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Config for calibrated lattice model.

Used in the notebooks

Used in the tutorials

A calibrated lattice model applies piecewise-linear and categorical calibration on the input feature, followed by a lattice model and an optional output piecewise-linear calibration.


model_config = tfl.configs.CalibratedLatticeConfig(
feature_analysis_input_fn = create_input_fn(num_epochs=1, ...)
train_input_fn = create_input_fn(num_epochs=100, ...)
estimator = tfl.estimators.CannedClassifier(

feature_configs A list of tfl.configs.FeatureConfig instances that specify configurations for each feature. If a configuration is not provided for a feature, a default configuration will be used.
interpolation One of 'hypercube' or 'simplex' interpolation. For a d-dimensional lattice, 'hypercube' interpolates 2^d parameters, whereas 'simplex' uses d+1 parameters and thus scales better. For details see tfl.lattice_lib.evaluate_with_simplex_interpolation and tfl.lattice_lib.evaluate_with_hypercube_interpolation.
parameterization The parameterization of the lattice function class to use. A lattice function is uniquely determined by specifying its value on every lattice vertex. A parameterization scheme is a mapping from a vector of parameters to a multidimensional array of lattice vertex values. It can be one of:

  • String 'all_vertices': This is the "traditional" parameterization that keeps one scalar parameter per lattice vertex where the mapping is essentially the identity map. With this scheme, the number of parameters scales exponentially with the number of inputs to the lattice. The underlying lattice used will be a tfl.layers.Lattice layer.
  • String 'kronecker_factored': With this parameterization, for each lattice input i we keep a collection of num_terms vectors each having feature_configs[0].lattice_size entries (note that all features must have the same lattice size). To obtain the tensor of lattice vertex values, for t=1,2,...,num_terms we compute the outer product of the t'th vector in each collection, multiply by a per-term scale, and sum the resulting tensors. Finally, we add a single shared bias parameter to each entry in the sum. With this scheme, the number of parameters grows linearly with len(feature_configs) (assuming lattice sizes and num_terms are held constant). Currently, only monotonicity shape constraint and bound constraint are supported for this scheme. Regularization is not currently supported. The underlying lattice used will be a tfl.layers.KroneckerFactoredLattice layer.
num_terms The number of terms in a lattice using 'kronecker_factored' parameterization. Ignored if parameterization is set to 'all_vertices'.
regularizer_configs A list of tfl.configs.RegularizerConfig instances that apply global regularization.
output_min Lower bound constraint on the output of the model.
output_max Upper bound constraint on the output of the model.
output_calibration If a piecewise-linear calibration should be used on the output of the lattice.
output_calibration_num_keypoints Number of keypoints to use for the output piecewise-linear calibration.
output_initialization The initial values to setup for the output of the model. When using output calibration, these values are used to initialize the output keypoints of the output piecewise-linear calibration. Otherwise the lattice parameters will be setup to form a linear function in the range of output_initialization. It can be one of:
  • String 'quantiles': Output is initliazed to label quantiles, if possible.
  • String 'uniform': Output is initliazed uniformly in label range.
  • A list of numbers: To be used for initialization of the output lattice or output calibrator.
  • random_seed Random seed to use for initialization of a lattice with 'kronecker_factored' parameterization. Ignored if parameterization is set to 'all_vertices'.



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    Returns a deserialized configuration dictionary.


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    Returns existing or default FeatureConfig with the given name.


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    Returns a configuration dictionary.


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    Returns existing or default RegularizerConfig with the given name.