# yellowbrick.features.pcoords
# Implementations of parallel coordinates for feature analysis.
#
# Author:   <NAME> <<EMAIL>>
# Created:  Mon Oct 03 21:46:06 2016 -0400
#
# Copyright (C) 2016 District Data Labs
# For license information, see LICENSE.txt
#
# ID: pcoords.py [] <EMAIL> $

"""
Implementations of parallel coordinates for multi-dimensional feature
analysis. There are a variety of parallel coordinates from Andrews Curves to
coordinates that optimize column order.
"""

##########################################################################
## Imports
##########################################################################

import numpy as np
import matplotlib.pyplot as plt

from yellowbrick.features.base import DataVisualizer
from yellowbrick.exceptions import YellowbrickTypeError
from yellowbrick.style.colors import resolve_colors, get_color_cycle

##########################################################################
## Quick Methods
##########################################################################

def parallel_coordinates(X, y=None, ax=None, features=None, classes=None,
                         color=None, colormap=None, vlines=True,
                         vlines_kwds=None, **kwargs):
    """Displays each feature as a vertical axis and each instance as a line.

    This helper function is a quick wrapper to utilize the ParallelCoordinates
    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, default: None
        The axes to plot the figure on.

    features : list of strings, default: None
        The names of the features or columns

    classes : list of strings, default: None
        The names of the classes in the target

    color : list or tuple of colors, default: None
        Specify the colors for each individual class

    colormap : string or matplotlib cmap, default: None
        Sequential colormap for continuous target

    vlines : bool, default: True
        Display the vertical azis lines

    vlines_kwds : dict, default: None
        Keyword arguments to draw the vlines

    kwargs : dict
        Keyword arguments that are passed to the base class and may influence
        the visualization as defined in other Visualizers.

    Returns
    -------
    ax : matplotlib axes
        Returns the axes that the parallel coordinates were drawn on.
    """
    # Instantiate the visualizer
    visualizer = ParallelCoordinates(
        ax, features, classes, color, colormap, vlines, vlines_kwds, **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


##########################################################################
## Static Parallel Coordinates Visualizer
##########################################################################

class ParallelCoordinates(DataVisualizer):
    """
    Parallel coordinates displays each feature as a vertical axis spaced
    evenly along the horizontal, and each instance as a line drawn between
    each individual axis.

    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).

    features : list, default: None
        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.

    classes : list, default: None
        a list of class names for the legend
        If classes is None and a y value is passed to fit then the classes
        are selected from the target vector.

    color : list or tuple, default: None
        optional list or tuple of colors to colorize lines
        Use either color to colorize the lines on a per class basis or
        colormap to color them on a continuous scale.

    colormap : string or cmap, default: None
        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.

    vlines : boolean, default: True
        flag to determine vertical line display

    vlines_kwds : dict, default: None
        options to style or display the vertical lines, default: None

    kwargs : dict
        Keyword arguments that are passed to the base class and may influence
        the visualization as defined in other Visualizers.

    Examples
    --------

    >>> visualizer = ParallelCoordinates()
    >>> 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.
    """

    def __init__(self, ax=None, features=None, classes=None, color=None,
                 colormap=None, vlines=True, vlines_kwds=None, **kwargs):
        super(ParallelCoordinates, self).__init__(
            ax, features, classes, color, colormap, **kwargs
        )

        # Visual Parameters
        self.show_vlines = vlines
        self.vlines_kwds = vlines_kwds or {
            'linewidth': 1, 'color': 'black'
        }

    def draw(self, X, y, **kwargs):
        """
        Called from the fit method, this method creates the parallel
        coordinates canvas and draws each instance and vertical lines on it.
        """

        # Get the shape of the data
        nrows, ncols = X.shape

        # Create the xticks for each column
        # TODO: Allow the user to specify this feature
        x = list(range(ncols))

        # Create the colors
        # TODO: Allow both colormap, listed colors, and palette definition
        # TODO: Make this an independent function or property for override!
        # color_values = resolve_colors(
        #     num_colors=len(self.classes_), colormap=self.colormap, color=self.color
        # )
        color_values = get_color_cycle()
        colors = dict(zip(self.classes_, color_values))

        # Track which labels are already in the legend
        used_legends = set([])

        # TODO: Make this function compatible with DataFrames!
        # TODO: Make an independent function to allow addition of instances!
        for idx, row in enumerate(X):
            # TODO: How to map classmap to labels?
            label = y[idx] # Get the label for the row
            label = self.classes_[label]

            if label not in used_legends:
                used_legends.add(label)
                self.ax.plot(x, row, color=colors[label], label=label, **kwargs)
            else:
                self.ax.plot(x, row, color=colors[label], **kwargs)

        # Add the vertical lines
        # TODO: Make an independent function for override!
        if self.show_vlines:
            for idx in x:
                self.ax.axvline(idx, **self.vlines_kwds)

        # Set the limits
        self.ax.set_xticks(x)
        self.ax.set_xticklabels(self.features_)
        self.ax.set_xlim(x[0], x[-1])

    def finalize(self, **kwargs):
        """
        Finalize executes any subclass-specific axes finalization steps.
        The user calls poof and poof calls finalize.

