Logistic Regression - Iris dataset

Author

Nipun Batra

Published

January 1, 2024

Open In Colab

import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
from latexify import *
from sklearn.linear_model import LogisticRegression
import matplotlib.patches as mpatches
%config InlineBackend.figure_format = 'retina'

from sklearn.datasets import load_iris

d = load_iris()
X = d['data'][:, :2]
y = d['target']

d['feature_names']

['sepal length (cm)',
 'sepal width (cm)',
 'petal length (cm)',
 'petal width (cm)']

latexify()
colours = ['blue', 'red', 'green']
species = ['I. setosa', 'I. versicolor', 'I. virginica']
for i in range(0, 3):    
    df_ = X[y == i]
    plt.scatter(        
        df_[:, 0],        
        df_[:, 1],
        color=colours[i],        
        alpha=0.5,        
        label=species[i] ,
        s=10
    )
format_axes(plt.gca())
plt.legend()
plt.xlabel(d['feature_names'][0])
plt.ylabel(d['feature_names'][1])


plt.savefig("../figures/logistic-regression/logisitic-iris.pdf", bbox_inches="tight", transparent=True)

clf = LogisticRegression(penalty='none',solver='newton-cg')

clf.fit(X, y)

LogisticRegression(penalty='none', solver='newton-cg')

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clf.coef_

array([[-55.82562338,  47.29592374],
       [ 26.96162409, -23.85029157],
       [ 28.86399931, -23.44563218]])

X.shape

(150, 2)

y.shape

(150,)

# create a mesh to plot in
x_min, x_max = X[:, 0].min() - 0.3, X[:, 0].max() + 0.3
y_min, y_max = X[:, 1].min() - 0.3, X[:, 1].max() + 0.3
h = 0.02
xx, yy = np.meshgrid(np.arange(x_min, x_max, h),
                     np.arange(y_min, y_max, h))





Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])

# Put the result into a color plot
Z = Z.reshape(xx.shape)
plt.contourf(xx, yy, Z, cmap=plt.cm.Paired, alpha=0.4)
plt.gca().set_aspect('equal')
#plt.scatter(X[:, 0], X[:, 1], c=y)
latexify()
for i in range(0, 3):    
    df_ = X[y == i]
    plt.scatter(        
        df_[:, 0],        
        df_[:, 1],
        color=colours[i],        
        alpha=0.5,        
        label=species[i],
        s=10
    )
format_axes(plt.gca())
plt.legend()
plt.xlabel(d['feature_names'][0])
plt.ylabel(d['feature_names'][1])
plt.savefig("../figures/logistic-regression/logisitic-iris-prediction.pdf", bbox_inches="tight", transparent=True)

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