mirror of
https://github.com/Doctorado-ML/Odte.git
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Update to scikit-learn 1.2
This commit is contained in:
Ricardo Montañana Gómez
parent
7300bd66db
commit
cabf926eb1
GPG Key ID:
46064262FD9A7ADE
@ -1,174 +1,174 @@
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{
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"cells": [
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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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"import datetime, time\n",
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"import numpy as np\n",
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"import pandas as pd\n",
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"from sklearn.model_selection import train_test_split, cross_validate\n",
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"from sklearn import tree\n",
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"from sklearn.metrics import classification_report, confusion_matrix, f1_score\n",
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"from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, BaggingClassifier\n",
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"from stree import Stree\n",
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"from odte import Odte\n",
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"\n",
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"random_state = 1"
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]
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"cells": [
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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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"import datetime, time\n",
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"import numpy as np\n",
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"import pandas as pd\n",
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"from sklearn.model_selection import train_test_split, cross_validate\n",
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"from sklearn import tree\n",
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"from sklearn.metrics import classification_report, confusion_matrix, f1_score\n",
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"from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, BaggingClassifier\n",
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"from stree import Stree\n",
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"from odte import Odte\n",
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"\n",
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"random_state = 1"
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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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"from sklearn.datasets import load_wine\n",
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"X, y = load_wine(return_X_y=True)\n",
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"Xtrain, Xtest, ytrain, ytest = train_test_split(X, y, test_size=.2, random_state=random_state)"
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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_estimators = 20\n",
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"clf = {}\n",
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"clf[\"stree\"] = Stree(random_state=random_state, max_depth=5)\n",
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"clf[\"stree\"].set_params(**dict(splitter=\"best\", kernel=\"linear\", max_features=\"auto\"))\n",
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"clf[\"odte\"] = Odte(n_jobs=-1, estimator=clf[\"stree\"], random_state=random_state, n_estimators=n_estimators, max_features=.8)\n",
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"clf[\"adaboost\"] = AdaBoostClassifier(estimator=clf[\"stree\"], n_estimators=n_estimators, random_state=random_state, algorithm=\"SAMME\")\n",
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"clf[\"bagging\"] = BaggingClassifier(estimator=clf[\"stree\"], n_estimators=n_estimators)"
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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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"tags": []
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},
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"outputs": [],
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"source": [
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"print(\"*\"*30,\"Results for wine\", \"*\"*30)\n",
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"for clf_type, item in clf.items():\n",
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" print(f\"Training {clf_type}...\")\n",
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" now = time.time()\n",
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" item.fit(Xtrain, ytrain)\n",
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" print(f\"Score: {item.score(Xtest, ytest) * 100:.3f} in {time.time()-now:.2f} seconds\")"
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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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"from sklearn.datasets import load_iris\n",
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"X, y = load_iris(return_X_y=True)\n",
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"Xtrain, Xtest, ytrain, ytest = train_test_split(X, y, test_size=.2, random_state=random_state)"
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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_estimators = 10\n",
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"clf = {}\n",
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"clf[\"stree\"] = Stree(random_state=random_state, max_depth=3)\n",
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"clf[\"odte\"] = Odte(n_jobs=-1, random_state=random_state, n_estimators=n_estimators, max_features=1.0)\n",
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"clf[\"adaboost\"] = AdaBoostClassifier(estimator=clf[\"stree\"], n_estimators=n_estimators, random_state=random_state, algorithm=\"SAMME\")\n",
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"clf[\"bagging\"] = BaggingClassifier(estimator=clf[\"stree\"], n_estimators=n_estimators)"
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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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"tags": []
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},
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"outputs": [],
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"source": [
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"print(\"*\"*30,\"Results for iris\", \"*\"*30)\n",
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"for clf_type, item in clf.items():\n",
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" print(f\"Training {clf_type}...\")\n",
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" now = time.time()\n",
