# 导入csv文件
def loadDataSet(filename):
    dataset = []
    with open(filename, 'r') as fr:
        for line in fr.readlines():
            if not line:
                continue
            lineArr = []
            for featrue in line.split(','):
                # strip()返回移除字符串头尾指定的字符生成的新字符串
                str_f = featrue.strip()
                if str_f.isdigit(): # 判断是否是数字
                    # 将数据集的第column列转换成float形式
                    lineArr.append(float(str_f))
                else:
                    # 添加分类标签
                    lineArr.append(str_f)
            dataset.append(lineArr)
    return dataset

def cross_validation_split(dataset, n_folds):
    """cross_validation_split(将数据集进行抽重抽样 n_folds 份，数据可以重复抽取)

    Args:
        dataset     原始数据集
        n_folds     数据集dataset分成n_flods份
    Returns:
        dataset_split    list集合，存放的是：将数据集进行抽重抽样 n_folds 份，数据可以重复抽取
    """
    dataset_split = list()
    dataset_copy = list(dataset)       # 复制一份 dataset,防止 dataset 的内容改变
    fold_size = len(dataset) / n_folds
    for i in range(n_folds):
        fold = list()                  # 每次循环 fold 清零，防止重复导入 dataset_split
        while len(fold) < fold_size:   # 这里不能用 if，if 只是在第一次判断时起作用，while 执行循环，直到条件不成立
            # 有放回的随机采样，有一些样本被重复采样，从而在训练集中多次出现，有的则从未在训练集中出现，此为自助采样法。从而保证每棵决策树训练集的差异性            
            index = randrange(len(dataset_copy))
            # 将对应索引 index 的内容从 dataset_copy 中导出，并将该内容从 dataset_copy 中删除。
            # pop() 函数用于移除列表中的一个元素（默认最后一个元素），并且返回该元素的值。
            fold.append(dataset_copy.pop(index))  # 无放回的方式
            # fold.append(dataset_copy[index])  # 有放回的方式
        dataset_split.append(fold)
    # 由dataset分割出的n_folds个数据构成的列表，为了用于交叉验证
    return dataset_split

# Create a random subsample from the dataset with replacement
def subsample(dataset, ratio):   # 创建数据集的随机子样本
    """random_forest(评估算法性能，返回模型得分)

    Args:
        dataset         训练数据集
        ratio           训练数据集的样本比例
    Returns:
        sample          随机抽样的训练样本
    """

    sample = list()
    # 训练样本的按比例抽样。
    # round() 方法返回浮点数x的四舍五入值。
    n_sample = round(len(dataset) * ratio)
    while len(sample) < n_sample:
        # 有放回的随机采样，有一些样本被重复采样，从而在训练集中多次出现，有的则从未在训练集中出现，此为自助采样法。从而保证每棵决策树训练集的差异性
        index = randrange(len(dataset))
        sample.append(dataset[index])
    return sample

# 找出分割数据集的最优特征，得到最优的特征 index，特征值 row[index]，以及分割完的数据 groups（left, right）
def get_split(dataset, n_features):
    class_values = list(set(row[-1] for row in dataset))  # class_values =[0, 1]
    b_index, b_value, b_score, b_groups = 999, 999, 999, None
    features = list()
    while len(features) < n_features:
        index = randrange(len(dataset[0])-1)  # 往 features 添加 n_features 个特征（ n_feature 等于特征数的个数），特征索引从 dataset 中随机取
        if index not in features:
            features.append(index)
    for index in features:                    # 在 n_features 个特征中选出最优的特征索引，并没有遍历所有特征，从而保证了每课决策树的差异性
        for row in dataset:
            groups = test_split(index, row[index], dataset)  # groups=(left, right), row[index] 遍历每一行 index 索引下的特征值作为分类值 value, 找出最优的分类特征和特征值
            gini = gini_index(groups, class_values)
            # 左右两边的数量越一样，说明数据区分度不高，gini系数越大
            if gini < b_score:
                b_index, b_value, b_score, b_groups = index, row[index], gini, groups  # 最后得到最优的分类特征 b_index,分类特征值 b_value,分类结果 b_groups。b_value 为分错的代价成本
    # print b_score
    return {'index': b_index, 'value': b_value, 'groups': b_groups}

