From 5afff1d72825d643b8b4adbfcbfc8fc8fe96a8ea Mon Sep 17 00:00:00 2001 From: oooo26 <1160141009@qq.com> Date: Fri, 13 Jan 2023 22:04:19 +0800 Subject: [PATCH 1/3] Update document --- docs/Changelog.rst | 4 ++-- docs/Contributing/AfterCodeDeveloping.rst | 2 +- docs/Contributing/AppendixArchitecture.rst | 2 +- docs/Contributing/Bug-NewFeatures.rst | 4 ++-- docs/Contributing/ContributeDocs.rst | 16 ++++++++-------- docs/Contributing/ContributePyR.rst | 2 +- docs/Contributing/DevelopNewFeatures.rst | 2 +- docs/Contributing/index.rst | 4 ++-- docs/Tutorial/1-glm/plot_4_CoxRegression.py | 8 +++----- .../causal_model.png | Bin 45677 -> 38525 bytes .../plot_2_geomstats.py | 4 ++-- .../5-scikit-learn-connection/plot_5_dowhy.py | 11 ++++++----- docs/conf.py | 2 +- 13 files changed, 30 insertions(+), 31 deletions(-) diff --git a/docs/Changelog.rst b/docs/Changelog.rst index 9214e98d..f143a4b1 100644 --- a/docs/Changelog.rst +++ b/docs/Changelog.rst @@ -16,7 +16,7 @@ Unreleased - Re-write logistic, poisson and gamma regression on the basis of GLM base model. Versions 0.4.2 -- 0.4.5 ----------- +----------------------- - R package @@ -52,7 +52,7 @@ Versions 0.4.2 -- 0.4.5 `Junhao Huang `__! Version 0.4.1 ----------- +------------- - R package diff --git a/docs/Contributing/AfterCodeDeveloping.rst b/docs/Contributing/AfterCodeDeveloping.rst index 0d86ed69..2f15c732 100644 --- a/docs/Contributing/AfterCodeDeveloping.rst +++ b/docs/Contributing/AfterCodeDeveloping.rst @@ -1,5 +1,5 @@ After Code Developing -=========== +===================== CodeFactor ---------- diff --git a/docs/Contributing/AppendixArchitecture.rst b/docs/Contributing/AppendixArchitecture.rst index d81f7a41..f4707e28 100644 --- a/docs/Contributing/AppendixArchitecture.rst +++ b/docs/Contributing/AppendixArchitecture.rst @@ -1,5 +1,5 @@ Appendix: Architecture of **abess** -======================== +=================================== In this page, we briefly introduce our core code of ``abess``, which is summarized in the Figure below. diff --git a/docs/Contributing/Bug-NewFeatures.rst b/docs/Contributing/Bug-NewFeatures.rst index db58a890..b6d4c4ee 100644 --- a/docs/Contributing/Bug-NewFeatures.rst +++ b/docs/Contributing/Bug-NewFeatures.rst @@ -1,5 +1,5 @@ Bug Report or New Feature Request -============ +================================= Bugs Report ----------- @@ -27,4 +27,4 @@ When suggesting a new feature, please: - explain in detail how it would work. - keep the scope as narrow as possible, to make it easier to understand and implementation. -- provide few important literatures if possible. \ No newline at end of file +- provide few important literatures if possible. diff --git a/docs/Contributing/ContributeDocs.rst b/docs/Contributing/ContributeDocs.rst index eace90e6..10e8af65 100644 --- a/docs/Contributing/ContributeDocs.rst +++ b/docs/Contributing/ContributeDocs.rst @@ -1,10 +1,10 @@ Contribute documentation -============ +======================== .. _general development procedure: General development procedure -~~~~~~~~~~~~~~~~~ +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ If you're a more experienced with the ``abess`` and are looking forward to improve your open source development skills, the next step up is to @@ -77,14 +77,14 @@ parameters it requires, such as Also note that the style of Python document is similar to `numpydoc `__. -The development of Python API's documentation relies on +The development of Python API's documentation mainly relies on `Sphinx `__, -`nbsphinx `__ (support jupyter -notebook for Sphinx), -`myst-parser `__ (support +`sphinx-gallery `__ (support markdown for Sphinx), `sphinx-rtd-theme `__ -(support “Read the Docs” theme for Sphinx). Make sure these packages +(support “Read the Docs” theme for Sphinx) and so on. + +Please make sure all packages in :code:`docs/requirements.txt` have been installed. 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z`P%SpFk@CV6J>|%>96dazLKx`?JinTYU=F@lcbP%>TA-rro4)X8#laV)WEp)TC?h1 zIaS%DzEuke{2f6a(;%@uCuuX zukwr&f`81~_1t2~v!3;W$J>QyK3!>Q=j0r(I81q7Q0e(C%iN4>?