.. _OpenMDAO-Recording: ========= Recording ========= This tutorial is builds on the :ref:`Optimization of the Paraboloid Tutorial ` by demonstrating how to save the data generated for future use. Consider the code below: .. testsetup:: recording_run import os if os.path.exists('paraboloid'): os.remove('paraboloid') .. testcode:: recording_run from openmdao.api import IndepVarComp, Component, Group, Problem, ScipyOptimizer, SqliteRecorder class Paraboloid(Component): """ Evaluates the equation f(x,y) = (x-3)^2 + xy + (y+4)^2 - 3 """ def __init__(self): super(Paraboloid, self).__init__() self.add_param('x', val=0.0) self.add_param('y', val=0.0) self.add_output('f_xy', val=0.0) def solve_nonlinear(self, params, unknowns, resids): """f(x,y) = (x-3)^2 + xy + (y+4)^2 - 3 Optimal solution (minimum): x = 6.6667; y = -7.3333 """ x = params['x'] y = params['y'] unknowns['f_xy'] = (x - 3.0) ** 2 + x * y + (y + 4.0) ** 2 - 3.0 def linearize(self, params, unknowns, resids): """ Jacobian for our paraboloid.""" x = params['x'] y = params['y'] J = {} J['f_xy', 'x'] = 2.0 * x - 6.0 + y J['f_xy', 'y'] = 2.0 * y + 8.0 + x return J top = Problem() root = top.root = Group() root.add('p1', IndepVarComp('x', 3.0)) root.add('p2', IndepVarComp('y', -4.0)) root.add('p', Paraboloid()) root.connect('p1.x', 'p.x') root.connect('p2.y', 'p.y') top.driver = ScipyOptimizer() top.driver.options['optimizer'] = 'SLSQP' top.driver.add_desvar('p1.x', lower=-50, upper=50) top.driver.add_desvar('p2.y', lower=-50, upper=50) top.driver.add_objective('p.f_xy') recorder = SqliteRecorder('paraboloid') recorder.options['record_params'] = True recorder.options['record_metadata'] = True top.driver.add_recorder(recorder) top.setup() top.run() top.cleanup() # this closes all recorders print('\n') print('Minimum of %f found at (%f, %f)' % (top['p.f_xy'], top['p.x'], top['p.y'])) .. testoutput:: recording_run :hide: :options: -ELLIPSIS, +NORMALIZE_WHITESPACE Optimization terminated successfully. (Exit mode 0) Current function value: [-27.33333333] Iterations: 5 Function evaluations: 6 Gradient evaluations: 5 Optimization Complete ----------------------------------- Minimum of -27.333333 found at (6.666667, -7.333333) .. Copy over the recorded file so we can test reading it later and so other testing code does not mess it up .. testcleanup:: recording_run import os if os.path.exists('paraboloid'): os.remove('paraboloid') .. testsetup:: recording1 import os if os.path.exists('paraboloid'): os.remove('paraboloid') from openmdao.api import SqliteRecorder, Problem, Group top = Problem() root = top.root = Group() These next four lines are all it takes to record the state of the problem as the optimizer progresses. Notice that because by default, recorders only record `Unknowns`, if we also want to record `Parameters` and `metadata`, we must set those recording options. (We could also record `Resids` by using the `record_metadata` option but this problem does not have residuals. ) .. testcode:: recording1 recorder = SqliteRecorder('paraboloid') recorder.options['record_params'] = True recorder.options['record_metadata'] = True top.driver.add_recorder(recorder) We initialize a `SqliteRecorder` by passing it a `filename` argument. This recorder indirectly uses Python's `sqlite3` module to store the data generated. In this case, `sqlite3` will open a database file named 'paraboloid' to use as a back-end. Actually, OpenMDAO's `SqliteRecorder` makes use of the `sqlitedict module `_ because it has a simple, Pythonic dict-like interface to Python’s sqlite3 database. We then add the recorder to the driver using `driver.add_recorder`. Depending on your needs, you are able to add more recorders by using additional `driver.add_recorder` calls. Solvers also have an `add_recorder` method that is invoked the same way. This allows you to record the evolution of variables at lower levels. While it might not be an issue, it is good practice to tell the `Problem` explicitly to clean things up before the program terminates. This will close all recorders and potentially release other operating system resources. This is simply done in this case by calling: .. testcode:: recording1 top.cleanup() .. testcleanup:: recording1 import os if os.path.exists('paraboloid'): os.remove('paraboloid') Includes and Excludes ===================== Over the course of an analysis or optimization, the model may generate a very large amount of data. Since you may not be interested in the value of every variable at every step, OpenMDAO allows you to filter which variables are recorded through the use of includes and excludes. The recorder will store