From b21244a7f0b4eb34601cb515dcfc3f94188be22b Mon Sep 17 00:00:00 2001 From: leavingseason Date: Mon, 30 Nov 2020 07:00:04 +0000 Subject: [PATCH 1/5] update sequential reco models: add serving example --- .../sequential_recsys_amazondataset.ipynb | 20902 +++++++++++++++- .../recommender/deeprec/deeprec_utils.py | 2 +- .../recommender/deeprec/models/base_model.py | 3 +- .../deeprec/models/sequential/caser.py | 4 +- .../sequential/sequential_base_model.py | 17 +- 5 files changed, 20798 insertions(+), 130 deletions(-) diff --git a/examples/00_quick_start/sequential_recsys_amazondataset.ipynb b/examples/00_quick_start/sequential_recsys_amazondataset.ipynb index 6fcfba3937..dad82f6581 100644 --- a/examples/00_quick_start/sequential_recsys_amazondataset.ipynb +++ b/examples/00_quick_start/sequential_recsys_amazondataset.ipynb @@ -39,16 +39,7 @@ "cell_type": "code", "execution_count": 1, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "System version: 3.6.8 |Anaconda, Inc.| (default, Feb 21 2019, 18:30:04) [MSC v.1916 64 bit (AMD64)]\n", - "Tensorflow version: 1.12.0\n" - ] - } - ], + "outputs": [], "source": [ "import sys\n", "sys.path.append(\"../../\")\n", @@ -60,25 +51,51 @@ "import tensorflow as tf\n", "import time\n", "\n", + "import numpy as np\n", + "\n", "from reco_utils.common.constants import SEED\n", "from reco_utils.recommender.deeprec.deeprec_utils import (\n", " prepare_hparams\n", ")\n", "from reco_utils.dataset.amazon_reviews import download_and_extract, data_preprocessing\n", "from reco_utils.dataset.download_utils import maybe_download\n", - "from reco_utils.recommender.deeprec.models.sequential.sli_rec import SLI_RECModel\n", + "\n", + "\n", + "from reco_utils.recommender.deeprec.models.sequential.sli_rec import SLI_RECModel as SeqModel\n", "#### to use the other model, use one of the following lines:\n", - "#from reco_utils.recommender.deeprec.models.sequential.asvd import A2SVDModel\n", - "#from reco_utils.recommender.deeprec.models.sequential.caser import CaserModel\n", - "#from reco_utils.recommender.deeprec.models.sequential.gru4rec import GRU4RecModel\n", + "# from reco_utils.recommender.deeprec.models.sequential.asvd import A2SVDModel as SeqModel\n", + "# from reco_utils.recommender.deeprec.models.sequential.caser import CaserModel as SeqModel\n", + "# from reco_utils.recommender.deeprec.models.sequential.gru4rec import GRU4RecModel as SeqModel\n", + "\n", "#from reco_utils.recommender.deeprec.models.sequential.nextitnet import NextItNetModel\n", "\n", "from reco_utils.recommender.deeprec.io.sequential_iterator import SequentialIterator\n", "#from reco_utils.recommender.deeprec.io.nextitnet_iterator import NextItNetIterator\n", "\n", + "\n", + "\n", + "## ATTENTION: change to the corresponding config file, e.g., caser.yaml for CaserModel \n", + "yaml_file = '../../reco_utils/recommender/deeprec/config/sli_rec.yaml' " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "System version: 3.6.8 |Anaconda, Inc.| (default, Dec 30 2018, 01:22:34) \n", + "[GCC 7.3.0]\n", + "Tensorflow version: 1.15.0\n" + ] + } + ], + "source": [ "print(\"System version: {}\".format(sys.version))\n", - "print(\"Tensorflow version: {}\".format(tf.__version__))\n", - "\n" + "print(\"Tensorflow version: {}\".format(tf.__version__))\n" ] }, { @@ -90,7 +107,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": { "tags": [ "parameters" @@ -101,7 +118,7 @@ "EPOCHS = 10\n", "BATCH_SIZE = 400\n", "RANDOM_SEED = SEED # Set None for non-deterministic result\n", - "yaml_file = '../../reco_utils/recommender/deeprec/config/sli_rec.yaml'\n", + "\n", "data_path = os.path.join(\"..\", \"..\", \"tests\", \"resources\", \"deeprec\", \"slirec\")" ] }, @@ -128,36 +145,11 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": { "scrolled": false }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████████████████████████████████████████████████████████████████████████| 692k/692k [02:17<00:00, 5.02kKB/s]\n", - "100%|████████████████████████████████████████████████████████████████████████████| 97.5k/97.5k [00:24<00:00, 4.00kKB/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "start reviews preprocessing...\n", - "start meta preprocessing...\n", - "start create instances...\n", - "creating item2cate dict\n", - "getting sampled data...\n", - "start data processing...\n", - "data generating...\n", - "vocab generating...\n", - "start valid negative sampling\n", - "start test negative sampling\n" - ] - } - ], + "outputs": [], "source": [ "\n", "# for test\n", @@ -205,16 +197,31 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": { "scrolled": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:\n", + "The TensorFlow contrib module will not be included in TensorFlow 2.0.\n", + "For more information, please see:\n", + " * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n", + " * https://github.com/tensorflow/addons\n", + " * https://github.com/tensorflow/io (for I/O related ops)\n", + "If you depend on functionality not listed there, please file an issue.\n", + "\n" + ] + } + ], "source": [ "hparams = prepare_hparams(yaml_file, \n", " embed_l2=0., \n", " layer_l2=0., \n", - " learning_rate=0.001, \n", + " learning_rate=0.001, # set to 0.01 if batch normalization is disable\n", " epochs=EPOCHS,\n", " batch_size=BATCH_SIZE,\n", " show_step=20,\n", @@ -241,7 +248,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -260,15 +267,48 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": { "scrolled": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:From ../../reco_utils/recommender/deeprec/models/sequential/sequential_base_model.py:43: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n", + "\n", + "WARNING:tensorflow:From ../../reco_utils/recommender/deeprec/models/sequential/sequential_base_model.py:64: The name tf.variable_scope is deprecated. Please use tf.compat.v1.variable_scope instead.\n", + "\n", + "WARNING:tensorflow:From ../../reco_utils/recommender/deeprec/models/sequential/sequential_base_model.py:253: The name tf.get_variable is deprecated. Please use tf.compat.v1.get_variable instead.\n", + "\n", + "WARNING:tensorflow:From ../../reco_utils/recommender/deeprec/models/sequential/sequential_base_model.py:275: The name tf.summary.histogram is deprecated. Please use tf.compat.v1.summary.histogram instead.\n", + "\n", + "WARNING:tensorflow:From ../../reco_utils/recommender/deeprec/models/sequential/sli_rec.py:64: dynamic_rnn (from tensorflow.python.ops.rnn) is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "Please use `keras.layers.RNN(cell)`, which is equivalent to this API\n", + "WARNING:tensorflow:From ../../reco_utils/recommender/deeprec/models/sequential/rnn_cell_implement.py:621: calling Constant.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "Call initializer instance with the dtype argument instead of passing it to the constructor\n", + "WARNING:tensorflow:From /home/v-xdeng/.conda/envs/reco_tf15/lib/python3.6/site-packages/tensorflow_core/python/ops/rnn.py:244: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "Use tf.where in 2.0, which has the same broadcast rule as np.where\n", + "WARNING:tensorflow:From ../../reco_utils/recommender/deeprec/models/base_model.py:677: batch_normalization (from tensorflow.python.layers.normalization) is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "Use keras.layers.BatchNormalization instead. In particular, `tf.control_dependencies(tf.GraphKeys.UPDATE_OPS)` should not be used (consult the `tf.keras.layers.batch_normalization` documentation).\n", + "WARNING:tensorflow:From /home/v-xdeng/.conda/envs/reco_tf15/lib/python3.6/site-packages/tensorflow_core/python/layers/normalization.py:327: Layer.apply (from tensorflow.python.keras.engine.base_layer) is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "Please use `layer.__call__` method instead.\n", + "WARNING:tensorflow:From ../../reco_utils/recommender/deeprec/models/base_model.py:340: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n", + "WARNING:tensorflow:From ../../reco_utils/recommender/deeprec/models/sequential/sequential_base_model.py:332: The name tf.trainable_variables is deprecated. Please use tf.compat.v1.trainable_variables instead.\n", + "\n" + ] + } + ], "source": [ - "model = SLI_RECModel(hparams, input_creator, seed=RANDOM_SEED)\n", - "## of course you can create models like ASVDModel, CaserModel and GRU4RecModel in the same manner\n", - "\n", + "model = SeqModel(hparams, input_creator, seed=RANDOM_SEED)\n", "\n", "## sometimes we don't want to train a model from scratch\n", "## then we can load a pre-trained model like this: \n", @@ -284,14 +324,14 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "{'auc': 0.5114, 'logloss': 0.6931, 'mean_mrr': 0.29, 'ndcg2': 0.4517, 'ndcg4': 0.4517, 'ndcg6': 0.4517, 'ndcg8': 0.4517, 'ndcg10': 0.4517, 'group_auc': 0.512}\n" + "{'auc': 0.5137, 'logloss': 0.6931, 'mean_mrr': 0.2893, 'ndcg@2': 0.161, 'ndcg@4': 0.248, 'ndcg@6': 0.3228, 'group_auc': 0.514}\n" ] } ], @@ -311,7 +351,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": { "scrolled": true }, @@ -320,39 +360,31 @@ "name": "stdout", "output_type": "stream", "text": [ - "step 20 , total_loss: 1.6097, data_loss: 1.6097\n", - "step 40 , total_loss: 1.6087, data_loss: 1.6087\n", - "eval valid at epoch 1: auc:0.4895,logloss:0.693,mean_mrr:0.4475,ndcg2:0.5827,ndcg4:0.5827,ndcg6:0.5827,ndcg8:0.5827,ndcg10:0.5827,group_auc:0.4907\n", - "step 20 , total_loss: 1.6069, data_loss: 1.6069\n", - "step 40 , total_loss: 1.4812, data_loss: 1.4812\n", - "eval valid at epoch 2: auc:0.5625,logloss:0.6931,mean_mrr:0.4916,ndcg2:0.6164,ndcg4:0.6164,ndcg6:0.6164,ndcg8:0.6164,ndcg10:0.6164,group_auc:0.5422\n", - "step 20 , total_loss: 1.4089, data_loss: 1.4089\n", - "step 40 , total_loss: 1.3968, data_loss: 1.3968\n", - "eval valid at epoch 3: auc:0.684,logloss:0.6957,mean_mrr:0.5984,ndcg2:0.6985,ndcg4:0.6985,ndcg6:0.6985,ndcg8:0.6985,ndcg10:0.6985,group_auc:0.6787\n", - "step 20 , total_loss: 1.2920, data_loss: 1.2920\n", - "step 40 , total_loss: 1.3227, data_loss: 1.3227\n", - "eval valid at epoch 4: auc:0.6965,logloss:0.6827,mean_mrr:0.6145,ndcg2:0.7107,ndcg4:0.7107,ndcg6:0.7107,ndcg8:0.7107,ndcg10:0.7107,group_auc:0.6914\n", - "step 20 , total_loss: 1.3205, data_loss: 1.3205\n", - "step 40 , total_loss: 1.2936, data_loss: 1.2936\n", - "eval valid at epoch 5: auc:0.6986,logloss:0.6657,mean_mrr:0.6192,ndcg2:0.7142,ndcg4:0.7142,ndcg6:0.7142,ndcg8:0.7142,ndcg10:0.7142,group_auc:0.6965\n", - "step 20 , total_loss: 1.2575, data_loss: 1.2575\n", - "step 40 , total_loss: 1.2785, data_loss: 1.2785\n", - "eval valid at epoch 6: auc:0.7055,logloss:0.6147,mean_mrr:0.6197,ndcg2:0.7146,ndcg4:0.7146,ndcg6:0.7146,ndcg8:0.7146,ndcg10:0.7146,group_auc:0.699\n", - "step 20 , total_loss: 1.2735, data_loss: 1.2735\n", - "step 40 , total_loss: 1.2838, data_loss: 1.2838\n", - "eval valid at epoch 7: auc:0.7205,logloss:0.6434,mean_mrr:0.6345,ndcg2:0.7257,ndcg4:0.7257,ndcg6:0.7257,ndcg8:0.7257,ndcg10:0.7257,group_auc:0.7092\n", - "step 20 , total_loss: 1.1849, data_loss: 1.1849\n", - "step 40 , total_loss: 1.1954, data_loss: 1.1954\n", - "eval valid at epoch 8: auc:0.7234,logloss:0.6514,mean_mrr:0.6413,ndcg2:0.7308,ndcg4:0.7308,ndcg6:0.7308,ndcg8:0.7308,ndcg10:0.7308,group_auc:0.715\n", - "step 20 , total_loss: 1.2023, data_loss: 1.2023\n", - "step 40 , total_loss: 1.1818, data_loss: 1.1818\n", - "eval valid at epoch 9: auc:0.7285,logloss:0.6794,mean_mrr:0.639,ndcg2:0.7292,ndcg4:0.7292,ndcg6:0.7292,ndcg8:0.7292,ndcg10:0.7292,group_auc:0.7152\n", - "step 20 , total_loss: 1.1680, data_loss: 1.1680\n", - "step 40 , total_loss: 1.1911, data_loss: 1.1911\n", - "eval valid at epoch 10: auc:0.7317,logloss:0.6242,mean_mrr:0.6454,ndcg2:0.7339,ndcg4:0.7339,ndcg6:0.7339,ndcg8:0.7339,ndcg10:0.7339,group_auc:0.7181\n", - "[(1, {'auc': 0.4895, 'logloss': 0.693, 'mean_mrr': 0.4475, 'ndcg2': 0.5827, 'ndcg4': 0.5827, 'ndcg6': 0.5827, 'ndcg8': 0.5827, 'ndcg10': 0.5827, 'group_auc': 0.4907}), (2, {'auc': 0.5625, 'logloss': 0.6931, 'mean_mrr': 0.4916, 'ndcg2': 0.6164, 'ndcg4': 0.6164, 'ndcg6': 0.6164, 'ndcg8': 0.6164, 'ndcg10': 0.6164, 'group_auc': 0.5422}), (3, {'auc': 0.684, 'logloss': 0.6957, 'mean_mrr': 0.5984, 'ndcg2': 0.6985, 'ndcg4': 0.6985, 'ndcg6': 0.6985, 'ndcg8': 0.6985, 'ndcg10': 0.6985, 'group_auc': 0.6787}), (4, {'auc': 0.6965, 'logloss': 0.6827, 'mean_mrr': 0.6145, 'ndcg2': 0.7107, 'ndcg4': 0.7107, 'ndcg6': 0.7107, 'ndcg8': 0.7107, 'ndcg10': 0.7107, 'group_auc': 0.6914}), (5, {'auc': 0.6986, 'logloss': 0.6657, 'mean_mrr': 0.6192, 'ndcg2': 0.7142, 'ndcg4': 0.7142, 'ndcg6': 0.7142, 'ndcg8': 0.7142, 'ndcg10': 0.7142, 'group_auc': 0.6965}), (6, {'auc': 0.7055, 'logloss': 0.6147, 'mean_mrr': 0.6197, 'ndcg2': 0.7146, 'ndcg4': 0.7146, 'ndcg6': 0.7146, 'ndcg8': 0.7146, 'ndcg10': 0.7146, 'group_auc': 0.699}), (7, {'auc': 0.7205, 'logloss': 0.6434, 'mean_mrr': 0.6345, 'ndcg2': 0.7257, 'ndcg4': 0.7257, 'ndcg6': 0.7257, 'ndcg8': 0.7257, 'ndcg10': 0.7257, 'group_auc': 0.7092}), (8, {'auc': 0.7234, 'logloss': 0.6514, 'mean_mrr': 0.6413, 'ndcg2': 0.7308, 'ndcg4': 0.7308, 'ndcg6': 0.7308, 'ndcg8': 0.7308, 'ndcg10': 0.7308, 'group_auc': 0.715}), (9, {'auc': 0.7285, 'logloss': 0.6794, 'mean_mrr': 0.639, 'ndcg2': 0.7292, 'ndcg4': 0.7292, 'ndcg6': 0.7292, 'ndcg8': 0.7292, 'ndcg10': 0.7292, 'group_auc': 0.7152}), (10, {'auc': 0.7317, 'logloss': 0.6242, 'mean_mrr': 0.6454, 'ndcg2': 0.7339, 'ndcg4': 0.7339, 'ndcg6': 0.7339, 'ndcg8': 0.7339, 'ndcg10': 0.7339, 'group_auc': 0.7181})]\n", + "WARNING:tensorflow:From ../../reco_utils/recommender/deeprec/models/sequential/sequential_base_model.py:105: The name tf.summary.FileWriter is deprecated. Please use tf.compat.v1.summary.FileWriter instead.\n", + "\n", + "step 20 , total_loss: 1.6122, data_loss: 1.6122\n", + "eval valid at epoch 1: auc:0.5088,logloss:0.6931,mean_mrr:0.4623,ndcg@2:0.3321,ndcg@4:0.5188,ndcg@6:0.594,group_auc:0.5071\n", + "step 20 , total_loss: 1.5740, data_loss: 1.5740\n", + "eval valid at epoch 2: auc:0.5709,logloss:0.6965,mean_mrr:0.5217,ndcg@2:0.4216,ndcg@4:0.5878,ndcg@6:0.6399,group_auc:0.5876\n", + "step 20 , total_loss: 1.4441, data_loss: 1.4441\n", + "eval valid at epoch 3: auc:0.6733,logloss:0.8291,mean_mrr:0.5962,ndcg@2:0.524,ndcg@4:0.6598,ndcg@6:0.6965,group_auc:0.6672\n", + "step 20 , total_loss: 1.3054, data_loss: 1.3054\n", + "eval valid at epoch 4: auc:0.7021,logloss:0.7882,mean_mrr:0.6362,ndcg@2:0.5794,ndcg@4:0.6974,ndcg@6:0.7269,group_auc:0.7085\n", + "step 20 , total_loss: 1.3181, data_loss: 1.3181\n", + "eval valid at epoch 5: auc:0.7119,logloss:0.6315,mean_mrr:0.6302,ndcg@2:0.5733,ndcg@4:0.6956,ndcg@6:0.7227,group_auc:0.71\n", + "step 20 , total_loss: 1.1659, data_loss: 1.1659\n", + "eval valid at epoch 6: auc:0.7277,logloss:0.6974,mean_mrr:0.6609,ndcg@2:0.6056,ndcg@4:0.7205,ndcg@6:0.7456,group_auc:0.7319\n", + "step 20 , total_loss: 1.1980, data_loss: 1.1980\n", + "eval valid at epoch 7: auc:0.7331,logloss:0.6623,mean_mrr:0.6615,ndcg@2:0.609,ndcg@4:0.7221,ndcg@6:0.7462,group_auc:0.7349\n", + "step 20 , total_loss: 1.2404, data_loss: 1.2404\n", + "eval valid at epoch 8: auc:0.7363,logloss:0.68,mean_mrr:0.6687,ndcg@2:0.6178,ndcg@4:0.7274,ndcg@6:0.7516,group_auc:0.7401\n", + "step 20 , total_loss: 1.1585, data_loss: 1.1585\n", + "eval valid at epoch 9: auc:0.7442,logloss:0.6297,mean_mrr:0.6706,ndcg@2:0.6201,ndcg@4:0.7275,ndcg@6:0.7529,group_auc:0.7396\n", + "step 20 , total_loss: 1.0657, data_loss: 1.0657\n", + "eval valid at epoch 10: auc:0.7402,logloss:0.6048,mean_mrr:0.6683,ndcg@2:0.6153,ndcg@4:0.7289,ndcg@6:0.7512,group_auc:0.7406\n", + "[(1, {'auc': 0.5088, 'logloss': 0.6931, 'mean_mrr': 0.4623, 'ndcg@2': 0.3321, 'ndcg@4': 0.5188, 'ndcg@6': 0.594, 'group_auc': 0.5071}), (2, {'auc': 0.5709, 'logloss': 0.6965, 'mean_mrr': 0.5217, 'ndcg@2': 0.4216, 'ndcg@4': 0.5878, 'ndcg@6': 0.6399, 'group_auc': 0.5876}), (3, {'auc': 0.6733, 'logloss': 0.8291, 'mean_mrr': 0.5962, 'ndcg@2': 0.524, 'ndcg@4': 0.6598, 'ndcg@6': 0.6965, 'group_auc': 0.6672}), (4, {'auc': 0.7021, 'logloss': 0.7882, 'mean_mrr': 0.6362, 'ndcg@2': 0.5794, 'ndcg@4': 0.6974, 'ndcg@6': 0.7269, 'group_auc': 0.7085}), (5, {'auc': 0.7119, 'logloss': 0.6315, 'mean_mrr': 0.6302, 'ndcg@2': 0.5733, 'ndcg@4': 0.6956, 'ndcg@6': 0.7227, 'group_auc': 0.71}), (6, {'auc': 0.7277, 'logloss': 0.6974, 'mean_mrr': 0.6609, 'ndcg@2': 0.6056, 'ndcg@4': 0.7205, 'ndcg@6': 0.7456, 'group_auc': 0.7319}), (7, {'auc': 0.7331, 'logloss': 0.6623, 'mean_mrr': 0.6615, 'ndcg@2': 0.609, 'ndcg@4': 0.7221, 'ndcg@6': 0.7462, 'group_auc': 0.7349}), (8, {'auc': 0.7363, 'logloss': 0.68, 'mean_mrr': 0.6687, 'ndcg@2': 0.6178, 'ndcg@4': 0.7274, 'ndcg@6': 0.7516, 'group_auc': 0.7401}), (9, {'auc': 0.7442, 'logloss': 0.6297, 'mean_mrr': 0.6706, 'ndcg@2': 0.6201, 'ndcg@4': 0.7275, 'ndcg@6': 0.7529, 'group_auc': 0.7396}), (10, {'auc': 0.7402, 'logloss': 0.6048, 'mean_mrr': 0.6683, 'ndcg@2': 0.6153, 'ndcg@4': 0.7289, 'ndcg@6': 0.7512, 'group_auc': 0.7406})]\n", "best epoch: 10\n", - "Time cost for training is 9.53 mins\n" + "Time cost for training is 2.68 mins\n" ] } ], @@ -376,29 +408,35 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "{'auc': 0.7111, 'logloss': 0.6447, 'mean_mrr': 0.4673, 'ndcg2': 0.5934, 'ndcg4': 0.5934, 'ndcg6': 0.5934, 'ndcg8': 0.5934, 'ndcg10': 0.5934, 'group_auc': 0.698}\n" + "{'auc': 0.7099, 'logloss': 0.6063, 'mean_mrr': 0.4902, 'ndcg@2': 0.4025, 'ndcg@4': 0.5056, 'ndcg@6': 0.5555, 'group_auc': 0.709}\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/v-xdeng/.conda/envs/reco_tf15/lib/python3.6/site-packages/ipykernel_launcher.py:3: DeprecationWarning: Function record is deprecated and will be removed in verison 1.0.0 (current version 0.19.1). Please see `scrapbook.glue` (nteract-scrapbook) as a replacement for this functionality.\n", + " This is separate from the ipykernel package so we can avoid doing imports until\n" ] }, { "data": { "application/papermill.record+json": { "res_syn": { - "auc": 0.7111, - "group_auc": 0.698, - "logloss": 0.6447, - "mean_mrr": 0.4673, - "ndcg10": 0.5934, - "ndcg2": 0.5934, - "ndcg4": 0.5934, - "ndcg6": 0.5934, - "ndcg8": 0.5934 + "auc": 0.7099, + "group_auc": 0.709, + "logloss": 0.6063, + "mean_mrr": 0.4902, + "ndcg@2": 0.4025, + "ndcg@4": 0.5056, + "ndcg@6": 0.5555 } } }, @@ -421,16 +459,25 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:From ../../reco_utils/recommender/deeprec/models/sequential/sequential_base_model.py:231: The name tf.gfile.GFile is deprecated. Please use tf.io.gfile.GFile instead.\n", + "\n" + ] + } + ], "source": [ "model = model.predict(test_file, output_file)" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -469,30 +516,20659 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Reference\n", - "\\[1\\] Zeping Yu, Jianxun Lian, Ahmad Mahmoody, Gongshen Liu, Xing Xie. Adaptive User Modeling with Long and Short-Term Preferences for Personailzed Recommendation. In Proceedings of the 28th International Joint Conferences on Artificial Intelligence, IJCAI’19, Pages 4213-4219. AAAI Press, 2019.\n", + "## 3. Online serving\n", + "In this section, we provide a simple example to illustrate how we can use the trained model to serve for production demand.\n", "\n", - "\\[2\\] Balázs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, Domonkos Tikk. Session-based Recommendations with Recurrent Neural Networks. ICLR (Poster) 2016\n", + "Suppose we are in a new session. First let's load a previous trained model:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "loading saved model in ../../tests/resources/deeprec/slirec/model/best_model\n", + "INFO:tensorflow:Restoring parameters from ../../tests/resources/deeprec/slirec/model/best_model\n" + ] + } + ], + "source": [ + "model_best_trained = SeqModel(hparams, input_creator, seed=RANDOM_SEED)\n", + "path_best_trained = os.path.join(hparams.MODEL_DIR, \"best_model\")\n", + "print('loading saved model in {0}'.format(path_best_trained))\n", + "model_best_trained.load_model(path_best_trained)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's see if we load the model correctly. The testing metrics should be close to the numbers we have in the training stage." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'auc': 0.7099,\n", + " 'logloss': 0.6063,\n", + " 'mean_mrr': 0.4902,\n", + " 'ndcg@2': 0.4025,\n", + " 'ndcg@4': 0.5056,\n", + " 'ndcg@6': 0.5555,\n", + " 'group_auc': 0.709}" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model_best_trained.run_eval(test_file, num_ngs=test_num_ngs)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And we make predictions using this model. In the next step, we will make predictions using a serving model. Then we can check if the two result files are consistent." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model_best_trained.predict(test_file, output_file)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For online serving, usually we only keep the necessary computation nodes and froze the TF model to ab sinle pb file, so that we can easily compute scores with both Python or Java:" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:From :6: convert_variables_to_constants (from tensorflow.python.framework.graph_util_impl) is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "Use `tf.compat.v1.graph_util.convert_variables_to_constants`\n", + "WARNING:tensorflow:From /home/v-xdeng/.conda/envs/reco_tf15/lib/python3.6/site-packages/tensorflow_core/python/framework/graph_util_impl.py:277: extract_sub_graph (from tensorflow.python.framework.graph_util_impl) is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "Use `tf.compat.v1.graph_util.extract_sub_graph`\n", + "INFO:tensorflow:Froze 61 variables.\n", + "INFO:tensorflow:Converted 61 variables to const ops.\n" + ] + } + ], + "source": [ "\n", - "\\[3\\] Tang, Jiaxi, and Ke Wang. Personalized top-n sequential recommendation via convolutional sequence embedding. Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining. ACM, 2018.\n", + "with model_best_trained.sess as sess:\n", + " graph_def = model_best_trained.graph.as_graph_def()\n", + " output_graph_def = tf.graph_util.convert_variables_to_constants(\n", + " sess,\n", + " graph_def,\n", + " [\"pred\"]\n", + " )\n", "\n", - "\\[4\\] Yuan, F., Karatzoglou, A., Arapakis, I., Jose, J. M., & He, X. A Simple Convolutional Generative Network for Next Item Recommendation. WSDM, 2019" + " outfilepath = os.path.join(hparams.MODEL_DIR, \"serving_model.pb\")\n", + " with tf.gfile.GFile(outfilepath, 'wb') as f:\n", + " f.write(output_graph_def.SerializeToString())\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The serving logic is as simple as feeding the feature values to the corresponding input nodes, and fetch the score from the output node. \n", + "\n", + "In our model, input nodes are some placeholders and control variables (such as is_training, layer_keeps). We can get the nodes by their name:" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "class LoadFrozedPredModel:\n", + " def __init__(self, graph):\n", + " self.pred = graph.get_tensor_by_name('import/pred:0') \n", + " self.items = graph.get_tensor_by_name('import/items:0') \n", + " self.cates = graph.get_tensor_by_name('import/cates:0') \n", + " self.item_history = graph.get_tensor_by_name('import/item_history:0') \n", + " self.item_cate_history = graph.get_tensor_by_name('import/item_cate_history:0') \n", + " self.mask = graph.get_tensor_by_name('import/mask:0') \n", + " self.time_from_first_action = graph.get_tensor_by_name('import/time_from_first_action:0') \n", + " self.time_to_now = graph.get_tensor_by_name('import/time_to_now:0') \n", + " self.layer_keeps = graph.get_tensor_by_name('import/layer_keeps:0') \n", + " self.is_training = graph.get_tensor_by_name('import/is_training:0') \n" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "def infer_as_serving(model, infile, outfile, hparams, iterator, sess):\n", + " preds = []\n", + " \n", + " for batch_data_input in iterator.load_data_from_file(infile, batch_num_ngs=0):\n", + " if batch_data_input:\n", + " feed_dict = {\n", + " model.layer_keeps:np.ones(3, dtype=np.float32),\n", + " model.is_training:False,\n", + " model.items: batch_data_input[iterator.items],\n", + " model.cates: batch_data_input[iterator.cates],\n", + " model.item_history: batch_data_input[iterator.item_history],\n", + " model.item_cate_history: batch_data_input[iterator.item_cate_history],\n", + " model.mask: batch_data_input[iterator.mask],\n", + " model.time_from_first_action: batch_data_input[iterator.time_from_first_action],\n", + " model.time_to_now: batch_data_input[iterator.time_to_now]\n", + " }\n", + " step_pred = sess.run(model.pred, feed_dict=feed_dict)\n", + " preds.extend(np.reshape(step_pred, -1))\n", + " \n", + " with open(outfile, \"w\") as wt:\n", + " for line in preds:\n", + " wt.write('{0}\\n'.format(line))\n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here is the main pipeline for inferring in an online serving manner. You can compare the 'output_serving.txt' with 'output.txt' to see if the results are consistent." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "graph_def_optimized = node {\n", + " name: \"items\"\n", + " op: \"Placeholder\"\n", + " attr {\n", + " key: \"dtype\"\n", + " value {\n", + " type: DT_INT32\n", + " }\n", + " }\n", + " attr {\n", + " key: \"shape\"\n", + " value {\n", + " shape {\n", + " dim {\n", + " size: -1\n", + " }\n", + " }\n", + " }\n", + " }\n", + "}\n", + "node {\n", + " name: \"cates\"\n", + " op: \"Placeholder\"\n", + " attr {\n", + " key: \"dtype\"\n", + " value {\n", + " type: DT_INT32\n", + " }\n", + " }\n", + " attr {\n", + " key: \"shape\"\n", + " value {\n", + " shape {\n", + " dim {\n", + " size: -1\n", + " }\n", + " }\n", + " }\n", + " }\n", + "}\n", + "node {\n", + " name: \"item_history\"\n", + " op: \"Placeholder\"\n", + " attr {\n", + " key: \"dtype\"\n", + " value {\n", + " type: DT_INT32\n", + " }\n", + " }\n", + " attr {\n", + " key: \"shape\"\n", + " value {\n", + " shape {\n", + " dim {\n", + " size: -1\n", + " }\n", + " dim {\n", + " size: 50\n", + " }\n", + " }\n", + " }\n", + " }\n", + "}\n", + "node {\n", + " name: \"item_cate_history\"\n", + " op: \"Placeholder\"\n", + " attr {\n", + " key: \"dtype\"\n", + " value {\n", + " type: DT_INT32\n", + " }\n", + " }\n", + " attr {\n", + " key: \"shape\"\n", + " value {\n", + " shape {\n", + " dim {\n", + " size: -1\n", + " }\n", + " dim {\n", + " size: 50\n", + " }\n", + " }\n", + " }\n", + " }\n", + "}\n", + "node {\n", + " name: \"mask\"\n", + " op: \"Placeholder\"\n", + " attr {\n", + " key: \"dtype\"\n", + " value {\n", + " type: DT_INT32\n", + " }\n", + " }\n", + " attr {\n", + " key: \"shape\"\n", + " value {\n", + " shape {\n", + " dim {\n", + " size: -1\n", + " }\n", + " dim {\n", + " size: 50\n", + " }\n", + " }\n", + " }\n", + " }\n", + "}\n", + "node {\n", + " 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type: DT_FLOAT\n", + " }\n", + " }\n", + " attr {\n", + " key: \"Tshape\"\n", + " value {\n", + " type: DT_INT32\n", + " }\n", + " }\n", + "}\n", + "node {\n", + " name: \"sequential/logit_fcn/nn_part/add_2\"\n", + " op: \"AddV2\"\n", + " input: \"sequential/logit_fcn/nn_part/Tensordot_2\"\n", + " input: \"sequential/logit_fcn/nn_part/b_nn_output/read\"\n", + " attr {\n", + " key: \"T\"\n", + " value {\n", + " type: DT_FLOAT\n", + " }\n", + " }\n", + "}\n", + "node {\n", + " name: \"Sigmoid\"\n", + " op: \"Sigmoid\"\n", + " input: \"sequential/logit_fcn/nn_part/add_2\"\n", + " attr {\n", + " key: \"T\"\n", + " value {\n", + " type: DT_FLOAT\n", + " }\n", + " }\n", + "}\n", + "node {\n", + " name: \"pred\"\n", + " op: \"Identity\"\n", + " input: \"Sigmoid\"\n", + " attr {\n", + " key: \"T\"\n", + " value {\n", + " type: DT_FLOAT\n", + " }\n", + " }\n", + "}\n", + "library {\n", + "}\n", + "\n" + ] + } + ], + "source": [ + "G = tf.Graph()\n", + "with tf.gfile.GFile(\n", + " os.path.join(hparams.MODEL_DIR, \"serving_model.pb\"),\n", + " 'rb'\n", + ") as f, G.as_default():\n", + " graph_def_optimized = tf.GraphDef()\n", + " graph_def_optimized.ParseFromString(f.read())\n", + " print('graph_def_optimized = ' + str(graph_def_optimized))\n", + "\n", + "\n", + "with tf.Session(graph=G) as sess:\n", + " tf.import_graph_def(graph_def_optimized)\n", + "\n", + " model = LoadFrozedPredModel(sess.graph)\n", + " \n", + " serving_output_file = os.path.join(data_path, r'output_serving.txt') \n", + " iterator = input_creator(hparams, tf.Graph())\n", + " infer_as_serving(model, test_file, serving_output_file, hparams, iterator, sess)\n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Reference\n", + "\\[1\\] Zeping Yu, Jianxun Lian, Ahmad Mahmoody, Gongshen Liu, Xing Xie. Adaptive User Modeling with Long and Short-Term Preferences for Personailzed Recommendation. In Proceedings of the 28th International Joint Conferences on Artificial Intelligence, IJCAI’19, Pages 4213-4219. AAAI Press, 2019.\n", + "\n", + "\\[2\\] Balázs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, Domonkos Tikk. Session-based Recommendations with Recurrent Neural Networks. ICLR (Poster) 2016\n", + "\n", + "\\[3\\] Tang, Jiaxi, and Ke Wang. Personalized top-n sequential recommendation via convolutional sequence embedding. Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining. ACM, 2018.\n", + "\n", + "\\[4\\] Yuan, F., Karatzoglou, A., Arapakis, I., Jose, J. M., & He, X. A Simple Convolutional Generative Network for Next Item Recommendation. WSDM, 2019" + ] } ], "metadata": { "celltoolbar": "Tags", "kernelspec": { - "display_name": "Python (reco)", + "display_name": "reco_tf15", "language": "python", - "name": "reco" + "name": "reco_tf15" }, "language_info": { "codemirror_mode": { @@ -509,4 +21185,4 @@ }, "nbformat": 4, "nbformat_minor": 2 -} \ No newline at end of file +} diff --git a/reco_utils/recommender/deeprec/deeprec_utils.py b/reco_utils/recommender/deeprec/deeprec_utils.py index 69c2f212ae..b5640ee5bd 100644 --- a/reco_utils/recommender/deeprec/deeprec_utils.py +++ b/reco_utils/recommender/deeprec/deeprec_utils.py @@ -438,7 +438,7 @@ def create_hparams(flags): need_sample=flags["need_sample"] if "need_sample" in flags else True, embedding_dropout=flags["embedding_dropout"] if "embedding_dropout" in flags - else 0.3, + else 0.0, user_vocab=flags["user_vocab"] if "user_vocab" in flags else None, item_vocab=flags["item_vocab"] if "item_vocab" in flags else None, cate_vocab=flags["cate_vocab"] if "cate_vocab" in flags else None, diff --git a/reco_utils/recommender/deeprec/models/base_model.py b/reco_utils/recommender/deeprec/models/base_model.py index ebfb8cc964..93652bf0fc 100644 --- a/reco_utils/recommender/deeprec/models/base_model.py +++ b/reco_utils/recommender/deeprec/models/base_model.py @@ -106,6 +106,7 @@ def _get_pred(self, logit, task): task ) ) + pred = tf.identity(pred, name='pred') return pred def _add_summaries(self): @@ -703,4 +704,4 @@ def _fcn_net(self, model_output, layer_sizes, scope): + b_nn_output ) self.logit = nn_output - return nn_output + return nn_output diff --git a/reco_utils/recommender/deeprec/models/sequential/caser.py b/reco_utils/recommender/deeprec/models/sequential/caser.py index d21e42e406..b253a7da78 100644 --- a/reco_utils/recommender/deeprec/models/sequential/caser.py +++ b/reco_utils/recommender/deeprec/models/sequential/caser.py @@ -17,7 +17,7 @@ class CaserModel(SequentialBaseModel): Web Search and Data Mining, ACM, 2018. """ - def __init__(self, hparams, iterator_creator): + def __init__(self, hparams, iterator_creator, seed=None): """Initialization of variables for caser Args: @@ -32,7 +32,7 @@ def __init__(self, hparams, iterator_creator): self.lengths = [ i + 1 for i in range(self.L) ] # horizonal convolution filter shape - super().__init__(hparams, iterator_creator) + super().__init__(hparams, iterator_creator, seed=seed) def _build_seq_graph(self): """The main function to create caser model. diff --git a/reco_utils/recommender/deeprec/models/sequential/sequential_base_model.py b/reco_utils/recommender/deeprec/models/sequential/sequential_base_model.py index 5caebf3186..d1d094df9e 100644 --- a/reco_utils/recommender/deeprec/models/sequential/sequential_base_model.py +++ b/reco_utils/recommender/deeprec/models/sequential/sequential_base_model.py @@ -169,6 +169,10 @@ def fit( sess=train_sess, save_path=self.hparams.MODEL_DIR + "epoch_" + str(epoch), ) + checkpoint_path = self.saver.save( + sess=train_sess, + save_path=os.path.join(self.hparams.MODEL_DIR, "best_model") + ) if self.hparams.write_tfevents: self.writer.close() @@ -321,19 +325,6 @@ def _lookup_from_embedding(self): ) tf.summary.histogram("target_item_embedding_output", self.target_item_embedding) - # dropout after embedding - self.user_embedding = self._dropout( - self.user_embedding, keep_prob=1 - self.hparams.embedding_dropout - ) - self.item_history_embedding = self._dropout( - self.item_history_embedding, keep_prob=1 - self.hparams.embedding_dropout - ) - self.cate_history_embedding = self._dropout( - self.cate_history_embedding, keep_prob=1 - self.hparams.embedding_dropout - ) - self.target_item_embedding = self._dropout( - self.target_item_embedding, keep_prob=1 - self.hparams.embedding_dropout - ) def _add_norm(self): """Regularization for embedding variables and other variables.""" From b21b7231e3b37b503cf394edbae8eb715405b3f8 Mon Sep 17 00:00:00 2001 From: leavingseason Date: Mon, 30 Nov 2020 07:26:49 +0000 Subject: [PATCH 2/5] update notebook --- .../00_quick_start/sequential_recsys_amazondataset.ipynb | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/examples/00_quick_start/sequential_recsys_amazondataset.ipynb b/examples/00_quick_start/sequential_recsys_amazondataset.ipynb index dad82f6581..3dbcf31a27 100644 --- a/examples/00_quick_start/sequential_recsys_amazondataset.ipynb +++ b/examples/00_quick_start/sequential_recsys_amazondataset.ipynb @@ -607,7 +607,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "For online serving, usually we only keep the necessary computation nodes and froze the TF model to ab sinle pb file, so that we can easily compute scores with both Python or Java:" + "Exciting. Now let's start our quick journey of online serving. \n", + "\n", + "For efficient and flexible serving, usually we only keep the necessary computation nodes and froze the TF model to a single pb file, so that we can easily compute scores with this unified pb file in both Python or Java:" ] }, { @@ -710,7 +712,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Here is the main pipeline for inferring in an online serving manner. You can compare the 'output_serving.txt' with 'output.txt' to see if the results are consistent." + "Here is the main pipeline for inferring in an online serving manner. You can compare the 'output_serving.txt' with 'output.txt' to see if the results are consistent.\n", + "\n", + "The input file format is the same as introduced in Section 1 'Input data format'. In serving stage, since we do not need a groundtrue lable, so for the label column, you can simply place any number like a zero. The iterator will parse the input file and convert into the required format for model's feed_dictionary. " ] }, { From 08fffb2d34dad4410e3688badfa7c4106ac78d1a Mon Sep 17 00:00:00 2001 From: leavingseason Date: Mon, 30 Nov 2020 07:57:51 +0000 Subject: [PATCH 3/5] update notebook --- .../sequential_recsys_amazondataset.ipynb | 27 +++++++------------ 1 file changed, 9 insertions(+), 18 deletions(-) diff --git a/examples/00_quick_start/sequential_recsys_amazondataset.ipynb b/examples/00_quick_start/sequential_recsys_amazondataset.ipynb index 3dbcf31a27..b9842b0ae0 100644 --- a/examples/00_quick_start/sequential_recsys_amazondataset.ipynb +++ b/examples/00_quick_start/sequential_recsys_amazondataset.ipynb @@ -25,7 +25,9 @@ "* It takes both time irregularity and semantic irregularity into consideration by modifying the gating logic in LSTM.\n", "* It uses an attention mechanism to dynamic fuse the long-term component and short-term component.\n", "\n", - "In this notebook, we test SLi_Rec on a subset of the public dataset: [Amazon_reviews](http://snap.stanford.edu/data/amazon/productGraph/categoryFiles/reviews_Movies_and_TV_5.json.gz) and [Amazon_metadata](http://snap.stanford.edu/data/amazon/productGraph/categoryFiles/meta_Movies_and_TV.json.gz)" + "In this notebook, we test SLi_Rec on a subset of the public dataset: [Amazon_reviews](http://snap.stanford.edu/data/amazon/productGraph/categoryFiles/reviews_Movies_and_TV_5.json.gz) and [Amazon_metadata](http://snap.stanford.edu/data/amazon/productGraph/categoryFiles/meta_Movies_and_TV.json.gz)\n", + "\n", + "This notebook is well tested under TF 1.15.0. " ] }, { @@ -131,6 +133,8 @@ "\n", "`1 A1QQ86H5M2LVW2 B0059XTU1S Movies 1377561600 B002ZG97WE,B004IK30PA,B000BNX3AU,B0017ANB08,B005LAIHW2 Movies,Movies,Movies,Movies,Movies 1304294400,1304812800,1315785600,1316304000,1356998400` \n", "\n", + "In data preprocessing stage, we have a script to generate some ID mapping dictionaries, so user_id, item_id and category_id will be mapped into interager index starting from 1. And you need to tell the input iterator where is the ID mapping files are. (For example, in the next section, we have some mapping files like user_vocab, item_vocab, and cate_vocab). The data preprocessing script is at https://github.com/microsoft/recommenders/blob/master/reco_utils/dataset/amazon_reviews.py, you need to call the `_create_vocab(train_file, user_vocab, item_vocab, cate_vocab)` function. Note that ID vocabulary only creates from the train_file, so the new IDs in valid_file or test_file will be regarded as unknown IDs and assigned with a defualt 0 index.\n", + "\n", "Only the SLi_Rec model is time-aware. For the other models, you can just pad some meaningless timestamp in the data files to fill up the format, the models will ignore these columns.