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Classification Probability in Scikit-learn

Plot the classification probability for different classifiers. We use a 3 class dataset, and we classify it with a Support Vector classifier, L1 and L2 penalized logistic regression with either a One-Vs-Rest or multinomial setting, and Gaussian process classification.

The logistic regression is not a multiclass classifier out of the box. As a result it can identify only the first class.

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In [1]:
import sklearn


This tutorial imports LogisticRegression, SVC, GaussianProcessClassifier and RBF.

In [2]:

import plotly.plotly as py
import plotly.graph_objs as go
from plotly import tools

import matplotlib.pyplot as plt
import as cm
import numpy as np

from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC
from sklearn.gaussian_process import GaussianProcessClassifier
from sklearn.gaussian_process.kernels import RBF
from sklearn import datasets
Automatically created module for IPython interactive environment


In [3]:
iris = datasets.load_iris()
X =[:, 0:2]  # we only take the first two features for visualization
y =

n_features = X.shape[1]

C = 1.0
kernel = 1.0 * RBF([1.0, 1.0])  # for GPC

# Create different classifiers. The logistic regression cannot do
# multiclass out of the box.
classifiers = {'L1 logistic': LogisticRegression(C=C, penalty='l1'),
               'L2 logistic (OvR)': LogisticRegression(C=C, penalty='l2'),
               'Linear SVC': SVC(kernel='linear', C=C, probability=True,
               'L2 logistic (Multinomial)': LogisticRegression(
                C=C, solver='lbfgs', multi_class='multinomial'),
               'GPC': GaussianProcessClassifier(kernel)

n_classifiers = len(classifiers)

xx = np.linspace(3, 9, 100)
yy = np.linspace(1, 5, 100).T
xx, yy = np.meshgrid(xx, yy)
Xfull = np.c_[xx.ravel(), yy.ravel()]


In [4]:
def matplotlib_to_plotly(cmap, pl_entries):
    h = 1.0/(pl_entries-1)
    pl_colorscale = []
    for k in range(pl_entries):
        C = map(np.uint8, np.array(cmap(k*h)[:3])*255)
        pl_colorscale.append([k*h, 'rgb'+str((C[0], C[1], C[2]))])
    return pl_colorscale

fig = tools.make_subplots(rows=5, cols=3, print_grid=False,
                          subplot_titles = ('Class 0', 'Class 1', 'Class 2',
                                            'Class 0', 'Class 1', 'Class 2',
                                            'Class 0', 'Class 1', 'Class 2',
                                            'Class 0', 'Class 1', 'Class 2',
                                            'Class 0', 'Class 1', 'Class 2'))

for index, (name, classifier) in enumerate(classifiers.items()):, y)

    y_pred = classifier.predict(X)
    classif_rate = np.mean(y_pred.ravel() == y.ravel()) * 100
    print("classif_rate for %s : %f " % (name, classif_rate))

    # View probabilities=
    probas = classifier.predict_proba(Xfull)
    n_classes = np.unique(y_pred).size
    for k in range(n_classes):
        idx = (y_pred == k)
        if idx.any():
            trace = go.Scatter(x=X[idx, 0], y=X[idx, 1],mode='markers', 
                               marker=dict(color='black', size=10))
        x_max, x_min= max(X[idx, 0]), min(X[idx, 0])
        y_max, y_min= max(X[idx, 1]), min(X[idx, 1])
        imshow_handle = go.Heatmap(z=probas[:, k].reshape((100, 100)),
                                   x=np.linspace(x_min, x_max, 100), 
                                   y=np.linspace(y_min, y_max, 100),
                                   colorscale=matplotlib_to_plotly(cm.jet, len(probas[:, k].reshape((100, 100)))))
        fig.append_trace(imshow_handle, i, k+1)
        fig.append_trace(trace, i, k+1)
for k in map(str,range(1,16)):
    x = 'xaxis' + k
    y = 'yaxis' + k
    fig['layout'][y].update(showticklabels=False, ticks='')
    fig['layout'][x].update(showticklabels=False, ticks='')

titles = ['GPC','L2 logistic (Multinomial)','Linear SVC','L2 logistic (OvR)','L1 logistic']
for l in map(str,range(1,16,3)):
    y = 'yaxis' + l
    fig['layout'][y].update(title = titles[i])
classif_rate for GPC : 82.666667 
classif_rate for L2 logistic (OvR) : 76.666667 
classif_rate for L1 logistic : 79.333333 
classif_rate for Linear SVC : 82.000000 
classif_rate for L2 logistic (Multinomial) : 82.000000 
In [5]:



    Alexandre Gramfort <>


    BSD 3 clause
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