def draw(clf, ds: DataSet, step):
X = ds.get_X()
y = ds.get_y()
x_min, y_min = np.amin(X, 0)
x_max, y_max = np.amax(X, 0)
xx, yy = np.meshgrid(np.arange(x_min, x_max, step),
np.arange(y_min, y_max, step))
grid = np.c_[xx.ravel(), yy.ravel()]
predict_z = np.array(pm_predict(clf.predict_single, grid,
name='predict')).reshape(xx.shape)
x0, y0 = X[y == -1].T
x1, y1 = X[y == 1].T
X_sup = X[clf.support_indices]
x_sup, y_sup = X_sup.T
def plot(_predict_z):
plt.figure(figsize=(10, 10))
plt.pcolormesh(xx,
yy,
_predict_z,
cmap=plt.get_cmap('seismic'),
shading='auto')
plt.scatter(x0, y0, color='red', s=100)
plt.scatter(x1, y1, color='blue', s=100)
plt.scatter(x_sup, y_sup, color='white', marker='x', s=60)
plt.show()
plot(predict_z)
def choose_best(ds: DataSet):
gs = GridSearchCV(estimator=SMO(),
param_grid=GRID,
cv=4,
scoring='accuracy',
verbose=1,
n_jobs=-1)
gs.fit(ds.get_X(), ds.get_y())
draw_metrics(gs.cv_results_)
print(f'Got best score {gs.best_score_} with params {gs.best_params_}')
return gs.best_params_