From 3f04d6524c88f07e481bba3a23b1492fcc2838ae Mon Sep 17 00:00:00 2001 From: amrit110 Date: Wed, 29 Nov 2023 18:52:57 -0500 Subject: [PATCH] deploy: 3a3f5e39dac39283df293e13ff2b867380eb160a --- 404.html | 2 +- api/_sources/intro.rst.txt | 158 +- .../mimiciv/mortality_prediction.ipynb.txt | 10 +- api/intro.html | 111 +- api/searchindex.js | 2 +- .../kaggle/heart_failure_prediction.html | 70 +- .../kaggle/heart_failure_prediction.ipynb | 3530 +-- .../kaggle/heart_failure_report_periodic.html | 182 +- .../mimiciv/mortality_prediction.html | 140 +- .../mimiciv/mortality_prediction.ipynb | 20642 ++++++++-------- .../mimiciv/mortality_report_periodic.html | 132 +- api/tutorials/nihcxr/cxr_classification.html | 132 +- api/tutorials/nihcxr/cxr_classification.ipynb | 3678 ++- api/tutorials/nihcxr/monitor_api.html | 22 +- api/tutorials/nihcxr/monitor_api.ipynb | 1880 +- .../nihcxr/nihcxr_report_periodic.html | 6 +- .../length_of_stay_report_periodic.html | 138 +- api/tutorials/synthea/los_prediction.html | 120 +- api/tutorials/synthea/los_prediction.ipynb | 6460 ++--- assets/js/59d1d05d.0341b085.js | 1 + assets/js/59d1d05d.98f3cfec.js | 1 - ...f21cd.515432ee.js => 91ff21cd.fc37e053.js} | 2 +- assets/js/d207b03a.74bc7f75.js | 1 + assets/js/d207b03a.89295f3c.js | 1 - ...n.dddfbb63.js => runtime~main.8613a517.js} | 2 +- blog/archive/index.html | 2 +- blog/atom.xml | 4 +- blog/cyclops-0.2.0-release/index.html | 8 +- blog/index.html | 6 +- blog/rss.xml | 4 +- blog/tags/0-2-0/index.html | 6 +- blog/tags/index.html | 2 +- docs/intro/index.html | 2 +- index.html | 2 +- markdown-page/index.html | 2 +- 35 files changed, 18850 insertions(+), 18611 deletions(-) create mode 100644 assets/js/59d1d05d.0341b085.js delete mode 100644 assets/js/59d1d05d.98f3cfec.js rename assets/js/{91ff21cd.515432ee.js => 91ff21cd.fc37e053.js} (80%) create mode 100644 assets/js/d207b03a.74bc7f75.js delete mode 100644 assets/js/d207b03a.89295f3c.js rename assets/js/{runtime~main.dddfbb63.js => runtime~main.8613a517.js} (95%) diff --git a/404.html b/404.html index d4550e591..ccde2feb1 100644 --- a/404.html +++ b/404.html @@ -5,7 +5,7 @@ Page Not Found | cyclops - + diff --git a/api/_sources/intro.rst.txt b/api/_sources/intro.rst.txt index 0be95a28a..0206124c0 100644 --- a/api/_sources/intro.rst.txt +++ b/api/_sources/intro.rst.txt @@ -1,36 +1,22 @@ -.. figure:: - https://github.com/VectorInstitute/cyclops/blob/main/docs/source/theme/static/cyclops_logo-dark.png?raw=true +.. figure:: https://github.com/VectorInstitute/cyclops/blob/main/docs/source/theme/static/cyclops_logo-dark.png?raw=true :alt: cyclops Logo -------------- -|PyPI| |PyPI - Python Version| |code checks| |integration tests| |docs| -|codecov| |docker| |license| - -``cyclops`` is a toolkit for facilitating research and deployment of ML -models for healthcare. It provides a few high-level APIs namely: - -- ``data`` - Create datasets for training, inference and evaluation. We - use the popular πŸ€— - `datasets `__ to efficiently - load and slice different modalities of data -- ``models`` - Use common model implementations using - `scikit-learn `__ and - `PyTorch `__ -- ``tasks`` - Use common ML task formulations such as binary - classification or multi-label classification on tabular, time-series - and image data +|PyPI| |PyPI - Python Version| |code checks| |integration tests| |docs| |codecov| |docker| |license| + +``cyclops`` is a toolkit for facilitating research and deployment of ML models for healthcare. It provides a few high-level APIs namely: + +- ``data`` - Create datasets for training, inference and evaluation. We use the popular πŸ€— `datasets `__ to efficiently load and slice different modalities of data +- ``models`` - Use common model implementations using `scikit-learn `__ and `PyTorch `__ +- ``tasks`` - Use common ML task formulations such as binary classification or multi-label classification on tabular, time-series and image data - ``evaluate`` - Evaluate models on clinical prediction tasks - ``monitor`` - Detect dataset shift relevant for clinical use cases -- ``report`` - Create `model report - cards `__ - for clinical ML models +- ``report`` - Create `model report cards `__ for clinical ML models -``cyclops`` also provides example end-to-end use case implementations on -clinical datasets such as +``cyclops`` also provides example end-to-end use case implementations on clinical datasets such as -- `NIH chest - x-ray `__ +- `NIH chest x-ray `__ - `MIMIC-IV `__ 🐣 Getting Started @@ -43,84 +29,25 @@ Installing cyclops using pip python3 -m pip install pycyclops -``cyclops`` has many optional dependencies that are used for specific -functionality. For example, the -`monai `__ library is used for -loading DICOM images to create datasets. All optional dependencies can -be installed with ``pycyclops[all]``, and specific sets of dependencies -are listed in the sections below. - -+-----------------------------+--------------------------+--------------+ -| Dependency | pip extra | Notes | -+=============================+==========================+==============+ -| xgboost | xgboost | Allows use | -| | | of | -| | | `XGBoos | -| | | t `__ | -| | | model | -+-----------------------------+--------------------------+--------------+ -| torch | torch | Allows use | -| | | of | -| | | `PyTorch < | -| | | https://pyto | -| | | rch.org/>`__ | -| | | models | -+-----------------------------+--------------------------+--------------+ -| torchvision | torchvision | Allows use | -| | | of | -| | | `T | -| | | orchvision < | -| | | https://pyto | -| | | rch.org/visi | -| | | on/stable/in | -| | | dex.html>`__ | -| | | library | -+-----------------------------+--------------------------+--------------+ -| torchxrayvision | torchxrayvision | Uses | -| | | `TorchXR | -| | | ayVision `__ | -| | | library | -+-----------------------------+--------------------------+--------------+ -| monai | monai | Uses | -| | | `M | -| | | ONAI `__ | -| | | to load and | -| | | transform | -| | | images | -+-----------------------------+--------------------------+--------------+ -| alibi | alibi | Uses | -| | | `Alibi `__ | -| | | for | -| | | additional | -| | | ex | -| | | plainability | -| | | f | -| | | unctionality | -+-----------------------------+--------------------------+--------------+ -| alibi-detect | alibi-detect | Uses `Alibi | -| | | Detect | -| | | `__ | -| | | for dataset | -| | | shift | -| | | detection | -+-----------------------------+--------------------------+--------------+ +``cyclops`` has many optional dependencies that are used for specific functionality. For example, the `monai `__ library is used for loading DICOM images to create datasets. All optional dependencies can be installed with ``pycyclops[all]``, and specific sets of dependencies are listed in the sections below. + ++-----------------------------+--------------------------+---------------------------------------------------------------------------------------------------------------+ +| Dependency | pip extra | Notes | ++=============================+==========================+===============================================================================================================+ +| xgboost | xgboost | Allows use of `XGBoost `__ model | ++-----------------------------+--------------------------+---------------------------------------------------------------------------------------------------------------+ +| torch | torch | Allows use of `PyTorch `__ models | ++-----------------------------+--------------------------+---------------------------------------------------------------------------------------------------------------+ +| torchvision | torchvision | Allows use of `Torchvision `__ library | ++-----------------------------+--------------------------+---------------------------------------------------------------------------------------------------------------+ +| torchxrayvision | torchxrayvision | Uses `TorchXRayVision `__ library | ++-----------------------------+--------------------------+---------------------------------------------------------------------------------------------------------------+ +| monai | monai | Uses `MONAI `__ to load and transform images | ++-----------------------------+--------------------------+---------------------------------------------------------------------------------------------------------------+ +| alibi | alibi | Uses `Alibi `__ for additional explainability functionality | ++-----------------------------+--------------------------+---------------------------------------------------------------------------------------------------------------+ +| alibi-detect | alibi-detect | Uses `Alibi Detect `__ for dataset shift detection | ++-----------------------------+--------------------------+---------------------------------------------------------------------------------------------------------------+ πŸ§‘πŸΏβ€πŸ’» Developing ======================= @@ -128,25 +55,20 @@ are listed in the sections below. Using poetry ------------ -The development environment can be set up using -`poetry `__. Hence, make -sure it is installed and then run: +The development environment can be set up using `poetry `__. Hence, make sure it is installed and then run: .. code:: bash python3 -m poetry install source $(poetry env info --path)/bin/activate -In order to install dependencies for testing (codestyle, unit tests, -integration tests), run: +In order to install dependencies for testing (codestyle, unit tests, integration tests), run: .. code:: bash python3 -m poetry install --with test -API documentation is built using -`Sphinx `__ and can be locally -built by: +API documentation is built using `Sphinx `__ and can be locally built by: .. code:: bash @@ -157,9 +79,7 @@ built by: Contributing ------------ -Contributing to cyclops is welcomed. See -`Contributing `__ -for guidelines. +Contributing to cyclops is welcomed. See `Contributing `__ for guidelines. πŸ“š `Documentation `__ ================================================================= @@ -167,22 +87,18 @@ for guidelines. πŸ““ Notebooks ============ -To use jupyter notebooks, the python virtual environment can be -installed and used inside an IPython kernel. After activating the -virtual environment, run: +To use jupyter notebooks, the python virtual environment can be installed and used inside an IPython kernel. After activating the virtual environment, run: .. code:: bash python3 -m ipykernel install --user --name -Now, you can navigate to the notebook’s ``Kernel`` tab and set it as -````. +Now, you can navigate to the notebook’s ``Kernel`` tab and set it as ````. πŸŽ“ Citation =========== -Reference to cite when you use ``cyclops`` in a project or a research -paper: +Reference to cite when you use ``cyclops`` in a project or a research paper: :: diff --git a/api/_sources/tutorials/mimiciv/mortality_prediction.ipynb.txt b/api/_sources/tutorials/mimiciv/mortality_prediction.ipynb.txt index 42dd280aa..299c08d21 100644 --- a/api/_sources/tutorials/mimiciv/mortality_prediction.ipynb.txt +++ b/api/_sources/tutorials/mimiciv/mortality_prediction.ipynb.txt @@ -36,6 +36,7 @@ "from cycquery import MIMICIVQuerier\n", "from datasets import Dataset\n", "from datasets.features import ClassLabel\n", + "from imblearn.over_sampling import SMOTE\n", "from sklearn.compose import ColumnTransformer\n", "from sklearn.impute import SimpleImputer\n", "from sklearn.pipeline import Pipeline\n", @@ -584,7 +585,12 @@ " ),\n", " ],\n", " remainder=\"passthrough\",\n", - ")" + ")\n", + "preprocessor_pipeline = [\n", + " (\"preprocessor\", preprocessor),\n", + " (\"oversampling\", SMOTE(random_state=RANDOM_SEED)),\n", + "]\n", + "preprocessor_pipeline = Pipeline(preprocessor_pipeline)" ] }, { @@ -692,7 +698,7 @@ "mortality_task.train(\n", " dataset[\"train\"],\n", " model_name=model_name,\n", - " transforms=preprocessor,\n", + " transforms=preprocessor_pipeline,\n", " best_model_params=best_model_params,\n", ")" ] diff --git a/api/intro.html b/api/intro.html index cc81215f5..22718a8b8 100644 --- a/api/intro.html +++ b/api/intro.html @@ -177,32 +177,19 @@ cyclops Logo
-

PyPI PyPI - Python Version code checks integration tests docs -codecov docker license

-

cyclops is a toolkit for facilitating research and deployment of ML -models for healthcare. It provides a few high-level APIs namely:

+

PyPI PyPI - Python Version code checks integration tests docs codecov docker license

+

cyclops is a toolkit for facilitating research and deployment of ML models for healthcare. It provides a few high-level APIs namely:

    -

  • data - Create datasets for training, inference and evaluation. We -use the popular πŸ€— -datasets to efficiently -load and slice different modalities of data

    • -

  • models - Use common model implementations using -scikit-learn and -PyTorch

    • -

  • tasks - Use common ML task formulations such as binary -classification or multi-label classification on tabular, time-series -and image data

    • +

  • data - Create datasets for training, inference and evaluation. We use the popular πŸ€— datasets to efficiently load and slice different modalities of data

    • +

  • models - Use common model implementations using scikit-learn and PyTorch

    • +

  • tasks - Use common ML task formulations such as binary classification or multi-label classification on tabular, time-series and image data

  • evaluate - Evaluate models on clinical prediction tasks

  • monitor - Detect dataset shift relevant for clinical use cases

    • -

  • report - Create model report -cards -for clinical ML models

    • +

  • report - Create model report cards for clinical ML models

-

cyclops also provides example end-to-end use case implementations on -clinical datasets such as

+

cyclops also provides example end-to-end use case implementations on clinical datasets such as

@@ -212,12 +199,7 @@

Installing cyclops using pip
python3 -m pip install pycyclops
-

cyclops has many optional dependencies that are used for specific -functionality. For example, the -monai library is used for -loading DICOM images to create datasets. All optional dependencies can -be installed with pycyclops[all], and specific sets of dependencies -are listed in the sections below.

+

cyclops has many optional dependencies that are used for specific functionality. For example, the monai library is used for loading DICOM images to create datasets. All optional dependencies can be installed with pycyclops[all], and specific sets of dependencies are listed in the sections below.

@@ -228,67 +210,31 @@

Installing cyclops using pip

- + - + - + - + - + - + - +

Dependency

xgboost

xgboost

Allows use -of -XGBoos -t -model

Allows use of XGBoost model

torch

torch

Allows use -of -`PyTorch < -https://pyto -rch.org/>`__ -models

Allows use of PyTorch models

torchvision

torchvision

Allows use -of -`T -orchvision < -https://pyto -rch.org/visi -on/stable/in -dex.html>`__ -library

Allows use of Torchvision library

torchxrayvision

torchxrayvision

Uses -TorchXR -ayVision -library

Uses TorchXRayVision library

monai

monai

Uses -M -ONAI -to load and -transform -images

Uses MONAI to load and transform images

alibi

alibi

Uses -Alibi -for -additional -ex -plainability -f -unctionality

Uses Alibi for additional explainability functionality

alibi-detect

alibi-detect

Uses Alibi -Detect -for dataset -shift -detection

Uses Alibi Detect for dataset shift detection
@@ -298,21 +244,16 @@

Installing cyclops using pip

Using poetry

-

The development environment can be set up using -poetry. Hence, make -sure it is installed and then run:

+

The development environment can be set up using poetry. Hence, make sure it is installed and then run:

python3 -m poetry install
 source $(poetry env info --path)/bin/activate
-

In order to install dependencies for testing (codestyle, unit tests, -integration tests), run:

+

In order to install dependencies for testing (codestyle, unit tests, integration tests), run:

python3 -m poetry install --with test
-

API documentation is built using -Sphinx and can be locally -built by:

+

API documentation is built using Sphinx and can be locally built by:

python3 -m poetry install --with docs
 cd docs
 make html SPHINXOPTS="-D nbsphinx_allow_errors=True"
@@ -321,9 +262,7 @@ 
Using poetry
 
Contributing

-
Contributing to cyclops is welcomed. See
-Contributing
-for guidelines.

+
Contributing to cyclops is welcomed. See Contributing for guidelines.

@@ -331,19 +270,15 @@

πŸ“š

πŸ““ Notebooks

-

To use jupyter notebooks, the python virtual environment can be -installed and used inside an IPython kernel. After activating the -virtual environment, run:

+

To use jupyter notebooks, the python virtual environment can be installed and used inside an IPython kernel. After activating the virtual environment, run:

python3 -m ipykernel install --user --name <name_of_kernel>
-

Now, you can navigate to the notebook’s Kernel tab and set it as -<name_of_kernel>.

+

Now, you can navigate to the notebook’s Kernel tab and set it as <name_of_kernel>.

πŸŽ“ Citation

-

Reference to cite when you use cyclops in a project or a research -paper:

+

Reference to cite when you use cyclops in a project or a research paper:

@article {Krishnan2022.12.02.22283021,
     author = {Krishnan, Amrit and Subasri, Vallijah and McKeen, Kaden and Kore, Ali and Ogidi, Franklin and Alinoori, Mahshid and Lalani, Nadim and Dhalla, Azra and Verma, Amol and Razak, Fahad and Pandya, Deval and Dolatabadi, Elham},
     title = {CyclOps: Cyclical development towards operationalizing ML models for health},
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"Load-Dataset"]], "Multilabel AUROC by Pathology and Sex": [[133, "Multilabel-AUROC-by-Pathology-and-Sex"]], "Multilabel AUROC by Pathology and Age": [[133, "Multilabel-AUROC-by-Pathology-and-Age"]], "Log Performance Metrics as Tests w/ Thresholds": [[133, "Log-Performance-Metrics-as-Tests-w/-Thresholds"]], "Populate Model Card Fields": [[133, "Populate-Model-Card-Fields"]], "NIHCXR Clinical Drift Experiments Tutorial": [[134, "NIHCXR-Clinical-Drift-Experiments-Tutorial"]], "Import Libraries and Load NIHCXR Dataset": [[134, "Import-Libraries-and-Load-NIHCXR-Dataset"]], "Example 1. Generate Source/Target Dataset for Experiments (1-2)": [[134, "Example-1.-Generate-Source/Target-Dataset-for-Experiments-(1-2)"]], "Example 2. Sensitivity test experiment with 3 dimensionality reduction techniques": [[134, "Example-2.-Sensitivity-test-experiment-with-3-dimensionality-reduction-techniques"]], "Example 3. Sensitivity test experiment with models trained on different datasets": [[134, "Example-3.-Sensitivity-test-experiment-with-models-trained-on-different-datasets"]], "Example 4. Sensitivity test experiment with different clinical shifts": [[134, "Example-4.-Sensitivity-test-experiment-with-different-clinical-shifts"]], "Example 5. Rolling window experiment with synthetic timestamps using biweekly window": [[134, "Example-5.-Rolling-window-experiment-with-synthetic-timestamps-using-biweekly-window"]], "Prolonged Length of Stay Prediction": [[135, "Prolonged-Length-of-Stay-Prediction"]], "Data Querying": [[135, "Data-Querying"]], "Compute length of stay (labels)": [[135, "Compute-length-of-stay-(labels)"]], "Length of stay distribution": [[135, "Length-of-stay-distribution"]], "monitor API": [[136, "monitor-api"]], "Example use cases": [[137, "example-use-cases"]], "Tabular data": [[137, "tabular-data"]], "Kaggle Heart Failure Prediction": [[137, "kaggle-heart-failure-prediction"]], "MIMICIV Mortality Prediction": [[137, "mimiciv-mortality-prediction"]], "Synthea Prolonged Length of Stay Prediction": [[137, "synthea-prolonged-length-of-stay-prediction"]], "Image data": [[137, "image-data"]], "NIH Chest X-ray classification": [[137, "nih-chest-x-ray-classification"]]}, "indexentries": 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"Identifying feature types": [[131, "Identifying-feature-types"], [132, "Identifying-feature-types"], [135, "Identifying-feature-types"]], "Creating data preprocessors": [[131, "Creating-data-preprocessors"], [132, "Creating-data-preprocessors"], [135, "Creating-data-preprocessors"]], "Creating Hugging Face Dataset": [[131, "Creating-Hugging-Face-Dataset"], [132, "Creating-Hugging-Face-Dataset"], [135, "Creating-Hugging-Face-Dataset"]], "Model Creation": [[131, "Model-Creation"], [132, "Model-Creation"], [133, "Model-Creation"], [135, "Model-Creation"]], "Task Creation": [[131, "Task-Creation"], [132, "Task-Creation"], [135, "Task-Creation"]], "Training": [[131, "Training"], [132, "Training"], [135, "Training"]], "Prediction": [[131, "Prediction"], [132, "Prediction"], [135, "Prediction"]], "Evaluation": [[131, "Evaluation"], [132, "Evaluation"], [135, "Evaluation"]], "Performance over time": [[131, "Performance-over-time"]], "Report Generation": [[131, "Report-Generation"], [132, "Report-Generation"], [135, "Report-Generation"]], "Mortality Prediction": [[132, "Mortality-Prediction"]], "Data Querying & Processing": [[132, "Data-Querying-&-Processing"]], "Compute mortality (labels)": [[132, "Compute-mortality-(labels)"]], "Data Inspection and Preprocessing": [[132, "Data-Inspection-and-Preprocessing"], [135, "Data-Inspection-and-Preprocessing"]], "Drop NaNs based on the NAN_THRESHOLD": [[132, "Drop-NaNs-based-on-the-NAN_THRESHOLD"], [135, "Drop-NaNs-based-on-the-NAN_THRESHOLD"]], "Gender distribution": [[132, "Gender-distribution"], [135, "Gender-distribution"]], "Chest X-Ray Disease Classification": [[133, "Chest-X-Ray-Disease-Classification"]], "Generate Historical Reports": [[133, "Generate-Historical-Reports"]], "Initialize Periodic Report": [[133, "Initialize-Periodic-Report"]], "Load Dataset": [[133, "Load-Dataset"]], "Multilabel AUROC by Pathology and Sex": [[133, "Multilabel-AUROC-by-Pathology-and-Sex"]], "Multilabel AUROC by Pathology and Age": [[133, "Multilabel-AUROC-by-Pathology-and-Age"]], "Log Performance Metrics as Tests w/ Thresholds": [[133, "Log-Performance-Metrics-as-Tests-w/-Thresholds"]], "Populate Model Card Fields": [[133, "Populate-Model-Card-Fields"]], "NIHCXR Clinical Drift Experiments Tutorial": [[134, "NIHCXR-Clinical-Drift-Experiments-Tutorial"]], "Import Libraries and Load NIHCXR Dataset": [[134, "Import-Libraries-and-Load-NIHCXR-Dataset"]], "Example 1. Generate Source/Target Dataset for Experiments (1-2)": [[134, "Example-1.-Generate-Source/Target-Dataset-for-Experiments-(1-2)"]], "Example 2. Sensitivity test experiment with 3 dimensionality reduction techniques": [[134, "Example-2.-Sensitivity-test-experiment-with-3-dimensionality-reduction-techniques"]], "Example 3. Sensitivity test experiment with models trained on different datasets": [[134, "Example-3.-Sensitivity-test-experiment-with-models-trained-on-different-datasets"]], "Example 4. Sensitivity test experiment with different clinical shifts": [[134, "Example-4.-Sensitivity-test-experiment-with-different-clinical-shifts"]], "Example 5. Rolling window experiment with synthetic timestamps using biweekly window": [[134, "Example-5.-Rolling-window-experiment-with-synthetic-timestamps-using-biweekly-window"]], "Prolonged Length of Stay Prediction": [[135, "Prolonged-Length-of-Stay-Prediction"]], "Data Querying": [[135, "Data-Querying"]], "Compute length of stay (labels)": [[135, "Compute-length-of-stay-(labels)"]], "Length of stay distribution": [[135, "Length-of-stay-distribution"]], "monitor API": [[136, "monitor-api"]], "Example use cases": [[137, "example-use-cases"]], "Tabular data": [[137, "tabular-data"]], "Kaggle Heart Failure Prediction": [[137, "kaggle-heart-failure-prediction"]], "MIMICIV Mortality Prediction": [[137, "mimiciv-mortality-prediction"]], "Synthea Prolonged Length of Stay Prediction": [[137, "synthea-prolonged-length-of-stay-prediction"]], "Image data": [[137, "image-data"]], "NIH Chest X-ray classification": [[137, "nih-chest-x-ray-classification"]]}, "indexentries": 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"add_state() (multilabelstatscores method)": [[109, "cyclops.evaluate.metrics.stat_scores.MultilabelStatScores.add_state"]], "clone() (multilabelstatscores method)": [[109, "cyclops.evaluate.metrics.stat_scores.MultilabelStatScores.clone"]], "compute() (multilabelstatscores method)": [[109, "cyclops.evaluate.metrics.stat_scores.MultilabelStatScores.compute"]], "reset_state() (multilabelstatscores method)": [[109, "cyclops.evaluate.metrics.stat_scores.MultilabelStatScores.reset_state"]], "update_state() (multilabelstatscores method)": [[109, "cyclops.evaluate.metrics.stat_scores.MultilabelStatScores.update_state"]], "statscores (class in cyclops.evaluate.metrics.stat_scores)": [[110, "cyclops.evaluate.metrics.stat_scores.StatScores"]], "__add__() (statscores method)": [[110, "cyclops.evaluate.metrics.stat_scores.StatScores.__add__"]], "__call__() (statscores method)": [[110, "cyclops.evaluate.metrics.stat_scores.StatScores.__call__"]], "__init__() (statscores method)": [[110, 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"cyclops.monitor.clinical_applicator.ClinicalShiftApplicator"]], "age() (clinicalshiftapplicator method)": [[112, "cyclops.monitor.clinical_applicator.ClinicalShiftApplicator.age"]], "apply_shift() (clinicalshiftapplicator method)": [[112, "cyclops.monitor.clinical_applicator.ClinicalShiftApplicator.apply_shift"]], "custom() (clinicalshiftapplicator method)": [[112, "cyclops.monitor.clinical_applicator.ClinicalShiftApplicator.custom"]], "hospital_type() (clinicalshiftapplicator method)": [[112, "cyclops.monitor.clinical_applicator.ClinicalShiftApplicator.hospital_type"]], "month() (clinicalshiftapplicator method)": [[112, "cyclops.monitor.clinical_applicator.ClinicalShiftApplicator.month"]], "sex() (clinicalshiftapplicator method)": [[112, "cyclops.monitor.clinical_applicator.ClinicalShiftApplicator.sex"]], "time() (clinicalshiftapplicator method)": [[112, "cyclops.monitor.clinical_applicator.ClinicalShiftApplicator.time"]], "cyclops.monitor.synthetic_applicator": [[113, "module-cyclops.monitor.synthetic_applicator"]], "syntheticshiftapplicator (class in cyclops.monitor.synthetic_applicator)": [[114, "cyclops.monitor.synthetic_applicator.SyntheticShiftApplicator"]], "apply_shift() (syntheticshiftapplicator method)": [[114, "cyclops.monitor.synthetic_applicator.SyntheticShiftApplicator.apply_shift"]], "binary_noise_shift() (in module cyclops.monitor.synthetic_applicator)": [[115, "cyclops.monitor.synthetic_applicator.binary_noise_shift"]], "feature_association_shift() (in module cyclops.monitor.synthetic_applicator)": [[116, "cyclops.monitor.synthetic_applicator.feature_association_shift"]], "feature_swap_shift() (in module cyclops.monitor.synthetic_applicator)": [[117, "cyclops.monitor.synthetic_applicator.feature_swap_shift"]], "gaussian_noise_shift() (in module cyclops.monitor.synthetic_applicator)": [[118, "cyclops.monitor.synthetic_applicator.gaussian_noise_shift"]], "knockout_shift() (in module cyclops.monitor.synthetic_applicator)": [[119, "cyclops.monitor.synthetic_applicator.knockout_shift"]], "cyclops.report.report": [[120, "module-cyclops.report.report"]], "modelcardreport (class in cyclops.report.report)": [[121, "cyclops.report.report.ModelCardReport"]], "export() (modelcardreport method)": [[121, "cyclops.report.report.ModelCardReport.export"]], "from_json_file() (modelcardreport class method)": [[121, "cyclops.report.report.ModelCardReport.from_json_file"]], "log_citation() (modelcardreport method)": [[121, "cyclops.report.report.ModelCardReport.log_citation"]], "log_dataset() (modelcardreport method)": [[121, "cyclops.report.report.ModelCardReport.log_dataset"]], "log_descriptor() (modelcardreport method)": [[121, "cyclops.report.report.ModelCardReport.log_descriptor"]], "log_fairness_assessment() (modelcardreport method)": [[121, "cyclops.report.report.ModelCardReport.log_fairness_assessment"]], "log_from_dict() (modelcardreport method)": [[121, "cyclops.report.report.ModelCardReport.log_from_dict"]], "log_image() (modelcardreport method)": [[121, "cyclops.report.report.ModelCardReport.log_image"]], "log_license() (modelcardreport method)": [[121, "cyclops.report.report.ModelCardReport.log_license"]], "log_model_parameters() (modelcardreport method)": [[121, "cyclops.report.report.ModelCardReport.log_model_parameters"]], "log_owner() (modelcardreport method)": [[121, "cyclops.report.report.ModelCardReport.log_owner"]], "log_performance_metrics() (modelcardreport method)": [[121, "cyclops.report.report.ModelCardReport.log_performance_metrics"]], "log_plotly_figure() (modelcardreport method)": [[121, "cyclops.report.report.ModelCardReport.log_plotly_figure"]], "log_quantitative_analysis() (modelcardreport method)": [[121, "cyclops.report.report.ModelCardReport.log_quantitative_analysis"]], "log_reference() (modelcardreport method)": [[121, "cyclops.report.report.ModelCardReport.log_reference"]], "log_regulation() (modelcardreport method)": [[121, "cyclops.report.report.ModelCardReport.log_regulation"]], "log_risk() (modelcardreport method)": [[121, "cyclops.report.report.ModelCardReport.log_risk"]], "log_use_case() (modelcardreport method)": [[121, "cyclops.report.report.ModelCardReport.log_use_case"]], "log_user() (modelcardreport method)": [[121, "cyclops.report.report.ModelCardReport.log_user"]], "log_version() (modelcardreport method)": [[121, "cyclops.report.report.ModelCardReport.log_version"]], "cyclops.tasks.classification": [[122, "module-cyclops.tasks.classification"]], "binarytabularclassificationtask (class in cyclops.tasks.classification)": [[123, "cyclops.tasks.classification.BinaryTabularClassificationTask"]], "__init__() (binarytabularclassificationtask method)": [[123, "cyclops.tasks.classification.BinaryTabularClassificationTask.__init__"]], "add_model() (binarytabularclassificationtask method)": [[123, "cyclops.tasks.classification.BinaryTabularClassificationTask.add_model"]], "data_type 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"cyclops.tasks.classification.MultilabelImageClassificationTask.models_count"]], "predict() (multilabelimageclassificationtask method)": [[124, "cyclops.tasks.classification.MultilabelImageClassificationTask.predict"]], "save_model() (multilabelimageclassificationtask method)": [[124, "cyclops.tasks.classification.MultilabelImageClassificationTask.save_model"]], "task_type (multilabelimageclassificationtask property)": [[124, "cyclops.tasks.classification.MultilabelImageClassificationTask.task_type"]], "cyclops.data": [[125, "module-cyclops.data"]], "cyclops.data.features": [[125, "module-cyclops.data.features"]], "cyclops.evaluate": [[126, "module-cyclops.evaluate"]], "cyclops.evaluate.fairness": [[126, "module-cyclops.evaluate.fairness"]], "cyclops.evaluate.metrics": [[126, "module-cyclops.evaluate.metrics"]], "cyclops.evaluate.metrics.functional": [[126, "module-cyclops.evaluate.metrics.functional"]], "cyclops.monitor": [[127, "module-cyclops.monitor"]], "cyclops.report": [[128, 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\ No newline at end of file
diff --git a/api/tutorials/kaggle/heart_failure_prediction.html b/api/tutorials/kaggle/heart_failure_prediction.html
index 8b2c34142..8024b91e5 100644
--- a/api/tutorials/kaggle/heart_failure_prediction.html
+++ b/api/tutorials/kaggle/heart_failure_prediction.html
@@ -549,7 +549,7 @@ 
Data Loading
 
-2023-11-29 09:17:22,135 INFO cyclops.utils.file - Loading DataFrame from ./data/heart.csv
+2023-11-29 18:39:12,833 INFO cyclops.utils.file - Loading DataFrame from ./data/heart.csv
@@ -631,9 +631,9 @@

Sex values

-
+
@@ -1128,9 +1128,9 @@

Training 
-2023-11-29 09:17:28,925 INFO cyclops.models.wrappers.sk_model - Best alpha: 0.001
-2023-11-29 09:17:28,926 INFO cyclops.models.wrappers.sk_model - Best eta0: 0.01
-2023-11-29 09:17:28,927 INFO cyclops.models.wrappers.sk_model - Best learning_rate: adaptive
+2023-11-29 18:39:19,915 INFO cyclops.models.wrappers.sk_model - Best alpha: 0.001
+2023-11-29 18:39:19,916 INFO cyclops.models.wrappers.sk_model - Best eta0: 0.01
+2023-11-29 18:39:19,916 INFO cyclops.models.wrappers.sk_model - Best learning_rate: adaptive
@@ -1194,7 +1194,7 @@

Prediction

-
+
@@ -1323,55 +1323,55 @@

Evaluation

-
+
-
+
-
+
-
+
-
+
-
+
-
+
-
+
-
+
[36]:
@@ -1395,25 +1395,25 @@ 
Evaluation
-
+
-
+
-
+
-
+

Log the performance metrics to the report.

