How to use lt method in stryker-parent

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ops_test.py

Source:ops_test.py

1# Copyright 2016 The TensorFlow Authors. All Rights Reserved.2#3# Licensed under the Apache License, Version 2.0 (the "License");4# you may not use this file except in compliance with the License.5# You may obtain a copy of the License at6#7#     http://www.apache.org/licenses/LICENSE-2.08#9# Unless required by applicable law or agreed to in writing, software10# distributed under the License is distributed on an "AS IS" BASIS,11# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.12# See the License for the specific language governing permissions and13# limitations under the License.14# ==============================================================================15from __future__ import absolute_import16from __future__ import division17from __future__ import print_function18import numpy as np19from six.moves import range  # pylint: disable=redefined-builtin20from tensorflow.contrib.labeled_tensor.python.ops import core21from tensorflow.contrib.labeled_tensor.python.ops import ops22from tensorflow.contrib.labeled_tensor.python.ops import test_util23from tensorflow.python.framework import constant_op24from tensorflow.python.framework import dtypes25from tensorflow.python.framework import errors_impl26from tensorflow.python.ops import array_ops27from tensorflow.python.ops import math_ops28from tensorflow.python.ops import string_ops29from tensorflow.python.platform import test as test_lib30class Base(test_util.Base):31  def setUp(self):32    super(Base, self).setUp()33    self.x_size = 734    self.channel_size = 335    self.z_size = 436    self.probs_size = 1137    tensor = math_ops.range(0, self.x_size * self.channel_size * self.z_size *38                            self.probs_size)39    tensor = array_ops.reshape(40        tensor, [self.x_size, self.channel_size, self.z_size, self.probs_size])41    a0 = ('x', range(self.x_size))42    a1 = ('channel', ['red', 'green', 'blue'])43    a2 = 'z'44    a3 = ('probs', np.linspace(0.0, 1.0, self.probs_size))45    self.tensor = tensor46    self.a0 = a047    self.a1 = a148    self.a2 = a249    self.a2_resolved = ('z', self.z_size)50    self.a3 = a351    self.original_lt = core.LabeledTensor(tensor, [a0, a1, a2, a3])52    self.x_probs_lt = core.slice_function(self.original_lt, {'z': 0})53    self.x_probs_lt = ops.select(self.x_probs_lt, {'channel': 'red'})54    self.channel_probs_lt = core.slice_function(self.original_lt,55                                                {'x': 3,56                                                 'z': 0})57class SelectTest(Base):58  def test_name(self):59    select_lt = ops.select(self.original_lt, {'channel': 'green'})60    self.assertIn('lt_select', select_lt.name)61  def test_scalar(self):62    select_lt = ops.select(self.original_lt, {'channel': 'green'})63    golden_lt = core.LabeledTensor(self.tensor[:, 1, :, :],64                                   [self.a0, self.a2, self.a3])65    self.assertLabeledTensorsEqual(select_lt, golden_lt)66  def test_slice(self):67    select_lt = ops.select(self.original_lt, {'channel': slice('red', 'green')})68    a1_sliced = ('channel', ['red', 'green'])69    golden_lt = core.LabeledTensor(self.tensor[:, :2, :, :],70                                   [self.a0, a1_sliced, self.a2, self.a3])71    self.assertLabeledTensorsEqual(select_lt, golden_lt)72  def test_slices(self):73    select_lt = ops.select(self.original_lt,74                           {'x': slice(1, 4),75                            'channel': slice('green', None)})76    a0_sliced = ('x', range(1, 5))77    a1_sliced = ('channel', ['green', 'blue'])78    golden_lt = core.LabeledTensor(self.tensor[1:5, 1:, :, :],79                                   [a0_sliced, a1_sliced, self.a2, self.a3])80    self.assertLabeledTensorsEqual(select_lt, golden_lt)81  def test_list(self):82    select_lt = ops.select(self.original_lt, {'channel': ['red', 'green']})83    a1_sliced = ('channel', ['red', 'green'])84    golden_lt = core.LabeledTensor(self.tensor[:, :2, :, :],85                                   [self.a0, a1_sliced, self.a2, self.a3])86    self.assertLabeledTensorsEqual(select_lt, golden_lt)87  def test_list_one_item(self):88    select_lt = ops.select(self.original_lt, {'channel': ['red']})89    a1_sliced = ('channel', ['red'])90    golden_lt = core.LabeledTensor(self.tensor[:, :1, :, :],91                                   [self.a0, a1_sliced, self.a2, self.a3])92    self.assertLabeledTensorsEqual(select_lt, golden_lt)93  def test_list_zero_items(self):94    select_lt = ops.select(self.original_lt, {'channel': []})95    golden_lt = core.LabeledTensor(self.tensor[:, :0, :, :],96                                   [self.a0, 'channel', self.a2, self.a3])97    self.assertLabeledTensorsEqual(select_lt, golden_lt)98  def test_scalars(self):99    select_lt = ops.select(self.original_lt, {'x': 1, 'channel': 'green'})100    golden_lt = core.LabeledTensor(self.tensor[1, 1, :, :], [self.a2, self.a3])101    self.assertLabeledTensorsEqual(select_lt, golden_lt)102  def test_tuple(self):103    original_lt = core.LabeledTensor(constant_op.constant([5, 6]),104                                     [('x', [(1, 2), (3, 4)])])105    select_lt = ops.select(original_lt, {'x': (1, 2)})106    golden_lt = core.LabeledTensor(constant_op.constant(5), [])107    self.assertLabeledTensorsEqual(select_lt, golden_lt)108  def test_invalid_input(self):109    with self.assertRaises(ValueError):110      ops.select(self.original_lt, {'foo': 1})111    with self.assertRaises(ValueError):112      ops.select(self.original_lt, {'z': 1})113    with self.assertRaises(KeyError):114      ops.select(self.original_lt, {'channel': 'purple'})115    with self.assertRaises(KeyError):116      ops.select(self.original_lt, {'channel': ['red', 'purple']})117    with self.assertRaises(NotImplementedError):118      ops.select(self.original_lt, {'channel': ['red'], 'x': [1]})119    with self.assertRaises(NotImplementedError):120      ops.select(self.original_lt, {'channel': ['red'], 'x': 1})121    with self.assertRaises(NotImplementedError):122      ops.select(self.original_lt, {'channel': slice('red', 'green', 2)})123class ConcatTest(Base):124  def setUp(self):125    super(ConcatTest, self).setUp()126    self.red_lt = ops.select(self.original_lt, {'channel': ['red']})127    self.green_lt = ops.select(self.original_lt, {'channel': ['green']})128    self.blue_lt = ops.select(self.original_lt, {'channel': ['blue']})129  def test_name(self):130    concat_lt = ops.concat([self.red_lt, self.blue_lt], 'channel')131    self.assertIn('lt_concat', concat_lt.name)132  def test(self):133    concat_lt = ops.concat([self.red_lt, self.green_lt], 'channel')134    golden_lt = ops.select(self.original_lt, {'channel': ['red', 'green']})135    self.assertLabeledTensorsEqual(concat_lt, golden_lt)136  def test_transposed(self):137    green_transposed = core.transpose(self.green_lt,138                                      ['probs', 'channel', 'z', 'x'])139    with self.assertRaises(ValueError):140      ops.concat([self.red_lt, green_transposed], 'channel')141  def test_invalid_input(self):142    with self.assertRaises(ValueError):143      ops.concat([], 'channel')144    with self.assertRaises(ValueError):145      ops.concat([self.red_lt, self.red_lt], 'channel')146    with self.assertRaises(ValueError):147      ops.concat([self.red_lt, self.red_lt], 'foo')148class PackTest(Base):149  def test_name(self):150    pack_lt = ops.pack([self.original_lt, self.original_lt], 'batch')151    self.assertIn('lt_pack', pack_lt.name)152  def test(self):153    pack_lt = ops.pack([self.original_lt, self.original_lt], 'batch')154    golden_lt = core.LabeledTensor(155        array_ops.stack([self.original_lt.tensor, self.original_lt.tensor]),156        ['batch', self.a0, self.a1, self.a2, self.a3])157    self.assertLabeledTensorsEqual(pack_lt, golden_lt)158  def test_axis(self):159    pack_lt = ops.pack(160        [self.original_lt, self.original_lt], new_axis='batch', axis_position=4)161    golden_lt = core.LabeledTensor(162        array_ops.stack(163            [self.original_lt.tensor, self.original_lt.tensor], axis=4),164        [self.a0, self.a1, self.a2, self.a3, 'batch'])165    self.assertLabeledTensorsEqual(pack_lt, golden_lt)166  def test_invalid_input(self):167    with self.assertRaises(ValueError):168      ops.pack([self.original_lt, self.original_lt], 'channel')169class UnpackTest(Base):170  def test_name(self):171    unpack_lts = ops.unpack(self.original_lt)172    for t in unpack_lts:173      self.assertIn('lt_unpack', t.name)174  def test(self):175    unpack_lt = ops.unpack(self.original_lt)[0]176    golden_lt = core.LabeledTensor(177        array_ops.unstack(self.original_lt.tensor)[0],178        [self.a1, self.a2, self.a3])179    self.assertLabeledTensorsEqual(unpack_lt, golden_lt)180  def test_axis(self):181    unpack_lt = ops.unpack(self.original_lt, axis_name='z')[0]182    golden_lt = core.LabeledTensor(183        array_ops.unstack(184            self.original_lt.tensor, axis=2)[0], [self.a0, self.a1, self.a3])185    self.assertLabeledTensorsEqual(unpack_lt, golden_lt)186  def test_invalid_input(self):187    with self.assertRaises(ValueError):188      ops.unpack(self.original_lt, axis_name='not_found')189class ReshapeTest(Base):190  def test_name(self):191    reshape_lt = ops.reshape(self.original_lt, ['channel'], ['foo'])192    self.assertIn('lt_reshape', reshape_lt.name)193  def test_identity(self):194    reshape_lt = ops.reshape(self.original_lt,195                             self.original_lt.axes.keys(),196                             self.original_lt.axes.values())197    self.assertLabeledTensorsEqual(reshape_lt, self.original_lt)198  def test_known_size(self):199    new_dim_size = self.channel_size * self.z_size * self.probs_size200    reshape_lt = ops.reshape(self.original_lt, ['channel', 'z', 'probs'],201                             [('new_dim', new_dim_size)])202    golden_lt = core.LabeledTensor(203        array_ops.reshape(self.original_lt.tensor, [self.x_size, -1]),204        [self.original_lt.axes['x'], 'new_dim'])205    self.assertLabeledTensorsEqual(reshape_lt, golden_lt)206  def test_unknown_size(self):207    reshape_lt = ops.reshape(self.original_lt, ['channel', 'z', 'probs'],208                             ['new_dim'])209    golden_lt = core.LabeledTensor(210        array_ops.reshape(self.original_lt.tensor, [self.x_size, -1]),211        [self.original_lt.axes['x'], 'new_dim'])212    self.assertLabeledTensorsEqual(reshape_lt, golden_lt)213  def test_unknown_dimension(self):214    orig_lt = core.LabeledTensor(215        array_ops.placeholder(dtypes.float32, [None]), ['x'])216    reshape_lt = ops.reshape(orig_lt, ['x'], ['y', ('z', 1)])217    self.assertEqual(reshape_lt.axes, core.Axes([('y', None), ('z', 1)]))218    with self.test_session() as sess:219      result = sess.run(reshape_lt, feed_dict={orig_lt.tensor: [1, 2]})220      np.testing.assert_array_equal(result, [[1], [2]])221  def test_with_labels(self):222    new_dim_size = self.channel_size * self.z_size * self.probs_size223    reshape_lt = ops.reshape(self.original_lt, ['channel', 'z', 'probs'],224                             [('new_dim', range(new_dim_size))])225    golden_lt = core.LabeledTensor(226        array_ops.reshape(self.original_lt.tensor, [self.x_size, -1]),227        [self.original_lt.axes['x'], ('new_dim', range(new_dim_size))])228    self.assertLabeledTensorsEqual(reshape_lt, golden_lt)229  def test_invalid_input(self):230    with self.assertRaisesRegexp(ValueError, 'not contained in the set'):231      ops.reshape(self.original_lt, ['foo'], ['bar'])232    with self.assertRaisesRegexp(core.AxisOrderError,233                                 'not a slice of axis names'):234      ops.reshape(self.original_lt, ['probs', 'z'], ['bar'])235    with self.assertRaisesRegexp(ValueError, 'at most one axis in new_axes'):236      ops.reshape(self.original_lt, ['probs'], ['foo', 'bar'])237class RenameAxisTest(Base):238  def test_name(self):239    rename_axis_lt = ops.rename_axis(self.original_lt, 'channel', 'foo')240    self.assertIn('lt_rename_axis', rename_axis_lt.name)241  def test_identity(self):242    rename_axis_lt = ops.rename_axis(self.original_lt, 'channel', 'channel')243    self.assertLabeledTensorsEqual(rename_axis_lt, self.original_lt)244  def test_new_name(self):245    rename_axis_lt = ops.rename_axis(self.original_lt, 'channel', 'foo')246    expected_axes = [(name if name != 'channel' else 'foo', axis.value)247                     for name, axis in self.original_lt.axes.items()]248    expected_lt = core.LabeledTensor(self.original_lt.tensor, expected_axes)249    self.assertLabeledTensorsEqual(rename_axis_lt, expected_lt)250  def test_invalid_input(self):251    with self.assertRaisesRegexp(ValueError, 'not contained in the set'):252      ops.rename_axis(self.original_lt, 'foo', 'bar')253class BatchTest(Base):254  def setUp(self):255    super(BatchTest, self).setUp()256    tensors = []257    for i in range(10):258      offset_lt = core.LabeledTensor(constant_op.constant(i), [])259      tensors.append(core.add(self.original_lt, offset_lt))260    self.pack_lt = ops.pack(tensors, 'batch')261  def test_name(self):262    batch_ops = ops.batch(263        [self.pack_lt, self.pack_lt], batch_size=2, enqueue_many=True)264    for bo in batch_ops:265      self.assertIn('lt_batch', bo.name)266  def test_enqueue_many(self):267    [batch_2_op] = ops.batch([self.pack_lt], batch_size=2, enqueue_many=True)268    self.assertEqual(len(batch_2_op.axes['batch']), 2)269    [batch_10_op] = ops.batch([batch_2_op], batch_size=10, enqueue_many=True)270    self.assertLabeledTensorsEqual(self.pack_lt, batch_10_op)271  def test_no_enqueue_many(self):272    [batch_2_op] = ops.batch([self.original_lt], batch_size=2)273    self.assertEqual(len(batch_2_op.axes['batch']), 2)274    [batch_10_op] = ops.batch([batch_2_op], batch_size=10, enqueue_many=True)275    self.assertLabeledTensorsEqual(276        ops.pack(10 * [self.original_lt], 'batch'), batch_10_op)277  def test_invalid_input(self):278    with self.assertRaises(ValueError):279      ops.batch([self.original_lt], 3, enqueue_many=True)280  def test_allow_smaller_final_batch(self):281    [batch_2_op] = ops.batch(282        [self.original_lt], batch_size=2, allow_smaller_final_batch=True)283    self.assertEqual(batch_2_op.axes['batch'].size, None)284class ShuffleBatchTest(Base):285  def setUp(self):286    super(ShuffleBatchTest, self).setUp()287    tensors = []288    for i in range(10):289      offset_lt = core.LabeledTensor(constant_op.constant(i), [])290      tensors.append(core.add(self.original_lt, offset_lt))291    self.pack_lt = ops.pack(tensors, 'batch')292  def test_name(self):293    batch_lts = ops.shuffle_batch(294        [self.pack_lt, self.pack_lt], batch_size=2, enqueue_many=True)295    for blt in batch_lts:296      self.assertIn('lt_shuffle_batch', blt.name)297  def test_enqueue_many(self):298    [batch_2_lt] = ops.shuffle_batch(299        [self.pack_lt],300        batch_size=2,301        enqueue_many=True,302        min_after_dequeue=8,303        seed=0)304    self.assertEqual(len(batch_2_lt.axes['batch']), 2)305    [batch_10_lt] = ops.batch([batch_2_lt], batch_size=10, enqueue_many=True)306    self.assertEqual(batch_10_lt.axes, self.pack_lt.axes)307    [batch_10, pack] = self.eval([batch_10_lt.tensor, self.pack_lt.tensor])308    self.assertFalse((batch_10 == pack).all())309  def test_allow_smaller_final_batch(self):310    [batch_2_op] = ops.shuffle_batch(311        [self.original_lt], batch_size=2, allow_smaller_final_batch=True)312    self.assertEqual(batch_2_op.axes['batch'].size, None)313class RandomCropTest(Base):314  def test_name(self):315    crop_lt = ops.random_crop(self.original_lt, {'probs': 3})316    self.assertIn('lt_random_crop', crop_lt.name)317  def test_single(self):318    crop_lt = ops.random_crop(self.original_lt, {'probs': 3})319    self.assertEqual(320        core.Axes([self.a0, self.a1, self.a2_resolved, ('probs', 3)]),321        crop_lt.axes)322  def test_double(self):323    crop_lt = ops.random_crop(self.original_lt, {'probs': 3, 'channel': 2})324    self.assertEqual(325        core.Axes([self.a0, ('channel', 2), self.a2_resolved, ('probs', 3)]),326        crop_lt.axes)327  def test_size1(self):328    crop_lt = ops.random_crop(self.original_lt, {'probs': 1})329    self.assertEqual(330        core.Axes([self.a0, self.a1, self.a2_resolved, ('probs', 1)]),331        crop_lt.axes)332  def test_different_seeds(self):333    crop_0_lt = ops.random_crop(334        self.original_lt, {'probs': 3,335                           'channel': 2}, seed=0)336    crop_1_lt = ops.random_crop(337        self.original_lt, {'probs': 3,338                           'channel': 2}, seed=1)339    self.assertEqual(crop_0_lt.axes, crop_1_lt.axes)340    [crop_0, crop_1] = self.eval([crop_0_lt.tensor, crop_1_lt.tensor])341    self.assertFalse((crop_0 == crop_1).all())342  def test_identical_seeds(self):343    crop_0_lt = ops.random_crop(344        self.original_lt, {'probs': 3,345                           'channel': 2}, seed=0)346    crop_1_lt = ops.random_crop(347        self.original_lt, {'probs': 3,348                           'channel': 2}, seed=0)349    self.assertLabeledTensorsEqual(crop_0_lt, crop_1_lt)350  def test_crop_idempotent(self):351    crop_0_lt = ops.random_crop(352        self.original_lt, {'probs': 3,353                           'channel': 2}, seed=0)354    crop_1_lt = ops.random_crop(crop_0_lt, {'probs': 3, 'channel': 2}, seed=1)355    self.assertLabeledTensorsEqual(crop_0_lt, crop_1_lt)356  def test_invalid_input(self):357    with self.assertRaises(ValueError):358      ops.random_crop(self.original_lt, {'foobar': 2})359class MapFnTest(Base):360  def test_name(self):361    map_lt = ops.map_fn(core.identity, self.original_lt)362    self.assertIn('lt_map_fn', map_lt.name)363  def test_identity(self):364    map_lt = ops.map_fn(core.identity, self.original_lt)365    self.assertLabeledTensorsEqual(map_lt, self.original_lt)366  