Python中支持向量机的libsvm特征示例

2条回答

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1楼 · 编辑于 2024-10-05 12:24:08

我赞同@MarcoPashkov的评论，但我将尝试详细说明LibSVM文件格式。我发现文档很全面但很难找到，对于Python库，我建议使用README on GitHub。

需要注意的一点是，存在一种稀疏格式，其中0的所有特征都将被删除，而密集格式中0的特征不会被删除。这两个都是自述文件中的等效示例。

# Dense data
>>> y, x = [1,-1], [[1,0,1], [-1,0,-1]]
# Sparse data
>>> y, x = [1,-1], [{1:1, 3:1}, {1:-1,3:-1}]

y变量存储数据的所有类别的列表。

x变量存储特征向量。

assert len(y) == len(x), "Both lists should be the same length"

在Heart Scale Example中找到的格式是一种稀疏格式，其中dictionary键是特征索引，dictionary值是特征值，而第一个值是类别。

稀疏格式在为特征向量使用Bag of Words Representation时非常有用。

As most documents will typically use a very small subset of the words used in the corpus, the resulting matrix will have many feature values that are zeros (typically more than 99% of them).
For instance a collection of 10,000 short text documents (such as emails) will use a vocabulary with a size in the order of 100,000 unique words in total while each document will use 100 to 1000 unique words individually.

作为一个使用你开始使用的特征向量的例子，我训练了一个基本的LibSVM 3.20模型。这段代码并不打算使用，但可能有助于展示如何创建和测试模型。

from collections import namedtuple
# Using namedtuples for descriptive purposes, in actual code a normal tuple would work fine.
Category = namedtuple("Category", ["index", "name"])
Feature = namedtuple("Feature", ["category_index", "distance_from_beginning", "distance_from_end", "contains_digit", "capitalized"])

# Separate up the set of categories, libsvm requires a numerical index so we associate each with an index.
categories = dict()
for index, name in enumerate("B M C S NA".split(' ')):
    # LibSVM expects index to start at 1, not 0.
    categories[name] = Category(index + 1, name)
categories

Out[0]: {'B': Category(index=1, name='B'),
   'C': Category(index=3, name='C'),
   'M': Category(index=2, name='M'),
   'NA': Category(index=5, name='NA'),
   'S': Category(index=4, name='S')}

# Faked set of CSV input for example purposes.
csv_input_lines = """category_index,distance_from_beginning,distance_from_end,contains_digit,capitalized
B,1,10,1,0
M,10,1,0,1
C,2,3,0,1
S,23,2,0,0
NA,12,0,0,1""".split("\n")
# We just ignore the header.
header = csv_input_lines[0]

# A list of Feature namedtuples, this will be trained as lists.
features = list()
for line in csv_input_lines[1:]:
    split_values = line.split(',')
    # Create a Feature with the values converted to integers.
    features.append(Feature(categories[split_values[0]].index, *map(int, split_values[1:])))

features

Out[1]: [Feature(category_index=1, distance_from_beginning=1, distance_from_end=10, contains_digit=1, capitalized=0),
 Feature(category_index=2, distance_from_beginning=10, distance_from_end=1, contains_digit=0, capitalized=1),
 Feature(category_index=3, distance_from_beginning=2, distance_from_end=3, contains_digit=0, capitalized=1),
 Feature(category_index=4, distance_from_beginning=23, distance_from_end=2, contains_digit=0, capitalized=0),
 Feature(category_index=5, distance_from_beginning=12, distance_from_end=0, contains_digit=0, capitalized=1)]

# Y is the category index used in training for each Feature. Now it is an array (order important) of all the trained indexes.
y = map(lambda f: f.category_index, features)
# X is the feature vector, for this we convert all the named tuple's values except the category which is at index 0.
x = map(lambda f: list(f)[1:], features)

from svmutil import svm_parameter, svm_problem, svm_train, svm_predict
# Barebones defaults for SVM
param = svm_parameter()
# The (Y,X) parameters should be the train dataset.
prob = svm_problem(y, x)
model=svm_train(prob, param)

