def __init__(self, **kwargs): r"""Initialize PSO feature selection algorithm. """ self._params = dict( C1 = ParameterDefinition(MinMax(1.5, 2.5), param_type=float), C2 = ParameterDefinition(MinMax(1.5, 2.5), param_type=float) ) self.__pso = PSO(NP=10)
def __init__(self, **kwargs): r"""Initialize BA feature selection algorithm. """ self._params = dict(A=ParameterDefinition(MinMax(0.5, 1.0), param_type=float), r=ParameterDefinition(MinMax(0.0, 0.5), param_type=float), Qmin=ParameterDefinition(MinMax(0.0, 1.0), param_type=float), Qmax=ParameterDefinition(MinMax(1.0, 2.0), param_type=float)) self.__ba = BA(NP=10)
def __init__(self, **kwargs): r"""Initialize SelectPercentile feature selection algorithm. """ self._params = dict(score_func=ParameterDefinition( [chi2, f_classif, mutual_info_classif]), percentile=ParameterDefinition( MinMax(10, 100), np.uint)) self.__select_percentile = SelectPerc()
def __init__(self, **kwargs): r"""Initialize RandomForestClassifier instance. """ warnings.filterwarnings(action='ignore', category=ChangedBehaviorWarning) warnings.filterwarnings(action='ignore', category=ConvergenceWarning) warnings.filterwarnings(action='ignore', category=DataConversionWarning) warnings.filterwarnings(action='ignore', category=DataDimensionalityWarning) warnings.filterwarnings(action='ignore', category=EfficiencyWarning) warnings.filterwarnings(action='ignore', category=FitFailedWarning) warnings.filterwarnings(action='ignore', category=NonBLASDotWarning) warnings.filterwarnings(action='ignore', category=UndefinedMetricWarning) self._params = dict( n_estimators=ParameterDefinition(MinMax(min=10, max=111), np.uint)) self.__random_forest_classifier = RF()
def select_features(self, x, y, **kwargs): r"""Perform the feature selection process. Arguments: x (pandas.core.frame.DataFrame): Array of original features. y (pandas.core.series.Series) Expected classifier results. Returns: numpy.ndarray[bool]: Mask of selected features. """ if self.__k is None: self.__k = x.shape[1] self._params['k'] = ParameterDefinition(MinMax(1, self.__k), np.int) val = np.int(np.around(np.random.uniform(1, self.__k))) self.__select_k_best.set_params(k=val) self.__select_k_best.fit(x, y) return self.__select_k_best.get_support()
def __init__(self, **kwargs): r"""Initialize LinearSVCClassifier instance. """ warnings.filterwarnings(action='ignore', category=ChangedBehaviorWarning) warnings.filterwarnings(action='ignore', category=ConvergenceWarning) warnings.filterwarnings(action='ignore', category=DataConversionWarning) warnings.filterwarnings(action='ignore', category=DataDimensionalityWarning) warnings.filterwarnings(action='ignore', category=EfficiencyWarning) warnings.filterwarnings(action='ignore', category=FitFailedWarning) warnings.filterwarnings(action='ignore', category=NonBLASDotWarning) warnings.filterwarnings(action='ignore', category=UndefinedMetricWarning) self._params = dict(penalty=ParameterDefinition(['l1', 'l2']), max_iter=ParameterDefinition( MinMax(min=300, max=2000), np.uint)) self.__linear_SVC = LSVC()
def __init__(self, **kwargs): r"""Initialize AdaBoost instance. """ warnings.filterwarnings(action='ignore', category=ChangedBehaviorWarning) warnings.filterwarnings(action='ignore', category=ConvergenceWarning) warnings.filterwarnings(action='ignore', category=DataConversionWarning) warnings.filterwarnings(action='ignore', category=DataDimensionalityWarning) warnings.filterwarnings(action='ignore', category=EfficiencyWarning) warnings.filterwarnings(action='ignore', category=FitFailedWarning) warnings.filterwarnings(action='ignore', category=NonBLASDotWarning) warnings.filterwarnings(action='ignore', category=UndefinedMetricWarning) self._params = dict(n_estimators=ParameterDefinition( MinMax(min=10, max=111), np.uint), algorithm=ParameterDefinition(['SAMME', 'SAMME.R'])) self.__ada_boost = AdaBoostClassifier()
def __init__(self, **kwargs): r"""Initialize Bagging instance. """ warnings.filterwarnings(action='ignore', category=ChangedBehaviorWarning) warnings.filterwarnings(action='ignore', category=ConvergenceWarning) warnings.filterwarnings(action='ignore', category=DataConversionWarning) warnings.filterwarnings(action='ignore', category=DataDimensionalityWarning) warnings.filterwarnings(action='ignore', category=EfficiencyWarning) warnings.filterwarnings(action='ignore', category=FitFailedWarning) warnings.filterwarnings(action='ignore', category=NonBLASDotWarning) warnings.filterwarnings(action='ignore', category=UndefinedMetricWarning) self._params = dict( n_estimators=ParameterDefinition(MinMax(min=10, max=111), np.uint), bootstrap=ParameterDefinition([True, False]), bootstrap_features=ParameterDefinition([True, False])) self.__bagging_classifier = BaggingClassifier()
def __init__(self, **kwargs): r"""Initialize GaussianProcess instance. """ warnings.filterwarnings(action='ignore', category=ChangedBehaviorWarning) warnings.filterwarnings(action='ignore', category=ConvergenceWarning) warnings.filterwarnings(action='ignore', category=DataConversionWarning) warnings.filterwarnings(action='ignore', category=DataDimensionalityWarning) warnings.filterwarnings(action='ignore', category=EfficiencyWarning) warnings.filterwarnings(action='ignore', category=FitFailedWarning) warnings.filterwarnings(action='ignore', category=NonBLASDotWarning) warnings.filterwarnings(action='ignore', category=UndefinedMetricWarning) self._params = dict( max_iter_predict=ParameterDefinition(MinMax(50, 200), np.uint), warm_start=ParameterDefinition([True, False]), multi_class=ParameterDefinition(['one_vs_rest', 'one_vs_one'])) self.__gaussian_process = GPC()
def __init__(self, **kwargs): r"""Initialize VarianceThreshold feature selection algorithm. """ self._params = dict( threshold=ParameterDefinition(MinMax(0, 0.1), np.float)) self.__variance_threshold = VarThr()