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| 1 | +# Copyright 2019 The TensorFlow Hub 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 at |
| 6 | +# |
| 7 | +# http://www.apache.org/licenses/LICENSE-2.0 |
| 8 | +# |
| 9 | +# Unless required by applicable law or agreed to in writing, software |
| 10 | +# 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 and |
| 13 | +# limitations under the License. |
| 14 | +# ============================================================================== |
| 15 | +"""Utils for module search functionality.""" |
| 16 | + |
| 17 | +from __future__ import absolute_import |
| 18 | +from __future__ import division |
| 19 | +from __future__ import print_function |
| 20 | + |
| 21 | +import numpy as np |
| 22 | +import tensorflow as tf |
| 23 | +import tensorflow_hub as hub |
| 24 | +import tensorflow_datasets as tfds |
| 25 | + |
| 26 | + |
| 27 | +def compute_distance_matrix(x_train, x_test, measure="squared_l2"): |
| 28 | + """Calculates the distance matrix between test and train. |
| 29 | +
|
| 30 | + Args: |
| 31 | + x_train: Matrix (NxD) where each row represents a training sample |
| 32 | + x_test: Matrix (MxD) where each row represents a test sample |
| 33 | + measure: Distance measure (not necessarly metric) to use |
| 34 | +
|
| 35 | + Raises: |
| 36 | + NotImplementedError: When the measure is not implemented |
| 37 | +
|
| 38 | + Returns: |
| 39 | + Matrix (MxN) where elemnt i,j is the distance between |
| 40 | + x_test_i and x_train_j. |
| 41 | + """ |
| 42 | + |
| 43 | + x_train = tf.convert_to_tensor(x_train, tf.float64) |
| 44 | + x_test = tf.convert_to_tensor(x_test, tf.float64) |
| 45 | + |
| 46 | + if measure == "squared_l2": |
| 47 | + x_xt = tf.matmul(x_test, tf.transpose(x_train)).numpy() |
| 48 | + |
| 49 | + x_train_2 = tf.reduce_sum(tf.math.square(x_train), 1).numpy() |
| 50 | + x_test_2 = tf.reduce_sum(tf.math.square(x_test), 1).numpy() |
| 51 | + |
| 52 | + for i in range(np.shape(x_xt)[0]): |
| 53 | + x_xt[i, :] = np.multiply(x_xt[i, :], -2) |
| 54 | + x_xt[i, :] = np.add(x_xt[i, :], x_test_2[i]) |
| 55 | + x_xt[i, :] = np.add(x_xt[i, :], x_train_2) |
| 56 | + |
| 57 | + else: |
| 58 | + raise NotImplementedError("Method '{}' is not implemented".format(measure)) |
| 59 | + |
| 60 | + return x_xt |
| 61 | + |
| 62 | + |
| 63 | +def compute_distance_matrix_loo(x, measure="squared_l2"): |
| 64 | + """Calculates the distance matrix for leave-one-out strategy. |
| 65 | +
|
| 66 | + Args: |
| 67 | + x: Matrix (NxD) where each row represents a sample |
| 68 | + measure: Distance measure (not necessarly metric) to use |
| 69 | +
|
| 70 | + Raises: |
| 71 | + NotImplementedError: When the measure is not implemented |
| 72 | +
|
| 73 | + Returns: |
| 74 | + Matrix (NxN) where elemnt i,j is the distance between x_i and x_j. |
| 75 | + The diagonal is set to infinity |
| 76 | + """ |
| 77 | + |
| 78 | + x = tf.convert_to_tensor(x, tf.float64) |
| 79 | + |
| 80 | + if measure == "squared_l2": |
| 81 | + x_xt = tf.matmul(x, tf.transpose(x)).numpy() |
| 82 | + diag = np.diag(x_xt) |
| 83 | + d = np.copy(x_xt) |
| 84 | + |
| 85 | + for i in range(np.shape(d)[0]): |
| 86 | + d[i, :] = np.multiply(d[i, :], -2) |