We can add a performance metric to the model card using the log_performance_metric method, which expects a dictionary where the keys are in the following format: slice_name/metric_name. For instance, overall/accuracy.

@@ -1528,9 +1528,9 @@

Evaluation
-

diff --git a/api/tutorials/kaggle/heart_failure_prediction.ipynb b/api/tutorials/kaggle/heart_failure_prediction.ipynb index c6b498fbb..9b5c8e1ef 100644 --- a/api/tutorials/kaggle/heart_failure_prediction.ipynb +++ b/api/tutorials/kaggle/heart_failure_prediction.ipynb @@ -21,10 +21,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2023-11-29T14:17:17.634124Z", - "iopub.status.busy": "2023-11-29T14:17:17.633607Z", - "iopub.status.idle": "2023-11-29T14:17:21.511786Z", - "shell.execute_reply": "2023-11-29T14:17:21.510846Z" + "iopub.execute_input": "2023-11-29T23:39:07.676070Z", + "iopub.status.busy": "2023-11-29T23:39:07.675451Z", + "iopub.status.idle": "2023-11-29T23:39:11.988839Z", + "shell.execute_reply": "2023-11-29T23:39:11.988079Z" }, "tags": [] }, @@ -83,10 +83,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2023-11-29T14:17:21.518328Z", - "iopub.status.busy": "2023-11-29T14:17:21.517627Z", - "iopub.status.idle": "2023-11-29T14:17:21.523839Z", - "shell.execute_reply": "2023-11-29T14:17:21.522537Z" + "iopub.execute_input": "2023-11-29T23:39:11.992948Z", + "iopub.status.busy": "2023-11-29T23:39:11.992430Z", + "iopub.status.idle": "2023-11-29T23:39:11.996294Z", + "shell.execute_reply": "2023-11-29T23:39:11.995523Z" } }, "outputs": [], @@ -106,10 +106,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2023-11-29T14:17:21.529747Z", - "iopub.status.busy": "2023-11-29T14:17:21.529248Z", - "iopub.status.idle": "2023-11-29T14:17:21.535603Z", - "shell.execute_reply": "2023-11-29T14:17:21.534371Z" + "iopub.execute_input": "2023-11-29T23:39:12.000390Z", + "iopub.status.busy": "2023-11-29T23:39:12.000005Z", + "iopub.status.idle": "2023-11-29T23:39:12.004649Z", + "shell.execute_reply": "2023-11-29T23:39:12.003779Z" }, "tags": [] }, @@ -135,10 +135,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2023-11-29T14:17:21.541504Z", - "iopub.status.busy": "2023-11-29T14:17:21.541007Z", - "iopub.status.idle": "2023-11-29T14:17:22.127428Z", - "shell.execute_reply": "2023-11-29T14:17:22.125863Z" + "iopub.execute_input": "2023-11-29T23:39:12.008791Z", + "iopub.status.busy": "2023-11-29T23:39:12.008267Z", + "iopub.status.idle": "2023-11-29T23:39:12.822103Z", + "shell.execute_reply": "2023-11-29T23:39:12.820649Z" }, "tags": [] }, @@ -158,10 +158,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2023-11-29T14:17:22.133397Z", - "iopub.status.busy": "2023-11-29T14:17:22.132987Z", - "iopub.status.idle": "2023-11-29T14:17:22.155948Z", - "shell.execute_reply": "2023-11-29T14:17:22.154840Z" + "iopub.execute_input": "2023-11-29T23:39:12.829891Z", + "iopub.status.busy": "2023-11-29T23:39:12.829251Z", + "iopub.status.idle": "2023-11-29T23:39:12.858994Z", + "shell.execute_reply": "2023-11-29T23:39:12.857916Z" }, "tags": [] }, @@ -170,7 +170,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-11-29 09:17:22,135 \u001b[1;37mINFO\u001b[0m cyclops.utils.file - Loading DataFrame from ./data/heart.csv\n" + "2023-11-29 18:39:12,833 \u001b[1;37mINFO\u001b[0m cyclops.utils.file - Loading DataFrame from ./data/heart.csv\n" ] }, { @@ -226,10 +226,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2023-11-29T14:17:22.190986Z", - "iopub.status.busy": "2023-11-29T14:17:22.190447Z", - "iopub.status.idle": "2023-11-29T14:17:22.440751Z", - "shell.execute_reply": "2023-11-29T14:17:22.440064Z" + "iopub.execute_input": "2023-11-29T23:39:12.887041Z", + "iopub.status.busy": "2023-11-29T23:39:12.886327Z", + "iopub.status.idle": "2023-11-29T23:39:13.161166Z", + "shell.execute_reply": "2023-11-29T23:39:13.160502Z" }, "tags": [] }, @@ -2037,9 +2037,9 @@ } }, "text/html": [ - "
+
@@ -1385,7 +1385,7 @@

Graphics

-
+
@@ -1393,7 +1393,7 @@

Graphics

-
+
@@ -1455,7 +1455,7 @@

Quantitative Analysis

- 1.0 + 0.81 @@ -1488,11 +1488,11 @@

Quantitative Analysis
- 0.58 + 0.82 - ▼ + ▲ @@ -1521,7 +1521,7 @@

Quantitative Analysis
- 0.63 + 0.58 @@ -1554,7 +1554,7 @@

Quantitative Analysis
- 0.69 + 0.49 @@ -1587,7 +1587,7 @@

Quantitative Analysis
- 1.0 + 0.94 @@ -1621,7 +1621,7 @@

Graphics

-
+
@@ -1629,7 +1629,7 @@

Graphics

-
+
@@ -1637,7 +1637,7 @@

Graphics

-
+
@@ -1691,7 +1691,7 @@

Graphics

-
+
@@ -1966,22 +1966,17 @@

Model Parameters

-

Fit_intercept

- True +

Average

+ False
- - - - -
-

Validation_fraction

- 0.1 +

Tol

+ 0.001
@@ -1989,26 +1984,22 @@

Validation_fraction

-

Learning_rate

- adaptive +

Random_state

+ 123
-
-

Class_weight

- balanced -
-

Max_iter

- 1000 +

Early_stopping

+ True
@@ -2025,8 +2016,8 @@

Power_t

-

Tol

- 0.001 +

Max_iter

+ 1000
@@ -2034,8 +2025,8 @@

Tol

-

Verbose

- 0 +

L1_ratio

+ 0.15
@@ -2043,7 +2034,7 @@

Verbose

-

Early_stopping

+

Fit_intercept

True
@@ -2052,8 +2043,8 @@

Early_stopping

-

Loss

- log_loss +

Verbose

+ 0
@@ -2061,8 +2052,8 @@

Loss

-

Random_state

- 123 +

Alpha

+ 0.001
@@ -2070,8 +2061,8 @@

Random_state

-

Eta0

- 0.01 +

Epsilon

+ 0.1
@@ -2079,8 +2070,8 @@

Eta0

-

Epsilon

- 0.1 +

Shuffle

+ True
@@ -2088,8 +2079,8 @@

Epsilon

-

Warm_start

- False +

Loss

+ log_loss
@@ -2106,7 +2097,7 @@

N_iter_no_change

-

Average

+

Warm_start

False
@@ -2124,8 +2115,8 @@

Penalty

-

Alpha

- 0.001 +

Validation_fraction

+ 0.1
@@ -2133,8 +2124,8 @@

Alpha

-

L1_ratio

- 0.15 +

Learning_rate

+ adaptive
@@ -2142,8 +2133,17 @@

L1_ratio

-

Shuffle

- True +

Class_weight

+ balanced +
+ + + + + +
+

Eta0

+ 0.01
@@ -2446,7 +2446,7 @@

Ethical Considerations

function generate_model_card_plot() { var model_card_plots = [] var overall_indices = [10, 11, 12, 13, 14] - var histories = JSON.parse("{\"0\": [\"0.8421052631578947\", \"0.730327541104787\", \"0.7030760279409386\", \"0.8579741762945138\", \"0.7478288442344909\"], \"1\": [\"0.8260869565217391\", \"0.6905609408639374\", \"0.7962916326810654\", \"0.8121129395576436\", \"0.6388998137840977\"], \"2\": [\"0.6785714285714286\", \"0.6943694225979045\", \"0.6702122624177528\", \"0.7882713316903022\", \"0.8666562328104871\"], \"3\": [\"0.7450980392156863\", \"0.8259786093290672\", \"0.974060141572785\", \"0.9928312400282496\", \"0.9172657552675775\"], \"4\": [\"0.8819444444444444\", \"0.8749460270110991\", \"0.7613036819197363\", \"0.7268856577549472\", \"0.6539097745080193\"], \"5\": [\"0.8623853211009175\", \"0.8767038375540659\", \"0.8908153948427936\", \"0.8806042782373351\", \"0.6322690589221415\"], \"6\": [\"0.8676470588235294\", \"0.9539197196883319\", \"0.985543471901814\", \"1.0\", \"1.0\"], \"7\": [\"0.9076923076923077\", \"0.8848615041817769\", \"0.7622628486624229\", \"0.6196368097170402\", \"0.45721339698824626\"], \"8\": [\"0.8872180451127819\", \"1.0\", \"1.0\", \"0.9841600220585406\", \"0.9455627531644175\"], \"9\": [\"0.927972027972028\", \"1.0\", \"1.0\", \"1.0\", \"1.0\"], \"10\": [\"0.842391304347826\", \"0.9569582132284947\", \"1.0\", \"1.0\", \"1.0\"], \"11\": [\"0.8686868686868687\", \"0.7158606114936176\", \"0.5243330604854617\", \"0.6065760734561116\", \"0.5803581005546472\"], \"12\": [\"0.8431372549019608\", \"0.6856955297647507\", \"0.687324213614098\", \"0.5188924732080029\", \"0.6308644986383557\"], \"13\": [\"0.8557213930348259\", \"0.7222492255001233\", \"0.7166415515322165\", \"0.7849792568929502\", \"0.6868530332587113\"], \"14\": [\"0.9152319464371114\", \"1.0\", \"1.0\", \"1.0\", \"1.0\"], \"15\": [\"0.796875\", \"0.7887949035617763\", \"nan\", \"0.8777348566341282\", \"0.7006120203282235\"]}"); + var histories = JSON.parse("{\"0\": [\"0.8421052631578947\", \"0.9876152707178263\", \"0.8372195166133398\", \"0.9662157320061161\", \"0.8999645657547848\"], \"1\": [\"0.8260869565217391\", \"0.6241901130124994\", \"0.6401987337246035\", \"0.7066895491176313\", \"0.615257658216848\"], \"2\": [\"0.6785714285714286\", \"0.49236320712167914\", \"0.42592801370925565\", \"0.34570117717801885\", \"0.14420694460888597\"], \"3\": [\"0.7450980392156863\", \"0.607758402538723\", \"0.46718526724371723\", \"0.47891800336148377\", \"0.3465274997464647\"], \"4\": [\"0.8819444444444444\", \"1.0\", \"1.0\", \"0.9227302457218001\", \"0.8637584476176359\"], \"5\": [\"0.8623853211009175\", \"0.9349053651847322\", \"1.0\", \"1.0\", \"1.0\"], \"6\": [\"0.8676470588235294\", \"0.9646686569969414\", \"0.9253221444746759\", \"0.9839592786209543\", \"0.8277587169636388\"], \"7\": [\"0.9076923076923077\", \"1.0\", \"1.0\", \"1.0\", \"1.0\"], \"8\": [\"0.8872180451127819\", \"0.978880758407258\", \"1.0\", \"0.9923067759709132\", \"1.0\"], \"9\": [\"0.927972027972028\", \"1.0\", \"0.8643177870297746\", \"0.8490360781596255\", \"0.894517585308534\"], \"10\": [\"0.842391304347826\", \"0.6894251659298075\", \"0.6702272038630812\", \"0.7616034873445499\", \"0.8096530316124602\"], \"11\": [\"0.8686868686868687\", \"0.7586171465469109\", \"0.7101729939723108\", \"0.6978653801673484\", \"0.8170462552594536\"], \"12\": [\"0.8431372549019608\", \"0.7896023147862168\", \"0.6775636180194909\", \"0.6717330471366704\", \"0.5831426588589941\"], \"13\": [\"0.8557213930348259\", \"0.7425707802704926\", \"0.7664274987346631\", \"0.6059799423050559\", \"0.4856203420097341\"], \"14\": [\"0.9152319464371114\", \"0.8890419779904989\", \"0.8339020215474601\", \"0.9221756875331447\", \"0.935944758925748\"], \"15\": [\"0.796875\", \"0.6393345597878707\", \"nan\", \"0.6214280752831901\", \"0.6987766560392897\"]}"); var thresholds = JSON.parse("{\"0\": \"0.7\", \"1\": \"0.7\", \"2\": \"0.7\", \"3\": \"0.7\", \"4\": \"0.7\", \"5\": \"0.7\", \"6\": \"0.7\", \"7\": \"0.7\", \"8\": \"0.7\", \"9\": \"0.7\", \"10\": \"0.7\", \"11\": \"0.7\", \"12\": \"0.7\", \"13\": \"0.7\", \"14\": \"0.7\", \"15\": \"0.7\"}"); var timestamps = JSON.parse("{\"0\": [\"2021-09-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\", \"2022-02-01\"], \"1\": [\"2021-09-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\", \"2022-02-01\"], \"2\": [\"2021-09-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\", \"2022-02-01\"], \"3\": [\"2021-09-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\", \"2022-02-01\"], \"4\": [\"2021-09-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\", \"2022-02-01\"], \"5\": [\"2021-09-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\", \"2022-02-01\"], \"6\": [\"2021-09-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\", \"2022-02-01\"], \"7\": [\"2021-09-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\", \"2022-02-01\"], \"8\": [\"2021-09-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\", \"2022-02-01\"], \"9\": [\"2021-09-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\", \"2022-02-01\"], \"10\": [\"2021-09-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\", \"2022-02-01\"], \"11\": [\"2021-09-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\", \"2022-02-01\"], \"12\": [\"2021-09-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\", \"2022-02-01\"], \"13\": [\"2021-09-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\", \"2022-02-01\"], \"14\": [\"2021-09-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\", \"2022-02-01\"], \"15\": [\"2021-09-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\", \"2022-02-01\"]}"); @@ -2727,10 +2727,10 @@

Ethical Considerations

} } var slices_all = JSON.parse("{\"0\": [\"metric:Accuracy\", \"Sex:F\", \"Age:overall_Age\"], \"1\": [\"metric:Precision\", \"Age:[30 - 50)\", \"Sex:overall_Sex\"], \"2\": [\"metric:Recall\", \"Age:[30 - 50)\", \"Sex:overall_Sex\"], \"3\": [\"metric:F1 Score\", \"Age:[30 - 50)\", \"Sex:overall_Sex\"], \"4\": [\"metric:AUROC\", \"Age:[30 - 50)\", \"Sex:overall_Sex\"], \"5\": [\"metric:Accuracy\", \"Age:[50 - 70)\", \"Sex:overall_Sex\"], \"6\": [\"metric:Precision\", \"Age:[50 - 70)\", \"Sex:overall_Sex\"], \"7\": [\"metric:Recall\", \"Age:[50 - 70)\", \"Sex:overall_Sex\"], \"8\": [\"metric:F1 Score\", \"Age:[50 - 70)\", \"Sex:overall_Sex\"], \"9\": [\"metric:AUROC\", \"Age:[50 - 70)\", \"Sex:overall_Sex\"], \"10\": [\"metric:Accuracy\", \"Sex:overall_Sex\", \"Age:overall_Age\"], \"11\": [\"metric:Precision\", \"Sex:overall_Sex\", \"Age:overall_Age\"], \"12\": [\"metric:Recall\", \"Sex:overall_Sex\", \"Age:overall_Age\"], \"13\": [\"metric:F1 Score\", \"Sex:overall_Sex\", \"Age:overall_Age\"], \"14\": [\"metric:AUROC\", \"Sex:overall_Sex\", \"Age:overall_Age\"], \"15\": [\"metric:Accuracy\", \"Age:[30 - 50)\", \"Sex:overall_Sex\"]}"); - var histories_all = JSON.parse("{\"0\": [\"0.8421052631578947\", \"0.730327541104787\", \"0.7030760279409386\", \"0.8579741762945138\", \"0.7478288442344909\"], \"1\": [\"0.8260869565217391\", \"0.6905609408639374\", \"0.7962916326810654\", \"0.8121129395576436\", \"0.6388998137840977\"], \"2\": [\"0.6785714285714286\", \"0.6943694225979045\", \"0.6702122624177528\", \"0.7882713316903022\", \"0.8666562328104871\"], \"3\": [\"0.7450980392156863\", \"0.8259786093290672\", \"0.974060141572785\", \"0.9928312400282496\", \"0.9172657552675775\"], \"4\": [\"0.8819444444444444\", \"0.8749460270110991\", \"0.7613036819197363\", \"0.7268856577549472\", \"0.6539097745080193\"], \"5\": [\"0.8623853211009175\", \"0.8767038375540659\", \"0.8908153948427936\", \"0.8806042782373351\", \"0.6322690589221415\"], \"6\": [\"0.8676470588235294\", \"0.9539197196883319\", \"0.985543471901814\", \"1.0\", \"1.0\"], \"7\": [\"0.9076923076923077\", \"0.8848615041817769\", \"0.7622628486624229\", \"0.6196368097170402\", \"0.45721339698824626\"], \"8\": [\"0.8872180451127819\", \"1.0\", \"1.0\", \"0.9841600220585406\", \"0.9455627531644175\"], \"9\": [\"0.927972027972028\", \"1.0\", \"1.0\", \"1.0\", \"1.0\"], \"10\": [\"0.842391304347826\", \"0.9569582132284947\", \"1.0\", \"1.0\", \"1.0\"], \"11\": [\"0.8686868686868687\", \"0.7158606114936176\", \"0.5243330604854617\", \"0.6065760734561116\", \"0.5803581005546472\"], \"12\": [\"0.8431372549019608\", \"0.6856955297647507\", \"0.687324213614098\", \"0.5188924732080029\", \"0.6308644986383557\"], \"13\": [\"0.8557213930348259\", \"0.7222492255001233\", \"0.7166415515322165\", \"0.7849792568929502\", \"0.6868530332587113\"], \"14\": [\"0.9152319464371114\", \"1.0\", \"1.0\", \"1.0\", \"1.0\"], \"15\": [\"0.796875\", \"0.7887949035617763\", \"nan\", \"0.8777348566341282\", \"0.7006120203282235\"]}"); + var histories_all = JSON.parse("{\"0\": [\"0.8421052631578947\", \"0.9876152707178263\", \"0.8372195166133398\", \"0.9662157320061161\", \"0.8999645657547848\"], \"1\": [\"0.8260869565217391\", \"0.6241901130124994\", \"0.6401987337246035\", \"0.7066895491176313\", \"0.615257658216848\"], \"2\": [\"0.6785714285714286\", \"0.49236320712167914\", \"0.42592801370925565\", \"0.34570117717801885\", \"0.14420694460888597\"], \"3\": [\"0.7450980392156863\", \"0.607758402538723\", \"0.46718526724371723\", \"0.47891800336148377\", \"0.3465274997464647\"], \"4\": [\"0.8819444444444444\", \"1.0\", \"1.0\", \"0.9227302457218001\", \"0.8637584476176359\"], \"5\": [\"0.8623853211009175\", \"0.9349053651847322\", \"1.0\", \"1.0\", \"1.0\"], \"6\": [\"0.8676470588235294\", \"0.9646686569969414\", \"0.9253221444746759\", \"0.9839592786209543\", \"0.8277587169636388\"], \"7\": [\"0.9076923076923077\", \"1.0\", \"1.0\", \"1.0\", \"1.0\"], \"8\": [\"0.8872180451127819\", \"0.978880758407258\", \"1.0\", \"0.9923067759709132\", \"1.0\"], \"9\": [\"0.927972027972028\", \"1.0\", \"0.8643177870297746\", \"0.8490360781596255\", \"0.894517585308534\"], \"10\": [\"0.842391304347826\", \"0.6894251659298075\", \"0.6702272038630812\", \"0.7616034873445499\", \"0.8096530316124602\"], \"11\": [\"0.8686868686868687\", \"0.7586171465469109\", \"0.7101729939723108\", \"0.6978653801673484\", \"0.8170462552594536\"], \"12\": [\"0.8431372549019608\", \"0.7896023147862168\", \"0.6775636180194909\", \"0.6717330471366704\", \"0.5831426588589941\"], \"13\": [\"0.8557213930348259\", \"0.7425707802704926\", \"0.7664274987346631\", \"0.6059799423050559\", \"0.4856203420097341\"], \"14\": [\"0.9152319464371114\", \"0.8890419779904989\", \"0.8339020215474601\", \"0.9221756875331447\", \"0.935944758925748\"], \"15\": [\"0.796875\", \"0.6393345597878707\", \"nan\", \"0.6214280752831901\", \"0.6987766560392897\"]}"); var thresholds_all = JSON.parse("{\"0\": \"0.7\", \"1\": \"0.7\", \"2\": \"0.7\", \"3\": \"0.7\", \"4\": \"0.7\", \"5\": \"0.7\", \"6\": \"0.7\", \"7\": \"0.7\", \"8\": \"0.7\", \"9\": \"0.7\", \"10\": \"0.7\", \"11\": \"0.7\", \"12\": \"0.7\", \"13\": \"0.7\", \"14\": \"0.7\", \"15\": \"0.7\"}"); - var trends_all = JSON.parse("{\"0\": \"neutral\", \"1\": \"negative\", \"2\": \"positive\", \"3\": \"positive\", \"4\": \"negative\", \"5\": \"negative\", \"6\": \"positive\", \"7\": \"negative\", \"8\": \"positive\", \"9\": \"positive\", \"10\": \"positive\", \"11\": \"negative\", \"12\": \"negative\", \"13\": \"negative\", \"14\": \"positive\", \"15\": \"neutral\"}"); - var passed_all = JSON.parse("{\"0\": true, \"1\": false, \"2\": true, \"3\": true, \"4\": false, \"5\": false, \"6\": true, \"7\": false, \"8\": true, \"9\": true, \"10\": true, \"11\": false, \"12\": false, \"13\": false, \"14\": true, \"15\": true}"); + var trends_all = JSON.parse("{\"0\": \"neutral\", \"1\": \"negative\", \"2\": \"negative\", \"3\": \"negative\", \"4\": \"negative\", \"5\": \"positive\", \"6\": \"neutral\", \"7\": \"positive\", \"8\": \"positive\", \"9\": \"negative\", \"10\": \"neutral\", \"11\": \"negative\", \"12\": \"negative\", \"13\": \"negative\", \"14\": \"neutral\", \"15\": \"neutral\"}"); + var passed_all = JSON.parse("{\"0\": true, \"1\": false, \"2\": false, \"3\": false, \"4\": true, \"5\": true, \"6\": true, \"7\": true, \"8\": true, \"9\": true, \"10\": true, \"11\": true, \"12\": false, \"13\": false, \"14\": true, \"15\": false}"); var names_all = JSON.parse("{\"0\": \"Accuracy\", \"1\": \"Precision\", \"2\": \"Recall\", \"3\": \"F1 Score\", \"4\": \"AUROC\", \"5\": \"Accuracy\", \"6\": \"Precision\", \"7\": \"Recall\", \"8\": \"F1 Score\", \"9\": \"AUROC\", \"10\": \"Accuracy\", \"11\": \"Precision\", \"12\": \"Recall\", \"13\": \"F1 Score\", \"14\": \"AUROC\", \"15\": \"Accuracy\"}"); var timestamps_all = JSON.parse("{\"0\": [\"2021-09-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\", \"2022-02-01\"], \"1\": [\"2021-09-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\", \"2022-02-01\"], \"2\": [\"2021-09-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\", \"2022-02-01\"], \"3\": [\"2021-09-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\", \"2022-02-01\"], \"4\": [\"2021-09-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\", \"2022-02-01\"], \"5\": [\"2021-09-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\", \"2022-02-01\"], \"6\": [\"2021-09-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\", \"2022-02-01\"], \"7\": [\"2021-09-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\", \"2022-02-01\"], \"8\": [\"2021-09-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\", \"2022-02-01\"], \"9\": [\"2021-09-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\", \"2022-02-01\"], \"10\": [\"2021-09-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\", \"2022-02-01\"], \"11\": [\"2021-09-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\", \"2022-02-01\"], \"12\": [\"2021-09-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\", \"2022-02-01\"], \"13\": [\"2021-09-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\", \"2022-02-01\"], \"14\": [\"2021-09-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\", \"2022-02-01\"], \"15\": [\"2021-09-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\", \"2022-02-01\"]}"); @@ -3003,10 +3003,10 @@

