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binning_test.py
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binning_test.py
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# Copyright 2018 The Exoplanet ML Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Tests for binning.py."""
from absl.testing import absltest
import numpy as np
from binning import bin_and_aggregate
class BinningTest(absltest.TestCase):
def testErrors(self):
# x size less than 2.
x = [1]
y = [2]
with self.assertRaises(ValueError):
bin_and_aggregate(x, y, num_bins=2, bin_width=1, x_min=0, x_max=2)
# x and y not the same size.
x = [1, 2]
y = [4, 5, 6]
with self.assertRaises(ValueError):
bin_and_aggregate(x, y, num_bins=2, bin_width=1, x_min=0, x_max=2)
# x_min not less than x_max.
x = [1, 2, 3]
with self.assertRaises(ValueError):
bin_and_aggregate(x, y, num_bins=2, bin_width=1, x_min=-1, x_max=-1)
# x_min greater than the last element of x.
with self.assertRaises(ValueError):
bin_and_aggregate(x, y, num_bins=2, bin_width=0.25, x_min=3.5, x_max=4)
# bin_width nonpositive.
with self.assertRaises(ValueError):
bin_and_aggregate(x, y, num_bins=2, bin_width=0, x_min=1, x_max=3)
# bin_width greater than or equal to x_max - x_min.
with self.assertRaises(ValueError):
bin_and_aggregate(x, y, num_bins=2, bin_width=1, x_min=1.5, x_max=2.5)
# num_bins less than 2.
x = [1, 2, 3]
with self.assertRaises(ValueError):
bin_and_aggregate(x, y, num_bins=1, bin_width=1, x_min=0, x_max=2)
def testBucketBoundaries(self):
x = np.array([-6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6])
y = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13])
result, bin_counts = bin_and_aggregate(x, y, num_bins=5, bin_width=2, x_min=-5, x_max=5)
np.testing.assert_array_equal([2.5, 4.5, 6.5, 8.5, 10.5], result)
np.testing.assert_array_equal([2, 2, 2, 2, 2], bin_counts)
def testMultiSizeBins(self):
# Construct bins with size 0, 1, 2, 3, 4, 5, 10, respectively.
x = np.array([1, 2, 2, 3, 3, 3, 4, 4, 4, 4, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6])
y = np.array([0, -1, 1, 4, 5, 6, 2, 2, 4, 4, 1, 1, 1, 1, -1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
result, bin_counts = bin_and_aggregate(x, y, num_bins=7, bin_width=1, x_min=0, x_max=7)
np.testing.assert_array_equal([0, 0, 0, 5, 3, 1, 5.5], result)
np.testing.assert_array_equal([0, 1, 2, 3, 4, 5, 10], bin_counts)
def testMedianAggr(self):
x = np.array([-4, -2, -2, 0, 0, 0, 2, 2, 2, 2, 3, 3, 3, 3, 3])
y = np.array([0, -1, 1, 4, 5, 6, 1, 3, 3, 9, 1, 1, 1, 1, -2])
result, bin_counts = bin_and_aggregate(x, y, num_bins=5, bin_width=2, x_min=-5, x_max=5)
np.testing.assert_array_equal([0, 0, 5, 3, 1], result)
np.testing.assert_array_equal([1, 2, 3, 4, 5], bin_counts)
def testMeanAggr(self):
x = np.array([-4, -2, -2, 0, 0, 0, 2, 2, 2, 2, 3, 3, 3, 3, 3])
y = np.array([0, -1, 1, 4, 5, 6, 1, 3, 3, 9, 1, 1, 1, 1, -2])
result, bin_counts = bin_and_aggregate(x, y, num_bins=5, bin_width=2, x_min=-5, x_max=5, aggr_fn=np.mean)
np.testing.assert_array_almost_equal([0, 0, 5, 4, 0.4], result)
np.testing.assert_array_equal([1, 2, 3, 4, 5], bin_counts)
def testMinAggr(self):
x = np.array([-4, -2, -2, 0, 0, 0, 2, 2, 2, 2, 3, 3, 3, 3, 3])
y = np.array([0, -1, 1, 4, 5, 6, 1, 3, 3, 9, 1, 1, 1, 1, -2])
result, bin_counts = bin_and_aggregate(x, y, num_bins=5, bin_width=2, x_min=-5, x_max=5, aggr_fn=np.min)
np.testing.assert_array_equal([0, -1, 4, 1, -2], result)
