def test_submission(self): train_data = extract_features(load_adult_train_data()) valid_data = extract_features(load_adult_valid_data()) model = submission(train_data) predictions = [predict(model, p) for p in train_data] print() print() print("Training Accuracy:", accuracy(train_data, predictions)) predictions = [predict(model, p) for p in valid_data] print("Validation Accuracy:", accuracy(valid_data, predictions)) print()
def test_submission(self): train_data = extract_features(load_adult_train_data()) valid_data = extract_features(load_adult_valid_data()) model = submission(train_data) predictions = [predict(model, p) for p in train_data] print print print "Training Accuracy:", accuracy(train_data, predictions) predictions = [predict(model, p) for p in valid_data] print "Validation Accuracy:", accuracy(valid_data, predictions) print
def test_submission(self): """Overall test. """ train_data = extract_features(load_adult_train_data()) valid_data = extract_features(load_adult_valid_data()) model = submission(train_data) predictions = [predict(model, p) for p in train_data] print("Training Accuracy: {0}".format( accuracy(train_data, predictions))) predictions = [predict(model, p) for p in valid_data] print("Validation Accuracy: {0}".format( accuracy(valid_data, predictions)))
def test_accuracy(self): data = extract_features(load_adult_train_data()) a = accuracy(data, [0.4]*len(data)) self.assertAlmostEqual(a, 0.751077514754)
def test_accuracy(self): data = extract_features(load_adult_train_data()) a = accuracy(data, [0]*len(data)) self.assertAlmostEqual(a, 0.751077514754)
max_num_faces = max(max_num_faces, len(item_dict)) # predict for result of one picture predictions = [] for i in range(0, len(item_dict)): face = {} face["faceRectangle"] = item_dict[i]["faceRectangle"] face["result"] = (predict( model, extract_features_single_point(item_dict[i]["scores"])) >= 0.5) predictions.append(face) predictions_all.append(predictions) print max_num_faces return predictions_all, max_num_faces if __name__ == "__main__": # prepare the model train_data = extract_features(load_adult_train_data()) model = submission(train_data) print model predictions = [predict(model, p) for p in train_data] print print print "Training Accuracy:", accuracy(train_data, predictions) app.run(host='ec2-52-206-17-234.compute-1.amazonaws.com', port=8000, threaded=True)
from sgd import extract_features from sgd import logistic, dot, predict, accuracy, submission, extract_features import csv import random with open('HR_comma_sep.csv', 'rb') as f: reader = csv.DictReader(f) data = list(reader) random.shuffle(data) train_separate = data[0:10500] test_separate = data[10500:] train_data = extract_features(train_separate) test_data = extract_features(test_separate) def test_logistic(): self.assertAlmostEqual(logistic(1), 0.7310585786300049) self.assertAlmostEqual(logistic(2), 0.8807970779778823) self.assertAlmostEqual(logistic(-1), 0.2689414213699951) def test_dot(): d = dot([1.1, 2, 3.5], [-1, 0.1, .08]) self.assertAlmostEqual(d, -.62) def test_accuracy(): data = train_data
def test_accuracy(self): """Tests the accuracy calculation. """ data = extract_features(load_adult_train_data()) a = accuracy(data, [0] * len(data)) self.assertAlmostEqual(a, 0.7636129)