\n", "\n", "We use Softmax to the loss function. In training and evalution stage, we group 1 positive instance with num_ngs negative instances. Pair-wise ranking can be regarded as a special case of Softmax ranking, where num_ngs is set to 1. \n", @@ -197,27 +201,14 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 22, "metadata": { "scrolled": true }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:\n", - "The TensorFlow contrib module will not be included in TensorFlow 2.0.\n", - "For more information, please see:\n", - " * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n", - " * https://github.com/tensorflow/addons\n", - " * https://github.com/tensorflow/io (for I/O related ops)\n", - "If you depend on functionality not listed there, please file an issue.\n", - "\n" - ] - } - ], + "outputs": [], "source": [ + "### NOTE: \n", + "### remember to use `_create_vocab(train_file, user_vocab, item_vocab, cate_vocab)` to generate the user_vocab, item_vocab and cate_vocab files, if you are using your own dataset rather than using our demo Amazon dataset.\n", "hparams = prepare_hparams(yaml_file, \n", " embed_l2=0., \n", " layer_l2=0., \n", From 13c47c80fc3f7c94cc0d1d581ea09e1c773da945 Mon Sep 17 00:00:00 2001 From: leavingseason Date: Tue, 1 Dec 2020 03:13:06 +0000 Subject: [PATCH 4/5] update notebook --- examples/00_quick_start/sequential_recsys_amazondataset.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/00_quick_start/sequential_recsys_amazondataset.ipynb b/examples/00_quick_start/sequential_recsys_amazondataset.ipynb index b9842b0ae0..88f49371f6 100644 --- a/examples/00_quick_start/sequential_recsys_amazondataset.ipynb +++ b/examples/00_quick_start/sequential_recsys_amazondataset.ipynb @@ -201,7 +201,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 23, "metadata": { "scrolled": true }, From 61818c0f5e46c2a4a98fa686b60acb2e4963aee0 Mon Sep 17 00:00:00 2001 From: leavingseason Date: Wed, 2 Dec 2020 05:56:09 +0000 Subject: [PATCH 5/5] update notebook --- .../sequential_recsys_amazondataset.ipynb | 20518 +--------------- 1 file changed, 65 insertions(+), 20453 deletions(-) diff --git a/examples/00_quick_start/sequential_recsys_amazondataset.ipynb b/examples/00_quick_start/sequential_recsys_amazondataset.ipynb index 88f49371f6..dfe9e57c70 100644 --- a/examples/00_quick_start/sequential_recsys_amazondataset.ipynb +++ b/examples/00_quick_start/sequential_recsys_amazondataset.ipynb @@ -201,11 +201,26 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 5, "metadata": { "scrolled": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:\n", + "The TensorFlow contrib module will not be included in TensorFlow 2.0.\n", + "For more information, please see:\n", + " * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n", + " * https://github.com/tensorflow/addons\n", + " * https://github.com/tensorflow/io (for I/O related ops)\n", + "If you depend on functionality not listed there, please file an issue.\n", + "\n" + ] + } + ], "source": [ "### NOTE: \n", "### remember to use `_create_vocab(train_file, user_vocab, item_vocab, cate_vocab)` to generate the user_vocab, item_vocab and cate_vocab files, if you are using your own dataset rather than using our demo Amazon dataset.\n", @@ -322,7 +337,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "{'auc': 0.5137, 'logloss': 0.6931, 'mean_mrr': 0.2893, 'ndcg@2': 0.161, 'ndcg@4': 0.248, 'ndcg@6': 0.3228, 'group_auc': 0.514}\n" + "{'auc': 0.5131, 'logloss': 0.6931, 'mean_mrr': 0.289, 'ndcg@2': 0.1609, 'ndcg@4': 0.2475, 'ndcg@6': 0.3219, 'group_auc': 0.5134}\n" ] } ], @@ -353,29 +368,32 @@ "text": [ "WARNING:tensorflow:From ../../reco_utils/recommender/deeprec/models/sequential/sequential_base_model.py:105: The name tf.summary.FileWriter is deprecated. Please use tf.compat.v1.summary.FileWriter instead.\n", "\n", - "step 20 , total_loss: 1.6122, data_loss: 1.6122\n", - "eval valid at epoch 1: auc:0.5088,logloss:0.6931,mean_mrr:0.4623,ndcg@2:0.3321,ndcg@4:0.5188,ndcg@6:0.594,group_auc:0.5071\n", - "step 20 , total_loss: 1.5740, data_loss: 1.5740\n", - "eval valid at epoch 2: auc:0.5709,logloss:0.6965,mean_mrr:0.5217,ndcg@2:0.4216,ndcg@4:0.5878,ndcg@6:0.6399,group_auc:0.5876\n", - "step 20 , total_loss: 1.4441, data_loss: 1.4441\n", - "eval valid at epoch 3: auc:0.6733,logloss:0.8291,mean_mrr:0.5962,ndcg@2:0.524,ndcg@4:0.6598,ndcg@6:0.6965,group_auc:0.6672\n", - "step 20 , total_loss: 1.3054, data_loss: 1.3054\n", - "eval valid at epoch 4: auc:0.7021,logloss:0.7882,mean_mrr:0.6362,ndcg@2:0.5794,ndcg@4:0.6974,ndcg@6:0.7269,group_auc:0.7085\n", - "step 20 , total_loss: 1.3181, data_loss: 1.3181\n", - "eval valid at epoch 5: auc:0.7119,logloss:0.6315,mean_mrr:0.6302,ndcg@2:0.5733,ndcg@4:0.6956,ndcg@6:0.7227,group_auc:0.71\n", - "step 20 , total_loss: 1.1659, data_loss: 1.1659\n", - "eval valid at epoch 6: auc:0.7277,logloss:0.6974,mean_mrr:0.6609,ndcg@2:0.6056,ndcg@4:0.7205,ndcg@6:0.7456,group_auc:0.7319\n", - "step 20 , total_loss: 1.1980, data_loss: 1.1980\n", - "eval valid at epoch 7: auc:0.7331,logloss:0.6623,mean_mrr:0.6615,ndcg@2:0.609,ndcg@4:0.7221,ndcg@6:0.7462,group_auc:0.7349\n", - "step 20 , total_loss: 1.2404, data_loss: 1.2404\n", - "eval valid at epoch 8: auc:0.7363,logloss:0.68,mean_mrr:0.6687,ndcg@2:0.6178,ndcg@4:0.7274,ndcg@6:0.7516,group_auc:0.7401\n", - "step 20 , total_loss: 1.1585, data_loss: 1.1585\n", - "eval valid at epoch 9: auc:0.7442,logloss:0.6297,mean_mrr:0.6706,ndcg@2:0.6201,ndcg@4:0.7275,ndcg@6:0.7529,group_auc:0.7396\n", - "step 20 , total_loss: 1.0657, data_loss: 1.0657\n", - "eval valid at epoch 10: auc:0.7402,logloss:0.6048,mean_mrr:0.6683,ndcg@2:0.6153,ndcg@4:0.7289,ndcg@6:0.7512,group_auc:0.7406\n", - "[(1, {'auc': 0.5088, 'logloss': 0.6931, 'mean_mrr': 0.4623, 'ndcg@2': 0.3321, 'ndcg@4': 0.5188, 'ndcg@6': 0.594, 'group_auc': 0.5071}), (2, {'auc': 0.5709, 'logloss': 0.6965, 'mean_mrr': 0.5217, 'ndcg@2': 0.4216, 'ndcg@4': 0.5878, 'ndcg@6': 0.6399, 'group_auc': 0.5876}), (3, {'auc': 0.6733, 'logloss': 0.8291, 'mean_mrr': 0.5962, 'ndcg@2': 0.524, 'ndcg@4': 0.6598, 'ndcg@6': 0.6965, 'group_auc': 0.6672}), (4, {'auc': 0.7021, 'logloss': 0.7882, 'mean_mrr': 0.6362, 'ndcg@2': 0.5794, 'ndcg@4': 0.6974, 'ndcg@6': 0.7269, 'group_auc': 0.7085}), (5, {'auc': 0.7119, 'logloss': 0.6315, 'mean_mrr': 0.6302, 'ndcg@2': 0.5733, 'ndcg@4': 0.6956, 'ndcg@6': 0.7227, 'group_auc': 0.71}), (6, {'auc': 0.7277, 'logloss': 0.6974, 'mean_mrr': 0.6609, 'ndcg@2': 0.6056, 'ndcg@4': 0.7205, 'ndcg@6': 0.7456, 'group_auc': 0.7319}), (7, {'auc': 0.7331, 'logloss': 0.6623, 