def linear_test():
X, y = make_regression(n_features=1, noise=20, random_state=1234)
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.33,
random_state=1234)
lr = LinearRegression()
lr.fit(X_train, y_train)
y_pred = lr.predict(X_test)
plt.figure(1, figsize=(5, 4))
plt.scatter(X_test, y_test, c="black")
plt.plot(X_test, lr.theta * X_test + lr.bias, linewidth=1, c="red")
plt.axhline(0.5, color=".5")
plt.ylabel("y")
plt.xlabel("X")
plt.legend(
("Linear Regression Model", ),
loc="lower right",
fontsize="small",
)
plt.tight_layout()
plt.show()
def one_hot_regression(proc, data):
"""
linear regression using a one hot representation
:proc processing object
:data tuple containing train/test (one hot encoded space)
return linear regression object
"""
print('one hot regression...')
"""ridge and random forest regression"""
# train and test
trainOneHotX, trainY, testOneHotX, testY = data
######### linear regression #########
linear_models = 'ridge'
print('ridge regression with one hot representation...')
linReg = LinearRegression(model=linear_models)
linReg.fit(trainOneHotX, trainY)
preds = linReg.predict(testOneHotX)
print('test r2 score: ', metrics.r2_score(testY, preds))
print('test mse: ', metrics.mse(testY, preds))
return linReg
# Add some noise to the observations
noise_var = 0.5
# Create random input and output data
X = lhs(D_in, N)
y = 5 * X + noise_var * np.random.randn(N, D_out)
# Define the model
model = LinearRegression(X, y)
# Define an optimizer
optimizer = SGD(model.num_params, lr=1e-3, momentum=0.9)
# optimizer = Adam(model.num_params, lr = 1e-3)
# optimizer = RMSprop(model.num_params, lr = 1e-3)
# Train the model
model.train(10000, optimizer)
# Print the learned parameters
print('w = %e, sigma_sq = %e' %
(model.theta[:-1], np.exp(model.theta[-1])))
# Make predictions
y_pred = model.predict(X)
# Plot
plt.figure(1)
plt.plot(X, y, 'o')
plt.plot(X, y_pred)
plt.show()
# Generate some noisy train data
noise = np.random.randn(n)
x = np.linspace(0, 20, n)
y = 32.3 + 5.2 * (x + noise)
x = scaleFeature(x)
y = scaleFeature(y)
x = x.reshape((-1, 1))
# Train the lm, print out the parameters, plot the fit
lm.train(x, y, 0.5, 100, 0)
print(lm.parameters)
# Plot the fit of the linear model
y_hat = lm.predict(x)
# Plot the fit of line and train data
plt.plot(x, y, 'o')
plt.plot(x, y_hat)
plt.ylabel("y")
plt.xlabel("x")
plt.title("Plot of the Models Fit")
plt.show()
wait()
# Plot the costs to check proper convergence of gradient descent
plotCosts(lm.costsOverTime)
import matplotlib.pyplot as plt
from dataset_income import Data
from metrics import MSE
from models import ConstantModel, LinearRegression, LinearRegressionWithB
from gradient_descent import stochastic_gradient_descent, gradient_descent, mini_batch_gradient_descent
if __name__ == '__main__':
dataset = Data(
r'C:\Users\Lautaro\PycharmProjects\ceia_intro_a_IA\clase_3\ejercicios\data\income.csv'
)
X_train, X_test, y_train, y_test = dataset.split(0.8)
linear_regression = LinearRegression()
linear_regression.fit(X_train, y_train)
lr_y_hat = linear_regression.predict(X_test)
linear_regression_b = LinearRegressionWithB()
linear_regression_b.fit(X_train, y_train)
lrb_y_hat = linear_regression_b.predict(X_test)
constant_model = ConstantModel()
constant_model.fit(X_train, y_train)
ct_y_hat = constant_model.predict(X_test)
mse = MSE()
lr_mse = mse(y_test, lr_y_hat)
lrb_mse = mse(y_test, lrb_y_hat)
ct_mse = mse(y_test, ct_y_hat)
x_plot = np.linspace(0, 10, 10)