setup pre-commit

.github/workflows/examples.yml → .github/workflows/main.yml

@@ -1,14 +1,30 @@
-name: Check Examples
+name: Tests
 
-on: [pull_request]
+on: [push, pull_request]
 
 jobs:
-  run:
+  Linting:
+    runs-on: ubuntu-latest
+
+    steps:
+      - uses: actions/checkout@v1
+      - name: Set up Python 3.7
+        uses: actions/setup-python@v1
+        with:
+          python-version: 3.7
+      - name: Linting
+        run: |
+          pip install pre-commit
+          pre-commit run --all-files
+
+  Linux:
+    needs: Linting
     runs-on: ubuntu-latest
     strategy:
       max-parallel: 4
       matrix:
         python-version: [3.6, 3.7, 3.8]
+
     steps:
     - uses: actions/checkout@v1
     - name: Set up Python ${{ matrix.python-version }}
@@ -18,12 +34,14 @@ jobs:
     - name: Install dependencies
       run: |
         pip install -r requirements-dev.txt
-        pip install papermill
-    - name: Execute notebooks with papermill
+    - name: Test with pytest
       run: |
+        py.test test --cov=pysindy
+    - name: Execute feature notebook with papermill
+      run: |
+        pip install papermill
         cd example
         papermill --report-mode feature_overview.ipynb out.json
-        # for nb in *.ipynb; do papermill --report-mode $nb out.json; done
     - uses: actions/cache@v1
       with:
         path: ~/.cache/pip

.pre-commit-config.yaml

@@ -14,3 +14,8 @@ repos:
   rev: stable
   hooks:
     - id: black
+- repo: https://gitlab.com/pycqa/flake8
+  rev: master
+  hooks:
+    - id: flake8
+      args: ["--config=setup.cfg"]

README.rst

@@ -42,7 +42,11 @@ Community guidelines
 
 Contributing code
 ^^^^^^^^^^^^^^^^^
-We welcome contributions to PySINDy. To contribute a new feature please submit a pull request. To be accepted your code should conform to PEP8 (you may choose to use flake8 to test this before submitting your pull request). Your contributed code should pass all unit tests. Upon submission of a pull request, your code will be tested automatically, but you may also choose to test it yourself by running
+We welcome contributions to PySINDy. To contribute a new feature please submit a pull request. To be accepted your code should conform to PEP8 (you may choose to use flake8 to test this before submitting your pull request). Your contributed code should pass all unit tests. Upon submission of a pull request, your code will be linted and tested automatically, but you may also choose to list it yourself invoking
+
+``pre-commit -a -v``
+
+as well as test it yourself by running
 
 ``pytest``
 

docs/conf.py

@@ -7,7 +7,7 @@
 project = "pysindy"  # package name
 
 
-#### no need to edit below this line ##
+# no need to edit below this line
 
 copyright = f"{datetime.datetime.now().year}, {author}"
 

pysindy/__init__.py

@@ -1,4 +1,5 @@
-from pkg_resources import get_distribution, DistributionNotFound
+from pkg_resources import DistributionNotFound
+from pkg_resources import get_distribution
 
 try:
     __version__ = get_distribution(__name__).version

pysindy/differentiation/base.py

@@ -1,7 +1,6 @@
 """
 Base class for numerical differentiation methods
 """
-
 import abc
 
 from pysindy.utils.base import validate_input

pysindy/feature_library/__init__.py

@@ -1,4 +1,4 @@
+from .custom_library import CustomLibrary
 from .feature_library import BaseFeatureLibrary
-from .polynomial_library import PolynomialLibrary
 from .fourier_library import FourierLibrary
-from .custom_library import CustomLibrary
+from .polynomial_library import PolynomialLibrary

pysindy/feature_library/custom_library.py

@@ -1,11 +1,11 @@
-from pysindy.feature_library import BaseFeatureLibrary
+from itertools import combinations
+from itertools import combinations_with_replacement as combinations_w_r
 
+import numpy as np
 from sklearn.utils import check_array
 from sklearn.utils.validation import check_is_fitted
 
