Source code for farm.eval

from tqdm import tqdm
import torch
import numbers
import logging
import numpy as np
from seqeval.metrics import classification_report as token_classification_report
from sklearn.metrics import classification_report
from sklearn.metrics import r2_score
from import DataLoader

from farm.metrics import compute_metrics
from farm.utils import to_numpy, format_log
from farm.utils import MLFlowLogger as MlLogger
from farm.modeling.adaptive_model import AdaptiveModel
from farm.visual.ascii.images import BUSH_SEP

logger = logging.getLogger(__name__)

[docs]class Evaluator: """Handles evaluation of a given model over a specified dataset."""
[docs] def __init__( self, data_loader, tasks, device, classification_report=True ): """ :param data_loader: The PyTorch DataLoader that will return batches of data from the evaluation dataset :type data_loader: DataLoader :param label_maps: :param device: The device on which the tensors should be processed. Choose from "cpu" and "cuda". :param metrics: The list of metrics which need to be computed, one for each prediction head. :param metrics: list :param classification_report: Whether a report on the classification performance should be generated. :type classification_report: bool """ self.data_loader = data_loader #self.label_maps = label_maps self.tasks = tasks self.device = device # Where should metric be defined? When dataset loaded? In config? #self.metrics = metrics self.classification_report = classification_report
[docs] def eval(self, model): """ Performs evaluation on a given model. :param model: The model on which to perform evaluation :type model: AdaptiveModel :return all_results: A list of dictionaries, one for each prediction head. Each dictionary contains the metrics and reports generated during evaluation. :rtype all_results: list of dicts """ model.eval() # init empty lists per prediction head loss_all = [0 for _ in model.prediction_heads] preds_all = [[] for _ in model.prediction_heads] label_all = [[] for _ in model.prediction_heads] ids_all = [[] for _ in model.prediction_heads] passage_start_t_all = [[] for _ in model.prediction_heads] for step, batch in enumerate( tqdm(self.data_loader, desc="Evaluating", mininterval=10) ): batch = {key: batch[key].to(self.device) for key in batch} with torch.no_grad(): logits = model.forward(**batch) losses_per_head = model.logits_to_loss_per_head(logits=logits, **batch) preds = model.logits_to_preds(logits=logits, **batch) labels = model.prepare_labels(**batch) # stack results of all batches per prediction head for head_num, head in enumerate(model.prediction_heads): loss_all[head_num] += np.sum(to_numpy(losses_per_head[head_num])) preds_all[head_num] += list(to_numpy(preds[head_num])) label_all[head_num] += list(to_numpy(labels[head_num])) if head.model_type == "span_classification": ids_all[head_num] += list(to_numpy(batch["id"])) passage_start_t_all[head_num] += list(to_numpy(batch["passage_start_t"])) # Evaluate per prediction head all_results = [] for head_num, head in enumerate(model.prediction_heads): if head.model_type == "multilabel_text_classification": # converting from string preds back to multi-hot encoding from sklearn.preprocessing import MultiLabelBinarizer mlb = MultiLabelBinarizer(classes=head.label_list) # TODO check why .fit() should be called on predictions, rather than on labels preds_all[head_num] = mlb.fit_transform(preds_all[head_num]) label_all[head_num] = mlb.transform(label_all[head_num]) if hasattr(head, 'aggregate_preds'): preds_all[head_num], label_all[head_num] = head.aggregate_preds(preds=preds_all[head_num], labels=label_all[head_num], passage_start_t=passage_start_t_all[head_num], ids=ids_all[head_num]) result = {"loss": loss_all[head_num] / len(self.data_loader.dataset), "task_name": head.task_name} result.update( compute_metrics(metric=head.metric, preds=preds_all[head_num], labels=label_all[head_num] ) ) # Select type of report depending on prediction head output type if self.classification_report: if head.ph_output_type == "per_token": report_fn = token_classification_report elif head.ph_output_type == "per_sequence": report_fn = classification_report elif head.ph_output_type == "per_token_squad": report_fn = lambda *args, **kwargs: "not Implemented" elif head.ph_output_type == "per_sequence_continuous": report_fn = r2_score else: raise NotImplementedError # CHANGE PARAMETERS, not all report_fn accept digits if head.ph_output_type in ["per_sequence_continuous","per_token"]: result["report"] = report_fn( label_all[head_num], preds_all[head_num] ) else: # supply labels as all possible combination because if ground truth labels do not cover # all values in label_list (maybe dev set is small), the report will break if head.model_type == "multilabel_text_classification": # For multilabel classification, we don't eval with string labels here, but with multihot vectors. # Therefore we need to supply all possible label ids instead of label values. all_possible_labels = list(range(len(head.label_list))) else: all_possible_labels = head.label_list result["report"] = report_fn( label_all[head_num], preds_all[head_num], digits=4, labels=all_possible_labels, target_names=head.label_list) all_results.append(result) return all_results
[docs] @staticmethod def log_results(results, dataset_name, steps, logging=True, print=True): # Print a header header = "\n\n" header += BUSH_SEP + "\n" header += "***************************************************\n" header += f"***** EVALUATION | {dataset_name.upper()} SET | AFTER {steps} BATCHES *****\n" header += "***************************************************\n" header += BUSH_SEP + "\n" for head_num, head in enumerate(results):"\n _________ {} _________".format(head['task_name'])) for metric_name, metric_val in head.items(): # log with ML framework (e.g. Mlflow) if logging: if isinstance(metric_val, numbers.Number): MlLogger.log_metrics( metrics={ f"{dataset_name}_{metric_name}_{head['task_name']}": metric_val }, step=steps, ) # print via standard python logger if print: if metric_name == "report": if isinstance(metric_val, str) and len(metric_val) > 8000: metric_val = metric_val[:7500] + "\n ............................. \n" + metric_val[-500:]"{}: \n {}".format(metric_name, metric_val)) else:"{}: {}".format(metric_name, metric_val))