def svm():
# ********************* load the dataset and divide to X&y ***********************
from sklearn.datasets import make_blobs
X, Y = make_blobs(cluster_std=0.9,
random_state=20,
n_samples=1000,
centers=10,
n_features=10)
from Algorithms.ML_.helper.data_helper import split_train_val_test
X, Xv, y, Yv, Xt, Yt = split_train_val_test(X, Y)
print(X.shape, y.shape, Xv.shape, Yv.shape, Xt.shape, Yt.shape)
# ********************* build model ***********************
from model import SVM
from activation import Activation, Softmax, Hinge
from regularization import Regularization, L1, L2, L12
from optimizer import Vanilla
model = SVM()
learning_rate, reg_rate = 1e-3, 5e-1
model.compile(alpha=learning_rate,
lambda_=reg_rate,
activation=Softmax(),
reg=L2(),
opt=Vanilla())
model.describe()
# ********************* train ***********************
loss_train, loss_val = model.train(X,
y,
val=(Xv, Yv),
iter_=1000,
return_loss=True,
verbose=True,
eps=1e-3)
import matplotlib.pyplot as plt
plt.plot(range(len(loss_train)), loss_train)
plt.plot(range(len(loss_val)), loss_val)
plt.legend(['train', 'val'])
plt.xlabel('Iteration')
plt.ylabel('Training loss')
plt.title('Training Loss history')
plt.show()
# ********************* predict ***********************
pred_train = model.predict(X)
pred_val = model.predict(Xv)
pred_test = model.predict(Xt)
import metrics
print('train accuracy=', metrics.accuracy(y, pred_train))
print('val accuracy=', metrics.accuracy(Yv, pred_val))
print('test accuracy=', metrics.accuracy(Yt, pred_test))
print('null accuracy=', metrics.null_accuracy(y))
import metrics
metrics.print_metrics(Yt, pred_test)
