Modeling Constrained Combinatorial Problems in Python
Project description
PyCSP3 v1.1.x
This is the version 1.1.0 of PyCSP3, a library in Python 3 (version 3.5 or later) for modeling constrained combinatorial problems. PyCSP3 is inspired from both JvCSP3 (a Javabased API) and Numberjack; it is also related to CPpy.
With PyCSP3, it is possible to generate instances of:
 CSPs (Constraint Satisfaction Problems)
 COPs (Constraint Optimization Problems)
in format XCSP3; see www.xcsp.org.
Note that:
 the code is available on Github
 a welldocumented guide is available
 PyCSP3 is available as a PyPi package here
Important: another version, 1.2.0, is planned to be published within a few weeks/months. Currently, our main goal is :
 to fix a few problems encountered with python 3.8 (with python 3.5, 3.6 and 3.7, things look good)
 to give more helpful messages when the user (modeler) writes something incorrect
At this stage, one can run:
 the constraint solver 'AbsCon', with the option solve or the option solver=abscon
 the constraint solver 'Choco, with the option solver=choco
Of course, it is possible to launch on generated XCSP3 instances (files) any solver that recognizes the XCSP3 format. In the medium/long term, we also plan to develop an interface that will allow users to pilot solvers with Python.
Installation
Installation instructions are currently given for Linux and Windows (instructions for Mac will be inserted soon)
Installing PyCSP3 (Linux)
For installing PyCSP3, you need to execute:
sudo apt install python3pip sudo pip3 install pycsp3
For using the solve or solver options, you need to install (at least) Java version 8:
sudo aptget install openjdk8jdk
Installing PyCSP3 (Windows)
For installing Python, you need to install the version 3.7:
https://www.python.org/downloads/release/python377/
Do not forget to tick the box 'Add Python 3.7 to PATH' during the installation.
Next, you have to open the console and upgrade pip:
python m pip install upgrade pip
And for installing pycsp3:
python m pip install pycsp3
For using the solve or solver options, you need to install (at least) Java version 8:
https://www.oracle.com/java/technologies/javasedownloads.html
And add in the PATH the java command, for example, temporally, with the command:
set path=%path%;C:/Program Files/Java/jdk14.0.1/bin/
You can check the java command by typing in your console:
java version
Updating PyCSP3
For updating your version of PyCSP3, simply execute:
For linux:
sudo pip3 install upgrade pycsp3
For Windows:
python m pip install upgrade pycsp3
Copying a pool of models
PyCSP3 is accompanied by more than 100 models.
To get them in a subdirectory problems
of your current directory, execute:
python3 m pycsp3 (For linux) python m pycsp3 (For Windows)
And you can test the compilation of one of the models, for example:
python3 problems/g0_school/Pic.py (For Linux) python problems\g0_school\Pic.py (For Windows)
Compiling PyCSP3 Models
For generating an XCSP3 file from a PyCSP3 model, you have to execute:
python3 <file> [options]
with:
 <file>: a Python file to be executed, describing a model in PyCSP3
 [options]: possible options to be used when compiling
Among the options, we find:

data=<data_value>
: allows us to specify the data to be used by the model. It can be: elementary: data=5
 a simple list: data=[9,0,0,3,9]
 a JSON file: data=Bibd346.json
Data can then be directly used in the PyCSP3 model by means of a predefined variable
data
. 
dataparser=<file>
: a Python file for reading/parsing data given under any arbitrary form (e.g., by a text file). See Example Nonogram below, for an illustration. 
dataexport
: exports (saves) the data in JSON format. See Example Nonogram below, for an illustration. 
variant=<variant_name>
: the name of a variant, to be used with functionvariant()
. See Example AllInterval below, for an illustration. 
solve
: attempts to solve the instance with the embedded solver 'AbsCon'. It requires that Java version 8 (at least) is installed. 
solver=<solver_name>
: attempts to solve the instance with the solver whose name is given. Currently, it can be 'abscon' or 'choco'. Important: it requires that Java version 8 (at least) is installed.
Some Examples
We succinctly introduce a few PyCSP3 models, showing how to compile them with different options.
Example 1: in console mode
Our first example shows how you can build basic models in console mode. In this example, we just post two variable and two simple binary constraints.
$ python3 Python 3.5.2 >>> from pycsp3 import * >>> x = Var(range(10)) >>> y = Var(range(10)) >>> satisfy( x < y, x + y > 15 ) >>> compile()
Note that to get an XCSP3 file, we call compile()
.
Example 2: Send+More=Money
This example shows how you can define a model when no data is required from the user. This is the classical cryptoarithmetic puzzle 'Send+More=Money'.
File SendMore.py
from pycsp3 import * # letters[i] is the digit of the ith letter involved in the equation s, e, n, d, m, o, r, y = letters = VarArray(size=8, dom=range(10)) satisfy( # letters are given different values AllDifferent(letters), # words cannot start with 0 [s > 0, m > 0], # respecting the mathematical equation [s, e, n, d] * [1000, 100, 10, 1] + [m, o, r, e] * [1000, 100, 10, 1] == [m, o, n, e, y] * [10000, 1000, 100, 10, 1] )
To generate the XCSP3 instance (file), the command is:
python3 SendMore.py
To generate and solve (with AbsCon) the XCSP3 instance, the command is:
python3 SendMore.py solve
To generate and solve with Choco the XCSP3 instance, the command is:
python3 SendMore.py solver=choco
Example 3: AllInterval Series
This example shows how you can simply specify an integer (as unique data) for a model. For our illustration, we consider the problem AllInterval Series.
A classical model is:
File AllInterval.py
(version 1)
from pycsp3 import * n = data # x[i] is the ith note of the series x = VarArray(size=n, dom=range(n)) satisfy( # notes must occur once, and so form a permutation AllDifferent(x), # intervals between neighbouring notes must form a permutation AllDifferent(abs(x[i]  x[i + 1]) for i in range(n  1)), # tag(symmetrybreaking) x[0] < x[n  1] )
Note the presence of a tag symmetrybreaking
that will be directly integrated into the XCSP3 file generated by the following command:
python3 AllInterval.py data=5
Suppose that you would prefer to declare a second array of variables for representing successive distances. This would give:
File AllInterval.py
(version 2)
from pycsp3 import * n = data # x[i] is the ith note of the series x = VarArray(size=n, dom=range(n)) # y[i] is the distance between x[i] and x[i+1] y = VarArray(size=n  1, dom=range(1, n)) satisfy( # notes must occur once, and so form a permutation AllDifferent(x), # intervals between neighbouring notes must form a permutation AllDifferent(y), # computing distances [y[i] == abs(x[i]  x[i + 1]) for i in range(n  1)], # tag(symmetrybreaking) [x[0] < x[n  1], y[0] < y[1]] )
However, sometimes, it may be relevant to combine different variants of a model in the same file. In our example, this would give:
File AllInterval.py
(version 3)
from pycsp3 import * n = data # x[i] is the ith note of the series x = VarArray(size=n, dom=range(n)) if not variant(): satisfy( # notes must occur once, and so form a permutation AllDifferent(x), # intervals between neighbouring notes must form a permutation AllDifferent(abs(x[i]  x[i + 1]) for i in range(n  1)), # tag(symmetrybreaking) x[0] < x[n  1] ) elif variant("aux"): # y[i] is the distance between x[i] and x[i+1] y = VarArray(size=n  1, dom=range(1, n)) satisfy( # notes must occur once, and so form a permutation AllDifferent(x), # intervals between neighbouring notes must form a permutation AllDifferent(y), # computing distances [y[i] == abs(x[i]  x[i + 1]) for i in range(n  1)], # tag(symmetrybreaking) [x[0] < x[n  1], y[0] < y[1]] )
For compiling the main model (variant), the command is:
python3 AllInterval.py data=5
For compiling the second model variant, using the option variant
, the command is:
python3 AllInterval.py data=5 variant=aux
To generate and solve (with AbsCon) the instance of order 10 and variant 'aux', the command is:
python3 AllInterval.py data=10 variant=aux solve
Example 4: BIBD
This example shows how you can specify a list of integers to be used as data for a model.
For our illustration, we consider the problem BIBD.
We need five integers v, b, r, k, l
for specifying a unique instance (possibly, b
and r
can be set to 0, so that these values are automatically computed according to a template for this problem).
