[实践]自行车租赁预测

认识数据

这是一个城市自行车租赁系统，提供的数据为2年内华盛顿按小时记录的自行车租赁数据，其中训练集由每个月的前19天组成，测试集由20号之后的时间组成（需要我们自己去预测）。数据来源：Kaggle自行车租赁预测比赛

项目数据描述如下： (1) datetime：日期，以年-月-日 小时的形式给出。 (2) season：季节。1 为春季, 2为夏季,3 为秋季,4 为冬季。(3) hodliday：是否为假期。1代表是，0代表不是。 (4) workingday：是否为工作日，1代表是，0代表不是。 (5) weather:天气：     1: 天气晴朗或者少云/部分有云。     2: 有雾和云/风等。     3: 小雪/小雨，闪电及多云。     4: 大雨/冰雹/闪电和大雾/大雪。   (6) temp - 摄氏温度。 (7) atemp - 人们感觉的温度。 (8) humidity - 湿度。 (9) windspeed - 风速。 (10) casual -随机预定自行车的人数 (11) registered - 登记预定自行车的人数。 (12) count - 总租车数，即casual+registered数目。 其中10~12不属于特征，12为我们需要预测的值。

数据预处理

导入相关数据分析包，将matplotlib的图表直接嵌入到Notebook之中，读取训练数据，观察训练集前十行，获取数据类型与数据集大小。

import numpy as npimport pandas as pdimport matplotlib.pyplot as pltimport seaborn as sns%matplotlib inlinedf_train = pd.read_csv('kaggle_bike_competition_train.csv',header = 0)df_train.head(10)

print "字段名称与类型：", '\n' , df_train.dtypesprint "数据集大小：", '\n' , df_train.shapeprint "列统计：", '\n' , df_train.count()

字段名称与类型： datetime       objectseason          int64holiday         int64workingday      int64weather         int64temp          float64atemp         float64humidity        int64windspeed     float64casual          int64registered      int64count           int64dtype: object数据集大小： (10886, 12)列统计： datetime      10886season        10886holiday       10886workingday    10886weather       10886temp          10886atemp         10886humidity      10886windspeed     10886casual        10886registered    10886count         10886dtype: int64

训练集有10886个样本，12个变量，没有缺省值。

发现datetime数值包含的信息很多，我们将月、日、和 小时单独拎出来，放到3列中，然后删除与模型学习无关的变量。

df_train['month'] = pd.DatetimeIndex(df_train.datetime).monthdf_train['day'] = pd.DatetimeIndex(df_train.datetime).dayofweekdf_train['hour'] = pd.DatetimeIndex(df_train.datetime).hourdf_train_origin = df_train #保存原数据集df_train = df_train.drop(['datetime','casual','registered'], axis = 1)

将数据集分为两部分：
1. df_train_target：目标，也就是count字段。
2. df_train_data：用于产出特征的数据

df_train_target = df_train['count'].valuesdf_train_data = df_train.drop(['count'],axis = 1).values

特征工程

应用机器学习算法的过程，多半是在调参，各种不同的参数会带来不同的结果（比如正则化系数，比如决策树类的算法的树深和棵树，比如距离判定准则等等等等）

我们使用交叉验证的方式（交叉验证集约占全部数据的20%）来看看模型的效果，我们会试 支持向量回归/Suport Vector Regression, 岭回归/Ridge Regression 和 随机森林回归/Random Forest Regressor。每个模型会跑3趟看平均的结果。

from sklearn import linear_modelfrom sklearn import cross_validationfrom sklearn import svmfrom sklearn.ensemble import RandomForestRegressorfrom sklearn.learning_curve import learning_curvefrom sklearn.grid_search import GridSearchCVfrom sklearn.metrics import explained_variance_score# 切分数据（训练集和测试集）cv = cross_validation.ShuffleSplit(len(df_train_data), n_iter=3, test_size=0.2,    random_state=0)print "岭回归"    for train, test in cv:        svc = linear_model.Ridge().fit(df_train_data[train], df_train_target[train])    print("train score: {0:.3f}, test score: {1:.3f}\n".format(        svc.score(df_train_data[train], df_train_target[train]), svc.score(df_train_data[test], df_train_target[test])))print "支持向量回归/SVR(kernel='rbf',C=10,gamma=.001)"for train, test in cv:       svc = svm.SVR(kernel ='rbf', C = 10, gamma = .001).fit(df_train_data[train], df_train_target[train])    print("train score: {0:.3f}, test score: {1:.3f}\n".format(        svc.score(df_train_data[train], df_train_target[train]), svc.score(df_train_data[test], df_train_target[test])))print "随机森林回归/Random Forest(n_estimators = 100)"    for train, test in cv:        svc = RandomForestRegressor(n_estimators = 100).fit(df_train_data[train], df_train_target[train])    print("train score: {0:.3f}, test score: {1:.3f}\n".format(        svc.score(df_train_data[train], df_train_target[train]), svc.score(df_train_data[test], df_train_target[test])))

