From 863e9f6e1aa04d8133ea406a7a19e837d7a8f92d Mon Sep 17 00:00:00 2001 From: KaisaridiSofia <115173948+KaisaridiSofia@users.noreply.github.com> Date: Mon, 6 Oct 2025 15:30:50 +0200 Subject: [PATCH 01/24] Documentation (#464) * complete mathematical background and to to go further * mixture model * fit algorithm * estimate algorithm * convergence * add link for rhat --- docs/algorithms.md | 114 +++++++++++++++++++++++++++++++++++--- docs/mathematics.md | 43 +++++++++++++- docs/models.md | 37 +++++++++++++ docs/models_evaluation.md | 12 +++- docs/references.bib | 44 +++++++++++++++ docs/to_go_further.md | 34 ++++++++++++ 6 files changed, 270 insertions(+), 14 deletions(-) diff --git a/docs/algorithms.md b/docs/algorithms.md index 0071f68b6..817965cd3 100644 --- a/docs/algorithms.md +++ b/docs/algorithms.md @@ -1,18 +1,78 @@ # Algorithms ## Fit + +In this section we describe how to fit a `leaspy` model in your data. Leaspy uses the MCMC-SAEM algorithm to fit a model by jointly estimating the fixed effects and the distribution of the random effects. It is particularly well suited to this kind of models where the likelihood involves latent variables and is not available in closed form. + +The algorithm is an adaptation of the Expectation-Maximisation (EM) algorithm that relies on an iterative procedure that alternates between the following main steps: + +- Expectation/Stochastic Approximation Step: the algorithm uses Markov Chain Monte Carlo (MCMC) to generate samples of the latent variables (random effects) conditional on the current parameter estimates, and compute the sufficient statistics of the complete-data log-likelihood using a stochastic approximation scheme. +- Maximization Step: Given the updated sufficient statistics, the fixed effects and variance components are re-estimated by maximizing the approximate complete-data log-likelihood. + +By iterating these steps, the MCMC-SAEM algorithm converges to the maximum likelihood estimates of the model parameters. + ### Prerequisites - - Data format and preprocessing steps. + +To fit a logistic model, you need a dataframe with the following columns: + +- `ID`: Patient identifier +- `TIME`: Time of measurement +- One or more columns representing the longitudinal outcomes (e.g., `FEATURE_1`, `FEATURE_2`, ...) + +For the importation of dataframe: + +```python +dataset = dataframe.set_index(["ID", "TIME"]).sort_index() +print(dataset.head()) + + FEATURE_1 FEATURE_2 + ID TIME +132-S2-0 81.661 0.44444 0.04000 + 82.136 0.60000 0.56000 + 82.682 0.39267 0.04000 + 83.139 0.58511 0.30000 + 83.691 0.57044 0.05040 + +data_leaspy = Data.from_dataframe(dataset, "visit") +``` ### Running Task - - How to run the fit algorithm. - - Example commands and code snippets. + +Then you will need to choose one from the existing models in `leaspy.models` . Let's use th logistic model as an example. + +```python +from leaspy.models import LogisticModel +``` + +We need to specify the arguments `name`, `dimension` (the number of features in your dataset) and the `obs_models` (valid choices for the logistic model are 'gaussian-diagonal' to estimate one noise coefficient per feature or 'gaussian-scalar' to estimate one noise coefficient for all the features). When we fit a multivariate model we also need to specify `source_dimension` that corresponds to the degrees of freedom of intermarker spacing parameters. We refer you to the [mathematical background section](./mathematics.md####Individual-trajectory-&Spatial-random-effects) for more details. We generally suggest a number of sources close to the square root of the number of features ($\sqrt(dimension)$). + +```python +model = LogisticModel(name="my-model", source_dimension=1, dimension=2, obs_models='gaussian-diagonal') +model.fit(data_leaspy, "mcmc_saem", n_iter=20000) +``` +You can also control add a `seed`or control other arguments for the output and the logs like `save_periodicity`, `path`, e.t.c. + ### Output - - What results to expect from the fitting process. - - DataFrame object - - Plotting object -### Setting Options (Different Models) - - How to set specific options for different types of models. - - Customizing parameters for logistic, joint, mixture, and covariate models. + +Once the iterations are done we can see the parameters that were estimated by the model and save them in a dedicated file. + +```python +model.save('my_path/my_model/model_parameters.json') +model.parameters +``` + +And we can also plot the estimated average trajectory. + +```python +import matplotlib.pyplot as plt +from leaspy.io.logs.visualization.plotting import Plotting +leaspy_plotting = Plotting(model) + +ax = leaspy_plotting.average_trajectory( + alpha=1, figsize=(14, 6), n_std_left=2, n_std_right=8 +) +plt.show() +``` + ## Personalize @@ -76,6 +136,42 @@ GS-200 1.152407 -0.171888 76.504517 0.770118 ## Estimate + +This sections describes the procedure for estimating a patient's trajectory. +Once the personalization is performed then we can estimate and visualize the trajectory of a specific subject using its individual parameters. + +```python +import numpy as np + +observations = dataframe.loc["GS-187"] +print(f"Seen ages: {observations.index.values}") +print("Individual Parameters : ", ip["GS-187"]) + +timepoints = np.linspace(60, 100, 100) +reconstruction = model.estimate({"GS-187": timepoints}, ip) +``` + +The reconstruction object contains the estimated feature values for the individual and we can plot them along with the actual observations. + +```python +ax = leaspy_plotting.patient_trajectories( + data_leaspy, + ip, + patients_idx=["GS-187"], + labels=["FEATURE_1", "FEATURE_2"], + alpha=1, + linestyle="-", + linewidth=2, + markersize=8, + obs_alpha=0.5, + figsize=(16, 6), + factor_past=0.5, + factor_future=5, +) +ax.set_xlim(45, 80) +plt.show() +``` + ## Simulate This section describes the procedure for simulating new patient data under the Spatiotemporal model structure. The simulation method, relying on a fitted Leaspy model and user-defined parameters, involves the following steps: diff --git a/docs/mathematics.md b/docs/mathematics.md index c91d6b9ce..52d972e91 100644 --- a/docs/mathematics.md +++ b/docs/mathematics.md @@ -1,10 +1,47 @@ # Mathematical Background -## Mixed effect models +## Nonlinear mixed effect models -*Talk about mixed-effect models and their ability to handle missing values* +Mixed effects models have become a powerful and widely used statistical tool for analyzing longitudinal, or more specifically repeated measurements, data, especially in the context of biomedical and epidemiological research. These models are particularly suited for studying neurodegenerative and brain diseases where patients are followed over time, and multiple clinical characteristics are measured repeatedly. Moreover these diseases have been known to be heterogenous with subtypes that are often difficult ot characterize. -## Riemanian framework +One of the key strengths of mixed-effects models lies in their ability to capture two types of variability: + +- **Within-subject variability (fixed effects)** + This reflects systematic influences that affect all patients in similar ways, such as the effect of treatment, demographic factors, or known disease subtypes. Fixed effects represent consistent trends or shifts in the data that apply across the **population** or across specific groups. +- **Between-subject variability (random effects)** + This accounts for differences among individual patients. For example, even if patients share the same diagnosis, their disease progression rates and onset time can vary considerably. By incorporating random effects, the model can capture this heterogeneity, allowing for personalized trajectory estimates at an **individual** level. + +With a mixed effects model, we can estimate the long-term progression of a disease by reconstructing individual patient trajectories over time, even when measurements are irregularly spaced or partially missing. This is crucial for understanding the natural history of a disease, identifying typical progression patterns, and recognizing outliers. Importantly, understanding how a disease evolves over time and quantifying the expected variability between individuals have significant clinical and research implications. Clinically, it supports personalized medicine approaches, tailoring treatment plans based on predicted progression. From a research standpoint, it helps identify factors driving heterogeneity in disease progression, which can inform new hypotheses about disease mechanisms or guide the development of targeted therapies. + +Brain and neurodegenerative diseases typically do not progress at a constant rate over time. In this context a non-linear formulation of mixed effects models provides a more realistic and flexible framework for representing the intricate and variable progression patterns. Many biomarkers and clinical symptoms in neurodegeneration follow nonlinear relationships with time or disease stage. Also, nonlinear models can flexibly accommodate this heterogeneity by allowing individual trajectories to differ not only in magnitude but also in shape and timing. For example, some patients may experience a rapid decline early on, while others decline more slowly but for a longer duration. + +### Parameter estimation + +We suppose that the parameters capturing the population and the individual variability, namely fixed and random effects respectively, are latent ($z$) follow some prior distributions. The parameters that define these distributions are called namely model parameters ($\theta$). We can also include hyperparmeters $\Pi$ that have known values. + +Estimating the parameters of the models can be done through maximisation of the likelihood. + +Assuming $y$ correspond to the observed data, $z$ the latent parameters, $\theta$ the model parameters and $\Pi$ the hyperparameters the likelihood estimated by the model is the following: + +$$ +p(y \mid \theta, \Pi) = \int_{z} p(y, z \mid \theta, \Pi) dz +$$ + +$p(y, z \mid \theta, \Pi)$ can be divided into two different terms: data attachment, which represents how well the model describes the data $y$, and a prior attachment, which prevents over-fitting: + +$$ +\log p(y, z \mid \theta ,\Pi) = \log {p(y \mid z, \theta, \Pi)} + \log p(z \mid \theta , \Pi ) +$$ + +The prior attachment term can be separated into two terms: two terms for the prior attachment of latent parameters (fixed and random). We end up with the following expression : + +$$ +\log p(y, z \mid \theta, \Pi) = \log {p(y \mid z, \theta, \Pi)} + \log p(z_{re} \mid z_{fe}, \theta, \Pi) + \log p(z_{fe} \mid \theta, \Pi) +$$ + +A common and well-defined algorithm for the likelihood maximization, proven to converge for non-linear mixed effects models is the MCMC-SAEM. + +## Riemannian framework ### Disease progression represented as trajectory diff --git a/docs/models.md b/docs/models.md index ab0f1c5d9..18bfbf47e 100644 --- a/docs/models.md +++ b/docs/models.md @@ -221,6 +221,43 @@ leaspy_joint.fit(data_joint, nb_iter=1000, nb_burnin=500) For estimation, it is the [CIF](./glossary.md#cif) that is outputted by the model. Note that for prediction purposes, the [CIF](./glossary.md#cif) is corrected using the survival probability at the time of the last observed visit, following common practice in other packages {cite}`andrinopoulou_combined_2017`. ## Mixture Model + +### Definition + +Mixture models are a class of statistical models that represent a population as a combination of underlying subpopulations, each described by its own probability distribution {cite}`mclachlan_finite_2000`. Standard mixed effects models assume a certain homogeneity in the sense that the inter-patient variability as random variations around a fixed reference. Mixture models explicitly recognize that observed data may arise from distinct latent groups—for example, patients may cluster into different onset times, rates of disease progression or more advanced clinical scores. This formulation captures heterogeneity in the data, allowing for flexible modeling of complex, multimodal patterns. Estimation is typically carried out using an adaptation of MCMC-SAEM for mixture models as described in {cite}`mclachlan_finite_2000`, which iteratively refine both the component parameters and the posterior probabilities of membership. By providing a probabilistic clustering framework, mixture models not only identify hidden structure but also quantify uncertainty in subgroup assignment, making them widely applicable in biomedical sciences. + +In Leaspy the mixture model is implemented as an adaptation of the spatio-temporal logistic model where the individual parameters (`tau`, `xi`and `sources`) come from a mixture of gaussian distributions with a number of components defined by the user. + +### Data + +The same rules apply as for the standard [logistic model](#logistic-model). + +### Mathematical background + +To build a mixture model, we add another layer upon the hierarchical structure of the model. We suppose that each subgroup $c$ can be described by a different set of $(\overline{\tau}^c, \overline{\xi}^c, \overline{s}^c)$ and has a respective probability of occurrence $\pi^c$. We suppose that the population parameters come from normal distributions as in the standard model, while the individual parameters are sampled from a mixture of gaussians with mixing probabilities $\pi^c$. + +The individual parameters (random effects): + +$$ +\begin{cases} +\xi_i \sim \sum_{c=1}^{n_c} \pi^c \mathcal{N} (\overline\xi^c, \sigma_{\xi}^c) \\ +\tau_i \sim \sum_{c=1}^{n_c} \pi^c \mathcal{N} (\overline\tau^c, \sigma_{\tau}^c)\\ +s_{il} \sim \sum_{c=1}^{n_c} \pi^c \mathcal{N} (\overline s^c, 1) +\end{cases} +$$ + + +### Model summary + +To use the mixture model in Leaspy you need to choose the number of cluster you wish to estimate beforehand. + +```python +from leaspy.models import LogisticMultivariateMixtureModel + +leaspy_mixture = LogisticMultivariateMixtureModel(source_dimension=1, n_clusters=2, dimension=3) +leaspy_mixture.fit(data_logistic, "mcmc_saem", n_iter=1000) +``` + ## Covariate Model ## References diff --git a/docs/models_evaluation.md b/docs/models_evaluation.md index 615261153..47655f2bf 100644 --- a/docs/models_evaluation.md +++ b/docs/models_evaluation.md @@ -2,14 +2,22 @@ ## Convergence diagnosis -## Likelihood based metrics +When using leaspy, you have to choose the number of iterations to estimate the model and hopefully to reach convergence of the MCMC-SAEM algorithm used. A famous way to monitor it is by running several MC chains with various random seeds and compute the statistic of Gelman-Rubin{cite}`gelman1992inference`, denoted $\hat{R}$. The $\hat{R}$ measures the ratio of the **within-chain variance** to the **between-chain variance**. If the chain has converged to the same stationary distribution, these two variances should be roughly equal{cite}`vehtari2021convergence`. + +$$ +\hat{R} = \frac{\hat{V}}{W} +$$ + +where $W$ is the within-chain variance and $\hat{V}$ is the posterior variance estimate for the pooled rank-traces. + +This could be computed thanks to the package `arviz` with the function [`rhat`](https://python.arviz.org/en/stable/api/generated/arviz.rhat.html). ## Fit metrics Leaspy stores three negative log likelihood (nll) values in the `fit_metrics` of the model’s json file: - `nll_attach`: $-\log p(y \mid z, \theta, \Pi)$, where $y$ are the observations, $z$ the latent parameters, $\theta$ the model parameters, and $\Pi$ the hyperparameters. It corresponds to the nll attached to the data. - `nll_regul_ind_sum`: $-\log p(z_{\text{re}} \mid z_{\text{fe}}, \theta, \Pi)$, where $z_{\text{re}}$ denotes the latent random effects and $z_{\text{fe}}$ the latent fixed effects. It corresponds to the nll from the random effects. -- `nll_tot`: $-\log p(y, z, \theta \mid \Pi)$. It corresponds to the total nll: nll_attach, nll_regul_ind_sum and the nll linked to the individual parameters (`v0`,`xi`,`tau`), that is not reported directly in the json file. +- `nll_tot`: $-\log p(y, z, \theta \mid \Pi)$. It corresponds to the total nll: nll_attach, nll_regul_ind_sum and the nll linked to the fixed effects $-\log p(z_{\text{fe}} \mid \theta, \Pi)$, that is not reported directly in the json file. The last conditional nll can be used for computing fit metrics. diff --git a/docs/references.bib b/docs/references.bib index d9c690f6e..a62215170 100644 --- a/docs/references.bib +++ b/docs/references.bib @@ -627,3 +627,47 @@ @article{willmott2005advantages year={2005}, publisher={Inter-Research} } + +@book{mclachlan_finite_2000, + title = {Finite Mixture Models}, + author = {McLachlan, Geoffrey J. and Peel, David}, + year = {2000}, + publisher = {Wiley}, + address = {New York}, + isbn = {978-0-471-00626-8} +} + +@article{dempster_maximum_1977, + title = {Maximum likelihood from incomplete data via the {EM} algorithm}, + author = {Dempster, Arthur P. and Laird, Nan M. and Rubin, Donald B.}, + journal = {Journal of the Royal Statistical Society: Series B (Methodological)}, + volume = {39}, + number = {1}, + pages = {1--38}, + year = {1977}, + publisher = {Wiley} +} + +@article{vehtari2021convergence, + title={Rank-Normalization, Folding, and Localization: An Improved Rˆ for Assessing Convergence of MCMC (with Discussion)}, + volume={16}, + ISSN={1936-0975}, + url={http://dx.doi.org/10.1214/20-BA1221}, + DOI={10.1214/20-ba1221}, + number={2}, + journal={Bayesian Analysis}, + publisher={Institute of Mathematical Statistics}, + author={Vehtari, Aki and Gelman, Andrew and Simpson, Daniel and Carpenter, Bob and Bürkner, Paul-Christian}, + year={2021}, + month=jun } + + @article{gelman1992inference, + title = {Inference from Iterative Simulation Using Multiple Sequences}, + author = {Gelman, Andrew and Rubin, Donald B.}, + journal = {Statistical Science}, + volume = {7}, + number = {4}, + pages = {457--472}, + year = {1992}, + publisher = {Institute of Mathematical Statistics} +} diff --git a/docs/to_go_further.md b/docs/to_go_further.md index d44964906..c8f8bb47a 100644 --- a/docs/to_go_further.md +++ b/docs/to_go_further.md @@ -1,6 +1,40 @@ # To Go Further ## Model Implementation + +This section aims to provide a survival guide for any potential futue developper who wishes to contribute to leaspy by implementing a new model. In order to implement a new model you should be able to answer the following questions : + +- Data + - What kind of data my model aims to analyze? +- Parameters + - What parameters my model needs to be fully specified? +- Algorithm + - Does my model require a new/modified version of the MCMC-SAEM algortihm to estimate the parameters? + +You could start by creating a *my_model.py* with the basic structure of you model. +What model class should be branched? + +### Data + +If other than longitudinal, events, covariates you should buid a *_data_reader.py* + +### Parameters + +- **DAG** + For models to be computationally sound, we needed to have a flow of dependency: a variable cannot depend on a variable that itself depends on it. Said differently, every model + should be laid out as a Directed Acyclic Graph (DAG). The graph specifies the properties of each variable, as well as the dependency flow and the update rules. Such structure can + optimise computations and has the advantage of being close to the mathematical formulation. +- **Parameter classes** + Decide on what class to attribute each parameter, *get_variable_specs* +- **Likelihood** + *distributions* for the latent parameters +- **Sufficient Statistics** + *utils* functions for new model parameters + +### Algortihm + +Probably *samplers* because *algo/mcmc_saem* provides the basic structure. + ### DAG Intuition and Structure for Models ## What kind of scientific question could be answered with leaspy? From 7e2e23d9bdaad98795a3e7258f44b7bb052182dc Mon Sep 17 00:00:00 2001 From: caglayantuna Date: Fri, 10 Oct 2025 13:48:05 +0200 Subject: [PATCH 02/24] improve examples (#465) --- docs/index.md | 56 ++++++++-------- docs/sg_execution_times.rst | 10 +-- examples/plot_parkinson_example.py | 32 +++++---- examples/plot_quickstart.py | 100 +++++++++-------------------- 4 files changed, 82 insertions(+), 116 deletions(-) diff --git a/docs/index.md b/docs/index.md index 97980174d..b252bc899 100644 --- a/docs/index.md +++ b/docs/index.md @@ -1,34 +1,5 @@ - ![leaspy logo](./_static/images/leaspy_logo.png) -# Welcome to Leaspy's documentation! - -```{toctree} -:caption: Getting Started -:maxdepth: 2 -install -``` - -```{toctree} -:caption: Examples -:maxdepth: 2 - -auto_examples/index - -``` - -```{toctree} -:caption: Documentation -:maxdepth: 3 - -user_guide -glossary -notations -references -changelog -license -``` - ## LEArning Spatiotemporal Patterns in Python @@ -79,3 +50,30 @@ More detailed explanations about the models themselves and about the estimation - **Intensive application for Alzheimer's Disease progression**: *AD Course Map charts Alzheimer's disease progression*, I. Koval, A. Bone, M. Louis, S. Bottani, A. Marcoux, J. Samper-Gonzalez, N. Burgos, B. Charlier, A. Bertrand, S. Epelbaum, O. Colliot, S. Allassonniere & S. Durrleman, Scientific Reports, 2021. 11(1):1-16 [Open Access](https://hal.inria.fr/hal-01964821) - [www.digital-brain.org](https://project.inria.fr/digitalbrain/) : Website related to the application of the model for Alzheimer's disease. - [Disease Course Mapping](https://disease-progression-modelling.github.io/pages/models/disease_course_mapping.html) webpage by Igor Koval + + +```{toctree} +:caption: Getting Started +:maxdepth: 2 +install +``` + +```{toctree} +:caption: Examples +:maxdepth: 2 + +auto_examples/index + +``` + +```{toctree} +:caption: Documentation +:maxdepth: 3 + +user_guide +glossary +notations +references +changelog +license +``` \ No newline at end of file diff --git a/docs/sg_execution_times.rst b/docs/sg_execution_times.rst index bb34db1cc..bf1e2eadc 100644 --- a/docs/sg_execution_times.rst +++ b/docs/sg_execution_times.rst @@ -6,7 +6,7 @@ Computation times ================= -**00:42.558** total execution time for 4 files **from all galleries**: +**00:30.526** total execution time for 4 files **from all galleries**: .. container:: @@ -33,14 +33,14 @@ Computation times - Time - Mem (MB) * - :ref:`sphx_glr_auto_examples_plot_quickstart.py` (``../examples/plot_quickstart.py``) - - 00:31.453 + - 00:20.482 - 0.0 * - :ref:`sphx_glr_auto_examples_plot_parkinson_example.py` (``../examples/plot_parkinson_example.py``) - - 00:05.162 + - 00:04.638 - 0.0 * - :ref:`sphx_glr_auto_examples_plot_mixture.py` (``../examples/plot_mixture.py``) - - 00:03.092 + - 00:02.755 - 0.0 * - :ref:`sphx_glr_auto_examples_plot_simulate.py` (``../examples/plot_simulate.py``) - - 00:02.850 + - 00:02.651 - 0.0 diff --git a/examples/plot_parkinson_example.py b/examples/plot_parkinson_example.py index a423f02a6..f32a3b1ab 100644 --- a/examples/plot_parkinson_example.py +++ b/examples/plot_parkinson_example.py @@ -33,6 +33,7 @@ data_train = Data.from_dataframe(df_train) data_test = Data.from_dataframe(df_test) + # %% # The logistic model is imported and initialized. # A two-dimensional source space is chosen to represent disease progression trajectories. @@ -40,16 +41,6 @@ model = LogisticModel(name="test-model", source_dimension=2) -# %% -# The model is fitted to the training data using the MCMC-SAEM algorithm. -# A fixed seed is used for reproducibility and 100 iterations are performed. -model.fit( - data_train, - "mcmc_saem", - n_iter=100, - progress_bar=False, -) - # %% # Visualization utilities from Leaspy and Matplotlib are imported. import matplotlib.pyplot as plt @@ -59,14 +50,28 @@ leaspy_plotting = Plotting(model) # %% -# An illustration of individual patient observations on the learned disease progression space is created. +# Data that will be used to fit the model can be illustrated as follows: + ax = leaspy_plotting.patient_observations(data_train, alpha=0.7, figsize=(14, 6)) ax.set_ylim(0, 0.8) # The y-axis is adjusted for better visibility. plt.show() + +# %% +# The model is fitted to the training data using the MCMC-SAEM algorithm. +# A fixed seed is used for reproducibility and 100 iterations are performed. +model.fit( + data_train, + "mcmc_saem", + seed=0, + n_iter=100, + progress_bar=False, +) + + # %% # The average trajectory estimated by the model is displayed. -# This figure shows the mean disease progression curve together with variability bands. +# This figure shows the mean disease progression curves for all features. ax = leaspy_plotting.average_trajectory( alpha=1, figsize=(14, 6), n_std_left=2, n_std_right=8 ) @@ -89,7 +94,6 @@ data_test, alpha=0.7, linestyle="-", - # patients_idx=list(data_test.individuals.keys())[:4], figsize=(14, 6), ) plt.show() @@ -110,7 +114,7 @@ ax = leaspy_plotting.patient_trajectories( data_test, ip, - patients_idx=["GS-187", "GS-180"], + patients_idx=["GS-187"], labels=["MDS1", "MDS2", "MDS3 (off)"], alpha=1, linestyle="-", diff --git a/examples/plot_quickstart.py b/examples/plot_quickstart.py index 0810362ba..fb6cec23b 100644 --- a/examples/plot_quickstart.py +++ b/examples/plot_quickstart.py @@ -2,13 +2,14 @@ Quickstart with Leaspy ====================== -This example shows how to quickly use Leaspy. +This example demonstrates how to quickly use Leaspy with properly formatted data. """ # %% -# Comprehensive example -# --------------------- -# We first load synthetic data in a long format to get of a grasp of longitudinal data: +# Leaspy uses its own data container. To use it correctly, you need to provide either +# a CSV file or a pandas.DataFrame in *long format*. +# +# Below is an example of synthetic longitudinal data illustrating how to use Leaspy: from leaspy.datasets import load_dataset @@ -20,15 +21,23 @@ # %% # The data correspond to repeated visits (`TIME` index) of different participants (`ID` index). # Each visit corresponds to the measurement of 4 different outcomes : the MMSE, the RAVLT, the FAQ and the FDG PET. -# If plotted, the data would look like the following: -# -# .. image:: ../alzheimer-observations.png -# :width: 600 -# :alt: Alzeimer observations + # %% -# Where each color corresponds to a variable, and the connected dots corresponds to the repeated visits of a single participant. -# Not very engaging, right ? To go a step further, let's first encapsulate the data into the main `Data` container: +# ```{warning} +# You **MUST** include both `ID` and `TIME`, either as indices or as columns. +# The remaining columns should correspond to the observed variables +# (also called features or endpoints). +# Each feature should have its own column, and each visit should occupy one row. +# ``` + + +# %% +# ```{warning} +# - Leaspy supports *linear* and *logistic* models. +# - The features **MUST** be increasing over time. +# - For logistic models, data must be rescaled between 0 and 1. +# ``` from leaspy.io.data import Data, Dataset @@ -36,8 +45,9 @@ dataset = Dataset(data) # %% -# Leaspy core functionality is to estimate the group-average trajectory of the different variables that are measured in a population. -# Clinical scores often have a ceiling and a floor effect, so let's initialize a multivariate logistic model: +# The core functionality of Leaspy is to estimate the group-average trajectory +# of the variables measured in a population. To do this, you need to choose a model. +# For example, a logistic model can be initialized and fitted as follows: from leaspy.models import LogisticModel @@ -51,67 +61,21 @@ ) # %% -# If we were to plot the measured average progression of the variables, see started example notebook for details, it would look like the following: -# -# .. image:: ../alzheimer-model.png -# :width: 600 -# :alt: Alzeimer model - -# %% -# We can also derive the individual trajectory of each subject. -# To do this, we use a personalization algorithm called `scipy_minimize`: +# Leaspy can also estimate the *individual trajectories* of each participant. +# This is done using a personalization algorithm, here `scipy_minimize`: individual_parameters = model.personalize( dataset, "scipy_minimize", seed=0, progress_bar=False ) print(individual_parameters.to_dataframe()) -# %% -# Plotting the input participant data against its personalization would give the following, see started example notebook for details. -# -# .. image:: ../alzheimer-subject_trajectories.png -# :width: 600 -# :alt: Alzeimer subject trajectories # %% -# Using my own data -# ----------------- -# -# Data format -# ........... -# -# Leaspy uses its own data container. To use it properly, you need to provide a csv file or a pandas.DataFrame in the right format: longitudinal data in a long format. -# Let's have a look at the data used in the previous example: - -print(alzheimer_df.head()) - -# %% [markdown] -# ```{warning} -# You **MUST** have `ID` and `TIME`, either in index or in the columns. The other columns must be the observed variables (also named features or endpoints). In this fashion, you have one column per feature and one line per visit. -# ``` - -# %% -# Data scale & constraints -# ........................ -# - -# %% [markdwon] -# ```{warning} -# - Leaspy uses linear and logistic models. -# - The features **MUST** be increasing with time. -# - For the logistic model, you need to rescale your data between 0 and 1. -# ``` - -# %% -# Missing data -# ............ -# Leaspy automatically handles missing data as long as they are encoded as nan in your pandas.DataFrame, or as empty values in your csv file. - -# %% -# Going further -# ............. -# You can check the [user_guide](../user_guide.md) and the full API documentation. -# -# You can also dive into the [examples](./index.rst). +# To go further; # -# The [Disease Progression Modelling](https://disease-progression-modelling.github.io/) website also hosts a [mathematical introduction](https://disease-progression-modelling.github.io/pages/models/disease_course_mapping.html) and [tutorials](https://disease-progression-modelling.github.io/pages/notebooks/disease_course_mapping/disease_course_mapping.html) for Leaspy. +# 1. See the [User Guide](../user_guide.md) and full API documentation. +# 2. Explore additional [examples](./index.rst). +# 3. Visit the [Disease Progression Modelling](https://disease-progression-modelling.github.io/) website, +# which provides a [mathematical introduction](https://disease-progression-modelling.github.io/pages/models/disease_course_mapping.html) +# and [tutorials](https://disease-progression-modelling.github.io/pages/notebooks/disease_course_mapping/disease_course_mapping.html) +# for earlier versions such as v1.5. From 04e202617df0360bd5647647dd6e6ee482126c2c Mon Sep 17 00:00:00 2001 From: caglayantuna Date: Fri, 10 Oct 2025 13:48:17 +0200 Subject: [PATCH 03/24] update license (#467) --- LICENSE | 703 +++++++++++++++++++++++++++++++++++++++++++++++++++++--- 1 file changed, 674 insertions(+), 29 deletions(-) diff --git a/LICENSE b/LICENSE index 6b4679d2e..047cd609d 100644 --- a/LICENSE +++ b/LICENSE @@ -1,29 +1,674 @@ -BSD 3-Clause License - -Copyright (c) 2021, INRIA / Aramis Lab -All rights reserved. - -Redistribution and use in source and binary forms, with or without -modification, are permitted provided that the following conditions are met: - -1. Redistributions of source code must retain the above copyright notice, this - list of conditions and the following disclaimer. - -2. Redistributions in binary form must reproduce the above copyright notice, - this list of conditions and the following disclaimer in the documentation - and/or other materials provided with the distribution. - -3. 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Interpretation of Sections 15 and 16. + + If the disclaimer of warranty and limitation of liability provided +above cannot be given local legal effect according to their terms, +reviewing courts shall apply local law that most closely approximates +an absolute waiver of all civil liability in connection with the +Program, unless a warranty or assumption of liability accompanies a +copy of the Program in return for a fee. + + END OF TERMS AND CONDITIONS + + How to Apply These Terms to Your New Programs + + If you develop a new program, and you want it to be of the greatest +possible use to the public, the best way to achieve this is to make it +free software which everyone can redistribute and change under these terms. + + To do so, attach the following notices to the program. It is safest +to attach them to the start of each source file to most effectively +state the exclusion of warranty; and each file should have at least +the "copyright" line and a pointer to where the full notice is found. + + leaspy + Copyright (C) 2019 icm-institute / AramisLab + + This program is free software: you can redistribute it and/or modify + it under the terms of the GNU General Public License as published by + the Free Software Foundation, either version 3 of the License, or + (at your option) any later version. + + This program is distributed in the hope that it will be useful, + but WITHOUT ANY WARRANTY; without even the implied warranty of + MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + GNU General Public License for more details. + + You should have received a copy of the GNU General Public License + along with this program. If not, see . + +Also add information on how to contact you by electronic and paper mail. + + If the program does terminal interaction, make it output a short +notice like this when it starts in an interactive mode: + + leaspy Copyright (C) 2019 icm-institute / AramisLab + This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. + This is free software, and you are welcome to redistribute it + under certain conditions; type `show c' for details. + +The hypothetical commands `show w' and `show c' should show the appropriate +parts of the General Public License. Of course, your program's commands +might be different; for a GUI interface, you would use an "about box". + + You should also get your employer (if you work as a programmer) or school, +if any, to sign a "copyright disclaimer" for the program, if necessary. +For more information on this, and how to apply and follow the GNU GPL, see +. + + The GNU General Public License does not permit incorporating your program +into proprietary programs. If your program is a subroutine library, you +may consider it more useful to permit linking proprietary applications with +the library. If this is what you want to do, use the GNU Lesser General +Public License instead of this License. But first, please read +. From 8282b215255ef71529280b45c288e7299aac35db Mon Sep 17 00:00:00 2001 From: caglayantuna Date: Fri, 10 Oct 2025 13:48:29 +0200 Subject: [PATCH 04/24] add_joint_example (#466) --- docs/models.md | 2 +- examples/plot_joint.py | 58 +++++++ .../data/simulated_data_for_joint.csv | 158 ++++++++++++++++++ 3 files changed, 217 insertions(+), 1 deletion(-) create mode 100644 examples/plot_joint.py create mode 100644 src/leaspy/datasets/data/simulated_data_for_joint.csv diff --git a/docs/models.md b/docs/models.md index 18bfbf47e..9008ea665 100644 --- a/docs/models.md +++ b/docs/models.md @@ -215,7 +215,7 @@ $$ In practice in leaspy, to use the joint model, you need to precise "joint" in Leaspy object creation, then you can use it to fit, personnalize, estimate and simulate. ```python -leaspy_joint = Joint(nb_events=2, source_dimension=3) +leaspy_joint = JointModel(nb_events=2, source_dimension=3) leaspy_joint.fit(data_joint, nb_iter=1000, nb_burnin=500) ``` For estimation, it is the [CIF](./glossary.md#cif) that is outputted by the model. Note that for prediction purposes, the [CIF](./glossary.md#cif) is corrected using the survival probability at the time of the last observed visit, following common practice in other packages {cite}`andrinopoulou_combined_2017`. diff --git a/examples/plot_joint.py b/examples/plot_joint.py new file mode 100644 index 000000000..2a436fb7d --- /dev/null +++ b/examples/plot_joint.py @@ -0,0 +1,58 @@ +""" +Joint Model +===================================================== +This notebook contains the code for a simple implementation of the Leaspy Joint model on synthetic data. +""" + +# %% +# The following imports are required libraries for numerical computation and data manipulation. +import os + +import pandas as pd + +import leaspy +from leaspy.io.data import Data + +leaspy_root = os.path.dirname(leaspy.__file__) + +data_path = os.path.join(leaspy_root, "datasets/data/simulated_data_for_joint.csv") + +df = pd.read_csv(data_path, dtype={"ID": str}, sep=";") +print(df.head()) + +# %% +# To use the Joint Model in Leaspy, your dataset must include the following columns: +# +# 1. **ID** : Patient identifier +# 2. **TIME** : Time of measurement +# 3. **EVENT_TIME** : Time of the event +# 4. **EVENT_BOOL** : Event indicator: +# - `1` if the event occurred +# - `0` if censored +# - `2` if a competing event occurred +# +# For one patient, the event time and event bool are the same for each row. + + +# %% +# We load the Joint Model from the `leaspy.models` and transform the dataset in a leaspy-compatible form with the built-in functions. +from leaspy.models import JointModel + +data = Data.from_dataframe(df, "joint") +model = JointModel(name="test_model", nb_events=1) + +# %% +# The parameter `nb_events` should match the number of distinct event types +# present in the `EVENT_BOOL` column: +# +# - If `EVENT_BOOL` contains values {0, 1}, then `nb_events=1`. +# - If it contains values {0, 1, 2}, then `nb_events=2`. +# +# Once the model is initialized, we can fit it to the data. + +model.fit(data, "mcmc_saem", seed=1312, n_iter=100, progress_bar=False) + + +# %% +# The Joint Model includes specific parameters such as `log_rho_mean` and `zeta_mean`. +print(model.parameters) diff --git a/src/leaspy/datasets/data/simulated_data_for_joint.csv b/src/leaspy/datasets/data/simulated_data_for_joint.csv new file mode 100644 index 000000000..e3684406d --- /dev/null +++ b/src/leaspy/datasets/data/simulated_data_for_joint.csv @@ -0,0 +1,158 @@ +ID;TIME;EVENT_TIME;EVENT_BOOL;Y0;Y1;Y2;Y3 +116;78.461;85.5;1;0.44443999999999995;0.04;0.0;0.0 +116;78.936;85.5;1;0.6;0.0;0.0;0.2 +116;79.482;85.5;1;0.39266999999999996;0.04;0.0;0.2 +116;79.939;85.5;1;0.58511;0.0;0.0;0.0 +116;80.491;85.5;1;0.57044;0.0;0.0;0.0 +116;81.455;85.5;1;0.55556;0.2;0.1;0.2 +116;82.491;85.5;1;0.71844;0.2;0.1;0.6 +116;83.463;85.5;1;0.71111;0.32;0.2;0.6 +116;84.439;85.5;1;0.9111100000000001;0.52;0.6;1.0 +142;77.794;85.777;0;0.326;0.0;0.0;0.0 +142;78.27199999999999;85.777;0;0.35556;0.0;0.0;0.0 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example/start/starter.ipynb | 711 -------- examples/GALLERY_HEADER.rst | 4 +- ...ot_quickstart.py => plot_01_quickstart.py} | 4 - ...xample.py => plot_02_parkinson_example.py} | 0 examples/{plot_joint.py => plot_03_joint.py} | 0 .../{plot_mixture.py => plot_04_mixture.py} | 0 .../{plot_simulate.py => plot_05_simulate.py} | 0 poetry.lock | 84 +- pyproject.toml | 1 + 42 files changed, 158 insertions(+), 5222 deletions(-) create mode 100644 docs/_static/custom.js delete mode 100644 docs/tutorials.md delete mode 100644 example/start/inputs/algorithm_settings_calibration.json delete mode 100644 example/start/inputs/algorithm_settings_personalization.json delete mode 100644 example/start/inputs/cofactor.csv delete mode 100644 example/start/inputs/data.csv delete mode 100644 example/start/inputs/data_normalized.csv delete mode 100644 example/start/inputs/data_univariate.csv delete mode 100644 example/start/inputs/simulation_settings.json delete mode 100644 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example/start/outputs/logs/parameter_convergence/xi.csv delete mode 100644 example/start/outputs/logs/parameter_convergence/xi_mean.csv delete mode 100644 example/start/outputs/logs/parameter_convergence/xi_std.csv delete mode 100644 example/start/outputs/model_parameters.json delete mode 100644 example/start/starter.ipynb rename examples/{plot_quickstart.py => plot_01_quickstart.py} (84%) rename examples/{plot_parkinson_example.py => plot_02_parkinson_example.py} (100%) rename examples/{plot_joint.py => plot_03_joint.py} (100%) rename examples/{plot_mixture.py => plot_04_mixture.py} (100%) rename examples/{plot_simulate.py => plot_05_simulate.py} (100%) diff --git a/Makefile b/Makefile index a6b112019..151a96831 100644 --- a/Makefile +++ b/Makefile @@ -13,6 +13,7 @@ clean.doc: @$(RM) -rf site/ @$(RM) -rf docs/_build/ @$(RM) -rf docs/auto_examples/ + @$(RM) -rf docs/sg_execution_times.rst .PHONY: clean.py clean.py: diff --git a/README.md b/README.md index 80ea780ac..41a208504 100644 --- a/README.md +++ b/README.md @@ -34,7 +34,7 @@ pip install leaspy #### Install in development mode -If you haven't done it already, create and activate a dedicated environment (see the beginning of the installation section). +If you haven't done it already, create and activate a dedicated environment. **Clone the repository** @@ -84,10 +84,8 @@ pre-commit install ### Examples & Tutorials -The `example/start/` folder contains a starting point if you want to launch your first scripts and notebook with the Leaspy package. -You can find additional walkthroughs in: -- this series of [online tutorials](https://disease-progression-modelling.github.io/pages/notebooks/disease_course_mapping/disease_course_mapping.html) from 2020 -- this [Medium post](https://medium.com/@igoroa/analysis-of-longitudinal-data-made-easy-with-leaspy-f8d529fcb5f8) of 2019 (_warning_: the plotter and the individual parameters described there have been deprecated since then) +The `examples` folder contains a starting point if you want to launch your first scripts and notebook with the Leaspy package. + ## Description Leaspy is a software package for the statistical analysis of **longitudinal data**, particularly **medical** data that comes in a form of **repeated observations** of patients at different time-points. @@ -99,7 +97,7 @@ Considering these series of short-term data, the software aims at : - predicting future observations - simulating virtual patients to un-bias the initial cohort or mimics its characteristics -The software package can be used with scalar multivariate data whose progression can be modelled by a logistic shape, an exponential decay or a linear progression. +The software package can be used with scalar multivariate data whose progression can be described by a logistic model, linear, joint or mixture model. The simplest type of data handled by the software are scalar data: they correspond to one (univariate) or multiple (multivariate) measurement(s) per patient observation. This includes, for instance, clinical scores, cognitive assessments, physiological measurements (e.g. blood markers, radioactive markers) but also imaging-derived data that are rescaled, for instance, between 0 and 1 to describe a logistic progression. @@ -113,15 +111,12 @@ This includes, for instance, clinical scores, cognitive assessments, physiologic More detailed explanations about the models themselves and about the estimation procedure can be found in the following articles : - **Mathematical framework**: *A Bayesian mixed-effects model to learn trajectories of changes from repeated manifold-valued observations*. Jean-Baptiste Schiratti, Stéphanie Allassonnière, Olivier Colliot, and Stanley Durrleman. The Journal of Machine Learning Research, 18:1–33, December 2017. [Open Access](https://hal.archives-ouvertes.fr/hal-01540367). -- **Application to imaging data**: *Statistical learning of spatiotemporal patterns from longitudinal manifold-valued networks*. I. Koval, J.-B. Schiratti, A. Routier, M. Bacci, O. Colliot, S. Allassonnière and S. Durrleman. MICCAI, September 2017. [Open Access](https://hal.archives-ouvertes.fr/hal-01540828) -- **Application to imaging data**: *Spatiotemporal Propagation of the Cortical Atrophy: Population and Individual Patterns*. Igor Koval, Jean-Baptiste Schiratti, Alexandre Routier, Michael Bacci, Olivier Colliot, Stéphanie Allassonnière, and Stanley Durrleman. Front Neurol. 2018 May 4;9:235. [Open Access](https://hal.inria.fr/hal-01910400) -- **Application to data with missing values**: *Learning disease progression models with longitudinal data and missing values*. R. Couronne, M. Vidailhet, JC. Corvol, S. Lehéricy, S. Durrleman. ISBI, April 2019. [Open Access](https://hal.archives-ouvertes.fr/hal-02091571) -- **Intensive application for Alzheimer's Disease progression**: *AD Course Map charts Alzheimer's disease progression*, I. Koval, A. Bone, M. Louis, S. Bottani, A. Marcoux, J. Samper-Gonzalez, N. Burgos, B. Charlier, A. Bertrand, S. Epelbaum, O. Colliot, S. Allassonniere & S. Durrleman, Scientific Reports, 2021. 11(1):1-16 [Open Access](https://hal.inria.fr/hal-01964821) +- **Mixture Model**: *A mixture model for subtype identification: Application to CADASIL* Sofia Kaisaridi, Juliette Ortholand, Caglayan Tuna, Nicolas Gensollen, and Sophie Tezenas Du Montcel. ISCB 46-46th Annual Conference of the International Society for Clinical Biostatistics, August 2025. [Open Access](https://hal.science/hal-05266776v1) +- **Joint Model**: *Joint model with latent disease age: overcoming the need for reference time* Juliette Ortholand, Nicolas Gensollen, Stanley Durrleman, Sophie Tezenas du Montcel. arXiv preprint arXiv:2401.17249. 2024 [Open Access](https://arxiv.org/abs/2401.17249) - [www.digital-brain.org](https://www.digital-brain.org): website related to the application of the model for Alzheimer's disease -- [Disease Course Mapping](https://disease-progression-modelling.github.io/pages/models/disease_course_mapping.html) webpage by Igor Koval ## License -The package is distributed under the BSD 3-Clause license. +The package is distributed under the GNU LESSER GENERAL PUBLIC LICENSE. ## Support The development of this software has been supported by the European Union H2020 program (project EuroPOND, grant number 666992, project HBP SGA1 grant number 720270), by the European Research Council (to Stanley Durrleman project LEASP, grant number 678304) and by the ICM Big Brain Theory Program (project DYNAMO). diff --git a/docs/_static/custom.css b/docs/_static/custom.css index f82ebaf39..e5645e0a5 100644 --- a/docs/_static/custom.css +++ b/docs/_static/custom.css @@ -1,3 +1,6 @@ .wy-nav-content { max-width: 96% !important; } +.sphx-glr-thumbcontainer .xref.std.std-ref { + display: none; +} \ No newline at end of file diff --git a/docs/_static/custom.js b/docs/_static/custom.js new file mode 100644 index 000000000..0febaaf58 --- /dev/null +++ b/docs/_static/custom.js @@ -0,0 +1,23 @@ +document.addEventListener("DOMContentLoaded", function() { + // Find all gallery thumbnails + document.querySelectorAll('.sphx-glr-thumbcontainer').forEach(function(el) { + const pyfileSpan = el.querySelector('.xref.std.std-ref'); + if (pyfileSpan) { + let pyfile = pyfileSpan.textContent.trim(); + + // Remove the sphx_glr_auto_examples_ prefix + pyfile = pyfile.replace(/^sphx_glr_auto_examples_/, ''); + + // Replace .py with .html + const htmlFile = pyfile.replace(/\.py$/, '.html'); + + // Attach click event + el.addEventListener('click', function() { + window.location.href = htmlFile; + }); + + // Change cursor to pointer + el.style.cursor = 'pointer'; + } + }); +}); \ No newline at end of file diff --git a/docs/conf.py b/docs/conf.py index 424be9ae6..df984314e 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -16,6 +16,7 @@ from datetime import date import sphinx +from sphinx_gallery.sorting import FileNameSortKey # -- Project information ----------------------------------------------------- @@ -45,7 +46,6 @@ def find_var(varname: str, *py_file_paths): "sphinx.ext.autosummary", "sphinx.ext.intersphinx", "numpydoc", - "sphinx_rtd_theme", "sphinx_gallery.gen_gallery", "myst_nb", "sphinxcontrib.bibtex", @@ -74,10 +74,11 @@ def find_var(varname: str, *py_file_paths): ] sphinx_gallery_conf = { - "examples_dirs": "../examples", # path to your example scripts - "gallery_dirs": "auto_examples", # path to where to save gallery generated output + "examples_dirs": ["../examples"], # path to your example scripts + "gallery_dirs": ["auto_examples"], # path to where to save gallery generated output "notebook_images": "https://leaspy.readthedocs.io/en/stable/_images/", "plot_gallery": True, + "within_subsection_order": FileNameSortKey, } # this is needed for some reason... @@ -140,7 +141,7 @@ def find_var(varname: str, *py_file_paths): # html_theme = 'alabaster' # html_theme = 'sphinx-theme' -html_theme = "sphinx_rtd_theme" +html_theme = "pydata_sphinx_theme" add_function_parentheses = True @@ -151,6 +152,7 @@ def find_var(varname: str, *py_file_paths): html_css_files = [ "custom.css", ] +html_js_files = ["custom.js"] # Theme options are theme-specific and customize the look and feel of a theme # further. For a list of options available for each theme, see the diff --git a/docs/index.md b/docs/index.md index b252bc899..7b4523dbd 100644 --- a/docs/index.md +++ b/docs/index.md @@ -18,13 +18,13 @@ Considering these series of short-term data, the software aims at : - Predicting future observations - Simulating virtual patients to un-bias the initial cohort or mimic its characteristics -The software package can be used with scalar multivariate data whose progression can be modelled by a logistic shape, an exponential decay or a linear progression. +The software package can be used with scalar multivariate data whose progression can be described by a logistic model, linear, joint or mixture model. The simplest type of data handled by the software are scalar data: they correspond to one (univariate) or multiple (multivariate) measurement(s) per patient observation. This includes, for instance, clinical scores, cognitive assessments, physiological measurements (e.g. blood markers, radioactive markers) but also imaging-derived data that are rescaled, for instance, between 0 and 1 to describe a logistic progression. #### Getting started -Information to install, test, and contribute to the package are available [here](./nutshell.md). +Information to install, test, and contribute to the package are available [here](./install.md). #### API Documentation @@ -37,28 +37,28 @@ The main documentation. This contains an in-depth description of all algorithms #### License -The package is distributed under the BSD 3-Clause license. +The package is distributed under the GNU LESSER GENERAL PUBLIC LICENSE. ### Further information More detailed explanations about the models themselves and about the estimation procedure can be found in the following articles : -- **Mathematical framework**: *A Bayesian mixed-effects model to learn trajectories of changes from repeated manifold-valued observations.* Jean-Baptiste Schiratti, Stéphanie Allassonnière, Olivier Colliot, and Stanley Durrleman. The Journal of Machine Learning Research, 18:1–33, December 2017. [Open Access](https: //hal.archives-ouvertes.fr/hal-01540367) -- **Application to imaging data**: *Statistical learning of spatiotemporal patterns from longitudinal manifold-valued networks.* I. Koval, J.-B. Schiratti, A. Routier, M. Bacci, O. Colliot, S. Allassonnière and S. Durrleman. MICCAI, September 2017. [Open Access](https://hal.archives-ouvertes.fr/hal-01540828) -- **Application to imaging data**: *Spatiotemporal Propagation of the Cortical Atrophy: Population and Individual Patterns.* Igor Koval, Jean-Baptiste Schiratti, Alexandre Routier, Michael Bacci, Olivier Colliot, Stéphanie Allassonnière, and Stanley Durrleman. Front Neurol. 2018 May 4;9:235. [Open Access](https://hal.inria.fr/hal-01910400) -- **Application to data with missing values**: *Learning disease progression models with longitudinal data and missing values*. R. Couronne, M. Vidailhet, JC. Corvol, S. Lehéricy, S. Durrleman. ISBI, April 2019. [Open Access](https://hal.archives-ouvertes.fr/hal-02091571) -- **Intensive application for Alzheimer's Disease progression**: *AD Course Map charts Alzheimer's disease progression*, I. Koval, A. Bone, M. Louis, S. Bottani, A. Marcoux, J. Samper-Gonzalez, N. Burgos, B. Charlier, A. Bertrand, S. Epelbaum, O. Colliot, S. Allassonniere & S. Durrleman, Scientific Reports, 2021. 11(1):1-16 [Open Access](https://hal.inria.fr/hal-01964821) +- **Mathematical framework**: *A Bayesian mixed-effects model to learn trajectories of changes from repeated manifold-valued observations.* Jean-Baptiste Schiratti, Stéphanie Allassonnière, Olivier Colliot, and Stanley Durrleman. The Journal of Machine Learning Research, 18:1–33, December 2017. [Open Access](https://hal.archives-ouvertes.fr/hal-01540367) +- **Mixture Model**: *A mixture model for subtype identification: Application to CADASIL* Sofia Kaisaridi, Juliette Ortholand, Caglayan Tuna, Nicolas Gensollen, and Sophie Tezenas Du Montcel +ISCB 46-46th Annual Conference of the International Society for Clinical Biostatistics, August 2025. [Open Access](https://hal.science/hal-05266776v1) +- **Joint Model**: *Joint model with latent disease age: overcoming the need for reference time* Juliette Ortholand, Nicolas Gensollen, Stanley Durrleman, Sophie Tezenas du Montcel. arXiv preprint arXiv:2401.17249. 2024 [Open Access](https://arxiv.org/abs/2401.17249) - [www.digital-brain.org](https://project.inria.fr/digitalbrain/) : Website related to the application of the model for Alzheimer's disease. -- [Disease Course Mapping](https://disease-progression-modelling.github.io/pages/models/disease_course_mapping.html) webpage by Igor Koval ```{toctree} + :hidden: :caption: Getting Started :maxdepth: 2 install ``` ```{toctree} +:hidden: :caption: Examples :maxdepth: 2 @@ -67,6 +67,7 @@ auto_examples/index ``` ```{toctree} +:hidden: :caption: Documentation :maxdepth: 3 diff --git a/docs/install.md b/docs/install.md index 1b6b46827..25d2c50ef 100644 --- a/docs/install.md +++ b/docs/install.md @@ -1,7 +1,7 @@ # Installation ```{warning} -Leaspy requires Python >= 3.9, < 3.13. +Leaspy requires Python >= 3.9, <= 3.13. ``` ```{tip} @@ -15,7 +15,7 @@ conda create --name leaspy python=3.10 conda activate leaspy ``` -## Install a released version +## 1. Install a released version To install the latest version of Leaspy: @@ -23,11 +23,11 @@ To install the latest version of Leaspy: pip install leaspy ``` -## Install in development mode +## 2. Install in development mode -If you haven't done it already, create and activate a dedicated environment (see the beginning of the installation section). +If you haven't done it already, create and activate a dedicated environment. -### Clone the repository +### 2.1 Clone the repository To install the project in development mode, you first need to get the source code by cloning the project's repository: @@ -36,7 +36,7 @@ git clone git@gitlab.com:icm-institute/aramislab/leaspy.git cd leaspy ``` -### Install poetry +### 2.2 Install poetry This project relies on [poetry](https://python-poetry.org) that you would need to install (see the [official instructions](https://python-poetry.org/docs/#installation)). @@ -54,7 +54,7 @@ pipx ensurepath pipx install poetry ``` -### Install Leaspy and its dependencies +### 2.3 Install Leaspy and its dependencies Install leaspy in development mode: @@ -62,7 +62,7 @@ Install leaspy in development mode: poetry install ``` -### Install the pre-commit hook +### 2.4 Install the pre-commit hook Once you have installed Leaspy in development mode, do not forget to install the [pre-commit](https://pre-commit.com) hook in order to automatically format and lint your commits: @@ -71,9 +71,9 @@ pipx install pre-commit pre-commit install ``` -## Notebook configuration +## 3. Notebook configuration -After installation, you can run the examples [here](./nutshell.md). +After installation, you can run the examples in the next section. To do so, in your ``leaspy`` environment, you can download ``ipykernel`` to use ``leaspy`` with ``jupyter`` notebooks: diff --git a/docs/sg_execution_times.rst b/docs/sg_execution_times.rst index bf1e2eadc..23ad02ae1 100644 --- a/docs/sg_execution_times.rst +++ b/docs/sg_execution_times.rst @@ -6,7 +6,7 @@ Computation times ================= -**00:30.526** total execution time for 4 files **from all galleries**: +**00:36.810** total execution time for 5 files **from all galleries**: .. container:: @@ -32,15 +32,18 @@ Computation times * - Example - Time - Mem (MB) - * - :ref:`sphx_glr_auto_examples_plot_quickstart.py` (``../examples/plot_quickstart.py``) - - 00:20.482 + * - :ref:`sphx_glr_auto_examples_plot_01_quickstart.py` (``../examples/plot_01_quickstart.py``) + - 00:20.917 - 0.0 - * - :ref:`sphx_glr_auto_examples_plot_parkinson_example.py` (``../examples/plot_parkinson_example.py``) - - 00:04.638 + * - :ref:`sphx_glr_auto_examples_plot_05_simulate.py` (``../examples/plot_05_simulate.py``) + - 00:05.703 - 0.0 - * - :ref:`sphx_glr_auto_examples_plot_mixture.py` (``../examples/plot_mixture.py``) - - 00:02.755 + * - :ref:`sphx_glr_auto_examples_plot_04_mixture.py` (``../examples/plot_04_mixture.py``) + - 00:04.900 - 0.0 - * - :ref:`sphx_glr_auto_examples_plot_simulate.py` (``../examples/plot_simulate.py``) - - 00:02.651 + * - :ref:`sphx_glr_auto_examples_plot_02_parkinson_example.py` (``../examples/plot_02_parkinson_example.py``) + - 00:04.716 + - 0.0 + * - :ref:`sphx_glr_auto_examples_plot_03_joint.py` (``../examples/plot_03_joint.py``) + - 00:00.574 - 0.0 diff --git a/docs/tutorials.md b/docs/tutorials.md deleted file mode 100644 index b53c79f6b..000000000 --- a/docs/tutorials.md +++ /dev/null @@ -1,5 +0,0 @@ -# Link to Jupyter Notebook Tutorial - -The [Disease Progression Modelling](https://disease-progression-modelling.github.io/) website hosts [tutorials](https://disease-progression-modelling.github.io/pages/notebooks/disease_course_mapping/disease_course_mapping.html) for Leaspy. - -*NOTE: do we reallly need to separate Tuto 2 and 3?"* diff --git a/docs/user_guide.md b/docs/user_guide.md index 64efc1028..9a917543a 100644 --- a/docs/user_guide.md +++ b/docs/user_guide.md @@ -13,6 +13,5 @@ mathematics models algorithms models_evaluation -tutorials to_go_further ``` diff --git a/example/start/inputs/algorithm_settings_calibration.json b/example/start/inputs/algorithm_settings_calibration.json deleted file mode 100644 index 6611ffd18..000000000 --- a/example/start/inputs/algorithm_settings_calibration.json +++ /dev/null @@ -1,35 +0,0 @@ -{ - "name": "mcmc_saem", - "seed": 0, - "algorithm_initialization_method": null, - "model_initialization_method": "default", - "device": "cpu", - "parameters": { - "progress_bar": true, - "n_iter": 3000, - "n_burn_in_iter": null, - "n_burn_in_iter_frac": 0.9, - "burn_in_step_power": 0.8, - "random_order_variables": true, - "sampler_ind": "Gibbs", - "sampler_ind_params": { - "acceptation_history_length": 25, - "mean_acceptation_rate_target_bounds": [0.2, 0.4], - "adaptive_std_factor": 0.1 - }, - "sampler_pop": "Gibbs", - "sampler_pop_params": { - "random_order_dimension": true, - "acceptation_history_length": 25, - "mean_acceptation_rate_target_bounds": [0.2, 0.4], - "adaptive_std_factor": 0.1 - }, - "annealing": { - "do_annealing": false, - "initial_temperature": 100, - "n_plateau": 10, - "n_iter": null, - "n_iter_frac": 0.5 - } - } -} diff --git a/example/start/inputs/algorithm_settings_personalization.json b/example/start/inputs/algorithm_settings_personalization.json deleted file mode 100644 index 20394fcaa..000000000 --- a/example/start/inputs/algorithm_settings_personalization.json +++ /dev/null @@ -1,9 +0,0 @@ -{ - "name": "scipy_minimize", - "seed": 0, - "parameters": { - "progress_bar": true, - "use_jacobian": true, - "n_jobs": 1 - } -} diff --git a/example/start/inputs/cofactor.csv b/example/start/inputs/cofactor.csv deleted file mode 100644 index 8667e20d0..000000000 --- a/example/start/inputs/cofactor.csv +++ /dev/null @@ -1,249 +0,0 @@ -ID,Cofactor 1 -132-S2-0,1 -184-S2-0,1 -238-S2-1,1 -256-S2-0,1 -300-S2-0,1 -350-S2-1,1 -492-S2-0,1 -514-S2-1,1 -654-S2-1,1 -796-S2-0,1 -992-S2-0,1 -1014-S2-1,1 -1152-S2-0,1 -1188-S2-0,1 -1246-S2-1,1 -1296-S2-0,1 -1318-S2-1,1 -1500-S2-0,1 -1504-S2-0,0 -1634-S2-1,1 -1716-S2-0,1 -1728-S2-0,1 -1738-S2-1,1 -1750-S2-1,1 -1878-S2-1,0 -1964-S2-0,1 -1968-S2-0,1 -1980-S2-0,1 -1990-S2-1,1 -1992-S2-0,1 -2004-S2-0,1 -2044-S2-0,1 -2048-S2-0,1 -2144-S2-0,1 -2162-S2-1,1 -2210-S2-1,1 -2272-S2-0,1 -2274-S2-1,1 -2336-S2-0,1 -2438-S2-1,1 -2500-S2-0,1 -2656-S2-0,1 -2716-S2-0,1 -2728-S2-0,1 -2734-S2-1,1 -2738-S2-1,1 -114-S2-1,1 -200-S2-0,1 -902-S2-1,1 -910-S2-1,1 -1258-S2-1,1 -1330-S2-1,1 -1392-S2-0,1 -1440-S2-0,1 -2136-S2-0,1 -2392-S2-0,1 -2420-S2-0,1 -2660-S2-0,1 -2736-S2-0,1 -3904-S2-0,1 -3952-S2-0,0 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diff --git a/example/start/outputs/logs/parameter_convergence/sources1.csv b/example/start/outputs/logs/parameter_convergence/sources1.csv deleted file mode 100644 index 9f0181fca..000000000 --- a/example/start/outputs/logs/parameter_convergence/sources1.csv +++ /dev/null @@ -1,100 +0,0 @@ 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"name": "logistic", - "features": [ - "Feature 1", - "Feature 2", - "Feature 3", - "Feature 4" - ], - "dimension": 4, - "source_dimension": 2, - "noise_model": { - "name": "gaussian-diagonal", - "scale": [ - 0.07424536347389221, - 0.04837438091635704, - 0.08233640342950821, - 0.07684528082609177 - ] - }, - "parameters": { - "g": [ - -0.035903580486774445, - 2.9895029067993164, - 2.455995798110962, - 1.8869861364364624 - ], - "v0": [ - -3.3233938217163086, - -4.126061916351318, - -4.114415645599365, - -3.9727160930633545 - ], - "betas": [ - [ - 0.057215407490730286, - 0.09361007809638977 - ], - [ - 0.03731274604797363, - 0.004822447430342436 - ], - [ - 0.061814554035663605, - -0.010769529268145561 - ] - ], - "tau_mean": 80.34884643554688, - "tau_std": 7.6457200050354, - "xi_mean": 0.0, - "xi_std": 0.7054909467697144, - "sources_mean": 0.0, - "sources_std": 1.0, - "mixing_matrix": [ - [ - -0.07631143182516098, - -0.08544891327619553 - ], - [ - -0.00807197205722332, - 0.02050524204969406 - ], - [ - 0.011360290460288525, - -0.024237534031271935 - ], - [ - 0.04969138279557228, - -0.024344317615032196 - ] - ] - } -} \ No newline at end of file diff --git a/example/start/starter.ipynb b/example/start/starter.ipynb deleted file mode 100644 index 2a1cc56f1..000000000 --- a/example/start/starter.ipynb +++ /dev/null @@ -1,711 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Imports" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import sys\n", - "\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "import pandas as pd\n", - "\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We do not recommand to run the notebook from within the Leaspy repo (to prevent it from interfering with Leaspy git versioning).\n", - "Therefore, copy paste the start folder elsewhere, and replace the `project_path` with the path to the leaspy folder." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# these 2 lines are not needed if you installed leaspy with pip\n", - "leaspy_path = os.path.join(os.getcwd(), \"..\", \"..\")\n", - "sys.path.append(leaspy_path)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from leaspy import AlgorithmSettings, Data, IndividualParameters, Leaspy, __watermark__\n", - "\n", - "# Watermark trace with all packages versions\n", - "__watermark__" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Step 1. Data\n", - "\n", - "You have to store your data into a `Data` object.\n", - "\n", - "### Option #1\n", - "You can load your data directly from a csv file, whose rows corresponds to subject visits, each column being a feature - except for the `ID` and `TIME` column. This result in multiple rows per subject. The input format has therefore to follow the following rules:\n", - "- A column named `ID`: corresponds to the subject indices\n", - "- A columns named `TIME`: corresponds to the subject's age at the corresponding visit\n", - "- One column per feature\n", - "- Each row is a visit, therefore the concatenation of the subject ID, the patient age at which the corresponding visit occured, and then the feature values.\n", - "\n", - "Here is an example :\n", - "\n", - "| ID | TIME | Feature 1 | Feature 2 | Feature 3 | Feature 4\n", - "| --- | --- | --- | --- | --- | --- |\n", - "| 309 | 70.4 | 25 | 0.12 | -0.19 | 3 |\n", - "| 309 | 73.4 | 10 | 0.13 | NaN | 1.3 |\n", - "| 309 | 75.7 | 24.3 | 0.0 | -0.44 | 0.8 |\n", - "| 40 | 60.1 | 5 | NaN | -0.12 | 2.3 |\n", - "| 40 | 62.5 | 23.4 | 0.8 | -0.142 | 0.94 |\n", - "| 918 | 88 | 9.3 | 0.9 | -0.3 | 0.23 |\n", - "| 918 | 92.3 | 33 | 0.88 | -0.423 | NaN |\n", - "| 918 | 95.3 | 34 | 0.58 | -0.523 | 0.34 |\n", - "\n", - "You can find additional example in the `inputs` folder: `data.csv` and `data_univariate.csv`\n", - "\n", - "You probably noticed that there are NaN: do not worry, Leaspy can handle them ;)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "data = Data.from_csv_file(os.path.join(\"inputs\", \"data_normalized.csv\"))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Option #2\n", - "\n", - "However, there are chances that your csv files contains data that either you don't want or that don't fit the model you are going to use. For instance, to build a logistic curve model, you need data that are normalized between 0 and 1 and that are increasing with time. Moreover, to calibrate the progression model, **we highly recommend to keep subjects that have been seen at least two times**. \n", - "\n", - "For that reason, you can load your data from a dataframe that you have preprocessed with pandas methods.\n", - "\n", - "Let's seen an example where:\n", - "- Feature 1 is increasing through time and is between 0 and 1: nothing to do\n", - "- Feature 2 is increasing through time and is between 0 and 100: need to normalize between 0 and 1\n", - "- Feature 3 is decreasing through time and is between 0 and 1: need to revert the temporal progression to be increasing\n", - "- Feature 4 is decreasing through time and is between 60 and 0: need to revert and to normalize between 0 and 1" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df = pd.read_csv(os.path.join(\"inputs\", \"data.csv\"))\n", - "\n", - "# —— Feature 1\n", - "df[\"Feature 1\"] = df[\"Feature 1\"]\n", - "\n", - "# —— Feature 2\n", - "df[\"Feature 2\"] = df[\"Feature 2\"] / 100\n", - "\n", - "# —— Feature 3\n", - "df[\"Feature 3\"] = -df[\"Feature 3\"] + 1\n", - "\n", - "# —— Feature 4\n", - "df[\"Feature 4\"] = -df[\"Feature 4\"] / 60 + 1\n", - "\n", - "# —— Let's select patient with at least two visits\n", - "df = df.set_index([\"ID\", \"TIME\"])\n", - "indices = [idx for idx in df.index.unique(\"ID\") if df.loc[idx].shape[0] >= 2]\n", - "df = df[df.index.get_level_values(0).isin(indices)]\n", - "\n", - "# —— Store the data into a Data object\n", - "data = Data.from_dataframe(df)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Remark & Tip:** while some data have natural minimum and maximum values (inducing ceiling and floor effects), other don't have natural bounds. In that case, normalize as best as you can so that you don't have values (current or future) higher than 1 or lower than 0. You can check that with `Feature 4` that does not reach maximum values or `Feature 1` that starts above minimum value" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fts_min_max = df.agg([\"min\", \"max\"])\n", - "\n", - "assert (fts_min_max.loc[\"min\"] >= 0).all()\n", - "assert (fts_min_max.loc[\"max\"] <= 1).all()\n", - "\n", - "pd.options.display.float_format = \"{:.2f}\".format\n", - "fts_min_max" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Step 2. Settings\n", - "\n", - "To run any algorithm, you need to specify the settings of the related algorithm thanks to the `AlgorithmSettings` object. \n", - "\n", - "### Option #1\n", - "\n", - "To ease Leaspy's usage for new users, we specified default values for each algorithm. Therefore, the name of the algorithm used is enough to run it. The one you need to fit your progression model is `mcmc_saem`. Let's see an example:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "algo_settings = AlgorithmSettings(\n", - " \"mcmc_saem\",\n", - " n_iter=3000, # n_iter defines the number of iterations\n", - " progress_bar=True,\n", - ") # To display a nice progression bar during calibration" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If you want to store the different logs of the model during the iterations, you can use the following method with the path of the folder where the logs will be stored" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "algo_settings.set_logs(\n", - " path=\"outputs/logs\", # Creates a logs file ; if existing, ask if rewrite it\n", - " save_periodicity=50, # Saves the values in csv files every N iterations\n", - " console_print_periodicity=100, # Displays logs in the console/terminal every N iterations, or None\n", - " plot_periodicity=1000, # Generates the convergence plots every N iterations\n", - " overwrite_logs_folder=True, # if True and the logs folder already exists, it entirely overwrites it\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Step 3. Fit model\n", - "\n", - "Now that you have your data and your settings, you can run the model. You first need to choose the type of progression shape you want to give to your data. You can either choose logistic or linear (soon exponential) with the possibility to enforce a \"parallelism\" between the features. The dedicated names are :\n", - "\n", - "`logistic`, `logistic_parallel`, `linear` and `linear_parallel`. You can also call the `univariate` model which is a single logistic function that you can run on the `input/data_univariate.csv`.\n", - "\n", - "Note : the model might rely on hyperparameters that you can define as shown below. There are optional. The most important one is the `source_dimension`. To be short, if you consider `N` features, these can be reordered as not everyone follows the same relative ordering of events. However, all reordering are not likely to exist, and there are some common patterns within a population. Therefore, the `source_dimension` is in a way a subspace that defines the degrees of freedom that you have to reorder you sequence of events. Still not clear? That's fine. It's related to the `space-shifts` in papers described in the repo. But don't worry if you didn't get it yet. Make it equal to somewhat higher than the log/square-root of the number of features." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "leaspy = Leaspy(\n", - " \"logistic\",\n", - " source_dimension=2, # Optional\n", - " noise_model=\"gaussian_diagonal\", # Optional: To get a noise estimate per feature keep it this way (default)\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Congrats! You are now ready to run the model" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "leaspy.fit(data, settings=algo_settings)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Did it run well?\n", - "\n", - "Before assuming that the model is estimated, you have to check that the convergence went went. For that, you can look the at the convergence during the iterations. To do so, you can explore the `logs` folder that shows the model convergence during the iterations. The first thing to look at is probably the `plots/convergence_1.pdf` and `plots/convergence_2.pdf` files : a run has had enough iterations to converge if the last 20 or even 30% of the iterations were stable for all the parameters. If not, you should provably rerun it with more iterations. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from IPython.display import IFrame\n", - "\n", - "IFrame(\"./outputs/logs/plots/convergence_1.pdf\", width=990, height=670)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Then, you can check out the parameters of the model that are stored here : " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "leaspy.model.parameters" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "They are probably not straightfoward for now. The most important one is probably `noise_std`. It corresponds to the standard deviation of the error for a given feature. The smallest, the better - up to the lower bound which is the intrinsic noise in the data. Let's display it:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "noise = leaspy.model.parameters[\"noise_std\"]\n", - "features = leaspy.model.features\n", - "\n", - "print(\"Standard deviation of the residual noise for feature:\")\n", - "for n, f in zip(noise, features):\n", - " print(f\"- {f}: {n:.2%}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Remark:** Usually, cognitive measurements have an intrinsic error (computed on test-retest exams) between 5 and 10%.\n", - "\n", - "### Save and load the model\n", - "\n", - "There are many reasons why one might want to save the output parameters of the model. To do so, just do: " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "leaspy.save(\"outputs/model_parameters.json\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Later, you can load the model if needed." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "leaspy = Leaspy.load(\"outputs/model_parameters.json\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Display the average model\n", - "\n", - "Now that we have sufficient evidence that the model has converged, let's output what the average progression look like! \n", - "\n", - "First, let's detail a bit what we are going to represent. We are going to display a trajectory: it corresponds to the temporal progression of the biomarkers. There is not only one trajectory for a cohort, as each subject has his or her own specific trajectory, meaning his or her disease progression. Each of these individual trajectories are rely on individual parameters that are subject-specific. We will see what this individual parameters a bit later, do not worry. For now, let's stick to the \"average\" trajectory.\n", - "\n", - "So what does the average trajectory corresponds to? The average trajectory corresponds to a \"virtual\" patient whose individual parameters are the average individual parameters. And these averages are already estimated during the calibration.\n", - "\n", - "So let's get these average individual parameters and store them in the `IndividualParameters` object that stores a collection of individual parameter, for many subjects. Here, it is used for only one subject, called `average`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# —— Get the average individual parameters\n", - "mean_xi = leaspy.model.parameters[\"xi_mean\"].numpy()\n", - "mean_tau = leaspy.model.parameters[\"tau_mean\"].numpy()\n", - "mean_source = leaspy.model.parameters[\"sources_mean\"].numpy().tolist()\n", - "number_of_sources = leaspy.model.source_dimension\n", - "mean_sources = [mean_source] * number_of_sources\n", - "\n", - "# —— Store the average individual parameters in a dedicated object\n", - "average_parameters = {\"xi\": mean_xi, \"tau\": mean_tau, \"sources\": mean_sources}\n", - "\n", - "ip_average = IndividualParameters()\n", - "ip_average.add_individual_parameters(\"average\", average_parameters)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The second very important function - after `leaspy.fit()` - is `leaspy.estimate`. Given some individual parameters and timepoints, the function estimates the values of the biomarkers at the given timepoints which derive from the individual trajectory encoded thanks to the individual parameters.\n", - "\n", - "You can check out the documentation and run it : " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "?leaspy.estimate" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "timepoints = np.linspace(55, 105, 100)\n", - "values = leaspy.estimate({\"average\": timepoints}, ip_average)\n", - "\n", - "\n", - "def plot_trajectory(\n", - " timepoints,\n", - " reconstruction,\n", - " observations=None,\n", - " *,\n", - " xlabel=\"Reparametrized age\",\n", - " ylabel=\"Normalized feature value\",\n", - "):\n", - " if observations is not None:\n", - " ages = observations.index.values\n", - "\n", - " plt.figure(figsize=(10, 5))\n", - " plt.ylim(0, 1)\n", - " colors = [\"#003f5c\", \"#7a5195\", \"#ef5675\", \"#ffa600\"]\n", - "\n", - " for c, name, val in zip(colors, leaspy.model.features, reconstruction.T):\n", - " plt.plot(timepoints, val, label=name, c=c, linewidth=3)\n", - " if observations is not None:\n", - " plt.plot(\n", - " ages,\n", - " observations[name],\n", - " c=c,\n", - " marker=\"o\",\n", - " markersize=12,\n", - " linewidth=1,\n", - " linestyle=\":\",\n", - " )\n", - "\n", - " plt.xlim(min(timepoints), max(timepoints))\n", - " plt.xlabel(xlabel)\n", - " plt.ylabel(ylabel)\n", - " plt.grid()\n", - " plt.legend()\n", - " plt.show()\n", - "\n", - "\n", - "plot_trajectory(timepoints, values[\"average\"])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Step 4. Personalize the model to individual data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The personalization procedure allows to estimate the individual parameters that allows to modify the average progression to individual observations. The variations from the average trajectory to the individual one are encoded within three individual parameters : \n", - "- $\\alpha_i = \\exp(\\xi_i)$ : the acceleration factor, that modulates the speed of progression : $\\alpha_i > 1$ means faster, $\\alpha_i < 1$ means slower than the average progression\n", - "- $\\tau_i$ : the time shift which delays the progression in a given number of years. It has to be compared to `tau_mean` $ = \\bar{\\tau} $ which is in the model parameters above. In fact, $ \\tau_i \\sim N( \\bar{\\tau}, \\sigma_{\\tau}^2)$ , so $\\tau_i > \\bar{\\tau}$ means that the patient has a disease that starts later than average, while $\\tau_i < \\bar{\\tau}$ means that the patient has a disease that starts earlier than average\n", - "- $w_i = (w_1, ..., w_N)$ ($N$ being the size of the feature space) : the space-shift which might, for a given individual, change the ordering of the conversion of the different features, compared to the mean trajectory.\n", - "\n", - "In a nutshell, the $k$-th feature at the $j$-th visit of subject $i$, which occurs at time $t_{ij}$ writes: \n", - "\n", - "$$y_{ijk} = f_\\theta ( w_{ik}+ \\exp(\\xi_i) * (t_{ij} - \\tau_i) ) + \\epsilon_{ijk}$$\n", - "\n", - "This writing is not exactly correct but helps understand the role of each individual parameters.\n", - "\n", - "**[ Advanced ]** Remember the `sources`, or the `source_dimension`? Well, $w_i$ is not estimated directly, but rather thanks to a Independant Component Analysis, such that $w_i = A s_i$ where $s_i$ is of size $N_s$ = `source_dimension`. See associated papers for further details.\n", - "\n", - "Now, let's estimate these individual parameters. The procedure relies on the `scipy_minimize` algorithm that you have to define (or to load from an appropriate json file) : " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Option 1\n", - "settings_personalization = AlgorithmSettings(\"scipy_minimize\", use_jacobian=True)\n", - "\n", - "# Option 2\n", - "settings_personalization = AlgorithmSettings.load(\n", - " \"inputs/algorithm_settings_personalization.json\"\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here is the third most important function of leaspy : `leaspy.personalize`. It estimates the individual parameters for the data you provide:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "?leaspy.personalize" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ip = leaspy.personalize(data, settings_personalization)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Note here that you can personalize your model on patients that have only one visit! You don't have to use the same `data` as previously. Especially, you can here personalize your model with patients that have only one visit.\n", - "\n", - "Once the personalization is done, you can check the different functions that the `IndividualParameters` provides (you can load and save them, transform them to dataframes, etc) : " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "?ip" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, let's see what you can do with the individual parameters.\n", - "\n", - "# Step 5. Impute missing values & predict individual trajectories\n", - "\n", - "The individual parameters entirely defines the individual trajectory, and thus, the biomarker values at any time. So you can reconstruct the individual biomarkers at different ages. \n", - "\n", - "You can reconstruct your observations at seen ages, i.e. at visits that have been used to personalize the model. There are two reasons you might want to do that:\n", - "- see how well the model fitted individual data\n", - "- impute missing values: as Leaspy handles missing values, it can then reconstruct them (note that this reconstruction will be noiseless)\n", - "\n", - "To do so, let's first retrieve the observations of subject '1634-S2-1' in the initial dataset. You can also get his/her individual parameters as shown here:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "subject_id = \"1634-S2-1\"\n", - "observations = df.loc[subject_id]\n", - "print(f\"Seen ages: {observations.index.values}\")\n", - "print(ip[subject_id])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, as done with the average trajectory, let's estimate the trajectory for this patient.\n", - "\n", - "**Remark**: The `estimate` first argument is a dictionary, so that you can estimate the trajectory of multiple individuals simultaneously (as long as the individual parameters of all your queried patients are in `ip`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "timepoints = np.linspace(77, 85, 100)\n", - "reconstruction = leaspy.estimate({subject_id: timepoints}, ip)\n", - "\n", - "plot_trajectory(timepoints, reconstruction[subject_id], observations, xlabel=\"Age\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "From this plot, we clearly see that the estimations at late ages, above 82 years old are clearly prediction. To estimate the quality of the prediction, you can hide some future visits in the calibration and personalization procedure, then estimate the values at these visits and see how good it performs by comparing the predicting value compared to the hidden one." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Step 6. Further Analysis\n", - "\n", - "Besides prediction, the individual parameters are interesting in the sense that they provide meaningful and interesting insights about the disease progression. For that reasons, these individual parameters can be correlated to other cofactors. Let's seen an example.\n", - "\n", - "Let's consider that you have a covariate `Cofactor 1` that encodes a genetic status: 1 if a specific mutation is present, 0 otherwise. Now, let's see if this mutation has an effect on the disease progression: " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# —— Convert individual parameters to dataframe\n", - "df_ip = ip.to_dataframe()\n", - "\n", - "# —— Merge with cofactors\n", - "cofactor = pd.read_csv(\"inputs/cofactor.csv\", index_col=[\"ID\"])\n", - "df_ip = df_ip.join(cofactor)\n", - "\n", - "# —— Separate the individual parameters with respect to the cofactor\n", - "carriers = df_ip[df_ip[\"Cofactor 1\"] == 0.0]\n", - "non_carriers = df_ip[df_ip[\"Cofactor 1\"] == 1.0]\n", - "\n", - "\n", - "def plot_histo(title, ft, bins_nb=10):\n", - " # compute bins (same for 2 carriers & non-carriers)\n", - " _, bins = np.histogram(df_ip[ft], bins=bins_nb)\n", - "\n", - " plt.title(title)\n", - " plt.hist(carriers[ft], density=True, bins=bins, edgecolor=\"white\", label=\"Carriers\")\n", - " plt.hist(\n", - " non_carriers[ft],\n", - " density=True,\n", - " bins=bins,\n", - " alpha=0.6,\n", - " edgecolor=\"white\",\n", - " label=\"Non carriers\",\n", - " )\n", - " plt.legend()\n", - " plt.show()\n", - "\n", - "\n", - "# —— Plot the time shifts in carriers and non-carriers\n", - "plot_histo(\"Time shift histogram\", \"tau\")\n", - "\n", - "# —— Plot the acceleration factor in carriers and non-carriers\n", - "plot_histo(\"Log-Acceleration factor histogram\", \"xi\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "It seems that the mutation has an effect on the disease onset, but not on its pace. Let's verify it with a statistical test" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from scipy import stats\n", - "\n", - "# —— Student t-test (under the asumption of a gaussian distribution only)\n", - "print(stats.ttest_ind(carriers[\"tau\"], non_carriers[\"tau\"]))\n", - "print(stats.ttest_ind(carriers[\"xi\"], non_carriers[\"xi\"]))\n", - "\n", - "# —— Mann-withney t-test\n", - "print(stats.mannwhitneyu(carriers[\"tau\"], non_carriers[\"tau\"]))\n", - "print(stats.mannwhitneyu(carriers[\"xi\"], non_carriers[\"xi\"]))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "It is now clear that the mutation has an effect on the disease onset, but not on its pace.\n", - "\n", - "\n", - "\n", - "# The end of this Notebook. But the beginning of your explorations.\n", - "\n", - "You've now seen most of the applications that you can run with Leaspy. There are many more to discover based on your experimental settings and analysis. But it's time for you to try it on your own and to enjoy the package.\n", - "\n", - "Have fun! " - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.12" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/examples/GALLERY_HEADER.rst b/examples/GALLERY_HEADER.rst index 1e0f0e469..2a0e7f4c3 100644 --- a/examples/GALLERY_HEADER.rst +++ b/examples/GALLERY_HEADER.rst @@ -1,4 +1,4 @@ -Example gallery for Leaspy -========================== +Examples gallery +=============== This is a gallery of examples of how to use Leaspy to conduct various kind of analysis. diff --git a/examples/plot_quickstart.py b/examples/plot_01_quickstart.py similarity index 84% rename from examples/plot_quickstart.py rename to examples/plot_01_quickstart.py index fb6cec23b..b3df2a4c4 100644 --- a/examples/plot_quickstart.py +++ b/examples/plot_01_quickstart.py @@ -75,7 +75,3 @@ # # 1. See the [User Guide](../user_guide.md) and full API documentation. # 2. Explore additional [examples](./index.rst). -# 3. Visit the [Disease Progression Modelling](https://disease-progression-modelling.github.io/) website, -# which provides a [mathematical introduction](https://disease-progression-modelling.github.io/pages/models/disease_course_mapping.html) -# and [tutorials](https://disease-progression-modelling.github.io/pages/notebooks/disease_course_mapping/disease_course_mapping.html) -# for earlier versions such as v1.5. diff --git a/examples/plot_parkinson_example.py b/examples/plot_02_parkinson_example.py similarity index 100% rename from examples/plot_parkinson_example.py rename to examples/plot_02_parkinson_example.py diff --git a/examples/plot_joint.py b/examples/plot_03_joint.py similarity index 100% rename from examples/plot_joint.py rename to examples/plot_03_joint.py diff --git a/examples/plot_mixture.py b/examples/plot_04_mixture.py similarity index 100% rename from examples/plot_mixture.py rename to examples/plot_04_mixture.py diff --git a/examples/plot_simulate.py b/examples/plot_05_simulate.py similarity index 100% rename from examples/plot_simulate.py rename to examples/plot_05_simulate.py diff --git a/poetry.lock b/poetry.lock index fc9e0bc00..71e7c5e03 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,5 +1,24 @@ # This file is automatically @generated by Poetry 2.1.1 and should not be changed by hand. +[[package]] +name = "accessible-pygments" +version = "0.0.5" +description = "A collection of accessible pygments styles" +optional = false +python-versions = ">=3.9" +groups = ["docs"] +files = [ + {file = "accessible_pygments-0.0.5-py3-none-any.whl", hash = "sha256:88ae3211e68a1d0b011504b2ffc1691feafce124b845bd072ab6f9f66f34d4b7"}, + {file = "accessible_pygments-0.0.5.tar.gz", hash = "sha256:40918d3e6a2b619ad424cb91e556bd3bd8865443d9f22f1dcdf79e33c8046872"}, +] + +[package.dependencies] +pygments = ">=1.5" + +[package.extras] +dev = ["pillow", "pkginfo (>=1.10)", "playwright", "pre-commit", "setuptools", "twine (>=5.0)"] +tests = 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"sphinx-theme-builder[cli]", "tox"] +doc = ["ablog (>=0.11.8)", "colorama", "graphviz", "ipykernel", "ipyleaflet", "ipywidgets", "jupyter_sphinx", "jupyterlite-sphinx", "linkify-it-py", "matplotlib", "myst-parser", "nbsphinx", "numpy", "numpydoc", "pandas", "plotly", "rich", "sphinx-autoapi (>=3.0.0)", "sphinx-copybutton", "sphinx-design", "sphinx-favicon (>=1.0.1)", "sphinx-sitemap", "sphinx-togglebutton", "sphinxcontrib-youtube (>=1.4.1)", "sphinxext-rediraffe", "xarray"] +i18n = ["Babel", "jinja2"] +test = ["pytest", "pytest-cov", "pytest-regressions", "sphinx[test]"] + [[package]] name = "pygments" version = "2.19.1" @@ -3433,6 +3503,18 @@ files = [ {file = "snowballstemmer-2.2.0.tar.gz", hash = "sha256:09b16deb8547d3412ad7b590689584cd0fe25ec8db3be37788be3810cbf19cb1"}, ] +[[package]] +name = "soupsieve" +version = "2.8" +description = "A modern CSS selector implementation for Beautiful Soup." +optional = false +python-versions = ">=3.9" +groups = ["docs"] +files = [ + {file = "soupsieve-2.8-py3-none-any.whl", hash = "sha256:0cc76456a30e20f5d7f2e14a98a4ae2ee4e5abdc7c5ea0aafe795f344bc7984c"}, + {file = "soupsieve-2.8.tar.gz", hash = "sha256:e2dd4a40a628cb5f28f6d4b0db8800b8f581b65bb380b97de22ba5ca8d72572f"}, +] + [[package]] name = "sphinx" version = "7.4.7" @@ -4274,4 +4356,4 @@ type = ["pytest-mypy"] [metadata] lock-version = "2.1" python-versions = ">=3.9,<=3.13" -content-hash = "dded7e9f869644ef5a44547c68609a5cd990a4ccec437bd43a8b9ec9ce57b97f" +content-hash = "04344c60e681098d672f73d8795863330ec059e0d794efa5a287bedf095a5d17" diff --git a/pyproject.toml b/pyproject.toml index 9c09d077b..fb4c228ed 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -71,6 +71,7 @@ sphinx_theme = ">=1.0" sphinx-autoapi = "*" myst-nb = "*" sphinxcontrib-bibtex = "*" +pydata-sphinx-theme = "^0.16.1" [tool.ruff] target-version = "py39" From 30b53e100c3396674c007552eb7b73e54dceec49 Mon Sep 17 00:00:00 2001 From: Sebastian Date: Wed, 26 Nov 2025 17:15:04 +0100 Subject: [PATCH 06/24] Docs: Migrate to Furo theme and improve gallery layout - Switch Sphinx theme to 'furo': cleaner interface - CSS Improvements: - Force white background on logos for visibility in dark mode. - Refactor example gallery to a simple bulleted list. - Add responsive styling for Pandas DataFrames (scrollable, dark mode colors). - JS: Disable hover tooltips on gallery links. - Makefile: Fix syntax error and improve doc build rule. - Dependencies: Update poetry.lock and pyproject.toml. --- Makefile | 4 ++ docs/_static/custom.css | 61 +++++++++++++++++++++++- docs/_static/custom.js | 3 ++ docs/conf.py | 3 +- docs/sg_execution_times.rst | 18 +++---- examples/plot_01_quickstart.py | 8 +++- examples/plot_02_parkinson_example.py | 5 ++ examples/plot_03_joint.py | 4 ++ examples/plot_04_mixture.py | 7 +++ examples/plot_05_simulate.py | 5 +- poetry.lock | 67 +++++++++++++++++++++------ pyproject.toml | 1 + 12 files changed, 157 insertions(+), 29 deletions(-) diff --git a/Makefile b/Makefile index 151a96831..903f89c6c 100644 --- a/Makefile +++ b/Makefile @@ -15,6 +15,10 @@ clean.doc: @$(RM) -rf docs/auto_examples/ @$(RM) -rf docs/sg_execution_times.rst +.PHONY: doc-quick +doc-quick: install.doc + @$(SPHINX) -b html docs/ docs/_build/html + .PHONY: clean.py clean.py: @find . -name __pycache__ -exec $(RM) -r {} + diff --git a/docs/_static/custom.css b/docs/_static/custom.css index e5645e0a5..5792b63d0 100644 --- a/docs/_static/custom.css +++ b/docs/_static/custom.css @@ -3,4 +3,63 @@ } .sphx-glr-thumbcontainer .xref.std.std-ref { display: none; -} \ No newline at end of file +} + +/* Hide images in the gallery */ +.sphx-glr-thumbcontainer img { + display: none !important; +} + +/* Reset container height to fit text only */ +.sphx-glr-thumbcontainer { + min-height: auto !important; + height: auto !important; +} + +/* Set background as white for the sidebar logo and the Leaspy logo in index */ +.sidebar-logo, img[src*="leaspy_logo.png"] { + background-color: white; + padding: 5px; /* Adds some space around the logo */ + border-radius: 4px; /* Optional: Rounds the corners of the background */ +} + +/* Pandas DataFrame styling for Furo */ +table.dataframe { + border: none !important; + border-collapse: collapse; + border-spacing: 0; + margin-bottom: 1em; + width: auto; + overflow-x: auto; + display: block; /* Makes it scrollable if wide */ +} + +table.dataframe thead { + border-bottom: 1px solid var(--color-border-primary); + vertical-align: bottom; +} + +table.dataframe tr, table.dataframe th, table.dataframe td { + text-align: right; + vertical-align: middle; + padding: 0.5em 0.5em; + line-height: normal; + white-space: normal; + max-width: none; + border: none; + color: var(--color-foreground-primary); /* Fix dark mode text */ +} + +table.dataframe th { + font-weight: bold; + background: var(--color-background-secondary); /* Header background */ +} + +table.dataframe tbody tr:nth-child(odd) { + background: var(--color-background-secondary); /* Zebra striping */ +} + +table.dataframe tbody tr:hover { + background: var(--color-background-hover); /* Hover effect */ +} + diff --git a/docs/_static/custom.js b/docs/_static/custom.js index 0febaaf58..103057d7e 100644 --- a/docs/_static/custom.js +++ b/docs/_static/custom.js @@ -19,5 +19,8 @@ document.addEventListener("DOMContentLoaded", function() { // Change cursor to pointer el.style.cursor = 'pointer'; } + + // Remove the tooltip attribute to prevent the description from popping up + el.removeAttribute('tooltip'); }); }); \ No newline at end of file diff --git a/docs/conf.py b/docs/conf.py index df984314e..f0030561b 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -141,7 +141,8 @@ def find_var(varname: str, *py_file_paths): # html_theme = 'alabaster' # html_theme = 'sphinx-theme' -html_theme = "pydata_sphinx_theme" +# html_theme = "pydata_sphinx_theme" +html_theme = "furo" add_function_parentheses = True diff --git a/docs/sg_execution_times.rst b/docs/sg_execution_times.rst index 23ad02ae1..018b76da9 100644 --- a/docs/sg_execution_times.rst +++ b/docs/sg_execution_times.rst @@ -6,7 +6,7 @@ Computation times ================= -**00:36.810** total execution time for 5 files **from all galleries**: +**00:08.619** total execution time for 5 files **from all galleries**: .. container:: @@ -32,18 +32,18 @@ Computation times * - Example - Time - Mem (MB) - * - :ref:`sphx_glr_auto_examples_plot_01_quickstart.py` (``../examples/plot_01_quickstart.py``) - - 00:20.917 - - 0.0 * - :ref:`sphx_glr_auto_examples_plot_05_simulate.py` (``../examples/plot_05_simulate.py``) - - 00:05.703 + - 00:08.619 - 0.0 - * - :ref:`sphx_glr_auto_examples_plot_04_mixture.py` (``../examples/plot_04_mixture.py``) - - 00:04.900 + * - :ref:`sphx_glr_auto_examples_plot_01_quickstart.py` (``../examples/plot_01_quickstart.py``) + - 00:00.000 - 0.0 * - :ref:`sphx_glr_auto_examples_plot_02_parkinson_example.py` (``../examples/plot_02_parkinson_example.py``) - - 00:04.716 + - 00:00.000 - 0.0 * - :ref:`sphx_glr_auto_examples_plot_03_joint.py` (``../examples/plot_03_joint.py``) - - 00:00.574 + - 00:00.000 + - 0.0 + * - :ref:`sphx_glr_auto_examples_plot_04_mixture.py` (``../examples/plot_04_mixture.py``) + - 00:00.000 - 0.0 diff --git a/examples/plot_01_quickstart.py b/examples/plot_01_quickstart.py index b3df2a4c4..a3f2c9bc7 100644 --- a/examples/plot_01_quickstart.py +++ b/examples/plot_01_quickstart.py @@ -69,9 +69,13 @@ ) print(individual_parameters.to_dataframe()) - # %% -# To go further; +# We have seen how to fit a model and personalize it to individuals. +# Leaspy also provides various plotting functions to visualize the results. +# Let's go to the next [section](./plot_02_parkinson_example) to see how to plot +# the group-average trajectory and the individual trajectories using the Parkinson's disease dataset. +# %% +# To go further: # # 1. See the [User Guide](../user_guide.md) and full API documentation. # 2. Explore additional [examples](./index.rst). diff --git a/examples/plot_02_parkinson_example.py b/examples/plot_02_parkinson_example.py index f32a3b1ab..8b150446c 100644 --- a/examples/plot_02_parkinson_example.py +++ b/examples/plot_02_parkinson_example.py @@ -127,3 +127,8 @@ ) ax.set_xlim(45, 120) plt.show() + +# %% +# This concludes the Parkinson's disease progression modeling example using Leaspy. +# Leaspy is also capable of handling various other types of models, as the Joint Models, +# which will be explored in the [next section](./plot_03_joint). \ No newline at end of file diff --git a/examples/plot_03_joint.py b/examples/plot_03_joint.py index 2a436fb7d..526fdb378 100644 --- a/examples/plot_03_joint.py +++ b/examples/plot_03_joint.py @@ -56,3 +56,7 @@ # %% # The Joint Model includes specific parameters such as `log_rho_mean` and `zeta_mean`. print(model.parameters) + +# %% +# We have seend how to fit a Joint Model using Leaspy. It also provides other models as the +# [Mixture Model](./plot_04_mixture) that can be explored in the next examples. \ No newline at end of file diff --git a/examples/plot_04_mixture.py b/examples/plot_04_mixture.py index ddaa730aa..7cb64a003 100644 --- a/examples/plot_04_mixture.py +++ b/examples/plot_04_mixture.py @@ -295,3 +295,10 @@ def get_ip(df_leaspy, model): plt.suptitle("Population progression", fontsize=18) plt.tight_layout() plt.show() + +# %% +# This concludes the Mixture Model example using Leaspy. We can also use these fit models to +# simulate new data according to the estimated parameters. This can be useful for +# validating the model, for generating synthetic datasets for further analysis or for +# generate a trajectory for a new individual given specific parameters. Let's check this +# in the [next example](./plot_05_simulate.py). diff --git a/examples/plot_05_simulate.py b/examples/plot_05_simulate.py index a9ae3ef59..6ba32858a 100644 --- a/examples/plot_05_simulate.py +++ b/examples/plot_05_simulate.py @@ -91,4 +91,7 @@ # %% # The simulated longitudinal dataset is displayed below. -print(df_sim) +df_sim.head(10) + +# %% +# This concludes the simulation example using Leaspy. Stay tuned for more examples on model fitting and analysis! \ No newline at end of file diff --git a/poetry.lock b/poetry.lock index 71e7c5e03..5e09a08b3 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 2.1.1 and should not be changed by hand. +# This file is automatically @generated by Poetry 2.2.1 and should not be changed by hand. [[package]] name = "accessible-pygments" @@ -501,7 +501,7 @@ description = "Python library for calculating contours of 2D quadrilateral grids optional = false python-versions = ">=3.10" groups = ["main"] -markers = "python_version >= \"3.11\"" +markers = "python_version >= \"3.11\" and python_version < \"3.13\" or python_full_version == \"3.13.0\"" files = [ {file = "contourpy-1.3.2-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:ba38e3f9f330af820c4b27ceb4b9c7feee5fe0493ea53a8720f4792667465934"}, {file = "contourpy-1.3.2-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:dc41ba0714aa2968d1f8674ec97504a8f7e334f48eeacebcaa6256213acb0989"}, @@ -931,6 +931,25 @@ test-downstream = ["aiobotocore (>=2.5.4,<3.0.0)", "dask[dataframe,test]", "moto test-full = ["adlfs", "aiohttp (!=4.0.0a0,!=4.0.0a1)", "cloudpickle", "dask", "distributed", "dropbox", "dropboxdrivefs", "fastparquet", "fusepy", "gcsfs", "jinja2", "kerchunk", "libarchive-c", "lz4", "notebook", "numpy", "ocifs", "pandas", "panel", "paramiko", "pyarrow", "pyarrow (>=1)", "pyftpdlib", "pygit2", "pytest", "pytest-asyncio (!=0.22.0)", "pytest-benchmark", "pytest-cov", "pytest-mock", "pytest-recording", "pytest-rerunfailures", "python-snappy", "requests", "smbprotocol", "tqdm", "urllib3", "zarr", "zstandard"] tqdm = ["tqdm"] +[[package]] +name = "furo" +version = "2025.9.25" +description = "A clean customisable Sphinx documentation theme." +optional = false +python-versions = ">=3.8" +groups = ["docs"] +files = [ + {file = "furo-2025.9.25-py3-none-any.whl", hash = "sha256:2937f68e823b8e37b410c972c371bc2b1d88026709534927158e0cb3fac95afe"}, + {file = "furo-2025.9.25.tar.gz", hash = "sha256:3eac05582768fdbbc2bdfa1cdbcdd5d33cfc8b4bd2051729ff4e026a1d7e0a98"}, +] + +[package.dependencies] +accessible-pygments = ">=0.0.5" +beautifulsoup4 = "*" +pygments = ">=2.7" +sphinx = ">=6.0,<9.0" +sphinx-basic-ng = ">=1.0.0.beta2" + [[package]] name = "greenlet" version = "3.2.1" @@ -1039,7 +1058,7 @@ files = [ {file = "importlib_metadata-8.7.0-py3-none-any.whl", hash = "sha256:e5dd1551894c77868a30651cef00984d50e1002d06942a7101d34870c5f02afd"}, {file = "importlib_metadata-8.7.0.tar.gz", hash = "sha256:d13b81ad223b890aa16c5471f2ac3056cf76c5f10f82d6f9292f0b415f389000"}, ] -markers = {dev = "python_version < \"3.10\""} +markers = {dev = "python_version == \"3.9\""} [package.dependencies] zipp = ">=3.20" @@ -1060,7 +1079,7 @@ description = "Read resources from Python packages" optional = false python-versions = ">=3.9" groups = ["main"] -markers = "python_version < \"3.10\"" +markers = "python_version == \"3.9\"" files = [ {file = "importlib_resources-6.5.2-py3-none-any.whl", hash = "sha256:789cfdc3ed28c78b67a06acb8126751ced69a3d5f79c095a98298cd8a760ccec"}, {file = "importlib_resources-6.5.2.tar.gz", hash = "sha256:185f87adef5bcc288449d98fb4fba07cea78bc036455dd44c5fc4a2fe78fed2c"}, @@ -1181,7 +1200,7 @@ description = "IPython: Productive Interactive Computing" optional = false python-versions = ">=3.11" groups = ["docs"] -markers = "python_version >= \"3.11\"" +markers = "python_version >= \"3.11\" and python_version < \"3.13\" or python_full_version == \"3.13.0\"" files = [ {file = "ipython-9.2.0-py3-none-any.whl", hash = "sha256:fef5e33c4a1ae0759e0bba5917c9db4eb8c53fee917b6a526bd973e1ca5159f6"}, {file = "ipython-9.2.0.tar.gz", hash = "sha256:62a9373dbc12f28f9feaf4700d052195bf89806279fc8ca11f3f54017d04751b"}, @@ -1215,7 +1234,7 @@ description = "Defines a variety of Pygments lexers for highlighting IPython cod optional = false python-versions = ">=3.8" groups = ["docs"] -markers = "python_version >= \"3.11\"" +markers = "python_version >= \"3.11\" and python_version < \"3.13\" or python_full_version == \"3.13.0\"" files = [ {file = "ipython_pygments_lexers-1.1.1-py3-none-any.whl", hash = "sha256:a9462224a505ade19a605f71f8fa63c2048833ce50abc86768a0d81d876dc81c"}, {file = "ipython_pygments_lexers-1.1.1.tar.gz", hash = "sha256:09c0138009e56b6854f9535736f4171d855c8c08a563a0dcd8022f78355c7e81"}, @@ -1516,7 +1535,7 @@ description = "A fast implementation of the Cassowary constraint solver" optional = false python-versions = ">=3.10" groups = ["main"] -markers = "python_version >= \"3.11\"" +markers = "python_version >= \"3.11\" and python_version < \"3.13\" or python_full_version == \"3.13.0\"" files = [ {file = "kiwisolver-1.4.8-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:88c6f252f6816a73b1f8c904f7bbe02fd67c09a69f7cb8a0eecdbf5ce78e63db"}, {file = "kiwisolver-1.4.8-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:c72941acb7b67138f35b879bbe85be0f6c6a70cab78fe3ef6db9c024d9223e5b"}, @@ -1803,7 +1822,7 @@ description = "Python plotting package" optional = false python-versions = ">=3.10" groups = ["main"] -markers = "python_version >= \"3.11\"" +markers = "python_version >= \"3.11\" and python_version < \"3.13\" or python_full_version == \"3.13.0\"" files = [ {file = "matplotlib-3.10.1-cp310-cp310-macosx_10_12_x86_64.whl", hash = "sha256:ff2ae14910be903f4a24afdbb6d7d3a6c44da210fc7d42790b87aeac92238a16"}, {file = "matplotlib-3.10.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:0721a3fd3d5756ed593220a8b86808a36c5031fce489adb5b31ee6dbb47dd5b2"}, @@ -1984,7 +2003,7 @@ description = "An extended [CommonMark](https://spec.commonmark.org/) compliant optional = false python-versions = ">=3.10" groups = ["docs"] -markers = "python_version >= \"3.11\"" +markers = "python_version >= \"3.11\" and python_version < \"3.13\" or python_full_version == \"3.13.0\"" files = [ {file = "myst_parser-4.0.1-py3-none-any.whl", hash = "sha256:9134e88959ec3b5780aedf8a99680ea242869d012e8821db3126d427edc9c95d"}, {file = "myst_parser-4.0.1.tar.gz", hash = "sha256:5cfea715e4f3574138aecbf7d54132296bfd72bb614d31168f48c477a830a7c4"}, @@ -2089,7 +2108,7 @@ description = "Python package for creating and manipulating graphs and networks" optional = false python-versions = ">=3.10" groups = ["main"] -markers = "python_version >= \"3.11\"" +markers = "python_version >= \"3.11\" and python_version < \"3.13\" or python_full_version == \"3.13.0\"" files = [ {file = "networkx-3.4.2-py3-none-any.whl", hash = "sha256:df5d4365b724cf81b8c6a7312509d0c22386097011ad1abe274afd5e9d3bbc5f"}, {file = "networkx-3.4.2.tar.gz", hash = "sha256:307c3669428c5362aab27c8a1260aa8f47c4e91d3891f48be0141738d8d053e1"}, @@ -3399,7 +3418,7 @@ description = "Fundamental algorithms for scientific computing in Python" optional = false python-versions = ">=3.10" groups = ["main"] -markers = "python_version >= \"3.11\"" +markers = "python_version >= \"3.11\" and python_version < \"3.13\" or python_full_version == \"3.13.0\"" files = [ {file = "scipy-1.15.2-cp310-cp310-macosx_10_13_x86_64.whl", hash = "sha256:a2ec871edaa863e8213ea5df811cd600734f6400b4af272e1c011e69401218e9"}, {file = "scipy-1.15.2-cp310-cp310-macosx_12_0_arm64.whl", hash = "sha256:6f223753c6ea76983af380787611ae1291e3ceb23917393079dcc746ba60cfb5"}, @@ -3468,7 +3487,7 @@ files = [ {file = "setuptools-80.3.1-py3-none-any.whl", hash = "sha256:ea8e00d7992054c4c592aeb892f6ad51fe1b4d90cc6947cc45c45717c40ec537"}, {file = "setuptools-80.3.1.tar.gz", hash = "sha256:31e2c58dbb67c99c289f51c16d899afedae292b978f8051efaf6262d8212f927"}, ] -markers = {main = "platform_system == \"Linux\" and platform_machine == \"x86_64\" or python_version >= \"3.12\"", docs = "python_version >= \"3.12\""} +markers = {main = "platform_system == \"Linux\" and platform_machine == \"x86_64\" or python_version == \"3.12\" or python_full_version == \"3.13.0\"", docs = "python_version == \"3.12\" or python_full_version == \"3.13.0\""} [package.extras] check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1) ; sys_platform != \"cygwin\"", "ruff (>=0.8.0) ; sys_platform != \"cygwin\""] @@ -3594,6 +3613,24 @@ docs = ["furo (>=2024.1.29)"] numpy = ["nptyping (>=2.5)"] testing = ["covdefaults (>=2.3)", "coverage (>=7.4.4)", "defusedxml (>=0.7.1)", "diff-cover (>=9)", "pytest (>=8.1.1)", "pytest-cov (>=5)", "sphobjinv (>=2.3.1)", "typing-extensions (>=4.11)"] +[[package]] +name = "sphinx-basic-ng" +version = "1.0.0b2" +description = "A modern skeleton for Sphinx themes." +optional = false +python-versions = ">=3.7" +groups = ["docs"] +files = [ + {file = "sphinx_basic_ng-1.0.0b2-py3-none-any.whl", hash = "sha256:eb09aedbabfb650607e9b4b68c9d240b90b1e1be221d6ad71d61c52e29f7932b"}, + {file = "sphinx_basic_ng-1.0.0b2.tar.gz", hash = "sha256:9ec55a47c90c8c002b5960c57492ec3021f5193cb26cebc2dc4ea226848651c9"}, +] + +[package.dependencies] +sphinx = ">=4.0" + +[package.extras] +docs = ["furo", "ipython", "myst-parser", "sphinx-copybutton", "sphinx-inline-tabs"] + [[package]] name = "sphinx-gallery" version = "0.19.0" @@ -3964,7 +4001,7 @@ description = "A list of Python Standard Libraries (2.7 through 3.13)." optional = false python-versions = ">=3.9" groups = ["docs"] -markers = "python_version < \"3.10\"" +markers = "python_version == \"3.9\"" files = [ {file = "stdlib_list-0.11.1-py3-none-any.whl", hash = "sha256:9029ea5e3dfde8cd4294cfd4d1797be56a67fc4693c606181730148c3fd1da29"}, {file = "stdlib_list-0.11.1.tar.gz", hash = "sha256:95ebd1d73da9333bba03ccc097f5bac05e3aa03e6822a0c0290f87e1047f1857"}, @@ -4343,7 +4380,7 @@ files = [ {file = "zipp-3.21.0-py3-none-any.whl", hash = "sha256:ac1bbe05fd2991f160ebce24ffbac5f6d11d83dc90891255885223d42b3cd931"}, {file = "zipp-3.21.0.tar.gz", hash = "sha256:2c9958f6430a2040341a52eb608ed6dd93ef4392e02ffe219417c1b28b5dd1f4"}, ] -markers = {main = "python_version < \"3.10\"", dev = "python_version < \"3.10\""} +markers = {main = "python_version == \"3.9\"", dev = "python_version == \"3.9\""} [package.extras] check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1) ; sys_platform != \"cygwin\""] @@ -4356,4 +4393,4 @@ type = ["pytest-mypy"] [metadata] lock-version = "2.1" python-versions = ">=3.9,<=3.13" -content-hash = "04344c60e681098d672f73d8795863330ec059e0d794efa5a287bedf095a5d17" +content-hash = "b449dfd70b9e2fb9cb29b3a7f11f0c1507964198f1af79138e65c6efde5af990" diff --git a/pyproject.toml b/pyproject.toml index fb4c228ed..be6e2294d 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -72,6 +72,7 @@ sphinx-autoapi = "*" myst-nb = "*" sphinxcontrib-bibtex = "*" pydata-sphinx-theme = "^0.16.1" +furo = "^2025.9.25" [tool.ruff] target-version = "py39" From eba36bf675ffde2a2af6cd126763790042900415 Mon Sep 17 00:00:00 2001 From: Sebastian Date: Mon, 1 Dec 2025 14:27:38 +0100 Subject: [PATCH 07/24] Adding code.view: it allows to watch the source code. Improve API reference styling for Sphinx docs Enhanced class and method separation with distinct vertical lines and highlight blocks Swapped primary and secondary colors for indentation and highlighting Reduced and tightened spacing between parameters, attributes, and returns for clarity Removed third-level indentation and unnecessary/old CSS rules Improved visibility and differentiation of important API sections Added bullet markers for parameter names Fixed alignment of vertical lines and highlight blocks for class/method headers Cleaned up and consolidated CSS for maintainability --- docs/_static/custom.css | 133 +++++++++++++++++++++++++++++++++++++++- docs/conf.py | 1 + 2 files changed, 132 insertions(+), 2 deletions(-) diff --git a/docs/_static/custom.css b/docs/_static/custom.css index 5792b63d0..a471469b7 100644 --- a/docs/_static/custom.css +++ b/docs/_static/custom.css @@ -7,7 +7,7 @@ /* Hide images in the gallery */ .sphx-glr-thumbcontainer img { - display: none !important; +display: none !important; } /* Reset container height to fit text only */ @@ -20,7 +20,7 @@ .sidebar-logo, img[src*="leaspy_logo.png"] { background-color: white; padding: 5px; /* Adds some space around the logo */ - border-radius: 4px; /* Optional: Rounds the corners of the background */ + border-radius: 4px; /* Rounds the corners of the background */ } /* Pandas DataFrame styling for Furo */ @@ -63,3 +63,132 @@ table.dataframe tbody tr:hover { background: var(--color-background-hover); /* Hover effect */ } +/* --- API Reference Styling --- */ + +/* 1. Class Separation (Big) */ +dl.py.class { + margin-bottom: 4rem !important; + padding-bottom: 2rem; + border-bottom: 2px solid var(--color-border-primary); /* Stronger separator */ +} + +/* 2. Method/Function Separation (Small) */ +dl.py.method, dl.py.function { + margin-bottom: 2rem !important; + padding-bottom: 1rem; + border-bottom: 1px solid var(--color-border-secondary); /* Lighter separator */ +} + +/* Remove the border from the last method to avoid double borders with the class end */ +dl.py.class > dd > dl.py.method:last-child { + border-bottom: none; + margin-bottom: 0 !important; + padding-bottom: 0; +} + +/* 3. Signature Highlighting - Split for differentiation */ + +/* Class Header (Level 1) */ +dl.py.class > dt { + background-color: var(--color-background-secondary); + padding: 0.8rem; + border-radius: 4px; + margin-top: 1.5rem; + font-weight: bold; + border-left: 5px solid var(--color-brand-primary); /* Brand Color (Blue) */ + display: block; + text-indent: 0 !important; + margin-left: 0 !important; + width: 100%; + box-sizing: border-box; +} + +/* Method/Function Header (Level 2) */ +dl.py.function > dt, +dl.py.method > dt { + background-color: var(--color-background-secondary); + padding: 0.6rem; + border-radius: 4px; + margin-top: 1.0rem; + font-weight: bold; + border-left: 3px solid var(--color-foreground-primary); /* Darkest/Thickest (Black/Dark Grey) */ + display: block; + text-indent: 0 !important; + margin-left: 0 !important; + width: 100%; + box-sizing: border-box; +} + +/* 4. Indentation and Vertical Lines */ + +/* Level 1: Class Body (Darkest Line, aligned left) */ +dl.py.class > dd { + margin-left: 0 !important; /* Move line to the left edge */ + padding-left: 1.5rem; /* Keep content indented */ + border-left: 4px solid var(--color-brand-primary); /* Brand Color (Blue) */ +} + +/* Level 2: Method/Function Body (Brand Color Line) */ +dl.py.method > dd, +dl.py.function > dd { + margin-left: 0 !important; /* Move line to the left edge */ + padding-left: 1.5rem; + border-left: 2px solid var(--color-foreground-primary); +} + +/* 5. Styling for Parameters, Returns, Attributes (Field Lists) */ +/* Removed Level 3 indentation/borders as requested, keeping only tight spacing */ + +/* The Headers (e.g. "Parameters", "Returns") - Simple bold, no box */ +dl.field-list > dt { + font-weight: bold; + font-size: 1.0em; + color: var(--color-foreground-primary); + margin-top: 1.0rem; + margin-bottom: 0.5rem; + padding: 0; + border: none; + background: none; +} + +/* Field Lists Content - No vertical line, just simple indentation */ +dl.field-list > dd { + margin-left: 0 !important; + padding-left: 1.0rem; + border-left: none; /* Removed the third level line */ +} + +/* The Items (e.g. parameter names like "settings", "name") */ +dl.field-list > dd > dl > dt { + background-color: transparent !important; + padding: 0 !important; + margin-top: 0.0rem !important; /* Zero margin */ + margin-bottom: 0.0rem !important; + font-weight: bold; + border: none !important; + color: var(--color-foreground-primary); + font-family: var(--font-stack-monospace); + line-height: 1.2; +} + +/* The Descriptions (e.g. "Name of the algorithm.") */ +dl.field-list > dd > dl > dd { + margin-top: 0.0rem !important; + margin-bottom: 0.1rem !important; /* Extremely tight */ + margin-left: 1.0rem !important; +} + +/* Remove default margins from paragraphs inside descriptions to fix spacing */ +dl.field-list > dd > dl > dd > p { + margin-top: 0 !important; + margin-bottom: 0.0rem !important; /* Zero margin */ + line-height: 1.3; +} + +/* Add a small bullet or marker to parameter names to make them distinct */ +dl.field-list > dd > dl > dt::before { + content: "•"; + color: var(--color-foreground-muted); + margin-right: 0.5rem; + font-weight: normal; +} diff --git a/docs/conf.py b/docs/conf.py index f0030561b..f6502fe3a 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -49,6 +49,7 @@ def find_var(varname: str, *py_file_paths): "sphinx_gallery.gen_gallery", "myst_nb", "sphinxcontrib.bibtex", + "sphinx.ext.viewcode", ] bibtex_bibfiles = ["references.bib"] From 680020d5dc6328247db20fccdb350c69a3ed222a Mon Sep 17 00:00:00 2001 From: Sebastian Date: Thu, 4 Dec 2025 13:57:45 +0100 Subject: [PATCH 08/24] Fix warnings, update examples and improve logging - Suppress deprecation warnings in . - Update calls in examples to use - Fix warnings, update examples and improve logging - Fix initialization in plot_03_joint.py by adding . - Silence seed setting log and improve duration formatting precision in . - Update execution times: now we show 2 decimals to the second. --- docs/sg_execution_times.rst | 16 ++++++++-------- examples/plot_01_quickstart.py | 2 +- examples/plot_02_parkinson_example.py | 2 +- examples/plot_03_joint.py | 2 +- src/leaspy/algo/algo_with_device.py | 9 +++++++-- src/leaspy/algo/base.py | 4 ++-- 6 files changed, 20 insertions(+), 15 deletions(-) diff --git a/docs/sg_execution_times.rst b/docs/sg_execution_times.rst index 018b76da9..d61d0c74c 100644 --- a/docs/sg_execution_times.rst +++ b/docs/sg_execution_times.rst @@ -6,7 +6,7 @@ Computation times ================= -**00:08.619** total execution time for 5 files **from all galleries**: +**00:08.804** total execution time for 5 files **from all galleries**: .. container:: @@ -32,18 +32,18 @@ Computation times * - Example - Time - Mem (MB) - * - :ref:`sphx_glr_auto_examples_plot_05_simulate.py` (``../examples/plot_05_simulate.py``) - - 00:08.619 - - 0.0 - * - :ref:`sphx_glr_auto_examples_plot_01_quickstart.py` (``../examples/plot_01_quickstart.py``) - - 00:00.000 - - 0.0 * - :ref:`sphx_glr_auto_examples_plot_02_parkinson_example.py` (``../examples/plot_02_parkinson_example.py``) - - 00:00.000 + - 00:08.118 - 0.0 * - :ref:`sphx_glr_auto_examples_plot_03_joint.py` (``../examples/plot_03_joint.py``) + - 00:00.687 + - 0.0 + * - :ref:`sphx_glr_auto_examples_plot_01_quickstart.py` (``../examples/plot_01_quickstart.py``) - 00:00.000 - 0.0 * - :ref:`sphx_glr_auto_examples_plot_04_mixture.py` (``../examples/plot_04_mixture.py``) - 00:00.000 - 0.0 + * - :ref:`sphx_glr_auto_examples_plot_05_simulate.py` (``../examples/plot_05_simulate.py``) + - 00:00.000 + - 0.0 diff --git a/examples/plot_01_quickstart.py b/examples/plot_01_quickstart.py index a3f2c9bc7..8d73547ea 100644 --- a/examples/plot_01_quickstart.py +++ b/examples/plot_01_quickstart.py @@ -65,7 +65,7 @@ # This is done using a personalization algorithm, here `scipy_minimize`: individual_parameters = model.personalize( - dataset, "scipy_minimize", seed=0, progress_bar=False + dataset, "scipy_minimize", seed=0, progress_bar=False, use_jacobian=False ) print(individual_parameters.to_dataframe()) diff --git a/examples/plot_02_parkinson_example.py b/examples/plot_02_parkinson_example.py index 8b150446c..94308d830 100644 --- a/examples/plot_02_parkinson_example.py +++ b/examples/plot_02_parkinson_example.py @@ -79,7 +79,7 @@ # %% # Individual parameters are obtained for the test data using the personalization step. -ip = model.personalize(data_test, "scipy_minimize", seed=0, progress_bar=False) +ip = model.personalize(data_test, "scipy_minimize", seed=0, progress_bar=False, use_jacobian=False) # %% # The test data with individually re-parametrized ages is plotted below. diff --git a/examples/plot_03_joint.py b/examples/plot_03_joint.py index 526fdb378..3c6da5c3c 100644 --- a/examples/plot_03_joint.py +++ b/examples/plot_03_joint.py @@ -39,7 +39,7 @@ from leaspy.models import JointModel data = Data.from_dataframe(df, "joint") -model = JointModel(name="test_model", nb_events=1) +model = JointModel(name="test_model", nb_events=1, source_dimension=2) # %% # The parameter `nb_events` should match the number of distinct event types diff --git a/src/leaspy/algo/algo_with_device.py b/src/leaspy/algo/algo_with_device.py index 23a75ce98..91ea89e45 100644 --- a/src/leaspy/algo/algo_with_device.py +++ b/src/leaspy/algo/algo_with_device.py @@ -1,4 +1,5 @@ import contextlib +import warnings import torch @@ -57,10 +58,14 @@ def _device_manager(self, model: McmcSaemCompatibleModel, dataset: Dataset): algorithm_tensor_type = "torch.cuda.FloatTensor" try: - yield torch.set_default_tensor_type(algorithm_tensor_type) + with warnings.catch_warnings(): + warnings.filterwarnings("ignore", message=".*torch.set_default_tensor_type.*") + yield torch.set_default_tensor_type(algorithm_tensor_type) finally: if self.algorithm_device != self._default_algorithm_device.type: model.move_to_device(self._default_algorithm_device) dataset.move_to_device(self._default_algorithm_device) - torch.set_default_tensor_type(self._default_algorithm_tensor_type) + with warnings.catch_warnings(): + warnings.filterwarnings("ignore", message=".*torch.set_default_tensor_type.*") + torch.set_default_tensor_type(self._default_algorithm_tensor_type) diff --git a/src/leaspy/algo/base.py b/src/leaspy/algo/base.py index 12f4a5f14..0c0ece79e 100644 --- a/src/leaspy/algo/base.py +++ b/src/leaspy/algo/base.py @@ -115,7 +115,7 @@ def _initialize_seed(seed: Optional[int]): np.random.seed(seed) torch.manual_seed(seed) # TODO: use logger instead (level=INFO) - print(f" ==> Setting seed to {seed}") + # print(f" ==> Setting seed to sdfsdf {seed}") # Silenced to reduce verbosity def run( self, model: ModelType, dataset: Optional[Dataset] = None, **kwargs @@ -262,7 +262,7 @@ def load_parameters(self, parameters: dict): self.algo_parameters[k] = v @staticmethod - def _duration_to_str(seconds: float, *, seconds_fmt=".0f") -> str: + def _duration_to_str(seconds: float, *, seconds_fmt=".2f") -> str: """ Convert a float representing computation time in seconds to a string giving time in hour, minutes and seconds ``%h %min %s``. From 7d19709e8c3eeee916395a55ad053ded848cc9f6 Mon Sep 17 00:00:00 2001 From: Sebastian Date: Fri, 5 Dec 2025 16:02:59 +0100 Subject: [PATCH 09/24] docs: improve examples and add Data/Dataset tutorial - Remove unused Dataset imports across examples - Fix PEP 8 import formatting (remove blank lines within import groups) - Simplify plot_02_parkinson patient_trajectories call - Add new data_summary.ipynb documenting Data and Dataset classes and link in quickstart --- docs/data_summary.ipynb | 251 ++++++++++++++++++++++++++ docs/sg_execution_times.rst | 14 +- examples/plot_01_quickstart.py | 14 +- examples/plot_02_parkinson_example.py | 7 - examples/plot_03_joint.py | 3 - examples/plot_04_mixture.py | 9 +- 6 files changed, 270 insertions(+), 28 deletions(-) create mode 100644 docs/data_summary.ipynb diff --git a/docs/data_summary.ipynb b/docs/data_summary.ipynb new file mode 100644 index 000000000..b24dea8db --- /dev/null +++ b/docs/data_summary.ipynb @@ -0,0 +1,251 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "6b089476", + "metadata": {}, + "source": [ + "# Understanding Leaspy's Data Containers: `Data` and `Dataset`\n", + "\n", + "In `leaspy`, transforming raw data (like a CSV) into a model-ready format involves two key classes: `Data` and `Dataset`. Understanding their distinct roles is crucial for having full control of your analysis.\n", + "\n", + "---\n", + "\n", + "## 1. The `Data` Class: The User Interface\n", + "\n", + "The `Data` class is your **primary tool** for loading, organizing, and inspecting data. It acts as a flexible, patient-centric container that bridges the gap between raw spreadsheets and the model.\n", + "\n", + "## Key Features & Methods\n", + "\n", + "### **Loading Data**\n", + "Use the factory method to load from a pandas DataFrame. Notice that there is a slight difference when you work with joint models." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "c6de245e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " ID TIME EVENT_TIME EVENT_BOOL Y0 Y1 Y2 Y3\n", + "0 116 78.461 85.5 1 0.44444 0.04 0.0 0.0\n", + "1 116 78.936 85.5 1 0.60000 0.00 0.0 0.2\n", + "2 116 79.482 85.5 1 0.39267 0.04 0.0 0.2\n" + ] + } + ], + "source": [ + "import os\n", + "import pandas as pd\n", + "import leaspy\n", + "from leaspy.io.data import Data\n", + "\n", + "leaspy_root = os.path.dirname(leaspy.__file__)\n", + "data_path = os.path.join(leaspy_root, \"datasets/data/simulated_data_for_joint.csv\")\n", + "df = pd.read_csv(data_path, dtype={\"ID\": str}, sep=\";\")\n", + "\n", + "data = Data.from_dataframe(df) \t\t\t\t\t\t\t# <-\n", + "# For joint models (longitudinal + time-to-event):\n", + "data_joint = Data.from_dataframe(df, data_type='joint')\t# <-\n", + "print(df.head(3))" + ] + }, + { + "cell_type": "markdown", + "id": "73706687", + "metadata": {}, + "source": [ + "### **Inspection**: \n", + "Access data naturally by patient ID or index. This is made thanks to the iterators handdling inside `Data`, it also allows you to iterate using for loops. So you can:\n", + "* select data usint the brackets (`data['116']`)\n", + "* check some attributes (`data.n_individuals`) \n", + "* convert the whole dataset or some individuals back into a dataframe object (`data[['116']].to_dataframe()`)\n", + "* iterate into each individual (`for individual in data:`)\n", + "* generate an iterator (`for i, individual in enumerate(data):`)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "6e92c403", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of patients: 17\n", + "patient data (observations shape): (9, 6)\n", + "patient data (dataframe):\n", + " ID TIME EVENT_TIME EVENT_BOOL Y0 Y1 Y2 Y3\n", + "0 116 78.461 85.5 1.0 0.44444 0.04 0.0 0.0\n", + "1 116 78.936 85.5 1.0 0.60000 0.00 0.0 0.2\n", + "2 116 79.482 85.5 1.0 0.39267 0.04 0.0 0.2\n", + "3 116 79.939 85.5 1.0 0.58511 0.00 0.0 0.0\n", + "4 116 80.491 85.5 1.0 0.57044 0.00 0.0 0.0\n", + "5 116 81.455 85.5 1.0 0.55556 0.20 0.1 0.2\n", + "6 116 82.491 85.5 1.0 0.71844 0.20 0.1 0.6\n", + "7 116 83.463 85.5 1.0 0.71111 0.32 0.2 0.6\n", + "8 116 84.439 85.5 1.0 0.91111 0.52 0.6 1.0\n", + "\n", + "Iterating over first 3 patients:\n", + " - Patient 116: 9 visits\n", + " - Patient 142: 11 visits\n", + " - Patient 169: 7 visits\n", + "Patient ID: 116\n", + "Patient ID: 142\n", + "Patient ID: 169\n" + ] + } + ], + "source": [ + "patient_data = data['116'] # Get a specific individual\n", + "n_patients = data.n_individuals # Get total count\n", + "print(f\"Number of patients: {n_patients}\")\n", + "print(f\"patient data (observations shape): {patient_data.observations.shape}\")\n", + "print(f\"patient data (dataframe):\\n{data[['116']].to_dataframe()}\")\n", + "print(\"\\nIterating over first 3 patients:\")\n", + "for individual in data:\n", + " if len(individual.timepoints) == 10: break\n", + " print(f\" - Patient {individual.idx}: {len(individual.timepoints)} visits\")\n", + "for i, individual in enumerate(data):\n", + " if i >= 3: break # Stop after 3 iterations using the index 'i'\n", + " print(f\"Patient ID: {individual.idx}\")" + ] + }, + { + "cell_type": "markdown", + "id": "12eb4a50", + "metadata": {}, + "source": [ + "### **Managing Cofactors**\n", + "Easily attach patient characteristics (e.g., genetics, demographics). It is used to group populations when using `plotter.plot_distribution`, so plotter can color different cofactors. Lets generate a dataset and its parameters to show how it works." + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "id": "8867dfe4", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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Njp911lkN/04tib17947DDjssXnrppdhxxx036L1SK+WYMWMa9lMLY9++fTe06AAAAGyqLqmjRo2KqVOnxsMPPxzbb7/9e167//7758c5c+bkxzS2cdGiRY2uqd9f17jH9u3bR8eOHRttAAAANKPAWKlUclj82c9+Fg899FAMGDDgfZ8za9as/JhaGpPBgwfH7373u1i8eHHDNQ888EAOgbvsssv6fwUAAABUv0tq6oY6efLkuPvuu/NajPVjDjt16hQdOnTI3U7T+aOOOiq6du2axzCef/75eQbVPfbYI1+bluFIwfC0006Lq666Kr/GuHHj8munlkQAAABqsIXxhhtuyDOjHnLIIbnFsH6744478vm0JEZaLiOFwoEDB8YFF1wQw4YNi3vuuafhNdq0aZO7s6bH1Nr42c9+Nq/DuPa6jQAAANRYC2Pqkvpe0kQ0jz766Pu+TppF9Re/+MX6vDUAAAC1tA4jAAAAWy6BEQAAgCKBEQAAgCKBEQAAgCKBEQAAgCKBEQAAgCKBEQAAgCKBEQAAgCKBEQAAgKK68mEA+P9GTpoRLcHoRUuqXQQAaFa0MAIAAFAkMAIAAFAkMAIAAFAkMAIAAFAkMAIAAFAkMAIAAFAkMAIAAFAkMAIAAFAkMAIAAFAkMAIAAFAkMAIAAFAkMAIAAFAkMAIAAFAkMAIAAFAkMAIAAFAkMAIAAFAkMAIAAFAkMAIAAFAkMAIAAFAkMAIAAFAkMAIAAFAkMAIAAFAkMAIAAFAkMAIAAFAkMAIAAFAkMAIAAFAkMAIAAFAkMAIAAFAkMAIAAFAkMAIAAFAkMAIAAFAkMAIAAFAkMAIAAFAkMAIAAFAkMAIAAFAkMAIAAFAkMAIAAFAkMAIAAFAkMAIAAFAkMAIAAFAkMAIAAFAkMAIAAFAkMAIAAFAkMAIAAFAkMAIAAFAkMAIAAFAkMAIAAFAkMAIAAFAkMAIAAFAkMAIAAFAkMAIAAFAkMAIAAFAkMAIAAFBUF+th4sSJMWXKlPjDH/4QHTp0iI9+9KNx5ZVXxs4779xwzYoVK+KCCy6IH//4x7Fy5coYOnRoXH/99dGzZ8+Ga+bNmxfnnHNOPPzww7HtttvG8OHD82vX1a1XcQCAzWD0onGb780md46acsod1S4BQPNpYXz00Ufj3HPPjSeffDIeeOCBeOutt+Lwww+P119/veGa888/P+65556488478/Uvv/xynHjiiQ3nV69eHUcffXSsWrUqnnjiibj11ltj0qRJMX78+Kb9ygAAANgorSqVSmVDn/zXv/41evTokYPhwQcfHEuXLo3u3bvH5MmT45Of/GS+JrVGDho0KKZPnx4HHHBA/PKXv4xjjjkmB8n6Vscbb7wxLr744vx67dq1e9/3XbZsWXTq1Cm/X8eOHTe0+AD8nUZOmhEtwWZtSaNor75aGAE2h783U23UGMb04kmXLl3y48yZM3Or45AhQxquGThwYPTr1y8HxiQ97r777o26qKZuq6nAzz//fPF9UtfWdH7tDQAAgE1rgwPjmjVr4rzzzosDDzwwdtttt3xs4cKFuYWwc+fGfx1M4TCdq79m7bBYf77+XEka35jSb/3Wt2/fDS02AAAAmzowprGMzz33XJ7cZlMbO3Zsbs2s3+bPn7/J3xMAAKCl26BpSUeNGhVTp06NX//617H99ts3HO/Vq1eezGbJkiWNWhkXLVqUz9Vf8/TTTzd6vXS+/lxJ+/bt8wYAAEAzbWFM8+OksPizn/0sHnrooRgwYECj8/vss0+0bds2pk2b1nBs9uzZeRmNwYMH5/30+Lvf/S4WL17ccE2acTUNtNxll102/isCAABg87cwpm6oaQbUu+++O7bbbruGMYdpXGFalzE9jhw5MsaMGZMnwkkhcPTo0TkkphlSk7QMRwqGp512Wlx11VX5NcaNG5dfWysiALRss+YviVpybTOYQfjmEftVuwjAFmy9AuMNN9yQHw855JBGx2+55ZYYMWJE/vc111wTrVu3jmHDhuXZTdMMqNdff33DtW3atMndWc8555wcJLfZZpsYPnx4XH755U3zFQEAALD5A+Pfs2TjVlttFdddd13e1qV///7xi1/8Yn3eGgAAgFqY9AaAGjb5pPV+yuhFtdVNEACo8rIaAAAAbNkERgAAAIoERgAAAIoERgAAAIoERgAAAIoERgAAAIoERgAAAIoERgAAAIoERgAAAIoERgAAAIoERgAAAIoERgAAAIoERgAAAIoERgAAAIoERgAAAIoERgAAAIoERgAAAIoERgAAAIoERgAAAIoERgAAAIoERgAAAIoERgAAAIoERgAAAIoERgAAAIoERgAAAIoERgAAAIoERgAAAIoERgAAAIoERgAAAIoERgAAAIoERgAAAIoERgAAAIoERgAAAIoERgAAAIoERgAAAIoERgAAAIoERgAAAIoERgAAAIoERgAAAIoERgAAAIoERgAAAIoERgAAAIoERgAAAIoERgAAAIoERgAAAIoERgAAAIoERgAAAIoERgAAAIoERgAAAIoERgAAAIoERgAAAIoERgAAAIoERgAAAIoERgAAAIoERgAAAIoERgAAAIoERgAAAIoERgAAAIoERgAAAIoERgAAAIoERgAAAIoERgAAAJomMP7617+OY489Nvr06ROtWrWKu+66q9H5ESNG5ONrb0cccUSja1599dU49dRTo2PHjtG5c+cYOXJkLF++fH2LAgAAQHMKjK+//nrsueeecd11163zmhQQFyxY0LD96Ec/anQ+hcXnn38+HnjggZg6dWoOoWedddaGfQUAAABsEnXr+4Qjjzwyb++lffv20atXr+K53//+93HffffFjBkzYt99983Hrr322jjqqKPi6quvzi2XAAAAbKFjGB955JHo0aNH7LzzznHOOefEK6+80nBu+vTpuRtqfVhMhgwZEq1bt46nnnqq+HorV66MZcuWNdoAAACoscCYuqPedtttMW3atLjyyivj0UcfzS2Sq1evzucXLlyYw+Ta6urqokuXLvlcycSJE6NTp04NW9++fZu62AAAAGxsl9T3c/LJJzf8e/fdd4899tgjdtxxx9zqeNhhh23Qa44dOzbGjBnTsJ9aGIVGAACAGl9WY4cddohu3brFnDlz8n4a27h48eJG17z99tt55tR1jXtMYyLTjKprbwAAANR4YPzLX/6SxzD27t077w8ePDiWLFkSM2fObLjmoYceijVr1sT++++/qYsDAADApuqSmtZLrG8tTObOnRuzZs3KYxDTNmHChBg2bFhuLXzppZfiS1/6UnzoQx+KoUOH5usHDRqUxzmeeeaZceONN8Zbb70Vo0aNyl1ZzZAKAABQwy2MzzzzTHzkIx/JW5LGFqZ/jx8/Ptq0aRPPPvts/PM//3PstNNOMXLkyNhnn33iN7/5Te5WWu/222+PgQMH5jGNaTmNgw46KG666aam/coAAADYvC2MhxxySFQqlXWev//++9/3NVJL5OTJk9f3rQEAANiSxjACAABQmwRGAAAAigRGAAAAigRGAAAAigRGAAAAigRGAAAAigRGAAAAigRGAAAAigRGAAAAigRGAAAAigRGAAAAigRGAAAAigRGAAAAigRGAAAAigRGAAAAigRGAAAAigRGAAAAigRGAAAAigRGAAAAigRGAAAAigRGAAAAigRGAAAAigRGAAAAigRGAAAAigRGAAAAigRGAAAAiurKhwEAeD+jF42rdhEiJneOZueUO6pdAqCJaGEEAACgSGAEAACgSGAEAACgSGAEAACgSGAEAACgSGAEAACgSGAEAACgSGAEAACgSGAEAACgSGAEAACgSGAEAACgSGAEAACgSGAEAACgSGAEAACgSGAEAACgqK58GGDLNHLSjGjpRi9aUu0iAAA1QgsjAAAARQIjAAAARQIjAAAARQIjAAAARQIjAAAARQIjAAAARQIjAAAARQIjAAAARQIjAAAARQIjAAAARQIjAAAARQIjAAAARQIjAAAARQIjAAAARQIjAAAARQIjAAAARQIjAAAARQIjAAAARQIjAAAARQIjAAAATRMYf/3rX8exxx4bffr0iVatWsVdd93V6HylUonx48dH7969o0OHDjFkyJB48cUXG13z6quvxqmnnhodO3aMzp07x8iRI2P58uXrWxQAAACaU2B8/fXXY88994zrrruueP6qq66K7373u3HjjTfGU089Fdtss00MHTo0VqxY0XBNCovPP/98PPDAAzF16tQcQs8666yN+0oAAABoUnXr+4QjjzwybyWpdfE73/lOjBs3Lo477rh87LbbbouePXvmlsiTTz45fv/738d9990XM2bMiH333Tdfc+2118ZRRx0VV199dW65BAAAYAsbwzh37txYuHBh7oZar1OnTrH//vvH9OnT8356TN1Q68Nikq5v3bp1bpEsWblyZSxbtqzRBgAAQA0FxhQWk9SiuLa0X38uPfbo0aPR+bq6uujSpUvDNe80ceLEHDzrt759+zZlsQEAAKjVWVLHjh0bS5cubdjmz59f7SIBAABs8Zo0MPbq1Ss/Llq0qNHxtF9/Lj0uXry40fm33347z5xaf807tW/fPs+ouvYGAABADQXGAQMG5NA3bdq0hmNpvGEamzh48OC8nx6XLFkSM2fObLjmoYceijVr1uSxjgAAANToLKlpvcQ5c+Y0muhm1qxZeQxiv3794rzzzouvfe1r8eEPfzgHyEsvvTTPfHr88cfn6wcNGhRHHHFEnHnmmXnpjbfeeitGjRqVZ1A1QyoAAEANB8ZnnnkmPv7xjzfsjxkzJj8OHz48Jk2aFF/60pfyWo1pXcXUknjQQQflZTS22mqrhufcfvvtOSQedthheXbUYcOG5bUbAQAAaD5aVdLiiTUmdXNNs6WmCXCMZwTWx8hJM6KlG71oXLWLADShvfp2jmbnlDuqXQKgiTJVTcySCgAAwOYnMAIAAFAkMAIAAFAkMAIAAFAkMAIAAFAkMAIAAFAkMAIAAFAkMAIAAFAkMAIAAFAkMAIAAFAkMAIAAFAkMAIAAFBUVz4MsAWYfNK7Do1etKQqRQEAqEVaGAEAACgSGAEAACgSGAEAACgSGAEAACgSGAEAACgSGAEAACgSGAEAACiyDiMAQA2bNb/5rS977aQZ0dzdPGK/ahcBaoIWRgAAAIoERgAAAIoERgAAAIoERgAAAIoERgAAAIoERgAAAIoERgAAAIoERgAAAIoERgAAAIoERgAAAIoERgAAAIoERgAAAIoERgAAAIoERgAAAIoERgAAAIoERgAAAIoERgAAAIoERgAAAIoERgAAAIoERgAAAIoERgAAAIoERgAAAIoERgAAAIoERgAAAIoERgAAAIoERgAAAIoERgAAAIoERgAAAIoERgAAAIoERgAAAIoERgAAAIoERgAAAIoERgAAAIrqyocBAGDDjF40Lpq9yZ03/3uecsfmf0/YSFoYAQAAKBIYAQAAKBIYAQAAKBIYAQAAKBIYAQAAKBIYAQAAKBIYAQAAKBIYAQAA2DyB8bLLLotWrVo12gYOHNhwfsWKFXHuuedG165dY9ttt41hw4bFokWLmroYAAAANMcWxl133TUWLFjQsD322GMN584///y455574s4774xHH300Xn755TjxxBM3RTEAAADYCHWb5EXr6qJXr17vOr506dK4+eabY/LkyXHooYfmY7fccksMGjQonnzyyTjggAM2RXEAAABoLi2ML774YvTp0yd22GGHOPXUU2PevHn5+MyZM+Ott96KIUOGNFybuqv269cvpk+fvs7XW7lyZSxbtqzRBgAAQI0Fxv333z8mTZoU9913X9xwww0xd+7c+NjHPhavvfZaLFy4MNq1axedO3du9JyePXvmc+syceLE6NSpU8PWt2/fpi42AAAAm7pL6pFHHtnw7z322CMHyP79+8dPfvKT6NChwwa95tixY2PMmDEN+6mFUWgEAACo8WU1UmviTjvtFHPmzMnjGletWhVLlixpdE2aJbU05rFe+/bto2PHjo02AAAAajwwLl++PF566aXo3bt37LPPPtG2bduYNm1aw/nZs2fnMY6DBw/e1EUBAACgml1SL7zwwjj22GNzN9S0ZMZXv/rVaNOmTXzmM5/J4w9HjhyZu5d26dIltxSOHj06h0UzpAIAAGzhgfEvf/lLDoevvPJKdO/ePQ466KC8ZEb6d3LNNddE69atY9iwYXn206FDh8b111/f1MUAAABgI7WqVCqVqDFp0pvUWpnWdTSeEVinySe969Cs+Y3HUAPQMu3Vt/Gs/ZvFKXds/veEjcxUm3wMIwAAALWpybukApvPyEkzql2EZm30Iq2JAAAbQwsjAAAARQIjAAAARQIjAAAARQIjAAAARQIjAAAARQIjAAAARQIjAAAARQIjAAAARQIjAAAARQIjAAAARQIjAAAARQIjAAAARQIjAAAARQIjAAAARQIjAAAARXXlw0AtGb1oXLWLAADAFkgLIwAAAEUCIwAAAEUCIwAAAEUCIwAAAEUCIwAAAEUCIwAAAEUCIwAAAEUCIwAAAEUCIwAAAEUCIwAAAEUCIwAAAEUCIwAAAEUCIwAAAEUCIwAAAEUCIwAAAEUCIwAAAEUCIwAAAEUCIwAAAEUCIwAAAEUCIwAAAEUCIwAAAEUCIwAAAEUCIwAAAEV15cMAAECTmnxStUvQfJ1yR7VLwDpoYQQAAKBIYAQAAKBIYAQAAKDIGEYAAFqcWfOXVLsINWevvp2rXQSqQAsjAAAARQIjAAAARQIjAAAARQIjAAAARQIjAAAARQIjAAAARQIjAAAARQIjAAAARQIjAAAARXXlw9AMTT6p2iVodkYvWlLtIgAAsAXTwggAAECRFkYAAOB9zZq/6Xo2XTtpRmypbh6xX9QyLYwAAAAUCYwAAAAUCYwAAAAUGcPYHJkNFAAAaAa0MAIAAFCkhZEtftYtAACgBgPjddddF9/61rdi4cKFseeee8a1114b//iP/xi1buRGTgtsMXYAAKBFd0m94447YsyYMfHVr341/vM//zMHxqFDh8bixYurVSQAAACaQwvjt7/97TjzzDPj9NNPz/s33nhj3HvvvfGDH/wgLrnkkkbXrly5Mm/1li5dmh+XLVsWzdGqN5dv1POXr3i7ycoCAADN3cb+/tycLWummaW+XJVK5T2va1V5vys2gVWrVsXWW28dP/3pT+P4449vOD58+PBYsmRJ3H333Y2uv+yyy2LChAmbu5gAAABbtPnz58f222/fvFoY//a3v8Xq1aujZ8+ejY6n/T/84Q/vun7s2LG5+2q9NWvWxKuvvhpdu3aNVq1aNVnC7tu3b/6GdezYsUlek01HfdUOdVVb1FftUFe1Q13VFvVVO9TVxknthq+99lr06dOn9mdJbd++fd7W1rlz503yXul/Nv/D1Q71VTvUVW1RX7VDXdUOdVVb1FftUFcbrlOnTs1z0ptu3bpFmzZtYtGiRY2Op/1evXpVo0gAAAA0h8DYrl272GeffWLatGmNupmm/cGDB1ejSAAAADSXLqlpTGKa5GbffffNay9+5zvfiddff71h1tTNLXV5TUt8vLPrK82T+qod6qq2qK/aoa5qh7qqLeqrdqirzaMqs6TW+973vhff+ta3YuHChbHXXnvFd7/73dh///2rVRwAAACaS2AEAACg+arKGEYAAACaP4ERAACAIoERAACAIoERAACAohYXGC+77LJo1apVo23gwIEN51esWBHnnntudO3aNbbddtsYNmxYLFq0qKplbqner64OOeSQd50/++yzq1rmlux//ud/4rOf/Wz+7HTo0CF23333eOaZZxrOp/m1xo8fH717987nhwwZEi+++GJVy9ySvV99jRgx4l2fryOOOKKqZW6JPvjBD76rHtKW7lOJe1Zt1Zf7VvOxevXquPTSS2PAgAH5Z+COO+4YV1xxRb5X1XPfqp26cs/aQtdhrKZdd901HnzwwYb9urr//204//zz4957740777wzOnXqFKNGjYoTTzwxHn/88SqVtmV7r7pKzjzzzLj88ssb9rfeeuvNWj7+r//93/+NAw88MD7+8Y/HL3/5y+jevXu+qX7gAx9ouOaqq67KS+fceuut+Yd++uE/dOjQeOGFF2Krrbaqavlbmr+nvpJ0s73lllsa9q1ztfnNmDEj/7JU77nnnotPfOIT8alPfSrvu2fVVn0l7lvNw5VXXhk33HBDviel3zXSH8zSWuDpc/Sv//qv+Rr3rdqpq8Q9a9NpkYExhY5evXq96/jSpUvj5ptvjsmTJ8ehhx6aj6X/8QYNGhRPPvlkHHDAAVUobcu2rrpa+0b7XufZfD/M+/bt2+gHdbq51kt/BfzOd74T48aNi+OOOy4fu+2226Jnz55x1113xcknn1yVcrdU71dfa99sfb6qK4X5tX3zm9/Mf13/p3/6J/esGquveu5bzcMTTzyR70dHH310Q+vwj370o3j66afzvvtW7dRVPfesTafFdUlN0l/S+/TpEzvssEOceuqpMW/evHx85syZ8dZbb+UuB/VSF8h+/frF9OnTq1jilmtddVXv9ttvj27dusVuu+0WY8eOjTfeeKNqZW3Jfv7zn8e+++6b/4reo0eP+MhHPhLf//73G87PnTs3Fi5c2Oizlf4yuP/++/tsNcP6qvfII4/k8zvvvHOcc8458corr1SlvPxfq1atih/+8Idxxhln5O5W7lm1VV/13Leah49+9KMxbdq0+OMf/5j3f/vb38Zjjz0WRx55ZN5336qduqrnnrXptLgWxvRBnzRpUv6facGCBTFhwoT42Mc+lruNpB8M7dq1i86dOzd6TvprUjpH86mr7bbbLk455ZTo379/DpTPPvtsXHzxxTF79uyYMmVKtYve4vz3f/937i4yZsyY+PKXv5y7ZaVuIunzNHz48IbPT/osrc1nq3nWV33XntS1MbU8vvTSS/m6dHNOvyi1adOm2l9Ci5RaNZYsWZLH6iTuWbVVX4n7VvNxySWXxLJly/IfWdLPtNSV+Otf/3r+43TivlU7dZW4Z21aLS4wrv3XiD322COHkvTD+yc/+UkeSEtt1NXIkSPjrLPOajifJuxIg9IPO+yw/IMidQFi81mzZk1usfrGN76R91OLVQr2N954Y0MAobbqa+3uVunzlT6D6XOV/oKbPmdsfqn7afq5mMIGtVlf7lvNR/pdIrX2pi7daVzcrFmz4rzzzsv15b5Ve3XlnrVptcguqWtLf5ndaaedYs6cObnfc+pCkv4iuLY045w+0c2rrkpSoEzWdZ5NJ/3Ss8suuzQ6lsZR1Xchrv/8vHP2Rp+t5llfJalbeOpG5/NVHX/+85/zBGCf//znG465Z9VWfZW4b1XPRRddlFuuUtBIAeO0007Lk0hNnDgxn3ffqp26KnHPalotPjAuX748/2Uv/QK1zz77RNu2bXM/6Xqpq0j6JWrw4MFVLSeN66ok/cUpWdd5Np0042b6rKwtjTVILcJJ6iKSbrBrf7ZS95KnnnrKZ6sZ1lfJX/7ylzwexOerOtJkNmlsTv2kD4l7Vm3VV4n7VvWksaOtWzf+NTh1XUw9MBL3rdqpqxL3rCZWaWEuuOCCyiOPPFKZO3du5fHHH68MGTKk0q1bt8rixYvz+bPPPrvSr1+/ykMPPVR55plnKoMHD84bzauu5syZU7n88stzHaXzd999d2WHHXaoHHzwwdUudov09NNPV+rq6ipf//rXKy+++GLl9ttvr2y99daVH/7whw3XfPOb36x07tw519Wzzz5bOe644yoDBgyovPnmm1Ute0v0fvX12muvVS688MLK9OnT8+frwQcfrOy9996VD3/4w5UVK1ZUu/gtzurVq/N96eKLL37XOfes2qkv963mZfjw4ZV/+Id/qEydOjXXx5QpU/LvGF/60pcarnHfqo26cs/a9FpcYDzppJMqvXv3rrRr1y7/z5f20w/xeumHwL/8y79UPvCBD+RfoE4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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "import torch\n", + "from leaspy.io.logs.visualization import Plotter\n", + "from leaspy.io.outputs import Result\n", + "\n", + "n_individuals = 2000\n", + "patient_ids = [str(i) for i in range(n_individuals)]\n", + "\n", + "df_longitudinal = pd.DataFrame({'ID': np.repeat(patient_ids, 3), 'TIME': np.tile([60, 70, 80], n_individuals), 'Y0': np.random.rand(n_individuals * 3)})\n", + "data = Data.from_dataframe(df_longitudinal)\n", + "\n", + "df_cofactors = pd.DataFrame({'gender': np.random.choice(['Male', 'Female'], size=n_individuals)}, index=patient_ids)\n", + "df_cofactors.index.name = 'ID'\n", + "data.load_cofactors(df_cofactors, cofactors=['gender'])\n", + "\n", + "individual_parameters = {'tau': torch.tensor(np.random.normal(70, 5, (n_individuals, 1))), 'xi': torch.tensor(np.random.normal(0, 0.5, (n_individuals, 1)))}\n", + "result_obj = Result(data, individual_parameters)\n", + "\n", + "Plotter().plot_distribution(result_obj, parameter='tau', cofactor='gender')" + ] + }, + { + "cell_type": "markdown", + "id": "d35263b9", + "metadata": {}, + "source": [ + "It is important to note that attaching external data to the class through `data.load_cofactors` is different from loading cofactors inside the model using `factory_kws`:\n", + "\n", + "| Feature | **Covariates** (via `factory_kws`) | **Cofactors** (via `load_cofactors`) |\n", + "| :--- | :--- | :--- |\n", + "| **Purpose** | Used **inside the model** to modulate parameters (e.g., in `CovariateLogisticModel`). | Used for **analysis/metadata** (e.g., plotting, stratification) but ignored by the model's math. |\n", + "| **Loading** | Loaded **during** `Data` creation. | Loaded **after** `Data` creation. |\n", + "| **Storage** | Stored as a `numpy.ndarray` in `individual.covariates`. | Stored as a `dict` in `individual.cofactors`. |\n", + "| **Constraints** | Must be **integers**, constant per individual, and have no missing values. | Can be any type (strings, floats, etc.). |\n" + ] + }, + { + "cell_type": "markdown", + "id": "689f4849", + "metadata": {}, + "source": [ + "### **Best Practice:**\n", + "Always create a `Data` object first. It validates your input and handles irregularities (missing visits, different timelines) gracefully.\n", + "\n", + "---\n", + "\n", + "## 2. The `Dataset` Class: The Internal Engine\n", + "\n", + "The `Dataset` class is the **high-performance numerical engine**. It converts the flexible `Data` object into rigid PyTorch Tensors optimized for mathematical computation.\n", + "\n", + "### What it does\n", + "* **Tensorization**: Converts all values to PyTorch tensors.\n", + "* **Padding**: Standardizes patient timelines by padding them to the maximum number of visits (creating a rectangular data block).\n", + "* **Masking**: Creates a binary mask to distinguish real data from padding.\n", + "\n", + "### When to use it explicitly?\n", + "You rarely need to instantiate `Dataset` yourself. However, it is useful for **optimization**:\n", + "1. **Memory Efficiency**: For massive datasets, convert `Data` $\\to$ `Dataset` and delete the original `Data`/`DataFrame` to free up RAM.\n", + "2. **Performance**: If you are running multiple models on the same data, creating a `Dataset` once prevents `leaspy` from repeating the conversion process for every `fit()` call.\n", + "\n", + "---\n", + "\n", + "## 3. Workflow & Best Practices\n", + "\n", + "### The Standard Workflow\n", + "The most common and recommended workflow is straightforward:\n", + "\n", + "```\n", + "[CSV / DataFrame] -> Data.from_dataframe() -> [Data Object] -> model.fit(data)\n", + "```\n", + "\n", + "Inside `model.fit(data)`, Leaspy automatically converts your `Data` object into a `Dataset` for computation.\n", + "\n", + "### Guidelines for `model.fit()`\n", + "\n", + "| Input Type | Verdict | Reason |\n", + "| :--- | :--- | :--- |\n", + "| **`Data` Object** | ✅ **Recommended** | **Safe & Standard.** Handles all model types (including Joint models) correctly. Easy to inspect before fitting. |\n", + "| **`Dataset` Object** | ⚡ **Optimization** | **Fast.** Use for heavy datasets or repeated experiments to skip internal conversion steps. |\n", + "| **`pd.DataFrame`** | ❌ **Avoid** | **Risky.** Fails for complex models (e.g., `JointModel`) that require specific loading parameters. Leads to inconsistent code. |" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "leaspy", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.19" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/sg_execution_times.rst b/docs/sg_execution_times.rst index d61d0c74c..ef8b3219e 100644 --- a/docs/sg_execution_times.rst +++ b/docs/sg_execution_times.rst @@ -6,7 +6,7 @@ Computation times ================= -**00:08.804** total execution time for 5 files **from all galleries**: +**00:37.949** total execution time for 5 files **from all galleries**: .. container:: @@ -32,16 +32,16 @@ Computation times * - Example - Time - Mem (MB) - * - :ref:`sphx_glr_auto_examples_plot_02_parkinson_example.py` (``../examples/plot_02_parkinson_example.py``) - - 00:08.118 + * - :ref:`sphx_glr_auto_examples_plot_01_quickstart.py` (``../examples/plot_01_quickstart.py``) + - 00:32.496 - 0.0 - * - :ref:`sphx_glr_auto_examples_plot_03_joint.py` (``../examples/plot_03_joint.py``) - - 00:00.687 + * - :ref:`sphx_glr_auto_examples_plot_04_mixture.py` (``../examples/plot_04_mixture.py``) + - 00:05.454 - 0.0 - * - :ref:`sphx_glr_auto_examples_plot_01_quickstart.py` (``../examples/plot_01_quickstart.py``) + * - :ref:`sphx_glr_auto_examples_plot_02_parkinson_example.py` (``../examples/plot_02_parkinson_example.py``) - 00:00.000 - 0.0 - * - :ref:`sphx_glr_auto_examples_plot_04_mixture.py` (``../examples/plot_04_mixture.py``) + * - :ref:`sphx_glr_auto_examples_plot_03_joint.py` (``../examples/plot_03_joint.py``) - 00:00.000 - 0.0 * - :ref:`sphx_glr_auto_examples_plot_05_simulate.py` (``../examples/plot_05_simulate.py``) diff --git a/examples/plot_01_quickstart.py b/examples/plot_01_quickstart.py index 8d73547ea..0795579f7 100644 --- a/examples/plot_01_quickstart.py +++ b/examples/plot_01_quickstart.py @@ -39,10 +39,16 @@ # - For logistic models, data must be rescaled between 0 and 1. # ``` -from leaspy.io.data import Data, Dataset +from leaspy.io.data import Data data = Data.from_dataframe(alzheimer_df) -dataset = Dataset(data) + +# %% +# ```{seealso} +# For a deeper understanding of the `Data` and `Dataset` classes, including +# iteration, cofactors, and best practices, see the +# [Data Containers Guide](../data_summary.ipynb). +# ``` # %% # The core functionality of Leaspy is to estimate the group-average trajectory @@ -53,7 +59,7 @@ model = LogisticModel(name="test-model", source_dimension=2) model.fit( - dataset, + data, "mcmc_saem", seed=42, n_iter=100, @@ -65,7 +71,7 @@ # This is done using a personalization algorithm, here `scipy_minimize`: individual_parameters = model.personalize( - dataset, "scipy_minimize", seed=0, progress_bar=False, use_jacobian=False + data, "scipy_minimize", seed=0, progress_bar=False, use_jacobian=False ) print(individual_parameters.to_dataframe()) diff --git a/examples/plot_02_parkinson_example.py b/examples/plot_02_parkinson_example.py index 94308d830..7e4282bd4 100644 --- a/examples/plot_02_parkinson_example.py +++ b/examples/plot_02_parkinson_example.py @@ -44,7 +44,6 @@ # %% # Visualization utilities from Leaspy and Matplotlib are imported. import matplotlib.pyplot as plt - from leaspy.io.logs.visualization.plotting import Plotting leaspy_plotting = Plotting(model) @@ -116,13 +115,7 @@ ip, patients_idx=["GS-187"], labels=["MDS1", "MDS2", "MDS3 (off)"], - alpha=1, - linestyle="-", - linewidth=2, - markersize=8, - obs_alpha=0.5, figsize=(16, 6), - factor_past=0.5, factor_future=5, ) ax.set_xlim(45, 120) diff --git a/examples/plot_03_joint.py b/examples/plot_03_joint.py index 3c6da5c3c..a4602e31d 100644 --- a/examples/plot_03_joint.py +++ b/examples/plot_03_joint.py @@ -7,14 +7,11 @@ # %% # The following imports are required libraries for numerical computation and data manipulation. import os - import pandas as pd - import leaspy from leaspy.io.data import Data leaspy_root = os.path.dirname(leaspy.__file__) - data_path = os.path.join(leaspy_root, "datasets/data/simulated_data_for_joint.csv") df = pd.read_csv(data_path, dtype={"ID": str}, sep=";") diff --git a/examples/plot_04_mixture.py b/examples/plot_04_mixture.py index 7cb64a003..d3eae551d 100644 --- a/examples/plot_04_mixture.py +++ b/examples/plot_04_mixture.py @@ -8,21 +8,18 @@ # %% # The following imports are required libraries for numerical computation, data manipulation, and visualization. import os - import matplotlib.pyplot as plt import numpy as np import pandas as pd import torch - import leaspy -from leaspy.io.data import Data, Dataset +from leaspy.io.data import Data # %% # This toy example is part of a simulation study, carried out by Sofia Kaisaridi that will be included in # an article to be submitted in a biostatistics journal. The dataset contains 1000 individuals each with 6 visits and 6 scores. leaspy_root = os.path.dirname(leaspy.__file__) - data_path = os.path.join(leaspy_root, "datasets/data/simulated_data_for_mixture.csv") all_data = pd.read_csv(data_path, sep=";", decimal=",") @@ -34,7 +31,6 @@ from leaspy.models import LogisticMultivariateMixtureModel leaspy_data = Data.from_dataframe(all_data) -leaspy_dataset = Dataset(leaspy_data) # %% # Then we fit a model with 3 clusters and 2 sources. Note that we have an extra argument `n_clusters` than the @@ -48,7 +44,7 @@ obs_models="gaussian-diagonal", ) -model.fit(leaspy_dataset, "mcmc_saem", seed=1312, n_iter=100, progress_bar=False) +model.fit(leaspy_data, "mcmc_saem", seed=1312, n_iter=100, progress_bar=False) # %% # First we take a look in the population parameters. @@ -63,7 +59,6 @@ from torch.distributions import Normal - def get_ip(df_leaspy, model): """ leaspy_data : the dataframe with the correct indexing From acdaa87b33a86f57449755a4ceef805e0ca3db03 Mon Sep 17 00:00:00 2001 From: Sebastian Date: Mon, 8 Dec 2025 17:28:51 +0100 Subject: [PATCH 10/24] first try to model.summary(), visible in examples gallery --- examples/plot_01_quickstart.py | 1 + examples/plot_03_joint.py | 1 + examples/plot_04_mixture.py | 1 + src/leaspy/models/base.py | 253 ++++++++++++++++++++++ src/leaspy/models/joint.py | 8 + src/leaspy/models/mcmc_saem_compatible.py | 29 +++ src/leaspy/models/mixture.py | 17 ++ 7 files changed, 310 insertions(+) diff --git a/examples/plot_01_quickstart.py b/examples/plot_01_quickstart.py index 0795579f7..504f23f83 100644 --- a/examples/plot_01_quickstart.py +++ b/examples/plot_01_quickstart.py @@ -65,6 +65,7 @@ n_iter=100, progress_bar=False, ) +model.summary() # %% # Leaspy can also estimate the *individual trajectories* of each participant. diff --git a/examples/plot_03_joint.py b/examples/plot_03_joint.py index a4602e31d..456ea1d1c 100644 --- a/examples/plot_03_joint.py +++ b/examples/plot_03_joint.py @@ -48,6 +48,7 @@ # Once the model is initialized, we can fit it to the data. model.fit(data, "mcmc_saem", seed=1312, n_iter=100, progress_bar=False) +model.summary() # %% diff --git a/examples/plot_04_mixture.py b/examples/plot_04_mixture.py index d3eae551d..8ef5ea3e5 100644 --- a/examples/plot_04_mixture.py +++ b/examples/plot_04_mixture.py @@ -45,6 +45,7 @@ ) model.fit(leaspy_data, "mcmc_saem", seed=1312, n_iter=100, progress_bar=False) +model.summary() # %% # First we take a look in the population parameters. diff --git a/src/leaspy/models/base.py b/src/leaspy/models/base.py index 268b974db..389e991d4 100644 --- a/src/leaspy/models/base.py +++ b/src/leaspy/models/base.py @@ -751,6 +751,259 @@ def fit( ) is None: return algorithm.run(self, dataset) + + def summary(self) -> None: + """Print a formatted summary of the model parameters. + + This method prints a statistical summary of the model. It displays the model's + population parameters (fixed effects), individual parameter + distributions (random effects), and noise estimates. + + Examples + -------- + >>> from leaspy.models import LogisticModel + >>> model = LogisticModel(name="test") + >>> model.fit(data, "mcmc_saem", n_iter=100) + >>> model.summary() + """ + if not self.is_initialized: + print("Model is not initialized. Call fit() first.") + return + + if self.parameters is None or len(self.parameters) == 0: + print("Model has no parameters. Call fit() first.") + return + + lines = [] + sep = "=" * 60 + + # Header + lines.append(sep) + lines.append(f"{'Model Summary':^60}") + lines.append(sep) + lines.append(f"Model Name: {self.name}") + lines.append(f"Model Type: {self.__class__.__name__}") + if self.features is not None: + lines.append(f"Features ({self.dimension}): {', '.join(self.features)}") + if (fm := getattr(self, "fit_metrics", None)) and (nll := fm.get("nll_tot")): + lines.append(f"Neg. Log-Likelihood: {nll:.4f}") + lines.append(sep) + + # Get parameter categories (from model or fallback) + if hasattr(self, "_param_categories"): + cats = self._param_categories + pop_params = cats.get("population", []) + ind_params = cats.get("individual_priors", []) + noise_params = cats.get("noise", []) + else: + # Fallback: all params as population + pop_params = sorted(self.parameters.keys()) + ind_params = [] + noise_params = [] + + # Population Parameters (Fixed Effects) + if pop_params: + lines.append("") + lines.append("Population Parameters") + lines.append("-" * 60) + lines.extend(self._format_parameter_group(pop_params)) + + # Individual Parameters (Random Effects) + if ind_params: + lines.append("") + lines.append("Individual Parameters") + lines.append("-" * 60) + lines.extend(self._format_parameter_group(ind_params)) + + # Noise Model + if noise_params: + lines.append("") + lines.append("Noise Model") + lines.append("-" * 60) + lines.extend(self._format_parameter_group(noise_params)) + + lines.append(sep) + + print("\n".join(lines)) + + def _format_parameter_group(self, param_names: list[str]) -> list[str]: + """Format a group of parameters, consolidating 1D parameters with the same axis. + + This method groups consecutive 1D parameters that share the same axis + to avoid repeating column headers. + + Parameters + ---------- + param_names : :obj:`list` [:obj:`str`] + List of parameter names to format. + + Returns + ------- + :obj:`list` [:obj:`str`] + List of formatted lines. + """ + lines = [] + axes_map = getattr(self, "_param_axes", {}) + + # Group consecutive 1D parameters with the same axis + i = 0 + while i < len(param_names): + name = param_names[i] + value = self.parameters[name] + axes = axes_map.get(name, ()) + + # Check if this is a 1D parameter with axis labels + if value.ndim == 1 and len(axes) >= 1: + axis_name = axes[0] + n = len(value) + + # Collect consecutive 1D params with the same axis and size + group = [(name, value)] + j = i + 1 + while j < len(param_names): + next_name = param_names[j] + next_value = self.parameters[next_name] + next_axes = axes_map.get(next_name, ()) + if (next_value.ndim == 1 and + len(next_axes) >= 1 and + next_axes[0] == axis_name and + len(next_value) == n): + group.append((next_name, next_value)) + j += 1 + else: + break + + # Format the group with a single header + if len(group) > 1: + col_labels = self._get_axis_labels(axis_name, n) + if col_labels: + header = " " * 20 + " ".join(f"{lbl:>8}" for lbl in col_labels) + lines.append(header) + for gname, gvalue in group: + row = f" {gname:<18}" + " ".join( + f"{v.item():>8.4f}" for v in gvalue + ) + lines.append(row) + i = j + continue + + # Default: format individually + lines.append(self._format_parameter(name, value)) + i += 1 + + return lines + + def _format_parameter(self, name: str, value: torch.Tensor) -> str: + """Format a single parameter for display in the summary. + + Uses axis metadata from `_param_axes` (if defined) to create + properly labeled tables for multi-dimensional parameters. + + Parameters + ---------- + name : :obj:`str` + The name of the parameter. + value : :class:`torch.Tensor` + The tensor value of the parameter. + + Returns + ------- + :obj:`str` + A formatted string representation of the parameter. + """ + # Get axis labels if available + axes = getattr(self, "_param_axes", {}).get(name, ()) + + if value.ndim == 0: + # Scalar + val_str = f"{value.item():.4f}" + return f" {name:<18} {val_str}" + + elif value.ndim == 1: + # 1D tensor - format as table with column headers + n = len(value) + if n > 10: + return f" {name:<18} Tensor of shape ({n},)" + + axis_name = axes[0] if len(axes) >= 1 else None + col_labels = self._get_axis_labels(axis_name, n) + + if col_labels: + # Create table format for 1D with labeled columns + header = " " * 20 + " ".join(f"{lbl:>8}" for lbl in col_labels) + values = f" {name:<18}" + " ".join( + f"{v.item():>8.4f}" for v in value + ) + return header + "\n" + values + else: + # Simple list format + val_str = "[" + ", ".join(f"{v.item():.4f}" for v in value) + "]" + return f" {name:<18} {val_str}" + + elif value.ndim == 2: + rows, cols = value.shape + if rows > 8 or cols > 8: + return f" {name:<18} Tensor of shape {tuple(value.shape)}" + + row_axis = axes[0] if len(axes) >= 1 else None + col_axis = axes[1] if len(axes) >= 2 else None + + row_labels = self._get_axis_labels(row_axis, rows) + col_labels = self._get_axis_labels(col_axis, cols) + + lines = [f" {name}:"] + + # Column headers + if col_labels: + header = " " * 12 + " ".join(f"{lbl:>8}" for lbl in col_labels) + lines.append(header) + + # Data rows + for i, row in enumerate(value): + row_lbl = row_labels[i] if row_labels else f"[{i}]" + row_str = f" {row_lbl:<8}" + " ".join(f"{v.item():>8.4f}" for v in row) + lines.append(row_str) + + return "\n".join(lines) + + else: + return f" {name:<18} Tensor of shape {tuple(value.shape)}" + + def _get_axis_labels(self, axis_name: Optional[str], size: int) -> Optional[list[str]]: + """Get human-readable labels for an axis dimension. + + Parameters + ---------- + axis_name : :obj:`str` or None + The semantic name of the axis (e.g., 'feature', 'source', 'cluster'). + size : :obj:`int` + The size of the axis. + + Returns + ------- + :obj:`list` [:obj:`str`] or None + A list of labels, or None if no meaningful labels available. + """ + if axis_name is None: + return None + + if axis_name == "feature": + if hasattr(self, "features") and self.features is not None: + # Use actual feature names (truncated), respecting the actual size + feats = self.features[:size] # In case size < len(features) + return [f[:8] if len(f) <= 8 else f[:7] + "." for f in feats] + return [f"f{i}" for i in range(size)] + elif axis_name == "source": + return [f"s{i}" for i in range(size)] + elif axis_name == "cluster": + return [f"c{i}" for i in range(size)] + elif axis_name == "event": + return [f"e{i}" for i in range(size)] + elif axis_name == "basis": + # For basis vectors (e.g., in betas_mean), use generic indices + return [f"b{i}" for i in range(size)] + else: + return [f"{axis_name[:1]}{i}" for i in range(size)] @staticmethod def _get_dataset( diff --git a/src/leaspy/models/joint.py b/src/leaspy/models/joint.py index 0810d74b8..7f080d9fe 100644 --- a/src/leaspy/models/joint.py +++ b/src/leaspy/models/joint.py @@ -49,6 +49,14 @@ class JointModel(LogisticModel): init_tolerance: float = 0.3 + # Extend axis definitions for JointModel-specific parameters + _param_axes = { + **LogisticModel._param_axes, + "n_log_nu_mean": ("event",), + "log_rho_mean": ("event",), + "zeta_mean": ("source", "event"), + } + def __init__(self, name: str, **kwargs): super().__init__(name, **kwargs) self._configure_observation_models() diff --git a/src/leaspy/models/mcmc_saem_compatible.py b/src/leaspy/models/mcmc_saem_compatible.py index ce32dc8c6..3f450eb4b 100644 --- a/src/leaspy/models/mcmc_saem_compatible.py +++ b/src/leaspy/models/mcmc_saem_compatible.py @@ -60,6 +60,35 @@ class McmcSaemCompatibleModel(StatefulModel): Private instance holding all values for model variables and their derived variables. """ + # Base parameter categories for summary display (override in subclasses) + _individual_prior_params: tuple[str, ...] = () + _noise_params: tuple[str, ...] = ("noise_std",) + + # Explicit axis labels for multi-dimensional parameters + # Maps param_name -> tuple of axis names, e.g., ("feature",) or ("feature", "source") + # Subclasses can extend this with: _param_axes = {**ParentClass._param_axes, "new_param": ("axis",)} + _param_axes: dict[str, tuple[str, ...]] = { + "log_g_mean": ("feature",), + "log_g_std": ("feature",), + "log_v0_mean": ("feature",), + "betas_mean": ("basis", "source"), # basis vectors, not features (dim-1) + "mixing_matrix": ("source", "feature"), + "noise_std": ("feature",), + } + + @property + def _param_categories(self) -> dict[str, list[str]]: + """Categorize parameters for summary display.""" + ind_priors = set(self._individual_prior_params) + noise = set(self._noise_params) + all_params = set(self.parameters.keys()) if self.parameters else set() + pop = all_params - ind_priors - noise + return { + "population": sorted(k for k in pop if k in all_params), + "individual_priors": sorted(k for k in ind_priors if k in all_params), + "noise": sorted(k for k in noise if k in all_params), + } + def __init__( self, name: str, diff --git a/src/leaspy/models/mixture.py b/src/leaspy/models/mixture.py index 4ece300f5..87f371d99 100644 --- a/src/leaspy/models/mixture.py +++ b/src/leaspy/models/mixture.py @@ -78,6 +78,23 @@ class TimeReparametrizedMixtureModel(McmcSaemCompatibleModel): _tau_std = 5.0 _noise_std = 0.1 _sources_mean = 0 + + # Override: in mixture models, tau/xi/sources means define cluster centers (population-level) + _individual_prior_params = ("tau_std", "xi_std", "sources_std") + _noise_params = ("noise_std",) + + # Extend base _param_axes with mixture-specific cluster-indexed parameters + _param_axes = { + **McmcSaemCompatibleModel._param_axes, + "tau_mean": ("cluster",), + "tau_std": ("cluster",), + "xi_mean": ("cluster",), + "xi_std": ("cluster",), + "sources_mean": ("source", "cluster"), + "sources_std": ("source", "cluster"), + "probs": ("cluster",), + } + _sources_std = 1.0 @property From 720b0c4b1775c90dfbbfc8c7531aed5e3464dd9e Mon Sep 17 00:00:00 2001 From: Sebastian Date: Wed, 10 Dec 2025 14:58:48 +0100 Subject: [PATCH 11/24] Implement `model.summary()` for formatted model inspection - Added `summary()` method to `BaseModel` to print a structured overview of model parameters. - Displays metadata including features, sources, clusters, and fit metrics (e.g., log-likelihood). - Categorizes parameters into Population, Individual (Random Effects), and Noise groups. - Implemented smart formatting: - Groups consecutive 1D parameters with shared axes. - Aligns columns for 2D parameters. - Sorts tables by column count to optimize layout. - Added support for ANSI bold text and text wrapping for long lists. --- src/leaspy/models/base.py | 63 +++++++++++++++++------ src/leaspy/models/mcmc_saem_compatible.py | 35 +++++++++++-- src/leaspy/models/mixture.py | 4 +- 3 files changed, 81 insertions(+), 21 deletions(-) diff --git a/src/leaspy/models/base.py b/src/leaspy/models/base.py index 389e991d4..6ffccd82c 100644 --- a/src/leaspy/models/base.py +++ b/src/leaspy/models/base.py @@ -775,18 +775,51 @@ def summary(self) -> None: return lines = [] - sep = "=" * 60 + width = 90 + sep = "=" * width + + # ANSI escape codes for bold text + BOLD = "\033[1m" + RESET = "\033[0m" + + def wrap_text(label: str, text: str, indent: int = 0) -> list[str]: + """Wrap text to fit within the table width.""" + import textwrap + prefix = f"{label}: " if label else "" + initial_indent = " " * indent + prefix + subsequent_indent = " " * (indent + len(prefix)) + wrapper = textwrap.TextWrapper( + width=width, + initial_indent=initial_indent, + subsequent_indent=" " * (indent + 4), # Fixed indent for subsequent lines + break_long_words=False, + break_on_hyphens=False + ) + return wrapper.wrap(text) # Header lines.append(sep) - lines.append(f"{'Model Summary':^60}") + lines.append(f"{BOLD}{'Model Summary':^{width}}{RESET}") lines.append(sep) - lines.append(f"Model Name: {self.name}") - lines.append(f"Model Type: {self.__class__.__name__}") + lines.append(f"{BOLD}Model Name:{RESET} {self.name}") + lines.append(f"{BOLD}Model Type:{RESET} {self.__class__.__name__}") + if self.features is not None: - lines.append(f"Features ({self.dimension}): {', '.join(self.features)}") + feat_str = ", ".join(self.features) + lines.extend(wrap_text(f"{BOLD}Features ({self.dimension}){RESET}", feat_str)) + + # Sources + if hasattr(self, "source_dimension") and self.source_dimension is not None: + sources = [f"Source {i} (s{i})" for i in range(self.source_dimension)] + lines.extend(wrap_text(f"{BOLD}Sources ({self.source_dimension}){RESET}", ", ".join(sources))) + + # Clusters (for Mixture models) + if hasattr(self, "n_clusters") and self.n_clusters is not None: + clusters = [f"Cluster {i} (c{i})" for i in range(self.n_clusters)] + lines.extend(wrap_text(f"{BOLD}Clusters ({self.n_clusters}){RESET}", ", ".join(clusters))) + if (fm := getattr(self, "fit_metrics", None)) and (nll := fm.get("nll_tot")): - lines.append(f"Neg. Log-Likelihood: {nll:.4f}") + lines.append(f"{BOLD}Neg. Log-Likelihood:{RESET} {nll:.4f}") lines.append(sep) # Get parameter categories (from model or fallback) @@ -804,22 +837,22 @@ def summary(self) -> None: # Population Parameters (Fixed Effects) if pop_params: lines.append("") - lines.append("Population Parameters") - lines.append("-" * 60) + lines.append(f"{BOLD}Population Parameters{RESET}") + lines.append("-" * width) lines.extend(self._format_parameter_group(pop_params)) # Individual Parameters (Random Effects) if ind_params: lines.append("") - lines.append("Individual Parameters") - lines.append("-" * 60) + lines.append(f"{BOLD}Individual Parameters{RESET}") + lines.append("-" * width) lines.extend(self._format_parameter_group(ind_params)) # Noise Model if noise_params: lines.append("") - lines.append("Noise Model") - lines.append("-" * 60) + lines.append(f"{BOLD}Noise Model{RESET}") + lines.append("-" * width) lines.extend(self._format_parameter_group(noise_params)) lines.append(sep) @@ -955,13 +988,13 @@ def _format_parameter(self, name: str, value: torch.Tensor) -> str: # Column headers if col_labels: - header = " " * 12 + " ".join(f"{lbl:>8}" for lbl in col_labels) + header = " " * 20 + " ".join(f"{lbl:>8}" for lbl in col_labels) lines.append(header) # Data rows for i, row in enumerate(value): row_lbl = row_labels[i] if row_labels else f"[{i}]" - row_str = f" {row_lbl:<8}" + " ".join(f"{v.item():>8.4f}" for v in row) + row_str = f" {row_lbl:<8}" + " ".join(f"{v.item():>8.4f}" for v in row) lines.append(row_str) return "\n".join(lines) @@ -998,7 +1031,7 @@ def _get_axis_labels(self, axis_name: Optional[str], size: int) -> Optional[list elif axis_name == "cluster": return [f"c{i}" for i in range(size)] elif axis_name == "event": - return [f"e{i}" for i in range(size)] + return None elif axis_name == "basis": # For basis vectors (e.g., in betas_mean), use generic indices return [f"b{i}" for i in range(size)] diff --git a/src/leaspy/models/mcmc_saem_compatible.py b/src/leaspy/models/mcmc_saem_compatible.py index 3f450eb4b..bf0bf9e7d 100644 --- a/src/leaspy/models/mcmc_saem_compatible.py +++ b/src/leaspy/models/mcmc_saem_compatible.py @@ -61,7 +61,17 @@ class McmcSaemCompatibleModel(StatefulModel): """ # Base parameter categories for summary display (override in subclasses) - _individual_prior_params: tuple[str, ...] = () + + _individual_prior_params: tuple[str, ...] = ( + "tau_mean", + "tau_std", + "xi_mean", + "xi_std", + "sources_mean", + "sources_std", + "zeta_mean" + ) + _noise_params: tuple[str, ...] = ("noise_std",) # Explicit axis labels for multi-dimensional parameters @@ -83,10 +93,27 @@ def _param_categories(self) -> dict[str, list[str]]: noise = set(self._noise_params) all_params = set(self.parameters.keys()) if self.parameters else set() pop = all_params - ind_priors - noise + + def sort_key(name: str) -> tuple[int, str, str]: + # Sort by number of columns (ascending), then primary axis, then name + val = self.parameters[name] + axes = self._param_axes.get(name, ()) + primary_axis = axes[0] if axes else "" + + n_cols = 1 + if val.ndim == 1 and axes: + # Check if this axis produces labeled columns + if self._get_axis_labels(primary_axis, len(val)) is not None: + n_cols = len(val) + elif val.ndim == 2: + n_cols = val.shape[1] + + return (n_cols, primary_axis, name) + return { - "population": sorted(k for k in pop if k in all_params), - "individual_priors": sorted(k for k in ind_priors if k in all_params), - "noise": sorted(k for k in noise if k in all_params), + "population": sorted((k for k in pop if k in all_params), key=sort_key), + "individual_priors": sorted((k for k in ind_priors if k in all_params), key=sort_key), + "noise": sorted((k for k in noise if k in all_params), key=sort_key), } def __init__( diff --git a/src/leaspy/models/mixture.py b/src/leaspy/models/mixture.py index 87f371d99..341e74ecb 100644 --- a/src/leaspy/models/mixture.py +++ b/src/leaspy/models/mixture.py @@ -59,7 +59,7 @@ class TimeReparametrizedMixtureModel(McmcSaemCompatibleModel): source_dimension : Optional[:obj:`int`] Number of sources. Dimension of spatial components (default is None). **kwargs: :obj:`dict` - Additional hyperparameters for the model. Must include: + Additional hyperparameters for the model. Must include: - 'n_clusters': int Number of mixture components (must be ≥ 2). - 'dimension' or 'features': int or list @@ -80,7 +80,7 @@ class TimeReparametrizedMixtureModel(McmcSaemCompatibleModel): _sources_mean = 0 # Override: in mixture models, tau/xi/sources means define cluster centers (population-level) - _individual_prior_params = ("tau_std", "xi_std", "sources_std") + _individual_prior_params = ("tau_std", "xi_std", "sources_std", "tau_mean", "xi_mean") _noise_params = ("noise_std",) # Extend base _param_axes with mixture-specific cluster-indexed parameters From b8325c1358268f60dfdbc87879cc277fb9aa9e7f Mon Sep 17 00:00:00 2001 From: Sebastian Date: Fri, 12 Dec 2025 11:46:38 +0100 Subject: [PATCH 12/24] Key changes: - `model.summary()` now returns a feat: Enhance model summary with rich object and programmatic access Refactors `model.summary()` to return a new `Summary` data object instead of printing directly. This provides a richer, more interactive experience. Key changes: - `model.summary()` now returns a object. - The object auto-prints when not assigned to a variable, preserving the old fire-and-forget behavior. - When assigned (), it provides programmatic access to attributes. - A new method allows fetching any model parameter by name. - A method is included to guide users on available attributes (`summary.help()`). - now captures dataset and training metadata. - and now persist and restore this new metadata. - The method was also added to provide a quick overview of model configuration and data statistics. --- src/leaspy/models/base.py | 640 +++++++++++++++++++++++++++++----- src/leaspy/models/settings.py | 5 +- 2 files changed, 556 insertions(+), 89 deletions(-) diff --git a/src/leaspy/models/base.py b/src/leaspy/models/base.py index 6ffccd82c..8bcfe0618 100644 --- a/src/leaspy/models/base.py +++ b/src/leaspy/models/base.py @@ -1,8 +1,10 @@ +import textwrap import warnings from abc import ABC, abstractmethod +from dataclasses import dataclass, field from enum import Enum from pathlib import Path -from typing import Optional, Union +from typing import Any, Optional, Union import numpy as np import pandas as pd @@ -19,9 +21,374 @@ "InitializationMethod", "BaseModel", "ModelInterface", + "Summary", ] +@dataclass +class Summary: + """A structured summary of a Leaspy model. + + Provides programmatic access to model metadata, parameters, and training + statistics. Calling `model.summary()` prints the summary; storing it in a + variable allows access to individual attributes. + + Use `s.help()` to see all available attributes and usage examples. + + Examples + -------- + >>> model.summary() # Prints the formatted summary + >>> s = model.summary() # Store to access attributes + >>> s.algorithm # 'mcmc_saem' + >>> s.seed # 42 + >>> s.n_subjects # 150 + >>> s.get_param('tau_std') # tensor([10.5]) + >>> s.help() # Show all available attributes + """ + + name: str + model_type: str + dimension: Optional[int] = None + features: Optional[list[str]] = None + source_dimension: Optional[int] = None + n_clusters: Optional[int] = None + obs_models: Optional[list[str]] = None + nll: Optional[float] = None + training_info: dict = field(default_factory=dict) + dataset_info: dict = field(default_factory=dict) + parameters: dict[str, dict[str, Any]] = field(default_factory=dict) + leaspy_version: Optional[str] = None + _param_axes: dict = field(default_factory=dict, repr=False) + _feature_names: Optional[list[str]] = field(default=None, repr=False) + _printed: bool = field(default=False, repr=False) + + # Formatting constants + _WIDTH: int = field(default=80, repr=False) + _BOLD: str = field(default="\033[1m", repr=False) + _RESET: str = field(default="\033[0m", repr=False) + + def __post_init__(self): + """Print the summary automatically when created (unless suppressed).""" + pass # Printing is handled in __del__ if not stored + + def __del__(self): + """Print summary if it was never accessed or printed.""" + if not self._printed: + print(str(self)) + + def __repr__(self) -> str: + """Mark as printed and return the formatted string.""" + self._printed = True + return str(self) + + def __getattribute__(self, name: str) -> Any: + """Mark as printed when any public attribute is accessed.""" + # Always use object.__getattribute__ to avoid recursion + value = object.__getattribute__(self, name) + # Mark as printed for public attribute access (not private/dunder) + if not name.startswith("_") and name not in ("help",): + object.__setattr__(self, "_printed", True) + return value + + def __str__(self) -> str: + """Return a formatted string representation of the summary.""" + # Don't mark as printed here - let __repr__ or __del__ handle it + lines = [] + sep = "=" * self._WIDTH + + # Header + lines.append(sep) + lines.append(f"{self._BOLD}{'Model Summary':^{self._WIDTH}}{self._RESET}") + lines.append(sep) + lines.append(f"{self._BOLD}Model Name:{self._RESET} {self.name}") + lines.append(f"{self._BOLD}Model Type:{self._RESET} {self.model_type}") + + if self.features is not None: + feat_str = ", ".join(self.features) + lines.extend(self._wrap_text(f"{self._BOLD}Features ({self.dimension}){self._RESET}", feat_str)) + + if self.source_dimension is not None: + sources = [f"Source {i} (s{i})" for i in range(self.source_dimension)] + lines.extend(self._wrap_text(f"{self._BOLD}Sources ({self.source_dimension}){self._RESET}", ", ".join(sources))) + + if self.n_clusters is not None: + clusters = [f"Cluster {i} (c{i})" for i in range(self.n_clusters)] + lines.extend(self._wrap_text(f"{self._BOLD}Clusters ({self.n_clusters}){self._RESET}", ", ".join(clusters))) + + if self.obs_models: + lines.extend(self._wrap_text(f"{self._BOLD}Observation Models{self._RESET}", ", ".join(self.obs_models))) + + if self.nll is not None: + lines.append(f"{self._BOLD}Neg. Log-Likelihood:{self._RESET} {self.nll:.4f}") + + # Training Metadata + if self.training_info: + lines.append("") + lines.append(f"{self._BOLD}Training Metadata{self._RESET}") + lines.append("-" * self._WIDTH) + ti = self.training_info + lines.append(f"Algorithm: {ti.get('algorithm', 'N/A')}") + if "seed" in ti: + lines.append(f"Seed: {ti['seed']}") + lines.append(f"Iterations: {ti.get('n_iter', 'N/A')}") + if ti.get("converged") is not None: + lines.append(f"Converged: {ti['converged']}") + if "duration" in ti: + lines.append(f"Duration: {ti['duration']}") + + # Data Context + if self.dataset_info: + lines.append("") + lines.append(f"{self._BOLD}Data Context{self._RESET}") + lines.append("-" * self._WIDTH) + di = self.dataset_info + lines.append(f"Subjects: {di.get('n_subjects', 'N/A')}") + lines.append(f"Visits: {di.get('n_visits', 'N/A')}") + lines.append(f"Total Observations: {di.get('n_observations', 'N/A')}") + + # Leaspy Version + if self.leaspy_version: + lines.append("") + lines.append(f"Leaspy Version: {self.leaspy_version}") + + lines.append(sep) + + # Parameters by category + for category, params in self.parameters.items(): + if params: + lines.append("") + lines.append(f"{self._BOLD}{category}{self._RESET}") + lines.append("-" * self._WIDTH) + lines.extend(self._format_parameter_group(params)) + + lines.append(sep) + return "\n".join(lines) + + # ------------------------------------------------------------------------- + # Convenience properties for common attributes + # ------------------------------------------------------------------------- + + @property + def sources(self) -> Optional[list[str]]: + """List of source names (e.g., ['s0', 's1']) or None if not applicable.""" + if self.source_dimension is None: + return None + return [f"s{i}" for i in range(self.source_dimension)] + + @property + def clusters(self) -> Optional[list[str]]: + """List of cluster names (e.g., ['c0', 'c1']) or None if not applicable.""" + if self.n_clusters is None: + return None + return [f"c{i}" for i in range(self.n_clusters)] + + @property + def algorithm(self) -> Optional[str]: + """Name of the algorithm used for training.""" + return self.training_info.get("algorithm") + + @property + def seed(self) -> Optional[int]: + """Random seed used for training.""" + return self.training_info.get("seed") + + @property + def n_iter(self) -> Optional[int]: + """Number of iterations used for training.""" + return self.training_info.get("n_iter") + + @property + def converged(self) -> Optional[bool]: + """Whether the training algorithm converged.""" + return self.training_info.get("converged") + + @property + def n_subjects(self) -> Optional[int]: + """Number of subjects in the training dataset.""" + return self.dataset_info.get("n_subjects") + + @property + def n_visits(self) -> Optional[int]: + """Total number of visits in the training dataset.""" + return self.dataset_info.get("n_visits") + + @property + def n_observations(self) -> Optional[int]: + """Total number of observations in the training dataset.""" + return self.dataset_info.get("n_observations") + + def get_param(self, name: str) -> Optional[Any]: + """Get a parameter value by name, searching across all categories. + + Parameters + ---------- + name : str + The parameter name (e.g., 'betas_mean', 'tau_std', 'noise_std'). + + Returns + ------- + The parameter value (typically a torch.Tensor), or None if not found. + """ + for category_params in self.parameters.values(): + if name in category_params: + return category_params[name] + return None + + def help(self) -> None: + """Print information about available Summary attributes and their meanings.""" + help_text = f""" +{self._BOLD}Summary Help{self._RESET} +{'=' * 60} + +The Summary object provides access to model metadata and parameters. + +{self._BOLD}Usage:{self._RESET} + model.summary() # Print the formatted summary + s = model.summary() # Store to access individual attributes + +{self._BOLD}Available Attributes:{self._RESET} + + {self._BOLD}Model Information:{self._RESET} + name Model name (str) + model_type Model class name, e.g., 'LogisticModel' (str) + dimension Number of features (int) + features List of feature names (list[str]) + sources List of source names, e.g., ['s0', 's1'] (list[str] or None) + clusters List of cluster names, e.g., ['c0', 'c1'] (list[str] or None) + source_dimension Number of sources (int or None) + n_clusters Number of clusters (int or None) + obs_models Observation model names (list[str] or None) + + {self._BOLD}Training:{self._RESET} + algorithm Algorithm name, e.g., 'mcmc_saem' (str) + seed Random seed used (int) + n_iter Number of iterations (int) + converged Whether training converged (bool or None) + nll Negative log-likelihood (float or None) + + {self._BOLD}Dataset:{self._RESET} + n_subjects Number of subjects in training data (int) + n_visits Total number of visits (int) + n_observations Total number of observations (int) + + {self._BOLD}Parameters:{self._RESET} + parameters All parameters grouped by category (dict) + get_param(name) Get a specific parameter by name + + {self._BOLD}Other:{self._RESET} + training_info Full training metadata dict + dataset_info Full dataset statistics dict + leaspy_version Leaspy version used (str) + +{self._BOLD}Note:{self._RESET} + Attributes returning None indicate the model does not support + that feature or the information was not generated. + +{self._BOLD}Examples:{self._RESET} + >>> s = model.summary() + >>> s.algorithm # 'mcmc_saem' + >>> s.seed # 42 + >>> s.n_subjects # 150 + >>> s.get_param('tau_std') # tensor([10.5]) +""" + print(help_text) + self._printed = True + + def _wrap_text(self, label: str, text: str, indent: int = 0) -> list[str]: + """Wrap text to fit within the display width.""" + prefix = f"{label}: " if label else "" + initial_indent = " " * indent + prefix + subsequent_indent = " " * (indent + 4) + wrapper = textwrap.TextWrapper( + width=self._WIDTH, + initial_indent=initial_indent, + subsequent_indent=subsequent_indent, + break_long_words=False, + break_on_hyphens=False + ) + return wrapper.wrap(text) + + def _format_parameter_group(self, params: dict[str, Any]) -> list[str]: + """Format a group of parameters for display.""" + lines = [] + for name, value in params.items(): + if isinstance(value, torch.Tensor): + lines.append(self._format_tensor(name, value)) + else: + lines.append(f" {name:<18} {value}") + return lines + + def _format_tensor(self, name: str, value: torch.Tensor) -> str: + """Format a tensor parameter for display.""" + axes = self._param_axes.get(name, ()) + + if value.ndim == 0: + return f" {name:<18} {value.item():.4f}" + + elif value.ndim == 1: + n = len(value) + if n > 10: + return f" {name:<18} Tensor of shape ({n},)" + + axis_name = axes[0] if len(axes) >= 1 else None + col_labels = self._get_axis_labels(axis_name, n) + + if col_labels: + header = " " * 20 + " ".join(f"{lbl:>8}" for lbl in col_labels) + values = f" {name:<18}" + " ".join(f"{v.item():>8.4f}" for v in value) + return header + "\n" + values + else: + val_str = "[" + ", ".join(f"{v.item():.4f}" for v in value) + "]" + return f" {name:<18} {val_str}" + + elif value.ndim == 2: + rows, cols = value.shape + if rows > 8 or cols > 8: + return f" {name:<18} Tensor of shape {tuple(value.shape)}" + + row_axis = axes[0] if len(axes) >= 1 else None + col_axis = axes[1] if len(axes) >= 2 else None + + row_labels = self._get_axis_labels(row_axis, rows) + col_labels = self._get_axis_labels(col_axis, cols) + + result = [f" {name}:"] + if col_labels: + header = " " * 20 + " ".join(f"{lbl:>8}" for lbl in col_labels) + result.append(header) + + for i, row in enumerate(value): + row_lbl = row_labels[i] if row_labels else f"[{i}]" + row_str = f" {row_lbl:<8}" + " ".join(f"{v.item():>8.4f}" for v in row) + result.append(row_str) + + return "\n".join(result) + + else: + return f" {name:<18} Tensor of shape {tuple(value.shape)}" + + def _get_axis_labels(self, axis_name: Optional[str], size: int) -> Optional[list[str]]: + """Get human-readable labels for an axis dimension.""" + if axis_name is None: + return None + + if axis_name == "feature": + if self._feature_names is not None: + feats = self._feature_names[:size] + return [f[:8] if len(f) <= 8 else f[:7] + "." for f in feats] + return [f"f{i}" for i in range(size)] + elif axis_name == "source": + return [f"s{i}" for i in range(size)] + elif axis_name == "cluster": + return [f"c{i}" for i in range(size)] + elif axis_name == "event": + return None + elif axis_name == "basis": + return [f"b{i}" for i in range(size)] + else: + return None + + class InitializationMethod(str, Enum): """Possible initialization methods for Leaspy models. @@ -388,6 +755,35 @@ def simulate( """ raise NotImplementedError + def _compute_dataset_statistics(self, dataset: Dataset) -> dict: + """Compute descriptive statistics of the dataset used for training.""" + stats = { + "n_subjects": dataset.n_individuals, + "n_scores": dataset.dimension, + "n_visits": dataset.n_visits, + "n_observations": int(dataset.mask.sum().item()), + } + + # Per-subject observations + visits_per_ind = np.array(dataset.n_visits_per_individual) + stats["visits_per_subject"] = { + "median": float(np.median(visits_per_ind)), + "min": int(np.min(visits_per_ind)), + "max": int(np.max(visits_per_ind)), + "iqr": float(np.percentile(visits_per_ind, 75) - np.percentile(visits_per_ind, 25)) + } + + # Missing data (Total possible points - observed points) + total_possible = stats["n_visits"] * stats["n_scores"] + stats["n_missing"] = total_possible - stats["n_observations"] + stats["pct_missing"] = (stats["n_missing"] / total_possible) * 100 if total_possible > 0 else 0.0 + + # Joint model specific + if getattr(dataset, "event_bool", None) is not None: + stats["n_events"] = int(dataset.event_bool.sum().item()) + + return stats + class BaseModel(ModelInterface): """Base model class from which all ``Leaspy`` models should inherit. @@ -403,6 +799,8 @@ def __init__(self, name: str, **kwargs): ) self._features: Optional[list[FeatureType]] = user_provided_features self._dimension: Optional[int] = user_provided_dimension + self.dataset_info = {} + self.training_info = {} self.initialization_method: InitializationMethod = InitializationMethod.DEFAULT if "initialization_method" in kwargs: self.initialization_method = InitializationMethod( @@ -664,6 +1062,8 @@ def to_dict(self, **kwargs) -> KwargsType: "parameters": { k: tensor_to_list(v) for k, v in (self.parameters or {}).items() }, + "dataset_info": self.dataset_info, + "training_info": self.training_info, } @classmethod @@ -692,6 +1092,11 @@ def load(cls, path_to_model_settings: Union[str, Path]): reader = ModelSettings(path_to_model_settings) instance = model_factory(reader.name, **reader.hyperparameters) instance.load_parameters(reader.parameters) + + # Load extra info if available + instance.dataset_info = reader.dataset_info + instance.training_info = reader.training_info + instance._is_initialized = True return instance @@ -750,115 +1155,174 @@ def fit( ) ) is None: return + + # Compute and store dataset statistics + self.dataset_info = self._compute_dataset_statistics(dataset) + + # Store training metadata + self.training_info = { + "algorithm": algorithm.name, + "seed": algorithm.seed, + "n_iter": algorithm.algo_parameters.get("n_iter"), + "converged": getattr(algorithm, "converged", None) # If algorithm tracks this + } + algorithm.run(self, dataset) - def summary(self) -> None: - """Print a formatted summary of the model parameters. - - This method prints a statistical summary of the model. It displays the model's - population parameters (fixed effects), individual parameter - distributions (random effects), and noise estimates. - - Examples - -------- - >>> from leaspy.models import LogisticModel - >>> model = LogisticModel(name="test") - >>> model.fit(data, "mcmc_saem", n_iter=100) - >>> model.summary() - """ - if not self.is_initialized: - print("Model is not initialized. Call fit() first.") - return - - if self.parameters is None or len(self.parameters) == 0: - print("Model has no parameters. Call fit() first.") - return - + def info(self) -> None: + """Print detailed information about the model configuration and training dataset.""" lines = [] - width = 90 + width = 80 sep = "=" * width - - # ANSI escape codes for bold text BOLD = "\033[1m" RESET = "\033[0m" - def wrap_text(label: str, text: str, indent: int = 0) -> list[str]: - """Wrap text to fit within the table width.""" - import textwrap - prefix = f"{label}: " if label else "" - initial_indent = " " * indent + prefix - subsequent_indent = " " * (indent + len(prefix)) - wrapper = textwrap.TextWrapper( - width=width, - initial_indent=initial_indent, - subsequent_indent=" " * (indent + 4), # Fixed indent for subsequent lines - break_long_words=False, - break_on_hyphens=False - ) - return wrapper.wrap(text) - - # Header lines.append(sep) - lines.append(f"{BOLD}{'Model Summary':^{width}}{RESET}") + lines.append(f"{BOLD}{'Model Information':^{width}}{RESET}") lines.append(sep) - lines.append(f"{BOLD}Model Name:{RESET} {self.name}") - lines.append(f"{BOLD}Model Type:{RESET} {self.__class__.__name__}") - if self.features is not None: - feat_str = ", ".join(self.features) - lines.extend(wrap_text(f"{BOLD}Features ({self.dimension}){RESET}", feat_str)) + # Statistical Model Info + lines.append(f"{BOLD}Statistical Model{RESET}") + lines.append("-" * width) + lines.append(f"Type: {self.__class__.__name__}") + lines.append(f"Name: {self.name}") + lines.append(f"Dimension: {self.dimension}") + if hasattr(self, "source_dimension"): + lines.append(f"Source Dimension: {self.source_dimension}") + + # Observation Models + if hasattr(self, "obs_models"): + obs_names = [om.to_string() for om in self.obs_models] + lines.append(f"Observation Models: {', '.join(obs_names)}") + + # Parameter count + if self.parameters: + n_params = sum(p.numel() for p in self.parameters.values()) + lines.append(f"Total Parameters: {n_params}") - # Sources - if hasattr(self, "source_dimension") and self.source_dimension is not None: - sources = [f"Source {i} (s{i})" for i in range(self.source_dimension)] - lines.extend(wrap_text(f"{BOLD}Sources ({self.source_dimension}){RESET}", ", ".join(sources))) - - # Clusters (for Mixture models) if hasattr(self, "n_clusters") and self.n_clusters is not None: - clusters = [f"Cluster {i} (c{i})" for i in range(self.n_clusters)] - lines.extend(wrap_text(f"{BOLD}Clusters ({self.n_clusters}){RESET}", ", ".join(clusters))) - - if (fm := getattr(self, "fit_metrics", None)) and (nll := fm.get("nll_tot")): - lines.append(f"{BOLD}Neg. Log-Likelihood:{RESET} {nll:.4f}") - lines.append(sep) - - # Get parameter categories (from model or fallback) - if hasattr(self, "_param_categories"): - cats = self._param_categories - pop_params = cats.get("population", []) - ind_params = cats.get("individual_priors", []) - noise_params = cats.get("noise", []) - else: - # Fallback: all params as population - pop_params = sorted(self.parameters.keys()) - ind_params = [] - noise_params = [] + clusters = self.n_clusters + lines.append(f"Clusters: {clusters}") - # Population Parameters (Fixed Effects) - if pop_params: - lines.append("") - lines.append(f"{BOLD}Population Parameters{RESET}") - lines.append("-" * width) - lines.extend(self._format_parameter_group(pop_params)) - # Individual Parameters (Random Effects) - if ind_params: + # Dataset Info + if self.dataset_info: lines.append("") - lines.append(f"{BOLD}Individual Parameters{RESET}") + lines.append(f"{BOLD}Training Dataset{RESET}") lines.append("-" * width) - lines.extend(self._format_parameter_group(ind_params)) - - # Noise Model - if noise_params: + di = self.dataset_info + lines.append(f"Subjects: {di.get('n_subjects', 'N/A')}") + lines.append(f"Visits: {di.get('n_visits', 'N/A')}") + lines.append(f"Scores (Features): {di.get('n_scores', 'N/A')}") + lines.append(f"Total Observations: {di.get('n_observations', 'N/A')}") + + if "visits_per_subject" in di: + vps = di["visits_per_subject"] + lines.append(f"Visits per Subject: Median {vps['median']:.1f} [Min {vps['min']}, Max {vps['max']}, IQR {vps['iqr']:.1f}]") + + if "n_missing" in di: + lines.append(f"Missing Data: {di['n_missing']} ({di.get('pct_missing', 0):.2f}%)") + + if "n_events" in di: + lines.append(f"Events Observed: {di['n_events']}") + + # Training Info + if self.training_info: lines.append("") - lines.append(f"{BOLD}Noise Model{RESET}") + lines.append(f"{BOLD}Training Details{RESET}") lines.append("-" * width) - lines.extend(self._format_parameter_group(noise_params)) + ti = self.training_info + lines.append(f"Algorithm: {ti.get('algorithm', 'N/A')}") + if "seed" in ti: + lines.append(f"Seed: {ti['seed']}") + lines.append(f"Iterations: {ti.get('n_iter', 'N/A')}") + if ti.get("converged") is not None: + lines.append(f"Converged: {ti['converged']}") lines.append(sep) - print("\n".join(lines)) + def summary(self) -> Summary: + """Generate a structured summary of the model. + + When called directly (e.g., `model.summary()`), prints a formatted summary. + When stored in a variable, provides programmatic access to model attributes. + + Returns + ------- + :class:`Summary` + A structured summary object. + + Raises + ------ + :exc:`.LeaspyModelInputError` + If the model is not initialized or has no parameters. + + Examples + -------- + >>> model.summary() # Prints the summary + >>> s = model.summary() # Store to access attributes + >>> s.nll # Get specific value + >>> s.help() # Show available attributes + """ + if not self.is_initialized: + raise LeaspyModelInputError("Model is not initialized. Call fit() first.") + + if self.parameters is None or len(self.parameters) == 0: + raise LeaspyModelInputError("Model has no parameters. Call fit() first.") + + # Get NLL if available + nll = None + if (fm := getattr(self, "fit_metrics", None)) and (nll_val := fm.get("nll_tot")): + nll = float(nll_val) + + # Get observation model names if available + obs_model_names = None + if hasattr(self, "obs_models"): + obs_model_names = [om.to_string() for om in self.obs_models] + + # Get leaspy version + try: + from leaspy import __version__ as version + except ImportError: + version = None + + # Build parameters dictionary grouped by category + params_by_category = {} + if hasattr(self, "_param_categories"): + cats = self._param_categories + cat_names = { + "population": "Population Parameters", + "individual_priors": "Individual Parameters", + "noise": "Noise Model", + } + for cat_key, display_name in cat_names.items(): + param_names = cats.get(cat_key, []) + if param_names: + params_by_category[display_name] = { + name: self.parameters[name] for name in param_names if name in self.parameters + } + else: + # Fallback: all params under "Parameters" + params_by_category["Parameters"] = dict(self.parameters) + + return Summary( + name=self.name, + model_type=self.__class__.__name__, + dimension=self.dimension, + features=self.features, + source_dimension=getattr(self, "source_dimension", None), + n_clusters=getattr(self, "n_clusters", None), + obs_models=obs_model_names, + nll=nll, + training_info=dict(self.training_info), + dataset_info=dict(self.dataset_info), + parameters=params_by_category, + leaspy_version=version, + _param_axes=getattr(self, "_param_axes", {}), + _feature_names=self.features, + ) + def _format_parameter_group(self, param_names: list[str]) -> list[str]: """Format a group of parameters, consolidating 1D parameters with the same axis. diff --git a/src/leaspy/models/settings.py b/src/leaspy/models/settings.py index 51183e7cb..8d2bd46d4 100644 --- a/src/leaspy/models/settings.py +++ b/src/leaspy/models/settings.py @@ -50,10 +50,13 @@ def __init__(self, path_to_model_settings_or_dict: Union[str, dict]): ModelSettings._check_settings(settings) self.name: str = settings["name"].lower() self.parameters: KwargsType = settings["parameters"] + self.dataset_info: dict = settings.get("dataset_info", {}) + self.training_info: dict = settings.get("training_info", {}) + self.hyperparameters: KwargsType = { k.lower(): v for k, v in settings.items() - if k not in ("name", "parameters", "hyperparameters", "leaspy_version") + if k not in ("name", "parameters", "hyperparameters", "leaspy_version", "dataset_info", "training_info") } @staticmethod From c42e13c90c5c3653ee801a1ef1bafe048fe49515 Mon Sep 17 00:00:00 2001 From: Sebastian Date: Mon, 15 Dec 2025 14:15:50 +0100 Subject: [PATCH 13/24] Patching the models tests: as we generate new info to use in summary(), the test failed. For now we will just skip testing the from the new keys in this test, TODO: create a separate test for summary() and info() --- tests/functional_tests/api/test_api_fit.py | 8 ++++++++ 1 file changed, 8 insertions(+) diff --git a/tests/functional_tests/api/test_api_fit.py b/tests/functional_tests/api/test_api_fit.py index 054c8842c..ba0df9211 100644 --- a/tests/functional_tests/api/test_api_fit.py +++ b/tests/functional_tests/api/test_api_fit.py @@ -124,6 +124,14 @@ def check_model_consistency( None, ) + # Don't compare training metadata - these are informational and vary by run + # The core model parameters are still fully verified + # TODO (Sebastian): we need to create a separated file to thest model.summary and + # model.info, as this file is centered to the model results. + for key in ["dataset_info", "training_info"]: + model_parameters_new.pop(key, None) + expected_model_parameters.pop(key, None) + self.assertDictAlmostEqual( model_parameters_new, expected_model_parameters, 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@@ def find_var(varname: str, *py_file_paths): ] html_js_files = ["custom.js"] +# Favicon +html_favicon = "_static/favicon.png" + # Theme options are theme-specific and customize the look and feel of a theme # further. For a list of options available for each theme, see the # documentation. From c1c9ebb577862050174064a2822de92f65f499c1 Mon Sep 17 00:00:00 2001 From: Sebastian Date: Mon, 5 Jan 2026 11:45:42 +0100 Subject: [PATCH 15/24] removing sg_execution_times.rst --- docs/sg_execution_times.rst | 49 ------------------------------------- 1 file changed, 49 deletions(-) delete mode 100644 docs/sg_execution_times.rst diff --git a/docs/sg_execution_times.rst b/docs/sg_execution_times.rst deleted file mode 100644 index ef8b3219e..000000000 --- a/docs/sg_execution_times.rst +++ /dev/null @@ -1,49 +0,0 @@ - -:orphan: - -.. _sphx_glr_sg_execution_times: - - -Computation times -================= -**00:37.949** total execution time for 5 files **from all galleries**: - -.. container:: - - .. raw:: html - - - - - - - - .. list-table:: - :header-rows: 1 - :class: table table-striped sg-datatable - - * - Example - - Time - - Mem (MB) - * - :ref:`sphx_glr_auto_examples_plot_01_quickstart.py` (``../examples/plot_01_quickstart.py``) - - 00:32.496 - - 0.0 - * - :ref:`sphx_glr_auto_examples_plot_04_mixture.py` (``../examples/plot_04_mixture.py``) - - 00:05.454 - - 0.0 - * - :ref:`sphx_glr_auto_examples_plot_02_parkinson_example.py` (``../examples/plot_02_parkinson_example.py``) - - 00:00.000 - - 0.0 - * - :ref:`sphx_glr_auto_examples_plot_03_joint.py` (``../examples/plot_03_joint.py``) - - 00:00.000 - - 0.0 - * - :ref:`sphx_glr_auto_examples_plot_05_simulate.py` (``../examples/plot_05_simulate.py``) - - 00:00.000 - - 0.0 From c9a42a7efa786206df4bb0075f8316c1e67858eb Mon Sep 17 00:00:00 2001 From: Sebastian Date: Mon, 23 Feb 2026 15:19:08 +0100 Subject: [PATCH 16/24] solving poetry issue --- poetry.lock | 65 +---------------------------------------------------- 1 file changed, 1 insertion(+), 64 deletions(-) diff --git a/poetry.lock b/poetry.lock index 312846b63..9f22a2817 100644 --- a/poetry.lock +++ b/poetry.lock @@ -167,29 +167,6 @@ charset-normalizer = ["charset-normalizer"] html5lib = ["html5lib"] lxml = ["lxml"] -[[package]] -name = "beautifulsoup4" -version = "4.14.2" -description = "Screen-scraping library" -optional = false -python-versions = ">=3.7.0" -groups = ["docs"] -files = [ - {file = "beautifulsoup4-4.14.2-py3-none-any.whl", hash = "sha256:5ef6fa3a8cbece8488d66985560f97ed091e22bbc4e9c2338508a9d5de6d4515"}, - {file = "beautifulsoup4-4.14.2.tar.gz", hash = "sha256:2a98ab9f944a11acee9cc848508ec28d9228abfd522ef0fad6a02a72e0ded69e"}, -] - -[package.dependencies] -soupsieve = ">1.2" -typing-extensions = ">=4.0.0" - -[package.extras] -cchardet = ["cchardet"] -chardet = ["chardet"] -charset-normalizer = ["charset-normalizer"] -html5lib = ["html5lib"] -lxml = ["lxml"] - [[package]] name = "certifi" version = "2025.4.26" @@ -3088,34 +3065,6 @@ doc = ["ablog (>=0.11.8)", "colorama", "graphviz", "ipykernel", "ipyleaflet", "i i18n = ["Babel", "jinja2"] test = ["pytest", "pytest-cov", "pytest-regressions", "sphinx[test]"] -[[package]] -name = "pydata-sphinx-theme" -version = "0.16.1" -description = "Bootstrap-based Sphinx theme from the PyData community" -optional = false -python-versions = ">=3.9" -groups = ["docs"] -files = [ - {file = "pydata_sphinx_theme-0.16.1-py3-none-any.whl", hash = "sha256:225331e8ac4b32682c18fcac5a57a6f717c4e632cea5dd0e247b55155faeccde"}, - {file = "pydata_sphinx_theme-0.16.1.tar.gz", hash = "sha256:a08b7f0b7f70387219dc659bff0893a7554d5eb39b59d3b8ef37b8401b7642d7"}, -] - -[package.dependencies] -accessible-pygments = "*" -Babel = "*" -beautifulsoup4 = "*" -docutils = "!=0.17.0" -pygments = ">=2.7" -sphinx = ">=6.1" -typing-extensions = "*" - -[package.extras] -a11y = ["pytest-playwright"] -dev = ["pandoc", "pre-commit", "pydata-sphinx-theme[doc,test]", "pyyaml", "sphinx-theme-builder[cli]", "tox"] -doc = ["ablog (>=0.11.8)", "colorama", "graphviz", "ipykernel", "ipyleaflet", "ipywidgets", "jupyter_sphinx", "jupyterlite-sphinx", "linkify-it-py", "matplotlib", "myst-parser", "nbsphinx", "numpy", "numpydoc", "pandas", "plotly", "rich", "sphinx-autoapi (>=3.0.0)", "sphinx-copybutton", "sphinx-design", "sphinx-favicon (>=1.0.1)", "sphinx-sitemap", "sphinx-togglebutton", "sphinxcontrib-youtube (>=1.4.1)", "sphinxext-rediraffe", "xarray"] -i18n = ["Babel", "jinja2"] -test = ["pytest", "pytest-cov", "pytest-regressions", "sphinx[test]"] - [[package]] name = "pygments" version = "2.19.1" @@ -3812,18 +3761,6 @@ files = [ {file = "soupsieve-2.8.tar.gz", hash = "sha256:e2dd4a40a628cb5f28f6d4b0db8800b8f581b65bb380b97de22ba5ca8d72572f"}, ] -[[package]] -name = "soupsieve" -version = "2.8" -description = "A modern CSS selector implementation for Beautiful Soup." -optional = false -python-versions = ">=3.9" -groups = ["docs"] -files = [ - {file = "soupsieve-2.8-py3-none-any.whl", hash = "sha256:0cc76456a30e20f5d7f2e14a98a4ae2ee4e5abdc7c5ea0aafe795f344bc7984c"}, - {file = "soupsieve-2.8.tar.gz", hash = "sha256:e2dd4a40a628cb5f28f6d4b0db8800b8f581b65bb380b97de22ba5ca8d72572f"}, -] - [[package]] name = "sphinx" version = "7.4.7" @@ -4684,4 +4621,4 @@ type = ["pytest-mypy"] [metadata] lock-version = "2.1" python-versions = ">=3.9,<=3.13" -content-hash = "04344c60e681098d672f73d8795863330ec059e0d794efa5a287bedf095a5d17" +content-hash = "9c44eaa747443634fc49d7da729eeb62e8f7d7296359809ef17965419bf3c2eb" From d6adb025931fd5df85a4d0e95efaa8471a0a7bf7 Mon Sep 17 00:00:00 2001 From: Sebastian Date: Mon, 23 Feb 2026 15:47:44 +0100 Subject: [PATCH 17/24] fixing warnings --- docs/conf.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/docs/conf.py b/docs/conf.py index 27d7e4483..30968c7fd 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -134,6 +134,7 @@ def find_var(varname: str, *py_file_paths): "auto_examples/*.ipynb", "auto_examples/*.py.md5", "auto_examples/*.codeobj.json", + "data_summary.ipynb", ] show_warning_types = True @@ -179,8 +180,6 @@ def find_var(varname: str, *py_file_paths): # further. For a list of options available for each theme, see the # documentation. html_theme_options = { - "collapse_navigation": True, - "navigation_depth": 4, # Logo and description # 'description': 'LEArning Spatiotemporal Patterns in Python', # 'logo_name': 'false', From 1105eff27d1284a00da3f7c69b9c03f7905857df Mon Sep 17 00:00:00 2001 From: Sebastian Date: Mon, 23 Feb 2026 15:56:45 +0100 Subject: [PATCH 18/24] fixing readthedocs and test problem? --- .readthedocs.yaml | 4 ++++ pyproject.toml | 2 +- 2 files changed, 5 insertions(+), 1 deletion(-) diff --git a/.readthedocs.yaml b/.readthedocs.yaml index 77d9965f2..b0409be55 100644 --- a/.readthedocs.yaml +++ b/.readthedocs.yaml @@ -10,6 +10,10 @@ build: - poetry config virtualenvs.create false - . "$READTHEDOCS_VIRTUALENV_PATH/bin/activate" && poetry install --with docs - pip freeze + post_install: + # RTD auto-upgrades sphinx (breaks furo/myst-parser <9) and setuptools + # (82+ removes pkg_resources, breaking pybtex). Re-pin to safe versions. + - pip install "sphinx<9" "setuptools<82" sphinx: configuration: docs/conf.py diff --git a/pyproject.toml b/pyproject.toml index 321f4f8ce..3d91dbad7 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -16,7 +16,7 @@ maintainers = [ ] readme = "README.md" packages = [{include = "leaspy", from = "src"}] -requires-python = ">=3.9,<=3.13" +requires-python = ">=3.9,<3.14" keywords = [ "leaspy", "longitudinal", From 5685fe297b42e3502671cb97fbf624a4c58902ca Mon Sep 17 00:00:00 2001 From: Sebastian Date: Mon, 23 Feb 2026 16:20:00 +0100 Subject: [PATCH 19/24] forgot to poetry lock --- poetry.lock | 28 ++++++++++++++-------------- 1 file changed, 14 insertions(+), 14 deletions(-) diff --git a/poetry.lock b/poetry.lock index 9f22a2817..cd55c24d8 100644 --- a/poetry.lock +++ b/poetry.lock @@ -501,7 +501,7 @@ description = "Python library for calculating contours of 2D quadrilateral grids optional = false python-versions = ">=3.10" groups = ["main"] -markers = "python_version >= \"3.11\" and python_version < \"3.13\" or python_full_version == \"3.13.0\"" +markers = "python_version >= \"3.11\"" files = [ {file = "contourpy-1.3.2-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:ba38e3f9f330af820c4b27ceb4b9c7feee5fe0493ea53a8720f4792667465934"}, {file = "contourpy-1.3.2-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:dc41ba0714aa2968d1f8674ec97504a8f7e334f48eeacebcaa6256213acb0989"}, @@ -793,7 +793,7 @@ description = "A platform independent file lock." optional = false python-versions = ">=3.10" groups = ["main"] -markers = "python_version >= \"3.11\" and python_version < \"3.13\" or python_full_version == \"3.13.0\"" +markers = "python_version >= \"3.11\"" files = [ {file = "filelock-3.20.3-py3-none-any.whl", hash = "sha256:4b0dda527ee31078689fc205ec4f1c1bf7d56cf88b6dc9426c4f230e46c2dce1"}, {file = "filelock-3.20.3.tar.gz", hash = "sha256:18c57ee915c7ec61cff0ecf7f0f869936c7c30191bb0cf406f1341778d0834e1"}, @@ -888,7 +888,7 @@ description = "Tools to manipulate font files" optional = false python-versions = ">=3.10" groups = ["main"] -markers = "python_version >= \"3.11\" and python_version < \"3.13\" or python_full_version == \"3.13.0\"" +markers = "python_version >= \"3.11\"" files = [ {file = "fonttools-4.61.1-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:7c7db70d57e5e1089a274cbb2b1fd635c9a24de809a231b154965d415d6c6d24"}, {file = "fonttools-4.61.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:5fe9fd43882620017add5eabb781ebfbc6998ee49b35bd7f8f79af1f9f99a958"}, @@ -1291,7 +1291,7 @@ description = "IPython: Productive Interactive Computing" optional = false python-versions = ">=3.11" groups = ["docs"] -markers = "python_version >= \"3.11\" and python_version < \"3.13\" or python_full_version == \"3.13.0\"" +markers = "python_version >= \"3.11\"" files = [ {file = "ipython-9.2.0-py3-none-any.whl", hash = "sha256:fef5e33c4a1ae0759e0bba5917c9db4eb8c53fee917b6a526bd973e1ca5159f6"}, {file = "ipython-9.2.0.tar.gz", hash = "sha256:62a9373dbc12f28f9feaf4700d052195bf89806279fc8ca11f3f54017d04751b"}, @@ -1325,7 +1325,7 @@ description = "Defines a variety of Pygments lexers for highlighting IPython cod optional = false python-versions = ">=3.8" groups = ["docs"] -markers = "python_version >= \"3.11\" and python_version < \"3.13\" or python_full_version == \"3.13.0\"" +markers = "python_version >= \"3.11\"" files = [ {file = "ipython_pygments_lexers-1.1.1-py3-none-any.whl", hash = "sha256:a9462224a505ade19a605f71f8fa63c2048833ce50abc86768a0d81d876dc81c"}, {file = "ipython_pygments_lexers-1.1.1.tar.gz", hash = "sha256:09c0138009e56b6854f9535736f4171d855c8c08a563a0dcd8022f78355c7e81"}, @@ -1626,7 +1626,7 @@ description = "A fast implementation of the Cassowary constraint solver" optional = false python-versions = ">=3.10" groups = ["main"] -markers = "python_version >= \"3.11\" and python_version < \"3.13\" or python_full_version == \"3.13.0\"" +markers = "python_version >= \"3.11\"" files = [ {file = "kiwisolver-1.4.8-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:88c6f252f6816a73b1f8c904f7bbe02fd67c09a69f7cb8a0eecdbf5ce78e63db"}, {file = "kiwisolver-1.4.8-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:c72941acb7b67138f35b879bbe85be0f6c6a70cab78fe3ef6db9c024d9223e5b"}, @@ -1913,7 +1913,7 @@ description = "Python plotting package" optional = false python-versions = ">=3.10" groups = ["main"] -markers = "python_version >= \"3.11\" and python_version < \"3.13\" or python_full_version == \"3.13.0\"" +markers = "python_version >= \"3.11\"" files = [ {file = "matplotlib-3.10.1-cp310-cp310-macosx_10_12_x86_64.whl", hash = "sha256:ff2ae14910be903f4a24afdbb6d7d3a6c44da210fc7d42790b87aeac92238a16"}, {file = "matplotlib-3.10.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:0721a3fd3d5756ed593220a8b86808a36c5031fce489adb5b31ee6dbb47dd5b2"}, @@ -2094,7 +2094,7 @@ description = "An extended [CommonMark](https://spec.commonmark.org/) compliant optional = false python-versions = ">=3.10" groups = ["docs"] -markers = "python_version >= \"3.11\" and python_version < \"3.13\" or python_full_version == \"3.13.0\"" +markers = "python_version >= \"3.11\"" files = [ {file = "myst_parser-4.0.1-py3-none-any.whl", hash = "sha256:9134e88959ec3b5780aedf8a99680ea242869d012e8821db3126d427edc9c95d"}, {file = "myst_parser-4.0.1.tar.gz", hash = "sha256:5cfea715e4f3574138aecbf7d54132296bfd72bb614d31168f48c477a830a7c4"}, @@ -2199,7 +2199,7 @@ description = "Python package for creating and manipulating graphs and networks" optional = false python-versions = ">=3.10" groups = ["main"] -markers = "python_version >= \"3.11\" and python_version < \"3.13\" or python_full_version == \"3.13.0\"" +markers = "python_version >= \"3.11\"" files = [ {file = "networkx-3.4.2-py3-none-any.whl", hash = "sha256:df5d4365b724cf81b8c6a7312509d0c22386097011ad1abe274afd5e9d3bbc5f"}, {file = "networkx-3.4.2.tar.gz", hash = "sha256:307c3669428c5362aab27c8a1260aa8f47c4e91d3891f48be0141738d8d053e1"}, @@ -2785,7 +2785,7 @@ description = "Python Imaging Library (fork)" optional = false python-versions = ">=3.10" groups = ["main", "docs"] -markers = "python_version >= \"3.11\" and python_version < \"3.13\" or python_full_version == \"3.13.0\"" +markers = "python_version >= \"3.11\"" files = [ {file = "pillow-12.1.0-cp310-cp310-macosx_10_10_x86_64.whl", hash = "sha256:fb125d860738a09d363a88daa0f59c4533529a90e564785e20fe875b200b6dbd"}, {file = "pillow-12.1.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:cad302dc10fac357d3467a74a9561c90609768a6f73a1923b0fd851b6486f8b0"}, @@ -3645,7 +3645,7 @@ description = "Fundamental algorithms for scientific computing in Python" optional = false python-versions = ">=3.10" groups = ["main"] -markers = "python_version >= \"3.11\" and python_version < \"3.13\" or python_full_version == \"3.13.0\"" +markers = "python_version >= \"3.11\"" files = [ {file = "scipy-1.15.2-cp310-cp310-macosx_10_13_x86_64.whl", hash = "sha256:a2ec871edaa863e8213ea5df811cd600734f6400b4af272e1c011e69401218e9"}, {file = "scipy-1.15.2-cp310-cp310-macosx_12_0_arm64.whl", hash = "sha256:6f223753c6ea76983af380787611ae1291e3ceb23917393079dcc746ba60cfb5"}, @@ -3714,7 +3714,7 @@ files = [ {file = "setuptools-80.3.1-py3-none-any.whl", hash = "sha256:ea8e00d7992054c4c592aeb892f6ad51fe1b4d90cc6947cc45c45717c40ec537"}, {file = "setuptools-80.3.1.tar.gz", hash = "sha256:31e2c58dbb67c99c289f51c16d899afedae292b978f8051efaf6262d8212f927"}, ] -markers = {main = "platform_system == \"Linux\" and platform_machine == \"x86_64\" or python_version == \"3.12\" or python_full_version == \"3.13.0\"", docs = "python_version == \"3.12\" or python_full_version == \"3.13.0\""} +markers = {main = "platform_system == \"Linux\" and platform_machine == \"x86_64\" or python_version >= \"3.12\"", docs = "python_version >= \"3.12\""} [package.extras] check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1) ; sys_platform != \"cygwin\"", "ruff (>=0.8.0) ; sys_platform != \"cygwin\""] @@ -4620,5 +4620,5 @@ type = ["pytest-mypy"] [metadata] lock-version = "2.1" -python-versions = ">=3.9,<=3.13" -content-hash = "9c44eaa747443634fc49d7da729eeb62e8f7d7296359809ef17965419bf3c2eb" +python-versions = ">=3.9,<3.14" +content-hash = "99bace1474c942e4a010f7dd77aa1ddeefee6e2609e07804b2534251fe89808e" From f81a1ebe9b07808830dc2d618a207d18a1271133 Mon Sep 17 00:00:00 2001 From: Sebastian Date: Thu, 26 Feb 2026 16:58:22 +0100 Subject: [PATCH 20/24] fixing minor but various visual effects, revert theme: we went back to pydata theme, furo aborted --- .readthedocs.yaml | 2 +- docs/_static/custom.css | 222 ++++++++++++++++++++++---------- docs/_templates/navbar-nav.html | 22 ++++ docs/conf.py | 75 +++++------ docs/index.md | 12 +- examples/plot_01_quickstart.py | 39 +++--- poetry.lock | 36 ++---- pyproject.toml | 2 +- 8 files changed, 253 insertions(+), 157 deletions(-) create mode 100644 docs/_templates/navbar-nav.html diff --git a/.readthedocs.yaml b/.readthedocs.yaml index b0409be55..f74581767 100644 --- a/.readthedocs.yaml +++ b/.readthedocs.yaml @@ -11,7 +11,7 @@ build: - . "$READTHEDOCS_VIRTUALENV_PATH/bin/activate" && poetry install --with docs - pip freeze post_install: - # RTD auto-upgrades sphinx (breaks furo/myst-parser <9) and setuptools + # RTD auto-upgrades sphinx (breaks myst-parser <9) and setuptools # (82+ removes pkg_resources, breaking pybtex). Re-pin to safe versions. - pip install "sphinx<9" "setuptools<82" diff --git a/docs/_static/custom.css b/docs/_static/custom.css index 703806fae..1c546ec34 100644 --- a/docs/_static/custom.css +++ b/docs/_static/custom.css @@ -1,29 +1,118 @@ -.wy-nav-content { - max-width: 96% !important; +/* ============================================================================= + Leaspy custom styles for PyData Sphinx Theme + CSS variables: https://pydata-sphinx-theme.readthedocs.io/en/stable/user_guide/styling.html + ============================================================================= */ + +/* --- Global: slightly larger base font to match upstream v2 look --- */ +html { + --pst-font-size-base: 1.06rem; +} + +/* --- Sphinx-Gallery: hide download note, timing, and download footer --- */ +.sphx-glr-download-link-note, +.sphx-glr-timing, +.sphx-glr-footer { + display: none !important; } + +/* --- Sphinx-Gallery: gallery card thumbnails --- */ .sphx-glr-thumbcontainer .xref.std.std-ref { display: none; } -/* Hide images in the gallery */ .sphx-glr-thumbcontainer img { -display: none !important; + display: none !important; } -/* Reset container height to fit text only */ .sphx-glr-thumbcontainer { min-height: auto !important; height: auto !important; } -/* Set background as white for the sidebar logo and the Leaspy logo in index */ -.sidebar-logo, img[src*="leaspy_logo.png"] { +/* --- Viewcode / highlighted line (hll): pastel blue instead of orange --- */ +html[data-theme="light"] .highlight .hll { + background-color: rgba(80, 150, 220, 0.18) !important; +} +html[data-theme="dark"] .highlight .hll { + background-color: rgba(80, 150, 220, 0.15) !important; +} + +/* --- Viewcode: source code block target highlight (pastel blue) --- */ +html[data-theme="light"] { + --pst-color-target: rgba(80, 150, 220, 0.18); +} +html[data-theme="dark"] { + --pst-color-target: rgba(80, 150, 220, 0.15); +} + +/* --- Code blocks: green left border (input code only) --- */ +div.highlight-Python, +div.highlight-python3, +div.highlight-python { + border-left: 4px solid var(--pst-color-success); +} + +/* --- Sphinx-Gallery: cell borders (code + output) --- */ +/* Input code blocks: rounded grey/white border */ +div.highlight-Python, +div.highlight-python3, +div.highlight-python { + border: 1px solid; + border-left: 4px solid var(--pst-color-success); + border-radius: 6px; + overflow: hidden; +} +html[data-theme="light"] div.highlight-Python, +html[data-theme="light"] div.highlight-python3, +html[data-theme="light"] div.highlight-python { + border-color: #c0c0c0; + border-left-color: var(--pst-color-success); +} +html[data-theme="dark"] div.highlight-Python, +html[data-theme="dark"] div.highlight-python3, +html[data-theme="dark"] div.highlight-python { + border-color: #555; + border-left-color: var(--pst-color-success); +} + +/* Output blocks: rounded grey/white border */ +.sphx-glr-script-out div.highlight-none { + border: 1px solid; + border-radius: 6px; + overflow: hidden; +} +html[data-theme="light"] .sphx-glr-script-out div.highlight-none { + border-color: #989898; +} +html[data-theme="dark"] .sphx-glr-script-out div.highlight-none { + border-color: #555; +} + +/* --- Sphinx-Gallery: script output blocks --- */ +/* Slightly darker bg in light mode, slightly brighter in dark mode */ +html[data-theme="light"] .sphx-glr-script-out .highlight, +html[data-theme="light"] .sphx-glr-script-out pre { + background: #e1e1e1 !important; +} +html[data-theme="dark"] .sphx-glr-script-out .highlight, +html[data-theme="dark"] .sphx-glr-script-out pre { + background: #272626 !important; +} + +.sphx-glr-script-out pre { + color: var(--pst-color-text-base) !important; + font-size: 0.875em; + line-height: 1.5; +} + +/* --- Logo: white background patch for transparency on dark bg --- */ +img[src*="leaspy_logo.png"] { background-color: white; - padding: 5px; /* Adds some space around the logo */ - border-radius: 4px; /* Rounds the corners of the background */ + padding: 5px; + border-radius: 4px; } -/* Pandas DataFrame styling for Furo */ +/* --- Pandas DataFrame styling --- */ table.dataframe { border: none !important; border-collapse: collapse; @@ -31,15 +120,17 @@ table.dataframe { margin-bottom: 1em; width: auto; overflow-x: auto; - display: block; /* Makes it scrollable if wide */ + display: block; } table.dataframe thead { - border-bottom: 1px solid var(--color-border-primary); + border-bottom: 1px solid var(--pst-color-border); vertical-align: bottom; } -table.dataframe tr, table.dataframe th, table.dataframe td { +table.dataframe tr, +table.dataframe th, +table.dataframe td { text-align: right; vertical-align: middle; padding: 0.5em 0.5em; @@ -47,56 +138,59 @@ table.dataframe tr, table.dataframe th, table.dataframe td { white-space: normal; max-width: none; border: none; - color: var(--color-foreground-primary); /* Fix dark mode text */ + color: var(--pst-color-text-base); } table.dataframe th { font-weight: bold; - background: var(--color-background-secondary); /* Header background */ + background: var(--pst-color-surface); } table.dataframe tbody tr:nth-child(odd) { - background: var(--color-background-secondary); /* Zebra striping */ + background: var(--pst-color-surface); } table.dataframe tbody tr:hover { - background: var(--color-background-hover); /* Hover effect */ + background: var(--pst-color-surface); + opacity: 0.85; } -/* --- API Reference Styling --- */ +/* ============================================================================= + API Reference Styling + ============================================================================= */ -/* 1. Class Separation (Big) */ +/* --- 1. Class block separation --- */ dl.py.class { margin-bottom: 4rem !important; padding-bottom: 2rem; - border-bottom: 2px solid var(--color-border-primary); /* Stronger separator */ + border-bottom: 2px solid var(--pst-color-border); } -/* 2. Method/Function Separation (Small) */ -dl.py.method, dl.py.function { +/* --- 2. Method / Function separation --- */ +dl.py.method, +dl.py.function { margin-bottom: 2rem !important; padding-bottom: 1rem; - border-bottom: 1px solid var(--color-border-secondary); /* Lighter separator */ + border-bottom: 1px solid var(--pst-color-border); } -/* Remove the border from the last method to avoid double borders with the class end */ +/* Remove border on last method to avoid double borders with class end */ dl.py.class > dd > dl.py.method:last-child { - border-bottom: none; margin-bottom: 0 !important; padding-bottom: 0; } -/* 3. Signature Highlighting - Split for differentiation */ +/* --- 3. Signature headers --- */ -/* Class Header (Level 1) */ +/* Class header (Level 1) */ dl.py.class > dt { - background-color: var(--color-background-secondary); + background-color: var(--pst-color-surface); padding: 0.8rem; border-radius: 4px; margin-top: 1.5rem; font-weight: bold; - border-left: 5px solid var(--color-brand-primary); /* Brand Color (Blue) */ + border-left: 5px solid var(--pst-color-primary); display: block; text-indent: 0 !important; margin-left: 0 !important; @@ -104,15 +198,15 @@ dl.py.class > dt { box-sizing: border-box; } -/* Method/Function Header (Level 2) */ +/* Method / Function header (Level 2) */ dl.py.function > dt, dl.py.method > dt { - background-color: var(--color-background-secondary); + background-color: var(--pst-color-surface); padding: 0.6rem; border-radius: 4px; margin-top: 1.0rem; font-weight: bold; - border-left: 3px solid var(--color-foreground-primary); /* Darkest/Thickest (Black/Dark Grey) */ + border-left: 3px solid var(--pst-color-text-base); display: block; text-indent: 0 !important; margin-left: 0 !important; @@ -120,31 +214,30 @@ dl.py.method > dt { box-sizing: border-box; } -/* 4. Indentation and Vertical Lines */ +/* --- 4. Body indentation with vertical lines --- */ -/* Level 1: Class Body (Darkest Line, aligned left) */ +/* Class body */ dl.py.class > dd { - margin-left: 0 !important; /* Move line to the left edge */ - padding-left: 1.5rem; /* Keep content indented */ - border-left: 4px solid var(--color-brand-primary); /* Brand Color (Blue) */ + margin-left: 0 !important; + padding-left: 1.5rem; + border-left: 4px solid var(--pst-color-primary); } -/* Level 2: Method/Function Body (Brand Color Line) */ +/* Method / Function body */ dl.py.method > dd, dl.py.function > dd { - margin-left: 0 !important; /* Move line to the left edge */ + margin-left: 0 !important; padding-left: 1.5rem; - border-left: 2px solid var(--color-foreground-primary); + border-left: 2px solid var(--pst-color-text-base); } -/* 5. Styling for Parameters, Returns, Attributes (Field Lists) */ -/* Removed Level 3 indentation/borders as requested, keeping only tight spacing */ +/* --- 5. Parameters / Returns / Attributes field lists --- */ -/* The Headers (e.g. "Parameters", "Returns") - Simple bold, no box */ +/* Section header (e.g. "Parameters", "Returns") */ dl.field-list > dt { font-weight: bold; font-size: 1.0em; - color: var(--color-foreground-primary); + color: var(--pst-color-text-base); margin-top: 1.0rem; margin-bottom: 0.5rem; padding: 0; @@ -152,44 +245,43 @@ dl.field-list > dt { background: none; } -/* Field Lists Content - No vertical line, just simple indentation */ +/* Field list content — no vertical line, simple indent */ dl.field-list > dd { margin-left: 0 !important; padding-left: 1.0rem; - border-left: none; /* Removed the third level line */ + border-left: none; } -/* The Items (e.g. parameter names like "settings", "name") */ +/* Parameter names (monospace, bold) */ dl.field-list > dd > dl > dt { background-color: transparent !important; padding: 0 !important; - margin-top: 0.0rem !important; /* Zero margin */ - margin-bottom: 0.0rem !important; + margin-top: 0 !important; + margin-bottom: 0 !important; font-weight: bold; border: none !important; - color: var(--color-foreground-primary); - font-family: var(--font-stack-monospace); + color: var(--pst-color-text-base); + font-family: var(--pst-font-family-monospace); line-height: 1.2; } -/* The Descriptions (e.g. "Name of the algorithm.") */ +/* Bullet before each parameter name */ +dl.field-list > dd > dl > dt::before { + content: "•"; + color: var(--pst-color-text-muted); + margin-right: 0.5rem; + font-weight: normal; +} + +/* Parameter descriptions */ dl.field-list > dd > dl > dd { - margin-top: 0.0rem !important; - margin-bottom: 0.1rem !important; /* Extremely tight */ + margin-top: 0 !important; + margin-bottom: 0.1rem !important; margin-left: 1.0rem !important; } -/* Remove default margins from paragraphs inside descriptions to fix spacing */ dl.field-list > dd > dl > dd > p { margin-top: 0 !important; - margin-bottom: 0.0rem !important; /* Zero margin */ + margin-bottom: 0 !important; line-height: 1.3; -} - -/* Add a small bullet or marker to parameter names to make them distinct */ -dl.field-list > dd > dl > dt::before { - content: "•"; - color: var(--color-foreground-muted); - margin-right: 0.5rem; - font-weight: normal; -} +} \ No newline at end of file diff --git a/docs/_templates/navbar-nav.html b/docs/_templates/navbar-nav.html new file mode 100644 index 000000000..ee1de80f7 --- /dev/null +++ b/docs/_templates/navbar-nav.html @@ -0,0 +1,22 @@ +{# Navbar navigation links. + On toctree-rooted pages, generate_header_nav_html produces the links. + On orphan pages (e.g. viewcode _modules/), it returns empty — fall back to + a hardcoded list so navigation is always visible. +#} +

 diff --git a/docs/conf.py b/docs/conf.py index 30968c7fd..7e6b980a8 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -50,8 +50,13 @@ def find_var(varname: str, *py_file_paths): "myst_nb", "sphinxcontrib.bibtex", "sphinx.ext.viewcode", + "sphinx_copybutton", ] +# sphinx-copybutton: strip shell prompts from copied text +copybutton_prompt_text = r">>> |\$ " +copybutton_prompt_is_regexp = True + bibtex_bibfiles = ["references.bib"] # -- autoapi configuration --------------------------------------------------- @@ -145,22 +150,17 @@ def find_var(varname: str, *py_file_paths): ] # The name of the Pygments (syntax highlighting) style to use. +# PyData supports separate styles for light and dark mode. highlight_language = "python3" -pygments_style = "sphinx" +pygments_style = "friendly" # light mode +pygments_dark_style = "monokai" # dark mode # -- Options for HTML output ------------------------------------------------- # The theme to use for HTML and HTML Help pages. See the documentation for # a list of builtin themes. # -# ---sphinx-themes----- -# html_theme = 'neo_rtd_theme' -# html_theme_path = [sphinx_theme.get_html_theme_path()] - -# html_theme = 'alabaster' -# html_theme = 'sphinx-theme' -# html_theme = "pydata_sphinx_theme" -html_theme = "furo" +html_theme = "pydata_sphinx_theme" add_function_parentheses = True @@ -176,47 +176,38 @@ def find_var(varname: str, *py_file_paths): # Favicon html_favicon = "_static/favicon.png" -# Theme options are theme-specific and customize the look and feel of a theme -# further. For a list of options available for each theme, see the -# documentation. +# Theme options html_theme_options = { - # Logo and description - # 'description': 'LEArning Spatiotemporal Patterns in Python', - # 'logo_name': 'false', - # 'logo_text_align': 'center', - # GitHub stuff - # 'github_banner': 'true', - # 'github_repo': 'pyts', - # 'github_type': 'star', - # 'github_user': 'johannfaouzi', - # Page and sidebar widths - # 'page_width': '1300px', - "body_max_width": "1000px", - # 'sidebar_width': '250px', - # Related links - # 'show_related': 'true', - # 'show_relbar_bottom': 'true', - # Font sizes - # 'font_size': '15px', - # 'code_font_size': '13px' + # Navbar + "navbar_align": "left", + "navbar_center": ["navbar-nav"], + "navbar_end": ["theme-switcher", "navbar-icon-links"], + "header_links_before_dropdown": 6, + # Secondary (right) sidebar + "secondary_sidebar_items": ["page-toc"], + "show_toc_level": 2, + # Logo + "logo": { + "image_light": "_static/images/leaspy_logo.png", + "image_dark": "_static/images/leaspy_logo.png", + }, + # GitHub icon in navbar + "icon_links": [ + { + "name": "GitHub", + "url": "https://github.com/aramis-lab/leaspy", + "icon": "fa-brands fa-github", + } + ], } html_context = { - "display_github": True, - "github_url": "https://github.com", "github_user": "aramis-lab", "github_repo": "leaspy", - "github_version": "v2/", - "conf_py_path": "/docs/", + "github_version": "v2", + "doc_path": "docs", } -# Custom CSS files -# html_css_files = [ -# 'custom.css', -# ]# The name of an image file (relative to this directory) to place at the top -# of the sidebar. -html_logo = "_static/images/leaspy_logo.png" - html_title = "Leaspy" # A shorter title for the navigation bar. Default is the same as html_title. html_short_title = "Leaspy documentation" diff --git a/docs/index.md b/docs/index.md index e3d229003..a3007c7c9 100644 --- a/docs/index.md +++ b/docs/index.md @@ -74,10 +74,18 @@ auto_examples/index :maxdepth: 3 user_guide +reference/api/index +changelog +references +``` + +```{toctree} + :hidden: + :caption: More + :maxdepth: 2 + glossary notations -references -changelog to_go_further license ``` diff --git a/examples/plot_01_quickstart.py b/examples/plot_01_quickstart.py index 504f23f83..a7dcd8f6a 100644 --- a/examples/plot_01_quickstart.py +++ b/examples/plot_01_quickstart.py @@ -24,31 +24,31 @@ # %% -# ```{warning} -# You **MUST** include both `ID` and `TIME`, either as indices or as columns. -# The remaining columns should correspond to the observed variables -# (also called features or endpoints). -# Each feature should have its own column, and each visit should occupy one row. -# ``` +# .. warning:: +# +# You **MUST** include both ``ID`` and ``TIME``, either as indices or as columns. +# The remaining columns should correspond to the observed variables +# (also called features or endpoints). +# Each feature should have its own column, and each visit should occupy one row. # %% -# ```{warning} -# - Leaspy supports *linear* and *logistic* models. -# - The features **MUST** be increasing over time. -# - For logistic models, data must be rescaled between 0 and 1. -# ``` +# .. warning:: +# +# - Leaspy supports *linear* and *logistic* models. +# - The features **MUST** be increasing over time. +# - For logistic models, data must be rescaled between 0 and 1. from leaspy.io.data import Data data = Data.from_dataframe(alzheimer_df) # %% -# ```{seealso} -# For a deeper understanding of the `Data` and `Dataset` classes, including -# iteration, cofactors, and best practices, see the -# [Data Containers Guide](../data_summary.ipynb). -# ``` +# .. seealso:: +# +# For a deeper understanding of the ``Data`` and ``Dataset`` classes, including +# iteration, cofactors, and best practices, refer to the Data Containers Guide +# in the documentation. # %% # The core functionality of Leaspy is to estimate the group-average trajectory @@ -79,10 +79,11 @@ # %% # We have seen how to fit a model and personalize it to individuals. # Leaspy also provides various plotting functions to visualize the results. -# Let's go to the next [section](./plot_02_parkinson_example) to see how to plot +# Let's go to the next :doc:`section ` to see how to plot # the group-average trajectory and the individual trajectories using the Parkinson's disease dataset. + # %% # To go further: # -# 1. See the [User Guide](../user_guide.md) and full API documentation. -# 2. Explore additional [examples](./index.rst). +# 1. See the :doc:`User Guide <../user_guide>` and full API documentation. +# 2. Explore additional :doc:`examples <./index>`. diff --git a/poetry.lock b/poetry.lock index cd55c24d8..039825755 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1022,25 +1022,6 @@ test-downstream = ["aiobotocore (>=2.5.4,<3.0.0)", "dask[dataframe,test]", "moto test-full = ["adlfs", "aiohttp (!=4.0.0a0,!=4.0.0a1)", "cloudpickle", "dask", "distributed", "dropbox", "dropboxdrivefs", "fastparquet", "fusepy", "gcsfs", "jinja2", "kerchunk", "libarchive-c", "lz4", "notebook", "numpy", "ocifs", "pandas", "panel", "paramiko", "pyarrow", "pyarrow (>=1)", "pyftpdlib", "pygit2", "pytest", "pytest-asyncio (!=0.22.0)", "pytest-benchmark", "pytest-cov", "pytest-mock", "pytest-recording", "pytest-rerunfailures", "python-snappy", "requests", "smbprotocol", "tqdm", "urllib3", "zarr", "zstandard"] tqdm = ["tqdm"] -[[package]] -name = "furo" -version = "2025.9.25" -description = "A clean customisable Sphinx documentation theme." -optional = false -python-versions = ">=3.8" -groups = ["docs"] -files = [ - {file = "furo-2025.9.25-py3-none-any.whl", hash = "sha256:2937f68e823b8e37b410c972c371bc2b1d88026709534927158e0cb3fac95afe"}, - {file = "furo-2025.9.25.tar.gz", hash = "sha256:3eac05582768fdbbc2bdfa1cdbcdd5d33cfc8b4bd2051729ff4e026a1d7e0a98"}, -] - -[package.dependencies] -accessible-pygments = ">=0.0.5" -beautifulsoup4 = "*" -pygments = ">=2.7" -sphinx = ">=6.0,<9.0" -sphinx-basic-ng = ">=1.0.0.beta2" - [[package]] name = "greenlet" version = "3.2.1" @@ -3841,22 +3822,23 @@ numpy = ["nptyping (>=2.5)"] testing = ["covdefaults (>=2.3)", "coverage (>=7.4.4)", "defusedxml (>=0.7.1)", "diff-cover (>=9)", "pytest (>=8.1.1)", "pytest-cov (>=5)", "sphobjinv (>=2.3.1)", "typing-extensions (>=4.11)"] [[package]] -name = "sphinx-basic-ng" -version = "1.0.0b2" -description = "A modern skeleton for Sphinx themes." +name = "sphinx-copybutton" +version = "0.5.2" +description = "Add a copy button to each of your code cells." optional = false python-versions = ">=3.7" groups = ["docs"] files = [ - {file = "sphinx_basic_ng-1.0.0b2-py3-none-any.whl", hash = "sha256:eb09aedbabfb650607e9b4b68c9d240b90b1e1be221d6ad71d61c52e29f7932b"}, - {file = "sphinx_basic_ng-1.0.0b2.tar.gz", hash = "sha256:9ec55a47c90c8c002b5960c57492ec3021f5193cb26cebc2dc4ea226848651c9"}, + {file = "sphinx-copybutton-0.5.2.tar.gz", hash = "sha256:4cf17c82fb9646d1bc9ca92ac280813a3b605d8c421225fd9913154103ee1fbd"}, + {file = "sphinx_copybutton-0.5.2-py3-none-any.whl", hash = "sha256:fb543fd386d917746c9a2c50360c7905b605726b9355cd26e9974857afeae06e"}, ] [package.dependencies] -sphinx = ">=4.0" +sphinx = ">=1.8" [package.extras] -docs = ["furo", "ipython", "myst-parser", "sphinx-copybutton", "sphinx-inline-tabs"] +code-style = ["pre-commit (==2.12.1)"] +rtd = ["ipython", "myst-nb", "sphinx", "sphinx-book-theme", "sphinx-examples"] [[package]] name = "sphinx-gallery" @@ -4621,4 +4603,4 @@ type = ["pytest-mypy"] [metadata] lock-version = "2.1" python-versions = ">=3.9,<3.14" -content-hash = "99bace1474c942e4a010f7dd77aa1ddeefee6e2609e07804b2534251fe89808e" +content-hash = "b56744a4283022f6f0f7c21fa58284b73750d4233834ff907ffbc63fcbfb522c" diff --git a/pyproject.toml b/pyproject.toml index 3d91dbad7..c1e3fa8aa 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -72,7 +72,7 @@ sphinx-autoapi = "*" myst-nb = "*" sphinxcontrib-bibtex = "*" pydata-sphinx-theme = "^0.16.1" -furo = "^2025.9.25" +sphinx-copybutton = "*" # Security fixes - prevent downgrades urllib3 = ">=2.6.3" requests = ">=2.32.5" From e830d1692775e90350de23321e9a911b9004d5ca Mon Sep 17 00:00:00 2001 From: Sebastian Date: Mon, 2 Mar 2026 11:51:50 +0100 Subject: [PATCH 21/24] fixing UI --- docs/_static/custom.css | 105 ++++++++++++---------------------------- 1 file changed, 32 insertions(+), 73 deletions(-) diff --git a/docs/_static/custom.css b/docs/_static/custom.css index 1c546ec34..0c52cdcb2 100644 --- a/docs/_static/custom.css +++ b/docs/_static/custom.css @@ -3,57 +3,44 @@ CSS variables: https://pydata-sphinx-theme.readthedocs.io/en/stable/user_guide/styling.html ============================================================================= */ -/* --- Global: slightly larger base font to match upstream v2 look --- */ -html { - --pst-font-size-base: 1.06rem; +/* --- Viewcode: pastel blue highlights (hll lines + target blocks) --- */ +html[data-theme="light"] { + --pst-color-target: rgba(80, 150, 220, 0.18); +} +html[data-theme="dark"] { + --pst-color-target: rgba(80, 150, 220, 0.15); +} +html[data-theme="light"] .highlight .hll { + background-color: rgba(80, 150, 220, 0.18) !important; +} +html[data-theme="dark"] .highlight .hll { + background-color: rgba(80, 150, 220, 0.15) !important; } -/* --- Sphinx-Gallery: hide download note, timing, and download footer --- */ +/* ============================================================================= + Sphinx-Gallery + ============================================================================= */ + +/* Hide download note, timing, and download footer */ .sphx-glr-download-link-note, .sphx-glr-timing, .sphx-glr-footer { display: none !important; } -/* --- Sphinx-Gallery: gallery card thumbnails --- */ +/* Gallery card thumbnails: text only, no images */ .sphx-glr-thumbcontainer .xref.std.std-ref { display: none; } - .sphx-glr-thumbcontainer img { display: none !important; } - .sphx-glr-thumbcontainer { min-height: auto !important; height: auto !important; } -/* --- Viewcode / highlighted line (hll): pastel blue instead of orange --- */ -html[data-theme="light"] .highlight .hll { - background-color: rgba(80, 150, 220, 0.18) !important; -} -html[data-theme="dark"] .highlight .hll { - background-color: rgba(80, 150, 220, 0.15) !important; -} - -/* --- Viewcode: source code block target highlight (pastel blue) --- */ -html[data-theme="light"] { - --pst-color-target: rgba(80, 150, 220, 0.18); -} -html[data-theme="dark"] { - --pst-color-target: rgba(80, 150, 220, 0.15); -} - -/* --- Code blocks: green left border (input code only) --- */ -div.highlight-Python, -div.highlight-python3, -div.highlight-python { - border-left: 4px solid var(--pst-color-success); -} - -/* --- Sphinx-Gallery: cell borders (code + output) --- */ -/* Input code blocks: rounded grey/white border */ +/* Input code blocks: green left border + grey/white outline */ div.highlight-Python, div.highlight-python3, div.highlight-python { @@ -75,7 +62,7 @@ html[data-theme="dark"] div.highlight-python { border-left-color: var(--pst-color-success); } -/* Output blocks: rounded grey/white border */ +/* Output blocks: grey/white outline, no green bar, tinted background */ .sphx-glr-script-out div.highlight-none { border: 1px solid; border-radius: 6px; @@ -87,9 +74,6 @@ html[data-theme="light"] .sphx-glr-script-out div.highlight-none { html[data-theme="dark"] .sphx-glr-script-out div.highlight-none { border-color: #555; } - -/* --- Sphinx-Gallery: script output blocks --- */ -/* Slightly darker bg in light mode, slightly brighter in dark mode */ html[data-theme="light"] .sphx-glr-script-out .highlight, html[data-theme="light"] .sphx-glr-script-out pre { background: #e1e1e1 !important; @@ -98,21 +82,24 @@ html[data-theme="dark"] .sphx-glr-script-out .highlight, html[data-theme="dark"] .sphx-glr-script-out pre { background: #272626 !important; } - .sphx-glr-script-out pre { color: var(--pst-color-text-base) !important; font-size: 0.875em; line-height: 1.5; } -/* --- Logo: white background patch for transparency on dark bg --- */ +/* ============================================================================= + Misc + ============================================================================= */ + +/* Logo: white background patch for transparency on dark bg */ img[src*="leaspy_logo.png"] { background-color: white; padding: 5px; border-radius: 4px; } -/* --- Pandas DataFrame styling --- */ +/* Pandas DataFrame styling */ table.dataframe { border: none !important; border-collapse: collapse; @@ -122,34 +109,29 @@ table.dataframe { overflow-x: auto; display: block; } - table.dataframe thead { border-bottom: 1px solid var(--pst-color-border); vertical-align: bottom; } - table.dataframe tr, table.dataframe th, table.dataframe td { text-align: right; vertical-align: middle; - padding: 0.5em 0.5em; + padding: 0.5em; line-height: normal; white-space: normal; max-width: none; border: none; color: var(--pst-color-text-base); } - table.dataframe th { font-weight: bold; background: var(--pst-color-surface); } - table.dataframe tbody tr:nth-child(odd) { background: var(--pst-color-surface); } - table.dataframe tbody tr:hover { background: var(--pst-color-surface); opacity: 0.85; @@ -159,31 +141,27 @@ table.dataframe tbody tr:hover { API Reference Styling ============================================================================= */ -/* --- 1. Class block separation --- */ +/* 1. Class block separation */ dl.py.class { margin-bottom: 4rem !important; padding-bottom: 2rem; border-bottom: 2px solid var(--pst-color-border); } -/* --- 2. Method / Function separation --- */ +/* 2. Method / Function separation */ dl.py.method, dl.py.function { margin-bottom: 2rem !important; padding-bottom: 1rem; border-bottom: 1px solid var(--pst-color-border); } - -/* Remove border on last method to avoid double borders with class end */ dl.py.class > dd > dl.py.method:last-child { border-bottom: none; margin-bottom: 0 !important; padding-bottom: 0; } -/* --- 3. Signature headers --- */ - -/* Class header (Level 1) */ +/* 3. Signature headers */ dl.py.class > dt { background-color: var(--pst-color-surface); padding: 0.8rem; @@ -197,8 +175,6 @@ dl.py.class > dt { width: 100%; box-sizing: border-box; } - -/* Method / Function header (Level 2) */ dl.py.function > dt, dl.py.method > dt { background-color: var(--pst-color-surface); @@ -214,16 +190,12 @@ dl.py.method > dt { box-sizing: border-box; } -/* --- 4. Body indentation with vertical lines --- */ - -/* Class body */ +/* 4. Body indentation with vertical lines */ dl.py.class > dd { margin-left: 0 !important; padding-left: 1.5rem; border-left: 4px solid var(--pst-color-primary); } - -/* Method / Function body */ dl.py.method > dd, dl.py.function > dd { margin-left: 0 !important; @@ -231,12 +203,9 @@ dl.py.function > dd { border-left: 2px solid var(--pst-color-text-base); } -/* --- 5. Parameters / Returns / Attributes field lists --- */ - -/* Section header (e.g. "Parameters", "Returns") */ +/* 5. Parameters / Returns / Attributes field lists */ dl.field-list > dt { font-weight: bold; - font-size: 1.0em; color: var(--pst-color-text-base); margin-top: 1.0rem; margin-bottom: 0.5rem; @@ -244,15 +213,10 @@ dl.field-list > dt { border: none; background: none; } - -/* Field list content — no vertical line, simple indent */ dl.field-list > dd { margin-left: 0 !important; padding-left: 1.0rem; - border-left: none; } - -/* Parameter names (monospace, bold) */ dl.field-list > dd > dl > dt { background-color: transparent !important; padding: 0 !important; @@ -264,22 +228,17 @@ dl.field-list > dd > dl > dt { font-family: var(--pst-font-family-monospace); line-height: 1.2; } - -/* Bullet before each parameter name */ dl.field-list > dd > dl > dt::before { content: "•"; color: var(--pst-color-text-muted); margin-right: 0.5rem; font-weight: normal; } - -/* Parameter descriptions */ dl.field-list > dd > dl > dd { margin-top: 0 !important; margin-bottom: 0.1rem !important; margin-left: 1.0rem !important; } - dl.field-list > dd > dl > dd > p { margin-top: 0 !important; margin-bottom: 0 !important; From fa8a35435784fbbde598de57e38c9977bf7bec8d Mon Sep 17 00:00:00 2001 From: Sebastian Date: Mon, 2 Mar 2026 14:01:33 +0100 Subject: [PATCH 22/24] refactor(models): extract summary and info logic to dedicated module This commit addresses structural concerns by moving the newly added summary and information features out of the core `base.py` file, keeping the original source code as clean and untouched as possible. Key changes: - Extracted Summary and Info dataclasses into a new summary.py module. - Moved all formatting logic out of BaseModel and into the new module. - Created AutoPrintMixin to standardize the auto-display behavior across both Info and Summary. - Updated BaseModel.fit() to calculate and store training loop duration. - Replaced dictionary annotations with DatasetInfo and TrainingInfo TypedDicts. - Removed unused imports. --- src/leaspy/models/base.py | 683 ++------------------ src/leaspy/models/mcmc_saem_compatible.py | 3 +- src/leaspy/models/settings.py | 6 +- src/leaspy/models/summary.py | 719 ++++++++++++++++++++++ 4 files changed, 785 insertions(+), 626 deletions(-) create mode 100644 src/leaspy/models/summary.py diff --git a/src/leaspy/models/base.py b/src/leaspy/models/base.py index be281b66f..469c219d3 100644 --- a/src/leaspy/models/base.py +++ b/src/leaspy/models/base.py @@ -1,10 +1,9 @@ -import textwrap +import time import warnings from abc import ABC, abstractmethod -from dataclasses import dataclass, field from enum import Enum from pathlib import Path -from typing import Any, Optional, Union +from typing import Optional, Union import numpy as np import pandas as pd @@ -17,378 +16,15 @@ from leaspy.io.outputs.result import Result from leaspy.utils.typing import DictParamsTorch, FeatureType, IDType, KwargsType +from .summary import DatasetInfo, Info, Summary, TrainingInfo + __all__ = [ "InitializationMethod", "BaseModel", "ModelInterface", - "Summary", ] -@dataclass -class Summary: - """A structured summary of a Leaspy model. - - Provides programmatic access to model metadata, parameters, and training - statistics. Calling `model.summary()` prints the summary; storing it in a - variable allows access to individual attributes. - - Use `s.help()` to see all available attributes and usage examples. - - Examples - -------- - >>> model.summary() # Prints the formatted summary - >>> s = model.summary() # Store to access attributes - >>> s.algorithm # 'mcmc_saem' - >>> s.seed # 42 - >>> s.n_subjects # 150 - >>> s.get_param('tau_std') # tensor([10.5]) - >>> s.help() # Show all available attributes - """ - - name: str - model_type: str - dimension: Optional[int] = None - features: Optional[list[str]] = None - source_dimension: Optional[int] = None - n_clusters: Optional[int] = None - obs_models: Optional[list[str]] = None - nll: Optional[float] = None - training_info: dict = field(default_factory=dict) - dataset_info: dict = field(default_factory=dict) - parameters: dict[str, dict[str, Any]] = field(default_factory=dict) - leaspy_version: Optional[str] = None - _param_axes: dict = field(default_factory=dict, repr=False) - _feature_names: Optional[list[str]] = field(default=None, repr=False) - _printed: bool = field(default=False, repr=False) - - # Formatting constants - _WIDTH: int = field(default=80, repr=False) - _BOLD: str = field(default="\033[1m", repr=False) - _RESET: str = field(default="\033[0m", repr=False) - - def __post_init__(self): - """Print the summary automatically when created (unless suppressed).""" - pass # Printing is handled in __del__ if not stored - - def __del__(self): - """Print summary if it was never accessed or printed.""" - if not self._printed: - print(str(self)) - - def __repr__(self) -> str: - """Mark as printed and return the formatted string.""" - self._printed = True - return str(self) - - def __getattribute__(self, name: str) -> Any: - """Mark as printed when any public attribute is accessed.""" - # Always use object.__getattribute__ to avoid recursion - value = object.__getattribute__(self, name) - # Mark as printed for public attribute access (not private/dunder) - if not name.startswith("_") and name not in ("help",): - object.__setattr__(self, "_printed", True) - return value - - def __str__(self) -> str: - """Return a formatted string representation of the summary.""" - # Don't mark as printed here - let __repr__ or __del__ handle it - lines = [] - sep = "=" * self._WIDTH - - # Header - lines.append(sep) - lines.append(f"{self._BOLD}{'Model Summary':^{self._WIDTH}}{self._RESET}") - lines.append(sep) - lines.append(f"{self._BOLD}Model Name:{self._RESET} {self.name}") - lines.append(f"{self._BOLD}Model Type:{self._RESET} {self.model_type}") - - if self.features is not None: - feat_str = ", ".join(self.features) - lines.extend(self._wrap_text(f"{self._BOLD}Features ({self.dimension}){self._RESET}", feat_str)) - - if self.source_dimension is not None: - sources = [f"Source {i} (s{i})" for i in range(self.source_dimension)] - lines.extend(self._wrap_text(f"{self._BOLD}Sources ({self.source_dimension}){self._RESET}", ", ".join(sources))) - - if self.n_clusters is not None: - clusters = [f"Cluster {i} (c{i})" for i in range(self.n_clusters)] - lines.extend(self._wrap_text(f"{self._BOLD}Clusters ({self.n_clusters}){self._RESET}", ", ".join(clusters))) - - if self.obs_models: - lines.extend(self._wrap_text(f"{self._BOLD}Observation Models{self._RESET}", ", ".join(self.obs_models))) - - if self.nll is not None: - lines.append(f"{self._BOLD}Neg. Log-Likelihood:{self._RESET} {self.nll:.4f}") - - # Training Metadata - if self.training_info: - lines.append("") - lines.append(f"{self._BOLD}Training Metadata{self._RESET}") - lines.append("-" * self._WIDTH) - ti = self.training_info - lines.append(f"Algorithm: {ti.get('algorithm', 'N/A')}") - if "seed" in ti: - lines.append(f"Seed: {ti['seed']}") - lines.append(f"Iterations: {ti.get('n_iter', 'N/A')}") - if ti.get("converged") is not None: - lines.append(f"Converged: {ti['converged']}") - if "duration" in ti: - lines.append(f"Duration: {ti['duration']}") - - # Data Context - if self.dataset_info: - lines.append("") - lines.append(f"{self._BOLD}Data Context{self._RESET}") - lines.append("-" * self._WIDTH) - di = self.dataset_info - lines.append(f"Subjects: {di.get('n_subjects', 'N/A')}") - lines.append(f"Visits: {di.get('n_visits', 'N/A')}") - lines.append(f"Total Observations: {di.get('n_observations', 'N/A')}") - - # Leaspy Version - if self.leaspy_version: - lines.append("") - lines.append(f"Leaspy Version: {self.leaspy_version}") - - lines.append(sep) - - # Parameters by category - for category, params in self.parameters.items(): - if params: - lines.append("") - lines.append(f"{self._BOLD}{category}{self._RESET}") - lines.append("-" * self._WIDTH) - lines.extend(self._format_parameter_group(params)) - - lines.append(sep) - return "\n".join(lines) - - # ------------------------------------------------------------------------- - # Convenience properties for common attributes - # ------------------------------------------------------------------------- - - @property - def sources(self) -> Optional[list[str]]: - """List of source names (e.g., ['s0', 's1']) or None if not applicable.""" - if self.source_dimension is None: - return None - return [f"s{i}" for i in range(self.source_dimension)] - - @property - def clusters(self) -> Optional[list[str]]: - """List of cluster names (e.g., ['c0', 'c1']) or None if not applicable.""" - if self.n_clusters is None: - return None - return [f"c{i}" for i in range(self.n_clusters)] - - @property - def algorithm(self) -> Optional[str]: - """Name of the algorithm used for training.""" - return self.training_info.get("algorithm") - - @property - def seed(self) -> Optional[int]: - """Random seed used for training.""" - return self.training_info.get("seed") - - @property - def n_iter(self) -> Optional[int]: - """Number of iterations used for training.""" - return self.training_info.get("n_iter") - - @property - def converged(self) -> Optional[bool]: - """Whether the training algorithm converged.""" - return self.training_info.get("converged") - - @property - def n_subjects(self) -> Optional[int]: - """Number of subjects in the training dataset.""" - return self.dataset_info.get("n_subjects") - - @property - def n_visits(self) -> Optional[int]: - """Total number of visits in the training dataset.""" - return self.dataset_info.get("n_visits") - - @property - def n_observations(self) -> Optional[int]: - """Total number of observations in the training dataset.""" - return self.dataset_info.get("n_observations") - - def get_param(self, name: str) -> Optional[Any]: - """Get a parameter value by name, searching across all categories. - - Parameters - ---------- - name : str - The parameter name (e.g., 'betas_mean', 'tau_std', 'noise_std'). - - Returns - ------- - The parameter value (typically a torch.Tensor), or None if not found. - """ - for category_params in self.parameters.values(): - if name in category_params: - return category_params[name] - return None - - def help(self) -> None: - """Print information about available Summary attributes and their meanings.""" - help_text = f""" -{self._BOLD}Summary Help{self._RESET} -{'=' * 60} - -The Summary object provides access to model metadata and parameters. - -{self._BOLD}Usage:{self._RESET} - model.summary() # Print the formatted summary - s = model.summary() # Store to access individual attributes - -{self._BOLD}Available Attributes:{self._RESET} - - {self._BOLD}Model Information:{self._RESET} - name Model name (str) - model_type Model class name, e.g., 'LogisticModel' (str) - dimension Number of features (int) - features List of feature names (list[str]) - sources List of source names, e.g., ['s0', 's1'] (list[str] or None) - clusters List of cluster names, e.g., ['c0', 'c1'] (list[str] or None) - source_dimension Number of sources (int or None) - n_clusters Number of clusters (int or None) - obs_models Observation model names (list[str] or None) - - {self._BOLD}Training:{self._RESET} - algorithm Algorithm name, e.g., 'mcmc_saem' (str) - seed Random seed used (int) - n_iter Number of iterations (int) - converged Whether training converged (bool or None) - nll Negative log-likelihood (float or None) - - {self._BOLD}Dataset:{self._RESET} - n_subjects Number of subjects in training data (int) - n_visits Total number of visits (int) - n_observations Total number of observations (int) - - {self._BOLD}Parameters:{self._RESET} - parameters All parameters grouped by category (dict) - get_param(name) Get a specific parameter by name - - {self._BOLD}Other:{self._RESET} - training_info Full training metadata dict - dataset_info Full dataset statistics dict - leaspy_version Leaspy version used (str) - -{self._BOLD}Note:{self._RESET} - Attributes returning None indicate the model does not support - that feature or the information was not generated. - -{self._BOLD}Examples:{self._RESET} - >>> s = model.summary() - >>> s.algorithm # 'mcmc_saem' - >>> s.seed # 42 - >>> s.n_subjects # 150 - >>> s.get_param('tau_std') # tensor([10.5]) -""" - print(help_text) - self._printed = True - - def _wrap_text(self, label: str, text: str, indent: int = 0) -> list[str]: - """Wrap text to fit within the display width.""" - prefix = f"{label}: " if label else "" - initial_indent = " " * indent + prefix - subsequent_indent = " " * (indent + 4) - wrapper = textwrap.TextWrapper( - width=self._WIDTH, - initial_indent=initial_indent, - subsequent_indent=subsequent_indent, - break_long_words=False, - break_on_hyphens=False - ) - return wrapper.wrap(text) - - def _format_parameter_group(self, params: dict[str, Any]) -> list[str]: - """Format a group of parameters for display.""" - lines = [] - for name, value in params.items(): - if isinstance(value, torch.Tensor): - lines.append(self._format_tensor(name, value)) - else: - lines.append(f" {name:<18} {value}") - return lines - - def _format_tensor(self, name: str, value: torch.Tensor) -> str: - """Format a tensor parameter for display.""" - axes = self._param_axes.get(name, ()) - - if value.ndim == 0: - return f" {name:<18} {value.item():.4f}" - - elif value.ndim == 1: - n = len(value) - if n > 10: - return f" {name:<18} Tensor of shape ({n},)" - - axis_name = axes[0] if len(axes) >= 1 else None - col_labels = self._get_axis_labels(axis_name, n) - - if col_labels: - header = " " * 20 + " ".join(f"{lbl:>8}" for lbl in col_labels) - values = f" {name:<18}" + " ".join(f"{v.item():>8.4f}" for v in value) - return header + "\n" + values - else: - val_str = "[" + ", ".join(f"{v.item():.4f}" for v in value) + "]" - return f" {name:<18} {val_str}" - - elif value.ndim == 2: - rows, cols = value.shape - if rows > 8 or cols > 8: - return f" {name:<18} Tensor of shape {tuple(value.shape)}" - - row_axis = axes[0] if len(axes) >= 1 else None - col_axis = axes[1] if len(axes) >= 2 else None - - row_labels = self._get_axis_labels(row_axis, rows) - col_labels = self._get_axis_labels(col_axis, cols) - - result = [f" {name}:"] - if col_labels: - header = " " * 20 + " ".join(f"{lbl:>8}" for lbl in col_labels) - result.append(header) - - for i, row in enumerate(value): - row_lbl = row_labels[i] if row_labels else f"[{i}]" - row_str = f" {row_lbl:<8}" + " ".join(f"{v.item():>8.4f}" for v in row) - result.append(row_str) - - return "\n".join(result) - - else: - return f" {name:<18} Tensor of shape {tuple(value.shape)}" - - def _get_axis_labels(self, axis_name: Optional[str], size: int) -> Optional[list[str]]: - """Get human-readable labels for an axis dimension.""" - if axis_name is None: - return None - - if axis_name == "feature": - if self._feature_names is not None: - feats = self._feature_names[:size] - return [f[:8] if len(f) <= 8 else f[:7] + "." for f in feats] - return [f"f{i}" for i in range(size)] - elif axis_name == "source": - return [f"s{i}" for i in range(size)] - elif axis_name == "cluster": - return [f"c{i}" for i in range(size)] - elif axis_name == "event": - return None - elif axis_name == "basis": - return [f"b{i}" for i in range(size)] - else: - return None - - class InitializationMethod(str, Enum): """Possible initialization methods for Leaspy models. @@ -754,7 +390,7 @@ def simulate( """ raise NotImplementedError - def _compute_dataset_statistics(self, dataset: Dataset) -> dict: + def _compute_dataset_statistics(self, dataset: Dataset) -> DatasetInfo: """Compute descriptive statistics of the dataset used for training.""" stats = { "n_subjects": dataset.n_individuals, @@ -798,8 +434,8 @@ def __init__(self, name: str, **kwargs): ) self._features: Optional[list[FeatureType]] = user_provided_features self._dimension: Optional[int] = user_provided_dimension - self.dataset_info = {} - self.training_info = {} + self.dataset_info: DatasetInfo = {} + self.training_info: TrainingInfo = {} self.initialization_method: InitializationMethod = InitializationMethod.DEFAULT if "initialization_method" in kwargs: self.initialization_method = InitializationMethod( @@ -1158,89 +794,69 @@ def fit( # Compute and store dataset statistics self.dataset_info = self._compute_dataset_statistics(dataset) - - # Store training metadata + + # Store training metadata (converged captured after run) self.training_info = { "algorithm": algorithm.name, "seed": algorithm.seed, "n_iter": algorithm.algo_parameters.get("n_iter"), - "converged": getattr(algorithm, "converged", None) # If algorithm tracks this } - + + t0 = time.perf_counter() algorithm.run(self, dataset) + elapsed = time.perf_counter() - t0 + + self.training_info["converged"] = getattr(algorithm, "converged", None) + self.training_info["duration"] = f"{elapsed:.1f}s" - def info(self) -> None: - """Print detailed information about the model configuration and training dataset.""" - lines = [] - width = 80 - sep = "=" * width - BOLD = "\033[1m" - RESET = "\033[0m" - - lines.append(sep) - lines.append(f"{BOLD}{'Model Information':^{width}}{RESET}") - lines.append(sep) - - # Statistical Model Info - lines.append(f"{BOLD}Statistical Model{RESET}") - lines.append("-" * width) - lines.append(f"Type: {self.__class__.__name__}") - lines.append(f"Name: {self.name}") - lines.append(f"Dimension: {self.dimension}") - if hasattr(self, "source_dimension"): - lines.append(f"Source Dimension: {self.source_dimension}") - - # Observation Models + def info(self) -> Info: + """Return model configuration and training context. + + When called directly (e.g. ``model.info()``), prints the information. + When stored in a variable, provides programmatic access. + + Returns + ------- + :class:`~leaspy.models.summary.Info` + Model configuration and training context. + + Examples + -------- + >>> model.info() # prints info + >>> i = model.info() # store for programmatic access + >>> i.n_subjects # 150 + >>> i.pct_missing # 2.5 + >>> i.help() # list available attributes + """ + # Observation model names + obs_model_names = None if hasattr(self, "obs_models"): - obs_names = [om.to_string() for om in self.obs_models] - lines.append(f"Observation Models: {', '.join(obs_names)}") - + obs_model_names = [om.to_string() for om in self.obs_models] + # Parameter count + n_total_params = None if self.parameters: - n_params = sum(p.numel() for p in self.parameters.values()) - lines.append(f"Total Parameters: {n_params}") - - if hasattr(self, "n_clusters") and self.n_clusters is not None: - clusters = self.n_clusters - lines.append(f"Clusters: {clusters}") - - - # Dataset Info - if self.dataset_info: - lines.append("") - lines.append(f"{BOLD}Training Dataset{RESET}") - lines.append("-" * width) - di = self.dataset_info - lines.append(f"Subjects: {di.get('n_subjects', 'N/A')}") - lines.append(f"Visits: {di.get('n_visits', 'N/A')}") - lines.append(f"Scores (Features): {di.get('n_scores', 'N/A')}") - lines.append(f"Total Observations: {di.get('n_observations', 'N/A')}") - - if "visits_per_subject" in di: - vps = di["visits_per_subject"] - lines.append(f"Visits per Subject: Median {vps['median']:.1f} [Min {vps['min']}, Max {vps['max']}, IQR {vps['iqr']:.1f}]") - - if "n_missing" in di: - lines.append(f"Missing Data: {di['n_missing']} ({di.get('pct_missing', 0):.2f}%)") - - if "n_events" in di: - lines.append(f"Events Observed: {di['n_events']}") - - # Training Info - if self.training_info: - lines.append("") - lines.append(f"{BOLD}Training Details{RESET}") - lines.append("-" * width) - ti = self.training_info - lines.append(f"Algorithm: {ti.get('algorithm', 'N/A')}") - if "seed" in ti: - lines.append(f"Seed: {ti['seed']}") - lines.append(f"Iterations: {ti.get('n_iter', 'N/A')}") - if ti.get("converged") is not None: - lines.append(f"Converged: {ti['converged']}") - - lines.append(sep) - print("\n".join(lines)) + n_total_params = sum(p.numel() for p in self.parameters.values()) + + # Leaspy version + try: + from leaspy import __version__ as version + except ImportError: + version = None + + return Info( + name=self.name, + model_type=self.__class__.__name__, + dimension=self.dimension, + features=self.features, + source_dimension=getattr(self, "source_dimension", None), + n_clusters=getattr(self, "n_clusters", None), + obs_models=obs_model_names, + n_total_params=n_total_params, + training_info=dict(self.training_info), + dataset_info=dict(self.dataset_info), + leaspy_version=version, + ) def summary(self) -> Summary: """Generate a structured summary of the model. @@ -1323,185 +939,6 @@ def summary(self) -> Summary: _feature_names=self.features, ) - def _format_parameter_group(self, param_names: list[str]) -> list[str]: - """Format a group of parameters, consolidating 1D parameters with the same axis. - - This method groups consecutive 1D parameters that share the same axis - to avoid repeating column headers. - - Parameters - ---------- - param_names : :obj:`list` [:obj:`str`] - List of parameter names to format. - - Returns - ------- - :obj:`list` [:obj:`str`] - List of formatted lines. - """ - lines = [] - axes_map = getattr(self, "_param_axes", {}) - - # Group consecutive 1D parameters with the same axis - i = 0 - while i < len(param_names): - name = param_names[i] - value = self.parameters[name] - axes = axes_map.get(name, ()) - - # Check if this is a 1D parameter with axis labels - if value.ndim == 1 and len(axes) >= 1: - axis_name = axes[0] - n = len(value) - - # Collect consecutive 1D params with the same axis and size - group = [(name, value)] - j = i + 1 - while j < len(param_names): - next_name = param_names[j] - next_value = self.parameters[next_name] - next_axes = axes_map.get(next_name, ()) - if (next_value.ndim == 1 and - len(next_axes) >= 1 and - next_axes[0] == axis_name and - len(next_value) == n): - group.append((next_name, next_value)) - j += 1 - else: - break - - # Format the group with a single header - if len(group) > 1: - col_labels = self._get_axis_labels(axis_name, n) - if col_labels: - header = " " * 20 + " ".join(f"{lbl:>8}" for lbl in col_labels) - lines.append(header) - for gname, gvalue in group: - row = f" {gname:<18}" + " ".join( - f"{v.item():>8.4f}" for v in gvalue - ) - lines.append(row) - i = j - continue - - # Default: format individually - lines.append(self._format_parameter(name, value)) - i += 1 - - return lines - - def _format_parameter(self, name: str, value: torch.Tensor) -> str: - """Format a single parameter for display in the summary. - - Uses axis metadata from `_param_axes` (if defined) to create - properly labeled tables for multi-dimensional parameters. - - Parameters - ---------- - name : :obj:`str` - The name of the parameter. - value : :class:`torch.Tensor` - The tensor value of the parameter. - - Returns - ------- - :obj:`str` - A formatted string representation of the parameter. - """ - # Get axis labels if available - axes = getattr(self, "_param_axes", {}).get(name, ()) - - if value.ndim == 0: - # Scalar - val_str = f"{value.item():.4f}" - return f" {name:<18} {val_str}" - - elif value.ndim == 1: - # 1D tensor - format as table with column headers - n = len(value) - if n > 10: - return f" {name:<18} Tensor of shape ({n},)" - - axis_name = axes[0] if len(axes) >= 1 else None - col_labels = self._get_axis_labels(axis_name, n) - - if col_labels: - # Create table format for 1D with labeled columns - header = " " * 20 + " ".join(f"{lbl:>8}" for lbl in col_labels) - values = f" {name:<18}" + " ".join( - f"{v.item():>8.4f}" for v in value - ) - return header + "\n" + values - else: - # Simple list format - val_str = "[" + ", ".join(f"{v.item():.4f}" for v in value) + "]" - return f" {name:<18} {val_str}" - - elif value.ndim == 2: - rows, cols = value.shape - if rows > 8 or cols > 8: - return f" {name:<18} Tensor of shape {tuple(value.shape)}" - - row_axis = axes[0] if len(axes) >= 1 else None - col_axis = axes[1] if len(axes) >= 2 else None - - row_labels = self._get_axis_labels(row_axis, rows) - col_labels = self._get_axis_labels(col_axis, cols) - - lines = [f" {name}:"] - - # Column headers - if col_labels: - header = " " * 20 + " ".join(f"{lbl:>8}" for lbl in col_labels) - lines.append(header) - - # Data rows - for i, row in enumerate(value): - row_lbl = row_labels[i] if row_labels else f"[{i}]" - row_str = f" {row_lbl:<8}" + " ".join(f"{v.item():>8.4f}" for v in row) - lines.append(row_str) - - return "\n".join(lines) - - else: - return f" {name:<18} Tensor of shape {tuple(value.shape)}" - - def _get_axis_labels(self, axis_name: Optional[str], size: int) -> Optional[list[str]]: - """Get human-readable labels for an axis dimension. - - Parameters - ---------- - axis_name : :obj:`str` or None - The semantic name of the axis (e.g., 'feature', 'source', 'cluster'). - size : :obj:`int` - The size of the axis. - - Returns - ------- - :obj:`list` [:obj:`str`] or None - A list of labels, or None if no meaningful labels available. - """ - if axis_name is None: - return None - - if axis_name == "feature": - if hasattr(self, "features") and self.features is not None: - # Use actual feature names (truncated), respecting the actual size - feats = self.features[:size] # In case size < len(features) - return [f[:8] if len(f) <= 8 else f[:7] + "." for f in feats] - return [f"f{i}" for i in range(size)] - elif axis_name == "source": - return [f"s{i}" for i in range(size)] - elif axis_name == "cluster": - return [f"c{i}" for i in range(size)] - elif axis_name == "event": - return None - elif axis_name == "basis": - # For basis vectors (e.g., in betas_mean), use generic indices - return [f"b{i}" for i in range(size)] - else: - return [f"{axis_name[:1]}{i}" for i in range(size)] - @staticmethod def _get_dataset( data: Optional[Union[pd.DataFrame, Data, Dataset]] = None, diff --git a/src/leaspy/models/mcmc_saem_compatible.py b/src/leaspy/models/mcmc_saem_compatible.py index 90002e766..c4c8a40c7 100644 --- a/src/leaspy/models/mcmc_saem_compatible.py +++ b/src/leaspy/models/mcmc_saem_compatible.py @@ -20,6 +20,7 @@ from .obs_models import ObservationModel from .stateful import StatefulModel +from .summary import get_axis_labels __all__ = ["McmcSaemCompatibleModel"] @@ -103,7 +104,7 @@ def sort_key(name: str) -> tuple[int, str, str]: n_cols = 1 if val.ndim == 1 and axes: # Check if this axis produces labeled columns - if self._get_axis_labels(primary_axis, len(val)) is not None: + if get_axis_labels(primary_axis, len(val), self.features) is not None: n_cols = len(val) elif val.ndim == 2: n_cols = val.shape[1] diff --git a/src/leaspy/models/settings.py b/src/leaspy/models/settings.py index 8d2bd46d4..2b74b2c8f 100644 --- a/src/leaspy/models/settings.py +++ b/src/leaspy/models/settings.py @@ -5,6 +5,8 @@ from leaspy.exceptions import LeaspyModelInputError from leaspy.utils.typing import KwargsType +from .summary import DatasetInfo, TrainingInfo + __all__ = ["ModelSettings"] @@ -50,8 +52,8 @@ def __init__(self, path_to_model_settings_or_dict: Union[str, dict]): ModelSettings._check_settings(settings) self.name: str = settings["name"].lower() self.parameters: KwargsType = settings["parameters"] - self.dataset_info: dict = settings.get("dataset_info", {}) - self.training_info: dict = settings.get("training_info", {}) + self.dataset_info: DatasetInfo = settings.get("dataset_info", {}) + self.training_info: TrainingInfo = settings.get("training_info", {}) self.hyperparameters: KwargsType = { k.lower(): v diff --git a/src/leaspy/models/summary.py b/src/leaspy/models/summary.py new file mode 100644 index 000000000..41ce37141 --- /dev/null +++ b/src/leaspy/models/summary.py @@ -0,0 +1,719 @@ +"""Model inspection objects for programmatic access to model metadata. + +This module provides :class:`Summary` and :class:`Info`, returned by +``model.summary()`` and ``model.info()`` respectively, along with the +:class:`~typing.TypedDict` schemas for training and dataset metadata. + +Both classes auto-print when their return value is discarded (e.g. +``model.summary()``) and stay silent when stored in a variable +(e.g. ``s = model.summary()``). See :class:`AutoPrintMixin` for details. +""" + +import textwrap +from dataclasses import dataclass, field +from typing import Any, Optional, TypedDict + +import torch + +__all__ = [ + "AutoPrintMixin", + "DatasetInfo", + "Info", + "Summary", + "TrainingInfo", + "VisitsPerSubject", + "get_axis_labels", +] + + +# --------------------------------------------------------------------------- +# TypedDict schemas for metadata +# --------------------------------------------------------------------------- + + +class VisitsPerSubject(TypedDict, total=False): + """Per-subject visit distribution statistics.""" + + median: float + min: int + max: int + iqr: float + + +class DatasetInfo(TypedDict, total=False): + """Statistics of the training dataset, computed during ``fit()``.""" + + n_subjects: int + n_scores: int + n_visits: int + n_observations: int + visits_per_subject: VisitsPerSubject + n_missing: int + pct_missing: float + n_events: int + + +class TrainingInfo(TypedDict, total=False): + """Metadata about the training process, captured during ``fit()``.""" + + algorithm: str + seed: int + n_iter: int + converged: bool + duration: str + + +# --------------------------------------------------------------------------- +# Shared utilities +# --------------------------------------------------------------------------- + +# ANSI formatting constants +_WIDTH = 80 +_BOLD = "\033[1m" +_RESET = "\033[0m" + + +def get_axis_labels( + axis_name: Optional[str], + size: int, + feature_names: Optional[list[str]] = None, +) -> Optional[list[str]]: + """Resolve human-readable labels for a parameter axis. + + Parameters + ---------- + axis_name : str or None + Semantic axis name (``"feature"``, ``"source"``, ``"cluster"``, + ``"basis"``). + size : int + Number of elements along the axis. + feature_names : list[str], optional + Feature names used when *axis_name* is ``"feature"``. + + Returns + ------- + list[str] or None + Labels for the axis, or ``None`` if no meaningful labels are available. + """ + if axis_name is None: + return None + + if axis_name == "feature": + if feature_names is not None: + feats = feature_names[:size] + return [f[:8] if len(f) <= 8 else f[:7] + "." for f in feats] + return [f"f{i}" for i in range(size)] + elif axis_name == "source": + return [f"s{i}" for i in range(size)] + elif axis_name == "cluster": + return [f"c{i}" for i in range(size)] + elif axis_name == "event": + return None + elif axis_name == "basis": + return [f"b{i}" for i in range(size)] + else: + return None + + +def _wrap_text(label: str, text: str, indent: int = 0) -> list[str]: + """Wrap *text* with a bold *label* prefix to fit within ``_WIDTH``.""" + prefix = f"{label}: " if label else "" + initial_indent = " " * indent + prefix + subsequent_indent = " " * (indent + 4) + wrapper = textwrap.TextWrapper( + width=_WIDTH, + initial_indent=initial_indent, + subsequent_indent=subsequent_indent, + break_long_words=False, + break_on_hyphens=False, + ) + return wrapper.wrap(text) + + +# --------------------------------------------------------------------------- +# Auto-print mixin +# --------------------------------------------------------------------------- + + +class AutoPrintMixin: + """Mixin that auto-prints when the object is discarded. + + Relies on CPython reference counting: when the return value of e.g. + ``model.summary()`` is not assigned, the object is immediately + garbage-collected, triggering ``__del__`` which prints it. + + When stored (``s = model.summary()``), any public attribute access + sets ``_printed = True``, suppressing the ``__del__`` output. + + Subclasses must define a ``_printed: bool`` field (via dataclass) + and a ``__str__`` method. + """ + + def __del__(self): + if not object.__getattribute__(self, "_printed"): + print(str(self)) + + def __repr__(self) -> str: + object.__setattr__(self, "_printed", True) + return str(self) + + def __getattribute__(self, name: str): + value = object.__getattribute__(self, name) + # Suppress auto-print once any public attribute is accessed + if not name.startswith("_") and name != "help": + object.__setattr__(self, "_printed", True) + return value + + +# --------------------------------------------------------------------------- +# Info +# --------------------------------------------------------------------------- + + +@dataclass(repr=False) +class Info(AutoPrintMixin): + """Model configuration and training context (no parameter values). + + Returned by ``model.info()``. Auto-prints when discarded; provides + programmatic access when stored in a variable. + + Examples + -------- + >>> model.info() # prints info + >>> i = model.info() # store for programmatic access + >>> i.n_subjects # 150 + >>> i.pct_missing # 2.5 + >>> i.help() # list available attributes + """ + + name: str + model_type: str + dimension: Optional[int] = None + features: Optional[list[str]] = None + source_dimension: Optional[int] = None + n_clusters: Optional[int] = None + obs_models: Optional[list[str]] = None + n_total_params: Optional[int] = None + training_info: TrainingInfo = field(default_factory=dict) + dataset_info: DatasetInfo = field(default_factory=dict) + leaspy_version: Optional[str] = None + _printed: bool = field(default=False, repr=False) + + # -- Convenience properties: training ------------------------------------ + + @property + def algorithm(self) -> Optional[str]: + """Algorithm name used for training.""" + return self.training_info.get("algorithm") + + @property + def seed(self) -> Optional[int]: + """Random seed used for training.""" + return self.training_info.get("seed") + + @property + def n_iter(self) -> Optional[int]: + """Number of iterations.""" + return self.training_info.get("n_iter") + + @property + def converged(self) -> Optional[bool]: + """Whether training converged.""" + return self.training_info.get("converged") + + @property + def duration(self) -> Optional[str]: + """Training duration.""" + return self.training_info.get("duration") + + # -- Convenience properties: dataset ------------------------------------- + + @property + def n_subjects(self) -> Optional[int]: + """Number of subjects in the training dataset.""" + return self.dataset_info.get("n_subjects") + + @property + def n_visits(self) -> Optional[int]: + """Total number of visits.""" + return self.dataset_info.get("n_visits") + + @property + def n_scores(self) -> Optional[int]: + """Number of scored features.""" + return self.dataset_info.get("n_scores") + + @property + def n_observations(self) -> Optional[int]: + """Total number of observed data points.""" + return self.dataset_info.get("n_observations") + + @property + def pct_missing(self) -> Optional[float]: + """Percentage of missing observations.""" + return self.dataset_info.get("pct_missing") + + @property + def n_missing(self) -> Optional[int]: + """Number of missing observations.""" + return self.dataset_info.get("n_missing") + + @property + def visits_per_subject(self) -> Optional[VisitsPerSubject]: + """Per-subject visit distribution statistics.""" + return self.dataset_info.get("visits_per_subject") + + @property + def n_events(self) -> Optional[int]: + """Number of observed events (joint models only).""" + return self.dataset_info.get("n_events") + + # -- Display ------------------------------------------------------------- + + def __str__(self) -> str: + lines = [] + sep = "=" * _WIDTH + + lines.append(sep) + lines.append(f"{_BOLD}{'Model Information':^{_WIDTH}}{_RESET}") + lines.append(sep) + + # Statistical Model + lines.append(f"{_BOLD}Statistical Model{_RESET}") + lines.append("-" * _WIDTH) + lines.append(f"Type: {self.model_type}") + lines.append(f"Name: {self.name}") + lines.append(f"Dimension: {self.dimension}") + if self.source_dimension is not None: + lines.append(f"Source Dimension: {self.source_dimension}") + if self.obs_models: + lines.append(f"Observation Models: {', '.join(self.obs_models)}") + if self.n_total_params is not None: + lines.append(f"Total Parameters: {self.n_total_params}") + if self.n_clusters is not None: + lines.append(f"Clusters: {self.n_clusters}") + + # Training Dataset + if self.dataset_info: + lines.append("") + lines.append(f"{_BOLD}Training Dataset{_RESET}") + lines.append("-" * _WIDTH) + di = self.dataset_info + lines.append(f"Subjects: {di.get('n_subjects', 'N/A')}") + lines.append(f"Visits: {di.get('n_visits', 'N/A')}") + lines.append(f"Scores (Features): {di.get('n_scores', 'N/A')}") + lines.append(f"Total Observations: {di.get('n_observations', 'N/A')}") + if "visits_per_subject" in di: + vps = di["visits_per_subject"] + lines.append( + f"Visits per Subject: Median {vps['median']:.1f} " + f"[Min {vps['min']}, Max {vps['max']}, IQR {vps['iqr']:.1f}]" + ) + if "n_missing" in di: + lines.append( + f"Missing Data: {di['n_missing']} " + f"({di.get('pct_missing', 0):.2f}%)" + ) + if "n_events" in di: + lines.append(f"Events Observed: {di['n_events']}") + + # Training Details + if self.training_info: + lines.append("") + lines.append(f"{_BOLD}Training Details{_RESET}") + lines.append("-" * _WIDTH) + ti = self.training_info + lines.append(f"Algorithm: {ti.get('algorithm', 'N/A')}") + if "seed" in ti: + lines.append(f"Seed: {ti['seed']}") + lines.append(f"Iterations: {ti.get('n_iter', 'N/A')}") + if ti.get("converged") is not None: + lines.append(f"Converged: {ti['converged']}") + if "duration" in ti: + lines.append(f"Duration: {ti['duration']}") + + # Leaspy Version + if self.leaspy_version: + lines.append("") + lines.append(f"Leaspy Version: {self.leaspy_version}") + + lines.append(sep) + return "\n".join(lines) + + def help(self) -> None: + """Print available attributes and their meanings.""" + help_text = f""" +{_BOLD}Info Help{_RESET} +{'=' * 60} + +The Info object provides access to model configuration and training context. + +{_BOLD}Usage:{_RESET} + model.info() # Print model information + i = model.info() # Store to access individual attributes + +{_BOLD}Available Attributes:{_RESET} + + {_BOLD}Model:{_RESET} + name Model name (str) + model_type Model class name (str) + dimension Number of features (int) + features Feature names (list[str]) + source_dimension Number of sources (int or None) + n_clusters Number of clusters (int or None) + obs_models Observation model names (list[str] or None) + n_total_params Total number of scalar parameters (int) + + {_BOLD}Training:{_RESET} + algorithm Algorithm name (str) + seed Random seed (int) + n_iter Number of iterations (int) + converged Whether training converged (bool or None) + duration Training duration (str) + + {_BOLD}Dataset:{_RESET} + n_subjects Number of subjects (int) + n_visits Total visits (int) + n_scores Number of scored features (int) + n_observations Total observations (int) + pct_missing Percent missing data (float) + n_missing Count of missing observations (int) + visits_per_subject Visit distribution stats (dict) + n_events Observed events, joint models only (int or None) + + {_BOLD}Other:{_RESET} + training_info Full training metadata (TrainingInfo) + dataset_info Full dataset statistics (DatasetInfo) + leaspy_version Leaspy version (str) + +{_BOLD}Examples:{_RESET} + >>> i = model.info() + >>> i.algorithm # 'mcmc_saem' + >>> i.n_subjects # 150 + >>> i.pct_missing # 2.5 +""" + print(help_text) + object.__setattr__(self, "_printed", True) + + +# --------------------------------------------------------------------------- +# Summary +# --------------------------------------------------------------------------- + + +@dataclass(repr=False) +class Summary(AutoPrintMixin): + """Structured summary of a Leaspy model including parameter values. + + Returned by ``model.summary()``. Auto-prints when discarded; provides + programmatic access when stored in a variable. + + Examples + -------- + >>> model.summary() # prints the formatted summary + >>> s = model.summary() # store for programmatic access + >>> s.algorithm # 'mcmc_saem' + >>> s.get_param('tau_std') # tensor([10.5]) + >>> s.help() # list available attributes + """ + + name: str + model_type: str + dimension: Optional[int] = None + features: Optional[list[str]] = None + source_dimension: Optional[int] = None + n_clusters: Optional[int] = None + obs_models: Optional[list[str]] = None + nll: Optional[float] = None + training_info: TrainingInfo = field(default_factory=dict) + dataset_info: DatasetInfo = field(default_factory=dict) + parameters: dict[str, dict[str, Any]] = field(default_factory=dict) + leaspy_version: Optional[str] = None + _param_axes: dict = field(default_factory=dict, repr=False) + _feature_names: Optional[list[str]] = field(default=None, repr=False) + _printed: bool = field(default=False, repr=False) + + # -- Convenience properties ---------------------------------------------- + + @property + def sources(self) -> Optional[list[str]]: + """Source names (e.g. ``['s0', 's1']``) or ``None``.""" + if self.source_dimension is None: + return None + return [f"s{i}" for i in range(self.source_dimension)] + + @property + def clusters(self) -> Optional[list[str]]: + """Cluster names (e.g. ``['c0', 'c1']``) or ``None``.""" + if self.n_clusters is None: + return None + return [f"c{i}" for i in range(self.n_clusters)] + + @property + def algorithm(self) -> Optional[str]: + """Algorithm name used for training.""" + return self.training_info.get("algorithm") + + @property + def seed(self) -> Optional[int]: + """Random seed used for training.""" + return self.training_info.get("seed") + + @property + def n_iter(self) -> Optional[int]: + """Number of iterations.""" + return self.training_info.get("n_iter") + + @property + def converged(self) -> Optional[bool]: + """Whether training converged.""" + return self.training_info.get("converged") + + @property + def n_subjects(self) -> Optional[int]: + """Number of subjects in the training dataset.""" + return self.dataset_info.get("n_subjects") + + @property + def n_visits(self) -> Optional[int]: + """Total number of visits.""" + return self.dataset_info.get("n_visits") + + @property + def n_observations(self) -> Optional[int]: + """Total number of observations.""" + return self.dataset_info.get("n_observations") + + def get_param(self, name: str) -> Optional[Any]: + """Get a parameter value by name, searching across all categories. + + Parameters + ---------- + name : str + Parameter name (e.g. ``'betas_mean'``, ``'tau_std'``). + + Returns + ------- + value + The parameter value (typically a ``torch.Tensor``), or ``None``. + """ + for category_params in self.parameters.values(): + if name in category_params: + return category_params[name] + return None + + # -- Display ------------------------------------------------------------- + + def __str__(self) -> str: + lines = [] + sep = "=" * _WIDTH + + # Header + lines.append(sep) + lines.append(f"{_BOLD}{'Model Summary':^{_WIDTH}}{_RESET}") + lines.append(sep) + lines.append(f"{_BOLD}Model Name:{_RESET} {self.name}") + lines.append(f"{_BOLD}Model Type:{_RESET} {self.model_type}") + + if self.features is not None: + feat_str = ", ".join(self.features) + lines.extend( + _wrap_text( + f"{_BOLD}Features ({self.dimension}){_RESET}", feat_str + ) + ) + + if self.source_dimension is not None: + sources = [f"Source {i} (s{i})" for i in range(self.source_dimension)] + lines.extend( + _wrap_text( + f"{_BOLD}Sources ({self.source_dimension}){_RESET}", + ", ".join(sources), + ) + ) + + if self.n_clusters is not None: + clusters = [f"Cluster {i} (c{i})" for i in range(self.n_clusters)] + lines.extend( + _wrap_text( + f"{_BOLD}Clusters ({self.n_clusters}){_RESET}", + ", ".join(clusters), + ) + ) + + if self.obs_models: + lines.extend( + _wrap_text( + f"{_BOLD}Observation Models{_RESET}", + ", ".join(self.obs_models), + ) + ) + + if self.nll is not None: + lines.append(f"{_BOLD}Neg. Log-Likelihood:{_RESET} {self.nll:.4f}") + + # Training Metadata + if self.training_info: + lines.append("") + lines.append(f"{_BOLD}Training Metadata{_RESET}") + lines.append("-" * _WIDTH) + ti = self.training_info + lines.append(f"Algorithm: {ti.get('algorithm', 'N/A')}") + if "seed" in ti: + lines.append(f"Seed: {ti['seed']}") + lines.append(f"Iterations: {ti.get('n_iter', 'N/A')}") + if ti.get("converged") is not None: + lines.append(f"Converged: {ti['converged']}") + if "duration" in ti: + lines.append(f"Duration: {ti['duration']}") + + # Data Context + if self.dataset_info: + lines.append("") + lines.append(f"{_BOLD}Data Context{_RESET}") + lines.append("-" * _WIDTH) + di = self.dataset_info + lines.append(f"Subjects: {di.get('n_subjects', 'N/A')}") + lines.append(f"Visits: {di.get('n_visits', 'N/A')}") + lines.append(f"Total Observations: {di.get('n_observations', 'N/A')}") + + # Leaspy Version + if self.leaspy_version: + lines.append("") + lines.append(f"Leaspy Version: {self.leaspy_version}") + + lines.append(sep) + + # Parameters by category + for category, params in self.parameters.items(): + if params: + lines.append("") + lines.append(f"{_BOLD}{category}{_RESET}") + lines.append("-" * _WIDTH) + lines.extend(self._format_parameter_group(params)) + + lines.append(sep) + return "\n".join(lines) + + def help(self) -> None: + """Print available attributes and their meanings.""" + help_text = f""" +{_BOLD}Summary Help{_RESET} +{'=' * 60} + +The Summary object provides access to model metadata and parameters. + +{_BOLD}Usage:{_RESET} + model.summary() # Print the formatted summary + s = model.summary() # Store to access individual attributes + +{_BOLD}Available Attributes:{_RESET} + + {_BOLD}Model Information:{_RESET} + name Model name (str) + model_type Model class name, e.g., 'LogisticModel' (str) + dimension Number of features (int) + features List of feature names (list[str]) + sources Source names, e.g., ['s0', 's1'] (list[str] or None) + clusters Cluster names, e.g., ['c0', 'c1'] (list[str] or None) + source_dimension Number of sources (int or None) + n_clusters Number of clusters (int or None) + obs_models Observation model names (list[str] or None) + + {_BOLD}Training:{_RESET} + algorithm Algorithm name, e.g., 'mcmc_saem' (str) + seed Random seed used (int) + n_iter Number of iterations (int) + converged Whether training converged (bool or None) + nll Negative log-likelihood (float or None) + + {_BOLD}Dataset:{_RESET} + n_subjects Number of subjects in training data (int) + n_visits Total number of visits (int) + n_observations Total number of observations (int) + + {_BOLD}Parameters:{_RESET} + parameters All parameters grouped by category (dict) + get_param(name) Get a specific parameter by name + + {_BOLD}Other:{_RESET} + training_info Full training metadata (TrainingInfo) + dataset_info Full dataset statistics (DatasetInfo) + leaspy_version Leaspy version used (str) + +{_BOLD}Examples:{_RESET} + >>> s = model.summary() + >>> s.algorithm # 'mcmc_saem' + >>> s.seed # 42 + >>> s.n_subjects # 150 + >>> s.get_param('tau_std') # tensor([10.5]) +""" + print(help_text) + object.__setattr__(self, "_printed", True) + + # -- Private formatting helpers ------------------------------------------ + + def _format_parameter_group(self, params: dict[str, Any]) -> list[str]: + """Format a group of parameters for display.""" + lines = [] + for name, value in params.items(): + if isinstance(value, torch.Tensor): + lines.append(self._format_tensor(name, value)) + else: + lines.append(f" {name:<18} {value}") + return lines + + def _format_tensor(self, name: str, value: torch.Tensor) -> str: + """Format a tensor parameter with axis labels.""" + param_axes = object.__getattribute__(self, "_param_axes") + feature_names = object.__getattribute__(self, "_feature_names") + axes = param_axes.get(name, ()) + + if value.ndim == 0: + return f" {name:<18} {value.item():.4f}" + + elif value.ndim == 1: + n = len(value) + if n > 10: + return f" {name:<18} Tensor of shape ({n},)" + + axis_name = axes[0] if len(axes) >= 1 else None + col_labels = get_axis_labels(axis_name, n, feature_names) + + if col_labels: + header = " " * 20 + " ".join(f"{lbl:>8}" for lbl in col_labels) + values = f" {name:<18}" + " ".join( + f"{v.item():>8.4f}" for v in value + ) + return header + "\n" + values + else: + val_str = "[" + ", ".join(f"{v.item():.4f}" for v in value) + "]" + return f" {name:<18} {val_str}" + + elif value.ndim == 2: + rows, cols = value.shape + if rows > 8 or cols > 8: + return f" {name:<18} Tensor of shape {tuple(value.shape)}" + + row_axis = axes[0] if len(axes) >= 1 else None + col_axis = axes[1] if len(axes) >= 2 else None + row_labels = get_axis_labels(row_axis, rows, feature_names) + col_labels = get_axis_labels(col_axis, cols, feature_names) + + result = [f" {name}:"] + if col_labels: + header = " " * 20 + " ".join(f"{lbl:>8}" for lbl in col_labels) + result.append(header) + + for i, row in enumerate(value): + row_lbl = row_labels[i] if row_labels else f"[{i}]" + row_str = ( + f" {row_lbl:<8}" + + " ".join(f"{v.item():>8.4f}" for v in row) + ) + result.append(row_str) + + return "\n".join(result) + + else: + return f" {name:<18} Tensor of shape {tuple(value.shape)}" From e01a01aac24ee35acb661bb0b5c7b0bf7cbcf229 Mon Sep 17 00:00:00 2001 From: Sebastian Date: Mon, 2 Mar 2026 15:01:31 +0100 Subject: [PATCH 23/24] fixing bold titles and duration: removed from summary and we added more decimals in info() --- src/leaspy/models/base.py | 2 +- src/leaspy/models/summary.py | 9 +++++++-- 2 files changed, 8 insertions(+), 3 deletions(-) diff --git a/src/leaspy/models/base.py b/src/leaspy/models/base.py index 469c219d3..d1ced52b0 100644 --- a/src/leaspy/models/base.py +++ b/src/leaspy/models/base.py @@ -807,7 +807,7 @@ def fit( elapsed = time.perf_counter() - t0 self.training_info["converged"] = getattr(algorithm, "converged", None) - self.training_info["duration"] = f"{elapsed:.1f}s" + self.training_info["duration"] = f"{elapsed:.3f}s" def info(self) -> Info: """Return model configuration and training context. diff --git a/src/leaspy/models/summary.py b/src/leaspy/models/summary.py index 41ce37141..dd01ff8f5 100644 --- a/src/leaspy/models/summary.py +++ b/src/leaspy/models/summary.py @@ -157,6 +157,13 @@ def __repr__(self) -> str: object.__setattr__(self, "_printed", True) return str(self) + def _repr_html_(self) -> str: + """Rich HTML display for Jupyter notebooks (enables bold rendering).""" + object.__setattr__(self, "_printed", True) + text = str(self) + text = text.replace("\033[1m", "").replace("\033[0m", "") + return f"
{text}
" + def __getattribute__(self, name: str): value = object.__getattribute__(self, name) # Suppress auto-print once any public attribute is accessed @@ -564,8 +571,6 @@ def __str__(self) -> str: lines.append(f"Iterations: {ti.get('n_iter', 'N/A')}") if ti.get("converged") is not None: lines.append(f"Converged: {ti['converged']}") - if "duration" in ti: - lines.append(f"Duration: {ti['duration']}") # Data Context if self.dataset_info: From e1758b4bd940349d6d908c80ffe5e93d85205adc Mon Sep 17 00:00:00 2001 From: Sebastian Date: Mon, 2 Mar 2026 15:03:04 +0100 Subject: [PATCH 24/24] showing properly the new methods in the examples --- examples/plot_01_quickstart.py | 9 +++++++++ examples/plot_03_joint.py | 4 ++++ 2 files changed, 13 insertions(+) diff --git a/examples/plot_01_quickstart.py b/examples/plot_01_quickstart.py index a7dcd8f6a..a24683e23 100644 --- a/examples/plot_01_quickstart.py +++ b/examples/plot_01_quickstart.py @@ -68,6 +68,15 @@ model.summary() # %% +# The ``fit`` method estimates the parameters of the model, which are then accessible +# through the ``summary`` method. The parameters are also stored in the ``parameters`` attribute of the model. + +model.info() + +# %% +# The method ``info`` provides the model configuration and the settings used for the fit, +# as well as the dataset information and the training information. +# # Leaspy can also estimate the *individual trajectories* of each participant. # This is done using a personalization algorithm, here `scipy_minimize`: diff --git a/examples/plot_03_joint.py b/examples/plot_03_joint.py index 456ea1d1c..dedbd37f8 100644 --- a/examples/plot_03_joint.py +++ b/examples/plot_03_joint.py @@ -50,6 +50,10 @@ model.fit(data, "mcmc_saem", seed=1312, n_iter=100, progress_bar=False) model.summary() +# %% +# We can also access the model information and parameters after fitting it to the data. +model.info() + # %% # The Joint Model includes specific parameters such as `log_rho_mean` and `zeta_mean`.