comparison COBRAxy/marea_cluster.py @ 428:8cd0c70b0084 draft

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427:4a385fdb9e58

                        type = str,
                        choices = ['kmeans', 'dbscan', 'hierarchy'],
                        default = 'kmeans',
                        help = 'choose clustering algorythm')
    
    parser.add_argument('-k1', '--k_min', 
                        type = int,
                        default = 2,
                        help = 'choose minimun cluster number to be generated')
    

    
    for i in X.columns:
        if any(val is None or np.isnan(val) for val in X[i]):
            X = X.drop(columns=[i])
            
    if args.k_max != None:
       numero_classi = X.shape[0]
       while args.k_max >= numero_classi:
          err = 'Skipping k = ' + str(args.k_max) + ' since it is >= number of classes of dataset'
          warning(err)

428:8cd0c70b0084

                        type = str,
                        choices = ['kmeans', 'dbscan', 'hierarchy'],
                        default = 'kmeans',
                        help = 'choose clustering algorythm')
    
    parser.add_argument('-sc', '--scaling',
                        type = str,
                        choices = ['true', 'false'],
                        default = 'true',
                        help = 'choose if you want to scaling the data')
    
    parser.add_argument('-k1', '--k_min', 
                        type = int,
                        default = 2,
                        help = 'choose minimun cluster number to be generated')
    

    
    for i in X.columns:
        if any(val is None or np.isnan(val) for val in X[i]):
            X = X.drop(columns=[i])
            
    if args.scaling == True:
        list_to_remove = []
        toll_std=1e-8
        for i in X.columns:
            mean_i = X[i].mean()
            std_i = X[i].std()
            if std_i >toll_std:
                #scaling with mean 0 and std 1
                X[i] = (X[i]-mean_i)/std_i
            else:
                #remove feature because std = 0 during clustering
                list_to_remove.append(i)
        if len(list_to_remove)>0:
            X = X.drop(columns=list_to_remove)

    if args.k_max != None:
       numero_classi = X.shape[0]
       while args.k_max >= numero_classi:
          err = 'Skipping k = ' + str(args.k_max) + ' since it is >= number of classes of dataset'
          warning(err)