How to use initialize_1 method in hypothesis

Best Python code snippet using hypothesis

principal.py

Source:principal.py

1from importlib import reload2from decimal import Decimal3from random import sample, uniform, randint4from datetime import datetime5import numpy as np6import pandas as pd7import logging8import problem9import evaluate10from problem import Problem11from evaluate import SolutionEvaluator12def log():13    logFormatter = logging.Formatter("[%(asctime)s] %(message)s", datefmt='%m/%d %I:%M:%S')14    rootLogger = logging.getLogger()15    rootLogger.setLevel(logging.DEBUG)16    17    if not rootLogger.handlers:18        fileHandler = logging.FileHandler(datetime.now().strftime('PSO_%d-%m_%H:%M.log'))19        fileHandler.setFormatter(logFormatter)20        rootLogger.addHandler(fileHandler)21        consoleHandler = logging.StreamHandler()22        consoleHandler.setFormatter(logFormatter)23        rootLogger.addHandler(consoleHandler)24    return rootLogger25rootLogger  = log()    26def minimize_comparator(individual_solution, global_solution) -> bool:27    return individual_solution < global_solution28def maximize_comparator(individual_solution, global_solution) -> bool:29    return individual_solution > global_solution30def create_population_uniform_strategy(X, num_particles):31    32    _, n_cols = X.shape33    lower = int((num_particles / 3) * 2)34    35    particles = np.zeros(shape=(num_particles, n_cols + 1))36    for i in range(lower):37        features = sample(range(n_cols), int(round(n_cols*0.2)))38        39        for j in features:40            particles[i,j] = round(Decimal(uniform(0.61,1.0)), 4)41            42    for i in range(lower, num_particles):43        qtd_high = sample(range(n_cols), randint(round(n_cols/2 + 1), n_cols))44        45        for j in qtd_high:46            particles[i,j] = round(Decimal(uniform(0.61, 1.0)),4)47            48    return particles49def create_population_20_50_strategy(X, num_particles):50    51    _, n_cols = X.shape52    particles = np.zeros(shape=(num_particles, n_cols + 1))    53    lower_group = int((num_particles / 3 ) * 2)54    55    for i in range(lower_group):56        features = sample(range(n_cols), int(round(n_cols * 0.2)))57    for j in features:58            particles[i,j] = round(Decimal(uniform(0.61,1.0)), 4)59    60    for i in range(lower_group, num_particles):61        features = sample(range(n_cols), randint(round(n_cols / 2 + 1), n_cols))62        for j in features:63             particles[i,j] = round(Decimal(uniform(0.61, 1.0)), 4)64                                                 65    return particles                                             66                                                 67class PSOException(Exception):68                                                 69      def __init__(self,message):70         super(PSOException, self).__init__()71         self.message = message72                                                 73      def __str__(self):74         75         return repr(self.message)                                        76class PSOSelector(object):77      78       def __init__(self, estimator, w=0.7298, c1=1.49618, c2=1.49618,79                    num_particles=30, max_iter=100, max_local_improvement=50,80                    maximize_objective=True, initialization='uniform',81                    fitness_method='type_2', cv = 3):82                                                 83           self.w = w84           self.c1 = c185           self.c2 = c286           self.num_particles = num_particles87           self.max_iter = max_iter88           self.cv = cv89           self.evaluator_ = None90           self.estimator = estimator91           self.velocity_ = None92           self.solution_ = None93           self.initialize = 094           self.initialize_1 = 095           self.N = None96           self.max_local_improvement = max_local_improvement97           self.local_improvement = 098           self.particles = None99           self.count = []100           self.N_ = 0101           self.iteration_ = 0102           self.pop_ = None103           self.count_global = 0104           self._final_cols = None105           self._final_index = None106           self._setup_initialization(initialization, fitness_method)107           self._setup_solution_comparator(maximize_objective)108           self.selected_features_ = None109           110       def _setup_initialization(self, initialization, type_search):111           112           init_method = {113                   'uniform': create_population_uniform_strategy,114                   '20_50': create_population_20_50_strategy115                         }116           117           init_search = {118                    'type_1': self.search_type_1,119                    'type_2': self.search_type_2,120                    'type_3': self.search_type_3,121                    'type_4': self.search_type_4122                          }     123                                                     124           self._initialization = initialization125           if initialization not in init_method:126               raise PSOException(f'Invalid method {initialization!r}')127           self.init_method_ = init_method[initialization]128           self.init_search_ = init_search[type_search]129           130       def _setup_solution_comparator(self, maximize_objective):131           132           self.maximize_objective = maximize_objective133           if self.maximize_objective:134               self.is_solution_better = maximize_comparator135           else:136               self.is_solution_better = minimize_comparator137               138       def model_baseline(self, prob):139           n_cols = prob.data.shape[1]140           particle = np.zeros(shape=(1, n_cols + 1))141           particle[:] = 1142           evaluator_ = SolutionEvaluator(prob, 1)143           score = evaluator_.evaluate(particle)144           return score[0]145       def fit(self, X, unused_y, **kargs):146           147           if not isinstance(X, pd.DataFrame):148               raise PSOException('The "X" parameter must be a data frame')149               150           self._initialize(X)151           prob = Problem(X, unused_y, self.estimator,152                          self.cv, **kargs)153           154           self.N_ = prob.n_cols155           self.evaluator_ = SolutionEvaluator(prob, self.num_particles)156           score_all = self.model_baseline(prob)157           rootLogger.info((158                       f'Score with all features - {score_all[-1]}'))159           self.velocity_ = np.zeros(shape=(self.num_particles, self.N_))160           self.best_global_, self.best_global_[:] = \161             np.zeros(shape=(1, self.N_ + 1)), 'nan'162            163           self.best_individual_, self.best_individual_[:] = \164             np.zeros(shape=(self.num_particles, self.N_ + 1)), 'nan'165           self.solution_ = np.zeros(shape=(self.max_iter + 1, self.N_ + 1))166           167           168           while not self._is_stop_criteria_accepted():169               self.init_search_()170               count_sel_feat = self.count_features(self.best_global_[0])171               172               best_glob = self.best_global_[0]173               self.selected_features_  = np.ma.masked_where(best_glob[:-1]>0.6, best_glob[:-1])174               self.selected_features_, = np.where(self.selected_features_.mask == True)175               colunas = list(prob.data.iloc[:, self.selected_features_].columns)176               rootLogger.info((177                       f'Iteration: {self.iteration_}/{self.max_iter} \n , '178                       f'Best global metric: {self.best_global_[:, -1]} \n , '179                       f'Index features_selected: {self.selected_features_} \n , '180                       f'Number of selected features: {count_sel_feat} \n , '181                       f'Columns selected: {colunas}'))182               183               184               for i in range(0, self.num_particles):185                   self.count.append(self.count_features(186                           self.best_individual_[i, :]))187               188           best_glob = self.best_global_[0]189           self.selected_features_ = np.ma.masked_where(best_glob[:-1]>0.6, best_glob[:-1])190           self.selected_features_, = np.where(self.selected_features_.mask == True)191           colunas = list(prob.data.iloc[:, self.selected_features_].columns)192           rootLogger.info((f'Final Index features selected: {self.selected_features_} /n, '193                            f'Final Columns selected: {colunas} \n'))194           195           self._final_cols = colunas     196           self._final_index = self.selected_features_197           198       def _initialize(self, X):199           200          self.iteration_ = 0201          self.pop_ = self.init_method_(X, self.num_particles)202              203       def _is_stop_criteria_accepted(self):204          205          no_global_improv = self.local_improvement >= self.max_local_improvement206          max_iter_reached = self.iteration_ >= self.max_iter207          return max_iter_reached or no_global_improv208      209       def search_type_1(self):210           211          self.pop_ = self.evaluator_.evaluate(self.pop_)212          self.calculate_best_individual_type_1(self.pop_)213          self.calculate_best_global_type_1()214          