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