MNT Use check_scalar in BIRCH and DBSCAN #20816

SanjayMarreddi · 2021-08-23T13:51:18Z

Reference Issues/PRs

Solves a part of the Issue #20724
See also PR #20723

What does this implement/fix? Explain your changes.

Using the helper function check_scalar from sklearn.utils to validate scalar parameters and making sure to get consistent error types and messages in different Clustering Algorithms:

DBSCAN algorithm.
BIRCH algorithm.
Validate the changes made and Perform System tests.

References:

docs

Any other comments?

Really excited to contribute to the Machine Learning library scikit-learn that I have been using in my projects from the past year!

glemaitre · 2021-09-02T15:45:28Z

Could you merge main into your branch. You should as well use a for loop. We found it clearer to make the function call directly.

SanjayMarreddi · 2021-09-02T16:11:05Z

@glemaitre Sure, I will do it and also try to finish the remaining components of this PR ( like testing & some modifications ) and make sure to make it available to merge by this Weekend/ in 1-2 days.
I am sorry if there is any inconvenience from my side. Thanks!

…nto check_scalar

SanjayMarreddi · 2021-09-03T13:32:36Z

@glemaitre I have made the necessary changes. Kindly review it. Thanks!

glemaitre · 2021-09-03T14:13:15Z

There is a bug in check_scalar that will be fixed soon: #20921
You can merge this PR into yours in order to use the right parameter naming. Alternatively, you can wait that it is merged in main.

glemaitre · 2021-09-03T14:15:01Z

You should check the CIs because the linter is failing. You might have forgotten to use black on the changed file.
I will look at the changes and check if the code is OK.

glemaitre · 2021-09-15T14:26:31Z

Concretely, this is the patch to correct the failure:

diff --git a/sklearn/cluster/_birch.py b/sklearn/cluster/_birch.py
index c688bc2f95..b418e0b5b7 100644
--- a/sklearn/cluster/_birch.py
+++ b/sklearn/cluster/_birch.py
@@ -13,9 +13,8 @@ from ..metrics import pairwise_distances_argmin
 from ..metrics.pairwise import euclidean_distances
 from ..base import TransformerMixin, ClusterMixin, BaseEstimator
 from ..utils.extmath import row_norms
-from ..utils import deprecated
-from ..utils import check_scalar
-from ..utils.validation import check_is_fitted
+from ..utils import check_scalar, deprecated
+from ..utils.validation import _num_samples, check_is_fitted
 from ..exceptions import ConvergenceWarning
 from . import AgglomerativeClustering
 from .._config import config_context
@@ -481,7 +480,7 @@ class Birch(ClusterMixin, TransformerMixin, BaseEstimator):
     # TODO: Remove in 1.2
     # mypy error: Decorated property not supported
     @deprecated(  # type: ignore
-        "`fit_` is deprecated in 1.0 and will be removed in 1.2."
+        "`fit_` is deprecated in 1.0 and will be removed in 1.2"
     )
     @property
     def fit_(self):
@@ -490,7 +489,7 @@ class Birch(ClusterMixin, TransformerMixin, BaseEstimator):
     # TODO: Remove in 1.2
     # mypy error: Decorated property not supported
     @deprecated(  # type: ignore
-        "`partial_fit_` is deprecated in 1.0 and will be removed in 1.2."
+        "`partial_fit_` is deprecated in 1.0 and will be removed in 1.2"
     )
     @property
     def partial_fit_(self):
@@ -519,22 +518,24 @@ class Birch(ClusterMixin, TransformerMixin, BaseEstimator):
             "threshold",
             target_type=numbers.Real,
             min_val=0.0,
-            closed="neither"
+            include_boundaries="neither",
         )
         check_scalar(
             self.branching_factor,
             "branching_factor",
             target_type=numbers.Integral,
             min_val=1,
-            closed="neither"
-        )
-        check_scalar(
-            self.n_clusters,
-            "n_clusters",
-            target_type=numbers.Integral,
-            min_val=1,
-            closed="left"
+            include_boundaries="neither",
         )
+        if isinstance(self.n_clusters, numbers.Number):
+            check_scalar(
+                self.n_clusters,
+                "n_clusters",
+                target_type=numbers.Integral,
+                min_val=1,
+                max_val=_num_samples(X),
+                include_boundaries="both",
+            )
 
