# -*- coding: utf-8 -*- """ Created on Wed Nov 6 15:08:38 2013 @author: pietro """ from __future__ import (absolute_import, division, print_function, unicode_literals) from gettext import lgettext as _ from sklearn.linear_model import SGDClassifier from sklearn.ensemble import (AdaBoostClassifier, ExtraTreesClassifier, GradientBoostingClassifier, RandomForestClassifier, RandomTreesEmbedding) from sklearn.neighbors import (NearestNeighbors, KNeighborsClassifier, RadiusNeighborsClassifier, NearestCentroid) from sklearn.tree import DecisionTreeClassifier from sklearn.naive_bayes import GaussianNB from sklearn import metrics from grass.pygrass.messages import get_msgr MSGR = get_msgr() try: import mlpy except ImportError: MSGR.warning(_("MLPY not found in the current python path" "check that is installed or set the python path." "Only `sklearn` will be used.")) mlpy = None COLS = [('cat', 'INTEGER PRIMARY KEY'), ('class', 'INTEGER'), ('color', 'VARCHAR(11)'), ] # Unsupervisioned #nbrs = NearestNeighbors(n_neighbors=8, # algorithm='ball_tree').fit(data) #distances, indices = nbrs.kneighbors(data) CLASSIFIERS = [ # # Stochastic Gradient Descent (SGD) # {'name': 'sgd_hinge_l2', 'classifier': SGDClassifier, 'kwargs': {'loss': "hinge", 'penalty': "l2"}}, {'name': 'sgd_huber_l2', 'classifier': SGDClassifier, 'kwargs': {'loss': "modified_huber", 'penalty': "l2"}}, {'name': 'sgd_log_l2', 'classifier': SGDClassifier, 'kwargs': {'loss': "log", 'penalty': "l2"}}, {'name': 'sgd_hinge_l1', 'classifier': SGDClassifier, 'kwargs': {'loss': "hinge", 'penalty': "l1"}}, {'name': 'sgd_huber_l1', 'classifier': SGDClassifier, 'kwargs': {'loss': "modified_huber", 'penalty': "l1"}}, {'name': 'sgd_log_l1', 'classifier': SGDClassifier, 'kwargs': {'loss': "log", 'penalty': "l1"}}, {'name': 'sgd_hinge_elastic', 'classifier': SGDClassifier, 'kwargs': {'loss': "hinge", 'penalty': "elasticnet"}}, {'name': 'sgd_huber_elastic', 'classifier': SGDClassifier, 'kwargs': {'loss': "modified_huber", 'penalty': "elasticnet"}}, {'name': 'sgd_log_elastic', 'classifier': SGDClassifier, 'kwargs': {'loss': "log", 'penalty': "elasticnet"}}, # # K-NN # # uniform {'name': 'knn2_uniform', 'classifier': KNeighborsClassifier, 'kwargs': {'n_neighbors': 2, 'weights': 'uniform'}}, {'name': 'knn4_uniform', 'classifier': KNeighborsClassifier, 'kwargs': {'n_neighbors': 4, 'weights': 'uniform'}}, {'name': 'knn8_uniform', 'classifier': KNeighborsClassifier, 'kwargs': {'n_neighbors': 8, 'weights': 'uniform'}}, {'name': 'knn16_uniform', 'classifier': KNeighborsClassifier, 'kwargs': {'n_neighbors': 16, 'weights': 'uniform'}}, # distance {'name': 'knn2_distance', 'classifier': KNeighborsClassifier, 'kwargs': {'n_neighbors': 2, 'weights': 'distance'}}, {'name': 'knn4_distance', 'classifier': KNeighborsClassifier, 'kwargs': {'n_neighbors': 4, 'weights': 'distance'}}, {'name': 'knn8_distance', 'classifier': KNeighborsClassifier, 'kwargs': {'n_neighbors': 8, 