text: # yellowbrick.features.rankd
  # Implements 1D (histograms) and 2D (joint plot) feature rankings.
  #
  # Author:   Benjamin Bengfort <bbengfort@districtdatalabs.com>
  # Created:  Fri Oct 07 15:14:01 2016 -0400
  #
  # Copyright (C) 2016 District Data Labs
  # For license information, see LICENSE.txt
  #
  # ID: rankd.py [ee754dc] benjamin@bengfort.com $
  
  """
  Implements 1D (histograms) and 2D (joint plot) feature rankings.
  """
  
  ##########################################################################
  ## Imports
  ##########################################################################
  
  import numpy as np
  from scipy.stats import shapiro
  from scipy.stats import spearmanr
  from scipy.stats import kendalltau as sp_kendalltau
  
  from yellowbrick.utils import is_dataframe
  from yellowbrick.features.base import MultiFeatureVisualizer
  from yellowbrick.exceptions import YellowbrickValueError
  
  
  __all__ = ["rank1d", "rank2d", "Rank1D", "Rank2D"]
  
  
  ##########################################################################
  ## Metrics
  ##########################################################################
  
  def kendalltau(X):
      """
      Accepts a matrix X and returns a correlation matrix so that each column
      is the variable and each row is the observations.
  
      Parameters
      ----------
      X : ndarray or DataFrame of shape n x m
          A matrix of n instances with m features
  
      """
      corrs = np.zeros((X.shape[1], X.shape[1]))
      for idx, cola in enumerate(X.T):
          for jdx, colb in enumerate(X.T):
              corrs[idx, jdx] = sp_kendalltau(cola,colb)[0]
      return corrs
  
  
  ##########################################################################
  ## Base Feature Visualizer
  ##########################################################################
  
  class RankDBase(MultiFeatureVisualizer):
      """
      Base visualizer for Rank1D and Rank2D
  
      Parameters
      ----------
      ax : matplotlib Axes, default: None
          The axis to plot the figure on. If None is passed in the current axes
          will be used (or generated if required).
  
      algorithm : string
          The ranking algorithm to use; options and defaults vary by subclass
  
      features : list
          A list of feature names to use.
          If a DataFrame is passed to fit and features is None, feature
          names are selected as the columns of the DataFrame.
  
      show_feature_names : boolean, default: True
          If True, the feature names are used to label the axis ticks in the
          plot.
  
      kwargs : dict
          Keyword arguments that are passed to the base class and may influence
          the visualization as defined in other Visualizers.
  
      Attributes
      ----------
      ranks_ : ndarray
          An n-dimensional, symmetric array of rank scores, where n is the
          number of features. E.g. for 1D ranking, it is (n,), for a
          2D ranking it is (n,n) and so forth.
  
      Examples
      --------
  
      >>> visualizer = Rank2D()
      >>> visualizer.fit(X, y)
      >>> visualizer.transform(X)
      >>> visualizer.poof()
  
      Notes
      -----
      These parameters can be influenced later on in the visualization
      process, but can and should be set as early as possible.
      """
  
      ranking_methods = {}
  
      def __init__(self, ax=None, algorithm=None, features=None,
                   show_feature_names=True, **kwargs):
          """
          Initialize the class with the options required to rank and
          order features as well as visualize the result.
          """
          super(RankDBase, self).__init__(ax=ax, features=features, **kwargs)
  
          # Data Parameters
          self.ranking_ = algorithm
  
          # Display parameters
          self.show_feature_names_ = show_feature_names
  
      def transform(self, X, **kwargs):
          """
          The transform method is the primary drawing hook for ranking classes.
  
          Parameters
          ----------
          X : ndarray or DataFrame of shape n x m
              A matrix of n instances with m features
  
          kwargs : dict
              Pass generic arguments to the drawing method
  
          Returns
          -------
          X : ndarray
              Typically a transformed matrix, X' is returned. However, this
              method performs no transformation on the original data, instead
              simply ranking the features that are in the input data and returns
              the original data, unmodified.
          """
          self.ranks_ = self.rank(X)
          self.draw(**kwargs)
  
          # Return the X matrix, unchanged
          return X
  
      def rank(self, X, algorithm=None):
          """
          Returns the feature ranking.
  
