Delete lib

lib/.ipynb_checkpoints/utils-checkpoint.py

@@ -1,206 +0,0 @@
-import numpy as np
-from scipy.special import softmax
-import collections
-import torch
-from torch.utils.data import DataLoader
-from transformers import default_data_collator
-import pandas as pd
-
-def preprocess_examples(examples, tokenizer , max_length = 384, stride = 128):
-    """
-    Preprocesses and tokenizes examples in preparation for inference
-    
-    Parameters:
-    -----------
-    examples : datasets.Dataset
-        The dataset of examples.  Must have columns:
-        'id', 'question', 'context'
-    tokenizer : transformers.AutoTokenizer
-        The tokenizer for the model
-    max_length : int
-        The max length for context truncation
-    stride : int
-        The stride for context truncation
-
-    Returns:
-    --------
-    inputs : dict
-        The tokenized and processed data dictionary with
-        keys 'input_ids', 'attention_mask', 'offset_ids', 'example_id'
-        All values are lists of length = # of inputs output by tokenizer
-            inputs['input_ids'][k] : list
-                token ids corresponding to tokens in feature k
-            inputs['attention_mask'][k] : list
-                attention mask for feature k
-            inputs['offset_ids'][k] : list
-                offset ids for feature k
-            inputs['example_id'][k] : int
-                id of example from which feature k originated
-    """
-    questions = [q.strip() for q in examples["question"]]
-    inputs = tokenizer(
-        questions,
-        examples['context'],
-        max_length=max_length,
-        truncation="only_second",
-        stride=stride,
-        return_overflowing_tokens=True,
-        return_offsets_mapping=True,
-        padding="max_length",
-    )
-
-    sample_map = inputs.pop("overflow_to_sample_mapping")
-    example_ids = []
-
-    for i in range(len(inputs["input_ids"])):
-        sample_idx = sample_map[i]
-        example_ids.append(examples["id"][sample_idx])
-
-        sequence_ids = inputs.sequence_ids(i)
-        offset = inputs["offset_mapping"][i]
-        inputs["offset_mapping"][i] = [
-            o if sequence_ids[k] == 1 else None for k, o in enumerate(offset)
-        ]
-
-    inputs["example_id"] = example_ids
-    return inputs
-
-
-def make_predictions(model,tokenizer,inputs,examples,
-                    n_best = 20,max_answer_length=30):
-    """
-    Generates a list of prediction data based on logits
-
-    Parameters:
-    -----------
-    model : transformers.AutoModelForQuestionAnswering
-        The trained model
-    tokenizer : transformers.AutoTokenizer
-        The model's tokenizer
-    inputs : dict
-        The tokenized and processed data dictionary with
-        keys 'input_ids', 'attention_mask', 'offset_ids', 'example_id'
-        All values are lists of length = # of inputs output by tokenizer
-            inputs['input_ids'][k] : list
-                token ids corresponding to tokens in feature k
-            inputs['attention_mask'][k] : list
-                attention mask for feature k
-            inputs['offset_ids'][k] : list
-                offset ids for feature k
-            inputs['example_id'][k] : int
-                id of example from which feature k originated
-    examples : datasets.Dataset
-        The dataset of examples.  Must have columns:
-        'id', 'question', 'context'
-    n_best : int
-        The number of top start/end (by logit) indices to consider
-    max_answer_length : int
-        The maximum length (in characters) allowed for a candidate answer
-
-    Returns:
-    --------
-    predicted_answers : list(dict)
-        predicted_answers[k] has keys 'id','prediction_text','confidence'
-        predicted_answers[k]['id'] : int
-            The unique id of the example
-        predicted_answers[k]['prediction_text'] : str
-            The predicted answer as a string
-        predicted_answers[k]['confidence'] : float
-            The predicted probability corresponding to the answer, i.e. the
-            corresponding output of a softmax function on logits          
-    """
-    assert n_best <= len(inputs['input_ids'][0]), 'n_best cannot be larger than max_length'
-    
-    if torch.backends.mps.is_available():
-        device = "mps"
-    elif torch.cuda.is_available():
-        device = "cuda"
-    else:
-        device = "cpu"
-    data_for_model = inputs.remove_columns(["example_id", "offset_mapping"])
-    data_for_model.set_format("torch",device=device)
-    dl = DataLoader(
-        data_for_model,
-        collate_fn=default_data_collator,
-        batch_size=len(inputs)
-    )
-    model = model.to(device)
-    for batch in dl:
-        outputs = model(**batch)
-        
-    start_logits = outputs.start_logits.cpu().detach().numpy()
-    end_logits = outputs.end_logits.cpu().detach().numpy()       
-    example_to_inputs = collections.defaultdict(list)
-    for idx, feature in enumerate(inputs):
-        example_to_inputs[feature["example_id"]].append(idx)
-    
-    predicted_answers = []
-    for example in examples:
-        example_id = example["id"]
-        context = example["context"]
-        answers = []
-
-        for feature_index in example_to_inputs[example_id]:
-            start_logit = start_logits[feature_index]
-            end_logit = end_logits[feature_index]
-            offsets = inputs[feature_index]['offset_mapping']
-            
-            start_indices = np.argsort(start_logit)[-1:-n_best-1:-1].tolist()
-            end_indices = np.argsort(end_logit)[-1 :-n_best-1: -1].tolist()
-
-            for start_index in start_indices:
-                for end_index in end_indices:
-                    # Skip answers with a length that is either < 0 or > max_answer_length.
