def main():
# load data into shuffle matrix
df = pd.read_csv("diabetes.csv")
df = np.array(df)
np.random.shuffle(df)
# split data into labels and design matrix
design = df[:, :-1].T
labels = df[:, -1]
labels = labels.reshape((labels.shape[0],1)).T # keep dimensionality to 2
_, m_tot = design.shape
# split into test and training data
frac_test = .6
split_idx = int(frac_test*m_tot)
train_design = design[:, :split_idx]
train_labels = labels[:, :split_idx]
test_design = design[:, split_idx:]
test_labels = labels[:, split_idx:]
# fit perceptron
perc = Perceptron()
perc.fit(X=train_design, Y=train_labels, alpha=1e-4,
lambd=0, epochs=100_000)
# get model accuracies
test_acc = perc.acc(X=test_design, Y=test_labels)
train_acc = perc.acc(X=train_design, Y=train_labels)
print("Test set accuracy: %.5f" % test_acc)
print("Training set accuracy: %.5f" % train_acc)
def main():
# load data into shuffle matrix
df = pd.read_csv("diabetes.csv")
data = np.array(df)
np.random.shuffle(data)
# split data into labels and design matrix
design = data[:, :-1].T
labels = data[:, -1]
labels = labels.reshape((labels.shape[0],1)).T # keep dims to 2
n, m_tot = design.shape
# normalize dataset
maxs = np.amax(design, axis=1)
mins = np.amin(design, axis=1)
ranges = maxs - mins
design -= mins.reshape(len(mins),1)
design /= ranges.reshape(len(ranges),1)
# split into test and training data
frac_test = .8
split_idx = int(frac_test*m_tot)
train_design = design[:, :split_idx]
train_labels = labels[:, :split_idx]
test_design = design[:, split_idx:]
test_labels = labels[:, split_idx:]
# fit neural network
nn = NN(ns=[n,5,1], acts=["ReLU","ReLU","sigmoid"])
nn.fit(train_design, train_labels, alpha=1e-2, epochs = 20_000)
test_acc = nn.evaluate(X=test_design, Y=test_labels)
train_acc = nn.evaluate(X=train_design, Y=train_labels)
print("Network test set accuracy: %.5f" % test_acc)
print("Network training set accuracy: %.5f" % train_acc)
print()
# fit perceptron
perc = Perceptron()
perc.fit(X=train_design, Y=train_labels, alpha=1e-4,
lambd=1e-2, epochs=100_000)
test_acc = perc.acc(X=test_design, Y=test_labels)
train_acc = perc.acc(X=train_design, Y=train_labels)
print("Own perceptron test set accuracy: %.5f" % test_acc)
print("Own perceptron training set accuracy: %.5f" % train_acc)
print()
# fit standard template perceptron from sklearn
clf = standardPerceptron(tol=1e-3, random_state=0)
clf.fit(train_design.T, train_labels.squeeze())
train_acc = clf.score(train_design.T, train_labels.squeeze())
test_acc = clf.score(test_design.T, test_labels.squeeze())
print("Sklearn perceptron test set accuracy: %.5f" % test_acc)
print("Sklearn perceptron training set accuracy: %.5f"%train_acc)
print()
