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Plot probabilities with different base classifiers
Plot the classification probability for ensemble models with different base classifiers.
We use a 3-class imbalanced dataset, and we classify it with a SelfPacedEnsembleClassifier
(ensemble size = 5).
We use Decision Tree, Support Vector Machine (rbf kernel), and Gaussian process classifier as the base classifier.
This example uses:
# Adapted from sklearn
# Author: Zhining Liu <zhining.liu@outlook.com>
# Alexandre Gramfort <alexandre.gramfort@inria.fr>
# License: BSD 3 clause
print(__doc__)
# Import imbalanced-ensemble
import imbens
# Import utilities
import numpy as np
from collections import Counter
import sklearn
from imbens.datasets import make_imbalance
from imbens.ensemble.base import sort_dict_by_key
RANDOM_STATE = 42
Preparation
Make 3 imbalanced iris classification tasks.
iris = sklearn.datasets.load_iris()
X = iris.data[:, 0:2] # we only take the first two features for visualization
y = iris.target
X, y = make_imbalance(
X, y, sampling_strategy={0: 50, 1: 30, 2: 10}, random_state=RANDOM_STATE
)
print(
'Class distribution of imbalanced iris dataset: \n%s' % sort_dict_by_key(Counter(y))
)
Class distribution of imbalanced iris dataset:
{0: 50, 1: 30, 2: 10}
Create SPE (ensemble size = 5) with different base classifiers.
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
from sklearn.gaussian_process import GaussianProcessClassifier
from sklearn.gaussian_process.kernels import RBF
classifiers = {
'SPE-DT': imbens.ensemble.SelfPacedEnsembleClassifier(
n_estimators=5,
estimator=DecisionTreeClassifier(),
),
'SPE-SVM-rbf': imbens.ensemble.SelfPacedEnsembleClassifier(
n_estimators=5,
estimator=SVC(kernel='rbf', probability=True),
),
'SPE-GPC': imbens.ensemble.SelfPacedEnsembleClassifier(
n_estimators=5,
estimator=GaussianProcessClassifier(1.0 * RBF([1.0, 1.0])),
),
}
n_classifiers = len(classifiers)
Plot classification probabilities
import matplotlib.pyplot as plt
n_features = X.shape[1]
plt.figure(figsize=(3 * 2, n_classifiers * 2))
plt.subplots_adjust(bottom=0.2, top=0.95)
xx = np.linspace(3, 9, 100)
yy = np.linspace(1, 5, 100).T
xx, yy = np.meshgrid(xx, yy)
Xfull = np.c_[xx.ravel(), yy.ravel()]
for index, (name, classifier) in enumerate(classifiers.items()):
classifier.fit(X, y)
y_pred = classifier.predict(X)
accuracy = sklearn.metrics.balanced_accuracy_score(y, y_pred)
print("Balanced Accuracy (train) for %s: %0.1f%% " % (name, accuracy * 100))
# View probabilities:
probas = classifier.predict_proba(Xfull)
n_classes = np.unique(y_pred).size
for k in range(n_classes):
plt.subplot(n_classifiers, n_classes, index * n_classes + k + 1)
plt.title("Class %d" % k)
if k == 0:
plt.ylabel(name)
imshow_handle = plt.imshow(
probas[:, k].reshape((100, 100)), extent=(3, 9, 1, 5), origin='lower'
)
plt.xticks(())
plt.yticks(())
idx = y_pred == k
if idx.any():
plt.scatter(X[idx, 0], X[idx, 1], marker='o', c='w', edgecolor='k')
ax = plt.axes([0.15, 0.04, 0.7, 0.05])
plt.title("Probability")
plt.colorbar(imshow_handle, cax=ax, orientation='horizontal')
plt.show()
Balanced Accuracy (train) for SPE-DT: 85.6%
Balanced Accuracy (train) for SPE-SVM-rbf: 77.8%
Balanced Accuracy (train) for SPE-GPC: 84.4%
Total running time of the script: ( 0 minutes 2.069 seconds)