'weights': 'distance'}}, {'name': 'knn16_distance', 'classifier': KNeighborsClassifier, 'kwargs': {'n_neighbors': 16, 'weights': 'distance'}}, # centroid # ‘euclidean’, ‘l2’, ‘l1’, ‘manhattan’, ‘cityblock’ # [‘braycurtis’, ‘canberra’, ‘chebyshev’, ‘correlation’, ‘cosine’, ‘dice’, ‘hamming’, ‘jaccard’, ‘kulsinski’, ‘mahalanobis’, ‘matching’, ‘minkowski’, ‘rogerstanimoto’, ‘russellrao’, ‘seuclidean’, ‘sokalmichener’, ‘sokalsneath’, ‘sqeuclidean’, ‘yule’] {'name': 'knn_centroid_euclidean_none', 'classifier': NearestCentroid, 'kwargs': {'metric': 'euclidean', 'shrink_threshold ': None}}, {'name': 'knn_centroid_euclidean_0p5', 'classifier': NearestCentroid, 'kwargs': {'metric': 'euclidean', 'shrink_threshold ': 0.5}}, {'name': 'knn_centroid_euclidean_1', 'classifier': NearestCentroid, 'kwargs': {'metric': 'euclidean', 'shrink_threshold ': 1.0}}, {'name': 'knn_centroid_euclidean_1p5', 'classifier': NearestCentroid, 'kwargs': {'metric': 'euclidean', 'shrink_threshold ': 1.5}}, {'name': 'knn_centroid_euclidean_2', 'classifier': NearestCentroid, 'kwargs': {'metric': 'euclidean', 'shrink_threshold ': 2.0}}, {'name': 'knn_centroid_l2_none', 'classifier': NearestCentroid, 'kwargs': {'metric': 'l2', 'shrink_threshold ': None}}, {'name': 'knn_centroid_l2_0p5', 'classifier': NearestCentroid, 'kwargs': {'metric': 'l2', 'shrink_threshold ': 0.5}}, {'name': 'knn_centroid_l2_1', 'classifier': NearestCentroid, 'kwargs': {'metric': 'l2', 'shrink_threshold ': 1.0}}, {'name': 'knn_centroid_l2_1p5', 'classifier': NearestCentroid, 'kwargs': {'metric': 'l2', 'shrink_threshold ': 1.5}}, {'name': 'knn_centroid_l2_2', 'classifier': NearestCentroid, 'kwargs': {'metric': 'l2', 'shrink_threshold ': 2.0}}, {'name': 'knn_centroid_l1_none', 'classifier': NearestCentroid, 'kwargs': {'metric': 'l1', 'shrink_threshold ': None}}, {'name': 'knn_centroid_l1_0p5', 'classifier': NearestCentroid, 'kwargs': {'metric': 'l1', 'shrink_threshold ': 0.5}}, {'name': 'knn_centroid_l1_1', 'classifier': NearestCentroid, 'kwargs': {'metric': 'l1', 'shrink_threshold ': 1.0}}, {'name': 'knn_centroid_l1_1p5', 'classifier': NearestCentroid, 'kwargs': {'metric': 'l1', 'shrink_threshold ': 1.5}}, {'name': 'knn_centroid_l1_2', 'classifier': NearestCentroid, 'kwargs': {'metric': 'l1', 'shrink_threshold ': 2.0}}, {'name': 'knn_centroid_manhattan_none', 'classifier': NearestCentroid, 'kwargs': {'metric': 'manhattan', 'shrink_threshold ': None}}, # {'name': 'knn_centroid_manhattan_0p5', 'classifier': NearestCentroid, # 'kwargs': {'metric': 'manhattan', 'shrink_threshold ': 0.5}}, # {'name': 'knn_centroid_manhattan_1', 'classifier': NearestCentroid, # 'kwargs': {'metric': 'manhattan', 'shrink_threshold ': 1.0}}, # {'name': 'knn_centroid_manhattan_1p5', 'classifier': NearestCentroid, # 'kwargs': {'metric': 'manhattan', 'shrink_threshold ': 1.5}}, # {'name': 'knn_centroid_manhattan_2', 'classifier': NearestCentroid, # 'kwargs': {'metric': 'manhattan', 'shrink_threshold ': 2.0}}, {'name': 'knn_centroid_cityblock_none', 'classifier': NearestCentroid, 'kwargs': {'metric': 'cityblock', 'shrink_threshold ': None}}, # {'name': 'knn_centroid_cityblock_0p5', 'classifier': NearestCentroid, # 'kwargs': {'metric': 'cityblock', 'shrink_threshold ': 0.5}}, # {'name': 'knn_centroid_cityblock_1', 'classifier': NearestCentroid, # 'kwargs': {'metric': 'cityblock', 'shrink_threshold ': 1.0}}, # {'name': 'knn_centroid_cityblock_1p5', 'classifier': NearestCentroid, # 'kwargs': {'metric': 'cityblock', 'shrink_threshold ': 1.5}}, # {'name': 'knn_centroid_cityblock_2', 'classifier': NearestCentroid, # 'kwargs': {'metric': 'cityblock', 'shrink_threshold ': 2.0}}, # # Tree # {'name': 'd_tree_gini', 'classifier': DecisionTreeClassifier, 'kwargs': {'criterion': 'gini', 'splitter': 'best', 'max_depth': None, 'min_samples_split': 2, 'min_samples_leaf': 1, 'max_features': None, 'random_state': None, 'min_density': None}}, {'name': 'd_tree_gini_sqrt', 'classifier': DecisionTreeClassifier, 'kwargs': {'criterion': 'gini', 'max_depth': 'sqrt'}}, {'name': 'd_tree_gini_log2', 'classifier': DecisionTreeClassifier, 'kwargs': {'criterion': 'gini', 'max_depth': 'log2'}}, # {'name': 'd_tree_gini_0p25', 'classifier': DecisionTreeClassifier, # 'kwargs': {'criterion': 'gini', 'max_depth': 0.25}}, # {'name': 'd_tree_gini_0p50', 'classifier': DecisionTreeClassifier, # 'kwargs': {'criterion': 'gini', 'max_depth': 0.5}}, # {'name': 'd_tree_gini_0p75', 'classifier': DecisionTreeClassifier, # 'kwargs': {'criterion': 'gini', 'max_depth': 0.75}}, {'name': 'd_tree_entropy', 'classifier': DecisionTreeClassifier, 'kwargs': {'criterion': 'entropy', 'splitter': 'best', 'max_depth': None, 'min_samples_split': 2, 'min_samples_leaf': 1, 'max_features': None, 'random_state': None, 'min_density': None}}, {'name': 'd_tree_entropy_sqrt', 'classifier': DecisionTreeClassifier, 'kwargs': {'criterion': 'entropy', 'max_depth': 'sqrt'}}, {'name': 'd_tree_entropy_log2', 'classifier': DecisionTreeClassifier, 'kwargs': {'criterion': 'entropy', 'max_depth': 'log2'}}, # {'name': 'd_tree_entropy_0p25', 'classifier': DecisionTreeClassifier, # 'kwargs': {'criterion': 'entropy', 'max_depth': 0.25}}, # {'name': 'd_tree_entropy_0p50', 'classifier': DecisionTreeClassifier, # 'kwargs': {'criterion': 'entropy', 'max_depth': 0.5}}, # {'name': 'd_tree_entropy_0p75', 'classifier': DecisionTreeClassifier, # 'kwargs': {'criterion': 'entropy', 'max_depth': 0.75}}, # # Forest # {'name': 'rand_tree_gini', 'classifier': RandomForestClassifier, 'kwargs': {'criterion': 'gini', 'max_depth': None, 'min_samples_split': 2, 'min_samples_leaf': 1, 'max_features': None, 'random_state': None, 'min_density': None}}, {'name': 'rand_tree_gini_sqrt', 'classifier': RandomForestClassifier, 'kwargs': {'criterion': 'gini', 'max_depth': None, 'min_samples_split': 2, 'min_samples_leaf': 1, 'max_features': 'sqrt', 'random_state': None, 'min_density': None}}, {'name': 'rand_tree_gini_log2', 'classifier': RandomForestClassifier, 'kwargs': {'criterion': 'gini', 'max_depth': None, 'min_samples_split': 2, 'min_samples_leaf': 1, 'max_features': 'log2', 'random_state': None, 'min_density': None}}, {'name': 'rand_tree_gini_0p05', 'classifier': RandomForestClassifier, 'kwargs': {'criterion': 'gini', 'max_features': 0.05}}, {'name': 'rand_tree_gini_0p25', 'classifier': RandomForestClassifier, 'kwargs': {'criterion': 'gini', 'max_features': 0.25}}, {'name': 'rand_tree_gini_0p50', 'classifier': RandomForestClassifier, 'kwargs': {'criterion': 'gini', 'max_features': 0.5}}, {'name': 'rand_tree_gini_0p75', 'classifier': RandomForestClassifier, 'kwargs': {'criterion': 'gini', 'max_features': 0.75}}, {'name': 'rand_tree_entropy', 'classifier': RandomForestClassifier, 'kwargs': {'criterion': 'entropy', 'max_depth': None, 'min_samples_split': 2, 'min_samples_leaf': 1, 'max_features': None, 'random_state': None, 'min_density': None}}, {'name': 'rand_tree_entropy_sqrt', 'classifier': RandomForestClassifier, 'kwargs': {'criterion': 'entropy', 'max_depth': None, 'min_samples_split': 2, 'min_samples_leaf': 1, 'max_features': 'sqrt', 'random_state': None, 'min_density': None}}, {'name': 'rand_tree_entropy_log2', 'classifier': RandomForestClassifier, 'kwargs': {'criterion': 'entropy', 'max_depth': None, 'min_samples_split': 2, 'min_samples_leaf': 1, 'max_features': 'log2', 'random_state': None, 'min_density': None}}, {'name': 'rand_tree_entropy_0p25', 'classifier': RandomForestClassifier, 'kwargs': {'criterion': 'entropy', 'max_features': 0.25}}, {'name': 'rand_tree_entropy_0p50', 'classifier': RandomForestClassifier, 'kwargs': {'criterion': 'entropy', 'max_features': 0.5}}, {'name': 'rand_tree_entropy_0p75', 'classifier': RandomForestClassifier, 'kwargs': {'criterion': 'entropy', 'max_features': 0.75}}, # # RandomTreesEmbedding # {'name': 'rand_tree_emb_10_5', 'classifier': RandomTreesEmbedding, # 'kwargs': dict(n_estimators=10, max_depth=5, min_samples_split=2, # min_samples_leaf=1, n_jobs=-1, random_state=None, verbose=0, # min_density=None)}, # {'name': 'rand_tree_emb_10_5_leaf3', 'classifier': RandomTreesEmbedding, # 'kwargs': dict(n_estimators=10, max_depth=5, min_samples_split=2, # min_samples_leaf=3, n_jobs=1, random_state=None, verbose=0, # min_density=None)}, # {'name': 'rand_tree_emb_10_50', 'classifier': RandomTreesEmbedding, # 'kwargs': dict(n_estimators=10, max_depth=50, min_samples_split=2, # min_samples_leaf=1, n_jobs=1, random_state=None, verbose=0, # min_density=None)}, # {'name': 'rand_tree_emb_100_50', 'classifier': RandomTreesEmbedding, # 'kwargs': dict(n_estimators=100, max_depth=50, min_samples_split=2, # min_samples_leaf=1, n_jobs=1, random_state=None, verbose=0, # min_density=None)}, # {'name': 'rand_tree_emb_100_50', 'classifier': RandomTreesEmbedding, # 'kwargs': dict(n_estimators=100, max_depth=50, min_samples_split=2, # min_samples_leaf=3, n_jobs=1, random_state=None, verbose=0, # min_density=None)}, # # AdaBoost classifier # {'name': 'ada_50_1.0', 'classifier': AdaBoostClassifier, 'kwargs': dict(base_estimator=DecisionTreeClassifier(compute_importances=None, criterion='gini', max_depth=1, max_features=None, min_density=None, min_samples_leaf=1, min_samples_split=2, random_state=None, splitter='best'), n_estimators=50, learning_rate=1.0, algorithm='SAMME.R', random_state=None)}, {'name': 'ada_50_1.0_minleaf3', 'classifier': AdaBoostClassifier, 'kwargs': dict(base_estimator=DecisionTreeClassifier(compute_importances=None, criterion='gini', max_depth=1, max_features=None, min_density=None, min_samples_leaf=3, min_samples_split=2, random_state=None, splitter='best'), n_estimators=50, learning_rate=1.0, algorithm='SAMME.R', random_state=None)}, {'name': 'ada_50_0.5', 'classifier': AdaBoostClassifier, 'kwargs': dict(base_estimator=DecisionTreeClassifier(compute_importances=None, criterion='gini', max_depth=1, max_features=None, min_density=None, min_samples_leaf=1, min_samples_split=2, random_state=None, splitter='best'), n_estimators=50, learning_rate=0.5, algorithm='SAMME.R', random_state=None)}, {'name': 'extra_tree_10_1', 'classifier': ExtraTreesClassifier, 'kwargs': dict(n_estimators=10, criterion='gini', max_depth=None, min_samples_split=2, min_samples_leaf=1, max_features='auto', bootstrap=False, oob_score=False, n_jobs=1, random_state=None, verbose=0, min_density=None, compute_importances=None)}, {'name': 'extra_tree_10_3', 'classifier': ExtraTreesClassifier, 'kwargs': dict(n_estimators=10, criterion='gini', max_depth=None, min_samples_split=2, min_samples_leaf=3, max_features='auto', bootstrap=False, oob_score=False, n_jobs=1, random_state=None, verbose=0, min_density=None, compute_importances=None)}, {'name': 'extra_tree_100_1', 'classifier': ExtraTreesClassifier, 'kwargs': dict(n_estimators=100, criterion='gini', max_depth=None, min_samples_split=2, min_samples_leaf=1, max_features='auto', bootstrap=False, oob_score=False, n_jobs=1, random_state=None, verbose=0, min_density=None, compute_importances=None)}, {'name': 'extra_tree_100_3', 'classifier': ExtraTreesClassifier, 'kwargs': dict(n_estimators=100, criterion='gini', max_depth=None, min_samples_split=2, min_samples_leaf=3, max_features='auto', bootstrap=False, oob_score=False, n_jobs=1, random_state=None, verbose=0, min_density=None, compute_importances=None)}, {'name': 'extra_tree_100_5', 'classifier': ExtraTreesClassifier, 'kwargs': dict(n_estimators=100, criterion='gini', max_depth=None, min_samples_split=2, min_samples_leaf=5, max_features='auto', bootstrap=False, oob_score=False, n_jobs=1, random_state=None, verbose=0, min_density=None, compute_importances=None)}, {'name': 'gradient_boost_100_minleaf1', 'classifier': GradientBoostingClassifier, 'kwargs': dict(loss='deviance', learning_rate=0.1, n_estimators=100, subsample=1.0, min_samples_split=2, min_samples_leaf=1, max_depth=3, init=None, random_state=None, max_features=None, verbose=0)}, {'name': 