def kaggle(alpha, c, epsilon):
X = temp["X"]
test = temp["X_test"]
x_labels = temp["y"]
X, x_labels = permute_dictionaries(X, x_labels)
X = scaler.transform(X)
test = scaler.transform(test)
X = np.hstack((X, np.ones((X.shape[0], 1))))
test = np.hstack((test, np.ones((test.shape[0], 1))))
w = np.zeros((13, 1))
epoch = 0
loss = np.inf
while loss >= epsilon:
x_predictions = sigmoid(np.matmul(X, w))
dw = calc_gradient(X, x_labels, x_predictions, w, c)
w = w - alpha * dw
loss = obj(x_labels, x_predictions, w, c)
epoch += 1
if epoch % 1000 == 0:
print(loss)
test_predictions = classify(test, w)
print(test_predictions)
results_to_csv(test_predictions.flatten())
def mnist_hyperparameter():
"""returns the best value of C"""
data = split_mnist_data()
training_data = data["training_data"][:10000]
training_labels = data["training_labels"][:10000]
val_true = data["validation_labels"]
C_vals = []
accuracies = []
for exp in range(-8, -1):
C = 10**(exp)
C_vals.append(C)
clf = svm.SVC(kernel='linear', C=C)
clf.fit(training_data, training_labels)
val_pred = clf.predict(data["validation_data"])
accuracy = accuracy_score(val_true, val_pred)
accuracies.append(accuracy)
top_acc = 0
top_ind = 0
top_C = 0
for ind in range(len(accuracies)):
if accuracies[ind] > top_acc:
top_ind = ind
top_acc = accuracies[ind]
top_C = C_vals[ind]
#print("Top C:", top_C)
mnist_data = scipy.io.loadmat('data/mnist_data.mat')
test_data = mnist_data["test_data"]
training_data, training_labels = shuffle(mnist_data["training_data"],
mnist_data["training_labels"])
clf = svm.LinearSVC(C=top_C)
clf.fit(training_data, training_labels)
test_preds = clf.predict(test_data)
results_to_csv(test_preds)
return C_vals, accuracies
def problem5(training_data, training_data_labels, test_data, C_value): classifier = svm.LinearSVC(dual = False, random_state = 10, C = C_value) classifier.fit(training_data, np.ravel(training_data_labels)) predict_training_results = classifier.predict(training_data) print(accuracy_score(np.ravel(training_data_labels), np.ravel(predict_training_results))) predict_test_results = classifier.predict(test_data) results_to_csv(predict_test_results)
def problem6(training_data, training_data_labels, test_data, C_Value=0):
classifier = svm.LinearSVC(random_state=40, C=10**C_Value)
classifier.fit(training_data, np.ravel(training_data_labels))
predict_training_results = classifier.predict(training_data)
print(
accuracy_score(np.ravel(training_data_labels),
np.ravel(predict_training_results)))
predict_test_results = classifier.predict(test_data)
results_to_csv(predict_test_results)
def problem6(training_data, training_data_labels, test_data, linear, C_Value = 0): classifier = svm.LinearSVC(dual = False, random_state = 10, verbose = 1, max_iter = 1000000) if(not linear): classifier = svm.SVC(kernel = "linear", random_state = 10, verbose = 0) classifier.fit(training_data, np.ravel(training_data_labels)) predict_training_results = classifier.predict(training_data) print(accuracy_score(np.ravel(training_data_labels), np.ravel(predict_training_results))) predict_test_results = classifier.predict(test_data) results_to_csv(predict_test_results)
def kaggle(c):
data = deskew_all(mnist_data["training_data"])
labels = mnist_data["training_labels"]
test_data = deskew_all(mnist_data["test_data"])
partitioned_data = partition_data(data, labels)
means = empirical_mean(partitioned_data)
partitioned_covariances = empirical_cov(partitioned_data)
priors = calc_priors(partitioned_data, len(data))
samples = {'training': data, 'test': test_data}