         # TODO: Remove deprected flags in 1.2
         self._deprecated_fit, self._deprecated_partial_fit = True, False
@@ -722,7 +723,7 @@ class Birch(ClusterMixin, TransformerMixin, BaseEstimator):
             if len(centroids) < self.n_clusters:
                 not_enough_centroids = True
         elif clusterer is not None and not hasattr(clusterer, "fit_predict"):
-            raise ValueError(
+            raise TypeError(
                 "n_clusters should be an instance of ClusterMixin or an int"
             )
 
diff --git a/sklearn/cluster/_dbscan.py b/sklearn/cluster/_dbscan.py
index f248d069f1..3422a20742 100644
--- a/sklearn/cluster/_dbscan.py
+++ b/sklearn/cluster/_dbscan.py
@@ -365,34 +365,32 @@ class DBSCAN(ClusterMixin, BaseEstimator):
             "eps",
             target_type=numbers.Real,
             min_val=0.0,
-            closed="neither"
+            include_boundaries="neither",
         )
         check_scalar(
             self.min_samples,
             "min_samples",
             target_type=numbers.Integral,
             min_val=1,
-            closed="left"
+            include_boundaries="left",
         )
         check_scalar(
             self.leaf_size,
             "leaf_size",
             target_type=numbers.Integral,
             min_val=1,
-            closed="left"
-        )
-        check_scalar(
-            self.p,
-            "p",
-            target_type=numbers.Real,
-            min_val=1.0,
-            closed="left"
-        )
-        check_scalar(
-            self.n_jobs,
-            "n_jobs",
-            target_type=numbers.Integral
+            include_boundaries="left",
         )
+        if self.p is not None:
+            check_scalar(
+                self.p,
+                "p",
+                target_type=numbers.Real,
+                min_val=1.0,
+                include_boundaries="left",
+            )
+        if self.n_jobs is not None:
+            check_scalar(self.n_jobs, "n_jobs", target_type=numbers.Integral)
 
         neighbors_model = NearestNeighbors(
             radius=self.eps,
diff --git a/sklearn/cluster/tests/test_birch.py b/sklearn/cluster/tests/test_birch.py
index fdc14e8560..5d8a3222ef 100644
--- a/sklearn/cluster/tests/test_birch.py
+++ b/sklearn/cluster/tests/test_birch.py
@@ -19,35 +19,6 @@ from sklearn.utils._testing import assert_array_equal
 from sklearn.utils._testing import assert_array_almost_equal
 
 
-@pytest.mark.parametrize(
-    "input, params, err_type, err_msg",
-    [
-        (X, {"threshold": -1.0}, ValueError,
-         "threshold == -1.0, must be a positive real number."),
-        (X, {"threshold": 0.0}, ValueError,
-         "threshold == 0.0, must be a positive real number."),
-
-        (X, {"branching_factor": 0}, ValueError,
-         "branching_factor == 0, must be a positive integer greater than 1."),
-        (X, {"branching_factor": 1}, ValueError,
-         "branching_factor == 1, must be a positive integer greater than 1."),
-        (X, {"branching_factor": 1.5}, ValueError,
-         "min_samples == 1.5, must be an integer."),
-        (X, {"branching_factor": -2}, ValueError,
-         "branching_factor == -2, must be a positive integer."),
-
-        (X, {"n_clusters": 0}, ValueError, "n_clusters == 0, must be a positive integer."),
-        (X, {"n_clusters": 2.5}, ValueError,  "n_clusters == 2.5, must be an integer."),
-        (X, {"n_clusters": -3}, ValueError,
-         "n_clusters == -2, must be a positive integer."),
-    ],
-)
-def test_birch_params_validation(input, params, err_type, err_msg):
-    """Check the parameters validation in `Birch`."""
-    with pytest.raises(err_type, match=err_msg):
-        Birch(**params).fit(input)
-
-
 def test_n_samples_leaves_roots():
     # Sanity check for the number of samples in leaves and roots
     X, y = make_blobs(n_samples=10)
@@ -114,7 +85,8 @@ def test_n_clusters():
     # Test that the wrong global clustering step raises an Error.
     clf = ElasticNet()
     brc3 = Birch(n_clusters=clf)
-    with pytest.raises(ValueError):
+    err_msg = "n_clusters should be an instance of ClusterMixin or an int"
+    with pytest.raises(TypeError, match=err_msg):
         brc3.fit(X)
 
