`step_embed`

creates a *specification* of a recipe step that
will convert a nominal (i.e. factor) predictor into a set of
scores derived from a tensorflow model via a word-embedding model.
`embed_control`

is a simple wrapper for setting default options.

step_embed( recipe, ..., role = "predictor", trained = FALSE, outcome = NULL, predictors = NULL, num_terms = 2, hidden_units = 0, options = embed_control(), mapping = NULL, history = NULL, skip = FALSE, id = rand_id("lencode_bayes") ) # S3 method for step_embed tidy(x, ...) embed_control( loss = "mse", metrics = NULL, optimizer = "sgd", epochs = 20, validation_split = 0, batch_size = 32, verbose = 0, callbacks = NULL )

recipe | A recipe object. The step will be added to the sequence of operations for this recipe. |
---|---|

... | One or more selector functions to choose variables.
For |

role | For model terms created by this step, what analysis role should they be assigned?. By default, the function assumes that the embedding variables created will be used as predictors in a model. |

trained | A logical to indicate if the quantities for preprocessing have been estimated. |

outcome | A call to |

predictors | An optional call to |

num_terms | An integer for the number of resulting variables. |

hidden_units | An integer for the number of hidden units in a dense ReLu layer between the embedding and output later. Use a value of zero for no intermediate layer (see Details below). |

options | A list of options for the model fitting process. |

mapping | A list of tibble results that define the
encoding. This is |

history | A tibble with the convergence statistics for
each term. This is |

skip | A logical. Should the step be skipped when the
recipe is baked by |

id | A character string that is unique to this step to identify it. |

x | A |

optimizer, loss, metrics | Arguments to pass to |

epochs, validation_split, batch_size, verbose, callbacks | Arguments to pass to |

An updated version of `recipe`

with the new step added
to the sequence of existing steps (if any). For the `tidy`

method, a tibble with columns `terms`

(the selectors or
variables for encoding), `level`

(the factor levels), and
several columns containing `embed`

in the name.

Factor levels are initially assigned at random to the new variables and these variables are used in a neural network to optimize both the allocation of levels to new columns as well as estimating a model to predict the outcome. See Section 6.1.2 of Francois and Allaire (2018) for more details.

The new variables are mapped to the specific levels seen at the time of model training and an extra instance of the variables are used for new levels of the factor.

One model is created for each call to `step_embed`

. All terms
given to the step are estimated and encoded in the same model
which would also contain predictors give in `predictors`

(if
any).

When the outcome is numeric, a linear activation function is used in the last layer while softmax is used for factor outcomes (with any number of levels).

For example, the `keras`

code for a numeric outcome, one
categorical predictor, and no hidden units used here would be

keras_model_sequential() %>% layer_embedding( input_dim = num_factor_levels_x + 1, output_dim = num_terms, input_length = 1 ) %>% layer_flatten() %>% layer_dense(units = 1, activation = 'linear')

If a factor outcome is used and hidden units were requested, the code would be

keras_model_sequential() %>% layer_embedding( input_dim = num_factor_levels_x + 1, output_dim = num_terms, input_length = 1 ) %>% layer_flatten() %>% layer_dense(units = hidden_units, activation = "relu") %>% layer_dense(units = num_factor_levels_y, activation = 'softmax')

Other variables specified by `predictors`

are added as an
additional dense layer after `layer_flatten`

and before the
hidden layer.

Also note that it may be difficult to obtain reproducible results using this step due to the nature of Tensorflow (see link in References).

tensorflow models cannot be run in parallel within the same
session (via `foreach`

or `futures`

) or the `parallel`

package.
If using a recipes with this step with `caret`

, avoid parallel
processing.

Francois C and Allaire JJ (2018)
*Deep Learning with R*, Manning

"How can I obtain reproducible results using Keras during development?" https://tinyurl.com/keras-repro

"Concatenate Embeddings for Categorical Variables with Keras" https://flovv.github.io/Embeddings_with_keras_part2/