diff --git a/.coveragerc b/.coveragerc
index 82ed0b8..b88aee5 100644
--- a/.coveragerc
+++ b/.coveragerc
@@ -15,5 +15,3 @@ exclude_lines =
     # Don't complain if non-runnable code isn't run:
     if 0:
     if __name__ == .__main__.
-
-
diff --git a/.github/workflows/pre-commit.yml b/.github/workflows/pre-commit.yml
new file mode 100644
index 0000000..df20a5f
--- /dev/null
+++ b/.github/workflows/pre-commit.yml
@@ -0,0 +1,62 @@
+name: pre-commit
+on:
+  push:
+    branches-ignore:
+      - 'master'
+  pull_request:
+    types: [opened, synchronize, reopened, ready_for_review]
+
+concurrency:
+  group: ${{ github.workflow }}-${{ github.ref }}
+  cancel-in-progress: true
+
+jobs:
+  pre-commit:
+    runs-on: ubuntu-latest
+
+    if: >-
+      !endsWith(github.event.head_commit.message, '# ci skip') &&
+      (
+        startsWith(github.ref, 'refs/heads') ||
+        github.event.pull_request.draft == false
+      )
+
+    steps:
+
+      - uses: actions/checkout@v3
+        with:
+          fetch-depth: 0
+
+      - name: Cache pre-commit
+        uses: actions/cache@v3
+        with:
+          path: ~/.cache/pre-commit
+          key: pre-commit-${{ hashFiles('.pre-commit-config.yaml') }}-
+
+      - name: Run pre-commit
+        id: precommit
+        uses: pre-commit/action@v3.0.0
+        continue-on-error: true
+
+      - name: Commit files
+        if: steps.precommit.outcome == 'failure' && startsWith(github.ref, 'refs/heads')
+        run: |
+          if [[ `git status --porcelain --untracked-files=no` ]]; then
+            git config --local user.email "41898282+github-actions[bot]@users.noreply.github.com"
+            git config --local user.name "github-actions[bot]"
+            git add .
+            git checkout -- .github/workflows
+            git commit -m "pre-commit" -a
+          fi
+        shell: bash -ex {0}
+
+      - name: Push changes
+        if: steps.precommit.outcome == 'failure' && startsWith(github.ref, 'refs/heads')
+        uses: ad-m/github-push-action@master
+        with:
+          github_token: ${{ secrets.GITHUB_TOKEN }}
+          branch: ${{ github.ref }}
+
+      - name: Check pre-commit
+        if: steps.precommit.outcome == 'failure'
+        uses: pre-commit/action@v3.0.0
diff --git a/.github/workflows/run_tests.yml b/.github/workflows/run_tests.yml
new file mode 100644
index 0000000..cc5dac8
--- /dev/null
+++ b/.github/workflows/run_tests.yml
@@ -0,0 +1,77 @@
+name: Unit Tests
+
+on:
+  push:
+    branches-ignore:
+      - 'test_deploy'
+  pull_request:
+    branches:
+      - '*'
+
+concurrency:
+  group: ${{ github.workflow }}-${{ github.ref }}
+  cancel-in-progress: true
+
+jobs:
+
+  run_tester:
+    runs-on: ${{ matrix.config.os }}
+    if: "!contains(github.event.head_commit.message, 'ci skip')"
+
+    strategy:
+      fail-fast: false
+      matrix:
+        config:
+        - {name: '3.10', os: ubuntu-latest, python: '3.10' }
+        - {name: '3.9', os: ubuntu-latest, python: '3.9' }
+        - {name: '3.8', os: ubuntu-latest, python: '3.8' }
+        - {name: '3.7', os: ubuntu-latest, python: '3.7' }
+
+    steps:
+
+    - uses: actions/checkout@v2
+      with:
+        fetch-depth: 0
+
+    - name: Install system dependencies
+      if: runner.os == 'Linux'
+      run: |
+        sudo apt-get update -qq
+        sudo apt-get install -y libhdf5-dev libhdf5-serial-dev pandoc gfortran libblas-dev liblapack-dev llvm-dev
+
+    - name: Set up Python
+      uses: actions/setup-python@v2
+      with:
+        python-version: ${{ matrix.config.python }}
+
+    - name: Cache Python packages
+      uses: actions/cache@v2
+      with:
+        path: ${{ env.pythonLocation }}
+        key: ${{runner.os}}-${{ matrix.config.python }}-pip-${{ env.pythonLocation }}-${{ hashFiles('setup.py') }}
+        restore-keys: ${{runner.os}}-${{ matrix.config.python }}-pip-${{ env.pythonLocation }}-
+
+    - name: Install package & dependencies
+      run: |
+        python -m pip install --upgrade pip
+        pip install -U wheel setuptools
+        pip install -U .[test]
+        python -c "import graphtools"
+
+    - name: Run tests
+      run: |
+        nose2 -vvv
+
+    - name: Coveralls
+      env:
+        GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+        COVERALLS_SERVICE_NAME: github
+      run: |
+        coveralls
+
+    - name: Upload check results on fail
+      if: failure()
+      uses: actions/upload-artifact@master
+      with:
+        name: ${{ matrix.config.name }}_results
+        path: check
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
new file mode 100644
index 0000000..152000b
--- /dev/null
+++ b/.pre-commit-config.yaml
@@ -0,0 +1,26 @@
+repos:
+  - repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v3.3.0
+    hooks:
+      - id: check-yaml
+      - id: end-of-file-fixer
+      - id: trailing-whitespace
+        exclude: \.(ai|gz)$
+  - repo: https://github.com/timothycrosley/isort
+    rev: 5.6.4
+    hooks:
+      - id: isort
+  - repo: https://github.com/psf/black
+    rev: 22.3.0
+    hooks:
+      - id: black
+        args: ['--target-version=py36']
+  - repo: https://github.com/pre-commit/mirrors-autopep8
+    rev: v1.5.4
+    hooks:
+      - id: autopep8
+#   - repo: https://gitlab.com/pycqa/flake8
+#     rev: 3.8.4
+#     hooks:
+#       - id: flake8
+#         additional_dependencies: ['hacking']
diff --git a/.travis.yml b/.travis.yml
deleted file mode 100644
index 3fd9f36..0000000
--- a/.travis.yml
+++ /dev/null
@@ -1,32 +0,0 @@
-language: python
-python:
-  - '3.5'
-  - '3.6'
-  - '3.7'
-  - '3.8'
-cache:
-  - pip
-  - apt
-addons:
-  apt:
-    packages: libjs-mathjax
-script:
-  - python -c "import graphtools"
-  - 'pip install -U .[test]'
-  - 'if [ "$TRAVIS_PYTHON_VERSION" != "3.5" ]; then black . --check --diff; fi'
-  - python setup.py test
-  - 'pip install -U .[doc]'
-  - cd doc; make html
-  - cd ..
-deploy:
-  provider: pypi
-  user: scottgigante
-  password: '${PYPI_PASSWORD}'
-  distributions: sdist bdist_wheel
-  skip_existing: true
-  cleanup: false
-  'on':
-    tags: true
-    branch: master
-after_success:
-  - coveralls
diff --git a/README.rst b/README.rst
index 133409a..62ae08f 100644
--- a/README.rst
+++ b/README.rst
@@ -8,9 +8,9 @@ graphtools
 .. image:: https://anaconda.org/conda-forge/graphtools/badges/version.svg
     :target: https://anaconda.org/conda-forge/graphtools/
     :alt: Latest Conda version
-.. image:: https://api.travis-ci.com/KrishnaswamyLab/graphtools.svg?branch=master
+.. image:: https://img.shields.io/github/workflow/status/KrishnaswamyLab/graphtools/Unit%20Tests/master?label=Github%20Actions
     :target: https://travis-ci.com/KrishnaswamyLab/graphtools
-    :alt: Travis CI Build
+    :alt: Github Actions Build
 .. image:: https://img.shields.io/readthedocs/graphtools.svg
     :target: https://graphtools.readthedocs.io/
     :alt: Read the Docs
diff --git a/autoblack.sh b/autoblack.sh
index cfbaf2b..3642ac8 100644
--- a/autoblack.sh
+++ b/autoblack.sh
@@ -11,4 +11,3 @@ for file in \$files; do
 done
 EOF
 chmod +x .git/hooks/pre-commit
-
diff --git a/doc/Makefile b/doc/Makefile
index acdb12e..c596553 100644
--- a/doc/Makefile
+++ b/doc/Makefile
@@ -17,4 +17,4 @@ help:
 # Catch-all target: route all unknown targets to Sphinx using the new
 # "make mode" option.  $(O) is meant as a shortcut for $(SPHINXOPTS).
 %: Makefile
-	@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
\ No newline at end of file
+	@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
diff --git a/graphtools/__init__.py b/graphtools/__init__.py
index 7384afc..0518e98 100644
--- a/graphtools/__init__.py
+++ b/graphtools/__init__.py
@@ -1,2 +1,4 @@
-from .api import Graph, from_igraph, read_pickle
+from .api import from_igraph
+from .api import Graph
+from .api import read_pickle
 from .version import __version__
diff --git a/graphtools/api.py b/graphtools/api.py
index b9a4b1b..d61e512 100644
--- a/graphtools/api.py
+++ b/graphtools/api.py
@@ -1,11 +1,12 @@
-import numpy as np
-import warnings
+from . import base
+from . import graphs
 from scipy import sparse
+
+import numpy as np
 import pickle
 import pygsp
 import tasklogger
-
-from . import base, graphs
+import warnings
 
 _logger = tasklogger.get_tasklogger("graphtools")
 
@@ -36,7 +37,7 @@ def Graph(
     graphtype="auto",
     use_pygsp=False,
     initialize=True,
-    **kwargs
+    **kwargs,
 ):
     """Create a graph built on data.
 
@@ -255,7 +256,7 @@ def Graph(
         else:
             msg = msg + " and PyGSP inheritance"
 
-    _logger.debug(msg)
+    _logger.log_debug(msg)
 
     class_names = [p.__name__.replace("Graph", "") for p in parent_classes]
     try:
@@ -273,7 +274,7 @@ def Graph(
                 pass
 
     # build graph and return
-    _logger.debug(
+    _logger.log_debug(
         "Initializing {} with arguments {}".format(
             parent_classes,
             ", ".join(
diff --git a/graphtools/base.py b/graphtools/base.py
index 4789b8a..1ed702a 100644
--- a/graphtools/base.py
+++ b/graphtools/base.py
@@ -1,21 +1,23 @@
-from future.utils import with_metaclass
+from . import matrix
+from . import utils
 from builtins import super
 from copy import copy as shallow_copy
-import numpy as np
-import abc
-import pygsp
+from future.utils import with_metaclass
 from inspect import signature
-from sklearn.decomposition import PCA, TruncatedSVD
-from sklearn.preprocessing import normalize
-from sklearn.utils.graph import graph_shortest_path
 from scipy import sparse
-import warnings
+from scipy.sparse.csgraph import shortest_path
+from sklearn.decomposition import PCA
+from sklearn.decomposition import TruncatedSVD
+from sklearn.preprocessing import normalize
+
+import abc
 import numbers
+import numpy as np
 import pickle
+import pygsp
 import sys
 import tasklogger
-
-from . import matrix, utils
+import warnings
 
 _logger = tasklogger.get_tasklogger("graphtools")
 
@@ -173,7 +175,7 @@ def _parse_n_pca_threshold(self, data, n_pca, rank_threshold):
             n_pca = None
         elif n_pca is True:  # notify that we're going to estimate rank.
             n_pca = "auto"
-            _logger.info(
+            _logger.log_info(
                 "Estimating n_pca from matrix rank. "
                 "Supply an integer n_pca "
                 "for fixed amount."
@@ -237,7 +239,7 @@ def _reduce_data(self):
         if self.n_pca is not None and (
             self.n_pca == "auto" or self.n_pca < self.data.shape[1]
         ):
-            with _logger.task("PCA"):
+            with _logger.log_task("PCA"):
                 n_pca = self.data.shape[1] - 1 if self.n_pca == "auto" else self.n_pca
                 if sparse.issparse(self.data):
                     if (
@@ -269,7 +271,7 @@ def _reduce_data(self):
                             "maximum singular value {} "
                             "for the data matrix".format(threshold, smax)
                         )
-                    _logger.info(
+                    _logger.log_info(
                         "Using rank estimate of {} as n_pca".format(self.n_pca)
                     )
                     # reset the sklearn operator
@@ -292,8 +294,7 @@ def _reduce_data(self):
             return data_nu
 
     def get_params(self):
-        """Get parameters from this object
-        """
+        """Get parameters from this object"""
         return {"n_pca": self.n_pca, "random_state": self.random_state}
 
     def set_params(self, **params):
@@ -469,7 +470,7 @@ def __init__(
         anisotropy=0,
         gamma=None,
         initialize=True,
-        **kwargs
+        **kwargs,
     ):
         if gamma is not None:
             warnings.warn(
@@ -498,10 +499,10 @@ def __init__(
         self.anisotropy = anisotropy
 
         if initialize:
-            _logger.debug("Initializing kernel...")
+            _logger.log_debug("Initializing kernel...")
             self.K
         else:
-            _logger.debug("Not initializing kernel.")
+            _logger.log_debug("Not initializing kernel.")
         super().__init__(**kwargs)
 
