# plot_decision_regions: Visualize the decision regions of a classifier

A function for plotting decision regions of classifiers in 1 or 2 dimensions.

from mlxtend.plotting import plot_decision_regions

## Example 1 - Decision regions in 2D

from mlxtend.plotting import plot_decision_regions
import matplotlib.pyplot as plt
from sklearn import datasets
from sklearn.svm import SVC

X = iris.data[:, [0, 2]]
y = iris.target

# Training a classifier
svm = SVC(C=0.5, kernel='linear')
svm.fit(X, y)

# Plotting decision regions
plot_decision_regions(X, y, clf=svm, legend=2)

plt.xlabel('sepal length [cm]')
plt.ylabel('petal length [cm]')
plt.title('SVM on Iris')
plt.show()


## Example 2 - Decision regions in 1D

from mlxtend.plotting import plot_decision_regions
import matplotlib.pyplot as plt
from sklearn import datasets
from sklearn.svm import SVC

X = iris.data[:, 2]
X = X[:, None]
y = iris.target

# Training a classifier
svm = SVC(C=0.5, kernel='linear')
svm.fit(X, y)

# Plotting decision regions
plot_decision_regions(X, y, clf=svm, legend=2)

plt.xlabel('sepal length [cm]')
plt.title('SVM on Iris')

plt.show()


## Example 3 - Decision Region Grids

from sklearn.linear_model import LogisticRegression
from sklearn.naive_bayes import GaussianNB
from sklearn.ensemble import RandomForestClassifier
from sklearn.svm import SVC
from sklearn import datasets
import numpy as np

# Initializing Classifiers
clf1 = LogisticRegression(random_state=1,
solver='newton-cg',
multi_class='multinomial')
clf2 = RandomForestClassifier(random_state=1, n_estimators=100)
clf3 = GaussianNB()
clf4 = SVC(gamma='auto')

X = iris.data[:, [0,2]]
y = iris.target

import matplotlib.pyplot as plt
from mlxtend.plotting import plot_decision_regions
import matplotlib.gridspec as gridspec
import itertools
gs = gridspec.GridSpec(2, 2)

fig = plt.figure(figsize=(10,8))

labels = ['Logistic Regression', 'Random Forest', 'Naive Bayes', 'SVM']
for clf, lab, grd in zip([clf1, clf2, clf3, clf4],
labels,
itertools.product([0, 1], repeat=2)):

clf.fit(X, y)
ax = plt.subplot(gs[grd[0], grd[1]])
fig = plot_decision_regions(X=X, y=y, clf=clf, legend=2)
plt.title(lab)

plt.show()


## Example 4 - Highlighting Test Data Points

from mlxtend.plotting import plot_decision_regions
from mlxtend.preprocessing import shuffle_arrays_unison
import matplotlib.pyplot as plt
from sklearn import datasets
from sklearn.svm import SVC

X, y = iris.data[:, [0,2]], iris.target
X, y = shuffle_arrays_unison(arrays=[X, y], random_seed=3)

X_train, y_train = X[:100], y[:100]
X_test, y_test = X[100:], y[100:]

# Training a classifier
svm = SVC(C=0.5, kernel='linear')
svm.fit(X_train, y_train)

# Plotting decision regions
plot_decision_regions(X, y, clf=svm, legend=2,
X_highlight=X_test)

plt.xlabel('sepal length [cm]')
plt.ylabel('petal length [cm]')
plt.title('SVM on Iris')
plt.show()


## Example 5 - Evaluating Classifier Behavior on Non-Linear Problems

from sklearn.linear_model import LogisticRegression
from sklearn.naive_bayes import GaussianNB
from sklearn.ensemble import RandomForestClassifier
from sklearn.svm import SVC

# Initializing Classifiers
clf1 = LogisticRegression(random_state=1, solver='lbfgs')
clf2 = RandomForestClassifier(n_estimators=100,
random_state=1)
clf3 = GaussianNB()
clf4 = SVC(gamma='auto')

# Loading Plotting Utilities
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
import itertools
from mlxtend.plotting import plot_decision_regions
import numpy as np


### XOR

xx, yy = np.meshgrid(np.linspace(-3, 3, 50),
np.linspace(-3, 3, 50))
rng = np.random.RandomState(0)
X = rng.randn(300, 2)
y = np.array(np.logical_xor(X[:, 0] > 0, X[:, 1] > 0),
dtype=int)

gs = gridspec.GridSpec(2, 2)

fig = plt.figure(figsize=(10,8))

labels = ['Logistic Regression', 'Random Forest', 'Naive Bayes', 'SVM']
for clf, lab, grd in zip([clf1, clf2, clf3, clf4],
labels,
itertools.product([0, 1], repeat=2)):

clf.fit(X, y)
ax = plt.subplot(gs[grd[0], grd[1]])
fig = plot_decision_regions(X=X, y=y, clf=clf, legend=2)
plt.title(lab)

plt.show()


### Half-Moons

from sklearn.datasets import make_moons
X, y = make_moons(n_samples=100, random_state=123)

gs = gridspec.GridSpec(2, 2)

fig = plt.figure(figsize=(10,8))

labels = ['Logistic Regression', 'Random Forest', 'Naive Bayes', 'SVM']
for clf, lab, grd in zip([clf1, clf2, clf3, clf4],
labels,
itertools.product([0, 1], repeat=2)):

clf.fit(X, y)
ax = plt.subplot(gs[grd[0], grd[1]])
fig = plot_decision_regions(X=X, y=y, clf=clf, legend=2)
plt.title(lab)

plt.show()


### Concentric Circles

from sklearn.datasets import make_circles
X, y = make_circles(n_samples=1000, random_state=123, noise=0.1, factor=0.2)

gs = gridspec.GridSpec(2, 2)

fig = plt.figure(figsize=(10,8))

labels = ['Logistic Regression', 'Random Forest', 'Naive Bayes', 'SVM']
for clf, lab, grd in zip([clf1, clf2, clf3, clf4],
labels,
itertools.product([0, 1], repeat=2)):

clf.fit(X, y)
ax = plt.subplot(gs[grd[0], grd[1]])
fig = plot_decision_regions(X=X, y=y, clf=clf, legend=2)
plt.title(lab)

plt.show()


## Example 6 - Working with existing axes objects using subplots

import matplotlib.pyplot as plt
from mlxtend.plotting import plot_decision_regions

from sklearn.linear_model import LogisticRegression
from sklearn.naive_bayes import GaussianNB
from sklearn import datasets
import numpy as np

X = iris.data[:, 2]
X = X[:, None]
y = iris.target

# Initializing and fitting classifiers
clf1 = LogisticRegression(random_state=1,
solver='lbfgs',
multi_class='multinomial')
clf2 = GaussianNB()
clf1.fit(X, y)
clf2.fit(X, y)

fig, axes = plt.subplots(1, 2, figsize=(10, 3))

fig = plot_decision_regions(X=X, y=y, clf=clf1, ax=axes[0], legend=2)
fig = plot_decision_regions(X=X, y=y, clf=clf2, ax=axes[1], legend=1)

plt.show()


## Example 7 - Decision regions with more than two training features

from mlxtend.plotting import plot_decision_regions
import matplotlib.pyplot as plt
from sklearn import datasets
from sklearn.svm import SVC

X, y = datasets.make_blobs(n_samples=600, n_features=3,
centers=[[2, 2, -2],[-2, -2, 2]],
cluster_std=[2, 2], random_state=2)

# Training a classifier
svm = SVC(gamma='auto')
svm.fit(X, y)

# Plotting decision regions
fig, ax = plt.subplots()
# Decision region for feature 3 = 1.5
value = 1.5
# Plot training sample with feature 3 = 1.5 +/- 0.75
width = 0.75
plot_decision_regions(X, y, clf=svm,
filler_feature_values={2: value},
filler_feature_ranges={2: width},
legend=2, ax=ax)
ax.set_xlabel('Feature 1')
ax.set_ylabel('Feature 2')
ax.set_title('Feature 3 = {}'.format(value))

fig.suptitle('SVM on make_blobs')
plt.show()


## Example 8 - Grid of decision region slices

from mlxtend.plotting import plot_decision_regions
import matplotlib.pyplot as plt
from sklearn import datasets
from sklearn.svm import SVC

X, y = datasets.make_blobs(n_samples=500, n_features=3, centers=[[2, 2, -2],[-2, -2, 2]],
cluster_std=[2, 2], random_state=2)