def test_callable_object(self):367    class Identity(object):368      def __call__(self, other):369        return other370    map_lt = ops.map_fn(Identity(), self.original_lt)371    self.assertLabeledTensorsEqual(map_lt, self.original_lt)372  def test_slice(self):373    map_lt = ops.map_fn(lambda t: core.slice_function(t, {'channel': 1}),374                        self.original_lt)375    slice_lt = core.slice_function(self.original_lt, {'channel': 1})376    self.assertLabeledTensorsEqual(map_lt, slice_lt)377  def test_string(self):378    def fn(entry_lt):379      op = string_ops.string_join([entry_lt, 'world'])380      return core.LabeledTensor(op, [])381    tensor_lt = ops.constant(['hi', 'bye'], axes=['batch'])382    map_lt = ops.map_fn(fn, tensor_lt)383    golden_lt = ops.constant(['hiworld', 'byeworld'], axes=['batch'])384    self.assertLabeledTensorsEqual(map_lt, golden_lt)385class FoldlTest(Base):386  def test_name(self):387    foldl_lt = ops.foldl(core.add, self.original_lt,388                         core.slice_function(self.original_lt, {'x': 0}))389    self.assertIn('lt_foldl', foldl_lt.name)390  def test_sum(self):391    initializer_lt = ops.constant([0, 10], axes=['y'])392    tensor_lt = ops.constant([[1, 2], [3, 4], [5, 6]], axes=['x', 'y'])393    foldl_lt = ops.foldl(core.add, tensor_lt, initializer_lt)394    golden_lt = ops.constant([9, 22], axes=['y'])395    self.assertLabeledTensorsEqual(foldl_lt, golden_lt)396class SqueezeTest(Base):397  def setUp(self):398    super(SqueezeTest, self).setUp()399    self.squeezable_lt = core.slice_function(400        self.original_lt, {'channel': slice(0, 1),401                           'probs': slice(0, 1)})402  def test_name(self):403    squeeze_lt = ops.squeeze(self.squeezable_lt)404    self.assertIn('lt_squeeze', squeeze_lt.name)405  def test_none(self):406    none_lt = ops.squeeze(self.squeezable_lt, None)407    axes_lt = ops.squeeze(self.squeezable_lt, ['channel', 'probs'])408    self.assertLabeledTensorsEqual(none_lt, axes_lt)409  def test(self):410    squeeze_lt = ops.squeeze(self.squeezable_lt, ['probs'])411    golden_lt = core.slice_function(self.squeezable_lt, {'probs': 0})412    self.assertLabeledTensorsEqual(squeeze_lt, golden_lt)413  def test_invalid_input(self):414    with self.assertRaises(ValueError):415      ops.squeeze(self.original_lt, ['channel'])416    with self.assertRaises(ValueError):417      ops.squeeze(self.squeezable_lt, ['foo'])418class MatMulTest(Base):419  def test_name(self):420    x_lt = core.LabeledTensor(array_ops.ones((3,)), ['x'])421    matmul_lt = ops.matmul(x_lt, x_lt)422    self.assertIn('lt_matmul', matmul_lt.name)423  def test_vector_vector(self):424    x_lt = core.LabeledTensor(math_ops.range(3), ['x'])425    matmul_lt = ops.matmul(x_lt, x_lt)426    golden_lt = core.convert_to_labeled_tensor(5)427    self.assertLabeledTensorsEqual(matmul_lt, golden_lt)428  def test_matrix_vector(self):429    xy_lt = core.LabeledTensor(430        array_ops.reshape(math_ops.range(6), (2, 3)), ['x', 'y'])431    y_lt = core.LabeledTensor(math_ops.range(3), ['y'])432    matmul_lt = ops.matmul(xy_lt, y_lt)433    golden_lt = core.LabeledTensor(434        math_ops.matmul(xy_lt.tensor, array_ops.reshape(y_lt.tensor,435                                                        (-1, 1)))[:, 0], ['x'])436    self.assertLabeledTensorsEqual(matmul_lt, golden_lt)437    matmul_lt = ops.matmul(y_lt, xy_lt)438    self.assertLabeledTensorsEqual(matmul_lt, golden_lt)439  def test_matrix_matrix(self):440    xy_lt = core.LabeledTensor(441        array_ops.reshape(math_ops.range(6), (2, 3)), ['x', 'y'])442    yz_lt = core.LabeledTensor(443        array_ops.reshape(math_ops.range(12), (3, 4)), ['y', 'z'])444    matmul_lt = ops.matmul(xy_lt, yz_lt)445    golden_lt = core.LabeledTensor(446        math_ops.matmul(xy_lt.tensor, yz_lt.tensor), ['x', 'z'])447    self.assertLabeledTensorsEqual(matmul_lt, golden_lt)448    transpose = lambda x: core.transpose(x, list(x.axes.keys())[::-1])449    matmul_lt = ops.matmul(xy_lt, transpose(yz_lt))450    self.assertLabeledTensorsEqual(matmul_lt, golden_lt)451    matmul_lt = ops.matmul(transpose(xy_lt), yz_lt)452    self.assertLabeledTensorsEqual(matmul_lt, golden_lt)453    matmul_lt = ops.matmul(transpose(xy_lt), transpose(yz_lt))454    self.assertLabeledTensorsEqual(matmul_lt, golden_lt)455    matmul_lt = ops.matmul(yz_lt, xy_lt)456    self.assertLabeledTensorsEqual(matmul_lt, transpose(golden_lt))457  def test_matrix_matrix_axis_order(self):458    xy_lt = core.LabeledTensor(459        array_ops.reshape(math_ops.range(6), (2, 3)), ['x', 'y'])460    yz_lt = core.LabeledTensor(461        array_ops.reshape(math_ops.range(12), (3, 4)), ['y', 'z'])462    golden_lt = core.LabeledTensor(463        math_ops.matmul(xy_lt.tensor, yz_lt.tensor), ['x', 'z'])464    with core.axis_order_scope(['x', 'y', 'z']):465      matmul_lt = ops.matmul(xy_lt, yz_lt)466      self.assertLabeledTensorsEqual(matmul_lt, golden_lt)467      matmul_lt = ops.matmul(yz_lt, xy_lt)468      self.assertLabeledTensorsEqual(matmul_lt, golden_lt)469  def test_invalid(self):470    scalar_lt = core.LabeledTensor(array_ops.ones(()), [])471    x_lt = core.LabeledTensor(array_ops.ones((2,)), ['x'])472    x2_lt = core.LabeledTensor(array_ops.ones((3,)), ['x'])473    y_lt = core.LabeledTensor(array_ops.ones((3,)), ['y'])474    xy_lt = core.LabeledTensor(array_ops.ones((2, 3)), ['x', 'y'])475    xyz_lt = core.LabeledTensor(array_ops.ones((2, 3, 1)), ['x', 'y', 'z'])476    with self.assertRaisesRegexp(ValueError, 'inputs with at least rank'):477      ops.matmul(x_lt, scalar_lt)478    with self.assertRaises(NotImplementedError):479      ops.matmul(x_lt, xyz_lt)480    with self.assertRaisesRegexp(ValueError, 'exactly one axis in common'):481      ops.matmul(x_lt, y_lt)482    with self.assertRaises(NotImplementedError):483      ops.matmul(xy_lt, xy_lt)484    with self.assertRaisesRegexp(ValueError, 'does not match'):485      ops.matmul(x_lt, x2_lt)486class ReduceSumTest(Base):487  def test_name(self):488    sum_lt = ops.reduce_sum(self.original_lt, {'channel'})489    self.assertIn('lt_reduce_sum', sum_lt.name)490  def test_drop_axis(self):491    sum_lt = ops.reduce_sum(self.original_lt, {'channel'})492    golden_lt = core.LabeledTensor(493        math_ops.reduce_sum(self.original_lt.tensor, 1),494        [self.a0, self.a2, self.a3])495    self.assertLabeledTensorsEqual(sum_lt, golden_lt)496  def test_drop_scalar_axis(self):497    sum_lt = ops.reduce_sum(self.original_lt, 'channel')498    golden_lt = core.LabeledTensor(499        math_ops.reduce_sum(self.original_lt.tensor, 1),500        [self.a0, self.a2, self.a3])501    self.assertLabeledTensorsEqual(sum_lt, golden_lt)502  def test_keep_axis(self):503    sum_lt = ops.reduce_sum(self.original_lt, {('channel', 'hihowareyou')})504    golden_lt = core.LabeledTensor(505        math_ops.reduce_sum(506            self.original_lt.tensor, 1, keep_dims=True),507        [self.a0, ('channel', ['hihowareyou']), self.a2, self.a3])508    self.assertLabeledTensorsEqual(sum_lt, golden_lt)509  def test_keep_scalar_axis(self):510    sum_lt = ops.reduce_sum(self.original_lt, ('channel', 'hihowareyou'))511    golden_lt = core.LabeledTensor(512        math_ops.reduce_sum(513            self.original_lt.tensor, 1, keep_dims=True),514        [self.a0, ('channel', ['hihowareyou']), self.a2, self.a3])515    self.assertLabeledTensorsEqual(sum_lt, golden_lt)516  def test_scalar(self):517    scalar_lt = core.LabeledTensor(constant_op.constant(42), [])518    reduce_lt = ops.reduce_sum(scalar_lt, [])519    self.assertLabeledTensorsEqual(reduce_lt, scalar_lt)520  def test_empty_list(self):521    reduce_lt = ops.reduce_sum(self.original_lt, [])522    self.assertLabeledTensorsEqual(reduce_lt, self.original_lt)523  def test_none(self):524    sum_lt = ops.reduce_sum(self.original_lt)525    golden_lt = core.LabeledTensor(526        math_ops.reduce_sum(self.original_lt.tensor), [])527    self.assertLabeledTensorsEqual(sum_lt, golden_lt)528  def test_function_docstring_and_name(self):529    self.assertIn('tf.reduce_sum', ops.reduce_sum.__doc__)530    self.assertEqual('reduce_sum', ops.reduce_sum.__name__)531class ReduceMeanTest(Base):532  def test_name(self):533    actual_lt = ops.reduce_mean(self.original_lt, {'channel'})534    self.assertIn('lt_reduce_mean', actual_lt.name)535  def test(self):536    actual_lt = ops.reduce_mean(self.original_lt, {'channel'})537    golden_lt = core.LabeledTensor(538        math_ops.reduce_mean(self.original_lt.tensor, 1),539        [self.a0, self.a2, self.a3])540    self.assertLabeledTensorsEqual(actual_lt, golden_lt)541class ReduceProdTest(Base):542  def test_name(self):543    result_lt = ops.reduce_prod(self.original_lt, {'channel'})544    self.assertIn('lt_reduce_prod', result_lt.name)545  def test(self):546    result_lt = ops.reduce_prod(self.original_lt, {'channel'})547    golden_lt = core.LabeledTensor(548        math_ops.reduce_prod(self.original_lt.tensor, 1),549        [self.a0, self.a2, self.a3])550    self.assertLabeledTensorsEqual(result_lt, golden_lt)551class ReduceMinTest(Base):552  def test_name(self):553    result_lt = ops.reduce_min(self.original_lt, {'channel'})554    self.assertIn('lt_reduce_min', result_lt.name)555  def test(self):556    result_lt = ops.reduce_min(self.original_lt, {'channel'})557    golden_lt = core.LabeledTensor(558        math_ops.reduce_min(self.original_lt.tensor, 1),559        [self.a0, self.a2, self.a3])560    self.assertLabeledTensorsEqual(result_lt, golden_lt)561class ReduceMaxTest(Base):562  def test_name(self):563    result_lt = ops.reduce_max(self.original_lt, {'channel'})564    self.assertIn('lt_reduce_max', result_lt.name)565  def test(self):566    result_lt = ops.reduce_max(self.original_lt, {'channel'})567    golden_lt = core.LabeledTensor(568        math_ops.reduce_max(self.original_lt.tensor, 1),569        [self.a0, self.a2, self.a3])570    self.assertLabeledTensorsEqual(result_lt, golden_lt)571class BaseReduceBoolean(Base):572  def setUp(self):573    super(BaseReduceBoolean, self).setUp()574    self.bool_tensor = math_ops.cast(self.original_lt.tensor > 5, dtypes.bool)575    self.bool_lt = core.LabeledTensor(self.bool_tensor, self.original_lt.axes)576class ReduceAllTest(BaseReduceBoolean):577  def test_name(self):578    result_lt = ops.reduce_all(self.bool_lt, {'channel'})579    self.assertIn('lt_reduce_all', result_lt.name)580  def test(self):581    result_lt = ops.reduce_all(self.bool_lt, {'channel'})582    golden_lt = core.LabeledTensor(583        math_ops.reduce_all(self.bool_tensor, 1), [self.a0, self.a2, self.a3])584    self.assertLabeledTensorsEqual(result_lt, golden_lt)585class ReduceAnyTest(BaseReduceBoolean):586  def test_name(self):587    result_lt = ops.reduce_any(self.bool_lt, {'channel'})588    self.assertIn('lt_reduce_any', result_lt.name)589  def test(self):590    result_lt = ops.reduce_any(self.bool_lt, {'channel'})591    golden_lt = core.LabeledTensor(592        math_ops.reduce_any(self.bool_tensor, 1), [self.a0, self.a2, self.a3])593    self.assertLabeledTensorsEqual(result_lt, golden_lt)594class TileTest(Base):595  def test_name(self):596    tile_lt = ops.tile(self.original_lt, {'z': 2})597    self.assertIn('lt_tile', tile_lt.name)598  def test(self):599    for multiple in [2, constant_op.constant(2)]:600      tile_lt = ops.tile(self.original_lt, {'z': multiple})601      golden_op = array_ops.tile(self.original_lt.tensor, [1, 1, multiple, 1])602      golden_axes = [603          'z' if axis.name == 'z' else axis604          for axis in self.original_lt.axes.values()605      ]606      golden_lt = core.LabeledTensor(golden_op, golden_axes)607      self.assertLabeledTensorsEqual(tile_lt, golden_lt)608  def test_invalid_input(self):609    with self.assertRaisesRegexp(ValueError, 'are not contained in the set'):610      ops.tile(self.original_lt, {'foo': 5})611    with self.assertRaisesRegexp(ValueError, 'axes with tick labels'):612      ops.tile(self.original_lt, {'x': 5})613class PadTest(Base):614  def test_name(self):615    pad_lt = ops.pad(self.original_lt,616                     {'x': (1, 1),617                      'channel': ([], ['alpha'])})618    self.assertIn('lt_pad', pad_lt.name)619  def test(self):620    pad_lt = ops.pad(self.original_lt,621                     {'x': (1, 1),622                      'channel': ([], ['alpha'])})623    golden_op = array_ops.pad(self.original_lt.tensor, [[1, 1], [0, 1], [0, 0],624                                                        [0, 0]])625    golden_axes = [('x', self.x_size + 2),626                   ('channel', ['red', 'green', 'blue', 'alpha']), self.a2,627                   self.a3]628    golden_lt = core.LabeledTensor(golden_op, golden_axes)629    self.assertLabeledTensorsEqual(pad_lt, golden_lt)630  def test_invalid_input(self):631    with self.assertRaisesRegexp(ValueError, 'are not contained in the set'):632      ops.pad(self.original_lt, {'foo': (1, 1), 'channel': ([], ['alpha'])})633class ConstantTest(Base):634  def test_name(self):635    constant_lt = ops.constant(1)636    self.assertIn('lt_constant', constant_lt.name)637  def test_scalar(self):638    constant_lt = ops.constant(1)639    golden_lt = core.LabeledTensor(constant_op.constant(1), [])640    self.assertLabeledTensorsEqual(constant_lt, golden_lt)641  def test_infer_shape(self):642    constant_lt = ops.constant([1, 2], axes=['x'])643    golden_lt = core.LabeledTensor(constant_op.constant([1, 2]), ['x'])644    self.assertLabeledTensorsEqual(constant_lt, golden_lt)645  def test_specify_shape(self):646    constant_lt = ops.constant(1, axes=[('x', 3)])647    golden_lt = core.LabeledTensor(constant_op.constant(1, shape=(3,)), ['x'])648    self.assertLabeledTensorsEqual(constant_lt, golden_lt)649  def test_existing_axes(self):650    golden_lt = core.LabeledTensor(constant_op.constant([1, 2]), ['x'])651    constant_lt = ops.constant([1, 2], axes=golden_lt.axes)652    self.assertLabeledTensorsEqual(constant_lt, golden_lt)653class ZerosLikeTest(Base):654  def test_name(self):655    like_lt = ops.zeros_like(self.original_lt)656    self.assertIn('lt_zeros_like', like_lt.name)657  def test(self):658    like_lt = ops.zeros_like(self.original_lt)659    golden_lt = core.LabeledTensor(660        array_ops.zeros_like(self.original_lt.tensor), self.original_lt.axes)661    self.assertLabeledTensorsEqual(like_lt, golden_lt)662class OnesLikeTest(Base):663  def test_name(self):664    like_lt = ops.ones_like(self.original_lt)665    self.assertIn('lt_ones_like', like_lt.name)666  def test(self):667    like_lt = ops.ones_like(self.original_lt)668    golden_lt = core.LabeledTensor(669        array_ops.ones_like(self.original_lt.tensor), self.original_lt.axes)670    self.assertLabeledTensorsEqual(like_lt, golden_lt)671class CastTest(Base):672  def test_name(self):673    cast_lt = ops.cast(self.original_lt, dtypes.float16)674    self.assertIn('lt_cast', cast_lt.name)675  def test(self):676    cast_lt = ops.cast(self.original_lt, dtypes.float16)677    golden_lt = core.LabeledTensor(678        math_ops.cast(self.original_lt.tensor, dtypes.float16),679        self.original_lt.axes)680    self.assertLabeledTensorsEqual(cast_lt, golden_lt)681class VerifyTensorAllFiniteTest(Base):682  def setUp(self):683    super(VerifyTensorAllFiniteTest, self).setUp()684    self.finite_lt = core.LabeledTensor(constant_op.constant(42.0), [])685    self.nan_lt = core.LabeledTensor(constant_op.constant(np.nan), [])686    self.checked_finite_lt = ops.verify_tensor_all_finite(self.finite_lt, '')687    self.checked_nan_lt = ops.verify_tensor_all_finite(self.nan_lt, '')688  def test_name(self):689    self.assertIn('lt_verify_tensor_all_finite', self.checked_finite_lt.name)690    self.assertIn('lt_verify_tensor_all_finite', self.checked_nan_lt.name)691  def test_finite(self):692    self.assertLabeledTensorsEqual(self.finite_lt, self.checked_finite_lt)693  def test_nan(self):694    with self.assertRaisesRegexp(errors_impl.InvalidArgumentError,695                                 'Tensor had NaN values'):696      self.eval([self.checked_nan_lt])697class BooleanMaskTest(Base):698  def test_name(self):699    mask = core.LabeledTensor(math_ops.range(7) > 3, [self.a0])700    masked_lt = ops.boolean_mask(self.original_lt, mask)701    self.assertIn('lt_boolean_mask', masked_lt.name)702  def test(self):703    mask = core.LabeledTensor(math_ops.range(7) > 3, [self.a0])704    masked_lt = ops.boolean_mask(self.original_lt, mask)705    golden_lt = core.LabeledTensor(706        array_ops.boolean_mask(self.original_lt.tensor, mask.tensor),707        ['x', self.a1, self.a2, self.a3])708    self.assertLabeledTensorsEqual(masked_lt, golden_lt)709  def test_invalid_rank(self):710    mask = core.LabeledTensor(array_ops.ones((7, 3)) > 3, [self.a0, self.a1])711    with self.assertRaises(NotImplementedError):712      ops.boolean_mask(self.original_lt, mask)713  def test_mismatched_axis(self):714    mask = core.LabeledTensor(math_ops.range(7) > 3, ['foo'])715    with self.assertRaisesRegexp(ValueError, 'not equal'):716      ops.boolean_mask(self.original_lt, mask)717class WhereTest(Base):718  def test_name(self):719    condition = core.LabeledTensor(math_ops.range(5) < 3, ['x'])720    where_lt = ops.where(condition, condition, condition)721    self.assertIn('lt_where', where_lt.name)722  def test(self):723    condition = core.LabeledTensor(math_ops.range(5) < 3, ['x'])724    x = core.LabeledTensor(array_ops.ones(5), ['x'])725    y = core.LabeledTensor(array_ops.zeros(5), ['x'])726    where_lt = ops.where(condition, x, y)727    golden_lt = core.LabeledTensor(728        array_ops.concat([array_ops.ones(3), array_ops.zeros(2)], 0), ['x'])729    self.assertLabeledTensorsEqual(where_lt, golden_lt)730  def test_mismatched_axes(self):731    condition = core.LabeledTensor(math_ops.range(5) < 3, ['x'])732    with self.assertRaisesRegexp(ValueError, 'equal axes'):733      ops.where(condition, condition[:3], condition)734    with self.assertRaisesRegexp(ValueError, 'equal axes'):735      ops.where(condition, condition, condition[:3])736if __name__ == '__main__':...

core_test.py

Source:core_test.py

1# Copyright 2016 The TensorFlow Authors. All Rights Reserved.2#3# Licensed under the Apache License, Version 2.0 (the "License");4# you may not use this file except in compliance with the License.5# You may obtain a copy of the License at6#7#     http://www.apache.org/licenses/LICENSE-2.08#9# Unless required by applicable law or agreed to in writing, software10# distributed under the License is distributed on an "AS IS" BASIS,11# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.12# See the License for the specific language governing permissions and13# limitations under the License.14# ==============================================================================15from __future__ import absolute_import16from __future__ import division17from __future__ import print_function18import operator19import re20import textwrap21import numpy as np22from six.moves import range  # pylint: disable=redefined-builtin23from tensorflow.contrib.labeled_tensor.python.ops import _typecheck as tc24from tensorflow.contrib.labeled_tensor.python.ops import core25from tensorflow.contrib.labeled_tensor.python.ops import test_util26from tensorflow.python.framework import constant_op27from tensorflow.python.framework import dtypes28from tensorflow.python.framework import ops29from tensorflow.python.framework import tensor_shape30from tensorflow.python.ops import array_ops31from tensorflow.python.ops import math_ops32from tensorflow.python.platform import test as test_lib33class AxisTest(test_lib.TestCase):34  def setUp(self):35    d_7 = tensor_shape.Dimension(7)36    p_rgb = ['red', 'green', 'blue']37    self.i_7 = core.Axis('7', d_7)38    self.i_7p = core.Axis('7prime', d_7)39    self.i_rgb = core.Axis('rgb', p_rgb)40    self.i_range = core.Axis('range', range(7))41    self.i_unknown = core.Axis('unknown', None)42  def test_equality(self):43    axes = [self.i_7, self.i_7p, self.i_rgb, self.i_range, self.i_unknown]44    for i, axis_0 in enumerate(axes):45      for j, axis_1 in enumerate(axes):46        if i == j:47          self.assertEqual(axis_0, axis_1)48        else:49          self.assertNotEqual(axis_0, axis_1)50  def test_axis_value(self):51    self.assertEqual(self.i_7.value, tensor_shape.Dimension(7))52    self.assertTrue(self.i_range.value == tuple(range(7)))53  def test_axis_input(self):54    axes = [self.i_7, self.i_7p, self.i_rgb, self.i_range, self.i_unknown]55    for axis in axes:56      self.assertEqual(axis, core.Axis(axis.name, axis.value))57  def test_axis_value_input(self):58    axis = self.i_range59    for value in [range(7), list(range(7)), np.arange(7)]:60      self.assertEqual(axis, core.Axis(axis.name, value))61  def test_size(self):62    self.assertEqual(len(self.i_7), 7)63    self.assertEqual(len(self.i_rgb), 3)64    self.assertEqual(len(self.i_range), 7)65    self.assertEqual(self.i_unknown.size, None)66  def test_concat_single(self):67    red = core.Axis('rgb', ['red'])68    self.assertEqual(core.concat_axes([red]), red)69  def test_concat_many(self):70    red = core.Axis('rgb', ['red'])71    green = core.Axis('rgb', ['green'])72    blue = core.Axis('rgb', ['blue'])73    red_green_blue = core.Axis('rgb', ['red', 'green', 'blue'])74    self.assertEqual(core.concat_axes([red, green, blue]), red_green_blue)75  def test_concat_different_names(self):76    red = core.Axis('red', ['red'])77    green = core.Axis('green', ['red'])78    with self.assertRaises(ValueError):79      core.concat_axes([red, green])80  def test_concat_unknown(self):81    red = core.Axis('rgb', None)82    green = core.Axis('rgb', None)83    self.assertEqual(core.concat_axes([red, green]), red)84  def test_repr(self):85    self.assertEqual("Axis('7', Dimension(7))", repr(self.i_7))86  def test_invalid_input(self):87    with self.assertRaises(TypeError):88      core.Axis('foo', [{}])89    with self.assertRaises(ValueError):90      core.Axis('foo', [1, 2, 3, 1])91    red = core.Axis('foo', ['red'])92    with self.assertRaises(tc.Error):93      core.concat_axes([red, 1])94  def test_as_axis(self):95    self.assertEqual(self.i_7, core.as_axis(('7', 7)))96    self.assertEqual(self.i_7, core.as_axis(self.i_7))97class AxesTest(test_lib.TestCase):98  def setUp(self):99    d_7 = tensor_shape.Dimension(7)100    d_8 = tensor_shape.Dimension(8)101    p_rgb = ['red', 'green', 'blue']102    p_range = range(7)103    self.i_8 = core.Axis('8', d_8)104    self.a0 = core.Axes([('d7', d_7)])105    self.a1 = core.Axes([('d7', d_7)])106    self.a2 = core.Axes([('d7', d_7), ('rgb', p_rgb)])107    self.a3 = core.Axes([('8', d_8), ('range', p_range)])108  def test_equality(self):109    self.assertEqual(self.a0, self.a0)110    self.assertEqual(self.a0, self.a1)111    self.assertNotEqual(self.a0, self.a2)112  def test_repr(self):113    self.assertEqual("Axes([('d7', Dimension(7))])", repr(self.a0))114  def test_remove(self):115    a = self.a3.remove('range')116    self.assertEqual(a, core.Axes([self.i_8]))117    with self.assertRaises(KeyError):118      self.a3.remove('foobar')119  def test_typecheck_error_message(self):120    pattern = ('List(Union(labeled_tensor.Axis, Tuple(..., '121               'Union(Union(numpy.ndarray, %s, list, tuple), '122               'Optional(Union(tensorflow.Dimension, int))))))' %123               range.__name__)124    regexp = re.escape(pattern).replace(re.escape('...'), '.*')125    with self.assertRaisesRegexp(tc.Error, 'allowed type ' + regexp):126      core.Axes(None)127class LabeledTensorTest(test_util.Base):128  def setUp(self):129    tensor = array_ops.ones([7, 3, 8, 1])130    a0 = ('x', range(7))131    a1 = ('channel', ['red', 'green', 'blue'])132    a2 = ('y', 8)133    a3 = ('z', tensor_shape.Dimension(1))134    self.lt = core.LabeledTensor(tensor, [a0, a1, a2, a3])135  def test_repr(self):136    pattern = textwrap.dedent("""\137    <LabeledTensor '...' shape=(7, 3, 8, 1) dtype=float32138     axes=[('x', ...),139           ('channel', ...),140           ('y', Dimension(8)),141           ('z', Dimension(1))]>""")142    regexp = re.escape(pattern).replace(re.escape('...'), '.*')143    self.assertRegexpMatches(repr(self.lt), regexp)144  def test_reuse_existing_axes(self):145    alt_lt = core.LabeledTensor(self.lt.tensor, self.lt.axes)146    self.assertLabeledTensorsEqual(alt_lt, self.lt)147  def test_reuse_existing_axis_objects(self):148    alt_lt = core.LabeledTensor(self.lt.tensor, self.lt.axes.values())149    self.assertLabeledTensorsEqual(alt_lt, self.lt)150  def test_indexing_scalars(self):151    actual = self.lt[:, :, :, 0]152    expected = core.LabeledTensor(self.lt.tensor[:, :, :, 0],153                                  list(self.lt.axes.values())[:-1])154    self.assertLabeledTensorsEqual(actual, expected)155    actual = self.lt[1, :, :, 0]156    expected = core.LabeledTensor(self.lt.tensor[1, :, :, 0],157                                  list(self.lt.axes.values())[1:-1])158    self.assertLabeledTensorsEqual(actual, expected)159    actual = self.lt[1, 2, :, 0]160    expected = core.LabeledTensor(self.lt.tensor[1, 2, :, 0],161                                  list(self.lt.axes.values())[2:-1])162    self.assertLabeledTensorsEqual(actual, expected)163  def test_indexing_1d(self):164    lt_1d = self.lt[1, 2, :, 0]165    actual = lt_1d[3]166    expected = core.LabeledTensor(lt_1d.tensor[3], [])167    self.assertLabeledTensorsEqual(actual, expected)168  def test_indexing_slices(self):169    actual = self.lt[:3, :, :, :]170    axes = [('x', range(3))] + list(self.lt.axes.values())[1:]171    expected = core.LabeledTensor(self.lt.tensor[:3, :, :, :], axes)172    self.assertLabeledTensorsEqual(actual, expected)173  def test_invalid_indexing(self):174    with self.assertRaises(ValueError):175      self.lt[0]  # pylint: disable=pointless-statement176    with self.assertRaises(ValueError):177      self.lt[:, :, :, :, 0]  # pylint: disable=pointless-statement178  def test_unknown_size(self):179    tensor = array_ops.placeholder(dtypes.string, [None])180    actual = core.LabeledTensor(tensor, ['x'])181    self.assertIsNone(actual.axes['x'].size)182    self.assertIsNone(actual.axes['x'].value.value)183  def test_eq(self):184    self.assertEqual(self.lt, self.lt)185    self.assertNotEqual(self.lt, self.lt.tensor)186    self.assertNotEqual(self.lt.tensor, self.lt)187  def test_hash(self):188    lt1 = self.lt189    lt2 = core.LabeledTensor(self.lt.tensor, self.lt.axes)190    self.assertEqual(lt1, lt2)191    self.assertEqual(hash(lt1), hash(lt2))192  def test_name(self):193    self.assertEqual(self.lt.name, self.lt.tensor.name)194  def test_dtype(self):195    self.assertEqual(self.lt.dtype, self.lt.tensor.dtype)196  def test_shape(self):197    self.assertEqual(self.lt.shape, self.lt.tensor.shape)198  def test_get_shape(self):199    self.assertEqual(self.lt.get_shape(), self.lt.tensor.get_shape())200  def test_convert_to_tensor(self):201    expected = self.lt.tensor202    actual = ops.convert_to_tensor(self.lt)203    self.assertIs(expected, actual)204class Base(test_util.Base):205  def setUp(self):206    self.x_size = 7207    self.channel_size = 3208    self.z_size = 4209    self.probs_size = 11210    tensor = math_ops.range(0, self.x_size * self.channel_size * self.z_size *211                            self.probs_size)212    tensor = array_ops.reshape(213        tensor, [self.x_size, self.channel_size, self.z_size, self.probs_size])214    a0 = ('x', range(self.x_size))215    a1 = ('channel', ['red', 'green', 'blue'])216    a2 = 'z'217    a3 = ('probs', np.linspace(0.0, 1.0, self.probs_size))218    self.tensor = tensor219    self.a0 = a0220    self.a1 = a1221    self.a2 = a2222    self.a3 = a3223    self.original_lt = core.LabeledTensor(tensor, [a0, a1, a2, a3])224    self.x_probs_lt = core.slice_function(self.original_lt,225                                          {'z': 0,226                                           'channel': 0})227    self.channel_probs_lt = core.slice_function(self.original_lt,228                                                {'x': 3,229                                                 'z': 0})230class IdentityTest(Base):231  def test_name(self):232    identity_lt = core.identity(self.original_lt)233    self.assertIn('lt_identity', identity_lt.name)234class SliceFunctionTest(Base):235  def test_name(self):236    select_lt = core.slice_function(self.original_lt, {'channel': 1})237    self.assertIn('lt_slice', select_lt.name)238  def test_scalar(self):239    select_lt = core.slice_function(self.original_lt, {'channel': 1})240    golden_lt = core.LabeledTensor(self.tensor[:, 1, :, :],241                                   [self.a0, self.a2, self.a3])242    self.assertLabeledTensorsEqual(select_lt, golden_lt)243  def test_slice(self):244    select_lt = core.slice_function(self.original_lt, {'channel': slice(0, 2)})245    a1_sliced = ('channel', ['red', 'green'])246    golden_lt = core.LabeledTensor(self.tensor[:, :2, :, :],247                                   [self.a0, a1_sliced, self.a2, self.a3])248    self.assertLabeledTensorsEqual(select_lt, golden_lt)249  def test_slices(self):250    select_lt = core.slice_function(251        self.original_lt, {'x': slice(1, 5),252                           'channel': slice(1, None)})253    a0_sliced = ('x', range(1, 5))254    a1_sliced = ('channel', ['green', 'blue'])255    golden_lt = core.LabeledTensor(self.tensor[1:5, 1:, :, :],256                                   [a0_sliced, a1_sliced, self.a2, self.a3])257    self.assertLabeledTensorsEqual(select_lt, golden_lt)258  def test_slice_unlabeled(self):259    select_lt = core.slice_function(self.original_lt, {'z': slice(1, 3)})260    a2_sliced = 'z'261    golden_lt = core.LabeledTensor(self.tensor[:, :, 1:3, :],262                                   [self.a0, self.a1, a2_sliced, self.a3])263    self.assertLabeledTensorsEqual(select_lt, golden_lt)264  def test_slice_unknown_shape(self):265    lt = core.LabeledTensor(266        array_ops.placeholder(dtypes.float32, [None, 1]), ['x', 'y'])267    sliced_lt = core.slice_function(lt, {'y': 0})268    self.assertEqual(list(sliced_lt.axes.values()), [lt.axes['x']])269class TransposeTest(Base):270  def test_name(self):271    transpose_lt = core.transpose(self.original_lt,272                                  self.original_lt.axes.keys())273    self.assertIn('lt_transpose', transpose_lt.name)274  def test_identity(self):275    transpose_lt = core.transpose(self.original_lt,276                                  self.original_lt.axes.keys())277    golden_lt = self.original_lt278    self.assertLabeledTensorsEqual(transpose_lt, golden_lt)279  def test(self):280    transpose_lt = core.transpose(self.original_lt,281                                  ['z', 'channel', 'x', 'probs'])282    golden_lt = core.LabeledTensor(283        array_ops.transpose(self.tensor, [2, 1, 0, 3]),284        [self.a2, self.a1, self.a0, self.a3])285    self.assertLabeledTensorsEqual(transpose_lt, golden_lt)286  def test_default_axis_order(self):287    transpose_lt = core.transpose(self.original_lt)288    golden_lt = core.LabeledTensor(289        array_ops.transpose(self.tensor, [3, 2, 1, 0]),290        list(reversed(list(self.original_lt.axes.values()))))291    self.assertLabeledTensorsEqual(transpose_lt, golden_lt)292  def test_invalid_input(self):293    with self.assertRaises(ValueError):294      core.transpose(self.original_lt, ['channel', 'x', 'probs'])295    with self.assertRaises(ValueError):296      core.transpose(self.original_lt, ['z', 'foo', 'x', 'probs'])297class ExpandDimsTest(Base):298  def test_name(self):299    expand_lt = core.expand_dims(self.original_lt, self.original_lt.axes.keys())300    self.assertIn('lt_expand', expand_lt.name)301  def test_identity(self):302    expand_lt = core.expand_dims(self.original_lt, self.original_lt.axes.keys())303    golden_lt = self.original_lt304    self.assertLabeledTensorsEqual(expand_lt, golden_lt)305  def test(self):306    expand_lt = core.expand_dims(307        self.original_lt, ['foo', 'x', 'bar', 'channel', 'z', 'probs', 'grok'])308    golden_lt = core.LabeledTensor(309        array_ops.reshape(self.tensor, [310            1, self.x_size, 1, self.channel_size, self.z_size, self.probs_size,311            1312        ]), ['foo', self.a0, 'bar', self.a1, self.a2, self.a3, 'grok'])313    self.assertLabeledTensorsEqual(expand_lt, golden_lt)314  def test_label(self):315    expand_lt = core.expand_dims(self.original_lt, [316        'x',317        'channel',318        ('foo', 'bar'),319        'z',320        'probs',321    ])322    golden_lt = core.LabeledTensor(323        array_ops.reshape(324            self.tensor,325            [self.x_size, self.channel_size, 1, self.z_size, self.probs_size]),326        [self.a0, self.a1, ('foo', ['bar']), self.a2, self.a3])327    self.assertLabeledTensorsEqual(expand_lt, golden_lt)328  def test_unknown_dimension(self):329    orig_lt = core.LabeledTensor(330        array_ops.placeholder(dtypes.float32, [None]), ['x'])331    expand_lt = core.expand_dims(orig_lt, ['x', 'y'])332    self.assertEqual(expand_lt.axes, core.Axes([('x', None), ('y', 1)]))333  def test_invalid_input(self):334    with self.assertRaises(core.AxisOrderError):335      core.expand_dims(self.original_lt,336                       ['foo', 'not_x', 'bar', 'channel', 'z', 'probs', 'grok'])337    with self.assertRaises(core.AxisOrderError):338      core.expand_dims(self.original_lt,339                       ['foo', 'z', 'bar', 'channel', 'x', 'probs', 'grok'])340class AxisOrderScopeTest(Base):341  def test(self):342    xyz = ['x', 'y', 'z']343    abc = ['a', 'b', 'c']344    self.assertIsNone(core.get_axis_order())345    with core.axis_order_scope(xyz):346      self.assertEqual(core.get_axis_order(), xyz)347      with core.axis_order_scope():348        self.assertIsNone(core.get_axis_order())349        with core.axis_order_scope(abc):350          self.assertEqual(core.get_axis_order(), abc)351        self.assertIsNone(core.get_axis_order())352      self.assertEqual(core.get_axis_order(), xyz)353    self.assertIsNone(core.get_axis_order())354class