predictions = QDA(means, partitioned_covariances, priors, samples, c)
train_predictions = predictions['training']
test_predictions = predictions['test']
print(error_rate(np.array([train_predictions]).T, labels))
results_to_csv(np.array(test_predictions))
return
def spam_hyperparameter():
C_vals = []
accuracies = []
data = split_spam_data_crossval(5)
for exp in range(-5, 4):
print(exp)
C = 10**exp
C_vals.append(C)
accuracy = 0
for k in range(5):
training_data = []
training_labels = []
for i in range(5):
if not i == k:
training_data.append(data[i]["data"])
training_labels.append(data[i]["labels"])
val_data = data[k]["data"]
val_true = data[k]["labels"]
clf = svm.LinearSVC(C=C)
for ind in range(len(training_data)):
clf.fit(training_data[ind], training_labels[ind])
val_pred = clf.predict(val_data)
accuracy += accuracy_score(val_true, val_pred)
accuracies.append(accuracy / 5)
top_acc = 0
top_ind = 0
top_C = 0
for ind in range(len(accuracies)):
if accuracies[ind] > top_acc:
top_ind = ind
top_acc = accuracies[ind]
top_C = C_vals[ind]
spam_data = scipy.io.loadmat('data/spam_data.mat')
test_data = spam_data["test_data"]
training_data, training_labels = shuffle(spam_data["training_data"],
spam_data["training_labels"])
clf = svm.LinearSVC(C=top_C)
clf.fit(training_data, training_labels)
test_preds = clf.predict(test_data)
results_to_csv(test_preds)
return C_vals, accuracies
def cifar10_hyperparameter():
"""returns the best value of C"""
data = split_cifar10_data()
training_data = data["training_data"][:2000]
training_labels = data["training_labels"][:2000]
val_true = data["validation_labels"]
C_vals = []
accuracies = []
for exp in range(-2, 2):
print(exp)
C = 10**(exp)
C_vals.append(C)
clf = svm.LinearSVC(C=C)
clf.fit(training_data, training_labels)
val_pred = clf.predict(data["validation_data"])
accuracy = accuracy_score(val_true, val_pred)
accuracies.append(accuracy)
top_acc = 0
top_ind = 0
top_C = 0
for ind in range(len(accuracies)):
if accuracies[ind] > top_acc:
top_ind = ind
top_acc = accuracies[ind]
top_C = C_vals[ind]
cifar10_data = scipy.io.loadmat('data/cifar10_data.mat')
test_data = cifar10_data["test_data"]
training_data, training_labels = shuffle(cifar10_data["training_data"],
cifar10_data["training_labels"])
clf = svm.LinearSVC(C=top_C)
clf.fit(training_data, training_labels)
test_preds = clf.predict(test_data)
results_to_csv(test_preds)
return C_vals, accuracies
#c, acc = cifar10_hyperparameter()
#print(c)
#print(acc)
def evaluateModel(X, y, split, model, filename=None, Z=None):
full = np.concatenate((X, y.reshape(-1, 1)), axis=1)
np.random.shuffle(full)
train = full[:split, :]
val = full[split:, :]
train_x, train_y = split_xy(train)
train_y = train_y.reshape(-1, )
val_x, val_y = split_xy(val)
val_y = val_y.reshape(-1)
model.fit(train_x, train_y)
prediction_train = model.predict(train_x)
prediction_val = model.predict(val_x)
if filename:
prediction_test = model.predict(Z)
results_to_csv(prediction_test, filename)
print("predictions saved")
return measure_accuracy(prediction_train,
train_y), measure_accuracy(prediction_val, val_y)
v_error = classifier.errorRate(predictions, yv)
depths.append(d)
v_errors.append(v_error)
plt.plot(depths, v_errors)
plt.show()
if spam_kaggle:
print("Making Kaggle Predictions for Spam...")
"""
Make predictions of the test data for kaggle submission.
Set maxDepth=22 based on the validation test.
"""
classifier = RandomForest(100, maxDepth=57)
classifier.fit(X, y)
predictions = np.array(classifier.predict(Z))
results_to_csv(predictions)
if spam_decision_tree:
"""
Generate a random 80/20 training/validation split.