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" item.fit(Xtrain, ytrain)\n",
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" print(f\"Score: {item.score(Xtest, ytest) * 100:.3f} in {time.time()-now:.2f} seconds\")"
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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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"tags": []
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},
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"outputs": [],
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"source": [
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"cross = cross_validate(estimator=clf[\"odte\"], X=X, y=y, n_jobs=-1, return_train_score=True)\n",
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"print(cross)\n",
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"print(f\"{np.mean(cross['test_score'])*100:.3f} +- {np.std(cross['test_score']):.3f}\")"
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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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"tags": []
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},
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"outputs": [],
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"source": [
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"cross = cross_validate(estimator=clf[\"adaboost\"], X=X, y=y, n_jobs=-1, return_train_score=True)\n",
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"print(cross)\n",
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"print(f\"{np.mean(cross['test_score'])*100:.3f} +- {np.std(cross['test_score']):.3f}\")"
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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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"tags": []
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},
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"outputs": [],
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"source": [
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"from sklearn.utils.estimator_checks import check_estimator\n",
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"# Make checks one by one\n",
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"c = 0\n",
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"checks = check_estimator(Odte(), generate_only=True)\n",
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"for check in checks:\n",
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" c += 1\n",
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" print(c, check[1])\n",
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" check[1](check[0])"
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]
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}
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],
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"metadata": {
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"interpreter": {
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"hash": "da86226729227d0e8962a5ec29ea906307507ca2c30ceaaf651c09a617630939"
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},
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"kernelspec": {
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"display_name": "Python 3.9.2 64-bit ('general': venv)",
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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": "3.9.7"
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},
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"orig_nbformat": 2
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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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"from sklearn.datasets import load_wine\n",
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"X, y = load_wine(return_X_y=True)\n",
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"Xtrain, Xtest, ytrain, ytest = train_test_split(X, y, test_size=.2, random_state=random_state)"
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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_estimators = 20\n",
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"clf = {}\n",
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"clf[\"stree\"] = Stree(random_state=random_state, max_depth=5)\n",
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"clf[\"stree\"].set_params(**dict(splitter=\"best\", kernel=\"linear\", max_features=\"auto\"))\n",
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"clf[\"odte\"] = Odte(n_jobs=-1, base_estimator=clf[\"stree\"], random_state=random_state, n_estimators=n_estimators, max_features=.8)\n",
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"clf[\"adaboost\"] = AdaBoostClassifier(base_estimator=clf[\"stree\"], n_estimators=n_estimators, random_state=random_state, algorithm=\"SAMME\")\n",
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"clf[\"bagging\"] = BaggingClassifier(base_estimator=clf[\"stree\"], n_estimators=n_estimators)"
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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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"tags": []
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},
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"outputs": [],
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"source": [
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"print(\"*\"*30,\"Results for wine\", \"*\"*30)\n",
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"for clf_type, item in clf.items():\n",
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" print(f\"Training {clf_type}...\")\n",
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" now = time.time()\n",
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" item.fit(Xtrain, ytrain)\n",
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" print(f\"Score: {item.score(Xtest, ytest) * 100:.3f} in {time.time()-now:.2f} seconds\")"
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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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"from sklearn.datasets import load_iris\n",
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"X, y = load_iris(return_X_y=True)\n",
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"Xtrain, Xtest, ytrain, ytest = train_test_split(X, y, test_size=.2, random_state=random_state)"
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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_estimators = 10\n",
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"clf = {}\n",
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"clf[\"stree\"] = Stree(random_state=random_state, max_depth=3)\n",
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"clf[\"odte\"] = Odte(n_jobs=-1, random_state=random_state, n_estimators=n_estimators, max_features=1.0)\n",
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"clf[\"adaboost\"] = AdaBoostClassifier(base_estimator=clf[\"stree\"], n_estimators=n_estimators, random_state=random_state, algorithm=\"SAMME\")\n",
