sklearn LinearModel、Ridge和Lasso是否产生不兼容的输出？

Question

我在波士顿之家的数据集上玩不同的回归模型。我发现，如果我使用正态线性模型或岭回归预测值的形状（102，1），而如果我使用相同的代码与套索的输出是形状（102，1）。为什么？这使得它随后与ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()行中的pearsonr崩溃

你知道如何让下面的代码顺利运行吗

from sklearn.datasets import load_boston
import numpy as np
import pandas as pd
import sys


def evalOneModel (model, name, X, y, nRuns):
    allMse = []
    allR2 = []
    all_rho_P = []
    ################ OLS ################
    for i in range(nRuns):
        x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=None)

        model = model.fit(x_train, y_train)
        predictions = model.predict(x_test)

        mse = mean_squared_error(y_test, predictions)
        r2 = r2_score(y_test, predictions)

        allMse.append(mse)
        allR2.append(r2)

        print(type(y_test))
        print(y_test.shape)
        print(type(predictions))
        print(predictions.shape)
        rhoP, pval = pearsonr(y_test, predictions)

        rhoP = rhoP[0]

        all_rho_P.append(rhoP)

        print("run{}={:0.3f}; ".format(i, rhoP), end="")
        print(model.coef_)


    myTitle = "{} mean={:0.3f}".format(name, np.mean(all_rho_P))
    print("")
    print(myTitle)


    print("")
    sys.stdout.flush()

####### MAIN #####


pd.set_option('expand_frame_repr', False)

bosten_data = load_boston()

df = pd.DataFrame(bosten_data.data, columns=bosten_data.feature_names)
df['MEDV'] = bosten_data.target  # add the target to the data frame

target = pd.DataFrame(bosten_data.target, columns=["MEDV"])

norm_df = (df - df.mean()) / df.std()
norm_target = (target - target.mean()) / target.std()



X = norm_df[["RM", "AGE", "PTRATIO", "LSTAT"]]
y = norm_target


from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error, r2_score
from scipy.stats import spearmanr, pearsonr


print("\n\nstarting runs ...\n")

from sklearn import linear_model
model = linear_model.LinearRegression()
evalOneModel (model, "OLS", X, y, 1)

from sklearn.linear_model import Ridge  # L2
model = linear_model.Ridge(alpha=1.0)
evalOneModel (model, "Ridge (alpha=1)", X, y, 1)

from sklearn.linear_model import Lasso  # L1
model = linear_model.Lasso(alpha=1.0)
evalOneModel (model, "Lasso (alpha=1)", X, y, 1)