Ethical Considerations

} } var slices_all = JSON.parse("{\"0\": [\"metric:Accuracy\", \"Sex:F\", \"Age:overall_Age\"], \"1\": [\"metric:Precision\", \"Age:[30 - 50)\", \"Sex:overall_Sex\"], \"2\": [\"metric:Recall\", \"Age:[30 - 50)\", \"Sex:overall_Sex\"], \"3\": [\"metric:F1 Score\", \"Age:[30 - 50)\", \"Sex:overall_Sex\"], \"4\": [\"metric:AUROC\", \"Age:[30 - 50)\", \"Sex:overall_Sex\"], \"5\": [\"metric:Accuracy\", \"Age:[50 - 70)\", \"Sex:overall_Sex\"], \"6\": [\"metric:Precision\", \"Age:[50 - 70)\", \"Sex:overall_Sex\"], \"7\": [\"metric:Recall\", \"Age:[50 - 70)\", \"Sex:overall_Sex\"], \"8\": [\"metric:F1 Score\", \"Age:[50 - 70)\", \"Sex:overall_Sex\"], \"9\": [\"metric:AUROC\", \"Age:[50 - 70)\", \"Sex:overall_Sex\"], \"10\": [\"metric:Accuracy\", \"Sex:overall_Sex\", \"Age:overall_Age\"], \"11\": [\"metric:Precision\", \"Sex:overall_Sex\", \"Age:overall_Age\"], \"12\": [\"metric:Recall\", \"Sex:overall_Sex\", \"Age:overall_Age\"], \"13\": [\"metric:F1 Score\", \"Sex:overall_Sex\", \"Age:overall_Age\"], \"14\": [\"metric:AUROC\", \"Sex:overall_Sex\", \"Age:overall_Age\"], \"15\": [\"metric:Accuracy\", \"Age:[30 - 50)\", \"Sex:overall_Sex\"]}"); - var histories_all = JSON.parse("{\"0\": [\"0.8421052631578947\", \"0.730327541104787\", \"0.7030760279409386\", \"0.8579741762945138\", \"0.7478288442344909\"], \"1\": [\"0.8260869565217391\", \"0.6905609408639374\", \"0.7962916326810654\", \"0.8121129395576436\", \"0.6388998137840977\"], \"2\": [\"0.6785714285714286\", \"0.6943694225979045\", \"0.6702122624177528\", \"0.7882713316903022\", \"0.8666562328104871\"], \"3\": [\"0.7450980392156863\", \"0.8259786093290672\", \"0.974060141572785\", \"0.9928312400282496\", \"0.9172657552675775\"], \"4\": [\"0.8819444444444444\", \"0.8749460270110991\", \"0.7613036819197363\", \"0.7268856577549472\", \"0.6539097745080193\"], \"5\": [\"0.8623853211009175\", \"0.8767038375540659\", \"0.8908153948427936\", \"0.8806042782373351\", \"0.6322690589221415\"], \"6\": [\"0.8676470588235294\", \"0.9539197196883319\", \"0.985543471901814\", \"1.0\", \"1.0\"], \"7\": [\"0.9076923076923077\", \"0.8848615041817769\", \"0.7622628486624229\", \"0.6196368097170402\", \"0.45721339698824626\"], \"8\": [\"0.8872180451127819\", \"1.0\", \"1.0\", \"0.9841600220585406\", \"0.9455627531644175\"], \"9\": [\"0.927972027972028\", \"1.0\", \"1.0\", \"1.0\", \"1.0\"], \"10\": [\"0.842391304347826\", \"0.9569582132284947\", \"1.0\", \"1.0\", \"1.0\"], \"11\": [\"0.8686868686868687\", \"0.7158606114936176\", \"0.5243330604854617\", \"0.6065760734561116\", \"0.5803581005546472\"], \"12\": [\"0.8431372549019608\", \"0.6856955297647507\", \"0.687324213614098\", \"0.5188924732080029\", \"0.6308644986383557\"], \"13\": [\"0.8557213930348259\", \"0.7222492255001233\", \"0.7166415515322165\", \"0.7849792568929502\", \"0.6868530332587113\"], \"14\": [\"0.9152319464371114\", \"1.0\", \"1.0\", \"1.0\", \"1.0\"], \"15\": [\"0.796875\", \"0.7887949035617763\", \"nan\", \"0.8777348566341282\", \"0.7006120203282235\"]}"); + var histories_all = JSON.parse("{\"0\": [\"0.8421052631578947\", \"0.9876152707178263\", \"0.8372195166133398\", \"0.9662157320061161\", \"0.8999645657547848\"], \"1\": [\"0.8260869565217391\", \"0.6241901130124994\", \"0.6401987337246035\", \"0.7066895491176313\", \"0.615257658216848\"], \"2\": [\"0.6785714285714286\", \"0.49236320712167914\", \"0.42592801370925565\", \"0.34570117717801885\", \"0.14420694460888597\"], \"3\": [\"0.7450980392156863\", \"0.607758402538723\", \"0.46718526724371723\", \"0.47891800336148377\", \"0.3465274997464647\"], \"4\": [\"0.8819444444444444\", \"1.0\", \"1.0\", \"0.9227302457218001\", \"0.8637584476176359\"], \"5\": [\"0.8623853211009175\", \"0.9349053651847322\", \"1.0\", \"1.0\", \"1.0\"], \"6\": [\"0.8676470588235294\", \"0.9646686569969414\", \"0.9253221444746759\", \"0.9839592786209543\", \"0.8277587169636388\"], \"7\": [\"0.9076923076923077\", \"1.0\", \"1.0\", \"1.0\", \"1.0\"], \"8\": [\"0.8872180451127819\", \"0.978880758407258\", \"1.0\", \"0.9923067759709132\", \"1.0\"], \"9\": [\"0.927972027972028\", \"1.0\", \"0.8643177870297746\", \"0.8490360781596255\", \"0.894517585308534\"], \"10\": [\"0.842391304347826\", \"0.6894251659298075\", \"0.6702272038630812\", \"0.7616034873445499\", \"0.8096530316124602\"], \"11\": [\"0.8686868686868687\", \"0.7586171465469109\", \"0.7101729939723108\", \"0.6978653801673484\", \"0.8170462552594536\"], \"12\": [\"0.8431372549019608\", \"0.7896023147862168\", \"0.6775636180194909\", \"0.6717330471366704\", \"0.5831426588589941\"], \"13\": [\"0.8557213930348259\", \"0.7425707802704926\", \"0.7664274987346631\", \"0.6059799423050559\", \"0.4856203420097341\"], \"14\": [\"0.9152319464371114\", \"0.8890419779904989\", \"0.8339020215474601\", \"0.9221756875331447\", \"0.935944758925748\"], \"15\": [\"0.796875\", \"0.6393345597878707\", \"nan\", \"0.6214280752831901\", \"0.6987766560392897\"]}"); var thresholds_all = JSON.parse("{\"0\": \"0.7\", \"1\": \"0.7\", \"2\": \"0.7\", \"3\": \"0.7\", \"4\": \"0.7\", \"5\": \"0.7\", \"6\": \"0.7\", \"7\": \"0.7\", \"8\": \"0.7\", \"9\": \"0.7\", \"10\": \"0.7\", \"11\": \"0.7\", \"12\": \"0.7\", \"13\": \"0.7\", \"14\": \"0.7\", \"15\": \"0.7\"}"); - var trends_all = JSON.parse("{\"0\": \"neutral\", \"1\": \"negative\", \"2\": \"positive\", \"3\": \"positive\", \"4\": \"negative\", \"5\": \"negative\", \"6\": \"positive\", \"7\": \"negative\", \"8\": \"positive\", \"9\": \"positive\", \"10\": \"positive\", \"11\": \"negative\", \"12\": \"negative\", \"13\": \"negative\", \"14\": \"positive\", \"15\": \"neutral\"}"); - var passed_all = JSON.parse("{\"0\": true, \"1\": false, \"2\": true, \"3\": true, \"4\": false, \"5\": false, \"6\": true, \"7\": false, \"8\": true, \"9\": true, \"10\": true, \"11\": false, \"12\": false, \"13\": false, \"14\": true, \"15\": true}"); + var trends_all = JSON.parse("{\"0\": \"neutral\", \"1\": \"negative\", \"2\": \"negative\", \"3\": \"negative\", \"4\": \"negative\", \"5\": \"positive\", \"6\": \"neutral\", \"7\": \"positive\", \"8\": \"positive\", \"9\": \"negative\", \"10\": \"neutral\", \"11\": \"negative\", \"12\": \"negative\", \"13\": \"negative\", \"14\": \"neutral\", \"15\": \"neutral\"}"); + var passed_all = JSON.parse("{\"0\": true, \"1\": false, \"2\": false, \"3\": false, \"4\": true, \"5\": true, \"6\": true, \"7\": true, \"8\": true, \"9\": true, \"10\": true, \"11\": true, \"12\": false, \"13\": false, \"14\": true, \"15\": false}"); var names_all = JSON.parse("{\"0\": \"Accuracy\", \"1\": \"Precision\", \"2\": \"Recall\", \"3\": \"F1 Score\", \"4\": \"AUROC\", \"5\": \"Accuracy\", \"6\": \"Precision\", \"7\": \"Recall\", \"8\": \"F1 Score\", \"9\": \"AUROC\", \"10\": \"Accuracy\", \"11\": \"Precision\", \"12\": \"Recall\", \"13\": \"F1 Score\", \"14\": \"AUROC\", \"15\": \"Accuracy\"}"); var timestamps_all = JSON.parse("{\"0\": [\"2021-09-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\", \"2022-02-01\"], \"1\": [\"2021-09-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\", \"2022-02-01\"], \"2\": [\"2021-09-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\", \"2022-02-01\"], \"3\": [\"2021-09-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\", \"2022-02-01\"], \"4\": [\"2021-09-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\", \"2022-02-01\"], \"5\": [\"2021-09-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\", \"2022-02-01\"], \"6\": [\"2021-09-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\", \"2022-02-01\"], \"7\": [\"2021-09-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\", \"2022-02-01\"], \"8\": [\"2021-09-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\", \"2022-02-01\"], \"9\": [\"2021-09-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\", \"2022-02-01\"], \"10\": [\"2021-09-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\", \"2022-02-01\"], \"11\": [\"2021-09-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\", \"2022-02-01\"], \"12\": [\"2021-09-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\", \"2022-02-01\"], \"13\": [\"2021-09-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\", \"2022-02-01\"], \"14\": [\"2021-09-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\", \"2022-02-01\"], \"15\": [\"2021-09-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\", \"2022-02-01\"]}"); diff --git a/api/tutorials/mimiciv/mortality_prediction.html b/api/tutorials/mimiciv/mortality_prediction.html index 2d99bbea7..f588e3835 100644 --- a/api/tutorials/mimiciv/mortality_prediction.html +++ b/api/tutorials/mimiciv/mortality_prediction.html @@ -465,6 +465,7 @@

Import Librariesfrom cycquery import MIMICIVQuerier from datasets import Dataset from datasets.features import ClassLabel +from imblearn.over_sampling import SMOTE from sklearn.compose import ColumnTransformer from sklearn.impute import SimpleImputer from sklearn.pipeline import Pipeline @@ -705,9 +706,9 @@

Compute mortality (labels) 
-2023-11-29 09:17:40,862 INFO cycquery.orm    - Database setup, ready to run queries!
-2023-11-29 09:17:47,656 INFO cycquery.orm    - Query returned successfully!
-2023-11-29 09:17:47,657 INFO cycquery.utils.profile - Finished executing function run_query in 4.768326 s
+2023-11-29 18:39:32,429 INFO cycquery.orm    - Database setup, ready to run queries!
+2023-11-29 18:39:39,538 INFO cycquery.orm    - Query returned successfully!
+2023-11-29 18:39:39,539 INFO cycquery.utils.profile - Finished executing function run_query in 5.097803 s

@@ -726,8 +727,8 @@

Compute mortality (labels) 
-2023-11-29 09:18:03,520 INFO cycquery.orm    - Query returned successfully!
-2023-11-29 09:18:03,522 INFO cycquery.utils.profile - Finished executing function run_query in 15.155576 s
+2023-11-29 18:40:12,334 INFO cycquery.orm    - Query returned successfully!
+2023-11-29 18:40:12,335 INFO cycquery.utils.profile - Finished executing function run_query in 32.061962 s

@@ -792,9 +793,9 @@

Drop NaNs based on the

-
+
@@ -1253,7 +1259,7 @@

Training mortality_task.train( dataset["train"], model_name=model_name, - transforms=preprocessor, + transforms=preprocessor_pipeline, best_model_params=best_model_params, )

@@ -1264,12 +1270,12 @@

Training 
-2023-11-29 09:19:26,103 INFO cyclops.models.wrappers.sk_model - Best reg_lambda: 10
-2023-11-29 09:19:26,104 INFO cyclops.models.wrappers.sk_model - Best n_estimators: 100
-2023-11-29 09:19:26,104 INFO cyclops.models.wrappers.sk_model - Best max_depth: 5
-2023-11-29 09:19:26,105 INFO cyclops.models.wrappers.sk_model - Best learning_rate: 0.1
-2023-11-29 09:19:26,105 INFO cyclops.models.wrappers.sk_model - Best gamma: 2
-2023-11-29 09:19:26,106 INFO cyclops.models.wrappers.sk_model - Best colsample_bytree: 1
+2023-11-29 18:42:54,325 INFO cyclops.models.wrappers.sk_model - Best reg_lambda: 0
+2023-11-29 18:42:54,325 INFO cyclops.models.wrappers.sk_model - Best n_estimators: 500
+2023-11-29 18:42:54,326 INFO cyclops.models.wrappers.sk_model - Best max_depth: 5
+2023-11-29 18:42:54,326 INFO cyclops.models.wrappers.sk_model - Best learning_rate: 0.1
+2023-11-29 18:42:54,326 INFO cyclops.models.wrappers.sk_model - Best gamma: 2
+2023-11-29 18:42:54,327 INFO cyclops.models.wrappers.sk_model - Best colsample_bytree: 0.7
@@ -1278,26 +1284,26 @@

Training 

XGBClassifier(base_score=None, booster=None, callbacks=None,
-              colsample_bylevel=None, colsample_bynode=None, colsample_bytree=1,
-              early_stopping_rounds=None, enable_categorical=False,
-              eval_metric='logloss', feature_types=None, gamma=2, gpu_id=None,
-              grow_policy=None, importance_type=None,
-              interaction_constraints=None, learning_rate=0.1, max_bin=None,
-              max_cat_threshold=None, max_cat_to_onehot=None,
-              max_delta_step=None, max_depth=5, max_leaves=None,
-              min_child_weight=3, missing=nan, monotone_constraints=None,
-              n_estimators=100, n_jobs=None, num_parallel_tree=None,
-              predictor=None, random_state=123, ...)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
+ colsample_bylevel=None, colsample_bynode=None, + colsample_bytree=0.7, early_stopping_rounds=None, + enable_categorical=False, eval_metric='logloss', + feature_types=None, gamma=2, gpu_id=None, grow_policy=None, + importance_type=None, interaction_constraints=None, + learning_rate=0.1, max_bin=None, max_cat_threshold=None, + max_cat_to_onehot=None, max_delta_step=None, max_depth=5, + max_leaves=None, min_child_weight=3, missing=nan, + monotone_constraints=None, n_estimators=500, n_jobs=None, + num_parallel_tree=None, predictor=None, random_state=123, ...)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org. 
[25]:
@@ -1313,7 +1319,7 @@ 
Training
 
-{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': 1, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': 'logloss', 'feature_types': None, 'gamma': 2, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': 3, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 100, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 123, 'reg_alpha': None, 'reg_lambda': 10, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': None, 'seed': 123}
+{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': 0.7, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': 'logloss', 'feature_types': None, 'gamma': 2, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': 3, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 500, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 123, 'reg_alpha': None, 'reg_lambda': 0, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': None, 'seed': 123}

Log the model parameters to the report.

@@ -1349,7 +1355,7 @@

Prediction

-
+
@@ -1477,85 +1483,85 @@

Evaluation

-
+
-
+
-
+
-
+
-
+
-
+
-
+
-
+
-
+
-
+
-
+
-
+
-
+
-
+

Log the performance metrics to the report.

We can add a performance metric to the model card using the log_performance_metric method, which expects a dictionary where the keys are in the following format: slice_name/metric_name. For instance, overall/accuracy.

@@ -1653,9 +1659,9 @@

Evaluation
-
diff --git a/api/tutorials/mimiciv/mortality_prediction.ipynb b/api/tutorials/mimiciv/mortality_prediction.ipynb index 16d51bf25..350162df7 100644 --- a/api/tutorials/mimiciv/mortality_prediction.ipynb +++ b/api/tutorials/mimiciv/mortality_prediction.ipynb @@ -21,10 +21,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2023-11-29T14:17:35.495168Z", - "iopub.status.busy": "2023-11-29T14:17:35.494569Z", - "iopub.status.idle": "2023-11-29T14:17:39.401560Z", - "shell.execute_reply": "2023-11-29T14:17:39.400363Z" + "iopub.execute_input": "2023-11-29T23:39:26.403501Z", + "iopub.status.busy": "2023-11-29T23:39:26.402887Z", + "iopub.status.idle": "2023-11-29T23:39:30.468458Z", + "shell.execute_reply": "2023-11-29T23:39:30.467536Z" } }, "outputs": [], @@ -43,6 +43,7 @@ "from cycquery import MIMICIVQuerier\n", "from datasets import Dataset\n", "from datasets.features import ClassLabel\n", + "from imblearn.over_sampling import SMOTE\n", "from sklearn.compose import ColumnTransformer\n", "from sklearn.impute import SimpleImputer\n", "from sklearn.pipeline import Pipeline\n", @@ -86,10 +87,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2023-11-29T14:17:39.406713Z", - "iopub.status.busy": "2023-11-29T14:17:39.406358Z", - "iopub.status.idle": "2023-11-29T14:17:39.410143Z", - "shell.execute_reply": "2023-11-29T14:17:39.409364Z" + "iopub.execute_input": "2023-11-29T23:39:30.473457Z", + "iopub.status.busy": "2023-11-29T23:39:30.472695Z", + "iopub.status.idle": "2023-11-29T23:39:30.477549Z", + "shell.execute_reply": "2023-11-29T23:39:30.476793Z" } }, "outputs": [], @@ -109,10 +110,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2023-11-29T14:17:39.415177Z", - "iopub.status.busy": "2023-11-29T14:17:39.414875Z", - "iopub.status.idle": "2023-11-29T14:17:39.418690Z", - "shell.execute_reply": "2023-11-29T14:17:39.418041Z" + "iopub.execute_input": "2023-11-29T23:39:30.481724Z", + "iopub.status.busy": "2023-11-29T23:39:30.481160Z", + "iopub.status.idle": "2023-11-29T23:39:30.486458Z", + "shell.execute_reply": "2023-11-29T23:39:30.485781Z" } }, "outputs": [], @@ -144,10 +145,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2023-11-29T14:17:39.424127Z", - "iopub.status.busy": "2023-11-29T14:17:39.423696Z", - "iopub.status.idle": "2023-11-29T14:18:48.099898Z", - "shell.execute_reply": "2023-11-29T14:18:48.098966Z" + "iopub.execute_input": "2023-11-29T23:39:30.491175Z", + "iopub.status.busy": "2023-11-29T23:39:30.490639Z", + "iopub.status.idle": "2023-11-29T23:41:19.110165Z", + "shell.execute_reply": "2023-11-29T23:41:19.109472Z" } }, "outputs": [ @@ -155,21 +156,21 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-11-29 09:17:40,862 \u001b[1;37mINFO\u001b[0m cycquery.orm - Database setup, ready to run queries!\n" + "2023-11-29 18:39:32,429 \u001b[1;37mINFO\u001b[0m cycquery.orm - Database setup, ready to run queries!\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "2023-11-29 09:17:47,656 \u001b[1;37mINFO\u001b[0m cycquery.orm - Query returned successfully!\n" + "2023-11-29 18:39:39,538 \u001b[1;37mINFO\u001b[0m cycquery.orm - Query returned successfully!\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "2023-11-29 09:17:47,657 \u001b[1;37mINFO\u001b[0m cycquery.utils.profile - Finished executing function run_query in 4.768326 s\n" + "2023-11-29 18:39:39,539 \u001b[1;37mINFO\u001b[0m cycquery.utils.profile - Finished executing function run_query in 5.097803 s\n" ] }, { @@ -186,14 +187,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-11-29 09:18:03,520 \u001b[1;37mINFO\u001b[0m cycquery.orm - Query returned successfully!\n" + "2023-11-29 18:40:12,334 \u001b[1;37mINFO\u001b[0m cycquery.orm - Query returned successfully!\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "2023-11-29 09:18:03,522 \u001b[1;37mINFO\u001b[0m cycquery.utils.profile - Finished executing function run_query in 15.155576 s\n" + "2023-11-29 18:40:12,335 \u001b[1;37mINFO\u001b[0m cycquery.utils.profile - Finished executing function run_query in 32.061962 s\n" ] }, { @@ -389,10 +390,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2023-11-29T14:18:48.105149Z", - "iopub.status.busy": "2023-11-29T14:18:48.104945Z", - "iopub.status.idle": "2023-11-29T14:18:48.245083Z", - "shell.execute_reply": "2023-11-29T14:18:48.244383Z" + "iopub.execute_input": "2023-11-29T23:41:19.114229Z", + "iopub.status.busy": "2023-11-29T23:41:19.114012Z", + "iopub.status.idle": "2023-11-29T23:41:19.267213Z", + "shell.execute_reply": "2023-11-29T23:41:19.266685Z" } }, "outputs": [ @@ -2390,9 +2391,9 @@ } }, "text/html": [ - "
+
@@ -1084,7 +1084,7 @@

Graphics

-
+
@@ -1092,7 +1092,7 @@

Graphics

-
+
@@ -1100,7 +1100,7 @@

Graphics

-
+
@@ -1182,7 +1182,7 @@

Quantitative Analysis

- 0.83 + 0.89 @@ -1215,7 +1215,7 @@

Quantitative Analysis
- 0.21 + 0.38 @@ -1248,7 +1248,7 @@

Quantitative Analysis
- 0.1 + 0.25 @@ -1281,7 +1281,7 @@

Quantitative Analysis
- 0.27 + 0.67 @@ -1314,7 +1314,7 @@

Quantitative Analysis
- 0.86 + 0.79 @@ -1346,7 +1346,7 @@

Graphics

-
+
@@ -1354,7 +1354,7 @@

Graphics

-
+
@@ -1362,7 +1362,7 @@

Graphics

-
+
@@ -1416,7 +1416,7 @@

Graphics

-
+
@@ -1690,6 +1690,10 @@

Model Parameters

+
+

Missing

+ nan +
@@ -1697,41 +1701,45 @@

Model Parameters

N_estimators

- 100 + 500
-
-

Learning_rate

- 0.1 -
-
-

Eval_metric

- logloss -
+
+

Objective

+ binary:logistic +
+
+

Colsample_bytree

+ 0.7 +
+
+

Learning_rate

+ 0.1 +
@@ -1757,10 +1765,6 @@

Eval_metric

-
-

Gamma

- 2 -
@@ -1771,10 +1775,6 @@

Gamma

-
-

Reg_lambda

- 10 -
@@ -1786,8 +1786,8 @@

Reg_lambda

-

Max_depth

- 5 +

Enable_categorical

+ False
@@ -1804,15 +1804,15 @@

Max_depth

-
-

Colsample_bytree

- 1 -
+
+

Reg_lambda

+ 0 +
@@ -1828,20 +1828,24 @@

Colsample_bytree

+
+

Eval_metric

+ logloss +
-
-

Random_state

- 123 -
+
+

Min_child_weight

+ 3 +
@@ -1872,18 +1876,14 @@

Random_state

-
-

Enable_categorical

- False -
-

Missing

- nan +

Seed

+ 123
@@ -1896,8 +1896,8 @@

Missing

-

Min_child_weight

- 3 +

Random_state

+ 123
@@ -1905,8 +1905,8 @@

Min_child_weight

-

Objective

- binary:logistic +

Max_depth

+ 5
@@ -1923,15 +1923,15 @@

Objective

-
-

Seed

- 123 -
+
+

Gamma

+ 2 +
@@ -2213,7 +2213,7 @@

Ethical Considerations

function generate_model_card_plot() { var model_card_plots = [] var overall_indices = [20, 21, 22, 23, 24] - var histories = JSON.parse("{\"0\": [\"0.9942279942279942\", \"1.0\", \"0.9772216244373779\", \"0.9856369246124833\", \"1.0\"], \"1\": [\"0.0\", \"0.0\", \"0.0\", \"0.013299620663894007\", \"0.0\"], \"2\": [\"0.0\", \"0.0\", \"0.0\", \"0.0809989668527891\", \"0.14445721819909296\"], \"3\": [\"0.0\", \"0.0\", \"0.020973815105886143\", \"0.010854554725201294\", \"0.07999137356712058\"], \"4\": [\"0.7714078374455733\", \"0.9255710323617534\", \"0.7945089448143359\", \"0.803954186226459\", \"0.801425933696679\"], \"5\": [\"0.9848771266540642\", \"0.8504471677661822\", \"0.8749386024431693\", \"0.9844580438445864\", \"1.0\"], \"6\": [\"0.4\", \"0.3822896737095082\", \"0.27484991788516855\", \"0.3363899934295726\", \"0.5667436488074996\"], \"7\": [\"0.08695652173913043\", \"0.13372470558538885\", \"0.1479401453349632\", \"0.12822785676290324\", \"0.0\"], \"8\": [\"0.14285714285714285\", \"0.12332398991443574\", \"0.1805536624197745\", \"0.07777050980102032\", \"0.05874753918482507\"], \"9\": [\"0.9017569220504837\", \"0.875569396475515\", \"0.919324204415494\", \"0.8431505226168358\", \"0.9433868075381606\"], \"10\": [\"0.9763663220088626\", \"0.8746009198921261\", \"1.0\", \"0.9548555400773429\", \"0.9825387051292276\"], \"11\": [\"0.5\", \"0.6165795038070493\", \"0.6423729046912994\", \"0.45555579951410063\", \"0.5871637628526757\"], \"12\": [\"0.0625\", \"0.0\", \"0.1520966077315517\", \"0.2295608568836467\", \"0.1493655900542773\"], \"13\": [\"0.1111111111111111\", \"0.0420674856243113\", \"0.0\", \"0.0\", \"0.12932932536952954\"], \"14\": [\"0.8699413767019667\", \"0.9912826396429929\", \"0.9544294676164752\", \"0.8852446236161613\", \"0.7467863972110218\"], \"15\": [\"0.9859906604402935\", \"0.9381671522505842\", \"0.932206806781172\", \"0.8483645910096131\", \"0.889359858178005\"], \"16\": [\"0.0\", \"0.0801605079717288\", \"0.01851684395049885\", \"0.04156759219474436\", \"0.0\"], \"17\": [\"0.0\", \"0.0\", \"0.03938882552373204\", \"0.0476752453423874\", \"0.060820877635413495\"], \"18\": [\"0.0\", \"0.0\", \"0.14887316477419074\", \"0.1310838427809166\", \"0.06904299847498857\"], \"19\": [\"0.8730440967283072\", \"1.0\", \"0.9297742620417593\", \"0.951609220285032\", \"0.9804722823112194\"], \"20\": [\"0.9814230634419909\", \"1.0\", \"0.8800386930779416\", \"0.9097563892160808\", \"0.8273054389631473\"], \"21\": [\"0.3333333333333333\", \"0.33075814040909934\", \"0.33325206024415976\", \"0.2060615956288472\", \"0.20756583671506454\"], \"22\": [\"0.0392156862745098\", \"0.062352922490084575\", \"0.05986752851404827\", \"0.0\", \"0.10427802438781027\"], \"23\": [\"0.07017543859649122\", \"0.0\", \"0.16072699958080439\", \"0.17704571483809475\", \"0.2658757656240651\"], \"24\": [\"0.8726889756616422\", \"0.8894083186308973\", \"0.8685380529009181\", \"0.8402425097015667\", \"0.8632987810626611\"]}"); + var histories = JSON.parse("{\"0\": [\"0.9942279942279942\", \"1.0\", \"1.0\", \"0.8783564126317389\", \"0.8179351114782751\"], \"1\": [\"0.5\", \"0.4689470696078733\", \"0.5521607962170983\", \"0.5117497031163375\", \"0.5468846482859442\"], \"2\": [\"0.25\", \"0.2410629185093236\", \"0.405558516251176\", \"0.4025122855315672\", \"0.5093469738551926\"], \"3\": [\"0.3333333333333333\", \"0.3401123168259615\", \"0.3512857658298257\", \"0.1737522166477075\", \"0.08968971556484677\"], \"4\": [\"0.8679245283018868\", \"0.898539976161913\", \"0.8285065072744036\", \"0.9167610439839499\", \"1.0\"], \"5\": [\"0.9722747321991179\", \"1.0\", \"1.0\", \"1.0\", \"1.0\"], \"6\": [\"0.16129032258064516\", \"0.08489904434145287\", \"0.24867878260814727\", \"0.1657314973006622\", \"0.1602462512471506\"], \"7\": [\"0.21739130434782608\", \"0.2593525012800042\", \"0.13355690779608997\", \"0.0\", \"0.09143021489807017\"], \"8\": [\"0.18518518518518517\", \"0.13756822475992295\", \"0.04595790125882031\", \"0.0\", \"0.13698694730361424\"], \"9\": [\"0.8229734237740465\", \"0.8310310701487316\", \"0.8887032435518512\", \"0.7309328829550903\", \"0.9007205597130927\"], \"10\": [\"0.9638109305760709\", \"0.9396910355865894\", \"0.9482425143984425\", \"1.0\", \"1.0\"], \"11\": [\"0.22580645161290322\", \"0.18647707292184534\", \"0.1920041938246677\", \"0.19589572070652728\", \"0.243423521298061\"], \"12\": [\"0.21875\", \"0.24586123701281476\", \"0.195436557744915\", \"0.12818992139307833\", \"0.22986683516546563\"], \"13\": [\"0.2222222222222222\", \"0.23787166210352517\", \"0.12863906766970545\", \"0.1772291896793858\", \"0.23743722619924515\"], \"14\": [\"0.815194780635401\", \"0.8164472035940429\", \"0.9210628710346137\", \"0.8726448468127834\", \"1.0\"], \"15\": [\"0.9779853235490327\", \"1.0\", \"0.9604537266522587\", \"0.8133761870303216\", \"0.8473061777124823\"], \"16\": [\"0.15\", \"0.22232893480816163\", \"0.26874972111014817\", \"0.18496642710854988\", \"0.01380296974436479\"], \"17\": [\"0.15789473684210525\", \"0.21159988290874593\", \"0.2070842984427737\", \"0.22264546244395172\", \"0.08819446743154422\"], \"18\": [\"0.15384615384615385\", \"0.2896589428944639\", \"0.3844075563070706\", \"0.2943054139466239\", \"0.12599121043336445\"], \"19\": [\"0.8182432432432432\", \"0.7949570687150005\", \"0.7272785530896604\", \"0.633452567709159\", \"0.6069223144240898\"], \"20\": [\"0.9712583245706274\", \"1.0\", \"1.0\", \"0.9410924151381872\", \"0.8885116361216804\"], \"21\": [\"0.19607843137254902\", \"0.32209395214097714\", \"0.2772620578820867\", \"0.3397048909527858\", \"0.38317261122723073\"], \"22\": [\"0.19607843137254902\", \"0.04730887674712522\", \"0.17279446242673727\", \"0.25606872753500287\", \"0.2457789180996587\"], \"23\": [\"0.19607843137254902\", \"0.38495560413767727\", \"0.6021387229110434\", \"0.4974601592658269\", \"0.6698374723441936\"], \"24\": [\"0.8188198905543659\", \"0.9832782328138028\", \"0.9507556939409646\", \"0.888083860111265\", \"0.7922109678314846\"]}"); var thresholds = JSON.parse("{\"0\": \"0.7\", \"1\": \"0.7\", \"2\": \"0.7\", \"3\": \"0.7\", \"4\": \"0.7\", \"5\": \"0.7\", \"6\": \"0.7\", \"7\": \"0.7\", \"8\": \"0.7\", \"9\": \"0.7\", \"10\": \"0.7\", \"11\": \"0.7\", \"12\": \"0.7\", \"13\": \"0.7\", \"14\": \"0.7\", \"15\": \"0.7\", \"16\": \"0.7\", \"17\": \"0.7\", \"18\": \"0.7\", \"19\": \"0.7\", \"20\": \"0.7\", \"21\": \"0.7\", \"22\": \"0.7\", \"23\": \"0.7\", \"24\": \"0.7\"}"); var timestamps = JSON.parse("{\"0\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"1\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"2\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"3\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"4\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"5\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"6\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"7\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"8\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"9\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"10\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"11\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"12\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"13\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"14\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"15\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"16\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"17\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"18\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"19\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"20\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"21\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"22\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"23\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"24\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"]}"); @@ -2494,10 +2494,10 @@

Ethical Considerations

} } var slices_all = JSON.parse("{\"0\": [\"metric:Accuracy\", \"age:[20 - 50)\", \"gender:overall_gender\"], \"1\": [\"metric:Precision\", \"age:[20 - 50)\", \"gender:overall_gender\"], \"2\": [\"metric:Recall\", \"age:[20 - 50)\", \"gender:overall_gender\"], \"3\": [\"metric:F1 Score\", \"age:[20 - 50)\", \"gender:overall_gender\"], \"4\": [\"metric:AUROC\", \"age:[20 - 50)\", \"gender:overall_gender\"], \"5\": [\"metric:Accuracy\", \"age:[50 - 80)\", \"gender:overall_gender\"], \"6\": [\"metric:Precision\", \"age:[50 - 80)\", \"gender:overall_gender\"], \"7\": [\"metric:Recall\", \"age:[50 - 80)\", \"gender:overall_gender\"], \"8\": [\"metric:F1 Score\", \"age:[50 - 80)\", \"gender:overall_gender\"], \"9\": [\"metric:AUROC\", \"age:[50 - 80)\", \"gender:overall_gender\"], \"10\": [\"metric:Accuracy\", \"gender:M\", \"age:overall_age\"], \"11\": [\"metric:Precision\", \"gender:M\", \"age:overall_age\"], \"12\": [\"metric:Recall\", \"gender:M\", \"age:overall_age\"], \"13\": [\"metric:F1 Score\", \"gender:M\", \"age:overall_age\"], \"14\": [\"metric:AUROC\", \"gender:M\", \"age:overall_age\"], \"15\": [\"metric:Accuracy\", \"gender:F\", \"age:overall_age\"], \"16\": [\"metric:Precision\", \"gender:F\", \"age:overall_age\"], \"17\": [\"metric:Recall\", \"gender:F\", \"age:overall_age\"], \"18\": [\"metric:F1 Score\", \"gender:F\", \"age:overall_age\"], \"19\": [\"metric:AUROC\", \"gender:F\", \"age:overall_age\"], \"20\": [\"metric:Accuracy\", \"age:overall_age\", \"gender:overall_gender\"], \"21\": [\"metric:Precision\", \"age:overall_age\", \"gender:overall_gender\"], \"22\": [\"metric:Recall\", \"age:overall_age\", \"gender:overall_gender\"], \"23\": [\"metric:F1 Score\", \"age:overall_age\", \"gender:overall_gender\"], \"24\": [\"metric:AUROC\", \"age:overall_age\", \"gender:overall_gender\"]}"); - var histories_all = JSON.parse("{\"0\": [\"0.9942279942279942\", \"1.0\", \"0.9772216244373779\", \"0.9856369246124833\", \"1.0\"], \"1\": [\"0.0\", \"0.0\", \"0.0\", \"0.013299620663894007\", \"0.0\"], \"2\": [\"0.0\", \"0.0\", \"0.0\", \"0.0809989668527891\", \"0.14445721819909296\"], \"3\": [\"0.0\", \"0.0\", \"0.020973815105886143\", \"0.010854554725201294\", \"0.07999137356712058\"], \"4\": [\"0.7714078374455733\", \"0.9255710323617534\", \"0.7945089448143359\", \"0.803954186226459\", \"0.801425933696679\"], \"5\": [\"0.9848771266540642\", \"0.8504471677661822\", \"0.8749386024431693\", \"0.9844580438445864\", \"1.0\"], \"6\": [\"0.4\", \"0.3822896737095082\", \"0.27484991788516855\", \"0.3363899934295726\", \"0.5667436488074996\"], \"7\": [\"0.08695652173913043\", \"0.13372470558538885\", \"0.1479401453349632\", \"0.12822785676290324\", \"0.0\"], \"8\": [\"0.14285714285714285\", \"0.12332398991443574\", \"0.1805536624197745\", \"0.07777050980102032\", \"0.05874753918482507\"], \"9\": [\"0.9017569220504837\", \"0.875569396475515\", \"0.919324204415494\", \"0.8431505226168358\", \"0.9433868075381606\"], \"10\": [\"0.9763663220088626\", \"0.8746009198921261\", \"1.0\", \"0.9548555400773429\", \"0.9825387051292276\"], \"11\": [\"0.5\", \"0.6165795038070493\", \"0.6423729046912994\", \"0.45555579951410063\", \"0.5871637628526757\"], \"12\": [\"0.0625\", \"0.0\", \"0.1520966077315517\", \"0.2295608568836467\", \"0.1493655900542773\"], \"13\": [\"0.1111111111111111\", \"0.0420674856243113\", \"0.0\", \"0.0\", \"0.12932932536952954\"], \"14\": [\"0.8699413767019667\", \"0.9912826396429929\", \"0.9544294676164752\", \"0.8852446236161613\", \"0.7467863972110218\"], \"15\": [\"0.9859906604402935\", \"0.9381671522505842\", \"0.932206806781172\", \"0.8483645910096131\", \"0.889359858178005\"], \"16\": [\"0.0\", \"0.0801605079717288\", \"0.01851684395049885\", \"0.04156759219474436\", \"0.0\"], \"17\": [\"0.0\", \"0.0\", \"0.03938882552373204\", \"0.0476752453423874\", \"0.060820877635413495\"], \"18\": [\"0.0\", \"0.0\", \"0.14887316477419074\", \"0.1310838427809166\", \"0.06904299847498857\"], \"19\": [\"0.8730440967283072\", \"1.0\", \"0.9297742620417593\", \"0.951609220285032\", \"0.9804722823112194\"], \"20\": [\"0.9814230634419909\", \"1.0\", \"0.8800386930779416\", \"0.9097563892160808\", \"0.8273054389631473\"], \"21\": [\"0.3333333333333333\", \"0.33075814040909934\", \"0.33325206024415976\", \"0.2060615956288472\", \"0.20756583671506454\"], \"22\": [\"0.0392156862745098\", \"0.062352922490084575\", \"0.05986752851404827\", \"0.0\", \"0.10427802438781027\"], \"23\": [\"0.07017543859649122\", \"0.0\", \"0.16072699958080439\", \"0.17704571483809475\", \"0.2658757656240651\"], \"24\": [\"0.8726889756616422\", \"0.8894083186308973\", \"0.8685380529009181\", \"0.8402425097015667\", \"0.8632987810626611\"]}"); + var histories_all = JSON.parse("{\"0\": [\"0.9942279942279942\", \"1.0\", \"1.0\", \"0.8783564126317389\", \"0.8179351114782751\"], \"1\": [\"0.5\", \"0.4689470696078733\", \"0.5521607962170983\", \"0.5117497031163375\", \"0.5468846482859442\"], \"2\": [\"0.25\", \"0.2410629185093236\", \"0.405558516251176\", \"0.4025122855315672\", \"0.5093469738551926\"], \"3\": [\"0.3333333333333333\", \"0.3401123168259615\", \"0.3512857658298257\", \"0.1737522166477075\", \"0.08968971556484677\"], \"4\": [\"0.8679245283018868\", \"0.898539976161913\", \"0.8285065072744036\", \"0.9167610439839499\", \"1.0\"], \"5\": [\"0.9722747321991179\", \"1.0\", \"1.0\", \"1.0\", \"1.0\"], \"6\": [\"0.16129032258064516\", \"0.08489904434145287\", \"0.24867878260814727\", \"0.1657314973006622\", \"0.1602462512471506\"], \"7\": [\"0.21739130434782608\", \"0.2593525012800042\", \"0.13355690779608997\", \"0.0\", \"0.09143021489807017\"], \"8\": [\"0.18518518518518517\", \"0.13756822475992295\", \"0.04595790125882031\", \"0.0\", \"0.13698694730361424\"], \"9\": [\"0.8229734237740465\", \"0.8310310701487316\", \"0.8887032435518512\", \"0.7309328829550903\", \"0.9007205597130927\"], \"10\": [\"0.9638109305760709\", \"0.9396910355865894\", \"0.9482425143984425\", \"1.0\", \"1.0\"], \"11\": [\"0.22580645161290322\", \"0.18647707292184534\", \"0.1920041938246677\", \"0.19589572070652728\", \"0.243423521298061\"], \"12\": [\"0.21875\", \"0.24586123701281476\", \"0.195436557744915\", \"0.12818992139307833\", \"0.22986683516546563\"], \"13\": [\"0.2222222222222222\", \"0.23787166210352517\", \"0.12863906766970545\", \"0.1772291896793858\", \"0.23743722619924515\"], \"14\": [\"0.815194780635401\", \"0.8164472035940429\", \"0.9210628710346137\", \"0.8726448468127834\", \"1.0\"], \"15\": [\"0.9779853235490327\", \"1.0\", \"0.9604537266522587\", \"0.8133761870303216\", \"0.8473061777124823\"], \"16\": [\"0.15\", \"0.22232893480816163\", \"0.26874972111014817\", \"0.18496642710854988\", \"0.01380296974436479\"], \"17\": [\"0.15789473684210525\", \"0.21159988290874593\", \"0.2070842984427737\", \"0.22264546244395172\", \"0.08819446743154422\"], \"18\": [\"0.15384615384615385\", \"0.2896589428944639\", \"0.3844075563070706\", \"0.2943054139466239\", \"0.12599121043336445\"], \"19\": [\"0.8182432432432432\", \"0.7949570687150005\", \"0.7272785530896604\", \"0.633452567709159\", \"0.6069223144240898\"], \"20\": [\"0.9712583245706274\", \"1.0\", \"1.0\", \"0.9410924151381872\", \"0.8885116361216804\"], \"21\": [\"0.19607843137254902\", \"0.32209395214097714\", \"0.2772620578820867\", \"0.3397048909527858\", \"0.38317261122723073\"], \"22\": [\"0.19607843137254902\", \"0.04730887674712522\", \"0.17279446242673727\", \"0.25606872753500287\", \"0.2457789180996587\"], \"23\": [\"0.19607843137254902\", \"0.38495560413767727\", \"0.6021387229110434\", \"0.4974601592658269\", \"0.6698374723441936\"], \"24\": [\"0.8188198905543659\", \"0.9832782328138028\", \"0.9507556939409646\", \"0.888083860111265\", \"0.7922109678314846\"]}"); var thresholds_all = JSON.parse("{\"0\": \"0.7\", \"1\": \"0.7\", \"2\": \"0.7\", \"3\": \"0.7\", \"4\": \"0.7\", \"5\": \"0.7\", \"6\": \"0.7\", \"7\": \"0.7\", \"8\": \"0.7\", \"9\": \"0.7\", \"10\": \"0.7\", \"11\": \"0.7\", \"12\": \"0.7\", \"13\": \"0.7\", \"14\": \"0.7\", \"15\": \"0.7\", \"16\": \"0.7\", \"17\": \"0.7\", \"18\": \"0.7\", \"19\": \"0.7\", \"20\": \"0.7\", \"21\": \"0.7\", \"22\": \"0.7\", \"23\": \"0.7\", \"24\": \"0.7\"}"); - var trends_all = JSON.parse("{\"0\": \"neutral\", \"1\": \"neutral\", \"2\": \"positive\", \"3\": \"positive\", \"4\": \"neutral\", \"5\": \"positive\", \"6\": \"positive\", \"7\": \"negative\", \"8\": \"negative\", \"9\": \"neutral\", \"10\": \"neutral\", \"11\": \"neutral\", \"12\": \"positive\", \"13\": \"neutral\", \"14\": \"negative\", \"15\": \"negative\", \"16\": \"neutral\", \"17\": \"positive\", \"18\": \"positive\", \"19\": \"positive\", \"20\": \"negative\", \"21\": \"negative\", \"22\": \"neutral\", \"23\": \"positive\", \"24\": \"neutral\"}"); - var passed_all = JSON.parse("{\"0\": true, \"1\": false, \"2\": false, \"3\": false, \"4\": true, \"5\": true, \"6\": false, \"7\": false, \"8\": false, \"9\": true, \"10\": true, \"11\": false, \"12\": false, \"13\": false, \"14\": true, \"15\": true, \"16\": false, \"17\": false, \"18\": false, \"19\": true, \"20\": true, \"21\": false, \"22\": false, \"23\": false, \"24\": true}"); + var trends_all = JSON.parse("{\"0\": \"negative\", \"1\": \"positive\", \"2\": \"positive\", \"3\": \"negative\", \"4\": \"positive\", \"5\": \"neutral\", \"6\": \"neutral\", \"7\": \"negative\", \"8\": \"negative\", \"9\": \"neutral\", \"10\": \"positive\", \"11\": \"neutral\", \"12\": \"neutral\", \"13\": \"neutral\", \"14\": \"positive\", \"15\": \"negative\", \"16\": \"negative\", \"17\": \"negative\", \"18\": \"neutral\", \"19\": \"negative\", \"20\": \"negative\", \"21\": \"positive\", \"22\": \"positive\", \"23\": \"positive\", \"24\": \"negative\"}"); + var passed_all = JSON.parse("{\"0\": true, \"1\": false, \"2\": false, \"3\": false, \"4\": true, \"5\": true, \"6\": false, \"7\": false, \"8\": false, \"9\": true, \"10\": true, \"11\": false, \"12\": false, \"13\": false, \"14\": true, \"15\": true, \"16\": false, \"17\": false, \"18\": false, \"19\": false, \"20\": true, \"21\": false, \"22\": false, \"23\": false, \"24\": true}"); var names_all = JSON.parse("{\"0\": \"Accuracy\", \"1\": \"Precision\", \"2\": \"Recall\", \"3\": \"F1 Score\", \"4\": \"AUROC\", \"5\": \"Accuracy\", \"6\": \"Precision\", \"7\": \"Recall\", \"8\": \"F1 Score\", \"9\": \"AUROC\", \"10\": \"Accuracy\", \"11\": \"Precision\", \"12\": \"Recall\", \"13\": \"F1 Score\", \"14\": \"AUROC\", \"15\": \"Accuracy\", \"16\": \"Precision\", \"17\": \"Recall\", \"18\": \"F1 Score\", \"19\": \"AUROC\", \"20\": \"Accuracy\", \"21\": \"Precision\", \"22\": \"Recall\", \"23\": \"F1 Score\", \"24\": \"AUROC\"}"); var timestamps_all = JSON.parse("{\"0\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"1\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"2\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"3\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"4\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"5\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"6\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"7\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"8\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"9\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"10\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"11\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"12\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"13\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"14\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"15\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"16\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"17\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"18\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"19\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"20\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"21\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"22\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"23\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"24\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"]}"); @@ -2770,10 +2770,10 @@

Ethical Considerations

} } var slices_all = JSON.parse("{\"0\": [\"metric:Accuracy\", \"age:[20 - 50)\", \"gender:overall_gender\"], \"1\": [\"metric:Precision\", \"age:[20 - 50)\", \"gender:overall_gender\"], \"2\": [\"metric:Recall\", \"age:[20 - 50)\", \"gender:overall_gender\"], \"3\": [\"metric:F1 Score\", \"age:[20 - 50)\", \"gender:overall_gender\"], \"4\": [\"metric:AUROC\", \"age:[20 - 50)\", \"gender:overall_gender\"], \"5\": [\"metric:Accuracy\", \"age:[50 - 80)\", \"gender:overall_gender\"], \"6\": [\"metric:Precision\", \"age:[50 - 80)\", \"gender:overall_gender\"], \"7\": [\"metric:Recall\", \"age:[50 - 80)\", \"gender:overall_gender\"], \"8\": [\"metric:F1 Score\", \"age:[50 - 80)\", \"gender:overall_gender\"], \"9\": [\"metric:AUROC\", \"age:[50 - 80)\", \"gender:overall_gender\"], \"10\": [\"metric:Accuracy\", \"gender:M\", \"age:overall_age\"], \"11\": [\"metric:Precision\", \"gender:M\", \"age:overall_age\"], \"12\": [\"metric:Recall\", \"gender:M\", \"age:overall_age\"], \"13\": [\"metric:F1 Score\", \"gender:M\", \"age:overall_age\"], \"14\": [\"metric:AUROC\", \"gender:M\", \"age:overall_age\"], \"15\": [\"metric:Accuracy\", \"gender:F\", \"age:overall_age\"], \"16\": [\"metric:Precision\", \"gender:F\", \"age:overall_age\"], \"17\": [\"metric:Recall\", \"gender:F\", \"age:overall_age\"], \"18\": [\"metric:F1 Score\", \"gender:F\", \"age:overall_age\"], \"19\": [\"metric:AUROC\", \"gender:F\", \"age:overall_age\"], \"20\": [\"metric:Accuracy\", \"age:overall_age\", \"gender:overall_gender\"], \"21\": [\"metric:Precision\", \"age:overall_age\", \"gender:overall_gender\"], \"22\": [\"metric:Recall\", \"age:overall_age\", \"gender:overall_gender\"], \"23\": [\"metric:F1 Score\", \"age:overall_age\", \"gender:overall_gender\"], \"24\": [\"metric:AUROC\", \"age:overall_age\", \"gender:overall_gender\"]}"); - var histories_all = JSON.parse("{\"0\": [\"0.9942279942279942\", \"1.0\", \"0.9772216244373779\", \"0.9856369246124833\", \"1.0\"], \"1\": [\"0.0\", \"0.0\", \"0.0\", \"0.013299620663894007\", \"0.0\"], \"2\": [\"0.0\", \"0.0\", \"0.0\", \"0.0809989668527891\", \"0.14445721819909296\"], \"3\": [\"0.0\", \"0.0\", \"0.020973815105886143\", \"0.010854554725201294\", \"0.07999137356712058\"], \"4\": [\"0.7714078374455733\", \"0.9255710323617534\", \"0.7945089448143359\", \"0.803954186226459\", \"0.801425933696679\"], \"5\": [\"0.9848771266540642\", \"0.8504471677661822\", \"0.8749386024431693\", \"0.9844580438445864\", \"1.0\"], \"6\": [\"0.4\", \"0.3822896737095082\", \"0.27484991788516855\", \"0.3363899934295726\", \"0.5667436488074996\"], \"7\": [\"0.08695652173913043\", \"0.13372470558538885\", \"0.1479401453349632\", \"0.12822785676290324\", \"0.0\"], \"8\": [\"0.14285714285714285\", \"0.12332398991443574\", \"0.1805536624197745\", \"0.07777050980102032\", \"0.05874753918482507\"], \"9\": [\"0.9017569220504837\", \"0.875569396475515\", \"0.919324204415494\", \"0.8431505226168358\", \"0.9433868075381606\"], \"10\": [\"0.9763663220088626\", \"0.8746009198921261\", \"1.0\", \"0.9548555400773429\", \"0.9825387051292276\"], \"11\": [\"0.5\", \"0.6165795038070493\", \"0.6423729046912994\", \"0.45555579951410063\", \"0.5871637628526757\"], \"12\": [\"0.0625\", \"0.0\", \"0.1520966077315517\", \"0.2295608568836467\", \"0.1493655900542773\"], \"13\": [\"0.1111111111111111\", \"0.0420674856243113\", \"0.0\", \"0.0\", \"0.12932932536952954\"], \"14\": [\"0.8699413767019667\", \"0.9912826396429929\", \"0.9544294676164752\", \"0.8852446236161613\", \"0.7467863972110218\"], \"15\": [\"0.9859906604402935\", \"0.9381671522505842\", \"0.932206806781172\", \"0.8483645910096131\", \"0.889359858178005\"], \"16\": [\"0.0\", \"0.0801605079717288\", \"0.01851684395049885\", \"0.04156759219474436\", \"0.0\"], \"17\": [\"0.0\", \"0.0\", \"0.03938882552373204\", \"0.0476752453423874\", \"0.060820877635413495\"], \"18\": [\"0.0\", \"0.0\", \"0.14887316477419074\", \"0.1310838427809166\", \"0.06904299847498857\"], \"19\": [\"0.8730440967283072\", \"1.0\", \"0.9297742620417593\", \"0.951609220285032\", \"0.9804722823112194\"], \"20\": [\"0.9814230634419909\", \"1.0\", \"0.8800386930779416\", \"0.9097563892160808\", \"0.8273054389631473\"], \"21\": [\"0.3333333333333333\", \"0.33075814040909934\", \"0.33325206024415976\", \"0.2060615956288472\", \"0.20756583671506454\"], \"22\": [\"0.0392156862745098\", \"0.062352922490084575\", \"0.05986752851404827\", \"0.0\", \"0.10427802438781027\"], \"23\": [\"0.07017543859649122\", \"0.0\", \"0.16072699958080439\", \"0.17704571483809475\", \"0.2658757656240651\"], \"24\": [\"0.8726889756616422\", \"0.8894083186308973\", \"0.8685380529009181\", \"0.8402425097015667\", \"0.8632987810626611\"]}"); + var histories_all = JSON.parse("{\"0\": [\"0.9942279942279942\", \"1.0\", \"1.0\", \"0.8783564126317389\", \"0.8179351114782751\"], \"1\": [\"0.5\", \"0.4689470696078733\", \"0.5521607962170983\", \"0.5117497031163375\", \"0.5468846482859442\"], \"2\": [\"0.25\", \"0.2410629185093236\", \"0.405558516251176\", \"0.4025122855315672\", \"0.5093469738551926\"], \"3\": [\"0.3333333333333333\", \"0.3401123168259615\", \"0.3512857658298257\", \"0.1737522166477075\", \"0.08968971556484677\"], \"4\": [\"0.8679245283018868\", \"0.898539976161913\", \"0.8285065072744036\", \"0.9167610439839499\", \"1.0\"], \"5\": [\"0.9722747321991179\", \"1.0\", \"1.0\", \"1.0\", \"1.0\"], \"6\": [\"0.16129032258064516\", \"0.08489904434145287\", \"0.24867878260814727\", \"0.1657314973006622\", \"0.1602462512471506\"], \"7\": [\"0.21739130434782608\", \"0.2593525012800042\", \"0.13355690779608997\", \"0.0\", \"0.09143021489807017\"], \"8\": [\"0.18518518518518517\", \"0.13756822475992295\", \"0.04595790125882031\", \"0.0\", \"0.13698694730361424\"], \"9\": [\"0.8229734237740465\", \"0.8310310701487316\", \"0.8887032435518512\", \"0.7309328829550903\", \"0.9007205597130927\"], \"10\": [\"0.9638109305760709\", \"0.9396910355865894\", \"0.9482425143984425\", \"1.0\", \"1.0\"], \"11\": [\"0.22580645161290322\", \"0.18647707292184534\", \"0.1920041938246677\", \"0.19589572070652728\", \"0.243423521298061\"], \"12\": [\"0.21875\", \"0.24586123701281476\", \"0.195436557744915\", \"0.12818992139307833\", \"0.22986683516546563\"], \"13\": [\"0.2222222222222222\", \"0.23787166210352517\", \"0.12863906766970545\", \"0.1772291896793858\", \"0.23743722619924515\"], \"14\": [\"0.815194780635401\", \"0.8164472035940429\", \"0.9210628710346137\", \"0.8726448468127834\", \"1.0\"], \"15\": [\"0.9779853235490327\", \"1.0\", \"0.9604537266522587\", \"0.8133761870303216\", \"0.8473061777124823\"], \"16\": [\"0.15\", \"0.22232893480816163\", \"0.26874972111014817\", \"0.18496642710854988\", \"0.01380296974436479\"], \"17\": [\"0.15789473684210525\", \"0.21159988290874593\", \"0.2070842984427737\", \"0.22264546244395172\", \"0.08819446743154422\"], \"18\": [\"0.15384615384615385\", \"0.2896589428944639\", \"0.3844075563070706\", \"0.2943054139466239\", \"0.12599121043336445\"], \"19\": [\"0.8182432432432432\", \"0.7949570687150005\", \"0.7272785530896604\", \"0.633452567709159\", \"0.6069223144240898\"], \"20\": [\"0.9712583245706274\", \"1.0\", \"1.0\", \"0.9410924151381872\", \"0.8885116361216804\"], \"21\": [\"0.19607843137254902\", \"0.32209395214097714\", \"0.2772620578820867\", \"0.3397048909527858\", \"0.38317261122723073\"], \"22\": [\"0.19607843137254902\", \"0.04730887674712522\", \"0.17279446242673727\", \"0.25606872753500287\", \"0.2457789180996587\"], \"23\": [\"0.19607843137254902\", \"0.38495560413767727\", \"0.6021387229110434\", \"0.4974601592658269\", \"0.6698374723441936\"], \"24\": [\"0.8188198905543659\", \"0.9832782328138028\", \"0.9507556939409646\", \"0.888083860111265\", \"0.7922109678314846\"]}"); var thresholds_all = JSON.parse("{\"0\": \"0.7\", \"1\": \"0.7\", \"2\": \"0.7\", \"3\": \"0.7\", \"4\": \"0.7\", \"5\": \"0.7\", \"6\": \"0.7\", \"7\": \"0.7\", \"8\": \"0.7\", \"9\": \"0.7\", \"10\": \"0.7\", \"11\": \"0.7\", \"12\": \"0.7\", \"13\": \"0.7\", \"14\": \"0.7\", \"15\": \"0.7\", \"16\": \"0.7\", \"17\": \"0.7\", \"18\": \"0.7\", \"19\": \"0.7\", \"20\": \"0.7\", \"21\": \"0.7\", \"22\": \"0.7\", \"23\": \"0.7\", \"24\": \"0.7\"}"); - var trends_all = JSON.parse("{\"0\": \"neutral\", \"1\": \"neutral\", \"2\": \"positive\", \"3\": \"positive\", \"4\": \"neutral\", \"5\": \"positive\", \"6\": \"positive\", \"7\": \"negative\", \"8\": \"negative\", \"9\": \"neutral\", \"10\": \"neutral\", \"11\": \"neutral\", \"12\": \"positive\", \"13\": \"neutral\", \"14\": \"negative\", \"15\": \"negative\", \"16\": \"neutral\", \"17\": \"positive\", \"18\": \"positive\", \"19\": \"positive\", \"20\": \"negative\", \"21\": \"negative\", \"22\": \"neutral\", \"23\": \"positive\", \"24\": \"neutral\"}"); - var passed_all = JSON.parse("{\"0\": true, \"1\": false, \"2\": false, \"3\": false, \"4\": true, \"5\": true, \"6\": false, \"7\": false, \"8\": false, \"9\": true, \"10\": true, \"11\": false, \"12\": false, \"13\": false, \"14\": true, \"15\": true, \"16\": false, \"17\": false, \"18\": false, \"19\": true, \"20\": true, \"21\": false, \"22\": false, \"23\": false, \"24\": true}"); + var trends_all = JSON.parse("{\"0\": \"negative\", \"1\": \"positive\", \"2\": \"positive\", \"3\": \"negative\", \"4\": \"positive\", \"5\": \"neutral\", \"6\": \"neutral\", \"7\": \"negative\", \"8\": \"negative\", \"9\": \"neutral\", \"10\": \"positive\", \"11\": \"neutral\", \"12\": \"neutral\", \"13\": \"neutral\", \"14\": \"positive\", \"15\": \"negative\", \"16\": \"negative\", \"17\": \"negative\", \"18\": \"neutral\", \"19\": \"negative\", \"20\": \"negative\", \"21\": \"positive\", \"22\": \"positive\", \"23\": \"positive\", \"24\": \"negative\"}"); + var passed_all = JSON.parse("{\"0\": true, \"1\": false, \"2\": false, \"3\": false, \"4\": true, \"5\": true, \"6\": false, \"7\": false, \"8\": false, \"9\": true, \"10\": true, \"11\": false, \"12\": false, \"13\": false, \"14\": true, \"15\": true, \"16\": false, \"17\": false, \"18\": false, \"19\": false, \"20\": true, \"21\": false, \"22\": false, \"23\": false, \"24\": true}"); var names_all = JSON.parse("{\"0\": \"Accuracy\", \"1\": \"Precision\", \"2\": \"Recall\", \"3\": \"F1 Score\", \"4\": \"AUROC\", \"5\": \"Accuracy\", \"6\": \"Precision\", \"7\": \"Recall\", \"8\": \"F1 Score\", \"9\": \"AUROC\", \"10\": \"Accuracy\", \"11\": \"Precision\", \"12\": \"Recall\", \"13\": \"F1 Score\", \"14\": \"AUROC\", \"15\": \"Accuracy\", \"16\": \"Precision\", \"17\": \"Recall\", \"18\": \"F1 Score\", \"19\": \"AUROC\", \"20\": \"Accuracy\", \"21\": \"Precision\", \"22\": \"Recall\", \"23\": \"F1 Score\", \"24\": \"AUROC\"}"); var timestamps_all = JSON.parse("{\"0\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"1\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"2\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"3\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"4\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"5\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"6\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"7\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"8\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"9\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"10\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"11\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"12\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"13\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"14\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"15\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"16\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"17\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"18\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"19\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"20\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"21\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"22\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"23\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"24\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"]}"); diff --git a/api/tutorials/nihcxr/cxr_classification.html b/api/tutorials/nihcxr/cxr_classification.html index acb9a1e8f..629c8d379 100644 --- a/api/tutorials/nihcxr/cxr_classification.html +++ b/api/tutorials/nihcxr/cxr_classification.html @@ -496,74 +496,74 @@

