def s3_7():
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()
def s3_6_3():
rf = RandomForestClassifier(criterion="entropy",
n_estimators=10,
random_state=1,
n_jobs=2)
rf.fit(X_train, y_train)
plot_decision_regions(X_combined,
y_combined,
classifier=rf,
test_idx=range(105, 150))
plt.xlabel("petal length [cm]")
plt.ylabel("petal width [cm]")
plt.legend(loc="upper left")
plt.show()
def s3_6_2():
tree = DecisionTreeClassifier(criterion="entropy",
max_depth=3,
random_state=0)
tree.fit(X_train, y_train)
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()
export_graphviz(tree,
out_file="tree.dot",
feature_names=["petal length", "petal width"])
def s5_2_4():
lda = LinearDiscriminantAnalysis(n_components=2)
X_train_lda = lda.fit_transform(X_train_std, y_train)
lr = LogisticRegression()
lr = lr.fit(X_train_lda, y_train)
plot_decision_regions(X_train_lda, y_train, classifier=lr)
plt.xlabel("LD 1")
plt.ylabel("LD 2")
plt.legend(loc="lower left")
plt.show()
X_test_lda = lda.transform(X_test_std)
plot_decision_regions(X_test_lda, y_test, classifier=lr)
plt.xlabel("LD 1")
plt.ylabel("LD 2")
plt.legend(loc="lower left")
plt.show()
def s5_1_3():
pca = PCA(n_components=2)
lr = LogisticRegression()
X_train_pca = pca.fit_transform(X_train_std)
X_test_pca = pca.transform(X_test_std)
lr.fit(X_train_pca, y_train)
plot_decision_regions(X_train_pca, y_train, classifier=lr)
plt.xlabel("PC1")
plt.ylabel("PC2")
plt.legend(loc="lower left")
plt.show()
plot_decision_regions(X_test_pca, y_test, classifier=lr)
plt.xlabel("PC1")
plt.ylabel("PC2")
plt.legend(loc="lower left")
plt.show()
pca = PCA(n_components=None)
X_train_pca = pca.fit_transform(X_train_std)
print(pca.explained_variance_ratio_)
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.3,
random_state=0)
sc = StandardScaler()
sc.fit(X_train)
X_train_std = sc.transform(X_train)
X_test_std = sc.transform(X_test)
ppn = Perceptron(max_iter=40, eta0=0.1, random_state=0, shuffle=True)
ppn.fit(X_train_std, y_train)
y_pred = ppn.predict(X_test_std)
print("Misclassified samples: {0:d}".format((y_test != y_pred).sum()))
print("Accuracy: {0:.2f}".format(accuracy_score(y_test, y_pred)))
X_combined_std = np.vstack((X_train_std, X_test_std))
y_combined = np.hstack((y_train, y_test))
plot_decision_regions(X=X_combined_std,
y=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()
import numpy as np from sklearn.neighbors import KNeighborsClassifier from sklearn import datasets from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler from util import plot_decision_regions iris = datasets.load_iris() x = iris.data[:, [2,3]] y = iris.target x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=0) sc = StandardScaler() sc.fit(x_train) x_train_std = sc.transform(x_train) x_test_std = sc.transform(x_test) knn = KNeighborsClassifier(n_neighbors=5, p=2, metric='minkowski') knn.fit(x_train_std, y_train) 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, knn) plt.show()
plt.scatter(X_xor[y_xor == -1, 0],
X_xor[y_xor == -1, 1],
c="r",
marker="s",
label="-1")
plt.xlim([-3, 3])
plt.ylim([-3, 3])
plt.legend(loc="best")
plt.show()
# ----
svm = SVC(kernel="rbf", random_state=0, gamma=0.10, C=10.0)
svm.fit(X_xor, y_xor)
plot_decision_regions(X_xor, y_xor, classifier=svm, test_idx=range(105, 150))
plt.legend(loc="upper left")
plt.show()
# ----
iris = datasets.load_iris()
X = iris.data[:, [2, 3]]
y = iris.target
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.3,
random_state=0)
sc = StandardScaler()
sc.fit(X_train)
y = np.where(y == 'Iris-setosa', -1, 1)
X = df.iloc[0:100, [0, 2]].values
plt.figure(100)
plt.scatter(X[:50, 0], X[:50, 1], color='red', marker='o', label='setosa')
plt.scatter(X[50:100, 0],
X[50:100, 1],
color='blue',
marker='x',
label='versicolor')
plt.xlabel('petal length')
plt.ylabel('sepal length')
plt.legend(loc="upper left")
ppn = SimplePerceptron(learning_rate=10, n_iterations=100)
ppn.fit(X, y)
plt.figure(200)
plt.plot(range(1, len(ppn.errors) + 1), ppn.errors, marker='o')
plt.xlabel('epochs')
plt.ylabel('number of misclassifications')
plt.figure(300)
plot_decision_regions(X, y, classifier=ppn)
