eriklindernoren--ml-from-scratch
eriklindernoren
115 行
3.7 KiB
Python
115 行
3.7 KiB
Python
import progressbar
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from mpl_toolkits.mplot3d import Axes3D
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import matplotlib.pyplot as plt
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import matplotlib.cm as cmx
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import matplotlib.colors as colors
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import numpy as np
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from mlfromscratch.utils.data_operation import calculate_covariance_matrix
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from mlfromscratch.utils.data_operation import calculate_correlation_matrix
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from mlfromscratch.utils.data_manipulation import standardize
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bar_widgets = [
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'Training: ', progressbar.Percentage(), ' ', progressbar.Bar(marker="-", left="[", right="]"),
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' ', progressbar.ETA()
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]
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class Plot():
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def __init__(self):
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self.cmap = plt.get_cmap('viridis')
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def _transform(self, X, dim):
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covariance = calculate_covariance_matrix(X)
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eigenvalues, eigenvectors = np.linalg.eig(covariance)
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# Sort eigenvalues and eigenvector by largest eigenvalues
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idx = eigenvalues.argsort()[::-1]
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eigenvalues = eigenvalues[idx][:dim]
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eigenvectors = np.atleast_1d(eigenvectors[:, idx])[:, :dim]
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# Project the data onto principal components
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X_transformed = X.dot(eigenvectors)
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return X_transformed
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def plot_regression(self, lines, title, axis_labels=None, mse=None, scatter=None, legend={"type": "lines", "loc": "lower right"}):
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if scatter:
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scatter_plots = scatter_labels = []
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for s in scatter:
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scatter_plots += [plt.scatter(s["x"], s["y"], color=s["color"], s=s["size"])]
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scatter_labels += [s["label"]]
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scatter_plots = tuple(scatter_plots)
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scatter_labels = tuple(scatter_labels)
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for l in lines:
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li = plt.plot(l["x"], l["y"], color=s["color"], linewidth=l["width"], label=l["label"])
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if mse:
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plt.suptitle(title)
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plt.title("MSE: %.2f" % mse, fontsize=10)
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else:
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plt.title(title)
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if axis_labels:
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plt.xlabel(axis_labels["x"])
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plt.ylabel(axis_labels["y"])
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if legend["type"] == "lines":
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plt.legend(loc="lower_left")
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elif legend["type"] == "scatter" and scatter:
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plt.legend(scatter_plots, scatter_labels, loc=legend["loc"])
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plt.show()
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# Plot the dataset X and the corresponding labels y in 2D using PCA.
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def plot_in_2d(self, X, y=None, title=None, accuracy=None, legend_labels=None):
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X_transformed = self._transform(X, dim=2)
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x1 = X_transformed[:, 0]
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x2 = X_transformed[:, 1]
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class_distr = []
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y = np.array(y).astype(int)
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colors = [self.cmap(i) for i in np.linspace(0, 1, len(np.unique(y)))]
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# Plot the different class distributions
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for i, l in enumerate(np.unique(y)):
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_x1 = x1[y == l]
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_x2 = x2[y == l]
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_y = y[y == l]
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class_distr.append(plt.scatter(_x1, _x2, color=colors[i]))
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# Plot legend
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if not legend_labels is None:
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plt.legend(class_distr, legend_labels, loc=1)
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# Plot title
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if title:
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if accuracy:
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perc = 100 * accuracy
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plt.suptitle(title)
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plt.title("Accuracy: %.1f%%" % perc, fontsize=10)
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else:
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plt.title(title)
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# Axis labels
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plt.xlabel('Principal Component 1')
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plt.ylabel('Principal Component 2')
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plt.show()
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# Plot the dataset X and the corresponding labels y in 3D using PCA.
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def plot_in_3d(self, X, y=None):
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X_transformed = self._transform(X, dim=3)
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x1 = X_transformed[:, 0]
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x2 = X_transformed[:, 1]
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x3 = X_transformed[:, 2]
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fig = plt.figure()
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ax = fig.add_subplot(111, projection='3d')
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ax.scatter(x1, x2, x3, c=y)
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plt.show()
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