root/MachineLearningWithMATLAB/Classification_RobotFailures/MainAnalysis.m @ 11
| 10 | anderm8 | %% Classify Robot Execution Failures
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% Robot Execution dataset contains force and torque measurements on a robot
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% after failure detection. Each failure is characterized by 15 force/torque
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% samples collected at regular time intervals starting immediately after
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% failure detection. The total observation window for each failure instance
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% was of 315 ms. All features are numeric and represent a force or a torque
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% measured after failure detection. The dataset used in the following
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% workbook is a modified version where the average of the 15 samples is
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% used to represent each failure. The goal of this analysis is to build a
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% model to automatically identify the failure type given the sensor
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% measurements.
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%
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% The data is stored in an excel file, as follows:
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% Fx Fy Fz Tx Ty Tz Fault
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%
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% The original dataset is courtesy of:
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% Luis Seabra Lopes and Luis M. Camarinha-Matos
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% Universidade Nova de Lisboa,
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% Monte da Caparica, Portugal
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% Date Donated: April 23, 1999
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%
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% Copyright 2015 The MathWorks, Inc.
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%% Import Existing Data
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% In this example, the data is imported from an Excel File. We can make use
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% of the interactive Import Tool to import the data and auto-generate the
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% code for the purpose of automation. The table data type allows us to
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% collect mixed-type data and metadata properties (such as variable names,
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% row names, descriptions, and variable units) in a single container.
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% Tables are suitable for column-oriented or tabular data that is often
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% stored as columns in a text file or in a spreadsheet. Since our dataset
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% contains experimental data with rows representing different observations
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% and columns representing different measured variables, tables are a
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% suitable choice.
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% Auto-generated code for importing data
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faultData = importFaultData('faultData.xlsx');
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%% Convert Categorical Data into Categorical Arrays
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% Categorical data contains discrete pieces of information, such as the
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% different classes of faults in this dataset. A categorical array provides
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% efficient storage and convenient manipulation of nonnumeric data while
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% also maintaining meaningful names for the values. We can open a variable
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% in the Variable Editor and convert categorical attributes into
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% categorical arrays interactively. MATLAB will echo the code necessary
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% to accomplish these interactive tasks in the Command Window.
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% Convert categorical variables into categorical arrays
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faultData.Fault = categorical(faultData.Fault);
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%% Visualize Data
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% By simply visualizing our data, we begin get insights into our dataset.
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% For example, we can see that the forces in Z direction are largely
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% responsible in determining the Obstruction fault class.
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% We can open the variable |faultData| in the Variable Editor and
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% interactively create various types of plots by selecting one or more
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% columns. As we create the plots, MATLAB echoes the corresponding commands
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% in the Command Window.
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% Display a pie chart to illustrate numerical distribution of faults
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pie(faultData.Fault)
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% Fz vs Tz plot, differentiated by Fault Class
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figure
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gscatter(faultData.Fz,faultData.Tz,faultData.Fault)
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xlabel('Fz')
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ylabel('Tz')
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title('Outcome')
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% Visualize data using a box plot
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figure
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boxplot(faultData.Fz,faultData.Fault)
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xlabel('Fault')
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ylabel('Fz')
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title('Fz per fault class')
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%% Summary of our dataset
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% We can gain some quick insights into the our data by using the |summary|
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% command.
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% The summary contains the following information on the variables:
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% Name (Size and Data Type)
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% Units (if any)
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% Description (if any)
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% Values
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% numeric variables ? minimum, median, and maximum values
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% logical variables ? number of values that are true and false
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% categorical variables ? number of elements from each category
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summary(faultData)
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%% Filter Data
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% From the summary results, notice that the 'lost' category of faults are
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% only represented by three samples. In machine learning, data is key. Our
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% model is only as good as the data we feed it. Since we do not have enough
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% data to represent the 'lost' category, we will remove it from our dataset
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% to increase the accuracy of our model.
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% Remove 'lost' category
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faultData(faultData.Fault == 'lost',:) = [];
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faultData.Fault = removecats(faultData.Fault);
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%% Apply Machine Learning Techniques
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% Statistics and Machine Learning Toolbox features a number of supervised
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% and unsupervised machine learning techniques. It supports both
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% classification and regression algorithms. The supervised learning
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% techniques range from non-linear regression, generalized linear
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% regression, discriminant analysis, SVMs to decision trees and ensemble
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% methods.
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%
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% Observe that once the data has been prepared, the syntax to utilize the
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% different modeling techniques is very similar and most of these
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% techniques can handle categorical predictors directly. The user can
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% conveniently supply information about different parameters associated
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% with the different algorithms.
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%
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% All of the classification techniques can be explored interactively using
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% our new Classification Learner App. Once we decide on an algorithm, the
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% App can generate code for the desired technique.
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%
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% For example, below we used the App to generate code for a Medium tree.
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% Train and view classifier
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[trainedClassifier, validationAccuracy, validationPredictions, validationScores]= ...
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trainClassifierComplexTree(faultData);
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view(trainedClassifier.ClassificationTree, 'Mode', 'graph')
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%% Predict responses for new data
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% After we create classification models interactively in the Classification
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% Learner App, we can export our best model to the workspace. We can then
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% use the trained model to make predictions using new data.
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% Predict a response using completely new data
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PredictedResponse = trainedClassifier.predictFcn(faultData(3,:))
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%% Evaluate classifier performance
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% After a classification algorithm has trained on data, we may want to
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% examine the performance of the algorithm on a specific set of test data.
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% Various performance measures (such as mean squared error, classification
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% error, or exponential loss) can summarize the predictive power of a
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% classifier in a single number. However, a performance curve offers more
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% information as it lets us explore the classifier performance across a
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% range of thresholds on its output. An ROC curve shows true positive rate
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% versus false positive rate for different thresholds of the classifier
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% output. We can use it, for example, to find the threshold that maximizes
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% the classification accuracy or to assess how the classifier performs in
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% regions of high sensitivity and high specificity. Let us plot the ROC
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% curve for the class 'normal'.
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% Create and plot the ROC curve for our classifier
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[X,Y,T,AUC] = perfcurve(faultData.Fault,validationScores(:,3),'normal');
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disp(AUC)
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figure
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plot(X,Y)
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title('Performance Curves (ROC) for ''normal class''');
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xlabel('False Positive Rate [ = FP/(TN+FP)]');
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ylabel('True Positive Rate [ = TP/(TP+FN)]');
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