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utils.py
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utils.py
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import matplotlib.pyplot as plt
from sklearn.datasets import make_classification
def make_dataset(n_samples=200, n_features=20, n_classes=2, random_state=None):
"""
Generate a random dataset for classification with n_classes classes. Uses
scikit-learn under the hood. Has n_features // 2 informative features.
Parameters:
-----------
n_samples: Specifies the number of datapoints in the generated dataset.
Default is 200.
n_features: Specifies the number of features in the generated dataset.
Default is 20.
n_classes: Specifies the number of classes (labels) in the generated
dataset. Default is 2.
random_state: Specifies the seed for random number generated for the
creation of the dataset.
Returns:
--------
X: Generated dataset samples of shape (n_samples, n_features).
y: Class labels of the datapoints in X. Has shape (n_samples, 1).
"""
X, y = make_classification(n_samples=n_samples, n_features=n_features,
n_classes=n_classes, n_redundant=0,
n_informative=n_features // 2,
random_state=random_state)
return X, y.reshape(-1, 1)
def plot_misclassification_error(clf, X, y, output_file):
"""
Plots the misclassification error of the classifier on the data provided
against the iterations. The passed classifier must have a param_log_
attribute and a score function. The plot is saved to output_file passed.
Parameters:
-----------
clf: Instance of the classifier for which the misclassification error needs
to be plotted.
X: Data vectors of shape (n_samples, n_features) for which misclassification
error needs to be computed.
y: True class labels for the samples in X. Has shape (n_samples, 1).
output_file: Specifies path to the file where the plot must be saved.
"""
# Calculate the misclassification error after each iteration on the data
misclassification_log = []
for iter_, weight in clf.param_log_:
score = 1 - clf.score(X, y, weight)
misclassification_log.append((iter_, score))
plt.rcParams['figure.figsize'] = [15, 10] # set the plot size
plt.plot(*tuple(zip(*misclassification_log))) # plot the data
plt.title('Misclassification error on data vs. Iteration #')
plt.xlabel('Iteration #')
plt.ylabel('Misclassification Error')
plt.savefig(output_file) # save the plot