From dc444415d997df69210bb991c77fa22c6410a99f Mon Sep 17 00:00:00 2001 From: Samwell Freeman Date: Tue, 29 Sep 2020 17:14:20 -0400 Subject: [PATCH 01/21] paired --- CONTRIBUTING.md | 192 ---------- README.md | 32 +- docker/terra_image/Dockerfile | 8 +- docker/terra_image/README.md | 8 +- docker/vm_boot_images/Dockerfile | 5 +- .../config/tensorflow-requirements.txt | 2 - ml4h/arguments.py | 19 +- ml4h/explorations.py | 61 ++++ ml4h/models.py | 133 ++++++- ml4h/plots.py | 194 ++++++++-- ml4h/recipes.py | 77 +++- ml4h/tensorize/tensor_writer_ukbb.py | 181 ++++------ ml4h/tensormap/mgb/dynamic.py | 15 +- ml4h/tensormap/ukb/by_script.py | 19 +- ml4h/tensormap/ukb/genetics.py | 10 +- ml4h/tensormap/ukb/mri.py | 52 ++- ml4h/test_utils.py | 1 + .../visualization_tools/annotation_storage.py | 36 +- ml4h/visualization_tools/annotations.py | 55 ++- .../batch_image_annotations.py | 236 ------------ .../dicom_interactive_plots.py | 74 ++-- ml4h/visualization_tools/dicom_plots.py | 122 +++---- .../ecg_interactive_plots.py | 22 +- ml4h/visualization_tools/ecg_reshape.py | 58 ++- ml4h/visualization_tools/ecg_static_plots.py | 11 +- ml4h/visualization_tools/facets.py | 13 +- ml4h/visualization_tools/hd5_mri_plots.py | 181 +++++----- .../paired_multimodal_autoencoder.ipynb | 113 +++++- .../paired_multimodal_segmenter_mri_ecg.ipynb | 210 +++++++---- notebooks/autoencoders/vae_mri_slice.ipynb | 2 +- notebooks/mnist_demo.ipynb | 90 ++++- .../mri/mri_cardiac_long_axis_sketch.ipynb | 8 +- .../mri/mri_cardiac_short_axis_sketch.ipynb | 14 +- .../identify_a_sample_to_review.ipynb | 6 +- .../review_results/image_annotations.ipynb | 268 -------------- .../review_results/review_one_sample.ipynb | 8 +- ...handling_for_notebook_visualizations.ipynb | 40 ++- ...ntify_a_sample_to_review_interactive.ipynb | 4 +- .../image_annotations_demo.ipynb | 259 -------------- .../review_one_sample_interactive.ipynb | 4 +- pylintrc | 337 ------------------ scripts/jupyter.sh | 3 +- tests/test_models.py | 69 +++- tests/test_recipes.py | 6 +- 44 files changed, 1289 insertions(+), 1969 deletions(-) delete mode 100644 CONTRIBUTING.md delete mode 100644 ml4h/visualization_tools/batch_image_annotations.py delete mode 100644 notebooks/review_results/image_annotations.ipynb delete mode 100644 notebooks/terra_featured_workspace/image_annotations_demo.ipynb delete mode 100644 pylintrc diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md deleted file mode 100644 index 2e2006c07..000000000 --- a/CONTRIBUTING.md +++ /dev/null @@ -1,192 +0,0 @@ -# Contributing - -1. Before making a substantial pull request, consider first [filing an issue](https://github.com/broadinstitute/ml/issues) describing the feature addition or change you wish to make. -1. [Get setup](#setup-for-code-contributions) -1. [Follow the coding style](#python-coding-style) -1. [Test your code](#testing) -1. Send a [pull request](https://github.com/broadinstitute/ml/pulls) - -## Setup for code contributions - -### Get setup for GitHub - -Small typos in code or documentation may be edited directly using the GitHub web interface. Otherwise: - -1. If you are new to GitHub, don't start here. Instead, work through a GitHub tutorial such as https://guides.github.com/activities/hello-world/. -1. Create a fork of https://github.com/broadinstitute/ml -1. Clone your fork. -1. Work from a feature branch. See the [Appendix](#appendix) for detailed `git` commands. - -### Install precommit - -[`pre-commit`](https://pre-commit.com/) is a framework for managing and maintaining multi-language pre-commit hooks. - -``` -# Install pre-commit -pip3 install pre-commit -# Install the git hook scripts by running this within the git clone directory -cd ${HOME}/ml -pre-commit install -``` - -See [.pre-commit-config.yaml](https://github.com/broadinstitute/ml/blob/master/.pre-commit-config.yaml) for the currently configured pre-commit hooks for ml4cvd. - -### Install git-secrets - -```git-secrets``` helps us avoid committing secrets (e.g. private keys) and other critical data (e.g. PHI) to our -repositories. ```git-secrets``` can be obtained via [github](https://github.com/awslabs/git-secrets) or on MacOS can be -installed with Homebrew by running ```brew install git-secrets```. - -To add hooks to all repositories that you initialize or clone in the future: - -```git secrets --install --global``` - -To add hooks to all local repositories: - -``` -git secrets --install ~/.git-templates/git-secrets -git config --global init.templateDir ~/.git-templates/git-secrets -``` - -We maintain our own custom "provider" to cover any private keys or other critical data that we would like to avoid -committing to our repositories. Feel free to add ```egrep```-compatible regular expressions to -```git_secrets_provider_ml4cvd.txt``` to match types of critical data that are not currently covered by the patterns in that -file. To register the patterns in this file with ```git-secrets```: - -``` -git secrets --add-provider -- cat ${HOME}/ml/git_secrets_provider_ml4cvd.txt -``` - -### Install pylint - -[`pylint`](https://www.pylint.org/) is a Python static code analysis tool which looks for programming errors, helps enforcing a coding standard, sniffs for code smells and offers simple refactoring suggestions. - -``` -# Install pylint -pip3 install pylint -``` - -See [pylintrc](https://github.com/broadinstitute/ml/blob/master/pylintrc) for the current lint configuration for ml4cvd. - -# Python coding style - -Changes to ml4cvd should conform to [PEP 8 -- Style Guide for Python Code](https://www.python.org/dev/peps/pep-0008/). See also [Google Python Style Guide](https://github.com/google/styleguide/blob/gh-pages/pyguide.md) as another decription of this coding style. - -Use `pylint` to check your Python changes: - -```bash -pylint --rcfile=${HOME}/ml/pylintrc myfile.py -``` - -Any messages returned by `pylint` are intended to be self-explanatory, but that isn't always the case. - -* Search for `pylint ` or `pylint ` for more details on the recommended code change to resolve the lint issue. -* Or add comment `# pylint: disable=` to the end of the line of code. - -# Testing - -## Testing of `recipes` - -Unit tests can be run in Docker with -``` -${HOME}/ml/scripts/tf.sh -T ${HOME}/ml/tests -``` -Unit tests can be run locally in a conda environment with -``` -python -m pytest ${HOME}/ml/tests -``` -Some of the unit tests are slow due to creating, saving and loading `tensorflow` models. -To skip those tests to move quickly, run -``` -python -m pytest ${HOME}/ml/tests -m "not slow" -``` -pytest can also run specific tests using `::`. For example - -``` -python -m pytest ${HOME}/ml/tests/test_models.py::TestMakeMultimodalMultitaskModel::test_u_connect_segment -``` - -For more pytest usage information, checkout the [usage guide](https://docs.pytest.org/en/latest/usage.html). - -## Testing of `visualization_tools` - -The code in [ml4cvd/visualization_tools](https://github.com/broadinstitute/ml/tree/master/ml4cvd/visualization_tools) is primarily interactive so we add test cases to notebook [test_error_handling_for_notebook_visualizations.ipynb](https://github.com/broadinstitute/ml/blob/master/notebooks/review_results/test_error_handling_for_notebook_visualizations.ipynb) and visually inspect the output of `Cells -> Run all`. - -# Appendix - -For the ml4cvd GitHub repository, we are doing ‘merge and squash’ of pull requests. So that means your fork does not match upstream after your pull request has been merged. The easiest way to manage this is to always work in a feature branch, instead of checking changes into your fork’s master branch. - - -## How to work on a new feature - -(1) Get the latest version of the upstream repo - -``` -git fetch upstream -``` - -Note: If you get an error saying that upstream is unknown, run the following remote add command and then re-run the fetch command. You only need to do this once per git clone. - -``` -git remote add upstream https://github.com/broadinstitute/ml.git -``` - -(2) Make sure your master branch is “even” with upstream. - -``` -git checkout master -git merge --ff-only upstream/master -git push -``` - -Now the master branch of your fork on GitHub should say *"This branch is even with broadinstitute:master."*. - - -(3) Create a feature branch for your change. - -``` -git checkout -b my-feature-branch-name -``` - -Because you created this feature branch from your master branch that was up to date with upstream (step 2), your feature branch is also up to date with upstream. Commit your changes to this branch until you are happy with them. - -(4) Push your changes to GitHub and send a pull request. - -``` -git push --set-upstream origin my-feature-branch-name -``` - -After your pull request is merged, its safe to delete your branch! - -## I accidentally checked a new change to my master branch instead of a feature branch. How to fix this? - -(1) Soft undo your change(s). This leaves the changes in the files on disk but undoes the commit. - -``` -git checkout master -# Moves pointer back to previous HEAD -git reset --soft HEAD@{1} -``` - -Or if you need to move back several commits to the most recent one in common with upstream, you can change ‘1’ to be however many commits back you need to go. - -(2) “stash” your now-unchecked-in changes so that you can get them back later. - -``` -git stash -``` - -(3) Now do the [How to work on a new feature](#how-to-work-on-a-new-feature) step to bring master up to date and create your new feature branch that is “even” with upstream. Here are those commands again: - -``` -git fetch upstream -git merge --ff-only upstream/master -git checkout -b my-feature-branch-name -``` - -(4) “unstash” your changes. - -``` -git stash pop -``` -Now you can proceed with your work! diff --git a/README.md b/README.md index 8996bfe39..0335a4885 100644 --- a/README.md +++ b/README.md @@ -1,7 +1,7 @@ # ml4h `ml4h` is a project aimed at using machine learning to model multi-modal cardiovascular time series and imaging data. `ml4h` began as a set of tools to make it easy to work -with the UK Biobank on Google Cloud Platform and has since expanded to include other data sources +with the UK Biobank on the Google Cloud and has since expanded to include other data sources and functionality. @@ -9,7 +9,6 @@ Getting Started * [Setting up your local environment](#setting-up-your-local-environment) * [Setting up a remote VM](#setting-up-a-remote-vm) * Modeling/Data Sources/Tests [(`ml4h/DATA_MODELING_TESTS.md`)](ml4h/DATA_MODELING_TESTS.md) -* [Contributing Code](#contributing-code) Advanced Topics: * Tensorizing Data (going from raw data to arrays suitable for modeling, in `ml4h/tensorize/README.md, TENSORIZE.md` ) @@ -20,7 +19,7 @@ Clone the repo ``` git clone git@github.com:broadinstitute/ml.git ``` -Make sure you have installed the [Google Cloud SDK (gcloud)](https://cloud.google.com/sdk/docs/downloads-interactive). With [Homebrew](https://brew.sh/), you can use +Make sure you have installed the [google cloud tools (gcloud)](https://cloud.google.com/storage/docs/gsutil_install). With [Homebrew](https://brew.sh/), you can use ``` brew cask install google-cloud-sdk ``` @@ -146,6 +145,29 @@ If you get a public key error run: `gcloud compute config-ssh` Now open a browser on your laptop and go to the URL `http://localhost:8888` -## Contributing code -Want to contribute code to this project? Please see [CONTRIBUTING](./CONTRIBUTING.md) for developer setup and other details. +### Installing git-secrets + +```git-secrets``` helps us avoid committing secrets (e.g. private keys) and other critical data (e.g. PHI) to our +repositories. ```git-secrets``` can be obtained via [github](https://github.com/awslabs/git-secrets) or on MacOS can be +installed with Homebrew by running ```brew install git-secrets```. + +To add hooks to all repositories that you initialize or clone in the future: + +```git secrets --install --global``` + +To add hooks to all local repositories: + +``` +git secrets --install ~/.git-templates/git-secrets +git config --global init.templateDir ~/.git-templates/git-secrets +``` + +We maintain our own custom "provider" to cover any private keys or other critical data that we would like to avoid +committing to our repositories. Feel free to add ```egrep```-compatible regular expressions to +```git_secrets_provider_ml4h.txt``` to match types of critical data that are not currently covered by the patterns in that +file. To register the patterns in this file with ```git-secrets```: + +``` +git secrets --add-provider -- cat ${HOME}/ml/git_secrets_provider_ml4h.txt +``` diff --git a/docker/terra_image/Dockerfile b/docker/terra_image/Dockerfile index a59ecd6ae..721f94500 100644 --- a/docker/terra_image/Dockerfile +++ b/docker/terra_image/Dockerfile @@ -1,4 +1,4 @@ -FROM us.gcr.io/broad-dsp-gcr-public/terra-jupyter-gatk:1.0.6 +FROM us.gcr.io/broad-dsp-gcr-public/terra-jupyter-gatk:1.0.0 # https://github.com/DataBiosphere/terra-docker/blob/master/terra-jupyter-gatk/CHANGELOG.md USER root @@ -19,10 +19,6 @@ RUN pip3 install --user -r $HOME/ml4h_pkg/config/tensorflow-requirements.txt \ # first few rows of the downloaded dataframe of query results. # Pin version due to https://github.com/googleapis/google-cloud-python/issues/9965 && pip3 install --upgrade --user google-cloud-bigquery[pandas]==1.22.0 \ - # Upgrade to a newer version. The one on the base Terra image was a bit too old. - && pip3 install --upgrade --user numpy \ # Configure notebook extensions. && jupyter nbextension install --user --py vega \ - && jupyter nbextension enable --user --py vega \ - && jupyter nbextension install --user --py ipycanvas \ - && jupyter nbextension enable --user --py ipycanvas + && jupyter nbextension enable --user --py vega diff --git a/docker/terra_image/README.md b/docker/terra_image/README.md index 9a81dc74a..71284c0bd 100644 --- a/docker/terra_image/README.md +++ b/docker/terra_image/README.md @@ -2,13 +2,13 @@ To build and push: ``` -mv ml4h ml4hBAK_$(date +"%Y%m%d_%H%M%S") \ +mv ml4cvd ml4cvdBAK_$(date +"%Y%m%d_%H%M%S") \ && mv config configBAK_$(date +"%Y%m%d_%H%M%S") \ - && cp -r ../../ml4h . \ + && cp -r ../../ml4cvd . \ && cp -r ../vm_boot_images/config . \ && gcloud --project uk-biobank-sek-data builds submit \ --timeout 20m \ - --tag gcr.io/uk-biobank-sek-data/ml4h_terra:`date +"%Y%m%d_%H%M%S"` . + --tag gcr.io/uk-biobank-sek-data/ml4cvd_terra:`date +"%Y%m%d_%H%M%S"` . ``` Notes: @@ -20,5 +20,5 @@ available to docker. cd notebooks find . -name "*.ipynb" -type f -print0 | \ xargs -0 perl -i -pe \ - 's/gcr.io\/uk-biobank-sek-data\/ml4h_terra:\d{8}_\d{6}/gcr.io\/uk-biobank-sek-data\/ml4h_terra:20200623_145127/g' + 's/gcr.io\/uk-biobank-sek-data\/ml4cvd_terra:\d{8}_\d{6}/gcr.io\/uk-biobank-sek-data\/ml4cvd_terra:20200623_145127/g' ``` diff --git a/docker/vm_boot_images/Dockerfile b/docker/vm_boot_images/Dockerfile index 59e5b32be..a62694ca0 100644 --- a/docker/vm_boot_images/Dockerfile +++ b/docker/vm_boot_images/Dockerfile @@ -34,7 +34,4 @@ RUN apt-get install python3-tk libgl1-mesa-glx libxt-dev -y # Requirements for the tensorflow project RUN pip3 install --upgrade pip RUN pip3 install -r pre_requirements.txt -RUN pip3 install -r tensorflow-requirements.txt \ - # Configure notebook extensions. - && jupyter nbextension install --user --py ipycanvas \ - && jupyter nbextension enable --user --py ipycanvas +RUN pip3 install -r tensorflow-requirements.txt diff --git a/docker/vm_boot_images/config/tensorflow-requirements.txt b/docker/vm_boot_images/config/tensorflow-requirements.txt index d782967af..bb6a1e777 100644 --- a/docker/vm_boot_images/config/tensorflow-requirements.txt +++ b/docker/vm_boot_images/config/tensorflow-requirements.txt @@ -28,5 +28,3 @@ altair facets-overview plotnine vega -ipycanvas==0.4.1 -ipyannotations==0.2.0 diff --git a/ml4h/arguments.py b/ml4h/arguments.py index f00c6f9f9..6692bcc74 100644 --- a/ml4h/arguments.py +++ b/ml4h/arguments.py @@ -20,7 +20,7 @@ import importlib import numpy as np import multiprocessing -from typing import Set, Dict, List, Optional +from typing import Set, Dict, List, Optional, Tuple from collections import defaultdict from ml4h.logger import load_config @@ -168,6 +168,10 @@ def parse_args(): '--u_connect', nargs=2, action='append', help='U-Net connect first TensorMap to second TensorMap. They must be the same shape except for number of channels. Can be provided multiple times.', ) + parser.add_argument( + '--pairs', nargs=2, action='append', + help='TensorMap pairs for paired autoencoder. The pair_loss metric will encourage similar embeddings for each two input TensorMap pairs. Can be provided multiple times.', + ) parser.add_argument('--aligned_dimension', default=16, type=int, help='Dimensionality of aligned embedded space for multi-modal alignment models.') parser.add_argument( '--max_parameters', default=9000000, type=int, @@ -201,6 +205,7 @@ def parse_args(): parser.add_argument('--validation_steps', default=18, type=int, help='Number of validation batches to examine in an epoch validation.') parser.add_argument('--learning_rate', default=0.0002, type=float, help='Learning rate during training.') parser.add_argument('--mixup_alpha', default=0, type=float, help='If positive apply mixup and sample from a Beta with this value as shape parameter alpha.') + parser.add_argument('--pair_loss', default='cosine', help='Distance metric between paired embeddings', choices=['euclid', 'cosine']) parser.add_argument( '--label_weights', nargs='*', type=float, help='List of per-label weights for weighted categorical cross entropy. If provided, must map 1:1 to number of labels.', @@ -219,6 +224,9 @@ def parse_args(): parser.add_argument('--anneal_rate', default=0., type=float, help='Annealing rate in epochs of loss terms during training') parser.add_argument('--anneal_shift', default=0., type=float, help='Annealing offset in epochs of loss terms during training') parser.add_argument('--anneal_max', default=2.0, type=float, help='Annealing maximum value') + parser.add_argument( + '--save_last_model', default=False, action='store_true', + help='If true saves the model weights from the last training epoch, otherwise the model with best validation loss is saved.') # Run specific and debugging arguments parser.add_argument('--id', default='no_id', help='Identifier for this run, user-defined string to keep experiments organized.') @@ -384,6 +392,14 @@ def _process_u_connect_args(u_connect: Optional[List[List]], tensormap_prefix) - return new_u_connect +def _process_pair_args(pairs: Optional[List[List]], tensormap_prefix) -> List[Tuple[TensorMap, TensorMap]]: + pairs = pairs or [] + new_pairs = [] + for pair in pairs: + new_pairs.append((tensormap_lookup(pair[0], tensormap_prefix), tensormap_lookup(pair[1], tensormap_prefix))) + return new_pairs + + def _process_args(args): now_string = datetime.datetime.now().strftime('%Y-%m-%d_%H-%M') args_file = os.path.join(args.output_folder, args.id, 'arguments_' + now_string + '.txt') @@ -396,6 +412,7 @@ def _process_args(args): f.write(k + ' = ' + str(v) + '\n') load_config(args.logging_level, os.path.join(args.output_folder, args.id), 'log_' + now_string, args.min_sample_id) args.u_connect = _process_u_connect_args(args.u_connect, args.tensormap_prefix) + args.pairs = _process_pair_args(args.pairs, args.tensormap_prefix) args.tensor_maps_in = [] args.tensor_maps_out = [] diff --git a/ml4h/explorations.py b/ml4h/explorations.py index dc21246db..4e82968ab 100644 --- a/ml4h/explorations.py +++ b/ml4h/explorations.py @@ -79,6 +79,21 @@ def predictions_to_pngs( ax.add_patch(matplotlib.patches.Rectangle(y_corner, y_width, y_height, linewidth=1, edgecolor='y', facecolor='none')) logging.info(f"True BBox: {corner}, {width}, {height} Predicted BBox: {y_corner}, {y_width}, {y_height} Vmin {vmin} Vmax{vmax}") plt.savefig(f"{folder}{sample_id}_bbox_batch_{i:02d}{IMAGE_EXT}") + elif tm.axes() == 2: + fig = plt.figure(figsize=(SUBPLOT_SIZE, SUBPLOT_SIZE * 3)) + for i in range(y.shape[0]): + sample_id = os.path.basename(paths[i]).replace(TENSOR_EXT, '') + title = f'{tm.name}_{sample_id}_reconstruction' + for j in range(tm.shape[1]): + plt.subplot(tm.shape[1], 1, j + 1) + plt.plot(labels[tm.output_name()][i, :, j], c='k', linestyle='--', label='original') + plt.plot(y[i, :, j], c='b', label='reconstruction') + if j == 0: + plt.title(title) + plt.legend() + plt.tight_layout() + plt.savefig(os.path.join(folder, title + IMAGE_EXT)) + plt.clf() elif len(tm.shape) == 3: for i in range(y.shape[0]): sample_id = os.path.basename(paths[i]).replace(TENSOR_EXT, '') @@ -1332,3 +1347,49 @@ def _get_occurrences(df, order, start, end): fpath = os.path.join(args.output_folder, args.id, 'summary_cohort_counts.csv') pd.DataFrame.from_dict(cohort_counts, orient='index', columns=['count']).rename_axis('description').to_csv(fpath) logging.info(f'Saved cohort counts to {fpath}') + + +def directions_in_latent_space(stratify_column, stratify_thresh, split_column, split_thresh, latent_cols, latent_df): + hit = latent_df.loc[latent_df[stratify_column] >= stratify_thresh][latent_cols].to_numpy() + miss = latent_df.loc[latent_df[stratify_column] < stratify_thresh][latent_cols].to_numpy() + miss_mean_vector = np.mean(miss, axis=0) + hit_mean_vector = np.mean(hit, axis=0) + strat_vector = hit_mean_vector - miss_mean_vector + + hit1 = latent_df.loc[(latent_df[stratify_column] >= stratify_thresh) + & (latent_df[split_column] >= split_thresh)][latent_cols].to_numpy() + miss1 = latent_df.loc[(latent_df[stratify_column] < stratify_thresh) + & (latent_df[split_column] >= split_thresh)][latent_cols].to_numpy() + hit2 = latent_df.loc[(latent_df[stratify_column] >= stratify_thresh) + & (latent_df[split_column] < split_thresh)][latent_cols].to_numpy() + miss2 = latent_df.loc[(latent_df[stratify_column] < stratify_thresh) + & (latent_df[split_column] < split_thresh)][latent_cols].to_numpy() + miss_mean_vector1 = np.mean(miss1, axis=0) + hit_mean_vector1 = np.mean(hit1, axis=0) + angle1 = angle_between(miss_mean_vector1, hit_mean_vector1) + miss_mean_vector2 = np.mean(miss2, axis=0) + hit_mean_vector2 = np.mean(hit2, axis=0) + angle2 = angle_between(miss_mean_vector2, hit_mean_vector2) + h1_vector = hit_mean_vector1 - miss_mean_vector1 + h2_vector = hit_mean_vector2 - miss_mean_vector2 + angle3 = angle_between(h1_vector, h2_vector) + print(f'\n Between {stratify_column}, and splits: {split_column}\n', + f'Angles h1 and m1: {angle1:.2f}, h2 and m2 {angle2:.2f} h1-m1 and h2-m2 {angle3:.2f} degrees.\n' + f'stratify threshold: {stratify_thresh}, split thresh: {split_thresh}, \n' + f'hit_mean_vector2 shape {miss_mean_vector1.shape}, miss1:{hit_mean_vector2.shape} \n' + f'Hit1 shape {hit1.shape}, miss1:{miss1.shape} threshold:{stratify_thresh}\n' + f'Hit2 shape {hit2.shape}, miss2:{miss2.shape}\n') + + return hit_mean_vector1, miss_mean_vector1, hit_mean_vector2, miss_mean_vector2 + + +def latent_space_dataframe(infer_hidden_tsv, explore_csv): + df = pd.read_csv(explore_csv) + df['fpath'] = pd.to_numeric(df['fpath'], errors='coerce') + df2 = pd.read_csv(infer_hidden_tsv, sep='\t') + df2['sample_id'] = pd.to_numeric(df2['sample_id'], errors='coerce') + latent_df = pd.merge(df, df2, left_on='fpath', right_on='sample_id', how='inner') + latent_df.info() + return latent_df + + diff --git a/ml4h/models.py b/ml4h/models.py index 493870ad1..3eb53ccbd 100755 --- a/ml4h/models.py +++ b/ml4h/models.py @@ -533,16 +533,9 @@ def l2_norm(x, axis=None): def pairwise_cosine_difference(t1, t2): - """ - A [batch x n x d] tensor of n rows with d dimensions - B [batch x m x d] tensor of n rows with d dimensions - - returns: - D [batch x n x m] tensor of cosine similarity scores between each point i Model: + opt = get_optimizer( + kwargs['optimizer'], kwargs['learning_rate'], steps_per_epoch=kwargs['training_steps'], + learning_rate_schedule=kwargs['learning_rate_schedule'], optimizer_kwargs=kwargs.get('optimizer_kwargs'), + ) + if 'model_file' in kwargs and kwargs['model_file'] is not None: + custom_dict = _get_custom_objects(kwargs['tensor_maps_out']) + encoders = {} + for tm in kwargs['tensor_maps_in']: + encoders[tm] = load_model(f"{os.path.dirname(kwargs['model_file'])}/encoder_{tm.name}.h5", custom_objects=custom_dict, compile=False) + decoders = {} + for tm in kwargs['tensor_maps_out']: + decoders[tm] = load_model(f"{os.path.dirname(kwargs['model_file'])}/decoder_{tm.name}.h5", custom_objects=custom_dict, compile=False) + logging.info(f"Attempting to load model file from: {kwargs['model_file']}") + m = load_model(kwargs['model_file'], custom_objects=custom_dict, compile=False) + m.compile(optimizer=opt, loss=custom_dict['loss']) + m.summary() + logging.info(f"Loaded model file from: {kwargs['model_file']}") + return m, encoders, decoders + + inputs = {tm: Input(shape=tm.shape, name=tm.input_name()) for tm in kwargs['tensor_maps_in']} + original_outputs = {tm: 1 for tm in kwargs['tensor_maps_out']} + real_serial_layers = kwargs['model_layers'] + kwargs['model_layers'] = None + multimodal_activations = [] + encoders = {} + decoders = {} + outputs = {} + losses = [] + for left, right in pairs: + kwargs['tensor_maps_in'] = [left] + left_model = make_multimodal_multitask_model(**kwargs) + encode_left = make_hidden_layer_model(left_model, [left], kwargs['hidden_layer']) + h_left = encode_left(inputs[left]) + + kwargs['tensor_maps_in'] = [right] + right_model = make_multimodal_multitask_model(**kwargs) + encode_right = make_hidden_layer_model(right_model, [right], kwargs['hidden_layer']) + h_right = encode_right(inputs[right]) + + if pair_loss == 'cosine': + loss_layer = CosineLossLayer(1.0) + elif pair_loss == 'euclid': + loss_layer = L2LossLayer(1.0) + + paired_embeddings = loss_layer([h_left, h_right]) + multimodal_activations.extend(paired_embeddings) + if left not in encoders: + encoders[left] = encode_left + if right not in encoders: + encoders[right] = encode_right + + multimodal_activation = Concatenate()(multimodal_activations) + multimodal_activation = Dense(units=kwargs['dense_layers'][0])(multimodal_activation) + #multimodal_activation = _activation_layer(kwargs['activation'])(multimodal_activation) + latent_inputs = Input(shape=(kwargs['dense_layers'][0]), name='input_concept_space') + + # build decoder models + for tm in kwargs['tensor_maps_out']: + if tm.axes() > 1: + shape = _calc_start_shape(num_upsamples=len(kwargs['dense_blocks']), output_shape=tm.shape, + upsample_rates=[kwargs['pool_x'], kwargs['pool_y'], kwargs['pool_z']], + channels=kwargs['dense_blocks'][-1]) + + restructure = FlatToStructure(output_shape=shape, activation=kwargs['activation'], + normalization=kwargs['dense_normalize']) + + decode = ConvDecoder( + tensor_map_out=tm, + filters_per_dense_block=kwargs['dense_blocks'][::-1], + conv_layer_type=kwargs['conv_type'], + conv_x=kwargs['conv_x'], + conv_y=kwargs['conv_y'], + conv_z=kwargs['conv_z'], + block_size=kwargs['block_size'], + activation=kwargs['activation'], + normalization=kwargs['conv_normalize'], + regularization=kwargs['conv_regularize'], + regularization_rate=kwargs['conv_regularize_rate'], + upsample_x=kwargs['pool_x'], + upsample_y=kwargs['pool_y'], + upsample_z=kwargs['pool_z'], + u_connect_parents=[tm_in for tm_in in kwargs['tensor_maps_in'] if tm in kwargs['u_connect'][tm_in]], + ) + reconstruction = decode(restructure(latent_inputs), {}, {}) + else: + decode = DenseDecoder(tensor_map_out=tm, parents=tm.parents, activation=kwargs['activation']) + reconstruction = decode(latent_inputs, {}, {}) + + decoder = Model(latent_inputs, reconstruction, name=tm.output_name()) + decoders[tm] = decoder + outputs[tm.output_name()] = decoder(multimodal_activation) + losses.append(tm.loss) + + kwargs['tensor_maps_out'] = list(original_outputs.keys()) + kwargs['tensor_maps_in'] = list(inputs.keys()) + + m = Model(inputs=list(inputs.values()), outputs=outputs) + my_metrics = {tm.output_name(): tm.metrics for tm in kwargs['tensor_maps_out']} + m.compile(optimizer=opt, loss=losses, metrics=my_metrics) + m.summary() + + if real_serial_layers is not None: + m.load_weights(kwargs['model_layers'], by_name=True) + logging.info(f"Loaded model weights from:{kwargs['model_layers']}") + + return m, encoders, decoders + + # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # ~~~~~~~ Training ~~~~~~~~~~~~~~~~ # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ @@ -1173,13 +1279,13 @@ def train_model_from_generators( inspect_show_labels: bool, return_history: bool = False, plot: bool = True, + save_last_model: bool = False, ) -> Union[Model, Tuple[Model, History]]: """Train a model from tensor generators for validation and training data. Training data lives on disk, it will be loaded by generator functions. Plots the metric history after training. Creates a directory to save weights, if necessary. Measures runtime and plots architecture diagram if inspect_model is True. - :param model: The model to optimize :param generate_train: Generator function that yields mini-batches of training data. :param generate_valid: Generator function that yields mini-batches of validation data. @@ -1193,6 +1299,9 @@ def train_model_from_generators( :param inspect_model: If True, measure training and inference runtime of the model and generate architecture plot. :param inspect_show_labels: If True, show labels on the architecture plot. :param return_history: If true return history from training and don't plot the training history + :param plot: If true, plots the metrics for train and validation set at the end of each epoch + :param save_last_model: If true saves the model weights from last epoch otherwise saves model with best validation loss + :return: The optimized model. """ model_file = os.path.join(output_folder, run_id, run_id + MODEL_EXT) @@ -1206,7 +1315,7 @@ def train_model_from_generators( history = model.fit( generate_train, steps_per_epoch=training_steps, epochs=epochs, verbose=1, validation_steps=validation_steps, validation_data=generate_valid, - callbacks=_get_callbacks(patience, model_file), + callbacks=_get_callbacks(patience, model_file, save_last_model), ) generate_train.kill_workers() generate_valid.kill_workers() @@ -1221,10 +1330,10 @@ def train_model_from_generators( def _get_callbacks( - patience: int, model_file: str, + patience: int, model_file: str, save_last_model: bool ) -> List[Callback]: callbacks = [ - ModelCheckpoint(filepath=model_file, verbose=1, save_best_only=True), + ModelCheckpoint(filepath=model_file, verbose=1, save_best_only=not save_last_model), EarlyStopping(monitor='val_loss', patience=patience * 3, verbose=1), ReduceLROnPlateau(monitor='val_loss', factor=0.5, patience=patience, verbose=1), ] diff --git a/ml4h/plots.py b/ml4h/plots.py index c353d1b94..c60282803 100755 --- a/ml4h/plots.py +++ b/ml4h/plots.py @@ -29,6 +29,7 @@ from matplotlib.ticker import AutoMinorLocator, MultipleLocator from sklearn import manifold +from sklearn.decomposition import PCA from sklearn.metrics import roc_curve, auc, precision_recall_curve, average_precision_score from sklearn.metrics import brier_score_loss, precision_score, recall_score, f1_score, roc_auc_score from sklearn.calibration import calibration_curve @@ -39,10 +40,6 @@ from scipy.ndimage.filters import gaussian_filter from scipy import stats -import ml4h.tensormap.ukb.ecg -import ml4h.tensormap.mgb.ecg -from ml4h.tensormap.mgb.dynamic import make_waveform_maps - from ml4h.TensorMap import TensorMap from ml4h.metrics import concordance_index, coefficient_of_determination from ml4h.defines import IMAGE_EXT, JOIN_CHAR, PDF_EXT, TENSOR_EXT, ECG_REST_LEADS, ECG_REST_MEDIAN_LEADS, PARTNERS_DATETIME_FORMAT, PARTNERS_DATE_FORMAT, HD5_GROUP_CHAR @@ -165,7 +162,7 @@ def evaluate_predictions( if tm.sentinel is not None: y_predictions = y_predictions[y_truth != tm.sentinel] y_truth = y_truth[y_truth != tm.sentinel] - _plot_reconstruction(tm, y_truth, y_predictions, folder, test_paths) + plot_reconstruction(tm, y_truth, y_predictions, folder, test_paths) if prediction_flat.shape[0] == truth_flat.shape[0]: performance_metrics.update(subplot_pearson_per_class(prediction_flat, truth_flat, tm.channel_map, protected, title, prefix=folder)) elif tm.is_continuous(): @@ -190,10 +187,15 @@ def plot_metric_history(history, training_steps: int, title: str, prefix='./figu cols = max(2, int(math.ceil(math.sqrt(total_plots)))) rows = max(2, int(math.ceil(total_plots / cols))) f, axes = plt.subplots(rows, cols, figsize=(int(cols*SUBPLOT_SIZE), int(rows*SUBPLOT_SIZE))) + logging.info(f'all keys {list(sorted(history.history.keys()))}') for k in sorted(history.history.keys()): if not k.startswith('val_'): if isinstance(history.history[k][0], LearningRateSchedule): history.history[k] = [history.history[k][0](i * training_steps) for i in range(len(history.history[k]))] + if len(np.array(history.history[k]).shape) > 1: # Hack for models with paired loss + history.history[k] = np.array(history.history[k])[:, 0, 0] + if 'val_' + k in history.history: + history.history['val_' + k] = np.array(history.history['val_' + k])[:, 0, 0] axes[row, col].plot(history.history[k]) k_split = str(k).replace('output_', '').split('_') k_title = " ".join(OrderedDict.fromkeys(k_split)) @@ -464,7 +466,7 @@ def subplot_pearson_per_class( os.makedirs(os.path.dirname(figure_path)) plt.savefig(figure_path, bbox_inches='tight') plt.clf() - logging.info(f"Saved Pearson correlations at: {figure_path} with {len(protected)} protected TensorMaps.") + logging.info(f"{label_text} saved at: {figure_path}{f' with {len(protected)} protected TensorMaps.' if len(protected) else '.'}") return labels_to_areas @@ -1225,15 +1227,16 @@ def _plot_partners_figure( def plot_partners_ecgs(args): plot_tensors = [ - ml4h.tensormap.mgb.ecg.partners_ecg_patientid, ml4h.tensormap.mgb.ecg.partners_ecg_firstname, ml4h.tensormap.mgb.ecg.partners_ecg_lastname, - ml4h.tensormap.mgb.ecg.partners_ecg_sex, ml4h.tensormap.mgb.ecg.partners_ecg_dob, ml4h.tensormap.mgb.ecg.partners_ecg_age, - ml4h.tensormap.mgb.ecg.partners_ecg_datetime, ml4h.tensormap.mgb.ecg.partners_ecg_sitename, ml4h.tensormap.mgb.ecg.partners_ecg_location, - ml4h.tensormap.mgb.ecg.partners_ecg_read_md, ml4h.tensormap.mgb.ecg.partners_ecg_taxis_md, ml4h.tensormap.mgb.ecg.partners_ecg_rate_md, - ml4h.tensormap.mgb.ecg.partners_ecg_pr_md, ml4h.tensormap.mgb.ecg.partners_ecg_qrs_md, ml4h.tensormap.mgb.ecg.partners_ecg_qt_md, - ml4h.tensormap.mgb.ecg.partners_ecg_paxis_md, ml4h.tensormap.mgb.ecg.partners_ecg_raxis_md, ml4h.tensormap.mgb.ecg.partners_ecg_qtc_md, + 'partners_ecg_patientid', 'partners_ecg_firstname', 'partners_ecg_lastname', + 'partners_ecg_sex', 'partners_ecg_dob', 'partners_ecg_age', + 'partners_ecg_datetime', 'partners_ecg_sitename', 'partners_ecg_location', + 'partners_ecg_read_md', 'partners_ecg_taxis_md', 'partners_ecg_rate_md', + 'partners_ecg_pr_md', 'partners_ecg_qrs_md', 'partners_ecg_qt_md', + 'partners_ecg_paxis_md', 'partners_ecg_raxis_md', 'partners_ecg_qtc_md', ] - voltage_tensor = make_waveform_maps('partners_ecg_2500_raw') - tensor_maps_in = plot_tensors + [voltage_tensor] + voltage_tensor = 'partners_ecg_2500_raw' + from ml4h.tensor_maps_partners_ecg_labels import TMAPS + tensor_maps_in = [TMAPS[it] for it in plot_tensors + [voltage_tensor]] tensor_paths = [os.path.join(args.tensors, tp) for tp in os.listdir(args.tensors) if os.path.splitext(tp)[-1].lower()==TENSOR_EXT] if 'clinical' == args.plot_mode: @@ -1500,12 +1503,13 @@ def plot_ecg_rest( :param is_blind: if True, the plot gets blinded (helpful for review and annotation) """ map_fields_to_tmaps = { - 'ramp': ml4h.tensormap.ukb.ecg.ecg_rest_ramplitude_raw, - 'samp': ml4h.tensormap.ukb.ecg.ecg_rest_samplitude_raw, - 'aVL': ml4h.tensormap.ukb.ecg.ecg_rest_lvh_avl, - 'Sokolow_Lyon': ml4h.tensormap.ukb.ecg.ecg_rest_lvh_sokolow_lyon, - 'Cornell': ml4h.tensormap.ukb.ecg.ecg_rest_lvh_cornell, - } + 'ramp': 'ecg_rest_ramplitude_raw', + 'samp': 'ecg_rest_samplitude_raw', + 'aVL': 'ecg_rest_lvh_avl', + 'Sokolow_Lyon': 'ecg_rest_lvh_sokolow_lyon', + 'Cornell': 'ecg_rest_lvh_cornell', + } + from ml4h.tensor_from_file import TMAPS raw_scale = 0.005 # Conversion from raw to mV default_yrange = ECG_REST_PLOT_DEFAULT_YRANGE # mV time_interval = 2.5 # time-interval per plot in seconds. ts_Reference data is in s, voltage measurement is 5 uv per lsb @@ -1517,7 +1521,7 @@ def plot_ecg_rest( with h5py.File(tensor_path, 'r') as hd5: traces, text = _ecg_rest_traces_and_text(hd5) for field in map_fields_to_tmaps: - tm = map_fields_to_tmaps[field] + tm = TMAPS[map_fields_to_tmaps[field]] patient_dic[field] = np.zeros(tm.shape) try: patient_dic[field][:] = tm.tensor_from_file(tm, hd5) @@ -2076,32 +2080,150 @@ def _text_on_plot(axes, x, y, text, alpha=0.8, background='white'): t.set_bbox({'facecolor': background, 'alpha': alpha, 'edgecolor': background}) -def _plot_reconstruction( +def plot_reconstruction( tm: TensorMap, y_true: np.ndarray, y_pred: np.ndarray, - folder: str, paths: List[str], + folder: str, paths: List[str], num_samples: int = 4, ): - num_samples = 3 logging.info(f'Plotting {num_samples} reconstructions of {tm}.') if None in tm.shape: # can't handle dynamic shapes return for i in range(num_samples): - title = f'{tm.name}_{os.path.basename(paths[i]).replace(TENSOR_EXT, "")}_reconstruction' + sample_id = os.path.basename(paths[i]).replace(TENSOR_EXT, '') + title = f'{tm.name}_{sample_id}_reconstruction' y = y_true[i].reshape(tm.shape) yp = y_pred[i].reshape(tm.shape) if tm.axes() == 2: - fig = plt.figure(figsize=(SUBPLOT_SIZE, SUBPLOT_SIZE * num_samples)) + index2channel = {v: k for k, v in tm.channel_map.items()} + fig, axes = plt.subplots(tm.shape[1], 2, figsize=(2 * SUBPLOT_SIZE, 6*SUBPLOT_SIZE)) for j in range(tm.shape[1]): - plt.subplot(tm.shape[1], 1, j + 1) - plt.plot(y[:, j], c='k', linestyle='--', label='original') - plt.plot(yp[:, j], c='b', label='reconstruction') - if j == 0: - plt.title(title) - plt.legend() + axes[j, 0].plot(y[:, j], c='k', linestyle='--', label='original') + axes[j, 1].plot(yp[:, j], c='b', label='reconstruction') + axes[j, 0].set_title(f'Lead: {index2channel[j]}') + axes[j, 0].legend() + axes[j, 1].legend() plt.tight_layout() - # TODO: implement 3d, 4d - plt.savefig(os.path.join(folder, title + IMAGE_EXT)) + plt.savefig(os.path.join(folder, title + IMAGE_EXT)) + elif tm.axes() == 3: + if tm.is_categorical(): + plt.imsave(f"{folder}{sample_id}_{tm.name}_truth_{i:02d}{IMAGE_EXT}", np.argmax(y, axis=-1), cmap='plasma') + plt.imsave(f"{folder}{sample_id}_{tm.name}_prediction_{i:02d}{IMAGE_EXT}", np.argmax(yp, axis=-1), cmap='plasma') + else: + plt.imsave(f'{folder}{sample_id}_{tm.name}_truth_{i:02d}{IMAGE_EXT}', y[:, :, 0], cmap='gray') + plt.imsave(f'{folder}{sample_id}_{tm.name}_prediction_{i:02d}{IMAGE_EXT}', yp[:, :, 0], cmap='gray') + elif tm.axes() == 4: + for j in range(y.shape[3]): + image_path_base = f'{folder}{sample_id}_{tm.name}_{i:03d}_{j:03d}' + if tm.is_categorical(): + truth = np.argmax(yp[tm.output_name()][:, :, j, :], axis=-1) + prediction = np.argmax(y[:, :, j, :], axis=-1) + plt.imsave(f'{image_path_base}_truth{IMAGE_EXT}', truth, cmap='plasma') + plt.imsave(f'{image_path_base}_prediction{IMAGE_EXT}', prediction, cmap='plasma') + else: + plt.imsave(f'{image_path_base}_truth{IMAGE_EXT}', y[:, :, j, 0], cmap='gray') + plt.imsave(f'{image_path_base}_prediction{IMAGE_EXT}', yp[:, :, j, :], cmap='gray') plt.clf() -if __name__ == '__main__': - plot_noisy() +def pca_on_matrix(matrix, pca_components, prefix='./figures/'): + pca = PCA() + pca.fit(matrix) + print(f'PCA explains {100 * np.sum(pca.explained_variance_ratio_[:pca_components]):0.1f}% of variance with {pca_components} top PCA components.') + matrix_reduced = pca.transform(matrix)[:, :pca_components] + print(f'PCA reduces matrix shape:{matrix_reduced.shape} from matrix shape: {matrix.shape}') + plot_scree(pca_components, 100 * pca.explained_variance_ratio_, prefix) + return pca, matrix_reduced + + +def plot_scree(pca_components, percent_explained, prefix='./figures/'): + _ = plt.figure(figsize=(6, 4)) + plt.plot(range(len(percent_explained)), percent_explained, 'g.-', linewidth=1) + plt.axvline(x=pca_components, c='r', linewidth=3) + label = f'{np.sum(percent_explained[:pca_components]):0.1f}% of variance explained by top {pca_components} of {len(percent_explained)} components' + plt.text(pca_components + 0.02 * len(percent_explained), percent_explained[1], label) + plt.title('Scree Plot') + plt.xlabel('Principal Components') + plt.ylabel('% of Variance Explained by Each Component') + figure_path = f'{prefix}pca_{pca_components}_of_{len(percent_explained)}.png' + if not os.path.exists(os.path.dirname(figure_path)): + os.makedirs(os.path.dirname(figure_path)) + plt.savefig(figure_path) + + +def unit_vector(vector): + """ Returns the unit vector of the vector. """ + return vector / np.linalg.norm(vector) + + +def angle_between(v1, v2): + """ Returns the angle in radians between vectors 'v1' and 'v2':: + angle_between((1, 0, 0), (0, 1, 0)) + 90 + angle_between((1, 0, 0), (1, 0, 0)) + 0.0 + angle_between((1, 0, 0), (-1, 0, 0)) + 180 + """ + v1_u = unit_vector(v1) + v2_u = unit_vector(v2) + return np.arccos(np.clip(np.dot(v1_u, v2_u), -1.0, 1.0)) * 180 / 3.141592 + + +def stratify_latent_space(stratify_column, stratify_thresh, latent_cols, latent_df): + hit = latent_df.loc[latent_df[stratify_column] >= stratify_thresh][latent_cols].to_numpy() + miss = latent_df.loc[latent_df[stratify_column] < stratify_thresh][latent_cols].to_numpy() + miss_mean_vector = np.mean(miss, axis=0) + hit_mean_vector = np.mean(hit, axis=0) + angle = angle_between(miss_mean_vector, hit_mean_vector) + print(f'Angle between {stratify_column} and all others: {angle}, \n' + f'Hit shape {hit.shape}, miss:{miss.shape} threshold:{stratify_thresh}\n' + f'Distance: {np.linalg.norm(hit_mean_vector - miss_mean_vector):.3f}, ' + f'Hit std {np.std(hit, axis=1).mean():.3f}, miss std:{np.std(miss, axis=1).mean():.3f}\n') + return hit_mean_vector, miss_mean_vector + + +def plot_hit_to_miss_transforms(latent_df, decoders, feature='Sex_Female_0_0', prefix='./figures/', + thresh=1.0, latent_dimension=256, samples=16, scalar=3.0, cmap='plasma'): + latent_cols = [f'latent_{i}' for i in range(latent_dimension)] + female, male = stratify_latent_space(feature, thresh, latent_cols, latent_df) + sex_vector = female - male + embeddings = latent_df.iloc[:samples][latent_cols].to_numpy() + sexes = latent_df.iloc[:samples][feature].to_numpy() + print(f'Embedding shape: {embeddings.shape} sexes shape: {sexes.shape}') + + sex_vectors = np.tile(sex_vector, (samples, 1)) + male_to_female = embeddings + (scalar * sex_vectors) + female_to_male = embeddings - (scalar * sex_vectors) + print(f'embeddings shape: {embeddings.shape} features vectors shape: {sex_vectors.shape}') + for dtm in decoders: + predictions = decoders[dtm].predict(embeddings) + m2f = decoders[dtm].predict(male_to_female) + f2m = decoders[dtm].predict(female_to_male) + print(f'prediction shape: {predictions.shape}') + if dtm.axes() == 3: + fig, axes = plt.subplots(samples, 2, figsize=(18, samples * 4)) + for i in range(samples): + axes[i, 0].set_title(f"{feature}: {sexes[i]} ?>== thresh: + axes[i, 1].imshow(np.argmax(f2m[i, ...], axis=-1), cmap=cmap) + axes[i, 1].set_title(f'{feature} to less than {thresh}') + else: + axes[i, 1].imshow(np.argmax(m2f[i, ...], axis=-1), cmap=cmap) + axes[i, 1].set_title(f'{feature} to more than or equal to {thresh}') + else: + axes[i, 0].imshow(predictions[i, ..., 0], cmap='gray') + if sexes[i] >= thresh: + axes[i, 1].imshow(f2m[i, ..., 0], cmap='gray') + axes[i, 1].set_title(f'{feature} to less than {thresh}') + else: + axes[i, 1].imshow(m2f[i, ..., 0], cmap='gray') + axes[i, 1].set_title(f'{feature} to more than or equal to {thresh}') + figure_path = f'{prefix}/{dtm.name}_{feature}_transform_scalar_{scalar}.png' + if not os.path.exists(os.path.dirname(figure_path)): + os.makedirs(os.path.dirname(figure_path)) + plt.savefig(figure_path) \ No newline at end of file diff --git a/ml4h/recipes.py b/ml4h/recipes.py index 80c22f93c..148c2c517 100755 --- a/ml4h/recipes.py +++ b/ml4h/recipes.py @@ -15,15 +15,16 @@ from ml4h.defines import TENSOR_EXT, MODEL_EXT from ml4h.tensormap.tensor_map_maker import write_tensor_maps from ml4h.tensorize.tensor_writer_mgb import write_tensors_mgb -from ml4h.explorations import test_labels_to_label_map, infer_with_pixels, explore +from ml4h.explorations import test_labels_to_label_map, infer_with_pixels, explore, latent_space_dataframe from ml4h.tensor_generators import BATCH_INPUT_INDEX, BATCH_OUTPUT_INDEX, BATCH_PATHS_INDEX from ml4h.explorations import mri_dates, ecg_dates, predictions_to_pngs, sample_from_language_model from ml4h.explorations import plot_while_learning, plot_histograms_of_tensors_in_pdf, cross_reference from ml4h.tensor_generators import TensorGenerator, test_train_valid_tensor_generators, big_batch_from_minibatch_generator -from ml4h.models import make_character_model_plus, embed_model_predict, make_siamese_model, make_multimodal_multitask_model +from ml4h.models import make_character_model_plus, embed_model_predict, make_siamese_model, make_multimodal_multitask_model, make_paired_autoencoder_model from ml4h.metrics import get_roc_aucs, get_precision_recall_aucs, get_pearson_coefficients, log_aucs, log_pearson_coefficients from ml4h.models import train_model_from_generators, get_model_inputs_outputs, make_shallow_model, make_hidden_layer_model, saliency_map -from ml4h.plots import evaluate_predictions, plot_scatters, plot_rocs, plot_precision_recalls, subplot_roc_per_class, plot_tsne, plot_prediction_calibrations +from ml4h.plots import evaluate_predictions, plot_scatters, plot_rocs, plot_precision_recalls, subplot_roc_per_class, plot_tsne, plot_prediction_calibrations, \ + plot_reconstruction, plot_hit_to_miss_transforms from ml4h.tensorize.tensor_writer_ukbb import write_tensors, append_fields_from_csv, append_gene_csv, write_tensors_from_dicom_pngs, write_tensors_from_ecg_pngs from ml4h.plots import subplot_rocs, subplot_comparison_rocs, subplot_scatters, subplot_comparison_scatters, plot_saliency_maps, plot_partners_ecgs, plot_ecg_rest_mp @@ -74,20 +75,20 @@ def run(args): ecg_dates(args.tensors, args.output_folder, args.id) elif 'plot_histograms' == args.mode: plot_histograms_of_tensors_in_pdf(args.id, args.tensors, args.output_folder, args.max_samples) - elif 'plot_heatmap' == args.mode: - plot_heatmap_of_tensors(args.id, args.tensors, args.output_folder, args.min_samples, args.max_samples) elif 'plot_resting_ecgs' == args.mode: plot_ecg_rest_mp(args.tensors, args.min_sample_id, args.max_sample_id, args.output_folder, args.num_workers) elif 'plot_partners_ecgs' == args.mode: plot_partners_ecgs(args) - elif 'tabulate_correlations' == args.mode: - tabulate_correlations_of_tensors(args.id, args.tensors, args.output_folder, args.min_samples, args.max_samples) elif 'train_shallow' == args.mode: train_shallow_model(args) elif 'train_char' == args.mode: train_char_model(args) elif 'train_siamese' == args.mode: train_siamese_model(args) + elif 'train_paired' == args.mode: + train_paired_model(args) + elif 'inspect_paired' == args.mode: + inspect_paired_model(args) elif 'write_tensor_maps' == args.mode: write_tensor_maps(args) elif 'append_continuous_csv' == args.mode: @@ -135,8 +136,8 @@ def train_multimodal_multitask(args): generate_train, generate_valid, generate_test = test_train_valid_tensor_generators(**args.__dict__) model = make_multimodal_multitask_model(**args.__dict__) model = train_model_from_generators( - model, generate_train, generate_valid, args.training_steps, args.validation_steps, args.batch_size, - args.epochs, args.patience, args.output_folder, args.id, args.inspect_model, args.inspect_show_labels, + model, generate_train, generate_valid, args.training_steps, args.validation_steps, args.batch_size, args.epochs, + args.patience, args.output_folder, args.id, args.inspect_model, args.inspect_show_labels, save_last_model=args.save_last_model ) out_path = os.path.join(args.output_folder, args.id + '/') @@ -285,14 +286,14 @@ def infer_multimodal_multitask(args): logging.info(f"Wrote:{stats['count']} rows of inference. Last tensor:{tensor_paths[0]}") -def hidden_inference_file_name(output_folder: str, id_: str) -> str: - return os.path.join(output_folder, id_, 'hidden_inference_' + id_ + '.tsv') +def _hidden_file_name(output_folder: str, prefix_: str, id_: str, extension_: str) -> str: + return os.path.join(output_folder, id_, prefix_ + id_ + extension_) def infer_hidden_layer_multimodal_multitask(args): stats = Counter() args.num_workers = 0 - inference_tsv = hidden_inference_file_name(args.output_folder, args.id) + inference_tsv = _hidden_file_name(args.output_folder, 'hidden_inference_', args.id, '.tsv') tsv_style_is_genetics = 'genetics' in args.tsv_style tensor_paths = [os.path.join(args.tensors, tp) for tp in sorted(os.listdir(args.tensors)) if os.path.splitext(tp)[-1].lower() == TENSOR_EXT] # hard code batch size to 1 so we can iterate over file names and generated tensors together in the tensor_paths for loop @@ -303,6 +304,7 @@ def infer_hidden_layer_multimodal_multitask(args): generate_test.set_worker_paths(tensor_paths) full_model = make_multimodal_multitask_model(**args.__dict__) embed_model = make_hidden_layer_model(full_model, args.tensor_maps_in, args.hidden_layer) + embed_model.save(_hidden_file_name(args.output_folder, f'{args.hidden_layer}_encoder_', args.id, '.h5')) dummy_input = {tm.input_name(): np.zeros((1,) + full_model.get_layer(tm.input_name()).input_shape[0][1:]) for tm in args.tensor_maps_in} dummy_out = embed_model.predict(dummy_input) latent_dimensions = int(np.prod(dummy_out.shape[1:])) @@ -392,6 +394,57 @@ def train_siamese_model(args): ) +def train_paired_model(args): + full_model, encoders, decoders = make_paired_autoencoder_model(**args.__dict__) + generate_train, generate_valid, generate_test = test_train_valid_tensor_generators(**args.__dict__) + train_model_from_generators( + full_model, generate_train, generate_valid, args.training_steps, args.validation_steps, args.batch_size, + args.epochs, args.patience, args.output_folder, args.id, args.inspect_model, args.inspect_show_labels, + save_last_model=True + ) + for tm in encoders: + encoders[tm].save(f'{args.output_folder}{args.id}/encoder_{tm.name}.h5') + for tm in decoders: + decoders[tm].save(f'{args.output_folder}{args.id}/decoder_{tm.name}.h5') + out_path = os.path.join(args.output_folder, args.id, 'reconstructions/') + test_data, test_labels, test_paths = big_batch_from_minibatch_generator(generate_test, args.test_steps) + samples = args.test_steps * args.batch_size + predictions_list = full_model.predict(test_data) + predictions_dict = {name: pred for name, pred in zip(full_model.output_names, predictions_list)} + logging.info(f'Predictions and shapes are: {[(p, predictions_dict[p].shape) for p in predictions_dict]}') + performance_metrics = {} + for i, etm in enumerate(encoders): + embed = encoders[etm].predict(test_data[etm.input_name()]) + plot_reconstruction(etm, test_data[etm.input_name()], predictions_dict[etm.output_name()], out_path, test_paths, samples) + for dtm in decoders: + if dtm.axes() > 1: + reconstruction = decoders[dtm].predict(embed) + logging.info(f'{dtm.name} has prediction shape: {reconstruction.shape} from embed shape: {embed.shape}') + my_out_path = os.path.join(out_path, f'decoding_{dtm.name}_from_{etm.name}/') + if not os.path.exists(os.path.dirname(my_out_path)): + os.makedirs(os.path.dirname(my_out_path)) + plot_reconstruction(dtm, test_data[dtm.input_name()], reconstruction, my_out_path, test_paths, samples) + else: + y_truth = np.array(test_labels[dtm.output_name()]) + performance_metrics.update(evaluate_predictions(dtm, decoders[dtm].predict(embed), y_truth, {}, dtm.name, os.path.join(args.output_folder, args.id), test_paths)) + return performance_metrics + + +def inspect_paired_model(args): + full_model, encoders, decoders = make_paired_autoencoder_model(**args.__dict__) + infer_hidden_tsv = _hidden_file_name(args.output_folder, 'hidden_inference_', args.id, '.tsv') + latent_df = latent_space_dataframe(infer_hidden_tsv, args.app_csv) + out_folder = os.path.join(args.output_folder, args.id, 'latent_transformations/') + for tm in args.tensor_maps_protected: + index2channel = {v: k for k, v in tm.channel_map.items()} + thresh = 1 if tm.is_categorical() else tm.normalization.mean + plot_hit_to_miss_transforms(latent_df, decoders, + feature=index2channel[0], + thresh=thresh, + latent_dimension=args.dense_layers[0], + prefix=out_folder) + + def plot_predictions(args): _, _, generate_test = test_train_valid_tensor_generators(**args.__dict__) model = make_multimodal_multitask_model(**args.__dict__) diff --git a/ml4h/tensorize/tensor_writer_ukbb.py b/ml4h/tensorize/tensor_writer_ukbb.py index ceb4389e8..64242bcbe 100644 --- a/ml4h/tensorize/tensor_writer_ukbb.py +++ b/ml4h/tensorize/tensor_writer_ukbb.py @@ -87,10 +87,6 @@ def write_tensors( mri_unzip: str, mri_field_ids: List[int], xml_field_ids: List[int], - zoom_x: int, - zoom_y: int, - zoom_width: int, - zoom_height: int, write_pngs: bool, min_sample_id: int, max_sample_id: int, @@ -109,13 +105,6 @@ def write_tensors( :param mri_unzip: Folder where zipped DICOM will be decompressed :param mri_field_ids: List of MRI field IDs from UKBB :param xml_field_ids: List of ECG field IDs from UKBB - :param x: Maximum x dimension of MRIs - :param y: Maximum y dimension of MRIs - :param z: Maximum z dimension of MRIs - :param zoom_x: x coordinate of the zoom - :param zoom_y: y coordinate of the zoom - :param zoom_width: width of the zoom - :param zoom_height: height of the zoom :param write_pngs: write MRIs as PNG images for debugging :param min_sample_id: Minimum sample id to generate, for parallelization :param max_sample_id: Maximum sample id to generate, for parallelization @@ -137,7 +126,7 @@ def write_tensors( continue try: with h5py.File(tp, 'w') as hd5: - _write_tensors_from_zipped_dicoms(zoom_x, zoom_y, zoom_width, zoom_height, write_pngs, tensors, mri_unzip, mri_field_ids, zip_folder, hd5, sample_id, stats) + _write_tensors_from_zipped_dicoms(write_pngs, tensors, mri_unzip, mri_field_ids, zip_folder, hd5, sample_id, stats) _write_tensors_from_zipped_niftis(zip_folder, mri_field_ids, hd5, sample_id, stats) _write_tensors_from_xml(xml_field_ids, xml_folder, hd5, sample_id, write_pngs, stats, continuous_stats) stats['Tensors written'] += 1 @@ -188,19 +177,26 @@ def write_tensors_from_dicom_pngs( continue stats[sample_header + '_' + sample_id] += 1 dicom_file = row[dicom_index] + try: png = imageio.imread(os.path.join(png_path, dicom_file + png_postfix)) + if len(png.shape) == 3 and png.mean() == png[:, :, 0].mean(): + png = png[:, :, 0] + elif len(png.shape) == 3: + raise ValueError(f'PNG has color information but no method to tensorize it {png.mean()}, 0ch :{png[:, :, 0].mean()}, 1ch :{png[:, :, 1].mean()}, 2ch :{png[:, :, 2].mean()}.') full_tensor = np.zeros((x, y), dtype=np.float32) full_tensor[:png.shape[0], :png.shape[1]] = png tensor_file = os.path.join(tensors, str(sample_id) + TENSOR_EXT) if not os.path.exists(os.path.dirname(tensor_file)): os.makedirs(os.path.dirname(tensor_file)) with h5py.File(tensor_file, 'a') as hd5: - tensor_name = series + '_annotated_' + row[instance_index] + tensor_name = series.lower() + '_annotated_' + row[instance_index] tp = tensor_path(path_prefix, tensor_name) if tp in hd5: tensor = first_dataset_at_path(hd5, tp) - tensor[:] = full_tensor + min_x = min(png.shape[0], tensor.shape[0]) + min_y = min(png.shape[1], tensor.shape[1]) + tensor[:min_x, :min_y] = full_tensor[:min_x, :min_y] stats['updated'] += 1 else: create_tensor_in_hd5(hd5, path_prefix, tensor_name, full_tensor, stats) @@ -328,7 +324,7 @@ def _dicts_and_plots_from_tensorization( continuous = {} value_counter = Counter() for k in sorted(list(stats.keys())): - logging.info("{} has {}".format(k, stats[k])) + #logging.info("{} has {}".format(k, stats[k])) if 'categorical' not in k and 'continuous' not in k: continue @@ -346,10 +342,10 @@ def _dicts_and_plots_from_tensorization( plot_value_counter(list(categories.keys()), value_counter, a_id + '_v_count', os.path.join(output_folder, a_id)) plot_histograms(continuous_stats, a_id, os.path.join(output_folder, a_id)) - logging.info("Continuous tensor map: {}".format(continuous)) - logging.info("Continuous Columns: {}".format(len(continuous))) - logging.info("Category tensor map: {}".format(categories)) - logging.info("Categories Columns: {}".format(len(categories))) + # logging.info("Continuous tensor map: {}".format(continuous)) + # logging.info("Continuous Columns: {}".format(len(continuous))) + # logging.info("Category tensor map: {}".format(categories)) + # logging.info("Categories Columns: {}".format(len(categories))) def _to_float_or_false(s): @@ -367,10 +363,6 @@ def _to_float_or_nan(s): def _write_tensors_from_zipped_dicoms( - zoom_x: int, - zoom_y: int, - zoom_width: int, - zoom_height: int, write_pngs: bool, tensors: str, dicoms: str, @@ -390,10 +382,8 @@ def _write_tensors_from_zipped_dicoms( os.makedirs(dicom_folder) with zipfile.ZipFile(zipped, "r") as zip_ref: zip_ref.extractall(dicom_folder) - _write_tensors_from_dicoms( - zoom_x, zoom_y, zoom_width, zoom_height, write_pngs, tensors, dicom_folder, - hd5, sample_str, stats, - ) + ukb_instance = zipped.split('_')[2] + _write_tensors_from_dicoms(write_pngs, tensors, dicom_folder, hd5, sample_str, ukb_instance, stats) stats['MRI fields written'] += 1 shutil.rmtree(dicom_folder) @@ -410,36 +400,31 @@ def _write_tensors_from_zipped_niftis(zip_folder: str, mri_field_ids: List[str], def _write_tensors_from_dicoms( - zoom_x: int, zoom_y: int, zoom_width: int, zoom_height: int, write_pngs: bool, tensors: str, - dicom_folder: str, hd5: h5py.File, sample_str: str, stats: Dict[str, int], + write_pngs: bool, tensors: str, dicom_folder: str, hd5: h5py.File, sample_str: str, ukb_instance: str, stats: Dict[str, int], ) -> None: """Convert a folder of DICOMs from a sample into tensors for each series Segmented dicoms require special processing and are written to tensor per-slice Arguments - :param x: Width of the tensors (actual MRI width will be padded with 0s or cropped to this number) - :param y: Height of the tensors (actual MRI width will be padded with 0s or cropped to this number) - :param z: Minimum number of slices to include in the each tensor if more slices are found they will be kept - :param zoom_x: x coordinate of the zoom - :param zoom_y: y coordinate of the zoom - :param zoom_width: width of the zoom - :param zoom_height: height of the zoom :param write_pngs: write MRIs as PNG images for debugging :param tensors: Folder where hd5 tensor files are being written :param dicom_folder: Folder with all dicoms associated with one sample. :param hd5: Tensor file in which to create datasets for each series and each segmented slice :param sample_str: The current sample ID as a string + :param ukb_instance: The UK Biobank assessment visit instance number :param stats: Counter to keep track of summary statistics """ views = defaultdict(list) + series_to_numbers = defaultdict(set) min_ideal_series = 9e9 for dicom in os.listdir(dicom_folder): if os.path.splitext(dicom)[-1] != DICOM_EXT: continue d = pydicom.read_file(os.path.join(dicom_folder, dicom)) series = d.SeriesDescription.lower().replace(' ', '_') + series_to_numbers[series].add(int(d.SeriesNumber)) if series + '_12bit' in MRI_LIVER_SERIES_12BIT and d.LargestImagePixelValue > 2048: views[series + '_12bit'].append(d) stats[series + '_12bit'] += 1 @@ -462,99 +447,61 @@ def _write_tensors_from_dicoms( else: mri_group = 'ukb_mri' + if len(series_to_numbers[v]) > 1 and v not in MRI_BRAIN_SERIES: + max_series = max(series_to_numbers[v]) + single_series = [dicom for dicom in views[v] if int(dicom.SeriesNumber) == max_series] + # for d in views[v]: + # logging.warning(f'{d.SeriesNumber} with Date: {_datetime_from_dicom(d)} Time {d.AcquisitionTime}') + logging.warning(f'{v} has {len(views[v])} series:{series_to_numbers[v]} Using only max series: {max_series} with {len(single_series)}') + views[v] = single_series if v == MRI_TO_SEGMENT: - _tensorize_short_and_long_axis_segmented_cardiac_mri(views[v], v, zoom_x, zoom_y, zoom_width, zoom_height, write_pngs, tensors, hd5, mri_date, mri_group, stats) + _tensorize_short_and_long_axis_segmented_cardiac_mri(views[v], v, ukb_instance, hd5, mri_date, mri_group, stats) elif v in MRI_BRAIN_SERIES: _tensorize_brain_mri(views[v], v, mri_date, mri_group, hd5) else: - mri_data = np.zeros((views[v][0].Rows, views[v][0].Columns, len(views[v])), dtype=np.float32) - for slicer in views[v]: - _save_pixel_dimensions_if_missing(slicer, v, hd5) - _save_slice_thickness_if_missing(slicer, v, hd5) - _save_series_orientation_and_position_if_missing(slicer, v, hd5) - slice_index = slicer.InstanceNumber - 1 - if v in MRI_LIVER_IDEAL_PROTOCOL: - slice_index = _slice_index_from_ideal_protocol(slicer, min_ideal_series) - mri_data[..., slice_index] = slicer.pixel_array.astype(np.float32) - create_tensor_in_hd5(hd5, mri_group, v, mri_data, stats, mri_date) + pass + # mri_data = np.zeros((views[v][0].Rows, views[v][0].Columns, len(views[v])), dtype=np.float32) + # for slicer in views[v]: + # _save_pixel_dimensions_if_missing(slicer, v, hd5) + # _save_slice_thickness_if_missing(slicer, v, hd5) + # _save_series_orientation_and_position_if_missing(slicer, v, hd5) + # slice_index = slicer.InstanceNumber - 1 + # if v in MRI_LIVER_IDEAL_PROTOCOL: + # slice_index = _slice_index_from_ideal_protocol(slicer, min_ideal_series) + # mri_data[..., slice_index] = slicer.pixel_array.astype(np.float32) + # create_tensor_in_hd5(hd5, mri_group, f'{v}/{ukb_instance}', mri_data, stats, mri_date) def _tensorize_short_and_long_axis_segmented_cardiac_mri( - slices: List[pydicom.Dataset], series: str, zoom_x: int, zoom_y: int, - zoom_width: int, zoom_height: int, write_pngs: bool, tensors: str, - hd5: h5py.File, mri_date: datetime.datetime, mri_group: str, - stats: Dict[str, int], + slices: List[pydicom.Dataset], series: str, instance: str, + hd5: h5py.File, mri_date: datetime.datetime, mri_group: str, stats: Dict[str, int], ) -> None: - systoles = {} - diastoles = {} - systoles_pix = {} - systoles_masks = {} - diastoles_masks = {} - for slicer in slices: - full_mask = np.zeros((slicer.Rows, slicer.Columns), dtype=np.float32) - full_slice = np.zeros((slicer.Rows, slicer.Columns), dtype=np.float32) - + #full_slice = np.zeros((slicer.Rows, slicer.Columns), dtype=np.float32) if _has_overlay(slicer): if _is_mitral_valve_segmentation(slicer): series = series.replace('sax', 'lax') else: series = series.replace('lax', 'sax') - series_segmented = f'{series}_segmented' - series_zoom = f'{series}_zoom' - series_zoom_segmented = f'{series}_zoom_segmented' + series_segmented = f'{series}_segmented' try: overlay, mask, ventricle_pixels, _ = _get_overlay_from_dicom(slicer) except KeyError: logging.exception(f'Got key error trying to make anatomical mask, skipping.') continue - _save_pixel_dimensions_if_missing(slicer, series, hd5) - _save_slice_thickness_if_missing(slicer, series, hd5) - _save_series_orientation_and_position_if_missing(slicer, series, hd5, str(slicer.InstanceNumber)) + # _save_pixel_dimensions_if_missing(slicer, series, hd5) + # _save_slice_thickness_if_missing(slicer, series, hd5) + # _save_series_orientation_and_position_if_missing(slicer, series, hd5, str(slicer.InstanceNumber)) _save_pixel_dimensions_if_missing(slicer, series_segmented, hd5) _save_slice_thickness_if_missing(slicer, series_segmented, hd5) _save_series_orientation_and_position_if_missing(slicer, series_segmented, hd5, str(slicer.InstanceNumber)) - - cur_angle = (slicer.InstanceNumber - 1) // MRI_FRAMES # dicom InstanceNumber is 1-based - full_slice[:] = slicer.pixel_array.astype(np.float32) - create_tensor_in_hd5(hd5, mri_group, f'{series}{HD5_GROUP_CHAR}{slicer.InstanceNumber}', full_slice, stats, mri_date) - create_tensor_in_hd5(hd5, mri_group, f'{series_zoom_segmented}{HD5_GROUP_CHAR}{slicer.InstanceNumber}', mask, stats, mri_date) - - zoom_slice = full_slice[zoom_x: zoom_x + zoom_width, zoom_y: zoom_y + zoom_height] - zoom_mask = mask[zoom_x: zoom_x + zoom_width, zoom_y: zoom_y + zoom_height] - create_tensor_in_hd5(hd5, mri_group, f'{series_zoom}{HD5_GROUP_CHAR}{slicer.InstanceNumber}', zoom_slice, stats, mri_date) - create_tensor_in_hd5(hd5, mri_group, f'{series_zoom_segmented}{HD5_GROUP_CHAR}{slicer.InstanceNumber}', zoom_mask, stats, mri_date) - - if (slicer.InstanceNumber - 1) % MRI_FRAMES == 0: # Diastole frame is always the first - diastoles[cur_angle] = slicer - diastoles_masks[cur_angle] = mask - if cur_angle not in systoles: - systoles[cur_angle] = slicer - systoles_pix[cur_angle] = ventricle_pixels - systoles_masks[cur_angle] = mask - else: - if ventricle_pixels < systoles_pix[cur_angle]: - systoles[cur_angle] = slicer - systoles_pix[cur_angle] = ventricle_pixels - systoles_masks[cur_angle] = mask - - for angle in diastoles: - logging.info(f'Found systole, instance:{systoles[angle].InstanceNumber} ventricle pixels:{systoles_pix[angle]}') - full_slice = diastoles[angle].pixel_array.astype(np.float32) - create_tensor_in_hd5(hd5, mri_group, f'diastole_frame_b{angle}', full_slice, stats, mri_date) - create_tensor_in_hd5(hd5, mri_group, f'diastole_mask_b{angle}', diastoles_masks[angle], stats, mri_date) - if write_pngs: - plt.imsave(tensors + 'diastole_frame_b' + str(angle) + IMAGE_EXT, full_slice) - plt.imsave(tensors + 'diastole_mask_b' + str(angle) + IMAGE_EXT, full_mask) - - full_slice = systoles[angle].pixel_array.astype(np.float32) - create_tensor_in_hd5(hd5, mri_group, f'systole_frame_b{angle}', full_slice, stats, mri_date) - create_tensor_in_hd5(hd5, mri_group, f'systole_mask_b{angle}', systoles_masks[angle], stats, mri_date) - if write_pngs: - plt.imsave(tensors + 'systole_frame_b' + str(angle) + IMAGE_EXT, full_slice) - plt.imsave(tensors + 'systole_mask_b' + str(angle) + IMAGE_EXT, full_mask) + # + # cur_angle = (slicer.InstanceNumber - 1) // MRI_FRAMES # dicom InstanceNumber is 1-based + #full_slice[:] = slicer.pixel_array.astype(np.float32) + #create_tensor_in_hd5(hd5, mri_group, f'{series}{HD5_GROUP_CHAR}{instance}', full_slice, stats, mri_date, slicer.InstanceNumber) + create_tensor_in_hd5(hd5, mri_group, f'{series_segmented}{HD5_GROUP_CHAR}{instance}', mask, stats, mri_date, slicer.InstanceNumber) def _tensorize_brain_mri(slices: List[pydicom.Dataset], series: str, mri_date: datetime.datetime, mri_group: str, hd5: h5py.File) -> None: @@ -588,13 +535,16 @@ def _save_slice_thickness_if_missing(slicer, series, hd5): def _save_series_orientation_and_position_if_missing(slicer, series, hd5, instance=None): orientation_ds_name = MRI_PATIENT_ORIENTATION + '_' + series position_ds_name = MRI_PATIENT_POSITION + '_' + series - if instance: - orientation_ds_name += HD5_GROUP_CHAR + instance - position_ds_name += HD5_GROUP_CHAR + instance - if orientation_ds_name not in hd5 and series in MRI_BRAIN_SERIES + MRI_CARDIAC_SERIES + MRI_CARDIAC_SERIES_SEGMENTED + MRI_LIVER_SERIES + MRI_LIVER_SERIES_12BIT: - hd5.create_dataset(orientation_ds_name, data=[float(x) for x in slicer.ImageOrientationPatient]) - if position_ds_name not in hd5 and series in MRI_BRAIN_SERIES + MRI_CARDIAC_SERIES + MRI_CARDIAC_SERIES_SEGMENTED + MRI_LIVER_SERIES + MRI_LIVER_SERIES_12BIT: - hd5.create_dataset(position_ds_name, data=[float(x) for x in slicer.ImagePositionPatient]) + if instance is not None: + orientation_ds_name = f'{orientation_ds_name}_{instance}' + position_ds_name = f'{position_ds_name}_{instance}' + try: + if orientation_ds_name not in hd5 and series in MRI_BRAIN_SERIES + MRI_CARDIAC_SERIES + MRI_CARDIAC_SERIES_SEGMENTED + MRI_LIVER_SERIES + MRI_LIVER_SERIES_12BIT: + hd5.create_dataset(orientation_ds_name, data=[float(x) for x in slicer.ImageOrientationPatient]) + if position_ds_name not in hd5 and series in MRI_BRAIN_SERIES + MRI_CARDIAC_SERIES + MRI_CARDIAC_SERIES_SEGMENTED + MRI_LIVER_SERIES + MRI_LIVER_SERIES_12BIT: + hd5.create_dataset(position_ds_name, data=[float(x) for x in slicer.ImagePositionPatient]) + except RuntimeError as e: + logging.warning(f' got error {e} \n orientation : {orientation_ds_name} {slicer.ImageOrientationPatient} and pos: {position_ds_name} {slicer.ImagePositionPatient}') def _has_overlay(d) -> bool: @@ -745,13 +695,16 @@ def _write_ecg_rest_tensors(ecgs, xml_field, hd5, sample_id, write_pngs, stats, def create_tensor_in_hd5( hd5: h5py.File, path_prefix: str, name: str, value, stats: Counter = None, date: datetime.datetime = None, - storage_type: StorageType = None, attributes: Dict[str, Any] = None, + instance: str = None, storage_type: StorageType = None, attributes: Dict[str, Any] = None, ): hd5_path = tensor_path(path_prefix, name) + if instance is not None: + hd5_path = f'{hd5_path}instance_{instance}/' if hd5_path in hd5: hd5_path = f'{hd5_path}instance_{len(hd5[hd5_path])}' - else: + elif instance is None: hd5_path = f'{hd5_path}instance_0' + if stats is not None: stats[hd5_path] += 1 if storage_type == StorageType.STRING: diff --git a/ml4h/tensormap/mgb/dynamic.py b/ml4h/tensormap/mgb/dynamic.py index 2708a8f85..8e858a654 100644 --- a/ml4h/tensormap/mgb/dynamic.py +++ b/ml4h/tensormap/mgb/dynamic.py @@ -23,7 +23,8 @@ INCIDENCE_CSV = '/media/erisone_snf13/lc_outcomes.csv' CARDIAC_SURGERY_OUTCOMES_CSV = '/data/sts-data/mgh-preop-ecg-outcome-labels.csv' PARTNERS_PREFIX = 'partners_ecg_rest' -WIDE_FILE = '/home/sam/ml/hf-wide-2020-08-18-with-lvh-and-lbbb.tsv' +WIDE_FILE = '/home/sam/ml/hf-wide-2020-09-15-with-lvh-and-lbbb.tsv' +#WIDE_FILE = '/home/sam/ml/mgh-wide-2020-06-25-with-mrn.tsv' def make_mgb_dynamic_tensor_maps(desired_map_name: str) -> TensorMap: @@ -511,6 +512,7 @@ def _days_to_years_float(s: str): except ValueError: return None + def _time_to_event_tensor_from_days(tm: TensorMap, has_disease: int, follow_up_days: int): tensor = np.zeros(tm.shape, dtype=np.float32) if follow_up_days > tm.days_window: @@ -535,7 +537,7 @@ def _survival_curve_tensor_from_dates(tm: TensorMap, has_disease: int, assessmen def tensor_from_wide( - file_name: str, patient_column: str = 'fpath', age_column: str = 'age', bmi_column: str = 'bmi', + file_name: str, patient_column: str = 'Mrn', age_column: str = 'age', bmi_column: str = 'bmi', sex_column: str = 'sex', hf_column: str = 'any_hf_age', start_column: str = 'start_fu', end_column: str = 'last_encounter', delimiter: str = '\t', population_normalize: int = 2000, target: str = 'ecg', skip_prevalent: bool = True, @@ -562,8 +564,11 @@ def tensor_from_wide( try: patient_key = int(float(row[patient_index])) patient_data[patient_key] = { - 'age': _days_to_years_float(row[age_index]), 'bmi': _to_float_or_none(row[bmi_index]), 'sex': row[sex_index], - 'hf_age': _days_to_years_float(row[hf_index]), 'end_age': _days_to_years_float(row[end_index]), + 'age': _days_to_years_float(row[age_index]), + 'bmi': _to_float_or_none(row[bmi_index]), + 'sex': row[sex_index], + 'hf_age': _days_to_years_float(row[hf_index]), + 'end_age': _days_to_years_float(row[end_index]), 'start_date': datetime.datetime.strptime(row[start_index], CARDIAC_SURGERY_DATE_FORMAT), } @@ -574,7 +579,7 @@ def tensor_from_wide( def tensor_from_file(tm: TensorMap, hd5: h5py.File, dependents=None): mrn_int = _hd5_filename_to_mrn_int(hd5.filename) if mrn_int not in patient_data: - raise KeyError(f'{tm.name} mrn not in legacy csv.') + raise KeyError(f'{tm.name} mrn not in csv.') if patient_data[mrn_int]['end_age'] is None or patient_data[mrn_int]['age'] is None: raise ValueError(f'{tm.name} could not find ages.') if patient_data[mrn_int]['end_age'] - patient_data[mrn_int]['age'] < 0: diff --git a/ml4h/tensormap/ukb/by_script.py b/ml4h/tensormap/ukb/by_script.py index 8f4591894..260f883e9 100644 --- a/ml4h/tensormap/ukb/by_script.py +++ b/ml4h/tensormap/ukb/by_script.py @@ -4275,8 +4275,10 @@ ukb_23098_0 = TensorMap('23098_Weight_0_0', loss='logcosh', path_prefix='continuous', normalization={'mean': 78.03287707120329, 'std': 15.89979106473436}, annotation_units=1, channel_map={'23098_Weight_0_0': 0, }) ukb_23099_0 = TensorMap('23099_Body-fat-percentage_0_0', loss='logcosh', path_prefix='continuous', normalization={'mean': 31.451786835511985, 'std': 8.547574421386416}, annotation_units=1, channel_map={'23099_Body-fat-percentage_0_0': 0, }) ukb_23099_1 = TensorMap('23099_Body-fat-percentage_1_0', loss='logcosh', path_prefix='continuous', normalization={'mean': 31.264657151412308, 'std': 8.292322694561557}, annotation_units=1, channel_map={'23099_Body-fat-percentage_1_0': 0, }) +ukb_23099_2 = TensorMap('23099_Body-fat-percentage_2_0', loss='logcosh', path_prefix='continuous', normalization={'mean': 31.264657151412308, 'std': 8.292322694561557}, annotation_units=1, channel_map={'23099_Body-fat-percentage_2_0': 0, }) ukb_23100_1 = TensorMap('23100_Whole-body-fat-mass_1_0', loss='logcosh', path_prefix='continuous', normalization={'mean': 24.313408909308837, 'std': 9.148610869548651}, annotation_units=1, channel_map={'23100_Whole-body-fat-mass_1_0': 0, }) -ukb_23100_0 = TensorMap('23100_Whole-body-fat-mass_0_0', loss='logcosh', path_prefix='continuous', normalization={'mean': 24.85684055488536, 'std': 9.565426323411884}, annotation_units=1, channel_map={'23100_Whole-body-fat-mass_0_0': 0, }) +ukb_23100_2 = TensorMap('23100_Whole-body-fat-mass_2_0', loss='logcosh', path_prefix='continuous', normalization={'mean': 24.85684055488536, 'std': 9.565426323411884}, annotation_units=1, channel_map={'23100_Whole-body-fat-mass_2_0': 0, }) +ukb_23101_2 = TensorMap('23101_Whole-body-fatfree-mass_2_0', loss='logcosh', path_prefix='continuous', normalization={'mean': 52.606156455797255, 'std': 11.126052649633138}, annotation_units=1, channel_map={'23101_Whole-body-fatfree-mass_2_0': 0, }) ukb_23101_1 = TensorMap('23101_Whole-body-fatfree-mass_1_0', loss='logcosh', path_prefix='continuous', normalization={'mean': 52.606156455797255, 'std': 11.126052649633138}, annotation_units=1, channel_map={'23101_Whole-body-fatfree-mass_1_0': 0, }) ukb_23101_0 = TensorMap('23101_Whole-body-fatfree-mass_0_0', loss='logcosh', path_prefix='continuous', normalization={'mean': 53.21773908875434, 'std': 11.496421819167042}, annotation_units=1, channel_map={'23101_Whole-body-fatfree-mass_0_0': 0, }) ukb_23102_0 = TensorMap('23102_Whole-body-water-mass_0_0', loss='logcosh', path_prefix='continuous', normalization={'mean': 38.947460814537706, 'std': 8.414129150401765}, annotation_units=1, channel_map={'23102_Whole-body-water-mass_0_0': 0, }) @@ -4300,44 +4302,59 @@ ukb_23110_1 = TensorMap('23110_Impedance-of-arm-left_1_0', loss='logcosh', path_prefix='continuous', normalization={'mean': 337.08795210775764, 'std': 57.39185814366964}, annotation_units=1, channel_map={'23110_Impedance-of-arm-left_1_0': 0, }) ukb_23110_2 = TensorMap('23110_Impedance-of-arm-left_2_0', loss='logcosh', path_prefix='continuous', normalization={'mean': 326.5779425175772, 'std': 54.696785486803726}, annotation_units=1, channel_map={'23110_Impedance-of-arm-left_2_0': 0, }) ukb_23110_0 = TensorMap('23110_Impedance-of-arm-left_0_0', loss='logcosh', path_prefix='continuous', normalization={'mean': 332.26580591961624, 'std': 56.83465434478377}, annotation_units=1, channel_map={'23110_Impedance-of-arm-left_0_0': 0, }) +ukb_23111_2 = TensorMap('23111_Leg-fat-percentage-right_2_0', loss='logcosh', path_prefix='continuous', normalization={'mean': 31.525313045647284, 'std': 10.627681452664147}, annotation_units=1, channel_map={'23111_Leg-fat-percentage-right_2_0': 0, }) ukb_23111_1 = TensorMap('23111_Leg-fat-percentage-right_1_0', loss='logcosh', path_prefix='continuous', normalization={'mean': 31.525313045647284, 'std': 10.627681452664147}, annotation_units=1, channel_map={'23111_Leg-fat-percentage-right_1_0': 0, }) ukb_23111_0 = TensorMap('23111_Leg-fat-percentage-right_0_0', loss='logcosh', path_prefix='continuous', normalization={'mean': 32.04635516524227, 'std': 10.694715982950983}, annotation_units=1, channel_map={'23111_Leg-fat-percentage-right_0_0': 0, }) +ukb_23112_2 = TensorMap('23112_Leg-fat-mass-right_2_0', loss='logcosh', path_prefix='continuous', normalization={'mean': 4.126630082314794, 'std': 1.8301929159686312}, annotation_units=1, channel_map={'23112_Leg-fat-mass-right_2_0': 0, }) ukb_23112_1 = TensorMap('23112_Leg-fat-mass-right_1_0', loss='logcosh', path_prefix='continuous', normalization={'mean': 4.126630082314794, 'std': 1.8301929159686312}, annotation_units=1, channel_map={'23112_Leg-fat-mass-right_1_0': 0, }) ukb_23112_0 = TensorMap('23112_Leg-fat-mass-right_0_0', loss='logcosh', path_prefix='continuous', normalization={'mean': 4.308648770510948, 'std': 1.898437368686469}, annotation_units=1, channel_map={'23112_Leg-fat-mass-right_0_0': 0, }) +ukb_23113_2 = TensorMap('23113_Leg-fatfree-mass-right_2_0', loss='logcosh', path_prefix='continuous', normalization={'mean': 8.792466949363929, 'std': 1.9185583701790712}, annotation_units=1, channel_map={'23113_Leg-fatfree-mass-right_2_0': 0, }) ukb_23113_1 = TensorMap('23113_Leg-fatfree-mass-right_1_0', loss='logcosh', path_prefix='continuous', normalization={'mean': 8.792466949363929, 'std': 1.9185583701790712}, annotation_units=1, channel_map={'23113_Leg-fatfree-mass-right_1_0': 0, }) ukb_23113_0 = TensorMap('23113_Leg-fatfree-mass-right_0_0', loss='logcosh', path_prefix='continuous', normalization={'mean': 8.96966001620808, 'std': 2.0246336838262913}, annotation_units=1, channel_map={'23113_Leg-fatfree-mass-right_0_0': 0, }) ukb_23114_0 = TensorMap('23114_Leg-predicted-mass-right_0_0', loss='logcosh', path_prefix='continuous', normalization={'mean': 8.48647434933969, 'std': 1.9273038036404626}, annotation_units=1, channel_map={'23114_Leg-predicted-mass-right_0_0': 0, }) ukb_23114_1 = TensorMap('23114_Leg-predicted-mass-right_1_0', loss='logcosh', path_prefix='continuous', normalization={'mean': 8.322803691693691, 'std': 1.828108029602454}, annotation_units=1, channel_map={'23114_Leg-predicted-mass-right_1_0': 0, }) +ukb_23115_2 = TensorMap('23115_Leg-fat-percentage-left_2_0', loss='logcosh', path_prefix='continuous', normalization={'mean': 31.41810426540285, 'std': 10.597272488375483}, annotation_units=1, channel_map={'23115_Leg-fat-percentage-left_2_0': 0, }) ukb_23115_1 = TensorMap('23115_Leg-fat-percentage-left_1_0', loss='logcosh', path_prefix='continuous', normalization={'mean': 31.41810426540285, 'std': 10.597272488375483}, annotation_units=1, channel_map={'23115_Leg-fat-percentage-left_1_0': 0, }) ukb_23115_0 = TensorMap('23115_Leg-fat-percentage-left_0_0', loss='logcosh', path_prefix='continuous', normalization={'mean': 31.951824770075714, 'std': 10.648351373024937}, annotation_units=1, channel_map={'23115_Leg-fat-percentage-left_0_0': 0, }) ukb_23116_0 = TensorMap('23116_Leg-fat-mass-left_0_0', loss='logcosh', path_prefix='continuous', normalization={'mean': 4.242730601973062, 'std': 1.871902668963224}, annotation_units=1, channel_map={'23116_Leg-fat-mass-left_0_0': 0, }) ukb_23116_1 = TensorMap('23116_Leg-fat-mass-left_1_0', loss='logcosh', path_prefix='continuous', normalization={'mean': 4.064025941631329, 'std': 1.8044984851208248}, annotation_units=1, channel_map={'23116_Leg-fat-mass-left_1_0': 0, }) +ukb_23116_2 = TensorMap('23116_Leg-fat-mass-left_2_0', loss='logcosh', path_prefix='continuous', normalization={'mean': 4.064025941631329, 'std': 1.8044984851208248}, annotation_units=1, channel_map={'23116_Leg-fat-mass-left_2_0': 0, }) +ukb_23117_2 = TensorMap('23117_Leg-fatfree-mass-left_2_0', loss='logcosh', path_prefix='continuous', normalization={'mean': 8.697755051134946, 'std': 1.9100971996154277}, annotation_units=1, channel_map={'23117_Leg-fatfree-mass-left_2_0': 0, }) ukb_23117_1 = TensorMap('23117_Leg-fatfree-mass-left_1_0', loss='logcosh', path_prefix='continuous', normalization={'mean': 8.697755051134946, 'std': 1.9100971996154277}, annotation_units=1, channel_map={'23117_Leg-fatfree-mass-left_1_0': 0, }) ukb_23117_0 = TensorMap('23117_Leg-fatfree-mass-left_0_0', loss='logcosh', path_prefix='continuous', normalization={'mean': 8.866400981491417, 'std': 2.01067768245527}, annotation_units=1, channel_map={'23117_Leg-fatfree-mass-left_0_0': 0, }) ukb_23118_1 = TensorMap('23118_Leg-predicted-mass-left_1_0', loss='logcosh', path_prefix='continuous', normalization={'mean': 8.23430281865802, 'std': 1.819746251807764}, annotation_units=1, channel_map={'23118_Leg-predicted-mass-left_1_0': 0, }) ukb_23118_0 = TensorMap('23118_Leg-predicted-mass-left_0_0', loss='logcosh', path_prefix='continuous', normalization={'mean': 8.389605651769429, 'std': 1.914070754426116}, annotation_units=1, channel_map={'23118_Leg-predicted-mass-left_0_0': 0, }) ukb_23119_0 = TensorMap('23119_Arm-fat-percentage-right_0_0', loss='logcosh', path_prefix='continuous', normalization={'mean': 29.531533189516836, 'std': 10.173278005428738}, annotation_units=1, channel_map={'23119_Arm-fat-percentage-right_0_0': 0, }) ukb_23119_1 = TensorMap('23119_Arm-fat-percentage-right_1_0', loss='logcosh', path_prefix='continuous', normalization={'mean': 29.08498378648042, 'std': 9.660940777028525}, annotation_units=1, channel_map={'23119_Arm-fat-percentage-right_1_0': 0, }) +ukb_23119_2 = TensorMap('23119_Arm-fat-percentage-right_2_0', loss='logcosh', path_prefix='continuous', normalization={'mean': 29.08498378648042, 'std': 9.660940777028525}, annotation_units=1, channel_map={'23119_Arm-fat-percentage-right_2_0': 0, }) +ukb_23120_2 = TensorMap('23120_Arm-fat-mass-right_2_0', loss='logcosh', path_prefix='continuous', normalization={'mean': 1.190551259665752, 'std': 0.5962184768943481}, annotation_units=1, channel_map={'23120_Arm-fat-mass-right_2_0': 0, }) ukb_23120_1 = TensorMap('23120_Arm-fat-mass-right_1_0', loss='logcosh', path_prefix='continuous', normalization={'mean': 1.190551259665752, 'std': 0.5962184768943481}, annotation_units=1, channel_map={'23120_Arm-fat-mass-right_1_0': 0, }) ukb_23120_0 = TensorMap('23120_Arm-fat-mass-right_0_0', loss='logcosh', path_prefix='continuous', normalization={'mean': 1.239808434567098, 'std': 0.6384610930020305}, annotation_units=1, channel_map={'23120_Arm-fat-mass-right_0_0': 0, }) +ukb_23121_2 = TensorMap('23121_Arm-fatfree-mass-right_2_0', loss='logcosh', path_prefix='continuous', normalization={'mean': 2.838897480668496, 'std': 0.7716166268521579}, annotation_units=1, channel_map={'23121_Arm-fatfree-mass-right_2_0': 0, }) ukb_23121_1 = TensorMap('23121_Arm-fatfree-mass-right_1_0', loss='logcosh', path_prefix='continuous', normalization={'mean': 2.838897480668496, 'std': 0.7716166268521579}, annotation_units=1, channel_map={'23121_Arm-fatfree-mass-right_1_0': 0, }) ukb_23121_0 = TensorMap('23121_Arm-fatfree-mass-right_0_0', loss='logcosh', path_prefix='continuous', normalization={'mean': 2.8931570158616644, 'std': 0.8215411754827264}, annotation_units=1, channel_map={'23121_Arm-fatfree-mass-right_0_0': 0, }) ukb_23122_0 = TensorMap('23122_Arm-predicted-mass-right_0_0', loss='logcosh', path_prefix='continuous', normalization={'mean': 2.7094604752076186, 'std': 0.7833267524137338}, annotation_units=1, channel_map={'23122_Arm-predicted-mass-right_0_0': 0, }) ukb_23122_1 = TensorMap('23122_Arm-predicted-mass-right_1_0', loss='logcosh', path_prefix='continuous', normalization={'mean': 2.6591219755550015, 'std': 0.7367273170117283}, annotation_units=1, channel_map={'23122_Arm-predicted-mass-right_1_0': 0, }) +ukb_23123_2 = TensorMap('23123_Arm-fat-percentage-left_2_0', loss='logcosh', path_prefix='continuous', normalization={'mean': 29.945781990521326, 'std': 9.82011062734792}, annotation_units=1, channel_map={'23123_Arm-fat-percentage-left_2_0': 0, }) ukb_23123_1 = TensorMap('23123_Arm-fat-percentage-left_1_0', loss='logcosh', path_prefix='continuous', normalization={'mean': 29.945781990521326, 'std': 9.82011062734792}, annotation_units=1, channel_map={'23123_Arm-fat-percentage-left_1_0': 0, }) ukb_23123_0 = TensorMap('23123_Arm-fat-percentage-left_0_0', loss='logcosh', path_prefix='continuous', normalization={'mean': 30.42513586266444, 'std': 10.267361725229156}, annotation_units=1, channel_map={'23123_Arm-fat-percentage-left_0_0': 0, }) ukb_23124_0 = TensorMap('23124_Arm-fat-mass-left_0_0', loss='logcosh', path_prefix='continuous', normalization={'mean': 1.3197648029289917, 'std': 0.7131871500892241}, annotation_units=1, channel_map={'23124_Arm-fat-mass-left_0_0': 0, }) ukb_23124_1 = TensorMap('23124_Arm-fat-mass-left_1_0', loss='logcosh', path_prefix='continuous', normalization={'mean': 1.2664488126122535, 'std': 0.6681665072338635}, annotation_units=1, channel_map={'23124_Arm-fat-mass-left_1_0': 0, }) +ukb_23124_2 = TensorMap('23124_Arm-fat-mass-left_2_0', loss='logcosh', path_prefix='continuous', normalization={'mean': 1.2664488126122535, 'std': 0.6681665072338635}, annotation_units=1, channel_map={'23124_Arm-fat-mass-left_2_0': 0, }) +ukb_23125_2 = TensorMap('23125_Arm-fatfree-mass-left_2_0', loss='logcosh', path_prefix='continuous', normalization={'mean': 2.8760987777500615, 'std': 0.7893732349099633}, annotation_units=1, channel_map={'23125_Arm-fatfree-mass-left_2_0': 0, }) ukb_23125_1 = TensorMap('23125_Arm-fatfree-mass-left_1_0', loss='logcosh', path_prefix='continuous', normalization={'mean': 2.8760987777500615, 'std': 0.7893732349099633}, annotation_units=1, channel_map={'23125_Arm-fatfree-mass-left_1_0': 0, }) ukb_23125_0 = TensorMap('23125_Arm-fatfree-mass-left_0_0', loss='logcosh', path_prefix='continuous', normalization={'mean': 2.9256461729453624, 'std': 0.8372290130379668}, annotation_units=1, channel_map={'23125_Arm-fatfree-mass-left_0_0': 0, }) ukb_23126_0 = TensorMap('23126_Arm-predicted-mass-left_0_0', loss='logcosh', path_prefix='continuous', normalization={'mean': 2.7400753431894462, 'std': 0.7977945107270243}, annotation_units=1, channel_map={'23126_Arm-predicted-mass-left_0_0': 0, }) ukb_23126_1 = TensorMap('23126_Arm-predicted-mass-left_1_0', loss='logcosh', path_prefix='continuous', normalization={'mean': 2.6941082564230485, 'std': 0.7532883471284786}, annotation_units=1, channel_map={'23126_Arm-predicted-mass-left_1_0': 0, }) +ukb_23127_2 = TensorMap('23127_Trunk-fat-percentage_2_0', loss='logcosh', path_prefix='continuous', normalization={'mean': 31.227587927163885, 'std': 7.733697629204932}, annotation_units=1, channel_map={'23127_Trunk-fat-percentage_2_0': 0, }) ukb_23127_1 = TensorMap('23127_Trunk-fat-percentage_1_0', loss='logcosh', path_prefix='continuous', normalization={'mean': 31.227587927163885, 'std': 7.733697629204932}, annotation_units=1, channel_map={'23127_Trunk-fat-percentage_1_0': 0, }) ukb_23127_0 = TensorMap('23127_Trunk-fat-percentage_0_0', loss='logcosh', path_prefix='continuous', normalization={'mean': 31.17342071305921, 'std': 8.008510881672985}, annotation_units=1, channel_map={'23127_Trunk-fat-percentage_0_0': 0, }) +ukb_23128_2 = TensorMap('23128_Trunk-fat-mass_2_0', loss='logcosh', path_prefix='continuous', normalization={'mean': 13.669508605637319, 'std': 5.019106497864643}, annotation_units=1, channel_map={'23128_Trunk-fat-mass_2_0': 0, }) ukb_23128_1 = TensorMap('23128_Trunk-fat-mass_1_0', loss='logcosh', path_prefix='continuous', normalization={'mean': 13.669508605637319, 'std': 5.019106497864643}, annotation_units=1, channel_map={'23128_Trunk-fat-mass_1_0': 0, }) ukb_23128_0 = TensorMap('23128_Trunk-fat-mass_0_0', loss='logcosh', path_prefix='continuous', normalization={'mean': 13.735808957116548, 'std': 5.1711708664366345}, annotation_units=1, channel_map={'23128_Trunk-fat-mass_0_0': 0, }) ukb_23129_0 = TensorMap('23129_Trunk-fatfree-mass_0_0', loss='logcosh', path_prefix='continuous', normalization={'mean': 29.585953690492165, 'std': 5.981176227799396}, annotation_units=1, channel_map={'23129_Trunk-fatfree-mass_0_0': 0, }) ukb_23129_1 = TensorMap('23129_Trunk-fatfree-mass_1_0', loss='logcosh', path_prefix='continuous', normalization={'mean': 29.42263906211025, 'std': 5.885068008414232}, annotation_units=1, channel_map={'23129_Trunk-fatfree-mass_1_0': 0, }) +ukb_23129_2 = TensorMap('23129_Trunk-fatfree-mass_2_0', loss='logcosh', path_prefix='continuous', normalization={'mean': 29.42263906211025, 'std': 5.885068008414232}, annotation_units=1, channel_map={'23129_Trunk-fatfree-mass_2_0': 0, }) ukb_23130_0 = TensorMap('23130_Trunk-predicted-mass_0_0', loss='logcosh', path_prefix='continuous', normalization={'mean': 28.36869182530567, 'std': 5.804851285767162}, annotation_units=1, channel_map={'23130_Trunk-predicted-mass_0_0': 0, }) ukb_23130_1 = TensorMap('23130_Trunk-predicted-mass_1_0', loss='logcosh', path_prefix='continuous', normalization={'mean': 28.2279920179596, 'std': 5.708378220103142}, annotation_units=1, channel_map={'23130_Trunk-predicted-mass_1_0': 0, }) ukb_23200_2 = TensorMap('23200_L1L4-area_2_0', loss='logcosh', path_prefix='continuous', normalization={'mean': 59.829805862939246, 'std': 8.280381408666097}, annotation_units=1, channel_map={'23200_L1L4-area_2_0': 0, }) diff --git a/ml4h/tensormap/ukb/genetics.py b/ml4h/tensormap/ukb/genetics.py index be785c8f5..4f2b6d86d 100644 --- a/ml4h/tensormap/ukb/genetics.py +++ b/ml4h/tensormap/ukb/genetics.py @@ -1,4 +1,5 @@ from ml4h.TensorMap import TensorMap, Interpretation +from ml4h.defines import StorageType from ml4h.metrics import weighted_crossentropy @@ -77,8 +78,11 @@ def _ttn_tensor_from_file(tm, hd5, dependents={}): }, ) -genetic_caucasian = TensorMap('Genetic-ethnic-grouping_Caucasian_0_0', Interpretation.CATEGORICAL, path_prefix='categorical', channel_map={'no_caucasian': 0, 'caucasian': 1}) +genetic_caucasian = TensorMap( + 'Genetic-ethnic-grouping_Caucasian_0_0', Interpretation.CATEGORICAL, path_prefix='categorical', storage_type=StorageType.CATEGORICAL_FLAG, + channel_map={'no_caucasian': 0, 'Genetic-ethnic-grouping_Caucasian_0_0': 1}) + genetic_caucasian_weighted = TensorMap( - 'Genetic-ethnic-grouping_Caucasian_0_0', Interpretation.CATEGORICAL, path_prefix='categorical', - channel_map={'no_caucasian': 0, 'caucasian': 1}, loss=weighted_crossentropy([10.0, 1.0], 'caucasian_loss'), + 'Genetic-ethnic-grouping_Caucasian_0_0', Interpretation.CATEGORICAL, path_prefix='categorical', storage_type=StorageType.CATEGORICAL_FLAG, + channel_map={'no_caucasian': 0, 'Genetic-ethnic-grouping_Caucasian_0_0': 1}, loss=weighted_crossentropy([10.0, 1.0], 'caucasian_loss'), ) diff --git a/ml4h/tensormap/ukb/mri.py b/ml4h/tensormap/ukb/mri.py index d81ff9c90..c8a6bb323 100644 --- a/ml4h/tensormap/ukb/mri.py +++ b/ml4h/tensormap/ukb/mri.py @@ -215,7 +215,7 @@ def _slice_tensor_from_file(tm, hd5, dependents={}): def _segmented_dicom_slices(dicom_key_prefix, path_prefix='ukb_cardiac_mri', step=1, total_slices=50): def _segmented_dicom_tensor_from_file(tm, hd5, dependents={}): tensor = np.zeros(tm.shape, dtype=np.float32) - if path_prefix == 'ukb_liver_mri': + if tm.axes() == 3 or path_prefix == 'ukb_liver_mri': categorical_index_slice = get_tensor_at_first_date(hd5, path_prefix, f'{dicom_key_prefix}1') categorical_one_hot = to_categorical(categorical_index_slice, len(tm.channel_map)) tensor[..., :] = pad_or_crop_array_to_shape(tensor[..., :].shape, categorical_one_hot) @@ -896,8 +896,9 @@ def _mri_slice_blackout_tensor_from_file(tm, hd5, dependents={}): ), ) lax_4ch_diastole_slice0_224_3d = TensorMap( - 'lax_4ch_diastole_slice0_224_3d', Interpretation.CONTINUOUS, shape=(160, 224, 1), - normalization=ZeroMeanStd1(), tensor_from_file=_slice_tensor('ukb_cardiac_mri/cine_segmented_lax_4ch/instance_0', 0), + 'lax_4ch_diastole_slice0_224_3d', Interpretation.CONTINUOUS, shape=(160, 224, 1), loss='logcosh', + normalization=ZeroMeanStd1(), + tensor_from_file=_slice_tensor('ukb_cardiac_mri/cine_segmented_lax_4ch/instance_0', 0), ) lax_4ch_diastole_slice0_256_3d = TensorMap( 'lax_4ch_diastole_slice0_256_3d', Interpretation.CONTINUOUS, shape=(192, 256, 1), @@ -953,8 +954,8 @@ def _mri_slice_blackout_tensor_from_file(tm, hd5, dependents={}): 'ukb_cardiac_mri/cine_segmented_ao_dist/instance_0', 0, ), ) -cine_segmented_ao_dist_slice0_3d = TensorMap( - 'cine_segmented_ao_dist_slice0_3d', Interpretation.CONTINUOUS, shape=(256, 256, 1), loss='logcosh', +aorta_diastole_slice0_3d = TensorMap( + 'aorta_diastole_slice0_3d', Interpretation.CONTINUOUS, shape=(192, 256, 1), loss='logcosh', normalization=ZeroMeanStd1(), tensor_from_file=_slice_tensor('ukb_cardiac_mri/cine_segmented_ao_dist/instance_0', 0), ) cine_segmented_lvot_slice0_3d = TensorMap( @@ -1179,6 +1180,10 @@ def _pad_crop_tensor(tm, hd5, dependents={}): channel_map=MRI_SAX_SEGMENTED_CHANNEL_MAP, ) +segmented_aorta_diastole = TensorMap( + 'segmented_aorta_diastole', Interpretation.CATEGORICAL, shape=(192, 256, len(MRI_AO_SEGMENTED_CHANNEL_MAP)), + tensor_from_file=_segmented_dicom_slices('cine_segmented_ao_dist_annotated_'), channel_map=MRI_AO_SEGMENTED_CHANNEL_MAP, +) cine_segmented_ao_dist = TensorMap( 'cine_segmented_ao_dist', Interpretation.CATEGORICAL, shape=(160, 192, 100, len(MRI_AO_SEGMENTED_CHANNEL_MAP)), tensor_from_file=_segmented_dicom_slices('cine_segmented_ao_dist_annotated_'), channel_map=MRI_AO_SEGMENTED_CHANNEL_MAP, @@ -1251,14 +1256,30 @@ def sax_tensor_from_file(tm, hd5, dependents={}): [1.0, 40.0, 40.0], 'sax_all_diastole_segmented', ), ) +sax_all_diastole_192_segmented_weighted = TensorMap( + 'sax_all_diastole_segmented', Interpretation.CATEGORICAL, shape=(192, 192, 13, 3), + channel_map=MRI_SEGMENTED_CHANNEL_MAP, + loss=weighted_crossentropy( + [1.0, 40.0, 40.0], 'sax_all_diastole_segmented', + ), +) sax_all_diastole = TensorMap( 'sax_all_diastole', shape=(256, 256, 13, 1), tensor_from_file=sax_tensor('diastole'), - dependent_map=sax_all_diastole_segmented, + path_prefix='ukb_cardiac_mri', ) sax_all_diastole_weighted = TensorMap( 'sax_all_diastole', shape=(256, 256, 13, 1), tensor_from_file=sax_tensor('diastole'), - dependent_map=sax_all_diastole_segmented_weighted, + dependent_map=sax_all_diastole_segmented_weighted, path_prefix='ukb_cardiac_mri', +) + +sax_all_diastole_192 = TensorMap( + 'sax_all_diastole', shape=(192, 192, 13, 1), tensor_from_file=sax_tensor('diastole'), + dependent_map=sax_all_diastole_segmented, path_prefix='ukb_cardiac_mri', +) +sax_all_diastole_192_weighted = TensorMap( + 'sax_all_diastole', shape=(192, 192, 13, 1), tensor_from_file=sax_tensor('diastole'), + dependent_map=sax_all_diastole_segmented_weighted, path_prefix='ukb_cardiac_mri', ) sax_all_systole_segmented = TensorMap( @@ -1271,6 +1292,7 @@ def sax_tensor_from_file(tm, hd5, dependents={}): loss=weighted_crossentropy([1.0, 40.0, 40.0], 'sax_all_systole_segmented'), ) + sax_all_systole = TensorMap( 'sax_all_systole', shape=(256, 256, 13, 1), tensor_from_file=sax_tensor('systole'), dependent_map=sax_all_systole_segmented, @@ -1351,6 +1373,14 @@ def _slice_tensor_from_file(tm, hd5, dependents={}): 'aorta_slice_nekoui', shape=(200, 240, 1), normalization=ZeroMeanStd1(), tensor_from_file=_slice_tensor_with_segmentation('cine_segmented_ao_dist/instance_0', 'cine_segmented_ao_dist_nekoui_annotated_'), ) +lvot_slice_jamesp = TensorMap( + 'lvot_slice_jamesp', shape=(200, 240, 1), normalization=ZeroMeanStd1(), + tensor_from_file=_slice_tensor_with_segmentation('cine_segmented_lvot/instance_0', 'cine_segmented_lvot_jamesp_annotated_'), +) +lvot_slice_nekoui = TensorMap( + 'lvot_slice_nekoui', shape=(200, 240, 1), normalization=ZeroMeanStd1(), + tensor_from_file=_slice_tensor_with_segmentation('cine_segmented_lvot/instance_0', 'cine_segmented_lvot_nekoui_annotated_'), +) lax_2ch_slice_jamesp = TensorMap( 'lax_2ch_slice_jamesp', shape=(192, 160, 1), normalization=ZeroMeanStd1(), tensor_from_file=_slice_tensor_with_segmentation('cine_segmented_lax_2ch/instance_0', 'cine_segmented_lax_2ch_jamesp_annotated_'), @@ -1387,6 +1417,14 @@ def _segmented_dicom_tensor_from_file(tm, hd5, dependents={}): 'cine_segmented_ao_dist', Interpretation.CATEGORICAL, shape=(200, 240, len(MRI_AO_SEGMENTED_CHANNEL_MAP)), tensor_from_file=_segmented_dicom_slice('cine_segmented_ao_dist_nekoui_annotated_'), channel_map=MRI_AO_SEGMENTED_CHANNEL_MAP, ) +cine_segmented_lvot_jamesp = TensorMap( + 'cine_segmented_lvot', Interpretation.CATEGORICAL, shape=(200, 240, len(MRI_LVOT_SEGMENTED_CHANNEL_MAP)), + tensor_from_file=_segmented_dicom_slice('cine_segmented_lvot_jamesp_annotated_'), channel_map=MRI_LVOT_SEGMENTED_CHANNEL_MAP, +) +cine_segmented_lvot_nekoui = TensorMap( + 'cine_segmented_lvot', Interpretation.CATEGORICAL, shape=(200, 240, len(MRI_LVOT_SEGMENTED_CHANNEL_MAP)), + tensor_from_file=_segmented_dicom_slice('cine_segmented_lvot_nekoui_annotated_'), channel_map=MRI_LVOT_SEGMENTED_CHANNEL_MAP, +) cine_segmented_lax_2ch_jamesp = TensorMap( 'cine_segmented_lax_2ch_slice', Interpretation.CATEGORICAL, shape=(192, 160, len(MRI_LAX_2CH_SEGMENTED_CHANNEL_MAP)), tensor_from_file=_segmented_dicom_slice('cine_segmented_lax_2ch_jamesp_annotated_'), channel_map=MRI_LAX_2CH_SEGMENTED_CHANNEL_MAP, diff --git a/ml4h/test_utils.py b/ml4h/test_utils.py index e8d53c7fe..d729f1c23 100644 --- a/ml4h/test_utils.py +++ b/ml4h/test_utils.py @@ -35,6 +35,7 @@ ), ] +TENSOR_MAP_PAIRS = [[(CONTINUOUS_TMAPS[2], CONTINUOUS_TMAPS[3])], [(CONTINUOUS_TMAPS[2], CONTINUOUS_TMAPS[3]), (CONTINUOUS_TMAPS[2], CATEGORICAL_TMAPS[4])]] TMAPS_UP_TO_4D = CONTINUOUS_TMAPS[:-1] + CATEGORICAL_TMAPS[:-1] TMAPS_5D = CONTINUOUS_TMAPS[-1:] + CATEGORICAL_TMAPS[-1:] MULTIMODAL_UP_TO_4D = [list(x) for x in product(CONTINUOUS_TMAPS[:-1], CATEGORICAL_TMAPS[:-1])] diff --git a/ml4h/visualization_tools/annotation_storage.py b/ml4h/visualization_tools/annotation_storage.py index d0020b1ec..ac8e89249 100644 --- a/ml4h/visualization_tools/annotation_storage.py +++ b/ml4h/visualization_tools/annotation_storage.py @@ -2,11 +2,9 @@ import abc import datetime -from typing import Optional, Union - +import pandas as pd from google.cloud import bigquery from google.cloud.bigquery import magics as bqmagics -import pandas as pd class AnnotationStorage(abc.ABC): @@ -16,14 +14,12 @@ class AnnotationStorage(abc.ABC): """ @abc.abstractmethod - def describe(self) -> str: + def describe(self): """Return a string describing how annotations are stored.""" + pass @abc.abstractmethod - def submit_annotation( - self, sample_id: Union[int, str], annotator: str, key: str, - value_numeric: Optional[Union[int, float]], value_string: Optional[str], comment: str, - ) -> bool: + def submit_annotation(self, sample_id, annotator, key, value_numeric, value_string, comment): """Add an annotation to the collection of annotations. Args: @@ -36,9 +32,10 @@ def submit_annotation( Returns: Whether the submission was successful. Throws an Exception on failure. """ + pass @abc.abstractmethod - def view_recent_submissions(self, count: int = 10) -> pd.DataFrame: + def view_recent_submissions(self, count=10): """View a dataframe of up to [count] most recent submissions. Args: @@ -47,6 +44,7 @@ def view_recent_submissions(self, count: int = 10) -> pd.DataFrame: Returns: A dataframe of the most recent annotations. """ + pass class TransientAnnotationStorage(AnnotationStorage): @@ -58,14 +56,11 @@ class TransientAnnotationStorage(AnnotationStorage): def __init__(self): self.annotations = [] - def describe(self) -> str: + def describe(self): return '''Annotations will be stored in memory only during the duration of this demo.\n For durable storage of annotations, use BigQueryAnnotationStorage instead.''' - def submit_annotation( - self, sample_id: Union[int, str], annotator: str, key: str, - value_numeric: Optional[Union[int, float]], value_string: Optional[str], comment: str, - ) -> bool: + def submit_annotation(self, sample_id, annotator, key, value_numeric, value_string, comment): """Add this annotation to our in-memory collection of annotations. Args: @@ -90,7 +85,7 @@ def submit_annotation( self.annotations.append(annotation) return True - def view_recent_submissions(self, count: int = 10) -> pd.DataFrame: + def view_recent_submissions(self, count=10): """View a dataframe of up to [count] most recent submissions. Args: @@ -115,17 +110,14 @@ class BigQueryAnnotationStorage(AnnotationStorage): annotations_schema.json """ - def __init__(self, table: str): + def __init__(self, table): """This table should already exist.""" self.table = table - def describe(self) -> str: + def describe(self): return f'''Annotations are stored in BigQuery table {self.table}''' - def submit_annotation( - self, sample_id: Union[int, str], annotator: str, key: str, - value_numeric: Optional[Union[int, float]], value_string: Optional[str], comment: str, - ) -> bool: + def submit_annotation(self, sample_id, annotator, key, value_numeric, value_string, comment): """Call a BigQuery INSERT statement to add a row containing annotation information. Args: @@ -158,7 +150,7 @@ def submit_annotation( # Return whether the submission completed. return submission.done() - def view_recent_submissions(self, count: int = 10) -> pd.DataFrame: + def view_recent_submissions(self, count=10): """View a dataframe of up to [count] most recent submissions. This is a convenience method for use within the annotation flow. For full access to the underlying annotations, diff --git a/ml4h/visualization_tools/annotations.py b/ml4h/visualization_tools/annotations.py index 9ca9c1b44..2400a07d8 100644 --- a/ml4h/visualization_tools/annotations.py +++ b/ml4h/visualization_tools/annotations.py @@ -2,11 +2,8 @@ import os import socket -from typing import Any, Dict, Union - from IPython.display import display from IPython.display import HTML -import pandas as pd import ipywidgets as widgets from ml4h.visualization_tools.annotation_storage import AnnotationStorage from ml4h.visualization_tools.annotation_storage import TransientAnnotationStorage @@ -14,18 +11,14 @@ DEFAULT_ANNOTATION_STORAGE = TransientAnnotationStorage() -def _get_df_sample(sample_info: pd.DataFrame, sample_id: Union[int, str]) -> pd.DataFrame: +def _get_df_sample(sample_info, sample_id): """Return a dataframe containing only the row for the indicated sample_id.""" df_sample = sample_info[sample_info['sample_id'] == str(sample_id)] - if df_sample.shape[0] == 0: df_sample = sample_info.query('sample_id == ' + str(sample_id)) + if 0 == df_sample.shape[0]: df_sample = sample_info.query('sample_id == ' + str(sample_id)) return df_sample -def display_annotation_collector( - sample_info: pd.DataFrame, sample_id: Union[int, str], - annotation_storage: AnnotationStorage = DEFAULT_ANNOTATION_STORAGE, - custom_annotation_key: str = None, -) -> None: +def display_annotation_collector(sample_info, sample_id, annotation_storage: AnnotationStorage = DEFAULT_ANNOTATION_STORAGE, custom_annotation_key=None): """Method to create a gui (set of widgets) through which the user can create an annotation and submit it to storage. Args: @@ -33,16 +26,15 @@ def display_annotation_collector( sample_id: The selected sample for which the values will be displayed. annotation_storage: An instance of AnnotationStorage. custom_annotation_key: The key for an annotation of data other than the tabular fields. + + Returns: + A notebook-friendly messages indicating the status of the submission. """ df_sample = _get_df_sample(sample_info, sample_id) if df_sample.shape[0] == 0: - display( - HTML(f'''
- Warning: Sample {sample_id} not present in sample_info DataFrame. -
'''), - ) - return + return HTML(f'''
+ Warning: Sample {sample_id} not present in sample_info DataFrame.
''') # Show the sample ID for this annotation. sample = widgets.HTML(value=f'For sample {sample_id}') @@ -90,7 +82,7 @@ def handle_key_change(change): submit_button = widgets.Button(description='Submit annotation', button_style='success') output = widgets.Output() - def cb_on_button_clicked(b): + def on_button_clicked(b): params = _format_annotation(sample_id=sample_id, key=key.value, keyvalue=keyvalue.value, comment=comment.value) try: success = annotation_storage.submit_annotation( @@ -101,38 +93,34 @@ def cb_on_button_clicked(b): value_string=params['value_string'], comment=params['comment'], ) - except Exception as e: # pylint: disable=broad-except + except Exception as e: display( HTML(f'''
- Warning: Unable to store annotation. - 

{e}

-
'''), + Warning: Unable to store annotation. + 

{e}

+ '''), ) - return + return() with output: if success: # Show the information that was submitted. display( HTML(f'''
- Submission successful\n[{annotation_storage.describe()}] -
'''), + Submission successful\n[{annotation_storage.describe()}]'''), ) display(annotation_storage.view_recent_submissions(1)) else: display( HTML('''
- Annotation not submitted. Please try again. -
'''), + Annotation not submitted. Please try again.'''), ) - submit_button.on_click(cb_on_button_clicked) + submit_button.on_click(on_button_clicked) # Display all the widgets. display(sample, box1, comment, submit_button, output) -def _format_annotation( - sample_id: Union[int, str], key: str, keyvalue: Union[int, float, str], comment: str, -) -> Dict[str, Any]: +def _format_annotation(sample_id, key, keyvalue, comment): """Helper method to clean and reshape info from the widgets and the environment into a dictionary representing the annotation.""" # Programmatically get the identity of the person running this Terra notebook. current_user = os.getenv('OWNER_EMAIL') @@ -140,10 +128,11 @@ def _format_annotation( if current_user is None: current_user = socket.gethostname() # By convention, we prefix the hostname with our username. - value_numeric = None - value_string = None # Check whether the value is string or numeric. - if keyvalue is not None: + if keyvalue is None: + value_numeric = None + value_string = None + else: try: value_numeric = float(keyvalue) # this will fail if the value is text value_string = None diff --git a/ml4h/visualization_tools/batch_image_annotations.py b/ml4h/visualization_tools/batch_image_annotations.py deleted file mode 100644 index 34ff731df..000000000 --- a/ml4h/visualization_tools/batch_image_annotations.py +++ /dev/null @@ -1,236 +0,0 @@ -"""Methods for batch annotations of images stored as 3D tensors, such as MRIs, from within notebooks.""" - -import json -import os -import socket -import tempfile -from typing import Any, Dict, List - -from IPython.display import display -import numpy as np -import pandas as pd -import h5py -from ipyannotations import PolygonAnnotator -import ipywidgets as widgets -from ml4h.visualization_tools.hd5_mri_plots import MRI_TMAPS -from ml4h.visualization_tools.annotation_storage import AnnotationStorage -from ml4h.visualization_tools.annotation_storage import TransientAnnotationStorage -from PIL import Image -import tensorflow as tf - - -class BatchImageAnnotator(): - """Annotate batches of images with polygons drawn over regions of interest.""" - - SUBMIT_BUTTON_DESCRIPTION = 'Submit polygons, goto next sample' - USE_INSTRUCTIONS = ''' -

    -
  • To draw a polygon, click anywhere you'd like to start. Continue to click - along the edge of the polygon until arrive back where you started. To - finish, simply click the first point (highlighted in red). It may be - helpful to increase the point size if you're struggling (using the slider).
    • - -
  • You can change the class of a polygon using the dropdown menu while the - polygon is still "open", or unfinished. If you make a mistake, use the Undo - button until the point that's wrong has disappeared. - -
  • You can move, but not add / subtract polygon points, by clicking the "Edit" - button. Simply drag a point you want to adjust. Again, if you have - difficulty aiming at the points, you can increase the point size.
    • - -
  • You can increase or decrease the contrast and brightness of the image - using the sliders to make it easier to annotate. Sometimes you need to see - what's behind already-created annotations, and for this purpose you can - make them more see-through using the "Opacity" slider.
    • -

- ''' - EXPECTED_COLUMN_NAMES = ['sample_id', 'tmap_name', 'instance_number', 'folder'] - DEFAULT_ANNOTATION_CLASSNAME = 'region_of_interest' - CSS = ''' - - ''' - - def __init__( - self, samples: pd.DataFrame, annotation_categories: List[str] = None, - zoom: float = 1.5, annotation_storage: AnnotationStorage = TransientAnnotationStorage(), - ): - """Initializes an instance of BatchImageAnnotator. - - Args: - samples: A dataframe of samples to annotate. Columns must include those - in BatchImageAnnotator.EXPECTED_COLUMN_NAMES. - annotation_categories: A list of one or more strings to serve as tags for the polygons. - zoom: Desired zoom level for the image. - annotation_storage: An instance of AnnotationStorage. This faciltates the use of a user-provided - strategy for the storage and processing of annotations. - - Raises: - ValueError: The provided dataframe does not contain the expected columns. - """ - if not set(self.EXPECTED_COLUMN_NAMES).issubset(samples.columns): - raise ValueError(f'samples Dataframe must contain columns {self.EXPECTED_COLUMN_NAMES}') - self.samples = samples - self.current_sample = 0 - # TODO(deflaux) remove this after https://github.com/janfreyberg/ipyannotations/issues/11 - self.zoom = zoom - self.annotation_storage = annotation_storage - if annotation_categories is None: - annotation_categories = [self.DEFAULT_ANNOTATION_CLASSNAME] - - self.annotation_widget = PolygonAnnotator( - options=annotation_categories, - canvas_size=(900, 280 * self.zoom), - ) - self.annotation_widget.on_submit(self._store_annotations) - self.annotation_widget.submit_button.description = self.SUBMIT_BUTTON_DESCRIPTION - self.annotation_widget.submit_button.layout = widgets.Layout(width='300px') - - self.title_widget = widgets.HTML('') - self.results_widget = widgets.HTML('') - - def _store_annotations(self, data: Dict[Any, Any]) -> None: - """Transfer widget state to the annotation storage and advance to the next sample.""" - if self.current_sample >= self.samples.shape[0]: - self.results_widget.value = '

Annotation batch complete!

Thank you for making the model better.' - return - - # Convert polygon points in canvas coordinates to tensor coordinates. - image_canvas_position = self.annotation_widget.canvas.image_extent - x_offset, y_offset, _, _ = image_canvas_position - tensor_coords = [ - ( - a['label'], - [( - int((p[0] - x_offset) / self.zoom), - int((p[1] - y_offset) / self.zoom), - ) for p in a['points']], - ) for a in data - ] - # Store the annotation using the provided annotation storage strategy. - self.annotation_storage.submit_annotation( - sample_id=self.samples.loc[self.current_sample, 'sample_id'], - annotator=os.getenv('OWNER_EMAIL') if os.getenv('OWNER_EMAIL') else socket.gethostname(), - key=self.samples.loc[self.current_sample, 'tmap_name'], - value_numeric=self.samples.loc[self.current_sample, 'instance_number'], - value_string=self.samples.loc[self.current_sample, 'folder'], - comment=json.dumps(tensor_coords), - ) - - # Display this annotation at the bottom of the widget. - results = f''' -
-

Prior sample's submitted annotations

- The {self.SUBMIT_BUTTON_DESCRIPTION} button is both printing out the polygons below and storing the polygons - via strategy {self.annotation_storage.__class__.__name__}.
- Details: {self.annotation_storage.describe()} -

sample info

- {self._format_info_for_current_sample()} -

canvas coordinates

- image extent {image_canvas_position} - {[f'
{json.dumps(x)}
' for x in data]} -

source tensor coordinates

- {[f'
{json.dumps(x)}
' for x in tensor_coords]} -
- ''' - self.results_widget.value = results - - # Advance to the next sample. - self.current_sample += 1 - self._annotate_image_for_current_sample() - - def _format_info_for_current_sample(self) -> str: - """Convert information about the current sample to an HTML table for display within the widget.""" - headings = ' '.join([f'{c}' for c in self.EXPECTED_COLUMN_NAMES] + ['TMAP shape']) - values = ' '.join([f'{self.samples.loc[self.current_sample, c]}' for c in self.EXPECTED_COLUMN_NAMES] - + [f'{MRI_TMAPS[self.samples.loc[self.current_sample, "tmap_name"]].shape}']) - return f''' - - {headings} - {values} -
- ''' - - def _annotate_image_for_current_sample(self) -> None: - """Retrieve the data for the current sample and display its image in the annotation widget. - - If all samples have been processed, display the completion message. - """ - if self.current_sample >= self.samples.shape[0]: - self.annotation_widget.canvas.clear() - # Note: the above command clears the canvas, but any incomplete polygons will be redrawn. Call this - # private method to clear those. TODO(deflaux) remove this after https://github.com/janfreyberg/ipyannotations/issues/15 - self.annotation_widget.canvas._init_empty_data() # pylint: disable=protected-access - self.title_widget.value = '

Annotation batch complete!

Thank you for making the model better.' - return - - sample_id = self.samples.loc[self.current_sample, 'sample_id'] - tmap_name = self.samples.loc[self.current_sample, 'tmap_name'] - instance_number = self.samples.loc[self.current_sample, 'instance_number'] - folder = self.samples.loc[self.current_sample, 'folder'] - - with tempfile.TemporaryDirectory() as tmpdirname: - sample_hd5 = str(sample_id) + '.hd5' - local_path = os.path.join(tmpdirname, sample_hd5) - try: - tf.io.gfile.copy(src=os.path.join(folder, sample_hd5), dst=local_path) - hd5 = h5py.File(local_path, mode='r') - except (tf.errors.NotFoundError, tf.errors.PermissionDeniedError) as e: - self.annotation_widget.canvas.clear() - # Note: the above command clears the canvas, but any incomplete polygons will be redrawn. Call this - # private method to clear those. TODO(deflaux) remove this after https://github.com/janfreyberg/ipyannotations/issues/15 - self.annotation_widget.canvas._init_empty_data() # pylint: disable=protected-access - self.title_widget.value = f''' -
-

Warning: MRI HD5 file not available for sample {sample_id} in folder {folder}

- Use the folder parameter to read HD5s from a different local directory or Cloud Storage bucket. - 

{e.message}

-
- ''' - return - - tensor = MRI_TMAPS[tmap_name].tensor_from_file(MRI_TMAPS[tmap_name], hd5) - tensor_instance = tensor[:, :, instance_number] - if self.zoom > 1.0: - # TODO(deflaux) remove this after https://github.com/janfreyberg/ipyannotations/issues/11 - img = Image.fromarray(tensor_instance) - zoomed_img = img.resize([int(self.zoom * s) for s in img.size], Image.LANCZOS) - tensor_instance = np.asarray(zoomed_img) - - self.annotation_widget.display(tensor_instance) - self.title_widget.value = f''' - {self.CSS} -
-

Batch annotation of {self.samples.shape[0]} samples

- {self.USE_INSTRUCTIONS} -
-

Current sample

- {self._format_info_for_current_sample()} -
- ''' - - def annotate_images(self) -> None: - """Begin the batch annotation task by displaying the annotation widget populated with the first sample. - - The submit button is used to proceed to the next sample until all samples have been processed. - """ - self._annotate_image_for_current_sample() - display(widgets.VBox([self.title_widget, self.annotation_widget, self.results_widget])) - - def view_recent_submissions(self, count: int = 10) -> pd.DataFrame: - """View a dataframe of up to [count] most recent submissions. - - Args: - count: The number of the most recent submissions to return. - - Returns: - A dataframe of the most recent annotations. - """ - return self.annotation_storage.view_recent_submissions(count=count) diff --git a/ml4h/visualization_tools/dicom_interactive_plots.py b/ml4h/visualization_tools/dicom_interactive_plots.py index ec9d63834..d9850e841 100644 --- a/ml4h/visualization_tools/dicom_interactive_plots.py +++ b/ml4h/visualization_tools/dicom_interactive_plots.py @@ -1,4 +1,4 @@ -"""Methods for integration of interactive DICOM plots within notebooks. +"""Methods for integration of interactive dicom plots within notebooks. TODO: * Continue to *pragmatically* improve this to make the visualization controls @@ -8,15 +8,14 @@ import collections import os import tempfile -from typing import Any, DefaultDict, Dict, Optional, Tuple import zipfile from IPython.display import display from IPython.display import HTML -import numpy as np import ipywidgets as widgets import matplotlib.pyplot as plt from ml4h.runtime_data_defines import get_mri_folders +import numpy as np import pydicom import tensorflow as tf @@ -28,12 +27,15 @@ MAX_COLOR_RANGE = 6000 -def choose_mri(sample_id, folder: Optional[str] = None) -> None: +def choose_mri(sample_id, folder=None): """Render widget to choose the MRI to plot. Args: sample_id: The id of the sample to retrieve. folder: The local or Cloud Storage folder under which the files reside. + + Returns: + ipywidget or HTML upon error. """ if folder is None: folders = get_mri_folders(sample_id) @@ -43,26 +45,22 @@ def choose_mri(sample_id, folder: Optional[str] = None) -> None: sample_mris = [] sample_mri_glob = str(sample_id) + '_*.zip' try: - for f in folders: - sample_mris.extend(tf.io.gfile.glob(pattern=os.path.join(f, sample_mri_glob))) + for folder in folders: + sample_mris.extend(tf.io.gfile.glob(pattern=os.path.join(folder, sample_mri_glob))) except (tf.errors.NotFoundError, tf.errors.PermissionDeniedError) as e: - display( - HTML(f'''
+ return HTML(f''' +
Warning: MRI not available for sample {sample_id} in {folders}: 

{e.message}

Use the folder parameter to read DICOMs from a different local directory or Cloud Storage bucket. -
'''), - ) - return +
''') if not sample_mris: - display( - HTML(f'''
+ return HTML(f''' +
Warning: MRI DICOMs not available for sample {sample_id} in {folders}.
Use the folder parameter to read DICOMs from a different local directory or Cloud Storage bucket. -
'''), - ) - return +
''') mri_chooser = widgets.Dropdown( options=sample_mris, @@ -79,11 +77,14 @@ def choose_mri(sample_id, folder: Optional[str] = None) -> None: display(file_controls_ui, file_controls_output) -def choose_mri_series(sample_mri: str) -> None: +def choose_mri_series(sample_mri): """Render widgets and interactive plots for MRIs. Args: sample_mri: The local or Cloud Storage path to the MRI file. + + Returns: + ipywidget or HTML upon error. """ with tempfile.TemporaryDirectory() as tmpdirname: local_path = os.path.join(tmpdirname, os.path.basename(sample_mri)) @@ -92,15 +93,13 @@ def choose_mri_series(sample_mri: str) -> None: with zipfile.ZipFile(local_path, 'r') as zip_ref: zip_ref.extractall(tmpdirname) except (tf.errors.NotFoundError, tf.errors.PermissionDeniedError) as e: - display( - HTML(f'''
+ return HTML(f''' +
Warning: Cardiac MRI not available for sample {os.path.basename(sample_mri)}: 

{e.message}

-
'''), - ) - return +
''') - unordered_dicoms: DefaultDict[Any, Any] = collections.defaultdict(dict) + unordered_dicoms = collections.defaultdict(dict) for dcm_file in os.listdir(tmpdirname): if not dcm_file.endswith('.dcm'): continue @@ -113,13 +112,8 @@ def choose_mri_series(sample_mri: str) -> None: unordered_dicoms[key1][key2] = dcm if not unordered_dicoms: - display( - HTML(f'''
- No series available in MRI for sample {os.path.basename(sample_mri)}. - Try a different MRI. -
'''), - ) - return + print(f'\n\nNo series available in MRI for sample {os.path.basename(sample_mri)}\n\nTry a different MRI.') + return None # Convert from dict of dicts to dict of ordered lists. dicoms = {} @@ -140,7 +134,7 @@ def choose_mri_series(sample_mri: str) -> None: style={'description_width': 'initial'}, layout=widgets.Layout(width='800px'), ) - # Slide through DICOM image instances using a slide bar. + # Slide through dicom image instances using a slide bar. instance_chooser = widgets.IntSlider( continuous_update=True, value=default_instance_value, @@ -218,25 +212,25 @@ def on_value_change(change): display(viz_controls_ui, viz_controls_output) -def compute_color_range(dicoms: Dict[str, Any], series_name: str) -> Tuple[int, int]: +def compute_color_range(dicoms, series_name): """Compute the mean values for the color ranges of instances in the series.""" vmin = np.mean([np.min(d.pixel_array) for d in dicoms[series_name]]) vmax = np.mean([np.max(d.pixel_array) for d in dicoms[series_name]]) - return (vmin, vmax) + return(vmin, vmax) -def compute_instance_range(dicoms: Dict[str, Any], series_name: str) -> Tuple[int, int]: +def compute_instance_range(dicoms, series_name): """Compute middle and max instances.""" middle_instance = int(len(dicoms[series_name]) / 2) max_instance = len(dicoms[series_name]) - return (middle_instance, max_instance) + return(middle_instance, max_instance) def dicom_animation( - dicoms: Dict[str, Any], series_name: str, instance: int, vmin: int, vmax: int, transpose: bool, - fig_width: int, title_prefix: str = '', -) -> None: - """Render one frame of a DICOM animation. + dicoms, series_name, instance, vmin, vmax, transpose, + fig_width, title_prefix='', +): + """Render one frame of a dicom animation. Args: dicoms: the dictionary DICOM series and instances lists @@ -256,7 +250,7 @@ def dicom_animation( dcm = dicoms[series_name][instance - 1] if instance != dcm.InstanceNumber: # Notice invalid input, but don't throw an error. - print(f'WARNING: Instance parameter {str(instance)} and instance number {str(dcm.InstanceNumber)} do not match.') + print(f'WARNING: Instance parameter {str(instance)} and dicom instance number {str(dcm.InstanceNumber)} do not match.') if transpose: height = dcm.pixel_array.T.shape[0] diff --git a/ml4h/visualization_tools/dicom_plots.py b/ml4h/visualization_tools/dicom_plots.py index 093691382..ce2b3e083 100644 --- a/ml4h/visualization_tools/dicom_plots.py +++ b/ml4h/visualization_tools/dicom_plots.py @@ -1,17 +1,16 @@ -"""Methods for integration of DICOM plots within notebooks.""" +"""Methods for integration of dicom plots within notebooks.""" import collections import os import tempfile -from typing import Dict, List, Optional, Tuple, Union import zipfile from IPython.display import display from IPython.display import HTML -import numpy as np import ipywidgets as widgets import matplotlib.pyplot as plt from ml4h.runtime_data_defines import get_cardiac_mri_folder +import numpy as np import pydicom from scipy.ndimage.morphology import binary_closing from scipy.ndimage.morphology import binary_erosion @@ -28,21 +27,21 @@ MRI_SEGMENTED_CHANNEL_MAP = {'background': 0, 'ventricle': 1, 'myocardium': 2} -def _is_mitral_valve_segmentation(d: pydicom.FileDataset) -> bool: - """Determine whether a DICOM has mitral valve segmentation. +def _is_mitral_valve_segmentation(d): # -> bool: + """Determine whether a dicom has mitral valve segmentation. This is used for visualization of CINE_segmented_SAX_InlineVF. Args: - d: the DICOM file + d: the dicom file Returns: - Whether or not the DICOM has mitral valve segmentation + Whether or not the dicom has mitral valve segmentation """ return d.SliceThickness == 6 -def _get_overlay_from_dicom(d: pydicom.FileDataset) -> Tuple[int, int, int]: +def _get_overlay_from_dicom(d): """Get an overlay from a DICOM file. Morphological operators are used to transform the pixel outline of the @@ -50,7 +49,7 @@ def _get_overlay_from_dicom(d: pydicom.FileDataset) -> Tuple[int, int, int]: is used for visualization of CINE_segmented_SAX_InlineVF. Args: - d: the DICOM file + d: the dicom file Returns: Raw overlay array with myocardium outline, anatomical mask (a pixel @@ -78,30 +77,29 @@ def _get_overlay_from_dicom(d: pydicom.FileDataset) -> Tuple[int, int, int]: byte >>= 1 bit += 1 overlay = overlay[:expected_bit_length] - if overlay_frames != 1: - raise ValueError(f'DICOM has {overlay_frames} overlay frames, but only one expected.') - overlay = overlay.reshape(rows, cols) - idx = np.where(overlay == 1) - min_pos = (np.min(idx[0]), np.min(idx[1])) - max_pos = (np.max(idx[0]), np.max(idx[1])) - short_side = min((max_pos[0] - min_pos[0]), (max_pos[1] - min_pos[1])) - small_radius = max(MRI_MIN_RADIUS, short_side * MRI_SMALL_RADIUS_FACTOR) - big_radius = max(MRI_MIN_RADIUS+1, short_side * MRI_BIG_RADIUS_FACTOR) - small_structure = _unit_disk(small_radius) - m1 = binary_closing(overlay, small_structure).astype(np.int) - big_structure = _unit_disk(big_radius) - m2 = binary_closing(overlay, big_structure).astype(np.int) - anatomical_mask = m1 + m2 - ventricle_pixels = np.count_nonzero(anatomical_mask == MRI_SEGMENTED_CHANNEL_MAP['ventricle']) - myocardium_pixels = np.count_nonzero(anatomical_mask == MRI_SEGMENTED_CHANNEL_MAP['myocardium']) - if ventricle_pixels == 0 and myocardium_pixels > MRI_MAX_MYOCARDIUM: - erode_structure = _unit_disk(small_radius*1.5) - anatomical_mask = anatomical_mask - binary_erosion(m1, erode_structure).astype(np.int) + if overlay_frames == 1: + overlay = overlay.reshape(rows, cols) + idx = np.where(overlay == 1) + min_pos = (np.min(idx[0]), np.min(idx[1])) + max_pos = (np.max(idx[0]), np.max(idx[1])) + short_side = min((max_pos[0] - min_pos[0]), (max_pos[1] - min_pos[1])) + small_radius = max(MRI_MIN_RADIUS, short_side * MRI_SMALL_RADIUS_FACTOR) + big_radius = max(MRI_MIN_RADIUS+1, short_side * MRI_BIG_RADIUS_FACTOR) + small_structure = _unit_disk(small_radius) + m1 = binary_closing(overlay, small_structure).astype(np.int) + big_structure = _unit_disk(big_radius) + m2 = binary_closing(overlay, big_structure).astype(np.int) + anatomical_mask = m1 + m2 ventricle_pixels = np.count_nonzero(anatomical_mask == MRI_SEGMENTED_CHANNEL_MAP['ventricle']) - return overlay, anatomical_mask, ventricle_pixels + myocardium_pixels = np.count_nonzero(anatomical_mask == MRI_SEGMENTED_CHANNEL_MAP['myocardium']) + if ventricle_pixels == 0 and myocardium_pixels > MRI_MAX_MYOCARDIUM: + erode_structure = _unit_disk(small_radius*1.5) + anatomical_mask = anatomical_mask - binary_erosion(m1, erode_structure).astype(np.int) + ventricle_pixels = np.count_nonzero(anatomical_mask == MRI_SEGMENTED_CHANNEL_MAP['ventricle']) + return overlay, anatomical_mask, ventricle_pixels -def _unit_disk(r: int) -> np.ndarray: +def _unit_disk(r): # -> np.ndarray: """Get the unit disk for a radius. This is used for visualization of CINE_segmented_SAX_InlineVF. @@ -116,9 +114,7 @@ def _unit_disk(r: int) -> np.ndarray: return (x ** 2 + y ** 2 <= r ** 2).astype(np.int) -def plot_cardiac_long_axis( - b_series: List[pydicom.FileDataset], sides: int = 7, fig_width: int = 18, title_prefix: str = '', -) -> None: +def plot_cardiac_long_axis(b_series, sides=7, fig_width=18, title_prefix=''): """Visualize CINE_segmented_SAX_InlineVF series. Args: @@ -172,9 +168,9 @@ def plot_cardiac_long_axis( def plot_cardiac_short_axis( - series: List[pydicom.FileDataset], transpose: bool = False, fig_width: int = 18, - title_prefix: str = '', -) -> None: + series, transpose=False, fig_width=18, + title_prefix='', +): """Visualize CINE_segmented_LAX series. Args: @@ -229,14 +225,14 @@ def plot_cardiac_short_axis( def plot_mri_series( - sample_mri: str, dicoms: Dict[str, pydicom.FileDataset], series_name: str, sax_sides: int, - lax_transpose: bool, fig_width: int, -) -> None: + sample_mri, dicoms, series_name, sax_sides, + lax_transpose, fig_width, +): """Visualize the applicable series within this DICOM. Args: sample_mri: The local or Cloud Storage path to the MRI file. - dicoms: A dictionary of DICOMs. + dicoms: A dictionary of dicoms. series_name: The name of the chosen series. sax_sides: How many sides to display for CINE_segmented_SAX_InlineVF. lax_transpose: Whether to transpose when plotting CINE_segmented_LAX. @@ -262,9 +258,10 @@ def plot_mri_series( ) else: print(f'Visualization not currently implemented for {series_name}.') + return None -def choose_mri_series(sample_mri: str) -> None: +def choose_mri_series(sample_mri): """Render widgets and plots for cardiac MRIs. Visualization is supported for CINE_segmented_SAX_InlineVF series and @@ -272,6 +269,9 @@ def choose_mri_series(sample_mri: str) -> None: Args: sample_mri: The local or Cloud Storage path to the MRI file. + + Returns: + ipywidget or HTML upon error. """ with tempfile.TemporaryDirectory() as tmpdirname: local_path = os.path.join(tmpdirname, os.path.basename(sample_mri)) @@ -280,13 +280,11 @@ def choose_mri_series(sample_mri: str) -> None: with zipfile.ZipFile(local_path, 'r') as zip_ref: zip_ref.extractall(tmpdirname) except (tf.errors.NotFoundError, tf.errors.PermissionDeniedError) as e: - display( - HTML(f'''
+ return HTML(f''' +
Warning: Cardiac MRI not available for sample {os.path.basename(sample_mri)}: 

{e.message}

-
'''), - ) - return +
''') filtered_dicoms = collections.defaultdict(list) series_descriptions = [] @@ -297,7 +295,7 @@ def choose_mri_series(sample_mri: str) -> None: series_descriptions.append(dcm.SeriesDescription) if 'cine_segmented_lax' in dcm.SeriesDescription.lower(): filtered_dicoms[dcm.SeriesDescription.lower()].append(dcm) - if dcm.SeriesDescription.lower() == 'cine_segmented_sax_inlinevf': + if 'cine_segmented_sax_inlinevf' == dcm.SeriesDescription.lower(): cur_angle = (dcm.InstanceNumber - 1) // MRI_FRAMES filtered_dicoms[f'{dcm.SeriesDescription.lower()}_angle_{str(cur_angle)}'].append(dcm) @@ -352,20 +350,22 @@ def choose_mri_series(sample_mri: str) -> None: ) display(viz_controls_ui, viz_controls_output) else: - display( - HTML(f'''
- Neither CINE_segmented_SAX_InlineVF nor CINE_segmented_LAX available in MRI for sample {os.path.basename(sample_mri)}. - Try a different MRI. -
'''), + print( + f'\n\nNeither CINE_segmented_SAX_InlineVF nor CINE_segmented_LAX available in MRI for sample {os.path.basename(sample_mri)}.', + '\n\nTry a different MRI.', ) + return None -def choose_cardiac_mri(sample_id: Union[int, str], folder: Optional[str] = None) -> None: +def choose_cardiac_mri(sample_id, folder=None): """Render widget to choose the cardiac MRI to plot. Args: sample_id: The id of the ECG sample to retrieve. folder: The local or Cloud Storage folder under which the files reside. + + Returns: + ipywidget or HTML upon error. """ if folder is None: folder = get_cardiac_mri_folder(sample_id) @@ -374,23 +374,19 @@ def choose_cardiac_mri(sample_id: Union[int, str], folder: Optional[str] = None) try: sample_mris = tf.io.gfile.glob(pattern=os.path.join(folder, sample_mri_glob)) except (tf.errors.NotFoundError, tf.errors.PermissionDeniedError) as e: - display( - HTML(f'''
+ return HTML(f''' +
Warning: Cardiac MRI not available for sample {sample_id} in {folder}: 

{e.message}

Use the folder parameter to read DICOMs from a different local directory or Cloud Storage bucket. -
'''), - ) - return +
''') if not sample_mris: - display( - HTML(f'''
+ return HTML(f''' +
Warning: Cardiac MRI DICOM not available for sample {sample_id} in {folder}.
Use the folder parameter to read DICOMs from a different local directory or Cloud Storage bucket. -
'''), - ) - return +
''') mri_chooser = widgets.Dropdown( options=[(os.path.basename(mri), mri) for mri in sample_mris], diff --git a/ml4h/visualization_tools/ecg_interactive_plots.py b/ml4h/visualization_tools/ecg_interactive_plots.py index 18ed39a9b..97a4e1547 100644 --- a/ml4h/visualization_tools/ecg_interactive_plots.py +++ b/ml4h/visualization_tools/ecg_interactive_plots.py @@ -2,12 +2,10 @@ import os import tempfile -from typing import Optional, Union -from IPython.display import HTML import altair as alt # Interactive data visualization for plots. -from ml4h.TensorMap import TensorMap -from ml4h.visualization_tools.ecg_reshape import DEFAULT_RESTING_ECG_SIGNAL_TMAP +from IPython.display import HTML +from ml4h.visualization_tools.ecg_reshape import DEFAULT_RESTING_ECG_SIGNAL_TMAP_NAME from ml4h.visualization_tools.ecg_reshape import reshape_exercise_ecg_to_tidy from ml4h.visualization_tools.ecg_reshape import reshape_resting_ecg_to_tidy @@ -33,21 +31,18 @@ ) -def resting_ecg_interactive_plot( - sample_id: Union[int, str], folder: Optional[str] = None, - tmap: TensorMap = DEFAULT_RESTING_ECG_SIGNAL_TMAP, -) -> Union[HTML, alt.Chart]: +def resting_ecg_interactive_plot(sample_id, folder=None, tmap_name=DEFAULT_RESTING_ECG_SIGNAL_TMAP_NAME): """Wrangle resting ECG data to tidy and present it as an interactive plot. Args: sample_id: The id of the ECG sample to retrieve. folder: The local or Cloud Storage folder under which the files reside. - tmap: The TensorMap to use for ECG input. + tmap_name: The name of the TMAP to use for ecg input. Returns: An Altair plot or a notebook-friendly error. """ - tidy_resting_ecg_signal = reshape_resting_ecg_to_tidy(sample_id, folder, tmap) + tidy_resting_ecg_signal = reshape_resting_ecg_to_tidy(sample_id, folder, tmap_name) if tidy_resting_ecg_signal.shape[0] == 0: return HTML(f'''
@@ -90,9 +85,7 @@ def resting_ecg_interactive_plot( return upper & lower -def exercise_ecg_interactive_plot( - sample_id: Union[int, str], folder: Optional[str] = None, time_interval_seconds: int = 10, -) -> Union[HTML, alt.Chart]: +def exercise_ecg_interactive_plot(sample_id, folder=None, time_interval_seconds=10): """Wrangle exercise ECG data to tidy and present it as an interactive plot. Args: @@ -147,8 +140,7 @@ def exercise_ecg_interactive_plot( lead_select, ).transform_filter( # https://github.com/altair-viz/altair/issues/1960 - f'''((toNumber({brush.name}.time) - {time_interval_seconds/2.0}) < datum.time) - && (datum.time < toNumber({brush.name}.time) + {time_interval_seconds/2.0})''', + f'((toNumber({brush.name}.time) - {time_interval_seconds/2.0}) < datum.time) && (datum.time < toNumber({brush.name}.time) + {time_interval_seconds/2.0})', ) return trend.encode(y='heartrate:Q') & trend.encode(y='load:Q') & signal diff --git a/ml4h/visualization_tools/ecg_reshape.py b/ml4h/visualization_tools/ecg_reshape.py index 167eb5012..b3213d359 100644 --- a/ml4h/visualization_tools/ecg_reshape.py +++ b/ml4h/visualization_tools/ecg_reshape.py @@ -1,57 +1,53 @@ """Methods for reshaping raw ECG signal data for use in the pandas ecosystem.""" import os import tempfile -from typing import Any, Dict, Optional, Tuple, Union -import numpy as np -import pandas as pd from biosppy.signals.tools import filter_signal import h5py from ml4h.defines import ECG_BIKE_LEADS from ml4h.defines import ECG_REST_LEADS from ml4h.runtime_data_defines import get_exercise_ecg_hd5_folder from ml4h.runtime_data_defines import get_resting_ecg_hd5_folder -from ml4h.TensorMap import TensorMap -import ml4h.tensormap.ukb.ecg as ecg_tmaps +from ml4h.tensor_maps_by_hand import TMAPS +import numpy as np +import pandas as pd import tensorflow as tf RAW_SCALE = 0.005 # Convert to mV. SAMPLING_RATE = 500.0 -DEFAULT_RESTING_ECG_SIGNAL_TMAP = ecg_tmaps.ecg_rest +DEFAULT_RESTING_ECG_SIGNAL_TMAP_NAME = 'ecg_rest' # TODO(deflaux): parameterize exercise ECG by TMAP name if there is similar ECG data from other studies. -EXERCISE_ECG_SIGNAL_TMAP = ecg_tmaps.ecg_bike_raw_full +EXERCISE_ECG_SIGNAL_TMAP = TMAPS['ecg-bike-raw-full'] EXERCISE_ECG_TREND_TMAPS = [ - ecg_tmaps.ecg_bike_raw_trend_hr, - ecg_tmaps.ecg_bike_raw_trend_load, - ecg_tmaps.ecg_bike_raw_trend_grade, - ecg_tmaps.ecg_bike_raw_trend_artifact, - ecg_tmaps.ecg_bike_raw_trend_mets, - ecg_tmaps.ecg_bike_raw_trend_pacecount, - ecg_tmaps.ecg_bike_raw_trend_phasename, - ecg_tmaps.ecg_bike_raw_trend_phasetime, - ecg_tmaps.ecg_bike_raw_trend_time, - ecg_tmaps.ecg_bike_raw_trend_vecount, + TMAPS['ecg-bike-raw-trend-hr'], + TMAPS['ecg-bike-raw-trend-load'], + TMAPS['ecg-bike-raw-trend-grade'], + TMAPS['ecg-bike-raw-trend-artifact'], + TMAPS['ecg-bike-raw-trend-mets'], + TMAPS['ecg-bike-raw-trend-pacecount'], + TMAPS['ecg-bike-raw-trend-phasename'], + TMAPS['ecg-bike-raw-trend-phasetime'], + TMAPS['ecg-bike-raw-trend-time'], + TMAPS['ecg-bike-raw-trend-vecount'], ] EXERCISE_PHASES = {0.0: 'Pretest', 1.0: 'Exercise', 2.0: 'Recovery'} -def _examine_available_keys(hd5: Dict[str, Any]) -> None: +def _examine_available_keys(hd5): print(f'hd5 ECG keys {[k for k in hd5.keys() if "ecg" in k]}') for key in [k for k in hd5.keys() if 'ecg' in k]: - print(f'hd5 {key} keys {k for k in hd5[key]}') + print(f'hd5 {key} keys {[k for k in hd5[key].keys()]}') -def reshape_resting_ecg_to_tidy( - sample_id: Union[int, str], folder: Optional[str] = None, tmap: TensorMap = DEFAULT_RESTING_ECG_SIGNAL_TMAP, -) -> pd.DataFrame: +def reshape_resting_ecg_to_tidy(sample_id, folder=None, tmap_name=DEFAULT_RESTING_ECG_SIGNAL_TMAP_NAME): """Wrangle resting ECG data to tidy. Args: sample_id: The id of the ECG sample to retrieve. folder: The local or Cloud Storage folder under which the files reside. - tmap: The TensorMap to use for ECG input. + tmap_name: The name of the TMAP to use for ecg input. Returns: A pandas dataframe in tidy format or print a notebook-friendly error and return an empty dataframe. @@ -59,7 +55,7 @@ def reshape_resting_ecg_to_tidy( if folder is None: folder = get_resting_ecg_hd5_folder(sample_id) - data: Dict[str, Any] = {'lead': [], 'raw': [], 'ts_reference': [], 'filtered': [], 'filtered_1': [], 'filtered_2': []} + data = {'lead': [], 'raw': [], 'ts_reference': [], 'filtered': [], 'filtered_1': [], 'filtered_2': []} with tempfile.TemporaryDirectory() as tmpdirname: sample_hd5 = str(sample_id) + '.hd5' @@ -73,10 +69,10 @@ def reshape_resting_ecg_to_tidy( with h5py.File(local_path, mode='r') as hd5: try: - signals = tmap.tensor_from_file(tmap, hd5) + signals = TMAPS[tmap_name].tensor_from_file(TMAPS[tmap_name], hd5) except (KeyError, ValueError) as e: - print(f'''Warning: Resting ECG TMAP {tmap.name} not available for sample {sample_id}. - Use the tmap parameter to choose a different TMAP.\n\n{e}''') + print(f'''Warning: Resting ECG TMAP {tmap_name} not available for sample {sample_id}. + Use the tmap_name parameter to choose a different TMAP.\n\n{e}''') _examine_available_keys(hd5) return pd.DataFrame(data) for (lead, channel) in ECG_REST_LEADS.items(): @@ -140,9 +136,7 @@ def reshape_resting_ecg_to_tidy( return tidy_signal_df -def reshape_exercise_ecg_to_tidy( - sample_id: Union[int, str], folder: Optional[str] = None, -) -> Tuple[pd.DataFrame, pd.DataFrame]: +def reshape_exercise_ecg_to_tidy(sample_id, folder=None): """Wrangle exercise ECG signal data to tidy format. Args: @@ -214,9 +208,7 @@ def reshape_exercise_ecg_to_tidy( return (trend_df, tidy_signal_df) -def reshape_exercise_ecg_and_trend_to_tidy( - sample_id: Union[int, str], folder: Optional[str] = None, -) -> Tuple[pd.DataFrame, pd.DataFrame]: +def reshape_exercise_ecg_and_trend_to_tidy(sample_id, folder=None): """Wrangle exercise ECG signal and trend data to tidy format. Args: diff --git a/ml4h/visualization_tools/ecg_static_plots.py b/ml4h/visualization_tools/ecg_static_plots.py index ac7283237..2ebcfc3e1 100644 --- a/ml4h/visualization_tools/ecg_static_plots.py +++ b/ml4h/visualization_tools/ecg_static_plots.py @@ -1,18 +1,17 @@ """Methods for integration of static plots within notebooks.""" import os import tempfile -from typing import List, Optional, Union from IPython.display import HTML from IPython.display import SVG -import numpy as np from ml4h.plots import plot_ecg_rest from ml4h.runtime_data_defines import get_resting_ecg_hd5_folder from ml4h.runtime_data_defines import get_resting_ecg_svg_folder +import numpy as np import tensorflow as tf -def display_resting_ecg(sample_id: Union[int, str], folder: Optional[str] = None) -> Union[HTML, SVG]: +def display_resting_ecg(sample_id, folder=None): """Retrieve (or render) and display the SVG of the resting ECG. Args: @@ -54,8 +53,8 @@ def display_resting_ecg(sample_id: Union[int, str], folder: Optional[str] = None try: # We don't need the resulting SVG, so send it to a temporary directory. with tempfile.TemporaryDirectory() as tmpdirname: - return plot_ecg_rest(tensor_paths=[local_path], rows=[0], out_folder=tmpdirname, is_blind=False) - except Exception as e: # pylint: disable=broad-except + plot_ecg_rest(tensor_paths = [local_path], rows=[0], out_folder=tmpdirname, is_blind=False) + except Exception as e: return HTML(f'''
Warning: Unable to render static plot of resting ECG for sample {sample_id} from {hd5_folder}: @@ -63,7 +62,7 @@ def display_resting_ecg(sample_id: Union[int, str], folder: Optional[str] = None
''') -def major_breaks_x_resting_ecg(limits: List[float]) -> np.array: +def major_breaks_x_resting_ecg(limits): """Method to compute breaks for plotnine plots of ECG resting data. Args: diff --git a/ml4h/visualization_tools/facets.py b/ml4h/visualization_tools/facets.py index 18f96327d..a45ea88da 100644 --- a/ml4h/visualization_tools/facets.py +++ b/ml4h/visualization_tools/facets.py @@ -2,7 +2,6 @@ import base64 import os -import pandas as pd from facets_overview.generic_feature_statistics_generator import GenericFeatureStatisticsGenerator FACETS_DEPENDENCIES = { @@ -26,10 +25,10 @@ FACETS_DEPENDENCIES[dep] = os.path.basename(url) -class FacetsOverview(): +class FacetsOverview(object): """Methods for Facets Overview notebook integration.""" - def __init__(self, data: pd.DataFrame): + def __init__(self, data): # This takes the dataframe and computes all the inputs to the Facets # Overview plots such as: # - numeric variables: histogram bins, mean, min, median, max, etc.. @@ -40,7 +39,7 @@ def __init__(self, data: pd.DataFrame): [{'name': 'data', 'table': data}], ) - def _repr_html_(self) -> str: + def _repr_html_(self): """Html representation of Facets Overview for use in a Jupyter notebook.""" protostr = base64.b64encode(self._proto.SerializeToString()).decode('utf-8') html_template = ''' @@ -58,14 +57,14 @@ def _repr_html_(self) -> str: return html -class FacetsDive(): +class FacetsDive(object): """Methods for Facets Dive notebook integration.""" - def __init__(self, data: pd.DataFrame, height: int = 1000): + def __init__(self, data, height=1000): self._data = data self.height = height - def _repr_html_(self) -> str: + def _repr_html_(self): """Html representation of Facets Dive for use in a Jupyter notebook.""" html_template = """ diff --git a/ml4h/visualization_tools/hd5_mri_plots.py b/ml4h/visualization_tools/hd5_mri_plots.py index d3894b39d..20b3305b1 100644 --- a/ml4h/visualization_tools/hd5_mri_plots.py +++ b/ml4h/visualization_tools/hd5_mri_plots.py @@ -1,34 +1,29 @@ """Methods for integration of plots of mri data processed to 3D tensors from within notebooks.""" -from collections import OrderedDict from enum import Enum, auto import os import tempfile -from typing import Any, Dict, List, Optional, Tuple, Union +import h5py from IPython.display import display from IPython.display import HTML -import numpy as np -import h5py import ipywidgets as widgets import matplotlib.pyplot as plt from ml4h.runtime_data_defines import get_mri_hd5_folder -import ml4h.tensormap.ukb.mri as ukb_mri -import ml4h.tensormap.ukb.mri_vtk as ukb_mri_vtk -from ml4h.TensorMap import Interpretation, TensorMap +from ml4h.tensor_maps_by_hand import TMAPS +from ml4h.TensorMap import Interpretation +import numpy as np import tensorflow as tf -# Discover applicable TensorMaps. -MRI_TMAPS = { - key: value for key, value in ukb_mri.__dict__.items() if isinstance(value, TensorMap) - and value.interpretation == Interpretation.CONTINUOUS and value.axes() == 3 -} -MRI_TMAPS.update( - { - key: value for key, value in ukb_mri_vtk.__dict__.items() - if isinstance(value, TensorMap) and value.interpretation == Interpretation.CONTINUOUS and value.axes() == 3 - }, +# Discover applicable TMAPS. +CARDIAC_MRI_TMAP_NAMES = [k for k in TMAPS.keys() if ('_lax_' in k or '_sax_' in k) and TMAPS[k].axes() == 3] +CARDIAC_MRI_TMAP_NAMES.extend( + [k for k in TMAPS.keys() if TMAPS[k].path_prefix == 'ukb_cardiac_mri' and TMAPS[k].axes() == 3], ) +LIVER_MRI_TMAP_NAMES = [k for k in TMAPS.keys() if TMAPS[k].path_prefix == 'ukb_liver_mri' and TMAPS[k].axes() == 3] +BRAIN_MRI_TMAP_NAMES = [k for k in TMAPS.keys() if TMAPS[k].path_prefix == 'ukb_brain_mri' and TMAPS[k].axes() == 3] +# This includes more than just MRI TMAPS, it is a best effort. +BEST_EFFORT_MRI_TMAP_NAMES = [k for k in TMAPS.keys() if TMAPS[k].interpretation == Interpretation.CONTINUOUS and TMAPS[k].axes() == 3] MIN_IMAGE_WIDTH = 8 DEFAULT_IMAGE_WIDTH = 12 @@ -46,30 +41,42 @@ class PlotType(Enum): class TensorMapCache: """Cache the tensor to display for reuse when re-plotting the same TMAP with different plot parameters.""" - def __init__(self, hd5: Dict[str, Any], tmap: TensorMap): + def __init__(self, hd5, tmap_name): self.hd5 = hd5 - self.tmap: Optional[TensorMap] = None + self.tmap_name = None self.tensor = None - _ = self.get(tmap) + _ = self.get(tmap_name) - def get(self, tmap: TensorMap) -> np.array: - if self.tmap != tmap: - self.tensor = tmap.tensor_from_file(tmap, self.hd5) - self.tmap = tmap + def get(self, tmap_name): + if self.tmap_name != tmap_name: + self.tensor = TMAPS[tmap_name].tensor_from_file(TMAPS[tmap_name], self.hd5) + self.tmap_name = tmap_name return self.tensor -def choose_mri_tmap( - sample_id: Union[int, str], folder: Optional[str] = None, tmap: Optional[TensorMap] = None, - default_tmaps: Dict[str, TensorMap] = MRI_TMAPS, -) -> None: +def choose_cardiac_mri_tmap(sample_id, folder=None, tmap_name='cine_lax_4ch_192', default_tmap_names=CARDIAC_MRI_TMAP_NAMES): + choose_mri_tmap(sample_id, folder, tmap_name, default_tmap_names) + + +def choose_brain_mri_tmap(sample_id, folder=None, tmap_name='t2_flair_sag_p2_1mm_fs_ellip_pf78_1', default_tmap_names=BRAIN_MRI_TMAP_NAMES): + choose_mri_tmap(sample_id, folder, tmap_name, default_tmap_names) + + +def choose_liver_mri_tmap(sample_id, folder=None, tmap_name='liver_shmolli_segmented', default_tmap_names=LIVER_MRI_TMAP_NAMES): + choose_mri_tmap(sample_id, folder, tmap_name, default_tmap_names) + + +def choose_mri_tmap(sample_id, folder=None, tmap_name=None, default_tmap_names=BEST_EFFORT_MRI_TMAP_NAMES): """Render widgets and plots for MRI tensors. Args: sample_id: The id of the sample to retrieve. folder: The local or Cloud Storage folder under which the files reside. - tmap: The TensorMap for the 3D MRI tensor to visualize. - default_tmaps: Other TensorMaps to offer for visualization, if present in the hd5. + tmap_name: The TMAP name for the 3D MRI tensor to visualize. + default_tmap_names: Other TMAP names to offer for visualization, if present in the hd5. + + Returns: + ipywidget or HTML upon error. """ if folder is None: folder = get_mri_hd5_folder(sample_id) @@ -81,45 +88,42 @@ def choose_mri_tmap( tf.io.gfile.copy(src=os.path.join(folder, sample_hd5), dst=local_path) hd5 = h5py.File(local_path, mode='r') except (tf.errors.NotFoundError, tf.errors.PermissionDeniedError) as e: - display( - HTML(f'''
+ return HTML(f''' +
Warning: MRI HD5 file not available for sample {sample_id} in folder {folder}: 

{e.message}

Use the folder parameter to read HD5s from a different local directory or Cloud Storage bucket. -
'''), - ) - return - - sample_tmaps = OrderedDict() - # Add the passed tmap parameter, if it is present in this hd5. - if tmap: - if tmap.hd5_key_guess() in hd5: - if len(tmap.shape) == 3: - sample_tmaps[tmap.name] = tmap +
''') + + sample_tmap_names = [] + # Add the passed tmap_name parameter, if it is present in this hd5. + if tmap_name: + if TMAPS[tmap_name].hd5_key_guess() in hd5: + if len(TMAPS[tmap_name].shape) == 3: + sample_tmap_names.append(tmap_name) else: - print(f'{tmap} is not a 3D tensor, skipping it') + print(f'{tmap_name} is not a 3D tensor, skipping it') else: - print(f'{tmap} is not available in {sample_id}') - # Also discover applicable TensorMaps for this particular sample's HD5 file. - sample_tmaps.update({n: t for n, t in sorted(default_tmaps.items(), key=lambda t: t[0]) if t.hd5_key_guess() in hd5}) - - if not sample_tmaps: - display( - HTML(f'''
- Neither {tmap.name} nor any of {default_tmaps.keys()} are present in this HD5 for sample {sample_id} in {folder}. - Use the tmap parameter to try a different TensorMap or the folder parameter to try a different hd5 for the sample. -
'''), - ) - return - - default_tmap_value = next(iter(sample_tmaps.values())) + print(f'{tmap_name} is not available in {sample_id}') + # Also discover applicable TMAPS for this particular sample's HD5 file. + sample_tmap_names.extend( + sorted(set([k for k in default_tmap_names if TMAPS[k].hd5_key_guess() in hd5])), + ) + + if not sample_tmap_names: + return HTML(f'''
+ Neither {tmap_name} nor any of {default_tmap_names} are present in this HD5 for sample {sample_id} in {folder}. + Use the tmap_name parameter to try a different TMAP or the folder parameter to try a different hd5 for the sample. +
''') + + default_tmap_name_value = sample_tmap_names[0] # Display the middle instance by default in the interactive view. - default_instance_value, max_instance_value = compute_instance_range(default_tmap_value) - default_vmin_value, default_vmax_value = compute_color_range(hd5, default_tmap_value) + default_instance_value, max_instance_value = compute_instance_range(default_tmap_name_value) + default_vmin_value, default_vmax_value = compute_color_range(hd5, default_tmap_name_value) - tmap_chooser = widgets.Dropdown( - options=sample_tmaps, - value=default_tmap_value, + tmap_name_chooser = widgets.Dropdown( + options=sample_tmap_names, + value=default_tmap_name_value, description='Choose the MRI tensor TMAP name to visualize:', style={'description_width': 'initial'}, layout=widgets.Layout(width='900px'), @@ -170,20 +174,20 @@ def choose_mri_tmap( viz_controls_ui = widgets.VBox( [ widgets.HTML('