     def _check_symmetrization(self, kernel_symm, theta):
@@ -556,18 +557,20 @@ def _build_kernel(self):
     def symmetrize_kernel(self, K):
         # symmetrize
         if self.kernel_symm == "+":
-            _logger.debug("Using addition symmetrization.")
+            _logger.log_debug("Using addition symmetrization.")
             K = (K + K.T) / 2
         elif self.kernel_symm == "*":
-            _logger.debug("Using multiplication symmetrization.")
+            _logger.log_debug("Using multiplication symmetrization.")
             K = K.multiply(K.T)
         elif self.kernel_symm == "mnn":
-            _logger.debug("Using mnn symmetrization (theta = {}).".format(self.theta))
+            _logger.log_debug(
+                "Using mnn symmetrization (theta = {}).".format(self.theta)
+            )
             K = self.theta * matrix.elementwise_minimum(K, K.T) + (
                 1 - self.theta
             ) * matrix.elementwise_maximum(K, K.T)
         elif self.kernel_symm is None:
-            _logger.debug("Using no symmetrization.")
+            _logger.log_debug("Using no symmetrization.")
             pass
         else:
             raise NotImplementedError
@@ -589,8 +592,7 @@ def apply_anisotropy(self, K):
         return K
 
     def get_params(self):
-        """Get parameters from this object
-        """
+        """Get parameters from this object"""
         return {
             "kernel_symm": self.kernel_symm,
             "theta": self.theta,
@@ -697,8 +699,7 @@ def diff_aff(self):
 
     @property
     def diff_op(self):
-        """Synonym for P
-        """
+        """Synonym for P"""
         return self.P
 
     @property
@@ -719,8 +720,7 @@ def K(self):
 
     @property
     def kernel(self):
-        """Synonym for K
-        """
+        """Synonym for K"""
         return self.K
 
     @property
@@ -850,10 +850,10 @@ def _check_shortest_path_distance(self, distance):
     def _default_shortest_path_distance(self):
         if not self.weighted:
             distance = "data"
-            _logger.info("Using ambient data distances.")
+            _logger.log_info("Using ambient data distances.")
         else:
             distance = "affinity"
-            _logger.info("Using negative log affinity distances.")
+            _logger.log_info("Using negative log affinity distances.")
         return distance
 
     def shortest_path(self, method="auto", distance=None):
@@ -903,7 +903,7 @@ def shortest_path(self, method="auto", distance=None):
                 "Got {}".format(distance)
             )
 
-        P = graph_shortest_path(D, method=method)
+        P = shortest_path(D, method=method)
         # symmetrize for numerical error
         P = (P + P.T) / 2
         # sklearn returns 0 if no path exists
@@ -1019,8 +1019,7 @@ def __init__(self, data, verbose=True, n_jobs=1, **kwargs):
         super().__init__(data, **kwargs)
 
     def get_params(self):
-        """Get parameters from this object
-        """
+        """Get parameters from this object"""
         params = Data.get_params(self)
         params.update(BaseGraph.get_params(self))
         return params
diff --git a/graphtools/estimator.py b/graphtools/estimator.py
index 5cb130f..b072693 100644
--- a/graphtools/estimator.py
+++ b/graphtools/estimator.py
@@ -1,12 +1,15 @@
-import numpy as np
-import tasklogger
-import pygsp
-import abc
-
+from . import api
+from . import base
+from . import graphs
+from . import matrix
+from . import utils
 from functools import partial
 from scipy import sparse
 
-from . import api, graphs, base, utils, matrix
+import abc
+import numpy as np
+import pygsp
+import tasklogger
 
 
 def attribute(attr, default=None, doc=None, on_set=None):
@@ -81,18 +84,18 @@ class GraphEstimator(object, metaclass=abc.ABCMeta):
 
     verbose : `int` or `boolean`, optional (default: 1)
         If `True` or `> 0`, print status messages
-        
+
     n_svd : int, optional (default: 100)
         number of singular vectors to compute for landmarking
-    
+
     thresh : float, optional (default: 1e-4)
         threshold below which to truncate kernel
-    
+
     kwargs : additional arguments for graphtools.Graph
-    
+
     Attributes
     ----------
-    
+
     graph : graphtools.Graph
     """
 
@@ -203,7 +206,7 @@ def __init__(
         n_jobs=1,
         verbose=1,
         thresh=1e-4,
-        **kwargs
+        **kwargs,
     ):
 
         if verbose is True:
@@ -248,13 +251,13 @@ def _set_graph_params(self, **params):
                     )
                 self.graph.set_params(**params)
             except ValueError as e:
-                _logger.debug("Reset graph due to {}".format(str(e)))
+                _logger.log_debug("Reset graph due to {}".format(str(e)))
                 self.graph = None
 
     @abc.abstractmethod
     def _reset_graph(self):
         """Trigger a reset of self.graph
-        
+
         Any downstream effects of resetting the graph should override this function
         """
         raise NotImplementedError
@@ -358,10 +361,10 @@ def _update_graph(self, X, precomputed, n_pca, n_landmark, **kwargs):
                 n_jobs=self.n_jobs,
                 thresh=self.thresh,
                 verbose=self.verbose,
-                **(self.kwargs)
+                **(self.kwargs),
             )
             if self.graph is not None:
-                _logger.info("Using precomputed graph and diffusion operator...")
+                _logger.log_info("Using precomputed graph and diffusion operator...")
 
     def fit(self, X, **kwargs):
         """Computes the graph
@@ -384,13 +387,13 @@ def fit(self, X, **kwargs):
         X, n_pca, n_landmark, precomputed, update_graph = self._parse_input(X)
 
         if precomputed is None:
-            _logger.info(
+            _logger.log_info(
                 "Building graph on {} samples and {} features.".format(
                     X.shape[0], X.shape[1]
                 )
             )
         else:
-            _logger.info(
+            _logger.log_info(
                 "Building graph on precomputed {} matrix with {} samples.".format(
                     precomputed, X.shape[0]
                 )
@@ -402,7 +405,7 @@ def fit(self, X, **kwargs):
         self.X = X
 
         if self.graph is None:
-            with _logger.task("graph and diffusion operator"):
+            with _logger.log_task("graph and diffusion operator"):
                 self.graph = api.Graph(
                     X,
                     n_pca=n_pca,
@@ -417,6 +420,6 @@ def fit(self, X, **kwargs):
                     thresh=self.thresh,
                     verbose=self.verbose,
                     **(self.kwargs),
-                    **kwargs
+                    **kwargs,
                 )
         return self
diff --git a/graphtools/graphs.py b/graphtools/graphs.py
index 2caa431..8f512a9 100644
--- a/graphtools/graphs.py
+++ b/graphtools/graphs.py
@@ -1,19 +1,23 @@
 from __future__ import division
+
+from . import matrix
+from . import utils
+from .base import DataGraph
+from .base import PyGSPGraph
 from builtins import super
-import numpy as np
+from scipy import sparse
+from scipy.spatial.distance import cdist
+from scipy.spatial.distance import pdist
+from scipy.spatial.distance import squareform
+from sklearn.cluster import MiniBatchKMeans
 from sklearn.neighbors import NearestNeighbors
-from sklearn.utils.extmath import randomized_svd
 from sklearn.preprocessing import normalize
-from sklearn.cluster import MiniBatchKMeans
-from scipy.spatial.distance import pdist, cdist
-from scipy.spatial.distance import squareform
-from scipy import sparse
+from sklearn.utils.extmath import randomized_svd
+
 import numbers
-import warnings
+import numpy as np
 import tasklogger
-
-from . import matrix, utils
-from .base import DataGraph, PyGSPGraph
+import warnings
 
