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symbolic_regression in energy engineering.ipynb
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copy-of-symbolic_regression.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/kokovariax/de9d6640b59fbd0e53475e15edd3da75/copy-of-symbolic_regression.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "dDlAghnJQJ_0"
},
"source": [
"# **Symbolic Regression**"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "CJj9EiXhQJ__"
},
"source": [
"### 4.1 Importing libraries"
]
},
{
"cell_type": "markdown",
"source": [],
"metadata": {
"id": "f9uYq5H6TGGz"
}
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "fuG9SuqYQJ__",
"outputId": "d3b2b979-5f71-4908-d7fd-c3bb92ca1308",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Requirement already satisfied: gplearn in /usr/local/lib/python3.10/dist-packages (0.4.2)\n",
"Requirement already satisfied: scikit-learn>=1.0.2 in /usr/local/lib/python3.10/dist-packages (from gplearn) (1.2.2)\n",
"Requirement already satisfied: joblib>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from gplearn) (1.4.0)\n",
"Requirement already satisfied: numpy>=1.17.3 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=1.0.2->gplearn) (1.25.2)\n",
"Requirement already satisfied: scipy>=1.3.2 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=1.0.2->gplearn) (1.11.4)\n",
"Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=1.0.2->gplearn) (3.4.0)\n"
]
}
],
"source": [
"!pip install gplearn\n",
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"from gplearn.genetic import SymbolicRegressor\n",
"import sklearn\n",
"from sklearn.ensemble import RandomForestRegressor\n",
"from sklearn.tree import DecisionTreeRegressor\n",
"from sympy import *\n",
"from sklearn.utils.random import check_random_state\n",
"import graphviz\n",
"import time"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "by_bqy6XQKAA"
},
"source": [
"### 4.2 Initial dataset and data analysis"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "nNbIbA0hQKAA"
},
"source": [
"Initial dataset:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "yqBy1TneQKAB"
},
"outputs": [],
"source": [
"nsample = 1000\n",
"sig = 0.2"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "jVjBg67gQKAB"
},
"outputs": [],
"source": [
"def Lw(Dc):\n",
" L_p = 40 # Example value for L_p\n",
" h_t = 90 # Example value for h_t\n",
" return L_p + 10 * np.log10(2 * np.pi * (h_t**2 + Dc**2)) + (0.005 * np.sqrt(h_t**2 + Dc**2))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "_MDZf14KQKAB"
},
"outputs": [],
"source": [
"rng = check_random_state(0)\n",
"Dc = rng.uniform(100, 1000, nsample)\n",
"Lt = Lw(Dc) + sig*np.random.normal(size=nsample)"
]
},
{
"cell_type": "code",
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},
"outputId": "0bdbabc9-5969-4d83-ab36-55234f74754b"
},
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{
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" Dc Lt\n",
"0 593.932154 106.382209\n",
"1 743.670430 109.097119\n",
"2 642.487038 107.295649\n",
"3 590.394865 106.392511\n",
"4 481.289319 104.215250"
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"execution_count": 87
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"\">\n",
" </div>\n",
" <script type=\"text/javascript\">\n",
" (() => {\n",
" const chartElement = document.getElementById(\"chart-d8967959-6e57-4950-aeed-2404e4bc119a\");\n",
" async function getCodeForChartHandler(event) {\n",
" const chartCodeResponse = await google.colab.kernel.invokeFunction(\n",
" 'getCodeForChart', [\"chart-d8967959-6e57-4950-aeed-2404e4bc119a\"], {});\n",
" const responseJson = chartCodeResponse.data['application/json'];\n",
" await google.colab.notebook.addCell(responseJson.code, 'code');\n",
" }\n",
" chartElement.onclick = getCodeForChartHandler;\n",
" })();\n",
" </script>\n",
" <style>\n",
" .colab-quickchart-chart-with-code {\n",
" display: block;\n",
" float: left;\n",
" border: 1px solid transparent;\n",
" }\n",
"\n",
" .colab-quickchart-chart-with-code:hover {\n",
" cursor: pointer;\n",
" border: 1px solid #aaa;\n",
" }\n",
" </style>"
]
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"from matplotlib import pyplot as plt\n",
"_df_1['Dc'].plot(kind='hist', bins=20, title='Dc')\n",
"plt.gca().spines[['top', 'right',]].set_visible(False)"
],
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"tqT2fxuVk9QAAAAASUVORK5CYII=\n",
"\">\n",
" </div>\n",
" <script type=\"text/javascript\">\n",
" (() => {\n",
" const chartElement = document.getElementById(\"chart-691ffd14-0546-4b0a-b956-ef0af9e07cd1\");\n",
" async function getCodeForChartHandler(event) {\n",
" const chartCodeResponse = await google.colab.kernel.invokeFunction(\n",
" 'getCodeForChart', [\"chart-691ffd14-0546-4b0a-b956-ef0af9e07cd1\"], {});\n",
" const responseJson = chartCodeResponse.data['application/json'];\n",
" await google.colab.notebook.addCell(responseJson.code, 'code');\n",
" }\n",
" chartElement.onclick = getCodeForChartHandler;\n",
