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Minor fixes to docs and setup.py

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This commit is contained in:
Alinson S. Xavier
2023-06-08 12:37:11 -05:00
parent 1ea989d48a
commit 8dd5bb416b
3 changed files with 31 additions and 102 deletions
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68
docs/tutorials/getting-started-gurobipy.ipynb
View File

@@ -33,6 +33,7 @@
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "02f0a927",
"metadata": {},
@@ -44,53 +45,17 @@
"- Python version, compatible with the Pyomo and Gurobipy modeling languages,\n",
"- Julia version, compatible with the JuMP modeling language.\n",
"\n",
"In this tutorial, we will demonstrate how to use and install the Python/Gurobipy version of the package. The first step is to install Python 3.8+ in your computer. See the [official Python website for more instructions](https://www.python.org/downloads/). After Python is installed, we proceed to install MIPLearn using `pip`:"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "cd8a69c1",
"metadata": {
"ExecuteTime": {
"end_time": "2023-06-06T20:18:02.381829278Z",
"start_time": "2023-06-06T20:18:02.381532300Z"
}
},
"outputs": [],
"source": [
"# !pip install MIPLearn==0.3.0"
]
},
{
"cell_type": "markdown",
"id": "e8274543",
"metadata": {},
"source": [
"In addition to MIPLearn itself, we will also install Gurobi 10.0, a state-of-the-art commercial MILP solver. This step also install a demo license for Gurobi, which should able to solve the small optimization problems in this tutorial. A license is required for solving larger-scale problems."
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "dcc8756c",
"metadata": {
"ExecuteTime": {
"end_time": "2023-06-06T20:18:15.537811992Z",
"start_time": "2023-06-06T20:18:13.449177860Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Requirement already satisfied: gurobipy<10.1,>=10 in /home/axavier/Software/anaconda3/envs/miplearn/lib/python3.8/site-packages (10.0.1)\n"
]
}
],
"source": [
"!pip install 'gurobipy>=10,<10.1'"
"In this tutorial, we will demonstrate how to use and install the Python/Gurobipy version of the package. The first step is to install Python 3.8+ in your computer. See the [official Python website for more instructions](https://www.python.org/downloads/). After Python is installed, we proceed to install MIPLearn using `pip`:\n",
"\n",
"```\n",
"$ pip install MIPLearn==0.3\n",
"```\n",
"\n",
"In addition to MIPLearn itself, we will also install Gurobi 10.0, a state-of-the-art commercial MILP solver. This step also install a demo license for Gurobi, which should able to solve the small optimization problems in this tutorial. A license is required for solving larger-scale problems.\n",
"\n",
"```\n",
"$ pip install 'gurobipy>=10,<10.1'\n",
"```"
]
},
{
@@ -214,6 +179,7 @@
"from miplearn.io import read_pkl_gz\n",
"from miplearn.solvers.gurobi import GurobiModel\n",
"\n",
"\n",
"def build_uc_model(data: Union[str, UnitCommitmentData]) -> GurobiModel:\n",
" if isinstance(data, str):\n",
" data = read_pkl_gz(data)\n",
@@ -223,9 +189,7 @@
" x = model._x = model.addVars(n, vtype=GRB.BINARY, name=\"x\")\n",
" y = model._y = model.addVars(n, name=\"y\")\n",
" model.setObjective(\n",
" quicksum(\n",
" data.cfix[i] * x[i] + data.cvar[i] * y[i] for i in range(n)\n",
" )\n",
" quicksum(data.cfix[i] * x[i] + data.cvar[i] * y[i] for i in range(n))\n",
" )\n",
" model.addConstrs(y[i] <= data.pmax[i] * x[i] for i in range(n))\n",
" model.addConstrs(y[i] >= data.pmin[i] * x[i] for i in range(n))\n",
@@ -588,7 +552,7 @@
"\n",
"solver_ml = LearningSolver(components=[comp])\n",
"solver_ml.fit(train_data)\n",
"solver_ml.optimize(test_data[0], build_uc_model);"
"solver_ml.optimize(test_data[0], build_uc_model)"
]
},
{
@@ -690,7 +654,7 @@
"source": [
"solver_baseline = LearningSolver(components=[])\n",
"solver_baseline.fit(train_data)\n",
"solver_baseline.optimize(test_data[0], build_uc_model);"
"solver_baseline.optimize(test_data[0], build_uc_model)"
]
},
{

