Fixed the limitation when "no of Workloads" become greater than "no o… #10
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…f performance matrics/no of features". It will now work for any number of workloads
karanrk
reviewed
Aug 15, 2022
similarity-analyzer/dopca.py
Outdated
| @@ -136,7 +136,11 @@ def dopca(dataset, colnames, n_components, cols): | |||
| num_val = StandardScaler().fit_transform(num_val) | |||
| logger.debug(f"Post normalizing metrics, num_val: {num_val}") | |||
| # PCA analysis, Create PCA model | |||
| #pca = PCA(n_components=n_components) #Commented by Faqeer ur Rehman: 11 Aug 2022. Limitation: If the n_components(no of workloads) are greater than num_val(the number of features), it will throw error. | |||
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@faqeerurrehmanIntel - could you please keep comments to bare minimum information. "Commented by" and date is already recorded by git, can be removed.
similarity-analyzer/dopca.py
Outdated
| @@ -136,7 +136,11 @@ def dopca(dataset, colnames, n_components, cols): | |||
| num_val = StandardScaler().fit_transform(num_val) | |||
| logger.debug(f"Post normalizing metrics, num_val: {num_val}") | |||
| # PCA analysis, Create PCA model | |||
| #pca = PCA(n_components=n_components) #Commented by Faqeer ur Rehman: 11 Aug 2022. Limitation: If the n_components(no of workloads) are greater than num_val(the number of features), it will throw error. | |||
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| n_components = len(num_val[0]) # Added by Faqeer ur Rehman: 11 Aug 2022. Solution: To scale it for any number of workloads, generate PCAs equivalent to number of features/performance matrics (instead of number of workloads) we have for each workload. | |||
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please get rid of "Added by" and date.
|
Removed the extra comments. |
karanrk
approved these changes
Aug 18, 2022
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Fixed the limitation when "no of workloads" become greater than "no of performance matrics / no of features". Now, It will work for similarity analysis of any "no of workloads".