" })();\n",
" </script>\n",
" <style>\n",
" .colab-quickchart-chart-with-code {\n",
" display: block;\n",
" float: left;\n",
" border: 1px solid transparent;\n",
" }\n",
"\n",
" .colab-quickchart-chart-with-code:hover {\n",
" cursor: pointer;\n",
" border: 1px solid #aaa;\n",
" }\n",
" </style>"
]
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"from matplotlib import pyplot as plt\n",
"_df_2['Lt'].plot(kind='hist', bins=20, title='Lt')\n",
"plt.gca().spines[['top', 'right',]].set_visible(False)"
],
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"\">\n",
" </div>\n",
" <script type=\"text/javascript\">\n",
" (() => {\n",
" const chartElement = document.getElementById(\"chart-1433be60-d4d7-43b6-9f42-f6974a5cf6de\");\n",
" async function getCodeForChartHandler(event) {\n",
" const chartCodeResponse = await google.colab.kernel.invokeFunction(\n",
" 'getCodeForChart', [\"chart-1433be60-d4d7-43b6-9f42-f6974a5cf6de\"], {});\n",
" const responseJson = chartCodeResponse.data['application/json'];\n",
" await google.colab.notebook.addCell(responseJson.code, 'code');\n",
" }\n",
" chartElement.onclick = getCodeForChartHandler;\n",
" })();\n",
" </script>\n",
" <style>\n",
" .colab-quickchart-chart-with-code {\n",
" display: block;\n",
" float: left;\n",
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"\n",
" .colab-quickchart-chart-with-code:hover {\n",
" cursor: pointer;\n",
" border: 1px solid #aaa;\n",
" }\n",
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]
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
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],
"text/html": [
"<h4 class=\"colab-quickchart-section-title\">2-d distributions</h4>\n",
"<style>\n",
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" clear: both;\n",
" }\n",
"</style>"
]
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"from matplotlib import pyplot as plt\n",
"_df_3.plot(kind='scatter', x='index', y='Dc', s=32, alpha=.8)\n",
"plt.gca().spines[['top', 'right',]].set_visible(False)"
],
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"\">\n",
" </div>\n",
" <script type=\"text/javascript\">\n",
" (() => {\n",
" const chartElement = document.getElementById(\"chart-820b2f05-a0e4-45a0-9f3d-6a333dd8fd55\");\n",
" async function getCodeForChartHandler(event) {\n",
" const chartCodeResponse = await google.colab.kernel.invokeFunction(\n",
" 'getCodeForChart', [\"chart-820b2f05-a0e4-45a0-9f3d-6a333dd8fd55\"], {});\n",
" const responseJson = chartCodeResponse.data['application/json'];\n",
" await google.colab.notebook.addCell(responseJson.code, 'code');\n",
" }\n",
" chartElement.onclick = getCodeForChartHandler;\n",
" })();\n",
" </script>\n",
" <style>\n",
" .colab-quickchart-chart-with-code {\n",
" display: block;\n",
" float: left;\n",
" border: 1px solid transparent;\n",
" }\n",
"\n",
" .colab-quickchart-chart-with-code:hover {\n",
" cursor: pointer;\n",
" border: 1px solid #aaa;\n",
" }\n",
" </style>"
]
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"from matplotlib import pyplot as plt\n",
"_df_4.plot(kind='scatter', x='Dc', y='Lt', s=32, alpha=.8)\n",
"plt.gca().spines[['top', 'right',]].set_visible(False)"
],
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" </div>\n",
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]
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
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],
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"<h4 class=\"colab-quickchart-section-title\">Time series</h4>\n",
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"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"from matplotlib import pyplot as plt\n",
"import seaborn as sns\n",
"def _plot_series(series, series_name, series_index=0):\n",
" palette = list(sns.palettes.mpl_palette('Dark2'))\n",
" xs = series['index']\n",
" ys = series['Dc']\n",
" \n",
" plt.plot(xs, ys, label=series_name, color=palette[series_index % len(palette)])\n",
"\n",
"fig, ax = plt.subplots(figsize=(10, 5.2), layout='constrained')\n",
"df_sorted = _df_5.sort_values('index', ascending=True)\n",
"_plot_series(df_sorted, '')\n",
"sns.despine(fig=fig, ax=ax)\n",
"plt.xlabel('index')\n",
"_ = plt.ylabel('Dc')"
],
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"\">\n",
" </div>\n",
" <script type=\"text/javascript\">\n",
" (() => {\n",
" const chartElement = document.getElementById(\"chart-83396298-0178-4ca5-996b-4dca15725ac0\");\n",
" async function getCodeForChartHandler(event) {\n",
" const chartCodeResponse = await google.colab.kernel.invokeFunction(\n",
" 'getCodeForChart', [\"chart-83396298-0178-4ca5-996b-4dca15725ac0\"], {});\n",
" const responseJson = chartCodeResponse.data['application/json'];\n",
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" }\n",
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" })();\n",
" </script>\n",
" <style>\n",
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" display: block;\n",
" float: left;\n",
