Dimensionality reduction #8590

Diegomangasco · 2023-03-31T10:52:54Z

Describe your change:

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

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machine_learning/dimensionality_reduction.py

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machine_learning/dimensionality_reduction.py

rohan472000 · 2023-03-31T11:47:51Z

@Diegomangasco , run ruff . to rectify the failure of ruff

machine_learning/dimensionality_reduction.py

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machine_learning/dimensionality_reduction.py

algorithms-keeper · 2023-03-31T17:53:58Z

machine_learning/dimensionality_reduction.py

+    return covariance_sum / features.shape[1]
+
+
+def covariance_between_classes(


As there is no test file in this pull request nor any test function or class in the file machine_learning/dimensionality_reduction.py, please provide doctest for the function covariance_between_classes

"""

features = np.array([[1, 2, 3], [4, 5, 6]])
labels = np.array([0, 1, 0])
covariance_between_classes(features, labels, 2)
output : array([[-1.5, -1.5],[-1.5, -1.5]])

"""

Pytest discovery is not finding/running these

See the GitHub Actions output.

machine_learning/data_transformations.py .. [ 54%] machine_learning/decision_tree.py . [ 54%] machine_learning/k_means_clust.py . [ 54%] machine_learning/k_nearest_neighbours.py .. [ 55%] machine_learning/linear_discriminant_analysis.py ....... [ 55%] machine_learning/multilayer_perceptron_classifier.py . [ 55%] machine_learning/scoring_functions.py ..... [ 56%] machine_learning/self_organizing_map.py .. [ 56%] machine_learning/similarity_search.py ... [ 56%] machine_learning/support_vector_machines.py ... [ 56%] machine_learning/word_frequency_functions.py .... [ 57%] machine_learning/xgboost_classifier.py .. [ 57%] machine_learning/xgboost_regressor.py ... [ 57%] machine_learning/forecasting/run.py ..... [ 57%] machine_learning/local_weighted_learning/local_weighted_learning.py .... [ 58%]

why it is not running these???

machine_learning/dimensionality_reduction.py

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machine_learning/dimensionality_reduction.py

rohan472000 · 2023-04-01T13:30:22Z

@Diegomangasco , I also tried but getting -ve sign and some different outputs everytime.

AssertionError: Expected [[ 6.92820323 8.66025404 10.39230485]
[ 3. 3. 3. ]],
but got [[ -6.92820323 -8.66025404 -10.39230485]
[ -2.2719232 -2.2719232 -2.2719232 ]]

Diegomangasco · 2023-04-01T14:04:24Z

@Diegomangasco , I also tried but getting -ve sign and some different outputs everytime.

AssertionError: Expected [[ 6.92820323 8.66025404 10.39230485]
[ 3. 3. 3. ]],
but got [[ -6.92820323 -8.66025404 -10.39230485]
[ -2.2719232 -2.2719232 -2.2719232 ]]

Yes because the projection could be done in whatever direction (plus or minus sign), the important things are the values.

Diegomangasco · 2023-04-01T14:15:33Z

I think that the round errors may due to machine rounding.
Because all the operation that I wrote are mathematical-deterministic.

rohan472000 · 2023-04-01T14:16:16Z

yes but values that we are getting are also different.

Diegomangasco · 2023-04-01T23:51:34Z

yes but values that we are getting are also different.

Can you provide me the input you gave?
Because it seems strange that math operation, like dot product between matrices, give different results.

rohan472000 · 2023-04-02T03:10:53Z

def test_pca():
    features = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
    dimensions = 2
    expected_output = np.array([[6.92820323, 8.66025404, 10.39230485], [3., 3., 3.]])
    output = principal_component_analysis(features, dimensions)
    assert np.allclose(expected_output, output), f"Expected {expected_output}, but got {output}"

test_pca()

error

AssertionError: Expected [[ 6.92820323 8.66025404 10.39230485]
[ 3. 3. 3. ]], but got [[ -6.92820323 -8.66025404 -10.39230485]
[ -2.2719232 -2.2719232 -2.2719232 ]]

Diegomangasco · 2023-04-02T17:05:44Z

@rohan472000 @cclauss
I share some trials on my local machine:

It seems to be deterministic, maybe there are some problems with doctests (?)

Diegomangasco · 2023-04-02T17:21:44Z

@rohan472000 @cclauss
I switched from doctest to an homemade test for pca, now all build tests pass.

rohan472000

I'm not sure about using different function to test the various cases comes under a good practice or not for this repo, other than that everything looks fine to me.

rohan472000 · 2023-03-31T18:33:06Z

machine_learning/dimensionality_reduction.py

+    return covariance_sum / features.shape[1]
+
+
+def covariance_between_classes(


"""

features = np.array([[1, 2, 3], [4, 5, 6]])
labels = np.array([0, 1, 0])
covariance_between_classes(features, labels, 2)
output : array([[-1.5, -1.5],[-1.5, -1.5]])

"""

machine_learning/dimensionality_reduction.py

rohan472000 · 2023-03-31T18:45:46Z

machine_learning/dimensionality_reduction.py

+    return covariance_sum / features.shape[1]
+
+
+def covariance_between_classes(


why it is not running these???

Shaquum · 2023-04-13T09:13:53Z

**_> projected_data = linear_discriminant_analysis(features, labels, classes, 3)

except AssertionError:
    pass
else:
    raise AssertionError("Did not raise AssertionError for dimensions > features")
def test_principal_component_analysis() -> None:
features = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
dimensions = 2
expected_output = np.array([[6.92820323, 8.66025404, 10.39230485], [3.0, 3.0, 3.0]])
output = principal_component_analysis(features, labels)
assert np.allclose(
expected_output, output
), f"Expected {expected_output}." ### " {output}"

if name == "main":
import doctest
doctest.testmod()_**

Shaquum

projected_data = linear_discriminant_analysis(features, labels, classes, 3)
except AssertionError:
pass
else:
raise AssertionError("Did not raise AssertionError for dimensions > features")

def test_principal_component_analysis() -> None:
features = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
dimensions = 2
expected_output = np.array([[6.92820323, 8.66025404, 10.39230485], [3.0, 3.0, 3.0]])
output = principal_component_analysis(features, labels)
assert np.allclose(
expected_output, output
), f"Expected {expected_output}." {output}"

if name ==dismissed "main":

machine_learning/dimensionality_reduction.py

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Diegomangasco added 7 commits March 28, 2023 19:28

First commit for dimensionality_reduction.py

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Some bug fixies

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Finish code for dimensionality_reduction.py

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PCA and LDA finished and tested

eb50e28

Add Copyright

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Add links to Wikipedia

5509e7d

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Dimensionality reduction (TheAlgorithms#8590)

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Dimensionality reduction #8590

Dimensionality reduction #8590

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		return covariance_sum / features.shape[1]


		def covariance_between_classes(

Dimensionality reduction #8590

Dimensionality reduction #8590

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Diegomangasco commented Mar 31, 2023

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