# Random Forest Algorithm
def random_forest(train, test, max_depth, min_size, sample_size, n_trees, n_features):
    """random_forest(评估算法性能，返回模型得分)

    Args:
        train           训练数据集
        test            测试数据集
        max_depth       决策树深度不能太深，不然容易导致过拟合
        min_size        叶子节点的大小
        sample_size     训练数据集的样本比例
        n_trees         决策树的个数
        n_features      选取的特征的个数
    Returns:
        predictions     每一行的预测结果，bagging 预测最后的分类结果
    """

    trees = list()
    # n_trees 表示决策树的数量
    for i in range(n_trees):
        # 随机抽样的训练样本， 随机采样保证了每棵决策树训练集的差异性
        sample = subsample(train, sample_size)
        # 创建一个决策树
        tree = build_tree(sample, max_depth, min_size, n_features)
        trees.append(tree)

    # 每一行的预测结果，bagging 预测最后的分类结果
    predictions = [bagging_predict(trees, row) for row in test]
    return predictions

# 评估算法性能，返回模型得分
def evaluate_algorithm(dataset, algorithm, n_folds, *args):
    """evaluate_algorithm(评估算法性能，返回模型得分)

    Args:
        dataset     原始数据集
        algorithm   使用的算法
        n_folds     数据的份数
        *args       其他的参数
    Returns:
        scores      模型得分
    """

    # 将数据集进行随机抽样，分成 n_folds 份，数据无重复的抽取
    folds = cross_validation_split(dataset, n_folds)
    scores = list()
    # 每次循环从 folds 从取出一个 fold 作为测试集，其余作为训练集，遍历整个 folds ，实现交叉验证
    for fold in folds:
        train_set = list(folds)
        train_set.remove(fold)
        # 将多个 fold 列表组合成一个 train_set 列表, 类似 union all
        """
        In [20]: l1=[[1, 2, 'a'], [11, 22, 'b']]
        In [21]: l2=[[3, 4, 'c'], [33, 44, 'd']]
        In [22]: l=[]
        In [23]: l.append(l1)
        In [24]: l.append(l2)
        In [25]: l
        Out[25]: [[[1, 2, 'a'], [11, 22, 'b']], [[3, 4, 'c'], [33, 44, 'd']]]
        In [26]: sum(l, [])
        Out[26]: [[1, 2, 'a'], [11, 22, 'b'], [3, 4, 'c'], [33, 44, 'd']]
        """
        train_set = sum(train_set, [])
        test_set = list()
        # fold 表示从原始数据集 dataset 提取出来的测试集
        for row in fold:
            row_copy = list(row)
            row_copy[-1] = None 
            test_set.append(row_copy)
        predicted = algorithm(train_set, test_set, *args)
        actual = [row[-1] for row in fold]

        # 计算随机森林的预测结果的正确率
        accuracy = accuracy_metric(actual, predicted)
        scores.append(accuracy)
    return scores

#Import Library
from sklearn.ensemble import RandomForestClassifier
 
#Assumed you have, X (predictor) and Y (target) for training data set and x_test(predictor) of test_dataset
# Create Random Forest object
model= RandomForestClassifier()
 
# Train the model using the training sets and check score
model.fit(X, y)
 
#Predict Output
predicted= model.predict(x_test)

# sklearn_rf.py
import pandas as pd
from sklearn.ensemble import RandomForestClassifier
 
df = pd.read_csv('sklearn_data.csv')
train, test = df.query("is_date != -1"), df.query("is_date == -1")
y_train, X_train = train['is_date'], train.drop(['is_date'], axis=1)
X_test = test.drop(['is_date'], axis=1)
 
model = RandomForestClassifier(n_estimators=50,
                               criterion='gini',
                               max_features="sqrt",
                               min_samples_leaf=1,
                               n_jobs=4,
                           )
model.fit(X_train, y_train)
print model.predict(X_test)
print zip(X_train.columns, model.feature_importances_)