The model is:
File Bibd.py
from pycsp3 import * v, b, r, k, l = data b = (l * v * (v  1)) // (k * (k  1)) if b == 0 else b r = (l * (v  1)) // (k  1) if r == 0 else r # x[i][j] is the value of the matrix at row i and column j x = VarArray(size=[v, b], dom={0, 1}) satisfy( # constraints on rows [Sum(row) == r for row in x], # constraints on columns [Sum(col) == k for col in columns(x)], # scalar constraints with respect to lambda [row1 * row2 == l for (row1, row2) in combinations(x, 2)] )
To generate an XCSP3 instance (file), we can for example execute a command like:
python3 Bibd.py data=[9,0,0,3,9]
With some command interpreters (shells), you may have to escape the characters '[' and ']', which gives:
python3 Bibd.py data=\[9,0,0,3,9\]
Example 5: Rack Configuration
This example shows how you can specify a JSON file to be used as data for a model. For our illustration, we consider the problem Rack Configuration. The data (for a specific instance) are then initially given in a JSON file, as for example:
File Rack_r2.json
{ "nRacks": 10, "models": [[150,8,150],[200,16,200]], "cardTypes": [[20,20],[40,8],[50,4],[75,2]] }
In the following model, we directly unpack the components of the variable data
(because it is automatically given under the form of a named tuple) whose fields are exactly those of the main object in the JSON file.
File Rack.py
from pycsp3 import * nRacks, models, cardTypes = data models.append([0, 0, 0]) # we add first a dummy model (0,0,0) powers, sizes, costs = zip(*models) cardPowers, cardDemands = zip(*cardTypes) nModels, nTypes = len(models), len(cardTypes) table = {(i, powers[i], sizes[i], costs[i]) for i in range(nModels)} # m[i] is the model used for the ith rack m = VarArray(size=nRacks, dom=range(nModels)) # p[i] is the power of the model used for the ith rack p = VarArray(size=nRacks, dom=powers) # s[i] is the size (number of connectors) of the model used for the ith rack s = VarArray(size=nRacks, dom=sizes) # c[i] is the cost (price) of the model used for the ith rack c = VarArray(size=nRacks, dom=costs) # nc[i][j] is the number of cards of type j put in the ith rack nc = VarArray(size=[nRacks, nTypes], dom=lambda i, j: range(min(max(sizes), cardDemands[j]) + 1)) satisfy( # linking rack models with powers, sizes and costs [(m[i], p[i], s[i], c[i]) in table for i in range(nRacks)], # connectorcapacity constraints [Sum(nc[i]) <= s[i] for i in range(nRacks)], # powercapacity constraints [nc[i] * cardPowers <= p[i] for i in range(nRacks)], # demand constraints [Sum(nc[:, j]) == cardDemands[j] for j in range(nTypes)], # tag(symmetrybreaking) [Decreasing(m), imply(m[0] == m[1], nc[0][0] >= nc[1][0])] ) minimize( # minimizing the total cost being paid for all racks Sum(c) )
To generate an XCSP3 instance (file), we execute the command:
python3 Rack.py data=Rack_r2.json
One might want to have the data in the JSON file with another structure, as for example:
File Rack_r2b.json
{ "nRacks": 10, "rackModels": [ {"power":150,"nConnectors":8,"price":150}, {"power":200,"nConnectors":16,"price":200} ], "cardTypes": [ {"power":20,"demand":20}, {"power":40,"demand":8}, {"power":50,"demand":4}, {"power":75,"demand":2} ] }
We only need to modify one line from the previous model:
File Rack2.py
models.append(models[0].__class__(0, 0, 0)) # we add first a dummy model (0,0,0) ; we get the class of the used named tuples to build a new one
To generate an XCSP3 instance (file), we execute the command:
python3 Rack2.py data=Rack_r2b.json
Example 6: Nonogram
This example shows how you can use an auxiliary Python file for parsing data that are not initially given under JSON format. For our illustration, we consider the problem Nonogram. The data (for a specific Nonogram puzzle) are initially given in a text file as follows:
 a line stating the numbers of rows and columns,
 then, for each row a line stating the number of blocks followed by the sizes of all these blocks (on the same line),
 then, for each column a line stating the number of blocks followed by the sizes of all these blocks (on the same line).
Below, here is an example of such a text file.
