From 3e0e3620a653162eccf2366a9c1d3d3f9bda863f Mon Sep 17 00:00:00 2001
From: johannes bilk <johannes.bilk-2@exp2.physik.uni-giessen.de>
Date: Thu, 7 Mar 2024 12:16:15 +0100
Subject: [PATCH] clean up
---
forrest-test.ipynb | 766 ++--
forrest-test.json | 6317 ++++++++++++++-------------
machineLearning/rf/leafFunction.py | 2 +
som-test.json | 6416 ++++++++++++++--------------
som/__init__.py | 4 -
som/error.py | 51 -
som/neighborhood.py | 101 -
som/som.py | 302 --
som/topology.py | 363 --
tree-test.ipynb | 2 +-
tree-test.json | 1310 +++---
11 files changed, 7637 insertions(+), 7997 deletions(-)
delete mode 100644 som/__init__.py
delete mode 100644 som/error.py
delete mode 100644 som/neighborhood.py
delete mode 100644 som/som.py
delete mode 100644 som/topology.py
diff --git a/forrest-test.ipynb b/forrest-test.ipynb
index 78285ca..598013e 100644
--- a/forrest-test.ipynb
+++ b/forrest-test.ipynb
@@ -149,134 +149,170 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "tree 1 |⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿| done ✔ | 13%\n",
- "tree 2 |⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿| done ✔ | 12%\n",
+ "tree 1 |⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿| done ✔ | 18%\n",
+ "tree 2 |⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿| done ✔ | 21%\n",
"tree 3 |⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿| done ✔ | 16%\n",
- "tree 4 |⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿| done ✔ | 17%\n",
+ "tree 4 |⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿| done ✔ | 16%\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: 35\n",
+ "split: CART, impurity: Entropy, leaf: Mode, nodes: 47\n",
"maxDepth: 7, reached depth: 7, minSamplesSplit: 2\n",
"························································\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: 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",
+ "â•´feat: 0 <= 2.65, samples: 9600\n",
+ " ├─feat: 0 <= 2.09, samples: 4855\n",
+ " │ ├─feat: 0 <= 1.38, samples: 4748\n",
+ " │ │ └─╴value: 0.0\n",
+ " │ │ └─╴feat: 1 <= 1.59, samples: 312\n",
+ " │ │ ├─feat: 3 <= 2.41, samples: 295\n",
+ " │ │ │ └─╴value: 0.0\n",
+ " │ │ │ └─╴feat: 0 <= 1.41, samples: 4\n",
+ " │ │ │ └─╴value: 1.0\n",
+ " │ │ │ └─╴value: 0.0\n",
+ " │ │ └─╴feat: 3 <= 0.60, samples: 17\n",
+ " │ │ ├─feat: 1 <= 1.76, samples: 10\n",
+ " │ │ │ └─╴value: 0.0\n",
+ " │ │ │ └─╴value: 0.0\n",
+ " │ │ └─╴value: 1.0\n",
+ " │ └─╴feat: 1 <= 1.22, samples: 107\n",
+ " │ ├─feat: 2 <= -1.25, samples: 58\n",
+ " │ │ └─╴value: 1.0\n",
+ " │ │ └─╴value: 0.0\n",
+ " │ └─╴feat: 1 <= 1.93, samples: 49\n",
+ " │ ├─feat: 2 <= 0.91, samples: 14\n",
+ " │ │ ├─feat: 0 <= 2.56, samples: 9\n",
+ " │ │ │ └─╴value: 0.0\n",
+ " │ │ │ └─╴value: 1.0\n",
+ " │ │ └─╴value: 1.0\n",
+ " │ └─╴feat: 3 <= -0.38, samples: 35\n",
+ " │ └─╴value: 0.0\n",
+ " │ └─╴value: 1.0\n",
+ " └─╴feat: 0 <= 3.03, samples: 4745\n",
+ " ├─feat: 1 <= 0.99, samples: 108\n",
+ " │ ├─feat: 4 <= 0.30, samples: 23\n",
+ " │ │ ├─feat: 2 <= 1.14, samples: 19\n",
" │ │ │ └─╴value: 0.0\n",
- " │ │ │ └─╴feat: 0 <= 0.75, samples: 17\n",
- " │ │ │ └─╴value: 0.0\n",
+ " │ │ │ └─╴feat: 0 <= 2.77, samples: 2\n",
" │ │ │ └─╴value: 1.0\n",
+ " │ │ │ └─╴value: 0.0\n",
" │ │ └─╴value: 1.0\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",
- " │ └─╴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: 1.0\n",
+ " └─╴feat: 1 <= 1.22, samples: 4637\n",
+ " ├─feat: 0 <= 3.91, samples: 459\n",
+ " │ ├─feat: 1 <= 1.20, samples: 56\n",
+ " │ │ ├─feat: 3 <= 0.82, samples: 52\n",
+ " │ │ │ └─╴value: 1.0\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: 33\n",
+ "split: CART, impurity: Entropy, leaf: Mode, nodes: 57\n",
"maxDepth: 7, reached depth: 7, minSamplesSplit: 2\n",
"························································\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",
+ "â•´feat: 0 <= 2.31, samples: 9600\n",
+ " ├─feat: 0 <= 1.74, samples: 4747\n",
+ " │ ├─feat: 3 <= 2.32, samples: 4590\n",
+ " │ │ └─╴value: 0.0\n",
+ " │ │ └─╴feat: 0 <= 1.24, samples: 51\n",
+ " │ │ └─╴value: 0.0\n",
+ " │ │ └─╴value: 1.0\n",
+ " │ └─╴feat: 1 <= 1.53, samples: 157\n",
+ " │ ├─feat: 3 <= 1.86, samples: 134\n",
" │ │ └─╴value: 0.0\n",
- " │ │ └─╴value: 1.0\n",
- " │ └─╴feat: 0 <= -1.24, samples: 18\n",
- " │ └─╴value: 0.0\n",
+ " │ │ └─╴feat: 0 <= 2.04, samples: 2\n",
+ " │ │ └─╴value: 0.0\n",
+ " │ │ └─╴value: 1.0\n",
+ " │ └─╴feat: 3 <= 0.90, samples: 23\n",
+ " │ ├─feat: 1 <= 1.64, samples: 15\n",
+ " │ │ ├─feat: 1 <= 1.53, samples: 6\n",
+ " │ │ │ └─╴value: 0.0\n",
+ " │ │ │ └─╴value: 1.0\n",
+ " │ │ └─╴feat: 0 <= 2.16, samples: 9\n",
+ " │ │ └─╴value: 0.0\n",
+ " │ │ └─╴value: 1.0\n",
" │ └─╴value: 1.0\n",
- " └─╴feat: 2 <= 3.35, samples: 4853\n",
- " ├─feat: 3 <= 1.41, samples: 320\n",
- " │ ├─feat: 0 <= 0.48, samples: 99\n",
+ " └─╴feat: 0 <= 2.81, samples: 4853\n",
+ " ├─feat: 1 <= 0.64, samples: 107\n",
+ " │ ├─feat: 3 <= 1.71, samples: 28\n",
" │ │ └─╴value: 0.0\n",
- " │ │ └─╴feat: 2 <= 2.99, samples: 61\n",
- " │ │ ├─feat: 4 <= 0.87, samples: 40\n",
- " │ │ │ └─╴value: 0.0\n",
- " │ │ │ └─╴value: 1.0\n",
+ " │ │ └─╴feat: 0 <= 2.65, samples: 2\n",
+ " │ │ └─╴value: 0.0\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",
- " └─╴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",
+ " │ └─╴feat: 3 <= 0.48, samples: 79\n",
+ " │ ├─feat: 0 <= 2.50, samples: 22\n",
+ " │ │ ├─feat: 1 <= 1.73, samples: 13\n",
+ " │ │ │ └─╴value: 0.0\n",
+ " │ │ │ └─╴value: 0.0\n",
+ " │ │ └─╴feat: 4 <= -0.92, samples: 9\n",
+ " │ │ └─╴value: 0.0\n",
+ " │ │ └─╴value: 1.0\n",
+ " │ └─╴feat: 2 <= 0.70, samples: 57\n",
+ " │ └─╴value: 1.0\n",
+ " │ └─╴feat: 2 <= 0.83, samples: 18\n",
+ " │ └─╴value: 0.0\n",
+ " │ └─╴value: 1.0\n",
+ " └─╴feat: 1 <= 0.42, samples: 4746\n",
+ " ├─feat: 0 <= 3.53, samples: 105\n",
+ " │ ├─feat: 3 <= 0.46, samples: 18\n",
+ " │ │ └─╴value: 0.0\n",
+ " │ │ └─╴feat: 2 <= 0.99, samples: 11\n",
+ " │ │ └─╴value: 1.0\n",
+ " │ │ └─╴value: 0.0\n",
" │ └─╴value: 1.0\n",
- " └─╴value: 1.0\n",
+ " └─╴feat: 1 <= 1.24, samples: 4641\n",
+ " ├─feat: 1 <= 1.20, samples: 408\n",
+ " │ └─╴value: 1.0\n",
+ " │ └─╴feat: 0 <= 3.88, samples: 32\n",
+ " │ └─╴value: 0.0\n",
+ " │ └─╴value: 1.0\n",
+ " └─╴value: 1.0\n",
"\n",
"—————————————————————— tree: 3/5 ———————————————————————\n",
"split: CART, impurity: Entropy, leaf: Mode, nodes: 43\n",
"maxDepth: 7, reached depth: 7, minSamplesSplit: 2\n",
"························································\n",
- "â•´feat: 2 <= 2.36, samples: 9600\n",
- " ├─feat: 2 <= 1.77, samples: 4747\n",
- " │ ├─feat: 2 <= 1.65, samples: 4591\n",
+ "â•´feat: 0 <= 2.60, samples: 9600\n",
+ " ├─feat: 0 <= 2.08, samples: 4747\n",
+ " │ ├─feat: 0 <= 1.41, samples: 4642\n",
" │ │ └─╴value: 0.0\n",
- " │ │ └─╴feat: 3 <= 3.06, samples: 51\n",
+ " │ │ └─╴feat: 1 <= 1.40, samples: 305\n",
" │ │ └─╴value: 0.0\n",
- " │ │ └─╴value: 1.0\n",
- " │ └─╴feat: 3 <= 1.01, samples: 156\n",
- " │ ├─feat: 2 <= 2.34, samples: 111\n",
+ " │ │ └─╴feat: 0 <= 1.42, samples: 23\n",
+ " │ │ └─╴value: 1.0\n",
+ " │ │ └─╴feat: 1 <= 1.58, samples: 22\n",
+ " │ │ └─╴value: 0.0\n",
+ " │ │ └─╴value: 0.0\n",
+ " │ └─╴feat: 1 <= 1.61, samples: 105\n",
+ " │ ├─feat: 3 <= 1.41, samples: 62\n",
" │ │ └─╴value: 0.0\n",
- " │ │ └─╴feat: 0 <= -1.20, samples: 2\n",
- " │ │ └─╴value: 0.0\n",
- " │ │ └─╴value: 1.0\n",
- " │ └─╴feat: 0 <= 0.47, samples: 45\n",
- " │ ├─feat: 1 <= -1.38, samples: 16\n",
- " │ │ └─╴value: 1.0\n",
+ " │ │ └─╴value: 1.0\n",
+ " │ └─╴feat: 3 <= 1.00, samples: 43\n",
+ " │ ├─feat: 3 <= 0.92, samples: 19\n",
+ " │ │ ├─feat: 3 <= 0.45, samples: 17\n",
