Fixed typos. Next is testing. Heavy testing.

solidago/src/solidago/pipeline/__init__.py

@@ -1,7 +1,7 @@
 from .trust_propagation import *
 from .preference_learning import *
 from .voting_rights import *
-# from .scaling import *
+from .scaling import *
 from .aggregation import *
 from .post_process import *
 

solidago/src/solidago/pipeline/aggregation/__init__.py

@@ -4,8 +4,5 @@
 The aggregation may also adjust the user models to the learned global model.
 """
 
-from .base import Aggregation
 from .average import Average
 from .entitywise_qr_quantile import EntitywiseQrQuantile
-# from .standardized_qr_quantile import StandardizedQrQuantile
-# from .standardized_qr_median import StandardizedQrMedian

solidago/src/solidago/pipeline/aggregation/average.py

@@ -1,12 +1,10 @@
 import pandas as pd
 
-from solidago.voting_rights import VotingRights
-from solidago.scoring_model import ScoringModel, DirectScoringModel
+from solidago.state import *
+from solidago.pipeline.base import StateFunction
 
-from .base import Aggregation
 
-
-class Average(Aggregation):
+class Average(StateFunction):
     def main(self, 
         entities: Entities,
         voting_rights: VotingRights,

solidago/src/solidago/pipeline/aggregation/entitywise_qr_quantile.py

@@ -1,13 +1,12 @@
 import pandas as pd
 import numpy as np
 
-from .base import Aggregation
-
-from solidago.state import *
 from solidago.primitives.lipschitz import qr_quantile, qr_uncertainty
+from solidago.state import *
+from solidago.pipeline.base import StateFunction
 
 
-class EntitywiseQrQuantile(Aggregation):
+class EntitywiseQrQuantile(StateFunction):
     def __init__(self, quantile=0.2, lipschitz=0.1, error=1e-5):
         """ Standardize scores so that only a fraction 1 - dev_quantile
         of the scores is further than 1 away from the median,
@@ -38,7 +37,7 @@ def main(self,
             for criterion, scores_list in all_scores.items():
                 if criterion not in rights:
                     continue
-                scores, left_uncs, right_uncs = [ np.array(l) for l in zip(*scores_list)) ]
+                scores, left_uncs, right_uncs = [ np.array(l) for l in zip(*scores_list) ]
                 score = qr_quantile(
                     lipschitz=self.lipschitz,
                     quantile=self.quantile,

solidago/src/solidago/pipeline/post_process/__init__.py

@@ -4,6 +4,4 @@
 of yielding more human-readible scores.
 """
 
-from .base import PostProcess
-from .no_post_process import NoPostProcess
 from .squash import Squash

solidago/src/solidago/pipeline/post_process/squash.py

@@ -4,10 +4,10 @@
 import pandas as pd
 
 from solidago.state import *
-from .base import PostProcess
+from solidago.pipeline.base import StateFunction
 
 
-class Squash(PostProcess):
+class Squash(StateFunction):
     def __init__(self, score_max: float = 100.0):
         assert score_max > 0
         self.score_max = score_max

solidago/src/solidago/pipeline/preference_learning/base.py

@@ -7,10 +7,10 @@
 logger = logging.getLogger(__name__)
 
 from solidago.state import *
+from solidago.pipeline.base import StateFunction
 
 
-
-class PreferenceLearning(ABC):
+class PreferenceLearning(StateFunction, ABC):
     def main(self, 
         users: Users,
         entities: Entities,