harisarang
commited on
Commit
•
e4a101c
1
Parent(s):
6a246ff
add: jsonl datasets
Browse files- trainer.py +0 -303
trainer.py
DELETED
@@ -1,303 +0,0 @@
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from os import environ, path
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from transformers import BertTokenizerFast, FlaxAutoModel
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import jax.numpy as jnp
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import jax
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from flax.training.train_state import TrainState
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import pandas as pd
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from tyrec.trainer import BaseTrainer, loss, HFConfig
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from tyrec.recommendations.model import RecommendationModel
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from tyrec.utils import compute_mean, logger
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from tyrec.evaluator import RetrivalEvaluator
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from tyrec.utils import trange
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class RecommendationsTrainer(BaseTrainer):
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def __init__(
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self,
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hf_config: HFConfig,
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data_dir,
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event_to_train,
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threshold=0,
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dimensions=0,
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other_features=False,
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*args,
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**kwargs,
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):
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self.model_name = hf_config.model_name
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self.data_dir = data_dir
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self.query_prompt = hf_config.query_prompt
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self.doc_prompt = hf_config.doc_prompt
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self.event_to_train = event_to_train
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self.other_features = other_features
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self.text_encoder = FlaxAutoModel.from_pretrained(self.model_name)
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self.tokenizer = BertTokenizerFast.from_pretrained(self.model_name)
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self.base_path = (
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"/content/drive/MyDrive/"
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if "COLAB_ENV" in environ and environ["COLAB_ENV"] == "true"
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else "./"
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)
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self.train_file = path.join(self.base_path, data_dir, "train.jsonl")
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self.eval_file = path.join(self.base_path, data_dir, "test.jsonl")
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self.items_file = path.join(self.base_path, data_dir, "items.jsonl")
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self.item_embeddings_file = path.join(
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self.base_path, data_dir, "item_embeds.jsonl"
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)
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self.train_user_embeddings_file = path.join(
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self.base_path, data_dir, "train_user_embeds.jsonl"
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)
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self.test_user_embeddings_file = path.join(
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self.base_path, data_dir, "test_user_embeds.jsonl"
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)
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self.dimensions = (
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dimensions if dimensions > 0 else self.text_encoder.config.hidden_size
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)
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model = RecommendationModel(self.dimensions)
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super().__init__(*args, model=model, **kwargs)
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self.dataloader = pd.read_json(self.train_file, lines=True)
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self.dataloader = self.dataloader[
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self.dataloader["event"] == self.event_to_train
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].reset_index(drop=True)
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self.test_dataset = pd.read_json(self.eval_file, lines=True)
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self.test_dataset = self.test_dataset[
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self.test_dataset["event"] == self.event_to_train
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].reset_index(drop=True)
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unique_did = (
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pd.concat([self.dataloader, self.test_dataset], ignore_index=True)["did"]
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.unique()
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.tolist()
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)
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self.items = pd.read_json(self.items_file, lines=True)
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self.items = self.items[self.items["did"].isin(unique_did)].reset_index(
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drop=True
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)
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self.threshold = threshold
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self.evaluator: RetrivalEvaluator | None = None
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self.item_embeds = []
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self.train_user_embeds = []
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self.train_label_embeds = []
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self.test_user_embeds = []
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self.test_label_embeds = []
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self.rng = jax.random.PRNGKey(0)
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def embed_items(self, examples):
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texts = [self.doc_prompt + x for x in examples["text"].tolist()]
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tokens = self.tokenizer(
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texts,
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truncation=True,
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padding="max_length",
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return_tensors="jax",
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max_length=70,
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)
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embeddings = self.text_encoder(**tokens).last_hidden_state
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embeddings = self.mean_pooling(embeddings, tokens["attention_mask"])
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embeddings = embeddings / jnp.linalg.norm(embeddings, axis=-1, keepdims=True)
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if self.other_features:
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embeddings = [embeddings[i] for i in range(embeddings.shape[0])]
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features = examples["features"].tolist()
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for i in range(len(embeddings)):
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embeddings[i] = jnp.concatenate([embeddings[i], jnp.array(features[i])])
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embeddings = jnp.array(embeddings)
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return [embeddings[i] for i in range(embeddings.shape[0])]
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def embed_events(self, df):
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user_vecs = []
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label_vecs = []
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for _, row in df.iterrows():
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label = self.items[self.items["did"] == row["label"]["did"]].index.tolist()[
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0
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]
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history = [x["did"] for x in row["data"]]
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multi_hot = [0] * len(self.item_embeds)
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indexes = (
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self.items[self.items["did"].isin(history)]
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.index.reindex(
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self.items[self.items["did"].isin(history)]["did"]
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.map(dict(zip(history, range(len(history)))))
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.sort_values()
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.index
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)[0]
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.tolist()
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)
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for idx in indexes:
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multi_hot[idx] = 1
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multi_hot = jnp.array(multi_hot)
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user_vecs.append(compute_mean(self.item_embeds, multi_hot))
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label_vecs.append(label)
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return jnp.array(user_vecs), jnp.array(label_vecs)
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def group_events(self, df):
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df = df.sort_values(["sid", "ts"])
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def group_to_dict_array(to_dict):
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return to_dict.drop(["sid", "event"], axis=1).to_dict("records")
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grouped_data = []
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for (sid,), group in df.groupby(["sid"]):
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data = group_to_dict_array(group)
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if len(data) > 2:
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grouped_data.append(
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{
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"sid": sid,
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"data": data[:-1],
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"label": data[-1],
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}
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)
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grouped_data = pd.DataFrame(grouped_data)
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return grouped_data
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@staticmethod
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def users_to_sessions(file_path, threshold):
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df = pd.read_json(file_path, lines=True)
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if threshold > 0:
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def create_intervals(group):
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group = group.copy()
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group["time_diff"] = group["ts"].diff()
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group["interval"] = (group["time_diff"] > threshold).cumsum()
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return group.drop("time_diff", axis=1)
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df_list = [create_intervals(group) for _, group in df.groupby("sid")]
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df = pd.concat(df_list, ignore_index=True)
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else:
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df["interval"] = 0
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return df
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def load_item_embeddings(self):
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item_embeds = pd.read_json(self.item_embeddings_file, lines=True)
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item_with_embeds = pd.merge(self.items, item_embeds, on="did", how="left")
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return [jnp.array(x) for x in item_with_embeds["embed"]]
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def load_user_embeddings(self, df, file_path):
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user_embeds = pd.read_json(file_path, lines=True)
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user_with_embeds = pd.merge(df, user_embeds, on="sid", how="left")
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return jnp.array([jnp.array(x) for x in user_with_embeds["embed"]]), jnp.array(
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[
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self.items[self.items["did"] == x["did"]].index.tolist()[0]
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for x in user_with_embeds["label"]
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]
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)
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def setup(self):
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corpus = {
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f"{self.items.loc[x]['did']}": self.items.loc[x]["text"]
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for x in range(len(self.items))
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}
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if path.exists(self.item_embeddings_file):
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logger.info("Found a saved item embedding file...")
