Adding module_search/utils.py

PiperOrigin-RevId: 275830769

Author: TensorFlow Hub Authors

tensorflow_hub/tools/module_search/BUILD

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+# Copyright 2019 The TensorFlow Hub Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+licenses(["notice"])  # Apache 2.0 License
+
+py_library(
+    name = "utils",
+    srcs = ["utils.py"],
+    deps = [
+        "//tensorflow_hub:expect_numpy_installed",
+        "//tensorflow_hub:expect_tensorflow_installed",
+    ],
+)

tensorflow_hub/tools/module_search/README.md

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+# Module search
+
+*WIP* - tool to rank a list of modules for use in a downstream task.

tensorflow_hub/tools/module_search/utils.py

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+# Copyright 2019 The TensorFlow Hub Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Utils for module search functionality."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+import tensorflow as tf
+import tensorflow_hub as hub
+import tensorflow_datasets as tfds
+
+
+def compute_distance_matrix(x_train, x_test, measure="squared_l2"):
+  """Calculates the distance matrix between test and train.
+
+  Args:
+    x_train: Matrix (NxD) where each row represents a training sample
+    x_test: Matrix (MxD) where each row represents a test sample
+    measure: Distance measure (not necessarly metric) to use
+
+  Raises:
+    NotImplementedError: When the measure is not implemented
+
+  Returns:
+    Matrix (MxN) where elemnt i,j is the distance between
+    x_test_i and x_train_j.
+  """
+
+  x_train = tf.convert_to_tensor(x_train, tf.float64)
+  x_test = tf.convert_to_tensor(x_test, tf.float64)
+
+  if measure == "squared_l2":
+    x_xt = tf.matmul(x_test, tf.transpose(x_train)).numpy()
+
+    x_train_2 = tf.reduce_sum(tf.math.square(x_train), 1).numpy()
+    x_test_2 = tf.reduce_sum(tf.math.square(x_test), 1).numpy()
+
+    for i in range(np.shape(x_xt)[0]):
+      x_xt[i, :] = np.multiply(x_xt[i, :], -2)
+      x_xt[i, :] = np.add(x_xt[i, :], x_test_2[i])
+      x_xt[i, :] = np.add(x_xt[i, :], x_train_2)
+
+  else:
+    raise NotImplementedError("Method '{}' is not implemented".format(measure))
+
+  return x_xt
+
+
+def compute_distance_matrix_loo(x, measure="squared_l2"):
+  """Calculates the distance matrix for leave-one-out strategy.
+
+  Args:
+    x: Matrix (NxD) where each row represents a sample
+    measure: Distance measure (not necessarly metric) to use
+
+  Raises:
+    NotImplementedError: When the measure is not implemented
+
+  Returns:
+    Matrix (NxN) where elemnt i,j is the distance between x_i and x_j.
+    The diagonal is set to infinity
+  """
+
+  x = tf.convert_to_tensor(x, tf.float64)
+
+  if measure == "squared_l2":
+    x_xt = tf.matmul(x, tf.transpose(x)).numpy()
+    diag = np.diag(x_xt)
+    d = np.copy(x_xt)
+
+    for i in range(np.shape(d)[0]):
+      d[i, :] = np.multiply(d[i, :], -2)
+      d[i, :] = np.add(d[i, :], x_xt[i, i])
+      d[i, :] = np.add(d[i, :], diag)
+      d[i, i] = float("inf")
+
+  elif measure == "cosine":
+    d = tf.matmul(x, tf.transpose(x)).numpy()
+    diag_sqrt = np.sqrt(np.diag(d))
+    outer = np.outer(diag_sqrt, diag_sqrt)
+    d = np.ones(np.shape(d)) - np.divide(d, outer)
+    np.fill_diagonal(d, float("inf"))
+
+  else:
+    raise NotImplementedError("Method '{}' is not implemented".format(measure))
+
+  return d
+
+
+def knn_errorrate(d, y_train, y_test, k=1):
+  """Calculate the knn error rate based on the distance matrix d.
+
+  Args:
+    d: distance matrix
+    y_train: label vector for the training samples
+    y_test: label vector for the test samples
+    k: number of direct neighbors for knn
+
+  Returns:
+    knn error rate (1 - accuracy)
+  """
+
+  if k == 1:
+    indices = np.argmin(d, axis=1)
+
+    cnt = 0
+    for i in range(len(indices)):
+      if y_test[i] != y_train[indices[i]]:
+        cnt += 1
+
+    return float(cnt) / len(indices)
+
+  indices = np.argpartition(d, k - 1, axis=1)
+  cnt = 0
+  for i in range(np.shape(d)[0]):
+    cnt_i = 0
+    for j in range(k):
+      if y_test[i] != y_train[indices[i, j]]:
+        cnt_i += 1
+    if cnt_i >= k / 2.0:
+      cnt += 1
+
+  return float(cnt) / np.shape(d)[0]
+
+
+def knn_errorrate_loo(d, y, k=1):
+  """Calculate the leave-one-out expected knn error rate based
+  on the distance matrix d.
+
+  Args:
+    d: distance matrix, the diagonal should be infinity
+    y: label matrix
+    k: number of direct neighbors for knn
+
+  Returns:
+    Expected leave-one-out knn error rate (1 - accuracy)
+  """
+
+  if k == 1:
+    indices = np.argmin(d, axis=1)
+
+    cnt = 0
+    for i in range(len(indices)):
+      if y[i] != y[indices[i]]:
+        cnt += 1
+
+    return float(cnt) / len(indices)
+
+  indices = np.argpartition(d, k - 1, axis=1)
+  cnt = 0
+  for i in range(np.shape(d)[0]):
+    cnt_i = 0
+    for j in range(k):
+      if y[i] != y[indices[i, j]]:
+        cnt_i += 1
+    if cnt_i >= k / 2.0:
+      cnt += 1
+
+  return float(cnt) / np.shape(d)[0]
+
+
+def load_data(dataset, split, num_examples=None):
+  ds = tfds.load(dataset, split=split, shuffle_files=False)
+  if num_examples:
+    ds = ds.take(num_examples)
+  return ds
+
+
+def load_embedding_fn(module):
+  m = hub.load(module, tags=[])
+  return lambda x: m.signatures["default"](x)["default"]