 _logger = tasklogger.get_tasklogger("graphtools")
 
@@ -76,7 +80,7 @@ def __init__(
         distance="euclidean",
         thresh=1e-4,
         n_pca=None,
-        **kwargs
+        **kwargs,
     ):
 
         if decay is not None:
@@ -132,8 +136,7 @@ def __init__(
         super().__init__(data, n_pca=n_pca, **kwargs)
 
     def get_params(self):
-        """Get parameters from this object
-        """
+        """Get parameters from this object"""
         params = super().get_params()
         params.update(
             {
@@ -347,19 +350,19 @@ def build_kernel_to_data(
 
         Y = self._check_extension_shape(Y)
         if self.decay is None or self.thresh == 1:
-            with _logger.task("KNN search"):
+            with _logger.log_task("KNN search"):
                 # binary connectivity matrix
                 K = self.knn_tree.kneighbors_graph(
                     Y, n_neighbors=knn, mode="connectivity"
                 )
         else:
-            with _logger.task("KNN search"):
+            with _logger.log_task("KNN search"):
                 # sparse fast alpha decay
                 knn_tree = self.knn_tree
                 search_knn = min(knn * self.search_multiplier, knn_max)
                 distances, indices = knn_tree.kneighbors(Y, n_neighbors=search_knn)
                 self._check_duplicates(distances, indices)
-            with _logger.task("affinities"):
+            with _logger.log_task("affinities"):
                 if bandwidth is None:
                     bandwidth = distances[:, knn - 1]
 
@@ -370,7 +373,7 @@ def build_kernel_to_data(
 
                 radius = bandwidth * np.power(-1 * np.log(self.thresh), 1 / self.decay)
                 update_idx = np.argwhere(np.max(distances, axis=1) < radius).reshape(-1)
-                _logger.debug(
+                _logger.log_debug(
                     "search_knn = {}; {} remaining".format(search_knn, len(update_idx))
                 )
                 if len(update_idx) > 0:
@@ -399,7 +402,7 @@ def build_kernel_to_data(
                             else radius[i]
                         )
                     ]
-                    _logger.debug(
+                    _logger.log_debug(
                         "search_knn = {}; {} remaining".format(
                             search_knn, len(update_idx)
                         )
@@ -412,7 +415,7 @@ def build_kernel_to_data(
                     ).fit(self.data_nu)
                 if len(update_idx) > 0:
                     if search_knn == knn_max:
-                        _logger.debug(
+                        _logger.log_debug(
                             "knn search to knn_max ({}) on {}".format(
                                 knn_max, len(update_idx)
                             )
@@ -425,7 +428,7 @@ def build_kernel_to_data(
                             distances[idx] = dist_new[i]
                             indices[idx] = ind_new[i]
                     else:
-                        _logger.debug("radius search on {}".format(len(update_idx)))
+                        _logger.log_debug("radius search on {}".format(len(update_idx)))
                         # give up - radius search
                         dist_new, ind_new = knn_tree.radius_neighbors(
                             Y[update_idx, :],
@@ -524,8 +527,7 @@ def __init__(self, data, n_landmark=2000, n_svd=100, **kwargs):
         super().__init__(data, **kwargs)
 
     def get_params(self):
-        """Get parameters from this object
-        """
+        """Get parameters from this object"""
         params = super().get_params()
         params.update({"n_landmark": self.n_landmark, "n_pca": self.n_pca})
         return params
@@ -653,19 +655,20 @@ def build_landmark_op(self):
         probabilities between cluster centers by using transition probabilities
         between samples assigned to each cluster.
         """
-        with _logger.task("landmark operator"):
+        with _logger.log_task("landmark operator"):
             is_sparse = sparse.issparse(self.kernel)
             # spectral clustering
-            with _logger.task("SVD"):
+            with _logger.log_task("SVD"):
                 _, _, VT = randomized_svd(
                     self.diff_aff,
                     n_components=self.n_svd,
                     random_state=self.random_state,
                 )
-            with _logger.task("KMeans"):
+            with _logger.log_task("KMeans"):
                 kmeans = MiniBatchKMeans(
                     self.n_landmark,
                     init_size=3 * self.n_landmark,
+                    n_init=1,
                     batch_size=10000,
                     random_state=self.random_state,
                 )
@@ -678,7 +681,8 @@ def build_landmark_op(self):
             pnm = pmn.transpose()
             pmn = normalize(pmn, norm="l1", axis=1)
             pnm = normalize(pnm, norm="l1", axis=1)
-            landmark_op = pmn.dot(pnm)  # sparsity agnostic matrix multiplication
+            # sparsity agnostic matrix multiplication
+            landmark_op = pmn.dot(pnm)
             if is_sparse:
                 # no need to have a sparse landmark operator
                 landmark_op = landmark_op.toarray()
@@ -834,7 +838,7 @@ def __init__(
         n_pca=None,
         thresh=1e-4,
         precomputed=None,
-        **kwargs
+        **kwargs,
     ):
         if decay is None and precomputed not in ["affinity", "adjacency"]:
             # decay high enough is basically a binary kernel
@@ -886,8 +890,7 @@ def __init__(
         super().__init__(data, n_pca=n_pca, **kwargs)
 