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"\n",
" .colab-quickchart-chart-with-code:hover {\n",
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" }\n",
" </style>"
]
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"from matplotlib import pyplot as plt\n",
"import seaborn as sns\n",
"def _plot_series(series, series_name, series_index=0):\n",
" palette = list(sns.palettes.mpl_palette('Dark2'))\n",
" xs = series['index']\n",
" ys = series['Lt']\n",
" \n",
" plt.plot(xs, ys, label=series_name, color=palette[series_index % len(palette)])\n",
"\n",
"fig, ax = plt.subplots(figsize=(10, 5.2), layout='constrained')\n",
"df_sorted = _df_6.sort_values('index', ascending=True)\n",
"_plot_series(df_sorted, '')\n",
"sns.despine(fig=fig, ax=ax)\n",
"plt.xlabel('index')\n",
"_ = plt.ylabel('Lt')"
],
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"\">\n",
" </div>\n",
" <script type=\"text/javascript\">\n",
" (() => {\n",
" const chartElement = document.getElementById(\"chart-bcede766-7b15-4cd8-8db9-a2659d445aa5\");\n",
" async function getCodeForChartHandler(event) {\n",
" const chartCodeResponse = await google.colab.kernel.invokeFunction(\n",
" 'getCodeForChart', [\"chart-bcede766-7b15-4cd8-8db9-a2659d445aa5\"], {});\n",
" const responseJson = chartCodeResponse.data['application/json'];\n",
" await google.colab.notebook.addCell(responseJson.code, 'code');\n",
" }\n",
" chartElement.onclick = getCodeForChartHandler;\n",
" })();\n",
" </script>\n",
" <style>\n",
" .colab-quickchart-chart-with-code {\n",
" display: block;\n",
" float: left;\n",
" border: 1px solid transparent;\n",
" }\n",
"\n",
" .colab-quickchart-chart-with-code:hover {\n",
" cursor: pointer;\n",
" border: 1px solid #aaa;\n",
" }\n",
" </style>"
]
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"from matplotlib import pyplot as plt\n",
"import seaborn as sns\n",
"def _plot_series(series, series_name, series_index=0):\n",
" palette = list(sns.palettes.mpl_palette('Dark2'))\n",
" counted = (series['index']\n",
" .value_counts()\n",
" .reset_index(name='counts')\n",
" .rename({'index': 'index'}, axis=1)\n",
" .sort_values('index', ascending=True))\n",
" xs = counted['index']\n",
" ys = counted['counts']\n",
" plt.plot(xs, ys, label=series_name, color=palette[series_index % len(palette)])\n",
"\n",
"fig, ax = plt.subplots(figsize=(10, 5.2), layout='constrained')\n",
"df_sorted = _df_7.sort_values('index', ascending=True)\n",
"_plot_series(df_sorted, '')\n",
"sns.despine(fig=fig, ax=ax)\n",
"plt.xlabel('index')\n",
"_ = plt.ylabel('count()')"
],
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"\">\n",
" </div>\n",
" <script type=\"text/javascript\">\n",
" (() => {\n",
" const chartElement = document.getElementById(\"chart-7c789967-9893-44b9-bf0c-a2e69a19bc7b\");\n",
" async function getCodeForChartHandler(event) {\n",
" const chartCodeResponse = await google.colab.kernel.invokeFunction(\n",
" 'getCodeForChart', [\"chart-7c789967-9893-44b9-bf0c-a2e69a19bc7b\"], {});\n",
" const responseJson = chartCodeResponse.data['application/json'];\n",
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" }\n",
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" })();\n",
" </script>\n",
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" display: block;\n",
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"\n",
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" }\n",
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]
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
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],
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"<h4 class=\"colab-quickchart-section-title\">Values</h4>\n",
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"</style>"
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},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"from matplotlib import pyplot as plt\n",
"_df_8['index'].plot(kind='line', figsize=(8, 4), title='index')\n",
"plt.gca().spines[['top', 'right']].set_visible(False)"
],
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"\">\n",
" </div>\n",
" <script type=\"text/javascript\">\n",
" (() => {\n",
" const chartElement = document.getElementById(\"chart-35d7a159-c294-4ab3-80bc-5e7e8d5fed90\");\n",
" async function getCodeForChartHandler(event) {\n",
" const chartCodeResponse = await google.colab.kernel.invokeFunction(\n",
" 'getCodeForChart', [\"chart-35d7a159-c294-4ab3-80bc-5e7e8d5fed90\"], {});\n",
" const responseJson = chartCodeResponse.data['application/json'];\n",
" await google.colab.notebook.addCell(responseJson.code, 'code');\n",
" }\n",
" chartElement.onclick = getCodeForChartHandler;\n",
" })();\n",
" </script>\n",
" <style>\n",
" .colab-quickchart-chart-with-code {\n",
" display: block;\n",
" float: left;\n",
" border: 1px solid transparent;\n",
" }\n",
"\n",
" .colab-quickchart-chart-with-code:hover {\n",
" cursor: pointer;\n",
" border: 1px solid #aaa;\n",
" }\n",
" </style>"
]
