pycharm中的sklearn 包dataset

PyCharm本身并没有提供sklearn包的数据集，但是你可以通过以下代码在PyCharm中获取和使用sklearn中的数据集：

from sklearn.datasets import load_iris
iris = load_iris()
X = iris.data
y = iris.target

这个例子使用了sklearn中的经典数据集iris。你可以通过调用load_iris函数来获取这个数据集，然后将数据集中的数据和标签分别赋值给X和y变量。这里的X和y可以用于训练和测试机器学习模型。

你也可以通过其他类似的函数来获取sklearn中的其他数据集，例如load_digits函数可以获取手写数字数据集。

相关问题

pycharm实现利用sklearn的决策树函数实现分类问题的预测

PyCharm是一款强大的Python集成开发环境，它可以帮助你在其中方便地使用scikit-learn库来构建决策树模型进行分类任务。以下是简单的步骤：

1. **安装依赖**：首先，确保已经安装了`pycharm`, `numpy`, `pandas`, 和 `scikit-learn`。如果没有安装，可以在PyCharm的终端或者命令行里通过`pip install`命令来安装。

2. **导入所需的库**：

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import accuracy_score

3. **数据预处理**：
- 加载数据集，例如从CSV文件、数据库或其他源加载。可以使用pandas的`read_csv()`函数读取CSV文件。
- 将数据划分为特征（X）和目标变量（y）。

   data = pd.read_csv('your_dataset.csv')
   X = data.drop('target_column', axis=1)
   y = data['target_column']

4. **划分训练集和测试集**：

   X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

5. **创建并训练决策树模型**：

   model = DecisionTreeClassifier()
   model.fit(X_train, y_train)

6. **进行预测**：

   predictions = model.predict(X_test)

7. **评估性能**：
使用`accuracy_score`计算模型在测试集上的准确率：

   accuracy = accuracy_score(y_test, predictions)
   print("Accuracy:", accuracy)

8. **保存和使用模型**：
如果需要长期使用，可以将模型序列化存储起来：

   from sklearn.externals import joblib
   joblib.dump(model, 'tree_model.pkl')

预测新数据时，可以读取模型进行预测。

修改一下这段代码在pycharm中的实现，import pandas as pd import numpy as np from sklearn.model_selection import train_test_split import torch import torch.nn as nn import torch.nn.functional as F import torch.optim as optim #from torchvision import datasets,transforms import torch.utils.data as data #from torch .nn:utils import weight_norm import matplotlib.pyplot as plt from sklearn.metrics import precision_score from sklearn.metrics import recall_score from sklearn.metrics import f1_score from sklearn.metrics import cohen_kappa_score data_ = pd.read_csv(open(r"C:\Users\zhangjinyue\Desktop\rice.csv"),header=None) data_ = np.array(data_).astype('float64') train_data =data_[:,:520] train_Data =np.array(train_data).astype('float64') train_labels=data_[:,520] train_labels=np.array(train_data).astype('float64') train_data,train_data,train_labels,train_labels=train_test_split(train_data,train_labels,test_size=0.33333) train_data=torch.Tensor(train_data) train_data=torch.LongTensor(train_labels) train_data=train_data.reshape(-1,1,20,26) train_data=torch.Tensor(train_data) train_data=torch.LongTensor(train_labels) train_data=train_data.reshape(-1,1,20,26) start_epoch=1 num_epoch=1 BATCH_SIZE=70 Ir=0.001 classes=('0','1','2','3','4','5') device=torch.device("cuda"if torch.cuda.is_available()else"cpu") torch.backends.cudnn.benchmark=True best_acc=0.0 train_dataset=data.TensorDataset(train_data,train_labels) test_dataset=data.TensorDataset(train_data,train_labels) train_loader=torch.utills.data.DataLoader(dtaset=train_dataset,batch_size=BATCH_SIZE,shuffle=True) test_loader=torch.utills.data.DataLoader(dtaset=train_dataset,batch_size=BATCH_SIZE,shuffle=True)

修改后的代码如下所示：

import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torch.utils.data as data
import matplotlib.pyplot as plt
from sklearn.metrics import precision_score, recall_score, f1_score, cohen_kappa_score

data_ = pd.read_csv(r"C:\Users\zhangjinyue\Desktop\rice.csv", header=None)
data_ = np.array(data_).astype('float64')

train_data = data_[:, :520]
train_labels = data_[:, 520]
train_data, _, train_labels, _ = train_test_split(train_data, train_labels, test_size=0.33333)

train_data = torch.Tensor(train_data)
train_labels = torch.LongTensor(train_labels)
train_data = train_data.reshape(-1, 1, 20, 26)

start_epoch = 1
num_epoch = 1
BATCH_SIZE = 70
Ir = 0.001
classes = ('0', '1', '2', '3', '4', '5')
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
torch.backends.cudnn.benchmark = True
best_acc = 0.0

train_dataset = data.TensorDataset(train_data, train_labels)
test_dataset = data.TensorDataset(train_data, train_labels)

train_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=BATCH_SIZE, shuffle=True)
test_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=BATCH_SIZE, shuffle=True)

请注意，我做了以下修改：

1. 移除了不需要的导入语句。
2. 修复了变量名拼写错误。
3. 移除了重复的代码行。
4. 修正了 `torch.utils.data.DataLoader` 的拼写错误。
5. 修正了数据集分割时的变量名错误。

请根据你的实际需求进一步调整代码。