How+to+make+targeted+offers+to+customers+Local.ipynb

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        {
            "source": "# How to make targeted offers to customers?\n\nThis tutorial includes everything you need to set up IBM Decision Optimization CPLEX Modeling for Python (DOcplex), build a Mathematical Programming model, and get its solution by solving the model with IBM ILOG CPLEX Optimizer.\n\nWhen you finish this tutorial, you'll have a foundational knowledge of _Prescriptive Analytics_.\n\n>This notebook is part of the [Prescriptive Analytics for Python](https://rawgit.com/IBMDecisionOptimization/docplex-doc/master/docs/index.html).\n\n>It requires a valid subscription to **Decision Optimization on Cloud** or a **local installation of CPLEX Optimizers**.\n\nDiscover us [here](https://developer.ibm.com/docloud).\n\n\nTable of contents:\n\n-  [Describe the business problem](#Describe-the-business-problem)\n*  [How decision optimization (prescriptive analytics) can help](#How-decision-optimization-can-help)\n*  [Prepare the data](#Prepare-the-data)\n*  [Use decision optimization](#Use-IBM-Decision-Optimization-CPLEX-Modeling-for-Python)\n    *  [Step 1: Download the library](#Step-1:-Download-the-library)\n    *  [Step 2: Set up the engines](#Step-2:-Set-up-the-prescriptive-engine)\n    -  [Step 3: Set up the prescriptive model](#Step-3:-Set-up-the-prescriptive-model)\n        * [Define the decision variables](#Define-the-decision-variables)\n        * [Set up the constraints](#Set-up-the-constraints)\n        * [Express the objective](#Express-the-objective)\n        * [Solve with the Decision Optimization solve service](#Solve-with-the-Decision-Optimization-solve-service)\n    *  [Step 4: Analyze the solution and run an example analysis](#Step-4:-Analyze-the-solution)\n", 
            "metadata": {}, 
            "cell_type": "markdown"
        }, 
        {
            "source": "## Describe the business problem\n* The Self-Learning Response Model (SLRM) node enables you to build a model that you can continually update. Such updates are useful in building a model that assists with predicting which offers are most appropriate for customers and the probability of the offers being accepted. These sorts of models are most beneficial in customer relationship management, such as marketing applications or call centers.\n* This example is based on a fictional banking company. \n* The marketing department wants to achieve more profitable results in future campaigns by matching the right offer of financial services to each customer. \n* Specifically, the datascience department identified the characteristics of customers who are most likely to respond favorably based on previous offers and responses and to promote the best current offer based on the results and now need to compute the best offerig plan.\n<br>\n\nA set of business constraints have to be respected:\n\n* We have a limited budget to run a marketing campaign based on \"gifts\", \"newsletter\", \"seminar\"...\n* We want to determine which is the best way to contact the customers.\n* We need to identify which customers to contact.", 
            "metadata": {}, 
            "cell_type": "markdown"
        }, 
        {
            "source": "## How decision optimization can help\n\n* Prescriptive analytics technology recommends actions based on desired outcomes, taking into account specific scenarios, resources, and knowledge of past and current events. This insight can help your organization make better decisions and have greater control of business outcomes.  \n\n* Prescriptive analytics is the next step on the path to insight-based actions. It creates value through synergy with predictive analytics, which analyzes data to predict future outcomes.  \n\n* Prescriptive analytics takes that insight to the next level by suggesting the optimal way to handle that future situation. Organizations that can act fast in dynamic conditions and make superior decisions in uncertain environments gain a strong competitive advantage.  \n<br/>\n\n+ For example:\n    + Automate complex decisions and trade-offs to better manage limited resources.\n    + Take advantage of a future opportunity or mitigate a future risk.\n    + Proactively update recommendations based on changing events.\n    + Meet operational goals, increase customer loyalty, prevent threats and fraud, and optimize business processes.", 
