【OSCP】使用SPU实现随机森林算法 #752
【OSCP】使用SPU实现随机森林算法 #752
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sml/forest/BUILD.bazel
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| # Copyright 2023 Ant Group Co., Ltd. | |||
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麻烦把forest.py移动到ensemble目录内，对应的tests和emulation和build文件等也移动一下哈～
sml/forest/forest.py
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| self, | ||
| n_estimators, | ||
| max_features, | ||
| n_features, |
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n_features 建议在 fit 里从数据集获取，max_features的校验或者具体值的计算也可以放到fit里
sml/forest/forest.py
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| self.splitter = splitter | ||
| self.max_depth = max_depth | ||
| self.bootstrap = bootstrap | ||
| self.max_samples = max_samples |
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同n_features和max_features，具体的校验和值的计算延迟到fit中
sml/forest/forest.py
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| bootstrap, | ||
| max_samples, | ||
| n_labels, | ||
| seed, |
sml/forest/forest.py
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| X_sample, y_sample = self._bootstrap_sample(X, y) | ||
| features = self._select_features() | ||
| # selected_indices = self._shuffle_indices(n_features) | ||
| print(y_sample) |
sml/forest/forest.py
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| for i, tree in enumerate(self.trees): | ||
| features = self.features_indices[i] | ||
| print(features) | ||
| tree_predictions = tree_predictions.at[:, i].set( |
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尽量少用.set，这种jax的update方法，会重新copy所有数据，可以先计算好，然后jnp.array一次性得到array
sml/forest/forest.py
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| return y_pred.ravel() | ||
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| def jax_mode_row(data): |
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不用这么复杂，，label取值为0,1,2,... （decision tree的要求），比如二分类，直接统计所有的tree里==0和==1的个数，返回其中大的即可。。（请尽量避免循环，，善用向量化的）
sml/forest/forest.py
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| class RandomForestClassifier: | ||
| """A random forest classifier.""" | ||
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__init__里的超参数需要解释；
以及对数据格式的要求也需要说明（可以参考决策树模型里的一些注释说明信息）
sml/forest/forest.py
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| from sml.tree.tree import DecisionTreeClassifier as sml_dtc | ||
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| # from functools import partial | ||
| # from jax import jit |
sml/forest/forest.py
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| Description : 基本完成函数编写的工作，目前测试结果基本正确，后面需要完成emul和test | ||
| bootstrap有问题,bootstrap后predict不输出1,bootstrap无1(因为不支持jax.random的api) | ||
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| !最终：bootstrap这个参数，不可用：在明文下bootstrap取样正确，但在forest_test.py时，无法取到标签1， |
Pull Request
What problem does this PR solve?
Issue Number: Fixed 254 #
Possible side effects?
Performance:
Backward compatibility: