Mlxtend简介

Mlxtend是一个基于Python的开源项目，主要为日常处理数据科学相关的任务提供了一些工具和扩展，项目的Github地址：https://github.com/rasbt/mlxtend

在项目文档的User Guide一栏可以看到，mlxtend主要提供如下几个大类的工具模块

classifier

• Adaline
• EnsembleVoteClassifier
• LogisticRegression
• NeuralNetMLP
• Perceptron

regressor

• LinearRegression

regression_utils

• plot linear regression

feature_selection

• SequentialFeatureSelector

evaluate

• Confusion Matrix
• Plot decision regions
• Plot learning curves
• Scoring

preprocesssing

• DenseTransformer
• MeanCenterer
• Minmax scaling
• Shuffle arrays unison
• Standardize

data

• AutoMPG data
• Boston housing data
• Iris data
• Mnist data
• Load mnist
• Wine data

file_io

• Find filegroups
• Find files

general plotting

• Category scatter
• Enrichment plot
• Stacked barplot

math

• Num combinations
• Num permutations

text

• Generalize names
• Generalize names duplcheck
• Tokenizer

utils

• Counter

general concepts

• Activation functions
• Gradient optimization
• Linear gradient derivative
• Regularization linear

以上每个工具模块都附有相应的example、API和source code，可方便查阅。

附上项目首页的example:

import numpy as np
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
import itertools
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC
from sklearn.ensemble import RandomForestClassifier
from mlxtend.classifier import EnsembleVoteClassifier
from mlxtend.data import iris_data
from mlxtend.evaluate import plot_decision_regions

# Initializing Classifiers
clf1 = LogisticRegression(random_state=0)
clf2 = RandomForestClassifier(random_state=0)
clf3 = SVC(random_state=0, probability=True)
eclf = EnsembleVoteClassifier(clfs=[clf1, clf2, clf3], weights=[2, 1, 1], voting='soft')

# Loading some example data
X, y = iris_data()
X = X[:,[0, 2]]

# Plotting Decision Regions
gs = gridspec.GridSpec(2, 2)
fig = plt.figure(figsize=(10, 8))
for clf, lab, grd in zip([clf1, clf2, clf3, eclf],
                         ['Logistic Regression', 'Random Forest', 'Naive Bayes', 'Ensemble'],
                         itertools.product([0, 1], repeat=2)):
    clf.fit(X, y)
    ax = plt.subplot(gs[grd[0], grd[1]])
    fig = plot_decision_regions(X=X, y=y, clf=clf, legend=2)
    plt.title(lab)
plt.show()