            "metadata": {}, 
            "cell_type": "markdown"
        }, 
        {
            "source": "## Prepare the data\n### Step 1 : Model the data\n\nThe predictions show which offers a customer is most likely to accept, and the confidence that they will accept, depending on each customer\u2019s details.\n\nFor example:\n(139987, \"Pension\", 0.13221, \"Mortgage\", 0.10675) indicates that customer Id=139987 will certainly not buy a _Pension_ as the level is only 13.2%, \nwhereas\n(140030, \"Savings\", 0.95678, \"Pension\", 0.84446) is more than likely to buy _Savings_ and a _Pension_ as the rates are 95.7% and 84.4%.\n\nThis data is taken from a SPSS example, except that the names of the customers were modified.\n\nA Python data analysis library, [pandas](http://pandas.pydata.org), is used to store the data. Let's set up and declare the data.", 
            "metadata": {}, 
            "cell_type": "markdown"
        }, 
        {
            "execution_count": 1, 
            "source": "import pandas as pd\n\nnames = {\n    139987 : \"Guadalupe J. Martinez\", 140030 : \"Michelle M. Lopez\", 140089 : \"Terry L. Ridgley\", \n    140097 : \"Miranda B. Roush\", 139068 : \"Sandra J. Wynkoop\", 139154 : \"Roland Gu\u00e9rette\", 139158 : \"Fabien Mailhot\", \n    139169 : \"Christian Austerlitz\", 139220 : \"Steffen Meister\", 139261 : \"Wolfgang Sanger\",\n    139416 : \"Lee Tsou\", 139422 : \"Sanaa' Hikmah Hakimi\", 139532 : \"Miroslav \u0160karoupka\", \n    139549 : \"George Blomqvist\", 139560 : \"Will Henderson\", 139577 : \"Yuina Ohira\", 139580 : \"Vlad Alekseeva\", \n    139636 : \"Cassio Lombardo\", 139647 : \"Trinity Zelaya Miramontes\", 139649 : \"Eldar Muravyov\", 139665 : \"Shu T'an\", \n    139667 : \"Jameel Abdul-Ghani Gerges\", 139696 : \"Zeeb Longoria Marrero\", 139752 : \"Matheus Azevedo Melo\", \n    139832 : \"Earl B. Wood\", 139859 : \"Gabrielly Sousa Martins\", 139881 : \"Franca Palermo\"}\n\n\ndata = [(139987, \"Pension\", 0.13221, \"Mortgage\", 0.10675), (140030, \"Savings\", 0.95678, \"Pension\", 0.84446), (140089, \"Savings\", 0.95678, \"Pension\", 0.80233), \n                        (140097, \"Pension\", 0.13221, \"Mortgage\", 0.10675), (139068, \"Pension\", 0.80506, \"Savings\", 0.28391), (139154, \"Pension\", 0.13221, \"Mortgage\", 0.10675), \n                        (139158, \"Pension\", 0.13221, \"Mortgage\", 0.10675),(139169, \"Pension\", 0.13221, \"Mortgage\", 0.10675), (139220, \"Pension\", 0.13221, \"Mortgage\", 0.10675), \n                        (139261, \"Pension\", 0.13221, \"Mortgage\", 0.10675), (139416, \"Pension\", 0.13221, \"Mortgage\", 0.10675), (139422, \"Pension\", 0.13221, \"Mortgage\", 0.10675), \n                        (139532, \"Savings\", 0.95676, \"Mortgage\", 0.82269), (139549, \"Savings\", 0.16428, \"Pension\", 0.13221), (139560, \"Savings\", 0.95678, \"Pension\", 0.86779), \n                        (139577, \"Pension\", 0.13225, \"Mortgage\", 0.10675), (139580, \"Pension\", 0.13221, \"Mortgage\", 0.10675), (139636, \"Pension\", 0.13221, \"Mortgage\", 0.10675), \n                        (139647, \"Savings\", 0.28934, \"Pension\", 0.13221), (139649, \"Pension\", 0.13221, \"Mortgage\", 0.10675), (139665, \"Savings\", 0.95675, \"Pension\", 0.27248), \n                        (139667, \"Pension\", 0.13221, \"Mortgage\", 0.10675), (139696, \"Savings\", 0.16188, \"Pension\", 0.13221), (139752, \"Pension\", 0.13221, \"Mortgage\", 0.10675), \n                        (139832, \"Savings\", 0.95678, \"Pension\", 0.83426), (139859, \"Savings\", 0.95678, \"Pension\", 0.75925), (139881, \"Pension\", 0.13221, \"Mortgage\", 0.10675)]\n\nproducts = [\"Car loan\", \"Savings\", \"Mortgage\", \"Pension\"]\nproductValue = [100, 200, 300, 400]\nbudgetShare = [0.6, 0.1, 0.2, 0.1]\n\navailableBudget = 500\nchannels =  pd.DataFrame(data=[(\"gift\", 20.0, 0.20), (\"newsletter\", 15.0, 0.05), (\"seminar\", 23.0, 0.30)], columns=[\"name\", \"cost\", \"factor\"])", 
            "metadata": {
                "collapsed": true
            }, 
            "outputs": [], 
            "cell_type": "code"
