堆栈内存溢出
  简体   繁体   English

我如何获得 cuML RandomForestClassifier 叶子？

[英]How can I get cuML RandomForestClassifier leafs?

 原文  2023-01-31 21:05:43  7   1    gpu/ random-forest/ rapids/ cuml

问题描述

I'm new to cuML and I have a decision tree classifier using scikit learn.我是cuML的新手，我有一个使用 scikit 学习的决策树分类器。 I would like to perform some hyperparameter search using the GPU, so I started looking cuML .我想使用 GPU 执行一些超参数搜索，所以我开始寻找cuML 。 There is no DecisionTreeClassifier in cuML, but it can be reproduced by using RandomForestClassifier with 1 tree and no bootstrap, as far as I read on other SO posts. cuML 中没有DecisionTreeClassifier ，但据我在其他 SO 帖子中所读，它可以通过使用具有 1 棵树且没有引导程序的RandomForestClassifier来重现。

My problem is how can I extract the tree and all the rules (the leafs and nodes) using cuML RandomForestClassifier ?我的问题是如何使用cuML RandomForestClassifier提取树和所有规则（叶子和节点）？ Or I should be looking to other algorithms like XGBoost ?或者我应该寻找像XGBoost这样的其他算法？

1 个解决方案

解决方案1
 0  2023-02-01 14:59:00

Access to the underlying decision trees or information isn't necessary to do hyperparameter optimization.进行超参数优化不需要访问底层决策树或信息。

With that said, you can access summary information about the underlying trees and leaf predictions like this:话虽如此，您可以像这样访问有关底层树和叶预测的摘要信息：

from cuml.ensemble import RandomForestClassifier
from cuml.datasets import make_classification

N = 100
K = 10

X, y = make_classification(
    n_samples=N,
    n_features=K,
    n_informative=K,
    n_redundant=0
)

clf = RandomForestClassifier(n_estimators=2)
clf.fit(X, y)

print(clf.get_summary_text())
print(clf.get_detailed_text())
print(clf.get_json())
Forest has 2 trees, max_depth 16, and max_leaves -1
Tree #0
 Decision Tree depth --> 9 and n_leaves --> 18
 Tree Fitting - Overall time --> 1.216 milliseconds

Tree #1
 Decision Tree depth --> 7 and n_leaves --> 16
 Tree Fitting - Overall time --> 1.919 milliseconds


Forest has 2 trees, max_depth 16, and max_leaves -1
Tree #0
 Decision Tree depth --> 9 and n_leaves --> 18
 Tree Fitting - Overall time --> 1.216 milliseconds

└(colid: 7, quesval: 2.73323, best_metric_val: 0.0407427)
    ├(colid: 9, quesval: -0.233239, best_metric_val: 0.116631)
    │   ├(colid: 2, quesval: -1.48028, best_metric_val: 0.045858)
    │   │   ├(colid: 8, quesval: -1.14041, best_metric_val: 0.28125)
    │   │   │   ├(leaf, prediction: [0, 1], best_metric_val: 0)
    │   │   │   └(colid: 1, quesval: 0.720062, best_metric_val: 0.375)
    │   │   │       ├(leaf, prediction: [1, 0], best_metric_val: 0)
    │   │   │       └(leaf, prediction: [0, 1], best_metric_val: 0)
    │   │   └(leaf, prediction: [0, 1], best_metric_val: 0)
    │   └(colid: 3, quesval: -1.01601, best_metric_val: 0.313368)
    │       ├(colid: 8, quesval: 1.68195, best_metric_val: 0.0131944)
    │       │   ├(leaf, prediction: [1, 0], best_metric_val: 0)
    │       │   └(colid: 6, quesval: -0.458985, best_metric_val: 0.32)
    │       │       ├(leaf, prediction: [0, 1], best_metric_val: 0)
    │       │       └(leaf, prediction: [1, 0], best_metric_val: 0)
    │       └(colid: 7, quesval: -2.86422, best_metric_val: 0.126263)
    │           ├(leaf, prediction: [1, 0], best_metric_val: 0)
    │           └(colid: 8, quesval: 1.3618, best_metric_val: 0.0198347)
    │               ├(colid: 9, quesval: 1.96266, best_metric_val: 0.142222)
    │               │   ├(colid: 5, quesval: -0.427346, best_metric_val: 0.0308642)
    │               │   │   ├(colid: 8, quesval: -0.295362, best_metric_val: 0.125)
    │               │   │   │   ├(leaf, prediction: [0, 1], best_metric_val: 0)
    │               │   │   │   └(colid: 6, quesval: 1.99819, best_metric_val: 0.5)
    │               │   │   │       ├(leaf, prediction: [1, 0], best_metric_val: 0)
    │               │   │   │       └(leaf, prediction: [0, 1], best_metric_val: 0)
    │               │   │   └(leaf, prediction: [0, 1], best_metric_val: 0)
    │               │   └(leaf, prediction: [1, 0], best_metric_val: 0)
    │               └(leaf, prediction: [0, 1], best_metric_val: 0)
    └(colid: 3, quesval: 1.4614, best_metric_val: 0.239645)
        ├(leaf, prediction: [1, 0], best_metric_val: 0)
        └(colid: 7, quesval: 3.80204, best_metric_val: 0.125)
            ├(leaf, prediction: [0, 1], best_metric_val: 0)
            └(colid: 8, quesval: 0.637938, best_metric_val: 0.5)
                ├(leaf, prediction: [0, 1], best_metric_val: 0)
                └(leaf, prediction: [1, 0], best_metric_val: 0)
Tree #1
 Decision Tree depth --> 7 and n_leaves --> 16
 Tree Fitting - Overall time --> 1.919 milliseconds

