python - 如何在非 scikit 模型的输出上使用 Yellowbrick

Question

如何在非 Scikit 模型的输出上使用Yellowbrick ？

我有一个 PyTorch 多类分类器网络，并希望在将此模型应用于数据的结果上使用ClassificationReport功能。我怎样才能做到这一点？

Answer 1

如果您使用skorch使 Pytorch 模型 sci-kit 学习兼容的库，那么您可以使用 Yellowbrick 的第三方包装器，那么您可以使您的模型工作。这是一些示例代码

import numpy as np
from sklearn.datasets import make_classification
from torch import nn
from sklearn.model_selection import train_test_split


from skorch import NeuralNetClassifier

X, y = make_classification(1000, 20, n_informative=10, random_state=0)
X = X.astype(np.float32)
y = y.astype(np.int64)

X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)


class MyModule(nn.Module):
    def __init__(self, num_units=10, nonlin=nn.ReLU()):
        super(MyModule, self).__init__()

        self.dense0 = nn.Linear(20, num_units)
        self.nonlin = nonlin
        self.dropout = nn.Dropout(0.5)
        self.dense1 = nn.Linear(num_units, num_units)
        self.output = nn.Linear(num_units, 2)
        self.softmax = nn.Softmax(dim=-1)

    def forward(self, X, **kwargs):
        X = self.nonlin(self.dense0(X))
        X = self.dropout(X)
        X = self.nonlin(self.dense1(X))
        X = self.softmax(self.output(X))
        return X


net = NeuralNetClassifier(
    MyModule,
    max_epochs=10,
    lr=0.1,
    # Shuffle training data on each epoch
    iterator_train__shuffle=True,
)


# Import the wrap function and a Yellowbrick visualizer
from yellowbrick.contrib.wrapper import wrap
from yellowbrick.classifier import classification_report

# Instantiate the third party estimator and wrap it, optionally fitting it
model = wrap(net)
model.fit(X_train, y_train)

# Use the visualizer
oz = classification_report(model, X_train, y_train, X_test=X_test, y_test=y_test, support=True, is_fitted=True)