train_data,test_data,train_label,test_label = model_selection.train_test_split(x,y, random_state=1, train_size=0.9,test_size=0.1)

import pandas as pd import numpy as np from sklearn.model_selection import train_test_split from sklearn import preprocessing from LAMDA_SSL.Algorithm.Regression.CoReg import CoReg from LAMDA_SSL.Evaluation.Regressor.Mean_Squared_Error import Mean_Squared_Error pd.set_option('display.max_columns', None) # 所有列 pd.set_option('display.max_rows', None) # 所有行 data = pd.read_excel('半监督数据.xlsx') X = data.drop(columns=['label']) # 特征矩阵 y = data['label'] # 标签列 # 划分数据集 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, stratify=None, shuffle=True, random_state=0) # 划分带标签数据集 labeled_size = 0.3 n_labeled = int(labeled_size * len(X_train)) indices = np.arange(len(X_train)) unlabeled_indices = np.delete(indices, y_train.index[:n_labeled]) X_unlabeled = X_train.iloc[unlabeled_indices] y_unlabeled = y_train.iloc[unlabeled_indices] X_labeled = X_train.iloc[y_train.index[:n_labeled]] y_labeled = y_train.iloc[y_train.index[:n_labeled]] # 数据预处理 pre_transform=preprocessing.StandardScaler() pre_transform.fit(np.vstack([X_train, X_test])) X_train = pre_transform.transform(X_train) X_test = pre_transform.transform(X_test) # 构建和训练模型 model = CoReg() model.fit(X=X_train, y=y_labeled, test_datas=X_unlabeled) pred_y = model.predict(X=X_test) # 计算性能指标 performance = Mean_Squared_Error().scoring(y_test, pred_y)代码运行不了，怎么修改？

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, stratify=None, shuffle=True, random_state=0) # 划分带标签数据集 labeled_size = 0.3 n_labeled = int(labeled_size * len(X_train)...

def train_test_datasplit(samples): from sklearn.model_selection import train_test_split data = samples.iloc[:, 0:(samples.shape[1] - 1)] label = samples.iloc[:, -1] data_train, data_test, label_train, label_test = train_test_split(data, label, test_size=0.33, random_state=42) return data_train, data_test, label_train, label_test代码讲解

- train_test_split：是 scikit-learn 中的一个函数，用于将数据集划分为训练集和测试集，该函数的参数包括： - data：数据集的输入特征，即前面所有列的数据。 - label：数据集的输出标签，即最后一列的...

from sklearn.model_selection import train_test_splittrain_data, test_data, train_label, test_label = train_test_split(X, y, test_size=0.2, random_state=42)。42是什么意思

在这里，random_state=42是用来设置随机数生成器的种子，以确保每次运行程序时都能得到相同的结果。种子的值可以是任何整数，只要它们在不同的运行之间是唯一的即可。在这个例子中，使用random_state=42是为了...

from sklearn.datasets import load_wine from sklearn.model_selection import train_test_split from sklearn.tree import DecisionTreeClassifier # 加载葡萄酒数据集 wine = load_wine() # 划分训练集和测试集 X_train, X_test, y_train, y_test = train_test_split(wine.data, wine.target, test_size=0.3, random_state=42) # 初始化决策树模型 clf = DecisionTreeClassifier() # 训练模型并预测测试集 clf.fit(X_train, y_train) y_pred = clf.predict(X_test) # 输出准确率 print("7:3的准确率：", clf.score(X_test, y_test)) # 重新划分训练集和测试集，比例为3:1 X_train, X_test, y_train, y_test = train_test_split(wine.data, wine.target, test_size=0.25, random_state=42) # 训练模型并预测测试集 clf.fit(X_train, y_train) y_pred1 = clf.predict(X_test) # 输出准确率 print("3:1的准确率：", clf.score(X_test, y_test)) # 重新划分训练集和测试集，比例为4:1 X_train, X_test, y_train, y_test = train_test_split(wine.data, wine.target, test_size=0.2, random_state=42) # 训练模型并预测测试集 clf.fit(X_train, y_train) y_pred2 = clf.predict(X_test) # 输出准确率 print("4:1的准确率：", clf.score(X_test, y_test)) # 重新划分训练集和测试集，比例为9:1 X_train, X_test, y_train, y_test = train_test_split(wine.data, wine.target, test_size=0.1, random_state=42) # 训练模型并预测测试集 clf.fit(X_train, y_train) y_pred3 = clf.predict(X_test) # 输出准确率 print("9:1的准确率：", clf.score(X_test, y_test))

acc_73 = clf.score(X_test, y_test) acc_31 = clf.score(X_test, y_test) acc_41 = clf.score(X_test, y_test) acc_91 = clf.score(X_test, y_test) # 绘制折线图 plt.plot([7, 3], [acc_73, acc_73], label="7:3...

import numpy as np import pandas as pd from sklearn.model_selection import train_test_split from keras.models import Sequential from keras.layers import Dense from pyswarm import pso import matplotlib.pyplot as plt from sklearn.preprocessing import StandardScaler file = "zhong.xlsx" data = pd.read_excel(file) #reading file X=np.array(data.loc[:,'种植密度':'有效积温']) y=np.array(data.loc[:,'产量']) y.shape=(185,1) # 将数据集分为训练集和测试集 X_train, X_test, y_train, y_test = train_test_split(X,y, test_size=0.25, random_state=10) SC=StandardScaler() X_train=SC.fit_transform(X_train) X_test=SC.fit_transform(X_test) y_train=SC.fit_transform(y_train) y_test=SC.fit_transform(y_test) print("X_train.shape:", X_train.shape) print("X_test.shape:", X_test.shape) print("y_train.shape:", y_train.shape) print("y_test.shape:", y_test.shape) # 定义BP神经网络模型 def nn_model(X): model = Sequential() model.add(Dense(8, input_dim=X_train.shape[1], activation='relu')) model.add(Dense(12, activation='relu')) model.add(Dense(1)) model.compile(loss='mean_squared_error', optimizer='adam') return model # 定义适应度函数 def fitness_func(X): model = nn_model(X) model.fit(X_train, y_train, epochs=60, verbose=2) score = model.evaluate(X_test, y_test, verbose=2) return score # 定义变量的下限和上限 lb = [5, 5] ub = [30, 30] # 利用PySwarm库实现改进的粒子群算法来优化BP神经网络预测模型 result = pso(fitness_func, lb, ub) # 输出最优解和函数值 print('最优解:', result[0]) print('最小函数值:', result[1]) # 绘制预测值和真实值对比图 model = nn_model(X) model.fit(X_train, y_train, epochs=60, verbose=0) y_pred = model.predict(X_test) y_true = SC.inverse_transform(y_test) y_pred=SC.inverse_transform(y_pred) plt.figure() plt.plot(y_true,"bo-",label = '真实值') plt.plot(y_pred,"ro-", label = '预测值') plt.title('神经网络预测展示') plt.xlabel('序号') plt.ylabel('产量') plt.legend(loc='upper right') plt.show() # 绘制损失函数曲线图 model = nn_model(X) history = model.fit(X_train, y_train, epochs=60, validation_data=(X_test, y_test), verbose=2) plt.plot(history.history['loss'], label='train') plt.plot(history.history['val_loss'], label='test') plt.legend() plt.show()

这是一个使用改进的粒子群算法优化BP神经网络预测模型的代码，通过读取Excel文件并将其分为训练集和测试集，利用StandardScaler进行数据标准化处理。然后定义BP神经网络模型和适应度函数，并定义变量的下限和上限。...

import pandas as pd from sklearn.linear_model import LogisticRegression from sklearn.model_selection import train_test_split from sklearn.metrics import accuracy_score # 读取数据集 data = pd.read_csv('train_binary.csv') # 提取特征和标签 X = data.drop(['label'], axis=1) y = data['label'] # 切分训练集和测试集 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42) # 定义逻辑斯蒂回归模型，并拟合训练集 model = LogisticRegression() model.fit(X_train, y_train) # 在测试集上进行预测，并计算准确率 y_pred = model.predict(X_test) acc = accuracy_score(y_test, y_pred) print('Accuracy:', acc)以上代码出现STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.的问题，请问如何解决

这个问题通常是由于逻辑回归模型的收敛条件未满足或数据存在问题...model = LogisticRegression(max_iter=1000) 如果增加迭代次数后仍然出现相同的错误，可能需要对数据进行进一步的处理，如标准化、特征选择等。

import tensorflow as tf import numpy as np import matplotlib.pyplot as plt import pandas as pd from sklearn.utils import shuffle from sklearn.preprocessing import scale df = pd.read_csv("C:\\boston.csv",header=0) ds = df.values from sklearn.datasets import load_boston boston = load_boston() X = boston.data y = boston.target from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) from sklearn.preprocessing import StandardScaler scaler = StandardScaler() X_train = scaler.fit_transform(X_train) X_test = scaler.transform(X_test) from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense model = Sequential([ Dense(64, activation='relu', input_shape=(X_train.shape[1],)), Dense(64, activation='relu'), Dense(64, activation='relu'), Dense(1) ]) model.compile(loss='mean_squared_error', optimizer='adam') history = model.fit(X_train, y_train, validation_split=0.1, epochs=100, batch_size=32) from sklearn.metrics import mean_squared_error y_pred = model.predict(x_test)mse = mean_squared_error(y_test, y_pred)print('MSE:’, mse) import matplotlib.pyplot as plt plt.plot(history.history['accuracy'], label='train') plt.plot(history.history['val_accuracy'], label='validation') plt.legend() plt.show()

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) 对训练集和测试集进行标准化处理： from sklearn.preprocessing import StandardScaler scaler = ...

上述代码报错，from sklearn.datasets import load_iris from sklearn.model_selection import train_test_split from sklearn.metrics import accuracy_score # 加载数据集 iris = load_iris() X, y = iris.data, iris.target # 将数据集分成训练集和测试集 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) # 初始化MAT模型 model = MAT(num_features=X.shape[1], num_classes=len(set(y))) # 训练模型 model.train(X_train, y_train) # 预测测试集 y_pred = [] for x in X_test: pred = model.predict(x) y_pred.append(pred) # 计算准确率 acc = accuracy_score(y_test, y_pred) print("Accuracy:", acc)

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) # 初始化MAT模型 model = MAT(num_features=X.shape[1], num_classes=len(set(y))) # 训练模型 model.train(X_train...

from data_process import get_data import torch from sklearn.model_selection import train_test_split from LeNet5 import LeNet5 X, y = get_data() # 获取数据【0.025，0.035】100*0.2 = 20 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, stratify=y) # 数据拆分 print(X_train.shape) #(1075, 227, 227, 1) 0 1 2 3 --- (1075, 1, 227, 227) 0 3 1 2 X_train_tensor = torch.tensor(X_train, dtype=torch.float32).permute(0, 3, 1, 2) # 将数据转成模型要求的形式 print(X_train_tensor.shape) X_test_tensor = torch.tensor(X_test, dtype=torch.float32).permute(0, 3, 1, 2) y_train_tensor = torch.tensor(y_train, dtype=torch.int64) train_ds = torch.utils.data.TensorDataset(X_train_tensor, y_train_tensor) # 将数据转为tensordata类型 train_dl = torch.utils.data.DataLoader(train_ds, batch_size=128, shuffle=True) # 对数据进行分批及打乱操作 network = LeNet5() # 实例化得到一个leNet-5网络模型 loss_fn = torch.nn.CrossEntropyLoss() # 损失函数（交差熵） optimizer = torch.optim.SGD(network.parameters(), lr=0.01) # 优化器 # 模型训练 for epoch in range(1): for image, label in train_dl: y_pre = network(image) # 模型计算(前向传播) loss = loss_fn(y_pre, label) # 计算损失值 network.zero_grad() # 将网络中的所有梯度清零 loss.backward() # 计算梯度项（反向求导） optimizer.step() # 参数优化（模型训练） print('第{}轮训练，当前批次的训练损失值为：{}'.format(epoch, loss.item())) predicted = network(X_test_tensor) # 模型预测 result = predicted.data.numpy().argmax(axis=1) # 预测标签 acc_test = (result == y_test).mean() # 模型测试精度 print(acc_test) torch.save(network.state_dict(), 'leNet5-1.pt') # 保存模型参数

1. 数据准备部分：使用get_data函数获取数据，然后使用train_test_split将数据集划分为训练集和测试集，并将数据转换成模型要求的格式； 2. 模型定义部分：实例化一个LeNet-5网络模型； 3. 损失函数和优化器的...

data.head(11) X=data.loc[:,:] y=data.loc[:,] from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) from sklearn.neighbors import KNeighborsClassifier clf = KNeighborsClassifier(n_neighbors=3) clf.fit(X_train, y_train.astype('int')) print("Test set predictions: {}".format(clf.predict(X_test))) print("Training set score:{:.2f}".format(clf.score(X_train,y_train))) print("Test set accuracy: {:.2f}".format(clf.score(X_test, y_test)))

这段代码中还有一个问题，即X和y的赋值语句中的语法不正确，需要指定具体的列名或索引。如果你想将所有列都作为特征输入到模型中，可以将X的赋值语句修改为： X = data.iloc[:, :-1] 这样就可以将除了最后...

import pandas as pd import numpy as np import matplotlib.pyplot as plt from sklearn.model_selection import train_test_split from sklearn.preprocessing import MinMaxScaler from sklearn.linear_model import LogisticRegression from sklearn.tree import DecisionTreeClassifier from sklearn.neighbors import KNeighborsClassifier from sklearn.svm import SVC from sklearn.metrics import classification_report from sklearn.metrics import confusion_matrix fruits = pd.read_table('fruit_data_with_colors.txt') fruits.head() fruits.shape print(fruits['fruit_name'].unique()) fruits.drop('fruit_label', axis=1).hist(bins=30, figsize=(9,9)) plt.suptitle("Histogram for each numeric input variable") plt.show() feature_names = ['mass', 'width', 'height', 'color_score'] x = fruits[feature_names] y = fruits['fruit_label'] X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=0) scaler = MinMaxScaler() X_train = scaler.fit_transform(X_train) X_test = scaler.transform(X_test) logreg = LogisticRegression() logreg.fit(X_train, y_train) clf = DecisionTreeClassifier().fit(X_train, y_train) knn = KNeighborsClassifier() knn.fit(X_train, y_train) svm = SVC() svm.fit(X_train, y_train) pred = knn.predict(X_test)

