From bef24bd47c44243c5e436638b0f571cd1383980d Mon Sep 17 00:00:00 2001
From: dmt <>
Date: Sun, 20 Oct 2019 23:08:36 +0200
Subject: [PATCH] Construct only unlabeled learnblocks.
---
cml/domain/construction.py | 57 ++++++++++++++++++++++----------------
1 file changed, 33 insertions(+), 24 deletions(-)
diff --git a/cml/domain/construction.py b/cml/domain/construction.py
index cddc6d0..0317d64 100644
--- a/cml/domain/construction.py
+++ b/cml/domain/construction.py
@@ -42,6 +42,7 @@ class Constructor:
self.ml_models = ml_models
self._logging_dir = None
self._construction = None
+ self._category = None
@log_construction
def construct(self, learnblock):
@@ -86,10 +87,10 @@ class Constructor:
@property
def construction_type(self):
- return self.settings.construct_type
+ return self._category
@construction_type.setter
- def construction_type(self, construct_type):
+ def construction_type(self, construction_type):
if self.settings.construct_type == "conceptual":
self._construction = partial(
self._construct_conceptual_knowledge,
@@ -108,34 +109,42 @@ class Constructor:
# TODO (dmt): Provide proper exception handling.
raise Exception("Provide valid construction type.")
+ self._category = construction_type
+
def _construct_conceptual_knowledge(self,
learnblock,
categorial_complexity=None,
min_category_size=None):
- for ml_model in self.ml_models:
- for cluster_number in range(2, categorial_complexity+1):
- ml_model.cluster = cluster_number
- trained_model = ml_model.train(learnblock)
- for cluster, size in trained_model.cluster_sizes.items():
- if size < min_category_size:
- continue
- labels = trained_model.get_labels()
- labeled_learnblock = learnblock.set_labels(labels)
- labeled_learnblock.n_cluster = cluster_number
- yield labeled_learnblock
+ if learnblock.labeled:
+ yield learnblock
+ else:
+ for ml_model in self.ml_models:
+ for cluster_number in range(2, categorial_complexity+1):
+ ml_model.cluster = cluster_number
+ trained_model = ml_model.train(learnblock)
+ for cluster, size in trained_model.cluster_sizes.items():
+ if size < min_category_size:
+ continue
+ labels = trained_model.get_labels()
+ labeled_learnblock = learnblock.set_labels(labels)
+ labeled_learnblock.n_cluster = cluster_number
+ yield labeled_learnblock
def _construct_procedural_knowledge(self,
learnblock,
procedural_complexity=None,
max_target_error=None):
- for ml_model in self.ml_models:
- for target_number in range(2, procedural_complexity+1):
- model = ml_model()
- model.io_shape = learnblock.learn_rows
- model.target_number = target_number
- trained_model = model.train(learnblock.as_numpy_array())
- if trained_model.target_error < max_target_error:
- for labels in trained_model.targets:
- labeled_learnblock = learnblock.set_labels(list(labels))
- labeled_learnblock.n_cluster = target_number
- yield labeled_learnblock
+ if learnblock.labeled:
+ yield learnblock
+ else:
+ for ml_model in self.ml_models:
+ for target_number in range(2, procedural_complexity+1):
+ model = ml_model()
+ model.io_shape = learnblock.learn_rows
+ model.target_number = target_number
+ trained_model = model.train(learnblock.as_numpy_array())
+ if trained_model.target_error < max_target_error:
+ for labels in trained_model.targets:
+ labeled_learnblock = learnblock.set_labels(list(labels))
+ labeled_learnblock.n_cluster = target_number
+ yield labeled_learnblock
--
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