# Copyright 2015 The TensorFlow Authors. All Rights Reserved. # # 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 tensorflow.ops.histogram_ops.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import numpy as np from tensorflow.python.framework import dtypes from tensorflow.python.framework import constant_op from tensorflow.python.ops import array_ops from tensorflow.python.ops import histogram_ops from tensorflow.python.platform import test class BinValuesFixedWidth(test.TestCase): def test_empty_input_gives_all_zero_counts(self): # Bins will be: # (-inf, 1), [1, 2), [2, 3), [3, 4), [4, inf) value_range = [0.0, 5.0] values = [] expected_bins = [] with self.cached_session(): bins = histogram_ops.histogram_fixed_width_bins( values, value_range, nbins=5) self.assertEqual(dtypes.int32, bins.dtype) self.assertAllClose(expected_bins, bins.eval()) def test_1d_values_int32_output(self): # Bins will be: # (-inf, 1), [1, 2), [2, 3), [3, 4), [4, inf) value_range = [0.0, 5.0] values = [-1.0, 0.0, 1.5, 2.0, 5.0, 15] expected_bins = [0, 0, 1, 2, 4, 4] with self.cached_session(): bins = histogram_ops.histogram_fixed_width_bins( values, value_range, nbins=5, dtype=dtypes.int64) self.assertEqual(dtypes.int32, bins.dtype) self.assertAllClose(expected_bins, bins.eval()) def test_1d_float64_values_int32_output(self): # Bins will be: # (-inf, 1), [1, 2), [2, 3), [3, 4), [4, inf) value_range = np.float64([0.0, 5.0]) values = np.float64([-1.0, 0.0, 1.5, 2.0, 5.0, 15]) expected_bins = [0, 0, 1, 2, 4, 4] with self.cached_session(): bins = histogram_ops.histogram_fixed_width_bins( values, value_range, nbins=5) self.assertEqual(dtypes.int32, bins.dtype) self.assertAllClose(expected_bins, bins.eval()) def test_2d_values(self): # Bins will be: # (-inf, 1), [1, 2), [2, 3), [3, 4), [4, inf) value_range = [0.0, 5.0] values = constant_op.constant( [[-1.0, 0.0, 1.5], [2.0, 5.0, 15]], shape=(2, 3)) expected_bins = [[0, 0, 1], [2, 4, 4]] with self.cached_session(): bins = histogram_ops.histogram_fixed_width_bins( values, value_range, nbins=5) self.assertEqual(dtypes.int32, bins.dtype) self.assertAllClose(expected_bins, bins.eval()) class HistogramFixedWidthTest(test.TestCase): def setUp(self): self.rng = np.random.RandomState(0) def test_with_invalid_value_range(self): values = [-1.0, 0.0, 1.5, 2.0, 5.0, 15] with self.assertRaisesRegexp( ValueError, "Shape must be rank 1 but is rank 0"): histogram_ops.histogram_fixed_width(values, 1.0) with self.assertRaisesRegexp(ValueError, "Dimension must be 2 but is 3"): histogram_ops.histogram_fixed_width(values, [1.0, 2.0, 3.0]) def test_with_invalid_nbins(self): values = [-1.0, 0.0, 1.5, 2.0, 5.0, 15] with self.assertRaisesRegexp( ValueError, "Shape must be rank 0 but is rank 1"): histogram_ops.histogram_fixed_width(values, [1.0, 5.0], nbins=[1, 2]) with self.assertRaisesRegexp( ValueError, "Requires nbins > 0"): histogram_ops.histogram_fixed_width(values, [1.0, 5.0], nbins=-5) def test_empty_input_gives_all_zero_counts(self): # Bins will be: # (-inf, 1), [1, 2), [2, 3), [3, 4), [4, inf) value_range = [0.0, 5.0] values = [] expected_bin_counts = [0, 0, 0, 0, 0] with self.test_session(use_gpu=True): hist = histogram_ops.histogram_fixed_width(values, value_range, nbins=5) self.assertEqual(dtypes.int32, hist.dtype) self.assertAllClose(expected_bin_counts, hist.eval()) def test_1d_values_int64_output(self): # Bins will be: # (-inf, 1), [1, 2), [2, 3), [3, 4), [4, inf) value_range = [0.0, 5.0] values = [-1.0, 0.0, 1.5, 2.0, 5.0, 15] expected_bin_counts = [2, 1, 1, 0, 2] with self.test_session(use_gpu=True): hist = histogram_ops.histogram_fixed_width( values, value_range, nbins=5, dtype=dtypes.int64) self.assertEqual(dtypes.int64, hist.dtype) self.assertAllClose(expected_bin_counts, hist.eval()) def test_1d_float64_values(self): # Bins will be: # (-inf, 1), [1, 2), [2, 3), [3, 4), [4, inf) value_range = np.float64([0.0, 5.0]) values = np.float64([-1.0, 0.0, 1.5, 2.0, 5.0, 15]) expected_bin_counts = [2, 1, 1, 0, 2] with self.test_session(use_gpu=True): hist = histogram_ops.histogram_fixed_width(values, value_range, nbins=5) self.assertEqual(dtypes.int32, hist.dtype) self.assertAllClose(expected_bin_counts, hist.eval()) def test_2d_values(self): # Bins will be: # (-inf, 1), [1, 2), [2, 3), [3, 4), [4, inf) value_range = [0.0, 5.0] values = [[-1.0, 0.0, 1.5], [2.0, 5.0, 15]] expected_bin_counts = [2, 1, 1, 0, 2] with self.test_session(use_gpu=True): hist = histogram_ops.histogram_fixed_width(values, value_range, nbins=5) self.assertEqual(dtypes.int32, hist.dtype) self.assertAllClose(expected_bin_counts, hist.eval()) def test_shape_inference(self): value_range = [0.0, 5.0] values = [[-1.0, 0.0, 1.5], [2.0, 5.0, 15]] expected_bin_counts = [2, 1, 1, 0, 2] placeholder = array_ops.placeholder(dtypes.int32) with self.test_session(use_gpu=True): hist = histogram_ops.histogram_fixed_width(values, value_range, nbins=5) self.assertAllEqual(hist.shape.as_list(), (5,)) self.assertEqual(dtypes.int32, hist.dtype) self.assertAllClose(expected_bin_counts, hist.eval()) hist = histogram_ops.histogram_fixed_width( values, value_range, nbins=placeholder) self.assertEquals(hist.shape.ndims, 1) self.assertIs(hist.shape[0].value, None) self.assertEqual(dtypes.int32, hist.dtype) self.assertAllClose(expected_bin_counts, hist.eval({placeholder: 5})) if __name__ == '__main__': test.main()