# Training a classifier
svm = SVC(gamma='auto')
svm.fit(X, y)

# Plotting decision regions
fig, axarr = plt.subplots(2, 2, figsize=(10,8), sharex=True, sharey=True)
values = [-4.0, -1.0, 1.0, 4.0]
width = 0.75
for value, ax in zip(values, axarr.flat):
plot_decision_regions(X, y, clf=svm,
filler_feature_values={2: value},
filler_feature_ranges={2: width},
legend=2, ax=ax)
ax.set_xlabel('Feature 1')
ax.set_ylabel('Feature 2')
ax.set_title('Feature 3 = {}'.format(value))

fig.suptitle('SVM on make_blobs')
plt.show()


## Example 9 - Customizing the plotting style

from mlxtend.plotting import plot_decision_regions
from mlxtend.preprocessing import shuffle_arrays_unison
import matplotlib.pyplot as plt
from sklearn import datasets
from sklearn.svm import SVC

X = iris.data[:, [0, 2]]
y = iris.target
X, y = shuffle_arrays_unison(arrays=[X, y], random_seed=3)
X_train, y_train = X[:100], y[:100]
X_test, y_test = X[100:], y[100:]

# Training a classifier
svm = SVC(C=0.5, kernel='linear')
svm.fit(X_train, y_train)

# Specify keyword arguments to be passed to underlying plotting functions
scatter_kwargs = {'s': 120, 'edgecolor': None, 'alpha': 0.7}
contourf_kwargs = {'alpha': 0.2}
scatter_highlight_kwargs = {'s': 120, 'label': 'Test data', 'alpha': 0.7}
# Plotting decision regions
plot_decision_regions(X, y, clf=svm, legend=2,
X_highlight=X_test,
scatter_kwargs=scatter_kwargs,
contourf_kwargs=contourf_kwargs,
scatter_highlight_kwargs=scatter_highlight_kwargs)

plt.xlabel('sepal length [cm]')
plt.ylabel('petal length [cm]')
plt.title('SVM on Iris')
plt.show()


## Example 10 - Providing your own legend labels

Custom legend labels can be provided by returning the axis object(s) from the plot_decision_region function and then getting the handles and labels of the legend. Custom handles (i.e., labels) can then be provided via ax.legend

ax = plot_decision_regions(X, y, clf=svm, legend=0)

handles, labels = ax.get_legend_handles_labels()
ax.legend(handles,
['class 0', 'class 1', 'class 2'],
framealpha=0.3, scatterpoints=1)



An example is shown below.

from mlxtend.plotting import plot_decision_regions
import matplotlib.pyplot as plt
from sklearn import datasets
from sklearn.svm import SVC

X = iris.data[:, [0, 2]]
y = iris.target

# Training a classifier
svm = SVC(C=0.5, kernel='linear')
svm.fit(X, y)

# Plotting decision regions
ax = plot_decision_regions(X, y, clf=svm, legend=0)

plt.xlabel('sepal length [cm]')
plt.ylabel('petal length [cm]')
plt.title('SVM on Iris')

handles, labels = ax.get_legend_handles_labels()
ax.legend(handles,
['class square', 'class triangle', 'class circle'],
framealpha=0.3, scatterpoints=1)

plt.show()


## Example 11 - Plots with different zoom factors

from mlxtend.plotting import plot_decision_regions
import matplotlib.pyplot as plt
from sklearn import datasets
from sklearn.svm import SVC

X = iris.data[:, [0, 2]]
y = iris.target

# Training a classifier
svm = SVC(C=0.5, kernel='linear')
svm.fit(X, y)

SVC(C=0.5, kernel='linear')


Default Zoom Factor

plot_decision_regions(X, y, clf=svm, zoom_factor=1.)
plt.show()


Zooming out

plot_decision_regions(X, y, clf=svm, zoom_factor=0.1)
plt.show()


Zooming in

Note that while zooming in (by choosing a zoom_factor > 1.0) the plots are still created such that all data points are shown in the plot.

plot_decision_regions(X, y, clf=svm, zoom_factor=2.0)
plt.show()


Cropping the axes

In order to zoom in further, which means that some training examples won't be shown, you can simply crop the axes as shown below:

plot_decision_regions(X, y, clf=svm, zoom_factor=2.0)
plt.xlim(5, 6)
plt.ylim(2, 5)
plt.show()


