=========================================================================== CS 529 - Intro to Machine Learning - Assignment 3 - Logistic Regression
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Source code of the project is hosted on github: https://github.com/vamshins/Logistic-Regression
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The executable "LogisticRegression.py" is provided in the assignment submission in UNM Learn.
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I have programmed in "Python 2.7.9 |Anaconda 2.2.0 (64-bit)|" installed in Windows 8.1. I installed numpy, scikit_learn, scipy, scikits_talkbox. Download all the files attached in UNM Learn submission and store them in "<HOME_DIR>" (This may be any folder on your OS) Copy the data into the "<HOME_DIR>/opihi.cs.uvic.ca" folder. The folder structure looks like - - <HOME_DIR> |--> /LogisticRegression.py |--> /fftdata.txt |--> /classesmatrix.txt |--> /mfccdata.txt |--> /opihi.cs.uvic.ca/sound/genres/ |--> /classical |--> /jazz |--> /country |--> /pop |--> /rock |--> /metal
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Execution of the Program: 4.1. Go to Run(Windows) or Terminal(Linux) 4.2. Navigate to <HOME_DIR> 4.3. Run the command -> python LogisticRegression.py - Give -fft or -fft20 or -mfcc -fft -> Generates fft for the data and performs the Logistic Regression with Gradient Ascent and stores the data in fftdata.txt file. If there is no fftdata.txt, the program will generate it. If it is already present, then program will continue with processing the data from text file. -fft20 -> Generates 20 best features per genre for the fft data and performs the Logistic Regression with Gradient Ascent If there is no fftdata.txt, the program will generate it. If it is already present, then program will continue with processing the data from text file. -mfcc -> Generates mfcc for the data and performs the Logistic Regression with Gradient Ascent If there is no mfccdata.txt, the program will generate it. If it is already present, then program will continue with processing the data from text file. - Path to the music data. Eg: <HOME_DIR>\opihi.cs.uvic.ca\sound\genres
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Output of the Program for each fold with highest accuracy: ============================================================================================== For FFT: Execution : python LogisticRegression.py -fft <HOME_DIR>\opihi.cs.uvic.ca\sound\genres
Fold 0 max accuracy : 0.483333333333
Confusion Matrix :
[[ 10. 0. 0. 0. 0. 0.]
[ 1. 1. 3. 0. 4. 1.]
[ 2. 0. 7. 0. 1. 0.]
[ 4. 0. 0. 0. 5. 1.]
[ 1. 0. 0. 0. 9. 0.]
[ 1. 3. 0. 0. 4. 2.]]
Fold 1 max accuracy : 0.566666666667
Confusion Matrix :
[[ 7. 0. 1. 0. 1. 1.]
[ 0. 2. 2. 3. 1. 2.]
[ 1. 1. 5. 1. 1. 1.]
[ 0. 0. 0. 9. 1. 0.]
[ 0. 1. 0. 0. 7. 2.]
[ 0. 3. 1. 1. 1. 4.]]
Fold 2 max accuracy : 0.466666666667
Confusion Matrix :
[[ 5. 1. 1. 0. 2. 1.]
[ 0. 2. 1. 0. 6. 1.]
[ 0. 0. 6. 0. 3. 1.]
[ 0. 0. 0. 3. 6. 1.]
[ 0. 0. 0. 0. 10. 0.]
[ 0. 1. 0. 1. 6. 2.]]
Fold 3 max accuracy : 0.5
Confusion Matrix :
[[ 9. 0. 0. 0. 0. 1.]
[ 4. 2. 0. 2. 2. 0.]
[ 1. 0. 4. 3. 1. 1.]
[ 0. 0. 0. 6. 3. 1.]
[ 1. 0. 0. 2. 6. 1.]
[ 1. 0. 0. 3. 3. 3.]]
Fold 4 max accuracy : 0.466666666667
Confusion Matrix :
[[ 7. 0. 1. 0. 1. 1.]
[ 0. 4. 1. 0. 4. 1.]
[ 2. 0. 4. 0. 1. 3.]
[ 1. 1. 0. 1. 2. 5.]
[ 0. 0. 0. 1. 6. 3.]
[ 0. 3. 0. 0. 1. 6.]]
Fold 5 max accuracy : 0.5
Confusion Matrix :
[[ 8. 0. 1. 0. 1. 0.]
[ 1. 1. 0. 6. 1. 1.]
[ 1. 0. 4. 3. 1. 1.]
[ 0. 0. 0. 7. 2. 1.]
[ 0. 1. 0. 2. 7. 0.]
[ 1. 1. 0. 4. 1. 3.]]
Fold 6 max accuracy : 0.583333333333
Confusion Matrix :
[[ 7. 0. 0. 2. 1. 0.]
[ 0. 3. 0. 2. 3. 2.]
[ 0. 1. 3. 2. 0. 4.]
[ 0. 0. 0. 8. 2. 0.]
[ 0. 0. 0. 2. 8. 0.]
