Added the only one entrypoint --- commands.py. Corrected imports.

README.md

@@ -47,7 +47,7 @@ poetry install
 If you want to train the chosen model and save it afterwards, run:
 
 ```
-poetry run python3 mlopscourse/train.py --model_type [chosen_model]
+poetry run python3 commands.py train --model_type [chosen_model]
 ```
 
 The available models are Random Forest (from the scikit-learn library) and CatBoost.
@@ -58,5 +58,5 @@ If you want to infer a previously trained model, make sure you've placed the che
 `checkpoints/` and then run
 
 ```
-poetry run python3 mlopscourse/infer.py --model_type [chosen_model] --ckpt [checkpoint_filename_with_extension]
+poetry run python3 commands.py infer --model_type [chosen_model] --ckpt [checkpoint_filename_with_extension]
 ```

commands.py

@@ -0,0 +1,37 @@
+import fire
+
+from mlopscourse.infer import infer
+from mlopscourse.train import train
+
+
+def outer_train(model_type: str) -> None:
+    """
+    Trains the chosen model on the train split of the dataset and saves the checkpoint.
+
+    Parameters
+    ----------
+    model_type : str
+        The type of model for training. Should be "rf" for RandomForest and "cb"
+        for CatBoost.
+    """
+    train(model_type)
+
+
+def outer_infer(model_type: str, ckpt: str) -> None:
+    """
+    Runs the chosen model on the test set of the dataset and calculates the R^2 metric.
+
+    Parameters
+    ----------
+    model_type : str
+        The type of model that was used for training. Should be "rf" for RandomForest
+        and "cb" for CatBoost.
+    ckpt : str
+        The filename inside 'checkpoint/' to load the model from. Should also contain the
+        the filename extension.
+    """
+    infer(model_type, ckpt)
+
+
+if __name__ == "__main__":
+    fire.Fire()

mlopscourse/infer.py

@@ -3,7 +3,7 @@
 
 import fire
 
-from data.prepare_dataset import prepare_dataset
+from .data.prepare_dataset import prepare_dataset
 
 
 def infer(model_type: str, ckpt: str) -> None:

mlopscourse/train.py

@@ -2,8 +2,8 @@
 
 import fire
 
-from data.prepare_dataset import prepare_dataset
-from models.models_zoo import prepare_model
+from .data.prepare_dataset import prepare_dataset
+from .models.models_zoo import prepare_model
 
 
 def train(model_type: str) -> None:
@@ -23,7 +23,7 @@ def train(model_type: str) -> None:
         _,
         numerical_features,
         categorical_features,
-    ) = prepare_dataset()
+    ) = prepare_dataset(print_info=True)
     model = prepare_model(model_type, numerical_features, categorical_features)
 
     print(f"Training the {model_type} model...")