85 lines
2.5 KiB
Python
85 lines
2.5 KiB
Python
# Sebastian Raschka 2014-2020
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# mlxtend Machine Learning Library Extensions
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#
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# Matplotlib wrapper for generating stacked barplots.
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# Author: Sebastian Raschka <sebastianraschka.com>
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#
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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 scatterplotmatrix(X, fig_axes=None, names=None,
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figsize=(8, 8), alpha=1.0, **kwargs):
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"""
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Lower triangular of a scatterplot matrix
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Parameters
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-----------
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X : array-like, shape={num_examples, num_features}
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Design matrix containing data instances (examples)
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with multiple exploratory variables (features).
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fix_axes : tuple (default: None)
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A `(fig, axes)` tuple, where fig is an figure object
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and axes is an axes object created via matplotlib,
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for example, by calling the pyplot `subplot` function
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`fig, axes = plt.subplots(...)`
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names : list (default: None)
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A list of string names, which should have the same number
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of elements as there are features (columns) in `X`.
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figsize : tuple (default: (8, 8))
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Height and width of the subplot grid. Ignored if
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fig_axes is not `None`.
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alpha : float (default: 1.0)
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Transparency for both the scatter plots and the
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histograms along the diagonal.
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**kwargs : kwargs
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Keyword arguments for the scatterplots.
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Returns
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--------
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fix_axes : tuple
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A `(fig, axes)` tuple, where fig is an figure object
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and axes is an axes object created via matplotlib,
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for example, by calling the pyplot `subplot` function
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`fig, axes = plt.subplots(...)`
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Examples
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----------
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For more usage examples, please see
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http://rasbt.github.io/mlxtend/user_guide/plotting/scatterplotmatrix/
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"""
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num_examples, num_features = X.shape
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if fig_axes is None:
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fig, axes = plt.subplots(nrows=num_features,
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ncols=num_features,
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figsize=figsize)
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else:
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fig, axes = fig_axes
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if names is None:
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names = ['X%d' % (i+1) for i in range(num_features)]
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for i, j in zip(*np.triu_indices_from(axes, k=1)):
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axes[j, i].scatter(X[:, j], X[:, i], alpha=alpha, **kwargs)
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axes[j, i].set_xlabel(names[j])
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axes[j, i].set_ylabel(names[i])
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axes[i, j].set_axis_off()
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for i in range(num_features):
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axes[i, i].hist(X[:, i], alpha=alpha)
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axes[i, i].set_ylabel('Count')
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axes[i, i].set_xlabel(names[i])
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return fig, axes
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