pylint (#95)

gpfit/classes.py

@@ -1,3 +1,7 @@
+"""
+Implements the Max Affine, Softmax Affine, and Implicit Softmax Affine function
+classes
+"""
 import numpy as np
 from .logsumexp import lse_scaled, lse_implicit
 

gpfit/constraint_set.py

@@ -4,11 +4,10 @@
 from gpkit import Variable, NomialArray, NamedVariables, VectorVariable
 from gpkit.small_scripts import initsolwarning, appendsolwarning
 
+
 # pylint: disable=too-many-instance-attributes, too-many-locals,
 # pylint: disable=too-many-branches, no-member, import-error
-# pylint: disable=too-many-arguments
-
-
+# pylint: disable=too-many-arguments, import-outside-toplevel
 class FitConstraintSet(ConstraintSet):
     """
     Constraint set for fitted functions
@@ -104,7 +103,7 @@ def process_result(self, result):
         """
         make sure fit result is within bounds of fitted data
         """
-        super(FitConstraintSet, self).process_result(result)
+        super().process_result(result)
         initsolwarning(result, "Fit Out-of-Bounds")
 
         if self.mfac not in result["sensitivities"]["constants"]:

gpfit/fit.py

@@ -66,8 +66,8 @@ def residual(params):
         r = yhat - ydata
         return r, drdp
 
-    ydata_col = ydata.reshape(ydata.size, 1)
-    ba = get_initial_parameters(xdata, ydata_col, K).flatten("F")
+    ba = get_initial_parameters(xdata, ydata.reshape(ydata.size, 1),
+                                K).flatten("F")
     if ftype == "ISMA":
         params, _ = levenberg_marquardt(residual, hstack((ba, alpha0*ones(K))))
     elif ftype == "SMA":

gpfit/least_squares.py

@@ -6,7 +6,7 @@
 from scipy.sparse import spdiags, issparse
 
 
-# pylint: disable=too-many-locals,too-many-arguments,too-many-branches,too-many-statements
+# pylint: disable=too-many-locals,too-many-arguments,too-many-branches,too-many-statements,no-else-break
 def levenberg_marquardt(
     residfun,
     initparams,
@@ -63,7 +63,7 @@ def levenberg_marquardt(
     # Define display formatting if required
     formatstr1 = "%5.0f        %9.6g        %9.3g\n"
     formatstr = (
-        "%5.0f        %9.6g        %9.3g        " "%12.4g        %12.4g        %8.4g\n"
+        "%5.0f        %9.6g        %9.3g        %12.4g        %12.4g        %8.4g\n"
     )
 
     # Get residual values and jacobian at initial point; extract size info
@@ -102,12 +102,10 @@ def levenberg_marquardt(
 
     # Main Loop
     while True:
-
         if itr == maxiter:
             if verbose:
                 print("Reached maximum number of iterations")
             break
-
         elif time() - t > maxtime:
             if verbose:
                 print("Reached maxtime (%s seconds)" % maxtime)

gpfit/tests/t_constraint_set.py

@@ -24,7 +24,7 @@ def test_fit_constraint_set(self):
         sol = m.solve(verbosity=0)
         self.assertAlmostEqual(sol["cost"], 0.7777593)
 
-    def test_fit_constraint_set_outside_bounds(self):
+    def test_outside_bounds(self):
         m = Model(self.wvar, [self.fcs, self.uvar <= 4])
         sol = m.solve(verbosity=0)
         self.assertTrue(len(sol['warnings']["Fit Out-of-Bounds"]) == 1)

gpfit/tests/t_fit.py

@@ -1,9 +1,11 @@
-#unit tests for gpfit.fit module"
+"""unit tests for fit module"""
 import unittest
 from numpy import logspace, log10, log, vstack
 from gpfit.fit import fit
 
+
 class TestFit(unittest.TestCase):
+    """Test fit function"""
 
     u = logspace(0, log10(3), 501)
     w = (u**2 + 3)/(u + 1)**2
@@ -12,11 +14,11 @@ class TestFit(unittest.TestCase):
     K = 3
 
     def test_rms_error(self):
-        cstrt, rms_error = fit(self.x, self.y, self.K, "SMA")
+        _, rms_error = fit(self.x, self.y, self.K, "SMA")
         self.assertTrue(rms_error < 1e-4)
-        cstrt, rms_error = fit(self.x, self.y, self.K, "ISMA")
+        _, rms_error = fit(self.x, self.y, self.K, "ISMA")
         self.assertTrue(rms_error < 1e-5)
-        cstrt, rms_error = fit(self.x, self.y, self.K, "MA")
+        _, rms_error = fit(self.x, self.y, self.K, "MA")
         self.assertTrue(rms_error < 1e-2)
 
     def test_incorrect_inputs(self):

gpfit/tests/t_plot_fit.py

@@ -6,12 +6,12 @@
 
 class TestPlotFit(unittest.TestCase):
     "Unit tests for plot_fit_1d"
+    N = 51
+    u = np.logspace(0, np.log10(3), N)
+    w = (u**2 + 3)/(u + 1)**2
 
     def test_plot_fit_1d(self):
-        N = 51
-        U = np.logspace(0, np.log10(3), N)
-        W = (U**2+3)/(U+1)**2
-        plot_fit_1d(U, W, K=2, fitclass='SMA', plotspace="linear")
+        plot_fit_1d(self.u, self.w, K=2, fitclass='SMA', plotspace="linear")
 
 
 TESTS = [TestPlotFit]

setup.py

@@ -30,7 +30,7 @@
     author_email="gpkit@mit.edu",
     url="https://github.com/convexengineering/gpfit",
     python_requires=">=3.4",
-    install_requires=["numpy", "scipy", "gpkit"],
+    install_requires=["numpy", "scipy", "gpkit", "matplotlib"],
     version="0.1.0",
     packages=["gpfit", "gpfit.tests"],
     license=LICENSE,