Repository: zoj613/pyloras (language: python)

Issue:
ENH: Improve runtime of the re-sampling
Currently, the runtime is less than ideal. I think its possible to improve the speed by cythonizing the loop in https://github.com/zoj613/pyloras/blob/b896367dac4e970bb31d2b4cac69fe26f7627a2d/pyloras/_loras.py#L163-L177

and maybe also rewriting https://github.com/zoj613/pyloras/blob/b896367dac4e970bb31d2b4cac69fe26f7627a2d/pyloras/_loras.py#L184 in Cython.

I will need to first profile and determine where the most time is spent.


Discussion / hints:
Seems like 98% of the runtime (at least for the example in the README) is spent computing the TSNE embedding. Not sure if this is even worth it anymore. Maybe it might make a difference if the dataset is large.

Fix patch:
diff --git a/README.md b/README.md
index 27d3369..e2bebf4 100644
--- a/README.md
+++ b/README.md
@@ -34,7 +34,7 @@ from collections import Counter
 from pyloras import LORAS
 from sklearn.datasets import make_classification
 
-X, y = make_classification(n_samples=5000, n_features=5, n_informative=5,
+X, y = make_classification(n_samples=20000, n_features=5, n_informative=5,
                            n_redundant=0, n_repeated=0, n_classes=3,
                            n_clusters_per_class=1,
                            weights=[0.01, 0.05, 0.94],
@@ -42,10 +42,10 @@ X, y = make_classification(n_samples=5000, n_features=5, n_informative=5,
 
 lrs = LORAS(random_state=0, embedding_params={'perplexity': 35, 'n_iter': 250})
 print(sorted(Counter(y).items()))
-# [(0, 58), (1, 265), (2, 4677)]
+# [(0, 270), (1, 1056), (2, 18674)]
 X_resampled, y_resampled = lrs.fit_resample(X, y)
-print(sorted(Counter(y_resampled).items()))
-# [(0, 4672), (1, 4454), (2, 4674)]
+print(sorted(Counter(y_resampled.astype(int)).items()))
+# [(0, 18674), (1, 18674), (2, 18674)]
 ```
 
 ## References
diff --git a/poetry.lock b/poetry.lock
index d2ab645..3449d3d 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -8,17 +8,17 @@ python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
 
 [[package]]
 name = "attrs"
-version = "20.3.0"
+version = "21.2.0"
 description = "Classes Without Boilerplate"
 category = "dev"
 optional = false
-python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
 
 [package.extras]
-dev = ["coverage[toml] (>=5.0.2)", "hypothesis", "pympler", "pytest (>=4.3.0)", "six", "zope.interface", "furo", "sphinx", "pre-commit"]
-docs = ["furo", "sphinx", "zope.interface"]
-tests = ["coverage[toml] (>=5.0.2)", "hypothesis", "pympler", "pytest (>=4.3.0)", "six", "zope.interface"]
-tests_no_zope = ["coverage[toml] (>=5.0.2)", "hypothesis", "pympler", "pytest (>=4.3.0)", "six"]
+dev = ["coverage[toml] (>=5.0.2)", "hypothesis", "pympler", "pytest (>=4.3.0)", "six", "mypy", "pytest-mypy-plugins", "zope.interface", "furo", "sphinx", "sphinx-notfound-page", "pre-commit"]
+docs = ["furo", "sphinx", "zope.interface", "sphinx-notfound-page"]
+tests = ["coverage[toml] (>=5.0.2)", "hypothesis", "pympler", "pytest (>=4.3.0)", "six", "mypy", "pytest-mypy-plugins", "zope.interface"]
+tests_no_zope = ["coverage[toml] (>=5.0.2)", "hypothesis", "pympler", "pytest (>=4.3.0)", "six", "mypy", "pytest-mypy-plugins"]
 
 [[package]]
 name = "colorama"
@@ -30,7 +30,7 @@ python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
 
 [[package]]
 name = "coverage"
-version = "5.4"
+version = "5.5"
 description = "Code coverage measurement for Python"
 category = "dev"
 optional = false
@@ -59,7 +59,7 @@ tests = ["pytest", "pytest-cov"]
 
 [[package]]
 name = "importlib-metadata"
-version = "3.4.0"
+version = "4.6.1"
 description = "Read metadata from Python packages"
 category = "dev"
 optional = false
@@ -71,7 +71,8 @@ zipp = ">=0.5"
 
 [package.extras]
 docs = ["sphinx", "jaraco.packaging (>=8.2)", "rst.linker (>=1.9)"]
-testing = ["pytest (>=3.5,!=3.7.3)", "pytest-checkdocs (>=1.2.3)", "pytest-flake8", "pytest-cov", "pytest-enabler", "packaging", "pep517", "pyfakefs", "flufl.flake8", "pytest-black (>=0.3.7)", "pytest-mypy", "importlib-resources (>=1.3)"]
+perf = ["ipython"]
+testing = ["pytest (>=4.6)", "pytest-checkdocs (>=2.4)", "pytest-flake8", "pytest-cov", "pytest-enabler (>=1.0.1)", "packaging", "pep517", "pyfakefs", "flufl.flake8", "pytest-perf (>=0.9.2)", "pytest-black (>=0.3.7)", "pytest-mypy", "importlib-resources (>=1.3)"]
 
 [[package]]
 name = "iniconfig"
@@ -83,7 +84,7 @@ python-versions = "*"
 
 [[package]]
 name = "joblib"
-version = "1.0.0"
+version = "1.0.1"
 description = "Lightweight pipelining with Python functions"
 category = "main"
 optional = false
@@ -99,11 +100,11 @@ python-versions = ">=3.6"
 
 [[package]]
 name = "packaging"
-version = "20.9"
+version = "21.0"
 description = "Core utilities for Python packages"
 category = "dev"
 optional = false
-python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
+python-versions = ">=3.6"
 
 [package.dependencies]
 pyparsing = ">=2.0.2"
@@ -140,7 +141,7 @@ python-versions = ">=2.6, !=3.0.*, !=3.1.*, !=3.2.*"
 
 [[package]]
 name = "pytest"
-version = "6.2.2"
+version = "6.2.4"
 description = "pytest: simple powerful testing with Python"
 category = "dev"
 optional = false
@@ -162,7 +163,7 @@ testing = ["argcomplete", "hypothesis (>=3.56)", "mock", "nose", "requests", "xm
 
 [[package]]
 name = "pytest-cov"
-version = "2.11.1"
+version = "2.12.1"
 description = "Pytest plugin for measuring coverage."
 category = "dev"
 optional = false
@@ -171,13 +172,14 @@ python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
 [package.dependencies]
 coverage = ">=5.2.1"
 pytest = ">=4.6"
+toml = "*"
 
 [package.extras]
-testing = ["fields", "hunter", "process-tests (==2.0.2)", "six", "pytest-xdist", "virtualenv"]
+testing = ["fields", "hunter", "process-tests", "six", "pytest-xdist", "virtualenv"]
 
 [[package]]
 name = "scikit-learn"
-version = "0.24.1"
+version = "0.24.2"
 description = "A set of python modules for machine learning and data mining"
 category = "main"
 optional = false
@@ -208,11 +210,11 @@ numpy = ">=1.14.5"
 
 [[package]]
 name = "threadpoolctl"
-version = "2.1.0"
+version = "2.2.0"
 description = "threadpoolctl"
 category = "main"
 optional = false
-python-versions = ">=3.5"
+python-versions = ">=3.6"
 
 [[package]]
 name = "toml"
@@ -224,7 +226,7 @@ python-versions = ">=2.6, !=3.0.*, !=3.1.*, !=3.2.*"
 
 [[package]]
 name = "typing-extensions"
-version = "3.7.4.3"
+version = "3.10.0.0"
 description = "Backported and Experimental Type Hints for Python 3.5+"
 category = "dev"
 optional = false
@@ -232,15 +234,15 @@ python-versions = "*"
 
 [[package]]
 name = "zipp"
-version = "3.4.0"
+version = "3.5.0"
 description = "Backport of pathlib-compatible object wrapper for zip files"
 category = "dev"
 optional = false
 python-versions = ">=3.6"
 
 [package.extras]
-docs = ["sphinx", "jaraco.packaging (>=3.2)", "rst.linker (>=1.9)"]
-testing = ["pytest (>=3.5,!=3.7.3)", "pytest-checkdocs (>=1.2.3)", "pytest-flake8", "pytest-cov", "jaraco.test (>=3.2.0)", "jaraco.itertools", "func-timeout", "pytest-black (>=0.3.7)", "pytest-mypy"]
+docs = ["sphinx", "jaraco.packaging (>=8.2)", "rst.linker (>=1.9)"]
+testing = ["pytest (>=4.6)", "pytest-checkdocs (>=2.4)", "pytest-flake8", "pytest-cov", "pytest-enabler (>=1.0.1)", "jaraco.itertools", "func-timeout", "pytest-black (>=0.3.7)", "pytest-mypy"]
 
 [metadata]
 lock-version = "1.1"
@@ -253,79 +255,82 @@ atomicwrites = [
     {file = "atomicwrites-1.4.0.tar.gz", hash = "sha256:ae70396ad1a434f9c7046fd2dd196fc04b12f9e91ffb859164193be8b6168a7a"},
 ]
 attrs = [
-    {file = "attrs-20.3.0-py2.py3-none-any.whl", hash = "sha256:31b2eced602aa8423c2aea9c76a724617ed67cf9513173fd3a4f03e3a929c7e6"},
-    {file = "attrs-20.3.0.tar.gz", hash = "sha256:832aa3cde19744e49938b91fea06d69ecb9e649c93ba974535d08ad92164f700"},
+    {file = "attrs-21.2.0-py2.py3-none-any.whl", hash = "sha256:149e90d6d8ac20db7a955ad60cf0e6881a3f20d37096140088356da6c716b0b1"},
+    {file = "attrs-21.2.0.tar.gz", hash = "sha256:ef6aaac3ca6cd92904cdd0d83f629a15f18053ec84e6432106f7a4d04ae4f5fb"},
 ]
 colorama = [
     {file = "colorama-0.4.4-py2.py3-none-any.whl", hash = "sha256:9f47eda37229f68eee03b24b9748937c7dc3868f906e8ba69fbcbdd3bc5dc3e2"},
     {file = "colorama-0.4.4.tar.gz", hash = "sha256:5941b2b48a20143d2267e95b1c2a7603ce057ee39fd88e7329b0c292aa16869b"},
 ]
 coverage = [
-    {file = "coverage-5.4-cp27-cp27m-macosx_10_9_x86_64.whl", hash = "sha256:6d9c88b787638a451f41f97446a1c9fd416e669b4d9717ae4615bd29de1ac135"},
-    {file = "coverage-5.4-cp27-cp27m-manylinux1_i686.whl", hash = "sha256:66a5aae8233d766a877c5ef293ec5ab9520929c2578fd2069308a98b7374ea8c"},
-    {file = "coverage-5.4-cp27-cp27m-manylinux1_x86_64.whl", hash = "sha256:9754a5c265f991317de2bac0c70a746efc2b695cf4d49f5d2cddeac36544fb44"},
-    {file = "coverage-5.4-cp27-cp27m-manylinux2010_i686.whl", hash = "sha256:fbb17c0d0822684b7d6c09915677a32319f16ff1115df5ec05bdcaaee40b35f3"},
-    {file = "coverage-5.4-cp27-cp27m-manylinux2010_x86_64.whl", hash = "sha256:b7f7421841f8db443855d2854e25914a79a1ff48ae92f70d0a5c2f8907ab98c9"},
-    {file = "coverage-5.4-cp27-cp27m-win32.whl", hash = "sha256:4a780807e80479f281d47ee4af2eb2df3e4ccf4723484f77da0bb49d027e40a1"},
-    {file = "coverage-5.4-cp27-cp27m-win_amd64.whl", hash = "sha256:87c4b38288f71acd2106f5d94f575bc2136ea2887fdb5dfe18003c881fa6b370"},
-    {file = "coverage-5.4-cp27-cp27mu-manylinux1_i686.whl", hash = "sha256:c6809ebcbf6c1049002b9ac09c127ae43929042ec1f1dbd8bb1615f7cd9f70a0"},
-    {file = "coverage-5.4-cp27-cp27mu-manylinux1_x86_64.whl", hash = "sha256:ba7ca81b6d60a9f7a0b4b4e175dcc38e8fef4992673d9d6e6879fd6de00dd9b8"},
-    {file = "coverage-5.4-cp27-cp27mu-manylinux2010_i686.whl", hash = "sha256:89fc12c6371bf963809abc46cced4a01ca4f99cba17be5e7d416ed7ef1245d19"},
-    {file = "coverage-5.4-cp27-cp27mu-manylinux2010_x86_64.whl", hash = "sha256:4a8eb7785bd23565b542b01fb39115a975fefb4a82f23d407503eee2c0106247"},
-    {file = "coverage-5.4-cp35-cp35m-macosx_10_9_x86_64.whl", hash = "sha256:7e40d3f8eb472c1509b12ac2a7e24158ec352fc8567b77ab02c0db053927e339"},
-    {file = "coverage-5.4-cp35-cp35m-manylinux1_i686.whl", hash = "sha256:1ccae21a076d3d5f471700f6d30eb486da1626c380b23c70ae32ab823e453337"},
-    {file = "coverage-5.4-cp35-cp35m-manylinux1_x86_64.whl", hash = "sha256:755c56beeacac6a24c8e1074f89f34f4373abce8b662470d3aa719ae304931f3"},
-    {file = "coverage-5.4-cp35-cp35m-manylinux2010_i686.whl", hash = "sha256:322549b880b2d746a7672bf6ff9ed3f895e9c9f108b714e7360292aa5c5d7cf4"},
-    {file = "coverage-5.4-cp35-cp35m-manylinux2010_x86_64.whl", hash = "sha256:60a3307a84ec60578accd35d7f0c71a3a971430ed7eca6567399d2b50ef37b8c"},
-    {file = "coverage-5.4-cp35-cp35m-win32.whl", hash = "sha256:1375bb8b88cb050a2d4e0da901001347a44302aeadb8ceb4b6e5aa373b8ea68f"},
-    {file = "coverage-5.4-cp35-cp35m-win_amd64.whl", hash = "sha256:16baa799ec09cc0dcb43a10680573269d407c159325972dd7114ee7649e56c66"},
-    {file = "coverage-5.4-cp36-cp36m-macosx_10_9_x86_64.whl", hash = "sha256:2f2cf7a42d4b7654c9a67b9d091ec24374f7c58794858bff632a2039cb15984d"},
-    {file = "coverage-5.4-cp36-cp36m-manylinux1_i686.whl", hash = "sha256:b62046592b44263fa7570f1117d372ae3f310222af1fc1407416f037fb3af21b"},