anything that matches the includes filter and that does not match the exclude filter. By default, the includes are set to `['*']` and the excludes are set to `[]`, i.e. include everything and exclude nothing. The includes and excludes filters are set via the `options` structure in the recorder. If we were only interested in the variable `x` from our Paraboloid model, we could record that by setting the includes as follows: .. testsetup:: recording3 import os if os.path.exists('paraboloid'): os.remove('paraboloid') from openmdao.api import SqliteRecorder, Problem, Group top = Problem() root = top.root = Group() .. testcode:: recording3 recorder = SqliteRecorder('paraboloid') recorder.options['includes'] = ['x'] top.driver.add_recorder(recorder) .. testcleanup:: recording3 top.cleanup() import os if os.path.exists('paraboloid'): os.remove('paraboloid') Similarly, if we were interested in everything except the value of `f_xy`, we could exclude that by doing the following: .. testsetup:: recording4 import os if os.path.exists('paraboloid'): os.remove('paraboloid') from openmdao.api import SqliteRecorder, Problem, Group top = Problem() root = top.root = Group() .. testcode:: recording4 recorder = SqliteRecorder('paraboloid') recorder.options['excludes'] = ['f_xy'] top.driver.add_recorder(recorder) The includes and excludes filters will accept glob arguments. For example, `recorder.options['excludes'] = ['comp1.*']` would exclude any variable that starts with "comp1.". .. testcleanup:: recording4 top.cleanup() import os if os.path.exists('paraboloid'): os.remove('paraboloid') Accessing Recorded Data ======================= While each recorder stores data differently in order to match the file format, the common theme for accessing data is the iteration coordinate. The iteration coordinate describes where and when in the execution hierarchy the data was collected. Iteration coordinates are strings formatted as pairs of names and iteration numbers separated by '/'. For example, 'rank0:SLSQP/1/root/2/G1/3' would describe the third iteration of 'G1' during the second iteration of 'root' during the first iteration of 'SLSQP'. Some solvers and drivers may have sub-steps that are recorded. In those cases, the iteration number may be of the form '1-3', indicating the third sub-step of the first iteration. Since our Paraboloid only has a recorder added to the driver, our 'paraboloid' sqlite file will contain keys of the form 'rank0:SLSQP/1', 'rank0:SLSQP/2', etc. To access the data from our run, we can use the following code: .. testsetup:: reading import os if os.path.exists('paraboloid'): os.remove('paraboloid') from openmdao.api import IndepVarComp, Component, Group, Problem, ScipyOptimizer, SqliteRecorder class Paraboloid(Component): """ Evaluates the equation f(x,y) = (x-3)^2 + xy + (y+4)^2 - 3 """ def __init__(self): super(Paraboloid, self).__init__() self.add_param('x', val=0.0) self.add_param('y', val=0.0) self.add_output('f_xy', val=0.0) def solve_nonlinear(self, params, unknowns, resids): """f(x,y) = (x-3)^2 + xy + (y+4)^2 - 3 Optimal solution (minimum): x = 6.6667; y = -7.3333 """ x = params['x'] y = params['y'] unknowns['f_xy'] = (x - 3.0) ** 2 + x * y + (y + 4.0) ** 2 - 3.0 def linearize(self, params, unknowns, resids): """ Jacobian for our paraboloid.""" x = params['x'] y = params['y'] J = {} J['f_xy', 'x'] = 2.0 * x - 6.0 + y J['f_xy', 'y'] = 2.0 * y + 8.0 + x return J # to keep the output of the run from doctest which does not handle output from setup well! import os import sys f = open(os.devnull, 'w') sys.stdout = f top = Problem() root = top.root = Group() root.add('p1', IndepVarComp('x', 3.0)) root.add('p2', IndepVarComp('y', -4.0)) root.add('p', Paraboloid()) root.connect('p1.x', 'p.x') root.connect('p2.y', 'p.y') top.driver = ScipyOptimizer() top.driver.options['optimizer'] = 'SLSQP' top.driver.add_desvar('p1.x', lower=-50, upper=50) top.driver.add_desvar('p2.y', lower=-50, upper=50) top.driver.add_objective('p.f_xy') recorder = SqliteRecorder('paraboloid') recorder.options['record_params'] = True recorder.options['record_metadata'] = True top.driver.add_recorder(recorder) top.setup() top.run() top.cleanup() .. testoutput:: reading :hide: :options: -ELLIPSIS, +NORMALIZE_WHITESPACE Optimization terminated successfully. (Exit mode 0) Current function value: [-27.33333333] Iterations: 5 Function evaluations: 6 Gradient evaluations: 5 Optimization Complete ----------------------------------- Minimum of -27.333333 found at (6.666667, -7.333333) .. testcode:: reading import sqlitedict from pprint import pprint db = sqlitedict.SqliteDict( 'paraboloid', 