From c2e174ca2cd4f4788175271b15a579b7c2835243 Mon Sep 17 00:00:00 2001 From: lshpaner Date: Sun, 28 Jul 2024 10:00:14 -0700 Subject: [PATCH] updated changelog to latest releases, changed classifiying esrd outcomes header --- docs/.doctrees/changelog.doctree | Bin 28497 -> 40773 bytes docs/.doctrees/citations.doctree | Bin 5621 -> 5623 bytes docs/.doctrees/environment.pickle | Bin 3823949 -> 3848612 bytes docs/.doctrees/references.doctree | Bin 10398 -> 12194 bytes docs/.doctrees/usage_guide.doctree | Bin 212801 -> 215337 bytes docs/_sources/changelog.rst.txt | 64 +++++++++-- docs/_sources/citations.rst.txt | 2 +- docs/_sources/references.rst.txt | 3 +- docs/_sources/usage_guide.rst.txt | 166 +++++++++++++---------------- docs/acknowledgements.html | 4 +- docs/changelog.html | 128 +++++++++++++++++----- docs/citations.html | 2 +- docs/genindex.html | 2 +- docs/index.html | 26 +++-- docs/objects.inv | Bin 540 -> 538 bytes docs/references.html | 8 +- docs/search.html | 2 +- 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Conversions (Adapted from Ali et al., 2021, BMC Nephrol) - - +`First 5 Rows of Biochemical Data with Conversions (Adapted from Ali et al., 2021, BMC Nephrol)` [1]_. .. table:: :align: left @@ -353,8 +349,8 @@ The ``upcr_uacr`` function is typically used in clinical data processing where a 744 NaN Name: uACR, Length: 745, dtype: float64 -Classifying ESRD Outcome by 2 and 5 Year Outcomes -================================================= +Classifying ESRD Outcomes +========================= .. code-block:: python @@ -403,13 +399,7 @@ This function creates a new column in the DataFrame which is populated with a `` ) -.. raw:: html - -