-    {file = "coverage-5.4-cp36-cp36m-manylinux1_x86_64.whl", hash = "sha256:812eaf4939ef2284d29653bcfee9665f11f013724f07258928f849a2306ea9f9"},
-    {file = "coverage-5.4-cp36-cp36m-manylinux2010_i686.whl", hash = "sha256:859f0add98707b182b4867359e12bde806b82483fb12a9ae868a77880fc3b7af"},
-    {file = "coverage-5.4-cp36-cp36m-manylinux2010_x86_64.whl", hash = "sha256:04b14e45d6a8e159c9767ae57ecb34563ad93440fc1b26516a89ceb5b33c1ad5"},
-    {file = "coverage-5.4-cp36-cp36m-win32.whl", hash = "sha256:ebfa374067af240d079ef97b8064478f3bf71038b78b017eb6ec93ede1b6bcec"},
-    {file = "coverage-5.4-cp36-cp36m-win_amd64.whl", hash = "sha256:84df004223fd0550d0ea7a37882e5c889f3c6d45535c639ce9802293b39cd5c9"},
-    {file = "coverage-5.4-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:1b811662ecf72eb2d08872731636aee6559cae21862c36f74703be727b45df90"},
-    {file = "coverage-5.4-cp37-cp37m-manylinux1_i686.whl", hash = "sha256:6b588b5cf51dc0fd1c9e19f622457cc74b7d26fe295432e434525f1c0fae02bc"},
-    {file = "coverage-5.4-cp37-cp37m-manylinux1_x86_64.whl", hash = "sha256:3fe50f1cac369b02d34ad904dfe0771acc483f82a1b54c5e93632916ba847b37"},
-    {file = "coverage-5.4-cp37-cp37m-manylinux2010_i686.whl", hash = "sha256:32ab83016c24c5cf3db2943286b85b0a172dae08c58d0f53875235219b676409"},
-    {file = "coverage-5.4-cp37-cp37m-manylinux2010_x86_64.whl", hash = "sha256:68fb816a5dd901c6aff352ce49e2a0ffadacdf9b6fae282a69e7a16a02dad5fb"},
-    {file = "coverage-5.4-cp37-cp37m-win32.whl", hash = "sha256:a636160680c6e526b84f85d304e2f0bb4e94f8284dd765a1911de9a40450b10a"},
-    {file = "coverage-5.4-cp37-cp37m-win_amd64.whl", hash = "sha256:bb32ca14b4d04e172c541c69eec5f385f9a075b38fb22d765d8b0ce3af3a0c22"},
-    {file = "coverage-5.4-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:6c4d7165a4e8f41eca6b990c12ee7f44fef3932fac48ca32cecb3a1b2223c21f"},
-    {file = "coverage-5.4-cp38-cp38-manylinux1_i686.whl", hash = "sha256:a565f48c4aae72d1d3d3f8e8fb7218f5609c964e9c6f68604608e5958b9c60c3"},
-    {file = "coverage-5.4-cp38-cp38-manylinux1_x86_64.whl", hash = "sha256:fff1f3a586246110f34dc762098b5afd2de88de507559e63553d7da643053786"},
-    {file = "coverage-5.4-cp38-cp38-manylinux2010_i686.whl", hash = "sha256:a839e25f07e428a87d17d857d9935dd743130e77ff46524abb992b962eb2076c"},
-    {file = "coverage-5.4-cp38-cp38-manylinux2010_x86_64.whl", hash = "sha256:6625e52b6f346a283c3d563d1fd8bae8956daafc64bb5bbd2b8f8a07608e3994"},
-    {file = "coverage-5.4-cp38-cp38-win32.whl", hash = "sha256:5bee3970617b3d74759b2d2df2f6a327d372f9732f9ccbf03fa591b5f7581e39"},
-    {file = "coverage-5.4-cp38-cp38-win_amd64.whl", hash = "sha256:03ed2a641e412e42cc35c244508cf186015c217f0e4d496bf6d7078ebe837ae7"},
-    {file = "coverage-5.4-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:14a9f1887591684fb59fdba8feef7123a0da2424b0652e1b58dd5b9a7bb1188c"},
-    {file = "coverage-5.4-cp39-cp39-manylinux1_i686.whl", hash = "sha256:9564ac7eb1652c3701ac691ca72934dd3009997c81266807aef924012df2f4b3"},
-    {file = "coverage-5.4-cp39-cp39-manylinux1_x86_64.whl", hash = "sha256:0f48fc7dc82ee14aeaedb986e175a429d24129b7eada1b7e94a864e4f0644dde"},
-    {file = "coverage-5.4-cp39-cp39-manylinux2010_i686.whl", hash = "sha256:107d327071061fd4f4a2587d14c389a27e4e5c93c7cba5f1f59987181903902f"},
-    {file = "coverage-5.4-cp39-cp39-manylinux2010_x86_64.whl", hash = "sha256:0cdde51bfcf6b6bd862ee9be324521ec619b20590787d1655d005c3fb175005f"},
-    {file = "coverage-5.4-cp39-cp39-win32.whl", hash = "sha256:c67734cff78383a1f23ceba3b3239c7deefc62ac2b05fa6a47bcd565771e5880"},
-    {file = "coverage-5.4-cp39-cp39-win_amd64.whl", hash = "sha256:c669b440ce46ae3abe9b2d44a913b5fd86bb19eb14a8701e88e3918902ecd345"},
-    {file = "coverage-5.4-pp36-none-any.whl", hash = "sha256:c0ff1c1b4d13e2240821ef23c1efb1f009207cb3f56e16986f713c2b0e7cd37f"},
-    {file = "coverage-5.4-pp37-none-any.whl", hash = "sha256:cd601187476c6bed26a0398353212684c427e10a903aeafa6da40c63309d438b"},
-    {file = "coverage-5.4.tar.gz", hash = "sha256:6d2e262e5e8da6fa56e774fb8e2643417351427604c2b177f8e8c5f75fc928ca"},
+    {file = "coverage-5.5-cp27-cp27m-macosx_10_9_x86_64.whl", hash = "sha256:b6d534e4b2ab35c9f93f46229363e17f63c53ad01330df9f2d6bd1187e5eaacf"},
+    {file = "coverage-5.5-cp27-cp27m-manylinux1_i686.whl", hash = "sha256:b7895207b4c843c76a25ab8c1e866261bcfe27bfaa20c192de5190121770672b"},
+    {file = "coverage-5.5-cp27-cp27m-manylinux1_x86_64.whl", hash = "sha256:c2723d347ab06e7ddad1a58b2a821218239249a9e4365eaff6649d31180c1669"},
+    {file = "coverage-5.5-cp27-cp27m-manylinux2010_i686.whl", hash = "sha256:900fbf7759501bc7807fd6638c947d7a831fc9fdf742dc10f02956ff7220fa90"},
+    {file = "coverage-5.5-cp27-cp27m-manylinux2010_x86_64.whl", hash = "sha256:004d1880bed2d97151facef49f08e255a20ceb6f9432df75f4eef018fdd5a78c"},
+    {file = "coverage-5.5-cp27-cp27m-win32.whl", hash = "sha256:06191eb60f8d8a5bc046f3799f8a07a2d7aefb9504b0209aff0b47298333302a"},
+    {file = "coverage-5.5-cp27-cp27m-win_amd64.whl", hash = "sha256:7501140f755b725495941b43347ba8a2777407fc7f250d4f5a7d2a1050ba8e82"},
+    {file = "coverage-5.5-cp27-cp27mu-manylinux1_i686.whl", hash = "sha256:372da284cfd642d8e08ef606917846fa2ee350f64994bebfbd3afb0040436905"},
+    {file = "coverage-5.5-cp27-cp27mu-manylinux1_x86_64.whl", hash = "sha256:8963a499849a1fc54b35b1c9f162f4108017b2e6db2c46c1bed93a72262ed083"},
+    {file = "coverage-5.5-cp27-cp27mu-manylinux2010_i686.whl", hash = "sha256:869a64f53488f40fa5b5b9dcb9e9b2962a66a87dab37790f3fcfb5144b996ef5"},
+    {file = "coverage-5.5-cp27-cp27mu-manylinux2010_x86_64.whl", hash = "sha256:4a7697d8cb0f27399b0e393c0b90f0f1e40c82023ea4d45d22bce7032a5d7b81"},
+    {file = "coverage-5.5-cp310-cp310-macosx_10_14_x86_64.whl", hash = "sha256:8d0a0725ad7c1a0bcd8d1b437e191107d457e2ec1084b9f190630a4fb1af78e6"},
