reports_avg = np.mean(reports_list, axis=0) matrices_avg = np.mean(matrices_list, axis=0) micro_auc_avg = np.mean(micro_auc_list) macro_auc_avg = np.mean(macro_auc_list) fpr_avg = np.mean(fpr_list, axis=0) tpr_avg = np.mean(tpr_list, axis=0) roc_auc_avg = np.mean(roc_auc_list, axis=0)出现ufunc 'add' did not contain a loop with signature matching types (dtype('<U326'), dtype('<U326')) -> None的问题怎么解决

这个错误通常是由于numpy数组中包含字符串类型的数据引起的。numpy中的大多数函数是针对数值类型的，不支持字符串类型的数据。解决方法是将字符串类型的数据转换为数值类型。你可以使用numpy中的astype()函数将字符串类型的数据转换为数值类型，例如：

reports_list = reports_list.astype(float)
matrices_list = matrices_list.astype(float)

如果你还有其他包含字符串类型数据的数组，也需要进行相应的转换。

相关问题

修改完善下列代码，得到十折交叉验证三分类的平均每一折的分类报告，三分类的每一类的平均每一折的混淆矩阵，平均每一折的“micro”和“macro”auc值和roc曲线。min_max_scaler = MinMaxScaler() X_train1, X_test1 = x[train_id], x[test_id] y_train1, y_test1 = y[train_id], y[test_id] # apply the same scaler to both sets of data X_train1 = min_max_scaler.fit_transform(X_train1) X_test1 = min_max_scaler.transform(X_test1) # convert to numpy arrays X_train1 = np.array(X_train1) X_test1 = np.array(X_test1) # train gcForest config = get_config() tree = gcForest(config) tree.fit(X_train1, y_train1)

这是一个使用gcForest进行分类的代码，需要补充一些内容才能完成您的要求。

首先，您需要导入必要的库：

from sklearn.preprocessing import MinMaxScaler
from sklearn.metrics import classification_report, confusion_matrix, roc_auc_score, roc_curve
import numpy as np
from gcforest.gcforest import GCForest
from gcforest.utils.config_utils import load_json, get_config

然后，您需要定义一个函数，该函数将返回每一折的分类报告，混淆矩阵，micro和macro auc值，以及roc曲线。

def evaluate_model(X_train, y_train, X_test, y_test):
    # apply the same scaler to both sets of data
    min_max_scaler = MinMaxScaler()
    X_train = min_max_scaler.fit_transform(X_train)
    X_test = min_max_scaler.transform(X_test)

    # train gcForest
    config = get_config()
    tree = GCForest(config)
    tree.fit(X_train, y_train)

    # predict on test set
    y_pred = tree.predict(X_test)

    # calculate classification report and confusion matrix for each class
    class_names = np.unique(y_train)
    reports = []
    matrices = []
    for class_name in class_names:
        mask_train = y_train == class_name
        mask_test = y_test == class_name
        y_train_class = np.zeros_like(y_train)
        y_train_class[mask_train] = 1
        y_test_class = np.zeros_like(y_test)
        y_test_class[mask_test] = 1
        y_pred_class = np.zeros_like(y_pred)
        y_pred_class[y_pred == class_name] = 1
        reports.append(classification_report(y_test_class, y_pred_class))
        matrices.append(confusion_matrix(y_test_class, y_pred_class))

    # calculate micro and macro AUC
    y_scores = tree.predict_proba(X_test)
    micro_auc = roc_auc_score(y_test, y_scores, multi_class='ovo', average='micro')
    macro_auc = roc_auc_score(y_test, y_scores, multi_class='ovo', average='macro')

    # calculate ROC curve
    fpr = dict()
    tpr = dict()
    roc_auc = dict()
    for i in range(len(class_names)):
        fpr[i], tpr[i], _ = roc_curve(y_test_class[:, i], y_scores[:, i])
        roc_auc[i] = auc(fpr[i], tpr[i])

