diff --git a/forrest-test.ipynb b/forrest-test.ipynb
index 9be0a85feea2e9ddde8caee4bc83d1a3b160b057..78285ca312d22c6610af94c22e62f4a3d0245f21 100644
--- a/forrest-test.ipynb
+++ b/forrest-test.ipynb
@@ -108,8 +108,8 @@
"outputs": [],
"source": [
"task = 'classifier' # 'classifier'/'regressor'\n",
- "forrest = RandomForest(bootstrapping=False, retrainFirst=False)\n",
- "#forrest.setComponent(GradientBoosting())\n",
+ "forrest = RandomForest(bootstrapping=True, retrainFirst=False)\n",
+ "#forrest.setComponent(AdaBoosting())\n",
"forrest.setComponent(Majority())\n",
"for i in range(5):\n",
" tree = DecisionTree(maxDepth=7, minSamplesSplit=2)\n",
@@ -142,273 +142,246 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "tree 1 |⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿| done ✔ | 18%\n",
- "tree 2 |⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿| done ✔ | 18%\n",
- "tree 3 |⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿| done ✔ | 18%\n",
- "tree 4 |⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿| done ✔ | 18%\n",
- "tree 5 |⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿| done ✔ | 18%\n",
- "â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â” forrest â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\n",
- "voting: Majority, booster: GradientBoosting, bootstrapping: False\n",
+ "tree 1 |\u001b[0m\u001b[31m\u001b[0m\u001b[0m\u001b[31m \u001b[0m | 00%\r"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "tree 1 |⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿| done ✔ | 13%\n",
+ "tree 2 |⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿| done ✔ | 12%\n",
+ "tree 3 |⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿| done ✔ | 16%\n",
+ "tree 4 |⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿| done ✔ | 17%\n",
+ "tree 5 |⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿| done ✔ | 17%\n",
+ "â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â” forrest â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\n",
+ "voting: Majority, booster: None, bootstrapping: True\n",
"\n",
"—————————————————————— tree: 1/5 ———————————————————————\n",
- "split: CART, impurity: Entropy, leaf: Mode, nodes: 47\n",
+ "split: CART, impurity: Entropy, leaf: Mode, nodes: 35\n",
"maxDepth: 7, reached depth: 7, minSamplesSplit: 2\n",
"························································\n",
- "â•´feat: 4 <= 2.79, samples: 9600\n",
- " ├─feat: 4 <= 1.98, samples: 4853\n",
- " │ ├─feat: 3 <= 1.43, samples: 4693\n",
- " │ │ └─╴value: 0.0\n",
- " │ │ └─╴feat: 0 <= 2.19, samples: 361\n",
- " │ │ ├─feat: 4 <= 1.18, samples: 357\n",
- " │ │ │ └─╴value: 0.0\n",
- " │ │ │ └─╴feat: 4 <= 1.18, samples: 44\n",
- " │ │ │ └─╴value: 1.0\n",
- " │ │ │ └─╴value: 0.0\n",
- " │ │ └─╴feat: 1 <= 0.47, samples: 4\n",
- " │ │ └─╴value: 1.0\n",
- " │ │ └─╴value: 0.0\n",
- " │ └─╴feat: 3 <= 1.25, samples: 160\n",
- " │ ├─feat: 0 <= 1.44, samples: 98\n",
- " │ │ ├─feat: 3 <= 1.14, samples: 92\n",
- " │ │ │ └─╴value: 0.0\n",
- " │ │ │ └─╴feat: 0 <= -0.86, samples: 2\n",
- " │ │ │ └─╴value: 1.0\n",
- " │ │ │ └─╴value: 0.0\n",
- " │ │ └─╴feat: 3 <= 0.10, samples: 6\n",
- " │ │ └─╴value: 0.0\n",
- " │ │ └─╴value: 1.0\n",
- " │ └─╴feat: 0 <= 0.77, samples: 62\n",
- " │ ├─feat: 4 <= 2.23, samples: 20\n",
- " │ │ ├─feat: 3 <= 2.03, samples: 6\n",
- " │ │ │ └─╴value: 0.0\n",
- " │ │ │ └─╴value: 1.0\n",
- " │ │ └─╴feat: 3 <= 1.28, samples: 14\n",
- " │ │ └─╴value: 0.0\n",
- " │ │ └─╴value: 1.0\n",
+ "â•´feat: 2 <= 2.14, samples: 9600\n",
+ " ├─feat: 3 <= 2.15, samples: 4747\n",
+ " │ └─╴value: 0.0\n",
+ " │ └─╴feat: 2 <= 1.65, samples: 104\n",
+ " │ └─╴value: 0.0\n",
+ " │ └─╴feat: 0 <= -1.22, samples: 8\n",
+ " │ └─╴value: 0.0\n",
" │ └─╴value: 1.0\n",
- " └─╴feat: 4 <= 3.40, samples: 4747\n",
- " ├─feat: 3 <= 0.61, samples: 209\n",
- " │ ├─feat: 0 <= 1.22, samples: 10\n",
- " │ │ └─╴value: 0.0\n",
+ " └─╴feat: 2 <= 3.24, samples: 4853\n",
+ " ├─feat: 3 <= 0.71, samples: 267\n",
+ " │ ├─feat: 0 <= 1.74, samples: 56\n",
+ " │ │ ├─feat: 4 <= 0.15, samples: 53\n",
+ " │ │ │ └─╴value: 0.0\n",
+ " │ │ │ └─╴feat: 0 <= 0.75, samples: 17\n",
+ " │ │ │ └─╴value: 0.0\n",
+ " │ │ │ └─╴value: 1.0\n",
" │ │ └─╴value: 1.0\n",
- " │ └─╴feat: 0 <= 0.28, samples: 199\n",
- " │ ├─feat: 3 <= 1.69, samples: 29\n",
- " │ │ ├─feat: 3 <= 1.37, samples: 6\n",
- " │ │ │ └─╴value: 1.0\n",
- " │ │ │ └─╴value: 0.0\n",
- " │ │ └─╴feat: 4 <= 2.91, samples: 23\n",
- " │ │ └─╴value: 0.0\n",
+ " │ └─╴feat: 3 <= 1.96, samples: 211\n",
+ " │ ├─feat: 2 <= 2.30, samples: 64\n",
+ " │ │ └─╴value: 0.0\n",
+ " │ │ └─╴feat: 1 <= 0.02, samples: 55\n",
" │ │ └─╴value: 1.0\n",
- " │ └─╴value: 1.0\n",
- " └─╴value: 1.0\n",
+ " │ │ └─╴value: 1.0\n",
+ " │ └─╴feat: 2 <= 2.34, samples: 147\n",
+ " │ ├─feat: 2 <= 2.34, samples: 20\n",
+ " │ │ └─╴value: 1.0\n",
+ " │ │ └─╴value: 0.0\n",
+ " │ └─╴value: 1.0\n",
+ " └─╴feat: 3 <= 0.22, samples: 4586\n",
+ " ├─feat: 2 <= 3.59, samples: 51\n",
+ " │ ├─feat: 3 <= -0.28, samples: 3\n",
+ " │ │ └─╴value: 1.0\n",
+ " │ │ └─╴value: 0.0\n",
+ " │ └─╴value: 1.0\n",
+ " └─╴value: 1.0\n",
"\n",
"—————————————————————— tree: 2/5 ———————————————————————\n",
- "split: CART, impurity: Entropy, leaf: Mode, nodes: 47\n",
+ "split: CART, impurity: Entropy, leaf: Mode, nodes: 33\n",
"maxDepth: 7, reached depth: 7, minSamplesSplit: 2\n",
"························································\n",
- "â•´feat: 4 <= 2.79, samples: 9600\n",
- " ├─feat: 4 <= 1.98, samples: 4853\n",
- " │ ├─feat: 3 <= 1.43, samples: 4693\n",
- " │ │ └─╴value: 0.0\n",
- " │ │ └─╴feat: 0 <= 2.19, samples: 361\n",
- " │ │ ├─feat: 4 <= 1.18, samples: 357\n",
- " │ │ │ └─╴value: 0.0\n",
- " │ │ │ └─╴feat: 4 <= 1.18, samples: 44\n",
- " │ │ │ └─╴value: 1.0\n",
- " │ │ │ └─╴value: 0.0\n",
- " │ │ └─╴feat: 1 <= 0.47, samples: 4\n",
- " │ │ └─╴value: 1.0\n",
- " │ │ └─╴value: 0.0\n",
- " │ └─╴feat: 3 <= 1.25, samples: 160\n",
- " │ ├─feat: 0 <= 1.44, samples: 98\n",
- " │ │ ├─feat: 3 <= 1.14, samples: 92\n",
- " │ │ │ └─╴value: 0.0\n",
- " │ │ │ └─╴feat: 1 <= -0.45, samples: 2\n",
- " │ │ │ └─╴value: 1.0\n",
- " │ │ │ └─╴value: 0.0\n",
- " │ │ └─╴feat: 3 <= 0.10, samples: 6\n",
- " │ │ └─╴value: 0.0\n",
- " │ │ └─╴value: 1.0\n",
- " │ └─╴feat: 0 <= 0.77, samples: 62\n",
- " │ ├─feat: 4 <= 2.23, samples: 20\n",
- " │ │ ├─feat: 3 <= 2.03, samples: 6\n",
- " │ │ │ └─╴value: 0.0\n",
- " │ │ │ └─╴value: 1.0\n",
- " │ │ └─╴feat: 3 <= 1.28, samples: 14\n",
- " │ │ └─╴value: 0.0\n",
- " │ │ └─╴value: 1.0\n",
+ "â•´feat: 2 <= 2.28, samples: 9600\n",
+ " ├─feat: 2 <= 1.88, samples: 4747\n",
+ " │ └─╴value: 0.0\n",
+ " │ └─╴feat: 3 <= 2.00, samples: 105\n",
+ " │ ├─feat: 0 <= 1.86, samples: 87\n",
+ " │ │ └─╴value: 0.0\n",
+ " │ │ └─╴value: 1.0\n",
+ " │ └─╴feat: 0 <= -1.24, samples: 18\n",
+ " │ └─╴value: 0.0\n",
" │ └─╴value: 1.0\n",
- " └─╴feat: 4 <= 3.40, samples: 4747\n",
- " ├─feat: 3 <= 0.61, samples: 209\n",
- " │ ├─feat: 0 <= 1.22, samples: 10\n",
+ " └─╴feat: 2 <= 3.35, samples: 4853\n",
+ " ├─feat: 3 <= 1.41, samples: 320\n",
+ " │ ├─feat: 0 <= 0.48, samples: 99\n",
" │ │ └─╴value: 0.0\n",
- " │ │ └─╴value: 1.0\n",
- " │ └─╴feat: 0 <= 0.28, samples: 199\n",
- " │ ├─feat: 3 <= 1.69, samples: 29\n",
- " │ │ ├─feat: 3 <= 1.37, samples: 6\n",
- " │ │ │ └─╴value: 1.0\n",
- " │ │ │ └─╴value: 0.0\n",
- " │ │ └─╴feat: 4 <= 2.91, samples: 23\n",
- " │ │ └─╴value: 0.0\n",
- " │ │ └─╴value: 1.0\n",
+ " │ │ └─╴feat: 2 <= 2.99, samples: 61\n",
+ " │ │ ├─feat: 4 <= 0.87, samples: 40\n",
+ " │ │ │ └─╴value: 0.0\n",
+ " │ │ │ └─╴value: 1.0\n",
+ " │ │ └─╴value: 1.0\n",
+ " │ └─╴feat: 2 <= 2.35, samples: 221\n",
+ " │ ├─feat: 4 <= 0.56, samples: 13\n",
+ " │ │ └─╴value: 1.0\n",
+ " │ │ └─╴value: 0.0\n",
" │ └─╴value: 1.0\n",
- " └─╴value: 1.0\n",
+ " └─╴feat: 3 <= 0.57, samples: 4533\n",
+ " ├─feat: 2 <= 3.65, samples: 150\n",
+ " │ ├─feat: 3 <= 0.40, samples: 9\n",
+ " │ │ ├─feat: 0 <= -0.57, samples: 4\n",
+ " │ │ │ └─╴value: 1.0\n",
+ " │ │ │ └─╴value: 0.0\n",