+    {file = "coverage-5.5-cp310-cp310-manylinux1_x86_64.whl", hash = "sha256:51cb9476a3987c8967ebab3f0fe144819781fca264f57f89760037a2ea191cb0"},
+    {file = "coverage-5.5-cp310-cp310-win_amd64.whl", hash = "sha256:c0891a6a97b09c1f3e073a890514d5012eb256845c451bd48f7968ef939bf4ae"},
+    {file = "coverage-5.5-cp35-cp35m-macosx_10_9_x86_64.whl", hash = "sha256:3487286bc29a5aa4b93a072e9592f22254291ce96a9fbc5251f566b6b7343cdb"},
+    {file = "coverage-5.5-cp35-cp35m-manylinux1_i686.whl", hash = "sha256:deee1077aae10d8fa88cb02c845cfba9b62c55e1183f52f6ae6a2df6a2187160"},
+    {file = "coverage-5.5-cp35-cp35m-manylinux1_x86_64.whl", hash = "sha256:f11642dddbb0253cc8853254301b51390ba0081750a8ac03f20ea8103f0c56b6"},
+    {file = "coverage-5.5-cp35-cp35m-manylinux2010_i686.whl", hash = "sha256:6c90e11318f0d3c436a42409f2749ee1a115cd8b067d7f14c148f1ce5574d701"},
+    {file = "coverage-5.5-cp35-cp35m-manylinux2010_x86_64.whl", hash = "sha256:30c77c1dc9f253283e34c27935fded5015f7d1abe83bc7821680ac444eaf7793"},
+    {file = "coverage-5.5-cp35-cp35m-win32.whl", hash = "sha256:9a1ef3b66e38ef8618ce5fdc7bea3d9f45f3624e2a66295eea5e57966c85909e"},
+    {file = "coverage-5.5-cp35-cp35m-win_amd64.whl", hash = "sha256:972c85d205b51e30e59525694670de6a8a89691186012535f9d7dbaa230e42c3"},
+    {file = "coverage-5.5-cp36-cp36m-macosx_10_9_x86_64.whl", hash = "sha256:af0e781009aaf59e25c5a678122391cb0f345ac0ec272c7961dc5455e1c40066"},
+    {file = "coverage-5.5-cp36-cp36m-manylinux1_i686.whl", hash = "sha256:74d881fc777ebb11c63736622b60cb9e4aee5cace591ce274fb69e582a12a61a"},
+    {file = "coverage-5.5-cp36-cp36m-manylinux1_x86_64.whl", hash = "sha256:92b017ce34b68a7d67bd6d117e6d443a9bf63a2ecf8567bb3d8c6c7bc5014465"},
+    {file = "coverage-5.5-cp36-cp36m-manylinux2010_i686.whl", hash = "sha256:d636598c8305e1f90b439dbf4f66437de4a5e3c31fdf47ad29542478c8508bbb"},
+    {file = "coverage-5.5-cp36-cp36m-manylinux2010_x86_64.whl", hash = "sha256:41179b8a845742d1eb60449bdb2992196e211341818565abded11cfa90efb821"},
+    {file = "coverage-5.5-cp36-cp36m-win32.whl", hash = "sha256:040af6c32813fa3eae5305d53f18875bedd079960822ef8ec067a66dd8afcd45"},
+    {file = "coverage-5.5-cp36-cp36m-win_amd64.whl", hash = "sha256:5fec2d43a2cc6965edc0bb9e83e1e4b557f76f843a77a2496cbe719583ce8184"},
+    {file = "coverage-5.5-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:18ba8bbede96a2c3dde7b868de9dcbd55670690af0988713f0603f037848418a"},
+    {file = "coverage-5.5-cp37-cp37m-manylinux1_i686.whl", hash = "sha256:2910f4d36a6a9b4214bb7038d537f015346f413a975d57ca6b43bf23d6563b53"},
+    {file = "coverage-5.5-cp37-cp37m-manylinux1_x86_64.whl", hash = "sha256:f0b278ce10936db1a37e6954e15a3730bea96a0997c26d7fee88e6c396c2086d"},
+    {file = "coverage-5.5-cp37-cp37m-manylinux2010_i686.whl", hash = "sha256:796c9c3c79747146ebd278dbe1e5c5c05dd6b10cc3bcb8389dfdf844f3ead638"},
+    {file = "coverage-5.5-cp37-cp37m-manylinux2010_x86_64.whl", hash = "sha256:53194af30d5bad77fcba80e23a1441c71abfb3e01192034f8246e0d8f99528f3"},
+    {file = "coverage-5.5-cp37-cp37m-win32.whl", hash = "sha256:184a47bbe0aa6400ed2d41d8e9ed868b8205046518c52464fde713ea06e3a74a"},
+    {file = "coverage-5.5-cp37-cp37m-win_amd64.whl", hash = "sha256:2949cad1c5208b8298d5686d5a85b66aae46d73eec2c3e08c817dd3513e5848a"},
+    {file = "coverage-5.5-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:217658ec7187497e3f3ebd901afdca1af062b42cfe3e0dafea4cced3983739f6"},
+    {file = "coverage-5.5-cp38-cp38-manylinux1_i686.whl", hash = "sha256:1aa846f56c3d49205c952d8318e76ccc2ae23303351d9270ab220004c580cfe2"},
+    {file = "coverage-5.5-cp38-cp38-manylinux1_x86_64.whl", hash = "sha256:24d4a7de75446be83244eabbff746d66b9240ae020ced65d060815fac3423759"},
+    {file = "coverage-5.5-cp38-cp38-manylinux2010_i686.whl", hash = "sha256:d1f8bf7b90ba55699b3a5e44930e93ff0189aa27186e96071fac7dd0d06a1873"},
+    {file = "coverage-5.5-cp38-cp38-manylinux2010_x86_64.whl", hash = "sha256:970284a88b99673ccb2e4e334cfb38a10aab7cd44f7457564d11898a74b62d0a"},
+    {file = "coverage-5.5-cp38-cp38-win32.whl", hash = "sha256:01d84219b5cdbfc8122223b39a954820929497a1cb1422824bb86b07b74594b6"},
+    {file = "coverage-5.5-cp38-cp38-win_amd64.whl", hash = "sha256:2e0d881ad471768bf6e6c2bf905d183543f10098e3b3640fc029509530091502"},
+    {file = "coverage-5.5-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:d1f9ce122f83b2305592c11d64f181b87153fc2c2bbd3bb4a3dde8303cfb1a6b"},
+    {file = "coverage-5.5-cp39-cp39-manylinux1_i686.whl", hash = "sha256:13c4ee887eca0f4c5a247b75398d4114c37882658300e153113dafb1d76de529"},
+    {file = "coverage-5.5-cp39-cp39-manylinux1_x86_64.whl", hash = "sha256:52596d3d0e8bdf3af43db3e9ba8dcdaac724ba7b5ca3f6358529d56f7a166f8b"},
+    {file = "coverage-5.5-cp39-cp39-manylinux2010_i686.whl", hash = "sha256:2cafbbb3af0733db200c9b5f798d18953b1a304d3f86a938367de1567f4b5bff"},
+    {file = "coverage-5.5-cp39-cp39-manylinux2010_x86_64.whl", hash = "sha256:44d654437b8ddd9eee7d1eaee28b7219bec228520ff809af170488fd2fed3e2b"},
+    {file = "coverage-5.5-cp39-cp39-win32.whl", hash = "sha256:d314ed732c25d29775e84a960c3c60808b682c08d86602ec2c3008e1202e3bb6"},
+    {file = "coverage-5.5-cp39-cp39-win_amd64.whl", hash = "sha256:13034c4409db851670bc9acd836243aeee299949bd5673e11844befcb0149f03"},
+    {file = "coverage-5.5-pp36-none-any.whl", hash = "sha256:f030f8873312a16414c0d8e1a1ddff2d3235655a2174e3648b4fa66b3f2f1079"},
+    {file = "coverage-5.5-pp37-none-any.whl", hash = "sha256:2a3859cb82dcbda1cfd3e6f71c27081d18aa251d20a17d87d26d4cd216fb0af4"},
+    {file = "coverage-5.5.tar.gz", hash = "sha256:ebe78fe9a0e874362175b02371bdfbee64d8edc42a044253ddf4ee7d3c15212c"},
 ]
 imbalanced-learn = [
     {file = "imbalanced-learn-0.7.0.tar.gz", hash = "sha256:da59de0d1c0fa66f62054dd9a0a295a182563aa1abbb3bf9224a3678fcfe8fa4"},