        Parameters
        ----------
        kwargs: generic keyword arguments.

        """
        # Set the title
        self.set_title(
            'Parallel Coordinates for {} Features'.format(len(self.features_))
        )

        # Set the legend and the grid
        self.ax.legend(loc='best')
        self.ax.grid()

# yellowbrick.features.pcoords
# Implementations of parallel coordinates for feature analysis.
#
# Author:   <NAME> <<EMAIL>>
# Created:  Mon Oct 03 21:46:06 2016 -0400
#
# Copyright (C) 2016 District Data Labs
# For license information, see LICENSE.txt
#
# ID: pcoords.py [] <EMAIL> $

"""
Implementations of parallel coordinates for multi-dimensional feature
analysis. There are a variety of parallel coordinates from Andrews Curves to
coordinates that optimize column order.
"""

##########################################################################
## Imports
##########################################################################

import numpy as np
import matplotlib.pyplot as plt

from yellowbrick.features.base import DataVisualizer
from yellowbrick.exceptions import YellowbrickTypeError
from yellowbrick.style.colors import resolve_colors, get_color_cycle

##########################################################################
## Quick Methods
##########################################################################

def parallel_coordinates(X, y=None, ax=None, features=None, classes=None,
                         color=None, colormap=None, vlines=True,
                         vlines_kwds=None, **kwargs):
    """Displays each feature as a vertical axis and each instance as a line.

    This helper function is a quick wrapper to utilize the ParallelCoordinates
    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, default: None
        The axes to plot the figure on.

    features : list of strings, default: None
        The names of the features or columns

    classes : list of strings, default: None
        The names of the classes in the target

    color : list or tuple of colors, default: None
        Specify the colors for each individual class

    colormap : string or matplotlib cmap, default: None
        Sequential colormap for continuous target

    vlines : bool, default: True
        Display the vertical azis lines

    vlines_kwds : dict, default: None
        Keyword arguments to draw the vlines

    kwargs : dict
        Keyword arguments that are passed to the base class and may influence
        the visualization as defined in other Visualizers.

    Returns
    -------
    ax : matplotlib axes
        Returns the axes that the parallel coordinates were drawn on.
    """
    # Instantiate the visualizer
    visualizer = ParallelCoordinates(
        ax, features, classes, color, colormap, vlines, vlines_kwds, **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


##########################################################################
## Static Parallel Coordinates Visualizer
##########################################################################

class ParallelCoordinates(DataVisualizer):
    """
    Parallel coordinates displays each feature as a vertical axis spaced
    evenly along the horizontal, and each instance as a line drawn between
    each individual axis.

    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).

    features : list, default: None
        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.

    classes : list, default: None
        a list of class names for the legend
        If classes is None and a y value is passed to fit then the classes
        are selected from the target vector.

    color : list or tuple, default: None
        optional list or tuple of colors to colorize lines
        Use either color to colorize the lines on a per class basis or
        colormap to color them on a continuous scale.

    colormap : string or cmap, default: None
        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.

    vlines : boolean, default: True
        flag to determine vertical line display

    vlines_kwds : dict, default: None
        options to style or display the vertical lines, default: None

    kwargs : dict
        Keyword arguments that are passed to the base class and may influence
        the visualization as defined in other Visualizers.

    Examples
    --------

    >>> visualizer = ParallelCoordinates()
    >>> 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.
    """

    def __init__(self, ax=None, features=None, classes=None, color=None,
                 colormap=None, vlines=True, vlines_kwds=None, **kwargs):
        super(ParallelCoordinates, self).__init__(
            ax, features, classes, color, colormap, **kwargs
        )

        # Visual Parameters
        self.show_vlines = vlines
        self.vlines_kwds = vlines_kwds or {
            'linewidth': 1, 'color': 'black'
        }

    def draw(self, X, y, **kwargs):
        """
        Called from the fit method, this method creates the parallel
        coordinates canvas and draws each instance and vertical lines on it.
        """

        # Get the shape of the data
        nrows, ncols = X.shape

        # Create the xticks for each column
        # TODO: Allow the user to specify this feature
        x = list(range(ncols))

