# yellowbrick.classifier.class_balance
# Class balance visualizer for showing per-class support.
#
# Author: Rebecca Bilbro <rbilbro@districtdatalabs.com>
# Author: Benjamin Bengfort <bbengfort@districtdatalabs.com>
# Author: Neal Humphrey
# Author: Larry Gray
# Created: Wed May 18 12:39:40 2016 -0400
#
# Copyright (C) 2017 District Data Labs
# For license information, see LICENSE.txt
#
# ID: class_balance.py [5388065] neal@nhumphrey.com $
"""
Class balance visualizer for showing per-class support.
"""
##########################################################################
## Imports
##########################################################################
import matplotlib.pyplot as plt
import numpy as np
from .base import ClassificationScoreVisualizer
from sklearn.model_selection import train_test_split
from sklearn.metrics import precision_recall_fscore_support
from sklearn.utils.multiclass import unique_labels
from sklearn.metrics.classification import _check_targets
from ..exceptions import ModelError, YellowbrickValueError
from ..style.colors import resolve_colors
##########################################################################
## Class Balance Chart
##########################################################################
[文档]class ClassBalance(ClassificationScoreVisualizer):
"""
Class balance chart that shows the support for each class in the
fitted classification model displayed as a bar plot. It is initialized
with a fitted model and generates a class balance chart on draw.
Parameters
----------
ax: axes
the axis to plot the figure on.
model: estimator
Scikit-Learn estimator object. Should be an instance of a classifier,
else ``__init__()`` will raise an exception.
classes: list
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 passed to the super class. Here, used
to colorize the bars in the histogram.
Notes
-----
These parameters can be influenced later on in the visualization
process, but can and should be set as early as possible.
"""
[文档] def score(self, X, y=None, **kwargs):
"""
Generates the Scikit-Learn precision_recall_fscore_support
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
Returns
-------
ax : the axis with the plotted figure
"""
y_pred = self.predict(X)
self.scores = precision_recall_fscore_support(y, y_pred)
self.support = dict(zip(self.classes_, self.scores[-1]))
return self.draw()
[文档] def draw(self):
"""
Renders the class balance chart across the axis.
Returns
-------
ax : the axis with the plotted figure
"""
#TODO: Would rather not have to set the colors with this method.
# Refactor to make better use of yb_palettes module?
colors = self.colors[0:len(self.classes_)]
self.ax.bar(
np.arange(len(self.support)), self.support.values(),
color=colors, align='center', width=0.5
)
return self.ax
[文档] 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('Class Balance for {}'.format(self.name))
# Set the x ticks with the class names
self.ax.set_xticks(np.arange(len(self.support)))
self.ax.set_xticklabels(self.support.keys())
# Compute the ceiling for the y limit
cmax = max(self.support.values())
self.ax.set_ylim(0, cmax + cmax* 0.1)
def class_balance(model, X, y=None, ax=None, classes=None, **kwargs):
"""Quick method:
Displays the support for each class in the
fitted classification model displayed as a bar plot.
This helper function is a quick wrapper to utilize the ClassBalance
ScoreVisualizer 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 axes to plot the figure on.
model : the Scikit-Learn estimator (should be a classifier)
classes : list of strings
The names of the classes in the target
Returns
-------
ax : matplotlib axes
Returns the axes that the class balance plot was drawn on.
"""
# Instantiate the visualizer
visualizer = ClassBalance(model, ax, classes, **kwargs)
# Create the train and test splits
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
# Fit and transform the visualizer (calls draw)
visualizer.fit(X_train, y_train, **kwargs)
visualizer.score(X_test, y_test)
# Return the axes object on the visualizer
return visualizer.ax
##########################################################################
## Class Prediction Error Chart
##########################################################################
class ClassPredictionError(ClassificationScoreVisualizer):
"""
Class Prediction Error chart that shows the support for each class in the
fitted classification model displayed as a stacked bar. Each bar is
segmented to show the distribution of predicted classes for each
class. It is initialized with a fitted model and generates a
class prediction error chart on draw.
Parameters
----------
ax: axes
the axis to plot the figure on.
model: estimator
Scikit-Learn estimator object. Should be an instance of a classifier,
else ``__init__()`` will raise an exception.
classes: list
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 passed to the super class. Here, used
to colorize the bars in the histogram.
Notes
-----
These parameters can be influenced later on in the visualization
process, but can and should be set as early as possible.
"""
def score(self, X, y, **kwargs):
"""
Generates a 2D array where each row is the count of the
predicted classes and each column is the true class
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
Returns
-------
ax : the axis with the plotted figure
"""
# We're replying on predict to raise NotFitted
y_pred = self.predict(X)
y_type, y_true, y_pred = _check_targets(y, y_pred)
if y_type not in ("binary", "multiclass"):
raise YellowbrickValueError("%s is not supported" % y_type)
indices = unique_labels(y_true, y_pred)
if len(self.classes_) > len(indices):
raise ModelError("y and y_pred contain zero values "
"for one of the specified classes")
elif len(self.classes_) < len(indices):
raise NotImplementedError("filtering classes is "
"currently not supported")
# Create a table of scores whose rows are the true classes
# and whose columns are the predicted classes; each element
# is the count of predictions for that class that match the true
# value of that class.
self.scores_ = np.array([
[
(y_pred[y == label_t] == label_p).sum()
for label_p in indices
]
for label_t in indices
])
return self.draw()
def draw(self):
"""
Renders the class prediction error across the axis.
Returns
-------
ax : the axis with the plotted figure
"""
indices = np.arange(len(self.classes_))
prev = np.zeros(len(self.classes_))
colors = resolve_colors(
colors=self.colors,
n_colors=len(self.classes_))
for idx, row in enumerate(self.scores_):
self.ax.bar(indices, row, label=self.classes_[idx],
bottom=prev, color=colors[idx])
prev += row
return self.ax
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.
"""
indices = np.arange(len(self.classes_))
# Set the title
self.set_title("Class Prediction Error for {}".format(self.name))
# Set the x ticks with the class names
self.ax.set_xticks(indices)
self.ax.set_xticklabels(self.classes_)
# Set the axes labels
self.ax.set_xlabel("actual class")
self.ax.set_ylabel("number of predicted class")
# Compute the ceiling for the y limit
cmax = max([sum(scores) for scores in self.scores_])
self.ax.set_ylim(0, cmax + cmax * 0.1)
# Put the legend outside of the graph
plt.legend(bbox_to_anchor=(1.04, 0.5), loc="center left")
plt.tight_layout(rect=[0, 0, 0.85, 1])
def class_prediction_error(model, X, y=None, ax=None, classes=None,
test_size=0.2, **kwargs):
"""Quick method:
Displays the support for each class in the
fitted classification model displayed as a stacked bar plot.
Each bar is segmented to show the distribution of predicted
classes for each class.
This helper function is a quick wrapper to utilize the ClassPredictionError
ScoreVisualizer 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 axes to plot the figure on.
model : the Scikit-Learn estimator (should be a classifier)
classes : list of strings
The names of the classes in the target
Returns
-------
ax : matplotlib axes
Returns the axes that the class prediction error plot was drawn on.
"""
# Instantiate the visualizer
visualizer = ClassPredictionError(model, ax, classes, **kwargs)
# Create the train and test splits
X_train, X_test, y_train, y_test = train_test_split(X, y,
test_size=test_size)
# Fit and transform the visualizer (calls draw)
visualizer.fit(X_train, y_train, **kwargs)
visualizer.score(X_test, y_test)
# Return the axes object on the visualizer
return visualizer.ax