From 695ad5da40d57e63e3720ce397ab84ac1119d12c Mon Sep 17 00:00:00 2001
From: dmt <>
Date: Tue, 12 Nov 2019 18:25:47 +0100
Subject: [PATCH] Clean api endpoints.
---
cml/controller/api.py | 154 ++++++++++++++++++------------------------
1 file changed, 67 insertions(+), 87 deletions(-)
diff --git a/cml/controller/api.py b/cml/controller/api.py
index 8a729f3..9575713 100644
--- a/cml/controller/api.py
+++ b/cml/controller/api.py
@@ -1,7 +1,8 @@
"""Entry point.
"""
-from os.path import join, expanduser
+import pickle
+from os.path import exists
from typing import List, Tuple
from cml.usecases.query import KnowledgeSearchUsecase
@@ -10,14 +11,12 @@ from cml.usecases.command import (
FeatureSelectionUsecase,
ReconstructionUsecase,
ConstructionUsecase,
- DeconstructionUsecase
-)
-from cml.ports.source_adapters import PandasAdapter
+ DeconstructionUsecase)
+from cml.ports.source_adapters import PandasAdapter, Adapter
from cml.ports.ml_adapter import (
ConstructionClusteringMLModel,
FilterMethod,
- EmbeddedMethod
-)
+ EmbeddedMethod)
from cml.shared.settings import Settings
from cml.shared.settings import specific_settings_factory, read_settings
from cml.shared.request import (
@@ -26,47 +25,34 @@ from cml.shared.request import (
ConstructionRequest,
FeatureSelectionRequest,
ReconstructionRequest,
- DeconstructionRequest
-)
+ DeconstructionRequest)
from cml.ports.ml_adapter import (
KernelDensityEstimator,
find_relative_extrema,
Autoencoder,
ReconstructionConceptualMLModel,
- ReconstructionProceduralMLModel
-)
+ ReconstructionProceduralMLModel)
+from cml.domain import *
__all__ = (
"load_settings",
"get_settings",
- "get_data_source",
+ "get_source",
"construction",
"reconstruction",
"reconstruction",
+ "deconstruction",
"search_knowledge",
- "visualizer",
- "save_knowledge",
+ "visualize",
"load_knowledge",
"feature_selection"
)
-def default_path(func):
- def wrapper(path: str = None):
- if not path:
- path = join(expanduser("~"), ".cml", "settings.ini")
-
- try:
- func(path)
- except FileNotFoundError as e:
- # TODO (dmt):Provide proper exception handling!
- pass
- return wrapper
-
-
-@default_path
def load_settings(path: str):
+ assert isinstance(path, str)
+ assert exists(path)
read_settings(path)
@@ -74,90 +60,84 @@ def get_settings():
return Settings
-def get_data_source(path: str = None):
+def load_knowledge(path: str):
+ assert isinstance(path, str)
+ assert exists(path)
+ with open(path, "rb") as file:
+ return pickle.load(file)
+
+
+def get_source(path: str = None):
general_settings = specific_settings_factory("general")
preprocessing_settings = specific_settings_factory("preprocessing")
block_processing_settings = specific_settings_factory("block_processing")
- if path:
- general_settings.input_file = path
- source_adapter = PandasAdapter.read_csv_data(general_settings.input_file)
- density_estimator = KernelDensityEstimator()
- relative_extrema = find_relative_extrema
-
- preprocessing_req = PreprocessRequest(source_adapter,
- preprocessing_settings,
- block_processing_settings,
- density_estimator,
- relative_extrema)
- preprocessing_usecase = PreprocessUsecase()
- return preprocessing_usecase.execute(preprocessing_req)
+ if path: general_settings.input_file = path
+ density_estimator_interface = KernelDensityEstimator
+ relative_extrema_interface = find_relative_extrema
+ request = PreprocessRequest(PandasAdapter,
+ general_settings,
+ preprocessing_settings,
+ block_processing_settings,
+ density_estimator_interface,
+ relative_extrema_interface)
+ usecase = PreprocessUsecase()
+ return usecase.execute(request)
def construction(mode: str, algorithms: List[Tuple]):
- if mode == "conceptual":
- interface = ConstructionClusteringMLModel
- elif mode == "procedural":
- interface = None
- raise ValueError()
- else:
- raise ValueError()
-
+ assert mode == "conceptual"
+ interface = ConstructionClusteringMLModel
settings = specific_settings_factory("construction")
request = ConstructionRequest(settings, algorithms, interface, mode)
usecase = ConstructionUsecase()
return usecase.execute(request)
-def feature_selection(filter_ml_model, embedded_ml_model):
- filter_method = FilterMethod(filter_ml_model)
- embedded_method = EmbeddedMethod(embedded_ml_model)
+def feature_selection(*, filter_method, embedded_method):
+ assert filter_method is not None
+ assert embedded_method is not None
+ filter_method = FilterMethod(filter_method)
+ embedded_method = EmbeddedMethod(embedded_method)
settings = specific_settings_factory("feature_selection")
- feature_selection_req = FeatureSelectionRequest(filter_method,
- embedded_method,
- settings)
- feature_selection_usecase = FeatureSelectionUsecase()
- return feature_selection_usecase.execute(feature_selection_req)
+ request = FeatureSelectionRequest(filter_method, embedded_method, settings)
+ usecase = FeatureSelectionUsecase()
+ return usecase.execute(request)
def reconstruction(mode, algorithms: List[Tuple]):
- if mode == "conceptual":
- interface = ReconstructionConceptualMLModel
- elif mode == "procedural":
- interface = ReconstructionProceduralMLModel
- else:
- raise ValueError()
-
+ assert mode == "conceptual"
+ interface = ReconstructionConceptualMLModel
settings = specific_settings_factory("reconstruction")
request = ReconstructionRequest(settings, algorithms, interface, mode)
usecase = ReconstructionUsecase()
return usecase.execute(request)
-def search_knowledge(constructor, feature_selector, reconstructor, data_source,
- stdout=False):
- deconstruction_settings = specific_settings_factory("deconstruction")
- deconstruction_req = DeconstructionRequest(data_source,
- deconstruction_settings,
- constructor.construction_type)
- deconstruction_usecase = DeconstructionUsecase()
- deconstructor = deconstruction_usecase.execute(deconstruction_req)
- knowledge_search_req = KnowledgeSearchRequest(constructor,
- feature_selector,
- reconstructor,
- deconstructor,
- data_source,
- stdout)
- knowledge_search_usecase = KnowledgeSearchUsecase()
- return knowledge_search_usecase.execute(knowledge_search_req)
-
-
-def visualizer():
- pass
+def deconstruction(mode, source, knowledge=None):
+ assert mode == "conceptual"
+ settings = specific_settings_factory("deconstruction")
+ request = DeconstructionRequest(mode, source, settings, knowledge)
+ usecase = DeconstructionUsecase()
+ return usecase.execute(request)
-def save_knowledge():
- pass
+def search_knowledge(constructor,
+ selector,
+ reconstructor,
+ deconstructor,
+ *,
+ stdout=False,
+ parallel=False):
+ assert isinstance(constructor, Constructor)
+ assert isinstance(selector, FeatureSelector)
+ assert isinstance(reconstructor, Reconstructor)
+ assert isinstance(deconstructor, Deconstructor)
+ assert isinstance(stdout, bool)
+ request = KnowledgeSearchRequest(constructor, selector, reconstructor,
+ deconstructor, stdout, parallel)
+ usecase = KnowledgeSearchUsecase()
+ return usecase.execute(request)
-def load_knowledge():
+def visualize():
pass
--
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