From a0eab2f7b957d05a5654eb05aedd7d0403686575 Mon Sep 17 00:00:00 2001
From: dmt <>
Date: Fri, 18 Oct 2019 23:09:32 +0200
Subject: [PATCH] Log the construction result.
---
cml/domain/construction.py | 56 +++++++++++++++++++++++++++-----------
1 file changed, 40 insertions(+), 16 deletions(-)
diff --git a/cml/domain/construction.py b/cml/domain/construction.py
index b223f01..cddc6d0 100644
--- a/cml/domain/construction.py
+++ b/cml/domain/construction.py
@@ -1,11 +1,32 @@
from functools import partial
+from cml.shared.parameter import PROTOCOL_LEVEL
+
__all__ = (
"Constructor",
)
+def log_construction(func):
+ def wrapper(self, learnblock):
+ for complete_learnblock in func(self, learnblock):
+ if self.logger and self.logger.level == PROTOCOL_LEVEL:
+ self.logger.protocol(
+ "\t".join([
+ "",
+ str(complete_learnblock.length),
+ str(complete_learnblock.min_timestamp),
+ str(complete_learnblock.max_timestamp),
+ str(complete_learnblock.relatives),
+ str(complete_learnblock.n_cluster),
+ str(complete_learnblock.feature_count)
+ ]))
+
+ yield complete_learnblock
+ return wrapper
+
+
def update_construction(func):
def wrapper(self, value):
func(self, value)
@@ -16,11 +37,13 @@ def update_construction(func):
class Constructor:
def __init__(self, ml_models, settings):
+ self.logger = None
self.settings = settings
self.ml_models = ml_models
+ self._logging_dir = None
self._construction = None
- self._construction_type = "conceptual"
+ @log_construction
def construct(self, learnblock):
for block in self._construction(learnblock):
yield block
@@ -63,20 +86,18 @@ class Constructor:
@property
def construction_type(self):
- return self._construction_type
+ return self.settings.construct_type
@construction_type.setter
def construction_type(self, construct_type):
- if construct_type == "conceptual":
- self._construction_type = construct_type
+ if self.settings.construct_type == "conceptual":
self._construction = partial(
self._construct_conceptual_knowledge,
categorial_complexity=self.settings.max_categories,
min_category_size=self.settings.min_category_size,
)
- elif construct_type == "procedural":
- self._construction_type = construct_type
+ elif self.settings.construct_type == "procedural":
self._construction = partial(
self._construct_procedural_knowledge,
procedural_complexity=self.settings.max_model_targets,
@@ -92,26 +113,29 @@ class Constructor:
categorial_complexity=None,
min_category_size=None):
for ml_model in self.ml_models:
- for cluster_number in range(2, categorial_complexity):
+ 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():
- print(cluster, size)
if size < min_category_size:
continue
labels = trained_model.get_labels()
- labeld_learnblock = learnblock.set_labels(labels)
- yield labeld_learnblock
+ 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):
- ml_model.target_number = target_number
- trained_model = ml_model.train(learnblock)
+ 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:
- labeld_learnblock = learnblock.set_labels(trained_model)
- yield labeld_learnblock
-
+ 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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