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236 def mean(self): 237 # TODO, there is a lot of copy-paste with the code above 238 # TODO, we should probably define groupby.aggregate 239 func = _accumulate_groupby_mean 240 start = (0, 0) 241 if isinstance(self.grouper, Streaming): 242 func = partial(func, index=self.index) 243 example = self.root.example.groupby(self.grouper.example) 244 if self.index is not None: 245 example = example[self.index] 246 example = example.mean() 247 stream = self.root.stream.zip(self.grouper.stream) 248 stream = stream.accumulate(func, start=start, returns_state=True) 249 else: 250 func = partial(func, grouper=self.grouper, index=self.index) 251 example = self.root.example.groupby(self.grouper) 252 if self.index is not None: 253 example = example[self.index] 254 example = example.mean() 255 stream = self.root.stream.accumulate(func, start=start, 256 returns_state=True) 257 if isinstance(example, pd.DataFrame): 258 return StreamingDataFrame(stream, example) 259 else: 260 return StreamingSeries(stream, example)
202 def aggregate(group_reports): 203 summary = {} 204 top_model = max(group_reports, key=lambda r: r['Dev First Sentence']) 205 summary['Avg First Sentence'] = np.mean([r['First Sentence'] for r in group_reports]) 206 summary['Std First Sentence'] = np.std([r['First Sentence'] for r in group_reports]) 207 summary['Avg Full Question'] = np.mean([r['Full Question'] for r in group_reports]) 208 summary['Std Full Question'] = np.std([r['Full Question'] for r in group_reports]) 209 summary['First Sentence'] = top_model['First Sentence'] 210 summary['Full Question'] = top_model['Full Question'] 211 summary['Dev First Sentence'] = top_model['Dev First Sentence'] 212 summary['Dev Full Question'] = top_model['Dev Full Question'] 213 summary['first_df'] = top_model['first_df'] 214 summary['full_df'] = top_model['full_df'] 215 summary['char_df'] = top_model['char_df'] 216 summary['fold'] = top_model['fold'] 217 summary['guesser_name'] = top_model['guesser_name'] 218 summary['random_seed'] = top_model['random_seed'] 219 summary['wiki'] = top_model['wiki'] 220 summary['training_time'] = top_model['training_time'] 221 222 stable_scores = [] 223 eager_scores = [] 224 for _, group in top_model['char_df'].sort_values('score', ascending=False).groupby('qanta_id'): 225 group = group.groupby(['char_index']).first().reset_index() 226 stable, eager = compute_curve_score(group) 227 stable_scores.append(stable) 228 eager_scores.append(eager) 229 summary['curve_score_stable'] = np.mean(stable_scores) 230 summary['curve_score_eager'] = np.mean(eager_scores) 231 return summary
59 def average(average_window, data): 60 window = [] 61 newdata = [] 62 for v in data: 63 window.append(v) 64 if len(window) == average_window: 65 newdata.append(sum(window)/average_window) 66 del window[0] 67 return newdata