Source code for lightwood.mixer.neural_ts

import time
from copy import deepcopy
from typing import Dict, Optional, List

import numpy as np
import pandas as pd

import torch
from torch import nn
import torch_optimizer as ad_optim
from torch.cuda.amp import GradScaler
from import DataLoader
from torch.optim.optimizer import Optimizer

from type_infer.dtype import dtype
from lightwood.api.types import PredictionArguments
from lightwood.encoder.base import BaseEncoder
from import EncodedDs, ConcatedEncodedDs
from lightwood.mixer.neural import Neural
from lightwood.mixer.helpers.ar_net import ArNet
from lightwood.mixer.helpers.default_net import DefaultNet
from lightwood.mixer.helpers.ts import _apply_stl_on_training, _stl_transform, _stl_inverse_transform
from lightwood.api.types import TimeseriesSettings

[docs]class NeuralTs(Neural): def __init__( self, stop_after: float, target: str, dtype_dict: Dict[str, str], timeseries_settings: TimeseriesSettings, target_encoder: BaseEncoder, net: str, fit_on_dev: bool, search_hyperparameters: bool, ts_analysis: Dict[str, Dict], n_epochs: Optional[int] = None, use_stl: Optional[bool] = False ): """ Subclassed Neural mixer used for time series forecasting scenarios. :param stop_after: How long the total fitting process should take :param target: Name of the target column :param dtype_dict: Data type dictionary :param timeseries_settings: TimeseriesSettings object for time-series tasks, refer to its documentation for available settings. :param target_encoder: Reference to the encoder used for the target :param net: The network type to use (`DeafultNet` or `ArNet`) :param fit_on_dev: If we should fit on the dev dataset :param search_hyperparameters: If the network should run a more through hyperparameter search (currently disabled) :param n_epochs: amount of epochs that the network will be trained for. Supersedes all other early stopping criteria if specified. """ # noqa super().__init__( stop_after, target, dtype_dict, target_encoder, net, fit_on_dev, search_hyperparameters, n_epochs, ) self.timeseries_settings = timeseries_settings assert self.timeseries_settings.is_timeseries self.ts_analysis = ts_analysis self.net_class = DefaultNet if net == 'DefaultNet' else ArNet self.stable = True self.use_stl = use_stl def _select_criterion(self) -> torch.nn.Module: if self.dtype_dict[] in (dtype.integer, dtype.float, dtype.num_tsarray, dtype.quantity): criterion = nn.L1Loss() else: criterion = super()._select_criterion() return criterion def _select_optimizer(self) -> Optimizer: optimizer = ad_optim.Ranger(self.model.parameters(), return optimizer def _fit(self, train_data: EncodedDs, dev_data: EncodedDs) -> None: """ :param train_data: The network is fit/trained on this :param dev_data: Data used for early stopping and hyperparameter determination """ # noqa self.started = time.time() original_train = deepcopy(train_data.data_frame) original_dev = deepcopy(dev_data.data_frame) # Use STL blocks if available if self.use_stl and self.ts_analysis.get('stl_transforms', False): _apply_stl_on_training(train_data, dev_data,, self.timeseries_settings, self.ts_analysis) # ConcatedEncodedDs self.batch_size = min(200, int(len(train_data) / 10)) self.batch_size = max(40, self.batch_size) dev_dl = DataLoader(dev_data, batch_size=self.batch_size, shuffle=False) train_dl = DataLoader(train_data, batch_size=self.batch_size, shuffle=False) = 1e-4 self.num_hidden = 1 # Find learning rate # keep the weights self._init_net(train_data), self.model = self._find_lr(train_dl) # Keep on training optimizer = self._select_optimizer() criterion = self._select_criterion() scaler = GradScaler() # Only 0.8 of the remaining time budget is used to allow some time for the final tuning and partial fit self.model, epoch_to_best_model, _ = self._max_fit( train_dl, dev_dl, criterion, optimizer, scaler, (self.stop_after - (time.time() - self.started)) * 0.8, return_model_after=20000) self.epochs_to_best += epoch_to_best_model # restore dfs train_data.data_frame = original_train dev_data.data_frame = original_dev if self.fit_on_dev: self.partial_fit(dev_data, train_data)
[docs] def fit(self, train_data: EncodedDs, dev_data: EncodedDs) -> None: self._fit(train_data, dev_data)
def __call__(self, ds: EncodedDs, args: PredictionArguments = PredictionArguments() ) -> pd.DataFrame: original_df = deepcopy(ds.data_frame) self.model = self.model.eval() decoded_predictions = [] all_probs: List[List[float]] = [] rev_map = {} length = sum(ds.encoded_ds_lenghts) if isinstance(ds, ConcatedEncodedDs) else len(ds) pred_cols = [f'prediction_{i}' for i in range(self.timeseries_settings.horizon)] ydf = pd.DataFrame(0, # zero-filled index=np.arange(length), dtype=object, columns=pred_cols) if self.use_stl and self.ts_analysis.get('stl_transforms', False): ds.data_frame = _stl_transform(ydf, ds,, self.timeseries_settings, self.ts_analysis) with torch.no_grad(): for idx, (X, Y) in enumerate(ds): X = Yh = self.model(X) Yh = torch.unsqueeze(Yh, 0) if len(Yh.shape) < 2 else Yh kwargs = {} for dep in self.target_encoder.dependencies: kwargs['dependency_data'] = {dep: ds.data_frame.iloc[idx][[dep]].values} if args.predict_proba and self.supports_proba: decoded_prediction, probs, rev_map = self.target_encoder.decode_probabilities(Yh, **kwargs) all_probs.append(probs) else: decoded_prediction = self.target_encoder.decode(Yh, **kwargs) decoded_predictions.extend(decoded_prediction) decoded_predictions = np.array(decoded_predictions) if len(decoded_predictions.shape) == 1: decoded_predictions = np.expand_dims(decoded_predictions, axis=1) ydf[pred_cols] = decoded_predictions if self.use_stl and self.ts_analysis.get('stl_transforms', False): ydf = _stl_inverse_transform(ydf, ds, self.timeseries_settings, self.ts_analysis) ydf['prediction'] = ydf.values.tolist() if self.timeseries_settings.horizon == 1: ydf['prediction'] = [p[0] for p in ydf['prediction']] if args.predict_proba and self.supports_proba: raw_predictions = np.array(all_probs).squeeze(axis=1) for idx, label in enumerate(rev_map.values()): ydf[f'__mdb_proba_{label}'] = raw_predictions[:, idx] # TODO: make this part of the base mixer class? to avoid repetitive code # and ensure other contribs don't accidentally modify the df ds.data_frame = original_df return ydf[['prediction']]