diff --git a/.gitignore b/.gitignore index 579989452..e76715761 100644 --- a/.gitignore +++ b/.gitignore @@ -183,6 +183,12 @@ airflow.yaml # temporary examples/tests +outputs ### d3rlpy logs -d3rlpy_logs/ \ No newline at end of file +d3rlpy_logs/ + +# datasets +replay_benchmarks/data +# logs and checkpoints +replay_benchmarks/artifacts \ No newline at end of file diff --git a/Dockerfile b/Dockerfile new file mode 100644 index 000000000..84a7bc24a --- /dev/null +++ b/Dockerfile @@ -0,0 +1,17 @@ +FROM python:3.11-slim as builder +RUN apt-get update \ + && apt-get install -y --no-install-recommends apt-utils \ + && apt-get install libgomp1 build-essential pandoc -y \ + && apt-get install git -y --no-install-recommends + +COPY --from=openjdk:11-jre-slim /usr/local/openjdk-11 /usr/local/openjdk-11 +ENV JAVA_HOME /usr/local/openjdk-11 +RUN update-alternatives --install /usr/bin/java java /usr/local/openjdk-11/bin/java 1 + +WORKDIR /root + +RUN pip install --no-cache-dir --upgrade pip wheel poetry==1.5.1 poetry-dynamic-versioning \ + && python -m poetry config virtualenvs.create false +COPY . RePlay-Accelerated/ +RUN cd RePlay-Accelerated && ./poetry_wrapper.sh install --all-extras +CMD ["/bin/bash"] \ No newline at end of file diff --git a/main.py b/main.py new file mode 100755 index 000000000..3663c99aa --- /dev/null +++ b/main.py @@ -0,0 +1,38 @@ +"""Main module""" + +import os +import logging +import warnings +import yaml + +from replay_benchmarks.utils.conf import load_config, seed_everything +from replay_benchmarks import TrainRunner, InferRunner + +logging.basicConfig( + level=logging.INFO, + format="%(asctime)s - %(levelname)s - %(message)s", +) +warnings.filterwarnings("ignore") + + +def main() -> None: + config_dir = "./replay_benchmarks/configs" + base_config_path = os.path.join(config_dir, "config.yaml") + config = load_config(base_config_path, config_dir) + logging.info("Configuration:\n%s", yaml.dump(config)) + + seed_everything(config["env"]["SEED"]) + logging.info(f"Fixing seed: {config['env']['SEED']}") + + if config["mode"]["name"] == "train": + runner = TrainRunner(config) + elif config["mode"]["name"] == "infer": + runner = InferRunner(config) + else: + raise ValueError(f"Unsupported mode: {config['mode']}") + + runner.run() + + +if __name__ == "__main__": + main() diff --git a/replay/models/nn/sequential/bert4rec/lightning.py b/replay/models/nn/sequential/bert4rec/lightning.py index 8b6a575b2..757808281 100644 --- a/replay/models/nn/sequential/bert4rec/lightning.py +++ b/replay/models/nn/sequential/bert4rec/lightning.py @@ -31,6 +31,10 @@ def __init__( loss_sample_count: Optional[int] = None, negative_sampling_strategy: str = "global_uniform", negatives_sharing: bool = False, + n_buckets: int = 100, + bucket_size_x: int = 100, + bucket_size_y: int = 100, + mix_x: bool = False, optimizer_factory: OptimizerFactory = FatOptimizerFactory(), lr_scheduler_factory: Optional[LRSchedulerFactory] = None, ): @@ -64,6 +68,14 @@ def __init__( Default: ``global_uniform``. :param negatives_sharing: Apply negative sharing in calculating sampled logits. Default: ``False``. + :param n_buckets: Number of buckets for SCE loss. + Default: ``100`` + :param bucket_size_x: Size of x buckets for SCE loss. + Default: ``100`` + :param bucket_size_y: Size of y buckets for SCE loss. + Default: ``100`` + :param mix_x: Mix states embeddings with random matrix for SCE loss. + Default: ``False`` :param optimizer_factory: Optimizer factory. Default: ``FatOptimizerFactory``. :param lr_scheduler_factory: Learning rate schedule factory. @@ -90,6 +102,10 @@ def __init__( self._lr_scheduler_factory = lr_scheduler_factory self._loss = self._create_loss() self._schema = tensor_schema + self._n_buckets = n_buckets + self._bucket_size_x = bucket_size_x + self._bucket_size_y = bucket_size_y + self._mix_x = mix_x assert negative_sampling_strategy in {"global_uniform", "inbatch"} item_count = tensor_schema.item_id_features.item().cardinality @@ -207,6 +223,8 @@ def _compute_loss(self, batch: Bert4RecTrainingBatch) -> torch.Tensor: loss_func = self._compute_loss_bce if self._loss_sample_count is None else self._compute_loss_bce_sampled elif self._loss_type == "CE": loss_func = self._compute_loss_ce if self._loss_sample_count is None else self._compute_loss_ce_sampled + elif self._loss_type == "SCE": + loss_func = self._compute_loss_scalable_ce else: msg = f"Not supported loss type: {self._loss_type}" raise ValueError(msg) @@ -325,6 +343,64 @@ def _compute_loss_ce_sampled( labels_flat = torch.zeros(positive_logits.size(0), dtype=torch.long, device=padding_mask.device) loss = self._loss(logits, labels_flat) return loss + + def _compute_loss_scalable_ce( + self, + feature_tensors: TensorMap, + positive_labels: torch.LongTensor, + padding_mask: torch.BoolTensor, + tokens_mask: torch.BoolTensor, + ) -> torch.Tensor: + + labels_mask = (~padding_mask) + tokens_mask + masked_tokens = ~labels_mask + + pad_token = feature_tensors[self._schema.item_id_feature_name].view(-1)[~padding_mask.view(-1)][0] + emb = self._model.forward_step(feature_tensors, padding_mask, tokens_mask) + hd = torch.tensor(emb.shape[-1]) + + x = emb.view(-1, hd) + y = positive_labels.view(-1) + w = self.get_all_embeddings()["item_embedding"] + + correct_class_logits_ = (x * torch.index_select(w, dim=0, index=y)).sum(dim=1) # (bs,) + + with torch.no_grad(): + if self._mix_x: + omega = 1/torch.sqrt(torch.sqrt(hd)) * torch.randn(x.shape[0], self._n_buckets, device=x.device) + buckets = omega.T @ x + del omega + else: + buckets = 1/torch.sqrt(torch.sqrt(hd)) * torch.randn(self._n_buckets, hd, device=x.device) # (n_b, hd) + + with torch.no_grad(): + x_bucket = buckets @ x.T # (n_b, hd) x (hd, b) -> (n_b, b) + x_bucket[:, ~padding_mask.view(-1)] = float('-inf') + _, top_x_bucket = torch.topk(x_bucket, dim=1, k=self._bucket_size_x) # (n_b, bs_x) + del x_bucket + + y_bucket = buckets @ w.T # (n_b, hd) x (hd, n_cl) -> (n_b, n_cl) + + y_bucket[:, pad_token] = float('-inf') + _, top_y_bucket = torch.topk(y_bucket, dim=1, k=self._bucket_size_y) # (n_b, bs_y) + del y_bucket + + x_bucket = torch.gather(x, 0, top_x_bucket.view(-1, 1).expand(-1, hd)).view(self._n_buckets, self._bucket_size_x, hd) # (n_b, bs_x, hd) + y_bucket = torch.gather(w, 0, top_y_bucket.view(-1, 1).expand(-1, hd)).view(self._n_buckets, self._bucket_size_y, hd) # (n_b, bs_y, hd) + + wrong_class_logits = (x_bucket @ y_bucket.transpose(-1, -2)) # (n_b, bs_x, bs_y) + mask = torch.index_select(y, dim=0, index=top_x_bucket.view(-1)).view(self._n_buckets, self._bucket_size_x)[:, :, None] == top_y_bucket[:, None, :] # (n_b, bs_x, bs_y) + wrong_class_logits = wrong_class_logits.masked_fill(mask, float('-inf')) # (n_b, bs_x, bs_y) + correct_class_logits = torch.index_select(correct_class_logits_, dim=0, index=top_x_bucket.view(-1)).view(self._n_buckets, self._bucket_size_x)[:, :, None] # (n_b, bs_x, 1) + logits = torch.cat((wrong_class_logits, correct_class_logits), dim=2) # (n_b, bs_x, bs_y + 1) + + loss_ = torch.nn.functional.cross_entropy(logits.view(-1, logits.shape[-1]), (logits.shape[-1] - 1) * torch.ones(logits.shape[0] * logits.shape[1], dtype=torch.int64, device=logits.device), reduction='none') # (n_b * bs_x,) + loss = torch.zeros(x.shape[0], device=x.device, dtype=x.dtype) + loss.scatter_reduce_(0, top_x_bucket.view(-1), loss_, reduce='amax', include_self=False) + loss = loss[(loss != 0) & (masked_tokens).view(-1)] + loss = torch.mean(loss) + + return loss def _get_sampled_logits( self, @@ -412,7 +488,7 @@ def _create_loss(self) -> Union[torch.nn.BCEWithLogitsLoss, torch.nn.CrossEntrop if self._loss_type == "BCE": return torch.nn.BCEWithLogitsLoss(reduction="sum") - if self._loss_type == "CE": + if self._loss_type == "CE" or self._loss_type == "SCE": return torch.nn.CrossEntropyLoss() msg = "Not supported loss_type" diff --git a/replay/models/nn/sequential/sasrec/lightning.py b/replay/models/nn/sequential/sasrec/lightning.py index eb03693a5..de0f061a0 100644 --- a/replay/models/nn/sequential/sasrec/lightning.py +++ b/replay/models/nn/sequential/sasrec/lightning.py @@ -33,6 +33,10 @@ def __init__( loss_sample_count: Optional[int] = None, negative_sampling_strategy: str = "global_uniform", negatives_sharing: bool = False, + n_buckets: int = 100, + bucket_size_x: int = 100, + bucket_size_y: int = 100, + mix_x: bool = False, optimizer_factory: OptimizerFactory = FatOptimizerFactory(), lr_scheduler_factory: Optional[LRSchedulerFactory] = None, ): @@ -62,6 +66,14 @@ def __init__( Default: ``global_uniform``. :param negatives_sharing: Apply negative sharing in calculating sampled logits. Default: ``False``. + :param n_buckets: Number of buckets for SCE loss. + Default: ``100`` + :param bucket_size_x: Size of x buckets for SCE loss. + Default: ``100`` + :param bucket_size_y: Size of y buckets for SCE loss. + Default: ``100`` + :param mix_x: Mix states embeddings with random matrix for SCE loss. + Default: ``False`` :param optimizer_factory: Optimizer factory. Default: ``FatOptimizerFactory``. :param lr_scheduler_factory: Learning rate schedule factory. @@ -87,6 +99,10 @@ def __init__( self._lr_scheduler_factory = lr_scheduler_factory self._loss = self._create_loss() self._schema = tensor_schema + self._n_buckets = n_buckets + self._bucket_size_x = bucket_size_x + self._bucket_size_y = bucket_size_y + self._mix_x = mix_x assert negative_sampling_strategy in {"global_uniform", "inbatch"} item_count = tensor_schema.item_id_features.item().cardinality @@ -190,6 +206,8 @@ def _compute_loss(self, batch: SasRecTrainingBatch) -> torch.Tensor: loss_func = self._compute_loss_bce if self._loss_sample_count is None else self._compute_loss_bce_sampled elif self._loss_type == "CE": loss_func = self._compute_loss_ce if self._loss_sample_count is None else self._compute_loss_ce_sampled + elif self._loss_type == "SCE": + loss_func = self._compute_loss_scalable_ce else: msg = f"Not supported loss type: {self._loss_type}" raise ValueError(msg) @@ -306,6 +324,61 @@ def _compute_loss_ce_sampled( labels_flat = torch.zeros(positive_logits.size(0), dtype=torch.long, device=padding_mask.device) loss = self._loss(logits, labels_flat) return loss + + def _compute_loss_scalable_ce( + self, + feature_tensors: TensorMap, + positive_labels: torch.LongTensor, + padding_mask: torch.BoolTensor, + target_padding_mask: torch.BoolTensor, + ) -> torch.Tensor: + + pad_token = feature_tensors[self._schema.item_id_feature_name].view(-1)[~padding_mask.view(-1)][0] + emb = self._model.forward_step(feature_tensors, padding_mask)[target_padding_mask] + hd = torch.tensor(emb.shape[-1]) + + x = emb.view(-1, hd) + y = positive_labels[target_padding_mask].view(-1) + w = self.get_all_embeddings()["item_embedding"] + + correct_class_logits_ = (x * torch.index_select(w, dim=0, index=y)).sum(dim=1) # (bs,) + + with torch.no_grad(): + if self._mix_x: + omega = 1/torch.sqrt(torch.sqrt(hd)) * torch.randn(x.shape[0], self._n_buckets, device=x.device) + buckets = omega.T @ x + del omega + else: + buckets = 1/torch.sqrt(torch.sqrt(hd)) * torch.randn(self._n_buckets, hd, device=x.device) # (n_b, hd) + + with torch.no_grad(): + x_bucket = buckets @ x.T # (n_b, hd) x (hd, b) -> (n_b, b) + x_bucket[:, ~padding_mask[target_padding_mask].view(-1)] = float('-inf') + _, top_x_bucket = torch.topk(x_bucket, dim=1, k=self._bucket_size_x) # (n_b, bs_x) + del x_bucket + + y_bucket = buckets @ w.T # (n_b, hd) x (hd, n_cl) -> (n_b, n_cl) + + y_bucket[:, pad_token] = float('-inf') + _, top_y_bucket = torch.topk(y_bucket, dim=1, k=self._bucket_size_y) # (n_b, bs_y) + del y_bucket + + x_bucket = torch.gather(x, 0, top_x_bucket.view(-1, 1).expand(-1, hd)).view(self._n_buckets, self._bucket_size_x, hd) # (n_b, bs_x, hd) + y_bucket = torch.gather(w, 0, top_y_bucket.view(-1, 1).expand(-1, hd)).view(self._n_buckets, self._bucket_size_y, hd) # (n_b, bs_y, hd) + + wrong_class_logits = (x_bucket @ y_bucket.transpose(-1, -2)) # (n_b, bs_x, bs_y) + mask = torch.index_select(y, dim=0, index=top_x_bucket.view(-1)).view(self._n_buckets, self._bucket_size_x)[:, :, None] == top_y_bucket[:, None, :] # (n_b, bs_x, bs_y) + wrong_class_logits = wrong_class_logits.masked_fill(mask, float('-inf')) # (n_b, bs_x, bs_y) + correct_class_logits = torch.index_select(correct_class_logits_, dim=0, index=top_x_bucket.view(-1)).view(self._n_buckets, self._bucket_size_x)[:, :, None] # (n_b, bs_x, 1) + logits = torch.cat((wrong_class_logits, correct_class_logits), dim=2) # (n_b, bs_x, bs_y + 1) + + loss_ = torch.nn.functional.cross_entropy(logits.view(-1, logits.shape[-1]), (logits.shape[-1] - 1) * torch.ones(logits.shape[0] * logits.shape[1], dtype=torch.int64, device=logits.device), reduction='none') # (n_b * bs_x,) + loss = torch.zeros(x.shape[0], device=x.device, dtype=x.dtype) + loss.scatter_reduce_(0, top_x_bucket.view(-1), loss_, reduce='amax', include_self=False) + loss = loss[loss != 0] + loss = torch.mean(loss) + + return loss def _get_sampled_logits( self, @@ -391,7 +464,7 @@ def _create_loss(self) -> Union[torch.nn.BCEWithLogitsLoss, torch.nn.CrossEntrop if self._loss_type == "BCE": return torch.nn.BCEWithLogitsLoss(reduction="sum") - if self._loss_type == "CE": + if self._loss_type == "CE" or self._loss_type == "SCE": return torch.nn.CrossEntropyLoss() msg = "Not supported loss_type" diff --git a/replay_benchmarks/09_sasrec_example_like_pipeline.ipynb b/replay_benchmarks/09_sasrec_example_like_pipeline.ipynb new file mode 100644 index 000000000..794e21e15 --- /dev/null +++ b/replay_benchmarks/09_sasrec_example_like_pipeline.ipynb @@ -0,0 +1,2339 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Example of the SASRec training and inference stages\n", + "Note that all the given examples can be run without using PySpark, using only Pandas" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# changing core directory\n", + "import os, sys\n", + "dir2 = os.path.abspath('')\n", + "dir1 = os.path.dirname(dir2)\n", + "if not dir1 in sys.path:\n", + " sys.path.append(dir1)\n", + "os.chdir('..')" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "os.environ[\"CUDA_DEVICE_ORDER\"] = \"PCI_BUS_ID\" \n", + "os.environ[\"OMP_NUM_THREADS\"] = \"4\"\n", + "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0\"\n", + "os.environ[\"KAGGLE_USERNAME\"] = \"recsysaccelerate\"\n", + "os.environ[\"KAGGLE_KEY\"] = \"6363e91b656fea576c39e4f55dcc1d00\"" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "import lightning as L\n", + "from lightning.pytorch.loggers import CSVLogger\n", + "from lightning.pytorch.callbacks import ModelCheckpoint\n", + "from torch.utils.data import DataLoader\n", + "import torch\n", + "\n", + "from replay.preprocessing.filters import MinCountFilter\n", + "from replay.metrics import OfflineMetrics, Recall, Precision, MAP, NDCG, HitRate, MRR\n", + "from replay.metrics.torch_metrics_builder import metrics_to_df\n", + "from replay.splitters import LastNSplitter, TimeSplitter\n", + "from replay.utils import get_spark_session\n", + "from replay.data import (\n", + " FeatureHint,\n", + " FeatureInfo,\n", + " FeatureSchema,\n", + " FeatureSource,\n", + " FeatureType,\n", + " Dataset,\n", + ")\n", + "from replay.models.nn.optimizer_utils import FatOptimizerFactory\n", + "from replay.models.nn.sequential.callbacks import (\n", + " ValidationMetricsCallback,\n", + " SparkPredictionCallback,\n", + " PandasPredictionCallback, \n", + " TorchPredictionCallback,\n", + " QueryEmbeddingsPredictionCallback\n", + ")\n", + "from replay.models.nn.sequential.postprocessors import RemoveSeenItems\n", + "from replay.data.nn import (\n", + " SequenceTokenizer,\n", + " SequentialDataset,\n", + " TensorFeatureSource,\n", + " TensorSchema,\n", + " TensorFeatureInfo\n", + ")\n", + "from replay.models.nn.sequential import SasRec\n", + "from replay.models.nn.sequential.sasrec import (\n", + " SasRecPredictionDataset,\n", + " SasRecTrainingDataset,\n", + " SasRecValidationDataset,\n", + " SasRecPredictionBatch,\n", + " SasRecModel\n", + ")\n", + "\n", + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "import random\n", + "import numpy as np\n", + "\n", + "SEED = 42\n", + "\n", + "torch.manual_seed(SEED)\n", + "torch.cuda.manual_seed(SEED)\n", + "np.random.seed(SEED)\n", + "random.seed(SEED)\n", + "\n", + "torch.backends.cudnn.deterministic=True" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Getting a spark session" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.11/site-packages/pyspark/bin/load-spark-env.sh: line 68: ps: command not found\n", + "Setting default log level to \"WARN\".\n", + "To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).\n", + "24/11/28 07:39:42 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable\n", + "24/11/28 07:39:42 WARN SparkConf: Note that spark.local.dir will be overridden by the value set by the cluster manager (via SPARK_LOCAL_DIRS in mesos/standalone/kubernetes and LOCAL_DIRS in YARN).\n", + "24/11/28 07:39:42 WARN Utils: Service 'SparkUI' could not bind on port 4040. Attempting port 4041.\n", + "24/11/28 07:39:42 WARN Utils: Service 'SparkUI' could not bind on port 4041. Attempting port 4042.\n", + "24/11/28 07:39:42 WARN Utils: Service 'SparkUI' could not bind on port 4042. Attempting port 4043.\n", + "24/11/28 07:39:42 WARN Utils: Service 'SparkUI' could not bind on port 4043. Attempting port 4044.\n", + "24/11/28 07:39:42 WARN Utils: Service 'SparkUI' could not bind on port 4044. Attempting port 4045.\n", + "24/11/28 07:39:42 WARN Utils: Service 'SparkUI' could not bind on port 4045. Attempting port 4046.\n", + "24/11/28 07:39:42 WARN Utils: Service 'SparkUI' could not bind on port 4046. Attempting port 4047.\n", + "24/11/28 07:39:42 WARN Utils: Service 'SparkUI' could not bind on port 4047. Attempting port 4048.\n", + "24/11/28 07:39:42 WARN Utils: Service 'SparkUI' could not bind on port 4048. Attempting port 4049.\n", + "24/11/28 07:39:42 WARN Utils: Service 'SparkUI' could not bind on port 4049. Attempting port 4050.\n", + "24/11/28 07:39:42 WARN Utils: Service 'SparkUI' could not bind on port 4050. Attempting port 4051.\n" + ] + } + ], + "source": [ + "spark_session = get_spark_session()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Prepare data\n", + "### Load raw movielens-1M interactions, item features and user features.\n", + "In the current implementation, the SASRec does not take into account the features of items or users. They are only used to get a complete list of users and items." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: rs-datasets in /usr/local/lib/python3.11/site-packages (0.5.1)\n", + "Requirement already satisfied: datatable in /usr/local/lib/python3.11/site-packages (from rs-datasets) (1.1.0)\n", + "Requirement already satisfied: pandas in /usr/local/lib/python3.11/site-packages (from rs-datasets) (2.0.3)\n", + "Requirement already satisfied: gdown in /usr/local/lib/python3.11/site-packages (from rs-datasets) (5.2.0)\n", + "Requirement already satisfied: pyarrow in /usr/local/lib/python3.11/site-packages (from rs-datasets) (16.0.0)\n", + "Requirement already satisfied: tqdm in /usr/local/lib/python3.11/site-packages (from rs-datasets) (4.66.4)\n", + "Requirement already satisfied: xlrd in /usr/local/lib/python3.11/site-packages (from rs-datasets) 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Use the --root-user-action option if you know what you are doing and want to suppress this warning.\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.2\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n" + ] + } + ], + "source": [ + "!pip install rs-datasets" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from rs_datasets import MovieLens" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "movielens = MovieLens(\"1m\")\n", + "interactions = movielens.ratings\n", + "user_features = movielens.users\n", + "item_features = movielens.items" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " user_id item_id rating timestamp\n", + "count 999611.000000 999611.000000 999611.000000 9.996110e+05\n", + "mean 3024.576537 1865.439428 3.581974 9.722409e+08\n", + "std 1728.436705 1095.976336 1.116894 1.214827e+07\n", + "min 1.000000 1.000000 1.000000 9.567039e+08\n", + "25% 1506.000000 1030.000000 3.000000 9.653025e+08\n", + "50% 3070.000000 1835.000000 4.000000 9.730170e+08\n", + "75% 4477.000000 2770.000000 4.000000 9.752208e+08\n", + "max 6040.000000 3952.000000 5.000000 1.046455e+09" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "interactions.describe()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Removing duplicates in the timestamp column without changing the original items order where timestamp is the same" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "# interactions[\"timestamp\"] = interactions[\"timestamp\"].astype(\"int64\")\n", + "# interactions = interactions.sort_values(by=\"timestamp\")\n", + "# interactions[\"timestamp\"] = interactions.groupby(\"user_id\").cumcount()\n", + "# interactions" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Split interactions into the train, validation and test datasets using LastNSplitter" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "# splitter = LastNSplitter(\n", + "# N=1,\n", + "# divide_column=\"user_id\",\n", + "# query_column=\"user_id\",\n", + "# strategy=\"interactions\",\n", + "# )\n", + "\n", + "# raw_test_events, raw_test_gt = splitter.split(interactions)\n", + "# raw_validation_events, raw_validation_gt = splitter.split(raw_test_events)\n", + "# raw_train_events = raw_validation_events\n", + "\n", + "\n", + "splitter = TimeSplitter(\n", + " time_threshold=0.1,\n", + " drop_cold_users=True,\n", + " drop_cold_items=True,\n", + " item_column='item_id',\n", + " query_column='user_id',\n", + " timestamp_column='timestamp',\n", + ")\n", + "\n", + "# train_events, validation_events, validation_gt, test_events, test_gt = (\n", + "# _split_data(splitter, interactions)\n", + "# )\n", + "\n", + "raw_test_events, raw_test_gt = splitter.split(interactions)\n", + "raw_validation_events, raw_validation_gt = splitter.split(raw_test_events)\n", + "raw_train_events = raw_validation_events\n", + "\n", + "raw_test_gt = raw_test_gt[raw_test_gt['item_id'].isin(raw_train_events['item_id'])]\n", + "raw_test_gt = raw_test_gt[raw_test_gt['user_id'].isin(raw_train_events['user_id'])]" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "def test_splitting(events, gt, name=''):\n", + " if events['timestamp'].max() > gt['timestamp'].min():\n", + " print(\"Problem with time points in\", name)\n", + " if len(set(gt['user_id'].unique().tolist()) - set(events['user_id'].unique().tolist())) > 0:\n", + " print(\"Problem with cold users in\", name)\n", + " if len(set(gt['item_id'].unique().tolist()) - set(events['item_id'].unique().tolist())) > 0:\n", + " print(\"Problem with cold items in\", name)\n", + "\n", + "\n", + "test_splitting(raw_train_events, raw_test_gt, \"train events, test gt\")\n", + "test_splitting(raw_train_events, raw_validation_gt, \"train events, valid gt\")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Prepare FeatureSchema required to create Dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "def prepare_feature_schema(is_ground_truth: bool) -> FeatureSchema:\n", + " base_features = FeatureSchema(\n", + " [\n", + " FeatureInfo(\n", + " column=\"user_id\",\n", + " feature_hint=FeatureHint.QUERY_ID,\n", + " feature_type=FeatureType.CATEGORICAL,\n", + " ),\n", + " FeatureInfo(\n", + " column=\"item_id\",\n", + " feature_hint=FeatureHint.ITEM_ID,\n", + " feature_type=FeatureType.CATEGORICAL,\n", + " ),\n", + " ]\n", + " )\n", + " if is_ground_truth:\n", + " return base_features\n", + "\n", + " all_features = base_features + FeatureSchema(\n", + " [\n", + " FeatureInfo(\n", + " column=\"timestamp\",\n", + " feature_type=FeatureType.NUMERICAL,\n", + " feature_hint=FeatureHint.TIMESTAMP,\n", + " ),\n", + " ]\n", + " )\n", + " return all_features" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Create Dataset for the training stage" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "user_features = None\n", + "item_features = None" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "train_dataset = Dataset(\n", + " feature_schema=prepare_feature_schema(is_ground_truth=False),\n", + " interactions=raw_train_events,\n", + " query_features=user_features,\n", + " item_features=item_features,\n", + " check_consistency=True,\n", + " categorical_encoded=False,\n", + ")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Create Datasets (events and ground_truth) for the validation stage" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "validation_dataset = Dataset(\n", + " feature_schema=prepare_feature_schema(is_ground_truth=False),\n", + " interactions=raw_validation_events,\n", + " query_features=user_features,\n", + " item_features=item_features,\n", + " check_consistency=True,\n", + " categorical_encoded=False,\n", + ")\n", + "validation_gt = Dataset(\n", + " feature_schema=prepare_feature_schema(is_ground_truth=True),\n", + " interactions=raw_validation_gt,\n", + " check_consistency=True,\n", + " categorical_encoded=False,\n", + ")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Create Datasets (events and ground_truth) for the testing stage" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "test_dataset = Dataset(\n", + " feature_schema=prepare_feature_schema(is_ground_truth=False),\n", + " interactions=raw_test_events,\n", + " query_features=user_features,\n", + " item_features=item_features,\n", + " check_consistency=True,\n", + " categorical_encoded=False,\n", + ")\n", + "test_gt = Dataset(\n", + " feature_schema=prepare_feature_schema(is_ground_truth=True),\n", + " interactions=raw_test_gt,\n", + " check_consistency=True,\n", + " categorical_encoded=False,\n", + ")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Create the tensor schema\n", + "A schema shows the correspondence of columns from the source dataset with the internal representation of tensors inside the model" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "ITEM_FEATURE_NAME = \"item_id_seq\"\n", + "\n", + "tensor_schema = TensorSchema(\n", + " TensorFeatureInfo(\n", + " name=ITEM_FEATURE_NAME,\n", + " is_seq=True,\n", + " feature_type=FeatureType.CATEGORICAL,\n", + " feature_sources=[TensorFeatureSource(FeatureSource.INTERACTIONS, train_dataset.feature_schema.item_id_column)],\n", + " feature_hint=FeatureHint.ITEM_ID,\n", + " )\n", + ")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Create sequential datasets using SequenceTokenizer\n", + "The SequentialDataset internally store data in the form of sequences of items sorted by increasing interaction time (timestamp). A SequenceTokenizer is used to convert to this format. In addition, the SequenceTokenizer encodes all categorical columns from the source dataset and stores mapping inside itself.