np.testing.assert_array_equal([1, 2, 3, 4, 5], bin_counts)
def testMaxAggr(self):
x = np.array([-4, -2, -2, 0, 0, 0, 2, 2, 2, 2, 3, 3, 3, 3, 3])
y = np.array([0, -1, 1, 4, 5, 6, 1, 3, 3, 9, 1, 1, 1, 1, -2])
result, bin_counts = bin_and_aggregate(x, y, num_bins=5, bin_width=2, x_min=-5, x_max=5, aggr_fn=np.max)
np.testing.assert_array_equal([0, 1, 6, 9, 1], result)
np.testing.assert_array_equal([1, 2, 3, 4, 5], bin_counts)
def testWideBins(self):
x = np.array([-6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6])
y = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13])
result, bin_counts = bin_and_aggregate(x, y, num_bins=5, bin_width=6, x_min=-7, x_max=7)
np.testing.assert_array_equal([3, 4.5, 6.5, 8.5, 10.5], result)
np.testing.assert_array_equal([5, 6, 6, 6, 6], bin_counts)
def testNarrowBins(self):
x = np.array([-6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6])
y = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13])
result, bin_counts = bin_and_aggregate(x, y, num_bins=5, bin_width=1, x_min=-4.5, x_max=4.5)
np.testing.assert_array_equal([3, 5, 7, 9, 11], result)
np.testing.assert_array_equal([1, 1, 1, 1, 1], bin_counts)
def testDefaultXMinMax(self):
x = np.array([-4, -2, -2, 0, 0, 0, 2, 2, 2, 2, 3, 3, 3, 3, 3])
y = np.array([7, -1, 3, 4, 5, 6, 2, 2, 4, 4, 1, 1, 1, 1, -1])
result, bin_counts = bin_and_aggregate(x, y, num_bins=5)
np.testing.assert_array_equal([7, 1, 5, 0, 3], result)
np.testing.assert_array_equal([1, 2, 3, 0, 4], bin_counts)
def test2D(self):
x = np.array([-4, -2, -2, 0, 0, 0, 2, 2, 2, 2, 3, 3, 3, 3, 3])
y = np.array(
[
[0, 123],
[-1, 0],
[1, 2],
[4, -4],
[5, 4],
[6, 6],
[1, 50],
[3, 100],
[3, 100],
[9, 110],
[1, 0],
[1, 0],
[1, 0],
[1, 0],
[-2, 5],
]
)
result, bin_counts = bin_and_aggregate(x, y, num_bins=5, bin_width=2, x_min=-5, x_max=5)
np.testing.assert_array_equal([[0, 123], [0, 1], [5, 4], [3, 100], [1, 0]], result)
np.testing.assert_array_equal([1, 2, 3, 4, 5], bin_counts)
def test2DMean(self):
x = np.array([-4, -2, -2, 0, 0, 0, 2, 2, 2, 2, 3, 3, 3, 3, 3])
y = np.array(
[
[0, 123],
[-1, 0],
[1, 2],
[4, -4],
[5, 4],
[6, 6],
[1, 50],
[3, 100],
[3, 100],
[9, 110],
[1, 0],
[1, 0],
[1, 0],
[1, 0],
[-2, 5],
]
)
result, bin_counts = bin_and_aggregate(x, y, num_bins=5, bin_width=2, x_min=-5, x_max=5, aggr_fn=np.mean)
np.testing.assert_array_almost_equal([[0, 123], [0, 1], [5, 2], [4, 90], [0.4, 1]], result)
np.testing.assert_array_equal([1, 2, 3, 4, 5], bin_counts)
def test2DMin(self):
x = np.array([-4, -2, -2, 0, 0, 0, 2, 2, 2, 2, 3, 3, 3, 3, 3])
y = np.array(
[
[0, 123],
[-1, 0],
[1, 2],
[4, -4],
[5, 4],
[6, 6],
[1, 50],
[3, 100],
[3, 100],
[9, 110],
[1, 0],
[1, 0],
[1, 0],
[1, 0],
[-2, 5],
]
)
result, bin_counts = bin_and_aggregate(x, y, num_bins=5, bin_width=2, x_min=-5, x_max=5, aggr_fn=np.min)
np.testing.assert_array_almost_equal([[0, 123], [-1, 0], [4, -4], [1, 50], [-2, 0]], result)
np.testing.assert_array_equal([1, 2, 3, 4, 5], bin_counts)
def test2DMax(self):
x = np.array([-4, -2, -2, 0, 0, 0, 2, 2, 2, 2, 3, 3, 3, 3, 3])
y = np.array(
[
[0, 123],
[-1, 0],
[1, 2],
[4, -4],
[5, 4],
[6, 6],
[1, 50],
[3, 100],
[3, 100],
[9, 110],
[1, 0],
[1, 0],
[1, 0],
[1, 0],
[-2, 5],
]
)
result, bin_counts = bin_and_aggregate(x, y, num_bins=5, bin_width=2, x_min=-5, x_max=5, aggr_fn=np.max)
np.testing.assert_array_almost_equal([[0, 123], [1, 2], [6, 6], [9, 110], [1, 5]], result)
np.testing.assert_array_equal([1, 2, 3, 4, 5], bin_counts)
if __name__ == "__main__":
absltest.main()