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Moved documentation for parametric models into own file

refactoring
Matthias Volk 7 years ago
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7 changed files with 119 additions and 102 deletions
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  1. 1
      doc/source/advanced_topics.rst
  2. 61
      doc/source/doc/parametric_models.rst
  3. 49
      doc/source/getting_started.rst
  4. 37
      examples/04-getting-started.py
  5. 26
      examples/06-getting-started.py
  6. 41
      examples/parametric_models/01-parametric-models.py
  7. 4
      examples/parametric_models/02-parametric-models.py

1
doc/source/advanced_topics.rst
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@ -11,3 +11,4 @@ This guide is a collection of examples meant to bridge the gap between the getti
doc/building_models
doc/reward_models
doc/shortest_paths
doc/parametric_models

61
doc/source/doc/parametric_models.rst
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@ -0,0 +1,61 @@
*****************
Parametric Models
*****************
Instantiating parametric models
===============================
.. seealso:: `01-parametric-models.py <https://github.com/moves-rwth/stormpy/blob/master/examples//parametric_models/01-parametric-models.py>`_
Input formats such as prism allow to specify programs with open constants. We refer to these open constants as parameters.
If the constants only influence the probabilities or rates, but not the topology of the underlying model, we can build these models as parametric models::
>>> import stormpy
>>> import stormpy.examples
>>> import stormpy.examples.files
>>> path = stormpy.examples.files.prism_dtmc_die
>>> prism_program = stormpy.parse_prism_program(path)
>>> formula_str = "P=? [F s=7 & d=2]"
>>> properties = stormpy.parse_properties_for_prism_program(formula_str, prism_program)
>>> model = stormpy.build_parametric_model(prism_program, properties)
>>> parameters = model.collect_probability_parameters()
>>> for x in parameters:
... print(x)
In order to obtain a standard DTMC, MDP or other Markov model, we need to instantiate these models by means of a model instantiator::
>>> import stormpy.pars
>>> instantiator = stormpy.pars.PDtmcInstantiator(model)
Before we obtain an instantiated model, we need to map parameters to values: We build such a dictionary as follows::
>>> point = dict()
>>> for x in parameters:
... print(x.name)
... point[x] = 0.4
>>> instantiated_model = instantiator.instantiate(point)
>>> result = stormpy.model_checking(instantiated_model, properties[0])
Initial states and labels are set as for the parameter-free case.
Checking parametric models
==========================
.. seealso:: `02-parametric-models.py <https://github.com/moves-rwth/stormpy/blob/master/examples//parametric_models/02-parametric-models.py>`_
It is also possible to check the parametric model directly, similar as before in :ref:`getting-started-checking-properties`::
>>> result = stormpy.model_checking(model, properties[0])
>>> initial_state = model.initial_states[0]
>>> func = result.at(initial_state)
We collect the constraints ensuring that underlying model is well-formed and the graph structure does not change.
>>> collector = stormpy.ConstraintCollector(model)
>>> for formula in collector.wellformed_constraints:
... print(formula)
>>> for formula in collector.graph_preserving_constraints:
... print(formula)

49
doc/source/getting_started.rst
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@ -126,58 +126,11 @@ A good way to get the result for the initial states is as follows::
>>> print(result.at(initial_state))
0.5
Instantiating parametric models
------------------------------------
.. seealso:: `04-getting-started.py <https://github.com/moves-rwth/stormpy/blob/master/examples/04-getting-started.py>`_
Input formats such as prism allow to specify programs with open constants. We refer to these open constants as parameters.
If the constants only influence the probabilities or rates, but not the topology of the underlying model, we can build these models as parametric models::
>>> model = stormpy.build_parametric_model(prism_program, properties)
>>> parameters = model.collect_probability_parameters()
>>> for x in parameters:
... print(x)
In order to obtain a standard DTMC, MDP or other Markov model, we need to instantiate these models by means of a model instantiator::
>>> import stormpy.pars
>>> instantiator = stormpy.pars.PDtmcInstantiator(model)
Before we obtain an instantiated model, we need to map parameters to values: We build such a dictionary as follows::
>>> point = dict()
>>> for x in parameters:
... print(x.name)
... point[x] = 0.4
>>> instantiated_model = instantiator.instantiate(point)
>>> result = stormpy.model_checking(instantiated_model, properties[0])
Initial states and labels are set as for the parameter-free case.
Checking parametric models
------------------------------------
.. seealso:: `05-getting-started.py <https://github.com/moves-rwth/stormpy/blob/master/examples/05-getting-started.py>`_
It is also possible to check the parametric model directly, similar as before in :ref:`getting-started-checking-properties`::
>>> result = stormpy.model_checking(model, properties[0])
>>> initial_state = model.initial_states[0]
>>> func = result.at(initial_state)
We collect the constraints ensuring that underlying model is well-formed and the graph structure does not change.
>>> collector = stormpy.ConstraintCollector(model)
>>> for formula in collector.wellformed_constraints:
... print(formula)
>>> for formula in collector.graph_preserving_constraints:
... print(formula)
.. _getting-started-investigating-the-model:
Investigating the model
-------------------------------------
.. seealso:: `06-getting-started.py <https://github.com/moves-rwth/stormpy/blob/master/examples/06-getting-started.py>`_
.. seealso:: `04-getting-started.py <https://github.com/moves-rwth/stormpy/blob/master/examples/04-getting-started.py>`_
One powerful part of the Storm model checker is to quickly create the Markov chain from higher-order descriptions, as seen above::

