Update docs for dev (build 3765)

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: fef05efec0e5752982a248f2c6f81c34
+config: be8c65d642d46f45d8c3b95be69377e5
 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

@@ -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, PLOT_KINDS\nfrom benchopt.plotting.plot_objective_curve import reset_solver_styles_idx\n\n\nBENCHMARK_PATH = (\n    Path().resolve().parent / 'benchmarks' / 'benchmark_logreg_l2'\n)\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\n\nkinds = list(PLOT_KINDS.keys())\nreset_solver_styles_idx()\nfigs = plot_benchmark(save_file, benchmark=Benchmark(BENCHMARK_PATH),\n                      kinds=kinds, html=False)\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, PLOT_KINDS\nfrom benchopt.plotting.plot_objective_curve import reset_solver_styles_idx\n\n\nBENCHMARK_PATH = (\n    Path().resolve().parent / 'benchmarks' / 'benchmark_logreg_l2'\n)\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\n\nkinds = list(PLOT_KINDS.keys())\nreset_solver_styles_idx()\nfigs = plot_benchmark(save_file, benchmark=Benchmark(BENCHMARK_PATH),\n                      kinds=kinds, html=False)\nplt.show()"
       ]
     }
   ],

dev/_downloads/6f1e7a639e0699d6164445b55e6c116d/auto_examples_jupyter.zip

@@ -20,7 +20,7 @@
 
 try:
 
-    save_file = run_benchmark(
+    _, save_file = run_benchmark(
         BENCHMARK_PATH,
         solver_names=['sklearn[liblinear]', 'sklearn[newton-cg]', 'lightning'],
         dataset_names=["Simulated[n_features=500,n_samples=200]"],

dev/_downloads/a3747ad75224b0e1c78c72c3113052a8/plot_run_benchmark_python_R.zip

@@ -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, PLOT_KINDS\nfrom benchopt.plotting.plot_objective_curve import reset_solver_styles_idx\n\n\nBENCHMARK_PATH = Path().resolve().parent / 'benchmarks' / 'benchmark_lasso'\n\nif not BENCHMARK_PATH.exists():\n    raise RuntimeError(\n        \"This example can only work when Lasso benchmark is cloned in the \"\n        \"example folder. Please run:\\n\"\n        \"$ git clone https://github.com/benchopt/benchmark_lasso \"\n        f\"{BENCHMARK_PATH.resolve()}\"\n    )\n\nsave_file = run_benchmark(\n    BENCHMARK_PATH,\n    solver_names=['Python-PGD[use_acceleration=False]', 'R-PGD'],\n    dataset_names=[\"Simulated[n_features=5000,n_samples=100,rho=0]\"],\n    objective_filters=['*[fit_intercept=False,reg=0.5]'],\n    max_runs=100, timeout=100, n_repetitions=5,\n    plot_result=False, show_progress=False\n)\n\n\nkinds = list(PLOT_KINDS.keys())\nreset_solver_styles_idx()\nfigs = plot_benchmark(\n    save_file, benchmark=Benchmark(BENCHMARK_PATH), kinds=kinds, 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, PLOT_KINDS\nfrom benchopt.plotting.plot_objective_curve import reset_solver_styles_idx\n\n\nBENCHMARK_PATH = Path().resolve().parent / 'benchmarks' / 'benchmark_lasso'\n\nif not BENCHMARK_PATH.exists():\n    raise RuntimeError(\n        \"This example can only work when Lasso benchmark is cloned in the \"\n        \"example folder. Please run:\\n\"\n        \"$ git clone https://github.com/benchopt/benchmark_lasso \"\n        f\"{BENCHMARK_PATH.resolve()}\"\n    )\n\n_, save_file = run_benchmark(\n    BENCHMARK_PATH,\n    solver_names=['Python-PGD[use_acceleration=False]', 'R-PGD'],\n    dataset_names=[\"Simulated[n_features=5000,n_samples=100,rho=0]\"],\n    objective_filters=['*[fit_intercept=False,reg=0.5]'],\n    max_runs=100, timeout=100, n_repetitions=5,\n    plot_result=False, show_progress=False\n)\n\n\nkinds = list(PLOT_KINDS.keys())\nreset_solver_styles_idx()\nfigs = plot_benchmark(\n    save_file, benchmark=Benchmark(BENCHMARK_PATH), kinds=kinds, html=False\n)\nplt.show()"
       ]
     }
   ],