def main():
print("-- Classification Tree --")
data = datasets.load_iris()
X = data.data
y = data.target
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4)
clf = ClassificationTree()
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print("Accuracy:", accuracy)
pca = PCA()
pca.plot_in_2d(X_test,
y_pred,
title="Decision Tree",
accuracy=accuracy,
legend_labels=data.target_names)
print("-- Regression Tree --")
X, y = datasets.make_regression(n_features=1,
n_samples=100,
bias=0,
noise=5)
X_train, X_test, y_train, y_test = train_test_split(standardize(X),
y,
test_size=0.3)
clf = RegressionTree()
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
mse = mean_squared_error(y_test, y_pred)
print("Mean Squared Error:", mse)
# Plot the results
plt.scatter(X_test[:, 0], y_test, color='black')
plt.scatter(X_test[:, 0], y_pred, color='green')
plt.title("Regression Tree (%.2f MSE)" % mse)
plt.show()
def main():
print("-- Gradient Boosting Classification --")
data = datasets.load_iris()
X = data.data
y = data.target
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4)
clf = GradientBoostingClassifier(debug=True)
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print("Accuracy:", accuracy)
Plot().plot_in_2d(X_test,
y_pred,
title="Gradient Boosting",
accuracy=accuracy,
legend_labels=data.target_names)
print("-- Gradient Boosting Regression --")
X, y = datasets.make_regression(n_features=1,
n_samples=150,
bias=0,
noise=5)
X_train, X_test, y_train, y_test = train_test_split(standardize(X),
y,
test_size=0.5)
clf = GradientBoostingRegressor(debug=True)
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
mse = mean_squared_error(y_test, y_pred)
print("Mean Squared Error:", mse)
# Plot the results
plt.scatter(X_test[:, 0], y_test, color='black')
plt.scatter(X_test[:, 0], y_pred, color='green')
plt.title("Gradient Boosting Regression (%.2f MSE)" % mse)
plt.show()
def main():
print("-- XGBoost --")
data = datasets.load_iris()
X = data.data
y = data.target
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.4,
seed=2)
clf = XGBoost(debug=True)
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print("Accuracy:", accuracy)
Plot().plot_in_2d(X_test,
y_pred,
title="XGBoost",
accuracy=accuracy,
legend_labels=data.target_names)
def main():
data = datasets.load_digits()
X = normalize(data.data)
y = data.target
n_samples, n_features = np.shape(X)
n_hidden, n_output = 50, 10
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, seed=1)
optimizer = GradientDescent_(learning_rate=0.0001, momentum=0.3)
# MLP
clf = MultilayerPerceptron(n_iterations=6000,
batch_size=200,
optimizer=optimizer,
val_error=True)
clf.add(DenseLayer(n_inputs=n_features, n_units=n_hidden))
clf.add(DenseLayer(n_inputs=n_hidden, n_units=n_hidden))
clf.add(DenseLayer(n_inputs=n_hidden, n_units=n_output, activation_function=Softmax))
clf.fit(X_train, y_train)
clf.plot_errors()
y_pred = clf.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print ("Accuracy:", accuracy)
# Reduce dimension to two using PCA and plot the results
pca = PCA()
pca.plot_in_2d(X_test, y_pred, title="Multilayer Perceptron", accuracy=accuracy, legend_labels=np.unique(y))
def main():
data = datasets.load_digits()
X = normalize(data.data)
y = data.target
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, seed=1)
# Optimization method for finding weights that minimizes loss
optimizer = RMSprop(learning_rate=0.01)
# Perceptron
clf = Perceptron(n_iterations=5000,
activation_function=ELU,
optimizer=optimizer,
early_stopping=True,
plot_errors=True)
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print ("Accuracy:", accuracy)
# Reduce dimension to two using PCA and plot the results
Plot().plot_in_2d(X_test, y_pred, title="Perceptron", accuracy=accuracy, legend_labels=np.unique(y))
def main():
# Load dataset
data = datasets.load_iris()
X = normalize(data.data[data.target != 0])
y = data.target[data.target != 0]
y[y == 1] = 0
y[y == 2] = 1
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.33,
seed=1)
clf = LogisticRegression(gradient_descent=True)
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print("Accuracy:", accuracy)
# Reduce dimension to two using PCA and plot the results
Plot().plot_in_2d(X_test,
y_pred,
title="Logistic Regression",
accuracy=accuracy)
def main():
data = datasets.load_digits()
X = normalize(data.data)
y = data.target
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, seed=1)
# MLP
clf = MultilayerPerceptron(n_hidden=12,
n_iterations=1000,
learning_rate=0.0001,
momentum=0.8,
activation_function=ExpLU,
early_stopping=True,
plot_errors=True)
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print ("Accuracy:", accuracy)
# Reduce dimension to two using PCA and plot the results
pca = PCA()
pca.plot_in_2d(X_test, y_pred, title="Multilayer Perceptron", accuracy=accuracy, legend_labels=np.unique(y))
def main():
data = datasets.load_digits()
X = normalize(data.data)
y = data.target
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.33,
seed=1)
# Optimization method for finding weights that minimizes loss
optimizer = RMSprop(learning_rate=0.01)
# Perceptron
clf = Perceptron(n_iterations=5000,
activation_function=ExpLU,
optimizer=optimizer,
early_stopping=True,
plot_errors=True)
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print("Accuracy:", accuracy)
# Reduce dimension to two using PCA and plot the results
pca = PCA()
pca.plot_in_2d(X_test,
y_pred,
title="Perceptron",
accuracy=accuracy,
legend_labels=np.unique(y))
def main():
data = datasets.load_digits()
X = normalize(data.data)
y = data.target
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.33,
seed=1)
# Perceptron
clf = Perceptron(n_iterations=5000,
