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Make work with export
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@yngve-sk
yngve-sk committed Dec 20, 2024
1 parent f51b30e commit 0ee4398
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Showing 7 changed files with 443 additions and 315 deletions.
6 changes: 3 additions & 3 deletions src/ert/run_models/everest_run_model.py
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Original file line number Diff line number Diff line change
Expand Up @@ -21,13 +21,13 @@
)

import numpy as np
from seba_sqlite import SqliteStorage, sqlite_storage
from numpy import float64
from numpy._typing import NDArray
from ropt.enums import EventType, OptimizerExitCode
from ropt.evaluator import EvaluatorContext, EvaluatorResult
from ropt.plan import BasicOptimizer
from ropt.plan import Event as OptimizerEvent
from seba_sqlite import SqliteStorage, sqlite_storage
from typing_extensions import TypedDict

from _ert.events import EESnapshot, EESnapshotUpdate, Event
Expand Down Expand Up @@ -130,7 +130,7 @@ def __call__(self) -> str | None: ...


@dataclass
class OptimalResult:
class OptimalResult: # noqa

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Name "OptimalResult" already defined (possibly by an import)
batch: int
controls: list[Any]
total_objective: float
Expand Down Expand Up @@ -297,7 +297,7 @@ def run_experiment(

# Seems ROPT batches are 1-indexed now,
# whereas seba has its own 0-indexed counter.
assert self._result == optimal_result_from_everstorage
assert self._result.__dict__ == optimal_result_from_everstorage.__dict__

self._exit_code = (
"max_batch_num_reached"
Expand Down
227 changes: 87 additions & 140 deletions src/everest/api/everest_data_api.py
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Original file line number Diff line number Diff line change
@@ -1,8 +1,9 @@
from collections import OrderedDict
from pathlib import Path

import polars
import polars as pl
from seba_sqlite.snapshot import SebaSnapshot
from ropt.enums import ConstraintType
from seba_sqlite import SebaSnapshot

from ert.storage import open_storage
from everest.config import EverestConfig, ServerConfig
Expand All @@ -20,42 +21,29 @@ def __init__(self, config: EverestConfig, filter_out_gradient=True):

@property
def batches(self):
batch_ids = list({opt.batch_id for opt in self._snapshot.optimization_data})
batch_ids2 = sorted(
return sorted(
b.batch_id
for b in self._ever_storage.data.batches
if b.batch_objectives is not None
)
assert batch_ids == batch_ids2
return sorted(batch_ids)

@property
def accepted_batches(self):
batch_ids = list(
{opt.batch_id for opt in self._snapshot.optimization_data if opt.merit_flag}
)
batch_ids2 = sorted(
return sorted(
b.batch_id for b in self._ever_storage.data.batches if b.is_improvement
)
assert batch_ids == batch_ids2

return sorted(batch_ids)

@property
def objective_function_names(self):
original = [fnc.name for fnc in self._snapshot.metadata.objectives.values()]
new = sorted(
return sorted(
self._ever_storage.data.objective_functions["objective_name"]
.unique()
.to_list()
)
assert original == new
return original

@property
def output_constraint_names(self):
original = [fnc.name for fnc in self._snapshot.metadata.constraints.values()]
new = (
return (
sorted(
self._ever_storage.data.nonlinear_constraints["constraint_name"]
.unique()
Expand All @@ -64,28 +52,16 @@ def output_constraint_names(self):
if self._ever_storage.data.nonlinear_constraints is not None
else []
)
assert original == new
return original

def input_constraint(self, control):
controls = [
con
for con in self._snapshot.metadata.controls.values()
if con.name == control
]

original = {"min": controls[0].min_value, "max": controls[0].max_value}

initial_values = self._ever_storage.data.initial_values
control_spec = initial_values.filter(
pl.col("control_name") == control
).to_dicts()[0]
new = {
return {
"min": control_spec.get("lower_bounds"),
"max": control_spec.get("upper_bounds"),
}
assert new == original
return original

def output_constraint(self, constraint):
"""
Expand All @@ -95,146 +71,122 @@ def output_constraint(self, constraint):
"right_hand_side" is a constant real number that indicates
the constraint bound/target.
"""
constraints = [
con
for con in self._snapshot.metadata.constraints.values()
if con.name == constraint
]

old = {
"type": constraints[0].constraint_type,
"right_hand_side": constraints[0].rhs_value,
}

constraint_dict = self._ever_storage.data.nonlinear_constraints.to_dicts()[0]
new = {
"type": constraint_dict["constraint_type"],
"right_hand_side": constraint_dict["rhs_value"],
return {
"type": ConstraintType(constraint_dict["constraint_type"]).name.lower(),
"right_hand_side": constraint_dict["constraint_rhs_value"],
}

assert old == new
return new

@property
def realizations(self):
old = list(
OrderedDict.fromkeys(
int(sim.realization) for sim in self._snapshot.simulation_data
)
)
new = sorted(
return sorted(
self._ever_storage.data.batches[0]
.realization_objectives["realization"]
.unique()
.to_list()
)
assert old == new
return new

