166 lines
7.4 KiB
Python
166 lines
7.4 KiB
Python
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# -*- coding: utf-8 -*-
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from __future__ import absolute_import, division, print_function, unicode_literals
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import numpy as np
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try:
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import matplotlib.pyplot as plt
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except ImportError:
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pass
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RC_PARAMS = {'axes.axisbelow': True,
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'axes.edgecolor': '.8',
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'axes.facecolor': 'white',
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'axes.grid': False,
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'axes.labelcolor': '.15',
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'axes.linewidth': 1.0,
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'axes.labelpad' : 10.0,
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'figure.facecolor': 'white',
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'font.family': ['sans-serif'],
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'font.sans-serif': ['Arial', 'Liberation Sans', 'sans-serif'],
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'grid.color': '.8', 'grid.linestyle': '-',
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'image.cmap': 'Greys',
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'legend.frameon': False,
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'legend.numpoints': 1, 'legend.scatterpoints': 1,
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'lines.solid_capstyle': 'round',
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'text.color': '1.0',
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'xtick.color': '1.0', 'xtick.direction': 'out',
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'xtick.major.size': 0.0, 'xtick.minor.size': 0.0,
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'xtick.major.pad' : 5,
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'ytick.color': '1.0', 'ytick.direction': 'out',
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'ytick.major.size': 0.0, 'ytick.minor.size': 0.0,
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'axes.ymargin' : 0.9,
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'ytick.major.pad': 5}
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COLOR_PAIRS = (((0.65098041296005249, 0.80784314870834351, 0.89019608497619629),
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(0.12572087695201239, 0.47323337360924367, 0.707327968232772)),
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((0.68899655751153521, 0.8681737867056154, 0.54376011946622071),
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(0.21171857311445125, 0.63326415104024547, 0.1812226118410335)),
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((0.98320646005518297, 0.5980161709820524, 0.59423301088459368),
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(0.89059593116535862, 0.10449827132271793, 0.11108035462744099)),
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((0.99175701702342312, 0.74648213716698619, 0.43401768935077328),
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(0.99990772780250103, 0.50099192647372981, 0.0051211073118098693)),
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((0.78329874347238004, 0.68724338552531095, 0.8336793640080622),
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(0.42485198495434734, 0.2511495584950722, 0.60386007743723258)),
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((0.99760092286502611, 0.99489427150464516, 0.5965244373854468),
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(0.69411766529083252, 0.3490196168422699, 0.15686275064945221)))
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def draw_termite(values_mat, col_labels, row_labels,
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highlight_cols=None, highlight_colors=None,
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save=False, pow_x = 0.66, pow_y = 0.8):
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"""
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Make a "termite" plot, typically used for assessing topic models with a tabular
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layout that promotes comparison of terms both within and across topics.
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Args:
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values_mat (``np.ndarray`` or matrix): matrix of values with shape
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(# row labels, # col labels) used to size the dots on the grid
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col_labels (seq[str]): labels used to identify x-axis ticks on the grid
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row_labels(seq[str]): labels used to identify y-axis ticks on the grid
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highlight_cols (int or seq[int], optional): indices for columns
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to visually highlight in the plot with contrasting colors
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highlight_colors (tuple of 2-tuples): each 2-tuple corresponds to a pair
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of (light/dark) matplotlib-friendly colors used to highlight a single
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column; if not specified (default), a good set of 6 pairs are used
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save (str, optional): give the full /path/to/fname on disk to save figure
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Returns:
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``matplotlib.axes.Axes.axis``: axis on which termite plot is plotted
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Raises:
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ValueError: if more columns are selected for highlighting than colors
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or if any of the inputs' dimensions don't match
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References:
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.. Chuang, Jason, Christopher D. Manning, and Jeffrey Heer. "Termite:
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Visualization techniques for assessing textual topic models."
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Proceedings of the International Working Conference on Advanced
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Visual Interfaces. ACM, 2012.
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.. seealso:: :func:`TopicModel.termite_plot <textacy.tm.TopicModel.termite_plot>`
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"""
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try:
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plt
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except NameError:
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raise ImportError(
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'matplotlib is not installed, so textacy.viz won\'t work; install it \
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individually, or along with textacy via `pip install textacy[viz]`')
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n_rows, n_cols = values_mat.shape
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max_val = np.max(values_mat)
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if n_rows != len(row_labels):
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msg = "values_mat and row_labels dimensions don't match: {} vs. {}".format(
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n_rows, len(row_labels))
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raise ValueError(msg)
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if n_cols != len(col_labels):
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msg = "values_mat and col_labels dimensions don't match: {} vs. {}".format(
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n_cols, len(col_labels))
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raise ValueError(msg)
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if highlight_colors is None:
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highlight_colors = COLOR_PAIRS
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if highlight_cols is not None:
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if isinstance(highlight_cols, int):
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highlight_cols = (highlight_cols,)
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elif len(highlight_cols) > len(highlight_colors):
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msg = 'no more than {} columns may be highlighted at once'.format(
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len(highlight_colors))
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raise ValueError(msg)
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highlight_colors = {hc: COLOR_PAIRS[i]
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for i, hc in enumerate(highlight_cols)}
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with plt.rc_context(RC_PARAMS):
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fig, ax = plt.subplots(figsize=(pow(n_cols, pow_y), pow(n_rows, pow_x))) #hier fesntergröße
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_ = ax.set_yticks(range(n_rows))
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yticklabels = ax.set_yticklabels(row_labels,
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fontsize=14, color='gray')
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if highlight_cols is not None:
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for i, ticklabel in enumerate(yticklabels):
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max_tick_val = max(values_mat[i, hc] for hc in highlight_cols)
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for hc in highlight_cols:
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if max_tick_val > 0 and values_mat[i, hc] == max_tick_val:
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ticklabel.set_color(highlight_colors[hc][1])
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ax.get_xaxis().set_ticks_position('top')
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_ = ax.set_xticks(range(n_cols))
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xticklabels = ax.set_xticklabels(col_labels,
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fontsize=14, color='gray',
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rotation=30, ha='left')
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if highlight_cols is not None:
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gridlines = ax.get_xgridlines()
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for i, ticklabel in enumerate(xticklabels):
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if i in highlight_cols:
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ticklabel.set_color(highlight_colors[i][1])
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gridlines[i].set_color(highlight_colors[i][0])
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gridlines[i].set_alpha(0.5)
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for col_ind in range(n_cols):
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if highlight_cols is not None and col_ind in highlight_cols:
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ax.scatter([col_ind for _ in range(n_rows)],
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[i for i in range(n_rows)],
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s=600 * (values_mat[:, col_ind] / max_val),
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alpha=0.5, linewidth=1,
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color=highlight_colors[col_ind][0],
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edgecolor=highlight_colors[col_ind][1])
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else:
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ax.scatter([col_ind for _ in range(n_rows)],
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[i for i in range(n_rows)],
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s=600 * (values_mat[:, col_ind] / max_val),
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alpha=0.5, linewidth=1,
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color='black', edgecolor='gray')
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_ = ax.set_xlim(left=-1, right=n_cols)
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_ = ax.set_ylim(bottom=-1, top=n_rows)
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ax.invert_yaxis() # otherwise, values/labels go from bottom to top
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#plt.ylim(ymax=5)
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if save:
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fig.savefig(save, bbox_inches='tight', dpi=100)
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return ax
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