     # Test that a small number of clusters raises a warning.
@@ -211,3 +183,39 @@ def test_birch_fit_attributes_deprecated(attribute):
 
     with pytest.warns(FutureWarning, match=msg):
         getattr(brc, attribute)
+
+
+@pytest.mark.parametrize(
+    "params, err_type, err_msg",
+    [
+        ({"threshold": -1.0}, ValueError, "threshold == -1.0, must be > 0.0."),
+        ({"threshold": 0.0}, ValueError, "threshold == 0.0, must be > 0.0."),
+        ({"branching_factor": 0}, ValueError, "branching_factor == 0, must be > 1."),
+        ({"branching_factor": 1}, ValueError, "branching_factor == 1, must be > 1."),
+        (
+            {"branching_factor": 1.5},
+            TypeError,
+            "branching_factor must be an instance of <class 'numbers.Integral'>, not"
+            " <class 'float'>.",
+        ),
+        ({"branching_factor": -2}, ValueError, "branching_factor == -2, must be > 1."),
+        ({"n_clusters": 0}, ValueError, "n_clusters == 0, must be >= 1."),
+        (
+            {"n_clusters": 2.5},
+            TypeError,
+            "n_clusters must be an instance of <class 'numbers.Integral'>, not <class"
+            " 'float'>.",
+        ),
+        (
+            {"n_clusters": "whatever"},
+            TypeError,
+            "n_clusters should be an instance of ClusterMixin or an int",
+        ),
+        ({"n_clusters": -3}, ValueError, "n_clusters == -3, must be >= 1."),
+    ],
+)
+def test_birch_params_validation(params, err_type, err_msg):
+    """Check the parameters validation in `Birch`."""
+    X, _ = make_blobs(n_samples=80, centers=4)
+    with pytest.raises(err_type, match=err_msg):
+        Birch(**params).fit(X)
diff --git a/sklearn/cluster/tests/test_dbscan.py b/sklearn/cluster/tests/test_dbscan.py
index 592745196d..b593d83d45 100644
--- a/sklearn/cluster/tests/test_dbscan.py
+++ b/sklearn/cluster/tests/test_dbscan.py
@@ -25,35 +25,6 @@ n_clusters = 3
 X = generate_clustered_data(n_clusters=n_clusters)
 
 
-@pytest.mark.parametrize(
-    "input, params, err_type, err_msg",
-    [
-        (X, {"eps": -1.0}, ValueError, "eps == -1.0, must be a positive real number."),
-        (X, {"eps": 0.0}, ValueError, "eps == 0.0, must be a positive real number."),
-
-        (X, {"min_samples": 0}, ValueError,
-         "min_samples == 0, must be a positive integer."),
-        (X, {"min_samples": 1.5}, ValueError, "min_samples == 1.5, must be an integer."),
-        (X, {"min_samples": -2}, ValueError,
-         "min_samples == -2, must be a positive integer."),
-
-        (X, {"leaf_size": 0},   ValueError, "leaf_size == 0, must be a positive integer."),
-        (X, {"leaf_size": 2.5}, ValueError, "leaf_size == 1.5, must be an integer."),
-        (X, {"leaf_size": -3},  ValueError,
-         "leaf_size == -2, must be a positive integer."),
-
-        (X, {"p": 0},  ValueError, "p == 0, must be >= 1"),
-        (X, {"p": -2}, ValueError, "p == -2, must be a positive real number."),
-
-        (X, {"n_jobs": 2.5}, ValueError, "n_jobs == 2.5, must be an integer."),
-    ],
-)
-def test_dbscan_params_validation(input, params, err_type, err_msg):
-    """Check the parameters validation in `DBSCAN`."""
-    with pytest.raises(err_type, match=err_msg):
-        dbscan(**params).fit(input)
-
-
 def test_dbscan_similarity():
     # Tests the DBSCAN algorithm with a similarity array.
     # Parameters chosen specifically for this task.
@@ -454,3 +425,40 @@ def test_dbscan_precomputed_metric_with_initial_rows_zero():
     matrix = sparse.csr_matrix(ar)
     labels = DBSCAN(eps=0.2, metric="precomputed", min_samples=2).fit(matrix).labels_
     assert_array_equal(labels, [-1, -1, 0, 0, 0, 1, 1])
+
+
+@pytest.mark.parametrize(
+    "params, err_type, err_msg",
+    [
+        ({"eps": -1.0}, ValueError, "eps == -1.0, must be > 0.0."),
+        ({"eps": 0.0}, ValueError, "eps == 0.0, must be > 0.0."),
+        ({"min_samples": 0}, ValueError, "min_samples == 0, must be >= 1."),
+        (
+            {"min_samples": 1.5},
+            TypeError,
+            "min_samples must be an instance of <class 'numbers.Integral'>, not <class"
+            " 'float'>.",
+        ),
+        ({"min_samples": -2}, ValueError, "min_samples == -2, must be >= 1."),
+        ({"leaf_size": 0}, ValueError, "leaf_size == 0, must be >= 1."),
+        (
+            {"leaf_size": 2.5},
+            TypeError,
+            "leaf_size must be an instance of <class 'numbers.Integral'>, not <class"
+            " 'float'>.",
+        ),
+        ({"leaf_size": -3}, ValueError, "leaf_size == -3, must be >= 1."),
+        ({"p": 0}, ValueError, "p == 0, must be >= 1.0."),
+        ({"p": -2}, ValueError, "p == -2, must be >= 1.0."),
+        (
+            {"n_jobs": 2.5},
+            TypeError,
+            "n_jobs must be an instance of <class 'numbers.Integral'>, not <class"
+            " 'float'>.",
+        ),
+    ],
+)
+def test_dbscan_params_validation(params, err_type, err_msg):
+    """Check the parameters validation in `DBSCAN`."""
+    with pytest.raises(err_type, match=err_msg):
+        DBSCAN(**params).fit(X)