learning_rate=0.001,
activation_function=Sigmoid)
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print("Accuracy:", accuracy)
# Reduce dimension to two using PCA and plot the results
Plot().plot_in_2d(X_test,
y_pred,
title="Perceptron",
accuracy=accuracy,
legend_labels=np.unique(y))
def main():
data = datasets.load_digits()
X = data.data
y = data.target
digit1 = 1
digit2 = 8
idx = np.append(np.where(y == digit1)[0], np.where(y == digit2)[0])
y = data.target[idx]
# Change labels to {-1, 1}
y[y == digit1] = -1
y[y == digit2] = 1
X = data.data[idx]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5)
# Adaboost classification with 5 weak classifiers
clf = Adaboost(n_clf=5)
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print ("Accuracy:", accuracy)
# Reduce dimensions to 2d using pca and plot the results
Plot().plot_in_2d(X_test, y_pred, title="Adaboost", accuracy=accuracy)
def main():
print("-- Gradient Boosting Regression --")
X, y = datasets.make_regression(n_features=1,
n_samples=150,
bias=0,
noise=5)
X_train, X_test, y_train, y_test = train_test_split(standardize(X),
y,
test_size=0.5)
clf = GradientBoostingRegressor(debug=True)
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
mse = mean_squared_error(y_test, y_pred)
print("Mean Squared Error:", mse)
# Plot the results
plt.scatter(X_test[:, 0], y_test, color='black')
plt.scatter(X_test[:, 0], y_pred, color='green')
plt.title("Gradient Boosting Regression (%.2f MSE)" % mse)
plt.show()
def main():
data = datasets.load_digits()
X = data.data
y = data.target
digit1 = 1
digit2 = 8
idx = np.append(np.where(y == digit1)[0], np.where(y == digit2)[0])
y = data.target[idx]
# Change labels to {-1, 1}
y[y == digit1] = -1
y[y == digit2] = 1
X = data.data[idx]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5)
# Adaboost classification with 5 weak classifiers
clf = Adaboost(n_clf=5)
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print("Accuracy:", accuracy)
# Reduce dimensions to 2d using pca and plot the results
Plot().plot_in_2d(X_test, y_pred, title="Adaboost", accuracy=accuracy)
def main():
data = datasets.load_digits()
X = normalize(data.data)
y = data.target
# Convert the nominal y values to binary
y = to_categorical(y)
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.33,
seed=1)
# MLP
clf = MultilayerPerceptron(n_hidden=12,
n_iterations=1000,
learning_rate=0.01)
clf.fit(X_train, y_train)
y_pred = np.argmax(clf.predict(X_test), axis=1)
y_test = np.argmax(y_test, axis=1)
accuracy = accuracy_score(y_test, y_pred)
print("Accuracy:", accuracy)
# Reduce dimension to two using PCA and plot the results
Plot().plot_in_2d(X_test,
y_pred,
title="Multilayer Perceptron",
accuracy=accuracy,
legend_labels=np.unique(y))
def main():
print ("-- Classification Tree --")
data = datasets.load_iris()
X = data.data
y = data.target
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4)
clf = ClassificationTree()
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print ("Accuracy:", accuracy)
Plot().plot_in_2d(X_test, y_pred,
title="Decision Tree",
accuracy=accuracy,
legend_labels=data.target_names)
print ("-- Regression Tree --")
X, y = datasets.make_regression(n_features=1, n_samples=100, bias=0, noise=5)
X_train, X_test, y_train, y_test = train_test_split(standardize(X), y, test_size=0.3)
clf = RegressionTree()
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
mse = mean_squared_error(y_test, y_pred)
print ("Mean Squared Error:", mse)
# Plot the results
plt.scatter(X_test[:, 0], y_test, color='black')
plt.scatter(X_test[:, 0], y_pred, color='green')
plt.title("Regression Tree (%.2f MSE)" % mse)
plt.show()
def main():
data = datasets.load_digits()
X = normalize(data.data)
y = data.target
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33)
clf = KNN(k=5)
y_pred = clf.predict(X_test, X_train, y_train)
accuracy = accuracy_score(y_test, y_pred)
print ("Accuracy:", accuracy)
# Reduce dimensions to 2d using pca and plot the results
Plot().plot_in_2d(X_test, y_pred, title="K Nearest Neighbors", accuracy=accuracy, legend_labels=data.target_names)
def fit(self, X, y):
X_train = X
y_train = y
if self.val_error:
# Split the data into training and validation sets
X_train, X_validate, y_train, y_validate = train_test_split(
X, y, test_size=0.1)
y_validate = categorical_to_binary(y_validate)
# Convert the nominal y values to binary
y_train = categorical_to_binary(y_train)
n_samples, n_features = np.shape(X_train)
n_batches = int(n_samples / self.batch_size)
bar = progressbar.ProgressBar(widgets=bar_widgets)
for i in bar(range(self.n_iterations)):
X_, y_ = shuffle_data(X_train, y_train)
batch_t_error = 0 # Mean batch training error
batch_v_error = 0 # Mean batch validation error
for idx in np.array_split(np.arange(n_samples), n_batches):
X_batch, y_batch = X_[idx], y_[idx]
# Calculate output
y_pred = self._forward_pass(X_batch)
# Calculate the cross entropy training loss
loss = np.mean(self.cross_ent.loss(y_batch, y_pred))
batch_t_error += loss
loss_grad = self.cross_ent.gradient(y_batch, y_pred)
# Update the NN weights
self._backward_pass(loss_grad=loss_grad)
if self.val_error:
# Calculate the validation error
y_val_pred = self._forward_pass(X_validate)
loss = np.mean(self.cross_ent.loss(y_validate, y_val_pred))
batch_v_error += loss
batch_t_error /= n_batches
batch_v_error /= n_batches
self.errors["training"].append(batch_t_error)
self.errors["validation"].append(batch_v_error)
def main():
data = datasets.load_iris()
X = data.data
y = data.target
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, seed=2)
clf = RandomForest(debug=True)
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print ("Accuracy:", accuracy)
