandas
> copying sklearn_pandas/features_generator.py ->
> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.10_sklearn-pandas/build/sklearn_pandas
> copying sklearn_pandas/cross_validation.py ->
> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.10_sklearn-pandas/build/sklearn_pandas
> copying sklearn_pandas/transformers.py ->
> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.10_sklearn-pandas/build/sklearn_pandas
> copying sklearn_pandas/dataframe_mapper.py ->
> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.10_sklearn-pandas/build/sklearn_pandas
> dh_auto_test -O--buildsystem=pybuild
> pybuild --test --test-pytest -i python{version} -p "3.9 3.10"
> I: pybuild base:239: cd
> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.9_sklearn-pandas/build; python3.9 -m
> pytest ; cd /<<PKGBUILDDIR>>; python3.9 -m doctest -v README.rst
> ============================= test session starts
> ==============================
> platform linux -- Python 3.9.13, pytest-6.2.5, py-1.10.0, pluggy-1.0.0
> rootdir: /<<PKGBUILDDIR>>, configfile: pytest.ini
> collected 69 items
>
> tests/test_dataframe_mapper.py ......................................... [
> 59%]
> .................. [
> 85%]
> tests/test_features_generator.py .... [
> 91%]
> tests/test_pipeline.py .... [
> 97%]
> tests/test_transformers.py ..
> [100%]
>
> =============================== warnings summary
> ===============================
> .pybuild/cpython3_3.9_sklearn-pandas/build/tests/test_dataframe_mapper.py: 13
> warnings
> /usr/lib/python3/dist-packages/sklearn/utils/deprecation.py:87:
> FutureWarning: Function get_feature_names is deprecated; get_feature_names is
> deprecated in 1.0 and will be removed in 1.2. Please use
> get_feature_names_out instead.
> warnings.warn(msg, category=FutureWarning)
>
> .pybuild/cpython3_3.9_sklearn-pandas/build/tests/test_dataframe_mapper.py::test_sparse_features
>
> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.9_sklearn-pandas/build/tests/test_dataframe_mapper.py:820:
> DeprecationWarning: Please use `csr_matrix` from the `scipy.sparse`
> namespace, the `scipy.sparse.csr` namespace is deprecated.
> assert type(dmatrix) == sparse.csr.csr_matrix
>
> .pybuild/cpython3_3.9_sklearn-pandas/build/tests/test_dataframe_mapper.py::test_sparse_off
>
> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.9_sklearn-pandas/build/tests/test_dataframe_mapper.py:834:
> DeprecationWarning: Please use `csr_matrix` from the `scipy.sparse`
> namespace, the `scipy.sparse.csr` namespace is deprecated.
> assert type(dmatrix) != sparse.csr.csr_matrix
>
> .pybuild/cpython3_3.9_sklearn-pandas/build/tests/test_transformers.py::test_common_numerical_transformer
> .pybuild/cpython3_3.9_sklearn-pandas/build/tests/test_transformers.py::test_numerical_transformer_serialization
>
> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.9_sklearn-pandas/build/sklearn_pandas/transformers.py:35:
> DeprecationWarning:
> NumericalTransformer will be deprecated in 3.0 version.
> Please use Sklearn.base.TransformerMixin to write
> customer transformers
>
> warnings.warn("""
>
> -- Docs: https://docs.pytest.org/en/stable/warnings.html
> ======================= 69 passed, 17 warnings in 3.20s
> ========================
> Trying:
> from sklearn_pandas import DataFrameMapper
> Expecting nothing
> ok
> Trying:
> import pandas as pd
> Expecting nothing
> ok
> Trying:
> import numpy as np
> Expecting nothing
> ok
> Trying:
> import sklearn.preprocessing, sklearn.decomposition, \
> sklearn.linear_model, sklearn.pipeline, sklearn.metrics, \
