diff --git a/docs/_static/florencestheme.css b/docs/_static/florencestheme.css index 7b7d13a4..37c52f8e 100644 --- a/docs/_static/florencestheme.css +++ b/docs/_static/florencestheme.css @@ -266,6 +266,13 @@ a:active { display: inline-block; } +.wy-menu-vertical li.toctree-l4.current li.toctree-l5 > a { + /* background: #cdf8be; */ + /*background: #ffe7fb;*/ + background: var(--lightpink); + display: inline-block; +} + .wy-menu-vertical { overflow-x: scroll; /*background-color: #ffe7fb; !* Background color for the side scroll *!*/ diff --git a/docs/_static/fusilli_pipeline_diagram.png b/docs/_static/fusilli_pipeline_diagram.png new file mode 100644 index 00000000..60ae2bb5 Binary files /dev/null and b/docs/_static/fusilli_pipeline_diagram.png differ diff --git a/docs/_static/modify_thumbnail.png b/docs/_static/modify_thumbnail.png new file mode 100644 index 00000000..cf7a0d12 Binary files /dev/null and b/docs/_static/modify_thumbnail.png differ diff --git a/docs/auto_examples/auto_examples_jupyter.zip b/docs/auto_examples/auto_examples_jupyter.zip index 9234ac5b..d4e092b8 100644 Binary files a/docs/auto_examples/auto_examples_jupyter.zip and b/docs/auto_examples/auto_examples_jupyter.zip differ diff --git a/docs/auto_examples/auto_examples_python.zip b/docs/auto_examples/auto_examples_python.zip index 704a0c2e..e5eb074e 100644 Binary files a/docs/auto_examples/auto_examples_python.zip and b/docs/auto_examples/auto_examples_python.zip differ diff --git a/docs/auto_examples/customising_behaviour/customising_training_parameters.ipynb b/docs/auto_examples/customising_behaviour/customising_training_parameters.ipynb index 99770534..c4d941ff 100644 --- a/docs/auto_examples/customising_behaviour/customising_training_parameters.ipynb +++ b/docs/auto_examples/customising_behaviour/customising_training_parameters.ipynb @@ -6,6 +6,17 @@ "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

Note

The ``extra_log_string_dict`` argument is also used to modify the logging behaviour of the model. For more information, see `wandb`.

\n" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# sphinx_gallery_thumbnail_path = '_static/pink_pasta_logo.png'" + ] } ], "metadata": { diff --git a/docs/auto_examples/customising_behaviour/customising_training_parameters.py b/docs/auto_examples/customising_behaviour/customising_training_parameters.py index 517e2737..d11bd9f2 100644 --- a/docs/auto_examples/customising_behaviour/customising_training_parameters.py +++ b/docs/auto_examples/customising_behaviour/customising_training_parameters.py @@ -153,3 +153,4 @@ The ``extra_log_string_dict`` argument is also used to modify the logging behaviour of the model. For more information, see :ref:`wandb`. """ +# sphinx_gallery_thumbnail_path = '_static/pink_pasta_logo.png' diff --git a/docs/auto_examples/customising_behaviour/customising_training_parameters.rst b/docs/auto_examples/customising_behaviour/customising_training_parameters.rst index 44d5b65d..eeb02b42 100644 --- a/docs/auto_examples/customising_behaviour/customising_training_parameters.rst +++ b/docs/auto_examples/customising_behaviour/customising_training_parameters.rst @@ -172,6 +172,12 @@ The checkpoint name would then be (if the model trained for 100 epochs): The ``extra_log_string_dict`` argument is also used to modify the logging behaviour of the model. For more information, see :ref:`wandb`. +.. GENERATED FROM PYTHON SOURCE LINES 156-157 + +.. code-block:: Python + + # sphinx_gallery_thumbnail_path = '_static/pink_pasta_logo.png' + .. _sphx_glr_download_auto_examples_customising_behaviour_customising_training_parameters.py: diff --git a/docs/auto_examples/customising_behaviour/images/thumb/sphx_glr_customising_training_parameters_thumb.png b/docs/auto_examples/customising_behaviour/images/thumb/sphx_glr_customising_training_parameters_thumb.png index 8a5fed58..30b5d4f3 100644 Binary files a/docs/auto_examples/customising_behaviour/images/thumb/sphx_glr_customising_training_parameters_thumb.png and b/docs/auto_examples/customising_behaviour/images/thumb/sphx_glr_customising_training_parameters_thumb.png differ diff --git a/docs/auto_examples/customising_behaviour/images/thumb/sphx_glr_plot_modify_layer_sizes_thumb.png b/docs/auto_examples/customising_behaviour/images/thumb/sphx_glr_plot_modify_layer_sizes_thumb.png index ffd78e0e..f860ea94 100644 Binary files a/docs/auto_examples/customising_behaviour/images/thumb/sphx_glr_plot_modify_layer_sizes_thumb.png and b/docs/auto_examples/customising_behaviour/images/thumb/sphx_glr_plot_modify_layer_sizes_thumb.png differ diff --git a/docs/auto_examples/customising_behaviour/plot_modify_layer_sizes.ipynb b/docs/auto_examples/customising_behaviour/plot_modify_layer_sizes.ipynb index f6655777..6099daa9 100644 --- a/docs/auto_examples/customising_behaviour/plot_modify_layer_sizes.ipynb +++ b/docs/auto_examples/customising_behaviour/plot_modify_layer_sizes.ipynb @@ -11,7 +11,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Setting up the experiment\n\nFirst, we will set up the experiment by importing the necessary packages, creating the simulated data, and setting the parameters for the experiment.\n\nFor a more detailed explanation of this process, please see the `train_test_examples` tutorials.\n\n\n" + "## Setting up the experiment\n\nFirst, we will set up the experiment by importing the necessary packages, creating the simulated data, and setting the parameters for the experiment.\n\nFor a more detailed explanation of this process, please see the example tutorials.\n\n\n" ] }, { @@ -22,14 +22,14 @@ }, "outputs": [], "source": [ - "import matplotlib.pyplot as plt\nimport os\nimport torch.nn as nn\nfrom torch_geometric.nn import GCNConv, ChebConv\n\nfrom docs.examples import generate_sklearn_simulated_data\nfrom fusilli.data import get_data_module\nfrom fusilli.eval import RealsVsPreds\nfrom fusilli.train import train_and_save_models\n\nfrom fusilli.fusionmodels.tabularfusion.attention_weighted_GNN import AttentionWeightedGNN\n\nparams = {\n \"test_size\": 0.2,\n \"kfold_flag\": False,\n \"log\": False,\n \"pred_type\": \"regression\",\n \"loss_log_dir\": \"loss_logs/modify_layers\", # where the csv of the loss is saved for plotting later\n \"checkpoint_dir\": \"checkpoints\",\n \"loss_fig_path\": \"loss_figures\",\n}\n\n# empty the loss log directory (only needed for this tutorial)\nfor dir in os.listdir(params[\"loss_log_dir\"]):\n for file in os.listdir(os.path.join(params[\"loss_log_dir\"], dir)):\n os.remove(os.path.join(params[\"loss_log_dir\"], dir, file))\n # remove dir\n os.rmdir(os.path.join(params[\"loss_log_dir\"], dir))\n\nparams = generate_sklearn_simulated_data(\n num_samples=100,\n num_tab1_features=10,\n num_tab2_features=15,\n img_dims=(1, 100, 100),\n params=params,\n)" + "# sphinx_gallery_thumbnail_path = '_static/modify_thumbnail.png'\nimport matplotlib.pyplot as plt\nimport os\nimport torch.nn as nn\nfrom torch_geometric.nn import GCNConv, ChebConv\n\nfrom docs.examples import generate_sklearn_simulated_data\nfrom fusilli.data import get_data_module\nfrom fusilli.eval import RealsVsPreds\nfrom fusilli.train import train_and_save_models\n\nfrom fusilli.fusionmodels.tabularfusion.attention_weighted_GNN import AttentionWeightedGNN\n\nparams = {\n \"test_size\": 0.2,\n \"kfold_flag\": False,\n \"log\": False,\n \"pred_type\": \"regression\",\n \"loss_log_dir\": \"loss_logs/modify_layers\", # where the csv of the loss is saved for plotting later\n \"checkpoint_dir\": \"checkpoints\",\n \"loss_fig_path\": \"loss_figures\",\n}\n\n# empty the loss log directory (only needed for this tutorial)\nfor dir in os.listdir(params[\"loss_log_dir\"]):\n for file in os.listdir(os.path.join(params[\"loss_log_dir\"], dir)):\n os.remove(os.path.join(params[\"loss_log_dir\"], dir, file))\n # remove dir\n os.rmdir(os.path.join(params[\"loss_log_dir\"], dir))\n\nparams = generate_sklearn_simulated_data(\n num_samples=100,\n num_tab1_features=10,\n num_tab2_features=15,\n img_dims=(1, 100, 100),\n params=params,\n)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Specifying the model modifications\n\nNow, we will specify the modifications we want to make to the model.\n\nWe are using the :class:`~fusilli.fusionmodels.tabularfusion.attention_weighted_GNN.AttentionWeightedGNN` model for this example.\nThis is a graph-based model which has a pretrained MLP (multi-layer perceptron) to get attention weights, and a graph neural network that uses the attention weights to perform the fusion.\n\nThe following modifications can be made to the method that makes the graph structure: :class:`~fusilli.fusionmodels.tabularfusion.attention_weighted_GNN.AttentionWeightedGraphMaker`:\n\n\n.. list-table::\n :widths: 40 60\n :header-rows: 1\n :stub-columns: 0\n\n * - Attribute\n - Guidance\n * - :attr:`~.AttentionWeightedGraphMaker.early_stop_callback`\n - ``EarlyStopping`` object from ``from lightning.pytorch.callbacks import EarlyStopping``\n * - :attr:`~.AttentionWeightedGraphMaker.edge_probability_threshold`\n - Integer between 0 and 100.\n * - :attr:`~.AttentionWeightedGraphMaker.attention_MLP_test_size`\n - Float between 0 and 1.\n * - :attr:`~.AttentionWeightedGraphMaker.AttentionWeightingMLPInstance.weighting_layers`\n - ``nn.ModuleDict``: final layer output size must be the same as the input layer input size.\n * - :attr:`~.AttentionWeightedGraphMaker.AttentionWeightingMLPInstance.fused_layers`\n - ``nn.Sequential``\n\n\nThe following modifications can be made to the **fusion** model :class:`~fusilli.fusionmodels.tabularfusion.attention_weighted_GNN.AttentionWeightedGNN`:\n\n.. list-table::\n :widths: 40 60\n :header-rows: 1\n :stub-columns: 0\n\n * - Attribute\n - Guidance\n * - :attr:`~.AttentionWeightedGNN.graph_conv_layers`\n - ``nn.Sequential`` of ``torch_geometric.nn` Layers.\n * - :attr:`~.AttentionWeightedGNN.dropout_prob`\n - Float between (not including) 0 and 1.\n\nLet's modify the model! More info about how to do this can be found in `modifying-models`.\n\n" + "## Specifying the model modifications\n\nNow, we will specify the modifications we want to make to the model.\n\nWe are using the :class:`~fusilli.fusionmodels.tabularfusion.attention_weighted_GNN.AttentionWeightedGNN` model for this example.\nThis is a graph-based model which has a pretrained MLP (multi-layer perceptron) to get attention weights, and a graph neural network that uses the attention weights to perform the fusion.\n\nThe following modifications can be made to the method that makes the graph structure: :class:`~fusilli.fusionmodels.tabularfusion.attention_weighted_GNN.AttentionWeightedGraphMaker`:\n\n\n.. list-table::\n :widths: 40 60\n :header-rows: 1\n :stub-columns: 0\n\n * - Attribute\n - Guidance\n * - :attr:`~.AttentionWeightedGraphMaker.early_stop_callback`\n - ``EarlyStopping`` object from ``from lightning.pytorch.callbacks import EarlyStopping``\n * - :attr:`~.AttentionWeightedGraphMaker.edge_probability_threshold`\n - Integer between 0 and 100.\n * - :attr:`~.AttentionWeightedGraphMaker.attention_MLP_test_size`\n - Float between 0 and 1.\n * - :attr:`~.AttentionWeightedGraphMaker.AttentionWeightingMLPInstance.weighting_layers`\n - ``nn.ModuleDict``: final layer output size must be the same as the input layer input size.\n * - :attr:`~.AttentionWeightedGraphMaker.AttentionWeightingMLPInstance.fused_layers`\n - ``nn.Sequential``\n\n\nThe following modifications can be made to the **fusion** model :class:`~fusilli.fusionmodels.tabularfusion.attention_weighted_GNN.AttentionWeightedGNN`:\n\n.. list-table::\n :widths: 40 60\n :header-rows: 1\n :stub-columns: 0\n\n * - Attribute\n - Guidance\n * - :attr:`~.AttentionWeightedGNN.graph_conv_layers`\n - ``nn.Sequential`` of ``torch_geometric.nn`` Layers.\n * - :attr:`~.AttentionWeightedGNN.dropout_prob`\n - Float between (not including) 0 and 1.\n\nLet's modify the model! More info about how to do this can be found in `modifying-models`.