---
type: concept
x-llmwiki:
  schemaVersion: "0.1"
  contentHash: b0d6d426cdb1931910239dc1647349fc0772cc019c09d9ae05f30d2996bb0a64
  pageDirectory: concepts
  sources:
    - parallelize-hyperopt-hyperparameter-tuning-databricks-on-aws.md
  confidence: 0.9
  provenanceState: extracted
  freshnessStatus: unverified
  aliases:
    - objective-function-pattern-for-hyperopt
    - OFPFH
  citations:
    - file: parallelize-hyperopt-hyperparameter-tuning-databricks-on-aws.md
title: Objective Function Pattern for Hyperopt
description: "The pattern for writing Hyperopt objective functions: define a function taking hyperparameters as arguments, train/evaluate a model, and return a dictionary with 'loss' (to minimize) and 'status'."
tags:
  - hyperopt
  - objective-function
  - pattern
timestamp: "2026-06-19T19:54:20.053Z"
---

# Objective Function Pattern for Hyperopt

The **Objective Function Pattern for Hyperopt** is a design pattern for defining objective functions in [Hyperopt](/concepts/hyperopt.md) hyperparameter tuning workflows. The pattern ensures that Hyperopt's `fmin()` function can properly evaluate and compare different hyperparameter configurations by returning a dictionary with specific required and optional keys.

## Pattern Structure

The objective function in Hyperopt must return a dictionary that contains at minimum two keys: `loss` and `status`. This dictionary structure is what Hyperopt's optimization algorithms use to evaluate and compare different hyperparameter settings during the tuning process. ^[parallelize-hyperopt-hyperparameter-tuning-databricks-on-aws.md]

### Required Keys

```python
def objective(params):
    # Model training and evaluation logic
    return {
        'loss': -accuracy,  # The value to minimize
        'status': STATUS_OK # Indicates successful evaluation
    }
```

- **`loss`**: A numeric value that Hyperopt attempts to minimize. Since most machine learning metrics (like accuracy) are higher-is-better, the pattern returns the negative of the metric so that Hyperopt's minimization logic finds the optimal configuration. ^[parallelize-hyperopt-hyperparameter-tuning-databricks-on-aws.md]
- **`status`**: Must be set to `STATUS_OK` to indicate that the evaluation completed successfully. This constant is imported from Hyperopt and signals to the optimization algorithm that the returned loss value is valid. ^[parallelize-hyperopt-hyperparameter-tuning-databricks-on-aws.md]

## Example with scikit-learn

The following example demonstrates the pattern using a Support Vector Classifier (SVC) from scikit-learn with cross-validation: ^[parallelize-hyperopt-hyperparameter-tuning-databricks-on-aws.md]

```python
from sklearn.datasets import load_iris
from sklearn.model_selection import cross_val_score
from sklearn.svm import SVC
from hyperopt import fmin, tpe, hp, SparkTrials, STATUS_OK, Trials

# Load dataset
iris = load_iris()
X = iris.data
y = iris.target

def objective(C):
    clf = SVC(C=C)
    accuracy = cross_val_score(clf, X, y).mean()
    return {'loss': -accuracy, 'status': STATUS_OK}
```

## Integration with MLflow Tracking

The pattern integrates with [MLflow Tracking](/concepts/mlflow-tracking.md) for automated experiment logging. When using MLflow, the objective function can include MLflow logging calls, and the distributed tuning with [SparkTrials](/concepts/sparktrials.md) automatically tracks each trial as an [MLflow Run](/concepts/mlflow-run.md): ^[parallelize-hyperopt-hyperparameter-tuning-databricks-on-aws.md]

```python
import mlflow
from hyperopt import fmin, tpe, hp, SparkTrials

search_space = hp.lognormal('C', 0, 1.0)
spark_trials = SparkTrials()

with mlflow.start_run():
    argmin = fmin(
        fn=objective,
        space=search_space,
        algo=tpe.suggest,
        max_evals=16,
        trials=spark_trials
    )
```

## Key Considerations

- The objective function should be self-contained, importing all necessary libraries and loading required data within its scope. ^[parallelize-hyperopt-hyperparameter-tuning-databricks-on-aws.md]
- For cross-validation metrics, return the mean metric value. ^[parallelize-hyperopt-hyperparameter-tuning-databricks-on-aws.md]
- When using accuracy or other higher-is-better metrics, negate the value in the `loss` key. ^[parallelize-hyperopt-hyperparameter-tuning-databricks-on-aws.md]
- The pattern works identically for both single-machine `Trials` and distributed `SparkTrials` objects. ^[parallelize-hyperopt-hyperparameter-tuning-databricks-on-aws.md]

## Related Concepts

- [Hyperopt fmin()](/concepts/hyperopt-fmin.md) — The function that consumes the objective function pattern
- [SparkTrials](/concepts/sparktrials.md) — Distributed trials class for parallelizing hyperparameter tuning
- Search Space Definition — How to define the `space` parameter for Hyperopt
- [Tree of Parzen Estimators (TPE)](/concepts/hyperopt-tree-of-parzen-estimators-tpe-algorithm.md) — The Bayesian optimization algorithm commonly used with this pattern

## Sources

- parallelize-hyperopt-hyperparameter-tuning-databricks-on-aws.md

# Citations

1. [parallelize-hyperopt-hyperparameter-tuning-databricks-on-aws.md](/references/parallelize-hyperopt-hyperparameter-tuning-databricks-on-aws-b91f741c.md)