Generate Historical Reports 
-Flattening the indices: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 1000/1000 [00:53<00:00, 18.84 examples/s]
-Flattening the indices: 100%|β–ˆβ–ˆβ–ˆβ–ˆ| 1000/1000 [00:00<00:00, 543374.01 examples/s]
-Filter: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 1000/1000 [00:00<00:00, 238055.74 examples/s]
-Map: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 400/400 [00:00<00:00, 1797.56 examples/s]
-Filter -> Patient Gender:M: 100%|β–ˆβ–ˆβ–ˆ| 400/400 [00:00<00:00, 43661.10 examples/s]
-Filter -> Patient Gender:F: 100%|β–ˆβ–ˆβ–ˆ| 400/400 [00:00<00:00, 43702.05 examples/s]
-Filter -> overall: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 400/400 [00:00<00:00, 46045.71 examples/s]
-Filter -> Patient Age:[19 - 35]: 100%|β–ˆ| 400/400 [00:00<00:00, 41547.30 examples
-Filter -> Patient Age:[35 - 65]: 100%|β–ˆ| 400/400 [00:00<00:00, 42940.33 examples
-Filter -> Patient Age:[65 - 100]: 100%|β–ˆ| 400/400 [00:00<00:00, 40465.05 example
+Flattening the indices: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 1000/1000 [00:52<00:00, 19.12 examples/s]
+Flattening the indices: 100%|β–ˆβ–ˆβ–ˆβ–ˆ| 1000/1000 [00:00<00:00, 554435.43 examples/s]
+Filter: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 1000/1000 [00:00<00:00, 238556.71 examples/s]
+Map: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 400/400 [00:00<00:00, 1790.78 examples/s]
+Filter -> Patient Gender:M: 100%|β–ˆβ–ˆβ–ˆ| 400/400 [00:00<00:00, 43326.23 examples/s]
+Filter -> Patient Gender:F: 100%|β–ˆβ–ˆβ–ˆ| 400/400 [00:00<00:00, 35970.96 examples/s]
+Filter -> overall: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 400/400 [00:00<00:00, 45044.34 examples/s]
+Filter -> Patient Age:[19 - 35]: 100%|β–ˆ| 400/400 [00:00<00:00, 41191.30 examples
+Filter -> Patient Age:[35 - 65]: 100%|β–ˆ| 400/400 [00:00<00:00, 42204.71 examples
+Filter -> Patient Age:[65 - 100]: 100%|β–ˆ| 400/400 [00:00<00:00, 41689.77 example
 Filter -> Patient Age:[19 - 35]&Patient Gender:M: 100%|β–ˆ| 400/400 [00:00<00:00,
 Filter -> Patient Age:[19 - 35]&Patient Gender:F: 100%|β–ˆ| 400/400 [00:00<00:00,
 Filter -> Patient Age:[35 - 65]&Patient Gender:M: 100%|β–ˆ| 400/400 [00:00<00:00,
 Filter -> Patient Age:[35 - 65]&Patient Gender:F: 100%|β–ˆ| 400/400 [00:00<00:00,
 Filter -> Patient Age:[65 - 100]&Patient Gender:M: 100%|β–ˆ| 400/400 [00:00<00:00,
 Filter -> Patient Age:[65 - 100]&Patient Gender:F: 100%|β–ˆ| 400/400 [00:00<00:00,
-Filter -> overall: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 400/400 [00:00<00:00, 46872.90 examples/s]
-Flattening the indices: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 1000/1000 [00:49<00:00, 20.05 examples/s]
-Flattening the indices: 100%|β–ˆβ–ˆβ–ˆβ–ˆ| 1000/1000 [00:00<00:00, 553849.73 examples/s]
-Filter: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 1000/1000 [00:00<00:00, 237691.49 examples/s]
-Map: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 396/396 [00:00<00:00, 1795.28 examples/s]
-Filter -> Patient Gender:M: 100%|β–ˆβ–ˆβ–ˆ| 396/396 [00:00<00:00, 43594.34 examples/s]
-Filter -> Patient Gender:F: 100%|β–ˆβ–ˆβ–ˆ| 396/396 [00:00<00:00, 43577.18 examples/s]
-Filter -> overall: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 396/396 [00:00<00:00, 41511.16 examples/s]
-Filter -> Patient Age:[19 - 35]: 100%|β–ˆ| 396/396 [00:00<00:00, 36106.70 examples
-Filter -> Patient Age:[35 - 65]: 100%|β–ˆ| 396/396 [00:00<00:00, 42604.70 examples
-Filter -> Patient Age:[65 - 100]: 100%|β–ˆ| 396/396 [00:00<00:00, 38818.00 example
+Filter -> overall: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 400/400 [00:00<00:00, 46542.61 examples/s]
+Flattening the indices: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 1000/1000 [00:55<00:00, 18.09 examples/s]
+Flattening the indices: 100%|β–ˆβ–ˆβ–ˆβ–ˆ| 1000/1000 [00:00<00:00, 549856.32 examples/s]
+Filter: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 1000/1000 [00:00<00:00, 236378.72 examples/s]
+Map: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 396/396 [00:00<00:00, 1778.59 examples/s]
+Filter -> Patient Gender:M: 100%|β–ˆβ–ˆβ–ˆ| 396/396 [00:00<00:00, 42951.76 examples/s]
+Filter -> Patient Gender:F: 100%|β–ˆβ–ˆβ–ˆ| 396/396 [00:00<00:00, 42136.70 examples/s]
+Filter -> overall: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 396/396 [00:00<00:00, 44147.05 examples/s]
+Filter -> Patient Age:[19 - 35]: 100%|β–ˆ| 396/396 [00:00<00:00, 40585.08 examples
+Filter -> Patient Age:[35 - 65]: 100%|β–ˆ| 396/396 [00:00<00:00, 40974.55 examples
+Filter -> Patient Age:[65 - 100]: 100%|β–ˆ| 396/396 [00:00<00:00, 40960.40 example
 Filter -> Patient Age:[19 - 35]&Patient Gender:M: 100%|β–ˆ| 396/396 [00:00<00:00,
 Filter -> Patient Age:[19 - 35]&Patient Gender:F: 100%|β–ˆ| 396/396 [00:00<00:00,
 Filter -> Patient Age:[35 - 65]&Patient Gender:M: 100%|β–ˆ| 396/396 [00:00<00:00,
 Filter -> Patient Age:[35 - 65]&Patient Gender:F: 100%|β–ˆ| 396/396 [00:00<00:00,
 Filter -> Patient Age:[65 - 100]&Patient Gender:M: 100%|β–ˆ| 396/396 [00:00<00:00,
 Filter -> Patient Age:[65 - 100]&Patient Gender:F: 100%|β–ˆ| 396/396 [00:00<00:00,
-Filter -> overall: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 396/396 [00:00<00:00, 46483.39 examples/s]
-Flattening the indices: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 1000/1000 [00:50<00:00, 19.81 examples/s]
-Flattening the indices: 100%|β–ˆβ–ˆβ–ˆβ–ˆ| 1000/1000 [00:00<00:00, 556126.23 examples/s]
-Filter: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 1000/1000 [00:00<00:00, 242669.75 examples/s]
-Map: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 383/383 [00:00<00:00, 1809.86 examples/s]
-Filter -> Patient Gender:M: 100%|β–ˆβ–ˆβ–ˆ| 383/383 [00:00<00:00, 39151.34 examples/s]
-Filter -> Patient Gender:F: 100%|β–ˆβ–ˆβ–ˆ| 383/383 [00:00<00:00, 43045.59 examples/s]
-Filter -> overall: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 383/383 [00:00<00:00, 45120.31 examples/s]
-Filter -> Patient Age:[19 - 35]: 100%|β–ˆ| 383/383 [00:00<00:00, 40864.35 examples
-Filter -> Patient Age:[35 - 65]: 100%|β–ˆ| 383/383 [00:00<00:00, 41867.61 examples
-Filter -> Patient Age:[65 - 100]: 100%|β–ˆ| 383/383 [00:00<00:00, 41341.80 example
+Filter -> overall: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 396/396 [00:00<00:00, 45915.42 examples/s]
+Flattening the indices: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 1000/1000 [00:53<00:00, 18.54 examples/s]
+Flattening the indices: 100%|β–ˆβ–ˆβ–ˆβ–ˆ| 1000/1000 [00:00<00:00, 558347.18 examples/s]
+Filter: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 1000/1000 [00:00<00:00, 237785.82 examples/s]
+Map: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 383/383 [00:00<00:00, 1761.69 examples/s]
+Filter -> Patient Gender:M: 100%|β–ˆβ–ˆβ–ˆ| 383/383 [00:00<00:00, 42496.72 examples/s]
+Filter -> Patient Gender:F: 100%|β–ˆβ–ˆβ–ˆ| 383/383 [00:00<00:00, 41270.64 examples/s]
+Filter -> overall: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 383/383 [00:00<00:00, 44025.94 examples/s]
+Filter -> Patient Age:[19 - 35]: 100%|β–ˆ| 383/383 [00:00<00:00, 39874.36 examples
+Filter -> Patient Age:[35 - 65]: 100%|β–ˆ| 383/383 [00:00<00:00, 41116.42 examples
+Filter -> Patient Age:[65 - 100]: 100%|β–ˆ| 383/383 [00:00<00:00, 40240.94 example
 Filter -> Patient Age:[19 - 35]&Patient Gender:M: 100%|β–ˆ| 383/383 [00:00<00:00,
 Filter -> Patient Age:[19 - 35]&Patient Gender:F: 100%|β–ˆ| 383/383 [00:00<00:00,
 Filter -> Patient Age:[35 - 65]&Patient Gender:M: 100%|β–ˆ| 383/383 [00:00<00:00,
 Filter -> Patient Age:[35 - 65]&Patient Gender:F: 100%|β–ˆ| 383/383 [00:00<00:00,
 Filter -> Patient Age:[65 - 100]&Patient Gender:M: 100%|β–ˆ| 383/383 [00:00<00:00,
 Filter -> Patient Age:[65 - 100]&Patient Gender:F: 100%|β–ˆ| 383/383 [00:00<00:00,
-Filter -> overall: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 383/383 [00:00<00:00, 45036.82 examples/s]
-Flattening the indices: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 1000/1000 [00:53<00:00, 18.72 examples/s]
-Flattening the indices: 100%|β–ˆβ–ˆβ–ˆβ–ˆ| 1000/1000 [00:00<00:00, 552318.15 examples/s]
-Filter: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 1000/1000 [00:00<00:00, 238217.98 examples/s]
-Map: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 411/411 [00:00<00:00, 1834.40 examples/s]
-Filter -> Patient Gender:M: 100%|β–ˆβ–ˆβ–ˆ| 411/411 [00:00<00:00, 41788.49 examples/s]
-Filter -> Patient Gender:F: 100%|β–ˆβ–ˆβ–ˆ| 411/411 [00:00<00:00, 43529.59 examples/s]
-Filter -> overall: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 411/411 [00:00<00:00, 46018.66 examples/s]
-Filter -> Patient Age:[19 - 35]: 100%|β–ˆ| 411/411 [00:00<00:00, 42057.65 examples
-Filter -> Patient Age:[35 - 65]: 100%|β–ˆ| 411/411 [00:00<00:00, 42661.33 examples
-Filter -> Patient Age:[65 - 100]: 100%|β–ˆ| 411/411 [00:00<00:00, 42489.93 example
+Filter -> overall: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 383/383 [00:00<00:00, 45243.58 examples/s]
+Flattening the indices: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 1000/1000 [00:50<00:00, 19.64 examples/s]
+Flattening the indices: 100%|β–ˆβ–ˆβ–ˆβ–ˆ| 1000/1000 [00:00<00:00, 549352.19 examples/s]
+Filter: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 1000/1000 [00:00<00:00, 234475.85 examples/s]
+Map: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 411/411 [00:00<00:00, 1788.75 examples/s]
+Filter -> Patient Gender:M: 100%|β–ˆβ–ˆβ–ˆ| 411/411 [00:00<00:00, 43002.94 examples/s]
+Filter -> Patient Gender:F: 100%|β–ˆβ–ˆβ–ˆ| 411/411 [00:00<00:00, 42734.30 examples/s]
+Filter -> overall: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 411/411 [00:00<00:00, 44711.68 examples/s]
+Filter -> Patient Age:[19 - 35]: 100%|β–ˆ| 411/411 [00:00<00:00, 41308.83 examples
+Filter -> Patient Age:[35 - 65]: 100%|β–ˆ| 411/411 [00:00<00:00, 43833.98 examples
+Filter -> Patient Age:[65 - 100]: 100%|β–ˆ| 411/411 [00:00<00:00, 41575.84 example
 Filter -> Patient Age:[19 - 35]&Patient Gender:M: 100%|β–ˆ| 411/411 [00:00<00:00,
 Filter -> Patient Age:[19 - 35]&Patient Gender:F: 100%|β–ˆ| 411/411 [00:00<00:00,
 Filter -> Patient Age:[35 - 65]&Patient Gender:M: 100%|β–ˆ| 411/411 [00:00<00:00,
 Filter -> Patient Age:[35 - 65]&Patient Gender:F: 100%|β–ˆ| 411/411 [00:00<00:00,
 Filter -> Patient Age:[65 - 100]&Patient Gender:M: 100%|β–ˆ| 411/411 [00:00<00:00,
 Filter -> Patient Age:[65 - 100]&Patient Gender:F: 100%|β–ˆ| 411/411 [00:00<00:00,
-Filter -> overall: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 411/411 [00:00<00:00, 46849.09 examples/s]
+Filter -> overall: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 411/411 [00:00<00:00, 47578.35 examples/s]

CyclOps offers a package for documentation of the model through a model report. The ModelCardReport class is used to populate and generate the model report as an HTML file. The model report has the following sections:

@@ -660,13 +660,13 @@

Model Creation

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+
@@ -782,19 +782,19 @@

Multilabel AUROC by Pathology and Sex

-
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-
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-
+
@@ -852,61 +852,61 @@

Multilabel AUROC by Pathology and Age
-
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[8]:
@@ -962,9 +962,9 @@ 
Multilabel AUROC by Pathology and Age
-

