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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 Load the Iris dataset from scikit-learn. The third column represents the petal length, and the fourth column the petal width of the flower samples. The classes are already converted to integer labels where 0=Iris-Setosa, 1=Iris-Versicolor, 2=Iris-Virginica.
Train different classifiers using off-the-shelf algorithms in scikit-learn.
Step1:  1. Training a perceptron via scikit-learn
Step2:  2. Training a logistic regression model with scikit-learn
Step3:  3. Training a support vector machine (SVM) with scikit-learn
Step4:  Training a kernel SVM with scikit-learn
Instead of calculating the dot product between two points explicitly, we define a kernel function, which computes the distance/similarity between two points in higher dimensional space. One of the most widely used kernels is the Radial Basis Function kernel (RBF kernel) or Gaussian kernel. The gamma parameter in rbf can be understood as a cut-off parameter for the Gaussian sphere. If we increase the value for , we increase the influence or reach of the training samples, which leads to a softer decision boundary.
Step5:  Since we chose a relatively small value for gamma, the resulting decision boundary of the RBF kernel SVM model will be relatively soft. When we increase gamma from 0.2 to 100, we observe the effect on the decision boundary below. Although the model fits the training dataset very well, such a classifier is likely to overfit and have a high generalization error on unseen data.
Step6:  4. Training a decision tree classifier with scikit-learn
Step7:  5. Training a random forest with scikit-learn
Step8:  6. Training a KNN with scikit-learn
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Python Code:
from sklearn import datasets
import numpy as np
iris = datasets.load_iris()
X = iris.data[:, [2, 3]]
y = iris.target
print('Class labels:', np.unique(y))
# Splitting data into 70% training and 30% test data:
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.3, random_state=0)
# Standardizing the features:
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
sc.fit(X_train)
X_train_std = sc.transform(X_train)
X_test_std = sc.transform(X_test)
Explanation: Load the Iris dataset from scikit-learn. The third column represents the petal length, and the fourth column the petal width of the flower samples. The classes are already converted to integer labels where 0=Iris-Setosa, 1=Iris-Versicolor, 2=Iris-Virginica.
Train different classifiers using off-the-shelf algorithms in scikit-learn.
End of explanation
from sklearn.linear_model import Perceptron
ppn = Perceptron(n_iter=40, eta0=0.1, random_state=0)
ppn.fit(X_train_std, y_train)
y_pred = ppn.predict(X_test_std)
print('Misclassified sample: %d' % (y_test != y_pred).sum())
from sklearn.metrics import accuracy_score
print('Accuracy: %.2f' % accuracy_score(y_test, y_pred))
from matplotlib.colors import ListedColormap
import matplotlib.pyplot as plt
def plot_decision_regions(X, y, classifier, test_idx=None, resolution=0.02):
    # setup marker generator and color map
    markers = ('s', 'x', 'o', '^', 'v')
    colors = ('red', 'blue', 'lightgreen', 'gray', 'cyan')
    cmap = ListedColormap(colors[:len(np.unique(y))])
    # plot the decision surface
    x1_min, x1_max = X[:, 0].min() - 1, X[:, 0].max() + 1
    x2_min, x2_max = X[:, 1].min() - 1, X[:, 1].max() + 1
    xx1, xx2 = np.meshgrid(np.arange(x1_min, x1_max, resolution),
                           np.arange(x2_min, x2_max, resolution))
    Z = classifier.predict(np.array([xx1.ravel(), xx2.ravel()]).T)
    Z = Z.reshape(xx1.shape)
    plt.contourf(xx1, xx2, Z, alpha=0.4, cmap=cmap)
    plt.xlim(xx1.min(), xx1.max())
    plt.ylim(xx2.min(), xx2.max())
    for idx, cl in enumerate(np.unique(y)):
        plt.scatter(x=X[y == cl, 0], y=X[y == cl, 1],
                    alpha=0.8, c=cmap(idx),
                    marker=markers[idx], label=cl)
    # highlight test samples
    if test_idx:
        # plot all samples
        X_test, y_test = X[test_idx, :], y[test_idx]
        plt.scatter(X_test[:, 0],
                    X_test[:, 1],
                    c='',
                    alpha=1.0,
                    linewidths=1,
                    marker='o',
                    s=55, label='test set')
X_combined_std = np.vstack((X_train_std, X_test_std))
y_combined = np.hstack((y_train, y_test))
plot_decision_regions(X_combined_std, y_combined,