     {file = "imbalanced_learn-0.7.0-py3-none-any.whl", hash = "sha256:487b3fbbf93db046db93e3164c9a038b98abcbf588f1baad8816b2815a1ee5e9"},
 ]
 importlib-metadata = [
-    {file = "importlib_metadata-3.4.0-py3-none-any.whl", hash = "sha256:ace61d5fc652dc280e7b6b4ff732a9c2d40db2c0f92bc6cb74e07b73d53a1771"},
-    {file = "importlib_metadata-3.4.0.tar.gz", hash = "sha256:fa5daa4477a7414ae34e95942e4dd07f62adf589143c875c133c1e53c4eff38d"},
+    {file = "importlib_metadata-4.6.1-py3-none-any.whl", hash = "sha256:9f55f560e116f8643ecf2922d9cd3e1c7e8d52e683178fecd9d08f6aa357e11e"},
+    {file = "importlib_metadata-4.6.1.tar.gz", hash = "sha256:079ada16b7fc30dfbb5d13399a5113110dab1aa7c2bc62f66af75f0b717c8cac"},
 ]
 iniconfig = [
     {file = "iniconfig-1.1.1-py2.py3-none-any.whl", hash = "sha256:011e24c64b7f47f6ebd835bb12a743f2fbe9a26d4cecaa7f53bc4f35ee9da8b3"},
     {file = "iniconfig-1.1.1.tar.gz", hash = "sha256:bc3af051d7d14b2ee5ef9969666def0cd1a000e121eaea580d4a313df4b37f32"},
 ]
 joblib = [
-    {file = "joblib-1.0.0-py3-none-any.whl", hash = "sha256:75ead23f13484a2a414874779d69ade40d4fa1abe62b222a23cd50d4bc822f6f"},
-    {file = "joblib-1.0.0.tar.gz", hash = "sha256:7ad866067ac1fdec27d51c8678ea760601b70e32ff1881d4dc8e1171f2b64b24"},
+    {file = "joblib-1.0.1-py3-none-any.whl", hash = "sha256:feeb1ec69c4d45129954f1b7034954241eedfd6ba39b5e9e4b6883be3332d5e5"},
+    {file = "joblib-1.0.1.tar.gz", hash = "sha256:9c17567692206d2f3fb9ecf5e991084254fe631665c450b443761c4186a613f7"},
 ]
 numpy = [
     {file = "numpy-1.19.5-cp36-cp36m-macosx_10_9_x86_64.whl", hash = "sha256:cc6bd4fd593cb261332568485e20a0712883cf631f6f5e8e86a52caa8b2b50ff"},
@@ -364,8 +369,8 @@ numpy = [
     {file = "numpy-1.19.5.zip", hash = "sha256:a76f502430dd98d7546e1ea2250a7360c065a5fdea52b2dffe8ae7180909b6f4"},
 ]
 packaging = [
-    {file = "packaging-20.9-py2.py3-none-any.whl", hash = "sha256:67714da7f7bc052e064859c05c595155bd1ee9f69f76557e21f051443c20947a"},
-    {file = "packaging-20.9.tar.gz", hash = "sha256:5b327ac1320dc863dca72f4514ecc086f31186744b84a230374cc1fd776feae5"},
+    {file = "packaging-21.0-py3-none-any.whl", hash = "sha256:c86254f9220d55e31cc94d69bade760f0847da8000def4dfe1c6b872fd14ff14"},
+    {file = "packaging-21.0.tar.gz", hash = "sha256:7dc96269f53a4ccec5c0670940a4281106dd0bb343f47b7471f779df49c2fbe7"},
 ]
 pluggy = [
     {file = "pluggy-0.13.1-py2.py3-none-any.whl", hash = "sha256:966c145cd83c96502c3c3868f50408687b38434af77734af1e9ca461a4081d2d"},
@@ -380,43 +385,47 @@ pyparsing = [
     {file = "pyparsing-2.4.7.tar.gz", hash = "sha256:c203ec8783bf771a155b207279b9bccb8dea02d8f0c9e5f8ead507bc3246ecc1"},
 ]
 pytest = [
-    {file = "pytest-6.2.2-py3-none-any.whl", hash = "sha256:b574b57423e818210672e07ca1fa90aaf194a4f63f3ab909a2c67ebb22913839"},
-    {file = "pytest-6.2.2.tar.gz", hash = "sha256:9d1edf9e7d0b84d72ea3dbcdfd22b35fb543a5e8f2a60092dd578936bf63d7f9"},
+    {file = "pytest-6.2.4-py3-none-any.whl", hash = "sha256:91ef2131a9bd6be8f76f1f08eac5c5317221d6ad1e143ae03894b862e8976890"},
+    {file = "pytest-6.2.4.tar.gz", hash = "sha256:50bcad0a0b9c5a72c8e4e7c9855a3ad496ca6a881a3641b4260605450772c54b"},
 ]
 pytest-cov = [
-    {file = "pytest-cov-2.11.1.tar.gz", hash = "sha256:359952d9d39b9f822d9d29324483e7ba04a3a17dd7d05aa6beb7ea01e359e5f7"},
-    {file = "pytest_cov-2.11.1-py2.py3-none-any.whl", hash = "sha256:bdb9fdb0b85a7cc825269a4c56b48ccaa5c7e365054b6038772c32ddcdc969da"},
+    {file = "pytest-cov-2.12.1.tar.gz", hash = "sha256:261ceeb8c227b726249b376b8526b600f38667ee314f910353fa318caa01f4d7"},
+    {file = "pytest_cov-2.12.1-py2.py3-none-any.whl", hash = "sha256:261bb9e47e65bd099c89c3edf92972865210c36813f80ede5277dceb77a4a62a"},
 ]
 scikit-learn = [
-    {file = "scikit-learn-0.24.1.tar.gz", hash = "sha256:a0334a1802e64d656022c3bfab56a73fbd6bf4b1298343f3688af2151810bbdf"},
-    {file = "scikit_learn-0.24.1-cp36-cp36m-macosx_10_13_x86_64.whl", hash = "sha256:9bed8a1ef133c8e2f13966a542cb8125eac7f4b67dcd234197c827ba9c7dd3e0"},
-    {file = "scikit_learn-0.24.1-cp36-cp36m-manylinux1_i686.whl", hash = "sha256:a36e159a0521e13bbe15ca8c8d038b3a1dd4c7dad18d276d76992e03b92cf643"},
-    {file = "scikit_learn-0.24.1-cp36-cp36m-manylinux1_x86_64.whl", hash = "sha256:c658432d8a20e95398f6bb95ff9731ce9dfa343fdf21eea7ec6a7edfacd4b4d9"},
-    {file = "scikit_learn-0.24.1-cp36-cp36m-manylinux2010_i686.whl", hash = "sha256:9dfa564ef27e8e674aa1cc74378416d580ac4ede1136c13dd555a87996e13422"},
-    {file = "scikit_learn-0.24.1-cp36-cp36m-manylinux2010_x86_64.whl", hash = "sha256:9c6097b6a9b2bafc5e0f31f659e6ab5e131383209c30c9e978c5b8abdac5ed2a"},
-    {file = "scikit_learn-0.24.1-cp36-cp36m-win32.whl", hash = "sha256:7b04691eb2f41d2c68dbda8d1bd3cb4ef421bdc43aaa56aeb6c762224552dfb6"},
-    {file = "scikit_learn-0.24.1-cp36-cp36m-win_amd64.whl", hash = "sha256:1adf483e91007a87171d7ce58c34b058eb5dab01b5fee6052f15841778a8ecd8"},
-    {file = "scikit_learn-0.24.1-cp37-cp37m-macosx_10_13_x86_64.whl", hash = "sha256:ddb52d088889f5596bc4d1de981f2eca106b58243b6679e4782f3ba5096fd645"},
-    {file = "scikit_learn-0.24.1-cp37-cp37m-manylinux1_i686.whl", hash = "sha256:a29460499c1e62b7a830bb57ca42e615375a6ab1bcad053cd25b493588348ea8"},
-    {file = "scikit_learn-0.24.1-cp37-cp37m-manylinux1_x86_64.whl", hash = "sha256:0567a2d29ad08af98653300c623bd8477b448fe66ced7198bef4ed195925f082"},
-    {file = "scikit_learn-0.24.1-cp37-cp37m-manylinux2010_i686.whl", hash = "sha256:99349d77f54e11f962d608d94dfda08f0c9e5720d97132233ebdf35be2858b2d"},
-    {file = "scikit_learn-0.24.1-cp37-cp37m-manylinux2010_x86_64.whl", hash = "sha256:83b21ff053b1ff1c018a2d24db6dd3ea339b1acfbaa4d9c881731f43748d8b3b"},
-    {file = "scikit_learn-0.24.1-cp37-cp37m-win32.whl", hash = "sha256:c3deb3b19dd9806acf00cf0d400e84562c227723013c33abefbbc3cf906596e9"},
-    {file = "scikit_learn-0.24.1-cp37-cp37m-win_amd64.whl", hash = "sha256:d54dbaadeb1425b7d6a66bf44bee2bb2b899fe3e8850b8e94cfb9c904dcb46d0"},
