From a774bc101e6d3c0a88b1e7847fccd0e96e584d4a Mon Sep 17 00:00:00 2001
From: dmt <>
Date: Thu, 24 Oct 2019 21:44:28 +0200
Subject: [PATCH] Initialize pragmatic machine learning models with meta
information.
---
cml/domain/reconstruction.py | 42 +++++++++++++++++++++++++++++++-----
1 file changed, 37 insertions(+), 5 deletions(-)
diff --git a/cml/domain/reconstruction.py b/cml/domain/reconstruction.py
index 830aab6..fef8f6e 100644
--- a/cml/domain/reconstruction.py
+++ b/cml/domain/reconstruction.py
@@ -95,10 +95,16 @@ class Reconstructor:
self.ml_models = ml_models
self.knowledge_domain = knowlege_domain
self._category = None
+ self._free_id = None
self.__reconstruction = None
- def reconstruct(self, learnblock):
- reliabilities_to_model = self.__reconstruction(learnblock)
+ def reconstruct(self, learnblock, which_models=None, meta=None):
+ if not which_models:
+ which_models = [m.abbreviation for m in self.ml_models]
+
+ reliabilities_to_model = self.__reconstruction(learnblock,
+ which_models,
+ meta)
if reliabilities_to_model.keys():
return determine_winner(reliabilities_to_model)
raise NoModelReconstructedError()
@@ -118,9 +124,22 @@ class Reconstructor:
else:
raise ValueError()
- def _reconstruct_conceptual(self, learnblock, krippen=None):
+ @property
+ def free_id(self):
+ return self._free_id
+
+ @free_id.setter
+ def free_id(self, value):
+ self._free_id = iter(value)
+
+ def _reconstruct_conceptual(self,
+ learnblock,
+ which_models,
+ krippen=None,
+ meta=None):
reliability_to_model = defaultdict(list)
for model in self.ml_models:
+ if model.abbreviation not in which_models: continue
# train model
train_block, eval_block = self.split(learnblock)
@@ -134,12 +153,25 @@ class Reconstructor:
learnblock,
krippen)
if reliability >= self.settings.min_reliability:
+ # TODO (dmt): Fix the knowledge tier after first iteration!
+ prag_meta_data = Metadata(
+ "C",
+ 1,
+ next(self.free_id),
+ learnblock.indexes,
+ learnblock.min_timestamp,
+ learnblock.max_timestamp,
+ [model.subject],
+ [".".join(["C", '1', learnblock.purpose])]
+ )
+
reliability_to_model[reliability].append(
- PragmaticMachineLearningModel(trained_model,
+ PragmaticMachineLearningModel(prag_meta_data,
+ trained_model,
learnblock))
return reliability_to_model
- def _reconstruct_procedural(self, learnblock, krippen=None):
+ def _reconstruct_procedural(self, learnblock, krippen=None, meta=None):
reliability_to_model = defaultdict(list)
for model in self.ml_models:
--
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