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