text: # yellowbrick.text.dispersion
  # Implementations of lexical dispersions for text visualization.
  #
  # Author:   Larry Gray
  # Created:  Fri Jun 22 15:40:49 2018 -0400
  #
  # Copyright (C) 2018 The scikit-yb developers
  # For license information, see LICENSE.txt
  #
  # ID: dispersion.py [3822dd6] lwgray@gmail.com $
  
  """
  Implementation of lexical dispersion for text visualization
  """
  
  
  ##########################################################################
  ## Imports
  ##########################################################################
  
  import itertools
  from collections import defaultdict
  
  import numpy as np
  
  from yellowbrick.text.base import TextVisualizer
  from yellowbrick.style.colors import resolve_colors
  from yellowbrick.exceptions import YellowbrickValueError
  
  
  ##########################################################################
  ## Dispersion Plot Visualizer
  ##########################################################################
  
  
  class DispersionPlot(TextVisualizer):
      """
      Lexical dispersion illustrates the homogeneity of a word (or set of words) across
      the documents of a corpus.
  
      DispersionPlot allows for visualization of the lexical dispersion of words in a
      corpus. This plot illustrates with vertical lines the occurrences of one or more
      search terms throughout the corpus, noting how many words relative to the beginning
      of the corpus it appears. If the target vector of the corpus documents is provided,
      the points will be colored with respect to their document category, which allows for
      additional analysis of relationships in search term homogeneity within and across
      document categories. If annotation is requested, document boundaries will be
      displayed as vertical lines in the plot.
  
      Parameters
      ----------
      search_terms : list
          A list of search terms whose dispersion across a corpus passed at fit
          should be visualized.
  
      ax : matplotlib axes, default: None
          The axes to plot the figure on.
  
      colors : list or tuple of colors
          Specify the colors for each individual class. Will override colormap if both are
          provided.
  
      colormap : string or matplotlib cmap
          Qualitative colormap for discrete target
  
      ignore_case : boolean, default: False
          Specify whether input will be case-sensitive.
  
      annotate_docs : boolean, default: False
          Specify whether document boundaries will be displayed. Vertical lines
          are positioned at the end of each document.
  
      labels : list of strings
          The names of the classes in the target, used to create a legend.
          Labels must match names of classes in sorted order.
  
      kwargs : dict
          Pass any additional keyword arguments to the super class.
  
      Attributes
      ----------
      self.classes_ : list
          A list of strings representing the unique classes in the target in sorted order.
          If ``y`` is provided, these are extracted from ``y``, unless a list of class
          labels is provided by the user on instantiation.
  
      self.boundaries_ : list
          A list of integers indicating the document boundaries with respect to
          word offsets.
  
      self.indexed_words_ : list
          A list of integers indicating the y position for each occurrence of each of
          the search terms.
  
      self.word_categories_ : list
          A list of strings indicating the corresponding document category of each search
          term occurrence.
      """
  
      # NOTE: cannot be np.nan
      NULL_CLASS = None
  
      def __init__(
          self,
          search_terms,
          ax=None,
          colors=None,
          colormap=None,
          ignore_case=False,
          annotate_docs=False,
          labels=None,
          **kwargs
      ):
          super(DispersionPlot, self).__init__(ax=ax, **kwargs)
  
          self.labels = labels
          self.colors = colors
          self.colormap = colormap
  
          self.ignore_case = ignore_case
          self.search_terms = search_terms
          self.annotate_docs = annotate_docs
  
      def _compute_dispersion(self, X, y):
          """
          Produces a generator containing the offset word count, y_coordinate, and
          label/category for each occurrance of the search terms.
  
