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1.0rc1
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f438124057 |
@@ -20,15 +20,13 @@ pip install git+https://github.com/doctorado-ml/stree
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- [](https://mybinder.org/v2/gh/Doctorado-ML/STree/master?urlpath=lab/tree/notebooks/benchmark.ipynb) Benchmark
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- [](https://mybinder.org/v2/gh/Doctorado-ML/STree/master?urlpath=lab/tree/notebooks/benchmark.ipynb) Benchmark
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- [](https://colab.research.google.com/github/Doctorado-ML/STree/blob/master/notebooks/benchmark.ipynb) Benchmark
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- [](https://colab.research.google.com/github/Doctorado-ML/STree/blob/master/notebooks/benchmark.ipynb) Benchmark
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- [](https://colab.research.google.com/github/Doctorado-ML/STree/blob/master/notebooks/features.ipynb) Test features
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- [](https://colab.research.google.com/github/Doctorado-ML/STree/blob/master/notebooks/features.ipynb) Some features
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- [](https://colab.research.google.com/github/Doctorado-ML/STree/blob/master/notebooks/adaboost.ipynb) Adaboost
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- [](https://colab.research.google.com/github/Doctorado-ML/STree/blob/master/notebooks/gridsearch.ipynb) Gridsearch
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- [](https://colab.research.google.com/github/Doctorado-ML/STree/blob/master/notebooks/gridsearch.ipynb) Gridsearch
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- [](https://colab.research.google.com/github/Doctorado-ML/STree/blob/master/notebooks/test_graphs.ipynb) Test Graphics
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- [](https://colab.research.google.com/github/Doctorado-ML/STree/blob/master/notebooks/ensemble.ipynb) Ensembles
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## Hyperparameters
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## Hyperparameters
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@@ -17,23 +17,25 @@
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},
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},
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{
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{
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"cell_type": "code",
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"cell_type": "code",
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"execution_count": 1,
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"execution_count": null,
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"metadata": {},
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"metadata": {},
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"outputs": [],
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"outputs": [],
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"source": [
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"source": [
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"#\n",
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"#\n",
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"# Google Colab setup\n",
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"# Google Colab setup\n",
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"#\n",
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"#\n",
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"#!pip install git+https://github.com/doctorado-ml/stree"
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"#!pip install git+https://github.com/doctorado-ml/stree\n",
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"!pip install pandas"
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]
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]
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},
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},
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{
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{
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"cell_type": "code",
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"cell_type": "code",
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"execution_count": 2,
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"execution_count": null,
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"metadata": {},
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"metadata": {},
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"outputs": [],
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"outputs": [],
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"source": [
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"source": [
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"import datetime, time\n",
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"import datetime, time\n",
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"import os\n",
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"import numpy as np\n",
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"import numpy as np\n",
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"import pandas as pd\n",
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"import pandas as pd\n",
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"from sklearn.model_selection import train_test_split\n",
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"from sklearn.model_selection import train_test_split\n",
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@@ -47,11 +49,10 @@
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},
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},
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{
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{
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"cell_type": "code",
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"cell_type": "code",
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"execution_count": 3,
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"execution_count": null,
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"metadata": {},
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"metadata": {},
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"outputs": [],
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"outputs": [],
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"source": [
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"source": [
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"import os\n",
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"if not os.path.isfile('data/creditcard.csv'):\n",
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"if not os.path.isfile('data/creditcard.csv'):\n",
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" !wget --no-check-certificate --content-disposition http://nube.jccm.es/index.php/s/Zs7SYtZQJ3RQ2H2/download\n",
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" !wget --no-check-certificate --content-disposition http://nube.jccm.es/index.php/s/Zs7SYtZQJ3RQ2H2/download\n",
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" !tar xzf creditcard.tgz"
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" !tar xzf creditcard.tgz"
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@@ -66,19 +67,11 @@
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},
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},
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{
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{
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"cell_type": "code",
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"cell_type": "code",
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"execution_count": 4,
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"execution_count": null,
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"metadata": {
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"metadata": {
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"tags": []
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"tags": []
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},
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},
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"outputs": [
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"outputs": [],
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"2021-01-14 11:30:51\n"
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]
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}
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],
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"source": [
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"source": [
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"print(datetime.date.today(), time.strftime(\"%H:%M:%S\"))"
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"print(datetime.date.today(), time.strftime(\"%H:%M:%S\"))"
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]
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]
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},
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},
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{
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{
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"cell_type": "code",
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"cell_type": "code",
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"execution_count": 5,
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"execution_count": null,
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"metadata": {},
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"metadata": {},
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"outputs": [],
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"outputs": [],
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"source": [
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"source": [
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},
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},
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{
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{
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"cell_type": "code",
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"cell_type": "code",
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"execution_count": 6,
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"execution_count": null,
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"metadata": {
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"metadata": {
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"tags": []
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"tags": []
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},
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},
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"outputs": [
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"outputs": [],
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Fraud: 0.173% 492\n",
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"Valid: 99.827% 284,315\n"
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]
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}
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],
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"source": [
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"source": [
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"print(\"Fraud: {0:.3f}% {1}\".format(df.Class[df.Class == 1].count()*100/df.shape[0], df.Class[df.Class == 1].count()))\n",
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"print(\"Fraud: {0:.3f}% {1}\".format(df.Class[df.Class == 1].count()*100/df.shape[0], df.Class[df.Class == 1].count()))\n",
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"print(\"Valid: {0:.3f}% {1:,}\".format(df.Class[df.Class == 0].count()*100/df.shape[0], df.Class[df.Class == 0].count()))"