self.solution_[self.iteration_, :] = self.best_global_215          self.update_velocity()216          self.iteration_ += 1217             218       def search_type_2(self):219         220          self.pop_ = self.evaluator_.evaluate(self.pop_)221          self.calculate_best_individual_type_2(self.pop_)222          self.calculate_best_global_type_2()223          self.solution_[self.iteration_, :] = self.best_global_224          self.update_velocity()225          self.iteration_ += 1226       def search_type_3(self):227         228          self.pop_ = self.evaluator_.evaluate(self.pop_)229          self.calculate_best_individual_type_3(self.pop_)230          self.calculate_best_global_type_3()231          self.solution_[self.iteration_, :] = self.best_global_232          self.update_velocity()233          self.iteration_ += 1234       def search_type_4(self):235         236          self.pop_ = self.evaluator_.evaluate(self.pop_)237          self.calculate_best_individual_type_4(self.pop_)238          self.calculate_best_global_type_4()239          self.solution_[self.iteration_, :] = self.best_global_240          self.update_velocity()241          self.iteration_ += 1        242         243       def update_velocity(self):244         245         w = self.w246         c1, c2 = self.c1, self.c2247         248         for i in range(0, len(self.pop_) - 1):249             for j in range(0, self.N_):250                 r1= round(uniform(0,1), 2)251                 r2 = round(uniform(0, 1), 2)252                 pop = self.pop_[i, j]253                 inertia = w * self.velocity_[i,j]254                 cognitive = c1 * r1 * (self.best_individual_[i,j] - pop)255                 social = c2 * r2 * (self.best_global_[0, j] - pop)256                 velocity = inertia + cognitive + social257                 self.velocity_[i,j] = velocity258                 self.pop_[i,j] += velocity259                 260       def calculate_best_individual_type_2(self, pop):261         262         if self.initialize == 0:263            for i in range(0, len(pop)):264                for j in range(0, self.N_ + 1):265                    self.best_individual_[i,j] = pop[i,j]266                         267            self.initialize = 1268            return269             270         for i in range(0, len(pop)):271             candidate_a = pop[i, self.N_]272             candidate_b = self.best_individual_[i, self.N_]273             if self.is_solution_better(candidate_a,candidate_b):274                 for j in range(0 , self.N_ + 1):275                     self.best_individual_[i,j] = pop[i,j]276                 continue    277             278             particle_count = self.count_features(self.pop_[i, :])279             count_best_individual = self.count_features(280                     self.best_individual_[i, :])281             282             if particle_count > 0:283                 if (candidate_a == candidate_b284                        and particle_count < count_best_individual):285                     286                     for j in range(0, self.N_ + 1):287                         self.best_individual_[i,j] = pop[i,j]288                         289                         290       def calculate_best_global_type_2(self):291         292         if self.initialize_1 == 0:293             for i in range(0, self.N_ + 1):294                 self.best_global_[0,i] = self.best_individual_[0, i]295                 296             self.initialize_1 = 1297             self.count_global = self.count_features(self.best_global_[0, :])298                        299         for i in range(0, self.num_particles): 300             best_ind = self.best_individual_[i, self.N_]301             best_global = self.best_global_[0, self.N_]302             if self.is_solution_better(best_ind,303                                        best_global):304                 305                self.local_improvement = 1306                for j in range(0, self.N_ + 1):307                    308                    self.best_global_[0,j] = self.best_individual_[i,j]309                 310                self.count_global = self.count_features(311                         self.best_global_[0, :])312                 313                continue314             315             count_best_individual = self.count_features(self.best_individual_[i, :])316             317             if (best_global == best_ind318                       and count_best_individual < self.count_global):319                 320                 self.local_improvement = 1321                 self.count_global = 0322                 for j in range(0, self.N_ + 1):323                     self.best_global_[0, j] = self.best_individual_[i,j]324                     