146 lines
5.0 KiB
Python
146 lines
5.0 KiB
Python
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# Sebastian Raschka 2014-2020
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# mlxtend Machine Learning Library Extensions
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# Author: Sebastian Raschka <sebastianraschka.com>
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#
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# A function for plotting a confusion matrix.
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# License: BSD 3 clause
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import matplotlib.pyplot as plt
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import numpy as np
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def plot_confusion_matrix(conf_mat,
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hide_spines=False,
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hide_ticks=False,
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figsize=None,
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cmap=None,
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colorbar=False,
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show_absolute=True,
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show_normed=False,
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class_names=None,
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figure=None,
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axis=None):
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"""Plot a confusion matrix via matplotlib.
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Parameters
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-----------
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conf_mat : array-like, shape = [n_classes, n_classes]
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Confusion matrix from evaluate.confusion matrix.
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hide_spines : bool (default: False)
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Hides axis spines if True.
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hide_ticks : bool (default: False)
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Hides axis ticks if True
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figsize : tuple (default: (2.5, 2.5))
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Height and width of the figure
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cmap : matplotlib colormap (default: `None`)
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Uses matplotlib.pyplot.cm.Blues if `None`
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colorbar : bool (default: False)
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Shows a colorbar if True
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show_absolute : bool (default: True)
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Shows absolute confusion matrix coefficients if True.
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At least one of `show_absolute` or `show_normed`
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must be True.
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show_normed : bool (default: False)
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Shows normed confusion matrix coefficients if True.
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The normed confusion matrix coefficients give the
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proportion of training examples per class that are
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assigned the correct label.
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At least one of `show_absolute` or `show_normed`
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must be True.
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class_names : array-like, shape = [n_classes] (default: None)
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List of class names.
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If not `None`, ticks will be set to these values.
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figure : None or Matplotlib figure (default: None)
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If None will create a new figure.
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axis : None or Matplotlib figure axis (default: None)
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If None will create a new axis.
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Returns
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-----------
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fig, ax : matplotlib.pyplot subplot objects
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Figure and axis elements of the subplot.
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Examples
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-----------
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For usage examples, please see
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http://rasbt.github.io/mlxtend/user_guide/plotting/plot_confusion_matrix/
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"""
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if not (show_absolute or show_normed):
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raise AssertionError('Both show_absolute and show_normed are False')
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if class_names is not None and len(class_names) != len(conf_mat):
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raise AssertionError('len(class_names) should be equal to number of'
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'classes in the dataset')
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total_samples = conf_mat.sum(axis=1)[:, np.newaxis]
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normed_conf_mat = conf_mat.astype('float') / total_samples
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if figure is None and axis is None:
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fig, ax = plt.subplots(figsize=figsize)
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elif axis is None:
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fig = figure
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ax = fig.add_subplot(1, 1, 1)
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else:
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fig, ax = figure, axis
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ax.grid(False)
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if cmap is None:
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cmap = plt.cm.Blues
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if figsize is None:
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figsize = (len(conf_mat)*1.25, len(conf_mat)*1.25)
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if show_normed:
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matshow = ax.matshow(normed_conf_mat, cmap=cmap)
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else:
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matshow = ax.matshow(conf_mat, cmap=cmap)
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if colorbar:
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fig.colorbar(matshow)
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for i in range(conf_mat.shape[0]):
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for j in range(conf_mat.shape[1]):
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cell_text = ""
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if show_absolute:
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cell_text += format(conf_mat[i, j], 'd')
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if show_normed:
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cell_text += "\n" + '('
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cell_text += format(normed_conf_mat[i, j], '.2f') + ')'
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else:
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cell_text += format(normed_conf_mat[i, j], '.2f')
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if show_normed:
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ax.text(x=j,
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y=i,
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s=cell_text,
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va='center',
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ha='center',
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color="white" if normed_conf_mat[i, j] > 0.5
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else "black")
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else:
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ax.text(x=j,
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y=i,
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s=cell_text,
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va='center',
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ha='center',
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color="white" if conf_mat[i, j] > np.max(conf_mat)/2
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else "black")
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if class_names is not None:
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tick_marks = np.arange(len(class_names))
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plt.xticks(tick_marks, class_names, rotation=45)
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plt.yticks(tick_marks, class_names)
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if hide_spines:
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ax.spines['right'].set_visible(False)
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ax.spines['top'].set_visible(False)
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ax.spines['left'].set_visible(False)
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ax.spines['bottom'].set_visible(False)
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ax.yaxis.set_ticks_position('left')
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ax.xaxis.set_ticks_position('bottom')
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if hide_ticks:
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ax.axes.get_yaxis().set_ticks([])
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ax.axes.get_xaxis().set_ticks([])
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plt.xlabel('predicted label')
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plt.ylabel('true label')
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return fig, ax
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