     def get_params(self):
-        """Get parameters from this object
-        """
+        """Get parameters from this object"""
         params = super().get_params()
         params.update(
             {
@@ -985,7 +988,7 @@ def build_kernel(self):
                 K = K.tolil()
             K = matrix.set_diagonal(K, 1)
         else:
-            with _logger.task("affinities"):
+            with _logger.log_task("affinities"):
                 if sparse.issparse(self.data_nu):
                     self.data_nu = self.data_nu.toarray()
                 if self.precomputed == "distance":
@@ -1091,7 +1094,7 @@ def build_kernel_to_data(self, Y, knn=None, bandwidth=None, bandwidth_scale=None
         if self.precomputed is not None:
             raise ValueError("Cannot extend kernel on precomputed graph")
         else:
-            with _logger.task("affinities"):
+            with _logger.log_task("affinities"):
                 Y = self._check_extension_shape(Y)
                 pdx = cdist(Y, self.data_nu, metric=self.distance)
                 if bandwidth is None:
@@ -1101,7 +1104,7 @@ def build_kernel_to_data(self, Y, knn=None, bandwidth=None, bandwidth_scale=None
                     bandwidth = bandwidth(pdx)
                 bandwidth = bandwidth_scale * bandwidth
                 pdx = (pdx.T / bandwidth).T
-                K = np.exp(-1 * pdx ** self.decay)
+                K = np.exp(-1 * pdx**self.decay)
                 # handle nan
                 K = np.where(np.isnan(K), 1, K)
                 K[K < self.thresh] = 0
@@ -1128,7 +1131,7 @@ def _check_shortest_path_distance(self, distance):
     def _default_shortest_path_distance(self):
         if self.precomputed is not None and not self.weighted:
             distance = "constant"
-            _logger.info("Using constant distances.")
+            _logger.log_info("Using constant distances.")
         else:
             distance = super()._default_shortest_path_distance()
         return distance
@@ -1178,7 +1181,7 @@ def __init__(
         distance="euclidean",
         thresh=1e-4,
         n_jobs=1,
-        **kwargs
+        **kwargs,
     ):
         self.beta = beta
         self.sample_idx = sample_idx
@@ -1222,8 +1225,7 @@ def _check_symmetrization(self, kernel_symm, theta):
             super()._check_symmetrization(kernel_symm, theta)
 
     def get_params(self):
-        """Get parameters from this object
-        """
+        """Get parameters from this object"""
         params = super().get_params()
         params.update(
             {
@@ -1296,13 +1298,13 @@ def build_kernel(self):
             symmetric matrix with ones down the diagonal
             with no non-negative entries.
         """
-        with _logger.task("subgraphs"):
+        with _logger.log_task("subgraphs"):
             self.subgraphs = []
             from .api import Graph
 
             # iterate through sample ids
             for i, idx in enumerate(self.samples):
-                _logger.debug(
+                _logger.log_debug(
                     "subgraph {}: sample {}, "
                     "n = {}, knn = {}".format(
                         i, idx, np.sum(self.sample_idx == idx), self.knn
@@ -1327,7 +1329,7 @@ def build_kernel(self):
                 )
                 self.subgraphs.append(graph)  # append to list of subgraphs
 
-        with _logger.task("MNN kernel"):
+        with _logger.log_task("MNN kernel"):
             if self.thresh > 0 or self.decay is None:
                 K = sparse.lil_matrix((self.data_nu.shape[0], self.data_nu.shape[0]))
             else:
@@ -1343,7 +1345,7 @@ def build_kernel(self):
                 for j, Y in enumerate(self.subgraphs):
                     if i == j:
                         continue
-                    with _logger.task(
+                    with _logger.log_task(
                         "kernel from sample {} to {}".format(
                             self.samples[i], self.samples[j]
                         )
diff --git a/graphtools/matrix.py b/graphtools/matrix.py
index 8c818f2..490155f 100644
--- a/graphtools/matrix.py
+++ b/graphtools/matrix.py
@@ -1,8 +1,8 @@
-import numpy as np
-import numbers
-
 from scipy import sparse
 
+import numbers
+import numpy as np
+
 
 def if_sparse(sparse_func, dense_func, *args, **kwargs):
     if sparse.issparse(args[0]):
diff --git a/graphtools/utils.py b/graphtools/utils.py
index 55e2bd3..96a992c 100644
--- a/graphtools/utils.py
+++ b/graphtools/utils.py
@@ -1,7 +1,8 @@
+from . import matrix
+from deprecated import deprecated
+
 import numbers
 import warnings
-from deprecated import deprecated
-from . import matrix
 
 try:
     import pandas as pd
diff --git a/graphtools/version.py b/graphtools/version.py
index 5197c5f..a06ff4e 100644
--- a/graphtools/version.py
+++ b/graphtools/version.py
@@ -1 +1 @@
-__version__ = "1.5.2"
+__version__ = "1.5.3"
diff --git a/setup.cfg b/setup.cfg
index 6372926..c4ba0c6 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -1,2 +1,29 @@
 [metadata]
 license-file = LICENSE
+
+[flake8]
+ignore =
+    # top-level module docstring
+    D100, D104,
+    # space before: conflicts with black
+    E203,
+    # import not in alphabetical: conflicts with isort
+    H306
+per-file-ignores =
+    # imported but unused
+    __init__.py: F401
+    # missing docstring in public function for methods, metrics, datasets
+    openproblems/tasks/*/*/*.py: D103, E203
+    openproblems/tasks/*/*/__init__.py: F401, D103
+max-line-length = 88
+exclude =
+    .git,
+    __pycache__,
+    build,
+    dist,
+    Snakefile
+
+[isort]
+profile = black
+force_single_line = true
+force_alphabetical_sort = true
diff --git a/setup.py b/setup.py
index 0586273..e548184 100644
--- a/setup.py
+++ b/setup.py
@@ -1,6 +1,7 @@
+from setuptools import setup
+
 import os
 import sys
-from setuptools import setup
 
 install_requires = [
     "numpy>=1.14.0",
@@ -44,7 +45,9 @@
     description="graphtools",
     author="Scott Gigante, Daniel Burkhardt, and Jay Stanley, Yale University",
     author_email="scott.gigante@yale.edu",
-    packages=["graphtools",],
+    packages=[
+        "graphtools",
+    ],
     license="GNU General Public License Version 2",
     install_requires=install_requires,
     extras_require={"test": test_requires, "doc": doc_requires},
@@ -54,7 +57,12 @@
     download_url="https://github.com/KrishnaswamyLab/graphtools/archive/v{}.tar.gz".format(
         version
     ),
-    keywords=["graphs", "big-data", "signal processing", "manifold-learning",],
+    keywords=[
+        "graphs",
+        "big-data",
+        "signal processing",
+        "manifold-learning",
+    ],
     classifiers=[
         "Development Status :: 4 - Beta",
         "Environment :: Console",
diff --git a/test/load_tests/__init__.py b/test/load_tests/__init__.py
index 1c6213f..8e8765c 100644
--- a/test/load_tests/__init__.py
+++ b/test/load_tests/__init__.py
@@ -1,16 +1,20 @@
-from sklearn.decomposition import PCA, TruncatedSVD
+from nose.tools import assert_raises_regex
+from nose.tools import assert_warns_regex
+from scipy.spatial.distance import cdist
+from scipy.spatial.distance import pdist
+from scipy.spatial.distance import squareform
 from sklearn import datasets
-from scipy.spatial.distance import pdist, cdist, squareform
-import pygsp
+from sklearn.decomposition import PCA
+from sklearn.decomposition import TruncatedSVD
+
 import graphtools
+import nose2
 import numpy as np
-import scipy.sparse as sp
-import warnings
 import pandas as pd
-
-import nose2
-from nose.tools import assert_raises_regex, assert_warns_regex
+import pygsp
 import re
+import scipy.sparse as sp
+import warnings
 