    +`First 5 Rows of Outcome Data (Adapted from Ali et al., 2021, BMC Nephrol)` [1]_. .. table:: :align: left @@ -512,19 +502,10 @@ This function is designed to compute the risk of chronic kidney disease (CKD) ov # The resulting DataFrame 'df' now includes new columns with risk # predictions for each model and time frame -.. raw:: html -
    - - - First 5 Rows of Kidney Failure Risk Data (Adapted from Ali et al., 2021, BMC Nephrol)
    -
    - -
    +`First 5 Rows of Kidney Failure Risk Data (Adapted from Ali et al., 2021, BMC Nephrol)` [1]_. -
    - -.. table:: +.. table:: :align: left ======== ====== ======================== ========================== ========= ============ ================ ================ ================ ================ ================ ================ ================ ================ @@ -538,10 +519,31 @@ This function is designed to compute the risk of chronic kidney disease (CKD) ov ======== ====== ======================== ========================== ========= ============ ================ ================ ================ ================ ================ ================ ================ ================ - Performance Assessment ======================= +AUC ROC & Precision-Recall Curves +--------------------------------- + +.. raw:: html + +
    + +.. image:: ../assets/performance_grid.svg + :alt: AUC_ROC_and_Precision_Recall + :align: left + :width: 900px + +.. raw:: html + +
    + +.. raw:: html + +
    + +\ + .. function:: plot_kfre_metrics(df, num_vars, fig_size=(12, 6), mode="both", image_path_png=None, image_path_svg=None, image_prefix=None, bbox_inches="tight", plot_type="all_plots", save_plots=False, show_years=[2, 5], plot_combinations=False, show_grids=False, decimal_places=2) :param DataFrame df: The input DataFrame containing the necessary columns for truth and predictions. @@ -597,67 +599,8 @@ This function generates the true labels and predicted probabilities for 2-year a ) -AUC ROC & Precision-Recall Curves ---------------------------------- - -.. raw:: html - -
    - -.. image:: ../assets/performance_grid.svg - :alt: AUC_ROC_and_Precision_Recall - :align: left - :width: 900px - -.. raw:: html - -
    - -.. raw:: html - -
    - -\ - - -.. function:: eval_kfre_metrics(df, n_var_list, outcome_years=[2, 5], decimal_places=6) - - :param DataFrame df: The input DataFrame containing the necessary columns for truth and predictions. Rows with NaN values will be dropped. - :param list of int n_var_list: List of variable numbers to consider, e.g., ``[4, 6, 8]``. - :param list, tuple, or int outcome_years: (`optional`) List, tuple, or single year to consider for outcomes. Default is ``[2, 5]``. - :param int decimal_places: (`optional`) Number of decimal places for the calculated metrics. Default is ``6``. - - :returns: ``pd.DataFrame``: A DataFrame containing the calculated metrics for each outcome. - - :raises: ``ValueError``: If required outcome columns are missing in the DataFrame. - ``ValueError``: If an invalid variable number is provided in ``n_var_list``. - - This function computes a set of performance metrics for multiple binary classification models given the true labels and the predicted probabilities for each outcome. The metrics calculated include precision (positive predictive value), average precision, sensitivity (recall), specificity, AUC ROC, and Brier score. - - Notes: - - Precision is calculated with a threshold of `0.5` for the predicted probabilities. - - Sensitivity is also known as recall. - - Specificity is calculated as the recall for the negative class. - - AUC ROC is calculated using the receiver operating characteristic curve. - - Brier score measures the mean squared difference between predicted probabilities and the true binary outcomes. - -**Example Usage** - -.. code-block:: python - - from kfre import eval_kfre_metrics - - -.. code-block:: python - - metrics_df_n_var = eval_kfre_metrics( - df=df, # Metrics-ready DataFrame as the first argument - n_var_list=[4, 6, 8], # Specify the list of variable numbers to consider - outcome_years=[2, 5], # Specify the list of outcome years to consider - ) - -Performance Metrics Explained ------------------------------ +Performance Metrics +-------------------- This section explains the various performance metrics calculated by the `eval_kfre_metrics` function. @@ -733,4 +676,47 @@ Where: - :math:`N` is the number of total observations. - :math:`f_i` is the predicted probability for the :math:`i`-th observation. - :math:`o_i` is the actual outcome for the :math:`i`-th observation (`0` or `1`). + + +---- + +.. function:: eval_kfre_metrics(df, n_var_list, outcome_years=[2, 5], decimal_places=6) + + :param DataFrame df: The input DataFrame containing the necessary columns for truth and predictions. Rows with NaN values will be dropped. + :param list of int n_var_list: List of variable numbers to consider, e.g., ``[4, 6, 8]``. + :param list, tuple, or int outcome_years: (`optional`) List, tuple, or single year to consider for outcomes. Default is ``[2, 5]``. + :param int decimal_places: (`optional`) Number of decimal places for the calculated metrics. Default is ``6``. + + :returns: ``pd.DataFrame``: A DataFrame containing the calculated metrics for each outcome. + + :raises: ``ValueError``: If required outcome columns are missing in the DataFrame. + ``ValueError``: If an invalid variable number is provided in ``n_var_list``. + + This function computes a set of performance metrics for multiple binary classification models given the true labels and the predicted probabilities for each outcome. The metrics calculated include precision (positive predictive value), average precision, sensitivity (recall), specificity, AUC ROC, and Brier score. + + Notes: + - Precision is calculated with a threshold of `0.5` for the predicted probabilities. + - Sensitivity is also known as recall. + - Specificity is calculated as the recall for the negative class. + - AUC ROC is calculated using the receiver operating characteristic curve. + - Brier score measures the mean squared difference between predicted probabilities and the true binary outcomes. + +**Example Usage** + +.. code-block:: python + + from kfre import eval_kfre_metrics + + +.. code-block:: python + + metrics_df_n_var = eval_kfre_metrics( + df=df, # Metrics-ready DataFrame as the first argument + n_var_list=[4, 6, 8], # Specify the list of variable numbers to consider + outcome_years=[2, 5], # Specify the list of outcome years to consider + ) + + +---- +.. [1] Ali, I., Donne, R. L., & Kalra, P. A. (2021). A validation study of the kidney failure risk equation in advanced chronic kidney disease according to disease aetiology with evaluation of discrimination, calibration and clinical utility. *BMC Nephrology, 22(1),* 194. https://doi.org/10.1186/s12882-021-02402-1 \ No newline at end of file diff --git a/docs/acknowledgements.html b/docs/acknowledgements.html index aed1558..96a5a11 100644 --- a/docs/acknowledgements.html +++ b/docs/acknowledgements.html @@ -59,7 +59,7 @@
  1. Usage Guide
  2. Single Patient Risk Calculation
  3. Conversion of Clinical Parameters
  4. -
  5. Classifying ESRD Outcome by 2 and 5 Year Outcomes
  6. +
  7. Classifying ESRD Outcomes
  8. Batch Risk Calculation for Multiple Patients
  9. Performance Assessment
  10. @@ -70,7 +70,7 @@
  11. Usage Guide
  12. Single Patient Risk Calculation
  13. Conversion of Clinical Parameters
  14. -
  15. Classifying ESRD Outcome by 2 and 5 Year Outcomes
  16. +
  17. Classifying ESRD Outcomes
  18. Batch Risk Calculation for Multiple Patients
  19. Performance Assessment
  20. diff --git a/docs/changelog.html b/docs/changelog.html index 9d595c0..604b68b 100644 --- a/docs/changelog.html +++ b/docs/changelog.html @@ -67,12 +67,16 @@
  21. Acknowledgements
  22. Citing KFRE
  23. Changelog
  24. References
  25. @@ -106,7 +110,80 @@

    Changelog

    -

    0.1.8

    +

    0.1.12

    +

    Patch Release

    +

    This release is a comprehensive stable build encompassing changes from version 0.1.8 forward.

    +
      +
    • Package version fix in min_readme.md citation section

    • +
    • Python version fix in min_readme.md

    • +
    • Python version fix from 3.74 to 3.7.4

    • +
    • +
      new functions for classifying outcomes and ckd stages
        +
      • class_esrd_outcome

      • +
      • class_ckd_stages

      • +
      +
      +
      +
    • +
    • +
      new functions for performance assessment
        +
      • plot_kfre_metrics

      • +
      • +
        eval_kfre_metrics
          +
        • roc_auc curves

        • +
        • precision-recall curves

        • +
        +
        +
        +
      • +
      +
      +
      +
    • +
    • +
      performance metrics:
        +
      • Precision/PPV

      • +
      • Average Precision

      • +
      • Sensitivity

      • +
      • Specificity

      • +
      • AUC ROC

      • +
      • Brier Score

      • +
      +
      +
      +
    • +
    +
    +
    +

    0.1.11

    +

    Patch Release

    +
      +
    • Package version fix in min_readme.md citation section

    • +
    +
    +
    +

    0.1.10

    +

    Patch Release

    +
      +
    • Python version fix in min_readme.md

    • +
    +
    +
    +

    0.1.9

    +
      +
    • Python version fix from 3.74 to 3.7.4

    • +
    • +
      updated functions for classifying outcomes and ckd stages
        +
      • class_esrd_outcome

      • +
      • class_ckd_stages

      • +
      +
      +
      +
    • +
    +
    +
    +

    0.1.8

    -
    -

    0.1.7

    +
    +

    0.1.7

    This release includes the following updates and improvements:

    • Acknowledgements for key influencers whose exceptional work on end-stage kidney disease has greatly inspired the creation of this library.

