community_engineering/DyMMMMultiObjectiveProblem.py at main · LMSE/community_engineering · GitHub

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import numpy as np
import math
from pymoo.model.problem import Problem
from scipy.spatial import distance
from sklearn.preprocessing import MinMaxScaler


def scale(X, x_min, x_max):
    nom = (X-X.min(axis=0))*(x_max-x_min)
    denom = X.max(axis=0) - X.min(axis=0)
    denom[denom==0] = 1
    return x_min + nom/denom


class DyMMMMultiObjectiveProblem(Problem):

    localWeight=1.0
    globalWeight=1.0
    train_X=None
    train_y=None
    surrogate=None
    scaler=None

    def __init__(self,
                 paramCount,
                 lowerBounds,
                 upperBounds,
                 train_X,
                 train_y,
                 scaler,
                 surrogate):
        super().__init__(n_var=paramCount,
                         n_obj=2, #number of objectives
                         n_constr=0, #number of constraints
                         xl=lowerBounds, #lower bounds of the design variables
                         xu=upperBounds) #upper bounds of the design variables

        self.train_X = train_X.to_numpy()
        self.train_y = train_y.to_numpy()
        self.surrogate=surrogate
        self.scaler=scaler

    # given an input from algorithm X between bounds create a measure for out
    def _evaluate(self, X, out, *args, **kwargs):

        #f1= maximize distance from training sample   (https://docs.scipy.org/doc/scipy/reference/spatial.distance.html)
        #f2= minimize error
        #train_X=self.train_X.reshape(-1,1)

        train_X=self.train_X

        #print("<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<")
        #print("X.shape={}".format(str(X.shape)))
        #print(X)
        # print(train_X)
        #print("<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<")
        X_n=np.copy(X)
        train_X_n=np.copy(train_X)
        #X_n=np.append(X,train_X, axis=0)
        for i in range(X.shape[1]):
            v=X[:,i]
            v=self.scaler[i].transform(v.reshape(-1,1))
            X_n[:,i]=v.reshape(v.shape[0],)
            #X_n[:,i]=self.scaler[i].transform([X[:,i]])
        for i in range(train_X.shape[1]):
            v=train_X[:,i]
            v=self.scaler[i].transform(v.reshape(-1,1))
            train_X_n[:,i]=v.reshape(v.shape[0],)
            #train_X_n[:,i]=self.scaler[i].transform([train_X[:,i]])
        f1_array=distance.cdist(X_n, train_X_n, 'cityblock')
        # print(">>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>")
        # print(f1_array.shape)
        # print(f1_array)
        # print(">>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>")
        # #f1=np.array(f1_array.shape[0],1)
        f1=-1.0*self.globalWeight*f1_array.min(axis=1).reshape(f1_array.shape[0],1)
        #print(X.shape)
        f2=-1.0*self.localWeight*self.surrogate.predict_variances(X_n)
        #greedy strategy
        tempvar=self.localWeight
        self.localWeight = self.globalWeight
        self.globalWeight = tempvar
        #print("f1={} f2={} {} {} ".format(str(f1), str(f2), str(self.localWeight), str(self.globalWeight)))
        out["F"] = np.column_stack([f1, f2])
        #print(out["F"])
        # print("=======================================================================================================================================================================================")
        #print(f1.shape)
        #print(f2.shape)
        # print("=======================================================================================================================================================================================")