mirror of
https://github.com/Doctorado-ML/STree.git
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New_version_sklearn (#56)
* test: 🧪 Update max_iter as int in test_multiclass_dataset * refactor: 📝 Rename base_estimator to estimator as the former is deprectated in notebook * refactor: 📌 Convert max_iter to int as needed in sklearn 1.2 * chore: 🔖 Update version info to 1.3.1
This commit is contained in:
Ricardo Montañana Gómez
committed by
GitHub
GitHub
parent
c37f044e3a
commit
5b791bc5bf
@@ -1,253 +1,253 @@
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Test Gridsearch\n",
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"with different kernels and different configurations"
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]
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Test Gridsearch\n",
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"with different kernels and different configurations"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Setup\n",
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"Uncomment the next cell if STree is not already installed"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"#\n",
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"# Google Colab setup\n",
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"#\n",
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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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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"colab": {},
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"colab_type": "code",
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"id": "zIHKVxthDZEa"
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},
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"outputs": [],
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"source": [
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"import random\n",
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"import os\n",
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"import pandas as pd\n",
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"import numpy as np\n",
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"from sklearn.ensemble import AdaBoostClassifier\n",
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"from sklearn.svm import LinearSVC\n",
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"from sklearn.model_selection import GridSearchCV, train_test_split\n",
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"from stree import Stree"
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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": null,
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"metadata": {
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"colab": {},
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"colab_type": "code",
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"id": "IEmq50QgDZEi"
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},
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"outputs": [],
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"source": [
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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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" !tar xzf creditcard.tgz"
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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": null,
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"metadata": {
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"colab": {},
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"colab_type": "code",
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"id": "z9Q-YUfBDZEq",
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"outputId": "afc822fb-f16a-4302-8a67-2b9e2880159b",
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"tags": []
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},
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"outputs": [],
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"source": [
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"random_state=1\n",
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"\n",
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"def load_creditcard(n_examples=0):\n",
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" df = pd.read_csv('data/creditcard.csv')\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()))\n",
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" y = df.Class\n",
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" X = df.drop(['Class', 'Time', 'Amount'], axis=1).values\n",
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" if n_examples > 0:\n",
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" # Take first n_examples samples\n",
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" X = X[:n_examples, :]\n",
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" y = y[:n_examples, :]\n",
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" else:\n",
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" # Take all the positive samples with a number of random negatives\n",
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" if n_examples < 0:\n",
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" Xt = X[(y == 1).ravel()]\n",
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" yt = y[(y == 1).ravel()]\n",
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" indices = random.sample(range(X.shape[0]), -1 * n_examples)\n",
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" X = np.append(Xt, X[indices], axis=0)\n",
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" y = np.append(yt, y[indices], axis=0)\n",
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" print(\"X.shape\", X.shape, \" y.shape\", y.shape)\n",
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" print(\"Fraud: {0:.3f}% {1}\".format(len(y[y == 1])*100/X.shape[0], len(y[y == 1])))\n",
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" print(\"Valid: {0:.3f}% {1}\".format(len(y[y == 0]) * 100 / X.shape[0], len(y[y == 0])))\n",
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" Xtrain, Xtest, ytrain, ytest = train_test_split(X, y, train_size=0.7, shuffle=True, random_state=random_state, stratify=y)\n",
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" return Xtrain, Xtest, ytrain, ytest\n",
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"\n",
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"data = load_creditcard(-1000) # Take all true samples + 1000 of the others\n",
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"# data = load_creditcard(5000) # Take the first 5000 samples\n",
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"# data = load_creditcard(0) # Take all the samples\n",
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"\n",
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"Xtrain = data[0]\n",
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"Xtest = data[1]\n",
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"ytrain = data[2]\n",
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"ytest = data[3]"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Tests"
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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": null,
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"metadata": {
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"colab": {},
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"colab_type": "code",
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"id": "HmX3kR4PDZEw"
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},
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"outputs": [],
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"source": [
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"parameters = [{\n",
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" 'base_estimator': [Stree(random_state=random_state)],\n",
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" 'n_estimators': [10, 25],\n",
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" 'learning_rate': [.5, 1],\n",
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" 'estimator__split_criteria': ['max_samples', 'impurity'],\n",
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" 'estimator__tol': [.1, 1e-02],\n",
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" 'estimator__max_depth': [3, 5, 7],\n",
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" 'estimator__C': [1, 7, 55],\n",
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" 'estimator__kernel': ['linear']\n",
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"},\n",
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"{\n",