          Attributes
          ----------
          self.boundaries_ : list
              A list of integers indicating the document boundaries with respect to
              word offsets.
          """
          self.boundaries_ = []
          offset = 0
  
          if y is None:
              y = itertools.repeat(None)
  
          for doc, category in zip(X, y):
              for word in doc:
                  if self.ignore_case:
                      word = word.lower()
  
                  # NOTE: this will find all indices if duplicate words are supplied
                  # In the case that word is not in target words, any empty list is
                  # returned and no data will be yielded
                  offset += 1
                  for y_coord in (self.indexed_words_ == word).nonzero()[0]:
                      y_coord = int(y_coord)
                      yield (offset, y_coord, category)
  
              if self.annotate_docs:
                  self.boundaries_.append(offset)
  
          self.boundaries_ = np.array(self.boundaries_, dtype=int)
  
      def _check_missing_words(self, points):
          """
          Helper method to raise an error if any of the requested search
          terms do not appear in the corpus.
          """
          for index in range(len(self.indexed_words_)):
              if index in points[:, 1]:
                  pass
              else:
                  raise YellowbrickValueError(
                      ("The search term '{}' is not found in " "this corpus").format(
                          self.indexed_words_[index]
                      )
                  )
  
      def fit(self, X, y=None, **kwargs):
          """
          The fit method is the primary drawing input for the dispersion
          visualization.
  
          Parameters
          ----------
          X : list or generator
              Should be provided as a list of documents or a generator
              that yields a list of documents that contain a list of
              words in the order they appear in the document.
  
          y : ndarray or Series of length n
              An optional array or series of target or class values for
              instances. If this is specified, then the points will be colored
              according to their class.
  
          Returns
          -------
          self : instance
              Returns the instance of the transformer/visualizer
  
          Attributes
          ----------
          self.classes_ : list
              A list of strings representing the unique classes in the target in sorted order.
              If ``y`` is provided, these are extracted from ``y``, unless a list of class
              labels is provided by the user on instantiation.
  
          self.indexed_words_ : list
              A list of integers indicating the y position for each occurrence of each of
              the search terms.
  
          self.word_categories_ : list
              A list of strings indicating the corresponding document category of each search
              term occurrence.
          """
  
          if y is not None:
              self.classes_ = np.unique(y)
          else:
              self.classes_ = np.array([self.NULL_CLASS])
  
          # Create an index (e.g. the y position) for the target words
          self.indexed_words_ = np.flip(self.search_terms, axis=0)
          if self.ignore_case:
              self.indexed_words_ = np.array([w.lower() for w in self.indexed_words_])
  
          # Stack is used to create a 2D array from the generator
          try:
              offsets_positions_categories = np.stack(self._compute_dispersion(X, y))
          except ValueError:
              raise YellowbrickValueError(("No search terms were found in the corpus"))
  
          word_positions = np.stack(
              zip(
                  offsets_positions_categories[:, 0].astype(int),
                  offsets_positions_categories[:, 1].astype(int),
              )
          )
  
          self.word_categories_ = offsets_positions_categories[:, 2]
  
          self._check_missing_words(word_positions)
  
          self.draw(word_positions, **kwargs)
          return self
  
      def draw(self, points, **kwargs):
          """
          Called from the fit method, this method creates the canvas and
          draws the plot on it.
  
          Parameters
          ----------
          kwargs: generic keyword arguments.
          """
  
          # Resolve the labels with the classes
          labels = self.labels if self.labels is not None else self.classes_
          if len(labels) != len(self.classes_):
              raise YellowbrickValueError(
                  (
                      "number of supplied labels ({}) does not "
                      "match the number of classes ({})"
                  ).format(len(labels), len(self.classes_))
              )
  
          # Create the color mapping for the labels.
          color_values = resolve_colors(
              n_colors=len(labels), colormap=self.colormap, colors=self.colors
          )
          colors = dict(zip(labels, color_values))
  