> sklearn.compose
> Expecting nothing
> ok
> Trying:
> from sklearn.feature_extraction.text import CountVectorizer
> Expecting nothing
> ok
> Trying:
> data = pd.DataFrame({'pet': ['cat', 'dog', 'dog', 'fish', 'cat',
> 'dog', 'cat', 'fish'],
> 'children': [4., 6, 3, 3, 2, 3, 5, 4],
> 'salary': [90., 24, 44, 27, 32, 59, 36, 27]})
> Expecting nothing
> ok
> Trying:
> mapper = DataFrameMapper([
> ('pet', sklearn.preprocessing.LabelBinarizer()),
> (['children'], sklearn.preprocessing.StandardScaler())
> ])
> Expecting nothing
> ok
> Trying:
> data['children'].shape
> Expecting:
> (8,)
> ok
> Trying:
> data[['children']].shape
> Expecting:
> (8, 1)
> ok
> Trying:
> np.round(mapper.fit_transform(data.copy()), 2)
> Expecting:
> array([[ 1. , 0. , 0. , 0.21],
> [ 0. , 1. , 0. , 1.88],
> [ 0. , 1. , 0. , -0.63],
> [ 0. , 0. , 1. , -0.63],
> [ 1. , 0. , 0. , -1.46],
> [ 0. , 1. , 0. , -0.63],
> [ 1. , 0. , 0. , 1.04],
> [ 0. , 0. , 1. , 0.21]])
> ok
> Trying:
> sample = pd.DataFrame({'pet': ['cat'], 'children': [5.]})
> Expecting nothing
> ok
> Trying:
> np.round(mapper.transform(sample), 2)
> Expecting:
> array([[1. , 0. , 0. , 1.04]])
> ok
> Trying:
> mapper.transformed_names_
> Expecting:
> ['pet_cat', 'pet_dog', 'pet_fish', 'children']
> ok
> Trying:
> mapper_alias = DataFrameMapper([
> (['children'], sklearn.preprocessing.StandardScaler(),
> {'alias': 'children_scaled'})
> ])
> Expecting nothing
> ok
> Trying:
> _ = mapper_alias.fit_transform(data.copy())
> Expecting nothing
> ok
> Trying:
> mapper_alias.transformed_names_
> Expecting:
> ['children_scaled']
> ok
> Trying:
> mapper_alias = DataFrameMapper([
> (['children'], sklearn.preprocessing.StandardScaler(), {'prefix':
> 'standard_scaled_'}),
> (['children'], sklearn.preprocessing.StandardScaler(), {'suffix':
> '_raw'})
> ])
> Expecting nothing
> ok
> Trying:
> _ = mapper_alias.fit_transform(data.copy())
> Expecting nothing
> ok
> Trying:
> mapper_alias.transformed_names_
> Expecting:
> ['standard_scaled_children', 'children_raw']
> ok
> Trying:
> class GetColumnsStartingWith:
> def __init__(self, start_str):
> self.pattern = start_str
>
> def __call__(self, X:pd.DataFrame=None):
> return [c for c in X.columns if c.startswith(self.pattern)]
> Expecting nothing
> ok
> Trying:
> df = pd.DataFrame({
> 'sepal length (cm)': [1.0, 2.0, 3.0],
> 'sepal width (cm)': [1.0, 2.0, 3.0],
> 'petal length (cm)': [1.0, 2.0, 3.0],
> 'petal width (cm)': [1.0, 2.0, 3.0]
> })
> Expecting nothing
> ok
> Trying:
> t = DataFrameMapper([
> (
> sklearn.compose.make_column_selector(dtype_include=float),
> sklearn.preprocessing.StandardScaler(),
> {'alias': 'x'}
> ),
> (
> GetColumnsStartingWith('petal'),
> None,
> {'alias': 'petal'}
> )], df_out=True, default=False)
> Expecting nothing
> ok
> Trying:
> t.fit(df).transform(df).shape
> Expecting:
> (3, 6)
> ok
> Trying:
> t.transformed_names_
> Expecting:
> ['x_0', 'x_1', 'x_2', 'x_3', 'petal_0', 'petal_1']
> ok
> Trying:
> from sklearn.base import TransformerMixin
> Expecting nothing
> ok
> Trying:
> class DateEncoder(TransformerMixin):
> def fit(self, X, y=None):
> return self
>
> def transform(self, X):
> dt = X.dt
> return pd.concat([dt.year, dt.month, dt.day], axis=1)
> Expecting nothing
> ok
> Trying:
> dates_df = pd.DataFrame(
> {'dates': pd.date_range('2015-10-30', '2015-11-02')})
> Expecting nothing
> ok
> Trying:
> mapper_dates = DataFrameMapper([
> ('dates', DateEncoder())
> ], input_df=True)
> Expecting nothing
> ok
> Trying:
> mapper_dates.fit_transform(dates_df)
> Expecting:
> array([[2015, 10, 30],
> [2015, 10, 31],
> [2015, 11, 1],
> [2015, 11, 2]])
> ok
> Trying:
> mapper_dates = DataFrameMapper([
> ('dates', DateEncoder(), {'input_df': True})
> ])
> Expecting nothing
> ok
> Trying:
> mapper_dates.fit_transform(dates_df)
> Expecting:
> array([[2015, 10, 30],
> [2015, 10, 31],
> [2015, 11, 1],
> [2015, 11, 2]])
> ok
> Trying:
> mapper_df = DataFrameMapper([
> ('pet', sklearn.preprocessing.LabelBinarizer()),
> (['children'], sklearn.preprocessing.StandardScaler())
> ], df_out=True)
> Expecting nothing
> ok
> Trying:
> np.round(mapper_df.fit_transform(data.copy()), 2)
> Expecting:
> pet_cat pet_dog pet_fish children
> 0 1 0 0 0.21
> 1 0 1 0 1.88
> 2 0 1 0 -0.63
> 3 0 0 1 -0.63
> 4 1 0 0 -1.46
> 5 0 1 0 -0.63
> 6 1 0 0 1.04
> 7 0 0 1 0.21
> ok
> Trying:
> mapper_df = DataFrameMapper([
> ('pet', sklearn.preprocessing.LabelBinarizer()),
> (['children'], sklearn.preprocessing.StandardScaler())
> ], drop_cols=['salary'])
> Expecting nothing
> ok
> Trying:
> np.round(mapper_df.fit_transform(data.copy()), 1)
> Expecting:
> array([[ 1. , 0. , 0. , 0.2],
> [ 0. , 1. , 0. , 1.9],
> [ 0. , 1. , 0. , -0.6],
> [ 0. , 0. , 1. , -0.6],
> [ 1. , 0. , 0. , -1.5],
> [ 0. , 1. , 0. , -0.6],
> [ 1. , 0. , 0. , 1. ],
> [ 0. , 0. , 1. , 0.2]])
> ok
> Trying:
> mapper2 = DataFrameMapper([
> (['children', 'salary'], sklearn.decomposition.PCA(1))
> ])
> Expecting nothing
> ok
> Trying:
> np.round(mapper2.fit_transform(data.copy()), 1)
> Expecting:
> array([[ 47.6],
> [-18.4],
> [ 1.6],
> [-15.4],
> [-10.4],
> [ 16.6],
> [ -6.4],
> [-15.4]])
> ok
> Trying:
> from sklearn.impute import SimpleImputer
> Expecting nothing
> ok
> Trying:
> mapper3 = DataFrameMapper([
> (['age'], [SimpleImputer(),
> sklearn.preprocessing.StandardScaler()])])
> Expecting nothing
> ok
> Trying:
> data_3 = pd.DataFrame({'age': [1, np.nan, 3]})
> Expecting nothing
> ok
> Trying:
> mapper3.fit_transform(data_3)
> Expecting:
> array([[-1.22474487],
> [ 0. ],
> [ 1.22474487]])
> ok
> Trying:
> mapper3 = DataFrameMapper([
> ('pet', sklearn.preprocessing.LabelBinarizer()),
> ('children', None)
> ])
> Expecting nothing
> ok
> Trying:
> np.round(mapper3.fit_transform(data.copy()))
> Expecting:
> array([[1., 0., 0., 4.],
> [0., 1., 0., 6.],
> [0., 1., 0., 3.],
> [0., 0., 1., 3.],
> [1., 0., 0., 2.],
> [0., 1., 0., 3.],
> [1., 0., 0., 5.],
> [0., 0., 1., 4.]])
> ok
> Trying:
> mapper4 = DataFrameMapper([
> ('pet', sklearn.preprocessing.LabelBinarizer()),
> ('children', None)
> ], default=sklearn.preprocessing.StandardScaler())
> Expecting nothing
> ok
> Trying:
> np.round(mapper4.fit_transform(data.copy()), 1)
> Expecting:
> array([[ 1. , 0. , 0. , 4. , 2.3],
> [ 0. , 1. , 0. , 6. , -0.9],
> [ 0. , 1. , 0. , 3. , 0.1],
> [ 0. , 0. , 1. , 3. , -0.7],
> [ 1. , 0. , 0. , 2. , -0.5],
> [ 0. , 1. , 0. , 3. , 0.8],
> [ 1. , 0. , 0. , 5. , -0.3],
> [ 0. , 0. , 1. , 4. , -0.7]])
> ok
> Trying:
> from sklearn_pandas import gen_features
> Expecting nothing
> ok
> Trying:
> feature_def = gen_features(
> columns=['col1', 'col2', 'col3'],
> classes=[sklearn.preprocessing.LabelEncoder]
> )
> Expecting nothing
> ok
> Trying:
> feature_def
> Expecting:
> [('col1', [LabelEncoder()], {}), ('col2', [LabelEncoder()], {}), ('col3',
> [LabelEncoder()], {})]
> ok
> Trying:
> mapper5 = DataFrameMapper(feature_def)
> Expecting nothing
> ok
> /usr/lib/python3/dist-packages/sklearn/utils/deprecation.py:87:
> FutureWarning: Function get_feature_names is deprecated; get_feature_names is
> deprecated in 1.0 and will be removed in 1.2. Please use
> get_feature_names_out instead.
