python - 如何解决关于预计算度量的错误需要形状n_queries，n_indexed的KPCA？

编程问答 2022-05-02

问题描述

我正在应用内核主成分分析 (KPCA) 来降低特征矩阵集的维数，以获得一组数据点。我浏览了 scikit 学习包中 KPCA 中使用的参数，并了解到如果选择其中之一，则有些参数应该可以工作（例如，如果选择了伽马，则不使用度数和系数），但是，当我尝试要将 hyperopt 包用于 KPCA，我不断收到以下错误：

错误信息：

ValueError: Precomputed metric requires shape (n_queries,n_indexed). Got (50,14) for 50 indexed.

我一直在尝试解决这个问题，但是，我一直收到这个错误。有人可以解释这个错误的原因和可能的解决方案吗？请找到以下代码：

代码：

from sklearn.decomposition import PCA,KernelPCA,SparsePCA,IncrementalPCA
from hyperopt import hp,tpe,atpe,fmin,Trials,rand,STATUS_OK

# Implementing Hyperparamater method:
models = {'pca'      : PCA,'kpca'     : KernelPCA,'spca'     : SparsePCA,# 'ipca'     : IncrementalPCA
          }

def search_space(model):
    # Initialising variables:
    model = model.lower()
    space = {}

    # Calling the models:
    if model == 'pca':
        space = {'svd_solver'        : hp.choice('svd_solver',["auto","full","arpack","randomized"]),}

    elif model == 'kpca':
        space = {'kernel'            : hp.choice('kernel',['linear','poly','rbf','sigmoid','cosine','precomputed']),'gamma'             : hp.choice('gamma',np.linspace(1,0.1,12)),'degree'            : hp.choice('degree',np.linspace(3,10,8)),'coef0'             : hp.choice('coef0',12))
                 }

    elif model == 'spca':
        space = {'alpha'             : hp.choice('alpha',np.arange(1.0,15.0,0.2)),'ridge_alpha'       : hp.choice('ridge_alpha',np.linspace(0.01,0.3,30)),'method'            : hp.choice('method',['lars','cd']),'max_iter'          : hp.choice('max_iter',[1000,1500,2000,2500,3000])
                 }

    # elif model == 'ipca':
    #     space = {'batch_size'        : hp.choice('batch_size',['gini','entropy']),#              }
    space['model'] = model
    return space

def obj_fnc(params):
    model = params.get('model').lower()
    # X_ = scale_normalize(params,X[:])
    del params['model']
    clf = models[model](**params)
    return (get_acc_status(clf,X))

def get_acc_status(clf,X):
    X_reduced = clf.fit_transform(X)
    # X_prereduced = clf.fit_inverse_transform(X_reduced)
    # acc = -1 * mean_squared_error(X,X_prereduced)
    X_prereduced = clf.inverse_transform(X_reduced)
    # acc = -1 * mean_absolute_error(X,X_prereduced)
    acc = -1 * r2_score(X,X_prereduced)
    # acc = cross_val_score(clf,X).mean()
    return {'loss': -acc,'status': STATUS_OK}

##### Hyperparameter optimisation:
# Running Bayesian Optimisation to get the best parameters:
start = time.time()

# Create the algorithms
tpe_algo = tpe.suggest
# rand_algo = rand.suggest
# atpe_algo = atpe.suggest

# Assigning model:
model = 'kpca'

# Creating the trial objects:
hypopt_trials = Trials()

# Getting the best parameters:
best_params = fmin(obj_fnc,search_space(model),algo=tpe_algo,max_evals=500,trials=hypopt_trials)
print("Best params: ",best_params)
print('Best accuracy: ',hypopt_trials.best_trial['result']['loss'])
print("[INFO] Baye. Opt. search took {:.2f} seconds".format(time.time() - start))

# Calling parameters:
## PCA:
svd_solver = ["auto","randomized"]
## KPCA:
kernel =  ["linear","poly","rbf","sigmoid","cosine","precomputed"]
gamma = np.linspace(1,12)
degree = np.linspace(3,8)
coef0 = np.linspace(1,12)
kernel_gamma = ["poly","sigmoid"]
kernel_degree = "poly"
kernel_coef0 = "sigmoid"
## SPCA:
alpha = np.arange(1.0,0.2)
ridge_alpha = np.linspace(0.01,30)
method = ['lars','cd']
max_iter = [1000,3000]

# Creating the PCA models:
# pca = PCA(n_components=2,svd_solver=svd_solver[best_params['svd_solver'])
if any(x in best_params for x in kernel_gamma):
    pca = KernelPCA(n_components=2,kernel=kernel[best_params['kernel']],gamma='{0}'.format(gamma[best_params['gamma']]))
    if any(x in best_params for x in kernel_degree):
        pca = KernelPCA(n_components=2,gamma='{0}'.format(gamma[best_params['gamma']]),degree='{0}'.format(degree[best_params['degree']]),coef0='{0}'.format(coef0[best_params['coef0']]))                  
    if any(x in best_params for x in kernel_coef0):
        pca = KernelPCA(n_components=2,coef0='{0}'.format(coef0[best_params['coef0']]))                  
# pca = SparsePCA(n_components=2,alpha='{0}'.format(alpha[best_params['alpha']]),ridge_alpha='{0}'.format(ridge_alpha[best_params['ridge_alpha']]),method=method[best_params['method']],max_iter='{0}'.format(max_iter[best_params['max_iter']]))
# pca = IncrementalPCA(n_components=2)
print('Model: ',pca)
PrincipalComponents = pca.fit_transform(X_std)
principalDf = pd.DataFrame(data = PrincipalComponents,columns = ['principal component 1','principal component 2'])
finalDf = pd.concat([principalDf,dataframe[['Label']]],axis = 1)
print('Principal Component Analysis: ')
print(principalDf)

解决方法

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