# Parsing Utilities

## Overview

Parsing utilities are a set of functions that helps generate parsing spec for `tf$parse_example` to be used with estimators. If users keep data in `tf$Example` format, they need to call `tf$parse_example` with a proper feature spec. There are two main things that these utility functions help:

- Users need to combine parsing spec of features with labels and weights (if any) since they are all parsed from same `tf$Example` instance. The utility functions combine these specs.

- It is difficult to map expected label by a estimator such as `dnn_classifier` to corresponding `tf$parse_example` spec. The utility functions encode it by getting related information from users (key, dtype).

## Example output of parsing spec

    parsing_spec <- classifier_parse_example_spec(
      feature_columns = column_numeric('a'),
      label_key = 'b',
      weight_column = 'c'
    )

For the above example, `classifier_parse_example_spec` would return the following:

    expected_spec <- list(
      a = tf$python$ops$parsing_ops$FixedLenFeature(reticulate::tuple(1L), dtype = tf$float32),
      c = tf$python$ops$parsing_ops$FixedLenFeature(reticulate::tuple(1L), dtype = tf$float32),
      b = tf$python$ops$parsing_ops$FixedLenFeature(reticulate::tuple(1L), dtype = tf$int64)
    )

    # This should be the same as the one we constructed using `classifier_parse_example_spec`
    testthat::expect_equal(parsing_spec, expected_spec)

## Example usage with a classifier

Firstly, define features transformations and initiailize your classifier similar to the following:

    fcs <- feature_columns(...)

    model <- dnn_classifier(
      n_classes = 1000,
      feature_columns = fcs,
      weight_column = 'example-weight',
      label_vocabulary= c('photos', 'keep', ...),
      hidden_units = c(256, 64, 16)
    )

Next, create the parsing configuration for `tf$parse_example` using `classifier_parse_example_spec` and the feature columns `fcs` we have just defined:

    parsing_spec <- classifier_parse_example_spec(
      feature_columns = fcs,
      label_key = 'my-label',
      label_dtype = tf$string,
      weight_column = 'example-weight'
    )

This label configuration tells the classifier the following:

- weights are retrieved with key ‘example-weight’
- label is string and can be one of the following `c('photos', 'keep', ...)`
- integer id for label ‘photos’ is 0, ‘keep’ is 1, etc

Then define your input function with the help of `read_batch_features` that reads the batches of features from files in `tf$Example` format with the parsing configuration `parsing_spec` we just defined:

    input_fn_train <- function() {
      features <- tf$contrib$learn$read_batch_features(
        file_pattern = train_files,
        batch_size = batch_size,
        features = parsing_spec,
        reader = tf$RecordIOReader)
      labels <- features[["my-label"]]
      return(list(features, labels))
    }

Finally we can train the model using the training input function parsed by `classifier_parse_example_spec`:

    train(model, input_fn = input_fn_train)