> warnings.warn(msg, category=FutureWarning)
> Trying:
> data5 = pd.DataFrame({
> 'col1': ['yes', 'no', 'yes'],
> 'col2': [True, False, False],
> 'col3': ['one', 'two', 'three']
> })
> Expecting nothing
> ok
> Trying:
> mapper5.fit_transform(data5)
> Expecting:
> array([[1, 1, 0],
> [0, 0, 2],
> [1, 0, 1]])
> ok
> Trying:
> from sklearn.impute import SimpleImputer
> Expecting nothing
> ok
> Trying:
> import numpy as np
> Expecting nothing
> ok
> Trying:
> feature_def = gen_features(
> columns=[['col1'], ['col2'], ['col3']],
> classes=[{'class': SimpleImputer, 'strategy':'most_frequent'}]
> )
> Expecting nothing
> ok
> Trying:
> mapper6 = DataFrameMapper(feature_def)
> Expecting nothing
> ok
> Trying:
> data6 = pd.DataFrame({
> 'col1': [np.nan, 1, 1, 2, 3],
> 'col2': [True, False, np.nan, np.nan, True],
> 'col3': [0, 0, 0, np.nan, np.nan]
> })
> Expecting nothing
> ok
> Trying:
> mapper6.fit_transform(data6)
> Expecting:
> array([[1.0, True, 0.0],
> [1.0, False, 0.0],
> [1.0, True, 0.0],
> [2.0, True, 0.0],
> [3.0, True, 0.0]], dtype=object)
> ok
> Trying:
> feature_def = gen_features(
> columns=['col1', 'col2', 'col3'],
> classes=[sklearn.preprocessing.LabelEncoder],
> prefix="lblencoder_"
> )
> Expecting nothing
> ok
> Trying:
> mapper5 = DataFrameMapper(feature_def)
> Expecting nothing
> ok
> Trying:
> data5 = pd.DataFrame({
> 'col1': ['yes', 'no', 'yes'],
> 'col2': [True, False, False],
> 'col3': ['one', 'two', 'three']
> })
> Expecting nothing
> ok
> Trying:
> _ = mapper5.fit_transform(data5)
> Expecting nothing
> ok
> Trying:
> mapper5.transformed_names_
> Expecting:
> ['lblencoder_col1', 'lblencoder_col2', 'lblencoder_col3']
> ok
> Trying:
> from sklearn.feature_selection import SelectKBest, chi2
> Expecting nothing
> ok
> Trying:
> mapper_fs = DataFrameMapper([(['children','salary'], SelectKBest(chi2,
> k=1))])
> Expecting nothing
> ok
> Trying:
> mapper_fs.fit_transform(data[['children','salary']], data['pet'])
> Expecting:
> array([[90.],
> [24.],
> [44.],
> [27.],
> [32.],
> [59.],
> [36.],
> [27.]])
> ok
> Trying:
> mapper5 = DataFrameMapper([
> ('pet', CountVectorizer()),
> ], sparse=True)
> Expecting nothing
> ok
> Trying:
> type(mapper5.fit_transform(data))
> Expecting:
> <class 'scipy.sparse.csr.csr_matrix'>
> **********************************************************************
> File "README.rst", line 475, in README.rst
> Failed example:
> type(mapper5.fit_transform(data))
> Expected:
> <class 'scipy.sparse.csr.csr_matrix'>
> Got:
> <class 'scipy.sparse._csr.csr_matrix'>
> Trying:
> from sklearn_pandas import NumericalTransformer
> Expecting nothing
> ok
> Trying:
> mapper5 = DataFrameMapper([
> ('children', NumericalTransformer('log')),
> ])
> Expecting nothing
> ok
> Trying:
> mapper5.fit_transform(data)
> Expecting:
> array([[1.38629436],
> [1.79175947],
> [1.09861229],
> [1.09861229],
> [0.69314718],
> [1.09861229],
> [1.60943791],
> [1.38629436]])
> ok
> Trying:
> import logging
> Expecting nothing
> ok
> Trying:
> logging.getLogger('sklearn_pandas').setLevel(logging.INFO)
> Expecting nothing
> ok
> **********************************************************************
> 1 items had failures:
> 1 of 72 in README.rst
> 72 tests in 1 items.
> 71 passed and 1 failed.
> ***Test Failed*** 1 failures.
> E: pybuild pybuild:369: test: plugin distutils failed with: exit code=1: cd
> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.9_sklearn-pandas/build; python3.9 -m
> pytest ; cd {dir}; python{version} -m doctest -v README.rst
> I: pybuild base:239: cd
> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.10_sklearn-pandas/build; python3.10 -m
> pytest ; cd /<<PKGBUILDDIR>>; python3.10 -m doctest -v README.rst
> ============================= test session starts
> ==============================
> platform linux -- Python 3.10.5, pytest-6.2.5, py-1.10.0, pluggy-1.0.0
> rootdir: /<<PKGBUILDDIR>>, configfile: pytest.ini
> collected 69 items
>
> tests/test_dataframe_mapper.py ......................................... [
> 59%]
> .................. [
> 85%]
> tests/test_features_generator.py .... [
> 91%]
> tests/test_pipeline.py .... [
> 97%]
> tests/test_transformers.py ..