325                 self.count_global = self.count_features(326                         self.best_global_[0, :])327                 328                           329       def calculate_best_individual_type_1(self,pop):330         if self.initialize == 0:331             for i in range(0, len(pop)):332                 for j in range(0, self.N_ + 1):333                     self.best_individual_[i,j] = pop[i,j]334             self.initialize = 1335             return336         337         for i in range(0, len(pop)):338             if self.is_solution_better(pop[i,self.N_],339                                        self.best_individual_[i, self.N_]): 340                 for j in range(0, self.N_ + 1):341                     self.best_individual_[i, j] = pop[i,j]342                    343                 344       def calculate_best_global_type_1(self):345          if self.initialize_1 == 0:346              for i in range(0, self.N_ + 1):347                  self.best_global_[0,i] = self.best_individual_[0,i]348              self.initialize_1 = 1    349          for i in range(0, len(self.pop_)):350              if self.is_solution_better(self.best_individual_[i, self.N_],351                                         self.best_global_[0, self.N_]):352                  self.local_improvement = 1353              354              for j in range(0, self.N_ + 1):355                  self.best_global_[0,j] = self.best_individual_[i,j]356                  357          self.local_improvement += 1358          359       def calculate_best_individual_type_3(self, pop):360         361         if self.initialize == 0:362            for i in range(0, len(pop)):363                for j in range(0, self.N_ + 1):364                    self.best_individual_[i,j] = pop[i,j]365                         366            self.initialize = 1367            return368             369         for i in range(0, len(pop)):370             candidate_a = pop[i, self.N_]371             candidate_b = self.best_individual_[i, self.N_]372             particle_count = self.count_features(self.pop_[i, :])373             count_best_individual = self.count_features(374                     self.best_individual_[i, :])375             376             if particle_count > 0:377                 if (self.is_solution_better(candidate_a,candidate_b)378                        and particle_count <= count_best_individual):379                     380                     for j in range(0, self.N_ + 1):381                         self.best_individual_[i,j] = pop[i,j]382                 elif (candidate_a == candidate_b 383                      and particle_count < count_best_individual):   384                     for j in range(0, self.N_ + 1):385                         self.best_individual_[i,j] = pop[i,j]    386                 else:387                     continue        388                         389       def calculate_best_global_type_3(self):390         391         if self.initialize_1 == 0:392             for i in range(0, self.N_ + 1):393                 self.best_global_[0,i] = self.best_individual_[0, i]394                 395             self.initialize_1 = 1396             self.count_global = self.count_features(self.best_global_[0, :])397                        398         for i in range(0, self.num_particles): 399             best_ind = self.best_individual_[i, self.N_]400             best_global = self.best_global_[0, self.N_]401             count_best_individual = self.count_features(self.best_individual_[i, :])402             403             if (self.is_solution_better(best_ind,best_global)404                 and count_best_individual <= self.count_global):405                 406                self.local_improvement = 1407                for j in range(0, self.N_ + 1):408                    self.best_global_[0,j] = self.best_individual_[i,j]409                 410                self.count_global = self.count_features(411                         self.best_global_[0, :])412                 413             elif (best_ind == best_global414                  and count_best_individual < self.count_global):415                 self.local_improvement = 1416                 self.count_global = 0417                 for j in range(0, self.N_ + 1):418                     self.best_global_[0, j] = self.best_individual_[i,j]419                     420                 self.count_global = self.count_features(421                         self.best_global_[0, :])422             else:423                 continue            424       def calculate_best_individual_type_4(self, pop):425         426         if self.initialize == 0:427            for i in range(0, len(pop)):428                for j