\n", + "SequentialDataset.keep_common_query_ids is used to leave only sequences from the same users" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "tokenizer = SequenceTokenizer(tensor_schema, allow_collect_to_master=True, handle_unknown_rule=\"drop\")\n", + "tokenizer.fit(train_dataset)\n", + "\n", + "sequential_train_dataset = tokenizer.transform(train_dataset)\n", + "\n", + "sequential_validation_dataset = tokenizer.transform(validation_dataset)\n", + "sequential_validation_gt = tokenizer.transform(validation_gt, [tensor_schema.item_id_feature_name])\n", + "\n", + "sequential_validation_dataset, sequential_validation_gt = SequentialDataset.keep_common_query_ids(\n", + " sequential_validation_dataset, sequential_validation_gt\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "test_query_ids = test_gt.query_ids\n", + "test_query_ids_np = tokenizer.query_id_encoder.transform(test_query_ids)[\"user_id\"].values\n", + "sequential_test_dataset = tokenizer.transform(test_dataset).filter_by_query_id(test_query_ids_np)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can get the user and item mapping and inverse mapping as follows" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'user_id': {6040: 0, 6039: 1, 6038: 2, 6037: 3, 6036: 4, 6035: 5, 6034: 6, 6033: 7, 6032: 8, 6031: 9, 6030: 10, 6029: 11, 6028: 12, 6027: 13, 6026: 14, 6025: 15, 6024: 16, 6023: 17, 6022: 18, 6021: 19, 6020: 20, 6019: 21, 6018: 22, 6017: 23, 6016: 24, 6015: 25, 6014: 26, 6013: 27, 6012: 28, 6011: 29, 6010: 30, 6009: 31, 6007: 32, 6008: 33, 6006: 34, 6005: 35, 6004: 36, 6003: 37, 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"print(len(tokenizer.query_id_encoder.mapping[\"user_id\"]), len(tokenizer.item_id_encoder.mapping['item_id']))" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "4" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tokenizer.item_id_encoder.mapping['item_id'][1961]" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Train model\n", + "### Create SASRec model instance and run the training stage using lightning\n", + "After each epoch validation metrics are shown. You can change the list of validation metrics in ValidationMetricsCallback\n", + "The model is determined to be the best and is saved if the metric updates its maximum during validation (see the ModelCheckpoint)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "GPU available: True (cuda), used: True\n", + "TPU available: False, using: 0 TPU cores\n", + "IPU available: False, using: 0 IPUs\n", + "HPU available: False, using: 0 HPUs\n", + "You are using a CUDA device ('NVIDIA L40') that has Tensor Cores. To properly utilize them, you should set `torch.set_float32_matmul_precision('medium' | 'high')` which will trade-off precision for performance. For more details, read https://pytorch.org/docs/stable/generated/torch.set_float32_matmul_precision.html#torch.set_float32_matmul_precision\n", + "/usr/local/lib/python3.11/site-packages/lightning/pytorch/callbacks/model_checkpoint.py:653: Checkpoint directory /root/RePlay-Accelerated/.checkpoints exists and is not empty.\n", + "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n", + "\n", + " | Name | Type | Params\n", + "--------------------------------------------\n", + "0 | _model | SasRecModel | 2.2 M \n", + "1 | _loss | CrossEntropyLoss | 0 \n", + "--------------------------------------------\n", + "2.2 M Trainable params\n", + "0 Non-trainable params\n", + "2.2 M Total params\n", + "8.681 Total estimated model params size (MB)\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "fac78134025246588af6ad38441f6af7", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Sanity Checking: | | 0/? 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user_iditem_idscore
0020128.138867
0016428.094225
003608.087744
0023257.976056
00477.485192
............
6039603912513.320609
6039603921743.301445
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6039603931413.280689
6039603928493.2665
\n", + "

604000 rows × 3 columns

\n", + "" + ], + "text/plain": [ + " user_id item_id score\n", + "0 0 2012 8.138867\n", + "0 0 1642 8.094225\n", + "0 0 360 8.087744\n", + "0 0 2325 7.976056\n", + "0 0 47 7.485192\n", + "... ... ... ...\n", + "6039 6039 1251 3.320609\n", + "6039 6039 2174 3.301445\n", + "6039 6039 3197 3.286182\n", + "6039 6039 3141 3.280689\n", + "6039 6039 2849 3.2665\n", + "\n", + "[604000 rows x 3 columns]" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pandas_res" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor(0) tensor([2012, 1642, 360, 2325, 47, 2020, 2016, 2009, 1020, 2014, 12, 1382,\n", + " 612, 2027, 3088, 2018, 1012, 1019, 3090, 2965, 547, 2068, 2021, 1990,\n", + " 2073, 2315, 2072, 700, 105, 1050, 582, 1346, 2011, 2918, 310, 1743,\n", + " 1977, 314, 3546, 1058, 974, 1010, 1850, 2023, 358, 1979, 259, 2631,\n", + " 667, 33, 2030, 2070, 2013, 1002, 2640, 3328, 2807, 592, 1964, 3144,\n", + " 2830, 2985, 3327, 3329, 1262, 1017, 1547, 476, 1, 2285, 1459, 1664,\n", + " 1018, 59, 2692, 1000, 1936, 1851, 3218, 3414, 1539, 826, 1011, 3106,\n", + " 999, 791, 2092, 1004, 2047, 236, 2731, 3676, 2677, 3682, 156, 2071,\n", + " 1008, 363, 1945, 2777]) tensor([8.1389, 8.0942, 8.0877, 7.9761, 7.4852, 7.3621, 7.1054, 7.0903, 7.0875,\n", + " 6.9929, 6.8924, 6.8650, 6.8604, 6.7333, 6.7214, 6.6113, 6.6022, 6.5607,\n", + " 6.4482, 6.4093, 6.3793, 6.3753, 6.2767, 6.2201, 6.2057, 6.1955, 6.1510,\n", + " 6.1112, 6.0373, 6.0356, 5.9954, 5.9927, 5.9892, 5.9694, 5.9151, 5.8761,\n", + " 5.8475, 5.8284, 5.7449, 5.7310, 5.7161, 5.6727, 5.6549, 5.6495, 5.6186,\n", + " 5.6085, 5.6031, 5.5880, 5.5866, 5.5690, 5.5257, 5.4499, 5.3953, 5.3695,\n", + " 5.3578, 5.3515, 5.3228, 5.3165, 5.3163, 5.3155, 5.2968, 5.2931, 5.2828,\n", + " 5.2674, 5.2641, 5.2480, 5.2354, 5.2027, 5.1985, 5.1930, 5.1861, 5.1736,\n", + " 5.1604, 5.1566, 5.1563, 5.1207, 5.0993, 5.0964, 5.0871, 5.0869, 5.0672,\n", + " 5.0649, 5.0559, 5.0418, 5.0282, 5.0174, 4.9985, 4.9898, 4.9881, 4.8736,\n", + " 4.8252, 4.7583, 4.7408, 4.7300, 4.6881, 4.6835, 4.6771, 4.6612, 4.6486,\n", + " 4.5319])\n" + ] + } + ], + "source": [ + "print(torch_user_ids[0], torch_item_ids[0], torch_scores[0])" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Suppose we want to get the recomendations in PySpark format. \n", + "Let's get the inverse representation of labels using inverse_transform method.\n", + "\n", + "Note that the reverse representation can only be obtained for PySpark and Pandas formats. When working with PyTorch tensors, the reverse representation must be done manually" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "recommendations = tokenizer.query_and_item_id_encoder.inverse_transform(spark_res)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+------------------+-------+-------+\n", + "| score|user_id|item_id|\n", + "+------------------+-------+-------+\n", + "| 8.138867378234863| 1| 2081|\n", + "| 8.09422492980957| 1| 1688|\n", + "| 8.087743759155273| 1| 364|\n", + "| 7.976056098937988| 1| 2394|\n", + "| 7.48519229888916| 1| 48|\n", + "| 7.362086296081543| 1| 2089|\n", + "| 7.105367660522461| 1| 2085|\n", + "| 7.090343475341797| 1| 2078|\n", + "| 7.08748197555542| 1| 1033|\n", + "| 6.992861747741699| 1| 2083|\n", + "| 6.892394542694092| 1| 13|\n", + "| 6.864995956420898| 1| 1405|\n", + "| 6.860413074493408| 1| 616|\n", + "| 6.73327112197876| 1| 2096|\n", + "| 6.721376419067383| 1| 3157|\n", + "| 6.611262321472168| 1| 2087|\n", + "| 6.602219581604004| 1| 1025|\n", + "|6.5607428550720215| 1| 1032|\n", + "| 6.448214530944824| 1| 3159|\n", + "| 6.409302711486816| 1| 3034|\n", + "+------------------+-------+-------+\n", + "only showing top 20 rows\n", + "\n" + ] + } + ], + "source": [ + "recommendations.show()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Calculating metrics" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "init_args = {\"query_column\": \"user_id\", \"rating_column\": \"score\"}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + } + ], + "source": [ + "result_metrics = OfflineMetrics(\n", + " [Recall(TOPK), Precision(TOPK), MAP(TOPK), NDCG(TOPK), MRR(TOPK), HitRate(TOPK)], **init_args\n", + ")(recommendations.toPandas(), raw_test_gt)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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k11020100
HitRate0.069040.2960260.4153970.680464
MAP0.069040.1264830.1346440.141227
MRR0.069040.1264830.1346440.141227
NDCG0.069040.1659980.1960250.244784
Precision0.069040.0296030.0207700.006805
Recall0.069040.2960260.4153970.680464
\n", + "
" + ], + "text/plain": [ + "k 1 10 20 100\n", + "HitRate 0.06904 0.296026 0.415397 0.680464\n", + "MAP 0.06904 0.126483 0.134644 0.141227\n", + "MRR 0.06904 0.126483 0.134644 0.141227\n", + "NDCG 0.06904 0.165998 0.196025 0.244784\n", + "Precision 0.06904 0.029603 0.020770 0.006805\n", + "Recall 0.06904 0.296026 0.415397 0.680464" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "metrics_to_df(result_metrics)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### User embeddings" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Got 6040 x 300 user embeddings, because among all 12 batches: \n", + "\n", + "11 batches contains 512 samples\n", + "\n", + "1 batch contains 408 left samples\n", + "\n", + "11 * 512 + 408 == 6040" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor([[ 0.3704, 1.9303, 1.9161, ..., -0.1143, 1.5672, -0.9027],\n", + " [-1.8589, -2.6053, 0.1639, ..., -0.4250, -2.5378, -0.8178],\n", + " [-0.0877, 0.3147, 2.4707, ..., -0.7766, -0.8095, -1.2838],\n", + " ...,\n", + " [-0.5727, 0.1162, 1.5556, ..., -1.8936, 1.4968, -1.3920],\n", + " [-0.3458, -1.6828, -0.5697, ..., -1.6742, 3.2049, 0.6597],\n", + " [-1.2831, 0.7636, 1.0783, ..., -1.1996, -0.0189, 1.4718]],\n", + " device='cuda:0')" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "user_embeddings" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "torch.Size([6040, 300])" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "user_embeddings.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can access user embeddings directly with `SasRecModel` class" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor([[-4.3240e-01, -1.0684e-01, -8.1389e-02, ..., 6.6867e-01,\n", + " 4.7710e-01, -1.7522e+00],\n", + " [ 7.8765e-02, 8.0354e-01, -9.5730e-01, ..., -1.9753e+00,\n", + " 1.4708e+00, -7.7084e-01],\n", + " [ 1.0037e+00, 6.5928e-01, -1.4148e+00, ..., 1.0593e+00,\n", + " -7.4484e-01, -2.7631e-01],\n", + " ...,\n", + " [-1.4800e+00, -1.9950e-01, -3.6084e-01, ..., 1.5323e+00,\n", + " 2.6428e-01, -8.7467e-01],\n", + " [ 1.1612e+00, -6.3325e-04, 4.0772e-01, ..., 2.5503e-01,\n", + " -3.4220e-01, 5.4465e-01],\n", + " [-6.3809e-01, 1.0407e+00, -2.1736e+00, ..., 8.7484e-01,\n", + " 1.4989e+00, -1.1730e+00]], device='cuda:0', grad_fn=)" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", + "\n", + "core_model = SasRecModel(\n", + " tensor_schema,\n", + " num_blocks=2,\n", + " num_heads=2,\n", + " max_len=MAX_SEQ_LEN,\n", + " hidden_size=300,\n", + " dropout=0.5\n", + ")\n", + "core_model.eval()\n", + "core_model = core_model.to(device)\n", + "\n", + "# Get first batch of data \n", + "data = next(iter(prediction_dataloader))\n", + "tensor_map, padding_mask = data.features, data.padding_mask\n", + "\n", + "# Ensure everything is on the same device\n", + "padding_mask = padding_mask.to(device)\n", + "tensor_map[\"item_id_seq\"] = tensor_map[\"item_id_seq\"].to(device)\n", + "\n", + "# Get user embeddings\n", + "user_embeddings_batch = core_model.get_query_embeddings(tensor_map, padding_mask)\n", + "user_embeddings_batch" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "torch.Size([512, 300])" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "user_embeddings_batch.