R_output_to_Python.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"authorship_tag":"ABX9TyOGlNlH7F2/xnV/uQtvittB"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"code","source":["%pip install gurobipy\n","%pip install pandas xlrd\n","import gurobipy as gp\n","import pandas as pd\n","from gurobipy import GRB\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"5-zRF50zAdPP","executionInfo":{"status":"ok","timestamp":1688841024857,"user_tz":300,"elapsed":17730,"user":{"displayName":"Raziye Aghapour","userId":"04149038362456817730"}},"outputId":"662c7215-4081-49ed-945f-025ec60673f9"},"execution_count":1,"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting gurobipy\n","  Downloading gurobipy-10.0.2-cp310-cp310-manylinux2014_x86_64.whl (12.7 MB)\n","\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m12.7/12.7 MB\u001b[0m \u001b[31m78.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hInstalling collected packages: gurobipy\n","Successfully installed gurobipy-10.0.2\n","Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (1.5.3)\n","Requirement already satisfied: xlrd in /usr/local/lib/python3.10/dist-packages (2.0.1)\n","Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas) (2.8.2)\n","Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas) (2022.7.1)\n","Requirement already satisfied: numpy>=1.21.0 in /usr/local/lib/python3.10/dist-packages (from pandas) (1.22.4)\n","Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas) (1.16.0)\n"]}]},{"cell_type":"code","execution_count":null,"metadata":{"id":"AF7Yv3znTj6-"},"outputs":[],"source":["import re\n","import pandas as pd\n","import numpy as np\n","\n","\n","# Example R output string\n","r_output = \"\"\"\n","## 1) root 80 42207120.0 4474.154\n","## 2) System 1: Heating Source=DX Coils 56 9451800.0 4139.805\n","## 4) Heater Fuel=Gas 12 761177.5 3726.917 *\n","## 5) Heater Fuel=Electricity 44 6086973.0 4252.411\n","## 10) Roof: Ext Finish/Color=Aluminum Paint,Uncolored 32 1204252.0 4078.056\n","## 20) AGW: Exterior Insulation>=5 23 237098.3 3991.113 *\n","## 21) AGW: Exterior Insulation< 5 9 348985.9 4300.244 *\n","## 11) Roof: Ext Finish/Color=Dark,Light 12 1315822.0 4717.358 *\n","## 3) System 1: Heating Source=Electric Resistance 24 11888070.0 5254.300\n","## 6) Heater Fuel=Gas 12 2210965.0 4666.217 *\n","## 7) Heater Fuel=Electricity 12 1376897.0 5842.383 *\n","\"\"\"\n","\n","# Regular expression patterns for extracting information\n","pattern = r\"([0-9]+)+\\)\\s?(.*?(?:(?:[<=])|(?:[> ])|(?:[=])).*)\\s(\\d+)\\s([\\d.]+)\\s([\\d.]+)\\s(\\*)?\"\n","\n","# Extract information from the R output string\n","matches = re.findall(pattern, r_output)\n","\n","# Create a dictionary to store the extracted information\n","data = {\n","    'Node-id': [],\n","    'Left_child': [],\n","    'Right_child': [],\n","    'Split': [],\n","    'Count': [],\n","    'Risk': [],\n","    'Prediction': [],\n","    'nodetype': []\n","\n","}\n","\n","# Parse the extracted information and populate the dictionary\n","for match in matches:\n","    node, split, count, risk, prediction, nodetype = match\n","    # nodetype='terminalnode' if  nodetype=='*' else \"splitnode\"\n","    nodetype = 'terminalnode' if nodetype == '*' else (\"root\" if split.strip() == 'root' else 'splitnode')\n","    left_child= int(node)*2       if  nodetype!='terminalnode'   else -2\n","    right_child=int(node)*2+1     if  nodetype!='terminalnode'   else -2\n","    data['Node-id'].append(int(node))\n","    data['Left_child'].append(int(left_child))\n","    data['Right_child'].append(int(right_child))\n","    data['Split'].append(split)\n","    data['Count'].append(int(count))\n","    data['Risk'].append(float(risk))\n","    data['Prediction'].append(float(prediction))\n","    data['nodetype'].append(nodetype)\n","\n","\n","# Create a pandas DataFrame from the dictionary\n","df = pd.DataFrame(data)\n","# print(df)"]},{"cell_type":"code","source":["n_nodes =len(df['Node-id'].tolist())\n","node_id=df['Node-id'].tolist()\n","children_left=df['Left_child'].tolist()\n","children_right=df['Right_child'].tolist()\n","# feature=tree_dataframe['feature'].tolist()\n","node_type=df['nodetype'].tolist()\n","criteria = df['Split'].tolist()\n","value = df['Prediction'].tolist()\n","n_samples= df['Count'].tolist()\n","paths = []\n","path = []\n","\n","def recurse(node, path, paths):\n","  node_index=df[df['Node-id'] == node].index.item()\n","  if node_type[node_index]!='terminalnode':\n","\n","       CriteriaL=criteria[df[df['Node-id'] == children_left[node_index]].index.item()]\n","       CriteriaR=criteria[df[df['Node-id'] == children_right[node_index]].index.item()]\n","\n","       p1, p2 = list(path), list(path)\n","\n","       p1 +=[CriteriaL]\n","\n","       recurse(children_left[node_index], p1, paths)\n","       p2 +=[CriteriaR]\n","       recurse(children_right[node_index], p2, paths)\n","  else:\n","      path += [(f\"Prediction={value[node_index]}\", f\"Terminalnode={node}\")]\n","      paths += [path]\n","      # print(path)\n","\n","recurse(1, path, paths)\n","print(paths)\n","\n","\n","TerminalNode_ID=[node_id[index] for index, element in enumerate(node_type) if element == 'terminalnode']\n","\n","\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"P5rwucAnXWSQ","executionInfo":{"status":"ok","timestamp":1688546191623,"user_tz":300,"elapsed":148,"user":{"displayName":"Raziye Aghapour","userId":"04149038362456817730"}},"outputId":"8ae9d2c5-a8b6-4512-8826-f01d19648ce4"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["[['System 1: Heating Source=DX Coils', 'Heater Fuel=Gas', ('Prediction=3726.917', 'Terminalnode=4')], ['System 1: Heating Source=DX Coils', 'Heater Fuel=Electricity', 'Roof: Ext Finish/Color=Aluminum Paint,Uncolored', 'AGW: Exterior Insulation>=5', ('Prediction=3991.113', 'Terminalnode=20')], ['System 1: Heating Source=DX Coils', 'Heater Fuel=Electricity', 'Roof: Ext Finish/Color=Aluminum Paint,Uncolored', 'AGW: Exterior Insulation< 5', ('Prediction=4300.244', 'Terminalnode=21')], ['System 1: Heating Source=DX Coils', 'Heater Fuel=Electricity', 'Roof: Ext Finish/Color=Dark,Light', ('Prediction=4717.358', 'Terminalnode=11')], ['System 1: Heating Source=Electric Resistance', 'Heater Fuel=Gas', ('Prediction=4666.217', 'Terminalnode=6')], ['System 1: Heating Source=Electric Resistance', 'Heater Fuel=Electricity', ('Prediction=5842.383', 'Terminalnode=7')]]\n"]}]},{"cell_type":"code","source":["from google.colab import files\n","\n","uploaded = files.upload()\n","# Read the Excel file into a DataFrame\n","import io\n","Factors=pd.read_excel('Vishnu_variables.xlsx', header=0)\n","# df = pd.read_excel('Runs2023-2022.xlsx', header=0)\n","#df = df.drop('Unnamed: 0', axis=1)\n","# Access the column headings\n","headings_Factors = Factors.columns\n","# headings_df = df.columns\n","\n","Factors.head()\n","# print(headings_df)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":263},"id":"z_8C77nn_-In","executionInfo":{"status":"ok","timestamp":1688531269876,"user_tz":300,"elapsed":7089,"user":{"displayName":"Raziye Aghapour","userId":"04149038362456817730"}},"outputId":"7bf3c83b-9989-468a-9f5b-db474a62f18d"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["\n","     <input type=\"file\" id=\"files-88b96171-8bde-4693-b9aa-16dac26e1a8f\" name=\"files[]\" multiple disabled\n","        style=\"border:none\" />\n","     <output id=\"result-88b96171-8bde-4693-b9aa-16dac26e1a8f\">\n","      Upload widget is only available when the cell has been executed in the\n","      current browser session. Please rerun this cell to enable.\n","      </output>\n","      <script>// Copyright 2017 Google LLC\n","//\n","// Licensed under the Apache License, Version 2.0 (the \"License\");\n","// you may not use this file except in compliance with the License.\n","// You may obtain a copy of the License at\n","//\n","//      http://www.apache.org/licenses/LICENSE-2.0\n","//\n","// Unless required by applicable law or agreed to in writing, software\n","// distributed under the License is distributed on an \"AS IS\" BASIS,\n","// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n","// See the License for the specific language governing permissions and\n","// limitations under the License.\n","\n","/**\n"," * @fileoverview Helpers for google.colab Python module.\n"," */\n","(function(scope) {\n","function span(text, styleAttributes = {}) {\n","  const element = document.createElement('span');\n","  element.textContent = text;\n","  for (const key of Object.keys(styleAttributes)) {\n","    element.style[key] = styleAttributes[key];\n","  }\n","  return element;\n","}\n","\n","// Max number of bytes which will be uploaded at a time.\n","const MAX_PAYLOAD_SIZE = 100 * 1024;\n","\n","function _uploadFiles(inputId, outputId) {\n","  const steps = uploadFilesStep(inputId, outputId);\n","  const outputElement = document.getElementById(outputId);\n","  // Cache steps on the outputElement to make it available for the next call\n","  // to uploadFilesContinue from Python.\n","  outputElement.steps = steps;\n","\n","  return _uploadFilesContinue(outputId);\n","}\n","\n","// This is roughly an async generator (not supported in the browser yet),\n","// where there are multiple asynchronous steps and the Python side is going\n","// to poll for completion of each step.\n","// This uses a Promise to block the python side on completion of each step,\n","// then passes the result of the previous step as the input to the next step.\n","function _uploadFilesContinue(outputId) {\n","  const outputElement = document.getElementById(outputId);\n","  const steps = outputElement.steps;\n","\n","  const next = steps.next(outputElement.lastPromiseValue);\n","  return Promise.resolve(next.value.promise).then((value) => {\n","    // Cache the last promise value to make it available to the next\n","    // step of the generator.\n","    outputElement.lastPromiseValue = value;\n","    return next.value.response;\n","  });\n","}\n","\n","/**\n"," * Generator function which is called between each async step of the upload\n"," * process.\n"," * @param {string} inputId Element ID of the input file picker element.\n"," * @param {string} outputId Element ID of the output display.\n"," * @return {!Iterable<!Object>} Iterable of next steps.\n"," */\n","function* uploadFilesStep(inputId, outputId) {\n","  const inputElement = document.getElementById(inputId);\n","  inputElement.disabled = false;\n","\n","  const outputElement = document.getElementById(outputId);\n","  outputElement.innerHTML = '';\n","\n","  const pickedPromise = new Promise((resolve) => {\n","    inputElement.addEventListener('change', (e) => {\n","      resolve(e.target.files);\n","    });\n","  });\n","\n","  const cancel = document.createElement('button');\n","  inputElement.parentElement.appendChild(cancel);\n","  cancel.textContent = 'Cancel upload';\n","  const cancelPromise = new Promise((resolve) => {\n","    cancel.onclick = () => {\n","      resolve(null);\n","    };\n","  });\n","\n","  // Wait for the user to pick the files.\n","  const files = yield {\n","    promise: Promise.race([pickedPromise, cancelPromise]),\n","    response: {\n","      action: 'starting',\n","    }\n","  };\n","\n","  cancel.remove();\n","\n","  // Disable the input element since further picks are not allowed.\n","  inputElement.disabled = true;\n","\n","  if (!files) {\n","    return {\n","      response: {\n","        action: 'complete',\n","      }\n","    };\n","  }\n","\n","  for (const file of files) {\n","    const li = document.createElement('li');\n","    li.append(span(file.name, {fontWeight: 'bold'}));\n","    li.append(span(\n","        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n","        `last modified: ${\n","            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n","                                    'n/a'} - `));\n","    const percent = span('0% done');\n","    li.appendChild(percent);\n","\n","    outputElement.appendChild(li);\n","\n","    const fileDataPromise = new Promise((resolve) => {\n","      const reader = new FileReader();\n","      reader.onload = (e) => {\n","        resolve(e.target.result);\n","      };\n","      reader.readAsArrayBuffer(file);\n","    });\n","    // Wait for the data to be ready.\n","    let fileData = yield {\n","      promise: fileDataPromise,\n","      response: {\n","        action: 'continue',\n","      }\n","    };\n","\n","    // Use a chunked sending to avoid message size limits. See b/62115660.\n","    let position = 0;\n","    do {\n","      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n","      const chunk = new Uint8Array(fileData, position, length);\n","      position += length;\n","\n","      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n","      yield {\n","        response: {\n","          action: 'append',\n","          file: file.name,\n","          data: base64,\n","        },\n","      };\n","\n","      let percentDone = fileData.byteLength === 0 ?\n","          100 :\n","          Math.round((position / fileData.byteLength) * 100);\n","      percent.textContent = `${percentDone}% done`;\n","\n","    } while (position < fileData.byteLength);\n","  }\n","\n","  // All done.\n","  yield {\n","    response: {\n","      action: 'complete',\n","    }\n","  };\n","}\n","\n","scope.google = scope.google || {};\n","scope.google.colab = scope.google.colab || {};\n","scope.google.colab._files = {\n","  _uploadFiles,\n","  _uploadFilesContinue,\n","};\n","})(self);\n","</script> "]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Saving Vishnu_variables.xlsx to Vishnu_variables (3).xlsx\n"]},{"output_type":"execute_result","data":{"text/plain":["   variable                description                type  \\\n","0         1     Roof: Ext Finish/Color         Categorical   \n","1         2  Roof: Exterior Insulation  Numerical-discrete   \n","2         3     Roof: Add'l Insulation  Numerical-discrete   \n","3         4      AGW: Ext Finish/Color         Categorical   \n","4         5   AGW: Exterior Insulation  Numerical-discrete   \n","\n","                                              values  \n","0     \"Light\", \"Dark\", \"Uncolored\", \"Aluminum Paint\"  \n","1  0, 4, 36, 35, 24, 20, 12, 9, 30, 28, 42, 18, 1...  \n","2    0, 19, 49, 38, 11, 3, 30, 13, 60, 21, 7, 15, 26  \n","3             \"Light\", \"Dark\", \"Uncolored\", \"Medium\"  \n","4     10.5, 21, 2, 1.3, 7, 14, 4, 12, 0, 18, 6, 9, 8  "],"text/html":["\n","  <div id=\"df-d0f4c828-65c4-4435-ba34-c8af2c414b2c\">\n","    <div class=\"colab-df-container\">\n","      <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>variable</th>\n","      <th>description</th>\n","      <th>type</th>\n","      <th>values</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1</td>\n","      <td>Roof: Ext Finish/Color</td>\n","      <td>Categorical</td>\n","      <td>\"Light\", \"Dark\", \"Uncolored\", \"Aluminum Paint\"</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2</td>\n","      <td>Roof: Exterior Insulation</td>\n","      <td>Numerical-discrete</td>\n","      <td>0, 4, 36, 35, 24, 20, 12, 9, 30, 28, 42, 18, 1...