-    {file = "scikit_learn-0.24.1-cp38-cp38-macosx_10_13_x86_64.whl", hash = "sha256:3c4f07f47c04e81b134424d53c3f5e16dfd7f494e44fd7584ba9ce9de2c5e6c1"},
-    {file = "scikit_learn-0.24.1-cp38-cp38-manylinux1_i686.whl", hash = "sha256:c13ebac42236b1c46397162471ea1c46af68413000e28b9309f8c05722c65a09"},
-    {file = "scikit_learn-0.24.1-cp38-cp38-manylinux1_x86_64.whl", hash = "sha256:4ddd2b6f7449a5d539ff754fa92d75da22de261fd8fdcfb3596799fadf255101"},
-    {file = "scikit_learn-0.24.1-cp38-cp38-manylinux2010_i686.whl", hash = "sha256:826b92bf45b8ad80444814e5f4ac032156dd481e48d7da33d611f8fe96d5f08b"},
-    {file = "scikit_learn-0.24.1-cp38-cp38-manylinux2010_x86_64.whl", hash = "sha256:259ec35201e82e2db1ae2496f229e63f46d7f1695ae68eef9350b00dc74ba52f"},
-    {file = "scikit_learn-0.24.1-cp38-cp38-win32.whl", hash = "sha256:8772b99d683be8f67fcc04789032f1b949022a0e6880ee7b75a7ec97dbbb5d0b"},
-    {file = "scikit_learn-0.24.1-cp38-cp38-win_amd64.whl", hash = "sha256:ed9d65594948678827f4ff0e7ae23344e2f2b4cabbca057ccaed3118fdc392ca"},
-    {file = "scikit_learn-0.24.1-cp39-cp39-macosx_10_13_x86_64.whl", hash = "sha256:8aa1b3ac46b80eaa552b637eeadbbce3be5931e4b5002b964698e33a1b589e1e"},
-    {file = "scikit_learn-0.24.1-cp39-cp39-manylinux1_i686.whl", hash = "sha256:c7f4eb77504ac586d8ac1bde1b0c04b504487210f95297235311a0ab7edd7e38"},
-    {file = "scikit_learn-0.24.1-cp39-cp39-manylinux1_x86_64.whl", hash = "sha256:087dfede39efb06ab30618f9ab55a0397f29c38d63cd0ab88d12b500b7d65fd7"},
-    {file = "scikit_learn-0.24.1-cp39-cp39-manylinux2010_i686.whl", hash = "sha256:895dbf2030aa7337649e36a83a007df3c9811396b4e2fa672a851160f36ce90c"},
-    {file = "scikit_learn-0.24.1-cp39-cp39-manylinux2010_x86_64.whl", hash = "sha256:9a24d1ccec2a34d4cd3f2a1f86409f3f5954cc23d4d2270ba0d03cf018aa4780"},
-    {file = "scikit_learn-0.24.1-cp39-cp39-win32.whl", hash = "sha256:fab31f48282ebf54dd69f6663cd2d9800096bad1bb67bbc9c9ac84eb77b41972"},
-    {file = "scikit_learn-0.24.1-cp39-cp39-win_amd64.whl", hash = "sha256:4562dcf4793e61c5d0f89836d07bc37521c3a1889da8f651e2c326463c4bd697"},
+    {file = "scikit-learn-0.24.2.tar.gz", hash = "sha256:d14701a12417930392cd3898e9646cf5670c190b933625ebe7511b1f7d7b8736"},
+    {file = "scikit_learn-0.24.2-cp36-cp36m-macosx_10_13_x86_64.whl", hash = "sha256:d5bf9c863ba4717b3917b5227463ee06860fc43931dc9026747de416c0a10fee"},
+    {file = "scikit_learn-0.24.2-cp36-cp36m-manylinux1_i686.whl", hash = "sha256:5beaeb091071625e83f5905192d8aecde65ba2f26f8b6719845bbf586f7a04a1"},
+    {file = "scikit_learn-0.24.2-cp36-cp36m-manylinux1_x86_64.whl", hash = "sha256:06ffdcaaf81e2a3b1b50c3ac6842cfb13df2d8b737d61f64643ed61da7389cde"},
+    {file = "scikit_learn-0.24.2-cp36-cp36m-manylinux2010_i686.whl", hash = "sha256:fec42690a2eb646b384eafb021c425fab48991587edb412d4db77acc358b27ce"},
+    {file = "scikit_learn-0.24.2-cp36-cp36m-manylinux2010_x86_64.whl", hash = "sha256:5ff3e4e4cf7592d36541edec434e09fb8ab9ba6b47608c4ffe30c9038d301897"},
+    {file = "scikit_learn-0.24.2-cp36-cp36m-manylinux2014_aarch64.whl", hash = "sha256:3cbd734e1aefc7c5080e6b6973fe062f97c26a1cdf1a991037ca196ce1c8f427"},
+    {file = "scikit_learn-0.24.2-cp36-cp36m-win32.whl", hash = "sha256:f74429a07fedb36a03c159332b914e6de757176064f9fed94b5f79ebac07d913"},
+    {file = "scikit_learn-0.24.2-cp36-cp36m-win_amd64.whl", hash = "sha256:dd968a174aa82f3341a615a033fa6a8169e9320cbb46130686562db132d7f1f0"},
+    {file = "scikit_learn-0.24.2-cp37-cp37m-macosx_10_13_x86_64.whl", hash = "sha256:49ec0b1361da328da9bb7f1a162836028e72556356adeb53342f8fae6b450d47"},
+    {file = "scikit_learn-0.24.2-cp37-cp37m-manylinux1_i686.whl", hash = "sha256:f18c3ed484eeeaa43a0d45dc2efb4d00fc6542ccdcfa2c45d7b635096a2ae534"},
+    {file = "scikit_learn-0.24.2-cp37-cp37m-manylinux1_x86_64.whl", hash = "sha256:cdf24c1b9bbeb4936456b42ac5bd32c60bb194a344951acb6bfb0cddee5439a4"},
+    {file = "scikit_learn-0.24.2-cp37-cp37m-manylinux2010_i686.whl", hash = "sha256:d177fe1ff47cc235942d628d41ee5b1c6930d8f009f1a451c39b5411e8d0d4cf"},
+    {file = "scikit_learn-0.24.2-cp37-cp37m-manylinux2010_x86_64.whl", hash = "sha256:f3ec00f023d84526381ad0c0f2cff982852d035c921bbf8ceb994f4886c00c64"},
+    {file = "scikit_learn-0.24.2-cp37-cp37m-manylinux2014_aarch64.whl", hash = "sha256:ae19ac105cf7ce8c205a46166992fdec88081d6e783ab6e38ecfbe45729f3c39"},
+    {file = "scikit_learn-0.24.2-cp37-cp37m-win32.whl", hash = "sha256:f0ed4483c258fb23150e31b91ea7d25ff8495dba108aea0b0d4206a777705350"},
+    {file = "scikit_learn-0.24.2-cp37-cp37m-win_amd64.whl", hash = "sha256:39b7e3b71bcb1fe46397185d6c1a5db1c441e71c23c91a31e7ad8cc3f7305f9a"},
+    {file = "scikit_learn-0.24.2-cp38-cp38-macosx_10_13_x86_64.whl", hash = "sha256:90a297330f608adeb4d2e9786c6fda395d3150739deb3d42a86d9a4c2d15bc1d"},
+    {file = "scikit_learn-0.24.2-cp38-cp38-manylinux1_i686.whl", hash = "sha256:f1d2108e770907540b5248977e4cff9ffaf0f73d0d13445ee938df06ca7579c6"},
+    {file = "scikit_learn-0.24.2-cp38-cp38-manylinux1_x86_64.whl", hash = "sha256:1eec963fe9ffc827442c2e9333227c4d49749a44e592f305398c1db5c1563393"},
+    {file = "scikit_learn-0.24.2-cp38-cp38-manylinux2010_i686.whl", hash = "sha256:2db429090b98045d71218a9ba913cc9b3fe78e0ba0b6b647d8748bc6d5a44080"},
+    {file = "scikit_learn-0.24.2-cp38-cp38-manylinux2010_x86_64.whl", hash = "sha256:62214d2954377fcf3f31ec867dd4e436df80121e7a32947a0b3244f58f45e455"},
+    {file = "scikit_learn-0.24.2-cp38-cp38-manylinux2014_aarch64.whl", hash = "sha256:8fac72b9688176922f9f54fda1ba5f7ffd28cbeb9aad282760186e8ceba9139a"},
+    {file = "scikit_learn-0.24.2-cp38-cp38-win32.whl", hash = "sha256:ae426e3a52842c6b6d77d00f906b6031c8c2cfdfabd6af7511bb4bc9a68d720e"},
+    {file = "scikit_learn-0.24.2-cp38-cp38-win_amd64.whl", hash = "sha256:038f4e9d6ef10e1f3fe82addc3a14735c299866eb10f2c77c090410904828312"},
