# Feature Spec interface

## Overview

In this document we will demonstrate the basic usage of the `feature_spec` interface in `tfdatasets`.

The `feature_spec` interface is a user friendly interface to `feature_columns`. It allows us to specify column transformations and representations when working with structured data.

We will use the `hearts` dataset and it can be loaded with `data(hearts)`.

    library(tfdatasets)
    library(dplyr)
    data(hearts)

    head(hearts)

We want to train a model to predict the `target` variable using Keras but, before that we need to prepare the data. We need to transform the categorical variables into some form of dense variable, we usually want to normalize all numeric columns too.

The feature spec interface works with `data.frame`s or TensorFlow datasets objects.

    ids_train <- sample.int(nrow(hearts), size = 0.75*nrow(hearts))
    hearts_train <- hearts[ids_train,]
    hearts_test <- hearts[-ids_train,]

Now let’s start creating our feature specification:

    spec <- feature_spec(hearts_train, target ~ .)

The first thing we need to do after creating the feature_spec is decide on the variables’ types.

We can do this by adding steps to the `spec` object.

    spec <- spec %>%
      step_numeric_column(
        all_numeric(), -cp, -restecg, -exang, -sex, -fbs,
        normalizer_fn = scaler_standard()
      ) %>%
      step_categorical_column_with_vocabulary_list(thal)

The following steps can be used to define the variable type:

- `step_numeric_column` to define numeric variables
- `step_categorical_with_vocabulary_list` for categorical variables with a fixed vocabulary
- `step_categorical_column_with_hash_bucket` for categorical variables using the hash trick
- `step_categorical_column_with_identity` to store categorical variables as integers
- `step_categorical_column_with_vocabulary_file` when you have the possible vocabulary in a file

When using `step_categorical_column_with_vocabulary_list` you can also provide a `vocabulary` argument with the fixed vocabulary. The recipe will find all the unique values in the dataset and use it as the vocabulary.

You can also specify a `normalizer_fn` to the `step_numeric_column`. In this case the variable will be transformed by the feature column. Note that the transformation will occur in the TensorFlow Graph, so it must use only TensorFlow ops. Like in the example we offer pre-made normalizers - and they will compute the normalizing function during the recipe preparation.

You can also use selectors like:

- `starts_with()`, `ends_with()`, `matches()` etc. (from tidyselect)
- `all_numeric()` to select all numeric variables
- `all_nominal()` to select all strings
- `has_type("float32")` to select based on TensorFlow variable type.

Now we can print the recipe:

    spec

After specifying the types of the columns you can add transformation steps. For example you may want to bucketize a numeric column:

    spec <- spec %>%
      step_bucketized_column(age, boundaries = c(18, 25, 30, 35, 40, 45, 50, 55, 60, 65))

You can also specify the kind of numeric representation that you want to use for your categorical variables.

    spec <- spec %>%
      step_indicator_column(thal) %>%
      step_embedding_column(thal, dimension = 2)

Another common transformation is to add interactions between variables using crossed columns.

    spec <- spec %>%
      step_crossed_column(thal_and_age = c(thal, bucketized_age), hash_bucket_size = 1000) %>%
      step_indicator_column(thal_and_age)

Note that the `crossed_column` is a categorical column, so we need to also specify what kind of numeric tranformation we want to use. Also note that we can name the transformed variables - each step uses a default naming for columns, eg. `bucketized_age` is the default name when you use `step_bucketized_column` with column called `age`.

With the above code we have created our recipe. Note we can also define the recipe by chaining a sequence of methods:

    spec <- feature_spec(hearts_train, target ~ .) %>%
      step_numeric_column(
        all_numeric(), -cp, -restecg, -exang, -sex, -fbs,
        normalizer_fn = scaler_standard()
      ) %>%
      step_categorical_column_with_vocabulary_list(thal) %>%
      step_bucketized_column(age, boundaries = c(18, 25, 30, 35, 40, 45, 50, 55, 60, 65)) %>%
      step_indicator_column(thal) %>%
      step_embedding_column(thal, dimension = 2) %>%
      step_crossed_column(c(thal, bucketized_age), hash_bucket_size = 10) %>%
      step_indicator_column(crossed_thal_bucketized_age)

After defining the recipe we need to `fit` it. It’s when fitting that we compute the vocabulary list for categorical variables or find the mean and standard deviation for the normalizing functions. Fitting involves evaluating the full dataset, so if you have provided the vocabulary list and your columns are already normalized you can skip the fitting step (TODO).

In our case, we will fit the feature spec, since we didn’t specify the vocabulary list for the categorical variables.

    spec_prep <- fit(spec)

After preparing we can see the list of dense features that were defined:

    str(spec_prep$dense_features())

Now we are ready to define our model in Keras. We will use a specialized `layer_dense_features` that knows what to do with the feature columns specification.

We also use a new `layer_input_from_dataset` that is useful to create a Keras input object copying the structure from a `data.frame` or TensorFlow dataset.

    library(keras)

    input <- layer_input_from_dataset(hearts_train %>% select(-target))

    output <- input %>%
      layer_dense_features(dense_features(spec_prep)) %>%
      layer_dense(units = 32, activation = "relu") %>%
      layer_dense(units = 1, activation = "sigmoid")

    model <- keras_model(input, output)

    model %>% compile(
      loss = loss_binary_crossentropy,
      optimizer = "adam",
      metrics = "binary_accuracy"
    )

We can finally train the model on the dataset:

    history <- model %>%
      fit(
        x = hearts_train %>% select(-target),
        y = hearts_train$target,
        epochs = 15,
        validation_split = 0.2
      )

    plot(history)

Finally we can make predictions in the test set and calculate performance metrics like the AUC of the ROC curve:

    hearts_test$pred <- predict(model, hearts_test %>% select(-target))
    Metrics::auc(hearts_test$target, hearts_test$pred)