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f8f5a2f | 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 | """Small executable example; it is not a claimed state-of-the-art baseline."""
from __future__ import annotations
from typing import Callable
import numpy as np
from datasets import Dataset
from sklearn.ensemble import RandomForestClassifier
def _features(dataset: Dataset, preprocess: Callable) -> np.ndarray:
rows: list[np.ndarray] = []
for row in dataset:
signal = preprocess(row["signal"])
diff = np.diff(signal, axis=0)
rows.append(
np.concatenate(
[
signal.min(axis=0),
signal.max(axis=0),
np.median(signal, axis=0),
np.quantile(signal, 0.25, axis=0),
np.quantile(signal, 0.75, axis=0),
np.mean(signal**2, axis=0),
diff.mean(axis=0),
diff.std(axis=0),
]
)
)
return np.vstack(rows)
class ExampleStatsRF:
def __init__(self, *, seed: int, task_view_id: str):
self.task_view_id = task_view_id
self.model = RandomForestClassifier(
n_estimators=200,
class_weight="balanced",
random_state=seed,
n_jobs=-1,
)
def fit(self, train: Dataset, validation: Dataset, *, preprocess: Callable) -> None:
del validation
self.model.fit(_features(train, preprocess), np.asarray(train["label"], dtype=object))
def predict(self, test: Dataset, *, preprocess: Callable) -> np.ndarray:
return self.model.predict(_features(test, preprocess))
def build(*, seed: int, task_view_id: str) -> ExampleStatsRF:
return ExampleStatsRF(seed=seed, task_view_id=task_view_id)
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