CheckAxisOrderTest(Base):355  def test_passes(self):356    axis_order = ['w', 'x', 'y', 'z']357    lt = core.LabeledTensor(array_ops.ones((1, 1, 1, 1)), axis_order)358    core.check_axis_order(lt, axis_order)359    lt = core.LabeledTensor(array_ops.ones((1, 1, 1)), axis_order[1:])360    core.check_axis_order(lt, axis_order)361    lt = core.LabeledTensor(array_ops.ones((1, 1, 1)), axis_order[:-1])362    core.check_axis_order(lt, axis_order)363  def test_invalid(self):364    axis_order = ['w', 'x', 'y', 'z']365    lt = core.LabeledTensor(array_ops.ones((1, 1, 1, 1)), axis_order)366    with self.assertRaises(core.AxisOrderError):367      core.check_axis_order(lt)368    with self.assertRaises(core.AxisOrderError):369      core.check_axis_order(lt, axis_order[:-1])370    with self.assertRaises(core.AxisOrderError):371      core.check_axis_order(lt, axis_order[::-1])372  def test_scope(self):373    axis_order = ['w', 'x', 'y', 'z']374    lt = core.LabeledTensor(array_ops.ones((1, 1, 1, 1)), axis_order)375    with core.axis_order_scope(axis_order):376      core.check_axis_order(lt)377class ImposeAxisOrderTest(Base):378  def test_identity(self):379    axis_order = ['w', 'x', 'y', 'z']380    lt = core.LabeledTensor(381        array_ops.reshape(math_ops.range(24), (1, 2, 3, 4)), axis_order)382    actual = core.impose_axis_order(lt, axis_order)383    self.assertLabeledTensorsEqual(lt, actual)384    lt = core.LabeledTensor(385        array_ops.reshape(math_ops.range(6), (1, 2, 3)), axis_order[:3])386    actual = core.impose_axis_order(lt, axis_order)387    self.assertLabeledTensorsEqual(lt, actual)388  def test_reverse(self):389    axis_order = ['w', 'x', 'y', 'z']390    lt = core.LabeledTensor(391        array_ops.reshape(math_ops.range(24), (1, 2, 3, 4)), axis_order)392    actual = core.impose_axis_order(lt, axis_order[::-1])393    expected = core.transpose(lt, axis_order[::-1])394    self.assertLabeledTensorsEqual(expected, actual)395    lt = core.LabeledTensor(396        array_ops.reshape(math_ops.range(6), (1, 2, 3)), axis_order[:3])397    actual = core.impose_axis_order(lt, axis_order[::-1])398    expected = core.transpose(lt, ['y', 'x', 'w'])399    self.assertLabeledTensorsEqual(expected, actual)400  def test_scope(self):401    axis_order = ['w', 'x', 'y', 'z']402    lt = core.LabeledTensor(403        array_ops.reshape(math_ops.range(24), (1, 2, 3, 4)), axis_order)404    expected = core.transpose(lt, axis_order[::-1])405    with core.axis_order_scope(axis_order[::-1]):406      actual = core.impose_axis_order(lt)407    self.assertLabeledTensorsEqual(expected, actual)408  def test_invalid(self):409    lt = core.LabeledTensor(410        array_ops.reshape(math_ops.range(2), (1, 2)), ['x', 'y'])411    with self.assertRaises(ValueError):412      core.impose_axis_order(lt)413    with self.assertRaises(ValueError):414      core.impose_axis_order(lt, ['x'])415class FindConsistentOrderingTest(Base):416  def test(self):417    cases = [418        ([], [], []),419        (['x'], [], ['x']),420        ([], ['x'], ['x']),421        (['x'], ['x'], ['x']),422        (['x'], ['y'], ['x', 'y']),423        (['y'], ['x'], ['y', 'x']),424        (['x', 'y'], ['x', 'y'], ['x', 'y']),425        (['x', 'y'], ['y', 'x'], None),426        (['x', 'y'], ['y', 'z'], ['x', 'y', 'z']),427        (['x', 'z'], ['y', 'z'], ['x', 'y', 'z']),428        (['x', 'y'], ['x', 'z'], ['x', 'y', 'z']),429        (['w', 'x'], ['y', 'z'], ['w', 'x', 'y', 'z']),430        (['x', 'y', 'z'], ['z', 'x'], None),431        (['x', 'y', 'z'], ['x'], ['x', 'y', 'z']),432        ([], ['x', 'y', 'z'], ['x', 'y', 'z']),433    ]434    for a, b, expected in cases:435      actual = core._find_consistent_ordering(a, b)436      msg = ('unexpected ordering between %r and %r:\nexpected: %r\nactual: %r'437             % (a, b, expected, actual))438      self.assertEqual(expected, actual, msg=msg)439class AlignTest(Base):440  def test_name(self):441    align_lt_0, align_lt_1, _ = core.align(self.original_lt, self.original_lt)442    self.assertIn('lt_align', align_lt_0.name)443    self.assertIn('/0', align_lt_0.name)444    self.assertIn('lt_align', align_lt_1.name)445    self.assertIn('/1', align_lt_1.name)446  def test_identical_shaped_inputs(self):447    offset_tensor = self.original_lt.tensor + 1448    offset_lt = core.LabeledTensor(offset_tensor, self.original_lt.axes)449    align_lt, align_offset_lt, broadcast_axes = core.align(self.original_lt,450                                                           offset_lt)451    self.assertLabeledTensorsEqual(align_lt, self.original_lt)452    self.assertLabeledTensorsEqual(align_offset_lt, offset_lt)453    self.assertEqual(broadcast_axes, self.original_lt.axes)454  def test_different_inputs(self):455    # The correct axis ordering is ['x', 'channel', 'probs'].456    align_x_probs_lt, align_channel_probs_lt, broadcast_axes = core.align(457        self.x_probs_lt, self.channel_probs_lt)458    x_probs_golden_lt = core.LabeledTensor(459        array_ops.reshape(self.x_probs_lt.tensor,460                          [self.x_size, 1, self.probs_size]),461        [self.a0, 'channel', self.a3])462    self.assertLabeledTensorsEqual(align_x_probs_lt, x_probs_golden_lt)463    channel_probs_golden_lt = core.LabeledTensor(464        array_ops.reshape(self.channel_probs_lt.tensor,465                          [1, self.channel_size, self.probs_size]),466        ['x', self.a1, self.a3])467    self.assertLabeledTensorsEqual(align_channel_probs_lt,468                                   channel_probs_golden_lt)469    self.assertEqual(broadcast_axes, core.Axes([self.a0, self.a1, self.a3]))470  def test_axis_order_scope(self):471    xz_lt = core.LabeledTensor(array_ops.ones((2, 3)), ['x', 'z'])472    yz_lt = core.LabeledTensor(array_ops.ones((4, 3)), ['y', 'z'])473    _, _, broadcast_axes = core.align(xz_lt, yz_lt)474    self.assertEqual(list(broadcast_axes.keys()), ['x', 'y', 'z'])475    _, _, broadcast_axes = core.align(yz_lt, xz_lt)476    self.assertEqual(list(broadcast_axes.keys()), ['y', 'x', 'z'])477    with core.axis_order_scope(['x', 'y', 'z']):478      _, _, broadcast_axes = core.align(yz_lt, xz_lt)479      self.assertEqual(list(broadcast_axes.keys()), ['x', 'y', 'z'])480    with core.axis_order_scope(['x', 'y']):481      with self.assertRaises(core.AxisOrderError):482        core.align(xz_lt, yz_lt)483      with self.assertRaises(core.AxisOrderError):484        core.align(yz_lt, xz_lt)485  def test_invalid_input(self):486    lt_0 = core.LabeledTensor(array_ops.zeros([5]), [('a', range(5))])487    lt_1 = core.LabeledTensor(array_ops.zeros([5]), [('a', range(1, 6))])488    with self.assertRaises(ValueError):489      core.align(lt_0, lt_1)490class ConvertToLabeledTensorTest(Base):491  # TODO(shoyer): Simplify these tests once we can reuse labeled tensors in492  # assertLabeledTensorsEqual.493  def test_labeled_tensor(self):494    actual = core.convert_to_labeled_tensor(self.original_lt)495    self.assertLabeledTensorsEqual(actual, self.original_lt)496  def test_python_scalar(self):497    actual = core.convert_to_labeled_tensor(42)498    golden_lt = core.LabeledTensor(ops.convert_to_tensor(42), [])499    self.assertLabeledTensorsEqual(actual, golden_lt)500  def test_numpy_array(self):501    actual = core.convert_to_labeled_tensor(np.array(42))502    golden_lt = core.LabeledTensor(ops.convert_to_tensor(42), [])503    self.assertLabeledTensorsEqual(actual, golden_lt)504  def test_tensor(self):505    actual = core.convert_to_labeled_tensor(constant_op.constant(42))506    golden_lt = core.LabeledTensor(ops.convert_to_tensor(42), [])507    self.assertLabeledTensorsEqual(actual, golden_lt)508  def test_invalid_input(self):509    with self.assertRaises(ValueError):510      core.convert_to_labeled_tensor(math_ops.range(5))511    with self.assertRaises(ValueError):512      core.convert_to_labeled_tensor(np.array([1, 2]))513class DocStringCheckMixin(object):514  # requires self.ops to be defined515  def test_function_docstring_and_name(self):516    for op_name, _, _, lt_op in self.ops:517      if lt_op is not None:518        self.assertIn('tf.%s' % op_name, lt_op.__doc__)519        self.assertEqual(op_name, lt_op.__name__)520class UnaryOpsTestsMixin(object):521  # requires self.ops and self.test_lt to be defined522  def test_core_op(self):523    for op_name, _, tf_op, lt_op in self.ops:524      if tf_op is not None:525        golden_lt = core.LabeledTensor(526            tf_op(self.test_lt.tensor), self.test_lt.axes)527        actual_lt = lt_op(self.test_lt)528        self.assertIn(op_name, actual_lt.name)529        self.assertLabeledTensorsEqual(golden_lt, actual_lt)530  def test_infix(self):531    for op_name, infix_op, _, _ in self.ops:532      if infix_op is not None:533        expected_lt = core.LabeledTensor(534            infix_op(self.test_lt.tensor), self.test_lt.axes)535        actual_lt = infix_op(self.test_lt)536        self.assertIn(op_name, actual_lt.name)537        self.assertLabeledTensorsEqual(expected_lt, actual_lt)538class CoreUnaryOpsTest(Base, DocStringCheckMixin, UnaryOpsTestsMixin):539  def setUp(self):540    super(CoreUnaryOpsTest, self).setUp()541    self.ops = [542        ('abs', operator.abs, math_ops.abs, core.abs_function),543        ('neg', operator.neg, math_ops.negative, core.neg),544        # TODO(shoyer): add unary + to core TensorFlow545        ('pos', None, None, None),546        ('sign', None, math_ops.sign, core.sign),547        ('reciprocal', None, math_ops.reciprocal, core.reciprocal),548        ('square', None, math_ops.square, core.square),549        ('round', None, math_ops.round, core.round_function),550        ('sqrt', None, math_ops.sqrt, core.sqrt),551        ('rsqrt', None, math_ops.rsqrt, core.rsqrt),552        ('log', None, math_ops.log, core.log),553        ('exp', None, math_ops.exp, core.exp),554        ('log', None, math_ops.log, core.log),555        ('ceil', None, math_ops.ceil, core.ceil),556        ('floor', None, math_ops.floor, core.floor),557        ('cos', None, math_ops.cos, core.cos),558        ('sin', None, math_ops.sin, core.sin),559        ('tan', None, math_ops.tan, core.tan),560        ('acos', None, math_ops.acos, core.acos),561        ('asin', None, math_ops.asin, core.asin),562        ('atan', None, math_ops.atan, core.atan),563        ('lgamma', None, math_ops.lgamma, core.lgamma),564        ('digamma', None, math_ops.digamma, core.digamma),565        ('erf', None, math_ops.erf, core.erf),566        ('erfc', None, math_ops.erfc, core.erfc),567        ('lgamma', None, math_ops.lgamma, core.lgamma),568    ]569    total_size = np.prod([v.size for v in self.original_lt.axes.values()])570    self.test_lt = core.LabeledTensor(571        math_ops.cast(self.original_lt, dtypes.float32) / total_size,572        self.original_lt.axes)573class LogicalNotTest(Base, DocStringCheckMixin, UnaryOpsTestsMixin):574  def setUp(self):575    super(LogicalNotTest, self).setUp()576    self.ops = [('logical_not', operator.invert, math_ops.logical_not,577                 core.logical_not),]578    self.test_lt = self.original_lt < 10579class BinaryOpsTestsMixin(object):580  # requires self.ops, self.test_lt_1, self.test_lt_2, self.test_lt_1_broadcast581  # and self.test_lt_2_broadcast to be defined582  def test_core_op(self):583    for op_name, _, tf_op, lt_op in self.ops:584      golden_tensor = tf_op(self.test_lt_1_broadcast, self.test_lt_2_broadcast)585      golden_lt = core.LabeledTensor(golden_tensor, self.broadcast_axes)586      actual_lt = lt_op(self.test_lt_1, self.test_lt_2)587      self.assertIn(op_name, actual_lt.name)588      self.assertLabeledTensorsEqual(golden_lt, actual_lt)589  def test_infix(self):590    for op_name, infix_op, _, lt_op in self.ops:591      if infix_op is not None:592        expected_lt = lt_op(self.test_lt_1, self.test_lt_2)593        actual_lt = infix_op(self.test_lt_1, self.test_lt_2)594        self.assertIn(op_name, actual_lt.name)595        self.assertLabeledTensorsEqual(expected_lt, actual_lt)596class CoreBinaryOpsTest(Base, DocStringCheckMixin, BinaryOpsTestsMixin):597  def setUp(self):598    super(CoreBinaryOpsTest, self).setUp()599    self.x_probs_broadcast_tensor = array_ops.reshape(600        self.x_probs_lt.tensor, [self.x_size, 1, self.probs_size])601    self.channel_probs_broadcast_tensor = array_ops.reshape(602        self.channel_probs_lt.tensor, [1, self.channel_size, self.probs_size])603    # == and != are not element-wise for tf.Tensor, so they shouldn't be604    # elementwise for LabeledTensor, either.605    self.ops = [606        ('add', operator.add, math_ops.add, core.add),607        ('sub', operator.sub, math_ops.subtract, core.sub),608        ('mul', operator.mul, math_ops.multiply, core.mul),609        ('div', operator.truediv, math_ops.div, core.div),610        ('mod', operator.mod, math_ops.mod, core.mod),611        ('pow', operator.pow, math_ops.pow, core.pow_function),612        ('equal', None, math_ops.equal, core.equal),613        ('less', operator.lt, math_ops.less, core.less),614        ('less_equal', operator.le, math_ops.less_equal, core.less_equal),615        ('not_equal', None, math_ops.not_equal, core.not_equal),616        ('greater', operator.gt, math_ops.greater, core.greater),617        ('greater_equal', operator.ge, math_ops.greater_equal,618         core.greater_equal),619    ]620    self.test_lt_1 = self.x_probs_lt621    self.test_lt_2 = self.channel_probs_lt622    self.test_lt_1_broadcast = self.x_probs_broadcast_tensor623    self.test_lt_2_broadcast = self.channel_probs_broadcast_tensor624    self.broadcast_axes = [self.a0, self.a1, self.a3]625  def test_reflexive(self):626    labeled_tensor = self.x_probs_lt + 1  # all elements must be >0 for division627    for op_name, infix_op, _, lt_op in self.ops:628      if infix_op is not None:629        expected_lt = lt_op(2, labeled_tensor)630        actual_lt = infix_op(2, labeled_tensor)631        # Python uses greater for the reflexive version of less (and vise-versa)632        if 'less' in op_name:633          op_name = op_name.replace('less', 'greater')634        elif 'greater' in op_name:635          op_name = op_name.replace('greater', 'less')636        self.assertIn(op_name, actual_lt.name)637        self.assertLabeledTensorsEqual(expected_lt, actual_lt)638class LogicalBinaryOpsTest(Base, DocStringCheckMixin, BinaryOpsTestsMixin):639  def setUp(self):640    super(LogicalBinaryOpsTest, self).setUp()641    self.ops = [642        ('logical_and', operator.and_, math_ops.logical_and, core.logical_and),643        ('logical_or', operator.or_, math_ops.logical_or, core.logical_or),644        ('logical_xor', operator.xor, math_ops.logical_xor, core.logical_xor),645    ]646    self.test_lt_1 = self.original_lt < 10647    self.test_lt_2 = self.original_lt < 5648    self.test_lt_1_broadcast = self.test_lt_1.tensor649    self.test_lt_2_broadcast = self.test_lt_2.tensor650    self.broadcast_axes = self.test_lt_1.axes651class FloatBinaryOpsTest(Base, DocStringCheckMixin, BinaryOpsTestsMixin):652  def setUp(self):653    super(FloatBinaryOpsTest, self).setUp()654    self.ops = [655        ('igamma', None, math_ops.igamma, core.igamma),656        ('igammac', None, math_ops.igammac, core.igammac),657        ('zeta', None, math_ops.zeta, core.zeta),658        ('polygamma', None, math_ops.polygamma, core.polygamma),659        ('maximum', None, math_ops.maximum, core.maximum),660        ('minimum', None, math_ops.minimum, core.minimum),661        ('squared_difference', None, math_ops.squared_difference,662         core.squared_difference),663    ]664    total_size = np.prod([v.size for v in self.original_lt.axes.values()])665    test_lt = core.LabeledTensor(666        math_ops.cast(self.original_lt, dtypes.float32) / total_size,667        self.original_lt.axes)668    self.test_lt_1 = test_lt669    self.test_lt_2 = 1.0 - test_lt670    self.test_lt_1_broadcast = self.test_lt_1.tensor671    self.test_lt_2_broadcast = self.test_lt_2.tensor672    self.broadcast_axes = self.test_lt_1.axes673if __name__ == '__main__':...

setup.py

Source:setup.py

1# coding: utf-82"""3    Telstra Messaging API4     # Introduction  Send and receive SMS and MMS messages globally using TelstraÃ¢â¬â¢s enterprise grade Messaging API. It also allows your application to track the delivery status of both sent and received messages. Get your dedicated Australian number, and start sending and receiving messages today.  # Features  <p>The Telstra Messaging API provides the features below. <table>   <thead>     <tr>       <th>Feature</th>       <th>Description</th>     </tr>   </thead>   <tbody>     <tr>       <td><code>Dedicated Number</code></td>       <td>Provision a mobile number for your account to be used as from address in the API</td>     </tr>     <tr>       <td><code>Send Messages</code></td>       <td>Sending SMS or MMS messages</td>     </tr>     <tr>       <td><code>Receive Messages</code></td>       <td>Telstra will deliver messages sent to a dedicated number or to the <code>notifyURL</code> defined by you</td>     </tr>     <tr>       <td><code>Broadcast Messages</code></td>       <td>Invoke a single API to send a message to a list of number provided in <code>to</code></td>     </tr>     <tr>       <td><code>Delivery Status</code></td>       <td>Query the delivery status of your messages</td>     </tr>     <tr>       <td><code>Callbacks</code></td>       <td>Provide a notification URL and Telstra will notify your app when messages status changes</td>     </tr>     <tr>       <td><code>Alphanumeric Identifier</code></td>       <td>Differentiate yourself by providing an alphanumeric string in <code>from</code>. This feature is only available on paid plans</td>     </tr>     <tr>       <td><code>Concatenation</code></td>       <td>Send messages up to 1900 characters long and Telstra will automaticaly segment and reassemble them</td>     </tr>     <tr>       <td><code>Reply Request</code></td>       <td>Create a chat session by associating <code>messageId</code> and <code>to</code> number to track responses received from a mobile number. We will store this association for 8 days</td>     </tr>     <tr>       <td><code>Character set</code></td>       <td>Accepts all Unicode characters as part of UTF-8</td>     </tr>     <tr>       <td><code>Bounce-back response</code></td>       <td>See if your SMS hits an unreachable or unallocated number (Australia Only)</td>     </tr>     <tr>       <td><code>Queuing</code></td>       <td>Messaging API will automatically queue and deliver each message at a compliant rate.</td>     </tr>     <tr>       <td><code>Emoji Encoding</code></td>       <td>The API supports the encoding of the full range of emojis. Emojis in the reply messages will be in their UTF-8 format.</td>     </tr>   </tbody> </table>   # Getting Access to the API  <ol>   <li>Register at <a href=\"https://dev.telstra.com\">https://dev.telstra.com</a>.   <li>After registration, login to <a href=\"https://dev.telstra.com\">https://dev.telstra.com</a>        and navigate to the &quot;My apps&quot; page.    <li>Create your application by clicking the &quot;Add new app&quot; button    <li>Select &quot;API Free Trial&quot; Product when configuring your application. This Product includes the Telstra Messaging API as well as other APIs. Your application will be approved automatically.   <li>There is a maximum of 1000 free messages per developer. Additional messages and features can be purchased from <a href=\"https://dev.telstra.com\">https://dev.telstra.com</a>.   <li>Note your <code>Client key</code> and <code>Client secret</code> as these will be needed to provision a number for your application and for authentication. </ol>  <p>Now head over to <b>Getting Started</b> where you can find a postman collection as well as some links to sample apps and SDKs to get you started. <p>Happy Messaging!   # Getting Started  <p>Below are the steps to get started with the Telstra Messaging API.</p>    <ol>     <li>Generate OAuth2 Token using your <code>Client key</code> and <code>Client secret</code>.</li>     <li>Create Subscription in order to receive a provisioned number.</li>     <li>Send Message to a specific mobile number.</li>   </ol>    <h2>Run in Postman</h2>    <p><a   href=\"https://app.getpostman.com/run-collection/ded00578f69a9deba256#?env%5BMessaging%20API%20Environments%5D=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\">   <img src=\"https://run.pstmn.io/button.svg\" alt=\"Run in Postman\" /></a></p>    <h2>Sample Apps</h2>   - <a href=\"https://github.com/telstra/MessagingAPI-perl-sample-app\">Perl Sample App</a>   - <a href=\"https://github.com/telstra/messaging-sample-code-happy-chat\">Happy Chat App</a>   - <a href=\"https://github.com/developersteve/telstra-messaging-php\">PHP Sample App</a>    <h2>SDK repos</h2>   - <a href=\"https://github.com/telstra/MessagingAPI-SDK-php\">Messaging API - PHP SDK</a>   - <a href=\"https://github.com/telstra/MessagingAPI-SDK-python\">Messaging API - Python SDK</a>   - <a href=\"https://github.com/telstra/MessagingAPI-SDK-ruby\">Messaging API - Ruby SDK</a>   - <a href=\"https://github.com/telstra/MessagingAPI-SDK-node\">Messaging API - NodeJS SDK</a>   - <a href=\"https://github.com/telstra/MessagingAPI-SDK-dotnet\">Messaging API - .Net2 SDK</a>   - <a href=\"https://github.com/telstra/MessagingAPI-SDK-Java\">Messaging API - Java SDK</a>  # Delivery Notification  The API provides several methods for notifying when a message has been delivered to the destination.  <ol>   <li>When you provision a number there is an opportunity to specify a <code>notifyURL</code>, when the message has been delivered the API will make a call to this URL to advise of the message status. If this is not provided then you can make use the Get Replies API to poll for messages.</li>   <li>If you do not specify a URL you can always call the <code>GET /sms</code> API get the latest replies to the message.</li> </ol>  <I>Please note that the notification URLs and the polling call are exclusive. If a notification URL has been set then the polling call will not provide any useful information.</I>  <h2>Notification URL Format</h2>  When a message has reached its final state, the API will send a POST to the URL that has been previously specified.   <h3>Notification URL Format for SMS</h3> <pre><code class=\"language-sh\">{     to: '+61418123456'     sentTimestamp: '2017-03-17T10:05:22+10:00'     receivedTimestamp: '2017-03-17T10:05:23+10:00'     messageId: /cccb284200035236000000000ee9d074019e0301/1261418123456     deliveryStatus: DELIVRD   } </code></pre> \\ The fields are:  <table>   <thead>     <tr>       <th>Field</th>       <th>Description</th>     </tr>   </thead>   <tbody>     <tr>       <td><code>to</code></td>       <td>The number the message was sent to.</td>     </tr>     <tr>       <td><code>receivedTimestamp</code></td>       <td>Time the message was sent to the API.</td>     </tr>     <tr>       <td><code>sentTimestamp</code></td>       <td>Time handling of the message ended.</td>     </tr>     <tr>       <td><code>deliveryStatus</code></td>       <td>The final state of the message.</td>     </tr>     <tr>       <td><code>messageId</code></td>       <td>The same reference that was returned when the original message was sent.</td>     </tr>     <tr>       <td><code>receivedTimestamp</code></td>       <td>Time the message was sent to the API.</td>     </tr>   </tbody> </table>  Upon receiving this call it is expected that your servers will give a 204 (No Content) response. Anything else will cause the API to reattempt the call 5 minutes later.   <h3>Notification URL Format for SMS Replies</h3> <pre><code class=\"language-sh\">{     \"status\": \"RECEIVED\"     \"destinationAddress\": \"+61418123456\"     \"senderAddress\": \"+61421987654\"     \"message\": \"Foo\"             \"sentTimestamp\": \"2018-03-23T12:10:06+10:00\"   }                              </code></pre>  \\ The fields are:  <table>   <thead>     <tr>       <th>Field</th>       <th>Description</th>     </tr>   </thead>   <tbody>     <tr>       <td><code>status</code></td>       <td>The final state of the message.</td>     </tr>     <tr>       <td><code>destinationAddress</code></td>       <td>The number the message was sent to.</td>     </tr>     <tr>       <td><code>senderAddress</code></td>       <td>The number the message was sent from.</td>     </tr>     <tr>       <td><code>message</code></td>       <td>The sontent of the SMS reply.</td>     </tr>     <tr>       <td><code>sentTimestamp</code></td>       <td>Time handling of the message ended.</td>     </tr>   </tbody> </table>  <h3>Notification URL Format for MMS Replies</h3> <pre><code class=\"language-sh\">{     \"status\": \"RECEIVED\",     \"destinationAddress\": \"+61418123456\",     \"senderAddress\": \"+61421987654\",     \"subject\": \"Foo\",     \"sentTimestamp\": \"2018-03-23T12:15:45+10:00\",     \"envelope\": \"string\",     \"MMSContent\":      [       {         \"type\": \"application/smil\",         \"filename\": \"smil.xml\",         \"payload\": \"string\"       },        {         \"type\": \"image/jpeg\",         \"filename\": \"sample.jpeg\",         \"payload\": \"string\"        }      ]    } </code></pre>  \\ The fields are:  <table>   <thead>     <tr>       <th>Field</th>       <th>Description</th>     </tr>   </thead>   <tbody>     <tr>       <td><code>status</code></td>       <td>The final state of the message.</td>     </tr>     <tr>       <td><code>destinationAddress</code></td>       <td>The number the message was sent to.</td>     </tr>     <tr>       <td><code>senderAddress</code></td>       <td>The number the message was sent from.</td>     </tr>     <tr>       <td><code>subject</code></td>       <td>The subject assigned to the message.</td>     </tr>     <tr>       <td><code>sentTimestamp</code></td>       <td>Time handling of the message ended.</td>     </tr>     <tr>       <td><code>envelope</code></td>       <td>Information about about terminal type and originating operator.</td>     </tr>     <tr>       <td><code>MMSContent</code></td>       <td>An array of the actual content of the reply message.</td>     </tr>     <tr>       <td><code>type</code></td>       <td>The content type of the message.</td>     </tr>     <tr>       <td><code>filename</code></td>       <td>The filename for the message content.</td>     </tr>     <tr>       <td><code>payload</code></td>       <td>The content of the message.</td>     </tr>   </tbody> </table>  # Frequently Asked Questions **Q: Can I send a broadcast message using the Telstra Messging API?** A. Yes. Recipient numbers can be in teh form of an array of strings if a broadcast message needs to be sent.  <h2>Notes</h2> <a href=\"http://petstore.swagger.io/?url=https://raw.githubusercontent.com/telstra/MessagingAPI-v2/master/docs/swagger/messaging-api-swagger.yaml\" target=\"_blank\">View messaging in Swagger UI</a>  # noqa: E5015    OpenAPI spec version: 2.2.66    7    Generated by: https://github.com/swagger-api/swagger-codegen.git8"""9from setuptools import setup, find_packages  # noqa: H30110NAME = "swagger-client"11VERSION = "1.0.0"12# To install the library, run the following13#14# python setup.py install15#16# prerequisite: setuptools17# http://pypi.python.org/pypi/setuptools18REQUIRES = ["urllib3 >= 1.15", "six >= 1.10", "certifi", "python-dateutil"]19setup(20    name=NAME,21    version=VERSION,22    description="Telstra Messaging API",23    author_email="",24    url="",25    keywords=["Swagger", "Telstra Messaging API"],26    install_requires=REQUIRES,27    packages=find_packages(),28    include_package_data=True,29    long_description="""\30     # Introduction  Send and receive SMS and MMS messages globally using TelstraÃ¢â¬â¢s enterprise grade Messaging API. It also allows your application to track the delivery status of both sent and received messages. Get your dedicated Australian number, and start sending and receiving messages today.  # Features  &lt;p&gt;The Telstra Messaging API provides the features below. &lt;table&gt;   &lt;thead&gt;     &lt;tr&gt;       &lt;th&gt;Feature&lt;/th&gt;       &lt;th&gt;Description&lt;/th&gt;     &lt;/tr&gt;   &lt;/thead&gt;   &lt;tbody&gt;     &lt;tr&gt;       &lt;td&gt;&lt;code&gt;Dedicated Number&lt;/code&gt;&lt;/td&gt;       &lt;td&gt;Provision a mobile number for your account to be used as from address in the API&lt;/td&gt;     &lt;/tr&gt;     &lt;tr&gt;       &lt;td&gt;&lt;code&gt;Send Messages&lt;/code&gt;&lt;/td&gt;       &lt;td&gt;Sending SMS or MMS messages&lt;/td&gt;     &lt;/tr&gt;     &lt;tr&gt;       &lt;td&gt;&lt;code&gt;Receive Messages&lt;/code&gt;&lt;/td&gt;       &lt;td&gt;Telstra will deliver messages sent to a dedicated number or to the &lt;code&gt;notifyURL&lt;/code&gt; defined by you&lt;/td&gt;     &lt;/tr&gt;     &lt;tr&gt;       &lt;td&gt;&lt;code&gt;Broadcast Messages&lt;/code&gt;&lt;/td&gt;       &lt;td&gt;Invoke a single API to send a message to a list of number provided in &lt;code&gt;to&lt;/code&gt;&lt;/td&gt;     &lt;/tr&gt;     &lt;tr&gt;       &lt;td&gt;&lt;code&gt;Delivery Status&lt;/code&gt;&lt;/td&gt;       &lt;td&gt;Query the delivery status of your messages&lt;/td&gt;     &lt;/tr&gt;     &lt;tr&gt;       &lt;td&gt;&lt;code&gt;Callbacks&lt;/code&gt;&lt;/td&gt;       &lt;td&gt;Provide a notification URL and Telstra will notify your app when messages status changes&lt;/td&gt;     &lt;/tr&gt;     &lt;tr&gt;       &lt;td&gt;&lt;code&gt;Alphanumeric Identifier&lt;/code&gt;&lt;/td&gt;       &lt;td&gt;Differentiate yourself by providing an alphanumeric string in &lt;code&gt;from&lt;/code&gt;. This feature is only available on paid plans&lt;/td&gt;     &lt;/tr&gt;     &lt;tr&gt;       &lt;td&gt;&lt;code&gt;Concatenation&lt;/code&gt;&lt;/td&gt;       &lt;td&gt;Send messages up to 1900 characters long and Telstra will automaticaly segment and reassemble them&lt;/td&gt;     &lt;/tr&gt;     &lt;tr&gt;       &lt;td&gt;&lt;code&gt;Reply Request&lt;/code&gt;&lt;/td&gt;       &lt;td&gt;Create a chat session by associating &lt;code&gt;messageId&lt;/code&gt; and &lt;code&gt;to&lt;/code&gt; number to track responses received from a mobile number. We will store this association for 8 days&lt;/td&gt;     &lt;/tr&gt;     &lt;tr&gt;       &lt;td&gt;&lt;code&gt;Character set&lt;/code&gt;&lt;/td&gt;       &lt;td&gt;Accepts all Unicode characters as part of UTF-8&lt;/td&gt;     &lt;/tr&gt;     &lt;tr&gt;       &lt;td&gt;&lt;code&gt;Bounce-back response&lt;/code&gt;&lt;/td&gt;       &lt;td&gt;See if your SMS hits an unreachable or unallocated number (Australia Only)&lt;/td&gt;     &lt;/tr&gt;     &lt;tr&gt;       &lt;td&gt;&lt;code&gt;Queuing&lt;/code&gt;&lt;/td&gt;       &lt;td&gt;Messaging API will automatically queue and deliver each message at a compliant rate.&lt;/td&gt;     &lt;/tr&gt;     &lt;tr&gt;       &lt;td&gt;&lt;code&gt;Emoji Encoding&lt;/code&gt;&lt;/td&gt;       &lt;td&gt;The API supports the encoding of the full range of emojis. Emojis in the reply messages will be in their UTF-8 format.&lt;/td&gt;     &lt;/tr&gt;   &lt;/tbody&gt; &lt;/table&gt;   # Getting Access to the API  &lt;ol&gt;   &lt;li&gt;Register at &lt;a href&#x3D;\&quot;https://dev.telstra.com\&quot;&gt;https://dev.telstra.com&lt;/a&gt;.   &lt;li&gt;After registration, login to &lt;a href&#x3D;\&quot;https://dev.telstra.com\&quot;&gt;https://dev.telstra.com&lt;/a&gt;        and navigate to the &amp;quot;My apps&amp;quot; page.    &lt;li&gt;Create your application by clicking the &amp;quot;Add new app&amp;quot; button    &lt;li&gt;Select &amp;quot;API Free Trial&amp;quot; Product when configuring your application. This Product includes the Telstra Messaging API as well as other APIs. Your application will be approved automatically.   &lt;li&gt;There is a maximum of 1000 free messages per developer. Additional messages and features can be purchased from &lt;a href&#x3D;\&quot;https://dev.telstra.com\&quot;&gt;https://dev.telstra.com&lt;/a&gt;.   &lt;li&gt;Note your &lt;code&gt;Client key&lt;/code&gt; and &lt;code&gt;Client secret&lt;/code&gt; as these will be needed to provision a number for your application and for authentication. &lt;/ol&gt;  &lt;p&gt;Now head over to &lt;b&gt;Getting Started&lt;/b&gt; where you can find a postman collection as well as some links to sample apps and SDKs to get you started. &lt;p&gt;Happy Messaging!   # Getting Started  &lt;p&gt;Below are the steps to get started with the Telstra Messaging API.&lt;/p&gt;    &lt;ol&gt;     &lt;li&gt;Generate OAuth2 Token using your &lt;code&gt;Client key&lt;/code&gt; and &lt;code&gt;Client secret&lt;/code&gt;.&lt;/li&gt;     &lt;li&gt;Create Subscription in order to receive a provisioned number.&lt;/li&gt;     &lt;li&gt;Send Message to a specific mobile number.&lt;/li&gt;   &lt;/ol&gt;    &lt;h2&gt;Run in Postman&lt;/h2&gt;    &lt;p&gt;&lt;a   href&#x3D;\&quot;https://app.getpostman.com/run-collection/ded00578f69a9deba256#?env%5BMessaging%20API%20Environments%5D&#x3D;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&#x3D;\&quot;&gt;   &lt;img src&#x3D;\&quot;https://run.pstmn.io/button.svg\&quot; alt&#x3D;\&quot;Run in Postman\&quot; /&gt;&lt;/a&gt;&lt;/p&gt;    &lt;h2&gt;Sample Apps&lt;/h2&gt;   - &lt;a href&#x3D;\&quot;https://github.com/telstra/MessagingAPI-perl-sample-app\&quot;&gt;Perl Sample App&lt;/a&gt;   - &lt;a href&#x3D;\&quot;https://github.com/telstra/messaging-sample-code-happy-chat\&quot;&gt;Happy Chat App&lt;/a&gt;   - &lt;a href&#x3D;\&quot;https://github.com/developersteve/telstra-messaging-php\&quot;&gt;PHP Sample App&lt;/a&gt;    &lt;h2&gt;SDK repos&lt;/h2&gt;   - &lt;a href&#x3D;\&quot;https://github.com/telstra/MessagingAPI-SDK-php\&quot;&gt;Messaging API - PHP SDK&lt;/a&gt;   - &lt;a href&#x3D;\&quot;https://github.com/telstra/MessagingAPI-SDK-python\&quot;&gt;Messaging API - Python SDK&lt;/a&gt;   - &lt;a href&#x3D;\&quot;https://github.com/telstra/MessagingAPI-SDK-ruby\&quot;&gt;Messaging API - Ruby SDK&lt;/a&gt;   - &lt;a href&#x3D;\&quot;https://github.com/telstra/MessagingAPI-SDK-node\&quot;&gt;Messaging API - NodeJS SDK&lt;/a&gt;   - &lt;a href&#x3D;\&quot;https://github.com/telstra/MessagingAPI-SDK-dotnet\&quot;&gt;Messaging API - .Net2 SDK&lt;/a&gt;   - &lt;a href&#x3D;\&quot;https://github.com/telstra/MessagingAPI-SDK-Java\&quot;&gt;Messaging API - Java SDK&lt;/a&gt;  # Delivery Notification  The API provides several methods for notifying when a message has been delivered to the destination.  &lt;ol&gt;   &lt;li&gt;When you provision a number there is an opportunity to specify a &lt;code&gt;notifyURL&lt;/code&gt;, when the message has been delivered the API will make a call to this URL to advise of the message status. If this is not provided then you can make use the Get Replies API to poll for messages.&lt;/li&gt;   &lt;li&gt;If you do not specify a URL you can always call the &lt;code&gt;GET /sms&lt;/code&gt; API get the latest replies to the message.&lt;/li&gt; &lt;/ol&gt;  &lt;I&gt;Please note that the notification URLs and the polling call are exclusive. If a notification URL has been set then the polling call will not provide any useful information.&lt;/I&gt;  &lt;h2&gt;Notification URL Format&lt;/h2&gt;  When a message has reached its final state, the API will send a POST to the URL that has been previously specified.   &lt;h3&gt;Notification URL Format for SMS&lt;/h3&gt; &lt;pre&gt;&lt;code class&#x3D;\&quot;language-sh\&quot;&gt;{     to: &#39;+61418123456&#39;     sentTimestamp: &#39;2017-03-17T10:05:22+10:00&#39;     receivedTimestamp: &#39;2017-03-17T10:05:23+10:00&#39;     messageId: /cccb284200035236000000000ee9d074019e0301/1261418123456     deliveryStatus: DELIVRD   } &lt;/code&gt;&lt;/pre&gt; \\ The fields are:  &lt;table&gt;   &lt;thead&gt;     &lt;tr&gt;       &lt;th&gt;Field&lt;/th&gt;       &lt;th&gt;Description&lt;/th&gt;     &lt;/tr&gt;   &lt;/thead&gt;   &lt;tbody&gt;     &lt;tr&gt;       &lt;td&gt;&lt;code&gt;to&lt;/code&gt;&lt;/td&gt;       &lt;td&gt;The number the message was sent to.&lt;/td&gt;     &lt;/tr&gt;     &lt;tr&gt;       &lt;td&gt;&lt;code&gt;receivedTimestamp&lt;/code&gt;&lt;/td&gt;       &lt;td&gt;Time the message was sent to the API.&lt;/td&gt;     &lt;/tr&gt;     &lt;tr&gt;       &lt;td&gt;&lt;code&gt;sentTimestamp&lt;/code&gt;&lt;/td&gt;       &lt;td&gt;Time handling of the message ended.&lt;/td&gt;     &lt;/tr&gt;     &lt;tr&gt;       &lt;td&gt;&lt;code&gt;deliveryStatus&lt;/code&gt;&lt;/td&gt;       &lt;td&gt;The final state of the message.&lt;/td&gt;     &lt;/tr&gt;     &lt;tr&gt;       &lt;td&gt;&lt;code&gt;messageId&lt;/code&gt;&lt;/td&gt;       &lt;td&gt;The same reference that was returned when the original message was sent.&lt;/td&gt;     &lt;/tr&gt;     &lt;tr&gt;       &lt;td&gt;&lt;code&gt;receivedTimestamp&lt;/code&gt;&lt;/td&gt;       &lt;td&gt;Time the message was sent to the API.&lt;/td&gt;     &lt;/tr&gt;   &lt;/tbody&gt; &lt;/table&gt;  Upon receiving this call it is expected that your servers will give a 204 (No Content) response. Anything else will cause the API to reattempt the call 5 minutes later.   &lt;h3&gt;Notification URL Format for SMS Replies&lt;/h3&gt; &lt;pre&gt;&lt;code class&#x3D;\&quot;language-sh\&quot;&gt;{     \&quot;status\&quot;: \&quot;RECEIVED\&quot;     \&quot;destinationAddress\&quot;: \&quot;+61418123456\&quot;     \&quot;senderAddress\&quot;: \&quot;+61421987654\&quot;     \&quot;message\&quot;: \&quot;Foo\&quot;             \&quot;sentTimestamp\&quot;: \&quot;2018-03-23T12:10:06+10:00\&quot;   }                              &lt;/code&gt;&lt;/pre&gt;  \\ The fields are:  &lt;table&gt;   &lt;thead&gt;     &lt;tr&gt;       &lt;th&gt;Field&lt;/th&gt;       &lt;th&gt;Description&lt;/th&gt;     &lt;/tr&gt;   &lt;/thead&gt;   &lt;tbody&gt;     &lt;tr&gt;       &lt;td&gt;&lt;code&gt;status&lt;/code&gt;&lt;/td&gt;       &lt;td&gt;The final state of the message.&lt;/td&gt;     &lt;/tr&gt;     &lt;tr&gt;       &lt;td&gt;&lt;code&gt;destinationAddress&lt;/code&gt;&lt;/td&gt;       &lt;td&gt;The number the message was sent to.&lt;/td&gt;     &lt;/tr&gt;     &lt;tr&gt;       &lt;td&gt;&lt;code&gt;senderAddress&lt;/code&gt;&lt;/td&gt;       &lt;td&gt;The number the message was sent from.&lt;/td&gt;     &lt;/tr&gt;     &lt;tr&gt;       &lt;td&gt;&lt;code&gt;message&lt;/code&gt;&lt;/td&gt;       &lt;td&gt;The sontent of the SMS reply.&lt;/td&gt;     &lt;/tr&gt;     &lt;tr&gt;       &lt;td&gt;&lt;code&gt;sentTimestamp&lt;/code&gt;&lt;/td&gt;       &lt;td&gt;Time handling of the message ended.&lt;/td&gt;     &lt;/tr&gt;   &lt;/tbody&gt; &lt;/table&gt;  &lt;h3&gt;Notification URL Format for MMS Replies&lt;/h3&gt; &lt;pre&gt;&lt;code class&#x3D;\&quot;language-sh\&quot;&gt;{     \&quot;status\&quot;: \&quot;RECEIVED\&quot;,     \&quot;destinationAddress\&quot;: \&quot;+61418123456\&quot;,     \&quot;senderAddress\&quot;: \&quot;+61421987654\&quot;,     \&quot;subject\&quot;: \&quot;Foo\&quot;,     \&quot;sentTimestamp\&quot;: \&quot;2018-03-23T12:15:45+10:00\&quot;,     \&quot;envelope\&quot;: \&quot;string\&quot;,     \&quot;MMSContent\&quot;:      [       {         \&quot;type\&quot;: \&quot;application/smil\&quot;,         \&quot;filename\&quot;: \&quot;smil.xml\&quot;,         \&quot;payload\&quot;: \&quot;string\&quot;       },        {         \&quot;type\&quot;: \&quot;image/jpeg\&quot;,         \&quot;filename\&quot;: \&quot;sample.jpeg\&quot;,         \&quot;payload\&quot;: \&quot;string\&quot;        }      ]    } &lt;/code&gt;&lt;/pre&gt;  \\ The fields are:  &lt;table&gt;   &lt;thead&gt;     &lt;tr&gt;       &lt;th&gt;Field&lt;/th&gt;       &lt;th&gt;Description&lt;/th&gt;     &lt;/tr&gt;   &lt;/thead&gt;   &lt;tbody&gt;     &lt;tr&gt;       &lt;td&gt;&lt;code&gt;status&lt;/code&gt;&lt;/td&gt;       &lt;td&gt;The final state of the message.&lt;/td&gt;     &lt;/tr&gt;     &lt;tr&gt;       &lt;td&gt;&lt;code&gt;destinationAddress&lt;/code&gt;&lt;/td&gt;       &lt;td&gt;The number the message was sent to.&lt;/td&gt;     &lt;/tr&gt;     &lt;tr&gt;       &lt;td&gt;&lt;code&gt;senderAddress&lt;/code&gt;&lt;/td&gt;       &lt;td&gt;The number the message was sent from.&lt;/td&gt;     &lt;/tr&gt;     &lt;tr&gt;       &lt;td&gt;&lt;code&gt;subject&lt;/code&gt;&lt;/td&gt;       &lt;td&gt;The subject assigned to the message.&lt;/td&gt;     &lt;/tr&gt;     &lt;tr&gt;       &lt;td&gt;&lt;code&gt;sentTimestamp&lt;/code&gt;&lt;/td&gt;       &lt;td&gt;Time handling of the message ended.&lt;/td&gt;     &lt;/tr&gt;     &lt;tr&gt;       &lt;td&gt;&lt;code&gt;envelope&lt;/code&gt;&lt;/td&gt;       &lt;td&gt;Information about about terminal type and originating operator.&lt;/td&gt;     &lt;/tr&gt;     &lt;tr&gt;       &lt;td&gt;&lt;code&gt;MMSContent&lt;/code&gt;&lt;/td&gt;       &lt;td&gt;An array of the actual content of the reply message.&lt;/td&gt;     &lt;/tr&gt;     &lt;tr&gt;       &lt;td&gt;&lt;code&gt;type&lt;/code&gt;&lt;/td&gt;       &lt;td&gt;The content type of the message.&lt;/td&gt;     &lt;/tr&gt;     &lt;tr&gt;       &lt;td&gt;&lt;code&gt;filename&lt;/code&gt;&lt;/td&gt;       &lt;td&gt;The filename for the message content.&lt;/td&gt;     &lt;/tr&gt;     &lt;tr&gt;       &lt;td&gt;&lt;code&gt;payload&lt;/code&gt;&lt;/td&gt;       &lt;td&gt;The content of the message.&lt;/td&gt;     &lt;/tr&gt;   &lt;/tbody&gt; &lt;/table&gt;  # Frequently Asked Questions **Q: Can I send a broadcast message using the Telstra Messging API?** A. Yes. Recipient numbers can be in teh form of an array of strings if a broadcast message needs to be sent.  &lt;h2&gt;Notes&lt;/h2&gt; &lt;a href&#x3D;\&quot;http://petstore.swagger.io/?url&#x3D;https://raw.githubusercontent.com/telstra/MessagingAPI-v2/master/docs/swagger/messaging-api-swagger.yaml\&quot; target&#x3D;\&quot;_blank\&quot;&gt;View messaging in Swagger UI&lt;/a&gt;  # noqa: E50131    """...

cpso.py

Source:cpso.py

1import requests2from bs4 import BeautifulSoup3import pandas as pd4from string import ascii_lowercase5import re6import time7from random import randint8import csv9import sys10import os11url_search = "https://www.cpso.on.ca/Public-Information-Services/Find-a-Doctor?search=general"12url_paging = "https://www.cpso.on.ca/Public-Register-Info-(1)/Doctor-Search-Results"13abs_file_path = os.path.abspath(__file__)14file_dir = os.path.dirname(os.path.abspath(__file__))15project_dir = os.path.dirname(file_dir)16# progess bar17def progress(count, total, status=''):18    bar_len = 6019    filled_len = int(round(bar_len * count / float(total)))20    percents = round(100.0 * count / float(total), 1)21    bar = '=' * filled_len + '-' * (bar_len - filled_len)22    sys.stdout.write('[%s] %s%s ...%s\r' % (bar, percents, '%', status[0:40].ljust(40)))23    sys.stdout.flush()24def scrape_doctors(soup):25    new_docs = {}26    for doctor in soup.find_all(class_ = 'doctor-search-results--result'):27        doc_str = doctor.find('h3').text28        new_docs[ (re.search("\d+", doc_str).group()) ] = doctor29    return new_docs30headers_search = {31    "Host": "www.cpso.on.ca",32    "User-Agent": "Adam Kowalczewski, Service Canada, adam.kowalczewski@servicecanada.gc.ca",33    "Accept": "text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8",34    "Accept-Language": "en-US,en;q=0.5",35    "Accept-Encoding": "gzip, deflate, br",36    "Referer": "https://www.cpso.on.ca/Public-Information-Services/Find-a-Doctor?search=general",37    "Content-Type": "application/x-www-form-urlencoded",38    "Connection": "keep-alive",39    "Cookie": "CMSPreferredCulture=en-CA; _ga=GA1.3.788028045.1540596057; _gid=GA1.3.1509832542.1540596057; CMSCsrfCookie=PB+DjsX3FE9SCgAk3jGZzc65Ld6NEaE755HpaDB9; ASP.NET_SessionId=pek14izhwnw4u0curdgkxygh; _gat_UA-36725164-1=1",40    "Upgrade-Insecure-Requests": "1"41}42headers_paging = {43    "Referer": "https://www.cpso.on.ca/Public-Register-Info-(1)/Doctor-Search-Results"44}45manScript = "%3b%3bAjaxControlToolkit%2c+Version%3d4.1.60919.0%2c+Culture%3dneutral%2c+PublicKeyToken%3d28f01b0e84b6d53e%3aen-CA%3aee051b62-9cd6-49a5-87bb-93c07bc43d63%3a475a4ef5%3aeffe2a26%3a7e63a579"46manScript = manScript.replace( "%3b", ";" )47manScript = manScript.replace( "%2c", "," )48manScript = manScript.replace( "%3d", "=" )49manScript = manScript.replace( "%3a", ":" )50manScript = manScript.replace( "en-CA", "en-US")51def crawl_cpso( city_code = '', fsa = '', char = '' ):52    doctors = {}53    # get HTML to generate POST data54    with requests.Session() as s:55        r = s.get(url_search)56        soup = BeautifulSoup(r.content, 'html.parser')57        payload = {58            "__CMSCsrfToken": soup.find("input", id="__CMSCsrfToken")['value'],59            "__VIEWSTATE": soup.find("input", id="__VIEWSTATE")['value'],60            "__VIEWSTATEGENERATOR": soup.find("input", id="__VIEWSTATEGENERATOR")['value'],61            "lng": 'en-CA',62            "manScript_HiddenField": manScript,63            "searchType":"general",64            "p$lt$ctl04$pageplaceholder$p$lt$ctl02$AllDoctorsSearch$advancedState":"closed",65            "p$lt$ctl04$pageplaceholder$p$lt$ctl02$AllDoctorsSearch$ConcernsState":"closed",66            "p$lt$ctl04$pageplaceholder$p$lt$ctl02$AllDoctorsSearch$ddCity": city_code,67            "p$lt$ctl04$pageplaceholder$p$lt$ctl02$AllDoctorsSearch$txtPostalCode": fsa,68            "p$lt$ctl04$pageplaceholder$p$lt$ctl02$AllDoctorsSearch$txtLastName": char,69            "p$lt$ctl04$pageplaceholder$p$lt$ctl02$AllDoctorsSearch$grpGender":"+",70            "p$lt$ctl04$pageplaceholder$p$lt$ctl02$AllDoctorsSearch$grpDocType":"rdoDocTypeAll",71            "p$lt$ctl04$pageplaceholder$p$lt$ctl02$AllDoctorsSearch$ddHospitalName":"-1",72            "p$lt$ctl04$pageplaceholder$p$lt$ctl02$AllDoctorsSearch$ddLanguage":"08",73            "p$lt$ctl04$pageplaceholder$p$lt$ctl02$AllDoctorsSearch$chkActiveDoctors":"on",74            "p$lt$ctl04$pageplaceholder$p$lt$ctl02$AllDoctorsSearch$chkPracticeRestrictions":"on",75            "p$lt$ctl04$pageplaceholder$p$lt$ctl02$AllDoctorsSearch$chkPendingHearings":"on",76            "p$lt$ctl04$pageplaceholder$p$lt$ctl02$AllDoctorsSearch$chkPastHearings":"on",77            "p$lt$ctl04$pageplaceholder$p$lt$ctl02$AllDoctorsSearch$chkHospitalNotices":"on",78            "p$lt$ctl04$pageplaceholder$p$lt$ctl02$AllDoctorsSearch$chkConcerns":"on",79            "p$lt$ctl04$pageplaceholder$p$lt$ctl02$AllDoctorsSearch$chkNotices":"on",80            "p$lt$ctl04$pageplaceholder$p$lt$ctl02$AllDoctorsSearch$btnSubmit1":"Submit"81        }82        # send POST request83        time.sleep(randint(1,3))84        r = s.post( url_search, data = payload )85        soup = BeautifulSoup(r.content, 'html.parser')86        doctors.update( scrape_doctors(soup) )87        while True:88            # stop if there's no pages89            try:90                n_in_group = len(soup.find('div', class_ = "doctor-search-paging").find_all('a', id = re.compile("rptPages")))91            except:92                break93            # page through the rest of the group94            for i in range(1, n_in_group):95                payload_paging = {96                    "__CMSCsrfToken": soup.find("input", id="__CMSCsrfToken")['value'],97                    "__VIEWSTATE": soup.find("input", id="__VIEWSTATE")['value'],98                    "__VIEWSTATEGENERATOR": soup.find("input", id="__VIEWSTATEGENERATOR")['value'],99                    "__EVENTTARGET": "p$lt$ctl04$pageplaceholder$p$lt$ctl03$CPSO_DoctorSearchResults$rptPages$ctl0{:1}$lnbPage",100                    "lng": 'en-CA',101                    "manScript_HiddenField": manScript,102                    "p$lt$ctl04$pageplaceholder$p$lt$ctl03$CPSO_DoctorSearchResults$hdnCurrentPage": 1103                    }104                payload_paging['__EVENTTARGET'] = payload_paging['__EVENTTARGET'].format(i)105                time.sleep(randint(1,3))106                r = s.post( url_paging, headers = headers_search.update(headers_paging), data = payload_paging )107                soup = BeautifulSoup(r.content, 'html.parser')108                #page_num = soup.find('div', class_ = 'doctor-search-count').find('div', class_ = 'text-align--right').text.strip()109                doctors.update( scrape_doctors(soup) )110                payload_paging['p$lt$ctl04$pageplaceholder$p$lt$ctl03$CPSO_DoctorSearchResults$hdnCurrentPage'] += 1111            # stop if there's no more groups112            if soup.find(class_ = "aspNetDisabled next") != None:113                break114            # switch to the next group115            payload_paging['__EVENTTARGET'] = 'p$lt$ctl04$pageplaceholder$p$lt$ctl03$CPSO_DoctorSearchResults$lnbNextGroup'116            time.sleep(randint(1,3))117            r = s.post( url_paging, data = payload_paging )118            soup = BeautifulSoup(r.content, 'html.parser')119    return doctors120def count_doctors( city_code = '', fsa = '', char = '' ):121    with requests.Session() as s:122        r = s.get(url_search)123        soup = BeautifulSoup(r.content, 'html.parser')124        payload = {125            "__CMSCsrfToken": soup.find("input", id="__CMSCsrfToken")['value'],126            "__VIEWSTATE": soup.find("input", id="__VIEWSTATE")['value'],127            "__VIEWSTATEGENERATOR": soup.find("input", id="__VIEWSTATEGENERATOR")['value'],128            "lng": 'en-CA',129            "manScript_HiddenField": manScript,130            "searchType":"general",131            "p$lt$ctl04$pageplaceholder$p$lt$ctl02$AllDoctorsSearch$advancedState":"closed",132            "p$lt$ctl04$pageplaceholder$p$lt$ctl02$AllDoctorsSearch$ConcernsState":"closed",133            "p$lt$ctl04$pageplaceholder$p$lt$ctl02$AllDoctorsSearch$ddCity": city_code,134            "p$lt$ctl04$pageplaceholder$p$lt$ctl02$AllDoctorsSearch$txtPostalCode": fsa,135            "p$lt$ctl04$pageplaceholder$p$lt$ctl02$AllDoctorsSearch$txtLastName": char,136            "p$lt$ctl04$pageplaceholder$p$lt$ctl02$AllDoctorsSearch$grpGender":"+",137            "p$lt$ctl04$pageplaceholder$p$lt$ctl02$AllDoctorsSearch$grpDocType":"rdoDocTypeAll",138            "p$lt$ctl04$pageplaceholder$p$lt$ctl02$AllDoctorsSearch$ddHospitalName":"-1",139            "p$lt$ctl04$pageplaceholder$p$lt$ctl02$AllDoctorsSearch$ddLanguage":"08",140            "p$lt$ctl04$pageplaceholder$p$lt$ctl02$AllDoctorsSearch$chkActiveDoctors":"on",141            "p$lt$ctl04$pageplaceholder$p$lt$ctl02$AllDoctorsSearch$chkPracticeRestrictions":"on",142            "p$lt$ctl04$pageplaceholder$p$lt$ctl02$AllDoctorsSearch$chkPendingHearings":"on",143            "p$lt$ctl04$pageplaceholder$p$lt$ctl02$AllDoctorsSearch$chkPastHearings":"on",144            "p$lt$ctl04$pageplaceholder$p$lt$ctl02$AllDoctorsSearch$chkHospitalNotices":"on",145            "p$lt$ctl04$pageplaceholder$p$lt$ctl02$AllDoctorsSearch$chkConcerns":"on",146            "p$lt$ctl04$pageplaceholder$p$lt$ctl02$AllDoctorsSearch$chkNotices":"on",147            "p$lt$ctl04$pageplaceholder$p$lt$ctl02$AllDoctorsSearch$btnSubmit1":"Submit"148        }149        # get doctor search result count150        time.sleep(randint(1,3))151        r = s.post(url_search, data = payload)152        soup = BeautifulSoup(r.content, 'html.parser')153        target = soup.find('div', class_ = "doctor-search-count").strong.text...

h52csv.py

Source:h52csv.py

1import pandas as pd2import numpy as np3import h5py4import os5import pandas as pd6import numpy as np7import h5py8import os9def h5_to_csv(surf_files, surf_name='test_loss'):10    surfs_and_acc = [11        # "lth_0_model_lt.pth.tar_lenet5_1337_250eval",12        # "lth_1_model_lt.pth.tar_lenet5_1337_250eval",13        # "lth_2_model_lt.pth.tar_lenet5_1337_250eval",14        # "lth_3_model_lt.pth.tar_lenet5_1337_250eval",15        # "lth_4_model_lt.pth.tar_lenet5_1337_250eval",16        # "lth_5_model_lt.pth.tar_lenet5_1337_250eval",17        # "lth_6_model_lt.pth.tar_lenet5_1337_250eval",18        # "lth_7_model_lt.pth.tar_lenet5_1337_250eval",19        # "lth_8_model_lt.pth.tar_lenet5_1337_250eval",20        # "lth_9_model_lt.pth.tar_lenet5_1337_250eval",21        ("lth_10_model_lt.pth.tar_lenet5_1337_250eval", "26%"),22        # "lth_11_model_lt.pth.tar_lenet5_1337_250eval",23        # "lth_12_model_lt.pth.tar_lenet5_1337_250eval",24        # "lth_13_model_lt.pth.tar_lenet5_1337_250eval",25        ("lth_14_model_lt.pth.tar_lenet5_1337_250eval", "24%"),26        # "lth_15_model_lt.pth.tar_lenet5_1337_250eval",27        # "lth_16_model_lt.pth.tar_lenet5_1337_250eval",28        # "lth_17_model_lt.pth.tar_lenet5_1337_250eval",29        ("lth_18_model_lt.pth.tar_lenet5_1337_250eval", "21%"),30        # "lth_19_model_lt.pth.tar_lenet5_1337_250eval",31        # "lth_20_model_lt.pth.tar_lenet5_1337_250eval",32        # "lth_21_model_lt.pth.tar_lenet5_1337_250eval",33        ("lth_22_model_lt.pth.tar_lenet5_1337_250eval", "15%"),34        # "lth_23_model_lt.pth.tar_lenet5_1337_250eval",35        # "lth_24_model_lt.pth.tar_lenet5_1337_250eval",36        # "lth_25_model_lt.pth.tar_lenet5_1337_250eval",37        ("lth_26_model_lt.pth.tar_lenet5_1337_250eval", "11%"),38        # "lth_27_model_lt.pth.tar_lenet5_1337_250eval",39        # "lth_28_model_lt.pth.tar_lenet5_1337_250eval",40        # "lth_29_model_lt.pth.tar_lenet5_1337_250eval",41        ("lth_30_model_lt.pth.tar_lenet5_1337_250eval", "10%"),42        # "lth_31_model_lt.pth.tar_lenet5_1337_250eval",43        # "lth_32_model_lt.pth.tar_lenet5_1337_250eval",44        # "lth_33_model_lt.pth.tar_lenet5_1337_250eval",45        # "lth_34_model_lt.pth.tar_lenet5_1337_250eval",46        # "lth_0_model_lt.pth.tar_lenet5_3717_250eval",47        # "lth_1_model_lt.pth.tar_lenet5_3717_250eval",48        # "lth_2_model_lt.pth.tar_lenet5_3717_250eval",49        # "lth_3_model_lt.pth.tar_lenet5_3717_250eval",50        # "lth_4_model_lt.pth.tar_lenet5_3717_250eval",51        # "lth_5_model_lt.pth.tar_lenet5_3717_250eval",52        # "lth_6_model_lt.pth.tar_lenet5_3717_250eval",53        # "lth_7_model_lt.pth.tar_lenet5_3717_250eval",54        # "lth_8_model_lt.pth.tar_lenet5_3717_250eval",55        # "lth_9_model_lt.pth.tar_lenet5_3717_250eval",56        ("lth_10_model_lt.pth.tar_lenet5_3717_250eval", "28%"),57        # "lth_11_model_lt.pth.tar_lenet5_3717_250eval",58        # "lth_12_model_lt.pth.tar_lenet5_3717_250eval",59        # "lth_13_model_lt.pth.tar_lenet5_3717_250eval",60        ("lth_14_model_lt.pth.tar_lenet5_3717_250eval", "25%"),61        # "lth_15_model_lt.pth.tar_lenet5_3717_250eval",62        # "lth_16_model_lt.pth.tar_lenet5_3717_250eval",63        # "lth_17_model_lt.pth.tar_lenet5_3717_250eval",64        ("lth_18_model_lt.pth.tar_lenet5_3717_250eval", "24%"),65        # "lth_19_model_lt.pth.tar_lenet5_3717_250eval",66        # "lth_20_model_lt.pth.tar_lenet5_3717_250eval",67        # "lth_21_model_lt.pth.tar_lenet5_3717_250eval",68        ("lth_22_model_lt.pth.tar_lenet5_3717_250eval", "25%"),69        # "lth_23_model_lt.pth.tar_lenet5_3717_250eval",70        # "lth_24_model_lt.pth.tar_lenet5_3717_250eval",71        # "lth_25_model_lt.pth.tar_lenet5_3717_250eval",72        ("lth_26_model_lt.pth.tar_lenet5_3717_250eval", "27%"),73        # "lth_27_model_lt.pth.tar_lenet5_3717_250eval",74        # "lth_28_model_lt.pth.tar_lenet5_3717_250eval",75        # "lth_29_model_lt.pth.tar_lenet5_3717_250eval",76        ("lth_30_model_lt.pth.tar_lenet5_3717_250eval", "28%"),77        # "lth_31_model_lt.pth.tar_lenet5_3717_250eval",78        # "lth_32_model_lt.pth.tar_lenet5_3717_250eval",79        # "lth_33_model_lt.pth.tar_lenet5_3717_250eval",80        # "lth_34_model_lt.pth.tar_lenet5_3717_250eval"81    ]82    df = pd.DataFrame(columns=['id', 'mask', "% unpruned", "test acc", 'x', 'y', 'z'])83    for j, (surf_file, acc) in enumerate(surfs_and_acc):84        if os.path.isdir(surf_file): continue85        if surf_file[5] == "_":86            p = surf_file[4]87        else:88            p = surf_file[4:6]89        prune_percent = 1090        p = ((1 - prune_percent / 100) ** float(p)) * 10091        p = round(p, 1)92        print(surf_file)93        f = h5py.File("/home/rbain/git/loss_landscape/2nd_paper/surfs/pruning_combo/" + surf_file, 'r')94        # seed for random masks was 133795        if '1337' in surf_file:96            pruning = "random"97        else:98            pruning = "IMP"99        [xcoordinates, ycoordinates] = np.meshgrid(f['xcoordinates'][:], f['ycoordinates'][:][:])100        vals = f[surf_name]101        x_array = xcoordinates[:].ravel()102        y_array = ycoordinates[:].ravel()103        z_array = np.log(vals[:].ravel())104        for i in range(len(z_array)):105            row = {'id': j, 'mask': pruning, '% unpruned': p, 'test acc': acc, 'x': x_array[i], 'y': y_array[i], 'z': z_array[i]}106            df = df.append(row, ignore_index=True)107    print("Total number of rows:", len(df.index))108    df.to_csv("lenet5_cifar10_bs9600_35iters_lth+random.csv", index=False)109        110if __name__ == "__main__":111    h5_dir = os.path.join("/home/rbain/git/loss_landscape/2nd_paper/surfs/pruning_combo")112    plots = sorted(os.listdir(h5_dir))113    # plots.insert(0, plots[-1])114    # plots = plots[:-1]115    #print(plots);exit()...

