scikit_regularizer.py

from sklearn import datasets
from sklearn.linear_model import LogisticRegression
from sklearn.cross_validation import train_test_split
from sklearn.preprocessing import StandardScaler
import numpy as np
import matplotlib.pyplot as plt


# load iris data-set
iris = datasets.load_iris()
# last two features of iris dataset
X = iris.data[:, [2, 3]]
# Iris-Setosa, Iris-Versicolor, Iris-Virginica
y = iris.target

# Split X and y in into 30 % test data and 70 % into trainining data
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3,
                                                    random_state=0)
sc = StandardScaler()
# Estimate mean and std using fit method
sc.fit(X_train)
# Standarize training and test data using mean and std via transform method
X_train_std = sc.transform(X_train)
X_test_std = sc.transform(X_test)

weights, params = [], []
for c in np.arange(-5, 5):
    lr = LogisticRegression(C=10 ** c, random_state=0)
    lr.fit(X_train_std, y_train)
    weights.append(lr.coef_[1])
    params.append(10 ** c)
weights = np.array(weights)
plt.plot(params, weights[:, 0], label='petal length')
plt.plot(params, weights[:, 1], label='petal width', linestyle='--')
plt.ylabel('weight coefficient')
plt.xlabel('C')
plt.legend(loc='upper left')
plt.xscale('log')
plt.show()