Distributional Program Synthesis

• Learns a joint probabilistic relational model in the form of distributional program for a relational database.
• It can make use of background knowledge (if available) written in DC language.
• Performs Stochastic EM if some fields are missing in the relational database. (Experimental)

The code is in beta, if you need help or find a bug please write an issue or contact me at niteshroyal (DOT) 30 (AT) gmail (DOT) com

Installation

1. Install dependencies
   $ apt install build-essential pkg-config libgsl-dev libreadline-dev libboost-all-dev python2-dev

2. Install Yap Prolog
   $ tar -xzvf yap-6.2.2.1.tar.gz
   $ cd yap-6.2.2
   $ mkdir arch
   $ cd arch
   $ ../configure --enable-tabling=yes --enable-dynamic-loading
   $ make
   $ sudo make install
   $ sudo make install_library

3. Install DC that is available in the main DiceML folder
   $ cd DC
   $ sh make.sh

4. Check if DC is correctly installed or not

   $ sh test.sh

   Expected Output:

	Testing example1.pl...
	Absolute error drawn(1) ~= 1: 0.00404777777777715
	Absolute error drawn(1) ~= 2: 0.00110174603175042
	Absolute error drawn(1) ~= 3: 0.000147500000002382
	Absolute error average g ~ Gaussian(0,0.1): 0.00116244998571192
	% 0.179 CPU in 0.183 seconds ( 97% CPU)

5. Install Numpy, Sklearn, Cython in python2

6. Build PyDC executable file and copy to the 'core' folder
   $ cd yapInterface
   $ python2 setup.py build_ext --inplace
   $ mv yapWrapper.so ../core/

Execution

1. Learning a distributional program from deterministic data. An example of declarative bias in shown in file '../data/FinancialData.pl'

 from core.DCLearner import DCLearner
 
 ## Output DC program
 outputFile = '../data/MyDCRules.pl'
 f = open(outputFile, 'w')

 ## Input Prolog program '../data/FinancialData.pl' contains example of 
 ## declarative bias needed for the deterministic case 
 obj = DCLearner('../data/FinancialData.pl', '', '')

 obj.learnRules()
 translateObj = obj.interface.translator
 for rule in obj.rules:
     rule = translateObj.translate(rule)
     f.write(rule + '\n')
 f.close()

2. Learning a distributional program from deterministic/probabilistic data as well as background theory. Two input files are needed for this case.

 from core.DCLearner import DCLearner
 
 ## Output DC program
 outputFile = '../data/MyDCRules.pl'
 f = open(outputFile, 'w')

 ## Input DC program and a helper Prolog program
 obj = DCLearner('../data/FinancialData_Enumerated.pl','../data/FinancialDataDC.pl','')

 obj.learnRules()
 translateObj = obj.interface.translator
 for rule in obj.rules:
     rule = translateObj.translate(rule)
     f.write(rule + '\n')
 f.close()

DiceML interface (py_dreaml_interface)

• Package allows to pass a set of related tables to the DiceML
• The package automatically generates declarative bias
• Calls DiceML to learn DC program
• Query the learned DC Program

Installation

1. Install Cython package in python3

2. First install Yap Prolog Wrapper

   $ cd yapInterfaceForPython3
   $ python3 setup.py build_ext
   $ sudo python3 setup.py install

3. Installing py_dreaml_interface

   ## From the main folder of DiceML
   $ pip3 install .

4. Add environment variables

   $ vim ~/.bashrc
   
   ## Add following lines in the 'bashrc' file
   
   ## Path to Python 2
   export DREAML_PYTHON2_BIN="/usr/bin/python2.7"
   
   ## Path to the (your) main DiceML folder
   export DREAML_PATH="/home/nitesh/eclipse-workspace/DiceML"
   
   ## Path to main DiceML folder
   export PYTHONPATH="/home/nitesh/eclipse-workspace/DiceML"

Execution

1. See an example

   $ vim py_dreaml_interface/example.py

2. Run the example

   $ python3 py_dreaml_interface/example.py