From e0fca9ecf04b98e3e9d86562c0ed84bbb312a38d Mon Sep 17 00:00:00 2001 From: Ritik Garg <53874982+rooky1905@users.noreply.github.com> Date: Sat, 3 Oct 2020 14:26:48 +0530 Subject: [PATCH 1/2] Adding Kernel PCA This is a dimensionality reduction technique. And I have demonstrated it using Logistic Regression as an example. --- kernel_pca.py | 75 +++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 75 insertions(+) create mode 100644 kernel_pca.py diff --git a/kernel_pca.py b/kernel_pca.py new file mode 100644 index 0000000..7547cc0 --- /dev/null +++ b/kernel_pca.py @@ -0,0 +1,75 @@ +# Kernel PCA + +# Importing the libraries +import numpy as np +import matplotlib.pyplot as plt +import pandas as pd + +# Importing the dataset +dataset = pd.read_csv('Social_Network_Ads.csv') +X = dataset.iloc[:, [2, 3]].values +y = dataset.iloc[:, -1].values + +# Feature Scaling +from sklearn.preprocessing import StandardScaler +sc = StandardScaler() +X = sc.fit_transform(X) + +# Splitting the dataset into the Training set and Test set +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) + +# Applying Kernel PCA +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) + +# Training the Logistic Regression model on the Training set +from sklearn.linear_model import LogisticRegression +classifier = LogisticRegression(random_state = 0) +classifier.fit(X_train, y_train) + +# Predicting the Test set results +y_pred = classifier.predict(X_test) + +# Making the Confusion Matrix +from sklearn.metrics import confusion_matrix +cm = confusion_matrix(y_test, y_pred) +print(cm) + +# Visualising the Training set results +from matplotlib.colors import ListedColormap +X_set, y_set = X_train, y_train +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'))) +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'))(i), label = j) +plt.title('Logistic Regression (Training set)') +plt.xlabel('Age') +plt.ylabel('Estimated Salary') +plt.legend() +plt.show() + +# Visualising the Test set results +from matplotlib.colors import ListedColormap +X_set, y_set = X_test, y_test +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'))) +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'))(i), label = j) +plt.title('Logistic Regression (Test set)') +plt.xlabel('Age') +plt.ylabel('Estimated Salary') +plt.legend() +plt.show() \ No newline at end of file From a835a1d11c1958ae1df6dc2892ee105e818a039e Mon Sep 17 00:00:00 2001 From: Ritik Garg <53874982+rooky1905@users.noreply.github.com> Date: Sat, 3 Oct 2020 14:27:50 +0530 Subject: [PATCH 2/2] Dataset for Kernel PCA --- Social_Network_Ads.csv | 401 +++++++++++++++++++++++++++++++++++++++++ 1 file changed, 401 insertions(+) create mode 100644 Social_Network_Ads.csv diff --git a/Social_Network_Ads.csv b/Social_Network_Ads.csv new file mode 100644 index 0000000..4e6dadd --- /dev/null +++ b/Social_Network_Ads.csv @@ -0,0 +1,401 @@ +User ID,Gender,Age,EstimatedSalary,Purchased +15624510,Male,19,19000,0 +15810944,Male,35,20000,0 +15668575,Female,26,43000,0 +15603246,Female,27,57000,0 +15804002,Male,19,76000,0 +15728773,Male,27,58000,0 +15598044,Female,27,84000,0 +15694829,Female,32,150000,1 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