    • -
    • Implemented comprehensive exception handling within the kfre_person() function to ensure proper parameter validation: -- Combined all exceptions into a single exception. -- Concatenated exceptions using a newline character for better readability. -- Added checks to ensure age, is_male, eGFR, and uACR parameters are supplied. -- Validated that the years parameter can only be 2 or 5. -- Ensured dm and htn parameters, if provided, are either 0, 1, True, or False. -- Added a check to ensure is_north_american is specified as either True or False.

    • +
    • Implemented comprehensive exception handling within the kfre_person() function to ensure proper parameter validation:

      +
        +
      • Combined all exceptions into a single exception.

      • +
      • Concatenated exceptions using a newline character for better readability.

      • +
      • Added checks to ensure age, is_male, eGFR, and uACR parameters are supplied.

      • +
      • Validated that the years parameter can only be 2 or 5.

      • +
      • Ensured dm and htn parameters, if provided, are either 0, 1, True, or False.

      • +
      • Added a check to ensure is_north_american is specified as either True or False.

      • +
      +
    -
    -

    0.1.6

    +
    +

    0.1.6

    This release includes the following updates and improvements:

    • Added version information to the __init__.py file. The version of this release is 0.1.6.

    -
    -

    0.1.5

    +
    +

    0.1.5

    This stable release, kfre 0.1.5, builds directly upon the foundations set in version 0.1.2 and 0.1.4 with no changes to the codebase. The key highlight of this update is a an update of citing version 0.1.5 under citations section on PyPI landing page.

    -
    -

    0.1.4

    +
    +

    0.1.4

    Documentation Enhancements

    Core Documentation Migration: All essential documentation has been transferred to this new site, available here at lshpaner.github.io/kfre_docs. This migration enhances accessibility and ease of navigation.

    Visual Updates: A new logo has been introduced, now featured on both the documentation site and the PyPI landing page to enhance brand recognition.

    @@ -182,8 +262,8 @@

    0.1.4<

    Why no version 0.1.3? In alignment with common superstitions, version 0.1.3 was skipped, much like how many buildings lack a 13th floor.

    -
    -

    0.1.2

    +
    +

    0.1.2

    This release, kfre 0.1.2, marks a substantial update from the preliminary alpha versions, introducing significant enhancements and features that elevate the tool’s flexibility, accuracy, and ease of use:

    Enhanced Core Functionality: A comprehensive overhaul from earlier minimal viable products to a more robust and feature-rich application.

    New Calculator Function: The introduction of the kfre_person() function enables risk metrics calculations for individuals one at a time, customizing the analysis to each unique dataset.

    diff --git a/docs/citations.html b/docs/citations.html index f7339b1..bd22a35 100644 --- a/docs/citations.html +++ b/docs/citations.html @@ -97,7 +97,7 @@

    Citing KFRE

    -

    Shpaner, L. (2024). kfre: A Python Library for Reproducing Kidney Failure Risk Equations (KFRE) (0.1.8). Zenodo. https://doi.org/10.5281/zenodo.11100222

    +

    Shpaner, L. (2024). kfre: A Python Library for Reproducing Kidney Failure Risk Equations (KFRE) (0.1.12). Zenodo. https://doi.org/10.5281/zenodo.11100222

    @software{shpaner_2024_11100222,
       author       = {Shpaner, Leonid},
       title        = {{kfre: A Python Library for Reproducing Kidney
    diff --git a/docs/genindex.html b/docs/genindex.html
    index 78199ad..3b3e5e4 100644
    --- a/docs/genindex.html
    +++ b/docs/genindex.html
    @@ -55,7 +55,7 @@
     
  26. Usage Guide
  27. Single Patient Risk Calculation
  28. Conversion of Clinical Parameters
  29. -
  30. Classifying ESRD Outcome by 2 and 5 Year Outcomes
  31. +
  32. Classifying ESRD Outcomes
  33. Batch Risk Calculation for Multiple Patients
  34. Performance Assessment
  35. diff --git a/docs/index.html b/docs/index.html index 02ed5b3..0d2af38 100644 --- a/docs/index.html +++ b/docs/index.html @@ -58,7 +58,7 @@
  36. Usage Guide
  37. Single Patient Risk Calculation
  38. Conversion of Clinical Parameters
  39. -
  40. Classifying ESRD Outcome by 2 and 5 Year Outcomes
  41. +
  42. Classifying ESRD Outcomes
  43. Batch Risk Calculation for Multiple Patients
  44. Performance Assessment
  45. @@ -127,7 +127,7 @@

    KFRE DocumentationClassifying ESRD Outcome by 2 and 5 Year Outcomes @@ -154,12 +156,16 @@

