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_submission():
valid_data = test_data
model = submission(train_data)
predictions = [predict(model, p) for p in train_data]
print
print
# print predictions
train_accuracy = accuracy(train_data, predictions)
print "Training Accuracy:", train_accuracy
# print train_data
predictions = [predict(model, p) for p in valid_data]
valid_accuracy = accuracy(valid_data, predictions)
print "Validation Accuracy:", valid_accuracy
print
return train_accuracy, valid_accuracy
def getPredictions(username, input_address):
# read all filenames in the input folder
data_list = compute_for_username(username)
max_num_faces = 0
# predictions for all pictures
predictions_all = []
# filename should be in format: <unique filename>_<number of faces in picture>
for data in data_list:
print(data)
item_dict = json.loads(data)
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
def test_predict(self):
"""Tests the predict function.
"""
model = [1, 2, 1, 0, 1]
point = {'features': [.4, 1, 3, .01, .1], 'label': 1}
p = predict(model, point)
self.assertAlmostEqual(p, 0.995929862284)
def test_predict(self):
model = [1,2,1,0,1]
point = {'features':[.4,1,3,.01,.1], 'label': 1}
p = predict(model, point)
self.assertAlmostEqual(p, 0.995929862284)
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
from test import test_submission, return_train_data, return_test_data
test_submission()
def predict():
train_data = return_train_data()
test_data = return_test_data()
model = submission(train_data)
print test_data[0]
# predictions = [predict(model, p) for p in train_data]
predict()
