BasePlasticity¶
-
class
model._base.BasePlasticity(outputs=100, num_epochs=100, activation='Linear', optimizer=<class 'plasticity.model.optimizer.Optimizer'>, batch_size=100, weights_init=<class 'plasticity.model.weights.BaseWeights'>, precision=1e-30, epochs_for_convergency=None, convergency_atol=0.01, random_state=None, verbose=True)[source]¶ Bases:
sklearn.base.BaseEstimator,sklearn.base.TransformerMixinAbstract base class for plasticity models
- Parameters
outputs (int (default=100)) – Number of hidden units
num_epochs (int (default=100)) – Maximum number of epochs for model convergency
batch_size (int (default=100)) – Size of the minibatch
weights_init (BaseWeights object) – Weights initialization strategy.
activation (str (default="Linear")) – Name of the activation function
optimizer (Optimizer (default=SGD)) – Optimizer object (derived by the base class Optimizer)
precision (float (default=1e-30)) – Parameter that controls numerical precision of the weight updates
epochs_for_convergency (int (default=None)) – Number of stable epochs requested for the convergency. If None the training proceeds up to the maximum number of epochs (num_epochs).
convergency_atol (float (default=0.01)) – Absolute tolerance requested for the convergency
random_state (int (default=None)) – Random seed for batch subdivisions
verbose (bool (default=True)) – Turn on/off the verbosity
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fit(X, y=None)[source]¶ Fit the Plasticity model weights.
- Parameters
X (array-like of shape (n_samples, n_features)) – The training input samples
y (array-like, default=None) – The array of labels
- Returns
self – Return self
- Return type
object
Notes
Note
The model tries to memorize the given input producing a valid encoding.
Warning
If the array of labels is provided, it will be considered as a set of new inputs for the neurons. The labels can be 1D array or multi-dimensional array: the given shape is internally reshaped according to the required dimensions.
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fit_transform(X, y=None)[source]¶ Fit the model model meta-transformer and apply the data encoding transformation.
- Parameters
X (array-like of shape (n_samples, n_features)) – The training input samples
y (array-like, shape (n_samples,)) – The target values
- Returns
Xnew – The data encoded according to the model weights.
- Return type
array-like of shape (n_samples, encoded_features)
Notes
Warning
If the array of labels is provided, it will be considered as a set of new inputs for the neurons. The labels can be 1D array or multi-dimensional array: the given shape is internally reshaped according to the required dimensions.
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get_params(deep=True)¶ Get parameters for this estimator.
- Parameters
deep (bool, default=True) – If True, will return the parameters for this estimator and contained subobjects that are estimators.
- Returns
params – Parameter names mapped to their values.
- Return type
mapping of string to any
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load_weights(filename)[source]¶ Load the weight matrix from a binary file.
- Parameters
filename (str) – Filename or path
- Returns
self – Return self
- Return type
object
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predict(X, y=None)[source]¶ Reduce X applying the Plasticity encoding.
- Parameters
X (array of shape (n_samples, n_features)) – The input samples
y (array-like, default=None) – The array of labels
- Returns
Xnew – The encoded features
- Return type
array of shape (n_values, n_samples)
Notes
Warning
If the array of labels is provided, it will be considered as a set of new inputs for the neurons. The labels can be 1D array or multi-dimensional array: the given shape is internally reshaped according to the required dimensions.
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save_weights(filename)[source]¶ Save the current weights to a binary file.
- Parameters
filename (str) – Filename or path
- Returns
- Return type
True if everything is ok
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set_params(**params)¶ Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form
<component>__<parameter>so that it’s possible to update each component of a nested object.- Parameters
**params (dict) – Estimator parameters.
- Returns
self – Estimator instance.
- Return type
object
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transform(X)[source]¶ Apply the data reduction according to the features in the best signature found.
- Parameters
X (array-like of shape (n_samples, n_features)) – The input samples
- Returns
Xnew – The data encoded according to the model weights.
- Return type
array-like of shape (n_samples, encoded_features)