↑ Date Features

Date features are like 2023-06-25 15:00:00, 2023-06-25, 6-25-2023, or 6/25/2023.

Date Features Preprocessing¶

name: date_feature_name
type: date
preprocessing:
  missing_value_strategy: fill_with_const
  fill_value: ''
  datetime_format: "%d %b %Y"

Ludwig will try to infer the date format automatically, but a specific format can be provided. The date string spec is the same as the one described in python's datetime.

Here are some preprocessing parameters.

missing_value_strategy (default fill_with_const): what strategy to follow when there's a missing value. The value should be one of fill_with_const (replaces the missing value with a specific value specified with the fill_value parameter). The special value backfill replaces the missing values with the next valid value.
fill_value (default ""): the value to replace missing values with when missing_value_strategy is fill_with_const. This can be a datetime string. If empty, the current datetime will be used.
datetime_format (default null): this parameter can either be a datetime format string, or null, in which case the datetime format will be inferred automatically.

Date Input Features and Encoders¶

Input date features are transformed into a int tensors of size N x 9 (where N is the size of the dataset and the 9 dimensions contain year, month, day, weekday, yearday, hour, minute, second, and second of day).

For example, the date 2022-06-25 09:30:59 would be deconstructed into:

[
  2022, # Year
  6,  # June
  25, # 25th day of the month
  5,  # Weekday: Saturday
  176,  # 176th day of the year
  9,  # Hour
  30, # Minute
  59, # Seconds
  34259,  # 34259th second of the day
]

Currently there are two encoders supported for dates: DateEmbed (default) and DateWave. The encoder can be set by specifying embed or wave in the feature's encoder parameter in the input feature's configuration.

name: date_feature_name
type: date
encoder: embed

Embed Encoder¶

name: date_column_name
type: date
encoder: embed
embedding_size: 10
embeddings_on_cpu: false
fc_layers: null
num_fc_layers: 0
output_size: 10
use_bias: true
weights_initializer: glorot_uniform
bias_initializer: zeros
norm: null
norm_params: null
activation: relu
dropout: 0

This encoder passes the year through a fully connected layer of one neuron and embeds all other elements for the date, concatenates them and passes the concatenated representation through fully connected layers.

It takes the following optional parameters:

embedding_size (default 10): it is the maximum embedding size adopted.
embeddings_on_cpu (default false): by default embeddings matrices are stored on GPU memory if a GPU is used, as it allows for faster access, but in some cases the embedding matrix may be really big and this parameter forces the placement of the embedding matrix in regular memory and the CPU is used to resolve them, slightly slowing down the process as a result of data transfer between CPU and GPU memory.
dropout (default 0): dropout rate.
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.
output_size (default 10): if a 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 weights 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.
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.
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 Torch's documentation on batch normalization or for layer see Torch's 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

Wave Encoder¶

name: date_column_name
type: date
encoder: wave
fc_layers: null
num_fc_layers: 0
output_size: 10
use_bias: true
weights_initializer: glorot_uniform
bias_initializer: zeros
norm: null
norm_params: null
activation: relu
dropout: 0

This encoder passes the year through a fully connected layer of one neuron and represents all other elements for the date by taking the cosine of their value with a different period (12 for months, 31 for days, etc.), concatenates them and passes the concatenated representation through fully connected layers.

It takes the following 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_fc_layers (default 0): This is the number of stacked fully connected layers.
output_size (default 10): if a 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 weights 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.
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.
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 Torch's documentation on batch normalization or for layer see Torch's 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

Date Output Features and Decoders¶

There is currently no support for date as an output feature. Consider using the TEXT type.