+ " │ │ └─╴value: 1.0\n",
+ " │ └─╴value: 1.0\n",
+ " └─╴value: 1.0\n",
"\n",
"—————————————————————— tree: 3/5 ———————————————————————\n",
- "split: CART, impurity: Entropy, leaf: Mode, nodes: 47\n",
+ "split: CART, impurity: Entropy, leaf: Mode, nodes: 43\n",
"maxDepth: 7, reached depth: 7, minSamplesSplit: 2\n",
"························································\n",
- "â•´feat: 4 <= 2.79, samples: 9600\n",
- " ├─feat: 4 <= 1.98, samples: 4853\n",
- " │ ├─feat: 3 <= 1.43, samples: 4693\n",
+ "â•´feat: 2 <= 2.36, samples: 9600\n",
+ " ├─feat: 2 <= 1.77, samples: 4747\n",
+ " │ ├─feat: 2 <= 1.65, samples: 4591\n",
" │ │ └─╴value: 0.0\n",
- " │ │ └─╴feat: 0 <= 2.19, samples: 361\n",
- " │ │ ├─feat: 4 <= 1.18, samples: 357\n",
- " │ │ │ └─╴value: 0.0\n",
- " │ │ │ └─╴feat: 4 <= 1.18, samples: 44\n",
- " │ │ │ └─╴value: 1.0\n",
- " │ │ │ └─╴value: 0.0\n",
- " │ │ └─╴feat: 1 <= 0.47, samples: 4\n",
- " │ │ └─╴value: 1.0\n",
- " │ │ └─╴value: 0.0\n",
- " │ └─╴feat: 3 <= 1.25, samples: 160\n",
- " │ ├─feat: 0 <= 1.44, samples: 98\n",
- " │ │ ├─feat: 3 <= 1.14, samples: 92\n",
- " │ │ │ └─╴value: 0.0\n",
- " │ │ │ └─╴feat: 0 <= -0.86, samples: 2\n",
- " │ │ │ └─╴value: 1.0\n",
- " │ │ │ └─╴value: 0.0\n",
- " │ │ └─╴feat: 3 <= 0.10, samples: 6\n",
+ " │ │ └─╴feat: 3 <= 3.06, samples: 51\n",
+ " │ │ └─╴value: 0.0\n",
+ " │ │ └─╴value: 1.0\n",
+ " │ └─╴feat: 3 <= 1.01, samples: 156\n",
+ " │ ├─feat: 2 <= 2.34, samples: 111\n",
+ " │ │ └─╴value: 0.0\n",
+ " │ │ └─╴feat: 0 <= -1.20, samples: 2\n",
" │ │ └─╴value: 0.0\n",
" │ │ └─╴value: 1.0\n",
- " │ └─╴feat: 0 <= 0.77, samples: 62\n",
- " │ ├─feat: 4 <= 2.23, samples: 20\n",
- " │ │ ├─feat: 3 <= 2.03, samples: 6\n",
- " │ │ │ └─╴value: 0.0\n",
- " │ │ │ └─╴value: 1.0\n",
- " │ │ └─╴feat: 3 <= 1.28, samples: 14\n",
- " │ │ └─╴value: 0.0\n",
- " │ │ └─╴value: 1.0\n",
- " │ └─╴value: 1.0\n",
- " └─╴feat: 4 <= 3.40, samples: 4747\n",
- " ├─feat: 3 <= 0.61, samples: 209\n",
- " │ ├─feat: 0 <= 1.22, samples: 10\n",
- " │ │ └─╴value: 0.0\n",
+ " │ └─╴feat: 0 <= 0.47, samples: 45\n",
+ " │ ├─feat: 1 <= -1.38, samples: 16\n",
+ " │ │ └─╴value: 1.0\n",
+ " │ │ └─╴value: 0.0\n",
+ " │ └─╴feat: 3 <= 1.77, samples: 29\n",
+ " │ ├─feat: 3 <= 1.16, samples: 10\n",
+ " │ │ └─╴value: 1.0\n",
+ " │ │ └─╴value: 0.0\n",
+ " │ └─╴value: 1.0\n",
+ " └─╴feat: 2 <= 2.87, samples: 4853\n",
+ " ├─feat: 3 <= 0.93, samples: 107\n",
+ " │ ├─feat: 0 <= 1.74, samples: 42\n",
+ " │ │ ├─feat: 2 <= 2.70, samples: 39\n",
+ " │ │ │ └─╴value: 0.0\n",
+ " │ │ │ └─╴feat: 0 <= -0.55, samples: 5\n",
+ " │ │ │ └─╴value: 0.0\n",
+ " │ │ │ └─╴value: 1.0\n",
" │ │ └─╴value: 1.0\n",
- " │ └─╴feat: 0 <= 0.28, samples: 199\n",
- " │ ├─feat: 3 <= 1.69, samples: 29\n",
- " │ │ ├─feat: 3 <= 1.37, samples: 6\n",
- " │ │ │ └─╴value: 1.0\n",
- " │ │ │ └─╴value: 0.0\n",
- " │ │ └─╴feat: 4 <= 2.91, samples: 23\n",
- " │ │ └─╴value: 0.0\n",
- " │ │ └─╴value: 1.0\n",
+ " │ └─╴feat: 0 <= -0.81, samples: 65\n",
+ " │ └─╴value: 0.0\n",
" │ └─╴value: 1.0\n",
- " └─╴value: 1.0\n",
+ " └─╴feat: 3 <= 0.37, samples: 4746\n",
+ " ├─feat: 2 <= 3.62, samples: 106\n",
+ " │ ├─feat: 4 <= -0.07, samples: 17\n",
+ " │ │ ├─feat: 1 <= -0.51, samples: 8\n",
+ " │ │ │ └─╴value: 0.0\n",
+ " │ │ │ └─╴value: 1.0\n",
+ " │ │ └─╴value: 0.0\n",
+ " │ └─╴value: 1.0\n",
+ " └─╴value: 1.0\n",
"\n",
"—————————————————————— tree: 4/5 ———————————————————————\n",
- "split: CART, impurity: Entropy, leaf: Mode, nodes: 47\n",
+ "split: CART, impurity: Entropy, leaf: Mode, nodes: 45\n",
"maxDepth: 7, reached depth: 7, minSamplesSplit: 2\n",
"························································\n",
- "â•´feat: 4 <= 2.79, samples: 9600\n",
- " ├─feat: 4 <= 1.98, samples: 4853\n",
- " │ ├─feat: 3 <= 1.43, samples: 4693\n",
+ "â•´feat: 2 <= 2.15, samples: 9600\n",
+ " ├─feat: 2 <= 2.00, samples: 4747\n",
+ " │ ├─feat: 3 <= 1.96, samples: 4694\n",
" │ │ └─╴value: 0.0\n",
- " │ │ └─╴feat: 0 <= 2.19, samples: 361\n",
- " │ │ ├─feat: 4 <= 1.18, samples: 357\n",
- " │ │ │ └─╴value: 0.0\n",
- " │ │ │ └─╴feat: 4 <= 1.18, samples: 44\n",
- " │ │ │ └─╴value: 1.0\n",
- " │ │ │ └─╴value: 0.0\n",
- " │ │ └─╴feat: 1 <= 0.47, samples: 4\n",
+ " │ │ └─╴feat: 2 <= 1.67, samples: 153\n",
+ " │ │ └─╴value: 0.0\n",
+ " │ │ └─╴feat: 1 <= -0.94, samples: 4\n",
" │ │ └─╴value: 1.0\n",
" │ │ └─╴value: 0.0\n",
- " │ └─╴feat: 3 <= 1.25, samples: 160\n",
- " │ ├─feat: 0 <= 1.44, samples: 98\n",
- " │ │ ├─feat: 3 <= 1.14, samples: 92\n",
- " │ │ │ └─╴value: 0.0\n",
- " │ │ │ └─╴feat: 0 <= -0.86, samples: 2\n",
- " │ │ │ └─╴value: 1.0\n",
- " │ │ │ └─╴value: 0.0\n",
- " │ │ └─╴feat: 3 <= 0.10, samples: 6\n",
- " │ │ └─╴value: 0.0\n",
- " │ │ └─╴value: 1.0\n",
- " │ └─╴feat: 0 <= 0.77, samples: 62\n",
- " │ ├─feat: 4 <= 2.23, samples: 20\n",
- " │ │ ├─feat: 3 <= 2.03, samples: 6\n",
- " │ │ │ └─╴value: 0.0\n",
- " │ │ │ └─╴value: 1.0\n",
- " │ │ └─╴feat: 3 <= 1.28, samples: 14\n",
- " │ │ └─╴value: 0.0\n",
- " │ │ └─╴value: 1.0\n",
+ " │ └─╴feat: 0 <= 1.46, samples: 53\n",
+ " │ ├─feat: 3 <= 2.13, samples: 42\n",
+ " │ │ └─╴value: 0.0\n",
+ " │ │ └─╴value: 1.0\n",
+ " │ └─╴feat: 3 <= -1.24, samples: 11\n",
+ " │ └─╴value: 0.0\n",
" │ └─╴value: 1.0\n",
- " └─╴feat: 4 <= 3.40, samples: 4747\n",
- " ├─feat: 3 <= 0.61, samples: 209\n",
- " │ ├─feat: 0 <= 1.22, samples: 10\n",
- " │ │ └─╴value: 0.0\n",
+ " └─╴feat: 2 <= 2.86, samples: 4853\n",
+ " ├─feat: 3 <= 0.92, samples: 163\n",
+ " │ ├─feat: 0 <= 1.79, samples: 62\n",
+ " │ │ ├─feat: 4 <= 0.21, samples: 59\n",
+ " │ │ │ └─╴value: 0.0\n",
+ " │ │ │ └─╴feat: 1 <= -0.21, samples: 17\n",
+ " │ │ │ └─╴value: 0.0\n",
+ " │ │ │ └─╴value: 0.0\n",
" │ │ └─╴value: 1.0\n",
- " │ └─╴feat: 0 <= 0.28, samples: 199\n",
- " │ ├─feat: 3 <= 1.69, samples: 29\n",
- " │ │ ├─feat: 3 <= 1.37, samples: 6\n",
- " │ │ │ └─╴value: 1.0\n",
- " │ │ │ └─╴value: 0.0\n",
- " │ │ └─╴feat: 4 <= 2.91, samples: 23\n",
- " │ │ └─╴value: 0.0\n",
- " │ │ └─╴value: 1.0\n",
- " │ └─╴value: 1.0\n",
- " └─╴value: 1.0\n",
+ " │ └─╴feat: 0 <= -0.76, samples: 101\n",
+ " │ └─╴value: 0.0\n",
+ " │ └─╴feat: 3 <= 1.41, samples: 97\n",
+ " │ ├─feat: 3 <= 1.38, samples: 14\n",
+ " │ │ └─╴value: 1.0\n",
+ " │ │ └─╴value: 0.0\n",
+ " │ └─╴feat: 4 <= 0.56, samples: 83\n",
+ " │ └─╴value: 1.0\n",
+ " │ └─╴value: 1.0\n",
+ " └─╴feat: 2 <= 3.66, samples: 4690\n",
+ " ├─feat: 3 <= 0.43, samples: 362\n",
+ " │ ├─feat: 4 <= -0.07, samples: 16\n",
+ " │ │ └─╴value: 1.0\n",
+ " │ │ └─╴value: 0.0\n",
+ " │ └─╴feat: 2 <= 2.87, samples: 346\n",
+ " │ ├─feat: 0 <= 0.91, samples: 5\n",
+ " │ │ └─╴value: 0.0\n",
+ " │ │ └─╴value: 1.0\n",
+ " │ └─╴value: 1.0\n",
+ " └─╴value: 1.0\n",
"\n",
"—————————————————————— tree: 5/5 ———————————————————————\n",
- "split: CART, impurity: Entropy, leaf: Mode, nodes: 47\n",
+ "split: CART, impurity: Entropy, leaf: Mode, nodes: 45\n",
"maxDepth: 7, reached depth: 7, minSamplesSplit: 2\n",
"························································\n",
- "â•´feat: 4 <= 2.79, samples: 9600\n",
- " ├─feat: 4 <= 1.98, samples: 4853\n",
- " │ ├─feat: 3 <= 1.43, samples: 4693\n",
+ "â•´feat: 2 <= 2.50, samples: 9600\n",
+ " ├─feat: 2 <= 2.05, samples: 4748\n",
+ " │ ├─feat: 3 <= 2.48, samples: 4643\n",
" │ │ └─╴value: 0.0\n",
- " │ │ └─╴feat: 0 <= 2.19, samples: 361\n",
- " │ │ ├─feat: 4 <= 1.18, samples: 357\n",
- " │ │ │ └─╴value: 0.0\n",
- " │ │ │ └─╴feat: 4 <= 1.18, samples: 44\n",
- " │ │ │ └─╴value: 1.0\n",
- " │ │ │ └─╴value: 0.0\n",
- " │ │ └─╴feat: 1 <= 0.47, samples: 4\n",
- " │ │ └─╴value: 1.0\n",
- " │ │ └─╴value: 0.0\n",
- " │ └─╴feat: 3 <= 1.25, samples: 160\n",
- " │ ├─feat: 0 <= 1.44, samples: 98\n",
- " │ │ ├─feat: 3 <= 1.14, samples: 92\n",
+ " │ │ └─╴feat: 2 <= 1.30, samples: 50\n",
+ " │ │ └─╴value: 0.0\n",
+ " │ │ └─╴value: 1.0\n",
+ " │ └─╴feat: 3 <= 1.61, samples: 105\n",
+ " │ ├─feat: 0 <= 1.12, samples: 69\n",
+ " │ │ ├─feat: 3 <= 0.92, samples: 60\n",
" │ │ │ └─╴value: 0.0\n",
- " │ │ │ └─╴feat: 1 <= -0.45, samples: 2\n",
- " │ │ │ └─╴value: 1.0\n",
+ " │ │ │ └─╴feat: 2 <= 2.30, samples: 9\n",
