Pipeline optimizer progress

niaaml/__init__.py

@@ -3,10 +3,12 @@
 from niaaml import preprocessing_algorithms
 from niaaml import feature_selection_algorithms
 from niaaml.utilities import get_label_encoder
+from niaaml.utilities import float_converter
 from niaaml.utilities import MinMax
 from niaaml.utilities import ParameterDefinition
 from niaaml.utilities import Factory
-from niaaml.task import Task
+from niaaml.pipeline_optimizer import PipelineOptimizer
+from niaaml.pipeline import Pipeline
 
 __all__ = [
     'data',
@@ -17,7 +19,8 @@
     'MinMax',
     'ParameterDefinition',
     'Factory',
-    'Task'
+    'PipelineOptimizer',
+    'Pipeline'
 ]
 __project__ = 'niaaml'
 __version__ = '0.1.0'

niaaml/classifiers/__init__.py

@@ -5,7 +5,7 @@
 from niaaml.classifiers.ada_boost import AdaBoost
 from niaaml.classifiers.extremely_randomized_trees import ExtremelyRandomizedTrees
 from niaaml.classifiers.bagging import Bagging
-from niaaml.classifiers.utility import ClassifierUtility
+from niaaml.classifiers.utility import ClassifierFactory
 
 __all__ = [
     'Classifier',
@@ -15,5 +15,5 @@
     'AdaBoost',
     'Bagging',
     'ExtremelyRandomizedTrees',
-    'ClassifierUtility'
+    'ClassifierFactory'
 ]

niaaml/classifiers/utility.py

@@ -2,10 +2,10 @@
 from niaaml import classifiers
 
 __all__ = [
-	'ClassifierUtility'
+	'ClassifierFactory'
 ]
 
-class ClassifierUtility(Factory):
+class ClassifierFactory(Factory):
 	r"""Class with string mappings to classifiers.
 
 	Attributes:

niaaml/feature_selection_algorithms/__init__.py

@@ -3,13 +3,13 @@
 from niaaml.feature_selection_algorithms.jDEFSTH import jDEFSTH
 from niaaml.feature_selection_algorithms.select_percentile_chi2 import SelectPercentileChi2
 from niaaml.feature_selection_algorithms.select_k_best_chi2 import SelectKBestChi2
-from niaaml.feature_selection_algorithms.utility import FeatureSelectionAlgorithmUtility
+from niaaml.feature_selection_algorithms.utility import FeatureSelectionAlgorithmFactory
 
 __all__ = [
     'FeatureSelectionAlgorithm',
     'VarianceThresholdFeatureSelection',
     'jDEFSTH',
     'SelectPercentileChi2',
     'SelectKBestChi2',
-    'FeatureSelectionAlgorithmUtility'
+    'FeatureSelectionAlgorithmFactory'
 ]

niaaml/feature_selection_algorithms/utility.py

@@ -2,10 +2,10 @@
 from niaaml import feature_selection_algorithms
 
 __all__ = [
-	'FeatureSelectionAlgorithmUtility'
+	'FeatureSelectionAlgorithmFactory'
 ]
 
-class FeatureSelectionAlgorithmUtility(Factory):
+class FeatureSelectionAlgorithmFactory(Factory):
 	r"""Class with string mappings to feature selection algorithms.
 
 	Attributes:

niaaml/pipeline.py

@@ -0,0 +1,45 @@
+__all__ = [
+    'Pipeline'
+]
+
+class Pipeline:
+    r"""Classification pipeline defined by optional preprocessing steps, feature selection algorithm and classifier.
+
+	Date:
+		2020
+
+	Author
+		Luka Pečnik
+
+	License:
+        MIT
+
+	Attributes:
+		__data (DataReader): Instance of any DataReader implementation.
+		__feature_selection_algorithm (FeatureSelectionAlgorithm): Feature selection algorithm implementation.
+		__preprocessing_algorithm (PreprocessingAlgorithm): Preprocessing algorithm implementation (optional).
+        __classifier (Classifier): Classifier implementation.
+    """
+    __data = None
+    __feature_selection_algorithm = None
+    __preprocessing_algorithm = None
+    __classifier = None
+
+    def __init__(self, **kwargs):
+        r"""Initialize task.
+        """
+        self._set_parameters(**kwargs)
+
+    def _set_parameters(self, data, feature_selection_algorithm, preprocessing_algorithm, classifier, **kwargs):
+        r"""Set the parameters/arguments of the task.
+
+		Arguments:
+            data (DataReader): Instance of any DataReader implementation.
+            feature_selection_algorithm (FeatureSelectionAlgorithm): Feature selection algorithm implementation.
+            preprocessing_algorithm (PreprocessingAlgorithm): Preprocessing algorithm implementation (optional).
+            classifier (Classifier): Classifier implementation.
+        """
+        self.__data = data
+        self.__feature_selection_algorithm = feature_selection_algorithm
+        self.__preprocessing_algorithm = preprocessing_algorithm
+        self.__classifier = classifier