Visualization controls

'), - tmap_chooser, + tmap_name_chooser, widgets.HBox([transpose_chooser, fig_width_chooser]), widgets.HBox([flip_chooser, color_range_chooser]), widgets.HBox([plot_type_chooser, instance_chooser]), ], layout=widgets.Layout(width='auto', border='solid 1px grey'), ) - tmap_cache = TensorMapCache(hd5=hd5, tmap=tmap_chooser.value) + tmap_cache = TensorMapCache(hd5=hd5, tmap_name=tmap_name_chooser.value) viz_controls_output = widgets.interactive_output( plot_mri_tmap, { 'sample_id': widgets.fixed(sample_id), 'tmap_cache': widgets.fixed(tmap_cache), - 'tmap': tmap_chooser, + 'tmap_name': tmap_name_chooser, 'plot_type': plot_type_chooser, 'instance': instance_chooser, 'color_range': color_range_chooser, @@ -205,36 +209,33 @@ def on_plot_type_change(change): else: instance_chooser.layout.visibility = 'hidden' - tmap_chooser.observe(on_tmap_value_change, names='value') + tmap_name_chooser.observe(on_tmap_value_change, names='value') plot_type_chooser.observe(on_plot_type_change, names='value') display(viz_controls_ui, viz_controls_output) -def compute_color_range(hd5: Dict[str, Any], tmap: TensorMap) -> List[int]: +def compute_color_range(hd5, tmap_name): """Compute the mean values for the color ranges of instances in the MRI series.""" - mri_tensor = tmap.tensor_from_file(tmap, hd5) + mri_tensor = TMAPS[tmap_name].tensor_from_file(TMAPS[tmap_name], hd5) vmin = np.mean([np.min(mri_tensor[:, :, i]) for i in range(0, mri_tensor.shape[2])]) vmax = np.mean([np.max(mri_tensor[:, :, i]) for i in range(0, mri_tensor.shape[2])]) - return [vmin, vmax] + return[vmin, vmax] -def compute_instance_range(tmap: TensorMap) -> Tuple[int, int]: +def compute_instance_range(tmap_name): """Compute middle and max instances.""" - middle_instance = int(tmap.shape[2] / 2) - max_instance = tmap.shape[2] - return (middle_instance, max_instance) + middle_instance = int(TMAPS[tmap_name].shape[2] / 2) + max_instance = TMAPS[tmap_name].shape[2] + return(middle_instance, max_instance) -def plot_mri_tmap( - sample_id: Union[int, str], tmap_cache: TensorMapCache, tmap: TensorMap, plot_type: PlotType, - instance: int, color_range: Tuple[int, int], transpose: bool, flip: bool, fig_width: int, -) -> None: +def plot_mri_tmap(sample_id, tmap_cache, tmap_name, plot_type, instance, color_range, transpose, flip, fig_width): """Visualize the applicable MRI series within this HD5 file. Args: sample_id: The local or Cloud Storage path to the MRI file. tmap_cache: The cache from which to retrieve the tensor to be plotted. - tmap: The chosen TensorMap for the MRI series. + tmap_name: The name of the chosen TMAP for the MRI series. plot_type: Whether to display instances interactively or in a panel view. instance: The particular instance to display, if interactive. color_range: Array of minimum and maximum value for the color range. @@ -242,9 +243,12 @@ def plot_mri_tmap( flip: Whether to flip the image on its vertical axis fig_width: The desired width of the figure. Note that height computed as the proportion of the width based on the data to be plotted. + + Returns: + The plot or a notebook-friendly error message. """ - title_prefix = f'{tmap.name} from MRI {sample_id}' - mri_tensor = tmap_cache.get(tmap) + title_prefix = f'{tmap_name} from MRI {sample_id}' + mri_tensor = tmap_cache.get(tmap_name) if plot_type == PlotType.INTERACTIVE: plot_mri_tensor_as_animation( mri_tensor=mri_tensor, @@ -271,13 +275,10 @@ def plot_mri_tmap( title_prefix=title_prefix, ) else: - HTML(f'''
Invalid plot type: {plot_type}
''') + return HTML(f'''
Invalid plot type: {plot_type}
''') -def plot_mri_tensor_as_panels( - mri_tensor: np.array, vmin: int, vmax: int, transpose: bool = False, flip: bool = False, - fig_width: int = DEFAULT_IMAGE_WIDTH, title_prefix: str = '', -) -> None: +def plot_mri_tensor_as_panels(mri_tensor, vmin, vmax, transpose=False, flip=False, fig_width=DEFAULT_IMAGE_WIDTH, title_prefix=''): """Visualize an MRI series from a 3D tensor as a panel of static plots. Args: @@ -313,7 +314,7 @@ def plot_mri_tensor_as_panels( axes[row, col].set_yticklabels([]) axes[row, col].set_xticklabels([]) fig.suptitle( - f'{title_prefix}\nColor range: {vmin}-{vmax}, Transpose: {transpose}, Flip: {flip}, Figure size:{fig_width}x{fig_height}', # pylint: disable=line-too-long + f'{title_prefix}\nColor range: {vmin}-{vmax}, Transpose: {transpose}, Flip: {flip}, Figure size:{fig_width}x{fig_height}', fontsize=fig_width, ) fig.subplots_adjust( @@ -325,11 +326,7 @@ def plot_mri_tensor_as_panels( ) -def plot_mri_tensor_as_animation( - mri_tensor: np.array, instance: int, vmin: int, vmax: int, - transpose: bool = False, flip: bool = False, - fig_width: int = DEFAULT_IMAGE_WIDTH, title_prefix: str = '', -) -> None: +def plot_mri_tensor_as_animation(mri_tensor, instance, vmin, vmax, transpose=False, flip=False, fig_width=DEFAULT_IMAGE_WIDTH, title_prefix=''): """Visualize an MRI series from a 3D tensor as an animation rendered one panel at a time. Args: @@ -361,7 +358,7 @@ def plot_mri_tensor_as_animation( _, ax = plt.subplots(figsize=(fig_width, fig_height), facecolor='beige') ax.imshow(pixels, cmap='gray', vmin=vmin, vmax=vmax) ax.set_title( - f'{title_prefix}, Instance: {instance}\nColor range: {vmin}-{vmax}, Transpose: {transpose}, Flip: {flip}, Figure size:{fig_width}x{fig_height}', # pylint: disable=line-too-long + f'{title_prefix}, Instance: {instance}\nColor range: {vmin}-{vmax}, Transpose: {transpose}, Flip: {flip}, Figure size:{fig_width}x{fig_height}', fontsize=fig_width, ) ax.set_yticklabels([]) diff --git a/notebooks/autoencoders/paired_multimodal_autoencoder.ipynb b/notebooks/autoencoders/paired_multimodal_autoencoder.ipynb index 67870c053..bd58f6d25 100644 --- a/notebooks/autoencoders/paired_multimodal_autoencoder.ipynb +++ b/notebooks/autoencoders/paired_multimodal_autoencoder.ipynb @@ -328,9 +328,11 @@ ") -> Model:\n", " inputs = {tm: Input(shape=tm.shape, name=tm.input_name()) for tm in args.tensor_maps_in}\n", " original_outputs = {tm:1 for tm in args.tensor_maps_out}\n", + " real_serial_layers = kwargs['model_layers']\n", + " args.model_layers = None\n", " multimodal_activations = []\n", - " desired_distance_tm = []\n", - " my_metrics = {}\n", + " encoders = {}\n", + " decoders = {}\n", " outputs = []\n", " losses = []\n", " for left, right in pairs:\n", @@ -345,15 +347,22 @@ " h_right = encode_right(inputs[right]) \n", " \n", " if pair_loss == 'cosine':\n", - " loss_layer = CosineLossLayer(100.0)\n", + " loss_layer = CosineLossLayer(1.0)\n", " elif pair_loss == 'euclid':\n", - " loss_layer = L2LossLayer(100.0)\n", + " loss_layer = L2LossLayer(1.0)\n", " \n", " paired_embeddings = loss_layer([h_left, h_right])\n", " multimodal_activations.extend(paired_embeddings)\n", + " if left not in encoders:\n", + " encoders[left] = encode_left\n", + " if right not in encoders:\n", + " encoders[right] = encode_right \n", " \n", " multimodal_activation = Concatenate()(multimodal_activations)\n", + " encoder = Model(inputs=list(inputs.values()), outputs=[multimodal_activation], name='encoder')\n", " \n", + " # build decoder models\n", + " latent_inputs = Input(shape=(args.dense_layers[0]*len(inputs)), name='input_concept_space')\n", " pre_decoder_shapes: Dict[TensorMap, Optional[Tuple[int, ...]]] = {}\n", " for tm in args.tensor_maps_out:\n", " shape = _calc_start_shape(num_upsamples=len(args.dense_blocks), output_shape=tm.shape, \n", @@ -380,24 +389,26 @@ " upsample_z=args.pool_z,\n", " )\n", " \n", - " outputs.append(decode(restructure(multimodal_activation)))\n", + " reconstruction = decode(restructure(latent_inputs))\n", + " decoder = Model(latent_inputs, reconstruction, name=tm.output_name())\n", + " decoders[tm] = decoder\n", + " outputs.append(decoder(multimodal_activation))\n", " losses.append(tm.loss)\n", "\n", - " args.tensor_maps_out = list(original_outputs.keys()) + desired_distance_tm\n", + " args.tensor_maps_out = list(original_outputs.keys())\n", " args.tensor_maps_in = list(inputs.keys())\n", " \n", - " opt = Adam(lr=0.0001, beta_1=0.9, beta_2=0.999, epsilon=1e-08)\n", - " #outputs.reverse() # Make paired loss last\n", - " #losses.reverse()\n", " m = Model(inputs=list(inputs.values()), outputs=outputs)\n", - " m.compile(optimizer=opt, loss=losses)\n", + " my_metrics = {tm.output_name(): tm.metrics for tm in args.tensor_maps_out}\n", + " opt = Adam(lr=kwargs['learning_rate'], beta_1=0.9, beta_2=0.999, epsilon=1e-08)\n", + " m.compile(optimizer=opt, loss=losses, metrics=my_metrics)\n", " m.summary()\n", " \n", - " if kwargs['model_layers'] is not None:\n", + " if real_serial_layers is not None:\n", " m.load_weights(kwargs['model_layers'], by_name=True)\n", " print(f\"Loaded model weights from:{kwargs['model_layers']}\")\n", " \n", - " return m" + " return m, encoders, decoders" ] }, { @@ -407,7 +418,7 @@ "outputs": [], "source": [ "sys.argv = ['train', \n", - " '--tensors', '/mnt/disks/segmented-sax-lax/2020-07-07/', \n", + " '--tensors', '/mnt/disks/sax-lax-40k-lvm/2020-01-29/', \n", " '--input_tensors', 'lax_2ch_diastole_slice0_3d', 'lax_3ch_diastole_slice0_3d', \n", " '--output_tensors', 'lax_2ch_diastole_slice0_3d', 'lax_3ch_diastole_slice0_3d',\n", " '--activation', 'swish',\n", @@ -417,13 +428,13 @@ " '--conv_z', '3', '3', '3', \n", " '--dense_blocks', '32', '32', '32',\n", " '--block_size', '3',\n", - " '--dense_layers', '512',\n", + " '--dense_layers', '64',\n", " '--pool_x', '2',\n", " '--pool_y', '2',\n", " '--batch_size', '1',\n", " '--patience', '32',\n", - " '--epochs', '248',\n", - " '--learning_rate', '0.001',\n", + " '--epochs', '292',\n", + " '--learning_rate', '0.0001',\n", " '--training_steps', '256',\n", " '--validation_steps', '30',\n", " '--test_steps', '2',\n", @@ -433,12 +444,17 @@ " '--id', 'lax_2ch_3ch_diastole_pair_cosine_loss']\n", "args = parse_args()\n", "pairs = [(args.tensor_maps_in[0], args.tensor_maps_in[1])]\n", - "overparameterized_model = make_paired_autoencoder_model(pairs, pair_loss='cosine', **args.__dict__)\n", + "overparameterized_model, encoders, decoders = make_paired_autoencoder_model(pairs, pair_loss='cosine', **args.__dict__)\n", "generate_train, generate_valid, generate_test = test_train_valid_tensor_generators(**args.__dict__)\n", "train_model_from_generators(\n", " overparameterized_model, generate_train, generate_valid, args.training_steps, args.validation_steps, args.batch_size,\n", " args.epochs, args.patience, args.output_folder, args.id, args.inspect_model, args.inspect_show_labels,\n", - ")" + " plot=False, save_last_model=True\n", + ")\n", + "for tm in encoders:\n", + " encoders[tm].save(f'{args.output_folder}{args.id}/encoder_{tm.name}.h5')\n", + "for tm in decoders:\n", + " decoders[tm].save(f'{args.output_folder}{args.id}/decoder_{tm.name}.h5')" ] }, { @@ -502,6 +518,55 @@ "latent_df.info()" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "out_path = os.path.join(args.output_folder, args.id + '/')\n", + "test_data, test_labels, test_paths = big_batch_from_minibatch_generator(generate_test, args.test_steps*5)\n", + "print(list(test_data.keys()))\n", + "\n", + "preds = overparameterized_model.predict(test_data)\n", + "print([p.shape for p in preds])\n", + "print([tm.name for tm in args.tensor_maps_out])\n", + "print(test_paths)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from ml4h.plots import _plot_reconstruction\n", + "_plot_reconstruction(args.tensor_maps_out[0], test_data['input_lax_2ch_diastole_slice0_3d_continuous'], preds[0], out_path, test_paths, num_samples=2)\n", + "from ml4h.explorations import predictions_to_pngs\n", + "predictions_to_pngs(preds, args.tensor_maps_in, args.tensor_maps_out, \n", + " test_data, test_labels, test_paths, out_path)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "for i, etm in enumerate(encoders):\n", + " embed = encoders[etm].predict(test_data[etm.input_name()])\n", + " double = np.tile(embed, 2)\n", + " print(f'embed shape: {embed.shape} double shape: {double.shape}')\n", + " for dtm in decoders:\n", + " predictions = decoders[dtm].predict(double)\n", + " print(f'prediction shape: {predictions.shape}')\n", + " out_path = os.path.join(args.output_folder, args.id, f'decoding_{dtm.name}_from_{etm.name}/')\n", + " if not os.path.exists(os.path.dirname(out_path)):\n", + " os.makedirs(os.path.dirname(out_path))\n", + " _plot_reconstruction(dtm, test_data[dtm.input_name()], predictions.copy(), out_path, test_paths, 8)" + ] + }, { "cell_type": "code", "execution_count": null, @@ -711,6 +776,18 @@ "display_name": "Python 3", "language": "python", "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.9" } }, "nbformat": 4, diff --git a/notebooks/autoencoders/paired_multimodal_segmenter_mri_ecg.ipynb b/notebooks/autoencoders/paired_multimodal_segmenter_mri_ecg.ipynb index 442e27a18..3ea3e32a2 100644 --- a/notebooks/autoencoders/paired_multimodal_segmenter_mri_ecg.ipynb +++ b/notebooks/autoencoders/paired_multimodal_segmenter_mri_ecg.ipynb @@ -38,6 +38,7 @@ "# ml4h Imports\n", "from ml4h.TensorMap import TensorMap\n", "from ml4h.arguments import parse_args\n", + "from ml4h.plots import _plot_reconstruction\n", "from ml4h.models import make_multimodal_multitask_model, train_model_from_generators, make_hidden_layer_model, _conv_layer_from_kind_and_dimension\n", "from ml4h.tensor_generators import TensorGenerator, big_batch_from_minibatch_generator, test_train_valid_tensor_generators\n", "from ml4h.recipes import plot_predictions, infer_hidden_layer_multimodal_multitask\n", @@ -271,13 +272,6 @@ " return norm\n", "\n", "def pairwise_cosine_difference(t1, t2):\n", - " \"\"\"\n", - " A [batch x n x d] tensor of n rows with d dimensions\n", - " B [batch x m x d] tensor of n rows with d dimensions\n", - "\n", - " returns:\n", - " D [batch x n x m] tensor of cosine similarity scores between each point i Model:\n", " inputs = {tm: Input(shape=tm.shape, name=tm.input_name()) for tm in args.tensor_maps_in}\n", " original_outputs = {tm:1 for tm in args.tensor_maps_out}\n", + " real_serial_layers = kwargs['model_layers']\n", + " args.model_layers = None\n", " multimodal_activations = []\n", + " encoders = {}\n", + " decoders = {}\n", " outputs = []\n", " losses = []\n", " for left, right in pairs:\n", @@ -348,9 +346,16 @@ " \n", " paired_embeddings = loss_layer([h_left, h_right])\n", " multimodal_activations.extend(paired_embeddings)\n", + " if left not in encoders:\n", + " encoders[left] = encode_left\n", + " if right not in encoders:\n", + " encoders[right] = encode_right \n", " \n", " multimodal_activation = Concatenate()(multimodal_activations)\n", + " encoder = Model(inputs=list(inputs.values()), outputs=[multimodal_activation], name='encoder')\n", " \n", + " # build decoder models\n", + " latent_inputs = Input(shape=(args.dense_layers[0]*len(inputs)), name='input_concept_space')\n", " pre_decoder_shapes: Dict[TensorMap, Optional[Tuple[int, ...]]] = {}\n", " for tm in args.tensor_maps_out:\n", " shape = _calc_start_shape(num_upsamples=len(args.dense_blocks), output_shape=tm.shape, \n", @@ -377,7 +382,10 @@ " upsample_z=args.pool_z,\n", " )\n", " \n", - " outputs.append(decode(restructure(multimodal_activation)))\n", + " reconstruction = decode(restructure(latent_inputs))\n", + " decoder = Model(latent_inputs, reconstruction, name=tm.output_name())\n", + " decoders[tm] = decoder\n", + " outputs.append(decoder(multimodal_activation))\n", " losses.append(tm.loss)\n", "\n", " args.tensor_maps_out = list(original_outputs.keys())\n", @@ -389,11 +397,11 @@ " m.compile(optimizer=opt, loss=losses, metrics=my_metrics)\n", " m.summary()\n", " \n", - " if kwargs['model_layers'] is not None:\n", + " if real_serial_layers is not None:\n", " m.load_weights(kwargs['model_layers'], by_name=True)\n", " print(f\"Loaded model weights from:{kwargs['model_layers']}\")\n", " \n", - " return m" + " return m, encoders, decoders" ] }, { @@ -406,35 +414,90 @@ " '--tensors', '/mnt/disks/sax-lax-40k-lvm/2020-01-29/', \n", " '--input_tensors', 'ecg.ecg_rest', 'mri.cine_segmented_lax_4ch_diastole', \n", " '--output_tensors', 'ecg.ecg_rest', 'mri.cine_segmented_lax_4ch_diastole',\n", - " '--activation', 'swish',\n", + " '--activation', 'selu',\n", " '--conv_layers', '32',\n", - " '--conv_x', '9', '9', '9',\n", + " '--conv_x', '15', '15', '15',\n", " '--conv_y', '3', '3', '3', \n", " '--conv_z', '3', '3', '3',\n", " '--dense_blocks', '32', '32', '32',\n", " '--block_size', '3',\n", - " '--dense_layers', '512',\n", + " '--dense_layers', '64',\n", " '--pool_x', '2',\n", " '--pool_y', '2',\n", " '--batch_size', '1',\n", - " '--patience', '44',\n", - " '--epochs', '496',\n", - " '--learning_rate', '0.0001',\n", + " '--patience', '94',\n", + " '--epochs', '396',\n", + " '--learning_rate', '0.00005',\n", " '--training_steps', '128',\n", " '--validation_steps', '30',\n", " '--test_steps', '8',\n", " '--num_workers', '4',\n", " '--inspect_model',\n", " '--tensormap_prefix', 'ml4h.tensormap.ukb',\n", - " '--id', 'ecg_mri_lax_4ch_diastole_euclid_paired_segmenter_512d']\n", + " '--model_layers', './recipes_output/paired_ecg_segmented_mri_lax_4ch_diastole_cosine_64d_selu/paired_ecg_segmented_mri_lax_4ch_diastole_cosine_64d_selu.h5',\n", + " '--id', 'paired_ecg_segmented_mri_lax_4ch_diastole_cosine_64d_selu']\n", "args = parse_args()\n", "pairs = [(args.tensor_maps_in[0], args.tensor_maps_in[1])]\n", - "overparameterized_model = make_paired_autoencoder_model(pairs, pair_loss='euclid', **args.__dict__)\n", + "overparameterized_model, encoders, decoders = make_paired_autoencoder_model(pairs, pair_loss='cosine', **args.__dict__)\n", "generate_train, generate_valid, generate_test = test_train_valid_tensor_generators(**args.__dict__)\n", "train_model_from_generators(\n", " overparameterized_model, generate_train, generate_valid, args.training_steps, args.validation_steps, args.batch_size,\n", " args.epochs, args.patience, args.output_folder, args.id, args.inspect_model, args.inspect_show_labels,\n", - ")" + " plot=False, save_last_model=True\n", + ")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "for tm in encoders:\n", + " encoders[tm].save(f'{args.output_folder}{args.id}/encoder_{tm.name}.h5')\n", + "for tm in decoders:\n", + " decoders[tm].save(f'{args.output_folder}{args.id}/decoder_{tm.name}.h5')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "sys.argv = ['train', \n", + " '--tensors', '/mnt/disks/sax-lax-40k-lvm/2020-01-29/', \n", + " '--input_tensors', 'ecg.ecg_rest', 'mri.cine_segmented_lax_4ch_diastole', \n", + " '--output_tensors', 'ecg.ecg_rest', 'mri.cine_segmented_lax_4ch_diastole',\n", + " '--activation', 'swish',\n", + " '--conv_layers', '32',\n", + " '--conv_x', '9', '9', '9',\n", + " '--conv_y', '3', '3', '3', \n", + " '--conv_z', '3', '3', '3', \n", + " '--dense_blocks', '32', '32', '32',\n", + " '--block_size', '3',\n", + " '--dense_layers', '256',\n", + " '--pool_x', '2',\n", + " '--pool_y', '2',\n", + " '--batch_size', '1',\n", + " '--patience', '44',\n", + " '--epochs', '532',\n", + " '--learning_rate', '0.0002',\n", + " '--training_steps', '72',\n", + " '--validation_steps', '30',\n", + " '--test_steps', '8',\n", + " '--num_workers', '4',\n", + " '--inspect_model',\n", + " '--tensormap_prefix', 'ml4h.tensormap.ukb',\n", + " '--hidden_layer', 'concatenate_36',\n", + " '--model_file', './recipes_output/paired_ecg_segmented_mri_lax_4ch_diastole_cosine_64d_selu/paired_ecg_segmented_mri_lax_4ch_diastole_cosine_64d_selu.h5',\n", + " #'--sample_csv', '/home/sam/lvh/lvh_hold_out.txt',\n", + " '--id', 'paired_ecg_segmented_mri_lax_4ch_diastole_cosine_64d_selu']\n", + "args = parse_args()\n", + "#overparameterized_model = make_multimodal_multitask_model(**args.__dict__)\n", + "generate_train, generate_valid, generate_test = test_train_valid_tensor_generators(**args.__dict__)\n", + "#plot_predictions(args)\n", + "#infer_hidden_layer_multimodal_multitask(args)" ] }, { @@ -480,7 +543,17 @@ "metadata": {}, "outputs": [], "source": [ - "print(list(test_data['input_strip_continuous'].shape))" + "for i, etm in enumerate(encoders):\n", + " embed = encoders[etm].predict(test_data[etm.input_name()])\n", + " double = np.tile(embed, 2)\n", + " print(f'embed shape: {embed.shape} double shape: {double.shape}')\n", + " for dtm in decoders:\n", + " predictions = decoders[dtm].predict(double)\n", + " print(f'prediction shape: {predictions.shape}')\n", + " out_path = os.path.join(args.output_folder, args.id, f'decoding_{dtm.name}_from_{etm.name}/')\n", + " if not os.path.exists(os.path.dirname(out_path)):\n", + " os.makedirs(os.path.dirname(out_path))\n", + " _plot_reconstruction(dtm, test_data[dtm.input_name()], predictions.copy(), out_path, test_paths, 8)" ] }, { @@ -515,7 +588,7 @@ " if i % sample_every == 0 and col < samples:\n", " for j in range(rows):\n", " if len(test_data[test_key].shape) == 4:\n", - " axes[j, col].imshow(test_data[test_key][j, :, :, 0], cmap = 'gray')\n", + " axes[j, col].imshow(test_data[test_key][j, :, :, 0], cmap = 'plasma')\n", " axes[j, col].set_yticks(())\n", " elif len(test_data[test_key].shape) == 3:\n", " for l in range(12):\n", @@ -541,14 +614,14 @@ "\n", "\n", "test_data, test_labels, test_paths = big_batch_from_minibatch_generator(generate_test, args.test_steps)\n", - "test_key = 'input_lax_4ch_diastole_slice0_224_3d_continuous'\n", + "test_key = 'input_cine_segmented_lax_4ch_diastole_categorical'\n", "test_shape = test_data[test_key].shape\n", "test_data[test_key] = np.random.random(test_shape)\n", "out_path = os.path.join(args.output_folder, args.id, test_key + '_noise/')\n", "if not os.path.exists(os.path.dirname(out_path)):\n", " os.makedirs(os.path.dirname(out_path))\n", "noise_preds = plot_ae_towards_attractor(overparameterized_model, test_data, test_labels, test_key, \n", - " test_index=1, rows=8, samples=4, steps = 18)\n", + " test_index=1, rows=8, samples=4, steps = 28)\n", "print(list(test_data.keys()))\n", "_plot_reconstruction(args.tensor_maps_out[0], test_data['input_strip_continuous'], \n", " noise_preds[0], out_path, test_paths)\n", @@ -592,7 +665,10 @@ "df = pd.read_csv('/home/sam/ml/trained_models/lax_4ch_diastole_autoencode_leaky_converge/tensors_all_union.csv')\n", "df['21003_Age-when-attended-assessment-centre_2_0'].plot.hist(bins=30)\n", "hidden_inference = './recipes_output/ecg_mri_lax_4ch_diastole_paired_autoencoder_2blocks_256d_200_samples/hidden_inference_ecg_mri_lax_4ch_diastole_paired_autoencoder_2blocks_256d_200_samples.tsv'\n", + "hidden_inference = './recipes_output/paired_ecg_segmented_mri_lax_4ch_diastole_euclid_256d_swish/hidden_inference_paired_ecg_segmented_mri_lax_4ch_diastole_euclid_256d_swish.tsv'\n", + "hidden_inference = './recipes_output/paired_ecg_segmented_mri_lax_4ch_diastole_cosine_256d_selu/hidden_inference_paired_ecg_segmented_mri_lax_4ch_diastole_cosine_256d_selu.tsv'\n", "\n", + "hidden_inference = './recipes_output/paired_ecg_segmented_mri_lax_4ch_diastole_cosine_64d_selu/hidden_inference_paired_ecg_segmented_mri_lax_4ch_diastole_cosine_64d_selu.tsv'\n", "\n", "df2 = pd.read_csv(hidden_inference, sep='\\t')\n", "df['fpath'] = pd.to_numeric(df['fpath'], errors='coerce')\n", @@ -714,7 +790,7 @@ "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", - "latent_dimension = 256\n", + "latent_dimension = 128\n", "latent_cols = [f'latent_{i}' for i in range(latent_dimension)]\n", "pca, matrix_reduce = pca_on_matrix(df2[latent_cols].to_numpy(), 10)\n", "for strat in ['Sex_Female_0_0', 'has_ttntv', 'atrial_fibrillation_or_flutter', \n", @@ -742,7 +818,7 @@ " stratify_latent_space(strat, 1.0, latent_cols, latent_df)\n", "strats = ['LVEF', 'LVM', 'LVEDV', 'sample_id',\n", " '21001_Body-mass-index-BMI_0_0', '21003_Age-when-attended-assessment-centre_2_0']\n", - "theshes = [45, 100, 150, 3500000, 27.5, 70]\n", + "theshes = [45, 100, 150, 3500000, 27.5, 65]\n", "for strat, thresh in zip(strats, theshes):\n", " stratify_latent_space(strat, thresh, latent_cols, latent_df)" ] @@ -756,12 +832,12 @@ "latent_dimension = 512\n", "latent_cols = [f'latent_{i}' for i in range(latent_dimension)]\n", "pca, matrix_reduce = pca_on_matrix(df2[latent_cols].to_numpy(), 10)\n", - "for strat in ['Sex_Female_0_0', 'atrial_fibrillation_or_flutter', \n", + "for strat in ['Sex_Female_0_0', 'has_ttntv', 'atrial_fibrillation_or_flutter', \n", " 'coronary_artery_disease', 'hypertension']:\n", " stratify_latent_space(strat, 1.0, latent_cols, latent_df)\n", "strats = ['LVEF', 'LVM', 'LVEDV', 'sample_id',\n", " '21001_Body-mass-index-BMI_0_0', '21003_Age-when-attended-assessment-centre_2_0']\n", - "theshes = [45, 100, 150, 3500000, 27.5, 70]\n", + "theshes = [45, 100, 150, 3500000, 27.5, 65]\n", "for strat, thresh in zip(strats, theshes):\n", " stratify_latent_space(strat, thresh, latent_cols, latent_df)" ] @@ -775,11 +851,11 @@ "latent_dimension = 256\n", "latent_cols = [f'latent_{256+i}' for i in range(latent_dimension)]\n", "pca, matrix_reduce = pca_on_matrix(df2[latent_cols].to_numpy(), 10)\n", - "c_strats = [ 'Sex_Female_0_0']\n", + "c_strats = [ 'Sex_Female_0_0', 'has_ttntv']\n", "for c_strat in c_strats:\n", " strats = ['LVEF', 'LVM', 'LVEDV', 'sample_id',\n", " '21001_Body-mass-index-BMI_0_0', '21003_Age-when-attended-assessment-centre_2_0']\n", - " theshes = [50, 100, 150, 3750000, 27.5, 70]\n", + " theshes = [50, 100, 150, 3750000, 27.5, 65]\n", " for strat, thresh in zip(strats, theshes):\n", " directions_in_latent_space(c_strat, 1.0, strat, thresh, latent_cols, latent_df)" ] @@ -824,7 +900,8 @@ "metadata": {}, "outputs": [], "source": [ - "print(f'{ecg_encode[:5,:5]} \\n{mri_encode[:5,:5]}')" + "print(f\"{np.mean(np.sqrt(np.einsum('ij, ij->ij', ecg_encode, ecg_encode)))}\")\n", + "print(f\"{np.mean(np.sqrt(np.einsum('ij, ij->ij', mri_encode, mri_encode)))}\")" ] }, { @@ -836,7 +913,7 @@ "latent_dimension = 256\n", "latent_cols = [f'latent_{i}' for i in range(latent_dimension)]\n", "ecg_encode = latent_df[latent_cols].to_numpy()\n", - "latent_cols = [f'latent_{18+i}' for i in range(latent_dimension)]\n", + "latent_cols = [f'latent_{250+i}' for i in range(latent_dimension)]\n", "mri_encode = latent_df[latent_cols].to_numpy()\n", "diff = np.sqrt(np.einsum('ij, ij->ij', ecg_encode - mri_encode, ecg_encode - mri_encode))\n", "print(diff.shape) \n", @@ -849,8 +926,8 @@ "metadata": {}, "outputs": [], "source": [ - "ch2_random = np.random.random((4452, 256))\n", - "ch3_random = np.random.random((4452, 256))\n", + "ch2_random = np.random.random((8520, 256))\n", + "ch3_random = np.random.random((8520, 256))\n", "diff = np.sqrt(np.einsum('ij, ij->ij', ch2_random - ch3_random, ch2_random - ch3_random))\n", "print(diff.shape) \n", "print(np.mean(diff))" @@ -862,44 +939,35 @@ "metadata": {}, "outputs": [], "source": [ - "sys.argv = ['train', \n", - " '--tensors', '/mnt/disks/sax-lax-40k-lvm/2020-01-29/', \n", - " '--input_tensors', 'ecg.ecg_rest', 'mri.lax_4ch_diastole_slice0_224_3d', \n", - " '--output_tensors', 'ecg.ecg_rest', 'mri.lax_4ch_diastole_slice0_224_3d',\n", - " '--activation', 'swish',\n", - " '--conv_layers', '32',\n", - " '--conv_x', '9', '9', '9',\n", - " '--conv_y', '3', '3', '3', \n", - " '--conv_z', '3', '3', '3', \n", - " '--dense_blocks', '32', '32', '32',\n", - " '--block_size', '3',\n", - " '--dense_layers', '256',\n", - " '--pool_x', '2',\n", - " '--pool_y', '2',\n", - " '--batch_size', '1',\n", - " '--patience', '44',\n", - " '--epochs', '532',\n", - " '--learning_rate', '0.0002',\n", - " '--training_steps', '72',\n", - " '--validation_steps', '30',\n", - " '--test_steps', '8',\n", - " '--num_workers', '4',\n", - " '--inspect_model',\n", - " '--tensormap_prefix', 'ml4h.tensormap.ukb',\n", - " '--hidden_layer', 'concatenate_36',\n", - " '--model_file', './recipes_output/ecg_mri_lax_4ch_diastole_paired_autoencoder_2blocks_256d_200_samples/ecg_mri_lax_4ch_diastole_paired_autoencoder_2blocks_256d_200_samples.h5',\n", - " '--train_csv', '/home/sam/lvh/small_set.csv',\n", - " #'--sample_csv', '/home/sam/lvh/lvh_hold_out.txt',\n", - " '--id', 'ecg_mri_lax_4ch_diastole_paired_autoencoder_2blocks_256d_200_samples']\n", - "args = parse_args()\n", - "\n", - "#overparameterized_model = make_multimodal_multitask_model(**args.__dict__)\n", - "#infer_hidden_layer_multimodal_multitask(args)\n", - "#generate_train, generate_valid, generate_test = test_train_valid_tensor_generators(**args.__dict__)\n", - "# train_model_from_generators(\n", - "# overparameterized_model, generate_train, generate_valid, args.training_steps, args.validation_steps, args.batch_size,\n", - "# args.epochs, args.patience, args.output_folder, args.id, args.inspect_model, args.inspect_show_labels,\n", - "# )" + "latent_dimension = 128\n", + "latent_cols = [f'latent_{i}' for i in range(latent_dimension)]\n", + "all_encode = latent_df[latent_cols].to_numpy()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from ml4h.plots import _plot_reconstruction\n", + "for tm in decoders:\n", + " predictions = decoders[tm].predict(all_encode[:4])\n", + " print(predictions.shape)\n", + " out_path = os.path.join(args.output_folder, args.id, 'decodings/')\n", + " if not os.path.exists(os.path.dirname(out_path)):\n", + " os.makedirs(os.path.dirname(out_path))\n", + " samples = list(map(str, list(latent_df['sample_id'])[:10]))\n", + " _plot_reconstruction(tm, predictions, predictions, out_path, samples)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print()" ] }, { diff --git a/notebooks/autoencoders/vae_mri_slice.ipynb b/notebooks/autoencoders/vae_mri_slice.ipynb index 21785359e..0aa84e195 100644 --- a/notebooks/autoencoders/vae_mri_slice.ipynb +++ b/notebooks/autoencoders/vae_mri_slice.ipynb @@ -286,7 +286,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.8" + "version": "3.6.9" } }, "nbformat": 4, diff --git a/notebooks/mnist_demo.ipynb b/notebooks/mnist_demo.ipynb index 88393f90f..b4fc5bcbf 100644 --- a/notebooks/mnist_demo.ipynb +++ b/notebooks/mnist_demo.ipynb @@ -9,7 +9,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -34,7 +34,7 @@ "from tensorflow.keras.layers import Dense, Conv2D, Flatten\n", "\n", "from ml4h.defines import StorageType\n", - "from ml4h.arguments import parse_args, TMAPS, _get_tmap\n", + "from ml4h.arguments import parse_args\n", "from ml4h.TensorMap import TensorMap, Interpretation\n", "from ml4h.tensor_generators import test_train_valid_tensor_generators\n", "from ml4h.models import train_model_from_generators, make_multimodal_multitask_model, _inspect_model\n", @@ -47,7 +47,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -58,7 +58,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -95,7 +95,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -113,9 +113,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "loading data...\n", + "(50000, 784)\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "plot_mnist(4)" ] @@ -139,7 +158,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -158,9 +177,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "loading data...\n", + "Wrote 5000 MNIST images and labels as HD5 files\n", + "Wrote 10000 MNIST images and labels as HD5 files\n", + "Wrote 15000 MNIST images and labels as HD5 files\n", + "Wrote 20000 MNIST images and labels as HD5 files\n", + "Wrote 25000 MNIST images and labels as HD5 files\n", + "Wrote 30000 MNIST images and labels as HD5 files\n", + "Wrote 35000 MNIST images and labels as HD5 files\n", + "Wrote 40000 MNIST images and labels as HD5 files\n", + "Wrote 45000 MNIST images and labels as HD5 files\n", + "Wrote 50000 MNIST images and labels as HD5 files\n" + ] + } + ], "source": [ "mnist_as_hd5(HD5_FOLDER)" ] @@ -210,12 +247,41 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2020-09-18 16:17:13,404 - logger:25 - INFO - Logging configuration was loaded. Log messages can be found at ./runs/learn_mnist/log_2020-09-18_16-17_0.log.\n", + "2020-09-18 16:17:13,410 - arguments:444 - INFO - Command Line was: \n", + "./scripts/tf.sh train --tensors ./mnist_hd5s/ --tensormap_prefix ml4h.tensormap.mnist --input_tensors mnist_image --output_tensors mnist_label --batch_size 64 --test_steps 64 --epochs 24 --output_folder ./runs/ --id learn_mnist\n", + "\n", + "2020-09-18 16:17:13,410 - arguments:445 - INFO - Arguments are Namespace(activation='relu', aligned_dimension=16, alpha=0.5, anneal_max=2.0, anneal_rate=0.0, anneal_shift=0.0, app_csv=None, b_slice_force=None, balance_csvs=[], batch_size=64, bigquery_credentials_file='/mnt/ml4cvd/projects/jamesp/bigquery/bigquery-viewer-credentials.json', bigquery_dataset='broad-ml4cvd.ukbb7089_r10data', block_size=3, bottleneck_type=, cache_size=437500000.0, categorical_field_ids=[], continuous_field_ids=[], continuous_file=None, continuous_file_column=None, continuous_file_discretization_bounds=[], continuous_file_normalize=False, conv_dilate=False, conv_layers=[32], conv_normalize=None, conv_regularize=None, conv_regularize_rate=0.0, conv_type='conv', conv_x=[3], conv_y=[3], conv_z=[2], debug=False, dense_blocks=[32, 24, 16], dense_layers=[16, 64], dense_normalize=None, dense_regularize=None, dense_regularize_rate=0.0, dicom_series='cine_segmented_sax_b6', dicoms='./dicoms/', eager=False, embed_visualization=None, epochs=24, explore_export_errors=False, freeze_model_layers=False, hidden_layer='embed', id='learn_mnist', imputation_method_for_continuous_fields='random', include_array=False, include_instance=False, include_missing_continuous_channel=False, input_tensors=['mnist_image'], inspect_model=False, inspect_show_labels=True, join_tensors=['partners_ecg_patientid_clean'], label_weights=None, language_layer='ecg_rest_text', language_prefix='ukb_ecg_rest', learning_rate=0.0002, learning_rate_schedule=None, logging_level='INFO', match_any_window=False, max_models=16, max_parameters=9000000, max_patients=999999, max_pools=[], max_sample_id=7000000, max_samples=None, max_slices=999999, min_sample_id=0, min_samples=3, min_values=10, mixup_alpha=0, mlp_concat=False, mode='mlp', model_file=None, model_files=[], model_layers=None, mri_field_ids=['20208', '20209'], num_workers=8, number_per_window=1, optimizer='radam', order_in_window=None, output_folder='./runs/', output_tensors=['mnist_label'], padding='same', patience=8, phecode_definitions='/mnt/ml4cvd/projects/jamesp/data/phecode_definitions1.2.csv', phenos_folder='gs://ml4cvd/phenotypes/', plot_hist=True, plot_mode='clinical', pool_type='max', pool_x=2, pool_y=2, pool_z=1, protected_tensors=[], random_seed=12878, reference_end_time_tensor=None, reference_join_tensors=None, reference_labels=None, reference_name='Reference', reference_start_time_tensor=None, reference_tensors=None, sample_csv=None, sample_weight=None, save_last_model=False, t=48, tensor_maps_in=[TensorMap(mnist_image, (28, 28, 1), continuous)], tensor_maps_out=[TensorMap(mnist_label, (10,), categorical)], tensor_maps_protected=[], tensormap_prefix='ml4h.tensormap.mnist', tensors='./mnist_hd5s/', tensors_name='Tensors', tensors_source=None, test_csv=None, test_ratio=0.1, test_steps=64, text_file=None, text_one_hot=False, text_window=32, time_frequency='3M', time_tensor='partners_ecg_datetime', train_csv=None, training_steps=72, tsv_style='standard', u_connect=defaultdict(, {}), valid_csv=None, valid_ratio=0.2, validation_steps=18, window_name=None, write_pngs=False, x=256, xml_field_ids=['20205', '6025'], xml_folder='/mnt/disks/ecg-rest-xml/', y=256, z=48, zip_folder='/mnt/disks/sax-mri-zip/', zoom_height=96, zoom_width=96, zoom_x=50, zoom_y=35)\n", + "\n", + "2020-09-18 16:17:13,411 - tensor_generators:661 - INFO - Found 0 train, 0 validation, and 0 testing tensors at: ./mnist_hd5s/\n" + ] + }, + { + "ename": "ValueError", + "evalue": "Not enough tensors at ./mnist_hd5s/\nFound 0 training, 0 validation, and 0 testing tensors\nDiscarded 0 tensors", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 12\u001b[0m ]\n\u001b[1;32m 13\u001b[0m \u001b[0margs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mparse_args\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m \u001b[0mtrain_multimodal_multitask\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m/home/sam/ml/ml4h/recipes.py\u001b[0m in \u001b[0;36mtrain_multimodal_multitask\u001b[0;34m(args)\u001b[0m\n\u001b[1;32m 129\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 130\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mtrain_multimodal_multitask\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 131\u001b[0;31m \u001b[0mgenerate_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgenerate_valid\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgenerate_test\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtest_train_valid_tensor_generators\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__dict__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 132\u001b[0m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmake_multimodal_multitask_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__dict__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 133\u001b[0m model = train_model_from_generators(\n", + "\u001b[0;32m/home/sam/ml/ml4h/tensor_generators.py\u001b[0m in \u001b[0;36mtest_train_valid_tensor_generators\u001b[0;34m(tensor_maps_in, tensor_maps_out, tensor_maps_protected, tensors, batch_size, num_workers, training_steps, validation_steps, cache_size, balance_csvs, keep_paths, keep_paths_test, mixup_alpha, sample_csv, valid_ratio, test_ratio, train_csv, valid_csv, test_csv, siamese, sample_weight, **kwargs)\u001b[0m\n\u001b[1;32m 796\u001b[0m \u001b[0mtrain_csv\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtrain_csv\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 797\u001b[0m \u001b[0mvalid_csv\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvalid_csv\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 798\u001b[0;31m \u001b[0mtest_csv\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtest_csv\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 799\u001b[0m )\n\u001b[1;32m 800\u001b[0m \u001b[0mweights\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/home/sam/ml/ml4h/tensor_generators.py\u001b[0m in \u001b[0;36mget_train_valid_test_paths\u001b[0;34m(tensors, sample_csv, valid_ratio, test_ratio, train_csv, valid_csv, test_csv)\u001b[0m\n\u001b[1;32m 663\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_paths\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalid_paths\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest_paths\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 664\u001b[0m raise ValueError(\n\u001b[0;32m--> 665\u001b[0;31m \u001b[0;34mf'Not enough tensors at {tensors}\\n'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 666\u001b[0m \u001b[0;34mf'Found {len(train_paths)} training, {len(valid_paths)} validation, and {len(test_paths)} testing tensors\\n'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 667\u001b[0m \u001b[0;34mf'Discarded {len(discard_paths)} tensors'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mValueError\u001b[0m: Not enough tensors at ./mnist_hd5s/\nFound 0 training, 0 validation, and 0 testing tensors\nDiscarded 0 tensors" + ] + } + ], "source": [ "sys.argv = ['train', \n", " '--tensors', HD5_FOLDER, \n", + " '--tensormap_prefix', 'ml4h.tensormap.mnist',\n", " '--input_tensors', 'mnist_image',\n", " '--output_tensors', 'mnist_label',\n", " '--batch_size', '64',\n", diff --git a/notebooks/mri/mri_cardiac_long_axis_sketch.ipynb b/notebooks/mri/mri_cardiac_long_axis_sketch.ipynb index f9f99b4ad..26c08c73a 100644 --- a/notebooks/mri/mri_cardiac_long_axis_sketch.ipynb +++ b/notebooks/mri/mri_cardiac_long_axis_sketch.ipynb @@ -67,8 +67,8 @@ "outputs": [], "source": [ "def plot_lax(series, transpose=False, size=18):\n", - " cols = 5\n", - " rows = 10\n", + " cols = 2\n", + " rows = 25\n", " _, axes = plt.subplots(rows, cols, figsize=(size, size))\n", " for dcm in series:\n", " col = (dcm.InstanceNumber-1)%cols\n", @@ -76,7 +76,7 @@ " if transpose:\n", " axes[row, col].imshow(dcm.pixel_array.T)\n", " else:\n", - " axes[row, col].imshow(dcm.pixel_array)\n", + " axes[row, col].imshow(dcm.pixel_array, cmap='gray')\n", " axes[row, col].set_yticklabels([])\n", " axes[row, col].set_xticklabels([])" ] @@ -132,7 +132,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.8" + "version": "3.6.9" } }, "nbformat": 4, diff --git a/notebooks/mri/mri_cardiac_short_axis_sketch.ipynb b/notebooks/mri/mri_cardiac_short_axis_sketch.ipynb index 20b93d12f..64493df15 100644 --- a/notebooks/mri/mri_cardiac_short_axis_sketch.ipynb +++ b/notebooks/mri/mri_cardiac_short_axis_sketch.ipynb @@ -32,8 +32,9 @@ "source": [ "!mkdir ./dcm_scratch\n", "!rm ./dcm_scratch/*\n", - "!cp /mnt/ml4cvd/projects/bulk/cardiac_mri/1000387_20209_2_0.zip ./dcm_scratch/\n", - "!unzip ./dcm_scratch/1000387_20209_2_0.zip -d ./dcm_scratch/" + "\n", + "!cp /mnt/ml4cvd/projects/bulk/cardiac_mri/2467677_20209_2_0.zip ./dcm_scratch/\n", + "!unzip ./dcm_scratch/2467677_20209_2_0.zip -d ./dcm_scratch/" ] }, { @@ -154,11 +155,12 @@ " if idx >= sides*sides:\n", " continue\n", " if _is_mitral_valve_segmentation(dcm):\n", - " axes[idx%sides, idx//sides].imshow(dcm.pixel_array)\n", + " axes[idx%sides, idx//sides].imshow(dcm.pixel_array, cmap='gray')\n", " else:\n", " try:\n", " overlay, anatomical_mask, ventricle_pixels = _get_overlay_from_dicom(dcm)\n", - " axes[idx%sides, idx//sides].imshow(np.ma.masked_where(anatomical_mask == 2, dcm.pixel_array))\n", + " #axes[idx%sides, idx//sides].imshow(np.ma.masked_where(anatomical_mask == 2, dcm.pixel_array), cmap='gray')\n", + " axes[idx%sides, idx//sides].imshow(dcm.pixel_array, cmap='gray')\n", " except KeyError:\n", " print(f'Could not get overlay at {dcm.InstanceNumber}, angle {s}')\n", " axes[idx, idx//sides].imshow(dcm.pixel_array)\n", @@ -172,7 +174,7 @@ "metadata": {}, "outputs": [], "source": [ - "plot_b_series(series[2], sides=7)" + "plot_b_series(series[4], sides=2)" ] }, { @@ -261,7 +263,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.8" + "version": "3.6.9" } }, "nbformat": 4, diff --git a/notebooks/review_results/identify_a_sample_to_review.ipynb b/notebooks/review_results/identify_a_sample_to_review.ipynb index f26d76309..a5c659d24 100644 --- a/notebooks/review_results/identify_a_sample_to_review.ipynb +++ b/notebooks/review_results/identify_a_sample_to_review.ipynb @@ -16,7 +16,7 @@ "
\n", " This notebook assumes:\n", "
    \n", - "
  • Terra is running custom Docker image gcr.io/uk-biobank-sek-data/ml4h_terra:20200918_091608.
    • \n", + "
  • Terra is running custom Docker image gcr.io/uk-biobank-sek-data/ml4h_terra:20200729_091732.
    • \n", "
  • ml4h is running custom Docker image gcr.io/broad-ml4cvd/deeplearning:tf2-latest-gpu.
    • \n", "
\n", "
" @@ -32,7 +32,7 @@ "from ml4h.runtime_data_defines import determine_runtime\n", "from ml4h.runtime_data_defines import Runtime\n", "\n", - "if Runtime.ML4H_VM == determine_runtime():\n", + "if Runtime.ml4h_VM == determine_runtime():\n", " !pip3 install --user --upgrade pandas_gbq pyarrow\n", " # Be sure to restart the kernel if pip installs anything." ] @@ -259,7 +259,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.8" + "version": "3.7.7" }, "toc": { "base_numbering": 1, diff --git a/notebooks/review_results/image_annotations.ipynb b/notebooks/review_results/image_annotations.ipynb deleted file mode 100644 index 6e644f15a..000000000 --- a/notebooks/review_results/image_annotations.ipynb +++ /dev/null @@ -1,268 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Image annotations for a batch of samples\n", - "\n", - "Using this notebook, cardiologists are able to quickly view and annotate MRI images for a batch of samples. These annotated images become the training data for the next round of modeling." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Setup\n", - "\n", - "
\n", - " This notebook assumes\n", - "
    \n", - "
  • Terra is running custom Docker image gcr.io/uk-biobank-sek-data/ml4h_terra:20200918_091608.
    • \n", - "
  • ml4h is running custom Docker image gcr.io/broad-ml4cvd/deeplearning:tf2-latest-gpu.
    • \n", - "
\n", - "
" - ] - }, - { - "attachments": { - "Screen%20Shot%202020-06-22%20at%202.50.48%20PM.png": { - "image/png": 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" - } - }, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Screen%20Shot%202020-06-22%20at%202.50.48%20PM.png](attachment:Screen%20Shot%202020-06-22%20at%202.50.48%20PM.png)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# TODO(deflaux): remove this cell after gcr.io/broad-ml4cvd/deeplearning:tf2-latest-gpu has this preinstalled.\n", - "from ml4h.runtime_data_defines import determine_runtime\n", - "from ml4h.runtime_data_defines import Runtime\n", - "\n", - "if Runtime.ML4H_VM == determine_runtime():\n", - " !pip3 install --user ipycanvas==0.4.1 ipyannotations==0.2.0\n", - " !jupyter nbextension install --user --py ipycanvas\n", - " !jupyter nbextension enable --user --py ipycanvas\n", - " # Be sure to restart the kernel if pip installs anything.\n", - " # Also, shift-reload the browser page after the notebook extension installation." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from ml4h.visualization_tools.annotation_storage import BigQueryAnnotationStorage\n", - "from ml4h.visualization_tools.batch_image_annotations import BatchImageAnnotator\n", - "import pandas as pd\n", - "import tensorflow as tf" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "code_folding": [] - }, - "outputs": [], - "source": [ - "%%javascript\n", - "// Display cell outputs to full height (no vertical scroll bar)\n", - "IPython.OutputArea.auto_scroll_threshold = 9999;" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pd.set_option('display.max_colwidth', -1)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "BIG_QUERY_ANNOTATIONS_STORAGE = BigQueryAnnotationStorage('uk-biobank-sek-data.ml_results.annotations')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Define the batch of samples to annotate\n", - "\n", - "
\n", - " Edit the CSV file path below, if needed, to either a local file or one in Cloud Storage.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#---[ EDIT AND RUN THIS CELL TO READ FROM A LOCAL FILE OR A FILE IN CLOUD STORAGE ]---\n", - "SAMPLE_BATCH_FILE = None" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "if SAMPLE_BATCH_FILE:\n", - " samples_df = pd.read_csv(tf.io.gfile.GFile(SAMPLE_BATCH_FILE))\n", - "\n", - "else:\n", - " # Normally these would all be the same or similar TMAP. We are using different ones here just to make it\n", - " # more obvious in this demo that we are processing different samples.\n", - " samples_df = pd.DataFrame(\n", - " columns=BatchImageAnnotator.EXPECTED_COLUMN_NAMES,\n", - " data=[\n", - " [1655349, 'cine_lax_3ch_192', 25, 'gs://ml4cvd/deflaux/ukbb_tensors/'],\n", - " [1655349, 't2_flair_sag_p2_1mm_fs_ellip_pf78_1', 50, 'gs://ml4cvd/deflaux/ukbb_tensors/'],\n", - " [1655349, 'cine_lax_4ch_192', 25, 'gs://ml4cvd/deflaux/ukbb_tensors/'],\n", - " [1655349, 't2_flair_sag_p2_1mm_fs_ellip_pf78_2', 50, 'gs://ml4cvd/deflaux/ukbb_tensors/'],\n", - " [2403657, 'cine_lax_3ch_192', 25, 'gs://ml4cvd/deflaux/ukbb_tensors/'],\n", - " ])\n", - "\n", - "samples_df.shape" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "samples_df.head(n = 10)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Annotate the batch! " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Note: a zoom level of 1.0 displays the tensor as-is. For higher zoom levels, this code currently\n", - "# use the PIL library to scale the image.\n", - "\n", - "annotator = BatchImageAnnotator(samples=samples_df,\n", - " zoom=2.0,\n", - " annotation_categories=['region_of_interest'],\n", - " annotation_storage=BIG_QUERY_ANNOTATIONS_STORAGE)\n", - "annotator.annotate_images()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# View the stored annotations " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "annotator.view_recent_submissions(count=10)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Provenance" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import datetime\n", - "print(datetime.datetime.now())" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%bash\n", - "pip3 freeze" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Questions about these particular notebooks? Reach out to Puneet Batra pbatra@broadinstitute.org, Paolo Di Achille pdiachil@broadinstitute.org, and Nicole Deflaux deflaux@verily.com." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.8" - }, - "toc": { - "base_numbering": 1, - "nav_menu": {}, - "number_sections": true, - "sideBar": true, - "skip_h1_title": false, - "title_cell": "Table of Contents", - "title_sidebar": "Contents", - "toc_cell": false, - "toc_position": { - "height": "calc(100% - 180px)", - "left": "10px", - "top": "150px", - "width": "199px" - }, - "toc_section_display": true, - "toc_window_display": true - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/notebooks/review_results/review_one_sample.ipynb b/notebooks/review_results/review_one_sample.ipynb index 9569380aa..9fb1fc2b2 100644 --- a/notebooks/review_results/review_one_sample.ipynb +++ b/notebooks/review_results/review_one_sample.ipynb @@ -16,7 +16,7 @@ "
\n", " This notebook assumes\n", "
    \n", - "
  • Terra is running custom Docker image gcr.io/uk-biobank-sek-data/ml4h_terra:20200918_091608.
    • \n", + "
  • Terra is running custom Docker image gcr.io/uk-biobank-sek-data/ml4h_terra:20200729_091732.
    • \n", "
  • ml4h is running custom Docker image gcr.io/broad-ml4cvd/deeplearning:tf2-latest-gpu.
    • \n", "
\n", "
" @@ -41,7 +41,7 @@ "from ml4h.runtime_data_defines import determine_runtime\n", "from ml4h.runtime_data_defines import Runtime\n", "\n", - "if Runtime.ML4H_VM == determine_runtime():\n", + "if Runtime.ml4h_VM == determine_runtime():\n", " !pip3 install --user --upgrade pandas_gbq pyarrow\n", " # Be sure to restart the kernel if pip installs anything." ] @@ -523,7 +523,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## MRI DICOM visualization" + "## MRI dicom visualization" ] }, { @@ -641,7 +641,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.8" + "version": "3.7.7" }, "toc": { "base_numbering": 1, diff --git a/notebooks/review_results/test_error_handling_for_notebook_visualizations.ipynb b/notebooks/review_results/test_error_handling_for_notebook_visualizations.ipynb index 97eee7d12..0c4a1c97a 100644 --- a/notebooks/review_results/test_error_handling_for_notebook_visualizations.ipynb +++ b/notebooks/review_results/test_error_handling_for_notebook_visualizations.ipynb @@ -21,7 +21,7 @@ "metadata": {}, "source": [ "
\n", - " Terra Users test with the most recent custom Docker image which has all the software dependencies preinstalled. (e.g., more recent than gcr.io/uk-biobank-sek-data/ml4h_terra:20200918_091608)\n", + " Terra Users test with the most recent custom Docker image which has all the software dependencies preinstalled. (e.g., more recent than gcr.io/uk-biobank-sek-data/ml4h_terra:20200729_091732)\n", "
" ] }, @@ -41,9 +41,7 @@ "from ml4h.visualization_tools.annotation_storage import BigQueryAnnotationStorage\n", "\n", "import pandas as pd\n", - "import tensorflow as tf\n", - "\n", - "%matplotlib inline" + "import tensorflow as tf" ] }, { @@ -64,7 +62,7 @@ "outputs": [], "source": [ "#---[ EDIT THIS VARIABLE VALUE IF YOU LIKE ]---\n", - "MODEL_RESULTS_FILE = 'gs://uk-biobank-sek-data-us-east1/phenotypes/ml4cvd/ukbiobank_query_results_plus_four_fake_samples.csv'" + "MODEL_RESULTS_FILE = 'gs://uk-biobank-sek-data-us-east1/phenotypes/ml4h/ukbiobank_query_results_plus_four_fake_samples.csv'" ] }, { @@ -257,7 +255,7 @@ "metadata": {}, "outputs": [], "source": [ - "#dicom_interactive_plots.choose_mri(sample_id=SAMPLE_TO_REVIEW)" + "dicom_interactive_plots.choose_mri(sample_id=SAMPLE_TO_REVIEW)" ] }, { @@ -266,7 +264,7 @@ "metadata": {}, "outputs": [], "source": [ - "#dicom_plots.choose_cardiac_mri(sample_id=SAMPLE_TO_REVIEW)" + "dicom_plots.choose_cardiac_mri(sample_id=SAMPLE_TO_REVIEW)" ] }, { @@ -283,7 +281,7 @@ "outputs": [], "source": [ "SAMPLE_TO_REVIEW = 5993648\n", - "folder = 'gs://deflaux-test-001/'" + "folder = 'gs://broad-ml4cvd-vcm/'" ] }, { @@ -328,7 +326,7 @@ "metadata": {}, "outputs": [], "source": [ - "#dicom_interactive_plots.choose_mri(sample_id=SAMPLE_TO_REVIEW, folder=folder)" + "dicom_interactive_plots.choose_mri(sample_id=SAMPLE_TO_REVIEW, folder=folder)" ] }, { @@ -337,7 +335,7 @@ "metadata": {}, "outputs": [], "source": [ - "#dicom_plots.choose_cardiac_mri(sample_id=SAMPLE_TO_REVIEW, folder=folder)" + "dicom_plots.choose_cardiac_mri(sample_id=SAMPLE_TO_REVIEW, folder=folder)" ] }, { @@ -460,6 +458,24 @@ "hd5_mri_plots.choose_mri_tmap(sample_id=SAMPLE_TO_REVIEW)" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dicom_interactive_plots.choose_mri(sample_id=SAMPLE_TO_REVIEW)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dicom_plots.choose_cardiac_mri(sample_id=SAMPLE_TO_REVIEW)" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -615,7 +631,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.8" + "version": "3.7.7" }, "toc": { "base_numbering": 1, @@ -630,7 +646,7 @@ "height": "calc(100% - 180px)", "left": "10px", "top": "150px", - "width": "197.756px" + "width": "342.756px" }, "toc_section_display": true, "toc_window_display": true diff --git a/notebooks/terra_featured_workspace/identify_a_sample_to_review_interactive.ipynb b/notebooks/terra_featured_workspace/identify_a_sample_to_review_interactive.ipynb index e17eb08e2..e166223b1 100644 --- a/notebooks/terra_featured_workspace/identify_a_sample_to_review_interactive.ipynb +++ b/notebooks/terra_featured_workspace/identify_a_sample_to_review_interactive.ipynb @@ -20,7 +20,7 @@ "
\n", " This notebook assumes:\n", "
    \n", - "
  • Terra is running custom Docker image gcr.io/uk-biobank-sek-data/ml4h_terra:20200918_091608.
    • \n", + "
  • Terra is running custom Docker image gcr.io/uk-biobank-sek-data/ml4h_terra:20200729_091732.
    • \n", "
  • ml4h is running custom Docker image gcr.io/broad-ml4cvd/deeplearning:tf2-latest-gpu.
    • \n", "
\n", "
" @@ -79,7 +79,7 @@ "source": [ "#---[ EDIT THIS VARIABLE VALUE IF YOU LIKE ]---\n", "# TODO(paolo and team): provide CSV with phenotypes and ML results for fake samples.\n", - "MODEL_RESULTS_FILE = 'gs://uk-biobank-sek-data-us-east1/phenotypes/ml4cvd/ukbiobank_query_results_plus_four_fake_samples.csv'" + "MODEL_RESULTS_FILE = 'gs://uk-biobank-sek-data-us-east1/phenotypes/ml4h/ukbiobank_query_results_plus_four_fake_samples.csv'" ] }, { diff --git a/notebooks/terra_featured_workspace/image_annotations_demo.ipynb b/notebooks/terra_featured_workspace/image_annotations_demo.ipynb deleted file mode 100644 index ce15e4d73..000000000 --- a/notebooks/terra_featured_workspace/image_annotations_demo.ipynb +++ /dev/null @@ -1,259 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Image annotations for a batch of samples\n", - "\n", - "Using this notebook, cardiologists are able to quickly view and annotate MRI images for a batch of samples. These annotated images become the training data for the next round of modeling." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Setup\n", - "\n", - "
\n", - " This notebook assumes\n", - "
    \n", - "
  • Terra is running custom Docker image gcr.io/uk-biobank-sek-data/ml4h_terra:20200918_091608.
    • \n", - "
  • ml4cvd is running custom Docker image gcr.io/broad-ml4cvd/deeplearning:tf2-latest-gpu.
    • \n", - "
\n", - "
" - ] - }, - { - "attachments": { - "Screen%20Shot%202020-06-22%20at%202.50.48%20PM.png": { - "image/png": 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" - } - }, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Screen%20Shot%202020-06-22%20at%202.50.48%20PM.png](attachment:Screen%20Shot%202020-06-22%20at%202.50.48%20PM.png)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from ml4cvd.visualization_tools.batch_image_annotations import BatchImageAnnotator\n", - "import pandas as pd" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "code_folding": [] - }, - "outputs": [], - "source": [ - "%%javascript\n", - "// Display cell outputs to full height (no vertical scroll bar)\n", - "IPython.OutputArea.auto_scroll_threshold = 9999;" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pd.set_option('display.max_colwidth', -1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Define the batch of samples to annotate\n", - "\n", - "In general, we would read in a CSV file but for this demo we define the batch right here." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Normally these would all be the same or similar TMAP. We are using different ones here just to make it\n", - "# more obvious in this demo that we are processing different samples.\n", - "samples_df = pd.DataFrame(\n", - " columns=BatchImageAnnotator.EXPECTED_COLUMN_NAMES,\n", - " data=[\n", - " ['fake_1', 'cine_lax_3ch_192', 25, 'gs://ml4cvd/projects/fake_hd5s/'],\n", - " ['fake_1', 't2_flair_sag_p2_1mm_fs_ellip_pf78_1', 50, 'gs://ml4cvd/projects/fake_hd5s/'],\n", - " ['fake_1', 'cine_lax_4ch_192', 25, 'gs://ml4cvd/projects/fake_hd5s/'],\n", - " ['fake_1', 't2_flair_sag_p2_1mm_fs_ellip_pf78_2', 50, 'gs://ml4cvd/projects/fake_hd5s/'],\n", - " ['fake_2', 'cine_lax_3ch_192', 25, 'gs://ml4cvd/projects/fake_hd5s/'],\n", - " ])\n", - "\n", - "samples_df" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Annotate the batch! " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Note: a zoom level of 1.0 displays the tensor as-is. For higher zoom levels, this code currently\n", - "# use the PIL library to scale the image.\n", - "\n", - "annotator = BatchImageAnnotator(samples=samples_df, zoom=2.0, annotation_categories=['region_of_interest'])\n", - "annotator.annotate_images()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## via BigQuery annotation storage " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from ml4cvd.visualization_tools.annotation_storage import BigQueryAnnotationStorage" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "BIG_QUERY_ANNOTATIONS_STORAGE = BigQueryAnnotationStorage('uk-biobank-sek-data.ml_results.annotations')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Note: a zoom level of 1.0 displays the tensor as-is. For higher zoom levels, this code currently\n", - "# use the PIL library to scale the image.\n", - "\n", - "annotator = BatchImageAnnotator(samples=samples_df,\n", - " zoom=2.0,\n", - " annotation_categories=['region_of_interest'],\n", - " annotation_storage=BIG_QUERY_ANNOTATIONS_STORAGE)\n", - "annotator.annotate_images()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# View the stored annotations " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "annotator.view_recent_submissions(count=10)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Provenance" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import datetime\n", - "print(datetime.datetime.now())" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%bash\n", - "pip3 freeze" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Questions about these particular notebooks? Reach out to Puneet Batra pbatra@broadinstitute.org, Paolo Di Achille pdiachil@broadinstitute.org, and Nicole Deflaux deflaux@verily.com." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.8" - }, - "toc": { - "base_numbering": 1, - "nav_menu": {}, - "number_sections": true, - "sideBar": true, - "skip_h1_title": false, - "title_cell": "Table of Contents", - "title_sidebar": "Contents", - "toc_cell": false, - "toc_position": { - "height": "calc(100% - 180px)", - "left": "10px", - "top": "150px", - "width": "199px" - }, - "toc_section_display": true, - "toc_window_display": true - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/notebooks/terra_featured_workspace/review_one_sample_interactive.ipynb b/notebooks/terra_featured_workspace/review_one_sample_interactive.ipynb index 8e863a7e4..44379f740 100644 --- a/notebooks/terra_featured_workspace/review_one_sample_interactive.ipynb +++ b/notebooks/terra_featured_workspace/review_one_sample_interactive.ipynb @@ -18,7 +18,7 @@ "
\n", " This notebook assumes\n", "
    \n", - "
  • Terra is running custom Docker image gcr.io/uk-biobank-sek-data/ml4h_terra:20200918_091608.
    • \n", + "
  • Terra is running custom Docker image gcr.io/uk-biobank-sek-data/ml4h_terra:20200729_091732.
    • \n", "
  • ml4h is running custom Docker image gcr.io/broad-ml4cvd/deeplearning:tf2-latest-gpu.
    • \n", "
\n", "
" @@ -84,7 +84,7 @@ "source": [ "#---[ EDIT THIS VARIABLE VALUE IF YOU LIKE ]---\n", "# TODO(paolo and team): provide CSV with phenotypes and ML results for fake samples.\n", - "MODEL_RESULTS_FILE = 'gs://uk-biobank-sek-data-us-east1/phenotypes/ml4cvd/ukbiobank_query_results_plus_four_fake_samples.csv'" + "MODEL_RESULTS_FILE = 'gs://uk-biobank-sek-data-us-east1/phenotypes/ml4h/ukbiobank_query_results_plus_four_fake_samples.csv'" ] }, { diff --git a/pylintrc b/pylintrc deleted file mode 100644 index 8a5e40122..000000000 --- a/pylintrc +++ /dev/null @@ -1,337 +0,0 @@ -# This configuration was copied from https://github.com/tensorflow/tensorflow/blob/18ebe824d2f6f20b09839cb0a0073032a2d6c5fe/tensorflow/tools/ci_build/pylintrc and then further modified. - -[MASTER] - -# Specify a configuration file. -#rcfile= - -# Python code to execute, usually for sys.path manipulation such as -# pygtk.require(). -#init-hook= - -# Profiled execution. -profile=no - -# Add files or directories to the denylist. They should be base names, not -# paths. -ignore=CVS - -# Pickle collected data for later comparisons. -persistent=yes - -# List of plugins (as comma separated values of python modules names) to load, -# usually to register additional checkers. -load-plugins= - - -[MESSAGES CONTROL] - -# Enable the message, report, category or checker with the given id(s). You can -# either give multiple identifier separated by comma (,) or put this option -# multiple time. See also the "--disable" option for examples. -enable=indexing-exception,old-raise-syntax - -# Disable the message, report, category or checker with the given id(s). You -# can either give multiple identifiers separated by comma (,) or put this -# option multiple times (only on the command line, not in the configuration -# file where it should appear only once).You can also use "--disable=all" to -# disable everything first and then reenable specific checks. For example, if -# you want to run only the similarities checker, you can use "--disable=all -# --enable=similarities". If you want to run only the classes checker, but have -# no Warning level messages displayed, use"--disable=all --enable=classes -# --disable=W" -disable=design,similarities,no-self-use,attribute-defined-outside-init,locally-disabled,star-args,pointless-except,bad-option-value,global-statement,fixme,suppressed-message,useless-suppression,locally-enabled,no-member,no-name-in-module,import-error,unsubscriptable-object,unbalanced-tuple-unpacking,undefined-variable,not-context-manager - - -# Set the cache size for astng objects. -cache-size=500 - - -[REPORTS] - -# Set the output format. Available formats are text, parseable, colorized, msvs -# (visual studio) and html. You can also give a reporter class, eg -# mypackage.mymodule.MyReporterClass. -output-format=text - -# Put messages in a separate file for each module / package specified on the -# command line instead of printing them on stdout. Reports (if any) will be -# written in a file name "pylint_global.[txt|html]". -files-output=no - -# Tells whether to display a full report or only the messages -reports=no - -# Python expression which should return a note less than 10 (10 is the highest -# note). You have access to the variables errors warning, statement which -# respectively contain the number of errors / warnings messages and the total -# number of statements analyzed. This is used by the global evaluation report -# (RP0004). -evaluation=10.0 - ((float(5 * error + warning + refactor + convention) / statement) * 10) - -# Add a comment according to your evaluation note. This is used by the global -# evaluation report (RP0004). -comment=no - -# Template used to display messages. This is a python new-style format string -# used to format the message information. See doc for all details -#msg-template= - - -[TYPECHECK] - -# Tells whether missing members accessed in mixin class should be ignored. A -# mixin class is detected if its name ends with "mixin" (case insensitive). -ignore-mixin-members=yes - -# List of classes names for which member attributes should not be checked -# (useful for classes with attributes dynamically set). -ignored-classes=SQLObject - -# When zope mode is activated, add a predefined set of Zope acquired attributes -# to generated-members. -zope=no - -# List of members which are set dynamically and missed by pylint inference -# system, and so shouldn't trigger E0201 when accessed. Python regular -# expressions are accepted. -generated-members=REQUEST,acl_users,aq_parent - -# List of decorators that create context managers from functions, such as -# contextlib.contextmanager. -contextmanager-decorators=contextlib.contextmanager,contextlib2.contextmanager - - -[VARIABLES] - -# Tells whether we should check for unused import in __init__ files. -init-import=no - -# A regular expression matching the beginning of the name of dummy variables -# (i.e. not used). -dummy-variables-rgx=^\*{0,2}(_$|unused_|dummy_) - -# List of additional names supposed to be defined in builtins. Remember that -# you should avoid to define new builtins when possible. -additional-builtins= - - -[BASIC] - -# Required attributes for module, separated by a comma -required-attributes= - -# List of builtins function names that should not be used, separated by a comma -bad-functions=apply,input,reduce - - -# Disable the report(s) with the given id(s). -# All non-Google reports are disabled by default. -disable-report=R0001,R0002,R0003,R0004,R0101,R0102,R0201,R0202,R0220,R0401,R0402,R0701,R0801,R0901,R0902,R0903,R0904,R0911,R0912,R0913,R0914,R0915,R0921,R0922,R0923 - -# Regular expression which should only match correct module names -module-rgx=(([a-z_][a-z0-9_]*)|([A-Z][a-zA-Z0-9]+))$ - -# Regular expression which should only match correct module level names -const-rgx=^(_?[A-Z][A-Z0-9_]*|__[a-z0-9_]+__|_?[a-z][a-z0-9_]*)$ - -# Regular expression which should only match correct class names -class-rgx=^_?[A-Z][a-zA-Z0-9]*$ - -# Regular expression which should only match correct function names -function-rgx=^(?:(?P_?[A-Z][a-zA-Z0-9]*)|(?P_?[a-z][a-z0-9_]*))$ - -# Regular expression which should only match correct method names -method-rgx=^(?:(?P__[a-z0-9_]+__|next)|(?P_{0,2}[A-Z][a-zA-Z0-9]*)|(?P_{0,2}[a-z][a-z0-9_]*))$ - -# Regular expression which should only match correct instance attribute names -attr-rgx=^_{0,2}[a-z][a-z0-9_]*$ - -# Regular expression which should only match correct argument names -argument-rgx=^[a-z][a-z0-9_]*$ - -# Regular expression which should only match correct variable names -variable-rgx=^[a-z][a-z0-9_]*$ - -# Regular expression which should only match correct attribute names in class -# bodies -class-attribute-rgx=^(_?[A-Z][A-Z0-9_]*|__[a-z0-9_]+__|_?[a-z][a-z0-9_]*)$ - -# Regular expression which should only match correct list comprehension / -# generator expression variable names -inlinevar-rgx=^[a-z][a-z0-9_]*$ - -# Good variable names which should always be accepted, separated by a comma -good-names=main,_ - -# Bad variable names which should always be refused, separated by a comma -bad-names= - -# Regular expression which should only match function or class names that do -# not require a docstring. -no-docstring-rgx=(__.*__|main) - -# Minimum line length for functions/classes that require docstrings, shorter -# ones are exempt. -docstring-min-length=10 - - -[FORMAT] - -# Maximum number of characters on a single line. -max-line-length=120 - -# Regexp for a line that is allowed to be longer than the limit. -ignore-long-lines=(?x) - (^\s*(import|from)\s - |\$Id:\s\/\/depot\/.+#\d+\s\$ - |^[a-zA-Z_][a-zA-Z0-9_]*\s*=\s*("[^"]\S+"|'[^']\S+') - |^\s*\#\ LINT\.ThenChange - |^[^#]*\#\ type:\ [a-zA-Z_][a-zA-Z0-9_.,[\] ]*$ - |pylint - |""" - |\# - |lambda - |(https?|ftp):) - -# Allow the body of an if to be on the same line as the test if there is no -# else. -single-line-if-stmt=y - -# List of optional constructs for which whitespace checking is disabled -no-space-check= - -# Maximum number of lines in a module -max-module-lines=99999 - -# String used as indentation unit. This is usually " " (4 spaces) or "\t" (1 -# tab). -indent-string=' ' - - -[SIMILARITIES] - -# Minimum lines number of a similarity. -min-similarity-lines=4 - -# Ignore comments when computing similarities. -ignore-comments=yes - -# Ignore docstrings when computing similarities. -ignore-docstrings=yes - -# Ignore imports when computing similarities. -ignore-imports=no - - -[MISCELLANEOUS] - -# List of note tags to take in consideration, separated by a comma. -notes= - - -[IMPORTS] - -# Deprecated modules which should not be used, separated by a comma -deprecated-modules=regsub,TERMIOS,Bastion,rexec,sets - -# Create a graph of every (i.e. internal and external) dependencies in the -# given file (report RP0402 must not be disabled) -import-graph= - -# Create a graph of external dependencies in the given file (report RP0402 must -# not be disabled) -ext-import-graph= - -# Create a graph of internal dependencies in the given file (report RP0402 must -# not be disabled) -int-import-graph= - - -[CLASSES] - -# List of interface methods to ignore, separated by a comma. This is used for -# instance to not check methods defines in Zope's Interface base class. -ignore-iface-methods=isImplementedBy,deferred,extends,names,namesAndDescriptions,queryDescriptionFor,getBases,getDescriptionFor,getDoc,getName,getTaggedValue,getTaggedValueTags,isEqualOrExtendedBy,setTaggedValue,isImplementedByInstancesOf,adaptWith,is_implemented_by - -# List of method names used to declare (i.e. assign) instance attributes. -defining-attr-methods=__init__,__new__,setUp - -# List of valid names for the first argument in a class method. -valid-classmethod-first-arg=cls,class_ - -# List of valid names for the first argument in a metaclass class method. -valid-metaclass-classmethod-first-arg=mcs - - -[DESIGN] - -# Maximum number of arguments for function / method -max-args=5 - -# Argument names that match this expression will be ignored. Default to name -# with leading underscore -ignored-argument-names=_.* - -# Maximum number of locals for function / method body -max-locals=15 - -# Maximum number of return / yield for function / method body -max-returns=6 - -# Maximum number of branch for function / method body -max-branches=12 - -# Maximum number of statements in function / method body -max-statements=50 - -# Maximum number of parents for a class (see R0901). -max-parents=7 - -# Maximum number of attributes for a class (see R0902). -max-attributes=7 - -# Minimum number of public methods for a class (see R0903). -min-public-methods=2 - -# Maximum number of public methods for a class (see R0904). -max-public-methods=20 - - -[EXCEPTIONS] - -# Exceptions that will emit a warning when being caught. Defaults to -# "Exception" -overgeneral-exceptions=Exception,StandardError,BaseException - - -[AST] - -# Maximum line length for lambdas -short-func-length=1 - -# List of module members that should be marked as deprecated. -# All of the string functions are listed in 4.1.4 Deprecated string functions -# in the Python 2.4 docs. -deprecated-members=string.atof,string.atoi,string.atol,string.capitalize,string.expandtabs,string.find,string.rfind,string.index,string.rindex,string.count,string.lower,string.split,string.rsplit,string.splitfields,string.join,string.joinfields,string.lstrip,string.rstrip,string.strip,string.swapcase,string.translate,string.upper,string.ljust,string.rjust,string.center,string.zfill,string.replace,sys.exitfunc - - -[DOCSTRING] - -# List of exceptions that do not need to be mentioned in the Raises section of -# a docstring. -ignore-exceptions=AssertionError,NotImplementedError,StopIteration,TypeError - - - -[TOKENS] - -# Number of spaces of indent required when the last token on the preceding line -# is an open (, [, or {. -indent-after-paren=4 - - -[GOOGLE LINES] - -# Regexp for a proper copyright notice. -copyright=Copyright \d{4} The TensorFlow Authors\. +All [Rr]ights [Rr]eserved\. diff --git a/scripts/jupyter.sh b/scripts/jupyter.sh index 32edfb1f3..a83fd582d 100755 --- a/scripts/jupyter.sh +++ b/scripts/jupyter.sh @@ -54,7 +54,7 @@ while getopts ":ip:ch" opt ; do ;; c) DOCKER_IMAGE=${DOCKER_IMAGE_NO_GPU} - GPU_DEVICE="" + GPU_DEVICE="" ;; :) echo "ERROR: Option -${OPTARG} requires an argument." 1>&2 @@ -99,7 +99,6 @@ ${DOCKER_COMMAND} run -it \ ${GPU_DEVICE} \ --rm \ --ipc=host \ ---hostname=$(hostname) \ -v /home/${USER}/:/home/${USER}/ \ -v /mnt/:/mnt/ \ -p 0.0.0.0:${PORT}:${PORT} \ diff --git a/tests/test_models.py b/tests/test_models.py index 61df950cb..976a6b846 100644 --- a/tests/test_models.py +++ b/tests/test_models.py @@ -7,7 +7,8 @@ from typing import List, Optional, Dict, Tuple, Iterator from ml4h.TensorMap import TensorMap -from ml4h.models import make_multimodal_multitask_model, parent_sort, BottleneckType, ACTIVATION_FUNCTIONS, MODEL_EXT, train_model_from_generators, check_no_bottleneck +from ml4h.models import make_multimodal_multitask_model, parent_sort, BottleneckType, ACTIVATION_FUNCTIONS, MODEL_EXT, train_model_from_generators, \ + check_no_bottleneck, make_paired_autoencoder_model from ml4h.test_utils import TMAPS_UP_TO_4D, MULTIMODAL_UP_TO_4D, CATEGORICAL_TMAPS, CONTINUOUS_TMAPS, SEGMENT_IN, SEGMENT_OUT, PARENT_TMAPS, CYCLE_PARENTS from ml4h.test_utils import LANGUAGE_TMAP_1HOT_WINDOW, LANGUAGE_TMAP_1HOT_SOFTMAX @@ -18,14 +19,13 @@ 'dense_layers': [4, 2], 'dense_blocks': [5, 3], 'block_size': 3, - 'conv_width': 3, 'learning_rate': 1e-3, 'optimizer': 'adam', 'conv_type': 'conv', 'conv_layers': [6, 5, 3], - 'conv_x': [3], - 'conv_y': [3], - 'conv_z': [2], + 'conv_x': [3]*5, + 'conv_y': [3]*5, + 'conv_z': [2]*5, 'padding': 'same', 'max_pools': [], 'pool_type': 'max', @@ -39,6 +39,16 @@ 'dense_regularize_rate': .1, 'dense_normalize': 'batch_norm', 'bottleneck_type': BottleneckType.FlattenRestructure, + 'pair_loss': 'cosine', + 'training_steps': 12, + 'learning_rate': 0.00001, + 'epochs': 6, + 'optimizer': 'adam', + 'learning_rate_schedule': None, + 'model_layers': None, + 'model_file': None, + 'hidden_layer': 'embed', + 'u_connect': {}, } @@ -54,19 +64,20 @@ def make_training_data(input_tmaps: List[TensorMap], output_tmaps: List[TensorMa ), ]) -def assert_model_trains(input_tmaps: List[TensorMap], output_tmaps: List[TensorMap], m: Optional[tf.keras.Model] = None): +def assert_model_trains(input_tmaps: List[TensorMap], output_tmaps: List[TensorMap], m: Optional[tf.keras.Model] = None, skip_shape_check: bool = False): if m is None: m = make_multimodal_multitask_model( input_tmaps, output_tmaps, **DEFAULT_PARAMS, ) - for tmap, tensor in zip(input_tmaps, m.inputs): - assert tensor.shape[1:] == tmap.shape - assert tensor.shape[1:] == tmap.shape - for tmap, tensor in zip(parent_sort(output_tmaps), m.outputs): - assert tensor.shape[1:] == tmap.shape - assert tensor.shape[1:] == tmap.shape + if not skip_shape_check: + for tmap, tensor in zip(input_tmaps, m.inputs): + assert tensor.shape[1:] == tmap.shape + assert tensor.shape[1:] == tmap.shape + for tmap, tensor in zip(parent_sort(output_tmaps), m.outputs): + assert tensor.shape[1:] == tmap.shape + assert tensor.shape[1:] == tmap.shape data = make_training_data(input_tmaps, output_tmaps) history = m.fit(data, steps_per_epoch=2, epochs=2, validation_data=data, validation_steps=2) for tmap in output_tmaps: @@ -294,8 +305,8 @@ def test_parents(self, output_tmaps): def test_language_models(self, input_output_tmaps, tmpdir): params = DEFAULT_PARAMS.copy() m = make_multimodal_multitask_model( - input_output_tmaps[0], - input_output_tmaps[1], + tensor_maps_in=input_output_tmaps[0], + tensor_maps_out=input_output_tmaps[1], **params ) assert_model_trains(input_output_tmaps[0], input_output_tmaps[1], m) @@ -309,6 +320,36 @@ def test_language_models(self, input_output_tmaps, tmpdir): **DEFAULT_PARAMS, ) + @pytest.mark.parametrize( + 'pairs', + [ + [(CONTINUOUS_TMAPS[2], CONTINUOUS_TMAPS[1])], + [(CATEGORICAL_TMAPS[2], CATEGORICAL_TMAPS[1])], + [(CONTINUOUS_TMAPS[2], CONTINUOUS_TMAPS[1]), (CONTINUOUS_TMAPS[2], CATEGORICAL_TMAPS[3])] + ], + ) + def test_paired_models(self, pairs, tmpdir): + params = DEFAULT_PARAMS.copy() + pair_list = list(set([p[0] for p in pairs] + [p[1] for p in pairs])) + params['u_connect'] = {tm: [] for tm in pair_list} + m, encoders, decoders = make_paired_autoencoder_model( + pairs=pairs, + tensor_maps_in=pair_list, + tensor_maps_out=pair_list, + **params + ) + assert_model_trains(pair_list, pair_list, m, skip_shape_check=True) + m.save(os.path.join(tmpdir, 'paired_ae.h5')) + path = os.path.join(tmpdir, f'm{MODEL_EXT}') + m.save(path) + make_paired_autoencoder_model( + pairs=pairs, + tensor_maps_in=pair_list, + tensor_maps_out=pair_list, + **params + ) + + @pytest.mark.parametrize( 'tmaps', [_rotate(PARENT_TMAPS, i) for i in range(len(PARENT_TMAPS))], diff --git a/tests/test_recipes.py b/tests/test_recipes.py index 468dc44c6..df8f4a1e3 100644 --- a/tests/test_recipes.py +++ b/tests/test_recipes.py @@ -3,7 +3,7 @@ import pandas as pd import numpy as np -from ml4h.recipes import inference_file_name, hidden_inference_file_name +from ml4h.recipes import inference_file_name, _hidden_file_name from ml4h.recipes import train_multimodal_multitask, compare_multimodal_multitask_models from ml4h.recipes import infer_multimodal_multitask, infer_hidden_layer_multimodal_multitask from ml4h.recipes import compare_multimodal_scalar_task_models, _find_learning_rate @@ -42,7 +42,7 @@ def test_infer_genetics(self, default_arguments): def test_infer_hidden(self, default_arguments): infer_hidden_layer_multimodal_multitask(default_arguments) - tsv = hidden_inference_file_name(default_arguments.output_folder, default_arguments.id) + tsv = _hidden_file_name(default_arguments.output_folder, default_arguments.id) inferred = pd.read_csv(tsv, sep='\t') assert len(set(inferred['sample_id'])) == pytest.N_TENSORS @@ -50,7 +50,7 @@ def test_infer_hidden_genetics(self, default_arguments): default_arguments.tsv_style = 'genetics' infer_hidden_layer_multimodal_multitask(default_arguments) default_arguments.tsv_style = 'standard' - tsv = hidden_inference_file_name(default_arguments.output_folder, default_arguments.id) + tsv = _hidden_file_name(default_arguments.output_folder, default_arguments.id) inferred = pd.read_csv(tsv, sep='\t') assert len(set(inferred['FID'])) == pytest.N_TENSORS From 46fe794081c3d6b42bc20cd7e97ac1ff1d508dad Mon Sep 17 00:00:00 2001 From: Samwell Freeman Date: Tue, 29 Sep 2020 17:15:39 -0400 Subject: [PATCH 02/21] paired --- CONTRIBUTING.md | 192 ++++++++++ .../batch_image_annotations.py | 236 ++++++++++++ .../review_results/image_annotations.ipynb | 268 ++++++++++++++ .../image_annotations_demo.ipynb | 259 ++++++++++++++ pylintrc | 337 ++++++++++++++++++ 5 files changed, 1292 insertions(+) create mode 100644 CONTRIBUTING.md create mode 100644 ml4h/visualization_tools/batch_image_annotations.py create mode 100644 notebooks/review_results/image_annotations.ipynb create mode 100644 notebooks/terra_featured_workspace/image_annotations_demo.ipynb create mode 100644 pylintrc diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md new file mode 100644 index 000000000..2e2006c07 --- /dev/null +++ b/CONTRIBUTING.md @@ -0,0 +1,192 @@ +# Contributing + +1. Before making a substantial pull request, consider first [filing an issue](https://github.com/broadinstitute/ml/issues) describing the feature addition or change you wish to make. +1. [Get setup](#setup-for-code-contributions) +1. [Follow the coding style](#python-coding-style) +1. [Test your code](#testing) +1. Send a [pull request](https://github.com/broadinstitute/ml/pulls) + +## Setup for code contributions + +### Get setup for GitHub + +Small typos in code or documentation may be edited directly using the GitHub web interface. Otherwise: + +1. If you are new to GitHub, don't start here. Instead, work through a GitHub tutorial such as https://guides.github.com/activities/hello-world/. +1. Create a fork of https://github.com/broadinstitute/ml +1. Clone your fork. +1. Work from a feature branch. See the [Appendix](#appendix) for detailed `git` commands. + +### Install precommit + +[`pre-commit`](https://pre-commit.com/) is a framework for managing and maintaining multi-language pre-commit hooks. + +``` +# Install pre-commit +pip3 install pre-commit +# Install the git hook scripts by running this within the git clone directory +cd ${HOME}/ml +pre-commit install +``` + +See [.pre-commit-config.yaml](https://github.com/broadinstitute/ml/blob/master/.pre-commit-config.yaml) for the currently configured pre-commit hooks for ml4cvd. + +### Install git-secrets + +```git-secrets``` helps us avoid committing secrets (e.g. private keys) and other critical data (e.g. PHI) to our +repositories. ```git-secrets``` can be obtained via [github](https://github.com/awslabs/git-secrets) or on MacOS can be +installed with Homebrew by running ```brew install git-secrets```. + +To add hooks to all repositories that you initialize or clone in the future: + +```git secrets --install --global``` + +To add hooks to all local repositories: + +``` +git secrets --install ~/.git-templates/git-secrets +git config --global init.templateDir ~/.git-templates/git-secrets +``` + +We maintain our own custom "provider" to cover any private keys or other critical data that we would like to avoid +committing to our repositories. Feel free to add ```egrep```-compatible regular expressions to +```git_secrets_provider_ml4cvd.txt``` to match types of critical data that are not currently covered by the patterns in that +file. To register the patterns in this file with ```git-secrets```: + +``` +git secrets --add-provider -- cat ${HOME}/ml/git_secrets_provider_ml4cvd.txt +``` + +### Install pylint + +[`pylint`](https://www.pylint.org/) is a Python static code analysis tool which looks for programming errors, helps enforcing a coding standard, sniffs for code smells and offers simple refactoring suggestions. + +``` +# Install pylint +pip3 install pylint +``` + +See [pylintrc](https://github.com/broadinstitute/ml/blob/master/pylintrc) for the current lint configuration for ml4cvd. + +# Python coding style + +Changes to ml4cvd should conform to [PEP 8 -- Style Guide for Python Code](https://www.python.org/dev/peps/pep-0008/). See also [Google Python Style Guide](https://github.com/google/styleguide/blob/gh-pages/pyguide.md) as another decription of this coding style. + +Use `pylint` to check your Python changes: + +```bash +pylint --rcfile=${HOME}/ml/pylintrc myfile.py +``` + +Any messages returned by `pylint` are intended to be self-explanatory, but that isn't always the case. + +* Search for `pylint ` or `pylint ` for more details on the recommended code change to resolve the lint issue. +* Or add comment `# pylint: disable=` to the end of the line of code. + +# Testing + +## Testing of `recipes` + +Unit tests can be run in Docker with +``` +${HOME}/ml/scripts/tf.sh -T ${HOME}/ml/tests +``` +Unit tests can be run locally in a conda environment with +``` +python -m pytest ${HOME}/ml/tests +``` +Some of the unit tests are slow due to creating, saving and loading `tensorflow` models. +To skip those tests to move quickly, run +``` +python -m pytest ${HOME}/ml/tests -m "not slow" +``` +pytest can also run specific tests using `::`. For example + +``` +python -m pytest ${HOME}/ml/tests/test_models.py::TestMakeMultimodalMultitaskModel::test_u_connect_segment +``` + +For more pytest usage information, checkout the [usage guide](https://docs.pytest.org/en/latest/usage.html). + +## Testing of `visualization_tools` + +The code in [ml4cvd/visualization_tools](https://github.com/broadinstitute/ml/tree/master/ml4cvd/visualization_tools) is primarily interactive so we add test cases to notebook [test_error_handling_for_notebook_visualizations.ipynb](https://github.com/broadinstitute/ml/blob/master/notebooks/review_results/test_error_handling_for_notebook_visualizations.ipynb) and visually inspect the output of `Cells -> Run all`. + +# Appendix + +For the ml4cvd GitHub repository, we are doing ‘merge and squash’ of pull requests. So that means your fork does not match upstream after your pull request has been merged. The easiest way to manage this is to always work in a feature branch, instead of checking changes into your fork’s master branch. + + +## How to work on a new feature + +(1) Get the latest version of the upstream repo + +``` +git fetch upstream +``` + +Note: If you get an error saying that upstream is unknown, run the following remote add command and then re-run the fetch command. You only need to do this once per git clone. + +``` +git remote add upstream https://github.com/broadinstitute/ml.git +``` + +(2) Make sure your master branch is “even” with upstream. + +``` +git checkout master +git merge --ff-only upstream/master +git push +``` + +Now the master branch of your fork on GitHub should say *"This branch is even with broadinstitute:master."*. + + +(3) Create a feature branch for your change. + +``` +git checkout -b my-feature-branch-name +``` + +Because you created this feature branch from your master branch that was up to date with upstream (step 2), your feature branch is also up to date with upstream. Commit your changes to this branch until you are happy with them. + +(4) Push your changes to GitHub and send a pull request. + +``` +git push --set-upstream origin my-feature-branch-name +``` + +After your pull request is merged, its safe to delete your branch! + +## I accidentally checked a new change to my master branch instead of a feature branch. How to fix this? + +(1) Soft undo your change(s). This leaves the changes in the files on disk but undoes the commit. + +``` +git checkout master +# Moves pointer back to previous HEAD +git reset --soft HEAD@{1} +``` + +Or if you need to move back several commits to the most recent one in common with upstream, you can change ‘1’ to be however many commits back you need to go. + +(2) “stash” your now-unchecked-in changes so that you can get them back later. + +``` +git stash +``` + +(3) Now do the [How to work on a new feature](#how-to-work-on-a-new-feature) step to bring master up to date and create your new feature branch that is “even” with upstream. Here are those commands again: + +``` +git fetch upstream +git merge --ff-only upstream/master +git checkout -b my-feature-branch-name +``` + +(4) “unstash” your changes. + +``` +git stash pop +``` +Now you can proceed with your work! diff --git a/ml4h/visualization_tools/batch_image_annotations.py b/ml4h/visualization_tools/batch_image_annotations.py new file mode 100644 index 000000000..34ff731df --- /dev/null +++ b/ml4h/visualization_tools/batch_image_annotations.py @@ -0,0 +1,236 @@ +"""Methods for batch annotations of images stored as 3D tensors, such as MRIs, from within notebooks.""" + +import json +import os +import socket +import tempfile +from typing import Any, Dict, List + +from IPython.display import display +import numpy as np +import pandas as pd +import h5py +from ipyannotations import PolygonAnnotator +import ipywidgets as widgets +from ml4h.visualization_tools.hd5_mri_plots import MRI_TMAPS +from ml4h.visualization_tools.annotation_storage import AnnotationStorage +from ml4h.visualization_tools.annotation_storage import TransientAnnotationStorage +from PIL import Image +import tensorflow as tf + + +class BatchImageAnnotator(): + """Annotate batches of images with polygons drawn over regions of interest.""" + + SUBMIT_BUTTON_DESCRIPTION = 'Submit polygons, goto next sample' + USE_INSTRUCTIONS = ''' +

