Delete run.py

run.py

@@ -1,163 +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
-
-model_selector = '0'
-queries_selector = '0'
-prompt_selector = '0'
-metric_selector = '0'
-prediction_strategy_selector = ['1','2.0','2.1','2.2','2.3','3.0','3.1','3.2','3.3']
-
-models = {
-	'0' : 'joeddav/xlm-roberta-large-xnli',
-	'1' : '',
-	}
-
-queries = {
-	'0' : 'SDG_Titles.csv',
-	'1' : 'SDG_Headlines.csv',
-	'2' : 'SDG_Subjects.csv',
-	'3' : 'SDG_Targets.csv',
-	'4' : 'SDG_Numbers.csv',
-	}
-	
-prompts = {
-	'0' : '',
-	'1' : 'This is ',
-	'2' : 'The subject is ',
-	'3' : 'The Sustainable Development Goal is ',
-	}
-
-metrics = {
-	'0' : 'gorkaartola/metric_for_tp_fp_samples',
-	}
-
-prediction_strategy_options = {
-	'1': ["argmax_max"],
-	'2.0': ["threshold", 0.05],
-	'2.1': ["threshold", 0.25],
-	'2.2': ["threshold", 0.5],
-	'2.3': ["threshold", 0.75],
-	'3.0': ["topk", 9],
-	'3.1': ["topk", 7],
-	'3.2': ["topk", 5],
-	'3.3': ["topk", 3],
-	}
-
-saved_inference_tables_path = 'Reports/ZS inference tables/'	
-
-#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[queries_selector]}
-queries_dataset = load_dataset('gorkaartola/SDG_queries', data_files = queries_data_files)['queries']
-
-#Load prompt_________________________________
-prompt = prompts[prompt_selector]
-
-#Load prediction strategias__________________
-prediction_strategies = [prediction_strategy_options[x] for x in 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(models[model_selector])
-#device = torch.device("cuda")
-#model.to(device)
-tokenizer = AutoTokenizer.from_pretrained(models[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:
-		#data = {k: v.to(device) for k, v in batch.items() if k not in ['labels', 'nli_label']}
-		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(saved_inference_tables_path + 'ZS-inference-table_Model-' + model_selector + '_Queries-' + queries_selector + '_Prompt-' + prompt_selector + '.csv', index = False)
-#'''
-#Load saved predictions____________________________
-'''
-results_test = pd.read_csv(saved_inference_tables_path + 'ZS-inference-table_Model-' + model_selector + '_Queries-' + queries_selector + '_Prompt-' + 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 = [prediction_strategy_options[x] for x in prediction_strategy_selector]
-
-metric = evaluate.load(metrics['0'])
-metric.add_batch(predictions = predictions, references = references)
-results = metric.compute(prediction_strategies = prediction_strategies)
-with open('Reports/report-Model-' + model_selector + '_Queries-' + queries_selector + '_Prompt-' + prompt_selector + '.csv', 'a') as results_file:
-	for result in results:
-		results[result].to_csv(results_file, mode='a', index_label = result)
-		print(results[result], '\n')
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