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b/tutorial/transport/0_transport-tutorial_platform-setup.ipynb new file mode 100644 index 00000000..098459a2 --- /dev/null +++ b/tutorial/transport/0_transport-tutorial_platform-setup.ipynb @@ -0,0 +1,221 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "9398de15-4422-46d5-815f-99bf4ef36673", + "metadata": {}, + "source": [ + "### Transport Tutorial - Notebook 0 \n", + "\n", + "# Set up an **ixmp4** platform to solve *Dantzig's Transport Problem*\n", + "\n", + "## Aim and scope of this tutorial\n", + "\n", + "This tutorial takes you through the steps to solve a simple optimization model\n", + "using the **ixmp4** database management package and the **linopy** optimization package.\n", + "\n", + "We use **Dantzig's transport problem**, which is used as a [tutorial for linopy](https://linopy.readthedocs.io/en/latest/transport-tutorial.html).\n", + "This problem solves for a least-cost shipping schedule that meets demand constraints at several markets (cities)\n", + "and supply constraints at factories.\n", + "\n", + "For reference of the transport problem, see:\n", + "> Dantzig, G B, Chapter 3.3. In Linear Programming and Extensions. \n", + "> Princeton University Press, Princeton, New Jersey, 1963.\n", + "\n", + "## Tutorial outline\n", + "\n", + "This tutorial consists of three Jupyter notebooks:\n", + "\n", + "0. Set up an `ixmp4.Platform` to store the scenario input data and solution\n", + "1. Implement the **baseline version of the transport problem** and solve it\n", + "2. Create an **alternative scenario** and solve it " + ] + }, + { + "cell_type": "markdown", + "id": "6f4eb5bd-095c-4342-b02b-666cef908c6d", + "metadata": {}, + "source": [ + "## Setting up the database instance via the Command Line Interface (CLI)\n", + "\n", + "An [**ixmp4.Platform**](https://docs.ece.iiasa.ac.at/projects/ixmp4/en/latest/devs/ixmp4.core/platform.html#ixmp4.core.platform.Platform)\n", + "is the connection to a database instance that can hold scenario data and relevant additional information.\n", + "\n", + "
\n", + "\n", + "You only need to run this notebook when you work through the transport tutorial for the first time
\n", + "(or when you decided to delete the local database)!\n", + "\n", + "
\n", + "\n", + "Run the following in the command-line to create a new SQLite database instance called `transport-tutorial`:\n", + "```\n", + "ixmp4 platforms add transport-tutorial\n", + "```\n", + "The prompt will ask whether you really want to create a local database instance, answer `yes` or `y`.\n", + "\n", + "\n", + "You can then check if the database was successfully created by running:\n", + "```\n", + "ixmp4 platforms list\n", + "```\n", + "\n", + "The log output will show the locally available databases (on your computer) as well as \n", + "the **ixmp4** databases hosted by the [IIASA Scenario Services](https://docs.ece.iiasa.ac.at) team.\n", + "\n", + "After creating the database, you can connect to it via an **ixmp4.Platform** instance." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5c93c843-e30d-45fd-b01d-f2d3213d9f30", + "metadata": {}, + "outputs": [], + "source": [ + "import ixmp4\n", + "\n", + "platform = ixmp4.Platform(\"transport-tutorial\")" + ] + }, + { + "cell_type": "markdown", + "id": "b6209e20-9979-4f3b-b6b7-fe37a1c09366", + "metadata": {}, + "source": [ + "## Defining units in the `Platform` and database\n", + "\n", + "The **ixmp4** package requires that units are defined explicitly before adding data using them.\n", + "\n", + "The transport tutorial uses the units \"cases\", \"1000 miles\" and \"dollars per case per 1000 miles\"." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "9be63ed0-aca6-44e3-a6c6-d8fdf8ec2549", + "metadata": {}, + "outputs": [], + "source": [ + "for unit in [\"cases\", \"1000 miles\", \"dollars per case per 1000 miles\"]:\n", + " platform.units.create(unit)" + ] + }, + { + "cell_type": "markdown", + "id": "85c380fb-b29c-4f26-b70d-9d3cdaa42a99", + "metadata": {}, + "source": [ + "You can get a list of all `Unit`s defined in the **ixmp4.Platform** using the `tabulate()` method." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "98f37ba9-7218-428e-953a-107534cbfc12", + "metadata": {}, + "outputs": [], + "source": [ + "platform.units.tabulate()" + ] + }, + { + "cell_type": "markdown", + "id": "5b67ad18-13d3-40b4-9502-f926e753cf07", + "metadata": {}, + "source": [ + "
