How can I visualize border/decision function of two classes using scikit-learn

Question

I am pretty new in machine learning, so I still don't understand how I can visualize the border between 2 classes in bag of words case.

I found the following exaplpe to plot data

plot a document tfidf 2D graph

from sklearn.datasets import fetch_20newsgroups
from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer
from sklearn.decomposition import PCA
from sklearn.pipeline import Pipeline
import matplotlib.pyplot as plt

newsgroups_train = fetch_20newsgroups(subset='train', 
                                      categories=['alt.atheism', 'sci.space'])
pipeline = Pipeline([
    ('vect', CountVectorizer()),
    ('tfidf', TfidfTransformer()),
])        
X = pipeline.fit_transform(newsgroups_train.data).todense()

pca = PCA(n_components=2).fit(X)
data2D = pca.transform(X)
plt.scatter(data2D[:,0], data2D[:,1], c=newsgroups_train.target)
plt.show()

In my project I use SVC estimator

clf = SVC(random_state=241, kernel = 'linear')
clf.fit(X,newsgroups_train.target)

I have tried to use the example http://scikit-learn.org/stable/auto_examples/svm/plot_iris.html but it didn't work in text clasification case

So how can I add the border of two classes to this plot?

Thank you!

seralouk · Accepted Answer · 2020-06-04T13:58:45.887

The problem is that you need to select only 2 features in order to create the 2-dimensional decision surface plot. I will provide 2 examples. The first using iris data and the second using your data.

I have also written an article about this here: https://towardsdatascience.com/support-vector-machines-svm-clearly-explained-a-python-tutorial-for-classification-problems-29c539f3ad8?source=friends_link&sk=80f72ab272550d76a0cc3730d7c8af35

In both cases, I select only 2 features in order to create the plot.

Example 1 using iris data:

from sklearn.svm import SVC
import numpy as np
import matplotlib.pyplot as plt
from sklearn import svm, datasets

iris = datasets.load_iris()
X = iris.data[:, :2]  # we only take the first two features.
y = iris.target

def make_meshgrid(x, y, h=.02):
    x_min, x_max = x.min() - 1, x.max() + 1
    y_min, y_max = y.min() - 1, y.max() + 1
    xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
    return xx, yy

def plot_contours(ax, clf, xx, yy, **params):
    Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
    Z = Z.reshape(xx.shape)
    out = ax.contourf(xx, yy, Z, **params)
    return out

model = svm.SVC(kernel='linear')
clf = model.fit(X, y)

fig, ax = plt.subplots()
# title for the plots
title = ('Decision surface of linear SVC ')
# Set-up grid for plotting.
X0, X1 = X[:, 0], X[:, 1]
xx, yy = make_meshgrid(X0, X1)

plot_contours(ax, clf, xx, yy, cmap=plt.cm.coolwarm, alpha=0.8)
ax.scatter(X0, X1, c=y, cmap=plt.cm.coolwarm, s=20, edgecolors='k')
ax.set_ylabel('y label here')
ax.set_xlabel('x label here')
ax.set_xticks(())
ax.set_yticks(())
ax.set_title(title)
ax.legend()
plt.show()

RESULTS

Example 2 using your data:

from sklearn.svm import SVC
import numpy as np
import matplotlib.pyplot as plt
from sklearn import svm, datasets
from sklearn.datasets import fetch_20newsgroups
from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer
from sklearn.decomposition import PCA
from sklearn.pipeline import Pipeline
import matplotlib.pyplot as plt

newsgroups_train = fetch_20newsgroups(subset='train', 
                                      categories=['alt.atheism', 'sci.space'])
pipeline = Pipeline([('vect', CountVectorizer()), ('tfidf', TfidfTransformer())])        
X = pipeline.fit_transform(newsgroups_train.data).todense()

# Select ONLY 2 features
X = np.array(X)
X = X[:, [0,1]]
y = newsgroups_train.target

def make_meshgrid(x, y, h=.02):
    x_min, x_max = x.min() - 1, x.max() + 1
    y_min, y_max = y.min() - 1, y.max() + 1
    xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
    return xx, yy

def plot_contours(ax, clf, xx, yy, **params):
    Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
    Z = Z.reshape(xx.shape)
    out = ax.contourf(xx, yy, Z, **params)
    return out

model = svm.SVC(kernel='linear')
clf = model.fit(X, y)

fig, ax = plt.subplots()
# title for the plots
title = ('Decision surface of linear SVC ')
# Set-up grid for plotting.
X0, X1 = X[:, 0], X[:, 1]
xx, yy = make_meshgrid(X0, X1)

plot_contours(ax, clf, xx, yy, cmap=plt.cm.coolwarm, alpha=0.8)
ax.scatter(X0, X1, c=y, cmap=plt.cm.coolwarm, s=20, edgecolors='k')
ax.set_ylabel('y label here')
ax.set_xlabel('x label here')
ax.set_xticks(())
ax.set_yticks(())
ax.set_title(title)
ax.legend()
plt.show()

RESULTS

Important note:

In the second case, the plot is not nice since we selected randomly only 2 features to create it. One way to make it nice is the following: You could use a univariate ranking method (e.g. ANOVA F-value test) and find the best top-2 features from the 22464 that you initially have. Then using these top-2 you could create a nice separating surface plot.

How can I visualize border/decision function of two classes using scikit-learn

1 Answers1

Example 1 using iris data:

Example 2 using your data:

Important note: