35 lines
1.3 KiB
Python
35 lines
1.3 KiB
Python
# %%
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import pandas as pd
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# following code computes final mapping + classification accuracy
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# %%
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def run(fold):
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data_path = f'../relevant_class/binary_classifier_desc_unit/classification_prediction/exports/result_group_{fold}.csv'
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df = pd.read_csv(data_path, skipinitialspace=True)
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p_mdm = df['p_mdm']
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# data_path = f'../train/mapping_t5_complete_desc_unit_name/mapping_prediction/exports/result_group_{fold}.csv'
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data_path = f'../train/modified_t5_decoder_4_layers/mapping_prediction/exports/result_group_{fold}.csv'
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df = pd.read_csv(data_path, skipinitialspace=True)
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actual_mdm = df['MDM']
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thing_correctness = df['thing'] == df['p_thing']
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property_correctness = df['property'] == df['p_property']
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answer = thing_correctness & property_correctness
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# if is non-MDM -> then should be unmapped
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# if is MDM -> then should be mapped correctly
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# out of correctly predicted relevant data, how many are mapped correctly?
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correct_positive_mdm_and_map = sum(p_mdm & actual_mdm & answer)
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# number of correctly predicted non-relevant data
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correct_negative_mdm = sum(~(p_mdm) & ~(actual_mdm))
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overall_correct = (correct_positive_mdm_and_map + correct_negative_mdm)/len(actual_mdm)
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print(overall_correct)
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# %%
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for fold in [1,2,3,4,5]:
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run(fold)
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