\n\n" ] }, { @@ -83,7 +83,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## What happens when the modifications are incorrect?\n\nLet's see what happens when we try to modify an **attribute that doesn't exist**.\n\n\n" + "You can see that the input features to the ``final_prediction`` layer changed to fit with our modification to the ``graph_conv_layers`` output features!\n\n## What happens when the modifications are incorrect?\n\nLet's see what happens when we try to modify an **attribute that doesn't exist**.\n\n\n" ] }, { @@ -130,7 +130,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## What about modifying multiple attributes with the **conflicting modifications**?\n\n" + "## What about modifying multiple attributes with the **conflicting modifications**?\n\n\nFor this, let's switch to looking at the :class:`~fusilli.fusionmodels.tabularfusion.concat_feature_maps.ConcatTabularFeatureMaps` model.\nThis model concatenates the feature maps of the two modalities and then passes them through a prediction layer.\n\nWe can modify the layers that each tabular modality goes through before being concatenated, as well as the layers that come after the concatenation.\n\nThe output features of our modified ``mod1_layers`` and ``mod2_layers`` are 100 and 128, so the input features of the ``fused_layers`` should be 228. However, we've set the input features of the ``fused_layers`` to be 25.\n\nLet's see what happens when we try to modify the model in this way. It should throw an error when the data is passed through the model.\n\n" ] }, { @@ -141,7 +141,7 @@ }, "outputs": [], "source": [ - "#\n# For this, let's switch to looking at the :class:`~fusilli.fusionmodels.tabularfusion.concat_feature_maps.ConcatTabularFeatureMaps` model.\n# This model concatenates the feature maps of the two modalities and then passes them through a prediction layer.\n#\n# We can modify the layers that each tabular modality goes through before being concatenated, as well as the layers that come after the concatenation.\n#\n# The output features of our modified ``mod1_layers`` and ``mod2_layers`` are 100 and 128, so the input features of the ``fused_layers`` should be 228. However, we've set the input features of the ``fused_layers`` to be 25.\n#\n# Let's see what happens when we try to modify the model in this way. It should throw an error when the data is passed through the model.\n\nlayer_mods = {\n \"ConcatTabularFeatureMaps\": {\n \"mod1_layers\": nn.ModuleDict(\n {\n \"layer 1\": nn.Sequential(\n nn.Linear(10, 32),\n nn.ReLU(),\n ),\n \"layer 2\": nn.Sequential(\n nn.Linear(32, 66),\n nn.ReLU(),\n ),\n \"layer 3\": nn.Sequential(\n nn.Linear(66, 128),\n nn.ReLU(),\n ),\n }\n ),\n \"mod2_layers\": nn.ModuleDict(\n {\n \"layer 1\": nn.Sequential(\n nn.Linear(15, 45),\n nn.ReLU(),\n ),\n \"layer 2\": nn.Sequential(\n nn.Linear(45, 70),\n nn.ReLU(),\n ),\n \"layer 3\": nn.Sequential(\n nn.Linear(70, 100),\n nn.ReLU(),\n ),\n }\n ),\n \"fused_layers\": nn.Sequential(\n nn.Linear(25, 150),\n nn.ReLU(),\n nn.Linear(150, 75),\n nn.ReLU(),\n nn.Linear(75, 50),\n nn.ReLU(),\n ),\n },\n}\n\n# get the data and train the model\n\nfrom fusilli.fusionmodels.tabularfusion.concat_feature_maps import ConcatTabularFeatureMaps\n\ndatamodule = get_data_module(ConcatTabularFeatureMaps, params, layer_mods=layer_mods)\ntrained_model_list = train_and_save_models(\n data_module=datamodule,\n params=params,\n fusion_model=ConcatTabularFeatureMaps,\n layer_mods=layer_mods,\n max_epochs=5,\n)" + "layer_mods = {\n \"ConcatTabularFeatureMaps\": {\n \"mod1_layers\": nn.ModuleDict(\n {\n \"layer 1\": nn.Sequential(\n nn.Linear(10, 32),\n nn.ReLU(),\n ),\n \"layer 2\": nn.Sequential(\n nn.Linear(32, 66),\n nn.ReLU(),\n ),\n \"layer 3\": nn.Sequential(\n nn.Linear(66, 128),\n nn.ReLU(),\n ),\n }\n ),\n \"mod2_layers\": nn.ModuleDict(\n {\n \"layer 1\": nn.Sequential(\n nn.Linear(15, 45),\n nn.ReLU(),\n ),\n \"layer 2\": nn.Sequential(\n nn.Linear(45, 70),\n nn.ReLU(),\n ),\n \"layer 3\": nn.Sequential(\n nn.Linear(70, 100),\n nn.ReLU(),\n ),\n }\n ),\n \"fused_layers\": nn.Sequential(\n nn.Linear(25, 150),\n nn.ReLU(),\n nn.Linear(150, 75),\n nn.ReLU(),\n nn.Linear(75, 50),\n nn.ReLU(),\n ),\n },\n}\n\n# get the data and train the model\n\nfrom fusilli.fusionmodels.tabularfusion.concat_feature_maps import ConcatTabularFeatureMaps\n\ndatamodule = get_data_module(ConcatTabularFeatureMaps, params, layer_mods=layer_mods)\ntrained_model_list = train_and_save_models(\n data_module=datamodule,\n params=params,\n fusion_model=ConcatTabularFeatureMaps,\n layer_mods=layer_mods,\n max_epochs=5,\n)" ] }, { diff --git a/docs/auto_examples/customising_behaviour/plot_modify_layer_sizes.py b/docs/auto_examples/customising_behaviour/plot_modify_layer_sizes.py index 06465246..1b8daf8a 100644 --- a/docs/auto_examples/customising_behaviour/plot_modify_layer_sizes.py +++ b/docs/auto_examples/customising_behaviour/plot_modify_layer_sizes.py @@ -20,9 +20,10 @@ # # First, we will set up the experiment by importing the necessary packages, creating the simulated data, and setting the parameters for the experiment. # -# For a more detailed explanation of this process, please see the :ref:`train_test_examples` tutorials. +# For a more detailed explanation of this process, please see the example tutorials. # +# sphinx_gallery_thumbnail_path = '_static/modify_thumbnail.png' import matplotlib.pyplot as plt import os import torch.nn as nn @@ -101,7 +102,7 @@ # * - Attribute # - Guidance # * - :attr:`~.AttentionWeightedGNN.graph_conv_layers` -# - ``nn.Sequential`` of ``torch_geometric.nn` Layers. +# - ``nn.Sequential`` of ``torch_geometric.nn`` Layers. # * - :attr:`~.AttentionWeightedGNN.dropout_prob` # - Float between (not including) 0 and 1. # @@ -164,6 +165,8 @@ print("Fusion model:\n", trained_model_list[0].model) # %% +# You can see that the input features to the ``final_prediction`` layer changed to fit with our modification to the ``graph_conv_layers`` output features! +# # What happens when the modifications are incorrect? # ---------------------------------------------------- # @@ -226,7 +229,7 @@ # %% # What about modifying multiple attributes with the **conflicting modifications**? # ------------------------------------------------------------------------------------- - +# # # For this, let's switch to looking at the :class:`~fusilli.fusionmodels.tabularfusion.concat_feature_maps.ConcatTabularFeatureMaps` model. # This model concatenates the feature maps of the two modalities and then passes them through a prediction layer. diff --git a/docs/auto_examples/customising_behaviour/plot_modify_layer_sizes.py.md5 b/docs/auto_examples/customising_behaviour/plot_modify_layer_sizes.py.md5 index 26868f3c..0fc34405 100644 --- a/docs/auto_examples/customising_behaviour/plot_modify_layer_sizes.py.md5 +++ b/docs/auto_examples/customising_behaviour/plot_modify_layer_sizes.py.md5 @@ -1 +1 @@ -4f81d32d36529eb1462f93d7149491a7 \ No newline at end of file +c0bde3e7f591a48db53b82f48441dc50 \ No newline at end of file diff --git a/docs/auto_examples/customising_behaviour/plot_modify_layer_sizes.rst b/docs/auto_examples/customising_behaviour/plot_modify_layer_sizes.rst index 0ea5f1cd..9031d150 100644 --- a/docs/auto_examples/customising_behaviour/plot_modify_layer_sizes.rst +++ b/docs/auto_examples/customising_behaviour/plot_modify_layer_sizes.rst @@ -38,14 +38,15 @@ Setting up the experiment First, we will set up the experiment by importing the necessary packages, creating the simulated data, and setting the parameters for the experiment. -For a more detailed explanation of this process, please see the :ref:`train_test_examples` tutorials. +For a more detailed explanation of this process, please see the example tutorials. -.. GENERATED FROM PYTHON SOURCE LINES 25-63 +.. GENERATED FROM PYTHON SOURCE LINES 25-64 .. code-block:: Python + # sphinx_gallery_thumbnail_path = '_static/modify_thumbnail.png' import matplotlib.pyplot as plt import os import torch.nn as nn @@ -90,7 +91,7 @@ For a more detailed explanation of this process, please see the :ref:`train_test -.. GENERATED FROM PYTHON SOURCE LINES 64-109 +.. GENERATED FROM PYTHON SOURCE LINES 65-110 Specifying the model modifications ---------------------------------- @@ -132,13 +133,13 @@ The following modifications can be made to the **fusion** model :class:`~fusilli * - Attribute - Guidance * - :attr:`~.AttentionWeightedGNN.graph_conv_layers` - - ``nn.Sequential`` of ``torch_geometric.nn` Layers. + - ``nn.Sequential`` of ``torch_geometric.nn`` Layers. * - :attr:`~.AttentionWeightedGNN.dropout_prob` - Float between (not including) 0 and 1. Let's modify the model! More info about how to do this can be found in :ref:`modifying-models`. -.. GENERATED FROM PYTHON SOURCE LINES 109-143 +.. GENERATED FROM PYTHON SOURCE LINES 110-144 .. code-block:: Python @@ -183,12 +184,12 @@ Let's modify the model! More info about how to do this can be found in :ref:`mod -.. GENERATED FROM PYTHON SOURCE LINES 144-146 +.. GENERATED FROM PYTHON SOURCE LINES 145-147 Loading the data and training the model --------------------------------------- -.. GENERATED FROM PYTHON SOURCE LINES 146-160 +.. GENERATED FROM PYTHON SOURCE LINES 147-161 .. code-block:: Python @@ -220,709 +221,489 @@ Loading the data and training the model Checked params in AttentionWeightedGraphMaker Reset fused layers in AttentionWeightedGraphMaker Checked params in AttentionWeightedGraphMaker - Training: | | 0/? [00:00 diff --git a/docs/auto_examples/training_and_testing/images/sphx_glr_plot_one_model_binary_kfold_001.png b/docs/auto_examples/training_and_testing/images/sphx_glr_plot_one_model_binary_kfold_001.png index 25c0a0ef..d343a9a3 100644 Binary files a/docs/auto_examples/training_and_testing/images/sphx_glr_plot_one_model_binary_kfold_001.png and b/docs/auto_examples/training_and_testing/images/sphx_glr_plot_one_model_binary_kfold_001.png differ diff --git a/docs/auto_examples/training_and_testing/images/sphx_glr_plot_one_model_binary_kfold_002.png b/docs/auto_examples/training_and_testing/images/sphx_glr_plot_one_model_binary_kfold_002.png index a8be5c51..32cec0b9 100644 Binary files a/docs/auto_examples/training_and_testing/images/sphx_glr_plot_one_model_binary_kfold_002.png and b/docs/auto_examples/training_and_testing/images/sphx_glr_plot_one_model_binary_kfold_002.png differ diff --git a/docs/auto_examples/training_and_testing/images/sphx_glr_plot_one_model_binary_kfold_003.png b/docs/auto_examples/training_and_testing/images/sphx_glr_plot_one_model_binary_kfold_003.png index 80db169b..60e63aab 100644 Binary files a/docs/auto_examples/training_and_testing/images/sphx_glr_plot_one_model_binary_kfold_003.png and b/docs/auto_examples/training_and_testing/images/sphx_glr_plot_one_model_binary_kfold_003.png differ diff --git a/docs/auto_examples/training_and_testing/images/sphx_glr_plot_one_model_binary_kfold_004.png b/docs/auto_examples/training_and_testing/images/sphx_glr_plot_one_model_binary_kfold_004.png index 018fcf87..2e15c71a 100644 Binary files a/docs/auto_examples/training_and_testing/images/sphx_glr_plot_one_model_binary_kfold_004.png and b/docs/auto_examples/training_and_testing/images/sphx_glr_plot_one_model_binary_kfold_004.png differ diff --git a/docs/auto_examples/training_and_testing/images/sphx_glr_plot_one_model_binary_kfold_005.png b/docs/auto_examples/training_and_testing/images/sphx_glr_plot_one_model_binary_kfold_005.png index 734a5b5e..c4e08fe3 100644 Binary files a/docs/auto_examples/training_and_testing/images/sphx_glr_plot_one_model_binary_kfold_005.png and b/docs/auto_examples/training_and_testing/images/sphx_glr_plot_one_model_binary_kfold_005.png differ diff --git a/docs/auto_examples/training_and_testing/images/sphx_glr_plot_one_model_binary_kfold_006.png b/docs/auto_examples/training_and_testing/images/sphx_glr_plot_one_model_binary_kfold_006.png index a9654639..03f1689f 100644 Binary files a/docs/auto_examples/training_and_testing/images/sphx_glr_plot_one_model_binary_kfold_006.png and b/docs/auto_examples/training_and_testing/images/sphx_glr_plot_one_model_binary_kfold_006.png differ diff --git a/docs/auto_examples/training_and_testing/images/thumb/sphx_glr_plot_one_model_binary_kfold_thumb.png b/docs/auto_examples/training_and_testing/images/thumb/sphx_glr_plot_one_model_binary_kfold_thumb.png index 709ffeea..54b6e51c 100644 Binary files a/docs/auto_examples/training_and_testing/images/thumb/sphx_glr_plot_one_model_binary_kfold_thumb.png and b/docs/auto_examples/training_and_testing/images/thumb/sphx_glr_plot_one_model_binary_kfold_thumb.png differ diff --git a/docs/auto_examples/training_and_testing/index.rst b/docs/auto_examples/training_and_testing/index.rst index 68f67d4d..7d193a2d 100644 --- a/docs/auto_examples/training_and_testing/index.rst +++ b/docs/auto_examples/training_and_testing/index.rst @@ -10,6 +10,9 @@ Running Fusilli on your own data These are examples of how to train and validate fusion models with Fusilli. + + + .. raw:: html
