Python point_prob_plot Examples

Example #1

File: spiral_rf.py Project: kenshohara/fertilized-forests

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

Example #2

File: spiral_svm.py Project: caomw/fertilized-forests

X, Y = make_spiral(n_arms=n_classes, noise=.4)

##############################################################################
parameters = {'kernel': ['rbf'],
              'C': [1, 10, 100, 1000, 10000, 100000],
              'gamma': [10 ** x for x in range(-5, 3)],
              'probability':[True]}
svr = svm.SVC()
clf = grid_search.GridSearchCV(svr, parameters)
clf.fit(X, Y.ravel())

svmp = probaproxy(clf.predict_proba)

plt.figure()
point_prob_plot(svmp, X, Y, plotx, ploty)
plt.title('RBF kernel SVM $(\gamma=%d, C=%f)$' % (clf.best_params_['gamma'], clf.best_params_['C']))
plt.savefig('spiral_svm.png')
plt.show()

##############################################################################
parameters = {'C': [1, 10, 100, 1000, 10000],
              'probability': [True]}
svr = svm.SVC(kernel='linear', max_iter=20000)
clf = grid_search.GridSearchCV(svr, parameters)
clf.fit(X, Y.ravel())

svmp = probaproxy(clf.predict_proba)

plt.figure()
point_prob_plot(svmp, X, Y, plotx, ploty)

Example #3

File: custom.py Project: caomw/fertilized-forests

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()

Example #4

##############################################################################
parameters = {
    'kernel': ['rbf'],
    'C': [1, 10, 100, 1000, 10000, 100000],
    'gamma': [10**x for x in range(-5, 3)],
    'probability': [True]
}
svr = svm.SVC()
clf = grid_search.GridSearchCV(svr, parameters)
clf.fit(X, Y.ravel())

svmp = probaproxy(clf.predict_proba)

plt.figure()
point_prob_plot(svmp, X, Y, plotx, ploty)
plt.title('RBF kernel SVM $(\gamma=%d, C=%f)$' %
          (clf.best_params_['gamma'], clf.best_params_['C']))
plt.savefig('spiral_svm.png')
plt.show()

##############################################################################
parameters = {'C': [1, 10, 100, 1000, 10000], 'probability': [True]}
svr = svm.SVC(kernel='linear', max_iter=20000)
clf = grid_search.GridSearchCV(svr, parameters)
clf.fit(X, Y.ravel())

svmp = probaproxy(clf.predict_proba)

plt.figure()
point_prob_plot(svmp, X, Y, plotx, ploty)

Example #5

File: spiral_ann.py Project: kenshohara/fertilized-forests

h = SimpleNode(sx, 2, 4)
h2 = SimpleNode(h, 4, 2)
out = SimpleNode(h2, 2, 1, nlin=pynnet.nlins.sigmoid)
cost = errors.mse(out, sy)

theano.config.blas.ldflags = ''
eval = theano.function([sx], out.output)
test = theano.function([sx, sy], cost.output)
train = theano.function([sx, sy],
                        cost.output,
                        updates=get_updates(cost.params, cost.output, 0.01))

print("Error at start:", test(X, Y))

for i in range(200000):
    train(X, Y)
print("Error after 200000:", test(X, Y))


def pfunc(x):
    return 1. - eval(x)


clp = probaproxy(pfunc)

plt.figure()
point_prob_plot(clp, X, Y, plotx, ploty)
plt.title('ANN (2 hidden layers, 4, 2)')
plt.savefig('spiral_ann.png')
plt.show()

Example #6

File: spiral_ann.py Project: caomw/fertilized-forests

#%% Build ANN.
sx = theano.tensor.matrix('x')
sy = theano.tensor.matrix('y')

h = SimpleNode(sx, 2, 4)
h2 = SimpleNode(h, 4, 2)
out = SimpleNode(h2, 2, 1, nlin=pynnet.nlins.sigmoid)
cost = errors.mse(out, sy)

theano.config.blas.ldflags=''
eval = theano.function([sx], out.output)
test = theano.function([sx, sy], cost.output)
train = theano.function([sx, sy], cost.output,
                        updates=get_updates(cost.params, cost.output, 0.01))

print("Error at start:", test(X, Y))

for i in range(200000):
    train(X, Y)
print("Error after 200000:", test(X, Y))

def pfunc(x):
  return 1. - eval(x)
clp = probaproxy(pfunc)

plt.figure()
point_prob_plot(clp, X, Y, plotx, ploty)
plt.title('ANN (2 hidden layers, 4, 2)')
plt.savefig('spiral_ann.png')
plt.show()