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Item embeddings" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`get_all_embeddings()` method in transformers can be used to get copies of all embeddings that are presented in model as a dict." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'item_embedding': tensor([[-0.0238, 0.0208, -0.0037, ..., 0.0340, 0.0224, -0.0369],\n", + " [-0.0103, 0.0099, 0.0112, ..., 0.0112, 0.0255, -0.0349],\n", + " [ 0.0090, -0.0228, -0.0544, ..., 0.0024, 0.0438, 0.0060],\n", + " ...,\n", + " [-0.0220, 0.0150, -0.0027, ..., 0.0562, 0.0170, -0.0025],\n", + " [ 0.0147, -0.0004, 0.0214, ..., -0.0098, -0.0017, 0.0299],\n", + " [-0.0095, 0.0142, -0.0030, ..., 0.0435, -0.0034, 0.0077]]),\n", + " 'positional_embedding': tensor([[ 0.1256, 0.0453, -0.0112, ..., -0.1254, 0.0047, -0.0691],\n", + " [-0.0208, -0.0014, 0.0146, ..., 0.0680, -0.0909, -0.0365],\n", + " [-0.0839, 0.0381, 0.0081, ..., -0.0139, -0.0645, -0.0350],\n", + " ...,\n", + " [ 0.0322, 0.0308, -0.0525, ..., 0.0426, 0.0454, 0.0101],\n", + " [-0.0335, -0.0006, 0.0082, ..., -0.0202, -0.0435, -0.0786],\n", + " [-0.0537, -0.0550, 0.1043, ..., 0.0992, 0.0194, 0.0321]])}" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "all_embeddings = best_model.get_all_embeddings()\n", + "all_embeddings" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can access item embeddings from this dictionary" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor([[-0.0238, 0.0208, -0.0037, ..., 0.0340, 0.0224, -0.0369],\n", + " [-0.0103, 0.0099, 0.0112, ..., 0.0112, 0.0255, -0.0349],\n", + " [ 0.0090, -0.0228, -0.0544, ..., 0.0024, 0.0438, 0.0060],\n", + " ...,\n", + " [-0.0220, 0.0150, -0.0027, ..., 0.0562, 0.0170, -0.0025],\n", + " [ 0.0147, -0.0004, 0.0214, ..., -0.0098, -0.0017, 0.0299],\n", + " [-0.0095, 0.0142, -0.0030, ..., 0.0435, -0.0034, 0.0077]])" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "item_embeddings = all_embeddings[\"item_embedding\"]\n", + "item_embeddings" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Item embeddings shape is (N_ITEMS, HIDDEN_SIZE)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "torch.Size([3883, 300])" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "item_embeddings.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Ensure we got correct dimension and ensure we got the copy of tensor" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "assert item_embeddings.shape[0] == len(tokenizer.item_id_encoder.mapping[\"item_id\"])\n", + "assert id(item_embeddings) != id(best_model._model.item_embedder.item_emb.weight.data)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For example we observe one new item id in our training data. We can easily expand our item embedder by one element" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In order to expand item embeddings by new size `set_item_embeddings_by_size` method is applied" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "best_model.set_item_embeddings_by_size(item_embeddings.shape[0] + 1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now our new item embeddings have one extra embedding" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "new_size = best_model.get_all_embeddings()[\"item_embedding\"].shape[0]\n", + "old_size = item_embeddings.shape[0]\n", + "\n", + "assert new_size == old_size + 1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Alternatively, we can pass our item embeddings that replace the existing ones by calling `set_item_embeddings_by_tensor`.\n", + "\n", + "If tensor contains new items, they will be added to item embedder." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "new_embeddings_weights = torch.rand((new_size + 1, 300)) # randint used for example only\n", + "\n", + "best_model.set_item_embeddings_by_tensor(new_embeddings_weights)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "At the moment we expanded our item embeddings by one more item and replace weights by passing `new_embeddings_weights`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "old_size = new_size\n", + "new_size = best_model.get_all_embeddings()[\"item_embedding\"].shape[0]\n", + "\n", + "assert new_size == old_size + 1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Similarly, we can append tensor for only new items with no replace for existing by calling `append_item_embeddings`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "new_item_weights = torch.rand((1, 300)) # randint used for example only\n", + "\n", + "best_model.append_item_embeddings(new_item_weights)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We passed one new example and its weights to item embeddings, thus expanded our vocabulary by one item again" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "old_size = new_size\n", + "new_size = best_model.get_all_embeddings()[\"item_embedding\"].shape[0]\n", + "\n", + "assert new_size == old_size + 1" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example of launching an inference for a single user without using a trainer (in order to speed up)\n", + "An example for the production of an online script\n", + "\n", + "Let's assume that the user's sequence consisted of a sequence of items [1, 2, 3, 4, 5]. \n", + "Сreate a padding mask corresponding to the sequence of items.\n", + "\n", + "It is important to take only the latest MAX_SEQ_LEN or less items." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "item_sequence = torch.arange(1, 5).unsqueeze(0)[:, -MAX_SEQ_LEN:]\n", + "padding_mask = torch.ones_like(item_sequence, dtype=torch.bool)\n", + "sequence_item_count = item_sequence.shape[1]" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Wrapping created tensors in the SasRecPredictionBatch entity" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "batch = SasRecPredictionBatch(\n", + " query_id=torch.arange(0, item_sequence.shape[0], 1).long(),\n", + " padding_mask=padding_mask.bool(),\n", + " features={ITEM_FEATURE_NAME: item_sequence.long()}\n", + ")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Run predict step of the SasRec and get scores from the model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor([[ 2.7048, 2.2736, 2.1556, ..., -0.2144, -1.3674, 1.9593]])" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "with torch.no_grad():\n", + " scores = best_model.predict_step(batch, 0)\n", + "scores" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Getting three items with the highest score" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor([[ 4, 2013, 3032]])" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "torch.topk(scores, k=3).indices" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.9" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "0857f111b041889635bea848a6a183706c3f1c18c9dafdb447caa5e8bea01452" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/replay_benchmarks/__init__.py b/replay_benchmarks/__init__.py new file mode 100755 index 000000000..6dedd0819 --- /dev/null +++ b/replay_benchmarks/__init__.py @@ -0,0 +1,3 @@ +from .base_runner import BaseRunner +from .infer_runner import InferRunner +from .train_runner import TrainRunner \ No newline at end of file diff --git a/replay_benchmarks/base_runner.py b/replay_benchmarks/base_runner.py new file mode 100755 index 000000000..deeba4a43 --- /dev/null +++ b/replay_benchmarks/base_runner.py @@ -0,0 +1,226 @@ +import logging +import os +from abc import ABC, abstractmethod +from typing import Tuple + +from replay_benchmarks.preprocessing import DatasetManager +from replay.data import ( + FeatureHint, + FeatureInfo, + FeatureSchema, + FeatureSource, + FeatureType, + Dataset, +) +from replay.utils import DataFrameLike +from replay.data.nn import ( + SequenceTokenizer, + SequentialDataset, + TensorFeatureSource, + TensorSchema, + TensorFeatureInfo, +) + + +class BaseRunner(ABC): + def __init__(self, config): + self.config = config + self.model_name = config["model"]["name"] + self.model_save_name = config["model"]["save_name"] + self.dataset_name = config["dataset"]["name"] + self.dataset_cfg = config["dataset"] + self.model_cfg = config["model"]["params"] + self.mode = config["mode"]["name"] + self.item_column = self.dataset_cfg["feature_schema"]["item_column"] + self.user_column = self.dataset_cfg["feature_schema"]["query_column"] + self.timestamp_column = self.dataset_cfg["feature_schema"]["timestamp_column"] + self.tokenizer = None + self.dataset_manager = DatasetManager(config) + self.tensor_schema = self.build_tensor_schema() + self.setup_environment() + + def load_data(self): + """Load preprocessed data splits.""" + splits = self.dataset_manager.load_data() + return ( + splits["train"], + splits["validation"], + splits["validation_gt"], + splits["test"], + splits["test_gt"], + ) + + def prepare_feature_schema(self, is_ground_truth: bool) -> FeatureSchema: + """Prepare the feature schema based on whether ground truth is needed.""" + base_features = FeatureSchema( + [ + FeatureInfo( + column=self.user_column, + feature_hint=FeatureHint.QUERY_ID, + feature_type=FeatureType.CATEGORICAL, + ), + FeatureInfo( + column=self.item_column, + feature_hint=FeatureHint.ITEM_ID, + feature_type=FeatureType.CATEGORICAL, + ), + ] + ) + if is_ground_truth: + return base_features + + return base_features + FeatureSchema( + [ + FeatureInfo( + column=self.timestamp_column, + feature_type=FeatureType.NUMERICAL, + feature_hint=FeatureHint.TIMESTAMP, + ), + ] + ) + + def build_tensor_schema(self) -> TensorSchema: + """Build TensorSchema for the sequential model.""" + embedding_dim = self.model_cfg["training_params"]["embedding_dim"] + item_feature_name = "item_id_seq" + + return TensorSchema( + TensorFeatureInfo( + name=item_feature_name, + is_seq=True, + feature_type=FeatureType.CATEGORICAL, + feature_sources=[ + TensorFeatureSource( + FeatureSource.INTERACTIONS, + self.item_column, + ) + ], + feature_hint=FeatureHint.ITEM_ID, + embedding_dim=embedding_dim, + ) + ) + + def prepare_datasets( + self, + train_events: DataFrameLike, + validation_events: DataFrameLike, + validation_gt: DataFrameLike, + test_events: DataFrameLike, + test_gt: DataFrameLike, + ) -> Tuple[Dataset, Dataset, Dataset, Dataset, Dataset]: + """Prepare Dataset objects for training, validation, and testing.""" + logging.info("Preparing Dataset objects...") + feature_schema = self.prepare_feature_schema(is_ground_truth=False) + ground_truth_schema = self.prepare_feature_schema(is_ground_truth=True) + + train_dataset = Dataset( + feature_schema=feature_schema, + interactions=train_events, + check_consistency=True, + categorical_encoded=False, + ) + validation_dataset = Dataset( + feature_schema=feature_schema, + interactions=validation_events, + check_consistency=True, + categorical_encoded=False, + ) + validation_gt_dataset = Dataset( + feature_schema=ground_truth_schema, + interactions=validation_gt, + check_consistency=True, + categorical_encoded=False, + ) + test_dataset = Dataset( + feature_schema=feature_schema, + interactions=test_events, + check_consistency=True, + categorical_encoded=False, + ) + test_gt_dataset = Dataset( + feature_schema=ground_truth_schema, + interactions=test_gt, + check_consistency=True, + categorical_encoded=False, + ) + + return ( + train_dataset, + validation_dataset, + validation_gt_dataset, + test_dataset, + test_gt_dataset, + ) + + def prepare_seq_datasets( + self, + train_dataset: Dataset, + validation_dataset: Dataset, + validation_gt: Dataset, + test_dataset: Dataset, + test_gt: Dataset, + ) -> Tuple[ + SequentialDataset, SequentialDataset, SequentialDataset, SequentialDataset + ]: + """Prepare SequentialDataset objects for training, validation, and testing.""" + logging.info("Preparing SequentialDataset objects...") + self.tokenizer = self.tokenizer or self._initialize_tokenizer(train_dataset) + + seq_train_dataset = self.tokenizer.transform(train_dataset) + seq_validation_dataset, seq_validation_gt = self._prepare_sequential_validation( + validation_dataset, validation_gt + ) + seq_test_dataset = self._prepare_sequential_test(test_dataset, test_gt) + + return ( + seq_train_dataset, + seq_validation_dataset, + seq_validation_gt, + seq_test_dataset, + ) + + def _initialize_tokenizer(self, train_dataset: Dataset) -> SequenceTokenizer: + """Initialize and fit the SequenceTokenizer.""" + logging.info("Initializing and fitting SequenceTokenizer.") + tokenizer = SequenceTokenizer( + self.tensor_schema, allow_collect_to_master=True, handle_unknown_rule="drop" + ) + tokenizer.fit(train_dataset) + return tokenizer + + def _prepare_sequential_validation( + self, validation_dataset: Dataset, validation_gt: Dataset + ) -> Tuple[SequentialDataset, SequentialDataset]: + """Prepare sequential datasets for validation.""" + seq_validation_dataset = self.tokenizer.transform(validation_dataset) + seq_validation_gt = self.tokenizer.transform( + validation_gt, [self.tensor_schema.item_id_feature_name] + ) + + return SequentialDataset.keep_common_query_ids( + seq_validation_dataset, seq_validation_gt + ) + + def _prepare_sequential_test( + self, test_dataset: Dataset, test_gt: Dataset + ) -> SequentialDataset: + """Prepare sequential dataset for testing.""" + test_query_ids = test_gt.query_ids + test_query_ids_np = self.tokenizer.query_id_encoder.transform(test_query_ids)[ + self.user_column + ].values + return self.tokenizer.transform(test_dataset).filter_by_query_id( + test_query_ids_np + ) + + def setup_environment(self): + os.environ["CUDA_DEVICE_ORDER"] = self.config["env"]["CUDA_DEVICE_ORDER"] + os.environ["OMP_NUM_THREADS"] = self.config["env"]["OMP_NUM_THREADS"] + os.environ["CUDA_VISIBLE_DEVICES"] = self.config["env"]["CUDA_VISIBLE_DEVICES"] + os.environ["KAGGLE_USERNAME"] = "recsysaccelerate" + os.environ["KAGGLE_KEY"] = "6363e91b656fea576c39e4f55dcc1d00" + + @abstractmethod + def run(self): + """Run method to be implemented in derived classes.""" + raise NotImplementedError diff --git a/replay_benchmarks/benchmark_test.ipynb b/replay_benchmarks/benchmark_test.ipynb new file mode 100644 index 000000000..e85628789 --- /dev/null +++ b/replay_benchmarks/benchmark_test.ipynb @@ -0,0 +1,1312 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# changing core directory\n", + "import os, sys\n", + "dir2 = os.path.abspath('')\n", + "dir1 = os.path.dirname(dir2)\n", + "if not dir1 in sys.path:\n", + " sys.path.append(dir1)\n", + "os.chdir('..')" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import os\n", + "import logging\n", + "import warnings\n", + "import yaml\n", + "\n", + "from replay_benchmarks.utils.conf import load_config, seed_everything\n", + "from replay_benchmarks import TrainRunner, InferRunner\n", + "\n", + "import logging\n", + "import os\n", + "from abc import ABC, abstractmethod\n", + "from typing import Tuple\n", + "\n", + "import pandas as pd\n", + "from rs_datasets import MovieLens, Netflix\n", + "\n", + "from replay.data import (\n", + " FeatureHint,\n", + " FeatureInfo,\n", + " FeatureSchema,\n", + " FeatureSource,\n", + " FeatureType,\n", + " Dataset,\n", + ")\n", + "from replay.preprocessing.filters import MinCountFilter, NumInteractionsFilter\n", + "from replay.splitters import TimeSplitter\n", + "from replay.utils import DataFrameLike\n", + "from replay.data.nn import (\n", + " SequenceTokenizer,\n", + " SequentialDataset,\n", + " TensorFeatureSource,\n", + " TensorSchema,\n", + " TensorFeatureInfo,\n", + ")\n", + "\n", + "from torch.utils.data import DataLoader\n", + "from replay.models.nn.sequential.sasrec import (\n", + " SasRecTrainingDataset,\n", + " SasRecValidationDataset,\n", + " SasRecPredictionDataset,\n", + ")\n", + "from replay.models.nn.sequential.bert4rec import (\n", + " Bert4RecTrainingDataset,\n", + " Bert4RecValidationDataset,\n", + " Bert4RecPredictionDataset,\n", + ")\n", + "\n", + "\n", + "\n", + "import logging\n", + "import os\n", + "\n", + "import lightning as L\n", + "from lightning.pytorch.loggers import CSVLogger\n", + "from lightning.pytorch.callbacks import ModelCheckpoint\n", + "from torch.utils.data import DataLoader\n", + "from torch.profiler import profile, record_function, ProfilerActivity, schedule\n", + "\n", + "from replay_benchmarks.base_runner import BaseRunner\n", + "from replay.metrics import OfflineMetrics, Recall, Precision, MAP, NDCG, HitRate, MRR\n", + "from replay.metrics.torch_metrics_builder import metrics_to_df\n", + "from replay.models.nn.sequential import SasRec, Bert4Rec\n", + "from replay.models.nn.optimizer_utils import FatOptimizerFactory\n", + "from replay.models.nn.sequential.callbacks import ValidationMetricsCallback\n", + "from replay.models.nn.sequential.postprocessors import RemoveSeenItems\n", + "from replay.models.nn.sequential.sasrec import (\n", + " SasRecTrainingDataset,\n", + " SasRecValidationDataset,\n", + " SasRecPredictionDataset,\n", + ")\n", + "from replay.models.nn.sequential.bert4rec import (\n", + " Bert4RecTrainingDataset,\n", + " Bert4RecValidationDataset,\n", + " Bert4RecPredictionDataset,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "config_dir = \"./replay_benchmarks/configs\"\n", + "base_config_path = os.path.join(config_dir, \"config.yaml\")\n", + "config = load_config(base_config_path, config_dir)\n", + "\n", + "seed_everything(config[\"env\"][\"SEED\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "config = config\n", + "model_name = config[\"model\"][\"name\"]\n", + "dataset_name = config[\"dataset\"][\"name\"]\n", + "dataset_cfg = config[\"dataset\"]\n", + "model_cfg = config[\"model\"][\"params\"]\n", + "mode = config[\"mode\"][\"name\"]\n", + "item_column = dataset_cfg[\"feature_schema\"][\"item_column\"]\n", + "user_column = dataset_cfg[\"feature_schema\"][\"query_column\"]\n", + "timestamp_column = dataset_cfg[\"feature_schema\"][\"timestamp_column\"]\n", + "tokenizer = None\n", + "interactions = None\n", + "user_features = None\n", + "item_features = None\n", + "\n", + "\n", + "os.environ[\"CUDA_DEVICE_ORDER\"] = config[\"env\"][\"CUDA_DEVICE_ORDER\"]\n", + "os.environ[\"OMP_NUM_THREADS\"] = config[\"env\"][\"OMP_NUM_THREADS\"]\n", + "os.environ[\"CUDA_VISIBLE_DEVICES\"] = config[\"env\"][\"CUDA_VISIBLE_DEVICES\"]\n", + "os.environ[\"KAGGLE_USERNAME\"] = \"recsysaccelerate\"\n", + "os.environ[\"KAGGLE_KEY\"] = \"6363e91b656fea576c39e4f55dcc1d00\"" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "embedding_dim = model_cfg[\"training_params\"][\"embedding_dim\"]\n", + "item_feature_name = \"item_id_seq\"\n", + "\n", + "tensor_schema = TensorSchema(\n", + " TensorFeatureInfo(\n", + " name=item_feature_name,\n", + " is_seq=True,\n", + " feature_type=FeatureType.CATEGORICAL,\n", + " feature_sources=[\n", + " TensorFeatureSource(\n", + " FeatureSource.INTERACTIONS,\n", + " item_column,\n", + " )\n", + " ],\n", + " feature_hint=FeatureHint.ITEM_ID,\n", + " embedding_dim=embedding_dim,\n", + " )\n", + ")\n", + "\n", + "tensor_schema" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "DATASET_MAPPINGS = {\n", + " \"zvuk\": {\"kaggle\": \"alexxl/zvuk-dataset\", \"file\": \"zvuk-interactions.parquet\"},\n", + " \"megamarket\": {\"kaggle\": \"alexxl/megamarket\", \"file\": \"megamarket.parquet\"},\n", + "}\n", + "SUPPORTED_RS_DATASETS = [\"movielens\", \"netflix\"]\n", + "\n", + "def _download_dataset(\n", + " data_path: str, dataset_name: str, interactions_file: str\n", + "):\n", + " \"\"\"Download dataset from Kaggle or rs_datasets.\"\"\"\n", + " if dataset_name in DATASET_MAPPINGS:\n", + " _download_kaggle_dataset(data_path, dataset_name, interactions_file)\n", + " elif any(ds in dataset_name for ds in SUPPORTED_RS_DATASETS):\n", + " _download_rs_dataset(data_path, dataset_name, interactions_file)\n", + " else:\n", + " raise ValueError(f\"Unsupported dataset: {dataset_name}\")\n", + "\n", + "def _download_kaggle_dataset(\n", + " data_path: str, dataset_name: str, interactions_file: str\n", + ") -> None:\n", + " from kaggle.api.kaggle_api_extended import KaggleApi\n", + "\n", + " \"\"\"Download dataset from Kaggle.\"\"\"\n", + " kaggle_info = DATASET_MAPPINGS[dataset_name]\n", + " kaggle_dataset = kaggle_info[\"kaggle\"]\n", + " raw_data_file = os.path.join(data_path, kaggle_info[\"file\"])\n", + "\n", + " os.environ.setdefault(\"KAGGLE_USERNAME\", \"recsysaccelerate\")\n", + " os.environ.setdefault(\"KAGGLE_KEY\", \"6363e91b656fea576c39e4f55dcc1d00\")\n", + "\n", + " api = KaggleApi()\n", + " api.authenticate()\n", + "\n", + " api.dataset_download_files(kaggle_dataset, path=data_path, unzip=True)\n", + " logging.info(f\"Dataset downloaded and extracted to {data_path}\")\n", + "\n", + " interactions = pd.read_parquet(raw_data_file)\n", + " interactions[timestamp_column] = interactions[\n", + " timestamp_column\n", + " ].astype(\"int64\")\n", + " if dataset_name == \"megamarket\":\n", + " interactions = interactions[interactions.event == 2] # take only purchase\n", + " interactions.to_parquet(interactions_file)\n", + "\n", + "def _download_rs_dataset(\n", + " data_path: str, dataset_name: str, interactions_file: str\n", + ") -> None:\n", + " \"\"\"Download dataset from rs_datasets.\"\"\"\n", + " if \"movielens\" in dataset_name:\n", + " version = dataset_name.split(\"_\")[1]\n", + " movielens = MovieLens(version=version, path=data_path)\n", + " interactions = movielens.ratings\n", + " interactions = interactions[interactions[dataset_cfg[\"feature_schema\"][\"rating_column\"]] > dataset_cfg[\"preprocess\"][\"min_rating\"]]\n", + " elif dataset_name == \"netflix\":\n", + " netflix = Netflix(path=data_path)\n", + " interactions = pd.concat([netflix.train, netflix.test]).fillna(5).reset_index(drop=True)\n", + " interactions = interactions[interactions[dataset_cfg[\"feature_schema\"][\"rating_column\"]] > dataset_cfg[\"preprocess\"][\"min_rating\"]]\n", + " interactions = interactions.sort_values(by=[user_column, timestamp_column])\n", + " interactions[timestamp_column] += interactions.groupby([user_column, timestamp_column]).cumcount()\n", + " else:\n", + " raise ValueError(f\"Unsupported dataset: {dataset_name}\")\n", + "\n", + " interactions[timestamp_column] = interactions[\n", + " timestamp_column\n", + " ].astype(\"int64\")\n", + " interactions.to_parquet(interactions_file)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
user_iditem_idratingtimestamp
0111935978300760
116613978302109
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" + ], + "text/plain": [ + " user_id item_id rating timestamp\n", + "0 1 1193 5 978300760\n", + "1 1 661 3 978302109\n", + "2 1 914 3 978301968\n", + "3 1 3408 4 978300275\n", + "4 1 2355 5 978824291" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dataset_name = dataset_cfg[\"name\"]\n", + "data_path = dataset_cfg[\"path\"]\n", + "interactions_file = os.path.join(data_path, \"interactions.parquet\")\n", + "\n", + "if not os.path.exists(interactions_file):\n", + " _download_dataset(data_path, dataset_name, interactions_file)\n", + "\n", + "interactions = pd.read_parquet(interactions_file)\n", + "interactions.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " user_id item_id rating timestamp\n", + "count 1.000209e+06 1.000209e+06 1.000209e+06 1.000209e+06\n", + "mean 3.024512e+03 1.865540e+03 3.581564e+00 9.722437e+08\n", + "std 1.728413e+03 1.096041e+03 1.117102e+00 1.215256e+07\n", + "min 1.000000e+00 1.000000e+00 1.000000e+00 9.567039e+08\n", + "25% 1.506000e+03 1.030000e+03 3.000000e+00 9.653026e+08\n", + "50% 3.070000e+03 1.835000e+03 4.000000e+00 9.730180e+08\n", + "75% 4.476000e+03 2.770000e+03 4.000000e+00 9.752209e+08\n", + "max 6.040000e+03 3.952000e+03 5.000000e+00 1.046455e+09\n" + ] + } + ], + "source": [ + "print(interactions.describe())" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "20 1\n" + ] + }, + { + "data": { + "text/plain": [ + "(18, 5)" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "print(interactions.groupby(user_column).size().min(), interactions.groupby(item_column).size().min())\n", + "\n", + "interactions = MinCountFilter(\n", + " num_entries=dataset_cfg[\"preprocess\"][\"min_users_per_item\"],\n", + " groupby_column=item_column,\n", + ").transform(interactions)\n", + "\n", + "interactions = MinCountFilter(\n", + " num_entries=dataset_cfg[\"preprocess\"][\"min_items_per_user\"],\n", + " groupby_column=user_column,\n", + ").transform(interactions)\n", + "\n", + "\n", + "interactions.groupby(user_column).size().min(), interactions.groupby(item_column).size().min()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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user_iditem_idratingtimestamp