> [100%]
>
> =============================== warnings summary
> ===============================
> .pybuild/cpython3_3.10_sklearn-pandas/build/tests/test_dataframe_mapper.py:
> 13 warnings
> /usr/lib/python3/dist-packages/sklearn/utils/deprecation.py:87:
> FutureWarning: Function get_feature_names is deprecated; get_feature_names is
> deprecated in 1.0 and will be removed in 1.2. Please use
> get_feature_names_out instead.
> warnings.warn(msg, category=FutureWarning)
>
> .pybuild/cpython3_3.10_sklearn-pandas/build/tests/test_dataframe_mapper.py::test_sparse_features
>
> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.10_sklearn-pandas/build/tests/test_dataframe_mapper.py:820:
> DeprecationWarning: Please use `csr_matrix` from the `scipy.sparse`
> namespace, the `scipy.sparse.csr` namespace is deprecated.
> assert type(dmatrix) == sparse.csr.csr_matrix
>
> .pybuild/cpython3_3.10_sklearn-pandas/build/tests/test_dataframe_mapper.py::test_sparse_off
>
> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.10_sklearn-pandas/build/tests/test_dataframe_mapper.py:834:
> DeprecationWarning: Please use `csr_matrix` from the `scipy.sparse`
> namespace, the `scipy.sparse.csr` namespace is deprecated.
> assert type(dmatrix) != sparse.csr.csr_matrix
>
> .pybuild/cpython3_3.10_sklearn-pandas/build/tests/test_transformers.py::test_common_numerical_transformer
> .pybuild/cpython3_3.10_sklearn-pandas/build/tests/test_transformers.py::test_numerical_transformer_serialization
>
> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.10_sklearn-pandas/build/sklearn_pandas/transformers.py:35:
> DeprecationWarning:
> NumericalTransformer will be deprecated in 3.0 version.
> Please use Sklearn.base.TransformerMixin to write
> customer transformers
>
> warnings.warn("""
>
> -- Docs: https://docs.pytest.org/en/stable/warnings.html
> ======================= 69 passed, 17 warnings in 3.78s
> ========================
> Trying:
> from sklearn_pandas import DataFrameMapper
> Expecting nothing
> ok
> Trying:
> import pandas as pd
> Expecting nothing
> ok
> Trying:
> import numpy as np
> Expecting nothing
> ok
> Trying:
> import sklearn.preprocessing, sklearn.decomposition, \
> sklearn.linear_model, sklearn.pipeline, sklearn.metrics, \
> sklearn.compose
> Expecting nothing
> ok
> Trying:
> from sklearn.feature_extraction.text import CountVectorizer
> Expecting nothing
> ok
> Trying:
> data = pd.DataFrame({'pet': ['cat', 'dog', 'dog', 'fish', 'cat',
> 'dog', 'cat', 'fish'],
> 'children': [4., 6, 3, 3, 2, 3, 5, 4],
> 'salary': [90., 24, 44, 27, 32, 59, 36, 27]})
> Expecting nothing
> ok
> Trying:
> mapper = DataFrameMapper([
> ('pet', sklearn.preprocessing.LabelBinarizer()),
> (['children'], sklearn.preprocessing.StandardScaler())
> ])
> Expecting nothing
> ok
> Trying:
> data['children'].shape
> Expecting:
> (8,)
> ok
> Trying:
> data[['children']].shape
> Expecting:
> (8, 1)
> ok
> Trying:
> np.round(mapper.fit_transform(data.copy()), 2)
> Expecting:
> array([[ 1. , 0. , 0. , 0.21],
> [ 0. , 1. , 0. , 1.88],
> [ 0. , 1. , 0. , -0.63],
> [ 0. , 0. , 1. , -0.63],
> [ 1. , 0. , 0. , -1.46],
