> For the complete documentation index, see [llms.txt](https://gilad-rubin.gitbook.io/hypster/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://gilad-rubin.gitbook.io/hypster/in-depth/values-and-overrides.md).

# Values & Overrides

`values=` is the dictionary you pass to `instantiate()`, `instantiate_with_params()`, or `explore()` to select concrete parameter values.

{% code overflow="wrap" %}

```python
from hypster import HP, instantiate

def child(hp: HP):
    return {
        "x": hp.int(10, name="x"),
        "y": hp.int(20, name="y"),
    }

def parent(hp: HP):
    return {"child": hp.nest(child, name="child")}
```

{% endcode %}

## Top-Level Values

{% code overflow="wrap" %}

```python
def config(hp: HP):
    return {"batch_size": hp.int(32, name="batch_size")}

instantiate(config, values={"batch_size": 64})
# => {"batch_size": 64}
```

{% endcode %}

## Dotted Keys

Use dotted keys for nested parameters:

{% code overflow="wrap" %}

```python
instantiate(parent, values={"child.x": 15})
# => {"child": {"x": 15, "y": 20}}
```

{% endcode %}

## Nested Dictionaries

Nested dictionaries are normalized to the same dotted paths:

{% code overflow="wrap" %}

```python
instantiate(parent, values={"child": {"x": 25}})
# => {"child": {"x": 25, "y": 20}}
```

{% endcode %}

You can mix dotted keys and nested dictionaries as long as each final parameter path appears once.

## Nested Scope Names Are Not Leaves

A nested scope name is a prefix for child parameters, not a parameter leaf by itself. These forms are valid because they target `child.x`:

{% code overflow="wrap" %}

```python
instantiate(parent, values={"child.x": 15})
instantiate(parent, values={"child": {"x": 15}})
```

{% endcode %}

This raises because `child` is a scope, not a selectable parameter:

{% code overflow="wrap" %}

```python
instantiate(parent, values={"child": 123})
# ValueError: Unknown or unreachable parameters
```

{% endcode %}

## Duplicate Paths

This raises because both entries target `child.x`:

{% code overflow="wrap" %}

```python
instantiate(
    parent,
    values={
        "child.x": 100,
        "child": {"x": 100},
    },
)
# ValueError: Duplicate value for 'child.x'
```

{% endcode %}

Hypster raises even when the duplicate values are identical. A single canonical path keeps experiment logs and replay payloads unambiguous.

## Conditional Reachability

Only parameters touched by the active branch may appear in `values=`.

{% code overflow="wrap" %}

```python
from sklearn.base import ClassifierMixin
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression

def linear_model(hp: HP) -> LogisticRegression:
    C = hp.float(1.0, name="C", min=1e-4, max=100.0)
    return LogisticRegression(C=C, max_iter=1000)

def forest_model(hp: HP) -> RandomForestClassifier:
    n_estimators = hp.int(200, name="n_estimators", min=10)
    return RandomForestClassifier(n_estimators=n_estimators, random_state=42)

model_options = {"linear": linear_model, "forest": forest_model}

def model_config(hp: HP) -> ClassifierMixin:
    selected_config = hp.select(model_options, name="family", default="linear", options_only=True)
    return hp.nest(selected_config, name="model")

instantiate(model_config, values={"family": "linear", "model.n_estimators": 500})
# ValueError: Unknown or unreachable parameters
```

{% endcode %}

Use `explore(model_config, values={"family": "forest"})` to inspect the branch before instantiating it.

## Unknown Policies

`instantiate()`, `instantiate_with_params()`, and `explore()` accept the same `on_unknown` policy:

| Policy     | Behavior                                         |
| ---------- | ------------------------------------------------ |
| `"raise"`  | Default. Raise on unknown or unreachable values. |
| `"warn"`   | Emit a warning and continue.                     |
| `"ignore"` | Ignore unknown or unreachable values.            |

Prefer the default for experiments and production replay. Softer policies are useful when migrating old payloads or rendering exploratory UIs.

### Conditional Options Under Each Policy

A later `hp.select`'s option list can depend on an earlier one, so a parameter name can exist in one branch and not another. Overriding a parameter that only exists in a branch you did not take is the same "unreachable" case as above, worked through under each `on_unknown` policy:

{% code overflow="wrap" %}

```python
from hypster import HP, instantiate

def pipeline_config(hp: HP) -> dict:
    stage = hp.select(["ingest", "transform"], name="stage", default="ingest", options_only=True)
    if stage == "ingest":
        source = hp.select(["csv", "json"], name="source", default="csv", options_only=True)
        return {"stage": stage, "source": source}
    else:
        strategy = hp.select(["normalize", "aggregate"], name="strategy", default="normalize", options_only=True)
        return {"stage": stage, "strategy": strategy}
```

{% endcode %}

Taking the `"ingest"` branch and overriding `strategy` — a parameter that only exists on the untaken `"transform"` branch:

{% code overflow="wrap" %}

```python
# on_unknown="raise" (default): raises
instantiate(pipeline_config, values={"stage": "ingest", "strategy": "aggregate"})
# ValueError: Unknown or unreachable parameters

# on_unknown="warn": warns, then returns the ingest branch's own default for "source"
instantiate(pipeline_config, values={"stage": "ingest", "strategy": "aggregate"}, on_unknown="warn")
# UserWarning: Unknown or unreachable parameters
# => {"stage": "ingest", "source": "csv"}

# on_unknown="ignore": same result, no warning
instantiate(pipeline_config, values={"stage": "ingest", "strategy": "aggregate"}, on_unknown="ignore")
# => {"stage": "ingest", "source": "csv"}
```

{% endcode %}

Under `"warn"` and `"ignore"`, the override for the untaken branch is dropped, not redirected or coerced onto the active branch: the active branch (`"ingest"`) still resolves `source` to its own default, `"csv"`. Nothing about `strategy="aggregate"` leaks into the result.

## Select Keys vs Complex Values

Nested dictionaries inside `values=` are interpreted as nested parameter paths. If you need a select option whose runtime value is a dictionary, use dict-backed `select`:

{% code overflow="wrap" %}

```python
def config(hp: HP):
    return hp.select(
        {
            "small": {"layers": 2},
            "large": {"layers": 4},
        },
        name="model",
        default="small",
    )

instantiate(config, values={"model": "large"})
# => {"layers": 4}
```

{% endcode %}

Do not pass `values={"model": {"layers": 4}}`; Hypster will treat that as a nested parameter path.


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