main_cold_citeu.py

import utils
import numpy as np
import pandas as pd
import tensorflow as tf
import datetime
from sklearn import datasets
import data
import model
import scipy.sparse as sp

import argparse
import os

n_users = 5551 + 1
n_items = 16980 + 1

def main():
    data_path = args.data_dir
    checkpoint_path = args.checkpoint_path
    tb_log_path = args.tb_log_path
    model_select = args.model_select

    rank_out = args.rank
    user_batch_size = 1000
    n_scores_user = 2500
    data_batch_size = 100
    dropout = args.dropout
    recall_at = range(10, 110, 10)
    eval_batch_size = 1000
    max_data_per_step = 2500000
    eval_every = args.eval_every
    num_epoch = 500

    _lr = args.lr
    _decay_lr_every = 100
    _lr_decay = 0.1

    experiment = '%s_%s' % (
        datetime.datetime.now().strftime('%Y-%m-%d-%H:%M:%S'),
        '-'.join(str(x / 100) for x in model_select) if model_select else 'simple'
    )
    _tf_ckpt_file = None if checkpoint_path is None else checkpoint_path + experiment + '/tf_checkpoint'

    print('running: ' + experiment)

    dat = load_data(data_path)
    u_pref_scaled = dat['u_pref_scaled']
    v_pref_scaled = dat['v_pref_scaled']
    eval_cold = dat['eval_cold']
    item_content = dat['item_content']
    u_pref = dat['u_pref']
    v_pref = dat['v_pref']
    user_indices = dat['user_indices']

    timer = utils.timer(name='main').tic()

    # append pref factors for faster dropout
    v_pref_expanded = np.vstack([v_pref_scaled, np.zeros_like(v_pref_scaled[0, :])])
    v_pref_last = v_pref_scaled.shape[0]
    u_pref_expanded = np.vstack([u_pref_scaled, np.zeros_like(u_pref_scaled[0, :])])
    u_pref_last = u_pref_scaled.shape[0]
    timer.toc('initialized numpy data for tf')

    # prep eval
    eval_batch_size = eval_batch_size
    timer.tic()
    eval_cold.init_tf(u_pref_scaled, v_pref_scaled, None, item_content, eval_batch_size)
    timer.toc('initialized eval for tf').tic()

    dropout_net = model.DeepCF(latent_rank_in=u_pref.shape[1],
                               user_content_rank=0,
                               item_content_rank=item_content.shape[1],
                               model_select=model_select,
                               rank_out=rank_out)

    config = tf.ConfigProto(allow_soft_placement=True)

    with tf.device(args.model_device):
        dropout_net.build_model()

    with tf.device(args.inf_device):
        dropout_net.build_predictor(recall_at, n_scores_user)

    if args.progress:
        from tqdm import tqdm
    with tf.Session(config=config) as sess:
        tf_saver = None if _tf_ckpt_file is None else tf.train.Saver()
        train_writer = None if tb_log_path is None else tf.summary.FileWriter(
            tb_log_path + experiment, sess.graph)
        tf.global_variables_initializer().run()
        tf.local_variables_initializer().run()
        timer.toc('initialized tf')

        row_index = np.copy(user_indices)
        n_step = 0
        best_cold = 0
        n_batch_trained = 0
        best_step = 0
        for epoch in range(num_epoch):
            np.random.shuffle(row_index)
            for b in utils.batch(row_index, user_batch_size):
                n_step += 1
                # prep targets
                target_users = np.repeat(b, n_scores_user)
                target_users_rand = np.repeat(np.arange(len(b)), n_scores_user)
                target_items_rand = [np.random.choice(v_pref.shape[0], n_scores_user) for _ in b]
                target_items_rand = np.array(target_items_rand).flatten()
                target_ui_rand = np.transpose(np.vstack([target_users_rand, target_items_rand]))
                [target_scores, target_items, random_scores] = sess.run(
                    [dropout_net.tf_topk_vals, dropout_net.tf_topk_inds, dropout_net.preds_random],
                    feed_dict={
                        dropout_net.U_pref_tf: u_pref[b, :],
                        dropout_net.V_pref_tf: v_pref,
                        dropout_net.rand_target_ui: target_ui_rand
                    }
                )
                # merge topN and randomN items per user
                target_scores = np.append(target_scores, random_scores)
                target_items = np.append(target_items, target_items_rand)
                target_users = np.append(target_users, target_users)