[ 0. 0. 1. 2. 1. 6.]]
Fold 7 max accuracy : 0.55
Confusion Matrix :
[[ 8. 0. 2. 0. 0. 0.]
[ 0. 5. 0. 0. 3. 2.]
[ 1. 0. 5. 1. 1. 2.]
[ 0. 0. 1. 4. 4. 1.]
[ 0. 1. 0. 0. 8. 1.]
[ 0. 0. 1. 2. 4. 3.]]
Fold 8 max accuracy : 0.566666666667
Confusion Matrix :
[[ 8. 0. 0. 0. 1. 1.]
[ 0. 6. 1. 0. 2. 1.]
[ 0. 2. 4. 0. 0. 4.]
[ 0. 1. 0. 1. 6. 2.]
[ 0. 0. 0. 0. 10. 0.]
[ 0. 2. 0. 0. 3. 5.]]
Fold 9 max accuracy : 0.516666666667
Confusion Matrix :
[[ 9. 0. 1. 0. 0. 0.]
[ 0. 5. 1. 0. 0. 4.]
[ 2. 0. 5. 0. 0. 3.]
[ 0. 1. 0. 3. 2. 4.]
[ 0. 1. 1. 2. 5. 1.]
[ 0. 4. 1. 1. 0. 4.]]
Avg of all folds accuracies : 0.52
============================================================================================== For FFT20: Execution : python LogisticRegression.py -fft20 <HOME_DIR>\opihi.cs.uvic.ca\sound\genres
Fold 0 max accuracy : 0.633333333333
Confusion Matrix :
[[ 9. 0. 0. 0. 1. 0.]
[ 2. 5. 2. 1. 0. 0.]
[ 2. 2. 5. 0. 1. 0.]
[ 1. 0. 0. 9. 0. 0.]
[ 0. 1. 2. 2. 5. 0.]
[ 2. 1. 0. 1. 1. 5.]]
Fold 1 max accuracy : 0.616666666667
Confusion Matrix :
[[ 7. 1. 2. 0. 0. 0.]
[ 3. 5. 2. 0. 0. 0.]
[ 0. 4. 6. 0. 0. 0.]
[ 0. 0. 1. 9. 0. 0.]
[ 1. 2. 2. 0. 5. 0.]
[ 0. 3. 1. 1. 0. 5.]]
Fold 2 max accuracy : 0.6
Confusion Matrix :
[[ 7. 2. 0. 0. 0. 1.]
[ 0. 6. 1. 0. 3. 0.]
[ 1. 1. 7. 0. 1. 0.]
[ 1. 0. 2. 5. 2. 0.]
[ 0. 2. 1. 0. 5. 2.]
[ 1. 1. 0. 2. 0. 6.]]
Fold 3 max accuracy : 0.6
Confusion Matrix :
[[ 8. 0. 1. 0. 1. 0.]
[ 4. 4. 1. 0. 1. 0.]
[ 1. 3. 4. 0. 2. 0.]
[ 1. 1. 3. 5. 0. 0.]
[ 0. 0. 1. 0. 9. 0.]
[ 1. 1. 1. 0. 1. 6.]]
Fold 4 max accuracy : 0.583333333333
Confusion Matrix :
[[ 9. 0. 1. 0. 0. 0.]
[ 0. 6. 3. 0. 1. 0.]
[ 3. 2. 4. 0. 0. 1.]
[ 2. 0. 3. 5. 0. 0.]
[ 1. 0. 5. 0. 3. 1.]
[ 1. 0. 1. 0. 0. 8.]]
Fold 5 max accuracy : 0.616666666667
Confusion Matrix :
[[ 8. 0. 2. 0. 0. 0.]
[ 1. 4. 2. 2. 1. 0.]
[ 1. 1. 7. 1. 0. 0.]
[ 2. 0. 1. 7. 0. 0.]
[ 1. 1. 3. 0. 4. 1.]
[ 3. 0. 0. 0. 0. 7.]]
Fold 6 max accuracy : 0.616666666667
Confusion Matrix :
[[ 6. 0. 4. 0. 0. 0.]
[ 3. 6. 1. 0. 0. 0.]
[ 2. 2. 5. 1. 0. 0.]
[ 1. 0. 4. 5. 0. 0.]
[ 0. 1. 2. 0. 7. 0.]
[ 0. 0. 2. 0. 0. 8.]]
Fold 7 max accuracy : 0.633333333333
Confusion Matrix :
[[ 8. 0. 1. 0. 1. 0.]
[ 0. 6. 2. 0. 2. 0.]
[ 3. 0. 6. 0. 1. 0.]
[ 0. 2. 2. 6. 0. 0.]
[ 1. 1. 3. 0. 5. 0.]
[ 1. 0. 0. 0. 2. 7.]]
Fold 8 max accuracy : 0.666666666667
Confusion Matrix :
[[ 8. 0. 2. 0. 0. 0.]
[ 2. 5. 3. 0. 0. 0.]
[ 1. 0. 8. 0. 1. 0.]
