create multi-hot SparseTensor by categorical feature array column from CSV in TensorFlow

问题

This is a typical way of handling sparse features (such as some ID features) in recommendation system. I'm looking for a convenient way to prepare the data for TensorFlow pipeline.

I did lots of search, but yet find the good solution yet.

Below is the one which seems to be close to what I need, but not working yet.

See ####### part below

The data file is like:

csv = [
  '1221,cc,1',
  '213,aa|cc|ff,1',
]

for the second row, i need some SparseTensor like multi-hot

 aa bb cc dd ee ff
| 0  0  1  0  0  0 |
| 1  0  1  0  0  1 |

The full version of code is:

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import argparse
import os
import shutil
import sys

import tensorflow as tf  # pylint: disable=g-bad-import-order

_CSV_COLUMNS = ['a_id', 'b_id', 'tags', 'label']
_CSV_COLUMN_DEFAULTS = [[0], [0], [''], [0]]


def input_fn(data_file, num_epochs, shuffle, batch_size):
    """Generate an input function for the Estimator."""

    assert tf.gfile.Exists(data_file), (
        '%s not found. Please make sure you have run data_download.py and '
        'set the --data_dir argument to the correct path.' % data_file)

    """
$ cat vocab.txt
a
b
c
d
e
f
g
h
i
j
k
l
m
n
    """
    table = tf.contrib.lookup.index_table_from_file(
        vocabulary_file='vocab.txt', num_oov_buckets=1)

    def parse_csv(value):
        print('Parsing', data_file)
        columns = tf.decode_csv(value, record_defaults=_CSV_COLUMN_DEFAULTS)
        features = dict(zip(_CSV_COLUMNS, columns))

        ########################  BEGIN  ###########################

        # support multi-hot sparse features
        split_tags = tf.string_split([columns[2]], '|')  # hard-coded 'tags' column index
        # Output: tags.indices Tensor("StringSplit:0", shape=(?, 2), dtype=int64)
        print('tags.indices', split_tags.indices)

        indice_idx = tf.map_fn(lambda x : x[0], split_tags.indices)
        # Output: indice_idx Tensor("map/TensorArrayStack/TensorArrayGatherV3:0", shape=(?,), dtype=int64)
        print('indice_idx', indice_idx)
        value_idx = tf.map_fn(lambda x : x[1], split_tags.indices)

        value_arr = tf.cast(tf.gather(split_tags.values, value_idx), tf.int64)
        # Output:  value_arr shape (?,)
        print('value_arr shape', value_arr.shape)

        # stack is doing: [1, 2, 3], [4, 5, 6] ==> [[1, 2], [3, 4], [5,6]]
        new_indices = tf.stack([indice_idx, value_arr], axis=1)
        print('new_indices', new_indices)

        new_values = tf.ones_like(value_arr)
        # Output:  new_values Tensor("ones_like:0", shape=(?,), dtype=int64)
        print('new_values', new_values)

        with tf.Session() as s1:
            s1.run([tf.global_variables_initializer(), tf.tables_initializer()])
            ##### FAIL here #####
            # InvalidArgumentError (see above for traceback): You must feed a value for placeholder tensor 'arg0' with dtype string
            # [[Node: arg0 = Placeholder[dtype=DT_STRING, shape=<unknown>, _device="/job:localhost/replica:0/task:0/device:CPU:0"]()]]
            print(split_tags.values.eval())
            print(indice_idx.eval())
            print('value_arr', value_arr.eval())
            print('new_values', new_values.eval())

        categorial_tensor = tf.SparseTensor(
            indices=new_indices,
            values=new_values,
            dense_shape=[new_indices.shape[1], 4])

        ########################   END   ###########################

        categorical_cols = {
            'tags': categorial_tensor}

        features.update(categorical_cols)

        labels = features.pop('label')
        return features, tf.equal(labels, 1)

    # Extract lines from input files using the Dataset API.
    dataset = tf.data.TextLineDataset(data_file)

    if shuffle:
        dataset = dataset.shuffle(buffer_size=6)  # num of lines in the file

    dataset = dataset.map(parse_csv, num_parallel_calls=5)

    # We call repeat after shuffling, rather than before, to prevent separate
    # epochs from blending together.
    dataset = dataset.repeat(num_epochs)
    dataset = dataset.batch(batch_size)
    return dataset


"""
$ cat data.csv
1,2,a|c|g,1
0,1,c|f,0
0,2,b|g,1
0,1,b|v,0
0,1,g|j|k|l,1
0,1,a,0
"""
train_file = 'data.csv'
epochs_between_evals = 2
batch_size = 40
ds = input_fn(train_file, epochs_between_evals, True, batch_size)

with tf.Session() as s:
    s.run([tf.global_variables_initializer(), tf.tables_initializer()])
    print(s.run(ds))

来源：https://stackoverflow.com/questions/49783804/create-multi-hot-sparsetensor-by-categorical-feature-array-column-from-csv-in-te