diff --git a/api/tutorials/nihcxr/cxr_classification.ipynb b/api/tutorials/nihcxr/cxr_classification.ipynb index 4d911746c..d6300e920 100644 --- a/api/tutorials/nihcxr/cxr_classification.ipynb +++ b/api/tutorials/nihcxr/cxr_classification.ipynb @@ -24,10 +24,10 @@ "id": "fc1eb72a", "metadata": { "execution": { - "iopub.execute_input": "2023-11-29T14:19:39.486256Z", - "iopub.status.busy": "2023-11-29T14:19:39.485752Z", - "iopub.status.idle": "2023-11-29T14:19:43.257026Z", - "shell.execute_reply": "2023-11-29T14:19:43.255828Z" + "iopub.execute_input": "2023-11-29T23:43:09.098901Z", + "iopub.status.busy": "2023-11-29T23:43:09.098263Z", + "iopub.status.idle": "2023-11-29T23:43:15.528569Z", + "shell.execute_reply": "2023-11-29T23:43:15.527877Z" } }, "outputs": [], @@ -71,10 +71,10 @@ "id": "25c2a16f", "metadata": { "execution": { - "iopub.execute_input": "2023-11-29T14:19:43.263220Z", - "iopub.status.busy": "2023-11-29T14:19:43.262879Z", - "iopub.status.idle": "2023-11-29T14:24:49.460488Z", - "shell.execute_reply": "2023-11-29T14:24:49.458514Z" + "iopub.execute_input": "2023-11-29T23:43:15.533776Z", + "iopub.status.busy": "2023-11-29T23:43:15.533439Z", + "iopub.status.idle": "2023-11-29T23:48:34.441186Z", + "shell.execute_reply": "2023-11-29T23:48:34.439253Z" } }, "outputs": [ @@ -91,14 +91,14 @@ "output_type": "stream", "text": [ "\r", - "Flattening the indices: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 1000/1000 [00:53<00:00, 18.84 examples/s]\r", - "Flattening the indices: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 1000/1000 [00:53<00:00, 18.84 examples/s]\r\n", + "Flattening the indices: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 1000/1000 [00:52<00:00, 19.12 examples/s]\r", + "Flattening the indices: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 1000/1000 [00:52<00:00, 19.12 examples/s]\r\n", "\r", "Flattening the indices: 0%| | 0/1000 [00:00 Patient Gender:M: 0%| | 0/400 [00:00 Patient Gender:M: 100%|β–ˆβ–ˆβ–ˆ| 400/400 [00:00<00:00, 43661.10 examples/s]\r\n" + "Filter -> Patient Gender:M: 100%|β–ˆβ–ˆβ–ˆ| 400/400 [00:00<00:00, 43326.23 examples/s]\r\n" ] }, { @@ -121,10 +121,7 @@ "text": [ "\r", "Filter -> Patient Gender:F: 0%| | 0/400 [00:00 Patient Gender:F: 100%|β–ˆβ–ˆβ–ˆ| 400/400 [00:00<00:00, 43702.05 examples/s]\r\n", - "\r", - "Filter -> overall: 0%| | 0/400 [00:00 overall: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 400/400 [00:00<00:00, 46045.71 examples/s]\r\n" + "Filter -> Patient Gender:F: 100%|β–ˆβ–ˆβ–ˆ| 400/400 [00:00<00:00, 35970.96 examples/s]\r\n" ] }, { @@ -132,8 +129,8 @@ "output_type": "stream", "text": [ "\r", - "Filter -> Patient Age:[19 - 35]: 0%| | 0/400 [00:00 Patient Age:[19 - 35]: 100%|β–ˆ| 400/400 [00:00<00:00, 41547.30 examples\r\n" + "Filter -> overall: 0%| | 0/400 [00:00 overall: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 400/400 [00:00<00:00, 45044.34 examples/s]\r\n" ] }, { @@ -141,10 +138,11 @@ "output_type": "stream", "text": [ "\r", - "Filter -> Patient Age:[35 - 65]: 0%| | 0/400 [00:00 Patient Age:[35 - 65]: 100%|β–ˆ| 400/400 [00:00<00:00, 42940.33 examples\r\n", + "Filter -> Patient Age:[19 - 35]: 0%| | 0/400 [00:00 Patient Age:[19 - 35]: 100%|β–ˆ| 400/400 [00:00<00:00, 41191.30 examples\r\n", "\r", - "Filter -> Patient Age:[65 - 100]: 0%| | 0/400 [00:00 Patient Age:[35 - 65]: 0%| | 0/400 [00:00 Patient Age:[35 - 65]: 100%|β–ˆ| 400/400 [00:00<00:00, 42204.71 examples\r\n" ] }, { @@ -152,40 +150,49 @@ "output_type": "stream", "text": [ "\r", - "Filter -> Patient Age:[65 - 100]: 100%|β–ˆ| 400/400 [00:00<00:00, 40465.05 example\r\n", + "Filter -> Patient Age:[65 - 100]: 0%| | 0/400 [00:00 Patient Age:[65 - 100]: 100%|β–ˆ| 400/400 [00:00<00:00, 41689.77 example\r\n", "\r", "Filter -> Patient Age:[19 - 35]&Patient Gender:M: 0%| | 0/400 [00:00 Patient Age:[19 - 35]&Patient Gender:M: 100%|β–ˆ| 400/400 [00:00<00:00, \r\n", - "\r", - "Filter -> Patient Age:[19 - 35]&Patient Gender:F: 0%| | 0/400 [00:00 Patient Age:[19 - 35]&Patient Gender:F: 100%|β–ˆ| 400/400 [00:00<00:00, \r\n" + "Filter -> Patient Age:[19 - 35]&Patient Gender:M: 100%|β–ˆ| 400/400 [00:00<00:00, \r\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ + "\r", + "Filter -> Patient Age:[19 - 35]&Patient Gender:F: 0%| | 0/400 [00:00 Patient Age:[19 - 35]&Patient Gender:F: 100%|β–ˆ| 400/400 [00:00<00:00, \r\n", "\r", "Filter -> Patient Age:[35 - 65]&Patient Gender:M: 0%| | 0/400 [00:00 Patient Age:[35 - 65]&Patient Gender:M: 100%|β–ˆ| 400/400 [00:00<00:00, \r\n", "\r", - "Filter -> Patient Age:[35 - 65]&Patient Gender:F: 0%| | 0/400 [00:00 Patient Age:[35 - 65]&Patient Gender:F: 100%|β–ˆ| 400/400 [00:00<00:00, \r\n" + "Filter -> Patient Age:[35 - 65]&Patient Gender:F: 0%| | 0/400 [00:00 Patient Age:[35 - 65]&Patient Gender:F: 100%|β–ˆ| 400/400 [00:00<00:00, \r\n", "\r", "Filter -> Patient Age:[65 - 100]&Patient Gender:M: 0%| | 0/400 [00:00 Patient Age:[65 - 100]&Patient Gender:M: 100%|β–ˆ| 400/400 [00:00<00:00,\r\n", "\r", - "Filter -> Patient Age:[65 - 100]&Patient Gender:F: 0%| | 0/400 [00:00 Patient Age:[65 - 100]&Patient Gender:F: 0%| | 0/400 [00:00 Patient Age:[65 - 100]&Patient Gender:F: 100%|β–ˆ| 400/400 [00:00<00:00,\r\n", "\r", "Filter -> overall: 0%| | 0/400 [00:00 overall: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 400/400 [00:00<00:00, 46872.90 examples/s]\r\n" + "Filter -> overall: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 400/400 [00:00<00:00, 46542.61 examples/s]\r\n" ] }, { @@ -201,13 +208,7 @@ "output_type": "stream", "text": [ "\r", - "Flattening the indices: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 1000/1000 [00:49<00:00, 20.05 examples/s]\r", - "Flattening the indices: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 1000/1000 [00:49<00:00, 20.05 examples/s]\r\n", - "\r", - "Flattening the indices: 0%| | 0/1000 [00:00 Patient Gender:M: 0%| | 0/396 [00:00 Patient Gender:M: 100%|β–ˆβ–ˆβ–ˆ| 396/396 [00:00<00:00, 43594.34 examples/s]\r\n" + "Map: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 396/396 [00:00<00:00, 1811.14 examples/s]\r", + "Map: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 396/396 [00:00<00:00, 1778.59 examples/s]\r\n" ] }, { @@ -237,10 +241,10 @@ "output_type": "stream", "text": [ "\r", - "Filter -> Patient Gender:F: 0%| | 0/396 [00:00 Patient Gender:F: 100%|β–ˆβ–ˆβ–ˆ| 396/396 [00:00<00:00, 43577.18 examples/s]\r\n", + "Filter -> Patient Gender:M: 0%| | 0/396 [00:00 Patient Gender:M: 100%|β–ˆβ–ˆβ–ˆ| 396/396 [00:00<00:00, 42951.76 examples/s]\r\n", "\r", - "Filter -> overall: 0%| | 0/396 [00:00 Patient Gender:F: 0%| | 0/396 [00:00 overall: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 396/396 [00:00<00:00, 41511.16 examples/s]\r\n", + "Filter -> Patient Gender:F: 100%|β–ˆβ–ˆβ–ˆ| 396/396 [00:00<00:00, 42136.70 examples/s]\r\n", "\r", - "Filter -> Patient Age:[19 - 35]: 0%| | 0/396 [00:00 overall: 0%| | 0/396 [00:00 overall: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 396/396 [00:00<00:00, 44147.05 examples/s]\r\n" ] }, { @@ -258,10 +263,10 @@ "output_type": "stream", "text": [ "\r", - "Filter -> Patient Age:[19 - 35]: 100%|β–ˆ| 396/396 [00:00<00:00, 36106.70 examples\r\n", + "Filter -> Patient Age:[19 - 35]: 0%| | 0/396 [00:00 Patient Age:[19 - 35]: 100%|β–ˆ| 396/396 [00:00<00:00, 40585.08 examples\r\n", "\r", - "Filter -> Patient Age:[35 - 65]: 0%| | 0/396 [00:00 Patient Age:[35 - 65]: 100%|β–ˆ| 396/396 [00:00<00:00, 42604.70 examples\r\n" + "Filter -> Patient Age:[35 - 65]: 0%| | 0/396 [00:00 Patient Age:[65 - 100]: 0%| | 0/396 [00:00 Patient Age:[65 - 100]: 100%|β–ˆ| 396/396 [00:00<00:00, 38818.00 example\r\n", - "\r", - "Filter -> Patient Age:[19 - 35]&Patient Gender:M: 0%| | 0/396 [00:00 Patient Age:[19 - 35]&Patient Gender:M: 100%|β–ˆ| 396/396 [00:00<00:00, \r\n", + "Filter -> Patient Age:[35 - 65]: 100%|β–ˆ| 396/396 [00:00<00:00, 40974.55 examples\r\n", "\r", - "Filter -> Patient Age:[19 - 35]&Patient Gender:F: 0%| | 0/396 [00:00 Patient Age:[65 - 100]: 0%| | 0/396 [00:00 Patient Age:[65 - 100]: 100%|β–ˆ| 396/396 [00:00<00:00, 40960.40 example\r\n" ] }, { @@ -283,13 +285,14 @@ "output_type": "stream", "text": [ "\r", + "Filter -> Patient Age:[19 - 35]&Patient Gender:M: 0%| | 0/396 [00:00 Patient Age:[19 - 35]&Patient Gender:M: 100%|β–ˆ| 396/396 [00:00<00:00, \r\n", + "\r", + "Filter -> Patient Age:[19 - 35]&Patient Gender:F: 0%| | 0/396 [00:00 Patient Age:[19 - 35]&Patient Gender:F: 100%|β–ˆ| 396/396 [00:00<00:00, \r\n", "\r", "Filter -> Patient Age:[35 - 65]&Patient Gender:M: 0%| | 0/396 [00:00 Patient Age:[35 - 65]&Patient Gender:M: 100%|β–ˆ| 396/396 [00:00<00:00, \r\n", - "\r", - "Filter -> Patient Age:[35 - 65]&Patient Gender:F: 0%| | 0/396 [00:00 Patient Age:[35 - 65]&Patient Gender:F: 100%|β–ˆ| 396/396 [00:00<00:00, \r\n" + "Filter -> Patient Age:[35 - 65]&Patient Gender:M: 100%|β–ˆ| 396/396 [00:00<00:00, \r\n" ] }, { @@ -297,20 +300,24 @@ "output_type": "stream", "text": [ "\r", - "Filter -> Patient Age:[65 - 100]&Patient Gender:M: 0%| | 0/396 [00:00 Patient Age:[65 - 100]&Patient Gender:M: 100%|β–ˆ| 396/396 [00:00<00:00,\r\n", + "Filter -> Patient Age:[35 - 65]&Patient Gender:F: 0%| | 0/396 [00:00 Patient Age:[35 - 65]&Patient Gender:F: 100%|β–ˆ| 396/396 [00:00<00:00, \r\n", "\r", - "Filter -> Patient Age:[65 - 100]&Patient Gender:F: 0%| | 0/396 [00:00 Patient Age:[65 - 100]&Patient Gender:F: 100%|β–ˆ| 396/396 [00:00<00:00,\r\n" + "Filter -> Patient Age:[65 - 100]&Patient Gender:M: 0%| | 0/396 [00:00 Patient Age:[65 - 100]&Patient Gender:M: 100%|β–ˆ| 396/396 [00:00<00:00,\r\n", + "\r", + "Filter -> Patient Age:[65 - 100]&Patient Gender:F: 0%| | 0/396 [00:00 Patient Age:[65 - 100]&Patient Gender:F: 100%|β–ˆ| 396/396 [00:00<00:00,\r\n", "\r", "Filter -> overall: 0%| | 0/396 [00:00 overall: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 396/396 [00:00<00:00, 46483.39 examples/s]\r\n" + "Filter -> overall: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 396/396 [00:00<00:00, 45915.42 examples/s]\r\n" ] }, { @@ -326,21 +333,14 @@ "output_type": "stream", "text": [ "\r", - "Flattening the indices: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 1000/1000 [00:50<00:00, 19.82 examples/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\r", - "Flattening the indices: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 1000/1000 [00:50<00:00, 19.81 examples/s]\r\n", + "Flattening the indices: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 1000/1000 [00:53<00:00, 18.54 examples/s]\r", + "Flattening the indices: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 1000/1000 [00:53<00:00, 18.54 examples/s]\r\n", "\r", "Flattening the indices: 0%| | 0/1000 [00:00 Patient Gender:M: 0%| | 0/383 [00:00 Patient Gender:M: 100%|β–ˆβ–ˆβ–ˆ| 383/383 [00:00<00:00, 39151.34 examples/s]\r\n", + "Filter -> Patient Gender:M: 100%|β–ˆβ–ˆβ–ˆ| 383/383 [00:00<00:00, 42496.72 examples/s]\r\n", "\r", - "Filter -> Patient Gender:F: 0%| | 0/383 [00:00 Patient Gender:F: 100%|β–ˆβ–ˆβ–ˆ| 383/383 [00:00<00:00, 43045.59 examples/s]\r\n" + "Filter -> Patient Gender:F: 0%| | 0/383 [00:00 Patient Gender:F: 100%|β–ˆβ–ˆβ–ˆ| 383/383 [00:00<00:00, 41270.64 examples/s]\r\n", "\r", "Filter -> overall: 0%| | 0/383 [00:00 overall: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 383/383 [00:00<00:00, 45120.31 examples/s]\r\n" + "Filter -> overall: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 383/383 [00:00<00:00, 44025.94 examples/s]\r\n" ] }, { @@ -381,10 +382,9 @@ "text": [ "\r", "Filter -> Patient Age:[19 - 35]: 0%| | 0/383 [00:00 Patient Age:[19 - 35]: 100%|β–ˆ| 383/383 [00:00<00:00, 40864.35 examples\r\n", + "Filter -> Patient Age:[19 - 35]: 100%|β–ˆ| 383/383 [00:00<00:00, 39874.36 examples\r\n", "\r", - "Filter -> Patient Age:[35 - 65]: 0%| | 0/383 [00:00 Patient Age:[35 - 65]: 100%|β–ˆ| 383/383 [00:00<00:00, 41867.61 examples\r\n" + "Filter -> Patient Age:[35 - 65]: 0%| | 0/383 [00:00 Patient Age:[65 - 100]: 0%| | 0/383 [00:00 Patient Age:[65 - 100]: 100%|β–ˆ| 383/383 [00:00<00:00, 41341.80 example\r\n", + "Filter -> Patient Age:[35 - 65]: 100%|β–ˆ| 383/383 [00:00<00:00, 41116.42 examples\r\n", "\r", - "Filter -> Patient Age:[19 - 35]&Patient Gender:M: 0%| | 0/383 [00:00 Patient Age:[19 - 35]&Patient Gender:M: 100%|β–ˆ| 383/383 [00:00<00:00, \r\n", + "Filter -> Patient Age:[65 - 100]: 0%| | 0/383 [00:00 Patient Age:[65 - 100]: 100%|β–ˆ| 383/383 [00:00<00:00, 40240.94 example\r\n", "\r", - "Filter -> Patient Age:[19 - 35]&Patient Gender:F: 0%| | 0/383 [00:00 Patient Age:[19 - 35]&Patient Gender:M: 0%| | 0/383 [00:00 Patient Age:[19 - 35]&Patient Gender:M: 100%|β–ˆ| 383/383 [00:00<00:00, \r\n", + "\r", + "Filter -> Patient Age:[19 - 35]&Patient Gender:F: 0%| | 0/383 [00:00 Patient Age:[19 - 35]&Patient Gender:F: 100%|β–ˆ| 383/383 [00:00<00:00, \r\n", "\r", "Filter -> Patient Age:[35 - 65]&Patient Gender:M: 0%| | 0/383 [00:00 Patient Age:[35 - 65]&Patient Gender:M: 100%|β–ˆ| 383/383 [00:00<00:00, \r\n", - "\r", - "Filter -> Patient Age:[35 - 65]&Patient Gender:F: 0%| | 0/383 [00:00 Patient Age:[35 - 65]&Patient Gender:M: 100%|β–ˆ| 383/383 [00:00<00:00, \r\n" ] }, { @@ -419,22 +419,23 @@ "output_type": "stream", "text": [ "\r", + "Filter -> Patient Age:[35 - 65]&Patient Gender:F: 0%| | 0/383 [00:00 Patient Age:[35 - 65]&Patient Gender:F: 100%|β–ˆ| 383/383 [00:00<00:00, \r\n", "\r", "Filter -> Patient Age:[65 - 100]&Patient Gender:M: 0%| | 0/383 [00:00 Patient Age:[65 - 100]&Patient Gender:M: 100%|β–ˆ| 383/383 [00:00<00:00,\r\n", - "\r", - "Filter -> Patient Age:[65 - 100]&Patient Gender:F: 0%| | 0/383 [00:00 Patient Age:[65 - 100]&Patient Gender:F: 100%|β–ˆ| 383/383 [00:00<00:00,\r\n" + "Filter -> Patient Age:[65 - 100]&Patient Gender:M: 100%|β–ˆ| 383/383 [00:00<00:00,\r\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ + "\r", + "Filter -> Patient Age:[65 - 100]&Patient Gender:F: 0%| | 0/383 [00:00 Patient Age:[65 - 100]&Patient Gender:F: 100%|β–ˆ| 383/383 [00:00<00:00,\r\n", "\r", "Filter -> overall: 0%| | 0/383 [00:00 overall: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 383/383 [00:00<00:00, 45036.82 examples/s]\r\n" + "Filter -> overall: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 383/383 [00:00<00:00, 45243.58 examples/s]\r\n" ] }, { @@ -450,21 +451,20 @@ "output_type": "stream", "text": [ "\r", - "Flattening the indices: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 1000/1000 [00:53<00:00, 18.73 examples/s]\r", - "Flattening the indices: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 1000/1000 [00:53<00:00, 18.72 examples/s]\r\n", + "Flattening the indices: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 1000/1000 [00:50<00:00, 19.65 examples/s]\r", + "Flattening the indices: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 1000/1000 [00:50<00:00, 19.64 examples/s]\r\n", "\r", - "Flattening the indices: 0%| | 0/1000 [00:00 Patient Gender:M: 0%| | 0/411 [00:00 Patient Gender:M: 100%|β–ˆβ–ˆβ–ˆ| 411/411 [00:00<00:00, 41788.49 examples/s]\r\n", + "Filter -> Patient Gender:M: 0%| | 0/411 [00:00 Patient Gender:M: 100%|β–ˆβ–ˆβ–ˆ| 411/411 [00:00<00:00, 43002.94 examples/s]\r\n", "\r", "Filter -> Patient Gender:F: 0%| | 0/411 [00:00 Patient Gender:F: 100%|β–ˆβ–ˆβ–ˆ| 411/411 [00:00<00:00, 43529.59 examples/s]\r\n" + "Filter -> Patient Gender:F: 100%|β–ˆβ–ˆβ–ˆ| 411/411 [00:00<00:00, 42734.30 examples/s]\r\n" ] }, { @@ -497,7 +496,7 @@ "text": [ "\r", "Filter -> overall: 0%| | 0/411 [00:00 overall: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 411/411 [00:00<00:00, 46018.66 examples/s]\r\n" + "Filter -> overall: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 411/411 [00:00<00:00, 44711.68 examples/s]\r\n" ] }, { @@ -506,10 +505,10 @@ "text": [ "\r", "Filter -> Patient Age:[19 - 35]: 0%| | 0/411 [00:00 Patient Age:[19 - 35]: 100%|β–ˆ| 411/411 [00:00<00:00, 42057.65 examples\r\n", + "Filter -> Patient Age:[19 - 35]: 100%|β–ˆ| 411/411 [00:00<00:00, 41308.83 examples\r\n", "\r", "Filter -> Patient Age:[35 - 65]: 0%| | 0/411 [00:00 Patient Age:[35 - 65]: 100%|β–ˆ| 411/411 [00:00<00:00, 42661.33 examples\r\n" + "Filter -> Patient Age:[35 - 65]: 100%|β–ˆ| 411/411 [00:00<00:00, 43833.98 examples\r\n" ] }, { @@ -518,24 +517,23 @@ "text": [ "\r", "Filter -> Patient Age:[65 - 100]: 0%| | 0/411 [00:00 Patient Age:[65 - 100]: 100%|β–ˆ| 411/411 [00:00<00:00, 42489.93 example\r\n", + "Filter -> Patient Age:[65 - 100]: 100%|β–ˆ| 411/411 [00:00<00:00, 41575.84 example\r\n", "\r", - "Filter -> Patient Age:[19 - 35]&Patient Gender:M: 0%| | 0/411 [00:00 Patient Age:[19 - 35]&Patient Gender:M: 100%|β–ˆ| 411/411 [00:00<00:00, \r\n" + "Filter -> Patient Age:[19 - 35]&Patient Gender:M: 0%| | 0/411 [00:00 Patient Age:[19 - 35]&Patient Gender:M: 100%|β–ˆ| 411/411 [00:00<00:00, \r\n", "\r", "Filter -> Patient Age:[19 - 35]&Patient Gender:F: 0%| | 0/411 [00:00 Patient Age:[19 - 35]&Patient Gender:F: 100%|β–ˆ| 411/411 [00:00<00:00, \r\n", "\r", "Filter -> Patient Age:[35 - 65]&Patient Gender:M: 0%| | 0/411 [00:00 Patient Age:[35 - 65]&Patient Gender:M: 100%|β–ˆ| 411/411 [00:00<00:00, \r\n", - "\r", - "Filter -> Patient Age:[35 - 65]&Patient Gender:F: 0%| | 0/411 [00:00 Patient Age:[35 - 65]&Patient Gender:M: 100%|β–ˆ| 411/411 [00:00<00:00, \r\n" ] }, { @@ -543,12 +541,11 @@ "output_type": "stream", "text": [ "\r", + "Filter -> Patient Age:[35 - 65]&Patient Gender:F: 0%| | 0/411 [00:00 Patient Age:[35 - 65]&Patient Gender:F: 100%|β–ˆ| 411/411 [00:00<00:00, \r\n", "\r", "Filter -> Patient Age:[65 - 100]&Patient Gender:M: 0%| | 0/411 [00:00 Patient Age:[65 - 100]&Patient Gender:M: 100%|β–ˆ| 411/411 [00:00<00:00,\r\n", - "\r", - "Filter -> Patient Age:[65 - 100]&Patient Gender:F: 0%| | 0/411 [00:00 Patient Age:[65 - 100]&Patient Gender:M: 100%|β–ˆ| 411/411 [00:00<00:00,\r\n" ] }, { @@ -556,10 +553,11 @@ "output_type": "stream", "text": [ "\r", + "Filter -> Patient Age:[65 - 100]&Patient Gender:F: 0%| | 0/411 [00:00 Patient Age:[65 - 100]&Patient Gender:F: 100%|β–ˆ| 411/411 [00:00<00:00,\r\n", "\r", "Filter -> overall: 0%| | 0/411 [00:00 overall: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 411/411 [00:00<00:00, 46849.09 examples/s]\r\n" + "Filter -> overall: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 411/411 [00:00<00:00, 47578.35 examples/s]\r\n" ] } ], @@ -607,10 +605,10 @@ "id": "03edf1c0", "metadata": { "execution": { - "iopub.execute_input": "2023-11-29T14:24:49.468013Z", - "iopub.status.busy": "2023-11-29T14:24:49.467419Z", - "iopub.status.idle": "2023-11-29T14:24:49.476449Z", - "shell.execute_reply": "2023-11-29T14:24:49.474701Z" + "iopub.execute_input": "2023-11-29T23:48:34.449090Z", + "iopub.status.busy": "2023-11-29T23:48:34.448521Z", + "iopub.status.idle": "2023-11-29T23:48:34.457315Z", + "shell.execute_reply": "2023-11-29T23:48:34.455589Z" } }, "outputs": [], @@ -632,10 +630,10 @@ "id": "6514120e", "metadata": { "execution": { - "iopub.execute_input": "2023-11-29T14:24:49.485734Z", - "iopub.status.busy": "2023-11-29T14:24:49.485220Z", - "iopub.status.idle": "2023-11-29T14:24:52.809542Z", - "shell.execute_reply": "2023-11-29T14:24:52.808643Z" + "iopub.execute_input": "2023-11-29T23:48:34.464249Z", + "iopub.status.busy": "2023-11-29T23:48:34.463627Z", + "iopub.status.idle": "2023-11-29T23:48:37.800142Z", + "shell.execute_reply": "2023-11-29T23:48:37.799474Z" } }, "outputs": [], @@ -679,17 +677,17 @@ "id": "5f624ed4", "metadata": { "execution": { - "iopub.execute_input": "2023-11-29T14:24:52.817523Z", - "iopub.status.busy": "2023-11-29T14:24:52.817314Z", - "iopub.status.idle": "2023-11-29T14:25:06.273155Z", - "shell.execute_reply": "2023-11-29T14:25:06.272503Z" + "iopub.execute_input": "2023-11-29T23:48:37.808659Z", + "iopub.status.busy": "2023-11-29T23:48:37.808286Z", + "iopub.status.idle": "2023-11-29T23:48:51.427047Z", + "shell.execute_reply": "2023-11-29T23:48:51.426393Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f811edb627af44ecbe9a07e16f8dde85", + "model_id": "01d328de08234bd3931eac6a5e4a4653", "version_major": 2, "version_minor": 0 }, @@ -703,7 +701,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b5b09288a3fd435b9cec7232658831f2", + "model_id": "9cdfd76e670f48c18ec7b2af10b452d4", "version_major": 2, "version_minor": 0 }, @@ -763,17 +761,17 @@ "id": "bff27cc1", "metadata": { "execution": { - "iopub.execute_input": "2023-11-29T14:25:06.281965Z", - "iopub.status.busy": "2023-11-29T14:25:06.281627Z", - "iopub.status.idle": "2023-11-29T14:25:06.497817Z", - "shell.execute_reply": "2023-11-29T14:25:06.497116Z" + "iopub.execute_input": "2023-11-29T23:48:51.435548Z", + "iopub.status.busy": "2023-11-29T23:48:51.435212Z", + "iopub.status.idle": "2023-11-29T23:48:51.668034Z", + "shell.execute_reply": "2023-11-29T23:48:51.667293Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a82c4f3c393844ecac905b2da6cbea66", + "model_id": "468d2ba3481f4e59af49e95442b45e8d", "version_major": 2, "version_minor": 0 }, @@ -787,7 +785,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "554333fb244c474ebdc21e8f9ffc4089", + "model_id": "6e62a1b643964824badc574ff7516cce", "version_major": 2, "version_minor": 0 }, @@ -801,7 +799,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a0c3d710533e4604963a8a5af0b05732", + "model_id": "ecb0b9b2a62e4120ac8855a19ac715ff", "version_major": 2, "version_minor": 0 }, @@ -921,17 +919,17 @@ "id": "8c38ef9e", "metadata": { "execution": { - "iopub.execute_input": "2023-11-29T14:25:06.503003Z", - "iopub.status.busy": "2023-11-29T14:25:06.502678Z", - "iopub.status.idle": "2023-11-29T14:25:07.012012Z", - "shell.execute_reply": "2023-11-29T14:25:07.011100Z" + "iopub.execute_input": "2023-11-29T23:48:51.673976Z", + "iopub.status.busy": "2023-11-29T23:48:51.673779Z", + "iopub.status.idle": "2023-11-29T23:48:52.155992Z", + "shell.execute_reply": "2023-11-29T23:48:52.155270Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0bba15c1acf6473fa5dbf7f8521a7379", + "model_id": "01917e6fc20e4a09b2b58563b9317767", "version_major": 2, "version_minor": 0 }, @@ -945,7 +943,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "874155c23c4a4050bfa745395123a1f4", + "model_id": "f6734f9d26094d8ba38dc9d2d4b36f02", "version_major": 2, "version_minor": 0 }, @@ -959,7 +957,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a6e9dc371a834ce9ad3c60fff0cf4b90", + "model_id": "c3b4998fb4fc4dab8cec2b8043dacccc", "version_major": 2, "version_minor": 0 }, @@ -973,7 +971,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "bb33d05da2784c22baa6f1bce352dc03", + "model_id": "c84de320ab244e32a573edefee6a5439", "version_major": 2, "version_minor": 0 }, @@ -987,7 +985,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "34ab22f041d04895a710f19fbc3a15ae", + "model_id": "bee59d970be84773819853dc9ceeddf2", "version_major": 2, "version_minor": 0 }, @@ -1001,7 +999,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "52d2b5c6498a44acbf3595ab38bd5ff2", + "model_id": "4464f951cdcd471dbe37fb483580ebcc", "version_major": 2, "version_minor": 0 }, @@ -1015,7 +1013,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "cba3be76cb184cc1b3af35e0cd5f5aca", + "model_id": "2cd7a2e377ed4983a64152d9407ba7b9", "version_major": 2, "version_minor": 0 }, @@ -1029,7 +1027,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "9dbee1da760144748e8a3b44f36aa497", + "model_id": "9c241b981a7548ce87b283886cd42428", "version_major": 2, "version_minor": 0 }, @@ -1043,7 +1041,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "bdc02af1ad4f4a0f99b27e03b1a975a7", + "model_id": "b396ab1fb636440480dffde19b6e8b8e", "version_major": 2, "version_minor": 0 }, @@ -1057,7 +1055,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "fc8c00c512884445886e9c54558e7af3", + "model_id": "39c0e5502eed4e4a8b3db90a80b9c966", "version_major": 2, "version_minor": 0 }, @@ -1120,10 +1118,10 @@ "id": "3e674b7a", "metadata": { "execution": { - "iopub.execute_input": "2023-11-29T14:25:07.016813Z", - "iopub.status.busy": "2023-11-29T14:25:07.016612Z", - "iopub.status.idle": "2023-11-29T14:25:07.257678Z", - "shell.execute_reply": "2023-11-29T14:25:07.257186Z" + "iopub.execute_input": "2023-11-29T23:48:52.160498Z", + "iopub.status.busy": "2023-11-29T23:48:52.160297Z", + "iopub.status.idle": "2023-11-29T23:48:52.386769Z", + "shell.execute_reply": "2023-11-29T23:48:52.386109Z" } }, "outputs": [ @@ -2020,9 +2018,9 @@ } }, "text/html": [ - "
+
@@ -671,49 +671,49 @@

Example 4. Sensitivity test experiment with different clinical shifts
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@@ -758,7 +758,7 @@

Example 5. Rolling window experiment with synthetic timestamps using biweekl

diff --git a/api/tutorials/nihcxr/monitor_api.ipynb b/api/tutorials/nihcxr/monitor_api.ipynb index 04022a94f..246cf75b4 100644 --- a/api/tutorials/nihcxr/monitor_api.ipynb +++ b/api/tutorials/nihcxr/monitor_api.ipynb @@ -22,17 +22,17 @@ "id": "8aa3302d", "metadata": { "execution": { - "iopub.execute_input": "2023-11-29T14:25:12.779399Z", - "iopub.status.busy": "2023-11-29T14:25:12.778888Z", - "iopub.status.idle": "2023-11-29T14:25:19.946442Z", - "shell.execute_reply": "2023-11-29T14:25:19.944951Z" + "iopub.execute_input": "2023-11-29T23:48:57.672997Z", + "iopub.status.busy": "2023-11-29T23:48:57.672388Z", + "iopub.status.idle": "2023-11-29T23:49:05.384557Z", + "shell.execute_reply": "2023-11-29T23:49:05.383922Z" } }, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 1, @@ -79,17 +79,17 @@ "id": "e11920db", "metadata": { "execution": { - "iopub.execute_input": "2023-11-29T14:25:19.952473Z", - "iopub.status.busy": "2023-11-29T14:25:19.951950Z", - "iopub.status.idle": "2023-11-29T14:25:20.567971Z", - "shell.execute_reply": "2023-11-29T14:25:20.566008Z" + "iopub.execute_input": "2023-11-29T23:49:05.389863Z", + "iopub.status.busy": "2023-11-29T23:49:05.389598Z", + "iopub.status.idle": "2023-11-29T23:49:06.015795Z", + "shell.execute_reply": "2023-11-29T23:49:06.014810Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "4aaf8865f8c846af8d5ec01f80fd10aa", + "model_id": "f4e238149eed4353900bee379f9b7f2e", "version_major": 2, "version_minor": 0 }, @@ -145,10 +145,10 @@ "id": "54a3523a", "metadata": { "execution": { - "iopub.execute_input": "2023-11-29T14:25:20.574311Z", - "iopub.status.busy": "2023-11-29T14:25:20.573841Z", - "iopub.status.idle": "2023-11-29T14:25:31.022245Z", - "shell.execute_reply": "2023-11-29T14:25:31.021585Z" + "iopub.execute_input": "2023-11-29T23:49:06.019602Z", + "iopub.status.busy": "2023-11-29T23:49:06.019306Z", + "iopub.status.idle": "2023-11-29T23:49:16.636671Z", + "shell.execute_reply": "2023-11-29T23:49:16.636025Z" } }, "outputs": [ @@ -213,10 +213,10 @@ "id": "40b5a90f", "metadata": { "execution": { - "iopub.execute_input": "2023-11-29T14:25:31.028126Z", - "iopub.status.busy": "2023-11-29T14:25:31.027743Z", - "iopub.status.idle": "2023-11-29T14:25:37.698738Z", - "shell.execute_reply": "2023-11-29T14:25:37.698079Z" + "iopub.execute_input": "2023-11-29T23:49:16.642278Z", + "iopub.status.busy": "2023-11-29T23:49:16.642077Z", + "iopub.status.idle": "2023-11-29T23:49:23.458499Z", + "shell.execute_reply": "2023-11-29T23:49:23.457862Z" } }, "outputs": [ @@ -271,17 +271,17 @@ "id": "9ba03fac", "metadata": { "execution": { - "iopub.execute_input": "2023-11-29T14:25:37.704546Z", - "iopub.status.busy": "2023-11-29T14:25:37.704182Z", - "iopub.status.idle": "2023-11-29T14:25:52.961213Z", - "shell.execute_reply": "2023-11-29T14:25:52.960502Z" + "iopub.execute_input": "2023-11-29T23:49:23.464486Z", + "iopub.status.busy": "2023-11-29T23:49:23.464108Z", + "iopub.status.idle": "2023-11-29T23:49:38.717848Z", + "shell.execute_reply": "2023-11-29T23:49:38.717197Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "9c63d4fa446844049a8f6d16a0213282", + "model_id": "ac6035f1bae84c7dbf86dda7129f3d68", "version_major": 2, "version_minor": 0 }, @@ -295,7 +295,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b79e713cc0de453fbf98c3402270d1c9", + "model_id": "0b703c70a0f7497a814126a55464b5f3", "version_major": 2, "version_minor": 0 }, @@ -309,7 +309,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d4052ebfbe6741dcb04518df0335c269", + "model_id": "f4b2015a78bf4ca29856b27b53d9cc78", "version_major": 2, "version_minor": 0 }, @@ -323,7 +323,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "cfd771536a3041518785e7aab8791e7a", + "model_id": "72eabf60591c4f4990a41bd56c9a1254", "version_major": 2, "version_minor": 0 }, @@ -337,7 +337,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ec7bef0e21a74ab9bea2b109cf91eeaa", + "model_id": "8452e7a5bba1455bb329be7a76ca0a34", "version_major": 2, "version_minor": 0 }, @@ -351,7 +351,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b63612fef4c5466782921d395912a7ad", + "model_id": "77aa3920eaab4e5497146d27f115f27d", "version_major": 2, "version_minor": 0 }, @@ -365,7 +365,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "53d7baae728744daa1d6f69152b22e0a", + "model_id": "e746b71613924f3c9d9896443ce27f85", "version_major": 2, "version_minor": 0 }, @@ -379,7 +379,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "7e4f02baf2ed4e6c9daf4ae4b39d46d6", + "model_id": "2ddeb09e511c4c8fbc3496b7002a1a3a", "version_major": 2, "version_minor": 0 }, @@ -456,10 +456,10 @@ "id": "77e4b383", "metadata": { "execution": { - "iopub.execute_input": "2023-11-29T14:25:52.966489Z", - "iopub.status.busy": "2023-11-29T14:25:52.966292Z", - "iopub.status.idle": "2023-11-29T14:25:54.831182Z", - "shell.execute_reply": "2023-11-29T14:25:54.830527Z" + "iopub.execute_input": "2023-11-29T23:49:38.724447Z", + "iopub.status.busy": "2023-11-29T23:49:38.724248Z", + "iopub.status.idle": "2023-11-29T23:49:40.690545Z", + "shell.execute_reply": "2023-11-29T23:49:40.689924Z" }, "tags": [] }, @@ -518,23 +518,7 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "0cf967a6f5e84d248fb97873e37c7ab6": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "0f17e24ba628450796ec539b34dcc1f3": { + "012dd04f9f084e64b3025dee6b5c39ae": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -587,51 +571,30 @@ "width": null } }, - "122ee0dc75344bc698a2992c71753cee": { + "0322e5e8465c45aeab7d751656acd6cc": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", + "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", + "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", + "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_3f7b03a1b1cd4e21b168f6f29cc78876", - "max": 25596.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_0cf967a6f5e84d248fb97873e37c7ab6", + "layout": "IPY_MODEL_3e4c147ac60b4f0aa6d185ee76eee3db", + "placeholder": "​", + "style": "IPY_MODEL_5c0ef1892dec4ad6be175c26845e5560", "tabbable": null, "tooltip": null, - "value": 25596.0 - } - }, - "16d08e51e0534f1b8744a22dc3a8c815": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - 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"_model_name": "HTMLStyleModel", + "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "bar_color": null, + "description_width": "" } } }, diff --git a/api/tutorials/nihcxr/nihcxr_report_periodic.html b/api/tutorials/nihcxr/nihcxr_report_periodic.html index 8fc347266..b1ec80291 100644 --- a/api/tutorials/nihcxr/nihcxr_report_periodic.html +++ b/api/tutorials/nihcxr/nihcxr_report_periodic.html @@ -6639,7 +6639,7 @@

Graphics

-
+
@@ -6647,7 +6647,7 @@

Graphics

-
+
@@ -6655,7 +6655,7 @@

Graphics

-
+
diff --git a/api/tutorials/synthea/length_of_stay_report_periodic.html b/api/tutorials/synthea/length_of_stay_report_periodic.html index ae032d4b6..7e1011736 100644 --- a/api/tutorials/synthea/length_of_stay_report_periodic.html +++ b/api/tutorials/synthea/length_of_stay_report_periodic.html @@ -679,7 +679,7 @@

A quick glance of your most import
- 0.89 + 0.93 @@ -712,7 +712,7 @@

A quick glance of your most import
- 0.87 + 0.89 @@ -745,7 +745,7 @@

A quick glance of your most import
- 0.81 + 1.0 @@ -778,11 +778,11 @@

A quick glance of your most import
- 0.43 + 0.91 - ▼ + ▲ @@ -811,7 +811,7 @@

A quick glance of your most import
- 0.82 + 0.95 @@ -1076,7 +1076,7 @@

Graphics

-
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Graphics

-
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Graphics

-
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Graphics

-
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Graphics

-
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Quantitative Analysis
- 0.89 + 0.93 @@ -1223,7 +1223,7 @@

Quantitative Analysis
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Quantitative Analysis
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Quantitative Analysis
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Quantitative Analysis
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Graphics

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Graphics

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Graphics

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Graphics

-
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Graphics

-
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@@ -1711,6 +1711,10 @@

Model Parameters

+
+

Eval_metric

+ logloss +
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Model Parameters

