def __init__(self):
self.pca_randomForest = None
self.pca_randomForest_norm = None
self.pca_randomForest_pca = None
self.rbm_lr_rbm = None
self.rbm_lr = None
self.texture_10_8 = None
self.texture_5_10 = None
self.texture_7_10 = None
self.texture_9_8 = None
self.texture_4_10 = None
self.texture_20_8 = None
self.ensemble_logistic_regression = None
self.edge_pca_lr = None
self.pca_edge_norm = None
self.pca_edge_pca = None
self.ip = ImagesProcessor()
# Agregamos las predicciones aca porque no logramos pasarlas por referencia
self.pca_randomForest_y_hat = None
self.rbm_lr_y_hat = None
self.texture_10_8_y_hat = None
self.texture_5_10_y_hat = None
from Constants import Constants
from ImagesProcessor import ImagesProcessor
from sklearn.cross_validation import train_test_split
from sklearn.grid_search import GridSearchCV
from sklearn.metrics import classification_report
import rlcompleter, readline
readline.parse_and_bind('tab:complete')
from sklearn.ensemble import RandomForestClassifier
# Setear Dataset
ip = ImagesProcessor('../imgs/test/medium/', training=True)
# Setear Features
X = ip.getTextureFeature(15, 15)
# Setear Parametros para tunear
tuned_parameters = [{'n_estimators': [300, 500, 850, 1000, 1500, 2000, 5000]}]
# Setear Clasificador para tuner
classifier = RandomForestClassifier()
#############################################################
######## DE ACA PARA ABAJO NO HACE FALTA TOCAR NADA #########
#############################################################
#scores = ['precision', 'recall']
scores = ['precision']
Y = ip.getImagesClass()
def printResult(names, y, confidence):
with open('grupoEitanTincho.txt', 'wb') as f:
for i in range(0, len(y)):
item_id = names[i].split('.')[0]
result = None
if (y[i] == 0):
result = 1
else:
result = 0
conf = confidence[i][y[i]]
f.write("%s,%d,%f\n" % (item_id, result, conf))
ip = ImagesProcessor()
images, y = ip.getImages('../imgs/test/dataset/', size=None, training=False)
# Esto es lo que hay que usar para predecir el resultado final
if True:
ensemble = Ensemble()
ensemble.load()
X_predictions = ensemble.predict_small(images)
y_hat = ensemble.predict_big(X_predictions)
confidence = ensemble.ensemble_logistic_regression.predict_proba(
X_predictions)
printResult(y, y_hat, confidence)
#score(y_hat, y)
# Esto es lo que hay que usar para calcular al regression lineal y gurdarla
if False:
