from patsy import dmatrices import matplotlib.pyplot as plt import numpy as np from sklearn import svm #Taken from http://nbviewer.jupyter.org/github/agconti/kaggle-titanic/blob/master/Titanic.ipynb def plot_svm(df): # set plotting parameters plt.figure(figsize=(8,6)) # # Create an acceptable formula for our machine learning algorithms formula_ml = 'Survived ~ C(Pclass) + C(Sex) + Age + SibSp + Parch + C(Embarked)' # create a regression friendly data frame y, x = dmatrices(formula_ml, data=df, return_type='matrix') # select which features we would like to analyze # try chaning the selection here for diffrent output. # Choose : [2,3] - pretty sweet DBs [3,1] --standard DBs [7,3] -very cool DBs, # [3,6] -- very long complex dbs, could take over an hour to calculate! feature_1 = 2 feature_2 = 3 X = np.asarray(x) X = X[:,[feature_1, feature_2]] y = np.asarray(y) # needs to be 1 dimensional so we flatten. it comes out of dmatrices with a shape. y = y.flatten() n_sample = len(X) np.random.seed(0) order = np.random.permutation(n_sample) X = X[order] y = y[order].astype(np.float) # do a cross validation nighty_precent_of_sample = int(.9 * n_sample) X_train = X[:nighty_precent_of_sample] y_train = y[:nighty_precent_of_sample] X_test = X[nighty_precent_of_sample:] y_test = y[nighty_precent_of_sample:] # create a list of the types of kerneks we will use for your analysis types_of_kernels = ['linear', 'rbf', 'poly'] # specify our color map for plotting the results color_map = plt.cm.RdBu_r # fit the model for fig_num, kernel in enumerate(types_of_kernels): clf = svm.SVC(kernel=kernel, gamma=3) clf.fit(X_train, y_train) plt.figure(fig_num) plt.scatter(X[:, 0], X[:, 1], c=y, zorder=10, cmap=color_map) # circle out the test data plt.scatter(X_test[:, 0], X_test[:, 1], s=80, facecolors='none', zorder=10) plt.axis('tight') x_min = X[:, 0].min() x_max = X[:, 0].max() y_min = X[:, 1].min() y_max = X[:, 1].max() XX, YY = np.mgrid[x_min:x_max:200j, y_min:y_max:200j] Z = clf.decision_function(np.c_[XX.ravel(), YY.ravel()]) # put the result into a color plot Z = Z.reshape(XX.shape) plt.pcolormesh(XX, YY, Z > 0, cmap=color_map) plt.contour(XX, YY, Z, colors=['k', 'k', 'k'], linestyles=['--', '-', '--'], levels=[-.5, 0, .5]) plt.title(kernel) plt.show()