\n", + "\n", + "Defining an **ixmp4.Unit** again (i.e., creating one that is already defined in the database) will raise an error!\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "1e7f631c-67d5-4170-858d-c96636c5e765", + "metadata": {}, + "source": [ + "One reason for defining units explicitly is so that we can assign docstrings to them.\n", + "\n", + "First, we get a specific **ixmp4.Unit** from the platform. Then, we add a docstring. Third, we illustrate how to retrieve the docstring. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "500b36df-c528-4109-bc62-ab326af34f5e", + "metadata": {}, + "outputs": [], + "source": [ + "cases = platform.units.get(\"cases\")\n", + "cases" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "6c1a395d-6c8a-489c-9aa8-39d24895ca51", + "metadata": {}, + "outputs": [], + "source": [ + "cases.docs = \"Unit of a good produced at canning plants and consumed at markets\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ca1ccd87-03ac-4c29-bd45-0f253a7826c4", + "metadata": {}, + "outputs": [], + "source": [ + "cases.docs" + ] + }, + { + "cell_type": "markdown", + "id": "9bae066c-07c4-4550-8b40-5db2fb9a7049", + "metadata": {}, + "source": [ + "## Cleaning up after doing this tutorial\n", + "\n", + "You can use the **ixmp4** Command Line Interface (CLI) to remove the database related to this tutorial.\n", + "\n", + "
\n", + "\n", + "You still need this database in order to run Notebook 1 and Notebook 2, so don't do this right away!\n", + "\n", + "
\n", + "\n", + "Run the following in the command-line:\n", + "```\n", + "ixmp4 platforms remove transport-tutorial\n", + "```\n", + "\n", + "The prompt will ask whether you also want to delete the SQLite database file from your computer, \n", + "answer `y` for a complete deletion." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "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": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/tutorial/transport/1_transport-tutorial.ipynb b/tutorial/transport/1_transport-tutorial.ipynb new file mode 100644 index 00000000..73dbb0c3 --- /dev/null +++ b/tutorial/transport/1_transport-tutorial.ipynb @@ -0,0 +1,573 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Transport Tutorial - Notebook 1\n", + "\n", + "# Solve *Dantzig's Transport Problem* using **ixmp4** and **linopy**\n", + "\n", + "## Aim and scope of this tutorial\n", + "\n", + "This tutorial takes you through the steps to solve a simple optimization model\n", + "using the **ixmp4** database management package and the **linopy** optimization package.\n", + "\n", + "We use **Dantzig's transport problem**, which is used as a [tutorial for linopy](https://linopy.readthedocs.io/en/latest/transport-tutorial.html).\n", + "This problem solves for a least-cost shipping schedule that meets demand constraints at several markets (cities)\n", + "and supply constraints at factories.\n", + "\n", + "For reference of the transport problem, see:\n", + "> Dantzig, G B, Chapter 3.3. In Linear Programming and Extensions. \n", + "> Princeton University Press, Princeton, New Jersey, 1963.\n", + "\n", + "## Tutorial outline\n", + "\n", + "This tutorial consists of three Jupyter notebooks:\n", + "\n", + "0. Set up an `ixmp4.Platform` to store the scenario input data and solution\n", + "1. Implement the **baseline version of the transport problem** and solve it\n", + "2. Create an **alternative scenario** and solve it " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "\n", + "This notebook requires that you set up a database and defined the units as shown in [**Notebook 0**](0_transport-tutorial_platform-setup.ipynb).\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## The `Platform` as a connection to the database\n", + "\n", + "An [**ixmp4.Platform**](https://docs.ece.iiasa.ac.at/projects/ixmp4/en/latest/devs/ixmp4.core/platform.html#ixmp4.core.platform.Platform)\n", + "is the connection to a database instance that can hold scenario data and relevant additional information." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import ixmp4\n", + "\n", + "platform = ixmp4.Platform(\"transport-tutorial\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "An [**ixmp4.Run**](https://docs.ece.iiasa.ac.at/projects/ixmp4/en/latest/devs/ixmp4.core/run.html#ixmp4.core.run.Run)\n", + "is an object that holds all relevant information for one quantification of a \"scenario\". \n", + "A `Run` is identified by a *model name*, a *scenario name* and an automatically assigned *version number*.\n", + "\n", + "As a first step to solve the **transport problem**, we create a new `Run`." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "run = platform.runs.create(model=\"transport problem\", scenario=\"standard\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Defining the structure of the optimization problem\n", + "\n", + "### The `IndexSet`s\n", + "\n", + "An `IndexSet` defines a list of elements with a name. These sets can be used for \"indexed assignment\" of `Parameter`s, `Variable`s and `Equation`s. \n", + "The entries of these `Parameter`s, etc. are then validated against the elements of the linked set. \n", + "In database terms, a column of a `Parameter`, etc. is \"foreign-keyed\" onto a `IndexSet`.\n", + "\n", + "Below, we first show the data as they would be written in the **GAMS** tutorial." + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "Sets\n", + " i canning plants / seattle, san-diego /\n", + " j markets / new-york, chicago, topeka / ;" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We now initialize these sets and add the data." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "i = run.optimization.indexsets.create(\"i\")\n", + "i.add([\"seattle\", \"san-diego\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can display the data (a.k.a elements) of any `IndexSet` as a Python list:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "i.data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "An `IndexSet` can have a docstring as a means for documentation." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "i.docs = \"Canning plants\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For simplicity, the steps of creating an `IndexSet` and assigning elements can be done in one line.\n", + "\n", + "We illustrate this for the second `IndexSet` of the transport problem." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "run.optimization.indexsets.create(\"j\").add([\"new-york\", \"chicago\", \"topeka\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To add the docstring, we now have to explicitly get the `IndexSet` *j* and add the documentation." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "run.optimization.indexsets.get(\"j\").docs = \"Markets\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Parameters of the optimization problem\n", + "\n", + "A `Parameter` is a table with a number of index columns (each constrained to an `IndexSet`) as well as *units* and *values* columns.\n", + "\n", + "As a next step to solving the transport problem, we define the parameters *capacity* and *demand*.\n", + "The parameters are assigned on the `IndexSet`s *i* and *j*, respectively." + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "Parameters\n", + " a(i) capacity of plant i in cases\n", + " / seattle 350\n", + " san-diego 600 /\n", + " b(j) demand at market j in cases\n", + " / new-york 325\n", + " chicago 300\n", + " topeka 275 / ;" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The data of the `Parameter` can be assigned as a dictionary." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "a = run.optimization.parameters.create(name=\"a\", constrained_to_indexsets=[\"i\"])\n", + "a.docs = \"Capacity of canning plant i\"\n", + "\n", + "a_data = {\n", + " \"i\": [\"seattle\", \"san-diego\"],\n", + " \"values\": [350, 600],\n", + " \"units\": [\"cases\", \"cases\"],\n", + "}\n", + "a.add(data=a_data)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Alternatively, the data can be passed as a `pandas.DataFrame`." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "\n", + "b = run.optimization.parameters.create(\"b\", constrained_to_indexsets=[\"j\"])\n", + "b.docs = \"Demand at market j\"\n", + "\n", + "b_data = pd.DataFrame(\n", + " [\n", + " [\"new-york\", 325, \"cases\"],\n", + " [\"chicago\", 300, \"cases\"],\n", + " [\"topeka\", 275, \"cases\"],\n", + " ],\n", + " columns=[\"j\", \"values\", \"units\"],\n", + ")\n", + "b.add(b_data)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice how the data has three columns but has only been linked to one `IndexSet`? That's on purpose: Every `Parameter` needs to have (the columns) *values* and *units*. The value(s) can be any number(s), but the units have to be defined a-priori in the `ixmp4.Platform`.\n", + "\n", + "Here's how to access `parameter.data` to e.g. quickly confirm that *b* is set correctly:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "b.data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We now turn to a multi-dimensional parameter..." + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "Table d(i,j) distance in thousands of miles\n", + " new-york chicago topeka\n", + " seattle 2.5 1.7 1.8\n", + " san-diego 2.5 1.8 1.4 ;" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It is possible to add data to a `Parameter` in several steps...\n", + "\n", + "Here, we first define the parameter *d* and add four datapoints." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "d = run.optimization.parameters.create(\"d\", constrained_to_indexsets=[\"i\", \"j\"])\n", + "d.docs = \"Distance between cities\"\n", + "\n", + "d_data = {\n", + " \"i\": [\"seattle\", \"seattle\", \"seattle\", \"san-diego\"],\n", + " \"j\": [\"new-york\", \"chicago\", \"topeka\", \"new-york\"],\n", + " \"values\": [2.5, 1.7, 1.8, 2.5],\n", + " \"units\": [\"1000 miles\", \"1000 miles\", \"1000 miles\", \"1000 miles\"],\n", + "}\n", + "d.add(d_data)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we add the two other datapoints. This step-by-step manipulation of parameter data can be helpful in large models." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "d.add({\"i\": [\"san-diego\"], \"j\": [\"chicago\"], \"values\": [1.8], \"units\": [\"1000 miles\"]})\n", + "d.add({\"i\": [\"san-diego\"], \"j\": [\"topeka\"], \"values\": [1.4], \"units\": [\"1000 miles\"]})" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "\n", + "Every time you add data, **all** columns of the `Parameter` must be present!\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Scalars\n", + "\n", + "Another type of input data for optimization problems is a `Scalar`. These are not linked to an `IndexSet`, but consist of only a *value* and a *unit* (and a docstring)." + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "Scalar f freight in dollars per case per thousand miles /90/ ; " + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "f = run.optimization.scalars.create(\n", + " name=\"f\", value=90, unit=\"dollars per case per 1000 miles\"\n", + ")\n", + "f.docs = \"Freight\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Defining the solution structure\n", + "\n", + "The solution of an optimization problem is a list of `Variable` and `Equation` objects.\n", + "\n", + "We first define the `Variable`s and `Equation`s in the `ixmp4.Run`. The values of the solution (level and marginal, mathematically speaking) are read from the **linopy** output after solving the problem." + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "Variables\n", + " x(i,j) shipment quantities in cases\n", + " z total transportation costs in thousands of dollars ;\n", + " \n", + "Equations\n", + " cost define objective function\n", + " supply(i) observe supply limit at plant i\n", + " demand(j) satisfy demand at market j ;" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here, *supply* can only come from the canning plant in `IndexSet` *i*, while *demand* needs to be met at the markets in `IndexSet` *j*.\n", + "\n", + "Shipment is defined from a canning plant to a market, so *x* needs to be assigned to both *i* and *j*." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "x = run.optimization.variables.create(\"x\", constrained_to_indexsets=[\"i\", \"j\"])\n", + "z = run.optimization.variables.create(\"z\")\n", + "\n", + "supply = run.optimization.equations.create(\"supply\", constrained_to_indexsets=[\"i\"])\n", + "demand = run.optimization.equations.create(\"demand\", constrained_to_indexsets=[\"j\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Solve the scenario\n", + "\n", + "In this tutorial, we solve the tutorial using the open-source solver [*highs*](https://github.com/ERGO-Code/HiGHS) in **linopy**. \n", + "\n", + "The ``create_dantzig_model()`` function is a convenience shortcut to retrieve the data from the `ixmp4.Run`\n", + "and set up a **linopy** model correctly to solve the transport problem. Please see ``linopy_model.py`` for details.\n", + "\n", + "The solution of the transport problem is stored with the model object automatically.\n", + "The function ``store_dantzig_solution()`` reads the solution and stores it in the respective `Variable` and `Equation` objects of the `ixmp4.Run`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from tutorial.transport.dantzig_model_linopy import (\n", + " create_dantzig_model,\n", + " read_dantzig_solution,\n", + ")\n", + "\n", + "linopy_model = create_dantzig_model(run=run)\n", + "linopy_model.solve(\"highs\")\n", + "read_dantzig_solution(model=linopy_model, run=run)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Display and analyze the results\n", + "\n", + "We can now retrieve and display the components of the solution.\n", + "\n", + "First, the variable *z* is the objective value of the transport problem: the minimum total cost of satisfying the demand at all markets." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "z.levels" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The variable *x* shows the least-cost (optimal) shipment from plants to markets." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pd.DataFrame(x.data)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The levels and marginals of the `Equation`s show the shipped quantities and shadow prices (\"dual variables\") of the least-cost solution." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "demand.data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "supply.data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setting a default version of a `Run`\n", + "\n", + "The key benefit of **ixmp4** is handling a large number of scenarios - aka `ixmp4.Run` objects - in a version-controlled database.\n", + "Each `Run` is identified by a *model name*, a *scenario name* and an automatically assigned *version number*.\n", + "\n", + "For every model-scenario combination, we can assign one `Run` as the *default version*.\n", + "This allows to keep previous versions in the database (for easy reference and comparison) but have a well-defined approach to get the \"right\" version (e.g., the latest version of a scenario)." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "run.set_as_default()" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": ".venv", + "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": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/tutorial/transport/2_transport-tutorial_variant.ipynb b/tutorial/transport/2_transport-tutorial_variant.ipynb new file mode 100644 index 00000000..a1b32d46 --- /dev/null +++ b/tutorial/transport/2_transport-tutorial_variant.ipynb @@ -0,0 +1,503 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Transport Tutorial - Notebook 2\n", + "\n", + "# Implement and solve a variation of *Dantzig's Transport Problem*\n", + "\n", + "## Aim and scope of this tutorial\n", + "\n", + "This tutorial takes you through the steps to solve a simple optimization model\n", + "using the **ixmp4** database management package and the **linopy** optimization package.\n", + "\n", + "We use **Dantzig's transport problem**, which is used as a [tutorial for linopy](https://linopy.readthedocs.io/en/latest/transport-tutorial.html).\n", + "This problem solves for a least-cost shipping schedule that meets demand constraints in several markets (cities)\n", + "and supply constraints at factories.\n", + "\n", + "For reference of the transport problem, see:\n", + "> Dantzig, G B, Chapter 3.3. In Linear Programming and Extensions. \n", + "> Princeton University Press, Princeton, New Jersey, 1963.\n", + "\n", + "## Tutorial outline\n", + "\n", + "This tutorial consists of three Jupyter notebooks:\n", + "\n", + "0. Set up an `ixmp4.Platform` to store the scenario input data and solution\n", + "1. Implement the **baseline version of the transport problem** and solve it\n", + "2. Create an **alternative scenario** and solve it " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "\n", + "This notebook requires that you created and solved the base transport problem as shown in [**Notebook 1**](1_transport-tutorial.ipynb).\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## The `Platform` as a connection to the database\n", + "\n", + "An [**ixmp4.Platform**](https://docs.ece.iiasa.ac.at/projects/ixmp4/en/latest/devs/ixmp4.core/platform.html#ixmp4.core.platform.Platform)\n", + "is the connection to a database instance that can hold scenario data and relevant additional information." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import ixmp4\n", + "\n", + "platform = ixmp4.Platform(\"transport-tutorial\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As a first step, we list all scenarios (aka [**ixmp4.Run**](https://docs.ece.iiasa.ac.at/projects/ixmp4/en/latest/devs/ixmp4.core/run.html#ixmp4.core.run.Run) instances)\n", + "available in the `Platform` connected to the database instance." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "platform.runs.tabulate()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "\n", + "If you are running this tutorial for the first time, you will only see the *standard* scenario of the *transport problem*. \n", + "If you have already done some experimentation in the `transport-tutorial` platform, you will see more scenarios.\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Retrieve a `Run` from the `Platform`\n", + "\n", + "As a first step in this notebook, you can load the `ixmp4.Run` that you created in [Notebook 1](1_transport-tutorial.ipynb)." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "run = platform.runs.get(model=\"transport problem\", scenario=\"standard\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can check that this is indeed the version shown above by comparing the *version number*." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "run.version" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can check that the solution from [Notebook 1](1_transport-tutorial.ipynb) was indeed saved by inspecting an `ixmp4.Variable`. \n", + "The `data` of any optimization object is stored internally as a dictionary." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "run.optimization.variables.get(\"z\").data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For instances of a `Variable` or a `Parameter` that have multiple dimensions,\n", + "you can use **pandas** for filtering and inspecting the data.\n", + "\n", + "The following cell shows how to retrieve the distance parameter *d* and cast the `Parameter.data` to a `pandas.DataFrame`." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "\n", + "d = run.optimization.parameters.get(\"d\")\n", + "distance = pd.DataFrame(d.data)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**pandas** enables easy and powerful filtering, e.g. showing only the distances for connections from Seattle:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "distance[distance[\"i\"] == \"seattle\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can also combine filters for more elaborate queries." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "distance.loc[(distance[\"i\"] == \"seattle\") & (distance[\"j\"].isin([\"chicago\", \"topeka\"]))]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Please note that **pandas** recommends to use [advanced indexing](https://pandas.pydata.org/pandas-docs/stable/user_guide/advanced.html#advanced-advanced-hierarchical) if you find yourself using more than three conditionals in `.loc[]`." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Make a clone of the baseline scenario\n", + "\n", + "For illustration of a typical scenario analysis workflow, we create a copy of the standard transportation problem\n", + "and make two modifications:\n", + "- Add a new demand location *detroit* and add a demand-level and freight costs to that city.\n", + "- Reduce the demand level in *chicago* to ensure a feasible problem, because the total production capacity does not allow much slack for increased production.\n", + "\n", + "The next cell creates a copy (`clone()`) of the standard transportation problem." + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [], + "source": [ + "run_detroit = run.clone(scenario=\"detroit\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Because the cell above did not specify a *model name*, the clone kept the original model name." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "run_detroit.model.name" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We now modify the structure and parameters of the *detroit* scenario.\n", + "First, we reduce the demand in *chicago* to ensure a feasible problem." + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [], + "source": [ + "run_detroit.optimization.parameters.get(\"b\").add(\n", + " {\"j\": [\"chicago\"], \"values\": [200], \"units\": [\"cases\"]}\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we add a new market *detroit* and add all relevant parameters." + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [], + "source": [ + "run_detroit.optimization.indexsets.get(\"j\").add(\"detroit\")\n", + "run_detroit.optimization.parameters.get(\"b\").add(\n", + " {\"j\": [\"detroit\"], \"values\": [150], \"units\": [\"cases\"]}\n", + ")\n", + "run_detroit.optimization.parameters.get(\"d\").add(\n", + " {\n", + " \"i\": [\"seattle\", \"san-diego\"],\n", + " \"j\": [\"detroit\", \"detroit\"],\n", + " \"values\": [1.7, 1.9],\n", + " \"units\": [\"1000 miles\", \"1000 miles\"],\n", + " }\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Solve the new scenario\n", + "\n", + "Now, we create a **linopy** model, solve it, and read the solution to store it\n", + "in the respective `Variable` and `Equation` objects of the `ixmp4.Run`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from tutorial.transport.dantzig_model_linopy import (\n", + " create_dantzig_model,\n", + " read_dantzig_solution,\n", + ")\n", + "\n", + "linopy_model_detroit = create_dantzig_model(run=run_detroit)\n", + "linopy_model_detroit.solve(\"highs\")\n", + "read_dantzig_solution(model=linopy_model_detroit, run=run_detroit)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setting a default version of the scenario\n", + "\n", + "As in [Notebook 1](1_transport-tutorial.ipynb), we set the *detroit* scenario as the default version so that we can easily retrieve it later." + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [], + "source": [ + "run_detroit.set_as_default()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Display and analyze the results\n", + "\n", + "We can now compare the *standard* instance of the **transport problem** and the *detroit* scenario.