Source code for yellowbrick.contrib.missing.bar

# yellowbrick.contrib.missing.bar
# Missing Values Bar Visualizer
#
# Author:  Nathan Danielsen
# Created: Fri Mar 29 5:17:36 2018 -0500
#
# Copyright (C) 2018 The scikit-yb developers
# For license information, see LICENSE.txt
#
# ID: bar.py [1443e16] ndanielsen@users.noreply.github.com $

"""
Bar visualizer of missing values by column.
"""

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

import numpy as np

from yellowbrick.style.palettes import color_palette
from .base import MissingDataVisualizer


##########################################################################
## MissingValues Visualizer
##########################################################################


[docs]class MissingValuesBar(MissingDataVisualizer): """The MissingValues Bar visualizer creates a bar graph that lists the total count of missing values for each selected feature column. When y targets are supplied to fit, the output is a stacked bar chart where each color corresponds to the total NaNs for the feature in that column. Parameters ---------- alpha : float, default: 0.5 A value for bending elments with the background. marker : matplotlib marker, default: | The marker used for each element coordinate in the plot color : string, default: black The color for drawing the bar chart when the y targets are not passed to fit. colors : list, default: None The color palette for drawing a stack bar chart when the y targets are passed to fit. 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. kwargs : dict Keyword arguments that are passed to the base class and may influence the visualization as defined in other Visualizers. Attributes ---------- features_ : np.array The feature labels ranked according to their importance classes_ : np.array The class labels for each of the target values Examples -------- >>> from yellowbrick.contrib.missing import MissingValuesBar >>> visualizer = MissingValuesBar() >>> visualizer.fit(X, y=y) >>> visualizer.show() """ def __init__(self, width=0.5, color=None, colors=None, classes=None, **kwargs): if "target_type" not in kwargs: kwargs["target_type"] = "single" super(MissingValuesBar, self).__init__(**kwargs) self.width = width # the width of the bars self.classes_ = classes self.ind = None # Convert to array if necessary to match estimator.classes_ if self.classes_ is not None: self.classes_ = np.array(classes) # Set up classifier score visualization properties self.color = color if self.classes_ is not None: n_colors = len(self.classes_) else: n_colors = None self.colors = color_palette(kwargs.pop("colors", None), n_colors)
[docs] def get_nan_col_counts(self, **kwargs): # where matrix contains strings, handle them if np.issubdtype(self.X.dtype, np.string_) or np.issubdtype( self.X.dtype, np.unicode_ ): mask = np.where(self.X == "") nan_matrix = np.zeros(self.X.shape) nan_matrix[mask] = np.nan else: nan_matrix = self.X.astype(np.float) if self.y is None: nan_col_counts = [np.count_nonzero(np.isnan(col)) for col in nan_matrix.T] return nan_col_counts else: # add in counting of np.nan per target y by column nan_counts = [] for target_value in np.unique(self.y): indices = np.argwhere(self.y == target_value) target_matrix = nan_matrix[indices.flatten()] nan_col_counts = np.array( [np.count_nonzero(np.isnan(col)) for col in target_matrix.T] ) nan_counts.append((target_value, nan_col_counts)) return nan_counts
[docs] def draw(self, X, y, **kwargs): """Called from the fit method, this method generated a horizontal bar plot. If y is none, then draws a simple horizontal bar chart. If y is not none, then draws a stacked horizontal bar chart for each nan count per target values. """ nan_col_counts = self.get_nan_col_counts() # the x locations for the groups self.ind = np.arange(len(self.features_)) if y is None: self.ax.barh( self.ind - self.width / 2, nan_col_counts, self.width, color=self.color, label=None, ) else: self.draw_stacked_bar(nan_col_counts)
[docs] def draw_stacked_bar(self, nan_col_counts): """Draws a horizontal stacked bar chart with different colors for each count of nan values per label. """ for index, nan_values in enumerate(nan_col_counts): label, nan_col_counts = nan_values if index == 0: # first draw should be at zero bottom_chart = np.zeros(nan_col_counts.shape) # if features passed in then, label as such if self.classes_ is not None: label = self.classes_[index] color = self.colors[index] self.ax.barh( self.ind - self.width / 2, nan_col_counts, self.width, color=color, label=label, left=bottom_chart, ) # keep track of counts to build on stacked bottom_chart = nan_col_counts
[docs] def finalize(self, **kwargs): """ Sets a title and x-axis labels and adds a legend. Also ensures that the y tick values are correctly set to feature names. Parameters ---------- kwargs: generic keyword arguments. Notes ----- Generally this method is called from show and not directly by the user. """ # Set the title self.set_title("Count of Missing Values by Column") tick_locations = np.arange( len(self.features_) ) # the x locations for the groups self.ax.set_yticks(tick_locations) self.ax.set_yticklabels(self.get_feature_names()) # Remove the ticks from the graph self.ax.set_xlabel("Count") self.ax.legend(loc="best")
########################################################################## ## Quick Method ########################################################################## def missing_bar(X, y=None, ax=None, classes=None, width=0.5, color="black", **kwargs): """The MissingValues Bar visualizer creates a bar graph that lists the total count of missing values for each selected feature column. When y targets are supplied to fit, the output is a stacked bar chart where each color corresponds to the total NaNs for the feature in that column. Parameters ---------- alpha : float, default: 0.5 A value for bending elments with the background. marker : matplotlib marker, default: | The marker used for each element coordinate in the plot color : string, default: black The color for drawing the bar chart when the y targets are not passed to fit. colors : list, default: None The color pallette for drawing a stack bar chart when the y targets are passed to fit. 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. kwargs : dict Keyword arguments that are passed to the base class and may influence the visualization as defined in other Visualizers. Attributes ---------- features_ : np.array The feature labels ranked according to their importance classes_ : np.array The class labels for each of the target values Examples -------- >>> from yellowbrick.contrib.missing import missing_bar >>> visualizer = missing_bar(X, y=y) """ # Instantiate the visualizer visualizer = MissingValuesBar( ax=ax, classes=classes, width=width, color=color, **kwargs ) # Fit and transform the visualizer (calls draw) visualizer.fit(X, y) visualizer.show() # Return the axes object on the visualizer return visualizer.ax