Metadata-Version: 2.1
Name: tinybaker
Version: 0.0.1
Summary: Lightweight file-to-file build tool built for production workloads
Home-page: https://github.com/pypa/tinybaker
Author: Evin Sellin
Author-email: evinism@gmail.com
License: UNKNOWN
Description: # tinybaker: Lightweight file-to-file build tool built for production workloads
        
        This is a "working" example of a script that builds an ml model from given train and test dataframes.
        
        ```py
        # train_step.py
        from tinybaker import StepDefinition
        import pandas as pd
        from some_cool_ml_library import train_model, test_model
        
        class TrainModelStep(StepDefinition):
          input_set = {"train_csv", "test_csv"}
          output_set = {"pickled_model"}
        
          def script():
            train_data = pd.read_csv(self.input_files["train_csv"])
            test_data = pd.read_csv(self.input_files["test_csv"])
            X = train_data.drop(["label"])
            Y = train_data[["label"]]
            model = train_model(X, Y, depth_or_something=self.config["depth"])
            model.test_model()
            pickle.dump(self.output_files["pickled_model"], model)
        
        ```
        
        ```py
        # script.py
        from .train_step import TrainModelStep
        
        [_, train_csv_path, test_csv_path, pickled_model_path] =  parse_args(os)
        TrainModelStep.build(
          input={
            "train_csv": train_csv_path,
            "test_csv": test_csv_path,
          },
          output={
            "pickled_model": pickled_model_path
          },
          config={"depth": 5}
        )
        ```
        
        That's it!!
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