count999611.000000999611.000000999611.0000009.996110e+05
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" + ], + "text/plain": [ + " user_id item_id rating timestamp\n", + "count 999611.000000 999611.000000 999611.000000 9.996110e+05\n", + "mean 3024.576537 1865.439428 3.581974 9.722409e+08\n", + "std 1728.436705 1095.976336 1.116894 1.214827e+07\n", + "min 1.000000 1.000000 1.000000 9.567039e+08\n", + "25% 1506.000000 1030.000000 3.000000 9.653025e+08\n", + "50% 3070.000000 1835.000000 4.000000 9.730170e+08\n", + "75% 4477.000000 2770.000000 4.000000 9.752208e+08\n", + "max 6040.000000 3952.000000 5.000000 1.046455e+09" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "interactions.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Distribution of seq_len in validation:\n", + "count 489.000000\n", + "mean 40.132924\n", + "std 84.792205\n", + "min 1.000000\n", + "25% 3.000000\n", + "50% 8.000000\n", + "75% 36.000000\n", + "max 1002.000000\n", + "Name: item_id, dtype: float64.\n", + "Distribution of seq_len in test:\n", + "count 1022.000000\n", + "mean 79.366928\n", + "std 111.068752\n", + "min 1.000000\n", + "25% 13.000000\n", + "50% 37.000000\n", + "75% 101.000000\n", + "max 1010.000000\n", + "Name: item_id, dtype: float64.\n" + ] + } + ], + "source": [ + "splitter = TimeSplitter(\n", + " time_threshold=dataset_cfg[\"preprocess\"][\"global_split_ratio\"],\n", + " drop_cold_users=True,\n", + " drop_cold_items=True,\n", + " item_column=item_column,\n", + " query_column=user_column,\n", + " timestamp_column=timestamp_column,\n", + ")\n", + "\n", + "# train_events, validation_events, validation_gt, test_events, test_gt = (\n", + "# _split_data(splitter, interactions)\n", + "# )\n", + "\n", + "test_events, test_gt = splitter.split(interactions)\n", + "validation_events, validation_gt = splitter.split(test_events)\n", + "train_events = validation_events\n", + "\n", + "test_gt = test_gt[test_gt[item_column].isin(train_events[item_column])]\n", + "test_gt = test_gt[test_gt[user_column].isin(train_events[user_column])]\n", + "\n", + "\n", + "# Limit number of gt events in val and test only if max_num_test_interactions is not null\n", + "max_test_interactions = dataset_cfg[\"preprocess\"][\"max_num_test_interactions\"]\n", + "print(f\"Distribution of seq_len in validation:\\n{validation_gt.groupby(user_column)[item_column].agg('count').describe()}.\")\n", + "print(f\"Distribution of seq_len in test:\\n{test_gt.groupby(user_column)[item_column].agg('count').describe()}.\")\n", + "if max_test_interactions is not None:\n", + " \n", + " validation_gt = NumInteractionsFilter(\n", + " num_interactions=max_test_interactions,\n", + " first=True,\n", + " query_column=user_column,\n", + " item_column=item_column,\n", + " timestamp_column=timestamp_column,\n", + " ).transform(validation_gt)\n", + " print(f\"Distribution of seq_len in validation after filtering:\\n{validation_gt.groupby(user_column)[item_column].agg('count').describe()}.\")\n", + "\n", + " test_gt = NumInteractionsFilter(\n", + " num_interactions=max_test_interactions,\n", + " first=True,\n", + " query_column=user_column,\n", + " item_column=item_column,\n", + " timestamp_column=timestamp_column,\n", + " ).transform(test_gt)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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user_iditem_idratingtimestamp
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" + ], + "text/plain": [ + " user_id item_id rating timestamp\n", + "153950 990 2706 3 978137357\n", + "153914 990 2826 3 978137357\n", + "153908 990 2492 3 978137357\n", + "153923 990 1127 4 978137357\n", + "153915 990 2683 4 978137392" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "test_gt.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "False" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "train_events['timestamp'].max() <= validation_events['timestamp'].min()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "('timestamp', 'item_id', 'user_id')" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "timestamp_column, item_column, user_column" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "def test_splitting(events, gt, name=''):\n", + " if events[timestamp_column].max() > gt[timestamp_column].min():\n", + " print(\"Problem with time points in\", name)\n", + " if len(set(gt[user_column].unique().tolist()) - set(events[user_column].unique().tolist())) > 0:\n", + " print(\"Problem with cold users in\", name)\n", + " if len(set(gt[item_column].unique().tolist()) - set(events[item_column].unique().tolist())) > 0:\n", + " print(\"Problem with cold items in\", name)\n", + "\n", + "\n", + "test_splitting(train_events, test_gt, \"train events, test gt\")\n", + "test_splitting(train_events, validation_gt, \"train events, valid gt\")" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "set()" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "set([1, 2, 3]) - set({1, 2, 3, 4})" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "def prepare_feature_schema(is_ground_truth: bool) -> FeatureSchema:\n", + " \"\"\"Prepare the feature schema based on whether ground truth is needed.\"\"\"\n", + " base_features = FeatureSchema(\n", + " [\n", + " FeatureInfo(\n", + " column=user_column,\n", + " feature_hint=FeatureHint.QUERY_ID,\n", + " feature_type=FeatureType.CATEGORICAL,\n", + " ),\n", + " FeatureInfo(\n", + " column=item_column,\n", + " feature_hint=FeatureHint.ITEM_ID,\n", + " feature_type=FeatureType.CATEGORICAL,\n", + " ),\n", + " ]\n", + " )\n", + " if is_ground_truth:\n", + " return base_features\n", + "\n", + " return base_features + FeatureSchema(\n", + " [\n", + " FeatureInfo(\n", + " column=timestamp_column,\n", + " feature_type=FeatureType.NUMERICAL,\n", + " feature_hint=FeatureHint.TIMESTAMP,\n", + " ),\n", + " ]\n", + " )\n", + "\n", + "feature_schema = prepare_feature_schema(is_ground_truth=False)\n", + "ground_truth_schema = prepare_feature_schema(is_ground_truth=True)\n", + "\n", + "train_dataset = Dataset(\n", + " feature_schema=feature_schema,\n", + " interactions=train_events,\n", + " query_features=user_features,\n", + " item_features=item_features,\n", + " check_consistency=True,\n", + " categorical_encoded=False,\n", + ")\n", + "validation_dataset = Dataset(\n", + " feature_schema=feature_schema,\n", + " interactions=validation_events,\n", + " query_features=user_features,\n", + " item_features=item_features,\n", + " check_consistency=True,\n", + " categorical_encoded=False,\n", + ")\n", + "validation_gt_dataset = Dataset(\n", + " feature_schema=ground_truth_schema,\n", + " interactions=validation_gt,\n", + " check_consistency=True,\n", + " categorical_encoded=False,\n", + ")\n", + "test_dataset = Dataset(\n", + " feature_schema=feature_schema,\n", + " interactions=test_events,\n", + " query_features=user_features,\n", + " item_features=item_features,\n", + " check_consistency=True,\n", + " categorical_encoded=False,\n", + ")\n", + "test_gt_dataset = Dataset(\n", + " feature_schema=ground_truth_schema,\n", + " interactions=test_gt,\n", + " check_consistency=True,\n", + " categorical_encoded=False,\n", + ")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "tokenizer = SequenceTokenizer(\n", + " tensor_schema, allow_collect_to_master=True, handle_unknown_rule=\"drop\"\n", + ")\n", + "tokenizer.fit(train_dataset)\n", + "\n", + "seq_train_dataset = tokenizer.transform(train_dataset)\n", + "# seq_validation_dataset, seq_validation_gt = _prepare_sequential_validation(\n", + "# validation_dataset, validation_gt\n", + "# )\n", + "\n", + "seq_validation_dataset = tokenizer.transform(validation_dataset)\n", + "seq_validation_gt = tokenizer.transform(\n", + " validation_gt_dataset, [tensor_schema.item_id_feature_name]\n", + ")\n", + "\n", + "seq_validation_dataset, seq_validation_gt = SequentialDataset.keep_common_query_ids(\n", + " seq_validation_dataset, seq_validation_gt\n", + ")\n", + "\n", + "\n", + "test_query_ids = test_gt_dataset.query_ids\n", + "test_query_ids_np = tokenizer.query_id_encoder.transform(test_query_ids)[\n", + " user_column\n", + "].values\n", + "seq_test_dataset = tokenizer.transform(test_dataset).filter_by_query_id(\n", + " test_query_ids_np\n", + ")\n", + "# seq_test_dataset = self._prepare_sequential_test(test_dataset, test_gt)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'user_id': {6040: 0, 6039: 1, 6038: 2, 6037: 3, 6036: 4, 6035: 5, 6034: 6, 6033: 7, 6032: 8, 6031: 9, 6030: 10, 6029: 11, 6028: 12, 6027: 13, 6026: 14, 6025: 15, 6024: 16, 6023: 17, 6022: 18, 6021: 19, 6020: 20, 6019: 21, 6018: 22, 6017: 23, 6016: 24, 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tokenizer.query_id_encoder.inverse_mapping)\n", + "print(tokenizer.item_id_encoder.mapping, tokenizer.item_id_encoder.inverse_mapping)\n", + "\n", + "print(len(tokenizer.query_id_encoder.mapping[\"user_id\"]), len(tokenizer.item_id_encoder.mapping['item_id']))" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "dataset_mapping = {\n", + " \"sasrec\": (\n", + " SasRecTrainingDataset,\n", + " SasRecValidationDataset,\n", + " SasRecPredictionDataset,\n", + " ),\n", + " \"bert4rec\": (\n", + " Bert4RecTrainingDataset,\n", + " Bert4RecValidationDataset,\n", + " Bert4RecPredictionDataset,\n", + " ),\n", + "}\n", + "\n", + "if model_name.lower() in dataset_mapping:\n", + " TrainingDataset, ValidationDataset, PredictionDataset = dataset_mapping[\n", + " model_name.lower()\n", + " ]\n", + "else:\n", + " raise ValueError(\n", + " f\"Unsupported model type for dataloaders: {model_name}\"\n", + " )\n", + "\n", + "common_params = {\n", + " \"batch_size\": model_cfg[\"training_params\"][\"batch_size\"],\n", + " \"num_workers\": model_cfg[\"training_params\"][\"num_workers\"],\n", + " \"pin_memory\": True,\n", + "}\n", + "\n", + "train_dataloader = DataLoader(\n", + " dataset=TrainingDataset(\n", + " seq_train_dataset,\n", + " max_sequence_length=model_cfg[\"model_params\"][\"max_seq_len\"],\n", + " ),\n", + " shuffle=True,\n", + " **common_params,\n", + ")\n", + "val_dataloader = DataLoader(\n", + " dataset=ValidationDataset(\n", + " seq_validation_dataset,\n", + " seq_validation_gt,\n", + " seq_train_dataset,\n", + " max_sequence_length=model_cfg[\"model_params\"][\"max_seq_len\"],\n", + " ),\n", + " **common_params,\n", + ")\n", + "prediction_dataloader = DataLoader(\n", + " dataset=PredictionDataset(\n", + " seq_test_dataset,\n", + " max_sequence_length=model_cfg[\"model_params\"][\"max_seq_len\"],\n", + " ),\n", + " **common_params,\n", + ")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "model_config = {\n", + " \"tensor_schema\": tensor_schema,\n", + " \"optimizer_factory\": FatOptimizerFactory(\n", + " learning_rate=model_cfg[\"training_params\"][\"learning_rate\"]\n", + " ),\n", + "}\n", + "model_config.update(model_cfg[\"model_params\"])\n", + "\n", + "if model_name.lower() == \"sasrec\":\n", + " model = SasRec(**model_config)\n", + "elif model_name.lower() == \"bert4rec\":\n", + " model = Bert4Rec(**model_config)\n", + "else:\n", + " raise ValueError(f\"Unsupported model type: {model_name}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(2, 2, 300, 200, 0.5)" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model._model.num_blocks, model._model.num_heads, model._model.hidden_size, model._model.max_len, model._model.dropout," + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "GPU available: True (cuda), used: True\n", + "TPU available: False, using: 0 TPU cores\n", + "IPU available: False, using: 0 IPUs\n", + "HPU available: False, using: 0 HPUs\n", + "/usr/local/lib/python3.11/site-packages/lightning/pytorch/trainer/connectors/logger_connector/logger_connector.py:75: Starting from v1.9.0, `tensorboardX` has been removed as a dependency of the `lightning.pytorch` package, due to potential conflicts with other packages in the ML ecosystem. For this reason, `logger=True` will use `CSVLogger` as the default logger, unless the `tensorboard` or `tensorboardX` packages are found. Please `pip install lightning[extra]` or one of them to enable TensorBoard support by default\n", + "You are using a CUDA device ('NVIDIA L40') that has Tensor Cores. To properly utilize them, you should set `torch.set_float32_matmul_precision('medium' | 'high')` which will trade-off precision for performance. For more details, read https://pytorch.org/docs/stable/generated/torch.set_float32_matmul_precision.html#torch.set_float32_matmul_precision\n", + "/usr/local/lib/python3.11/site-packages/lightning/pytorch/callbacks/model_checkpoint.py:653: Checkpoint directory /root/RePlay-Accelerated/replay_benchmarks/artifacts/checkpoints exists and is not empty.