找出下面代码错误from sklearn.model_selection import train_test_split train_idx, test_idx = train_test_split(range(len(data)), test_size=0.1, random_state=42) train_idx, val_idx = train_test_split(train_idx, test_size=0.2, random_state=42 train_adj, train_features, train_labels = adj[train_idx], features[train_idx], data.iloc[train_idx]['LogS'] val_adj, val_features, val_labels = adj[val_idx], features[val_idx], data.iloc[val_idx]['LogS'] test_adj, test_features, test_labels = adj[test_idx], features[test_idx], data.iloc[test_idx]['LogS'] optimizer = tf.keras.optimizers.Adam(learning_rate=0.01) loss_fn = tf.keras.losses.MeanSquaredError() metrics=[tf.keras.metrics.MeanAbsoluteError(), tf.keras.metrics.RootMeanSquaredError()] model = GCNModel(hidden_dim=64, output_dim=32) model.compile(optimizer=optimizer, loss=loss_fn, metrics=metrics) history=model.fit((train_features,train_adj),train_labels,epochs=50,validation_data=((val_features, val_adj), val_labels)) test_scores = model.evaluate((test_features, test_adj), test_labels, verbose=0)

代码中的错误是 train_idx, val_idx = train_test_split(train_idx, test_size=0.2, random_state=42 没有正确的闭合括号。正确的代码应该是 train_idx, val_idx = train_test_split(train_idx, test_size=0.2, random_state=42)。

相关问题

from sklearn.datasets import load_breast_cancer from sklearn.model_selection import train_test_split from sklearn.ensemble import RandomForestClassifier from sklearn.metrics import accuracy_score from lime.lime_tabular import LimeTabularExplainer import numpy as np import pandas as pd # 准备数据 data = load_breast_cancer() # df=pd.DataFrame(data.data,columns=data.feature_names) # df['target']=data.target # print(df.head()) X = data.data y = data.target # 划分训练集和测试集 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42) # 训练模型 rfc = RandomForestClassifier(n_estimators=100, random_state=42) rfc.fit(X_train, y_train) # 预测结果 y_pred = rfc.predict(X_test) accuracy = accuracy_score(y_test, y_pred) print(f"Accuracy:{accuracy:.3f}") # 解释模型结果 def explain_sample(x, model, feature_names): explainer = LimeTabularExplainer(X_train, feature_names=feature_names, class_names=data.target_names) exp = explainer.explain_instance(x, model.predict_proba, num_features=len(feature_names)) return exp # 随机选择一个测试样本并解释 idx = np.random.randint(len(X_test)) x=X_test[idx] exp=explain_sample(x,rfc,data.feature_names) fig=exp.as_pyplot_figure() print(f"Sample index:{idx}") fig.show()优化一下这段代码，让可视化图片不要一闪而过

可以使用`input()`函数让程序等待用户输入后再继续执行，这样就可以让可视化图片在用户输入后再显示。代码如下：

from sklearn.datasets import load_breast_cancer 
from sklearn.model_selection import train_test_split 
from sklearn.ensemble import RandomForestClassifier 
from sklearn.metrics import accuracy_score 
from lime.lime_tabular import LimeTabularExplainer 
import numpy as np 
import pandas as pd 
import matplotlib.pyplot as plt 

# 准备数据 
data = load_breast_cancer() 
X = data.data 
y = data.target 

# 划分训练集和测试集 
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42) 

# 训练模型 
rfc = RandomForestClassifier(n_estimators=100, random_state=42) 
rfc.fit(X_train, y_train) 

# 预测结果 
y_pred = rfc.predict(X_test) 
accuracy = accuracy_score(y_test, y_pred) 
print(f"Accuracy:{accuracy:.3f}") 

# 解释模型结果 
def explain_sample(x, model, feature_names): 
    explainer = LimeTabularExplainer(X_train, feature_names=feature_names, class_names=data.target_names) 
    exp = explainer.explain_instance(x, model.predict_proba, num_features=len(feature_names)) 
    return exp 

# 随机选择一个测试样本并解释 
idx = np.random.randint(len(X_test)) 
x=X_test[idx] 
exp=explain_sample(x,rfc,data.feature_names) 
fig=exp.as_pyplot_figure() 

print(f"Sample index:{idx}") 
fig.show() 
input("Press Enter to continue...")

这样，当程序运行到`fig.show()`时，会显示可视化图片，并等待用户输入后才会继续执行下去。用户可以在图片显示的状态下仔细观察，直到输入回车键后程序才会结束。

import os import pickle import cv2 import matplotlib.pyplot as plt import numpy as np from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout from keras.models import Sequential from keras.optimizers import adam_v2 from keras_preprocessing.image import ImageDataGenerator from sklearn.model_selection import train_test_split from sklearn.preprocessing import LabelEncoder, OneHotEncoder, LabelBinarizer def load_data(filename=r'/root/autodl-tmp/RML2016.10b.dat'): with open(r'/root/autodl-tmp/RML2016.10b.dat', 'rb') as p_f: Xd = pickle.load(p_f, encoding="latin-1") # 提取频谱图数据和标签 spectrograms = [] labels = [] train_idx = [] val_idx = [] test_idx = [] np.random.seed(2016) a = 0 for (mod, snr) in Xd: X_mod_snr = Xd[(mod, snr)] for i in range(X_mod_snr.shape[0]): data = X_mod_snr[i, 0] frequency_spectrum = np.fft.fft(data) power_spectrum = np.abs(frequency_spectrum) ** 2 spectrograms.append(power_spectrum) labels.append(mod) train_idx += list(np.random.choice(range(a * 6000, (a + 1) * 6000), size=3600, replace=False)) val_idx += list(np.random.choice(list(set(range(a * 6000, (a + 1) * 6000)) - set(train_idx)), size=1200, replace=False)) a += 1 # 数据预处理 # 1. 将频谱图的数值范围调整到0到1之间 spectrograms_normalized = spectrograms / np.max(spectrograms) # 2. 对标签进行独热编码 label_binarizer = LabelBinarizer() labels_encoded= label_binarizer.fit_transform(labels) # transfor the label form to one-hot # 3. 划分训练集、验证集和测试集 # X_train, X_temp, y_train, y_temp = train_test_split(spectrograms_normalized, labels_encoded, test_size=0.15, random_state=42) # X_val, X_test, y_val, y_test = train_test_split(X_temp, y_temp, test_size=0.5, random_state=42) spectrogramss = np.array(spectrograms_normalized) print(spectrogramss.shape) labels = np.array(labels) X = np.vstack(spectrogramss) n_examples = X.shape[0] test_idx = list(set(range(0, n_examples)) - set(train_idx) - set(val_idx)) np.random.shuffle(train_idx) np.random.shuffle(val_idx) np.random.shuffle(test_idx) X_train = X[train_idx] X_val = X[val_idx] X_test = X[test_idx] print(X_train.shape) print(X_val.shape) print(X_test.shape) y_train = labels[train_idx] y_val = labels[val_idx] y_test = labels[test_idx] print(y_train.shape) print(y_val.shape) print(y_test.shape) # X_train = np.expand_dims(X_train,axis=-1) # X_test = np.expand_dims(X_test,axis=-1) # print(X_train.shape) return (mod, snr), (X_train, y_train), (X_val, y_val), (X_test, y_test) 这是我的数据预处理代码

你的数据预处理代码看起来很完整。它首先加载pickle文件，然后提取频谱图数据和标签。接下来，它对频谱图数据进行归一化处理，并对标签进行独热编码。最后，它将数据集划分为训练集、验证集和测试集，并返回这些数据集。

在代码中，你还有一些注释掉的代码段，例如对数据集进行划分的部分。如果你打算使用这些划分代码，你可以取消注释相应的行。另外，如果你的频谱图数据维度是二维的，你可能需要使用`np.expand_dims`函数将其扩展为三维，以便与Conv2D层兼容。

如果你还有其他问题或需要进一步帮助，请随时问我。