def run_training(save_result: bool = True):
images_df = dm.load_image_paths(config.DATA_FOLDER)
X_train, X_test, y_train, y_test = dm.get_train_test_target(images_df)
enc = pp.TargetEncoder()
enc.fit(y_train)
y_train = enc.transform(y_train)
pipe.pipe.fit(X_train, y_train)
if save_result:
joblib.dump(enc, config.ENCODER_PATH)
dm.save_pipeline_keras(pipe.pipe)
import data_management as dm
import config
def make_prediction(*, path_to_images) -> float:
"""Make a prediction using the saved model pipeline."""
# Load data
# create a dataframe with columns = ['image', 'target']
# column "image" contains path to image
# columns target can contain all zeros, it doesn't matter
dataframe = path_to_images # needs to load as above described
pipe = dm.load_pipeline_keras()
predictions = pipe.pipe.predict(dataframe)
#response = {'predictions': predictions, 'version': _version}
return predictions
if __name__ == '__main__':
from sklearn.externals import joblib
images_df = dm.load_image_paths(config.DATA_FOLDER)
X_train, X_test, y_train, y_test = dm.get_train_test_target(images_df)
pipe = joblib.load(config.PIPELINE_PATH)
predictions = pipe.predict(X_test)
print(predictions)