Plot().plot_in_2d(X_test, y_pred, title="Random Forest", accuracy=accuracy, legend_labels=data.target_names)
def main():
data = datasets.load_iris()
X = normalize(data.data[data.target != 0])
y = data.target[data.target != 0]
y[y == 1] = -1
y[y == 2] = 1
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33)
clf = SupportVectorMachine(kernel=polynomial_kernel, power=4, coef=1)
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print ("Accuracy:", accuracy)
# Reduce dimension to two using PCA and plot the results
Plot().plot_in_2d(X_test, y_pred, title="Support Vector Machine", accuracy=accuracy)
def main():
data = datasets.load_iris()
X = normalize(data.data)
y = data.target
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5)
clf = NaiveBayes()
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print ("Accuracy:", accuracy)
# Reduce dimension to two using PCA and plot the results
pca = PCA()
pca.plot_in_2d(X_test, y_pred, title="Naive Bayes", accuracy=accuracy, legend_labels=data.target_names)
def main():
data = datasets.load_digits()
X = normalize(data.data)
y = data.target
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33)
clf = KNN(k=5)
y_pred = clf.predict(X_test, X_train, y_train)
accuracy = accuracy_score(y_test, y_pred)
print("Accuracy:", accuracy)
# Reduce dimensions to 2d using pca and plot the results
Plot().plot_in_2d(X_test,
y_pred,
title="K Nearest Neighbors",
accuracy=accuracy,
legend_labels=data.target_names)
def main():
data = datasets.load_digits()
X = normalize(data.data)
y = data.target
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, seed=1)
# Perceptron
clf = Perceptron(n_iterations=5000,
learning_rate=0.001,
activation_function=Sigmoid)
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print ("Accuracy:", accuracy)
# Reduce dimension to two using PCA and plot the results
Plot().plot_in_2d(X_test, y_pred, title="Perceptron", accuracy=accuracy, legend_labels=np.unique(y))
def main():
data = datasets.load_iris()
X = normalize(data.data[data.target != 0])
y = data.target[data.target != 0]
y[y == 1] = -1
y[y == 2] = 1
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33)
clf = SupportVectorMachine(kernel=polynomial_kernel, power=4, coef=1)
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print ("Accuracy:", accuracy)
# Reduce dimension to two using PCA and plot the results
pca = PCA()
pca.plot_in_2d(X_test, y_pred, title="Support Vector Machine", accuracy=accuracy)
def main():
# Load temperature data
data = pd.read_csv('mlfromscratch/data/TempLinkoping2016.txt', sep="\t")
time = np.atleast_2d(data["time"].as_matrix()).T
temp = np.atleast_2d(data["temp"].as_matrix()).T
X = time # Time. Fraction of the year [0, 1]
y = temp[:, 0] # Temperature. Reduce to one-dim
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4)
n_samples, n_features = np.shape(X)
clf = LinearRegression()
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
mse = mean_squared_error(y_test, y_pred)
print("Mean squared error: %s" % (mse))
y_pred_line = clf.predict(X)
# Color map
cmap = plt.get_cmap('viridis')
# Plot the results
m1 = plt.scatter(366 * X_train, y_train, color=cmap(0.9), s=10)
m2 = plt.scatter(366 * X_test, y_test, color=cmap(0.5), s=10)
plt.plot(366 * X,
y_pred_line,
color='black',
linewidth=2,
label="Prediction")
plt.suptitle("Polynomial Ridge Regression")
plt.title("MSE: %.2f" % mse, fontsize=10)
plt.xlabel('Day')
plt.ylabel('Temperature in Celcius')
plt.legend((m1, m2), ("Training data", "Test data"), loc='lower right')
plt.show()
def main():
# Load dataset
data = datasets.load_iris()
X = normalize(data.data[data.target != 0])
y = data.target[data.target != 0]
y[y == 1] = 0
y[y == 2] = 1
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, seed=1)
clf = LogisticRegression(gradient_descent=True)
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print ("Accuracy:", accuracy)
# Reduce dimension to two using PCA and plot the results
Plot().plot_in_2d(X_test, y_pred, title="Logistic Regression", accuracy=accuracy)
def main():
print("-- Regression Tree --")
# Load temperature data
data = pd.read_csv('mlfromscratch/data/TempLinkoping2016.txt', sep="\t")
time = np.atleast_2d(data["time"].as_matrix()).T
temp = np.atleast_2d(data["temp"].as_matrix()).T
X = standardize(time) # Time. Fraction of the year [0, 1]
y = temp[:, 0] # Temperature. Reduce to one-dim
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)
clf = RegressionTree()
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
y_pred_line = clf.predict(X)
# Color map
cmap = plt.get_cmap('viridis')
mse = mean_squared_error(y_test, y_pred)
print("Mean Squared Error:", mse)
# Plot the results
# Plot the results
m1 = plt.scatter(366 * X_train, y_train, color=cmap(0.9), s=10)
m2 = plt.scatter(366 * X_test, y_test, color=cmap(0.5), s=10)
m3 = plt.scatter(366 * X_test, y_pred, color='black', s=10)
plt.suptitle("Regression Tree")
plt.title("MSE: %.2f" % mse, fontsize=10)
plt.xlabel('Day')
plt.ylabel('Temperature in Celcius')
plt.legend((m1, m2, m3), ("Training data", "Test data", "Prediction"),
loc='lower right')
plt.show()
def main():
print ("-- Classification Tree --")
data = datasets.load_iris()
X = data.data
y = data.target
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4)
clf = ClassificationTree()
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print ("Accuracy:", accuracy)
Plot().plot_in_2d(X_test, y_pred,
title="Decision Tree",
accuracy=accuracy,
legend_labels=data.target_names)
def main():
# Load the dataset
data = datasets.load_iris()
X = data.data
y = data.target
# Three -> two classes
X = X[y != 2]