</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>3</td>\n","      <td>Roof: Add'l Insulation</td>\n","      <td>Numerical-discrete</td>\n","      <td>0, 19, 49, 38, 11, 3, 30, 13, 60, 21, 7, 15, 26</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>4</td>\n","      <td>AGW: Ext Finish/Color</td>\n","      <td>Categorical</td>\n","      <td>\"Light\", \"Dark\", \"Uncolored\", \"Medium\"</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>5</td>\n","      <td>AGW: Exterior Insulation</td>\n","      <td>Numerical-discrete</td>\n","      <td>10.5, 21, 2, 1.3, 7, 14, 4, 12, 0, 18, 6, 9, 8</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d0f4c828-65c4-4435-ba34-c8af2c414b2c')\"\n","              title=\"Convert this 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re.findall(r'\"([^\"]+)\"', row[3]):\n","     extracted_list= extracted_list = row[3].split(\", \")\n","\n","    Var_lib[key[0]] = extracted_list\n","    Var_name_type[row[1]]=key[1]\n","    Var_index_type[key[0]]=key[1]\n","    Var_name_index[row[1]]=key[0]\n","\n","\n","print(Var_index_type)\n","\n","result_cat = {}\n","result_num_disc = {}\n","result_num_cont_LB = {}\n","result_num_cont_UB = {}\n","\n","for item in paths:\n","\n","\n","    split_index = len(item) - 1\n","\n","    part1 = item[:split_index]\n","    part2 = item[-1]\n","\n","    terminalnode=terminal_node = part2[1].split('=')[1]\n","\n","    for entry in part1:\n","\n","      variables = {}\n","\n","      if re.findall(r\"(.*?(?:(?:<\\s)).*)\", entry):\n","\n","              variable, value = re.split('<\\s', entry)\n","              variables[variable] = value.split(',')\n","\n","              if Var_name_type.get(variable) == 'Numerical-continuous':\n","                 result_num_cont_U B[(terminalnode,variable)] = variables[variable]\n","\n","              elif Var_name_type.get(variable) == 'Numerical-discrete':\n","                sortedvariables=sorted(Var_lib[Var_name_index[variable]])\n","                variables[variable]= sortedvariables[:int(variables[variable][0])]\n","                result_num_disc[(terminalnode,variable)] = variables[variable]\n","\n","      if re.findall(r\"(.*?(?<![>=])=.*)\", entry):\n","\n","\n","              variable, value = re.split('=', entry)\n","\n","              variables[variable] = value.split(',')\n","              result_cat[(terminalnode,variable)] = variables[variable]\n","\n","\n","      if re.findall(r\"(.*?(?:(?:>=)).*)\", entry):\n","\n","              variable, value = re.split('>=', entry)\n","              variables[variable] = value.split(',')\n","\n","              if Var_name_type.get(variable) == 'Numerical-continuous':\n","                 result_num_cont_LB[(terminalnode,variable)] = variables[variable]\n","\n","              elif Var_name_type.get(variable) == 'Numerical-discrete':\n","                sortedvariables=sorted(Var_lib[Var_name_index[variable]])\n","                variables[variable]= sortedvariables[int(variables[variable][0]):]\n","                result_num_disc[(terminalnode,variable)] = variables[variable]\n","\n","\n","\n","print(result_cat)\n","print(result_num_disc)\n","# print(result_num_cont_UB)"],"metadata":{"id":"NPyOr3UY70xR","executionInfo":{"status":"ok","timestamp":1688546245740,"user_tz":300,"elapsed":146,"user":{"displayName":"Raziye Aghapour","userId":"04149038362456817730"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"a39b5548-12a8-44cc-e9d9-bd3244164033"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["{'1': 'Categorical', '2': 'Numerical-discrete', '3': 'Numerical-discrete', '4': 'Categorical', '5': 'Numerical-discrete', '6': 'Numerical-discrete', '7': 'Numerical-discrete', '8': 'Numerical-discrete', '9': 'Numerical-discrete', '10': 'Categorical', '11': 'Numerical-discrete', '12': 'Categorical', '13': 'Categorical', '14': 'Categorical', '15': 'Categorical', '16': 