aitomatic 1.2.15
aitomatic library to interact with Aitomatic product
Requires Python: >=3.7

# WebModel Library User Manual

The WebModel library is a tool for building, tuning, and inference of models that are built with the Aitomatic system. The target users of this library are AI Engineers who use the Aitomatic system.

## Requirements

- Python 3.9 or higher
- `requests` library
- `pandas` library
- `numpy` library
- `tqdm` library

## Installation

The WebModel library can be installed using pip:

```bash
pip install 'aitomatic>=1.2.0' --index-url https://test.pypi.org/simple/ --extra-index-url https://pypi.org/simple
```

## Quick Start

To get started, you can create a WebModel object by passing in the model name and API token:

```python
from aitomatic.api.web_model import WebModel

# load model
API_ACCESS_TOKEN = '<API_ACCESS_TOKEN>'
project_name="<project name>"
model_name = "<model name>"

model = WebModel(api_token=API_ACCESS_TOKEN, project_name=project_name, model_name=model_name)
model.load()

# view model training statistics and info
print(model.stats)

# run model inference
data = {'X':  my_dataframe}
response = model.predict({'X': data})
print(response['predictions'])
```

## Methods

The `WebModel` class provides several methods for working with the model:

- **Constructor**

  ```python
  model_names = WebModel.get_model_names(api_token="YOUR_API_TOKEN", project_name="MyProject")
  ```

- **Load model**
  `load` set up the model ready by loading all parameter from Aitomatic model repo.

  ```python
  model.load()
  ```

  - **Return** the model with loaded params

- **Predict**
  The `predict` method takes a dictionary as input with the data you want to make predictions on. The input data should be a pandas DataFrame, Series, or numpy array with the key "X". The method returns a dictionary with the predictions, with the key "predictions".

  ```python
  response = model.predict(input_data={'X': df})
  ```

  - `input_data`: input data for prediction, dictionary with data under key 'X'
  - **Return**: result of the prediction call in a dictionary where the actual result is under `prediction` key

- **Tuning**
  `tune_model` is a statis method to generate multiple versions of a given model with the set of input params

  ```python
  tune_model(
      project_name=PROJECT_NAME,
      base_model=BASE_MODEL_NAME,
      conclusion_tuning_range=conclusion_threshold_ranges,
      ml_tuning_params=ML_MODELS_PARAMS,
      output_model_df_path='tuning.parquet',
      wait_for_tuning_to_complete=True,
      prefix="[HUNG7]",
  )
  ```

  - `project_name`: A string containing the name of the Aitomatic project to use.
  - `base_model`:A string containing the name of the base model to use.
  - `conclusion_tuning_range`: A dictionary specifying the range of values to use for the final layer of the tuned model.
  - `ml_tuning_params`: A dictionary specifying the AutoML tuning parameters to use.
  - `output_model_df_path`: A string specifying the path to save the resulting DataFrame
    containing the tuned model's hyperparameters and performance.
  - `wait_for_tuning_to_complete`: A boolean specifying whether to wait for the tuning process
    to complete before returning. Default is True.
  - `prefix`: A string containing a prefix to add to the name of the new model. Default is "finetune".
  - **Return** A Pandas DataFrame containing the hyperparameters and performance of the tuned model.

- **Log model metrics**
  `log_metrics` is to save the model metric after evaluation

  ```python
  model.log_metrics("accuracy", 0.95)
  ```

- **Get models in project `static`**

  ```python
  model_names = WebModel.get_model_names(api_token="YOUR_API_TOKEN", project_name="MyProject")
  ```

  - The `api_token` A string containing the access token for the Aitomatic API. If not provided, the AITOMATIC_API_TOKEN environment variable will be used.
  - The `project_name` A string containing the name of the Aitomatic project to use. If not provided, the AITOMATIC_PROJECT_ID environment variable will be used.
  - **Return** a list of the names of all models in the specified project.