from image import Image from preprocess import Preprocess from classifier import Classifier from log_loss import log_loss from postprocess import PostProcess genders = Image.genders() d, _ = Image.data() matrix = Preprocess.to_matrix(d) print matrix.shape matrix = Preprocess.remove_constants(matrix) print matrix.shape matrix = Preprocess.scale(matrix) matrix = Preprocess.polynomial(matrix, 2) matrix = Preprocess.scale(matrix) print matrix.shape matrix = matrix.tolist() half = len(matrix)/2 train, cv = matrix[:half], matrix[half:] train_genders, cv_genders = genders[:half], genders[half:] cv_genders = cv_genders[0::4] preds = Classifier.ensemble_preds(train, train_genders, cv) print "Score: ", log_loss(preds, cv_genders)
from image import Image from preprocess import Preprocess from classifier import Classifier from postprocess import PostProcess genders = Image.genders() all_data, ids = Image.all() matrix = Preprocess.to_matrix(all_data) matrix = Preprocess.remove_constants(matrix) matrix = Preprocess.scale(matrix) matrix = Preprocess.polynomial(matrix, 2) matrix = Preprocess.scale(matrix) matrix = matrix.tolist() train = matrix[:1128] test = matrix[1128:] test_ids = ids[1128:] print len(train) print len(test) print len(test_ids) print len(ids) print len(matrix) preds = Classifier.ensemble_preds(train, genders, test) # real # preds = Classifier.ensemble_preds(train, genders, train) # fake # for creating submission file PostProcess.submission(test_ids, preds)