+    {file = "scikit_learn-0.24.2-cp39-cp39-macosx_10_13_x86_64.whl", hash = "sha256:48f273836e19901ba2beecd919f7b352f09310ce67c762f6e53bc6b81cacf1f0"},
+    {file = "scikit_learn-0.24.2-cp39-cp39-manylinux1_i686.whl", hash = "sha256:a2a47449093dcf70babc930beba2ca0423cb7df2fa5fd76be5260703d67fa574"},
+    {file = "scikit_learn-0.24.2-cp39-cp39-manylinux1_x86_64.whl", hash = "sha256:0e71ce9c7cbc20f6f8b860107ce15114da26e8675238b4b82b7e7cd37ca0c087"},
+    {file = "scikit_learn-0.24.2-cp39-cp39-manylinux2010_i686.whl", hash = "sha256:2754c85b2287333f9719db7f23fb7e357f436deed512db3417a02bf6f2830aa5"},
+    {file = "scikit_learn-0.24.2-cp39-cp39-manylinux2010_x86_64.whl", hash = "sha256:7be1b88c23cfac46e06404582215a917017cd2edaa2e4d40abe6aaff5458f24b"},
+    {file = "scikit_learn-0.24.2-cp39-cp39-manylinux2014_aarch64.whl", hash = "sha256:4e6198675a6f9d333774671bd536668680eea78e2e81c0b19e57224f58d17f37"},
+    {file = "scikit_learn-0.24.2-cp39-cp39-win32.whl", hash = "sha256:cbdb0b3db99dd1d5f69d31b4234367d55475add31df4d84a3bd690ef017b55e2"},
+    {file = "scikit_learn-0.24.2-cp39-cp39-win_amd64.whl", hash = "sha256:40556bea1ef26ef54bc678d00cf138a63069144a0b5f3a436eecd8f3468b903e"},
 ]
 scipy = [
     {file = "scipy-1.5.4-cp36-cp36m-macosx_10_9_x86_64.whl", hash = "sha256:4f12d13ffbc16e988fa40809cbbd7a8b45bc05ff6ea0ba8e3e41f6f4db3a9e47"},
@@ -446,19 +455,19 @@ scipy = [
     {file = "scipy-1.5.4.tar.gz", hash = "sha256:4a453d5e5689de62e5d38edf40af3f17560bfd63c9c5bd228c18c1f99afa155b"},
 ]
 threadpoolctl = [
-    {file = "threadpoolctl-2.1.0-py3-none-any.whl", hash = "sha256:38b74ca20ff3bb42caca8b00055111d74159ee95c4370882bbff2b93d24da725"},
-    {file = "threadpoolctl-2.1.0.tar.gz", hash = "sha256:ddc57c96a38beb63db45d6c159b5ab07b6bced12c45a1f07b2b92f272aebfa6b"},
+    {file = "threadpoolctl-2.2.0-py3-none-any.whl", hash = "sha256:e5a995e3ffae202758fa8a90082e35783b9370699627ae2733cd1c3a73553616"},
+    {file = "threadpoolctl-2.2.0.tar.gz", hash = "sha256:86d4b6801456d780e94681d155779058759eaef3c3564758b17b6c99db5f81cb"},
 ]
 toml = [
     {file = "toml-0.10.2-py2.py3-none-any.whl", hash = "sha256:806143ae5bfb6a3c6e736a764057db0e6a0e05e338b5630894a5f779cabb4f9b"},
     {file = "toml-0.10.2.tar.gz", hash = "sha256:b3bda1d108d5dd99f4a20d24d9c348e91c4db7ab1b749200bded2f839ccbe68f"},
 ]
 typing-extensions = [
-    {file = "typing_extensions-3.7.4.3-py2-none-any.whl", hash = "sha256:dafc7639cde7f1b6e1acc0f457842a83e722ccca8eef5270af2d74792619a89f"},
-    {file = "typing_extensions-3.7.4.3-py3-none-any.whl", hash = "sha256:7cb407020f00f7bfc3cb3e7881628838e69d8f3fcab2f64742a5e76b2f841918"},
-    {file = "typing_extensions-3.7.4.3.tar.gz", hash = "sha256:99d4073b617d30288f569d3f13d2bd7548c3a7e4c8de87db09a9d29bb3a4a60c"},
+    {file = "typing_extensions-3.10.0.0-py2-none-any.whl", hash = "sha256:0ac0f89795dd19de6b97debb0c6af1c70987fd80a2d62d1958f7e56fcc31b497"},
+    {file = "typing_extensions-3.10.0.0-py3-none-any.whl", hash = "sha256:779383f6086d90c99ae41cf0ff39aac8a7937a9283ce0a414e5dd782f4c94a84"},
+    {file = "typing_extensions-3.10.0.0.tar.gz", hash = "sha256:50b6f157849174217d0656f99dc82fe932884fb250826c18350e159ec6cdf342"},
 ]
 zipp = [
-    {file = "zipp-3.4.0-py3-none-any.whl", hash = "sha256:102c24ef8f171fd729d46599845e95c7ab894a4cf45f5de11a44cc7444fb1108"},
-    {file = "zipp-3.4.0.tar.gz", hash = "sha256:ed5eee1974372595f9e416cc7bbeeb12335201d8081ca8a0743c954d4446e5cb"},
+    {file = "zipp-3.5.0-py3-none-any.whl", hash = "sha256:957cfda87797e389580cb8b9e3870841ca991e2125350677b2ca83a0e99390a3"},
+    {file = "zipp-3.5.0.tar.gz", hash = "sha256:f5812b1e007e48cff63449a5e9f4e7ebea716b4111f9c4f9a645f91d579bf0c4"},
 ]
diff --git a/pyloras/_loras.py b/pyloras/_loras.py
index 2626663..04e19a2 100644
--- a/pyloras/_loras.py
+++ b/pyloras/_loras.py
@@ -2,7 +2,7 @@
 Copyright (c) 2021, Zolisa Bleki
 SPDX-License-Identifier: BSD-3-Clause
 """
-from functools import partial
+from collections import defaultdict
 from math import ceil
 
 from imblearn.over_sampling.base import BaseOverSampler
@@ -12,10 +12,9 @@
     _n_jobs_docstring,
 )
 from imblearn.utils._validation import check_neighbors_object
-from joblib import Parallel, delayed
 import numpy as np
 from sklearn.manifold import TSNE
-from sklearn.utils import check_random_state, parallel_backend
+from sklearn.utils import check_random_state
 
 
 @Substitution(
@@ -29,7 +28,7 @@ class LORAS(BaseOverSampler):
     This class implements the LoRAS oversampling technique for imbalanced
     datasets. This technique generates Gaussian noise in small neighborhoods
     around the minority class samples and then the finaly synthetic samples
-    are obtained by a convex combination of multipke noisy data points
+    are obtained by a convex combination of multiple noisy data points
     (shadowsamples).
 
     Parameters
@@ -37,7 +36,7 @@ class LORAS(BaseOverSampler):
     {sampling_strategy}
     n_neighbors : int or estimator object, default=None
         If ``int``, number of nearest neighbours to used to construct synthetic
-        samples.  If object, an estimator that inherits from
+        samples. If object, an estimator that inherits from
         :class:`~sklearn.neighbors.base.KNeighborsMixin` that will be used to
         find the k_neighbors.
     n_shadow : int, default=None
@@ -50,7 +49,7 @@ class LORAS(BaseOverSampler):
         will be used for all shadow samples generated.
     n_affine : int, default=None
         The number of shadow samples to use when generating the synthetic
-        samples through random affine combination. If given, the value must be
+        samples through random affine combinations. If given, the value must be
         between ``2`` and the number of features used in the fitting data.
