99 lines
3.2 KiB
Python
99 lines
3.2 KiB
Python
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# Sebastian Raschka 2014-2020
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# mlxtend Machine Learning Library Extensions
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#
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# Algorithm for plotting sequential feature selection.
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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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def plot_sequential_feature_selection(metric_dict,
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figsize=None,
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kind='std_dev',
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color='blue',
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bcolor='steelblue',
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marker='o',
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alpha=0.2,
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ylabel='Performance',
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confidence_interval=0.95):
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"""Plot feature selection results.
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Parameters
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----------
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metric_dict : mlxtend.SequentialFeatureSelector.get_metric_dict() object
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figsize : tuple (default: None)
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Height and width of the figure
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kind : str (default: "std_dev")
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The kind of error bar or confidence interval in
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{'std_dev', 'std_err', 'ci', None}.
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color : str (default: "blue")
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Color of the lineplot (accepts any matplotlib color name)
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bcolor : str (default: "steelblue").
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Color of the error bars / confidence intervals
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(accepts any matplotlib color name).
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marker : str (default: "o")
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Marker of the line plot
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(accepts any matplotlib marker name).
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alpha : float in [0, 1] (default: 0.2)
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Transparency of the error bars / confidence intervals.
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ylabel : str (default: "Performance")
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Y-axis label.
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confidence_interval : float (default: 0.95)
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Confidence level if `kind='ci'`.
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Returns
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----------
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fig : matplotlib.pyplot.figure() object
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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_sequential_feature_selection/
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"""
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allowed = {'std_dev', 'std_err', 'ci', None}
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if kind not in allowed:
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raise AttributeError('kind not in %s' % allowed)
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# fig = plt.figure()
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if figsize is not None:
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fig = plt.subplots(figsize=figsize)
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else:
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fig = plt.subplots()
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k_feat = sorted(metric_dict.keys())
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avg = [metric_dict[k]['avg_score'] for k in k_feat]
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if kind:
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upper, lower = [], []
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if kind == 'ci':
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kind = 'ci_bound'
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for k in k_feat:
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upper.append(metric_dict[k]['avg_score'] +
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metric_dict[k][kind])
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lower.append(metric_dict[k]['avg_score'] -
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metric_dict[k][kind])
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plt.fill_between(k_feat,
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upper,
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lower,
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alpha=alpha,
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color=bcolor,
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lw=1)
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if kind == 'ci_bound':
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kind = 'Confidence Interval (%d%%)' % (confidence_interval * 100)
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plt.plot(k_feat, avg, color=color, marker=marker)
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plt.ylabel(ylabel)
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plt.xlabel('Number of Features')
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feature_min = len(metric_dict[k_feat[0]]['feature_idx'])
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feature_max = len(metric_dict[k_feat[-1]]['feature_idx'])
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plt.xticks(range(feature_min, feature_max + 1),
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range(feature_min, feature_max + 1))
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return fig
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