+ " │ │ │ └─╴value: 1.0\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 <= -0.81, samples: 65\n",
- " │ └─╴value: 0.0\n",
+ " │ └─╴value: 1.0\n",
+ " └─╴feat: 1 <= 0.58, samples: 4853\n",
+ " ├─feat: 0 <= 3.55, samples: 160\n",
+ " │ ├─feat: 3 <= 1.50, samples: 27\n",
+ " │ │ └─╴value: 0.0\n",
+ " │ │ └─╴feat: 2 <= -0.65, samples: 6\n",
+ " │ │ └─╴value: 0.0\n",
+ " │ │ └─╴value: 1.0\n",
+ " │ └─╴feat: 0 <= 3.88, samples: 133\n",
+ " │ ├─feat: 2 <= -0.66, samples: 9\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",
+ " └─╴feat: 1 <= 1.25, samples: 4693\n",
+ " ├─feat: 0 <= 3.88, samples: 414\n",
+ " │ ├─feat: 0 <= 3.87, samples: 61\n",
+ " │ │ ├─feat: 0 <= 2.73, samples: 58\n",
" │ │ │ └─╴value: 0.0\n",
" │ │ │ └─╴value: 1.0\n",
" │ │ └─╴value: 0.0\n",
@@ -284,100 +320,98 @@
" └─╴value: 1.0\n",
"\n",
"—————————————————————— tree: 4/5 ———————————————————————\n",
- "split: CART, impurity: Entropy, leaf: Mode, nodes: 45\n",
+ "split: CART, impurity: Entropy, leaf: Mode, nodes: 43\n",
"maxDepth: 7, reached depth: 7, minSamplesSplit: 2\n",
"························································\n",
- "â•´feat: 2 <= 2.15, samples: 9600\n",
- " ├─feat: 2 <= 2.00, samples: 4747\n",
- " │ ├─feat: 3 <= 1.96, samples: 4694\n",
+ "â•´feat: 0 <= 2.51, samples: 9600\n",
+ " ├─feat: 0 <= 1.63, samples: 4747\n",
+ " │ ├─feat: 3 <= 2.37, samples: 4486\n",
" │ │ └─╴value: 0.0\n",
- " │ │ └─╴feat: 2 <= 1.67, samples: 153\n",
+ " │ │ └─╴feat: 0 <= 1.21, samples: 47\n",
" │ │ └─╴value: 0.0\n",
- " │ │ └─╴feat: 1 <= -0.94, samples: 4\n",
+ " │ │ └─╴feat: 0 <= 1.41, samples: 5\n",
" │ │ └─╴value: 1.0\n",
" │ │ └─╴value: 0.0\n",
- " │ └─╴feat: 0 <= 1.46, samples: 53\n",
- " │ ├─feat: 3 <= 2.13, samples: 42\n",
+ " │ └─╴feat: 1 <= 1.51, samples: 261\n",
+ " │ ├─feat: 3 <= 1.46, samples: 218\n",
" │ │ └─╴value: 0.0\n",
- " │ │ └─╴value: 1.0\n",
- " │ └─╴feat: 3 <= -1.24, samples: 11\n",
+ " │ │ └─╴feat: 3 <= 2.23, samples: 15\n",
+ " │ │ ├─feat: 0 <= 2.38, samples: 11\n",
+ " │ │ │ └─╴value: 0.0\n",
+ " │ │ │ └─╴value: 1.0\n",
+ " │ │ └─╴value: 1.0\n",
+ " │ └─╴feat: 3 <= -0.40, samples: 43\n",
" │ └─╴value: 0.0\n",
- " │ └─╴value: 1.0\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 <= -0.76, 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",
+ " │ └─╴feat: 3 <= 0.50, samples: 37\n",
+ " │ ├─feat: 3 <= 0.28, samples: 13\n",
+ " │ │ └─╴value: 1.0\n",
+ " │ │ └─╴value: 0.0\n",
+ " │ └─╴value: 1.0\n",
+ " └─╴feat: 0 <= 2.99, samples: 4853\n",
+ " ├─feat: 1 <= 1.10, samples: 107\n",
+ " │ ├─feat: 1 <= 0.44, samples: 27\n",
+ " │ │ └─╴value: 0.0\n",
+ " │ │ └─╴feat: 2 <= 0.67, samples: 9\n",
+ " │ │ └─╴value: 1.0\n",
+ " │ │ └─╴value: 0.0\n",
+ " │ └─╴value: 1.0\n",
+ " └─╴feat: 0 <= 3.89, samples: 4746\n",
+ " ├─feat: 1 <= 1.24, samples: 471\n",
+ " │ ├─feat: 3 <= 0.81, samples: 57\n",
+ " │ │ ├─feat: 3 <= 0.60, samples: 11\n",
+ " │ │ │ └─╴value: 1.0\n",
+ " │ │ │ └─╴value: 0.0\n",
+ " │ │ └─╴feat: 0 <= 3.84, samples: 46\n",
+ " │ │ └─╴value: 1.0\n",
+ " │ │ └─╴value: 0.0\n",
+ " │ └─╴value: 1.0\n",
" └─╴value: 1.0\n",
"\n",
"—————————————————————— tree: 5/5 ———————————————————————\n",
"split: CART, impurity: Entropy, leaf: Mode, nodes: 45\n",
"maxDepth: 7, reached depth: 7, minSamplesSplit: 2\n",
"························································\n",
- "â•´feat: 2 <= 2.50, samples: 9600\n",
- " ├─feat: 2 <= 2.05, samples: 4748\n",
- " │ ├─feat: 3 <= 2.48, samples: 4643\n",
+ "â•´feat: 0 <= 2.43, samples: 9600\n",
+ " ├─feat: 0 <= 1.73, samples: 4853\n",
+ " │ ├─feat: 1 <= 2.24, samples: 4639\n",
" │ │ └─╴value: 0.0\n",
- " │ │ └─╴feat: 2 <= 1.30, samples: 50\n",
+ " │ │ └─╴feat: 3 <= 2.00, samples: 51\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",
+ " │ │ └─╴feat: 1 <= 2.61, samples: 2\n",
+ " │ │ └─╴value: 1.0\n",
+ " │ │ └─╴value: 0.0\n",
+ " │ └─╴feat: 1 <= 1.97, samples: 214\n",
+ " │ ├─feat: 3 <= 2.59, samples: 185\n",
+ " │ │ ├─feat: 1 <= 1.45, samples: 180\n",
" │ │ │ └─╴value: 0.0\n",
- " │ │ │ └─╴feat: 2 <= 2.30, samples: 9\n",
- " │ │ │ └─╴value: 0.0\n",
+ " │ │ │ └─╴feat: 4 <= 0.46, samples: 14\n",
" │ │ │ └─╴value: 1.0\n",
- " │ │ └─╴feat: 4 <= -0.46, samples: 9\n",
- " │ │ └─╴value: 0.0\n",
- " │ │ └─╴value: 1.0\n",
- " │ └─╴feat: 4 <= 0.62, samples: 36\n",
+ " │ │ │ └─╴value: 0.0\n",
+ " │ │ └─╴value: 1.0\n",
+ " │ └─╴feat: 3 <= 0.34, samples: 29\n",
+ " │ ├─feat: 4 <= 1.07, samples: 10\n",
+ " │ │ └─╴value: 0.0\n",
+ " │ │ └─╴value: 1.0\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",
+ " └─╴feat: 0 <= 2.99, samples: 4747\n",
+ " ├─feat: 1 <= 1.21, samples: 107\n",
+ " │ ├─feat: 1 <= 0.23, samples: 31\n",
+ " │ │ └─╴value: 0.0\n",
+ " │ │ └─╴feat: 3 <= 0.30, samples: 15\n",
+ " │ │ └─╴value: 0.0\n",
" │ │ └─╴value: 1.0\n",
- " │ └─╴feat: 1 <= 2.00, samples: 69\n",
+ " │ └─╴feat: 0 <= 2.51, samples: 76\n",
+ " │ ├─feat: 3 <= 0.34, samples: 4\n",
+ " │ │ └─╴value: 0.0\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",
+ " └─╴feat: 1 <= 1.23, samples: 4640\n",
+ " ├─feat: 0 <= 3.89, samples: 460\n",
+ " │ ├─feat: 3 <= 0.81, samples: 51\n",
+ " │ │ ├─feat: 2 <= 0.44, samples: 10\n",
+ " │ │ │ └─╴value: 1.0\n",
+ " │ │ │ └─╴value: 0.0\n",
+ " │ │ └─╴feat: 0 <= 3.82, samples: 41\n",
" │ │ └─╴value: 1.0\n",
" │ │ └─╴value: 0.0\n",
" │ └─╴value: 1.0\n",
@@ -405,11 +439,11 @@
{
"data": {
"text/plain": [
- "[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=0.9965625)]"
+ "[Accuracy(name='tree: 0', accuracy=0.9959375),\n",
+ " Accuracy(name='tree: 1', accuracy=0.9946875),\n",
+ " Accuracy(name='tree: 2', accuracy=0.9953125),\n",
+ " Accuracy(name='tree: 3', accuracy=0.9946875),\n",
+ " Accuracy(name='tree: 4', accuracy=0.9946875)]"
]
},
"execution_count": 5,
@@ -428,7 +462,7 @@
"outputs": [
{
"data": {
- "image/png": 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",
+ "image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
@@ -476,19 +510,19 @@
"————————— confusion matrix —————————\n",
" Class 0 Class 1 \n",
"····································\n",
- " Class 0 1596 4 \n",
+ " Class 0 1595 5 \n",
" 49% 0% \n",
"····································\n",
- " Class 1 3 1597 \n",
+ " Class 1 7 1593 \n",
" 0% 49% \n",
"\n",
"———————————————————————————————— scores ———————————————————————————————\n",
" accuracy precision sensitivity miss rate \n",
"·······································································\n",
- " Class 0 0.998 0.998 0.998 0.003 \n",
- " Class 1 0.998 0.998 0.998 0.002 \n",
+ " Class 0 0.996 0.996 0.997 0.003 \n",
+ " Class 1 0.996 0.997 0.996 0.004 \n",
"·······································································\n",
- " total 0.998 0.998 0.998 0.002 \n"
+ " total 0.996 0.996 0.996 0.004 \n"
]
}
],
@@ -528,125 +562,161 @@
"voting: Majority, booster: None, bootstrapping: True\n",
"\n",
"————————————————————— tree: 01/15 ——————————————————————\n",
- "split: CART, impurity: Entropy, leaf: Mode, nodes: 35\n",
+ "split: CART, impurity: Entropy, leaf: Mode, nodes: 47\n",
"maxDepth: 7, reached depth: 7, minSamplesSplit: 2\n",
"························································\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: 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",
+ "â•´feat: 0 <= 2.65, samples: 9600\n",
+ " ├─feat: 0 <= 2.09, samples: 4855\n",
+ " │ ├─feat: 0 <= 1.38, samples: 4748\n",
+ " │ │ └─╴value: 0.0\n",
+ " │ │ └─╴feat: 1 <= 1.59, samples: 312\n",
+ " │ │ ├─feat: 3 <= 2.41, samples: 295\n",
+ " │ │ │ └─╴value: 0.0\n",
+ " │ │ │ └─╴feat: 0 <= 1.41, samples: 4\n",
+ " │ │ │ └─╴value: 1.0\n",
+ " │ │ │ └─╴value: 0.0\n",
+ " │ │ └─╴feat: 3 <= 0.60, samples: 17\n",