    +
  • To draw a polygon, click anywhere you'd like to start. Continue to click + along the edge of the polygon until arrive back where you started. To + finish, simply click the first point (highlighted in red). It may be + helpful to increase the point size if you're struggling (using the slider).
    • + +
  • You can change the class of a polygon using the dropdown menu while the + polygon is still "open", or unfinished. If you make a mistake, use the Undo + button until the point that's wrong has disappeared. + +
  • You can move, but not add / subtract polygon points, by clicking the "Edit" + button. Simply drag a point you want to adjust. Again, if you have + difficulty aiming at the points, you can increase the point size.
    • + +
  • You can increase or decrease the contrast and brightness of the image + using the sliders to make it easier to annotate. Sometimes you need to see + what's behind already-created annotations, and for this purpose you can + make them more see-through using the "Opacity" slider.
    • +

+ ''' + EXPECTED_COLUMN_NAMES = ['sample_id', 'tmap_name', 'instance_number', 'folder'] + DEFAULT_ANNOTATION_CLASSNAME = 'region_of_interest' + CSS = ''' + + ''' + + def __init__( + self, samples: pd.DataFrame, annotation_categories: List[str] = None, + zoom: float = 1.5, annotation_storage: AnnotationStorage = TransientAnnotationStorage(), + ): + """Initializes an instance of BatchImageAnnotator. + + Args: + samples: A dataframe of samples to annotate. Columns must include those + in BatchImageAnnotator.EXPECTED_COLUMN_NAMES. + annotation_categories: A list of one or more strings to serve as tags for the polygons. + zoom: Desired zoom level for the image. + annotation_storage: An instance of AnnotationStorage. This faciltates the use of a user-provided + strategy for the storage and processing of annotations. + + Raises: + ValueError: The provided dataframe does not contain the expected columns. + """ + if not set(self.EXPECTED_COLUMN_NAMES).issubset(samples.columns): + raise ValueError(f'samples Dataframe must contain columns {self.EXPECTED_COLUMN_NAMES}') + self.samples = samples + self.current_sample = 0 + # TODO(deflaux) remove this after https://github.com/janfreyberg/ipyannotations/issues/11 + self.zoom = zoom + self.annotation_storage = annotation_storage + if annotation_categories is None: + annotation_categories = [self.DEFAULT_ANNOTATION_CLASSNAME] + + self.annotation_widget = PolygonAnnotator( + options=annotation_categories, + canvas_size=(900, 280 * self.zoom), + ) + self.annotation_widget.on_submit(self._store_annotations) + self.annotation_widget.submit_button.description = self.SUBMIT_BUTTON_DESCRIPTION + self.annotation_widget.submit_button.layout = widgets.Layout(width='300px') + + self.title_widget = widgets.HTML('') + self.results_widget = widgets.HTML('') + + def _store_annotations(self, data: Dict[Any, Any]) -> None: + """Transfer widget state to the annotation storage and advance to the next sample.""" + if self.current_sample >= self.samples.shape[0]: + self.results_widget.value = '

Annotation batch complete!

Thank you for making the model better.' + return + + # Convert polygon points in canvas coordinates to tensor coordinates. + image_canvas_position = self.annotation_widget.canvas.image_extent + x_offset, y_offset, _, _ = image_canvas_position + tensor_coords = [ + ( + a['label'], + [( + int((p[0] - x_offset) / self.zoom), + int((p[1] - y_offset) / self.zoom), + ) for p in a['points']], + ) for a in data + ] + # Store the annotation using the provided annotation storage strategy. + self.annotation_storage.submit_annotation( + sample_id=self.samples.loc[self.current_sample, 'sample_id'], + annotator=os.getenv('OWNER_EMAIL') if os.getenv('OWNER_EMAIL') else socket.gethostname(), + key=self.samples.loc[self.current_sample, 'tmap_name'], + value_numeric=self.samples.loc[self.current_sample, 'instance_number'], + value_string=self.samples.loc[self.current_sample, 'folder'], + comment=json.dumps(tensor_coords), + ) + + # Display this annotation at the bottom of the widget. + results = f''' +
+

Prior sample's submitted annotations

+ The {self.SUBMIT_BUTTON_DESCRIPTION} button is both printing out the polygons below and storing the polygons + via strategy {self.annotation_storage.__class__.__name__}.
+ Details: {self.annotation_storage.describe()} +

sample info

+ {self._format_info_for_current_sample()} +

canvas coordinates

+ image extent {image_canvas_position} + {[f'
{json.dumps(x)}
' for x in data]} +

source tensor coordinates

+ {[f'
{json.dumps(x)}
' for x in tensor_coords]} +
+ ''' + self.results_widget.value = results + + # Advance to the next sample. + self.current_sample += 1 + self._annotate_image_for_current_sample() + + def _format_info_for_current_sample(self) -> str: + """Convert information about the current sample to an HTML table for display within the widget.""" + headings = ' '.join([f'{c}' for c in self.EXPECTED_COLUMN_NAMES] + ['TMAP shape']) + values = ' '.join([f'{self.samples.loc[self.current_sample, c]}' for c in self.EXPECTED_COLUMN_NAMES] + + [f'{MRI_TMAPS[self.samples.loc[self.current_sample, "tmap_name"]].shape}']) + return f''' + + {headings} + {values} +
+ ''' + + def _annotate_image_for_current_sample(self) -> None: + """Retrieve the data for the current sample and display its image in the annotation widget. + + If all samples have been processed, display the completion message. + """ + if self.current_sample >= self.samples.shape[0]: + self.annotation_widget.canvas.clear() + # Note: the above command clears the canvas, but any incomplete polygons will be redrawn. Call this + # private method to clear those. TODO(deflaux) remove this after https://github.com/janfreyberg/ipyannotations/issues/15 + self.annotation_widget.canvas._init_empty_data() # pylint: disable=protected-access + self.title_widget.value = '

Annotation batch complete!

Thank you for making the model better.' + return + + sample_id = self.samples.loc[self.current_sample, 'sample_id'] + tmap_name = self.samples.loc[self.current_sample, 'tmap_name'] + instance_number = self.samples.loc[self.current_sample, 'instance_number'] + folder = self.samples.loc[self.current_sample, 'folder'] + + with tempfile.TemporaryDirectory() as tmpdirname: + sample_hd5 = str(sample_id) + '.hd5' + local_path = os.path.join(tmpdirname, sample_hd5) + try: + tf.io.gfile.copy(src=os.path.join(folder, sample_hd5), dst=local_path) + hd5 = h5py.File(local_path, mode='r') + except (tf.errors.NotFoundError, tf.errors.PermissionDeniedError) as e: + self.annotation_widget.canvas.clear() + # Note: the above command clears the canvas, but any incomplete polygons will be redrawn. Call this + # private method to clear those. TODO(deflaux) remove this after https://github.com/janfreyberg/ipyannotations/issues/15 + self.annotation_widget.canvas._init_empty_data() # pylint: disable=protected-access + self.title_widget.value = f''' +
+

Warning: MRI HD5 file not available for sample {sample_id} in folder {folder}

+ Use the folder parameter to read HD5s from a different local directory or Cloud Storage bucket. + 

{e.message}

+
+ ''' + return + + tensor = MRI_TMAPS[tmap_name].tensor_from_file(MRI_TMAPS[tmap_name], hd5) + tensor_instance = tensor[:, :, instance_number] + if self.zoom > 1.0: + # TODO(deflaux) remove this after https://github.com/janfreyberg/ipyannotations/issues/11 + img = Image.fromarray(tensor_instance) + zoomed_img = img.resize([int(self.zoom * s) for s in img.size], Image.LANCZOS) + tensor_instance = np.asarray(zoomed_img) + + self.annotation_widget.display(tensor_instance) + self.title_widget.value = f''' + {self.CSS} +
+