        # Create the colors
        # TODO: Allow both colormap, listed colors, and palette definition
        # TODO: Make this an independent function or property for override!
        # color_values = resolve_colors(
        #     num_colors=len(self.classes_), colormap=self.colormap, color=self.color
        # )
        color_values = get_color_cycle()
        colors = dict(zip(self.classes_, color_values))

        # Track which labels are already in the legend
        used_legends = set([])

        # TODO: Make this function compatible with DataFrames!
        # TODO: Make an independent function to allow addition of instances!
        for idx, row in enumerate(X):
            # TODO: How to map classmap to labels?
            label = y[idx] # Get the label for the row
            label = self.classes_[label]

            if label not in used_legends:
                used_legends.add(label)
                self.ax.plot(x, row, color=colors[label], label=label, **kwargs)
            else:
                self.ax.plot(x, row, color=colors[label], **kwargs)

        # Add the vertical lines
        # TODO: Make an independent function for override!
        if self.show_vlines:
            for idx in x:
                self.ax.axvline(idx, **self.vlines_kwds)

        # Set the limits
        self.ax.set_xticks(x)
        self.ax.set_xticklabels(self.features_)
        self.ax.set_xlim(x[0], x[-1])

    def finalize(self, **kwargs):
        """
        Finalize executes any subclass-specific axes finalization steps.
        The user calls poof and poof calls finalize.

        Parameters
        ----------
        kwargs: generic keyword arguments.

        """
        # Set the title
        self.set_title(
            'Parallel Coordinates for {} Features'.format(len(self.features_))
        )

        # Set the legend and the grid
        self.ax.legend(loc='best')
        self.ax.grid()

# yellowbrick.features.pcoords # Implementations of parallel coordinates for feature analysis. # # Author:   <NAME> <<EMAIL>> # Created:  Mon Oct 03 21:46:06 2016 -0400 # # Copyright (C) 2016 District Data Labs # For license information, see LICENSE.txt # # ID: pcoords.py [] <EMAIL> $ Implementations of parallel coordinates for multi-dimensional feature analysis. There are a variety of parallel coordinates from Andrews Curves to coordinates that optimize column order. ########################################################################## ## Imports ########################################################################## ########################################################################## ## Quick Methods ########################################################################## Displays each feature as a vertical axis and each instance as a line.      This helper function is a quick wrapper to utilize the ParallelCoordinates     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, default: None         The axes to plot the figure on.      features : list of strings, default: None         The names of the features or columns      classes : list of strings, default: None         The names of the classes in the target      color : list or tuple of colors, default: None         Specify the colors for each individual class      colormap : string or matplotlib cmap, default: None         Sequential colormap for continuous target      vlines : bool, default: True         Display the vertical azis lines      vlines_kwds : dict, default: None         Keyword arguments to draw the vlines      kwargs : dict         Keyword arguments that are passed to the base class and may influence         the visualization as defined in other Visualizers.      Returns     -------     ax : matplotlib axes         Returns the axes that the parallel coordinates were drawn on. # Instantiate the visualizer # Fit and transform the visualizer (calls draw) # Return the axes object on the visualizer ########################################################################## ## Static Parallel Coordinates Visualizer ########################################################################## Parallel coordinates displays each feature as a vertical axis spaced     evenly along the horizontal, and each instance as a line drawn between     each individual axis.      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).      features : list, default: None         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.      classes : list, default: None         a list of class names for the legend         If classes is None and a y value is passed to fit then the classes         are selected from the target vector.      color : list or tuple, default: None         optional list or tuple of colors to colorize lines         Use either color to colorize the lines on a per class basis or         colormap to color them on a continuous scale.      colormap : string or cmap, default: None         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.      vlines : boolean, default: True         flag to determine vertical line display      vlines_kwds : dict, default: None         options to style or display the vertical lines, default: None      kwargs : dict         Keyword arguments that are passed to the base class and may influence         the visualization as defined in other Visualizers.      Examples     --------      >>> visualizer = ParallelCoordinates()     >>> 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. # Visual Parameters Called from the fit method, this method creates the parallel         coordinates canvas and draws each instance and vertical lines on it. # Get the shape of the data # Create the xticks for each column # TODO: Allow the user to specify this feature # Create the colors # TODO: Allow both colormap, listed colors, and palette definition # TODO: Make this an independent function or property for override! # color_values = resolve_colors( #     num_colors=len(self.classes_), colormap=self.colormap, color=self.color # ) # Track which labels are already in the legend # TODO: Make this function compatible with DataFrames! # TODO: Make an independent function to allow addition of instances! # TODO: How to map classmap to labels? # Get the label for the row # Add the vertical lines # TODO: Make an independent function for override! # Set the limits Finalize executes any subclass-specific axes finalization steps.         The user calls poof and poof calls finalize.          Parameters         ----------         kwargs: generic keyword arguments. # Set the title # Set the legend and the grid