def main():
lasso_data_fname = "lasso_data.pickle"
x_train, y_train, x_val, y_val, target_fn, coefs_true, featurize = setup_problem.load_problem(lasso_data_fname)
# Generate features
X_train = featurize(x_train)
X_val = featurize(x_val)
pred_fns = []
x = np.sort(np.concatenate([np.arange(0,1,.001), x_train]))
X = featurize(x)
l2reg = 1
estimator = RidgeRegression(l2_reg=l2reg, step_size=0.00005, max_num_epochs=2000)
estimator.fit(X_train, y_train)
name = "Ridge with L2Reg="+str(l2reg)
pred_fns.append({"name":name, "preds": estimator.predict(X) })
l2reg = 0
estimator = RidgeRegression(l2_reg=l2reg, step_size=0.0005, max_num_epochs=500)
estimator.fit(X_train, y_train)
name = "Ridge with L2Reg="+str(l2reg)
pred_fns.append({"name":name, "preds": estimator.predict(X) })
# Let's plot prediction functions and compare coefficients for several fits
# and the target function.
pred_fns.append({"name": "Target Parameter Values (i.e. Bayes Optimal)", "coefs": coefs_true, "preds": target_fn(x)})
plot_utils.plot_prediction_functions(x, pred_fns, x_train, y_train, legend_loc="best")
def main():
lasso_data_fname = "lasso_data.pickle"
x_train, y_train, x_val, y_val, target_fn, coefs_true, featurize = setup_problem.load_problem(lasso_data_fname)
# Generate features
X_train = featurize(x_train)
X_val = featurize(x_val)
# Let's plot prediction functions and compare coefficients for several fits
# and the target function.
pred_fns = []
x = np.sort(np.concatenate([np.arange(0,1,.001), x_train]))
pred_fns.append({"name": "Target Parameter Values (i.e. Bayes Optimal)", "coefs": coefs_true, "preds": target_fn(x)})
estimator = MLPRegression(num_hidden_units=10, step_size=0.001, init_param_scale=.0005, max_num_epochs=5000)
x_train_as_column_vector = x_train.reshape(x_train.shape[0],1) # fit expects a 2-dim array
x_as_column_vector = x.reshape(x.shape[0],1) # fit expects a 2-dim array
estimator.fit(x_train_as_column_vector, y_train)
name = "MLP regression - no features"
pred_fns.append({"name":name, "preds": estimator.predict(x_as_column_vector) })
X = featurize(x)
estimator = MLPRegression(num_hidden_units=10, step_size=0.0005, init_param_scale=.01, max_num_epochs=500)
estimator.fit(X_train, y_train)
name = "MLP regression - with features"
pred_fns.append({"name":name, "preds": estimator.predict(X) })
plot_utils.plot_prediction_functions(x, pred_fns, x_train, y_train, legend_loc="best")
def main():
lasso_data_fname = "lasso_data.pkl"
x_train, y_train, x_val, y_val, target_fn, coefs_true, featurize = setup_problem.load_problem(
lasso_data_fname
)
# Generate features
X_train = featurize(x_train)
# X_val = featurize(x_val)
# Let's plot prediction functions and compare coefficients for several fits
# and the target function.
pred_fns = []
x = np.sort(np.concatenate([np.arange(0, 1, 0.001), x_train]))
pred_fns.append(
{
"name": "Target Parameter Values (i.e. Bayes Optimal)",
"coefs": coefs_true,
"preds": target_fn(x),
}
)
X = featurize(x)
estimator = LinearRegression(step_size=0.001, max_num_epochs=1000)
estimator.fit(X_train, y_train, print_every=100)
name = "Linear regression"
pred_fns.append({"name": name, "preds": estimator.predict(X)})
plot_utils.plot_prediction_functions(
x, pred_fns, x_train, y_train, legend_loc="best"
)
os.makedirs("img", exist_ok=True)
plt.savefig(os.path.join("img", "linear_regression.png"))
plt.show()
def main():
lasso_data_fname = "lasso_data.pickle"
x_train, y_train, x_val, y_val, target_fn, coefs_true, featurize = setup_problem.load_problem(lasso_data_fname)
# Generate features
X_train = featurize(x_train)#这里featurize是setup_problem.py里get_target_and_featurizer（）return的函数
X_val = featurize(x_val)
# Let's plot prediction functions and compare coefficients for several fits
# and the target function.
pred_fns = []
x = np.sort(np.concatenate([np.arange(0,1,.001), x_train]))
pred_fns.append({"name": "Target Parameter Values (i.e. Bayes Optimal)", "coefs": coefs_true, "preds": target_fn(x)})
X = featurize(x)
estimator = LinearRegression(step_size=0.001, max_num_epochs=1000)
estimator.fit(X_train, y_train)
name = "Linear regression"
pred_fns.append({"name":name, "preds": estimator.predict(X) })
plot_utils.plot_prediction_functions(x, pred_fns, x_train, y_train, legend_loc="best")
def main():
lasso_data_fname = "c:/Users/jack/ml_homework_2020/homework7/code/lasso_data.pickle"
#lasso_data_fname = "/Users/shunshun/github/ml_homework_2020/homework7/code/lasso_data.pickle"
#lasso_data_fname = "lasso_data.pickle"
x_train, y_train, x_val, y_val, target_fn, coefs_true, featurize = setup_problem.load_problem(
lasso_data_fname)
# Generate features
X_train = featurize(x_train)
X_val = featurize(x_val)
print(y_train[1])
print(X_train.shape, y_train.shape)
# Let's plot prediction functions and compare coefficients for several fits
# and the target function.
pred_fns = []
x = np.sort(np.concatenate([np.arange(0, 1, .001), x_train]))
pred_fns.append({
"name": "Target Parameter Values (i.e. Bayes Optimal)",
"coefs": coefs_true,
"preds": target_fn(x)
})
X = featurize(x)
estimator = LinearRegression(step_size=0.001, max_num_epochs=100)
estimator.fit(X_train, y_train)
name = "Linear regression"
pred_fns.append({"name": name, "preds": estimator.predict(X)})
plot_utils.plot_prediction_functions(x,
pred_fns,
x_train,
y_train,
legend_loc="best")
