ML之sklearn：sklearn.metrics中常用的函数参数(比如confusion_matrix等 )解释及其用法说明之详细攻略
ML之sklearn：sklearn.metrics中常用的函数参数(比如confusion_matrix等 )解释及其用法说明之详细攻略推荐文章ML：分类预测问题中评价指标(ER/混淆矩阵P-R-F1/ROC-AUC/RP/mAP)简介、使用方法、代码实现、案例应用之详细攻略CNN之性能指标：卷积神经网络中常用的性能指标(IOU/AP/mAP、混淆矩阵)简介、使用方法之详细攻略sklearn.metrics中常用的函数参数confusion_matrix函数解释返回值：混淆矩阵，其第i行和第j列条目表示真实标签为第i类、预测标签为第j类的样本数。预测0 1真实 01def confusion_matrix Found at: sklearn.metrics._classification@_deprecate_positional_argsdef confusion_matrix(y_true, y_pred, *, labels=None, sample_weight=None, normalize=None):"""Compute confusion matrix to evaluate the accuracy of a classification.By definition a confusion matrix :math:`C` is such that :math:`C_{i, j}` is equal to the number of observations known to be in group :math:`i` and predicted to be in group :math:`j`.Thus in binary classification, the count of true negatives is:math:`C_{0,0}`, false negatives is :math:`C_{1,0}`, true positives is:math:`C_{1,1}` and false positives is :math:`C_{0,1}`.Read more in the :ref:`User Guide <confusion_matrix>`.Parameters----------y_true : array-like of shape (n_samples,) Ground truth (correct) target values.y_pred : array-like of shape (n_samples,) Estimated targets as returned by a classifier.labels : array-like of shape (n_classes), default=None. List of labels to index the matrix. This may be used to reorderor select a subset of labels. If ``None`` is given, those that appear at least once in ``y_true`` or ``y_pred`` are used in sorted order.sample_weight : array-like of shape (n_samples,), default=None. Sample weights... versionadded:: 0.18normalize : {'true', 'pred', 'all'}, default=None. Normalizes confusion matrix over the true (rows), predicted (columns)conditions or all the population. If None, confusion matrix will not be normalized.Returns-------C : ndarray of shape (n_classes, n_classes)Confusion matrix whose i-th row and j-th column entry indicates the number of samples with true label being i-th class and prediced label being j-th class.References----------.. [1] `Wikipedia entry for the Confusion matrix <https://en.wikipedia.org/wiki/Confusion_matrix>`_ (Wikipedia and other references may use a different convention for axes)在:sklear. metrics._classification找到的def confusion_matrix@_deprecate_positional_argsdefconfusion_matrix (y_true, y_pred， *， label =None, sample_weight=None， normalize= None):计算混淆矩阵来评估分类的准确性。根据定义，一个混淆矩阵:math: ' C '是这样的:math: ' C_{i, j} '等于已知在:math: ' i '组和预测在:math: ' j '组的观测数。因此，在二元分类法中，true negatives的数量是:math:`C_{0,0}`, false negatives is :math:`C_{1,0}`, true positives is:math:`C_{1,1}` and false positives is :math:`C_{0,1}`.更多信息见:ref: ' User Guide <confusion_matrix> '。</confusion_matrix>参数----------y_true:类数组形状(n_samples，) Ground truth (correct)目标值。y_pred:分类器返回的估计目标的类数组形状(n_samples，)。标签:类数组形状(n_classes)，默认=无。索引矩阵的标签列表。这可以用于重新排序或者选择标签的子集。如果给出了' ' None ' '，则在' ' y_true ' '或' ' y_pred ' '中至少出现一次的值将按排序顺序使用。sample_weight:类似数组的形状(n_samples，)，默认=None。样本权重。. .versionadded:: 0.18{'true'， 'pred'， 'all'}， default=None。对真实(行)、预测(列)的混淆矩阵进行规范化条件或所有的人口。如果没有，混淆矩阵将不会被标准化。返回-------C:形状的ndarray (n_classes, n_classes)第i行和第j列项表示真标签样本个数为第i类，谓词标签样本个数为第j类的混淆矩阵。 引用----------. .[1] '用于混淆矩阵的维基百科条目<https: en.wikipedia.org="" wiki="" confusion_matrix=""> ' _(维基百科和其他引用可能对轴使用不同的约定)</https:>Examples-------->>> from sklearn.metrics import confusion_matrix>>> y_true = [2, 0, 2, 2, 0, 1]>>> y_pred = [0, 0, 2, 2, 0, 2]>>> confusion_matrix(y_true, y_pred)array([[2, 0, 0],[0, 0, 1],[1, 0, 2]])>>> y_true = ["cat", "ant", "cat", "cat", "ant", "bird"]>>> y_pred = ["ant", "ant", "cat", "cat", "ant", "cat"]>>> confusion_matrix(y_true, y_pred, labels=["ant", "bird", "cat"])array([[2, 0, 0],[0, 0, 1],[1, 0, 2]])In the binary case, we can extract true positives, etc as follows:>>> tn, fp, fn, tp = confusion_matrix([0, 1, 0, 1], [1, 1, 1, 0]).ravel()>>> (tn, fp, fn, tp)(0, 2, 1, 1)"""y_type, y_true, y_pred = _check_targets(y_true, y_pred)if y_type not in ("binary", "multiclass"):raise ValueError("%s is not supported" % y_type)if labels is None:labels = unique_labels(y_true, y_pred)else:labels = np.asarray(labels)n_labels = labels.sizeif n_labels == 0:raise ValueError("'labels' should contains at least one label.")elif y_true.size == 0:return np.zeros((n_labels, n_labels), dtype=np.int)elif np.all([l not in y_true for l in labels]):raise ValueError("At least one label specified must be in y_true")if sample_weight is None:sample_weight = np.ones(y_true.shape[0], dtype=np.int64)else:sample_weight = np.asarray(sample_weight)check_consistent_length(y_true, y_pred, sample_weight)if normalize not in ['true', 'pred', 'all', None]:raise ValueError("normalize must be one of {'true', 'pred', ""'all', None}")n_labels = labels.sizelabel_to_ind = {y:x for x, y in enumerate(labels)}# convert yt, yp into indexy_pred = np.array([label_to_ind.get(x, n_labels + 1) for x in y_pred])y_true = np.array([label_to_ind.get(x, n_labels + 1) for x in y_true])# intersect y_pred, y_true with labels, eliminate items not in labelsind = np.logical_and(y_pred < n_labels, y_true < n_labels)y_pred = y_pred[ind]y_true = y_true[ind] # also eliminate weights of eliminated itemssample_weight = sample_weight[ind]# Choose the accumulator dtype to always have high precisionif sample_weight.dtype.kind in {'i', 'u', 'b'}:dtype = np.int64else:dtype = np.float64cm = coo_matrix((sample_weight, (y_true, y_pred)), shape=(n_labels,n_labels), dtype=dtype).toarray()with np.errstate(all='ignore'):if normalize == 'true':cm = cm / cm.sum(axis=1, keepdims=True)elif normalize == 'pred':cm = cm / cm.sum(axis=0, keepdims=True)elif normalize == 'all':cm = cm / cm.sum()cm = np.nan_to_num(cm)return cm