@property
def simulations(self):
old = list(
OrderedDict.fromkeys(
[int(sim.simulation) for sim in self._snapshot.simulation_data]
)
)

new = sorted(
return sorted(
self._ever_storage.data.batches[0]
.realization_objectives["result_id"]
.realization_objectives["simulation_id"]
.unique()
.to_list()
)
assert old == new
return new

@property
def control_names(self):
old = [con.name for con in self._snapshot.metadata.controls.values()]
new = sorted(
return sorted(
self._ever_storage.data.initial_values["control_name"].unique().to_list()
)
assert old == new
return new

@property
def control_values(self):
controls = [con.name for con in self._snapshot.metadata.controls.values()]
return [
{"control": con, "batch": sim.batch, "value": sim.controls[con]}
for sim in self._snapshot.simulation_data
for con in controls
if con in sim.controls
]
all_control_names = self._ever_storage.data.initial_values[
"control_name"
].to_list()
new = []
for batch in self._ever_storage.data.batches:
if batch.realization_controls is None:
continue

for controls_dict in batch.realization_controls.to_dicts():
for name in all_control_names:
new.append(
{
"control": name,
"batch": batch.batch_id,
"value": controls_dict[name],
}
)

return new

@property
def objective_values(self):
old = [
{
"function": objective.name,
"batch": sim.batch,
"realization": sim.realization,
"simulation": sim.simulation,
"value": sim.objectives[objective.name],
"weight": objective.weight,
"norm": objective.normalization,
}
for sim in self._snapshot.simulation_data
for objective in self._snapshot.metadata.objectives.values()
if objective.name in sim.objectives
]

new = [
return [
b for b in self._ever_storage.data.batches if b.batch_objectives is not None
]

assert old == new

return old

@property
def single_objective_values(self):
single_obj = [
{
"batch": optimization_el.batch_id,
"objective": optimization_el.objective_value,
"accepted": optimization_el.merit_flag,
}
for optimization_el in self._snapshot.optimization_data
]
metadata = {
func.name: {"weight": func.weight, "norm": func.normalization}
for func in self._snapshot.metadata.functions.values()
if func.function_type == func.FUNCTION_OBJECTIVE_TYPE
}
if len(metadata) == 1:
return single_obj
objectives = []
for name, values in self._snapshot.expected_objectives.items():
for idx, val in enumerate(values):
factor = metadata[name]["weight"] * metadata[name]["norm"]
if len(objectives) > idx:
objectives[idx].update({name: val * factor})
else:
objectives.append({name: val * factor})
for idx, obj in enumerate(single_obj):
obj.update(objectives[idx])
batch_datas = polars.concat(
[
b.batch_objectives.select(
c for c in b.batch_objectives.columns if c != "merit_value"
).with_columns(
polars.lit(1 if b.is_improvement else 0).alias("accepted")
)
for b in self._ever_storage.data.batches
if b.realization_controls is not None
]
)
objectives = self._ever_storage.data.objective_functions

return single_obj
for o in objectives.to_dicts():
batch_datas = batch_datas.with_columns(
polars.col(o["objective_name"]) * o["weight"] * o["normalization"]
)

return (
batch_datas.rename(
{"total_objective_value": "objective", "batch_id": "batch"}
)
.select("batch", "objective", "accepted")
.to_dicts()
)

@property
def gradient_values(self):
return [
{
"batch": optimization_el.batch_id,
"function": function,
"control": control,
"value": value,
}
for optimization_el in self._snapshot.optimization_data
for function, info in optimization_el.gradient_info.items()
for control, value in info.items()
all_batch_data = [
b.batch_objective_gradient
for b in self._ever_storage.data.batches
if b.batch_objective_gradient is not None
]
if not all_batch_data:
return []

all_info = polars.concat(all_batch_data).drop("result_id")
objective_columns = [
c
for c in all_info.drop(["batch_id", "control_name"]).columns
if not c.endswith(".total")
]
return (
all_info.select("batch_id", "control_name", *objective_columns)
.unpivot(
on=objective_columns,
index=["batch_id", "control_name"],
variable_name="function",
value_name="value",
)
.rename({"control_name": "control", "batch_id": "batch"})
.sort(by=["batch", "control"])
.select(["batch", "function", "control", "value"])
.to_dicts()
)

def summary_values(self, batches=None, keys=None):
if batches is None:
Expand Down Expand Up @@ -265,13 +217,8 @@ def summary_values(self, batches=None, keys=None):
summary = summary.with_columns(
pl.Series("batch", [batch_id] * summary.shape[0])
)
# The realization ID as defined by Everest must be
# retrieved via the seba snapshot.
realization_map = {
sim.simulation: sim.realization
for sim in self._snapshot.simulation_data
if sim.batch == batch_id
}

realization_map = self._ever_storage.data.simulation_to_realization_map
realizations = pl.Series(
"realization",
[realization_map.get(str(sim)) for sim in summary["simulation"]],
Expand Down
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