" │ │ │ └─╴value: 0.0\n",
- " │ │ └─╴feat: 3 <= 0.10, samples: 6\n",
+ " │ │ │ └─╴value: 1.0\n",
+ " │ │ └─╴feat: 4 <= -0.46, samples: 9\n",
" │ │ └─╴value: 0.0\n",
" │ │ └─╴value: 1.0\n",
- " │ └─╴feat: 0 <= 0.77, samples: 62\n",
- " │ ├─feat: 4 <= 2.23, samples: 20\n",
- " │ │ ├─feat: 3 <= 2.03, samples: 6\n",
- " │ │ │ └─╴value: 0.0\n",
- " │ │ │ └─╴value: 1.0\n",
- " │ │ └─╴feat: 3 <= 1.28, samples: 14\n",
- " │ │ └─╴value: 0.0\n",
- " │ │ └─╴value: 1.0\n",
+ " │ └─╴feat: 4 <= 0.62, samples: 36\n",
" │ └─╴value: 1.0\n",
- " └─╴feat: 4 <= 3.40, samples: 4747\n",
- " ├─feat: 3 <= 0.61, samples: 209\n",
- " │ ├─feat: 0 <= 1.22, samples: 10\n",
- " │ │ └─╴value: 0.0\n",
- " │ │ └─╴value: 1.0\n",
- " │ └─╴feat: 0 <= 0.28, samples: 199\n",
- " │ ├─feat: 3 <= 1.69, samples: 29\n",
- " │ │ ├─feat: 3 <= 1.37, samples: 6\n",
- " │ │ │ └─╴value: 1.0\n",
- " │ │ │ └─╴value: 0.0\n",
- " │ │ └─╴feat: 4 <= 2.91, samples: 23\n",
- " │ │ └─╴value: 0.0\n",
- " │ │ └─╴value: 1.0\n",
+ " │ └─╴feat: 3 <= 2.60, samples: 5\n",
+ " │ └─╴value: 0.0\n",
+ " │ └─╴value: 1.0\n",
+ " └─╴feat: 2 <= 3.02, samples: 4852\n",
+ " ├─feat: 3 <= 1.41, samples: 107\n",
+ " │ ├─feat: 0 <= 0.52, samples: 38\n",
+ " │ │ ├─feat: 1 <= -1.30, samples: 25\n",
+ " │ │ │ └─╴value: 1.0\n",
+ " │ │ │ └─╴value: 0.0\n",
+ " │ │ └─╴feat: 0 <= 0.94, samples: 13\n",
+ " │ │ ├─feat: 0 <= 0.67, samples: 5\n",
+ " │ │ │ └─╴value: 1.0\n",
+ " │ │ │ └─╴value: 0.0\n",
+ " │ │ └─╴value: 1.0\n",
+ " │ └─╴feat: 1 <= 2.00, samples: 69\n",
" │ └─╴value: 1.0\n",
- " └─╴value: 1.0\n",
+ " │ └─╴value: 0.0\n",
+ " └─╴feat: 3 <= 0.53, samples: 4745\n",
+ " ├─feat: 2 <= 3.63, samples: 106\n",
+ " │ ├─feat: 4 <= 0.15, samples: 13\n",
+ " │ │ └─╴value: 1.0\n",
+ " │ │ └─╴feat: 2 <= 3.07, samples: 4\n",
+ " │ │ └─╴value: 1.0\n",
+ " │ │ └─╴value: 0.0\n",
+ " │ └─╴value: 1.0\n",
+ " └─╴value: 1.0\n",
"\n",
"\n"
]
@@ -432,11 +405,11 @@
{
"data": {
"text/plain": [
- "[Accuracy(name='tree: 0', accuracy=0.998125),\n",
- " Accuracy(name='tree: 1', accuracy=0.998125),\n",
- " Accuracy(name='tree: 2', accuracy=0.998125),\n",
+ "[Accuracy(name='tree: 0', accuracy=0.996875),\n",
+ " Accuracy(name='tree: 1', accuracy=0.9978125),\n",
+ " Accuracy(name='tree: 2', accuracy=0.9965625),\n",
" Accuracy(name='tree: 3', accuracy=0.998125),\n",
- " Accuracy(name='tree: 4', accuracy=0.998125)]"
+ " Accuracy(name='tree: 4', accuracy=0.9965625)]"
]
},
"execution_count": 5,
@@ -455,7 +428,7 @@
"outputs": [
{
"data": {
- "image/png": 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",
+ "image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
@@ -506,14 +479,14 @@
" Class 0 1596 4 \n",
" 49% 0% \n",
"····································\n",
- " Class 1 2 1598 \n",
+ " Class 1 3 1597 \n",
" 0% 49% \n",
"\n",
"———————————————————————————————— scores ———————————————————————————————\n",
" accuracy precision sensitivity miss rate \n",
"·······································································\n",
- " Class 0 0.998 0.999 0.998 0.003 \n",
- " Class 1 0.998 0.998 0.999 0.001 \n",
+ " Class 0 0.998 0.998 0.998 0.003 \n",
+ " Class 1 0.998 0.998 0.998 0.002 \n",
"·······································································\n",
" total 0.998 0.998 0.998 0.002 \n"
]
@@ -551,268 +524,234 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â” forrest â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\n",
- "voting: Majority, booster: GradientBoosting, bootstrapping: False\n",
+ "â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â” forrest â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\n",
+ "voting: Majority, booster: None, bootstrapping: True\n",
"\n",
"————————————————————— tree: 01/15 ——————————————————————\n",
- "split: CART, impurity: Entropy, leaf: Mode, nodes: 47\n",
+ "split: CART, impurity: Entropy, leaf: Mode, nodes: 35\n",
"maxDepth: 7, reached depth: 7, minSamplesSplit: 2\n",
"························································\n",
- "â•´feat: 4 <= 2.79, samples: 9600\n",
- " ├─feat: 4 <= 1.98, samples: 4853\n",
- " │ ├─feat: 3 <= 1.43, samples: 4693\n",
- " │ │ └─╴value: 0.0\n",
- " │ │ └─╴feat: 0 <= 2.19, samples: 361\n",
- " │ │ ├─feat: 4 <= 1.18, samples: 357\n",
- " │ │ │ └─╴value: 0.0\n",
- " │ │ │ └─╴feat: 4 <= 1.18, samples: 44\n",
- " │ │ │ └─╴value: 1.0\n",
- " │ │ │ └─╴value: 0.0\n",
- " │ │ └─╴feat: 1 <= 0.47, samples: 4\n",
- " │ │ └─╴value: 1.0\n",
- " │ │ └─╴value: 0.0\n",
- " │ └─╴feat: 3 <= 1.25, samples: 160\n",
- " │ ├─feat: 0 <= 1.44, samples: 98\n",
- " │ │ ├─feat: 3 <= 1.14, samples: 92\n",
- " │ │ │ └─╴value: 0.0\n",
- " │ │ │ └─╴feat: 0 <= -0.86, samples: 2\n",
- " │ │ │ └─╴value: 1.0\n",
- " │ │ │ └─╴value: 0.0\n",
- " │ │ └─╴feat: 3 <= 0.10, samples: 6\n",
- " │ │ └─╴value: 0.0\n",
- " │ │ └─╴value: 1.0\n",
- " │ └─╴feat: 0 <= 0.77, samples: 62\n",
- " │ ├─feat: 4 <= 2.23, samples: 20\n",
- " │ │ ├─feat: 3 <= 2.03, samples: 6\n",
- " │ │ │ └─╴value: 0.0\n",
- " │ │ │ └─╴value: 1.0\n",
- " │ │ └─╴feat: 3 <= 1.28, samples: 14\n",
- " │ │ └─╴value: 0.0\n",
- " │ │ └─╴value: 1.0\n",
+ "â•´feat: 2 <= 2.14, samples: 9600\n",
+ " ├─feat: 3 <= 2.15, samples: 4747\n",
+ " │ └─╴value: 0.0\n",
+ " │ └─╴feat: 2 <= 1.65, samples: 104\n",
+ " │ └─╴value: 0.0\n",
+ " │ └─╴feat: 0 <= -1.22, samples: 8\n",
+ " │ └─╴value: 0.0\n",
" │ └─╴value: 1.0\n",
- " └─╴feat: 4 <= 3.40, samples: 4747\n",
- " ├─feat: 3 <= 0.61, samples: 209\n",
- " │ ├─feat: 0 <= 1.22, samples: 10\n",
- " │ │ └─╴value: 0.0\n",
+ " └─╴feat: 2 <= 3.24, samples: 4853\n",
+ " ├─feat: 3 <= 0.71, samples: 267\n",
+ " │ ├─feat: 0 <= 1.74, samples: 56\n",
+ " │ │ ├─feat: 4 <= 0.15, samples: 53\n",
+ " │ │ │ └─╴value: 0.0\n",
+ " │ │ │ └─╴feat: 0 <= 0.75, samples: 17\n",
+ " │ │ │ └─╴value: 0.0\n",
+ " │ │ │ └─╴value: 1.0\n",
" │ │ └─╴value: 1.0\n",
- " │ └─╴feat: 0 <= 0.28, samples: 199\n",
- " │ ├─feat: 3 <= 1.69, samples: 29\n",
- " │ │ ├─feat: 3 <= 1.37, samples: 6\n",
- " │ │ │ └─╴value: 1.0\n",
- " │ │ │ └─╴value: 0.0\n",
- " │ │ └─╴feat: 4 <= 2.91, samples: 23\n",
- " │ │ └─╴value: 0.0\n",
+ " │ └─╴feat: 3 <= 1.96, samples: 211\n",
+ " │ ├─feat: 2 <= 2.30, samples: 64\n",
+ " │ │ └─╴value: 0.0\n",
+ " │ │ └─╴feat: 1 <= 0.02, samples: 55\n",
" │ │ └─╴value: 1.0\n",
- " │ └─╴value: 1.0\n",
- " └─╴value: 1.0\n",
+ " │ │ └─╴value: 1.0\n",
+ " │ └─╴feat: 2 <= 2.34, samples: 147\n",
+ " │ ├─feat: 2 <= 2.34, samples: 20\n",
+ " │ │ └─╴value: 1.0\n",
+ " │ │ └─╴value: 0.0\n",
+ " │ └─╴value: 1.0\n",
+ " └─╴feat: 3 <= 0.22, samples: 4586\n",
+ " ├─feat: 2 <= 3.59, samples: 51\n",
+ " │ ├─feat: 3 <= -0.28, samples: 3\n",
+ " │ │ └─╴value: 1.0\n",
+ " │ │ └─╴value: 0.0\n",
+ " │ └─╴value: 1.0\n",
+ " └─╴value: 1.0\n",
"\n",
"————————————————————— tree: 02/15 ——————————————————————\n",
- "split: CART, impurity: Entropy, leaf: Mode, nodes: 47\n",
+ "split: CART, impurity: Entropy, leaf: Mode, nodes: 33\n",
"maxDepth: 7, reached depth: 7, minSamplesSplit: 2\n",
"························································\n",
- "â•´feat: 4 <= 2.79, samples: 9600\n",
- " ├─feat: 4 <= 1.98, samples: 4853\n",
- " │ ├─feat: 3 <= 1.43, samples: 4693\n",
- " │ │ └─╴value: 0.0\n",
- " │ │ └─╴feat: 0 <= 2.19, samples: 361\n",
- " │ │ ├─feat: 4 <= 1.18, samples: 357\n",
- " │ │ │ └─╴value: 0.0\n",
- " │ │ │ └─╴feat: 4 <= 1.18, samples: 44\n",
- " │ │ │ └─╴value: 1.0\n",
- " │ │ │ └─╴value: 0.0\n",
- " │ │ └─╴feat: 1 <= 0.47, samples: 4\n",
- " │ │ └─╴value: 1.0\n",
- " │ │ └─╴value: 0.0\n",
- " │ └─╴feat: 3 <= 1.25, samples: 160\n",
- " │ ├─feat: 0 <= 1.44, samples: 98\n",
- " │ │ ├─feat: 3 <= 1.14, samples: 92\n",
- " │ │ │ └─╴value: 0.0\n",
- " │ │ │ └─╴feat: 1 <= -0.45, samples: 2\n",
- " │ │ │ └─╴value: 1.0\n",
- " │ │ │ └─╴value: 0.0\n",
- " │ │ └─╴feat: 3 <= 0.10, samples: 6\n",
- " │ │ └─╴value: 0.0\n",
- " │ │ └─╴value: 1.0\n",
- " │ └─╴feat: 0 <= 0.77, samples: 62\n",
- " │ ├─feat: 4 <= 2.23, samples: 20\n",
- " │ │ ├─feat: 3 <= 2.03, samples: 6\n",
- " │ │ │ └─╴value: 0.0\n",
