72 lines
1.5 KiB
Python
72 lines
1.5 KiB
Python
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# %%
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import pandas as pd
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# %%
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# import training file
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data_path = '../data_import/train.csv'
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train_df = pd.read_csv(data_path, skipinitialspace=True)
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# import test file
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data_path = '../data_import/test.csv'
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test_df = pd.read_csv(data_path, skipinitialspace=True)
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# import entity file
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data_path = '../data_import/entity.csv'
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entity_df = pd.read_csv(data_path, skipinitialspace=True)
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id2label = {}
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for _, row in entity_df.iterrows():
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id2label[row['id']] = row['name']
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# %%
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data_path = '../train/class_bert_process/classification_prediction/exports/result.csv'
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prediction_df = pd.read_csv(data_path)
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# %%
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predicted_entity_list = []
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for element in prediction_df['class_prediction']:
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predicted_entity_list.append(id2label[element])
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prediction_df['predicted_name'] = predicted_entity_list
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# %%
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new_df = pd.concat((test_df, prediction_df ), axis=1)
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# %%
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mismatch_mask = new_df['entity_id'] != new_df['class_prediction']
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mismatch_df = new_df[mismatch_mask]
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# %%
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# print the top 10 offending classes
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print(mismatch_df['entity_id'].value_counts()[:10])
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# %%
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# Convert the whole dataframe as a string and display
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# print the mismatch_df
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print(mismatch_df.to_markdown())
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# %%
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# let us see the test mentions
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select_value = 434
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select_mask = mismatch_df['entity_id'] == select_value
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mismatch_df[select_mask]
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# %%
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# let us see the train mentions
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select_value = 434
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select_mask = train_df['entity_id'] == select_value
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train_df[select_mask]
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# %%
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mismatch_df[select_mask]['class_prediction'].to_list()
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# %%
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# %%
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