y = y[y != 2]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33)
# Fit and predict using LDA
lda = LDA()
lda.fit(X_train, y_train)
y_pred = lda.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print("Accuracy:", accuracy)
Plot().plot_in_2d(X_test, y_pred, title="LDA", accuracy=accuracy)
def main():
# Load the dataset
data = datasets.load_iris()
X = data.data
y = data.target
# Three -> two classes
X = X[y != 2]
y = y[y != 2]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33)
# Fit and predict using LDA
lda = LDA()
lda.fit(X_train, y_train)
y_pred = lda.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print ("Accuracy:", accuracy)
Plot().plot_in_2d(X_test, y_pred, title="LDA", accuracy=accuracy)
def main():
print ("-- Gradient Boosting Classification --")
data = datasets.load_iris()
X = data.data
y = data.target
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4)
clf = GradientBoostingClassifier(debug=True)
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print ("Accuracy:", accuracy)
Plot().plot_in_2d(X_test, y_pred,
title="Gradient Boosting",
accuracy=accuracy,
legend_labels=data.target_names)
def main():
data = datasets.load_iris()
X = data.data
y = data.target
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.4,
seed=2)
clf = RandomForest(debug=True)
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print("Accuracy:", accuracy)
pca = PCA()
pca.plot_in_2d(X_test,
y_pred,
title="Random Forest",
accuracy=accuracy,
legend_labels=data.target_names)
def main():
data = datasets.load_digits()
X = data.data
y = data.target
n_samples = np.shape(X)
n_hidden = 512
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.4,
seed=1)
optimizer = Adam()
#-----
# MLP
#-----
# clf = NeuralNetwork(optimizer=optimizer,
# loss=CrossEntropy,
# validation_data=(X_test, y_test))
# clf.add(Dense(n_hidden, input_shape=(8*8,)))
# clf.add(Activation('leaky_relu'))
# clf.add(Dense(n_hidden))
# clf.add(Activation('leaky_relu'))
# clf.add(Dropout(0.25))
# clf.add(Dense(n_hidden))
# clf.add(Activation('leaky_relu'))
# clf.add(Dropout(0.25))
# clf.add(Dense(n_hidden))
# clf.add(Activation('leaky_relu'))
# clf.add(Dropout(0.25))
# clf.add(Dense(10))
# clf.add(Activation('softmax'))
# clf.fit(X_train, y_train, n_iterations=50, batch_size=256)
# clf.plot_errors()
# y_pred = np.argmax(clf.predict(X_test), axis=1)
# accuracy = accuracy_score(y_test, y_pred)
# print ("Accuracy:", accuracy)
#----------
# Conv Net
#----------
X_train = X_train.reshape((-1, 1, 8, 8))
X_test = X_test.reshape((-1, 1, 8, 8))
clf = NeuralNetwork(optimizer=optimizer,
loss=CrossEntropy,
validation_data=(X_test, y_test))
clf.add(
Conv2D(n_filters=16,
filter_shape=(3, 3),
input_shape=(1, 8, 8),
padding='same'))
clf.add(Activation('relu'))
clf.add(Conv2D(n_filters=32, filter_shape=(3, 3), padding='same'))
clf.add(Activation('relu'))
clf.add(Flatten())
clf.add(Dense(128))
clf.add(Activation('relu'))
clf.add(Dense(10))
clf.add(Activation('softmax'))
clf.fit(X_train, y_train, n_iterations=50, batch_size=256)
clf.plot_errors()
y_pred = np.argmax(clf.predict(X_test), axis=1)
accuracy = accuracy_score(y_test, y_pred)
print("Accuracy:", accuracy)
X_test = X_test.reshape(-1, 8 * 8)
# Reduce dimension to two using PCA and plot the results
Plot().plot_in_2d(X_test,
y_pred,
title="Convolutional Neural Network",
accuracy=accuracy,
legend_labels=np.unique(y))
def main():
optimizer = Adam()
def gen_mult_ser(nums):
""" Method which generates multiplication series """
X = np.zeros([nums, 10, 61], dtype=float)
y = np.zeros([nums, 10, 61], dtype=float)
for i in range(nums):
start = np.random.randint(2, 7)
mult_ser = np.linspace(start, start * 10, num=10, dtype=int)
X[i] = to_categorical(mult_ser, n_col=61)
y[i] = np.roll(X[i], -1, axis=0)
y[:, -1, 1] = 1 # Mark endpoint as 1
return X, y
def gen_num_seq(nums):
""" Method which generates sequence of numbers """
X = np.zeros([nums, 10, 20], dtype=float)
y = np.zeros([nums, 10, 20], dtype=float)
for i in range(nums):
start = np.random.randint(0, 10)
num_seq = np.arange(start, start + 10)
X[i] = to_categorical(num_seq, n_col=20)
y[i] = np.roll(X[i], -1, axis=0)
y[:, -1, 1] = 1 # Mark endpoint as 1
return X, y
X, y = gen_mult_ser(3000)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4)
# Model definition
clf = NeuralNetwork(optimizer=optimizer, loss=CrossEntropy)
clf.add(RNN(10, activation="tanh", bptt_trunc=5, input_shape=(10, 61)))
clf.add(Activation('softmax'))
clf.summary("RNN")
# Print a problem instance and the correct solution
tmp_X = np.argmax(X_train[0], axis=1)
tmp_y = np.argmax(y_train[0], axis=1)
print("Number Series Problem:")
print("X = [" + " ".join(tmp_X.astype("str")) + "]")
print("y = [" + " ".join(tmp_y.astype("str")) + "]")
print()
train_err, _ = clf.fit(X_train, y_train, n_epochs=500, batch_size=512)
# Predict labels of the test data
y_pred = np.argmax(clf.predict(X_test), axis=2)
y_test = np.argmax(y_test, axis=2)
print()
print("Results:")
for i in range(5):
# Print a problem instance and the correct solution
tmp_X = np.argmax(X_test[i], axis=1)
tmp_y1 = y_test[i]
tmp_y2 = y_pred[i]
print("X = [" + " ".join(tmp_X.astype("str")) + "]")
print("y_true = [" + " ".join(tmp_y1.astype("str")) + "]")
print("y_pred = [" + " ".join(tmp_y2.astype("str")) + "]")
print()
accuracy = np.mean(accuracy_score(y_test, y_pred))
print("Accuracy:", accuracy)
training = plt.plot(range(500), train_err, label="Training Error")
plt.title("Error Plot")
plt.ylabel('Training Error')
plt.xlabel('Iterations')
plt.show()
def fit(self, X, y):
X_train = X
y_train = y
if self.early_stopping:
# Split the data into training and validation sets
X_train, X_validate, y_train, y_validate = train_test_split(X, y, test_size=0.1)
