Old engine for Continuous Time Bayesian Networks. Superseded by reCTBN. 🐍
https://github.com/madlabunimib/PyCTBN
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324 lines
11 KiB
324 lines
11 KiB
import numpy as np
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import pytest
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import pandas as pd
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from pandas import DataFrame, Index, MultiIndex, Series
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import pandas._testing as tm
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from pandas.core.util.hashing import hash_tuples
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from pandas.util import hash_array, hash_pandas_object
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@pytest.fixture(
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params=[
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Series([1, 2, 3] * 3, dtype="int32"),
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Series([None, 2.5, 3.5] * 3, dtype="float32"),
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Series(["a", "b", "c"] * 3, dtype="category"),
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Series(["d", "e", "f"] * 3),
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Series([True, False, True] * 3),
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Series(pd.date_range("20130101", periods=9)),
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Series(pd.date_range("20130101", periods=9, tz="US/Eastern")),
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Series(pd.timedelta_range("2000", periods=9)),
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]
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)
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def series(request):
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return request.param
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@pytest.fixture(params=[True, False])
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def index(request):
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return request.param
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def _check_equal(obj, **kwargs):
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"""
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Check that hashing an objects produces the same value each time.
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Parameters
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----------
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obj : object
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The object to hash.
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kwargs : kwargs
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Keyword arguments to pass to the hashing function.
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"""
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a = hash_pandas_object(obj, **kwargs)
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b = hash_pandas_object(obj, **kwargs)
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tm.assert_series_equal(a, b)
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def _check_not_equal_with_index(obj):
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"""
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Check the hash of an object with and without its index is not the same.
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Parameters
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----------
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obj : object
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The object to hash.
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"""
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if not isinstance(obj, Index):
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a = hash_pandas_object(obj, index=True)
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b = hash_pandas_object(obj, index=False)
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if len(obj):
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assert not (a == b).all()
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def test_consistency():
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# Check that our hash doesn't change because of a mistake
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# in the actual code; this is the ground truth.
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result = hash_pandas_object(Index(["foo", "bar", "baz"]))
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expected = Series(
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np.array(
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[3600424527151052760, 1374399572096150070, 477881037637427054],
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dtype="uint64",
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),
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index=["foo", "bar", "baz"],
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)
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tm.assert_series_equal(result, expected)
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def test_hash_array(series):
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arr = series.values
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tm.assert_numpy_array_equal(hash_array(arr), hash_array(arr))
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@pytest.mark.parametrize(
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"arr2", [np.array([3, 4, "All"]), np.array([3, 4, "All"], dtype=object)]
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)
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def test_hash_array_mixed(arr2):
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result1 = hash_array(np.array(["3", "4", "All"]))
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result2 = hash_array(arr2)
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tm.assert_numpy_array_equal(result1, result2)
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@pytest.mark.parametrize("val", [5, "foo", pd.Timestamp("20130101")])
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def test_hash_array_errors(val):
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msg = "must pass a ndarray-like"
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with pytest.raises(TypeError, match=msg):
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hash_array(val)
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def test_hash_tuples():
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tuples = [(1, "one"), (1, "two"), (2, "one")]
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result = hash_tuples(tuples)
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expected = hash_pandas_object(MultiIndex.from_tuples(tuples)).values
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tm.assert_numpy_array_equal(result, expected)
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result = hash_tuples(tuples[0])
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assert result == expected[0]
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@pytest.mark.parametrize("val", [5, "foo", pd.Timestamp("20130101")])
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def test_hash_tuples_err(val):
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msg = "must be convertible to a list-of-tuples"
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with pytest.raises(TypeError, match=msg):
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hash_tuples(val)
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def test_multiindex_unique():
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mi = MultiIndex.from_tuples([(118, 472), (236, 118), (51, 204), (102, 51)])
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assert mi.is_unique is True
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result = hash_pandas_object(mi)
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assert result.is_unique is True
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def test_multiindex_objects():
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mi = MultiIndex(
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levels=[["b", "d", "a"], [1, 2, 3]],
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codes=[[0, 1, 0, 2], [2, 0, 0, 1]],
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names=["col1", "col2"],
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)
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recons = mi._sort_levels_monotonic()
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# These are equal.
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assert mi.equals(recons)
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assert Index(mi.values).equals(Index(recons.values))
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@pytest.mark.parametrize(
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"obj",
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[
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Series([1, 2, 3]),
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Series([1.0, 1.5, 3.2]),
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Series([1.0, 1.5, np.nan]),
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Series([1.0, 1.5, 3.2], index=[1.5, 1.1, 3.3]),
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Series(["a", "b", "c"]),
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Series(["a", np.nan, "c"]),
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Series(["a", None, "c"]),
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Series([True, False, True]),
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Series(dtype=object),
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Index([1, 2, 3]),
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Index([True, False, True]),
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DataFrame({"x": ["a", "b", "c"], "y": [1, 2, 3]}),
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DataFrame(),
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tm.makeMissingDataframe(),
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tm.makeMixedDataFrame(),
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tm.makeTimeDataFrame(),
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tm.makeTimeSeries(),
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tm.makeTimedeltaIndex(),
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tm.makePeriodIndex(),
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Series(tm.makePeriodIndex()),
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Series(pd.date_range("20130101", periods=3, tz="US/Eastern")),
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MultiIndex.from_product(
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[range(5), ["foo", "bar", "baz"], pd.date_range("20130101", periods=2)]
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),
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MultiIndex.from_product([pd.CategoricalIndex(list("aabc")), range(3)]),
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],
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)
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def test_hash_pandas_object(obj, index):
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_check_equal(obj, index=index)
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_check_not_equal_with_index(obj)
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def test_hash_pandas_object2(series, index):
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_check_equal(series, index=index)
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_check_not_equal_with_index(series)
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@pytest.mark.parametrize(
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"obj", [Series([], dtype="float64"), Series([], dtype="object"), Index([])]
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)
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def test_hash_pandas_empty_object(obj, index):
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# These are by-definition the same with
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# or without the index as the data is empty.