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"print(\"Valid: {0:.3f}% {1:,}\".format(df.Class[df.Class == 0].count()*100/df.shape[0], df.Class[df.Class == 0].count()))"
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@@ -125,7 +109,7 @@
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},
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},
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{
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{
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"cell_type": "code",
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"cell_type": "code",
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"execution_count": 7,
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"execution_count": null,
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"metadata": {},
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"metadata": {},
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"outputs": [],
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"outputs": [],
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"source": [
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"source": [
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},
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},
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{
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"cell_type": "code",
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"cell_type": "code",
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"execution_count": 8,
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"execution_count": null,
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"metadata": {
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"metadata": {
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"tags": []
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"tags": []
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},
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"outputs": [
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"outputs": [],
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"X shape: (284807, 29)\n",
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"y shape: (284807,)\n"
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]
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}
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],
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"source": [
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"source": [
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"# Remove unneeded features\n",
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"# Remove unneeded features\n",
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"y = df.Class.values\n",
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"y = df.Class.values\n",
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},
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"execution_count": null,
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"metadata": {},
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"metadata": {},
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"outputs": [],
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"outputs": [],
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"source": [
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"source": [
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},
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},
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{
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{
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {},
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"metadata": {},
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"outputs": [],
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"outputs": [],
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"source": [
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},
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"metadata": {},
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"metadata": {},
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"outputs": [],
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"outputs": [],
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"source": [
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},
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},
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{
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"cell_type": "code",
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"metadata": {},
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"outputs": [],
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{
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"outputs": [],
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},
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{
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"cell_type": "code",
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"outputs": [],
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{
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"cell_type": "code",
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"outputs": [],
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},
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{
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"cell_type": "code",
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"execution_count": 16,
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"execution_count": null,
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"metadata": {
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"metadata": {
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"tags": []
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"tags": []
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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||||||
"************************** Linear Tree **********************\n",
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"Train Model Linear Tree took: 10.25 seconds\n",
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"=========== Linear Tree - Train 199,364 samples =============\n",
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" precision recall f1-score support\n",
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"\n",
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" 0 1.000000 1.000000 1.000000 199020\n",
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" 1 1.000000 1.000000 1.000000 344\n",
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"\n",
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" accuracy 1.000000 199364\n",
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" macro avg 1.000000 1.000000 1.000000 199364\n",
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"weighted avg 1.000000 1.000000 1.000000 199364\n",
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"\n",
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"=========== Linear Tree - Test 85,443 samples =============\n",
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" precision recall f1-score support\n",
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"\n",
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" 0 0.999578 0.999613 0.999596 85295\n",
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" 1 0.772414 0.756757 0.764505 148\n",
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"\n",
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" accuracy 0.999192 85443\n",
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" macro avg 0.885996 0.878185 0.882050 85443\n",
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"weighted avg 0.999184 0.999192 0.999188 85443\n",
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"\n",
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"Confusion Matrix in Train\n",
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"[[199020 0]\n",
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" [ 0 344]]\n",
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"Confusion Matrix in Test\n",
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"[[85262 33]\n",
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" [ 36 112]]\n",
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"************************** Naive Bayes **********************\n",
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"Train Model Naive Bayes took: 0.09943 seconds\n",
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"=========== Naive Bayes - Train 199,364 samples =============\n",
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" precision recall f1-score support\n",
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"\n",
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" 0 0.999692 0.978238 0.988849 199020\n",
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" 1 0.061538 0.825581 0.114539 344\n",
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"\n",
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" accuracy 0.977975 199364\n",
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" macro avg 0.530615 0.901910 0.551694 199364\n",
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"weighted avg 0.998073 0.977975 0.987340 199364\n",
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"\n",
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"=========== Naive Bayes - Test 85,443 samples =============\n",
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" precision recall f1-score support\n",
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"\n",
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" 0 0.999712 0.977994 0.988734 85295\n",
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" 1 0.061969 0.837838 0.115403 148\n",
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"\n",
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" accuracy 0.977751 85443\n",
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" macro avg 0.530841 0.907916 0.552068 85443\n",
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"weighted avg 0.998088 0.977751 0.987221 85443\n",
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"\n",
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"Confusion Matrix in Train\n",
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"[[194689 4331]\n",
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" [ 60 284]]\n",
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"Confusion Matrix in Test\n",
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"[[83418 1877]\n",
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" [ 24 124]]\n",
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||||||
"************************** Stree (SVM Tree) **********************\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/Users/rmontanana/.virtualenvs/general/lib/python3.8/site-packages/sklearn/svm/_base.py:976: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
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" warnings.warn(\"Liblinear failed to converge, increase \"\n",
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"/Users/rmontanana/.virtualenvs/general/lib/python3.8/site-packages/sklearn/svm/_base.py:976: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
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" warnings.warn(\"Liblinear failed to converge, increase \"\n",
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"/Users/rmontanana/.virtualenvs/general/lib/python3.8/site-packages/sklearn/svm/_base.py:976: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
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" warnings.warn(\"Liblinear failed to converge, increase \"\n"
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]
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},
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"output_type": "stream",
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"text": [
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"Train Model Stree (SVM Tree) took: 28.47 seconds\n",
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"=========== Stree (SVM Tree) - Train 199,364 samples =============\n",