y_validate = categorical_to_binary(y_validate)
# Convert the nominal y values to binary
y_train = categorical_to_binary(y_train)
n_samples, n_features = np.shape(X_train)
n_outputs = np.shape(y_train)[1]
# Initial weights between [-1/sqrt(N), 1/sqrt(N)]
a = -1 / math.sqrt(n_features)
b = -a
self.W = (b - a) * np.random.random((n_features, self.n_hidden)) + a
self.w0 = (b - a) * np.random.random((1, self.n_hidden)) + a
self.V = (b - a) * np.random.random((self.n_hidden, n_outputs)) + a
self.v0 = (b - a) * np.random.random((1, n_outputs)) + a
# Error history
training_errors = []
validation_errors = []
iter_with_rising_val_error = 0
for i in range(self.n_iterations):
# Calculate hidden layer
hidden_layer_input = X_train.dot(self.W) + self.w0
hidden_layer_output = self.activation.function(hidden_layer_input)
# Calculate output layer
output_layer_input = hidden_layer_output.dot(self.V) + self.v0
output = self.activation.function(output_layer_input)
# Calculate the error
error = y_train - output
mse = np.mean(np.power(error, 2))
training_errors.append(mse)
# Calculate the loss gradient
output_gradient = -2 * (y_train - output) * \
self.activation.gradient(output_layer_input)
hidden_gradient = output_gradient.dot(
self.V.T) * self.activation.gradient(hidden_layer_input)
# Calcualte the gradient with respect to each weight term
grad_wrt_v = hidden_layer_output.T.dot(output_gradient)
grad_wrt_v0 = np.ones((1, n_samples)).dot(output_gradient)
grad_wrt_w = X_train.T.dot(hidden_gradient)
grad_wrt_w0 = np.ones((1, n_samples)).dot(hidden_gradient)
# Update weights
# Move against the gradient to minimize loss
self.V = self.v_opt.update(w=self.V, grad_wrt_w=grad_wrt_v)
self.v0 = self.v0_opt.update(w=self.v0, grad_wrt_w=grad_wrt_v0)
self.W = self.w_opt.update(w=self.W, grad_wrt_w=grad_wrt_w)
self.w0 = self.w0_opt.update(w=self.w0, grad_wrt_w=grad_wrt_w0)
if self.early_stopping:
# Calculate the validation error
error = y_validate - self._calculate_output(X_validate)
mse = np.mean(np.power(error, 2))
validation_errors.append(mse)
# If the validation error is larger than the previous iteration increase
# the counter
if len(validation_errors) > 1 and validation_errors[-1] > validation_errors[-2]:
iter_with_rising_val_error += 1
# # If the validation error has been for more than 50 iterations
# # stop training to avoid overfitting
if iter_with_rising_val_error > 50:
break
else:
iter_with_rising_val_error = 0
# Plot the training error
if self.plot_errors:
if self.early_stopping:
# Training and validation error plot
training, = plt.plot(range(i+1), training_errors, label="Training Error")
validation, = plt.plot(range(i+1), validation_errors, label="Validation Error")
plt.legend(handles=[training, validation])
else:
training, = plt.plot(range(i+1), training_errors, label="Training Error")
plt.legend(handles=[training])
plt.title("Error Plot")
plt.ylabel('Error')
plt.xlabel('Iterations')
plt.show()
X = normalize(X)
print("Dataset: The Digit Dataset (digits %s and %s)" % (digit1, digit2))
# ..........................
# DIMENSIONALITY REDUCTION
# ..........................
pca = PCA()
X = pca.transform(X, n_components=5) # Reduce to 5 dimensions
n_samples, n_features = np.shape(X)
# ..........................
# TRAIN / TEST SPLIT
# ..........................
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5)
# Rescaled labels {-1, 1}
rescaled_y_train = 2 * y_train - np.ones(np.shape(y_train))
rescaled_y_test = 2 * y_test - np.ones(np.shape(y_test))
# .......
# SETUP
# .......
adaboost = Adaboost(n_clf=8)
naive_bayes = NaiveBayes()
knn = KNN(k=4)
logistic_regression = LogisticRegression()
mlp = NeuralNetwork(optimizer=Adam(), loss=CrossEntropy)
mlp.add(Dense(input_shape=(n_features, ), n_units=64))
mlp.add(Activation('relu'))
mlp.add(Dense(n_units=64))
X = normalize(X)
print ("Dataset: The Digit Dataset (digits %s and %s)" % (digit1, digit2))
# ..........................
# DIMENSIONALITY REDUCTION
# ..........................
pca = PCA()
X = pca.transform(X, n_components=5) # Reduce to 5 dimensions
n_samples, n_features = np.shape(X)
# ..........................
# TRAIN / TEST SPLIT
# ..........................
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5)
# Rescaled labels {-1, 1}
rescaled_y_train = 2*y_train - np.ones(np.shape(y_train))
rescaled_y_test = 2*y_test - np.ones(np.shape(y_test))
# .......
# SETUP
# .......
adaboost = Adaboost(n_clf = 8)
naive_bayes = NaiveBayes()
knn = KNN(k=4)
logistic_regression = LogisticRegression()
mlp = NeuralNetwork(optimizer=Adam(),
loss=CrossEntropy)
mlp.add(Dense(input_shape=(n_features,), n_units=64))
mlp.add(Activation('relu'))
def fit(self, X, y):
X_train = X
y_train = y
if self.early_stopping:
# Split the data into training and validation sets
X_train, X_validate, y_train, y_validate = train_test_split(
X, y, test_size=0.1)
y_validate = categorical_to_binary(y_validate)
# Convert the nominal y values to binary
y_train = categorical_to_binary(y_train)
n_samples, n_features = np.shape(X_train)
n_outputs = np.shape(y_train)[1]
# Initial weights between [-1/sqrt(N), 1/sqrt(N)]
a = -1 / math.sqrt(n_features)
b = -a
self.W = (b - a) * np.random.random((n_features, n_outputs)) + a
self.w0 = (b - a) * np.random.random((1, n_outputs)) + a
# Error history
training_errors = []
validation_errors = []
iter_with_rising_val_error = 0
# Lambda function that calculates the neuron outputs
neuron_output = lambda w, b: self.activation.function(
np.dot(X_train, w) + b)
# Lambda function that calculates the loss gradient
loss_grad = lambda w, b: -2 * (y_train - neuron_output(w, b)) * \
self.activation.gradient(np.dot(X_train, w) + b)