    KFRE DocumentationCiting KFRE
  46. Changelog
  47. References
  48. diff --git a/docs/objects.inv b/docs/objects.inv index e067bb5799bf8af5b164f58b8d6d894cb4e490fc..77590e6c412210c22b2511c40e0a90dbf5d08f0d 100644 GIT binary patch delta 433 zcmV;i0Z#s$1eyepbAMCGPQx$|z2_^8+G~({?G3RB0a8_jI7Y5LO{_R}u*Ze|J&v6; z3#|yZ_|02p#t!8?EB6L6BW#&5ElGV`@10^Ov?43wEX^Uyo#1d?U?mqzk|s=2YcsHh zXWAjt70gk=fy$Uw0UE*V98uucV63OipnK+Co5~NO4qoJ$vwu=*sqx>fxtf{GQD)|l zDJzx!lE8DQK%pfT`I>7+(iNH3f{NTs(DN3O?m%8NiMLfym4npzF!Kb)u-oo9VV~^B z{OBu?V%YG|w*y;WfkNfv`C)rM;rGrXMR3kR3B}=iOzhC;{M3AUCN=??MQRZEu#-tC zsy;x5=ZczO0e_({vN>YmFYZO`3^hk`rMA zqfAftQYph>M&8ozq8B8U=4X~yAJeW7b6+)g652J^1`K>UVFiMn6}cHIl7aglvF)-) z=?3=ROfeX}q#ljR?dd!foYE$e@Rlr4!-_F4V$t?3)+#stpH2LTe%w?FRgMMc|NkBw b_C4j2_XZ%34e(2IIUxXPWTwdvZ_fuH!AR7o delta 435 zcmV;k0Zjgy1e^qrbAMDzPsAV)zV}yP+-ptt>dp9yQIn0vcx(`6TT%)Y26g|v6xuHB zuGxAkAKznUfKtwjdTXFC!qx@TlC-djP2(F;UtZ*yi+@sDsqx>Xxt5v2QD&Bq zDXW$KmcUDBK%pfT`6btk)(rj6K>< z`O#M(#W3O2cK~}|fkKt!@$Thz#@{C!rr>Yrw##6ILMDMUk6SktOaYV*6oF z(hY3AnPM=xNqaOZU&i%ZFs98U;VxOAhE-Eu#G>tM+$`VtH=FqueY?37sy=x~lUJTr dJ%9YThe kfre library is based on the risk prediction models developed in the following studies. Please refer to these studies for an in-depth understanding of the kidney failure risk prediction models used within this library.

    References

    -

    Ali, I., Donne, R. L., & Kalra, P. A. (2021). A validation study of the kidney failure risk equation in advanced chronic kidney disease according to disease aetiology with evaluation of discrimination, calibration and clinical utility. BMC Nephrology, 22(1), 194. doi: 10.1186/s12882-021-02402-1

    +

    Ali, I., Donne, R. L., & Kalra, P. A. (2021). A validation study of the kidney failure risk equation in advanced chronic kidney disease according to disease aetiology with evaluation of discrimination, calibration and clinical utility. BMC Nephrology, 22(1), 194. https://doi.org/10.1186/s12882-021-02402-1 [1]

    Sumida, K., Nadkarni, G. N., Grams, M. E., Sang, Y., Ballew, S. H., Coresh, J., Matsushita, K., Surapaneni, A., Brunskill, N., Chadban, S. J., Chang, A. R., Cirillo, M., Daratha, K. B., Gansevoort, R. T., Garg, A. X., Iacoviello, L., Kayama, T., Konta, T., Kovesdy, C. P., Lash, J., Lee, B. J., Major, R. W., Metzger, M., Miura, K., Naimark, D. M. J., Nelson, R. G., Sawhney, S., Stempniewicz, N., Tang, M., Townsend, R. R., Traynor, J. P., Valdivielso, J. M., Wetzels, J., Polkinghorne, K. R., & Heerspink, H. J. L. (2020). Conversion of urine protein-creatinine ratio or urine dipstick protein to urine albumin-creatinine ratio for use in chronic kidney disease screening and prognosis. Annals of Internal Medicine, 173 (6), 426-435. https://doi.org/10.7326/M20-0529

    Tangri, N., Grams, M. E., Levey, A. S., Coresh, J., Appel, L. J., Astor, B. C., Chodick, G., Collins, A. J., Djurdjev, O., Elley, C. R., Evans, M., Garg, A. X., Hallan, S. I., Inker, L. A., Ito, S., Jee, S. H., Kovesdy, C. P., Kronenberg, F., Heerspink, H. J. L., Marks, A., Nadkarni, G. N., Navaneethan, S. D., Nelson, R. G., Titze, S., Sarnak, M. J., Stengel, B., Woodward, M., Iseki, K., & for the CKD Prognosis Consortium. (2016). Multinational assessment of accuracy of equations for predicting risk of kidney failure: A meta-analysis. JAMA, 315 (2), 164–174. https://doi.org/10.1001/jama.2015.18202

    Tangri, N., Stevens, L. A., Griffith, J., Tighiouart, H., Djurdjev, O., Naimark, D., Levin, A., & Levey, A. S. (2011). A predictive model for progression of chronic kidney disease to kidney failure. JAMA, 305 (15), 1553-1559. https://doi.org/10.1001/jama.2011.451

    +
    diff --git a/docs/search.html b/docs/search.html index 0fb0a3d..e5bcda9 100644 --- a/docs/search.html +++ b/docs/search.html @@ -58,7 +58,7 @@
  49. Usage Guide
  50. Single Patient Risk Calculation
  51. Conversion of Clinical Parameters
  52. -
  53. Classifying ESRD Outcome by 2 and 5 Year Outcomes
  54. +
  55. Classifying ESRD Outcomes
  56. Batch Risk Calculation for Multiple Patients
  57. Performance Assessment
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"prerequisit": 3, "python": 3, "recal": 6, "refer": 5, "risk": 6, "roc": 6, "row": [], "singl": 6, "start": 4, "test": [], "uacr": 6, "upcr": 6, "usag": [4, 6], "welcom": 3, "year": []}}) \ No newline at end of file diff --git a/docs/usage_guide.html b/docs/usage_guide.html index f56df34..2160f7d 100644 --- a/docs/usage_guide.html +++ b/docs/usage_guide.html @@ -73,7 +73,7 @@ -
  59. Classifying ESRD Outcome by 2 and 5 Year Outcomes
  60. Performance Assessment
  61. @@ -327,8 +329,8 @@

    Conversion FunctionsThese parameters provide the flexibility to tailor the unit conversion process to specific data needs, enabling precise and appropriate conversions crucial for accurate data analysis and interpretation in clinical or scientific research.

