def main():
"""Testing file to show neural network can learn linearly separable
data."""
data = np.genfromtxt("training.csv", delimiter=',').tolist()
shuffle(data)
# NOTE: We have to wrap every target value into a tuple, for the
# purpose of being able to classify n-tuples later
targets = list((sample[-1] if sample[-1] == 1 else 0,) for sample in data)
features = list(sample[:-1] for sample in data)
print "Starting to train..."
start = time()
num_features = len(features[0]) # Subtract one because of target values
nn = NeuralNet(num_features, max_epochs=2, default_bias="random",
learn_rate=.85, scale=0.1, verbose=True)
nn.train(features, targets)
print "Done with training. Took {0} seconds to train." \
.format(round(time() - start, 2))
print "Beginning with scoring..."
start = time()
scored_data = np.genfromtxt("data_features.csv", delimiter=",")
correct = np.genfromtxt("data_targets.csv", delimiter=",")
prediction = nn.score_data(scored_data)
print "Done with scoring. Took {0} seconds to score the dataset" \
.format(round(time() - start, 2))
num_incorrect = sum(1 for i in xrange(len(correct)) \
if correct[i] != prediction[i])
print "Total number incorrect: {0}".format(num_incorrect)
def main():
print "Loading in the data..."
text = open("data/lemon_training.csv").read().split("\n")
data = list(map(int, sample.strip().split(",")) for sample in text
if sample.strip() != "")
print "Shuffling..."
shuffle(data)
# NOTE: We have to wrap every target value into a tuple, for the
# purpose of being able to classify n-tuples later
targets = tuple((1 if sample[-1] == 1 else 0,) for sample in data)
features = tuple(sample[:-1] for sample in data)
print "Starting to train..."
start = time()
num_features = len(features[0]) # Subtract one because of target values
nn = NeuralNet(num_features, max_epochs=20, learn_rate=.7, scale=3,
hidden_layer=[4], verbose=True, activation=("expit", 2))
nn.train(features, targets)
print "Done with training. Took {0} seconds to train." \
.format(round(time() - start, 2))
print "Beginning with scoring..."
start = time()
scored_text = open("data/lemon_testing.csv").read().split("\n")
testing = list(map(int, sample.strip().split(',')) for sample in scored_text
if sample.strip() != "")
predictions = nn.score_data(testing)
print "Done with scoring. Took {0} seconds to score the dataset" \
.format(round(time() - start, 2))
with open("results.txt", "w") as f:
f.write("IsBadBuy\n")
for pred in predictions:
f.write(str(pred[0, 0]) + "\n")