\n", + "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n", + "\n", + " | Name | Type | Params\n", + "--------------------------------------------\n", + "0 | _model | SasRecModel | 2.2 M \n", + "1 | _loss | CrossEntropyLoss | 0 \n", + "--------------------------------------------\n", + "2.2 M Trainable params\n", + "0 Non-trainable params\n", + "2.2 M Total params\n", + "8.682 Total estimated model params size (MB)\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "e3e5c19fde084b18abb543f8a39f1b10", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Sanity Checking: | | 0/? [00:00> /etc/sudoers && \ + echo '%sudo ALL=(ALL) NOPASSWD:ALL' >> /etc/sudoers + +# Add developers group and users +RUN addgroup --gid 1000 developers && \ + for user in $(getent passwd | cut -d: -f1); do \ + if [[ $user == "da" ]] || [[ $(id -u $user) -ge 1000 ]] && [[ $(eval echo "~$user") == /home* ]]; then \ + addgroup --gid $(id -g $user) $user && \ + adduser --disabled-password --gecos "" --uid $(id -u $user) --gid $(id -g $user) --shell $(getent passwd $user | cut -d: -f7) --home $(eval echo "~$user") $user && \ + adduser $user sudo && adduser $user developers; \ + fi; \ + done + +# Set up Miniconda for environment and package management +RUN curl -sLo ~/miniconda.sh https://repo.anaconda.com/miniconda/Miniconda3-py39_23.1.0-1-Linux-x86_64.sh \ + && chmod +x ~/miniconda.sh \ + && ~/miniconda.sh -b -p /opt/miniconda3 \ + && rm ~/miniconda.sh + +# Update PATH to include Miniconda +ENV PATH="/opt/miniconda3/bin:$PATH" + +# Install Mamba +RUN conda install -n base -c conda-forge mamba + +# Copy and install environment using Mamba +COPY environment.yml /tmp/environment.yml +RUN mamba env create --file=/tmp/environment.yml \ + && conda clean -ya \ + && rm /tmp/environment.yml + +# Install additional Python packages via pip +RUN conda run -n env pip install hydra-core implicit lightfm torch pytorch-lightning sqlalchemy hdfs redis msgpack_numpy msgpack lz4 nvitop + +# Set up default Conda environment and activate it +RUN echo "source activate env" > ~/.bashrc +ENV PATH /opt/conda/envs/env/bin:$PATH +SHELL ["conda", "run", "-n", "env", "/bin/bash", "-c"] + +# Download and setup Hadoop +RUN wget https://archive.apache.org/dist/hadoop/common/hadoop-3.3.1/hadoop-3.3.1.tar.gz && \ + tar -xzf hadoop-3.3.1.tar.gz -C /opt && \ + rm hadoop-3.3.1.tar.gz + +# Set environment variables for Hadoop +ENV HADOOP_HOME=/opt/hadoop-3.3.1 +ENV HADOOP_CONF_DIR=$HADOOP_HOME/etc/hadoop +ENV LD_LIBRARY_PATH=$HADOOP_HOME/lib/native:$LD_LIBRARY_PATH +ENV PATH=$HADOOP_HOME/bin:$PATH + +# Download and setup Spark +RUN wget https://archive.apache.org/dist/spark/spark-3.4.1/spark-3.4.1-bin-hadoop3.tgz && \ + tar -xzf spark-3.4.1-bin-hadoop3.tgz -C /opt && \ + ln -s /opt/spark-3.4.1-bin-hadoop3 /opt/spark && \ + rm spark-3.4.1-bin-hadoop3.tgz + +# Set environment variables for Spark +ENV SPARK_HOME /opt/spark +ENV PATH $SPARK_HOME/bin:$PATH \ No newline at end of file diff --git a/replay_benchmarks/environment/environment.yml b/replay_benchmarks/environment/environment.yml new file mode 100755 index 000000000..f01368e29 --- /dev/null +++ b/replay_benchmarks/environment/environment.yml @@ -0,0 +1,22 @@ +name: env +channels: + - defaults +dependencies: + - python=3.9 + - numpy + - pandas + - pyspark + - hdfs3 + - scikit-learn + - seaborn + - scipy + - sqlite + - h5py + - ipykernel + - matplotlib + - pip + - pyparsing + - pytest + - redis + - requests + - setuptools \ No newline at end of file diff --git a/replay_benchmarks/infer_runner.py b/replay_benchmarks/infer_runner.py new file mode 100755 index 000000000..534b35bd5 --- /dev/null +++ b/replay_benchmarks/infer_runner.py @@ -0,0 +1,36 @@ +from replay.models.nn.sequential import SasRec, Bert4Rec +from replay.utils import get_spark_session +from .base_runner import BaseRunner + + +class InferRunner(BaseRunner): + def __init__(self, config): + super().__init__(config) + self.spark_session = get_spark_session() + self.model = self.load_model() + + def load_model(self): + """Load the appropriate model based on the config.""" + model_config = { + "tensor_schema": self.model_cfg["tensor_schema"], + "block_count": self.model_cfg["block_count"], + "head_count": self.model_cfg["head_count"], + "max_seq_len": self.model_cfg["max_sequence_length"], + "hidden_size": self.model_cfg["hidden_size"], + "dropout_rate": self.model_cfg["dropout_rate"], + } + + if self.model_name.lower() == "sasrec": + model = SasRec(**model_config) + elif self.model_name.lower() == "bert4rec": + model = Bert4Rec(**model_config) + else: + raise ValueError(f"Model {self.model_name} not recognized.") + + model = model.load_from_checkpoint(self.config["paths"]["checkpoint_dir"]) + model.eval() + return model + + def run(self): + """Main inference logic.""" + pass diff --git a/replay_benchmarks/preprocessing/__init__.py b/replay_benchmarks/preprocessing/__init__.py new file mode 100644 index 000000000..bcf89d573 --- /dev/null +++ b/replay_benchmarks/preprocessing/__init__.py @@ -0,0 +1 @@ +from .dataset_manager import DatasetManager \ No newline at end of file diff --git a/replay_benchmarks/preprocessing/dataset_manager.py b/replay_benchmarks/preprocessing/dataset_manager.py new file mode 100644 index 000000000..8da908ab1 --- /dev/null +++ b/replay_benchmarks/preprocessing/dataset_manager.py @@ -0,0 +1,228 @@ +import os +import pandas as pd +import logging + +from rs_datasets import MovieLens, Netflix + +from replay.splitters import TimeSplitter +from replay.preprocessing.filters import MinCountFilter, NumInteractionsFilter + +DATASET_MAPPINGS = { + "zvuk": {"kaggle": "alexxl/zvuk-dataset", "file": "zvuk-interactions.parquet"}, + "megamarket": {"kaggle": "alexxl/megamarket", "file": "megamarket.parquet"}, +} +SUPPORTED_RS_DATASETS = ["movielens", "netflix"] + + +class DatasetManager: + def __init__(self, config): + self.config = config + self.data_path = config["dataset"]["path"] + self.dataset_name = config["dataset"]["name"] + self.item_column = config["dataset"]["feature_schema"]["item_column"] + self.user_column = config["dataset"]["feature_schema"]["query_column"] + self.timestamp_column = config["dataset"]["feature_schema"]["timestamp_column"] + self.split_cache_dir = os.path.join(self.data_path, "split_cache") + + os.makedirs(self.split_cache_dir, exist_ok=True) + + def load_data(self): + split_paths = { + "train": os.path.join(self.split_cache_dir, "train.parquet"), + "validation": os.path.join(self.split_cache_dir, "validation.parquet"), + "validation_gt": os.path.join(self.split_cache_dir, "validation_gt.parquet"), + "test": os.path.join(self.split_cache_dir, "test.parquet"), + "test_gt": os.path.join(self.split_cache_dir, "test_gt.parquet"), + } + + if all(os.path.exists(path) for path in split_paths.values()): + logging.info("Loading preprocessed splits from cache.") + return {key: pd.read_parquet(path) for key, path in split_paths.items()} + + logging.info("Preprocessing data as splits are not cached.") + raw_data = self._load_raw_data() + filtered_data = self._filter_data(raw_data) + splits = self._split_data(filtered_data) + + # Save preprocessed splits + for split_name, split_data in splits.items(): + split_data.to_parquet(split_paths[split_name], index=False) + + return splits + + def _load_raw_data(self): + interactions_file = os.path.join(self.data_path, "interactions.parquet") + if not os.path.exists(interactions_file): + logging.info(f"Dataset not found at {interactions_file}. Downloading...") + self._download_dataset(self.data_path, self.dataset_name, interactions_file) + logging.info(f"Loading raw dataset from {interactions_file}") + return pd.read_parquet(interactions_file) + + def _download_dataset( + self, data_path: str, dataset_name: str, interactions_file: str + ): + """Download dataset from Kaggle or rs_datasets.""" + if dataset_name in DATASET_MAPPINGS: + self._download_kaggle_dataset(data_path, dataset_name, interactions_file) + elif any(ds in dataset_name for ds in SUPPORTED_RS_DATASETS): + self._download_rs_dataset(data_path, dataset_name, interactions_file) + else: + raise ValueError(f"Unsupported dataset: {dataset_name}") + + def _download_kaggle_dataset( + self, data_path: str, dataset_name: str, interactions_file: str + ) -> None: + from kaggle.api.kaggle_api_extended import KaggleApi + + """Download dataset from Kaggle.""" + kaggle_info = DATASET_MAPPINGS[dataset_name] + kaggle_dataset = kaggle_info["kaggle"] + raw_data_file = os.path.join(data_path, kaggle_info["file"]) + + os.environ.setdefault("KAGGLE_USERNAME", "recsysaccelerate") + os.environ.setdefault("KAGGLE_KEY", "6363e91b656fea576c39e4f55dcc1d00") + + api = KaggleApi() + api.authenticate() + + api.dataset_download_files(kaggle_dataset, path=data_path, unzip=True) + logging.info(f"Dataset downloaded and extracted to {data_path}") + + interactions = pd.read_parquet(raw_data_file) + interactions[self.timestamp_column] = interactions[ + self.timestamp_column + ].astype("int64") + if dataset_name == "megamarket": + interactions = interactions[interactions.event == 2] # take only purchase + interactions.to_parquet(interactions_file) + + def _download_rs_dataset( + self, data_path: str, dataset_name: str, interactions_file: str + ) -> None: + """Download dataset from rs_datasets.""" + if "movielens" in dataset_name: + version = dataset_name.split("_")[1] + movielens = MovieLens(version=version, path=data_path) + interactions = movielens.ratings + interactions = interactions[ + interactions[self.config["dataset"]["feature_schema"]["rating_column"]] + > self.config["dataset"]["preprocess"]["min_rating"] + ] + elif dataset_name == "netflix": + netflix = Netflix(path=data_path) + interactions = ( + pd.concat([netflix.train, netflix.test]) + .fillna(5) + .reset_index(drop=True) + ) + interactions = interactions[ + interactions[self.config["dataset"]["feature_schema"]["rating_column"]] + > self.config["dataset"]["preprocess"]["min_rating"] + ] + interactions = interactions.sort_values( + by=[self.user_column, self.timestamp_column] + ) + interactions[self.timestamp_column] = pd.to_datetime(interactions[self.timestamp_column]) + interactions[self.timestamp_column] += pd.to_timedelta( + interactions.groupby([self.user_column, self.timestamp_column]).cumcount(), + unit="s" + ) + else: + raise ValueError(f"Unsupported dataset: {dataset_name}") + + interactions[self.timestamp_column] = interactions[ + self.timestamp_column + ].astype("int64") + interactions.to_parquet(interactions_file) + + def _filter_data(self, interactions: pd.DataFrame): + """Filters raw data based on minimum interaction counts.""" + + def log_min_counts(data: pd.DataFrame, message_prefix: str): + user_min = data.groupby(self.user_column).size().min() + item_min = data.groupby(self.item_column).size().min() + logging.info( + f"{message_prefix} - Min items per user: {user_min}, Min users per item: {item_min}" + ) + + log_min_counts(interactions, "Before filtering") + + interactions = MinCountFilter( + num_entries=self.config["dataset"]["preprocess"]["min_users_per_item"], + groupby_column=self.item_column, + ).transform(interactions) + + interactions = MinCountFilter( + num_entries=self.config["dataset"]["preprocess"]["min_items_per_user"], + groupby_column=self.user_column, + ).transform(interactions) + + log_min_counts(interactions, "After filtering") + + return interactions + + def _split_data(self, interactions): + """Split data for training, validation, and testing.""" + splitter = TimeSplitter( + time_threshold=self.config["dataset"]["preprocess"]["global_split_ratio"], + drop_cold_users=True, + drop_cold_items=True, + item_column=self.item_column, + query_column=self.user_column, + timestamp_column=self.timestamp_column, + ) + + test_events, test_gt = splitter.split(interactions) + validation_events, validation_gt = splitter.split(test_events) + train_events = validation_events + + test_gt = test_gt[ + test_gt[self.item_column].isin(train_events[self.item_column]) + ] + test_gt = test_gt[ + test_gt[self.user_column].isin(train_events[self.user_column]) + ] + + # Limit number of gt events in val and test only if max_num_test_interactions is not null + max_test_interactions = self.config["dataset"]["preprocess"][ + "max_num_test_interactions" + ] + logging.info( + f"Distribution of seq_len in validation:\n{validation_gt.groupby(self.user_column)[self.item_column].agg('count').describe()}." + ) + logging.info( + f"Distribution