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" precision recall f1-score support\n",
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"\n",
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" 0 0.999623 0.999864 0.999744 199020\n",
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" 1 0.908784 0.781977 0.840625 344\n",
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"\n",
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" accuracy 0.999488 199364\n",
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" macro avg 0.954204 0.890921 0.920184 199364\n",
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"weighted avg 0.999467 0.999488 0.999469 199364\n",
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"\n",
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"=========== Stree (SVM Tree) - Test 85,443 samples =============\n",
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" precision recall f1-score support\n",
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"\n",
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" 0 0.999637 0.999918 0.999777 85295\n",
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" 1 0.943548 0.790541 0.860294 148\n",
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"\n",
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" accuracy 0.999555 85443\n",
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" macro avg 0.971593 0.895229 0.930036 85443\n",
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"weighted avg 0.999540 0.999555 0.999536 85443\n",
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"\n",
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"Confusion Matrix in Train\n",
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"[[198993 27]\n",
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" [ 75 269]]\n",
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"Confusion Matrix in Test\n",
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"[[85288 7]\n",
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" [ 31 117]]\n",
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"************************** Neural Network **********************\n",
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"Train Model Neural Network took: 9.76 seconds\n",
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"=========== Neural Network - Train 199,364 samples =============\n",
|
|
||||||
" precision recall f1-score support\n",
|
|
||||||
"\n",
|
|
||||||
" 0 0.999247 0.999844 0.999545 199020\n",
|
|
||||||
" 1 0.862222 0.563953 0.681898 344\n",
|
|
||||||
"\n",
|
|
||||||
" accuracy 0.999092 199364\n",
|
|
||||||
" macro avg 0.930734 0.781899 0.840722 199364\n",
|
|
||||||
"weighted avg 0.999010 0.999092 0.998997 199364\n",
|
|
||||||
"\n",
|
|
||||||
"=========== Neural Network - Test 85,443 samples =============\n",
|
|
||||||
" precision recall f1-score support\n",
|
|
||||||
"\n",
|
|
||||||
" 0 0.999356 0.999871 0.999613 85295\n",
|
|
||||||
" 1 0.894231 0.628378 0.738095 148\n",
|
|
||||||
"\n",
|
|
||||||
" accuracy 0.999228 85443\n",
|
|
||||||
" macro avg 0.946793 0.814125 0.868854 85443\n",
|
|
||||||
"weighted avg 0.999173 0.999228 0.999160 85443\n",
|
|
||||||
"\n",
|
|
||||||
"Confusion Matrix in Train\n",
|
|
||||||
"[[198989 31]\n",
|
|
||||||
" [ 150 194]]\n",
|
|
||||||
"Confusion Matrix in Test\n",
|
|
||||||
"[[85284 11]\n",
|
|
||||||
" [ 55 93]]\n",
|
|
||||||
"************************** SVC (linear) **********************\n"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"name": "stderr",
|
|
||||||
"output_type": "stream",
|
|
||||||
"text": [
|
|
||||||
"/Users/rmontanana/.virtualenvs/general/lib/python3.8/site-packages/sklearn/svm/_base.py:976: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
|
|
||||||
" warnings.warn(\"Liblinear failed to converge, increase \"\n"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"name": "stdout",
|
|
||||||
"output_type": "stream",
|
|
||||||
"text": [
|
|
||||||
"Train Model SVC (linear) took: 8.207 seconds\n",
|
|
||||||
"=========== SVC (linear) - Train 199,364 samples =============\n",
|
|
||||||
" precision recall f1-score support\n",
|
|
||||||
"\n",
|
|
||||||
" 0 0.999237 0.999859 0.999548 199020\n",
|
|
||||||
" 1 0.872727 0.558140 0.680851 344\n",
|
|
||||||
"\n",
|
|
||||||
" accuracy 0.999097 199364\n",
|
|
||||||
" macro avg 0.935982 0.778999 0.840199 199364\n",
|
|
||||||
"weighted avg 0.999018 0.999097 0.998998 199364\n",
|
|
||||||
"\n",
|
|
||||||
"=========== SVC (linear) - Test 85,443 samples =============\n",
|
|
||||||
" precision recall f1-score support\n",
|
|
||||||
"\n",
|
|
||||||
" 0 0.999344 0.999894 0.999619 85295\n",
|
|
||||||
" 1 0.910891 0.621622 0.738956 148\n",
|
|
||||||
"\n",
|
|
||||||
" accuracy 0.999239 85443\n",
|
|
||||||
" macro avg 0.955117 0.810758 0.869287 85443\n",
|
|
||||||
"weighted avg 0.999191 0.999239 0.999168 85443\n",
|
|
||||||
"\n",
|
|
||||||
"Confusion Matrix in Train\n",
|
|
||||||
"[[198992 28]\n",
|
|
||||||
" [ 152 192]]\n",
|
|
||||||
"Confusion Matrix in Test\n",
|
|
||||||
"[[85286 9]\n",
|
|
||||||
" [ 56 92]]\n"
|
|
||||||
]
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"source": [
|
"source": [
|
||||||
"# Train & Test models\n",
|
"# Train & Test models\n",
|
||||||
"models = {\n",
|
"models = {\n",
|
||||||
" 'Linear Tree':linear_tree, 'Naive Bayes': naive_bayes, 'Stree (SVM Tree)': stree, \n",
|
" 'Linear Tree':linear_tree, 'Naive Bayes': naive_bayes, 'Stree ': stree, \n",
|
||||||
" 'Neural Network': mlp, 'SVC (linear)': svc\n",
|
" 'Neural Network': mlp, 'SVC (linear)': svc\n",
|
||||||
"}\n",
|
"}\n",
|
||||||
"\n",
|
"\n",
|
||||||
@@ -464,26 +260,11 @@
|
|||||||
},
|
},
|
||||||
{
|
{
|
||||||
"cell_type": "code",
|
"cell_type": "code",
|
||||||
"execution_count": 17,
|
"execution_count": null,
|
||||||
"metadata": {
|
"metadata": {
|
||||||
"tags": []
|
"tags": []
|
||||||
},
|
},
|
||||||
"outputs": [
|
"outputs": [],
|
||||||
{
|
|
||||||
"name": "stdout",
|
|
||||||
"output_type": "stream",
|
|
||||||
"text": [
|
|
||||||
"**************************************************************************************************************\n",
|
|
||||||
"*The best f1 model is Stree (SVM Tree), with a f1 score: 0.8603 in 28.4743 seconds with 0.7 samples in train dataset\n",
|
|
||||||
"**************************************************************************************************************\n",
|
|
||||||
"Model: Linear Tree\t Time: 10.25 seconds\t f1: 0.7645\n",
|
|
||||||
"Model: Naive Bayes\t Time: 0.10 seconds\t f1: 0.1154\n",
|
|
||||||
"Model: Stree (SVM Tree)\t Time: 28.47 seconds\t f1: 0.8603\n",
|
|
||||||
"Model: Neural Network\t Time: 9.76 seconds\t f1: 0.7381\n",
|
|
||||||
"Model: SVC (linear)\t Time: 8.21 seconds\t f1: 0.739\n"
|
|
||||||
]
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"source": [
|
"source": [
|
||||||
"print(\"*\"*110)\n",
|
"print(\"*\"*110)\n",
|
||||||
"print(f\"*The best f1 model is {best_model}, with a f1 score: {best_f1:.4} in {best_time:.6} seconds with {train_size:,} samples in train dataset\")\n",
|
"print(f\"*The best f1 model is {best_model}, with a f1 score: {best_f1:.4} in {best_time:.6} seconds with {train_size:,} samples in train dataset\")\n",
|
||||||
@@ -508,32 +289,9 @@
|
|||||||
},
|
},
|
||||||
{
|
{
|
||||||
"cell_type": "code",
|
"cell_type": "code",
|
||||||
"execution_count": 18,
|
"execution_count": null,
|
||||||
"metadata": {},
|
"metadata": {},
|
||||||
"outputs": [
|
"outputs": [],
|
||||||
{
|
|
||||||
"data": {
|
|
||||||
"text/plain": [
|
|
||||||
"{'C': 0.01,\n",
|
|
||||||
" 'criterion': 'entropy',\n",
|
|
||||||
" 'degree': 3,\n",
|
|
||||||
" 'gamma': 'scale',\n",
|
|
||||||
" 'kernel': 'linear',\n",
|
|
||||||
" 'max_depth': None,\n",
|
|
||||||
" 'max_features': None,\n",
|
|
||||||
" 'max_iter': 1000.0,\n",
|
|
||||||
" 'min_samples_split': 0,\n",
|
|
||||||
" 'random_state': 2020,\n",
|
|
||||||
" 'split_criteria': 'impurity',\n",
|
|
||||||
" 'splitter': 'random',\n",
|
|
||||||
" 'tol': 0.0001}"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
"execution_count": 18,
|
|
||||||
"metadata": {},
|
|
||||||
"output_type": "execute_result"
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"source": [
|
"source": [
|
||||||
"stree.get_params()"
|
"stree.get_params()"
|
||||||
]
|
]
|
||||||
@@ -556,7 +314,7 @@
|
|||||||
"name": "python",
|
"name": "python",
|
||||||
"nbconvert_exporter": "python",
|
"nbconvert_exporter": "python",
|
||||||
"pygments_lexer": "ipython3",
|
"pygments_lexer": "ipython3",
|
||||||
"version": "3.8.2"
|
"version": "3.8.2-final"
|
||||||
},
|
},
|
||||||
"toc": {
|
"toc": {
|
||||||
"base_numbering": 1,
|
"base_numbering": 1,
|
||||||
|
|||||||
@@ -17,38 +17,43 @@
|
|||||||
},
|
},
|
||||||
{
|
{
|
||||||
"cell_type": "code",
|
"cell_type": "code",
|
||||||
"execution_count": 1,
|
"execution_count": null,
|
||||||
"metadata": {},
|
"metadata": {},
|
||||||
"outputs": [],
|
"outputs": [],
|
||||||
"source": [
|
"source": [
|
||||||
"#\n",
|
"#\n",
|
||||||
"# Google Colab setup\n",
|
"# Google Colab setup\n",
|
||||||
"#\n",
|
"#\n",
|
||||||
"#!pip install git+https://github.com/doctorado-ml/stree"
|
"#!pip install git+https://github.com/doctorado-ml/stree\n",
|
||||||
|
"!pip install pandas"
|
||||||
]
|
]
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
"cell_type": "code",
|
"cell_type": "code",
|
||||||
"execution_count": 2,
|
"execution_count": null,
|
||||||
"metadata": {},
|
"metadata": {},
|
||||||
"outputs": [],
|
"outputs": [],
|
||||||
"source": [
|
"source": [
|
||||||
"import time\n",
|
"import time\n",
|
||||||
|
"import os\n",
|
||||||
|
"import random\n",
|
||||||
"import warnings\n",
|
"import warnings\n",
|
||||||
|
"import pandas as pd\n",
|
||||||
|
"import numpy as np\n",
|
||||||
"from sklearn.ensemble import AdaBoostClassifier, BaggingClassifier\n",
|
"from sklearn.ensemble import AdaBoostClassifier, BaggingClassifier\n",
|
||||||
"from sklearn.model_selection import train_test_split\n",
|
"from sklearn.model_selection import train_test_split\n",
|
||||||
"from sklearn.exceptions import ConvergenceWarning\n",
|
"from sklearn.exceptions import ConvergenceWarning\n",
|
||||||
"from stree import Stree\n",
|
"from stree import Stree\n",
|
||||||
|
"\n",
|
||||||
"warnings.filterwarnings(\"ignore\", category=ConvergenceWarning)"
|
"warnings.filterwarnings(\"ignore\", category=ConvergenceWarning)"
|
||||||
]
|
]
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
"cell_type": "code",
|
"cell_type": "code",
|
||||||
"execution_count": 3,
|
"execution_count": null,
|
||||||
"metadata": {},
|
"metadata": {},
|
||||||
"outputs": [],
|
"outputs": [],
|
||||||
"source": [
|
"source": [
|
||||||
"import os\n",
|
|
||||||
"if not os.path.isfile('data/creditcard.csv'):\n",
|
"if not os.path.isfile('data/creditcard.csv'):\n",
|
||||||
" !wget --no-check-certificate --content-disposition http://nube.jccm.es/index.php/s/Zs7SYtZQJ3RQ2H2/download\n",
|
" !wget --no-check-certificate --content-disposition http://nube.jccm.es/index.php/s/Zs7SYtZQJ3RQ2H2/download\n",
|
||||||
" !tar xzf creditcard.tgz"
|
" !tar xzf creditcard.tgz"
|
||||||
@@ -56,30 +61,15 @@
|
|||||||
},
|
},
|
||||||
{
|
{
|
||||||
"cell_type": "code",
|
"cell_type": "code",
|
||||||
"execution_count": 4,
|
"execution_count": null,
|
||||||
"metadata": {
|
"metadata": {
|
||||||
"tags": []
|
"tags": []
|
||||||
},
|
},
|
||||||
"outputs": [
|
"outputs": [],
|
||||||
{
|
|
||||||
"name": "stdout",
|
|
||||||
"output_type": "stream",
|
|
||||||
"text": [
|
|
||||||
"Fraud: 0.173% 492\n",
|
|
||||||
"Valid: 99.827% 284315\n",
|
|
||||||
"X.shape (100492, 28) y.shape (100492,)\n",
|
|
||||||
"Fraud: 0.651% 654\n",
|
|
||||||
"Valid: 99.349% 99838\n"
|
|
||||||
]
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"source": [
|
"source": [
|
||||||
"random_state=1\n",
|
"random_state=1\n",
|
||||||
"\n",
|
"\n",
|
||||||
"def load_creditcard(n_examples=0):\n",
|
"def load_creditcard(n_examples=0):\n",
|
||||||
" import pandas as pd\n",
|
|
||||||
" import numpy as np\n",
|
|
||||||
" import random\n",
|
|
||||||
" df = pd.read_csv('data/creditcard.csv')\n",
|
" df = pd.read_csv('data/creditcard.csv')\n",
|
||||||
" print(\"Fraud: {0:.3f}% {1}\".format(df.Class[df.Class == 1].count()*100/df.shape[0], df.Class[df.Class == 1].count()))\n",
|
" print(\"Fraud: {0:.3f}% {1}\".format(df.Class[df.Class == 1].count()*100/df.shape[0], df.Class[df.Class == 1].count()))\n",
|
||||||
" print(\"Valid: {0:.3f}% {1}\".format(df.Class[df.Class == 0].count()*100/df.shape[0], df.Class[df.Class == 0].count()))\n",
|
" print(\"Valid: {0:.3f}% {1}\".format(df.Class[df.Class == 0].count()*100/df.shape[0], df.Class[df.Class == 0].count()))\n",
|
||||||
@@ -130,21 +120,11 @@
|
|||||||
},
|
},
|
||||||
{
|
{
|
||||||
"cell_type": "code",
|
"cell_type": "code",
|
||||||
"execution_count": 5,
|
"execution_count": null,
|
||||||
"metadata": {
|
"metadata": {
|
||||||
"tags": []
|
"tags": []
|
||||||
},
|
},
|
||||||
"outputs": [
|
"outputs": [],
|
||||||
{
|
|
||||||
"name": "stdout",
|
|
||||||
"output_type": "stream",
|
|
||||||
"text": [
|
|
||||||
"Score Train: 0.9984504719663368\n",
|
|
||||||
"Score Test: 0.9983415151917209\n",
|
|
||||||
"Took 26.09 seconds\n"
|
|
||||||
]
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"source": [
|
"source": [
|
||||||
"now = time.time()\n",
|
"now = time.time()\n",
|
||||||
"clf = Stree(max_depth=3, random_state=random_state, max_iter=1e3)\n",
|
"clf = Stree(max_depth=3, random_state=random_state, max_iter=1e3)\n",
|
||||||
@@ -163,7 +143,7 @@
|
|||||||
},
|
},
|
||||||
{
|
{
|
||||||
"cell_type": "code",
|
"cell_type": "code",
|
||||||
"execution_count": 6,
|
"execution_count": null,
|
||||||
"metadata": {},
|
"metadata": {},
|
||||||
"outputs": [],
|
"outputs": [],
|
||||||
"source": [
|
"source": [
|
||||||
@@ -174,21 +154,11 @@
|
|||||||
},
|
},
|
||||||
{
|
{
|
||||||
"cell_type": "code",
|
"cell_type": "code",
|
||||||
"execution_count": 7,
|
"execution_count": null,
|
||||||
"metadata": {
|
"metadata": {
|
||||||
"tags": []
|
"tags": []
|
||||||
},
|
},
|
||||||
"outputs": [
|
"outputs": [],
|
||||||
{
|
|
||||||
"name": "stdout",
|
|
||||||
"output_type": "stream",
|
|
||||||
"text": [
|
|
||||||
"Kernel: linear\tTime: 43.49 seconds\tScore Train: 0.9980098\tScore Test: 0.9980762\n",
|
|
||||||
"Kernel: rbf\tTime: 8.86 seconds\tScore Train: 0.9934891\tScore Test: 0.9934987\n",
|
|
||||||
"Kernel: poly\tTime: 41.14 seconds\tScore Train: 0.9972279\tScore Test: 0.9973133\n"
|
|
||||||
]
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"source": [
|
"source": [
|
||||||
"for kernel in ['linear', 'rbf', 'poly']:\n",
|
"for kernel in ['linear', 'rbf', 'poly']:\n",
|
||||||
" now = time.time()\n",
|
" now = time.time()\n",
|
||||||
@@ -208,7 +178,7 @@
|
|||||||
},
|
},
|
||||||
{
|
{
|
||||||
"cell_type": "code",
|
"cell_type": "code",
|
||||||
"execution_count": 8,
|
"execution_count": null,
|
||||||
"metadata": {},
|
"metadata": {},
|
||||||
"outputs": [],
|
"outputs": [],
|
||||||
"source": [
|
"source": [
|
||||||
@@ -219,21 +189,11 @@
|
|||||||
},
|
},
|
||||||
{
|
{
|
||||||
"cell_type": "code",
|
"cell_type": "code",
|
||||||
"execution_count": 9,
|
"execution_count": null,
|
||||||
"metadata": {
|
"metadata": {
|
||||||
"tags": []
|
"tags": []
|
||||||
},
|
},
|
||||||
"outputs": [
|
"outputs": [],
|
||||||
{
|
|
||||||
"name": "stdout",
|
|
||||||
"output_type": "stream",
|
|
||||||
"text": [
|
|
||||||
"Kernel: linear\tTime: 187.51 seconds\tScore Train: 0.9984505\tScore Test: 0.9983083\n",
|
|
||||||
"Kernel: rbf\tTime: 73.65 seconds\tScore Train: 0.9993461\tScore Test: 0.9985074\n",
|
|
||||||
"Kernel: poly\tTime: 52.19 seconds\tScore Train: 0.9993461\tScore Test: 0.9987727\n"
|
|
||||||
]
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"source": [
|
"source": [
|
||||||
"for kernel in ['linear', 'rbf', 'poly']:\n",
|
"for kernel in ['linear', 'rbf', 'poly']:\n",
|
||||||
" now = time.time()\n",
|
" now = time.time()\n",
|
||||||
@@ -261,7 +221,7 @@
|
|||||||
"name": "python",
|
"name": "python",
|
||||||
"nbconvert_exporter": "python",
|
"nbconvert_exporter": "python",
|
||||||
"pygments_lexer": "ipython3",
|
"pygments_lexer": "ipython3",
|
||||||
"version": "3.8.2"
|
"version": "3.8.2-final"
|
||||||
}
|
}
|
||||||
},
|
},
|
||||||
"nbformat": 4,
|
"nbformat": 4,
|
||||||
|
|||||||
@@ -17,24 +17,27 @@
|
|||||||
},
|
},
|
||||||
{
|
{
|
||||||
"cell_type": "code",
|
"cell_type": "code",
|
||||||
"execution_count": 1,
|
"execution_count": null,
|
||||||
"metadata": {},
|
"metadata": {},
|
||||||
"outputs": [],
|
"outputs": [],
|
||||||
"source": [
|
"source": [
|
||||||
"#\n",
|
"#\n",
|
||||||
"# Google Colab setup\n",
|
"# Google Colab setup\n",
|
||||||
"#\n",
|
"#\n",
|
||||||
"#!pip install git+https://github.com/doctorado-ml/stree"
|
"#!pip install git+https://github.com/doctorado-ml/stree\n",
|
||||||
|
"!pip install pandas"
|
||||||
]
|
]
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
"cell_type": "code",
|
"cell_type": "code",
|
||||||
"execution_count": 2,
|
"execution_count": null,
|
||||||
"metadata": {},
|
"metadata": {},
|
||||||
"outputs": [],
|
"outputs": [],
|
||||||
"source": [
|
"source": [
|
||||||
"import time\n",
|
"import time\n",
|
||||||
|
"import random\n",
|
||||||
"import warnings\n",
|
"import warnings\n",
|
||||||
|
"import os\n",
|
||||||
"import numpy as np\n",
|
"import numpy as np\n",
|
||||||
"import pandas as pd\n",
|
"import pandas as pd\n",
|
||||||
"from sklearn.svm import SVC\n",
|
"from sklearn.svm import SVC\n",
|
||||||
@@ -42,6 +45,7 @@
|
|||||||
"from sklearn.utils.estimator_checks import check_estimator\n",
|
"from sklearn.utils.estimator_checks import check_estimator\n",
|
||||||
"from sklearn.datasets import make_classification, load_iris, load_wine\n",
|
"from sklearn.datasets import make_classification, load_iris, load_wine\n",
|
||||||
"from sklearn.model_selection import train_test_split\n",
|
"from sklearn.model_selection import train_test_split\n",
|
||||||
|
"from sklearn.utils.class_weight import compute_sample_weight\n",
|
||||||
"from sklearn.exceptions import ConvergenceWarning\n",
|
"from sklearn.exceptions import ConvergenceWarning\n",
|
||||||
"from stree import Stree\n",
|
"from stree import Stree\n",
|
||||||
"warnings.filterwarnings(\"ignore\", category=ConvergenceWarning)"
|
"warnings.filterwarnings(\"ignore\", category=ConvergenceWarning)"
|
||||||
@@ -49,13 +53,12 @@
|
|||||||
},
|
},
|
||||||
{
|
{
|
||||||
"cell_type": "code",
|
"cell_type": "code",
|
||||||
"execution_count": 3,
|
"execution_count": null,
|
||||||
"metadata": {
|
"metadata": {
|
||||||
"tags": []
|
"tags": []
|
||||||
},
|
},
|
||||||
"outputs": [],
|
"outputs": [],
|
||||||
"source": [
|
"source": [
|
||||||
"import os\n",
|
|
||||||
"if not os.path.isfile('data/creditcard.csv'):\n",
|
"if not os.path.isfile('data/creditcard.csv'):\n",
|
||||||
" !wget --no-check-certificate --content-disposition http://nube.jccm.es/index.php/s/Zs7SYtZQJ3RQ2H2/download\n",
|
" !wget --no-check-certificate --content-disposition http://nube.jccm.es/index.php/s/Zs7SYtZQJ3RQ2H2/download\n",
|
||||||
" !tar xzf creditcard.tgz"
|
" !tar xzf creditcard.tgz"
|
||||||
@@ -63,31 +66,15 @@
|
|||||||
},
|
},
|
||||||
{
|
{
|
||||||
"cell_type": "code",
|
"cell_type": "code",
|
||||||
"execution_count": 4,
|
"execution_count": null,
|
||||||
"metadata": {
|
"metadata": {
|
||||||
"tags": []
|
"tags": []
|
||||||
},
|
},
|
||||||
"outputs": [
|
"outputs": [],
|
||||||
{
|
|
||||||
"name": "stdout",
|
|
||||||
"output_type": "stream",
|
|
||||||
"text": [
|
|
||||||
"Fraud: 0.173% 492\n",
|
|
||||||
"Valid: 99.827% 284315\n",
|
|
||||||
"X.shape (5492, 28) y.shape (5492,)\n",
|
|
||||||
"Fraud: 9.086% 499\n",
|
|
||||||
"Valid: 90.914% 4993\n",
|
|
||||||
"[0.09079084 0.09079084 0.09079084 0.09079084] [0.09101942 0.09101942 0.09101942 0.09101942]\n"
|
|
||||||
]
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"source": [
|
"source": [
|
||||||
"random_state=1\n",
|
"random_state=1\n",
|
||||||
"\n",
|
"\n",
|
||||||
"def load_creditcard(n_examples=0):\n",
|
"def load_creditcard(n_examples=0):\n",
|
||||||
" import pandas as pd\n",
|
|
||||||
" import numpy as np\n",
|
|
||||||
" import random\n",
|
|
||||||
" df = pd.read_csv('data/creditcard.csv')\n",
|
" df = pd.read_csv('data/creditcard.csv')\n",
|
||||||
" print(\"Fraud: {0:.3f}% {1}\".format(df.Class[df.Class == 1].count()*100/df.shape[0], df.Class[df.Class == 1].count()))\n",
|
" print(\"Fraud: {0:.3f}% {1}\".format(df.Class[df.Class == 1].count()*100/df.shape[0], df.Class[df.Class == 1].count()))\n",
|
||||||
" print(\"Valid: {0:.3f}% {1}\".format(df.Class[df.Class == 0].count()*100/df.shape[0], df.Class[df.Class == 0].count()))\n",
|
" print(\"Valid: {0:.3f}% {1}\".format(df.Class[df.Class == 0].count()*100/df.shape[0], df.Class[df.Class == 0].count()))\n",
|
||||||
@@ -119,17 +106,8 @@
|
|||||||
"Xtest = data[1]\n",
|
"Xtest = data[1]\n",
|
||||||
"ytrain = data[2]\n",
|
"ytrain = data[2]\n",
|
||||||
"ytest = data[3]\n",
|
"ytest = data[3]\n",
|
||||||
"_, data = np.unique(ytrain, return_counts=True)\n",
|
"weights = compute_sample_weight(\"balanced\", ytrain)\n",
|
||||||
"wtrain = (data[1] / np.sum(data), data[0] / np.sum(data))\n",
|
"weights_test = compute_sample_weight(\"balanced\", ytest)\n",
|
||||||
"_, data = np.unique(ytest, return_counts=True)\n",
|
|
||||||
"wtest = (data[1] / np.sum(data), data[0] / np.sum(data))\n",
|
|
||||||
"# Set weights inverse to its count class in dataset\n",
|
|
||||||
"weights = np.ones(Xtrain.shape[0],)\n",
|
|
||||||
"weights[ytrain==0] = wtrain[0]\n",
|
|
||||||
"weights[ytrain==1] = wtrain[1]\n",
|
|
||||||
"weights_test = np.ones(Xtest.shape[0],)\n",
|
|
||||||
"weights_test[ytest==0] = wtest[0]\n",
|
|
||||||
"weights_test[ytest==1] = wtest[1]\n",
|
|
||||||
"print(weights[:4], weights_test[:4])"
|
"print(weights[:4], weights_test[:4])"
|
||||||
]
|
]
|
||||||
},
|
},
|
||||||
@@ -150,22 +128,11 @@
|
|||||||
},
|
},
|
||||||
{
|
{
|
||||||
"cell_type": "code",
|
"cell_type": "code",
|
||||||
"execution_count": 5,
|
"execution_count": null,
|
||||||
"metadata": {
|
"metadata": {
|
||||||
"tags": []
|
"tags": []
|
||||||
},
|
},
|
||||||
"outputs": [
|
"outputs": [],
|
||||||
{
|
|
||||||
"name": "stdout",
|
|
||||||
"output_type": "stream",
|
|
||||||
"text": [
|
|
||||||
"Accuracy of Train without weights 0.9849115504682622\n",
|
|
||||||
"Accuracy of Train with weights 0.9849115504682622\n",
|
|
||||||
"Accuracy of Tests without weights 0.9848300970873787\n",
|
|
||||||
"Accuracy of Tests with weights 0.9805825242718447\n"
|
|
||||||
]
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"source": [
|
"source": [
|
||||||
"C = 23\n",
|
"C = 23\n",
|
||||||
"print(\"Accuracy of Train without weights\", Stree(C=C, random_state=1).fit(Xtrain, ytrain).score(Xtrain, ytrain))\n",
|
"print(\"Accuracy of Train without weights\", Stree(C=C, random_state=1).fit(Xtrain, ytrain).score(Xtrain, ytrain))\n",
|
||||||
@@ -184,21 +151,11 @@
|
|||||||
},
|
},
|
||||||
{
|
{
|
||||||
"cell_type": "code",
|
"cell_type": "code",
|
||||||
"execution_count": 6,
|
"execution_count": null,
|
||||||
"metadata": {
|
"metadata": {
|
||||||
"tags": []
|
"tags": []
|
||||||
},
|
},
|
||||||
"outputs": [
|
"outputs": [],
|
||||||
{
|
|
||||||
"name": "stdout",
|
|
||||||
"output_type": "stream",
|
|
||||||
"text": [
|
|
||||||
"Time: 26.59s\tKernel: linear\tAccuracy_train: 0.9846514047866806\tAccuracy_test: 0.9848300970873787\n",
|
|
||||||
"Time: 0.56s\tKernel: rbf\tAccuracy_train: 0.9947970863683663\tAccuracy_test: 0.9866504854368932\n",
|
|
||||||
"Time: 0.23s\tKernel: poly\tAccuracy_train: 0.9955775234131113\tAccuracy_test: 0.9824029126213593\n"
|
|
||||||
]
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"source": [
|
"source": [
|
||||||
"random_state=1\n",
|
"random_state=1\n",
|
||||||
"for kernel in ['linear', 'rbf', 'poly']:\n",
|
"for kernel in ['linear', 'rbf', 'poly']:\n",
|
||||||
@@ -219,77 +176,11 @@
|
|||||||
},
|
},
|
||||||
{
|
{
|
||||||
"cell_type": "code",
|
"cell_type": "code",
|
||||||
"execution_count": 7,
|
"execution_count": null,
|
||||||
"metadata": {
|
"metadata": {
|
||||||
"tags": []
|
"tags": []
|
||||||
},
|
},
|
||||||
"outputs": [
|
"outputs": [],
|
||||||
{
|
|
||||||
"name": "stdout",
|
|
||||||
"output_type": "stream",
|
|
||||||
"text": [
|
|
||||||
"************** C=0.001 ****************************\n",
|
|
||||||
"Classifier's accuracy (train): 0.9823\n",
|
|
||||||
"Classifier's accuracy (test) : 0.9836\n",
|
|
||||||
"root feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) impurity=0.4391 counts=(array([0, 1]), array([3495, 349]))\n",
|
|
||||||
"root - Down, <cgaf> - Leaf class=0 belief= 0.981455 impurity=0.1332 counts=(array([0, 1]), array([3493, 66]))\n",
|
|
||||||
"root - Up, <cgaf> - Leaf class=1 belief= 0.992982 impurity=0.0603 counts=(array([0, 1]), array([ 2, 283]))\n",
|
|
||||||
"\n",
|
|
||||||
"**************************************************\n",
|
|
||||||
"************** C=0.01 ****************************\n",
|
|
||||||
"Classifier's accuracy (train): 0.9834\n",
|
|
||||||
"Classifier's accuracy (test) : 0.9842\n",
|
|
||||||
"root feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) impurity=0.4391 counts=(array([0, 1]), array([3495, 349]))\n",
|
|
||||||
"root - Down, <cgaf> - Leaf class=0 belief= 0.982288 impurity=0.1284 counts=(array([0, 1]), array([3494, 63]))\n",
|
|
||||||
"root - Up, <cgaf> - Leaf class=1 belief= 0.996516 impurity=0.0335 counts=(array([0, 1]), array([ 1, 286]))\n",
|
|
||||||
"\n",
|
|
||||||
"**************************************************\n",
|
|
||||||
"************** C=1 ****************************\n",
|
|
||||||
"Classifier's accuracy (train): 0.9844\n",
|
|
||||||
"Classifier's accuracy (test) : 0.9848\n",
|
|
||||||
"root feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) impurity=0.4391 counts=(array([0, 1]), array([3495, 349]))\n",
|
|
||||||
"root - Down feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) impurity=0.1236 counts=(array([0, 1]), array([3493, 60]))\n",
|
|
||||||
"root - Down - Down, <cgaf> - Leaf class=0 belief= 0.983108 impurity=0.1236 counts=(array([0, 1]), array([3492, 60]))\n",
|
|
||||||
"root - Down - Up, <pure> - Leaf class=0 belief= 1.000000 impurity=0.0000 counts=(array([0]), array([1]))\n",
|
|
||||||
"root - Up feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) impurity=0.0593 counts=(array([0, 1]), array([ 2, 289]))\n",
|
|
||||||
"root - Up - Down, <pure> - Leaf class=0 belief= 1.000000 impurity=0.0000 counts=(array([0]), array([2]))\n",
|
|
||||||
"root - Up - Up, <pure> - Leaf class=1 belief= 1.000000 impurity=0.0000 counts=(array([1]), array([289]))\n",
|
|
||||||
"\n",
|
|
||||||
"**************************************************\n",
|
|
||||||
"************** C=5 ****************************\n",
|
|
||||||
"Classifier's accuracy (train): 0.9847\n",
|
|
||||||
"Classifier's accuracy (test) : 0.9848\n",
|
|
||||||
"root feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) impurity=0.4391 counts=(array([0, 1]), array([3495, 349]))\n",
|
|
||||||
"root - Down feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) impurity=0.1236 counts=(array([0, 1]), array([3493, 60]))\n",
|
|
||||||
"root - Down - Down feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) impurity=0.1236 counts=(array([0, 1]), array([3492, 60]))\n",
|
|
||||||
"root - Down - Down - Down, <cgaf> - Leaf class=0 belief= 0.983385 impurity=0.1220 counts=(array([0, 1]), array([3492, 59]))\n",
|
|
||||||
"root - Down - Down - Up, <pure> - Leaf class=1 belief= 1.000000 impurity=0.0000 counts=(array([1]), array([1]))\n",
|
|
||||||
"root - Down - Up, <pure> - Leaf class=0 belief= 1.000000 impurity=0.0000 counts=(array([0]), array([1]))\n",
|
|
||||||
"root - Up feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) impurity=0.0593 counts=(array([0, 1]), array([ 2, 289]))\n",
|
|
||||||
"root - Up - Down, <pure> - Leaf class=0 belief= 1.000000 impurity=0.0000 counts=(array([0]), array([2]))\n",
|
|
||||||
"root - Up - Up, <pure> - Leaf class=1 belief= 1.000000 impurity=0.0000 counts=(array([1]), array([289]))\n",
|
|
||||||
"\n",
|
|
||||||
"**************************************************\n",
|
|
||||||
"************** C=17 ****************************\n",
|
|
||||||
"Classifier's accuracy (train): 0.9847\n",
|
|
||||||
"Classifier's accuracy (test) : 0.9848\n",
|
|
||||||
"root feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) impurity=0.4391 counts=(array([0, 1]), array([3495, 349]))\n",
|
|
||||||
"root - Down feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) impurity=0.1236 counts=(array([0, 1]), array([3493, 60]))\n",
|
|
||||||
"root - Down - Down feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) impurity=0.1220 counts=(array([0, 1]), array([3492, 59]))\n",
|
|
||||||
"root - Down - Down - Down, <cgaf> - Leaf class=0 belief= 0.983380 impurity=0.1220 counts=(array([0, 1]), array([3491, 59]))\n",
|
|
||||||
"root - Down - Down - Up, <pure> - Leaf class=0 belief= 1.000000 impurity=0.0000 counts=(array([0]), array([1]))\n",
|
|
||||||
"root - Down - Up feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) impurity=1.0000 counts=(array([0, 1]), array([1, 1]))\n",
|
|
||||||
"root - Down - Up - Down, <pure> - Leaf class=0 belief= 1.000000 impurity=0.0000 counts=(array([0]), array([1]))\n",
|
|
||||||
"root - Down - Up - Up, <pure> - Leaf class=1 belief= 1.000000 impurity=0.0000 counts=(array([1]), array([1]))\n",
|
|
||||||
"root - Up feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) impurity=0.0593 counts=(array([0, 1]), array([ 2, 289]))\n",
|
|
||||||
"root - Up - Down, <pure> - Leaf class=0 belief= 1.000000 impurity=0.0000 counts=(array([0]), array([2]))\n",
|
|
||||||
"root - Up - Up, <pure> - Leaf class=1 belief= 1.000000 impurity=0.0000 counts=(array([1]), array([289]))\n",
|
|
||||||
"\n",
|
|
||||||
"**************************************************\n",
|
|
||||||
"59.0161 secs\n"
|
|
||||||
]
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"source": [
|
"source": [
|
||||||
"t = time.time()\n",
|
"t = time.time()\n",
|
||||||
"for C in (.001, .01, 1, 5, 17):\n",
|
"for C in (.001, .01, 1, 5, 17):\n",
|
||||||
@@ -313,29 +204,11 @@
|
|||||||
},
|
},
|
||||||
{
|
{
|
||||||
"cell_type": "code",
|
"cell_type": "code",
|
||||||
"execution_count": 8,
|
"execution_count": null,
|
||||||
"metadata": {
|
"metadata": {
|
||||||
"tags": []
|
"tags": []
|
||||||
},
|
},
|
||||||
"outputs": [
|
"outputs": [],
|
||||||
{
|
|
||||||
"name": "stdout",
|
|
||||||
"output_type": "stream",
|
|
||||||
"text": [
|
|
||||||
"root feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) impurity=0.4391 counts=(array([0, 1]), array([3495, 349]))\n",
|
|
||||||
"root - Down feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) impurity=0.1236 counts=(array([0, 1]), array([3493, 60]))\n",
|
|
||||||
"root - Down - Down feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) impurity=0.1220 counts=(array([0, 1]), array([3492, 59]))\n",
|
|
||||||
"root - Down - Down - Down, <cgaf> - Leaf class=0 belief= 0.983380 impurity=0.1220 counts=(array([0, 1]), array([3491, 59]))\n",
|
|
||||||
"root - Down - Down - Up, <pure> - Leaf class=0 belief= 1.000000 impurity=0.0000 counts=(array([0]), array([1]))\n",
|
|
||||||
"root - Down - Up feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) impurity=1.0000 counts=(array([0, 1]), array([1, 1]))\n",
|
|
||||||
"root - Down - Up - Down, <pure> - Leaf class=0 belief= 1.000000 impurity=0.0000 counts=(array([0]), array([1]))\n",
|
|
||||||
"root - Down - Up - Up, <pure> - Leaf class=1 belief= 1.000000 impurity=0.0000 counts=(array([1]), array([1]))\n",
|
|
||||||
"root - Up feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) impurity=0.0593 counts=(array([0, 1]), array([ 2, 289]))\n",
|
|
||||||
"root - Up - Down, <pure> - Leaf class=0 belief= 1.000000 impurity=0.0000 counts=(array([0]), array([2]))\n",
|
|
||||||
"root - Up - Up, <pure> - Leaf class=1 belief= 1.000000 impurity=0.0000 counts=(array([1]), array([289]))\n"
|
|
||||||
]
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"source": [
|
"source": [
|
||||||
"#check iterator\n",
|
"#check iterator\n",
|
||||||
"for i in list(clf):\n",
|
"for i in list(clf):\n",
|
||||||
@@ -344,29 +217,11 @@
|
|||||||
},
|
},
|
||||||
{
|
{
|
||||||
"cell_type": "code",
|
"cell_type": "code",
|
||||||
"execution_count": 9,
|
"execution_count": null,
|
||||||
"metadata": {
|
"metadata": {
|
||||||
"tags": []
|
"tags": []
|
||||||
},
|
},
|
||||||
"outputs": [
|
"outputs": [],
|
||||||
{
|
|
||||||
"name": "stdout",
|
|
||||||
"output_type": "stream",
|
|
||||||
"text": [
|
|
||||||
"root feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) impurity=0.4391 counts=(array([0, 1]), array([3495, 349]))\n",
|
|
||||||
"root - Down feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) impurity=0.1236 counts=(array([0, 1]), array([3493, 60]))\n",
|
|
||||||
"root - Down - Down feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) impurity=0.1220 counts=(array([0, 1]), array([3492, 59]))\n",
|
|
||||||
"root - Down - Down - Down, <cgaf> - Leaf class=0 belief= 0.983380 impurity=0.1220 counts=(array([0, 1]), array([3491, 59]))\n",
|
|
||||||
"root - Down - Down - Up, <pure> - Leaf class=0 belief= 1.000000 impurity=0.0000 counts=(array([0]), array([1]))\n",
|
|
||||||
"root - Down - Up feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) impurity=1.0000 counts=(array([0, 1]), array([1, 1]))\n",
|
|
||||||
"root - Down - Up - Down, <pure> - Leaf class=0 belief= 1.000000 impurity=0.0000 counts=(array([0]), array([1]))\n",
|
|
||||||
"root - Down - Up - Up, <pure> - Leaf class=1 belief= 1.000000 impurity=0.0000 counts=(array([1]), array([1]))\n",
|
|
||||||
"root - Up feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) impurity=0.0593 counts=(array([0, 1]), array([ 2, 289]))\n",
|
|
||||||
"root - Up - Down, <pure> - Leaf class=0 belief= 1.000000 impurity=0.0000 counts=(array([0]), array([2]))\n",
|
|
||||||
"root - Up - Up, <pure> - Leaf class=1 belief= 1.000000 impurity=0.0000 counts=(array([1]), array([289]))\n"
|
|
||||||
]
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"source": [
|
"source": [
|
||||||
"#check iterator again\n",
|
"#check iterator again\n",
|
||||||
"for i in clf:\n",
|
"for i in clf:\n",
|
||||||
@@ -382,73 +237,17 @@
|
|||||||
},
|
},
|
||||||
{
|
{
|
||||||
"cell_type": "code",
|
"cell_type": "code",
|
||||||
"execution_count": 14,
|
"execution_count": null,
|
||||||
"metadata": {
|
"metadata": {
|
||||||
"tags": []
|
"tags": []
|
||||||
},
|
},
|
||||||
"outputs": [
|
"outputs": [],
|
||||||
{
|
|
||||||
"name": "stdout",
|
|
||||||
"output_type": "stream",
|
|
||||||
"text": [
|
|
||||||
"1 functools.partial(<function check_no_attributes_set_in_init at 0x16817f670>, 'Stree')\n",
|
|
||||||
"2 functools.partial(<function check_estimators_dtypes at 0x168179820>, 'Stree')\n",
|
|
||||||
"3 functools.partial(<function check_fit_score_takes_y at 0x168179700>, 'Stree')\n",
|
|
||||||
"4 functools.partial(<function check_sample_weights_pandas_series at 0x168174040>, 'Stree')\n",
|
|
||||||
"5 functools.partial(<function check_sample_weights_not_an_array at 0x168174160>, 'Stree')\n",
|
|
||||||
"6 functools.partial(<function check_sample_weights_list at 0x168174280>, 'Stree')\n",
|
|
||||||
"7 functools.partial(<function check_sample_weights_shape at 0x1681743a0>, 'Stree')\n",
|
|
||||||
"8 functools.partial(<function check_sample_weights_invariance at 0x1681744c0>, 'Stree', kind='ones')\n",
|
|
||||||
"10 functools.partial(<function check_estimators_fit_returns_self at 0x16817b8b0>, 'Stree')\n",
|
|
||||||
"11 functools.partial(<function check_estimators_fit_returns_self at 0x16817b8b0>, 'Stree', readonly_memmap=True)\n",
|
|
||||||
"12 functools.partial(<function check_complex_data at 0x168174670>, 'Stree')\n",
|
|
||||||
"13 functools.partial(<function check_dtype_object at 0x1681745e0>, 'Stree')\n",
|
|
||||||
"14 functools.partial(<function check_estimators_empty_data_messages at 0x1681799d0>, 'Stree')\n",
|
|
||||||
"15 functools.partial(<function check_pipeline_consistency at 0x1681795e0>, 'Stree')\n",
|
|
||||||
"16 functools.partial(<function check_estimators_nan_inf at 0x168179af0>, 'Stree')\n",
|
|
||||||
"17 functools.partial(<function check_estimators_overwrite_params at 0x16817f550>, 'Stree')\n",
|
|
||||||
"18 functools.partial(<function check_estimator_sparse_data at 0x168172ee0>, 'Stree')\n",
|
|
||||||
"19 functools.partial(<function check_estimators_pickle at 0x168179d30>, 'Stree')\n",
|
|
||||||
"20 functools.partial(<function check_estimator_get_tags_default_keys at 0x168181790>, 'Stree')\n",
|
|
||||||
"21 functools.partial(<function check_classifier_data_not_an_array at 0x16817f8b0>, 'Stree')\n",
|
|
||||||
"22 functools.partial(<function check_classifiers_one_label at 0x16817b430>, 'Stree')\n",
|
|
||||||
"23 functools.partial(<function check_classifiers_classes at 0x16817bd30>, 'Stree')\n",
|
|
||||||
"24 functools.partial(<function check_estimators_partial_fit_n_features at 0x168179e50>, 'Stree')\n",
|
|
||||||
"25 functools.partial(<function check_classifiers_train at 0x16817b550>, 'Stree')\n",
|
|
||||||
"26 functools.partial(<function check_classifiers_train at 0x16817b550>, 'Stree', readonly_memmap=True)\n",
|
|
||||||
"27 functools.partial(<function check_classifiers_train at 0x16817b550>, 'Stree', readonly_memmap=True, X_dtype='float32')\n",
|
|
||||||
"28 functools.partial(<function check_classifiers_regression_target at 0x168181280>, 'Stree')\n",
|
|
||||||
"29 functools.partial(<function check_supervised_y_no_nan at 0x1681720d0>, 'Stree')\n",
|
|
||||||
"30 functools.partial(<function check_supervised_y_2d at 0x16817baf0>, 'Stree')\n",
|
|
||||||
"31 functools.partial(<function check_estimators_unfitted at 0x16817b9d0>, 'Stree')\n",
|
|
||||||
"32 functools.partial(<function check_non_transformer_estimators_n_iter at 0x16817fdc0>, 'Stree')\n",
|
|
||||||
"33 functools.partial(<function check_decision_proba_consistency at 0x1681813a0>, 'Stree')\n",
|
|
||||||
"34 functools.partial(<function check_parameters_default_constructible at 0x16817fb80>, 'Stree')\n",
|
|
||||||
"35 functools.partial(<function check_methods_sample_order_invariance at 0x168174d30>, 'Stree')\n",
|
|
||||||
"36 functools.partial(<function check_methods_subset_invariance at 0x168174c10>, 'Stree')\n",
|
|
||||||
"37 functools.partial(<function check_fit2d_1sample at 0x168174e50>, 'Stree')\n",
|
|
||||||
"38 functools.partial(<function check_fit2d_1feature at 0x168174f70>, 'Stree')\n",
|
|
||||||
"39 functools.partial(<function check_get_params_invariance at 0x168181040>, 'Stree')\n",
|
|
||||||
"40 functools.partial(<function check_set_params at 0x168181160>, 'Stree')\n",
|
|
||||||
"41 functools.partial(<function check_dict_unchanged at 0x168174790>, 'Stree')\n",
|
|
||||||
"42 functools.partial(<function check_dont_overwrite_parameters at 0x168174940>, 'Stree')\n",
|
|
||||||
"43 functools.partial(<function check_fit_idempotent at 0x168181550>, 'Stree')\n",
|
|
||||||
"44 functools.partial(<function check_n_features_in at 0x1681815e0>, 'Stree')\n",
|
|
||||||
"45 functools.partial(<function check_fit1d at 0x1681790d0>, 'Stree')\n",
|
|
||||||
"46 functools.partial(<function check_fit2d_predict1d at 0x168174a60>, 'Stree')\n",
|
|
||||||
"47 functools.partial(<function check_requires_y_none at 0x168181670>, 'Stree')\n"
|
|
||||||
]
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"source": [
|
"source": [
|
||||||
"# Make checks one by one\n",
|
"# Make checks one by one\n",
|
||||||
"c = 0\n",
|
"c = 0\n",
|
||||||
"checks = check_estimator(Stree(), generate_only=True)\n",
|
"checks = check_estimator(Stree(), generate_only=True)\n",
|
||||||
"for check in checks:\n",
|
"for check in checks:\n",
|
||||||
" c += 1\n",
|
" c += 1\n",
|
||||||
" if c == 9:\n",
|
|
||||||
" pass\n",
|
|
||||||
" else:\n",
|
|
||||||
" print(c, check[1])\n",
|
" print(c, check[1])\n",
|
||||||
" check[1](check[0])"
|
" check[1](check[0])"
|
||||||
]
|
]
|
||||||
@@ -552,7 +351,7 @@
|
|||||||
"name": "python",
|
"name": "python",
|
||||||
"nbconvert_exporter": "python",
|
"nbconvert_exporter": "python",
|
||||||
"pygments_lexer": "ipython3",
|
"pygments_lexer": "ipython3",
|
||||||
"version": "3.9.1"
|
"version": "3.8.2-final"
|
||||||
}
|
}
|
||||||
},
|
},
|
||||||
"nbformat": 4,
|
"nbformat": 4,
|
||||||
|
|||||||
@@ -18,19 +18,20 @@
|
|||||||
},
|
},
|
||||||
{
|
{
|
||||||
"cell_type": "code",
|
"cell_type": "code",
|
||||||
"execution_count": 1,
|
"execution_count": null,
|
||||||
"metadata": {},
|
"metadata": {},
|
||||||
"outputs": [],
|
"outputs": [],
|
||||||
"source": [
|
"source": [
|
||||||
"#\n",
|
"#\n",
|
||||||
"# Google Colab setup\n",
|
"# Google Colab setup\n",
|
||||||
"#\n",
|
"#\n",
|
||||||
"#!pip install git+https://github.com/doctorado-ml/stree"
|
"#!pip install git+https://github.com/doctorado-ml/stree\n",
|
||||||
|
"!pip install pandas"
|
||||||
]
|
]
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
"cell_type": "code",
|
"cell_type": "code",
|
||||||
"execution_count": 2,
|
"execution_count": null,
|
||||||
"metadata": {
|
"metadata": {
|
||||||
"colab": {},
|
"colab": {},
|
||||||
"colab_type": "code",
|
"colab_type": "code",
|
||||||
@@ -38,6 +39,10 @@
|
|||||||
},
|
},
|
||||||
"outputs": [],
|
"outputs": [],
|
||||||
"source": [
|
"source": [
|
||||||
|
"import random\n",
|
||||||
|
"import os\n",
|
||||||
|
"import pandas as pd\n",
|
||||||
|
"import numpy as np\n",
|
||||||
"from sklearn.ensemble import AdaBoostClassifier\n",
|
"from sklearn.ensemble import AdaBoostClassifier\n",
|
||||||
"from sklearn.svm import LinearSVC\n",
|
"from sklearn.svm import LinearSVC\n",
|
||||||
"from sklearn.model_selection import GridSearchCV, train_test_split\n",
|
"from sklearn.model_selection import GridSearchCV, train_test_split\n",
|
||||||
@@ -46,7 +51,7 @@
|
|||||||
},
|
},
|
||||||
{
|
{
|
||||||
"cell_type": "code",
|
"cell_type": "code",
|
||||||
"execution_count": 3,
|
"execution_count": null,
|
||||||
"metadata": {
|
"metadata": {
|
||||||
"colab": {},
|
"colab": {},
|
||||||
"colab_type": "code",
|
"colab_type": "code",
|
||||||
@@ -54,7 +59,6 @@
|
|||||||
},
|
},
|
||||||
"outputs": [],
|
"outputs": [],
|
||||||
"source": [
|
"source": [
|
||||||
"import os\n",
|
|
||||||
"if not os.path.isfile('data/creditcard.csv'):\n",
|
"if not os.path.isfile('data/creditcard.csv'):\n",
|
||||||
" !wget --no-check-certificate --content-disposition http://nube.jccm.es/index.php/s/Zs7SYtZQJ3RQ2H2/download\n",
|
" !wget --no-check-certificate --content-disposition http://nube.jccm.es/index.php/s/Zs7SYtZQJ3RQ2H2/download\n",
|
||||||
" !tar xzf creditcard.tgz"
|
" !tar xzf creditcard.tgz"
|
||||||
@@ -62,7 +66,7 @@
|
|||||||
},
|
},
|
||||||
{
|
{
|
||||||
"cell_type": "code",
|
"cell_type": "code",
|
||||||
"execution_count": 4,
|
"execution_count": null,
|
||||||
"metadata": {
|
"metadata": {
|
||||||
"colab": {},
|
"colab": {},
|
||||||
"colab_type": "code",
|
"colab_type": "code",
|
||||||
@@ -70,26 +74,11 @@
|
|||||||
"outputId": "afc822fb-f16a-4302-8a67-2b9e2880159b",
|
"outputId": "afc822fb-f16a-4302-8a67-2b9e2880159b",
|
||||||
"tags": []
|
"tags": []
|
||||||
},
|
},
|
||||||
"outputs": [
|
"outputs": [],
|
||||||
{
|
|
||||||
"name": "stdout",
|
|
||||||
"output_type": "stream",
|
|
||||||
"text": [
|
|
||||||
"Fraud: 0.173% 492\n",
|
|
||||||
"Valid: 99.827% 284315\n",
|
|
||||||
"X.shape (1492, 28) y.shape (1492,)\n",
|
|
||||||
"Fraud: 33.177% 495\n",
|
|
||||||
"Valid: 66.823% 997\n"
|
|
||||||
]
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"source": [
|
"source": [
|
||||||
"random_state=1\n",
|
"random_state=1\n",
|
||||||
"\n",
|
"\n",
|
||||||
"def load_creditcard(n_examples=0):\n",
|
"def load_creditcard(n_examples=0):\n",
|
||||||
" import pandas as pd\n",
|
|
||||||
" import numpy as np\n",
|
|
||||||
" import random\n",
|
|
||||||
" df = pd.read_csv('data/creditcard.csv')\n",
|
" df = pd.read_csv('data/creditcard.csv')\n",
|
||||||
" print(\"Fraud: {0:.3f}% {1}\".format(df.Class[df.Class == 1].count()*100/df.shape[0], df.Class[df.Class == 1].count()))\n",
|
" print(\"Fraud: {0:.3f}% {1}\".format(df.Class[df.Class == 1].count()*100/df.shape[0], df.Class[df.Class == 1].count()))\n",
|
||||||
" print(\"Valid: {0:.3f}% {1}\".format(df.Class[df.Class == 0].count()*100/df.shape[0], df.Class[df.Class == 0].count()))\n",
|
" print(\"Valid: {0:.3f}% {1}\".format(df.Class[df.Class == 0].count()*100/df.shape[0], df.Class[df.Class == 0].count()))\n",
|
||||||
@@ -132,7 +121,7 @@
|
|||||||
},
|
},
|
||||||
{
|
{
|
||||||
"cell_type": "code",
|
"cell_type": "code",
|
||||||
"execution_count": 5,
|
"execution_count": null,
|
||||||
"metadata": {
|
"metadata": {
|
||||||
"colab": {},
|
"colab": {},
|
||||||
"colab_type": "code",
|
"colab_type": "code",
|
||||||
@@ -176,39 +165,16 @@
|
|||||||
},
|
},
|
||||||
{
|
{
|
||||||
"cell_type": "code",
|
"cell_type": "code",
|
||||||
"execution_count": 6,
|
"execution_count": null,
|
||||||
"metadata": {},
|
"metadata": {},
|
||||||
"outputs": [
|
"outputs": [],
|
||||||
{
|
|
||||||
"data": {
|
|
||||||
"text/plain": [
|
|
||||||
"{'C': 1.0,\n",
|
|
||||||
" 'criterion': 'entropy',\n",
|
|
||||||
" 'degree': 3,\n",
|
|
||||||
" 'gamma': 'scale',\n",
|
|
||||||
" 'kernel': 'linear',\n",
|
|
||||||
" 'max_depth': None,\n",
|
|
||||||
" 'max_features': None,\n",
|
|
||||||
" 'max_iter': 100000.0,\n",
|
|
||||||
" 'min_samples_split': 0,\n",
|
|
||||||
" 'random_state': None,\n",
|
|
||||||
" 'split_criteria': 'impurity',\n",
|
|
||||||
" 'splitter': 'random',\n",
|
|
||||||
" 'tol': 0.0001}"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
"execution_count": 6,
|
|
||||||
"metadata": {},
|
|
||||||
"output_type": "execute_result"
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"source": [
|
"source": [
|
||||||
"Stree().get_params()"
|
"Stree().get_params()"
|
||||||
]
|
]
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
"cell_type": "code",
|
"cell_type": "code",
|
||||||
"execution_count": 7,
|
"execution_count": null,
|
||||||
"metadata": {
|
"metadata": {
|
||||||
"colab": {},
|
"colab": {},
|
||||||
"colab_type": "code",
|
"colab_type": "code",
|
||||||
@@ -216,69 +182,7 @@
|
|||||||
"outputId": "7703413a-d563-4289-a13b-532f38f82762",
|
"outputId": "7703413a-d563-4289-a13b-532f38f82762",
|
||||||
"tags": []
|
"tags": []
|
||||||
},
|
},
|
||||||
"outputs": [
|
"outputs": [],
|
||||||
{
|
|
||||||
"name": "stdout",
|
|
||||||
"output_type": "stream",
|
|
||||||
"text": [
|
|
||||||
"Fitting 5 folds for each of 1008 candidates, totalling 5040 fits\n"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"name": "stderr",
|
|
||||||
"output_type": "stream",
|
|
||||||
"text": [
|
|
||||||
"[Parallel(n_jobs=-1)]: Using backend LokyBackend with 16 concurrent workers.\n",
|
|
||||||
"[Parallel(n_jobs=-1)]: Done 40 tasks | elapsed: 1.6s\n",
|
|
||||||
"[Parallel(n_jobs=-1)]: Done 130 tasks | elapsed: 3.1s\n",
|
|
||||||
"[Parallel(n_jobs=-1)]: Done 256 tasks | elapsed: 5.5s\n",
|
|
||||||
"[Parallel(n_jobs=-1)]: Done 418 tasks | elapsed: 9.3s\n",
|
|
||||||
"[Parallel(n_jobs=-1)]: Done 616 tasks | elapsed: 18.6s\n",
|
|
||||||
"[Parallel(n_jobs=-1)]: Done 850 tasks | elapsed: 28.2s\n",
|
|
||||||
"[Parallel(n_jobs=-1)]: Done 1120 tasks | elapsed: 35.4s\n",
|
|
||||||
"[Parallel(n_jobs=-1)]: Done 1426 tasks | elapsed: 43.5s\n",
|
|
||||||
"[Parallel(n_jobs=-1)]: Done 1768 tasks | elapsed: 51.3s\n",
|
|
||||||
"[Parallel(n_jobs=-1)]: Done 2146 tasks | elapsed: 1.0min\n",
|
|
||||||
"[Parallel(n_jobs=-1)]: Done 2560 tasks | elapsed: 1.2min\n",
|
|
||||||
"[Parallel(n_jobs=-1)]: Done 3010 tasks | elapsed: 1.4min\n",
|
|
||||||
"[Parallel(n_jobs=-1)]: Done 3496 tasks | elapsed: 1.7min\n",
|
|
||||||
"[Parallel(n_jobs=-1)]: Done 4018 tasks | elapsed: 2.1min\n",
|
|
||||||
"[Parallel(n_jobs=-1)]: Done 4576 tasks | elapsed: 2.6min\n",
|
|
||||||
"[Parallel(n_jobs=-1)]: Done 5040 out of 5040 | elapsed: 2.9min finished\n"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"data": {
|
|
||||||
"text/plain": [
|
|
||||||
"GridSearchCV(estimator=AdaBoostClassifier(algorithm='SAMME', random_state=1),\n",
|
|
||||||
" n_jobs=-1,\n",
|
|
||||||
" param_grid=[{'base_estimator': [Stree(C=55, max_depth=7,\n",
|
|
||||||
" random_state=1,\n",
|
|
||||||
" split_criteria='max_samples',\n",
|
|
||||||
" tol=0.1)],\n",
|
|
||||||
" 'base_estimator__C': [1, 7, 55],\n",
|
|
||||||
" 'base_estimator__kernel': ['linear'],\n",
|
|
||||||
" 'base_estimator__max_depth': [3, 5, 7],\n",
|
|
||||||
" 'base_estimator__split_criteria': ['max_samples',\n",
|
|
||||||