> [ 0. , 1. , 0. , -0.63],
> [ 1. , 0. , 0. , 1.04],
> [ 0. , 0. , 1. , 0.21]])
> ok
> Trying:
> sample = pd.DataFrame({'pet': ['cat'], 'children': [5.]})
> Expecting nothing
> ok
> Trying:
> np.round(mapper.transform(sample), 2)
> Expecting:
> array([[1. , 0. , 0. , 1.04]])
> ok
> Trying:
> mapper.transformed_names_
> Expecting:
> ['pet_cat', 'pet_dog', 'pet_fish', 'children']
> ok
> Trying:
> mapper_alias = DataFrameMapper([
> (['children'], sklearn.preprocessing.StandardScaler(),
> {'alias': 'children_scaled'})
> ])
> Expecting nothing
> ok
> Trying:
> _ = mapper_alias.fit_transform(data.copy())
> Expecting nothing
> ok
> Trying:
> mapper_alias.transformed_names_
> Expecting:
> ['children_scaled']
> ok
> Trying:
> mapper_alias = DataFrameMapper([
> (['children'], sklearn.preprocessing.StandardScaler(), {'prefix':
> 'standard_scaled_'}),
> (['children'], sklearn.preprocessing.StandardScaler(), {'suffix':
> '_raw'})
> ])
> Expecting nothing
> ok
> Trying:
> _ = mapper_alias.fit_transform(data.copy())
> Expecting nothing
> ok
> Trying:
> mapper_alias.transformed_names_
> Expecting:
> ['standard_scaled_children', 'children_raw']
> ok
> Trying:
> class GetColumnsStartingWith:
> def __init__(self, start_str):
> self.pattern = start_str
>
> def __call__(self, X:pd.DataFrame=None):
> return [c for c in X.columns if c.startswith(self.pattern)]
> Expecting nothing
> ok
> Trying:
> df = pd.DataFrame({
> 'sepal length (cm)': [1.0, 2.0, 3.0],
> 'sepal width (cm)': [1.0, 2.0, 3.0],
> 'petal length (cm)': [1.0, 2.0, 3.0],
> 'petal width (cm)': [1.0, 2.0, 3.0]
> })
> Expecting nothing
> ok
> Trying:
> t = DataFrameMapper([
> (
> sklearn.compose.make_column_selector(dtype_include=float),
> sklearn.preprocessing.StandardScaler(),
> {'alias': 'x'}
> ),
> (
> GetColumnsStartingWith('petal'),
> None,
> {'alias': 'petal'}
> )], df_out=True, default=False)
> Expecting nothing
> ok
> Trying:
> t.fit(df).transform(df).shape
> Expecting:
> (3, 6)
> ok
> Trying:
> t.transformed_names_
> Expecting:
> ['x_0', 'x_1', 'x_2', 'x_3', 'petal_0', 'petal_1']
> ok
> Trying:
> from sklearn.base import TransformerMixin
> Expecting nothing
> ok
> Trying:
> class DateEncoder(TransformerMixin):
> def fit(self, X, y=None):
> return self
>
> def transform(self, X):
> dt = X.dt
> return pd.concat([dt.year, dt.month, dt.day], axis=1)
> Expecting nothing
> ok
> Trying:
> dates_df = pd.DataFrame(
> {'dates': pd.date_range('2015-10-30', '2015-11-02')})
> Expecting nothing
> ok
> Trying:
> mapper_dates = DataFrameMapper([
> ('dates', DateEncoder())
> ], input_df=True)
> Expecting nothing
> ok
> Trying:
> mapper_dates.fit_transform(dates_df)
> Expecting:
> array([[2015, 10, 30],
> [2015, 10, 31],
> [2015, 11, 1],
> [2015, 11, 2]])
> ok
> Trying:
> mapper_dates = DataFrameMapper([
> ('dates', DateEncoder(), {'input_df': True})
> ])
> Expecting nothing
> ok
> Trying:
> mapper_dates.fit_transform(dates_df)
> Expecting:
> array([[2015, 10, 30],
> [2015, 10, 31],
> [2015, 11, 1],
> [2015, 11, 2]])
> ok
> Trying:
> mapper_df = DataFrameMapper([
> ('pet', sklearn.preprocessing.LabelBinarizer()),
> (['children'], sklearn.preprocessing.StandardScaler())
> ], df_out=True)
> Expecting nothing
> ok
> Trying:
> np.round(mapper_df.fit_transform(data.copy()), 2)
> Expecting:
> pet_cat pet_dog pet_fish children
> 0 1 0 0 0.21
> 1 0 1 0 1.88
> 2 0 1 0 -0.63
> 3 0 0 1 -0.63
> 4 1 0 0 -1.46
> 5 0 1 0 -0.63
> 6 1 0 0 1.04
> 7 0 0 1 0.21
> ok
> Trying:
> mapper_df = DataFrameMapper([
> ('pet', sklearn.preprocessing.LabelBinarizer()),
> (['children'], sklearn.preprocessing.StandardScaler())
> ], drop_cols=['salary'])
> Expecting nothing
> ok
> Trying:
> np.round(mapper_df.fit_transform(data.copy()), 1)
> Expecting:
> array([[ 1. , 0. , 0. , 0.2],
> [ 0. , 1. , 0. , 1.9],
> [ 0. , 1. , 0. , -0.6],
> [ 0. , 0. , 1. , -0.6],
> [ 1. , 0. , 0. , -1.5],
> [ 0. , 1. , 0. , -0.6],
> [ 1. , 0. , 0. , 1. ],
> [ 0. , 0. , 1. , 0.2]])
> ok
> Trying:
> mapper2 = DataFrameMapper([
> (['children', 'salary'], sklearn.decomposition.PCA(1))
> ])
> Expecting nothing
> ok
> Trying:
> np.round(mapper2.fit_transform(data.copy()), 1)
> Expecting:
> array([[ 47.6],
> [-18.4],
> [ 1.6],
> [-15.4],
> [-10.4],
> [ 16.6],
> [ -6.4],
> [-15.4]])
> ok
> Trying:
> from sklearn.impute import SimpleImputer
> Expecting nothing
> ok
> Trying:
> mapper3 = DataFrameMapper([
> (['age'], [SimpleImputer(),
> sklearn.preprocessing.StandardScaler()])])
> Expecting nothing
> ok
> Trying:
> data_3 = pd.DataFrame({'age': [1, np.nan, 3]})
> Expecting nothing
> ok
> Trying:
> mapper3.fit_transform(data_3)
> Expecting:
> array([[-1.22474487],
> [ 0. ],
> [ 1.22474487]])
> ok
> Trying:
> mapper3 = DataFrameMapper([
> ('pet', sklearn.preprocessing.LabelBinarizer()),
> ('children', None)
> ])
> Expecting nothing
> ok
> Trying:
> np.round(mapper3.fit_transform(data.copy()))
> Expecting:
> array([[1., 0., 0., 4.],
> [0., 1., 0., 6.],
> [0., 1., 0., 3.],
> [0., 0., 1., 3.],
> [1., 0., 0., 2.],
> [0., 1., 0., 3.],
> [1., 0., 0., 5.],
> [0., 0., 1., 4.]])
> ok
> Trying:
> mapper4 = DataFrameMapper([
> ('pet', sklearn.preprocessing.LabelBinarizer()),
> ('children', None)
> ], default=sklearn.preprocessing.StandardScaler())
> Expecting nothing
> ok
> Trying:
> np.round(mapper4.fit_transform(data.copy()), 1)
> Expecting:
> array([[ 1. , 0. , 0. , 4. , 2.3],
> [ 0. , 1. , 0. , 6. , -0.9],
> [ 0. , 1. , 0. , 3. , 0.1],
> [ 0. , 0. , 1. , 3. , -0.7],
> [ 1. , 0. , 0. , 2. , -0.5],
> [ 0. , 1. , 0. , 3. , 0.8],
> [ 1. , 0. , 0. , 5. , -0.3],
> [ 0. , 0. , 1. , 4. , -0.7]])
> ok
> Trying:
> from sklearn_pandas import gen_features
> Expecting nothing
> ok
> Trying:
> feature_def = gen_features(
> columns=['col1', 'col2', 'col3'],
> classes=[sklearn.preprocessing.LabelEncoder]
> )
> Expecting nothing
> ok
> Trying:
> feature_def
> Expecting:
> [('col1', [LabelEncoder()], {}), ('col2', [LabelEncoder()], {}), ('col3',
> [LabelEncoder()], {})]
> ok
> Trying:
> mapper5 = DataFrameMapper(feature_def)
> Expecting nothing
> ok
> /usr/lib/python3/dist-packages/sklearn/utils/deprecation.py:87:
> FutureWarning: Function get_feature_names is deprecated; get_feature_names is
> deprecated in 1.0 and will be removed in 1.2. Please use
> get_feature_names_out instead.
