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()