File Nonogram_example.txt
24 24
0
1 5
2 3 3
2 1 2
2 2 1
2 1 1
2 3 3
3 1 5 1
3 1 1 1
3 2 1 1
3 1 1 2
3 3 1 3
3 1 3 1
3 1 1 1
3 2 1 2
3 1 1 1
1 5
3 1 1 1
3 1 1 1
3 1 1 1
3 5 1 1
2 1 2
3 2 2 4
2 4 9
0
0
0
1 1
1 2
1 2
2 6 1
3 3 1 3
3 1 1 4
4 2 1 1 7
5 1 1 1 1 1
3 1 12 1
5 1 1 1 1 1
4 2 1 1 7
4 1 1 4 1
4 2 1 2 2
2 8 3
2 1 1
2 1 2
1 4
1 3
1 2
1 1
0
First, we need to write a piece of code in Python for building a dictionary data
that will be then used in our model (after having been automatically converted to a named tuple).
We have first to import everything (*) from pycsp3.problems.data.parsing
.
We can then add any new arbitrary item to the dictionary data
(which is initially empty).
This is what we do below with two items whose keys are called rowPatterns
and colPatterns
.
The values associated with these two keys are defined as twodimensional arrays (lists) of integers, defining the sizes of blocks.
The function next_int()
can be called for reading the next integer in a text file, which will be specified on the command line (see later).
File Nonogram_Parser.py
from pycsp3.problems.data.parsing import * nRows, nCols = next_int(), next_int() data["rowPatterns"] = [[next_int() for _ in range(next_int())] for _ in range(nRows)] data["colPatterns"] = [[next_int() for _ in range(next_int())] for _ in range(nRows)]
Then, we just write the model by getting data from the variable data
.
The model is totally independent of the way data were initially given (from a text file or a JSON file, for example).
In the code below, note how an object Automaton
is defined from a specified pattern (list of blocks).
Also, for a regular
constraint, we just write something like scope in automaton
.
Finally, x[:, j]
denotes the jth column of x
.
File Nonogram.py
from pycsp3 import * rows, cols = data # patterns for row and columns nRows, nCols = len(rows), len(cols) def automaton(pattern): q = Automaton.q # for building state names transitions = [] if len(pattern) == 0: n_states = 1 transitions.append((q(0), 0, q(0))) else: n_states = sum(pattern) + len(pattern) num = 0 for i in range(len(pattern)): transitions.append((q(num), 0, q(num))) for j in range(pattern[i]): transitions.append((q(num), 1, q(num + 1))) num += 1 if i < len(pattern)  1: transitions.append((q(num), 0, q(num + 1))) num += 1 transitions.append((q(num), 0, q(num))) return Automaton(start=q(0), final=q(n_states  1), transitions=transitions) # x[i][j] is 1 iff the cell at row i and col j is colored in black x = VarArray(size=[nRows, nCols], dom={0, 1}) satisfy( [x[i] in automaton(rows[i]) for i in range(nRows)], [x[:, j] in automaton(cols[j]) for j in range(nCols)] )
To generate the XCSP3 instance (file), we just need to specify the name of the text file (option data
) and the name of the Python parser (option dataparser
).
python3 Nonogram.py data=Nonogram_example.txt dataparser=Nonogram_Parser.py
Maybe, you think that it would be simpler to have directly the data in JSON file.
You can generate such a file with the option dataexport
.
The command is as follows:
python3 Nonogram.py data=Nonogram_example.txt dataparser=Nonogram_Parser.py dataexport
A file Nonogram_example.json
is generated, whose content is:
{ "colPatterns":[[],[],[],[1],[2],[2],[6,1],[3,1,3],[1,1,4],[2,1,1,7],[1,1,1,1,1],[1,12,1],[1,1,1,1,1],[2,1,1,7],[1,1,4,1],[2,1,2,2],[8,3],[1,1],[1,2],[4],[3],[2],[1],[]], "rowPatterns":[[],[5],[3,3],[1,2],[2,1],[1,1],[3,3],[1,5,1],[1,1,1],[2,1,1],[1,1,2],[3,1,3],[1,3,1],[1,1,1],[2,1,2],[1,1,1],[5],[1,1,1],[1,1,1],[1,1,1],[5,1,1],[1,2],[2,2,4],[4,9]] }
With this new file, you can directly generate the XCSP3 file with:
python3 Nonogram.py data=Nonogram_example.json
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