+
+

Objective

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Model Parameters

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Gamma

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-

Objective

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Learning_rate

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Objective

+
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Gamma

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Eval_metric

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-

Random_state

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Enable_categorical

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Random_state

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N_estimators

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Missing

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Random_state

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Colsample_bytree

- 1 -
-
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N_estimators

- 100 -
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N_estimators

-
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Seed

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Seed

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Max_depth

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Min_child_weight

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Seed

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-

Reg_lambda

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Colsample_bytree

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Reg_lambda

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-

Missing

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Missing

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Learning_rate

- 0.1 -
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Learning_rate

-

Min_child_weight

- 3 +

Reg_lambda

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Min_child_weight

+
+

Max_depth

+ 5 +
-

Enable_categorical

- False +

Random_state

+ 123
@@ -2229,7 +2229,7 @@

Ethical Considerations

function generate_model_card_plot() { var model_card_plots = [] var overall_indices = [20, 21, 22, 23, 24] - var histories = JSON.parse("{\"0\": [\"0.8407079646017699\", \"1.0\", \"0.9535555373183686\", \"1.0\", \"1.0\"], \"1\": [\"0.9298245614035088\", \"1.0\", \"1.0\", \"1.0\", \"1.0\"], \"2\": [\"0.7910447761194029\", \"0.5926933018663191\", \"0.7804670449245223\", \"0.7376407871492293\", \"0.5465348419641338\"], \"3\": [\"0.8548387096774194\", \"0.8153626474813961\", \"0.8514536200701625\", \"0.7768058779525989\", \"0.9854854312948707\"], \"4\": [\"0.9404607397793641\", \"0.8949636650610477\", \"0.906660291459166\", \"1.0\", \"1.0\"], \"5\": [\"0.8301886792452831\", \"0.8563736847647581\", \"0.8159982068421708\", \"0.8976179235519609\", \"1.0\"], \"6\": [\"0.8076923076923077\", \"0.7489210047899432\", \"0.8053678177629117\", \"0.8245493123311662\", \"0.8602189635308329\"], \"7\": [\"0.84\", \"0.754932693657288\", \"0.8060551802168078\", \"0.8583120562770775\", \"0.7654831866690714\"], \"8\": [\"0.8235294117647058\", \"0.8678531368225659\", \"0.7177282163127113\", \"0.5624705834178083\", \"0.5616873017920673\"], \"9\": [\"0.9507142857142857\", \"1.0\", \"0.9465364681982098\", \"1.0\", \"1.0\"], \"10\": [\"0.8833333333333333\", \"0.8134373089391121\", \"0.8094512573637288\", \"0.926398911190699\", \"0.8356058060491123\"], \"11\": [\"0.92\", \"0.985763050396293\", \"1.0\", \"0.9981546744973446\", \"0.9214991146376794\"], \"12\": [\"0.8961038961038961\", \"0.9053193773895977\", \"0.9347053929206671\", \"0.9982678399334953\", \"0.9423920940755381\"], \"13\": [\"0.9078947368421053\", \"0.9631849389365184\", \"0.9115969990336185\", \"1.0\", \"1.0\"], \"14\": [\"0.9655693144065237\", \"0.9338487311886668\", \"0.8562982565514443\", \"0.9169671708467616\", \"1.0\"], \"15\": [\"0.839622641509434\", \"0.9909832332361683\", \"0.8561460131501516\", \"0.9618850129643587\", \"0.906302716196539\"], \"16\": [\"0.9322033898305084\", \"0.9619683130407325\", \"0.90319102893862\", \"0.9550894165208688\", \"0.7316873930091807\"], \"17\": [\"0.8088235294117647\", \"0.801040203984305\", \"0.5784983418354932\", \"0.8580198138648689\", \"0.8461423924959066\"], \"18\": [\"0.8661417322834646\", \"0.7935975730984981\", \"0.7540499044764887\", \"0.7601284940848867\", \"0.7811671348977473\"], \"19\": [\"0.9498839009287925\", \"0.9962061190255244\", \"1.0\", \"1.0\", \"1.0\"], \"20\": [\"0.8628318584070797\", \"0.737794983624541\", \"0.9184235579351778\", \"0.926906430307438\", \"0.8918726769134402\"], \"21\": [\"0.9253731343283582\", \"0.8136738471845688\", \"0.7582821532532101\", \"0.8641482557796041\", \"0.8728445339677502\"], \"22\": [\"0.8551724137931035\", \"1.0\", \"0.8748691363088327\", \"0.7434526661116921\", \"0.807609405356592\"], \"23\": [\"0.8888888888888888\", \"0.8206604807347216\", \"0.9141701918855193\", \"0.738897020529353\", \"0.4267928290546515\"], \"24\": [\"0.9565346956151555\", \"0.8544008378981168\", \"0.8690954806184567\", \"0.8444433614751495\", \"0.8193378299658584\"]}"); + var histories = JSON.parse("{\"0\": [\"0.8898305084745762\", \"0.8503665091179831\", \"0.9297893524213725\", \"0.9968649240360706\", \"0.7822091484783841\"], \"1\": [\"0.9076923076923077\", \"1.0\", \"0.9509263047426283\", \"0.822297690478797\", \"0.8505985356131066\"], \"2\": [\"0.8939393939393939\", \"0.7693094653236543\", \"0.857554969295508\", \"0.8282108223025169\", \"0.9483171901689129\"], \"3\": [\"0.9007633587786259\", \"0.947750980693716\", \"1.0\", \"0.9782977085649905\", \"1.0\"], \"4\": [\"0.9763986013986015\", \"0.8799968936240172\", \"0.9899524971821883\", \"1.0\", \"1.0\"], \"5\": [\"0.8627450980392157\", \"0.8375093723886025\", \"0.669219656542432\", \"0.5774399949368754\", \"0.8513074631970157\"], \"6\": [\"0.9545454545454546\", \"0.9422570049130763\", \"0.8574708500081492\", \"0.6909954069345352\", \"0.638113357974244\"], \"7\": [\"0.7777777777777778\", \"0.6717003359337058\", \"0.7136402491180235\", \"0.7664181016107113\", \"0.8691103024576424\"], \"8\": [\"0.8571428571428571\", \"0.9190515536816062\", \"0.8958017802138195\", \"0.9032199382279366\", \"0.9743762460470176\"], \"9\": [\"0.9629629629629629\", \"0.8297611919690245\", \"0.8008443684149205\", \"0.8629042201151046\", \"1.0\"], \"10\": [\"0.890625\", \"1.0\", \"0.9848587671065657\", \"1.0\", \"1.0\"], \"11\": [\"0.9375\", \"0.9206221922549943\", \"0.9500162535923238\", \"0.8310571410227544\", \"0.780674650584581\"], \"12\": [\"0.8928571428571429\", \"1.0\", \"1.0\", \"1.0\", \"0.8773269912469804\"], \"13\": [\"0.9146341463414634\", \"0.9851122655596797\", \"0.9412701945324977\", \"0.892033287798631\", \"0.825499671513943\"], \"14\": [\"0.9724025974025974\", \"0.9336867901149665\", \"0.8990109038544556\", \"0.9758490692558033\", \"0.9082361030605376\"], \"15\": [\"0.8979591836734694\", \"0.8904362342089817\", \"1.0\", \"0.897424859655856\", \"1.0\"], \"16\": [\"0.9636363636363636\", \"1.0\", \"0.9874804701903391\", \"0.9369851224345842\", \"0.7753682688195168\"], \"17\": [\"0.8688524590163934\", \"0.6267373915643667\", \"0.6155239788832984\", \"0.8026722384348319\", \"0.8498116096512208\"], \"18\": [\"0.9137931034482759\", \"0.8075796411912217\", \"0.8178161462088959\", \"0.724906288361865\", \"0.7165394046442614\"], \"19\": [\"0.9709791758972087\", \"0.9442408675109836\", \"1.0\", \"1.0\", \"0.9855426996664309\"], \"20\": [\"0.8938053097345132\", \"0.87235241164168\", \"0.9502525766474152\", \"0.9891285897021805\", \"0.9325922357451697\"], \"21\": [\"0.9481481481481482\", \"1.0\", \"0.8271202175124691\", \"0.8487388093255679\", \"0.89054754569421\"], \"22\": [\"0.8827586206896552\", \"0.9187154413270038\", \"0.9592043988393847\", \"1.0\", \"1.0\"], \"23\": [\"0.9142857142857143\", \"0.7819232779227007\", \"0.8834517715341084\", \"0.9191318127346609\", \"0.9073776138358788\"], \"24\": [\"0.9724137931034482\", \"1.0\", \"0.9178292117210768\", \"1.0\", \"0.9490257063743417\"]}"); var thresholds = JSON.parse("{\"0\": \"0.7\", \"1\": \"0.7\", \"2\": \"0.7\", \"3\": \"0.7\", \"4\": \"0.7\", \"5\": \"0.7\", \"6\": \"0.7\", \"7\": \"0.7\", \"8\": \"0.7\", \"9\": \"0.7\", \"10\": \"0.7\", \"11\": \"0.7\", \"12\": \"0.7\", \"13\": \"0.7\", \"14\": \"0.7\", \"15\": \"0.7\", \"16\": \"0.7\", \"17\": \"0.7\", \"18\": \"0.7\", \"19\": \"0.7\", \"20\": \"0.7\", \"21\": \"0.7\", \"22\": \"0.7\", \"23\": \"0.7\", \"24\": \"0.7\"}"); var timestamps = JSON.parse("{\"0\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"1\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"2\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"3\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"4\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"5\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"6\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"7\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"8\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"9\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"10\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"11\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"12\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"13\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"14\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"15\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"16\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"17\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"18\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"19\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"20\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"21\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"22\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"23\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"24\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"]}"); @@ -2510,10 +2510,10 @@

Ethical Considerations

} } var slices_all = JSON.parse("{\"0\": [\"metric:Accuracy\", \"age:[20 - 50)\", \"gender:overall_gender\"], \"1\": [\"metric:Precision\", \"age:[20 - 50)\", \"gender:overall_gender\"], \"2\": [\"metric:Recall\", \"age:[20 - 50)\", \"gender:overall_gender\"], \"3\": [\"metric:F1 Score\", \"age:[20 - 50)\", \"gender:overall_gender\"], \"4\": [\"metric:AUROC\", \"age:[20 - 50)\", \"gender:overall_gender\"], \"5\": [\"metric:Accuracy\", \"age:[50 - 80)\", \"gender:overall_gender\"], \"6\": [\"metric:Precision\", \"age:[50 - 80)\", \"gender:overall_gender\"], \"7\": [\"metric:Recall\", \"age:[50 - 80)\", \"gender:overall_gender\"], \"8\": [\"metric:F1 Score\", \"age:[50 - 80)\", \"gender:overall_gender\"], \"9\": [\"metric:AUROC\", \"age:[50 - 80)\", \"gender:overall_gender\"], \"10\": [\"metric:Accuracy\", \"gender:M\", \"age:overall_age\"], \"11\": [\"metric:Precision\", \"gender:M\", \"age:overall_age\"], \"12\": [\"metric:Recall\", \"gender:M\", \"age:overall_age\"], \"13\": [\"metric:F1 Score\", \"gender:M\", \"age:overall_age\"], \"14\": [\"metric:AUROC\", \"gender:M\", \"age:overall_age\"], \"15\": [\"metric:Accuracy\", \"gender:F\", \"age:overall_age\"], \"16\": [\"metric:Precision\", \"gender:F\", \"age:overall_age\"], \"17\": [\"metric:Recall\", \"gender:F\", \"age:overall_age\"], \"18\": [\"metric:F1 Score\", \"gender:F\", \"age:overall_age\"], \"19\": [\"metric:AUROC\", \"gender:F\", \"age:overall_age\"], \"20\": [\"metric:Accuracy\", \"age:overall_age\", \"gender:overall_gender\"], \"21\": [\"metric:Precision\", \"age:overall_age\", \"gender:overall_gender\"], \"22\": [\"metric:Recall\", \"age:overall_age\", \"gender:overall_gender\"], \"23\": [\"metric:F1 Score\", \"age:overall_age\", \"gender:overall_gender\"], \"24\": [\"metric:AUROC\", \"age:overall_age\", \"gender:overall_gender\"]}"); - var histories_all = JSON.parse("{\"0\": [\"0.8407079646017699\", \"1.0\", \"0.9535555373183686\", \"1.0\", \"1.0\"], \"1\": [\"0.9298245614035088\", \"1.0\", \"1.0\", \"1.0\", \"1.0\"], \"2\": [\"0.7910447761194029\", \"0.5926933018663191\", \"0.7804670449245223\", \"0.7376407871492293\", \"0.5465348419641338\"], \"3\": [\"0.8548387096774194\", \"0.8153626474813961\", \"0.8514536200701625\", \"0.7768058779525989\", \"0.9854854312948707\"], \"4\": [\"0.9404607397793641\", \"0.8949636650610477\", \"0.906660291459166\", \"1.0\", \"1.0\"], \"5\": [\"0.8301886792452831\", \"0.8563736847647581\", \"0.8159982068421708\", \"0.8976179235519609\", \"1.0\"], \"6\": [\"0.8076923076923077\", \"0.7489210047899432\", \"0.8053678177629117\", \"0.8245493123311662\", \"0.8602189635308329\"], \"7\": [\"0.84\", \"0.754932693657288\", \"0.8060551802168078\", \"0.8583120562770775\", \"0.7654831866690714\"], \"8\": [\"0.8235294117647058\", \"0.8678531368225659\", \"0.7177282163127113\", \"0.5624705834178083\", \"0.5616873017920673\"], \"9\": [\"0.9507142857142857\", \"1.0\", \"0.9465364681982098\", \"1.0\", \"1.0\"], \"10\": [\"0.8833333333333333\", \"0.8134373089391121\", \"0.8094512573637288\", \"0.926398911190699\", \"0.8356058060491123\"], \"11\": [\"0.92\", \"0.985763050396293\", \"1.0\", \"0.9981546744973446\", \"0.9214991146376794\"], \"12\": [\"0.8961038961038961\", \"0.9053193773895977\", \"0.9347053929206671\", \"0.9982678399334953\", \"0.9423920940755381\"], \"13\": [\"0.9078947368421053\", \"0.9631849389365184\", \"0.9115969990336185\", \"1.0\", \"1.0\"], \"14\": [\"0.9655693144065237\", \"0.9338487311886668\", \"0.8562982565514443\", \"0.9169671708467616\", \"1.0\"], \"15\": [\"0.839622641509434\", \"0.9909832332361683\", \"0.8561460131501516\", \"0.9618850129643587\", \"0.906302716196539\"], \"16\": [\"0.9322033898305084\", \"0.9619683130407325\", \"0.90319102893862\", \"0.9550894165208688\", \"0.7316873930091807\"], \"17\": [\"0.8088235294117647\", \"0.801040203984305\", \"0.5784983418354932\", \"0.8580198138648689\", \"0.8461423924959066\"], \"18\": [\"0.8661417322834646\", \"0.7935975730984981\", \"0.7540499044764887\", \"0.7601284940848867\", \"0.7811671348977473\"], \"19\": [\"0.9498839009287925\", \"0.9962061190255244\", \"1.0\", \"1.0\", \"1.0\"], \"20\": [\"0.8628318584070797\", \"0.737794983624541\", \"0.9184235579351778\", \"0.926906430307438\", \"0.8918726769134402\"], \"21\": [\"0.9253731343283582\", \"0.8136738471845688\", \"0.7582821532532101\", \"0.8641482557796041\", \"0.8728445339677502\"], \"22\": [\"0.8551724137931035\", \"1.0\", \"0.8748691363088327\", \"0.7434526661116921\", \"0.807609405356592\"], \"23\": [\"0.8888888888888888\", \"0.8206604807347216\", \"0.9141701918855193\", \"0.738897020529353\", \"0.4267928290546515\"], \"24\": [\"0.9565346956151555\", \"0.8544008378981168\", \"0.8690954806184567\", \"0.8444433614751495\", \"0.8193378299658584\"]}"); + var histories_all = JSON.parse("{\"0\": [\"0.8898305084745762\", \"0.8503665091179831\", \"0.9297893524213725\", \"0.9968649240360706\", \"0.7822091484783841\"], \"1\": [\"0.9076923076923077\", \"1.0\", \"0.9509263047426283\", \"0.822297690478797\", \"0.8505985356131066\"], \"2\": [\"0.8939393939393939\", \"0.7693094653236543\", \"0.857554969295508\", \"0.8282108223025169\", \"0.9483171901689129\"], \"3\": [\"0.9007633587786259\", \"0.947750980693716\", \"1.0\", \"0.9782977085649905\", \"1.0\"], \"4\": [\"0.9763986013986015\", \"0.8799968936240172\", \"0.9899524971821883\", \"1.0\", \"1.0\"], \"5\": [\"0.8627450980392157\", \"0.8375093723886025\", \"0.669219656542432\", \"0.5774399949368754\", \"0.8513074631970157\"], \"6\": [\"0.9545454545454546\", \"0.9422570049130763\", \"0.8574708500081492\", \"0.6909954069345352\", \"0.638113357974244\"], \"7\": [\"0.7777777777777778\", \"0.6717003359337058\", \"0.7136402491180235\", \"0.7664181016107113\", \"0.8691103024576424\"], \"8\": [\"0.8571428571428571\", \"0.9190515536816062\", \"0.8958017802138195\", \"0.9032199382279366\", \"0.9743762460470176\"], \"9\": [\"0.9629629629629629\", \"0.8297611919690245\", \"0.8008443684149205\", \"0.8629042201151046\", \"1.0\"], \"10\": [\"0.890625\", \"1.0\", \"0.9848587671065657\", \"1.0\", \"1.0\"], \"11\": [\"0.9375\", \"0.9206221922549943\", \"0.9500162535923238\", \"0.8310571410227544\", \"0.780674650584581\"], \"12\": [\"0.8928571428571429\", \"1.0\", \"1.0\", \"1.0\", \"0.8773269912469804\"], \"13\": [\"0.9146341463414634\", \"0.9851122655596797\", \"0.9412701945324977\", \"0.892033287798631\", \"0.825499671513943\"], \"14\": [\"0.9724025974025974\", \"0.9336867901149665\", \"0.8990109038544556\", \"0.9758490692558033\", \"0.9082361030605376\"], \"15\": [\"0.8979591836734694\", \"0.8904362342089817\", \"1.0\", \"0.897424859655856\", \"1.0\"], \"16\": [\"0.9636363636363636\", \"1.0\", \"0.9874804701903391\", \"0.9369851224345842\", \"0.7753682688195168\"], \"17\": [\"0.8688524590163934\", \"0.6267373915643667\", \"0.6155239788832984\", \"0.8026722384348319\", \"0.8498116096512208\"], \"18\": [\"0.9137931034482759\", \"0.8075796411912217\", \"0.8178161462088959\", \"0.724906288361865\", \"0.7165394046442614\"], \"19\": [\"0.9709791758972087\", \"0.9442408675109836\", \"1.0\", \"1.0\", \"0.9855426996664309\"], \"20\": [\"0.8938053097345132\", \"0.87235241164168\", \"0.9502525766474152\", \"0.9891285897021805\", \"0.9325922357451697\"], \"21\": [\"0.9481481481481482\", \"1.0\", \"0.8271202175124691\", \"0.8487388093255679\", \"0.89054754569421\"], \"22\": [\"0.8827586206896552\", \"0.9187154413270038\", \"0.9592043988393847\", \"1.0\", \"1.0\"], \"23\": [\"0.9142857142857143\", \"0.7819232779227007\", \"0.8834517715341084\", \"0.9191318127346609\", \"0.9073776138358788\"], \"24\": [\"0.9724137931034482\", \"1.0\", \"0.9178292117210768\", \"1.0\", \"0.9490257063743417\"]}"); var thresholds_all = JSON.parse("{\"0\": \"0.7\", \"1\": \"0.7\", \"2\": \"0.7\", \"3\": \"0.7\", \"4\": \"0.7\", \"5\": \"0.7\", \"6\": \"0.7\", \"7\": \"0.7\", \"8\": \"0.7\", \"9\": \"0.7\", \"10\": \"0.7\", \"11\": \"0.7\", \"12\": \"0.7\", \"13\": \"0.7\", \"14\": \"0.7\", \"15\": \"0.7\", \"16\": \"0.7\", \"17\": \"0.7\", \"18\": \"0.7\", \"19\": \"0.7\", \"20\": \"0.7\", \"21\": \"0.7\", \"22\": \"0.7\", \"23\": \"0.7\", \"24\": \"0.7\"}"); - var trends_all = JSON.parse("{\"0\": \"positive\", \"1\": \"positive\", \"2\": \"negative\", \"3\": \"positive\", \"4\": \"positive\", \"5\": \"positive\", \"6\": \"positive\", \"7\": \"neutral\", \"8\": \"negative\", \"9\": \"neutral\", \"10\": \"neutral\", \"11\": \"neutral\", \"12\": \"positive\", \"13\": \"positive\", \"14\": \"neutral\", \"15\": \"positive\", \"16\": \"negative\", \"17\": \"positive\", \"18\": \"negative\", \"19\": \"positive\", \"20\": \"positive\", \"21\": \"neutral\", \"22\": \"negative\", \"23\": \"negative\", \"24\": \"negative\"}"); - var passed_all = JSON.parse("{\"0\": true, \"1\": true, \"2\": false, \"3\": true, \"4\": true, \"5\": true, \"6\": true, \"7\": true, \"8\": false, \"9\": true, \"10\": true, \"11\": true, \"12\": true, \"13\": true, \"14\": true, \"15\": true, \"16\": true, \"17\": true, \"18\": true, \"19\": true, \"20\": true, \"21\": true, \"22\": true, \"23\": false, \"24\": true}"); + var trends_all = JSON.parse("{\"0\": \"neutral\", \"1\": \"negative\", \"2\": \"positive\", \"3\": \"positive\", \"4\": \"positive\", \"5\": \"negative\", \"6\": \"negative\", \"7\": \"positive\", \"8\": \"positive\", \"9\": \"positive\", \"10\": \"positive\", \"11\": \"negative\", \"12\": \"neutral\", \"13\": \"negative\", \"14\": \"neutral\", \"15\": \"positive\", \"16\": \"negative\", \"17\": \"positive\", \"18\": \"negative\", \"19\": \"neutral\", \"20\": \"positive\", \"21\": \"negative\", \"22\": \"positive\", \"23\": \"positive\", \"24\": \"neutral\"}"); + var passed_all = JSON.parse("{\"0\": true, \"1\": true, \"2\": true, \"3\": true, \"4\": true, \"5\": true, \"6\": false, \"7\": true, \"8\": true, \"9\": true, \"10\": true, \"11\": true, \"12\": true, \"13\": true, \"14\": true, \"15\": true, \"16\": true, \"17\": true, \"18\": true, \"19\": true, \"20\": true, \"21\": true, \"22\": true, \"23\": true, \"24\": true}"); var names_all = JSON.parse("{\"0\": \"Accuracy\", \"1\": \"Precision\", \"2\": \"Recall\", \"3\": \"F1 Score\", \"4\": \"AUROC\", \"5\": \"Accuracy\", \"6\": \"Precision\", \"7\": \"Recall\", \"8\": \"F1 Score\", \"9\": \"AUROC\", \"10\": \"Accuracy\", \"11\": \"Precision\", \"12\": \"Recall\", \"13\": \"F1 Score\", \"14\": \"AUROC\", \"15\": \"Accuracy\", \"16\": \"Precision\", \"17\": \"Recall\", \"18\": \"F1 Score\", \"19\": \"AUROC\", \"20\": \"Accuracy\", \"21\": \"Precision\", \"22\": \"Recall\", \"23\": \"F1 Score\", \"24\": \"AUROC\"}"); var timestamps_all = JSON.parse("{\"0\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"1\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"2\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"3\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"4\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"5\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"6\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"7\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"8\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"9\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"10\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"11\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"12\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"13\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"14\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"15\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"16\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"17\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"18\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"19\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"20\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"21\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"22\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"23\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"24\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"]}"); @@ -2786,10 +2786,10 @@

Ethical Considerations

} } var slices_all = JSON.parse("{\"0\": [\"metric:Accuracy\", \"age:[20 - 50)\", \"gender:overall_gender\"], \"1\": [\"metric:Precision\", \"age:[20 - 50)\", \"gender:overall_gender\"], \"2\": [\"metric:Recall\", \"age:[20 - 50)\", \"gender:overall_gender\"], \"3\": [\"metric:F1 Score\", \"age:[20 - 50)\", \"gender:overall_gender\"], \"4\": [\"metric:AUROC\", \"age:[20 - 50)\", \"gender:overall_gender\"], \"5\": [\"metric:Accuracy\", \"age:[50 - 80)\", \"gender:overall_gender\"], \"6\": [\"metric:Precision\", \"age:[50 - 80)\", \"gender:overall_gender\"], \"7\": [\"metric:Recall\", \"age:[50 - 80)\", \"gender:overall_gender\"], \"8\": [\"metric:F1 Score\", \"age:[50 - 80)\", \"gender:overall_gender\"], \"9\": [\"metric:AUROC\", \"age:[50 - 80)\", \"gender:overall_gender\"], \"10\": [\"metric:Accuracy\", \"gender:M\", \"age:overall_age\"], \"11\": [\"metric:Precision\", \"gender:M\", \"age:overall_age\"], \"12\": [\"metric:Recall\", \"gender:M\", \"age:overall_age\"], \"13\": [\"metric:F1 Score\", \"gender:M\", \"age:overall_age\"], \"14\": [\"metric:AUROC\", \"gender:M\", \"age:overall_age\"], \"15\": [\"metric:Accuracy\", \"gender:F\", \"age:overall_age\"], \"16\": [\"metric:Precision\", \"gender:F\", \"age:overall_age\"], \"17\": [\"metric:Recall\", \"gender:F\", \"age:overall_age\"], \"18\": [\"metric:F1 Score\", \"gender:F\", \"age:overall_age\"], \"19\": [\"metric:AUROC\", \"gender:F\", \"age:overall_age\"], \"20\": [\"metric:Accuracy\", \"age:overall_age\", \"gender:overall_gender\"], \"21\": [\"metric:Precision\", \"age:overall_age\", \"gender:overall_gender\"], \"22\": [\"metric:Recall\", \"age:overall_age\", \"gender:overall_gender\"], \"23\": [\"metric:F1 Score\", \"age:overall_age\", \"gender:overall_gender\"], \"24\": [\"metric:AUROC\", \"age:overall_age\", \"gender:overall_gender\"]}"); - var histories_all = JSON.parse("{\"0\": [\"0.8407079646017699\", \"1.0\", \"0.9535555373183686\", \"1.0\", \"1.0\"], \"1\": [\"0.9298245614035088\", \"1.0\", \"1.0\", \"1.0\", \"1.0\"], \"2\": [\"0.7910447761194029\", \"0.5926933018663191\", \"0.7804670449245223\", \"0.7376407871492293\", \"0.5465348419641338\"], \"3\": [\"0.8548387096774194\", \"0.8153626474813961\", \"0.8514536200701625\", \"0.7768058779525989\", \"0.9854854312948707\"], \"4\": [\"0.9404607397793641\", \"0.8949636650610477\", \"0.906660291459166\", \"1.0\", \"1.0\"], \"5\": [\"0.8301886792452831\", \"0.8563736847647581\", \"0.8159982068421708\", \"0.8976179235519609\", \"1.0\"], \"6\": [\"0.8076923076923077\", \"0.7489210047899432\", \"0.8053678177629117\", \"0.8245493123311662\", \"0.8602189635308329\"], \"7\": [\"0.84\", \"0.754932693657288\", \"0.8060551802168078\", \"0.8583120562770775\", \"0.7654831866690714\"], \"8\": [\"0.8235294117647058\", \"0.8678531368225659\", \"0.7177282163127113\", \"0.5624705834178083\", \"0.5616873017920673\"], \"9\": [\"0.9507142857142857\", \"1.0\", \"0.9465364681982098\", \"1.0\", \"1.0\"], \"10\": [\"0.8833333333333333\", \"0.8134373089391121\", \"0.8094512573637288\", \"0.926398911190699\", \"0.8356058060491123\"], \"11\": [\"0.92\", \"0.985763050396293\", \"1.0\", \"0.9981546744973446\", \"0.9214991146376794\"], \"12\": [\"0.8961038961038961\", \"0.9053193773895977\", \"0.9347053929206671\", \"0.9982678399334953\", \"0.9423920940755381\"], \"13\": [\"0.9078947368421053\", \"0.9631849389365184\", \"0.9115969990336185\", \"1.0\", \"1.0\"], \"14\": [\"0.9655693144065237\", \"0.9338487311886668\", \"0.8562982565514443\", \"0.9169671708467616\", \"1.0\"], \"15\": [\"0.839622641509434\", \"0.9909832332361683\", \"0.8561460131501516\", \"0.9618850129643587\", \"0.906302716196539\"], \"16\": [\"0.9322033898305084\", \"0.9619683130407325\", \"0.90319102893862\", \"0.9550894165208688\", \"0.7316873930091807\"], \"17\": [\"0.8088235294117647\", \"0.801040203984305\", \"0.5784983418354932\", \"0.8580198138648689\", \"0.8461423924959066\"], \"18\": [\"0.8661417322834646\", \"0.7935975730984981\", \"0.7540499044764887\", \"0.7601284940848867\", \"0.7811671348977473\"], \"19\": [\"0.9498839009287925\", \"0.9962061190255244\", \"1.0\", \"1.0\", \"1.0\"], \"20\": [\"0.8628318584070797\", \"0.737794983624541\", \"0.9184235579351778\", \"0.926906430307438\", \"0.8918726769134402\"], \"21\": [\"0.9253731343283582\", \"0.8136738471845688\", \"0.7582821532532101\", \"0.8641482557796041\", \"0.8728445339677502\"], \"22\": [\"0.8551724137931035\", \"1.0\", \"0.8748691363088327\", \"0.7434526661116921\", \"0.807609405356592\"], \"23\": [\"0.8888888888888888\", \"0.8206604807347216\", \"0.9141701918855193\", \"0.738897020529353\", \"0.4267928290546515\"], \"24\": [\"0.9565346956151555\", \"0.8544008378981168\", \"0.8690954806184567\", \"0.8444433614751495\", \"0.8193378299658584\"]}"); + var histories_all = JSON.parse("{\"0\": [\"0.8898305084745762\", \"0.8503665091179831\", \"0.9297893524213725\", \"0.9968649240360706\", \"0.7822091484783841\"], \"1\": [\"0.9076923076923077\", \"1.0\", \"0.9509263047426283\", \"0.822297690478797\", \"0.8505985356131066\"], \"2\": [\"0.8939393939393939\", \"0.7693094653236543\", \"0.857554969295508\", \"0.8282108223025169\", \"0.9483171901689129\"], \"3\": [\"0.9007633587786259\", \"0.947750980693716\", \"1.0\", \"0.9782977085649905\", \"1.0\"], \"4\": [\"0.9763986013986015\", \"0.8799968936240172\", \"0.9899524971821883\", \"1.0\", \"1.0\"], \"5\": [\"0.8627450980392157\", \"0.8375093723886025\", \"0.669219656542432\", \"0.5774399949368754\", \"0.8513074631970157\"], \"6\": [\"0.9545454545454546\", \"0.9422570049130763\", \"0.8574708500081492\", \"0.6909954069345352\", \"0.638113357974244\"], \"7\": [\"0.7777777777777778\", \"0.6717003359337058\", \"0.7136402491180235\", \"0.7664181016107113\", \"0.8691103024576424\"], \"8\": [\"0.8571428571428571\", \"0.9190515536816062\", \"0.8958017802138195\", \"0.9032199382279366\", \"0.9743762460470176\"], \"9\": [\"0.9629629629629629\", \"0.8297611919690245\", \"0.8008443684149205\", \"0.8629042201151046\", \"1.0\"], \"10\": [\"0.890625\", \"1.0\", \"0.9848587671065657\", \"1.0\", \"1.0\"], \"11\": [\"0.9375\", \"0.9206221922549943\", \"0.9500162535923238\", \"0.8310571410227544\", \"0.780674650584581\"], \"12\": [\"0.8928571428571429\", \"1.0\", \"1.0\", \"1.0\", \"0.8773269912469804\"], \"13\": [\"0.9146341463414634\", \"0.9851122655596797\", \"0.9412701945324977\", \"0.892033287798631\", \"0.825499671513943\"], \"14\": [\"0.9724025974025974\", \"0.9336867901149665\", \"0.8990109038544556\", \"0.9758490692558033\", \"0.9082361030605376\"], \"15\": [\"0.8979591836734694\", \"0.8904362342089817\", \"1.0\", \"0.897424859655856\", \"1.0\"], \"16\": [\"0.9636363636363636\", \"1.0\", \"0.9874804701903391\", \"0.9369851224345842\", \"0.7753682688195168\"], \"17\": [\"0.8688524590163934\", \"0.6267373915643667\", \"0.6155239788832984\", \"0.8026722384348319\", \"0.8498116096512208\"], \"18\": [\"0.9137931034482759\", \"0.8075796411912217\", \"0.8178161462088959\", \"0.724906288361865\", \"0.7165394046442614\"], \"19\": [\"0.9709791758972087\", \"0.9442408675109836\", \"1.0\", \"1.0\", \"0.9855426996664309\"], \"20\": [\"0.8938053097345132\", \"0.87235241164168\", \"0.9502525766474152\", \"0.9891285897021805\", \"0.9325922357451697\"], \"21\": [\"0.9481481481481482\", \"1.0\", \"0.8271202175124691\", \"0.8487388093255679\", \"0.89054754569421\"], \"22\": [\"0.8827586206896552\", \"0.9187154413270038\", \"0.9592043988393847\", \"1.0\", \"1.0\"], \"23\": [\"0.9142857142857143\", \"0.7819232779227007\", \"0.8834517715341084\", \"0.9191318127346609\", \"0.9073776138358788\"], \"24\": [\"0.9724137931034482\", \"1.0\", \"0.9178292117210768\", \"1.0\", \"0.9490257063743417\"]}"); var thresholds_all = JSON.parse("{\"0\": \"0.7\", \"1\": \"0.7\", \"2\": \"0.7\", \"3\": \"0.7\", \"4\": \"0.7\", \"5\": \"0.7\", \"6\": \"0.7\", \"7\": \"0.7\", \"8\": \"0.7\", \"9\": \"0.7\", \"10\": \"0.7\", \"11\": \"0.7\", \"12\": \"0.7\", \"13\": \"0.7\", \"14\": \"0.7\", \"15\": \"0.7\", \"16\": \"0.7\", \"17\": \"0.7\", \"18\": \"0.7\", \"19\": \"0.7\", \"20\": \"0.7\", \"21\": \"0.7\", \"22\": \"0.7\", \"23\": \"0.7\", \"24\": \"0.7\"}"); - var trends_all = JSON.parse("{\"0\": \"positive\", \"1\": \"positive\", \"2\": \"negative\", \"3\": \"positive\", \"4\": \"positive\", \"5\": \"positive\", \"6\": \"positive\", \"7\": \"neutral\", \"8\": \"negative\", \"9\": \"neutral\", \"10\": \"neutral\", \"11\": \"neutral\", \"12\": \"positive\", \"13\": \"positive\", \"14\": \"neutral\", \"15\": \"positive\", \"16\": \"negative\", \"17\": \"positive\", \"18\": \"negative\", \"19\": \"positive\", \"20\": \"positive\", \"21\": \"neutral\", \"22\": \"negative\", \"23\": \"negative\", \"24\": \"negative\"}"); - var passed_all = JSON.parse("{\"0\": true, \"1\": true, \"2\": false, \"3\": true, \"4\": true, \"5\": true, \"6\": true, \"7\": true, \"8\": false, \"9\": true, \"10\": true, \"11\": true, \"12\": true, \"13\": true, \"14\": true, \"15\": true, \"16\": true, \"17\": true, \"18\": true, \"19\": true, \"20\": true, \"21\": true, \"22\": true, \"23\": false, \"24\": true}"); + var trends_all = JSON.parse("{\"0\": \"neutral\", \"1\": \"negative\", \"2\": \"positive\", \"3\": \"positive\", \"4\": \"positive\", \"5\": \"negative\", \"6\": \"negative\", \"7\": \"positive\", \"8\": \"positive\", \"9\": \"positive\", \"10\": \"positive\", \"11\": \"negative\", \"12\": \"neutral\", \"13\": \"negative\", \"14\": \"neutral\", \"15\": \"positive\", \"16\": \"negative\", \"17\": \"positive\", \"18\": \"negative\", \"19\": \"neutral\", \"20\": \"positive\", \"21\": \"negative\", \"22\": \"positive\", \"23\": \"positive\", \"24\": \"neutral\"}"); + var passed_all = JSON.parse("{\"0\": true, \"1\": true, \"2\": true, \"3\": true, \"4\": true, \"5\": true, \"6\": false, \"7\": true, \"8\": true, \"9\": true, \"10\": true, \"11\": true, \"12\": true, \"13\": true, \"14\": true, \"15\": true, \"16\": true, \"17\": true, \"18\": true, \"19\": true, \"20\": true, \"21\": true, \"22\": true, \"23\": true, \"24\": true}"); var names_all = JSON.parse("{\"0\": \"Accuracy\", \"1\": \"Precision\", \"2\": \"Recall\", \"3\": \"F1 Score\", \"4\": \"AUROC\", \"5\": \"Accuracy\", \"6\": \"Precision\", \"7\": \"Recall\", \"8\": \"F1 Score\", \"9\": \"AUROC\", \"10\": \"Accuracy\", \"11\": \"Precision\", \"12\": \"Recall\", \"13\": \"F1 Score\", \"14\": \"AUROC\", \"15\": \"Accuracy\", \"16\": \"Precision\", \"17\": \"Recall\", \"18\": \"F1 Score\", \"19\": \"AUROC\", \"20\": \"Accuracy\", \"21\": \"Precision\", \"22\": \"Recall\", \"23\": \"F1 Score\", \"24\": \"AUROC\"}"); var timestamps_all = JSON.parse("{\"0\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"1\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"2\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"3\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"4\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"5\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"6\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"7\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"8\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"9\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"10\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"11\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"12\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"13\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"14\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"15\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"16\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"17\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"18\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"19\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"20\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"21\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"22\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"23\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"24\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"]}"); diff --git a/api/tutorials/synthea/los_prediction.html b/api/tutorials/synthea/los_prediction.html index 63cb33dbd..ddfd922fa 100644 --- a/api/tutorials/synthea/los_prediction.html +++ b/api/tutorials/synthea/los_prediction.html @@ -677,17 +677,17 @@