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" 'base_estimator': [Stree(random_state=random_state)],\n",
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" 'n_estimators': [10, 25],\n",
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" 'learning_rate': [.5, 1],\n",
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" 'estimator__split_criteria': ['max_samples', 'impurity'],\n",
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" 'estimator__tol': [.1, 1e-02],\n",
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" 'estimator__max_depth': [3, 5, 7],\n",
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" 'estimator__C': [1, 7, 55],\n",
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" 'estimator__degree': [3, 5, 7],\n",
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" 'estimator__kernel': ['poly']\n",
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"},\n",
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"{\n",
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" 'base_estimator': [Stree(random_state=random_state)],\n",
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" 'n_estimators': [10, 25],\n",
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" 'learning_rate': [.5, 1],\n",
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" 'estimator__split_criteria': ['max_samples', 'impurity'],\n",
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" 'estimator__tol': [.1, 1e-02],\n",
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" 'estimator__max_depth': [3, 5, 7],\n",
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" 'estimator__C': [1, 7, 55],\n",
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" 'estimator__gamma': [.1, 1, 10],\n",
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" 'estimator__kernel': ['rbf']\n",
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"Stree().get_params()"
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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": null,
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"metadata": {
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"colab": {},
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"colab_type": "code",
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"id": "CrcB8o6EDZE5",
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"outputId": "7703413a-d563-4289-a13b-532f38f82762",
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"tags": []
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},
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"outputs": [],
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"source": [
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"clf = AdaBoostClassifier(random_state=random_state, algorithm=\"SAMME\")\n",
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"grid = GridSearchCV(clf, parameters, verbose=5, n_jobs=-1, return_train_score=True)\n",
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"grid.fit(Xtrain, ytrain)"
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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": null,
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"metadata": {
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"colab": {},
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"colab_type": "code",
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"id": "ZjX88NoYDZE8",
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"outputId": "285163c8-fa33-4915-8ae7-61c4f7844344",
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"tags": []
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},
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"outputs": [],
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"source": [
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"print(\"Best estimator: \", grid.best_estimator_)\n",
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"print(\"Best hyperparameters: \", grid.best_params_)\n",
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"print(\"Best accuracy: \", grid.best_score_)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Best estimator: AdaBoostClassifier(algorithm='SAMME',\n",
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" base_estimator=Stree(C=55, max_depth=7, random_state=1,\n",
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" split_criteria='max_samples', tol=0.1),\n",
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" learning_rate=0.5, n_estimators=25, random_state=1)\n",
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"Best hyperparameters: {'base_estimator': Stree(C=55, max_depth=7, random_state=1, split_criteria='max_samples', tol=0.1), 'estimator__C': 55, 'estimator__kernel': 'linear', 'estimator__max_depth': 7, 'estimator__split_criteria': 'max_samples', 'estimator__tol': 0.1, 'learning_rate': 0.5, 'n_estimators': 25}"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Best accuracy: 0.9511777695988222"
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]
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}
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],
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"metadata": {
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"colab": {
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"name": "gridsearch.ipynb",
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"provenance": []
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},
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.2-final"
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}
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
|
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"# Setup\n",
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"Uncomment the next cell if STree is not already installed"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"#\n",
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"# Google Colab setup\n",
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"#\n",
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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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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"colab": {},
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"colab_type": "code",
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"id": "zIHKVxthDZEa"
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},
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"outputs": [],
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"source": [
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"import random\n",
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"import os\n",
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"import pandas as pd\n",
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"import numpy as np\n",
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"from sklearn.ensemble import AdaBoostClassifier\n",
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"from sklearn.svm import LinearSVC\n",
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"from sklearn.model_selection import GridSearchCV, train_test_split\n",
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"from stree import Stree"
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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": null,
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"metadata": {
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"colab": {},
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"colab_type": "code",
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"id": "IEmq50QgDZEi"
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},
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"outputs": [],
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"source": [
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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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" !tar xzf creditcard.tgz"
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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": null,
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"metadata": {
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"colab": {},
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"colab_type": "code",
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"id": "z9Q-YUfBDZEq",
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"outputId": "afc822fb-f16a-4302-8a67-2b9e2880159b",
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"tags": []
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},
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"outputs": [],
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"source": [