        }, 
        {
            "source": "Offers are stored in a [pandas DataFrame](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.html).", 
            "metadata": {}, 
            "cell_type": "markdown"
        }, 
        {
            "execution_count": 2, 
            "source": "try: # Python 2\n    offers = pd.DataFrame(data=data, index=xrange(0, len(data)), columns=[\"customerid\", \"Product1\", \"Confidence1\", \"Product2\", \"Confidence2\"])\nexcept: # Python 3\n    offers = pd.DataFrame(data=data, index=range(0, len(data)), columns=[\"customerid\", \"Product1\", \"Confidence1\", \"Product2\", \"Confidence2\"])\noffers.insert(0,'name',pd.Series(names[i[0]] for i in data))", 
            "metadata": {
                "collapsed": false
            }, 
            "outputs": [], 
            "cell_type": "code"
        }, 
        {
            "source": "Let's customize the display of this data and show the confidence forecast for each customer.", 
            "metadata": {}, 
            "cell_type": "markdown"
        }, 
        {
            "execution_count": 3, 
            "source": "CSS = \"\"\"\nbody {\n    margin: 0;\n    font-family: Helvetica;\n}\ntable.dataframe {\n    border-collapse: collapse;\n    border: none;\n}\ntable.dataframe tr {\n    border: none;\n}\ntable.dataframe td, table.dataframe th {\n    margin: 0;\n    border: 1px solid white;\n    padding-left: 0.25em;\n    padding-right: 0.25em;\n}\ntable.dataframe th:not(:empty) {\n    background-color: #fec;\n    text-align: left;\n    font-weight: normal;\n}\ntable.dataframe tr:nth-child(2) th:empty {\n    border-left: none;\n    border-right: 1px dashed #888;\n}\ntable.dataframe td {\n    border: 2px solid #ccf;\n    background-color: #f4f4ff;\n}\n    table.dataframe thead th:first-child {\n        display: none;\n    }\n    table.dataframe tbody th {\n        display: none;\n    }\n\"\"\"", 
            "metadata": {
                "collapsed": true
            }, 
            "outputs": [], 
            "cell_type": "code"
        }, 
        {
            "execution_count": 4, 
            "source": "from IPython.core.display import HTML\nHTML('<style>{}</style>'.format(CSS))\n\nfrom IPython.display import display\ntry: \n    display(offers.drop('customerid',1).sort_values(by='name')) #Pandas >= 0.17\nexcept:\n    display(offers.drop('customerid',1).sort('name')) #Pandas < 0.17", 
            "metadata": {
                "collapsed": false
            }, 
            "outputs": [
                {
                    "metadata": {}, 
                    "output_type": "display_data", 
                    "data": {
                        "text/plain": "                         name Product1  Confidence1  Product2  Confidence2\n17            Cassio Lombardo  Pension      0.13221  Mortgage      0.10675\n7        Christian Austerlitz  Pension      0.13221  Mortgage      0.10675\n24               Earl B. Wood  Savings      0.95678   Pension      0.83426\n19             Eldar Muravyov  Pension      0.13221  Mortgage      0.10675\n6              Fabien Mailhot  Pension      0.13221  Mortgage      0.10675\n26             Franca Palermo  Pension      0.13221  Mortgage      0.10675\n25    Gabrielly Sousa Martins  Savings      0.95678   Pension      0.75925\n13           George Blomqvist  Savings      0.16428   Pension      0.13221\n0       Guadalupe J. Martinez  Pension      0.13221  Mortgage      0.10675\n21  Jameel Abdul-Ghani Gerges  Pension      0.13221  Mortgage      0.10675\n10                   Lee Tsou  Pension      0.13221  Mortgage      0.10675\n23       Matheus Azevedo Melo  Pension      0.13221  Mortgage      0.10675\n1           Michelle M. Lopez  Savings      0.95678   Pension      0.84446\n3            Miranda B. Roush  Pension      0.13221  Mortgage      0.10675\n12         Miroslav \u0160karoupka  Savings      0.95676  Mortgage      0.82269\n5             Roland Gu\u00e9rette  Pension      0.13221  Mortgage      0.10675\n11       Sanaa' Hikmah Hakimi  Pension      0.13221  Mortgage      0.10675\n4           Sandra J. Wynkoop  Pension      0.80506   Savings      0.28391\n20                   Shu T'an  Savings      0.95675   Pension      0.27248\n8             Steffen Meister  Pension      0.13221  Mortgage      0.10675\n2            Terry L. Ridgley  Savings      0.95678   Pension      0.80233\n18  Trinity Zelaya Miramontes  Savings      0.28934   Pension      0.13221\n16             Vlad Alekseeva  Pension      0.13221  Mortgage      0.10675\n14             Will Henderson  Savings      0.95678   Pension      0.86779\n9             Wolfgang Sanger  Pension      0.13221  Mortgage      0.10675\n15                Yuina Ohira  Pension      0.13225  Mortgage      0.10675\n22      Zeeb Longoria Marrero  Savings      0.16188   Pension      0.13221", 
