File size: 8,666 Bytes
1be89f3 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 |
import heapq
import json
import os
import time
from typing import Any
import click
import numpy as np
import polars as pl
import scipy.sparse as sp
import torch
from sansa import SANSA, ICFGramianFactorizerConfig, SANSAConfig, UMRUnitLowerTriangleInverterConfig
from tqdm import tqdm
from yambda.constants import Constants
from yambda.evaluation import metrics, ranking
from yambda.processing import timesplit
RANDOM_SEED = 42
@click.command()
@click.option(
'--data_dir', required=True, type=str, default="../../data/flat", show_default=True, help="Expects flat data"
)
@click.option(
'--size',
required=True,
type=click.Choice(['50m', '500m']),
default="50m",
multiple=False,
show_default=True,
)
@click.option(
'--interaction',
required=True,
type=click.Choice(['likes', 'listens']),
default="likes",
multiple=False,
show_default=True,
)
@click.option('--report_metrics', required=True, type=str, default=Constants.METRICS, multiple=True, show_default=True)
@click.option('--device', required=True, type=str, default="cuda:0", show_default=True)
def main(
data_dir: str,
size: str,
interaction: str,
report_metrics: list[str],
device: str,
):
print(f"REPORT METRICS: {report_metrics}")
print(f"SIZE {size}, INTERACTION {interaction}")
result = train_sansa_model(
data_dir,
size=size,
dataset_type=interaction,
device=device,
report_metrics=report_metrics,
)
print(json.dumps(result, indent=2))
def train_sansa_model(
data_path: str,
size: str,
dataset_type: str,
device: str,
report_metrics: list[str],
) -> dict[str, Any]:
np.random.seed(RANDOM_SEED)
curr_time = time.time()
print()
print(curr_time)
print(f"Size: {size}, Dataset: {dataset_type}")
df, grouped_test, train, test = get_train_val_test_matrices(
data_path=data_path,
size=size,
dataset_type=dataset_type,
)
data_finished = time.time()
print(f"Data is loaded in {data_finished - curr_time} seconds")
model = get_sansa_model()
model.fit(train)
train_finished = time.time()
print(f"Model is trained in {train_finished - data_finished}")
print(model)
if report_metrics:
calculated_metrics = evaluate_sansa(
df=df,
model=model,
device=device,
report_metrics=report_metrics,
grouped_test=grouped_test,
sparse_train=train,
sparse_test=test,
)
print(f"Model is evaluated in {time.time() - train_finished}")
return calculated_metrics
return {}
def get_train_val_test_matrices(
data_path: str,
size: str = "50m",
dataset_type: str = "likes",
) -> tuple[pl.LazyFrame, pl.LazyFrame, sp.csr_matrix, sp.csr_matrix]:
df = pl.scan_parquet(os.path.join(os.path.join(data_path, size, f"{dataset_type}.parquet")))
if dataset_type == "listens":
df = df.filter(pl.col("played_ratio_pct") >= Constants.TRACK_LISTEN_THRESHOLD)
flat_train, _, flat_test = timesplit.flat_split_train_val_test(
df, val_size=0, test_timestamp=Constants.TEST_TIMESTAMP
)
all_uids = set(flat_train.collect().get_column("uid").to_list())
all_items = set(flat_train.collect().get_column("item_id").to_list())
print(f"Dataset, users_num: {len(all_uids)}, items_num: {len(all_items)}")
# Create mapping to create sparse matrix
uid_to_idx = {uid: i for i, uid in enumerate(all_uids)}
item_id_to_idx = {item_id: i for i, item_id in enumerate(all_items)}
sparse_train, _ = get_sparse_data(flat_train, uid_to_idx, item_id_to_idx)
sparse_test, grouped_test = get_sparse_data(flat_test, uid_to_idx, item_id_to_idx)
print(f"Sparse train shape: {sparse_train.shape}, test shape: {sparse_test.shape}")
return df, grouped_test, sparse_train, sparse_test
def get_sparse_data(
df: pl.LazyFrame, uid_to_idx: dict[int, int], item_id_to_idx: dict[int, int]
) -> tuple[sp.csr_matrix, pl.LazyFrame]:
df = df.with_columns(
pl.col("uid").replace_strict(uid_to_idx).alias("uid"),
pl.col("item_id").replace_strict(item_id_to_idx, default=len(item_id_to_idx)).alias("item_id"),
