import pprint
import featurize
import util
if __name__ == "__main__":
# compsci = util.load_comments_from_file("data/compsci.csv")
hiphop = util.load_comments_from_file("data/hiphopheads.csv")
hiphop = sorted(hiphop, key=lambda x: int(x["score"]), reverse=True)
print 'Number of comments: {}'.format(len(hiphop))
print 'Highest score: {}'.format(hiphop[0]["score"])
print 'Top five comments:'
def load_data(input_file_name):
return f.transform(util.load_comments_from_file(input_file_name))
args = parser.parse_args()
print 'Loading and processing comments...'
data = load_data(args.input_file)
features = data[:, :-1]
scores = data[:, -1]
N = features.shape[0]
split = int(math.floor(0.6 * N))
print 'Loaded and processed {} comments'.format(N)
train_features = features[:split, :]
train_scores = scores[:split]
test_features = features[split:, :]
test_scores = scores[split:]
print 'Training model...'
clf = linear_model.LinearRegression()
clf.fit(train_features, train_scores)
print 'Mean Squared Error: '
error = mean_squared_error(test_scores, clf.predict(test_features))
print error
# DEBUG
raw = util.load_comments_from_file(args.input_file)
for c in raw[-5:]:
util.pretty_print_comment(c)
prediction = clf.predict(f.transform_comment(c))
print 'Real score: {}, Predicted: {}'.format(c.get("score"),
prediction)
def load_data(input_file_name):
return f.transform(util.load_comments_from_file(input_file_name))
args = parser.parse_args()
print 'Loading and processing comments...'
data = load_data(args.input_file)
features = data[:, :-1]
scores = data[:, -1]
N = features.shape[0]
split = int(math.floor(0.6*N))
print 'Loaded and processed {} comments'.format(N)
train_features = features[:split, :]
train_scores = scores[:split]
test_features = features[split:, :]
test_scores = scores[split:]
print 'Training model...'
clf = linear_model.LinearRegression()
clf.fit(train_features, train_scores)
print 'Mean Squared Error: '
error = mean_squared_error(test_scores,
clf.predict(test_features))
print error
# DEBUG
raw = util.load_comments_from_file(args.input_file)
for c in raw[-5:]:
util.pretty_print_comment(c)
prediction = clf.predict(f.transform_comment(c))
print 'Real score: {}, Predicted: {}'.format(c.get("score"),
prediction)
import pprint
import featurize
import util
if __name__ == "__main__":
# compsci = util.load_comments_from_file("data/compsci.csv")
hiphop = util.load_comments_from_file("data/hiphopheads.csv")
hiphop = sorted(hiphop, key=lambda x: int(x["score"]), reverse=True)
print 'Number of comments: {}'.format(len(hiphop))
print 'Highest score: {}'.format(hiphop[0]["score"])
print 'Top five comments:'