## Example 12 - Using classifiers that expect onehot-encoded outputs (Keras)

Most objects for classification that mimick the scikit-learn estimator API should be compatible with the plot_decision_regions function. However, if the classification model (e.g., a typical Keras model) output onehot-encoded predictions, we have to use an additional trick. I.e., for onehot encoded outputs, we need to wrap the Keras model into a class that converts these onehot encoded variables into integers. Such a wrapper class can be as simple as the following:

class Onehot2Int(object):

def __init__(self, model):
self.model = model

def predict(self, X):
y_pred = self.model.predict(X)
return np.argmax(y_pred, axis=1)


The example below illustrates how the Onehot2Int class can be used with a Keras model that outputs onehot encoded labels:

import keras
from keras.models import Sequential
from keras.layers import Dense
import matplotlib.pyplot as plt
import numpy as np
from mlxtend.data import iris_data
from mlxtend.preprocessing import standardize
from mlxtend.plotting import plot_decision_regions
from keras.utils import to_categorical

X, y = iris_data()
X = X[:, [2, 3]]

X = standardize(X)

# OneHot encoding
y_onehot = to_categorical(y)

# Create the model
np.random.seed(123)
model = Sequential()

# Configure the model and start training
history = model.fit(X, y_onehot, epochs=10, batch_size=5, verbose=1, validation_split=0.1)

# Wrap keras model
model_no_ohe = Onehot2Int(model)

# Plot decision boundary
plot_decision_regions(X, y, clf=model_no_ohe)
plt.show()


# API

plot_decision_regions(X, y, clf, feature_index=None, filler_feature_values=None, filler_feature_ranges=None, ax=None, X_highlight=None, zoom_factor=1.0, legend=1, hide_spines=True, markers='s^oxv<>', colors='#1f77b4,#ff7f0e,#3ca02c,#d62728,#9467bd,#8c564b,#e377c2,#7f7f7f,#bcbd22,#17becf', scatter_kwargs=None, contourf_kwargs=None, contour_kwargs=None, scatter_highlight_kwargs=None)

Plot decision regions of a classifier.

Please note that this functions assumes that class labels are
labeled consecutively, e.g,. 0, 1, 2, 3, 4, and 5. If you have class
labels with integer labels > 4, you may want to provide additional colors
and/or markers as colors and markers arguments.
See http://matplotlib.org/examples/color/named_colors.html for more
information.


Parameters

• X : array-like, shape = [n_samples, n_features]

Feature Matrix.

• y : array-like, shape = [n_samples]

True class labels.

• clf : Classifier object.

Must have a .predict method.

• feature_index : array-like (default: (0,) for 1D, (0, 1) otherwise)

Feature indices to use for plotting. The first index in feature_index will be on the x-axis, the second index will be on the y-axis.

• filler_feature_values : dict (default: None)

Only needed for number features > 2. Dictionary of feature index-value pairs for the features not being plotted.

• filler_feature_ranges : dict (default: None)

Only needed for number features > 2. Dictionary of feature index-value pairs for the features not being plotted. Will use the ranges provided to select training samples for plotting.

• ax : matplotlib.axes.Axes (default: None)

An existing matplotlib Axes. Creates one if ax=None.

• X_highlight : array-like, shape = [n_samples, n_features] (default: None)

An array with data points that are used to highlight samples in X.

• zoom_factor : float (default: 1.0)

Controls the scale of the x- and y-axis of the decision plot.

• hide_spines : bool (default: True)

Hide axis spines if True.

• legend : int (default: 1)

Integer to specify the legend location. No legend if legend is 0.

• markers : str (default: 's^oxv<>')

Scatterplot markers.

• colors : str (default: 'red,blue,limegreen,gray,cyan')

Comma separated list of colors.

• scatter_kwargs : dict (default: None)

Keyword arguments for underlying matplotlib scatter function.

• contourf_kwargs : dict (default: None)

Keyword arguments for underlying matplotlib contourf function.

• contour_kwargs : dict (default: None)

Keyword arguments for underlying matplotlib contour function (which draws the lines between decision regions).

• scatter_highlight_kwargs : dict (default: None)

Keyword arguments for underlying matplotlib scatter function.

Returns

• ax : matplotlib.axes.Axes object

Examples

For usage examples, please see http://rasbt.github.io/mlxtend/user_guide/plotting/plot_decision_regions/

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