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)
时间: 2023-06-06 07:08:07 浏览: 68
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:
```python
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。
#倒入相关库文件 import numpy as np import pandas as pd import matplotlib.pyplot as plt import seaborn as sns from sklearn.preprocessing import MinMaxScaler from sklearn.metrics import accuracy_score from sklearn.metrics import recall_score from sklearn.metrics import precision_score from sklearn.metrics import f1_score from sklearn.model_selection import train_test_split #首先我们先观察一下数据的总体描述 data = pd.read_csv('data.csv') data.describe(include='all') #观察数据的任意五行 data.sample(5) sns.countplot(data["target"]) plt.show() #target一共9个类别。由于是字符型,定义一个函数将target的类别标签转为index表示,方便后面计算交叉熵 def target2idx(targets): target_idx = [] target_labels = ['Class_1', 'Class_2', 'Class_3', 'Class_4', 'Class_5', 'Class_6', 'Class_7', 'Class_8', 'Class_9','Class_10'] for target in targets: target_idx.append(target_labels.index(target)) return target_idx #向量转化函数(提供参考,自行选择是否使用) def convert_to_vectors(c): m = len(c) k = np.max(c) + 1 y = np.zeros(m * k).reshape(m,k) for i in range(m): y[i][c[i]] = 1 return y #特征处理函数(提供参考,自行选择是否使用) def process_features(X): scaler = MinMaxScaler(feature_range=(0,1)) X = scaler.fit_transform(1.0*X) m, n = X.shape X = np.c_[np.ones((m, 1)), X] return X #数据获取样例,可自行处理 X = np.array(data)[:,1:-1].astype(float) c = target2idx(data['target']) y = convert_to_vectors(c) #划分训练集和测试集比例在0.1-0.9之间 X_train, X_test, y_train, y_test, c_train, c_test = train_test_split(X, y, c, random_state = 0, test_size = 0.2) #模型训练及预测 #计算指标,本指标使用加权的方式计算多分类问题,accuracy和recall相等,可将其原因写入报告 accuracy = accuracy_score(c_test, c_pred) precision = precision_score(c_test, c_pred,average = 'weighted') recall = recall_score(c_test, c_pred,average = 'weighted') f1 = f1_score(c_test, c_pred,average = 'weighted') print("accuracy = {}".format(accuracy)) print("precision = {}".format(precision)) print("recall = {}".format(recall)) print("f1 = {}".format(f1))补全代码
以下是代码补全:
```
#导入相关库文件
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.preprocessing import MinMaxScaler
from sklearn.metrics import accuracy_score
from sklearn.metrics import recall_score
from sklearn.metrics import precision_score
from sklearn.metrics import f1_score
from sklearn.model_selection import train_test_split
#首先我们先观察一下数据的总体描述
data = pd.read_csv('data.csv')
data.describe(include='all')
#观察数据的任意五行
data.sample(5)
sns.countplot(data["target"])
plt.show()
#target一共9个类别。由于是字符型,定义一个函数将target的类别标签转为index表示,方便后面计算交叉熵
def target2idx(targets):
target_idx = []
target_labels = ['Class_1', 'Class_2', 'Class_3', 'Class_4', 'Class_5', 'Class_6', 'Class_7', 'Class_8', 'Class_9','Class_10']
for target in targets:
target_idx.append(target_labels.index(target))
return target_idx
#向量转化函数(提供参考,自行选择是否使用)
def convert_to_vectors(c):
m = len(c)
k = np.max(c) + 1
y = np.zeros(m * k).reshape(m,k)
for i in range(m):
y[i][c[i]] = 1
return y
#特征处理函数(提供参考,自行选择是否使用)
def process_features(X):
scaler = MinMaxScaler(feature_range=(0,1))
X = scaler.fit_transform(1.0*X)
m, n = X.shape
X = np.c_[np.ones((m, 1)), X]
return X
#数据获取样例,可自行处理
X = np.array(data)[:,1:-1].astype(float)
c = target2idx(data['target'])
y = convert_to_vectors(c)
#划分训练集和测试集比例在0.1-0.9之间
X_train, X_test, y_train, y_test, c_train, c_test = train_test_split(X, y, c, random_state = 0, test_size = 0.2)
#模型训练及预测
#计算指标,本指标使用加权的方式计算多分类问题,accuracy和recall相等,可将其原因写入报告
accuracy = accuracy_score(c_test, c_pred)
precision = precision_score(c_test, c_pred,average = 'weighted')
recall = recall_score(c_test, c_pred,average = 'weighted')
f1 = f1_score(c_test, c_pred,average = 'weighted')
print("accuracy = {}".format(accuracy))
print("precision = {}".format(precision))
print("recall = {}".format(recall))
print("f1 = {}".format(f1))
```