How to use score_function method in hypothesis

Best Python code snippet using hypothesis

needlemanWunschN3.py

Source:needlemanWunschN3.py

...43            for k in range(len(self.sequence_a) + 1)]44        # initalize matrix45        for i in range(1, len(self.sequence_a) + 1):46            self.computation_matrix[i][0][0] = self.computation_matrix[i - 1][0][0] \47                                               + self.score_function("", "", self.sequence_a[i - 1])48        for i in range(1, len(self.sequence_b) + 1):49            self.computation_matrix[0][i][0] = self.computation_matrix[0][i - 1][0] \50                                               + self.score_function("", "", self.sequence_b[i - 1])51        for i in range(1, len(self.sequence_c) + 1):52            self.computation_matrix[0][0][i] = self.computation_matrix[0][0][i - 1] \53                                               + self.score_function("", "", self.sequence_c[i - 1])54        for i in range(1, len(self.sequence_a) + 1):55            for j in range(1, len(self.sequence_b) + 1):56                self.computation_matrix[i][j][0] = self.computation_matrix[i - 1][j - 1][0] \57                                                   + self.score_function(self.sequence_a[i - 1], self.sequence_b[j - 1],58                                                                         "")59        for i in range(1, len(self.sequence_a) + 1):60            for k in range(1, len(self.sequence_c) + 1):61                self.computation_matrix[i][0][k] = self.computation_matrix[i - 1][0][k - 1] \62                                                   + self.score_function(self.sequence_a[i - 1], "",63                                                                         self.sequence_c[k - 1])64        for j in range(1, len(self.sequence_b) + 1):65            for k in range(1, len(self.sequence_c) + 1):66                self.computation_matrix[0][j][k] = self.computation_matrix[0][j - 1][k - 1] \67                                                   + self.score_function("", self.sequence_b[j - 1],68                                                                         self.sequence_c[k - 1])69        for i in range(1, len(self.sequence_a) + 1):70            for j in range(1, len(self.sequence_b) + 1):71                for k in range(1, len(self.sequence_c) + 1):72                    self.computation_matrix[i][j][k] = self.compute_minimum(i, j, k)73    def compute_minimum(self, i, j, k):74        """Compute the minimal value for a given cell of the matrix.75            The minimum is choosen of the following values:76                D(i-1, j-1, k-1) + w(a_i-1, b_j-1, c_k-1)77                D(i, j-1, k-1) + w(a_i, b_j-1, c_k-1)78                D(i-1, j, k-1) + w(a_i-1, b_j, c_k-1)79                D(i-1, j-1, k) + w(a_i-1, b_j-1, c_k)80                D(i, j, k-1) + w(a_i, b_j, c_k-1)81                D(i-1, j, k) + w(a_i-1, b_j, c_k)82                D(i, j-1, k) + w(a_i, b_j-1, c_k)83            i: index of sequence A84            j: index of sequence B85            k: index of sequence C86        """87        # no gap88        no_gap = self.computation_matrix[i - 1][j - 1][k - 1] \89                 + self.score_function(self.sequence_a[i - 1], self.sequence_b[j - 1], self.sequence_c[k - 1])90        # one gap91        gap_a = self.computation_matrix[i][j - 1][k - 1] \92                + self.score_function("", self.sequence_b[j - 1], self.sequence_c[k - 1])93        gap_b = self.computation_matrix[i - 1][j][k - 1] \94                + self.score_function(self.sequence_a[i - 1], "", self.sequence_c[k - 1])95        gap_c = self.computation_matrix[i - 1][j - 1][k] \96                + self.score_function(self.sequence_a[i - 1], self.sequence_b[j - 1], "")97        # two gaps98        gap_ab = self.computation_matrix[i][j][k - 1] + self.score_function("", "", self.sequence_c[k - 1])99        gap_bc = self.computation_matrix[i - 1][j][k] + self.score_function(self.sequence_a[i - 1], "", "")100        gap_ac = self.computation_matrix[i][j - 1][k] + self.score_function("", self.sequence_b[j - 1], "")101        possible_values = [no_gap, gap_a, gap_b, gap_c, gap_ab, gap_bc, gap_ac]102        return min(possible_values)103    def traceback(self, maximal_optimal_solutions=-1):104        """Computes the traceback for the Needleman-Wunsch n=3 matrix."""105        self.traceback_stack = [[]]106        self.indices_stack = [[len(self.computation_matrix) - 1, len(self.computation_matrix[0]) - 1,107                               len(self.computation_matrix[0][0]) - 1]]108        self.traceback_stack_index = 0109        traceback_done = False110        optimal_solutions_count = 0111        while not traceback_done:112            i = self.indices_stack[self.traceback_stack_index][0]113            j = self.indices_stack[self.traceback_stack_index][1]114            k = self.indices_stack[self.traceback_stack_index][2]115            optimal_solutions_count += 1116            