in range(0, self.N_ + 1):429                    self.best_individual_[i,j] = pop[i,j]430                         431            self.initialize = 1432            return433             434         for i in range(0, len(pop)):435             candidate_a = pop[i, self.N_]436             candidate_b = self.best_individual_[i, self.N_]437             particle_count = self.count_features(self.pop_[i, :])438             count_best_individual = self.count_features(439                     self.best_individual_[i, :])440             441             if particle_count > 0:442                 if (self.is_solution_better(candidate_a,candidate_b)443                        and particle_count <= count_best_individual):444                     445                     for j in range(0, self.N_ + 1):446                         self.best_individual_[i,j] = pop[i,j]447                 elif (self.is_solution_better(candidate_a, 0.95 * candidate_b) 448                      and particle_count < count_best_individual):   449                     for j in range(0, self.N_ + 1):450                         self.best_individual_[i,j] = pop[i,j]    451                 else:452                     continue        453                         454       def calculate_best_global_type_4(self):455         456         if self.initialize_1 == 0:457             for i in range(0, self.N_ + 1):458                 self.best_global_[0,i] = self.best_individual_[0, i]459                 460             self.initialize_1 = 1461             self.count_global = self.count_features(self.best_global_[0, :])462                        463         for i in range(0, self.num_particles): 464             best_ind = self.best_individual_[i, self.N_]465             best_global = self.best_global_[0, self.N_]466             count_best_individual = self.count_features(self.best_individual_[i, :])467             468             if (self.is_solution_better(best_ind,best_global)469                 and count_best_individual <= self.count_global):470                 471                self.local_improvement = 1472                for j in range(0, self.N_ + 1):473                    self.best_global_[0,j] = self.best_individual_[i,j]474                 475                self.count_global = self.count_features(476                         self.best_global_[0, :])477                 478             elif (self.is_solution_better(best_ind, 0.95 * best_global)479                  and count_best_individual < self.count_global):480                 self.local_improvement = 1481                 self.count_global = 0482                 for j in range(0, self.N_ + 1):483                     self.best_global_[0, j] = self.best_individual_[i,j]484                     485                 self.count_global = self.count_features(486                         self.best_global_[0, :])487             else:488                 continue   489       def count_features(self, particle_proportions, threshold=0.6):490         491         count = 0492         for i in range(0, self.N_):493             if particle_proportions[i] > threshold:494                 count = count + 1495         return count        496         497       @property   498       def final_cols(self):           499           return self._final_cols 500       @property501       def final_index(self):502           return self._final_index      503              504              505            506                 507                 508                 509                510                511                512                513                514                515                516                517                     518            519                 520                 521                 522                 523                 524                 525                 526                 527                 528         529         530         531         532          533          534          535          536          537          538          539          540          541          542          543          544          545          546        547          548          549          550          551          552           553           554               555               556               557               558               559               560               561               562               563               564           565           566           567           568           569           570           571           572           573           574           575           576           577           578                                                 579                    580                    581                    582                                                 ...