 
 def assert_warns_message(expected_warning, expected_message, *args, **kwargs):
@@ -110,7 +114,7 @@ def build_graph(
     sparse=False,
     graph_class=graphtools.Graph,
     verbose=0,
-    **kwargs
+    **kwargs,
 ):
     if sparse:
         data = sp.coo_matrix(data)
@@ -122,5 +126,5 @@ def build_graph(
         knn=knn,
         random_state=42,
         verbose=verbose,
-        **kwargs
+        **kwargs,
     )
diff --git a/test/test_api.py b/test/test_api.py
index 64d5ae9..1ff7dfe 100644
--- a/test/test_api.py
+++ b/test/test_api.py
@@ -1,13 +1,16 @@
 from __future__ import print_function
-from load_tests import data, build_graph, assert_raises_message, assert_warns_message
 
+from load_tests import assert_raises_message
+from load_tests import assert_warns_message
+from load_tests import build_graph
+from load_tests import data
 
+import graphtools
 import igraph
 import numpy as np
-import graphtools
-import tempfile
 import os
 import pickle
+import tempfile
 
 
 def test_from_igraph():
diff --git a/test/test_data.py b/test/test_data.py
index 24f6dd2..09e34cb 100644
--- a/test/test_data.py
+++ b/test/test_data.py
@@ -1,16 +1,16 @@
 from __future__ import print_function
-from load_tests import (
-    np,
-    sp,
-    pd,
-    graphtools,
-    nose2,
-    data,
-    build_graph,
-    squareform,
-    pdist,
-)
-from load_tests import assert_raises_message, assert_warns_message
+
+from load_tests import assert_raises_message
+from load_tests import assert_warns_message
+from load_tests import build_graph
+from load_tests import data
+from load_tests import graphtools
+from load_tests import nose2
+from load_tests import np
+from load_tests import pd
+from load_tests import pdist
+from load_tests import sp
+from load_tests import squareform
 from nose.tools import assert_raises_regex
 
 import numbers
@@ -219,7 +219,7 @@ def test_anndata():
     except NameError:
         # not installed
         return
-    G = build_graph(anndata.AnnData(data))
+    G = build_graph(anndata.AnnData(data, dtype=data.dtype))
     assert isinstance(G, graphtools.base.BaseGraph)
     assert isinstance(G.data, np.ndarray)
 
@@ -230,7 +230,7 @@ def test_anndata_sparse():
     except NameError:
         # not installed
         return
-    G = build_graph(anndata.AnnData(sp.csr_matrix(data)))
+    G = build_graph(anndata.AnnData(sp.csr_matrix(data), dtype=data.dtype))
     assert isinstance(G, graphtools.base.BaseGraph)
     assert isinstance(G.data, sp.csr_matrix)
 
diff --git a/test/test_estimator.py b/test/test_estimator.py
index c66aceb..f3120cb 100644
--- a/test/test_estimator.py
+++ b/test/test_estimator.py
@@ -1,12 +1,14 @@
+from load_tests import assert_raises_message
+from load_tests import data
+from parameterized import parameterized
+from scipy import sparse
+
+import anndata
 import graphtools
 import graphtools.estimator
+import numpy as np
 import pygsp
-import anndata
 import warnings
-import numpy as np
-from load_tests import data, assert_raises_message
-from scipy import sparse
-from parameterized import parameterized
 
 
 class Estimator(graphtools.estimator.GraphEstimator):
@@ -97,7 +99,7 @@ def test_anndata_input():
     E = Estimator(verbose=0)
     E.fit(X.astype(np.float32))
     E2 = Estimator(verbose=0)
-    E2.fit(anndata.AnnData(X))
+    E2.fit(anndata.AnnData(X, dtype=X.dtype))
     np.testing.assert_allclose(
         E.graph.K.toarray(), E2.graph.K.toarray(), rtol=1e-6, atol=2e-7
     )
diff --git a/test/test_exact.py b/test/test_exact.py
index 07faab0..0044359 100644
--- a/test/test_exact.py
+++ b/test/test_exact.py
@@ -1,21 +1,20 @@
 from __future__ import print_function
-from sklearn.utils.graph import graph_shortest_path
-from load_tests import (
-    graphtools,
-    np,
-    sp,
-    pygsp,
-    nose2,
-    data,
-    build_graph,
-    squareform,
-    pdist,
-    PCA,
-    TruncatedSVD,
-    assert_raises_message,
-    assert_warns_message,
-)
+
+from load_tests import assert_raises_message
+from load_tests import assert_warns_message
+from load_tests import build_graph
+from load_tests import data
+from load_tests import graphtools
+from load_tests import nose2
+from load_tests import np
+from load_tests import PCA
+from load_tests import pdist
+from load_tests import pygsp
+from load_tests import sp
+from load_tests import squareform
+from load_tests import TruncatedSVD
 from nose.tools import assert_warns_regex
+from scipy.sparse.csgraph import shortest_path
 
 #####################################################
 # Check parameters
@@ -141,7 +140,7 @@ def test_exact_graph():
     knn_dist = np.partition(pdx, k, axis=1)[:, :k]
     epsilon = np.max(knn_dist, axis=1) * bandwidth_scale
     weighted_pdx = (pdx.T / epsilon).T
-    K = np.exp(-1 * weighted_pdx ** a)
+    K = np.exp(-1 * weighted_pdx**a)
     W = K + K.T
     W = np.divide(W, 2)
     np.fill_diagonal(W, 0)
@@ -218,7 +217,7 @@ def test_truncated_exact_graph():
     knn_dist = np.partition(pdx, k, axis=1)[:, :k]
     epsilon = np.max(knn_dist, axis=1)
     weighted_pdx = (pdx.T / epsilon).T
-    K = np.exp(-1 * weighted_pdx ** a)
+    K = np.exp(-1 * weighted_pdx**a)
     K[K < thresh] = 0
     W = K + K.T
     W = np.divide(W, 2)
@@ -289,7 +288,7 @@ def test_truncated_exact_graph_sparse():
     knn_dist = np.partition(pdx, k, axis=1)[:, :k]
     epsilon = np.max(knn_dist, axis=1)
     weighted_pdx = (pdx.T / epsilon).T
-    K = np.exp(-1 * weighted_pdx ** a)
+    K = np.exp(-1 * weighted_pdx**a)
     K[K < thresh] = 0
     W = K + K.T
     W = np.divide(W, 2)
@@ -360,7 +359,7 @@ def test_truncated_exact_graph_no_pca():
     knn_dist = np.partition(pdx, k, axis=1)[:, :k]
     epsilon = np.max(knn_dist, axis=1)
     weighted_pdx = (pdx.T / epsilon).T
-    K = np.exp(-1 * weighted_pdx ** a)
+    K = np.exp(-1 * weighted_pdx**a)
     K[K < thresh] = 0
     W = K + K.T
     W = np.divide(W, 2)
@@ -525,7 +524,7 @@ def test_exact_graph_anisotropy():
     knn_dist = np.partition(pdx, k, axis=1)[:, :k]
     epsilon = np.max(knn_dist, axis=1)
     weighted_pdx = (pdx.T / epsilon).T
-    K = np.exp(-1 * weighted_pdx ** a)
+    K = np.exp(-1 * weighted_pdx**a)
     K = K + K.T
     K = np.divide(K, 2)
     d = K.sum(1)
@@ -590,30 +589,36 @@ def test_exact_graph_anisotropy():
 
 
 def test_shortest_path_affinity():
+    np.random.seed(42)
     data_small = data[np.random.choice(len(data), len(data) // 4, replace=False)]
     G = build_graph(data_small, knn=5, decay=15)
     D = -1 * np.where(G.K != 0, np.log(np.where(G.K != 0, G.K, np.nan)), 0)
-    P = graph_shortest_path(D)
+    P = shortest_path(D)
     # sklearn returns 0 if no path exists
     P[np.where(P == 0)] = np.inf
     # diagonal should actually be zero
     np.fill_diagonal(P, 0)
-    np.testing.assert_allclose(P, G.shortest_path(distance="affinity"))
-    np.testing.assert_allclose(P, G.shortest_path())
+    np.testing.assert_allclose(
+        P, G.shortest_path(distance="affinity"), atol=1e-4, rtol=1e-3
+    )
+    np.testing.assert_allclose(P, G.shortest_path(), atol=1e-4, rtol=1e-3)
 
 
 def test_shortest_path_affinity_precomputed():
+    np.random.seed(42)
     data_small = data[np.random.choice(len(data), len(data) // 4, replace=False)]
     G = build_graph(data_small, knn=5, decay=15)
     G = graphtools.Graph(G.K, precomputed="affinity")
     D = -1 * np.where(G.K != 0, np.log(np.where(G.K != 0, G.K, np.nan)), 0)
-    P = graph_shortest_path(D)
+    P = shortest_path(D)
     # sklearn returns 0 if no path exists
     P[np.where(P == 0)] = np.inf
     # diagonal should actually be zero
     np.fill_diagonal(P, 0)
-    np.testing.assert_allclose(P, G.shortest_path(distance="affinity"))
-    np.testing.assert_allclose(P, G.shortest_path())
+    np.testing.assert_allclose(
+        P, G.shortest_path(distance="affinity"), atol=1e-4, rtol=1e-3
+    )
+    np.testing.assert_allclose(P, G.shortest_path(), atol=1e-4, rtol=1e-3)
 
 
 def test_shortest_path_decay_constant():
diff --git a/test/test_knn.py b/test/test_knn.py
index fe47c07..9dae5f9 100644
--- a/test/test_knn.py
+++ b/test/test_knn.py
@@ -1,21 +1,24 @@
-from __future__ import print_function, division
-from sklearn.utils.graph import graph_shortest_path
-from scipy.spatial.distance import pdist, squareform
-from load_tests import assert_raises_message, assert_warns_message
-from nose.tools import assert_raises_regex, assert_warns_regex
-import warnings
-from load_tests import (
-    graphtools,
-    np,
-    sp,
-    pygsp,
-    data,
-    datasets,
-    build_graph,
-    PCA,
-    TruncatedSVD,
-)
+from __future__ import division
+from __future__ import print_function
+
+from load_tests import assert_raises_message
+from load_tests import assert_warns_message
+from load_tests import build_graph
+from load_tests import data
+from load_tests import datasets
+from load_tests import graphtools
+from load_tests import np
+from load_tests import PCA
+from load_tests import pygsp
+from load_tests import sp
+from load_tests import TruncatedSVD
+from nose.tools import assert_raises_regex
+from nose.tools import assert_warns_regex
+from scipy.sparse.csgraph import shortest_path
+from scipy.spatial.distance import pdist
+from scipy.spatial.distance import squareform
 
+import warnings
 
 #####################################################
 # Check parameters
@@ -51,7 +54,7 @@ def test_duplicate_data():
         RuntimeWarning,
         r"Detected zero distance between samples ([0-9and,\s]*). Consider removing duplicates to avoid errors in downstream processing.",
     ):
-        build_graph(np.vstack([data, data[:9]]), n_pca=20, decay=10, thresh=1e-4)
+        build_graph(np.vstack([data, data[:9]]), n_pca=None, decay=10, thresh=1e-4)
 
 
 def test_duplicate_data_many():
@@ -59,7 +62,7 @@ def test_duplicate_data_many():
         RuntimeWarning,
         "Detected zero distance between ([0-9]*) pairs of samples. Consider removing duplicates to avoid errors in downstream processing.",
     ):
-        build_graph(np.vstack([data, data[:21]]), n_pca=20, decay=10, thresh=1e-4)
+        build_graph(np.vstack([data, data[:21]]), n_pca=None, decay=10, thresh=1e-4)
 
 
 def test_balltree_cosine():
@@ -156,7 +159,8 @@ def test_knn_graph():
         ),
     ):
         G2.build_kernel_to_data(
-            Y=G2.data_nu, knn=data.shape[0] + 1,
+            Y=G2.data_nu,
+            knn=data.shape[0] + 1,
         )
 
