Local Weighted Learning #5615

venkateshtantravahi · 2021-10-26T16:49:48Z

Describe your change:

Add an algorithm?
Fix a bug or typo in an existing algorithm?
Documentation change?

Checklist:

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machine_learning/local_weighted_learning/local_weighted_learning.py

cclauss · 2021-10-26T17:41:49Z

@poyea @dhruvmanila How do you folks feel about our requirements? We currently have a list of 20 and it takes time and space to install all these requirements when we test both locally and in our GitHub Actions. For this reason, I have been trying to limit our requirements to PyPI modules that help build great algorithms.

I struggle when someone wants to add new requirements like this PR that wants to add seaborn.

For this PR, I would recommend that seaborn not be imported at global scope. This is because seaborn is used for data display, not calculations. There could be plotting functions that would import seaborn at local scope but these would not be run in our CI or tests. In CONTRIBUTING.md and in my pull request reviews, I have been trying to emphasize the differences algorithmic functions that do not print() or plot vs. helper functions that display the results calculated from those algorithmic functions. Please let me know if you agree with this approach or not.

venkateshtantravahi · 2021-10-26T17:52:30Z

I try to run further tests without plots and display results based in algorithm Thanks Class for your suggestions

…

On Tue, 26 Oct, 2021, 11:12 pm Christian Clauss, ***@***.***> wrote: @poyea <https://github.com/poyea> @dhruvmanila <https://github.com/dhruvmanila> How do you folks feel about our requirements <https://github.com/TheAlgorithms/Python/blob/master/requirements.txt>? We currently have a list of 20 and it takes time and space to install all these requirements when we test both locally and in our GitHub Actions. For this reason, I have been trying to limit our requirements to PyPI modules that help build great algorithms. I struggle when someone wants to add new requirements like this PR that wants to add seaborn. For this PR, I would *recommend that seaborn not be imported at global scope.* This is because seaborn is used for data display, not calculations. There could be plotting functions that would import seaborn at local scope but these would not be run in our CI or tests. In CONTRIBUTING.md and in my pull request reviews, I have been trying to emphasize the differences *algorithmic functions* that do not print() or plot vs. helper functions that display the results calculated from those algorithmic functions. Please let me know if you agree with this approach or not. — You are receiving this because you authored the thread. Reply to this email directly, view it on GitHub <#5615 (comment)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/APKUHXG3576W3VSLUHYE6ILUI3Y7TANCNFSM5GYI22BA> . Triage notifications on the go with GitHub Mobile for iOS <https://apps.apple.com/app/apple-store/id1477376905?ct=notification-email&mt=8&pt=524675> or Android <https://play.google.com/store/apps/details?id=com.github.android&referrer=utm_campaign%3Dnotification-email%26utm_medium%3Demail%26utm_source%3Dgithub>.

machine_learning/local_weighted_learning/local_weighted_learning.py

poyea · 2021-10-27T03:41:16Z

@poyea @dhruvmanila How do you folks feel about our requirements? We currently have a list of 20 and it takes time and space to install all these requirements when we test both locally and in our GitHub Actions. For this reason, I have been trying to limit our requirements to PyPI modules that help build great algorithms.

I struggle when someone wants to add new requirements like this PR that wants to add seaborn.

For this PR, I would recommend that seaborn not be imported at global scope. This is because seaborn is used for data display, not calculations. There could be plotting functions that would import seaborn at local scope but these would not be run in our CI or tests. In CONTRIBUTING.md and in my pull request reviews, I have been trying to emphasize the differences algorithmic functions that do not print() or plot vs. helper functions that display the results calculated from those algorithmic functions. Please let me know if you agree with this approach or not.

I agree. Algorithms like this rely heavily on display (like images, graphs, lines, etc.). I suggest we review those existing ones and make it clear later. Should we allow .csv or .txt files?