'gradient_boost_100_meanleaf3', 'classifier': GradientBoostingClassifier, 'kwargs': dict(loss='deviance', learning_rate=0.1, n_estimators=100, subsample=1.0, min_samples_split=2, min_samples_leaf=3, max_depth=3, init=None, random_state=None, max_features=None, verbose=0)}, {'name': 'gradient_boost_100_meanleaf5', 'classifier': GradientBoostingClassifier, 'kwargs': dict(loss='deviance', learning_rate=0.1, n_estimators=100, subsample=1.0, min_samples_split=2, min_samples_leaf=5, max_depth=3, init=None, random_state=None, max_features=None, verbose=0)}, # # Gausian # {'name': 'gaussianNB', 'classifier': GaussianNB}, ] class MLPYWrapper(object): def __init__(self, cls): self.cls = cls self.mlcls = None self.wrap = dict(fit='learn', predict='pred') def __getattr__(self, name): if self.mlcls and name in self.wrap.keys(): return getattr(self.mlcls, self.wrap[name]) return super(MLPYWrapper, self).__getattr__(self, name) def __call__(self, *args, **kwargs): self.mlcls = self.cls(*args, **kwargs) if mlpy is not None: MLPY_CLS = [ # # Linear Discriminant Analysis Classifier (LDAC) # {'name': 'mlpy_LDAC_1', 'classifier': MLPYWrapper(mlpy.LDAC)}, # # Elastic Net Classifier # {'name': 'mlpy_ElasticNetC_0.1_0.1', 'classifier': MLPYWrapper(mlpy.ElasticNetC), 'kwargs': {'lmb': 0.1, 'eps': 0.1, 'supp': True, 'tol': 0.01}}, {'name': 'mlpy_ElasticNetC_0.1_0.01', 'classifier': MLPYWrapper(mlpy.ElasticNetC), 'kwargs': {'lmb': 0.1, 'eps': 0.01, 'supp': True, 'tol': 0.01}}, {'name': 'mlpy_ElasticNetC_0.1_0.001', 'classifier': MLPYWrapper(mlpy.ElasticNetC), 'kwargs': {'lmb': 0.1, 'eps': 0.001, 'supp': True, 'tol': 0.01}}, {'name': 'mlpy_ElasticNetC_0.01_0.1', 'classifier': MLPYWrapper(mlpy.ElasticNetC), 'kwargs': {'lmb': 0.01, 'eps': 0.1, 'supp': True, 'tol': 0.01}}, {'name': 'mlpy_ElasticNetC_0.01_0.01', 'classifier': MLPYWrapper(mlpy.ElasticNetC), 'kwargs': {'lmb': 0.01, 'eps': 0.01, 'supp': True, 'tol': 0.01}}, {'name': 'mlpy_ElasticNetC_0.01_0.001', 'classifier': MLPYWrapper(mlpy.ElasticNetC), 'kwargs': {'lmb': 0.01, 'eps': 0.001, 'supp': True, 'tol': 0.01}}, {'name': 'mlpy_ElasticNetC_0.001_0.1', 'classifier': MLPYWrapper(mlpy.ElasticNetC), 'kwargs': {'lmb': 0.001, 'eps': 0.1, 'supp': True, 'tol': 0.01}}, {'name': 'mlpy_ElasticNetC_0.001_0.01', 'classifier': MLPYWrapper(mlpy.ElasticNetC), 'kwargs': {'lmb': 0.001, 'eps': 0.01, 'supp': True, 'tol': 0.01}}, {'name': 'mlpy_ElasticNetC_0.001_0.001', 'classifier': MLPYWrapper(mlpy.ElasticNetC), 'kwargs': {'lmb': 0.001, 'eps': 0.001, 'supp': True, 'tol': 0.01}}, # # Diagonal Linear Discriminant Analysis (DLDA) # {'name': 'mlpy_DLDA_0.01', 'classifier': MLPYWrapper(mlpy.DLDA), 'kwargs': {'delta': 0.01}}, {'name': 'mlpy_DLDA_0.05', 'classifier': MLPYWrapper(mlpy.DLDA), 'kwargs': {'delta': 0.05}}, {'name': 'mlpy_DLDA_0.1', 'classifier': MLPYWrapper(mlpy.DLDA), 'kwargs': {'delta': 0.1}}, {'name': 'mlpy_DLDA_0.5', 'classifier': MLPYWrapper(mlpy.DLDA), 'kwargs': {'delta': 0.5}}, # # mlpy.Golub # {'name': 'mlpy_Golub', 'classifier': MLPYWrapper(mlpy.Golub)}, # # LibLinear # {'name': 'mlpy_liblin_l2r_lr', 'classifier': MLPYWrapper(mlpy.LibLinear), 'kwargs': {'solver_type': 'l2r_lr', 'C': 1, 'eps': 0.01}}, {'name': 'mlpy_liblin_l2r_l2loss_svc', 'classifier': MLPYWrapper(mlpy.LibLinear), 'kwargs': {'solver_type': 'l2r_l2loss_svc', 'C': 1, 'eps': 0.01}}, {'name': 'mlpy_liblin_l2r_l1loss_svc_dual', 'classifier': MLPYWrapper(mlpy.LibLinear), 'kwargs': {'solver_type': 'l2r_l1loss_svc_dual', 'C': 1, 'eps': 0.01}}, {'name': 'mlpy_liblin_mcsvm_cs', 'classifier': MLPYWrapper(mlpy.LibLinear), 'kwargs': {'solver_type': 'mcsvm_cs', 'C': 1, 'eps': 0.01}}, {'name': 'mlpy_liblin_l1r_l2loss_svc', 'classifier': MLPYWrapper(mlpy.LibLinear), 'kwargs': {'solver_type': 'l1r_l2loss_svc', 'C': 1, 'eps': 0.01}}, {'name': 'mlpy_liblin_l1r_lr', 'classifier': MLPYWrapper(mlpy.LibLinear), 'kwargs': {'solver_type': 'l1r_lr', 'C': 1, 'eps': 0.01}}, {'name': 'mlpy_liblin_l2r_lr_dual', 'classifier': MLPYWrapper(mlpy.LibLinear), 'kwargs': {'solver_type': 'l2r_lr_dual', 'C': 1, 'eps': 0.01}}, # # K-NN # {'name': 'mlpy_KNN_1', 'classifier': MLPYWrapper(mlpy.KNN), 'kwargs': {'k': 1}}, {'name': 'mlpy_KNN_2', 'classifier': MLPYWrapper(mlpy.KNN), 'kwargs': {'k': 2}}, {'name': 'mlpy_KNN_3', 'classifier': MLPYWrapper(mlpy.KNN), 'kwargs': {'k': 3}}, {'name': 'mlpy_KNN_4', 'classifier': MLPYWrapper(mlpy.KNN), 'kwargs': {'k': 4}}, {'name': 'mlpy_KNN_8', 'classifier': MLPYWrapper(mlpy.KNN), 'kwargs': {'k': 8}}, {'name': 'mlpy_KNN_8', 'classifier': MLPYWrapper(mlpy.KNN), 'kwargs': {'k': 16}}, # # Tree # {'name': 'mlpy_tree_0_0', 'classifier': MLPYWrapper(mlpy.ClassTree), 'kwargs': {'stumps': 0, 'minsize': 0}}, {'name': 'mlpy_tree_0_5', 'classifier': MLPYWrapper(mlpy.ClassTree), 'kwargs': {'stumps': 0, 'minsize': 5}}, {'name': 'mlpy_tree_0_10', 'classifier': MLPYWrapper(mlpy.ClassTree), 'kwargs': {'stumps': 0, 'minsize': 10}}, {'name': 'mlpy_tree_0_20', 'classifier': MLPYWrapper(mlpy.ClassTree), 'kwargs': {'stumps': 0, 'minsize': 20}}, {'name': 'mlpy_tree_0_40', 'classifier': MLPYWrapper(mlpy.ClassTree), 'kwargs': {'stumps': 0, 'minsize': 40}}, {'name': 'mlpy_tree_1_', 'classifier': MLPYWrapper(mlpy.ClassTree), 'kwargs': {'stumps': 1, 'minsize': 0}}, {'name': 'mlpy_tree_1_5', 'classifier': MLPYWrapper(mlpy.ClassTree), 'kwargs': {'stumps': 1, 'minsize': 5}}, {'name': 'mlpy_tree_1_10', 'classifier': MLPYWrapper(mlpy.ClassTree), 'kwargs': {'stumps': 1, 'minsize': 10}}, {'name': 'mlpy_tree_1_20', 'classifier': MLPYWrapper(mlpy.ClassTree), 'kwargs': {'stumps': 1, 'minsize': 20}}, {'name': 'mlpy_tree_1_40', 'classifier': MLPYWrapper(mlpy.ClassTree), 'kwargs': {'stumps': 1, 'minsize': 40}}, # # mlpy.MaximumLikelihoodC # #{'name': 'mlpy_maximumlike', # 'classifier': MLPYWrapper(mlpy.MaximumLikelihoodC)}, ] # add MLPY #CLASSIFIERS.extend(MLPY_CLS)