def main():
model = "ringnorm"
a, b, y_test = data.create_dataset(5000, model)
X_test = np.array(np.c_[a, b])
y_test = np.array(y_test)[:, 0]
#########################################
##### DATA CLASSIFICATION #####
#########################################
n_trees = 100
times = 100
scores_clf = []
for perc in tqdm(np.arange(0.1, 0.9, 0.01)):
### Classifier generation ###
clf = ClasificadorRuido(n_trees=n_trees, perc=perc)
score_clf = 0
for _ in range(times):
### Training data generation ###
a, b, y_train = data.create_dataset(300, model)
X_train = np.array(np.c_[a, b])
y_train = np.array(y_train)[:, 0]
### Classifiers training and classification ###
clf.fit(X_train, y_train, random_perc=False)
score_clf += (1 - clf.score(X_test, y_test))
scores_clf.append(score_clf / times)
### Random Forest ###
rfclf = RandomForestClassifier(n_estimators=n_trees)
rfscore = 0
for _ in range(times):
a, b, y_train = data.create_dataset(300, model)
X_train = np.array(np.c_[a, b])
y_train = np.array(y_train)[:, 0]
rfclf.fit(X_train, y_train)
rfscore += 1 - rfclf.score(X_test, y_test)
plt.axhline(y=rfscore/times, color='m', linestyle='-')
plt.plot(np.arange(0.1, 0.9, 0.01), scores_clf, linestyle='-.')
plt.savefig("../plots/noise-variation_"+model+".png")
def main():
model = "threenorm"
a, b, y_test = data.create_dataset(100, model)
X_test = np.array(np.c_[a, b])
y_test = np.array(y_test)[:, 0]
#########################################
##### DATA CLASSIFICATION #####
#########################################
n_trees = 100
for perc in tqdm(np.arange(0.1, 0.91, 0.1)):
### Classifier generation ###
clf = ClasificadorRuido(n_trees=n_trees, perc=perc)
clf_scores = np.zeros((100, n_trees))
for i in range(100):
### Training data generation ###
a, b, y_train = data.create_dataset(300, model)
X_train = np.array(np.c_[a, b])
y_train = np.array(y_train)[:, 0]
### Classifiers training and classification ###
clf.fit(X_train, y_train, random_perc=False)
clf.predict_proba_error(X_test)
for n_tree in range(1, n_trees + 1):
clf_scores[i, n_tree - 1] += 1 - clf.score_error(X_test, y_test, n_classifiers=n_tree)
plt.plot(range(1, n_trees + 1), clf_scores.mean(axis=0), linestyle='-.')
print()
print("Perc: ", np.round(perc, 1), " - ", clf_scores.mean(axis=0)[-1])
### Random Forest ###
rfclf = RandomForestClassifier(n_estimators=n_trees)
a, b, y_train = data.create_dataset(300, model)
X_train = np.array(np.c_[a, b])
y_train = np.array(y_train)[:, 0]
rfclf.fit(X_train, y_train)
rfscore = 1 - rfclf.score(X_test, y_test)
plt.axhline(y=rfscore, color='m', linestyle='-')
plt.legend(('perc=0.1', 'perc=0.2', 'perc=0.3', 'perc=0.4', 'perc=0.5', 'perc=0.6', 'perc=0.7', 'perc=0.8', 'perc=0.9', 'RF'),
loc='upper right')
plt.savefig("../plots/noise-error_"+model+"_100trees.png")