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)
