android-10.0.0_r47/s

# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
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# ==============================================================================
"""Skip-gram sampling ops from https://arxiv.org/abs/1301.3781."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import csv

from tensorflow.contrib import lookup
from tensorflow.contrib.text.python.ops import gen_skip_gram_ops
from tensorflow.contrib.util import loader
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.framework import random_seed
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import random_ops
from tensorflow.python.platform import gfile
from tensorflow.python.platform import resource_loader
from tensorflow.python.training import input as input_ops

_checkpoint_ops_so = loader.load_op_library(
    resource_loader.get_path_to_datafile("_skip_gram_ops.so"))

ops.NotDifferentiable("SkipGramGenerateCandidates")


def skip_gram_sample(input_tensor,
                     min_skips=1,
                     max_skips=5,
                     start=0,
                     limit=-1,
                     emit_self_as_target=False,
                     vocab_freq_table=None,
                     vocab_min_count=None,
                     vocab_subsampling=None,
                     corpus_size=None,
                     batch_size=None,
                     batch_capacity=None,
                     seed=None,
                     name=None):
  """Generates skip-gram token and label paired Tensors from the input tensor.

  Generates skip-gram `("token", "label")` pairs using each element in the
  rank-1 `input_tensor` as a token. The window size used for each token will be
  randomly selected from the range specified by `[min_skips, max_skips]`,
  inclusive. See https://arxiv.org/abs/1301.3781 for more details about
  skip-gram.

  For example, given `input_tensor = ["the", "quick", "brown", "fox", "jumps"]`,
  `min_skips = 1`, `max_skips = 2`, `emit_self_as_target = False`, the output
  `(tokens, labels)` pairs for the token "quick" will be randomly selected from
  either `(tokens=["quick", "quick"], labels=["the", "brown"])` for 1 skip, or
  `(tokens=["quick", "quick", "quick"], labels=["the", "brown", "fox"])` for 2
  skips.

  If `emit_self_as_target = True`, each token will also be emitted as a label
  for itself. From the previous example, the output will be either
  `(tokens=["quick", "quick", "quick"], labels=["the", "quick", "brown"])` for 1
  skip, or `(tokens=["quick", "quick", "quick", "quick"], labels=["the",
  "quick", "brown", "fox"])` for 2 skips.

  The same process is repeated for each element of `input_tensor` and
  concatenated together into the two output rank-1 `Tensors` (one for all the
  tokens, another for all the labels).

  If `vocab_freq_table` is specified, tokens in `input_tensor` that are not
  present in the vocabulary are discarded. Tokens whose frequency counts are
  below `vocab_min_count` are also discarded. Tokens whose frequency proportions
  in the corpus exceed `vocab_subsampling` may be randomly down-sampled. See
  Eq. 5 in http://arxiv.org/abs/1310.4546 for more details about subsampling.

  Due to the random window sizes used for each token, the lengths of the outputs
  are non-deterministic, unless `batch_size` is specified to batch the outputs
  to always return `Tensors` of length `batch_size`.

