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    Wendelin - HowTo Create Basic Prediction with scikit-learn

    HowTo Create Basic Prediction with scikit-learn
    • Last Update:2020-08-17
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    Agenda

    • Create Data Operation
    • Create Data Product
    • Create Transformation Script
    • Create Data Transformation
    • See Data Analysis
    • See Data Array
    • Plot Data

     

    This tutorial shows an example of Machine Learning with Wendelin. 

    Based on data from previous tutorials we will predict air pressure from humidity using Linear Regression by scikit-learn and visualize results. 

    Before doing this tutorial make sure you read and completed the following tutorials 

     

    Data Operation

    Open your Wendelin dashboard. 

    In Modules click on Data Operations Module

    Add Data Operation

    Click on Add to add a new Data Operation.

    Add Data Operation

    Click Proceed to continue.

    Fill the Form

    Fill the form to create Data Operation.

    Title -  we name it Predict Pressure

    Reference  - data-operation-predict-pressure

    Script ID DataAnalysisLine_predictPressureFromHumidity: this script will do all the magic. It doesn't exist yet, we will create it later in this tutorial. 

    At the don't forget to Save the changes.

    Validate

    Click on Validate on left side panel to validate Data Operation.

    Confirm Validation

    Click Validate to confirm validation.

    Data Product

     

    Now we need to create a new Data Product which will the output Data Product of the Transformation.

    Create Data Product

     

    Create a new Data Product as described in HowTo Create Data Product tutorial with following values

    Title -  Predicted Data Array

    Quantity Unit -  Unit/Piece

    Reference - environment-predicted-array

    Item Types

    • Data Array

    Use

    • Big Data/Ingestion/Stream Ingestion

    At the end don't forget to save the changes and Validate.

     

    Portal Callables

    After Data Product is created and validated, navigate to page called Portal Callables by adding /#/portal_callables at the end of the URL of your wendelin instance. 

    There we will create and store a the prediction script.  

     

    Add Transformation Script

    Click on Add button to add a new script.

    Add Transformation Script Cont.

    Choose Python Script as Document Type and click on Create Document to create an empty python script.

    Fill The Form

    Define ID, Title and Reference of your script.

    We name it DataAnalysisLine_predictPressureFromHumidity as we did in Data Operation at the beginning of this tutorial.

    Next we define the parameters we will give to our script. 

    in_array - the input dictionary that contains Data Array where raw data is stored after the previous transformation.

    out_array  - the output Data Array where results of the prediction will be stored. 

    At the end click Save to save the changes.

    Transformation Script

    The script we write in the textbox area at the bottom of the page.

    Transformation Script Cont.

    
    import numpy as np
    import pandas as pd
    from sklearn.model_selection import train_test_split
    from sklearn.linear_model import LinearRegression
    
    def predict(zbigarray):
    
      humidity = zbigarray[:]["humidity_mean"]
      pressure = zbigarray[:]["pressure_mean"]
    
      humidity = humidity[humidity !=0 ]
      pressure = pressure[pressure !=0 ]
    
      humid_train, humid_test, pressure_train, pressure_test = train_test_split(humidity.reshape(-1,1), pressure.reshape(-1,1), test_size=0.2, random_state=0)
    
      regressor = LinearRegression()
      regressor.fit(humid_train, pressure_train) #training the algorithm
    
      pressure_pred = regressor.predict( zbigarray[:]["humidity_mean"].reshape(-1,1))
    
      df = pd.DataFrame({'Date': zbigarray[:]["date"],'Actual':zbigarray[:]["pressure_mean"], 'Predicted': pressure_pred.flatten()})
    
      return  df
    
    
    out_data_array = out_array["Data Array"]
    
    in_data_array = in_array["Data Array"]
    
    in_zbigarray = in_data_array.getArray()
    if in_zbigarray is None:
      return
    
    if in_zbigarray.shape[0] == 0:
      return
    
    
    df = predict(in_zbigarray)
    
    ndarray = df.to_records(convert_datetime64=False)
    dtype = [('Date', '<M8[ns]'),('Actual', '<f8'), ('Predicted', '<f8')]
    
    zbigarray = out_data_array.getArray()
    if zbigarray is None:
      zbigarray = out_data_array.initArray(shape=(0,), dtype=dtype)
      zbigarray.append(ndarray)
    else:
      start_index = len(zbigarray)
      array_len =  len(ndarray)
      if start_index < array_len:
        zbigarray.append(ndarray[start_index:])
    
    

    This script takes as an input data from resampled Data Array, splits data into training and tests set. 

