Update docs for dev (build 3999)

Author: Circle CI

dev/.buildinfo

@@ -1,4 +1,4 @@
 # Sphinx build info version 1
 # This file records the configuration used when building these files. When it is not found, a full rebuild will be done.
-config: d0bb3e61215340052b6b83f7c53f4cfe
+config: faf4775a86fa92dc2678830c5729b41f
 tags: 645f666f9bcd5a90fca523b33c5a78b7

dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

@@ -23,7 +23,7 @@
         f"{BENCHMARK_PATH.resolve()}"
     )
 
-_, save_file = run_benchmark(
+save_file = run_benchmark(
     BENCHMARK_PATH,
     solver_names=['Python-PGD[use_acceleration=False]', 'R-PGD'],
     dataset_names=["Simulated[n_features=5000,n_samples=100,rho=0]"],

dev/_downloads/46d3545d2de3fcee6756b7d2ee7fb39f/plot_run_benchmark_python_R.py

@@ -0,0 +1,86 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Running an existing benchmark\n\nThis Example demonstrates how to run an existing benchmark with benchopt.\nIt uses the `benchopt_run` helper function to run the benchmark, which runs\nprogrammatically the equivalent of the command line interface:\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from benchopt.helpers.run_examples import benchopt_run"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "To run the benchmark, just execute:\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "benchopt_run('minimal_benchmark', n=20, r=2)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "This runs the benchmark named ``minimal_benchmark`` located in the\n``examples`` folder.\n\nThe parameters ``n``  controls the number of point on the curves, while ``r``\ncontrols the number of repetitions for each solver. The repetitions are used\nto compute the median and quartiles of the curves.\n\nTo get a more precise curve, you can increase ``n`` and ``r``:\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "benchopt_run('minimal_benchmark', n=30, r=5)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Here, the display is not ideal because both solvers reach convergence very\nquickly. You can change the display by selecting the scale of the axis in\nthe plot configuration panel.\nGo to the *Change plot* banner at the top of the HTML file, and change the\nscale to ``loglog`` on the drop down menu.\n\nNote that for convenience, this can be saved as a view in the configuration\nof the benchmark. See `here <plot_configs>` for more details.\nHere, clicking on ``Subopt. (log)`` in the *Available plots* above the\nfigure will take you to a view with the right scale, and looking at\nsuboptimality instead of objective value.\n\n"
+      ]
+    }
+  ],
+  "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": "3.12.12"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}

dev/_downloads/586c9ec48a37fd397fb981a992a54250/run_minimal_benchmark.zip

@@ -0,0 +1,40 @@
+"""Running an existing benchmark
+=============================
+
+This Example demonstrates how to run an existing benchmark with benchopt.
+It uses the `benchopt_run` helper function to run the benchmark, which runs
+programmatically the equivalent of the command line interface:
+"""
+
+from benchopt.helpers.run_examples import benchopt_run
+
+# %%
+# To run the benchmark, just execute:
+#
+
+benchopt_run('minimal_benchmark', n=20, r=2)
+
+# %%
+# This runs the benchmark named ``minimal_benchmark`` located in the
+# ``examples`` folder.
+#
+# The parameters ``n``  controls the number of point on the curves, while ``r``
+# controls the number of repetitions for each solver. The repetitions are used
+# to compute the median and quartiles of the curves.
+#
+# To get a more precise curve, you can increase ``n`` and ``r``:
+
+benchopt_run('minimal_benchmark', n=30, r=5)
+
+# %%
+# Here, the display is not ideal because both solvers reach convergence very
+# quickly. You can change the display by selecting the scale of the axis in
+# the plot configuration panel.
+# Go to the *Change plot* banner at the top of the HTML file, and change the
+# scale to ``loglog`` on the drop down menu.
+#
+# Note that for convenience, this can be saved as a view in the configuration
+# of the benchmark. See :ref:`here <plot_configs>` for more details.
+# Here, clicking on ``Subopt. (log)`` in the *Available plots* above the
+# figure will take you to a view with the right scale, and looking at
+# suboptimality instead of objective value.

dev/_downloads/6a65fd6b50c38318be8d3d22e519e51b/run_minimal_benchmark.ipynb

@@ -15,7 +15,7 @@
       },
       "outputs": [],
       "source": [
-        "from pathlib import Path\nimport matplotlib.pyplot as plt\nfrom benchopt import run_benchmark\nfrom benchopt.benchmark import Benchmark\nfrom benchopt.plotting import plot_benchmark\nfrom benchopt.plotting.helpers import reset_solver_styles\n\n\nBENCHMARK_PATH = (\n    Path().resolve().parent / 'benchmarks' / 'benchmark_logreg_l2'\n)\n\ntry:\n\n    _, save_file = run_benchmark(\n        BENCHMARK_PATH,\n        solver_names=['sklearn[liblinear]', 'sklearn[newton-cg]', 'lightning'],\n        dataset_names=[\"Simulated[n_features=500,n_samples=200]\"],\n        objective_filters=['L2 Logistic Regression[lmbd=1.0]'],\n        max_runs=100, timeout=20, n_repetitions=15,\n        plot_result=False, show_progress=True\n    )\n\nexcept RuntimeError:\n    raise RuntimeError(\n        \"This example can only work when Logreg-l2 benchmark is cloned in a \"\n        \"`benchmarks` folder. Please run:\\n\"\n        \"$ git clone https://github.com/benchopt/benchmark_logreg_l2 \"\n        f\"{BENCHMARK_PATH.resolve()}\"\n    )\n\nreset_solver_styles()\n\nfigs = plot_benchmark(\n    save_file, benchmark=Benchmark(BENCHMARK_PATH), html=False\n)\nplt.show()"
+        "from pathlib import Path\nimport matplotlib.pyplot as plt\nfrom benchopt import run_benchmark\nfrom benchopt.benchmark import Benchmark\nfrom benchopt.plotting import plot_benchmark\nfrom benchopt.plotting.helpers import reset_solver_styles\n\n\nBENCHMARK_PATH = (\n    Path().resolve().parent / 'benchmarks' / 'benchmark_logreg_l2'\n)\n\ntry:\n\n    save_file = run_benchmark(\n        BENCHMARK_PATH,\n        solver_names=['sklearn[liblinear]', 'sklearn[newton-cg]', 'lightning'],\n        dataset_names=[\"Simulated[n_features=500,n_samples=200]\"],\n        objective_filters=['L2 Logistic Regression[lmbd=1.0]'],\n        max_runs=100, timeout=20, n_repetitions=15,\n        plot_result=False, show_progress=True\n    )\n\nexcept RuntimeError:\n    raise RuntimeError(\n        \"This example can only work when Logreg-l2 benchmark is cloned in a \"\n        \"`benchmarks` folder. Please run:\\n\"\n        \"$ git clone https://github.com/benchopt/benchmark_logreg_l2 \"\n        f\"{BENCHMARK_PATH.resolve()}\"\n    )\n\nreset_solver_styles()\n\nfigs = plot_benchmark(\n    save_file, benchmark=Benchmark(BENCHMARK_PATH), html=False\n)\nplt.show()"
       ]
     }
   ],