> 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/examples/experiment-tracking.md).

# Experiment Tracking

Use `instantiate_with_params()` when you need both the runtime object and the exact selected parameters to log to MLflow, Weights & Biases, a database, or a JSON file.

## Capture The Value And Params

{% code overflow="wrap" %}

```python
from hypster import HP, instantiate, instantiate_with_params
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from my_app.experiments import TrainingJob
from my_app.features import FeatureSelector

def feature_config(hp: HP) -> FeatureSelector:
    numeric = hp.multi_text(["age", "income"], name="numeric")
    categorical = hp.multi_text(["country"], name="categorical")
    scale = hp.bool(True, name="scale")
    return FeatureSelector(numeric=numeric, categorical=categorical, scale=scale)

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 tree_model(hp: HP) -> RandomForestClassifier:
    max_depth = hp.int(6, name="max_depth", min=1, max=64)
    return RandomForestClassifier(max_depth=max_depth, random_state=42)

model_options = {
    "linear": linear_model,
    "tree": tree_model,
}

def training_job_config(hp: HP) -> TrainingJob:
    selected_model = hp.select(model_options, name="model_family", default="linear", options_only=True)
    features = hp.nest(feature_config, name="features")
    model = hp.nest(selected_model, name="model")
    return TrainingJob(features=features, model=model)

run = instantiate_with_params(
    training_job_config,
    values={
        "model_family": "tree",
        "model.max_depth": 12,
        "features.numeric": ["age", "income", "days_active"],
    },
)

assert run.value.model.max_depth == 12
assert run.params == {
    "model_family": "tree",
    "features.numeric": ["age", "income", "days_active"],
    "features.categorical": ["country"],
    "features.scale": True,
    "model.max_depth": 12,
}
replayed = instantiate(training_job_config, values=run.params)
assert replayed.model.max_depth == run.value.model.max_depth
```

{% endcode %}

## Log Params In Your Tracker

{% code overflow="wrap" %}

```python
def log_to_tracker(tracker, run):
    for path, value in run.params.items():
        tracker.log_param(path, value)
```

{% endcode %}

The important detail is that `run.params` contains defaulted values as well as user overrides. That makes it suitable for exact replay.

For run-record structure, versioning metadata, and replay-recovery guidance, see [Experiment Tracking (Reproducibility)](/hypster/reproducibility/experiment-tracking.md).


---

# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.

## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter:

```
GET https://gilad-rubin.gitbook.io/hypster/examples/experiment-tracking.md?ask=<question>&goal=<endgoal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal.

The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.