elif dec_name == 'quadratic':
feat_calc = soil.QuadraticSurfaceCalculator(
2,
np.array([X[:, 0].min(), X[:, 0].max(), X[:, 1].min(), X[:, 1].max()],
dtype='float32'),
random_seed=rs)
cls = []
lm = []
for i in range(n_trees):
cls.append(
soil.ThresholdDecider(
feat_sel_prov, feat_calc,
soil.RandomizedClassificationThresholdOptimizer(
suggestions,
n_classes,
soil.EntropyGain(soil.ShannonEntropy()),
random_seed=rs)))
lm.append(soil.ClassificationLeafManager(n_classes))
forest = soil.Forest(depth, 1, 2, n_trees, cls, lm,
soil.ClassicTraining(soil.NoBagging()))
forest.fit(X, Y)
if INTERACTIVE:
plt.figure()
point_prob_plot(forest, X, Y, plotx, ploty)
plt.title('Random Forest $(T=%d, D=%d, |\Theta_j|=%d)$' %
(n_trees, depth, suggestions * 2))
plt.savefig('spiral_rf.png')
plt.show()
assert accuracy_score(Y, np.argmax(forest.predict(X), axis=1)) > 0.95
n_trees = 200
# These variables will contain the classifiers and leaf managers for each tree.
cls = []
lm = []
for i in range(n_trees):
# Classifier:
cls.append(soil.ThresholdDecider(
soil.StandardFeatureSelectionProvider(1, 2, 2, 2, random_seed=1+i),
soil.LinearSurfaceCalculator(400, random_seed=1+i),
soil.RandomizedClassificationThresholdOptimizer(
True,
2,
soil.EntropyGain(soil.ShannonEntropy()), random_seed=1+i)))
# Leaf manager:
lm.append(soil.ClassificationLeafManager(2))
forest = soil.Forest(depth,
1,
2,
n_trees,
cls,
lm,
soil.ClassicTraining(soil.NoBagging()))
forest.fit(X, Y)
assert accuracy_score(Y, np.argmax(forest.predict(X), axis=1)) > 0.95
if INTERACTIVE:
plt.figure()
point_prob_plot(forest, X, Y, plotx, ploty)
plt.savefig('custom.png')
plt.show()