glemaitre

Tagging as "Request changes" to acknowledge that the PR has been reviewed

SanjayMarreddi · 2021-09-15T17:29:04Z

@glemaitre I will try to resolve all the issues by this weekend. Thanks!

SanjayMarreddi · 2021-09-20T10:18:07Z

@glemaitre I have done the suggested changes. Could you guide me in resolving the failing tests? I am using auto formatting with autopep8. Thanks!

glemaitre · 2021-09-23T12:00:37Z

I am using auto formatting with autopep8. Thanks!

We are using black and you can check the failing CI there:

https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=32760&view=logs&jobId=32e2e1bb-a28f-5b18-6cfc-3f01273f5609&j=32e2e1bb-a28f-5b18-6cfc-3f01273f5609&t=fc67071d-c3d4-58b8-d38e-cafc0d3c731a

Basically, you should reformat the file sklearn/cluster/tests/test_birch.py such that no file is reformatted by the CI, as mentioned in the contributing guide: https://scikit-learn.org/stable/developers/contributing.html#pull-request-checklist

An automatic way for the lining is to install pre-commit that will use black and flake8 before committing.

…nto check_scalar

sklearn/cluster/_birch.py

glemaitre

LGTM

SanjayMarreddi · 2021-09-23T15:53:00Z

Thanks a lot, @glemaitre for the review and also for guiding me throughout my first PR at scikit-learn!

SanjayMarreddi · 2021-09-24T05:10:39Z

Kind request for a review from @jeremiedbb and @ogrisel so that my PR can be merged!
Thanks!

jjerphan

Nothing to change: LGTM! 👌

Thank you @SanjayMarreddi.

SanjayMarreddi · 2021-09-30T12:45:37Z

Thanks for the approval @jjerphan!
Will be waiting for the PR to get merged.

thomasjpfan

Thank you for PR @SanjayMarreddi !

sklearn/cluster/_birch.py

sklearn/cluster/_dbscan.py

Co-authored-by: Thomas J. Fan <thomasjpfan@gmail.com>

SanjayMarreddi · 2021-10-04T19:00:56Z

@thomasjpfan Thanks a lot for the review. I modified the code to implement the suggestions on the GitHub UI itself. Will do the linting by tonight. Thanks!

…nto check_scalar

SanjayMarreddi · 2021-10-05T07:34:16Z

@thomasjpfan I have fixed the linting errors. But there are new tests that are failing. I went through the details but could not understand. Can u help me in resolving this? Thanks!

jjerphan · 2021-10-05T08:59:53Z

sklearn/cluster/tests/test_dbscan.py

+        ({"p": 0}, ValueError, "p == 0, must be >= 1.0."),
+        ({"p": -2}, ValueError, "p == -2, must be >= 1.0."),


@SanjayMarreddi: This should fix tests failures.

Suggested change

({"p": 0}, ValueError, "p == 0, must be >= 1.0."),

({"p": -2}, ValueError, "p == -2, must be >= 1.0."),

({"p": -2}, ValueError, "p == -2, must be >= 0.0."),

@jjerphan Thanks! I got the bug.

thomasjpfan

One small comment. Otherwise LGTM

sklearn/cluster/_birch.py

…it-learn; branch 'main' of https://github.com/scikit-learn/scikit-learn into check_scalar

SanjayMarreddi · 2021-10-09T10:39:42Z

@thomasjpfan Done with the changes. Thanks for the review. Waiting for the PR to get merged!!

Co-authored-by: Thomas J. Fan <thomasjpfan@gmail.com> Co-authored-by: Guillaume Lemaitre <g.lemaitre58@gmail.com>

SanjayMarreddi added 4 commits August 23, 2021 18:23

use check_scalar to validate scalar inputs in DBSCAN algorithm

68132df

use check_scalar to validate scalar inputs in BIRCH algorithm

358adbe

use check_scalar to validate scalar inputs in DBSCAN algorithm

d0781c2

use check_scalar to validate scalar inputs in BIRCH algorithm

07b022f

github-actions bot added the module:cluster label Aug 23, 2021

SanjayMarreddi added 3 commits September 3, 2021 18:53

Removed for loops and called check_scalar separately

efb2d21

Merge branch 'main' of https://github.com/scikit-learn/scikit-learn i…

fee4af1

…nto check_scalar

Removed redundant commented lines

4e36c4b

SanjayMarreddi marked this pull request as ready for review September 3, 2021 13:30

SanjayMarreddi changed the title ~~[WIP] Using check_scalar to validate scalars in different Clustering algorithms~~ [MRG] Using check_scalar to validate scalars in different Clustering algorithms Sep 3, 2021

glemaitre added the No Changelog Needed label Sep 3, 2021

glemaitre self-requested a review September 15, 2021 11:43

glemaitre changed the title ~~[MRG] Using check_scalar to validate scalars in different Clustering algorithms~~ MNT Use check_scalar in BIRCH and DBSCAN Sep 15, 2021

glemaitre requested changes Sep 15, 2021

View reviewed changes

SanjayMarreddi added 5 commits September 20, 2021 15:20

Made the suggested changes in DBSCAN & BIRCH algos

11a42ce

Resolved merge conflicts

db8ca61

Removed Merge Conflicts

2ba8d71

Implemented all the suggested changes

714e5a0

Corrected the typo

87c582f

SanjayMarreddi added 2 commits September 23, 2021 18:19

Merge branch 'main' of https://github.com/scikit-learn/scikit-learn i…

ce3604f

…nto check_scalar

Reformatted the file to resolve failing tests

4136d7b

glemaitre reviewed Sep 23, 2021

View reviewed changes

sklearn/cluster/_birch.py Outdated Show resolved Hide resolved

glemaitre reviewed Sep 23, 2021

View reviewed changes

sklearn/cluster/_birch.py Outdated Show resolved Hide resolved

Apply suggestions from code review

93cb702

glemaitre approved these changes Sep 23, 2021

View reviewed changes

jjerphan approved these changes Sep 30, 2021

View reviewed changes

thomasjpfan reviewed Oct 4, 2021

View reviewed changes

sklearn/cluster/_birch.py Outdated Show resolved Hide resolved

sklearn/cluster/_dbscan.py Outdated Show resolved Hide resolved

SanjayMarreddi and others added 2 commits October 5, 2021 00:27

Removed max_val check as suggested.

3cfbd8c

Co-authored-by: Thomas J. Fan <thomasjpfan@gmail.com>

Corrected the value of min_val

c83ee0e

Co-authored-by: Thomas J. Fan <thomasjpfan@gmail.com>

SanjayMarreddi added 3 commits October 5, 2021 12:04

Merge branch 'main' of https://github.com/scikit-learn/scikit-learn i…

dae5f1e

…nto check_scalar

Added comments and used Linting

6575587

Removed unnecessary imports

6e4b649

jjerphan reviewed Oct 5, 2021

View reviewed changes

Made corrections according to p limits

545ebd0

SanjayMarreddi requested a review from thomasjpfan October 5, 2021 09:51

thomasjpfan approved these changes Oct 8, 2021

View reviewed changes

sklearn/cluster/_birch.py Outdated Show resolved Hide resolved

SanjayMarreddi added 2 commits October 9, 2021 15:19

Merge branch 'check_scalar' of https://github.com/SanjayMarreddi/scik…

cecd8a5

…it-learn; branch 'main' of https://github.com/scikit-learn/scikit-learn into check_scalar

