Download Why Python?

Building Applications with
Python & Gurobi
Kostja Siefen
Building optimization models with Gurobi
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Option 1 – use a modeling language
◦ Very easy to build models
– Optimization modeling constructs built into language
◦ Attractive choice for non-programmers
◦ Alternatives: AMPL, GAMS, Pyomo, …
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Option 2 – use a full programming language
◦ Much more powerful and flexible development environment
– Complete access to solver functionality
– Richer set of language features
◦ Natural choice when deploying models and/or integrating them into
applications for others to use
◦ Alternatives: C, C++, C#, Java, MATLAB, Python, R, VB
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But what if users didn't have to choose between easy and
powerful?
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Gurobi Python Environment
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High-level optimization modeling constructs embedded in
Python programming language
◦ Combines expressiveness of a modeling language with the power and
flexibility of a programming language
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Design goals:
Require minimal programming skills to get started
Bring "feel" of a modeling language to the Python interface
Allow for code that is easy to write and maintain
Maintain unified design across all of our interfaces
Remain lightweight and efficient (memory & CPU) when compared with
solver alone
◦ Support all solver and programming needs
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Consistently earns high praise from our users
© 2015 Gurobi Optimization
Why Python?
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Fast, powerful and easy-to-use
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Extendible with vast library of prewritten modules (over 50,000)
◦ Statistical analysis, visualization, GUIs, web development, web services,
data connections, file compression, data encryption, …
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One of top-10 most popular programming languages, according to
TIOBE Programming Community Index
◦ http://www.tiobe.com/index.php/content/paperinfo/tpci/index.html
◦ Large and rapidly growing Python community with great training resources
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Interactive shell is useful for prototyping
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Can be deployed to create full-featured optimization applications
◦ GUI, server, web, …
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Free and open source with license that's business-friendly
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Getting started with Python
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Don't need to be an expert to start modeling in Python
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Nothing extra to buy or install
◦ Gurobi distribution includes Python interpreter and basic modules
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Gurobi Interactive Shell is Python shell with Gurobi extensions
◦ Powerful environment for interacting with existing models
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To launch the shell:
◦ Windows
– Double-click the Gurobi icon on the desktop
– Or run gurobi from a Command Prompt window
◦ Linux
– Run the gurobi.sh command from a terminal
◦ Mac
– Click on the Gurobi icon in the Launchpad
– Or run the gurobi.sh command from a terminal
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Programming in Python
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Python is a cross-platform, high-level programming language
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Object-oriented
Interpreted
Dynamically typed
Automatic memory management
Includes a large standard library
Documentation tightly integrated with the code
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Python programs are typically stored in text files with .py extension
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Most functionality in Python is provided by modules
◦ To use a module in Python programs, include an import statement
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To use the Gurobi interface, import the gurobipy module:
from gurobipy import *
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Editing and running Python programs
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Any text editor can be used to work with .py files
◦ Most programming editors have Python mode with syntax highlighting,
etc.
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To run a program:
◦ Windows
– At a Command Prompt window, enter
gurobi [program_name]
◦ Linux/Mac
– At a terminal, enter
gurobi.sh [program_name]
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Many IDEs available
◦ https://wiki.python.org/moin/IntegratedDevelopmentEnvironments
◦ Easy to install Gurobi in other Python distributions
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Gurobi recommends Anaconda
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Windows, Mac, Linux; 32/64-bit
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Python 2.x, 3.x — your choice
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~150 packages in the default installation
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conda package and environment management
◦ ~350 packages available with a simple conda install
◦ Create isolated “silos” for multiple projects
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Gurobi provides/maintains Anaconda packages
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Essential Gurobi modeling constructs
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Model
a model
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Var
a decision variable
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Constr
a constraint
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Overloaded operators
◦ Basic arithmetic (+, -, ×, ÷)
◦ Constraint (≤, =, ≥)
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Aggregate sum operator (quicksum)
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Python provides the rest!
