[{'role': 'system', 'content': "You are an expert in machine learning and your task is to classify the code snippets into categories from the provided list of categories. List of categories: ['helper_functions', 'load_data', 'data_exploration', 'data_preprocessing', 'evaluation', 'modelling', 'prediction', 'result_visualization', 'save_results', 'comment_only']"}, {'role': 'user', 'content': 'Identify to which category the provided code belongs and assign the code snippet to at least 1 but up to 5 categories based on the probabilities, ignore if the probability of the category is negligible. Strictly provide your response as a list. Avoid providing any additional information, commentary, or personal opinions.\\nPython Code:class MF_RS():\ndef __init__(self, numUsers, numSongs, embedding_dim, reg_lambda=0.01, conf_lambda=1.0):\nself.batch_size = np.min([5000, numUsers, numSongs]);\nself.numUsers = numUsers\nself.numSongs = numSongs\nself.epochs = 5\nself.reg_lambda = reg_lambda\nself.conf_lambda = conf_lambda\nself.userMat = tf.Variable(tf.random_normal([numUsers, embedding_dim]))\nself.songMat = tf.Variable(tf.random_normal([numSongs, embedding_dim]))\nself.userBias = tf.Variable(tf.random_normal([numUsers]))\nself.songBias = tf.Variable(tf.random_normal([numSongs]))\nself.overallBias = tf.Variable(tf.random_normal([1]))\nself.C_user = tf.Variable(.1*tf.ones([numUsers]))\nself.C_song = tf.Variable(.1*tf.ones([numSongs]))\nself.users = tf.placeholder(tf.int32, shape =(self.batch_size))\nself.songs = tf.placeholder(tf.int32, shape =(self.batch_size))\nself.rating = tf.placeholder(tf.float32, shape = (self.batch_size))\nself.U = tf.nn.embedding_lookup(self.userMat, self.users)\nself.W = tf.nn.embedding_lookup(self.songMat, self.songs)\nself.U_bias = tf.nn.embedding_lookup(self.userBias, self.users)\nself.W_bias = tf.nn.embedding_lookup(self.songBias, self.songs)\nself.C_ui = tf.maximum(0.0, tf.nn.embedding_lookup(self.C_user, self.users))\nself.C_sj = tf.maximum(0.0, tf.nn.embedding_lookup(self.C_song, self.songs))\nbias = self.U_bias+self.W_bias+self.overallBias\npq = tf.reduce_sum(tf.mul(self.U, self.W), 1)\nself.yhat = pq + bias\nself.confidence_reg = self.conf_lambda * tf.reduce_sum(tf.exp(-self.C_ui) + tf.exp(-self.C_sj))\nself.l2_reg = self.reg_lambda * ( tf.reduce_sum((tf.square(self.U) + tf.square(self.W))) +\ntf.reduce_sum(tf.square(self.U_bias) + tf.square(self.W_bias)))\nself.reg = self.confidence_reg + self.l2_reg\nself.error = tf.reduce_mean(self.C_ui * self.C_sj * tf.nn.l2_loss(self.yhat - self.rating))\nself.cost = (self.error + self.reg)/1e7\nself.optimizer = tf.train.GradientDescentOptimizer(learning_rate = .01).minimize(self.cost)\nself.session = tf.Session()\nself.session.run(tf.initialize_all_variables())\ndef train(self, users, songs, ratings):\nfor i in range(self.epochs):\navg_cost = 0\nperm = np.random.permutation(len(ratings))\nnum_batches = len(ratings) // self.batch_size\nfor b_idx in range(num_batches):\nbatch = perm[self.batch_size * b_idx:self.batch_size * (b_idx + 1)]\nusers_batch = users[batch]\nsongs_batch = songs[batch]\nratings_batch = ratings[batch]\navg_cost += self.session.run([self.cost, self.optimizer],\n{self.users:users_batch, self.songs:songs_batch, self.rating:ratings_batch})[0]\nprint(avg_cost/num_batches)'}, {'role': 'assistant', 'content': "['data_preprocessing']"}]