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365 lines
13 KiB
365 lines
13 KiB
import numpy as np
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import pytest
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from pandas import (
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DataFrame,
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Index,
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Series,
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)
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import pandas._testing as tm
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import pandas.core.common as com
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class TestSample:
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@pytest.fixture(params=[Series, DataFrame])
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def obj(self, request):
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klass = request.param
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if klass is Series:
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arr = np.random.randn(10)
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else:
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arr = np.random.randn(10, 10)
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return klass(arr, dtype=None)
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@pytest.mark.parametrize("test", list(range(10)))
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def test_sample(self, test, obj):
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# Fixes issue: 2419
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# Check behavior of random_state argument
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# Check for stability when receives seed or random state -- run 10
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# times.
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seed = np.random.randint(0, 100)
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tm.assert_equal(
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obj.sample(n=4, random_state=seed), obj.sample(n=4, random_state=seed)
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)
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tm.assert_equal(
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obj.sample(frac=0.7, random_state=seed),
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obj.sample(frac=0.7, random_state=seed),
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)
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tm.assert_equal(
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obj.sample(n=4, random_state=np.random.RandomState(test)),
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obj.sample(n=4, random_state=np.random.RandomState(test)),
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)
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tm.assert_equal(
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obj.sample(frac=0.7, random_state=np.random.RandomState(test)),
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obj.sample(frac=0.7, random_state=np.random.RandomState(test)),
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)
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tm.assert_equal(
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obj.sample(frac=2, replace=True, random_state=np.random.RandomState(test)),
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obj.sample(frac=2, replace=True, random_state=np.random.RandomState(test)),
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)
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os1, os2 = [], []
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for _ in range(2):
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np.random.seed(test)
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os1.append(obj.sample(n=4))
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os2.append(obj.sample(frac=0.7))
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tm.assert_equal(*os1)
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tm.assert_equal(*os2)
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def test_sample_lengths(self, obj):
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# Check lengths are right
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assert len(obj.sample(n=4) == 4)
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assert len(obj.sample(frac=0.34) == 3)
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assert len(obj.sample(frac=0.36) == 4)
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def test_sample_invalid_random_state(self, obj):
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# Check for error when random_state argument invalid.
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msg = (
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"random_state must be an integer, array-like, a BitGenerator, Generator, "
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"a numpy RandomState, or None"
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)
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with pytest.raises(ValueError, match=msg):
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obj.sample(random_state="a_string")
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def test_sample_wont_accept_n_and_frac(self, obj):
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# Giving both frac and N throws error
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msg = "Please enter a value for `frac` OR `n`, not both"
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with pytest.raises(ValueError, match=msg):
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obj.sample(n=3, frac=0.3)
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def test_sample_requires_positive_n_frac(self, obj):
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with pytest.raises(
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ValueError,
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match="A negative number of rows requested. Please provide `n` >= 0",
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):
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obj.sample(n=-3)
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with pytest.raises(
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ValueError,
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match="A negative number of rows requested. Please provide `frac` >= 0",
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):
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obj.sample(frac=-0.3)
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def test_sample_requires_integer_n(self, obj):
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# Make sure float values of `n` give error
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with pytest.raises(ValueError, match="Only integers accepted as `n` values"):
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obj.sample(n=3.2)
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def test_sample_invalid_weight_lengths(self, obj):
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# Weight length must be right
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msg = "Weights and axis to be sampled must be of same length"
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with pytest.raises(ValueError, match=msg):
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obj.sample(n=3, weights=[0, 1])
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with pytest.raises(ValueError, match=msg):
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bad_weights = [0.5] * 11
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obj.sample(n=3, weights=bad_weights)
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with pytest.raises(ValueError, match="Fewer non-zero entries in p than size"):
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bad_weight_series = Series([0, 0, 0.2])
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obj.sample(n=4, weights=bad_weight_series)
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def test_sample_negative_weights(self, obj):
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# Check won't accept negative weights
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bad_weights = [-0.1] * 10
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msg = "weight vector many not include negative values"
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with pytest.raises(ValueError, match=msg):
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obj.sample(n=3, weights=bad_weights)
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def test_sample_inf_weights(self, obj):
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# Check inf and -inf throw errors:
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weights_with_inf = [0.1] * 10
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weights_with_inf[0] = np.inf
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msg = "weight vector may not include `inf` values"
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with pytest.raises(ValueError, match=msg):
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obj.sample(n=3, weights=weights_with_inf)
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weights_with_ninf = [0.1] * 10
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weights_with_ninf[0] = -np.inf
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with pytest.raises(ValueError, match=msg):
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obj.sample(n=3, weights=weights_with_ninf)
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def test_sample_zero_weights(self, obj):
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# All zeros raises errors
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zero_weights = [0] * 10
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with pytest.raises(ValueError, match="Invalid weights: weights sum to zero"):
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obj.sample(n=3, weights=zero_weights)
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def test_sample_missing_weights(self, obj):
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# All missing weights
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nan_weights = [np.nan] * 10
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with pytest.raises(ValueError, match="Invalid weights: weights sum to zero"):
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obj.sample(n=3, weights=nan_weights)
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def test_sample_none_weights(self, obj):
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# Check None are also replaced by zeros.