`(9tL$_O--z~Kb zv6L{P)U5V##a=;5R>Rend0I!jT)}tLw{yC^NUnNB+XW4%SKi-IxDgA1p_g diff --git a/docs/Tutorial/5-scikit-learn-connection/plot_2_geomstats.py b/docs/Tutorial/5-scikit-learn-connection/plot_2_geomstats.py index 8f7b001e..ac1b8937 100644 --- a/docs/Tutorial/5-scikit-learn-connection/plot_2_geomstats.py +++ b/docs/Tutorial/5-scikit-learn-connection/plot_2_geomstats.py @@ -1,11 +1,11 @@ """ Work with geomstats -====================== +=================== """ # %% # The package `geomstats` is used for computations and statistics on nonlinear manifolds, -#such as Hypersphere,Hyperbolic Space, Symmetric-Positive-Definite (SPD) Matrices Space and Skew-Symmetric Matrices Space. +# such as Hypersphere,Hyperbolic Space, Symmetric-Positive-Definite (SPD) Matrices Space and Skew-Symmetric Matrices Space. # `abess` also works well with the package `geomstats`. # Here is an example of using `abess` to do logistic regression of samples on Hypersphere, # and we will compare the precision score, the recall score and the running time with `abess` and with `scikit-learn`. diff --git a/docs/Tutorial/5-scikit-learn-connection/plot_5_dowhy.py b/docs/Tutorial/5-scikit-learn-connection/plot_5_dowhy.py index f3c85efb..84cd09b6 100644 --- a/docs/Tutorial/5-scikit-learn-connection/plot_5_dowhy.py +++ b/docs/Tutorial/5-scikit-learn-connection/plot_5_dowhy.py @@ -6,13 +6,13 @@ this section, we will use ``abess`` to cope with high-dimensional mediation analysis problem, which is a popular topic in the research of causal inference. High-Dimensional mediation model is crucial in biomedical research studies. People always want to know by what mechanism the genes with differential expression, distinct genotypes or various epigeneticmarkers affect -the outcome or phenotype. Such a mechanistic process is what a mediation analysis aims to characterize (Huang and Pan, 2016). +the outcome or phenotype. Such a mechanistic process is what a mediation analysis aims to characterize (Huang and Pan, 2016 [2]_ ). """ ############################################################################### # # A typical example of the high-dimensional mediators is high-dimensional DNA methylation markers. This model can be represented -# by the following figure (Zhang et al. 2016), +# by the following figure (Zhang et al. 2016 [1]_ ), # # .. image:: ../../Tutorial/figure/med_model.png # @@ -34,7 +34,7 @@ ############################################################################### # Continuous Outcome -# """"""""""""""" +# """""""""""""""""" # We will follow the simulation settings and the data generating process in the R document of the R package HIMA # (Zhang et al. 2016). :math:`X` is generated from :math:`N(0,1.5)`, the first 8 elements of :math:`\beta\left(\beta_{k}, k=1, \cdots, 8\right)` # are :math:`(0.55,0.6,0.65,0.7,0.8,0.8,0,0)^{\mathrm{T}}`, and the first 8 elements of :math:`a\left(\alpha_{k}, k=1, \cdots, 8\right)` @@ -93,7 +93,7 @@ def simhima (n,p,alpha,beta,seed,binary=False): # variables. In this case, continuing to naively use ``abess`` may lead to philosophical causal issues and cannot have good theoretical # guarantees, since we have to treat :math:`X` as an "dependent variable" and treat :math:`M_i` as "independent variables", which is contrary # to the interpretation in reality. However, this naive approach performs well in this task of selecting true mediators, and this -# kind of idea has already been used in some existing methods, such as Coordinate-wise Mediation Filter (van Kesteren and Oberski, 2019). +# kind of idea has already been used in some existing methods, such as Coordinate-wise Mediation Filter (Van Kesteren and Oberski, 2019 [3]_ ). # Therefore, we will still use this kind of idea here, and the main task is to show the power of ``abess``. # # We will first apply BESS with a fixed support size to one of these subproblems, conducting a preliminary screening to @@ -197,6 +197,7 @@ def simhima (n,p,alpha,beta,seed,binary=False): # %% # After doing 1000 replications of the process mentioned above, we can get this list. The left number in each row is the # index, and the right number is the times that this mediator been selected. We can find that: +# # - The true mediators (indices 0-3) can always be selected during all the 1000 replications. # - The bewildering false mediators (indices 4-7) may be occasionally selected, but FDR can be controlled at a low level. # - It is almost impossible for other mediators (indices 8-199) to be selected by our method. @@ -302,4 +303,4 @@ def simhima (n,p,alpha,beta,seed,binary=False): # # # sphinx_gallery_thumbnail_path = '../../Tutorial/figure/dowhy.png' -# \ No newline at end of file +# diff --git a/docs/conf.py b/docs/conf.py index c780ba43..d28a0051 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -75,7 +75,7 @@ # directories to ignore when looking for source files. # This pattern also affects html_static_path and html_extra_path. source_suffix = ".rst" -exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store'] +exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store', '**/README.rst'] master_doc = "index" source_encoding = "utf-8" From 47a43086c77aa5559abad4f7595c77eb87a25b66 Mon Sep 17 00:00:00 2001 From: oooo26 <1160141009@qq.com> Date: Fri, 13 Jan 2023 22:32:56 +0800 Subject: [PATCH 2/3] Fix warnings on document --- .../Tutorial/1-glm/plot_1_LinearRegression.py | 4 ++-- docs/Tutorial/1-glm/plot_4_CoxRegression.py | 6 +++--- docs/Tutorial/1-glm/plot_a1_power_of_abess.py | 4 ++-- .../causal_model.png | Bin 38525 -> 45677 bytes .../plot_1_scikit_learn.py | 4 ---- .../plot_2_geomstats.py | 8 ++------ .../plot_3_double_machine_learning.py | 18 +++++++----------- .../5-scikit-learn-connection/plot_4_pyts.py | 12 ++++++------ .../5-scikit-learn-connection/plot_5_dowhy.py | 6 +----- 9 files changed, 23 insertions(+), 39 deletions(-) diff --git a/docs/Tutorial/1-glm/plot_1_LinearRegression.py b/docs/Tutorial/1-glm/plot_1_LinearRegression.py index 9c08575e..452131ef 100644 --- a/docs/Tutorial/1-glm/plot_1_LinearRegression.py +++ b/docs/Tutorial/1-glm/plot_1_LinearRegression.py @@ -21,10 +21,10 @@ # Next, we present an example to show the ``abess`` package can get an optimal estimation. # # Toward optimality: adaptive best-subset selection -# ^^^^^^^^^^^^^^^^^^^^^^ +# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ # # Synthetic dataset -# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +# ~~~~~~~~~~~~~~~~~ # # We generate a design matrix :math:`X` containing :math:`n = 300` observations and each observation has :math:`p = 1000` predictors. # The response variable :math:`y` is linearly related to the first, second, and fifth predictors in :math:`X`: diff --git a/docs/Tutorial/1-glm/plot_4_CoxRegression.py b/docs/Tutorial/1-glm/plot_4_CoxRegression.py index 47b0963a..de561172 100644 --- a/docs/Tutorial/1-glm/plot_4_CoxRegression.py +++ b/docs/Tutorial/1-glm/plot_4_CoxRegression.py @@ -1,7 +1,7 @@ """ -============== +================================= Survival Analysis: Cox Regression -============== +================================= """ ############################################################################### # Cox Proportional Hazards Regression @@ -49,7 +49,7 @@ # which is independent of time. # # Lung Cancer Dataset Analysis -# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ # We are going to apply best subset selection to the NCCTG Lung Cancer Dataset from https://www.kaggle.com/ukveteran/ncctg-lung-cancer-data. # This dataset consists of survival information of patients with advanced lung cancer from the North Central Cancer Treatment Group. # The proportional hazards model allows the analysis of survival data by regression modeling. diff --git a/docs/Tutorial/1-glm/plot_a1_power_of_abess.py b/docs/Tutorial/1-glm/plot_a1_power_of_abess.py index e7fe71de..311caaa1 100644 --- a/docs/Tutorial/1-glm/plot_a1_power_of_abess.py +++ b/docs/Tutorial/1-glm/plot_a1_power_of_abess.py @@ -24,7 +24,7 @@ ###################################### # Simulation Setting -# ^^^^^^^^^^ +# ^^^^^^^^^^^^^^^^^^ # # Both packages are compared in three aspects including the prediction # performance, the variable selection performance, and the computation @@ -66,7 +66,7 @@ ############################################################## # Numerical Results -# ^^^^^^^^^^^^^^^^^^^^ +# ^^^^^^^^^^^^^^^^^ # # For linear regression, we compare three methods in the two packages: # Lasso, OMP and abess. 