text stringlengths 1 93.6k |
|---|
return evaluation
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def main():
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args = parse_arguments()
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data = pd.read_json(args.input_file, lines=True)
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data["label"] = data["robust_metric"]
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num_ppl= int(args.input_file.split("/")[-1].split("_")[0].replace("people",""))
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print(num_ppl)
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if args.no_balance_label==False:
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# Separate the data by label
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data_0 = data[data["label"] == 0]
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data_1 = data[data["label"] == 1]
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# Determine the size of the smaller class
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min_size = min(len(data_0), len(data_1))
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# Sample from each class to balance the dataset
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balanced_data_0 = data_0.sample(n=min_size, random_state=42)
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balanced_data_1 = data_1.sample(n=min_size, random_state=42)
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# Concatenate the balanced datasets
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balanced_data = pd.concat([balanced_data_0, balanced_data_1])
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# Shuffle the balanced dataset
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data = balanced_data.sample(frac=1, random_state=42).reset_index(drop=True)
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train, test = prepare_cls_data(data, args.train_split)
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methods=[]
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if args.method=="combine":
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methods=["tfidf", "bow", "wordlength" , "charlength",]
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else:
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methods=[args.method]
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train_feature_list=[]
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test_feature_list=[]
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if args.text_field =="all_fields":
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text_fields = ["statements", "quiz" ,"response" , "cot_repeat_steps", "cot_steps", ]
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else:
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text_fields = [args.text_field]
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for text_field in text_fields:
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for method in methods:
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print(f"Processing {text_field} with {method}")
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train_feature, test_feature = vectorize_text(
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train, test, text_field=text_field, method=method, num_ppl=num_ppl
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)
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train_feature_list.append(train_feature)
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test_feature_list.append(test_feature)
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# Initialize an empty array
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concatenated_features = train_feature_list[0]
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print(len(train_feature_list))
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# Use a for loop to concatenate the features
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if len(train_feature_list)>1:
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for i, feature in enumerate(train_feature_list[1:]):
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concatenated_features = np.concatenate((concatenated_features, feature), axis=1)
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train_feature=concatenated_features
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print(len(test_feature_list))
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concatenated_features = test_feature_list[0]
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if len(test_feature_list)>1:
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for feature in test_feature_list[1:]:
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concatenated_features = np.concatenate((concatenated_features, feature), axis=1)
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test_feature=concatenated_features
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print("Train_feature shape", train_feature.shape)
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print("Test_feature shape", test_feature.shape)
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evaluation={}
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evaluation["method"] = args.method
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evaluation["text_field"] = args.text_field
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evaluation["input_file"] = args.input_file
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evaluation_results = train_and_evaluate(
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train_feature,
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test_feature,
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train["label"],
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test["label"],
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)
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evaluation.update(evaluation_results)
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print(evaluation)
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# # TODO: save eval results
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os.makedirs(args.output_dir, exist_ok=True)
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if args.no_balance_label:
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output_file = os.path.join(args.output_dir, f"results_{num_ppl}_unbalanced.jsonl")
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else:
|
output_file = os.path.join(args.output_dir, f"results_{num_ppl}_balanced.jonsl")
|
# Read existing data
|
existing_data = []
|
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