def main():
os.makedirs(DOWNSTREAM_DIR, exist_ok=True)
params = get_params()
_full_data = save_helper.load_data(DATA_FILEPATH)
_data = [d[:4] for d in _full_data]
_target = [d[4] for d in _full_data]
data = split_dataset(_data, _target, params['test_rate'])
for k, v in data.items():
np.save(os.path.join(DOWNSTREAM_DIR, f'{k}.npy'), v)
def main():
os.makedirs(MODEL_DIR, exist_ok=True)
labels = save_helper.load_labels(LABEL_FILEPATH)
_full_data = save_helper.load_data(DATA_FILEPATH)
_data = [d[:4] for d in _full_data]
_target = [d[4] for d in _full_data]
data = split_dataset(_data, _target)
svc_pipeline = define_svc_pipeline()
svc_modelname = "iris_svc"
svc_model_filename = f"{svc_modelname}.pkl"
svc_sklearn_interface_filename = f"{svc_modelname}_sklearn.yaml"
train_model(svc_pipeline, data["x_train"], data["y_train"])
evaluate_model(svc_pipeline, data["x_test"], data["y_test"])
save_helper.dump_sklearn(svc_pipeline,
os.path.join(MODEL_DIR, svc_model_filename))
save_helper.save_interface(
svc_modelname,
os.path.join(MODEL_DIR, svc_sklearn_interface_filename),
[1, 4],
str(data["x_train"].dtype).split(".")[-1],
[1, 3],
"float32",
DATA_TYPE.ARRAY,
[{
svc_model_filename: MODEL_RUNTIME.SKLEARN
}],
PREDICTION_TYPE.CLASSIFICATION,
"src.app.ml.iris.iris_predictor_sklearn",
label_filepath=LABEL_FILEPATH,
)
svc_onnx_filename = f"{svc_modelname}.onnx"
svc_onnx_interface_filename = f"{svc_modelname}_onnx_runtime.yaml"
save_helper.save_onnx(svc_pipeline,
os.path.join(MODEL_DIR, svc_onnx_filename))
save_helper.save_interface(
svc_modelname,
os.path.join(MODEL_DIR, svc_onnx_interface_filename),
[1, 4],
str(data["x_train"].dtype).split(".")[-1],
[1, 3],
"float32",
DATA_TYPE.ARRAY,
[{
svc_onnx_filename: MODEL_RUNTIME.ONNX_RUNTIME
}],
PREDICTION_TYPE.CLASSIFICATION,
"src.app.ml.iris.iris_predictor_onnx",
label_filepath=LABEL_FILEPATH,
)
tree_pipeline = define_tree_pipeline()
tree_modelname = "iris_tree"
tree_model_filename = f"{tree_modelname}.pkl"
tree_sklearn_interface_filename = f"{tree_modelname}_sklearn.yaml"
train_model(tree_pipeline, data["x_train"], data["y_train"])
evaluate_model(tree_pipeline, data["x_test"], data["y_test"])
save_helper.dump_sklearn(tree_pipeline,
os.path.join(MODEL_DIR, tree_model_filename))
save_helper.save_interface(
tree_modelname,
os.path.join(MODEL_DIR, tree_sklearn_interface_filename),
[1, 4],
str(data["x_train"].dtype).split(".")[-1],
[1, 3],
"float32",
DATA_TYPE.ARRAY,
[{
tree_model_filename: MODEL_RUNTIME.SKLEARN
}],
PREDICTION_TYPE.CLASSIFICATION,
"src.app.ml.iris.iris_predictor_sklearn",
label_filepath=LABEL_FILEPATH,
)
tree_onnx_filename = f"{tree_modelname}.onnx"
tree_onnx_interface_filename = f"{tree_modelname}_onnx_runtime.yaml"
save_helper.save_onnx(tree_pipeline,
os.path.join(MODEL_DIR, tree_onnx_filename))
save_helper.save_interface(
tree_modelname,
os.path.join(MODEL_DIR, tree_onnx_interface_filename),
[1, 4],
str(data["x_train"].dtype).split(".")[-1],
[1, 3],
"float32",
DATA_TYPE.ARRAY,
[{
tree_onnx_filename: MODEL_RUNTIME.ONNX_RUNTIME
}],
PREDICTION_TYPE.CLASSIFICATION,
"src.app.ml.iris.iris_predictor_onnx",
label_filepath=LABEL_FILEPATH,
)