# Lambda functions that calculates the gradient of the loss with
# respect to each weight term. Allows for computation of loss gradient at different
# coordinates.
grad_func_wrt_w = lambda w: X_train.T.dot(loss_grad(w, self.w0))
grad_func_wrt_w0 = lambda b: np.ones(
(1, n_samples)).dot(loss_grad(self.W, b))
# Optimize paramaters for n_iterations
for i in range(self.n_iterations):
# Training error
error = y_train - neuron_output(self.W, self.w0)
mse = np.mean(np.power(error, 2))
training_errors.append(mse)
# Update weights
self.W = self.w_opt.update(w=self.W, grad_func=grad_func_wrt_w)
self.w0 = self.w0_opt.update(w=self.w0, grad_func=grad_func_wrt_w0)
if self.early_stopping:
# Calculate the validation error
error = y_validate - self._calculate_output(X_validate)
mse = np.mean(np.power(error, 2))
validation_errors.append(mse)
# If the validation error is larger than the previous iteration increase
# the counter
if len(validation_errors
) > 1 and validation_errors[-1] > validation_errors[-2]:
iter_with_rising_val_error += 1
# If the validation error has been for more than 50 iterations
# stop training to avoid overfitting
if iter_with_rising_val_error > 50:
break
else:
iter_with_rising_val_error = 0
# Plot the training error
if self.plot_errors:
if self.early_stopping:
# Training and validation error plot
training, = plt.plot(range(i + 1),
training_errors,
label="Training Error")
validation, = plt.plot(range(i + 1),
validation_errors,
label="Validation Error")
plt.legend(handles=[training, validation])
else:
training, = plt.plot(range(i + 1),
training_errors,
label="Training Error")
plt.legend(handles=[training])
plt.title("Error Plot")
plt.ylabel('Error')
plt.xlabel('Iterations')
plt.show()
def main():
optimizer = Adam()
#-----
# MLP
#-----
data = datasets.load_digits()
X = data.data
y = data.target
# Convert to one-hot encoding
y = to_categorical(y.astype("int"))
n_samples = np.shape(X)
n_hidden = 512
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.4,
seed=1)
clf = NeuralNetwork(optimizer=optimizer,
loss=CrossEntropy,
validation_data=(X_test, y_test))
clf.add(Dense(n_hidden, input_shape=(8 * 8, )))
clf.add(Activation('leaky_relu'))
clf.add(Dense(n_hidden))
clf.add(Activation('leaky_relu'))
clf.add(Dropout(0.25))
clf.add(Dense(n_hidden))
clf.add(Activation('leaky_relu'))
clf.add(Dropout(0.25))
clf.add(Dense(n_hidden))
clf.add(Activation('leaky_relu'))
clf.add(Dropout(0.25))
clf.add(Dense(10))
clf.add(Activation('softmax'))
print()
clf.summary(name="MLP")
train_err, val_err = clf.fit(X_train, y_train, n_epochs=50, batch_size=256)
# Training and validation error plot
n = len(train_err)
training, = plt.plot(range(n), train_err, label="Training Error")
validation, = plt.plot(range(n), val_err, label="Validation Error")
plt.legend(handles=[training, validation])
plt.title("Error Plot")
plt.ylabel('Error')
plt.xlabel('Iterations')
plt.show()
# Predict labels of the test data
y_pred = np.argmax(clf.predict(X_test), axis=1)
y_test = np.argmax(y_test, axis=1)
accuracy = accuracy_score(y_test, y_pred)
print("Accuracy:", accuracy)
# Reduce dimension to 2D using PCA and plot the results
Plot().plot_in_2d(X_test,
y_pred,
title="Multilayer Perceptron",
accuracy=accuracy,
legend_labels=range(10))
def main():
data = datasets.load_digits()
X = data.data
y = data.target
n_samples = np.shape(X)
n_hidden = 512
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.4,
seed=1)
optimizer = Adam()
#-----
# MLP
#-----
# clf = NeuralNetwork(optimizer=optimizer,
# loss=CrossEntropy,
# validation_data=(X_test, y_test))
# clf.add(Dense(n_hidden, input_shape=(8*8,)))
# clf.add(Activation('leaky_relu'))
# clf.add(Dense(n_hidden))
# clf.add(Activation('leaky_relu'))
# clf.add(Dropout(0.25))
# clf.add(Dense(n_hidden))
# clf.add(Activation('leaky_relu'))
# clf.add(Dropout(0.25))
# clf.add(Dense(n_hidden))
# clf.add(Activation('leaky_relu'))
# clf.add(Dropout(0.25))
# clf.add(Dense(10))
# clf.add(Activation('softmax'))
# print ()
# clf.summary(name="MLP")
# clf.fit(X_train, y_train, n_epochs=50, batch_size=256)
# clf.plot_errors()
# y_pred = np.argmax(clf.predict(X_test), axis=1)
# accuracy = accuracy_score(y_test, y_pred)
# print ("Accuracy:", accuracy)
#----------
# Conv Net
#----------
X_train = X_train.reshape((-1, 1, 8, 8))
X_test = X_test.reshape((-1, 1, 8, 8))
clf = NeuralNetwork(optimizer=optimizer,
loss=CrossEntropy,
validation_data=(X_test, y_test))
clf.add(
Conv2D(n_filters=16,
filter_shape=(3, 3),
input_shape=(1, 8, 8),
padding='same'))
clf.add(Activation('relu'))
clf.add(Dropout(0.25))
clf.add(BatchNormalization())
clf.add(Conv2D(n_filters=32, filter_shape=(3, 3), padding='same'))
clf.add(Activation('relu'))
clf.add(Dropout(0.25))
clf.add(BatchNormalization())
clf.add(Flatten())
clf.add(Dense(256))
clf.add(Activation('relu'))
clf.add(Dropout(0.5))
clf.add(BatchNormalization())
clf.add(Dense(10))
clf.add(Activation('softmax'))
print()
clf.summary(name="ConvNet")
train_err, val_err = clf.fit(X_train, y_train, n_epochs=50, batch_size=256)
# Training and validation error plot
n = len(train_err)
training, = plt.plot(range(n), train_err, label="Training Error")
validation, = plt.plot(range(n), val_err, label="Validation Error")
plt.legend(handles=[training, validation])
plt.title("Error Plot")
plt.ylabel('Error')
plt.xlabel('Iterations')
plt.show()
# Make a prediction of the test set
y_pred = np.argmax(clf.predict(X_test), axis=1)
accuracy = accuracy_score(y_test, y_pred)
print("Accuracy:", accuracy)
X_test = X_test.reshape(-1, 8 * 8)
# Reduce dimension to 2D using PCA and plot the results
Plot().plot_in_2d(X_test,
y_pred,
title="Convolutional Neural Network",
accuracy=accuracy,
legend_labels=np.unique(y))
def main():
# Load temperature data
data = pd.read_csv('mlfromscratch/data/TempLinkoping2016.txt', sep="\t")
time = np.atleast_2d(data["time"].values).T
temp = np.atleast_2d(data["temp"].values).T
X = time # fraction of the year [0, 1]
y = temp
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4)
n_samples, n_features = np.shape(X)