63
docs/tutorials/getting-started-pyomo.ipynb
View File

@@ -33,6 +33,7 @@
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "02f0a927",
"metadata": {},
@@ -44,53 +45,17 @@
"- Python version, compatible with the Pyomo and Gurobipy modeling languages,\n",
"- Julia version, compatible with the JuMP modeling language.\n",
"\n",
"In this tutorial, we will demonstrate how to use and install the Python/Pyomo version of the package. The first step is to install Python 3.8+ in your computer. See the [official Python website for more instructions](https://www.python.org/downloads/). After Python is installed, we proceed to install MIPLearn using `pip`:"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "cd8a69c1",
"metadata": {
"ExecuteTime": {
"end_time": "2023-06-06T19:57:33.202580815Z",
"start_time": "2023-06-06T19:57:33.198341886Z"
}
},
"outputs": [],
"source": [
"# !pip install MIPLearn==0.3.0"
]
},
{
"cell_type": "markdown",
"id": "e8274543",
"metadata": {},
"source": [
"In addition to MIPLearn itself, we will also install Gurobi 10.0, a state-of-the-art commercial MILP solver. This step also install a demo license for Gurobi, which should able to solve the small optimization problems in this tutorial. A license is required for solving larger-scale problems."
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "dcc8756c",
"metadata": {
"ExecuteTime": {
"end_time": "2023-06-06T19:57:35.756831801Z",
"start_time": "2023-06-06T19:57:33.201767088Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Requirement already satisfied: gurobipy<10.1,>=10 in /home/axavier/Software/anaconda3/envs/miplearn/lib/python3.8/site-packages (10.0.1)\n"
]
}
],
"source": [
"!pip install 'gurobipy>=10,<10.1'"
"In this tutorial, we will demonstrate how to use and install the Python/Pyomo version of the package. The first step is to install Python 3.8+ in your computer. See the [official Python website for more instructions](https://www.python.org/downloads/). After Python is installed, we proceed to install MIPLearn using `pip`:\n",
"\n",
"```\n",
"$ pip install MIPLearn==0.3\n",
"```\n",
"\n",
"In addition to MIPLearn itself, we will also install Gurobi 10.0, a state-of-the-art commercial MILP solver. This step also install a demo license for Gurobi, which should able to solve the small optimization problems in this tutorial. A license is required for solving larger-scale problems.\n",
"\n",
"```\n",
"$ pip install 'gurobipy>=10,<10.1'\n",
"```"
]
},
{
@@ -600,7 +565,7 @@
"\n",
"solver_ml = LearningSolver(components=[comp])\n",
"solver_ml.fit(train_data)\n",
"solver_ml.optimize(test_data[0], build_uc_model);"
"solver_ml.optimize(test_data[0], build_uc_model)"
]
},
{
@@ -706,7 +671,7 @@
"source": [
"solver_baseline = LearningSolver(components=[])\n",
"solver_baseline.fit(train_data)\n",
"solver_baseline.optimize(test_data[0], build_uc_model);"
"solver_baseline.optimize(test_data[0], build_uc_model)"
]
},
{

2
setup.py
View File

@@ -6,7 +6,7 @@ from setuptools import setup, find_namespace_packages
setup(
name="miplearn",
version="0.3.0.dev1",
version="0.3.0",
author="Alinson S. Xavier",
author_email="axavier@anl.gov",
description="Extensible Framework for Learning-Enhanced Mixed-Integer Optimization",