         If not given, the value will be set to the total number of features in
         fitting data.
@@ -81,7 +80,7 @@ class LORAS(BaseOverSampler):
     Original dataset shape Counter({{1: 400, 2: 200, 0: 1400}})
     >>> X_res, y_res = l.fit_resample(X, y)
     >>> print(f"Resampled dataset shape % Counter(y_res))
-    Resampled dataset shape Counter({{1: 1200, 2: 1400, 0: 1400}})
+    Resampled dataset shape Counter({{1: 1400, 2: 1400, 0: 1400}})
 
     """
     def __init__(
@@ -109,7 +108,7 @@ def _initialize_params(self, X, y, rng):
         """Initialize the parameter values to their appropriate values."""
         f_size = X.shape[1]
         self.n_affine_ = f_size if self.n_affine is None else self.n_affine
-        self.tsne_ = TSNE(n_components=2, n_jobs=self.n_jobs, random_state=rng)
+        self.tsne_ = TSNE(n_components=2, random_state=rng)
 
         if self.embedding_params is not None:
             self.tsne_.set_params(**self.embedding_params)
@@ -123,115 +122,59 @@ def _initialize_params(self, X, y, rng):
         if self.n_jobs is not None:
             self.nn_.set_params(n_jobs=self.n_jobs)
 
-        if self.n_affine_ >= self.nn_.n_neighbors * f_size:
-            raise ValueError(
-                "The number of shadow samples used to construct synthetic "
-                "samples must be less than `n_neighbors * number of features`"
-            )
-
         if self.n_shadow is None:
             self.n_shadow_ = max(ceil(2 * f_size / self.nn_.n_neighbors), 40)
         else:
             self.n_shadow_ = self.n_shadow
 
+        if self.n_affine_ >= self.nn_.n_neighbors * self.n_shadow_:
+            raise ValueError(
+                "The number of shadow samples used to create an affine random "
+                "combination must be less than `n_neighbors * n_shadow`."
+            )
+
         try:
             iter(self.std)
             self.std_ = self.std
         except TypeError:
-            self.std_ = [self.std] * X.shape[1]
+            self.std_ = [self.std] * f_size
 
     def _fit_resample(self, X, y):
-        X_res = X.copy()
-        y_res = y.copy()
-
         random_state = check_random_state(self.random_state)
         self._initialize_params(X, y, random_state)
-        X_embedded = self.tsne_.fit_transform(X_res)
-        self.nn_.fit(X_embedded)
-
-        func = partial(
-            _make_samples,
-            n_shadow=self.n_shadow_,
-            std=self.std_,
-            n_affine=self.n_affine_,
-            random_state=random_state,
-            dirichlet_param=[1] * self.n_affine_,
-            n_features=X.shape[1],
-        )
-        X_res = [X_res.copy()]
-        y_res = [y_res.copy()]
-        for class_sample, n_samples in self.sampling_strategy_.items():
-            data_indices = np.flatnonzero(y == class_sample)
-            # number of synthetic samples per neighborhood group
-            n_gen = n_samples // data_indices.shape[0]
-            neighborhood_groups = self.nn_.kneighbors(
-                X_embedded[data_indices],
-                return_distance=False
-            )
-            with parallel_backend('loky', n_jobs=self.n_jobs):
-                samples = Parallel()(
-                    delayed(func)(X[i], class_sample, n_gen)
-                    for i in neighborhood_groups
+        n_features = X.shape[1]
+
+        X_res = [X.copy()]
+        y_res = [y.copy()]
+        dirichlet_param = [1] * self.n_affine_
+        loras_samples = defaultdict(lambda : [])
+
+        for minority_class, samples_to_make in self.sampling_strategy_.items():
+            X_embedded = self.tsne_.fit_transform(X[y == minority_class])
+            self.nn_.fit(X_embedded)
+            neighborhoods = self.nn_.kneighbors(X_embedded, return_distance=False)
+            num_loras = ceil(samples_to_make / X_embedded.shape[0])
+            for neighbor_group in neighborhoods:
+                shadow_sample_size = (self.n_shadow_, self.nn_.n_neighbors, n_features)
+                total_shadow_samples = (
+                    X[neighbor_group] +
+                    random_state.normal(scale=self.std_, size=shadow_sample_size)
+                ).reshape(self.n_shadow_ * self.nn_.n_neighbors, n_features)
+                random_index = random_state.randint(
+                    0, total_shadow_samples.shape[0], size=(num_loras, self.n_affine_)
                 )
-            X_res.extend(samples)
-            y_res.extend([class_sample] * n_gen * neighborhood_groups.shape[0])
-        X_res = np.vstack(X_res)
-        y_res = np.hstack(y_res)
-
-        return X_res, y_res
-
-
-def _make_samples(
-    X_neighbors,
-    class_sample,
-    n_gen,
-    n_shadow,
-    std,
-    n_affine,
-    random_state,
-    dirichlet_param,
-    n_features
-):
-    """
-    Make LoRAS samples given data point and its nearest neighbors,
-
-    This function generates `n_gen` LoRAS synthetic samples given a data point
-    and its nearest neighbors in the 2d manifold created by t-SNE.
-
-    X_neighbors : numpy.ndarray
-        The neighborhood group (including the data point).
-    class_sample : scalar
-        The assigned class of the neighborhood group.
-    n_gen : int
-        The number of synthetic samples to generate.
-    n_shadow: int
-        The number of shadow samples to use.
-    std : list
-        Standard deviations per feature of the data point. These values are
-        used to sample the Gaussian noise used to create shadow samples.
-    n_affine : int
-        The size of the subset of shadow samples used to greate the affine
-        convex combination of augmented data points.
-    random_state : numpy.random.RandomState
-        The random number generating object.
-    dirichlet_param : list
-        A list of 1's whose size is equal to `n_affine`. It is used to
-        generate the affine combination weights who follow a Dirichlet
-        distribution.
-    n_features : int
-        Number of features per data point in the neighborhood group.
+                weights = random_state.dirichlet(dirichlet_param, size=num_loras)
+                loras_samples[minority_class].append(
+                    (weights[:, None] @ total_shadow_samples[random_index])
+                    .reshape(num_loras, n_features)
+                )
+            # keep only ``samples_to_make`` synthetic samples from the generated.
+            samples_to_drop = X_embedded.shape[0] * num_loras - samples_to_make
+            if samples_to_drop:
+                random_state.shuffle(loras_samples[minority_class])
+                X_res.append(
+                    np.concatenate(loras_samples[minority_class])[samples_to_drop:]
+                )
+                y_res.append([minority_class] * samples_to_make)
 
-    """
-    size = (n_shadow, X_neighbors.shape[0], n_features)
-    shadow_points = random_state.normal(scale=std, size=size)
-    shadow_sample = X_neighbors + shadow_points
-    shadow_sample = shadow_sample.reshape(
-        shadow_sample.shape[0] * shadow_sample.shape[1], -1
-    )
-    random_index = random_state.randint(
-        0, shadow_sample.shape[0], size=(n_gen, n_affine)
-    )
-    weights = random_state.dirichlet(dirichlet_param, size=n_gen)
-    samples = weights[:, None] @ shadow_sample[random_index]
-    samples = np.squeeze(samples)
-    return samples
+        return np.concatenate(X_res), np.concatenate(y_res)


Test patch:
diff --git a/tests/test_loras.py b/tests/test_loras.py
index 33e3c6d..7b00261 100644
--- a/tests/test_loras.py
+++ b/tests/test_loras.py
@@ -54,7 +54,7 @@ def test_loras(data):
     assert lrs.n_shadow_ == 40
     assert isinstance(lrs.std_, list)
     assert np.allclose(lrs.std_, 0.005)
-    assert len(np.argwhere(y_res == 1)) == 18
+    assert len(y_res[y_res == 1]) == 21
 
     # test reporducibility
     lrs = LORAS(random_state=np.random.RandomState(0))