of seq_len in test:\n{test_gt.groupby(self.user_column)[self.item_column].agg('count').describe()}." + ) + if max_test_interactions is not None: + + validation_gt = NumInteractionsFilter( + num_interactions=max_test_interactions, + first=True, + query_column=self.user_column, + item_column=self.item_column, + timestamp_column=self.timestamp_column, + ).transform(validation_gt) + logging.info( + f"Distribution of seq_len in validation after filtering:\n{validation_gt.groupby(self.user_column)[self.item_column].agg('count').describe()}." + ) + + test_gt = NumInteractionsFilter( + num_interactions=max_test_interactions, + first=True, + query_column=self.user_column, + item_column=self.item_column, + timestamp_column=self.timestamp_column, + ).transform(test_gt) + logging.info( + f"Distribution of seq_len in test after filtering:\n{test_gt.groupby(self.user_column)[self.item_column].agg('count').describe()}." + ) + else: + logging.info("max_num_test_interactions is null. Skipping filtration.") + + return { + "train": train_events, + "validation": validation_events, + "validation_gt": validation_gt, + "test": test_events, + "test_gt": test_gt, + } diff --git a/replay_benchmarks/train_runner.py b/replay_benchmarks/train_runner.py new file mode 100755 index 000000000..2cbed048e --- /dev/null +++ b/replay_benchmarks/train_runner.py @@ -0,0 +1,313 @@ +import logging +import os +import yaml +from pathlib import Path + +import torch +import lightning as L +from lightning.pytorch.loggers import CSVLogger, TensorBoardLogger +from lightning.pytorch.callbacks import ModelCheckpoint, EarlyStopping +from lightning.pytorch.profilers import SimpleProfiler +from torch.utils.data import DataLoader +from torch.profiler import profile, ProfilerActivity + +from replay_benchmarks.base_runner import BaseRunner +from replay.metrics import ( + OfflineMetrics, + Recall, + Precision, + MAP, + NDCG, + HitRate, + MRR, + Coverage, +) +from replay.metrics.torch_metrics_builder import metrics_to_df +from replay.models.nn.sequential import SasRec, Bert4Rec +from replay.models.nn.optimizer_utils import FatOptimizerFactory +from replay.models.nn.sequential.callbacks import ( + ValidationMetricsCallback, + PandasPredictionCallback, +) +from replay.models.nn.sequential.postprocessors import RemoveSeenItems +from replay.models.nn.sequential.sasrec import ( + SasRecTrainingDataset, + SasRecValidationDataset, + SasRecPredictionDataset, +) +from replay.models.nn.sequential.bert4rec import ( + Bert4RecTrainingDataset, + Bert4RecValidationDataset, + Bert4RecPredictionDataset, +) + + +class TrainRunner(BaseRunner): + def __init__(self, config): + super().__init__(config) + self.item_count = None + self.raw_test_gt = None + self.seq_val_dataset = None + self.seq_test_dataset = None + + # Loggers + self.log_dir = Path(config["paths"]["log_dir"]) / self.dataset_name / self.model_save_name + self.csv_logger = CSVLogger(save_dir=self.log_dir / "csv_logs") + self.tb_logger = TensorBoardLogger(save_dir=self.log_dir / "tb_logs") + + self._check_paths() + + def _check_paths(self): + """Ensure all required directories exist.""" + required_paths = [ + self.config["paths"]["log_dir"], + self.config["paths"]["checkpoint_dir"], + self.config["paths"]["results_dir"], + ] + for path in required_paths: + Path(path).mkdir(parents=True, exist_ok=True) + + def _initialize_model(self): + """Initialize the model based on the configuration.""" + model_config = { + "tensor_schema": self.tensor_schema, + "optimizer_factory": FatOptimizerFactory( + learning_rate=self.model_cfg["training_params"]["learning_rate"] + ), + } + model_config.update(self.model_cfg["model_params"]) + + if "sasrec" in self.model_name.lower(): + return SasRec(**model_config) + elif "bert4rec" in self.model_name.lower(): + return Bert4Rec(**model_config) + else: + raise ValueError(f"Unsupported model type: {self.model_name}") + + def _prepare_dataloaders( + self, + seq_train_dataset, + seq_validation_dataset, + seq_validation_gt, + seq_test_dataset, + ): + """Initialize dataloaders for training, validation, and testing.""" + logging.info("Preparing dataloaders...") + + dataset_mapping = { + "sasrec": ( + SasRecTrainingDataset, + SasRecValidationDataset, + SasRecPredictionDataset, + ), + "bert4rec": ( + Bert4RecTrainingDataset, + Bert4RecValidationDataset, + Bert4RecPredictionDataset, + ), + } + + datasets = dataset_mapping.get(self.model_name.lower()) + if not datasets: + raise ValueError( + f"Unsupported model type for dataloaders: {self.model_name}" + ) + + TrainingDataset, ValidationDataset, PredictionDataset = datasets + common_params = { + "batch_size": self.model_cfg["training_params"]["batch_size"], + "num_workers": self.model_cfg["training_params"]["num_workers"], + "pin_memory": True, + } + + train_dataloader = DataLoader( + dataset=TrainingDataset( + seq_train_dataset, + max_sequence_length=self.model_cfg["model_params"]["max_seq_len"], + ), + shuffle=True, + **common_params, + ) + val_dataloader = DataLoader( + dataset=ValidationDataset( + seq_validation_dataset, + seq_validation_gt, + seq_train_dataset, + max_sequence_length=self.model_cfg["model_params"]["max_seq_len"], + ), + **common_params, + ) + prediction_dataloader = DataLoader( + dataset=PredictionDataset( + seq_test_dataset, + max_sequence_length=self.model_cfg["model_params"]["max_seq_len"], + ), + **common_params, + ) + + return train_dataloader, val_dataloader, prediction_dataloader + + def _load_dataloaders(self): + """Loads data and prepares dataloaders.""" + logging.info("Preparing datasets for training.") + train_events, validation_events, validation_gt, test_events, test_gt = ( + self.load_data() + ) + self.raw_test_gt = test_gt + + train_dataset, val_dataset, val_gt_dataset, test_dataset, test_gt_dataset = ( + self.prepare_datasets( + train_events, validation_events, validation_gt, test_events, test_gt + ) + ) + self.item_count = train_dataset.item_count + + ( + seq_train_dataset, + seq_validation_dataset, + seq_validation_gt, + seq_test_dataset, + ) = self.prepare_seq_datasets( + train_dataset, val_dataset, val_gt_dataset, test_dataset, test_gt_dataset + ) + self.seq_val_dataset = seq_validation_dataset + self.seq_test_dataset = seq_test_dataset + + return self._prepare_dataloaders( + seq_train_dataset, + seq_validation_dataset, + seq_validation_gt, + seq_test_dataset, + ) + + def calculate_metrics(self, predictions, ground_truth): + """Calculate and return the desired metrics based on the predictions.""" + top_k = self.config["metrics"]["ks"] + metrics_list = [ + Recall(top_k), + Precision(top_k), + MAP(top_k), + NDCG(top_k), + MRR(top_k), + HitRate(top_k), + ] + metrics = OfflineMetrics( + metrics_list, query_column="user_id", rating_column="score" + )(predictions, ground_truth) + return metrics_to_df(metrics) + + def save_model(self, trainer, best_model): + """Save the best model checkpoint to the specified directory.""" + save_path = os.path.join( + self.config["paths"]["checkpoint_dir"], + f"{self.model_save_name}_{self.dataset_name}", + ) + torch.save( + { + "model_state_dict": best_model.state_dict(), + "optimizer_state_dict": trainer.optimizers[0].state_dict(), + "config": self.model_cfg, + }, + f"{save_path}/{self.model_save_name}_checkpoint.pth", + ) + + self.tokenizer.save(f"{save_path}/sequence_tokenizer") + logging.info(f"Best model saved at: {save_path}") + + def run(self): + """Execute the training pipeline.""" + train_dataloader, val_dataloader, prediction_dataloader = ( + self._load_dataloaders() + ) + + logging.info("Initializing model...") + model = self._initialize_model() + + checkpoint_callback = ModelCheckpoint( + dirpath=os.path.join( + self.config["paths"]["checkpoint_dir"], + f"{self.model_save_name}_{self.dataset_name}", + ), + save_top_k=1, + verbose=True, + monitor="ndcg@10", + mode="max", + ) + + early_stopping = EarlyStopping( + monitor="ndcg@10", + patience=self.model_cfg["training_params"]["max_epochs"] // 10, + mode="max", + verbose=True, + ) + + validation_metrics_callback = ValidationMetricsCallback( + metrics=self.config["metrics"]["types"], + ks=self.config["metrics"]["ks"], + item_count=self.item_count, + postprocessors=[RemoveSeenItems(self.seq_val_dataset)], + ) + + profiler = SimpleProfiler(dirpath = self.csv_logger.log_dir, filename = 'simple_profiler') + + trainer = L.Trainer( + max_epochs=self.model_cfg["training_params"]["max_epochs"], + callbacks=[checkpoint_callback, early_stopping, validation_metrics_callback], + logger=[self.csv_logger, self.tb_logger], + profiler=profiler + ) + + logging.info("Starting training...") + if self.config["mode"]["profiler"]["enabled"]: + with profile( + activities=[ProfilerActivity.CPU, ProfilerActivity.CUDA], + record_shapes=True, + with_flops=True, + profile_memory=True, + ) as prof: + trainer.fit(model, train_dataloader, val_dataloader) + logging.info( + prof.key_averages().table( + sort_by="self_cuda_time_total", + row_limit=self.config["mode"]["profiler"].get("row_limit", 10), + ) + ) + prof.export_chrome_trace( + os.path.join( + self.config["paths"]["log_dir"], + f"{self.model_save_name}_{self.dataset_name}_profile.json", + ) + ) + else: + trainer.fit(model, train_dataloader, val_dataloader) + if self.model_name.lower() == "sasrec": + best_model = SasRec.load_from_checkpoint(checkpoint_callback.best_model_path) + elif self.model_name.lower() == "bert4rec": + best_model = Bert4Rec.load_from_checkpoint(checkpoint_callback.best_model_path) + self.save_model(trainer, best_model) + + logging.info("Evaluating on test set...") + pandas_prediction_callback = PandasPredictionCallback( + top_k=max(self.config["metrics"]["ks"]), + query_column="user_id", + item_column="item_id", + rating_column="score", + postprocessors=[RemoveSeenItems(self.seq_test_dataset)], + ) + L.Trainer(callbacks=[pandas_prediction_callback], inference_mode=True).predict( + best_model, dataloaders=prediction_dataloader, return_predictions=False + ) + + result = pandas_prediction_callback.get_result() + recommendations = self.tokenizer.query_and_item_id_encoder.inverse_transform( + result + ) + test_metrics = self.calculate_metrics(recommendations, self.raw_test_gt) + logging.info(test_metrics) + test_metrics.to_csv( + os.path.join( + self.config["paths"]["results_dir"], + f"{self.model_save_name}_{self.dataset_name}_test_metrics.csv", + ), + ) + diff --git a/replay_benchmarks/utils/__init__.py b/replay_benchmarks/utils/__init__.py new file mode 100755 index 000000000..e94810ef6 --- /dev/null +++ b/replay_benchmarks/utils/__init__.py @@ -0,0 +1 @@ +from .profiler import Profiler \ No newline at end of file diff --git a/replay_benchmarks/utils/conf.py b/replay_benchmarks/utils/conf.py new file mode 100644 index 000000000..41982f094 --- /dev/null +++ b/replay_benchmarks/utils/conf.py @@ -0,0 +1,52 @@ +import os +import yaml +import random +from typing import Dict + +import torch +import numpy as np + + +def load_yaml(file_path: str) -> Dict: + """Load a single YAML file.""" + with open(file_path, "r") as f: + return yaml.safe_load(f) + + +def seed_everything(seed: int) -> None: + """Fix seed everywhere.""" + torch.manual_seed(seed) + torch.cuda.manual_seed(seed) + np.random.seed(seed) + random.seed(seed) + + torch.backends.cudnn.deterministic=True + + +def merge_dicts(base: Dict, update: Dict) -> Dict: + """Recursively merge two dictionaries.""" + for key, value in update.items(): + if isinstance(value, dict) and key in base: + base[key] = merge_dicts(base[key], value) + else: + base[key] = value + return base + + +def load_config(base_config_path: str, config_dir: str) -> Dict: + """Load base configuration and merge sub-configurations.""" + base_config = load_yaml(base_config_path) + defaults = base_config.get("defaults", []) + + for default in defaults: + for key, subconfig in default.items(): + if subconfig is None: + continue + subconfig_path = os.path.join(config_dir, key, f"{subconfig}.yaml") + if not os.path.exists(subconfig_path): + raise FileNotFoundError(f"Configuration file not found: {subconfig_path}") + subconfig_data = load_yaml(subconfig_path) + base_config = merge_dicts(base_config, subconfig_data) + + base_config.pop("defaults", None) + return base_config \ No newline at end of file diff --git a/replay_benchmarks/utils/profiler.py b/replay_benchmarks/utils/profiler.py new file mode 100755 index 000000000..d859f1d88 --- /dev/null +++ b/replay_benchmarks/utils/profiler.py @@ -0,0 +1,4 @@ +class Profiler: + def __init__(self): + """Initialize profiler with placeholders for system and GPU metrics.""" + pass