63 lines
1.8 KiB
Python
63 lines
1.8 KiB
Python
# %%
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import pandas as pd
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import numpy as np
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# %%
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data_path = '../../data_import/exports/data_mapping_mdm.csv'
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full_df = pd.read_csv(data_path, skipinitialspace=True)
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mdm_list = sorted(list((set(full_df['thing'] + full_df['property']))))
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# %%
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fold = 5
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file_path = f'../../train/classification_bert_complete_desc_unit/classification_prediction/exports/result_group_{fold}.csv'
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df_bert = pd.read_csv(file_path)
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# %%
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file_path = f'../../train/mapping_t5_complete_desc_unit/mapping_prediction/exports/result_group_{fold}.csv'
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# file_path = f'../../train/mapping_t5-base_desc_unit/mapping_prediction/exports/result_group_{fold}.csv'
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df_t5 = pd.read_csv(file_path)
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df_t5 = df_t5[df_t5['MDM']].reset_index(drop=True)
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df_t5['class_prediction'] = (df_t5['p_thing'] + df_t5['p_property'])
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df_t5['in_vocab'] = df_t5['class_prediction'].isin(mdm_list)
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# %%
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df_t5['bert_prediction'] = df_bert['class_prediction']
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df_bert['t5_prediction'] = df_t5['class_prediction']
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# %%
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bert_correct = (df_bert['thing'] + df_bert['property']) == df_bert['class_prediction']
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# %%
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t5_correct = (df_t5['thing'] + df_t5['property']) == (df_t5['p_thing'] + df_t5['p_property'])
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# %%
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sum(t5_correct)/len(t5_correct)
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# %%
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# replace t5 not in vocab with bert values
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t5_correct_modified = t5_correct.copy()
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condition = ~df_t5['in_vocab']
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t5_correct_modified[condition] = np.array(bert_correct[condition])
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# %%
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# new replacement correctness
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sum(t5_correct_modified)/len(t5_correct_modified)
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# %%
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# when bert is correct and t5 is wrong
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cond_mask = bert_correct & (~t5_correct)
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print(sum(cond_mask))
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print(df_t5[cond_mask].to_string())
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# %%
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# when bert is wrong and t5 is correct
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cond_mask = (~bert_correct) & (t5_correct)
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print(sum(cond_mask))
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print(df_bert[cond_mask].to_string())
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# %%
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# when both are wrong
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cond_mask = (~bert_correct) & (~t5_correct)
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print(sum(cond_mask))
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# %%
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