writer = csv.writer(open(BASE_PATH + "results_sparse.csv", "wb"), delimiter=",")
writer_full = csv.writer(open(BASE_PATH + "results_full.csv", "wb"), delimiter=",")
csv_header = ['Lambda', 'Lasso', 'Lasso Red', 'ElasticNet', 'ElasticNet Red', 'Graph', 'Graph Red', 'GraKe', 'GraKe Red']
writer_full.writerow(csv_header)
writer.writerow(csv_header)
lambda_range = [0.1, 1.1] # inclusive 1.0
n_fold = 10
n_iter = 1000
p_val = 0.05
num_examples_sparse = 40
num_examples_big = 2000
response = "PackagingCycleTime"
# *************** Load Data ************** #
mm = ModelManager()
file_list = [BASE_PATH + "foerdern.txt", BASE_PATH + "foerdern_ind.txt",
BASE_PATH + "testen.txt", BASE_PATH + "beladen.txt",
BASE_PATH + "verpacken.txt"]
mm.load_data(file_list)
k_sem_reduced = mm.load_kernel_laplacian(BASE_PATH + "kernel.csv")
k_full = mm.load_kernel_laplacian(BASE_PATH + "full_kernel.csv")
k_reg_reduced = mm.load_kernel_laplacian(BASE_PATH + "p_value_kernel.csv")
dependency_graph_full = mm.load_kernel_laplacian(BASE_PATH + "dependency_full.csv")
dependency_graph_sem_reduced = mm.load_kernel_laplacian(BASE_PATH + "dependency.csv")
index_sparse = np.ones(num_examples_sparse, dtype=bool)
index_sparse = np.concatenate((index_sparse, np.zeros(mm.num_examples() - num_examples_sparse - 1, dtype=bool)))
np.random.shuffle(index_sparse)
__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)