The repo contain Lloyd's algorithm for K-Means clustering, which consists of two steps:
- Assignment step: Each observation in the data is assigned to the nearest cluster by use of the Euclidean distance from the point in the data to the cluster centroid.
- Update step: The mean of each cluster is calculated and assigned as the new centroid.
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Run implementation of K-means on iris_train.csv dataset for both L2 and L1 norm.
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Submit a csv file that is the source dataset with an appended column of classification.
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Submit images of the points plotted in multivariate space and color coded based on classification (for both norms).
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Now import both iris_train.csv and iris_test.csv , and produce a model on the train dataset for each norm above. Finally give metrics of prediction accuracy on the test data for each norm.
A sript that generates the files listed above is provided. Run
python train_iris.pyAn 80/20 split is used for training and testing.
Implementation of K-Means can be found inside cluster/Kmeans.py, while L1 and L2 norms can be found in
clustering/util/distance.py.
The following files were generated on a previous run:
data/results/train_results_l2.csv: Results for training using L2-Normdata/results/train_results_l1.csv: Results for training using L1-Normdata/results/test_results_l2.csv: Test results using L2-Norm.data/results/test_results_l1.csv: Test results using L1-Norm.
The below table is a sample of the file data/results/train_results_l2.csv, where the column name
ground is the ground truth and predicted is a converted version of the predicted results from K-Means. The K-means
algorithm provided returns the predictions as one hot labels (in string format, can be converted back by int(label)). The
labels are then matched to the labels corresponding to the ground truth.
| one | two | three | four | ground | predicted |
|---|---|---|---|---|---|
| 4.8 | 3.4 | 1.9 | 0.2 | Iris-setosa | Iris-setosa |
| 6.4 | 3.1 | 5.5 | 1.8 | Iris-virginica | Iris-virginica |
| 5.4 | 3.9 | 1.7 | 0.4 | Iris-setosa | Iris-setosa |
| 6.4 | 2.7 | 5.3 | 1.9 | Iris-virginica | Iris-virginica |
| 7.4 | 2.8 | 6.1 | 1.9 | Iris-virginica | Iris-virginica |
| 4.9 | 3.1 | 1.5 | 0.1 | Iris-setosa | Iris-setosa |
| 5.1 | 3.5 | 1.4 | 0.3 | Iris-setosa | Iris-setosa |
| 4.9 | 3.1 | 1.5 | 0.1 | Iris-setosa | Iris-setosa |
| 6.3 | 2.3 | 4.4 | 1.3 | Iris-versicolor | Iris-versicolor |
| 5.0 | 3.4 | 1.5 | 0.2 | Iris-setosa | Iris-setosa |
| 5.5 | 4.2 | 1.4 | 0.2 | Iris-setosa | Iris-setosa |
| 6.4 | 3.2 | 4.5 | 1.5 | Iris-versicolor | Iris-versicolor |
| 6.7 | 3.0 | 5.0 | 1.7 | Iris-versicolor | Iris-virginica |
| 6.7 | 3.3 | 5.7 | 2.5 | Iris-virginica | Iris-virginica |
| 6.0 | 2.9 | 4.5 | 1.5 | Iris-versicolor | Iris-versicolor |
| 5.2 | 3.5 | 1.5 | 0.2 | Iris-setosa | Iris-setosa |
| 4.4 | 3.2 | 1.3 | 0.2 | Iris-setosa | Iris-setosa |
| 7.2 | 3.0 | 5.8 | 1.6 | Iris-virginica | Iris-virginica |
| 6.9 | 3.1 | 5.1 | 2.3 | Iris-virginica | Iris-virginica |
| 5.0 | 3.2 | 1.2 | 0.2 | Iris-setosa | Iris-setosa |
Graph using L2-Norm (Training)
Two views of L1-Norm (Training)
Test visualization using L2-Norm
TODO: Add evaluation metrics such as Recall, Precession, F-Score, and/or Percentage of Variance Explained (PVE).