Source code for lightwood.mixer.unit


For encoders that already fine-tune on the targets (namely text)
the unity mixer just arg-maxes the output of the encoder.

from typing import List, Optional

import torch
import pandas as pd

from lightwood.helpers.log import log
from lightwood.mixer.base import BaseMixer
from lightwood.encoder.base import BaseEncoder
from import EncodedDs
from lightwood.api.types import PredictionArguments

[docs]class Unit(BaseMixer): def __init__(self, stop_after: float, target_encoder: BaseEncoder): super().__init__(stop_after) self.target_encoder = target_encoder self.supports_proba = False self.stable = True
[docs] def fit(self, train_data: EncodedDs, dev_data: EncodedDs) -> None:"Unit Mixer just borrows from encoder")
[docs] def partial_fit(self, train_data: EncodedDs, dev_data: EncodedDs, args: Optional[dict] = None) -> None: pass
def __call__(self, ds: EncodedDs, args: PredictionArguments = PredictionArguments()) -> pd.DataFrame: if args.predict_proba: # @TODO: depending on the target encoder, this might be enabled log.warning('This model does not output probability estimates') decoded_predictions: List[object] = [] for X, _ in ds: decoded_prediction = self.target_encoder.decode(torch.unsqueeze(X, 0)) decoded_predictions.extend(decoded_prediction) ydf = pd.DataFrame({"prediction": decoded_predictions}) return ydf