48 lines
1.3 KiB
Python
48 lines
1.3 KiB
Python
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"""Test fast_dict."""
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import numpy as np
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from numpy.testing import assert_allclose, assert_array_equal
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from sklearn.utils._fast_dict import IntFloatDict, argmin
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def test_int_float_dict():
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rng = np.random.RandomState(0)
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keys = np.unique(rng.randint(100, size=10).astype(np.intp))
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values = rng.rand(len(keys))
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d = IntFloatDict(keys, values)
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for key, value in zip(keys, values):
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assert d[key] == value
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assert len(d) == len(keys)
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d.append(120, 3.0)
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assert d[120] == 3.0
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assert len(d) == len(keys) + 1
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for i in range(2000):
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d.append(i + 1000, 4.0)
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assert d[1100] == 4.0
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def test_int_float_dict_argmin():
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# Test the argmin implementation on the IntFloatDict
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keys = np.arange(100, dtype=np.intp)
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values = np.arange(100, dtype=np.float64)
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d = IntFloatDict(keys, values)
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assert argmin(d) == (0, 0)
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def test_to_arrays():
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# Test that an IntFloatDict is converted into arrays
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# of keys and values correctly
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keys_in = np.array([1, 2, 3], dtype=np.intp)
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values_in = np.array([4, 5, 6], dtype=np.float64)
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d = IntFloatDict(keys_in, values_in)
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keys_out, values_out = d.to_arrays()
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assert keys_out.dtype == keys_in.dtype
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assert values_in.dtype == values_out.dtype
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assert_array_equal(keys_out, keys_in)
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assert_allclose(values_out, values_in)
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