                        "text/html": "<div>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>name</th>\n      <th>Product1</th>\n      <th>Confidence1</th>\n      <th>Product2</th>\n      <th>Confidence2</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>17</th>\n      <td>Cassio Lombardo</td>\n      <td>Pension</td>\n      <td>0.13221</td>\n      <td>Mortgage</td>\n      <td>0.10675</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>Christian Austerlitz</td>\n      <td>Pension</td>\n      <td>0.13221</td>\n      <td>Mortgage</td>\n      <td>0.10675</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>Earl B. Wood</td>\n      <td>Savings</td>\n      <td>0.95678</td>\n      <td>Pension</td>\n      <td>0.83426</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>Eldar Muravyov</td>\n      <td>Pension</td>\n      <td>0.13221</td>\n      <td>Mortgage</td>\n      <td>0.10675</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>Fabien Mailhot</td>\n      <td>Pension</td>\n      <td>0.13221</td>\n      <td>Mortgage</td>\n      <td>0.10675</td>\n    </tr>\n    <tr>\n      <th>26</th>\n      <td>Franca Palermo</td>\n      <td>Pension</td>\n      <td>0.13221</td>\n      <td>Mortgage</td>\n      <td>0.10675</td>\n    </tr>\n    <tr>\n      <th>25</th>\n      <td>Gabrielly Sousa Martins</td>\n      <td>Savings</td>\n      <td>0.95678</td>\n      <td>Pension</td>\n      <td>0.75925</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>George Blomqvist</td>\n      <td>Savings</td>\n      <td>0.16428</td>\n      <td>Pension</td>\n      <td>0.13221</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>Guadalupe J. Martinez</td>\n      <td>Pension</td>\n      <td>0.13221</td>\n      <td>Mortgage</td>\n      <td>0.10675</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>Jameel Abdul-Ghani Gerges</td>\n      <td>Pension</td>\n      <td>0.13221</td>\n      <td>Mortgage</td>\n      <td>0.10675</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>Lee Tsou</td>\n      <td>Pension</td>\n      <td>0.13221</td>\n      <td>Mortgage</td>\n      <td>0.10675</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>Matheus Azevedo Melo</td>\n      <td>Pension</td>\n      <td>0.13221</td>\n      <td>Mortgage</td>\n      <td>0.10675</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Michelle M. Lopez</td>\n      <td>Savings</td>\n      <td>0.95678</td>\n      <td>Pension</td>\n      <td>0.84446</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Miranda B. Roush</td>\n      <td>Pension</td>\n      <td>0.13221</td>\n      <td>Mortgage</td>\n      <td>0.10675</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>Miroslav \u0160karoupka</td>\n      <td>Savings</td>\n      <td>0.95676</td>\n      <td>Mortgage</td>\n      <td>0.82269</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Roland Gu\u00e9rette</td>\n      <td>Pension</td>\n      <td>0.13221</td>\n      <td>Mortgage</td>\n      <td>0.10675</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>Sanaa' Hikmah Hakimi</td>\n      <td>Pension</td>\n      <td>0.13221</td>\n      <td>Mortgage</td>\n      <td>0.10675</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Sandra J. Wynkoop</td>\n      <td>Pension</td>\n      <td>0.80506</td>\n      <td>Savings</td>\n      <td>0.28391</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>Shu T'an</td>\n      <td>Savings</td>\n      <td>0.95675</td>\n      <td>Pension</td>\n      <td>0.27248</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>Steffen Meister</td>\n      <td>Pension</td>\n      <td>0.13221</td>\n      <td>Mortgage</td>\n      <td>0.10675</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Terry L. Ridgley</td>\n      <td>Savings</td>\n      <td>0.95678</td>\n      <td>Pension</td>\n      <td>0.80233</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>Trinity Zelaya Miramontes</td>\n      <td>Savings</td>\n      <td>0.28934</td>\n      <td>Pension</td>\n      <td>0.13221</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>Vlad Alekseeva</td>\n      <td>Pension</td>\n      <td>0.13221</td>\n      <td>Mortgage</td>\n      <td>0.10675</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>Will Henderson</td>\n      <td>Savings</td>\n      <td>0.95678</td>\n      <td>Pension</td>\n      <td>0.86779</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>Wolfgang Sanger</td>\n      <td>Pension</td>\n      <td>0.13221</td>\n      <td>Mortgage</td>\n      <td>0.10675</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>Yuina Ohira</td>\n      <td>Pension</td>\n      <td>0.13225</td>\n      <td>Mortgage</td>\n      <td>0.10675</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>Zeeb Longoria Marrero</td>\n      <td>Savings</td>\n      <td>0.16188</td>\n      <td>Pension</td>\n      <td>0.13221</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
                    }
                }
            ], 
            "cell_type": "code"
        }, 
        {
            "source": "## Use IBM Decision Optimization CPLEX Modeling for Python\n\nLet's create the optimization model to select the best ways to contact customers and stay within the limited budget.", 
            "metadata": {}, 
            "cell_type": "markdown"
        }, 
        {
            "source": "### Step 1: Download the library\n\nFirst install *docplex* if needed.", 
            "metadata": {}, 
            "cell_type": "markdown"
        }, 
        {
            "execution_count": 5, 
            "source": "import sys\ntry:\n    import docplex.mp\nexcept:\n    if hasattr(sys, 'real_prefix'):\n        #we are in a virtual env.\n        !pip install docplex\n    else:\n        !pip install --user docplex", 
            "metadata": {
                "collapsed": false
            }, 
            "outputs": [], 
            "cell_type": "code"
        }, 
        {
            "source": "### Step 2: Set up the prescriptive engine\n\n* Subscribe to our private cloud offer or Decision Optimization on Cloud solve service [here](https://developer.ibm.com/docloud) if you do not want to use a local solver.\n* Get the service URL and your personal API key and enter your credentials here if accurate:", 
            "metadata": {}, 
            "cell_type": "markdown"
        }, 
        {
            "execution_count": 6, 
            "source": "url = None\nkey = None", 
            "metadata": {
                "collapsed": true
            }, 
            "outputs": [], 
            "cell_type": "code"
        }, 
        {
            "source": "### Step 3: Set up the prescriptive model\n#### Create the model", 
            "metadata": {}, 
            "cell_type": "markdown"
        }, 
        {
            "execution_count": 7, 
            "source": "from docplex.mp.model import Model\n\nmdl = Model(name=\"marketing_campaign\")", 
            "metadata": {
                "collapsed": false
            }, 
            "outputs": [], 
            "cell_type": "code"
        }, 
        {
            "source": "#### Define the decision variables\n- The integer decision variables `channelVars`, represent whether or not a customer will be made an offer for a particular product via a particular channel.\n- The integer decision variable `totaloffers` represents the total number of offers made.\n- The continuous variable `budgetSpent` represents the total cost of the offers made.", 
            "metadata": {}, 
            "cell_type": "markdown"
        }, 
        {
            "execution_count": 8, 
            "source": "try: # Python 2\n    offersR = xrange(0, len(offers))\n    productsR = xrange(0, len(products))\n    channelsR = xrange(0, len(channels))\nexcept: # Python 3\n    offersR = range(0, len(offers))\n    productsR = range(0, len(products))\n    channelsR = range(0, len(channels))\n\nchannelVars = mdl.binary_var_cube(offersR, productsR, channelsR)\ntotaloffers = mdl.integer_var(lb=0)\nbudgetSpent = mdl.continuous_var()", 
            "metadata": {
                "collapsed": false
            }, 
            "outputs": [], 
            "cell_type": "code"
        }, 
        {
            "source": "#### Set up the constraints\n- Offer only one product per customer.\n- Compute the budget and set a maximum on it.\n- Compute the number of offers to be made.", 
            "metadata": {}, 
            "cell_type": "markdown"
        }, 
        {
            "execution_count": 9, 
            "source": "# Only 1 product is offered to each customer     \nmdl.add_constraints( mdl.sum(channelVars[o,p,c] for p in productsR for c in channelsR) <=1\n                   for o in offersR)\n\nmdl.add_constraint( totaloffers == mdl.sum(channelVars[o,p,c] \n                                           for o in offersR \n                                           for p in productsR \n                                           for c in channelsR) )\n\nmdl.add_constraint( budgetSpent == mdl.sum(channelVars[o,p,c]*channels.get_value(index=c, col=\"cost\") \n                                           for o in offersR \n                                           for p in productsR \n                                           for c in channelsR) )\n\n# Balance the offers among products   \nfor p in productsR:\n    mdl.add_constraint( mdl.sum(channelVars[o,p,c] for o in offersR for c in channelsR) \n                       <= budgetShare[p] * totaloffers )\n            \n# Do not exceed the budget\nmdl.add_constraint( mdl.sum(channelVars[o,p,c]*channels.get_value(index=c, col=\"cost\") \n                            for o in offersR \n                            for p in productsR \n                            for c in channelsR)  <= availableBudget )  \n\nmdl.print_information()", 
            "metadata": {
                "collapsed": false
            }, 
            "outputs": [
                {
                    "name": "stdout", 
                    "output_type": "stream", 
                    "text": "Model: marketing_campaign\n - number of variables: 326\n   - binary=324, integer=1, continuous=1\n - number of constraints: 34\n   - linear=34\n - parameters: defaults\n"
                }
            ], 
            "cell_type": "code"
        }, 
        {
            "source": "#### Express the objective\n\nWe want to maximize the expected revenue.", 
            "metadata": {}, 
            "cell_type": "markdown"
        }, 
        {
            "execution_count": 10, 
            "source": "mdl.maximize(\n    mdl.sum( channelVars[idx,p,idx2] * c.factor * productValue[p]* o.Confidence1  \n            for p in productsR \n            for idx,o in offers[offers['Product1'] == products[p]].iterrows()  \n            for idx2, c in channels.iterrows())\n    +\n    mdl.sum( channelVars[idx,p,idx2] * c.factor * productValue[p]* o.Confidence2 \n            for p in productsR \n            for idx,o in offers[offers['Product2'] == products[p]].iterrows() \n            for idx2, c in channels.iterrows())\n    )", 
            "metadata": {
                "collapsed": true
            }, 
            "outputs": [], 
            "cell_type": "code"
        }, 
        {
            "source": "#### Solve with the Decision Optimization solve service\n\nIf url and key are None, the Modeling layer will look for a local runtime, otherwise will use the credentials.\nLook at the documentation for a good understanding of the various solving/generation modes.\n\nIf you're using a Community Edition of CPLEX runtimes, depending on the size of the problem, the solve stage may fail and will need a paying subscription or product installation.", 
            "metadata": {}, 
            "cell_type": "markdown"
        }, 
        {
            "execution_count": 11, 
            "source": "s = mdl.solve(url=url, key=key)\nassert s, \"No Solution !!!\"", 
            "metadata": {
                "collapsed": false
            }, 
            "outputs": [], 
            "cell_type": "code"
        }, 
        {
            "source": "### Step 4: Analyze the solution\n\nFirst, let's display the **Optimal Marketing Channel per customer**.", 
            "metadata": {}, 
            "cell_type": "markdown"
        }, 
        {
            "execution_count": 12, 
            "source": "report = [(channels.get_value(index=c, col=\"name\"), products[p], names[offers.get_value(o, \"customerid\")]) \n          for c in channelsR \n          for p in productsR \n          for o in offersR  if channelVars[o,p,c].solution_value==1]\n\nassert len(report) == totaloffers.solution_value\n\nprint(\"Marketing plan has {0} offers costing {1}\".format(totaloffers.solution_value, budgetSpent.solution_value))\n\nreport_bd = pd.DataFrame(report, columns=['channel', 'product', 'customer'])\ndisplay(report_bd)", 
            "metadata": {
                "collapsed": false
            }, 
            "outputs": [
                {
                    "name": "stdout", 
                    "output_type": "stream", 
                    "text": "Marketing plan has 20 offers costing 364.0\n"
                }, 
                {
                    "metadata": {}, 
                    "output_type": "display_data", 
                    "data": {
                        "text/plain": "       channel   product                   customer\n0   newsletter  Car loan             Fabien Mailhot\n1   newsletter  Car loan       Christian Austerlitz\n2   newsletter  Car loan                   Lee Tsou\n3   newsletter  Car loan       Sanaa' Hikmah Hakimi\n4   newsletter  Car loan           George Blomqvist\n5   newsletter  Car loan                Yuina Ohira\n6   newsletter  Car loan             Vlad Alekseeva\n7   newsletter  Car loan            Cassio Lombardo\n8   newsletter  Car loan  Trinity Zelaya Miramontes\n9   newsletter  Car loan             Eldar Muravyov\n10  newsletter  Car loan  Jameel Abdul-Ghani Gerges\n11  newsletter  Car loan      Zeeb Longoria Marrero\n12     seminar   Savings           Terry L. Ridgley\n13     seminar   Savings    Gabrielly Sousa Martins\n14     seminar  Mortgage           Miranda B. Roush\n15     seminar  Mortgage         Miroslav \u0160karoupka\n16     seminar  Mortgage       Matheus Azevedo Melo\n17     seminar  Mortgage             Franca Palermo\n18     seminar   Pension          Michelle M. Lopez\n19     seminar   Pension             Will Henderson", 
                        "text/html": "<div>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>channel</th>\n      <th>product</th>\n      <th>customer</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>newsletter</td>\n      <td>Car loan</td>\n      <td>Fabien Mailhot</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>newsletter</td>\n      <td>Car loan</td>\n      <td>Christian Austerlitz</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>newsletter</td>\n      <td>Car loan</td>\n      <td>Lee Tsou</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>newsletter</td>\n      <td>Car loan</td>\n      <td>Sanaa' Hikmah Hakimi</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>newsletter</td>\n      <td>Car loan</td>\n      <td>George Blomqvist</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>newsletter</td>\n      <td>Car loan</td>\n      <td>Yuina Ohira</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>newsletter</td>\n      <td>Car loan</td>\n      <td>Vlad Alekseeva</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>newsletter</td>\n      <td>Car loan</td>\n      <td>Cassio Lombardo</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>newsletter</td>\n      <td>Car loan</td>\n      <td>Trinity Zelaya Miramontes</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>newsletter</td>\n      <td>Car loan</td>\n      <td>Eldar Muravyov</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>newsletter</td>\n      <td>Car loan</td>\n      <td>Jameel Abdul-Ghani Gerges</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>newsletter</td>\n      <td>Car loan</td>\n      <td>Zeeb Longoria Marrero</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>seminar</td>\n      <td>Savings</td>\n      <td>Terry L. Ridgley</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>seminar</td>\n      <td>Savings</td>\n      <td>Gabrielly Sousa Martins</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>seminar</td>\n      <td>Mortgage</td>\n      <td>Miranda B. Roush</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>seminar</td>\n      <td>Mortgage</td>\n      <td>Miroslav \u0160karoupka</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>seminar</td>\n      <td>Mortgage</td>\n      <td>Matheus Azevedo Melo</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>seminar</td>\n      <td>Mortgage</td>\n      <td>Franca Palermo</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>seminar</td>\n      <td>Pension</td>\n      <td>Michelle M. Lopez</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>seminar</td>\n      <td>Pension</td>\n      <td>Will Henderson</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
