models
/
official
/
recommend
/
deepfm
/
src
/
dataset.py

# Copyright 2020 Huawei Technologies Co., Ltd
#
# 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.
# ============================================================================
"""
Create train or eval dataset.
"""
import os
import math
from enum import Enum

import numpy as np
import pandas as pd
import mindspore.dataset as ds
import mindspore.common.dtype as mstype
from .model_utils.config import config


class DataType(Enum):
    """
    Enumerate supported dataset format.
    """
    MINDRECORD = 1
    TFRECORD = 2
    H5 = 3


class H5Dataset():
    """
    Create dataset with H5 format.

    Args:
        data_path (str): Dataset directory.
        train_mode (bool): Whether dataset is used for train or eval (default=True).
        train_num_of_parts (int): The number of train data file (default=21).
        test_num_of_parts (int): The number of test data file (default=3).
    """
    max_length = 39

    def __init__(self, data_path, train_mode=True,
                 train_num_of_parts=config.train_num_of_parts,
                 test_num_of_parts=config.test_num_of_parts):
        self._hdf_data_dir = data_path
        self._is_training = train_mode
        if self._is_training:
            self._file_prefix = 'train'
            self._num_of_parts = train_num_of_parts
        else:
            self._file_prefix = 'test'
            self._num_of_parts = test_num_of_parts
        self.data_size = self._bin_count(self._hdf_data_dir, self._file_prefix, self._num_of_parts)
        print("data_size: {}".format(self.data_size))

    def _bin_count(self, hdf_data_dir, file_prefix, num_of_parts):
        size = 0
        for part in range(num_of_parts):
            _y = pd.read_hdf(os.path.join(hdf_data_dir, f'{file_prefix}_output_part_{str(part)}.h5'))
            size += _y.shape[0]
        return size

    def _iterate_hdf_files_(self, num_of_parts=None,
                            shuffle_block=False):
        """
        iterate among hdf files(blocks). when the whole data set is finished, the iterator restarts
            from the beginning, thus the data stream will never stop
        :param train_mode: True or false,false is eval_mode,
            this file iterator will go through the train set
        :param num_of_parts: number of files
        :param shuffle_block: shuffle block files at every round
        :return: input_hdf_file_name, output_hdf_file_name, finish_flag
        """
        parts = np.arange(num_of_parts)
        while True:
            if shuffle_block:
                for _ in range(int(shuffle_block)):
                    np.random.shuffle(parts)
            for i, p in enumerate(parts):
                yield os.path.join(self._hdf_data_dir, f'{self._file_prefix}_input_part_{str(p)}.h5'), \
                      os.path.join(self._hdf_data_dir, f'{self._file_prefix}_output_part_{str(p)}.h5'), \
                      i + 1 == len(parts)

    def _generator(self, X, y, batch_size, shuffle=True):
        """
        should be accessed only in private
        :param X:
        :param y:
        :param batch_size:
        :param shuffle:
        :return:
        """
        number_of_batches = np.ceil(1. * X.shape[0] / batch_size)
        counter = 0
        finished = False
        sample_index = np.arange(X.shape[0])
        if shuffle:
            for _ in range(int(shuffle)):
                np.random.shuffle(sample_index)
        assert X.shape[0] > 0
        while True:
            batch_index = sample_index[batch_size * counter: batch_size * (counter + 1)]
            X_batch = X[batch_index]
            y_batch = y[batch_index]
            counter += 1
            yield X_batch, y_batch, finished
            if counter == number_of_batches:
                counter = 0
                finished = True

    def batch_generator(self, batch_size=1000,
                        random_sample=False, shuffle_block=False):
        """
        :param train_mode: True or false,false is eval_mode,
        :param batch_size
        :param num_of_parts: number of files
        :param random_sample: if True, will shuffle
        :param shuffle_block: shuffle file blocks at every round
        :return:
        """

        for hdf_in, hdf_out, _ in self._iterate_hdf_files_(self._num_of_parts,
                                                           shuffle_block):
            start = stop = None
            X_all = pd.read_hdf(hdf_in, start=start, stop=stop).values
            y_all = pd.read_hdf(hdf_out, start=start, stop=stop).values
            data_gen = self._generator(X_all, y_all, batch_size,
                                       shuffle=random_sample)
            finished = False

            while not finished:
                X, y, finished = data_gen.__next__()
                X_id = X[:, 0:self.max_length]
                X_va = X[:, self.max_length:]
                yield np.array(X_id.astype(dtype=np.int32)), \
                      np.array(X_va.astype(dtype=np.float32)), \
                      np.array(y.astype(dtype=np.float32))


def _get_h5_dataset(directory, train_mode=True, epochs=1, batch_size=1000):
    """
    Get dataset with h5 format.

    Args:
        directory (str): Dataset directory.
        train_mode (bool): Whether dataset is use for train or eval (default=True).
        epochs (int): Dataset epoch size (default=1).
        batch_size (int): Dataset batch size (default=1000)

    Returns:
        Dataset.
    """
    data_para = {'batch_size': batch_size}
    if train_mode:
        data_para['random_sample'] = True
        data_para['shuffle_block'] = True

    h5_dataset = H5Dataset(data_path=directory, train_mode=train_mode)
    numbers_of_batch = math.ceil(h5_dataset.data_size / batch_size)

    def _iter_h5_data():
        train_eval_gen = h5_dataset.batch_generator(**data_para)
        for _ in range(0, numbers_of_batch, 1):
            yield train_eval_gen.__next__()

    data_set = ds.GeneratorDataset(_iter_h5_data, ["ids", "weights", "labels"])
    data_set = data_set.repeat(epochs)
    return data_set


def _get_mindrecord_dataset(directory, train_mode=True, epochs=1, batch_size=1000,
                            line_per_sample=1000, rank_size=None, rank_id=None):
    """
    Get dataset with mindrecord format.