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--- a/docs/Tutorial/5-scikit-learn-connection/plot_1_scikit_learn.py +++ b/docs/Tutorial/5-scikit-learn-connection/plot_1_scikit_learn.py @@ -99,8 +99,4 @@ plt.show() # %% -# -# -# # sphinx_gallery_thumbnail_path = 'Tutorial/figure/scikit_learn.png' -# diff --git a/docs/Tutorial/5-scikit-learn-connection/plot_2_geomstats.py b/docs/Tutorial/5-scikit-learn-connection/plot_2_geomstats.py index ac1b8937..9e3f0014 100644 --- a/docs/Tutorial/5-scikit-learn-connection/plot_2_geomstats.py +++ b/docs/Tutorial/5-scikit-learn-connection/plot_2_geomstats.py @@ -27,7 +27,7 @@ ############################################################################### # An Example -# --------------------- +# ---------- # Two sets of samples on Hypersphere in 3-dimensional Euclidean Space are created. # The sample points in `data0` are distributed around $[-3/5, 0, 4/5]$, and the sample points in `data1` are distributed around $[3/5, 0, 4/5]$. # The sample size of both is set to 100, and the precision of both is set to 5. @@ -107,7 +107,7 @@ ############################################################################### # Comparison -# ------------- +# ---------- # Here is the comparison of the precision score and the recall score with `abess` and `scikit-learn`, and # the comparison of the running time with `abess` and `scikit-learn`. # @@ -212,8 +212,4 @@ # And the running time with `abess` is only slightly slower than that without `abess`. # %% -# -# -# # sphinx_gallery_thumbnail_path = 'Tutorial/figure/geomstats.png' -# diff --git a/docs/Tutorial/5-scikit-learn-connection/plot_3_double_machine_learning.py b/docs/Tutorial/5-scikit-learn-connection/plot_3_double_machine_learning.py index b99bbd60..4fce87f9 100644 --- a/docs/Tutorial/5-scikit-learn-connection/plot_3_double_machine_learning.py +++ b/docs/Tutorial/5-scikit-learn-connection/plot_3_double_machine_learning.py @@ -2,9 +2,9 @@ ================================ Work with DoubleML ================================ -Double machine learning offer a debiased way for estimating low-dimensional parameter of interest in the presence of +Double machine learning [1]_ offer a debiased way for estimating low-dimensional parameter of interest in the presence of high-dimensional nuisance. Many machine learning methods can be used to estimate the nuisance parameters, such as random -forests, lasso or post-lasso, neural nets, boosted regression trees, and so on. The Python package ``DoubleML`` provide an +forests, lasso or post-lasso, neural nets, boosted regression trees, and so on. The Python package ``DoubleML`` [2]_ provide an implementation of the double machine learning. It's built on top of scikit-learn and is an excellent package. The object-oriented implementation of ``DoubleML`` is very flexible, in particular functionalities to estimate double machine learning models and to perform statistical inference via the methods fit, bootstrap, confint, p_adjust and tune. @@ -12,7 +12,7 @@ ############################################################################### # -# In fact, ``abess`` also works well with the package ``DoubleML``. Here is an example of using ``abess`` to solve such +# In fact, ``abess`` [3]_ also works well with the package ``DoubleML``. Here is an example of using ``abess`` to solve such # a problem, and we will compare it to the lasso regression. @@ -28,7 +28,7 @@ ############################################################################### # Partially linear regression (PLR) model -# ^^^^^^^^^^^^^^^^^ +# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ # PLR models take the form # # .. math:: @@ -47,7 +47,7 @@ ############################################################################### # Data -# """"""""""""""" +# """" # We simulate the data from a PLR model, which both :math:`m_0` and :math:`g_0` are low-dimensional linear combinations # of :math:`X`, and we save the data as ``DoubleMLData`` class. @@ -67,7 +67,7 @@ ############################################################################### # Model fitting with ``abess`` -# """"""""""""""" +# """""""""""""""""""""""""""" # Based on the simulated data, now we are going to illustrate how to integrate the ``abess`` with ``DoubleML``. To # estimate the PLR model with the double machine learning algorithm, first we need to choose a learner to estimate the # nuisance parameters :math:`\eta_0 = (m_0, g_0)`. Considering the sparsity of the data, we can use the adaptive best @@ -90,7 +90,7 @@ ############################################################################### # Comparison with lasso -# ^^^^^^^^^^^^^^^^^ +# ^^^^^^^^^^^^^^^^^^^^^ # The lasso regression is a shrinkage and variable selection method for regression models, which can also be used in # high-dimensional setting. Here, we compare the abess regression with the lasso regression at different variable # dimensions. @@ -190,8 +190,4 @@ # # %% -# -# -# # sphinx_gallery_thumbnail_path = 'Tutorial/figure/doubleml.png' -# diff --git a/docs/Tutorial/5-scikit-learn-connection/plot_4_pyts.py b/docs/Tutorial/5-scikit-learn-connection/plot_4_pyts.py index 3c5d0313..ae93bef9 100644 --- a/docs/Tutorial/5-scikit-learn-connection/plot_4_pyts.py +++ b/docs/Tutorial/5-scikit-learn-connection/plot_4_pyts.py @@ -1,7 +1,7 @@ """ -================================ +============== Work with pyts -================================ +============== ``pyts`` is a Python package dedicated to time series classification. It aims to make time series classification easily accessible by providing preprocessing and utility tools, and implementations of several time series classification algorithms. In this example, we will mainly focus on the shapelets-based algorithms. @@ -29,7 +29,7 @@ # %% # Data -# """"""""""""""" +# """" # In this example, we use the buint-in coffee dataset in ``pyts`` to perform shapelets learning. It has two classes, # 0 and 1. So, this is a binary classification task. Both train dataset and test dataset have 28 time series and the # dimension of each time series is 286. We plot the time series in the train dataset. @@ -53,7 +53,7 @@ # %% # Learning shapelets with ``abess`` -# """"""""""""""""""""""""""""""""""" +# """"""""""""""""""""""""""""""""" # To select discriminant shapelets, we first collect all subsequences with predefined length and step as the candidates. # Then we transform the original time series by computing the distance between them to each subsequence. Therefore, # the original time series are transformed to some ultra high dimensional vectors. Finally, we perform binary @@ -132,7 +132,7 @@ def fit_predict(self, size=None): # %% # Learning shapelets with ``pyts`` -# """""""""""""""""""""""""""""""""" +# """""""""""""""""""""""""""""""" # We compare our method with the one implemented in ``pyts``, which is a two-step procedure. First, it selects discriminant # shapelets based on mutual information. Then, a support vector machine is applied to perform binary classification with # transformed time series based on those selected shapelets. Analogously, we print the performance and execution time. @@ -154,7 +154,7 @@ def fit_predict(self, size=None): # %% # Plot: learned shapelets -# """"""""""""""""""""""""" +# """"""""""""""""""""""" # The following figure shows the discriminant shapelets selected by these two methods. # %% diff --git a/docs/Tutorial/5-scikit-learn-connection/plot_5_dowhy.py b/docs/Tutorial/5-scikit-learn-connection/plot_5_dowhy.py index 84cd09b6..d7847be8 100644 --- a/docs/Tutorial/5-scikit-learn-connection/plot_5_dowhy.py +++ b/docs/Tutorial/5-scikit-learn-connection/plot_5_dowhy.py @@ -299,8 +299,4 @@ def simhima (n,p,alpha,beta,seed,binary=False): # # %% -# -# -# -# sphinx_gallery_thumbnail_path = '../../Tutorial/figure/dowhy.png' -# +# sphinx_gallery_thumbnail_path = 'Tutorial/figure/dowhy.png' From 8a964e8b296e211b7558928767db411e73cf68af Mon Sep 17 00:00:00 2001 From: oooo26 <1160141009@qq.com> Date: Fri, 13 Jan 2023 22:41:52 +0800 Subject: [PATCH 3/3] Update changelog --- docs/Changelog.rst | 9 ++++++--- 1 file changed, 6 insertions(+), 3 deletions(-) diff --git a/docs/Changelog.rst b/docs/Changelog.rst index f143a4b1..03181403 100644 --- a/docs/Changelog.rst +++ b/docs/Changelog.rst @@ -1,18 +1,21 @@ Changelog ========= -Unreleased ----------- +Version 0.4.6 +------------- - R package - Python package + - Support `score` function for all GLM estimators. - Rearrange some arguments to improve legibility. Please check `here `__ for the latest API. + - Better docstring, e.g. move important arguments to the front. + - Combine `metrics.py` and `functions.py`. - C++ - - Support the base model for GLM. The Sparse GLM model can be implemented easilier. + - Support the base model for GLM. The Sparse GLM model can be implemented much easilier. - Re-write logistic, poisson and gamma regression on the basis of GLM base model. Versions 0.4.2 -- 0.4.5