\n", + "\n", + "The next two cells show the objective values of the two `Run`s." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "jupyter": { + "name": "scen-z" + } + }, + "outputs": [], + "source": [ + "run.optimization.variables.get(\"z\").levels" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "jupyter": { + "name": "scen-detroit-z" + } + }, + "outputs": [], + "source": [ + "run_detroit.optimization.variables.get(\"z\").levels" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You see that the *detroit* scenario has higher total cost.\n", + "\n", + "The next two cells compare the optimal assignment of shipments from plants to markets in the two `Run`s." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pd.DataFrame(run.optimization.variables.get(\"x\").data)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pd.DataFrame(run_detroit.optimization.variables.get(\"x\").data)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pd.DataFrame(run_detroit.optimization.equations.get(\"demand\").data)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Scenario version management\n", + "\n", + "As above, you can now list all scenarios available in the database instance to which the `ixmp4.Platform` is connected.\n", + "\n", + "You should now see (at least) two scenarios." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "platform.runs.tabulate()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "When developing scenarios, we often make minor variations to numerous scenarios, and changing the *scenario name* every time is not practical.\n", + "\n", + "The **ixmp4** package provides a more efficient approach to scenario version management: Every time that you `create()` a new `Run` or `clone()` an existing `Run` and the *model-scenario-name* combination already exists, a new *version* will be created.\n", + "\n", + "You can get a specific *version* by including the version number in the call to get an `ixmp4.Run` like\n", + "\n", + "```python\n", + "platforms.runs.get(model, scenario, version)\n", + "```\n", + "\n", + "You also can assign one of the versions as *default*, so that this `Run` will be retrieved when you call\n", + "\n", + "```python\n", + "platforms.runs.get(model, scenario)\n", + "```\n", + "\n", + "without a version number.\n", + "\n", + "All versions will be kept in the database and are accessible via the `ixmp4.Platform`.\n", + "\n", + "If you have run this tutorial multiple times, you can tabulate all `Run`s - including the non-default versions - as illustrated below." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "platform.runs.tabulate(default_only=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Cleaning up after doing this tutorial\n", + "\n", + "You can use the **ixmp4** Command Line Interface (CLI) to remove the database related to this tutorial.\n", + "\n", + "Run the following in the command-line:\n", + "```\n", + "ixmp4 platforms remove transport-tutorial\n", + "```\n", + "\n", + "The prompt will ask whether you also want to delete the SQLite database file from your computer, answer `y` for a complete deletion." + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": ".venv", + "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": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/tutorial/transport/_static/python.png b/tutorial/transport/_static/python.png deleted file mode 100644 index 738f6ed4..00000000 Binary files a/tutorial/transport/_static/python.png and /dev/null differ diff --git a/tutorial/transport/dantzig_model_linopy.py b/tutorial/transport/dantzig_model_linopy.py new file mode 100644 index 00000000..3cfaffd3 --- /dev/null +++ b/tutorial/transport/dantzig_model_linopy.py @@ -0,0 +1,119 @@ +import linopy +import pandas as pd + +from ixmp4.core import Parameter, Run + + +def create_parameter( + parameter: Parameter, + index: "pd.Index[str] | list[pd.Index[str]]", + index_name: str | list[str], + name: str, +) -> "pd.Series[float]": + if isinstance(index, list): + index = pd.MultiIndex.from_product(index) + + # Ensure parameter.data are aligned with the index + sorted_values = ( + pd.DataFrame(parameter.data).set_index(index_name).loc[index.values]["values"] + ) + + return pd.Series(data=sorted_values, index=index, name=name) + + +def create_dantzig_model(run: Run) -> linopy.Model: + """Creates new linopy.Model for Dantzig's problem based on ixmp4.Run.""" + m = linopy.Model() + i = pd.Index( + run.optimization.indexsets.get("i").data, name="Canning Plants" + ).astype("string") + j = pd.Index(run.optimization.indexsets.get("j").data, name="Markets").astype( + "string" + ) + a = create_parameter( + parameter=run.optimization.parameters.get("a"), + index=i, + index_name="i", + name="capacity of plant i in cases", + ) + b = create_parameter( + parameter=run.optimization.parameters.get("b"), + index=j, + index_name="j", + name="demand at market j in cases", + ) + d = create_parameter( + parameter=run.optimization.parameters.get("d"), + index=[i, j], + index_name=["i", "j"], + name="distance in thousands of miles", + ) + f = run.optimization.scalars.get("f").value + + c = d * f / 1000 + c.name = "transport cost in thousands of dollars per case" + + x = m.add_variables(lower=0.0, coords=[i, j], name="Shipment quantities in cases") + + m.add_constraints( + lhs=x.sum(dim="Markets"), + sign="<=", + rhs=a, + name="Observe supply limit at plant i", + ) + + m.add_constraints( + lhs=x.sum(dim="Canning Plants"), + sign=">=", + rhs=b, + name="Satisfy demand at market j", + ) + + obj = c.to_xarray() * x + m.add_objective(obj) + + return m + + +def read_dantzig_solution(model: linopy.Model, run: Run) -> None: + """Reads the Dantzig solution from linopy.Model to ixmp4.Run.""" + # Handle objective + # TODO adding fake marginals here until Variables don't require this column anymore + # Can't add units if this column was not declared above. Better stored as Scalar + # maybe? + run.optimization.variables.get("z").add( + data={"levels": [model.objective.value], "marginals": [-0.0]} + ) + + # Handle shipment quantities + x_data = model.solution.to_dataframe() + x_data.reset_index(inplace=True) + x_data.rename( + columns={ + "Shipment quantities in cases": "levels", + "Canning Plants": "i", + "Markets": "j", + }, + inplace=True, + ) + # x_data["units"] = "cases" + # TODO Again setting fake marginals until they are optional for variables + x_data["marginals"] = -0.0 + run.optimization.variables.get("x").add(data=x_data) + + # The following don't seem to be typed correctly by linopy + # Add supply data + supply_data = { + "i": run.optimization.indexsets.get("i").data, + "levels": model.constraints["Observe supply limit at plant i"].data.rhs, + "marginals": model.constraints["Observe supply limit at plant i"].data.dual, + } + run.optimization.equations.get("supply").add(data=supply_data) + + # Add demand data + demand_data = { + "j": run.optimization.indexsets.get("j").data, + "levels": model.constraints["Satisfy demand at market j"].data.rhs, + "marginals": model.constraints["Satisfy demand at market j"].data.dual, + } + run.optimization.equations.get("demand").add(data=demand_data) diff --git a/tutorial/transport/utils.py b/tutorial/transport/utils.py new file mode 100644 index 00000000..4b81d037 --- /dev/null +++ b/tutorial/transport/utils.py @@ -0,0 +1,53 @@ +import pandas as pd + +import ixmp4 +from ixmp4.core import Run, Unit + + +def create_default_dantzig_run(platform: ixmp4.Platform) -> Run: + """Creates new ixmp4.Run holding all data for Dantzig's problem.""" + try: + cases = platform.units.get("cases") + km = platform.units.get("km") + unit_cost_per_case = platform.units.get("USD/km") + except Unit.NotFound: + cases = platform.units.create("cases") + km = platform.units.create("km") + unit_cost_per_case = platform.units.create("USD/km") + run = platform.runs.create(model="transport problem", scenario="standard") + run.set_as_default() + run.optimization.indexsets.create("i").add(["seattle", "san-diego"]) + run.optimization.indexsets.create("j").add(["new-york", "chicago", "topeka"]) + a_data = { + "i": ["seattle", "san-diego"], + "values": [350, 600], + "units": [cases.name, cases.name], + } + run.optimization.parameters.create(name="a", constrained_to_indexsets=["i"]).add( + data=a_data + ) + b_data = pd.DataFrame( + [ + ["new-york", 325, cases.name], + ["chicago", 300, cases.name], + ["topeka", 275, cases.name], + ], + columns=["j", "values", "units"], + ) + run.optimization.parameters.create("b", constrained_to_indexsets=["j"]).add(b_data) + d_data = { + "i": ["seattle", "seattle", "seattle", "san-diego", "san-diego", "san-diego"], + "j": ["new-york", "chicago", "topeka", "new-york", "chicago", "topeka"], + "values": [2.5, 1.7, 1.8, 2.5, 1.8, 1.4], + "units": [km.name] * 6, + } + run.optimization.parameters.create("d", constrained_to_indexsets=["i", "j"]).add( + d_data + ) + run.optimization.scalars.create(name="f", value=90, unit=unit_cost_per_case) + run.optimization.variables.create("z") + run.optimization.variables.create("x", constrained_to_indexsets=["i", "j"]) + run.optimization.equations.create("supply", constrained_to_indexsets=["i"]) + run.optimization.equations.create("demand", constrained_to_indexsets=["j"]) + + return run