- " │ │ │ └─╴value: 1.0\n",
- " │ │ └─╴feat: 3 <= 1.28, samples: 14\n",
- " │ │ └─╴value: 0.0\n",
- " │ │ └─╴value: 1.0\n",
+ "â•´feat: 2 <= 2.28, samples: 9600\n",
+ " ├─feat: 2 <= 1.88, samples: 4747\n",
+ " │ └─╴value: 0.0\n",
+ " │ └─╴feat: 3 <= 2.00, samples: 105\n",
+ " │ ├─feat: 0 <= 1.86, samples: 87\n",
+ " │ │ └─╴value: 0.0\n",
+ " │ │ └─╴value: 1.0\n",
+ " │ └─╴feat: 0 <= -1.24, samples: 18\n",
+ " │ └─╴value: 0.0\n",
" │ └─╴value: 1.0\n",
- " └─╴feat: 4 <= 3.40, samples: 4747\n",
- " ├─feat: 3 <= 0.61, samples: 209\n",
- " │ ├─feat: 0 <= 1.22, samples: 10\n",
+ " └─╴feat: 2 <= 3.35, samples: 4853\n",
+ " ├─feat: 3 <= 1.41, samples: 320\n",
+ " │ ├─feat: 0 <= 0.48, samples: 99\n",
" │ │ └─╴value: 0.0\n",
- " │ │ └─╴value: 1.0\n",
- " │ └─╴feat: 0 <= 0.28, samples: 199\n",
- " │ ├─feat: 3 <= 1.69, samples: 29\n",
- " │ │ ├─feat: 3 <= 1.37, samples: 6\n",
- " │ │ │ └─╴value: 1.0\n",
- " │ │ │ └─╴value: 0.0\n",
- " │ │ └─╴feat: 4 <= 2.91, samples: 23\n",
- " │ │ └─╴value: 0.0\n",
- " │ │ └─╴value: 1.0\n",
+ " │ │ └─╴feat: 2 <= 2.99, samples: 61\n",
+ " │ │ ├─feat: 4 <= 0.87, samples: 40\n",
+ " │ │ │ └─╴value: 0.0\n",
+ " │ │ │ └─╴value: 1.0\n",
+ " │ │ └─╴value: 1.0\n",
+ " │ └─╴feat: 2 <= 2.35, samples: 221\n",
+ " │ ├─feat: 4 <= 0.56, samples: 13\n",
+ " │ │ └─╴value: 1.0\n",
+ " │ │ └─╴value: 0.0\n",
" │ └─╴value: 1.0\n",
- " └─╴value: 1.0\n",
+ " └─╴feat: 3 <= 0.57, samples: 4533\n",
+ " ├─feat: 2 <= 3.65, samples: 150\n",
+ " │ ├─feat: 3 <= 0.40, samples: 9\n",
+ " │ │ ├─feat: 0 <= -0.57, samples: 4\n",
+ " │ │ │ └─╴value: 1.0\n",
+ " │ │ │ └─╴value: 0.0\n",
+ " │ │ └─╴value: 1.0\n",
+ " │ └─╴value: 1.0\n",
+ " └─╴value: 1.0\n",
"\n",
"————————————————————— tree: 03/15 ——————————————————————\n",
- "split: CART, impurity: Entropy, leaf: Mode, nodes: 47\n",
+ "split: CART, impurity: Entropy, leaf: Mode, nodes: 43\n",
"maxDepth: 7, reached depth: 7, minSamplesSplit: 2\n",
"························································\n",
- "â•´feat: 4 <= 2.79, samples: 9600\n",
- " ├─feat: 4 <= 1.98, samples: 4853\n",
- " │ ├─feat: 3 <= 1.43, samples: 4693\n",
+ "â•´feat: 2 <= 2.36, samples: 9600\n",
+ " ├─feat: 2 <= 1.77, samples: 4747\n",
+ " │ ├─feat: 2 <= 1.65, samples: 4591\n",
" │ │ └─╴value: 0.0\n",
- " │ │ └─╴feat: 0 <= 2.19, samples: 361\n",
- " │ │ ├─feat: 4 <= 1.18, samples: 357\n",
- " │ │ │ └─╴value: 0.0\n",
- " │ │ │ └─╴feat: 4 <= 1.18, samples: 44\n",
- " │ │ │ └─╴value: 1.0\n",
- " │ │ │ └─╴value: 0.0\n",
- " │ │ └─╴feat: 1 <= 0.47, samples: 4\n",
- " │ │ └─╴value: 1.0\n",
- " │ │ └─╴value: 0.0\n",
- " │ └─╴feat: 3 <= 1.25, samples: 160\n",
- " │ ├─feat: 0 <= 1.44, samples: 98\n",
- " │ │ ├─feat: 3 <= 1.14, samples: 92\n",
- " │ │ │ └─╴value: 0.0\n",
- " │ │ │ └─╴feat: 0 <= -0.86, samples: 2\n",
- " │ │ │ └─╴value: 1.0\n",
- " │ │ │ └─╴value: 0.0\n",
- " │ │ └─╴feat: 3 <= 0.10, samples: 6\n",
+ " │ │ └─╴feat: 3 <= 3.06, samples: 51\n",
+ " │ │ └─╴value: 0.0\n",
+ " │ │ └─╴value: 1.0\n",
+ " │ └─╴feat: 3 <= 1.01, samples: 156\n",
+ " │ ├─feat: 2 <= 2.34, samples: 111\n",
+ " │ │ └─╴value: 0.0\n",
+ " │ │ └─╴feat: 0 <= -1.20, samples: 2\n",
" │ │ └─╴value: 0.0\n",
" │ │ └─╴value: 1.0\n",
- " │ └─╴feat: 0 <= 0.77, samples: 62\n",
- " │ ├─feat: 4 <= 2.23, samples: 20\n",
- " │ │ ├─feat: 3 <= 2.03, samples: 6\n",
- " │ │ │ └─╴value: 0.0\n",
- " │ │ │ └─╴value: 1.0\n",
- " │ │ └─╴feat: 3 <= 1.28, samples: 14\n",
- " │ │ └─╴value: 0.0\n",
- " │ │ └─╴value: 1.0\n",
- " │ └─╴value: 1.0\n",
- " └─╴feat: 4 <= 3.40, samples: 4747\n",
- " ├─feat: 3 <= 0.61, samples: 209\n",
- " │ ├─feat: 0 <= 1.22, samples: 10\n",
- " │ │ └─╴value: 0.0\n",
+ " │ └─╴feat: 0 <= 0.47, samples: 45\n",
+ " │ ├─feat: 1 <= -1.38, samples: 16\n",
+ " │ │ └─╴value: 1.0\n",
+ " │ │ └─╴value: 0.0\n",
+ " │ └─╴feat: 3 <= 1.77, samples: 29\n",
+ " │ ├─feat: 3 <= 1.16, samples: 10\n",
+ " │ │ └─╴value: 1.0\n",
+ " │ │ └─╴value: 0.0\n",
+ " │ └─╴value: 1.0\n",
+ " └─╴feat: 2 <= 2.87, samples: 4853\n",
+ " ├─feat: 3 <= 0.93, samples: 107\n",
+ " │ ├─feat: 0 <= 1.74, samples: 42\n",
+ " │ │ ├─feat: 2 <= 2.70, samples: 39\n",
+ " │ │ │ └─╴value: 0.0\n",
+ " │ │ │ └─╴feat: 0 <= -0.55, samples: 5\n",
+ " │ │ │ └─╴value: 0.0\n",
+ " │ │ │ └─╴value: 1.0\n",
" │ │ └─╴value: 1.0\n",
- " │ └─╴feat: 0 <= 0.28, samples: 199\n",
- " │ ├─feat: 3 <= 1.69, samples: 29\n",
- " │ │ ├─feat: 3 <= 1.37, samples: 6\n",
- " │ │ │ └─╴value: 1.0\n",
- " │ │ │ └─╴value: 0.0\n",
- " │ │ └─╴feat: 4 <= 2.91, samples: 23\n",
- " │ │ └─╴value: 0.0\n",
- " │ │ └─╴value: 1.0\n",
+ " │ └─╴feat: 0 <= -0.81, samples: 65\n",
+ " │ └─╴value: 0.0\n",
" │ └─╴value: 1.0\n",
- " └─╴value: 1.0\n",
+ " └─╴feat: 3 <= 0.37, samples: 4746\n",
+ " ├─feat: 2 <= 3.62, samples: 106\n",
+ " │ ├─feat: 4 <= -0.07, samples: 17\n",
+ " │ │ ├─feat: 1 <= -0.51, samples: 8\n",
+ " │ │ │ └─╴value: 0.0\n",
+ " │ │ │ └─╴value: 1.0\n",
+ " │ │ └─╴value: 0.0\n",
+ " │ └─╴value: 1.0\n",
+ " └─╴value: 1.0\n",
"\n",
"————————————————————— tree: 04/15 ——————————————————————\n",
- "split: CART, impurity: Entropy, leaf: Mode, nodes: 47\n",
+ "split: CART, impurity: Entropy, leaf: Mode, nodes: 45\n",
"maxDepth: 7, reached depth: 7, minSamplesSplit: 2\n",
"························································\n",
- "â•´feat: 4 <= 2.79, samples: 9600\n",
- " ├─feat: 4 <= 1.98, samples: 4853\n",
- " │ ├─feat: 3 <= 1.43, samples: 4693\n",
+ "â•´feat: 2 <= 2.15, samples: 9600\n",
+ " ├─feat: 2 <= 2.00, samples: 4747\n",
+ " │ ├─feat: 3 <= 1.96, samples: 4694\n",
" │ │ └─╴value: 0.0\n",
- " │ │ └─╴feat: 0 <= 2.19, samples: 361\n",
- " │ │ ├─feat: 4 <= 1.18, samples: 357\n",
- " │ │ │ └─╴value: 0.0\n",
- " │ │ │ └─╴feat: 4 <= 1.18, samples: 44\n",
- " │ │ │ └─╴value: 1.0\n",
- " │ │ │ └─╴value: 0.0\n",
- " │ │ └─╴feat: 1 <= 0.47, samples: 4\n",
+ " │ │ └─╴feat: 2 <= 1.67, samples: 153\n",
+ " │ │ └─╴value: 0.0\n",
+ " │ │ └─╴feat: 1 <= -0.94, samples: 4\n",
" │ │ └─╴value: 1.0\n",
" │ │ └─╴value: 0.0\n",
- " │ └─╴feat: 3 <= 1.25, samples: 160\n",
- " │ ├─feat: 0 <= 1.44, samples: 98\n",
- " │ │ ├─feat: 3 <= 1.14, samples: 92\n",
- " │ │ │ └─╴value: 0.0\n",
- " │ │ │ └─╴feat: 0 <= -0.86, samples: 2\n",
- " │ │ │ └─╴value: 1.0\n",
- " │ │ │ └─╴value: 0.0\n",
- " │ │ └─╴feat: 3 <= 0.10, samples: 6\n",
- " │ │ └─╴value: 0.0\n",
- " │ │ └─╴value: 1.0\n",
- " │ └─╴feat: 0 <= 0.77, samples: 62\n",
- " │ ├─feat: 4 <= 2.23, samples: 20\n",
- " │ │ ├─feat: 3 <= 2.03, samples: 6\n",
- " │ │ │ └─╴value: 0.0\n",
- " │ │ │ └─╴value: 1.0\n",
- " │ │ └─╴feat: 3 <= 1.28, samples: 14\n",
- " │ │ └─╴value: 0.0\n",
- " │ │ └─╴value: 1.0\n",
+ " │ └─╴feat: 0 <= 1.46, samples: 53\n",
+ " │ ├─feat: 3 <= 2.13, samples: 42\n",
+ " │ │ └─╴value: 0.0\n",
+ " │ │ └─╴value: 1.0\n",
+ " │ └─╴feat: 3 <= -1.24, samples: 11\n",
+ " │ └─╴value: 0.0\n",
" │ └─╴value: 1.0\n",
- " └─╴feat: 4 <= 3.40, samples: 4747\n",
- " ├─feat: 3 <= 0.61, samples: 209\n",
- " │ ├─feat: 0 <= 1.22, samples: 10\n",
- " │ │ └─╴value: 0.0\n",
+ " └─╴feat: 2 <= 2.86, samples: 4853\n",
+ " ├─feat: 3 <= 0.92, samples: 163\n",
+ " │ ├─feat: 0 <= 1.79, samples: 62\n",
+ " │ │ ├─feat: 4 <= 0.21, samples: 59\n",
+ " │ │ │ └─╴value: 0.0\n",
+ " │ │ │ └─╴feat: 1 <= -0.21, samples: 17\n",
+ " │ │ │ └─╴value: 0.0\n",
+ " │ │ │ └─╴value: 0.0\n",
" │ │ └─╴value: 1.0\n",
- " │ └─╴feat: 0 <= 0.28, samples: 199\n",
- " │ ├─feat: 3 <= 1.69, samples: 29\n",
- " │ │ ├─feat: 3 <= 1.37, samples: 6\n",
- " │ │ │ └─╴value: 1.0\n",
- " │ │ │ └─╴value: 0.0\n",
- " │ │ └─╴feat: 4 <= 2.91, samples: 23\n",
- " │ │ └─╴value: 0.0\n",
- " │ │ └─╴value: 1.0\n",
- " │ └─╴value: 1.0\n",
- " └─╴value: 1.0\n",
+ " │ └─╴feat: 0 <= -0.76, samples: 101\n",
+ " │ └─╴value: 0.0\n",
+ " │ └─╴feat: 3 <= 1.41, samples: 97\n",
+ " │ ├─feat: 3 <= 1.38, samples: 14\n",
+ " │ │ └─╴value: 1.0\n",
+ " │ │ └─╴value: 0.0\n",
+ " │ └─╴feat: 4 <= 0.56, samples: 83\n",
+ " │ └─╴value: 1.0\n",
+ " │ └─╴value: 1.0\n",
+ " └─╴feat: 2 <= 3.66, samples: 4690\n",
+ " ├─feat: 3 <= 0.43, samples: 362\n",
+ " │ ├─feat: 4 <= -0.07, samples: 16\n",
