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%matplotlib inline
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# yellowbrick.features.jointplot
# Implementations of joint plots for univariate and bivariate analysis.
#
# Author: Prema Damodaran Roman
# Created:
#
# Copyright (C) 2017 District Data Labs
# For license information, see LICENSE.txt
#
# ID: jointplot.py
##########################################################################
## Imports
##########################################################################
import numpy as np
import pandas as pd
import matplotlib as mpl
import matplotlib.pyplot as plt
from yellowbrick.features.base import FeatureVisualizer
from yellowbrick.exceptions import YellowbrickValueError
from yellowbrick.bestfit import draw_best_fit
from yellowbrick.utils import is_dataframe
class JointPlotVisualizer(FeatureVisualizer):
"""
JointPlotVisualizer allows for a simultaneous visualization of the relationship
between two variables and the distrbution of each individual variable. The
relationship is plotted along the joint axis and univariate distributions
are plotted on top of the x axis and to the right of the y axis.
"""
def __init__(self, ax=None, feature=None, target=None,
joint_plot='scatter', joint_args=None,
xy_plot='hist', xy_args=None,
size=6, ratio=5, space=.2, **kwargs):
"""
Initialize the visualization with many of the options required
in order to make most visualizations work.
These parameters can be influenced later on in the visualization
process, but can and should be set as early as possible.
Parameters
----------
ax: This is inherited from FeatureVisualizer but is defined within
JointPlotVisualizer since there are three axes objects.
feature: The name of the X variable
If a DataFrame is passed to fit and feature is None, feature
is selected as the column of the DataFrame. There must be only
one column in the DataFrame.
target: The name of the Y variable
If target is None and a y value is passed to fit then the target
is selected from the target vector.
joint_plot: The type of plot to render in the joint axis. Currently,
the choices are scatter and hex. Use scatter for small datasets
and hex for large datasets
joint_args: Keyword arguments used for customizing the joint plot.
Property Description
alpha transparency
facecolor background color of the joint axis
aspect aspect ratio
fit used if scatter is selected for joint_plot to draw a
best fit line - values can be True or False.
Uses Yellowbrick.bestfit
estimator used if scatter is selected for joint_plot to determine
the type of best fit line to use. Refer to
Yellowbrick.bestfit for types of estimators that can be used.
x_bins used if hex is selected to set the number of bins for the x value
y_bins used if hex is selected to set the number of bins for the y value
cmap 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.
xy_plot: The type of plot to render along the x and y axes.
Currently, the choice is hist
xy_args: Keyword arguments used for customizing the x and y plots.
Property Description
alpha transparency
facecolor_x background color of the x axis
facecolor_y background color of the y axis
bins used to set up the number of bins for the hist plot
histcolor_x used to set the color for the histogram on the x axis
histcolor_y used to set the color for the histogram on the y axis
size: Size of each side of the figure in inches.
ratio: Ratio of joint axis size to the x and y axes height.
space: Space between the joint axis and the x and y axes.
kwargs: Keyword arguments passed to the super class.
"""
#check matplotlib version - needs to be version 2.0.0
if mpl.__version__ == '2.0.0':
pass
else:
print('This Visualizer requires Matplotlib version 2.0.0. Please upgrade to continue.')
super(JointPlotVisualizer, self).__init__(ax, **kwargs)
self.feature = feature
self.target = target
self.joint_plot = joint_plot
self.joint_args = joint_args
self.xy_plot = xy_plot
self.xy_args = xy_args
self.size = size
self.ratio = ratio
self.space = space
def fit(self, X, y, **kwargs):
"""
Sets up the X and y variables for the jointplot
and checks to ensure that X and y are of the
correct data type
Fit calls draw
Parameters
----------
X : ndarray or DataFrame of shape n x 1
A matrix of n instances with 1 feature
y : ndarray or Series of length n
An array or series of the target value
kwargs: dict
keyword arguments passed to Scikit-Learn API.
"""
#throw an error if X has more than 1 column
if is_dataframe(X):
nrows, ncols = X.shape
if ncols > 1:
raise YellowbrickValueError(
"X needs to be an ndarray or DataFrame with one feature, please select one feature from the DataFrame"
)
#throw an error is y is None
if y is None:
raise YellowbrickValueError(
"Joint plots are useful for classification and regression problems, which require a target variable")
# Handle the feature name if it is None.
if self.feature is None:
# If X is a data frame, get the columns off it.
if is_dataframe(X):
self.feature = X.columns
else:
self.feature = ['x']