pl.lit(1).alias("action"),
)
grouped_df = df.group_by('uid', maintain_order=True).agg(
[pl.col('item_id').alias('item_id'), pl.col('action').alias('actions')]
)
rows = []
cols = []
values = []
for user_id, item_ids, actions in tqdm(grouped_df.select('uid', 'item_id', 'actions').collect().rows()):
rows.extend([user_id] * len(item_ids))
cols.extend(item_ids)
values.extend(actions)
user_item_data = sp.csr_matrix(
(values, (rows, cols)),
dtype=np.float32,
shape=(len(uid_to_idx), len(item_id_to_idx) + 1), # +1 for default unknown test items
)
return user_item_data, grouped_df
def get_sansa_model() -> SANSA:
factorizer_config = ICFGramianFactorizerConfig(
# reordering_use_long=True,
factorization_shift_step=1e-3, # initial diagonal shift if incomplete factorization fails
factorization_shift_multiplier=2.0, # multiplier for the shift for subsequent attempts
)
inverter_config = UMRUnitLowerTriangleInverterConfig(
scans=1, # number of scans through all columns of the matrix
finetune_steps=15, # number of finetuning steps, targeting worst columns
)
config = SANSAConfig(
l2=10.0, # regularization strength
weight_matrix_density=5e-5, # desired density of weights
gramian_factorizer_config=factorizer_config, # factorizer configuration
lower_triangle_inverter_config=inverter_config, # inverter configuration
)
print(config)
model = SANSA(config)
return model
def evaluate_sansa(
df: pl.LazyFrame,
model: SANSA,
device: str,
report_metrics: list[str],
grouped_test: pl.LazyFrame,
sparse_train: sp.csr_matrix,
sparse_test: sp.csr_matrix,
) -> dict[str, Any]:
num_items_for_metrics = len(set(df.collect().get_column("item_id").to_list()))
print(num_items_for_metrics)
test_targets = ranking.Targets.from_sequential(grouped_test, device=device)
print(len(test_targets.user_ids))
# to free some RAM
del df, grouped_test
train_pred_sparse = model.forward(sparse_train)
print(f"Train prediction shape: {train_pred_sparse.shape}")
A = train_pred_sparse
num_users = A.shape[0]
num_items_k = 150
# 0 if there is no such item
top_items_idx = np.full((num_users, num_items_k), 0, dtype=int)
# -1 score if there is no such item
top_items_score = np.full((num_users, num_items_k), -1, dtype=A.data.dtype)
for row in tqdm(range(num_users)):
start, end = A.indptr[row], A.indptr[row + 1]
row_scores = A.data[start:end]
row_cols = A.indices[start:end]
if len(row_scores) == 0:
continue
k_here = min(num_items_k, len(row_scores))
top_k = heapq.nlargest(k_here, zip(row_scores, row_cols), key=lambda x: x[0])
# Fill in
for i, (score, idx) in enumerate(top_k):
top_items_idx[row, i] = idx
top_items_score[row, i] = score
user_ids = torch.arange(top_items_idx.shape[0], dtype=torch.int32, device="cpu")
print(user_ids.shape)
scores = torch.as_tensor(top_items_score, dtype=torch.float32, device="cpu")
print(scores.shape)
scores_indices = torch.as_tensor(top_items_idx, dtype=torch.long, device="cpu")
print(scores_indices.shape)
targets = torch.as_tensor(sparse_test.toarray(), dtype=torch.bool, device="cpu")
print(targets.shape)
targets = targets.to(dtype=torch.bool, device=device)
not_zero_user_indices = targets.any(dim=1)
print(torch.sum(not_zero_user_indices))
not_zero_user_indices = not_zero_user_indices.to(dtype=torch.bool, device="cpu")
user_ids = user_ids[not_zero_user_indices]
scores = scores[not_zero_user_indices]
print(f"After removing zero users scores shape: {scores.shape}, targets shape: {targets.shape}")
scores_indices = scores_indices[not_zero_user_indices]
print(scores_indices.shape)
test_ranked = ranking.Ranked(
user_ids=user_ids.to(device),
scores=scores.to(device),
item_ids=scores_indices.to(device),
num_item_ids=num_items_for_metrics,
)
calculated_metrics = metrics.calc_metrics(test_ranked, test_targets, report_metrics)
print(calculated_metrics)
return calculated_metrics
if __name__ == "__main__":
main()
|