+ " │ │ └─╴value: 1.0\n",
+ " │ │ └─╴value: 0.0\n",
+ " │ └─╴feat: 2 <= 2.87, samples: 346\n",
+ " │ ├─feat: 0 <= 0.91, samples: 5\n",
+ " │ │ └─╴value: 0.0\n",
+ " │ │ └─╴value: 1.0\n",
+ " │ └─╴value: 1.0\n",
+ " └─╴value: 1.0\n",
"\n",
"————————————————————— tree: 05/15 ——————————————————————\n",
- "split: CART, impurity: Entropy, leaf: Mode, nodes: 47\n",
+ "split: CART, impurity: Entropy, leaf: Mode, nodes: 45\n",
"maxDepth: 7, reached depth: 7, minSamplesSplit: 2\n",
"························································\n",
- "â•´feat: 4 <= 2.79, samples: 9600\n",
- " ├─feat: 4 <= 1.98, samples: 4853\n",
- " │ ├─feat: 3 <= 1.43, samples: 4693\n",
+ "â•´feat: 2 <= 2.50, samples: 9600\n",
+ " ├─feat: 2 <= 2.05, samples: 4748\n",
+ " │ ├─feat: 3 <= 2.48, samples: 4643\n",
" │ │ └─╴value: 0.0\n",
- " │ │ └─╴feat: 0 <= 2.19, samples: 361\n",
- " │ │ ├─feat: 4 <= 1.18, samples: 357\n",
- " │ │ │ └─╴value: 0.0\n",
- " │ │ │ └─╴feat: 4 <= 1.18, samples: 44\n",
- " │ │ │ └─╴value: 1.0\n",
- " │ │ │ └─╴value: 0.0\n",
- " │ │ └─╴feat: 1 <= 0.47, samples: 4\n",
- " │ │ └─╴value: 1.0\n",
- " │ │ └─╴value: 0.0\n",
- " │ └─╴feat: 3 <= 1.25, samples: 160\n",
- " │ ├─feat: 0 <= 1.44, samples: 98\n",
- " │ │ ├─feat: 3 <= 1.14, samples: 92\n",
+ " │ │ └─╴feat: 2 <= 1.30, samples: 50\n",
+ " │ │ └─╴value: 0.0\n",
+ " │ │ └─╴value: 1.0\n",
+ " │ └─╴feat: 3 <= 1.61, samples: 105\n",
+ " │ ├─feat: 0 <= 1.12, samples: 69\n",
+ " │ │ ├─feat: 3 <= 0.92, samples: 60\n",
" │ │ │ └─╴value: 0.0\n",
- " │ │ │ └─╴feat: 1 <= -0.45, samples: 2\n",
- " │ │ │ └─╴value: 1.0\n",
+ " │ │ │ └─╴feat: 2 <= 2.30, samples: 9\n",
" │ │ │ └─╴value: 0.0\n",
- " │ │ └─╴feat: 3 <= 0.10, samples: 6\n",
+ " │ │ │ └─╴value: 1.0\n",
+ " │ │ └─╴feat: 4 <= -0.46, samples: 9\n",
" │ │ └─╴value: 0.0\n",
" │ │ └─╴value: 1.0\n",
- " │ └─╴feat: 0 <= 0.77, samples: 62\n",
- " │ ├─feat: 4 <= 2.23, samples: 20\n",
- " │ │ ├─feat: 3 <= 2.03, samples: 6\n",
- " │ │ │ └─╴value: 0.0\n",
- " │ │ │ └─╴value: 1.0\n",
- " │ │ └─╴feat: 3 <= 1.28, samples: 14\n",
- " │ │ └─╴value: 0.0\n",
- " │ │ └─╴value: 1.0\n",
+ " │ └─╴feat: 4 <= 0.62, samples: 36\n",
" │ └─╴value: 1.0\n",
- " └─╴feat: 4 <= 3.40, samples: 4747\n",
- " ├─feat: 3 <= 0.61, samples: 209\n",
- " │ ├─feat: 0 <= 1.22, samples: 10\n",
- " │ │ └─╴value: 0.0\n",
- " │ │ └─╴value: 1.0\n",
- " │ └─╴feat: 0 <= 0.28, samples: 199\n",
- " │ ├─feat: 3 <= 1.69, samples: 29\n",
- " │ │ ├─feat: 3 <= 1.37, samples: 6\n",
- " │ │ │ └─╴value: 1.0\n",
- " │ │ │ └─╴value: 0.0\n",
- " │ │ └─╴feat: 4 <= 2.91, samples: 23\n",
- " │ │ └─╴value: 0.0\n",
- " │ │ └─╴value: 1.0\n",
+ " │ └─╴feat: 3 <= 2.60, samples: 5\n",
+ " │ └─╴value: 0.0\n",
+ " │ └─╴value: 1.0\n",
+ " └─╴feat: 2 <= 3.02, samples: 4852\n",
+ " ├─feat: 3 <= 1.41, samples: 107\n",
+ " │ ├─feat: 0 <= 0.52, samples: 38\n",
+ " │ │ ├─feat: 1 <= -1.30, samples: 25\n",
+ " │ │ │ └─╴value: 1.0\n",
+ " │ │ │ └─╴value: 0.0\n",
+ " │ │ └─╴feat: 0 <= 0.94, samples: 13\n",
+ " │ │ ├─feat: 0 <= 0.67, samples: 5\n",
+ " │ │ │ └─╴value: 1.0\n",
+ " │ │ │ └─╴value: 0.0\n",
+ " │ │ └─╴value: 1.0\n",
+ " │ └─╴feat: 1 <= 2.00, samples: 69\n",
" │ └─╴value: 1.0\n",
- " └─╴value: 1.0\n",
+ " │ └─╴value: 0.0\n",
+ " └─╴feat: 3 <= 0.53, samples: 4745\n",
+ " ├─feat: 2 <= 3.63, samples: 106\n",
+ " │ ├─feat: 4 <= 0.15, samples: 13\n",
+ " │ │ └─╴value: 1.0\n",
+ " │ │ └─╴feat: 2 <= 3.07, samples: 4\n",
+ " │ │ └─╴value: 1.0\n",
+ " │ │ └─╴value: 0.0\n",
+ " │ └─╴value: 1.0\n",
+ " └─╴value: 1.0\n",
"\n",
"\n"
]
@@ -840,14 +779,14 @@
" Class 0 1596 4 \n",
" 49% 0% \n",
"····································\n",
- " Class 1 2 1598 \n",
+ " Class 1 3 1597 \n",
" 0% 49% \n",
"\n",
"———————————————————————————————— scores ———————————————————————————————\n",
" accuracy precision sensitivity miss rate \n",
"·······································································\n",
- " Class 0 0.998 0.999 0.998 0.003 \n",
- " Class 1 0.998 0.998 0.999 0.001 \n",
+ " Class 0 0.998 0.998 0.998 0.003 \n",
+ " Class 1 0.998 0.998 0.998 0.002 \n",
"·······································································\n",
" total 0.998 0.998 0.998 0.002 \n"
]
@@ -895,7 +834,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.11.5"
+ "version": "3.12.2"
},
"vscode": {
"interpreter": {
diff --git a/forrest-test.json b/forrest-test.json
index adabfa583a9c3bb4401091a100c84e880bcca8da..9bdd65f0570d8a1503dfef8a3b9e7023d32383a2 100644
--- a/forrest-test.json
+++ b/forrest-test.json
@@ -1,5 +1,10 @@
{
- "bootstrapping": false,
+ "datetime": "2024-03-05T17:00:54.049104",
+ "qualifiedName": [
+ "machineLearning.rf.randomForrest",
+ "RandomForest"
+ ],
+ "bootstrapping": true,
"trained": true,
"baked": true,
"boosting": false,
@@ -30,8 +35,8 @@
},
"nodes": {
"0": {
- "threshold": 2.7910068664087704,
- "feature": 4,
+ "threshold": 2.138594686343364,
+ "feature": 2,
"leftID": 1,
"rightID": 2,
"id": 0,
@@ -41,8 +46,8 @@
"bakedValues": null
},
"1": {
- "threshold": 1.9802450082752243,
- "feature": 4,
+ "threshold": 2.153123334244971,
+ "feature": 3,
"leftID": 3,
"rightID": 4,
"id": 1,
@@ -52,10 +57,10 @@
"bakedValues": null
},
"2": {
- "threshold": 3.4047001207427647,
- "feature": 4,
- "leftID": 33,
- "rightID": 34,
+ "threshold": 3.23503187450122,
+ "feature": 2,
+ "leftID": 9,
+ "rightID": 10,
"id": 2,
"isRoot": false,
"parent": 0,
@@ -63,4686 +68,14 @@
"bakedValues": null
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"3": {
- "threshold": 1.4312225608480555,
- "feature": 3,
- "leftID": 5,
- "rightID": 6,
- "id": 3,
- "isRoot": false,
- "parent": 1,
- "rawValues": null,
- "bakedValues": null
- },
- "4": {
- "threshold": 1.2462741364847558,
- "feature": 3,
- "leftID": 15,
- "rightID": 16,
- "id": 4,
- "isRoot": false,
- "parent": 1,
- "rawValues": null,
- "bakedValues": null
- },
- "33": {
- "threshold": 0.6061906851568859,
- "feature": 3,
- "leftID": 35,
- "rightID": 36,
- "id": 33,
- "isRoot": false,
- "parent": 2,
- "rawValues": null,
- "bakedValues": null
- },
- "34": {
"threshold": null,
"feature": null,
"leftID": null,
"rightID": null,
- "id": 34,
+ "id": 3,
"isRoot": false,
- "parent": 2,
+ "parent": 1,
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diff --git a/machineLearning/rf/decisionTree.py b/machineLearning/rf/decisionTree.py
index a70755d9660de8d17f149c7d22f80db88112b7d0..42f05939c6531b8855bc6d607698d8b5ac2961e4 100644
--- a/machineLearning/rf/decisionTree.py
+++ b/machineLearning/rf/decisionTree.py
@@ -2,8 +2,8 @@ import numpy as np
from numpy.typing import ArrayLike
from .node import Node
from .impurityMeasure import ImpurityMeasure, ODD
-from .leafFunction import LeafFunction
-from .splitAlgorithm import SplitAlgorithm, RandomSplitAlgorithm
+from .leafFunction import LeafFunction, AnomalyDetection
+from .splitAlgorithm import SplitAlgorithm, RSA
from .featureSelection import FeatureSelection
from importlib import import_module
from inspect import signature
@@ -237,7 +237,7 @@ class DecisionTree():
if not isinstance(data, DataSet):
if targets is None:
raise ValueError("When providing raw data as np.ndarray, 'targets' must also be provided.")
- data = DataSet(data, targets=targets, weights=weights) # Convert to DataSet
+ data = DataSet(data, targets=targets, classWeights=weights) # Convert to DataSet
else:
# Data is an instance of DataSet, check if targets were unnecessarily provided
if targets is not None:
@@ -248,7 +248,7 @@ class DecisionTree():
# set the root node of the tree
id = str(self.id+1).zfill(len(str(self.treeID+1)))
self.bar = Progressbar(f'tree {id}', self._maxNodes)
- self.root = Node(level=0, isRoot=True)
+ self.root = Node(level=1, isRoot=True)
self.bar.step()
self.totalData, self.numFeatures = data.shape
@@ -277,7 +277,9 @@ class DecisionTree():
# iterating over raw predictions
with ThreadPoolExecutor(max_workers=None) as executor:
- predictions = list(executor.map(self._leafFunction, rawPredictions))
+ # Assuming 'rawPredictions' is a list of np.ndarray objects and you don't have 'nodes' yet.
