Merge pull request #14 from florencejt/refactor/params

Changing the inputs to be separate parameter arguments rather than a parameter dictionary

docs/auto_examples/customising_behaviour/customising_training_parameters.ipynb

@@ -4,7 +4,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n# How to customise the training in Fusilli\n\nThis tutorial will show you how to customise the training of your fusion model.\n\nWe will cover the following topics:\n\n* Early stopping\n* Batch size\n* Number of epochs\n* Checkpoint suffix modification\n\n## Early stopping\n\nEarly stopping is implemented in Fusilli using the PyTorch Lightning\n[EarlyStopping](https://lightning.ai/docs/pytorch/stable/api/lightning.pytorch.callbacks.EarlyStopping.html#lightning.pytorch.callbacks.EarlyStopping)\ncallback. This callback can be passed to the\n:func:`~fusilli.model_utils.train_and_save_models` function using the\n``early_stopping_callback`` argument. For example:\n\n```python\nfrom fusilli.data import get_data_module\nfrom fusilli.train import train_and_save_models\n\nfrom lightning.pytorch.callbacks import EarlyStopping\n\nmodified_early_stopping_callback = EarlyStopping(\n    monitor=\"val_loss\",\n    min_delta=0.00,\n    patience=3,\n    verbose=True,\n    mode=\"min\",\n)\n\ndatamodule = get_data_module(\n        fusion_model=example_model,\n        params=params,\n        own_early_stopping_callback=modified_early_stopping_callback,\n    )\n\ntrained_model_list = train_and_save_models(\n    data_module=datamodule,\n    params=params,\n    fusion_model=example_model,\n    )\n```\nNote that you only need to pass the callback to the :func:`~.fusilli.data.get_data_module` and **not** to the :func:`~.fusilli.train.train_and_save_models` function. The new early stopping measure will be saved within the data module and accessed during training.\n\n\n-----\n\n## Batch size\n\nThe batch size can be set using the ``batch_size`` argument in the :func:`~.fusilli.data.get_data_module` function. By default, the batch size is 8.\n\n```python\nfrom fusilli.data import get_data_module\nfrom fusilli.train import train_and_save_models\n\ndatamodule = get_data_module(\n        fusion_model=example_model,\n        params=params,\n        batch_size=32,\n    )\n\ntrained_model_list = train_and_save_models(\n        data_module=datamodule,\n        params=params,\n        fusion_model=example_model,\n        batch_size=32,\n    )\n```\n-----\n\n## Number of epochs\n\nYou can change the maximum number of epochs using the ``max_epochs`` argument in the :func:`~.fusilli.data.get_data_module` and :func:`~.fusilli.train.train_and_save_models` functions. By default, the maximum number of epochs is 1000.\n\nYou also pass it to the :func:`~.fusilli.data.get_data_module` function because some of the fusion models require pre-training.\n\nChanging the ``max_epochs`` parameter is especially useful when wanting to run a quick test of your model. For example, you can set ``max_epochs=5`` to run a quick test of your model.\n\n```python\nfrom fusilli.data import get_data_module\nfrom fusilli.train import train_and_save_models\n\ndatamodule = get_data_module(\n        fusion_model=example_model,\n        params=params,\n        max_epochs=5,\n    )\n\ntrained_model_list = train_and_save_models(\n        data_module=datamodule,\n        params=params,\n        fusion_model=example_model,\n        max_epochs=5,\n    )\n```\nSetting ``max_epochs`` to -1 will train the model until early stopping is triggered.\n\n-----\n\n## Checkpoint suffix modification\n\nBy default, Fusilli saves the model checkpoints in the following format:\n\n    ``{fusion_model.__name__}_epoch={epoch_n}.ckpt``\n\nIf the checkpoint is for a pre-trained model, then the following format is used:\n\n    ``subspace_{fusion_model.__name__}_{pretrained_model.__name__}.ckpt``\n\nYou can add suffixes to the checkpoint names by passing a string to the ``extra_log_string_dict`` argument in the :func:`~.fusilli.data.get_data_module` and :func:`~.fusilli.train.train_and_save_models` functions. For example, I could add a suffix to denote that I've changed the batch size for this particular run:\n\n```python\nfrom fusilli.data import get_data_module\nfrom fusilli.train import train_and_save_models\n\nextra_suffix_dict = {\"batchsize\": 32}\n\ndatamodule = get_data_module(\n        fusion_model=example_model,\n        params=params,\n        batch_size=32,\n        extra_log_string_dict=extra_suffix_dict,\n    )\n\ntrained_model_list = train_and_save_models(\n        data_module=datamodule,\n        params=params,\n        fusion_model=example_model,\n        batch_size=32,\n        extra_log_string_dict=extra_suffix_dict,\n    )\n```\nThe checkpoint name would then be (if the model trained for 100 epochs):\n\n    ``ExampleModel_epoch=100_batchsize_32.ckpt``\n\n\n<div class=\"alert alert-info\"><h4>Note</h4><p>The ``extra_log_string_dict`` argument is also used to modify the logging behaviour of the model. For more information, see `wandb`.</p></div>\n"
+        "\n# How to customise the training in Fusilli\n\nThis tutorial will show you how to customise the training of your fusion model.\n\nWe will cover the following topics:\n\n* Early stopping\n* Batch size\n* Number of epochs\n* Checkpoint suffix modification\n\n## Early stopping\n\nEarly stopping is implemented in Fusilli using the PyTorch Lightning\n[EarlyStopping](https://lightning.ai/docs/pytorch/stable/api/lightning.pytorch.callbacks.EarlyStopping.html#lightning.pytorch.callbacks.EarlyStopping)\ncallback. This callback can be passed to the\n:func:`~fusilli.model_utils.train_and_save_models` function using the\n``early_stopping_callback`` argument. For example:\n\n```python\nfrom fusilli.data import prepare_fusion_data\nfrom fusilli.train import train_and_save_models\n\nfrom lightning.pytorch.callbacks import EarlyStopping\n\nmodified_early_stopping_callback = EarlyStopping(\n    monitor=\"val_loss\",\n    min_delta=0.00,\n    patience=3,\n    verbose=True,\n    mode=\"min\",\n)\n\ndatamodule = prepare_fusion_data(\n        prediction_task=\"binanry\",\n        fusion_model=example_model,\n        data_paths=data_paths,\n        output_paths=output_path,\n        own_early_stopping_callback=modified_early_stopping_callback,\n    )\n\ntrained_model_list = train_and_save_models(\n    data_module=datamodule,\n    fusion_model=example_model,\n    )\n```\nNote that you only need to pass the callback to the :func:`~.fusilli.data.prepare_fusion_data` and **not** to the :func:`~.fusilli.train.train_and_save_models` function. The new early stopping measure will be saved within the data module and accessed during training.\n\n\n-----\n\n## Batch size\n\nThe batch size can be set using the ``batch_size`` argument in the :func:`~.fusilli.data.prepare_fusion_data` function. By default, the batch size is 8.\n\n```python\nfrom fusilli.data import prepare_fusion_data\nfrom fusilli.train import train_and_save_models\n\ndatamodule = prepare_fusion_data(\n        prediction_task=\"binary\",\n        fusion_model=example_model,\n        data_paths=data_paths,\n        output_paths=output_path,\n        batch_size=32\n    )\n\ntrained_model_list = train_and_save_models(\n        data_module=datamodule,\n        fusion_model=example_model,\n        batch_size=32,\n    )\n```\n-----\n\n## Number of epochs\n\nYou can change the maximum number of epochs using the ``max_epochs`` argument in the :func:`~.fusilli.data.prepare_fusion_data` and :func:`~.fusilli.train.train_and_save_models` functions. By default, the maximum number of epochs is 1000.\n\nYou also pass it to the :func:`~.fusilli.data.prepare_fusion_data` function because some of the fusion models require pre-training.\n\nChanging the ``max_epochs`` parameter is especially useful when wanting to run a quick test of your model. For example, you can set ``max_epochs=5`` to run a quick test of your model.\n\n```python\nfrom fusilli.data import prepare_fusion_data\nfrom fusilli.train import train_and_save_models\n\ndatamodule = prepare_fusion_data(\n        prediction_task=\"binary\",\n        fusion_model=example_model,\n        data_paths=data_paths,\n        output_paths=output_path,\n        max_epochs=5,\n    )\n\ntrained_model_list = train_and_save_models(\n        data_module=datamodule,\n        fusion_model=example_model,\n        max_epochs=5,\n    )\n```\nSetting ``max_epochs`` to -1 will train the model until early stopping is triggered.\n\n-----\n\n## Checkpoint suffix modification\n\nBy default, Fusilli saves the model checkpoints in the following format:\n\n    ``{fusion_model.__name__}_epoch={epoch_n}.ckpt``\n\nIf the checkpoint is for a pre-trained model, then the following format is used:\n\n    ``subspace_{fusion_model.__name__}_{pretrained_model.__name__}.ckpt``\n\nYou can add suffixes to the checkpoint names by passing a string to the ``extra_log_string_dict`` argument in the :func:`~.fusilli.data.prepare_fusion_data` and :func:`~.fusilli.train.train_and_save_models` functions. For example, I could add a suffix to denote that I've changed the batch size for this particular run:\n\n```python\nfrom fusilli.data import prepare_fusion_data\nfrom fusilli.train import train_and_save_models\n\nextra_suffix_dict = {\"batchsize\": 32}\n\ndatamodule = prepare_fusion_data(\n        prediction_task=\"binary\",\n        fusion_model=example_model,\n        data_paths=data_paths,\n        output_paths=output_path,\n        batch_size=32,\n        extra_log_string_dict=extra_suffix_dict,\n    )\n\ntrained_model_list = train_and_save_models(\n        data_module=datamodule,\n        fusion_model=example_model,\n        batch_size=32,\n        extra_log_string_dict=extra_suffix_dict,\n    )\n```\nThe checkpoint name would then be (if the model trained for 100 epochs):\n\n    ``ExampleModel_epoch=100_batchsize_32.ckpt``\n\n\n<div class=\"alert alert-info\"><h4>Note</h4><p>The ``extra_log_string_dict`` argument is also used to modify the logging behaviour of the model. For more information, see `wandb`.</p></div>\n"
       ]
     },
     {
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.9.13"
+      "version": "3.9.16"
     }
   },
   "nbformat": 4,

docs/auto_examples/customising_behaviour/customising_training_parameters.py

@@ -22,7 +22,7 @@
 
 .. code-block:: python
 
-    from fusilli.data import get_data_module
+    from fusilli.data import prepare_fusion_data
     from fusilli.train import train_and_save_models
 
     from lightning.pytorch.callbacks import EarlyStopping
@@ -35,42 +35,44 @@
         mode="min",
     )
 
-    datamodule = get_data_module(
+    datamodule = prepare_fusion_data(
+            prediction_task="binanry",
             fusion_model=example_model,
-            params=params,
+            data_paths=data_paths,
+            output_paths=output_path,
             own_early_stopping_callback=modified_early_stopping_callback,
         )
 
     trained_model_list = train_and_save_models(
         data_module=datamodule,
-        params=params,
         fusion_model=example_model,
         )
 
-Note that you only need to pass the callback to the :func:`~.fusilli.data.get_data_module` and **not** to the :func:`~.fusilli.train.train_and_save_models` function. The new early stopping measure will be saved within the data module and accessed during training.
+Note that you only need to pass the callback to the :func:`~.fusilli.data.prepare_fusion_data` and **not** to the :func:`~.fusilli.train.train_and_save_models` function. The new early stopping measure will be saved within the data module and accessed during training.
 