# Prior parameters
# - Weights are assumed distr. according to a Normal distribution
# - The variance of the weights are assumed distributed according to
# a scaled inverse chi-squared distribution.
# High prior uncertainty!
# Normal
mu0 = np.array([0] * n_features)
omega0 = np.diag([.0001] * n_features)
# Scaled inverse chi-squared
nu0 = 1
sigma_sq0 = 100
# The credible interval
cred_int = 10
clf = BayesianRegression(n_draws=2000,
poly_degree=4,
mu0=mu0,
omega0=omega0,
nu0=nu0,
sigma_sq0=sigma_sq0,
cred_int=cred_int)
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
mse = mean_squared_error(y_test, y_pred)
# Get prediction line
y_pred_, y_lower_, y_upper_ = clf.predict(X=X, eti=True)
# Print the mean squared error
print ("Mean Squared Error:", mse)
# Color map
cmap = plt.get_cmap('viridis')
# Plot the results
m1 = plt.scatter(366 * X_train, y_train, color=cmap(0.9), s=10)
m2 = plt.scatter(366 * X_test, y_test, color=cmap(0.5), s=10)
p1 = plt.plot(366 * X, y_pred_, color="black", linewidth=2, label="Prediction")
p2 = plt.plot(366 * X, y_lower_, color="gray", linewidth=2, label="{0}% Credible Interval".format(cred_int))
p3 = plt.plot(366 * X, y_upper_, color="gray", linewidth=2)
plt.axis((0, 366, -20, 25))
plt.suptitle("Bayesian Regression")
plt.title("MSE: %.2f" % mse, fontsize=10)
plt.xlabel('Day')
plt.ylabel('Temperature in Celcius')
plt.legend(loc='lower right')
# plt.legend((m1, m2), ("Training data", "Test data"), loc='lower right')
plt.legend(loc='lower right')
plt.show()
def main():
data = datasets.load_digits()
X = data.data
y = data.target
n_samples = np.shape(X)
n_hidden = 512
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, seed=1)
optimizer = Adam()
#-----
# MLP
#-----
# clf = NeuralNetwork(optimizer=optimizer,
# loss=CrossEntropy,
# validation_data=(X_test, y_test))
# clf.add(Dense(n_hidden, input_shape=(8*8,)))
# clf.add(Activation('leaky_relu'))
# clf.add(Dense(n_hidden))
# clf.add(Activation('leaky_relu'))
# clf.add(Dropout(0.25))
# clf.add(Dense(n_hidden))
# clf.add(Activation('leaky_relu'))
# clf.add(Dropout(0.25))
# clf.add(Dense(n_hidden))
# clf.add(Activation('leaky_relu'))
# clf.add(Dropout(0.25))
# clf.add(Dense(10))
# clf.add(Activation('softmax'))
# clf.fit(X_train, y_train, n_iterations=50, batch_size=256)
# clf.plot_errors()
# y_pred = np.argmax(clf.predict(X_test), axis=1)
# accuracy = accuracy_score(y_test, y_pred)
# print ("Accuracy:", accuracy)
#----------
# Conv Net
#----------
X_train = X_train.reshape((-1,1,8,8))
X_test = X_test.reshape((-1,1,8,8))
clf = NeuralNetwork(optimizer=optimizer,
loss=CrossEntropy,
validation_data=(X_test, y_test))
clf.add(Conv2D(n_filters=16, filter_shape=(3,3), input_shape=(1,8,8), padding='same'))
clf.add(Activation('relu'))
clf.add(Conv2D(n_filters=32, filter_shape=(3,3), padding='same'))
clf.add(Activation('relu'))
clf.add(Flatten())
clf.add(Dense(128))
clf.add(Activation('relu'))
clf.add(Dense(10))
clf.add(Activation('softmax'))
clf.fit(X_train, y_train, n_iterations=50, batch_size=256)
clf.plot_errors()
y_pred = np.argmax(clf.predict(X_test), axis=1)
accuracy = accuracy_score(y_test, y_pred)
print ("Accuracy:", accuracy)
X_test = X_test.reshape(-1, 8*8)
# Reduce dimension to two using PCA and plot the results
Plot().plot_in_2d(X_test, y_pred, title="Convolutional Neural Network", accuracy=accuracy, legend_labels=np.unique(y))
def main():
# Load temperature data
data = pd.read_csv('mlfromscratch/data/TempLinkoping2016.txt', sep="\t")
time = np.atleast_2d(data["time"].as_matrix()).T
temp = np.atleast_2d(data["temp"].as_matrix()).T
X = time # fraction of the year [0, 1]
y = temp
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4)
n_samples, n_features = np.shape(X)
