{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "from pycaret.classification import *\n", "\n", "data = pd.read_csv('data/data_all.csv')\n", "print(data)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "m_setup = setup(data=data, target='label', normalize=True, #bin_numeric_features=['illum'],\n", " feature_interaction=False, \n", " feature_ratio=False,\n", " trigonometry_features=True,\n", " use_gpu=True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "m_setup" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "best_model = compare_models(n_select=6)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# tuned_top = [tune_model(x) for x in best_model]\n", "bagged_top = [ensemble_model(x) for x in tuned_top]\n", "blender = blend_models(estimator_list=best_model)\n", "best = automl(optimize = 'F1')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "m = create_model('rf')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "m_tune = tune_model(m, optimize='Kappa')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# plot_model(tuned_dt, plot='auc')\n", "evaluate_model(m)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plot_model(tuned_dt, plot='feature')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "interpreter": { "hash": "916dbcbb3f70747c44a77c7bcd40155683ae19c65e1c03b4aa3499c5328201f1" }, "kernelspec": { "display_name": "Python 3.8.10 64-bit", "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": "3.8.8" }, "orig_nbformat": 4 }, "nbformat": 4, "nbformat_minor": 2 }