岭回归train score: 0.339, test score: 0.332train score: 0.330, test score: 0.370train score: 0.342, test score: 0.320支持向量回归/SVR(kernel='rbf',C=10,gamma=.001)train score: 0.417, test score: 0.408train score: 0.406, test score: 0.452train score: 0.419, test score: 0.390随机森林回归/Random Forest(n_estimators = 100)train score: 0.981, test score: 0.866train score: 0.981, test score: 0.880train score: 0.981, test score: 0.870

模型调参

随机森林回归获得了最佳结果，利用GridSearch尝试寻找最优参数，大概耗时2分钟左右的时间。

X = df_train_datay = df_train_targetX_train, X_test, y_train, y_test = cross_validation.train_test_split(    X, y, test_size=0.2, random_state=0)tuned_parameters = [{'n_estimators':[10,100,500]}]   scores = ['r2']for score in scores:    print score    clf = GridSearchCV(RandomForestRegressor(), tuned_parameters, cv=5, scoring=score)    clf.fit(X_train, y_train)    #最优模型    print(clf.best_estimator_)    print ""    print("得分分别是:")    for params, mean_score, scores in clf.grid_scores_:        print("%0.3f (+/-%0.03f) for %r"              % (mean_score, scores.std() / 2, params))

RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,           max_features='auto', max_leaf_nodes=None,           min_impurity_split=1e-07, min_samples_leaf=1,           min_samples_split=2, min_weight_fraction_leaf=0.0,           n_estimators=500, n_jobs=1, oob_score=False, random_state=None,           verbose=0, warm_start=False)得分分别是:0.846 (+/-0.008) for {'n_estimators': 10}0.862 (+/-0.006) for {'n_estimators': 100}0.863 (+/-0.005) for {'n_estimators': 500}

再看看模型的学习曲线，是否过拟合或欠拟合

def plot_learning_curve(estimator, title, X, y, ylim=None, cv=None,                        n_jobs=1, train_sizes=np.linspace(.1, 1.0, 5)):    plt.figure()    plt.title(title)    if ylim is not None:        plt.ylim(*ylim)    plt.xlabel("Training examples")    plt.ylabel("Score")    train_sizes, train_scores, test_scores = learning_curve(        estimator, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes)    train_scores_mean = np.mean(train_scores, axis=1)    train_scores_std = np.std(train_scores, axis=1)    test_scores_mean = np.mean(test_scores, axis=1)    test_scores_std = np.std(test_scores, axis=1)    plt.grid()    plt.fill_between(train_sizes, train_scores_mean - train_scores_std,                     train_scores_mean + train_scores_std, alpha=0.1,                     color="r")    plt.fill_between(train_sizes, test_scores_mean - test_scores_std,                     test_scores_mean + test_scores_std, alpha=0.1, color="g")    plt.plot(train_sizes, train_scores_mean, 'o-', color="r",             label="Training score")    plt.plot(train_sizes, test_scores_mean, 'o-', color="g",             label="Cross-validation score")    plt.legend(loc="best")    return plttitle = "Learning Curves (Random Forest, n_estimators = 100)"cv = cross_validation.ShuffleSplit(df_train_data.shape[0], n_iter=10,test_size=0.2, random_state=0)estimator = RandomForestRegressor(n_estimators = 100)plot_learning_curve(estimator, title, X, y, (0.0, 1.01), cv=cv, n_jobs=4)plt.show()

随机森林的算法学习能力比较强，由图可以发现，训练集和测试集的得分差距也是蛮大的，过拟合还比较明显，尝试一下缓解过拟合，效果不是太好。

print "随机森林回归/Random Forest(n_estimators=200, max_features=0.6, max_depth=15)"for train, test in cv:     svc = RandomForestRegressor(n_estimators = 200, max_features=0.6, max_depth=15).fit(df_train_data[train], df_train_target[train])    print("train score: {0:.3f}, test score: {1:.3f}\n".format(        svc.score(df_train_data[train], df_train_target[train]), svc.score(df_train_data[test], df_train_target[test])))

随机森林回归/Random Forest(n_estimators=200, max_features=0.6, max_depth=15)train score: 0.965, test score: 0.870train score: 0.966, test score: 0.885train score: 0.965, test score: 0.872train score: 0.965, test score: 0.877train score: 0.967, test score: 0.870train score: 0.965, test score: 0.872train score: 0.966, test score: 0.864train score: 0.966, test score: 0.873train score: 0.965, test score: 0.873train score: 0.966, test score: 0.870