          Parameters
          ----------
          X : ndarray or DataFrame of shape n x m
              A matrix of n instances with m features
  
          algorithm : str or None
              The ranking mechanism to use, or None for the default
  
          Returns
          -------
          ranks : ndarray
              An n-dimensional, symmetric array of rank scores, where n is the
              number of features. E.g. for 1D ranking, it is (n,), for a
              2D ranking it is (n,n) and so forth.
          """
          algorithm = algorithm or self.ranking_
          algorithm = algorithm.lower()
  
          if algorithm not in self.ranking_methods:
              raise YellowbrickValueError(
                  "'{}' is unrecognized ranking method".format(algorithm)
              )
  
          # Extract matrix from dataframe if necessary
          if is_dataframe(X):
              X = X.values
  
          return self.ranking_methods[algorithm](X)
  
      def finalize(self, **kwargs):
          """
          Finalize executes any subclass-specific axes finalization steps.
          The user calls poof and poof calls finalize.
  
          Parameters
          ----------
          kwargs: dict
              generic keyword arguments
  
          """
          # Set the title
          self.set_title(
              "{} Ranking of {} Features".format(
                  self.ranking_.title(), len(self.features_)
              )
          )
  
  
  ##########################################################################
  ## Rank 1D Feature Visualizer
  ##########################################################################
  
  class Rank1D(RankDBase):
      """
      Rank1D computes a score for each feature in the data set with a specific
      metric or algorithm (e.g. Shapiro-Wilk) then returns the features ranked
      as a bar plot.
  
      Parameters
      ----------
      ax : matplotlib Axes, default: None
          The axis to plot the figure on. If None is passed in the current axes
          will be used (or generated if required).
  
      algorithm : one of {'shapiro', }, default: 'shapiro'
          The ranking algorithm to use, default is 'Shapiro-Wilk.
  
      features : list
          A list of feature names to use.
          If a DataFrame is passed to fit and features is None, feature
          names are selected as the columns of the DataFrame.
  
      orient : 'h' or 'v', default='h'
          Specifies a horizontal or vertical bar chart.
  
      show_feature_names : boolean, default: True
          If True, the feature names are used to label the x and y ticks in the
          plot.
  
      kwargs : dict
          Keyword arguments that are passed to the base class and may influence
          the visualization as defined in other Visualizers.
  
      Attributes
      ----------
      ranks_ : ndarray
          An array of rank scores with shape (n,), where n is the
          number of features. It is computed during `fit`.
  
      Examples
      --------
      >>> visualizer = Rank1D()
      >>> visualizer.fit(X, y)
      >>> visualizer.transform(X)
      >>> visualizer.poof()
      """
  
      ranking_methods = {
          'shapiro': lambda X: np.array([shapiro(x)[0] for x in X.T]),
      }
  
      def __init__(self, ax=None, algorithm='shapiro', features=None,
                   orient='h', show_feature_names=True, **kwargs):
          """
          Initialize the class with the options required to rank and
          order features as well as visualize the result.
          """
          super(Rank1D, self).__init__(
              ax=ax, algorithm=algorithm, features=features,
              show_feature_names=show_feature_names, **kwargs
          )
          self.orientation_ = orient
  
      def draw(self, **kwargs):
          """
          Draws the bar plot of the ranking array of features.
          """
          if self.orientation_ == 'h':
              # Make the plot
              self.ax.barh(np.arange(len(self.ranks_)), self.ranks_, color='b')
  
              # Add ticks and tick labels
              self.ax.set_yticks(np.arange(len(self.ranks_)))
              if self.show_feature_names_:
                  self.ax.set_yticklabels(self.features_)
              else:
                  self.ax.set_yticklabels([])
  