diff --git a/docs/auto_examples/training_and_testing/plot_model_comparison_loop_kfold.ipynb b/docs/auto_examples/training_and_testing/plot_model_comparison_loop_kfold.ipynb index ad630f76..686320f9 100644 --- a/docs/auto_examples/training_and_testing/plot_model_comparison_loop_kfold.ipynb +++ b/docs/auto_examples/training_and_testing/plot_model_comparison_loop_kfold.ipynb @@ -87,7 +87,7 @@ }, "outputs": [], "source": [ - "all_trained_models = {}\n\nfor i, fusion_model in enumerate(fusion_models):\n fusion_model_name = fusion_model.__name__\n print(f\"Running model {fusion_model_name}\")\n\n # Get data module\n data_module = get_data_module(fusion_model, params, batch_size=params[\"batch_size\"])\n\n # Train and test\n single_model_list = train_and_save_models(\n data_module=data_module,\n params=params,\n fusion_model=fusion_model,\n enable_checkpointing=False, # False for the example notebooks\n show_loss_plot=True, # True for the example notebooks\n )\n\n # Save to all_trained_models\n all_trained_models[fusion_model_name] = single_model_list" + "# Using %%capture to hide the progress bar and plots (there are a lot of them!)\n\nall_trained_models = {}\n\nfor i, fusion_model in enumerate(fusion_models):\n fusion_model_name = fusion_model.__name__\n print(f\"Running model {fusion_model_name}\")\n\n # Get data module\n data_module = get_data_module(fusion_model, params, batch_size=params[\"batch_size\"])\n\n # Train and test\n single_model_list = train_and_save_models(\n data_module=data_module,\n params=params,\n fusion_model=fusion_model,\n enable_checkpointing=False, # False for the example notebooks\n show_loss_plot=True, # True for the example notebooks\n )\n\n # Save to all_trained_models\n all_trained_models[fusion_model_name] = single_model_list\n\n plt.close(\"all\")" ] }, { diff --git a/docs/auto_examples/training_and_testing/plot_model_comparison_loop_kfold.py b/docs/auto_examples/training_and_testing/plot_model_comparison_loop_kfold.py index a6dc1f95..8515a5d0 100644 --- a/docs/auto_examples/training_and_testing/plot_model_comparison_loop_kfold.py +++ b/docs/auto_examples/training_and_testing/plot_model_comparison_loop_kfold.py @@ -127,6 +127,8 @@ # In this section, we train all the fusion models using the generated data and specified parameters. # We store the results of each model for later analysis. +# Using %%capture to hide the progress bar and plots (there are a lot of them!) + all_trained_models = {} for i, fusion_model in enumerate(fusion_models): @@ -148,6 +150,8 @@ # Save to all_trained_models all_trained_models[fusion_model_name] = single_model_list + plt.close("all") + # %% # 5. Plotting the results of the individual models # ------------------------------------------------- diff --git a/docs/auto_examples/training_and_testing/plot_model_comparison_loop_kfold.rst b/docs/auto_examples/training_and_testing/plot_model_comparison_loop_kfold.rst index 176a04aa..fc451f42 100644 --- a/docs/auto_examples/training_and_testing/plot_model_comparison_loop_kfold.rst +++ b/docs/auto_examples/training_and_testing/plot_model_comparison_loop_kfold.rst @@ -208,11 +208,13 @@ This function also simulated image data which we aren't using here. In this section, we train all the fusion models using the generated data and specified parameters. We store the results of each model for later analysis. -.. GENERATED FROM PYTHON SOURCE LINES 129-151 +.. GENERATED FROM PYTHON SOURCE LINES 129-155 .. code-block:: Python + # Using %%capture to hide the progress bar and plots (there are a lot of them!) + all_trained_models = {} for i, fusion_model in enumerate(fusion_models): @@ -234,8 +236,10 @@ We store the results of each model for later analysis. # Save to all_trained_models all_trained_models[fusion_model_name] = single_model_list + plt.close("all") + -.. GENERATED FROM PYTHON SOURCE LINES 152-159 +.. GENERATED FROM PYTHON SOURCE LINES 156-163 5. Plotting the results of the individual models ------------------------------------------------- @@ -245,7 +249,7 @@ If you want to save the figures rather than show them, you can use the :meth:`~. This will save the figures in a timestamped folder in the current working directory with the method name and plot type in the filename. You can add an extra suffix to the filename by passing a string to the ``extra_string`` argument of the :meth:`~fusilli.eval.Plotter.save_to_local` method. -.. GENERATED FROM PYTHON SOURCE LINES 159-164 +.. GENERATED FROM PYTHON SOURCE LINES 163-168 .. code-block:: Python @@ -255,13 +259,13 @@ You can add an extra suffix to the filename by passing a string to the ``extra_s plt.show() -.. GENERATED FROM PYTHON SOURCE LINES 165-168 +.. GENERATED FROM PYTHON SOURCE LINES 169-172 6. Plotting comparison of the models ------------------------------------- In this section, we visualize the results of each individual model. -.. GENERATED FROM PYTHON SOURCE LINES 168-172 +.. GENERATED FROM PYTHON SOURCE LINES 172-176 .. code-block:: Python @@ -270,13 +274,13 @@ In this section, we visualize the results of each individual model. plt.show() -.. GENERATED FROM PYTHON SOURCE LINES 173-176 +.. GENERATED FROM PYTHON SOURCE LINES 177-180 7. Saving the results of the models ------------------------------------- In this section, we compare the performance of all the trained models using a violin chart, providing an overview of how each model performed as a distribution over the different cross-validation folds. -.. GENERATED FROM PYTHON SOURCE LINES 176-179 +.. GENERATED FROM PYTHON SOURCE LINES 180-183 .. code-block:: Python @@ -287,7 +291,7 @@ In this section, we compare the performance of all the trained models using a vi .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 0.006 seconds) + **Total running time of the script:** (0 minutes 0.080 seconds) .. _sphx_glr_download_auto_examples_training_and_testing_plot_model_comparison_loop_kfold.py: diff --git a/docs/auto_examples/training_and_testing/plot_model_comparison_loop_kfold_codeobj.pickle b/docs/auto_examples/training_and_testing/plot_model_comparison_loop_kfold_codeobj.pickle index 21cc2fdb..82a9b9c7 100644 Binary files a/docs/auto_examples/training_and_testing/plot_model_comparison_loop_kfold_codeobj.pickle and b/docs/auto_examples/training_and_testing/plot_model_comparison_loop_kfold_codeobj.pickle differ diff --git a/docs/auto_examples/training_and_testing/plot_one_model_binary_kfold.ipynb b/docs/auto_examples/training_and_testing/plot_one_model_binary_kfold.ipynb index a7b8a8fc..e473d50c 100644 --- a/docs/auto_examples/training_and_testing/plot_one_model_binary_kfold.ipynb +++ b/docs/auto_examples/training_and_testing/plot_one_model_binary_kfold.ipynb @@ -15,7 +15,7 @@ }, "outputs": [], "source": [ - "import matplotlib.pyplot as plt\nfrom tqdm.auto import tqdm\nimport os\n\nfrom docs.examples import generate_sklearn_simulated_data\nfrom fusilli.data import get_data_module\nfrom fusilli.eval import ConfusionMatrix\nfrom fusilli.train import train_and_save_models" + "import matplotlib.pyplot as plt\nfrom tqdm.auto import tqdm\nimport os\n\nfrom docs.examples import generate_sklearn_simulated_data\nfrom fusilli.data import get_data_module\nfrom fusilli.eval import ConfusionMatrix\nfrom fusilli.train import train_and_save_models\n\n# sphinx_gallery_thumbnail_number = -1" ] }, { @@ -105,7 +105,7 @@ }, "outputs": [], "source": [ - "confusion_matrix_fig = ConfusionMatrix.from_final_val_data(\n single_model_list\n)\n\nplt.show()" + "confusion_matrix_fig = ConfusionMatrix.from_final_val_data(\n single_model_list\n)\nplt.show()" ] } ], diff --git a/docs/auto_examples/training_and_testing/plot_one_model_binary_kfold.py b/docs/auto_examples/training_and_testing/plot_one_model_binary_kfold.py index 995ebb84..fd75925a 100644 --- a/docs/auto_examples/training_and_testing/plot_one_model_binary_kfold.py +++ b/docs/auto_examples/training_and_testing/plot_one_model_binary_kfold.py @@ -24,6 +24,8 @@ from fusilli.eval import ConfusionMatrix from fusilli.train import train_and_save_models +# sphinx_gallery_thumbnail_number = -1 + # %% # 1. Import the fusion model 🔍 # -------------------------------- @@ -124,5 +126,4 @@ confusion_matrix_fig = ConfusionMatrix.from_final_val_data( single_model_list ) - plt.show() diff --git a/docs/auto_examples/training_and_testing/plot_one_model_binary_kfold.py.md5 b/docs/auto_examples/training_and_testing/plot_one_model_binary_kfold.py.md5 index 0cc15905..9510df63 100644 --- a/docs/auto_examples/training_and_testing/plot_one_model_binary_kfold.py.md5 +++ b/docs/auto_examples/training_and_testing/plot_one_model_binary_kfold.py.md5 @@ -1 +1 @@ -c6dda228d1218472134a61e603a0a2ba \ No newline at end of file +36b51b90e2f0212151c0237326c907e2 \ No newline at end of file diff --git a/docs/auto_examples/training_and_testing/plot_one_model_binary_kfold.rst b/docs/auto_examples/training_and_testing/plot_one_model_binary_kfold.rst index 3b66c7ef..874644da 100644 --- a/docs/auto_examples/training_and_testing/plot_one_model_binary_kfold.rst +++ b/docs/auto_examples/training_and_testing/plot_one_model_binary_kfold.rst @@ -33,7 +33,7 @@ Key Features: - 📈 Plotting the loss curves of each fold. - 📊 Visualising the results of a single K-Fold model using the :class:`~.ConfusionMatrix` class. -.. GENERATED FROM PYTHON SOURCE LINES 17-27 +.. GENERATED FROM PYTHON SOURCE LINES 17-29 .. code-block:: Python @@ -47,6 +47,7 @@ Key Features: from fusilli.eval import ConfusionMatrix from fusilli.train import train_and_save_models + # sphinx_gallery_thumbnail_number = -1 @@ -54,14 +55,15 @@ Key Features: -.. GENERATED FROM PYTHON SOURCE LINES 28-32 + +.. GENERATED FROM PYTHON SOURCE LINES 30-34 1. Import the fusion model 🔍 -------------------------------- We're importing only one model for this example, the :class:`~.TabularCrossmodalMultiheadAttention` model. Instead of using the :func:`~fusilli.utils.model_chooser.import_chosen_fusion_models` function, we're importing the model directly like with any other library method. -.. GENERATED FROM PYTHON SOURCE LINES 32-38 +.. GENERATED FROM PYTHON SOURCE LINES 34-40 .. code-block:: Python @@ -78,7 +80,7 @@ Instead of using the :func:`~fusilli.utils.model_chooser.import_chosen_fusion_mo -.. GENERATED FROM PYTHON SOURCE LINES 39-51 +.. GENERATED FROM PYTHON SOURCE LINES 41-53 2. Set the training parameters 🎯 ----------------------------------- @@ -93,7 +95,7 @@ For using k-fold cross validation, the necessary parameters are: We're also setting our own batch_size for this example. -.. GENERATED FROM PYTHON SOURCE LINES 51-68 +.. GENERATED FROM PYTHON SOURCE LINES 53-70 .. code-block:: Python @@ -121,14 +123,14 @@ We're also setting our own batch_size for this example. -.. GENERATED FROM PYTHON SOURCE LINES 69-73 +.. GENERATED FROM PYTHON SOURCE LINES 71-75 3. Generating simulated data 🔮 -------------------------------- Time to create some simulated data for our models to work their wonders on. This function also simulated image data which we aren't using here. -.. GENERATED FROM PYTHON SOURCE LINES 73-82 +.. GENERATED FROM PYTHON SOURCE LINES 75-84 .. code-block:: Python @@ -148,7 +150,7 @@ This function also simulated image data which we aren't using here. -.. GENERATED FROM PYTHON SOURCE LINES 83-96 +.. GENERATED FROM PYTHON SOURCE LINES 85-98 4. Training the fusion model 🏁 -------------------------------------- @@ -164,7 +166,7 @@ This function takes the following parameters: Then we pass the data module, the parameters, and the fusion model to the :func:`~fusilli.train.train_and_save_models` function. We're not using checkpointing for this example, so we set ``enable_checkpointing=False``. We're also setting ``show_loss_plot=True`` to plot the loss curves for each fold. -.. GENERATED FROM PYTHON SOURCE LINES 96-117 +.. GENERATED FROM PYTHON SOURCE LINES 98-119 .. code-block:: Python @@ -238,51 +240,51 @@ We're not using checkpointing for this example, so we set ``enable_checkpointing method_name: Tabular Crossmodal multi-head attention modality_type: tabular_tabular fusion_type: attention - Training: | | 0/? [00:00 + File "/Users/florencetownend/Library/CloudStorage/OneDrive-UniversityCollegeLondon/Projects/fusilli/docs/examples/training_and_testing/plot_two_models_traintest.py", line 47, in fusion_models = import_chosen_fusion_models(model_conditions) File "/Users/florencetownend/Library/CloudStorage/OneDrive-UniversityCollegeLondon/Projects/fusilli/fusilli/utils/model_chooser.py", line 323, in import_chosen_fusion_models imported_models = get_models(model_conditions, skip_models) @@ -109,7 +111,7 @@ We're importing ConcatTabularData and TabularChannelWiseMultiAttention models fo -.. GENERATED FROM PYTHON SOURCE LINES 48-59 +.. GENERATED FROM PYTHON SOURCE LINES 50-61 2. Set the training parameters 🎯 ----------------------------------- @@ -123,7 +125,7 @@ For training and testing, the necessary parameters are: - ``pred_type``: the type of prediction to be performed. This is either ``regression``, ``binary``, or ``classification``. For this example we're using regression. - ``loss_log_dir``: the directory to save the loss logs to. This is used for plotting the loss curves. -.. GENERATED FROM PYTHON SOURCE LINES 59-75 +.. GENERATED FROM PYTHON SOURCE LINES 61-77 .. code-block:: Python @@ -144,14 +146,14 @@ For training and testing, the necessary parameters are: os.rmdir(os.path.join(params["loss_log_dir"], dir)) -.. GENERATED FROM PYTHON SOURCE LINES 76-80 +.. GENERATED FROM PYTHON SOURCE LINES 78-82 3. Generating simulated data 🔮 -------------------------------- Time to create some simulated data for our models to work their wonders on. This function also simulated image data which we aren't using here. -.. GENERATED FROM PYTHON SOURCE LINES 80-89 +.. GENERATED FROM PYTHON SOURCE LINES 82-91 .. code-block:: Python @@ -165,7 +167,7 @@ This function also simulated image data which we aren't using here. ) -.. GENERATED FROM PYTHON SOURCE LINES 90-101 +.. GENERATED FROM PYTHON SOURCE LINES 92-103 4. Training the first fusion model 🏁 -------------------------------------- @@ -179,14 +181,14 @@ This function takes the following inputs: First we'll create a dictionary to store both the trained models so we can compare them later. -.. GENERATED FROM PYTHON SOURCE LINES 101-103 +.. GENERATED FROM PYTHON SOURCE LINES 103-105 .. code-block:: Python all_trained_models = {} # create dictionary to store trained models -.. GENERATED FROM PYTHON SOURCE LINES 104-111 +.. GENERATED FROM PYTHON SOURCE LINES 106-113 To train the first model we need to: @@ -196,7 +198,7 @@ To train the first model we need to: 4. *Train and test the model*: This is done with the :func:`~fusilli.train.train_and_save_models` function. This function takes the datamodule, the parameters, the fusion model, and the initialised model as inputs. It returns a list of the trained models (in this case, only one model). 5. *Add the trained model to the ``all_trained_models`` dictionary*: This is so we can compare the results of the two models later. -.. GENERATED FROM PYTHON SOURCE LINES 111-133 +.. GENERATED FROM PYTHON SOURCE LINES 113-135 .. code-block:: Python @@ -223,7 +225,7 @@ To train the first model we need to: all_trained_models[fusion_model.__name__] = model_1_list -.. GENERATED FROM PYTHON SOURCE LINES 134-139 +.. GENERATED FROM PYTHON SOURCE LINES 136-141 5. Plotting the results of the first model 📊 ----------------------------------------------- @@ -231,7 +233,7 @@ Let's unveil the results of our first model's hard work. We're using the :class: This class takes the trained model as an input and returns a plot of the real values vs the predicted values from the final validation data (when using from_final_val_data). If you want to plot the results from the test data, you can use from_new_data instead. See the example notebook on plotting with new data for more detail. -.. GENERATED FROM PYTHON SOURCE LINES 139-144 +.. GENERATED FROM PYTHON SOURCE LINES 141-146 .. code-block:: Python @@ -241,17 +243,17 @@ If you want to plot the results from the test data, you can use from_new_data in plt.show() -.. GENERATED FROM PYTHON SOURCE LINES 145-148 +.. GENERATED FROM PYTHON SOURCE LINES 147-150 6. Training the second fusion model 🏁 --------------------------------------- It's time for our second fusion model to shine! Here we train the second fusion model: TabularChannelWiseMultiAttention. We're using the same steps as before, but this time we're using the second model in the ``fusion_models`` list. -.. GENERATED FROM PYTHON SOURCE LINES 151-152 +.. GENERATED FROM PYTHON SOURCE LINES 153-154 Choose the model -.. GENERATED FROM PYTHON SOURCE LINES 152-173 +.. GENERATED FROM PYTHON SOURCE LINES 154-175 .. code-block:: Python @@ -277,12 +279,12 @@ Choose the model all_trained_models[fusion_model.__name__] = model_2_list -.. GENERATED FROM PYTHON SOURCE LINES 174-176 +.. GENERATED FROM PYTHON SOURCE LINES 176-178 7. Plotting the results of the second model 📊 ----------------------------------------------- -.. GENERATED FROM PYTHON SOURCE LINES 176-181 +.. GENERATED FROM PYTHON SOURCE LINES 178-183 .. code-block:: Python @@ -292,7 +294,7 @@ Choose the model plt.show() -.. GENERATED FROM PYTHON SOURCE LINES 182-187 +.. GENERATED FROM PYTHON SOURCE LINES 184-189 8. Comparing the results of the two models 📈 ---------------------------------------------- @@ -300,7 +302,7 @@ Let the ultimate showdown begin! We're comparing the results of our two models. We're using the :class:`~fusilli.eval.ModelComparison` class to compare the results of the two models. This class takes the trained models as an input and returns a plot of the results of the two models and a Pandas DataFrame of the metrics of the two models. -.. GENERATED FROM PYTHON SOURCE LINES 187-194 +.. GENERATED FROM PYTHON SOURCE LINES 189-196 .. code-block:: Python @@ -312,13 +314,13 @@ This class takes the trained models as an input and returns a plot of the result plt.show() -.. GENERATED FROM PYTHON SOURCE LINES 195-198 +.. GENERATED FROM PYTHON SOURCE LINES 197-200 9. Saving the metrics of the two models 💾 ------------------------------------------- Time to archive our models' achievements. We're using the :class:`~fusilli.eval.ModelComparison` class to save the metrics of the two models. -.. GENERATED FROM PYTHON SOURCE LINES 198-200 +.. GENERATED FROM PYTHON SOURCE LINES 200-202 .. code-block:: Python @@ -328,7 +330,7 @@ Time to archive our models' achievements. We're using the :class:`~fusilli.eval. .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 0.002 seconds) + **Total running time of the script:** (0 minutes 0.003 seconds) .. _sphx_glr_download_auto_examples_training_and_testing_plot_two_models_traintest.py: diff --git a/docs/auto_examples/training_and_testing/plot_two_models_traintest_codeobj.pickle b/docs/auto_examples/training_and_testing/plot_two_models_traintest_codeobj.pickle index 0111840c..e794df78 100644 Binary files a/docs/auto_examples/training_and_testing/plot_two_models_traintest_codeobj.pickle and b/docs/auto_examples/training_and_testing/plot_two_models_traintest_codeobj.pickle differ diff --git a/docs/auto_examples/training_and_testing/sg_execution_times.rst b/docs/auto_examples/training_and_testing/sg_execution_times.rst index c0eb2b2f..0077a9cb 100644 --- a/docs/auto_examples/training_and_testing/sg_execution_times.rst +++ b/docs/auto_examples/training_and_testing/sg_execution_times.rst @@ -6,7 +6,7 @@ Computation times ================= -**00:00.008** total execution time for 3 files **from auto_examples/training_and_testing**: +**00:12.208** total execution time for 3 files **from auto_examples/training_and_testing**: .. container:: @@ -32,12 +32,12 @@ Computation times * - Example - Time - Mem (MB) + * - :ref:`sphx_glr_auto_examples_training_and_testing_plot_one_model_binary_kfold.py` (``plot_one_model_binary_kfold.py``) + - 00:12.125 + - 0.0 * - :ref:`sphx_glr_auto_examples_training_and_testing_plot_model_comparison_loop_kfold.py` (``plot_model_comparison_loop_kfold.py``) - - 00:00.006 + - 00:00.080 - 0.0 * - :ref:`sphx_glr_auto_examples_training_and_testing_plot_two_models_traintest.py` (``plot_two_models_traintest.py``) - - 00:00.002 - - 0.0 - * - :ref:`sphx_glr_auto_examples_training_and_testing_plot_one_model_binary_kfold.py` (``plot_one_model_binary_kfold.py``) - - 00:00.000 + - 00:00.003 - 0.0 diff --git a/docs/conf.py b/docs/conf.py index bd79ee73..0fee62cc 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -115,6 +115,7 @@ ], ), "within_subsection_order": FileNameSortKey, + 'default_thumb_file': '_static/pink_pasta_logo.png', } diff --git a/docs/contributing_examples/A_template_other_fusion_codeobj.pickle b/docs/contributing_examples/A_template_other_fusion_codeobj.pickle index 7ec59e8d..0c11f908 100644 Binary files a/docs/contributing_examples/A_template_other_fusion_codeobj.pickle and b/docs/contributing_examples/A_template_other_fusion_codeobj.pickle differ diff --git a/docs/contributing_examples/template_graph_fusion_codeobj.pickle b/docs/contributing_examples/template_graph_fusion_codeobj.pickle index 1dad9f1a..db1eb3b3 100644 Binary files a/docs/contributing_examples/template_graph_fusion_codeobj.pickle and b/docs/contributing_examples/template_graph_fusion_codeobj.pickle differ diff --git a/docs/data_loading.rst b/docs/data_loading.rst index 470b44d9..c4bd7189 100644 --- a/docs/data_loading.rst +++ b/docs/data_loading.rst @@ -58,9 +58,9 @@ Tabular and Image Data Tabular data should follow the format specified above. Image data should be in a ``.pt`` file format with dimensions ``(num_samples, num_channels, height, width)``. -For example, for 100 2D 28x28 grey-scale images, my images.pt file would have the dimensions ``(100, 1, 28, 28)``. +For example, for 100 2D 28x28 grey-scale images, my images.pt file would have the dimensions ``(100, 1, 28, 28)`` when I use ``torch.load()``. -For 100 3D 32x32x32 RGB images, my images.pt file would have the dimensions ``(100, 3, 32, 32, 32)``. +For 100 3D 32x32x32 RGB images, my images.pt file would have the dimensions ``(100, 3, 32, 32, 32)`` when I use ``torch.load()``. **Example of loading tabular and image data:** @@ -122,12 +122,12 @@ If you use a different suffix than the default "_test", you must pass the suffix } # Using the training data (params["tabular1_source"], params["tabular2_source"], and params["img_source"]) - data_module = get_data_module(params) + data_module = get_data_module(fusion_model=some_example_model, params=params) # Train the model on params["tabular1_source"], params["tabular2_source"], and params["img_source"] - trained_model_dict = train_and_save_models(data_module, params, some_example_model) + trained_model= train_and_save_models(data_module, params, some_example_model) # Evaluate the model on the external test data: # params["tabular1_source_testing"], params["tabular2_source_testing"], and params["img_source_testing"] - RealsVsPreds.from_new_data(model, params, data_file_suffix="_testing") + RealsVsPreds.from_new_data(trained_model, params, data_file_suffix="_testing") diff --git a/docs/examples/customising_behaviour/customising_training_parameters.py b/docs/examples/customising_behaviour/customising_training_parameters.py index 517e2737..d11bd9f2 100644 --- a/docs/examples/customising_behaviour/customising_training_parameters.py +++ b/docs/examples/customising_behaviour/customising_training_parameters.py @@ -153,3 +153,4 @@ The ``extra_log_string_dict`` argument is also used to modify the logging behaviour of the model. For more information, see :ref:`wandb`. """ +# sphinx_gallery_thumbnail_path = '_static/pink_pasta_logo.png' diff --git a/docs/examples/customising_behaviour/loss_figures/AttentionWeightedGNN.png b/docs/examples/customising_behaviour/loss_figures/AttentionWeightedGNN.png index 01c47a6e..4728d2b9 100644 Binary files a/docs/examples/customising_behaviour/loss_figures/AttentionWeightedGNN.png and b/docs/examples/customising_behaviour/loss_figures/AttentionWeightedGNN.png differ diff --git a/docs/examples/customising_behaviour/loss_figures/ConcatTabularFeatureMaps.png b/docs/examples/customising_behaviour/loss_figures/ConcatTabularFeatureMaps.png index afcdb6d3..961a5545 100644 Binary files a/docs/examples/customising_behaviour/loss_figures/ConcatTabularFeatureMaps.png and b/docs/examples/customising_behaviour/loss_figures/ConcatTabularFeatureMaps.png differ diff --git a/docs/examples/customising_behaviour/loss_logs/modify_layers/AttentionWeightedGNN/metrics.csv b/docs/examples/customising_behaviour/loss_logs/modify_layers/AttentionWeightedGNN/metrics.csv index c24c2a30..6bf58b09 100644 --- a/docs/examples/customising_behaviour/loss_logs/modify_layers/AttentionWeightedGNN/metrics.csv +++ b/docs/examples/customising_behaviour/loss_logs/modify_layers/AttentionWeightedGNN/metrics.csv @@ -1,12 +1,12 @@ -val_loss,step,R2_val,MAE_val,epoch,R2_train,train_loss,MAE_train -63.55864334106445,0,-0.06568336486816406,6.7931671142578125,0,,, -,0,,,0,-0.01151895523071289,74.97073364257812,6.880713939666748 -63.6014289855957,1,-0.06640076637268066,6.7944135665893555,1,,, -,1,,,1,-0.009949326515197754,74.8543930053711,6.875021457672119 -63.642791748046875,2,-0.06709432601928711,6.7956414222717285,2,,, -,2,,,2,-0.011220455169677734,74.94860076904297,6.879720687866211 -63.685707092285156,3,-0.06781387329101562,6.79683780670166,3,,, -,3,,,3,-0.0103987455368042,74.88771057128906,6.879384994506836 -63.727577209472656,4,-0.06851589679718018,6.798135280609131,4,,, -,4,,,4,-0.01016998291015625,74.8707504272461,6.8800506591796875 -63.727577209472656,5,-0.06851589679718018,6.798135280609131,5,,, +val_loss,epoch,MAE_val,step,R2_val,train_loss,R2_train,MAE_train +167.5082550048828,0,10.717531204223633,0,-0.004385232925415039,,, +,0,,0,,105.55506896972656,-0.003792405128479004,8.279817581176758 +167.48513793945312,1,10.717973709106445,1,-0.00424647331237793,,, +,1,,1,,105.5949478149414,-0.004171609878540039,8.286035537719727 +167.4565887451172,2,10.718071937561035,2,-0.004075288772583008,,, +,2,,2,,105.68045806884766,-0.004984736442565918,8.289670944213867 +167.42849731445312,3,10.718225479125977,3,-0.003906846046447754,,, +,3,,3,,105.51983642578125,-0.003457307815551758,8.283498764038086 +167.40155029296875,4,10.718470573425293,4,-0.0037453174591064453,,, +,4,,4,,105.49263000488281,-0.0031986236572265625,8.279643058776855 +167.40155029296875,5,10.718470573425293,5,-0.0037453174591064453,,, diff --git a/docs/examples/customising_behaviour/loss_logs/modify_layers/ConcatTabularFeatureMaps/metrics.csv b/docs/examples/customising_behaviour/loss_logs/modify_layers/ConcatTabularFeatureMaps/metrics.csv index 1cd6205a..52f822ee 100644 --- a/docs/examples/customising_behaviour/loss_logs/modify_layers/ConcatTabularFeatureMaps/metrics.csv +++ b/docs/examples/customising_behaviour/loss_logs/modify_layers/ConcatTabularFeatureMaps/metrics.csv @@ -1,12 +1,12 @@ -val_loss,step,R2_val,MAE_val,epoch,R2_train,train_loss,MAE_train -63.1016845703125,9,-0.0239332914352417,6.413779258728027,0,,, -,9,,,0,-0.28295496106147766,75.14381408691406,6.9738664627075195 -63.12262725830078,19,-0.024272918701171875,6.4155426025390625,1,,, -,19,,,1,-0.10543932020664215,75.0732650756836,6.972143650054932 -63.1646728515625,29,-0.024955391883850098,6.419016361236572,2,,, -,29,,,2,-0.5992171168327332,75.05265045166016,6.971622467041016 -63.20314407348633,39,-0.025579452514648438,6.422268867492676,3,,, -,39,,,3,-0.07159312069416046,75.01472473144531,6.969532012939453 -63.29044723510742,49,-0.026996254920959473,6.4294538497924805,4,,, -,49,,,4,-0.20253725349903107,74.99994659423828,6.9703569412231445 -63.29044723510742,50,-0.026996254920959473,6.4294538497924805,5,,, +val_loss,epoch,MAE_val,step,R2_val,train_loss,R2_train,MAE_train +115.2126693725586,0,8.956426620483398,9,-0.14734911918640137,,, +,0,,9,,118.92069244384766,-0.1175713986158371,8.718819618225098 +115.06486511230469,1,8.952523231506348,19,-0.14587712287902832,,, +,1,,19,,118.92134857177734,-0.11324342340230942,8.719310760498047 +115.07295227050781,2,8.95265007019043,29,-0.14595770835876465,,, +,2,,29,,118.88328552246094,-0.13166308403015137,8.718779563903809 +115.05082702636719,3,8.951873779296875,39,-0.14573729038238525,,, +,3,,39,,118.8876953125,-0.2592984735965729,8.718381881713867 +114.9400634765625,4,8.948206901550293,49,-0.14463436603546143,,, +,4,,49,,118.8436050415039,-0.20507344603538513,8.717238426208496 +114.9400634765625,5,8.948206901550293,50,-0.14463436603546143,,, diff --git a/docs/examples/customising_behaviour/plot_modify_layer_sizes.py b/docs/examples/customising_behaviour/plot_modify_layer_sizes.py index 06465246..1b8daf8a 100644 --- a/docs/examples/customising_behaviour/plot_modify_layer_sizes.py +++ b/docs/examples/customising_behaviour/plot_modify_layer_sizes.py @@ -20,9 +20,10 @@ # # First, we will set up the experiment by importing the necessary packages, creating the simulated data, and setting the parameters for the experiment. # -# For a more detailed explanation of this process, please see the :ref:`train_test_examples` tutorials. +# For a more detailed explanation of this process, please see the example tutorials. # +# sphinx_gallery_thumbnail_path = '_static/modify_thumbnail.png' import matplotlib.pyplot as plt import os import torch.nn as nn @@ -101,7 +102,7 @@ # * - Attribute # - Guidance # * - :attr:`~.AttentionWeightedGNN.graph_conv_layers` -# - ``nn.Sequential`` of ``torch_geometric.nn` Layers. +# - ``nn.Sequential`` of ``torch_geometric.nn`` Layers. # * - :attr:`~.AttentionWeightedGNN.dropout_prob` # - Float between (not including) 0 and 1. # @@ -164,6 +165,8 @@ print("Fusion model:\n", trained_model_list[0].model) # %% +# You can see that the input features to the ``final_prediction`` layer changed to fit with our modification to the ``graph_conv_layers`` output features! +# # What happens when the modifications are incorrect? # ---------------------------------------------------- # @@ -226,7 +229,7 @@ # %% # What about modifying multiple attributes with the **conflicting modifications**? # ------------------------------------------------------------------------------------- - +# # # For this, let's switch to looking at the :class:`~fusilli.fusionmodels.tabularfusion.concat_feature_maps.ConcatTabularFeatureMaps` model. # This model concatenates the feature maps of the two modalities and then passes them through a prediction layer. diff --git a/docs/examples/training_and_testing/README.rst b/docs/examples/training_and_testing/README.rst index f91b4609..0170c6ed 100644 --- a/docs/examples/training_and_testing/README.rst +++ b/docs/examples/training_and_testing/README.rst @@ -3,4 +3,6 @@ Running Fusilli on your own data ========================================== -These are examples of how to train and validate fusion models with Fusilli. \ No newline at end of file +These are examples of how to train and validate fusion models with Fusilli. + + diff --git a/docs/examples/training_and_testing/loss_logs/one_model_binary_kfold/TabularCrossmodalMultiheadAttention_fold_0/metrics.csv b/docs/examples/training_and_testing/loss_logs/one_model_binary_kfold/TabularCrossmodalMultiheadAttention_fold_0/metrics.csv index e719134d..ab4e8f54 100644 --- a/docs/examples/training_and_testing/loss_logs/one_model_binary_kfold/TabularCrossmodalMultiheadAttention_fold_0/metrics.csv +++ b/docs/examples/training_and_testing/loss_logs/one_model_binary_kfold/TabularCrossmodalMultiheadAttention_fold_0/metrics.csv @@ -1,90 +1,56 @@ -epoch,binary_auroc_val,step,val_loss,binary_accuracy_val,train_loss,binary_auroc_train,binary_accuracy_train -0,0.6422275304794312,12,0.7012183666229248,0.5199999809265137,,, -0,,12,,,0.7143906950950623,0.6695048809051514,0.4975000023841858 -1,0.7087339758872986,25,0.6870812177658081,0.5199999809265137,,, -1,,25,,,0.6884341239929199,0.7467377185821533,0.4975000023841858 -2,0.7367788553237915,38,0.6741982102394104,0.5199999809265137,,, -2,,38,,,0.6693103909492493,0.8109018206596375,0.4975000023841858 -3,0.7932692766189575,51,0.6504029035568237,0.75,,, -3,,51,,,0.6390573978424072,0.8407748341560364,0.6675000190734863 -4,0.8381410241127014,64,0.6215775012969971,0.7599999904632568,,, -4,,64,,,0.5966801643371582,0.9005104899406433,0.8100000023841858 -5,0.8313301801681519,77,0.6141452193260193,0.800000011920929,,, 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b/docs/examples/training_and_testing/loss_logs/one_model_binary_kfold/TabularCrossmodalMultiheadAttention_fold_2/metrics.csv @@ -1,80 +1,68 @@ -epoch,binary_auroc_val,step,val_loss,binary_accuracy_val,train_loss,binary_auroc_train,binary_accuracy_train -0,0.7600643634796143,12,0.7048887014389038,0.5400000214576721,,, -0,,12,,,0.7133395671844482,0.68536376953125,0.48750001192092896 -1,0.8248792290687561,25,0.6843693256378174,0.5400000214576721,,, -1,,25,,,0.686348557472229,0.7720392346382141,0.4925000071525574 -2,0.852254331111908,38,0.6602023839950562,0.5400000214576721,,, -2,,38,,,0.6720725893974304,0.8102962374687195,0.4925000071525574 -3,0.8663446307182312,51,0.6329968571662903,0.7900000214576721,,, -3,,51,,,0.6395697593688965,0.8585124015808105,0.5975000262260437 -4,0.8679549694061279,64,0.5955969095230103,0.8199999928474426,,, -4,,64,,,0.5988562107086182,0.878251850605011,0.8149999976158142 -5,0.8655394911766052,77,0.6038033962249756,0.8199999928474426,,, 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+,,246,18,,0.5550589561462402,0.9200000166893005,0.9303997159004211 +0.5695570707321167,0.8500000238418579,259,19,0.9328000545501709,,, +,,259,19,,0.5546401739120483,0.9225000143051147,0.9299198389053345 +0.5603280067443848,0.8799999952316284,272,20,0.9264000654220581,,, +,,272,20,,0.5535286068916321,0.925000011920929,0.9332154989242554 +0.5666249394416809,0.8399999737739563,285,21,0.9308000802993774,,, +,,285,21,,0.5561432838439941,0.9175000190734863,0.9329145550727844 +0.5630534887313843,0.8700000047683716,298,22,0.9372000694274902,,, +,,298,22,,0.5566124320030212,0.9100000262260437,0.9306011199951172 +0.5630534887313843,0.8700000047683716,299,23,0.9372000694274902,,, diff --git a/docs/examples/training_and_testing/plot_model_comparison_loop_kfold.py b/docs/examples/training_and_testing/plot_model_comparison_loop_kfold.py index a6dc1f95..96a39e96 100644 --- a/docs/examples/training_and_testing/plot_model_comparison_loop_kfold.py +++ b/docs/examples/training_and_testing/plot_model_comparison_loop_kfold.py @@ -54,6 +54,8 @@ from fusilli.train import train_and_save_models from fusilli.utils.model_chooser import import_chosen_fusion_models +# sphinx_gallery_thumbnail_number = -1 + # from IPython.utils import io # for hiding the tqdm progress bar # %% @@ -127,6 +129,8 @@ # In this section, we train all the fusion models using the