" 'impuri...\n",
|
|
||||||
" {'base_estimator': [Stree(random_state=1)],\n",
|
|
||||||
" 'base_estimator__C': [1, 7, 55],\n",
|
|
||||||
" 'base_estimator__gamma': [0.1, 1, 10],\n",
|
|
||||||
" 'base_estimator__kernel': ['rbf'],\n",
|
|
||||||
" 'base_estimator__max_depth': [3, 5, 7],\n",
|
|
||||||
" 'base_estimator__split_criteria': ['max_samples',\n",
|
|
||||||
" 'impurity'],\n",
|
|
||||||
" 'base_estimator__tol': [0.1, 0.01],\n",
|
|
||||||
" 'learning_rate': [0.5, 1],\n",
|
|
||||||
" 'n_estimators': [10, 25]}],\n",
|
|
||||||
" return_train_score=True, verbose=5)"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
"execution_count": 7,
|
|
||||||
"metadata": {},
|
|
||||||
"output_type": "execute_result"
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"source": [
|
"source": [
|
||||||
"clf = AdaBoostClassifier(random_state=random_state, algorithm=\"SAMME\")\n",
|
"clf = AdaBoostClassifier(random_state=random_state, algorithm=\"SAMME\")\n",
|
||||||
"grid = GridSearchCV(clf, parameters, verbose=5, n_jobs=-1, return_train_score=True)\n",
|
"grid = GridSearchCV(clf, parameters, verbose=5, n_jobs=-1, return_train_score=True)\n",
|
||||||
@@ -287,7 +191,7 @@
|
|||||||
},
|
},
|
||||||
{
|
{
|
||||||
"cell_type": "code",
|
"cell_type": "code",
|
||||||
"execution_count": 8,
|
"execution_count": null,
|
||||||
"metadata": {
|
"metadata": {
|
||||||
"colab": {},
|
"colab": {},
|
||||||
"colab_type": "code",
|
"colab_type": "code",
|
||||||
@@ -295,20 +199,7 @@
|
|||||||
"outputId": "285163c8-fa33-4915-8ae7-61c4f7844344",
|
"outputId": "285163c8-fa33-4915-8ae7-61c4f7844344",
|
||||||
"tags": []
|
"tags": []
|
||||||
},
|
},
|
||||||
"outputs": [
|
"outputs": [],
|
||||||
{
|
|
||||||
"name": "stdout",
|
|
||||||
"output_type": "stream",
|
|
||||||
"text": [
|
|
||||||
"Best estimator: AdaBoostClassifier(algorithm='SAMME',\n",
|
|
||||||
" base_estimator=Stree(C=55, max_depth=7, random_state=1,\n",
|
|
||||||
" split_criteria='max_samples', tol=0.1),\n",
|
|
||||||
" learning_rate=0.5, n_estimators=25, random_state=1)\n",
|
|
||||||
"Best hyperparameters: {'base_estimator': Stree(C=55, max_depth=7, random_state=1, split_criteria='max_samples', tol=0.1), 'base_estimator__C': 55, 'base_estimator__kernel': 'linear', 'base_estimator__max_depth': 7, 'base_estimator__split_criteria': 'max_samples', 'base_estimator__tol': 0.1, 'learning_rate': 0.5, 'n_estimators': 25}\n",
|
|
||||||
"Best accuracy: 0.9511777695988222\n"
|
|
||||||
]
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"source": [
|
"source": [
|
||||||
"print(\"Best estimator: \", grid.best_estimator_)\n",
|
"print(\"Best estimator: \", grid.best_estimator_)\n",
|
||||||
"print(\"Best hyperparameters: \", grid.best_params_)\n",
|
"print(\"Best hyperparameters: \", grid.best_params_)\n",
|
||||||
@@ -354,7 +245,7 @@
|
|||||||
"name": "python",
|
"name": "python",
|
||||||
"nbconvert_exporter": "python",
|
"nbconvert_exporter": "python",
|
||||||
"pygments_lexer": "ipython3",
|
"pygments_lexer": "ipython3",
|
||||||
"version": "3.8.2"
|
"version": "3.8.2-final"
|
||||||
}
|
}
|
||||||
},
|
},
|
||||||
"nbformat": 4,
|
"nbformat": 4,
|
||||||
|
|||||||
@@ -1,4 +1 @@
|
|||||||
numpy
|
scikit-learn>0.24
|
||||||
scikit-learn
|
|
||||||
pandas
|
|
||||||
ipympl
|
|
||||||
100
stree/Strees.py
100
stree/Strees.py
@@ -15,6 +15,7 @@ from typing import Optional
|
|||||||
import numpy as np
|
import numpy as np
|
||||||
from sklearn.base import BaseEstimator, ClassifierMixin
|
from sklearn.base import BaseEstimator, ClassifierMixin
|
||||||
from sklearn.svm import SVC, LinearSVC
|
from sklearn.svm import SVC, LinearSVC
|
||||||
|
from sklearn.preprocessing import StandardScaler
|
||||||
from sklearn.utils import check_consistent_length
|
from sklearn.utils import check_consistent_length
|
||||||
from sklearn.utils.multiclass import check_classification_targets
|
from sklearn.utils.multiclass import check_classification_targets
|
||||||
from sklearn.exceptions import ConvergenceWarning
|
from sklearn.exceptions import ConvergenceWarning
|
||||||
@@ -41,6 +42,7 @@ class Snode:
|
|||||||
impurity: float,
|
impurity: float,
|
||||||
title: str,
|
title: str,
|
||||||
weight: np.ndarray = None,
|
weight: np.ndarray = None,
|
||||||
|
scaler: StandardScaler = None,
|
||||||
):
|
):
|
||||||
self._clf = clf
|
self._clf = clf
|
||||||
self._title = title
|
self._title = title
|
||||||
@@ -58,6 +60,7 @@ class Snode:
|
|||||||
self._features = features
|
self._features = features
|
||||||
self._impurity = impurity
|
self._impurity = impurity
|
||||||
self._partition_column: int = -1
|
self._partition_column: int = -1
|
||||||
|
self._scaler = scaler
|
||||||
|
|
||||||
@classmethod
|
@classmethod
|
||||||
def copy(cls, node: "Snode") -> "Snode":
|
def copy(cls, node: "Snode") -> "Snode":
|
||||||
@@ -68,6 +71,8 @@ class Snode:
|
|||||||
node._features,
|
node._features,
|
||||||
node._impurity,
|
node._impurity,
|
||||||
node._title,
|
node._title,
|
||||||
|
node._sample_weight,
|
||||||
|
node._scaler,
|
||||||
)
|
)
|
||||||
|
|
||||||
def set_partition_column(self, col: int):
|
def set_partition_column(self, col: int):
|
||||||
@@ -79,6 +84,30 @@ class Snode:
|
|||||||
def set_down(self, son):
|
def set_down(self, son):
|
||||||
self._down = son
|
self._down = son
|
||||||
|
|
||||||
|
def set_title(self, title):
|
||||||
|
self._title = title
|
||||||
|
|
||||||
|
def set_classifier(self, clf):
|
||||||
|
self._clf = clf
|
||||||
|
|
||||||
|
def set_features(self, features):
|
||||||
|
self._features = features
|
||||||
|
|
||||||
|
def set_impurity(self, impurity):
|
||||||
|
self._impurity = impurity
|
||||||
|
|
||||||
|
def get_title(self) -> str:
|
||||||
|
return self._title
|
||||||
|
|
||||||
|
def get_classifier(self) -> SVC:
|
||||||
|
return self._clf
|
||||||
|
|
||||||
|
def get_impurity(self) -> float:
|
||||||
|
return self._impurity
|
||||||
|
|
||||||
|
def get_features(self) -> np.array:
|
||||||
|
return self._features
|
||||||
|
|
||||||
def set_up(self, son):
|
def set_up(self, son):
|
||||||
self._up = son
|
self._up = son
|
||||||
|
|
||||||
@@ -154,6 +183,7 @@ class Splitter:
|
|||||||
criteria: str = None,
|
criteria: str = None,
|
||||||
min_samples_split: int = None,
|
min_samples_split: int = None,
|
||||||
random_state=None,
|
random_state=None,
|
||||||
|
normalize=False,
|
||||||
):
|
):
|
||||||
self._clf = clf
|
self._clf = clf
|
||||||
self._random_state = random_state
|
self._random_state = random_state
|
||||||
@@ -163,6 +193,7 @@ class Splitter:
|
|||||||
self._min_samples_split = min_samples_split
|
self._min_samples_split = min_samples_split
|
||||||
self._criteria = criteria
|
self._criteria = criteria
|
||||||
self._splitter_type = splitter_type
|
self._splitter_type = splitter_type
|
||||||
|
self._normalize = normalize
|
||||||
|
|
||||||
if clf is None:
|
if clf is None:
|
||||||
raise ValueError(f"clf has to be a sklearn estimator, got({clf})")
|
raise ValueError(f"clf has to be a sklearn estimator, got({clf})")
|
||||||
@@ -462,8 +493,7 @@ class Splitter:
|
|||||||
origin[down] if any(down) else None,
|
origin[down] if any(down) else None,
|
||||||
]
|
]
|
||||||
|
|
||||||
@staticmethod
|
def _distances(self, node: Snode, data: np.ndarray) -> np.array:
|
||||||
def _distances(node: Snode, data: np.ndarray) -> np.array:
|
|
||||||
"""Compute distances of the samples to the hyperplane of the node
|
"""Compute distances of the samples to the hyperplane of the node
|
||||||
|
|
||||||
Parameters
|
Parameters
|
||||||
@@ -479,7 +509,10 @@ class Splitter:
|
|||||||
array of shape (m, nc) with the distances of every sample to
|
array of shape (m, nc) with the distances of every sample to
|
||||||
the hyperplane of every class. nc = # of classes
|
the hyperplane of every class. nc = # of classes
|
||||||
"""
|
"""
|
||||||
return node._clf.decision_function(data[:, node._features])
|
X_transformed = data[:, node._features]
|
||||||
|
if self._normalize:
|
||||||
|
X_transformed = node._scaler.transform(X_transformed)
|
||||||
|
return node._clf.decision_function(X_transformed)
|
||||||
|
|
||||||
|
|
||||||
class Stree(BaseEstimator, ClassifierMixin):
|
class Stree(BaseEstimator, ClassifierMixin):
|
||||||
@@ -505,6 +538,7 @@ class Stree(BaseEstimator, ClassifierMixin):
|
|||||||
min_samples_split: int = 0,
|
min_samples_split: int = 0,
|
||||||
max_features=None,
|
max_features=None,
|
||||||
splitter: str = "random",
|
splitter: str = "random",
|
||||||
|
normalize: bool = False,
|
||||||
):
|
):
|
||||||
self.max_iter = max_iter
|
self.max_iter = max_iter
|
||||||
self.C = C
|
self.C = C
|
||||||
@@ -519,6 +553,7 @@ class Stree(BaseEstimator, ClassifierMixin):
|
|||||||
self.max_features = max_features
|
self.max_features = max_features
|
||||||
self.criterion = criterion
|
self.criterion = criterion
|
||||||
self.splitter = splitter
|
self.splitter = splitter
|
||||||
|
self.normalize = normalize
|
||||||
|
|
||||||
def _more_tags(self) -> dict:
|
def _more_tags(self) -> dict:
|
||||||
"""Required by sklearn to supply features of the classifier
|
"""Required by sklearn to supply features of the classifier
|
||||||
@@ -582,6 +617,7 @@ class Stree(BaseEstimator, ClassifierMixin):
|
|||||||
criteria=self.split_criteria,
|
criteria=self.split_criteria,
|
||||||
random_state=self.random_state,
|
random_state=self.random_state,
|
||||||
min_samples_split=self.min_samples_split,
|
min_samples_split=self.min_samples_split,
|
||||||
|
normalize=self.normalize,
|
||||||
)
|
)
|
||||||
if self.random_state is not None:
|
if self.random_state is not None:
|
||||||
random.seed(self.random_state)
|
random.seed(self.random_state)
|
||||||
@@ -635,41 +671,39 @@ class Stree(BaseEstimator, ClassifierMixin):
|
|||||||
X = X[~indices_zero, :]
|
X = X[~indices_zero, :]
|
||||||
y = y[~indices_zero]
|
y = y[~indices_zero]
|
||||||
sample_weight = sample_weight[~indices_zero]
|
sample_weight = sample_weight[~indices_zero]
|
||||||
|
self.depth_ = max(depth, self.depth_)
|
||||||
|
scaler = StandardScaler()
|
||||||
|
node = Snode(None, X, y, X.shape[1], 0.0, title, sample_weight, scaler)
|
||||||
if np.unique(y).shape[0] == 1:
|
if np.unique(y).shape[0] == 1:
|
||||||
# only 1 class => pure dataset
|
# only 1 class => pure dataset
|
||||||
return Snode(
|
node.set_title(title + ", <pure>")
|
||||||
clf=None,
|
return node
|
||||||
X=X,
|
|
||||||
y=y,
|
|
||||||
features=X.shape[1],
|
|
||||||
impurity=0.0,
|
|
||||||
title=title + ", <pure>",
|
|
||||||
weight=sample_weight,
|
|
||||||
)
|
|
||||||
# Train the model
|
# Train the model
|
||||||
clf = self._build_clf()
|
clf = self._build_clf()
|
||||||
Xs, features = self.splitter_.get_subspace(X, y, self.max_features_)
|
Xs, features = self.splitter_.get_subspace(X, y, self.max_features_)
|
||||||
|
if self.normalize:
|
||||||
|
scaler.fit(Xs)
|
||||||
|
Xs = scaler.transform(Xs)
|
||||||
clf.fit(Xs, y, sample_weight=sample_weight)
|
clf.fit(Xs, y, sample_weight=sample_weight)
|
||||||
impurity = self.splitter_.partition_impurity(y)
|
node.set_impurity(self.splitter_.partition_impurity(y))
|
||||||
node = Snode(clf, X, y, features, impurity, title, sample_weight)
|
node.set_classifier(clf)
|
||||||
self.depth_ = max(depth, self.depth_)
|
node.set_features(features)
|
||||||
self.splitter_.partition(X, node, True)
|
self.splitter_.partition(X, node, True)
|
||||||
X_U, X_D = self.splitter_.part(X)
|
X_U, X_D = self.splitter_.part(X)
|
||||||
y_u, y_d = self.splitter_.part(y)
|
y_u, y_d = self.splitter_.part(y)
|
||||||
sw_u, sw_d = self.splitter_.part(sample_weight)
|
sw_u, sw_d = self.splitter_.part(sample_weight)
|
||||||
if X_U is None or X_D is None:
|
if X_U is None or X_D is None:
|
||||||
# didn't part anything
|
# didn't part anything
|
||||||
return Snode(
|
node.set_title(title + ", <cgaf>")
|
||||||
clf,
|
return node
|
||||||
X,
|
node.set_up(
|
||||||
y,
|
self.train(X_U, y_u, sw_u, depth + 1, title + f" - Up({depth+1})")
|
||||||
features=X.shape[1],
|
)
|
||||||
impurity=impurity,
|
node.set_down(
|
||||||
title=title + ", <cgaf>",
|
self.train(
|
||||||
weight=sample_weight,
|
X_D, y_d, sw_d, depth + 1, title + f" - Down({depth+1})"
|
||||||
|
)
|
||||||
)
|
)
|
||||||
