test_optimagic.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3ecf3b40",
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "050a279a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import math\n",
    "\n",
    "import optimagic as om\n",
    "import scipy\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "363eaf46",
   "metadata": {},
   "source": [
    "# Numerical Optimization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "513e9f45",
   "metadata": {},
   "outputs": [],
   "source": [
    "def square(params):\n",
    "    x = params\n",
    "    return (x-4)**2\n",
    "\n",
    "def sq_gradient(params):\n",
    "    x = params\n",
    "    return 2*(x-4)\n",
    "\n",
    "def sphere(params):\n",
    "    return params @ params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8000546f",
   "metadata": {},
   "outputs": [],
   "source": [
    "bounds = om.Bounds(lower = 5, upper = 10)\n",
    "lbfgsb_res = om.minimize(\n",
    "    fun=square,\n",
    "    params=9,\n",
    "    algorithm=\"scipy_lbfgsb\",\n",
    "    jac = sq_gradient,\n",
    "    bounds = bounds,\n",
    "    logging = 'my_log.db',\n",
    "    log_options = {'if_database_exists':\"replace\"},\n",
    ")\n",
    "\n",
    "lbfgsb_res.params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "de829bff",
   "metadata": {},
   "outputs": [],
   "source": [
    "lbfgsb_res = om.minimize(\n",
    "    fun=sphere,\n",
    "    params=np.arange(5),\n",
    "    algorithm=\"scipy_lbfgsb\",\n",
    "    constraints = om.FixedConstraint(selector = lambda params : params[4]),\n",
    ")\n",
    "\n",
    "lbfgsb_res.params.round(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ca6f956a",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = om.params_plot(lbfgsb_res)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "14d981b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "lbfgsb_res = om.minimize(\n",
    "    fun=sphere,\n",
    "    params=np.array([0.2,0.8]),\n",
    "    algorithm=\"scipy_lbfgsb\",\n",
    "    constraints = om.ProbabilityConstraint(selector = lambda params : params),\n",
    ")\n",
    "\n",
    "lbfgsb_res.params.round(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b72ea427",
   "metadata": {},
   "outputs": [],
   "source": [
    "nm_res = om.minimize(\n",
    "    fun=square,\n",
    "    params=9,\n",
    "    algorithm=\"scipy_neldermead\",\n",
    "    bounds = bounds,\n",
    ")\n",
    "\n",
    "lbfgsb_res.params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "204341a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "@om.mark.least_squares\n",
    "def ls_sphere(params):\n",
    "    return params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "93db4eda",
   "metadata": {},
   "outputs": [],
   "source": [
    "res = om.minimize(\n",
    "    fun = ls_sphere,\n",
    "    params =  np.arange(5),\n",
    "    algorithm  = 'scipy_neldermead',\n",
    ")\n",
    "res.params.round(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7044bbf4",
   "metadata": {},
   "outputs": [],
   "source": [
    "results = {\"lbfgsb\": lbfgsb_res, \"nelder_mead\": nm_res}\n",
    "om.criterion_plot(results, max_evaluations = 30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4febb153",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = om.params_plot(\n",
    "    lbfgsb_res,\n",
    "    max_evaluations=300,\n",
    "    # optionally select a subset of parameters to plot\n",
    "    selector=lambda params: params,\n",
    ")\n",
    "fig.show(renderer=\"png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d82affd6",
   "metadata": {},
   "source": [
    "reader = om.OptimizeLogReader(\"my_log.db\")\n",
    "reader.read_history()['params']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f38b7fb4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "b017ba89",
   "metadata": {},
   "source": [
    "# Numerical Differerntiation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8fdf9ea8",
   "metadata": {},
   "outputs": [],
   "source": [
    "def sphere(params):\n",
    "    return params @ params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f3ec89a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "params = np.arange(5)\n",
    "fd = om.first_derivative(\n",
    "    func = sphere,\n",
    "    params = params,\n",
    "    method = 'backward',\n",
    "    bounds = om.Bounds(lower = params, upper = params + 5)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "89a7e5de",
   "metadata": {},
   "outputs": [],
   "source": [
    "fd.derivative"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "25d10fd3",
   "metadata": {},
   "outputs": [],
   "source": [
    "sd = om.second_derivative(\n",
    "    func = sphere,\n",
    "    params = np.arange(5),\n",
    "    n_cores = 6,\n",
    "    method = 'forward',\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1ce5b593",
   "metadata": {},
   "outputs": [],
   "source": [
    "sd.derivative.round(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37884f39",
   "metadata": {},
   "source": [
    "##  Objective functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "064f8faf",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "def sphere(params):\n",
    "    return (params[\"value\"]**2).sum()\n",
    "\n",
    "@om.mark.least_squares\n",
    "def ls_spheres(params):\n",
    "    return params['value']\n",
    "params = pd.DataFrame(\n",
    "    data = {\"value\" : [1,2] , \"bounds\" : [0,1]}\n",
    ")\n",
    "om.minimize(\n",
    "    fun = ls_sphere,\n",
    "    params = params,\n",
    "    algorithm = 'scipy_lbfgsb',\n",
    "    bounds = om.Bounds(lower = params['bounds'])\n",
    ").params.round(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cbfef9ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "def shifted_sphere(x, offset):\n",
    "    return (x-offset) @ (x - offset)\n",
    "\n",
    "om.minimize(\n",
    "    fun = shifted_sphere,\n",
    "    params = np.arange(5),\n",
    "    algorithm = 'scipy_lbfgsb',\n",
    "    fun_kwargs = {'offset': 2}\n",
    ").params.round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c3d9ae24",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.arange(20)\n",
    "x = np.append(x,0)\n",
    "x = np.delete(x, [0])\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "000f3d71",
   "metadata": {},
   "outputs": [],
   "source": [
    "om.algos.GradientBased.Scalar.Available"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6d2cd1ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "x0 = np.array([2,3,4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3cfd7f00",
   "metadata": {},
   "outputs": [],
   "source": [
    "x1 = np.ones_like(x0)\n",
    "x1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc92ad08",
   "metadata": {},
   "source": [
    "# Logging"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "60b79f31",
   "metadata": {},
   "outputs": [],
   "source": [
    "log_options = om.SQLiteLogOptions(\n",
    "    \"my_log.db\",\n",
    "    fast_logging=True,\n",
    "    if_database_exists=om.ExistenceStrategy.REPLACE,\n",
    ")\n",
    "\n",
    "res = om.minimize(\n",
    "    fun=sphere,\n",
    "    params=np.arange(5),\n",
    "    algorithm=\"scipy_lbfgsb\",\n",
    "    logging=log_options,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f82c8b90",
   "metadata": {},
   "outputs": [],
   "source": [
    "reader = res.logger\n",
    "reader.read_start_params()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "52d87e1e",
   "metadata": {},
   "outputs": [],
   "source": [
    "reader.read_history()['fun']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0c50be34",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.insert(0, \"/home/gaurav/Documents/\")\n",
    "import os\n",
    "import optimagic as om\n",
    "fig = om.criterion_plot(res)\n",
    "fig.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "031db83c",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(om.__file__)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "40adccb4",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = om.params_plot(\"my_log.db\")\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3250cfab",
   "metadata": {},
   "outputs": [],
   "source": [
    "params = {\"alpha\": 0, \"beta\": 0, \"gamma\": 0, \"delta\": 0}\n",
    "for key in params:\n",
    "    print(key)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dbd11ed4",
   "metadata": {},
   "outputs": [],
   "source": [
    "res = om.minimize(\n",
    "    fun= lambda x : x @ x,\n",
    "    params=np.arange(5),\n",
    "    algorithm=\"scipy_lbfgsb\",\n",
    "    logging= om.SQLiteLogOptions(\n",
    "    \"my_log.db\", if_database_exists=om.ExistenceStrategy.REPLACE,),\n",
    ")\n",
    "fig = om.criterion_plot(\"my_log.db\")\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "07ed3045",
   "metadata": {},
   "outputs": [],
   "source": [
    "import optimagic as om\n",
    "import numpy as np\n",
    "log_options = om.SQLiteLogOptions(\"my_log.db\", if_database_exists=om.ExistenceStrategy.REPLACE,)\n",
    "res = om.minimize(fun=lambda x: x @ x, params=np.arange(5), algorithm=\"scipy_lbfgsb\", logging=log_options,)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4ca154e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "om.minimize(\n",
    "    fun= lambda x : x@x,\n",
    "    params=np.arange(5),\n",
    "    algorithm=\"scipy_lbfgsb\",\n",
    "    logging=om.SQLiteLogOptions(\n",
    "    \"my_log.db\", if_database_exists=om.ExistenceStrategy.REPLACE\n",
    "),)\n",
    "fig = om.criterion_plot(\"my_log.db\")\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "24b26ccc",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "path = Path(\"test_log.db\")\n",
    "\n",
    "om.minimize(\n",
    "    fun= lambda x : x@x,\n",
    "    params=np.arange(10),\n",
    "    algorithm=\"scipy_lbfgsb\",\n",
    "    logging=om.SQLiteLogOptions(\n",
    "    path, if_database_exists=om.ExistenceStrategy.REPLACE\n",
    "),)\n",
    "fig = om.criterion_plot(path)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "470c69ee",
   "metadata": {},
   "source": [
    "# Benchmark test bug scipy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1d758868",
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy\n",
    "import pickle\n",
    "import optimagic as om"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2608dd66",
   "metadata": {},
   "outputs": [],
   "source": [
    "# print(scipy.__version__)\n",
    "# problems = om.get_benchmark_problems(\"more_wild\")\n",
    "# optimizers = {\n",
    "#     \"scipy_lbfgsb\"\n",
    "# }\n",
    "# results_scipy_1_15_2 = om.run_benchmark(problems,\n",
    "#                        optimizers)\n",
    "# with open(\"results_scipy_1_15_2\", \"wb\") as f:\n",
    "#     pickle.dump(results_scipy_1_15_2, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a5ed276a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.14.0\n"
     ]
    }
   ],
   "source": [
    "# print(scipy.__version__)\n",
    "# problems = om.get_benchmark_problems(\"more_wild\")\n",
    "# optimizers = {\n",
    "#     \"scipy_lbfgsb\"\n",
    "# }\n",
    "# results_scipy_1_14_0 = om.run_benchmark(problems,\n",
    "#                        optimizers)\n",
    "# with open(\"results_scipy_1_14_0\", \"wb\") as f:\n",
    "#     pickle.dump(results_scipy_1_14_0, f)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b7243997",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys([('linear_full_rank_good_start', 'scipy_1-15_2_lbfgsb'), ('linear_full_rank_bad_start', 'scipy_1-15_2_lbfgsb'), ('linear_rank_one_good_start', 'scipy_1-15_2_lbfgsb'), ('linear_rank_one_bad_start', 'scipy_1-15_2_lbfgsb'), ('linear_rank_one_zero_columns_rows_good_start', 'scipy_1-15_2_lbfgsb'), ('linear_rank_one_zero_columns_rows_bad_start', 'scipy_1-15_2_lbfgsb'), ('rosenbrock_good_start', 'scipy_1-15_2_lbfgsb'), ('rosenbrock_bad_start', 'scipy_1-15_2_lbfgsb'), ('helical_valley_good_start', 'scipy_1-15_2_lbfgsb'), ('helical_valley_bad_start', 'scipy_1-15_2_lbfgsb'), ('powell_singular_good_start', 'scipy_1-15_2_lbfgsb'), ('powell_singular_bad_start', 'scipy_1-15_2_lbfgsb'), ('freudenstein_roth_good_start', 'scipy_1-15_2_lbfgsb'), ('freudenstein_roth_bad_start', 'scipy_1-15_2_lbfgsb'), ('bard_good_start', 'scipy_1-15_2_lbfgsb'), ('bard_bad_start', 'scipy_1-15_2_lbfgsb'), ('kowalik_osborne', 'scipy_1-15_2_lbfgsb'), ('meyer', 'scipy_1-15_2_lbfgsb'), ('watson_6_good_start', 'scipy_1-15_2_lbfgsb'), ('watson_6_bad_start', 'scipy_1-15_2_lbfgsb'), ('watson_9_good_start', 'scipy_1-15_2_lbfgsb'), ('watson_9_bad_start', 'scipy_1-15_2_lbfgsb'), ('watson_12_good_start', 'scipy_1-15_2_lbfgsb'), ('watson_12_bad_start', 'scipy_1-15_2_lbfgsb'), ('box_3d', 'scipy_1-15_2_lbfgsb'), ('jennrich_sampson', 'scipy_1-15_2_lbfgsb'), ('brown_dennis_good_start', 'scipy_1-15_2_lbfgsb'), ('brown_dennis_bad_start', 'scipy_1-15_2_lbfgsb'), ('chebyquad_6', 'scipy_1-15_2_lbfgsb'), ('chebyquad_7', 'scipy_1-15_2_lbfgsb'), ('chebyquad_8', 'scipy_1-15_2_lbfgsb'), ('chebyquad_9', 'scipy_1-15_2_lbfgsb'), ('chebyquad_10', 'scipy_1-15_2_lbfgsb'), ('chebyquad_11', 'scipy_1-15_2_lbfgsb'), ('brown_almost_linear', 'scipy_1-15_2_lbfgsb'), ('osborne_one', 'scipy_1-15_2_lbfgsb'), ('osborne_two_good_start', 'scipy_1-15_2_lbfgsb'), ('osborne_two_bad_start', 'scipy_1-15_2_lbfgsb'), ('bdqrtic_8', 'scipy_1-15_2_lbfgsb'), ('bdqrtic_10', 'scipy_1-15_2_lbfgsb'), ('bdqrtic_11', 'scipy_1-15_2_lbfgsb'), ('bdqrtic_12', 'scipy_1-15_2_lbfgsb'), ('cube_5', 'scipy_1-15_2_lbfgsb'), ('cube_6', 'scipy_1-15_2_lbfgsb'), ('cube_8', 'scipy_1-15_2_lbfgsb'), ('mancino_5_good_start', 'scipy_1-15_2_lbfgsb'), ('mancino_5_bad_start', 'scipy_1-15_2_lbfgsb'), ('mancino_8', 'scipy_1-15_2_lbfgsb'), ('mancino_10', 'scipy_1-15_2_lbfgsb'), ('mancino_12_good_start', 'scipy_1-15_2_lbfgsb'), ('mancino_12_bad_start', 'scipy_1-15_2_lbfgsb'), ('heart_eight_good_start', 'scipy_1-15_2_lbfgsb'), ('heart_eight_bad_start', 'scipy_1-15_2_lbfgsb'), ('brown_almost_linear_medium', 'scipy_1-15_2_lbfgsb')])\n",
      "dict_keys([('linear_full_rank_good_start', 'scipy_1-14_0_lbfgsb'), ('linear_full_rank_bad_start', 'scipy_1-14_0_lbfgsb'), ('linear_rank_one_good_start', 'scipy_1-14_0_lbfgsb'), ('linear_rank_one_bad_start', 'scipy_1-14_0_lbfgsb'), ('linear_rank_one_zero_columns_rows_good_start', 'scipy_1-14_0_lbfgsb'), ('linear_rank_one_zero_columns_rows_bad_start', 'scipy_1-14_0_lbfgsb'), ('rosenbrock_good_start', 'scipy_1-14_0_lbfgsb'), ('rosenbrock_bad_start', 'scipy_1-14_0_lbfgsb'), ('helical_valley_good_start', 'scipy_1-14_0_lbfgsb'), ('helical_valley_bad_start', 'scipy_1-14_0_lbfgsb'), ('powell_singular_good_start', 'scipy_1-14_0_lbfgsb'), ('powell_singular_bad_start', 'scipy_1-14_0_lbfgsb'), ('freudenstein_roth_good_start', 'scipy_1-14_0_lbfgsb'), ('freudenstein_roth_bad_start', 'scipy_1-14_0_lbfgsb'), ('bard_good_start', 'scipy_1-14_0_lbfgsb'), ('bard_bad_start', 'scipy_1-14_0_lbfgsb'), ('kowalik_osborne', 'scipy_1-14_0_lbfgsb'), ('meyer', 'scipy_1-14_0_lbfgsb'), ('watson_6_good_start', 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('bdqrtic_12', 'scipy_1-14_0_lbfgsb'), ('cube_5', 'scipy_1-14_0_lbfgsb'), ('cube_6', 'scipy_1-14_0_lbfgsb'), ('cube_8', 'scipy_1-14_0_lbfgsb'), ('mancino_5_good_start', 'scipy_1-14_0_lbfgsb'), ('mancino_5_bad_start', 'scipy_1-14_0_lbfgsb'), ('mancino_8', 'scipy_1-14_0_lbfgsb'), ('mancino_10', 'scipy_1-14_0_lbfgsb'), ('mancino_12_good_start', 'scipy_1-14_0_lbfgsb'), ('mancino_12_bad_start', 'scipy_1-14_0_lbfgsb'), ('heart_eight_good_start', 'scipy_1-14_0_lbfgsb'), ('heart_eight_bad_start', 'scipy_1-14_0_lbfgsb'), ('brown_almost_linear_medium', 'scipy_1-14_0_lbfgsb'), ('linear_full_rank_good_start', 'scipy_1-15_2_lbfgsb'), ('linear_full_rank_bad_start', 'scipy_1-15_2_lbfgsb'), ('linear_rank_one_good_start', 'scipy_1-15_2_lbfgsb'), ('linear_rank_one_bad_start', 'scipy_1-15_2_lbfgsb'), ('linear_rank_one_zero_columns_rows_good_start', 'scipy_1-15_2_lbfgsb'), ('linear_rank_one_zero_columns_rows_bad_start', 'scipy_1-15_2_lbfgsb'), ('rosenbrock_good_start', 'scipy_1-15_2_lbfgsb'), ('rosenbrock_bad_start', 'scipy_1-15_2_lbfgsb'), ('helical_valley_good_start', 'scipy_1-15_2_lbfgsb'), ('helical_valley_bad_start', 'scipy_1-15_2_lbfgsb'), ('powell_singular_good_start', 'scipy_1-15_2_lbfgsb'), ('powell_singular_bad_start', 'scipy_1-15_2_lbfgsb'), ('freudenstein_roth_good_start', 'scipy_1-15_2_lbfgsb'), ('freudenstein_roth_bad_start', 'scipy_1-15_2_lbfgsb'), ('bard_good_start', 'scipy_1-15_2_lbfgsb'), ('bard_bad_start', 'scipy_1-15_2_lbfgsb'), ('kowalik_osborne', 'scipy_1-15_2_lbfgsb'), ('meyer', 'scipy_1-15_2_lbfgsb'), ('watson_6_good_start', 'scipy_1-15_2_lbfgsb'), ('watson_6_bad_start', 'scipy_1-15_2_lbfgsb'), ('watson_9_good_start', 'scipy_1-15_2_lbfgsb'), ('watson_9_bad_start', 'scipy_1-15_2_lbfgsb'), ('watson_12_good_start', 'scipy_1-15_2_lbfgsb'), ('watson_12_bad_start', 'scipy_1-15_2_lbfgsb'), ('box_3d', 'scipy_1-15_2_lbfgsb'), ('jennrich_sampson', 'scipy_1-15_2_lbfgsb'), ('brown_dennis_good_start', 'scipy_1-15_2_lbfgsb'), ('brown_dennis_bad_start', 'scipy_1-15_2_lbfgsb'), ('chebyquad_6', 'scipy_1-15_2_lbfgsb'), ('chebyquad_7', 'scipy_1-15_2_lbfgsb'), ('chebyquad_8', 'scipy_1-15_2_lbfgsb'), ('chebyquad_9', 'scipy_1-15_2_lbfgsb'), ('chebyquad_10', 'scipy_1-15_2_lbfgsb'), ('chebyquad_11', 'scipy_1-15_2_lbfgsb'), ('brown_almost_linear', 'scipy_1-15_2_lbfgsb'), ('osborne_one', 'scipy_1-15_2_lbfgsb'), ('osborne_two_good_start', 'scipy_1-15_2_lbfgsb'), ('osborne_two_bad_start', 'scipy_1-15_2_lbfgsb'), ('bdqrtic_8', 'scipy_1-15_2_lbfgsb'), ('bdqrtic_10', 'scipy_1-15_2_lbfgsb'), ('bdqrtic_11', 'scipy_1-15_2_lbfgsb'), ('bdqrtic_12', 'scipy_1-15_2_lbfgsb'), ('cube_5', 'scipy_1-15_2_lbfgsb'), ('cube_6', 'scipy_1-15_2_lbfgsb'), ('cube_8', 'scipy_1-15_2_lbfgsb'), ('mancino_5_good_start', 'scipy_1-15_2_lbfgsb'), ('mancino_5_bad_start', 'scipy_1-15_2_lbfgsb'), ('mancino_8', 'scipy_1-15_2_lbfgsb'), ('mancino_10', 'scipy_1-15_2_lbfgsb'), ('mancino_12_good_start', 'scipy_1-15_2_lbfgsb'), ('mancino_12_bad_start', 'scipy_1-15_2_lbfgsb'), ('heart_eight_good_start', 'scipy_1-15_2_lbfgsb'), ('heart_eight_bad_start', 'scipy_1-15_2_lbfgsb'), ('brown_almost_linear_medium', 'scipy_1-15_2_lbfgsb')])\n"
     ]
    }
   ],
   "source": [
    "with open(\"scipy_old/results_scipy_1_14_0\", \"rb\") as f:\n",
    "    results_scipy_1_14_0 = pickle.load(f)\n",
    "with open(\"scipy_old/results_scipy_1_15_2\", \"rb\") as f:\n",
    "    results_scipy_1_15_2 = pickle.load(f)\n",
    "problems = om.get_benchmark_problems(\"more_wild\")\n",
    "updated_results_scipy_1_14_0 = {}\n",
    "updated_results_scipy_1_15_2 = {}\n",
    "for key, value in results_scipy_1_14_0.items():\n",
    "    (a,b) = key\n",
    "    updated_results_scipy_1_14_0[(a,'scipy_1-14_0_lbfgsb')] = results_scipy_1_14_0[key]\n",
    "for key, value in results_scipy_1_15_2.items():\n",
    "    (a,b) = key\n",
    "    updated_results_scipy_1_15_2[(a,'scipy_1-15_2_lbfgsb')] = results_scipy_1_15_2[key]\n",
    "\n",
    "print(updated_results_scipy_1_15_2.keys())\n",
    "results = updated_results_scipy_1_14_0 | updated_results_scipy_1_15_2\n",
    "print(results.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "60c3d283",
   "metadata": {},
   "outputs": [
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          0.07544581618655694,
          0.09465020576131689
         ],
         "title": {
          "text": "Share of Function Distance to Optimum<br>Missing From Best So Far"
         }
        },
        "yaxis5": {
         "anchor": "x5",
         "domain": [
          0.9053497942386832,
          0.9245541838134431
         ],
         "title": {
          "text": "Share of Function Distance to Optimum<br>Missing From Best So Far"
         }
        },
        "yaxis50": {
         "anchor": "x50",
         "domain": [
          0.07544581618655694,
          0.09465020576131689
         ],
         "title": {
          "text": "Share of Function Distance to Optimum<br>Missing From Best So Far"
         }
        },
        "yaxis51": {
         "anchor": "x51",
         "domain": [
          0.03772290809327847,
          0.05692729766803842
         ],
         "title": {
          "text": "Share of Function Distance to Optimum<br>Missing From Best So Far"
         }
        },
        "yaxis52": {
         "anchor": "x52",
         "domain": [
          0.03772290809327847,
          0.05692729766803842
         ],
         "title": {
          "text": "Share of Function Distance to Optimum<br>Missing From Best So Far"
         }
        },
        "yaxis53": {
         "anchor": "x53",
         "domain": [
          0,
          0.019204389574759947
         ],
         "title": {
          "text": "Share of Function Distance to Optimum<br>Missing From Best So Far"
         }
        },
        "yaxis54": {
         "anchor": "x54",
         "domain": [
          0,
          0.019204389574759947
         ],
         "title": {
          "text": "Share of Function Distance to Optimum<br>Missing From Best So Far"
         }
        },
        "yaxis6": {
         "anchor": "x6",
         "domain": [
          0.9053497942386832,
          0.9245541838134431
         ],
         "title": {
          "text": "Share of Function Distance to Optimum<br>Missing From Best So Far"
         }
        },
        "yaxis7": {
         "anchor": "x7",
         "domain": [
          0.8676268861454047,
          0.8868312757201646
         ],
         "title": {
          "text": "Share of Function Distance to Optimum<br>Missing From Best So Far"
         }
        },
        "yaxis8": {
         "anchor": "x8",
         "domain": [
          0.8676268861454047,
          0.8868312757201646
         ],
         "title": {
          "text": "Share of Function Distance to Optimum<br>Missing From Best So Far"
         }
        },
        "yaxis9": {
         "anchor": "x9",
         "domain": [
          0.8299039780521262,
          0.8491083676268861
         ],
         "title": {
          "text": "Share of Function Distance to Optimum<br>Missing From Best So Far"
         }
        }
       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = om.convergence_plot(\n",
    "    problems=problems,\n",
    "    results=results,\n",
    "    n_cols=2,\n",
    "    )\n",
    "\n",
    "# fig.write_image(\"scipy_old/convplot.png\", scale=2)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "9bf7b070",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>scipy_lbfgsb</th>\n",
       "      <th>dimensionality</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>problem</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>bard_bad_start</th>\n",
       "      <td>success</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bard_good_start</th>\n",
       "      <td>success</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bdqrtic_10</th>\n",
       "      <td>success</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bdqrtic_11</th>\n",
       "      <td>success</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bdqrtic_12</th>\n",
       "      <td>success</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bdqrtic_8</th>\n",
       "      <td>success</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>box_3d</th>\n",
       "      <td>success</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>brown_almost_linear</th>\n",
       "      <td>success</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>brown_almost_linear_medium</th>\n",
       "      <td>success</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>brown_dennis_bad_start</th>\n",
       "      <td>success</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>brown_dennis_good_start</th>\n",
       "      <td>success</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chebyquad_10</th>\n",
       "      <td>failed</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chebyquad_11</th>\n",
       "      <td>success</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chebyquad_6</th>\n",
       "      <td>success</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chebyquad_7</th>\n",
       "      <td>success</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chebyquad_8</th>\n",
       "      <td>success</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chebyquad_9</th>\n",
       "      <td>success</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cube_5</th>\n",
       "      <td>success</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cube_6</th>\n",