niaaml/pipeline_optimizer.py

@@ -0,0 +1,118 @@
+import numpy as np
+from niaaml.pipeline import Pipeline
+from niaaml.classifiers import ClassifierFactory
+from niaaml.feature_selection_algorithms import FeatureSelectionAlgorithmFactory
+from niaaml.preprocessing_algorithms import PreprocessingAlgorithmFactory
+from NiaPy.task import StoppingTask
+from NiaPy.benchmarks import Benchmark
+from NiaPy.algorithms.basic import ParticleSwarmOptimization
+
+__all__ = [
+    'PipelineOptimizer',
+    'PipelineOptimizerBenchmark'
+]
+
+class PipelineOptimizer():
+    r"""Optimization task that finds the best classification pipeline according to the given input.
+
+	Date:
+		2020
+
+	Author
+		Luka Pečnik
+
+	License:
+        MIT
+
+	Attributes:
+		__data (DataReader): Instance of any DataReader implementation.
+		__feature_selection_algorithms (Iterable[FeatureSelectionAlgorithm]): Array of possible feature selection algorithms.
+		__preprocessing_algorithms (Iterable[PreprocessingAlgorithm]): Array of possible preprocessing algorithms.
+        __classifiers (Iterable[Classifier]): Array of possible classifiers.
+		__pipelines_numeric (numpy.ndarray[float]): Numeric representation of pipelines.
+		__pipelines (Iterable[Pipeline]): Actual pipelines.
+        __pop_size (int): Number of individuals in the pipeline optimizer's population.
+
+        __classifier_factory (ClassifierFactory): Factory for classifier instances.
+        __preprocessing_algorithm_factory (ClassifierFactory): Factory for preprocessing algorithm instances.
+        __feature_selection_algorithm_factory (ClassifierFactory): Factory for feature selection algorithm instances.
+    """
+    __data = None
+    __feature_selection_algorithms = None
+    __preprocessing_algorithms = None
+    __classifiers = None
+
+    __pop_size = None
+    __pipelines_numeric = None
+    __pipelines = None
+
+    __classifier_factory = ClassifierFactory()
+    __preprocessing_algorithm_factory = PreprocessingAlgorithmFactory()
+    __feature_selection_algorithm_factory = FeatureSelectionAlgorithmFactory()
+
+    def __init__(self, **kwargs):
+        r"""Initialize task.
+        """
+        self._set_parameters(**kwargs)
+        self.__initialize_population(self.__pop_size)
+
+    def _set_parameters(self, data, feature_selection_algorithms, preprocessing_algorithms, classifiers, pop_size, **kwargs):
+        r"""Set the parameters/arguments of the task.
+
+		Arguments:
+            data (DataReader): Instance of any DataReader implementation.
+            feature_selection_algorithms (Iterable[FeatureSelectionAlgorithm]): Array of possible feature selection algorithms.
+            preprocessing_algorithms (Iterable[PreprocessingAlgorithm]): Array of possible preprocessing algorithms.
+            classifiers (Iterable[Classificator]): Array of possible classifiers.
+            pop_size (int): Number of individuals in the pipeline optimizer's population.
+        """
+        self.__data = data
+
+        self.__preprocessing_algorithms = preprocessing_algorithms
+        try:
+            self.__preprocessing_algorithms.index(None)
+        except:
+            self.__preprocessing_algorithms.insert(0, None)
+
+        self.__classifiers = classifiers
+        self.__feature_selection_algorithms = feature_selection_algorithms
+        self.__pop_size = pop_size
+
+    def __initialize_population(self, pop_size):
+        r"""Initialize population of pipelines to find the best setup.
+
+        Arguments:
+            pop_size (int): Number of individuals.
+        """
+
+        self.__pipelines_numeric = np.random.uniform(size=(pop_size, 3))
+        self.__pipelines = [
+            Pipeline(
+                data=self.__data,
+                feature_selection_algorithm=self.__float_to_instance(i[0], self.__feature_selection_algorithms, self.__feature_selection_algorithm_factory) if self.__feature_selection_algorithms is not None and len(self.__feature_selection_algorithms) > 0 else None,
+                preprocessing_algorithm=self.__float_to_instance(i[1], self.__preprocessing_algorithms, self.__preprocessing_algorithm_factory) if self.__preprocessing_algorithms is not None and len(self.__preprocessing_algorithms) > 0 else None,
+                classifier=self.__float_to_instance(i[2], self.__classifiers, self.__classifier_factory)
+                ) for i in self.__pipelines_numeric
+        ]
+
+    def __float_to_instance(self, value, collection, factory):
+        r"""TODO
+        """
+        name = collection[np.int(np.round(value * (len(collection) - 1)))]
+        return factory.get_result(name) if name is not None else None
+
+    def optimize_pipeline(self, n_p, n_fes):
+        algo = ParticleSwarmOptimization(NP=n_p)    # TODO define InitPopFunc
+        task = StoppingTask(D=3, nFES=n_fes, benchmark=PipelineOptimizerBenchmark())
+        best = algo.run(task)
+        return best
+
+class PipelineOptimizerBenchmark(Benchmark):
+    def __init__(self):
+        Benchmark.__init__(self, 0.0, 1.0)
+
+    def function(self):
+        # TODO
+        def evaluate(D, sol):
+            return 0.0
+        return evaluate

niaaml/preprocessing_algorithms/__init__.py

@@ -1,11 +1,11 @@
 from niaaml.preprocessing_algorithms.preprocessing_algorithm import PreprocessingAlgorithm
 from niaaml.preprocessing_algorithms.normalizer import Normalizer
 from niaaml.preprocessing_algorithms.standard_scaler import StandardScaler
-from niaaml.preprocessing_algorithms.utility import PreprocessingAlgorithmUtility
+from niaaml.preprocessing_algorithms.utility import PreprocessingAlgorithmFactory
 
 __all__ = [
     'PreprocessingAlgorithm',
     'Normalizer',
     'StandardScaler',
-    'PreprocessingAlgorithmUtility'
+    'PreprocessingAlgorithmFactory'
 ]

niaaml/preprocessing_algorithms/utility.py

@@ -2,10 +2,10 @@
 from niaaml import preprocessing_algorithms
 
 __all__ = [
-	'PreprocessingAlgorithmUtility'
+	'PreprocessingAlgorithmFactory'
 ]
 
-class PreprocessingAlgorithmUtility(Factory):
+class PreprocessingAlgorithmFactory(Factory):
 	r"""Class with string mappings to preprocessing algorithms.
 
 	Attributes: