auger.ai.predict 1.1.13
Auger ML predict python and command line interface
License: Apache
Requires Python: >=3
Keywords: augerai auger ai machine learning automl deeplearn api sdk prediction predict

# Install
```
pip install auger.ai.predict
```

# Auger.ai.predict
Auger ML predict Python API and command line interface


# Download exported model

To download exported model you can use:

- Auger.ai web : https://app.auger.ai
- auger.ai command line interface: https://pypi.org/project/auger.ai/

# Predict using exported model

- Unzip file with model
- Run client.py from model folder:

python <model_path>/client.py --path_to_predict <data_path> --model_path model_path

--path_to_predict - path to file with data to predict. Should contain features used to train model
--model_path - folder which contain model.pkl.gz file

For example:

python ./models/export_9BB0BFA3D368454/client.py --path_to_predict ./files/baseball_predict.csv --model_path ./models/export_9BB0BFA3D368454/model

## Client.py command line parameters

  --path_to_predict Path to file for predict

  --model_path Path to folder with model

  --threshold Threshold to use for calculate target using predict_proba

  --score 0/1 Build scores after prediction if prediction data contain actual target

# Auger.ai.predict Python API
## auger_ml.model_exporter.ModelExporter
ModelExporter provides interface to Auger predict API.

- **ModelExporter(options)** - constructs ModelExporter instance.
  - options - optional parameters. Must be {} for now

- **predict_by_model(model_path, path_to_predict=None, records=None, features=None, threshold=None)** - produce prediction based on exported model and data
  - model_path - folder which contain model.pkl.gz file
  - path_to_predict - data to predict
  - records - data to predict: list of lists. path_to_predict should be None in this case. For example: [[0.1,0.2],[0.1, 0.3]]
  - features - feature names for records. Used only when records is not None
  - threshold - set threshold to produce prediction for classification based on probabilities. proba_ column will be added to prediction result for each target class

  - RETURN: predictions - if path_to_predict is not None, then file in same directory with predcitions, or pandas dataframe

  Example:
  ```
  def predict_by_model_example(path_to_predict=None, threshold=None, model_path=None):
      #features is an array mapping your data to the feature, your feature and data should be
      #the same that you trained your model with.
      #If it is None, features read from model/options.json file
      #['feature1', 'feature2']
      features = None 

      # data is an array of arrays to get predictions for, input your data below
      # each record should contain values for each feature
      records = [[],[]]

      if path_to_predict:
          path_to_predict=os.path.abspath(path_to_predict)

      predictions = ModelExporter({}).predict_by_model(
          records=records,
          model_path=model_path,
          path_to_predict=path_to_predict,
          features=features,
          threshold=threshold
      )

      return predictions
  ```

- **load_model(model_path)** - load model from file.
  - model_path - folder which contain model.pkl.gz file

  - RETURN: model, timeseries_model
    - model - ML model to call predict    
    - timeseries_model - flag is this timeseries model or not

- **preprocess_data(model_path, data_path, records=None, features=None)** - preprocess data for predict. It will process data same way as train data used for model
  - model_path - folder which contain model.pkl.gz file
  - data_path - data to preprocess
  - records - data to predict: list of lists. data_path should be None in this case. For example: [[0.1,0.2],[0.1, 0.3]]
  - features - feature names for records. Used only when records is not None

  - RETURN: X_test, Y_test, target_categoricals
    - X_test - data to call predict    
    - Y_test - array with target values
    - target_categoricals - dict with categories for target, may be used to get actual target values

  Example:
  ```
  def predict_by_model_example(path_to_predict=None, model_path=None):
      model_exporter = ModelExporter({})
      model, timeseries_model = model_exporter.load_model(model_path)
      X_test, Y_test, target_categoricals = model_exporter.preprocess_data(model_path, 
          data_path=path_to_predict)

      results = model.predict(X_test)

      # If your target is categorical you can translate predicted values back to original:
      # target_feature = "target"
      # categories = target_categoricals[target_feature]['categories']
      # results = map(lambda x: categories[int(x)], results)
  ```

  Example for timeseries data:
  ```
  def predict_by_model_timeseries_example(path_to_predict=None, model_path=None):
      model_exporter = ModelExporter({})
      model, timeseries_model = model_exporter.load_model(model_path)
      X_test, Y_test, target_categoricals = model_exporter.preprocess_data(model_path, 
          data_path=path_to_predict)

      if timeseries_model:
          results = model.predict((X_test, Y_test, False))[-1:]
      else:
          results = model.predict(X_test.iloc[-1:])
  ```