Compute length of stay (labels) 
-2023-11-29 09:26:02,994 INFO cycquery.orm    - Database setup, ready to run queries!
-2023-11-29 09:26:06,250 INFO cycquery.orm    - Query returned successfully!
-2023-11-29 09:26:06,251 INFO cycquery.utils.profile - Finished executing function run_query in 2.746726 s
-2023-11-29 09:26:08,026 INFO cycquery.orm    - Query returned successfully!
-2023-11-29 09:26:08,027 INFO cycquery.utils.profile - Finished executing function run_query in 1.774474 s
-2023-11-29 09:26:09,566 INFO cycquery.orm    - Query returned successfully!
-2023-11-29 09:26:09,568 INFO cycquery.utils.profile - Finished executing function run_query in 0.397723 s
-2023-11-29 09:26:10,062 INFO cycquery.orm    - Query returned successfully!
-2023-11-29 09:26:10,063 INFO cycquery.utils.profile - Finished executing function run_query in 0.489374 s
-2023-11-29 09:26:10,171 INFO cycquery.orm    - Query returned successfully!
-2023-11-29 09:26:10,172 INFO cycquery.utils.profile - Finished executing function run_query in 0.107587 s
+2023-11-29 18:49:49,298 INFO cycquery.orm    - Database setup, ready to run queries!
+2023-11-29 18:49:54,098 INFO cycquery.orm    - Query returned successfully!
+2023-11-29 18:49:54,099 INFO cycquery.utils.profile - Finished executing function run_query in 3.737664 s
+2023-11-29 18:49:55,881 INFO cycquery.orm    - Query returned successfully!
+2023-11-29 18:49:55,883 INFO cycquery.utils.profile - Finished executing function run_query in 1.782846 s
+2023-11-29 18:49:57,410 INFO cycquery.orm    - Query returned successfully!
+2023-11-29 18:49:57,411 INFO cycquery.utils.profile - Finished executing function run_query in 0.359313 s
+2023-11-29 18:49:57,835 INFO cycquery.orm    - Query returned successfully!
+2023-11-29 18:49:57,836 INFO cycquery.utils.profile - Finished executing function run_query in 0.423024 s
+2023-11-29 18:49:57,929 INFO cycquery.orm    - Query returned successfully!
+2023-11-29 18:49:57,930 INFO cycquery.utils.profile - Finished executing function run_query in 0.093229 s

@@ -740,9 +740,9 @@

Drop NaNs based on the

-
+
@@ -1304,12 +1304,12 @@

Training 

-2023-11-29 09:26:17,479 INFO cyclops.models.wrappers.sk_model - Best reg_lambda: 0
-2023-11-29 09:26:17,480 INFO cyclops.models.wrappers.sk_model - Best n_estimators: 100
-2023-11-29 09:26:17,480 INFO cyclops.models.wrappers.sk_model - Best max_depth: 2
-2023-11-29 09:26:17,481 INFO cyclops.models.wrappers.sk_model - Best learning_rate: 0.1
-2023-11-29 09:26:17,482 INFO cyclops.models.wrappers.sk_model - Best gamma: 0
-2023-11-29 09:26:17,483 INFO cyclops.models.wrappers.sk_model - Best colsample_bytree: 1
+2023-11-29 18:50:06,745 INFO cyclops.models.wrappers.sk_model - Best reg_lambda: 0
+2023-11-29 18:50:06,746 INFO cyclops.models.wrappers.sk_model - Best n_estimators: 100
+2023-11-29 18:50:06,746 INFO cyclops.models.wrappers.sk_model - Best max_depth: 5
+2023-11-29 18:50:06,746 INFO cyclops.models.wrappers.sk_model - Best learning_rate: 0.1
+2023-11-29 18:50:06,747 INFO cyclops.models.wrappers.sk_model - Best gamma: 1
+2023-11-29 18:50:06,747 INFO cyclops.models.wrappers.sk_model - Best colsample_bytree: 1
@@ -1320,21 +1320,21 @@

Training 
XGBClassifier(base_score=None, booster=None, callbacks=None,
               colsample_bylevel=None, colsample_bynode=None, colsample_bytree=1,
               early_stopping_rounds=None, enable_categorical=False,
-              eval_metric='logloss', feature_types=None, gamma=0, gpu_id=None,
+              eval_metric='logloss', feature_types=None, gamma=1, gpu_id=None,
               grow_policy=None, importance_type=None,
               interaction_constraints=None, learning_rate=0.1, max_bin=None,
               max_cat_threshold=None, max_cat_to_onehot=None,
-              max_delta_step=None, max_depth=2, max_leaves=None,
+              max_delta_step=None, max_depth=5, max_leaves=None,
               min_child_weight=3, missing=nan, monotone_constraints=None,
               n_estimators=100, n_jobs=None, num_parallel_tree=None,
               predictor=None, random_state=123, ...)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.

@@ -1353,7 +1353,7 @@

Training 

-{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': 1, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': 'logloss', 'feature_types': None, 'gamma': 0, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 2, 'max_leaves': None, 'min_child_weight': 3, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 100, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 123, 'reg_alpha': None, 'reg_lambda': 0, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': None, 'seed': 123}
+{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': 1, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': 'logloss', 'feature_types': None, 'gamma': 1, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': 3, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 100, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 123, 'reg_alpha': None, 'reg_lambda': 0, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': None, 'seed': 123}

Log the model parameters to the report.

@@ -1389,7 +1389,7 @@

Prediction

-
+

@@ -1518,85 +1518,85 @@

Evaluation

-
+
-
+
-
+
-
+
-
+
-
+
-
+
-
+
-
+
-
+
-
+
-
+
-
+
-
+

Log the performance metrics to the report.

We can add a performance metric to the model card using the log_performance_metric method, which expects a dictionary where the keys are in the following format: slice_name/metric_name. For instance, overall/accuracy.

@@ -1700,9 +1700,9 @@

Evaluation
-
diff --git a/api/tutorials/synthea/los_prediction.ipynb b/api/tutorials/synthea/los_prediction.ipynb index 982e37c31..4d1b52b5a 100644 --- a/api/tutorials/synthea/los_prediction.ipynb +++ b/api/tutorials/synthea/los_prediction.ipynb @@ -33,10 +33,10 @@ "id": "53009e6b", "metadata": { "execution": { - "iopub.execute_input": "2023-11-29T14:25:58.454492Z", - "iopub.status.busy": "2023-11-29T14:25:58.453997Z", - "iopub.status.idle": "2023-11-29T14:26:02.347632Z", - "shell.execute_reply": "2023-11-29T14:26:02.346626Z" + "iopub.execute_input": "2023-11-29T23:49:44.158745Z", + "iopub.status.busy": "2023-11-29T23:49:44.158120Z", + "iopub.status.idle": "2023-11-29T23:49:48.192318Z", + "shell.execute_reply": "2023-11-29T23:49:48.191623Z" } }, "outputs": [], @@ -96,10 +96,10 @@ "id": "afae58a8-5708-4e05-8695-25ba3ce1a71f", "metadata": { "execution": { - "iopub.execute_input": "2023-11-29T14:26:02.353911Z", - "iopub.status.busy": "2023-11-29T14:26:02.353185Z", - "iopub.status.idle": "2023-11-29T14:26:02.357858Z", - "shell.execute_reply": "2023-11-29T14:26:02.357136Z" + "iopub.execute_input": "2023-11-29T23:49:48.196826Z", + "iopub.status.busy": "2023-11-29T23:49:48.195846Z", + "iopub.status.idle": "2023-11-29T23:49:48.202886Z", + "shell.execute_reply": "2023-11-29T23:49:48.201479Z" }, "tags": [] }, @@ -122,10 +122,10 @@ "id": "739b109a-011b-4e6e-a3de-964edeffddbd", "metadata": { "execution": { - "iopub.execute_input": "2023-11-29T14:26:02.362976Z", - "iopub.status.busy": "2023-11-29T14:26:02.362564Z", - "iopub.status.idle": "2023-11-29T14:26:02.367521Z", - "shell.execute_reply": "2023-11-29T14:26:02.366586Z" + "iopub.execute_input": "2023-11-29T23:49:48.207103Z", + "iopub.status.busy": "2023-11-29T23:49:48.206400Z", + "iopub.status.idle": "2023-11-29T23:49:48.212674Z", + "shell.execute_reply": "2023-11-29T23:49:48.211265Z" }, "tags": [] }, @@ -156,10 +156,10 @@ "id": "e497df9f-0f3d-4e9c-845c-539627a37f67", "metadata": { "execution": { - "iopub.execute_input": "2023-11-29T14:26:02.373034Z", - "iopub.status.busy": "2023-11-29T14:26:02.372485Z", - "iopub.status.idle": "2023-11-29T14:26:10.209350Z", - "shell.execute_reply": "2023-11-29T14:26:10.208075Z" + "iopub.execute_input": "2023-11-29T23:49:48.217658Z", + "iopub.status.busy": "2023-11-29T23:49:48.217068Z", + "iopub.status.idle": "2023-11-29T23:49:57.965818Z", + "shell.execute_reply": "2023-11-29T23:49:57.965127Z" }, "tags": [] }, @@ -168,77 +168,77 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-11-29 09:26:02,994 \u001b[1;37mINFO\u001b[0m cycquery.orm - Database setup, ready to run queries!\n" + "2023-11-29 18:49:49,298 \u001b[1;37mINFO\u001b[0m cycquery.orm - Database setup, ready to run queries!\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "2023-11-29 09:26:06,250 \u001b[1;37mINFO\u001b[0m cycquery.orm - Query returned successfully!\n" + "2023-11-29 18:49:54,098 \u001b[1;37mINFO\u001b[0m cycquery.orm - Query returned successfully!\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "2023-11-29 09:26:06,251 \u001b[1;37mINFO\u001b[0m cycquery.utils.profile - Finished executing function run_query in 2.746726 s\n" + "2023-11-29 18:49:54,099 \u001b[1;37mINFO\u001b[0m cycquery.utils.profile - Finished executing function run_query in 3.737664 s\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "2023-11-29 09:26:08,026 \u001b[1;37mINFO\u001b[0m cycquery.orm - Query returned successfully!\n" + "2023-11-29 18:49:55,881 \u001b[1;37mINFO\u001b[0m cycquery.orm - Query returned successfully!\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "2023-11-29 09:26:08,027 \u001b[1;37mINFO\u001b[0m cycquery.utils.profile - Finished executing function run_query in 1.774474 s\n" + "2023-11-29 18:49:55,883 \u001b[1;37mINFO\u001b[0m cycquery.utils.profile - Finished executing function run_query in 1.782846 s\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "2023-11-29 09:26:09,566 \u001b[1;37mINFO\u001b[0m cycquery.orm - Query returned successfully!\n" + "2023-11-29 18:49:57,410 \u001b[1;37mINFO\u001b[0m cycquery.orm - Query returned successfully!\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "2023-11-29 09:26:09,568 \u001b[1;37mINFO\u001b[0m cycquery.utils.profile - Finished executing function run_query in 0.397723 s\n" + "2023-11-29 18:49:57,411 \u001b[1;37mINFO\u001b[0m cycquery.utils.profile - Finished executing function run_query in 0.359313 s\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "2023-11-29 09:26:10,062 \u001b[1;37mINFO\u001b[0m cycquery.orm - Query returned successfully!\n" + "2023-11-29 18:49:57,835 \u001b[1;37mINFO\u001b[0m cycquery.orm - Query returned successfully!\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "2023-11-29 09:26:10,063 \u001b[1;37mINFO\u001b[0m cycquery.utils.profile - Finished executing function run_query in 0.489374 s\n" + "2023-11-29 18:49:57,836 \u001b[1;37mINFO\u001b[0m cycquery.utils.profile - Finished executing function run_query in 0.423024 s\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "2023-11-29 09:26:10,171 \u001b[1;37mINFO\u001b[0m cycquery.orm - Query returned successfully!\n" + "2023-11-29 18:49:57,929 \u001b[1;37mINFO\u001b[0m cycquery.orm - Query returned successfully!\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "2023-11-29 09:26:10,172 \u001b[1;37mINFO\u001b[0m cycquery.utils.profile - Finished executing function run_query in 0.107587 s\n" + "2023-11-29 18:49:57,930 \u001b[1;37mINFO\u001b[0m cycquery.utils.profile - Finished executing function run_query in 0.093229 s\n" ] } ], @@ -397,10 +397,10 @@ "id": "c576ee51-e825-4970-86e8-3e5f221f145c", "metadata": { "execution": { - "iopub.execute_input": "2023-11-29T14:26:10.213563Z", - "iopub.status.busy": "2023-11-29T14:26:10.213171Z", - "iopub.status.idle": "2023-11-29T14:26:10.301336Z", - "shell.execute_reply": "2023-11-29T14:26:10.300459Z" + "iopub.execute_input": "2023-11-29T23:49:57.971033Z", + "iopub.status.busy": "2023-11-29T23:49:57.970733Z", + "iopub.status.idle": "2023-11-29T23:49:58.072632Z", + "shell.execute_reply": "2023-11-29T23:49:58.071959Z" }, "tags": [] }, @@ -1496,9 +1496,9 @@ } }, "text/html": [ - "
+ diff --git a/blog/atom.xml b/blog/atom.xml index c654e3f21..68c182b33 100644 --- a/blog/atom.xml +++ b/blog/atom.xml @@ -32,8 +32,8 @@ Our new version follows the release of 
import numpy as np
import torch
from torchmetrics.functional.classification.confusion_matrix import (
   binary_confusion_matrix as torch_binary_confusion_matrix,
)
from cyclops.evaluate.metrics.experimental.functional.confusion_matrix import (
   binary_confusion_matrix,
)


y_true = np.random.randint(0, 2, size=10)
y_pred = np.random.rand(10)
target = torch.asarray(y_true)
preds = torch.asarray(y_pred)

print("cyclops confusion matrix\n", binary_confusion_matrix(y_true, y_pred_discrete))
print("torchmetrics confusion matrix\n", torch_binary_confusion_matrix(y_true, y_pred_discrete))

results in

cyclops confusion matrix
 [[2 3]
 [4 1]]
torchmetrics confusion matrix
 [[2 3]
 [4 1]]
-

A simple and unified data API​

-

To process and create datasets for training, inference and evaluation. We use the popular πŸ€— datasets library for efficiently slicing different modalities of data to create subsets for evaluation and monitoring. It has new methods, improved generalizability, and uses Pandas 2.0.

+

A simple and unified data package​

+

We are introducing a simple and unified python package to process and create datasets for training, inference and evaluation. We use the popular πŸ€— datasets library for efficiently slicing different modalities (tabular, time-series and images) of data to create subsets for evaluation and monitoring. The package has new methods, improved generalizability, and uses Pandas 2.0.

Check out an example tutorial that showcases the use of the cyclops.data API to create slices of data for evaluation!

Next steps​

We believe that the model report will enable increased transparency around model development, and help with auditing of ML systems that are deployed in clinical settings. We are working with early adopter stakeholders on a few different use cases to pilot the model report. Stay tuned for updates and improvements as we learn more about how clinical ML teams use cyclops!

]]> diff --git a/blog/cyclops-0.2.0-release/index.html b/blog/cyclops-0.2.0-release/index.html index 083d48e87..afc1eee03 100644 --- a/blog/cyclops-0.2.0-release/index.html +++ b/blog/cyclops-0.2.0-release/index.html @@ -5,7 +5,7 @@ cyclops 0.2.0 release | cyclops - + @@ -28,10 +28,10 @@ 

import numpy as np
import torch
from torchmetrics.functional.classification.confusion_matrix import (
   binary_confusion_matrix as torch_binary_confusion_matrix,
)
from cyclops.evaluate.metrics.experimental.functional.confusion_matrix import (
   binary_confusion_matrix,
)


y_true = np.random.randint(0, 2, size=10)
y_pred = np.random.rand(10)
target = torch.asarray(y_true)
preds = torch.asarray(y_pred)

print("cyclops confusion matrix\n", binary_confusion_matrix(y_true, y_pred_discrete))
print("torchmetrics confusion matrix\n", torch_binary_confusion_matrix(y_true, y_pred_discrete))

results in

cyclops confusion matrix
 [[2 3]
 [4 1]]
torchmetrics confusion matrix
 [[2 3]
 [4 1]]
-

A simple and unified data API​

-

To process and create datasets for training, inference and evaluation. We use the popular πŸ€— datasets library for efficiently slicing different modalities of data to create subsets for evaluation and monitoring. It has new methods, improved generalizability, and uses Pandas 2.0.

+

A simple and unified data package​

+

We are introducing a simple and unified python package to process and create datasets for training, inference and evaluation. We use the popular πŸ€— datasets library for efficiently slicing different modalities (tabular, time-series and images) of data to create subsets for evaluation and monitoring. The package has new methods, improved generalizability, and uses Pandas 2.0.

Check out an example tutorial that showcases the use of the cyclops.data API to create slices of data for evaluation!

Next steps​

-

We believe that the model report will enable increased transparency around model development, and help with auditing of ML systems that are deployed in clinical settings. We are working with early adopter stakeholders on a few different use cases to pilot the model report. Stay tuned for updates and improvements as we learn more about how clinical ML teams use cyclops!

+

We believe that the model report will enable increased transparency around model development, and help with auditing of ML systems that are deployed in clinical settings. We are working with early adopter stakeholders on a few different use cases to pilot the model report. Stay tuned for updates and improvements as we learn more about how clinical ML teams use cyclops!

\ No newline at end of file diff --git a/blog/index.html b/blog/index.html index 06dd7e3d1..a535a4259 100644 --- a/blog/index.html +++ b/blog/index.html @@ -5,7 +5,7 @@ Blog | cyclops - + @@ -28,8 +28,8 @@ 

import numpy as np
import torch
from torchmetrics.functional.classification.confusion_matrix import (
   binary_confusion_matrix as torch_binary_confusion_matrix,
)
from cyclops.evaluate.metrics.experimental.functional.confusion_matrix import (
   binary_confusion_matrix,
)


y_true = np.random.randint(0, 2, size=10)
y_pred = np.random.rand(10)
target = torch.asarray(y_true)
preds = torch.asarray(y_pred)

print("cyclops confusion matrix\n", binary_confusion_matrix(y_true, y_pred_discrete))
print("torchmetrics confusion matrix\n", torch_binary_confusion_matrix(y_true, y_pred_discrete))

results in

cyclops confusion matrix
 [[2 3]
 [4 1]]
torchmetrics confusion matrix
 [[2 3]
 [4 1]]
-

A simple and unified data API​

-

To process and create datasets for training, inference and evaluation. We use the popular πŸ€— datasets library for efficiently slicing different modalities of data to create subsets for evaluation and monitoring. It has new methods, improved generalizability, and uses Pandas 2.0.

+

A simple and unified data package​

+

We are introducing a simple and unified python package to process and create datasets for training, inference and evaluation. We use the popular πŸ€— datasets library for efficiently slicing different modalities (tabular, time-series and images) of data to create subsets for evaluation and monitoring. The package has new methods, improved generalizability, and uses Pandas 2.0.

Check out an example tutorial that showcases the use of the cyclops.data API to create slices of data for evaluation!

Next steps​

We believe that the model report will enable increased transparency around model development, and help with auditing of ML systems that are deployed in clinical settings. We are working with early adopter stakeholders on a few different use cases to pilot the model report. Stay tuned for updates and improvements as we learn more about how clinical ML teams use cyclops!

diff --git a/blog/rss.xml b/blog/rss.xml index 25920ba87..4e8328b98 100644 --- a/blog/rss.xml +++ b/blog/rss.xml @@ -33,8 +33,8 @@ Our new version follows the release of 
import numpy as np
import torch
from torchmetrics.functional.classification.confusion_matrix import (
   binary_confusion_matrix as torch_binary_confusion_matrix,
)
from cyclops.evaluate.metrics.experimental.functional.confusion_matrix import (
   binary_confusion_matrix,
)


y_true = np.random.randint(0, 2, size=10)
y_pred = np.random.rand(10)
target = torch.asarray(y_true)
preds = torch.asarray(y_pred)

print("cyclops confusion matrix\n", binary_confusion_matrix(y_true, y_pred_discrete))
print("torchmetrics confusion matrix\n", torch_binary_confusion_matrix(y_true, y_pred_discrete))

results in

cyclops confusion matrix
 [[2 3]
 [4 1]]
torchmetrics confusion matrix
 [[2 3]
 [4 1]]
-

A simple and unified data API​

-

To process and create datasets for training, inference and evaluation. We use the popular πŸ€— datasets library for efficiently slicing different modalities of data to create subsets for evaluation and monitoring. It has new methods, improved generalizability, and uses Pandas 2.0.

+

A simple and unified data package​

+

We are introducing a simple and unified python package to process and create datasets for training, inference and evaluation. We use the popular πŸ€— datasets library for efficiently slicing different modalities (tabular, time-series and images) of data to create subsets for evaluation and monitoring. The package has new methods, improved generalizability, and uses Pandas 2.0.

Check out an example tutorial that showcases the use of the cyclops.data API to create slices of data for evaluation!

Next steps​

We believe that the model report will enable increased transparency around model development, and help with auditing of ML systems that are deployed in clinical settings. We are working with early adopter stakeholders on a few different use cases to pilot the model report. Stay tuned for updates and improvements as we learn more about how clinical ML teams use cyclops!

]]> diff --git a/blog/tags/0-2-0/index.html b/blog/tags/0-2-0/index.html index ba819a202..1276bc586 100644 --- a/blog/tags/0-2-0/index.html +++ b/blog/tags/0-2-0/index.html @@ -5,7 +5,7 @@ One post tagged with "0.2.0" | cyclops - + @@ -28,8 +28,8 @@ 

import numpy as np
import torch
from torchmetrics.functional.classification.confusion_matrix import (
   binary_confusion_matrix as torch_binary_confusion_matrix,
)
from cyclops.evaluate.metrics.experimental.functional.confusion_matrix import (
   binary_confusion_matrix,
)


y_true = np.random.randint(0, 2, size=10)
y_pred = np.random.rand(10)
target = torch.asarray(y_true)
preds = torch.asarray(y_pred)

print("cyclops confusion matrix\n", binary_confusion_matrix(y_true, y_pred_discrete))
print("torchmetrics confusion matrix\n", torch_binary_confusion_matrix(y_true, y_pred_discrete))

results in

cyclops confusion matrix
 [[2 3]
 [4 1]]
torchmetrics confusion matrix
 [[2 3]
 [4 1]]
-

A simple and unified data API​

-

To process and create datasets for training, inference and evaluation. We use the popular πŸ€— datasets library for efficiently slicing different modalities of data to create subsets for evaluation and monitoring. It has new methods, improved generalizability, and uses Pandas 2.0.

+

A simple and unified data package​

+

We are introducing a simple and unified python package to process and create datasets for training, inference and evaluation. We use the popular πŸ€— datasets library for efficiently slicing different modalities (tabular, time-series and images) of data to create subsets for evaluation and monitoring. The package has new methods, improved generalizability, and uses Pandas 2.0.

Check out an example tutorial that showcases the use of the cyclops.data API to create slices of data for evaluation!

Next steps​

We believe that the model report will enable increased transparency around model development, and help with auditing of ML systems that are deployed in clinical settings. We are working with early adopter stakeholders on a few different use cases to pilot the model report. Stay tuned for updates and improvements as we learn more about how clinical ML teams use cyclops!

diff --git a/blog/tags/index.html b/blog/tags/index.html index 24d94dbed..d6f03f559 100644 --- a/blog/tags/index.html +++ b/blog/tags/index.html @@ -5,7 +5,7 @@ Tags | cyclops - + diff --git a/docs/intro/index.html b/docs/intro/index.html index 54bcad3bf..a88c7f33a 100644 --- a/docs/intro/index.html +++ b/docs/intro/index.html @@ -5,7 +5,7 @@ intro | cyclops - + diff --git a/index.html b/index.html index 47fbbe8e8..65e72180a 100644 --- a/index.html +++ b/index.html @@ -5,7 +5,7 @@ cyclops | cyclops - + diff --git a/markdown-page/index.html b/markdown-page/index.html index efafa5458..1d1cdf51e 100644 --- a/markdown-page/index.html +++ b/markdown-page/index.html @@ -5,7 +5,7 @@ Markdown page example | cyclops - +