    # return evaluation results
    return reports, matrices, micro_auc, macro_auc, fpr, tpr, roc_auc

最后，您需要将数据分成10折，依次对每一折进行评估，并计算平均值。

# load data
X = np.load('X.npy')
y = np.load('y.npy')

# split data into 10 folds
from sklearn.model_selection import KFold
kf = KFold(n_splits=10, shuffle=True, random_state=42)
reports_list = []
matrices_list = []
micro_auc_list = []
macro_auc_list = []
fpr_list = []
tpr_list = []
roc_auc_list = []
for train_id, test_id in kf.split(X):
    X_train, X_test = X[train_id], X[test_id]
    y_train, y_test = y[train_id], y[test_id]
    reports, matrices, micro_auc, macro_auc, fpr, tpr, roc_auc = evaluate_model(X_train, y_train, X_test, y_test)
    reports_list.append(reports)
    matrices_list.append(matrices)
    micro_auc_list.append(micro_auc)
    macro_auc_list.append(macro_auc)
    fpr_list.append(fpr)
    tpr_list.append(tpr)
    roc_auc_list.append(roc_auc)

# calculate average evaluation results
reports_avg = np.mean(reports_list, axis=0)
matrices_avg = np.mean(matrices_list, axis=0)
micro_auc_avg = np.mean(micro_auc_list)
macro_auc_avg = np.mean(macro_auc_list)
fpr_avg = np.mean(fpr_list, axis=0)
tpr_avg = np.mean(tpr_list, axis=0)
roc_auc_avg = np.mean(roc_auc_list, axis=0)

请注意，上面的代码是示例代码，需要根据您的数据进行适当修改。

下面 tensorflow代码转化为paddlepaddle2.2.2代码：import tensorflow as tf import numpy as np def ccm(): """Generates random RGB -> Camera color correction matrices.""" # Fixed XYZ -> Camera CCM. cam2xyz = [[0.4679,0.2145,0.3176],[0.1433,0.8236,0.0331],[0.0003,-0.3607,1.3604]] xyz2cam = tf.convert_to_tensor(np.linalg.inv(np.array(cam2xyz)),dtype = np.float32) # Multiplies with RGB -> XYZ to get RGB -> Camera CCM. rgb2xyz = tf.to_float([[0.4124564, 0.3575761, 0.1804375], [0.2126729, 0.7151522, 0.0721750], [0.0193339, 0.1191920, 0.9503041]]) rgb2cam = tf.matmul(xyz2cam, rgb2xyz) # Normalizes each row. rgb2cam = rgb2cam / tf.reduce_sum(rgb2cam, axis=-1, keepdims=True) return rgb2cam def gains(): """Generates random gains for brightening and white balance.""" # RGB gain represents brightening. rgb_gain = 1.0 # Red and blue gains represent white balance. red_gain = 1.0 / 0.5527 blue_gain = 1.0 / 0.4844 return rgb_gain, red_gain, blue_gain

这是一样的问题，上一个回答已经给出了代码转换的结果。以下是转换后的代码：

import paddle.fluid as fluid
import numpy as np

def ccm():
    """Generates random RGB -> Camera color correction matrices."""
    # Fixed XYZ -> Camera CCM.
    cam2xyz = np.array([[0.4679,0.2145,0.3176],[0.1433,0.8236,0.0331],[0.0003,-0.3607,1.3604]], dtype=np.float32)
    xyz2cam = fluid.layers.create_tensor(data=np.linalg.inv(cam2xyz), dtype='float32')
    
    # Multiplies with RGB -> XYZ to get RGB -> Camera CCM.
    rgb2xyz = np.array([[0.4124564, 0.3575761, 0.1804375],[0.2126729, 0.7151522, 0.0721750],[0.0193339, 0.1191920, 0.9503041]], dtype=np.float32)
    rgb2cam = fluid.layers.matmul(xyz2cam, rgb2xyz)
    
    # Normalizes each row.
    rgb2cam = rgb2cam / fluid.layers.reduce_sum(rgb2cam, dim=-1, keep_dim=True)
    
    return rgb2cam

def gains():
    """Generates random gains for brightening and white balance."""
    # RGB gain represents brightening.
    rgb_gain = 1.0

    # Red and blue gains represent white balance.
    red_gain = 1.0 / 0.5527
    blue_gain = 1.0 / 0.4844
    
    return rgb_gain, red_gain, blue_gain