+ " │ │ ├─feat: 1 <= 1.76, samples: 10\n",
+ " │ │ │ └─╴value: 0.0\n",
+ " │ │ │ └─╴value: 0.0\n",
+ " │ │ └─╴value: 1.0\n",
+ " │ └─╴feat: 1 <= 1.22, samples: 107\n",
+ " │ ├─feat: 2 <= -1.25, samples: 58\n",
+ " │ │ └─╴value: 1.0\n",
+ " │ │ └─╴value: 0.0\n",
+ " │ └─╴feat: 1 <= 1.93, samples: 49\n",
+ " │ ├─feat: 2 <= 0.91, samples: 14\n",
+ " │ │ ├─feat: 0 <= 2.56, samples: 9\n",
+ " │ │ │ └─╴value: 0.0\n",
+ " │ │ │ └─╴value: 1.0\n",
+ " │ │ └─╴value: 1.0\n",
+ " │ └─╴feat: 3 <= -0.38, samples: 35\n",
+ " │ └─╴value: 0.0\n",
+ " │ └─╴value: 1.0\n",
+ " └─╴feat: 0 <= 3.03, samples: 4745\n",
+ " ├─feat: 1 <= 0.99, samples: 108\n",
+ " │ ├─feat: 4 <= 0.30, samples: 23\n",
+ " │ │ ├─feat: 2 <= 1.14, samples: 19\n",
" │ │ │ └─╴value: 0.0\n",
- " │ │ │ └─╴feat: 0 <= 0.75, samples: 17\n",
- " │ │ │ └─╴value: 0.0\n",
+ " │ │ │ └─╴feat: 0 <= 2.77, samples: 2\n",
" │ │ │ └─╴value: 1.0\n",
+ " │ │ │ └─╴value: 0.0\n",
" │ │ └─╴value: 1.0\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",
- " │ └─╴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: 1.0\n",
+ " └─╴feat: 1 <= 1.22, samples: 4637\n",
+ " ├─feat: 0 <= 3.91, samples: 459\n",
+ " │ ├─feat: 1 <= 1.20, samples: 56\n",
+ " │ │ ├─feat: 3 <= 0.82, samples: 52\n",
+ " │ │ │ └─╴value: 1.0\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: 33\n",
+ "split: CART, impurity: Entropy, leaf: Mode, nodes: 57\n",
"maxDepth: 7, reached depth: 7, minSamplesSplit: 2\n",
"························································\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",
+ "â•´feat: 0 <= 2.31, samples: 9600\n",
+ " ├─feat: 0 <= 1.74, samples: 4747\n",
+ " │ ├─feat: 3 <= 2.32, samples: 4590\n",
+ " │ │ └─╴value: 0.0\n",
+ " │ │ └─╴feat: 0 <= 1.24, samples: 51\n",
+ " │ │ └─╴value: 0.0\n",
+ " │ │ └─╴value: 1.0\n",
+ " │ └─╴feat: 1 <= 1.53, samples: 157\n",
+ " │ ├─feat: 3 <= 1.86, samples: 134\n",
" │ │ └─╴value: 0.0\n",
- " │ │ └─╴value: 1.0\n",
- " │ └─╴feat: 0 <= -1.24, samples: 18\n",
- " │ └─╴value: 0.0\n",
+ " │ │ └─╴feat: 0 <= 2.04, samples: 2\n",
+ " │ │ └─╴value: 0.0\n",
+ " │ │ └─╴value: 1.0\n",
+ " │ └─╴feat: 3 <= 0.90, samples: 23\n",
+ " │ ├─feat: 1 <= 1.64, samples: 15\n",
+ " │ │ ├─feat: 1 <= 1.53, samples: 6\n",
+ " │ │ │ └─╴value: 0.0\n",
+ " │ │ │ └─╴value: 1.0\n",
+ " │ │ └─╴feat: 0 <= 2.16, samples: 9\n",
+ " │ │ └─╴value: 0.0\n",
+ " │ │ └─╴value: 1.0\n",
" │ └─╴value: 1.0\n",
- " └─╴feat: 2 <= 3.35, samples: 4853\n",
- " ├─feat: 3 <= 1.41, samples: 320\n",
- " │ ├─feat: 0 <= 0.48, samples: 99\n",
+ " └─╴feat: 0 <= 2.81, samples: 4853\n",
+ " ├─feat: 1 <= 0.64, samples: 107\n",
+ " │ ├─feat: 3 <= 1.71, samples: 28\n",
" │ │ └─╴value: 0.0\n",
- " │ │ └─╴feat: 2 <= 2.99, samples: 61\n",
- " │ │ ├─feat: 4 <= 0.87, samples: 40\n",
- " │ │ │ └─╴value: 0.0\n",
- " │ │ │ └─╴value: 1.0\n",
+ " │ │ └─╴feat: 0 <= 2.65, samples: 2\n",
+ " │ │ └─╴value: 0.0\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",
- " └─╴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",
+ " │ └─╴feat: 3 <= 0.48, samples: 79\n",
+ " │ ├─feat: 0 <= 2.50, samples: 22\n",
+ " │ │ ├─feat: 1 <= 1.73, samples: 13\n",
+ " │ │ │ └─╴value: 0.0\n",
+ " │ │ │ └─╴value: 0.0\n",
+ " │ │ └─╴feat: 4 <= -0.92, samples: 9\n",
+ " │ │ └─╴value: 0.0\n",
+ " │ │ └─╴value: 1.0\n",
+ " │ └─╴feat: 2 <= 0.70, samples: 57\n",
+ " │ └─╴value: 1.0\n",
+ " │ └─╴feat: 2 <= 0.83, samples: 18\n",
+ " │ └─╴value: 0.0\n",
+ " │ └─╴value: 1.0\n",
+ " └─╴feat: 1 <= 0.42, samples: 4746\n",
+ " ├─feat: 0 <= 3.53, samples: 105\n",
+ " │ ├─feat: 3 <= 0.46, samples: 18\n",
+ " │ │ └─╴value: 0.0\n",
+ " │ │ └─╴feat: 2 <= 0.99, samples: 11\n",
+ " │ │ └─╴value: 1.0\n",
+ " │ │ └─╴value: 0.0\n",
" │ └─╴value: 1.0\n",
- " └─╴value: 1.0\n",
+ " └─╴feat: 1 <= 1.24, samples: 4641\n",
+ " ├─feat: 1 <= 1.20, samples: 408\n",
+ " │ └─╴value: 1.0\n",
+ " │ └─╴feat: 0 <= 3.88, samples: 32\n",
+ " │ └─╴value: 0.0\n",
+ " │ └─╴value: 1.0\n",
+ " └─╴value: 1.0\n",
"\n",
"————————————————————— tree: 03/15 ——————————————————————\n",
"split: CART, impurity: Entropy, leaf: Mode, nodes: 43\n",
"maxDepth: 7, reached depth: 7, minSamplesSplit: 2\n",
"························································\n",
- "â•´feat: 2 <= 2.36, samples: 9600\n",
- " ├─feat: 2 <= 1.77, samples: 4747\n",
- " │ ├─feat: 2 <= 1.65, samples: 4591\n",
+ "â•´feat: 0 <= 2.60, samples: 9600\n",
+ " ├─feat: 0 <= 2.08, samples: 4747\n",
+ " │ ├─feat: 0 <= 1.41, samples: 4642\n",
" │ │ └─╴value: 0.0\n",
- " │ │ └─╴feat: 3 <= 3.06, samples: 51\n",
+ " │ │ └─╴feat: 1 <= 1.40, samples: 305\n",
" │ │ └─╴value: 0.0\n",
- " │ │ └─╴value: 1.0\n",
- " │ └─╴feat: 3 <= 1.01, samples: 156\n",
- " │ ├─feat: 2 <= 2.34, samples: 111\n",
+ " │ │ └─╴feat: 0 <= 1.42, samples: 23\n",
+ " │ │ └─╴value: 1.0\n",
+ " │ │ └─╴feat: 1 <= 1.58, samples: 22\n",
+ " │ │ └─╴value: 0.0\n",
+ " │ │ └─╴value: 0.0\n",
+ " │ └─╴feat: 1 <= 1.61, samples: 105\n",
+ " │ ├─feat: 3 <= 1.41, samples: 62\n",
" │ │ └─╴value: 0.0\n",
- " │ │ └─╴feat: 0 <= -1.20, samples: 2\n",
- " │ │ └─╴value: 0.0\n",
- " │ │ └─╴value: 1.0\n",
- " │ └─╴feat: 0 <= 0.47, samples: 45\n",
- " │ ├─feat: 1 <= -1.38, samples: 16\n",
- " │ │ └─╴value: 1.0\n",
+ " │ │ └─╴value: 1.0\n",
+ " │ └─╴feat: 3 <= 1.00, samples: 43\n",
+ " │ ├─feat: 3 <= 0.92, samples: 19\n",
+ " │ │ ├─feat: 3 <= 0.45, samples: 17\n",
+ " │ │ │ └─╴value: 1.0\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 <= -0.81, samples: 65\n",
- " │ └─╴value: 0.0\n",
+ " │ └─╴value: 1.0\n",
+ " └─╴feat: 1 <= 0.58, samples: 4853\n",
+ " ├─feat: 0 <= 3.55, samples: 160\n",
+ " │ ├─feat: 3 <= 1.50, samples: 27\n",
+ " │ │ └─╴value: 0.0\n",
+ " │ │ └─╴feat: 2 <= -0.65, samples: 6\n",
+ " │ │ └─╴value: 0.0\n",
+ " │ │ └─╴value: 1.0\n",
+ " │ └─╴feat: 0 <= 3.88, samples: 133\n",
+ " │ ├─feat: 2 <= -0.66, samples: 9\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",
+ " └─╴feat: 1 <= 1.25, samples: 4693\n",
+ " ├─feat: 0 <= 3.88, samples: 414\n",
+ " │ ├─feat: 0 <= 3.87, samples: 61\n",
+ " │ │ ├─feat: 0 <= 2.73, samples: 58\n",
" │ │ │ └─╴value: 0.0\n",
" │ │ │ └─╴value: 1.0\n",
" │ │ └─╴value: 0.0\n",
@@ -654,100 +724,98 @@
" └─╴value: 1.0\n",
"\n",
"————————————————————— tree: 04/15 ——————————————————————\n",
- "split: CART, impurity: Entropy, leaf: Mode, nodes: 45\n",
+ "split: CART, impurity: Entropy, leaf: Mode, nodes: 43\n",
"maxDepth: 7, reached depth: 7, minSamplesSplit: 2\n",
"························································\n",
- "â•´feat: 2 <= 2.15, samples: 9600\n",
- " ├─feat: 2 <= 2.00, samples: 4747\n",
- " │ ├─feat: 3 <= 1.96, samples: 4694\n",
+ "â•´feat: 0 <= 2.51, samples: 9600\n",
+ " ├─feat: 0 <= 1.63, samples: 4747\n",
+ " │ ├─feat: 3 <= 2.37, samples: 4486\n",
" │ │ └─╴value: 0.0\n",
- " │ │ └─╴feat: 2 <= 1.67, samples: 153\n",
+ " │ │ └─╴feat: 0 <= 1.21, samples: 47\n",
" │ │ └─╴value: 0.0\n",
- " │ │ └─╴feat: 1 <= -0.94, samples: 4\n",
+ " │ │ └─╴feat: 0 <= 1.41, samples: 5\n",
" │ │ └─╴value: 1.0\n",
" │ │ └─╴value: 0.0\n",
- " │ └─╴feat: 0 <= 1.46, samples: 53\n",
- " │ ├─feat: 3 <= 2.13, samples: 42\n",
+ " │ └─╴feat: 1 <= 1.51, samples: 261\n",
+ " │ ├─feat: 3 <= 1.46, samples: 218\n",
" │ │ └─╴value: 0.0\n",
- " │ │ └─╴value: 1.0\n",
- " │ └─╴feat: 3 <= -1.24, samples: 11\n",
+ " │ │ └─╴feat: 3 <= 2.23, samples: 15\n",
+ " │ │ ├─feat: 0 <= 2.38, samples: 11\n",
+ " │ │ │ └─╴value: 0.0\n",
+ " │ │ │ └─╴value: 1.0\n",
+ " │ │ └─╴value: 1.0\n",
+ " │ └─╴feat: 3 <= -0.40, samples: 43\n",
" │ └─╴value: 0.0\n",
- " │ └─╴value: 1.0\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 <= -0.76, 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",
+ " │ └─╴feat: 3 <= 0.50, samples: 37\n",