+ predictions = list(executor.map(lambda args: self._leafFunction(*args),
+ zip([None] * len(rawPredictions), rawPredictions)))
else:
predictions = [0] * len(data)
@@ -318,11 +320,11 @@ class DecisionTree():
for node in self.breadthFirst():
if node._rawValues is not None:
- node._bakedValues = self._leafFunction(node._rawValues)
+ node._bakedValues = self._leafFunction(node, node._rawValues)
self._baked = True
- def _traverse(self, node: Node, dataPoint: np.ndarray, raw: bool = True) -> ArrayLike:
+ def _traverse(self, node: Node, dataPoint: np.ndarray, raw: bool = True, returnNode: bool = False) -> ArrayLike:
"""
recursive function to traverse the (trained) tree
"""
@@ -339,9 +341,9 @@ class DecisionTree():
else:
if raw is True:
# return the leaf value
- return node._rawValues
+ return (node._rawValues, node) if returnNode else node._rawValues
else:
- return node._bakedValues
+ return (node._bakedValues, node) if returnNode else node._bakedValues
def accuracy(self, data: np.ndarray | DataSet, targets: np.ndarray = None) -> float:
if isinstance(data, DataSet) is False:
diff --git a/machineLearning/rf/impurityMeasure.py b/machineLearning/rf/impurityMeasure.py
index f725b384d67ac157e75ef7894ea0393f81598f9d..c88adb30dfad9f91b3bbd63713ba9c8bd2563aa6 100644
--- a/machineLearning/rf/impurityMeasure.py
+++ b/machineLearning/rf/impurityMeasure.py
@@ -93,4 +93,4 @@ class ODD(ImpurityMeasure):
super().__init__()
def _loss(self, targets: np.ndarray, weights: np.ndarray = None) -> float:
- return 1.
+ return np.random.random() # return some default value
diff --git a/machineLearning/rf/leafFunction.py b/machineLearning/rf/leafFunction.py
index 97297fe36b341d3ea88a3b6fb459f9c8bfd8aadd..d99b77d27c5001533f79520e01d35d3ca6624306 100644
--- a/machineLearning/rf/leafFunction.py
+++ b/machineLearning/rf/leafFunction.py
@@ -1,5 +1,6 @@
import numpy as np
from abc import ABC, abstractmethod
+from .node import Node
class LeafFunction(ABC):
@@ -9,14 +10,14 @@ class LeafFunction(ABC):
def __init__(self) -> None:
self.name = self.__class__.__name__
- def __call__(self, rawPrediction) -> np.ndarray:
+ def __call__(self, node: Node, rawPrediction: np.ndarray) -> np.ndarray:
"""
calls '_leafFunc', makes working with this class easier
"""
- return self._leafFunc(rawPrediction)
+ return self._leafFunc(node=node, rawPrediction=rawPrediction)
@abstractmethod
- def _leafFunc(self, rawPrediction) -> np.ndarray:
+ def _leafFunc(self, node: Node, rawPrediction: np.ndarray) -> np.ndarray:
"""
needs to be implemented with every daughter class
"""
@@ -30,7 +31,7 @@ class Mode(LeafFunction):
def __init__(self) -> None:
super().__init__()
- def _leafFunc(self, rawPrediction) -> np.ndarray:
+ def _leafFunc(self, node: Node, rawPrediction: np.ndarray) -> np.ndarray:
# If the rawPrediction is one-hot encoded, convert it to categorical
if len(rawPrediction.shape) == 2 and rawPrediction.shape[1] > 1:
targetsCategorical = np.argmax(rawPrediction, axis=1)
@@ -50,7 +51,7 @@ class Mean(LeafFunction):
def __init__(self) -> None:
super().__init__()
- def _leafFunc(self, rawPrediction) -> np.ndarray:
+ def _leafFunc(self, node: Node, rawPrediction: np.ndarray) -> np.ndarray:
return np.mean(rawPrediction)
@@ -61,7 +62,7 @@ class Median(LeafFunction):
def __init__(self) -> None:
super().__init__()
- def _leafFunc(self, rawPrediction) -> np.ndarray:
+ def _leafFunc(self, node: Node, rawPrediction: np.ndarray) -> np.ndarray:
return np.median(rawPrediction)
@@ -74,7 +75,7 @@ class Probabilities(LeafFunction):
super().__init__()
self.numClasses = numClasses
- def _leafFunc(self, rawPrediction) -> np.ndarray:
+ def _leafFunc(self, node: Node, rawPrediction: np.ndarray) -> np.ndarray:
# If rawPrediction is one-hot encoded, convert it to categorical
if len(rawPrediction.shape) == 2 and rawPrediction.shape[1] > 1:
targetsCategorical = np.argmax(rawPrediction, axis=1)
@@ -101,7 +102,7 @@ class Confidence(LeafFunction):
def __init__(self) -> None:
super().__init__()
- def _leafFunc(self, rawPrediction) -> np.ndarray:
+ def _leafFunc(self, node: Node, rawPrediction: np.ndarray) -> np.ndarray:
# Convert one-hot encoded rawPrediction to categorical if applicable
if len(rawPrediction.shape) == 2 and rawPrediction.shape[1] > 1:
targetsCategorical = np.argmax(rawPrediction, axis=1)
@@ -120,11 +121,18 @@ class Confidence(LeafFunction):
return np.array([modeValue, modeCounts, confidence])
-class AnomalyLeafFunction(LeafFunction):
+class AnomalyDetection(LeafFunction):
"""
- Leaf function for anamaly detections using ODD trees
+ Leaf function for anomaly detections using ODD trees.
"""
- def _leafFunc(self, rawPrediction):
- # Implement custom logic for anomalies
- # This could be as simple as returning a fixed value or more complex statistics
- pass
+ def __init__(self, strategy: str = 'level') -> None:
+ super().__init__()
+ self.strategy = strategy # Determines which node attribute to use (e.g., 'level')
+
+ def _leafFunc(self, node: Node, rawPrediction: np.ndarray) -> np.ndarray:
+ # Use self.strategy to access the corresponding attribute from the node
+ # Assuming 'level' is an attribute of the node indicating its depth or similar
+ if hasattr(node, self.strategy):
+ return np.array([getattr(node, self.strategy)])
+ else:
+ raise AttributeError(f"Node does not have attribute '{self.strategy}'")
diff --git a/machineLearning/rf/randomForrest.py b/machineLearning/rf/randomForrest.py
index e374ca46d171b49032e7b65a9a72a5d3710f8ad8..a28b48d54c467a70c51e539cd8c02d9841149083 100644
--- a/machineLearning/rf/randomForrest.py
+++ b/machineLearning/rf/randomForrest.py
@@ -217,7 +217,7 @@ class RandomForest(object):
"""
treeStrings = [str(tree) for tree in self.trees]
- componentString = f'voting: {self.voting.name}, booster: {self._booster.name}, bootstrapping: {self.bootstrapping}\n'
+ componentString = f'voting: {self.voting.name}, booster: {self._booster.name if self._boosting else None}, bootstrapping: {self.bootstrapping}\n'
printString = ' forrest '.center(len(componentString), 'â”')
printString += '\n'
printString += componentString
@@ -251,4 +251,4 @@ class RandomForest(object):
return accuarcies
def __len__(self) -> int:
- return len(self.trees)
\ No newline at end of file
+ return len(self.trees)
diff --git a/machineLearning/rf/splitAlgorithm.py b/machineLearning/rf/splitAlgorithm.py
index 5568069aa28de57ab3314bf834eae8d3da0e049c..44943e8ced351f4aad39fc66de35cb44c74824b2 100644
--- a/machineLearning/rf/splitAlgorithm.py
+++ b/machineLearning/rf/splitAlgorithm.py
@@ -46,7 +46,7 @@ class SplitAlgorithm(ABC):
pass
@abstractmethod
- def goodGain(self, currentInfoGain, maxInfoGain) -> bool:
+ def _goodGain(self, currentInfoGain, maxInfoGain) -> bool:
pass
def findBestSplit(self, data: np.ndarray, targets: np.ndarray, weights: np.ndarray = None) -> tuple[float, int]:
@@ -64,7 +64,7 @@ class SplitAlgorithm(ABC):
currentInfoGain, currentFeature, currentThreshold = future.result() # This will re-raise any exceptions that occurred in the thread
except TypeError:
continue
- if self.goodGain(currentInfoGain, maxInfoGain):
+ if self._goodGain(currentInfoGain, maxInfoGain):
maxInfoGain = currentInfoGain
feature = currentFeature
threshold = currentThreshold
@@ -110,7 +110,7 @@ class CART(SplitAlgorithm):
return currentInfoGain, feat, splits[minInfoGainIndex]
- def goodGain(self, currentInfoGain, maxInfoGain) -> bool:
+ def _goodGain(self, currentInfoGain, maxInfoGain) -> bool:
return currentInfoGain < maxInfoGain
@@ -152,7 +152,7 @@ class ID3(SplitAlgorithm):
return infoGain[maxIndex], feat, splits[maxIndex]
- def goodGain(self, currentInfoGain, maxInfoGain) -> bool:
+ def _goodGain(self, currentInfoGain, maxInfoGain) -> bool:
return currentInfoGain > maxInfoGain
@@ -203,29 +203,30 @@ class C45(SplitAlgorithm):
return infoGainRatio[maxIndex], feat, splits[maxIndex]
- def goodGain(self, currentInfoGain, maxInfoGain) -> bool:
+ def _goodGain(self, currentInfoGain, maxInfoGain) -> bool:
return currentInfoGain > maxInfoGain
-class RandomSplitAlgorithm(SplitAlgorithm):
+class RSA(SplitAlgorithm):
"""
+ RSA - Random Split Algorithm
Implementaion of ODD tree splitting algorithm
This is the point where inheritince breaks down.
"""
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
- def _calculateInfoGain(self, feat, data, targets, weights=None):
+ def _calculateInfoGain(self, feat, data, targets, weights=None) -> None:
# Randomly select a feature and a split value for that feature
# This method simplifies to choosing a feature and then a random point as the split
pass
- def goodGain(self, currentInfoGain, maxInfoGain):
+ def _goodGain(self, currentInfoGain, maxInfoGain) -> None:
# Since we're doing random splits, the concept of "good gain" might be irrelevant
# Or it could be redefined to fit the anomaly detection context
pass
- def findBestSplit(self, data, targets, weights=None):
+ def findBestSplit(self, data, targets, weights=None) -> tuple:
"""
selecting a random feature and threshold
the most basic implementation
diff --git a/som-test.json b/som-test.json
index 6dc8ee8114d2b801a716520daa3a7657612d4d32..36adcf61efa32077f217aa256533cf605506892c 100644
--- a/som-test.json
+++ b/som-test.json
@@ -1,4 +1,9 @@
{
+ "datetime": "2024-03-05T14:12:22.428111",
+ "qualifiedName": [
+ "machineLearning.som.som",
+ "SOM"
+ ],
"numFeatures": 5,
"gridSize": [
15,
@@ -11,1169 +16,1169 @@
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diff --git a/tree-test.ipynb b/tree-test.ipynb
index 92442ed5834b90ac5ff81c682426ee25cc978a70..021d8e39e8d951c3a9e95a0d55187d7e495c2eea 100644
--- a/tree-test.ipynb
+++ b/tree-test.ipynb
@@ -1 +1 @@
-{"cells":[{"cell_type":"markdown","metadata":{},"source":["# Testing the Tree\n","\n","## Importing the Basics"]},{"cell_type":"code","execution_count":1,"metadata":{"trusted":false},"outputs":[],"source":["import numpy as np\n","from matplotlib import pyplot as plt\n","from machineLearning.metric import ConfusionMatrix, RegressionScores\n","from machineLearning.utility import ModelIO\n","from machineLearning.rf import (\n"," DecisionTree,\n"," Gini, Entropy, MSE, MAE,\n"," Mode, Mean, Confidence, Probabilities,\n"," CART, ID3, C45,\n"," ReducedError, CostComplexity, PessimisticError\n",")"]},{"cell_type":"markdown","metadata":{},"source":["## Generating Test Data\n","\n","Here I generate random test data. It's two blocks shifted very slightly in some dimensions. For classifier tasks each block gets a label, for regressor tasks each block gets the average coordinates plus some random value as a traget. It's a very simple dummy data set meant for testing the code.\n","\n","Here one can change the dimensionallity and amount of the data."]},{"cell_type":"code","execution_count":2,"metadata":{"trusted":false},"outputs":[],"source":["def dataShift(dims):\n"," offSet = [5, 1.5, 2.5]\n"," diffLen = abs(len(offSet) - dims)\n"," offSet.extend([0] * diffLen)\n"," np.random.shuffle(offSet)\n"," return offSet[:dims]\n","\n","# Initialize some parameters\n","totalAmount = 64000\n","dims = 7\n","evalAmount = totalAmount // 4\n","trainAmount = totalAmount - evalAmount\n","offSet = dataShift(dims)\n","\n","# Create covariance matrix\n","cov = np.eye(dims) # This creates a covariance matrix with variances 1 and covariances 0\n","\n","# Generate random multivariate data\n","oneData = np.random.multivariate_normal(np.zeros(dims), cov, totalAmount)\n","twoData = np.random.multivariate_normal(offSet, cov, totalAmount)\n","\n","# Split the data into training and evaluation sets\n","trainData = np.vstack((oneData[:trainAmount], twoData[:trainAmount]))\n","validData = np.vstack((oneData[trainAmount:], twoData[trainAmount:]))\n","\n","# Labels for classification tasks\n","trainLabels = np.hstack((np.zeros(trainAmount), np.ones(trainAmount)))\n","validLabels = np.hstack((np.zeros(evalAmount), np.ones(evalAmount)))\n","\n","# Targets for regression tasks\n","trainTargets = np.sum(trainData, axis=1) + np.random.normal(0, 0.1, 2*trainAmount)\n","validTargets = np.sum(validData, axis=1) + np.random.normal(0, 0.1, 2*evalAmount)\n","\n","# Shuffle the training data\n","trainIndex = np.random.permutation(len(trainData))\n","trainData = trainData[trainIndex]\n","trainLabels = trainLabels[trainIndex]\n","trainTargets = trainTargets[trainIndex]"]},{"cell_type":"code","execution_count":3,"metadata":{},"outputs":[],"source":["def scatterPairwise(data, labels, size: float = 10):\n"," num_dims = data.shape[1]\n"," fig, axes = plt.subplots(num_dims, num_dims, figsize=(12, 12))\n","\n"," if len(labels.shape) > 1:\n"," labels = np.argmax(labels, axis=1)\n"," \n"," colors = ['tab:blue', 'tab:orange', 'tab:green', 'tab:red']\n"," point_colors = [colors[label] for label in labels]\n","\n"," for i in range(num_dims):\n"," for j in range(num_dims):\n"," if i == j:\n"," axes[i][j].axis('off')\n"," else:\n"," axes[i][j].scatter(data[:, i], data[:, j], c=point_colors, s=size, alpha=0.5,label='data')\n"," axes[i][j].set_xlabel(f\"Dim {i}\")\n"," axes[i][j].set_ylabel(f\"Dim {j}\")\n"," plt.tight_layout()\n"," plt.show()"]},{"cell_type":"code","execution_count":4,"metadata":{},"outputs":[],"source":["#scatterPairwise(trainData, trainLabels.astype('int'))"]},{"cell_type":"markdown","metadata":{},"source":["## Creating the Tree\n","\n","Here the tree is created. One can set the maximum depth of the tree. Depending on the task, we add a different impurity function and a different leaf function. Finally we add the split algorithm and set the feature percentile. Higher numbers look at more possible splits, but decreases speed. Lower numbers look at less possible splits, speeding up the algorithm. Depending on the data set this can have a strong impact on the performance."]},{"cell_type":"code","execution_count":5,"metadata":{"trusted":false},"outputs":[],"source":["task = 'classifier' # 'classifier'/'regressor'\n","tree = DecisionTree(maxDepth=5, minSamplesSplit=12)\n","if task == 'regressor':\n"," tree.setComponent(MSE())\n"," tree.setComponent(Mean())\n","elif task == 'classifier':\n"," tree.setComponent(Entropy())\n"," tree.setComponent(Mode())\n"," #tree.setComponent(Confidence())\n"," #tree.setComponent(Probabilities(2))\n","tree.setComponent(CART(featurePercentile=90))"]},{"cell_type":"markdown","metadata":{},"source":["## Trainining the tree\n","\n","Again, depending on the task we train the tree with targets or labels. Then we make a prediction and plot the tree."]},{"cell_type":"code","execution_count":6,"metadata":{"trusted":false},"outputs":[{"name":"stdout","output_type":"stream","text":["tree 1 |⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿| done ✔ | 47%\n","—————————————————————— tree: 1/1 ———————————————————————\n","split: CART, impurity: Entropy, leaf: Mode, nodes: 31\n","maxDepth: 5, reached depth: 5, minSamplesSplit: 12\n","························································\n","â•´feat: 4 <= 2.21, samples: 96000\n"," ├─feat: 4 <= 1.43, samples: 47473\n"," │ ├─feat: 5 <= 2.31, samples: 44342\n"," │ │ ├─feat: 4 <= 0.78, samples: 43854\n"," │ │ │ └─╴value: 0.0\n"," │ │ │ └─╴value: 0.0\n"," │ │ └─╴feat: 4 <= 0.69, samples: 488\n"," │ │ └─╴value: 0.0\n"," │ │ └─╴value: 0.0\n"," │ └─╴feat: 5 <= 1.77, samples: 3131\n"," │ ├─feat: 5 <= 0.75, samples: 2924\n"," │ │ └─╴value: 0.0\n"," │ │ └─╴value: 0.0\n"," │ └─╴feat: 0 <= 0.72, samples: 207\n"," │ └─╴value: 0.0\n"," │ └─╴value: 1.0\n"," └─╴feat: 4 <= 3.04, samples: 48527\n"," ├─feat: 5 <= 1.14, samples: 1600\n"," │ ├─feat: 0 <= 0.73, samples: 616\n"," │ │ └─╴value: 0.0\n"," │ │ └─╴value: 0.0\n"," │ └─╴feat: 5 <= 1.77, samples: 984\n"," │ └─╴value: 1.0\n"," │ └─╴value: 1.0\n"," └─╴feat: 4 <= 3.40, samples: 46927\n"," ├─feat: 5 <= 0.76, samples: 1548\n"," │ └─╴value: 1.0\n"," │ └─╴value: 1.0\n"," └─╴feat: 5 <= 1.27, samples: 45379\n"," └─╴value: 1.0\n"," └─╴value: 1.0\n"]}],"source":["if task == 'regressor':\n"," tree.train(trainData, trainTargets)\n","elif task == 'classifier':\n"," tree.train(trainData, trainLabels)\n","print(tree)"]},{"cell_type":"code","execution_count":7,"metadata":{},"outputs":[],"source":["tree.bake()"]},{"cell_type":"code","execution_count":8,"metadata":{},"outputs":[],"source":["prediction = tree.eval(validData)"]},{"cell_type":"markdown","metadata":{},"source":["## Evaluating predictions\n","\n","Depending on the task at hand we create a confusion matrix (classification) or simple metrics (regression). Since the number of classes is fixed to two, we don't need to change anything here."]},{"cell_type":"code","execution_count":9,"metadata":{"trusted":false},"outputs":[{"name":"stdout","output_type":"stream","text":["â”â”â”â”â”â”â”â”â”â”â”â” evaluation â”â”â”â”â”â”â”â”â”â”â”â”\n","————————— confusion matrix —————————\n"," Class 0 Class 1 \n","····································\n"," Class 0 15964 36 \n"," 49% 0% \n","····································\n"," Class 1 45 15955 \n"," 0% 49% \n","\n","———————————————————————————————— scores ———————————————————————————————\n"," accuracy precision sensitivity miss rate \n","·······································································\n"," Class 0 0.997 0.997 0.998 0.002 \n"," Class 1 0.997 0.998 0.997 0.003 \n","·······································································\n"," total 0.997 0.997 0.997 0.003 \n"]}],"source":["if task == 'regressor':\n"," metrics = RegressionScores(numClasses=2)\n"," metrics.calcScores(prediction, validTargets, validLabels)\n"," print(metrics)\n","elif task == 'classifier':\n"," confusion = ConfusionMatrix(numClasses=2)\n"," confusion.update(prediction, validLabels)\n"," confusion.percentages()\n"," confusion.calcScores()\n"," print(confusion)"]},{"cell_type":"markdown","metadata":{},"source":["## Saving and Loading a Tree\n","\n","Trees can be converted to dictionaries and then saved as a json file. This allows us to load them and re-use them. Also json is a raw text format, which is neat."]},{"cell_type":"code","execution_count":10,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["—————————————————————— tree: 1/2 ———————————————————————\n","split: CART, impurity: Entropy, leaf: Mode, nodes: 31\n","maxDepth: 5, reached depth: 5, minSamplesSplit: 12\n","························································\n","â•´feat: 4 <= 2.21, samples: 96000\n"," ├─feat: 4 <= 1.43, samples: 47473\n"," │ ├─feat: 5 <= 2.31, samples: 44342\n"," │ │ ├─feat: 4 <= 0.78, samples: 43854\n"," │ │ │ └─╴value: 0.0\n"," │ │ │ └─╴value: 0.0\n"," │ │ └─╴feat: 4 <= 0.69, samples: 488\n"," │ │ └─╴value: 0.0\n"," │ │ └─╴value: 0.0\n"," │ └─╴feat: 5 <= 1.77, samples: 3131\n"," │ ├─feat: 5 <= 0.75, samples: 2924\n"," │ │ └─╴value: 0.0\n"," │ │ └─╴value: 0.0\n"," │ └─╴feat: 0 <= 0.72, samples: 207\n"," │ └─╴value: 0.0\n"," │ └─╴value: 1.0\n"," └─╴feat: 4 <= 3.04, samples: 48527\n"," ├─feat: 5 <= 1.14, samples: 1600\n"," │ ├─feat: 0 <= 0.73, samples: 616\n"," │ │ └─╴value: 0.0\n"," │ │ └─╴value: 0.0\n"," │ └─╴feat: 5 <= 1.77, samples: 984\n"," │ └─╴value: 1.0\n"," │ └─╴value: 1.0\n"," └─╴feat: 4 <= 3.40, samples: 46927\n"," ├─feat: 5 <= 0.76, samples: 1548\n"," │ └─╴value: 1.0\n"," │ └─╴value: 1.0\n"," └─╴feat: 5 <= 1.27, samples: 45379\n"," └─╴value: 1.0\n"," └─╴value: 1.0\n"]}],"source":["ModelIO.save(tree, 'tree-test')\n","newTree = ModelIO.load('tree-test')\n","print(newTree)"]},{"cell_type":"code","execution_count":11,"metadata":{"trusted":false},"outputs":[{"name":"stdout","output_type":"stream","text":["â”â”â”â”â”â”â”â”â”â”â”â” evaluation â”â”â”â”â”â”â”â”â”â”â”â”\n","————————— confusion matrix —————————\n"," Class 0 Class 1 \n","····································\n"," Class 0 15964 36 \n"," 49% 0% \n","····································\n"," Class 1 45 15955 \n"," 0% 49% \n","\n","———————————————————————————————— scores ———————————————————————————————\n"," accuracy precision sensitivity miss rate \n","·······································································\n"," Class 0 0.997 0.997 0.998 0.002 \n"," Class 1 0.997 0.998 0.997 0.003 \n","·······································································\n"," total 0.997 0.997 0.997 0.003 \n"]}],"source":["prediction = newTree.eval(validData)\n","\n","if task == 'regressor':\n"," newMetrics = RegressionScores(numClasses=2)\n"," newMetrics.calcScores(prediction, validTargets, validLabels)\n"," print(newMetrics)\n","elif task == 'classifier':\n"," newConfusion = ConfusionMatrix(numClasses=2)\n"," newConfusion.update(prediction, validLabels)\n"," newConfusion.percentages()\n"," newConfusion.calcScores()\n"," print(newConfusion)"]},{"cell_type":"markdown","metadata":{},"source":["## Comment\n","\n","The tree works pretty well with both regression and classification tasks. Labels shouldn't be one-hot encoded, it works but it's still rather iffy. Targets should 1D, I haven't tested with 2D, it might work. Training can be really fast with a percentile set in the split algorithm, otherwise it can be rather slow. Making predictions work fast and well enough."]}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.12.2"},"vscode":{"interpreter":{"hash":"aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49"}}},"nbformat":4,"nbformat_minor":2}
+{"cells":[{"cell_type":"markdown","metadata":{},"source":["# Testing the Tree\n","\n","## Importing the Basics"]},{"cell_type":"code","execution_count":1,"metadata":{"trusted":false},"outputs":[],"source":["import numpy as np\n","from matplotlib import pyplot as plt\n","from machineLearning.metric import ConfusionMatrix, RegressionScores\n","from machineLearning.utility import ModelIO\n","from machineLearning.data import DataSet\n","from machineLearning.rf import (\n"," DecisionTree,\n"," Gini, Entropy, MSE, MAE, ODD,\n"," Mode, Mean, Confidence, Probabilities,\n"," CART, ID3, C45, RSA,\n"," ReducedError, CostComplexity, PessimisticError\n",")"]},{"cell_type":"markdown","metadata":{},"source":["## Generating Test Data\n","\n","Here I generate random test data. It's two blocks shifted very slightly in some dimensions. For classifier tasks each block gets a label, for regressor tasks each block gets the average coordinates plus some random value as a traget. It's a very simple dummy data set meant for testing the code.\n","\n","Here one can change the dimensionallity and amount of the data."]},{"cell_type":"code","execution_count":2,"metadata":{"trusted":false},"outputs":[],"source":["def dataShift(dims):\n"," offSet = [5, 1.5, 2.5]\n"," diffLen = abs(len(offSet) - dims)\n"," offSet.extend([0] * diffLen)\n"," np.random.shuffle(offSet)\n"," return offSet[:dims]\n","\n","# Initialize some parameters\n","totalAmount = 64000\n","dims = 7\n","evalAmount = totalAmount // 4\n","trainAmount = totalAmount - evalAmount\n","offSet = dataShift(dims)\n","\n","# Create covariance matrix\n","cov = np.eye(dims) # This creates a covariance matrix with variances 1 and covariances 0\n","\n","# Generate random multivariate data\n","oneData = np.random.multivariate_normal(np.zeros(dims), cov, totalAmount)\n","twoData = np.random.multivariate_normal(offSet, cov, totalAmount)\n","\n","# Split the data into training and evaluation sets\n","trainData = np.vstack((oneData[:trainAmount], twoData[:trainAmount]))\n","validData = np.vstack((oneData[trainAmount:], twoData[trainAmount:]))\n","\n","# Labels for classification tasks\n","trainLabels = np.hstack((np.zeros(trainAmount), np.ones(trainAmount)))\n","validLabels = np.hstack((np.zeros(evalAmount), np.ones(evalAmount)))\n","\n","# Targets for regression tasks\n","trainTargets = np.sum(trainData, axis=1) + np.random.normal(0, 0.1, 2*trainAmount)\n","validTargets = np.sum(validData, axis=1) + np.random.normal(0, 0.1, 2*evalAmount)\n","\n","# Shuffle the training data\n","trainIndex = np.random.permutation(len(trainData))\n","trainData = trainData[trainIndex]\n","trainLabels = trainLabels[trainIndex]\n","trainTargets = trainTargets[trainIndex]\n","\n","trainSet = DataSet(trainData, targets=trainLabels)\n","validSet = DataSet(validData, targets=validLabels)"]},{"cell_type":"code","execution_count":3,"metadata":{},"outputs":[],"source":["def scatterPairwise(data, labels, size: float = 10):\n"," num_dims = data.shape[1]\n"," fig, axes = plt.subplots(num_dims, num_dims, figsize=(12, 12))\n","\n"," if len(labels.shape) > 1:\n"," labels = np.argmax(labels, axis=1)\n"," \n"," colors = ['tab:blue', 'tab:orange', 'tab:green', 'tab:red']\n"," point_colors = [colors[label] for label in labels]\n","\n"," for i in range(num_dims):\n"," for j in range(num_dims):\n"," if i == j:\n"," axes[i][j].axis('off')\n"," else:\n"," axes[i][j].scatter(data[:, i], data[:, j], c=point_colors, s=size, alpha=0.5,label='data')\n"," axes[i][j].set_xlabel(f\"Dim {i}\")\n"," axes[i][j].set_ylabel(f\"Dim {j}\")\n"," plt.tight_layout()\n"," plt.show()"]},{"cell_type":"code","execution_count":4,"metadata":{},"outputs":[],"source":["#scatterPairwise(trainData, trainLabels.astype('int'))"]},{"cell_type":"markdown","metadata":{},"source":["## Creating the Tree\n","\n","Here the tree is created. One can set the maximum depth of the tree. Depending on the task, we add a different impurity function and a different leaf function. Finally we add the split algorithm and set the feature percentile. Higher numbers look at more possible splits, but decreases speed. Lower numbers look at less possible splits, speeding up the algorithm. Depending on the data set this can have a strong impact on the performance."]},{"cell_type":"code","execution_count":5,"metadata":{"trusted":false},"outputs":[],"source":["task = 'classifier' # 'classifier'/'regressor'\n","tree = DecisionTree(maxDepth=5, minSamplesSplit=12)\n","if task == 'regressor':\n"," tree.setComponent(MSE())\n"," tree.setComponent(Mean())\n","elif task == 'classifier':\n"," tree.setComponent(Entropy())\n"," tree.setComponent(Mode())\n"," #tree.setComponent(Confidence())\n"," #tree.setComponent(Probabilities(2))\n","tree.setComponent(CART(featurePercentile=90))"]},{"cell_type":"markdown","metadata":{},"source":["## Trainining the tree\n","\n","Again, depending on the task we train the tree with targets or labels. Then we make a prediction and plot the tree."]},{"cell_type":"code","execution_count":6,"metadata":{"trusted":false},"outputs":[{"name":"stdout","output_type":"stream","text":["tree 1 |⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿| done ✔ | 47%\n","—————————————————————— tree: 1/1 ———————————————————————\n","split: CART, impurity: Entropy, leaf: Mode, nodes: 31\n","maxDepth: 5, reached depth: 5, minSamplesSplit: 12\n","························································\n","â•´feat: 5 <= 2.80, samples: 96000\n"," ├─feat: 5 <= 1.98, samples: 48527\n"," │ ├─feat: 5 <= 1.42, samples: 46927\n"," │ │ ├─feat: 6 <= 1.85, samples: 44348\n"," │ │ │ └─╴value: 0.0\n"," │ │ │ └─╴value: 0.0\n"," │ │ └─╴feat: 6 <= 2.21, samples: 2579\n"," │ │ └─╴value: 0.0\n"," │ │ └─╴value: 1.0\n"," │ └─╴feat: 6 <= 1.17, samples: 1600\n"," │ ├─feat: 1 <= 0.86, samples: 949\n"," │ │ └─╴value: 0.0\n"," │ │ └─╴value: 0.0\n"," │ └─╴feat: 1 <= 0.54, samples: 651\n"," │ └─╴value: 0.0\n"," │ └─╴value: 1.0\n"," └─╴feat: 6 <= 0.64, samples: 47473\n"," ├─feat: 5 <= 3.41, samples: 1566\n"," │ ├─feat: 1 <= 0.21, samples: 138\n"," │ │ └─╴value: 0.0\n"," │ │ └─╴value: 1.0\n"," │ └─╴feat: 5 <= 4.25, samples: 1428\n"," │ └─╴value: 1.0\n"," │ └─╴value: 1.0\n"," └─╴feat: 5 <= 3.20, samples: 45907\n"," ├─feat: 6 <= 1.31, samples: 1009\n"," │ └─╴value: 1.0\n"," │ └─╴value: 1.0\n"," └─╴feat: 5 <= 3.70, samples: 44898\n"," └─╴value: 1.0\n"," └─╴value: 1.0\n"]}],"source":["tree.train(trainSet)\n","print(tree)"]},{"cell_type":"code","execution_count":7,"metadata":{},"outputs":[],"source":["tree.bake()"]},{"cell_type":"code","execution_count":8,"metadata":{},"outputs":[],"source":["prediction = tree.eval(validSet)"]},{"cell_type":"markdown","metadata":{},"source":["## Evaluating predictions\n","\n","Depending on the task at hand we create a confusion matrix (classification) or simple metrics (regression). Since the number of classes is fixed to two, we don't need to change anything here."]},{"cell_type":"code","execution_count":9,"metadata":{"trusted":false},"outputs":[{"name":"stdout","output_type":"stream","text":["â”â”â”â”â”â”â”â”â”â”â”â” evaluation â”â”â”â”â”â”â”â”â”â”â”â”\n","————————— confusion matrix —————————\n"," Class 0 Class 1 \n","····································\n"," Class 0 15951 49 \n"," 49% 0% \n","····································\n"," Class 1 55 15945 \n"," 0% 49% \n","\n","———————————————————————————————— scores ———————————————————————————————\n"," accuracy precision sensitivity miss rate \n","·······································································\n"," Class 0 0.997 0.997 0.997 0.003 \n"," Class 1 0.997 0.997 0.997 0.003 \n","·······································································\n"," total 0.997 0.997 0.997 0.003 \n"]}],"source":["if task == 'regressor':\n"," metrics = RegressionScores(numClasses=2)\n"," metrics.calcScores(prediction, validTargets, validLabels)\n"," print(metrics)\n","elif task == 'classifier':\n"," confusion = ConfusionMatrix(numClasses=2)\n"," confusion.update(prediction, validLabels)\n"," confusion.percentages()\n"," confusion.calcScores()\n"," print(confusion)"]},{"cell_type":"markdown","metadata":{},"source":["## Saving and Loading a Tree\n","\n","Trees can be converted to dictionaries and then saved as a json file. This allows us to load them and re-use them. Also json is a raw text format, which is neat."]},{"cell_type":"code","execution_count":10,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["—————————————————————— tree: 1/2 ———————————————————————\n","split: CART, impurity: Entropy, leaf: Mode, nodes: 31\n","maxDepth: 5, reached depth: 5, minSamplesSplit: 12\n","························································\n","â•´feat: 5 <= 2.80, samples: 96000\n"," ├─feat: 5 <= 1.98, samples: 48527\n"," │ ├─feat: 5 <= 1.42, samples: 46927\n"," │ │ ├─feat: 6 <= 1.85, samples: 44348\n"," │ │ │ └─╴value: 0.0\n"," │ │ │ └─╴value: 0.0\n"," │ │ └─╴feat: 6 <= 2.21, samples: 2579\n"," │ │ └─╴value: 0.0\n"," │ │ └─╴value: 1.0\n"," │ └─╴feat: 6 <= 1.17, samples: 1600\n"," │ ├─feat: 1 <= 0.86, samples: 949\n"," │ │ └─╴value: 0.0\n"," │ │ └─╴value: 0.0\n"," │ └─╴feat: 1 <= 0.54, samples: 651\n"," │ └─╴value: 0.0\n"," │ └─╴value: 1.0\n"," └─╴feat: 6 <= 0.64, samples: 47473\n"," ├─feat: 5 <= 3.41, samples: 1566\n"," │ ├─feat: 1 <= 0.21, samples: 138\n"," │ │ └─╴value: 0.0\n"," │ │ └─╴value: 1.0\n"," │ └─╴feat: 5 <= 4.25, samples: 1428\n"," │ └─╴value: 1.0\n"," │ └─╴value: 1.0\n"," └─╴feat: 5 <= 3.20, samples: 45907\n"," ├─feat: 6 <= 1.31, samples: 1009\n"," │ └─╴value: 1.0\n"," │ └─╴value: 1.0\n"," └─╴feat: 5 <= 3.70, samples: 44898\n"," └─╴value: 1.0\n"," └─╴value: 1.0\n"]}],"source":["ModelIO.save(tree, 'tree-test')\n","newTree = ModelIO.load('tree-test')\n","print(newTree)"]},{"cell_type":"code","execution_count":11,"metadata":{"trusted":false},"outputs":[{"name":"stdout","output_type":"stream","text":["â”â”â”â”â”â”â”â”â”â”â”â” evaluation â”â”â”â”â”â”â”â”â”â”â”â”\n","————————— confusion matrix —————————\n"," Class 0 Class 1 \n","····································\n"," Class 0 15951 49 \n"," 49% 0% \n","····································\n"," Class 1 55 15945 \n"," 0% 49% \n","\n","———————————————————————————————— scores ———————————————————————————————\n"," accuracy precision sensitivity miss rate \n","·······································································\n"," Class 0 0.997 0.997 0.997 0.003 \n"," Class 1 0.997 0.997 0.997 0.003 \n","·······································································\n"," total 0.997 0.997 0.997 0.003 \n"]}],"source":["prediction = newTree.eval(validData)\n","\n","if task == 'regressor':\n"," newMetrics = RegressionScores(numClasses=2)\n"," newMetrics.calcScores(prediction, validTargets, validLabels)\n"," print(newMetrics)\n","elif task == 'classifier':\n"," newConfusion = ConfusionMatrix(numClasses=2)\n"," newConfusion.update(prediction, validLabels)\n"," newConfusion.percentages()\n"," newConfusion.calcScores()\n"," print(newConfusion)"]},{"cell_type":"markdown","metadata":{},"source":["## Comment\n","\n","The tree works pretty well with both regression and classification tasks. Labels shouldn't be one-hot encoded, it works but it's still rather iffy. Targets should 1D, I haven't tested with 2D, it might work. Training can be really fast with a percentile set in the split algorithm, otherwise it can be rather slow. Making predictions work fast and well enough."]}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.12.2"},"vscode":{"interpreter":{"hash":"aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49"}}},"nbformat":4,"nbformat_minor":2}
diff --git a/tree-test.json b/tree-test.json
index 3d1d0208011458bb916508111dddd672a797c080..fc5caddab5b98f31b01b1a009016377cda9a3775 100644
--- a/tree-test.json
+++ b/tree-test.json
@@ -1,11 +1,16 @@
{
+ "datetime": "2024-03-06T11:32:47.341188",
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