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"from matplotlib import pyplot as plt\n",
"_df_9['Dc'].plot(kind='line', figsize=(8, 4), title='Dc')\n",
"plt.gca().spines[['top', 'right']].set_visible(False)"
],
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"\">\n",
" </div>\n",
" <script type=\"text/javascript\">\n",
" (() => {\n",
" const chartElement = document.getElementById(\"chart-ff81ec20-4626-464f-8760-40545bed7e84\");\n",
" async function getCodeForChartHandler(event) {\n",
" const chartCodeResponse = await google.colab.kernel.invokeFunction(\n",
" 'getCodeForChart', [\"chart-ff81ec20-4626-464f-8760-40545bed7e84\"], {});\n",
" const responseJson = chartCodeResponse.data['application/json'];\n",
" await google.colab.notebook.addCell(responseJson.code, 'code');\n",
" }\n",
" chartElement.onclick = getCodeForChartHandler;\n",
" })();\n",
" </script>\n",
" <style>\n",
" .colab-quickchart-chart-with-code {\n",
" display: block;\n",
" float: left;\n",
" border: 1px solid transparent;\n",
" }\n",
"\n",
" .colab-quickchart-chart-with-code:hover {\n",
" cursor: pointer;\n",
" border: 1px solid #aaa;\n",
" }\n",
" </style>"
]
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"from matplotlib import pyplot as plt\n",
"_df_10['Lt'].plot(kind='line', figsize=(8, 4), title='Lt')\n",
"plt.gca().spines[['top', 'right']].set_visible(False)"
],
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"QmCC\n",
"\">\n",
" </div>\n",
" <script type=\"text/javascript\">\n",
" (() => {\n",
" const chartElement = document.getElementById(\"chart-d76ae346-8b2b-4e64-9f1d-ea9c23700897\");\n",
" async function getCodeForChartHandler(event) {\n",
" const chartCodeResponse = await google.colab.kernel.invokeFunction(\n",
" 'getCodeForChart', [\"chart-d76ae346-8b2b-4e64-9f1d-ea9c23700897\"], {});\n",
" const responseJson = chartCodeResponse.data['application/json'];\n",
" await google.colab.notebook.addCell(responseJson.code, 'code');\n",
" }\n",
" chartElement.onclick = getCodeForChartHandler;\n",
" })();\n",
" </script>\n",
" <style>\n",
" .colab-quickchart-chart-with-code {\n",
" display: block;\n",
" float: left;\n",
" border: 1px solid transparent;\n",
" }\n",
"\n",
" .colab-quickchart-chart-with-code:hover {\n",
" cursor: pointer;\n",
" border: 1px solid #aaa;\n",
" }\n",
" </style>"
]
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Error: Runtime no longer has a reference to this dataframe, please re-run this cell and try again.\n"
]
}
],
"source": [
"data = np.array([Dc, Lt]).T\n",
"columns = ['Dc', 'Lt']\n",
"df = pd.DataFrame(data = data, columns = columns)\n",
"df.head()"
]
},
{
"source": [
"# @title Dc vs Lt\n",
"\n",
"from matplotlib import pyplot as plt\n",
"df.plot(kind='scatter', x='Lt', y='Dc', s=32, alpha=.8)\n",
"plt.gca().spines[['top', 'right',]].set_visible(False)"
],
"cell_type": "code",
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
}
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 449
},
"id": "nUpLIC65W1N-",
"outputId": "1c1ac06f-7a6d-405e-c8d3-6d88d1bd9df8"
}
},
{
"cell_type": "markdown",
"metadata": {
"id": "hXMtog9SQKAC"
},
"source": [
"Data visualization:"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "jCaVg-HtQKAD"
},
"source": [
"### 4.3 ML getting data:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "pxUR-JxyQKAD",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "710a1517-b1f0-400a-b1de-4458ba2ab6b6"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"((700, 1), (300, 1), (700,), (300,))"
]
},
"metadata": {},
"execution_count": 89
}
],
"source": [
"X = df[['Lt']]\n",
"y = df['Dc']\n",
"y_true = y\n",
"X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split(X, y, test_size=0.30, random_state=42)\n",
"X_train.shape, X_test.shape, y_train.shape, y_test.shape"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "s_Dkrf4vQKAD"
},
"source": [
"### 4.4 GPlearn implementation"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "FN-2qpfKQKAD"
},
"source": [
"#### 4.4.1 First test:\n",
"\n",
"a) Choosing just some functions"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "vdnud0IRQKAD"
},
"outputs": [],
"source": [
"function_set = ['add', 'sub', 'mul','log','cos','sin']"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "S6LWpWt0QKAE"
},
"outputs": [],
"source": [
"est_gp = SymbolicRegressor(population_size=5000,function_set=function_set,\n",
" generations=40, stopping_criteria=0.01,\n",
" p_crossover=0.7, p_subtree_mutation=0.1,\n",
" p_hoist_mutation=0.05, p_point_mutation=0.1,\n",
" max_samples=0.9, verbose=1,\n",
" parsimony_coefficient=0.01, random_state=0)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "gjwEJofFQKAE"
},
"source": [
"b) Fit:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "tKm7HB0YQKAE",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "8eb4aa2a-26ff-4fdb-cfab-736eaf466083"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
" | Population Average | Best Individual |\n",
"---- ------------------------- ------------------------------------------ ----------\n",
