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# Ask-and-Tell Interface

Optuna has an Ask-and-Tell interface, which provides a more flexible interface for hyperparameter optimization. This tutorial explains three use-cases when the ask-and-tell interface is beneficial:

## Apply Optuna to an existing optimization problem with minimum modifications

Let’s consider the traditional supervised classification problem; you aim to maximize the validation accuracy. To do so, you train LogisticRegression as a simple model.

```
import numpy as np
from sklearn.datasets import make_classification
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
import optuna
X, y = make_classification(n_features=10)
X_train, X_test, y_train, y_test = train_test_split(X, y)
C = 0.01
clf = LogisticRegression(C=C)
clf.fit(X_train, y_train)
val_accuracy = clf.score(X_test, y_test) # the objective
```

Then you try to optimize hyperparameters `C`

and `solver`

of the classifier by using optuna.
When you introduce optuna naively, you define an `objective`

function
such that it takes `trial`

and calls `suggest_*`

methods of `trial`

to sample the hyperparameters:

```
def objective(trial):
X, y = make_classification(n_features=10)
X_train, X_test, y_train, y_test = train_test_split(X, y)
C = trial.suggest_float("C", 1e-7, 10.0, log=True)
solver = trial.suggest_categorical("solver", ("lbfgs", "saga"))
clf = LogisticRegression(C=C, solver=solver)
clf.fit(X_train, y_train)
val_accuracy = clf.score(X_test, y_test)
return val_accuracy
study = optuna.create_study(direction="maximize")
study.optimize(objective, n_trials=10)
```

This interface is not flexible enough.
For example, if `objective`

requires additional arguments other than `trial`

,
you need to define a class as in
How to define objective functions that have own arguments?.
The ask-and-tell interface provides a more flexible syntax to optimize hyperparameters.
The following example is equivalent to the previous code block.

```
study = optuna.create_study(direction="maximize")
n_trials = 10
for _ in range(n_trials):
trial = study.ask() # `trial` is a `Trial` and not a `FrozenTrial`.
C = trial.suggest_float("C", 1e-7, 10.0, log=True)
solver = trial.suggest_categorical("solver", ("lbfgs", "saga"))
clf = LogisticRegression(C=C, solver=solver)
clf.fit(X_train, y_train)
val_accuracy = clf.score(X_test, y_test)
study.tell(trial, val_accuracy) # tell the pair of trial and objective value
```

The main difference is to use two methods: `optuna.study.Study.ask()`

and `optuna.study.Study.tell()`

.
`optuna.study.Study.ask()`

creates a trial that can sample hyperparameters, and
`optuna.study.Study.tell()`

finishes the trial by passing `trial`

and an objective value.
You can apply Optuna’s hyperparameter optimization to your original code
without an `objective`

function.

If you want to make your optimization faster with a pruner, you need to explicitly pass the state of trial
to the argument of `optuna.study.Study.tell()`

method as follows:

```
import numpy as np
from sklearn.datasets import load_iris
from sklearn.linear_model import SGDClassifier
from sklearn.model_selection import train_test_split
import optuna
X, y = load_iris(return_X_y=True)
X_train, X_valid, y_train, y_valid = train_test_split(X, y)
classes = np.unique(y)
n_train_iter = 100
# define study with hyperband pruner.
study = optuna.create_study(
direction="maximize",
pruner=optuna.pruners.HyperbandPruner(
min_resource=1, max_resource=n_train_iter, reduction_factor=3
),
)
for _ in range(20):
trial = study.ask()
alpha = trial.suggest_float("alpha", 0.0, 1.0)
clf = SGDClassifier(alpha=alpha)
pruned_trial = False
for step in range(n_train_iter):
clf.partial_fit(X_train, y_train, classes=classes)
intermediate_value = clf.score(X_valid, y_valid)
trial.report(intermediate_value, step)
if trial.should_prune():
pruned_trial = True
break
if pruned_trial:
study.tell(trial, state=optuna.trial.TrialState.PRUNED) # tell the pruned state
else:
score = clf.score(X_valid, y_valid)
study.tell(trial, score) # tell objective value
```

Note

`optuna.study.Study.tell()`

method can take a trial number rather than the trial object.
`study.tell(trial.number, y)`

is equivalent to `study.tell(trial, y)`

.

## Define-and-Run

The ask-and-tell interface supports both define-by-run and define-and-run APIs. This section shows the example of the define-and-run API in addition to the define-by-run example above.

Define distributions for the hyperparameters before calling the
`optuna.study.Study.ask()`

method for define-and-run API.
For example,

```
distributions = {
"C": optuna.distributions.FloatDistribution(1e-7, 10.0, log=True),
"solver": optuna.distributions.CategoricalDistribution(("lbfgs", "saga")),
}
```

Pass `distributions`

to `optuna.study.Study.ask()`

method at each call.
The retuned `trial`

contains the suggested hyperparameters.

```
study = optuna.create_study(direction="maximize")
n_trials = 10
for _ in range(n_trials):
trial = study.ask(distributions) # pass the pre-defined distributions.
# two hyperparameters are already sampled from the pre-defined distributions
C = trial.params["C"]
solver = trial.params["solver"]
clf = LogisticRegression(C=C, solver=solver)
clf.fit(X_train, y_train)
val_accuracy = clf.score(X_test, y_test)
study.tell(trial, val_accuracy)
```

## Batch Optimization

The ask-and-tell interface enables us to optimize a batched objective for faster optimization. For example, parallelizable evaluation, operation over vectors, etc.

The following objective takes batched hyperparameters `xs`

and `ys`

instead of a single
pair of hyperparameters `x`

and `y`

and calculates the objective over the full vectors.

```
def batched_objective(xs: np.ndarray, ys: np.ndarray):
return xs**2 + ys
```

In the following example, the number of pairs of hyperparameters in a batch is \(10\),
and `batched_objective`

is evaluated three times.
Thus, the number of trials is \(30\).
Note that you need to store either `trial_numbers`

or `trial`

to call
`optuna.study.Study.tell()`

method after the batched evaluations.

```
batch_size = 10
study = optuna.create_study(sampler=optuna.samplers.CmaEsSampler())
for _ in range(3):
# create batch
trial_numbers = []
x_batch = []
y_batch = []
for _ in range(batch_size):
trial = study.ask()
trial_numbers.append(trial.number)
x_batch.append(trial.suggest_float("x", -10, 10))
y_batch.append(trial.suggest_float("y", -10, 10))
# evaluate batched objective
x_batch = np.array(x_batch)
y_batch = np.array(y_batch)
objectives = batched_objective(x_batch, y_batch)
# finish all trials in the batch
for trial_number, objective in zip(trial_numbers, objectives):
study.tell(trial_number, objective)
```

**Total running time of the script:** ( 0 minutes 0.139 seconds)