99_count-docs-by-spec_city.py

Source:99_count-docs-by-spec_city.py

1import requests2from bs4 import BeautifulSoup3import re4import time5from random import randint6import pickle7import csv8import sys9import os10url_search = "https://www.cpso.on.ca/Public-Information-Services/Find-a-Doctor?search=general"11abs_file_path = os.path.abspath(__file__)12file_dir = os.path.dirname(os.path.abspath(__file__))13project_dir = os.path.dirname(file_dir)14headers_search = {15    "Host": "www.cpso.on.ca",16    "User-Agent": "Adam Kowalczewski, Service Canada, adam.kowalczewski@servicecanada.gc.ca",17    "Accept": "text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8",18    "Accept-Language": "en-US,en;q=0.5",19    "Accept-Encoding": "gzip, deflate, br",20    "Referer": "https://www.cpso.on.ca/Public-Information-Services/Find-a-Doctor?search=general",21    "Content-Type": "application/x-www-form-urlencoded",22    "Connection": "keep-alive",23    "Cookie": "CMSPreferredCulture=en-CA; _ga=GA1.3.788028045.1540596057; _gid=GA1.3.1509832542.1540596057; CMSCsrfCookie=PB+DjsX3FE9SCgAk3jGZzc65Ld6NEaE755HpaDB9; ASP.NET_SessionId=pek14izhwnw4u0curdgkxygh; _gat_UA-36725164-1=1",24    "Upgrade-Insecure-Requests": "1"25}26manScript = "%3b%3bAjaxControlToolkit%2c+Version%3d4.1.60919.0%2c+Culture%3dneutral%2c+PublicKeyToken%3d28f01b0e84b6d53e%3aen-CA%3aee051b62-9cd6-49a5-87bb-93c07bc43d63%3a475a4ef5%3aeffe2a26%3a7e63a579"27manScript = manScript.replace( "%3b", ";" )28manScript = manScript.replace( "%2c", "," )29manScript = manScript.replace( "%3d", "=" )30manScript = manScript.replace( "%3a", ":" )31manScript = manScript.replace( "en-CA", "en-US")32def progress(count, total, status=''):33    bar_len = 6034    filled_len = int(round(bar_len * count / float(total)))35    percents = round(100.0 * count / float(total), 1)36    bar = '=' * filled_len + '-' * (bar_len - filled_len)37    sys.stdout.write('[%s] %s%s ...%s\r' % (bar, percents, '%', status[0:40].ljust(40)))38    sys.stdout.flush()39def ping_specialization( city, special ):40    with requests.Session() as s:41        r = s.get(url_search)42        soup = BeautifulSoup(r.content, 'html.parser')43        payload = {44            "__CMSCsrfToken": soup.find("input", id="__CMSCsrfToken")['value'],45            "__VIEWSTATE": soup.find("input", id="__VIEWSTATE")['value'],46            "__VIEWSTATEGENERATOR": soup.find("input", id="__VIEWSTATEGENERATOR")['value'],47            "lng": 'en-CA',48            "manScript_HiddenField": manScript,49            "searchType":"general",50            "p$lt$ctl04$pageplaceholder$p$lt$ctl02$AllDoctorsSearch$advancedState":"closed",51            "p$lt$ctl04$pageplaceholder$p$lt$ctl02$AllDoctorsSearch$ConcernsState":"closed",52            #"p$lt$ctl04$pageplaceholder$p$lt$ctl02$AllDoctorsSearch$txtPostalCode": fsa,53            "p$lt$ctl04$pageplaceholder$p$lt$ctl02$AllDoctorsSearch$ddCity": city,54            "p$lt$ctl04$pageplaceholder$p$lt$ctl02$AllDoctorsSearch$grpGender":"+",55            #"p$lt$ctl04$pageplaceholder$p$lt$ctl02$AllDoctorsSearch$grpDocType":"rdoDocTypeAll",56            "p$lt$ctl04$pageplaceholder$p$lt$ctl02$AllDoctorsSearch$grpDocType": "rdoDocTypeSpecialist",57            "p$lt$ctl04$pageplaceholder$p$lt$ctl02$AllDoctorsSearch$ddSpecialist": special,58            "p$lt$ctl04$pageplaceholder$p$lt$ctl02$AllDoctorsSearch$ddHospitalName":"-1",59            "p$lt$ctl04$pageplaceholder$p$lt$ctl02$AllDoctorsSearch$ddLanguage":"08",60            "p$lt$ctl04$pageplaceholder$p$lt$ctl02$AllDoctorsSearch$chkActiveDoctors":"on",61            "p$lt$ctl04$pageplaceholder$p$lt$ctl02$AllDoctorsSearch$chkPracticeRestrictions":"on",62            "p$lt$ctl04$pageplaceholder$p$lt$ctl02$AllDoctorsSearch$chkPendingHearings":"on",63            "p$lt$ctl04$pageplaceholder$p$lt$ctl02$AllDoctorsSearch$chkPastHearings":"on",64            "p$lt$ctl04$pageplaceholder$p$lt$ctl02$AllDoctorsSearch$chkHospitalNotices":"on",65            "p$lt$ctl04$pageplaceholder$p$lt$ctl02$AllDoctorsSearch$chkConcerns":"on",66            "p$lt$ctl04$pageplaceholder$p$lt$ctl02$AllDoctorsSearch$chkNotices":"on",67            "p$lt$ctl04$pageplaceholder$p$lt$ctl02$AllDoctorsSearch$btnSubmit1":"Submit"68        }69        # get doctor search result count70        time.sleep(randint(1,3))71        r = s.post(url_search, data = payload)72        soup = BeautifulSoup(r.content, 'html.parser')73        target = soup.find('div', class_ = "doctor-search-count").strong.text74        return re.search( "\d+", target ).group()75# list all specializations76specs = {}77results = {}78# for the big cities in Ontario79cities = [80    [1127, 'Brampton'],81    [1425, 'Hamilton'],82    [1561, "London"],83    [1630, "Mississauga"],84    [1711, "Ottawa"],85    [1977, 'Toronto']86 ]87with requests.Session() as s:88    r = s.get(url_search)89    soup = BeautifulSoup(r.content, 'html.parser')90    # get list of all specializations91    options = soup.find(id = 'ddSpecialist').find_all('option')92    for option in options :93        specs[ option['value'] ] = option.text94del specs['']95# progress bar settings96total = len(specs) - 197for city_code, city in cities:98    i = 099    print( 'processing', city )100    for key, val in specs.items():101        progress(i, total, status= 'pinging ' + val )102        i += 1103        try:104            results[ key ] = ping_specialization( city_code, key )105        except:106            results[ key ] = 0107    with open( project_dir + '/data/count-doc_spec_' + city + '.csv', 'w' ) as csv_file:108        writer = csv.writer(csv_file)109        writer.writerow( [ "city_code", "specialty_code", "num_doctors"] )110        for key, val in results.items():111            writer.writerow( [city_code, key, val] )...

99_count-docs-by-spec_fsa.py

Source:99_count-docs-by-spec_fsa.py

1import requests2from bs4 import BeautifulSoup3import re4import time5from random import randint6import pickle7import csv8import sys9import os10url_search = "https://www.cpso.on.ca/Public-Information-Services/Find-a-Doctor?search=general"11abs_file_path = os.path.abspath(__file__)12file_dir = os.path.dirname(os.path.abspath(__file__))13project_dir = os.path.dirname(file_dir)14headers_search = {15    "Host": "www.cpso.on.ca",16    "User-Agent": "Adam Kowalczewski, Service Canada, adam.kowalczewski@servicecanada.gc.ca",17    "Accept": "text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8",18    "Accept-Language": "en-US,en;q=0.5",19    "Accept-Encoding": "gzip, deflate, br",20    "Referer": "https://www.cpso.on.ca/Public-Information-Services/Find-a-Doctor?search=general",21    "Content-Type": "application/x-www-form-urlencoded",22    "Connection": "keep-alive",23    "Cookie": "CMSPreferredCulture=en-CA; _ga=GA1.3.788028045.1540596057; _gid=GA1.3.1509832542.1540596057; CMSCsrfCookie=PB+DjsX3FE9SCgAk3jGZzc65Ld6NEaE755HpaDB9; ASP.NET_SessionId=pek14izhwnw4u0curdgkxygh; _gat_UA-36725164-1=1",24    "Upgrade-Insecure-Requests": "1"25}26manScript = "%3b%3bAjaxControlToolkit%2c+Version%3d4.1.60919.0%2c+Culture%3dneutral%2c+PublicKeyToken%3d28f01b0e84b6d53e%3aen-CA%3aee051b62-9cd6-49a5-87bb-93c07bc43d63%3a475a4ef5%3aeffe2a26%3a7e63a579"27manScript = manScript.replace( "%3b", ";" )28manScript = manScript.replace( "%2c", "," )29manScript = manScript.replace( "%3d", "=" )30manScript = manScript.replace( "%3a", ":" )31manScript = manScript.replace( "en-CA", "en-US")32def progress(count, total, status=''):33    bar_len = 6034    filled_len = int(round(bar_len * count / float(total)))35    percents = round(100.0 * count / float(total), 1)36    bar = '=' * filled_len + '-' * (bar_len - filled_len)37    sys.stdout.write('[%s] %s%s ...%s\r' % (bar, percents, '%', status[0:40].ljust(40)))38    sys.stdout.flush()39def ping_specialization( fsa, special ):40    with requests.Session() as s:41        r = s.get(url_search)42        soup = BeautifulSoup(r.content, 'html.parser')43        payload = {44            "__CMSCsrfToken": soup.find("input", id="__CMSCsrfToken")['value'],45            "__VIEWSTATE": soup.find("input", id="__VIEWSTATE")['value'],46            "__VIEWSTATEGENERATOR": soup.find("input", id="__VIEWSTATEGENERATOR")['value'],47            "lng": 'en-CA',48            "manScript_HiddenField": manScript,49            "searchType":"general",50            "p$lt$ctl04$pageplaceholder$p$lt$ctl02$AllDoctorsSearch$advancedState":"closed",51            "p$lt$ctl04$pageplaceholder$p$lt$ctl02$AllDoctorsSearch$ConcernsState":"closed",52            "p$lt$ctl04$pageplaceholder$p$lt$ctl02$AllDoctorsSearch$txtPostalCode": fsa,53            #"p$lt$ctl04$pageplaceholder$p$lt$ctl02$AllDoctorsSearch$ddCity": city,54            "p$lt$ctl04$pageplaceholder$p$lt$ctl02$AllDoctorsSearch$grpGender":"+",55            #"p$lt$ctl04$pageplaceholder$p$lt$ctl02$AllDoctorsSearch$grpDocType":"rdoDocTypeAll",56            "p$lt$ctl04$pageplaceholder$p$lt$ctl02$AllDoctorsSearch$grpDocType": "rdoDocTypeSpecialist",57            "p$lt$ctl04$pageplaceholder$p$lt$ctl02$AllDoctorsSearch$ddSpecialist": special,58            "p$lt$ctl04$pageplaceholder$p$lt$ctl02$AllDoctorsSearch$ddHospitalName":"-1",59            "p$lt$ctl04$pageplaceholder$p$lt$ctl02$AllDoctorsSearch$ddLanguage":"08",60            "p$lt$ctl04$pageplaceholder$p$lt$ctl02$AllDoctorsSearch$chkActiveDoctors":"on",61            "p$lt$ctl04$pageplaceholder$p$lt$ctl02$AllDoctorsSearch$chkPracticeRestrictions":"on",62            "p$lt$ctl04$pageplaceholder$p$lt$ctl02$AllDoctorsSearch$chkPendingHearings":"on",63            "p$lt$ctl04$pageplaceholder$p$lt$ctl02$AllDoctorsSearch$chkPastHearings":"on",64            "p$lt$ctl04$pageplaceholder$p$lt$ctl02$AllDoctorsSearch$chkHospitalNotices":"on",65            "p$lt$ctl04$pageplaceholder$p$lt$ctl02$AllDoctorsSearch$chkConcerns":"on",66            "p$lt$ctl04$pageplaceholder$p$lt$ctl02$AllDoctorsSearch$chkNotices":"on",67            "p$lt$ctl04$pageplaceholder$p$lt$ctl02$AllDoctorsSearch$btnSubmit1":"Submit"68        }69        # get doctor search result count70        time.sleep(randint(1,3))71        r = s.post(url_search, data = payload)72        soup = BeautifulSoup(r.content, 'html.parser')73        target = soup.find('div', class_ = "doctor-search-count").strong.text74        return re.search( "\d+", target ).group()75# list all specializations76specs = {}77results = {}78# for an fsa79fsas = ['K1H', 'M5T', 'N6A']80with requests.Session() as s:81    r = s.get(url_search)82    soup = BeautifulSoup(r.content, 'html.parser')83    # get list of all specializations84    options = soup.find(id = 'ddSpecialist').find_all('option')85    for option in options :86        specs[ option['value'] ] = option.text87del specs['']88# progress bar settings89total = len(specs) - 190for fsa in fsas:91    print()92    print( 'processing', fsa )93    i = 094    for key, val in specs.items():95        progress(i, total, status= 'pinging ' + val )96        i += 197        try:98            results[ key ] = ping_specialization( fsa, key )99        except:100            results[ key ] = 0101    with open( project_dir + '/data/count-doc_spec_fsa-' + fsa + '.csv', 'w' ) as csv_file:102        writer = csv.writer(csv_file)103        writer.writerow( [ "fsa", "specialty_code", "num_doctors"] )104        for key, val in results.items():105            writer.writerow( [fsa, key, val] )...

99_count-docs_by-fsa-lastname.py

Source:99_count-docs_by-fsa-lastname.py

1import requests2from bs4 import BeautifulSoup3from string import ascii_lowercase4import pandas as pd5import re6import time7from random import randint8import pickle9import sys10import os11url_search = "https://www.cpso.on.ca/Public-Information-Services/Find-a-Doctor?search=general"12abs_file_path = os.path.abspath(__file__)13file_dir = os.path.dirname(os.path.abspath(__file__))14project_dir = os.path.dirname(file_dir)15headers_search = {16    "Host": "www.cpso.on.ca",17    "User-Agent": "Adam Kowalczewski, Service Canada, adam.kowalczewski@servicecanada.gc.ca",18    "Accept": "text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8",19    "Accept-Language": "en-US,en;q=0.5",20    "Accept-Encoding": "gzip, deflate, br",21    "Referer": "https://www.cpso.on.ca/Public-Information-Services/Find-a-Doctor?search=general",22    "Content-Type": "application/x-www-form-urlencoded",23    "Connection": "keep-alive",24    "Cookie": "CMSPreferredCulture=en-CA; _ga=GA1.3.788028045.1540596057; _gid=GA1.3.1509832542.1540596057; CMSCsrfCookie=PB+DjsX3FE9SCgAk3jGZzc65Ld6NEaE755HpaDB9; ASP.NET_SessionId=pek14izhwnw4u0curdgkxygh; _gat_UA-36725164-1=1",25    "Upgrade-Insecure-Requests": "1"26}27manScript = "%3b%3bAjaxControlToolkit%2c+Version%3d4.1.60919.0%2c+Culture%3dneutral%2c+PublicKeyToken%3d28f01b0e84b6d53e%3aen-CA%3aee051b62-9cd6-49a5-87bb-93c07bc43d63%3a475a4ef5%3aeffe2a26%3a7e63a579"28manScript = manScript.replace( "%3b", ";" )29manScript = manScript.replace( "%2c", "," )30manScript = manScript.replace( "%3d", "=" )31manScript = manScript.replace( "%3a", ":" )32manScript = manScript.replace( "en-CA", "en-US")33def progress(count, total, status=''):34    bar_len = 6035    filled_len = int(round(bar_len * count / float(total)))36    percents = round(100.0 * count / float(total), 1)37    bar = '=' * filled_len + '-' * (bar_len - filled_len)38    sys.stdout.write('[%s] %s%s ...%s\r' % (bar, percents, '%', status[0:40].ljust(40)))39    sys.stdout.flush()40# return number of doctors found for an fsa41def count_doc_by_fsa_char( fsa, char ):42    with requests.Session() as s:43        # prepare payload44        r = s.get(url_search)45        soup = BeautifulSoup(r.content, 'html.parser')46        payload = {47            "__CMSCsrfToken": soup.find("input", id="__CMSCsrfToken")['value'],48            "__VIEWSTATE": soup.find("input", id="__VIEWSTATE")['value'],49            "__VIEWSTATEGENERATOR": soup.find("input", id="__VIEWSTATEGENERATOR")['value'],50            "lng": 'en-CA',51            "manScript_HiddenField": manScript,52            "searchType":"general",53            "p$lt$ctl04$pageplaceholder$p$lt$ctl02$AllDoctorsSearch$advancedState":"closed",54            "p$lt$ctl04$pageplaceholder$p$lt$ctl02$AllDoctorsSearch$ConcernsState":"closed",55            "p$lt$ctl04$pageplaceholder$p$lt$ctl02$AllDoctorsSearch$txtPostalCode": fsa,56            "p$lt$ctl04$pageplaceholder$p$lt$ctl02$AllDoctorsSearch$txtLastName": char,57            "p$lt$ctl04$pageplaceholder$p$lt$ctl02$AllDoctorsSearch$grpGender":"+",58            "p$lt$ctl04$pageplaceholder$p$lt$ctl02$AllDoctorsSearch$grpDocType":"rdoDocTypeAll",59            "p$lt$ctl04$pageplaceholder$p$lt$ctl02$AllDoctorsSearch$ddHospitalName":"-1",60            "p$lt$ctl04$pageplaceholder$p$lt$ctl02$AllDoctorsSearch$ddLanguage":"08",61            "p$lt$ctl04$pageplaceholder$p$lt$ctl02$AllDoctorsSearch$chkActiveDoctors":"on",62            "p$lt$ctl04$pageplaceholder$p$lt$ctl02$AllDoctorsSearch$chkPracticeRestrictions":"on",63            "p$lt$ctl04$pageplaceholder$p$lt$ctl02$AllDoctorsSearch$chkPendingHearings":"on",64            "p$lt$ctl04$pageplaceholder$p$lt$ctl02$AllDoctorsSearch$chkPastHearings":"on",65            "p$lt$ctl04$pageplaceholder$p$lt$ctl02$AllDoctorsSearch$chkHospitalNotices":"on",66            "p$lt$ctl04$pageplaceholder$p$lt$ctl02$AllDoctorsSearch$chkConcerns":"on",67            "p$lt$ctl04$pageplaceholder$p$lt$ctl02$AllDoctorsSearch$chkNotices":"on",68            "p$lt$ctl04$pageplaceholder$p$lt$ctl02$AllDoctorsSearch$btnSubmit1":"Submit"69        }70        # get doctor search result count71        time.sleep(randint(1,3))72        r = s.post(url_search, data = payload)73        soup = BeautifulSoup(r.content, 'html.parser')74        target = soup.find('div', class_ = "doctor-search-count").strong.text75        return re.search( "\d+", target ).group()76# scrape an fsa for alphabet77fsas = ['K1H', 'M5T', 'N6A']78for fsa in fsas:79    fsa_idx = []80    char_idx = []81    count = []82    # init progress bar83    total = 2684    i_fsa = 085    start_time = time.time()86    for char in ascii_lowercase:87        progress(i_fsa, total, status= 'scraping ' + fsa + ' ' + char )88        try:89            fsa_idx.append(fsa)90            char_idx.append(char)91            count.append( count_doc_by_fsa_char( fsa, char ) )92        except:93            fsa_idx.append(fsa)94            char_idx.append(char)95            count.append( 'NA' )96        finally:97            i_fsa += 198    progress(i_fsa, total, status= 'finished ' + fsa )99    print()100    fsa_result = pd.DataFrame({101        "fsa_idx": fsa_idx,102        "char_idx": char_idx,103        "count": count })104    fsa_result.to_csv( project_dir + '/data/' + 'count-doc_' + fsa + '-lastname.csv', index = False )105elapsed_time = time.time() - start_time106print()...

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