Python DatasetSubset.column_values方法代码示例

# 需要导入模块: from opus_core.datasets.dataset import DatasetSubset [as 别名]
# 或者: from opus_core.datasets.dataset.DatasetSubset import column_values [as 别名]
    def run(self, n=500, 
            realestate_dataset_name = 'building',
            current_year=None,
            occupied_spaces_variable="occupied_spaces",
            total_spaces_variable="total_spaces",
            minimum_spaces_attribute="minimum_spaces",
            within_parcel_selection_weight_string=None,
            within_parcel_selection_n=0,
            within_parcel_selection_compete_among_types=False,
            within_parcel_selection_threshold=75,
            within_parcel_selection_MU_same_weight=False,
            within_parcel_selection_transpose_interpcl_weight=True,
            run_config=None,
            debuglevel=0):
        """
        run method of the Development Project Proposal Sampling Model
        
        **Parameters**
        
            **n** : int, sample size for each iteration
                   
                   sample n proposals at a time, which are then evaluated one by one until the 
                   target vacancies are satisfied or proposals are running out
                   
            **realestate_dataset_name** : string, name of real estate dataset
            
            **current_year**: int, simulation year. If None, get value from SimulationState
            
            **occupied_spaces_variable** : string, variable name for calculating how much spaces are currently occupied
                                        
                                          It can either be a variable for real_estate dataset that returns 
                                          the amount spaces being occupied or a target_vacancy attribute 
                                          that contains the name of real_estate variables.   
            
            **total_spaces_variable** : string, variable name for calculating total existing spaces
            
        **Returns**
        
            **proposal_set** : indices to proposal_set that are accepted 
            
            **demolished_buildings** : buildings to be demolished for re-development
        """

        self.accepted_proposals = []
        self.demolished_buildings = []  #id of buildings to be demolished

        if self.proposal_set.n <= 0:
            logger.log_status("The size of proposal_set is 0; no proposals to consider, skipping DPPSM.")
            return (self.proposal_set, self.demolished_buildings)

        target_vacancy = self.dataset_pool.get_dataset('target_vacancy')

        if current_year is None:
            year = SimulationState().get_current_time()
        else:
            year = current_year
        this_year_index = where(target_vacancy['year']==year)[0]
        target_vacancy_for_this_year = DatasetSubset(target_vacancy, this_year_index)
        if target_vacancy_for_this_year.size() == 0:
            raise IOError, 'No target vacancy defined for year %s.' % year
        
        ## current_target_vacancy.target_attribute_name = 'target_vacancy_rate'
        ## each column provides a category for which a target vacancy is specified
        self.column_names = list(set( target_vacancy.get_known_attribute_names() ) - \
                            set( [ target_vacancy.target_attribute_name, 
                                   'year', '_hidden_id_', minimum_spaces_attribute,
                                   occupied_spaces_variable, total_spaces_variable
                                   ] )
                            )
        self.column_names.sort(reverse=True)
            
        ## buildings table provides existing stocks
        self.realestate_dataset = self.dataset_pool.get_dataset(realestate_dataset_name)
        
        occupied_spaces_variables = [occupied_spaces_variable]
        total_spaces_variables = [total_spaces_variable]
        if occupied_spaces_variable in target_vacancy_for_this_year.get_known_attribute_names():
            occupied_spaces_variables += unique(target_vacancy_for_this_year[occupied_spaces_variable]).tolist()
        if total_spaces_variable in target_vacancy_for_this_year.get_known_attribute_names():
            total_spaces_variables += unique(target_vacancy_for_this_year[total_spaces_variable]).tolist()
            
        self._compute_variables_for_dataset_if_needed(self.realestate_dataset, self.column_names + occupied_spaces_variables + total_spaces_variables)
        self._compute_variables_for_dataset_if_needed(self.proposal_component_set, self.column_names + total_spaces_variables)
        self.proposal_set.compute_variables(["urbansim_parcel.development_project_proposal.number_of_components", 
                                             "urbansim_parcel.development_project_proposal.land_area_taken"],
                                            dataset_pool=self.dataset_pool)
        
        n_column = len(self.column_names)
        self.column_names_index = {}
        for iname in range(n_column):
            self.column_names_index[self.column_names[iname]] = iname
 
        target_vacancy_for_this_year.column_values = target_vacancy_for_this_year.get_multiple_attributes(self.column_names).reshape((-1, n_column))
        self.realestate_dataset.column_values = self.realestate_dataset.get_multiple_attributes(self.column_names).reshape((-1, n_column))
        self.proposal_component_set.column_values = self.proposal_component_set.get_multiple_attributes(self.column_names).reshape((-1, n_column))
        #defaults, can be changed later by spaces_variable specified in target_vacancy rates
        self.realestate_dataset.total_spaces = self.realestate_dataset[total_spaces_variable]
        self.proposal_component_set.total_spaces = self.proposal_component_set[total_spaces_variable]
        self.realestate_dataset.occupied_spaces = self.realestate_dataset[occupied_spaces_variable]
        
#.........这里部分代码省略.........