  Args:
    input_tensor: A rank-1 `Tensor` from which to generate skip-gram candidates.
    min_skips: `int` or scalar `Tensor` specifying the minimum window size to
      randomly use for each token. Must be >= 0 and <= `max_skips`. If
      `min_skips` and `max_skips` are both 0, the only label outputted will be
      the token itself when `emit_self_as_target = True` - or no output
      otherwise.
    max_skips: `int` or scalar `Tensor` specifying the maximum window size to
      randomly use for each token. Must be >= 0.
    start: `int` or scalar `Tensor` specifying the position in
      `input_tensor` from which to start generating skip-gram candidates.
    limit: `int` or scalar `Tensor` specifying the maximum number of
      elements in `input_tensor` to use in generating skip-gram candidates. -1
      means to use the rest of the `Tensor` after `start`.
    emit_self_as_target: `bool` or scalar `Tensor` specifying whether to emit
      each token as a label for itself.
    vocab_freq_table: (Optional) A lookup table (subclass of
      `lookup.InitializableLookupTableBase`) that maps tokens to their raw
      frequency counts. If specified, any token in `input_tensor` that is not
      found in `vocab_freq_table` will be filtered out before generating
      skip-gram candidates. While this will typically map to integer raw
      frequency counts, it could also map to float frequency proportions.
      `vocab_min_count` and `corpus_size` should be in the same units as this.
    vocab_min_count: (Optional) `int`, `float`, or scalar `Tensor` specifying
      minimum frequency threshold (from `vocab_freq_table`) for a token to be
      kept in `input_tensor`. If this is specified, `vocab_freq_table` must also
      be specified - and they should both be in the same units.
    vocab_subsampling: (Optional) `float` specifying frequency proportion
      threshold for tokens from `input_tensor`. Tokens that occur more
      frequently (based on the ratio of the token's `vocab_freq_table` value to
      the `corpus_size`) will be randomly down-sampled. Reasonable starting
      values may be around 1e-3 or 1e-5. If this is specified, both
      `vocab_freq_table` and `corpus_size` must also be specified. See Eq. 5
      in http://arxiv.org/abs/1310.4546 for more details.
    corpus_size: (Optional) `int`, `float`, or scalar `Tensor` specifying the
      total number of tokens in the corpus (e.g., sum of all the frequency
      counts of `vocab_freq_table`). Used with `vocab_subsampling` for
      down-sampling frequently occurring tokens. If this is specified,
      `vocab_freq_table` and `vocab_subsampling` must also be specified.
    batch_size: (Optional) `int` specifying batch size of returned `Tensors`.
    batch_capacity: (Optional) `int` specifying batch capacity for the queue
      used for batching returned `Tensors`. Only has an effect if
      `batch_size` > 0. Defaults to 100 * `batch_size` if not specified.
    seed: (Optional) `int` used to create a random seed for window size and
      subsampling. See `set_random_seed` docs for behavior.
    name: (Optional) A `string` name or a name scope for the operations.

  Returns:
    A `tuple` containing (token, label) `Tensors`. Each output `Tensor` is of
    rank-1 and has the same type as `input_tensor`. The `Tensors` will be of
    length `batch_size`; if `batch_size` is not specified, they will be of
    random length, though they will be in sync with each other as long as they
    are evaluated together.

  Raises:
    ValueError: If `vocab_freq_table` is not provided, but `vocab_min_count`,
      `vocab_subsampling`, or `corpus_size` is specified. If `vocab_subsampling`
      and `corpus_size` are not both present or both absent.
  """

  if vocab_freq_table is None and (vocab_min_count is not None or
                                   vocab_subsampling is not None or
                                   corpus_size is not None):
    raise ValueError(
        "vocab_freq_table is not provided, but vocab_min_count={}, "
        "vocab_subsampling={}, or corpus_size={} is not None. These settings "
        "are useless without a vocab_freq_table.".format(
            vocab_min_count, vocab_subsampling, corpus_size))

  if (vocab_subsampling is None) != (corpus_size is None):
    raise ValueError(
        "vocab_subsampling is {} while corpus_size is {} - both must be "
        "provided in order for subsampling to work.".format(
            vocab_subsampling, corpus_size))

  with ops.name_scope(
      name,
      "skip_gram_sample",
      values=[input_tensor, min_skips, max_skips, start, limit]):

    input_tensor = _filter_input(
        input_tensor=input_tensor,
        vocab_freq_table=vocab_freq_table,
        vocab_min_count=vocab_min_count,
        vocab_subsampling=vocab_subsampling,
        corpus_size=corpus_size,
        seed=seed)

    seed1, seed2 = random_seed.get_seed(seed)
    tokens, labels = gen_skip_gram_ops.skip_gram_generate_candidates(
        input_tensor=input_tensor,
        min_skips=min_skips,
        max_skips=max_skips,
        start=start,
        limit=limit,
        emit_self_as_target=emit_self_as_target,
        # Note that seed here should be seed1! This is due to
        # GuardedPhiloxRandom's hard-coded attributes of "seed" and "seed2".
        seed=seed1,
        seed2=seed2)

    # TODO(weiho): If the need arises, add support for sparse input_tensor that
    # figures out sentence boundaries, then calls
    # skip_gram_generate_candidates() on each sentence.