> warnings.warn(msg, category=FutureWarning)
> Trying:
> data5 = pd.DataFrame({
> 'col1': ['yes', 'no', 'yes'],
> 'col2': [True, False, False],
> 'col3': ['one', 'two', 'three']
> })
> Expecting nothing
> ok
> Trying:
> mapper5.fit_transform(data5)
> Expecting:
> array([[1, 1, 0],
> [0, 0, 2],
> [1, 0, 1]])
> ok
> Trying:
> from sklearn.impute import SimpleImputer
> Expecting nothing
> ok
> Trying:
> import numpy as np
> Expecting nothing
> ok
> Trying:
> feature_def = gen_features(
> columns=[['col1'], ['col2'], ['col3']],
> classes=[{'class': SimpleImputer, 'strategy':'most_frequent'}]
> )
> Expecting nothing
> ok
> Trying:
> mapper6 = DataFrameMapper(feature_def)
> Expecting nothing
> ok
> Trying:
> data6 = pd.DataFrame({
> 'col1': [np.nan, 1, 1, 2, 3],
> 'col2': [True, False, np.nan, np.nan, True],
> 'col3': [0, 0, 0, np.nan, np.nan]
> })
> Expecting nothing
> ok
> Trying:
> mapper6.fit_transform(data6)
> Expecting:
> array([[1.0, True, 0.0],
> [1.0, False, 0.0],
> [1.0, True, 0.0],
> [2.0, True, 0.0],
> [3.0, True, 0.0]], dtype=object)
> ok
> Trying:
> feature_def = gen_features(
> columns=['col1', 'col2', 'col3'],
> classes=[sklearn.preprocessing.LabelEncoder],
> prefix="lblencoder_"
> )
> Expecting nothing
> ok
> Trying:
> mapper5 = DataFrameMapper(feature_def)
> Expecting nothing
> ok
> Trying:
> data5 = pd.DataFrame({
> 'col1': ['yes', 'no', 'yes'],
> 'col2': [True, False, False],
> 'col3': ['one', 'two', 'three']
> })
> Expecting nothing
> ok
> Trying:
> _ = mapper5.fit_transform(data5)
> Expecting nothing
> ok
> Trying:
> mapper5.transformed_names_
> Expecting:
> ['lblencoder_col1', 'lblencoder_col2', 'lblencoder_col3']
> ok
> Trying:
> from sklearn.feature_selection import SelectKBest, chi2
> Expecting nothing
> ok
> Trying:
> mapper_fs = DataFrameMapper([(['children','salary'], SelectKBest(chi2,
> k=1))])
> Expecting nothing
> ok
> Trying:
> mapper_fs.fit_transform(data[['children','salary']], data['pet'])
> Expecting:
> array([[90.],
> [24.],
> [44.],
> [27.],
> [32.],
> [59.],
> [36.],
> [27.]])
> ok
> Trying:
> mapper5 = DataFrameMapper([
> ('pet', CountVectorizer()),
> ], sparse=True)
> Expecting nothing
> ok
> Trying:
> type(mapper5.fit_transform(data))
> Expecting:
> <class 'scipy.sparse.csr.csr_matrix'>
> **********************************************************************
> File "README.rst", line 475, in README.rst
> Failed example:
> type(mapper5.fit_transform(data))
> Expected:
> <class 'scipy.sparse.csr.csr_matrix'>
> Got:
> <class 'scipy.sparse._csr.csr_matrix'>
> Trying:
> from sklearn_pandas import NumericalTransformer
> Expecting nothing
> ok
> Trying:
> mapper5 = DataFrameMapper([
> ('children', NumericalTransformer('log')),
> ])
> Expecting nothing
> ok
> Trying:
> mapper5.fit_transform(data)
> Expecting:
> array([[1.38629436],
> [1.79175947],
> [1.09861229],
> [1.09861229],
> [0.69314718],
> [1.09861229],
> [1.60943791],
> [1.38629436]])
> ok
> Trying:
> import logging
> Expecting nothing
> ok
> Trying:
> logging.getLogger('sklearn_pandas').setLevel(logging.INFO)
> Expecting nothing
> ok
> **********************************************************************
> 1 items had failures:
> 1 of 72 in README.rst
> 72 tests in 1 items.
> 71 passed and 1 failed.
> ***Test Failed*** 1 failures.
> E: pybuild pybuild:369: test: plugin distutils failed with: exit code=1: cd
> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.10_sklearn-pandas/build; python3.10 -m
> pytest ; cd {dir}; python{version} -m doctest -v README.rst
> rm -fr -- /tmp/dh-xdg-rundir-fKyp54YC
> dh_auto_test: error: pybuild --test --test-pytest -i python{version} -p "3.9
> 3.10" returned exit code 13
The full build log is available from:
http://qa-logs.debian.net/2022/06/24/sklearn-pandas_2.2.0-1_unstable.log
All bugs filed during this archive rebuild are listed at:
https://bugs.debian.org/cgi-bin/pkgreport.cgi?tag=ftbfs-20220624;users=lu...@debian.org
or:
https://udd.debian.org/bugs/?release=na&merged=ign&fnewerval=7&flastmodval=7&fusertag=only&fusertagtag=ftbfs-20220624&fusertaguser=lu...@debian.org&allbugs=1&cseverity=1&ctags=1&caffected=1#results
A list of current common problems and possible solutions is available at
http://wiki.debian.org/qa.debian.org/FTBFS . You're welcome to contribute!
If you reassign this bug to another package, please marking it as 'affects'-ing
this package. See https://www.debian.org/Bugs/server-control#affects
If you fail to reproduce this, please provide a build log and diff it with mine
so that we can identify if something relevant changed in the meantime.
