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Add option to skip model statistics collection before solve (context.solver.collect_model_statistics) #42

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Add option to skip model statistics collection before solve (context.solver.collect_model_statistics) #42

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34f5f65
Add model statistics context option in context.py
Sep 3, 2026
a554d60
Allow skipping docplex model statistics before solve in solve_env.py
Sep 3, 2026
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2 changes: 2 additions & 0 deletions docplex/mp/context.py
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Original file line number Diff line number Diff line change
Expand Up @@ -250,6 +250,7 @@ def __init__(self, **kwargs):
self.log_output = False
self.max_threads = get_environment().get_available_core_count()
self.auto_publish = create_default_auto_publish_context()
self.collect_model_statistics = True
self.kpi_reporting = BaseContext()
from docplex.mp.progress import ProgressClock
self.kpi_reporting.filter_level = ProgressClock.Gap
Expand Down Expand Up @@ -322,6 +323,7 @@ class Context(BaseContext):
are saved as a table with KPI name and values. Currently only
csv files are supported. This can be a list of filenames if
multiple KPIs files are to be published.
solver.collect_model_statistics: If ``True``, model statistics are collected before solving.
context.solver.auto_publish.kpis_output_field_name: Name of field for KPI names in KPI output
table. Defaults to 'Name'
context.solver.auto_publish.kpis_output_field_value: Name of field for KPI values for KPI output
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49 changes: 26 additions & 23 deletions docplex/mp/solve_env.py
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Expand Up @@ -85,29 +85,32 @@ def env_kpi_hookfn(kpd):
# It is now modified so that notify start is always performed,
# then we update solve details only if they need to be published
# [[[
self_stats = mdl.statistics
# implementation for https://github.ibm.com/IBMDecisionOptimization/dd-planning/issues/2491
problem_type = mdl._get_cplex_problem_type()
kpis = make_new_kpis_dict(allkpis=mdl._allkpis[:],
sense=mdl._objective_sense.verb,
model_type=problem_type,
int_vars=self_stats.number_of_integer_variables,
continuous_vars=self_stats.number_of_continuous_variables,
semicontinuous_vars = self_stats._number_of_semicontinuous_variables,
semiinteger_vars = self_stats._number_of_semiinteger_variables,
linear_constraints=self_stats.number_of_linear_constraints,
bin_vars=self_stats.number_of_binary_variables,
quadratic_constraints=self_stats.number_of_quadratic_constraints,
total_constraints=self_stats.number_of_constraints,
total_variables=self_stats.number_of_variables)
if the_env.is_wmlworker:
from docplex_wml.worker.worker_utils import make_cplex_new_kpis_dict
new_kpis = make_cplex_new_kpis_dict(mdl)
kpis.update(new_kpis)

the_env.notify_start_solve(kpis)
if auto_publish_details:
the_env.update_solve_details(kpis, transaction=self._transaction)
if context.solver.collect_model_statistics:
self_stats = mdl.statistics
# implementation for https://github.ibm.com/IBMDecisionOptimization/dd-planning/issues/2491
problem_type = mdl._get_cplex_problem_type()
kpis = make_new_kpis_dict(allkpis=mdl._allkpis[:],
sense=mdl._objective_sense.verb,
model_type=problem_type,
int_vars=self_stats.number_of_integer_variables,
continuous_vars=self_stats.number_of_continuous_variables,
semicontinuous_vars = self_stats._number_of_semicontinuous_variables,
semiinteger_vars = self_stats._number_of_semiinteger_variables,
linear_constraints=self_stats.number_of_linear_constraints,
bin_vars=self_stats.number_of_binary_variables,
quadratic_constraints=self_stats.number_of_quadratic_constraints,
total_constraints=self_stats.number_of_constraints,
total_variables=self_stats.number_of_variables)
if the_env.is_wmlworker:
from docplex_wml.worker.worker_utils import make_cplex_new_kpis_dict
new_kpis = make_cplex_new_kpis_dict(mdl)
kpis.update(new_kpis)

the_env.notify_start_solve(kpis)
if auto_publish_details:
the_env.update_solve_details(kpis, transaction=self._transaction)
else:
the_env.notify_start_solve({})

# ---
# parameters override if necessary...
Expand Down