    # Batches the (tokens, labels) outputs so that they will be of deterministic
    # batch_size, to facilitate feeding them into the rest of the network.
    if batch_size is not None and batch_size > 0:
      batch_capacity = (batch_capacity
                        if (batch_capacity is not None and batch_capacity > 0)
                        else 100 * batch_size)
      return input_ops.batch(
          [tokens, labels],
          batch_size,
          capacity=batch_capacity,
          enqueue_many=True)

    return tokens, labels


def skip_gram_sample_with_text_vocab(input_tensor,
                                     vocab_freq_file,
                                     vocab_token_index=0,
                                     vocab_token_dtype=dtypes.string,
                                     vocab_freq_index=1,
                                     vocab_freq_dtype=dtypes.float64,
                                     vocab_delimiter=",",
                                     vocab_min_count=0,
                                     vocab_subsampling=None,
                                     corpus_size=None,
                                     min_skips=1,
                                     max_skips=5,
                                     start=0,
                                     limit=-1,
                                     emit_self_as_target=False,
                                     batch_size=None,
                                     batch_capacity=None,
                                     seed=None,
                                     name=None):
  """Skip-gram sampling with a text vocabulary file.

  Wrapper around `skip_gram_sample()` for use with a text vocabulary file. The
  vocabulary file is expected to be a plain-text file, with lines of
  `vocab_delimiter`-separated columns. The `vocab_token_index` column should
  contain the vocabulary term, while the `vocab_freq_index` column should
  contain the number of times that term occurs in the corpus. For example, with
  a text vocabulary file of:

    ```
    bonjour,fr,42
    hello,en,777
    hola,es,99
    ```

  You should set `vocab_delimiter=","`, `vocab_token_index=0`, and
  `vocab_freq_index=2`.

  See `skip_gram_sample()` documentation for more details about the skip-gram
  sampling process.

  Args:
    input_tensor: A rank-1 `Tensor` from which to generate skip-gram candidates.
    vocab_freq_file: `string` specifying full file path to the text vocab file.
    vocab_token_index: `int` specifying which column in the text vocab file
      contains the tokens.
    vocab_token_dtype: `DType` specifying the format of the tokens in the text
      vocab file.
    vocab_freq_index: `int` specifying which column in the text vocab file
      contains the frequency counts of the tokens.
    vocab_freq_dtype: `DType` specifying the format of the frequency counts in
      the text vocab file.
    vocab_delimiter: `string` specifying the delimiter used in the text vocab
      file.
    vocab_min_count: `int`, `float`, or scalar `Tensor` specifying
      minimum frequency threshold (from `vocab_freq_file`) for a token to be
      kept in `input_tensor`. This should correspond with `vocab_freq_dtype`.
    vocab_subsampling: (Optional) `float` specifying frequency proportion
      threshold for tokens from `input_tensor`. Tokens that occur more
      frequently will be randomly down-sampled. Reasonable starting values may
      be around 1e-3 or 1e-5. See Eq. 5 in http://arxiv.org/abs/1310.4546 for
      more details.
    corpus_size: (Optional) `int`, `float`, or scalar `Tensor` specifying the
      total number of tokens in the corpus (e.g., sum of all the frequency
      counts of `vocab_freq_file`). Used with `vocab_subsampling` for
      down-sampling frequently occurring tokens. If this is specified,
      `vocab_freq_file` and `vocab_subsampling` must also be specified.
      If `corpus_size` is needed but not supplied, then it will be calculated
      from `vocab_freq_file`. You might want to supply your own value if you
      have already eliminated infrequent tokens from your vocabulary files
      (where frequency < vocab_min_count) to save memory in the internal token
      lookup table. Otherwise, the unused tokens' variables will waste memory.
      The user-supplied `corpus_size` value must be greater than or equal to the
      sum of all the frequency counts of `vocab_freq_file`.
    min_skips: `int` or scalar `Tensor` specifying the minimum window size to
      randomly use for each token. Must be >= 0 and <= `max_skips`. If
      `min_skips` and `max_skips` are both 0, the only label outputted will be
      the token itself.
    max_skips: `int` or scalar `Tensor` specifying the maximum window size to
      randomly use for each token. Must be >= 0.
    start: `int` or scalar `Tensor` specifying the position in `input_tensor`
      from which to start generating skip-gram candidates.
    limit: `int` or scalar `Tensor` specifying the maximum number of elements in
      `input_tensor` to use in generating skip-gram candidates. -1 means to use
      the rest of the `Tensor` after `start`.
    emit_self_as_target: `bool` or scalar `Tensor` specifying whether to emit
      each token as a label for itself.
    batch_size: (Optional) `int` specifying batch size of returned `Tensors`.
    batch_capacity: (Optional) `int` specifying batch capacity for the queue
      used for batching returned `Tensors`. Only has an effect if
      `batch_size` > 0. Defaults to 100 * `batch_size` if not specified.
    seed: (Optional) `int` used to create a random seed for window size and
      subsampling. See
      [`set_random_seed`](../../g3doc/python/constant_op.md#set_random_seed)
      for behavior.
    name: (Optional) A `string` name or a name scope for the operations.

