def plot_gp(X, y, nstd, lscale, order, n_azi_buckets, param_var, cv_dir):
basisfns, b, B = setup_azi_basisfns(order, n_azi_buckets, param_var)
sgp = SparseGP(X=X, y=y, basisfns = basisfns, param_mean=b, param_cov=B, hyperparams=[nstd, 1.0, lscale, lscale], sta="FITZ", dfn_str="lld", wfn_str=wfn_str)
sgp.save_trained_model(os.path.join(cv_dir, "fitz%d_gp%d_%d.sgp" % (lscale, order, n_azi_buckets)))
from matplotlib.figure import Figure
from matplotlib.backends.backend_agg import FigureCanvasAgg
f = Figure()
ax = f.add_subplot(111)
plot_distance(X, y, sgp, ax, nstd)
ax.set_xlim([1900, 10000])
ax.set_ylim([-2, 4])
canvas = FigureCanvasAgg(f)
canvas.draw()
f.savefig(os.path.join(cv_dir, "gp%d_%d_%d.png" % (lscale, order, n_azi_buckets)), bbox_inches='tight')
def main(n_max=18000):
rundir = sys.argv[1]
task_name = sys.argv[2]
basedir = os.path.join(os.getenv('SIGVISA_HOME'), 'papers', 'product_tree', 'run', rundir)
basename = os.path.join(basedir, task_name)
X_train = np.loadtxt(basename + '_X_train.txt', delimiter=',')
y_train = np.loadtxt(basename + '_y_train.txt', delimiter=',')
n_X = X_train.shape[0]
if n_X > n_max:
X_train = np.array(X_train[:n_max,:], copy=True)
y_train = np.array(y_train[:n_max], copy=True)
print "using restricted subset of %d training points" % (n_max)
actual_n = min(n_X, n_max)
X_test = np.loadtxt(basename + '_X_test.txt', delimiter=',')
y_test = np.loadtxt(basename + '_y_test.txt', delimiter=',')
"""
#for nu in (90,):
nu = 90
csficbase = basename + '_csfic_%d' % nu if nu is not None else basename + '_csfic'
csficmodel_dir = basename + "_py_csfic_%d" % nu if nu is not None else basename + "_py_csfic"
print csficmodel_dir
#if not os.path.exists(csficbase):
# continue
csgp = os.path.join(csficmodel_dir, 'trained_%d.gp' % actual_n)
mkdir_p(csficmodel_dir)
if os.path.exists(csgp):
gp_csfic = SparseGP_CSFIC(fname=csgp, build_tree=True, leaf_bin_size=15)
else:
gp_csfic = load_matlab_csficmodel(csficbase, X_train, y_train)
gp_csfic.save_trained_model(csgp)
#print "testing predictions"
#test_predict(csficmodel_dir, sgp=gp_csfic, testX=X_test, testy=y_test)
#print "testing cutoff rule 0"
#eval_gp(bdir=csficmodel_dir, testX=X_test, test_n=200, gp=gp_csfic, cutoff_rule=0)
#print "testing cutoff rule 1"
#eval_gp(bdir=csficmodel_dir, testX=X_test, test_n=200, gp=gp_csfic, cutoff_rule=1)
#print "testing cutoff rule 2"
#eval_gp(bdir=csficmodel_dir, testX=X_test, test_n=200, gp=gp_csfic, cutoff_rule=2)
print "testing leaf bins 15"
gp_csfic10 = SparseGP_CSFIC(fname=csgp, build_tree=True, leaf_bin_size=10)
eval_gp(bdir=csficmodel_dir, testX=X_test, test_n=200, gp=gp_csfic10, cutoff_rule=2, flag="_bin15")
#print "testing leaf bins 100"
#gp_csfic50 = SparseGP_CSFIC(fname=csgp, build_tree=True, leaf_bin_size=50)
#eval_gp(bdir=csficmodel_dir, testX=X_test, test_n=200, gp=gp_csfic50, cutoff_rule=2, flag="_bin50")
"""
semodel_dir = basename + "_py_se"
segp = os.path.join(semodel_dir, 'trained.gp')
mkdir_p(semodel_dir)
if os.path.exists(segp):
gp_se = SparseGP(fname=segp, build_tree=True, leaf_bin_size=0, sparse_invert=False)
else:
gp_se = load_matlab_semodel(basename, X_train, y_train)
gp_se.save_trained_model(segp)
test_predict(semodel_dir, sgp=gp_se, testX=X_test, testy=y_test)
eval_gp(bdir=semodel_dir, testX=X_test, test_n=200, gp=gp_se)