| 87 | + d[i, :] = np.add(d[i, :], x_xt[i, i]) |
| 88 | + d[i, :] = np.add(d[i, :], diag) |
| 89 | + d[i, i] = float("inf") |
| 90 | + |
| 91 | + elif measure == "cosine": |
| 92 | + d = tf.matmul(x, tf.transpose(x)).numpy() |
| 93 | + diag_sqrt = np.sqrt(np.diag(d)) |
| 94 | + outer = np.outer(diag_sqrt, diag_sqrt) |
| 95 | + d = np.ones(np.shape(d)) - np.divide(d, outer) |
| 96 | + np.fill_diagonal(d, float("inf")) |
| 97 | + |
| 98 | + else: |
| 99 | + raise NotImplementedError("Method '{}' is not implemented".format(measure)) |
| 100 | + |
| 101 | + return d |
| 102 | + |
| 103 | + |
| 104 | +def knn_errorrate(d, y_train, y_test, k=1): |
| 105 | + """Calculate the knn error rate based on the distance matrix d. |
| 106 | +
|
| 107 | + Args: |
| 108 | + d: distance matrix |
| 109 | + y_train: label vector for the training samples |
| 110 | + y_test: label vector for the test samples |
| 111 | + k: number of direct neighbors for knn |
| 112 | +
|
| 113 | + Returns: |
| 114 | + knn error rate (1 - accuracy) |
| 115 | + """ |
| 116 | + |
| 117 | + if k == 1: |
| 118 | + indices = np.argmin(d, axis=1) |
| 119 | + |
| 120 | + cnt = 0 |
| 121 | + for i in range(len(indices)): |
| 122 | + if y_test[i] != y_train[indices[i]]: |
| 123 | + cnt += 1 |
| 124 | + |
| 125 | + return float(cnt) / len(indices) |
| 126 | + |
| 127 | + indices = np.argpartition(d, k - 1, axis=1) |
| 128 | + cnt = 0 |
| 129 | + for i in range(np.shape(d)[0]): |
| 130 | + cnt_i = 0 |
| 131 | + for j in range(k): |
| 132 | + if y_test[i] != y_train[indices[i, j]]: |
| 133 | + cnt_i += 1 |
| 134 | + if cnt_i >= k / 2.0: |
| 135 | + cnt += 1 |
| 136 | + |
| 137 | + return float(cnt) / np.shape(d)[0] |
| 138 | + |
| 139 | + |
| 140 | +def knn_errorrate_loo(d, y, k=1): |
| 141 | + """Calculate the leave-one-out expected knn error rate based |
| 142 | + on the distance matrix d. |
| 143 | +
|
| 144 | + Args: |
| 145 | + d: distance matrix, the diagonal should be infinity |
| 146 | + y: label matrix |
| 147 | + k: number of direct neighbors for knn |
| 148 | +
|
| 149 | + Returns: |
| 150 | + Expected leave-one-out knn error rate (1 - accuracy) |
| 151 | + """ |
| 152 | + |
| 153 | + if k == 1: |
| 154 | + indices = np.argmin(d, axis=1) |
| 155 | + |
| 156 | + cnt = 0 |
| 157 | + for i in range(len(indices)): |
| 158 | + if y[i] != y[indices[i]]: |
| 159 | + cnt += 1 |
| 160 | + |
| 161 | + return float(cnt) / len(indices) |
| 162 | + |
| 163 | + indices = np.argpartition(d, k - 1, axis=1) |
| 164 | + cnt = 0 |
| 165 | + for i in range(np.shape(d)[0]): |
| 166 | + cnt_i = 0 |
| 167 | + for j in range(k): |
| 168 | + if y[i] != y[indices[i, j]]: |
| 169 | + cnt_i += 1 |
| 170 | + if cnt_i >= k / 2.0: |
| 171 | + cnt += 1 |
| 172 | + |
| 173 | + return float(cnt) / np.shape(d)[0] |
| 174 | + |
| 175 | + |
| 176 | +def load_data(dataset, split, num_examples=None): |
| 177 | + ds = tfds.load(dataset, split=split, shuffle_files=False) |
| 178 | + if num_examples: |
| 179 | + ds = ds.take(num_examples) |
| 180 | + return ds |
| 181 | + |
| 182 | + |
| 183 | +def load_embedding_fn(module): |
| 184 | + m = hub.load(module, tags=[]) |
| 185 | + return lambda x: m.signatures["default"](x)["default"] |
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