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:])

Release files for auger.ai.predict 1.1.13

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Built distribution (wheel)

Table of built distributions (wheels) for auger.ai.predict 1.1.13
File	Interpreter	ABI	Platform	Reset
auger.ai.predict-1.1.13-py3-none-any.whl 134.5 kB Dec 21, 2024	Python 3	none	any	Details

Release files / auger.ai.predict-1.1.13-py3-none-any.whl

Download URL	auger.ai.predict-1.1.13-py3-none-any.whl
Size	134.5 kB
Tags	Python 3
SHA-256 checksum How to use checksums	`66aaeabd0c83f7f3750d97c76c117c008bce1d0baa28762ff131cfcacfc57589`
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