def feature_selection_extra_trees(self):
# Load Data
file = open("models.obj", 'r')
models = pickle.load(file)
samples = models[0]
responses = models[1]
# Using ExtraTreesClassifier
forest = FeatureSelectionScikit(n_estimators=10, criterion="gini")
forest.fit(samples=samples, response=responses)
importances = forest.importance()
std = np.std([tree.feature_importances_ for tree in forest.model.estimators_], axis=0)
indices = np.argsort(importances)[::-1]
# Print the feature ranking
print("Feature ranking:")
to_plot = []
to_indices = []
for f in range(50):
to_plot.append(importances[indices[f]])
to_indices.append(indices[f])
print("%d. feature %d (%f)" % (f + 1, indices[f], importances[indices[f]]))
# Plot the feature importances of the forest
plt.figure()
plt.title("Feature importances")
plt.bar(range(50), to_plot, color="b", yerr=std[to_indices], align="center")
locs, labels = plt.xticks(range(50), indices[50:])
plt.setp(labels, rotation=90)
plt.xlim([-1, 50])
plt.show()
def upper(self):
model = FeatureSelectionScikit()
rdmForestPre = RandomForest_scikit()
rdmForest = RandomForest_scikit()
file = open("models.obj", 'r')
models = pickle.load(file)
samples = models[0]
responses = models[1]
'''
pca = decomposition.PCA()
X_digits = samples
y_digits = responses
pca.fit(X_digits)
plt.figure(1, figsize=(4, 3))
plt.clf()
plt.axes([.2, .2, .7, .7])
plt.plot(pca.explained_variance_, linewidth=2)
plt.axis('tight')
plt.xlabel('n_components')
plt.ylabel('explained_variance_')
plt.show()
'''
# Scaled data
#samplesScaled = preprocessing.scale(samples)
samplesScaled = samples
model.fit(samplesScaled, responses)
variablesImportance = model.importance()
mean = np.mean(variablesImportance)
std = np.std(variablesImportance)
fig1 = plt.figure(1, figsize=(4, 3))
ax1 = fig1.add_subplot(111)
ax1.plot(variablesImportance, linewidth=2)
basicPre = []
indices = []
minimo = min(variablesImportance)
for i, value in enumerate(variablesImportance):
if value > minimo:
basicPre.append(value)
indices.append(i)
print('Escogi %d' % (len(basicPre)))
fig2 = plt.figure(2, figsize=(4, 3))
ax2 = fig2.add_subplot(111)
ax2.plot(basicPre, linewidth=2)
newSample = []
for i, fila in enumerate(samplesScaled):
newSample.append([val for is_good, val in izip(indices, fila) if is_good])
t0 = time()
rdmForestPre.train(newSample, responses)
a, confusionPre = rdmForestPre.test(newSample, responses, True)
preTiempo = (time() - t0)
print("With Preprocessing %0.3fs" % (preTiempo))
sumPre = 0
for idx, fila in enumerate(confusionPre):
for jdx, entrada in enumerate(fila):
if idx != jdx:
sumPre += entrada
t0 = time()
rdmForest.train(samples, responses)
a, confusion = rdmForest.test(samples, responses, True)
Tiempo = time() - t0
print("Without Preprocessing %0.3fs" % (Tiempo))
print("Preprocessing/Without = %0.3fs" % (1.0 * preTiempo / Tiempo))
sum = 0
for idx, fila in enumerate(confusion):
for jdx, entrada in enumerate(fila):
if idx != jdx:
sum += entrada
print(str(sumPre), str(sum), float(1.0 * sumPre / sum))
plt.show()