plt.xlabel('sepal length [cm]')
plt.ylabel('petal length [cm]')
plt.legend(loc='upper left')
plt.show()
from sklearn.svm import SVC
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from util import plot_decision_regions
iris = datasets.load_iris()
x = iris.data[:, [2, 3]]
y = iris.target
x_train, x_test, y_train, y_test = train_test_split(x,
y,
test_size=0.3,
random_state=0)
sc = StandardScaler()
sc.fit(x_train)
x_train_std = sc.transform(x_train)
x_test_std = sc.transform(x_test)
svm = SVC(kernel='linear', C=10.0, random_state=0)
svm.fit(x_train_std, y_train)
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, svm)
plt.show()
print('Labels counts in y_test:', np.bincount(y_test))
# GaussianNB
# https://scikit-learn.org/stable/modules/generated/sklearn.naive_bayes.GaussianNB.html
#
gnb_model = GaussianNB()
gnb_model.fit(X_train, y_train)
# test prediction
y_pred = gnb_model.predict(X_test)
print('Misclassified samples: %d' % (y_test != y_pred).sum())
print('Accuracy: %.2f%%' % (100.0 * gnb_model.score(X_test, y_test)))
# decision boundary
X_combined = np.vstack((X_train, X_test))
y_combined = np.hstack((y_train, y_test))
train_len = X_train.shape[0]
combined_len = X_combined.shape[0]
plt.figure(figsize=(3, 3), dpi=300)
plot_decision_regions(X=X_combined, y=y_combined, classifier=gnb_model, test_idx=range(train_len, combined_len))
plt.xlabel('petal length [cm]')
plt.ylabel('petal width [cm]')
plt.legend(loc='upper left')
plt.tight_layout()
#plt.savefig('images/03_01.png', dpi=300)
plt.show()
import matplotlib
matplotlib.use('TkAgg')
import matplotlib.pyplot as plt
import numpy as np
from sklearn.svm import SVC
from util import plot_decision_regions
np.random.seed(0)
x_xor = np.random.randn(200, 2)
y_xor = np.logical_xor(x_xor[:, 0] > 0, x_xor[:, 1] > 0)
y_xor = np.where(y_xor, 1, -1)
svm = SVC(kernel='rbf', random_state=0, gamma=0.1, C=10.0)
svm.fit(x_xor, y_xor)
plot_decision_regions(x_xor, y_xor, classifier=svm)
svm = SVC(kernel='rbf', random_state=0, gamma=100.0, C=10.0)
svm.fit(x_xor, y_xor)
plot_decision_regions(x_xor, y_xor, classifier=svm)
plt.show()
from sklearn.preprocessing import StandardScaler
from util import plot_decision_regions
iris = datasets.load_iris()
x = iris.data[:, [2, 3]]
y = iris.target
x_train, x_test, y_train, y_test = train_test_split(x,
y,
test_size=0.3,
random_state=0)
sc = StandardScaler()
sc.fit(x_train)
x_train_std = sc.transform(x_train)
x_test_std = sc.transform(x_test)
forest = RandomForestClassifier(criterion='entropy',
n_estimators=10,
random_state=1,
n_jobs=2)
forest.fit(x_train_std, y_train)
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, forest)
plt.show()
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from .perceptron import AdalinePreceptron
from util import plot_decision_regions
df = pd.read_csv(
'https://archive.ics.uci.edu/ml/'
'machine-learning-databases/iris/iris.data',
header=None)
y = df.iloc[0:100, 4].values
y = np.where(y == 'Iris-setosa', -1, 1)
X = df.iloc[0:100, [0, 2]].values
X_std = np.copy(X)
X_std[:, 0] = (X[:, 0] - X[:, 0].mean()) / X[:, 0].std()
X_std[:, 1] = (X[:, 1] - X[:, 1].mean()) / X[:, 1].std()
ada = AdalinePreceptron(n_iter=15)
ada = ada.fit(X_std, y)
plt.plot(range(1, len(ada.cost) + 1), ada.cost, marker='o')
plot_decision_regions(X_std, y, classifier=ada)
plt.title('Adaline - Gradient Descent')
plt.xlabel('sepal length [standardized]')
plt.ylabel('petal length [standardized]')
plt.legend(loc='upper left')
plt.tight_layout()
plt.show()
import numpy as np from sklearn.tree import DecisionTreeClassifier from sklearn import datasets from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler from util import plot_decision_regions iris = datasets.load_iris() x = iris.data[:, [2,3]] y = iris.target x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=0) sc = StandardScaler() sc.fit(x_train) x_train_std = sc.transform(x_train) x_test_std = sc.transform(x_test) tree = DecisionTreeClassifier(criterion='entropy', max_depth=3, random_state=0) tree.fit(x_train_std, y_train) 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, tree) plt.show()