'Categorical', '17': 'Categorical'}\n","{('4', 'System 1: Heating Source'): ['DX Coils'], ('4', 'Heater Fuel'): ['Gas'], ('20', 'System 1: Heating Source'): ['DX Coils'], ('20', 'Heater Fuel'): ['Electricity'], ('20', 'Roof: Ext Finish/Color'): ['Aluminum Paint', 'Uncolored'], ('21', 'System 1: Heating Source'): ['DX Coils'], ('21', 'Heater Fuel'): ['Electricity'], ('21', 'Roof: Ext Finish/Color'): ['Aluminum Paint', 'Uncolored'], ('11', 'System 1: Heating Source'): ['DX Coils'], ('11', 'Heater Fuel'): ['Electricity'], ('11', 'Roof: Ext Finish/Color'): ['Dark', 'Light'], ('6', 'System 1: Heating Source'): ['Electric Resistance'], ('6', 'Heater Fuel'): ['Gas'], ('7', 'System 1: Heating Source'): ['Electric Resistance'], ('7', 'Heater Fuel'): ['Electricity']}\n","{('20', 'AGW: Exterior Insulation'): ['18', '2', '21', '4', '6', '7', '8', '9'], ('21', 'AGW: Exterior Insulation'): ['0', '1.3', '10.5', '12', '14']}\n"]}]},{"cell_type":"code","source":["#Define Sets\n","# decision variables\n","Var_number=len(Var_index_type)\n","\n","count_dict = {}\n","\n","# Create an empty dictionary to store the counts\n","count_dict = {}\n","\n","# Iterate through the elements in the dictionary\n","for valuetype in Var_index_type.values():\n","    # Get the second element of the tuple key\n","    second_element = valuetype\n","\n","    # Check if the second element exists in the count dictionary\n","    if second_element in count_dict:\n","        # Increment the count for that second element\n","        count_dict[second_element] += 1\n","    else:\n","        # Add the second element to the count dictionary with a count of 1\n","        count_dict[second_element] = 1\n","\n","count_dict\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"1kkQxaWl9-s8","executionInfo":{"status":"ok","timestamp":1688546200287,"user_tz":300,"elapsed":111,"user":{"displayName":"Raziye Aghapour","userId":"04149038362456817730"}},"outputId":"2715f549-80a7-4d9a-c2fc-973a42556207"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["{'Categorical': 9, 'Numerical-discrete': 8}"]},"metadata":{},"execution_count":118}]},{"cell_type":"code","source":["\n","\n","# numerical decision variables\n","J=[str(i) for i in range (1,Var_number)]\n","TerminalNode_count= node_type.count('terminalnode')\n","\n","J_num=  [key for key, value in Var_index_type.items() if (value == 'Numerical-discrete' or value == 'Numerical-continuous')]\n","J_num_disc= [key for key, value in Var_index_type.items() if value == 'Numerical-discrete']\n","J_num_cont= [key for key  , value in Var_index_type.items() if  value == 'Numerical-continuous']\n","\n","# Categorical decision variables\n","J_cat=[key for key, value in Var_index_type.items() if value == 'Categorical']\n","\n","\n","#response variables\n","R=['1']\n","\n","#terminal nodes\n","R, T=gp.multidict({\n","    '1': [[node_id[index] for index, element in enumerate(node_type) if element == 'terminalnode']]\n","})\n","\n","\n"],"metadata":{"id":"I6euLBPu90bU"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["###Fill in the dictionaries"],"metadata":{"id":"vY24S2Q7E3XJ"}},{"cell_type":"code","source":["#Input data\n","# Mean of the responses for those observations in the terminal nodes\n","# R tree, t terminal node\n","\n","# Regression Intercept\n","Beta_Zero=[value[index] for index, element in enumerate(node_type) if element == 'terminalnode']\n","RT = [(key, str(value)) for key in T.keys() for value in T[key]]\n","\n","RT, Beta_Zero=gp.multidict(dict(zip(RT, Beta_Zero)))\n","my_lib={key[0]: [value] for key, value in Var_lib.items() if key[0] in J_num_cont}\n","\n","J_cat, cat_vls=gp.multidict({key: [value] for key  , value in Var_lib.items() if key in J_cat}) if J_cat else [], {}\n","J_num_disc, dic_vls=gp.multidict({key: [value] for key  , value in Var_lib.items() if key in J_num_disc}) if J_num_disc else [], {}\n","J_num_cont, con_vls = gp.multidict({key: [value] for key  , value in  Var_lib.items() if key in J_num_cont }) if J_num_cont else [], {}\n","\n","print(dic_vls)\n","\n","\n","# LB={}\n","# for j in J_num_cont:\n","#  LB.update(dict.fromkeys([(j,r,t) for r in R for t in T[r] ], lw_b[j]) )\n","\n","# UB={}\n","# for j in J_num:\n","#  UB.update(dict.fromkeys([(j,r,t) for r in R for t in T[r] ], up_b[j]) )\n","\n","# For discrete variables, the values they can actually take\n","# the calculation for R-... Batt come from Marjan's thesis page 35-36\n","# J_num_disc, dic_vls=gp.multidict({\n","#     '1': [[2,4,6,8]],\n","#     '2': [[1,1.5,2,3]],\n","#     '3': [[1.25,2,3,4]],\n","#     '4': [[1,2,3,4]],\n","#     '5': [[1,1.5,2,3]],\n","#     '6': [[0.9677,2.2580,3.5484,4.1935]],\n","#     '7': [[0.1,0.15,0.2,0.25]],\n","#     '8': [[0.1,0.15,0.2,0.25]],\n","#     '9': [[0.1,0.15,0.2,0.25]],\n","#     '10': [[0.1,0.15,0.2,0.25]],\n","#     '11': [[2.2580,6.1290,6.7742,9.6774]],\n","#     '12': [[13,19,21,30]],\n","#     '13': [[1,1.5,2,3]],\n","#     '14': [[100,70.7,62.5,50]],\n","#     '15': [[3,4,5,6]],\n","#     '16': [[2,2.3,2.7,3]],\n","# })\n","\n","DISC_Val={}\n","for j in J_num_disc:\n"," for v in range(1,len(dic_vls[j])+1):\n","  DISC_Val.update(dict.fromkeys([(j,str(v))], dic_vls[j][v-1]) )\n","\n","\n","Jdisc_V, DISC_ValS=gp.multidict(DISC_Val)\n","\n","print(DISC_ValS)\n","# bounds of numerical variables\n","J_num_cont, lw_b, up_b=gp.multidict({\n","    '1':  [min(dic_vls['1']),max(dic_vls['1'])],\n","    '2':  [min(dic_vls['2']),max(dic_vls['2'])],\n","    '3':  [min(dic_vls['3']),max(dic_vls['3'])],\n","    '4':  [min(dic_vls['4']),max(dic_vls['4'])],\n","    '5':  [min(dic_vls['5']),max(dic_vls['5'])],\n","    '6':  [min(dic_vls['6']),max(dic_vls['6'])],\n","    '7':  [min(dic_vls['7']),max(dic_vls['7'])],\n","    '8':  [min(dic_vls['8']),max(dic_vls['8'])],\n","    '9':  [min(dic_vls['9']),max(dic_vls['9'])],\n","    '10': [min(dic_vls['10']),max(dic_vls['10'])],\n","    '11': [min(dic_vls['11']),max(dic_vls['11'])],\n","    '12': [min(dic_vls['12']),max(dic_vls['12'])],\n","    '13': [min(dic_vls['13']),max(dic_vls['13'])],\n","    '14': [min(dic_vls['14']),max(dic_vls['14'])],\n","    '15': [min(dic_vls['15']),max(dic_vls['15'])],\n","    '16': [min(dic_vls['16']),max(dic_vls['16'])],\n","    '17': [575,675],\n","    '18': [10,30],\n","    '19': [575,675],\n","    '20': [10,30],\n","    '21': [575,675],\n","    '22': [10,30],\n","    '23': [5,105],\n","    '24': [10,30],\n","    '25': [250,350],\n","    '26': [5,25],\n","    '27': [100,200],\n","    '28': [5,25],\n","    '29': [100,200],\n","    '30': [15,35],\n","    '31': [100,200],\n","    '32': [5,25],\n","})\n","\n","#Initialize dictionaries for upper and lower bound of variables at each terminal load of each tree\n","\n","LB={}\n","for j in J_num:\n"," LB.update(dict.fromkeys([(j,r,t) for r in R for t in T[r] ], lw_b[j]) )\n","\n","UB={}\n","for j in J_num:\n"," UB.update(dict.fromkeys([(j,r,t) for r in R for t in T[r] ], up_b[j]) )\n","\n","# Update Upper bound a lower bounds of variables accroding to Regression Tree (Refer to Dickson's article)\n","\n","# limits of the variables in respose 1 tree\n","\n","UB.update({('11','1','1'): 5})\n","UB.update({('11','1','2'): 5})\n","UB.update({('11','1','3'): 5})\n","UB.update({('11','1','4'): 5})\n","LB.update({('11','1','5'): 5})\n","LB.update({('11','1','6'): 5})\n","LB.update({('11','1','7'): 5})\n","LB.update({('11','1','8'): 5})\n","UB.update({('18','1','1'): 20})\n","LB.update({('18','1','2'): 20})\n","UB.update({('19','1','5'): 651.2})\n","UB.update({('19','1','6'): 651.2})\n","UB.update({('19','1','7'): 651.2})\n","LB.update({('19','1','8'): 651.2})\n","UB.update({('29','1','5'): 144.55})\n","LB.update({('29','1','6'): 144.55})\n","LB.update({('29','1','7'): 144.55})\n","UB.update({('30','1','6'): 23.985})\n","LB.update({('30','1','7'): 23.985})\n","\n","# limits of the variables in respose 2 tree\n","\n","UB.update({('23','2','1'): 104.6})\n","UB.update({('23','2','2'): 104.6})\n","UB.update({('23','2','3'): 104.6})\n","UB.update({('23','2','4'): 104.6})\n","UB.update({('23','2','5'): 104.6})\n","UB.update({('23','2','6'): 104.6})\n","LB.update({('23','2','7'): 104.6})\n","UB.update({('24','2','1'): 26.91})\n","UB.update({('24','2','2'): 26.91})\n","UB.update({('24','2','3'): 26.91})\n","UB.update({('24','2','4'): 26.91})\n","UB.update({('24','2','5'): 26.91})\n","LB.update({('24','2','6'): 26.91})\n","UB.update({('25','2','1'): 255.9})\n","LB.update({('25','2','2'): 255.9})\n","LB.update({('25','2','3'): 255.9})\n","LB.update({('25','2','4'): 255.9})\n","LB.update({('25','2','5'): 255.9})\n","UB.update({('19','2','2'): 654.3})\n","UB.update({('19','2','3'): 654.3})\n","UB.update({('19','2','4'): 654.3})\n","LB.update({('19','2','5'): 654.3})\n","UB.update({('23','2','2'): 29.6})\n","LB.update({('23','2','3'): 29.6})\n","LB.update({('23','2','4'): 29.6})\n","UB.update({('32','2','3'): 12.575})\n","LB.update({('32','2','4'): 12.575})\n","\n","# limits of the variables in respose 3 tree\n","\n","UB.update({('11','3','1'): 5})\n","UB.update({('11','3','2'): 5})\n","UB.update({('11','3','3'): 5})\n","UB.update({('11','3','4'): 5})\n","LB.update({('11','3','5'): 5})\n","LB.update({('11','3','6'): 5})\n","LB.update({('11','3','7'): 5})\n","LB.update({('11','3','8'): 5})\n","UB.update({('18','3','1'): 20})\n","LB.update({('18','3','2'): 20})\n","UB.update({('19','3','5'): 651.2})\n","UB.update({('19','3','6'): 651.2})\n","UB.update({('19','3','7'): 651.2})\n","LB.update({('19','3','8'): 651.2})\n","UB.update({('29','3','5'): 144.55})\n","LB.update({('29','3','6'): 144.55})\n","LB.update({('29','3','7'): 144.55})\n","UB.update({('30','3','6'): 23.985})\n","LB.update({('30','3','7'): 23.985})\n","\n","jrt, l_jrt = gp.multidict(LB)\n","jrt, u_jrt=gp.multidict(UB)\n","\n","\n","\n","R, D=gp.multidict({\n","    '1': [600],\n","    '2': [30],\n","    '3': [11000]\n","})"],"metadata":{"id":"90uCEbUMFTUJ","executionInfo":{"status":"error","timestamp":1688546743031,"user_tz":300,"elapsed":193,"user":{"displayName":"Raziye Aghapour","userId":"04149038362456817730"}},"colab":{"base_uri":"https://localhost:8080/","height":258},"outputId":"7d531309-2388-49eb-ae68-e67b9530d352"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["{}\n"]},{"output_type":"error","ename":"TypeError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)","\u001b[0;32m<ipython-input-128-499d90e77a41>\u001b[0m in \u001b[0;36m<cell line: 49>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     48\u001b[0m \u001b[0mDISC_Val\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     49\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mj\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mJ_num_disc\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 50\u001b[0;31m  \u001b[0;32mfor\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdic_vls\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mj\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     51\u001b[0m   \u001b[0mDISC_Val\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdict\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfromkeys\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mj\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdic_vls\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mj\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     52\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mTypeError\u001b[0m: unhashable type: 'tuplelist'"]}]}]}