Batch annotation of {self.samples.shape[0]} samples

+ {self.USE_INSTRUCTIONS} +
+

Current sample

+ {self._format_info_for_current_sample()} +
+ ''' + + def annotate_images(self) -> None: + """Begin the batch annotation task by displaying the annotation widget populated with the first sample. + + The submit button is used to proceed to the next sample until all samples have been processed. + """ + self._annotate_image_for_current_sample() + display(widgets.VBox([self.title_widget, self.annotation_widget, self.results_widget])) + + def view_recent_submissions(self, count: int = 10) -> pd.DataFrame: + """View a dataframe of up to [count] most recent submissions. + + Args: + count: The number of the most recent submissions to return. + + Returns: + A dataframe of the most recent annotations. + """ + return self.annotation_storage.view_recent_submissions(count=count) diff --git a/notebooks/review_results/image_annotations.ipynb b/notebooks/review_results/image_annotations.ipynb new file mode 100644 index 000000000..6e644f15a --- /dev/null +++ b/notebooks/review_results/image_annotations.ipynb @@ -0,0 +1,268 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Image annotations for a batch of samples\n", + "\n", + "Using this notebook, cardiologists are able to quickly view and annotate MRI images for a batch of samples. These annotated images become the training data for the next round of modeling." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Setup\n", + "\n", + "
\n", + " This notebook assumes\n", + "
    \n", + "
  • Terra is running custom Docker image gcr.io/uk-biobank-sek-data/ml4h_terra:20200918_091608.
    • \n", + "
  • ml4h is running custom Docker image gcr.io/broad-ml4cvd/deeplearning:tf2-latest-gpu.
    • \n", + "
\n", + "
" + ] + }, + { + "attachments": { + "Screen%20Shot%202020-06-22%20at%202.50.48%20PM.png": { + "image/png": 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" + } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Screen%20Shot%202020-06-22%20at%202.50.48%20PM.png](attachment:Screen%20Shot%202020-06-22%20at%202.50.48%20PM.png)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# TODO(deflaux): remove this cell after gcr.io/broad-ml4cvd/deeplearning:tf2-latest-gpu has this preinstalled.\n", + "from ml4h.runtime_data_defines import determine_runtime\n", + "from ml4h.runtime_data_defines import Runtime\n", + "\n", + "if Runtime.ML4H_VM == determine_runtime():\n", + " !pip3 install --user ipycanvas==0.4.1 ipyannotations==0.2.0\n", + " !jupyter nbextension install --user --py ipycanvas\n", + " !jupyter nbextension enable --user --py ipycanvas\n", + " # Be sure to restart the kernel if pip installs anything.\n", + " # Also, shift-reload the browser page after the notebook extension installation." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from ml4h.visualization_tools.annotation_storage import BigQueryAnnotationStorage\n", + "from ml4h.visualization_tools.batch_image_annotations import BatchImageAnnotator\n", + "import pandas as pd\n", + "import tensorflow as tf" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "code_folding": [] + }, + "outputs": [], + "source": [ + "%%javascript\n", + "// Display cell outputs to full height (no vertical scroll bar)\n", + "IPython.OutputArea.auto_scroll_threshold = 9999;" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pd.set_option('display.max_colwidth', -1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "BIG_QUERY_ANNOTATIONS_STORAGE = BigQueryAnnotationStorage('uk-biobank-sek-data.ml_results.annotations')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Define the batch of samples to annotate\n", + "\n", + "
\n", + " Edit the CSV file path below, if needed, to either a local file or one in Cloud Storage.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#---[ EDIT AND RUN THIS CELL TO READ FROM A LOCAL FILE OR A FILE IN CLOUD STORAGE ]---\n", + "SAMPLE_BATCH_FILE = None" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "if SAMPLE_BATCH_FILE:\n", + " samples_df = pd.read_csv(tf.io.gfile.GFile(SAMPLE_BATCH_FILE))\n", + "\n", + "else:\n", + " # Normally these would all be the same or similar TMAP. We are using different ones here just to make it\n", + " # more obvious in this demo that we are processing different samples.\n", + " samples_df = pd.DataFrame(\n", + " columns=BatchImageAnnotator.EXPECTED_COLUMN_NAMES,\n", + " data=[\n", + " [1655349, 'cine_lax_3ch_192', 25, 'gs://ml4cvd/deflaux/ukbb_tensors/'],\n", + " [1655349, 't2_flair_sag_p2_1mm_fs_ellip_pf78_1', 50, 'gs://ml4cvd/deflaux/ukbb_tensors/'],\n", + " [1655349, 'cine_lax_4ch_192', 25, 'gs://ml4cvd/deflaux/ukbb_tensors/'],\n", + " [1655349, 't2_flair_sag_p2_1mm_fs_ellip_pf78_2', 50, 'gs://ml4cvd/deflaux/ukbb_tensors/'],\n", + " [2403657, 'cine_lax_3ch_192', 25, 'gs://ml4cvd/deflaux/ukbb_tensors/'],\n", + " ])\n", + "\n", + "samples_df.shape" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "samples_df.head(n = 10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Annotate the batch! " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Note: a zoom level of 1.0 displays the tensor as-is. For higher zoom levels, this code currently\n", + "# use the PIL library to scale the image.\n", + "\n", + "annotator = BatchImageAnnotator(samples=samples_df,\n", + " zoom=2.0,\n", + " annotation_categories=['region_of_interest'],\n", + " annotation_storage=BIG_QUERY_ANNOTATIONS_STORAGE)\n", + "annotator.annotate_images()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# View the stored annotations " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "annotator.view_recent_submissions(count=10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Provenance" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import datetime\n", + "print(datetime.datetime.now())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%bash\n", + "pip3 freeze" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Questions about these particular notebooks? Reach out to Puneet Batra pbatra@broadinstitute.org, Paolo Di Achille pdiachil@broadinstitute.org, and Nicole Deflaux deflaux@verily.com." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.8" + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": false, + "toc_position": { + "height": "calc(100% - 180px)", + "left": "10px", + "top": "150px", + "width": "199px" + }, + "toc_section_display": true, + "toc_window_display": true + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/notebooks/terra_featured_workspace/image_annotations_demo.ipynb b/notebooks/terra_featured_workspace/image_annotations_demo.ipynb new file mode 100644 index 000000000..ce15e4d73 --- /dev/null +++ b/notebooks/terra_featured_workspace/image_annotations_demo.ipynb @@ -0,0 +1,259 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Image annotations for a batch of samples\n", + "\n", + "Using this notebook, cardiologists are able to quickly view and annotate MRI images for a batch of samples. These annotated images become the training data for the next round of modeling." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Setup\n", + "\n", + "
\n", + " This notebook assumes\n", + "
    \n", + "
  • Terra is running custom Docker image gcr.io/uk-biobank-sek-data/ml4h_terra:20200918_091608.
    • \n", + "
  • ml4cvd is running custom Docker image gcr.io/broad-ml4cvd/deeplearning:tf2-latest-gpu.
    • \n", + "
\n", + "
" + ] + }, + { + "attachments": { + "Screen%20Shot%202020-06-22%20at%202.50.48%20PM.png": { + "image/png": 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" + } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Screen%20Shot%202020-06-22%20at%202.50.48%20PM.png](attachment:Screen%20Shot%202020-06-22%20at%202.50.48%20PM.png)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from ml4cvd.visualization_tools.batch_image_annotations import BatchImageAnnotator\n", + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "code_folding": [] + }, + "outputs": [], + "source": [ + "%%javascript\n", + "// Display cell outputs to full height (no vertical scroll bar)\n", + "IPython.OutputArea.auto_scroll_threshold = 9999;" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pd.set_option('display.max_colwidth', -1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Define the batch of samples to annotate\n", + "\n", + "In general, we would read in a CSV file but for this demo we define the batch right here." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Normally these would all be the same or similar TMAP. We are using different ones here just to make it\n", + "# more obvious in this demo that we are processing different samples.\n", + "samples_df = pd.DataFrame(\n", + " columns=BatchImageAnnotator.EXPECTED_COLUMN_NAMES,\n", + " data=[\n", + " ['fake_1', 'cine_lax_3ch_192', 25, 'gs://ml4cvd/projects/fake_hd5s/'],\n", + " ['fake_1', 't2_flair_sag_p2_1mm_fs_ellip_pf78_1', 50, 'gs://ml4cvd/projects/fake_hd5s/'],\n", + " ['fake_1', 'cine_lax_4ch_192', 25, 'gs://ml4cvd/projects/fake_hd5s/'],\n", + " ['fake_1', 't2_flair_sag_p2_1mm_fs_ellip_pf78_2', 50, 'gs://ml4cvd/projects/fake_hd5s/'],\n", + " ['fake_2', 'cine_lax_3ch_192', 25, 'gs://ml4cvd/projects/fake_hd5s/'],\n", + " ])\n", + "\n", + "samples_df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Annotate the batch! " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Note: a zoom level of 1.0 displays the tensor as-is. For higher zoom levels, this code currently\n", + "# use the PIL library to scale the image.\n", + "\n", + "annotator = BatchImageAnnotator(samples=samples_df, zoom=2.0, annotation_categories=['region_of_interest'])\n", + "annotator.annotate_images()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## via BigQuery annotation storage " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from ml4cvd.visualization_tools.annotation_storage import BigQueryAnnotationStorage" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "BIG_QUERY_ANNOTATIONS_STORAGE = BigQueryAnnotationStorage('uk-biobank-sek-data.ml_results.annotations')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Note: a zoom level of 1.0 displays the tensor as-is. For higher zoom levels, this code currently\n", + "# use the PIL library to scale the image.\n", + "\n", + "annotator = BatchImageAnnotator(samples=samples_df,\n", + " zoom=2.0,\n", + " annotation_categories=['region_of_interest'],\n", + " annotation_storage=BIG_QUERY_ANNOTATIONS_STORAGE)\n", + "annotator.annotate_images()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# View the stored annotations " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "annotator.view_recent_submissions(count=10)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Provenance" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import datetime\n", + "print(datetime.datetime.now())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%bash\n", + "pip3 freeze" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Questions about these particular notebooks? Reach out to Puneet Batra pbatra@broadinstitute.org, Paolo Di Achille pdiachil@broadinstitute.org, and Nicole Deflaux deflaux@verily.com." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.8" + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": false, + "toc_position": { + "height": "calc(100% - 180px)", + "left": "10px", + "top": "150px", + "width": "199px" + }, + "toc_section_display": true, + "toc_window_display": true + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/pylintrc b/pylintrc new file mode 100644 index 000000000..8a5e40122 --- /dev/null +++ b/pylintrc @@ -0,0 +1,337 @@ +# This configuration was copied from https://github.com/tensorflow/tensorflow/blob/18ebe824d2f6f20b09839cb0a0073032a2d6c5fe/tensorflow/tools/ci_build/pylintrc and then further modified. + +[MASTER] + +# Specify a configuration file. +#rcfile= + +# Python code to execute, usually for sys.path manipulation such as +# pygtk.require(). +#init-hook= + +# Profiled execution. +profile=no + +# Add files or directories to the denylist. They should be base names, not +# paths. +ignore=CVS + +# Pickle collected data for later comparisons. +persistent=yes + +# List of plugins (as comma separated values of python modules names) to load, +# usually to register additional checkers. +load-plugins= + + +[MESSAGES CONTROL] + +# Enable the message, report, category or checker with the given id(s). You can +# either give multiple identifier separated by comma (,) or put this option +# multiple time. See also the "--disable" option for examples. +enable=indexing-exception,old-raise-syntax + +# Disable the message, report, category or checker with the given id(s). You +# can either give multiple identifiers separated by comma (,) or put this +# option multiple times (only on the command line, not in the configuration +# file where it should appear only once).You can also use "--disable=all" to +# disable everything first and then reenable specific checks. For example, if +# you want to run only the similarities checker, you can use "--disable=all +# --enable=similarities". If you want to run only the classes checker, but have +# no Warning level messages displayed, use"--disable=all --enable=classes +# --disable=W" +disable=design,similarities,no-self-use,attribute-defined-outside-init,locally-disabled,star-args,pointless-except,bad-option-value,global-statement,fixme,suppressed-message,useless-suppression,locally-enabled,no-member,no-name-in-module,import-error,unsubscriptable-object,unbalanced-tuple-unpacking,undefined-variable,not-context-manager + + +# Set the cache size for astng objects. +cache-size=500 + + +[REPORTS] + +# Set the output format. Available formats are text, parseable, colorized, msvs +# (visual studio) and html. You can also give a reporter class, eg +# mypackage.mymodule.MyReporterClass. +output-format=text + +# Put messages in a separate file for each module / package specified on the +# command line instead of printing them on stdout. Reports (if any) will be +# written in a file name "pylint_global.[txt|html]". +files-output=no + +# Tells whether to display a full report or only the messages +reports=no + +# Python expression which should return a note less than 10 (10 is the highest +# note). You have access to the variables errors warning, statement which +# respectively contain the number of errors / warnings messages and the total +# number of statements analyzed. This is used by the global evaluation report +# (RP0004). +evaluation=10.0 - ((float(5 * error + warning + refactor + convention) / statement) * 10) + +# Add a comment according to your evaluation note. This is used by the global +# evaluation report (RP0004). +comment=no + +# Template used to display messages. This is a python new-style format string +# used to format the message information. See doc for all details +#msg-template= + + +[TYPECHECK] + +# Tells whether missing members accessed in mixin class should be ignored. A +# mixin class is detected if its name ends with "mixin" (case insensitive). +ignore-mixin-members=yes + +# List of classes names for which member attributes should not be checked +# (useful for classes with attributes dynamically set). +ignored-classes=SQLObject + +# When zope mode is activated, add a predefined set of Zope acquired attributes +# to generated-members. +zope=no + +# List of members which are set dynamically and missed by pylint inference +# system, and so shouldn't trigger E0201 when accessed. Python regular +# expressions are accepted. +generated-members=REQUEST,acl_users,aq_parent + +# List of decorators that create context managers from functions, such as +# contextlib.contextmanager. +contextmanager-decorators=contextlib.contextmanager,contextlib2.contextmanager + + +[VARIABLES] + +# Tells whether we should check for unused import in __init__ files. +init-import=no + +# A regular expression matching the beginning of the name of dummy variables +# (i.e. not used). +dummy-variables-rgx=^\*{0,2}(_$|unused_|dummy_) + +# List of additional names supposed to be defined in builtins. Remember that +# you should avoid to define new builtins when possible. +additional-builtins= + + +[BASIC] + +# Required attributes for module, separated by a comma +required-attributes= + +# List of builtins function names that should not be used, separated by a comma +bad-functions=apply,input,reduce + + +# Disable the report(s) with the given id(s). +# All non-Google reports are disabled by default. +disable-report=R0001,R0002,R0003,R0004,R0101,R0102,R0201,R0202,R0220,R0401,R0402,R0701,R0801,R0901,R0902,R0903,R0904,R0911,R0912,R0913,R0914,R0915,R0921,R0922,R0923 + +# Regular expression which should only match correct module names +module-rgx=(([a-z_][a-z0-9_]*)|([A-Z][a-zA-Z0-9]+))$ + +# Regular expression which should only match correct module level names +const-rgx=^(_?[A-Z][A-Z0-9_]*|__[a-z0-9_]+__|_?[a-z][a-z0-9_]*)$ + +# Regular expression which should only match correct class names +class-rgx=^_?[A-Z][a-zA-Z0-9]*$ + +# Regular expression which should only match correct function names +function-rgx=^(?:(?P_?[A-Z][a-zA-Z0-9]*)|(?P_?[a-z][a-z0-9_]*))$ + +# Regular expression which should only match correct method names +method-rgx=^(?:(?P__[a-z0-9_]+__|next)|(?P_{0,2}[A-Z][a-zA-Z0-9]*)|(?P_{0,2}[a-z][a-z0-9_]*))$ + +# Regular expression which should only match correct instance attribute names +attr-rgx=^_{0,2}[a-z][a-z0-9_]*$ + +# Regular expression which should only match correct argument names +argument-rgx=^[a-z][a-z0-9_]*$ + +# Regular expression which should only match correct variable names +variable-rgx=^[a-z][a-z0-9_]*$ + +# Regular expression which should only match correct attribute names in class +# bodies +class-attribute-rgx=^(_?[A-Z][A-Z0-9_]*|__[a-z0-9_]+__|_?[a-z][a-z0-9_]*)$ + +# Regular expression which should only match correct list comprehension / +# generator expression variable names +inlinevar-rgx=^[a-z][a-z0-9_]*$ + +# Good variable names which should always be accepted, separated by a comma +good-names=main,_ + +# Bad variable names which should always be refused, separated by a comma +bad-names= + +# Regular expression which should only match function or class names that do +# not require a docstring. +no-docstring-rgx=(__.*__|main) + +# Minimum line length for functions/classes that require docstrings, shorter +# ones are exempt. +docstring-min-length=10 + + +[FORMAT] + +# Maximum number of characters on a single line. +max-line-length=120 + +# Regexp for a line that is allowed to be longer than the limit. +ignore-long-lines=(?x) + (^\s*(import|from)\s + |\$Id:\s\/\/depot\/.+#\d+\s\$ + |^[a-zA-Z_][a-zA-Z0-9_]*\s*=\s*("[^"]\S+"|'[^']\S+') + |^\s*\#\ LINT\.ThenChange + |^[^#]*\#\ type:\ [a-zA-Z_][a-zA-Z0-9_.,[\] ]*$ + |pylint + |""" + |\# + |lambda + |(https?|ftp):) + +# Allow the body of an if to be on the same line as the test if there is no +# else. +single-line-if-stmt=y + +# List of optional constructs for which whitespace checking is disabled +no-space-check= + +# Maximum number of lines in a module +max-module-lines=99999 + +# String used as indentation unit. This is usually " " (4 spaces) or "\t" (1 +# tab). +indent-string=' ' + + +[SIMILARITIES] + +# Minimum lines number of a similarity. +min-similarity-lines=4 + +# Ignore comments when computing similarities. +ignore-comments=yes + +# Ignore docstrings when computing similarities. +ignore-docstrings=yes + +# Ignore imports when computing similarities. +ignore-imports=no + + +[MISCELLANEOUS] + +# List of note tags to take in consideration, separated by a comma. +notes= + + +[IMPORTS] + +# Deprecated modules which should not be used, separated by a comma +deprecated-modules=regsub,TERMIOS,Bastion,rexec,sets + +# Create a graph of every (i.e. internal and external) dependencies in the +# given file (report RP0402 must not be disabled) +import-graph= + +# Create a graph of external dependencies in the given file (report RP0402 must +# not be disabled) +ext-import-graph= + +# Create a graph of internal dependencies in the given file (report RP0402 must +# not be disabled) +int-import-graph= + + +[CLASSES] + +# List of interface methods to ignore, separated by a comma. This is used for +# instance to not check methods defines in Zope's Interface base class. +ignore-iface-methods=isImplementedBy,deferred,extends,names,namesAndDescriptions,queryDescriptionFor,getBases,getDescriptionFor,getDoc,getName,getTaggedValue,getTaggedValueTags,isEqualOrExtendedBy,setTaggedValue,isImplementedByInstancesOf,adaptWith,is_implemented_by + +# List of method names used to declare (i.e. assign) instance attributes. +defining-attr-methods=__init__,__new__,setUp + +# List of valid names for the first argument in a class method. +valid-classmethod-first-arg=cls,class_ + +# List of valid names for the first argument in a metaclass class method. +valid-metaclass-classmethod-first-arg=mcs + + +[DESIGN] + +# Maximum number of arguments for function / method +max-args=5 + +# Argument names that match this expression will be ignored. Default to name +# with leading underscore +ignored-argument-names=_.* + +# Maximum number of locals for function / method body +max-locals=15 + +# Maximum number of return / yield for function / method body +max-returns=6 + +# Maximum number of branch for function / method body +max-branches=12 + +# Maximum number of statements in function / method body +max-statements=50 + +# Maximum number of parents for a class (see R0901). +max-parents=7 + +# Maximum number of attributes for a class (see R0902). +max-attributes=7 + +# Minimum number of public methods for a class (see R0903). +min-public-methods=2 + +# Maximum number of public methods for a class (see R0904). +max-public-methods=20 + + +[EXCEPTIONS] + +# Exceptions that will emit a warning when being caught. Defaults to +# "Exception" +overgeneral-exceptions=Exception,StandardError,BaseException + + +[AST] + +# Maximum line length for lambdas +short-func-length=1 + +# List of module members that should be marked as deprecated. +# All of the string functions are listed in 4.1.4 Deprecated string functions +# in the Python 2.4 docs. +deprecated-members=string.atof,string.atoi,string.atol,string.capitalize,string.expandtabs,string.find,string.rfind,string.index,string.rindex,string.count,string.lower,string.split,string.rsplit,string.splitfields,string.join,string.joinfields,string.lstrip,string.rstrip,string.strip,string.swapcase,string.translate,string.upper,string.ljust,string.rjust,string.center,string.zfill,string.replace,sys.exitfunc + + +[DOCSTRING] + +# List of exceptions that do not need to be mentioned in the Raises section of +# a docstring. +ignore-exceptions=AssertionError,NotImplementedError,StopIteration,TypeError + + + +[TOKENS] + +# Number of spaces of indent required when the last token on the preceding line +# is an open (, [, or {. +indent-after-paren=4 + + +[GOOGLE LINES] + +# Regexp for a proper copyright notice. +copyright=Copyright \d{4} The TensorFlow Authors\. +All [Rr]ights [Rr]eserved\. From 837d019b5090b87827c804e6184ebf5149ca6709 Mon Sep 17 00:00:00 2001 From: Samwell Freeman Date: Tue, 29 Sep 2020 17:26:25 -0400 Subject: [PATCH 03/21] paired --- README.md | 32 +--- docker/terra_image/Dockerfile | 8 +- docker/terra_image/README.md | 8 +- docker/vm_boot_images/Dockerfile | 5 +- .../config/tensorflow-requirements.txt | 2 + ml4h/plots.py | 36 ++-- ml4h/tensorize/tensor_writer_ukbb.py | 181 +++++++++++------- 7 files changed, 154 insertions(+), 118 deletions(-) diff --git a/README.md b/README.md index 0335a4885..8996bfe39 100644 --- a/README.md +++ b/README.md @@ -1,7 +1,7 @@ # ml4h `ml4h` is a project aimed at using machine learning to model multi-modal cardiovascular time series and imaging data. `ml4h` began as a set of tools to make it easy to work -with the UK Biobank on the Google Cloud and has since expanded to include other data sources +with the UK Biobank on Google Cloud Platform and has since expanded to include other data sources and functionality. @@ -9,6 +9,7 @@ Getting Started * [Setting up your local environment](#setting-up-your-local-environment) * [Setting up a remote VM](#setting-up-a-remote-vm) * Modeling/Data Sources/Tests [(`ml4h/DATA_MODELING_TESTS.md`)](ml4h/DATA_MODELING_TESTS.md) +* [Contributing Code](#contributing-code) Advanced Topics: * Tensorizing Data (going from raw data to arrays suitable for modeling, in `ml4h/tensorize/README.md, TENSORIZE.md` ) @@ -19,7 +20,7 @@ Clone the repo ``` git clone git@github.com:broadinstitute/ml.git ``` -Make sure you have installed the [google cloud tools (gcloud)](https://cloud.google.com/storage/docs/gsutil_install). With [Homebrew](https://brew.sh/), you can use +Make sure you have installed the [Google Cloud SDK (gcloud)](https://cloud.google.com/sdk/docs/downloads-interactive). With [Homebrew](https://brew.sh/), you can use ``` brew cask install google-cloud-sdk ``` @@ -145,29 +146,6 @@ If you get a public key error run: `gcloud compute config-ssh` Now open a browser on your laptop and go to the URL `http://localhost:8888` +## Contributing code -### Installing git-secrets - -```git-secrets``` helps us avoid committing secrets (e.g. private keys) and other critical data (e.g. PHI) to our -repositories. ```git-secrets``` can be obtained via [github](https://github.com/awslabs/git-secrets) or on MacOS can be -installed with Homebrew by running ```brew install git-secrets```. - -To add hooks to all repositories that you initialize or clone in the future: - -```git secrets --install --global``` - -To add hooks to all local repositories: - -``` -git secrets --install ~/.git-templates/git-secrets -git config --global init.templateDir ~/.git-templates/git-secrets -``` - -We maintain our own custom "provider" to cover any private keys or other critical data that we would like to avoid -committing to our repositories. Feel free to add ```egrep```-compatible regular expressions to -```git_secrets_provider_ml4h.txt``` to match types of critical data that are not currently covered by the patterns in that -file. To register the patterns in this file with ```git-secrets```: - -``` -git secrets --add-provider -- cat ${HOME}/ml/git_secrets_provider_ml4h.txt -``` +Want to contribute code to this project? Please see [CONTRIBUTING](./CONTRIBUTING.md) for developer setup and other details. diff --git a/docker/terra_image/Dockerfile b/docker/terra_image/Dockerfile index 721f94500..a59ecd6ae 100644 --- a/docker/terra_image/Dockerfile +++ b/docker/terra_image/Dockerfile @@ -1,4 +1,4 @@ -FROM us.gcr.io/broad-dsp-gcr-public/terra-jupyter-gatk:1.0.0 +FROM us.gcr.io/broad-dsp-gcr-public/terra-jupyter-gatk:1.0.6 # https://github.com/DataBiosphere/terra-docker/blob/master/terra-jupyter-gatk/CHANGELOG.md USER root @@ -19,6 +19,10 @@ RUN pip3 install --user -r $HOME/ml4h_pkg/config/tensorflow-requirements.txt \ # first few rows of the downloaded dataframe of query results. # Pin version due to https://github.com/googleapis/google-cloud-python/issues/9965 && pip3 install --upgrade --user google-cloud-bigquery[pandas]==1.22.0 \ + # Upgrade to a newer version. The one on the base Terra image was a bit too old. + && pip3 install --upgrade --user numpy \ # Configure notebook extensions. && jupyter nbextension install --user --py vega \ - && jupyter nbextension enable --user --py vega + && jupyter nbextension enable --user --py vega \ + && jupyter nbextension install --user --py ipycanvas \ + && jupyter nbextension enable --user --py ipycanvas diff --git a/docker/terra_image/README.md b/docker/terra_image/README.md index 71284c0bd..9a81dc74a 100644 --- a/docker/terra_image/README.md +++ b/docker/terra_image/README.md @@ -2,13 +2,13 @@ To build and push: ``` -mv ml4cvd ml4cvdBAK_$(date +"%Y%m%d_%H%M%S") \ +mv ml4h ml4hBAK_$(date +"%Y%m%d_%H%M%S") \ && mv config configBAK_$(date +"%Y%m%d_%H%M%S") \ - && cp -r ../../ml4cvd . \ + && cp -r ../../ml4h . \ && cp -r ../vm_boot_images/config . \ && gcloud --project uk-biobank-sek-data builds submit \ --timeout 20m \ - --tag gcr.io/uk-biobank-sek-data/ml4cvd_terra:`date +"%Y%m%d_%H%M%S"` . + --tag gcr.io/uk-biobank-sek-data/ml4h_terra:`date +"%Y%m%d_%H%M%S"` . ``` Notes: @@ -20,5 +20,5 @@ available to docker. cd notebooks find . -name "*.ipynb" -type f -print0 | \ xargs -0 perl -i -pe \ - 's/gcr.io\/uk-biobank-sek-data\/ml4cvd_terra:\d{8}_\d{6}/gcr.io\/uk-biobank-sek-data\/ml4cvd_terra:20200623_145127/g' + 's/gcr.io\/uk-biobank-sek-data\/ml4h_terra:\d{8}_\d{6}/gcr.io\/uk-biobank-sek-data\/ml4h_terra:20200623_145127/g' ``` diff --git a/docker/vm_boot_images/Dockerfile b/docker/vm_boot_images/Dockerfile index a62694ca0..59e5b32be 100644 --- a/docker/vm_boot_images/Dockerfile +++ b/docker/vm_boot_images/Dockerfile @@ -34,4 +34,7 @@ RUN apt-get install python3-tk libgl1-mesa-glx libxt-dev -y # Requirements for the tensorflow project RUN pip3 install --upgrade pip RUN pip3 install -r pre_requirements.txt -RUN pip3 install -r tensorflow-requirements.txt +RUN pip3 install -r tensorflow-requirements.txt \ + # Configure notebook extensions. + && jupyter nbextension install --user --py ipycanvas \ + && jupyter nbextension enable --user --py ipycanvas diff --git a/docker/vm_boot_images/config/tensorflow-requirements.txt b/docker/vm_boot_images/config/tensorflow-requirements.txt index bb6a1e777..d782967af 100644 --- a/docker/vm_boot_images/config/tensorflow-requirements.txt +++ b/docker/vm_boot_images/config/tensorflow-requirements.txt @@ -28,3 +28,5 @@ altair facets-overview plotnine vega +ipycanvas==0.4.1 +ipyannotations==0.2.0 diff --git a/ml4h/plots.py b/ml4h/plots.py index c60282803..6b5ad6a78 100755 --- a/ml4h/plots.py +++ b/ml4h/plots.py @@ -40,6 +40,9 @@ from scipy.ndimage.filters import gaussian_filter from scipy import stats +import ml4h.tensormap.ukb.ecg +import ml4h.tensormap.mgb.ecg +from ml4h.tensormap.mgb.dynamic import make_waveform_maps from ml4h.TensorMap import TensorMap from ml4h.metrics import concordance_index, coefficient_of_determination from ml4h.defines import IMAGE_EXT, JOIN_CHAR, PDF_EXT, TENSOR_EXT, ECG_REST_LEADS, ECG_REST_MEDIAN_LEADS, PARTNERS_DATETIME_FORMAT, PARTNERS_DATE_FORMAT, HD5_GROUP_CHAR @@ -1227,16 +1230,15 @@ def _plot_partners_figure( def plot_partners_ecgs(args): plot_tensors = [ - 'partners_ecg_patientid', 'partners_ecg_firstname', 'partners_ecg_lastname', - 'partners_ecg_sex', 'partners_ecg_dob', 'partners_ecg_age', - 'partners_ecg_datetime', 'partners_ecg_sitename', 'partners_ecg_location', - 'partners_ecg_read_md', 'partners_ecg_taxis_md', 'partners_ecg_rate_md', - 'partners_ecg_pr_md', 'partners_ecg_qrs_md', 'partners_ecg_qt_md', - 'partners_ecg_paxis_md', 'partners_ecg_raxis_md', 'partners_ecg_qtc_md', + ml4h.tensormap.mgb.ecg.partners_ecg_patientid, ml4h.tensormap.mgb.ecg.partners_ecg_firstname, ml4h.tensormap.mgb.ecg.partners_ecg_lastname, + ml4h.tensormap.mgb.ecg.partners_ecg_sex, ml4h.tensormap.mgb.ecg.partners_ecg_dob, ml4h.tensormap.mgb.ecg.partners_ecg_age, + ml4h.tensormap.mgb.ecg.partners_ecg_datetime, ml4h.tensormap.mgb.ecg.partners_ecg_sitename, ml4h.tensormap.mgb.ecg.partners_ecg_location, + ml4h.tensormap.mgb.ecg.partners_ecg_read_md, ml4h.tensormap.mgb.ecg.partners_ecg_taxis_md, ml4h.tensormap.mgb.ecg.partners_ecg_rate_md, + ml4h.tensormap.mgb.ecg.partners_ecg_pr_md, ml4h.tensormap.mgb.ecg.partners_ecg_qrs_md, ml4h.tensormap.mgb.ecg.partners_ecg_qt_md, + ml4h.tensormap.mgb.ecg.partners_ecg_paxis_md, ml4h.tensormap.mgb.ecg.partners_ecg_raxis_md, ml4h.tensormap.mgb.ecg.partners_ecg_qtc_md, ] - voltage_tensor = 'partners_ecg_2500_raw' - from ml4h.tensor_maps_partners_ecg_labels import TMAPS - tensor_maps_in = [TMAPS[it] for it in plot_tensors + [voltage_tensor]] + voltage_tensor = make_waveform_maps('partners_ecg_2500_raw') + tensor_maps_in = plot_tensors + [voltage_tensor] tensor_paths = [os.path.join(args.tensors, tp) for tp in os.listdir(args.tensors) if os.path.splitext(tp)[-1].lower()==TENSOR_EXT] if 'clinical' == args.plot_mode: @@ -1503,13 +1505,13 @@ def plot_ecg_rest( :param is_blind: if True, the plot gets blinded (helpful for review and annotation) """ map_fields_to_tmaps = { - 'ramp': 'ecg_rest_ramplitude_raw', - 'samp': 'ecg_rest_samplitude_raw', - 'aVL': 'ecg_rest_lvh_avl', - 'Sokolow_Lyon': 'ecg_rest_lvh_sokolow_lyon', - 'Cornell': 'ecg_rest_lvh_cornell', - } - from ml4h.tensor_from_file import TMAPS + 'ramp': ml4h.tensormap.ukb.ecg.ecg_rest_ramplitude_raw, + 'samp': ml4h.tensormap.ukb.ecg.ecg_rest_samplitude_raw, + 'aVL': ml4h.tensormap.ukb.ecg.ecg_rest_lvh_avl, + 'Sokolow_Lyon': ml4h.tensormap.ukb.ecg.ecg_rest_lvh_sokolow_lyon, + 'Cornell': ml4h.tensormap.ukb.ecg.ecg_rest_lvh_cornell, + } + raw_scale = 0.005 # Conversion from raw to mV default_yrange = ECG_REST_PLOT_DEFAULT_YRANGE # mV time_interval = 2.5 # time-interval per plot in seconds. ts_Reference data is in s, voltage measurement is 5 uv per lsb @@ -1521,7 +1523,7 @@ def plot_ecg_rest( with h5py.File(tensor_path, 'r') as hd5: traces, text = _ecg_rest_traces_and_text(hd5) for field in map_fields_to_tmaps: - tm = TMAPS[map_fields_to_tmaps[field]] + tm = map_fields_to_tmaps[field] patient_dic[field] = np.zeros(tm.shape) try: patient_dic[field][:] = tm.tensor_from_file(tm, hd5) diff --git a/ml4h/tensorize/tensor_writer_ukbb.py b/ml4h/tensorize/tensor_writer_ukbb.py index 64242bcbe..ceb4389e8 100644 --- a/ml4h/tensorize/tensor_writer_ukbb.py +++ b/ml4h/tensorize/tensor_writer_ukbb.py @@ -87,6 +87,10 @@ def write_tensors( mri_unzip: str, mri_field_ids: List[int], xml_field_ids: List[int], + zoom_x: int, + zoom_y: int, + zoom_width: int, + zoom_height: int, write_pngs: bool, min_sample_id: int, max_sample_id: int, @@ -105,6 +109,13 @@ def write_tensors( :param mri_unzip: Folder where zipped DICOM will be decompressed :param mri_field_ids: List of MRI field IDs from UKBB :param xml_field_ids: List of ECG field IDs from UKBB + :param x: Maximum x dimension of MRIs + :param y: Maximum y dimension of MRIs + :param z: Maximum z dimension of MRIs + :param zoom_x: x coordinate of the zoom + :param zoom_y: y coordinate of the zoom + :param zoom_width: width of the zoom + :param zoom_height: height of the zoom :param write_pngs: write MRIs as PNG images for debugging :param min_sample_id: Minimum sample id to generate, for parallelization :param max_sample_id: Maximum sample id to generate, for parallelization @@ -126,7 +137,7 @@ def write_tensors( continue try: with h5py.File(tp, 'w') as hd5: - _write_tensors_from_zipped_dicoms(write_pngs, tensors, mri_unzip, mri_field_ids, zip_folder, hd5, sample_id, stats) + _write_tensors_from_zipped_dicoms(zoom_x, zoom_y, zoom_width, zoom_height, write_pngs, tensors, mri_unzip, mri_field_ids, zip_folder, hd5, sample_id, stats) _write_tensors_from_zipped_niftis(zip_folder, mri_field_ids, hd5, sample_id, stats) _write_tensors_from_xml(xml_field_ids, xml_folder, hd5, sample_id, write_pngs, stats, continuous_stats) stats['Tensors written'] += 1 @@ -177,26 +188,19 @@ def write_tensors_from_dicom_pngs( continue stats[sample_header + '_' + sample_id] += 1 dicom_file = row[dicom_index] - try: png = imageio.imread(os.path.join(png_path, dicom_file + png_postfix)) - if len(png.shape) == 3 and png.mean() == png[:, :, 0].mean(): - png = png[:, :, 0] - elif len(png.shape) == 3: - raise ValueError(f'PNG has color information but no method to tensorize it {png.mean()}, 0ch :{png[:, :, 0].mean()}, 1ch :{png[:, :, 1].mean()}, 2ch :{png[:, :, 2].mean()}.') full_tensor = np.zeros((x, y), dtype=np.float32) full_tensor[:png.shape[0], :png.shape[1]] = png tensor_file = os.path.join(tensors, str(sample_id) + TENSOR_EXT) if not os.path.exists(os.path.dirname(tensor_file)): os.makedirs(os.path.dirname(tensor_file)) with h5py.File(tensor_file, 'a') as hd5: - tensor_name = series.lower() + '_annotated_' + row[instance_index] + tensor_name = series + '_annotated_' + row[instance_index] tp = tensor_path(path_prefix, tensor_name) if tp in hd5: tensor = first_dataset_at_path(hd5, tp) - min_x = min(png.shape[0], tensor.shape[0]) - min_y = min(png.shape[1], tensor.shape[1]) - tensor[:min_x, :min_y] = full_tensor[:min_x, :min_y] + tensor[:] = full_tensor stats['updated'] += 1 else: create_tensor_in_hd5(hd5, path_prefix, tensor_name, full_tensor, stats) @@ -324,7 +328,7 @@ def _dicts_and_plots_from_tensorization( continuous = {} value_counter = Counter() for k in sorted(list(stats.keys())): - #logging.info("{} has {}".format(k, stats[k])) + logging.info("{} has {}".format(k, stats[k])) if 'categorical' not in k and 'continuous' not in k: continue @@ -342,10 +346,10 @@ def _dicts_and_plots_from_tensorization( plot_value_counter(list(categories.keys()), value_counter, a_id + '_v_count', os.path.join(output_folder, a_id)) plot_histograms(continuous_stats, a_id, os.path.join(output_folder, a_id)) - # logging.info("Continuous tensor map: {}".format(continuous)) - # logging.info("Continuous Columns: {}".format(len(continuous))) - # logging.info("Category tensor map: {}".format(categories)) - # logging.info("Categories Columns: {}".format(len(categories))) + logging.info("Continuous tensor map: {}".format(continuous)) + logging.info("Continuous Columns: {}".format(len(continuous))) + logging.info("Category tensor map: {}".format(categories)) + logging.info("Categories Columns: {}".format(len(categories))) def _to_float_or_false(s): @@ -363,6 +367,10 @@ def _to_float_or_nan(s): def _write_tensors_from_zipped_dicoms( + zoom_x: int, + zoom_y: int, + zoom_width: int, + zoom_height: int, write_pngs: bool, tensors: str, dicoms: str, @@ -382,8 +390,10 @@ def _write_tensors_from_zipped_dicoms( os.makedirs(dicom_folder) with zipfile.ZipFile(zipped, "r") as zip_ref: zip_ref.extractall(dicom_folder) - ukb_instance = zipped.split('_')[2] - _write_tensors_from_dicoms(write_pngs, tensors, dicom_folder, hd5, sample_str, ukb_instance, stats) + _write_tensors_from_dicoms( + zoom_x, zoom_y, zoom_width, zoom_height, write_pngs, tensors, dicom_folder, + hd5, sample_str, stats, + ) stats['MRI fields written'] += 1 shutil.rmtree(dicom_folder) @@ -400,31 +410,36 @@ def _write_tensors_from_zipped_niftis(zip_folder: str, mri_field_ids: List[str], def _write_tensors_from_dicoms( - write_pngs: bool, tensors: str, dicom_folder: str, hd5: h5py.File, sample_str: str, ukb_instance: str, stats: Dict[str, int], + zoom_x: int, zoom_y: int, zoom_width: int, zoom_height: int, write_pngs: bool, tensors: str, + dicom_folder: str, hd5: h5py.File, sample_str: str, stats: Dict[str, int], ) -> None: """Convert a folder of DICOMs from a sample into tensors for each series Segmented dicoms require special processing and are written to tensor per-slice Arguments + :param x: Width of the tensors (actual MRI width will be padded with 0s or cropped to this number) + :param y: Height of the tensors (actual MRI width will be padded with 0s or cropped to this number) + :param z: Minimum number of slices to include in the each tensor if more slices are found they will be kept + :param zoom_x: x coordinate of the zoom + :param zoom_y: y coordinate of the zoom + :param zoom_width: width of the zoom + :param zoom_height: height of the zoom :param write_pngs: write MRIs as PNG images for debugging :param tensors: Folder where hd5 tensor files are being written :param dicom_folder: Folder with all dicoms associated with one sample. :param hd5: Tensor file in which to create datasets for each series and each segmented slice :param sample_str: The current sample ID as a string - :param ukb_instance: The UK Biobank assessment visit instance number :param stats: Counter to keep track of summary statistics """ views = defaultdict(list) - series_to_numbers = defaultdict(set) min_ideal_series = 9e9 for dicom in os.listdir(dicom_folder): if os.path.splitext(dicom)[-1] != DICOM_EXT: continue d = pydicom.read_file(os.path.join(dicom_folder, dicom)) series = d.SeriesDescription.lower().replace(' ', '_') - series_to_numbers[series].add(int(d.SeriesNumber)) if series + '_12bit' in MRI_LIVER_SERIES_12BIT and d.LargestImagePixelValue > 2048: views[series + '_12bit'].append(d) stats[series + '_12bit'] += 1 @@ -447,61 +462,99 @@ def _write_tensors_from_dicoms( else: mri_group = 'ukb_mri' - if len(series_to_numbers[v]) > 1 and v not in MRI_BRAIN_SERIES: - max_series = max(series_to_numbers[v]) - single_series = [dicom for dicom in views[v] if int(dicom.SeriesNumber) == max_series] - # for d in views[v]: - # logging.warning(f'{d.SeriesNumber} with Date: {_datetime_from_dicom(d)} Time {d.AcquisitionTime}') - logging.warning(f'{v} has {len(views[v])} series:{series_to_numbers[v]} Using only max series: {max_series} with {len(single_series)}') - views[v] = single_series if v == MRI_TO_SEGMENT: - _tensorize_short_and_long_axis_segmented_cardiac_mri(views[v], v, ukb_instance, hd5, mri_date, mri_group, stats) + _tensorize_short_and_long_axis_segmented_cardiac_mri(views[v], v, zoom_x, zoom_y, zoom_width, zoom_height, write_pngs, tensors, hd5, mri_date, mri_group, stats) elif v in MRI_BRAIN_SERIES: _tensorize_brain_mri(views[v], v, mri_date, mri_group, hd5) else: - pass - # mri_data = np.zeros((views[v][0].Rows, views[v][0].Columns, len(views[v])), dtype=np.float32) - # for slicer in views[v]: - # _save_pixel_dimensions_if_missing(slicer, v, hd5) - # _save_slice_thickness_if_missing(slicer, v, hd5) - # _save_series_orientation_and_position_if_missing(slicer, v, hd5) - # slice_index = slicer.InstanceNumber - 1 - # if v in MRI_LIVER_IDEAL_PROTOCOL: - # slice_index = _slice_index_from_ideal_protocol(slicer, min_ideal_series) - # mri_data[..., slice_index] = slicer.pixel_array.astype(np.float32) - # create_tensor_in_hd5(hd5, mri_group, f'{v}/{ukb_instance}', mri_data, stats, mri_date) + mri_data = np.zeros((views[v][0].Rows, views[v][0].Columns, len(views[v])), dtype=np.float32) + for slicer in views[v]: + _save_pixel_dimensions_if_missing(slicer, v, hd5) + _save_slice_thickness_if_missing(slicer, v, hd5) + _save_series_orientation_and_position_if_missing(slicer, v, hd5) + slice_index = slicer.InstanceNumber - 1 + if v in MRI_LIVER_IDEAL_PROTOCOL: + slice_index = _slice_index_from_ideal_protocol(slicer, min_ideal_series) + mri_data[..., slice_index] = slicer.pixel_array.astype(np.float32) + create_tensor_in_hd5(hd5, mri_group, v, mri_data, stats, mri_date) def _tensorize_short_and_long_axis_segmented_cardiac_mri( - slices: List[pydicom.Dataset], series: str, instance: str, - hd5: h5py.File, mri_date: datetime.datetime, mri_group: str, stats: Dict[str, int], + slices: List[pydicom.Dataset], series: str, zoom_x: int, zoom_y: int, + zoom_width: int, zoom_height: int, write_pngs: bool, tensors: str, + hd5: h5py.File, mri_date: datetime.datetime, mri_group: str, + stats: Dict[str, int], ) -> None: + systoles = {} + diastoles = {} + systoles_pix = {} + systoles_masks = {} + diastoles_masks = {} + for slicer in slices: - #full_slice = np.zeros((slicer.Rows, slicer.Columns), dtype=np.float32) + full_mask = np.zeros((slicer.Rows, slicer.Columns), dtype=np.float32) + full_slice = np.zeros((slicer.Rows, slicer.Columns), dtype=np.float32) + if _has_overlay(slicer): if _is_mitral_valve_segmentation(slicer): series = series.replace('sax', 'lax') else: series = series.replace('lax', 'sax') - series_segmented = f'{series}_segmented' + series_zoom = f'{series}_zoom' + series_zoom_segmented = f'{series}_zoom_segmented' + try: overlay, mask, ventricle_pixels, _ = _get_overlay_from_dicom(slicer) except KeyError: logging.exception(f'Got key error trying to make anatomical mask, skipping.') continue - # _save_pixel_dimensions_if_missing(slicer, series, hd5) - # _save_slice_thickness_if_missing(slicer, series, hd5) - # _save_series_orientation_and_position_if_missing(slicer, series, hd5, str(slicer.InstanceNumber)) + _save_pixel_dimensions_if_missing(slicer, series, hd5) + _save_slice_thickness_if_missing(slicer, series, hd5) + _save_series_orientation_and_position_if_missing(slicer, series, hd5, str(slicer.InstanceNumber)) _save_pixel_dimensions_if_missing(slicer, series_segmented, hd5) _save_slice_thickness_if_missing(slicer, series_segmented, hd5) _save_series_orientation_and_position_if_missing(slicer, series_segmented, hd5, str(slicer.InstanceNumber)) - # - # cur_angle = (slicer.InstanceNumber - 1) // MRI_FRAMES # dicom InstanceNumber is 1-based - #full_slice[:] = slicer.pixel_array.astype(np.float32) - #create_tensor_in_hd5(hd5, mri_group, f'{series}{HD5_GROUP_CHAR}{instance}', full_slice, stats, mri_date, slicer.InstanceNumber) - create_tensor_in_hd5(hd5, mri_group, f'{series_segmented}{HD5_GROUP_CHAR}{instance}', mask, stats, mri_date, slicer.InstanceNumber) + + cur_angle = (slicer.InstanceNumber - 1) // MRI_FRAMES # dicom InstanceNumber is 1-based + full_slice[:] = slicer.pixel_array.astype(np.float32) + create_tensor_in_hd5(hd5, mri_group, f'{series}{HD5_GROUP_CHAR}{slicer.InstanceNumber}', full_slice, stats, mri_date) + create_tensor_in_hd5(hd5, mri_group, f'{series_zoom_segmented}{HD5_GROUP_CHAR}{slicer.InstanceNumber}', mask, stats, mri_date) + + zoom_slice = full_slice[zoom_x: zoom_x + zoom_width, zoom_y: zoom_y + zoom_height] + zoom_mask = mask[zoom_x: zoom_x + zoom_width, zoom_y: zoom_y + zoom_height] + create_tensor_in_hd5(hd5, mri_group, f'{series_zoom}{HD5_GROUP_CHAR}{slicer.InstanceNumber}', zoom_slice, stats, mri_date) + create_tensor_in_hd5(hd5, mri_group, f'{series_zoom_segmented}{HD5_GROUP_CHAR}{slicer.InstanceNumber}', zoom_mask, stats, mri_date) + + if (slicer.InstanceNumber - 1) % MRI_FRAMES == 0: # Diastole frame is always the first + diastoles[cur_angle] = slicer + diastoles_masks[cur_angle] = mask + if cur_angle not in systoles: + systoles[cur_angle] = slicer + systoles_pix[cur_angle] = ventricle_pixels + systoles_masks[cur_angle] = mask + else: + if ventricle_pixels < systoles_pix[cur_angle]: + systoles[cur_angle] = slicer + systoles_pix[cur_angle] = ventricle_pixels + systoles_masks[cur_angle] = mask + + for angle in diastoles: + logging.info(f'Found systole, instance:{systoles[angle].InstanceNumber} ventricle pixels:{systoles_pix[angle]}') + full_slice = diastoles[angle].pixel_array.astype(np.float32) + create_tensor_in_hd5(hd5, mri_group, f'diastole_frame_b{angle}', full_slice, stats, mri_date) + create_tensor_in_hd5(hd5, mri_group, f'diastole_mask_b{angle}', diastoles_masks[angle], stats, mri_date) + if write_pngs: + plt.imsave(tensors + 'diastole_frame_b' + str(angle) + IMAGE_EXT, full_slice) + plt.imsave(tensors + 'diastole_mask_b' + str(angle) + IMAGE_EXT, full_mask) + + full_slice = systoles[angle].pixel_array.astype(np.float32) + create_tensor_in_hd5(hd5, mri_group, f'systole_frame_b{angle}', full_slice, stats, mri_date) + create_tensor_in_hd5(hd5, mri_group, f'systole_mask_b{angle}', systoles_masks[angle], stats, mri_date) + if write_pngs: + plt.imsave(tensors + 'systole_frame_b' + str(angle) + IMAGE_EXT, full_slice) + plt.imsave(tensors + 'systole_mask_b' + str(angle) + IMAGE_EXT, full_mask) def _tensorize_brain_mri(slices: List[pydicom.Dataset], series: str, mri_date: datetime.datetime, mri_group: str, hd5: h5py.File) -> None: @@ -535,16 +588,13 @@ def _save_slice_thickness_if_missing(slicer, series, hd5): def _save_series_orientation_and_position_if_missing(slicer, series, hd5, instance=None): orientation_ds_name = MRI_PATIENT_ORIENTATION + '_' + series position_ds_name = MRI_PATIENT_POSITION + '_' + series - if instance is not None: - orientation_ds_name = f'{orientation_ds_name}_{instance}' - position_ds_name = f'{position_ds_name}_{instance}' - try: - if orientation_ds_name not in hd5 and series in MRI_BRAIN_SERIES + MRI_CARDIAC_SERIES + MRI_CARDIAC_SERIES_SEGMENTED + MRI_LIVER_SERIES + MRI_LIVER_SERIES_12BIT: - hd5.create_dataset(orientation_ds_name, data=[float(x) for x in slicer.ImageOrientationPatient]) - if position_ds_name not in hd5 and series in MRI_BRAIN_SERIES + MRI_CARDIAC_SERIES + MRI_CARDIAC_SERIES_SEGMENTED + MRI_LIVER_SERIES + MRI_LIVER_SERIES_12BIT: - hd5.create_dataset(position_ds_name, data=[float(x) for x in slicer.ImagePositionPatient]) - except RuntimeError as e: - logging.warning(f' got error {e} \n orientation : {orientation_ds_name} {slicer.ImageOrientationPatient} and pos: {position_ds_name} {slicer.ImagePositionPatient}') + if instance: + orientation_ds_name += HD5_GROUP_CHAR + instance + position_ds_name += HD5_GROUP_CHAR + instance + if orientation_ds_name not in hd5 and series in MRI_BRAIN_SERIES + MRI_CARDIAC_SERIES + MRI_CARDIAC_SERIES_SEGMENTED + MRI_LIVER_SERIES + MRI_LIVER_SERIES_12BIT: + hd5.create_dataset(orientation_ds_name, data=[float(x) for x in slicer.ImageOrientationPatient]) + if position_ds_name not in hd5 and series in MRI_BRAIN_SERIES + MRI_CARDIAC_SERIES + MRI_CARDIAC_SERIES_SEGMENTED + MRI_LIVER_SERIES + MRI_LIVER_SERIES_12BIT: + hd5.create_dataset(position_ds_name, data=[float(x) for x in slicer.ImagePositionPatient]) def _has_overlay(d) -> bool: @@ -695,16 +745,13 @@ def _write_ecg_rest_tensors(ecgs, xml_field, hd5, sample_id, write_pngs, stats, def create_tensor_in_hd5( hd5: h5py.File, path_prefix: str, name: str, value, stats: Counter = None, date: datetime.datetime = None, - instance: str = None, storage_type: StorageType = None, attributes: Dict[str, Any] = None, + storage_type: StorageType = None, attributes: Dict[str, Any] = None, ): hd5_path = tensor_path(path_prefix, name) - if instance is not None: - hd5_path = f'{hd5_path}instance_{instance}/' if hd5_path in hd5: hd5_path = f'{hd5_path}instance_{len(hd5[hd5_path])}' - elif instance is None: + else: hd5_path = f'{hd5_path}instance_0' - if stats is not None: stats[hd5_path] += 1 if storage_type == StorageType.STRING: From 6021abb4b65527349b8042cd52a29c25f3d0532e Mon Sep 17 00:00:00 2001 From: Samwell Freeman Date: Tue, 29 Sep 2020 17:27:41 -0400 Subject: [PATCH 04/21] paired --- .../visualization_tools/annotation_storage.py | 36 ++-- ml4h/visualization_tools/annotations.py | 55 +++--- .../dicom_interactive_plots.py | 74 +++---- ml4h/visualization_tools/dicom_plots.py | 122 ++++++------ .../ecg_interactive_plots.py | 22 ++- ml4h/visualization_tools/ecg_reshape.py | 58 +++--- ml4h/visualization_tools/ecg_static_plots.py | 11 +- ml4h/visualization_tools/facets.py | 13 +- ml4h/visualization_tools/hd5_mri_plots.py | 181 +++++++++--------- 9 files changed, 311 insertions(+), 261 deletions(-) diff --git a/ml4h/visualization_tools/annotation_storage.py b/ml4h/visualization_tools/annotation_storage.py index ac8e89249..d0020b1ec 100644 --- a/ml4h/visualization_tools/annotation_storage.py +++ b/ml4h/visualization_tools/annotation_storage.py @@ -2,9 +2,11 @@ import abc import datetime -import pandas as pd +from typing import Optional, Union + from google.cloud import bigquery from google.cloud.bigquery import magics as bqmagics +import pandas as pd class AnnotationStorage(abc.ABC): @@ -14,12 +16,14 @@ class AnnotationStorage(abc.ABC): """ @abc.abstractmethod - def describe(self): + def describe(self) -> str: """Return a string describing how annotations are stored.""" - pass @abc.abstractmethod - def submit_annotation(self, sample_id, annotator, key, value_numeric, value_string, comment): + def submit_annotation( + self, sample_id: Union[int, str], annotator: str, key: str, + value_numeric: Optional[Union[int, float]], value_string: Optional[str], comment: str, + ) -> bool: """Add an annotation to the collection of annotations. Args: @@ -32,10 +36,9 @@ def submit_annotation(self, sample_id, annotator, key, value_numeric, value_stri Returns: Whether the submission was successful. Throws an Exception on failure. """ - pass @abc.abstractmethod - def view_recent_submissions(self, count=10): + def view_recent_submissions(self, count: int = 10) -> pd.DataFrame: """View a dataframe of up to [count] most recent submissions. Args: @@ -44,7 +47,6 @@ def view_recent_submissions(self, count=10): Returns: A dataframe of the most recent annotations. """ - pass class TransientAnnotationStorage(AnnotationStorage): @@ -56,11 +58,14 @@ class TransientAnnotationStorage(AnnotationStorage): def __init__(self): self.annotations = [] - def describe(self): + def describe(self) -> str: return '''Annotations will be stored in memory only during the duration of this demo.\n For durable storage of annotations, use BigQueryAnnotationStorage instead.''' - def submit_annotation(self, sample_id, annotator, key, value_numeric, value_string, comment): + def submit_annotation( + self, sample_id: Union[int, str], annotator: str, key: str, + value_numeric: Optional[Union[int, float]], value_string: Optional[str], comment: str, + ) -> bool: """Add this annotation to our in-memory collection of annotations. Args: @@ -85,7 +90,7 @@ def submit_annotation(self, sample_id, annotator, key, value_numeric, value_stri self.annotations.append(annotation) return True - def view_recent_submissions(self, count=10): + def view_recent_submissions(self, count: int = 10) -> pd.DataFrame: """View a dataframe of up to [count] most recent submissions. Args: @@ -110,14 +115,17 @@ class BigQueryAnnotationStorage(AnnotationStorage): annotations_schema.json """ - def __init__(self, table): + def __init__(self, table: str): """This table should already exist.""" self.table = table - def describe(self): + def describe(self) -> str: return f'''Annotations are stored in BigQuery table {self.table}''' - def submit_annotation(self, sample_id, annotator, key, value_numeric, value_string, comment): + def submit_annotation( + self, sample_id: Union[int, str], annotator: str, key: str, + value_numeric: Optional[Union[int, float]], value_string: Optional[str], comment: str, + ) -> bool: """Call a BigQuery INSERT statement to add a row containing annotation information. Args: @@ -150,7 +158,7 @@ def submit_annotation(self, sample_id, annotator, key, value_numeric, value_stri # Return whether the submission completed. return submission.done() - def view_recent_submissions(self, count=10): + def view_recent_submissions(self, count: int = 10) -> pd.DataFrame: """View a dataframe of up to [count] most recent submissions. This is a convenience method for use within the annotation flow. For full access to the underlying annotations, diff --git a/ml4h/visualization_tools/annotations.py b/ml4h/visualization_tools/annotations.py index 2400a07d8..9ca9c1b44 100644 --- a/ml4h/visualization_tools/annotations.py +++ b/ml4h/visualization_tools/annotations.py @@ -2,8 +2,11 @@ import os import socket +from typing import Any, Dict, Union + from IPython.display import display from IPython.display import HTML +import pandas as pd import ipywidgets as widgets from ml4h.visualization_tools.annotation_storage import AnnotationStorage from ml4h.visualization_tools.annotation_storage import TransientAnnotationStorage @@ -11,14 +14,18 @@ DEFAULT_ANNOTATION_STORAGE = TransientAnnotationStorage() -def _get_df_sample(sample_info, sample_id): +def _get_df_sample(sample_info: pd.DataFrame, sample_id: Union[int, str]) -> pd.DataFrame: """Return a dataframe containing only the row for the indicated sample_id.""" df_sample = sample_info[sample_info['sample_id'] == str(sample_id)] - if 0 == df_sample.shape[0]: df_sample = sample_info.query('sample_id == ' + str(sample_id)) + if df_sample.shape[0] == 0: df_sample = sample_info.query('sample_id == ' + str(sample_id)) return df_sample -def display_annotation_collector(sample_info, sample_id, annotation_storage: AnnotationStorage = DEFAULT_ANNOTATION_STORAGE, custom_annotation_key=None): +def display_annotation_collector( + sample_info: pd.DataFrame, sample_id: Union[int, str], + annotation_storage: AnnotationStorage = DEFAULT_ANNOTATION_STORAGE, + custom_annotation_key: str = None, +) -> None: """Method to create a gui (set of widgets) through which the user can create an annotation and submit it to storage. Args: @@ -26,15 +33,16 @@ def display_annotation_collector(sample_info, sample_id, annotation_storage: Ann sample_id: The selected sample for which the values will be displayed. annotation_storage: An instance of AnnotationStorage. custom_annotation_key: The key for an annotation of data other than the tabular fields. - - Returns: - A notebook-friendly messages indicating the status of the submission. """ df_sample = _get_df_sample(sample_info, sample_id) if df_sample.shape[0] == 0: - return HTML(f'''
- Warning: Sample {sample_id} not present in sample_info DataFrame.
''') + display( + HTML(f'''
+ Warning: Sample {sample_id} not present in sample_info DataFrame. +
'''), + ) + return # Show the sample ID for this annotation. sample = widgets.HTML(value=f'For sample {sample_id}') @@ -82,7 +90,7 @@ def handle_key_change(change): submit_button = widgets.Button(description='Submit annotation', button_style='success') output = widgets.Output() - def on_button_clicked(b): + def cb_on_button_clicked(b): params = _format_annotation(sample_id=sample_id, key=key.value, keyvalue=keyvalue.value, comment=comment.value) try: success = annotation_storage.submit_annotation( @@ -93,34 +101,38 @@ def on_button_clicked(b): value_string=params['value_string'], comment=params['comment'], ) - except Exception as e: + except Exception as e: # pylint: disable=broad-except display( HTML(f'''
- Warning: Unable to store annotation. - 

{e}

-
'''), + Warning: Unable to store annotation. + 

{e}

+
'''), ) - return() + return with output: if success: # Show the information that was submitted. display( HTML(f'''
- Submission successful\n[{annotation_storage.describe()}]
'''), + Submission successful\n[{annotation_storage.describe()}] + '''), ) display(annotation_storage.view_recent_submissions(1)) else: display( HTML('''
- Annotation not submitted. Please try again.
'''), + Annotation not submitted. Please try again. + '''), ) - submit_button.on_click(on_button_clicked) + submit_button.on_click(cb_on_button_clicked) # Display all the widgets. display(sample, box1, comment, submit_button, output) -def _format_annotation(sample_id, key, keyvalue, comment): +def _format_annotation( + sample_id: Union[int, str], key: str, keyvalue: Union[int, float, str], comment: str, +) -> Dict[str, Any]: """Helper method to clean and reshape info from the widgets and the environment into a dictionary representing the annotation.""" # Programmatically get the identity of the person running this Terra notebook. current_user = os.getenv('OWNER_EMAIL') @@ -128,11 +140,10 @@ def _format_annotation(sample_id, key, keyvalue, comment): if current_user is None: current_user = socket.gethostname() # By convention, we prefix the hostname with our username. + value_numeric = None + value_string = None # Check whether the value is string or numeric. - if keyvalue is None: - value_numeric = None - value_string = None - else: + if keyvalue is not None: try: value_numeric = float(keyvalue) # this will fail if the value is text value_string = None diff --git a/ml4h/visualization_tools/dicom_interactive_plots.py b/ml4h/visualization_tools/dicom_interactive_plots.py index d9850e841..ec9d63834 100644 --- a/ml4h/visualization_tools/dicom_interactive_plots.py +++ b/ml4h/visualization_tools/dicom_interactive_plots.py @@ -1,4 +1,4 @@ -"""Methods for integration of interactive dicom plots within notebooks. +"""Methods for integration of interactive DICOM plots within notebooks. TODO: * Continue to *pragmatically* improve this to make the visualization controls @@ -8,14 +8,15 @@ import collections import os import tempfile +from typing import Any, DefaultDict, Dict, Optional, Tuple import zipfile from IPython.display import display from IPython.display import HTML +import numpy as np import ipywidgets as widgets import matplotlib.pyplot as plt from ml4h.runtime_data_defines import get_mri_folders -import numpy as np import pydicom import tensorflow as tf @@ -27,15 +28,12 @@ MAX_COLOR_RANGE = 6000 -def choose_mri(sample_id, folder=None): +def choose_mri(sample_id, folder: Optional[str] = None) -> None: """Render widget to choose the MRI to plot. Args: sample_id: The id of the sample to retrieve. folder: The local or Cloud Storage folder under which the files reside. - - Returns: - ipywidget or HTML upon error. """ if folder is None: folders = get_mri_folders(sample_id) @@ -45,22 +43,26 @@ def choose_mri(sample_id, folder=None): sample_mris = [] sample_mri_glob = str(sample_id) + '_*.zip' try: - for folder in folders: - sample_mris.extend(tf.io.gfile.glob(pattern=os.path.join(folder, sample_mri_glob))) + for f in folders: + sample_mris.extend(tf.io.gfile.glob(pattern=os.path.join(f, sample_mri_glob))) except (tf.errors.NotFoundError, tf.errors.PermissionDeniedError) as e: - return HTML(f''' -
+ display( + HTML(f'''
Warning: MRI not available for sample {sample_id} in {folders}: 

{e.message}

Use the folder parameter to read DICOMs from a different local directory or Cloud Storage bucket. -
''') +
'''), + ) + return if not sample_mris: - return HTML(f''' -
+ display( + HTML(f'''
Warning: MRI DICOMs not available for sample {sample_id} in {folders}.
Use the folder parameter to read DICOMs from a different local directory or Cloud Storage bucket. -
''') +
'''), + ) + return mri_chooser = widgets.Dropdown( options=sample_mris, @@ -77,14 +79,11 @@ def choose_mri(sample_id, folder=None): display(file_controls_ui, file_controls_output) -def choose_mri_series(sample_mri): +def choose_mri_series(sample_mri: str) -> None: """Render widgets and interactive plots for MRIs. Args: sample_mri: The local or Cloud Storage path to the MRI file. - - Returns: - ipywidget or HTML upon error. """ with tempfile.TemporaryDirectory() as tmpdirname: local_path = os.path.join(tmpdirname, os.path.basename(sample_mri)) @@ -93,13 +92,15 @@ def choose_mri_series(sample_mri): with zipfile.ZipFile(local_path, 'r') as zip_ref: zip_ref.extractall(tmpdirname) except (tf.errors.NotFoundError, tf.errors.PermissionDeniedError) as e: - return HTML(f''' -
+ display( + HTML(f'''
Warning: Cardiac MRI not available for sample {os.path.basename(sample_mri)}: 

{e.message}

-
''') +
'''), + ) + return - unordered_dicoms = collections.defaultdict(dict) + unordered_dicoms: DefaultDict[Any, Any] = collections.defaultdict(dict) for dcm_file in os.listdir(tmpdirname): if not dcm_file.endswith('.dcm'): continue @@ -112,8 +113,13 @@ def choose_mri_series(sample_mri): unordered_dicoms[key1][key2] = dcm if not unordered_dicoms: - print(f'\n\nNo series available in MRI for sample {os.path.basename(sample_mri)}\n\nTry a different MRI.') - return None + display( + HTML(f'''
+ No series available in MRI for sample {os.path.basename(sample_mri)}. + Try a different MRI. +
'''), + ) + return # Convert from dict of dicts to dict of ordered lists. dicoms = {} @@ -134,7 +140,7 @@ def choose_mri_series(sample_mri): style={'description_width': 'initial'}, layout=widgets.Layout(width='800px'), ) - # Slide through dicom image instances using a slide bar. + # Slide through DICOM image instances using a slide bar. instance_chooser = widgets.IntSlider( continuous_update=True, value=default_instance_value, @@ -212,25 +218,25 @@ def on_value_change(change): display(viz_controls_ui, viz_controls_output) -def compute_color_range(dicoms, series_name): +def compute_color_range(dicoms: Dict[str, Any], series_name: str) -> Tuple[int, int]: """Compute the mean values for the color ranges of instances in the series.""" vmin = np.mean([np.min(d.pixel_array) for d in dicoms[series_name]]) vmax = np.mean([np.max(d.pixel_array) for d in dicoms[series_name]]) - return(vmin, vmax) + return (vmin, vmax) -def compute_instance_range(dicoms, series_name): +def compute_instance_range(dicoms: Dict[str, Any], series_name: str) -> Tuple[int, int]: """Compute middle and max instances.""" middle_instance = int(len(dicoms[series_name]) / 2) max_instance = len(dicoms[series_name]) - return(middle_instance, max_instance) + return (middle_instance, max_instance) def dicom_animation( - dicoms, series_name, instance, vmin, vmax, transpose, - fig_width, title_prefix='', -): - """Render one frame of a dicom animation. + dicoms: Dict[str, Any], series_name: str, instance: int, vmin: int, vmax: int, transpose: bool, + fig_width: int, title_prefix: str = '', +) -> None: + """Render one frame of a DICOM animation. Args: dicoms: the dictionary DICOM series and instances lists @@ -250,7 +256,7 @@ def dicom_animation( dcm = dicoms[series_name][instance - 1] if instance != dcm.InstanceNumber: # Notice invalid input, but don't throw an error. - print(f'WARNING: Instance parameter {str(instance)} and dicom instance number {str(dcm.InstanceNumber)} do not match.') + print(f'WARNING: Instance parameter {str(instance)} and instance number {str(dcm.InstanceNumber)} do not match.') if transpose: height = dcm.pixel_array.T.shape[0] diff --git a/ml4h/visualization_tools/dicom_plots.py b/ml4h/visualization_tools/dicom_plots.py index ce2b3e083..093691382 100644 --- a/ml4h/visualization_tools/dicom_plots.py +++ b/ml4h/visualization_tools/dicom_plots.py @@ -1,16 +1,17 @@ -"""Methods for integration of dicom plots within notebooks.""" +"""Methods for integration of DICOM plots within notebooks.""" import collections import os import tempfile +from typing import Dict, List, Optional, Tuple, Union import zipfile from IPython.display import display from IPython.display import HTML +import numpy as np import ipywidgets as widgets import matplotlib.pyplot as plt from ml4h.runtime_data_defines import get_cardiac_mri_folder -import numpy as np import pydicom from scipy.ndimage.morphology import binary_closing from scipy.ndimage.morphology import binary_erosion @@ -27,21 +28,21 @@ MRI_SEGMENTED_CHANNEL_MAP = {'background': 0, 'ventricle': 1, 'myocardium': 2} -def _is_mitral_valve_segmentation(d): # -> bool: - """Determine whether a dicom has mitral valve segmentation. +def _is_mitral_valve_segmentation(d: pydicom.FileDataset) -> bool: + """Determine whether a DICOM has mitral valve segmentation. This is used for visualization of CINE_segmented_SAX_InlineVF. Args: - d: the dicom file + d: the DICOM file Returns: - Whether or not the dicom has mitral valve segmentation + Whether or not the DICOM has mitral valve segmentation """ return d.SliceThickness == 6 -def _get_overlay_from_dicom(d): +def _get_overlay_from_dicom(d: pydicom.FileDataset) -> Tuple[int, int, int]: """Get an overlay from a DICOM file. Morphological operators are used to transform the pixel outline of the @@ -49,7 +50,7 @@ def _get_overlay_from_dicom(d): is used for visualization of CINE_segmented_SAX_InlineVF. Args: - d: the dicom file + d: the DICOM file Returns: Raw overlay array with myocardium outline, anatomical mask (a pixel @@ -77,29 +78,30 @@ def _get_overlay_from_dicom(d): byte >>= 1 bit += 1 overlay = overlay[:expected_bit_length] - if overlay_frames == 1: - overlay = overlay.reshape(rows, cols) - idx = np.where(overlay == 1) - min_pos = (np.min(idx[0]), np.min(idx[1])) - max_pos = (np.max(idx[0]), np.max(idx[1])) - short_side = min((max_pos[0] - min_pos[0]), (max_pos[1] - min_pos[1])) - small_radius = max(MRI_MIN_RADIUS, short_side * MRI_SMALL_RADIUS_FACTOR) - big_radius = max(MRI_MIN_RADIUS+1, short_side * MRI_BIG_RADIUS_FACTOR) - small_structure = _unit_disk(small_radius) - m1 = binary_closing(overlay, small_structure).astype(np.int) - big_structure = _unit_disk(big_radius) - m2 = binary_closing(overlay, big_structure).astype(np.int) - anatomical_mask = m1 + m2 + if overlay_frames != 1: + raise ValueError(f'DICOM has {overlay_frames} overlay frames, but only one expected.') + overlay = overlay.reshape(rows, cols) + idx = np.where(overlay == 1) + min_pos = (np.min(idx[0]), np.min(idx[1])) + max_pos = (np.max(idx[0]), np.max(idx[1])) + short_side = min((max_pos[0] - min_pos[0]), (max_pos[1] - min_pos[1])) + small_radius = max(MRI_MIN_RADIUS, short_side * MRI_SMALL_RADIUS_FACTOR) + big_radius = max(MRI_MIN_RADIUS+1, short_side * MRI_BIG_RADIUS_FACTOR) + small_structure = _unit_disk(small_radius) + m1 = binary_closing(overlay, small_structure).astype(np.int) + big_structure = _unit_disk(big_radius) + m2 = binary_closing(overlay, big_structure).astype(np.int) + anatomical_mask = m1 + m2 + ventricle_pixels = np.count_nonzero(anatomical_mask == MRI_SEGMENTED_CHANNEL_MAP['ventricle']) + myocardium_pixels = np.count_nonzero(anatomical_mask == MRI_SEGMENTED_CHANNEL_MAP['myocardium']) + if ventricle_pixels == 0 and myocardium_pixels > MRI_MAX_MYOCARDIUM: + erode_structure = _unit_disk(small_radius*1.5) + anatomical_mask = anatomical_mask - binary_erosion(m1, erode_structure).astype(np.int) ventricle_pixels = np.count_nonzero(anatomical_mask == MRI_SEGMENTED_CHANNEL_MAP['ventricle']) - myocardium_pixels = np.count_nonzero(anatomical_mask == MRI_SEGMENTED_CHANNEL_MAP['myocardium']) - if ventricle_pixels == 0 and myocardium_pixels > MRI_MAX_MYOCARDIUM: - erode_structure = _unit_disk(small_radius*1.5) - anatomical_mask = anatomical_mask - binary_erosion(m1, erode_structure).astype(np.int) - ventricle_pixels = np.count_nonzero(anatomical_mask == MRI_SEGMENTED_CHANNEL_MAP['ventricle']) - return overlay, anatomical_mask, ventricle_pixels + return overlay, anatomical_mask, ventricle_pixels -def _unit_disk(r): # -> np.ndarray: +def _unit_disk(r: int) -> np.ndarray: """Get the unit disk for a radius. This is used for visualization of CINE_segmented_SAX_InlineVF. @@ -114,7 +116,9 @@ def _unit_disk(r): # -> np.ndarray: return (x ** 2 + y ** 2 <= r ** 2).astype(np.int) -def plot_cardiac_long_axis(b_series, sides=7, fig_width=18, title_prefix=''): +def plot_cardiac_long_axis( + b_series: List[pydicom.FileDataset], sides: int = 7, fig_width: int = 18, title_prefix: str = '', +) -> None: """Visualize CINE_segmented_SAX_InlineVF series. Args: @@ -168,9 +172,9 @@ def plot_cardiac_long_axis(b_series, sides=7, fig_width=18, title_prefix=''): def plot_cardiac_short_axis( - series, transpose=False, fig_width=18, - title_prefix='', -): + series: List[pydicom.FileDataset], transpose: bool = False, fig_width: int = 18, + title_prefix: str = '', +) -> None: """Visualize CINE_segmented_LAX series. Args: @@ -225,14 +229,14 @@ def plot_cardiac_short_axis( def plot_mri_series( - sample_mri, dicoms, series_name, sax_sides, - lax_transpose, fig_width, -): + sample_mri: str, dicoms: Dict[str, pydicom.FileDataset], series_name: str, sax_sides: int, + lax_transpose: bool, fig_width: int, +) -> None: """Visualize the applicable series within this DICOM. Args: sample_mri: The local or Cloud Storage path to the MRI file. - dicoms: A dictionary of dicoms. + dicoms: A dictionary of DICOMs. series_name: The name of the chosen series. sax_sides: How many sides to display for CINE_segmented_SAX_InlineVF. lax_transpose: Whether to transpose when plotting CINE_segmented_LAX. @@ -258,10 +262,9 @@ def plot_mri_series( ) else: print(f'Visualization not currently implemented for {series_name}.') - return None -def choose_mri_series(sample_mri): +def choose_mri_series(sample_mri: str) -> None: """Render widgets and plots for cardiac MRIs. Visualization is supported for CINE_segmented_SAX_InlineVF series and @@ -269,9 +272,6 @@ def choose_mri_series(sample_mri): Args: sample_mri: The local or Cloud Storage path to the MRI file. - - Returns: - ipywidget or HTML upon error. """ with tempfile.TemporaryDirectory() as tmpdirname: local_path = os.path.join(tmpdirname, os.path.basename(sample_mri)) @@ -280,11 +280,13 @@ def choose_mri_series(sample_mri): with zipfile.ZipFile(local_path, 'r') as zip_ref: zip_ref.extractall(tmpdirname) except (tf.errors.NotFoundError, tf.errors.PermissionDeniedError) as e: - return HTML(f''' -
+ display( + HTML(f'''
Warning: Cardiac MRI not available for sample {os.path.basename(sample_mri)}: 

{e.message}

-
''') +
'''), + ) + return filtered_dicoms = collections.defaultdict(list) series_descriptions = [] @@ -295,7 +297,7 @@ def choose_mri_series(sample_mri): series_descriptions.append(dcm.SeriesDescription) if 'cine_segmented_lax' in dcm.SeriesDescription.lower(): filtered_dicoms[dcm.SeriesDescription.lower()].append(dcm) - if 'cine_segmented_sax_inlinevf' == dcm.SeriesDescription.lower(): + if dcm.SeriesDescription.lower() == 'cine_segmented_sax_inlinevf': cur_angle = (dcm.InstanceNumber - 1) // MRI_FRAMES filtered_dicoms[f'{dcm.SeriesDescription.lower()}_angle_{str(cur_angle)}'].append(dcm) @@ -350,22 +352,20 @@ def choose_mri_series(sample_mri): ) display(viz_controls_ui, viz_controls_output) else: - print( - f'\n\nNeither CINE_segmented_SAX_InlineVF nor CINE_segmented_LAX available in MRI for sample {os.path.basename(sample_mri)}.', - '\n\nTry a different MRI.', + display( + HTML(f'''
+ Neither CINE_segmented_SAX_InlineVF nor CINE_segmented_LAX available in MRI for sample {os.path.basename(sample_mri)}. + Try a different MRI. +
'''), ) - return None -def choose_cardiac_mri(sample_id, folder=None): +def choose_cardiac_mri(sample_id: Union[int, str], folder: Optional[str] = None) -> None: """Render widget to choose the cardiac MRI to plot. Args: sample_id: The id of the ECG sample to retrieve. folder: The local or Cloud Storage folder under which the files reside. - - Returns: - ipywidget or HTML upon error. """ if folder is None: folder = get_cardiac_mri_folder(sample_id) @@ -374,19 +374,23 @@ def choose_cardiac_mri(sample_id, folder=None): try: sample_mris = tf.io.gfile.glob(pattern=os.path.join(folder, sample_mri_glob)) except (tf.errors.NotFoundError, tf.errors.PermissionDeniedError) as e: - return HTML(f''' -
+ display( + HTML(f'''
Warning: Cardiac MRI not available for sample {sample_id} in {folder}: 

