from .calculate_dependencies import *
class calculate_base():
    def __init__(self,
                 listDict_I=None,
                 data_I = None,
                 data_train_I = None,
                 data_test_I = None,
                 data_model_I = None, random_state_I = None,
                 model_score_I = None, feature_union_I = None,
                 pipeline_I = None):
        if listDict_I: self.listDict=listDict_I;
        else: self.listDict = None;

        if data_I: self.data=data_I;
        else: self.data = {};

        if data_train_I: self.data_train=data_train_I;
        else: self.data_train = {}; #nsamples x nfeatures

        if data_test_I:self.data_test=data_test_I;
        else: self.data_test = {};

        if data_model_I: self.data_model=data_model_I;
        else: self.data_model = None;

        self.data_hyperparameterCV = None;

        self.data_featureSelection = None;
        
        if random_state_I:
            npr.seed(random_state_I);
            self.random_state = random_state_I;
        else:
            npr.seed(0);
            self.random_state = 0;
        
        if model_score_I: self.model_score=model_score_I;
        else: self.model_score = None;
        
        if feature_union_I: self.feature_union=feature_union_I;
        else: self.feature_union = None;
        
        if pipeline_I: self.pipeline=pipeline_I;
        else: self.pipeline = None;

    def make_dataAndLabels(self,row_labels_I,column_labels_I):
        '''make the data and labels dictionary
        and row/column labels
        '''

        self.data = {};
        self.data['data']=self.listDict.get_dataMatrix();
        #self.data['row_labels']=self.listDict.get_rowLabels_asArray();
        self.data['row_labels']=self.listDict.get_rowLabels_asDataFrame();
        self.data['row_indexes']=self.listDict.get_rowLabels_asUniqueIndexes();
        #self.data['row_labels']=self.listDict.get_rowLabels(row_labels_I);
        #self.data['column_labels']=self.listDict.get_columnLabels_asArray();
        self.data['column_labels']=self.listDict.get_columnLabels_asDataFrame();
        #self.data['column_labels']=self.listDict.get_columnLabels(column_labels_I);

    def make_dataFactorFromRowLabels_v1(self,factor_index_I):
        '''make a data factor array for Row Labels
        INPUT:
        factor_index_I = integer to slice out the column of the row labels
        OUTPUT:
        array of strings
        '''
        factors_O=self.data['row_labels'][:,factor_index_I];
        return factors_O;
    
    def make_dataFactorFromRowLabels(self,factor_label_I):
        '''make a data factor array for Row Labels
        INPUT:
        factor_label_I = label to slice out the column of the row labels
        OUTPUT:
        array of strings
        '''
        factors_O=self.data['row_labels'][factor_label_I].ravel();
        return factors_O;

    def make_dataResponseFromRowLabels(self,response_index_I):
        '''make a data response array
        INPUT:
        response_index_I
        OUTPUT:
        array of numerical values
        '''
        self.data['response']=self.data['row_labels'][:,response_index_I];

    def get_uniqueResponses(self):
        '''get an arra of unique response in order
        '''
        return pd.Series(self.data_train['response']).unique();

    def get_finalEstimator(self,
            data_model_I,
            raise_I=False):
        '''
        retrieve the final estimator from a pipeline object
        INPUT:
        OUTPUT:
        '''
        estimator_O = data_model_I;
        try:
            if hasattr(data_model_I, "_final_estimator"): #pipeline
                estimator_O = data_model_I._final_estimator;
        except Exception as e:
            if raise_I: raise;
            else: print(e);
        return estimator_O;

    def set_listDict(self,listDict_I):
        self.listDict = listDict_I;

    def clear_data(self):
        self.listDict = None;
        self.data = {};
        self.data_train = {};
        self.data_test = {};
        self.data_model = None;
        self.random_state = 0;
        self.model_score = None;
        self.feature_union = None;
        self.pipeline = None;

    # other
    def null(self, A, eps=1e-6):
        u, s, vh = np.linalg.svd(A,full_matrices=1,compute_uv=1)
        null_rows = [];
        rank = np.linalg.matrix_rank(A)
        for i in range(A.shape[1]):
            if i<rank:
                null_rows.append(False);
            else:
                null_rows.append(True);
        null_space = scipy.compress(null_rows, vh, axis=0)
        return null_space.T
from .calculate_dependencies import *
class calculate_base():
    def __init__(self,
                 listDict_I=None,
                 data_I = None,
                 data_train_I = None,
                 data_test_I = None,
                 data_model_I = None, random_state_I = None,
                 model_score_I = None, feature_union_I = None,
                 pipeline_I = None):
        if listDict_I: self.listDict=listDict_I;
        else: self.listDict = None;

        if data_I: self.data=data_I;
        else: self.data = {};

        if data_train_I: self.data_train=data_train_I;
        else: self.data_train = {}; #nsamples x nfeatures

        if data_test_I:self.data_test=data_test_I;
        else: self.data_test = {};

        if data_model_I: self.data_model=data_model_I;
        else: self.data_model = None;

        self.data_hyperparameterCV = None;

        self.data_featureSelection = None;
        
        if random_state_I:
            npr.seed(random_state_I);
            self.random_state = random_state_I;
        else:
            npr.seed(0);
            self.random_state = 0;
        
        if model_score_I: self.model_score=model_score_I;
        else: self.model_score = None;
        
        if feature_union_I: self.feature_union=feature_union_I;
        else: self.feature_union = None;
        
        if pipeline_I: self.pipeline=pipeline_I;
        else: self.pipeline = None;

    def make_dataAndLabels(self,row_labels_I,column_labels_I):
        '''make the data and labels dictionary
        and row/column labels
        '''

        self.data = {};
        self.data['data']=self.listDict.get_dataMatrix();
        #self.data['row_labels']=self.listDict.get_rowLabels_asArray();
        self.data['row_labels']=self.listDict.get_rowLabels_asDataFrame();
        self.data['row_indexes']=self.listDict.get_rowLabels_asUniqueIndexes();
        #self.data['row_labels']=self.listDict.get_rowLabels(row_labels_I);
        #self.data['column_labels']=self.listDict.get_columnLabels_asArray();
        self.data['column_labels']=self.listDict.get_columnLabels_asDataFrame();
        #self.data['column_labels']=self.listDict.get_columnLabels(column_labels_I);

    def make_dataFactorFromRowLabels_v1(self,factor_index_I):
        '''make a data factor array for Row Labels
        INPUT:
        factor_index_I = integer to slice out the column of the row labels
        OUTPUT:
        array of strings
        '''
        factors_O=self.data['row_labels'][:,factor_index_I];
        return factors_O;
    
    def make_dataFactorFromRowLabels(self,factor_label_I):
        '''make a data factor array for Row Labels
        INPUT:
        factor_label_I = label to slice out the column of the row labels
        OUTPUT:
        array of strings
        '''
        factors_O=self.data['row_labels'][factor_label_I].ravel();
        return factors_O;

    def make_dataResponseFromRowLabels(self,response_index_I):
        '''make a data response array
        INPUT:
        response_index_I
        OUTPUT:
        array of numerical values
        '''
        self.data['response']=self.data['row_labels'][:,response_index_I];

    def get_uniqueResponses(self):
        '''get an arra of unique response in order
        '''
        return pd.Series(self.data_train['response']).unique();

    def get_finalEstimator(self,
            data_model_I,
            raise_I=False):
        '''
        retrieve the final estimator from a pipeline object
        INPUT:
        OUTPUT:
        '''
        estimator_O = data_model_I;
        try:
            if hasattr(data_model_I, "_final_estimator"): #pipeline
                estimator_O = data_model_I._final_estimator;
        except Exception as e:
            if raise_I: raise;
            else: print(e);
        return estimator_O;

    def set_listDict(self,listDict_I):
        self.listDict = listDict_I;

    def clear_data(self):
        self.listDict = None;
        self.data = {};
        self.data_train = {};
        self.data_test = {};
        self.data_model = None;
        self.random_state = 0;
        self.model_score = None;
        self.feature_union = None;
        self.pipeline = None;

    # other
    def null(self, A, eps=1e-6):
        u, s, vh = np.linalg.svd(A,full_matrices=1,compute_uv=1)
        null_rows = [];
        rank = np.linalg.matrix_rank(A)
        for i in range(A.shape[1]):
            if i<rank:
                null_rows.append(False);
            else:
                null_rows.append(True);
        null_space = scipy.compress(null_rows, vh, axis=0)
        return null_space.T
#nsamples x nfeatures make the data and labels dictionary         and row/column labels #self.data['row_labels']=self.listDict.get_rowLabels_asArray(); #self.data['row_labels']=self.listDict.get_rowLabels(row_labels_I); #self.data['column_labels']=self.listDict.get_columnLabels_asArray(); #self.data['column_labels']=self.listDict.get_columnLabels(column_labels_I); make a data factor array for Row Labels         INPUT:         factor_index_I = integer to slice out the column of the row labels         OUTPUT:         array of strings make a data factor array for Row Labels         INPUT:         factor_label_I = label to slice out the column of the row labels         OUTPUT:         array of strings make a data response array         INPUT:         response_index_I         OUTPUT:         array of numerical values get an arra of unique response in order retrieve the final estimator from a pipeline object         INPUT:         OUTPUT: #pipeline # other