'mean_mrr': 0.6615, 'ndcg@2': 0.609, 'ndcg@4': 0.7221, 'ndcg@6': 0.7462, 'group_auc': 0.7349}), (8, {'auc': 0.7363, 'logloss': 0.68, 'mean_mrr': 0.6687, 'ndcg@2': 0.6178, 'ndcg@4': 0.7274, 'ndcg@6': 0.7516, 'group_auc': 0.7401}), (9, {'auc': 0.7442, 'logloss': 0.6297, 'mean_mrr': 0.6706, 'ndcg@2': 0.6201, 'ndcg@4': 0.7275, 'ndcg@6': 0.7529, 'group_auc': 0.7396}), (10, {'auc': 0.7402, 'logloss': 0.6048, 'mean_mrr': 0.6683, 'ndcg@2': 0.6153, 'ndcg@4': 0.7289, 'ndcg@6': 0.7512, 'group_auc': 0.7406})]\n", + "step 20 , total_loss: 1.6105, data_loss: 1.6105\n", + "eval valid at epoch 1: auc:0.4977,logloss:0.6933,mean_mrr:0.4526,ndcg@2:0.3198,ndcg@4:0.51,ndcg@6:0.5866,group_auc:0.4972\n", + "step 20 , total_loss: 1.5950, data_loss: 1.5950\n", + "eval valid at epoch 2: auc:0.5648,logloss:0.7007,mean_mrr:0.4957,ndcg@2:0.3825,ndcg@4:0.553,ndcg@6:0.6197,group_auc:0.5484\n", + "step 20 , total_loss: 1.4578, data_loss: 1.4578\n", + "eval valid at epoch 3: auc:0.6493,logloss:0.816,mean_mrr:0.5831,ndcg@2:0.507,ndcg@4:0.6476,ndcg@6:0.6866,group_auc:0.6532\n", + "step 20 , total_loss: 1.2790, data_loss: 1.2790\n", + "eval valid at epoch 4: auc:0.7018,logloss:0.7818,mean_mrr:0.6176,ndcg@2:0.5572,ndcg@4:0.6838,ndcg@6:0.7131,group_auc:0.6969\n", + "step 20 , total_loss: 1.3249, data_loss: 1.3249\n", + "eval valid at epoch 5: auc:0.7208,logloss:0.6877,mean_mrr:0.6466,ndcg@2:0.5921,ndcg@4:0.7101,ndcg@6:0.7349,group_auc:0.722\n", + "step 20 , total_loss: 1.2396, data_loss: 1.2396\n", + "eval valid at epoch 6: auc:0.7336,logloss:0.6063,mean_mrr:0.6554,ndcg@2:0.6022,ndcg@4:0.7173,ndcg@6:0.7416,group_auc:0.7298\n", + "step 20 , total_loss: 1.1432, data_loss: 1.1432\n", + "eval valid at epoch 7: auc:0.7408,logloss:0.611,mean_mrr:0.6659,ndcg@2:0.614,ndcg@4:0.7267,ndcg@6:0.7494,group_auc:0.7383\n", + "step 20 , total_loss: 1.1373, data_loss: 1.1373\n", + "eval valid at epoch 8: auc:0.7454,logloss:0.6499,mean_mrr:0.6721,ndcg@2:0.6216,ndcg@4:0.7334,ndcg@6:0.7541,group_auc:0.7445\n", + "step 20 , total_loss: 1.1958, data_loss: 1.1958\n", + "eval valid at epoch 9: auc:0.7536,logloss:0.5951,mean_mrr:0.6715,ndcg@2:0.6222,ndcg@4:0.7323,ndcg@6:0.7537,group_auc:0.7454\n", + "step 20 , total_loss: 1.1403, data_loss: 1.1403\n", + "eval valid at epoch 10: auc:0.7553,logloss:0.5822,mean_mrr:0.6753,ndcg@2:0.6254,ndcg@4:0.7357,ndcg@6:0.7566,group_auc:0.7486\n", + "WARNING:tensorflow:From /home/v-xdeng/.conda/envs/reco_tf15/lib/python3.6/site-packages/tensorflow_core/python/training/saver.py:963: remove_checkpoint (from tensorflow.python.training.checkpoint_management) is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "Use standard file APIs to delete files with this prefix.\n", + "[(1, {'auc': 0.4977, 'logloss': 0.6933, 'mean_mrr': 0.4526, 'ndcg@2': 0.3198, 'ndcg@4': 0.51, 'ndcg@6': 0.5866, 'group_auc': 0.4972}), (2, {'auc': 0.5648, 'logloss': 0.7007, 'mean_mrr': 0.4957, 'ndcg@2': 0.3825, 'ndcg@4': 0.553, 'ndcg@6': 0.6197, 'group_auc': 0.5484}), (3, {'auc': 0.6493, 'logloss': 0.816, 'mean_mrr': 0.5831, 'ndcg@2': 0.507, 'ndcg@4': 0.6476, 'ndcg@6': 0.6866, 'group_auc': 0.6532}), (4, {'auc': 0.7018, 'logloss': 0.7818, 'mean_mrr': 0.6176, 'ndcg@2': 0.5572, 'ndcg@4': 0.6838, 'ndcg@6': 0.7131, 'group_auc': 0.6969}), (5, {'auc': 0.7208, 'logloss': 0.6877, 'mean_mrr': 0.6466, 'ndcg@2': 0.5921, 'ndcg@4': 0.7101, 'ndcg@6': 0.7349, 'group_auc': 0.722}), (6, {'auc': 0.7336, 'logloss': 0.6063, 'mean_mrr': 0.6554, 'ndcg@2': 0.6022, 'ndcg@4': 0.7173, 'ndcg@6': 0.7416, 'group_auc': 0.7298}), (7, {'auc': 0.7408, 'logloss': 0.611, 'mean_mrr': 0.6659, 'ndcg@2': 0.614, 'ndcg@4': 0.7267, 'ndcg@6': 0.7494, 'group_auc': 0.7383}), (8, {'auc': 0.7454, 'logloss': 0.6499, 'mean_mrr': 0.6721, 'ndcg@2': 0.6216, 'ndcg@4': 0.7334, 'ndcg@6': 0.7541, 'group_auc': 0.7445}), (9, {'auc': 0.7536, 'logloss': 0.5951, 'mean_mrr': 0.6715, 'ndcg@2': 0.6222, 'ndcg@4': 0.7323, 'ndcg@6': 0.7537, 'group_auc': 0.7454}), (10, {'auc': 0.7553, 'logloss': 0.5822, 'mean_mrr': 0.6753, 'ndcg@2': 0.6254, 'ndcg@4': 0.7357, 'ndcg@6': 0.7566, 'group_auc': 0.7486})]\n", "best epoch: 10\n", - "Time cost for training is 2.68 mins\n" + "Time cost for training is 2.63 mins\n" ] } ], @@ -406,7 +424,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "{'auc': 0.7099, 'logloss': 0.6063, 'mean_mrr': 0.4902, 'ndcg@2': 0.4025, 'ndcg@4': 0.5056, 'ndcg@6': 0.5555, 'group_auc': 0.709}\n" + "{'auc': 0.7249, 'logloss': 0.5924, 'mean_mrr': 0.4946, 'ndcg@2': 0.4075, 'ndcg@4': 0.5107, 'ndcg@6': 0.5607, 'group_auc': 0.7133}\n" ] }, { @@ -421,13 +439,13 @@ "data": { "application/papermill.record+json": { "res_syn": { - "auc": 0.7099, - "group_auc": 0.709, - "logloss": 0.6063, - "mean_mrr": 0.4902, - "ndcg@2": 0.4025, - "ndcg@4": 0.5056, - "ndcg@6": 0.5555 + "auc": 0.7249, + "group_auc": 0.7133, + "logloss": 0.5924, + "mean_mrr": 0.4946, + "ndcg@2": 0.4075, + "ndcg@4": 0.5107, + "ndcg@6": 0.5607 } } }, @@ -549,13 +567,13 @@ { "data": { "text/plain": [ - "{'auc': 0.7099,\n", - " 'logloss': 0.6063,\n", - " 'mean_mrr': 0.4902,\n", - " 'ndcg@2': 0.4025,\n", - " 'ndcg@4': 0.5056,\n", - " 'ndcg@6': 0.5555,\n", - " 'group_auc': 0.709}" + "{'auc': 0.7249,\n", + " 'logloss': 0.5924,\n", + " 'mean_mrr': 0.4946,\n", + " 'ndcg@2': 0.4075,\n", + " 'ndcg@4': 0.5107,\n", + " 'ndcg@6': 0.5607,\n", + " 'group_auc': 0.7133}" ] }, "execution_count": 14, @@ -582,7 +600,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 15, @@ -670,7 +688,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ @@ -710,20417 +728,9 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 19, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "graph_def_optimized = node {\n", - " name: \"items\"\n", - " op: \"Placeholder\"\n", - " attr {\n", - " key: \"dtype\"\n", - " value {\n", - " type: DT_INT32\n", - " }\n", - " }\n", - " attr {\n", - " key: \"shape\"\n", - " value {\n", - " shape {\n", - " dim {\n", - " 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-21129,7 +739,9 @@ ") as f, G.as_default():\n", " graph_def_optimized = tf.GraphDef()\n", " graph_def_optimized.ParseFromString(f.read())\n", - " print('graph_def_optimized = ' + str(graph_def_optimized))\n", + " \n", + " #### uncomment this line if you want to check what conent is included in the graph\n", + " #print('graph_def_optimized = ' + str(graph_def_optimized))\n", "\n", "\n", "with tf.Session(graph=G) as sess:\n",