[ 1. 0. 2. 7. 0. 0.]
[ 0. 0. 4. 0. 6. 0.]
[ 1. 1. 2. 0. 0. 6.]]
Fold 9 max accuracy : 0.666666666667
Confusion Matrix :
[[ 8. 0. 1. 0. 0. 1.]
[ 0. 5. 5. 0. 0. 0.]
[ 0. 1. 8. 0. 1. 0.]
[ 0. 1. 0. 8. 1. 0.]
[ 0. 2. 2. 0. 5. 1.]
[ 0. 2. 2. 0. 0. 6.]]
Avg of all folds accuracies : 0.623333333333
============================================================================================== For MFCC: Execution : python LogisticRegression.py -mfcc <HOME_DIR>\opihi.cs.uvic.ca\sound\genres
Fold 0 max accuracy : 0.733333333333
Confusion Matrix :
[[ 10. 0. 0. 0. 0. 0.]
[ 0. 6. 0. 1. 1. 2.]
[ 3. 0. 6. 1. 0. 0.]
[ 0. 1. 0. 9. 0. 0.]
[ 0. 1. 0. 0. 8. 1.]
[ 0. 0. 1. 4. 0. 5.]]
Fold 1 max accuracy : 0.633333333333
Confusion Matrix :
[[ 8. 1. 0. 1. 0. 0.]
[ 0. 5. 3. 0. 2. 0.]
[ 2. 3. 2. 2. 1. 0.]
[ 0. 0. 0. 9. 0. 1.]
[ 1. 1. 0. 0. 8. 0.]
[ 0. 4. 0. 0. 0. 6.]]
Fold 2 max accuracy : 0.666666666667
Confusion Matrix :
[[ 8. 0. 0. 0. 0. 2.]
[ 0. 4. 3. 1. 1. 1.]
[ 2. 0. 7. 0. 1. 0.]
[ 0. 0. 1. 9. 0. 0.]
[ 0. 1. 0. 0. 8. 1.]
[ 0. 1. 0. 4. 1. 4.]]
Fold 3 max accuracy : 0.683333333333
Confusion Matrix :
[[ 8. 1. 1. 0. 0. 0.]
[ 0. 6. 0. 0. 1. 3.]
[ 1. 3. 5. 0. 0. 1.]
[ 0. 0. 0. 10. 0. 0.]
[ 0. 2. 0. 0. 7. 1.]
[ 0. 2. 0. 1. 2. 5.]]
Fold 4 max accuracy : 0.666666666667
Confusion Matrix :
[[ 7. 1. 2. 0. 0. 0.]
[ 1. 4. 2. 0. 2. 1.]
[ 2. 2. 5. 0. 0. 1.]
[ 0. 0. 0. 10. 0. 0.]
[ 0. 0. 0. 0. 9. 1.]
[ 1. 0. 0. 4. 0. 5.]]
Fold 5 max accuracy : 0.683333333333
Confusion Matrix :
[[ 9. 0. 1. 0. 0. 0.]
[ 0. 6. 2. 0. 1. 1.]
[ 2. 2. 5. 0. 0. 1.]
[ 0. 1. 0. 9. 0. 0.]
[ 0. 0. 0. 0. 9. 1.]
[ 1. 1. 3. 2. 0. 3.]]
Fold 6 max accuracy : 0.716666666667
Confusion Matrix :
[[ 8. 1. 1. 0. 0. 0.]
[ 2. 5. 3. 0. 0. 0.]
[ 3. 0. 4. 0. 2. 1.]
[ 0. 0. 1. 9. 0. 0.]
[ 0. 0. 0. 0. 10. 0.]
[ 0. 0. 1. 2. 0. 7.]]
Fold 7 max accuracy : 0.783333333333
Confusion Matrix :
[[ 8. 0. 2. 0. 0. 0.]
[ 1. 7. 1. 0. 0. 1.]
[ 0. 1. 8. 0. 1. 0.]
[ 0. 0. 1. 9. 0. 0.]
[ 0. 0. 0. 0. 10. 0.]
[ 0. 2. 0. 3. 0. 5.]]
Fold 8 max accuracy : 0.7
Confusion Matrix :
[[ 9. 0. 0. 1. 0. 0.]
[ 2. 6. 0. 1. 0. 1.]
[ 3. 1. 5. 0. 1. 0.]
[ 0. 0. 0. 10. 0. 0.]
[ 0. 0. 0. 0. 10. 0.]
[ 1. 1. 0. 5. 1. 2.]]
Fold 9 max accuracy : 0.733333333333
Confusion Matrix :
[[ 8. 0. 1. 0. 0. 1.]
[ 1. 7. 0. 1. 1. 0.]
[ 2. 3. 5. 0. 0. 0.]
[ 0. 0. 0. 10. 0. 0.]
[ 0. 1. 0. 0. 8. 1.]
[ 0. 0. 2. 0. 2. 6.]]
Avg of all folds accuracies : 0.7
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