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"clf[\"bagging\"] = BaggingClassifier(base_estimator=clf[\"stree\"], n_estimators=n_estimators)"
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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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"tags": []
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},
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"outputs": [],
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"source": [
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"print(\"*\"*30,\"Results for iris\", \"*\"*30)\n",
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"for clf_type, item in clf.items():\n",
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" print(f\"Training {clf_type}...\")\n",
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" now = time.time()\n",
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" item.fit(Xtrain, ytrain)\n",
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" print(f\"Score: {item.score(Xtest, ytest) * 100:.3f} in {time.time()-now:.2f} seconds\")"
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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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"tags": []
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},
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"outputs": [],
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"source": [
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"cross = cross_validate(estimator=clf[\"odte\"], X=X, y=y, n_jobs=-1, return_train_score=True)\n",
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"print(cross)\n",
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"print(f\"{np.mean(cross['test_score'])*100:.3f} +- {np.std(cross['test_score']):.3f}\")"
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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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"tags": []
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},
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"outputs": [],
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"source": [
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"cross = cross_validate(estimator=clf[\"adaboost\"], X=X, y=y, n_jobs=-1, return_train_score=True)\n",
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"print(cross)\n",
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"print(f\"{np.mean(cross['test_score'])*100:.3f} +- {np.std(cross['test_score']):.3f}\")"
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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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"tags": []
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},
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"outputs": [],
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"source": [
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"from sklearn.utils.estimator_checks import check_estimator\n",
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"# Make checks one by one\n",
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"c = 0\n",
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"checks = check_estimator(Odte(), generate_only=True)\n",
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"for check in checks:\n",
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" c += 1\n",
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" print(c, check[1])\n",
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" check[1](check[0])"
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]
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}
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],
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"metadata": {
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"interpreter": {
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"hash": "da86226729227d0e8962a5ec29ea906307507ca2c30ceaaf651c09a617630939"
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},
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"kernelspec": {
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"display_name": "Python 3.9.2 64-bit ('general': venv)",
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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": "3.9.7"
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},
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"orig_nbformat": 2
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},
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"nbformat": 4,
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"nbformat_minor": 2
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"nbformat": 4,
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"nbformat_minor": 2
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}
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16
odte/Odte.py
16
odte/Odte.py
@ -31,7 +31,7 @@ class Odte(BaseEnsemble, ClassifierMixin):
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self,
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# n_jobs = -1 to use all available cores
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n_jobs: int = -1,
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base_estimator: BaseEstimator = None,
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estimator: BaseEstimator = None,
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random_state: int = 0,
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max_features: Optional[Union[str, int, float]] = None,
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max_samples: Optional[Union[int, float]] = None,
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@ -39,10 +39,10 @@ class Odte(BaseEnsemble, ClassifierMixin):
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be_hyperparams: str = "{}",
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):
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super().__init__(
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base_estimator=base_estimator,
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estimator=estimator,
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n_estimators=n_estimators,
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)
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self.base_estimator = base_estimator
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self.estimator = estimator
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self.n_jobs = n_jobs
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self.n_estimators = n_estimators
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self.random_state = random_state
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@ -55,7 +55,7 @@ class Odte(BaseEnsemble, ClassifierMixin):
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return __version__
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def _validate_estimator(self) -> None:
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"""Check the estimator and set the base_estimator_ attribute."""
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"""Check the estimator and set the estimator_ attribute."""
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super()._validate_estimator(
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default=Stree(random_state=self.random_state)
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)
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@ -79,7 +79,7 @@ class Odte(BaseEnsemble, ClassifierMixin):
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# Initialize computed parameters
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# Build the estimator
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self.max_features_ = self._initialize_max_features()
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# build base_estimator_
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# build estimator_
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self._validate_estimator()
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self.classes_, y = np.unique(y, return_inverse=True)
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self.n_classes_: int = self.classes_.shape[0]
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@ -108,7 +108,7 @@ class Odte(BaseEnsemble, ClassifierMixin):
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) -> Tuple[List[BaseEstimator], List[Tuple[int, ...]]]:
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n_samples = X.shape[0]
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boot_samples = self._get_bootstrap_n_samples(n_samples)
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estimator = clone(self.base_estimator_)
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estimator = clone(self.estimator_)
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return Parallel(n_jobs=self.n_jobs, prefer="threads")( # type: ignore
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delayed(Odte._parallel_build_tree)(
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estimator,
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@ -127,7 +127,7 @@ class Odte(BaseEnsemble, ClassifierMixin):
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@staticmethod
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def _parallel_build_tree(
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base_estimator_: BaseEstimator,
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estimator_: BaseEstimator,
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X: np.ndarray,
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y: np.ndarray,
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weights: np.ndarray,
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@ -136,7 +136,7 @@ class Odte(BaseEnsemble, ClassifierMixin):
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max_features: int,
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hyperparams: str,
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) -> Tuple[BaseEstimator, Tuple[int, ...]]:
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clf = clone(base_estimator_)
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clf = clone(estimator_)
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hyperparams_ = json.loads(hyperparams)
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hyperparams_.update(dict(random_state=random_seed))
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clf.set_params(**hyperparams_)
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@ -76,15 +76,15 @@ class Odte_test(unittest.TestCase):
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X, y = [[1, 2], [5, 6], [9, 10], [16, 17]], [0, 1, 1, 2]
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expected = [0, 1, 1, 2]
|
||||
tclf = Odte(
|
||||
base_estimator=Stree(),
|
||||
estimator=Stree(),
|
||||
random_state=self._random_state,
|
||||
n_estimators=10,
|
||||
n_jobs=-1,
|
||||
)
|
||||
tclf.set_params(
|
||||
**dict(
|
||||
base_estimator__kernel="rbf",
|
||||
base_estimator__random_state=self._random_state,
|
||||
estimator__kernel="rbf",
|
||||
estimator__random_state=self._random_state,
|
||||
)
|
||||
)
|
||||
computed = tclf.fit(X, y).predict(X)
|
||||
@ -96,14 +96,14 @@ class Odte_test(unittest.TestCase):
|
||||
X, y = load_dataset(self._random_state)
|
||||
expected = y
|
||||
tclf = Odte(
|
||||
base_estimator=Stree(),
|
||||
estimator=Stree(),
|
||||
random_state=self._random_state,
|
||||
max_features=1.0,
|
||||
max_samples=0.1,
|
||||
)
|
||||
tclf.set_params(
|
||||
**dict(
|
||||
base_estimator__kernel="linear",
|
||||
estimator__kernel="linear",
|
||||
)
|
||||
)
|
||||
computed = tclf.fit(X, y).predict(X)
|
||||
@ -146,16 +146,16 @@ class Odte_test(unittest.TestCase):
|
||||
"cfs",
|
||||
]:
|
||||
tclf = Odte(
|
||||
base_estimator=Stree(),
|
||||
estimator=Stree(),
|
||||
random_state=self._random_state,
|
||||
n_estimators=3,
|
||||
n_jobs=1,
|
||||
)
|
||||
tclf.set_params(
|
||||
**dict(
|
||||
base_estimator__max_features=max_features,
|
||||
base_estimator__splitter=splitter,
|
||||
base_estimator__random_state=self._random_state,
|
||||
estimator__max_features=max_features,
|
||||
estimator__splitter=splitter,
|
||||
estimator__random_state=self._random_state,
|
||||
)
|
||||
)
|
||||
expected = results.pop(0)
|
||||
@ -182,7 +182,7 @@ class Odte_test(unittest.TestCase):
|
||||
|
||||
def test_nodes_leaves_not_fitted(self):
|
||||
tclf = Odte(
|
||||
base_estimator=Stree(),
|
||||
estimator=Stree(),
|
||||
random_state=self._random_state,
|
||||
n_estimators=3,
|
||||
)
|
||||
@ -191,13 +191,13 @@ class Odte_test(unittest.TestCase):
|
||||
|
||||
def test_nodes_leaves_depth(self):
|
||||
tclf = Odte(
|
||||
base_estimator=Stree(),
|
||||
estimator=Stree(),
|
||||
random_state=self._random_state,
|
||||
n_estimators=5,
|
||||
n_jobs=1,
|
||||
)
|
||||
tclf_p = Odte(
|
||||
base_estimator=Stree(),
|
||||
estimator=Stree(),
|
||||
random_state=self._random_state,
|
||||
n_estimators=5,
|
||||
n_jobs=-1,
|
||||
@ -215,7 +215,7 @@ class Odte_test(unittest.TestCase):
|
||||
|
||||
def test_nodes_leaves_SVC(self):
|
||||
tclf = Odte(
|
||||
base_estimator=SVC(),
|
||||
estimator=SVC(),
|
||||
random_state=self._random_state,
|
||||
n_estimators=3,
|
||||
)
|
||||
@ -227,7 +227,7 @@ class Odte_test(unittest.TestCase):
|
||||
self.assertAlmostEqual(0.0, leaves)
|
||||
self.assertAlmostEqual(0.0, nodes)
|
||||
|
||||
def test_base_estimator_hyperparams(self):
|
||||
def test_estimator_hyperparams(self):
|
||||
data = [
|
||||
(Stree(), {"max_features": 7, "max_depth": 2}),
|
||||
(SVC(), {"kernel": "linear", "cache_size": 100}),
|
||||
@ -235,7 +235,7 @@ class Odte_test(unittest.TestCase):
|
||||
for clf, hyperparams in data:
|
||||
hyperparams_ = json.dumps(hyperparams)
|
||||
tclf = Odte(
|
||||
base_estimator=clf,
|
||||
estimator=clf,
|
||||
random_state=self._random_state,
|
||||
n_estimators=3,
|
||||
be_hyperparams=hyperparams_,
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user