       "      <td>success</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cube_8</th>\n",
       "      <td>success</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freudenstein_roth_bad_start</th>\n",
       "      <td>success</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freudenstein_roth_good_start</th>\n",
       "      <td>success</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>heart_eight_bad_start</th>\n",
       "      <td>success</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>heart_eight_good_start</th>\n",
       "      <td>success</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>helical_valley_bad_start</th>\n",
       "      <td>success</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>helical_valley_good_start</th>\n",
       "      <td>success</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>jennrich_sampson</th>\n",
       "      <td>error</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>kowalik_osborne</th>\n",
       "      <td>error</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>linear_full_rank_bad_start</th>\n",
       "      <td>success</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>linear_full_rank_good_start</th>\n",
       "      <td>success</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>linear_rank_one_bad_start</th>\n",
       "      <td>success</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>linear_rank_one_good_start</th>\n",
       "      <td>success</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>linear_rank_one_zero_columns_rows_bad_start</th>\n",
       "      <td>success</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>linear_rank_one_zero_columns_rows_good_start</th>\n",
       "      <td>success</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mancino_10</th>\n",
       "      <td>success</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mancino_12_bad_start</th>\n",
       "      <td>success</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mancino_12_good_start</th>\n",
       "      <td>success</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mancino_5_bad_start</th>\n",
       "      <td>success</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mancino_5_good_start</th>\n",
       "      <td>success</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mancino_8</th>\n",
       "      <td>success</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>meyer</th>\n",
       "      <td>success</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>osborne_one</th>\n",
       "      <td>error</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>osborne_two_bad_start</th>\n",
       "      <td>failed</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>osborne_two_good_start</th>\n",
       "      <td>success</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>powell_singular_bad_start</th>\n",
       "      <td>success</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>powell_singular_good_start</th>\n",
       "      <td>success</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rosenbrock_bad_start</th>\n",
       "      <td>success</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rosenbrock_good_start</th>\n",
       "      <td>success</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>watson_12_bad_start</th>\n",
       "      <td>success</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>watson_12_good_start</th>\n",
       "      <td>success</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>watson_6_bad_start</th>\n",
       "      <td>success</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>watson_6_good_start</th>\n",
       "      <td>success</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>watson_9_bad_start</th>\n",
       "      <td>success</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>watson_9_good_start</th>\n",
       "      <td>success</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             scipy_lbfgsb  dimensionality\n",
       "problem                                                                  \n",
       "bard_bad_start                                    success               3\n",
       "bard_good_start                                   success               3\n",
       "bdqrtic_10                                        success              10\n",
       "bdqrtic_11                                        success              11\n",
       "bdqrtic_12                                        success              12\n",
       "bdqrtic_8                                         success               8\n",
       "box_3d                                            success               3\n",
       "brown_almost_linear                               success              10\n",
       "brown_almost_linear_medium                        success             100\n",
       "brown_dennis_bad_start                            success               4\n",
       "brown_dennis_good_start                           success               4\n",
       "chebyquad_10                                       failed              10\n",
       "chebyquad_11                                      success              11\n",
       "chebyquad_6                                       success               6\n",
       "chebyquad_7                                       success               7\n",
       "chebyquad_8                                       success               8\n",
       "chebyquad_9                                       success               9\n",
       "cube_5                                            success               5\n",
       "cube_6                                            success               6\n",
       "cube_8                                            success               8\n",
       "freudenstein_roth_bad_start                       success               2\n",
       "freudenstein_roth_good_start                      success               2\n",
       "heart_eight_bad_start                             success               8\n",
       "heart_eight_good_start                            success               8\n",
       "helical_valley_bad_start                          success               3\n",
       "helical_valley_good_start                         success               3\n",
       "jennrich_sampson                                    error               2\n",
       "kowalik_osborne                                     error               4\n",
       "linear_full_rank_bad_start                        success               9\n",
       "linear_full_rank_good_start                       success               9\n",
       "linear_rank_one_bad_start                         success               7\n",
       "linear_rank_one_good_start                        success               7\n",
       "linear_rank_one_zero_columns_rows_bad_start       success               7\n",
       "linear_rank_one_zero_columns_rows_good_start      success               7\n",
       "mancino_10                                        success              10\n",
       "mancino_12_bad_start                              success              12\n",
       "mancino_12_good_start                             success              12\n",
       "mancino_5_bad_start                               success               5\n",
       "mancino_5_good_start                              success               5\n",
       "mancino_8                                         success               8\n",
       "meyer                                             success               3\n",
       "osborne_one                                         error               5\n",
       "osborne_two_bad_start                              failed              11\n",
       "osborne_two_good_start                            success              11\n",
       "powell_singular_bad_start                         success               4\n",
       "powell_singular_good_start                        success               4\n",
       "rosenbrock_bad_start                              success               2\n",
