├── LICENSE ├── README.md ├── __init__.py ├── py_fcm ├── __init__.py ├── fcm.py ├── fcm_estimator.py ├── learning │ ├── __init__.py │ ├── association.py │ ├── discretization │ │ ├── __init__.py │ │ ├── clusters_estimation.py │ │ ├── fuzzy_cmeans.py │ │ └── rl_fuzzy_cmeans.py │ └── utils.py ├── loader.py └── utils │ ├── __const.py │ ├── __init__.py │ └── functions.py ├── setup.py └── tests ├── __init__.py ├── test_estimator.py ├── test_fcm.py ├── test_functions.py ├── test_generator.py └── test_public_library_functions.py /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Copyright (C) 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # PyFCM 2 | Fuzzy cognitive maps python library. Also, supports the topology generation from data to solve classification problems. 3 | The details associated to the generation process are described in [this paper](https://link.springer.com/chapter/10.1007/978-3-030-89691-1_25). 4 | ### Installation 5 | 6 | #### From source: 7 | 8 | 1. Clone repository: 9 | ``` 10 | $ git clone https://github.com/J41R0/PyFCM.git 11 | $ cd PyFCM 12 | ``` 13 | 2. Install setup tools and package: 14 | ``` 15 | $ pip install setuptools 16 | $ python setup.py install 17 | ``` 18 | #### From PyPi: 19 | 1. Install package using pip: 20 | ``` 21 | $ pip install py-fcm 22 | ``` 23 | 24 | ### Example usage 25 | 26 | #### Inference: 27 | ``` 28 | from py_fcm import from_json 29 | 30 | fcm_json = """{ 31 | "max_iter": 500, 32 | "decision_function": "LAST", 33 | "activation_function": "sigmoid", 34 | "memory_influence": False, 35 | "stability_diff": 0.001, 36 | "stop_at_stabilize": True, 37 | "extra_steps": 5, 38 | "weight": 1, 39 | "concepts": 40 | [ 41 | { 42 | "id": "concept_1", 43 | "is_active": True, 44 | "type": "SIMPLE", 45 | "activation": 0.5 46 | }, 47 | { 48 | "id": "concept_2", "is_active": True, 49 | "type": "DECISION", "activation": 0.0, 50 | "custom_function": "gceq", 51 | "custom_function_args": {"weight": 0.3} 52 | }, 53 | { 54 | "id": "concept_3", 55 | "is_active": True, 56 | "type": "SIMPLE", 57 | "activation": 0.0, 58 | "use_memory": True 59 | }, 60 | { 61 | "id": "concept_4", 62 | "is_active": True, 63 | "type": "SIMPLE", 64 | "activation": 0.3, 65 | "custom_function": "saturation" 66 | } 67 | ], 68 | "relations": 69 | [ 70 | {"origin": "concept_4", "destiny": "concept_2", "weight": -0.1}, 71 | {"origin": "concept_1", "destiny": "concept_3", "weight": 0.59}, 72 | {"origin": "concept_3", "destiny": "concept_2", "weight": 0.8911} 73 | ], 74 | 'activation_function_args': {'lambda_val': 1}, 75 | """ 76 | my_fcm = from_json(fcm_json) 77 | my_fcm.run_inference() 78 | result = my_fcm.get_final_state(concept_type='any') 79 | print(result) 80 | ``` 81 | 82 | #### Generation: 83 | ``` 84 | import pandas 85 | from py_fcm import FcmEstimator 86 | 87 | data_dict = { 88 | 'F1': ['x', 'x', 'y', 'y'], 89 | 'F2': [9.8, 7.3, 1.1, 3.6], 90 | 'class': ['a', 'a', 'r', 'r'] 91 | } 92 | 93 | train = pandas.DataFrame(data_dict) 94 | x_train = train.loc[:, train.columns != 'class'] 95 | y_train = train.loc[:, 'class'] 96 | 97 | estimator = FcmEstimator() 98 | estimator.fit(x_train, y_train) 99 | print(estimator.predict(x_train)) 100 | print("Accuracy: ",estimator.score(x_train, y_train)) 101 | print(estimator.get_fcm().to_json()) 102 | 103 | ``` -------------------------------------------------------------------------------- /__init__.py: -------------------------------------------------------------------------------- 1 | __version__ = '0.3.0' 2 | -------------------------------------------------------------------------------- /py_fcm/__init__.py: -------------------------------------------------------------------------------- 1 | from py_fcm.utils.functions import Relation 2 | from py_fcm.fcm_estimator import FcmEstimator 3 | from py_fcm.loader import from_json, join_maps, FuzzyCognitiveMap 4 | from py_fcm.utils.__const import TYPE_SIMPLE, TYPE_DECISION, TYPE_FUZZY 5 | 6 | 7 | def load_csv_dataset(dataset_dir, ds_name, factorize=False): 8 | import pandas 9 | from collections import OrderedDict 10 | data_dict = OrderedDict() 11 | with open(dataset_dir + "/" + ds_name) as file: 12 | content = [] 13 | for line in file: 14 | content.append(line.strip()) 15 | # first line describe the atributes names 16 | attributes = str(content[0]).split(',') 17 | for current_att in attributes: 18 | data_dict[current_att] = [] 19 | 20 | # print(data_dict) 21 | for line in range(1, len(content)): 22 | data_line = str(content[line]).split(',') 23 | # avoid rows with different attributes length 24 | if len(data_line) == len(attributes): 25 | # the missing data must be identified 26 | for data in range(0, len(data_line)): 27 | # reusing for value type inference 28 | current_att = data_line[data] 29 | data_dict[attributes[data]].append(current_att) 30 | else: 31 | # Handle errors in dataset matrix 32 | print("Errors in line: ", line, len(data_line), len(attributes)) 33 | # adding data set 34 | print("\n===> Dataset for test: ", ds_name) 35 | try: 36 | dataset_frame = pandas.DataFrame(data_dict).drop_duplicates() 37 | except Exception as err: 38 | for key, value in data_dict.items(): 39 | print(key, value) 40 | pass 41 | raise Exception(ds_name + " " + str(err)) 42 | # Transform the columns disc values in 0..N values 43 | if factorize: 44 | # for current_col in dataset_frame. 45 | dataset_frame = dataset_frame.apply(lambda x: pandas.factorize(x)[0]) 46 | return dataset_frame 47 | -------------------------------------------------------------------------------- /py_fcm/fcm_estimator.py: -------------------------------------------------------------------------------- 1 | from collections import defaultdict 2 | 3 | import numpy as np 4 | from pandas import DataFrame, Series 5 | 6 | from py_fcm.utils.functions import Relation 7 | from py_fcm.learning.association import AssociationBasedFCM, FuzzyCognitiveMap 8 | 9 | 10 | class FcmEstimator: 11 | def __init__(self, concept_str_separator="___", concept_exclusion_val=-1, fit_inclination=True, 12 | double_relation=True, features_relation=True, infer_concept_type=True, discretization="cmeans-cfe", 13 | causal_eval_function=Relation.supp, causal_threshold=0.0, causality_function=Relation.pos_inf, 14 | causal_sign_function=None, causal_sign_threshold=0, fcm_mem_influence=True, fcm_max_it=250, 15 | fcm_extra_steps=5, fcm_stability_diff=0.001, fcm_decision_function="MEAN", 16 | fcm_excitation_function='KOSKO', fcm_activation_function="sigmoid_hip", vectorized_run=True, **kwargs): 17 | self.__is_fcm_generated = False 18 | # init FCM 19 | self.__fcm = FuzzyCognitiveMap(max_it=fcm_max_it, 20 | extra_steps=fcm_extra_steps, 21 | stability_diff=fcm_stability_diff, 22 | decision_function=fcm_decision_function, 23 | mem_influence=fcm_mem_influence, 24 | activation_function=fcm_activation_function, 25 | **kwargs) 26 | self.__fcm.set_default_concept_properties(excitation_function=fcm_excitation_function) 27 | self.__fcm.debug = not vectorized_run 28 | # init generator 29 | self.__generator = AssociationBasedFCM(str_separator=concept_str_separator, 30 | discretization=discretization, 31 | fit_inclination=fit_inclination, 32 | double_relation=double_relation, 33 | features_relation=features_relation, 34 | exclusion_val=concept_exclusion_val, 35 | causality_function=causality_function, 36 | causal_eval_function=causal_eval_function, 37 | causal_threshold=causal_threshold, 38 | sign_function=causal_sign_function, 39 | sign_threshold=causal_sign_threshold) 40 | self.__generator.infer_concept_type = infer_concept_type 41 | 42 | def __to_data_frame(self, x, start=0): 43 | if type(x) == dict: 44 | return DataFrame(x) 45 | if type(x) == list: 46 | x = np.array(x) 47 | if type(x) == np.ndarray: 48 | col_names = [] 49 | if len(x.shape) == 1: 50 | col_names.append(str(start)) 51 | else: 52 | col_names = [str(i + start) for i in range(x.shape[1])] 53 | return DataFrame(x, columns=col_names) 54 | if type(x) == Series: 55 | return x.to_frame() 56 | return x 57 | 58 | def __validate_dual_input(self, x, y): 59 | if type(x) == list: 60 | x = np.array(x) 61 | max_col = x.shape[1] 62 | x = self.__to_data_frame(x) 63 | y = self.__to_data_frame(y, start=max_col) 64 | if type(x) != DataFrame: 65 | raise Exception("Cannot load provided x structure, please use a pandas DataFrame like object.") 66 | if type(y) != DataFrame: 67 | raise Exception("Cannot load provided y structure, please use a pandas DataFrame like object.") 68 | return x, y 69 | 70 | def fit(self, x, y, plot=False, plot_dir='.'): 71 | # expect a dataframe or a matrix shaped in a list of rows 72 | x, y = self.__validate_dual_input(x, y) 73 | target_feat = [] 74 | for col in y.columns: 75 | target_feat.append(col) 76 | input_data = x.join(y, rsuffix='_class') 77 | self.__generator.reset() 78 | self.__generator.build_fcm(input_data, target_features=target_feat, fcm=self.__fcm, 79 | plot=plot, plot_dir=plot_dir) 80 | self.__is_fcm_generated = True 81 | 82 | def predict(self, x: DataFrame, plot=False, plot_dir='.'): 83 | if type(x) != DataFrame: 84 | x = self.__to_data_frame(x) 85 | if type(x) != DataFrame: 86 | raise Exception("Cannot load provided x structure, please use a pandas DataFrame like object.") 87 | result = defaultdict(list) 88 | if self.__is_fcm_generated: 89 | for index, row in x.iterrows(): 90 | self.__fcm.clear_execution() 91 | for feat_name in x.columns: 92 | self.__generator.init_concept_by_feature_data(feat_name, row[feat_name]) 93 | 94 | prediction = self.__generator.get_inference_result(plot=plot, plot_dir=plot_dir) 95 | for feat_name in prediction: 96 | result[feat_name].append(prediction[feat_name]) 97 | else: 98 | raise Exception("There is no FCM generated, the fit method mus be called first") 99 | return DataFrame(result) 100 | 101 | def score(self, x, y): 102 | # TODO: Add other scoring functions besides accuracy 103 | if self.__is_fcm_generated: 104 | x, y = self.__validate_dual_input(x, y) 105 | predicted_result = self.predict(x) 106 | right_predicted = 0 107 | total = 0 108 | for feat_name in y.columns: 109 | pos = 0 110 | for val in y[feat_name]: 111 | total += 1 112 | if val == predicted_result[feat_name][pos]: 113 | right_predicted += 1 114 | pos += 1 115 | return right_predicted / total 116 | else: 117 | raise Exception("There is no FCM generated, the fit method mus be called first") 118 | 119 | def get_fcm(self): 120 | return self.__fcm 121 | -------------------------------------------------------------------------------- /py_fcm/learning/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/J41R0/PyFCM/01c4630acc9784d94a27232b2b9a0cdac7ba6f4c/py_fcm/learning/__init__.py -------------------------------------------------------------------------------- /py_fcm/learning/association.py: -------------------------------------------------------------------------------- 1 | import warnings 2 | from collections import defaultdict 3 | 4 | from pandas import DataFrame 5 | 6 | from py_fcm.utils.__const import * 7 | from py_fcm.learning.utils import * 8 | from py_fcm.fcm import FuzzyCognitiveMap 9 | from py_fcm.utils.functions import Relation, fuzzy_set 10 | from py_fcm.learning.discretization import fuzzy_feature_discretization 11 | 12 | # USED CONST DEFINITION 13 | # features 14 | NP_ARRAY_DATA = "np array data" 15 | CONCEPT_DESC = "feature concepts description" 16 | CONCEPT_NAMES = "feature concept names" 17 | TYPE = "concept type" 18 | FEATURE_CONCEPTS = 0 19 | FEATURE_DESC = 1 20 | 21 | # np unique output consts 22 | UNIQUE_ARRAY = 0 23 | 24 | # coefficient calc output 25 | P_Q_COEFF = 0 26 | Q_P_COEFF = 1 27 | 28 | 29 | class AssociationBasedFCM: 30 | def __init__(self, str_separator="___", discretization="cmeans-gap", exclusion_val=-1, fit_inclination=True, 31 | double_relation=True, features_relation=True, causality_function=Relation.conf, 32 | causal_eval_function=Relation.supp, causal_threshold=0.0, sign_function=None, 33 | sign_threshold=0): 34 | self.__features = {} 35 | self.__processed_features = set() 36 | 37 | # vars 38 | self.__fcm = FuzzyCognitiveMap() 39 | self.__exclusion_val = exclusion_val 40 | self.__causality_value_function = causality_function 41 | self.__causality_evaluation_function = causal_eval_function 42 | self.__causal_threshold = causal_threshold 43 | self.__sign_function = sign_function 44 | self.__sign_function_cut_val = sign_threshold 45 | self.__str_separator = str_separator 46 | self.__double_relation = double_relation 47 | self.__features_relation = features_relation 48 | self.__discretization_method = discretization 49 | 50 | if fit_inclination: 51 | self.__fcm.fit_inclination = 0.75 52 | self.infer_concept_type = True 53 | 54 | def __name_concept(self, feat_name, value): 55 | return str(value) + str(self.__str_separator) + str(feat_name) 56 | 57 | def __gen_continuous_concepts(self, feat_name, target_feats, plot, plot_dir): 58 | if feat_name in target_feats: 59 | self.__features[feat_name][TYPE] = TYPE_REGRESOR 60 | else: 61 | self.__features[feat_name][TYPE] = TYPE_FUZZY 62 | 63 | n_clusters, val_list, memberships = fuzzy_feature_discretization(self.__features[feat_name][NP_ARRAY_DATA], 64 | strategy=self.__discretization_method, 65 | att_name=feat_name, plot=plot, 66 | plot_dir=plot_dir) 67 | names = [] 68 | final_membership = np.zeros((n_clusters, self.__features[feat_name][NP_ARRAY_DATA].size)) 69 | for curr_cluster in range(n_clusters): 70 | concept_name = self.__name_concept(feat_name, curr_cluster) 71 | names.append(concept_name) 72 | fun_args = {'membership': memberships[curr_cluster], 73 | 'val_list': val_list} 74 | self.__fcm.add_concept(concept_name, self.__features[feat_name][TYPE], is_active=True, 75 | activation_dict=fun_args) 76 | for val_pos in range(self.__features[feat_name][NP_ARRAY_DATA].size): 77 | curr_value = self.__features[feat_name][NP_ARRAY_DATA][val_pos] 78 | final_membership[curr_cluster][val_pos] = fuzzy_set(value=curr_value, 79 | membership=fun_args['membership'], 80 | val_list=fun_args['val_list']) 81 | 82 | self.__features[feat_name][CONCEPT_NAMES] = names 83 | self.__features[feat_name][CONCEPT_DESC] = ([i for i in range(n_clusters)], final_membership) 84 | 85 | def __gen_discrete_concepts(self, feat_name, target_feats, uniques_data=None): 86 | if feat_name in target_feats: 87 | self.__features[feat_name][TYPE] = TYPE_DECISION 88 | else: 89 | self.__features[feat_name][TYPE] = TYPE_SIMPLE 90 | 91 | if uniques_data is None: 92 | uniques_data = np.unique(self.__features[feat_name][NP_ARRAY_DATA], return_counts=True) 93 | 94 | feat_matrix = gen_discrete_feature_matrix(self.__features[feat_name][NP_ARRAY_DATA], uniques_data[UNIQUE_ARRAY]) 95 | 96 | names = [] 97 | for val_pos in range(uniques_data[UNIQUE_ARRAY].size): 98 | name = self.__name_concept(feat_name, uniques_data[UNIQUE_ARRAY][val_pos]) 99 | names.append(name) 100 | self.__fcm.add_concept(name, self.__features[feat_name][TYPE], is_active=True) 101 | self.__features[feat_name][CONCEPT_NAMES] = names 102 | self.__features[feat_name][CONCEPT_DESC] = (uniques_data[UNIQUE_ARRAY], feat_matrix) 103 | 104 | def __def_inner_feat_relations(self, feat_name): 105 | # FIXME: This function only works for fuzzy and discrete features, use the function type instead. 106 | if self.__exclusion_val != 0: 107 | related_concepts = self.__features[feat_name][CONCEPT_DESC][FEATURE_CONCEPTS] 108 | for concept1_pos in range(len(related_concepts) - 1): 109 | for concept2_pos in range(concept1_pos + 1, len(related_concepts)): 110 | self.__fcm.add_relation(self.__features[feat_name][CONCEPT_NAMES][concept1_pos], 111 | self.__features[feat_name][CONCEPT_NAMES][concept2_pos], 112 | self.__exclusion_val) 113 | self.__fcm.add_relation(self.__features[feat_name][CONCEPT_NAMES][concept2_pos], 114 | self.__features[feat_name][CONCEPT_NAMES][concept1_pos], 115 | self.__exclusion_val) 116 | 117 | def __def_relation_weight(self, feat_name_a, concept_a_pos, feat_name_b, concept_b_pos, sign, p_q, p_nq, np_q, 118 | np_nq): 119 | causality_p_q = self.__causality_evaluation_function(p_q, p_nq, np_q, np_nq) 120 | if abs(causality_p_q) > self.__causal_threshold: 121 | relation_weight = sign * self.__causality_value_function(p_q, p_nq, np_q, np_nq) 122 | if relation_weight != 0: 123 | self.__fcm.add_relation(self.__features[feat_name_a][CONCEPT_NAMES][concept_a_pos], 124 | self.__features[feat_name_b][CONCEPT_NAMES][concept_b_pos], 125 | relation_weight) 126 | 127 | def __def_two_feat_relation(self, feat_name_a, feat_name_b): 128 | for concept_p_pos in range(len(self.__features[feat_name_a][CONCEPT_NAMES])): 129 | for concept_q_pos in range(len(self.__features[feat_name_b][CONCEPT_NAMES])): 130 | relation_coefficients = calc_concepts_coefficient( 131 | self.__features[feat_name_a][CONCEPT_DESC][FEATURE_DESC][concept_p_pos], 132 | self.__features[feat_name_b][CONCEPT_DESC][FEATURE_DESC][concept_q_pos] 133 | ) 134 | if relation_coefficients is None: 135 | raise Exception("Invalid relation input data") 136 | # define relation sign 137 | sign_p_q = 1 138 | sign_q_p = 1 139 | if self.__sign_function is not None: 140 | if self.__sign_function_cut_val > self.__sign_function(*relation_coefficients[P_Q_COEFF]): 141 | sign_p_q = -1 142 | if self.__sign_function_cut_val > self.__sign_function(*relation_coefficients[Q_P_COEFF]): 143 | sign_q_p = -1 144 | if self.__double_relation: 145 | # define causality degree p -> q 146 | self.__def_relation_weight(feat_name_a, concept_p_pos, feat_name_b, concept_q_pos, sign_p_q, 147 | *relation_coefficients[P_Q_COEFF]) 148 | # define causality degree q -> p 149 | self.__def_relation_weight(feat_name_b, concept_q_pos, feat_name_a, concept_p_pos, sign_q_p, 150 | *relation_coefficients[Q_P_COEFF]) 151 | else: 152 | if self.__are_same_feature_group(feat_name_a, feat_name_b): 153 | p_q = abs(self.__causality_value_function(*relation_coefficients[P_Q_COEFF])) 154 | q_p = abs(self.__causality_value_function(*relation_coefficients[Q_P_COEFF])) 155 | if p_q > q_p: 156 | # define causality degree p -> q 157 | self.__def_relation_weight(feat_name_a, concept_p_pos, feat_name_b, concept_q_pos, 158 | sign_p_q, *relation_coefficients[P_Q_COEFF]) 159 | if q_p > p_q: 160 | # define causality degree q -> p 161 | self.__def_relation_weight(feat_name_b, concept_q_pos, feat_name_a, concept_p_pos, 162 | sign_q_p, *relation_coefficients[Q_P_COEFF]) 163 | else: 164 | if not self.__is_target_concept(feat_name_a): 165 | self.__def_relation_weight(feat_name_a, concept_p_pos, feat_name_b, concept_q_pos, 166 | sign_p_q, *relation_coefficients[P_Q_COEFF]) 167 | else: 168 | self.__def_relation_weight(feat_name_b, concept_q_pos, feat_name_a, concept_p_pos, 169 | sign_q_p, *relation_coefficients[Q_P_COEFF]) 170 | 171 | def __def_all_feat_relations(self, feat_name): 172 | for other_feat in self.__processed_features: 173 | if other_feat != feat_name: 174 | self.__def_two_feat_relation(feat_name, other_feat) 175 | 176 | self.__processed_features.add(feat_name) 177 | 178 | def __is_target_concept(self, feat_name): 179 | if self.__features[feat_name][TYPE] == TYPE_REGRESOR or self.__features[feat_name][TYPE] == TYPE_DECISION: 180 | return True 181 | return False 182 | 183 | def __are_same_feature_group(self, name_feat1, name_feat2): 184 | return ((self.__is_target_concept(name_feat1) and self.__is_target_concept(name_feat2)) or 185 | not self.__is_target_concept(name_feat1) and not self.__is_target_concept(name_feat2)) 186 | 187 | def __def_feat_target_relation(self, feat_name): 188 | for other_feat in self.__processed_features: 189 | if other_feat != feat_name and not self.__are_same_feature_group(feat_name, other_feat): 190 | self.__def_two_feat_relation(feat_name, other_feat) 191 | self.__processed_features.add(feat_name) 192 | 193 | def build_fcm(self, dataset: DataFrame, target_features=None, fcm=None, plot=False, 194 | plot_dir='.') -> FuzzyCognitiveMap: 195 | # TODO: handle features multivalued and with missing values 196 | if fcm is not None and type(fcm) == FuzzyCognitiveMap: 197 | self.__fcm = fcm 198 | else: 199 | self.__fcm = FuzzyCognitiveMap() 200 | if target_features is None: 201 | target_features = set() 202 | # define map concepts 203 | for feat_name in dataset.loc[:, ]: 204 | self.__features[feat_name] = {NP_ARRAY_DATA: np.array(dataset.loc[:, feat_name].values)} 205 | # discrete features 206 | # TODO: extend to categorical series 207 | if dataset[feat_name].dtype == np.object or dataset[feat_name].dtype == np.bool: 208 | self.__gen_discrete_concepts(feat_name, target_features) 209 | 210 | # possible continuous feature 211 | elif self.infer_concept_type: 212 | uniques_data = np.unique(self.__features[feat_name][NP_ARRAY_DATA], return_counts=True) 213 | if uniques_data[UNIQUE_ARRAY].size < 10: 214 | self.__gen_discrete_concepts(feat_name, target_features, uniques_data) 215 | else: 216 | # TODO: review type inference 217 | if (uniques_data[UNIQUE_ARRAY].size / self.__features[feat_name][NP_ARRAY_DATA].size) > 0.2: 218 | warnings.warn("Possible discrete feature behavior for " + feat_name + " feature.") 219 | self.__features[feat_name][NP_ARRAY_DATA] = self.__features[feat_name][ 220 | NP_ARRAY_DATA].astype( 221 | np.float64) 222 | self.__gen_continuous_concepts(feat_name, target_features, plot, plot_dir) 223 | else: 224 | self.__gen_continuous_concepts(feat_name, target_features, plot, plot_dir) 225 | 226 | # TODO: identify feature type to define the inner concept's relation properly 227 | self.__def_inner_feat_relations(feat_name) 228 | self.__processed_features.add(feat_name) 229 | 230 | if self.__features_relation: 231 | self.__def_all_feat_relations(feat_name) 232 | else: 233 | self.__def_feat_target_relation(feat_name) 234 | 235 | return self.__fcm 236 | 237 | def init_concept_by_feature_data(self, feat_name, value): 238 | try: 239 | if self.__features[feat_name][TYPE] == TYPE_FUZZY or self.__features[feat_name][TYPE] == TYPE_REGRESOR: 240 | for concept in self.__features[feat_name][CONCEPT_NAMES]: 241 | self.__fcm.init_concept(concept, value, required_presence=False) 242 | else: 243 | if type(value) != str: 244 | value = int(value) 245 | self.__fcm.init_concept(self.__name_concept(feat_name, value), 1.0, required_presence=False) 246 | except Exception: 247 | raise Exception("Cannot init concept") 248 | 249 | def __get_feature_and_info(self, concept: str): 250 | res = concept.split(self.__str_separator) 251 | return res[1], res[0] 252 | 253 | def __get_discrete_feature_result(self, fcm_results): 254 | final_result = {} 255 | for feat_name in fcm_results: 256 | max_actv = -1 257 | res_pos = 0 258 | curr_pos = 0 259 | for value, output in fcm_results[feat_name]: 260 | if output > max_actv: 261 | max_actv = output 262 | res_pos = curr_pos 263 | curr_pos += 1 264 | # return result with highest activation 265 | result = fcm_results[feat_name][res_pos][0] 266 | if result.isnumeric(): 267 | result = int(result) 268 | final_result[feat_name] = result 269 | return final_result 270 | 271 | def get_inference_result(self, plot=False, map_name="fcm", plot_dir='.'): 272 | self.__fcm.run_inference() 273 | fcm_result = self.__fcm.get_final_state() 274 | cont_res_feat = defaultdict(list) 275 | disc_res_feat = defaultdict(list) 276 | for concept in fcm_result: 277 | feat_name, info = self.__get_feature_and_info(concept) 278 | if self.__features[feat_name][TYPE] == TYPE_FUZZY or self.__features[feat_name][TYPE] == TYPE_REGRESOR: 279 | cont_res_feat[feat_name].append((info, fcm_result[concept])) 280 | else: 281 | disc_res_feat[feat_name].append((info, fcm_result[concept])) 282 | if plot: 283 | self.__fcm.plot_execution(fig_name=map_name, plot_dir=plot_dir) 284 | # TODO: handle continuous features output for regression problems 285 | return self.__get_discrete_feature_result(disc_res_feat) 286 | 287 | def reset(self): 288 | self.__features = {} 289 | self.__processed_features = set() 290 | self.__fcm.clear_all() 291 | -------------------------------------------------------------------------------- /py_fcm/learning/discretization/__init__.py: -------------------------------------------------------------------------------- 1 | import warnings 2 | 3 | import numpy as np 4 | 5 | from py_fcm.learning.discretization.clusters_estimation import estimate_clusters 6 | from py_fcm.learning.discretization.rl_fuzzy_cmeans import rl_fuzzy_cmeans 7 | from py_fcm.learning.discretization.fuzzy_cmeans import fuzzy_cmeans 8 | 9 | 10 | def __create_three_clusters(val_list): 11 | if len(val_list) % 2 == 0: 12 | mid = int(len(val_list) / 2) - 1 13 | new_value = val_list[mid] + abs(val_list[mid] - val_list[mid + 1]) / 2 14 | new_list = np.concatenate((val_list[:mid + 1], [new_value], val_list[mid + 1:])) 15 | val_list = new_list.copy() 16 | else: 17 | new_list = val_list.copy() 18 | 19 | if new_list[0] <= 0: 20 | abs_min = 1 + abs(new_list[0]) 21 | new_list = new_list + abs_min 22 | clusters_desc = np.zeros((len(new_list), 3)) 23 | mid = int(len(new_list) / 2) + 1 24 | max = len(clusters_desc) - 1 25 | for pos in range(mid): 26 | clusters_desc[pos][2] = 0.0 27 | clusters_desc[pos][1] = pos / mid # new_list[pos] / mid_val 28 | clusters_desc[pos][0] = 1.0 - clusters_desc[pos][1] 29 | 30 | clusters_desc[max - pos][0] = 0.0 31 | clusters_desc[max - pos][2] = clusters_desc[pos][0] 32 | clusters_desc[max - pos][1] = clusters_desc[pos][1] 33 | # start 34 | clusters_desc[0][0] = 1.0 35 | clusters_desc[0][1] = 0.0 36 | clusters_desc[0][2] = 0.0 37 | # mid 38 | clusters_desc[mid][0] = 0.0 39 | clusters_desc[mid][1] = 1.0 40 | clusters_desc[mid][2] = 0.0 41 | # end 42 | clusters_desc[-1][0] = 0.0 43 | clusters_desc[-1][1] = 0.0 44 | clusters_desc[-1][2] = 1.0 45 | clusters_desc = clusters_desc.T 46 | return val_list, clusters_desc 47 | 48 | 49 | def fuzzy_feature_discretization(val_list, max_clusters=7, max_iter=200, seed=None, 50 | strategy="cmeans-gap", plot=False, att_name=None, plot_dir="."): 51 | """ 52 | Estimate fuzzy clusters that define a continuous feature. Propose the amount of clusters to be used and return the 53 | membership degree of each provided point using fuzzy cmeans algorithm as kernel 54 | 55 | Args: 56 | val_list: 57 | max_clusters: 58 | gen_init_state: 59 | max_iter: 60 | seed: 61 | strategy: 'cmeans-gap' or 'cmeans-sil' for fuzzy cmeans or 'rl-cmeans' for robust learning fuzzy cmeans. 62 | force_clusters: 63 | plot: 64 | att_name: 65 | plot_dir: 66 | 67 | Returns: Membership dict of each provided value to each cluster. e.g. {'1.3':[0.01,0.57,0.23]} 68 | 69 | """ 70 | # TODO: add a possiblistic fuzzy cmeans approach 71 | val_list = np.unique(val_list) 72 | if len(val_list) < 10: 73 | raise Exception("Too few (" + str(len(val_list)) + ") variations for fuzzy clustering on feature: " + att_name) 74 | val_list.sort() 75 | input_values = np.vstack(val_list) 76 | kwargs = { 77 | 'maxiter': max_iter, 78 | 'seed': seed 79 | } 80 | if strategy == 'cmeans-gap': 81 | num_clusters, clusters_desc = estimate_clusters(input_values, max_clusters, method='gap_concentration', 82 | **kwargs) 83 | elif strategy == 'cmeans-sil': 84 | num_clusters, clusters_desc = estimate_clusters(input_values, max_clusters, method='fuzzy_silhouette', 85 | **kwargs) 86 | elif strategy == 'cmeans-cfe': 87 | num_clusters, clusters_desc = estimate_clusters(input_values, max_clusters, method='combined_fuzzy_entropy', 88 | **kwargs) 89 | elif strategy == 'rl-cmeans': 90 | centroids, clusters_desc, alpha, t = rl_fuzzy_cmeans(input_values, max_iter=max_iter) 91 | clusters_desc = clusters_desc.T 92 | num_clusters = len(alpha) 93 | if num_clusters > max_clusters: 94 | warnings.warn("Unexpected number of clusters found for " + att_name + ": " + str(num_clusters)) 95 | else: 96 | raise Exception("Unsupported clustering method: " + strategy) 97 | # forcing clusters 98 | if num_clusters < 2: 99 | num_clusters = 3 100 | val_list, clusters_desc = __create_three_clusters(val_list) 101 | 102 | if plot: 103 | import matplotlib.pyplot as plt 104 | colors = ['b', 'orange', 'g', 'r', 'c', 'm', 'y', 'k', 'Brown', 'ForestGreen'] 105 | 106 | # plotting membership degree of values over found clusters 107 | for current in range(0, num_clusters): 108 | # plt.plot(val_list, clusters_desc[:, current], colors[current]) 109 | plt.plot(range(0, len(val_list)), clusters_desc[current], colors[current]) 110 | # show plotted values 111 | if att_name is not None: 112 | plt.savefig(plot_dir + '/' + att_name + '.png') 113 | else: 114 | plt.savefig(plot_dir + '/cluster_test.png') 115 | plt.close() 116 | 117 | return num_clusters, val_list, clusters_desc 118 | -------------------------------------------------------------------------------- /py_fcm/learning/discretization/clusters_estimation.py: -------------------------------------------------------------------------------- 1 | import warnings 2 | 3 | import numpy as np 4 | from numba import njit 5 | 6 | from py_fcm.learning.utils import one_dimension_distance 7 | from py_fcm.learning.discretization.fuzzy_cmeans import fuzzy_cmeans 8 | 9 | 10 | @njit 11 | def __estimate_change_points(values: np.ndarray, max_clusters: int): 12 | """ 13 | Curve fit approach to find the amount of clusters to be used. Execute a curve fit process to input data and try to 14 | find the change points in input data, the amount of change points -1 is the number of clusters to be found and the 15 | change points can be used as algorithm initial state. 16 | Args: 17 | values: 18 | max_clusters: 19 | 20 | Returns: 21 | 22 | """ 23 | n_samples = values.size 24 | 25 | min_interval = float(1 / max_clusters) 26 | mean = 0.0 27 | 28 | x_change_points = [] 29 | y_change_points = [] 30 | flag_grow = False 31 | for value in range(n_samples - 1): 32 | if abs(values[value] - values[value + 1]) > abs(mean) and not flag_grow: 33 | flag_grow = True 34 | x_change_points.append(value) 35 | y_change_points.append(values[value]) 36 | 37 | if abs(values[value] - values[value + 1]) < abs(mean) and flag_grow: 38 | flag_grow = False 39 | x_change_points.append(value) 40 | y_change_points.append(values[value]) 41 | 42 | if len(x_change_points) > 1 and value - x_change_points[-2] < n_samples * min_interval: 43 | del x_change_points[-1] 44 | del y_change_points[-1] 45 | 46 | mean = (abs(values[value] - values[value + 1]) + mean) / 2 47 | 48 | if len(x_change_points) > 1 and n_samples - 1 - x_change_points[-1] < n_samples * min_interval: 49 | del x_change_points[-1] 50 | del y_change_points[-1] 51 | x_change_points.append(n_samples - 1) 52 | y_change_points.append(values[-1]) 53 | elif len(x_change_points) > 1: 54 | x_change_points.append(n_samples - 1) 55 | y_change_points.append(values[-1]) 56 | 57 | return x_change_points, y_change_points 58 | 59 | 60 | def gap_concentration(data: np.array, max_clusters: int): 61 | n_samples = data.size 62 | poly = np.polyfit(list(range(n_samples)), data[:, 0], max_clusters) 63 | func = np.poly1d(poly) 64 | image = func(np.linspace(0, data.size - 1, n_samples)) 65 | change_points = __estimate_change_points(image, max_clusters) 66 | return image, change_points 67 | 68 | 69 | def fuzzy_silhouette(data: np.array, max_clusters: int, alpha=2, function=fuzzy_cmeans, **kwargs): 70 | from sklearn.metrics import pairwise_distances_chunked 71 | if function is not None: 72 | dist_matrix = next(pairwise_distances_chunked(data)) 73 | clusters = 0 74 | final_fuzzy_silhouette = 0 75 | final_desc = data 76 | for n_clusters in range(2, max_clusters + 1): 77 | clusters_desc, centroids = function(data, n_clusters, **kwargs) 78 | silhouette = np.zeros(len(data)) 79 | weight = np.zeros(len(data)) 80 | for i in range(len(data)): 81 | curr_membership = clusters_desc[:, i] 82 | if curr_membership[0] > curr_membership[1]: 83 | max_val = curr_membership[0] 84 | max_pos = 0 85 | sec_max = curr_membership[1] 86 | sec_max_pos = 1 87 | else: 88 | max_val = curr_membership[1] 89 | max_pos = 1 90 | sec_max = curr_membership[0] 91 | sec_max_pos = 0 92 | for j in range(2, n_clusters): 93 | if curr_membership[j] > max_val: 94 | sec_max = max_val 95 | sec_max_pos = max_pos 96 | max_val = curr_membership[j] 97 | max_pos = j 98 | elif sec_max < curr_membership[j] != max_val: 99 | sec_max = curr_membership[j] 100 | sec_max_pos = j 101 | weight[i] = (max_val - sec_max) ** alpha 102 | a = np.sum(np.multiply(dist_matrix[i], clusters_desc[max_pos])) / (len(data) - 1) 103 | b = np.sum(np.multiply(dist_matrix[i], clusters_desc[sec_max_pos])) / (len(data) - 1) 104 | silhouette[i] = (b - a) / max(b, a) 105 | fuzzy_silhouette = np.sum(np.multiply(weight, silhouette)) / np.sum(weight) 106 | if fuzzy_silhouette > final_fuzzy_silhouette: 107 | final_fuzzy_silhouette = fuzzy_silhouette 108 | clusters = n_clusters 109 | final_desc = clusters_desc 110 | return clusters, final_desc, final_fuzzy_silhouette 111 | 112 | 113 | def __fuzzy_cross_entropy(u_i, u_j): 114 | return np.sum( 115 | u_i * np.log2(u_i / (u_i / 2 + u_j / 2)) + 116 | (1 - u_i) * np.log2((1 - u_i) / (1 - (u_i + u_j) / 2)) 117 | ) 118 | 119 | 120 | def cfe(data: np.array, max_clusters: int, function=fuzzy_cmeans, **kwargs): 121 | if function is not None: 122 | n = len(data) 123 | clusters = 0 124 | final_cfe = 0 125 | final_desc = data 126 | absolute_center = np.sum(data) / len(data) 127 | for n_clusters in range(2, max_clusters + 1): 128 | clusters_desc, centroids = function(data, n_clusters, **kwargs) 129 | sum_hui = wsgd = bgds = sfce = 0.0 130 | for cluster_pos in range(n_clusters): 131 | curr_cluster = clusters_desc[cluster_pos] 132 | a_i = np.sum(curr_cluster) 133 | sum_val = np.sum(np.multiply(curr_cluster, np.log2(curr_cluster)) + 134 | np.multiply(1 - curr_cluster, np.log2(1 - curr_cluster))) 135 | h_u_i = -sum_val / (n * np.log2(2)) 136 | sum_hui += h_u_i 137 | 138 | wsgd += np.sum(curr_cluster * (np.linalg.norm(data - centroids[cluster_pos]) ** 2)) 139 | bgds += a_i * np.linalg.norm(centroids[cluster_pos] - absolute_center) ** 2 140 | for j in range(n_clusters): 141 | if cluster_pos != j: 142 | sfce += (__fuzzy_cross_entropy(clusters_desc[cluster_pos], clusters_desc[j]) + 143 | __fuzzy_cross_entropy(clusters_desc[j], clusters_desc[cluster_pos])) 144 | # final calculations 145 | sfce = (2 / (n_clusters * (n_clusters - 1))) * sfce 146 | fe = sum_hui / n_clusters 147 | ch = (bgds / (n_clusters - 1)) * ((n - n_clusters) / wsgd) 148 | mc = sfce / fe 149 | cfe = (mc + ch) / 2 150 | if cfe > final_cfe: 151 | final_cfe = cfe 152 | clusters = n_clusters 153 | final_desc = clusters_desc 154 | return clusters, final_desc, final_cfe 155 | 156 | 157 | def estimate_clusters(data: np.array, max_clusters: int, method='gap_concentration', gen_init_state=True, 158 | function=fuzzy_cmeans, **kwargs): 159 | available_methods = {'gap_concentration', 'fuzzy_silhouette', 'combined_fuzzy_entropy'} 160 | clusters_desc = None 161 | clusters = 1 162 | init_state = None 163 | kwargs['m'] = 2.5 164 | kwargs['error'] = 0.0001 165 | if max_clusters >= data.size: 166 | max_clusters = data.size / 2 167 | if method not in available_methods or method != 'gap_concentration' and function is None: 168 | warnings.warn("Unsupported clusters estimation function " + method + ". Used gap concentration instead.") 169 | method = gap_concentration 170 | if method == 'gap_concentration': 171 | image, change_points = gap_concentration(data, max_clusters) 172 | clusters = len(change_points[1]) - 1 173 | if gen_init_state: 174 | # generating initial state for cluster iterations 175 | init_list = np.reshape(data, (len(data), 1)) 176 | # exclude last element if greater than 2 177 | if len(change_points[1]) > 2: 178 | init_cntrs = np.reshape(change_points[1][:-1], (len(change_points[1]) - 1, 1)) 179 | else: 180 | init_cntrs = np.reshape(change_points[1], (len(change_points[1]), 1)) 181 | init_state = one_dimension_distance(init_list, init_cntrs) 182 | if 'init' not in kwargs or kwargs['init'] is None: 183 | kwargs['init'] = init_state 184 | # clusters_desc, centrs = function(data, clusters, m=kwargs['m'], error=kwargs['error'], 185 | # maxiter=kwargs['maxiter'], init=kwargs['init'], seed=kwargs['seed']) 186 | if clusters > 1: 187 | clusters_desc, centrs = function(data, clusters, **kwargs) 188 | else: 189 | clusters = 1 190 | clusters_desc = np.ones(len(data), dtype=np.float64) 191 | if method == 'fuzzy_silhouette': 192 | clusters, clusters_desc, final_fuzzy_silhouette = fuzzy_silhouette(data, max_clusters, alpha=2, 193 | function=function, **kwargs) 194 | if method == 'combined_fuzzy_entropy': 195 | clusters, clusters_desc, final_cfe = cfe(data, max_clusters, function=function, **kwargs) 196 | return clusters, clusters_desc 197 | -------------------------------------------------------------------------------- /py_fcm/learning/discretization/fuzzy_cmeans.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | from numba import njit 3 | 4 | from py_fcm.learning.utils import normalize_columns, normalize_power_columns, one_dimension_distance 5 | 6 | 7 | @njit 8 | def _cmeans0(data, u_old, m): 9 | """ 10 | Single step in generic fuzzy c-means clustering algorithm. 11 | Modified from Ross, Fuzzy Logic w/Engineering Applications (2010), 12 | pages 352-353, equations 10.28 - 10.35. 13 | Parameters inherited from cmeans() 14 | """ 15 | # Normalizing, then eliminating any potential zero values. 16 | u_old = normalize_columns(u_old) 17 | 18 | u_old = np.fmax(u_old, np.finfo(np.float64).eps) 19 | 20 | um = u_old ** m 21 | # Calculate cluster centers 22 | # data = data.T 23 | cntr = um.dot(data) / np.atleast_2d(um.sum(axis=1)).T 24 | 25 | d = one_dimension_distance(data, cntr) 26 | d = np.fmax(d, np.finfo(np.float64).eps) 27 | 28 | jm = (um * d ** 2).sum() 29 | 30 | u = normalize_power_columns(d, - 2. / (m - 1)) 31 | 32 | return cntr, u, jm, d 33 | 34 | 35 | @njit 36 | def _fp_coeff(u): 37 | """ 38 | Fuzzy partition coefficient `fpc` relative to fuzzy c-partitioned 39 | matrix `u`. Measures 'fuzziness' in partitioned clustering. 40 | Parameters 41 | ---------- 42 | u : 2d array (C, N) 43 | Fuzzy c-partitioned matrix; N = number of data points and C = number 44 | of clusters. 45 | Returns 46 | ------- 47 | fpc : float 48 | Fuzzy partition coefficient. 49 | """ 50 | n = u.shape[1] 51 | 52 | return np.trace(u.dot(u.T)) / float(n) 53 | 54 | 55 | @njit 56 | def cmeans(data, c, m, error, maxiter, 57 | init=None, seed=None): 58 | """ 59 | Fuzzy c-means clustering algorithm [1]. 60 | Parameters 61 | ---------- 62 | data : 2d array, size (S, N) 63 | Data to be clustered. N is the number of data sets; S is the number 64 | of features within each sample vector. 65 | c : int 66 | Desired number of clusters or classes. 67 | m : float 68 | Array exponentiation applied to the membership function u_old at each 69 | iteration, where U_new = u_old ** m. 70 | error : float 71 | Stopping criterion; stop early if the norm of (u[p] - u[p-1]) < error. 72 | maxiter : int 73 | Maximum number of iterations allowed. 74 | metric: string 75 | By default is set to euclidean. Passes any option accepted by 76 | ``scipy.spatial.distance.cdist``. 77 | init : 2d array, size (c, N) 78 | Initial fuzzy c-partitioned matrix. If none provided, algorithm is 79 | randomly initialized. 80 | seed : int 81 | If provided, sets random seed of init. No effect if init is 82 | provided. Mainly for debug/testing purposes. 83 | Returns 84 | ------- 85 | cntr : 2d array, size (c, S) 86 | Cluster centers. Data for each center along each feature provided 87 | for every cluster (of the `c` requested clusters). 88 | u : 2d array, (c, N) 89 | Final fuzzy c-partitioned matrix. 90 | u0 : 2d array, (c, N) 91 | Initial guess at fuzzy c-partitioned matrix (either provided init or 92 | random guess used if init was not provided). 93 | d : 2d array, (c, N) 94 | Final Euclidian distance matrix. 95 | jm : 1d array, length P 96 | Objective function history. 97 | p : int 98 | Number of iterations run. 99 | fpc : float 100 | Final fuzzy partition coefficient. 101 | Notes 102 | ----- 103 | The algorithm implemented is from Ross et al. [1]_. 104 | Fuzzy C-Means has a known problem with high dimensionality datasets, where 105 | the majority of cluster centers are pulled into the overall center of 106 | gravity. If you are clustering data with very high dimensionality and 107 | encounter this issue, another clustering method may be required. For more 108 | information and the theory behind this, see Winkler et al. [2]_. 109 | References 110 | ---------- 111 | .. [1] Ross, Timothy J. Fuzzy Logic With Engineering Applications, 3rd ed. 112 | Wiley. 2010. ISBN 978-0-470-74376-8 pp 352-353, eq 10.28 - 10.35. 113 | .. [2] Winkler, R., Klawonn, F., & Kruse, R. Fuzzy c-means in high 114 | dimensional spaces. 2012. Contemporary Theory and Pragmatic 115 | Approaches in Fuzzy Computing Utilization, 1. 116 | """ 117 | # Setup u0 118 | n = data.shape[0] 119 | if init is None: 120 | if seed is not None: 121 | np.random.seed(seed=seed) 122 | u0 = np.random.rand(c, n) 123 | u0 = normalize_columns(u0) 124 | init = u0.copy() 125 | # print(init) 126 | u0 = init 127 | u = np.zeros((c, n), dtype=np.float64) 128 | for i in range(c): 129 | for j in range(n): 130 | u[i, j] = max(u0[i, j], np.finfo(np.float64).eps) 131 | 132 | # Initialize loop parameters 133 | jm = np.zeros(0) 134 | p = 0 135 | 136 | while p < maxiter - 1: 137 | u2 = u.copy() 138 | cntr, u, Jjm, d = _cmeans0(data, u2, m) 139 | jm = np.append(jm, Jjm) 140 | p += 1 141 | 142 | # Stopping rule 143 | if np.linalg.norm(u - u2) < error: 144 | break 145 | 146 | # error = np.linalg.norm(u - u2) 147 | fpc = _fp_coeff(u) 148 | return cntr, u, u0, d, jm, p, fpc 149 | 150 | 151 | @njit 152 | def fuzzy_cmeans(data: np.array, c: int, m=2.5, error=0.0001, maxiter=250, init=None, seed=None): 153 | """Simplify the clustering algorithm function call""" 154 | cntr, u, u0, d, jm, p, fpc = cmeans(data, c, m, error, maxiter, init, seed) 155 | return u, cntr 156 | -------------------------------------------------------------------------------- /py_fcm/learning/discretization/rl_fuzzy_cmeans.py: -------------------------------------------------------------------------------- 1 | import math 2 | import numpy as np 3 | from numba import njit 4 | 5 | 6 | @njit 7 | def _compute_u(data, v, alpha, r1, r2): 8 | u = np.zeros((len(data), len(v)), dtype=np.float64) 9 | c = len(alpha) 10 | for i in range(u.shape[0]): 11 | denom = 0.0 12 | for t in range(c): 13 | d_it = np.linalg.norm(data[i] - v[t]) 14 | denom += math.exp((-(d_it ** 2) + (r1 * math.log(alpha[t]))) / r2) 15 | for k in range(u.shape[1]): 16 | d_ik = np.linalg.norm(data[i] - v[k]) 17 | u[i][k] = math.exp((-(d_ik ** 2) + (r1 * math.log(alpha[k]))) / r2) / denom 18 | return u 19 | 20 | 21 | @njit 22 | def _update_alpha(u, alpha, r1, r3): 23 | new_alpha = alpha.copy() 24 | n = len(u) 25 | c = len(alpha) 26 | ln_accum = np.sum(np.multiply(alpha, np.log2(alpha))) 27 | for k in range(c): 28 | par_content = np.log2(alpha[k]) - ln_accum 29 | pre_val = 0.0 30 | for i in range(len(u)): 31 | pre_val += u[i][k] + ((r3 / r1) * alpha[k] * par_content) 32 | new_alpha[k] = pre_val / n 33 | return new_alpha 34 | 35 | 36 | @njit 37 | def _update_r3(alpha_old, new_alpha, u): 38 | # TODO: add niu function 39 | niu = 1 40 | n = len(u) 41 | c = len(alpha_old) 42 | num = 0.0 43 | ku_sum = [] 44 | v2_denom_accum = [] 45 | 46 | alpha_old_sum = 0.0 47 | for t in range(c): 48 | alpha_old_sum += alpha_old[t] * math.log(alpha_old[t]) 49 | for k in range(c): 50 | num += math.exp(-niu * n * abs(new_alpha[k] - alpha_old[k])) 51 | ku_sum.append(u.sum(0)[k] / n) 52 | v2_denom_accum.append(alpha_old[k] * alpha_old_sum) 53 | v1 = num / c 54 | v2 = (1 - max(ku_sum)) / (-max(v2_denom_accum)) 55 | 56 | return min(v1, v2) 57 | 58 | 59 | @njit 60 | def _resize_references(alpha, u): 61 | j = 0 62 | n = len(u) 63 | c = alpha.size 64 | for t in range(c): 65 | if alpha[t] < (1 / n): 66 | j += 1 67 | new_c = c - j 68 | resized_alpha = np.empty(new_c, dtype=alpha.dtype) 69 | resized_u = np.zeros((n, new_c), dtype=u.dtype) 70 | j = 0 71 | for t in range(c): 72 | if alpha[t] >= 1 / n: 73 | resized_alpha[j] = alpha[t] 74 | for i in range(n): 75 | resized_u[i][j] = u[i][t] 76 | j += 1 77 | resized_alpha = resized_alpha / resized_alpha.sum() 78 | for i in range(n): 79 | resized_u[i] = resized_u[i] / resized_u[i].sum() 80 | 81 | return resized_alpha, resized_u 82 | 83 | 84 | @njit 85 | def _update_v(data, u, c): 86 | new_v = np.zeros((c, data.shape[1]), dtype=u.dtype) 87 | u_sum = u.sum(0) 88 | for k in range(c): 89 | num = np.zeros(data.shape[1], dtype=u.dtype) 90 | for i in range(len(data)): 91 | num = num + (data[i] * u[i][k]) 92 | new_v[k] = num / u_sum[k] 93 | return new_v 94 | 95 | 96 | @njit 97 | def rl_fuzzy_cmeans(data, error=0.0005, max_iter=110): 98 | u = None 99 | c = data.shape[0] 100 | r1 = r2 = r3 = t = 1 101 | v = data.copy() 102 | alpha = np.full(c, 1 / c, dtype=np.float64) 103 | while t < max_iter: 104 | u = _compute_u(data, v, alpha, r1, r2) 105 | r1 = math.exp(-t / 10) 106 | r2 = math.exp(-t / 100) 107 | new_alpha = _update_alpha(u, alpha, r1, r3) 108 | r3 = _update_r3(alpha, new_alpha, u) 109 | new_alpha, u = _resize_references(new_alpha, u) 110 | c = len(new_alpha) 111 | if t >= 100 and (len(alpha) - len(new_alpha)) == 0: 112 | r3 = 0 113 | new_v = _update_v(data, u, c) 114 | if len(new_alpha) == 1 or len(new_v) == len(v) and np.linalg.norm(new_v - v) < error: 115 | v = new_v 116 | alpha = new_alpha 117 | break 118 | v = new_v 119 | alpha = new_alpha 120 | t += 1 121 | 122 | return v, u, alpha, t 123 | 124 | 125 | class RlFuzzyCmeans: 126 | centroids = None 127 | 128 | def fit(self, X): 129 | v, u, alpha, t = rl_fuzzy_cmeans(X) 130 | print("Clusters: ", len(v)) 131 | self.centroids = v 132 | 133 | def predict(self, X): 134 | y_pred = [] 135 | for element in X: 136 | min_dist = np.linalg.norm(self.centroids[0] - element) 137 | res = 0 138 | for cent_pos in range(1, len(self.centroids)): 139 | curr_dist = np.linalg.norm(self.centroids[cent_pos] - element) 140 | if curr_dist < min_dist: 141 | min_dist = curr_dist 142 | res = cent_pos 143 | y_pred.append(res) 144 | return y_pred 145 | -------------------------------------------------------------------------------- /py_fcm/learning/utils.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | from numba import njit 3 | 4 | 5 | # TODO: use njit decorator 6 | def gen_discrete_feature_matrix(val_list, unique_values): 7 | val_num = len(val_list) 8 | feat_num = len(unique_values) 9 | matrix = np.zeros((feat_num, val_num), dtype=np.int8) 10 | for val_pos in range(val_num): 11 | for single_val_pos in range(feat_num): 12 | if val_list[val_pos] == unique_values[single_val_pos]: 13 | matrix[single_val_pos][val_pos] = 1 14 | return matrix 15 | 16 | 17 | @njit 18 | def calc_concepts_coefficient(array1: np.array, array2: np.array): 19 | if len(array1) == len(array2): 20 | a_b = 0 21 | na_b = 0 22 | a_nb = 0 23 | na_nb = 0 24 | for val_pos in range(len(array1)): 25 | a_b += array1[val_pos] * array2[val_pos] 26 | a_nb += array1[val_pos] * (1 - array2[val_pos]) 27 | na_b += (1 - array1[val_pos]) * array2[val_pos] 28 | na_nb += (1 - array1[val_pos]) * (1 - array2[val_pos]) 29 | # return: a->b relation coefficients, b->a relation coefficients 30 | return (a_b, a_nb, na_b, na_nb), (a_b, na_b, a_nb, na_nb) 31 | 32 | 33 | @njit 34 | def one_dimension_distance(data: np.ndarray, centers: np.ndarray): 35 | res = np.empty((len(centers), len(data)), dtype=np.float64) 36 | for c_pos in range(len(centers)): 37 | for d_pos in range(len(data)): 38 | res[c_pos][d_pos] = np.linalg.norm(data[d_pos] - centers[c_pos]) 39 | return res 40 | 41 | 42 | @njit 43 | def normalize_columns(columns): 44 | """ 45 | Normalize columns of matrix. 46 | Parameters 47 | ---------- 48 | columns : 2d array (M x N) 49 | Matrix with columns 50 | Returns 51 | ------- 52 | normalized_columns : 2d array (M x N) 53 | columns/np.sum(columns, axis=0, keepdims=1) 54 | """ 55 | 56 | # broadcast sum over columns 57 | normalized_columns = columns / np.sum(columns, axis=0) 58 | 59 | return normalized_columns 60 | 61 | 62 | @njit 63 | def normalize_power_columns(columns: np.ndarray, exponent: float): 64 | """ 65 | Calculate normalize_columns(x**exponent) 66 | in a numerically safe manner. 67 | Parameters 68 | ---------- 69 | columns : 2d array (M x N) 70 | Matrix with columns 71 | exponent : float 72 | Exponent 73 | Returns 74 | ------- 75 | result : 2d array (M x N) 76 | normalize_columns(x**exponent) but safe 77 | """ 78 | 79 | assert np.all(columns >= 0.0) 80 | 81 | columns = columns.astype(np.float64) 82 | 83 | # values in range [0, 1] 84 | # columns = columns / np.max(columns, axis=0) 85 | for col in range(columns.shape[1]): 86 | columns[:, col] /= np.max(columns[:, col]) 87 | 88 | # values in range [eps, 1] 89 | 90 | columns = np.fmax(columns, np.finfo(columns.dtype).eps) 91 | 92 | if exponent < 0: 93 | # values in range [1, 1/eps] 94 | # columns /= np.min(columns, axis=0) 95 | for col in range(columns.shape[1]): 96 | columns[:, col] /= np.min(columns[:, col]) 97 | 98 | # values in range [1, (1/eps)**exponent] where exponent < 0 99 | # this line might trigger an underflow warning 100 | # if (1/eps)**exponent becomes zero, but that's ok 101 | columns = columns ** exponent 102 | else: 103 | # values in range [eps**exponent, 1] where exponent >= 0 104 | columns = columns ** exponent 105 | 106 | result = normalize_columns(columns) 107 | 108 | return result 109 | 110 | 111 | # ensure compilation 112 | # gen_discrete_feature_matrix(np.array(['1', '1', '2', '2']), np.array(['1', '2'])) 113 | calc_concepts_coefficient(np.array([1, 0, 0, 1]), np.array([0, 0, 1, 1])) 114 | one_dimension_distance(np.array([[-1], [-1.5], [-2]]), np.array([[0.1], [0.3]])) 115 | normalize_columns(np.array([[1.0, 1.8], [0.3, 0.7]])) 116 | normalize_power_columns(np.array([[1.0, 1.8], [0.3, 0.7]]), 2) 117 | -------------------------------------------------------------------------------- /py_fcm/loader.py: -------------------------------------------------------------------------------- 1 | import json 2 | 3 | from py_fcm.utils.__const import * 4 | from py_fcm.fcm import FuzzyCognitiveMap 5 | 6 | 7 | def from_json(str_json: str): 8 | """ 9 | Function to genrate a FCM object form a JSON like: 10 | { 11 | "max_iter": 500, 12 | "activation_function": "sigmoid", 13 | "actv_func_args": {"lambda_val":1}, 14 | "memory_influence": false, 15 | "decision_function": "LAST", 16 | "concepts" : 17 | [ 18 | {"id": "concept_1", "type": "SIMPLE", "activation": 0.5}, 19 | {"id": "concept_2", "type": "DECISION", "custom_function": "gceq", "custom_function_args": {"weight":0.3}}, 20 | {"id": "concept_3", "type": "SIMPLE", "memory_influence":true }, 21 | {"id": "concept_4", "type": "SIMPLE", "custom_function": "saturation", "activation": 0.3} 22 | ], 23 | "relations": 24 | [ 25 | {"origin": "concept_4", "destiny": "concept_2", "weight": -0.1}, 26 | {"origin": "concept_1", "destiny": "concept_3", "weight": 0.59}, 27 | {"origin": "concept_3", "destiny": "concept_2", "weight": 0.8911} 28 | ] 29 | } 30 | Structure: 31 | * iter: max map iterations, required. 32 | * activation_function: defalt activation function, required 33 | * activation_function_args: object (JSON serializable) to describe required function params, optional value 34 | * memory_influence: use memory or not, required 35 | * stability_diff: difference to consider a stable FCM state, optional with 0.001 by default 36 | * stop_at_stabilize: stop the inference process when the FCM reach a stable state, optional True by default 37 | * extra_steps: additional steps to execute after reach a stable state, optionay with 5 by default 38 | * weight: FCM weight ti be used in joint map process, optional with 1 by default 39 | * decision_function: define the decision function to get the final value, required: 40 | - "LAST": last inference value 41 | - "MEAN": whole execution average value 42 | - "EXITED": Highest last execution value in decision nodes 43 | * concepts: a concept list describing each concept, required 44 | * relations: a relations list between defined concepts, required 45 | 46 | Concept descrption: 47 | * id: concept id 48 | * type: node type => "SIMPLE": regular node and default ,"DECISION": target for a classification problems 49 | * active: define if node is active or not, by default is considered active 50 | * custom_function: custom node function, by default use map defined function 51 | * custom_function_args: object (JSON serializable) to describe custom_function params 52 | * memory_influence: use memory or not, by default use FCM memory definition 53 | * exitation_function: node exitation function, KOSKO by default 54 | * activation: initial node activation value, by default 0 55 | 56 | Relation descrption: 57 | * origin: start concept id 58 | * destiny: destiny concept id 59 | * weight: relaion weight in range => [-1,1] 60 | 61 | Exitation functions: 62 | * "MEAN": Mean values of all neighbors that influence the node 63 | * "KOSKO": B. Kosko proposed activation function 64 | * "PAPAGEORGIUS": E. Papageorgius proposed function to avoid saturation 65 | 66 | Activation functions: 67 | * "saturation": 1 if value is > 1, 0 if values is < 0 and value otherwise. Domain => [0,1] 68 | * "biestate": 1 if value is > 0, 0 otherwise. Domain => {0,1} 69 | * "threestate": 0 if value is < 0.25, 0.5 if 0.25 <= value <= 0.75, 1 otherwise. Domain => {0,0.5,1} 70 | * "gceq": weight(float), return value if > weight, 0 otherwise. Domain => [-1,1] 71 | * "sigmoid": lambda_val(int), sigmoid function => [0,1] 72 | * "sigmoid_hip": lambda_val(int), sigmoid hyperbolic function => [-1,1] 73 | 74 | Args: 75 | str_json: string JSON 76 | 77 | Returns: FCM object 78 | 79 | """ 80 | try: 81 | data_dict = json.loads(str_json) 82 | actv_param = {} 83 | stability_diff = 0.001 84 | stop_at_stabilize = True 85 | extra_steps = 5 86 | weight = 1 87 | if 'activation_function_args' in data_dict: 88 | actv_param = data_dict['activation_function_args'] 89 | 90 | if 'stability_diff' in data_dict: 91 | stability_diff = data_dict['stability_diff'] 92 | 93 | if 'stop_at_stabilize' in data_dict: 94 | stop_at_stabilize = data_dict['stop_at_stabilize'] 95 | 96 | if 'extra_steps' in data_dict: 97 | extra_steps = data_dict['extra_steps'] 98 | 99 | if 'weight' in data_dict: 100 | weight = data_dict['weight'] 101 | 102 | my_fcm = FuzzyCognitiveMap(max_it=data_dict['max_iter'], 103 | decision_function=data_dict['decision_function'], 104 | mem_influence=data_dict['memory_influence'], 105 | activation_function=data_dict['activation_function'], 106 | stability_diff=stability_diff, 107 | stabilize=stop_at_stabilize, 108 | extra_steps=extra_steps, 109 | **actv_param) 110 | my_fcm.weight = weight 111 | # adding concepts 112 | for concept in data_dict['concepts']: 113 | use_mem = None 114 | if 'memory_influence' in concept: 115 | use_mem = concept['memory_influence'] 116 | exitation = 'KOSKO' 117 | if 'exitation_function' in concept: 118 | exitation = concept['exitation_function'] 119 | active = True 120 | if 'active' in concept: 121 | active = concept['active'] 122 | custom_function = None 123 | if 'custom_function' in concept: 124 | custom_function = concept['custom_function'] 125 | custom_func_args = {} 126 | if 'custom_function_args' in concept: 127 | custom_func_args = concept['custom_function_args'] 128 | activation_dict = None 129 | if 'activation_dict' in concept: 130 | activation_dict = concept['activation_dict'] 131 | concept_type = TYPE_SIMPLE 132 | if concept['type'] == 'DECISION': 133 | concept_type = TYPE_DECISION 134 | my_fcm.add_concept(concept['id'], 135 | concept_type=concept_type, 136 | is_active=active, 137 | use_memory=use_mem, 138 | excitation_function=exitation, 139 | activation_function=custom_function, 140 | activation_dict=activation_dict, 141 | **custom_func_args) 142 | if 'activation' in concept: 143 | my_fcm.init_concept(concept['id'], concept['activation']) 144 | 145 | # adding relations 146 | for relation in data_dict['relations']: 147 | my_fcm.add_relation(origin_concept=relation['origin'], 148 | destiny_concept=relation['destiny'], 149 | weight=relation['weight']) 150 | return my_fcm 151 | except Exception as err: 152 | raise Exception("Cannot load json data due: " + str(err)) 153 | 154 | 155 | def join_maps(map_set, concept_strategy='union', value_strategy="average", relation_strategy="average", 156 | ignore_zeros=False): 157 | """ 158 | Join a set of FuzzyCognitiveMap in a new one according to defined strategy. All nodes will be set to default 159 | behaviour to avid mixing issues in the result. The final map also will be created with default behavior so, is 160 | required to update the map behavior after join process. Default setting will be updated on future library versions. 161 | Args: 162 | map_set: An iterable object that contains the FCMs 163 | concept_strategy: Strategy to join all maps nodes 164 | union: the new FuzzyCognitiveMap will have the set union of nodes in map_set 165 | intersection: the new FuzzyCognitiveMap will have the set intersection of nodes in map_set 166 | value_strategy: Strategy to define the initial state of map nodes 167 | highest: Select the highest node value as initial node state 168 | lowest: Select the lowest node value as initial node state 169 | average: Select the average of node values as initial node state 170 | relation_strategy: Strategy to define the value for repeated relations weight in map topology 171 | highest: Select the highest relation value as new relation value 172 | lowest: Select the lowest relation value as new relation value 173 | average: Select the average of relations values as new relation value 174 | ignore_zeros: Ignore zero evaluated concepts in value_strategy selected 175 | 176 | Returns: A new FuzzyCognitiveMap generated using defined strategies 177 | 178 | """ 179 | concept_strategies = {'union', 'intersection'} 180 | value_strategies = {'highest', 'lowest', 'average'} 181 | relation_strategies = {'highest', 'lowest', 'average'} 182 | if concept_strategy not in concept_strategies: 183 | raise Exception("Unknown concept strategy: " + concept_strategy) 184 | if value_strategy not in value_strategies: 185 | raise Exception("Unknown value strategy: " + value_strategy) 186 | if relation_strategy not in relation_strategies: 187 | raise Exception("Unknown relation strategy: " + relation_strategy) 188 | 189 | nodes_desc = {} 190 | relations = [] 191 | is_first = True 192 | final_map = {} 193 | if len(map_set) > 0: 194 | for fcm in map_set: 195 | map_desc = json.loads(fcm.to_json()) 196 | for relation in map_desc['relations']: 197 | relations.append(relation) 198 | if is_first: 199 | is_first = False 200 | final_map = map_desc 201 | for concept in map_desc['concepts']: 202 | nodes_desc[concept['id']] = concept 203 | nodes_desc[concept['id']]['accumulation'] = [nodes_desc[concept['id']]['activation']] 204 | else: 205 | new_node_set = {} 206 | for concept in map_desc['concepts']: 207 | new_node_set[concept['id']] = concept 208 | if concept_strategy == 'union': 209 | for key in new_node_set: 210 | if key in nodes_desc: 211 | nodes_desc[key]['accumulation'].append(new_node_set[key]['activation']) 212 | else: 213 | nodes_desc[key] = new_node_set[key] 214 | nodes_desc[key]['accumulation'] = [nodes_desc[key]['activation']] 215 | if concept_strategy == 'intersection': 216 | node_set = set(nodes_desc.keys()) 217 | node_set = node_set.intersection(new_node_set.keys()) 218 | to_remove = [] 219 | for key in nodes_desc: 220 | if key not in node_set: 221 | to_remove.append(key) 222 | else: 223 | nodes_desc[key]['accumulation'].append(new_node_set[key]['activation']) 224 | for key in to_remove: 225 | nodes_desc.pop(key) 226 | final_concepts = [] 227 | for key in nodes_desc: 228 | if value_strategy == "highest": 229 | nodes_desc[key]['activation'] = max(nodes_desc[key]['accumulation']) 230 | if value_strategy == "lowest": 231 | nodes_desc[key]['activation'] = min(nodes_desc[key]['accumulation']) 232 | if value_strategy == "average": 233 | num_elements = len(nodes_desc[key]['accumulation']) 234 | if num_elements > 0: 235 | nodes_desc[key]['activation'] = sum(nodes_desc[key]['accumulation']) / num_elements 236 | nodes_desc[key].pop('accumulation') 237 | if nodes_desc[key]['activation'] != 0: 238 | final_concepts.append(nodes_desc[key]) 239 | elif not ignore_zeros: 240 | final_concepts.append(nodes_desc[key]) 241 | 242 | relation_data = {} 243 | rel_separator = ' |=====> ' 244 | for curr_relation in relations: 245 | relation_name = curr_relation['origin'] + rel_separator + curr_relation['destiny'] 246 | if relation_name not in relation_data: 247 | relation_data[relation_name] = [curr_relation['weight']] 248 | else: 249 | relation_data[relation_name].append(curr_relation['weight']) 250 | 251 | final_relations = [] 252 | for curr_relation in relation_data: 253 | origin = curr_relation.split(rel_separator)[0] 254 | destiny = curr_relation.split(rel_separator)[1] 255 | if origin in nodes_desc and destiny in nodes_desc: 256 | new_relation = { 257 | 'origin': origin, 258 | 'destiny': destiny 259 | } 260 | if relation_strategy == "highest": 261 | new_relation['weight'] = max(relation_data[curr_relation]) 262 | if relation_strategy == "lowest": 263 | new_relation['weight'] = min(relation_data[curr_relation]) 264 | if relation_strategy == "average": 265 | new_relation['weight'] = sum(relation_data[curr_relation]) / len(relation_data[curr_relation]) 266 | final_relations.append(new_relation) 267 | 268 | final_map['concepts'] = final_concepts 269 | final_map['relations'] = final_relations 270 | final_json = json.dumps(final_map) 271 | return from_json(final_json) 272 | return FuzzyCognitiveMap() 273 | -------------------------------------------------------------------------------- /py_fcm/utils/__const.py: -------------------------------------------------------------------------------- 1 | # Saturation-type function 2 | FUNC_SATURATION = 0 3 | # Bistate-type function 4 | FUNC_BISTATE = 1 5 | # Tristate-type function 6 | FUNC_THREESTATE = 2 7 | # Sigmoid-type function 8 | FUNC_SIGMOID = 3 9 | # Sigmoid Hyperbolic / Tangent Hyperbolic -type function 10 | FUNC_SIGMOID_HIP = 4 11 | # Fuzzy-type function 12 | FUNC_FUZZY = 5 13 | # Greater conditional equality 14 | FUNC_GCEQ = 6 15 | # Lesser conditional equality 16 | FUNC_LCEQ = 7 17 | # Rectified Linear Activation Modified 18 | FUNC_RELM = 8 19 | 20 | # relations const 21 | RELATION_DESTINY = 0 22 | RELATION_WEIGHT = 1 23 | RELATION_ORIGIN = 2 24 | 25 | # topology const 26 | NODE_ACTIVE = "current node is active or not" # non active nodes do not activate neighbors 27 | NODE_ARCS = "current node outgoing arcs" 28 | NODE_TYPE = "type of node" 29 | NODE_VALUE = "current node value" 30 | NODE_USE_MEM = "use memory in current node execution" 31 | NODE_ACTV_SUM = "maximum node activation input value" 32 | NODE_AUX = "auxiliary influence value list for new value calculation" 33 | NODE_USE_MAP_FUNC = "use map activation function" 34 | NODE_EXEC_FUNC = "custom execution function" 35 | NODE_EXEC_FUNC_NAME = "custom execution function name" 36 | NODE_ACTV_FUNC = "custom activation function" 37 | NODE_ACTV_FUNC_NAME = "custom activation function name" 38 | NODE_ACTV_FUNC_ARGS = "custom activation function arguments" 39 | NODE_ACTV_FUNC_ARGS_VECT = "custom activation function arguments in vector" 40 | NODE_FUZZY_ACTIVATION = "activation relation for continuous values" 41 | NODE_FUZZY_MIN = "minimum continuous value" 42 | NODE_FUZZY_MAX = "maximum continuous value" 43 | 44 | # node types 45 | TYPE_SIMPLE = "default node type" 46 | TYPE_DECISION = "node type for classification problems" 47 | TYPE_FUZZY = "node type for fuzzy treatment of continuous values" 48 | TYPE_REGRESOR = "node type for regression problems" 49 | TYPE_MUTI = "node type for multivalues fields" 50 | TYPE_MUTI_DESC = "node type for multivalues fields in decision nodes" 51 | 52 | 53 | def is_valid_type(node_type) -> bool: 54 | type_set = set() 55 | type_set.add(TYPE_SIMPLE) 56 | type_set.add(TYPE_DECISION) 57 | type_set.add(TYPE_FUZZY) 58 | type_set.add(TYPE_REGRESOR) 59 | type_set.add(TYPE_MUTI) 60 | type_set.add(TYPE_MUTI_DESC) 61 | return node_type in type_set 62 | -------------------------------------------------------------------------------- /py_fcm/utils/__init__.py: -------------------------------------------------------------------------------- 1 | import ast 2 | from collections import OrderedDict 3 | 4 | import arff 5 | import pandas 6 | 7 | 8 | def load_dataset(ds_path, factorize=False, max_int_uniques=10, int_factor=0.2): 9 | ATT_NAME = 0 10 | data_dict = OrderedDict() 11 | ds_name = ds_path.split('/')[-1] 12 | ds_ext = ds_name.split('.')[-1] 13 | known_format = False 14 | last_feat = '' 15 | try: 16 | if 'arff' == ds_ext: 17 | with open(ds_path) as file: 18 | text = file.read() 19 | arff_dict = arff.loads(text) 20 | for attribute in arff_dict['attributes']: 21 | data_dict[attribute[ATT_NAME]] = [] 22 | for row in arff_dict['data']: 23 | for att_pos in range(0, len(arff_dict['attributes'])): 24 | try: 25 | data_dict[arff_dict['attributes'][att_pos][ATT_NAME]].append(ast.literal_eval(row[att_pos])) 26 | last_feat = arff_dict['attributes'][att_pos][ATT_NAME] 27 | except: 28 | data_dict[arff_dict['attributes'][att_pos][ATT_NAME]].append(row[att_pos]) 29 | known_format = True 30 | if 'csv' == ds_ext: 31 | with open(ds_path) as file: 32 | content = [] 33 | for line in file: 34 | content.append(line.strip()) 35 | # first line describe the atributes names 36 | attributes = str(content[0]).split(',') 37 | for current_att in attributes: 38 | data_dict[current_att] = [] 39 | for line in range(1, len(content)): 40 | data_line = str(content[line]).split(',') 41 | # avoid rows with different attributes length 42 | if len(data_line) == len(attributes): 43 | # the missing data must be identified 44 | for data in range(0, len(data_line)): 45 | # reusing for value type inference 46 | try: 47 | data_dict[attributes[data]].append(ast.literal_eval(data_line[data])) 48 | last_feat = attributes[data] 49 | except: 50 | data_dict[attributes[data]].append(data_line[data]) 51 | else: 52 | # Handle errors in dataset matrix 53 | raise Exception("Errors in line: ", line, len(data_line), len(attributes)) 54 | known_format = True 55 | except Exception as err: 56 | print("Error: ", str(err)) 57 | if not known_format: 58 | raise Exception("Unknown dataset format") 59 | else: 60 | if 'class' in data_dict: 61 | codes, uniques = pandas.factorize(data_dict['class']) 62 | classes = len(uniques) 63 | objects = len(codes) 64 | else: 65 | codes, uniques = pandas.factorize(data_dict[last_feat]) 66 | classes = len(uniques) 67 | objects = len(codes) 68 | print("\n===> Dataset for test: ", ds_name) 69 | print('Features: ', len(data_dict), " | Objects: ", objects, " | Classes: ", classes) 70 | 71 | try: 72 | if factorize: 73 | for key in data_dict: 74 | if type(data_dict[key][0]) == str: 75 | codes, uniques = pandas.factorize(data_dict[key]) 76 | data_dict[key] = codes 77 | elif type(data_dict[key][0]) == int: 78 | codes, uniques = pandas.factorize(data_dict[key]) 79 | if len(uniques) <= max_int_uniques or (len(uniques) / len(codes)) < int_factor: 80 | data_dict[key] = codes 81 | # remove equal entries 82 | dataset_frame = pandas.DataFrame(data_dict).drop_duplicates() 83 | except Exception as err: 84 | for key, value in data_dict.items(): 85 | print(key, value) 86 | pass 87 | raise Exception(ds_name + " " + str(err)) 88 | 89 | return dataset_frame 90 | -------------------------------------------------------------------------------- /py_fcm/utils/functions.py: -------------------------------------------------------------------------------- 1 | import math 2 | from math import exp 3 | 4 | import numpy as np 5 | from numba.typed import List 6 | from numba import njit 7 | 8 | from py_fcm.utils.__const import * 9 | 10 | 11 | @njit 12 | def __partition(array: np.array, start: int, end: int, mirrored_array: np.array): 13 | pivot = array[start] 14 | low = start + 1 15 | high = end 16 | 17 | while True: 18 | while low <= high and array[high] >= pivot: 19 | high = high - 1 20 | 21 | while low <= high and array[low] <= pivot: 22 | low = low + 1 23 | if low <= high: 24 | array[low], array[high] = array[high], array[low] 25 | mirrored_array[low], mirrored_array[high] = mirrored_array[high], mirrored_array[low] 26 | else: 27 | break 28 | 29 | array[start], array[high] = array[high], array[start] 30 | mirrored_array[start], mirrored_array[high] = mirrored_array[high], mirrored_array[start] 31 | 32 | return high 33 | 34 | 35 | @njit 36 | def dual_quick_sort(main_array: np.array, start: int, end: int, mirrored_array: np.array): 37 | if start >= end: 38 | return 39 | p = __partition(main_array, start, end, mirrored_array) 40 | dual_quick_sort(main_array, start, p - 1, mirrored_array) 41 | dual_quick_sort(main_array, p + 1, end, mirrored_array) 42 | 43 | 44 | @njit 45 | def __exec_actv_function(function_id: int, val: float, args=np.empty(1, dtype=np.float64)) -> float: 46 | if function_id == FUNC_SATURATION: 47 | return saturation(val) 48 | if function_id == FUNC_BISTATE: 49 | return bistate(val) 50 | if function_id == FUNC_THREESTATE: 51 | return threestate(val) 52 | if function_id == FUNC_GCEQ: 53 | if args.size == 0: 54 | return greater_cond_equality(val) 55 | else: 56 | return greater_cond_equality(val, weight=args[0]) 57 | if function_id == FUNC_LCEQ: 58 | if args.size == 0: 59 | return lower_cond_equality(val) 60 | else: 61 | return lower_cond_equality(val, weight=args[0]) 62 | if function_id == FUNC_SIGMOID: 63 | if args.size == 0: 64 | return sigmoid(val) 65 | else: 66 | return sigmoid(val, lambda_val=args[0]) 67 | if function_id == FUNC_SIGMOID_HIP: 68 | if args.size == 0: 69 | return sigmoid_hip(val) 70 | else: 71 | return sigmoid_hip(val, lambda_val=args[0]) 72 | if function_id == FUNC_RELM: 73 | if args.size == 0: 74 | return relm(val) 75 | else: 76 | return relm(val, lambda_val=args[0]) 77 | if function_id == FUNC_FUZZY: 78 | membership = args[:int(args.size / 2)] 79 | val_list = args[int(args.size / 2):] 80 | return fuzzy_set(val, membership, val_list) 81 | 82 | 83 | # vectorized inference process 84 | @njit 85 | def vectorized_run(state_vector: np.ndarray, relation_matrix: np.ndarray, functions: np.ndarray, func_args: List, 86 | reduce_values: np.ndarray, memory_usage: List, avoid_saturation: List, max_iterations: int, 87 | min_diff: float, extra_steps: int): 88 | output = np.full((state_vector.size, max_iterations), 2.0) 89 | keep_execution = True 90 | extra_steps_counter = extra_steps 91 | it_counter = max_iterations 92 | 93 | for val_pos in range(state_vector.size): 94 | output[val_pos][0] = state_vector[val_pos] 95 | if avoid_saturation[val_pos]: 96 | state_vector[val_pos] = (2 * state_vector[val_pos]) - 1 97 | 98 | current_step = 1 99 | difference = min_diff 100 | while keep_execution: 101 | it_counter = it_counter - 1 102 | if it_counter <= 0: 103 | keep_execution = False 104 | new_state = np.dot(state_vector, relation_matrix) 105 | for val_pos in range(state_vector.size): 106 | if memory_usage[val_pos]: 107 | new_state[val_pos] = new_state[val_pos] + state_vector[val_pos] 108 | if reduce_values[val_pos] > 0: 109 | new_state[val_pos] = new_state[val_pos] / reduce_values[val_pos] 110 | new_state[val_pos] = __exec_actv_function(functions[val_pos], new_state[val_pos], func_args[val_pos]) 111 | output[val_pos][current_step] = new_state[val_pos] 112 | 113 | if avoid_saturation[val_pos]: 114 | new_state[val_pos] = 2 * new_state[val_pos] - 1 115 | 116 | state_vector = new_state 117 | current_step = current_step + 1 118 | 119 | if current_step > 1: 120 | difference = abs(np.sum(output[:, current_step - 1]) - np.sum(output[:, current_step - 2])) 121 | if difference < min_diff: 122 | extra_steps_counter = extra_steps_counter - 1 123 | if extra_steps_counter == 0: 124 | keep_execution = False 125 | else: 126 | extra_steps_counter = extra_steps 127 | return output 128 | 129 | 130 | # activation functions relations 131 | 132 | @njit 133 | def sigmoid(val: float, lambda_val=1.0) -> float: 134 | return 1.0 / (1.0 + exp(-1 * lambda_val * val)) 135 | 136 | 137 | @njit 138 | def sigmoid_lambda(x: float, y: float) -> float: 139 | res = -(math.log((1 / y) - 1) / x) 140 | return res 141 | 142 | 143 | @njit 144 | def sigmoid_hip(val: float, lambda_val=2.0) -> float: 145 | # avoiding estimation errors 146 | if (-1 * lambda_val * val) > 500: 147 | return (1.0 - exp(500)) / (1.0 + exp(500)) 148 | else: 149 | return (1.0 - exp(-1 * lambda_val * val)) / (1.0 + exp(-1 * lambda_val * val)) 150 | 151 | 152 | @njit 153 | def sigmoid_hip_lambda(x: float, y: float) -> float: 154 | res = -(math.log((1 - y) / (1 + y)) / x) 155 | return res 156 | 157 | 158 | @njit 159 | def relm(val: float, lambda_val=1.0) -> float: 160 | if val < 0: 161 | return 0 162 | res = val / lambda_val 163 | if res > 1: 164 | return 1 165 | return res 166 | 167 | 168 | @njit 169 | def saturation(val: float) -> float: 170 | if val < 0: 171 | return 0.0 172 | elif val > 1: 173 | return 1.0 174 | else: 175 | return val 176 | 177 | 178 | @njit 179 | def bistate(val: float) -> float: 180 | if val <= 0.0: 181 | return 0.0 182 | return 1.0 183 | 184 | 185 | @njit 186 | def threestate(val: float) -> float: 187 | if val <= 1.0 / 3.0: 188 | return 0.0 189 | elif val <= 2.0 / 3.0: 190 | return 0.5 191 | return 1.0 192 | 193 | 194 | @njit 195 | def greater_cond_equality(val: float, weight=-1.0) -> float: 196 | if val >= weight: 197 | if val > 1: 198 | return 1 199 | if val < -1: 200 | return -1 201 | return val 202 | return 0 203 | 204 | 205 | @njit 206 | def lower_cond_equality(val: float, weight=1.0) -> float: 207 | if val <= weight: 208 | if val > 1: 209 | return 1 210 | if val < -1: 211 | return -1 212 | return val 213 | return 0 214 | 215 | 216 | @njit 217 | def fuzzy_set(value: float, membership=np.empty(1, dtype=np.float64), 218 | val_list=np.empty(1, dtype=np.float64)) -> float: 219 | # is assumed that the list of values (val_list) is sorted from lowest to greatest and with no repetitions 220 | 221 | negative_activation = False 222 | if 0.0 <= val_list.min() <= 1.0 and 0.0 <= val_list.max() <= 1.0 and value < 0.0: 223 | negative_activation = True 224 | value = abs(value) 225 | 226 | # result positions 227 | prev_pos = 0 228 | 229 | # find nearest values index 230 | index = np.searchsorted(val_list, value) 231 | if index == val_list.size: 232 | index = index - 1 233 | if val_list[index] == value: 234 | if not negative_activation: 235 | return membership[index] 236 | else: 237 | return -1 * (1 - membership[index]) 238 | if index == 0: 239 | if val_list[index] > value: 240 | if not negative_activation: 241 | return membership[index] 242 | else: 243 | return -1 * (1 - membership[index]) 244 | else: 245 | next_pos = 1 246 | elif index == val_list.size - 1: 247 | if val_list[index] < value: 248 | if not negative_activation: 249 | return membership[index] 250 | else: 251 | return -1 * (1 - membership[index]) 252 | else: 253 | prev_pos = index - 1 254 | next_pos = index 255 | else: 256 | if (value - val_list[index]) > 0: 257 | prev_pos = index 258 | next_pos = index + 1 259 | else: 260 | prev_pos = index - 1 261 | next_pos = index 262 | 263 | sign = 1.0 264 | if value != 0: 265 | sign = value / abs(value) 266 | value = abs(value) 267 | 268 | # f(Xi) = (f(Xi-1)*Xi/Xi-1)*Xi-1_Xi_coef + (f(Xi+1)*Xi/Xi+1)*Xi+1_Xi_coef 269 | # inf_estimation = (membership[prev_pos] * value) / float(val_list[prev_pos]) 270 | # sup_estimation = (membership[next_pos] * value) / float(val_list[next_pos]) 271 | inf_estimation = membership[prev_pos] 272 | sup_estimation = membership[next_pos] 273 | diff = val_list[next_pos] - val_list[prev_pos] 274 | # calc influence coefficents 275 | inf_coef = 1 - ((value - val_list[prev_pos]) / diff) 276 | # 1 - inf_coef 277 | sup_coef = 1 - ((val_list[next_pos] - value) / diff) 278 | # result estimation according to distance between extremes 279 | estimation = sign * ((inf_coef * inf_estimation) + (sup_coef * sup_estimation)) 280 | 281 | if not negative_activation: 282 | if estimation > 1: 283 | estimation = 1 284 | if estimation < -1: 285 | estimation = -1 286 | return estimation 287 | return -1 * (1 - estimation) 288 | 289 | 290 | # ensure functions numba compilation 291 | dual_quick_sort(np.array([2, 5, 1]), 0, 2, np.array([2, 5, 1])) 292 | __empt_arr = np.ones(2, np.float64) 293 | __empt_mat = np.ones((2, 2), np.float64) 294 | vectorized_run(__empt_arr, __empt_mat, __empt_arr, List([__empt_arr, __empt_arr]), 295 | __empt_arr, List([True, False]), List([True, False]), 296 | max_iterations=3, min_diff=0.0001, extra_steps=0) 297 | 298 | sigmoid(10, 1.5) 299 | sigmoid_lambda(500, 0.8) 300 | sigmoid_hip(10) 301 | sigmoid_hip_lambda(500, 0.85) 302 | relm(5, 5) 303 | bistate(10) 304 | threestate(10) 305 | saturation(10) 306 | greater_cond_equality(10, 0.5) 307 | fuzzy_set(10, np.array([0.0, 1.0]), np.array([5, 15])) 308 | 309 | 310 | class Activation: 311 | """ 312 | Class to map all activation functions that can be used by FCM concepts. The function args structure will be the 313 | next one: val, arg_list 314 | Where: 315 | val: is the value to apply the function 316 | arg_list: is a numpy array that contains the list of arguments values sorted 317 | Note: If some function require more than one argument will be assumed that the values will be sorted according to 318 | the alphabetical sort of arguments names 319 | """ 320 | 321 | @staticmethod 322 | def get_function_by_name(func_name: str): 323 | """ 324 | Get the function callable object from the function name 325 | Args: 326 | func_name: Activation function name 327 | 328 | Returns: Function callable object if func_name is found, None otherwise 329 | 330 | """ 331 | if func_name == "biestate": 332 | return bistate 333 | if func_name == "threestate": 334 | return threestate 335 | if func_name == "saturation": 336 | return saturation 337 | if func_name == "tan_hip": 338 | return sigmoid_hip 339 | if func_name == "sigmoid": 340 | return sigmoid 341 | if func_name == "sigmoid_hip": 342 | return sigmoid_hip 343 | if func_name == "fuzzy": 344 | return fuzzy_set 345 | if func_name == "gceq": 346 | return greater_cond_equality 347 | if func_name == "lceq": 348 | return lower_cond_equality 349 | if func_name == "relm": 350 | return relm 351 | return None 352 | 353 | @staticmethod 354 | def get_const_by_name(func_name: str): 355 | """ 356 | Get the function const value from the function name 357 | Args: 358 | func_name: Activation function name 359 | 360 | Returns: Function cont value if func_name is found, None otherwise 361 | 362 | """ 363 | if func_name == "biestate": 364 | return FUNC_BISTATE 365 | if func_name == "threestate": 366 | return FUNC_THREESTATE 367 | if func_name == "saturation": 368 | return FUNC_SATURATION 369 | if func_name == "tan_hip": 370 | return FUNC_SIGMOID_HIP 371 | if func_name == "sigmoid": 372 | return FUNC_SIGMOID 373 | if func_name == "sigmoid_hip": 374 | return FUNC_SIGMOID_HIP 375 | if func_name == "relm": 376 | return FUNC_RELM 377 | if func_name == "fuzzy": 378 | return FUNC_FUZZY 379 | if func_name == "gceq": 380 | return FUNC_GCEQ 381 | if func_name == "lceq": 382 | return FUNC_LCEQ 383 | return None 384 | 385 | @staticmethod 386 | def get_function_names() -> set: 387 | """ 388 | Get available activation function names 389 | Returns: Set of names 390 | 391 | """ 392 | names = set() 393 | names.add("biestate") 394 | names.add("threestate") 395 | names.add("saturation") 396 | names.add("tan_hip") 397 | names.add("sigmoid") 398 | names.add("sigmoid_hip") 399 | names.add("relm") 400 | names.add("gceq") 401 | names.add("lceq") 402 | names.add("proportion") 403 | return names 404 | 405 | 406 | class Excitation: 407 | """ 408 | All exitation functions must get a node as parameter 409 | """ 410 | 411 | # node: node dict for all functions 412 | @staticmethod 413 | def kosko(node): 414 | """ TeX functions: 415 | not memory: A^{(t+1)}_i = f\left(\sum_{j=1}^N w_{ij}*A^{(t)}_j \right) , i \neq j 416 | use memory: A^{(t+1)}_i = f\left(A^{(t)}_i+\sum_{j=1}^N w_{ij}*A^{(t)}_j \right) , i \neq j 417 | """ 418 | neighbors_val = node[NODE_AUX] 419 | node_val = node[NODE_VALUE] 420 | use_memory = node[NODE_USE_MEM] 421 | res = sum(neighbors_val) 422 | if use_memory: 423 | res += node_val 424 | return res 425 | 426 | @staticmethod 427 | def papageorgius(node): 428 | # to avoid saturation 429 | neighbors_val = node[NODE_AUX] 430 | node_val = node[NODE_VALUE] 431 | use_memory = node[NODE_USE_MEM] 432 | res = sum(neighbors_val) 433 | if use_memory: 434 | res += (2 * node_val) - 1 435 | return res 436 | 437 | @staticmethod 438 | def get_by_name(func_name: str): 439 | """ 440 | Get the function callable object from the function name 441 | Args: 442 | func_name: Excitation function name 443 | 444 | Returns: Function callable object if func_name is found, None otherwise 445 | 446 | """ 447 | if func_name == "KOSKO": 448 | return Excitation.kosko 449 | if func_name == "PAPAGEORGIUS": 450 | return Excitation.papageorgius 451 | return None 452 | 453 | @staticmethod 454 | def get_function_names() -> set: 455 | """ 456 | Get available excitation function names 457 | Returns: Set of names 458 | 459 | """ 460 | names = set() 461 | names.add("KOSKO") 462 | names.add("PAPAGEORGIUS") 463 | names.add("MEAN") 464 | return names 465 | 466 | 467 | class Decision: 468 | @staticmethod 469 | def last(val_list: list, last_pos=0) -> float: 470 | # return last value 471 | if last_pos > 0: 472 | return val_list[last_pos] 473 | return val_list[-1] 474 | 475 | @staticmethod 476 | def mean(val_list: list, last_pos=0) -> float: 477 | # return average execution value 478 | result = 0 479 | if last_pos <= 0: 480 | last_pos = len(val_list) - 1 481 | for elem_pos in range(last_pos + 1): 482 | result += val_list[elem_pos] 483 | return result / (last_pos + 1) 484 | 485 | @staticmethod 486 | def exited(val_list: list, last_pos=0) -> float: 487 | # return highest execution value 488 | if last_pos >= 0: 489 | res = val_list[:last_pos] 490 | else: 491 | res = val_list 492 | return max(res) 493 | 494 | @staticmethod 495 | def get_by_name(func_name: str): 496 | """ 497 | Get the function callable object from the function name 498 | Args: 499 | func_name: Decision function name 500 | 501 | Returns: Function callable object if func_name is found, None otherwise 502 | 503 | """ 504 | if func_name == "LAST": 505 | return Decision.last 506 | if func_name == "MEAN": 507 | return Decision.mean 508 | if func_name == "EXITED": 509 | return Decision.exited 510 | return None 511 | 512 | @staticmethod 513 | def get_function_names() -> set: 514 | """ 515 | Get available excitation function names 516 | Returns: Set of names 517 | 518 | """ 519 | names = set() 520 | names.add("LAST") 521 | names.add("MEAN") 522 | names.add("EXITED") 523 | return names 524 | 525 | 526 | class Fuzzy: 527 | @staticmethod 528 | def defuzzyfication(memb_val, min_scale, norm_val, membership=[], val_list=[]): 529 | """ 530 | Estimate possibles neron outputs according to activation. 531 | Args: 532 | memb_val: activation value 533 | min_scale: minimum value in values list for scale data 534 | norm_val: maximum value of scaled data for normalization 535 | membership: membership degree 536 | val_list: result values associated to membership 537 | 538 | Returns: List of possibles results 539 | 540 | """ 541 | raise NotImplementedError("Not implemented function") 542 | 543 | @staticmethod 544 | def fuzzyfication(value, min_scale, norm_val, membership=[], val_list=[]): 545 | """ 546 | Estimate the neuron activation according to described discrete fuzzy set. 547 | Args: 548 | value: value for estimate activation 549 | min_scale: minimum value in values list for scale data 550 | norm_val: maximum value of scaled data for normalization 551 | membership: membership degree list, each value must belong to domain [-1,1] 552 | val_list: result values associated to membership and len(val_list) = len(membership) 553 | 554 | Returns: Estimated activation 555 | 556 | """ 557 | value = (float(value) + min_scale) / norm_val 558 | 559 | # cmp values 560 | prev_value = 1 561 | next_value = -1 562 | 563 | # result positions 564 | prev_pos = 0 565 | next_pos = 0 566 | 567 | # calc result 568 | for elem_pos in range(0, len(val_list)): 569 | if value == val_list[elem_pos]: 570 | return membership[elem_pos] 571 | 572 | diff = float(value) - float(val_list[elem_pos]) 573 | if diff > 0: 574 | if diff < prev_value: 575 | prev_value = diff 576 | prev_pos = elem_pos 577 | else: 578 | if diff > next_value: 579 | next_value = diff 580 | next_pos = elem_pos 581 | # minimum value 582 | if prev_value == 1: 583 | return min(membership) 584 | # maximum value 585 | if next_value == -1: 586 | return max(membership) 587 | # estimate value 588 | inf_estimation = membership[prev_pos] 589 | sup_estimation = membership[next_pos] 590 | diff = float(val_list[next_pos]) - float(val_list[prev_pos]) 591 | if diff == 0: 592 | return membership[next_pos] 593 | # calc influence coefficents 594 | inf_coef = 1 - ((value - float(val_list[prev_pos])) / diff) 595 | # 1 - inf_coef 596 | sup_coef = 1 - ((float(val_list[next_pos]) - value) / diff) 597 | # result estimation according to distance between extremes 598 | estimation = (inf_coef * inf_estimation) + (sup_coef * sup_estimation) 599 | if estimation > 1: 600 | estimation = 1 601 | if estimation < -1: 602 | estimation = -1 603 | return estimation 604 | 605 | 606 | class Relation: 607 | """ 608 | All execution function must have the same params described in supp function, even if not used 609 | """ 610 | 611 | # support 612 | @staticmethod 613 | def supp(p_q, p_nq, np_q, np_nq): 614 | return p_q / (p_q + p_nq + np_q + np_nq) 615 | 616 | # confidence 617 | @staticmethod 618 | def conf(p_q, p_nq, np_q, np_nq): 619 | if (p_q + p_nq) != 0: 620 | return p_q / (p_q + p_nq) 621 | return 0 622 | 623 | # lift 624 | @staticmethod 625 | def lift(p_q, p_nq, np_q, np_nq): 626 | return (p_q / (p_q + p_nq)) / ((p_q + np_q) / (p_q + p_nq + np_q + np_nq)) 627 | 628 | # red odss ratio 629 | @staticmethod 630 | def rodr(p_q, p_nq, np_q, np_nq): 631 | if (p_nq + np_q) != 0: 632 | return (p_q + np_nq) / (p_nq + np_q) 633 | else: 634 | # zero div behavior 635 | return p_q + np_nq 636 | 637 | # odss ratio 638 | @staticmethod 639 | def odr(p_q, p_nq, np_q, np_nq): 640 | if (p_nq * np_q) != 0: 641 | return (p_q * np_nq) / (p_nq * np_q) 642 | else: 643 | # zero div behavior 644 | return p_q * np_nq 645 | 646 | # positive influence 647 | @staticmethod 648 | def pos_inf(p_q, p_nq, np_q, np_nq): 649 | total = p_q + p_nq + np_q + np_nq 650 | if (p_nq + np_q) != 0: 651 | pos_inf = (p_q / (p_nq + np_q)) / (p_q + p_nq) 652 | else: 653 | # zero div behavior 654 | pos_inf = p_q / (p_q + p_nq) 655 | 656 | if (p_q + np_nq) != 0: 657 | neg_inf = (p_nq / (p_q + np_nq)) / (p_q + p_nq) 658 | else: 659 | # zero div behavior 660 | neg_inf = p_nq / (p_q + p_nq) 661 | if pos_inf > neg_inf: 662 | return pos_inf 663 | if pos_inf < neg_inf: 664 | return -1 * neg_inf 665 | return 0 666 | 667 | @staticmethod 668 | def bayes(p_q, p_nq, np_q, np_nq): 669 | total = p_q + p_nq + np_q + np_nq 670 | prob_p = (p_q + p_nq) / total 671 | prob_q = (p_q + np_q) / total 672 | if p_q > np_q: 673 | return (((p_q - np_q) / total) * prob_q) / prob_p 674 | return 0 675 | 676 | @staticmethod 677 | def simple(p_q, p_nq, np_q, np_nq): 678 | total = p_q + p_nq + np_q + np_nq 679 | if p_q == p_nq: 680 | return 0 681 | if p_q - p_nq != 0: 682 | return ((p_q - p_nq) + (np_nq + np_q)) / total 683 | else: 684 | # zero div behavior 685 | return ((p_q - p_nq) - (np_nq + np_q)) / total 686 | -------------------------------------------------------------------------------- /setup.py: -------------------------------------------------------------------------------- 1 | import setuptools 2 | 3 | with open("README.md", "r") as fh: 4 | long_description = fh.read() 5 | 6 | setuptools.setup( 7 | name='py_fcm', 8 | version='1.0.0', 9 | scripts=[], 10 | author="Jairo Lefebre", 11 | author_email="jairo.lefebre@gmail.com", 12 | description="Fuzzy cognitive maps python library", 13 | long_description=long_description, 14 | long_description_content_type="text/markdown", 15 | url="https://github.com/J41R0/PyFCM", 16 | packages=setuptools.find_packages("py_fcm", exclude=["tests"]), 17 | install_requires=[ 18 | 'pandas >= 0.24.2', 19 | 'matplotlib >= 3.1.0', 20 | 'networkx >= 2.3', 21 | 'numpy >= 1.19.1', 22 | 'numba >= 0.51.2', 23 | ], 24 | python_requires='>=3.7', 25 | classifiers=[ 26 | "Programming Language :: Python :: 3", 27 | "Operating System :: OS Independent", 28 | ], 29 | ) 30 | -------------------------------------------------------------------------------- /tests/__init__.py: -------------------------------------------------------------------------------- 1 | from py_fcm.utils.__const import * 2 | from py_fcm.utils.functions import Excitation, Activation 3 | 4 | 5 | def create_concept(node_type=TYPE_SIMPLE, is_active=True, use_memory=True, 6 | exitation_function='KOSKO', activ_function=None, **kwargs): 7 | test_concept = {NODE_ACTIVE: is_active, NODE_ARCS: [], NODE_AUX: [1, 1, 1], NODE_VALUE: 1.0} 8 | test_concept[NODE_TYPE] = node_type 9 | test_concept[NODE_EXEC_FUNC] = Excitation.get_by_name(exitation_function) 10 | test_concept[NODE_USE_MEM] = use_memory 11 | test_concept[NODE_ACTV_FUNC] = Activation.get_function_by_name(activ_function) 12 | test_concept[NODE_ACTV_FUNC_ARGS] = kwargs 13 | return test_concept 14 | -------------------------------------------------------------------------------- /tests/test_estimator.py: -------------------------------------------------------------------------------- 1 | import json 2 | import unittest 3 | import pandas as pd 4 | from py_fcm.learning.association import AssociationBasedFCM 5 | 6 | 7 | class GeneratorTests(unittest.TestCase): 8 | @staticmethod 9 | def gen_fcm(): 10 | generator = AssociationBasedFCM() 11 | 12 | test_input = [ 13 | ['x', 5, 2.3, 'v1'], 14 | ['y', 7, 4.8, 'v1'], 15 | ['z', 3, 28.01, 'v2'], 16 | ['w', 1, 15.7, 'v2'] 17 | ] 18 | 19 | my_ds = pd.DataFrame(test_input, columns=['f1', 'f2', 'f3', 'class']) 20 | generated_fcm = generator.build_fcm(my_ds, target_features=['class']) 21 | return generated_fcm 22 | 23 | def test_association_generator_concepts(self): 24 | expected_json = {"concepts": [{"id": "w___f1", "is_active": True, "type": "SIMPLE", "activation": 0.0}, 25 | {"id": "x___f1", "is_active": True, "type": "SIMPLE", "activation": 0.0}, 26 | {"id": "y___f1", "is_active": True, "type": "SIMPLE", "activation": 0.0}, 27 | {"id": "z___f1", "is_active": True, "type": "SIMPLE", "activation": 0.0}, 28 | {"id": "0___f2", "is_active": True, "type": "SIMPLE", "activation": 0.0, 29 | "custom_function": "fuzzy", "activation_dict": { 30 | "membership": [0.14285714285714285, 0.42857142857142855, 0.7142857142857143, 31 | 1.0], "val_list": [1.0, 3.0, 5.0, 7.0]}}, 32 | {"id": "0___f3", "is_active": True, "type": "SIMPLE", "activation": 0.0, 33 | "custom_function": "fuzzy", "activation_dict": { 34 | "membership": [0.0821135308818279, 0.17136736879685824, 0.5605141021063905, 35 | 1.0], "val_list": [2.3, 4.8, 15.7, 28.01]}}, 36 | {"id": "v1___class", "is_active": True, "type": "DECISION", "activation": 0.0}, 37 | {"id": "v2___class", "is_active": True, "type": "DECISION", "activation": 0.0}] 38 | } 39 | 40 | generated_fcm = GeneratorTests.gen_fcm() 41 | json_fcm = json.loads(generated_fcm.to_json()) 42 | self.assertEqual(expected_json['concepts'], json_fcm['concepts']) 43 | 44 | def test_association_generator_relations(self): 45 | expected_json = {"relations": [{"origin": "w___f1", "destiny": "x___f1", "weight": -1}, 46 | {"origin": "x___f1", "destiny": "w___f1", "weight": -1}, 47 | {"origin": "w___f1", "destiny": "y___f1", "weight": -1}, 48 | {"origin": "y___f1", "destiny": "w___f1", "weight": -1}, 49 | {"origin": "w___f1", "destiny": "z___f1", "weight": -1}, 50 | {"origin": "z___f1", "destiny": "w___f1", "weight": -1}, 51 | {"origin": "x___f1", "destiny": "y___f1", "weight": -1}, 52 | {"origin": "y___f1", "destiny": "x___f1", "weight": -1}, 53 | {"origin": "x___f1", "destiny": "z___f1", "weight": -1}, 54 | {"origin": "z___f1", "destiny": "x___f1", "weight": -1}, 55 | {"origin": "y___f1", "destiny": "z___f1", "weight": -1}, 56 | {"origin": "z___f1", "destiny": "y___f1", "weight": -1}, 57 | {"origin": "0___f2", "destiny": "w___f1", "weight": 0.4375}, 58 | {"origin": "w___f1", "destiny": "0___f2", "weight": 1.0}, 59 | {"origin": "0___f2", "destiny": "x___f1", "weight": 0.0625}, 60 | {"origin": "x___f1", "destiny": "0___f2", "weight": 0.14285714285714285}, 61 | {"origin": "0___f2", "destiny": "y___f1", "weight": 0.1875}, 62 | {"origin": "y___f1", "destiny": "0___f2", "weight": 0.42857142857142855}, 63 | {"origin": "0___f2", "destiny": "z___f1", "weight": 0.3125}, 64 | {"origin": "z___f1", "destiny": "0___f2", "weight": 0.7142857142857143}, 65 | {"origin": "0___f3", "destiny": "w___f1", "weight": 0.5512694351505609}, 66 | {"origin": "w___f1", "destiny": "0___f3", "weight": 1.0}, 67 | {"origin": "0___f3", "destiny": "x___f1", "weight": 0.045266679787443406}, 68 | {"origin": "x___f1", "destiny": "0___f3", "weight": 0.0821135308818279}, 69 | {"origin": "0___f3", "destiny": "y___f1", "weight": 0.09446959259988191}, 70 | {"origin": "y___f1", "destiny": "0___f3", "weight": 0.17136736879685824}, 71 | {"origin": "0___f3", "destiny": "z___f1", "weight": 0.30899429246211374}, 72 | {"origin": "z___f1", "destiny": "0___f3", "weight": 0.5605141021063905}, 73 | {"origin": "0___f3", "destiny": "0___f2", "weight": 0.8189332808502263}, 74 | {"origin": "0___f2", "destiny": "0___f3", "weight": 0.6499241342377723}, 75 | {"origin": "v1___class", "destiny": "v2___class", "weight": -1}, 76 | {"origin": "v2___class", "destiny": "v1___class", "weight": -1}, 77 | {"origin": "v1___class", "destiny": "0___f3", "weight": 0.12674044983934307}, 78 | {"origin": "0___f3", "destiny": "v1___class", "weight": 0.13973627238732533}, 79 | {"origin": "v2___class", "destiny": "0___f3", "weight": 0.7802570510531952}, 80 | {"origin": "0___f3", "destiny": "v2___class", "weight": 0.8602637276126747}, 81 | {"origin": "v1___class", "destiny": "x___f1", "weight": 0.5}, 82 | {"origin": "x___f1", "destiny": "v1___class", "weight": 1.0}, 83 | {"origin": "v1___class", "destiny": "y___f1", "weight": 0.5}, 84 | {"origin": "y___f1", "destiny": "v1___class", "weight": 1.0}, 85 | {"origin": "v2___class", "destiny": "w___f1", "weight": 0.5}, 86 | {"origin": "w___f1", "destiny": "v2___class", "weight": 1.0}, 87 | {"origin": "v2___class", "destiny": "z___f1", "weight": 0.5}, 88 | {"origin": "z___f1", "destiny": "v2___class", "weight": 1.0}, 89 | {"origin": "v1___class", "destiny": "0___f2", "weight": 0.2857142857142857}, 90 | {"origin": "0___f2", "destiny": "v1___class", "weight": 0.25}, 91 | {"origin": "v2___class", "destiny": "0___f2", "weight": 0.8571428571428572}, 92 | {"origin": "0___f2", "destiny": "v2___class", "weight": 0.7500000000000001}] 93 | } 94 | 95 | generated_fcm = GeneratorTests.gen_fcm() 96 | json_fcm = json.loads(generated_fcm.to_json()) 97 | for elment_pos in range(len(expected_json['relations'])): 98 | self.assertIn(expected_json['relations'][elment_pos], json_fcm['relations']) 99 | -------------------------------------------------------------------------------- /tests/test_fcm.py: -------------------------------------------------------------------------------- 1 | import json 2 | import unittest 3 | from py_fcm import FuzzyCognitiveMap, TYPE_DECISION, TYPE_FUZZY, TYPE_SIMPLE 4 | 5 | 6 | class FuzzyCognitiveMapTests(unittest.TestCase): 7 | def setUp(self) -> None: 8 | self.fcm = FuzzyCognitiveMap() 9 | 10 | def __init_complex_fcm(self): 11 | fcm = FuzzyCognitiveMap() 12 | 13 | fcm.add_concept('result_1', concept_type=TYPE_DECISION) 14 | fcm.add_concept('result_2', concept_type=TYPE_DECISION) 15 | 16 | fcm.add_concept('input_1') 17 | fcm.init_concept('input_1', 0.5) 18 | fcm.add_concept('input_2') 19 | fcm.init_concept('input_2', 0.2) 20 | fcm.add_concept('input_3') 21 | fcm.init_concept('input_3', 1) 22 | fcm.add_concept('input_4') 23 | fcm.init_concept('input_4', -0.2) 24 | fcm.add_concept('input_5') 25 | fcm.init_concept('input_5', -0.5) 26 | 27 | fcm.add_relation('input_1', 'result_1', 0.5) 28 | fcm.add_relation('input_2', 'result_1', 1) 29 | 30 | fcm.add_relation('input_4', 'result_2', 0.5) 31 | fcm.add_relation('input_5', 'result_2', 1) 32 | 33 | fcm.add_relation('input_1', 'input_4', -0.3) 34 | fcm.add_relation('input_1', 'input_3', 0.7) 35 | fcm.add_relation('input_5', 'input_2', -0.3) 36 | fcm.add_relation('input_5', 'input_3', 0.7) 37 | 38 | fcm.add_relation('result_2', 'input_3', 0.5) 39 | fcm.add_relation('result_1', 'input_3', 0.5) 40 | return fcm 41 | 42 | def test_default_and_to_json(self) -> None: 43 | expected_json = { 44 | "max_iter": 200, 45 | "decision_function": "MEAN", 46 | "activation_function": "sigmoid_hip", 47 | "memory_influence": False, 48 | "stability_diff": 0.001, 49 | "stop_at_stabilize": True, 50 | "extra_steps": 5, 51 | "weight": 1, 52 | "concepts": [], 53 | "relations": [] 54 | } 55 | json_fcm = json.loads(self.fcm.to_json()) 56 | self.assertEqual(expected_json, json_fcm) 57 | 58 | def test_set_map_fuctions(self) -> None: 59 | expected_json = { 60 | "max_iter": 200, 61 | "decision_function": "LAST", 62 | "activation_function": "sigmoid", 63 | 'activation_function_args': {'lambda_val': 10}, 64 | "memory_influence": False, 65 | "stability_diff": 0.001, 66 | "stop_at_stabilize": True, 67 | "extra_steps": 5, 68 | "weight": 1, 69 | "concepts": [], 70 | "relations": [] 71 | } 72 | self.fcm.set_map_decision_function('LAST') 73 | self.fcm.set_map_activation_function('sigmoid', lambda_val=10) 74 | json_fcm = json.loads(self.fcm.to_json()) 75 | self.assertEqual(expected_json, json_fcm) 76 | 77 | def test_concept_addition_default(self) -> None: 78 | expected_json = { 79 | "concepts": [{'id': 'test', 'is_active': True, 'type': 'SIMPLE', 'activation': 0.0}] 80 | } 81 | self.fcm.add_concept('test') 82 | json_fcm = json.loads(self.fcm.to_json()) 83 | self.assertEqual(expected_json["concepts"], json_fcm["concepts"]) 84 | 85 | def test_concept_init_and_get_value(self) -> None: 86 | self.fcm.add_concept('test') 87 | self.fcm.init_concept('test', 0.5) 88 | self.assertEqual(0.5, self.fcm.get_concept_value('test')) 89 | 90 | def test_concept_redefinition(self) -> None: 91 | expected_json = { 92 | "concepts": [{'id': 'test', 'is_active': True, 'type': 'SIMPLE', 'activation': 0.0}] 93 | } 94 | self.fcm.add_concept('test') 95 | json_fcm = json.loads(self.fcm.to_json()) 96 | self.assertEqual(expected_json["concepts"], json_fcm["concepts"]) 97 | expected_json["concepts"][0]['type'] = 'DECISION' 98 | 99 | self.fcm.add_concept('test', concept_type=TYPE_DECISION) 100 | json_fcm = json.loads(self.fcm.to_json()) 101 | self.assertEqual(expected_json["concepts"], json_fcm["concepts"]) 102 | 103 | def test_concept_addition_custom_values(self) -> None: 104 | expected_json = { 105 | "concepts": [{'id': 'test', 'is_active': False, 'type': 'DECISION', 'activation': 0.0, 'use_memory': True, 106 | 'custom_function': 'gceq', 'custom_function_args': {'weight': 0.3}}] 107 | } 108 | self.fcm.add_concept('test', is_active=False, concept_type=TYPE_DECISION, use_memory=True, 109 | excitation_function='PAPAGEORGIUS', activation_function='gceq', weight=0.3) 110 | json_fcm = json.loads(self.fcm.to_json()) 111 | self.assertEqual(expected_json["concepts"], json_fcm["concepts"]) 112 | 113 | def test_concept_addition_with_default_definition(self) -> None: 114 | expected_json = { 115 | "concepts": [{'id': 'test', 'is_active': True, 'type': 'DECISION', 'activation': 0.0, 'use_memory': True, 116 | 'custom_function': 'gceq', 'custom_function_args': {'weight': 0.3}}] 117 | } 118 | self.fcm.set_default_concept_properties(concept_type=TYPE_DECISION, use_memory=True, 119 | excitation_function='PAPAGEORGIUS', activation_function='gceq', 120 | weight=0.3) 121 | self.fcm.add_concept('test') 122 | json_fcm = json.loads(self.fcm.to_json()) 123 | self.assertEqual(expected_json["concepts"], json_fcm["concepts"]) 124 | 125 | def test_concept_property_update(self) -> None: 126 | expected_json = { 127 | "concepts": [{'id': 'test', 'is_active': True, 'type': 'SIMPLE', 'activation': 0.0}] 128 | } 129 | self.fcm.add_concept('test') 130 | json_fcm = json.loads(self.fcm.to_json()) 131 | self.assertEqual(expected_json["concepts"], json_fcm["concepts"]) 132 | self.fcm.set_concept_properties('test', is_active=False, concept_type=TYPE_DECISION, use_memory=True, 133 | excitation_function='PAPAGEORGIUS', activation_function='gceq', weight=0.3) 134 | expected_json = { 135 | "concepts": [{'id': 'test', 'is_active': False, 'type': 'DECISION', 'activation': 0.0, 'use_memory': True, 136 | 'custom_function': 'gceq', 'custom_function_args': {'weight': 0.3}}] 137 | } 138 | json_fcm = json.loads(self.fcm.to_json()) 139 | self.assertEqual(expected_json["concepts"], json_fcm["concepts"]) 140 | 141 | def test_relation_addition(self) -> None: 142 | expected_json = { 143 | "concepts": [{'id': 'test', 'is_active': True, 'type': 'SIMPLE', 'activation': 0.0}, 144 | {'id': 'test_1', 'is_active': True, 'type': 'SIMPLE', 'activation': 0.0}], 145 | "relations": [{'origin': 'test', 'destiny': 'test_1', 'weight': 0.5}] 146 | } 147 | self.fcm.add_concept('test') 148 | self.fcm.add_concept('test_1') 149 | self.fcm.add_relation('test', 'test_1', 0.5) 150 | json_fcm = json.loads(self.fcm.to_json()) 151 | self.assertEqual(expected_json["relations"], json_fcm["relations"]) 152 | 153 | def test_clear_all(self) -> None: 154 | expected_json = { 155 | "max_iter": 200, 156 | "decision_function": "MEAN", 157 | "activation_function": "sigmoid_hip", 158 | "memory_influence": False, 159 | "stability_diff": 0.001, 160 | "stop_at_stabilize": True, 161 | "extra_steps": 5, 162 | "weight": 1, 163 | "concepts": [], 164 | "relations": [] 165 | } 166 | self.fcm.add_concept('test') 167 | self.fcm.add_concept('test_1') 168 | self.fcm.add_relation('test', 'test_1', 0.5) 169 | self.fcm.clear_all() 170 | json_fcm = json.loads(self.fcm.to_json()) 171 | self.assertEqual(expected_json, json_fcm) 172 | 173 | def test_inference_default(self): 174 | # sigmoid_hip 175 | expected_json = { 176 | 'concepts': [{'id': 'result_1', 'is_active': True, 'type': 'DECISION', 'activation': 0.056969360711939906}, 177 | {'id': 'result_2', 'is_active': True, 'type': 'DECISION', 'activation': -0.06113548755394814}, 178 | {'id': 'input_1', 'is_active': True, 'type': 'SIMPLE', 'activation': 0.05}, 179 | {'id': 'input_2', 'is_active': True, 'type': 'SIMPLE', 'activation': 0.034888503362331805}, 180 | {'id': 'input_3', 'is_active': True, 'type': 'SIMPLE', 'activation': 0.09792163723261006}, 181 | {'id': 'input_4', 'is_active': True, 'type': 'SIMPLE', 'activation': -0.034888503362331805}, 182 | {'id': 'input_5', 'is_active': True, 'type': 'SIMPLE', 'activation': -0.05}] 183 | } 184 | 185 | fcm = self.__init_complex_fcm() 186 | fcm.run_inference() 187 | json_fcm = json.loads(fcm.to_json()) 188 | self.assertEqual(expected_json["concepts"], json_fcm["concepts"]) 189 | fcm.debug = False 190 | fcm.run_inference() 191 | json_fcm = json.loads(fcm.to_json()) 192 | self.assertEqual(expected_json["concepts"], json_fcm["concepts"]) 193 | 194 | def test_is_stable(self): 195 | fcm = self.__init_complex_fcm() 196 | fcm.run_inference() 197 | self.assertEqual(True, fcm.is_stable()) 198 | fcm.debug = False 199 | fcm.run_inference() 200 | self.assertEqual(True, fcm.is_stable()) 201 | 202 | def test_get_final_state_default(self): 203 | fcm = self.__init_complex_fcm() 204 | fcm.run_inference() 205 | expected_result = {'result_1': 0.056969360711939906, 'result_2': -0.06113548755394814} 206 | self.assertEqual(expected_result, fcm.get_final_state()) 207 | fcm.debug = False 208 | fcm.run_inference() 209 | self.assertEqual(expected_result, fcm.get_final_state()) 210 | 211 | def test_search_concept_final_state_arg(self): 212 | fcm = self.__init_complex_fcm() 213 | fcm.run_inference() 214 | expected_result = {'result_1': 0.056969360711939906} 215 | self.assertEqual(expected_result, fcm.get_final_state(names=['result_1'])) 216 | fcm.debug = False 217 | fcm.run_inference() 218 | self.assertEqual(expected_result, fcm.get_final_state(names=['result_1'])) 219 | 220 | def test_get_final_state_any(self): 221 | fcm = self.__init_complex_fcm() 222 | fcm.run_inference() 223 | expected_result = {'result_1': 0.056969360711939906, 'result_2': -0.06113548755394814, 224 | 'input_1': 0.05, 'input_2': 0.034888503362331805, 225 | 'input_3': 0.09792163723261006, 'input_4': -0.034888503362331805, 226 | 'input_5': -0.05} 227 | self.assertEqual(expected_result, fcm.get_final_state("any")) 228 | fcm.debug = False 229 | fcm.run_inference() 230 | self.assertEqual(expected_result, fcm.get_final_state("any")) 231 | 232 | def test_get_final_state_custom_type(self): 233 | fcm = self.__init_complex_fcm() 234 | fcm.run_inference() 235 | expected_result = {'result_1': 0.056969360711939906, 'result_2': -0.06113548755394814} 236 | self.assertEqual(expected_result, fcm.get_final_state(TYPE_DECISION)) 237 | fcm.debug = False 238 | fcm.run_inference() 239 | self.assertEqual(expected_result, fcm.get_final_state(TYPE_DECISION)) 240 | 241 | def test_reset_execution(self): 242 | fcm = self.__init_complex_fcm() 243 | fcm.run_inference() 244 | fcm.reset_execution() 245 | expected_result = {'input_1': 0.5} 246 | self.assertEqual(expected_result, fcm.get_final_state(names=['input_1'])) 247 | 248 | def test_clear_execution(self): 249 | fcm = self.__init_complex_fcm() 250 | fcm.run_inference() 251 | fcm.clear_execution() 252 | expected_result = {'input_1': 0.0} 253 | self.assertEqual(expected_result, fcm.get_final_state(names=['input_1'])) 254 | 255 | def test_inference_sigmoid(self): 256 | expected_json = { 257 | 'concepts': [{'id': 'result_1', 'is_active': True, 'type': 'DECISION', 'activation': 0.5994700184028905}, 258 | {'id': 'result_2', 'is_active': True, 'type': 'DECISION', 'activation': 0.5755041378442186}, 259 | {'id': 'input_1', 'is_active': True, 'type': 'SIMPLE', 'activation': 0.5}, 260 | {'id': 'input_2', 'is_active': True, 'type': 'SIMPLE', 'activation': 0.44379910825937535}, 261 | {'id': 'input_3', 'is_active': True, 'type': 'SIMPLE', 'activation': 0.7851790048472618}, 262 | {'id': 'input_4', 'is_active': True, 'type': 'SIMPLE', 'activation': 0.3963131391906254}, 263 | {'id': 'input_5', 'is_active': True, 'type': 'SIMPLE', 'activation': 0.4}] 264 | } 265 | 266 | fcm = self.__init_complex_fcm() 267 | fcm.set_map_activation_function('sigmoid') 268 | fcm.run_inference() 269 | json_fcm = json.loads(fcm.to_json()) 270 | self.assertEqual(expected_json["concepts"], json_fcm["concepts"]) 271 | fcm.debug = False 272 | fcm.run_inference() 273 | json_fcm = json.loads(fcm.to_json()) 274 | self.assertEqual(expected_json["concepts"], json_fcm["concepts"]) 275 | 276 | def test_inference_biestate(self): 277 | expected_json = { 278 | 'concepts': [{'id': 'result_1', 'is_active': True, 'type': 'DECISION', 'activation': 0.2}, 279 | {'id': 'result_2', 'is_active': True, 'type': 'DECISION', 'activation': 0.0}, 280 | {'id': 'input_1', 'is_active': True, 'type': 'SIMPLE', 'activation': 0.05}, 281 | {'id': 'input_2', 'is_active': True, 'type': 'SIMPLE', 'activation': 0.12}, 282 | {'id': 'input_3', 'is_active': True, 'type': 'SIMPLE', 'activation': 0.3}, 283 | {'id': 'input_4', 'is_active': True, 'type': 'SIMPLE', 'activation': -0.02}, 284 | {'id': 'input_5', 'is_active': True, 'type': 'SIMPLE', 'activation': -0.05}] 285 | 286 | } 287 | 288 | fcm = self.__init_complex_fcm() 289 | fcm.set_map_activation_function('biestate') 290 | fcm.run_inference() 291 | json_fcm = json.loads(fcm.to_json()) 292 | self.assertEqual(expected_json["concepts"], json_fcm["concepts"]) 293 | fcm.debug = False 294 | fcm.run_inference() 295 | json_fcm = json.loads(fcm.to_json()) 296 | self.assertEqual(expected_json["concepts"], json_fcm["concepts"]) 297 | 298 | def test_inference_threestate(self): 299 | expected_json = { 300 | 'concepts': [{'id': 'result_1', 'is_active': True, 'type': 'DECISION', 'activation': 0.0625}, 301 | {'id': 'result_2', 'is_active': True, 'type': 'DECISION', 'activation': 0.0}, 302 | {'id': 'input_1', 'is_active': True, 'type': 'SIMPLE', 'activation': 0.0625}, 303 | {'id': 'input_2', 'is_active': True, 'type': 'SIMPLE', 'activation': 0.025}, 304 | {'id': 'input_3', 'is_active': True, 'type': 'SIMPLE', 'activation': 0.125}, 305 | {'id': 'input_4', 'is_active': True, 'type': 'SIMPLE', 'activation': -0.025}, 306 | {'id': 'input_5', 'is_active': True, 'type': 'SIMPLE', 'activation': -0.0625}] 307 | } 308 | 309 | fcm = self.__init_complex_fcm() 310 | fcm.set_map_activation_function('threestate') 311 | fcm.run_inference() 312 | json_fcm = json.loads(fcm.to_json()) 313 | self.assertEqual(expected_json["concepts"], json_fcm["concepts"]) 314 | fcm.debug = False 315 | fcm.run_inference() 316 | json_fcm = json.loads(fcm.to_json()) 317 | self.assertEqual(expected_json["concepts"], json_fcm["concepts"]) 318 | 319 | def test_inference_saturation(self): 320 | expected_json = { 321 | 'concepts': [{'id': 'result_1', 'is_active': True, 'type': 'DECISION', 'activation': 0.06}, 322 | {'id': 'result_2', 'is_active': True, 'type': 'DECISION', 'activation': 0.0}, 323 | {'id': 'input_1', 'is_active': True, 'type': 'SIMPLE', 'activation': 0.05}, 324 | {'id': 'input_2', 'is_active': True, 'type': 'SIMPLE', 'activation': 0.034999999999999996}, 325 | {'id': 'input_3', 'is_active': True, 'type': 'SIMPLE', 'activation': 0.13}, 326 | {'id': 'input_4', 'is_active': True, 'type': 'SIMPLE', 'activation': -0.02}, 327 | {'id': 'input_5', 'is_active': True, 'type': 'SIMPLE', 'activation': -0.05}] 328 | } 329 | 330 | fcm = self.__init_complex_fcm() 331 | fcm.set_map_activation_function('saturation') 332 | fcm.run_inference() 333 | json_fcm = json.loads(fcm.to_json()) 334 | self.assertEqual(expected_json["concepts"], json_fcm["concepts"]) 335 | fcm.debug = False 336 | fcm.run_inference() 337 | json_fcm = json.loads(fcm.to_json()) 338 | self.assertEqual(expected_json["concepts"], json_fcm["concepts"]) 339 | 340 | def test_inference_gceq(self): 341 | expected_json = { 342 | 'concepts': [{'id': 'result_1', 'is_active': True, 'type': 'DECISION', 'activation': 0.06}, 343 | {'id': 'result_2', 'is_active': True, 'type': 'DECISION', 'activation': 0.0}, 344 | {'id': 'input_1', 'is_active': True, 'type': 'SIMPLE', 'activation': 0.05}, 345 | {'id': 'input_2', 'is_active': True, 'type': 'SIMPLE', 'activation': 0.034999999999999996}, 346 | {'id': 'input_3', 'is_active': True, 'type': 'SIMPLE', 'activation': 0.13}, 347 | {'id': 'input_4', 'is_active': True, 'type': 'SIMPLE', 'activation': -0.02}, 348 | {'id': 'input_5', 'is_active': True, 'type': 'SIMPLE', 'activation': -0.05}] 349 | } 350 | 351 | fcm = self.__init_complex_fcm() 352 | fcm.set_map_activation_function('gceq', weight=0.0001) 353 | fcm.run_inference() 354 | json_fcm = json.loads(fcm.to_json()) 355 | self.assertEqual(expected_json["concepts"], json_fcm["concepts"]) 356 | fcm.debug = False 357 | fcm.run_inference() 358 | json_fcm = json.loads(fcm.to_json()) 359 | self.assertEqual(expected_json["concepts"], json_fcm["concepts"]) 360 | 361 | def test_inference_lceq(self): 362 | expected_json = { 363 | 'concepts': [{'id': 'result_1', 'is_active': True, 'type': 'DECISION', 'activation': 0.0}, 364 | {'id': 'result_2', 'is_active': True, 'type': 'DECISION', 'activation': -0.06749999999999999}, 365 | {'id': 'input_1', 'is_active': True, 'type': 'SIMPLE', 'activation': 0.05}, 366 | {'id': 'input_2', 'is_active': True, 'type': 'SIMPLE', 'activation': 0.02}, 367 | {'id': 'input_3', 'is_active': True, 'type': 'SIMPLE', 'activation': 0.06625}, 368 | {'id': 'input_4', 'is_active': True, 'type': 'SIMPLE', 'activation': -0.034999999999999996}, 369 | {'id': 'input_5', 'is_active': True, 'type': 'SIMPLE', 'activation': -0.05}] 370 | } 371 | 372 | fcm = self.__init_complex_fcm() 373 | fcm.set_map_activation_function('lceq', weight=0.0001) 374 | fcm.run_inference() 375 | json_fcm = json.loads(fcm.to_json()) 376 | self.assertEqual(expected_json["concepts"], json_fcm["concepts"]) 377 | fcm.debug = False 378 | fcm.run_inference() 379 | json_fcm = json.loads(fcm.to_json()) 380 | self.assertEqual(expected_json["concepts"], json_fcm["concepts"]) 381 | 382 | def test_inference_fuzzy(self): 383 | expected_json = { 384 | 'concepts': [{'id': 'r1', 'is_active': True, 'type': 'DECISION', 'activation': 0.4687996143920417}, 385 | {'id': 'r2', 'is_active': True, 'type': 'DECISION', 'activation': 0.4625401367231719}, 386 | {'id': 'c1', 'is_active': True, 'type': 'SIMPLE', 'activation': 0.3, 387 | 'custom_function': 'threestate'}, 388 | {'id': 'c2', 'is_active': True, 'type': 'SIMPLE', 'activation': 0.3, 389 | 'custom_function': 'threestate'}, 390 | {'id': 'c3', 'is_active': True, 'type': 'SIMPLE', 'activation': 0.3, 391 | 'custom_function': 'fuzzy', 'activation_dict': {'membership': [0.25, 0.5, 1.0, 0.5, 0.25], 392 | 'val_list': [1.0, 2.0, 3.0, 4.0, 5.0]}}] 393 | } 394 | 395 | fcm = FuzzyCognitiveMap(activation_function='sigmoid') 396 | 397 | fcm.add_concept('r1', TYPE_DECISION) 398 | fcm.add_concept('r2', TYPE_DECISION) 399 | fcm.add_concept('c1', TYPE_SIMPLE, activation_function="threestate") 400 | fcm.add_concept('c2', TYPE_SIMPLE, activation_function="threestate") 401 | fcm.add_concept('c3', TYPE_FUZZY, activation_dict={ 402 | 'membership': [0.25, 0.5, 1.0, 0.5, 0.25], 403 | 'val_list': [1, 2, 3, 4, 5] 404 | }) 405 | 406 | fcm.add_relation('c3', 'r1', 0.5) 407 | fcm.add_relation('c3', 'r2', 0.5) 408 | 409 | fcm.add_relation('r1', 'c1', 1.0) 410 | fcm.add_relation('r2', 'c2', 1.0) 411 | 412 | fcm.add_relation('c1', 'c3', 0.7) 413 | fcm.add_relation('c1', 'r2', -0.3) 414 | 415 | fcm.add_relation('c2', 'c3', 0.8) 416 | fcm.add_relation('c2', 'r1', -0.2) 417 | 418 | fcm.init_concept('c1', -1.0) 419 | fcm.init_concept('c2', -1.0) 420 | 421 | fcm.run_inference() 422 | 423 | json_fcm = json.loads(fcm.to_json()) 424 | self.assertEqual(expected_json["concepts"], json_fcm["concepts"]) 425 | fcm.debug = False 426 | fcm.run_inference() 427 | json_fcm = json.loads(fcm.to_json()) 428 | self.assertEqual(expected_json["concepts"], json_fcm["concepts"]) 429 | 430 | def test_inference_several_functions(self): 431 | expected_json = { 432 | 'concepts': [ 433 | {'id': 'result_1', 'is_active': True, 'type': 'DECISION', 'activation': 0.0, 'use_memory': True, 434 | 'custom_function': 'gceq', 'custom_function_args': {'weight': 0.003}}, 435 | {'id': 'result_2', 'is_active': True, 'type': 'DECISION', 'activation': 0.00012648385157387873, 436 | 'use_memory': True, 'custom_function': 'sigmoid', 'custom_function_args': {'lambda_val': 10}}, 437 | {'id': 'input_1', 'is_active': True, 'type': 'SIMPLE', 'activation': 0.07375, 'use_memory': True, 438 | 'custom_function': 'threestate'}, 439 | {'id': 'input_2', 'is_active': True, 'type': 'SIMPLE', 'activation': 0.04361062920291475}, 440 | {'id': 'input_3', 'is_active': True, 'type': 'SIMPLE', 'activation': -0.6816478125020062}, 441 | {'id': 'input_4', 'is_active': True, 'type': 'SIMPLE', 'activation': 0.18674101269496457}, 442 | {'id': 'input_5', 'is_active': True, 'type': 'SIMPLE', 'activation': -0.0625}] 443 | } 444 | 445 | fcm = self.__init_complex_fcm() 446 | fcm.add_concept('result_1', concept_type=TYPE_DECISION, use_memory=True, 447 | excitation_function='PAPAGEORGIUS', activation_function='gceq', weight=0.003) 448 | 449 | fcm.add_concept('result_2', concept_type=TYPE_DECISION, use_memory=True, 450 | excitation_function='PAPAGEORGIUS', activation_function='sigmoid', lambda_val=10) 451 | 452 | fcm.add_concept('input_1', use_memory=True, excitation_function='PAPAGEORGIUS', 453 | activation_function='threestate') 454 | fcm.init_concept('input_1', 0.59) 455 | fcm.run_inference() 456 | json_fcm = json.loads(fcm.to_json()) 457 | self.assertEqual(expected_json["concepts"], json_fcm["concepts"]) 458 | fcm.debug = False 459 | fcm.run_inference() 460 | json_fcm = json.loads(fcm.to_json()) 461 | self.assertEqual(expected_json["concepts"], json_fcm["concepts"]) 462 | -------------------------------------------------------------------------------- /tests/test_functions.py: -------------------------------------------------------------------------------- 1 | import unittest 2 | 3 | from tests import create_concept 4 | from py_fcm.utils.functions import * 5 | 6 | 7 | class ExitationFunctionsTests(unittest.TestCase): 8 | def test_kosko(self): 9 | test_concept = create_concept("test", exitation_function='KOSKO') 10 | res = test_concept[NODE_EXEC_FUNC](test_concept) 11 | self.assertEqual(4, res) 12 | test_concept = create_concept("test", exitation_function='KOSKO', use_memory=False) 13 | res = test_concept[NODE_EXEC_FUNC](test_concept) 14 | self.assertEqual(3, res) 15 | 16 | def test_papageorgius(self): 17 | test_concept = create_concept("test", exitation_function='PAPAGEORGIUS') 18 | res = test_concept[NODE_EXEC_FUNC](test_concept) 19 | self.assertEqual(4, res) 20 | test_concept = create_concept("test", exitation_function='PAPAGEORGIUS', use_memory=False) 21 | res = test_concept[NODE_EXEC_FUNC](test_concept) 22 | self.assertEqual(3, res) 23 | 24 | 25 | class ActivationFunctionsTests(unittest.TestCase): 26 | def test_biestate(self): 27 | test_concept = create_concept(activ_function='biestate') 28 | res = test_concept[NODE_ACTV_FUNC](0.5) 29 | self.assertEqual(1.0, res) 30 | res = test_concept[NODE_ACTV_FUNC](-0.5) 31 | self.assertEqual(0.0, res) 32 | 33 | def test_threestate(self): 34 | test_concept = create_concept(activ_function='threestate') 35 | res = test_concept[NODE_ACTV_FUNC](0.30) 36 | self.assertEqual(0.0, res) 37 | res = test_concept[NODE_ACTV_FUNC](0.60) 38 | self.assertEqual(0.5, res) 39 | res = test_concept[NODE_ACTV_FUNC](0.75) 40 | self.assertEqual(1.0, res) 41 | 42 | def test_saturation(self): 43 | test_concept = create_concept(activ_function='saturation') 44 | res = test_concept[NODE_ACTV_FUNC](-60) 45 | self.assertEqual(0.0, res) 46 | res = test_concept[NODE_ACTV_FUNC](75) 47 | self.assertEqual(1.0, res) 48 | 49 | def test_sigmoid(self): 50 | test_concept = create_concept(activ_function='sigmoid') 51 | res = test_concept[NODE_ACTV_FUNC](1) 52 | self.assertEqual(0.7310585786300049, res) 53 | res = test_concept[NODE_ACTV_FUNC](-1) 54 | self.assertEqual(0.2689414213699951, res) 55 | res = test_concept[NODE_ACTV_FUNC](-1, 50) 56 | self.assertEqual(1.928749847963918e-22, res) 57 | 58 | def test_sigmoid_and_tan_hip(self): 59 | test_concept = create_concept(activ_function='sigmoid_hip') 60 | res = test_concept[NODE_ACTV_FUNC](1) 61 | self.assertEqual(0.7615941559557649, res) 62 | res = test_concept[NODE_ACTV_FUNC](-1) 63 | self.assertEqual(-0.7615941559557649, res) 64 | test_concept = create_concept("test", activ_function='tan_hip') 65 | res = test_concept[NODE_ACTV_FUNC](1) 66 | self.assertEqual(0.7615941559557649, res) 67 | res = test_concept[NODE_ACTV_FUNC](-1) 68 | self.assertEqual(-0.7615941559557649, res) 69 | 70 | def test_gceq(self): 71 | test_concept = create_concept(activ_function='gceq') 72 | res = test_concept[NODE_ACTV_FUNC](0.7, 0.5) 73 | self.assertEqual(0.7, res) 74 | res = test_concept[NODE_ACTV_FUNC](7, 0.5) 75 | self.assertEqual(1.0, res) 76 | res = test_concept[NODE_ACTV_FUNC](0.3, 0.5) 77 | self.assertEqual(0.0, res) 78 | res = test_concept[NODE_ACTV_FUNC](-7, -3.5) 79 | self.assertEqual(0.0, res) 80 | res = test_concept[NODE_ACTV_FUNC](-2, -3.5) 81 | self.assertEqual(-1.0, res) 82 | 83 | def test_lceq(self): 84 | test_concept = create_concept(activ_function='lceq') 85 | res = test_concept[NODE_ACTV_FUNC](0.7, 0.5) 86 | self.assertEqual(0.0, res) 87 | res = test_concept[NODE_ACTV_FUNC](7, 0.5) 88 | self.assertEqual(0.0, res) 89 | res = test_concept[NODE_ACTV_FUNC](0.3, 0.5) 90 | self.assertEqual(0.3, res) 91 | res = test_concept[NODE_ACTV_FUNC](-7, -3.5) 92 | self.assertEqual(-1.0, res) 93 | res = test_concept[NODE_ACTV_FUNC](-2, -3.5) 94 | self.assertEqual(0.0, res) 95 | 96 | def test_fuzzy(self): 97 | test_concept = create_concept(activ_function='fuzzy') 98 | res = test_concept[NODE_ACTV_FUNC](10, np.array([0.0, 1.0]), np.array([5, 15])) 99 | self.assertEqual(0.5, res) 100 | res = test_concept[NODE_ACTV_FUNC](4, np.array([0.0, 1.0]), np.array([5, 15])) 101 | self.assertEqual(0.0, res) 102 | res = test_concept[NODE_ACTV_FUNC](16, np.array([0.0, 1.0]), np.array([5, 15])) 103 | self.assertEqual(1.0, res) 104 | res = test_concept[NODE_ACTV_FUNC](7, np.array([0.0, 0.5, 1.0]), np.array([5, 15, 20])) 105 | self.assertEqual(0.09999999999999998, res) 106 | res = test_concept[NODE_ACTV_FUNC](16, np.array([0.0, 0.5, 1.0]), np.array([5, 15, 20])) 107 | self.assertEqual(0.6, res) 108 | res = test_concept[NODE_ACTV_FUNC](-1.0, np.array([0.25, 0.5, 1.0, 0.5, 0.25]), 109 | np.array([0.2, 0.4, 0.6, 0.8, 1.0])) 110 | self.assertEqual(-0.75, res) 111 | res = test_concept[NODE_ACTV_FUNC](-0.5, np.array([0.25, 0.5, 1.0, 0.5, 0.25]), 112 | np.array([0.2, 0.4, 0.6, 0.8, 1.0])) 113 | self.assertEqual(-0.25, res) 114 | 115 | 116 | class RelationFunctionsTests(unittest.TestCase): 117 | def test_supp(self): 118 | res = Relation.supp(1, 2, 2, 1) 119 | self.assertEqual(0.16666666666666666, res) 120 | 121 | def test_conf(self): 122 | res = Relation.conf(1, 1, 1, 1) 123 | self.assertEqual(0.5, res) 124 | 125 | def test_lift(self): 126 | res = Relation.lift(1, 2, 2, 1) 127 | self.assertEqual(0.6666666666666666, res) 128 | 129 | def test_odr(self): 130 | res = Relation.odr(1, 2, 2, 1) 131 | self.assertEqual(0.25, res) 132 | 133 | def test_rodr(self): 134 | res = Relation.rodr(1, 2, 2, 1) 135 | self.assertEqual(0.5, res) 136 | 137 | def test_pos_inf(self): 138 | res = Relation.pos_inf(1, 2, 2, 1) 139 | self.assertEqual(-0.3333333333333333, res) 140 | 141 | def test_simple(self): 142 | res = Relation.simple(1, 2, 2, 1) 143 | self.assertEqual(0.3333333333333333, res) 144 | 145 | 146 | class OtherFucntionsTest(unittest.TestCase): 147 | def test_dual_quick_sort(self): 148 | array = np.array([2, 6, 1, 5]) 149 | mirror = np.array([2, 6, 1, 5]) 150 | dual_quick_sort(array, 0, len(array) - 1, mirror) 151 | for pos in range(len(array)): 152 | self.assertEqual(array[pos], mirror[pos]) 153 | -------------------------------------------------------------------------------- /tests/test_generator.py: -------------------------------------------------------------------------------- 1 | import json 2 | import unittest 3 | import pandas as pd 4 | from py_fcm.learning.association import AssociationBasedFCM 5 | 6 | 7 | class GeneratorTests(unittest.TestCase): 8 | @staticmethod 9 | def gen_fcm(): 10 | generator = AssociationBasedFCM() 11 | 12 | test_input = [ 13 | ['x', 5, 2.3, 'v1'], 14 | ['y', 7, 4.8, 'v1'], 15 | ['z', 3, 28.01, 'v2'], 16 | ['w', 1, 15.7, 'v2'] 17 | ] 18 | 19 | my_ds = pd.DataFrame(test_input, columns=['f1', 'f2', 'f3', 'class']) 20 | generated_fcm = generator.build_fcm(my_ds, target_features=['class']) 21 | return generated_fcm 22 | 23 | def test_association_generator_concepts(self): 24 | expected_json = {"concepts": [{"id": "w___f1", "is_active": True, "type": "SIMPLE", "activation": 0.0}, 25 | {"id": "x___f1", "is_active": True, "type": "SIMPLE", "activation": 0.0}, 26 | {"id": "y___f1", "is_active": True, "type": "SIMPLE", "activation": 0.0}, 27 | {"id": "z___f1", "is_active": True, "type": "SIMPLE", "activation": 0.0}, 28 | {"id": "0___f2", "is_active": True, "type": "SIMPLE", "activation": 0.0, 29 | "custom_function": "fuzzy", "activation_dict": { 30 | "membership": [0.14285714285714285, 0.42857142857142855, 0.7142857142857143, 31 | 1.0], "val_list": [1.0, 3.0, 5.0, 7.0]}}, 32 | {"id": "0___f3", "is_active": True, "type": "SIMPLE", "activation": 0.0, 33 | "custom_function": "fuzzy", "activation_dict": { 34 | "membership": [0.0821135308818279, 0.17136736879685824, 0.5605141021063905, 35 | 1.0], "val_list": [2.3, 4.8, 15.7, 28.01]}}, 36 | {"id": "v1___class", "is_active": True, "type": "DECISION", "activation": 0.0}, 37 | {"id": "v2___class", "is_active": True, "type": "DECISION", "activation": 0.0}] 38 | } 39 | 40 | generated_fcm = GeneratorTests.gen_fcm() 41 | json_fcm = json.loads(generated_fcm.to_json()) 42 | self.assertEqual(expected_json['concepts'], json_fcm['concepts']) 43 | 44 | def test_association_generator_relations(self): 45 | expected_json = {"relations": [{"origin": "w___f1", "destiny": "x___f1", "weight": -1}, 46 | {"origin": "x___f1", "destiny": "w___f1", "weight": -1}, 47 | {"origin": "w___f1", "destiny": "y___f1", "weight": -1}, 48 | {"origin": "y___f1", "destiny": "w___f1", "weight": -1}, 49 | {"origin": "w___f1", "destiny": "z___f1", "weight": -1}, 50 | {"origin": "z___f1", "destiny": "w___f1", "weight": -1}, 51 | {"origin": "x___f1", "destiny": "y___f1", "weight": -1}, 52 | {"origin": "y___f1", "destiny": "x___f1", "weight": -1}, 53 | {"origin": "x___f1", "destiny": "z___f1", "weight": -1}, 54 | {"origin": "z___f1", "destiny": "x___f1", "weight": -1}, 55 | {"origin": "y___f1", "destiny": "z___f1", "weight": -1}, 56 | {"origin": "z___f1", "destiny": "y___f1", "weight": -1}, 57 | {"origin": "0___f2", "destiny": "w___f1", "weight": 0.4375}, 58 | {"origin": "w___f1", "destiny": "0___f2", "weight": 1.0}, 59 | {"origin": "0___f2", "destiny": "x___f1", "weight": 0.0625}, 60 | {"origin": "x___f1", "destiny": "0___f2", "weight": 0.14285714285714285}, 61 | {"origin": "0___f2", "destiny": "y___f1", "weight": 0.1875}, 62 | {"origin": "y___f1", "destiny": "0___f2", "weight": 0.42857142857142855}, 63 | {"origin": "0___f2", "destiny": "z___f1", "weight": 0.3125}, 64 | {"origin": "z___f1", "destiny": "0___f2", "weight": 0.7142857142857143}, 65 | {"origin": "0___f3", "destiny": "w___f1", "weight": 0.5512694351505609}, 66 | {"origin": "w___f1", "destiny": "0___f3", "weight": 1.0}, 67 | {"origin": "0___f3", "destiny": "x___f1", "weight": 0.045266679787443406}, 68 | {"origin": "x___f1", "destiny": "0___f3", "weight": 0.0821135308818279}, 69 | {"origin": "0___f3", "destiny": "y___f1", "weight": 0.09446959259988191}, 70 | {"origin": "y___f1", "destiny": "0___f3", "weight": 0.17136736879685824}, 71 | {"origin": "0___f3", "destiny": "z___f1", "weight": 0.30899429246211374}, 72 | {"origin": "z___f1", "destiny": "0___f3", "weight": 0.5605141021063905}, 73 | {"origin": "0___f3", "destiny": "0___f2", "weight": 0.8189332808502263}, 74 | {"origin": "0___f2", "destiny": "0___f3", "weight": 0.6499241342377723}, 75 | {"origin": "v1___class", "destiny": "v2___class", "weight": -1}, 76 | {"origin": "v2___class", "destiny": "v1___class", "weight": -1}, 77 | {"origin": "v1___class", "destiny": "0___f3", "weight": 0.12674044983934307}, 78 | {"origin": "0___f3", "destiny": "v1___class", "weight": 0.13973627238732533}, 79 | {"origin": "v2___class", "destiny": "0___f3", "weight": 0.7802570510531952}, 80 | {"origin": "0___f3", "destiny": "v2___class", "weight": 0.8602637276126747}, 81 | {"origin": "v1___class", "destiny": "x___f1", "weight": 0.5}, 82 | {"origin": "x___f1", "destiny": "v1___class", "weight": 1.0}, 83 | {"origin": "v1___class", "destiny": "y___f1", "weight": 0.5}, 84 | {"origin": "y___f1", "destiny": "v1___class", "weight": 1.0}, 85 | {"origin": "v2___class", "destiny": "w___f1", "weight": 0.5}, 86 | {"origin": "w___f1", "destiny": "v2___class", "weight": 1.0}, 87 | {"origin": "v2___class", "destiny": "z___f1", "weight": 0.5}, 88 | {"origin": "z___f1", "destiny": "v2___class", "weight": 1.0}, 89 | {"origin": "v1___class", "destiny": "0___f2", "weight": 0.2857142857142857}, 90 | {"origin": "0___f2", "destiny": "v1___class", "weight": 0.25}, 91 | {"origin": "v2___class", "destiny": "0___f2", "weight": 0.8571428571428572}, 92 | {"origin": "0___f2", "destiny": "v2___class", "weight": 0.7500000000000001}] 93 | } 94 | 95 | generated_fcm = GeneratorTests.gen_fcm() 96 | json_fcm = json.loads(generated_fcm.to_json()) 97 | for elment_pos in range(len(expected_json['relations'])): 98 | self.assertIn(expected_json['relations'][elment_pos], json_fcm['relations']) 99 | -------------------------------------------------------------------------------- /tests/test_public_library_functions.py: -------------------------------------------------------------------------------- 1 | import unittest 2 | import json 3 | 4 | from py_fcm import join_maps, from_json 5 | 6 | 7 | class FromJsonTests(unittest.TestCase): 8 | def setUp(self) -> None: 9 | default_fcm = """ 10 | { 11 | "max_iter": 500, 12 | "activation_function": "sigmoid", 13 | "activation_function_args": {"lambda_val":1}, 14 | "memory_influence": false, 15 | "decision_function": "LAST", 16 | "concepts" : 17 | [ 18 | {"id": "concept_1", "type": "SIMPLE", "activation": 0.5}, 19 | {"id": "concept_2", "type": "DECISION", "custom_function": "gceq", "custom_function_args": {"weight":0.3}}, 20 | {"id": "concept_3", "type": "SIMPLE", "memory_influence":true }, 21 | {"id": "concept_4", "type": "SIMPLE", "custom_function": "saturation", "activation": 0.3} 22 | ], 23 | "relations": 24 | [ 25 | {"origin": "concept_4", "destiny": "concept_2", "weight": -0.1}, 26 | {"origin": "concept_1", "destiny": "concept_3", "weight": 0.59}, 27 | {"origin": "concept_3", "destiny": "concept_2", "weight": 0.8911} 28 | ] 29 | }""" 30 | self.fcm = from_json(default_fcm) 31 | 32 | def test_default(self): 33 | expected = { 34 | "max_iter": 500, 35 | "decision_function": "LAST", 36 | "activation_function": "sigmoid", 37 | "memory_influence": False, 38 | "stability_diff": 0.001, 39 | "stop_at_stabilize": True, 40 | "extra_steps": 5, 41 | "weight": 1, 42 | "concepts": 43 | [ 44 | { 45 | "id": "concept_1", 46 | "is_active": True, 47 | "type": "SIMPLE", 48 | "activation": 0.5 49 | }, 50 | { 51 | "id": "concept_2", "is_active": True, 52 | "type": "DECISION", "activation": 0.0, 53 | "custom_function": "gceq", 54 | "custom_function_args": {"weight": 0.3} 55 | }, 56 | { 57 | "id": "concept_3", 58 | "is_active": True, 59 | "type": "SIMPLE", 60 | "activation": 0.0, 61 | "use_memory": True 62 | }, 63 | { 64 | "id": "concept_4", 65 | "is_active": True, 66 | "type": "SIMPLE", 67 | "activation": 0.3, 68 | "custom_function": "saturation" 69 | } 70 | ], 71 | "relations": 72 | [ 73 | {"origin": "concept_4", "destiny": "concept_2", "weight": -0.1}, 74 | {"origin": "concept_1", "destiny": "concept_3", "weight": 0.59}, 75 | {"origin": "concept_3", "destiny": "concept_2", "weight": 0.8911} 76 | ], 77 | 'activation_function_args': {'lambda_val': 1}, 78 | } 79 | fcm_json = json.loads(self.fcm.to_json()) 80 | self.assertEqual(expected, fcm_json) 81 | 82 | 83 | class JoinMapsTests(unittest.TestCase): 84 | def setUp(self) -> None: 85 | fcm_json1 = """ 86 | { 87 | "max_iter": 500, 88 | "activation_function": "sigmoid", 89 | "actv_func_args": {"lambda_val":1}, 90 | "memory_influence": false, 91 | "decision_function": "LAST", 92 | "concepts" : 93 | [ 94 | {"id": "concept_1", "type": "SIMPLE", "activation": 0.25}, 95 | {"id": "concept_2", "type": "DECISION", "custom_function": "gceq", "custom_function_args": {"weight":0.3}} 96 | ], 97 | "relations": 98 | [ 99 | {"origin": "concept_1", "destiny": "concept_2", "weight": 0.25} 100 | ] 101 | }""" 102 | 103 | fcm_json2 = """ 104 | { 105 | "max_iter": 500, 106 | "activation_function": "sigmoid", 107 | "actv_func_args": {"lambda_val":1}, 108 | "memory_influence": false, 109 | "decision_function": "LAST", 110 | "concepts" : 111 | [ 112 | {"id": "concept_1", "type": "SIMPLE", "activation": 0.5}, 113 | {"id": "concept_2", "type": "DECISION", "custom_function": "gceq", "custom_function_args": {"weight":0.3}}, 114 | {"id": "concept_4", "type": "SIMPLE", "custom_function": "saturation", "activation": 0.3} 115 | ], 116 | "relations": 117 | [ 118 | {"origin": "concept_4", "destiny": "concept_2", "weight": 0.2}, 119 | {"origin": "concept_1", "destiny": "concept_2", "weight": 0.75} 120 | ] 121 | }""" 122 | 123 | fcm_json3 = """ 124 | { 125 | "max_iter": 500, 126 | "activation_function": "sigmoid", 127 | "actv_func_args": {"lambda_val":1}, 128 | "memory_influence": false, 129 | "decision_function": "LAST", 130 | "concepts" : 131 | [ 132 | {"id": "concept_1", "type": "SIMPLE", "activation": 0.75}, 133 | {"id": "concept_2", "type": "DECISION", "custom_function": "gceq", "custom_function_args": {"weight":0.3}}, 134 | {"id": "concept_3", "type": "SIMPLE", "memory_influence":true } 135 | ], 136 | "relations": 137 | [ 138 | {"origin": "concept_1", "destiny": "concept_4", "weight": -0.3911}, 139 | {"origin": "concept_2", "destiny": "concept_3", "weight": 0.8911} 140 | ] 141 | }""" 142 | self.fcm1 = from_json(fcm_json1) 143 | self.fcm2 = from_json(fcm_json2) 144 | self.fcm3 = from_json(fcm_json3) 145 | 146 | def test_default(self): 147 | fcm = join_maps([self.fcm1, self.fcm2, self.fcm3]) 148 | expected = { 149 | "max_iter": 500, 150 | "decision_function": "LAST", 151 | "activation_function": "sigmoid", 152 | "memory_influence": False, 153 | "stability_diff": 0.001, 154 | "stop_at_stabilize": True, 155 | "extra_steps": 5, 156 | "weight": 1, 157 | "concepts": [ 158 | {"id": "concept_1", "is_active": True, "type": "SIMPLE", "activation": 0.5}, 159 | {"id": "concept_2", "is_active": True, "type": "DECISION", 'activation': 0.0, 160 | "custom_function": "gceq", "custom_function_args": {"weight": 0.3}}, 161 | {"id": "concept_4", "is_active": True, "type": "SIMPLE", "activation": 0.3, 162 | "custom_function": "saturation"}, 163 | {"id": "concept_3", "is_active": True, "type": "SIMPLE", "activation": 0.0}, 164 | ], 165 | "relations": [ 166 | {'origin': 'concept_1', 'destiny': 'concept_2', 'weight': 0.5}, 167 | {'origin': 'concept_4', 'destiny': 'concept_2', 'weight': 0.2}, 168 | {'origin': 'concept_2', 'destiny': 'concept_3', 'weight': 0.8911} 169 | ] 170 | } 171 | 172 | fcm_json = json.loads(fcm.to_json()) 173 | self.assertEqual(expected, fcm_json) 174 | 175 | def test_inference_after_join(self): 176 | expected = {"max_iter": 500, "decision_function": "LAST", "activation_function": "sigmoid", 177 | "memory_influence": False, 178 | "stability_diff": 0.001, "stop_at_stabilize": True, "extra_steps": 5, "weight": 1, 179 | "concepts": [ 180 | {"id": "concept_1", "is_active": True, "type": "SIMPLE", "activation": 0.5}, 181 | {"id": "concept_2", "is_active": True, "type": "DECISION", "activation": 0.0, 182 | "custom_function": "gceq", "custom_function_args": {"weight": 0.3}}, 183 | {"id": "concept_4", "is_active": True, "type": "SIMPLE", "activation": 0.0, 184 | "custom_function": "saturation"}, 185 | {"id": "concept_3", "is_active": True, "type": "SIMPLE", "activation": 0.5} 186 | ], 187 | "relations": [ 188 | {"origin": "concept_1", "destiny": "concept_2", "weight": 0.5}, 189 | {"origin": "concept_4", "destiny": "concept_2", "weight": 0.2}, 190 | {"origin": "concept_2", "destiny": "concept_3", "weight": 0.8911} 191 | ]} 192 | fcm = join_maps([self.fcm1, self.fcm2, self.fcm3]) 193 | fcm.run_inference() 194 | fcm_json = json.loads(fcm.to_json()) 195 | self.assertEqual(expected, fcm_json) 196 | fcm.debug = False 197 | fcm.run_inference() 198 | fcm_json = json.loads(fcm.to_json()) 199 | self.assertEqual(expected, fcm_json) 200 | 201 | def test_intersection(self): 202 | fcm = join_maps([self.fcm1, self.fcm2, self.fcm3], concept_strategy='intersection') 203 | expected = { 204 | "concepts": [ 205 | {'id': 'concept_1', 'is_active': True, 'type': 'SIMPLE', 'activation': 0.5}, 206 | {'id': 'concept_2', 'is_active': True, 'type': 'DECISION', 'activation': 0.0, 207 | 'custom_function': 'gceq', 'custom_function_args': {'weight': 0.3}} 208 | ], 209 | "relations": [ 210 | {'origin': 'concept_1', 'destiny': 'concept_2', 'weight': 0.5} 211 | ] 212 | } 213 | 214 | fcm_json = json.loads(fcm.to_json()) 215 | self.assertEqual(expected['concepts'], fcm_json['concepts']) 216 | self.assertEqual(expected['relations'], fcm_json['relations']) 217 | 218 | def test_highest_strategies(self): 219 | fcm = join_maps([self.fcm1, self.fcm2, self.fcm3], 220 | concept_strategy='intersection', 221 | value_strategy='highest', 222 | relation_strategy='highest') 223 | expected = { 224 | "concepts": [ 225 | {'id': 'concept_1', 'is_active': True, 'type': 'SIMPLE', 'activation': 0.75}, 226 | {'id': 'concept_2', 'is_active': True, 'type': 'DECISION', 'activation': 0.0, 227 | 'custom_function': 'gceq', 'custom_function_args': {'weight': 0.3}} 228 | ], 229 | "relations": [ 230 | {'origin': 'concept_1', 'destiny': 'concept_2', 'weight': 0.75} 231 | ] 232 | } 233 | 234 | fcm_json = json.loads(fcm.to_json()) 235 | self.assertEqual(expected['concepts'], fcm_json['concepts']) 236 | self.assertEqual(expected['relations'], fcm_json['relations']) 237 | 238 | def test_lowest_strategies(self): 239 | fcm = join_maps([self.fcm1, self.fcm2, self.fcm3], 240 | concept_strategy='intersection', 241 | value_strategy='lowest', 242 | relation_strategy='lowest') 243 | expected = { 244 | "concepts": [ 245 | {'id': 'concept_1', 'is_active': True, 'type': 'SIMPLE', 'activation': 0.25}, 246 | {'id': 'concept_2', 'is_active': True, 'type': 'DECISION', 'activation': 0.0, 247 | 'custom_function': 'gceq', 'custom_function_args': {'weight': 0.3}} 248 | ], 249 | "relations": [ 250 | {'origin': 'concept_1', 'destiny': 'concept_2', 'weight': 0.25} 251 | ] 252 | } 253 | 254 | fcm_json = json.loads(fcm.to_json()) 255 | self.assertEqual(expected['concepts'], fcm_json['concepts']) 256 | self.assertEqual(expected['relations'], fcm_json['relations']) 257 | 258 | def test_exeptions(self): 259 | res = '' 260 | expected = 'Unknown concept strategy: aaaa' 261 | try: 262 | fcm = join_maps([self.fcm1, self.fcm2, self.fcm3], concept_strategy='aaaa') 263 | except Exception as err: 264 | res = str(err) 265 | self.assertEqual(expected, res) 266 | 267 | expected = 'Unknown value strategy: aaaa' 268 | try: 269 | fcm = join_maps([self.fcm1, self.fcm2, self.fcm3], value_strategy='aaaa') 270 | except Exception as err: 271 | res = str(err) 272 | self.assertEqual(expected, res) 273 | 274 | expected = 'Unknown relation strategy: aaaa' 275 | try: 276 | fcm = join_maps([self.fcm1, self.fcm2, self.fcm3], relation_strategy='aaaa') 277 | except Exception as err: 278 | res = str(err) 279 | self.assertEqual(expected, res) 280 | 281 | def test_join_empty_map_set(self): 282 | fcm = join_maps([], 283 | concept_strategy='intersection', 284 | value_strategy='highest', 285 | relation_strategy='highest') 286 | 287 | expected = {"max_iter": 200, "decision_function": "MEAN", "activation_function": "sigmoid_hip", 288 | "memory_influence": False, "stability_diff": 0.001, "stop_at_stabilize": True, 289 | "extra_steps": 5, "weight": 1, "concepts": [], "relations": []} 290 | 291 | fcm_json = json.loads(fcm.to_json()) 292 | self.assertEqual(expected, fcm_json) 293 | --------------------------------------------------------------------------------