split = False117            while i > 0 or j > 0 or k > 0:118                path_variable_i = i119                path_variable_j = j120                path_variable_k = k121                # no gap122                if i > 0 and j > 0 and k > 0:123                    if self.computation_matrix[i][j][k] == self.computation_matrix[i - 1][j - 1][k - 1] \124                            + self.score_function(self.sequence_a[i - 1], self.sequence_b[j - 1],125                                                  self.sequence_c[k - 1]):126                        self.traceback_stack[self.traceback_stack_index].append(mah.noGap)127                        path_variable_i -= 1  # change i128                        path_variable_j -= 1  # change j129                        path_variable_k -= 1  # change k130                        split = True131                # a gap in sequence a132                if j > 0 and k > 0:133                    if self.computation_matrix[i][j][k] == self.computation_matrix[i][j - 1][k - 1] \134                            + self.score_function("", self.sequence_b[j - 1], self.sequence_c[k - 1]):135                        if split == False:136                            self.traceback_stack[self.traceback_stack_index].append(mah.gapA)137                            path_variable_j -= 1138                            path_variable_k -= 1139                            split = True140                        else:141                            self.split([i, j - 1, k - 1], mah.gapA)142                # a gap in sequence b143                if i > 0 and k > 0:144                    if self.computation_matrix[i][j][k] == self.computation_matrix[i - 1][j][k - 1] \145                            + self.score_function(self.sequence_a[i - 1], "", self.sequence_c[k - 1]):146                        if split == False:147                            self.traceback_stack[self.traceback_stack_index].append(mah.gapB)148                            path_variable_i -= 1149                            path_variable_k -= 1150                        elif split == True:151                            self.split([i - 1, j, k - 1], mah.gapB)152                # a gap in sequence c153                if i > 0 and j > 0:154                    if self.computation_matrix[i][j][k] == self.computation_matrix[i - 1][j - 1][k] \155                            + self.score_function(self.sequence_a[i - 1], self.sequence_b[j - 1], ""):156                        if split == False:157                            self.traceback_stack[self.traceback_stack_index].append(mah.gapC)158                            path_variable_i -= 1159                            path_variable_j -= 1160                        elif split == True:161                            self.split([i - 1, j - 1, k], mah.gapC)162                # a gap in sequence a and b163                if k > 0:164                    if self.computation_matrix[i][j][k] == self.computation_matrix[i][j][k - 1] \165                            + self.score_function("", "", self.sequence_c[k - 1]):166                        if split == False:167                            self.traceback_stack[self.traceback_stack_index].append(mah.gapAB)168                            path_variable_k -= 1169                        elif split == True:170                            self.split([i, j, k - 1], mah.gapAB)171                # a gap in sequence a and c172                if j > 0:173                    if self.computation_matrix[i][j][k] == self.computation_matrix[i][j - 1][k] \174                            + self.score_function("", self.sequence_b[j - 1], ""):175                        if split == False:176                            self.traceback_stack[self.traceback_stack_index].append(mah.gapAC)177                            path_variable_j -= 1178                        elif split == True:179                            self.split([i, j - 1, k], mah.gapAC)180                # a gap in sequence b and c181                if i > 0:182                    if self.computation_matrix[i][j][k] == self.computation_matrix[i - 1][j][k] \183                            + self.score_function(self.sequence_a[i - 1], "", ""):184                        if split == False:185                            self.traceback_stack[self.traceback_stack_index].append(mah.gapBC)186                            path_variable_i -= 1187                        elif split == True:188                            self.split([i - 1, j, k], mah.gapBC)189                split = False190                i = path_variable_i191                j = path_variable_j192                k = path_variable_k193            if maximal_optimal_solutions != -1 and optimal_solutions_count >= maximal_optimal_solutions:194                break195            self.indices_stack[self.traceback_stack_index][0] = i196            self.indices_stack[self.traceback_stack_index][1] = j197            self.indices_stack[self.traceback_stack_index][2] = k...