generated data and specified parameters. # We store the results of each model for later analysis. +# Using %%capture to hide the progress bar and plots (there are a lot of them!) + all_trained_models = {} for i, fusion_model in enumerate(fusion_models): @@ -148,6 +152,8 @@ # Save to all_trained_models all_trained_models[fusion_model_name] = single_model_list + plt.close("all") + # %% # 5. Plotting the results of the individual models # ------------------------------------------------- diff --git a/docs/examples/training_and_testing/plot_one_model_binary_kfold.py b/docs/examples/training_and_testing/plot_one_model_binary_kfold.py index 995ebb84..fd75925a 100644 --- a/docs/examples/training_and_testing/plot_one_model_binary_kfold.py +++ b/docs/examples/training_and_testing/plot_one_model_binary_kfold.py @@ -24,6 +24,8 @@ from fusilli.eval import ConfusionMatrix from fusilli.train import train_and_save_models +# sphinx_gallery_thumbnail_number = -1 + # %% # 1. Import the fusion model 🔍 # -------------------------------- @@ -124,5 +126,4 @@ confusion_matrix_fig = ConfusionMatrix.from_final_val_data( single_model_list ) - plt.show() diff --git a/docs/examples/training_and_testing/plot_two_models_traintest.py b/docs/examples/training_and_testing/plot_two_models_traintest.py index 7833343e..5a4b7869 100644 --- a/docs/examples/training_and_testing/plot_two_models_traintest.py +++ b/docs/examples/training_and_testing/plot_two_models_traintest.py @@ -27,6 +27,8 @@ from fusilli.train import train_and_save_models from fusilli.utils.model_chooser import import_chosen_fusion_models +# sphinx_gallery_thumbnail_number = -1 + # %% # 1. Import fusion models 🔍 # -------------------------------- diff --git a/docs/experiment_setup.rst b/docs/experiment_setup.rst index c3748c46..038ab5d8 100644 --- a/docs/experiment_setup.rst +++ b/docs/experiment_setup.rst @@ -25,7 +25,7 @@ Here is the dictionary structure for defining necessary directories: .. warning:: - Fusilli utilizes predetermined file names to save files in these directories. Overwriting may occur if files with the same names exist. It's highly recommended to maintain separate directories for each Fusilli run to manage files belonging to each run effectively. + Fusilli utilises predetermined file names to save files in these directories. Overwriting may occur if files with the same names exist. **It's highly recommended to maintain separate directories for each Fusilli experiment** to manage files belonging to each run effectively. Example for Creating Directory Structure @@ -36,7 +36,6 @@ Here's an example block to set up the necessary directory structure: .. code-block:: python import os - from datetime import datetime # Create a timestamp for the run run_name = "Run1" diff --git a/docs/fusion_model_explanations.rst b/docs/fusion_model_explanations.rst index c6f42415..eea6a908 100644 --- a/docs/fusion_model_explanations.rst +++ b/docs/fusion_model_explanations.rst @@ -250,7 +250,7 @@ Incoming! Graph-based ----------- .. warning:: - It is not possible to use graph-based models with any evaluation with completely unseen data, such as in the method :meth:`.RealsVsPreds.from_new_data`. + ⚠️ It is not possible to use external test set data with graph-based fusion models. Trying to use a "from new data" method such as :meth:`.RealsVsPreds.from_new_data` will result in an error. :class:`.EdgeCorrGNN` ~~~~~~~~~~~~~~~~~~~~~~~~~~ diff --git a/docs/index.rst b/docs/index.rst index 9123603e..5cb112d1 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -10,6 +10,9 @@ Don't be silly, use fusilli! 🍝 Have you got multimodal data but not sure how to combine them for your machine learning task? Look no further! This library provides a way to compare different fusion methods for your multimodal data. +.. image:: _static/fusilli_pipeline_diagram.png + :align: left + ----- Why would you want to use fusilli? @@ -33,7 +36,7 @@ Why would you want to use fusilli? .. toctree:: :maxdepth: 1 - :caption: 🌸 Table of Contents 🌸 + :caption: 🌸 Getting Started 🌸 introduction fusion_model_explanations @@ -41,10 +44,16 @@ Why would you want to use fusilli? data_loading experiment_setup quick_start + +----- + +.. toctree:: + :maxdepth: 1 + :caption: 🌸 Further Guidance 🌸 + choosing_model modifying_models logging_with_wandb - developers_guide glossary ----- @@ -60,8 +69,9 @@ Why would you want to use fusilli? .. toctree:: :maxdepth: 2 - :caption: 🌸 How to contribute 🌸 + :caption: 🌸 Contributing 🌸 + developers_guide contributing_examples/index ----- diff --git a/docs/logging_with_wandb.rst b/docs/logging_with_wandb.rst index e00f4975..03161165 100644 --- a/docs/logging_with_wandb.rst +++ b/docs/logging_with_wandb.rst @@ -49,4 +49,4 @@ Upon training and inspecting Weights and Biases, the run will be labeled as ``Ed **What if you're not using Weights and Biases?** -When not utilizing Weights and Biases, Fusilli plots loss curves and saves them locally as PNG files. In this scenario, the WandB project name is replaced by user-specified tags in the PNG file name. For instance, running the same fusion model without using Weights and Biases will produce a PNG file named ``EdgeCorrGNN_dropout_prob_0.2.png``, saved in ``params["loss_fig_path"]``. \ No newline at end of file +When not using Weights and Biases, Fusilli plots loss curves and saves them locally as PNG files. In this scenario, the WandB project name is replaced by user-specified tags in the PNG file name. For instance, running the same fusion model without using Weights and Biases will produce a PNG file named ``EdgeCorrGNN_dropout_prob_0.2.png``, saved in ``params["loss_fig_path"]``. \ No newline at end of file diff --git a/docs/modifying_models.rst b/docs/modifying_models.rst index 2ee539bd..cbb966fc 100644 --- a/docs/modifying_models.rst +++ b/docs/modifying_models.rst @@ -109,7 +109,7 @@ Modifiable Attributes * - Attribute - Guidance * - :attr:`~.AttentionWeightedGNN.graph_conv_layers` - - ``nn.Sequential`` of ``torch_geometric.nn` Layers. + - ``nn.Sequential`` of ``torch_geometric.nn`` Layers. * - :attr:`~.AttentionWeightedGNN.dropout_prob` - Float between (not including) 0 and 1. diff --git a/docs/quick_start.rst b/docs/quick_start.rst index 86e1e6b3..9975fd93 100644 --- a/docs/quick_start.rst +++ b/docs/quick_start.rst @@ -5,7 +5,7 @@ This script provides a simple setup to train a model using ``fusilli`` on a sing .. note:: - For a more detailed guide on using Fusilli, refer to the :ref:`example_notebooks`. + For a more detailed guide on using Fusilli, refer to the :ref:`train_test_examples`. This code showcases the necessary steps to execute Fusilli on a single dataset. @@ -27,7 +27,7 @@ Ensure the elements in the ``params`` dictionary contain specific keys; you can # Import the example fusion model from fusilli.fusionmodels.tabularfusion.example_model import ExampleModel - # Set paths to your tabular datasets (CSV files with study_id and pred_label columns) + # Set paths to the data tabular_1_path = "path/to/tabular_1.csv" tabular_2_path = "path/to/tabular_2.csv" image_path = "path/to/image_file.pt" diff --git a/docs/sg_execution_times.rst b/docs/sg_execution_times.rst index 82865512..84f05b94 100644 --- a/docs/sg_execution_times.rst +++ b/docs/sg_execution_times.rst @@ -6,7 +6,7 @@ Computation times ================= -**00:07.276** total execution time for 8 files **from all galleries**: +**00:12.208** total execution time for 8 files **from all galleries**: .. container:: @@ -32,19 +32,19 @@ Computation times * - Example - Time - Mem (MB) - * - :ref:`sphx_glr_auto_examples_customising_behaviour_plot_modify_layer_sizes.py` (``examples/customising_behaviour/plot_modify_layer_sizes.py``) - - 00:07.268 + * - :ref:`sphx_glr_auto_examples_training_and_testing_plot_one_model_binary_kfold.py` (``examples/training_and_testing/plot_one_model_binary_kfold.py``) + - 00:12.125 - 0.0 * - :ref:`sphx_glr_auto_examples_training_and_testing_plot_model_comparison_loop_kfold.py` (``examples/training_and_testing/plot_model_comparison_loop_kfold.py``) - - 00:00.006 + - 00:00.080 - 0.0 * - :ref:`sphx_glr_auto_examples_training_and_testing_plot_two_models_traintest.py` (``examples/training_and_testing/plot_two_models_traintest.py``) - - 00:00.002 + - 00:00.003 - 0.0 * - :ref:`sphx_glr_auto_examples_customising_behaviour_customising_training_parameters.py` (``examples/customising_behaviour/customising_training_parameters.py``) - 00:00.000 - 0.0 - * - :ref:`sphx_glr_auto_examples_training_and_testing_plot_one_model_binary_kfold.py` (``examples/training_and_testing/plot_one_model_binary_kfold.py``) + * - :ref:`sphx_glr_auto_examples_customising_behaviour_plot_modify_layer_sizes.py` (``examples/customising_behaviour/plot_modify_layer_sizes.py``) - 00:00.000 - 0.0 * - :ref:`sphx_glr_contributing_examples_A_template_other_fusion.py` (``how_to_contribute/A_template_other_fusion.py``) diff --git a/fusilli/data.py b/fusilli/data.py index f3374d5d..e7ebf557 100644 --- a/fusilli/data.py +++ b/fusilli/data.py @@ -316,7 +316,7 @@ def load_img(self): return dataset, [None, None, img_dim] - def load_tabular_tabularular(self): + def load_tabular_tabular(self): """ Loads the tabular1 and tabular2 multimodal dataset @@ -473,7 +473,7 @@ def __init__( "img": LoadDatasets(self.sources, image_downsample_size).load_img, "tabular_tabular": LoadDatasets( self.sources, image_downsample_size - ).load_tabular_tabularular, + ).load_tabular_tabular, "tabular_image": LoadDatasets( self.sources, image_downsample_size ).load_tab_and_img, @@ -736,7 +736,7 @@ def __init__( "img": LoadDatasets(self.sources, self.image_downsample_size).load_img, "tabular_tabular": LoadDatasets( self.sources, self.image_downsample_size - ).load_tabular_tabularular, + ).load_tabular_tabular, "tabular_image": LoadDatasets( self.sources, self.image_downsample_size ).load_tab_and_img, @@ -1050,7 +1050,7 @@ def __init__( "img": LoadDatasets(self.sources, self.image_downsample_size).load_img, "tabular_tabular": LoadDatasets( self.sources, self.image_downsample_size - ).load_tabular_tabularular, + ).load_tabular_tabular, "tabular_image": LoadDatasets( self.sources, self.image_downsample_size ).load_tab_and_img, @@ -1206,7 +1206,7 @@ def __init__( "img": LoadDatasets(self.sources, self.image_downsample_size).load_img, "tabular_tabular": LoadDatasets( self.sources, self.image_downsample_size - ).load_tabular_tabularular, + ).load_tabular_tabular, "tabular_image": LoadDatasets( self.sources, self.image_downsample_size ).load_tab_and_img, diff --git a/fusilli/eval.py b/fusilli/eval.py index 0c78e034..dc2276e1 100644 --- a/fusilli/eval.py +++ b/fusilli/eval.py @@ -1,7 +1,7 @@ """ This module contains classes and functions for evaluating trained models (i.e. plotting results from training). The setup for this module has been inspired by the scikit-learn API for plotting results, which involves each plot -being a class with a ``from_model`` method that takes in a trained model and returns a plot with the validation data, +being a class with a ``from_final_val_data`` method that takes in a trained model and returns a plot with the validation data, and a ``from_new_data`` method that takes in a trained model and new data and returns a plot. """ @@ -643,7 +643,7 @@ def from_new_data( overall_kfold_metrics, ) - figure.suptitle("From new data") + figure.suptitle("Evaluation: External Test Data") elif len(model_list) == 1: # isinstance(model, nn.Module): # train/test model @@ -675,7 +675,7 @@ def from_new_data( metric_values, ) - figure.suptitle("From new data") + figure.suptitle("Evaluation: External Test Data") else: raise ValueError("Argument 'model_list' is an empty list. ") @@ -746,7 +746,7 @@ def from_final_val_data(cls, model_list): overall_kfold_metrics, ) - figure.suptitle("From final val data") + figure.suptitle("Evaluation: Validation Data") elif len(model_list) == 1: # isinstance(model[0], nn.Module): # train/test model @@ -777,7 +777,7 @@ def