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weights_with_None = [None] * 10
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weights_with_None[5] = 0.5
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tm.assert_equal(
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obj.sample(n=1, axis=0, weights=weights_with_None), obj.iloc[5:6]
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)
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@pytest.mark.parametrize(
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"func_str,arg",
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[
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("np.array", [2, 3, 1, 0]),
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("np.random.MT19937", 3),
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("np.random.PCG64", 11),
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],
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)
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def test_sample_random_state(self, func_str, arg, frame_or_series):
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# GH#32503
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obj = DataFrame({"col1": range(10, 20), "col2": range(20, 30)})
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obj = tm.get_obj(obj, frame_or_series)
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result = obj.sample(n=3, random_state=eval(func_str)(arg))
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expected = obj.sample(n=3, random_state=com.random_state(eval(func_str)(arg)))
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tm.assert_equal(result, expected)
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def test_sample_generator(self, frame_or_series):
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# GH#38100
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obj = frame_or_series(np.arange(100))
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rng = np.random.default_rng()
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# Consecutive calls should advance the seed
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result1 = obj.sample(n=50, random_state=rng)
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result2 = obj.sample(n=50, random_state=rng)
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assert not (result1.index.values == result2.index.values).all()
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# Matching generator initialization must give same result
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# Consecutive calls should advance the seed
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result1 = obj.sample(n=50, random_state=np.random.default_rng(11))
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result2 = obj.sample(n=50, random_state=np.random.default_rng(11))
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tm.assert_equal(result1, result2)
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def test_sample_upsampling_without_replacement(self, frame_or_series):
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# GH#27451
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obj = DataFrame({"A": list("abc")})
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obj = tm.get_obj(obj, frame_or_series)
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msg = (
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"Replace has to be set to `True` when "
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"upsampling the population `frac` > 1."
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)
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with pytest.raises(ValueError, match=msg):
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obj.sample(frac=2, replace=False)
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class TestSampleDataFrame:
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# Tests which are relevant only for DataFrame, so these are
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# as fully parametrized as they can get.
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def test_sample(self):
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# GH#2419
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# additional specific object based tests
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# A few dataframe test with degenerate weights.
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easy_weight_list = [0] * 10
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easy_weight_list[5] = 1
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df = DataFrame(
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{
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"col1": range(10, 20),
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"col2": range(20, 30),
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"colString": ["a"] * 10,
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"easyweights": easy_weight_list,
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}
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)
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sample1 = df.sample(n=1, weights="easyweights")
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tm.assert_frame_equal(sample1, df.iloc[5:6])
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# Ensure proper error if string given as weight for Series or
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# DataFrame with axis = 1.
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ser = Series(range(10))
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msg = "Strings cannot be passed as weights when sampling from a Series."
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with pytest.raises(ValueError, match=msg):
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ser.sample(n=3, weights="weight_column")
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msg = (
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"Strings can only be passed to weights when sampling from rows on a "
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"DataFrame"
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)
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with pytest.raises(ValueError, match=msg):
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df.sample(n=1, weights="weight_column", axis=1)
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# Check weighting key error
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with pytest.raises(
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KeyError, match="'String passed to weights not a valid column'"
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):
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df.sample(n=3, weights="not_a_real_column_name")
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# Check that re-normalizes weights that don't sum to one.
