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⇅ Number Features

Number Features Preprocessing

Number features are directly transformed into a float valued vector of length n (where n is the size of the dataset) and added to the HDF5 with a key that reflects the name of column in the dataset. No additional information about them is available in the JSON metadata file.

Parameters available for preprocessing are

  • missing_value_strategy (default fill_with_const): what strategy to follow when there's a missing value in a number column. The value should be one of fill_with_const (replaces the missing value with a specific value specified with the fill_value parameter), fill_with_mode (replaces the missing values with the most frequent value in the column), fill_with_mean (replaces the missing values with the mean of the values in the column), bfill (replaces the missing values with the next valid value), ffill (replaces the missing values with the previous valid value).
  • fill_value (default 0): the value to replace the missing values with in case the missing_value_strategy is fill_with_const.
  • normalization (default null): technique to be used when normalizing the number feature types. The available options are null, zscore, minmax and log1p. If the value is null no normalization is performed. If the value is zscore, the mean and standard deviation are computed so that values are shifted to have zero mean and 1 standard deviation. If the value is minmax, the minimum is subtracted from values and the result is divided by difference between maximum and minimum. If normalization is log1p the value returned is the natural log of 1 plus the original value. Note: log1p is defined only for positive values.

Configuration example:

name: click_count
type: number
preprocessing:
    missing_value_strategy: fill_with_const
    fill_value: 0
    normalization: null

Preprocessing parameters can also be defined once and applied to all number input features using the Type-Global Preprocessing section.

Number Input Features and Encoders

Number features have two encoders. One encoder (passthrough) simply returns the raw numerical values coming from the input placeholders as outputs. Inputs are of size b while outputs are of size b x 1 where b is the batch size. The other encoder (dense) passes the raw numerical values through fully connected layers. In this case the inputs of size b are transformed to size b x h.

The encoder parameters specified at the feature level are:

  • tied (default null): name of the input feature to tie the weights of the encoder with. It needs to be the name of a feature of the same type and with the same encoder parameters.

Example number feature entry in the input features list:

name: number_column_name
type: number
tied: null
encoder: 
    type: dense

The available encoder parameters:

  • type (default passthrough): the possible values are passthrough, dense and sparse. passthrough outputs the raw integer values unaltered. dense randomly initializes a trainable embedding matrix, sparse uses one-hot encoding.

Encoder type and encoder parameters can also be defined once and applied to all number input features using the Type-Global Encoder section.

Passthrough Encoder

There are no additional parameters for the passthrough encoder.

Dense Encoder

For the dense encoder these are the available parameters.

  • fc_layers (default null): a list of dictionaries containing the parameters of all the fully connected layers. The length of the list determines the number of stacked fully connected layers and the content of each dictionary determines the parameters for a specific layer. The available parameters for each layer are: activation, dropout, norm, norm_params, output_size, use_bias, bias_initializer and weights_initializer. If any of those values is missing from the dictionary, the default one specified as a parameter of the encoder will be used instead.
  • num_layers (default 1): this is the number of stacked fully connected layers that the input to the feature passes through. Their output is projected in the feature's output space.
  • output_size (default 256): if output_size is not already specified in fc_layers this is the default output_size that will be used for each layer. It indicates the size of the output of a fully connected layer.
  • use_bias (default true): boolean, whether the layer uses a bias vector.
  • weights_initializer (default glorot_uniform): initializer for the weight matrix. Options are: constant, identity, zeros, ones, orthogonal, normal, uniform, truncated_normal, variance_scaling, glorot_normal, glorot_uniform, xavier_normal, xavier_uniform, he_normal, he_uniform, lecun_normal, lecun_uniform. To see the parameters of each initializer, please refer to torch.nn.init.
  • bias_initializer (default zeros): initializer for the bias vector. Options are: constant, identity, zeros, ones, orthogonal, normal, uniform, truncated_normal, variance_scaling, glorot_normal, glorot_uniform, xavier_normal, xavier_uniform, he_normal, he_uniform, lecun_normal, lecun_uniform. Alternatively it is possible to specify a dictionary with a key type that identifies the type of initializer and other keys for its parameters, e.g. {type: normal, mean: 0, stddev: 0}. To know the parameters of each initializer, please refer to torch.nn.init.
  • norm (default null): if a norm is not already specified in fc_layers this is the default norm that will be used for each layer. It indicates the norm of the output and it can be null, batch or layer.
  • norm_params (default null): parameters used if norm is either batch or layer. For information on parameters used with batch see the Torch documentation on batch normalization or for layer see the Torch documentation on layer normalization.
  • activation (default relu): if an activation is not already specified in fc_layers this is the default activation that will be used for each layer. It indicates the activation function applied to the output.
  • dropout (default 0): dropout rate

Example number feature entry in the input features list:

name: number_column_name
type: number
norm: null
tied: null
encoder: 
    type: dense
    num_layers: 1
    output_size: 256
    use_bias: true
    weights_initializer: glorot_uniform
    bias_initializer: zeros
    activation: relu
    dropout: 0

Number Output Features and Decoders

Number features can be used when a regression needs to be performed. There is only one decoder available for number features: a (potentially empty) stack of fully connected layers, followed by a projection to a single number.

