NeuralClassificationTransformer¶
- class proglearn.NeuralClassificationTransformer(network, euclidean_layer_idx, optimizer, loss='categorical_crossentropy', pretrained=False, compile_kwargs={'metrics': ['acc']}, fit_kwargs={'callbacks': [<keras.callbacks.EarlyStopping object>], 'epochs': 100, 'validation_split': 0.33, 'verbose': False})[source]¶
A class used to transform data from a category to a specialized representation.
- Parameters
network : object
A neural network used in the classification transformer.
euclidean_layer_idx : int
An integer to represent the final layer of the transformer.
optimizer : str or keras.optimizers instance
An optimizer used when compiling the neural network.
loss : str, default="categorical_crossentropy"
A loss function used when compiling the neural network.
pretrained : bool, default=False
A boolean used to identify if the network is pretrained.
compile_kwargs : dict, default={"metrics": ["acc"]}
A dictionary containing metrics for judging network performance.
fit_kwargs : dict, default={
"epochs": 100, "callbacks": [keras.callbacks.EarlyStopping(patience=5, monitor="val_acc")], "verbose": False, "validation_split": 0.33,
},
A dictionary to hold epochs, callbacks, verbose, and validation split for the network.
Attributes
encoder_
(object) A Keras model with inputs and outputs based on the network attribute. Output layers are determined by the euclidean_layer_idx parameter.
fitted_
(boolean) A boolean flag initialized after the model is fitted.
Methods Summary
Fits the transformer to data X with labels y. |
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Fit to data, then transform it. |
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Get parameters for this estimator. |
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Set the parameters of this estimator. |
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Performs inference using the transformer. |
- NeuralClassificationTransformer.fit(X, y)[source]¶
Fits the transformer to data X with labels y.
- Parameters
X : ndarray
Input data matrix.
y : ndarray
Output (i.e. response data matrix).
- Returns
self : NeuralClassificationTransformer
The object itself.
- NeuralClassificationTransformer.fit_transform(X, y=None, **fit_params)¶
Fit to data, then transform it.
Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.
- Parameters
X : array-like of shape (n_samples, n_features)
Input samples.
y : array-like of shape (n_samples,) or (n_samples, n_outputs), default=None
Target values (None for unsupervised transformations).
**fit_params : dict
Additional fit parameters.
- Returns
X_new : ndarray array of shape (n_samples, n_features_new)
Transformed array.
- NeuralClassificationTransformer.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 : dict
Parameter names mapped to their values.
- NeuralClassificationTransformer.set_params(**params)¶
Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as
Pipeline). 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
Estimator instance.