### Set up AIMO Evaluation API in Python

Source: https://github.com/nathfavour/kaggle-mathematica/blob/main/aimo-submission-example.ipynb

Initializes the AIMO evaluation API environment and creates an iterator for the test set. This setup is crucial for interacting with the competition's backend and retrieving test problems.

```python
# Set up the evaluation API
import aimo

env = aimo.make_env()
iter_test = env.iter_test()
```

--------------------------------

### Initialize AIMO Environment

Source: https://context7.com/nathfavour/kaggle-mathematica/llms.txt

Creates and returns the AIMO evaluation environment. This function must be called before any other interaction with the API.

```python
import aimo

# Initialize the evaluation environment
env = aimo.make_env()
```

--------------------------------

### Iterate Through Test Problems

Source: https://context7.com/nathfavour/kaggle-mathematica/llms.txt

Returns an iterator for test problems and submission templates. Each iteration yields a tuple containing problem data and a sample submission dictionary.

```python
import aimo

env = aimo.make_env()
iter_test = env.iter_test()

# Iterate through test problems
for test, sample_submission in iter_test:
    # test contains the problem data with 'problem' key
    problem_text = test['problem']
    print(f"Problem: {problem_text}")
    print(f"Submission template: {sample_submission}")
```

--------------------------------

### Iterate and Predict with AIMO API in Python

Source: https://github.com/nathfavour/kaggle-mathematica/blob/main/aimo-submission-example.ipynb

Iterates through the test set provided by the AIMO evaluation API. For each test problem, it uses the defined model to make a prediction, updates the sample submission, and sends the prediction back to the environment. It also prints the test problem and the corresponding submission.

```python
# Iterate through the test set and use the model make predictions
for test, sample_submission in iter_test:
    sample_submission['answer'] = model.predict(test['problem'])
    env.predict(sample_submission)
    print(test)
    print(sample_submission, '\n')
```

--------------------------------

### Define Prediction Model in Python

Source: https://github.com/nathfavour/kaggle-mathematica/blob/main/aimo-submission-example.ipynb

Defines a simple Python class 'Model' with a 'predict' method. This method takes an input 'x' and returns a prediction. This serves as a placeholder for a more complex prediction logic.

```python
# Define your model
class Model:
    def predict(self, x):
        return 0

# What am I doing?
model = Model()
```

--------------------------------

### Submit Model Predictions

Source: https://context7.com/nathfavour/kaggle-mathematica/llms.txt

Submits the model's prediction for the current problem. This must be called once per problem before the next problem is served. Predictions cannot be modified after submission.

```python
import aimo

# Define a model class with predict method
class Model:
    def predict(self, x):
        # Process the problem and return an answer
        # Return 0 as placeholder - replace with actual logic
        return 0

model = Model()

# Set up evaluation environment
env = aimo.make_env()
iter_test = env.iter_test()

# Process each test problem and submit predictions
for test, sample_submission in iter_test:
    # Get model prediction for the problem
    sample_submission['answer'] = model.predict(test['problem'])

    # Submit the prediction
    env.predict(sample_submission)

    # Optional: print for debugging
    print(test)
    print(sample_submission, '\n')
```

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