                      classifier=ppn, test_idx=range(105, 150))
plt.xlabel('petal length [standardized]')
plt.ylabel('petal width [standardized]')
plt.legend(loc='upper left')
plt.show()
Explanation: 1. Training a perceptron via scikit-learn
End of explanation
from sklearn.linear_model import LogisticRegression
lr = LogisticRegression(C=1000, random_state=0)
lr.fit(X_train_std, y_train)
plot_decision_regions(X_combined_std, y_combined, classifier=lr, test_idx=range(105,150))
plt.xlabel('petal length [standardized]')
plt.ylabel('petal width [standardized]')
plt.legend(loc='upper left')
plt.show()
lr.predict_proba(X_test_std[0, :].reshape(1,-1))
Explanation: 2. Training a logistic regression model with scikit-learn
End of explanation
from sklearn.svm import SVC
svm = SVC(kernel='linear', C=1.0, random_state=0)
svm.fit(X_train_std, y_train)
plot_decision_regions(X_combined_std, y_combined,
                      classifier=svm, test_idx=range(105, 150))
plt.xlabel('petal length [standardized]')
plt.ylabel('petal width [standardized]')
plt.legend(loc='upper left')
plt.show()
Explanation: 3. Training a support vector machine (SVM) with scikit-learn
End of explanation
svm = SVC(kernel='rbf', random_state=0, gamma=0.2, C=1.0)
svm.fit(X_train_std, y_train)
plot_decision_regions(X_combined_std, y_combined,
                      classifier=svm, test_idx=range(105, 150))
plt.xlabel('petal length [standardized]')
plt.ylabel('petal width [standardized]')
plt.legend(loc='upper left')
plt.show()
Explanation: Training a kernel SVM with scikit-learn
Instead of calculating the dot product between two points explicitly, we define a kernel function, which computes the distance/similarity between two points in higher dimensional space. One of the most widely used kernels is the Radial Basis Function kernel (RBF kernel) or Gaussian kernel. The gamma parameter in rbf can be understood as a cut-off parameter for the Gaussian sphere. If we increase the value for , we increase the influence or reach of the training samples, which leads to a softer decision boundary.
End of explanation
svm = SVC(kernel='rbf', random_state=0, gamma=100.0, C=1.0)
svm.fit(X_train_std, y_train)
plot_decision_regions(X_combined_std, y_combined, 
                      classifier=svm, test_idx=range(105, 150))
plt.xlabel('petal length [standardized]')
plt.ylabel('petal width [standardized]')
plt.legend(loc='upper left')
plt.show()
Explanation: Since we chose a relatively small value for gamma, the resulting decision boundary of the RBF kernel SVM model will be relatively soft. When we increase gamma from 0.2 to 100, we observe the effect on the decision boundary below. Although the model fits the training dataset very well, such a classifier is likely to overfit and have a high generalization error on unseen data.
End of explanation
from sklearn.tree import DecisionTreeClassifier
tree = DecisionTreeClassifier(criterion='entropy', max_depth=3, random_state=0)
tree.fit(X_train, y_train)
X_combined = np.vstack((X_train, X_test))
y_combined = np.hstack((y_train, y_test))
plot_decision_regions(X_combined, y_combined, 
                      classifier=tree, test_idx=range(105, 150))
plt.xlabel('petal length [cm]')
plt.ylabel('petal width [cm]')
plt.legend(loc='upper left')
plt.show()
Explanation: 4. Training a decision tree classifier with scikit-learn
End of explanation
from sklearn.ensemble import RandomForestClassifier
forest = RandomForestClassifier(criterion='entropy',
                                n_estimators=10, 
                                random_state=1,
                                n_jobs=2)
forest.fit(X_train, y_train)
plot_decision_regions(X_combined, y_combined, 
                      classifier=forest, test_idx=range(105, 150))
plt.xlabel('petal length [cm]')
plt.ylabel('petal width [cm]')
plt.legend(loc='upper left')
plt.show()
Explanation: 5. Training a random forest with scikit-learn
End of explanation
from sklearn.neighbors import KNeighborsClassifier
knn = KNeighborsClassifier(n_neighbors=5, p=2, metric='minkowski')
knn.fit(X_train_std, y_train)
plot_decision_regions(X_combined_std, y_combined, 
                      classifier=knn, test_idx=range(105, 150))
plt.xlabel('petal length [standardized]')
plt.ylabel('petal width [standardized]')
plt.legend(loc='upper left')
plt.show()
Explanation: 6. Training a KNN with scikit-learn
End of explanation
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