你给出的代码是一个简单的机器学习分类问题的示例代码，使用的是...其中，MinMaxScaler用于对数据进行归一化处理，train_test_split用于将数据集分为训练集和测试集。最后使用K近邻分类器进行预测，并输出预测结果。

import pandas as pd from sklearn.model_selection import train_test_split from sklearn.tree import DecisionTreeClassifier, plot_tree from sklearn.preprocessing import LabelEncoder import matplotlib.pyplot as plt # 加载csv文件 data = pd.read_csv("data填补.csv") # 将标签进行编码 le = LabelEncoder() data['label'] = le.fit_transform(data['label']) # 划分自变量和因变量 X = data.drop(columns=["label"]) y = data["label"] # 将数据集划分为训练集和测试集 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) # 定义决策树模型 dt_model = DecisionTreeClassifier() # 训练决策树模型 dt_model.fit(X_train, y_train) # 计算测试集的准确率 accuracy = dt_model.score(X_test, y_test) print("测试集准确率：", accuracy) # 可视化决策树 plt.figure(figsize=(30, 30)) plot_tree(dt_model, filled=True, feature_names=X.columns, class_names=le.classes_) plt.show()我想使这段代码生成的决策图只显示置信度大于0.95的区间应该如何更改

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) # 定义决策树模型 dt_model = DecisionTreeClassifier() # 训练决策树模型 dt_model.fit(X_train, y_train) #...

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...

# 导入需要的库import pandas as pdfrom sklearn.model_selection import train_test_splitfrom sklearn.svm import SVCfrom sklearn.metrics import accuracy_score, confusion_matrix# 读取 Excel 表格数据df = pd.read_excel('data.xlsx')# 分离特征变量和因变量X = df.iloc[:, :-1]y = df.iloc[:, -1]# 将数据分为训练集和验证集X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)# 训练支持向量机模型svm = SVC(kernel='linear')svm.fit(X_train, y_train)# 预测新的数据new_data = pd.read_excel('new_data.xlsx')y_pred = svm.predict(new_data)# 输出预测结果到 Excel 表格new_data['Prediction'] = y_prednew_data.to_excel('predictions.xlsx', index=False)# 输出模型精度和混淆矩阵print('Accuracy:', accuracy_score(y_test, svm.predict(X_test)))print('Confusion Matrix:', confusion_matrix(y_test, svm.predict(X_test)))输出混淆矩阵图片

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2) # 训练支持向量机模型 svm = SVC(kernel='linear') svm.fit(X_train, y_train) # 预测新的数据 new_data = pd.read_excel('new_data....

import numpy as np import pandas as pd import matplotlib. pyplot as plt from sklearn.datasets import load_boston from sklearn.model_selection import train_test_split from linear_model_03.closed_form_sol.LinearRegression_CFSol import LinearRegressionClosedFormSol # 数据2，读取数据，先进行数据转换 df = pd.read_table('../data/mpg.data', header=0) # data换成txt,即可转化txt格式 df.to_csv('../data/mpg.csv', index=False) mpg = pd. read_csv("../data/mpg.csv"). dropna() # ..表示上两级目录 X, y = np. asarray(mpg. loc[:, "horsepower"]), np. asarray(mpg. loc[:, "displacement"]) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0, shuffle=True) lr_cfs = LinearRegressionClosedFormSol(fit_intercept=True, normalized=True) # 默认训练偏置项和进行标准化 lr_cfs.fit(X_train, y_train) # 训练模型 theta = lr_cfs.get_params() print(theta) y_test_pred = lr_cfs.predict(x_test=X_test) mse, r2, r2c = lr_cfs.cal_mse_r2(y_test, y_test_pred) print("均方误差：%.5f,判决系数：%.5f,修正判决系数：%.5f" % (mse, r2, r2c)) # lr_cfs. plt_predict(y_test, y_test_pred, is_sort=False) lr_cfs.plt_predict(y_test, y_test_pred, is_sort=True) plt. figure(figsize=(7,5)) plt. plot(X_test, y_test, "ro", label="Test Samples") plt. plot(X_test, y_test_pred, "k-", lw=1.5, label="Fit Model") plt. legend(frameon=False) plt. show()

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0, shuffle=True) # 训练模型 lr_cfs = LinearRegressionClosedFormSol(fit_intercept=True, normalized=True) lr_cfs....

train_data,test_data,train_label,test_label = model_selection.train_test_split(x,y, random_state=1, train_size=0.9,test_size=0.1)

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