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class proficloud.ml.SomReconstructionError.SomReconstructionErrorModel

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proficloud.ml.SomReconstructionError.SomReconstructionErrorModel proficloud.ml.SomReconstructionError.SomReconstructionErrorModel

Bases: proficloud.ml.AbstractModel.AbstractModelClass

Self-Organizing Map model with reconstruction error anomaly detection. https://www.sciencedirect.com/science/article/pii/S221282711830307X

static load

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proficloud.ml.SomReconstructionError.SomReconstructionErrorModel.load proficloud.ml.SomReconstructionError.SomReconstructionErrorModel.load

(file_name)

Load model from json file.

Parameters:	file_name (str) – Source file name.
Returns:	Model instance
Return type:	AbstractModelClass

predict(data, n_jobs=None)

This is the method to predict a whole dataset. The data parameter is the dataframe to use. Must be implemented by Model.

Parameters:	data (pandas.DataFrame) – Dataframe to evaluate
Returns:	Returns list with an entry for each row of the dataframe.
Return type:	list

predict_raw = None

Parameter for prediction. if True reconstruction error is returned. If false, output > 0 marks an anomaly.

predict_sample(sample)

This is the method to predict a single sample. The sample parameter is sample to predict. Must be implemented by Model.

Parameters:	sample (pandas.Series / numpy.array) – Single sample to predict
Returns:	Returns prediction for sample
Return type:	any

save(file_name)

Save model to json.

Parameters:

file_name (str) – Target file name.

scaler = None

Data scaling, created during training. Used to scale data to range [0,1].

signal_names = None

Signal names

som = None

Self-Organizing Map neural network

summary()

A summary method for the model

Returns:	Summary text
Return type:	str

threshold = None

Threshold for anomaly detection. Calculated from training data.

train(data)

This is the method to train the model. The data parameter is the dataframe to use. Must be implemented by Model.

Parameters:	data (pandas.DataFrame) – Dataframe to train the model.
Returns:	Does not return. Throw on exception during training.
Return type:	void

training_epochs = None

Hyperparameter, number of training epochs

training_learning_rate = None

Hyperparameter, Initial learning rate

training_side_length = None

Hyperparameter, side length of SOM (x and y are equal)

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