# Prior parameters
# - Weights are assumed distr. according to a Normal distribution
# - The variance of the weights are assumed distributed according to
# a scaled inverse chi-squared distribution.
# High prior uncertainty!
# Normal
mu0 = np.array([0] * n_features)
omega0 = np.diag([.0001] * n_features)
# Scaled inverse chi-squared
nu0 = 1
sigma_sq0 = 100
# The credible interval
cred_int = 10
clf = BayesianRegression(n_draws=2000,
poly_degree=4,
mu0=mu0,
omega0=omega0,
nu0=nu0,
sigma_sq0=sigma_sq0,
cred_int=cred_int)
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
mse = mean_squared_error(y_test, y_pred)
# Get prediction line
y_pred_, y_lower_, y_upper_ = clf.predict(X=X, eti=True)
# Print the mean squared error
print("Mean Squared Error:", mse)
# Color map
cmap = plt.get_cmap('viridis')
# Plot the results
m1 = plt.scatter(366 * X_train, y_train, color=cmap(0.9), s=10)
m2 = plt.scatter(366 * X_test, y_test, color=cmap(0.5), s=10)
p1 = plt.plot(366 * X,
y_pred_,
color="black",
linewidth=2,
label="Prediction")
p2 = plt.plot(366 * X,
y_lower_,
color="gray",
linewidth=2,
label="{0}% Credible Interval".format(cred_int))
p3 = plt.plot(366 * X, y_upper_, color="gray", linewidth=2)
plt.axis((0, 366, -20, 25))
plt.suptitle("Bayesian Regression")
plt.title("MSE: %.2f" % mse, fontsize=10)
plt.xlabel('Day')
plt.ylabel('Temperature in Celcius')
plt.legend(loc='lower right')
# plt.legend((m1, m2), ("Training data", "Test data"), loc='lower right')
plt.legend(loc='lower right')
plt.show()
def main():
# Load temperature data
data = pd.read_csv('mlfromscratch/data/TempLinkoping2016.txt', sep="\t")
time = np.atleast_2d(data["time"].as_matrix()).T
temp = data["temp"].as_matrix()
X = time # fraction of the year [0, 1]
y = temp
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4)
poly_degree = 11
# Finding regularization constant using cross validation
lowest_error = float("inf")
best_reg_factor = None
print ("Finding regularization constant using cross validation:")
k = 10
for reg_factor in np.arange(0, 0.1, 0.01):
cross_validation_sets = k_fold_cross_validation_sets(
X_train, y_train, k=k)
mse = 0
for _X_train, _X_test, _y_train, _y_test in cross_validation_sets:
clf = PolynomialRidgeRegression(degree=poly_degree,
reg_factor=reg_factor,
learning_rate=0.001,
n_iterations=10000)
clf.fit(_X_train, _y_train)
y_pred = clf.predict(_X_test)
_mse = mean_squared_error(_y_test, y_pred)
mse += _mse
mse /= k
# Print the mean squared error
print ("\tMean Squared Error: %s (regularization: %s)" % (mse, reg_factor))
# Save reg. constant that gave lowest error
if mse < lowest_error:
best_reg_factor = reg_factor
lowest_error = mse
# Make final prediction
clf = PolynomialRidgeRegression(degree=poly_degree,
reg_factor=best_reg_factor,
learning_rate=0.001,
n_iterations=10000)
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
mse = mean_squared_error(y_test, y_pred)
print ("Mean squared error: %s (given by reg. factor: %s)" % (lowest_error, best_reg_factor))
y_pred_line = clf.predict(X)
# Color map
cmap = plt.get_cmap('viridis')
# Plot the results
m1 = plt.scatter(366 * X_train, y_train, color=cmap(0.9), s=10)
m2 = plt.scatter(366 * X_test, y_test, color=cmap(0.5), s=10)
plt.plot(366 * X, y_pred_line, color='black', linewidth=2, label="Prediction")
plt.suptitle("Polynomial Ridge Regression")
plt.title("MSE: %.2f" % mse, fontsize=10)
plt.xlabel('Day')
plt.ylabel('Temperature in Celcius')
plt.legend((m1, m2), ("Training data", "Test data"), loc='lower right')
plt.show()
def fit(self, X, y):
X_train = X
y_train = y
if self.early_stopping:
# Split the data into training and validation sets
X_train, X_validate, y_train, y_validate = train_test_split(X, y, test_size=0.1)
y_validate = categorical_to_binary(y_validate)
# Convert the nominal y values to binary
y_train = categorical_to_binary(y_train)
n_samples, n_features = np.shape(X_train)
n_outputs = np.shape(y_train)[1]
# Initial weights between [-1/sqrt(N), 1/sqrt(N)]
a = -1 / math.sqrt(n_features)
b = -a
self.W = (b - a) * np.random.random((n_features, n_outputs)) + a
self.w0 = (b - a) * np.random.random((1, n_outputs)) + a
# Error history
training_errors = []
validation_errors = []
iter_with_rising_val_error = 0
# Lambda function that calculates the neuron outputs
neuron_output = lambda w, b: self.activation.function(np.dot(X_train, w) + b)
# Lambda function that calculates the loss gradient
loss_grad = lambda w, b: -2 * (y_train - neuron_output(w, b)) * \
self.activation.gradient(np.dot(X_train, w) + b)
# Lambda functions that calculates the gradient of the loss with
# respect to each weight term. Allows for computation of loss gradient at different
# coordinates.
grad_func_wrt_w = lambda w: X_train.T.dot(loss_grad(w, self.w0))
grad_func_wrt_w0 = lambda b: np.ones((1, n_samples)).dot(loss_grad(self.W, b))
# Optimize paramaters for n_iterations
for i in range(self.n_iterations):
# Training error
error = y_train - neuron_output(self.W, self.w0)
mse = np.mean(np.power(error, 2))
training_errors.append(mse)
# Update weights
self.W = self.w_opt.update(w=self.W, grad_func=grad_func_wrt_w)
self.w0 = self.w0_opt.update(w=self.w0, grad_func=grad_func_wrt_w0)
if self.early_stopping:
# Calculate the validation error
error = y_validate - self._calculate_output(X_validate)
mse = np.mean(np.power(error, 2))
validation_errors.append(mse)
# If the validation error is larger than the previous iteration increase
# the counter
if len(validation_errors) > 1 and validation_errors[-1] > validation_errors[-2]:
iter_with_rising_val_error += 1
# If the validation error has been for more than 50 iterations
# stop training to avoid overfitting
if iter_with_rising_val_error > 50:
break
else:
iter_with_rising_val_error = 0
# Plot the training error
if self.plot_errors:
if self.early_stopping:
# Training and validation error plot
training, = plt.plot(range(i+1), training_errors, label="Training Error")
validation, = plt.plot(range(i+1), validation_errors, label="Validation Error")
plt.legend(handles=[training, validation])
else:
training, = plt.plot(range(i+1), training_errors, label="Training Error")
plt.legend(handles=[training])
plt.title("Error Plot")
plt.ylabel('Error')
plt.xlabel('Iterations')
plt.show()