this repo has no description
Update follow parsing logic
+1412
-1
+4
pyproject.toml
+4
pyproject.toml
+138
scripts/split_follows.py
+138
scripts/split_follows.py
···
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import argparse
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import gzip
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import json
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import logging
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import os
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import random
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import sys
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import pandas as pd
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logger = logging.getLogger(__name__)
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logger.setLevel(logging.INFO)
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# Create formatter
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formatter = logging.Formatter(
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"%(asctime)s | %(levelname)s | %(message)s", datefmt="%Y-%m-%d %H:%M:%S"
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)
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# Console handler
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console_handler = logging.StreamHandler(sys.stdout)
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console_handler.setFormatter(formatter)
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logger.addHandler(console_handler)
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def main(follow_dir: str, output_dir: str, val_split: float, file_size: int):
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if val_split >= 1.0 or val_split <= 0.0:
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raise ValueError("Validation split must be between 0 and 1 exclusive")
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if not os.path.exists(output_dir):
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os.makedirs(output_dir)
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did_id_map: dict[str, int] = dict()
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logger.info("Reading follows...")
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train_file_idx = 1
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val_file_idx = 1
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train_follows: list[tuple[int, int]] = []
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val_follows: list[tuple[int, int]] = []
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for file in os.listdir(follow_dir):
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source_did = file[:-3] # Remove .gz extension
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if source_did not in did_id_map:
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did_id_map[source_did] = len(did_id_map)
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for line in gzip.open(follow_dir + file, "rt"):
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target_did = json.loads(line)["value"]["subject"]
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if target_did not in did_id_map:
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did_id_map[target_did] = len(did_id_map)
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if random.random() < val_split:
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val_follows.append((did_id_map[source_did], did_id_map[target_did]))
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if len(val_follows) >= file_size:
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logger.info(f"Saving validation file: {val_file_idx}")
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val_df = pd.DataFrame(
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val_follows, columns=["source", "target"], index=None
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)
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val_df.to_parquet(
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os.path.join(output_dir, f"val_follows_{val_file_idx}.parquet")
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)
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val_follows = []
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val_file_idx += 1
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del val_df
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else:
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train_follows.append((did_id_map[source_did], did_id_map[target_did]))
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if len(train_follows) >= file_size:
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logger.info(f"Saving training file: {train_file_idx}")
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train_df = pd.DataFrame(
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train_follows, columns=["source", "target"], index=None
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)
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train_df.to_parquet(
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os.path.join(
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output_dir, f"train_follows_{train_file_idx}.parquet"
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)
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)
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train_follows = []
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train_file_idx += 1
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del train_df
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logger.info("Saving remnant files...")
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if len(val_follows):
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val_df = pd.DataFrame(val_follows, columns=["source", "target"], index=None)
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val_df.to_parquet(
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os.path.join(output_dir, f"val_follows_{val_file_idx}.parquet")
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)
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if len(train_follows):
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train_df = pd.DataFrame(train_follows, columns=["source", "target"], index=None)
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train_df.to_parquet(
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os.path.join(output_dir, f"train_follows_{train_file_idx}.parquet")
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)
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logger.info("Finished reading follows")
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# Write did_id_map out
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logger.info("Writing id map...")
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with open(os.path.join(output_dir, "did_id_map.json"), "w") as out_file:
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out_file.write(json.dumps(did_id_map))
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"--follow-dir",
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dest="follow_dir",
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required=True,
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help="Path to folder with files",
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)
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parser.add_argument(
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"--output-dir",
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dest="output_dir",
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required=True,
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help="Path to folder for output",
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)
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parser.add_argument(
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"--val-split",
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dest="val_split",
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required=True,
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type=float,
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help="Percent of data for validation",
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)
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parser.add_argument(
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"--file-size",
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dest="file_size",
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required=True,
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type=int,
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help="Max rows per output file",
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)
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args = parser.parse_args()
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main(
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follow_dir=args.follow_dir,
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output_dir=args.output_dir,
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val_split=args.val_split,
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file_size=args.file_size,
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)
+46
scripts/training/data.py
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scripts/training/data.py
···
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import json
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import random
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import polars as pl
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from torch import Dataset
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class FollowDataset(Dataset):
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def __init__(
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self, did_id_map_path: str, parquet_file_path, negative_sample_count: int
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):
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with open(did_id_map_path, "r") as in_file:
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self.did_id_map: dict[str, int] = json.load(in_file)
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self.id_arr: list[int] = list(self.did_id_map.keys())
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self.follows = pl.read_parquet(parquet_file_path)
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self.follow_map: dict[int, list[int]] = {}
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for row in self.follows.rows():
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if row[0] not in self.follow_map:
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self.follow_map[row[0]] = []
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self.follow_map[row[0]].append(row[1])
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self.negative_sample_count = negative_sample_count
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def __len__(self):
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return self.follows.shape[0]
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def __get_item__(self, idx: int) -> list[tuple[int, int]]:
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"""
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Grab follow connection and create negative samples (follows that dont exist)
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"""
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sample = self.follows.item(idx)
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negative_samples = []
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for _ in range(self.negative_sample_count):
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attempt = 0
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while attempt < 5:
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rand_idx = random.randint(0, len(self.id_arr) - 1)
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rand_user = self.id_arr[rand_idx]
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if rand_user not in self.follow_map[sample[0]]:
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break
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attempt += 1
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if attempt == 5:
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raise Exception(f"Failed to find negative sample for id: {sample[0]}")
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negative_samples.append((sample[0], rand_user))
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return [(sample[0], sample[1])] + negative_samples
+24
scripts/training/models.py
+24
scripts/training/models.py
···
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import torch
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from torch import nn
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class FollowEmbed(nn.Module):
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def __init__(self, num_embeds: int, size: int):
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super().__init__()
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self.source_embed = nn.Embedding(num_embeddings=num_embeds, embedding_dim=size)
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self.target_embed = nn.Embedding(num_embeddings=num_embeds, embedding_dim=size)
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self.decision_layer = nn.Linear(1, 1)
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def forward(self, batch):
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source: torch.tensor = self.source_embed(batch[:, 0])
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target: torch.tensor = self.target_embed(batch[:, 1])
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return torch.bmm(source.unsqueeze(1), target.unsqueeze(2)).squeeze()
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if __name__ == "__main__":
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embeds = FollowEmbed(10, 4)
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x = torch.tensor([[1, 2], [3, 4], [5, 6]])
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print(f"Input tensor: {x.shape}")
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y = embeds(x)
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print(f"Output shape: {y.shape}")
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print(y)
+1199
-1
uv.lock
+1199
-1
uv.lock
···
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version = 1
2
-
revision = 2
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revision = 3
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requires-python = ">=3.9"
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resolution-markers = [
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"python_full_version >= '3.12'",
···
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]
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350
635
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