Deepak Mallubhotla
bb7f87239f
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112 lines
3.2 KiB
Python
112 lines
3.2 KiB
Python
import pytest
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import tantri.binning.binning as binning
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import numpy
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def test_bin_construction_faulty_min():
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x_list = numpy.array([5, 6, 7, 8])
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bin_config = binning.BinConfig(log_scale=False, bin_width=0.8, bin_min=5.5)
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with pytest.raises(ValueError):
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binning._construct_bins(x_list, bin_config)
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def test_bin_construction_force_min():
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x_list = numpy.array([4.5, 5.5, 6.5, 7.5, 8.5])
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bin_config = binning.BinConfig(log_scale=False, bin_width=1, bin_min=2)
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expected_bins = numpy.array([2, 3, 4, 5, 6, 7, 8, 9])
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actual_bins = binning._construct_bins(x_list, bin_config=bin_config)
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numpy.testing.assert_allclose(
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actual_bins, expected_bins, err_msg="The bins were not as expected"
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)
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def test_bin_construction_even():
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x_list = numpy.array([1, 2.8, 8, 12.2, 13.6, 17, 19.71, 20, 24, 33])
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bin_config = binning.BinConfig(log_scale=False, bin_width=8)
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expected_bins = numpy.array([1, 9, 17, 25, 33])
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actual_bins = binning._construct_bins(x_list, bin_config=bin_config)
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numpy.testing.assert_allclose(
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actual_bins, expected_bins, err_msg="The bins were not as expected"
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)
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def test_bin_construction_uneven():
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x_list = numpy.array([1, 2.8, 8, 12.2, 13.6, 17, 19.71, 20, 24, 33])
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bin_config = binning.BinConfig(log_scale=False, bin_width=7)
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expected_bins = numpy.array([1, 8, 15, 22, 29, 36])
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actual_bins = binning._construct_bins(x_list, bin_config=bin_config)
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numpy.testing.assert_allclose(
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actual_bins, expected_bins, err_msg="The bins were not as expected"
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)
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def test_bin_construction_uneven_non_integer():
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x_list = numpy.array([1, 2.8, 8, 12.2, 13.6, 17, 19.71, 20, 24, 33])
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bin_config = binning.BinConfig(log_scale=False, bin_width=7.5)
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expected_bins = numpy.array([1, 8.5, 16, 23.5, 31, 38.5])
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actual_bins = binning._construct_bins(x_list, bin_config=bin_config)
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numpy.testing.assert_allclose(
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actual_bins, expected_bins, err_msg="The bins were not as expected"
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)
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def test_group_x_bins(snapshot):
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x_list = numpy.array([1, 2.8, 8, 12.2, 13.6, 17, 19.71, 20, 24, 33])
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y_dict = {
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"identity_plus_one": (
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numpy.array([1, 2.8, 8, 12.2, 13.6, 17, 19.71, 20, 24, 33]) + 2
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)
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}
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bin_config = binning.BinConfig(log_scale=False, bin_width=8)
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# expected_bins = numpy.array([1, 9, 17, 25, 33])
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binned = binning.bin_lists(x_list, y_dict, bin_config)
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assert binned == snapshot
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def test_group_x_bins_mean(snapshot):
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x_list = numpy.array([1, 2.8, 8, 12.2, 13.6, 17, 19.71, 20, 24, 33])
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y_dict = {
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"identity_plus_one": (
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numpy.array([1, 2.8, 8, 12.2, 13.6, 17, 19.71, 20, 24, 33]) + 2
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)
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}
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bin_config = binning.BinConfig(log_scale=False, bin_width=8)
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# expected_bins = numpy.array([1, 9, 17, 25, 33])
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binned = binning.bin_lists(x_list, y_dict, bin_config)
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mean_binned = [bin.mean_point() for bin in binned]
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assert mean_binned == snapshot
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def test_group_x_bins_summary(snapshot):
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x_list = numpy.array([1, 2.8, 8, 12.2, 13.6, 17, 19.71, 20, 24, 33])
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y_dict = {
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"identity_plus_one": (
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numpy.array([1, 2.8, 8, 12.2, 13.6, 17, 19.71, 20, 24, 33]) + 2
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)
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}
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bin_config = binning.BinConfig(log_scale=False, bin_width=8)
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# expected_bins = numpy.array([1, 9, 17, 25, 33])
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binned = binning.bin_lists(x_list, y_dict, bin_config)
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summary = [bin.summary_point() for bin in binned]
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assert summary == snapshot
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