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"random_state=1\n",
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"\n",
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"def load_creditcard(n_examples=0):\n",
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" df = pd.read_csv('data/creditcard.csv')\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()))\n",
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" y = df.Class\n",
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" X = df.drop(['Class', 'Time', 'Amount'], axis=1).values\n",
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" if n_examples > 0:\n",
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" # Take first n_examples samples\n",
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" X = X[:n_examples, :]\n",
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" y = y[:n_examples, :]\n",
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" else:\n",
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" # Take all the positive samples with a number of random negatives\n",
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" if n_examples < 0:\n",
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" Xt = X[(y == 1).ravel()]\n",
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" yt = y[(y == 1).ravel()]\n",
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" indices = random.sample(range(X.shape[0]), -1 * n_examples)\n",
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" X = np.append(Xt, X[indices], axis=0)\n",
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" y = np.append(yt, y[indices], axis=0)\n",
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" print(\"X.shape\", X.shape, \" y.shape\", y.shape)\n",
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" print(\"Fraud: {0:.3f}% {1}\".format(len(y[y == 1])*100/X.shape[0], len(y[y == 1])))\n",
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" print(\"Valid: {0:.3f}% {1}\".format(len(y[y == 0]) * 100 / X.shape[0], len(y[y == 0])))\n",
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" Xtrain, Xtest, ytrain, ytest = train_test_split(X, y, train_size=0.7, shuffle=True, random_state=random_state, stratify=y)\n",
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" return Xtrain, Xtest, ytrain, ytest\n",
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"\n",
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"data = load_creditcard(-1000) # Take all true samples + 1000 of the others\n",
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"# data = load_creditcard(5000) # Take the first 5000 samples\n",
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"# data = load_creditcard(0) # Take all the samples\n",
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"\n",
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"Xtrain = data[0]\n",
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"Xtest = data[1]\n",
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"ytrain = data[2]\n",
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"ytest = data[3]"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Tests"
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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": null,
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"metadata": {
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"colab": {},
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"colab_type": "code",
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"id": "HmX3kR4PDZEw"
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},
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"outputs": [],
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"source": [
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"parameters = [{\n",
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" 'base_estimator': [Stree(random_state=random_state)],\n",
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" 'n_estimators': [10, 25],\n",
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" 'learning_rate': [.5, 1],\n",
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" 'base_estimator__split_criteria': ['max_samples', 'impurity'],\n",
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" 'base_estimator__tol': [.1, 1e-02],\n",
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" 'base_estimator__max_depth': [3, 5, 7],\n",
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" 'base_estimator__C': [1, 7, 55],\n",
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" 'base_estimator__kernel': ['linear']\n",
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"},\n",
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"{\n",
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" 'base_estimator': [Stree(random_state=random_state)],\n",
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" 'n_estimators': [10, 25],\n",
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" 'learning_rate': [.5, 1],\n",
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" 'base_estimator__split_criteria': ['max_samples', 'impurity'],\n",
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" 'base_estimator__tol': [.1, 1e-02],\n",
|
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" 'base_estimator__max_depth': [3, 5, 7],\n",
|
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" 'base_estimator__C': [1, 7, 55],\n",
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" 'base_estimator__degree': [3, 5, 7],\n",
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" 'base_estimator__kernel': ['poly']\n",
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"},\n",
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"{\n",
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" 'base_estimator': [Stree(random_state=random_state)],\n",
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" 'n_estimators': [10, 25],\n",
|
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" 'learning_rate': [.5, 1],\n",
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" 'base_estimator__split_criteria': ['max_samples', 'impurity'],\n",
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" 'base_estimator__tol': [.1, 1e-02],\n",
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" 'base_estimator__max_depth': [3, 5, 7],\n",
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" 'base_estimator__C': [1, 7, 55],\n",
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" 'base_estimator__gamma': [.1, 1, 10],\n",
|
||||
" 'base_estimator__kernel': ['rbf']\n",
|
||||
"}]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"Stree().get_params()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"colab": {},
|
||||
"colab_type": "code",
|
||||
"id": "CrcB8o6EDZE5",
|
||||
"outputId": "7703413a-d563-4289-a13b-532f38f82762",
|
||||
"tags": []
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"clf = AdaBoostClassifier(random_state=random_state, algorithm=\"SAMME\")\n",
|
||||
"grid = GridSearchCV(clf, parameters, verbose=5, n_jobs=-1, return_train_score=True)\n",
|
||||
"grid.fit(Xtrain, ytrain)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"colab": {},
|
||||
"colab_type": "code",
|
||||
"id": "ZjX88NoYDZE8",
|
||||
"outputId": "285163c8-fa33-4915-8ae7-61c4f7844344",
|
||||
"tags": []
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(\"Best estimator: \", grid.best_estimator_)\n",
|
||||
"print(\"Best hyperparameters: \", grid.best_params_)\n",
|
||||
"print(\"Best accuracy: \", grid.best_score_)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"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}"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Best accuracy: 0.9511777695988222"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"colab": {
|
||||
"name": "gridsearch.ipynb",
|
||||
"provenance": []
|
||||
},
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.8.2-final"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 4
|
||||
}
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 4
|
||||
}
|
||||
|
||||
@@ -139,7 +139,7 @@ class Stree(BaseEstimator, ClassifierMixin):
|
||||
self,
|
||||
C: float = 1.0,
|
||||
kernel: str = "linear",
|
||||
max_iter: int = 1e5,
|
||||
max_iter: int = int(1e5),
|
||||
random_state: int = None,
|
||||
max_depth: int = None,
|
||||
tol: float = 1e-4,
|
||||
|
||||
@@ -1 +1 @@
|
||||
__version__ = "1.3.0"
|
||||
__version__ = "1.3.1"
|
||||
|
||||
@@ -306,7 +306,7 @@ class Stree_test(unittest.TestCase):
|
||||
for criteria in ["max_samples", "impurity"]:
|
||||
for kernel in self._kernels:
|
||||
clf = Stree(
|
||||
max_iter=1e4,
|
||||
max_iter=int(1e4),
|
||||
multiclass_strategy="ovr"
|
||||
if kernel == "liblinear"
|
||||
else "ovo",
|
||||
|
||||
Reference in New Issue
Block a user