调用RandomForestClassifier时的参数说明：
n_estimators：指定森林中树的颗数，越多越好，只是不要超过内存；
criterion：指定在分裂使用的决策算法；
max_features：指定了在分裂时，随机选取的特征数目，sqrt即为全部特征的平均根；
min_samples_leaf：指定每颗决策树完全生成，即叶子只包含单一的样本；
n_jobs：指定并行使用的进程数；

import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
from sklearn.cross_validation import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.datasets import make_moons, make_circles, make_classification
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.lda import LDA
from sklearn.qda import QDA

h = .02  # step size in the mesh

names = ["Nearest Neighbors", "Linear SVM", "RBF SVM", "Decision Tree",
         "Random Forest", "AdaBoost", "Naive Bayes", "LDA", "QDA"]
classifiers = [
    KNeighborsClassifier(3),
    SVC(kernel="linear", C=0.025),
    SVC(gamma=2, C=1),
    DecisionTreeClassifier(max_depth=5),
    RandomForestClassifier(max_depth=5, n_estimators=10, max_features=1),
    AdaBoostClassifier(),
    GaussianNB(),
    LDA(),
    QDA()]

X, y = make_classification(n_features=2, n_redundant=0, n_informative=2,
                           random_state=1, n_clusters_per_class=1)
rng = np.random.RandomState(2)
X += 2 * rng.uniform(size=X.shape)
linearly_separable = (X, y)

datasets = [make_moons(noise=0.3, random_state=0),
            make_circles(noise=0.2, factor=0.5, random_state=1),
            linearly_separable
            ]

figure = plt.figure(figsize=(27, 9))
i = 1
# iterate over datasets
for ds in datasets:
    # preprocess dataset, split into training and test part
    X, y = ds
    X = StandardScaler().fit_transform(X)
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.4)

    x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5
    y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5
    xx, yy = np.meshgrid(np.arange(x_min, x_max, h),
                         np.arange(y_min, y_max, h))

    # just plot the dataset first
    cm = plt.cm.RdBu
    cm_bright = ListedColormap(['#FF0000', '#0000FF'])
    ax = plt.subplot(len(datasets), len(classifiers) + 1, i)
    # Plot the training points
    ax.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm_bright)
    # and testing points
    ax.scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=cm_bright, alpha=0.6)
    ax.set_xlim(xx.min(), xx.max())
    ax.set_ylim(yy.min(), yy.max())
    ax.set_xticks(())
    ax.set_yticks(())
    i += 1

    # iterate over classifiers
    for name, clf in zip(names, classifiers):
        ax = plt.subplot(len(datasets), len(classifiers) + 1, i)
        clf.fit(X_train, y_train)
        score = clf.score(X_test, y_test)

        # Plot the decision boundary. For that, we will assign a color to each
        # point in the mesh [x_min, m_max]x[y_min, y_max].
        if hasattr(clf, "decision_function"):
            Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()])
        else:
            Z = clf.predict_proba(np.c_[xx.ravel(), yy.ravel()])[:, 1]

        # Put the result into a color plot
        Z = Z.reshape(xx.shape)
        ax.contourf(xx, yy, Z, cmap=cm, alpha=.8)

        # Plot also the training points
        ax.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm_bright)
        # and testing points
        ax.scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=cm_bright,
                   alpha=0.6)

        ax.set_xlim(xx.min(), xx.max())
        ax.set_ylim(yy.min(), yy.max())
        ax.set_xticks(())
        ax.set_yticks(())
        ax.set_title(name)
        ax.text(xx.max() - .3, yy.min() + .3, ('%.2f' % score).lstrip('0'),
                size=15, horizontalalignment='right')
        i += 1

figure.subplots_adjust(left=.02, right=.98)
plt.show()

from pprint import pprint
from pyspark import SparkContext
from pyspark.mllib.tree import RandomForest
from pyspark.mllib.regression import LabeledPoint
 
sc = SparkContext()
data = sc.textFile('spark_data.csv').map(lambda x: x.split(',')).map(lambda x: (float(x[0]), int(x[1]), int(x[2]), float(x[3]), int(x[4]), int(x[5])))
train = data.filter(lambda x: x[5]!=-1).map(lambda v: LabeledPoint(v[-1], v[:-1]))
test = data.filter(lambda x: x[5]==-1)#.map(lambda v: LabeledPoint(v[-1], v[:-1]))
 
model = RandomForest.trainClassifier(train,
                                     numClasses=2,
                                     numTrees=50,
                                     categoricalFeaturesInfo={1:2, 2:2, 4:3},
                                     impurity='gini',
                                     maxDepth=5,
                                 )
 
print 'The predict is:', model.predict(test).collect()
print 'The Decision tree is:', model.toDebugString()