 
@@ -232,7 +236,7 @@ def test_sparse_alpha_knn_graph():
     knn_dist = np.partition(pdx, k, axis=1)[:, :k]
     epsilon = np.max(knn_dist, axis=1) * bandwidth_scale
     pdx = (pdx.T / epsilon).T
-    K = np.exp(-1 * pdx ** a)
+    K = np.exp(-1 * pdx**a)
     K = K + K.T
     W = np.divide(K, 2)
     np.fill_diagonal(W, 0)
@@ -278,7 +282,7 @@ def test_knnmax():
     knn_max_dist = np.max(np.partition(pdx, k_max, axis=1)[:, :k_max], axis=1)
     epsilon = np.max(knn_dist, axis=1)
     pdx_scale = (pdx.T / epsilon).T
-    K = np.where(pdx <= knn_max_dist[:, None], np.exp(-1 * pdx_scale ** a), 0)
+    K = np.where(pdx <= knn_max_dist[:, None], np.exp(-1 * pdx_scale**a), 0)
     K = K + K.T
     W = np.divide(K, 2)
     np.fill_diagonal(W, 0)
@@ -430,7 +434,7 @@ def test_knn_graph_anisotropy():
     knn_dist = np.partition(pdx, k, axis=1)[:, :k]
     epsilon = np.max(knn_dist, axis=1)
     weighted_pdx = (pdx.T / epsilon).T
-    K = np.exp(-1 * weighted_pdx ** a)
+    K = np.exp(-1 * weighted_pdx**a)
     K[K < thresh] = 0
     K = K + K.T
     K = np.divide(K, 2)
@@ -525,7 +529,7 @@ def test_knn_interpolate_wrong_shape():
 def test_shortest_path_constant():
     data_small = data[np.random.choice(len(data), len(data) // 4, replace=False)]
     G = build_graph(data_small, knn=5, decay=None)
-    P = graph_shortest_path(G.K)
+    P = shortest_path(G.K)
     # sklearn returns 0 if no path exists
     P[np.where(P == 0)] = np.inf
     # diagonal should actually be zero
@@ -537,7 +541,7 @@ def test_shortest_path_precomputed_constant():
     data_small = data[np.random.choice(len(data), len(data) // 4, replace=False)]
     G = build_graph(data_small, knn=5, decay=None)
     G = graphtools.Graph(G.K, precomputed="affinity")
-    P = graph_shortest_path(G.K)
+    P = shortest_path(G.K)
     # sklearn returns 0 if no path exists
     P[np.where(P == 0)] = np.inf
     # diagonal should actually be zero
@@ -550,7 +554,7 @@ def test_shortest_path_data():
     data_small = data[np.random.choice(len(data), len(data) // 4, replace=False)]
     G = build_graph(data_small, knn=5, decay=None)
     D = squareform(pdist(G.data_nu)) * np.where(G.K.toarray() > 0, 1, 0)
-    P = graph_shortest_path(D)
+    P = shortest_path(D)
     # sklearn returns 0 if no path exists
     P[np.where(P == 0)] = np.inf
     # diagonal should actually be zero
diff --git a/test/test_landmark.py b/test/test_landmark.py
index aa2f9cc..489d864 100644
--- a/test/test_landmark.py
+++ b/test/test_landmark.py
@@ -1,17 +1,16 @@
 from __future__ import print_function
-from load_tests import (
-    graphtools,
-    np,
-    nose2,
-    data,
-    digits,
-    build_graph,
-    generate_swiss_roll,
-    assert_raises_message,
-    assert_warns_message,
-)
-import pygsp
 
+from load_tests import assert_raises_message
+from load_tests import assert_warns_message
+from load_tests import build_graph
+from load_tests import data
+from load_tests import digits
+from load_tests import generate_swiss_roll
+from load_tests import graphtools
+from load_tests import nose2
+from load_tests import np
+
+import pygsp
 
 #####################################################
 # Check parameters
@@ -68,13 +67,17 @@ def test_landmark_exact_graph():
 
 
 def test_landmark_knn_graph():
+    np.random.seed(42)
     n_landmark = 500
     # knn graph
     G = build_graph(
         data, n_landmark=n_landmark, n_pca=20, decay=None, knn=5 - 1, random_state=42
     )
-    assert G.transitions.shape == (data.shape[0], n_landmark)
-    assert G.landmark_op.shape == (n_landmark, n_landmark)
+    n_landmark_out = G.landmark_op.shape[0]
+    assert n_landmark_out <= n_landmark
+    assert n_landmark_out >= n_landmark - 3
+    assert G.transitions.shape == (data.shape[0], n_landmark_out), G.transitions.shape
+    assert G.landmark_op.shape == (n_landmark_out, n_landmark_out)
     assert isinstance(G, graphtools.graphs.kNNGraph)
     assert isinstance(G, graphtools.graphs.LandmarkGraph)
 
diff --git a/test/test_matrix.py b/test/test_matrix.py
index aac45a7..41cf757 100644
--- a/test/test_matrix.py
+++ b/test/test_matrix.py
@@ -1,11 +1,12 @@
-import graphtools.matrix
-import graphtools.utils
+from load_tests import assert_warns_message
+from load_tests import data
 from parameterized import parameterized
 from scipy import sparse
-import numpy as np
+
 import graphtools
-from load_tests import data
-from load_tests import assert_warns_message
+import graphtools.matrix
+import graphtools.utils
+import numpy as np
 
 
 @parameterized(
diff --git a/test/test_mnn.py b/test/test_mnn.py
index 30e3a77..592b227 100644
--- a/test/test_mnn.py
+++ b/test/test_mnn.py
@@ -1,21 +1,20 @@
 from __future__ import print_function
-import warnings
-from load_tests import (
-    graphtools,
-    np,
-    pd,
-    pygsp,
-    nose2,
-    data,
-    digits,
-    build_graph,
-    generate_swiss_roll,
-    assert_raises_message,
-    assert_warns_message,
-    cdist,
-)
+
+from load_tests import assert_raises_message
+from load_tests import assert_warns_message
+from load_tests import build_graph
+from load_tests import cdist
+from load_tests import data
+from load_tests import digits
+from load_tests import generate_swiss_roll
+from load_tests import graphtools
+from load_tests import nose2
+from load_tests import np
+from load_tests import pd
+from load_tests import pygsp
 from scipy.linalg import norm
 
+import warnings
 
 #####################################################
 # Check parameters
@@ -420,7 +419,7 @@ def test_mnn_graph_decay():
             kdx_ij = np.sort(pdx_ij, axis=1)  # get kNN
             e_ij = kdx_ij[:, batch_k]  # dist to kNN
             pdxe_ij = pdx_ij / e_ij[:, np.newaxis]  # normalize
-            k_ij = np.exp(-1 * (pdxe_ij ** a))  # apply alpha-decaying kernel
+            k_ij = np.exp(-1 * (pdxe_ij**a))  # apply alpha-decaying kernel
             if si == sj:
                 K.iloc[sample_idx == si, sample_idx == sj] = (k_ij + k_ij.T) / 2
             else:
diff --git a/test/test_utils.py b/test/test_utils.py
index 1aadd82..da379b4 100644
--- a/test/test_utils.py
+++ b/test/test_utils.py
@@ -1,6 +1,7 @@
-import graphtools
 from load_tests import assert_raises_message
 
+import graphtools
+
 
 def test_check_in():
     graphtools.utils.check_in(["hello", "world"], foo="hello")