37
examples/04-getting-started.py
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@ -1,40 +1,27 @@
import stormpy
import stormpy.core
import pycarl
import pycarl.core
import stormpy.examples
import stormpy.examples.files
import stormpy._config as config
def example_getting_started_04():
# Check support for parameters
if not config.storm_with_pars:
print("Support parameters is missing. Try building storm-pars.")
return
import stormpy.pars
path = stormpy.examples.files.prism_pdtmc_die
path = stormpy.examples.files.prism_dtmc_die
prism_program = stormpy.parse_prism_program(path)
formula_str = "P=? [F s=7 & d=2]"
properties = stormpy.parse_properties_for_prism_program(formula_str, prism_program)
model = stormpy.build_parametric_model(prism_program, properties)
print("Model supports parameters: {}".format(model.supports_parameters))
parameters = model.collect_probability_parameters()
assert len(parameters) == 2
instantiator = stormpy.pars.PDtmcInstantiator(model)
point = dict()
for x in parameters:
print(x.name)
point[x] = stormpy.RationalRF(0.4)
instantiated_model = instantiator.instantiate(point)
result = stormpy.model_checking(instantiated_model, properties[0])
print(result)
model = stormpy.build_model(prism_program, properties)
print(model.model_type)
for state in model.states:
if state.id in model.initial_states:
print(state)
for action in state.actions:
for transition in action.transitions:
print("From state {}, with probability {}, go to state {}".format(state, transition.value(),
transition.column))
if __name__ == '__main__':

26
examples/06-getting-started.py
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@ -1,26 +0,0 @@
import stormpy
import stormpy.core
import stormpy.examples
import stormpy.examples.files
def example_getting_started_06():
path = stormpy.examples.files.prism_dtmc_die
prism_program = stormpy.parse_prism_program(path)
formula_str = "P=? [F s=7 & d=2]"
properties = stormpy.parse_properties_for_prism_program(formula_str, prism_program)
model = stormpy.build_model(prism_program, properties)
print(model.model_type)
for state in model.states:
if state.id in model.initial_states:
print(state)
for action in state.actions:
for transition in action.transitions:
print("From state {}, with probability {}, go to state {}".format(state, transition.value(), transition.column))
if __name__ == '__main__':
example_getting_started_06()

41
examples/parametric_models/01-parametric-models.py
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@ -0,0 +1,41 @@
import stormpy
import stormpy.core
import pycarl
import pycarl.core
import stormpy.examples
import stormpy.examples.files
import stormpy._config as config
def example_parametric_models_01():
# Check support for parameters
if not config.storm_with_pars:
print("Support parameters is missing. Try building storm-pars.")
return
import stormpy.pars
path = stormpy.examples.files.prism_pdtmc_die
prism_program = stormpy.parse_prism_program(path)
formula_str = "P=? [F s=7 & d=2]"
properties = stormpy.parse_properties_for_prism_program(formula_str, prism_program)
model = stormpy.build_parametric_model(prism_program, properties)
print("Model supports parameters: {}".format(model.supports_parameters))
parameters = model.collect_probability_parameters()
assert len(parameters) == 2
instantiator = stormpy.pars.PDtmcInstantiator(model)
point = dict()
for x in parameters:
print(x.name)
point[x] = stormpy.RationalRF(0.4)
instantiated_model = instantiator.instantiate(point)
result = stormpy.model_checking(instantiated_model, properties[0])
print(result)
if __name__ == '__main__':
example_parametric_models_01()

4
examples/05-getting-started.py → examples/parametric_models/02-parametric-models.py
View File

@ -11,7 +11,7 @@ import stormpy.examples.files
import stormpy._config as config
def example_getting_started_05():
def example_parametric_models_02():
# Check support for parameters
if not config.storm_with_pars:
print("Support parameters is missing. Try building storm-pars.")
@ -45,4 +45,4 @@ def example_getting_started_05():
if __name__ == '__main__':
example_getting_started_05()
example_parametric_models_02()
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