Source code for lightwood.analysis.nn_conf.temp_scale

from copy import deepcopy
from typing import Dict, Tuple
from types import SimpleNamespace

import torch
import pandas as pd
from torch import nn, optim
from sklearn.preprocessing import OrdinalEncoder

from lightwood.helpers.log import log
from lightwood.analysis.base import BaseAnalysisBlock

[docs]class TempScaler(BaseAnalysisBlock): """ Original reference (MIT Licensed): NB: Output of the neural network should be the classification logits, NOT the softmax (or log softmax)! TODO """ def __init__(self, deps=tuple()): super().__init__(deps=deps) self.temperature = nn.Parameter(torch.ones(1)) self.ordenc = OrdinalEncoder() self._softmax = torch.nn.Softmax(dim=1) = False def temperature_scale(self, logits): temperature = self.temperature.unsqueeze(1).expand(logits.size(0), logits.size(1)) return logits / temperature def softmax(self, logits): return self._softmax(self.temperature_scale(logits))
[docs] def analyze(self, info: Dict[str, object], **kwargs) -> Dict[str, object]: """ Tune and set the temperature of a neural model optimizing NLL using validation set logits. """ ns = SimpleNamespace(**kwargs) if ns.predictor.mixers[ns.predictor.indexes_by_accuracy[0]].supports_proba: self.n_cls = len(ns.stats_info.train_observed_classes) nll_criterion = nn.CrossEntropyLoss()[[val] for val in ns.stats_info.train_observed_classes]) # collect logits and labels for the validation set logits_list = [] labels_list = [] with torch.no_grad(): prob_cols = [col for col in ns.normal_predictions.columns if '__mdb_proba' in col and '__mdb_unknown_cat' not in col] if not prob_cols: return info # early stop if no proba info is available for logits, label in zip(ns.normal_predictions[prob_cols].values,[]): logits_list.append(logits.tolist()) labels_list.append(int(self.ordenc.transform([[label]]).flatten()[0])) logits = torch.tensor(logits_list) labels = torch.tensor(labels_list).long() # NLL and ECE before temp scaling before_temperature_nll = nll_criterion(logits, labels).item()'Before calibration - NLL: {round(before_temperature_nll, 3)}') # optimize w.r.t. NLL optimizer = optim.LBFGS([self.temperature], lr=0.001, max_iter=1000) def eval_loss(): optimizer.zero_grad() loss = nll_criterion(self.temperature_scale(logits), labels) loss.backward() return loss optimizer.step(eval_loss) # NLL and ECE after temp scaling after_temperature_nll = nll_criterion(self.temperature_scale(logits), labels).item()'Optimal temperature: {round(self.temperature.item(), 3)}')'After calibration - NLL: {round(after_temperature_nll, 3)}') output = deepcopy(ns.normal_predictions) output['confidence'] = torch.max(self.softmax(logits), dim=1).values.detach().numpy() info['result_df'] = output = True return info
[docs] def explain(self, row_insights: pd.DataFrame, global_insights: Dict[str, object], **kwargs) -> Tuple[pd.DataFrame, Dict[str, object]]: """ Perform temperature scaling on logits """ prob_cols = [col for col in row_insights.columns if '__mdb_proba' in col and '__mdb_unknown_cat' not in col] if and prob_cols: logits = torch.tensor(row_insights[prob_cols].values) confs = self.softmax(logits) row_insights['confidence'] = torch.max(confs, dim=1).values.detach().numpy().reshape(-1, 1) else: row_insights['confidence'] = torch.max( torch.tensor(row_insights[prob_cols].values), dim=1).values.detach().numpy().reshape(-1, 1) return row_insights, global_insights