node.set_up(self.train(X_U, y_u, sw_u, depth + 1, title + " - Up"))
|
|
||||||
node.set_down(self.train(X_D, y_d, sw_d, depth + 1, title + " - Down"))
|
|
||||||
return node
|
return node
|
||||||
|
|
||||||
def _build_predictor(self):
|
def _build_predictor(self):
|
||||||
@@ -812,6 +846,22 @@ class Stree(BaseEstimator, ClassifierMixin):
|
|||||||
score = y_true == y_pred
|
score = y_true == y_pred
|
||||||
return _weighted_sum(score, sample_weight, normalize=True)
|
return _weighted_sum(score, sample_weight, normalize=True)
|
||||||
|
|
||||||
|
def nodes_leaves(self) -> tuple:
|
||||||
|
"""Compute the number of nodes and leaves in the built tree
|
||||||
|
|
||||||
|
Returns
|
||||||
|
-------
|
||||||
|
[tuple]
|
||||||
|
tuple with the number of nodes and the number of leaves
|
||||||
|
"""
|
||||||
|
nodes = 0
|
||||||
|
leaves = 0
|
||||||
|
for node in self:
|
||||||
|
nodes += 1
|
||||||
|
if node.is_leaf():
|
||||||
|
leaves += 1
|
||||||
|
return nodes, leaves
|
||||||
|
|
||||||
def __iter__(self) -> Siterator:
|
def __iter__(self) -> Siterator:
|
||||||
"""Create an iterator to be able to visit the nodes of the tree in
|
"""Create an iterator to be able to visit the nodes of the tree in
|
||||||
preorder, can make a list with all the nodes in preorder
|
preorder, can make a list with all the nodes in preorder
|
||||||
|
|||||||
@@ -1,8 +1,6 @@
|
|||||||
import os
|
import os
|
||||||
import unittest
|
import unittest
|
||||||
|
|
||||||
import numpy as np
|
import numpy as np
|
||||||
|
|
||||||
from stree import Stree, Snode
|
from stree import Stree, Snode
|
||||||
from .utils import load_dataset
|
from .utils import load_dataset
|
||||||
|
|
||||||
@@ -69,6 +67,31 @@ class Snode_test(unittest.TestCase):
|
|||||||
self.assertEqual(0.75, test._belief)
|
self.assertEqual(0.75, test._belief)
|
||||||
self.assertEqual(-1, test._partition_column)
|
self.assertEqual(-1, test._partition_column)
|
||||||
|
|
||||||
|
def test_set_title(self):
|
||||||
|
test = Snode(None, [1, 2, 3, 4], [1, 0, 1, 1], [], 0.0, "test")
|
||||||
|
self.assertEqual("test", test.get_title())
|
||||||
|
test.set_title("another")
|
||||||
|
self.assertEqual("another", test.get_title())
|
||||||
|
|
||||||
|
def test_set_classifier(self):
|
||||||
|
test = Snode(None, [1, 2, 3, 4], [1, 0, 1, 1], [], 0.0, "test")
|
||||||
|
clf = Stree()
|
||||||
|
self.assertIsNone(test.get_classifier())
|
||||||
|
test.set_classifier(clf)
|
||||||
|
self.assertEqual(clf, test.get_classifier())
|
||||||
|
|
||||||
|
def test_set_impurity(self):
|
||||||
|
test = Snode(None, [1, 2, 3, 4], [1, 0, 1, 1], [], 0.0, "test")
|
||||||
|
self.assertEqual(0.0, test.get_impurity())
|
||||||
|
test.set_impurity(54.7)
|
||||||
|
self.assertEqual(54.7, test.get_impurity())
|
||||||
|
|
||||||
|
def test_set_features(self):
|
||||||
|
test = Snode(None, [1, 2, 3, 4], [1, 0, 1, 1], [0, 1], 0.0, "test")
|
||||||
|
self.assertListEqual([0, 1], test.get_features())
|
||||||
|
test.set_features([1, 2])
|
||||||
|
self.assertListEqual([1, 2], test.get_features())
|
||||||
|
|
||||||
def test_make_predictor_on_not_leaf(self):
|
def test_make_predictor_on_not_leaf(self):
|
||||||
test = Snode(None, [1, 2, 3, 4], [1, 0, 1, 1], [], 0.0, "test")
|
test = Snode(None, [1, 2, 3, 4], [1, 0, 1, 1], [], 0.0, "test")
|
||||||
test.set_up(Snode(None, [1], [1], [], 0.0, "another_test"))
|
test.set_up(Snode(None, [1], [1], [], 0.0, "another_test"))
|
||||||
@@ -94,3 +117,5 @@ class Snode_test(unittest.TestCase):
|
|||||||
self.assertEqual("test", computed._title)
|
self.assertEqual("test", computed._title)
|
||||||
self.assertIsInstance(computed._clf, Stree)
|
self.assertIsInstance(computed._clf, Stree)
|
||||||
self.assertEqual(test._partition_column, computed._partition_column)
|
self.assertEqual(test._partition_column, computed._partition_column)
|
||||||
|
self.assertEqual(test._sample_weight, computed._sample_weight)
|
||||||
|
self.assertEqual(test._scaler, computed._scaler)
|
||||||
|
|||||||
@@ -103,20 +103,20 @@ class Stree_test(unittest.TestCase):
|
|||||||
def test_iterator_and_str(self):
|
def test_iterator_and_str(self):
|
||||||
"""Check preorder iterator"""
|
"""Check preorder iterator"""
|
||||||
expected = [
|
expected = [
|
||||||
"root feaures=(0, 1, 2) impurity=1.0000 counts=(array([0, 1]), arr"
|
"root feaures=(0, 1, 2) impurity=1.0000 counts=(array([0, 1]), "
|
||||||
"ay([750, 750]))",
|
"array([750, 750]))",
|
||||||
"root - Down, <cgaf> - Leaf class=0 belief= 0.928297 impurity=0.37"
|
"root - Down(2), <cgaf> - Leaf class=0 belief= 0.928297 impurity="
|
||||||
"22 counts=(array([0, 1]), array([725, 56]))",
|
"0.3722 counts=(array([0, 1]), array([725, 56]))",
|
||||||
"root - Up feaures=(0, 1, 2) impurity=0.2178 counts=(array([0, 1])"
|
"root - Up(2) feaures=(0, 1, 2) impurity=0.2178 counts=(array([0, "
|
||||||
", array([ 25, 694]))",
|
"1]), array([ 25, 694]))",
|
||||||
"root - Up - Down feaures=(0, 1, 2) impurity=0.8454 counts=(array("
|
"root - Up(2) - Down(3) feaures=(0, 1, 2) impurity=0.8454 counts="
|
||||||
"[0, 1]), array([8, 3]))",
|
"(array([0, 1]), array([8, 3]))",
|
||||||
"root - Up - Down - Down, <pure> - Leaf class=0 belief= 1.000000 i"
|
"root - Up(2) - Down(3) - Down(4), <pure> - Leaf class=0 belief= "
|
||||||
"mpurity=0.0000 counts=(array([0]), array([7]))",
|
"1.000000 impurity=0.0000 counts=(array([0]), array([7]))",
|
||||||
"root - Up - Down - Up, <cgaf> - Leaf class=1 belief= 0.750000 imp"
|
"root - Up(2) - Down(3) - Up(4), <cgaf> - Leaf class=1 belief= "
|
||||||
"urity=0.8113 counts=(array([0, 1]), array([1, 3]))",
|
"0.750000 impurity=0.8113 counts=(array([0, 1]), array([1, 3]))",
|
||||||
"root - Up - Up, <cgaf> - Leaf class=1 belief= 0.975989 impurity=0"
|
"root - Up(2) - Up(3), <cgaf> - Leaf class=1 belief= 0.975989 "
|
||||||
".1634 counts=(array([0, 1]), array([ 17, 691]))",
|
"impurity=0.1634 counts=(array([0, 1]), array([ 17, 691]))",
|
||||||
]
|
]
|
||||||
computed = []
|
computed = []
|
||||||
expected_string = ""
|
expected_string = ""
|
||||||
@@ -198,10 +198,10 @@ class Stree_test(unittest.TestCase):
|
|||||||
"Synt": {
|
"Synt": {
|
||||||
"max_samples linear": 0.9606666666666667,
|
"max_samples linear": 0.9606666666666667,
|
||||||
"max_samples rbf": 0.7133333333333334,
|
"max_samples rbf": 0.7133333333333334,
|
||||||
"max_samples poly": 0.49066666666666664,
|
"max_samples poly": 0.618,
|
||||||
"impurity linear": 0.9606666666666667,
|
"impurity linear": 0.9606666666666667,
|
||||||
"impurity rbf": 0.7133333333333334,
|
"impurity rbf": 0.7133333333333334,
|
||||||
"impurity poly": 0.49066666666666664,
|
"impurity poly": 0.618,
|
||||||
},
|
},
|
||||||
"Iris": {
|
"Iris": {
|
||||||
"max_samples linear": 1.0,
|
"max_samples linear": 1.0,
|
||||||
@@ -378,9 +378,14 @@ class Stree_test(unittest.TestCase):
|
|||||||
n_samples=500,
|
n_samples=500,
|
||||||
)
|
)
|
||||||
clf = Stree(kernel="rbf", random_state=self._random_state)
|
clf = Stree(kernel="rbf", random_state=self._random_state)
|
||||||
self.assertEqual(0.824, clf.fit(X, y).score(X, y))
|
clf2 = Stree(
|
||||||
|
kernel="rbf", random_state=self._random_state, normalize=True
|
||||||
|
)
|
||||||
|
self.assertEqual(0.768, clf.fit(X, y).score(X, y))
|
||||||
|
self.assertEqual(0.814, clf2.fit(X, y).score(X, y))
|
||||||
X, y = load_wine(return_X_y=True)
|
X, y = load_wine(return_X_y=True)
|
||||||
self.assertEqual(0.6741573033707865, clf.fit(X, y).score(X, y))
|
self.assertEqual(0.6741573033707865, clf.fit(X, y).score(X, y))
|
||||||
|
self.assertEqual(1.0, clf2.fit(X, y).score(X, y))
|
||||||
|
|
||||||
def test_score_multiclass_poly(self):
|
def test_score_multiclass_poly(self):
|
||||||
X, y = load_dataset(
|
X, y = load_dataset(
|
||||||
@@ -392,9 +397,16 @@ class Stree_test(unittest.TestCase):
|
|||||||
clf = Stree(
|
clf = Stree(
|
||||||
kernel="poly", random_state=self._random_state, C=10, degree=5
|
kernel="poly", random_state=self._random_state, C=10, degree=5
|
||||||
)
|
)
|
||||||
|
clf2 = Stree(
|
||||||
|
kernel="poly",
|
||||||
|
random_state=self._random_state,
|
||||||
|
normalize=True,
|
||||||
|
)
|
||||||
self.assertEqual(0.786, clf.fit(X, y).score(X, y))
|
self.assertEqual(0.786, clf.fit(X, y).score(X, y))
|
||||||
|
self.assertEqual(0.818, clf2.fit(X, y).score(X, y))
|
||||||
X, y = load_wine(return_X_y=True)
|
X, y = load_wine(return_X_y=True)
|
||||||
self.assertEqual(0.702247191011236, clf.fit(X, y).score(X, y))
|
self.assertEqual(0.702247191011236, clf.fit(X, y).score(X, y))
|
||||||
|
self.assertEqual(0.6067415730337079, clf2.fit(X, y).score(X, y))
|
||||||
|
|
||||||
def test_score_multiclass_linear(self):
|
def test_score_multiclass_linear(self):
|
||||||
X, y = load_dataset(
|
X, y = load_dataset(
|
||||||
@@ -405,8 +417,14 @@ class Stree_test(unittest.TestCase):
|
|||||||
)
|
)
|
||||||
clf = Stree(kernel="linear", random_state=self._random_state)
|
clf = Stree(kernel="linear", random_state=self._random_state)
|
||||||
self.assertEqual(0.9533333333333334, clf.fit(X, y).score(X, y))
|
self.assertEqual(0.9533333333333334, clf.fit(X, y).score(X, y))
|
||||||
|
# Check with context based standardization
|
||||||
|
clf2 = Stree(
|
||||||
|
kernel="linear", random_state=self._random_state, normalize=True
|
||||||
|
)
|
||||||
|
self.assertEqual(0.9526666666666667, clf2.fit(X, y).score(X, y))
|
||||||
X, y = load_wine(return_X_y=True)
|
X, y = load_wine(return_X_y=True)
|
||||||
self.assertEqual(0.9550561797752809, clf.fit(X, y).score(X, y))
|
self.assertEqual(0.9831460674157303, clf.fit(X, y).score(X, y))
|
||||||
|
self.assertEqual(1.0, clf2.fit(X, y).score(X, y))
|
||||||
|
|
||||||
def test_zero_all_sample_weights(self):
|
def test_zero_all_sample_weights(self):
|
||||||
X, y = load_dataset(self._random_state)
|
X, y = load_dataset(self._random_state)
|
||||||
@@ -439,3 +457,55 @@ class Stree_test(unittest.TestCase):
|
|||||||
self.assertEqual(model1.score(X, y), 1)
|
self.assertEqual(model1.score(X, y), 1)
|
||||||
self.assertAlmostEqual(model2.score(X, y), 0.66666667)
|
self.assertAlmostEqual(model2.score(X, y), 0.66666667)
|
||||||
self.assertEqual(model2.score(X, y, w), 1)
|
self.assertEqual(model2.score(X, y, w), 1)
|
||||||
|
|
||||||
|
def test_depth(self):
|
||||||
|
X, y = load_dataset(
|
||||||
|
random_state=self._random_state,
|
||||||
|
n_classes=3,
|
||||||
|
n_features=5,
|
||||||
|
n_samples=1500,
|
||||||
|
)
|
||||||
|
clf = Stree(random_state=self._random_state)
|
||||||
|
clf.fit(X, y)
|
||||||
|
self.assertEqual(6, clf.depth_)
|
||||||
|
X, y = load_wine(return_X_y=True)
|
||||||
|
clf = Stree(random_state=self._random_state)
|
||||||
|
clf.fit(X, y)
|
||||||
|
self.assertEqual(4, clf.depth_)
|
||||||
|
|
||||||
|
def test_nodes_leaves(self):
|
||||||
|
X, y = load_dataset(
|
||||||
|
random_state=self._random_state,
|
||||||
|
n_classes=3,
|
||||||
|
n_features=5,
|
||||||
|
n_samples=1500,
|
||||||
|
)
|
||||||
|
clf = Stree(random_state=self._random_state)
|
||||||
|
clf.fit(X, y)
|
||||||
|
nodes, leaves = clf.nodes_leaves()
|
||||||
|
self.assertEqual(25, nodes)
|
||||||
|
self.assertEquals(13, leaves)
|
||||||
|
X, y = load_wine(return_X_y=True)
|
||||||
|
clf = Stree(random_state=self._random_state)
|
||||||
|
clf.fit(X, y)
|
||||||
|
nodes, leaves = clf.nodes_leaves()
|
||||||
|
self.assertEqual(9, nodes)
|
||||||
|
self.assertEquals(5, leaves)
|
||||||
|
|
||||||
|
def test_nodes_leaves_artificial(self):
|
||||||
|
n1 = Snode(None, [1, 2, 3, 4], [1, 0, 1, 1], [], 0.0, "test1")
|
||||||
|
n2 = Snode(None, [1, 2, 3, 4], [1, 0, 1, 1], [], 0.0, "test2")
|
||||||
|
n3 = Snode(None, [1, 2, 3, 4], [1, 0, 1, 1], [], 0.0, "test3")
|
||||||
|
n4 = Snode(None, [1, 2, 3, 4], [1, 0, 1, 1], [], 0.0, "test4")
|
||||||
|
n5 = Snode(None, [1, 2, 3, 4], [1, 0, 1, 1], [], 0.0, "test5")
|
||||||
|
n6 = Snode(None, [1, 2, 3, 4], [1, 0, 1, 1], [], 0.0, "test6")
|
||||||
|
n1.set_up(n2)
|
||||||
|
n2.set_up(n3)
|
||||||
|
n2.set_down(n4)
|
||||||
|
n3.set_up(n5)
|
||||||
|
n4.set_down(n6)
|
||||||
|
clf = Stree(random_state=self._random_state)
|
||||||
|
clf.tree_ = n1
|
||||||
|
nodes, leaves = clf.nodes_leaves()
|
||||||
|
self.assertEqual(6, nodes)
|
||||||
|
self.assertEqual(2, leaves)
|
||||||
|
|||||||
Reference in New Issue
Block a user