06.py

Source:06.py

...5    {6      'cases': [7        {8          'code': r"""9          >>> score_function("speling", "spelling")10          111          >>> score_function("used", "use")12          113          >>> score_function("misspelled", "spelling")14          615          >>> score_function("spelling", "spelling")16          017          >>> score_function("wird", "bird")18          119          >>> score_function("wird", "wire")20          121          """,22          'hidden': False,23          'locked': False24        },25        {26          'code': r"""27          >>> score_function("hello", "goodbye")28          729          >>> score_function("ate", "apple")30          331          >>> score_function("from", "form")32          233          >>> score_function("first", "flashy")34          435          >>> score_function("hash", "ash")36          137          >>> score_function("ash", "hash")38          139          """,40          'hidden': False,41          'locked': False42        },43        {44          'code': r"""45          >>> small_words_list = ["spell", "nest", "test", "pest", "best", "bird", "wired",46          ...                     "abstraction", "abstract", "peeling", "gestate", "west",47          ...                     "spelling", "bastion"]48          >>> autocorrect("speling", small_words_list, score_function)49          'spelling'50          >>> autocorrect("abstrction", small_words_list, score_function)51          'abstraction'...

__init__.py

Source:__init__.py

1# flake8: noqa2# metric class3from deepchem.metrics.metric import Metric4# metrics utils5from deepchem.metrics.metric import threshold_predictions6from deepchem.metrics.metric import normalize_weight_shape7from deepchem.metrics.metric import normalize_labels_shape8from deepchem.metrics.metric import normalize_prediction_shape9from deepchem.metrics.metric import handle_classification_mode10from deepchem.metrics.metric import to_one_hot11from deepchem.metrics.metric import from_one_hot12# sklearn & scipy score function13from deepchem.metrics.score_function import matthews_corrcoef14from deepchem.metrics.score_function import recall_score15from deepchem.metrics.score_function import kappa_score16from deepchem.metrics.score_function import cohen_kappa_score17from deepchem.metrics.score_function import r2_score18from deepchem.metrics.score_function import mean_squared_error19from deepchem.metrics.score_function import mean_absolute_error20from deepchem.metrics.score_function import precision_score21from deepchem.metrics.score_function import precision_recall_curve22from deepchem.metrics.score_function import auc23from deepchem.metrics.score_function import jaccard_score24from deepchem.metrics.score_function import f1_score25from deepchem.metrics.score_function import roc_auc_score26from deepchem.metrics.score_function import accuracy_score27from deepchem.metrics.score_function import balanced_accuracy_score28from deepchem.metrics.score_function import top_k_accuracy_score29from deepchem.metrics.score_function import pearsonr30# original score function31from deepchem.metrics.score_function import pearson_r2_score32from deepchem.metrics.score_function import jaccard_index33from deepchem.metrics.score_function import pixel_error34from deepchem.metrics.score_function import prc_auc_score35from deepchem.metrics.score_function import rms_score36from deepchem.metrics.score_function import mae_score37from deepchem.metrics.score_function import bedroc_score...

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