Delete run.py

run.py

@@ -1,123 +0,0 @@
-from transformers import AutoTokenizer, AutoModelForSequenceClassification, DataCollatorWithPadding
-from datasets import load_dataset, load_metric
-import evaluate
-from torch.utils.data import DataLoader
-import torch
-import numpy as np
-import pandas as pd
-import options as op
-
-def tp_tf_test(model_selector, queries_selector, prompt_selector, metric_selector, prediction_strategy_selector):
-	
-	#Load test dataset___________________________
-	test_dataset = load_dataset('gorkaartola/SC-ZS-test_AURORA-Gold-SDG_True-Positives-and-False-Positives')['test']
-	
-	#Load queries________________________________
-	queries_data_files = {'queries': queries_selector}
-	queries_dataset = load_dataset('gorkaartola/SDG_queries', data_files = queries_data_files)['queries']
-	
-	#Load prompt_________________________________
-	prompt = prompt_selector
-	
-	#Load prediction strategias__________________
-	prediction_strategies = prediction_strategy_selector
-	
-	#Calculate and save predictions_______________________
-	#'''
-	def tokenize_function(example, prompt = '', query = ''):
-		queries = []
-		for i in range(len(example['title'])):
-			queries.append(prompt + query)
-		tokenize = tokenizer(example['title'], queries, truncation='only_first')
-		#tokenize['query'] = queries
-		return tokenize
-	
-	model = AutoModelForSequenceClassification.from_pretrained(model_selector)
-	if torch.cuda.is_available():
-		device = torch.device("cuda")
-		model.to(device)
-	tokenizer = AutoTokenizer.from_pretrained(model_selector)	
-	data_collator = DataCollatorWithPadding(tokenizer=tokenizer)
-	
-	results_test = pd.DataFrame()
-	for query_data in queries_dataset:
-		query = query_data['SDGquery']
-		tokenized_test_dataset = test_dataset.map(tokenize_function, batched = True, fn_kwargs = {'prompt' : prompt, 'query' : query})
-		columns_to_remove = test_dataset.column_names
-		for column_name in ['label_ids', 'nli_label']:
-			columns_to_remove.remove(column_name)	
-		tokenized_test_dataset_for_inference = tokenized_test_dataset.remove_columns(columns_to_remove)
-		tokenized_test_dataset_for_inference.set_format('torch')
-		dataloader = DataLoader(
-				tokenized_test_dataset_for_inference,
-				batch_size=8,
-				collate_fn = data_collator,
-				)
-		values = []
-		labels = []
-		nli_labels =[]
-		for batch in dataloader:
-			if torch.cuda.is_available():
-				data = {k: v.to(device) for k, v in batch.items() if k not in ['labels', 'nli_label']}
-			else:
-				data = {k: v for k, v in batch.items() if k not in ['labels', 'nli_label']}
-			with torch.no_grad():
-				outputs = model(**data)
-			logits = outputs.logits			
-			entail_contradiction_logits = logits[:,[0,2]]
-			probs = entail_contradiction_logits.softmax(dim=1)
-			predictions = probs[:,1].tolist()
-			label_ids = batch['labels'].tolist()
-			nli_label_ids = batch['nli_label'].tolist()
-			for prediction, label, nli_label in zip(predictions, label_ids, nli_label_ids):
-				values.append(prediction)
-				labels.append(label)
-				nli_labels.append(nli_label)
-		results_test['dataset_labels'] = labels
-		results_test['nli_labels'] = nli_labels
-		results_test[query] = values
-	
-	results_test.to_csv('Reports/ZS inference tables/ZS-inference-table_Model-' + op.models[model_selector] + '_Queries-' + op.queries[queries_selector] + '_Prompt-' + op.prompts[prompt_selector] + '.csv', index = False)
-	#'''
-	#Load saved predictions____________________________
-	'''
-	results_test = pd.read_csv('Reports/ZS inference tables/ZS-inference-table_Model-' + op.models[model_selector] + '_Queries-' + op.queries[queries_selector] + '_Prompt-' + op.prompts[prompt_selector] + '.csv')
-	'''
-	#Analize predictions_______________________________
-	def logits_labels(raw): 
-		raw_logits = raw.iloc[:,2:]
-		logits = np.zeros(shape=(len(raw_logits.index),17))
-		for i in range(17):
-			queries = queries_dataset.filter(lambda x: x['label_ids'] == i)['SDGquery']
-			logits[:,i]=raw_logits[queries].max(axis=1)
-		labels = raw[["dataset_labels","nli_labels"]]
-		labels = np.array(labels).astype(int)
-		return logits, labels
-	
-	predictions, references = logits_labels(results_test)
-	prediction_strategies = [op.prediction_strategy_options[x] for x in prediction_strategy_selector]
-	
-	metric = evaluate.load(metric_selector)
-	metric.add_batch(predictions = predictions, references = references)
-	results = metric.compute(prediction_strategies = prediction_strategies)
-	prediction_strategies_names = '-'.join(prediction_strategy_selector).replace(" ", "")	
-	output_filename = 'Reports/report-Model-' + op.models[model_selector] + '_Queries-' + op.queries[queries_selector] + '_Prompt-' + op.prompts[prompt_selector] + '_Strategies-'+ prediction_strategies_names +'.csv'
-	with open(output_filename, 'a') as results_file:
-		for result in results:
-			results[result].to_csv(results_file, mode='a', index_label = result)
-			print(results[result], '\n')
-	return output_filename
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