Spaces:
Running
Running
| # Copyright 2018 The TensorFlow Authors All Rights Reserved. | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| # ============================================================================== | |
| """Utilities for working with Seq2Label datasets and models. | |
| This library provides utilities for parsing and generating Seq2Label protos. | |
| """ | |
| from __future__ import absolute_import | |
| from __future__ import division | |
| from __future__ import print_function | |
| import numpy as np | |
| from protos import seq2label_pb2 | |
| def get_all_label_values(dataset_info): | |
| """Retrieves possible values for modeled labels from a `Seq2LabelDatasetInfo`. | |
| Args: | |
| dataset_info: a `Seq2LabelDatasetInfo` message. | |
| Returns: | |
| A dictionary mapping each label name to a tuple of its permissible values. | |
| """ | |
| return { | |
| label_info.name: tuple(label_info.values) | |
| for label_info in dataset_info.labels | |
| } | |
| def construct_seq2label_model_info(hparams, model_type, targets, metadata_path, | |
| batch_size, num_filters, | |
| training_noise_rate): | |
| """Constructs a Seq2LabelModelInfo proto with the given properties. | |
| Args: | |
| hparams: initialized tf.contrib.training.Hparams object. | |
| model_type: string; descriptive tag indicating type of model, ie. "conv". | |
| targets: list of names of the targets the model is trained to predict. | |
| metadata_path: string; full path to Seq2LabelDatasetInfo text proto used | |
| to initialize the model. | |
| batch_size: int; number of reads per mini-batch. | |
| num_filters: int; number of filters for convolutional model. | |
| training_noise_rate: float; rate [0.0, 1.0] of base-flipping noise injected | |
| into input read sequenced at training time. | |
| Returns: | |
| The Seq2LabelModelInfo proto with the hparams, model_type, targets, | |
| num_filters, batch_size, metadata_path, and training_noise_rate fields | |
| set to the given values. | |
| """ | |
| return seq2label_pb2.Seq2LabelModelInfo( | |
| hparams_string=hparams.to_json(), | |
| model_type=model_type, | |
| targets=sorted(targets), | |
| num_filters=num_filters, | |
| batch_size=batch_size, | |
| metadata_path=metadata_path, | |
| training_noise_rate=training_noise_rate) | |
| def add_read_noise(read, base_flip_probability=0.01): | |
| """Adds base-flipping noise to the given read sequence. | |
| Args: | |
| read: string; the read sequence to which to add noise. | |
| base_flip_probability: float; probability of a base flip at each position. | |
| Returns: | |
| The given read with base-flipping noise added at the provided | |
| base_flip_probability rate. | |
| """ | |
| base_flips = np.random.binomial(1, base_flip_probability, len(read)) | |
| if sum(base_flips) == 0: | |
| return read | |
| read = np.array(list(read)) | |
| possible_mutations = np.char.replace(['ACTG'] * sum(base_flips), | |
| read[base_flips == 1], '') | |
| mutations = map(np.random.choice, map(list, possible_mutations)) | |
| read[base_flips == 1] = mutations | |
| return ''.join(read) | |