Added basic structure of details data processing and testing. For downloading huggingface details dataset files

details_data_processor.py

@@ -0,0 +1,155 @@
+import pandas as pd
+import os
+import fnmatch
+import json
+import re
+import numpy as np
+
+class DetailsDataProcessor:
+    
+    def __init__(self, directory='results', pattern='results*.json'):
+        self.directory = directory
+        self.pattern = pattern
+        # self.data = self.process_data()
+        # self.ranked_data = self.rank_data()
+
+    # @staticmethod
+    # def _find_files(directory, pattern):
+    #     for root, dirs, files in os.walk(directory):
+    #         for basename in files:
+    #             if fnmatch.fnmatch(basename, pattern):
+    #                 filename = os.path.join(root, basename)
+    #                 yield filename
+    
+    # def _read_and_transform_data(self, filename):
+    #     with open(filename) as f:
+    #         data = json.load(f)
+    #     df = pd.DataFrame(data['results']).T
+    #     return df
+    
+    # def _cleanup_dataframe(self, df, model_name):
+    #     df = df.rename(columns={'acc': model_name})
+    #     df.index = (df.index.str.replace('hendrycksTest-', 'MMLU_', regex=True)
+    #                       .str.replace('harness\|', '', regex=True)
+    #                       .str.replace('\|5', '', regex=True))
+    #     return df[[model_name]]
+    
+    # def _extract_mc1(self, df, model_name):
+    #     df = df.rename(columns={'mc1': model_name})
+    #     # rename row harness|truthfulqa:mc|0 to truthfulqa:mc1
+    #     df.index = (df.index.str.replace('mc\|0', 'mc1', regex=True))
+    #     # just return the harness|truthfulqa:mc1 row
+    #     df = df.loc[['harness|truthfulqa:mc1']]
+    #     return df[[model_name]]
+    
+    # def _extract_mc2(self, df, model_name):
+    #     # rename row harness|truthfulqa:mc|0 to truthfulqa:mc2
+    #     df = df.rename(columns={'mc2': model_name})
+    #     df.index = (df.index.str.replace('mc\|0', 'mc2', regex=True))
+    #     df = df.loc[['harness|truthfulqa:mc2']]
+    #     return df[[model_name]]
+    
+    # # remove extreme outliers from column harness|truthfulqa:mc1
+    # def _remove_mc1_outliers(self, df):
+    #     mc1 = df['harness|truthfulqa:mc1']
+    #     # Identify the outliers
+    #     # outliers_condition = mc1 > mc1.quantile(.95)
+    #     outliers_condition = mc1 == 1.0
+    #     # Replace the outliers with NaN
+    #     df.loc[outliers_condition, 'harness|truthfulqa:mc1'] = np.nan
+    #     return df
+
+
+    
+    # @staticmethod
+    # def _extract_parameters(model_name):
+    #     """
+    #     Function to extract parameters from model name.
+    #     It handles names with 'b/B' for billions and 'm/M' for millions. 
+    #     """
+    #     # pattern to match a number followed by 'b' (representing billions) or 'm' (representing millions)
+    #     pattern = re.compile(r'(\d+\.?\d*)([bBmM])')
+        
+    #     match = pattern.search(model_name)
+        
+    #     if match:
+    #         num, magnitude = match.groups()
+    #         num = float(num)
+            
+    #         # convert millions to billions
+    #         if magnitude.lower() == 'm':
+    #             num /= 1000
+            
+    #         return num
+        
+    #     # return NaN if no match
+    #     return np.nan
+
+    
+    # def process_data(self):
+        
+    #     dataframes = []
+    #     organization_names = []
+    #     for filename in self._find_files(self.directory, self.pattern):
+    #         raw_data = self._read_and_transform_data(filename)
+    #         split_path = filename.split('/')
+    #         model_name = split_path[2]
+    #         organization_name = split_path[1]
+    #         cleaned_data = self._cleanup_dataframe(raw_data, model_name)
+    #         mc1 = self._extract_mc1(raw_data, model_name)
+    #         mc2 = self._extract_mc2(raw_data, model_name)
+    #         cleaned_data = pd.concat([cleaned_data, mc1])
+    #         cleaned_data = pd.concat([cleaned_data, mc2])
+    #         organization_names.append(organization_name)
+    #         dataframes.append(cleaned_data)
+
+
+    #     data = pd.concat(dataframes, axis=1).transpose()
+
+    #     # Add organization column
+    #     data['organization'] = organization_names
+
+    #     # Add Model Name and rearrange columns
+    #     data['Model Name'] = data.index
+    #     cols = data.columns.tolist()
+    #     cols = cols[-1:] + cols[:-1]
+    #     data = data[cols]
+
+    #     # Remove the 'Model Name' column
+    #     data = data.drop(columns=['Model Name'])
+        
+    #     # Add average column
+    #     data['MMLU_average'] = data.filter(regex='MMLU').mean(axis=1)
+
+    #     # Reorder columns to move 'MMLU_average' to the third position
+    #     cols = data.columns.tolist()
+    #     cols = cols[:2] + cols[-1:] + cols[2:-1]
+    #     data = data[cols]
+
+    #     # Drop specific columns
+    #     data = data.drop(columns=['all', 'truthfulqa:mc|0'])
+
+    #     # Add parameter count column using extract_parameters function
+    #     data['Parameters'] = data.index.to_series().apply(self._extract_parameters)
+
+    #     # move the parameters column to the front of the dataframe
+    #     cols = data.columns.tolist()
+    #     cols = cols[-1:] + cols[:-1]
+    #     data = data[cols]
+
+    #     # remove extreme outliers from column harness|truthfulqa:mc1
+    #     data = self._remove_mc1_outliers(data)
+
+    #     return data
+    
+    # def rank_data(self):
+    #     # add rank for each column to the dataframe
+    #     # copy the data dataframe to avoid modifying the original dataframe
+    #     rank_data = self.data.copy()
+    #     for col in list(rank_data.columns):
+    #         rank_data[col + "_rank"] = rank_data[col].rank(ascending=False, method='min')
+
+    #     return rank_data
+
+    # def get_data(self, selected_models):
+    #     return self.data[self.data.index.isin(selected_models)]

test_details_data_processing.py

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+import unittest
+from details_data_processor import DetailsDataProcessor
+import pandas as pd
+
+class TestDetailsDataProcessor(unittest.TestCase):
+
+    def setUp(self):
+        self.processor = DetailsDataProcessor()
+
+    # check that the result is a pandas dataframe
+    def test_process_data(self):
+        pass
+        # data = self.processor.data
+        # self.assertIsInstance(data, pd.DataFrame)
+
+        
+if __name__ == '__main__':
+    unittest.main()