<commit_before>import numpy as np

def dcg(scores):
	return np.sum([
						(np.power(2, scores[i]) - 1) / np.log2(i + 1)
						for i in xrange(len(scores) + 1)
					])

def compute_lambda()

class LambdaMART:

	def __init__(self, training_data, number_of_trees, leaves_per_tree, learning_rate):
		'''
		The format for training data is as follows:
			[relevance, q_id, [feature vector]]
		'''
		self.training_data = training_data
		self.number_of_trees = number_of_trees
		self.leaves_per_tree = self.leaves_per_tree
		self.learning_rate = learning_rate

	def fit(self):

		for k in self.number_of_trees:
			for i in xrange(len(training_data)):


def main():
	

if __name__ == '__main__':
	main()<commit_msg>added predict framework<commit_after>import numpy as np

def dcg(scores):
	return np.sum([
						(np.power(2, scores[i]) - 1) / np.log2(i + 1)
						for i in xrange(len(scores) + 1)
					])

def compute_lambda()

class LambdaMART:

	def __init__(self, training_data, number_of_trees, leaves_per_tree, learning_rate):
		'''
		The format for training data is as follows:
			[relevance, q_id, [feature vector]]
		'''
		self.training_data = training_data
		self.number_of_trees = number_of_trees
		self.leaves_per_tree = self.leaves_per_tree
		self.learning_rate = learning_rate

	def fit(self):

		for k in self.number_of_trees:
			for i in xrange(len(training_data)):

	def predict(self, data):


def main():


if __name__ == '__main__':
	main()
{'fraction_non_alphanumeric': 0.11586206896551725, 'fraction_numerical': 0.006896551724137931, 'mean_word_length': 4.055749128919861, 'pattern_counts': {'":': 0, '<': 3, '<?xml version=': 0, '>': 3, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'dir': 'git-commits-cleaned', 'id': '1621527', 'n_tokens_mistral': 608, 'n_tokens_neox': 523, 'n_words': 132}