from_final_val_data(cls, model_list): metric_values, ) - figure.suptitle("From final val data") + figure.suptitle("Evaluation: Validation Data") else: raise ValueError(("Argument 'model_list' is an empty list. ")) diff --git a/fusilli/fusionmodels/tabularfusion/activation.py b/fusilli/fusionmodels/tabularfusion/activation.py index 33208c7f..4890d5a0 100644 --- a/fusilli/fusionmodels/tabularfusion/activation.py +++ b/fusilli/fusionmodels/tabularfusion/activation.py @@ -1,3 +1,7 @@ +""" +Activation-function fusion model for tabular data. +""" + import torch.nn as nn from fusilli.fusionmodels.base_model import ParentFusionModel import torch @@ -15,10 +19,10 @@ class ActivationFusion(ParentFusionModel, nn.Module): ---------- pred_type : str Type of prediction to be performed. - mod1_layers : dict + mod1_layers : nn.ModuleDict Dictionary containing the layers of the first modality. Calculated in the :meth:`~ParentFusionModel.set_mod1_layers` method. - mod2_layers : dict + mod2_layers : nn.ModuleDict Dictionary containing the layers of the second modality. Calculated in the :meth:`~ParentFusionModel.set_mod2_layers` method. fused_dim : int @@ -33,11 +37,11 @@ class ActivationFusion(ParentFusionModel, nn.Module): :meth:`~ActivationFusion.calc_fused_layers` method. """ - # str: Name of the method. + #: str: Name of the method. method_name = "Activation function map fusion" - # str: Type of modality. + #: str: Type of modality. modality_type = "tabular_tabular" - # str: Type of fusion. + #: str: Type of fusion. fusion_type = "operation" def __init__(self, pred_type, data_dims, params): @@ -49,7 +53,7 @@ def __init__(self, pred_type, data_dims, params): data_dims : list Dictionary containing the dimensions of the data. params : dict - Dictionary containing the parameters of the model. + Dictionary containing the user-specified parameters. """ ParentFusionModel.__init__(self, pred_type, data_dims, params) diff --git a/fusilli/fusionmodels/tabularfusion/attention_and_activation.py b/fusilli/fusionmodels/tabularfusion/attention_and_activation.py index 55653417..a3f22f86 100644 --- a/fusilli/fusionmodels/tabularfusion/attention_and_activation.py +++ b/fusilli/fusionmodels/tabularfusion/attention_and_activation.py @@ -1,3 +1,7 @@ +""" +Using activation functions to fuse tabular data, with self-attention on the second tabular modality. +""" + import torch.nn as nn from fusilli.fusionmodels.base_model import ParentFusionModel @@ -17,10 +21,10 @@ class AttentionAndSelfActivation(ParentFusionModel, nn.Module): ---------- pred_type : str Type of prediction to be performed. - mod1_layers : dict + mod1_layers : nn.ModuleDict Dictionary containing the layers of the first modality. Calculated in the :meth:`~ParentFusionModel.set_mod1_layers` method. - mod2_layers : dict + mod2_layers : nn.ModuleDict Dictionary containing the layers of the second modality. Calculated in the :meth:`~ParentFusionModel.set_mod2_layers` method. fused_dim : int @@ -37,11 +41,11 @@ class AttentionAndSelfActivation(ParentFusionModel, nn.Module): Reduction ratio of the channel attention module. """ - # str: Name of the method. + #: str: Name of the method. method_name = "Activation function and tabular self-attention" - # str: Type of modality. + #: str: Type of modality. modality_type = "tabular_tabular" - # str: Type of fusion. + #: str: Type of fusion. fusion_type = "operation" def __init__(self, pred_type, data_dims, params): diff --git a/fusilli/fusionmodels/tabularfusion/attention_weighted_GNN.py b/fusilli/fusionmodels/tabularfusion/attention_weighted_GNN.py index 69d458f9..a66955b3 100644 --- a/fusilli/fusionmodels/tabularfusion/attention_weighted_GNN.py +++ b/fusilli/fusionmodels/tabularfusion/attention_weighted_GNN.py @@ -1,10 +1,5 @@ """ -Inspired by the multi-modal brain age paper: -- Pretraining a model on concatenated tabular modalities -- Getting out the attention weights from the model: what does this mean? -- Make the graph structure: -- node features are second modality, -- edges are based on the attention weights: get weighted phenotypes by multiplying the attention weights by the multiple- +Attention weighted GNN model: the edge weights are the attention weights from a pre-trained MLP and the node features are the second modality. """ import torch.nn as nn from fusilli.fusionmodels.base_model import ParentFusionModel @@ -434,6 +429,9 @@ class AttentionWeightedGNN(ParentFusionModel, nn.Module): """ Graph neural network with the edge weighting as the distances between each nodes' weighted phenotypes and the node features as the second tabular modality features. + This is a model inspired by method in `Bintsi et al. (2023) `_ : *Multimodal brain age estimation using interpretable adaptive population-graph learning*. + + Attributes ---------- pred_type : str @@ -447,13 +445,13 @@ class AttentionWeightedGNN(ParentFusionModel, nn.Module): """ - # str: Name of the method. + #: str: Name of the method. method_name = "Attention-weighted GNN" - # str: Type of modality. + #: str: Type of modality. modality_type = "tabular_tabular" - # str: Type of fusion. + #: str: Type of fusion. fusion_type = "graph" # class: Graph maker class. diff --git a/fusilli/fusionmodels/tabularfusion/channelwise_att.py b/fusilli/fusionmodels/tabularfusion/channelwise_att.py index 43a968c7..8d0ace3d 100644 --- a/fusilli/fusionmodels/tabularfusion/channelwise_att.py +++ b/fusilli/fusionmodels/tabularfusion/channelwise_att.py @@ -34,15 +34,14 @@ class TabularChannelWiseMultiAttention(ParentFusionModel, nn.Module): Springer International Publishing. https://doi.org/10.1007/978-3-030-59713-9_24 Accompanying code: (our model is inspired by the work of Duanmu et al. (2020) [1]) - https://github.com/HongyiDuanmu26/Prediction-of-pCR-with-Integrative-Deep-Learning/ - blob/main/CustomNet.py + https://github.com/HongyiDuanmu26/Prediction-of-pCR-with-Integrative-Deep-Learning/blob/main/CustomNet.py Attributes ---------- - mod1_layers : dict + mod1_layers : nn.ModuleDict Dictionary containing the layers of the 1st type of tabular data. - mod2_layers : dict + mod2_layers : nn.ModuleDict Dictionary containing the layers of the 2nd type of tabular data. match_dim_layers : nn.ModuleDict Module dictionary containing the linear layers to make the dimensions of the two types of @@ -59,11 +58,11 @@ class TabularChannelWiseMultiAttention(ParentFusionModel, nn.Module): """ - # str: Name of the method. + #: str: Name of the method. method_name = "Channel-wise multiplication net (tabular)" - # str: Type of modality. + #: str: Type of modality. modality_type = "tabular_tabular" - # str: Type of fusion. + #: str: Type of fusion. fusion_type = "attention" def __init__(self, pred_type, data_dims, params): diff --git a/fusilli/fusionmodels/tabularfusion/concat_data.py b/fusilli/fusionmodels/tabularfusion/concat_data.py index 5366ed3f..72853bd4 100644 --- a/fusilli/fusionmodels/tabularfusion/concat_data.py +++ b/fusilli/fusionmodels/tabularfusion/concat_data.py @@ -29,11 +29,11 @@ class ConcatTabularData(ParentFusionModel, nn.Module): Calculated in the :meth:`~ParentFusionModel.set_final_pred_layers` method. """ - # str: Name of the method. + #: str: Name of the method. method_name = "Concatenating tabular data" - # str: Type of modality. + #: str: Type of modality. modality_type = "tabular_tabular" - # str: Type of fusion. + #: str: Type of fusion. fusion_type = "operation" def __init__(self, pred_type, data_dims, params): diff --git a/fusilli/fusionmodels/tabularfusion/concat_feature_maps.py b/fusilli/fusionmodels/tabularfusion/concat_feature_maps.py index 8145b130..0a032dd5 100644 --- a/fusilli/fusionmodels/tabularfusion/concat_feature_maps.py +++ b/fusilli/fusionmodels/tabularfusion/concat_feature_maps.py @@ -17,10 +17,10 @@ class ConcatTabularFeatureMaps(ParentFusionModel, nn.Module): ---------- pred_type : str Type of prediction to be performed. - mod1_layers : dict + mod1_layers : nn.ModuleDict Dictionary containing the layers of the first modality. Calculated in the :meth:`~ParentFusionModel.set_mod1_layers` method. - mod2_layers : dict + mod2_layers : nn.ModuleDict Dictionary containing the layers of the second modality. Calculated in the :meth:`~ParentFusionModel.set_mod2_layers` method. fused_dim : int @@ -35,11 +35,11 @@ class ConcatTabularFeatureMaps(ParentFusionModel, nn.Module): :meth:`~ConcatTabularFeatureMaps.calc_fused_layers` method. """ - # str: Name of the method. + #: str: Name of the method. method_name = "Concatenating tabular feature maps" - # str: Type of modality. + #: str: Type of modality. modality_type = "tabular_tabular" - # str: Type of fusion. + #: str: Type of fusion. fusion_type = "operation" def __init__(self, pred_type, data_dims, params): diff --git a/fusilli/fusionmodels/tabularfusion/crossmodal_att.py b/fusilli/fusionmodels/tabularfusion/crossmodal_att.py index 7a662db4..2a2754b0 100644 --- a/fusilli/fusionmodels/tabularfusion/crossmodal_att.py +++ b/fusilli/fusionmodels/tabularfusion/crossmodal_att.py @@ -33,9 +33,9 @@ class TabularCrossmodalMultiheadAttention(ParentFusionModel, nn.Module): Type of prediction to be performed. attention_embed_dim : int Number of features of the multihead attention layer. - mod1_layers : dict + mod1_layers : nn.ModuleDict Dictionary containing the layers of the first modality. - mod2_layers : dict + mod2_layers : nn.ModuleDict Dictionary containing the layers of the second modality. fused_dim : int Number of features of the fused layers. This is the flattened output size of the @@ -55,11 +55,11 @@ class TabularCrossmodalMultiheadAttention(ParentFusionModel, nn.Module): """ - # str: Name of the method. + #: str: Name of the method. method_name = "Tabular Crossmodal multi-head attention" - # str: Type of modality. + #: str: Type of modality. modality_type = "tabular_tabular" - # str: Type of fusion. + #: str: Type of fusion. fusion_type = "attention" def __init__(self, pred_type, data_dims, params): diff --git a/fusilli/fusionmodels/tabularfusion/decision.py b/fusilli/fusionmodels/tabularfusion/decision.py index 40687bf6..e3b076e9 100644 --- a/fusilli/fusionmodels/tabularfusion/decision.py +++ b/fusilli/fusionmodels/tabularfusion/decision.py @@ -16,9 +16,9 @@ class TabularDecision(ParentFusionModel, nn.Module): Attributes ---------- - mod1_layers : dict + mod1_layers : nn.ModuleDict Dictionary containing the layers of the 1st type of tabular data. - mod2_layers : dict + mod2_layers : nn.ModuleDict Dictionary containing the layers of the 2nd type of tabular data. fused_layers : nn.Sequential Sequential layer containing the fused layers. @@ -31,11 +31,11 @@ class TabularDecision(ParentFusionModel, nn.Module): """ - # str: Name of the method. + #: str: Name of the method. method_name = "Tabular decision" - # str: Type of modality. + #: str: Type of modality. modality_type = "tabular_tabular" - # str: Type of fusion. + #: str: Type of fusion. fusion_type = "operation" def __init__(self, pred_type, data_dims, params): diff --git a/fusilli/fusionmodels/tabularfusion/edge_corr_gnn.py b/fusilli/fusionmodels/tabularfusion/edge_corr_gnn.py index c3d0ef3a..49e79f51 100644 --- a/fusilli/fusionmodels/tabularfusion/edge_corr_gnn.py +++ b/fusilli/fusionmodels/tabularfusion/edge_corr_gnn.py @@ -1,5 +1,5 @@ """ -Edge correlation GNN model. +Edge correlation GNN model: edges are weighted by the correlation between the nodes' first tabular modality features. """ import torch.nn as nn @@ -108,11 +108,11 @@ class EdgeCorrGNN(ParentFusionModel, nn.Module): take in 256 features. """ - # str: Name of the method. + #: str: Name of the method. method_name = "Edge Correlation GNN" - # str: Type of modality. + #: str: Type of modality. modality_type = "tabular_tabular" - # str: Type of fusion. + #: str: Type of fusion. fusion_type = "graph" # class: Graph maker class. graph_maker = EdgeCorrGraphMaker diff --git a/fusilli/fusionmodels/tabularfusion/mcvae_model.py