       "rosenbrock_good_start                             success               2\n",
       "watson_12_bad_start                               success              12\n",
       "watson_12_good_start                              success              12\n",
       "watson_6_bad_start                                success               6\n",
       "watson_6_good_start                               success               6\n",
       "watson_9_bad_start                                success               9\n",
       "watson_9_good_start                               success               9"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = om.convergence_report(\n",
    "    problems=problems,\n",
    "    results=results_scipy_1_15_2,\n",
    "    stopping_criterion=\"y\",\n",
    "    x_precision=1e-4,\n",
    "    y_precision=1e-4,\n",
    ")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "74b12cc4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "fbed4a24",
   "metadata": {},
   "source": [
    "# Make fun optional"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a1f78d98",
   "metadata": {},
   "outputs": [],
   "source": [
    "def sphere(x):\n",
    "    return x @ x\n",
    "def grad_sphere(x):\n",
    "    return 2*x\n",
    "def sphere_and_grad(x):\n",
    "    return sphere(x), grad_sphere(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7c10560c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.00000000e+00, -5.55111512e-16, -1.11022302e-15, -2.22044605e-15,\n",
       "       -2.22044605e-15])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "res = om.minimize(\n",
    "    # fun = sphere,\n",
    "    params = np.arange(5),\n",
    "    # jac = grad_sphere,\n",
    "    fun_and_jac = lambda x: (x@x, 2*x),\n",
    "    algorithm='scipy_lbfgsb',   \n",
    "    \n",
    ")\n",
    "res.params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8acf4945",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.False_"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "expected = om.minimize(\n",
    "        fun=lambda x: x @ x,\n",
    "        jac = lambda x: 2*x,\n",
    "        params=np.arange(5),\n",
    "        algorithm=\"scipy_lbfgsb\",\n",
    "    )\n",
    "res = om.minimize(\n",
    "        fun_and_jac=lambda x: (x @ x, 2 * x),\n",
    "        params=np.arange(5),\n",
    "        algorithm=\"scipy_lbfgsb\",\n",
    "    )\n",
    "np.all(res.x != expected.x)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f3422602",
   "metadata": {},
   "outputs": [
    {
     "ename": "InvalidFunctionError",
     "evalue": "Least-squares solvers can only be used with least-squares problems.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mInvalidFunctionError\u001b[0m                      Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[11], line 22\u001b[0m\n\u001b[1;32m     20\u001b[0m results \u001b[38;5;241m=\u001b[39m {}\n\u001b[1;32m     21\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m algorithm \u001b[38;5;129;01min\u001b[39;00m [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mscipy_ls_lm\u001b[39m\u001b[38;5;124m\"\u001b[39m]:\n\u001b[0;32m---> 22\u001b[0m     results[algorithm] \u001b[38;5;241m=\u001b[39m \u001b[43mom\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mminimize\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m     23\u001b[0m \u001b[43m        \u001b[49m\u001b[43mparams\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43marange\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m5\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     24\u001b[0m \u001b[43m        \u001b[49m\u001b[43malgorithm\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43malgorithm\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     25\u001b[0m \u001b[43m        \u001b[49m\u001b[43mfun_and_jac\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mlambda\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m:\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[38;5;129;43m@x\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     26\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     28\u001b[0m fig \u001b[38;5;241m=\u001b[39m om\u001b[38;5;241m.\u001b[39mcriterion_plot(results)\n\u001b[1;32m     29\u001b[0m fig\u001b[38;5;241m.\u001b[39mshow(renderer\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpng\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "File \u001b[0;32m~/Documents/optimagic/src/optimagic/optimization/optimize.py:432\u001b[0m, in \u001b[0;36mminimize\u001b[0;34m(fun, params, algorithm, bounds, constraints, fun_kwargs, algo_options, jac, jac_kwargs, fun_and_jac, fun_and_jac_kwargs, numdiff_options, logging, error_handling, error_penalty, scaling, multistart, collect_history, skip_checks, x0, method, args, hess, hessp, callback, options, tol, criterion, criterion_kwargs, derivative, derivative_kwargs, criterion_and_derivative, criterion_and_derivative_kwargs, log_options, lower_bounds, upper_bounds, soft_lower_bounds, soft_upper_bounds, scaling_options, multistart_options)\u001b[0m\n\u001b[1;32m    284\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mminimize\u001b[39m(\n\u001b[1;32m    285\u001b[0m     fun: FunType \u001b[38;5;241m|\u001b[39m CriterionType \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m    286\u001b[0m     params: PyTree \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    330\u001b[0m     multistart_options: \u001b[38;5;28mdict\u001b[39m[\u001b[38;5;28mstr\u001b[39m, Any] \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m    331\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m OptimizeResult:\n\u001b[1;32m    332\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Minimize criterion using algorithm subject to constraints.\u001b[39;00m\n\u001b[1;32m    333\u001b[0m \n\u001b[1;32m    334\u001b[0m \u001b[38;5;124;03m    Args:\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    429\u001b[0m \n\u001b[1;32m    430\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 432\u001b[0m     problem \u001b[38;5;241m=\u001b[39m \u001b[43mcreate_optimization_problem\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    433\u001b[0m \u001b[43m        \u001b[49m\u001b[43mdirection\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mDirection\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mMINIMIZE\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    434\u001b[0m \u001b[43m        \u001b[49m\u001b[43mfun\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfun\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    435\u001b[0m \u001b[43m        \u001b[49m\u001b[43mparams\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mparams\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    436\u001b[0m \u001b[43m        \u001b[49m\u001b[43malgorithm\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43malgorithm\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    437\u001b[0m \u001b[43m        \u001b[49m\u001b[43mbounds\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbounds\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    438\u001b[0m \u001b[43m        \u001b[49m\u001b[43mfun_kwargs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfun_kwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    439\u001b[0m \u001b[43m        \u001b[49m\u001b[43mconstraints\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconstraints\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    440\u001b[0m \u001b[43m        \u001b[49m\u001b[43malgo_options\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43malgo_options\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    441\u001b[0m \u001b[43m        \u001b[49m\u001b[43mjac\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mjac\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    442\u001b[0m \u001b[43m        \u001b[49m\u001b[43mjac_kwargs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mjac_kwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    443\u001b[0m \u001b[43m        \u001b[49m\u001b[43mfun_and_jac\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfun_and_jac\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    