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weights_less_than_1 = [0] * 10
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weights_less_than_1[0] = 0.5
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tm.assert_frame_equal(df.sample(n=1, weights=weights_less_than_1), df.iloc[:1])
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###
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# Test axis argument
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###
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# Test axis argument
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df = DataFrame({"col1": range(10), "col2": ["a"] * 10})
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second_column_weight = [0, 1]
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tm.assert_frame_equal(
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df.sample(n=1, axis=1, weights=second_column_weight), df[["col2"]]
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)
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# Different axis arg types
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tm.assert_frame_equal(
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df.sample(n=1, axis="columns", weights=second_column_weight), df[["col2"]]
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)
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weight = [0] * 10
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weight[5] = 0.5
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tm.assert_frame_equal(df.sample(n=1, axis="rows", weights=weight), df.iloc[5:6])
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tm.assert_frame_equal(
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df.sample(n=1, axis="index", weights=weight), df.iloc[5:6]
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)
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# Check out of range axis values
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msg = "No axis named 2 for object type DataFrame"
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with pytest.raises(ValueError, match=msg):
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df.sample(n=1, axis=2)
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msg = "No axis named not_a_name for object type DataFrame"
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with pytest.raises(ValueError, match=msg):
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df.sample(n=1, axis="not_a_name")
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ser = Series(range(10))
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with pytest.raises(ValueError, match="No axis named 1 for object type Series"):
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ser.sample(n=1, axis=1)
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# Test weight length compared to correct axis
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msg = "Weights and axis to be sampled must be of same length"
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with pytest.raises(ValueError, match=msg):
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df.sample(n=1, axis=1, weights=[0.5] * 10)
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def test_sample_axis1(self):
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# Check weights with axis = 1
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easy_weight_list = [0] * 3
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easy_weight_list[2] = 1
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df = DataFrame(
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{"col1": range(10, 20), "col2": range(20, 30), "colString": ["a"] * 10}
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)
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sample1 = df.sample(n=1, axis=1, weights=easy_weight_list)
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tm.assert_frame_equal(sample1, df[["colString"]])
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# Test default axes
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tm.assert_frame_equal(
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df.sample(n=3, random_state=42), df.sample(n=3, axis=0, random_state=42)
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)
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def test_sample_aligns_weights_with_frame(self):
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# Test that function aligns weights with frame
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df = DataFrame({"col1": [5, 6, 7], "col2": ["a", "b", "c"]}, index=[9, 5, 3])
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ser = Series([1, 0, 0], index=[3, 5, 9])
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tm.assert_frame_equal(df.loc[[3]], df.sample(1, weights=ser))
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# Weights have index values to be dropped because not in
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# sampled DataFrame
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ser2 = Series([0.001, 0, 10000], index=[3, 5, 10])
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tm.assert_frame_equal(df.loc[[3]], df.sample(1, weights=ser2))
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# Weights have empty values to be filed with zeros
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ser3 = Series([0.01, 0], index=[3, 5])
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tm.assert_frame_equal(df.loc[[3]], df.sample(1, weights=ser3))
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# No overlap in weight and sampled DataFrame indices
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ser4 = Series([1, 0], index=[1, 2])
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with pytest.raises(ValueError, match="Invalid weights: weights sum to zero"):
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df.sample(1, weights=ser4)
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def test_sample_is_copy(self):
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# GH#27357, GH#30784: ensure the result of sample is an actual copy and
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# doesn't track the parent dataframe / doesn't give SettingWithCopy warnings
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df = DataFrame(np.random.randn(10, 3), columns=["a", "b", "c"])
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df2 = df.sample(3)
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with tm.assert_produces_warning(None):
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df2["d"] = 1
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def test_sample_does_not_modify_weights(self):
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# GH-42843
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result = np.array([np.nan, 1, np.nan])
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expected = result.copy()
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ser = Series([1, 2, 3])
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# Test numpy array weights won't be modified in place
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ser.sample(weights=result)
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tm.assert_numpy_array_equal(result, expected)
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# Test DataFrame column won't be modified in place
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df = DataFrame({"values": [1, 1, 1], "weights": [1, np.nan, np.nan]})
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expected = df["weights"].copy()
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df.sample(frac=1.0, replace=True, weights="weights")
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result = df["weights"]
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tm.assert_series_equal(result, expected)
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def test_sample_ignore_index(self):
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# GH 38581
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df = DataFrame(
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{"col1": range(10, 20), "col2": range(20, 30), "colString": ["a"] * 10}
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)
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result = df.sample(3, ignore_index=True)
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expected_index = Index(range(3))
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tm.assert_index_equal(result.index, expected_index, exact=True)
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