ValueError: x и y должны иметь одинаковое первое измерение, но иметь формы (3,) и (1,)

Я новичок в питоне и мне нужна помощь Вот мой код

import matplotlib.pyplot as plt
import mpl_toolkits.mplot3d
import pandas as pd
import numpy as np
from sklearn.datasets import load_iris
from sklearn.decomposition import PCA
from keras.datasets import mnist
from sklearn.naive_bayes import GaussianNB
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
from sklearn.metrics import accuracy_score, confusion_matrix
from sklearn.impute import SimpleImputer
from sklearn.impute import KNNImputer
from sklearn.preprocessing import StandardScaler, MinMaxScaler

# import some data to play with
iris = load_iris()
mnist = mnist.load_data()
data = iris.data
print(f'number of elements: {np.count_nonzero(data)}')
print(data)

#1.5/ Make 5% values in IRIS to nan
nan_percentage = 5
nan_data = np.where(np.random.random(data.shape) < (nan_percentage/100), np.nan,data)
print(nan_data)

print(f'number of NaN values: {np.count_nonzero(np.isnan(nan_data))}')

#1.6 Preproces missing data (i.e. nan) by using all the methods in the lecture
imputers = [SimpleImputer(strategy='mean'), SimpleImputer(strategy='median'),SimpleImputer(strategy='most_frequent'), SimpleImputer(strategy='constant')]
scalers = [StandardScaler(), MinMaxScaler()]
for imputer in imputers:
    for scaler in scalers:
        X = imputer.fit_transform(nan_data)
        X = scaler.fit_transform(X)
        y = iris.target

 #1.7/ For each preprocessing method, use a classification model (e.g., naive,Bayes)
# and evaluate the accuracy.
        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3,random_state=42)
        clf = GaussianNB()
        clf.fit(X_train, y_train)
        y_pred = clf.predict(X_test)
        accuracy = accuracy_score(y_test, y_pred)
        print(f"Imputer: {imputer.strategy}, Scaler: {type(scaler).__name__}, Accuracy:{accuracy:.2f}")

 # 1.8+ 1.9+ 1.10. Repeat step 1.6 and 1.7 with 10%,15%, 20% nan values.
nan_percentages = [10, 15, 20]
accuracies = []
for nan_percentage in nan_percentages:
    nan_data = np.where(np.random.random(data.shape) < nan_percentage/100, np.nan, data)
for imputer in imputers:
    for scaler in scalers:
        X = imputer.fit_transform(nan_data)
        X = scaler.fit_transform(X)
        y = iris.target
        X_train, X_test, y_train, y_test = train_test_split(X, y,test_size=0.3, random_state=42)
        clf = GaussianNB()
        clf.fit(X_train, y_train)
        y_pred = clf.predict(X_test)
        accuracy = accuracy_score(y_test, y_pred)
        accuracies.append(accuracy)
        print(f"NaN Percentage: {nan_percentage}%, Imputer: {imputer.strategy}, Scaler: {type(scaler).__name__}, Accuracy: {accuracy:.2f}")

 # Generate the line plot of accuracy for each combination of imputer and scaler,across different NaN percentages
fig, ax = plt.subplots(figsize=(8, 6))
colors = ['r', 'b', 'g', 'k']
for i, imputer in enumerate(imputers):
    for j, scaler in enumerate(scalers):
        idx = i * len(scalers) + j
        label = f"{imputer.strategy}-{type(scaler).__name__}"
        ax.plot(nan_percentages, accuracies[idx::len(imputers)*len(scalers)], color=colors[j], linestyle='-', label=label)
        ax.legend()
        ax.set_xlabel('NaN Percentage')
        ax.set_ylabel('Accuracy')
        ax.set_title('Accuracy by Imputer and Scaler for Different NaN Percentages')
        plt.show()

Весь код от begin до 1.7 верен, потому что я трижды проверял его. Вот полный след:

  File "C:\Users\nhatd\PycharmProjects\pythonProject1\main.py", line 75, in <module>
    ax.plot(nan_percentages, accuracies[idx::len(imputers)*len(scalers)], color=colors[j], linestyle='-', label=label)
  File "C:\Users\nhatd\PycharmProjects\pythonProject1\venv\Lib\site-packages\matplotlib\axes\_axes.py", line 1688, in plot
    lines = [*self._get_lines(*args, data=data, **kwargs)]
            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\nhatd\PycharmProjects\pythonProject1\venv\Lib\site-packages\matplotlib\axes\_base.py", line 311, in __call__
    yield from self._plot_args(
               ^^^^^^^^^^^^^^^^
  File "C:\Users\nhatd\PycharmProjects\pythonProject1\venv\Lib\site-packages\matplotlib\axes\_base.py", line 504, in _plot_args
    raise ValueError(f"x and y must have same first dimension, but "
ValueError: x and y must have same first dimension, but have shapes (3,) and (1,)

Я пытаюсь использовать np.unique() для X и y, но это не сработало. Может мне кто-нибудь помочь :( Я пытаюсь исправить диапазон X и Y и прочитал много сообщений, но это не сработало :(