在Scikit-Learn中設置多個算法試驗的問題

我正嘗試使用sklearn文檔中的this示例。我真的不知道是什麼代碼做什麼，儘管我認爲我輸入我的數據集走錯了路，我最近獲得了這個錯誤：在Scikit-Learn中設置多個算法試驗的問題

<ipython-input-26-3c3c0763766b> in <module>() 
    49 for ds in datasets: 
    50  # preprocess dataset, split into training and test part 
---> 51  X, y = ds 
    52  X = StandardScaler().fit_transform(X) 
    53  X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.4)

ValueError異常：值過多解壓任何想法，我怎麼可以修改代碼以使用我的數據集（這是來自熊貓數據框的多維numpy數組）並修復錯誤？

dataURL = "peridotites_clean_complete.csv" 
pd_data = pd.read_csv(dataURL) 
rock_names = pd_data['ROCK NAME'] 
rock_compositions = pd_data.columns[1:] 
rock_data = np.vstack([pd_data[x] for x in rock_compositions]) 

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 = [rock_data] 

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()

來源

2014-10-28 Evan Mosseri

的事情是ds是如下所示的一個具有兩個以上值的列表：

>>> ds=['rockatr1','rockatr2','rockatr','rocktype'] 
>>> X,y=ds 
Traceback (most recent call last): 
    File "<stdin>", line 1, in <module> 
ValueError: too many values to unpack

您必須指定哪一部分是X，如圖所示這是y下面。通常在分類數據中，最後一列用作標籤，這就是我在這裏所假設的。

>>> X,y=ds[:-1],ds[-1] 
>>> X 
['rockatr1', 'rockatr2', 'rockatr'] 
>>> y 
'rocktype'

來源

2014-10-28 05:16:51

在Scikit-Learn中設置多個算法試驗的問題

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