def test_fail_minibatches():
lr = TfSoftmaxRegression(epochs=100,
eta=0.5,
minibatches=13,
random_seed=1)
lr.fit(X, y)
assert((y == lr.predict(X)).all())
def test_fail_minibatches():
lr = TfSoftmaxRegression(epochs=100,
eta=0.5,
minibatches=13,
random_seed=1)
lr.fit(X, y)
assert ((y == lr.predict(X)).all())
def test_multi_logistic_regression_gd_acc():
lr = TfSoftmaxRegression(epochs=100,
eta=0.5,
minibatches=1,
random_seed=1)
lr.fit(X, y)
assert((y == lr.predict(X)).all())
def test_binary_logistic_regression_gd():
t = np.array([[-0.28, 0.95], [-2.23, 2.4]])
lr = TfSoftmaxRegression(epochs=100, eta=0.5, minibatches=1, random_seed=1)
lr.fit(X_bin, y_bin)
np.testing.assert_almost_equal(lr.weights_, t, 2)
assert (y_bin == lr.predict(X_bin)).all()
def test_init_params():
t = np.array([[-0.28, 0.95], [-2.23, 2.4]])
lr = TfSoftmaxRegression(epochs=50, eta=0.5, minibatches=1, random_seed=1)
lr.fit(X_bin, y_bin)
lr.fit(X_bin, y_bin, init_params=False)
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([[0.35, 0.32], [-7.14, 7.3]])
lr = TfSoftmaxRegression(epochs=100,
eta=0.5,
minibatches=len(y_bin),
random_seed=1)
lr.fit(X_bin, y_bin) # 0, 1 class
np.testing.assert_almost_equal(lr.weights_, t, 2)
assert (y_bin == lr.predict(X_bin)).all()
def test_binary_logistic_regression_sgd():
t = np.array([[0.35, 0.32],
[-7.14, 7.3]])
lr = TfSoftmaxRegression(epochs=100,
eta=0.5,
minibatches=len(y_bin),
random_seed=1)
lr.fit(X_bin, y_bin) # 0, 1 class
np.testing.assert_almost_equal(lr.weights_, t, 2)
assert((y_bin == lr.predict(X_bin)).all())
def test_binary_logistic_regression_gd():
t = np.array([[-0.28, 0.95],
[-2.23, 2.4]])
lr = TfSoftmaxRegression(epochs=100,
eta=0.5,
minibatches=1,
random_seed=1)
lr.fit(X_bin, y_bin)
np.testing.assert_almost_equal(lr.weights_, t, 2)
assert((y_bin == lr.predict(X_bin)).all())
def test_init_params():
t = np.array([[-0.28, 0.95],
[-2.23, 2.4]])
lr = TfSoftmaxRegression(epochs=50,
eta=0.5,
minibatches=1,
random_seed=1)
lr.fit(X_bin, y_bin)
lr.fit(X_bin, y_bin, init_params=False)
np.testing.assert_almost_equal(lr.w_, t, 2)
assert (y_bin == lr.predict(X_bin)).all()
def _clf_softmax(trX, teX, trY, teY):
print "factors", factors(trX.shape[0])
print "enter no of mini batch"
trY = trY.astype(int)
teY = teY.astype(int)
mini_batch = int(input())
clf = TfSoftmaxRegression(eta=0.75,
epochs=100,
print_progress=True,
minibatches=mini_batch,
random_seed=1)
clf.fit(trX, trY)
pred = clf.predict(teX)
print _f_count(teY), "test f count"
pred = pred.astype(np.int32)
print _f_count(pred), "pred f count"
conf_mat = confusion_matrix(teY, pred)
process_cm(conf_mat, to_print=True)
print precision_score(teY, pred), "Precision Score"
print recall_score(teY, pred), "Recall Score"
print roc_auc_score(teY, pred), "ROC_AUC"
def _clf_softmax(trX,teX,trY,teY):
print "factors",factors(trX.shape[0])
print "enter no of mini batch"
trY=trY.astype(int)
teY=teY.astype(int)
mini_batch=int(input())
clf = TfSoftmaxRegression(eta=0.75,
epochs=100,
print_progress=True,
minibatches=mini_batch,
random_seed=1)
clf.fit(trX, trY)
pred=clf.predict(teX)
print _f_count(teY),"test f count"
pred=pred.astype(np.int32)
print _f_count(pred),"pred f count"
conf_mat=confusion_matrix(teY, pred)
process_cm(conf_mat, to_print=True)
print precision_score(teY,pred),"Precision Score"
print recall_score(teY,pred),"Recall Score"
print roc_auc_score(teY,pred), "ROC_AUC"
def test_multi_logistic_regression_gd_acc():
lr = TfSoftmaxRegression(epochs=100, eta=0.5, minibatches=1, random_seed=1)
lr.fit(X, y)
assert (y == lr.predict(X)).all()
