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