Upload evaluation.py

evaluation.py

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+import numpy as np
+import torch
+
+from evaluate import load as load_metric
+
+from sklearn.metrics import mean_squared_error
+from tqdm.auto import tqdm
+
+MAX_TARGET_LENGTH = 128
+
+# load evaluation metrics
+sacrebleu = load_metric('sacrebleu')
+rouge = load_metric('rouge')
+meteor = load_metric('meteor')
+bertscore = load_metric('bertscore')
+
+# use gpu if it's available
+device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
+
+def flatten_list(l):
+    """
+    Utility function to convert a list of lists into a flattened list
+    Params:
+        l (list of lists): list to be flattened
+    Returns:
+        A flattened list with the elements of the original list
+    """
+    return [item for sublist in l for item in sublist]
+
+def parse_float(value):
+    """
+    Utility function to parse a string into a float
+    Params:
+        value (string): value to be converted to float
+    Returns:
+        The float representation of the given string, or None if the string could
+        not be converted to a float
+    """
+    try:
+        float_value = float(value)
+        return float_value
+    except ValueError:
+        return None
+
+def extract_scores(predictions):
+    """
+    Utility function to extract the scores from the predictions of the model
+    Params:
+        predictions (list): complete model predictions
+    Returns:
+        scores (list): extracted scores from the model's predictions
+    """
+    scores = []
+    # iterate through predictions and try to extract predicted score;
+    # if score could not be extracted, set it to None
+    for pred in predictions:
+        try:
+            score_string = pred.split(' ', 1)[0].strip()
+            score = parse_float(score_string)
+        except IndexError:
+            score = None
+        scores.append(score)
+    
+    return scores
+
+def extract_feedback(predictions):
+    """
+    Utility function to extract the feedback from the predictions of the model
+    Params:
+        predictions (list): complete model predictions
+    Returns:
+        feedback (list): extracted feedback from the model's predictions
+    """
+    feedback = []
+    # iterate through predictions and try to extract predicted feedback
+    for pred in predictions:
+        try:
+            fb = pred.split(':', 1)[1]
+        except IndexError:
+            try:
+                fb = pred.split(' ', 1)[1]
+            except IndexError:
+                fb = pred
+        feedback.append(fb.strip())
+    
+    return feedback
+
+def compute_rmse(predictions, labels):
+    """
+    Utility function to compute the root mean squared error of the
+    score predictions in relation to the golden label scores
+    Params:
+        predictions (list): model score predictions
+        labels (list): golden label scores
+    Returns:
+        (float, int): rmse of valid samples and number of invalid samples
+    """
+    # get indexes of valid score predictions
+    # (i.e., where the score is not None)
+    idx = np.where(np.array(predictions) != None)
+
+    # get size of the golden labels list and of
+    # the valid predictions array
+    labels_size = np.array(labels).size
+    valid_predictions_size = idx[0].size
+
+    # only compute rmse if valid score predictions were generated,
+    # otherwise set mse to 1
+    if valid_predictions_size > 0:
+        # calculate rmse from labels and predictions
+        valid_predictions = np.array(predictions)[idx]
+        score_labels = np.array(labels)[idx]
+        rmse = mean_squared_error(score_labels, valid_predictions, squared=False)
+
+        # cap mse at 1
+        if rmse > 1:
+            return 1, labels_size - valid_predictions_size
+        
+        # return computed rmse and number of invalid samples
+        return rmse, labels_size - valid_predictions_size
+    else:
+        return 1, labels_size - valid_predictions_size
+
+def compute_metrics(predictions, labels):
+    """
+    Compute evaluation metrics from the predictions of the model
+    Params:
+        predictions (list): complete model predictions
+        labels (list): golden labels (previously tokenized)
+    Returns:
+        results (dict): dictionary with the computed evaluation metrics
+    """
+    # extract feedback and labels from the model's predictions
+    predicted_feedback = extract_feedback(predictions)
+    predicted_scores = extract_scores(predictions)
+
+    # extract feedback and labels from the golden labels
+    reference_feedback = [x.split('Feedback:', 1)[1].strip() for x in labels]
+    reference_scores = [float(x.split('Feedback:', 1)[0].strip()) for x in labels]
+
+    # compute HF metrics
+    sacrebleu_score = sacrebleu.compute(predictions=predicted_feedback, references=[[x] for x in reference_feedback])['score']
+    rouge_score = rouge.compute(predictions=predicted_feedback, references=reference_feedback)['rouge2']
+    meteor_score = meteor.compute(predictions=predicted_feedback, references=reference_feedback)['meteor']
+    bert_score = bertscore.compute(
+        predictions=predicted_feedback,
+        references=reference_feedback,
+        lang='de',
+        model_type='bert-base-multilingual-cased',
+        rescale_with_baseline=True)
+    
+    # compute rmse of score predictions
+    rmse, _ = compute_rmse(predicted_scores, reference_scores)
+
+    results = {
+        'sacrebleu': sacrebleu_score,
+        'rouge': rouge_score,
+        'meteor': meteor_score,
+        'bert_score': np.array(bert_score['f1']).mean().item(),
+        'rmse': rmse
+        }
+    
+    return results
+
+def evaluate(model, tokenizer, dataloader):
+    """
+    Evaluate model on the given dataset
+    Params:
+        model (PreTrainedModel): seq2seq model
+        tokenizer (PreTrainedTokenizer): tokenizer from HuggingFace
+        dataloader (torch Dataloader): dataloader of the dataset to be used for evaluation
+    Returns:
+        results (dict): dictionary with the computed evaluation metrics
+        predictions (list): list of the decoded predictions of the model
+    """
+    decoded_preds, decoded_labels = [], []
+
+    model.eval()
+    # iterate through batchs in the dataloader
+    for batch in tqdm(dataloader):
+        with torch.no_grad():
+            batch = {k: v.to(device) for k, v in batch.items()}
+            # generate tokens from batch
+            generated_tokens = model.generate(
+                batch['input_ids'],
+                attention_mask=batch['attention_mask'],
+                max_length=MAX_TARGET_LENGTH
+            )
+            # get golden labels from batch
+            labels_batch = batch['labels']
+            
+            # decode model predictions and golden labels
+            decoded_preds_batch = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
+            decoded_labels_batch = tokenizer.batch_decode(labels_batch, skip_special_tokens=True)
+
+            decoded_preds.append(decoded_preds_batch)
+            decoded_labels.append(decoded_labels_batch)
+
+    # convert predictions and golden labels into flattened lists
+    predictions = flatten_list(decoded_preds)
+    labels = flatten_list(decoded_labels)
+
+    # compute metrics based on predictions and golden labels
+    results = compute_metrics(predictions, labels)
+
+    return results, predictions