# Evaluation of Execution Results
The execution result returned by the {py:func}`~amplify.solve` function contains various information about the solutions, model conversion, and execution time.
This page explains how to obtain and use this information.
The following is an example of obtaining the optimization result for a model consisting of an objective function and a constraint using the {py:class}`~amplify.FixstarsClient`.
```{testcode}
from datetime import timedelta
from amplify import VariableGenerator, equal_to, FixstarsClient, solve
# Create an array of decision variables
gen = VariableGenerator()
q = gen.array("Binary", 5)
# Create an objective function and a constraint
objective = q[0] * q[1] - q[2]
constraint = equal_to(q[0] + q[1] + q[2], 1)
# Define a model
model = objective + constraint
# Create a solver client
client = FixstarsClient()
# client.token = "xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"
client.parameters.timeout = timedelta(milliseconds=1000)
# Obtaining the result of the run
result = solve(model, client)
```
## The Result class
The following explains how to obtain information about solutions, model conversion, and execution time from an instance of the {py:attr}`~amplify.Result` class returned by the {py:func}`~amplify.solve` function.
(result-info-on-solutions)=
### Information on the solutions
The {py:attr}`~amplify.Result.solutions` attribute of the {py:class}`~amplify.Result` class stores the solutions to the input model. The {py:attr}`~amplify.Result.solutions` attribute is an instance of the {py:class}`~amplify.Result.SolutionList` class, which behaves like a list with the {py:class}`~amplify.Result.Solution` class as an element.
```{doctest}
>>> type(result.solutions[0])
```
The {py:class}`~amplify.Result.Solution` class represents a solution and has the following attributes.
```{list-table}
:header-rows: 1
- * Attribute name
* Type
* Summary
- * {py:attr}`~amplify.Result.Solution.objective`
* {py:class}`float`
* Objective function value.
- * {py:attr}`~amplify.Result.Solution.values`
* {py:class}`amplify.Values`
* Values of variables in the solution.
- * {py:attr}`~amplify.Result.Solution.feasible`
* {py:class}`bool`
* Whether the solution meet the constraints.
- * {py:attr}`~amplify.Result.Solution.time`
* {py:class}`datetime.timedelta`
* The timestamp at which the solution is obtained.
```
The {py:class}`~amplify.Values` class, which is the type of the {py:class}`~amplify.Result.Solution.values` attribute, is a class that represents solution values and acts like a dictionary with variables as keys and solution values as values.
```{doctest}
>>> solution = result.solutions[0]
>>> solution.values
Values({Poly(q_0): 0, Poly(q_1): 0, Poly(q_2): 1})
>>> solution.values[q[0]]
0.0
>>> solution.values[q[2]]
1.0
```
You can also use the {py:meth}`~amplify.Poly.evaluate` methods of the variable array and polynomial classes and the {py:meth}`~amplify.Constraint.is_satisfied` method of the constraint class to assign the solution values represented by the {py: class}`~amplify.Values` class to the variable array, polynomial or constraint conditions. See [](#decision-variable-evaluation), [](#polynomial-evaluation), and [](#constraint-evaluation) for more details.
The {py:class}`~amplify.Result` class provides several shortcuts to accessing the solution besides the {py:class}`~amplify.Result.solutions` attribute. First, the {py:attr}`~amplify.Result.best` attribute of the {py:class}`~amplify.Result` class provides the best solution. Accessing the result directly from the {py:class}`~amplify.Result` class by index is also possible.
```{doctest}
>>> result.best.values
Values({Poly(q_0): 0, Poly(q_1): 0, Poly(q_2): 1})
>>> len(result)
1
>>> result[0].values
Values({Poly(q_0): 0, Poly(q_1): 0, Poly(q_2): 1})
```
### Information on the model conversion
The following attributes can obtain information about the model conversion. See [](conversion.md) for details.
```{list-table}
* - {py:attr}`~amplify.Result.intermediate`
- The information on the intermediate model
* - {py:attr}`~amplify.Result.embedding`
- The information on the graph embedding
```
### Information on the response of the solver
The {py:attr}`~amplify.solve` function calls the `solve(...)` method of a solver client to run the solver after the model conversion and graph embedding have been performed. The object returned by the solver client can be obtained from the {py:attr}`~amplify.Result.client_result` attribute.
```{doctest}
>>> type(result.client_result)
```
The type of the {py:attr}`~amplify.Result.client_result` depends on the type of the solver client. See [](solvers.md) and its subpages for more information about the result type of each solver client.
### Information on the execution time
You can obtain information about various execution times through the following attributes. See [](timing.md) for more information.
```{list-table}
* - {py:attr}`~amplify.Result.total_time`
- Time taken to {py:func}`~amplify.solve`
* - {py:attr}`~amplify.Result.response_time`
- Time between sending a request to the solver and receiving a response
* - {py:attr}`~amplify.Result.execution_time`
- Time spent by the solver in optimization
```
## Decision variable evaluation
The solution values can be assigned to a variable array used in the formulation. The assigned result is returned as a NumPy array with the same shape as the variable array.
The following performs the assignment to evaluate a variable array. The {py:attr}`~amplify.Result.Solution.values` attribute of the {py:class}`~amplify.Result.Solution` object is passed to the {py:meth}`~amplify.PolyArray.evaluate` method of the {py:class}`~amplify.PolyArray` class.
```{tip}
The solution in the result can be obtained by retrieving the best result of the run with the {py:attr}`~amplify.Result.best` attribute or by accessing the elements in the same way as for a list.
```
```{doctest}
>>> print(result.best.values)
{q_0: 0, q_1: 0, q_2: 1}
>>> q_values = q.evaluate(result.best.values)
>>> print(q_values)
[0. 0. 1. 0. 0.]
```
If a variable not used in the formulation is included, as in `q[3]`{l=python} or `q[4]`{l=python} above, it is assigned one of its possible values by default. In the above example, the Amplify SDK assigns `0`{l=python} as the default value for the binary variable.
The `default` keyword argument to the {py:meth}`~amplify.PolyArray.evaluate` method can change the value used if no evaluation is performed; if the `default` keyword argument is given as a number, variables not passed to the solver are assigned that value.
```{testcode}
:hide:
q_values = q.evaluate(result.best.values, default=3)
```
```{doctest}
>>> q_values = q.evaluate(result.best.values, default=3) # doctest: +SKIP
Warning: Substituting variable q_3 with 3 is out of bounds.
Warning: Substituting variable q_4 with 3 is out of bounds.
>>> print(q_values)
[0. 0. 1. 3. 3.]
```
```{note}
A warning is printed if a value outside the bounds of the variable is given, such as `default=3`{l=python}, as shown above.
```
If the `default` keyword argument is {py:obj}`None`, variables not passed to the solver remain as they are. Only in this case the {py:meth}`~amplify.PolyArray.evaluate` method return a {py:class}`~amplify.PolyArray`.
```{doctest}
>>> q_values = q.evaluate(result.best.values, default=None)
>>> print(q_values)
[ 0, 0, 1, q_3, q_4]
```
```{attention}
Some variables may not be passed to the solver even if they are included in the model. This is because model conversions such as penalty function generation or graph embedding can cause terms to cancel each other out.
```
## Polynomial evaluation
The result of evaluating the objective function of the input model with the solution returned by the {py:func}`~amplify.solve` function can be obtained using the {py:attr}`~amplify.Result.Solution.objective` attribute of the {py:class}`~amplify.Result.Solution` object.
```{doctest}
>>> solution = result.best
>>> solution.objective
-1.0
```
On the other hand, there are cases where you want to evaluate a polynomial other than the objective function with the solution returned by the solver, for example, when the objective function is expressed as the sum of several polynomials. In this case, we pass the {py:attr}`~amplify.Result.Solution.values` attribute of the {py:class}`~amplify.Result.Solution` object to {py:class}`~amplify.Poly`'s {py:meth}`~amplify.Poly.evaluate` method, just as we would evaluate an array of variables.
```{doctest}
>>> objective_1 = q[0] * q[1] # The first term of the objective function
>>> objective_1.evaluate(solution.values)
0.0
```
```{hint}
The behavior when the polynomial contains variables not passed to the solver is similar to the {py:meth}`~amplify.PolyArray.evaluate` method of the {py:class}`~amplify.PolyArray` class; the `default` keyword argument can change this behavior.
```
## Constraint evaluation
If you want to know if the resulting solution satisfies the constraints, you can check with the {py:attr}`~amplify.Result.Solution.feasible` attribute of the {py:class}`~amplify.Result.Solution` class.
```{doctest}
>>> solution.feasible
True
```
By default, this will always be {py:obj}`True` because the {py:func}`~amplify.solve` function retrieves only solutions that satisfy all constraints in the model. To change this behavior and allow the solver to retrieve solutions that do not satisfy the constraints, pass a {py:class}`bool` to the `filter_solution` keyword argument of the {py:func}`~amplify.solve` function upfront or set {py:attr}`~amplify.Result.filter_solution` of the {py:class}`~amplify.Result` class to {py:obj}`False` afterward.
Let's test this by adding a constraint to the model that cannot be satisfied inconsistently.
```{testcode}
# Create an objective function and constraints
objective = q[0] * q[1] - q[2]
constraint1 = equal_to(q[0] + q[1] + q[2], 1, label="sum equals one")
constraint2 = equal_to(q[0] + q[1] + q[2], 2, label="sum equals two")
# Define a model (with conflicting constraints)
model = objective + constraint1 + constraint2
# Get the result of the run
result = solve(model, client)
```
You cannot retrieve the solution from {py:class}`~amplify.Result` if the solution filter is enabled.
```python
>>> result.best.feasible
Traceback (most recent call last):
File "", line 1, in
RuntimeError: result has no feasible solution
```
Turning off the solution filter allows us to obtain solutions that do not satisfy the constraints.
```{doctest}
>>> result.filter_solution = False
>>> result.best.feasible
False
```
Suppose you obtained a solution that does not satisfy a constraint for some reason, such as model configuration, penalty function weights, or solver settings. In that case, it may be desirable to determine which constraint was not satisfied.
You can check whether the constraint conditions are satisfied by passing the solution returned by the {py:func}`~amplify.solve` function to the {py:meth}`~amplify.Constraint.is_satisfied` method of the {py:class}`~amplify.Constraint` class.
```python
>>> constraint1.is_satisfied(result.best.values)
True
>>> constraint2.is_satisfied(result.best.values)
False
```
In the example above, we see that `constraint2` could not be satisfied.
We can also mechanically identify constraints from the list of constraints in the model that are not satisfied, as follows. This method is useful when adjusting and rerunning the penalty function weights.
```python
>>> list(c for c in model.constraints if not c.is_satisfied(result.best.values))
[Constraint({conditional: q_0 + q_1 + q_2 == 2, weight: 1, label: "sum equals two"})]
```