    Then it learns on training set and predicts air pressure based on humidity. 

    In output array it stored the true and predicted values. 

    Don't forget to save the changes after editing the script. 

    Data Transformations

    After your transformation script is ready, it's time to create Data Transformation.

    On Modules page click on Data Transformations.

    Add Data Transformation

    Click on Add to add a new Data Transformation.

    Create Data Transformation

    Click on Create Document to continue.

    Fill The Form

    Chose descriptive title and reference. For example

    Title : Predict Pressure

    Reference : predict-environment-pressure-data

    For Initial Product we choose the Data Product we created in  HowTo Resample Data tutorial, as we want to do the prediction on the resampled data.

    At the end click Save to save the changes

    Fill The Form Cont.

    After filling in the Initial Product and saving, values will appear in Initial Product Variation field.

    Choose Resolution/60 S as we want to use 60 seconds resampled data.

    At the end click Save to save the changes

    Data Transformation Lines

    Click on add button to add a Data Transformation Line.

    Create Operation Line

     

    Choose Data Transformation Operation Line for Document Type and click on Create Document. 

    Fill The Form

    Fill the form with followig values

    Title: Predict Pressure

    reference: data_operation

    Data Operation: put the name of the Data operation that we created at the beginning of this tutorial - Predict Pressure

    Trade Phase: Data/Convert

    Quantity: 1

    Quantity Unit: Unit/Piece

    At the end click on Save to save the changes. 

    Fill The Form Cont.

    After saving the changes a new field Item Types will appear.

    Choose Data Acquisition Unit for Item Types and save.

    Back To Data Transformation

    Once needed fields are filled and saved go back to Data Transformations view by clicking on the upper panel.

    Data Transformation Lines

    Add another Data Transformation Line.

    Create Transformation Line

    Now for Document Type choose Data Transformation Resource Line and click on Create Document.

    Fill The Form

    Fill the form with following values

    Title: Resampled Data

    Reference: in_array

    Data Product: put the name of the Data Product that we created in HowTo Resample Data tutorial - Generic Interval Resampled Array

    After filling in the Data Product name click Save to save the intermediate changes. 

    After saving a new field Item Types will appear. 

    Fill The Form Cont.

    Continue filling the form as shown on the screenshot

    Item Types: Data Array

    Trade Phase: Data/Convert

    Default Variation: Resolution/60 S

    Quantity: -1

    Quantity Unit: Unit/Piece

    At the end don't forget to save the changes.

    Add Output Line

    Head back to Data Transformation and add one more Transformation line.

    Create Transformation Line

    Again for Document Type choose Data Transformation Resource Line and click on Create Document.

    Fill The Form

    Fill the form with following values

    Title: Predicted Data

    Reference: out_array

    Data Product: put the name of the Data Product that we created earlier in this tutorial - Predicted Data Array.

    Then click Save to save the intermediate changes. 

    After saving a new field Item Types will appear. 

    Fill The Form Cont.

    Continue filling the form as shown on the screenshot

    Item Types: Data Array

    Trade Phase: Data/Convert

    Use: Big Data/Ingestion/Stream Ingestion

    Quantity: 1

    Quantity Unit: Unit/Piece

    At the end don't forget to save the changes.

     

    Data Transformation

    Head back to Data Transformation.

    Now you can see that we have 3 Data Transformation Lines.

    The first line defines what operation will be done. 

    The second line defines on what the operation will be done - the input.

    The last line defines the output.

    The very last step is to validate the Data Transformation. 

    Click on Validate on the left side panel to validate the Data Transformation.

    Confirm Validation

    Click on Validate to confirm the Validation.

    Data Analysis

    Go to Data Analyses module.

    Data Analysis

    After few minutes in Data Analysis Module a new Data Analysis called Predict Pressure will appear. 

    Data Array Module

    Go to Data Array Module to see the data.  

    Data Array

    Here we can see the newly created Data Arrays with prediction results - Predict Pressure.

    Click on it to navigate to the array.

    Data Array

    Click on Preview to see the data.

    Note it might take few minutes (<10) until data appears.

    Data Array

    Data is here!

    Notebook Module

    On Modules page click on Notebooks.

    Add Notebook

    Click on Add to add a new Notebook.

    Create Notebook

    Click on Create Document to continue.

    Notebook

    Fill in the Title.

    The visualisation code will be at the bottom text area.

    Notebook uses the same technology as Iodide

    To learn more about programming in Iodide environment checkout Iodide homepage.