{e.message}

Use the folder parameter to read DICOMs from a different local directory or Cloud Storage bucket. -
''') +
'''), + ) + return if not sample_mris: - return HTML(f''' -
+ display( + HTML(f'''
Warning: Cardiac MRI DICOM not available for sample {sample_id} in {folder}.
Use the folder parameter to read DICOMs from a different local directory or Cloud Storage bucket. -
''') +
'''), + ) + return mri_chooser = widgets.Dropdown( options=[(os.path.basename(mri), mri) for mri in sample_mris], diff --git a/ml4h/visualization_tools/ecg_interactive_plots.py b/ml4h/visualization_tools/ecg_interactive_plots.py index 97a4e1547..18ed39a9b 100644 --- a/ml4h/visualization_tools/ecg_interactive_plots.py +++ b/ml4h/visualization_tools/ecg_interactive_plots.py @@ -2,10 +2,12 @@ import os import tempfile +from typing import Optional, Union -import altair as alt # Interactive data visualization for plots. from IPython.display import HTML -from ml4h.visualization_tools.ecg_reshape import DEFAULT_RESTING_ECG_SIGNAL_TMAP_NAME +import altair as alt # Interactive data visualization for plots. +from ml4h.TensorMap import TensorMap +from ml4h.visualization_tools.ecg_reshape import DEFAULT_RESTING_ECG_SIGNAL_TMAP from ml4h.visualization_tools.ecg_reshape import reshape_exercise_ecg_to_tidy from ml4h.visualization_tools.ecg_reshape import reshape_resting_ecg_to_tidy @@ -31,18 +33,21 @@ ) -def resting_ecg_interactive_plot(sample_id, folder=None, tmap_name=DEFAULT_RESTING_ECG_SIGNAL_TMAP_NAME): +def resting_ecg_interactive_plot( + sample_id: Union[int, str], folder: Optional[str] = None, + tmap: TensorMap = DEFAULT_RESTING_ECG_SIGNAL_TMAP, +) -> Union[HTML, alt.Chart]: """Wrangle resting ECG data to tidy and present it as an interactive plot. Args: sample_id: The id of the ECG sample to retrieve. folder: The local or Cloud Storage folder under which the files reside. - tmap_name: The name of the TMAP to use for ecg input. + tmap: The TensorMap to use for ECG input. Returns: An Altair plot or a notebook-friendly error. """ - tidy_resting_ecg_signal = reshape_resting_ecg_to_tidy(sample_id, folder, tmap_name) + tidy_resting_ecg_signal = reshape_resting_ecg_to_tidy(sample_id, folder, tmap) if tidy_resting_ecg_signal.shape[0] == 0: return HTML(f'''
@@ -85,7 +90,9 @@ def resting_ecg_interactive_plot(sample_id, folder=None, tmap_name=DEFAULT_RESTI return upper & lower -def exercise_ecg_interactive_plot(sample_id, folder=None, time_interval_seconds=10): +def exercise_ecg_interactive_plot( + sample_id: Union[int, str], folder: Optional[str] = None, time_interval_seconds: int = 10, +) -> Union[HTML, alt.Chart]: """Wrangle exercise ECG data to tidy and present it as an interactive plot. Args: @@ -140,7 +147,8 @@ def exercise_ecg_interactive_plot(sample_id, folder=None, time_interval_seconds= lead_select, ).transform_filter( # https://github.com/altair-viz/altair/issues/1960 - f'((toNumber({brush.name}.time) - {time_interval_seconds/2.0}) < datum.time) && (datum.time < toNumber({brush.name}.time) + {time_interval_seconds/2.0})', + f'''((toNumber({brush.name}.time) - {time_interval_seconds/2.0}) < datum.time) + && (datum.time < toNumber({brush.name}.time) + {time_interval_seconds/2.0})''', ) return trend.encode(y='heartrate:Q') & trend.encode(y='load:Q') & signal diff --git a/ml4h/visualization_tools/ecg_reshape.py b/ml4h/visualization_tools/ecg_reshape.py index b3213d359..167eb5012 100644 --- a/ml4h/visualization_tools/ecg_reshape.py +++ b/ml4h/visualization_tools/ecg_reshape.py @@ -1,53 +1,57 @@ """Methods for reshaping raw ECG signal data for use in the pandas ecosystem.""" import os import tempfile +from typing import Any, Dict, Optional, Tuple, Union +import numpy as np +import pandas as pd from biosppy.signals.tools import filter_signal import h5py from ml4h.defines import ECG_BIKE_LEADS from ml4h.defines import ECG_REST_LEADS from ml4h.runtime_data_defines import get_exercise_ecg_hd5_folder from ml4h.runtime_data_defines import get_resting_ecg_hd5_folder -from ml4h.tensor_maps_by_hand import TMAPS -import numpy as np -import pandas as pd +from ml4h.TensorMap import TensorMap +import ml4h.tensormap.ukb.ecg as ecg_tmaps import tensorflow as tf RAW_SCALE = 0.005 # Convert to mV. SAMPLING_RATE = 500.0 -DEFAULT_RESTING_ECG_SIGNAL_TMAP_NAME = 'ecg_rest' +DEFAULT_RESTING_ECG_SIGNAL_TMAP = ecg_tmaps.ecg_rest # TODO(deflaux): parameterize exercise ECG by TMAP name if there is similar ECG data from other studies. -EXERCISE_ECG_SIGNAL_TMAP = TMAPS['ecg-bike-raw-full'] +EXERCISE_ECG_SIGNAL_TMAP = ecg_tmaps.ecg_bike_raw_full EXERCISE_ECG_TREND_TMAPS = [ - TMAPS['ecg-bike-raw-trend-hr'], - TMAPS['ecg-bike-raw-trend-load'], - TMAPS['ecg-bike-raw-trend-grade'], - TMAPS['ecg-bike-raw-trend-artifact'], - TMAPS['ecg-bike-raw-trend-mets'], - TMAPS['ecg-bike-raw-trend-pacecount'], - TMAPS['ecg-bike-raw-trend-phasename'], - TMAPS['ecg-bike-raw-trend-phasetime'], - TMAPS['ecg-bike-raw-trend-time'], - TMAPS['ecg-bike-raw-trend-vecount'], + ecg_tmaps.ecg_bike_raw_trend_hr, + ecg_tmaps.ecg_bike_raw_trend_load, + ecg_tmaps.ecg_bike_raw_trend_grade, + ecg_tmaps.ecg_bike_raw_trend_artifact, + ecg_tmaps.ecg_bike_raw_trend_mets, + ecg_tmaps.ecg_bike_raw_trend_pacecount, + ecg_tmaps.ecg_bike_raw_trend_phasename, + ecg_tmaps.ecg_bike_raw_trend_phasetime, + ecg_tmaps.ecg_bike_raw_trend_time, + ecg_tmaps.ecg_bike_raw_trend_vecount, ] EXERCISE_PHASES = {0.0: 'Pretest', 1.0: 'Exercise', 2.0: 'Recovery'} -def _examine_available_keys(hd5): +def _examine_available_keys(hd5: Dict[str, Any]) -> None: print(f'hd5 ECG keys {[k for k in hd5.keys() if "ecg" in k]}') for key in [k for k in hd5.keys() if 'ecg' in k]: - print(f'hd5 {key} keys {[k for k in hd5[key].keys()]}') + print(f'hd5 {key} keys {k for k in hd5[key]}') -def reshape_resting_ecg_to_tidy(sample_id, folder=None, tmap_name=DEFAULT_RESTING_ECG_SIGNAL_TMAP_NAME): +def reshape_resting_ecg_to_tidy( + sample_id: Union[int, str], folder: Optional[str] = None, tmap: TensorMap = DEFAULT_RESTING_ECG_SIGNAL_TMAP, +) -> pd.DataFrame: """Wrangle resting ECG data to tidy. Args: sample_id: The id of the ECG sample to retrieve. folder: The local or Cloud Storage folder under which the files reside. - tmap_name: The name of the TMAP to use for ecg input. + tmap: The TensorMap to use for ECG input. Returns: A pandas dataframe in tidy format or print a notebook-friendly error and return an empty dataframe. @@ -55,7 +59,7 @@ def reshape_resting_ecg_to_tidy(sample_id, folder=None, tmap_name=DEFAULT_RESTIN if folder is None: folder = get_resting_ecg_hd5_folder(sample_id) - data = {'lead': [], 'raw': [], 'ts_reference': [], 'filtered': [], 'filtered_1': [], 'filtered_2': []} + data: Dict[str, Any] = {'lead': [], 'raw': [], 'ts_reference': [], 'filtered': [], 'filtered_1': [], 'filtered_2': []} with tempfile.TemporaryDirectory() as tmpdirname: sample_hd5 = str(sample_id) + '.hd5' @@ -69,10 +73,10 @@ def reshape_resting_ecg_to_tidy(sample_id, folder=None, tmap_name=DEFAULT_RESTIN with h5py.File(local_path, mode='r') as hd5: try: - signals = TMAPS[tmap_name].tensor_from_file(TMAPS[tmap_name], hd5) + signals = tmap.tensor_from_file(tmap, hd5) except (KeyError, ValueError) as e: - print(f'''Warning: Resting ECG TMAP {tmap_name} not available for sample {sample_id}. - Use the tmap_name parameter to choose a different TMAP.\n\n{e}''') + print(f'''Warning: Resting ECG TMAP {tmap.name} not available for sample {sample_id}. + Use the tmap parameter to choose a different TMAP.\n\n{e}''') _examine_available_keys(hd5) return pd.DataFrame(data) for (lead, channel) in ECG_REST_LEADS.items(): @@ -136,7 +140,9 @@ def reshape_resting_ecg_to_tidy(sample_id, folder=None, tmap_name=DEFAULT_RESTIN return tidy_signal_df -def reshape_exercise_ecg_to_tidy(sample_id, folder=None): +def reshape_exercise_ecg_to_tidy( + sample_id: Union[int, str], folder: Optional[str] = None, +) -> Tuple[pd.DataFrame, pd.DataFrame]: """Wrangle exercise ECG signal data to tidy format. Args: @@ -208,7 +214,9 @@ def reshape_exercise_ecg_to_tidy(sample_id, folder=None): return (trend_df, tidy_signal_df) -def reshape_exercise_ecg_and_trend_to_tidy(sample_id, folder=None): +def reshape_exercise_ecg_and_trend_to_tidy( + sample_id: Union[int, str], folder: Optional[str] = None, +) -> Tuple[pd.DataFrame, pd.DataFrame]: """Wrangle exercise ECG signal and trend data to tidy format. Args: diff --git a/ml4h/visualization_tools/ecg_static_plots.py b/ml4h/visualization_tools/ecg_static_plots.py index 2ebcfc3e1..ac7283237 100644 --- a/ml4h/visualization_tools/ecg_static_plots.py +++ b/ml4h/visualization_tools/ecg_static_plots.py @@ -1,17 +1,18 @@ """Methods for integration of static plots within notebooks.""" import os import tempfile +from typing import List, Optional, Union from IPython.display import HTML from IPython.display import SVG +import numpy as np from ml4h.plots import plot_ecg_rest from ml4h.runtime_data_defines import get_resting_ecg_hd5_folder from ml4h.runtime_data_defines import get_resting_ecg_svg_folder -import numpy as np import tensorflow as tf -def display_resting_ecg(sample_id, folder=None): +def display_resting_ecg(sample_id: Union[int, str], folder: Optional[str] = None) -> Union[HTML, SVG]: """Retrieve (or render) and display the SVG of the resting ECG. Args: @@ -53,8 +54,8 @@ def display_resting_ecg(sample_id, folder=None): try: # We don't need the resulting SVG, so send it to a temporary directory. with tempfile.TemporaryDirectory() as tmpdirname: - plot_ecg_rest(tensor_paths = [local_path], rows=[0], out_folder=tmpdirname, is_blind=False) - except Exception as e: + return plot_ecg_rest(tensor_paths=[local_path], rows=[0], out_folder=tmpdirname, is_blind=False) + except Exception as e: # pylint: disable=broad-except return HTML(f'''
Warning: Unable to render static plot of resting ECG for sample {sample_id} from {hd5_folder}: @@ -62,7 +63,7 @@ def display_resting_ecg(sample_id, folder=None):
''') -def major_breaks_x_resting_ecg(limits): +def major_breaks_x_resting_ecg(limits: List[float]) -> np.array: """Method to compute breaks for plotnine plots of ECG resting data. Args: diff --git a/ml4h/visualization_tools/facets.py b/ml4h/visualization_tools/facets.py index a45ea88da..18f96327d 100644 --- a/ml4h/visualization_tools/facets.py +++ b/ml4h/visualization_tools/facets.py @@ -2,6 +2,7 @@ import base64 import os +import pandas as pd from facets_overview.generic_feature_statistics_generator import GenericFeatureStatisticsGenerator FACETS_DEPENDENCIES = { @@ -25,10 +26,10 @@ FACETS_DEPENDENCIES[dep] = os.path.basename(url) -class FacetsOverview(object): +class FacetsOverview(): """Methods for Facets Overview notebook integration.""" - def __init__(self, data): + def __init__(self, data: pd.DataFrame): # This takes the dataframe and computes all the inputs to the Facets # Overview plots such as: # - numeric variables: histogram bins, mean, min, median, max, etc.. @@ -39,7 +40,7 @@ def __init__(self, data): [{'name': 'data', 'table': data}], ) - def _repr_html_(self): + def _repr_html_(self) -> str: """Html representation of Facets Overview for use in a Jupyter notebook.""" protostr = base64.b64encode(self._proto.SerializeToString()).decode('utf-8') html_template = ''' @@ -57,14 +58,14 @@ def _repr_html_(self): return html -class FacetsDive(object): +class FacetsDive(): """Methods for Facets Dive notebook integration.""" - def __init__(self, data, height=1000): + def __init__(self, data: pd.DataFrame, height: int = 1000): self._data = data self.height = height - def _repr_html_(self): + def _repr_html_(self) -> str: """Html representation of Facets Dive for use in a Jupyter notebook.""" html_template = """ diff --git a/ml4h/visualization_tools/hd5_mri_plots.py b/ml4h/visualization_tools/hd5_mri_plots.py index 20b3305b1..d3894b39d 100644 --- a/ml4h/visualization_tools/hd5_mri_plots.py +++ b/ml4h/visualization_tools/hd5_mri_plots.py @@ -1,29 +1,34 @@ """Methods for integration of plots of mri data processed to 3D tensors from within notebooks.""" +from collections import OrderedDict from enum import Enum, auto import os import tempfile +from typing import Any, Dict, List, Optional, Tuple, Union -import h5py from IPython.display import display from IPython.display import HTML +import numpy as np +import h5py import ipywidgets as widgets import matplotlib.pyplot as plt from ml4h.runtime_data_defines import get_mri_hd5_folder -from ml4h.tensor_maps_by_hand import TMAPS -from ml4h.TensorMap import Interpretation -import numpy as np +import ml4h.tensormap.ukb.mri as ukb_mri +import ml4h.tensormap.ukb.mri_vtk as ukb_mri_vtk +from ml4h.TensorMap import Interpretation, TensorMap import tensorflow as tf -# Discover applicable TMAPS. -CARDIAC_MRI_TMAP_NAMES = [k for k in TMAPS.keys() if ('_lax_' in k or '_sax_' in k) and TMAPS[k].axes() == 3] -CARDIAC_MRI_TMAP_NAMES.extend( - [k for k in TMAPS.keys() if TMAPS[k].path_prefix == 'ukb_cardiac_mri' and TMAPS[k].axes() == 3], +# Discover applicable TensorMaps. +MRI_TMAPS = { + key: value for key, value in ukb_mri.__dict__.items() if isinstance(value, TensorMap) + and value.interpretation == Interpretation.CONTINUOUS and value.axes() == 3 +} +MRI_TMAPS.update( + { + key: value for key, value in ukb_mri_vtk.__dict__.items() + if isinstance(value, TensorMap) and value.interpretation == Interpretation.CONTINUOUS and value.axes() == 3 + }, ) -LIVER_MRI_TMAP_NAMES = [k for k in TMAPS.keys() if TMAPS[k].path_prefix == 'ukb_liver_mri' and TMAPS[k].axes() == 3] -BRAIN_MRI_TMAP_NAMES = [k for k in TMAPS.keys() if TMAPS[k].path_prefix == 'ukb_brain_mri' and TMAPS[k].axes() == 3] -# This includes more than just MRI TMAPS, it is a best effort. -BEST_EFFORT_MRI_TMAP_NAMES = [k for k in TMAPS.keys() if TMAPS[k].interpretation == Interpretation.CONTINUOUS and TMAPS[k].axes() == 3] MIN_IMAGE_WIDTH = 8 DEFAULT_IMAGE_WIDTH = 12 @@ -41,42 +46,30 @@ class PlotType(Enum): class TensorMapCache: """Cache the tensor to display for reuse when re-plotting the same TMAP with different plot parameters.""" - def __init__(self, hd5, tmap_name): + def __init__(self, hd5: Dict[str, Any], tmap: TensorMap): self.hd5 = hd5 - self.tmap_name = None + self.tmap: Optional[TensorMap] = None self.tensor = None - _ = self.get(tmap_name) + _ = self.get(tmap) - def get(self, tmap_name): - if self.tmap_name != tmap_name: - self.tensor = TMAPS[tmap_name].tensor_from_file(TMAPS[tmap_name], self.hd5) - self.tmap_name = tmap_name + def get(self, tmap: TensorMap) -> np.array: + if self.tmap != tmap: + self.tensor = tmap.tensor_from_file(tmap, self.hd5) + self.tmap = tmap return self.tensor -def choose_cardiac_mri_tmap(sample_id, folder=None, tmap_name='cine_lax_4ch_192', default_tmap_names=CARDIAC_MRI_TMAP_NAMES): - choose_mri_tmap(sample_id, folder, tmap_name, default_tmap_names) - - -def choose_brain_mri_tmap(sample_id, folder=None, tmap_name='t2_flair_sag_p2_1mm_fs_ellip_pf78_1', default_tmap_names=BRAIN_MRI_TMAP_NAMES): - choose_mri_tmap(sample_id, folder, tmap_name, default_tmap_names) - - -def choose_liver_mri_tmap(sample_id, folder=None, tmap_name='liver_shmolli_segmented', default_tmap_names=LIVER_MRI_TMAP_NAMES): - choose_mri_tmap(sample_id, folder, tmap_name, default_tmap_names) - - -def choose_mri_tmap(sample_id, folder=None, tmap_name=None, default_tmap_names=BEST_EFFORT_MRI_TMAP_NAMES): +def choose_mri_tmap( + sample_id: Union[int, str], folder: Optional[str] = None, tmap: Optional[TensorMap] = None, + default_tmaps: Dict[str, TensorMap] = MRI_TMAPS, +) -> None: """Render widgets and plots for MRI tensors. Args: sample_id: The id of the sample to retrieve. folder: The local or Cloud Storage folder under which the files reside. - tmap_name: The TMAP name for the 3D MRI tensor to visualize. - default_tmap_names: Other TMAP names to offer for visualization, if present in the hd5. - - Returns: - ipywidget or HTML upon error. + tmap: The TensorMap for the 3D MRI tensor to visualize. + default_tmaps: Other TensorMaps to offer for visualization, if present in the hd5. """ if folder is None: folder = get_mri_hd5_folder(sample_id) @@ -88,42 +81,45 @@ def choose_mri_tmap(sample_id, folder=None, tmap_name=None, default_tmap_names=B tf.io.gfile.copy(src=os.path.join(folder, sample_hd5), dst=local_path) hd5 = h5py.File(local_path, mode='r') except (tf.errors.NotFoundError, tf.errors.PermissionDeniedError) as e: - return HTML(f''' -
+ display( + HTML(f'''
Warning: MRI HD5 file not available for sample {sample_id} in folder {folder}: 

{e.message}

Use the folder parameter to read HD5s from a different local directory or Cloud Storage bucket. -
''') - - sample_tmap_names = [] - # Add the passed tmap_name parameter, if it is present in this hd5. - if tmap_name: - if TMAPS[tmap_name].hd5_key_guess() in hd5: - if len(TMAPS[tmap_name].shape) == 3: - sample_tmap_names.append(tmap_name) +
'''), + ) + return + + sample_tmaps = OrderedDict() + # Add the passed tmap parameter, if it is present in this hd5. + if tmap: + if tmap.hd5_key_guess() in hd5: + if len(tmap.shape) == 3: + sample_tmaps[tmap.name] = tmap else: - print(f'{tmap_name} is not a 3D tensor, skipping it') + print(f'{tmap} is not a 3D tensor, skipping it') else: - print(f'{tmap_name} is not available in {sample_id}') - # Also discover applicable TMAPS for this particular sample's HD5 file. - sample_tmap_names.extend( - sorted(set([k for k in default_tmap_names if TMAPS[k].hd5_key_guess() in hd5])), - ) - - if not sample_tmap_names: - return HTML(f'''
- Neither {tmap_name} nor any of {default_tmap_names} are present in this HD5 for sample {sample_id} in {folder}. - Use the tmap_name parameter to try a different TMAP or the folder parameter to try a different hd5 for the sample. -
''') - - default_tmap_name_value = sample_tmap_names[0] + print(f'{tmap} is not available in {sample_id}') + # Also discover applicable TensorMaps for this particular sample's HD5 file. + sample_tmaps.update({n: t for n, t in sorted(default_tmaps.items(), key=lambda t: t[0]) if t.hd5_key_guess() in hd5}) + + if not sample_tmaps: + display( + HTML(f'''
+ Neither {tmap.name} nor any of {default_tmaps.keys()} are present in this HD5 for sample {sample_id} in {folder}. + Use the tmap parameter to try a different TensorMap or the folder parameter to try a different hd5 for the sample. +
'''), + ) + return + + default_tmap_value = next(iter(sample_tmaps.values())) # Display the middle instance by default in the interactive view. - default_instance_value, max_instance_value = compute_instance_range(default_tmap_name_value) - default_vmin_value, default_vmax_value = compute_color_range(hd5, default_tmap_name_value) + default_instance_value, max_instance_value = compute_instance_range(default_tmap_value) + default_vmin_value, default_vmax_value = compute_color_range(hd5, default_tmap_value) - tmap_name_chooser = widgets.Dropdown( - options=sample_tmap_names, - value=default_tmap_name_value, + tmap_chooser = widgets.Dropdown( + options=sample_tmaps, + value=default_tmap_value, description='Choose the MRI tensor TMAP name to visualize:', style={'description_width': 'initial'}, layout=widgets.Layout(width='900px'), @@ -174,20 +170,20 @@ def choose_mri_tmap(sample_id, folder=None, tmap_name=None, default_tmap_names=B viz_controls_ui = widgets.VBox( [ widgets.HTML('

Visualization controls

'), - tmap_name_chooser, + tmap_chooser, widgets.HBox([transpose_chooser, fig_width_chooser]), widgets.HBox([flip_chooser, color_range_chooser]), widgets.HBox([plot_type_chooser, instance_chooser]), ], layout=widgets.Layout(width='auto', border='solid 1px grey'), ) - tmap_cache = TensorMapCache(hd5=hd5, tmap_name=tmap_name_chooser.value) + tmap_cache = TensorMapCache(hd5=hd5, tmap=tmap_chooser.value) viz_controls_output = widgets.interactive_output( plot_mri_tmap, { 'sample_id': widgets.fixed(sample_id), 'tmap_cache': widgets.fixed(tmap_cache), - 'tmap_name': tmap_name_chooser, + 'tmap': tmap_chooser, 'plot_type': plot_type_chooser, 'instance': instance_chooser, 'color_range': color_range_chooser, @@ -209,33 +205,36 @@ def on_plot_type_change(change): else: instance_chooser.layout.visibility = 'hidden' - tmap_name_chooser.observe(on_tmap_value_change, names='value') + tmap_chooser.observe(on_tmap_value_change, names='value') plot_type_chooser.observe(on_plot_type_change, names='value') display(viz_controls_ui, viz_controls_output) -def compute_color_range(hd5, tmap_name): +def compute_color_range(hd5: Dict[str, Any], tmap: TensorMap) -> List[int]: """Compute the mean values for the color ranges of instances in the MRI series.""" - mri_tensor = TMAPS[tmap_name].tensor_from_file(TMAPS[tmap_name], hd5) + mri_tensor = tmap.tensor_from_file(tmap, hd5) vmin = np.mean([np.min(mri_tensor[:, :, i]) for i in range(0, mri_tensor.shape[2])]) vmax = np.mean([np.max(mri_tensor[:, :, i]) for i in range(0, mri_tensor.shape[2])]) - return[vmin, vmax] + return [vmin, vmax] -def compute_instance_range(tmap_name): +def compute_instance_range(tmap: TensorMap) -> Tuple[int, int]: """Compute middle and max instances.""" - middle_instance = int(TMAPS[tmap_name].shape[2] / 2) - max_instance = TMAPS[tmap_name].shape[2] - return(middle_instance, max_instance) + middle_instance = int(tmap.shape[2] / 2) + max_instance = tmap.shape[2] + return (middle_instance, max_instance) -def plot_mri_tmap(sample_id, tmap_cache, tmap_name, plot_type, instance, color_range, transpose, flip, fig_width): +def plot_mri_tmap( + sample_id: Union[int, str], tmap_cache: TensorMapCache, tmap: TensorMap, plot_type: PlotType, + instance: int, color_range: Tuple[int, int], transpose: bool, flip: bool, fig_width: int, +) -> None: """Visualize the applicable MRI series within this HD5 file. Args: sample_id: The local or Cloud Storage path to the MRI file. tmap_cache: The cache from which to retrieve the tensor to be plotted. - tmap_name: The name of the chosen TMAP for the MRI series. + tmap: The chosen TensorMap for the MRI series. plot_type: Whether to display instances interactively or in a panel view. instance: The particular instance to display, if interactive. color_range: Array of minimum and maximum value for the color range. @@ -243,12 +242,9 @@ def plot_mri_tmap(sample_id, tmap_cache, tmap_name, plot_type, instance, color_r flip: Whether to flip the image on its vertical axis fig_width: The desired width of the figure. Note that height computed as the proportion of the width based on the data to be plotted. - - Returns: - The plot or a notebook-friendly error message. """ - title_prefix = f'{tmap_name} from MRI {sample_id}' - mri_tensor = tmap_cache.get(tmap_name) + title_prefix = f'{tmap.name} from MRI {sample_id}' + mri_tensor = tmap_cache.get(tmap) if plot_type == PlotType.INTERACTIVE: plot_mri_tensor_as_animation( mri_tensor=mri_tensor, @@ -275,10 +271,13 @@ def plot_mri_tmap(sample_id, tmap_cache, tmap_name, plot_type, instance, color_r title_prefix=title_prefix, ) else: - return HTML(f'''
Invalid plot type: {plot_type}
''') + HTML(f'''
Invalid plot type: {plot_type}
''') -def plot_mri_tensor_as_panels(mri_tensor, vmin, vmax, transpose=False, flip=False, fig_width=DEFAULT_IMAGE_WIDTH, title_prefix=''): +def plot_mri_tensor_as_panels( + mri_tensor: np.array, vmin: int, vmax: int, transpose: bool = False, flip: bool = False, + fig_width: int = DEFAULT_IMAGE_WIDTH, title_prefix: str = '', +) -> None: """Visualize an MRI series from a 3D tensor as a panel of static plots. Args: @@ -314,7 +313,7 @@ def plot_mri_tensor_as_panels(mri_tensor, vmin, vmax, transpose=False, flip=Fals axes[row, col].set_yticklabels([]) axes[row, col].set_xticklabels([]) fig.suptitle( - f'{title_prefix}\nColor range: {vmin}-{vmax}, Transpose: {transpose}, Flip: {flip}, Figure size:{fig_width}x{fig_height}', + f'{title_prefix}\nColor range: {vmin}-{vmax}, Transpose: {transpose}, Flip: {flip}, Figure size:{fig_width}x{fig_height}', # pylint: disable=line-too-long fontsize=fig_width, ) fig.subplots_adjust( @@ -326,7 +325,11 @@ def plot_mri_tensor_as_panels(mri_tensor, vmin, vmax, transpose=False, flip=Fals ) -def plot_mri_tensor_as_animation(mri_tensor, instance, vmin, vmax, transpose=False, flip=False, fig_width=DEFAULT_IMAGE_WIDTH, title_prefix=''): +def plot_mri_tensor_as_animation( + mri_tensor: np.array, instance: int, vmin: int, vmax: int, + transpose: bool = False, flip: bool = False, + fig_width: int = DEFAULT_IMAGE_WIDTH, title_prefix: str = '', +) -> None: """Visualize an MRI series from a 3D tensor as an animation rendered one panel at a time. Args: @@ -358,7 +361,7 @@ def plot_mri_tensor_as_animation(mri_tensor, instance, vmin, vmax, transpose=Fal _, ax = plt.subplots(figsize=(fig_width, fig_height), facecolor='beige') ax.imshow(pixels, cmap='gray', vmin=vmin, vmax=vmax) ax.set_title( - f'{title_prefix}, Instance: {instance}\nColor range: {vmin}-{vmax}, Transpose: {transpose}, Flip: {flip}, Figure size:{fig_width}x{fig_height}', + f'{title_prefix}, Instance: {instance}\nColor range: {vmin}-{vmax}, Transpose: {transpose}, Flip: {flip}, Figure size:{fig_width}x{fig_height}', # pylint: disable=line-too-long fontsize=fig_width, ) ax.set_yticklabels([]) From 6ff7a2d2ca558c2f77e4cf5af94553b0fb4379a2 Mon Sep 17 00:00:00 2001 From: Samwell Freeman Date: Tue, 29 Sep 2020 17:28:20 -0400 Subject: [PATCH 05/21] paired --- .../paired_multimodal_autoencoder.ipynb | 113 ++-------- .../paired_multimodal_segmenter_mri_ecg.ipynb | 210 ++++++------------ notebooks/autoencoders/vae_mri_slice.ipynb | 2 +- notebooks/mnist_demo.ipynb | 90 +------- .../mri/mri_cardiac_long_axis_sketch.ipynb | 8 +- .../mri/mri_cardiac_short_axis_sketch.ipynb | 14 +- .../identify_a_sample_to_review.ipynb | 6 +- .../review_results/review_one_sample.ipynb | 8 +- ...handling_for_notebook_visualizations.ipynb | 40 +--- ...ntify_a_sample_to_review_interactive.ipynb | 4 +- .../review_one_sample_interactive.ipynb | 4 +- 11 files changed, 135 insertions(+), 364 deletions(-) diff --git a/notebooks/autoencoders/paired_multimodal_autoencoder.ipynb b/notebooks/autoencoders/paired_multimodal_autoencoder.ipynb index bd58f6d25..67870c053 100644 --- a/notebooks/autoencoders/paired_multimodal_autoencoder.ipynb +++ b/notebooks/autoencoders/paired_multimodal_autoencoder.ipynb @@ -328,11 +328,9 @@ ") -> Model:\n", " inputs = {tm: Input(shape=tm.shape, name=tm.input_name()) for tm in args.tensor_maps_in}\n", " original_outputs = {tm:1 for tm in args.tensor_maps_out}\n", - " real_serial_layers = kwargs['model_layers']\n", - " args.model_layers = None\n", " multimodal_activations = []\n", - " encoders = {}\n", - " decoders = {}\n", + " desired_distance_tm = []\n", + " my_metrics = {}\n", " outputs = []\n", " losses = []\n", " for left, right in pairs:\n", @@ -347,22 +345,15 @@ " h_right = encode_right(inputs[right]) \n", " \n", " if pair_loss == 'cosine':\n", - " loss_layer = CosineLossLayer(1.0)\n", + " loss_layer = CosineLossLayer(100.0)\n", " elif pair_loss == 'euclid':\n", - " loss_layer = L2LossLayer(1.0)\n", + " loss_layer = L2LossLayer(100.0)\n", " \n", " paired_embeddings = loss_layer([h_left, h_right])\n", " multimodal_activations.extend(paired_embeddings)\n", - " if left not in encoders:\n", - " encoders[left] = encode_left\n", - " if right not in encoders:\n", - " encoders[right] = encode_right \n", " \n", " multimodal_activation = Concatenate()(multimodal_activations)\n", - " encoder = Model(inputs=list(inputs.values()), outputs=[multimodal_activation], name='encoder')\n", " \n", - " # build decoder models\n", - " latent_inputs = Input(shape=(args.dense_layers[0]*len(inputs)), name='input_concept_space')\n", " pre_decoder_shapes: Dict[TensorMap, Optional[Tuple[int, ...]]] = {}\n", " for tm in args.tensor_maps_out:\n", " shape = _calc_start_shape(num_upsamples=len(args.dense_blocks), output_shape=tm.shape, \n", @@ -389,26 +380,24 @@ " upsample_z=args.pool_z,\n", " )\n", " \n", - " reconstruction = decode(restructure(latent_inputs))\n", - " decoder = Model(latent_inputs, reconstruction, name=tm.output_name())\n", - " decoders[tm] = decoder\n", - " outputs.append(decoder(multimodal_activation))\n", + " outputs.append(decode(restructure(multimodal_activation)))\n", " losses.append(tm.loss)\n", "\n", - " args.tensor_maps_out = list(original_outputs.keys())\n", + " args.tensor_maps_out = list(original_outputs.keys()) + desired_distance_tm\n", " args.tensor_maps_in = list(inputs.keys())\n", " \n", + " opt = Adam(lr=0.0001, beta_1=0.9, beta_2=0.999, epsilon=1e-08)\n", + " #outputs.reverse() # Make paired loss last\n", + " #losses.reverse()\n", " m = Model(inputs=list(inputs.values()), outputs=outputs)\n", - " my_metrics = {tm.output_name(): tm.metrics for tm in args.tensor_maps_out}\n", - " opt = Adam(lr=kwargs['learning_rate'], beta_1=0.9, beta_2=0.999, epsilon=1e-08)\n", - " m.compile(optimizer=opt, loss=losses, metrics=my_metrics)\n", + " m.compile(optimizer=opt, loss=losses)\n", " m.summary()\n", " \n", - " if real_serial_layers is not None:\n", + " if kwargs['model_layers'] is not None:\n", " m.load_weights(kwargs['model_layers'], by_name=True)\n", " print(f\"Loaded model weights from:{kwargs['model_layers']}\")\n", " \n", - " return m, encoders, decoders" + " return m" ] }, { @@ -418,7 +407,7 @@ "outputs": [], "source": [ "sys.argv = ['train', \n", - " '--tensors', '/mnt/disks/sax-lax-40k-lvm/2020-01-29/', \n", + " '--tensors', '/mnt/disks/segmented-sax-lax/2020-07-07/', \n", " '--input_tensors', 'lax_2ch_diastole_slice0_3d', 'lax_3ch_diastole_slice0_3d', \n", " '--output_tensors', 'lax_2ch_diastole_slice0_3d', 'lax_3ch_diastole_slice0_3d',\n", " '--activation', 'swish',\n", @@ -428,13 +417,13 @@ " '--conv_z', '3', '3', '3', \n", " '--dense_blocks', '32', '32', '32',\n", " '--block_size', '3',\n", - " '--dense_layers', '64',\n", + " '--dense_layers', '512',\n", " '--pool_x', '2',\n", " '--pool_y', '2',\n", " '--batch_size', '1',\n", " '--patience', '32',\n", - " '--epochs', '292',\n", - " '--learning_rate', '0.0001',\n", + " '--epochs', '248',\n", + " '--learning_rate', '0.001',\n", " '--training_steps', '256',\n", " '--validation_steps', '30',\n", " '--test_steps', '2',\n", @@ -444,17 +433,12 @@ " '--id', 'lax_2ch_3ch_diastole_pair_cosine_loss']\n", "args = parse_args()\n", "pairs = [(args.tensor_maps_in[0], args.tensor_maps_in[1])]\n", - "overparameterized_model, encoders, decoders = make_paired_autoencoder_model(pairs, pair_loss='cosine', **args.__dict__)\n", + "overparameterized_model = make_paired_autoencoder_model(pairs, pair_loss='cosine', **args.__dict__)\n", "generate_train, generate_valid, generate_test = test_train_valid_tensor_generators(**args.__dict__)\n", "train_model_from_generators(\n", " overparameterized_model, generate_train, generate_valid, args.training_steps, args.validation_steps, args.batch_size,\n", " args.epochs, args.patience, args.output_folder, args.id, args.inspect_model, args.inspect_show_labels,\n", - " plot=False, save_last_model=True\n", - ")\n", - "for tm in encoders:\n", - " encoders[tm].save(f'{args.output_folder}{args.id}/encoder_{tm.name}.h5')\n", - "for tm in decoders:\n", - " decoders[tm].save(f'{args.output_folder}{args.id}/decoder_{tm.name}.h5')" + ")" ] }, { @@ -518,55 +502,6 @@ "latent_df.info()" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "out_path = os.path.join(args.output_folder, args.id + '/')\n", - "test_data, test_labels, test_paths = big_batch_from_minibatch_generator(generate_test, args.test_steps*5)\n", - "print(list(test_data.keys()))\n", - "\n", - "preds = overparameterized_model.predict(test_data)\n", - "print([p.shape for p in preds])\n", - "print([tm.name for tm in args.tensor_maps_out])\n", - "print(test_paths)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from ml4h.plots import _plot_reconstruction\n", - "_plot_reconstruction(args.tensor_maps_out[0], test_data['input_lax_2ch_diastole_slice0_3d_continuous'], preds[0], out_path, test_paths, num_samples=2)\n", - "from ml4h.explorations import predictions_to_pngs\n", - "predictions_to_pngs(preds, args.tensor_maps_in, args.tensor_maps_out, \n", - " test_data, test_labels, test_paths, out_path)\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "for i, etm in enumerate(encoders):\n", - " embed = encoders[etm].predict(test_data[etm.input_name()])\n", - " double = np.tile(embed, 2)\n", - " print(f'embed shape: {embed.shape} double shape: {double.shape}')\n", - " for dtm in decoders:\n", - " predictions = decoders[dtm].predict(double)\n", - " print(f'prediction shape: {predictions.shape}')\n", - " out_path = os.path.join(args.output_folder, args.id, f'decoding_{dtm.name}_from_{etm.name}/')\n", - " if not os.path.exists(os.path.dirname(out_path)):\n", - " os.makedirs(os.path.dirname(out_path))\n", - " _plot_reconstruction(dtm, test_data[dtm.input_name()], predictions.copy(), out_path, test_paths, 8)" - ] - }, { "cell_type": "code", "execution_count": null, @@ -776,18 +711,6 @@ "display_name": "Python 3", "language": "python", "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.9" } }, "nbformat": 4, diff --git a/notebooks/autoencoders/paired_multimodal_segmenter_mri_ecg.ipynb b/notebooks/autoencoders/paired_multimodal_segmenter_mri_ecg.ipynb index 3ea3e32a2..442e27a18 100644 --- a/notebooks/autoencoders/paired_multimodal_segmenter_mri_ecg.ipynb +++ b/notebooks/autoencoders/paired_multimodal_segmenter_mri_ecg.ipynb @@ -38,7 +38,6 @@ "# ml4h Imports\n", "from ml4h.TensorMap import TensorMap\n", "from ml4h.arguments import parse_args\n", - "from ml4h.plots import _plot_reconstruction\n", "from ml4h.models import make_multimodal_multitask_model, train_model_from_generators, make_hidden_layer_model, _conv_layer_from_kind_and_dimension\n", "from ml4h.tensor_generators import TensorGenerator, big_batch_from_minibatch_generator, test_train_valid_tensor_generators\n", "from ml4h.recipes import plot_predictions, infer_hidden_layer_multimodal_multitask\n", @@ -272,6 +271,13 @@ " return norm\n", "\n", "def pairwise_cosine_difference(t1, t2):\n", + " \"\"\"\n", + " A [batch x n x d] tensor of n rows with d dimensions\n", + " B [batch x m x d] tensor of n rows with d dimensions\n", + "\n", + " returns:\n", + " D [batch x n x m] tensor of cosine similarity scores between each point i Model:\n", " inputs = {tm: Input(shape=tm.shape, name=tm.input_name()) for tm in args.tensor_maps_in}\n", " original_outputs = {tm:1 for tm in args.tensor_maps_out}\n", - " real_serial_layers = kwargs['model_layers']\n", - " args.model_layers = None\n", " multimodal_activations = []\n", - " encoders = {}\n", - " decoders = {}\n", " outputs = []\n", " losses = []\n", " for left, right in pairs:\n", @@ -346,16 +348,9 @@ " \n", " paired_embeddings = loss_layer([h_left, h_right])\n", " multimodal_activations.extend(paired_embeddings)\n", - " if left not in encoders:\n", - " encoders[left] = encode_left\n", - " if right not in encoders:\n", - " encoders[right] = encode_right \n", " \n", " multimodal_activation = Concatenate()(multimodal_activations)\n", - " encoder = Model(inputs=list(inputs.values()), outputs=[multimodal_activation], name='encoder')\n", " \n", - " # build decoder models\n", - " latent_inputs = Input(shape=(args.dense_layers[0]*len(inputs)), name='input_concept_space')\n", " pre_decoder_shapes: Dict[TensorMap, Optional[Tuple[int, ...]]] = {}\n", " for tm in args.tensor_maps_out:\n", " shape = _calc_start_shape(num_upsamples=len(args.dense_blocks), output_shape=tm.shape, \n", @@ -382,10 +377,7 @@ " upsample_z=args.pool_z,\n", " )\n", " \n", - " reconstruction = decode(restructure(latent_inputs))\n", - " decoder = Model(latent_inputs, reconstruction, name=tm.output_name())\n", - " decoders[tm] = decoder\n", - " outputs.append(decoder(multimodal_activation))\n", + " outputs.append(decode(restructure(multimodal_activation)))\n", " losses.append(tm.loss)\n", "\n", " args.tensor_maps_out = list(original_outputs.keys())\n", @@ -397,11 +389,11 @@ " m.compile(optimizer=opt, loss=losses, metrics=my_metrics)\n", " m.summary()\n", " \n", - " if real_serial_layers is not None:\n", + " if kwargs['model_layers'] is not None:\n", " m.load_weights(kwargs['model_layers'], by_name=True)\n", " print(f\"Loaded model weights from:{kwargs['model_layers']}\")\n", " \n", - " return m, encoders, decoders" + " return m" ] }, { @@ -414,90 +406,35 @@ " '--tensors', '/mnt/disks/sax-lax-40k-lvm/2020-01-29/', \n", " '--input_tensors', 'ecg.ecg_rest', 'mri.cine_segmented_lax_4ch_diastole', \n", " '--output_tensors', 'ecg.ecg_rest', 'mri.cine_segmented_lax_4ch_diastole',\n", - " '--activation', 'selu',\n", + " '--activation', 'swish',\n", " '--conv_layers', '32',\n", - " '--conv_x', '15', '15', '15',\n", + " '--conv_x', '9', '9', '9',\n", " '--conv_y', '3', '3', '3', \n", " '--conv_z', '3', '3', '3',\n", " '--dense_blocks', '32', '32', '32',\n", " '--block_size', '3',\n", - " '--dense_layers', '64',\n", + " '--dense_layers', '512',\n", " '--pool_x', '2',\n", " '--pool_y', '2',\n", " '--batch_size', '1',\n", - " '--patience', '94',\n", - " '--epochs', '396',\n", - " '--learning_rate', '0.00005',\n", + " '--patience', '44',\n", + " '--epochs', '496',\n", + " '--learning_rate', '0.0001',\n", " '--training_steps', '128',\n", " '--validation_steps', '30',\n", " '--test_steps', '8',\n", " '--num_workers', '4',\n", " '--inspect_model',\n", " '--tensormap_prefix', 'ml4h.tensormap.ukb',\n", - " '--model_layers', './recipes_output/paired_ecg_segmented_mri_lax_4ch_diastole_cosine_64d_selu/paired_ecg_segmented_mri_lax_4ch_diastole_cosine_64d_selu.h5',\n", - " '--id', 'paired_ecg_segmented_mri_lax_4ch_diastole_cosine_64d_selu']\n", + " '--id', 'ecg_mri_lax_4ch_diastole_euclid_paired_segmenter_512d']\n", "args = parse_args()\n", "pairs = [(args.tensor_maps_in[0], args.tensor_maps_in[1])]\n", - "overparameterized_model, encoders, decoders = make_paired_autoencoder_model(pairs, pair_loss='cosine', **args.__dict__)\n", + "overparameterized_model = make_paired_autoencoder_model(pairs, pair_loss='euclid', **args.__dict__)\n", "generate_train, generate_valid, generate_test = test_train_valid_tensor_generators(**args.__dict__)\n", "train_model_from_generators(\n", " overparameterized_model, generate_train, generate_valid, args.training_steps, args.validation_steps, args.batch_size,\n", " args.epochs, args.patience, args.output_folder, args.id, args.inspect_model, args.inspect_show_labels,\n", - " plot=False, save_last_model=True\n", - ")\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "for tm in encoders:\n", - " encoders[tm].save(f'{args.output_folder}{args.id}/encoder_{tm.name}.h5')\n", - "for tm in decoders:\n", - " decoders[tm].save(f'{args.output_folder}{args.id}/decoder_{tm.name}.h5')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "sys.argv = ['train', \n", - " '--tensors', '/mnt/disks/sax-lax-40k-lvm/2020-01-29/', \n", - " '--input_tensors', 'ecg.ecg_rest', 'mri.cine_segmented_lax_4ch_diastole', \n", - " '--output_tensors', 'ecg.ecg_rest', 'mri.cine_segmented_lax_4ch_diastole',\n", - " '--activation', 'swish',\n", - " '--conv_layers', '32',\n", - " '--conv_x', '9', '9', '9',\n", - " '--conv_y', '3', '3', '3', \n", - " '--conv_z', '3', '3', '3', \n", - " '--dense_blocks', '32', '32', '32',\n", - " '--block_size', '3',\n", - " '--dense_layers', '256',\n", - " '--pool_x', '2',\n", - " '--pool_y', '2',\n", - " '--batch_size', '1',\n", - " '--patience', '44',\n", - " '--epochs', '532',\n", - " '--learning_rate', '0.0002',\n", - " '--training_steps', '72',\n", - " '--validation_steps', '30',\n", - " '--test_steps', '8',\n", - " '--num_workers', '4',\n", - " '--inspect_model',\n", - " '--tensormap_prefix', 'ml4h.tensormap.ukb',\n", - " '--hidden_layer', 'concatenate_36',\n", - " '--model_file', './recipes_output/paired_ecg_segmented_mri_lax_4ch_diastole_cosine_64d_selu/paired_ecg_segmented_mri_lax_4ch_diastole_cosine_64d_selu.h5',\n", - " #'--sample_csv', '/home/sam/lvh/lvh_hold_out.txt',\n", - " '--id', 'paired_ecg_segmented_mri_lax_4ch_diastole_cosine_64d_selu']\n", - "args = parse_args()\n", - "#overparameterized_model = make_multimodal_multitask_model(**args.__dict__)\n", - "generate_train, generate_valid, generate_test = test_train_valid_tensor_generators(**args.__dict__)\n", - "#plot_predictions(args)\n", - "#infer_hidden_layer_multimodal_multitask(args)" + ")" ] }, { @@ -543,17 +480,7 @@ "metadata": {}, "outputs": [], "source": [ - "for i, etm in enumerate(encoders):\n", - " embed = encoders[etm].predict(test_data[etm.input_name()])\n", - " double = np.tile(embed, 2)\n", - " print(f'embed shape: {embed.shape} double shape: {double.shape}')\n", - " for dtm in decoders:\n", - " predictions = decoders[dtm].predict(double)\n", - " print(f'prediction shape: {predictions.shape}')\n", - " out_path = os.path.join(args.output_folder, args.id, f'decoding_{dtm.name}_from_{etm.name}/')\n", - " if not os.path.exists(os.path.dirname(out_path)):\n", - " os.makedirs(os.path.dirname(out_path))\n", - " _plot_reconstruction(dtm, test_data[dtm.input_name()], predictions.copy(), out_path, test_paths, 8)" + "print(list(test_data['input_strip_continuous'].shape))" ] }, { @@ -588,7 +515,7 @@ " if i % sample_every == 0 and col < samples:\n", " for j in range(rows):\n", " if len(test_data[test_key].shape) == 4:\n", - " axes[j, col].imshow(test_data[test_key][j, :, :, 0], cmap = 'plasma')\n", + " axes[j, col].imshow(test_data[test_key][j, :, :, 0], cmap = 'gray')\n", " axes[j, col].set_yticks(())\n", " elif len(test_data[test_key].shape) == 3:\n", " for l in range(12):\n", @@ -614,14 +541,14 @@ "\n", "\n", "test_data, test_labels, test_paths = big_batch_from_minibatch_generator(generate_test, args.test_steps)\n", - "test_key = 'input_cine_segmented_lax_4ch_diastole_categorical'\n", + "test_key = 'input_lax_4ch_diastole_slice0_224_3d_continuous'\n", "test_shape = test_data[test_key].shape\n", "test_data[test_key] = np.random.random(test_shape)\n", "out_path = os.path.join(args.output_folder, args.id, test_key + '_noise/')\n", "if not os.path.exists(os.path.dirname(out_path)):\n", " os.makedirs(os.path.dirname(out_path))\n", "noise_preds = plot_ae_towards_attractor(overparameterized_model, test_data, test_labels, test_key, \n", - " test_index=1, rows=8, samples=4, steps = 28)\n", + " test_index=1, rows=8, samples=4, steps = 18)\n", "print(list(test_data.keys()))\n", "_plot_reconstruction(args.tensor_maps_out[0], test_data['input_strip_continuous'], \n", " noise_preds[0], out_path, test_paths)\n", @@ -665,10 +592,7 @@ "df = pd.read_csv('/home/sam/ml/trained_models/lax_4ch_diastole_autoencode_leaky_converge/tensors_all_union.csv')\n", "df['21003_Age-when-attended-assessment-centre_2_0'].plot.hist(bins=30)\n", "hidden_inference = './recipes_output/ecg_mri_lax_4ch_diastole_paired_autoencoder_2blocks_256d_200_samples/hidden_inference_ecg_mri_lax_4ch_diastole_paired_autoencoder_2blocks_256d_200_samples.tsv'\n", - "hidden_inference = './recipes_output/paired_ecg_segmented_mri_lax_4ch_diastole_euclid_256d_swish/hidden_inference_paired_ecg_segmented_mri_lax_4ch_diastole_euclid_256d_swish.tsv'\n", - "hidden_inference = './recipes_output/paired_ecg_segmented_mri_lax_4ch_diastole_cosine_256d_selu/hidden_inference_paired_ecg_segmented_mri_lax_4ch_diastole_cosine_256d_selu.tsv'\n", "\n", - "hidden_inference = './recipes_output/paired_ecg_segmented_mri_lax_4ch_diastole_cosine_64d_selu/hidden_inference_paired_ecg_segmented_mri_lax_4ch_diastole_cosine_64d_selu.tsv'\n", "\n", "df2 = pd.read_csv(hidden_inference, sep='\\t')\n", "df['fpath'] = pd.to_numeric(df['fpath'], errors='coerce')\n", @@ -790,7 +714,7 @@ "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", - "latent_dimension = 128\n", + "latent_dimension = 256\n", "latent_cols = [f'latent_{i}' for i in range(latent_dimension)]\n", "pca, matrix_reduce = pca_on_matrix(df2[latent_cols].to_numpy(), 10)\n", "for strat in ['Sex_Female_0_0', 'has_ttntv', 'atrial_fibrillation_or_flutter', \n", @@ -818,7 +742,7 @@ " stratify_latent_space(strat, 1.0, latent_cols, latent_df)\n", "strats = ['LVEF', 'LVM', 'LVEDV', 'sample_id',\n", " '21001_Body-mass-index-BMI_0_0', '21003_Age-when-attended-assessment-centre_2_0']\n", - "theshes = [45, 100, 150, 3500000, 27.5, 65]\n", + "theshes = [45, 100, 150, 3500000, 27.5, 70]\n", "for strat, thresh in zip(strats, theshes):\n", " stratify_latent_space(strat, thresh, latent_cols, latent_df)" ] @@ -832,12 +756,12 @@ "latent_dimension = 512\n", "latent_cols = [f'latent_{i}' for i in range(latent_dimension)]\n", "pca, matrix_reduce = pca_on_matrix(df2[latent_cols].to_numpy(), 10)\n", - "for strat in ['Sex_Female_0_0', 'has_ttntv', 'atrial_fibrillation_or_flutter', \n", + "for strat in ['Sex_Female_0_0', 'atrial_fibrillation_or_flutter', \n", " 'coronary_artery_disease', 'hypertension']:\n", " stratify_latent_space(strat, 1.0, latent_cols, latent_df)\n", "strats = ['LVEF', 'LVM', 'LVEDV', 'sample_id',\n", " '21001_Body-mass-index-BMI_0_0', '21003_Age-when-attended-assessment-centre_2_0']\n", - "theshes = [45, 100, 150, 3500000, 27.5, 65]\n", + "theshes = [45, 100, 150, 3500000, 27.5, 70]\n", "for strat, thresh in zip(strats, theshes):\n", " stratify_latent_space(strat, thresh, latent_cols, latent_df)" ] @@ -851,11 +775,11 @@ "latent_dimension = 256\n", "latent_cols = [f'latent_{256+i}' for i in range(latent_dimension)]\n", "pca, matrix_reduce = pca_on_matrix(df2[latent_cols].to_numpy(), 10)\n", - "c_strats = [ 'Sex_Female_0_0', 'has_ttntv']\n", + "c_strats = [ 'Sex_Female_0_0']\n", "for c_strat in c_strats:\n", " strats = ['LVEF', 'LVM', 'LVEDV', 'sample_id',\n", " '21001_Body-mass-index-BMI_0_0', '21003_Age-when-attended-assessment-centre_2_0']\n", - " theshes = [50, 100, 150, 3750000, 27.5, 65]\n", + " theshes = [50, 100, 150, 3750000, 27.5, 70]\n", " for strat, thresh in zip(strats, theshes):\n", " directions_in_latent_space(c_strat, 1.0, strat, thresh, latent_cols, latent_df)" ] @@ -900,8 +824,7 @@ "metadata": {}, "outputs": [], "source": [ - "print(f\"{np.mean(np.sqrt(np.einsum('ij, ij->ij', ecg_encode, ecg_encode)))}\")\n", - "print(f\"{np.mean(np.sqrt(np.einsum('ij, ij->ij', mri_encode, mri_encode)))}\")" + "print(f'{ecg_encode[:5,:5]} \\n{mri_encode[:5,:5]}')" ] }, { @@ -913,7 +836,7 @@ "latent_dimension = 256\n", "latent_cols = [f'latent_{i}' for i in range(latent_dimension)]\n", "ecg_encode = latent_df[latent_cols].to_numpy()\n", - "latent_cols = [f'latent_{250+i}' for i in range(latent_dimension)]\n", + "latent_cols = [f'latent_{18+i}' for i in range(latent_dimension)]\n", "mri_encode = latent_df[latent_cols].to_numpy()\n", "diff = np.sqrt(np.einsum('ij, ij->ij', ecg_encode - mri_encode, ecg_encode - mri_encode))\n", "print(diff.shape) \n", @@ -926,8 +849,8 @@ "metadata": {}, "outputs": [], "source": [ - "ch2_random = np.random.random((8520, 256))\n", - "ch3_random = np.random.random((8520, 256))\n", + "ch2_random = np.random.random((4452, 256))\n", + "ch3_random = np.random.random((4452, 256))\n", "diff = np.sqrt(np.einsum('ij, ij->ij', ch2_random - ch3_random, ch2_random - ch3_random))\n", "print(diff.shape) \n", "print(np.mean(diff))" @@ -939,35 +862,44 @@ "metadata": {}, "outputs": [], "source": [ - "latent_dimension = 128\n", - "latent_cols = [f'latent_{i}' for i in range(latent_dimension)]\n", - "all_encode = latent_df[latent_cols].to_numpy()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from ml4h.plots import _plot_reconstruction\n", - "for tm in decoders:\n", - " predictions = decoders[tm].predict(all_encode[:4])\n", - " print(predictions.shape)\n", - " out_path = os.path.join(args.output_folder, args.id, 'decodings/')\n", - " if not os.path.exists(os.path.dirname(out_path)):\n", - " os.makedirs(os.path.dirname(out_path))\n", - " samples = list(map(str, list(latent_df['sample_id'])[:10]))\n", - " _plot_reconstruction(tm, predictions, predictions, out_path, samples)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print()" + "sys.argv = ['train', \n", + " '--tensors', '/mnt/disks/sax-lax-40k-lvm/2020-01-29/', \n", + " '--input_tensors', 'ecg.ecg_rest', 'mri.lax_4ch_diastole_slice0_224_3d', \n", + " '--output_tensors', 'ecg.ecg_rest', 'mri.lax_4ch_diastole_slice0_224_3d',\n", + " '--activation', 'swish',\n", + " '--conv_layers', '32',\n", + " '--conv_x', '9', '9', '9',\n", + " '--conv_y', '3', '3', '3', \n", + " '--conv_z', '3', '3', '3', \n", + " '--dense_blocks', '32', '32', '32',\n", + " '--block_size', '3',\n", + " '--dense_layers', '256',\n", + " '--pool_x', '2',\n", + " '--pool_y', '2',\n", + " '--batch_size', '1',\n", + " '--patience', '44',\n", + " '--epochs', '532',\n", + " '--learning_rate', '0.0002',\n", + " '--training_steps', '72',\n", + " '--validation_steps', '30',\n", + " '--test_steps', '8',\n", + " '--num_workers', '4',\n", + " '--inspect_model',\n", + " '--tensormap_prefix', 'ml4h.tensormap.ukb',\n", + " '--hidden_layer', 'concatenate_36',\n", + " '--model_file', './recipes_output/ecg_mri_lax_4ch_diastole_paired_autoencoder_2blocks_256d_200_samples/ecg_mri_lax_4ch_diastole_paired_autoencoder_2blocks_256d_200_samples.h5',\n", + " '--train_csv', '/home/sam/lvh/small_set.csv',\n", + " #'--sample_csv', '/home/sam/lvh/lvh_hold_out.txt',\n", + " '--id', 'ecg_mri_lax_4ch_diastole_paired_autoencoder_2blocks_256d_200_samples']\n", + "args = parse_args()\n", + "\n", + "#overparameterized_model = make_multimodal_multitask_model(**args.__dict__)\n", + "#infer_hidden_layer_multimodal_multitask(args)\n", + "#generate_train, generate_valid, generate_test = test_train_valid_tensor_generators(**args.__dict__)\n", + "# train_model_from_generators(\n", + "# overparameterized_model, generate_train, generate_valid, args.training_steps, args.validation_steps, args.batch_size,\n", + "# args.epochs, args.patience, args.output_folder, args.id, args.inspect_model, args.inspect_show_labels,\n", + "# )" ] }, { diff --git a/notebooks/autoencoders/vae_mri_slice.ipynb b/notebooks/autoencoders/vae_mri_slice.ipynb index 0aa84e195..21785359e 100644 --- a/notebooks/autoencoders/vae_mri_slice.ipynb +++ b/notebooks/autoencoders/vae_mri_slice.ipynb @@ -286,7 +286,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.9" + "version": "3.6.8" } }, "nbformat": 4, diff --git a/notebooks/mnist_demo.ipynb b/notebooks/mnist_demo.ipynb index b4fc5bcbf..88393f90f 100644 --- a/notebooks/mnist_demo.ipynb +++ b/notebooks/mnist_demo.ipynb @@ -9,7 +9,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -34,7 +34,7 @@ "from tensorflow.keras.layers import Dense, Conv2D, Flatten\n", "\n", "from ml4h.defines import StorageType\n", - "from ml4h.arguments import parse_args\n", + "from ml4h.arguments import parse_args, TMAPS, _get_tmap\n", "from ml4h.TensorMap import TensorMap, Interpretation\n", "from ml4h.tensor_generators import test_train_valid_tensor_generators\n", "from ml4h.models import train_model_from_generators, make_multimodal_multitask_model, _inspect_model\n", @@ -47,7 +47,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -58,7 +58,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -95,7 +95,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -113,28 +113,9 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loading data...\n", - "(50000, 784)\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plot_mnist(4)" ] @@ -158,7 +139,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -177,27 +158,9 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loading data...\n", - "Wrote 5000 MNIST images and labels as HD5 files\n", - "Wrote 10000 MNIST images and labels as HD5 files\n", - "Wrote 15000 MNIST images and labels as HD5 files\n", - "Wrote 20000 MNIST images and labels as HD5 files\n", - "Wrote 25000 MNIST images and labels as HD5 files\n", - "Wrote 30000 MNIST images and labels as HD5 files\n", - "Wrote 35000 MNIST images and labels as HD5 files\n", - "Wrote 40000 MNIST images and labels as HD5 files\n", - "Wrote 45000 MNIST images and labels as HD5 files\n", - "Wrote 50000 MNIST images and labels as HD5 files\n" - ] - } - ], + "outputs": [], "source": [ "mnist_as_hd5(HD5_FOLDER)" ] @@ -247,41 +210,12 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2020-09-18 16:17:13,404 - logger:25 - INFO - Logging configuration was loaded. Log messages can be found at ./runs/learn_mnist/log_2020-09-18_16-17_0.log.\n", - "2020-09-18 16:17:13,410 - arguments:444 - INFO - Command Line was: \n", - "./scripts/tf.sh train --tensors ./mnist_hd5s/ --tensormap_prefix ml4h.tensormap.mnist --input_tensors mnist_image --output_tensors mnist_label --batch_size 64 --test_steps 64 --epochs 24 --output_folder ./runs/ --id learn_mnist\n", - "\n", - "2020-09-18 16:17:13,410 - arguments:445 - INFO - Arguments are Namespace(activation='relu', aligned_dimension=16, alpha=0.5, anneal_max=2.0, anneal_rate=0.0, anneal_shift=0.0, app_csv=None, b_slice_force=None, balance_csvs=[], batch_size=64, bigquery_credentials_file='/mnt/ml4cvd/projects/jamesp/bigquery/bigquery-viewer-credentials.json', bigquery_dataset='broad-ml4cvd.ukbb7089_r10data', block_size=3, bottleneck_type=, cache_size=437500000.0, categorical_field_ids=[], continuous_field_ids=[], continuous_file=None, continuous_file_column=None, continuous_file_discretization_bounds=[], continuous_file_normalize=False, conv_dilate=False, conv_layers=[32], conv_normalize=None, conv_regularize=None, conv_regularize_rate=0.0, conv_type='conv', conv_x=[3], conv_y=[3], conv_z=[2], debug=False, dense_blocks=[32, 24, 16], dense_layers=[16, 64], dense_normalize=None, dense_regularize=None, dense_regularize_rate=0.0, dicom_series='cine_segmented_sax_b6', dicoms='./dicoms/', eager=False, embed_visualization=None, epochs=24, explore_export_errors=False, freeze_model_layers=False, hidden_layer='embed', id='learn_mnist', imputation_method_for_continuous_fields='random', include_array=False, include_instance=False, include_missing_continuous_channel=False, input_tensors=['mnist_image'], inspect_model=False, inspect_show_labels=True, join_tensors=['partners_ecg_patientid_clean'], label_weights=None, language_layer='ecg_rest_text', language_prefix='ukb_ecg_rest', learning_rate=0.0002, learning_rate_schedule=None, logging_level='INFO', match_any_window=False, max_models=16, max_parameters=9000000, max_patients=999999, max_pools=[], max_sample_id=7000000, max_samples=None, max_slices=999999, min_sample_id=0, min_samples=3, min_values=10, mixup_alpha=0, mlp_concat=False, mode='mlp', model_file=None, model_files=[], model_layers=None, mri_field_ids=['20208', '20209'], num_workers=8, number_per_window=1, optimizer='radam', order_in_window=None, output_folder='./runs/', output_tensors=['mnist_label'], padding='same', patience=8, phecode_definitions='/mnt/ml4cvd/projects/jamesp/data/phecode_definitions1.2.csv', phenos_folder='gs://ml4cvd/phenotypes/', plot_hist=True, plot_mode='clinical', pool_type='max', pool_x=2, pool_y=2, pool_z=1, protected_tensors=[], random_seed=12878, reference_end_time_tensor=None, reference_join_tensors=None, reference_labels=None, reference_name='Reference', reference_start_time_tensor=None, reference_tensors=None, sample_csv=None, sample_weight=None, save_last_model=False, t=48, tensor_maps_in=[TensorMap(mnist_image, (28, 28, 1), continuous)], tensor_maps_out=[TensorMap(mnist_label, (10,), categorical)], tensor_maps_protected=[], tensormap_prefix='ml4h.tensormap.mnist', tensors='./mnist_hd5s/', tensors_name='Tensors', tensors_source=None, test_csv=None, test_ratio=0.1, test_steps=64, text_file=None, text_one_hot=False, text_window=32, time_frequency='3M', time_tensor='partners_ecg_datetime', train_csv=None, training_steps=72, tsv_style='standard', u_connect=defaultdict(, {}), valid_csv=None, valid_ratio=0.2, validation_steps=18, window_name=None, write_pngs=False, x=256, xml_field_ids=['20205', '6025'], xml_folder='/mnt/disks/ecg-rest-xml/', y=256, z=48, zip_folder='/mnt/disks/sax-mri-zip/', zoom_height=96, zoom_width=96, zoom_x=50, zoom_y=35)\n", - "\n", - "2020-09-18 16:17:13,411 - tensor_generators:661 - INFO - Found 0 train, 0 validation, and 0 testing tensors at: ./mnist_hd5s/\n" - ] - }, - { - "ename": "ValueError", - "evalue": "Not enough tensors at ./mnist_hd5s/\nFound 0 training, 0 validation, and 0 testing tensors\nDiscarded 0 tensors", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 12\u001b[0m ]\n\u001b[1;32m 13\u001b[0m \u001b[0margs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mparse_args\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m \u001b[0mtrain_multimodal_multitask\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m/home/sam/ml/ml4h/recipes.py\u001b[0m in \u001b[0;36mtrain_multimodal_multitask\u001b[0;34m(args)\u001b[0m\n\u001b[1;32m 129\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 130\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mtrain_multimodal_multitask\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 131\u001b[0;31m \u001b[0mgenerate_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgenerate_valid\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgenerate_test\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtest_train_valid_tensor_generators\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__dict__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 132\u001b[0m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmake_multimodal_multitask_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__dict__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 133\u001b[0m model = train_model_from_generators(\n", - "\u001b[0;32m/home/sam/ml/ml4h/tensor_generators.py\u001b[0m in \u001b[0;36mtest_train_valid_tensor_generators\u001b[0;34m(tensor_maps_in, tensor_maps_out, tensor_maps_protected, tensors, batch_size, num_workers, training_steps, validation_steps, cache_size, balance_csvs, keep_paths, keep_paths_test, mixup_alpha, sample_csv, valid_ratio, test_ratio, train_csv, valid_csv, test_csv, siamese, sample_weight, **kwargs)\u001b[0m\n\u001b[1;32m 796\u001b[0m \u001b[0mtrain_csv\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtrain_csv\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 797\u001b[0m \u001b[0mvalid_csv\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvalid_csv\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 798\u001b[0;31m \u001b[0mtest_csv\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtest_csv\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 799\u001b[0m )\n\u001b[1;32m 800\u001b[0m \u001b[0mweights\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/home/sam/ml/ml4h/tensor_generators.py\u001b[0m in \u001b[0;36mget_train_valid_test_paths\u001b[0;34m(tensors, sample_csv, valid_ratio, test_ratio, train_csv, valid_csv, test_csv)\u001b[0m\n\u001b[1;32m 663\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_paths\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalid_paths\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest_paths\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 664\u001b[0m raise ValueError(\n\u001b[0;32m--> 665\u001b[0;31m \u001b[0;34mf'Not enough tensors at {tensors}\\n'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 666\u001b[0m \u001b[0;34mf'Found {len(train_paths)} training, {len(valid_paths)} validation, and {len(test_paths)} testing tensors\\n'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 667\u001b[0m \u001b[0;34mf'Discarded {len(discard_paths)} tensors'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mValueError\u001b[0m: Not enough tensors at ./mnist_hd5s/\nFound 0 training, 0 validation, and 0 testing tensors\nDiscarded 0 tensors" - ] - } - ], + "outputs": [], "source": [ "sys.argv = ['train', \n", " '--tensors', HD5_FOLDER, \n", - " '--tensormap_prefix', 'ml4h.tensormap.mnist',\n", " '--input_tensors', 'mnist_image',\n", " '--output_tensors', 'mnist_label',\n", " '--batch_size', '64',\n", diff --git a/notebooks/mri/mri_cardiac_long_axis_sketch.ipynb b/notebooks/mri/mri_cardiac_long_axis_sketch.ipynb index 26c08c73a..f9f99b4ad 100644 --- a/notebooks/mri/mri_cardiac_long_axis_sketch.ipynb +++ b/notebooks/mri/mri_cardiac_long_axis_sketch.ipynb @@ -67,8 +67,8 @@ "outputs": [], "source": [ "def plot_lax(series, transpose=False, size=18):\n", - " cols = 2\n", - " rows = 25\n", + " cols = 5\n", + " rows = 10\n", " _, axes = plt.subplots(rows, cols, figsize=(size, size))\n", " for dcm in series:\n", " col = (dcm.InstanceNumber-1)%cols\n", @@ -76,7 +76,7 @@ " if transpose:\n", " axes[row, col].imshow(dcm.pixel_array.T)\n", " else:\n", - " axes[row, col].imshow(dcm.pixel_array, cmap='gray')\n", + " axes[row, col].imshow(dcm.pixel_array)\n", " axes[row, col].set_yticklabels([])\n", " axes[row, col].set_xticklabels([])" ] @@ -132,7 +132,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.9" + "version": "3.6.8" } }, "nbformat": 4, diff --git a/notebooks/mri/mri_cardiac_short_axis_sketch.ipynb b/notebooks/mri/mri_cardiac_short_axis_sketch.ipynb index 64493df15..20b93d12f 100644 --- a/notebooks/mri/mri_cardiac_short_axis_sketch.ipynb +++ b/notebooks/mri/mri_cardiac_short_axis_sketch.ipynb @@ -32,9 +32,8 @@ "source": [ "!mkdir ./dcm_scratch\n", "!rm ./dcm_scratch/*\n", - "\n", - "!cp /mnt/ml4cvd/projects/bulk/cardiac_mri/2467677_20209_2_0.zip ./dcm_scratch/\n", - "!unzip ./dcm_scratch/2467677_20209_2_0.zip -d ./dcm_scratch/" + "!cp /mnt/ml4cvd/projects/bulk/cardiac_mri/1000387_20209_2_0.zip ./dcm_scratch/\n", + "!unzip ./dcm_scratch/1000387_20209_2_0.zip -d ./dcm_scratch/" ] }, { @@ -155,12 +154,11 @@ " if idx >= sides*sides:\n", " continue\n", " if _is_mitral_valve_segmentation(dcm):\n", - " axes[idx%sides, idx//sides].imshow(dcm.pixel_array, cmap='gray')\n", + " axes[idx%sides, idx//sides].imshow(dcm.pixel_array)\n", " else:\n", " try:\n", " overlay, anatomical_mask, ventricle_pixels = _get_overlay_from_dicom(dcm)\n", - " #axes[idx%sides, idx//sides].imshow(np.ma.masked_where(anatomical_mask == 2, dcm.pixel_array), cmap='gray')\n", - " axes[idx%sides, idx//sides].imshow(dcm.pixel_array, cmap='gray')\n", + " axes[idx%sides, idx//sides].imshow(np.ma.masked_where(anatomical_mask == 2, dcm.pixel_array))\n", " except KeyError:\n", " print(f'Could not get overlay at {dcm.InstanceNumber}, angle {s}')\n", " axes[idx, idx//sides].imshow(dcm.pixel_array)\n", @@ -174,7 +172,7 @@ "metadata": {}, "outputs": [], "source": [ - "plot_b_series(series[4], sides=2)" + "plot_b_series(series[2], sides=7)" ] }, { @@ -263,7 +261,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.9" + "version": "3.6.8" } }, "nbformat": 4, diff --git a/notebooks/review_results/identify_a_sample_to_review.ipynb b/notebooks/review_results/identify_a_sample_to_review.ipynb index a5c659d24..f26d76309 100644 --- a/notebooks/review_results/identify_a_sample_to_review.ipynb +++ b/notebooks/review_results/identify_a_sample_to_review.ipynb @@ -16,7 +16,7 @@ "
\n", " This notebook assumes:\n", "
    \n", - "
  • Terra is running custom Docker image gcr.io/uk-biobank-sek-data/ml4h_terra:20200729_091732.
    • \n", + "
  • Terra is running custom Docker image gcr.io/uk-biobank-sek-data/ml4h_terra:20200918_091608.
    • \n", "
  • ml4h is running custom Docker image gcr.io/broad-ml4cvd/deeplearning:tf2-latest-gpu.
    • \n", "
\n", "
" @@ -32,7 +32,7 @@ "from ml4h.runtime_data_defines import determine_runtime\n", "from ml4h.runtime_data_defines import Runtime\n", "\n", - "if Runtime.ml4h_VM == determine_runtime():\n", + "if Runtime.ML4H_VM == determine_runtime():\n", " !pip3 install --user --upgrade pandas_gbq pyarrow\n", " # Be sure to restart the kernel if pip installs anything." ] @@ -259,7 +259,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.7" + "version": "3.7.8" }, "toc": { "base_numbering": 1, diff --git a/notebooks/review_results/review_one_sample.ipynb b/notebooks/review_results/review_one_sample.ipynb index 9fb1fc2b2..9569380aa 100644 --- a/notebooks/review_results/review_one_sample.ipynb +++ b/notebooks/review_results/review_one_sample.ipynb @@ -16,7 +16,7 @@ "
\n", " This notebook assumes\n", "
    \n", - "
  • Terra is running custom Docker image gcr.io/uk-biobank-sek-data/ml4h_terra:20200729_091732.
    • \n", + "
  • Terra is running custom Docker image gcr.io/uk-biobank-sek-data/ml4h_terra:20200918_091608.
    • \n", "
  • ml4h is running custom Docker image gcr.io/broad-ml4cvd/deeplearning:tf2-latest-gpu.
    • \n", "
\n", "
" @@ -41,7 +41,7 @@ "from ml4h.runtime_data_defines import determine_runtime\n", "from ml4h.runtime_data_defines import Runtime\n", "\n", - "if Runtime.ml4h_VM == determine_runtime():\n", + "if Runtime.ML4H_VM == determine_runtime():\n", " !pip3 install --user --upgrade pandas_gbq pyarrow\n", " # Be sure to restart the kernel if pip installs anything." ] @@ -523,7 +523,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## MRI dicom visualization" + "## MRI DICOM visualization" ] }, { @@ -641,7 +641,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.7" + "version": "3.7.8" }, "toc": { "base_numbering": 1, diff --git a/notebooks/review_results/test_error_handling_for_notebook_visualizations.ipynb b/notebooks/review_results/test_error_handling_for_notebook_visualizations.ipynb index 0c4a1c97a..97eee7d12 100644 --- a/notebooks/review_results/test_error_handling_for_notebook_visualizations.ipynb +++ b/notebooks/review_results/test_error_handling_for_notebook_visualizations.ipynb @@ -21,7 +21,7 @@ "metadata": {}, "source": [ "
\n", - " Terra Users test with the most recent custom Docker image which has all the software dependencies preinstalled. (e.g., more recent than gcr.io/uk-biobank-sek-data/ml4h_terra:20200729_091732)\n", + " Terra Users test with the most recent custom Docker image which has all the software dependencies preinstalled. (e.g., more recent than gcr.io/uk-biobank-sek-data/ml4h_terra:20200918_091608)\n", "
" ] }, @@ -41,7 +41,9 @@ "from ml4h.visualization_tools.annotation_storage import BigQueryAnnotationStorage\n", "\n", "import pandas as pd\n", - "import tensorflow as tf" + "import tensorflow as tf\n", + "\n", + "%matplotlib inline" ] }, { @@ -62,7 +64,7 @@ "outputs": [], "source": [ "#---[ EDIT THIS VARIABLE VALUE IF YOU LIKE ]---\n", - "MODEL_RESULTS_FILE = 'gs://uk-biobank-sek-data-us-east1/phenotypes/ml4h/ukbiobank_query_results_plus_four_fake_samples.csv'" + "MODEL_RESULTS_FILE = 'gs://uk-biobank-sek-data-us-east1/phenotypes/ml4cvd/ukbiobank_query_results_plus_four_fake_samples.csv'" ] }, { @@ -255,7 +257,7 @@ "metadata": {}, "outputs": [], "source": [ - "dicom_interactive_plots.choose_mri(sample_id=SAMPLE_TO_REVIEW)" + "#dicom_interactive_plots.choose_mri(sample_id=SAMPLE_TO_REVIEW)" ] }, { @@ -264,7 +266,7 @@ "metadata": {}, "outputs": [], "source": [ - "dicom_plots.choose_cardiac_mri(sample_id=SAMPLE_TO_REVIEW)" + "#dicom_plots.choose_cardiac_mri(sample_id=SAMPLE_TO_REVIEW)" ] }, { @@ -281,7 +283,7 @@ "outputs": [], "source": [ "SAMPLE_TO_REVIEW = 5993648\n", - "folder = 'gs://broad-ml4cvd-vcm/'" + "folder = 'gs://deflaux-test-001/'" ] }, { @@ -326,7 +328,7 @@ "metadata": {}, "outputs": [], "source": [ - "dicom_interactive_plots.choose_mri(sample_id=SAMPLE_TO_REVIEW, folder=folder)" + "#dicom_interactive_plots.choose_mri(sample_id=SAMPLE_TO_REVIEW, folder=folder)" ] }, { @@ -335,7 +337,7 @@ "metadata": {}, "outputs": [], "source": [ - "dicom_plots.choose_cardiac_mri(sample_id=SAMPLE_TO_REVIEW, folder=folder)" + "#dicom_plots.choose_cardiac_mri(sample_id=SAMPLE_TO_REVIEW, folder=folder)" ] }, { @@ -458,24 +460,6 @@ "hd5_mri_plots.choose_mri_tmap(sample_id=SAMPLE_TO_REVIEW)" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dicom_interactive_plots.choose_mri(sample_id=SAMPLE_TO_REVIEW)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dicom_plots.choose_cardiac_mri(sample_id=SAMPLE_TO_REVIEW)" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -631,7 +615,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.7" + "version": "3.7.8" }, "toc": { "base_numbering": 1, @@ -646,7 +630,7 @@ "height": "calc(100% - 180px)", "left": "10px", "top": "150px", - "width": "342.756px" + "width": "197.756px" }, "toc_section_display": true, "toc_window_display": true diff --git a/notebooks/terra_featured_workspace/identify_a_sample_to_review_interactive.ipynb b/notebooks/terra_featured_workspace/identify_a_sample_to_review_interactive.ipynb index e166223b1..e17eb08e2 100644 --- a/notebooks/terra_featured_workspace/identify_a_sample_to_review_interactive.ipynb +++ b/notebooks/terra_featured_workspace/identify_a_sample_to_review_interactive.ipynb @@ -20,7 +20,7 @@ "
\n", " This notebook assumes:\n", "
    \n", - "
  • Terra is running custom Docker image gcr.io/uk-biobank-sek-data/ml4h_terra:20200729_091732.
    • \n", + "
  • Terra is running custom Docker image gcr.io/uk-biobank-sek-data/ml4h_terra:20200918_091608.
    • \n", "
  • ml4h is running custom Docker image gcr.io/broad-ml4cvd/deeplearning:tf2-latest-gpu.
    • \n", "
\n", "
" @@ -79,7 +79,7 @@ "source": [ "#---[ EDIT THIS VARIABLE VALUE IF YOU LIKE ]---\n", "# TODO(paolo and team): provide CSV with phenotypes and ML results for fake samples.\n", - "MODEL_RESULTS_FILE = 'gs://uk-biobank-sek-data-us-east1/phenotypes/ml4h/ukbiobank_query_results_plus_four_fake_samples.csv'" + "MODEL_RESULTS_FILE = 'gs://uk-biobank-sek-data-us-east1/phenotypes/ml4cvd/ukbiobank_query_results_plus_four_fake_samples.csv'" ] }, { diff --git a/notebooks/terra_featured_workspace/review_one_sample_interactive.ipynb b/notebooks/terra_featured_workspace/review_one_sample_interactive.ipynb index 44379f740..8e863a7e4 100644 --- a/notebooks/terra_featured_workspace/review_one_sample_interactive.ipynb +++ b/notebooks/terra_featured_workspace/review_one_sample_interactive.ipynb @@ -18,7 +18,7 @@ "
\n", " This notebook assumes\n", "
    \n", - "
  • Terra is running custom Docker image gcr.io/uk-biobank-sek-data/ml4h_terra:20200729_091732.
    • \n", + "
  • Terra is running custom Docker image gcr.io/uk-biobank-sek-data/ml4h_terra:20200918_091608.
    • \n", "
  • ml4h is running custom Docker image gcr.io/broad-ml4cvd/deeplearning:tf2-latest-gpu.
    • \n", "
\n", "
" @@ -84,7 +84,7 @@ "source": [ "#---[ EDIT THIS VARIABLE VALUE IF YOU LIKE ]---\n", "# TODO(paolo and team): provide CSV with phenotypes and ML results for fake samples.\n", - "MODEL_RESULTS_FILE = 'gs://uk-biobank-sek-data-us-east1/phenotypes/ml4h/ukbiobank_query_results_plus_four_fake_samples.csv'" + "MODEL_RESULTS_FILE = 'gs://uk-biobank-sek-data-us-east1/phenotypes/ml4cvd/ukbiobank_query_results_plus_four_fake_samples.csv'" ] }, { From 509b32ff1e9e64f16cef4dfc5a6dbf30f511c76c Mon Sep 17 00:00:00 2001 From: Samwell Freeman Date: Wed, 30 Sep 2020 06:41:16 -0400 Subject: [PATCH 06/21] paired --- tests/conftest.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/tests/conftest.py b/tests/conftest.py index b553cf848..b288c57bb 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -7,8 +7,9 @@ from ml4h.test_utils import build_hdf5s -def pytest_configure(): +def pytest_configure(config): pytest.N_TENSORS = 50 + config.addinivalue_line("markers", "env(slow): mark tests as slow") #@mock.patch.dict(TMAPS, MOCK_TMAPS) From cf7a04278cecc8633b5307314988d9a9bdf4b055 Mon Sep 17 00:00:00 2001 From: Samwell Freeman Date: Wed, 30 Sep 2020 06:44:51 -0400 Subject: [PATCH 07/21] paired --- tests/conftest.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/conftest.py b/tests/conftest.py index b288c57bb..1b5cfde2e 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -9,7 +9,7 @@ def pytest_configure(config): pytest.N_TENSORS = 50 - config.addinivalue_line("markers", "env(slow): mark tests as slow") + config.addinivalue_line("markers", "slow: mark tests as slow") #@mock.patch.dict(TMAPS, MOCK_TMAPS) From a145dac7622a2966a4b97953bc8038132580e3cf Mon Sep 17 00:00:00 2001 From: Samwell Freeman Date: Wed, 30 Sep 2020 06:58:00 -0400 Subject: [PATCH 08/21] paired --- tests/test_models.py | 36 ++++++++++++++++++++++++++++++++++++ 1 file changed, 36 insertions(+) diff --git a/tests/test_models.py b/tests/test_models.py index 976a6b846..e3195df1d 100644 --- a/tests/test_models.py +++ b/tests/test_models.py @@ -349,6 +349,42 @@ def test_paired_models(self, pairs, tmpdir): **params ) + @pytest.mark.parametrize( + 'pairs', + [ + [(CONTINUOUS_TMAPS[2], CONTINUOUS_TMAPS[1])], + [(CATEGORICAL_TMAPS[2], CATEGORICAL_TMAPS[1])], + [(CONTINUOUS_TMAPS[2], CONTINUOUS_TMAPS[1]), (CONTINUOUS_TMAPS[2], CATEGORICAL_TMAPS[3])] + ], + ) + @pytest.mark.parametrize( + 'output_tmaps', + [ + [CONTINUOUS_TMAPS[0]], + [CATEGORICAL_TMAPS[0]], + [CONTINUOUS_TMAPS[0], CATEGORICAL_TMAPS[0]], + ], + ) + def test_semi_supervised_paired_models(self, pairs, output_tmaps, tmpdir): + params = DEFAULT_PARAMS.copy() + pair_list = list(set([p[0] for p in pairs] + [p[1] for p in pairs])) + params['u_connect'] = {tm: [] for tm in pair_list} + m, encoders, decoders = make_paired_autoencoder_model( + pairs=pairs, + tensor_maps_in=pair_list, + tensor_maps_out=pair_list+output_tmaps, + **params + ) + assert_model_trains(pair_list, pair_list+output_tmaps, m, skip_shape_check=True) + m.save(os.path.join(tmpdir, 'paired_ae.h5')) + path = os.path.join(tmpdir, f'm{MODEL_EXT}') + m.save(path) + make_paired_autoencoder_model( + pairs=pairs, + tensor_maps_in=pair_list, + tensor_maps_out=pair_list+output_tmaps, + **params + ) @pytest.mark.parametrize( 'tmaps', From 9010e1bf4d25ca8f8910256427951333b41e8cb2 Mon Sep 17 00:00:00 2001 From: Samwell Freeman Date: Thu, 1 Oct 2020 07:38:51 -0400 Subject: [PATCH 09/21] paired --- ml4h/models.py | 1 - ml4h/plots.py | 8 +++----- ml4h/recipes.py | 3 +-- 3 files changed, 4 insertions(+), 8 deletions(-) diff --git a/ml4h/models.py b/ml4h/models.py index 3eb53ccbd..ac2f7a350 100755 --- a/ml4h/models.py +++ b/ml4h/models.py @@ -1206,7 +1206,6 @@ def make_paired_autoencoder_model( multimodal_activation = Concatenate()(multimodal_activations) multimodal_activation = Dense(units=kwargs['dense_layers'][0])(multimodal_activation) - #multimodal_activation = _activation_layer(kwargs['activation'])(multimodal_activation) latent_inputs = Input(shape=(kwargs['dense_layers'][0]), name='input_concept_space') # build decoder models diff --git a/ml4h/plots.py b/ml4h/plots.py index 6b5ad6a78..c5da87886 100755 --- a/ml4h/plots.py +++ b/ml4h/plots.py @@ -2205,10 +2205,8 @@ def plot_hit_to_miss_transforms(latent_df, decoders, feature='Sex_Female_0_0', p fig, axes = plt.subplots(samples, 2, figsize=(18, samples * 4)) for i in range(samples): axes[i, 0].set_title(f"{feature}: {sexes[i]} ?>== thresh: @@ -2228,4 +2226,4 @@ def plot_hit_to_miss_transforms(latent_df, decoders, feature='Sex_Female_0_0', p figure_path = f'{prefix}/{dtm.name}_{feature}_transform_scalar_{scalar}.png' if not os.path.exists(os.path.dirname(figure_path)): os.makedirs(os.path.dirname(figure_path)) - plt.savefig(figure_path) \ No newline at end of file + plt.savefig(figure_path) diff --git a/ml4h/recipes.py b/ml4h/recipes.py index 148c2c517..c6b0581a1 100755 --- a/ml4h/recipes.py +++ b/ml4h/recipes.py @@ -421,8 +421,7 @@ def train_paired_model(args): reconstruction = decoders[dtm].predict(embed) logging.info(f'{dtm.name} has prediction shape: {reconstruction.shape} from embed shape: {embed.shape}') my_out_path = os.path.join(out_path, f'decoding_{dtm.name}_from_{etm.name}/') - if not os.path.exists(os.path.dirname(my_out_path)): - os.makedirs(os.path.dirname(my_out_path)) + os.makedirs(os.path.dirname(my_out_path), exist_ok=True) plot_reconstruction(dtm, test_data[dtm.input_name()], reconstruction, my_out_path, test_paths, samples) else: y_truth = np.array(test_labels[dtm.output_name()]) From c50a11643b3b0305a470206ce063d50929ae1f31 Mon Sep 17 00:00:00 2001 From: Samwell Freeman Date: Thu, 15 Oct 2020 07:27:38 -0400 Subject: [PATCH 10/21] lots of fixes --- ml4h/arguments.py | 6 ++-- ml4h/models.py | 84 +++++++++++++++++++++++++++-------------------- ml4h/plots.py | 4 --- ml4h/recipes.py | 20 +++++++---- 4 files changed, 65 insertions(+), 49 deletions(-) diff --git a/ml4h/arguments.py b/ml4h/arguments.py index 6692bcc74..3a8e53516 100644 --- a/ml4h/arguments.py +++ b/ml4h/arguments.py @@ -148,6 +148,7 @@ def parse_args(): parser.add_argument('--dense_normalize', default=None, choices=list(NORMALIZATION_CLASSES), help='Type of normalization layer for dense layers.') parser.add_argument('--activation', default='relu', help='Activation function for hidden units in neural nets dense layers.') parser.add_argument('--conv_layers', nargs='*', default=[32], type=int, help='List of number of kernels in convolutional layers.') + parser.add_argument('--conv_width', default=[71], nargs='*', type=int, help='X dimension of convolutional kernel for 1D models. Filter sizes are specified per layer given by conv_layers and per block given by dense_blocks. Filter sizes are repeated if there are less than the number of layers/blocks.') parser.add_argument('--conv_x', default=[3], nargs='*', type=int, help='X dimension of convolutional kernel. Filter sizes are specified per layer given by conv_layers and per block given by dense_blocks. Filter sizes are repeated if there are less than the number of layers/blocks.') parser.add_argument('--conv_y', default=[3], nargs='*', type=int, help='Y dimension of convolutional kernel. Filter sizes are specified per layer given by conv_layers and per block given by dense_blocks. Filter sizes are repeated if there are less than the number of layers/blocks.') parser.add_argument('--conv_z', default=[2], nargs='*', type=int, help='Z dimension of convolutional kernel. Filter sizes are specified per layer given by conv_layers and per block given by dense_blocks. Filter sizes are repeated if there are less than the number of layers/blocks.') @@ -172,7 +173,9 @@ def parse_args(): '--pairs', nargs=2, action='append', help='TensorMap pairs for paired autoencoder. The pair_loss metric will encourage similar embeddings for each two input TensorMap pairs. Can be provided multiple times.', ) - parser.add_argument('--aligned_dimension', default=16, type=int, help='Dimensionality of aligned embedded space for multi-modal alignment models.') + parser.add_argument('--pair_loss', default='cosine', help='Distance metric between paired embeddings', choices=['euclid', 'cosine']) + parser.add_argument('--pair_loss_weight', type=float, default=1.0, help='Weight on the pair loss term relative to other losses') + parser.add_argument('--multimodal_merge', default='average', choices=['average', 'concatenate'], help='How to merge modality specific encodings.') parser.add_argument( '--max_parameters', default=9000000, type=int, help='Maximum number of trainable parameters in a model during hyperparameter optimization.', @@ -205,7 +208,6 @@ def parse_args(): parser.add_argument('--validation_steps', default=18, type=int, help='Number of validation batches to examine in an epoch validation.') parser.add_argument('--learning_rate', default=0.0002, type=float, help='Learning rate during training.') parser.add_argument('--mixup_alpha', default=0, type=float, help='If positive apply mixup and sample from a Beta with this value as shape parameter alpha.') - parser.add_argument('--pair_loss', default='cosine', help='Distance metric between paired embeddings', choices=['euclid', 'cosine']) parser.add_argument( '--label_weights', nargs='*', type=float, help='List of per-label weights for weighted categorical cross entropy. If provided, must map 1:1 to number of labels.', diff --git a/ml4h/models.py b/ml4h/models.py index ac2f7a350..67996f272 100755 --- a/ml4h/models.py +++ b/ml4h/models.py @@ -525,10 +525,8 @@ def l2_norm(x, axis=None): """ takes an input tensor and returns the l2 norm along specified axis """ - square_sum = K.sum(K.square(x), axis=axis, keepdims=True) norm = K.sqrt(K.maximum(square_sum, K.epsilon())) - return norm @@ -536,12 +534,11 @@ def pairwise_cosine_difference(t1, t2): t1_norm = t1 / l2_norm(t1, axis=-1) t2_norm = t2 / l2_norm(t2, axis=-1) dot = K.clip(K.batch_dot(t1_norm, t2_norm), -1, 1) - return tf.acos(dot) + return K.mean(tf.acos(dot)) class CosineLossLayer(Layer): """Layer that creates an Cosine loss.""" - def __init__(self, weight, **kwargs): super(CosineLossLayer, self).__init__(**kwargs) self.weight = weight @@ -552,15 +549,13 @@ def get_config(self): return config def call(self, inputs): - # We use `add_loss` to create a regularization loss - # that depends on the inputs. + # We use `add_loss` to create a regularization loss that depends on the inputs. self.add_loss(self.weight * pairwise_cosine_difference(inputs[0], inputs[1])) return inputs class L2LossLayer(Layer): """Layer that creates an L2 loss.""" - def __init__(self, weight, **kwargs): super(L2LossLayer, self).__init__(**kwargs) self.weight = weight @@ -573,6 +568,7 @@ def get_config(self): def call(self, inputs): self.add_loss(self.weight * tf.reduce_sum(tf.square(inputs[0] - inputs[1]))) return inputs + return inputs class VariationalDiagNormal(Layer): @@ -1150,7 +1146,9 @@ def _make_multimodal_multitask_model( def make_paired_autoencoder_model( pairs: List[Tuple[TensorMap, TensorMap]], - pair_loss='cosine', + pair_loss: str = 'cosine', + pair_loss_weight: float = 1.0, + multimodal_merge: str = 'average', **kwargs ) -> Model: opt = get_optimizer( @@ -1172,8 +1170,7 @@ def make_paired_autoencoder_model( logging.info(f"Loaded model file from: {kwargs['model_file']}") return m, encoders, decoders - inputs = {tm: Input(shape=tm.shape, name=tm.input_name()) for tm in kwargs['tensor_maps_in']} - original_outputs = {tm: 1 for tm in kwargs['tensor_maps_out']} + inputs = {tm.input_name(): Input(shape=tm.shape, name=tm.input_name()) for tm in kwargs['tensor_maps_in']} real_serial_layers = kwargs['model_layers'] kwargs['model_layers'] = None multimodal_activations = [] @@ -1182,30 +1179,40 @@ def make_paired_autoencoder_model( outputs = {} losses = [] for left, right in pairs: - kwargs['tensor_maps_in'] = [left] - left_model = make_multimodal_multitask_model(**kwargs) - encode_left = make_hidden_layer_model(left_model, [left], kwargs['hidden_layer']) - h_left = encode_left(inputs[left]) + if left in encoders: + encode_left = encoders[left] + else: + kwargs['tensor_maps_in'] = [left] + left_model = make_multimodal_multitask_model(**kwargs) + encode_left = make_hidden_layer_model(left_model, [left], kwargs['hidden_layer']) + h_left = encode_left(inputs[left.input_name()]) - kwargs['tensor_maps_in'] = [right] - right_model = make_multimodal_multitask_model(**kwargs) - encode_right = make_hidden_layer_model(right_model, [right], kwargs['hidden_layer']) - h_right = encode_right(inputs[right]) + if right in encoders: + encode_right = encoders[right] + else: + kwargs['tensor_maps_in'] = [right] + right_model = make_multimodal_multitask_model(**kwargs) + encode_right = make_hidden_layer_model(right_model, [right], kwargs['hidden_layer']) + h_right = encode_right(inputs[right.input_name()]) if pair_loss == 'cosine': - loss_layer = CosineLossLayer(1.0) + loss_layer = CosineLossLayer(pair_loss_weight) elif pair_loss == 'euclid': - loss_layer = L2LossLayer(1.0) + loss_layer = L2LossLayer(pair_loss_weight) - paired_embeddings = loss_layer([h_left, h_right]) - multimodal_activations.extend(paired_embeddings) - if left not in encoders: - encoders[left] = encode_left - if right not in encoders: - encoders[right] = encode_right + multimodal_activations.extend(loss_layer([h_left, h_right])) + encoders[left] = encode_left + encoders[right] = encode_right - multimodal_activation = Concatenate()(multimodal_activations) - multimodal_activation = Dense(units=kwargs['dense_layers'][0])(multimodal_activation) + kwargs['tensor_maps_in'] = list(inputs.keys()) + if multimodal_merge == 'average': + multimodal_activation = Average()(multimodal_activations) + elif multimodal_merge == 'concatenate': + multimodal_activation = Concatenate()(multimodal_activations) + multimodal_activation = Dense(units=kwargs['dense_layers'][0], use_bias=False)(multimodal_activation) + multimodal_activation = _activation_layer(kwargs['activation'])(multimodal_activation) + else: + raise NotImplementedError(f'No merge architecture for method: {multimodal_merge}') latent_inputs = Input(shape=(kwargs['dense_layers'][0]), name='input_concept_space') # build decoder models @@ -1222,7 +1229,7 @@ def make_paired_autoencoder_model( tensor_map_out=tm, filters_per_dense_block=kwargs['dense_blocks'][::-1], conv_layer_type=kwargs['conv_type'], - conv_x=kwargs['conv_x'], + conv_x=kwargs['conv_x'] if tm.axes() > 2 else kwargs['conv_width'], conv_y=kwargs['conv_y'], conv_z=kwargs['conv_z'], block_size=kwargs['block_size'], @@ -1237,25 +1244,30 @@ def make_paired_autoencoder_model( ) reconstruction = decode(restructure(latent_inputs), {}, {}) else: + dense_block = FullyConnectedBlock( + widths=kwargs['dense_layers'], + activation=kwargs['activation'], + normalization=kwargs['dense_normalize'], + regularization=kwargs['dense_regularize'], + regularization_rate=kwargs['dense_regularize_rate'], + is_encoder=False, + ) decode = DenseDecoder(tensor_map_out=tm, parents=tm.parents, activation=kwargs['activation']) - reconstruction = decode(latent_inputs, {}, {}) + reconstruction = decode(dense_block(latent_inputs), {}, {}) decoder = Model(latent_inputs, reconstruction, name=tm.output_name()) decoders[tm] = decoder outputs[tm.output_name()] = decoder(multimodal_activation) losses.append(tm.loss) - kwargs['tensor_maps_out'] = list(original_outputs.keys()) - kwargs['tensor_maps_in'] = list(inputs.keys()) - - m = Model(inputs=list(inputs.values()), outputs=outputs) + m = Model(inputs=list(inputs.values()), outputs=list(outputs.values())) my_metrics = {tm.output_name(): tm.metrics for tm in kwargs['tensor_maps_out']} m.compile(optimizer=opt, loss=losses, metrics=my_metrics) m.summary() if real_serial_layers is not None: - m.load_weights(kwargs['model_layers'], by_name=True) - logging.info(f"Loaded model weights from:{kwargs['model_layers']}") + m.load_weights(real_serial_layers, by_name=True) + logging.info(f"Loaded model weights from:{real_serial_layers}") return m, encoders, decoders diff --git a/ml4h/plots.py b/ml4h/plots.py index c5da87886..d3b61fe89 100755 --- a/ml4h/plots.py +++ b/ml4h/plots.py @@ -195,10 +195,6 @@ def plot_metric_history(history, training_steps: int, title: str, prefix='./figu if not k.startswith('val_'): if isinstance(history.history[k][0], LearningRateSchedule): history.history[k] = [history.history[k][0](i * training_steps) for i in range(len(history.history[k]))] - if len(np.array(history.history[k]).shape) > 1: # Hack for models with paired loss - history.history[k] = np.array(history.history[k])[:, 0, 0] - if 'val_' + k in history.history: - history.history['val_' + k] = np.array(history.history['val_' + k])[:, 0, 0] axes[row, col].plot(history.history[k]) k_split = str(k).replace('output_', '').split('_') k_title = " ".join(OrderedDict.fromkeys(k_split)) diff --git a/ml4h/recipes.py b/ml4h/recipes.py index c6b0581a1..56ea2a00a 100755 --- a/ml4h/recipes.py +++ b/ml4h/recipes.py @@ -408,24 +408,30 @@ def train_paired_model(args): decoders[tm].save(f'{args.output_folder}{args.id}/decoder_{tm.name}.h5') out_path = os.path.join(args.output_folder, args.id, 'reconstructions/') test_data, test_labels, test_paths = big_batch_from_minibatch_generator(generate_test, args.test_steps) - samples = args.test_steps * args.batch_size + samples = min(args.test_steps * args.batch_size, 12) predictions_list = full_model.predict(test_data) predictions_dict = {name: pred for name, pred in zip(full_model.output_names, predictions_list)} logging.info(f'Predictions and shapes are: {[(p, predictions_dict[p].shape) for p in predictions_dict]}') performance_metrics = {} + for tm in args.tensor_maps_out: + if tm.axes() == 1: + y = predictions_dict[tm.output_name()] + y_truth = np.array(test_labels[tm.output_name()]) + metrics = evaluate_predictions(tm, y, y_truth, {}, tm.name, os.path.join(args.output_folder, args.id), test_paths) + performance_metrics.update(metrics) for i, etm in enumerate(encoders): embed = encoders[etm].predict(test_data[etm.input_name()]) plot_reconstruction(etm, test_data[etm.input_name()], predictions_dict[etm.output_name()], out_path, test_paths, samples) for dtm in decoders: + reconstruction = decoders[dtm].predict(embed) + logging.info(f'{dtm.name} has prediction shape: {reconstruction.shape} from embed shape: {embed.shape}') + my_out_path = os.path.join(out_path, f'decoding_{dtm.name}_from_{etm.name}/') + if not os.path.exists(os.path.dirname(my_out_path)): + os.makedirs(os.path.dirname(my_out_path)) if dtm.axes() > 1: - reconstruction = decoders[dtm].predict(embed) - logging.info(f'{dtm.name} has prediction shape: {reconstruction.shape} from embed shape: {embed.shape}') - my_out_path = os.path.join(out_path, f'decoding_{dtm.name}_from_{etm.name}/') - os.makedirs(os.path.dirname(my_out_path), exist_ok=True) plot_reconstruction(dtm, test_data[dtm.input_name()], reconstruction, my_out_path, test_paths, samples) else: - y_truth = np.array(test_labels[dtm.output_name()]) - performance_metrics.update(evaluate_predictions(dtm, decoders[dtm].predict(embed), y_truth, {}, dtm.name, os.path.join(args.output_folder, args.id), test_paths)) + evaluate_predictions(dtm, reconstruction, test_labels[dtm.output_name()], {}, dtm.name, my_out_path, test_paths) return performance_metrics From a7a42703678c1f60fc1e7d70ab5b3e6c8b3c7fd4 Mon Sep 17 00:00:00 2001 From: Samwell Freeman Date: Thu, 15 Oct 2020 07:31:51 -0400 Subject: [PATCH 11/21] lots of fixes --- scripts/tf.sh | 31 +++++++++++++++---------------- 1 file changed, 15 insertions(+), 16 deletions(-) diff --git a/scripts/tf.sh b/scripts/tf.sh index 779a0daca..d3adb6b10 100755 --- a/scripts/tf.sh +++ b/scripts/tf.sh @@ -35,19 +35,7 @@ done export GROUP_NAMES GROUP_IDS # Create string to be called in Docker's bash shell via eval; -# this creates a user, adds groups, adds user to groups, then calls the Python script -CALL_DOCKER_AS_USER=" - apt-get -y install sudo; - useradd -u $(id -u) ${USER}; - GROUP_NAMES_ARR=( \${GROUP_NAMES} ); - GROUP_IDS_ARR=( \${GROUP_IDS} ); - for (( i=0; i<\${#GROUP_NAMES_ARR[@]}; ++i )); do - echo \"Creating group\" \${GROUP_NAMES_ARR[i]} \"with gid\" \${GROUP_IDS_ARR[i]}; - groupadd -f -g \${GROUP_IDS_ARR[i]} \${GROUP_NAMES_ARR[i]}; - echo \"Adding user ${USER} to group\" \${GROUP_NAMES_ARR[i]} - usermod -aG \${GROUP_NAMES_ARR[i]} ${USER} - done; - sudo -u ${USER}" +CALL_DOCKER_AS_USER= ################### HELP TEXT ############################################ @@ -85,7 +73,7 @@ USAGE_MESSAGE ################### OPTION PARSING ####################################### -while getopts ":i:d:m:ctjrhT" opt ; do +while getopts ":i:d:m:ctjuhT" opt ; do case ${opt} in h) usage @@ -113,8 +101,19 @@ while getopts ":i:d:m:ctjrhT" opt ; do mkdir -p /home/${USER}/jupyter/root/ mkdir -p /mnt/ml4cvd/projects/${USER}/projects/jupyter/auto/ ;; - r) # Output owned by root - CALL_DOCKER_AS_USER="" + u) # this creates a user, adds groups, adds user to groups, then calls the Python script + CALL_DOCKER_AS_USER=" + apt-get -y install sudo; + useradd -u $(id -u) ${USER}; + GROUP_NAMES_ARR=( \${GROUP_NAMES} ); + GROUP_IDS_ARR=( \${GROUP_IDS} ); + for (( i=0; i<\${#GROUP_NAMES_ARR[@]}; ++i )); do + echo \"Creating group\" \${GROUP_NAMES_ARR[i]} \"with gid\" \${GROUP_IDS_ARR[i]}; + groupadd -f -g \${GROUP_IDS_ARR[i]} \${GROUP_NAMES_ARR[i]}; + echo \"Adding user ${USER} to group\" \${GROUP_NAMES_ARR[i]} + usermod -aG \${GROUP_NAMES_ARR[i]} ${USER} + done; + sudo -u ${USER}" ;; T) PYTHON_COMMAND=${TEST_COMMAND} From 5258555124ececd4ba25f1d3d4df321c7f6f9297 Mon Sep 17 00:00:00 2001 From: Samwell Freeman Date: Thu, 15 Oct 2020 07:33:03 -0400 Subject: [PATCH 12/21] lots of fixes --- ml4h/models.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ml4h/models.py b/ml4h/models.py index 67996f272..3224faa02 100755 --- a/ml4h/models.py +++ b/ml4h/models.py @@ -25,7 +25,7 @@ from tensorflow.keras.layers import SpatialDropout1D, SpatialDropout2D, SpatialDropout3D, add, concatenate from tensorflow.keras.layers import Input, Dense, Dropout, BatchNormalization, Activation, Flatten, LSTM, RepeatVector from tensorflow.keras.layers import Conv1D, Conv2D, Conv3D, UpSampling1D, UpSampling2D, UpSampling3D, MaxPooling1D -from tensorflow.keras.layers import MaxPooling2D, MaxPooling3D, AveragePooling1D, AveragePooling2D, AveragePooling3D, Layer +from tensorflow.keras.layers import MaxPooling2D, MaxPooling3D, Average, AveragePooling1D, AveragePooling2D, AveragePooling3D, Layer from tensorflow.keras.layers import SeparableConv1D, SeparableConv2D, DepthwiseConv2D, Concatenate, Add from tensorflow.keras.layers import GlobalAveragePooling1D, GlobalAveragePooling2D, GlobalAveragePooling3D import tensorflow_probability as tfp From e4a2dc0c9ddeddfeebaa57a7e8cc8dd2c9cb0320 Mon Sep 17 00:00:00 2001 From: Samwell Freeman Date: Thu, 15 Oct 2020 07:42:50 -0400 Subject: [PATCH 13/21] lots of fixes --- ml4h/plots.py | 13 +++++++------ 1 file changed, 7 insertions(+), 6 deletions(-) diff --git a/ml4h/plots.py b/ml4h/plots.py index d3b61fe89..15a1fff55 100755 --- a/ml4h/plots.py +++ b/ml4h/plots.py @@ -2085,16 +2085,17 @@ def plot_reconstruction( logging.info(f'Plotting {num_samples} reconstructions of {tm}.') if None in tm.shape: # can't handle dynamic shapes return + os.makedirs(os.path.dirname(folder), exist_ok=True) for i in range(num_samples): sample_id = os.path.basename(paths[i]).replace(TENSOR_EXT, '') title = f'{tm.name}_{sample_id}_reconstruction' - y = y_true[i].reshape(tm.shape) - yp = y_pred[i].reshape(tm.shape) + y = y_true[i] + yp = y_pred[i] if tm.axes() == 2: index2channel = {v: k for k, v in tm.channel_map.items()} - fig, axes = plt.subplots(tm.shape[1], 2, figsize=(2 * SUBPLOT_SIZE, 6*SUBPLOT_SIZE)) + fig, axes = plt.subplots(tm.shape[1], 2, figsize=(2 * SUBPLOT_SIZE, 6*SUBPLOT_SIZE)) #, sharey=True) for j in range(tm.shape[1]): - axes[j, 0].plot(y[:, j], c='k', linestyle='--', label='original') + axes[j, 0].plot(y[:, j], c='k', label='original') axes[j, 1].plot(yp[:, j], c='b', label='reconstruction') axes[j, 0].set_title(f'Lead: {index2channel[j]}') axes[j, 0].legend() @@ -2112,13 +2113,13 @@ def plot_reconstruction( for j in range(y.shape[3]): image_path_base = f'{folder}{sample_id}_{tm.name}_{i:03d}_{j:03d}' if tm.is_categorical(): - truth = np.argmax(yp[tm.output_name()][:, :, j, :], axis=-1) + truth = np.argmax(yp[:, :, j, :], axis=-1) prediction = np.argmax(y[:, :, j, :], axis=-1) plt.imsave(f'{image_path_base}_truth{IMAGE_EXT}', truth, cmap='plasma') plt.imsave(f'{image_path_base}_prediction{IMAGE_EXT}', prediction, cmap='plasma') else: plt.imsave(f'{image_path_base}_truth{IMAGE_EXT}', y[:, :, j, 0], cmap='gray') - plt.imsave(f'{image_path_base}_prediction{IMAGE_EXT}', yp[:, :, j, :], cmap='gray') + plt.imsave(f'{image_path_base}_prediction{IMAGE_EXT}', yp[:, :, j, 0], cmap='gray') plt.clf() From 08b357abd5f429bafc6b91889edbb1a7b6845817 Mon Sep 17 00:00:00 2001 From: Samwell Freeman Date: Thu, 15 Oct 2020 07:46:20 -0400 Subject: [PATCH 14/21] lots of fixes --- ml4h/plots.py | 18 +++++++++++++----- 1 file changed, 13 insertions(+), 5 deletions(-) diff --git a/ml4h/plots.py b/ml4h/plots.py index 15a1fff55..28ef0d5e8 100755 --- a/ml4h/plots.py +++ b/ml4h/plots.py @@ -22,6 +22,9 @@ from tensorflow.keras.optimizers.schedules import LearningRateSchedule import matplotlib + +from ml4h.tensor_generators import _sample_csv_to_set + matplotlib.use('Agg') # Need this to write images from the GSA servers. Order matters: import matplotlib.pyplot as plt # First import matplotlib, then use Agg, then import plt from matplotlib.ticker import NullFormatter @@ -2181,25 +2184,30 @@ def stratify_latent_space(stratify_column, stratify_thresh, latent_cols, latent_ def plot_hit_to_miss_transforms(latent_df, decoders, feature='Sex_Female_0_0', prefix='./figures/', - thresh=1.0, latent_dimension=256, samples=16, scalar=3.0, cmap='plasma'): + thresh=1.0, latent_dimension=256, samples=16, scalar=3.0, cmap='plasma', test_csv=None): latent_cols = [f'latent_{i}' for i in range(latent_dimension)] female, male = stratify_latent_space(feature, thresh, latent_cols, latent_df) sex_vector = female - male + if test_csv is not None: + sample_ids = [int(s) for s in _sample_csv_to_set(test_csv) if len(s) > 4 and int(s) in latent_df.index] + latent_df = latent_df.loc[sample_ids] + latent_df.info() + logging.info(f'Subset to test set with {len(sample_ids)} samples') + + samples = min(len(latent_df.index), samples) embeddings = latent_df.iloc[:samples][latent_cols].to_numpy() sexes = latent_df.iloc[:samples][feature].to_numpy() - print(f'Embedding shape: {embeddings.shape} sexes shape: {sexes.shape}') + logging.info(f'Embedding shape: {embeddings.shape} sexes shape: {sexes.shape}') sex_vectors = np.tile(sex_vector, (samples, 1)) male_to_female = embeddings + (scalar * sex_vectors) female_to_male = embeddings - (scalar * sex_vectors) - print(f'embeddings shape: {embeddings.shape} features vectors shape: {sex_vectors.shape}') for dtm in decoders: predictions = decoders[dtm].predict(embeddings) m2f = decoders[dtm].predict(male_to_female) f2m = decoders[dtm].predict(female_to_male) - print(f'prediction shape: {predictions.shape}') if dtm.axes() == 3: - fig, axes = plt.subplots(samples, 2, figsize=(18, samples * 4)) + fig, axes = plt.subplots(max(2, samples), 2, figsize=(18, samples * 4)) for i in range(samples): axes[i, 0].set_title(f"{feature}: {sexes[i]} ?>= Date: Thu, 15 Oct 2020 08:04:57 -0400 Subject: [PATCH 15/21] lots of fixes --- tests/test_models.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/tests/test_models.py b/tests/test_models.py index e3195df1d..5c19868b1 100644 --- a/tests/test_models.py +++ b/tests/test_models.py @@ -23,6 +23,7 @@ 'optimizer': 'adam', 'conv_type': 'conv', 'conv_layers': [6, 5, 3], + 'conv_width': [71]*5, 'conv_x': [3]*5, 'conv_y': [3]*5, 'conv_z': [2]*5, @@ -48,7 +49,7 @@ 'model_layers': None, 'model_file': None, 'hidden_layer': 'embed', - 'u_connect': {}, + 'u_connect': defaultdict(dict), } From 442a87a56901af11d13b6c15fdda1d550f531d04 Mon Sep 17 00:00:00 2001 From: Samwell Freeman Date: Thu, 15 Oct 2020 08:18:17 -0400 Subject: [PATCH 16/21] lots of fixes --- tests/test_models.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tests/test_models.py b/tests/test_models.py index 5c19868b1..0e4b3c5cb 100644 --- a/tests/test_models.py +++ b/tests/test_models.py @@ -332,7 +332,7 @@ def test_language_models(self, input_output_tmaps, tmpdir): def test_paired_models(self, pairs, tmpdir): params = DEFAULT_PARAMS.copy() pair_list = list(set([p[0] for p in pairs] + [p[1] for p in pairs])) - params['u_connect'] = {tm: [] for tm in pair_list} + params['u_connect'] = {tm.input_name(): [] for tm in pair_list} m, encoders, decoders = make_paired_autoencoder_model( pairs=pairs, tensor_maps_in=pair_list, @@ -369,7 +369,7 @@ def test_paired_models(self, pairs, tmpdir): def test_semi_supervised_paired_models(self, pairs, output_tmaps, tmpdir): params = DEFAULT_PARAMS.copy() pair_list = list(set([p[0] for p in pairs] + [p[1] for p in pairs])) - params['u_connect'] = {tm: [] for tm in pair_list} + params['u_connect'] = {tm.input_name(): [] for tm in pair_list} m, encoders, decoders = make_paired_autoencoder_model( pairs=pairs, tensor_maps_in=pair_list, From 0b76a95fde9169c72a62ac4ce5f08e7153f24ae8 Mon Sep 17 00:00:00 2001 From: Samwell Freeman Date: Thu, 15 Oct 2020 08:22:45 -0400 Subject: [PATCH 17/21] lots of fixes --- ml4h/models.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ml4h/models.py b/ml4h/models.py index 3224faa02..360afd94c 100755 --- a/ml4h/models.py +++ b/ml4h/models.py @@ -1170,7 +1170,7 @@ def make_paired_autoencoder_model( logging.info(f"Loaded model file from: {kwargs['model_file']}") return m, encoders, decoders - inputs = {tm.input_name(): Input(shape=tm.shape, name=tm.input_name()) for tm in kwargs['tensor_maps_in']} + inputs = {tm: Input(shape=tm.shape, name=tm.input_name()) for tm in kwargs['tensor_maps_in']} real_serial_layers = kwargs['model_layers'] kwargs['model_layers'] = None multimodal_activations = [] From 377a467b6a268f62ce12de9c39faec1bec1cce57 Mon Sep 17 00:00:00 2001 From: Samwell Freeman Date: Thu, 15 Oct 2020 08:23:37 -0400 Subject: [PATCH 18/21] lots of fixes --- tests/test_models.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tests/test_models.py b/tests/test_models.py index 0e4b3c5cb..5c19868b1 100644 --- a/tests/test_models.py +++ b/tests/test_models.py @@ -332,7 +332,7 @@ def test_language_models(self, input_output_tmaps, tmpdir): def test_paired_models(self, pairs, tmpdir): params = DEFAULT_PARAMS.copy() pair_list = list(set([p[0] for p in pairs] + [p[1] for p in pairs])) - params['u_connect'] = {tm.input_name(): [] for tm in pair_list} + params['u_connect'] = {tm: [] for tm in pair_list} m, encoders, decoders = make_paired_autoencoder_model( pairs=pairs, tensor_maps_in=pair_list, @@ -369,7 +369,7 @@ def test_paired_models(self, pairs, tmpdir): def test_semi_supervised_paired_models(self, pairs, output_tmaps, tmpdir): params = DEFAULT_PARAMS.copy() pair_list = list(set([p[0] for p in pairs] + [p[1] for p in pairs])) - params['u_connect'] = {tm.input_name(): [] for tm in pair_list} + params['u_connect'] = {tm: [] for tm in pair_list} m, encoders, decoders = make_paired_autoencoder_model( pairs=pairs, tensor_maps_in=pair_list, From 23c438c734ce6294d5f9ac522ff4d191fd8b0093 Mon Sep 17 00:00:00 2001 From: Samwell Freeman Date: Thu, 15 Oct 2020 08:27:50 -0400 Subject: [PATCH 19/21] lots of fixes --- ml4h/models.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/ml4h/models.py b/ml4h/models.py index 360afd94c..cbe0efc57 100755 --- a/ml4h/models.py +++ b/ml4h/models.py @@ -1185,7 +1185,7 @@ def make_paired_autoencoder_model( kwargs['tensor_maps_in'] = [left] left_model = make_multimodal_multitask_model(**kwargs) encode_left = make_hidden_layer_model(left_model, [left], kwargs['hidden_layer']) - h_left = encode_left(inputs[left.input_name()]) + h_left = encode_left(inputs[left]) if right in encoders: encode_right = encoders[right] @@ -1193,7 +1193,7 @@ def make_paired_autoencoder_model( kwargs['tensor_maps_in'] = [right] right_model = make_multimodal_multitask_model(**kwargs) encode_right = make_hidden_layer_model(right_model, [right], kwargs['hidden_layer']) - h_right = encode_right(inputs[right.input_name()]) + h_right = encode_right(inputs[right]) if pair_loss == 'cosine': loss_layer = CosineLossLayer(pair_loss_weight) From 5aeebe8fb3c4eebf6bb41af955d30d5fc5c23fa5 Mon Sep 17 00:00:00 2001 From: Samwell Freeman Date: Thu, 15 Oct 2020 08:32:49 -0400 Subject: [PATCH 20/21] lots of fixes --- tests/test_models.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/test_models.py b/tests/test_models.py index 5c19868b1..c7039b540 100644 --- a/tests/test_models.py +++ b/tests/test_models.py @@ -16,7 +16,7 @@ MEAN_PRECISION_EPS = .02 # how much mean precision degradation is acceptable DEFAULT_PARAMS = { 'activation': 'relu', - 'dense_layers': [4, 2], + 'dense_layers': [4], 'dense_blocks': [5, 3], 'block_size': 3, 'learning_rate': 1e-3, From 0fd4138549fa4873cdfce3cf827af0a954c53205 Mon Sep 17 00:00:00 2001 From: Samwell Freeman Date: Thu, 15 Oct 2020 08:34:55 -0400 Subject: [PATCH 21/21] lots of fixes --- ml4h/models.py | 2 +- tests/test_models.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/ml4h/models.py b/ml4h/models.py index cbe0efc57..0fde8d284 100755 --- a/ml4h/models.py +++ b/ml4h/models.py @@ -1213,7 +1213,7 @@ def make_paired_autoencoder_model( multimodal_activation = _activation_layer(kwargs['activation'])(multimodal_activation) else: raise NotImplementedError(f'No merge architecture for method: {multimodal_merge}') - latent_inputs = Input(shape=(kwargs['dense_layers'][0]), name='input_concept_space') + latent_inputs = Input(shape=(kwargs['dense_layers'][-1]), name='input_concept_space') # build decoder models for tm in kwargs['tensor_maps_out']: diff --git a/tests/test_models.py b/tests/test_models.py index c7039b540..5c19868b1 100644 --- a/tests/test_models.py +++ b/tests/test_models.py @@ -16,7 +16,7 @@ MEAN_PRECISION_EPS = .02 # how much mean precision degradation is acceptable DEFAULT_PARAMS = { 'activation': 'relu', - 'dense_layers': [4], + 'dense_layers': [4, 2], 'dense_blocks': [5, 3], 'block_size': 3, 'learning_rate': 1e-3,