    Args:
        directory (str): Dataset directory.
        train_mode (bool): Whether dataset is use for train or eval (default=True).
        epochs (int): Dataset epoch size (default=1).
        batch_size (int): Dataset batch size (default=1000).
        line_per_sample (int): The number of sample per line (default=1000).
        rank_size (int): The number of device, not necessary for single device (default=None).
        rank_id (int): Id of device, not necessary for single device (default=None).

    Returns:
        Dataset.
    """
    file_prefix_name = 'train_input_part.mindrecord' if train_mode else 'test_input_part.mindrecord'
    file_suffix_name = '00' if train_mode else '0'
    shuffle = train_mode

    if rank_size is not None and rank_id is not None:
        data_set = ds.MindDataset(os.path.join(directory, file_prefix_name + file_suffix_name),
                                  columns_list=['feat_ids', 'feat_vals', 'label'],
                                  num_shards=rank_size, shard_id=rank_id, shuffle=shuffle,
                                  num_parallel_workers=8)
    else:
        data_set = ds.MindDataset(os.path.join(directory, file_prefix_name + file_suffix_name),
                                  columns_list=['feat_ids', 'feat_vals', 'label'],
                                  shuffle=shuffle, num_parallel_workers=8)
    data_set = data_set.batch(int(batch_size / line_per_sample), drop_remainder=True)
    data_set = data_set.map(operations=(lambda x, y, z: (np.array(x).flatten().reshape(batch_size, 39),
                                                         np.array(y).flatten().reshape(batch_size, 39),
                                                         np.array(z).flatten().reshape(batch_size, 1))),
                            input_columns=['feat_ids', 'feat_vals', 'label'],
                            column_order=['feat_ids', 'feat_vals', 'label'],
                            num_parallel_workers=8)
    data_set = data_set.repeat(epochs)
    return data_set


def _get_tf_dataset(directory, train_mode=True, epochs=1, batch_size=1000,
                    line_per_sample=1000, rank_size=None, rank_id=None):
    """
    Get dataset with tfrecord format.

    Args:
        directory (str): Dataset directory.
        train_mode (bool): Whether dataset is use for train or eval (default=True).
        epochs (int): Dataset epoch size (default=1).
        batch_size (int): Dataset batch size (default=1000).
        line_per_sample (int): The number of sample per line (default=1000).
        rank_size (int): The number of device, not necessary for single device (default=None).
        rank_id (int): Id of device, not necessary for single device (default=None).

    Returns:
        Dataset.
    """
    dataset_files = []
    file_prefixt_name = 'train' if train_mode else 'test'
    shuffle = train_mode
    for (dir_path, _, filenames) in os.walk(directory):
        for filename in filenames:
            if file_prefixt_name in filename and 'tfrecord' in filename:
                dataset_files.append(os.path.join(dir_path, filename))
    schema = ds.Schema()
    schema.add_column('feat_ids', de_type=mstype.int32)
    schema.add_column('feat_vals', de_type=mstype.float32)
    schema.add_column('label', de_type=mstype.float32)
    if rank_size is not None and rank_id is not None:
        data_set = ds.TFRecordDataset(dataset_files=dataset_files, shuffle=shuffle,
                                      schema=schema, num_parallel_workers=8,
                                      num_shards=rank_size, shard_id=rank_id,
                                      shard_equal_rows=True)
    else:
        data_set = ds.TFRecordDataset(dataset_files=dataset_files, shuffle=shuffle,
                                      schema=schema, num_parallel_workers=8)
    data_set = data_set.batch(int(batch_size / line_per_sample), drop_remainder=True)
    data_set = data_set.map(operations=(lambda x, y, z: (
        np.array(x).flatten().reshape(batch_size, 39),
        np.array(y).flatten().reshape(batch_size, 39),
        np.array(z).flatten().reshape(batch_size, 1))),
                            input_columns=['feat_ids', 'feat_vals', 'label'],
                            column_order=['feat_ids', 'feat_vals', 'label'],
                            num_parallel_workers=8)
    data_set = data_set.repeat(epochs)
    return data_set


def create_dataset(directory, train_mode=True, epochs=1, batch_size=1000,
                   data_type=DataType.TFRECORD, line_per_sample=1000,
                   rank_size=None, rank_id=None):
    """
    Get dataset.

    Args:
        directory (str): Dataset directory.
        train_mode (bool): Whether dataset is use for train or eval (default=True).
        epochs (int): Dataset epoch size (default=1).
        batch_size (int): Dataset batch size (default=1000).
        data_type (DataType): The type of dataset which is one of H5, TFRECORE, MINDRECORD (default=TFRECORD).
        line_per_sample (int): The number of sample per line (default=1000).
        rank_size (int): The number of device, not necessary for single device (default=None).
        rank_id (int): Id of device, not necessary for single device (default=None).

    Returns:
        Dataset.
    """
    if data_type == DataType.MINDRECORD:
        return _get_mindrecord_dataset(directory, train_mode, epochs,
                                       batch_size, line_per_sample,
                                       rank_size, rank_id)
    if data_type == DataType.TFRECORD:
        return _get_tf_dataset(directory, train_mode, epochs, batch_size,
                               line_per_sample, rank_size=rank_size, rank_id=rank_id)

    if rank_size is not None and rank_size > 1:
        raise ValueError('Please use mindrecord dataset.')
    return _get_h5_dataset(directory, train_mode, epochs, batch_size)