In [1]:
# Kernel PCA
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
%matplotlib inline

In [2]:
# Veri Seti
url='https://raw.githubusercontent.com/cagriemreakin/Machine-Learning/master/4%20-%20Dimensionality%20Reduction/3%20-%20Kernel%20Principal%20Component%20Analysis/sosyal_ag_reklamlari.csv'
X = dataset.iloc[:, [2,3]].values
y = dataset.iloc[:, 4].values

# Boyut Sayısı
size=X.shape[1]

In [3]:
# Eğitim ve Test Set Görselleştirme için Kullanılacak
def visualize(X,y,title):
from matplotlib.colors import ListedColormap
plt.figure(figsize=(12,7))
X_set, y_set = X, y
X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),
np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))
plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),
alpha = 0.75, cmap = ListedColormap(('red', 'green', 'blue')))
plt.xlim(X1.min(), X1.max())
plt.ylim(X2.min(), X2.max())
for i, j in enumerate(np.unique(y_set)):
plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],
c = ListedColormap(('red', 'green', 'blue'))(i), label = j)
plt.title('Logistic Regression ' + title)
plt.xlabel('PC1')
plt.ylabel('PC2')
plt.legend()
plt.show()

In [4]:
# Eğitim ve Test Setine Ayırma
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)

In [5]:
# Feature Scaling
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)

C:\Users\ceakn\Anaconda3\lib\site-packages\sklearn\utils\validation.py:429: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.
warnings.warn(msg, _DataConversionWarning)

In [6]:
from sklearn.decomposition import KernelPCA
kpca = KernelPCA(n_components = 2, kernel = 'rbf')
X_train = kpca.fit_transform(X_train)
X_test = kpca.transform(X_test)

In [7]:
# Logistic Regression Modeli ile Veri Setini Eğitme
from sklearn.linear_model import LogisticRegression
classifier = LogisticRegression(random_state = 0)
classifier.fit(X_train, y_train)

Out[7]:
LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,
penalty='l2', random_state=0, solver='liblinear', tol=0.0001,
verbose=0, warm_start=False)
In [8]:
# Sonuçların Tahmini
y_pred = classifier.predict(X_test)

In [9]:
# Confusion Matrix
from sklearn.metrics import confusion_matrix
cm1 = confusion_matrix(y_test, y_pred)

In [10]:
print("\n Confusion Matrix for 2 Component\n",cm1)

 Confusion Matrix for 2 Component
[[54  4]
[ 4 18]]

In [11]:
from sklearn.metrics import accuracy_score
print("n_components = 2 -> Prediciton: %",accuracy_score(y_pred,y_test)*100)

n_components = 2 -> Prediciton: % 90.0

In [12]:
visualize(X_train,y_train,'Training Set for 2 Components')
visualize(X_test,y_test,'Test Set for 2 Components')

In [ ]: