ECE

Exposer is a data structure drawing from both histogram and a scatter plot. Like in histogram, the range of values is divided into a series of intervals, but like in a scatter plot the combination of features is analyzed. The rule of bin adjacency is here broken, so object may fall into more than one of them.

Quick start

Exposer

To create an exposer, all you need is to load a dataset, prepare dictionary with demanded configuration and use them to initiate object.

dataset = Dataset('data/iris.csv','iris')
configuration = {
    'radius': .5,
    'grain': 15,
    'chosenLambda': [2, 3]
}
exposer = Exposer(dataset, configuration)

For a process of classification, first is it required to clear supports for all samples in dataset. Later you can use exposer to create predictions. Dictionary with scores is provided by a function score() being a member of dataset object.

dataset.clearSupports()
exposer.predict()
scores = dataset.score()

Ensemble of Exposers [ECE]

To create an ensemble, all you need is to load a dataset, prepare dictionary with demanded configuration and use them to initiate object.

dataset = Dataset('data/iris.csv','iris')
configuration = {
    'radius': radius,
    'grain': grain,
    'limit': limit,
    'dimensions': dimensions,
    'eecApproach': ECEApproach.random,
    'exposerVotingMethod': ExposerVotingMethod.lone
}
ensemble = ECE(dataset,configuration)

For a process of classification you can simply use ensemble to create predictions. Dictionary with scores is provided by a function score() being a member of dataset object.

ensemble.predict()
scores = dataset.score()

Models

ExposerVotingMethod

• lone
• theta1
• theta2
• theta3
• thetas

ECEApproach

• brutal
• random
• heuristic