'openmdao' ) There are two arguments to create an instance of SqliteDict. The first, `'paraboloid'`, is the name of the sqlite database file. The second, `'openmdao'`, is the name of the table in the sqlite database. For the SqliteRecorder in OpenMDAO, all the recording is done to the `'openmdao'` table. Now, we can access the data using an iteration coordinate. It is not always obvious what are the iteration coordinates. To see what iteration coordinates were recorded, use the `keys` method on the `db` object: .. testcode:: reading print( list( db.keys() ) ) # list() needed for compatibility with Python 3. Not needed for Python 2 which will print out: .. testoutput:: reading :hide: :options: -ELLIPSIS, +NORMALIZE_WHITESPACE ['metadata', 'rank0:SLSQP/1', 'rank0:SLSQP/1/derivs', 'rank0:SLSQP/2', 'rank0:SLSQP/2/derivs', 'rank0:SLSQP/3', 'rank0:SLSQP/4', 'rank0:SLSQP/4/derivs', 'rank0:SLSQP/5', 'rank0:SLSQP/5/derivs', 'rank0:SLSQP/6', 'rank0:SLSQP/6/derivs'] :: ['metadata', 'rank0:SLSQP/1', 'rank0:SLSQP/1/derivs', 'rank0:SLSQP/2', 'rank0:SLSQP/2/derivs', 'rank0:SLSQP/3', 'rank0:SLSQP/4', 'rank0:SLSQP/4/derivs', 'rank0:SLSQP/5', 'rank0:SLSQP/5/derivs', 'rank0:SLSQP/6', 'rank0:SLSQP/6/derivs'] Note that we have three kinds of output data here. The entry for the key 'metadata' contains individual variable metadata such as 'units'. Entries that look like 'rank0:SLSQP/1' contain the value of the recorded variables at a specific iteration. Finally, entries that look like 'rank0:SLSQP/1/derivs' contain the derivative at that iteration if one was calculated. So for this example, the iteration coordinates are: :: ['rank0:SLSQP/1', 'rank0:SLSQP/2', 'rank0:SLSQP/3', 'rank0:SLSQP/4', 'rank0:SLSQP/5', 'rank0:SLSQP/6'] Now we can get the values for the first iteration coordinate: .. testcode:: reading data = db['rank0:SLSQP/1'] This `data` variable has four keys, 'timestamp', 'Parameters', 'Unknowns', and 'Residuals'. 'timestamp' yields the time at which data was recorded: .. testcode:: reading p = data['timestamp'] print(p) .. testoutput:: reading :hide: :options: +ELLIPSIS ... The remaining keys will yield a dictionary containing variable names mapped to values. Generally, the variables of interest will be contained in the 'Unknowns' key since that will contain the objective function values and the values controlled by the optimizer. For example, .. testcode:: reading u = data['Unknowns'] pprint(u) .. testoutput:: reading :hide: :options: -ELLIPSIS, +NORMALIZE_WHITESPACE {'p.f_xy': -15.0, 'p1.x': 3.0, 'p2.y': -4.0} will print out the dictionary: :: {'f_xy': -15.0, 'x': 3.0, 'y': -4.0} You can also access the values for the `Parameters`: .. testcode:: reading p = data['Parameters'] pprint(p) .. testoutput:: reading :hide: :options: -ELLIPSIS, +NORMALIZE_WHITESPACE {'p.x': 3.0, 'p.y': -4.0} Which will print out the dictionary: :: {'p.x': 3.0, 'p.y': -4.0} Finally, since our code told the recorder to record metadata, we can read that from the file as well. Notice that since metadata is only recorded once, it is a top level element of the dictionary, rather than a sub-dictionary of an interation coordinate. It contains sub-dictionaries for metadata about `Unknowns`, `Parameters`, `Resids`. .. testcode:: reading data = db['metadata'] u_meta = data['Unknowns'] pprint(u_meta) p_meta = data['Parameters'] pprint(p_meta) .. testoutput:: reading :hide: :options: -ELLIPSIS, +NORMALIZE_WHITESPACE {'p.f_xy': {'is_objective': True, 'pathname': 'p.f_xy', 'shape': 1, 'size': 1, 'top_promoted_name': 'p.f_xy', 'val': 0.0}, 'p1.x': {'is_desvar': True, 'pathname': 'p1.x', 'shape': 1, 'size': 1, 'top_promoted_name': 'p1.x', 'val': 3.0}, 'p2.y': {'is_desvar': True, 'pathname': 'p2.y', 'shape': 1, 'size': 1, 'top_promoted_name': 'p2.y', 'val': -4.0}} {'p.x': {'pathname': 'p.x', 'shape': 1, 'size': 1, 'top_promoted_name': 'p.x', 'val': 0.0}, 'p.y': {'pathname': 'p.y', 'shape': 1, 'size': 1, 'top_promoted_name': 'p.y', 'val': 0.0}} This code prints out the following: :: {'p.f_xy': {'is_objective': True, 'pathname': 'p.f_xy', 'shape': 1, 'size': 1, 'top_promoted_name': 'p.f_xy', 'val': 0.0}, 'p1.x': {'is_desvar': True, 'pathname': 'p1.x', 'shape': 1, 'size': 1, 'top_promoted_name': 'p1.x', 'val': 3.0}, 'p2.y': {'is_desvar': True, 'pathname': 'p2.y', 'shape': 1, 'size': 1, 'top_promoted_name': 'p2.y', 'val': -4.0}} {'p.x': {'pathname': 'p.x', 'shape': 1, 'size': 1, 'top_promoted_name': 'p.x', 'val': 0.0}, 'p.y': {'pathname': 'p.y', 'shape': 1, 'size': 1, 'top_promoted_name': 'p.y', 'val': 0.0}} .. testcleanup:: reading db.close() import os if os.path.exists('paraboloid'): os.remove('paraboloid')