  Returns:
    A `tuple` containing (token, label) `Tensors`. Each output `Tensor` is of
    rank-1 and has the same type as `input_tensor`. The `Tensors` will be of
    length `batch_size`; if `batch_size` is not specified, they will be of
    random length, though they will be in sync with each other as long as they
    are evaluated together.

  Raises:
    ValueError: If `vocab_token_index` or `vocab_freq_index` is less than 0 or
      exceeds the number of columns in `vocab_freq_file`. If `vocab_token_index`
      and `vocab_freq_index` are both set to the same column. If any token in
      `vocab_freq_file` has a negative frequency.
  """

  if vocab_token_index < 0 or vocab_freq_index < 0:
    raise ValueError(
        "vocab_token_index={} and vocab_freq_index={} must both be >= 0.".
        format(vocab_token_index, vocab_freq_index))
  if vocab_token_index == vocab_freq_index:
    raise ValueError(
        "vocab_token_index and vocab_freq_index should be different, but are "
        "both {}.".format(vocab_token_index))

  # Iterates through the vocab file and calculates the number of vocab terms as
  # well as the total corpus size (by summing the frequency counts of all the
  # vocab terms).
  calculated_corpus_size = 0.0
  vocab_size = 0
  with gfile.GFile(vocab_freq_file, mode="r") as f:
    reader = csv.reader(f, delimiter=vocab_delimiter)
    for row in reader:
      if vocab_token_index >= len(row) or vocab_freq_index >= len(row):
        raise ValueError(
            "Row in vocab file only has {} columns, so vocab_token_index={} or "
            "vocab_freq_index={} is out of bounds. Row content: {}".format(
                len(row), vocab_token_index, vocab_freq_index, row))
      vocab_size += 1
      freq = vocab_freq_dtype.as_numpy_dtype(row[vocab_freq_index])
      if freq < 0:
        raise ValueError(
            "Row in vocab file has negative frequency of {}. Row content: {}".
            format(freq, row))
      # Note: tokens whose frequencies are below vocab_min_count will still
      # contribute to the total corpus size used for vocab subsampling.
      calculated_corpus_size += freq

  if not corpus_size:
    corpus_size = calculated_corpus_size
  elif calculated_corpus_size - corpus_size > 1e-6:
    raise ValueError(
        "`corpus_size`={} must be greater than or equal to the sum of all the "
        "frequency counts ({}) of `vocab_freq_file` ({}).".format(
            corpus_size, calculated_corpus_size, vocab_freq_file))