b/fusilli/fusionmodels/tabularfusion/mcvae_model.py index 7eff1d7c..bba5e2e6 100644 --- a/fusilli/fusionmodels/tabularfusion/mcvae_model.py +++ b/fusilli/fusionmodels/tabularfusion/mcvae_model.py @@ -355,11 +355,11 @@ class MCVAE_tab(ParentFusionModel, nn.Module): """ - # str: Name of the method. + #: str: Name of the method. method_name = "MCVAE Tabular" - # str: Type of modality. + #: str: Type of modality. modality_type = "tabular_tabular" - # str: Type of fusion. + #: str: Type of fusion. fusion_type = "subspace" # class: Subspace method class. subspace_method = MCVAESubspaceMethod diff --git a/fusilli/fusionmodels/tabularimagefusion/channelwise_att.py b/fusilli/fusionmodels/tabularimagefusion/channelwise_att.py index 1b47a919..9a423d1c 100644 --- a/fusilli/fusionmodels/tabularimagefusion/channelwise_att.py +++ b/fusilli/fusionmodels/tabularimagefusion/channelwise_att.py @@ -33,15 +33,14 @@ class ImageChannelWiseMultiAttention(ParentFusionModel, nn.Module): Springer International Publishing. https://doi.org/10.1007/978-3-030-59713-9_24 Accompanying code: (our model is inspired by the work of Duanmu et al. (2020) [1]) - https://github.com/HongyiDuanmu26/Prediction-of-pCR-with-Integrative-Deep-Learning/ - blob/main/CustomNet.py + https://github.com/HongyiDuanmu26/Prediction-of-pCR-with-Integrative-Deep-Learning/blob/main/CustomNet.py Attributes ---------- - mod1_layers : dict + mod1_layers : nn.ModuleDict Dictionary containing the layers of the 1st type of tabular data. - img_layers : dict + img_layers : nn.ModuleDict Dictionary containing the layers of the image data. match_dim_layers : nn.ModuleDict Module dictionary containing the linear layers to make the dimensions of the two types of @@ -56,11 +55,11 @@ class ImageChannelWiseMultiAttention(ParentFusionModel, nn.Module): Sequential layer containing the final prediction layers. """ - # str: Name of the method. + #: str: Name of the method. method_name = "Channel-wise Image attention" - # str: Type of modality. + #: str: Type of modality. modality_type = "tabular_image" - # str: Type of fusion. + #: str: Type of fusion. fusion_type = "attention" def __init__(self, pred_type, data_dims, params): diff --git a/fusilli/fusionmodels/tabularimagefusion/concat_img_latent_tab_doubleloss.py b/fusilli/fusionmodels/tabularimagefusion/concat_img_latent_tab_doubleloss.py index 8cb188c2..ac0018fe 100644 --- a/fusilli/fusionmodels/tabularimagefusion/concat_img_latent_tab_doubleloss.py +++ b/fusilli/fusionmodels/tabularimagefusion/concat_img_latent_tab_doubleloss.py @@ -68,13 +68,13 @@ class ConcatImgLatentTabDoubleLoss(ParentFusionModel, nn.Module): Size of the fused layers: latent dimension size + tabular data dimension size. """ - # str: Name of the method. + #: str: Name of the method. method_name = ( "Trained Together Latent Image + Tabular Data" ) - # str: Type of modality. + #: str: Type of modality. modality_type = "tabular_image" - # str: Type of fusion. + #: str: Type of fusion. fusion_type = "subspace" def __init__(self, pred_type, data_dims, params): diff --git a/fusilli/fusionmodels/tabularimagefusion/concat_img_latent_tab_doubletrain.py b/fusilli/fusionmodels/tabularimagefusion/concat_img_latent_tab_doubletrain.py index ee0b8be0..a527c1af 100644 --- a/fusilli/fusionmodels/tabularimagefusion/concat_img_latent_tab_doubletrain.py +++ b/fusilli/fusionmodels/tabularimagefusion/concat_img_latent_tab_doubletrain.py @@ -441,13 +441,13 @@ class ConcatImgLatentTabDoubleTrain(ParentFusionModel, nn.Module): """ - # str: Name of the method. + #: str: Name of the method. method_name = "Pretrained Latent Image + Tabular Data" - # str: Type of modality. + #: str: Type of modality. modality_type = "tabular_image" - # str: Type of fusion. + #: str: Type of fusion. fusion_type = "subspace" - # class: Class containing the method to train the latent image space. + #: class: Class containing the method to train the latent image space. subspace_method = concat_img_latent_tab_subspace_method def __init__(self, pred_type, data_dims, params): diff --git a/fusilli/fusionmodels/tabularimagefusion/concat_img_maps_tabular_data.py b/fusilli/fusionmodels/tabularimagefusion/concat_img_maps_tabular_data.py index 673b358b..c9c16d17 100644 --- a/fusilli/fusionmodels/tabularimagefusion/concat_img_maps_tabular_data.py +++ b/fusilli/fusionmodels/tabularimagefusion/concat_img_maps_tabular_data.py @@ -20,7 +20,7 @@ class ConcatImageMapsTabularData(ParentFusionModel, nn.Module): ---------- pred_type : str Type of prediction to be performed. - img_layers : dict + img_layers : nn.ModuleDict Dictionary containing the layers of the image data. fused_layers : nn.Sequential Sequential layer containing the fused layers. Calculated in the diff --git a/fusilli/fusionmodels/tabularimagefusion/concat_img_maps_tabular_maps.py b/fusilli/fusionmodels/tabularimagefusion/concat_img_maps_tabular_maps.py index 0abe4682..07369791 100644 --- a/fusilli/fusionmodels/tabularimagefusion/concat_img_maps_tabular_maps.py +++ b/fusilli/fusionmodels/tabularimagefusion/concat_img_maps_tabular_maps.py @@ -19,10 +19,10 @@ class ConcatImageMapsTabularMaps(ParentFusionModel, nn.Module): ---------- pred_type : str Type of prediction to be performed. - mod1_layers : dict + mod1_layers : nn.ModuleDict Dictionary containing the layers of the first modality. Calculated in the :meth:`~ParentFusionModel.set_mod1_layers` method. - img_layers : dict + img_layers : nn.ModuleDict Dictionary containing the layers of the image data. Calculated in the :meth:`~ParentFusionModel.set_img_layers` method. fused_dim : int @@ -37,11 +37,11 @@ class ConcatImageMapsTabularMaps(ParentFusionModel, nn.Module): Calculated in the :meth:`~ConcatImageMapsTabularMaps.calc_fused_layers` method. """ - # str: Name of the method. + #: str: Name of the method. method_name = "Concatenating tabular and image feature maps" - # str: Type of modality. + #: str: Type of modality. modality_type = "tabular_image" - # str: Type of fusion. + #: str: Type of fusion. fusion_type = "operation" def __init__(self, pred_type, data_dims, params): diff --git a/fusilli/fusionmodels/tabularimagefusion/crossmodal_att.py b/fusilli/fusionmodels/tabularimagefusion/crossmodal_att.py index 478c560f..026bbd2d 100644 --- a/fusilli/fusionmodels/tabularimagefusion/crossmodal_att.py +++ b/fusilli/fusionmodels/tabularimagefusion/crossmodal_att.py @@ -32,9 +32,9 @@ class CrossmodalMultiheadAttention(ParentFusionModel, nn.Module): Type of prediction to be performed. attention_embed_dim : int Number of features of the multihead attention layer. - mod1_layers : dict + mod1_layers : nn.ModuleDict Dictionary containing the layers of the first modality. - img_layers : dict + img_layers : nn.ModuleDict Dictionary containing the layers of the image data. fused_dim : int Number of features of the fused layers. This is the flattened output size of the @@ -57,11 +57,11 @@ class CrossmodalMultiheadAttention(ParentFusionModel, nn.Module): """ - # str: Name of the method. + #: str: Name of the method. method_name = "Crossmodal multi-head attention" - # str: Type of modality. + #: str: Type of modality. modality_type = "tabular_image" - # str: Type of fusion. + #: str: Type of fusion. fusion_type = "attention" def __init__(self, pred_type, data_dims, params): diff --git a/fusilli/fusionmodels/tabularimagefusion/decision.py b/fusilli/fusionmodels/tabularimagefusion/decision.py index bf65b97d..73e9f88e 100644 --- a/fusilli/fusionmodels/tabularimagefusion/decision.py +++ b/fusilli/fusionmodels/tabularimagefusion/decision.py @@ -18,9 +18,9 @@ class ImageDecision(ParentFusionModel, nn.Module): Attributes ---------- - mod1_layers : dict + mod1_layers : nn.ModuleDict Dictionary containing the layers of the 1st type of tabular data. - img_layers : dict + img_layers : nn.ModuleDict Dictionary containing the layers of the image data. fused_layers : nn.Sequential Sequential layer containing the fused layers. @@ -40,11 +40,11 @@ class ImageDecision(ParentFusionModel, nn.Module): """ - # str: Name of the method. + #: str: Name of the method. method_name = "Image decision fusion" - # str: Type of modality. + #: str: Type of modality. modality_type = "tabular_image" - # str: Type of fusion. + #: str: Type of fusion. fusion_type = "operation" def __init__(self, pred_type, data_dims, params): diff --git a/fusilli/fusionmodels/tabularimagefusion/denoise_tab_img_maps.py b/fusilli/fusionmodels/tabularimagefusion/denoise_tab_img_maps.py index 8ddc4874..736cbc29 100644 --- a/fusilli/fusionmodels/tabularimagefusion/denoise_tab_img_maps.py +++ b/fusilli/fusionmodels/tabularimagefusion/denoise_tab_img_maps.py @@ -711,11 +711,11 @@ class DAETabImgMaps(ParentFusionModel, nn.Module): Final prediction layers. """ - # str: Name of the method. + #: str: Name of the method. method_name = "Denoising tabular autoencoder with image maps" - # str: Type of modality. + #: str: Type of modality. modality_type = "tabular_image" - # str: Type of fusion. + #: str: Type of fusion. fusion_type = "subspace" # class: Subspace method. subspace_method = denoising_autoencoder_subspace_method diff --git a/fusilli/fusionmodels/unimodal/image.py b/fusilli/fusionmodels/unimodal/image.py index 3b1f95b8..407a185f 100644 --- a/fusilli/fusionmodels/unimodal/image.py +++ b/fusilli/fusionmodels/unimodal/image.py @@ -1,5 +1,5 @@ """ -unimodal model using only the image data. +Unimodal model using only the image data. """ import torch.nn as nn @@ -11,11 +11,11 @@ class ImgUnimodal(ParentFusionModel, nn.Module): """ - This class implements a uni-modal model using only the image data. + A uni-modal model using only the image data. Attributes ---------- - img_layers : dict + img_layers : nn.ModuleDict Dictionary containing the layers of the image data. fused_dim : int Number of features of the fused layers. This is the flattened output size of the @@ -26,11 +26,11 @@ class ImgUnimodal(ParentFusionModel, nn.Module): Sequential layer containing the final prediction layers. """ - # str: Name of the method. + #: str: Name of the method. method_name = "Image unimodal" - # str: Type of modality. + #: str: Type of modality. modality_type = "img" - # str: Type of fusion. + #: str: Type of fusion. fusion_type = "unimodal" def __init__(self, pred_type, data_dims, params): diff --git a/fusilli/fusionmodels/unimodal/tabular1.py b/fusilli/fusionmodels/unimodal/tabular1.py index 3b426631..790534bc 100644 --- a/fusilli/fusionmodels/unimodal/tabular1.py +++ b/fusilli/fusionmodels/unimodal/tabular1.py @@ -14,7 +14,7 @@ class Tabular1Unimodal(ParentFusionModel, nn.Module): Attributes ---------- - mod1_layers : dict + mod1_layers : nn.ModuleDict Dictionary containing the layers of the 1st type of tabular data. fused_layers : nn.Sequential Sequential layer containing the fused layers. diff --git a/fusilli/fusionmodels/unimodal/tabular2.py b/fusilli/fusionmodels/unimodal/tabular2.py index e73fc085..8ff65be8 100644 --- a/fusilli/fusionmodels/unimodal/tabular2.py +++ b/fusilli/fusionmodels/unimodal/tabular2.py @@ -14,7 +14,7 @@ class Tabular2Unimodal(ParentFusionModel, nn.Module): Attributes ---------- - mod2_layers : dict + mod2_layers : nn.ModuleDict Dictionary containing the layers of the 2nd type of tabular data. fused_dim : int Dimension of the fused layer. @@ -27,11 +27,11 @@ class Tabular2Unimodal(ParentFusionModel, nn.Module): """ - # str: Name of the method. + #: str: Name of the method. method_name = "Tabular2 uni-modal" - # str: Modality type. + #: str: Modality type. modality_type = "tabular2" - # str: Fusion type. + #: str: Fusion type. fusion_type = "unimodal" def __init__(self, pred_type, data_dims, params): diff --git a/fusilli/utils/simulated_data/tabular1data.csv b/fusilli/utils/simulated_data/tabular1data.csv index bf5b6f9c..6269bbb8 100644 --- a/fusilli/utils/simulated_data/tabular1data.csv +++ b/fusilli/utils/simulated_data/tabular1data.csv @@ -1,101 +1,501 @@ 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