These are the available parameters of a number output feature

  • reduce_input (default sum): defines how to reduce an input that is not a vector, but a matrix or a higher order tensor, on the first dimension (second if you count the batch dimension). Available values are: sum, mean or avg, max, concat (concatenates along the first dimension), last (returns the last vector of the first dimension).
  • dependencies (default []): the output features this one is dependent on. For a detailed explanation refer to Output Feature Dependencies.
  • reduce_dependencies (default sum): defines how to reduce the output of a dependent feature that is not a vector, but a matrix or a higher order tensor, on the first dimension (second if you count the batch dimension). Available values are: sum, mean or avg, max, concat (concatenates along the first dimension), last (returns the last vector of the first dimension).
  • loss (default {type: mean_squared_error}): is a dictionary containing a loss type. The available loss types are mean_squared_error and mean_absolute_error.

Loss type and loss related parameters can also be defined once and applied to all number output features using the Type-Global Loss section.

These are the available parameters of a number output feature decoder

  • fc_layers (default null): a list of dictionaries containing the parameters of all the fully connected layers. The length of the list determines the number of stacked fully connected layers and the content of each dictionary determines the parameters for a specific layer. The available parameters for each layer are: activation, dropout, norm, norm_params, output_size, use_bias, bias_initializer and weights_initializer. If any of those values is missing from the dictionary, the default one specified as a parameter of the encoder will be used instead.
  • num_fc_layers (default 0): this is the number of stacked fully connected layers that the input to the feature passes through. Their output is projected in the feature's output space.
  • output_size (default 256): if output_size is not already specified in fc_layers this is the default output_size that will be used for each layer. It indicates the size of the output of a fully connected layer.
  • activation (default relu): if an activation is not already specified in fc_layers this is the default activation that will be used for each layer. It indicates the activation function applied to the output.
  • norm (default null): if a norm is not already specified in fc_layers this is the default norm that will be used for each layer. It indicates how the output should be normalized and may be one of null, batch or layer.
  • norm_params (default null): parameters used if norm is either batch or layer. For information on parameters used with batch see the Torch documentation on batch normalization or for layer see the Torch documentation on layer normalization.
  • dropout (default 0): dropout rate
  • use_bias (default true): boolean, whether the layer uses a bias vector.
  • weights_initializer (default xavier_uniform): initializer for the weight matrix. Options are: constant, identity, zeros, ones, orthogonal, normal, uniform, truncated_normal, variance_scaling, glorot_normal, glorot_uniform, xavier_normal, xavier_uniform, he_normal, he_uniform, lecun_normal, lecun_uniform. To see the parameters of each initializer, please refer to torch.nn.init.
  • bias_initializer (default zeros): initializer for the bias vector. Options are: constant, identity, zeros, ones, orthogonal, normal, uniform, truncated_normal, variance_scaling, glorot_normal, glorot_uniform, xavier_normal, xavier_uniform, he_normal, he_uniform, lecun_normal, lecun_uniform. Alternatively it is possible to specify a dictionary with a key type that identifies the type of initializer and other keys for its parameters, e.g. {type: normal, mean: 0, stddev: 0}. To know the parameters of each initializer, please refer to torch.nn.init.
  • clip (default null): If not null it specifies a minimum and maximum value the predictions will be clipped to. The value can be either a list or a tuple of length 2, with the first value representing the minimum and the second the maximum. For instance (-5,5) will make it so that all predictions will be clipped to the [-5,5] interval.

Decoder type and decoder parameters can also be defined once and applied to all number output features using the Type-Global Decoder section.

Example number feature entry (with default parameters) in the output features list:

name: number_column_name
type: number
reduce_input: sum
dependencies: []
reduce_dependencies: sum
loss:
    type: mean_squared_error
decoder:
    fc_layers: null
    num_fc_layers: 0
    output_size: 256
    activation: relu
    norm: null
    norm_params: null
    dropout: 0
    use_bias: true
    weights_initializer: glorot_uniform
    bias_initializer: zeros
    clip: null

Number Features Metrics

The metrics that are calculated every epoch and are available for number features are mean_squared_error, mean_absolute_error, root_mean_squared_error, root_mean_squared_percentage_error and the loss itself. You can set either of them as validation_metric in the training section of the configuration if you set the validation_field to be the name of a number feature.