444\u001b[0m \u001b[43m        \u001b[49m\u001b[43mfun_and_jac_kwargs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfun_and_jac_kwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    445\u001b[0m \u001b[43m        \u001b[49m\u001b[43mnumdiff_options\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnumdiff_options\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    446\u001b[0m \u001b[43m        \u001b[49m\u001b[43mlogging\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlogging\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    447\u001b[0m \u001b[43m        \u001b[49m\u001b[43merror_handling\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merror_handling\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    448\u001b[0m \u001b[43m        \u001b[49m\u001b[43merror_penalty\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merror_penalty\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    449\u001b[0m \u001b[43m        \u001b[49m\u001b[43mscaling\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mscaling\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    450\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmultistart\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmultistart\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    451\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcollect_history\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcollect_history\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    452\u001b[0m \u001b[43m        \u001b[49m\u001b[43mskip_checks\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mskip_checks\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    453\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;66;43;03m# scipy aliases\u001b[39;49;00m\n\u001b[1;32m    454\u001b[0m \u001b[43m        \u001b[49m\u001b[43mx0\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mx0\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    455\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmethod\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    456\u001b[0m \u001b[43m        \u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    457\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;66;43;03m# scipy arguments that are not yet supported\u001b[39;49;00m\n\u001b[1;32m    458\u001b[0m \u001b[43m        \u001b[49m\u001b[43mhess\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mhess\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    459\u001b[0m \u001b[43m        \u001b[49m\u001b[43mhessp\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mhessp\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    460\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcallback\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcallback\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    461\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;66;43;03m# scipy arguments that will never be supported\u001b[39;49;00m\n\u001b[1;32m    462\u001b[0m \u001b[43m        \u001b[49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    463\u001b[0m \u001b[43m        \u001b[49m\u001b[43mtol\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtol\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    464\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;66;43;03m# deprecated arguments\u001b[39;49;00m\n\u001b[1;32m    465\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcriterion\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcriterion\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    466\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcriterion_kwargs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcriterion_kwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    467\u001b[0m \u001b[43m        \u001b[49m\u001b[43mderivative\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mderivative\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    468\u001b[0m \u001b[43m        \u001b[49m\u001b[43mderivative_kwargs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mderivative_kwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    469\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcriterion_and_derivative\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcriterion_and_derivative\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    470\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcriterion_and_derivative_kwargs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcriterion_and_derivative_kwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    471\u001b[0m \u001b[43m        \u001b[49m\u001b[43mlower_bounds\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlower_bounds\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    472\u001b[0m \u001b[43m        \u001b[49m\u001b[43mlog_options\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlog_options\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    473\u001b[0m \u001b[43m        \u001b[49m\u001b[43mupper_bounds\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mupper_bounds\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    474\u001b[0m \u001b[43m        \u001b[49m\u001b[43msoft_lower_bounds\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msoft_lower_bounds\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    475\u001b[0m \u001b[43m        \u001b[49m\u001b[43msoft_upper_bounds\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msoft_upper_bounds\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    476\u001b[0m \u001b[43m        \u001b[49m\u001b[43mscaling_options\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mscaling_options\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    477\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmultistart_options\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmultistart_options\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    478\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    479\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m _optimize(problem)\n",
      "File \u001b[0;32m~/Documents/optimagic/src/optimagic/optimization/create_optimization_problem.py:434\u001b[0m, in \u001b[0;36mcreate_optimization_problem\u001b[0;34m(direction, fun, params, algorithm, bounds, fun_kwargs, constraints, algo_options, jac, jac_kwargs, fun_and_jac, fun_and_jac_kwargs, numdiff_options, logging, error_handling, error_penalty, scaling, multistart, collect_history, skip_checks, x0, method, args, hess, hessp, callback, options, tol, criterion, criterion_kwargs, derivative, derivative_kwargs, criterion_and_derivative, criterion_and_derivative_kwargs, lower_bounds, log_options, upper_bounds, soft_lower_bounds, soft_upper_bounds, scaling_options, multistart_options)\u001b[0m\n\u001b[1;32m    432\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m algorithm\u001b[38;5;241m.\u001b[39malgo_info\u001b[38;5;241m.\u001b[39msolver_type \u001b[38;5;241m==\u001b[39m AggregationLevel\u001b[38;5;241m.\u001b[39mLEAST_SQUARES:\n\u001b[1;32m    433\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m problem_type \u001b[38;5;241m!=\u001b[39m AggregationLevel\u001b[38;5;241m.\u001b[39mLEAST_SQUARES:\n\u001b[0;32m--> 434\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m InvalidFunctionError(\n\u001b[1;32m    435\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mLeast-squares solvers can only be used with least-squares problems.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    436\u001b[0m         )\n\u001b[1;32m    438\u001b[0m \u001b[38;5;66;03m# ==================================================================================\u001b[39;00m\n\u001b[1;32m    439\u001b[0m \u001b[38;5;66;03m# select the correct derivative functions\u001b[39;00m\n\u001b[1;32m    440\u001b[0m \u001b[38;5;66;03m# ==================================================================================\u001b[39;00m\n\u001b[1;32m    442\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m jac \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
      "\u001b[0;31mInvalidFunctionError\u001b[0m: Least-squares solvers can only be used with least-squares problems."
     ]
    }
   ],
   "source": [
    "@om.mark.least_squares\n",
    "def ls_sphere(params):\n",
    "    return params\n",
    "\n",
    "\n",
    "@om.mark.least_squares\n",
    "def ls_sphere_jac(params):\n",
    "    return np.eye(len(params))\n",
    "\n",
    "def sphere_gradient(x):\n",
    "    return 2*x\n",
    "\n",
    "res = om.minimize(\n",
    "    fun=ls_sphere,\n",
    "    params=np.arange(3),\n",
    "    algorithm=\"scipy_ls_lm\",\n",
    "    jac=ls_sphere_jac,\n",
    ")\n",
    "res.params.round(5)\n",
    "results = {}\n",
    "for algorithm in [\"scipy_ls_lm\"]:\n",
    "    results[algorithm] = om.minimize(\n",
    "        params=np.arange(5),\n",
    "        algorithm=algorithm,\n",
    "        fun_and_jac=lambda x:(x@x, 2*x),\n",
    "    )\n",
    "\n",
    "fig = om.criterion_plot(results)\n",
    "fig.show(renderer=\"png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0381f9c7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8a171cc0",
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7245261",
   "metadata": {},
   "source": [
    "# Benchmark"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b267e983",
   "metadata": {},
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            "gridcolor": "rgb(232,232,232)",
            "linecolor": "rgb(36,36,36)",
            "showgrid": false,
            "showline": true,
            "ticks": "outside"
           },
           "bgcolor": "white",
           "caxis": {
            "gridcolor": "rgb(232,232,232)",
            "linecolor": "rgb(36,36,36)",
            "showgrid": false,
            "showline": true,
            "ticks": "outside"
           }
          },
          "title": {
           "x": 0.05
          },
          "xaxis": {
           "automargin": true,
           "gridcolor": "rgb(232,232,232)",
           "linecolor": "rgb(36,36,36)",
           "showgrid": false,
           "showline": true,
           "ticks": "outside",
           "title": {
            "standoff": 15
           },
           "zeroline": false,
           "zerolinecolor": "rgb(36,36,36)"
          },
          "yaxis": {
           "automargin": true,
           "gridcolor": "rgb(232,232,232)",
           "linecolor": "rgb(36,36,36)",
           "showgrid": false,
           "showline": true,
           "ticks": "outside",
           "title": {
            "standoff": 15
           },
           "zeroline": false,
           "zerolinecolor": "rgb(36,36,36)"
          }
         }
        },
        "title": {},
        "width": 500,
        "xaxis": {
         "anchor": "y",
         "domain": [
          0,
          1
         ],
         "title": {
          "text": "Number of Function Evaluations"
         }
        },
        "yaxis": {
         "anchor": "x",
         "domain": [
          0,
          1
         ],
         "title": {
          "text": "Share of Problems Solved"
         }
        }
       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import optimagic as om\n",
    "problems = om.get_benchmark_problems(\"example\")\n",
    "a= om.algorithms.EnsmallenLBFGS\n",
    "b = om.algos.scipy_cobyla\n",
    "optimizers = {\n",
    "    a,\n",
    "    b,\n",
    "}\n",
    "results = om.run_benchmark(\n",
    "    problems, \n",
    "    optimizers\n",
    ")\n",
    "# a = list(results.keys())[0]\n",
    "# results[a]\n",
    "\n",
    "fig = om.profile_plot(\n",
    "    problems = problems,\n",
    "    results=results,\n",
    ")\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c345c0f0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "eb210ce5",
   "metadata": {},
   "source": [
    "# Ensmallen Module"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cab1e7dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"Implement ensmallen optimizers.\"\"\"\n",
    "\n",
    "from dataclasses import dataclass\n",
    "\n",
    "import numpy as np\n",
    "from numpy.typing import NDArray\n",
    "\n",
    "from optimagic import mark\n",
    "from optimagic.config import IS_PYENSMALLEN_INSTALLED\n",
    "from optimagic.optimization.algo_options import (\n",
    "    CONVERGENCE_FTOL_REL,\n",
    "    CONVERGENCE_GTOL_ABS,\n",
    "    MAX_LINE_SEARCH_STEPS,\n",
    "    STOPPING_MAXITER,\n",
    ")\n",
    "from optimagic.optimization.algorithm import Algorithm, InternalOptimizeResult\n",
    "from optimagic.optimization.internal_optimization_problem import (\n",
    "    InternalOptimizationProblem,\n",
    ")\n",
    "from optimagic.typing import AggregationLevel, NonNegativeFloat, PositiveInt\n",
    "\n",
    "if IS_PYENSMALLEN_INSTALLED:\n",
    "    import pyensmallen as pye\n",
    "\n",
    "LIMITED_MEMORY_MAX_HISTORY = 10\n",
    "\"\"\"Number of memory points to be stored (default 10).\"\"\"\n",
    "MIN_LINE_SEARCH_STEPS = 1e-20\n",
    "\"\"\"The minimum step of the line search.\"\"\"\n",
    "MAX_LINE_SEARCH_TRIALS = 50\n",
    "\"\"\"The maximum number of trials for the line search (before giving up).\"\"\"\n",
    "ARMIJO_CONSTANT = 1e-4\n",
    "\"\"\"Controls the accuracy of the line search routine for determining the Armijo\n",
    "condition.\"\"\"\n",
    "WOLFE_CONDITION = 0.9\n",
    "\"\"\"Parameter for detecting the Wolfe condition.\"\"\"\n",
    "\n",
    "\n",
    "@mark.minimizer(\n",
    "    name=\"ensmallen_lbfgs\",\n",
    "    solver_type=AggregationLevel.SCALAR,\n",
    "    is_available=IS_PYENSMALLEN_INSTALLED,\n",
    "    is_global=False,\n",
    "    needs_jac=True,\n",
    "    needs_hess=False,\n",
    "    supports_parallelism=False,\n",
    "    supports_bounds=False,\n",
    "    supports_linear_constraints=False,\n",
    "    supports_nonlinear_constraints=False,\n",
    "    disable_history=False,\n",
    ")\n",
    "@dataclass(frozen=True)\n",
    "class EnsmallenLBFGS(Algorithm):\n",
    "    limited_memory_max_history: PositiveInt = LIMITED_MEMORY_MAX_HISTORY\n",
    "    stopping_maxiter: PositiveInt = STOPPING_MAXITER\n",
    "    armijo_constant: NonNegativeFloat = ARMIJO_CONSTANT  # needs review\n",
    "    wolfe_condition: NonNegativeFloat = WOLFE_CONDITION  # needs review\n",
    "    convergence_gtol_abs: NonNegativeFloat = CONVERGENCE_GTOL_ABS\n",
    "    convergence_ftol_rel: NonNegativeFloat = CONVERGENCE_FTOL_REL\n",
    "    max_line_search_trials: PositiveInt = MAX_LINE_SEARCH_TRIALS\n",
    "    min_step_for_line_search: NonNegativeFloat = MIN_LINE_SEARCH_STEPS\n",
    "    max_step_for_line_search: NonNegativeFloat = MAX_LINE_SEARCH_STEPS\n",
    "\n",
    "    def _solve_internal_problem(\n",
    "        self, problem: InternalOptimizationProblem, x0: NDArray[np.float64]\n",
    "    ) -> InternalOptimizeResult:\n",
    "        optimizer = pye.L_BFGS(\n",
    "            numBasis=self.limited_memory_max_history,\n",
    "            maxIterations=self.stopping_maxiter,\n",
    "            armijoConstant=self.armijo_constant,\n",
    "            wolfe=self.wolfe_condition,\n",
    "            minGradientNorm=self.convergence_gtol_abs,\n",
