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pca_graph.py
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pca_graph.py
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#!/usr/bin/env python
import matplotlib.pyplot as plt
import numpy as np
from sklearn.decomposition import PCA
def get_pca_graph(samples, labels, out_file, plot_negative=True,
title=None, caption=None):
pca = PCA(n_components=2)
points = pca.fit_transform(samples)
positive_x = [p for p, l in zip(points[:,0], labels) if l == 1]
positive_y = [p for p, l in zip(points[:,1], labels) if l == 1]
negative_x = [p for p, l in zip(points[:,0], labels) if l == 0]
negative_y = [p for p, l in zip(points[:,1], labels) if l == 0]
legend_points = ()
legend_labels = ()
if plot_negative:
negative = plt.scatter(negative_x, negative_y, color='blue', alpha=.5)
legend_points += (negative,)
legend_labels += ('Clean',)
positive = plt.scatter(positive_x, positive_y, color='red', alpha=.5)
legend_points += (positive,)
legend_labels += ('Insult',)
if title:
plt.title(title)
plt.legend(legend_points, legend_labels)
plt.savefig(out_file)
plt.clf()