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_check_equal(obj, index=index)
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@pytest.mark.parametrize(
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"s1",
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[
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Series(["a", "b", "c", "d"]),
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Series([1000, 2000, 3000, 4000]),
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Series(pd.date_range(0, periods=4)),
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],
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)
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@pytest.mark.parametrize("categorize", [True, False])
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def test_categorical_consistency(s1, categorize):
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# see gh-15143
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#
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# Check that categoricals hash consistent with their values,
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# not codes. This should work for categoricals of any dtype.
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s2 = s1.astype("category").cat.set_categories(s1)
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s3 = s2.cat.set_categories(list(reversed(s1)))
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# These should all hash identically.
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h1 = hash_pandas_object(s1, categorize=categorize)
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h2 = hash_pandas_object(s2, categorize=categorize)
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h3 = hash_pandas_object(s3, categorize=categorize)
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tm.assert_series_equal(h1, h2)
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tm.assert_series_equal(h1, h3)
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def test_categorical_with_nan_consistency():
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c = pd.Categorical.from_codes(
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[-1, 0, 1, 2, 3, 4], categories=pd.date_range("2012-01-01", periods=5, name="B")
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)
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expected = hash_array(c, categorize=False)
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c = pd.Categorical.from_codes([-1, 0], categories=[pd.Timestamp("2012-01-01")])
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result = hash_array(c, categorize=False)
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assert result[0] in expected
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assert result[1] in expected
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@pytest.mark.parametrize("obj", [pd.Timestamp("20130101")])
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def test_pandas_errors(obj):
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msg = "Unexpected type for hashing"
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with pytest.raises(TypeError, match=msg):
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hash_pandas_object(obj)
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def test_hash_keys():
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# Using different hash keys, should have
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# different hashes for the same data.
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#
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# This only matters for object dtypes.
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obj = Series(list("abc"))
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a = hash_pandas_object(obj, hash_key="9876543210123456")
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b = hash_pandas_object(obj, hash_key="9876543210123465")
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assert (a != b).all()
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def test_invalid_key():
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# This only matters for object dtypes.
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msg = "key should be a 16-byte string encoded"
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with pytest.raises(ValueError, match=msg):
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hash_pandas_object(Series(list("abc")), hash_key="foo")
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def test_already_encoded(index):
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# If already encoded, then ok.
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obj = Series(list("abc")).str.encode("utf8")
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_check_equal(obj, index=index)
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def test_alternate_encoding(index):
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obj = Series(list("abc"))
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_check_equal(obj, index=index, encoding="ascii")
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@pytest.mark.parametrize("l_exp", range(8))
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@pytest.mark.parametrize("l_add", [0, 1])
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def test_same_len_hash_collisions(l_exp, l_add):
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length = 2 ** (l_exp + 8) + l_add
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s = tm.rands_array(length, 2)
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result = hash_array(s, "utf8")
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assert not result[0] == result[1]
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def test_hash_collisions():
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# Hash collisions are bad.
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#
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# https://github.com/pandas-dev/pandas/issues/14711#issuecomment-264885726
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hashes = [
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"Ingrid-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", # noqa: E501
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"Tim-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", # noqa: E501
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]
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# These should be different.
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result1 = hash_array(np.asarray(hashes[0:1], dtype=object), "utf8")
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expected1 = np.array([14963968704024874985], dtype=np.uint64)
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tm.assert_numpy_array_equal(result1, expected1)
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result2 = hash_array(np.asarray(hashes[1:2], dtype=object), "utf8")
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expected2 = np.array([16428432627716348016], dtype=np.uint64)
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tm.assert_numpy_array_equal(result2, expected2)
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result = hash_array(np.asarray(hashes, dtype=object), "utf8")
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tm.assert_numpy_array_equal(result, np.concatenate([expected1, expected2], axis=0))
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def test_hash_with_tuple():
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# GH#28969 array containing a tuple raises on call to arr.astype(str)
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# apparently a numpy bug github.com/numpy/numpy/issues/9441
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df = pd.DataFrame({"data": [tuple("1"), tuple("2")]})
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result = hash_pandas_object(df)
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expected = pd.Series([10345501319357378243, 8331063931016360761], dtype=np.uint64)
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tm.assert_series_equal(result, expected)
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df2 = pd.DataFrame({"data": [tuple([1]), tuple([2])]})
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result = hash_pandas_object(df2)
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expected = pd.Series([9408946347443669104, 3278256261030523334], dtype=np.uint64)
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tm.assert_series_equal(result, expected)
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# require that the elements of such tuples are themselves hashable
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df3 = pd.DataFrame({"data": [tuple([1, []]), tuple([2, {}])]})
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with pytest.raises(TypeError, match="unhashable type: 'list'"):
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hash_pandas_object(df3)
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def test_hash_object_none_key():
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# https://github.com/pandas-dev/pandas/issues/30887
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result = pd.util.hash_pandas_object(pd.Series(["a", "b"]), hash_key=None)
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expected = pd.Series([4578374827886788867, 17338122309987883691], dtype="uint64")
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tm.assert_series_equal(result, expected)
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