def test_multi_logistic_regression_gd_acc():
lr = SoftmaxRegression(epochs=200,
eta=0.005,
minibatches=1,
random_seed=1)
lr.fit(X, y)
assert (y == lr.predict(X)).all()
def test_multi_logistic_regression_gd_acc():
lr = SoftmaxRegression(epochs=200,
eta=0.005,
minibatches=1,
random_seed=1)
lr.fit(X, y)
assert (y == lr.predict(X)).all()
def test_binary_logistic_regression_gd():
t = np.array([[0.13, -0.12], [-3.07, 3.05]])
lr = SoftmaxRegression(epochs=200, eta=0.005, minibatches=1, random_seed=1)
lr.fit(X_bin, y_bin)
np.testing.assert_almost_equal(lr.w_, t, 2)
assert (y_bin == lr.predict(X_bin)).all()
def test_binary_logistic_regression_sgd():
t = np.array([[-0.68, 0.68], [-3.2, 3.2]])
lr = SoftmaxRegression(epochs=200,
eta=0.005,
minibatches=len(y_bin),
random_seed=1)
lr.fit(X_bin, y_bin) # 0, 1 class
np.testing.assert_almost_equal(lr.w_, t, 2)
assert (y_bin == lr.predict(X_bin)).all()
def test_binary_logistic_regression_sgd():
t = np.array([[0.13, -0.12],
[-3.06, 3.05]])
lr = SoftmaxRegression(epochs=200,
eta=0.005,
minibatches=len(y_bin),
random_seed=1)
lr.fit(X_bin, y_bin) # 0, 1 class
np.testing.assert_almost_equal(lr.w_, t, 2)
assert (y_bin == lr.predict(X_bin)).all()
def test_binary_l2_regularization_gd():
t = np.array([[-0.17, 0.17], [-2.26, 2.26]])
lr = SoftmaxRegression(epochs=200,
eta=0.005,
l2=1.0,
minibatches=1,
random_seed=1)
lr.fit(X_bin, y_bin)
np.testing.assert_almost_equal(lr.w_, t, 2)
assert (y_bin == lr.predict(X_bin)).all()
def test_binary_logistic_regression_gd():
t = np.array([[-0.2, 0.2],
[-3.09, 3.09]])
lr = SoftmaxRegression(epochs=200,
eta=0.005,
minibatches=1,
random_seed=1)
lr.fit(X_bin, y_bin)
np.testing.assert_almost_equal(lr.w_, t, 2)
assert((y_bin == lr.predict(X_bin)).all())
def test_binary_l2_regularization_gd():
t = np.array([[-0.17, 0.17],
[-2.26, 2.26]])
lr = SoftmaxRegression(epochs=200,
eta=0.005,
l2=1.0,
minibatches=1,
random_seed=1)
lr.fit(X_bin, y_bin)
np.testing.assert_almost_equal(lr.w_, t, 2)
assert (y_bin == lr.predict(X_bin)).all()
def test_binary_l2_regularization_gd():
lr = SoftmaxRegression(eta=0.005,
epochs=200,
minibatches=1,
l2_lambda=1.0,
random_seed=1)
lr.fit(X_bin, y_bin)
y_pred = lr.predict(X_bin)
expect_weights = np.array([[-0.316, 0.317], [-2.265, 2.265]])
np.testing.assert_almost_equal(lr.w_, expect_weights, 3)
acc = sum(y_pred == y_bin) / len(y_bin)
assert acc == 1.0
def test_binary_l2_regularization_gd():
lr = SoftmaxRegression(eta=0.005,
epochs=200,
minibatches=1,
l2_lambda=1.0,
random_seed=1)
lr.fit(X_bin, y_bin)
y_pred = lr.predict(X_bin)
expect_weights = np.array([[-0.316, 0.317],
[-2.265, 2.265]])
np.testing.assert_almost_equal(lr.w_, expect_weights, 3)
acc = sum(y_pred == y_bin) / len(y_bin)
assert(acc == 1.0)
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report, confusion_matrix
from mlxtend.classifier import SoftmaxRegression
from sklearn import datasets
iris = datasets.load_iris()
#X = iris.data[:, [2, 3]]
X = iris.data
y = iris.target
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.3,
random_state=0)
############# softmax Regresion ###############
# Fitting softmax regression to the tranning set
foft_regressor = SoftmaxRegression()
foft_regressor.fit(X_train, y_train)
# Predicting the test set result
y_pred = foft_regressor.predict(X_test)
print("############ softmax Regression ############")
print(confusion_matrix(y_test, y_pred))
print(classification_report(y_test, y_pred))