" Gen Length Fitness Length Fitness OOB Fitness Time Left\n",
" 0 16.04 295124 66 200.169 250.983 7.72m\n",
" 1 18.96 60041.2 54 168.894 189.947 8.73m\n",
" 2 25.24 15242.2 58 168.215 185.9 7.58m\n",
" 3 27.00 91985.9 33 164.545 190.708 3.61m\n",
" 4 37.19 633796 74 115.157 101.553 4.30m\n",
" 5 56.93 4.85768e+06 126 100.017 95.136 4.80m\n",
" 6 62.04 1.18988e+06 134 77.875 84.6707 5.17m\n",
" 7 80.07 669673 97 32.8696 37.7755 6.10m\n",
" 8 90.62 164126 96 31.0653 32.0302 6.43m\n",
" 9 115.79 914145 104 18.3931 18.1155 7.65m\n",
" 10 112.18 1.02548e+06 100 18.0228 21.5686 7.37m\n",
" 11 105.18 2.05979e+06 102 18.0036 19.7513 6.12m\n",
" 12 105.02 1.09517e+06 146 17.3752 19.5777 5.75m\n",
" 13 114.64 1.65476e+10 151 15.2798 15.4408 5.63m\n",
" 14 101.32 2.15345e+06 101 14.2345 15.7877 4.70m\n",
" 15 105.57 2.68088e+06 281 13.9111 18.6695 4.83m\n",
" 16 140.74 7.27245e+06 281 10.931 11.7914 5.74m\n",
" 17 150.47 2.79481e+06 220 10.4213 11.2445 5.49m\n",
" 18 141.22 4.47533e+06 223 9.91018 12.2101 5.33m\n",
" 19 216.36 9.83396e+06 208 9.70008 10.253 7.15m\n",
" 20 234.91 3.60888e+09 228 9.42292 10.5664 6.59m\n",
" 21 213.53 2.7053e+06 267 9.0507 9.10106 6.14m\n",
" 22 208.21 4.97763e+09 212 8.49818 8.16594 5.39m\n",
" 23 210.54 2.39698e+07 211 8.34073 8.89891 5.52m\n",
" 24 203.30 3.1091e+09 211 8.19075 10.1262 5.01m\n",
" 25 201.01 1.52676e+07 207 7.988 10.5286 4.15m\n",
" 26 202.97 8.71708e+08 211 7.94545 11.2122 4.16m\n",
" 27 200.33 8.5528e+07 216 7.83392 9.73861 3.63m\n",
" 28 198.47 4.80232e+06 201 7.89913 9.96624 3.15m\n",
" 29 194.19 6.57077e+08 201 7.82542 9.10365 3.00m\n",
" 30 189.79 2.31527e+11 217 7.75277 10.4626 2.72m\n",
" 31 186.96 1.37211e+07 203 7.71183 9.93904 2.47m\n",
" 32 186.10 1.40748e+07 199 7.72835 9.77239 1.94m\n",
" 33 185.66 6.12874e+07 181 7.68912 11.1074 1.65m\n",
" 34 182.99 2.35271e+09 190 7.64002 9.70899 1.40m\n",
" 35 181.52 9.27444e+06 185 7.62309 10.3469 1.03m\n",
" 36 180.25 1.20555e+12 178 7.67701 9.76618 48.20s\n",
" 37 176.30 1.54635e+09 191 7.6046 10.0266 30.27s\n",
" 38 175.54 5.54465e+07 187 7.61679 10.4381 15.55s\n",
" 39 173.21 2.60671e+08 169 7.55206 10.7751 0.00s\n",
"Time to fit: 607.3658051490784 seconds\n"
]
}
],
"source": [
"t0 = time.time()\n",
"est_gp.fit(X_train, y_train)\n",
"print('Time to fit:', time.time() - t0, 'seconds')"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "7pH43nzpQKAE"
},
"source": [
"c) Prediction"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "BwtJq8QxQKAE",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "dbae1948-ce4a-431e-cc0f-35c033adc5c4"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Time to predict: 0.0041408538818359375 seconds\n"
]
}
],
"source": [
"t0 = time.time()\n",
"y_gp1 = est_gp.predict(X_test)\n",
"print('Time to predict:', time.time() - t0, 'seconds')"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "UOIgUAMwQKAF"
},
"source": [
"d) Score"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "JLSjSo48QKAF",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "702cb7ca-679b-4f02-82a2-acdc7d26711f"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"R2: 0.9983602524874564\n"
]
}
],
"source": [
"score_gp1 = est_gp.score(X_test, y_test)\n",
"print('R2:', score_gp1)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "lxZjEHcKQKAF"
},
"source": [
"##### 4.4.1.1. **Visualizing the symbolic function**"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "rgbW8Fv5QKAF"
},
"source": [
"a) Equation"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "KqwXyA6TQKAF"
},
"outputs": [],
"source": [
"converter = {\n",
" 'add': lambda x, y : x + y,\n",
" 'sub': lambda x, y : x - y,\n",
" 'mul': lambda x, y : x*y,\n",
" 'div': lambda x, y : x/y,\n",
" 'sqrt': lambda x : x**0.5,\n",
" 'log': lambda x : log(x),\n",
" 'abs': lambda x : abs(x),\n",
" 'neg': lambda x : -x,\n",
" 'inv': lambda x : 1/x,\n",
" 'max': lambda x, y : max(x, y),\n",
" 'min': lambda x, y : min(x, y),\n",
" 'sin': lambda x : sin(x),\n",
" 'cos': lambda x : cos(x),\n",
" 'pow': lambda x, y : x**y,\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "MsE6A6_QQKAF",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 103
},
"outputId": "26e32810-1acb-43eb-a952-a03ed8bda923"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"0.0720000000000001*X0*(X0 - 0.54) - 1.732252*X0 + (0.0720000000000001*X0*(X0 + 0.54) - 0.07082341344*X0 - (0.099 - X0)*cos(0.044*X0) + 0.00348192*(X0 - 0.54)*(2*X0 + 0.526) + 0.09672*(0.18*X0*(X0 - 0.54) + 0.382)*cos(0.044*X0) - 0.09672*sin(X0) + 0.33755784206944)*cos(0.044*X0) - log(X0) - log(X0 + 0.526) + log(cos(0.044*X0)) - 0.551472948"
],
"text/latex": "$\\displaystyle 0.0720000000000001 X_{0} \\left(X_{0} - 0.54\\right) - 1.732252 X_{0} + \\left(0.0720000000000001 X_{0} \\left(X_{0} + 0.54\\right) - 0.07082341344 X_{0} - \\left(0.099 - X_{0}\\right) \\cos{\\left(0.044 X_{0} \\right)} + 0.00348192 \\left(X_{0} - 0.54\\right) \\left(2 X_{0} + 0.526\\right) + 0.09672 \\cdot \\left(0.18 X_{0} \\left(X_{0} - 0.54\\right) + 0.382\\right) \\cos{\\left(0.044 X_{0} \\right)} - 0.09672 \\sin{\\left(X_{0} \\right)} + 0.33755784206944\\right) \\cos{\\left(0.044 X_{0} \\right)} - \\log{\\left(X_{0} \\right)} - \\log{\\left(X_{0} + 0.526 \\right)} + \\log{\\left(\\cos{\\left(0.044 X_{0} \\right)} \\right)} - 0.551472948$"
