def automl_predict( input: Path, output: Path = Path("output.csv"), model: Path = Path("automl.bin"), ignore_cols: List[int] = typer.Option([]), format: str = None, ): """ 🔮 Predict with a previously trained AutoML instance. """ try: dataset = _load_dataset(format, input, ignore_cols) except ValueError as e: logger.error(f"⚠️ Error: {str(e)}") return try: with model.open("rb") as fp: automl = AutoML.load(fp) except TypeError as e: logger.error(f"⚠️ Error: {str(e)}") return console.print(f"🔮 Predicting {len(dataset)} items with the pipeline:") console.print(repr(automl.best_pipeline_)) X = dataset.values y = automl.predict(X) with output.open("wt") as fp: df = pd.DataFrame(y, columns=["y"]) df.to_csv(fp) console.print(f"💾 Predictions saved to [blue]{output.absolute()}[/]")
def automl_inspect(model: Path = Path("automl.bin")): """ 🔍 Inspect a trained AutoML model. """ with model.open("rb") as fp: automl = AutoML.load(fp) console.print(f"🔍 Inspecting AutoML model: [green]{model.absolute()}[/]") console.print(f"⭐ Best pipeline (score={automl.best_score_:0.3f}):") console.print(repr(automl.best_pipeline_))
def test_automl_save_load(): X, y = dummy.generate(seed=0) automl = AutoML(search_iterations=3, registry=[DummyAlgorithm]) automl.fit(X, y) pipe = automl.best_pipeline_ fp = BytesIO() automl.save(fp) fp.seek(0) automl2 = AutoML.load(fp) pipe2 = automl2.best_pipeline_ assert repr(pipe) == repr(pipe2)
def test_automl_save_load(): X, y = dummy.generate(seed=0) automl = AutoML( input=(MatrixContinuousDense, Supervised[VectorCategorical]), output=VectorCategorical, search_iterations=3, registry=[DummyAlgorithm], ) automl.fit(X, y) pipe = automl.best_pipeline_ fp = BytesIO() automl.save(fp) fp.seek(0) automl2 = AutoML.load(fp) pipe2 = automl2.best_pipeline_ assert repr(pipe) == repr(pipe2)