129 lines
3.4 KiB
Python
129 lines
3.4 KiB
Python
import numpy as np
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from sklearn.preprocessing import FunctionTransformer
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from sklearn.utils.testing import assert_equal, assert_array_equal
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from sklearn.utils.testing import assert_warns_message
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def _make_func(args_store, kwargs_store, func=lambda X, *a, **k: X):
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def _func(X, *args, **kwargs):
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args_store.append(X)
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args_store.extend(args)
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kwargs_store.update(kwargs)
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return func(X)
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return _func
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def test_delegate_to_func():
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# (args|kwargs)_store will hold the positional and keyword arguments
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# passed to the function inside the FunctionTransformer.
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args_store = []
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kwargs_store = {}
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X = np.arange(10).reshape((5, 2))
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assert_array_equal(
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FunctionTransformer(_make_func(args_store, kwargs_store)).transform(X),
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X, 'transform should have returned X unchanged',
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)
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# The function should only have received X.
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assert_equal(
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args_store,
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[X],
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'Incorrect positional arguments passed to func: {args}'.format(
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args=args_store,
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),
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)
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assert_equal(
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kwargs_store,
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{},
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'Unexpected keyword arguments passed to func: {args}'.format(
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args=kwargs_store,
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),
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)
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# reset the argument stores.
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args_store[:] = [] # python2 compatible inplace list clear.
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kwargs_store.clear()
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y = object()
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transformed = assert_warns_message(
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DeprecationWarning, "pass_y is deprecated",
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FunctionTransformer(
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_make_func(args_store, kwargs_store),
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pass_y=True).transform, X, y)
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assert_array_equal(transformed, X,
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err_msg='transform should have returned X unchanged')
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# The function should have received X and y.
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assert_equal(
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args_store,
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[X, y],
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'Incorrect positional arguments passed to func: {args}'.format(
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args=args_store,
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),
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)
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assert_equal(
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kwargs_store,
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{},
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'Unexpected keyword arguments passed to func: {args}'.format(
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args=kwargs_store,
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),
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)
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def test_np_log():
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X = np.arange(10).reshape((5, 2))
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# Test that the numpy.log example still works.
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assert_array_equal(
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FunctionTransformer(np.log1p).transform(X),
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np.log1p(X),
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)
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def test_kw_arg():
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X = np.linspace(0, 1, num=10).reshape((5, 2))
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F = FunctionTransformer(np.around, kw_args=dict(decimals=3))
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# Test that rounding is correct
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assert_array_equal(F.transform(X),
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np.around(X, decimals=3))
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def test_kw_arg_update():
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X = np.linspace(0, 1, num=10).reshape((5, 2))
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F = FunctionTransformer(np.around, kw_args=dict(decimals=3))
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F.kw_args['decimals'] = 1
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# Test that rounding is correct
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assert_array_equal(F.transform(X), np.around(X, decimals=1))
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def test_kw_arg_reset():
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X = np.linspace(0, 1, num=10).reshape((5, 2))
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F = FunctionTransformer(np.around, kw_args=dict(decimals=3))
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F.kw_args = dict(decimals=1)
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# Test that rounding is correct
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assert_array_equal(F.transform(X), np.around(X, decimals=1))
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def test_inverse_transform():
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X = np.array([1, 4, 9, 16]).reshape((2, 2))
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# Test that inverse_transform works correctly
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F = FunctionTransformer(
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func=np.sqrt,
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inverse_func=np.around, inv_kw_args=dict(decimals=3),
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)
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assert_array_equal(
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F.inverse_transform(F.transform(X)),
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np.around(np.sqrt(X), decimals=3),
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)
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