},
"metadata": {},
"execution_count": 96
}
],
"source": [
"next_e = sympify(str(est_gp._program), locals=converter)\n",
"next_e"
]
},
{
"cell_type": "code",
"source": [],
"metadata": {
"id": "x9n26pW1mhul"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "MjsxH5U2QKAG"
},
"source": [
"b) Score"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "9cvqC-DgQKAG",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "1cfc5fb5-d555-4b4c-d718-fdf8ffd347eb"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"0.9983602524874564"
]
},
"metadata": {},
"execution_count": 97
}
],
"source": [
"y_gp = est_gp.predict(X_test)\n",
"score_gp1 = est_gp.score(X_test, y_test)\n",
"score_gp1"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "jgFM-WeAQKAG"
},
"source": [
"c) Plot"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "G4M3zPlQQKAG",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 489
},
"outputId": "1cf06ba7-3773-4b89-db77-9a62150721c5"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Text(0.5, 0, 'Turbine Sound Power')"
]
},
"metadata": {},
"execution_count": 98
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 640x480 with 2 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
}
],
"source": [
"fig = plt.figure(constrained_layout=False, dpi=100)\n",
"gs = fig.add_gridspec(nrows=7, ncols=1)\n",
"f_ax1 = fig.add_subplot(gs[0:5,0])\n",
"plt.title('Test comparison')\n",
"plt.scatter(X_test, y_test, label = 'True function')\n",
"plt.scatter(X_test, y_gp1, marker = 'v', s = 10, label = 'Symbolic function')\n",
"plt.legend()\n",
"plt.ylabel('Distance')\n",
"f_ax2 = fig.add_subplot(gs[5:7, 0])\n",
"plt.scatter(X_test, 1. - y_gp1/y_test, marker = 's', s = 10)\n",
"plt.ylabel('Residual')\n",
"plt.xlabel('Turbine Sound Power')"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "4FRAPqHpQKAL"
},
"source": [
"d) Tree"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "n9Bn0jWHQKAM",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"outputId": "51b68f1e-6006-4db9-b608-a9e19e91eee7"
},
"outputs": [
{
"output_type": "execute_result",
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"text/plain": [
"<graphviz.sources.Source at 0x7b90b8d32800>"
]
},
"metadata": {},
"execution_count": 99
}
],
"source": [
"dot_data = est_gp._program.export_graphviz()\n",
"graph = graphviz.Source(dot_data)\n",
"graph.render('images/ex1', format='png', cleanup=True)\n",
"graph"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "54ub8-RhQKAM"
},
"source": [
"#### 4.4.2 Second test:\n",
"\n",
"a) Don't imposing any function"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "8BdHwtbUQKAM"
},
"outputs": [],
"source": [
"est_gp = SymbolicRegressor(population_size=5000,\n",
" generations=20, stopping_criteria=0.01,\n",
" p_crossover=0.7, p_subtree_mutation=0.1,\n",
" p_hoist_mutation=0.05, p_point_mutation=0.1,\n",
" max_samples=0.9, verbose=1,\n",
" parsimony_coefficient=0.01, random_state=0)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "pbqixeI1QKAM"
},
"source": [
"b) Fit"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "YJ-dJwSfQKAM",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "c7cc4b6c-74a4-4bab-ac9f-d3e119709d84"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
" | Population Average | Best Individual |\n",
"---- ------------------------- ------------------------------------------ ----------\n",
" Gen Length Fitness Length Fitness OOB Fitness Time Left\n",
" 0 48.81 8.52241e+15 31 178.587 176.079 5.49m\n",
" 1 35.89 1.44673e+06 127 167.094 146.856 4.88m\n",
" 2 58.26 7.55176e+13 127 159.622 169.186 2.40m\n",
" 3 77.94 2.07507e+09 127 119.287 124.611 2.77m\n",
" 4 79.81 4.21719e+08 125 120.157 132.514 2.51m\n",
" 5 114.77 1.38739e+07 127 109.75 103.729 3.03m\n",
" 6 130.68 3.35583e+12 119 104.325 103.097 3.21m\n",
" 7 134.71 1.11187e+12 181 84.4247 95.0787 2.82m\n",
" 8 136.53 1.10583e+12 181 68.5256 60.1965 2.73m\n",
" 9 140.18 3.89048e+12 181 63.3789 61.905 2.57m\n",
" 10 151.43 5.00596e+14 177 53.4422 51.031 2.39m\n",
" 11 172.72 2.91279e+14 181 47.7753 49.3711 2.24m\n",
" 12 193.90 6.88187e+14 233 37.0866 36.8706 2.23m\n",
" 13 192.98 1.70582e+13 233 31.5903 32.2154 1.83m\n",
" 14 191.55 2.03828e+13 245 28.5935 27.1434 1.69m\n",
" 15 200.23 3.5104e+15 243 25.4437 32.7962 1.25m\n",
" 16 244.42 1.31594e+16 243 25.9793 27.502 1.12m\n",
" 17 264.20 1.47044e+14 201 24.6301 22.9548 47.39s\n",
" 18 249.45 2.00149e+14 185 24.4818 23.8717 22.95s\n",
" 19 235.13 1.72148e+12 207 24.2462 26.2606 0.00s\n",
"Time to fit: 335.9563603401184 seconds\n"
]
}
],
"source": [
"t0 = time.time()\n",
"est_gp.fit(X_train, y_train)\n",