              # Order the features from top to bottom on the y axis
              self.ax.invert_yaxis()
  
              # Turn off y grid lines
              self.ax.yaxis.grid(False)
  
          elif self.orientation_ == 'v':
              # Make the plot
              self.ax.bar(np.arange(len(self.ranks_)), self.ranks_, color='b')
  
              # Add ticks and tick labels
              self.ax.set_xticks(np.arange(len(self.ranks_)))
              if self.show_feature_names_:
                  self.ax.set_xticklabels(self.features_, rotation=90)
              else:
                  self.ax.set_xticklabels([])
  
              # Turn off x grid lines
              self.ax.xaxis.grid(False)
  
          else:
              raise YellowbrickValueError(
                  "Orientation must be 'h' or 'v'"
              )
  
  
  ##########################################################################
  ## Rank 2D Feature Visualizer
  ##########################################################################
  
  class Rank2D(RankDBase):
      """
      Rank2D performs pairwise comparisons of each feature in the data set with
      a specific metric or algorithm (e.g. Pearson correlation) then returns
      them ranked as a lower left triangle diagram.
  
      Parameters
      ----------
      ax : matplotlib Axes, default: None
          The axis to plot the figure on. If None is passed in the current axes
          will be used (or generated if required).
  
      algorithm : str, default: 'pearson'
          The ranking algorithm to use, one of:
          'pearson', 'covariance', 'spearman', 'kendalltau'.
  
      features : list
          A list of feature names to use.
          If a DataFrame is passed to fit and features is None, feature
          names are selected as the columns of the DataFrame.
  
      colormap : string or cmap, default: 'RdBu_r'
          optional string or matplotlib cmap to colorize lines
          Use either color to colorize the lines on a per class basis or
          colormap to color them on a continuous scale.
  
      show_feature_names : boolean, default: True
          If True, the feature names are used to label the axis ticks in the
          plot.
  
      kwargs : dict
          Keyword arguments that are passed to the base class and may influence
          the visualization as defined in other Visualizers.
  
      Attributes
      ----------
      ranks_ : ndarray
          An array of rank scores with shape (n,n), where n is the
          number of features. It is computed during `fit`.
  
      Examples
      --------
  
      >>> visualizer = Rank2D()
      >>> visualizer.fit(X, y)
      >>> visualizer.transform(X)
      >>> visualizer.poof()
  
      Notes
      -----
      These parameters can be influenced later on in the visualization
      process, but can and should be set as early as possible.
      """
  
      ranking_methods = {
          'pearson': lambda X: np.corrcoef(X.transpose()),
          'covariance': lambda X: np.cov(X.transpose()),
          'spearman': lambda X: spearmanr(X, axis=0)[0],
          'kendalltau': lambda X: kendalltau(X)
      }
  
      def __init__(self, ax=None, algorithm='pearson', features=None,
                   colormap='RdBu_r', show_feature_names=True, **kwargs):
          """
          Initialize the class with the options required to rank and
          order features as well as visualize the result.
          """
          super(Rank2D, self).__init__(
              ax=ax, algorithm=algorithm, features=features,
              show_feature_names=show_feature_names, **kwargs
          )
          self.colormap=colormap
  
      def draw(self, **kwargs):
          """
          Draws the heatmap of the ranking matrix of variables.
          """
          # Set the axes aspect to be equal
          self.ax.set_aspect("equal")
  
          # Generate a mask for the upper triangle
          mask = np.zeros_like(self.ranks_, dtype=np.bool)
          mask[np.triu_indices_from(mask)] = True
  
          # Draw the heatmap
          # TODO: Move mesh to a property so the colorbar can be finalized
          data = np.ma.masked_where(mask, self.ranks_)
          mesh = self.ax.pcolormesh(data, cmap=self.colormap, vmin=-1, vmax=1)
  