-import numpy as np
-from itertools import combinations
-from itertools import combinations_with_replacement as combinations_w_r
+from pysindy.feature_library import BaseFeatureLibrary
 
 
 class CustomLibrary(BaseFeatureLibrary):
@@ -23,9 +23,9 @@ class CustomLibrary(BaseFeatureLibrary):
         a variable name), and output a string depiction of the respective
         mathematical function applied to that variable. For example, if the
         first library function is sine, the name function might return
-        :math:`\sin(x)` given :math:`x` as input. The function_names list must be the
+        :math:`\\sin(x)` given :math:`x` as input. The function_names list must be the
         same length as library_functions. If no list of function names is
-        provided, defaults to using :math:`[ f_0(x),f_1(x), f_2(x), \ldots ]`.
+        provided, defaults to using :math:`[ f_0(x),f_1(x), f_2(x), \\ldots ]`.
 
     Attributes
     ----------

pysindy/feature_library/fourier_library.py

@@ -1,9 +1,8 @@
-from pysindy.feature_library import BaseFeatureLibrary
-
+import numpy as np
 from sklearn.utils import check_array
 from sklearn.utils.validation import check_is_fitted
 
-import numpy as np
+from pysindy.feature_library import BaseFeatureLibrary
 
 
 class FourierLibrary(BaseFeatureLibrary):
@@ -38,9 +37,7 @@ class FourierLibrary(BaseFeatureLibrary):
     def __init__(self, n_frequencies=1, include_sin=True, include_cos=True):
         super(FourierLibrary, self).__init__()
         if not (include_sin or include_cos):
-            raise ValueError(
-                "include_sin and include_cos cannot both be False"
-            )
+            raise ValueError("include_sin and include_cos cannot both be False")
         if n_frequencies < 1 or not isinstance(n_frequencies, int):
             raise ValueError("n_frequencies must be a positive integer")
         self.n_frequencies = n_frequencies
@@ -68,13 +65,9 @@ def get_feature_names(self, input_features=None):
         for i in range(self.n_frequencies):
             for feature in input_features:
                 if self.include_sin:
-                    feature_names.append(
-                        "sin(" + str(i + 1) + " " + feature + ")"
-                    )
+                    feature_names.append("sin(" + str(i + 1) + " " + feature + ")")
                 if self.include_cos:
-                    feature_names.append(
-                        "cos(" + str(i + 1) + " " + feature + ")"
-                    )
+                    feature_names.append("cos(" + str(i + 1) + " " + feature + ")")
         return feature_names
 
     def fit(self, X, y=None):

pysindy/feature_library/polynomial_library.py

@@ -1,11 +1,14 @@
+from itertools import chain
+from itertools import combinations
+from itertools import combinations_with_replacement as combinations_w_r
+
 import numpy as np
 from scipy import sparse
-from itertools import chain, combinations
-from itertools import combinations_with_replacement as combinations_w_r
+from sklearn.preprocessing import _csr_polynomial_expansion
 from sklearn.preprocessing import PolynomialFeatures
 from sklearn.utils import check_array
-from sklearn.utils.validation import check_is_fitted, FLOAT_DTYPES
-from sklearn.preprocessing import _csr_polynomial_expansion
+from sklearn.utils.validation import check_is_fitted
+from sklearn.utils.validation import FLOAT_DTYPES
 
 from pysindy.feature_library import BaseFeatureLibrary
 

pysindy/optimizers/__init__.py

@@ -1,5 +1,5 @@
 from .base import BaseOptimizer
-from .stlsq import STLSQ
-from .sr3 import SR3
-from .lasso import LASSO
 from .elastic_net import ElasticNet
+from .lasso import LASSO
+from .sr3 import SR3
+from .stlsq import STLSQ

pysindy/optimizers/base.py

@@ -1,28 +1,23 @@
 """
 Base class for SINDy optimizers.
 """
-
 import abc
 
 import numpy as np
 from scipy import sparse
 from sklearn.linear_model import LinearRegression
-from sklearn.utils.validation import check_X_y
 from sklearn.utils.extmath import safe_sparse_dot
+from sklearn.utils.validation import check_X_y
 