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self.item_embeds = self.load_item_embeddings()
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else:
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for start in trange(0, len(self.items), 128, desc="Embedding items"):
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end = min(start + 128, len(self.items))
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e = self.embed_items(self.items.loc[start : end - 1])
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self.item_embeds.extend(e)
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self.item_embeds = jnp.array(self.item_embeds)
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self.dataloader = self.group_events(self.dataloader)
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self.dataset_len = len(self.dataloader)
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self.test_dataset = self.group_events(self.test_dataset)
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self.dataset_len = len(self.dataloader)
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if path.exists(self.train_user_embeddings_file):
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logger.info("Found a saved train embedding file...")
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self.train_user_embeds, self.train_label_embeds = self.load_user_embeddings(
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self.dataloader, self.train_user_embeddings_file
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)
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else:
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self.train_user_embeds, self.train_label_embeds = self.embed_events(
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self.dataloader
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)
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if path.exists(self.test_user_embeddings_file):
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logger.info("Found a saved test embedding file...")
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self.test_user_embeds, self.test_label_embeds = self.load_user_embeddings(
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self.test_dataset, self.test_user_embeddings_file
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)
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else:
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self.test_user_embeds, self.test_label_embeds = self.embed_events(
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self.test_dataset
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)
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users = {
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f"{self.test_dataset.loc[x]['sid']}": self.test_dataset.loc[x]["sid"]
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for x in range(len(self.test_dataset))
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}
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relevant_docs = {
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f"{self.test_dataset.loc[x]['sid']}": [
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f"{self.test_dataset.loc[x]['label']['did']}"
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]
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for x in range(len(self.test_dataset))
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}
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self.evaluator = RetrivalEvaluator(
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queries=users,
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corpus=corpus,
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relevant_docs=relevant_docs,
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corpus_chunk_size=40000,
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batch_size=512,
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show_progress_bar=True,
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)
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def get_initial_params(self):
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batch = jnp.array([jnp.zeros(self.text_encoder.config.hidden_size)])
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params = self.model.init(jax.random.PRNGKey(0), batch, batch, training=False)
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return params["params"]
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def train_step(self, _batch, start, end):
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self.rng, rng = jax.random.split(self.rng)
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batch = jax.random.permutation(rng, jnp.array(self.train_user_embeds))[
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start:end
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]
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labels = jax.random.permutation(rng, jnp.array(self.train_label_embeds))[
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start:end
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]
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user_vec = jnp.array(batch)
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items_vec = jnp.array(self.item_embeds)
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state, l = train_step(self.state, user_vec, items_vec, labels, rng)
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q, d = self.model.apply(
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{"params": self.state.params},
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jnp.array(self.test_user_embeds),
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jnp.array(self.item_embeds),
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training=False,
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rngs=rng,
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)
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q = q / jnp.linalg.norm(q, axis=1, keepdims=True)
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d = d / jnp.linalg.norm(d, axis=1, keepdims=True)
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val_l = loss.sparse_categorical_cross_entropy(
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q,
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d,
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self.test_label_embeds,
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)
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self.val_loss.append(val_l)
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return state, l
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def eval_step(self):
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q, d = self.model.apply(
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{"params": self.state.params},
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jnp.array(self.test_user_embeds),
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jnp.array(self.item_embeds),
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training=False,
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)
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q = q / jnp.linalg.norm(q, axis=1, keepdims=True)
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d = d / jnp.linalg.norm(d, axis=1, keepdims=True)
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self.evaluator(query_embeddings=q, corpus_embeddings=d, metrics=["recall"])
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@jax.jit
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def train_step(state: TrainState, user_embeds, item_embeds, labels, rng):
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def loss_fn(params):
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u = user_embeds / jnp.linalg.norm(user_embeds, axis=-1, keepdims=True)
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i = item_embeds / jnp.linalg.norm(item_embeds, axis=-1, keepdims=True)
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u, i = state.apply_fn(
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{"params": params},
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u,
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i,
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training=True,
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rngs={"dropout": rng},
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)
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u = u / jnp.linalg.norm(u, axis=-1, keepdims=True)
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i = i / jnp.linalg.norm(i, axis=-1, keepdims=True)
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l = loss.sparse_categorical_cross_entropy(u, i, labels)
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return l
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grad_fn = jax.value_and_grad(loss_fn)
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l, grads = grad_fn(state.params)
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state = state.apply_gradients(grads=grads)
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return state, l
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