def gmrf_adj_matrix(name=None, highlight=None):
print("Adj matrix")
Q = np.load("../results/gmrf_prec.npy")
fig, ax = newfig(1.0, ratio=1)
pl.bin_precision_matrix(Q, df.columns.values, ax, add_color=True, interleave=True, highlight=highlight)
if name:
savefig(name)
else:
savefig('gmrf_con')
def main(method, transform, temporal, layout, hybrid, threshold, output):
variables = getattr(layouts, layout)
K = list(variables.keys())
df = utils.prep_dataframe(keep=K)
if temporal:
df_shifted = utils.create_shifted_features(df)
df = df.join(df_shifted, how="outer")
df = df.dropna()
if transform:
print("* Tranform data")
X = tr.to_normal(df.values)
df = pd.DataFrame(X, index=df.index.values, columns=df.columns.values)
X = df.values
if hybrid:
model = HRF(k=5, k_star=10, variables_names=df.columns.values)
else:
model = GMRF(method=method[0])
model.fit(X)
if not hybrid:
print("* Selected alpha = {}".format(model.alpha_))
else:
print("* Selected k = {}, k star = {}".format(model.k, model.k_star))
if threshold:
Q = model.precision_.copy()
ts = np.arange(0., 1., 0.001)
bics = np.empty(len(ts))
connectivity = np.empty(len(ts))
n = Q.shape[0]
gmrf_test = GMRF()
gmrf_test.mean_ = np.mean(X, axis=0)
for i, t in enumerate(ts):
Q[Q < t] = 0
gmrf_test.precision_ = Q
bics[i], _ = gmrf_test.bic(X)
connectivity[i] = 1 - np.size(np.where(Q == 0)[0]) / (n * n)
fig, (ax, ax1) = plt.subplots(2, 1)
ax.plot(connectivity)
ax1.plot(bics)
if output:
results_name = os.path.join(os.path.dirname(__file__), "../results/")
if hybrid:
BNs = np.empty(len(model.variables_names), dtype=object)
for i in range(len(BNs)):
BNs[i] = (model.bns[i].variables_names, model.bns[i].nodes, model.bns[i].edges)
np.save(results_name + output + "_bns", BNs)
else:
np.save(results_name + output + "_prec", model.precision_)
np.save(results_name + output + "_mean", model.mean_)
np.save(results_name + output + "_bic_scores", model.bic_scores)
if not hybrid:
plt.figure()
plt.plot(model.bic_scores)
fig, ax = plt.subplots(1, 1)
pl.bin_precision_matrix(model.precision_, df.columns.values, ax)
plt.show()
def adj_static():
print("Adj static")
Q = np.load("../results/gmrf_static_prec.npy")
fig, ax = newfig(1.0, ratio=1)
pl.bin_precision_matrix(Q, df_static.columns.values, ax, add_color=True)
savefig('adj_static')