 
 -----
 
 Batch size
 ----------
 
-The batch size can be set using the ``batch_size`` argument in the :func:`~.fusilli.data.get_data_module` function. By default, the batch size is 8.
+The batch size can be set using the ``batch_size`` argument in the :func:`~.fusilli.data.prepare_fusion_data` function. By default, the batch size is 8.
 
 .. code-block:: python
 
-    from fusilli.data import get_data_module
+    from fusilli.data import prepare_fusion_data
     from fusilli.train import train_and_save_models
 
-    datamodule = get_data_module(
+    datamodule = prepare_fusion_data(
+            prediction_task="binary",
             fusion_model=example_model,
-            params=params,
-            batch_size=32,
+            data_paths=data_paths,
+            output_paths=output_path,
+            batch_size=32
         )
 
     trained_model_list = train_and_save_models(
             data_module=datamodule,
-            params=params,
             fusion_model=example_model,
             batch_size=32,
         )
@@ -81,26 +83,27 @@
 Number of epochs
 -------------------
 
-You can change the maximum number of epochs using the ``max_epochs`` argument in the :func:`~.fusilli.data.get_data_module` and :func:`~.fusilli.train.train_and_save_models` functions. By default, the maximum number of epochs is 1000.
+You can change the maximum number of epochs using the ``max_epochs`` argument in the :func:`~.fusilli.data.prepare_fusion_data` and :func:`~.fusilli.train.train_and_save_models` functions. By default, the maximum number of epochs is 1000.
 
-You also pass it to the :func:`~.fusilli.data.get_data_module` function because some of the fusion models require pre-training.
+You also pass it to the :func:`~.fusilli.data.prepare_fusion_data` function because some of the fusion models require pre-training.
 
 Changing the ``max_epochs`` parameter is especially useful when wanting to run a quick test of your model. For example, you can set ``max_epochs=5`` to run a quick test of your model.
 
 .. code-block:: python
 
-    from fusilli.data import get_data_module
+    from fusilli.data import prepare_fusion_data
     from fusilli.train import train_and_save_models
 
-    datamodule = get_data_module(
+    datamodule = prepare_fusion_data(
+            prediction_task="binary",
             fusion_model=example_model,
-            params=params,
+            data_paths=data_paths,
+            output_paths=output_path,
             max_epochs=5,
         )
 
     trained_model_list = train_and_save_models(
             data_module=datamodule,
-            params=params,
             fusion_model=example_model,
             max_epochs=5,
         )
@@ -120,25 +123,26 @@
 
     ``subspace_{fusion_model.__name__}_{pretrained_model.__name__}.ckpt``
 
-You can add suffixes to the checkpoint names by passing a string to the ``extra_log_string_dict`` argument in the :func:`~.fusilli.data.get_data_module` and :func:`~.fusilli.train.train_and_save_models` functions. For example, I could add a suffix to denote that I've changed the batch size for this particular run:
+You can add suffixes to the checkpoint names by passing a string to the ``extra_log_string_dict`` argument in the :func:`~.fusilli.data.prepare_fusion_data` and :func:`~.fusilli.train.train_and_save_models` functions. For example, I could add a suffix to denote that I've changed the batch size for this particular run:
 
 .. code-block:: python
 
-    from fusilli.data import get_data_module
+    from fusilli.data import prepare_fusion_data
     from fusilli.train import train_and_save_models
 
     extra_suffix_dict = {"batchsize": 32}
 
-    datamodule = get_data_module(
+    datamodule = prepare_fusion_data(
+            prediction_task="binary",
             fusion_model=example_model,
-            params=params,
+            data_paths=data_paths,
+            output_paths=output_path,
             batch_size=32,
             extra_log_string_dict=extra_suffix_dict,
         )
 
     trained_model_list = train_and_save_models(
             data_module=datamodule,
-            params=params,
             fusion_model=example_model,
             batch_size=32,
             extra_log_string_dict=extra_suffix_dict,