-                    if(
-                        end_index < start_index
-                        or end_index - start_index + 1 > max_answer_length
-                    ):
-                        continue
-                    
-                    if (offsets[start_index] is None)^(offsets[end_index] is None):
-                        continue
-                    if (offsets[start_index] is None)&(offsets[end_index] is None):
-                        answers.append(
-                            {
-                                    "text": '',
-                                    "logit_score": start_logit[start_index] + end_logit[end_index],
-                            }
-                        )
-                    else:
-                        answers.append(
-                            {
-                                "text": context[offsets[start_index][0] : offsets[end_index][1]],
-                                "logit_score": start_logit[start_index] + end_logit[end_index],
-                            }
-                        )
-            answer_logits = [a['logit_score'] for a in answers]
-            answer_probs = softmax(answer_logits)
-            
-            if len(answers)>0:
-                best_answer = max(answers, key=lambda x:x['logit_score'])
-                predicted_answers.append(
-                    {'id':example_id, 'prediction_text':best_answer['text'], 'confidence':answer_probs[0]}
-                )
-            else:
-                predicted_answers.append({'id':example_id, 'prediction_text':'','confidence':answer_probs[0]})
-            for pred in predicted_answers:
-                if pred['prediction_text'] == '':
-                    pred['prediction_text'] = "I don't have an answer based on the context provided."
-    return predicted_answers
-
-def get_examples():
-    """
-    Retrieve pre-made examples from a .csv file
-    Parameters: None
-    -----------
-    Returns:
-    --------
-    questions, contexts : list, list
-        Lists of examples of corresponding question-context pairs
-        
-    """
-    examples = pd.read_csv('examples.csv')
-    questions = list(examples['question'])
-    contexts = list(examples['context'])
-    return questions, contexts
-    

lib/utils.py

@@ -1,210 +0,0 @@
-import numpy as np
-from scipy.special import softmax
-import collections
-import torch
-from torch.utils.data import DataLoader
-from transformers import default_data_collator
-import pandas as pd
-
-def preprocess_examples(examples, tokenizer , max_length = 384, stride = 128):
-    """
-    Preprocesses and tokenizes examples in preparation for inference
-    
-    Parameters:
-    -----------
-    examples : datasets.Dataset
-        The dataset of examples.  Must have columns:
-        'id', 'question', 'context'
-    tokenizer : transformers.AutoTokenizer
-        The tokenizer for the model
-    max_length : int
-        The max length for context truncation
-    stride : int
-        The stride for context truncation
-
-    Returns:
-    --------
-    inputs : dict
-        The tokenized and processed data dictionary with
-        keys 'input_ids', 'attention_mask', 'offset_mapping', 'example_id'
-        All values are lists of length = # of inputs output by tokenizer
-            inputs['input_ids'][k] : list
-                token ids corresponding to tokens in feature k
-            inputs['attention_mask'][k] : list
-                attention mask for feature k
-            inputs['offset_mapping'][k] : list
-                offset_mapping for feature k
-            inputs['example_id'][k] : int
-                id of example from which feature k originated
-    """
-    questions = [q.strip() for q in examples["question"]]
-    inputs = tokenizer(
-        questions,
-        examples['context'],
-        max_length=max_length,
-        truncation="only_second",
-        stride=stride,
-        return_overflowing_tokens=True,
-        return_offsets_mapping=True,
-        padding="max_length",
-    )
-
-    sample_map = inputs.pop("overflow_to_sample_mapping")
-    example_ids = []
-
-    for i in range(len(inputs["input_ids"])):
-        sample_idx = sample_map[i]
-        example_ids.append(examples["id"][sample_idx])
-
-        sequence_ids = inputs.sequence_ids(i)
-        offset = inputs["offset_mapping"][i]
-        inputs["offset_mapping"][i] = [
-            o if sequence_ids[k] == 1 else None for k, o in enumerate(offset)
-        ]
-
-    inputs["example_id"] = example_ids
-    return inputs
-
-
-def make_predictions(model,tokenizer,inputs,examples,
-                    n_best = 20,max_answer_length=30):
-    """
-    Generates a list of prediction data based on logits
-
-    Parameters:
-    -----------
-    model : transformers.AutoModelForQuestionAnswering
-        The trained model
-    tokenizer : transformers.AutoTokenizer
-        The model's tokenizer
-    inputs : dict
-        The tokenized and processed data dictionary with
-        keys 'input_ids', 'attention_mask', 'offset_ids', 'example_id'
-        All values are lists of length = # of inputs output by tokenizer
-            inputs['input_ids'][k] : list
-                token ids corresponding to tokens in feature k
