brew.metrics.diversity package¶
Submodules¶
brew.metrics.diversity.base module¶
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class
brew.metrics.diversity.base.Diversity(metric='')[source]¶ Bases:
objectEnsemble Diversity Calculator.
The class calculates the diversity of ensemble of classifiers.
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`metric` function, receives the oracle output and returns float – Function used to calculate the metric.
Parameters: metric ({'e', 'kw', 'q', 'p', 'disagreement', 'agreement', 'df'}, optional) – Metric used to compute the ensemble diversity:
- ’e’ (Entropy Measure e) will use
kuncheva_entropy_measure() - ’kw’ (Kohavi Wolpert Variance) will use
kuncheva_kw() - ’q’ (Q Statistics) will use
kuncheva_q_statistics() - ’p’ (Correlation Coefficient p) will use
kuncheva_correlation_coefficient_p()# noqa - ’disagreement’ (Disagreement Measure) will use
kuncheva_disagreement_measure()# noqa - ’agreement’ (Agreement Measure) will use
kuncheva_agreement_measure()# noqa - ’df’ (Double Fault Measure) will use
kuncheva_double_fault_measure()# noqa
Examples
>>> from brew.metrics.diversity.base import Diversity >>> from brew.generation.bagging import Bagging >>> >>> from sklearn.tree import DecisionTreeClassifier >>> import numpy as np >>> >>> X = np.array([[-1, 0], [-0.8, 1], [-0.8, -1], [-0.5, 0], [0.5, 0], [1, 0], [0.8, 1], [0.8, -1]]) >>> y = np.array([1, 1, 1, 2, 1, 2, 2, 2]) >>> tree = DecisionTreeClassifier(max_depth=1, min_samples_leaf=1) >>> bag = Bagging(base_classifier=tree, n_classifiers=10) >>> bag.fit(X, y) >>> >>> div = Diversity(metric='q') >>> q = div.calculate(bag.ensemble, Xtst, ytst) >>> q < 1.01 and q > -1.01 True
See also
brew.metrics.diversity.paired- Paired diversity metrics.
brew.metrics.diversity.non_paired- Non-paired diversity metrics.
References
Brown, Gavin, et al. “Diversity creation methods: a survey and categorisation.” Information Fusion 6.1 (2005): 5-20.
Kuncheva, Ludmila I., and Christopher J. Whitaker. “Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy.” Machine learning 51.2 (2003): 181-207.
Tang, E. Ke, Ponnuthurai N. Suganthan, and Xin Yao. “An analysis of diversity measures.” Machine Learning 65.1 (2006): 247-271.
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brew.metrics.diversity.non_paired module¶
brew.metrics.diversity.paired module¶
Module contents¶
-
class
brew.metrics.diversity.Diversity(metric='')[source]¶ Bases:
objectEnsemble Diversity Calculator.
The class calculates the diversity of ensemble of classifiers.
-
`metric` function, receives the oracle output and returns float – Function used to calculate the metric.
Parameters: metric ({'e', 'kw', 'q', 'p', 'disagreement', 'agreement', 'df'}, optional) – Metric used to compute the ensemble diversity:
- ’e’ (Entropy Measure e) will use
kuncheva_entropy_measure() - ’kw’ (Kohavi Wolpert Variance) will use
kuncheva_kw() - ’q’ (Q Statistics) will use
kuncheva_q_statistics() - ’p’ (Correlation Coefficient p) will use
kuncheva_correlation_coefficient_p()# noqa - ’disagreement’ (Disagreement Measure) will use
kuncheva_disagreement_measure()# noqa - ’agreement’ (Agreement Measure) will use
kuncheva_agreement_measure()# noqa - ’df’ (Double Fault Measure) will use
kuncheva_double_fault_measure()# noqa
Examples
>>> from brew.metrics.diversity.base import Diversity >>> from brew.generation.bagging import Bagging >>> >>> from sklearn.tree import DecisionTreeClassifier >>> import numpy as np >>> >>> X = np.array([[-1, 0], [-0.8, 1], [-0.8, -1], [-0.5, 0], [0.5, 0], [1, 0], [0.8, 1], [0.8, -1]]) >>> y = np.array([1, 1, 1, 2, 1, 2, 2, 2]) >>> tree = DecisionTreeClassifier(max_depth=1, min_samples_leaf=1) >>> bag = Bagging(base_classifier=tree, n_classifiers=10) >>> bag.fit(X, y) >>> >>> div = Diversity(metric='q') >>> q = div.calculate(bag.ensemble, Xtst, ytst) >>> q < 1.01 and q > -1.01 True
See also
brew.metrics.diversity.paired- Paired diversity metrics.
brew.metrics.diversity.non_paired- Non-paired diversity metrics.
References
Brown, Gavin, et al. “Diversity creation methods: a survey and categorisation.” Information Fusion 6.1 (2005): 5-20.
Kuncheva, Ludmila I., and Christopher J. Whitaker. “Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy.” Machine learning 51.2 (2003): 181-207.
Tang, E. Ke, Ponnuthurai N. Suganthan, and Xin Yao. “An analysis of diversity measures.” Machine Learning 65.1 (2006): 247-271.
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