    "            factr=self.convergence_ftol_rel,\n",
    "            maxLineSearchTrials=self.max_line_search_trials,\n",
    "            minStep=self.min_step_for_line_search,\n",
    "            maxStep=self.max_step_for_line_search,\n",
    "        )\n",
    "\n",
    "        def objective_function(\n",
    "            x: NDArray[np.float64], grad: NDArray[np.float64]\n",
    "        ) -> np.float64:\n",
    "            grad[:] = problem.jac(x)\n",
    "            return np.float64(problem.fun(x))\n",
    "\n",
    "        raw = optimizer.optimize(objective_function, x0)\n",
    "\n",
    "        res = InternalOptimizeResult(\n",
    "            x=raw,  # only best x is available\n",
    "            fun=problem.fun(raw),  # best f(x) value is not available\n",
    "        )\n",
    "\n",
    "        return res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8b1fa4c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "#########################################################################################\n",
    "\n",
    "@mark.minimizer(\n",
    "    name=\"ensmallen_adam\",\n",
    "    solver_type=AggregationLevel.SCALAR,\n",
    "    is_available=IS_PYENSMALLEN_INSTALLED,\n",
    "    is_global=False,\n",
    "    needs_jac=True,\n",
    "    needs_hess=False,\n",
    "    supports_parallelism=False,\n",
    "    supports_bounds=False,\n",
    "    supports_linear_constraints=False,\n",
    "    supports_nonlinear_constraints=False,\n",
    "    disable_history=False,\n",
    ")\n",
    "@dataclass(frozen=True)\n",
    "class EnsmallenLBFGS(Algorithm):\n",
    "    step_size: NonNegativeFloat = STEP_SIZE\n",
    "    batch_size: PositiveInt = BATCH_SIZE\n",
    "    beta1: NonNegativeFloat = EXP_DECAY_RATE_FOR_FIRST_MOMENT\n",
    "    beta2: NonNegativeFloat = EXP_DECAY_RATE_FOR_WEIGHTED_INF_NORM\n",
    "    stopping_maxiter: PositiveInt = STOPPING_MAXITER\n",
    "    convergence_ftol_abs: NonNegativeFloat = CONVERGENCE_FTOL_ABS\n",
    "    shuffle: bool = True\n",
    "    reset_policy: bool = True\n",
    "    exact_objective: bool = False\n",
    "\n",
    "\n",
    "    def _solve_internal_problem(\n",
    "        self, problem: InternalOptimizationProblem, x0: NDArray[np.float64]\n",
    "    ) -> InternalOptimizeResult:\n",
    "        optimizer = pye.Adam(stepSize=self.step_size, batchSize=self.batch_size, beta1=self.beta1, beta2=self.beta2, eps=self.eps, maxIterations=self.stopping_maxiter, tolerance=self.convergence_ftol_abs, shuffle=self.shuffle, resetPolicy=self.reset_policy, exactObjective=self.exact_objective)\n",
    "        def objective_function(\n",
    "            x: NDArray[np.float64], grad: NDArray[np.float64]\n",
    "        ) -> np.float64:\n",
    "            fun, jac = problem.fun_and_jac(x)\n",
    "            grad[:] = jac\n",
    "            return np.float64(fun)\n",
    "\n",
    "        raw = optimizer.optimize(objective_function, x0)\n",
    "\n",
    "        res = InternalOptimizeResult(\n",
    "            x=raw,  # only best x is available\n",
    "            fun=problem.fun(raw),  # best f(x) value is not available\n",
    "        )\n",
    "\n",
    "        return res\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e15acc8",
   "metadata": {},
   "source": [
    "# Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0755b8cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "import optimagic as om\n",
    "import numpy as np\n",
    "import pyensmallen as pye\n",
    "from optimagic.optimizers.pyensmallen_optimizers import EnsmallenLBFGS\n",
    "\n",
    "x0 = np.arange(5)\n",
    "\n",
    "def sphere(x):\n",
    "    return x@x\n",
    "\n",
    "def sphere_grad(x):\n",
    "    return 2*x\n",
    "\n",
    "def sphere_and_grad(x):\n",
    "    return x@x, 2*x\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7de9b9f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 0., 0., 0., 0.])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "res = om.minimize(\n",
    "    fun = sphere,\n",
    "    params = x0,\n",
    "    algorithm = 'scipy_lbfgsb',\n",
    "    bounds = om.Bounds(lower = np.zeros_like(x0))\n",
    "\n",
    ")\n",
    "res.params.round(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "09858af7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 0., 0., 0., 0.])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "algo = EnsmallenLBFGS(stopping_maxiter=100,)\n",
    "# algo = om.algorithms.EnsmallenLBFGS()\n",
    "res = om.minimize(\n",
    "    fun = sphere,\n",
    "    # jac = sphere_grad,\n",
    "    params = x0,\n",
    "    algorithm = algo,\n",
    "    fun_and_jac = sphere_and_grad\n",
    ")\n",
    "res.params.round(3)\n",
    "# om.criterion_plot(res)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d05bfe5d",
   "metadata": {},
   "source": [
    "# Rosenbrock function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7dca56c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def rosenbrock(x, a=2):\n",
    "    return np.sum((a - x[:-1]) ** 2 + 100.0 * (x[1:] - x[:-1] ** 2) ** 2)\n",
    "\n",
    "\n",
    "def rosenbrock_gradient(x, a=2):\n",
    "    grad = np.zeros_like(x)\n",
    "    # Gradient for the first element\n",
    "    grad[0] = -2 * (a - x[0]) - 400 * x[0] * (x[1] - x[0] ** 2)\n",
    "    # Gradient for the middle elements\n",
    "    grad[1:-1] = (\n",
    "        -2 * (a - x[1:-1])\n",
    "        + 200 * (x[1:-1] - x[:-2] ** 2)\n",
    "        - 400 * x[1:-1] * (x[2:] - x[1:-1] ** 2)\n",
    "    )\n",
    "    # Gradient for the last element\n",
    "    grad[-1] = 200 * (x[-1] - x[-2] ** 2)\n",
    "    return grad\n",
    "\n",
    "\n",
    "def objective_function(x, grad):\n",
    "    grad[:] = rosenbrock_gradient(x)\n",
    "    return rosenbrock(x)\n",
    "\n",
    "\n",
    "\n",
    "x0 = np.array([-1.2, 1.0])\n",
    "\n",
    "optimizer = pye.L_BFGS()\n",
    "res= optimizer.optimize(objective_function, x0)\n",
    "type(res)\n",
    "optimizer.maxIterations"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9cf26bf",
   "metadata": {},
   "source": [
    "# Other"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "39478938",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([ 0.00000000e+00, -2.19792142e-07, -4.39584284e-07, -1.26862263e-07,\n",
       "       -6.57024213e-09])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "algo = om.algos.ensmallen_lbfgs(stopping_maxiter=2)\n",
    "res = om.minimize(\n",
    "    fun = lambda x: x@x,\n",
    "    params = np.arange(5),\n",
    "    algorithm=algo,\n",
    ")\n",
    "expected= np.array([ 0.000000100e+00, -2.197921242e-07, -4.39584284e-07, -1.268622263e-07,-6.57024213e-09])\n",
    "print(np.allclose(res.params, expected))\n",
    "res.x\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ff51b12d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "69a68900",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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