    Visualisation Script

    
    %% md
    # Prediction
    <div class="prediction_plot_div" id="prediction_plot_div"/>
    
    
    %% fetch
    js: jio.js
    js: ndarray_bundle.js
    js: wendelin.js
    js: https://cdn.plot.ly/plotly-latest.min.js
    
    
    %% js
    hateoas_url = "https://softinst133633.host.vifib.net/erp5/web_site_module/renderjs_runner/hateoas/";
    jio = jIO.createJIO({
      type: "erp5",
      url: hateoas_url,
      default_view_reference: "view"
    });
    
    gadget = {
      getSetting: function(property) {
        return new RSVP.Queue()
          .push(function () {
            if (property == "hateoas_url") {
              return hateoas_url;
            }
            return;
          });
      },
      jio_getAttachment: function(id, url, parameter_dict) {
        return jio.getAttachment(id, url, parameter_dict);
      },
      jio_get: function(id) {return jio.get(id);}
    }
    
    var prediction_label_list = ["Date", "Actual", "Predicted"];
    var prediction_graph = document.getElementById('prediction_plot_div');
    
    plot_prediction();
    
    function plot_prediction() {
      return getPredictionData()
        .push(function (data) {
          console.log("data")
          console.log(data)
          var layout = {barmode: 'stack','title' :'Predict Presure Based on Humidity'};
          Plotly.plot(prediction_graph,data, layout);
        });
    }
    
    function getPredictionData(start_date, stop_date){
      function unpack(rows, key) {
        return rows.map(function(row) { return row[key]; });
      }
    
      array_id = "data_array_module/90";
      prediction_graph_data=[];
    
      var start_index = 0;
      var stop_index = undefined;
    
      return jio.allDocs({
        query: 'portal_type:"Data Analysis Line" AND ' +
               'title: "Predicted Data" AND ' +
               'resource_reference:"environment-predicted-array" AND ' +
               'simulation_state:"started"'
      })
        .push(function (result) {
          var data_analysis_line_id = result.data.rows[0].id;
          return jio.allDocs({
            query: 'portal_type:"Data Array" AND ' +
                   'aggregate_related_relative_url:"' + data_analysis_line_id +'"'
          });
        })
        .push(function (result) {
          array_id = result.data.rows[0].id;
          return wendelin.getArrayRawSlice(gadget, array_id, 0, 1);
        })
        .push(function (result) {
          array_start_date = wendelin.convertFirstColToDate([[result.data[0]]])[0][0];
          if (start_index === undefined) {
            start_index = Math.max(0, Math.ceil((start_date - array_start_date) / (frequency*1000))),
            stop_index = Math.ceil((stop_date - array_start_date) / (frequency*1000));
          }
          return wendelin.getArrayRawSlice(gadget, array_id, start_index, stop_index);
        })
        .push(function(result) {
          for (i = 0; i < prediction_label_list.length; i += 1) {
            prediction_graph_data = prediction_graph_data.concat(nj.unpack(result.pick( null, prediction_label_list[i])));
          }
          return prediction_graph_data
        })
        .push(function(result){
          var filtered_graph_data = [];
          for (var i=0; i<result.length; i++) {
            if (result[i][0] != 0) {
              filtered_graph_data.push(result[i]);
            }
          }
          return wendelin.convertFirstColToDate(filtered_graph_data)
        })
        .push(function (graph_data) {
          var actual_data = {
              type: "scattergl",
              mode: "markers",
              name: 'Real',
              x: unpack(graph_data, prediction_label_list.indexOf("Date")),
              y: unpack(graph_data, prediction_label_list.indexOf("Actual")),
              line: {color: '#17BECF'}
          };
          var predicted_data = {
              type: "scattergl",
              mode: "markers",
              name: 'Predicted',
              x: unpack(graph_data, prediction_label_list.indexOf("Date")),
              y: unpack(graph_data, prediction_label_list.indexOf("Predicted")),
              line: {color: '#7F7F7F'}
          };
          var data = [actual_data,predicted_data];
          return data
        });
    }
    

    Copy/paste the code to your notebook and save the changes. 

    Note: you will need to provide the URL of your instance in the script. 

    For that change the line :

     

    hateoas_url = "https://softinstXXX.host.vifib.net/erp5/web_site_module/renderjs_runner/hateoas/";
    

     

    The script will find automatically the Data Array with prediction results based on the Data Product and plot the data using plotly library.

    Notebook

    After writing the script and saving the changes click on the Editable checkbox to (uncheck it) on the left side panel to see the result.

    Data Visualisation

    If everything was done correctly you will see the plot of your data.