                tf.local_variables_initializer().run()
                n_targets = len(target_scores)
                perm = np.random.permutation(n_targets)
                n_targets = min(n_targets, max_data_per_step)
                data_batch = [(n, min(n + data_batch_size, n_targets)) for n in xrange(0, n_targets, data_batch_size)]
                f_batch = 0
                gen = data_batch
                if args.progress:
                    gen = tqdm(gen)
                for (start, stop) in gen:
                    batch_perm = perm[start:stop]
                    batch_users = target_users[batch_perm]
                    batch_items = target_items[batch_perm]
                    if dropout != 0:
                        n_to_drop = int(np.floor(dropout * len(batch_perm)))
                        perm_user = np.random.permutation(len(batch_perm))[:n_to_drop]
                        perm_item = np.random.permutation(len(batch_perm))[:n_to_drop]
                        batch_v_pref = np.copy(batch_items)
                        batch_u_pref = np.copy(batch_users)
                        batch_v_pref[perm_user] = v_pref_last
                        batch_u_pref[perm_item] = u_pref_last
                    else:
                        batch_v_pref = batch_items
                        batch_u_pref = batch_users
                    item_content_batch = item_content[batch_items, :]
                    if sp.issparse(item_content):
                        item_content_batch = item_content_batch.todense()

                    _, _, loss_out = sess.run(
                        [dropout_net.preds, dropout_net.updates, dropout_net.loss],
                        feed_dict={
                            dropout_net.Uin: u_pref_expanded[batch_u_pref, :],
                            dropout_net.Vin: v_pref_expanded[batch_v_pref, :],
                            dropout_net.Vcontent: item_content_batch,
                            #
                            dropout_net.target: target_scores[batch_perm],
                            dropout_net.lr_placeholder: _lr,
                            dropout_net.phase: 1
                        }
                    )
                    f_batch += loss_out
                    if np.isnan(f_batch):
                        raise Exception('f is nan')

                n_batch_trained += len(data_batch)
                if n_step % _decay_lr_every == 0:
                    _lr = _lr_decay * _lr
                    print('decayed lr:' + str(_lr))
                if n_step % eval_every == 0:
                    recall_cold = utils.batch_eval_recall(
                        sess, dropout_net.eval_preds_cold, eval_feed_dict=dropout_net.get_eval_dict,
                        recall_k=recall_at, eval_data=eval_cold)

                    # checkpoint
                    if np.sum(recall_cold) > np.sum(best_cold):
                        best_cold = recall_cold
                        best_step = n_step
                        if tf_saver is not None:
                            tf_saver.save(sess, _tf_ckpt_file)

                    timer.toc('%d [%d]b [%d]tot f=%.2f best[%d]' % (
                        n_step, len(data_batch), n_batch_trained, f_batch, best_step
                    )).tic()
                    print ('\t\t\t'+' '.join([('@'+str(i)).ljust(6) for i in recall_at]))
                    print('cold start\t%s' % (
                        ' '.join(['%.4f' % i for i in recall_cold]),
                    ))
                    print('best epoch[%d]\t%s' % (
                        best_step,
                        ' '.join(['%.4f' % i for i in best_cold] ),
                    ))
                    summaries = []
                    for i, k in enumerate(recall_at):
                        if k % 100 == 0:
                            summaries.extend([
                                tf.Summary.Value(tag="recall@" + str(k) + " cold", simple_value=recall_cold[i]),
                            ])
                    recall_summary = tf.Summary(value=summaries)
                    if train_writer is not None:
                        train_writer.add_summary(recall_summary, n_step)

def tfidf(R):
    row = R.shape[0]
    col = R.shape[1]
    Rbin = R.copy()
    Rbin[Rbin!=0]=1.0
    R = R + Rbin
    tf = R.copy()
    tf.data = np.log(tf.data)
    idf = np.sum(Rbin,0)
    idf = np.log(row/(1+idf))
    idf = sp.spdiags(idf,0,col,col)
    return tf * idf

