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Description:

auger.ai.predict 1.1.12

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:])