Contains our pattern recognition project files, which is about performing a dimensional reduction using the KLDA technique and performing a classification by employing a probabilistic approach.
The operations are performed on a dataset named "Ultrasonic flowmeter diagnostics Data Set" hosted in the UCI Machine Learning Repository.
The Dataset can be found here.
| Task to be done | Classification |
|---|---|
| Count of data available | 540 |
The data is collected from four different ultrasonic flow meters(USM) named Meter A, Meter B, Meter C, and Meter D are separated into four different files. The Attributes collected for the different meters also differs.
The data is collected from a 8-path USM.
| Data Count | 87 |
|---|---|
| Number of Attributes | 37 |
| Number of Classes | 2 |
| Class '1' | Healthy |
| Class '2' | Installation effects (mostly defects) |
Attribute Information:
| Columns | Attribute |
|---|---|
| 1 | Flatness ratio |
| 2 | Symmetry |
| 3 | Crossflow |
| 4-11 | Flow velocity in each of the eight paths |
| 12-19 | Speed of sound in each of the eight paths |
| 20 | Average speed of sound in all eight paths |
| 21-36 | Gain at both ends of each of the eight paths |
| 37 | Class Attribute (1,2) |
The data is collected from a 4-path USM.
| Data Count | 92 |
|---|---|
| Number of Attributes | 52 |
| Number of Classes | 3 |
| Class '1' | Healthy |
| Class '2' | Gas Injection |
| Class '3' | Waxing |
Attribute Information:
| Columns | Attribute |
|---|---|
| 1 | Flatness ratio |
| 2 | Symmetry |
| 3 | Crossflow |
| 4 | Swirl angle |
| 5-8 | Flow velocity in each of the four paths |
| 9 | Average flow velocity in all four paths |
| 10-13 | Speed of sound in each of the four paths |
| 14 | Average speed of sound in all four paths |
| 15-22 | Signal strength at both ends of each of the four paths |
| 23 -26 | Turbulence in each of the four paths |
| 27 | Meter performance |
| 28-35 | Signal quality at both ends of each of the four paths |
| 36-43 | Gain at both ends of each of the four paths |
| 44-51 | Transit time at both ends of each of the four paths |
| 52 | Class Attribute (1,2,3) |
The data collected for both the meters is from from a 4-path USM.
| Data Count for Meter C | 181 |
|---|---|
| Data Count for Meter D | 180 |
The attributes of Meter C and Meter D is the same.
| Number of Attributes | 44 |
|---|---|
| Number of Classes | 4 |
| Class '1' | Healthy |
| Class '2' | Gas Injection |
| Class '3' | Installation effects (mostly defects) |
| Class '4' | Waxing |
Attribute Information:
| Columns | Attribute |
|---|---|
| 1 | Flatness ratio |
| 2 | Symmetry |
| 3 | Crossflow |
| 4-7 | Flow velocity in each of the four paths |
| 8-11 | Speed of sound in each of the four paths |
| 12-19 | Signal strength at both ends of each of the four paths |
| 20-27 | Signal quality at both ends of each of the four paths |
| 28-35 | Gain at both ends of each of the four paths |
| 36-43 | Transit time at both ends of each of the four paths |
| 44 | Class attribute (1,2,3,4) |
Install packages from requirements.txt.
pip install -r requirements.txt-
Install R
On Debian based distributions:$ apt-get install r-base
-
Open R with super user privileges
$ sudo R
-
Install the packages listed below
install.packages("caret") install.packages("lfda") install.packages("kernlab")
- Run
classify.pyon all 4 meters and note down the results for each meter. - Run
r_prep.pyfor all the 4 meters to obtain data compatible with R code. - Choose a meter and do the following steps
a. Run
klda.pyand run classify.py on the resulting saved file obtained. b. Runklda.Rand run classify.py on the resulting saved file obtained. c. Compare the results for no dimensionality reduction, dimensionality reduction using native Python and dimensionality reduction using R package. - Repeat step 3 for different values of the target dimension
- Repeat steps 3 and 4 for different kernel functions (Different Kernel functions can be found in a link given in
klda.R)
For flowmeter A, we get a score of 0.6667 after 8310 epochs with a run time of 0.4124 seconds
For flowmeter B, we get a score of 1.0000 after 1240 epochs with a run time of 0.1948 seconds
For flowmeter C, we get a score of 0.8421 after 4000 epochs with a run time of 1.3044 seconds
For flowmeter D, we get a score of 0.7778 after 6000 epochs with a run time of 1.9976 seconds