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{ "cells": [ { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "# Hyperparameter optimization" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Superconductivty Data Set: Predict the critical temperature based on 81 material features." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.metrics import mean_squared_error" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "filename = \"https://www.physi.uni-heidelberg.de/~reygers/lectures/2021/ml/data/train_critical_temp.csv\"\n", "df = pd.read_csv(filename, engine='python')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "df.head()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "y = df['critical_temp'].values\n", "X = df[[col for col in df.columns if col!=\"critical_temp\"]]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, shuffle=True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from sklearn.neural_network import MLPRegressor\n", "import time\n", "\n", "mlpr = MLPRegressor(hidden_layer_sizes=(50,50), activation='relu', random_state=1, max_iter=5000)\n", "\n", "start_time = time.time()\n", "mlpr.fit(X_train, y_train)\n", "run_time = time.time() - start_time\n", "\n", "print(run_time)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "y_pred = mlpr.predict(X_test)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plt.scatter(y_test, y_pred, s=2)\n", "plt.xlabel(\"true critical temperature (K)\", fontsize=14)\n", "plt.ylabel(\"predicted critical temperature (K)\", fontsize=14)\n", "plt.savefig(\"critical_temperature.pdf\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "rms = np.sqrt(mean_squared_error(y_test, y_pred))\n", "print(f\"root mean square error {rms:.2f}\")" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "## Now try to optimize the hyperparameters" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from sklearn.model_selection import GridSearchCV\n", "\n", "### Your code here\n" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.11" } }, "nbformat": 4, "nbformat_minor": 4}
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