wehub-resource-sync
125 lines
3.7 KiB
Python
125 lines
3.7 KiB
Python
import numpy as np
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import pandas as pd
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import pytest
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import scipy.sparse as ssp
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import shap
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@pytest.mark.parametrize(
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"arr",
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[
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np.arange(100),
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["zz"] * 100,
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pd.Series(range(100), name="test"),
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pd.DataFrame(np.random.RandomState(0).randn(100, 2), columns=["a", "b"]),
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],
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)
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def test_sample_basic(arr):
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"""Tests the basic functionality of `sample()` on a variety of array-like objects."""
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new_arr = shap.utils.sample(arr, 30, random_state=42)
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assert len(new_arr) == 30
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def test_sample_basic_sparse():
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"""Tests the basic functionality of `sample()` on sparse objects."""
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arr = ssp.csr_matrix((100, 3), dtype=np.int8)
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new_arr = shap.utils.sample(arr, 30, random_state=42)
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assert new_arr.shape[0] == 30
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def test_sample_no_op():
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"""Ensures that `sample()` is a no-op when numsamples is larger
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than the size of X.
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"""
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arr = np.arange(50)
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new_arr = shap.utils.sample(arr, 100, random_state=42)
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assert len(arr) == len(new_arr)
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def test_sample_sampling_without_replacement():
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"""Ensures that `sample()` is performing sampling without replacement.
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See GH dsgibbons#36.
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"""
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arr = np.arange(100)
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new_arr = shap.utils.sample(arr, 99, random_state=0)
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assert len(new_arr) == 99
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assert len(np.unique(new_arr)) == 99
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def test_sample_can_be_zipped():
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"""Ensures that the sampling is done via indexing.
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That is, sampling X and y separately would give the same result as sampling
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concat(X, y), up to a random state. Our `datasets` module relies on
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this behaviour.
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"""
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arr1 = pd.Series(np.arange(100))
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arr2 = pd.Series(np.repeat(np.arange(25), 4))
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combined = pd.DataFrame(
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{
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"arr1": arr1,
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"arr2": arr2,
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}
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)
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new_arr1 = shap.utils.sample(arr1, 75, random_state=42)
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new_arr2 = shap.utils.sample(arr2, 75, random_state=42)
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new_combined = shap.utils.sample(combined, 75, random_state=42)
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assert (new_arr1 == new_combined["arr1"]).all()
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assert (new_arr2 == new_combined["arr2"]).all()
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def test_opchain_repr():
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"""Ensures OpChain repr is working properly"""
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opchain = (
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shap.utils.OpChain("shap.DummyExplanation")
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.foo.foo(0, "big_blue_bear")
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.foo(0, v1=10)
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.foo(k1="alpha", k2="beta")
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.baz
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)
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expected_repr = "shap.DummyExplanation.foo.foo(0, 'big_blue_bear').foo(0, v1=10).foo(k1='alpha', k2='beta').baz"
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assert repr(opchain) == expected_repr
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def test_format_value_empty_string():
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"""Tests that format_value() handles empty strings without raising IndexError."""
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# Test with empty string
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result = shap.utils._general.format_value("", "%0.03f")
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assert result == ""
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def test_format_value_negative_number():
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"""Tests that format_value() correctly formats negative numbers with unicode minus sign."""
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result = shap.utils._general.format_value(-1.5, "%0.03f")
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assert result == "\u2212" + "1.5"
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def test_format_value_positive_number():
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"""Tests that format_value() correctly formats positive numbers."""
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result = shap.utils._general.format_value(1.5, "%0.03f")
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assert result == "1.5"
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def test_format_value_trailing_zeros():
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"""Tests that format_value() removes trailing zeros."""
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result = shap.utils._general.format_value(1.5000, "%0.03f")
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assert result == "1.5"
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def test_format_value_string_input():
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"""Tests that format_value() handles string inputs correctly."""
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# Test with non-empty string
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result = shap.utils._general.format_value("test_string", "%0.03f")
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assert result == "test_string"
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# Test with string that starts with minus
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result = shap.utils._general.format_value("-123", "%0.03f")
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assert result == "\u2212" + "123"
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