          # Transform labels into a map of class to label
          labels = dict(zip(self.classes_, labels))
  
          # Define boundaries with a vertical line
          if self.annotate_docs:
              for xcoords in self.boundaries_:
                  self.ax.axvline(x=xcoords, color="lightgray", linestyle="dashed")
  
          series = defaultdict(lambda: {"x": [], "y": []})
  
          if self.word_categories_ is not None:
              for point, t in zip(points, self.word_categories_):
                  label = labels[t]
                  series[label]["x"].append(point[0])
                  series[label]["y"].append(point[1])
          else:
              label = self.classes_[0]
              for x, y in points:
                  series[label]["x"].append(x)
                  series[label]["y"].append(y)
  
          for label, points in series.items():
              self.ax.scatter(
                  points["x"],
                  points["y"],
                  marker="|",
                  c=colors[label],
                  zorder=100,
                  label=label,
              )
  
          self.ax.set_yticks(list(range(len(self.indexed_words_))))
          self.ax.set_yticklabels(self.indexed_words_)
  
          return self.ax
  
      def finalize(self, **kwargs):
          """
          Prepares the figure for rendering by adding a title, axis labels, and
          managing the limits of the text labels. Adds a legend outside of the plot.
  
          Parameters
          ----------
          kwargs: generic keyword arguments.
  
          Notes
          -----
          Generally this method is called from show and not directly by the user.
          """
          self.set_title("Lexical Dispersion Plot")
          self.ax.set_ylim(-1, len(self.indexed_words_))
          self.ax.set_xlabel("Word Offset")
          self.ax.grid(False)
  
          # Add the legend outside of the figure box.
          if not all(self.classes_ == np.array([self.NULL_CLASS])):
              box = self.ax.get_position()
              self.ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])
              self.ax.legend(loc="center left", bbox_to_anchor=(1, 0.5))
  
  
  ##########################################################################
  ## Quick Method
  ##########################################################################
  
  
  def dispersion(
      search_terms,
      corpus,
      y=None,
      ax=None,
      colors=None,
      colormap=None,
      annotate_docs=False,
      ignore_case=False,
      labels=None,
      show=True,
      **kwargs
  ):
      """ Displays lexical dispersion plot for words in a corpus
  
      This helper function is a quick wrapper to utilize the DispersionPlot
      Visualizer for one-off analysis
  
      Parameters
      ----------
  
      search_terms : list
          A list of words whose dispersion will be examined within a corpus
  
      corpus : list
          Should be provided as a list of documents that contain
          a list of words in the order they appear in the document.
  
      y : ndarray or Series of length n
          An optional array or series of target or class values for
          instances. If this is specified, then the points will be colored
          according to their class.
  
      ax : matplotlib axes, default: None
          The axes to plot the figure on.
  
      colors : list or tuple of colors
          Specify the colors for each individual class. Will override colormap if both are
          provided.
  
      colormap : string or matplotlib cmap
          Qualitative colormap for discrete target
  
      annotate_docs : boolean, default: False
          Specify whether document boundaries will be displayed.  Vertical lines
          are positioned at the end of each document.
  
      ignore_case : boolean, default: False
          Specify whether input  will be case-sensitive.
  
      labels : list of strings
          The names of the classes in the target, used to create a legend.
          Labels must match names of classes in sorted order.
  
      show : bool, default: True
          If True, calls ``show()``, which in turn calls ``plt.show()`` however
          you cannot call ``plt.savefig`` from this signature, nor
          ``clear_figure``. If False, simply calls ``finalize()``
  
      kwargs : dict
          Pass any additional keyword arguments to the super class.
  
      Returns
      -------
      viz: DispersionPlot
          Returns the fitted, finalized visualizer
      """
  
      # Instantiate the visualizer
      visualizer = DispersionPlot(
          search_terms,
          ax=ax,
          colors=colors,
          colormap=colormap,
          ignore_case=ignore_case,
          labels=labels,
          annotate_docs=annotate_docs,
          **kwargs
      )
  
      visualizer.fit(corpus, y, **kwargs)
  
      if show:
          visualizer.show()
      else:
          visualizer.finalize()
  
      return visualizer
  