c06_overloading.py

Source:c06_overloading.py

...20# import overloading21class Duration:22    """Represents durations in H:M:S format."""23    24    def initialize_1(self, hours, mins=None, secs=None): # WITH OPTIONAL ARGUMENTS25        """Initializes Duration instance. Must be able to process either integer26        H:M:S format or decimal number of hours as inputs. Implementation #1."""27        if mins or secs: # H:M:S format28            if (not isinstance(hours, int) # check type29                    or isinstance(hours, bool)):30                msg = "arguments must be integers to use H:M:S format"31                raise TypeError(msg)32            self._hours = hours33        34            if mins:35                if (not isinstance(mins, int) # check type36                        or isinstance(mins, bool)):37                    msg = "arguments must be integers to use H:M:S format"38                    raise TypeError(msg)39                self._mins = mins40            41            else:42                self._mins = 043            if secs:44                if (not isinstance(secs, int) # check type45                        or isinstance(secs, bool)):46                    msg = "arguments must be integers to use H:M:S format"47                    raise TypeError(msg)48                self._secs = secs49            50            else:51                self._secs = 052        else: # decimal format53            if (not isinstance(hours, (int, float)) # check type54                    or isinstance(hours, bool)):55                msg = "argument must be a real number to use decimal format"56                raise TypeError(msg) 57            (self._hours, self._mins, self._secs) = self.dec2hms(hours)58    def initialize_2(self, *args): # WITH VARIABLE LENGTH ARGUMENT59        """Initializes Duration instance. Must be able to process either integer60        H:M:S format or decimal number of hours as inputs. Implementation #2."""61        # Create canvas62        self._hours = None63        self._mins = None64        self._secs = None65        # Parse arguments66        if len(args) > 3:67            raise TypeError("expected 1 to 3 arguments")68        69        elif len(args) > 1: # H:M:S format70            for (idx, field) in enumerate(vars(self)):71                72                if idx < len(args): # argument was provided73                    if (not isinstance(args[idx], int) # check type74                            or isinstance(args[idx], bool)):75                        msg = "arguments must be integers to use H:M:S format"76                        raise TypeError(msg)77                    vars(self)[field] = args[idx] # self.field does not work,78                                                  # as field is a string79                80                else:81                    vars(self)[field] = 082            83        elif len(args) > 0: # decimal duration (in hours)84            if (not isinstance(args[0], (int, float)) # check type85                    or isinstance(args[0], bool)):86                msg = "argument must be a real number to use decimal format"87                raise TypeError(msg)88            (self._hours, self._mins, self._secs) = self.dec2hms(args[0])89    90    # @overloading91    # def initialize_3(self, hours): # WITH OVERLOADING92    #     pass93    def __repr__(self):94        """Returns string representation of calling Duration instance."""95        formstr = "{:02}:{:02}:{:02}"96        return formstr.format(self._hours, self._mins, self._secs)97    @staticmethod98    def dec2hms(number):99        """Converts decimal number of hours to Duration object."""100        if (not isinstance(number, (int, float)) # check type101                or isinstance(number, bool)):102                raise TypeError("argument must be a decimal number")103        104        # Handle sign separately105        if number < 0:106            sign = -1107            number = -number108        else:109            sign = 1110            111        # Compute (% and // operators expect positive numbers)112        hours = sign * round(number // 1)113        mins = sign * round(((number % 1) * 60) // 1)114        secs = sign * round((((number % 1) * 60) % 1) * 60)115        return (hours, mins, secs)116    def hms2dec(self):117        """Converts Duration object to decimal number of hours."""118        return (self._hours + self._mins/60 + self._secs/3600)119   120	121	122	123# -------------------------------- TEST SCRIPT --------------------------------124import misctest as mt125duration_1 = Duration()126duration_2 = Duration()127decimal_1 = 13.25128decimal_2 = 2.425129mt.headprint("Implementation #1")130duration_1.initialize_1(decimal_1)131print(Duration.dec2hms(decimal_2))132duration_2.initialize_1(*Duration.dec2hms(decimal_2))133print("TIME\tHOURS\tHH:MM:SS")134print("duration_1\t{}\t{}".format(decimal_1, duration_1))135print("duration_2\t{}\t{}".format(decimal_2, duration_2))136mt.headprint("Implementation #2")137duration_1.initialize_2(decimal_1)138duration_2.initialize_2(*Duration.dec2hms(decimal_2))139print("TIME\tHOURS\tHH:MM:SS")140print("duration_1\t{}\t{}".format(decimal_1, duration_1))...

test_core.py

Source:test_core.py

...40api_token: thisisarandomvalueofapitoken41            """, file=fp)42        with var_dir.as_cwd():43            assert not config.initialized44            initialize_1(config_path=str(test_config))45        assert config.initialized46        assert config.dropbox.api_token == 'thisisarandomvalueofapitoken'47    def test_initialize_1_without_config(self, tmpdir_factory):48        var_dir = tmpdir_factory.mktemp('temp_var')49        test_config = var_dir.join('paper-git.cfg')50        with test_config.open(ensure=True, mode='w') as fp:51            print("""52[dropbox]53api_token: thisisadifferentapitoken54            """, file=fp)55        with var_dir.as_cwd():56            initialize_1()57        assert config.initialized58        assert config.dropbox.api_token == 'thisisadifferentapitoken'59    def test_initialize_2(self, tmpdir_factory):60        var_dir = tmpdir_factory.mktemp('temp_var')61        test_config = var_dir.join('paper-git.cfg')62        with test_config.open(ensure=True, mode='w') as fp:63            print("""64[dropbox]65api_token: thisisanotherapikey66            """, file=fp)67        assert config.dbox is None68        assert config.db.path is None69        with pytest.raises(peewee.OperationalError):70            config.db.db.connect()...

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