└(colid: 8, quesval: -1.19294, best_metric_val: 0.111478)
    ├(colid: 7, quesval: -2.32102, best_metric_val: 0.0867768)
    │   ├(leaf, prediction: [1, 0], best_metric_val: 0)
    │   └(leaf, prediction: [0, 1], best_metric_val: 0)
    └(colid: 3, quesval: 0.590359, best_metric_val: 0.180291)
        ├(colid: 6, quesval: -2.11692, best_metric_val: 0.126613)
        │   ├(leaf, prediction: [0, 1], best_metric_val: 0)
        │   └(colid: 5, quesval: -1.94796, best_metric_val: 0.0655193)
        │       ├(colid: 6, quesval: 1.18255, best_metric_val: 0.489796)
        │       │   ├(leaf, prediction: [0, 1], best_metric_val: 0)
        │       │   └(leaf, prediction: [1, 0], best_metric_val: 0)
        │       └(colid: 8, quesval: 3.48108, best_metric_val: 0.0196773)
        │           ├(colid: 5, quesval: 0.71779, best_metric_val: 0.00283446)
        │           │   ├(colid: 4, quesval: 1.85633, best_metric_val: 1.19209e-07)
        │           │   │   ├(leaf, prediction: [1, 0], best_metric_val: 0)
        │           │   │   └(leaf, prediction: [1, 0], best_metric_val: 0)
        │           │   └(colid: 5, quesval: 0.815552, best_metric_val: 0.152778)
        │           │       ├(leaf, prediction: [0, 1], best_metric_val: 0)
        │           │       └(leaf, prediction: [1, 0], best_metric_val: 0)
        │           └(colid: 9, quesval: 0.690919, best_metric_val: 0.5)
        │               ├(leaf, prediction: [0, 1], best_metric_val: 0)
        │               └(leaf, prediction: [1, 0], best_metric_val: 0)
        └(colid: 6, quesval: 2.16413, best_metric_val: 0.071035)
            ├(colid: 7, quesval: 3.80204, best_metric_val: 0.0818594)
            │   ├(colid: 9, quesval: 1.33454, best_metric_val: 0.02)
            │   │   ├(leaf, prediction: [0, 1], best_metric_val: 0)
            │   │   └(colid: 5, quesval: 0.0840077, best_metric_val: 0.375)
            │   │       ├(leaf, prediction: [0, 1], best_metric_val: 0)
            │   │       └(leaf, prediction: [1, 0], best_metric_val: 0)
            │   └(leaf, prediction: [1, 0], best_metric_val: 0)
            └(leaf, prediction: [1, 0], best_metric_val: 0)

