add gallery-sphinx

classo/solver.py

@@ -650,7 +650,11 @@ def __init__(self, method = "not specified"):
     def __repr__(self):
         string = "\n     numerical_method = " + str(self.numerical_method)
         string += "\n     rescaled lam : " + str(self.rescaled_lam)
-        string += "\n     threshold = " + str(round(self.threshold, 3))
+        if self.threshold is None:
+            string += "\n     threshold : average of the absolute value of beta"
+        else:
+            string += "\n     threshold = " + str(round(self.threshold, 3))
+
         if type(self.lam) is str:
             string += "\n     lam : " + self.lam
         else:

docs/source/auto_examples/index.rst

@@ -0,0 +1,83 @@
+:orphan:
+
+
+
+.. _sphx_glr_auto_examples:
+
+
+Examples Gallery
+================
+
+Bellow is a gallery of examples.
+
+
+
+.. raw:: html
+
+    <div class="sphx-glr-thumbcontainer" tooltip="Let&#x27;s present what classo does when using its default parameters on synthetic data">
+
+.. only:: html
+
+ .. figure:: /auto_examples/images/thumb/sphx_glr_plot_basic_example_thumb.png
+     :alt: Basic example
+
+     :ref:`sphx_glr_auto_examples_plot_basic_example.py`
+
+.. raw:: html
+
+    </div>
+
+
+.. toctree::
+   :hidden:
+
+   /auto_examples/plot_basic_example
+
+.. raw:: html
+
+    <div class="sphx-glr-thumbcontainer" tooltip="Let&#x27;s present how one can specify different aspects of the problem  formulation and model selec...">
+
+.. only:: html
+
+ .. figure:: /auto_examples/images/thumb/sphx_glr_plot_advanced_example_thumb.png
+     :alt: Advanced example
+
+     :ref:`sphx_glr_auto_examples_plot_advanced_example.py`
+
+.. raw:: html
+
+    </div>
+
+
+.. toctree::
+   :hidden:
+
+   /auto_examples/plot_advanced_example
+.. raw:: html
+
+    <div class="sphx-glr-clear"></div>
+
+
+
+.. only :: html
+
+ .. container:: sphx-glr-footer
+    :class: sphx-glr-footer-gallery
+
+
+  .. container:: sphx-glr-download sphx-glr-download-python
+
+    :download:`Download all examples in Python source code: auto_examples_python.zip </auto_examples/auto_examples_python.zip>`
+
+
+
+  .. container:: sphx-glr-download sphx-glr-download-jupyter
+
+    :download:`Download all examples in Jupyter notebooks: auto_examples_jupyter.zip </auto_examples/auto_examples_jupyter.zip>`
+
+
+.. only:: html
+
+ .. rst-class:: sphx-glr-signature
+
+    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_

docs/source/auto_examples/plot_advanced_example.ipynb

@@ -0,0 +1,162 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "%matplotlib inline"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# Advanced example\n\nLet's present how one can specify different aspects of the problem \nformulation and model selection strategy on classo, using synthetic data\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from classo import classo_problem, random_data\nimport numpy as np"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Generate the data\nThis code snippet generates a problem instance with sparse \u00df in dimension\nd=100 (sparsity d_nonzero=5). The design matrix X comprises n=100 samples generated from an i.i.d standard normal\ndistribution. The dimension of the constraint matrix C is d x k matrix. The noise level is \u03c3=0.5. \nThe input `zerosum=True` implies that C is the all-ones vector and C\u00df=0. The n-dimensional outcome vector y\nand the regression vector \u00df is then generated to satisfy the given constraints. \n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "m, d, d_nonzero, k, sigma = 100, 200, 5, 1, 0.5\n(X, C, y), sol = random_data(m, d, d_nonzero, k, sigma, zerosum=True, seed=1)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Define the classo instance\nNext we can define a default c-lasso problem instance with the generated data:\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "problem = classo_problem(X, y, C)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Change the parameters\nLet's see some example of change in the parameters\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "problem.formulation.huber                   = True\nproblem.formulation.concomitant             = False\nproblem.model_selection.CV                  = True\nproblem.model_selection.LAMfixed            = True\nproblem.model_selection.PATH                = True\nproblem.model_selection.StabSelparameters.method = 'max'\nproblem.model_selection.CVparameters.seed = 1\nproblem.model_selection.LAMfixedparameters.rescaled_lam = True\nproblem.model_selection.LAMfixedparameters.lam = .1"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Check parameters\nYou can look at the generated problem instance by typing:\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "print(problem)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Solve optimization problems\n We only use stability selection as default model selection strategy. \nThe command also allows you to inspect the computed stability profile for all variables \nat the theoretical \u03bb\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "problem.solve()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Visualisation\nAfter completion, the results of the optimization and model selection routines \ncan be visualized using\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "print(problem.solution)"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.9.0"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}

docs/source/auto_examples/plot_advanced_example.py

@@ -0,0 +1,71 @@
+r"""
+Advanced example
+===============
+
+Let's present how one can specify different aspects of the problem 
+formulation and model selection strategy on classo, using synthetic data
+"""
+
+from classo import classo_problem, random_data
+import numpy as np
+
+# %%
+#  Generate the data
+# ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+# This code snippet generates a problem instance with sparse ß in dimension
+# d=100 (sparsity d_nonzero=5). The design matrix X comprises n=100 samples generated from an i.i.d standard normal
+# distribution. The dimension of the constraint matrix C is d x k matrix. The noise level is σ=0.5. 
+# The input `zerosum=True` implies that C is the all-ones vector and Cß=0. The n-dimensional outcome vector y
+# and the regression vector ß is then generated to satisfy the given constraints. 
+
+m, d, d_nonzero, k, sigma = 100, 200, 5, 1, 0.5
+(X, C, y), sol = random_data(m, d, d_nonzero, k, sigma, zerosum=True, seed=1)
+
+# %%
+# Define the classo instance
+# ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+# Next we can define a default c-lasso problem instance with the generated data:
+
+problem = classo_problem(X, y, C) 
+
+
+# %%
+# Change the parameters
+# ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+# Let's see some example of change in the parameters
+
+problem.formulation.huber                   = True
+problem.formulation.concomitant             = False
+problem.model_selection.CV                  = True
+problem.model_selection.LAMfixed            = True
+problem.model_selection.PATH                = True
+problem.model_selection.StabSelparameters.method = 'max'
+problem.model_selection.CVparameters.seed = 1
+problem.model_selection.LAMfixedparameters.rescaled_lam = True
+problem.model_selection.LAMfixedparameters.lam = .1
+
+
+
+# %%
+# Check parameters
+# ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+# You can look at the generated problem instance by typing:
+
+print(problem)
+
+# %%
+#  Solve optimization problems
+# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+#  We only use stability selection as default model selection strategy. 
+# The command also allows you to inspect the computed stability profile for all variables 
+# at the theoretical λ
+
+problem.solve()
+
+# %%
+# Visualisation
+# ^^^^^^^^^^^^^^^
+# After completion, the results of the optimization and model selection routines 
+# can be visualized using
+
+print(problem.solution)