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        "#%%\n",
        "\"\"\" File 01logit_allAlgos.py\n",
        "\n",
        ":author: Michel Bierlaire, EPFL\n",
        ":date: Sat Sep  7 17:57:16 2019\n",
        "\n",
        " Logit model\n",
        " Three alternatives: Train, Car and Swissmetro\n",
        " SP data\n",
        "\"\"\"\n",
        "\n",
        "import pandas as pd\n",
        "import biogeme.biogeme as bio\n",
        "import biogeme.optimization as opt\n",
        "import biogeme.database as db\n",
        "from biogeme import models\n",
        "import biogeme.messaging as msg\n",
        "from biogeme.expressions import Beta\n",
        "\n",
        "# Read the data\n",
        "df = pd.read_csv('swissmetro.dat', sep='\\t')\n",
        "database = db.Database('swissmetro', df)\n",
        "\n",
        "# The following statement allows you to use the names of the variable\n",
        "# as Python variable.\n",
        "globals().update(database.variables)\n",
        "\n",
        "\n",
        "# Removing some observations\n",
        "exclude = ((PURPOSE != 1) * (PURPOSE != 3) + (CHOICE == 0)) > 0\n",
        "database.remove(exclude)\n",
        "\n",
        "# Parameters to be estimated\n",
        "ASC_CAR = Beta('ASC_CAR', 0, None, None, 0)\n",
        "ASC_TRAIN = Beta('ASC_TRAIN', 0, None, None, 0)\n",
        "ASC_SM = Beta('ASC_SM', 0, None, None, 1)\n",
        "B_TIME = Beta('B_TIME', 0, None, None, 0)\n",
        "B_COST = Beta('B_COST', 0, None, None, 0)\n",
        "\n",
        "# Definition of new variables\n",
        "SM_COST = SM_CO * (GA == 0)\n",
        "TRAIN_COST = TRAIN_CO * (GA == 0)\n",
        "CAR_AV_SP = CAR_AV * (SP != 0)\n",
        "TRAIN_AV_SP = TRAIN_AV * (SP != 0)\n",
        "TRAIN_TT_SCALED = TRAIN_TT / 100.0\n",
        "TRAIN_COST_SCALED = TRAIN_COST / 100\n",
        "SM_TT_SCALED = SM_TT / 100.0\n",
        "SM_COST_SCALED = SM_COST / 100\n",
        "CAR_TT_SCALED = CAR_TT / 100\n",
        "CAR_CO_SCALED = CAR_CO / 100\n",
        "\n",
        "# Definition of the utility functions\n",
        "V1 = ASC_TRAIN + B_TIME * TRAIN_TT_SCALED + B_COST * TRAIN_COST_SCALED\n",
        "V2 = ASC_SM + B_TIME * SM_TT_SCALED + B_COST * SM_COST_SCALED\n",
        "V3 = ASC_CAR + B_TIME * CAR_TT_SCALED + B_COST * CAR_CO_SCALED\n",
        "\n",
        "# Associate utility functions with the numbering of alternatives\n",
        "V = {1: V1, 2: V2, 3: V3}\n",
        "\n",
        "# Associate the availability conditions with the alternatives\n",
        "av = {1: TRAIN_AV_SP, 2: SM_AV, 3: CAR_AV_SP}\n",
        "\n",
        "# The following statement allows you to use the names of the variable\n",
        "# as Python variable.\n",
        "globals().update(database.variables)\n",
        "\n",
        "# Definition of the model. This is the contribution of each\n",
        "# observation to the log likelihood function.\n",
        "logprob = models.loglogit(V, av, CHOICE)\n",
        "\n",
        "# Define level of verbosity\n",
        "logger = msg.bioMessage()\n",
        "logger.setSilent()\n",
        "# logger.setWarning()\n",
        "# logger.setGeneral()\n",
        "# logger.setDetailed()\n",
        "\n",
        "# Create the Biogeme object\n",
        "biogeme = bio.BIOGEME(database, logprob)\n",
        "\n",
        "algos = {\n",
        "    'scipy                ': opt.scipy,\n",
        "    'Line search          ': opt.newtonLineSearchForBiogeme,\n",
        "    'Trust region (dogleg)': opt.newtonTrustRegionForBiogeme,\n",
        "    'Trust region (cg)    ': opt.newtonTrustRegionForBiogeme,\n",
        "    'LS-BFGS              ': opt.bfgsLineSearchForBiogeme,\n",
        "    'TR-BFGS              ': opt.bfgsTrustRegionForBiogeme,\n",
        "    'Simple bounds Newton ': opt.simpleBoundsNewtonAlgorithmForBiogeme,\n",
        "    'Simple bounds BFGS   ': opt.simpleBoundsNewtonAlgorithmForBiogeme,\n",
        "    'Simple bounds hybrid ': opt.simpleBoundsNewtonAlgorithmForBiogeme,\n",
        "}\n",
        "\n",
        "algoParameters = {\n",
        "    'Trust region (dogleg)': {'dogleg': True},\n",
        "    'Trust region (cg)': {'dogleg': False},\n",
        "    'Simple bounds Newton ': {'proportionAnalyticalHessian': 1.0},\n",
        "    'Simple bounds BFGS   ': {'proportionAnalyticalHessian': 0.0},\n",
        "    'Simple bounds hybrid ': {'proportionAnalyticalHessian': 0.5},\n",
        "}\n",
        "\n",
        "results = {}\n",
        "print(\"Algorithm\\t\\tloglike\\t\\tnormg\\ttime\\t\\tdiagnostic\")\n",
        "print(\"+++++++++\\t\\t+++++++\\t\\t+++++\\t++++\\t\\t++++++++++\")\n",
        "\n",
        "for name, algo in algos.items():\n",
        "    biogeme.modelName = f'01logit_allAlgos_{name}'.strip()\n",
        "    p = algoParameters.get(name)\n",
        "    results[name] = biogeme.estimate(algorithm=algo, algoParameters=p)\n",
        "    print(\n",
        "        f'{name}\\t{results[name].data.logLike:.2f}\\t'\n",
        "        f'{results[name].data.gradientNorm:.2g}\\t'\n",
        "        f'{results[name].data.optimizationMessages[\"Optimization time\"]}'\n",
        "        f'\\t{results[name].data.optimizationMessages[\"Cause of termination\"]}'\n",
        "    )\n"
      ],
      "outputs": [],
      "execution_count": null
    }
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