+ " │ ├─feat: 3 <= 0.28, samples: 13\n",
+ " │ │ └─╴value: 1.0\n",
+ " │ │ └─╴value: 0.0\n",
+ " │ └─╴value: 1.0\n",
+ " └─╴feat: 0 <= 2.99, samples: 4853\n",
+ " ├─feat: 1 <= 1.10, samples: 107\n",
+ " │ ├─feat: 1 <= 0.44, samples: 27\n",
+ " │ │ └─╴value: 0.0\n",
+ " │ │ └─╴feat: 2 <= 0.67, samples: 9\n",
+ " │ │ └─╴value: 1.0\n",
+ " │ │ └─╴value: 0.0\n",
+ " │ └─╴value: 1.0\n",
+ " └─╴feat: 0 <= 3.89, samples: 4746\n",
+ " ├─feat: 1 <= 1.24, samples: 471\n",
+ " │ ├─feat: 3 <= 0.81, samples: 57\n",
+ " │ │ ├─feat: 3 <= 0.60, samples: 11\n",
+ " │ │ │ └─╴value: 1.0\n",
+ " │ │ │ └─╴value: 0.0\n",
+ " │ │ └─╴feat: 0 <= 3.84, samples: 46\n",
+ " │ │ └─╴value: 1.0\n",
+ " │ │ └─╴value: 0.0\n",
+ " │ └─╴value: 1.0\n",
" └─╴value: 1.0\n",
"\n",
"————————————————————— tree: 05/15 ——————————————————————\n",
"split: CART, impurity: Entropy, leaf: Mode, nodes: 45\n",
"maxDepth: 7, reached depth: 7, minSamplesSplit: 2\n",
"························································\n",
- "â•´feat: 2 <= 2.50, samples: 9600\n",
- " ├─feat: 2 <= 2.05, samples: 4748\n",
- " │ ├─feat: 3 <= 2.48, samples: 4643\n",
+ "â•´feat: 0 <= 2.43, samples: 9600\n",
+ " ├─feat: 0 <= 1.73, samples: 4853\n",
+ " │ ├─feat: 1 <= 2.24, samples: 4639\n",
" │ │ └─╴value: 0.0\n",
- " │ │ └─╴feat: 2 <= 1.30, samples: 50\n",
+ " │ │ └─╴feat: 3 <= 2.00, samples: 51\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",
+ " │ │ └─╴feat: 1 <= 2.61, samples: 2\n",
+ " │ │ └─╴value: 1.0\n",
+ " │ │ └─╴value: 0.0\n",
+ " │ └─╴feat: 1 <= 1.97, samples: 214\n",
+ " │ ├─feat: 3 <= 2.59, samples: 185\n",
+ " │ │ ├─feat: 1 <= 1.45, samples: 180\n",
" │ │ │ └─╴value: 0.0\n",
- " │ │ │ └─╴feat: 2 <= 2.30, samples: 9\n",
- " │ │ │ └─╴value: 0.0\n",
+ " │ │ │ └─╴feat: 4 <= 0.46, samples: 14\n",
" │ │ │ └─╴value: 1.0\n",
- " │ │ └─╴feat: 4 <= -0.46, samples: 9\n",
- " │ │ └─╴value: 0.0\n",
- " │ │ └─╴value: 1.0\n",
- " │ └─╴feat: 4 <= 0.62, samples: 36\n",
+ " │ │ │ └─╴value: 0.0\n",
+ " │ │ └─╴value: 1.0\n",
+ " │ └─╴feat: 3 <= 0.34, samples: 29\n",
+ " │ ├─feat: 4 <= 1.07, samples: 10\n",
+ " │ │ └─╴value: 0.0\n",
+ " │ │ └─╴value: 1.0\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",
+ " └─╴feat: 0 <= 2.99, samples: 4747\n",
+ " ├─feat: 1 <= 1.21, samples: 107\n",
+ " │ ├─feat: 1 <= 0.23, samples: 31\n",
+ " │ │ └─╴value: 0.0\n",
+ " │ │ └─╴feat: 3 <= 0.30, samples: 15\n",
+ " │ │ └─╴value: 0.0\n",
" │ │ └─╴value: 1.0\n",
- " │ └─╴feat: 1 <= 2.00, samples: 69\n",
+ " │ └─╴feat: 0 <= 2.51, samples: 76\n",
+ " │ ├─feat: 3 <= 0.34, samples: 4\n",
+ " │ │ └─╴value: 0.0\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",
+ " └─╴feat: 1 <= 1.23, samples: 4640\n",
+ " ├─feat: 0 <= 3.89, samples: 460\n",
+ " │ ├─feat: 3 <= 0.81, samples: 51\n",
+ " │ │ ├─feat: 2 <= 0.44, samples: 10\n",
+ " │ │ │ └─╴value: 1.0\n",
+ " │ │ │ └─╴value: 0.0\n",
+ " │ │ └─╴feat: 0 <= 3.82, samples: 41\n",
" │ │ └─╴value: 1.0\n",
" │ │ └─╴value: 0.0\n",
" │ └─╴value: 1.0\n",
@@ -776,19 +844,19 @@
"————————— confusion matrix —————————\n",
" Class 0 Class 1 \n",
"····································\n",
- " Class 0 1596 4 \n",
+ " Class 0 1595 5 \n",
" 49% 0% \n",
"····································\n",
- " Class 1 3 1597 \n",
+ " Class 1 7 1593 \n",
" 0% 49% \n",
"\n",
"———————————————————————————————— scores ———————————————————————————————\n",
" accuracy precision sensitivity miss rate \n",
"·······································································\n",
- " Class 0 0.998 0.998 0.998 0.003 \n",
- " Class 1 0.998 0.998 0.998 0.002 \n",
+ " Class 0 0.996 0.996 0.997 0.003 \n",
+ " Class 1 0.996 0.997 0.996 0.004 \n",
"·······································································\n",
- " total 0.998 0.998 0.998 0.002 \n"
+ " total 0.996 0.996 0.996 0.004 \n"
]
}
],
diff --git a/forrest-test.json b/forrest-test.json
index 9bdd65f..8b223f8 100644
--- a/forrest-test.json
+++ b/forrest-test.json
@@ -1,5 +1,5 @@
{
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],
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},
- "198": {
- "threshold": 3.066764897426065,
- "feature": 2,
- "leftID": 199,
- "rightID": 200,
- "id": 198,
- "isRoot": false,
- "parent": 195,
- "rawValues": null,
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"feature": null,
"leftID": null,
"rightID": null,
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+ "id": 208,
"isRoot": false,
- "parent": 170,
+ "parent": 206,
"rawValues": [
0.0,
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@@ -50362,77 +50722,108 @@
0.0,
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+ 0.0,
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0.0
],
"bakedValues": 0
},
- "172": {
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- "parent": 170,
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- },
- "189": {
+ "231": {
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"feature": null,
"leftID": null,
"rightID": null,
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+ "id": 231,
"isRoot": false,
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"rawValues": [
1.0,
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],
"bakedValues": 1
},
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"feature": null,
"leftID": null,
"rightID": null,
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+ "id": 232,
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+ "parent": 229,
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},
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+ "233": {
"threshold": null,
"feature": null,
"leftID": null,
"rightID": null,
- "id": 199,
+ "id": 233,
"isRoot": false,
- "parent": 198,
+ "parent": 230,
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"feature": null,
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diff --git a/machineLearning/rf/leafFunction.py b/machineLearning/rf/leafFunction.py
index 18ab3ff..cbf5a30 100644
--- a/machineLearning/rf/leafFunction.py
+++ b/machineLearning/rf/leafFunction.py
@@ -144,6 +144,8 @@ class AnomalyDetection(LeafFunction):
def _leafFunc(self, node: Node) -> 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
+ assert isinstance(node, Node), "input type needs to be of Node"
+
if hasattr(node, self.strategy):
return np.array([getattr(node, self.strategy)])
else:
diff --git a/som-test.json b/som-test.json
index 36adcf6..9178d86 100644
--- a/som-test.json
+++ b/som-test.json
@@ -1,5 +1,5 @@
{
- "datetime": "2024-03-05T14:12:22.428111",
+ "datetime": "2024-03-07T11:43:10.483832",
"qualifiedName": [
"machineLearning.som.som",
"SOM"
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+ 56.0
],
[
- -112.0,
- 96.0,
- 390.0,
- 498.0,
- -342.0
+ 288.0,
+ -532.0,
+ -634.0,
+ 448.0,
+ 314.0
],
[
- 465.0,
- 555.0,
- 125.0,
- -353.0,
- 443.0
+ -66.0,
+ -450.0,
+ 280.0,
+ 320.0,
+ -320.0
],
[
- -71.0,
- 387.0,
- 451.0,
- -157.0,
- 51.0
+ -8.0,
+ 36.0,
+ -174.0,
+ -410.0,
+ -350.0
],
[
- 43.0,
- 271.0,
- -385.0,
- -377.0,
- -3.0
+ 295.0,
+ -373.0,
+ -215.0,
+ 147.0,
+ 343.0
]
],
"minimalDistances": [
diff --git a/som/__init__.py b/som/__init__.py
deleted file mode 100644
index 8a8b54f..0000000
--- a/som/__init__.py
+++ /dev/null
@@ -1,4 +0,0 @@
-from .error import *
-from .neighborhood import *
-from .som import *
-from .topology import *
\ No newline at end of file
diff --git a/som/error.py b/som/error.py
deleted file mode 100644
index 1a97516..0000000
--- a/som/error.py
+++ /dev/null
@@ -1,51 +0,0 @@
-import numpy as np
-from abc import ABC, abstractmethod
-
-
-class Error(ABC):
- """
- Class representing a generic error in the context of a Self-Organizing Map (SOM).
- """
- def __init__(self):
- self.name = self.__class__.__name__
-
- def __call__(self, *args) -> float:
- return self._calcError(*args)
-
- @abstractmethod
- def _calcError(self, *args) -> float:
- pass
-
-
-class Topological(Error):
- """
- Class representing a Topological error in a Self-Organizing Map (SOM). This error is computed
- based on the topological structure of the SOM.
- """
- def __init__(self):
- super().__init__()
-
- def _calcError(self, bestMatchingIndices: np.ndarray, secondMatchingIndices: np.ndarray) -> float:
- """
- Compute the Topological error. The error is calculated as the mean distance between
- the best matching indices and the second best matching indices, minus one.
- """
- distance = np.sum(np.abs(bestMatchingIndices - secondMatchingIndices), axis=-1) - 1
- return np.mean(distance)
-
-
-class Quantazation(Error):
- """
- Class representing a Quantization error in a Self-Organizing Map (SOM). This error is computed
- based on the discrepancy between the best matching units and the original data.
- """
- def __init__(self):
- super().__init__()
-
- def _calcError(self, bestMatchingUnits: np.ndarray, dataBatch: np.ndarray) -> float:
- """
- Compute the Quantization error. The error is calculated as the mean Euclidean distance
- between the best matching units and the original data batch.
- """
- distance = np.linalg.norm(bestMatchingUnits - dataBatch, axis=-1)
- return np.mean(distance)
\ No newline at end of file
diff --git a/som/neighborhood.py b/som/neighborhood.py
deleted file mode 100644
index db7610a..0000000
--- a/som/neighborhood.py
+++ /dev/null
@@ -1,101 +0,0 @@
-import numpy as np
-from abc import ABC, abstractmethod
-
-
-class NeighborhoodFunction(ABC):
- """
- Abstract base class for neighborhood functions, such as Gaussian, Bubble, and Mexican Hat.
- """
- def __init__(self, scale: float) -> None:
- self.name = self.__class__.__name__
- self.scale = scale
-
- def __call__(self, distance: np.ndarray) -> np.ndarray:
- """
- Calls the _compute method to compute the value of the neighborhood function for the input distance and radius.
- """
- return self._compute(distance)
-
- @abstractmethod
- def _compute(self, distance: np.ndarray) -> np.ndarray:
- """
- Computes the value of the neighborhood function for the input distance and radius.
- """
- pass
-
-
-class GuassianNeighborhood(NeighborhoodFunction):
- def __init__(self, scale: float) -> None:
- super().__init__(scale)
-
- def _compute(self, distance: np.ndarray) -> np.ndarray:
- """
- Computes the value of a Gaussian neighborhood function for a given distance and radius.
- """
- return np.exp(-distance ** 2 / (2 * self.scale ** 2))
-
-
-class BubbleNeighborhood(NeighborhoodFunction):
- def __init__(self, scale: float) -> None:
- super().__init__(scale)
-
- def _compute(self, distance: np.ndarray) -> np.ndarray:
- """
- Computes the value of a Bubble neighborhood function for a given distance and radius.
- """
- return np.where(distance <= self.scale, 1, 0)
-
-
-class MexicanHatNeighborhood(NeighborhoodFunction):
- def __init__(self, scale: float) -> None:
- super().__init__(scale)
-
- def _compute(self, distance: np.ndarray) -> np.ndarray:
- """
- Computes the value of a Mexican hat neighborhood function for a given distance and radius.
- """
- return (1 - (distance / self.scale) ** 2) * np.exp(-distance ** 2 / (2 * self.scale ** 2))
-
-
-class LinearNeighborhood(NeighborhoodFunction):
- def __init__(self, scale: float) -> None:
- super().__init__(scale)
-
- def _compute(self, distance: np.ndarray) -> np.ndarray:
- """
- Computes the value of a linear neighborhood function for a given distance and radius.
- """
- return np.where(distance <= self.scale, 1 - distance / self.scale, 0)
-
-
-class CosineNeighborhood(NeighborhoodFunction):
- def __init__(self, scale: float) -> None:
- super().__init__(scale)
-
- def _compute(self, distance: np.ndarray) -> np.ndarray:
- """
- Computes the value of a cosine neighborhood function for a given distance and radius.
- """
- return np.cos(np.pi * distance / self.scale)
-
-
-class CauchyNeighborhood(NeighborhoodFunction):
- def __init__(self, scale: float) -> None:
- super().__init__(scale)
-
- def _compute(self, distance: np.ndarray) -> np.ndarray:
- """
- Computes the value of a Cauchy neighborhood function for a given distance and radius.
- """
- return 1 / (1 + (distance / self.scale) ** 2)
-
-
-class EpanechnikovNeighborhood(NeighborhoodFunction):
- def __init__(self, scale: float) -> None:
- super().__init__(scale)
-
- def _compute(self, distance: np.ndarray) -> np.ndarray:
- """
- Computes the value of an Epanechnikov neighborhood function for a given distance and radius.
- """
- return np.where(distance <= self.scale, 1 - (distance / self.scale) ** 2, 0)
\ No newline at end of file
diff --git a/som/som.py b/som/som.py
deleted file mode 100644
index bb564e6..0000000
--- a/som/som.py
+++ /dev/null
@@ -1,302 +0,0 @@
-import numpy as np
-from numpy.typing import ArrayLike
-from data.dataLoader import DataLoader, DataSet
-from utility.progressbar import Progressbar
-from .neighborhood import NeighborhoodFunction
-from nn.scheduler import Scheduler
-from importlib import import_module
-from .observable import SOMobservable
-from .error import Topological, Quantazation
-from .topology import Topology
-
-
-class SOM(object):
- """
- Main code for SOM
- """
- def __init__(self, learningRate: float, gridSteps: int = 1, decreaseEvery: int = None, growth: bool = False) -> None:
- self.name = self.__class__.__name__
-
- self.topology = None
- self.gridSteps = gridSteps
- self.decreaseEvery = decreaseEvery
- self.growth = growth
- self._initedWeights = False
-
- # this holds counts per neuron per class
- self.counts = []
-
- # parameters for training
- self.learningRate = learningRate
- self._scheduler = None
-
- # error estimation for training
- self._topoError = Topological()
- self._quantError = Quantazation()
-
- # neighborhood
- self.neighborhoodFunc = None
-
- @property
- def qualifiedName(self) -> tuple:
- """
- This is needed for saving the SOM as a json file
- """
- return self.__class__.__module__, self.__class__.__name__
-
- def toDict(self) -> dict:
- """
- This converts all values and features of the SOM into a dict
- Than it can be saved using built-in functions as json file
- """
- saveDict = {}
- saveDict['numFeatures'] = self.topology.numFeatures
- saveDict['gridSize'] = self.topology.gridSize
- saveDict['topology'] = self.topology.name
- saveDict['initedWeights'] = self._initedWeights
-
- saveDict['learningRate'] = self.learningRate
- saveDict['gridSteps'] = self.gridSteps
- saveDict['counts'] = []
- for item in self.counts:
- saveDict['counts'].append(item.tolist())
- if self._scheduler is not None:
- saveDict['scheduler'] = self._scheduler.name
- saveDict['neighborhoodFunc'] = self.neighborhoodFunc.name
- saveDict['scale'] = self.neighborhoodFunc.scale
- saveDict['weights'] = self.topology.weights.tolist()
-
- saveDict['movementCounts'] = self.topology._movementCounts.tolist()
- saveDict['directionCounts'] = self.topology._directionCounts.tolist()
- saveDict['minimalDistances'] = self.topology._minimalDistances.tolist()
-
- return saveDict
-
- @classmethod
- def fromDict(cls, loadDict: dict) -> object:
- """
- This instantiates a new SOM from a dict, which was loaded from a json file
- """
- instance = cls(loadDict['learningRate'], loadDict['gridSteps'])
-
- Module = import_module('som.neighborhood') # dynamically import module
- Class = getattr(Module, loadDict['neighborhoodFunc']) # get class from imported module
- instance.neighborhoodFunc = Class(loadDict['scale'])
-
- Module = import_module('som.topology') # dynamically import module
- Class = getattr(Module, loadDict['topology']) # get class from imported module
- instance.topology = Class(loadDict['gridSize'], loadDict['numFeatures'])
- instance._initedWeights = loadDict['initedWeights']
-
- instance.topology.weights = np.array(loadDict['weights'])
- instance.topology._movementCounts = np.array(loadDict['movementCounts'])
- instance.topology._directionCounts = np.array(loadDict['directionCounts'])
- instance.topology._minimalDistances = np.array(loadDict['minimalDistances'])
- for item in loadDict['counts']:
- instance.counts.append(item)
-
- return instance
-
- def setComponent(self, component: NeighborhoodFunction | Scheduler | Topology) -> None:
- """
- this allows setting a neighborhood functions and if the user
- wishes also a learning rate schdeuler
- """
- if isinstance(component, NeighborhoodFunction):
- self.neighborhoodFunc = component
- elif isinstance(component, Scheduler):
- self._scheduler = component
- elif isinstance(component, Topology):
- self.topology = component
- else:
- raise TypeError("the provided component is not a valid one")
-
- def initWeights(self, data: DataLoader | np.ndarray) -> None:
- # setting up the weight grid/neurons
- self.topology.initWeights(data)
- self._initedWeights = True
-
- def _findBestMatch(self, dataBatch: np.ndarray) -> np.ndarray:
- """
- Find the best matching unit (BMU) for each data point in the batch.
- """
- batchSize, numFeatures = dataBatch.shape
-
- # Repeat data points and tile weights
- repeatedData = np.repeat(dataBatch, self.topology.numNeurons, axis=0)
- tiledWeights = np.tile(self.topology.weights, (batchSize, 1))
-
- # Compute Euclidean distances between data points and weights
- norms = np.linalg.norm(tiledWeights - repeatedData, axis=1).reshape(batchSize, -1)
-
- # Partition distances so the two smallest distances are at the beginning
- partitioned = np.argpartition(norms, 2, axis=1)
-
- # Extract the indices of the best and second best matching units
- bestMatches, secondMatches = partitioned[:, 0], partitioned[:, 1]
-
- return bestMatches, secondMatches
-
- def _findNeighbors(self, bestMatches: ArrayLike) -> np.ndarray:
- """
- Given a list of best matching units (BMUs) on a grid, this function finds the indices of their neighboring units
- within a certain number of steps from them.