Removed default arg of include_boundaries

468c5f2

thomasjpfan merged commit 2f2364d into scikit-learn:main Oct 10, 2021

glemaitre mentioned this pull request Oct 23, 2021

Release 1.0.1 #21404

Merged

10 tasks

samronsin pushed a commit to samronsin/scikit-learn that referenced this pull request Nov 30, 2021

MNT Use check_scalar in BIRCH and DBSCAN (scikit-learn#20816)

f9bf1e8

Co-authored-by: Thomas J. Fan <thomasjpfan@gmail.com> Co-authored-by: Guillaume Lemaitre <g.lemaitre58@gmail.com>

reshamas mentioned this pull request Dec 8, 2021

Use the function check_scalar for parameters validation #21927

Closed

41 tasks

Provide feedback

Saved searches

Use saved searches to filter your results more quickly

MNT Use check_scalar in BIRCH and DBSCAN #20816

MNT Use check_scalar in BIRCH and DBSCAN #20816

SanjayMarreddi commented Aug 23, 2021 •

edited by glemaitre

Loading

glemaitre commented Sep 2, 2021

SanjayMarreddi commented Sep 2, 2021 •

edited

Loading

SanjayMarreddi commented Sep 3, 2021

glemaitre commented Sep 3, 2021

glemaitre commented Sep 3, 2021

glemaitre commented Sep 15, 2021 •

edited

Loading

glemaitre left a comment

SanjayMarreddi commented Sep 15, 2021

SanjayMarreddi commented Sep 20, 2021

glemaitre commented Sep 23, 2021 •

edited

Loading

glemaitre left a comment

SanjayMarreddi commented Sep 23, 2021

SanjayMarreddi commented Sep 24, 2021 •

edited

Loading

jjerphan left a comment

SanjayMarreddi commented Sep 30, 2021

thomasjpfan left a comment

SanjayMarreddi commented Oct 4, 2021 •

edited

Loading

SanjayMarreddi commented Oct 5, 2021

jjerphan Oct 5, 2021 •

edited

Loading

SanjayMarreddi Oct 5, 2021

thomasjpfan left a comment

SanjayMarreddi commented Oct 9, 2021

		({"p": 0}, ValueError, "p == 0, must be >= 1.0."),
		({"p": -2}, ValueError, "p == -2, must be >= 1.0."),

MNT Use check_scalar in BIRCH and DBSCAN #20816

MNT Use check_scalar in BIRCH and DBSCAN #20816

Conversation

SanjayMarreddi commented Aug 23, 2021 • edited by glemaitre Loading

Reference Issues/PRs

What does this implement/fix? Explain your changes.

Any other comments?

glemaitre commented Sep 2, 2021

SanjayMarreddi commented Sep 2, 2021 • edited Loading

SanjayMarreddi commented Sep 3, 2021

glemaitre commented Sep 3, 2021

glemaitre commented Sep 3, 2021

glemaitre commented Sep 15, 2021 • edited Loading

glemaitre left a comment

Choose a reason for hiding this comment

SanjayMarreddi commented Sep 15, 2021

SanjayMarreddi commented Sep 20, 2021

glemaitre commented Sep 23, 2021 • edited Loading

glemaitre left a comment

Choose a reason for hiding this comment

SanjayMarreddi commented Sep 23, 2021

SanjayMarreddi commented Sep 24, 2021 • edited Loading

jjerphan left a comment

Choose a reason for hiding this comment

SanjayMarreddi commented Sep 30, 2021

thomasjpfan left a comment

Choose a reason for hiding this comment

SanjayMarreddi commented Oct 4, 2021 • edited Loading

SanjayMarreddi commented Oct 5, 2021

jjerphan Oct 5, 2021 • edited Loading

Choose a reason for hiding this comment

SanjayMarreddi Oct 5, 2021

Choose a reason for hiding this comment

thomasjpfan left a comment

Choose a reason for hiding this comment

SanjayMarreddi commented Oct 9, 2021

SanjayMarreddi commented Aug 23, 2021 •

edited by glemaitre

Loading

SanjayMarreddi commented Sep 2, 2021 •

edited

Loading

glemaitre commented Sep 15, 2021 •

edited

Loading

glemaitre commented Sep 23, 2021 •

edited

Loading

SanjayMarreddi commented Sep 24, 2021 •

edited

Loading

SanjayMarreddi commented Oct 4, 2021 •

edited

Loading

jjerphan Oct 5, 2021 •

edited

Loading