# Handle the target name if it is None.
if self.target is None:
self.target = ['y']
self.draw(X, y, **kwargs)
return self
def draw(self, X, y, **kwargs):
"""
Sets up the layout for the joint plot
draw calls draw_joint and draw_xy
to render the visualizations
"""
fig = plt.figure(figsize=(self.size, self.size))
gs = plt.GridSpec(self.ratio + 1, self.ratio + 1)
#Set up the 3 axes objects
joint_ax = fig.add_subplot(gs[1:, :-1])
x_ax = fig.add_subplot(gs[0, :-1], sharex=joint_ax)
y_ax = fig.add_subplot(gs[1:, -1], sharey=joint_ax)
fig.tight_layout()
fig.subplots_adjust(hspace=self.space, wspace=self.space)
self.fig = fig
self.joint_ax = joint_ax
self.x_ax = x_ax
self.y_ax = y_ax
self.draw_joint(X, y, **kwargs)
self.draw_xy(X, y, **kwargs)
def draw_joint(self, X, y, **kwargs):
"""
Draws the visualization for the joint axis
"""
if self.joint_args is None:
self.joint_args = {}
self.joint_args.setdefault("alpha", 0.4)
facecolor = self.joint_args.pop("facecolor", "#dddddd")
self.joint_ax.set_facecolor(facecolor)
if self.joint_plot == "scatter":
aspect = self.joint_args.pop("aspect", "auto")
self.joint_ax.set_aspect(aspect)
self.joint_ax.scatter(X, y, **self.joint_args)
fit = self.joint_args.pop("fit", True)
if fit:
estimator = self.joint_args.pop("estimator", "linear")
draw_best_fit(X, y, self.joint_ax, estimator)
elif self.joint_plot == "hex":
x_bins = self.joint_args.pop("x_bins", 50)
y_bins = self.joint_args.pop("y_bins", 50)
colormap = self.joint_args.pop("cmap", 'Blues')
gridsize = int(np.mean([x_bins, y_bins]))
xmin = X.min()
xmax = X.max()
ymin = y.min()
ymax = y.max()
self.joint_ax.hexbin(X, y, gridsize=gridsize, cmap=colormap,
mincnt=1, **self.joint_args)
self.joint_ax.axis([xmin, xmax, ymin, ymax])
def draw_xy(self, X, y, **kwargs):
"""
Draws the visualization for the x and y axes
"""
if self.xy_args is None:
self.xy_args = {}
facecolor_x = self.xy_args.pop("facecolor_x", "#dddddd")
self.x_ax.set_facecolor(facecolor_x)
facecolor_y = self.xy_args.pop("facecolor_y", "#dddddd")
self.y_ax.set_facecolor(facecolor_y)
if self.xy_plot == "hist":
hist_bins = self.xy_args.pop("bins", 50)
self.xy_args.setdefault("alpha", 0.4)
histcolor_x = self.xy_args.pop("histcolor_x", "#6897bb")
self.x_ax.set_facecolor(facecolor_x)
histcolor_y = self.xy_args.pop("histcolor_y", "#6897bb")
self.y_ax.set_facecolor(facecolor_y)
self.x_ax.hist(X, bins=hist_bins, color=histcolor_x, **self.xy_args)
self.y_ax.hist(y, bins=hist_bins, color=histcolor_y,
orientation='horizontal', **self.xy_args)
def show(self, **kwargs):
"""
Creates the labels for the feature and target variables
"""
self.joint_ax.set_xlabel(self.feature)
self.joint_ax.set_ylabel(self.target)
self.finalize(**kwargs)
def finalize(self, **kwargs):
"""
Finalize executes any subclass-specific axes finalization steps.
The user calls show and show calls finalize.
Parameters
----------
kwargs: generic keyword arguments.
"""
plt.setp(self.x_ax.get_xticklabels(), visible=False)
plt.setp(self.y_ax.get_yticklabels(), visible=False)
plt.setp(self.x_ax.yaxis.get_majorticklines(), visible=False)
plt.setp(self.x_ax.yaxis.get_minorticklines(), visible=False)
plt.setp(self.y_ax.xaxis.get_majorticklines(), visible=False)
plt.setp(self.y_ax.xaxis.get_minorticklines(), visible=False)
plt.setp(self.x_ax.get_yticklabels(), visible=False)
plt.setp(self.y_ax.get_xticklabels(), visible=False)
self.x_ax.yaxis.grid(False)
self.y_ax.xaxis.grid(False)
self.fig.suptitle("Joint Plot of {} vs {}"
.format(self.feature, self.target), y=1.05)
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import os
import sys
import pandas as pd
## The path to the test data sets
FIXTURES = os.path.join(os.getcwd(), "data")
## Dataset loading mechanisms
datasets = {
"credit": os.path.join(FIXTURES, "credit", "credit.csv"),
"concrete": os.path.join(FIXTURES, "concrete", "concrete.csv"),
"occupancy": os.path.join(FIXTURES, "occupancy", "occupancy.csv"),
}
def load_data(name):
"""
Loads and wrangles the passed in dataset by name.
If download is specified, this method will download any missing files.
"""
# Get the path from the datasets
path = datasets[name]
# Return the data frame
return pd.read_csv(path)
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# Load the data
df = load_data('concrete')
features = ['cement', 'slag', 'ash', 'water', 'splast', 'coarse', 'fine', 'age']
feature = 'cement'
target = 'strength'
# Get the X and y data from the DataFrame
X = df[feature].as_matrix()
y = df[target].as_matrix()
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visualizer = JointPlotVisualizer(feature=feature, target=target, joint_plot="hex")
visualizer.fit(X, y) # Fit the data to the visualizer
g = visualizer.show() # Draw/show/show the data
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