© 2015 Gurobi Optimization
Typical steps for building a model
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Create empty Model object with Model() constructor
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Add variables with Model.addVar()
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Call Model.update() once to process pending model updates
◦ Gurobi provides efficient "lazy updates"
◦ Only call Model.update() when necessary to reference new elements
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Call Model.setObjective() to set objective function
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Add constraints with Model.addConstr()
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Solve the model with Model.optimize()
◦ Will process any pending model updates
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Example: simple model
# Create empty Model
m = Model()
# Add variables
x = m.addVar(vtype=GRB.BINARY)
y = m.addVar(vtype=GRB.BINARY)
z = m.addVar(vtype=GRB.BINARY)
# Process pending updates
m.update()
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Example: simple model
# Set objective function
m.setObjective(x + y + 2*z, GRB.MAXIMIZE)
# Add constraints
m.addConstr(x + 2*y + 3*z <= 4)
m.addConstr(x + y >= 1)
# Solve model
m.optimize()
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Indices and subscripts in Python
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List is a mutable sequence of elements
◦ Useful when representing index sets
◦ Elements of any type (integers, strings, …) indexed by integers (0 to n-1)
◦ Ex: cities=['CHI', 'NYC', 'ATL']; cities.append('MIA');
cities[2]='BOS'; cities.remove('NYC'); cities.sort()
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Tuple is an immutable, compound grouping of elements
◦ Ideal for representing multidimensional subscripts
◦ Ex: arc=('CHI','NYC'); j=arc[1]
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Dictionary is a mutable mapping from keys to values
◦ Useful when representing coefficients or variables indexed by subscripts
◦ Keys must be immutable (integers, strings, tuples, …)
◦ Ex: cost={('CHI','NYC'):100, ('ATL','MIA'):150};
cost[('NYC','MIA')]=200; c=cost['ATL','MIA']
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© 2015 Gurobi Optimization
Iterating in Python and aggregate sums
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Loops iterate over collections of elements (list, dictionary, …)
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List comprehension efficiently builds lists via set notation
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Ex: penaltyarcs=[a for a in arcs if cost[a]>1000]
quicksum() is direct replacement for Python's sum() function
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Useful when representing the for-all modeling construct
Ex:
for c in cities:
print c # must indent all statements in loop
Gurobi feature that is more efficient when working with Var objects
Ex: obj=quicksum(cost[a]*x[a] for a in arcs)
Combining loops with sums is ideal when building constraints
Ex:
∑
j∈J
xij ≤ 5, ∀i ∈ I
can be built with
for i in I:
m.addConstr(quicksum(x[i,j] for j in J) <= 5)
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© 2015 Gurobi Optimization
Building a Gurobi Model: Six Steps
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Create a model
m = Model()
Add variables
x = m.addVar(...)
Commit changes
m.update()
Set objective
m.setObjective(...)
Add constraints
m.addConstr(...)
Optimize
m.optimize()
© 2015 Gurobi Optimization
Spyder
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Scientific Python Development EnviRonment
Familiar integrated development environment (IDE) for users of
MATLAB, Visual Studio, etc.
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Built-in editor, file explorer, documentation browser
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Graphical interface for debugging (ipdb)
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IPython console for interactive code execution
http://pythonhosted.org/spyder
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Latest Python-related enhancements in v6.5
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Interactive examples for commonly faced business problems
◦ http://www.gurobi.com/resources/examples/example-models-overview
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Expression building is more than 4X faster
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Lazy update mode
◦ Set new UpdateMode parameter to 1
◦ Refer to new variables and constraints without calling Model.update()
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More control over when Gurobi environments are created/released
◦ Default environment not created until first used
◦ Released with new disposeDefaultEnv() method
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New Conda package simplifies installation for the Anaconda Python distribution
$ conda config --add channels http://conda.anaconda.org/gurobi
$ conda install gurobi
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Interactive Examples
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Jupyter (formerly IPython)
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A web application for interactive data analysis
Create notebooks containing live code, text, equations, and
visualizations
A great way to prototype new algorithms, document experiments,
share reproducible results
Originally for Python, now there are kernels for over 50 different
languages
http://www.jupyter.org
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Pandas
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Fast and efficient DataFrame and Series objects
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Read/write CSV, HDF5, Excel, SQL, plain text
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Missing data handling
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Slicing, fancy indexing, subsetting
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Merges, joins
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Split-apply-combine operations (groupby)
http://pandas.pydata.org
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Bokeh
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Interactive visualization for web browsers
◦ High performance with streaming data and big data
◦ In-browser interactivity on otherwise static pages
◦ Bindings for Python, Scala, Julia, R
http://bokeh.pydata.org
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Jupyter Demo
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References
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Visit http://www.gurobi.com/documentation/ for more information on
the Gurobi Python Interface
◦ Quick Start Guide
◦ Reference Manual
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Explore our functional code examples to learn more about advanced
features
◦ Working with sparse models: netflow, workforce
◦ Accessing data sources (databases, spreadsheets, …): diet
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Visit https://docs.python.org/ learn more about Python
◦ Official Python website
◦ Tutorials, documentation, examples, …
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Visit https://www.continuum.io to get started with the Anaconda Python
distribution, which includes iPython and Jupyter
© 2015 Gurobi Optimization