- """
- batchSize = len(bestMatches)
- uu, vv = np.unravel_index(bestMatches, self.topology.gridSize)
- indices = np.vstack((uu, vv)).T
-
- # broadcasting arrays to match each other
- tiledGrid = np.tile(self.topology.gridIndices, (batchSize, 1))
- repeatedIndices = np.repeat(indices, self.topology.numNeurons, axis=0)
-
- # distances understood as steps on a grid
- distances = np.sum(abs(tiledGrid - repeatedIndices), axis=-1).reshape(batchSize, -1)
-
- # the 'topological correction' is needed to find diagonal neighbors on a hexagonal grid
- inputVectors, neighbors = np.where(distances <= self.gridSteps + self.topology.stepCorrection)
-
- return inputVectors, neighbors
-
- def _adjustWeights(self, dataBatch: ArrayLike) -> None:
- """
- Adjusts the weights of a self-organizing map (SOM) given a batch of input vectors.
- """
-
- # Find the bestMatches for each input vector in the batch
- bestMatches, secondMatches = self._findBestMatch(dataBatch)
-
- # calculating and updating errors
- topoError = self._topoError(bestMatches, secondMatches)
- quantError = self._quantError(self.topology.weights[bestMatches], dataBatch)
- self.metrics.update('topologyError', topoError, len(dataBatch) * self.topology.numNeurons)
- self.metrics.update('quantazationError', quantError, len(dataBatch))
-
- # Find the indices of the neighboring neurons
- inputVectors, neighbors = self._findNeighbors(bestMatches)
-
- # Adjust the weights of the winning neuron and its neighbors
- deltaWeights = self.learningRate * (dataBatch - self.topology.weights[bestMatches])
- self.topology[bestMatches] += deltaWeights
-
- # Calculate the Gaussian neighborhood function for each neighbor
- neighborIndices = self.topology.gridIndices[neighbors]
- bestMatchIndices = self.topology.gridIndices[bestMatches[inputVectors]]
- topologicalDistances = np.linalg.norm(neighborIndices - bestMatchIndices, axis=-1)
-
- # finding the neighbors and determining weight changes
- neighborhoodWeights = self.neighborhoodFunc(topologicalDistances)
- neighborhoodWeights = np.repeat(neighborhoodWeights,self.topology.numFeatures).reshape(-1,self.topology.numFeatures)
-
- # updating neighborhood neurons
- deltaNeighbors = self.learningRate * neighborhoodWeights * (dataBatch[inputVectors] - self.topology.weights[neighbors])
- self.topology[neighbors] += deltaNeighbors
-
- # counting neuron movements
- self.topology.countMovements(bestMatches, neighbors, deltaWeights, deltaNeighbors)
-
- def train(self, data: DataLoader | np.ndarray, epochs: int, batchSize: int = None) -> None:
- """
- Trains the self-organizing map using the given data for the specified number of epochs and steps.
- """
- if self._initedWeights is False:
- self.initWeights(data)
-
- # testing input arguments
- self._checkInputDataType(data, batchSize)
-
- # converting numpy arrays into a DataLoader
- if isinstance(data, DataLoader) is False:
- data = DataSet(data)
- data = DataLoader(data, self._batchSize)
-
- self.metrics = SOMobservable(epochs)
- # beginn training
- for i in range(epochs):
- length = len(data)
- bar = Progressbar(f'epoch {str(i+1).zfill(len(str(epochs)))}/{epochs}', length, 65) # setting up a progress bar
-
- # running over data batches
- for item in data:
- self._adjustWeights(item['data'])
- bar.step()
-
- # making a step with LR scheduler
- if self._scheduler is not None:
- self._scheduler.step()
-
- # growing the topology, this code is ugly
- if self.gridSteps > 1 and self.decreaseEvery:
- if i > 0 and i % self.decreaseEvery == 0:
- self.gridSteps -= 1
- if i > 0 and i % 5 == 0 and self.growth:
- self.topology.grow()
-
- # updating and printing training metrics
- self.metrics.update('learningRate', self.learningRate)
- self.metrics.update('gridSteps', self.gridSteps)
- self.metrics.print()
- self.metrics.step()
-
- def _checkInputDataType(self, data, batchSize):
- """
- checking if data is given as a DataLoader or
- if data is given as numpy arrays together with a batchsize
- """
- if batchSize is None:
- assert type(data) == DataLoader, "if no batch size is provided, data should be data loader"
- elif batchSize is not None and type(data) == DataLoader:
- raise ValueError('batch size is already determined by data loader')
- else:
- self._batchSize = batchSize
-
- def eval(self, data: DataLoader | np.ndarray, labels: np.ndarray = None, batchSize: int = None, keepCounts: bool = True) -> None:
- """
- evaluates the class by counting the number of classes per neuron
- """
-
- # checking if weights were initilized
- if self._initedWeights is False:
- self.initWeights(data)
-
- # testing input arguments
- self._checkInputDataType(data, batchSize)
-
- # converting numpy arrays into a DataLoader
- if isinstance(data, DataLoader) is False:
- data = DataSet(data, labels=labels)
- data = DataLoader(data, self._batchSize)
-
- # setting up the counting arrays for evaluation
- uniqueLabels = data.dataSet.uniques
- self._counts = np.zeros((self.topology.numNeurons, len(uniqueLabels)))
-
- # running of data batches
- for item in data:
- self._eval(item['data'], item['labels'])
-
- if keepCounts is True:
- self.counts.append(self._counts)
-
- def _eval(self, data, labels):
- # Find the bestMatches for each input vector in the batch
- bestMatches = self._findBestMatch(data)
-
- # Use np.add.at() to handle repeated indices correctly
- if len(labels.shape) > 1:
- categoryLabels = np.argmax(labels, axis=1)
- np.add.at(self._counts, (bestMatches, categoryLabels), 1)
- else:
- np.add.at(self._counts, (bestMatches, labels), 1)
-
- @property
- def weightMatrix(self) -> np.ndarray:
- return self.topology.weightMatrix
-
- @property
- def uMatrix(self) -> np.ndarray:
- return self.topology.uMatrix
-
- @property
- def weights(self) -> np.ndarray:
- return self.topology.weights
diff --git a/som/topology.py b/som/topology.py
deleted file mode 100644
index a5c1ac8..0000000
--- a/som/topology.py
+++ /dev/null
@@ -1,363 +0,0 @@
-import numpy as np
-from numpy.typing import ArrayLike
-from abc import ABC, abstractmethod
-from data.dataLoader import DataLoader
-
-
-def mapTo(values: np.ndarray, arange: list = [0,1]) -> np.ndarray:
- """
- this function maps any input values into a given range
- """
- assert len(arange) == 2, 'arange must be of length 2'
- assert arange[0] < arange[1], 'arange must start at a lower value than it ends'
-
- c, d = arange[0], arange[1]
- a, b = np.min(values), np.max(values)
-
- return c + ((d - c) / (b - a)) * (values - a)
-
-
-class Topology(ABC):
- """
- An abstract base class that represents the topology of a Self-Organizing Map (SOM). The topology determines
- the arrangement of neurons in the SOM.
- """
- def __init__(self, gridSize: tuple[int, int], numFeatures: int):
- self.name = self.__class__.__name__
- self.stepCorrection = 0
-
- # basic parameters of the topology
- self.gridSize = list(gridSize)
- self.gridIndices = [] # used for visualizing the map as a scatter plot
- self.numFeatures = numFeatures
- self.numNeurons = np.prod(gridSize)
-
- # neighborhood relations
- self.neighborIndices = []
- self.neuronIndices = []
-
- # weights and neuron init
- self.weights = np.zeros((self.numNeurons, self.numFeatures)) # weights are in a flat list
- self.genIndices() # generating indices, used to find neighborhood relations
-
- # these arrays will be used assessing the topology
- self._movementCounts = np.zeros((self.numNeurons), dtype=int)
- self._directionCounts = np.zeros((self.numNeurons, self.numFeatures))
- self._travelDistance = np.zeros((self.numNeurons, self.numFeatures))
- self._minimalDistances = np.zeros((self.numNeurons, self.numFeatures))
-
- def initWeights(self, data: DataLoader | np.ndarray) -> None:
- """
- Initialize the weights of the neurons in the SOM using the given data.
- """
- # checking if data is given as numpy array or as a dataloader
- if isinstance(data, DataLoader):
- minValues = np.min(data.dataSet, axis=0)
- maxValues = np.max(data.dataSet, axis=0)
- else:
- minValues = np.min(data, axis=0)
- maxValues = np.max(data, axis=0)
-
- # Initialize weights within the range for each feature
- for i in range(self.numFeatures):
- self.weights[:, i] = np.random.uniform(minValues[i], maxValues[i], self.numNeurons)
-
- @property
- def weightMatrix(self) -> np.ndarray:
- """
- generates the weight matrix from neurons in order to plot it
- """
- return np.linalg.norm(self.weights,axis=-1).reshape(*self.gridSize)
-
- @property
- def uMatrix(self) -> np.ndarray:
- """
- generates the u-matrix from neurons in order to plot it
- """
- matrix = np.zeros(self.numNeurons)
- for index, weight in enumerate(self.weights):
- distances = np.linalg.norm(weight - self.weights[self.getNeighbors(index)], axis=-1)
- matrix[index] = np.mean(distances)
-
- return matrix.reshape(*self.gridSize)
-
- def __getitem__(self, index: int | ArrayLike) -> np.ndarray:
- """
- this allows getting values directly from the topology
- """
- if isinstance(index, (int | np.integer)):
- return self.weights[index]
- index = np.array(index)
-
- # here we check if an index is 2d and ravel it to match the shape of the topology
- if len(index.shape) == 2:
- index = np.ravel_multi_index(index, self.gridSize)
- return self.weights[index]
-
- def __setitem__(self, index: int | ArrayLike, value) -> None:
- """
- this allows setting values directly from the topology
- """
- if isinstance(index, int | np.integer):
- self.weights[index] = np.array(value)
- return
- index = np.array(index)
-
- # here we check if an index is 2d and ravel it to match the shape of the topology
- if len(index.shape) == 2:
- index = np.ravel_multi_index(index, self.gridSize)
- self.weights[index] = np.array(value)
-
- def __iter__(self):
- """
- this allows 'for ... in Topology'
- """
- return iter(self.weights)
-
- @abstractmethod
- def genIndices(self) -> None:
- raise NotImplementedError('this has not been implemented')
-
- def getNeighbors(self, index) -> np.ndarray:
- """
- Get the indices of the neurons that are neighbors to the neuron at the given index.
- """
-
- # checking if index is a single number
- if isinstance(index, (int, np.integer)):
- index = np.array([index])
-
- # broadcasting indices arrays
- tiledIndices = np.tile(self.neuronIndices, len(index))
- repeatedIndex = np.repeat(index, len(self.neuronIndices))
-
- # finding the equal values
- bools = (tiledIndices == repeatedIndex).reshape(len(index), len(self.neuronIndices))
- return self.neighborIndices[np.any(bools, axis=0)]
-
- def countMovements(self, bestMatches, neighbors, directions, neighborDirection) -> None:
- """
- Update the internal counts related to the movements and directions of the neurons.
- """
- np.add.at(self._movementCounts, bestMatches, 1)
- np.add.at(self._movementCounts, neighbors, 1)
-
- self._directionCounts[bestMatches] += np.sign(directions).astype('int')
- self._directionCounts[neighbors] += np.sign(neighborDirection).astype('int')
-
- #self._travelDistance[bestMatches] += directions
- #self._travelDistance[neighbors] += neighborDirection
-
- def grow(self):
- """
- Grow the topology of the SOM based on the internal counts of movements and directions.
- The growth happens in the direction where there is maximum movement and direction.
-
- This is still very much a toy/proof of concept model and needs lots of work
- """
-
- # storing current size/shape of the topology, these are lists because,
- # python allows jagged/ragged lists, while numpy doesn't, this makes
- # growing/inserting new neurons easier
- weightsShape = list((*self.gridSize, self.numFeatures))
- movementsShape = list(self.gridSize)
-
- # squeezing the movement counts into the range [0,1]
- movements = mapTo(self._movementCounts)
-
- # counting the directional changes
- directions = np.sum(abs(self._directionCounts),axis=1)
- directions = abs(mapTo(directions, [-1,0]))
-
- # bringing both together, the idea is that, if a neurons have high movement count,
- # but low directional change, these values should be low, I call it jittering... not an official term
- one, two = np.argpartition(movements + directions, -2)[-2:]
-
- # unraveling movement+direction counts
- x1, y1 = np.unravel_index(one, shape=(self.gridSize))
- x2, y2 = np.unravel_index(two, shape=(self.gridSize))
-
- # finding the biggest changes
- xDiff = abs(x1 - x2)
- yDiff = abs(y1 - y2)
-
- # adjusting new size of the topology
- if xDiff > yDiff == 0:
- weightsShape[1] += 1
- movementsShape[1] += 1
- else:
- weightsShape[0] += 1
- movementsShape[0] += 1
-
- # creating new arrays for metrics and weights
- newMovements = np.zeros(movementsShape, dtype=int)
- newWeights = np.zeros(weightsShape)
- newDirections = np.zeros(weightsShape, dtype=int)
-
- if xDiff > yDiff == 0:
- # adding neurons in the x-direction
- for i in range(newMovements.shape[0]):
- weightLine = self.weights.reshape(*self.gridSize, self.numFeatures)[i]
- movementLine = self._movementCounts.reshape(*self.gridSize)[i]
- directionLine = self._directionCounts.reshape(*self.gridSize, self.numFeatures)[i]
-
- mappedMovements = mapTo(movementLine)
- mappedDirections = abs(mapTo(np.sum(abs(directionLine)), [-1,0]))
-
- # finding the neuron with biggest jittering
- maxIndex = np.argmax(mappedMovements + mappedDirections)
-
- # checking if maxindex is at the edge
- if 0 < maxIndex < len(movementLine) - 1:
- left = movementLine[maxIndex - 1]
- right = movementLine[maxIndex + 1]
- if left > right:
- maxIndex -= 1
-
- # checking if maxindex is at the edge
- if maxIndex == 0:
- maxIndex += 1
-
- # inserting newest neuron
- newMovementLine = np.insert(movementLine, maxIndex, 0)
- value = (weightLine[maxIndex-1] + weightLine[maxIndex])/2
- newWeightLine = np.insert(weightLine, maxIndex, value).reshape(-1,self.numFeatures)
- newDirectionLine = np.insert(directionLine, maxIndex, np.zeros(self.numFeatures,dtype=int)).reshape(-1,self.numFeatures)
-
- newMovements[i] = newMovementLine
- newWeights[i] = newWeightLine
- newDirections[i] = newDirectionLine
- else:
- # adding neurons in the y-direction
- for i in range(newMovements.shape[1]):
- weightLine = self.weights.reshape(*self.gridSize, self.numFeatures)[:,i]
- movementLine = self._movementCounts.reshape(*self.gridSize)[:,i]
- directionLine = self._directionCounts.reshape(*self.gridSize, self.numFeatures)[:,i]
-
- mappedMovements = mapTo(movementLine)
- mappedDirections = abs(mapTo(np.sum(abs(directionLine)), [-1,0]))
-
- # finding the neuron with biggest jittering
- maxIndex = np.argmax(mappedMovements + mappedDirections)
-
- # checking if maxindex is at the edge
- if 0 < maxIndex < len(movementLine) - 1:
- left = movementLine[maxIndex - 1]
- right = movementLine[maxIndex + 1]
- if left > right:
- maxIndex -= 1
-
- # checking if maxindex is at the edge
- if maxIndex == 0:
- maxIndex += 1
-
- # inserting newest neuron
- newMovementLine = np.insert(movementLine, maxIndex, 0)
- value = (weightLine[maxIndex-1] + weightLine[maxIndex])/2
- newWeightLine = np.insert(weightLine, maxIndex, value).reshape(-1,self.numFeatures)
- newDirectionLine = np.insert(directionLine, maxIndex, np.zeros(self.numFeatures,dtype=int)).reshape(-1,self.numFeatures)
-
- newMovements[:,i] = newMovementLine
- newWeights[:,i] = newWeightLine
- newDirections[:,i] = newDirectionLine
-
- # overwriting old arrays with grown arrays
- self.gridSize = movementsShape
- self.numNeurons = np.prod(movementsShape)
- self._movementCounts = newMovements.reshape(self.numNeurons)
- self.weights = newWeights.reshape(self.numNeurons, self.numFeatures)
- self._directionCounts = newDirections.reshape(self.numNeurons, self.numFeatures)
-
- # updating grid indices after growing the topology
- self.genIndices()
-
-
-class Rectangular(Topology):
- """
- A class used to represent a Rectangular topology of a Self Organizing Map (SOM).
- """
- def __init__(self, gridSize: tuple[int, int], numFeatures: int):
- super().__init__(gridSize, numFeatures)
-
- def genIndices(self) -> None:
- """
- Generates the indices for neurons and their respective neighbors in the rectangular grid.
- """
- self.gridIndices = []
- self.neighborIndices = []
- self.neuronIndices = []
-
- # Link the Neurons together in a grid pattern
- for index in range(self.numNeurons):
- position = [index // self.gridSize[0], index % self.gridSize[1]]
- self.gridIndices.append(position)
-
- neighbors = []
- if index >= self.gridSize[1]:
- neighbors.append(index-self.gridSize[1]) # up
- if index < self.numNeurons - self.gridSize[1]:
- neighbors.append(index+self.gridSize[1]) # down
- if index % self.gridSize[1] != 0:
- neighbors.append(index-1) # left
- if (index+1) % self.gridSize[1] != 0:
- neighbors.append(index+1) # right
-
- self.neighborIndices.extend(neighbors)
- self.neuronIndices.extend([index] * len(neighbors))
-
- self.gridIndices = np.array(self.gridIndices)
- self.neighborIndices = np.array(self.neighborIndices)
- self.neuronIndices = np.array(self.neuronIndices)
-
-
-class Hexagonal(Topology):
- """
- A class used to represent a Hexagonal topology of a Self Organizing Map (SOM).
- """
- def __init__(self, gridSize: tuple[int, int], numFeatures: int):
- super().__init__(gridSize, numFeatures)
- self.stepCorrection = 0.5
-
- def genIndices(self) -> None:
- """
- Generates the indices for neurons and their respective neighbors in the hexagonal grid.
- """
- self.gridIndices = []
- self.neighborIndices = []
- self.neuronIndices = []
-
- # Link the Neurons together in a hexagonal pattern
- for index in range(self.numNeurons):
- position = [index // self.gridSize[0], index % self.gridSize[1]]
- if position[0] % 2 == 0:
- position[1] += 0.5
- self.gridIndices.append(position)
-
- neighbors = []
- if index >= self.gridSize[1]:
- neighbors.append(index-self.gridSize[1]) # up
- if index < self.numNeurons - self.gridSize[1]:
- neighbors.append(index+self.gridSize[1]) # down
- if index % self.gridSize[1] != 0:
- neighbors.append(index-1) # left
- if (index+1) % self.gridSize[1] != 0:
- neighbors.append(index+1) # right
-
- # diagonal neighbors (based on even or odd row)
- row = index // self.gridSize[1]
- if row % 2 == 0: # even rows
- if index >= self.gridSize[1] and (index+1) % self.gridSize[1] != 0:
- neighbors.append(index-self.gridSize[1]+1) # upper right
- if index < self.numNeurons - self.gridSize[1] and (index+1) % self.gridSize[1] != 0:
- neighbors.append(index+self.gridSize[1]+1) # lower right
- else: # odd rows
- if index >= self.gridSize[1] and index % self.gridSize[1] != 0:
- neighbors.append(index-self.gridSize[1]-1) # upper left
- if index < self.numNeurons - self.gridSize[1] and index % self.gridSize[1] != 0:
- neighbors.append(index+self.gridSize[1]-1) # lower left
-
- self.neighborIndices.extend(neighbors)
- self.neuronIndices.extend([index] * len(neighbors))
-
- self.gridIndices = np.array(self.gridIndices)
- self.neighborIndices = np.array(self.neighborIndices)
\ No newline at end of file
diff --git a/tree-test.ipynb b/tree-test.ipynb
index bc16db9..be929b9 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.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: 3 <= 2.81, samples: 96000\n"," ├─feat: 3 <= 2.00, samples: 48527\n"," │ ├─feat: 2 <= 2.32, samples: 46927\n"," │ │ ├─feat: 3 <= 1.35, samples: 46411\n"," │ │ │ └─╴value: 0.0\n"," │ │ │ └─╴value: 0.0\n"," │ │ └─╴feat: 3 <= 1.10, samples: 516\n"," │ │ └─╴value: 0.0\n"," │ │ └─╴value: 0.0\n"," │ └─╴feat: 2 <= 1.45, samples: 1600\n"," │ ├─feat: 4 <= 1.10, samples: 1020\n"," │ │ └─╴value: 0.0\n"," │ │ └─╴value: 0.0\n"," │ └─╴feat: 2 <= 2.12, samples: 580\n"," │ └─╴value: 1.0\n"," │ └─╴value: 1.0\n"," └─╴feat: 3 <= 3.50, samples: 47473\n"," ├─feat: 2 <= 0.48, samples: 2609\n"," │ ├─feat: 2 <= -0.15, samples: 144\n"," │ │ └─╴value: 0.0\n"," │ │ └─╴value: 1.0\n"," │ └─╴feat: 2 <= 2.00, samples: 2465\n"," │ └─╴value: 1.0\n"," │ └─╴value: 1.0\n"," └─╴feat: 3 <= 3.90, samples: 44864\n"," ├─feat: 2 <= 1.14, samples: 3452\n"," │ └─╴value: 1.0\n"," │ └─╴value: 1.0\n"," └─╴feat: 2 <= 0.20, samples: 41412\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 15950 50 \n"," 49% 0% \n","····································\n"," Class 1 58 15942 \n"," 0% 49% \n","\n","———————————————————————————————— scores ———————————————————————————————\n"," accuracy precision sensitivity miss rate \n","·······································································\n"," Class 0 0.997 0.996 0.997 0.003 \n"," Class 1 0.997 0.997 0.996 0.004 \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: 3 <= 2.81, samples: 96000\n"," ├─feat: 3 <= 2.00, samples: 48527\n"," │ ├─feat: 2 <= 2.32, samples: 46927\n"," │ │ ├─feat: 3 <= 1.35, samples: 46411\n"," │ │ │ └─╴value: 0.0\n"," │ │ │ └─╴value: 0.0\n"," │ │ └─╴feat: 3 <= 1.10, samples: 516\n"," │ │ └─╴value: 0.0\n"," │ │ └─╴value: 0.0\n"," │ └─╴feat: 2 <= 1.45, samples: 1600\n"," │ ├─feat: 4 <= 1.10, samples: 1020\n"," │ │ └─╴value: 0.0\n"," │ │ └─╴value: 0.0\n"," │ └─╴feat: 2 <= 2.12, samples: 580\n"," │ └─╴value: 1.0\n"," │ └─╴value: 1.0\n"," └─╴feat: 3 <= 3.50, samples: 47473\n"," ├─feat: 2 <= 0.48, samples: 2609\n"," │ ├─feat: 2 <= -0.15, samples: 144\n"," │ │ └─╴value: 0.0\n"," │ │ └─╴value: 1.0\n"," │ └─╴feat: 2 <= 2.00, samples: 2465\n"," │ └─╴value: 1.0\n"," │ └─╴value: 1.0\n"," └─╴feat: 3 <= 3.90, samples: 44864\n"," ├─feat: 2 <= 1.14, samples: 3452\n"," │ └─╴value: 1.0\n"," │ └─╴value: 1.0\n"," └─╴feat: 2 <= 0.20, samples: 