                    }
                }
            ], 
            "cell_type": "code"
        }, 
        {
            "source": "\nThen let's **focus on seminar**.", 
            "metadata": {}, 
            "cell_type": "markdown"
        }, 
        {
            "execution_count": 13, 
            "source": "display(report_bd[report_bd['channel'] == \"seminar\"].drop('channel',1))", 
            "metadata": {
                "collapsed": false
            }, 
            "outputs": [
                {
                    "metadata": {}, 
                    "output_type": "display_data", 
                    "data": {
                        "text/plain": "     product                 customer\n12   Savings         Terry L. Ridgley\n13   Savings  Gabrielly Sousa Martins\n14  Mortgage         Miranda B. Roush\n15  Mortgage       Miroslav \u0160karoupka\n16  Mortgage     Matheus Azevedo Melo\n17  Mortgage           Franca Palermo\n18   Pension        Michelle M. Lopez\n19   Pension           Will Henderson", 
                        "text/html": "<div>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>product</th>\n      <th>customer</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>12</th>\n      <td>Savings</td>\n      <td>Terry L. Ridgley</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>Savings</td>\n      <td>Gabrielly Sousa Martins</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>Mortgage</td>\n      <td>Miranda B. Roush</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>Mortgage</td>\n      <td>Miroslav \u0160karoupka</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>Mortgage</td>\n      <td>Matheus Azevedo Melo</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>Mortgage</td>\n      <td>Franca Palermo</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>Pension</td>\n      <td>Michelle M. Lopez</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>Pension</td>\n      <td>Will Henderson</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
                    }
                }
            ], 
            "cell_type": "code"
        }, 
        {
            "source": "\n## Summary\n\n\nYou learned how to set up and use IBM Decision Optimization CPLEX Modeling for Python to formulate a Mathematical Programming model and solve it with IBM Decision Optimization on Cloud.\n", 
            "metadata": {}, 
            "cell_type": "markdown"
        }, 
        {
            "source": "## References\n* [CPLEX Modeling for Python documentation](https://rawgit.com/IBMDecisionOptimization/docplex-doc/master/docs/index.html)\n* [Decision Optimization on Cloud](https://developer.ibm.com/docloud/)\n* Need help with DOcplex or to report a bug? Please go [here](https://developer.ibm.com/answers/smartspace/docloud).\n* Contact us at dofeedback@wwpdl.vnet.ibm.com.", 
            "metadata": {
                "collapsed": true
            }, 
            "cell_type": "markdown"
        }, 
        {
            "source": "<div class=\"alert alert-block alert-info\"> Note: To save resources and get the best performance please use the code below to stop the kernel before exiting your notebook.</div>", 
            "metadata": {}, 
            "cell_type": "markdown"
        }, 
        {
            "execution_count": null, 
            "source": "%%javascript\nJupyter.notebook.session.delete();", 
            "metadata": {
                "collapsed": true
            }, 
            "outputs": [], 
            "cell_type": "code"
        }, 
        {
            "source": "<hr>\nCopyright &copy; IBM Corp. 2017. Released as licensed Sample Materials.", 
            "metadata": {}, 
            "cell_type": "markdown"
        }
    ]
}