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我如何獲得 cuML RandomForestClassifier 葉子？

[英]How can I get cuML RandomForestClassifier leafs?

 原文  2023-01-31 21:05:43  0   1    gpu/ random-forest/ rapids/ cuml

問題描述

我是cuML的新手，我有一個使用 scikit 學習的決策樹分類器。 我想使用 GPU 執行一些超參數搜索，所以我開始尋找cuML 。 cuML 中沒有DecisionTreeClassifier ，但據我在其他 SO 帖子中所讀，它可以通過使用具有 1 棵樹且沒有引導程序的RandomForestClassifier來重現。

我的問題是如何使用cuML RandomForestClassifier提取樹和所有規則（葉子和節點）？ 或者我應該尋找像XGBoost這樣的其他算法？

1 個解決方案

解決方案1
 0  2023-02-01 14:59:00

進行超參數優化不需要訪問底層決策樹或信息。

話雖如此，您可以像這樣訪問有關底層樹和葉預測的摘要信息：

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)

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]

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