41412\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 15950 50 \n"," 49% 0% \n","····································\n"," Class 1 58 15942 \n"," 0% 49% \n","\n","———————————————————————————————— scores ———————————————————————————————\n"," accuracy precision sensitivity miss rate \n","·······································································\n"," Class 0 0.997 0.996 0.997 0.003 \n"," Class 1 0.997 0.997 0.996 0.004 \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, AnomalyDetection,\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":"code","execution_count":3,"metadata":{},"outputs":[],"source":["tree = DecisionTree(maxDepth=5, minSamplesSplit=12)\n","tree.setComponent(RSA())\n","tree.setComponent(ODD())\n","tree.setComponent(AnomalyDetection())"]},{"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":4,"metadata":{"trusted":false},"outputs":[{"name":"stdout","output_type":"stream","text":["tree 1 |⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿| done ✔ | 28%\n","—————————————————————————— tree: 1/1 ——————————————————————————\n","split: RSA, impurity: ODD, leaf: AnomalyDetection, nodes: 19\n","maxDepth: 5, reached depth: 5, minSamplesSplit: 12\n","·······························································\n","â•´feat: 6 <= -2.99, samples: 96000\n"," ├─feat: 0 <= 2.69, samples: 140\n"," │ └─╴value: 0.0\n"," │ └─╴value: 1.0\n"," └─╴feat: 6 <= -2.10, samples: 95860\n"," ├─feat: 2 <= -0.94, samples: 1609\n"," │ ├─feat: 5 <= 3.07, samples: 283\n"," │ │ └─╴value: 1.0\n"," │ │ └─╴value: 1.0\n"," │ └─╴feat: 6 <= -2.85, samples: 1326\n"," │ └─╴value: 0.0\n"," │ └─╴value: 1.0\n"," └─╴feat: 2 <= -3.31, samples: 94251\n"," ├─feat: 3 <= -1.47, samples: 44\n"," │ └─╴value: 1.0\n"," │ └─╴value: 1.0\n"," └─╴feat: 5 <= 0.55, samples: 94207\n"," └─╴value: 0.0\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":5,"metadata":{},"outputs":[{"ename":"AttributeError","evalue":"Node does not have attribute 'level'","output_type":"error","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)","Cell \u001b[0;32mIn[5], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m prediction \u001b[38;5;241m=\u001b[39m \u001b[43mtree\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43meval\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalidSet\u001b[49m\u001b[43m)\u001b[49m\n","File \u001b[0;32m~/Documents/neural network/machineLearning/rf/decisionTree.py:281\u001b[0m, in \u001b[0;36mDecisionTree.eval\u001b[0;34m(self, data)\u001b[0m\n\u001b[1;32m 278\u001b[0m \u001b[38;5;66;03m# iterating over raw predictions\u001b[39;00m\n\u001b[1;32m 279\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m ThreadPoolExecutor(max_workers\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m) \u001b[38;5;28;01mas\u001b[39;00m executor:\n\u001b[1;32m 280\u001b[0m \u001b[38;5;66;03m# Assuming 'rawPredictions' is a list of np.ndarray objects and you don't have 'nodes' yet.\u001b[39;00m\n\u001b[0;32m--> 281\u001b[0m predictions \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mlist\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mexecutor\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmap\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_leafFunction\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrawPredictions\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 282\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 283\u001b[0m predictions \u001b[38;5;241m=\u001b[39m [\u001b[38;5;241m0\u001b[39m] \u001b[38;5;241m*\u001b[39m \u001b[38;5;28mlen\u001b[39m(data)\n","File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/concurrent/futures/_base.py:619\u001b[0m, in \u001b[0;36mExecutor.map.<locals>.result_iterator\u001b[0;34m()\u001b[0m\n\u001b[1;32m 616\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m fs:\n\u001b[1;32m 617\u001b[0m \u001b[38;5;66;03m# Careful not to keep a reference to the popped future\u001b[39;00m\n\u001b[1;32m 618\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m timeout \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 619\u001b[0m \u001b[38;5;28;01myield\u001b[39;00m \u001b[43m_result_or_cancel\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpop\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 620\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 621\u001b[0m \u001b[38;5;28;01myield\u001b[39;00m _result_or_cancel(fs\u001b[38;5;241m.\u001b[39mpop(), end_time \u001b[38;5;241m-\u001b[39m time\u001b[38;5;241m.\u001b[39mmonotonic())\n","File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/concurrent/futures/_base.py:317\u001b[0m, in \u001b[0;36m_result_or_cancel\u001b[0;34m(***failed resolving arguments***)\u001b[0m\n\u001b[1;32m 315\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 316\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 317\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfut\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mresult\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtimeout\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 318\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m 319\u001b[0m fut\u001b[38;5;241m.\u001b[39mcancel()\n","File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/concurrent/futures/_base.py:449\u001b[0m, in \u001b[0;36mFuture.result\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m 447\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m CancelledError()\n\u001b[1;32m 448\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_state \u001b[38;5;241m==\u001b[39m FINISHED:\n\u001b[0;32m--> 449\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m__get_result\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 451\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_condition\u001b[38;5;241m.\u001b[39mwait(timeout)\n\u001b[1;32m 453\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_state \u001b[38;5;129;01min\u001b[39;00m [CANCELLED, CANCELLED_AND_NOTIFIED]:\n","File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/concurrent/futures/_base.py:401\u001b[0m, in \u001b[0;36mFuture.__get_result\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 399\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_exception:\n\u001b[1;32m 400\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 401\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_exception\n\u001b[1;32m 402\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m 403\u001b[0m \u001b[38;5;66;03m# Break a reference cycle with the exception in self._exception\u001b[39;00m\n\u001b[1;32m 404\u001b[0m \u001b[38;5;28mself\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n","File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/concurrent/futures/thread.py:58\u001b[0m, in \u001b[0;36m_WorkItem.run\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 55\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m\n\u001b[1;32m 57\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m---> 58\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 59\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mBaseException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m exc:\n\u001b[1;32m 60\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfuture\u001b[38;5;241m.\u001b[39mset_exception(exc)\n","File \u001b[0;32m~/Documents/neural network/machineLearning/rf/leafFunction.py:142\u001b[0m, in \u001b[0;36mAnomalyDetection.__call__\u001b[0;34m(self, node)\u001b[0m\n\u001b[1;32m 141\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__call__\u001b[39m(\u001b[38;5;28mself\u001b[39m, node: Node \u001b[38;5;241m|\u001b[39m np\u001b[38;5;241m.\u001b[39mndarray) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m np\u001b[38;5;241m.\u001b[39mndarray:\n\u001b[0;32m--> 142\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_leafFunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnode\u001b[49m\u001b[43m)\u001b[49m\n","File \u001b[0;32m~/Documents/neural network/machineLearning/rf/leafFunction.py:150\u001b[0m, in \u001b[0;36mAnomalyDetection._leafFunc\u001b[0;34m(self, node)\u001b[0m\n\u001b[1;32m 148\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m np\u001b[38;5;241m.\u001b[39marray([\u001b[38;5;28mgetattr\u001b[39m(node, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstrategy)])\n\u001b[1;32m 149\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 150\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mAttributeError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNode does not have attribute \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstrategy\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n","\u001b[0;31mAttributeError\u001b[0m: Node does not have attribute 'level'"]}],"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 15950 50 \n"," 49% 0% \n","····································\n"," Class 1 58 15942 \n"," 0% 49% \n","\n","———————————————————————————————— scores ———————————————————————————————\n"," accuracy precision sensitivity miss rate \n","·······································································\n"," Class 0 0.997 0.996 0.997 0.003 \n"," Class 1 0.997 0.997 0.996 0.004 \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: 3 <= 2.81, samples: 96000\n"," ├─feat: 3 <= 2.00, samples: 48527\n"," │ ├─feat: 2 <= 2.32, samples: 46927\n"," │ │ ├─feat: 3 <= 1.35, samples: 46411\n"," │ │ │ └─╴value: 0.0\n"," │ │ │ └─╴value: 0.0\n"," │ │ └─╴feat: 3 <= 1.10, samples: 516\n"," │ │ └─╴value: 0.0\n"," │ │ └─╴value: 0.0\n"," │ └─╴feat: 2 <= 1.45, samples: 1600\n"," │ ├─feat: 4 <= 1.10, samples: 1020\n"," │ │ └─╴value: 0.0\n"," │ │ └─╴value: 0.0\n"," │ └─╴feat: 2 <= 2.12, samples: 580\n"," │ └─╴value: 1.0\n"," │ └─╴value: 1.0\n"," └─╴feat: 3 <= 3.50, samples: 47473\n"," ├─feat: 2 <= 0.48, samples: 2609\n"," │ ├─feat: 2 <= -0.15, samples: 144\n"," │ │ └─╴value: 0.0\n"," │ │ └─╴value: 1.0\n"," │ └─╴feat: 2 <= 2.00, samples: 2465\n"," │ └─╴value: 1.0\n"," │ └─╴value: 1.0\n"," └─╴feat: 3 <= 3.90, samples: 44864\n"," ├─feat: 2 <= 1.14, samples: 3452\n"," │ └─╴value: 1.0\n"," │ └─╴value: 1.0\n"," └─╴feat: 2 <= 0.20, samples: 41412\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 15950 50 \n"," 49% 0% \n","····································\n"," Class 1 58 15942 \n"," 0% 49% \n","\n","———————————————————————————————— scores ———————————————————————————————\n"," accuracy precision sensitivity miss rate \n","·······································································\n"," Class 0 0.997 0.996 0.997 0.003 \n"," Class 1 0.997 0.997 0.996 0.004 \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 99be817..129bc14 100644
--- a/tree-test.json
+++ b/tree-test.json
@@ -1,16 +1,16 @@
{
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