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machine_learning/local_weighted_learning/local_weighted_learning.py

venkateshtantravahi · 2021-10-28T11:34:09Z

Hi @ClausS even after the import change it was not able to pass test cases and im unable to merge it here by I'm attaching my file here once please have a look and suggest me what else I can do

…

On Tue, 26 Oct 2021 at 23:31, Christian Clauss ***@***.***> wrote: ***@***.**** requested changes on this pull request. ------------------------------ In machine_learning/local_weighted_learning/local_weighted_learning.py <#5615 (comment)>: > @@ -0,0 +1,116 @@ +# Required imports to run this file +import matplotlib.pyplot as plt +import numpy as np +import seaborn as sns ⬇️ Suggested change -import seaborn as sns ------------------------------ In machine_learning/local_weighted_learning/local_weighted_learning.py <#5615 (comment)>: > + m, n = np.shape(training_data_x) + ypred = np.zeros(m) + + for i, item in enumerate(training_data_x): + ypred[i] = item * local_weight( + item, training_data_x, training_data_y, bandwidth + ) + + return ypred + + +def load_data(dataset_name: str, cola_name: str, colb_name: str) -> np.mat: + """ + Function used for loading data from the seaborn splitting into x and y points + """ + data = sns.load_dataset(dataset_name) ⬇️ Suggested change - data = sns.load_dataset(dataset_name) + import seaborn as sns + + data = sns.load_dataset(dataset_name) — You are receiving this because you authored the thread. Reply to this email directly, view it on GitHub <#5615 (review)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/APKUHXF5ENIHSOEYFTF7CELUI33IBANCNFSM5GYI22BA> . Triage notifications on the go with GitHub Mobile for iOS <https://apps.apple.com/app/apple-store/id1477376905?ct=notification-email&mt=8&pt=524675> or Android <https://play.google.com/store/apps/details?id=com.github.android&referrer=utm_campaign%3Dnotification-email%26utm_medium%3Demail%26utm_source%3Dgithub>.

machine_learning/local_weighted_learning/local_weighted_learning.py

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machine_learning/local_weighted_learning/local_weighted_learning.py

ghost

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machine_learning/local_weighted_learning/local_weighted_learning.py

venkateshtantravahi · 2021-10-30T16:35:52Z

Hi clauss as requested by you changes has been made and pull requested review and approve

cclauss

Awesome work! Thanks for your persistance.

venkateshtantravahi · 2021-10-30T23:04:20Z

Hi clauss this is why I have added backslash in doctests because of it the run got failed and pr didn't merged to repo

…

On Sun, 31 Oct, 2021, 2:55 am Christian Clauss, ***@***.***> wrote: ***@***.**** approved this pull request. Awesome work! Thanks for your persistance. — You are receiving this because you authored the thread. Reply to this email directly, view it on GitHub <#5615 (review)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/APKUHXCKVUSBOECEXFOH4YTUJRWGDANCNFSM5GYI22BA> . Triage notifications on the go with GitHub Mobile for iOS <https://apps.apple.com/app/apple-store/id1477376905?ct=notification-email&mt=8&pt=524675> or Android <https://play.google.com/store/apps/details?id=com.github.android&referrer=utm_campaign%3Dnotification-email%26utm_medium%3Demail%26utm_source%3Dgithub>.

venkateshtantravahi · 2021-10-30T23:05:20Z

But great bow and thanks to you for consistently helping me for this pr

…

On Sun, 31 Oct, 2021, 2:55 am Christian Clauss, ***@***.***> wrote: ***@***.**** approved this pull request. Awesome work! Thanks for your persistance. — You are receiving this because you authored the thread. Reply to this email directly, view it on GitHub <#5615 (review)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/APKUHXCKVUSBOECEXFOH4YTUJRWGDANCNFSM5GYI22BA> . Triage notifications on the go with GitHub Mobile for iOS <https://apps.apple.com/app/apple-store/id1477376905?ct=notification-email&mt=8&pt=524675> or Android <https://play.google.com/store/apps/details?id=com.github.android&referrer=utm_campaign%3Dnotification-email%26utm_medium%3Demail%26utm_source%3Dgithub>.

cclauss · 2021-10-31T05:00:19Z

machine_learning/local_weighted_learning/local_weighted_learning.py

+    >>> weighted_matrix(np.array([1., 1.]),np.mat([[16.99, 10.34], [21.01,23.68],
+                        [24.59,25.69]]), 0.6)


You just need to use the ... to show doctest that we are continuing the previous line.