[
{"nodeid": 0, "split_feature": 7, "split_threshold": 2.7332263, "gain": 0.0407427214, "instance_count": 100, "yes": 1, "no": 2, "children": [
  {"nodeid": 1, "split_feature": 9, "split_threshold": -0.233238578, "gain": 0.116630867, "instance_count": 87, "yes": 3, "no": 4, "children": [
    {"nodeid": 3, "split_feature": 2, "split_threshold": -1.48028064, "gain": 0.0458579995, "instance_count": 39, "yes": 7, "no": 8, "children": [
      {"nodeid": 7, "split_feature": 8, "split_threshold": -1.1404053, "gain": 0.28125, "instance_count": 8, "yes": 13, "no": 14, "children": [
        {"nodeid": 13, "leaf_value": [0, 1], "instance_count": 4},
        {"nodeid": 14, "split_feature": 1, "split_threshold": 0.720061541, "gain": 0.375, "instance_count": 4, "yes": 21, "no": 22, "children": [
          {"nodeid": 21, "leaf_value": [1, 0], "instance_count": 3},
          {"nodeid": 22, "leaf_value": [0, 1], "instance_count": 1}
        ]}
      ]},
      {"nodeid": 8, "leaf_value": [0, 1], "instance_count": 31}
    ]},
    {"nodeid": 4, "split_feature": 3, "split_threshold": -1.01600909, "gain": 0.313368142, "instance_count": 48, "yes": 9, "no": 10, "children": [
      {"nodeid": 9, "split_feature": 8, "split_threshold": 1.68195295, "gain": 0.0131943803, "instance_count": 24, "yes": 15, "no": 16, "children": [
        {"nodeid": 15, "leaf_value": [1, 0], "instance_count": 19},
        {"nodeid": 16, "split_feature": 6, "split_threshold": -0.458984971, "gain": 0.320000023, "instance_count": 5, "yes": 23, "no": 24, "children": [
          {"nodeid": 23, "leaf_value": [0, 1], "instance_count": 1},
          {"nodeid": 24, "leaf_value": [1, 0], "instance_count": 4}
        ]}
      ]},
      {"nodeid": 10, "split_feature": 7, "split_threshold": -2.86421776, "gain": 0.126262575, "instance_count": 24, "yes": 17, "no": 18, "children": [
        {"nodeid": 17, "leaf_value": [1, 0], "instance_count": 2},
        {"nodeid": 18, "split_feature": 8, "split_threshold": 1.36179876, "gain": 0.0198347215, "instance_count": 22, "yes": 25, "no": 26, "children": [
          {"nodeid": 25, "split_feature": 9, "split_threshold": 1.96266103, "gain": 0.142222196, "instance_count": 10, "yes": 27, "no": 28, "children": [
            {"nodeid": 27, "split_feature": 5, "split_threshold": -0.427345634, "gain": 0.0308641735, "instance_count": 9, "yes": 29, "no": 30, "children": [
              {"nodeid": 29, "split_feature": 8, "split_threshold": -0.295361876, "gain": 0.125, "instance_count": 4, "yes": 31, "no": 32, "children": [
                {"nodeid": 31, "leaf_value": [0, 1], "instance_count": 2},
                {"nodeid": 32, "split_feature": 6, "split_threshold": 1.99819326, "gain": 0.5, "instance_count": 2, "yes": 33, "no": 34, "children": [
                  {"nodeid": 33, "leaf_value": [1, 0], "instance_count": 1},
                  {"nodeid": 34, "leaf_value": [0, 1], "instance_count": 1}
                ]}
              ]},
              {"nodeid": 30, "leaf_value": [0, 1], "instance_count": 5}
            ]},
            {"nodeid": 28, "leaf_value": [1, 0], "instance_count": 1}
          ]},
          {"nodeid": 26, "leaf_value": [0, 1], "instance_count": 12}
        ]}
      ]}
    ]}
  ]},
  {"nodeid": 2, "split_feature": 3, "split_threshold": 1.46139979, "gain": 0.239644989, "instance_count": 13, "yes": 5, "no": 6, "children": [
    {"nodeid": 5, "leaf_value": [1, 0], "instance_count": 9},
    {"nodeid": 6, "split_feature": 7, "split_threshold": 3.8020432, "gain": 0.125, "instance_count": 4, "yes": 11, "no": 12, "children": [
      {"nodeid": 11, "leaf_value": [0, 1], "instance_count": 2},
      {"nodeid": 12, "split_feature": 8, "split_threshold": 0.637937546, "gain": 0.5, "instance_count": 2, "yes": 19, "no": 20, "children": [
        {"nodeid": 19, "leaf_value": [0, 1], "instance_count": 1},
        {"nodeid": 20, "leaf_value": [1, 0], "instance_count": 1}
      ]}
    ]}
  ]}
]},
{"nodeid": 0, "split_feature": 8, "split_threshold": -1.19294095, "gain": 0.111478344, "instance_count": 100, "yes": 1, "no": 2, "children": [
  {"nodeid": 1, "split_feature": 7, "split_threshold": -2.3210218, "gain": 0.0867768154, "instance_count": 22, "yes": 3, "no": 4, "children": [
    {"nodeid": 3, "leaf_value": [1, 0], "instance_count": 1},
    {"nodeid": 4, "leaf_value": [0, 1], "instance_count": 21}
  ]},
  {"nodeid": 2, "split_feature": 3, "split_threshold": 0.590358853, "gain": 0.180290893, "instance_count": 78, "yes": 5, "no": 6, "children": [
    {"nodeid": 5, "split_feature": 6, "split_threshold": -2.1169188, "gain": 0.12661314, "instance_count": 56, "yes": 7, "no": 8, "children": [
      {"nodeid": 7, "leaf_value": [0, 1], "instance_count": 5},
      {"nodeid": 8, "split_feature": 5, "split_threshold": -1.94796324, "gain": 0.065519318, "instance_count": 51, "yes": 11, "no": 12, "children": [
        {"nodeid": 11, "split_feature": 6, "split_threshold": 1.18254995, "gain": 0.489795923, "instance_count": 7, "yes": 15, "no": 16, "children": [
          {"nodeid": 15, "leaf_value": [0, 1], "instance_count": 4},
          {"nodeid": 16, "leaf_value": [1, 0], "instance_count": 3}
        ]},
        {"nodeid": 12, "split_feature": 8, "split_threshold": 3.48108315, "gain": 0.0196772516, "instance_count": 44, "yes": 17, "no": 18, "children": [
          {"nodeid": 17, "split_feature": 5, "split_threshold": 0.717789888, "gain": 0.00283446093, "instance_count": 42, "yes": 21, "no": 22, "children": [
            {"nodeid": 21, "split_feature": 4, "split_threshold": 1.85632861, "gain": 1.1920929e-07, "instance_count": 30, "yes": 27, "no": 28, "children": [
              {"nodeid": 27, "leaf_value": [1, 0], "instance_count": 19},
              {"nodeid": 28, "leaf_value": [1, 0], "instance_count": 11}
            ]},
            {"nodeid": 22, "split_feature": 5, "split_threshold": 0.815551639, "gain": 0.152777761, "instance_count": 12, "yes": 29, "no": 30, "children": [
              {"nodeid": 29, "leaf_value": [0, 1], "instance_count": 1},
              {"nodeid": 30, "leaf_value": [1, 0], "instance_count": 11}
            ]}
          ]},
          {"nodeid": 18, "split_feature": 9, "split_threshold": 0.690918803, "gain": 0.5, "instance_count": 2, "yes": 23, "no": 24, "children": [
            {"nodeid": 23, "leaf_value": [0, 1], "instance_count": 1},
            {"nodeid": 24, "leaf_value": [1, 0], "instance_count": 1}
          ]}
        ]}
      ]}
    ]},
    {"nodeid": 6, "split_feature": 6, "split_threshold": 2.1641295, "gain": 0.0710349679, "instance_count": 22, "yes": 9, "no": 10, "children": [
      {"nodeid": 9, "split_feature": 7, "split_threshold": 3.8020432, "gain": 0.0818593949, "instance_count": 21, "yes": 13, "no": 14, "children": [
        {"nodeid": 13, "split_feature": 9, "split_threshold": 1.33453584, "gain": 0.0200000368, "instance_count": 20, "yes": 19, "no": 20, "children": [
          {"nodeid": 19, "leaf_value": [0, 1], "instance_count": 16},
          {"nodeid": 20, "split_feature": 5, "split_threshold": 0.08400774, "gain": 0.375, "instance_count": 4, "yes": 25, "no": 26, "children": [
            {"nodeid": 25, "leaf_value": [0, 1], "instance_count": 3},
            {"nodeid": 26, "leaf_value": [1, 0], "instance_count": 1}
          ]}
        ]},
        {"nodeid": 14, "leaf_value": [1, 0], "instance_count": 1}
      ]},
      {"nodeid": 10, "leaf_value": [1, 0], "instance_count": 1}
    ]}
  ]}
]}
]

