# Running AI Firewall on the Fraud Example with the RIME SDK

This tutorial will guide you through how to get started with the RIME SDK by
running AI Firewall on a model trained over a slightly modified version
of the <a target="_blank" rel="noopener"
href="https://www.kaggle.com/c/ieee-fraud-detection">IEEE-CIS Fraud
Detection</a> dataset.

Before you begin, make sure:
- You have completed the installation
- You are in the Python environment with the RIME Python SDK installed
- You have [downloaded](https://www.dropbox.com/s/bv0epg6fbkts62j/ri-public-examples.tar.gz?dl=0) the example datasets and configuration files

### Setup

The RIME SDK reads data and model from AWS S3. To begin using the RIME
SDK, please first upload the `fraud_continuous_testing` folder (found inside zip file downloaded
above) to an S3 bucket that you have access to.

### Running AI Firewall on the Fraud Example

For the basic example, we will run AI Firewall with a model. If you do
not want to run RIME with the model, and instead want to run in a different
mode (e.g. using prediction logs instead), see the end of this document for
different workflows. The model provided in the examples folder is a Catboost
model and we will need to install extra dependencies (in order to call that
model). To do this, please run:

```bash
pip install -r workshop_requirements.txt
```

{{ sdk_client_setup }}

Next, start python and run the following:

```python
import json

project = rime_client.create_project("FooBar's Test Project", "DESCRIPTION")

# Load the configuration file
f = open("examples/fraud_continuous_testing/trial_config.json")
config = json.load(f)

config["data_info"]["ref_path"] = "s3://path/to/examples/fraud_continuous_testing/ref.csv"
config["data_info"]["eval_path"] = "s3://path/to/examples/fraud_continuous_testing/eval_2021_04_01_to_2021_05_01.csv"

# Start the tests
job = rime_client.start_firewall_from_reference(test_run_config=config, project_id=project.project_id)
job.get_status(poll_rate_sec=1.0, verbose=True, wait_until_finish=True)
```

This will start running the tests and wait for them to finish. Once they
finish, you will be able to see the results through the RIME interface on your
web browser.

### Running AI Firewall on an Incremental Batch of Data
Similar to the above steps, but instead the command you will want to run is:

```python
# Load the configuration file
f = open("examples/fraud_continuous_testing/stress_testing_config.json")
ot_config = json.load(f)
ot_config["data_info"]["ref_path"] = "s3://path/to/examples/fraud_continuous_testing/ref.csv"
ot_config["data_info"]["eval_path"] = "s3://path/to/examples/fraud_continuous_testing/eval_2021_04_01_to_2021_04_29.csv"

# Start the stress test
job = rime_client.start_stress_test(test_run_config=ot_config, project_id=project.project_id)
job.get_status(poll_rate_sec=1.0, verbose=True, wait_until_finish=True)
ot_test_run_id = job.get_test_run_result()['metadata.id'][0]

# Create Firewall
firewall = rime_client.create_firewall("fw sdk test", 86400, ot_test_run_id, project.project_id)

# Run Firewall on an Incremental Batch of Data
incremental_config = {
    "eval_path": "s3://path/to/examples/fraud_continuous_testing/eval_2021_04_30_to_2021_05_01.csv",
    "timestamp_col": "timestamp"
}
job = firewall.run_firewall_incremental_data(incremental_config)
```