Suggested change

>>> weighted_matrix(np.array([1., 1.]),np.mat([[16.99, 10.34], [21.01,23.68],

[24.59,25.69]]), 0.6)

>>> weighted_matrix(np.array([1., 1.]),np.mat([[16.99, 10.34], [21.01,23.68],

... [24.59,25.69]]), 0.6)

cclauss · 2021-10-31T05:00:54Z

machine_learning/local_weighted_learning/local_weighted_learning.py

+    >>> local_weight(np.array([1., 1.]),np.mat([[16.99, 10.34], [21.01,23.68],
+                     [24.59,25.69]]),np.mat([[1.01, 1.66, 3.5]]), 0.6)


Suggested change

>>> local_weight(np.array([1., 1.]),np.mat([[16.99, 10.34], [21.01,23.68],

[24.59,25.69]]),np.mat([[1.01, 1.66, 3.5]]), 0.6)

>>> local_weight(np.array([1., 1.]),np.mat([[16.99, 10.34], [21.01,23.68],

... [24.59,25.69]]),np.mat([[1.01, 1.66, 3.5]]), 0.6)

cclauss · 2021-10-31T05:01:26Z

machine_learning/local_weighted_learning/local_weighted_learning.py

+    >>> local_weight_regression(np.mat([[16.99, 10.34], [21.01,23.68],
+                                [24.59,25.69]]),np.mat([[1.01, 1.66, 3.5]]), 0.6)


Suggested change

>>> local_weight_regression(np.mat([[16.99, 10.34], [21.01,23.68],

[24.59,25.69]]),np.mat([[1.01, 1.66, 3.5]]), 0.6)

>>> local_weight_regression(np.mat([[16.99, 10.34], [21.01,23.68],

... [24.59,25.69]]),np.mat([[1.01, 1.66, 3.5]]), 0.6)

cclauss · 2021-10-31T05:14:05Z

machine_learning/local_weighted_learning/local_weighted_learning.py

+def load_data(dataset_name: str, cola_name: str, colb_name: str) -> np.mat:
+    """
+    Function used for loading data from the seaborn splitting into x and y points
+    """


Let's silence @algorithms-keeper

Suggested change

"""

>>> pass # This function has no doctest.

"""

cclauss · 2021-10-31T05:15:16Z

machine_learning/local_weighted_learning/local_weighted_learning.py

+) -> plt.plot:
+    """
+    This function used to plot predictions and display the graph
+    """


Suggested change

"""

>>> pass # This function has no doctest.

"""

venkateshtantravahi and others added 11 commits October 22, 2021 18:10

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Local Weighted Learning #5615

Local Weighted Learning #5615

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		>>> weighted_matrix(np.array([1., 1.]),np.mat([[16.99, 10.34], [21.01,23.68],
		[24.59,25.69]]), 0.6)

		>>> local_weight(np.array([1., 1.]),np.mat([[16.99, 10.34], [21.01,23.68],
		[24.59,25.69]]),np.mat([[1.01, 1.66, 3.5]]), 0.6)

		>>> local_weight_regression(np.mat([[16.99, 10.34], [21.01,23.68],
		[24.59,25.69]]),np.mat([[1.01, 1.66, 3.5]]), 0.6)

Local Weighted Learning #5615

Local Weighted Learning #5615

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venkateshtantravahi commented Oct 26, 2021

Describe your change:

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