  vocab_freq_table = lookup.HashTable(
      lookup.TextFileInitializer(
          filename=vocab_freq_file,
          key_dtype=vocab_token_dtype,
          key_index=vocab_token_index,
          value_dtype=vocab_freq_dtype,
          value_index=vocab_freq_index,
          vocab_size=vocab_size,
          delimiter=vocab_delimiter),
      # For vocab terms not in vocab file, use a default value of -1.
      default_value=-1)

  return skip_gram_sample(
      input_tensor,
      min_skips=min_skips,
      max_skips=max_skips,
      start=start,
      limit=limit,
      emit_self_as_target=emit_self_as_target,
      vocab_freq_table=vocab_freq_table,
      vocab_min_count=vocab_min_count,
      vocab_subsampling=vocab_subsampling,
      # corpus_size is not used unless vocab_subsampling is specified.
      corpus_size=None if vocab_subsampling is None else corpus_size,
      batch_size=batch_size,
      batch_capacity=batch_capacity,
      seed=seed,
      name=name)


def _filter_input(input_tensor, vocab_freq_table, vocab_min_count,
                  vocab_subsampling, corpus_size, seed):
  """Filters input tensor based on vocab freq, threshold, and subsampling."""
  if vocab_freq_table is None:
    return input_tensor

  if not isinstance(vocab_freq_table, lookup.InitializableLookupTableBase):
    raise ValueError(
        "vocab_freq_table must be a subclass of "
        "InitializableLookupTableBase (such as HashTable) instead of type "
        "{}.".format(type(vocab_freq_table)))

  with ops.name_scope(
      "filter_vocab", values=[vocab_freq_table, input_tensor, vocab_min_count]):
    freq = vocab_freq_table.lookup(input_tensor)
    # Filters out elements in input_tensor that are not found in
    # vocab_freq_table (table returns a default value of -1 specified above when
    # an element is not found).
    mask = math_ops.not_equal(freq, vocab_freq_table.default_value)

    # Filters out elements whose vocab frequencies are less than the threshold.
    if vocab_min_count is not None:
      cast_threshold = math_ops.cast(vocab_min_count, freq.dtype)
      mask = math_ops.logical_and(mask,
                                  math_ops.greater_equal(freq, cast_threshold))

    input_tensor = array_ops.boolean_mask(input_tensor, mask)
    freq = array_ops.boolean_mask(freq, mask)

  if not vocab_subsampling:
    return input_tensor

  if vocab_subsampling < 0 or vocab_subsampling > 1:
    raise ValueError(
        "Invalid vocab_subsampling={} - it should be within range [0, 1].".
        format(vocab_subsampling))

  # Subsamples the input tokens based on vocabulary frequency and
  # vocab_subsampling threshold (ie randomly discard commonly appearing
  # tokens).
  with ops.name_scope(
      "subsample_vocab", values=[input_tensor, freq, vocab_subsampling]):
    corpus_size = math_ops.cast(corpus_size, dtypes.float64)
    freq = math_ops.cast(freq, dtypes.float64)
    vocab_subsampling = math_ops.cast(vocab_subsampling, dtypes.float64)

    # From tensorflow_models/tutorials/embedding/word2vec_kernels.cc, which is
    # suppose to correlate with Eq. 5 in http://arxiv.org/abs/1310.4546.
    keep_prob = ((math_ops.sqrt(freq /
                                (vocab_subsampling * corpus_size)) + 1.0) *
                 (vocab_subsampling * corpus_size / freq))
    random_prob = random_ops.random_uniform(
        array_ops.shape(freq),
        minval=0,
        maxval=1,
        dtype=dtypes.float64,
        seed=seed)

    mask = math_ops.less_equal(random_prob, keep_prob)
    return array_ops.boolean_mask(input_tensor, mask)