"print('Time to fit:', time.time() - t0, 'seconds')"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "PSjEhQKmQKAN"
},
"source": [
"c) Prediction"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "0V4oydsnQKAN",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "f0c27a23-7428-4dd8-e95c-203a620e9d62"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Time to predict: 0.006814002990722656 seconds\n"
]
}
],
"source": [
"t0 = time.time()\n",
"y_gp2 = est_gp.predict(X_test)\n",
"print('Time to predict:', time.time() - t0, 'seconds')"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "uFCnJytyQKAN"
},
"source": [
"d) Score"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "nXlCH-EJQKAN",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "9cdf5fdc-1bd6-45e0-b4a4-0947068d45fc"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"R2: 0.9857463519752963\n"
]
}
],
"source": [
"score_gp2 = est_gp.score(X_test, y_test)\n",
"print('R2:', score_gp2)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ydRnKpfoQKAN"
},
"source": [
"##### 4.4.2.1 **Visualizing the symbolic function**"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ayiDH-ezQKAO"
},
"source": [
"a) Equation"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "v-Sxaqy3QKAO",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 173
},
"outputId": "267843d3-4768-40a8-9547-48b5e70fb17c"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"-(8.54700854700855*X0 + 4.85470085470086)*(X0**2/(X0 - 0.647) + 0.862*X0 + 0.395)/(0.874*X0*(-0.305167 - 0.663/X0) + (-X0 - 0.604)/(-0.305167 - 0.663/X0)) + 0.000207882347076548*(-X0 - 0.042)**2*(X0 - 0.946)**2*(0.416025*(X0 + 0.328682170542636)**2)*(0.085*X0**2 + X0 - 2.30193650793651 - 0.044619*(2.484*X0 + 0.945/X0)/(X0*(-2*X0 - 1.57868304278922 - 0.297/X0)))/(X0**2*(X0 + 0.914)**2)"
],
"text/latex": "$\\displaystyle - \\frac{\\left(8.54700854700855 X_{0} + 4.85470085470086\\right) \\left(\\frac{X_{0}^{2}}{X_{0} - 0.647} + 0.862 X_{0} + 0.395\\right)}{0.874 X_{0} \\left(-0.305167 - \\frac{0.663}{X_{0}}\\right) + \\frac{- X_{0} - 0.604}{-0.305167 - \\frac{0.663}{X_{0}}}} + \\frac{0.000207882347076548 \\left(- X_{0} - 0.042\\right)^{2} \\left(X_{0} - 0.946\\right)^{2} \\cdot 0.416025 \\left(X_{0} + 0.328682170542636\\right)^{2} \\cdot \\left(0.085 X_{0}^{2} + X_{0} - 2.30193650793651 - \\frac{0.044619 \\cdot \\left(2.484 X_{0} + \\frac{0.945}{X_{0}}\\right)}{X_{0} \\left(- 2 X_{0} - 1.57868304278922 - \\frac{0.297}{X_{0}}\\right)}\\right)}{X_{0}^{2} \\left(X_{0} + 0.914\\right)^{2}}$"
},
"metadata": {},
"execution_count": 104
}
],
"source": [
"next_e = sympify(str(est_gp._program), locals=converter)\n",
"next_e"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Fs7Mx9_nQKAO"
},
"source": [
"b) Plot"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "kOtoUibxQKAO",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 489
},
"outputId": "4d295dfd-34b2-4b97-e4dd-bf04d8fff479"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Text(0.5, 0, 'D_c')"
]
},
"metadata": {},
"execution_count": 105
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 640x480 with 2 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
}
],
"source": [
"fig = plt.figure(constrained_layout=False, dpi=100)\n",
"gs = fig.add_gridspec(nrows=7, ncols=1)\n",
"f_ax1 = fig.add_subplot(gs[0:5,0])\n",
"plt.title('H (D_c) comparison')\n",
"plt.scatter(X_test, y_test, label = 'True function')\n",
"plt.scatter(X_test, y_gp2, marker = 'v', s = 10, label = 'Symbolic function')\n",
"plt.legend()\n",
"plt.ylabel('Turbine Sound Level')\n",
"f_ax2 = fig.add_subplot(gs[5:7, 0])\n",
"plt.scatter(X_test, 1. - y_gp2/y_test, marker = 's', s = 10)\n",
"plt.ylabel('Residual')\n",
"plt.xlabel('D_c')"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "2H-M9YdzQKAO"
},
"source": [
"c) Tree"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "R-KIhOeWQKAO",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"outputId": "3270a9c5-4fe1-470b-bf01-ca696c681c68"
},
"outputs": [
{
"output_type": "execute_result",
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"text/plain": [
"<graphviz.sources.Source at 0x7b90bcd844c0>"
]
},
"metadata": {},
"execution_count": 106
}
],
"source": [
"dot_data = est_gp._program.export_graphviz()\n",
"graph = graphviz.Source(dot_data)\n",
"graph.render('images/ex2', format='png', cleanup=True)\n",
"graph"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "3OdLnD-jQKAP"
},
"source": [
"### 4.5 Comparing GPlearn to traditional ML approaches"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "5G3SUVrwQKAP"
},
"source": [
"#### 4.5.1 Decision Tree Regressor"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "3-PNNeBNQKAP"
},
"source": [
"a) Model and fit"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "OR3UgDqFQKAP",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "634fdd9b-2cd2-4647-bd08-48aca517e148"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Time to fit: 0.004815101623535156 seconds\n"
]
}
],
"source": [
"est_tree = DecisionTreeRegressor(max_depth=5)\n",