    Example Usage

    The following is an example to illustrate the usage of the perform_conversions function. This example shows how to convert values from mmol to mg for various clinical parameters within a DataFrame.

    - - +

    First 5 Rows of Biochemical Data (Adapted from Ali et al., 2021, BMC Nephrol) [1].

    +
    First 5 Rows of Biochemical Data (Adapted from Ali et al., 2021, BMC Nephrol)
    @@ -388,11 +390,8 @@

    Conversion Functions 'Albumin (g/l)' to new column 'Albumin_g_dl' with factor 0.1 -
    - -First 5 Rows of Biochemical Data with Conversions (Adapted from Ali et al., 2021, BMC Nephrol) - -

    uPCR

    Calcium (mmol/L)

    +

    First 5 Rows of Biochemical Data with Conversions (Adapted from Ali et al., 2021, BMC Nephrol) [1].

    +
    @@ -502,8 +501,8 @@

    Conversion Functions -
    -

    Classifying ESRD Outcome by 2 and 5 Year Outcomes

    +
    +

    Classifying ESRD Outcomes

    from kfre import class_esrd_outcome
     
    @@ -553,11 +552,8 @@

    Classifying ESRD Outcome by 2 and 5 Year Outcomes) -
    - -First 5 Rows of Outcome Data - -

    uPCR

    Calcium (mmol/L)

    +

    First 5 Rows of Outcome Data (Adapted from Ali et al., 2021, BMC Nephrol) [1].

    +
    @@ -671,15 +667,8 @@

    Batch Risk Calculation for Multiple Patients# predictions for each model and time frame -
    - - -First 5 Rows of Kidney Failure Risk Data (Adapted from Ali et al., 2021, BMC Nephrol)
    -
    - -
    - -

    Index

    2_year_outcome

    +

    First 5 Rows of Kidney Failure Risk Data (Adapted from Ali et al., 2021, BMC Nephrol) [1].

    +
    @@ -778,6 +767,10 @@

    Batch Risk Calculation for Multiple Patients

    Performance Assessment

    +
    +

    AUC ROC & Precision-Recall Curves

    +
    AUC_ROC_and_Precision_Recall +

    plot_kfre_metrics(df, num_vars, fig_size=(12, 6), mode='both', image_path_png=None, image_path_svg=None, image_prefix=None, bbox_inches='tight', plot_type='all_plots', save_plots=False, show_years=[2, 5], plot_combinations=False, show_grids=False, decimal_places=2)
    @@ -836,57 +829,9 @@

    Performance Assessment) -
    -

    AUC ROC & Precision-Recall Curves

    -
    AUC_ROC_and_Precision_Recall -

    -
    -
    -eval_kfre_metrics(df, n_var_list, outcome_years=[2, 5], decimal_places=6)
    -
    -
    Parameters:
    -
      -
    • df (DataFrame) – The input DataFrame containing the necessary columns for truth and predictions. Rows with NaN values will be dropped.

    • -
    • n_var_list (list of int) – List of variable numbers to consider, e.g., [4, 6, 8].

    • -
    • outcome_years (list, tuple, or int) – (optional) List, tuple, or single year to consider for outcomes. Default is [2, 5].

    • -
    • decimal_places (int) – (optional) Number of decimal places for the calculated metrics. Default is 6.

    • -
    -
    -
    Returns:
    -

    pd.DataFrame: A DataFrame containing the calculated metrics for each outcome.

    -
    -
    Raises:
    -

    ValueError: If required outcome columns are missing in the DataFrame. -ValueError: If an invalid variable number is provided in n_var_list.

    -
    -
    -

    This function computes a set of performance metrics for multiple binary classification models given the true labels and the predicted probabilities for each outcome. The metrics calculated include precision (positive predictive value), average precision, sensitivity (recall), specificity, AUC ROC, and Brier score.

    -
    -
    Notes:
      -
    • Precision is calculated with a threshold of 0.5 for the predicted probabilities.

    • -
    • Sensitivity is also known as recall.

    • -
    • Specificity is calculated as the recall for the negative class.

    • -
    • AUC ROC is calculated using the receiver operating characteristic curve.

    • -
    • Brier score measures the mean squared difference between predicted probabilities and the true binary outcomes.

    • -
    -
    -
    -
    - -

    Example Usage

    -
    from kfre import eval_kfre_metrics
    -
    -
    -
    metrics_df_n_var = eval_kfre_metrics(
    -    df=df,                 # Metrics-ready DataFrame as the first argument
    -    n_var_list=[4, 6, 8],  # Specify the list of variable numbers to consider
    -    outcome_years=[2, 5],  # Specify the list of outcome years to consider
    -)
    -
    -
    -
    -

    Performance Metrics Explained

    +
    +

    Performance Metrics

    This section explains the various performance metrics calculated by the eval_kfre_metrics function.