 
 def _rescale_data(X, y, sample_weight):
     """Rescale data so as to support sample_weight"""
     n_samples = X.shape[0]
     sample_weight = np.asarray(sample_weight)
     if sample_weight.ndim == 0:
-        sample_weight = np.full(
-            n_samples, sample_weight, dtype=sample_weight.dtype
-        )
+        sample_weight = np.full(n_samples, sample_weight, dtype=sample_weight.dtype)
     sample_weight = np.sqrt(sample_weight)
-    sw_matrix = sparse.dia_matrix(
-        (sample_weight, 0), shape=(n_samples, n_samples)
-    )
+    sw_matrix = sparse.dia_matrix((sample_weight, 0), shape=(n_samples, n_samples))
     X = safe_sparse_dot(sw_matrix, X)
     y = safe_sparse_dot(sw_matrix, y)
     return X, y
@@ -95,9 +90,7 @@ def fit(self, x_, y, sample_weight=None, **reduce_kws):
         -------
         self : returns an instance of self
         """
-        x_, y = check_X_y(
-            x_, y, accept_sparse=[], y_numeric=True, multi_output=False
-        )
+        x_, y = check_X_y(x_, y, accept_sparse=[], y_numeric=True, multi_output=False)
 
         x, y, X_offset, y_offset, X_scale = self._preprocess_data(
             x_,

pysindy/optimizers/elastic_net.py

@@ -40,12 +40,7 @@ class ElasticNet(BaseOptimizer):
     """
 
     def __init__(
-        self,
-        alpha=1.0,
-        l1_ratio=0.5,
-        max_iter=1000,
-        elastic_net_kw={},
-        **kwargs
+        self, alpha=1.0, l1_ratio=0.5, max_iter=1000, elastic_net_kw={}, **kwargs
     ):
         super(ElasticNet, self).__init__(**kwargs)
 

pysindy/optimizers/sr3.py

@@ -1,7 +1,8 @@
 import warnings
 
 import numpy as np
-from scipy.linalg import cho_factor, cho_solve
+from scipy.linalg import cho_factor
+from scipy.linalg import cho_solve
 from sklearn.exceptions import ConvergenceWarning
 
 from pysindy.optimizers import BaseOptimizer
@@ -54,13 +55,7 @@ class SR3(BaseOptimizer):
     """
 
     def __init__(
-        self,
-        threshold=0.1,
-        nu=1.0,
-        tol=1e-5,
-        thresholder="l0",
-        max_iter=30,
-        **kwargs
+        self, threshold=0.1, nu=1.0, tol=1e-5, thresholder="l0", max_iter=30, **kwargs
     ):
         super(SR3, self).__init__(**kwargs)
 
@@ -115,9 +110,7 @@ def _reduce(self, x, y):
 
         # Precompute some objects for upcoming least-squares solves.
         # Assumes that self.nu is fixed throughout optimization procedure.
-        cho = cho_factor(
-            np.dot(x.T, x) + np.diag(np.full(x.shape[1], 1.0 / self.nu))
-        )
+        cho = cho_factor(np.dot(x.T, x) + np.diag(np.full(x.shape[1], 1.0 / self.nu)))
         x_transpose_y = np.dot(x.T, y)
 
         for _ in range(self.max_iter):

pysindy/optimizers/stlsq.py

@@ -1,8 +1,8 @@
 import warnings
 
 import numpy as np
-from sklearn.linear_model import ridge_regression
 from sklearn.exceptions import ConvergenceWarning
+from sklearn.linear_model import ridge_regression
 
 from pysindy.optimizers import BaseOptimizer
 
@@ -42,9 +42,7 @@ class STLSQ(BaseOptimizer):
         weight vector have not been masked out.
     """
 
-    def __init__(
-        self, threshold=0.1, alpha=0.0, max_iter=20, ridge_kw=None, **kwargs
-    ):
+    def __init__(self, threshold=0.1, alpha=0.0, max_iter=20, ridge_kw=None, **kwargs):
         super(STLSQ, self).__init__(**kwargs)
 
         if threshold < 0:
@@ -108,9 +106,7 @@ def _reduce(self, x, y):
                 break
 
             coef = self._regress(x[:, ind], y)
-            coef, ind = self._sparse_coefficients(
-                n_features, ind, coef, self.threshold
-            )
+            coef, ind = self._sparse_coefficients(n_features, ind, coef, self.threshold)
 
             if sum(ind) == n_features_selected or self._no_change():
                 # could not (further) select important features