104 lines
3.2 KiB
Python
104 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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# Implementation of the mulitnomial logistic regression algorithm for
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# classification.
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#
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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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from matplotlib.pyplot import subplots
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from matplotlib.table import Table
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import numpy as np
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def checkerboard_plot(ary,
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cell_colors=('white', 'black'),
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font_colors=('black', 'white'),
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fmt='%.1f',
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figsize=None,
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row_labels=None,
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col_labels=None,
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fontsize=None):
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"""
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Plot a checkerboard table / heatmap via matplotlib.
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Parameters
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-----------
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ary : array-like, shape = [n, m]
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A 2D Nnumpy array.
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cell_colors : tuple or list (default: ('white', 'black'))
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Tuple or list containing the two colors of the
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checkerboard pattern.
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font_colors : tuple or list (default: ('black', 'white'))
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Font colors corresponding to the cell colors.
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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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fmt : str (default: '%.1f')
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Python string formatter for cell values.
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The default '%.1f' results in floats with 1 digit after
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the decimal point. Use '%d' to show numbers as integers.
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row_labels : list (default: None)
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List of the row labels. Uses the array row
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indices 0 to n by default.
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col_labels : list (default: None)
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List of the column labels. Uses the array column
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indices 0 to m by default.
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fontsize : int (default: None)
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Specifies the font size of the checkerboard table.
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Uses matplotlib's default if None.
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Returns
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-----------
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fig : matplotlib 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/checkerboard_plot/
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"""
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fig, ax = subplots(figsize=figsize)
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ax.set_axis_off()
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tb = Table(ax, bbox=[0, 0, 1, 1])
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n_rows, n_cols = ary.shape
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if row_labels is None:
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row_labels = np.arange(n_rows)
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if col_labels is None:
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col_labels = np.arange(n_cols)
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width, height = 1.0 / n_cols, 1.0 / n_rows
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for (row_idx, col_idx), cell_val in np.ndenumerate(ary):
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idx = (col_idx + row_idx) % 2
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tb.add_cell(row_idx, col_idx, width, height,
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text=fmt % cell_val,
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loc='center',
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facecolor=cell_colors[idx])
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for row_idx, label in enumerate(row_labels):
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tb.add_cell(row_idx, -1,
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width, height,
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text=label, loc='right',
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edgecolor='none', facecolor='none')
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for col_idx, label in enumerate(col_labels):
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tb.add_cell(-1, col_idx,
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width, height / 2.,
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text=label, loc='center',
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edgecolor='none', facecolor='none')
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for (row_idx, col_idx), cell_val in np.ndenumerate(ary):
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idx = (col_idx + row_idx) % 2
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tb._cells[(row_idx, col_idx)]._text.set_color(font_colors[idx])
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ax.add_table(tb)
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tb.set_fontsize(fontsize)
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return fig
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