    Precision (Positive Predictive Value)

    Precision, also known as Positive Predictive Value (PPV), is the ratio of correctly predicted positive observations to the total predicted positives. It is calculated as:

    @@ -955,6 +900,59 @@

    Performance Metrics Explained +
    +
    +eval_kfre_metrics(df, n_var_list, outcome_years=[2, 5], decimal_places=6)
    +
    +
    Parameters:
    +
      +
    • df (DataFrame) – The input DataFrame containing the necessary columns for truth and predictions. Rows with NaN values will be dropped.

    • +
    • n_var_list (list of int) – List of variable numbers to consider, e.g., [4, 6, 8].

    • +
    • outcome_years (list, tuple, or int) – (optional) List, tuple, or single year to consider for outcomes. Default is [2, 5].

    • +
    • decimal_places (int) – (optional) Number of decimal places for the calculated metrics. Default is 6.

    • +
    +
    +
    Returns:
    +

    pd.DataFrame: A DataFrame containing the calculated metrics for each outcome.

    +
    +
    Raises:
    +

    ValueError: If required outcome columns are missing in the DataFrame. +ValueError: If an invalid variable number is provided in n_var_list.

    +
    +
    +

    This function computes a set of performance metrics for multiple binary classification models given the true labels and the predicted probabilities for each outcome. The metrics calculated include precision (positive predictive value), average precision, sensitivity (recall), specificity, AUC ROC, and Brier score.

    +
    +
    Notes:
      +
    • Precision is calculated with a threshold of 0.5 for the predicted probabilities.

    • +
    • Sensitivity is also known as recall.

    • +
    • Specificity is calculated as the recall for the negative class.

    • +
    • AUC ROC is calculated using the receiver operating characteristic curve.

    • +
    • Brier score measures the mean squared difference between predicted probabilities and the true binary outcomes.

    • +
    +
    +
    +
    + +

    Example Usage

    +
    from kfre import eval_kfre_metrics
    +
    +
    +
    metrics_df_n_var = eval_kfre_metrics(
    +    df=df,                 # Metrics-ready DataFrame as the first argument
    +    n_var_list=[4, 6, 8],  # Specify the list of variable numbers to consider
    +    outcome_years=[2, 5],  # Specify the list of outcome years to consider
    +)
    +
    +
    +
    +