"t0 = time.time()\n",
"est_tree.fit(X_train, y_train)\n",
"print('Time to fit:', time.time() - t0, 'seconds')"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Z1J6Sxz3QKAQ"
},
"source": [
"b) Prediction and score"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "qYXwYV91QKAQ",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "947f31f8-98f4-49fb-df2f-f9c279422f60"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Time to predict: 0.005401611328125 seconds\n"
]
}
],
"source": [
"t0 = time.time()\n",
"y_tree = est_tree.predict(X_test)\n",
"print('Time to predict:', time.time() - t0, 'seconds')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "58DAzBcsQKAQ",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "ae844c54-4c2c-4f7f-efde-746a73ccf382"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"DT: 0.9971088171211464\n"
]
}
],
"source": [
"#Score\n",
"score_tree = est_tree.score(X_test, y_test)\n",
"print('DT:', score_tree)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "o8X27nsFQKAQ"
},
"source": [
"c) Plot"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "ZO4gJ5U5QKAQ",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 489
},
"outputId": "cea2c66e-3506-474e-d3b1-07a8283af24e"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Text(0.5, 0, 'D_c')"
]
},
"metadata": {},
"execution_count": 110
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 640x480 with 2 Axes>"
],
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\n"
},
"metadata": {}
}
],
"source": [
"fig = plt.figure(constrained_layout=False, dpi=100)\n",
"gs = fig.add_gridspec(nrows=7, ncols=1)\n",
"f_ax1 = fig.add_subplot(gs[0:5,0])\n",
"plt.title('H (D_c) comparison')\n",
"plt.scatter(X_test, y_test, label = 'True function')\n",
"plt.scatter(X_test, y_tree, marker = 'v', s = 10, label = 'Decision Tree')\n",
"plt.legend()\n",
"plt.ylabel('H (D_c)')\n",
"f_ax2 = fig.add_subplot(gs[5:7, 0])\n",
"plt.scatter(X_test, 1. - y_tree/y_test, marker = 's', s = 10)\n",
"plt.ylabel('Residual')\n",
"plt.xlabel('D_c')"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "EwF-pGygQKAQ"
},
"source": [
"#### 4.5.2 Random Forest Regressor"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "iYdK4pbzQKAR"
},
"source": [
"a) Model and Fit"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "aGY5JnpoQKAR",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "2a82dabf-4e03-4fc0-aeeb-93c4c9f27af4"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Time to fit: 0.19230151176452637 seconds\n"
]
}
],
"source": [
"est_rf = RandomForestRegressor(n_estimators=100,max_depth=5)\n",
"t0 = time.time()\n",
"est_rf.fit(X_train, y_train)\n",
"print('Time to fit:', time.time() - t0, 'seconds')"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "XW0Y_TgxQKAR"
},
"source": [
"b) Prediction and Score"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "DMywfiJvQKAR",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "c2c001a2-0a56-4ff5-8c6f-da0fb8f48e3b"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Time to predict: 0.011875152587890625 seconds\n"
]
}
],
"source": [
"t0 = time.time()\n",
"y_rf = est_rf.predict(X_test)\n",
"print('Time to predict:', time.time() - t0, 'seconds')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "3oxAgHTDQKAR",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "59d75349-43ff-4d07-cf41-e9c47ce0cfb7"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"RF: 0.9978694916455902\n"
]
}
],
"source": [
"score_rf = est_rf.score(X_test, y_test)\n",
"print('RF:', score_rf)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "bwjYjfIHQKAS"
},
"source": [
"c) Plot"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "he4Gm8zNQKAS",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 489
},
"outputId": "472a4906-bf9f-44e0-ed1d-e2c199e58317"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Text(0.5, 0, 'z')"
]
},
"metadata": {},
"execution_count": 114
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 640x480 with 2 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
}
],
"source": [
"#Plot\n",
"fig = plt.figure(constrained_layout=False, dpi=100)\n",
"gs = fig.add_gridspec(nrows=7, ncols=1)\n",
"f_ax1 = fig.add_subplot(gs[0:5,0])\n",
"plt.title('H (z) comparison')\n",
"plt.scatter(X_test, y_test, label = 'True function')\n",
"plt.scatter(X_test, y_rf, marker = 'v', s = 10, label = 'Random Forest')\n",
"plt.legend()\n",
"plt.ylabel('H (z)')\n",
"f_ax2 = fig.add_subplot(gs[5:7, 0])\n",
"plt.scatter(X_test, 1. - y_rf/y_test, marker = 's', s = 10)\n",
"plt.ylabel('Residual')\n",
"plt.xlabel('z')"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "5eAaLSsFQKAS"
},
"source": [
"#### 4.5.3 All together"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "Mk3IBXxFQKAS",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 741
},
"outputId": "7807214c-af1c-4acd-b281-781bfba60b95"
},
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 1200x1000 with 4 Axes>"
],
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