暂无
暂无

声明:本站的技术帖子网页，遵循CC BY-SA 4.0协议，如果您需要转载，请注明本站网址或者原文地址。任何问题请咨询:yoyou2525@163.com.

相关问题 TfIdfVectorizer 在 GPU 上较慢（cuml 与 sklearn 实现） - TfIdfVectorizer is slower on GPU (cuml vs sklearn implementation) cuml DBSCAN 使用 numba 设备阵列运行缓慢 - cuml DBSCAN running slow with numba device array 如何获得连接显示器的GPU卡列表？ - How can I get the list of GPU cards to which are connected monitors? 我如何让我的Tensorflow脚本在我的GPU上运行？ - How can I get my tensorflow script to run on my GPU? 如何在 Colab 中获得 V100 GPU？ - How can I get V100 GPU in Colab? 有没有办法使用我的 GPU 的整个 memory 进行 CUML 计算？ - Is there a way of using the entire memory of my GPU for CUML calculations? 使用half_floats获取顶点属性可获得多少穿孔？ - How much perf can I get using half_floats for vertex attribs? 如何在x86程序中以编程方式获取Nvidia驱动程序版本？ - How can I get the Nvidia driver version programatically in x86 program? 如何使用 Python 和 Numba 获取 GPU 中的 CUDA 内核数量？ - How can I get the number of CUDA cores in my GPU using Python and Numba? 为什么使用 dask_cudf DataFrame 的 KNearestNeighbors 的 cuml predict() 方法需要这么长时间？ - Why is cuml predict() method for KNearestNeighbors taking so long with dask_cudf DataFrame?
相关标签
 
粤ICP备18138465号  © 2020-2024 STACKOOM.COM