    diff --git a/source/changelog.rst b/source/changelog.rst index 0a9563a..c84660f 100644 --- a/source/changelog.rst +++ b/source/changelog.rst @@ -26,6 +26,51 @@ Changelog ========= +0.1.12 +------ + +**Patch Release** + +This release is a comprehensive stable build encompassing changes from version ``0.1.8`` forward. + +- Package version fix in ``min_readme.md`` citation section +- Python version fix in ``min_readme.md`` +- Python version fix from ``3.74`` to ``3.7.4`` +- new functions for classifying outcomes and ckd stages + - ``class_esrd_outcome`` + - ``class_ckd_stages`` +- new functions for performance assessment + - ``plot_kfre_metrics`` + - ``eval_kfre_metrics`` + - roc_auc curves + - precision-recall curves +- performance metrics: + - Precision/PPV + - Average Precision + - Sensitivity + - Specificity + - AUC ROC + - Brier Score + +0.1.11 +------ +**Patch Release** + +- Package version fix in ``min_readme.md`` citation section + +0.1.10 +------ +**Patch Release** + +- Python version fix in ``min_readme.md`` + +0.1.9 +----- +- Python version fix from ``3.74`` to ``3.7.4`` +- updated functions for classifying outcomes and ckd stages + - ``class_esrd_outcome`` + - ``class_ckd_stages`` + 0.1.8 ----- @@ -48,17 +93,18 @@ Changelog 0.1.7 ----- -This release includes the following updates and improvements: +This release includes the following updates and improvements: + +- Acknowledgements for key influencers whose exceptional work on end-stage kidney disease has greatly inspired the creation of this library. -- Acknowledgements for key influencers whose exceptional work on end-stage kidney disease has greatly inspired the creation of this library. +- Implemented comprehensive exception handling within the ``kfre_person()`` function to ensure proper parameter validation: -- Implemented comprehensive exception handling within the ``kfre_person()`` function to ensure proper parameter validation: - - Combined all exceptions into a single exception. - - Concatenated exceptions using a newline character for better readability. - - Added checks to ensure ``age``, ``is_male``, ``eGFR``, and ``uACR`` parameters are supplied. - - Validated that the ``years`` parameter can only be 2 or 5. - - Ensured ``dm`` and ``htn`` parameters, if provided, are either ``0``, ``1``, ``True``, or ``False``. - - Added a check to ensure ``is_north_american`` is specified as either ``True`` or ``False``. + - Combined all exceptions into a single exception. + - Concatenated exceptions using a newline character for better readability. + - Added checks to ensure ``age``, ``is_male``, ``eGFR``, and ``uACR`` parameters are supplied. + - Validated that the ``years`` parameter can only be ``2`` or ``5``. + - Ensured ``dm`` and ``htn`` parameters, if provided, are either ``0``, ``1``, ``True``, or ``False``. + - Added a check to ensure ``is_north_american`` is specified as either ``True`` or ``False``. 0.1.6 diff --git a/source/citations.rst b/source/citations.rst index df73956..3797e37 100644 --- a/source/citations.rst +++ b/source/citations.rst @@ -25,7 +25,7 @@ Citing KFRE ============ -Shpaner, L. (2024). kfre: A Python Library for Reproducing Kidney Failure Risk Equations (KFRE) (0.1.8). Zenodo. https://doi.org/10.5281/zenodo.11100222 +Shpaner, L. (2024). kfre: A Python Library for Reproducing Kidney Failure Risk Equations (KFRE) (0.1.12). Zenodo. https://doi.org/10.5281/zenodo.11100222 .. code:: bash diff --git a/source/references.rst b/source/references.rst index 6391ff8..3171c44 100644 --- a/source/references.rst +++ b/source/references.rst @@ -27,7 +27,7 @@ The ``kfre`` library is based on the risk prediction models developed in the fol References =========== -Ali, I., Donne, R. L., & Kalra, P. A. (2021). A validation study of the kidney failure risk equation in advanced chronic kidney disease according to disease aetiology with evaluation of discrimination, calibration and clinical utility. *BMC Nephrology, 22(1),* 194. doi: 10.1186/s12882-021-02402-1 +Ali, I., Donne, R. L., & Kalra, P. A. (2021). A validation study of the kidney failure risk equation in advanced chronic kidney disease according to disease aetiology with evaluation of discrimination, calibration and clinical utility. *BMC Nephrology, 22(1),* 194. https://doi.org/10.1186/s12882-021-02402-1 [1]_ Sumida, K., Nadkarni, G. N., Grams, M. E., Sang, Y., Ballew, S. H., Coresh, J., Matsushita, K., Surapaneni, A., Brunskill, N., Chadban, S. J., Chang, A. R., Cirillo, M., Daratha, K. B., Gansevoort, R. T., Garg, A. X., Iacoviello, L., Kayama, T., Konta, T., Kovesdy, C. P., Lash, J., Lee, B. J., Major, R. W., Metzger, M., Miura, K., Naimark, D. M. J., Nelson, R. G., Sawhney, S., Stempniewicz, N., Tang, M., Townsend, R. R., Traynor, J. P., Valdivielso, J. M., Wetzels, J., Polkinghorne, K. R., & Heerspink, H. J. L. (2020). Conversion of urine protein-creatinine ratio or urine dipstick protein to urine albumin-creatinine ratio for use in chronic kidney disease screening and prognosis. *Annals of Internal Medicine, 173* (6), 426-435. https://doi.org/10.7326/M20-0529 @@ -35,3 +35,4 @@ Tangri, N., Grams, M. E., Levey, A. S., Coresh, J., Appel, L. J., Astor, B. C., Tangri, N., Stevens, L. A., Griffith, J., Tighiouart, H., Djurdjev, O., Naimark, D., Levin, A., & Levey, A. S. (2011). A predictive model for progression of chronic kidney disease to kidney failure. *JAMA, 305* (15), 1553-1559. https://doi.org/10.1001/jama.2011.451 +.. [1] This article is licensed under a Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. diff --git a/source/usage_guide.rst b/source/usage_guide.rst index ce3efec..0462460 100644 --- a/source/usage_guide.rst +++ b/source/usage_guide.rst @@ -244,7 +244,9 @@ These parameters provide the flexibility to tailor the unit conversion process t The following is an example to illustrate the usage of the ``perform_conversions`` function. This example shows how to convert values from mmol to mg for various clinical parameters within a DataFrame. -.. table:: First 5 Rows of Biochemical Data (Adapted from Ali et al., 2021, BMC Nephrol) +`First 5 Rows of Biochemical Data (Adapted from Ali et al., 2021, BMC Nephrol)` [1]_. + +.. table:: ====== =================== ================ ================== uPCR Calcium (mmol/L) Albumin (g/l) Phosphate (mmol/L) @@ -286,13 +288,7 @@ The following is an example to illustrate the usage of the ``perform_conversions Converted 'Albumin (g/l)' to new column 'Albumin_g_dl' with factor 0.1 -.. raw:: html - -
    - - First 5 Rows of Biochemical Data with Conversions (Adapted from Ali et al., 2021, BMC Nephrol) - -
    +`First 5 Rows of Biochemical Data with Conversions (Adapted from Ali et al., 2021, BMC Nephrol)` [1]_. .. table:: :align: left @@ -353,8 +349,8 @@ The ``upcr_uacr`` function is typically used in clinical data processing where a 744 NaN Name: uACR, Length: 745, dtype: float64 -Classifying ESRD Outcome by 2 and 5 Year Outcomes -================================================= +Classifying ESRD Outcomes +========================= .. code-block:: python @@ -403,13 +399,7 @@ This function creates a new column in the DataFrame which is populated with a `` ) -.. raw:: html - -
    - - First 5 Rows of Outcome Data - -
    +`First 5 Rows of Outcome Data (Adapted from Ali et al., 2021, BMC Nephrol)` [1]_. .. table:: :align: left @@ -512,19 +502,10 @@ This function is designed to compute the risk of chronic kidney disease (CKD) ov # The resulting DataFrame 'df' now includes new columns with risk # predictions for each model and time frame -.. raw:: html -
    - - - First 5 Rows of Kidney Failure Risk Data (Adapted from Ali et al., 2021, BMC Nephrol)
    -
    - -
    +`First 5 Rows of Kidney Failure Risk Data (Adapted from Ali et al., 2021, BMC Nephrol)` [1]_. -
    - -.. table:: +.. table:: :align: left ======== ====== ======================== ========================== ========= ============ ================ ================ ================ ================ ================ ================ ================ ================ @@ -538,10 +519,31 @@ This function is designed to compute the risk of chronic kidney disease (CKD) ov ======== ====== ======================== ========================== ========= ============ ================ ================ ================ ================ ================ ================ ================ ================ - Performance Assessment ======================= +AUC ROC & Precision-Recall Curves +--------------------------------- + +.. raw:: html + +
    + +.. image:: ../assets/performance_grid.svg + :alt: AUC_ROC_and_Precision_Recall + :align: left + :width: 900px + +.. raw:: html + +
    + +.. raw:: html + +
    + +\ + .. function:: plot_kfre_metrics(df, num_vars, fig_size=(12, 6), mode="both", image_path_png=None, image_path_svg=None, image_prefix=None, bbox_inches="tight", plot_type="all_plots", save_plots=False, show_years=[2, 5], plot_combinations=False, show_grids=False, decimal_places=2) :param DataFrame df: The input DataFrame containing the necessary columns for truth and predictions. @@ -597,67 +599,8 @@ This function generates the true labels and predicted probabilities for 2-year a ) -AUC ROC & Precision-Recall Curves ---------------------------------- - -.. raw:: html - -
    - -.. image:: ../assets/performance_grid.svg - :alt: AUC_ROC_and_Precision_Recall - :align: left - :width: 900px - -.. raw:: html - -
    - -.. raw:: html - -
    - -\ - - -.. function:: eval_kfre_metrics(df, n_var_list, outcome_years=[2, 5], decimal_places=6) - - :param DataFrame df: The input DataFrame containing the necessary columns for truth and predictions. Rows with NaN values will be dropped. - :param list of int n_var_list: List of variable numbers to consider, e.g., ``[4, 6, 8]``. - :param list, tuple, or int outcome_years: (`optional`) List, tuple, or single year to consider for outcomes. Default is ``[2, 5]``. - :param int decimal_places: (`optional`) Number of decimal places for the calculated metrics. Default is ``6``. - - :returns: ``pd.DataFrame``: A DataFrame containing the calculated metrics for each outcome. - - :raises: ``ValueError``: If required outcome columns are missing in the DataFrame. - ``ValueError``: If an invalid variable number is provided in ``n_var_list``. - - This function computes a set of performance metrics for multiple binary classification models given the true labels and the predicted probabilities for each outcome. The metrics calculated include precision (positive predictive value), average precision, sensitivity (recall), specificity, AUC ROC, and Brier score. - - Notes: - - Precision is calculated with a threshold of `0.5` for the predicted probabilities. - - Sensitivity is also known as recall. - - Specificity is calculated as the recall for the negative class. - - AUC ROC is calculated using the receiver operating characteristic curve. - - Brier score measures the mean squared difference between predicted probabilities and the true binary outcomes. - -**Example Usage** - -.. code-block:: python - - from kfre import eval_kfre_metrics - - -.. code-block:: python - - metrics_df_n_var = eval_kfre_metrics( - df=df, # Metrics-ready DataFrame as the first argument - n_var_list=[4, 6, 8], # Specify the list of variable numbers to consider - outcome_years=[2, 5], # Specify the list of outcome years to consider - ) - -Performance Metrics Explained ------------------------------ +Performance Metrics +-------------------- This section explains the various performance metrics calculated by the `eval_kfre_metrics` function. @@ -733,4 +676,47 @@ Where: - :math:`N` is the number of total observations. - :math:`f_i` is the predicted probability for the :math:`i`-th observation. - :math:`o_i` is the actual outcome for the :math:`i`-th observation (`0` or `1`). + + +---- + +.. function:: eval_kfre_metrics(df, n_var_list, outcome_years=[2, 5], decimal_places=6) + + :param DataFrame df: The input DataFrame containing the necessary columns for truth and predictions. Rows with NaN values will be dropped. + :param list of int n_var_list: List of variable numbers to consider, e.g., ``[4, 6, 8]``. + :param list, tuple, or int outcome_years: (`optional`) List, tuple, or single year to consider for outcomes. Default is ``[2, 5]``. + :param int decimal_places: (`optional`) Number of decimal places for the calculated metrics. Default is ``6``. + + :returns: ``pd.DataFrame``: A DataFrame containing the calculated metrics for each outcome. + + :raises: ``ValueError``: If required outcome columns are missing in the DataFrame. + ``ValueError``: If an invalid variable number is provided in ``n_var_list``. + + This function computes a set of performance metrics for multiple binary classification models given the true labels and the predicted probabilities for each outcome. The metrics calculated include precision (positive predictive value), average precision, sensitivity (recall), specificity, AUC ROC, and Brier score. + + Notes: + - Precision is calculated with a threshold of `0.5` for the predicted probabilities. + - Sensitivity is also known as recall. + - Specificity is calculated as the recall for the negative class. + - AUC ROC is calculated using the receiver operating characteristic curve. + - Brier score measures the mean squared difference between predicted probabilities and the true binary outcomes. + +**Example Usage** + +.. code-block:: python + + from kfre import eval_kfre_metrics + + +.. code-block:: python + + metrics_df_n_var = eval_kfre_metrics( + df=df, # Metrics-ready DataFrame as the first argument + n_var_list=[4, 6, 8], # Specify the list of variable numbers to consider + outcome_years=[2, 5], # Specify the list of outcome years to consider + ) + + +---- +.. [1] Ali, I., Donne, R. L., & Kalra, P. A. (2021). A validation study of the kidney failure risk equation in advanced chronic kidney disease according to disease aetiology with evaluation of discrimination, calibration and clinical utility. *BMC Nephrology, 22(1),* 194. https://doi.org/10.1186/s12882-021-02402-1 \ No newline at end of file

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