log('{} dataset: size={}'.format(tag, len(dataset)))
时间: 2024-04-28 20:10:51 浏览: 8
As an AI language model, I cannot run this code as I don't have access to the `log` function and `dataset` variable. However, I can explain what this code does:
It is a logging statement that formats a string with the `tag` and the length of the `dataset` variable. The `tag` variable is expected to be a string that describes the dataset, such as "training" or "validation". The `len()` function returns the number of elements in the `dataset` variable.
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def load_data(args): if args.dataset == "cora": return citegrh.load_cora() elif args.dataset == "citeseer": return citegrh.load_citeseer() elif args.dataset == "pubmed": return citegrh.load_pubmed() elif args.dataset is not None and args.dataset.startswith("reddit"): return RedditDataset(self_loop=("self-loop" in args.dataset)) else: raise ValueError("Unknown dataset: {}".format(args.dataset))
这段代码是一个数据加载函数`load_data`,根据传入的参数`args`中的`dataset`值来加载不同的数据集。
下面是对代码的解释:
- `def load_data(args):`:这是一个方法定义,接受一个参数`args`,表示加载数据需要的配置参数。
- `if args.dataset == "cora":`:如果`args.dataset`等于"cora",则执行下面的代码块。
- `return citegrh.load_cora()`:调用`citegrh`模块中的`load_cora`函数,加载Cora数据集,并将加载的数据返回。
- `elif args.dataset == "citeseer":`:如果`args.dataset`等于"citeseer",则执行下面的代码块。
- `return citegrh.load_citeseer()`:调用`citegrh`模块中的`load_citeseer`函数,加载Citeseer数据集,并将加载的数据返回。
- `elif args.dataset == "pubmed":`:如果`args.dataset`等于"pubmed",则执行下面的代码块。
- `return citegrh.load_pubmed()`:调用`citegrh`模块中的`load_pubmed`函数,加载Pubmed数据集,并将加载的数据返回。
- `elif args.dataset is not None and args.dataset.startswith("reddit"):`
- 如果`args.dataset`不为`None`且以"reddit"开头,则执行下面的代码块。
- `return RedditDataset(self_loop=("self-loop" in args.dataset))`:创建一个Reddit数据集对象,并根据参数`args.dataset`中是否包含"self-loop"来决定是否添加自环边。
- `else:`:如果以上条件都不满足,则执行下面的代码块。
- `raise ValueError("Unknown dataset: {}".format(args.dataset))`:抛出一个值错误异常,指示未知的数据集。
通过这段代码,根据传入的参数`args.dataset`的值,选择加载对应的数据集,并将加载的数据返回。如果`args.dataset`的值不在预定义的数据集中,则会抛出一个异常。
已知# 计算信息熵 def cal_entropy(dataset): numEntries = len(dataset) labelCounts = {} # 给所有可能分类创建字典 for featVec in dataset: currentlabel = featVec[-1] if currentlabel not in labelCounts.keys(): labelCounts[currentlabel] = 0 labelCounts[currentlabel] += 1 Ent = 0.0 # *** START CODE HERE *** for key in labelCounts: prob = float(labelCounts[key])/numEntries Ent -= prob*log(prob,2) # 计算信息熵Ent # *** END CODE HERE *** return Ent # 划分数据集 def splitdataset(dataset, axis, value): retdataset = [] # 创建返回的数据集列表 for featVec in dataset: # 抽取符合划分特征的值 if featVec[axis] == value: reducedfeatVec = featVec[:axis] # 去掉axis特征 reducedfeatVec.extend(featVec[axis + 1:]) # 将符合条件的特征添加到返回的数据集列表 retdataset.append(reducedfeatVec) return retdataset请补全# CART算法 def CART_chooseBestFeatureToSplit(dataset): numFeatures = len(dataset[0]) - 1 bestGini = 999999.0 bestFeature = -1 # *** START CODE HERE *** # 利用CART的原理以及仿照ID3_chooseBestFeatureToSplit代码进行编码 # *** END CODE HERE *** return bestFeature
请补全代码如下:
```python
import numpy as np
# 计算基尼不纯度
def cal_gini(dataset):
numEntries = len(dataset)
labelCounts = {}
for featVec in dataset:
currentlabel = featVec[-1]
if currentlabel not in labelCounts.keys():
labelCounts[currentlabel] = 0
labelCounts[currentlabel] += 1
Gini = 1.0
for key in labelCounts:
prob = float(labelCounts[key]) / numEntries
Gini -= prob * prob
return Gini
# 划分数据集
def splitdataset(dataset, axis, value):
retdataset = []
for featVec in dataset:
if featVec[axis] == value:
reducedfeatVec = featVec[:axis]
reducedfeatVec.extend(featVec[axis + 1:])
retdataset.append(reducedfeatVec)
return retdataset
# CART算法选择最优特征
def CART_chooseBestFeatureToSplit(dataset):
numFeatures = len(dataset[0]) - 1
bestGini = 999999.0
bestFeature = -1
for i in range(numFeatures):
featList = [example[i] for example in dataset]
uniqueVals = set(featList)
newGini = 0.0
for value in uniqueVals:
subdataset = splitdataset(dataset, i, value)
prob = len(subdataset) / float(len(dataset))
newGini += prob * cal_gini(subdataset)
if newGini < bestGini:
bestGini = newGini
bestFeature = i
return bestFeature
```
其中,cal_gini()函数用来计算基尼不纯度,CART_chooseBestFeatureToSplit()函数用来选择最优划分特征。