          # Set the Axis limits
          self.ax.set(
              xlim=(0, data.shape[1]), ylim=(0, data.shape[0])
          )
  
          # Add the colorbar
          cb = self.ax.figure.colorbar(mesh, None, self.ax)
          cb.outline.set_linewidth(0)
  
          # Reverse the rows to get the lower left triangle
          self.ax.invert_yaxis()
  
          # Add ticks and tick labels
          self.ax.set_xticks(np.arange(len(self.ranks_)) + 0.5)
          self.ax.set_yticks(np.arange(len(self.ranks_)) + 0.5)
          if self.show_feature_names_:
              self.ax.set_xticklabels(self.features_, rotation=90)
              self.ax.set_yticklabels(self.features_)
          else:
              self.ax.set_xticklabels([])
              self.ax.set_yticklabels([])
  
  
  ##########################################################################
  ## Quick Methods
  ##########################################################################
  
  def rank1d(X, y=None, ax=None, algorithm='shapiro', features=None,
             orient='h', show_feature_names=True, **kwargs):
      """Scores each feature with the algorithm and ranks them in a bar plot.
  
      This helper function is a quick wrapper to utilize the Rank1D Visualizer
      (Transformer) for one-off analysis.
  
      Parameters
      ----------
      X : ndarray or DataFrame of shape n x m
          A matrix of n instances with m features
  
      y : ndarray or Series of length n
          An array or series of target or class values
  
      ax : matplotlib axes
          the axis to plot the figure on.
  
      algorithm : one of {'shapiro', }, default: 'shapiro'
          The ranking algorithm to use, default is 'Shapiro-Wilk.
  
      features : list
          A list of feature names to use.
          If a DataFrame is passed to fit and features is None, feature
          names are selected as the columns of the DataFrame.
  
      orient : 'h' or 'v'
          Specifies a horizontal or vertical bar chart.
  
      show_feature_names : boolean, default: True
          If True, the feature names are used to label the axis ticks in the
          plot.
  
      Returns
      -------
      ax : matplotlib axes
          Returns the axes that the parallel coordinates were drawn on.
  
      """
      # Instantiate the visualizer
      visualizer = Rank1D(ax, algorithm, features, orient, show_feature_names,
                          **kwargs)
  
      # Fit and transform the visualizer (calls draw)
      visualizer.fit(X, y, **kwargs)
      visualizer.transform(X)
  
      # Return the axes object on the visualizer
      return visualizer.ax
  
  def rank2d(X, y=None, ax=None, algorithm='pearson', features=None,
             show_feature_names=True, colormap='RdBu_r', **kwargs):
      """Displays pairwise comparisons of features with the algorithm and ranks
      them in a lower-left triangle heatmap plot.
  
      This helper function is a quick wrapper to utilize the Rank2D Visualizer
      (Transformer) for one-off analysis.
  
      Parameters
      ----------
      X : ndarray or DataFrame of shape n x m
          A matrix of n instances with m features
  
      y : ndarray or Series of length n
          An array or series of target or class values
  
      ax : matplotlib axes
          the axis to plot the figure on.
  
      algorithm : one of {pearson, covariance, spearman, kendalltau}
          the ranking algorithm to use, default is Pearson correlation.
  
      features : list
          A list of feature names to use.
          If a DataFrame is passed to fit and features is None, feature
          names are selected as the columns of the DataFrame.
  
      show_feature_names : boolean, default: True
          If True, the feature names are used to label the axis ticks in the
          plot.
  
      colormap : string or cmap
          optional string or matplotlib cmap to colorize lines
          Use either color to colorize the lines on a per class basis or
          colormap to color them on a continuous scale.
  
      Returns
      -------
      ax : matplotlib axes
          Returns the axes that the parallel coordinates were drawn on.
  
      """
      # Instantiate the visualizer
      visualizer = Rank2D(ax, algorithm, features, colormap, show_feature_names,
                          **kwargs)
  
      # Fit and transform the visualizer (calls draw)
      visualizer.fit(X, y, **kwargs)
      visualizer.transform(X)
  
      # Return the axes object on the visualizer
      return visualizer.ax