-            inputs['attention_mask'][k] : list
-                attention mask for feature k
-            inputs['offset_ids'][k] : list
-                offset ids for feature k
-            inputs['example_id'][k] : int
-                id of example from which feature k originated
-    examples : datasets.Dataset
-        The dataset of examples.  Must have columns:
-        'id', 'question', 'context'
-    n_best : int
-        The number of top start/end (by logit) indices to consider
-    max_answer_length : int
-        The maximum length (in characters) allowed for a candidate answer
-
-    Returns:
-    --------
-    predicted_answers : list(dict)
-        predicted_answers[k] has keys 'id','prediction_text','confidence'
-        predicted_answers[k]['id'] : int
-            The unique id of the example
-        predicted_answers[k]['prediction_text'] : str
-            The predicted answer as a string
-        predicted_answers[k]['confidence'] : float
-            The predicted probability corresponding to the answer, i.e. the
-            corresponding output of a softmax function on logits          
-    """
-    assert n_best <= len(inputs['input_ids'][0]), 'n_best cannot be larger than max_length'
-    
-    if torch.backends.mps.is_available():
-        device = "mps"
-    elif torch.cuda.is_available():
-        device = "cuda"
-    else:
-        device = "cpu"
-    data_for_model = inputs.remove_columns(["example_id", "offset_mapping"])
-    data_for_model.set_format("torch",device=device)
-    dl = DataLoader(
-        data_for_model,
-        collate_fn=default_data_collator,
-        batch_size=len(inputs)
-    )
-    model = model.to(device)
-    for batch in dl:
-        outputs = model(**batch)
-        
-    start_logits = outputs.start_logits.cpu().detach().numpy()
-    end_logits = outputs.end_logits.cpu().detach().numpy()       
-    example_to_inputs = collections.defaultdict(list)
-    for idx, feature in enumerate(inputs):
-        example_to_inputs[feature["example_id"]].append(idx)
-    
-    predicted_answers = []
-    for example in examples:
-        example_id = example["id"]
-        context = example["context"]
-        answers = []
-
-        for feature_index in example_to_inputs[example_id]:
-            start_logit = start_logits[feature_index]
-            end_logit = end_logits[feature_index]
-            offsets = inputs[feature_index]['offset_mapping']
-            
-            start_indices = np.argsort(start_logit)[-1:-n_best-1:-1].tolist()
-            end_indices = np.argsort(end_logit)[-1 :-n_best-1: -1].tolist()
-
-            for start_index in start_indices:
-                for end_index in end_indices:
-                    # Skip answers with a length that is either < 0 or > max_answer_length.
-                    if(
-                        end_index < start_index
-                        or end_index - start_index + 1 > max_answer_length
-                    ):
-                        continue
-                    
-                    if (offsets[start_index] is None)^(offsets[end_index] is None):
-                        continue
-                    if (offsets[start_index] is None)&(offsets[end_index] is None):
-                        answers.append(
-                            {
-                                    "text": '',
-                                    "logit_score": start_logit[start_index] + end_logit[end_index],
-                            }
-                        )
-                    else:
-                        answers.append(
-                            {
-                                "text": context[offsets[start_index][0] : offsets[end_index][1]],
-                                "logit_score": start_logit[start_index] + end_logit[end_index],
-                            }
-                        )
-            answer_logits = [a['logit_score'] for a in answers]
-            answer_probs = softmax(answer_logits)
-            
-            if len(answers)>0:
-                # best_answer = max(answers, key=lambda x:x['logit_score'])
-                best_answer_idx = np.argmax(answer_logits)
-                predicted_answers.append(
-                    {'id':example_id,
-                     'prediction_text':answers[best_answer_idx]['text'],
-                     'confidence':answer_probs[best_answer_idx]}
-                )
-            else:
-                predicted_answers.append({'id':example_id, 'prediction_text':'',
-                                          'confidence':answer_probs[best_answer_idx]})
-            for pred in predicted_answers:
-                if pred['prediction_text'] == '':
-                    pred['prediction_text'] = "I don't have an answer based on the context provided."
-    return predicted_answers
-
-def get_examples():
-    """
-    Retrieve pre-made examples from a .csv file
-    Parameters: None
-    -----------
-    Returns:
-    --------
-    questions, contexts : list, list
-        Lists of examples of corresponding question-context pairs
-        
-    """
-    examples = pd.read_csv('examples.csv')
-    questions = list(examples['question'])
-    contexts = list(examples['context'])
-    return questions, contexts
-