File size: 2,814 Bytes
ab8be8f | 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 | import random
from pathlib import Path
import numpy as np
import pandas as pd
import torch
from model import Kronos, KronosPredictor, KronosTokenizer
TEST_DATA_ROOT = Path(__file__).parent
INPUT_DATA_PATH = TEST_DATA_ROOT / "regression_input.csv"
OUTPUT_DATA_DIR = TEST_DATA_ROOT
MAX_CTX_LEN = 512
TEST_CTX_LEN = [512, 256]
PRED_LEN = 8
FEATURE_NAMES = ["open", "high", "low", "close", "volume", "amount"]
MODEL_REVISION = "901c26c1332695a2a8f243eb2f37243a37bea320"
TOKENIZER_REVISION = "0e0117387f39004a9016484a186a908917e22426"
SEED = 123
DEVICE = "cpu"
def set_seed(seed: int) -> None:
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
if torch.backends.cudnn.is_available():
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
def generate_output(ctx_len: int) -> None:
if ctx_len > MAX_CTX_LEN:
raise ValueError(
f"Context length for output generation ({ctx_len}) "
f"cannot exceed maximum context length ({MAX_CTX_LEN})."
)
context_df = df.iloc[:ctx_len].copy()
future_timestamps = df["timestamps"].iloc[
ctx_len : ctx_len + PRED_LEN
].reset_index(drop=True)
tokenizer = KronosTokenizer.from_pretrained("NeoQuasar/Kronos-Tokenizer-base", revision=TOKENIZER_REVISION)
model = Kronos.from_pretrained("NeoQuasar/Kronos-small", revision=MODEL_REVISION)
tokenizer.eval()
model.eval()
predictor = KronosPredictor(
model, tokenizer, device=DEVICE, max_context=MAX_CTX_LEN
)
with torch.no_grad():
pred_df = predictor.predict(
df=context_df[FEATURE_NAMES].reset_index(drop=True),
x_timestamp=context_df["timestamps"].reset_index(drop=True),
y_timestamp=future_timestamps,
pred_len=PRED_LEN,
T=1.0,
top_k=1,
top_p=1.0,
verbose=False,
sample_count=1,
)
if pred_df.shape != (PRED_LEN, len(FEATURE_NAMES)):
raise ValueError(f"Unexpected prediction shape: {pred_df.shape}")
output_df = pred_df.reset_index(drop=True)
output_df["timestamps"] = future_timestamps
output_df = output_df[["timestamps"] + FEATURE_NAMES]
output_df.to_csv(OUTPUT_DATA_DIR / f"regression_output_{ctx_len}.csv", index=False)
print(f"Saved {ctx_len} fixture to {OUTPUT_DATA_DIR / f'regression_output_{ctx_len}.csv'}")
if __name__ == "__main__":
set_seed(SEED)
df = pd.read_csv(INPUT_DATA_PATH, parse_dates=["timestamps"])
if df.shape[0] < MAX_CTX_LEN + PRED_LEN:
raise ValueError(
f"Input data must have at least {MAX_CTX_LEN + PRED_LEN} rows, "
f"found {df.shape[0]} instead."
)
for ctx_len in TEST_CTX_LEN:
generate_output(ctx_len)
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