X_all = mm.get_all_features_except_response(response, index_big)
num_features_all = X_all.shape[1]
y_all = mm.get_data().ix[index_big, response]
mean_y_all = np.mean(y_all)
F, p_vals = f_regression(X_all, y_all)
index_reg_reduced = p_vals <= p_val
X_reg = X_all.ix[:, index_reg_reduced]
num_features_reg = X_reg.shape[1]
print("P-value reduced features: ", k_full.columns.values[index_reg_reduced])
for alpha in np.arange(0.1, 2.1, 0.1):
# **************************** Full Data Set *************************** #
grake_lasso = GraKeLasso(k_full.as_matrix(), alpha)
glasso = GraKeLasso(dependency_graph_full.as_matrix(), alpha)
lasso = Lasso(alpha=alpha, fit_intercept=True, normalize=True, precompute='auto', copy_X=True,
max_iter=n_iter, tol=1e-4, positive=False)
elasticNet = ElasticNet(alpha=alpha, l1_ratio=0.5, fit_intercept=True, normalize=True, precompute='auto',
max_iter=n_iter, copy_X=True, tol=1e-4, positive=False)
lambda_glasso = glasso.cross_val_lambda(X_all, y_all, n_fold, n_iter, lambda_range, model=None)
lambda_grake_lasso = grake_lasso.cross_val_lambda(X_all, y_all, n_fold, n_iter, lambda_range, model=None)
lambda_elastic_net = grake_lasso.cross_val_lambda(X_all, y_all, n_fold, n_iter, lambda_range, model=elasticNet)
print("Full Data Set:")
print("Evaluating Lasso:")
lasso_full, lasso_full_red = grake_lasso.cross_val(X_all, y_all, n_fold, n_iter, 0, model=lasso)
print("Evaluating Elastic Net:")
eNet_full, eNet_full_red = grake_lasso.cross_val(X_all, y_all, n_fold, n_iter, lambda_elastic_net, model=elasticNet)
print("Evaluating GraKeLasso:")
grake_full, grake_full_red = grake_lasso.cross_val(X_all, y_all, n_fold, n_iter, lambda_grake_lasso, model=None)
__author__ = 'martin'
from learning.grakelasso import GraKeLasso, ModelManager
import numpy as np
lambd = 0.1
alpha = 1
num_examples = 1000
response = "TestingProduct"
# *************** Load Data ************** #
mm = ModelManager()
mm.load_data(["../data/test.txt"])
kernel_lap = mm.load_kernel_laplacian("../data/laplacian.csv")
data = mm.get_data()
index_sparse = np.ones(num_examples, dtype=bool)
index_sparse = np.concatenate((index_sparse, np.zeros(mm.num_examples() - num_examples - 1, dtype=bool)))
np.random.shuffle(index_sparse)
X_sparse = mm.get_all_features_except_response(response, index_sparse)
y_sparse = data.ix[index_sparse, response]
# Evaluate GraKeLasso
klasso = GraKeLasso(kernel_lap.as_matrix(), alpha)
rmse, avg_theta = klasso.cross_val(X_sparse, y_sparse, 10, 10000, lambd)
print("MSE and Coefficient Reduction ", rmse, avg_theta)