def main():
relieff_file = open(RELIEFF_FILENAME, "r")
relieff_features = parse_relieff_list(relieff_file.readlines())
relieff_features.sort(key=lambda x: x[0], reverse=True)
results, features, feature_names = read_data(FEATURE_FILENAME)
#features = np.hstack((features, results.reshape((len(results), 1))))
correlations = find_correlation(features)
#print("\n".join("{}: {}".format(x, correlations[x]) for x in correlations))
#return
for threshold in frange(1.0, 0.0, -0.1):
selected = select_features(features, relieff_features, correlations,
threshold)
print("============================================================")
print("THRESHOLD =", threshold)
print("Count:", len(selected))
print("\n".join(
["{} ({})".format(x, feature_names[x]) for x in selected]))
print()
print()
print()
def main():
name, fmt = correlation.parse_options(argv)
results, features, feature_names = correlation.read_data(name, fmt)
features = N.hstack((features, results.reshape((len(results), 1))))
correlations = correlation.find_correlation(features)
g = nx.Graph()
#g.add_nodes_from(range(len(results) + 1))
threshold = 0.8
for (f1, f2) in [c for c in correlations.keys() if correlations[c][0] > threshold]:
g.add_edge(f1, f2)
cliques = sorted(nx.find_cliques(g), key=len, reverse=True)
cliques = [c for c in cliques if len(c) >= 3]
print("=====CLIQUES=====")
for clique in cliques:
print()
print("Clique, length:", len(clique))
for node in sorted(clique):
print("{:>3} ({})".format(node, feature_names[node]))
print()
print()
print("=====CLIQUE COUNT=====")
print()
cliquecount = {}
for clique in cliques:
for node in clique:
cliquecount[node] = cliquecount.get(node, 0) + 1
cliquesizelist = [(c, cliquecount[c]) for c in sorted(cliquecount, key=lambda x: (cliquecount.get(x), -1 * x), reverse=True)]
for c in cliquesizelist: print("{:>3}: {:>3} ({})".format(c[1], c[0], feature_names[c[0]]))
def main():
relieff_file = open(RELIEFF_FILENAME, "r")
relieff_features = parse_relieff_list(relieff_file.readlines())
relieff_features.sort(key=lambda x: x[0], reverse=True)
results, features, feature_names = read_data(FEATURE_FILENAME)
#features = np.hstack((features, results.reshape((len(results), 1))))
correlations = find_correlation(features)
#print("\n".join("{}: {}".format(x, correlations[x]) for x in correlations))
#return
for threshold in frange(1.0, 0.0, -0.1):
selected = select_features(features, relieff_features, correlations, threshold)
print("============================================================")
print("THRESHOLD =", threshold)
print("Count:", len(selected))
print("\n".join(["{} ({})".format(x, feature_names[x]) for x in selected]))
print()
print()
print()