"""
np.random.seed(42)
np.random.shuffle(X)
np.random.seed(42)
np.random.shuffle(y)
i = math.ceil(len(y)*0.8)
Xt = X[:i]
yt = y[:i]
Xv = X[i:]
yv = y[i:]
if p < threshold:
results.append(0)
else:
results.append(1)
return np.array(results)
learning_rate, lambda_, iter_ = 0.00001, 0.01, 1500
w = np.zeros((x_train.shape[1], 1))
lost = []
for i in range(iter_):
alpha = learning_rate / (i + 1)
cost = cost_fn(x_train, y_train, w, lambda_)
lost.append(cost)
idx = np.random.randint(x_train.shape[0])
y_hat = np.dot(x_train[idx:], w)
grad = 2 * lambda_ * w - np.dot(x_train[idx:].T, (y_train[idx:] - sigmoid(y_hat)))
w -= alpha * grad
pred = predict(w, x_val)
accuracy = sum(pred == y_val)/pred.shape[0]
print("val accuracy", accuracy)
test_data_norm = preprocessing.scale(data["X_test"])
predictions = predict(w, test_data_norm)
save_csv.results_to_csv(predictions)
print("accuracies:", bv_score)
plt.plot(C, bv_score, 'yo')
# plt.xscale('log')
plt.xlabel('C values')
plt.ylabel('accuracy_score')
plt.title('spam dataset K-Fold Cross-Validation')
# plt.show()
plt.savefig('figure_5.png')
plt.close()
############################
# Problem 6: Kaggle
############################
import save_csv
# (a) For the MNIST dataset, use C=10^-6.
model = svm.LinearSVC(C=0.000001)
model.fit(training_data, training_labels.ravel())
save_csv.results_to_csv(model.predict(data["test_data"]))
# (b) For the spam dataset, use C=14
model = svm.LinearSVC(C=14)
model.fit(training_data, training_labels.ravel())
save_csv.results_to_csv(model.predict(data["test_data"]))
# (c) For the cifar10 dataset
model = svm.LinearSVC()
model.fit(training_data, training_labels.ravel())
save_csv.results_to_csv(model.predict(data["test_data"]))
xi = X[ind]
yi = y[ind]
update = (delta / it) * ((yi - s_stoch(xi, w)) * xi - lamb * w)
w = w + update
return w
dsgd_costs = []
for it in dif_iters:
print(it)
w_dsgd = stochasticGradientDescent(data, labels, w_dsgd, sgd_regularizer,
sgd_step_size, it)
J_val = J(data, labels, w_dsgd, 0)
print(J_val)
dsgd_costs.append(J_val)
plt.plot(num_iters, dsgd_costs, 'ro-')
plt.ylabel("Cost Function Value")
plt.xlabel("Number of Training Iterations")
plt.title(
"Cost Value vs Training Iterations for Dynamic Stochastic Gradient Descent"
)
plt.show()
# Part 6 ////////////////////////////////////////////////////////////////////////////////////////////////////////
# train the model and run it on the test data
w = batchGradientDescent(data, labels, w_bgd, bgd_regularizer, bgd_step_size,
50)
preds = s(test_data, w)
results_to_csv(preds[:, 0])
else:
raise NotImplementedError("Dataset %s not handled" % dataset)
print("Features:", features)
print("Train/test size:", X.shape, Z.shape)
print("\n\nPart 0: constant classifier")
print("Accuracy", 1 - np.sum(y) / y.size)
# Basic decision tree
print("\n\nPart (a-b): simplified decision tree")
dt = DecisionTree(max_depth=3, feature_labels=features)
dt.fit(X, y)
print("Predictions", dt.predict(Z))
save_csv.results_to_csv(dt.predict(Z))
print("kfold")
"""
ss = ShuffleSplit(n_splits=5)
totals = []
for train_index, test_index in ss.split(X):
dt = DecisionTree(max_depth=3, feature_labels=features)
dt.fit(X[train_index], y[train_index])
predictions = dt.predict(X[test_index])
total = 0
for i in range(len(predictions)):
total += predictions[i] == y[test_index][i]
totals.append(total/len(predictions))
print(np.mean(totals))