# For actual accuracy checking, the (Y,X) parameters should be the test dataset.
p_labels, p_acc, p_vals = svm_predict(y, x, model)

Out[3]: Accuracy = 100% (5/5) (classification)

我希望这个例子有用，它不应该用于你的训练。它之所以被当作一个例子，仅仅是因为它效率低下。

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2楼 · 编辑于 2024-10-05 12:24:08

下面是一个分步指导，教你如何使用数据训练支持向量机，然后使用相同的数据集进行评估。也可以在http://nbviewer.ipython.org/gist/anonymous/2cf3b993aab10bf26d5f找到。在url中，您还可以看到中间数据的输出和结果的准确性（这是一个iPython notebook）

步骤0：安装依赖项

您需要安装以下库：

熊猫
scikit学习

从命令行：

pip install pandas
pip install scikit-learn

步骤1：加载数据

我们将使用熊猫来加载我们的数据。 pandas是一个易于加载数据的库。为了说明这一点，我们首先保存示例数据到csv，然后加载它。

我们将用train.csv训练支持向量机，并用test.csv获取测试标签

import pandas as pd

train_data_contents = """
class_label,distance_from_beginning,distance_from_end,contains_digit,capitalized
B,1,10,1,0
M,10,1,0,1
C,2,3,0,1
S,23,2,0,0
N,12,0,0,1"""


with open('train.csv', 'w') as output:
    output.write(train_data_contents)

train_dataframe = pd.read_csv('train.csv')

步骤2：处理数据

我们将把数据帧转换成numpy数组，这是scikit的格式- 学会理解。

我们需要转换标签“B”、“M”、“C”，。。。也因为支持向量机不懂弦乐。

然后用数据训练一个线性支持向量机

import numpy as np

train_labels = train_dataframe.class_label
labels = list(set(train_labels))
train_labels = np.array([labels.index(x) for x in train_labels])
train_features = train_dataframe.iloc[:,1:]
train_features = np.array(train_features)

print "train labels: "
print train_labels
print 
print "train features:"
print train_features

我们在这里看到，train_labels（5）的长度正好匹配多少行我们有trainfeatures。train_labels中的每个项对应一行。

步骤3：训练SVM

from sklearn import svm
classifier = svm.SVC()
classifier.fit(train_features, train_labels)

步骤4：在一些测试数据上评估支持向量机

test_data_contents = """
class_label,distance_from_beginning,distance_from_end,contains_digit,capitalized
B,1,10,1,0
M,10,1,0,1
C,2,3,0,1
S,23,2,0,0
N,12,0,0,1
"""

with open('test.csv', 'w') as output:
    output.write(test_data_contents)

test_dataframe = pd.read_csv('test.csv')

test_labels = test_dataframe.class_label
labels = list(set(test_labels))
test_labels = np.array([labels.index(x) for x in test_labels])

test_features = test_dataframe.iloc[:,1:]
test_features = np.array(test_features)

results = classifier.predict(test_features)
num_correct = (results == test_labels).sum()
recall = num_correct / len(test_labels)
print "model accuracy (%): ", recall * 100, "%"

链接和提示

如何加载LinearSVC的示例代码：http://scikitlearn.org/stable/modules/svm.html#svm
scikit学习示例的长列表：http://scikitlearn.org/stable/auto_examples/index.html。我发现这些有点帮助，但是经常弄糊涂自己。
如果您发现SVM需要很长时间来训练，请尝试LinearSVC 取而代之：http://scikitlearn.org/stable/modules/generated/sklearn.svm.LinearSVC.html
下面是另一个熟悉机器学习模型的教程：http://scikit-learn.org/stable/tutorial/basic/tutorial.html

您应该能够使用此代码并用您的培训数据替换train.csv，用您的测试数据替换test.csv，并获得测试数据的预测和准确结果。

请注意，由于您使用的是您培训过的数据，因此评估的准确性将异常高。

步骤0：安装依赖项

步骤1：加载数据

步骤2：处理数据

步骤3：训练SVM

步骤4：在一些测试数据上评估支持向量机

链接和提示

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