def load_data(data_path):
    timer = utils.timer(name='main').tic()
    split_folder = os.path.join(data_path, 'cold')

    u_file = os.path.join(data_path, 'trained/cold/WRMF_cold_rank200_reg1_alpha10_iter10.U.txt')
    v_file = os.path.join(data_path, 'trained/cold/WRMF_cold_rank200_reg1_alpha10_iter10.V.txt')
    item_content_file = os.path.join(data_path, 'item_features_0based.txt')
    train_file = os.path.join(split_folder, 'train.csv')
    test_cold_file = os.path.join(split_folder, 'test.csv')
    test_cold_iid_file = os.path.join(split_folder, 'test_item_ids.csv')

    dat = {}
    # load preference data
    timer.tic()
#    u_pref = np.fromfile(u_file, dtype='>f4').reshape(n_users, 200)
#    v_pref = np.fromfile(v_file, dtype='>f4').reshape(n_items, 200)

    u_pref = np.loadtxt(u_file).reshape(n_users,200)
    v_pref = np.loadtxt(v_file).reshape(n_items,200)

    dat['u_pref'] = u_pref
    dat['v_pref'] = v_pref

    timer.toc('loaded U:%s,V:%s' % (str(u_pref.shape), str(v_pref.shape))).tic()

    # pre-process
    _, dat['u_pref_scaled'] = utils.prep_standardize(u_pref)
    _, dat['v_pref_scaled'] = utils.prep_standardize(v_pref)

    timer.toc('standardized U,V').tic()

    # load content data
    timer.tic()
    item_content, _ = datasets.load_svmlight_file(item_content_file, zero_based=True, dtype=np.float32)

    item_content = tfidf(item_content)

    from sklearn.utils.extmath import randomized_svd
    u,s,_ = randomized_svd(item_content, n_components=300, n_iter=5)
    item_content = u * s
    _, item_content = utils.prep_standardize(item_content)

    if sp.issparse(item_content):
        dat['item_content'] = item_content.tolil(copy=False)
    else:
        dat['item_content'] = item_content
    timer.toc('loaded item feature sparse matrix: %s' % (str(item_content.shape))).tic()

    # load split
    timer.tic()
    train = pd.read_csv(train_file, delimiter=",", header=-1, dtype=np.int32).values.ravel().view(
        dtype=[('uid', np.int32), ('iid', np.int32), ('inter', np.int32)])
    dat['user_indices'] = np.unique(train['uid'])
    timer.toc('read train triplets %s' % train.shape).tic()

    dat['eval_cold'] = data.load_eval_data(test_cold_file, test_cold_iid_file, name='eval_cold', cold=True,
                                           train_data=train,citeu=True)
    return dat


if __name__ == "__main__":
    parser = argparse.ArgumentParser(description="Demo script to run DropoutNet on RecSys data",
                                     formatter_class=argparse.ArgumentDefaultsHelpFormatter)
    parser.add_argument('--data-dir', type=str, required=True, help='path to eval in the downloaded folder')

    parser.add_argument('--model-device', type=str, default='/gpu:0', help='device to use for training')
    parser.add_argument('--inf-device', type=str, default='/cpu:0', help='device to use for inference')
    parser.add_argument('--checkpoint-path', type=str, default=None,
                        help='path to dump checkpoint data from TensorFlow')
    parser.add_argument('--tb-log-path', type=str, default=None,
                        help='path to dump TensorBoard logs')
    parser.add_argument('--model-select', nargs='+', type=int,
                        default=[200],
                        help='specify the fully-connected architecture, starting from input,'
                             ' numbers indicate numbers of hidden units',
                        )
    parser.add_argument('--rank', type=int, default=200, help='output rank of latent model')
    parser.add_argument('--dropout', type=float, default=0.5, help='DropoutNet dropout')
    parser.add_argument('--eval-every', type=int, default=1, help='evaluate every X user-batch')
    parser.add_argument('--lr', type=float, default=0.005, help='starting learning rate')
    parser.add_argument('--progress', action='store_true', help='show tqdm progress (requires tqdm) during training')

    args = parser.parse_args()
    args, _ = parser.parse_known_args()
    for key in vars(args):
        print(key + ":" + str(vars(args)[key]))
    main()