Python sklearn df问题-字段Cady示例代码问题

我正在阅读Field Cady的《数据科学手册》，这里有示例代码：

我从代码的第23行得到语法错误，即：

File "<ipython-input-4-02028cc326e3>", line 2
    X, Y = df[df.columns[:3]], (df['species']=='virginica') X_train, X_test, 
Y_train, Y_test = train_test_split(X, Y, test_size=.8)
                                                                  ^
SyntaxError: invalid syntax

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您必须首先加载

iris

数据集。这是您的更新代码

    from matplotlib import pyplot as plt
    import sklearn
    from sklearn.metrics import roc_curve, auc
    from sklearn.cross_validation import train_test_split
    from sklearn.linear_model import LogisticRegression
    from sklearn.tree import DecisionTreeClassifier
    from sklearn.ensemble import RandomForestClassifier
    from sklearn.naive_bayes import GaussianNB
    # name -> (line format, classifier)
    from sklearn.datasets import load_iris
    import pandas as pd
    data = load_iris()
    df = pd.DataFrame(data['data'], columns=data['feature_names'])
    df['species'] = pd.Categorical.from_codes(iris.target, iris.target_names)


    CLASS_MAP = {
        'LogisticRegression':
            ('-', LogisticRegression()),
        'Naive Bayes': ('--', GaussianNB()),
        'Decision Tree':
            ('.-', DecisionTreeClassifier(max_depth=5)),
        'Random Forest':
            (':', RandomForestClassifier(
                max_depth=5, n_estimators=10,
                max_features=1)),
    }
    # Divide cols by independent/dependent, rows by test/ train
    X, Y = df[df.columns[:3]], (df['species']=='virginica') 
    X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=.8)
    for name, (line_fmt, model) in CLASS_MAP.items():
        model.fit(X_train, Y_train)
        # array w one col per label
        preds = model.predict_proba(X_test)
        pred = pd.Series(preds[:,1])
        fpr, tpr, thresholds = roc_curve(Y_test, pred)
        auc_score = auc(fpr, tpr)
        label='%s: auc=%f' % (name, auc_score)
        plt.plot(fpr, tpr, line_fmt,
            linewidth=5, label=label)
    plt.legend(loc="lower right")
    plt.title('Comparing Classifiers')
    plt.plot([0, 1], [0, 1], 'k--') #x=y line.  Visual aid
    plt.xlim([0.0, 1.0])
    plt.ylim([0.0, 1.05])
    plt.xlabel('False Positive Rate') 
    plt.ylabel('True Positive Rate')
    plt.show()

---

X\u train

之前缺少一个逗号。非常感谢，这给了我新的错误。。。不确定它在评论中是否显示得很好，^指向第一个=符号X，Y=df[df.columns[：3]，（df['species']='virginica'），X_train，X_test，Y_train，Y_test=train_test_split（X，Y，test_size=.8）^语法错误：无法分配到比较请发布代码，不仅仅是错误，所以我们可以给你更好的指导。谢谢你，萨默拉，我也在原始帖子的底部添加了它。

    from matplotlib import pyplot as plt
    import sklearn
    from sklearn.metrics import roc_curve, auc
    from sklearn.cross_validation import train_test_split
    from sklearn.linear_model import LogisticRegression
    from sklearn.tree import DecisionTreeClassifier
    from sklearn.ensemble import RandomForestClassifier
    from sklearn.naive_bayes import GaussianNB
    # name -> (line format, classifier)
    from sklearn.datasets import load_iris
    import pandas as pd
    data = load_iris()
    df = pd.DataFrame(data['data'], columns=data['feature_names'])
    df['species'] = pd.Categorical.from_codes(iris.target, iris.target_names)


    CLASS_MAP = {
        'LogisticRegression':
            ('-', LogisticRegression()),
        'Naive Bayes': ('--', GaussianNB()),
        'Decision Tree':
            ('.-', DecisionTreeClassifier(max_depth=5)),
        'Random Forest':
            (':', RandomForestClassifier(
                max_depth=5, n_estimators=10,
                max_features=1)),
    }
    # Divide cols by independent/dependent, rows by test/ train
    X, Y = df[df.columns[:3]], (df['species']=='virginica') 
    X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=.8)
    for name, (line_fmt, model) in CLASS_MAP.items():
        model.fit(X_train, Y_train)
        # array w one col per label
        preds = model.predict_proba(X_test)
        pred = pd.Series(preds[:,1])
        fpr, tpr, thresholds = roc_curve(Y_test, pred)
        auc_score = auc(fpr, tpr)
        label='%s: auc=%f' % (name, auc_score)
        plt.plot(fpr, tpr, line_fmt,
            linewidth=5, label=label)
    plt.legend(loc="lower right")
    plt.title('Comparing Classifiers')
    plt.plot([0, 1], [0, 1], 'k--') #x=y line.  Visual aid
    plt.xlim([0.0, 1.0])
    plt.ylim([0.0, 1.05])
    plt.xlabel('False Positive Rate') 
    plt.ylabel('True Positive Rate')
    plt.show()