Fix code quality issues (#59)

* test first corrected file

* update tests to include/exclude

* fixes for remaining docstrings

* fix import orders

.landscape.yaml

@@ -1 +1,11 @@
-doc-warnings: yes
+strictness: medium
+test-warnings: false
+doc-warnings: true
+
+pep257:
+  disable:
+    - D211 # No blank lines allowed before class docstring
+    - D213 # Multi-line docstring summary should start at the second line
+  enable:
+    - D203 # 1 blank line required before class docstring
+    - D212 # Multi-line docstring summary should start at the first line

.travis.yml

@@ -7,7 +7,7 @@ env:
     - PYTHON_VERSION="2.7" SKLEARN_VERSION="0.18.1"
     - PYTHON_VERSION="3.4" LATEST="true"
     - PYTHON_VERSION="3.5" LATEST="true"
-    - PYTHON_VERSION="3.6" QUALITY="true" LATEST="true"
+    - PYTHON_VERSION="3.6" LATEST="true"
 install: source continuous_integration/install.sh
 script: bash continuous_integration/test_script.sh
 after_success:

continuous_integration/test_script.sh

@@ -13,14 +13,14 @@ python -c "import numpy; print('numpy %s' % numpy.__version__)"
 python -c "import scipy; print('scipy %s' % scipy.__version__)"
 python -c "import sklearn; print('sklearn %s' % sklearn.__version__)"
 
+if [[ "$QUALITY" == "true" ]]; then
+    prospector --profile $TRAVIS_BUILD_DIR/.landscape gplearn || true;
+fi
+
 if [[ "$COVERAGE" == "true" ]]; then
     nosetests -s -v --with-coverage --cover-package=gplearn
 else
     nosetests -s -v gplearn
 fi
 
-if [[ "$QUALITY" == "true" ]]; then
-    prospector --doc-warnings gplearn || true;
-fi
-
 #make test-doc test-sphinxext

gplearn/__init__.py

@@ -1,6 +1,7 @@
 """Genetic Programming in Python, with a scikit-learn inspired API
 
 ``gplearn`` is a set of algorithms for learning genetic programming models.
+
 """
 __version__ = '0.3.dev0'
 

gplearn/_program.py

@@ -9,10 +9,9 @@
 #
 # License: BSD 3 clause
 
-import numpy as np
-
 from copy import deepcopy
 
+import numpy as np
 from sklearn.utils.random import sample_without_replacement
 
 from .functions import _Function
@@ -104,6 +103,7 @@ class _Program(object):
 
     length_ : int
         The number of functions and terminals in the program.
+
     """
 
     def __init__(self,
@@ -156,9 +156,10 @@ def build_program(self, random_state):
         -------
         program : list
             The flattened tree representation of the program.
+
         """
         if self.init_method == 'half and half':
-            method = ['grow', 'full'][random_state.randint(2)]
+            method = ('full' if random_state.randint(2) else 'grow')
         else:
             method = self.init_method
         max_depth = random_state.randint(*self.init_depth)
@@ -169,7 +170,7 @@ def build_program(self, random_state):
         program = [function]
         terminal_stack = [function.arity]
 
-        while len(terminal_stack) != 0:
+        while terminal_stack:
             depth = len(terminal_stack)
             choice = self.n_features + len(self.function_set)
             choice = random_state.randint(choice)
@@ -189,7 +190,7 @@ def build_program(self, random_state):
                 terminal_stack[-1] -= 1
                 while terminal_stack[-1] == 0:
                     terminal_stack.pop()
-                    if len(terminal_stack) == 0:
+                    if not terminal_stack:
                         return program
                     terminal_stack[-1] -= 1
 
@@ -244,6 +245,7 @@ def export_graphviz(self, fade_nodes=None):
         -------
         output : string
             The Graphviz script to plot the tree representation of the program.
+
         """
         terminals = []
         if fade_nodes is None:
@@ -278,7 +280,7 @@ def export_graphviz(self, fade_nodes=None):
                     if len(terminals[-1]) == 2:
                         parent = terminals[-1][-1]
                         terminals.pop()
-                        if len(terminals) == 0:
+                        if not terminals:
                             return output + "}"
                         terminals[-1].append(parent)
                         terminals[-1][0] -= 1
@@ -318,6 +320,7 @@ def execute(self, X):
         -------
         y_hats : array-like, shape = [n_samples]
             The result of executing the program on X.
+
         """
         # Check for single-node programs
         node = self.program[0]
@@ -374,6 +377,7 @@ def get_all_indices(self, n_samples=None, max_samples=None,
 
         not_indices : array-like, shape = [n_samples]
             The out-of-sample indices.
+
         """
         if self._indices_state is None and random_state is None:
             raise ValueError('The program has not been evaluated for fitness '
@@ -421,6 +425,7 @@ def raw_fitness(self, X, y, sample_weight):
         -------
         raw_fitness : float
             The raw fitness of the program.
+
         """
         y_pred = self.execute(X)
         raw_fitness = self.metric(y, y_pred, sample_weight)
@@ -440,6 +445,7 @@ def fitness(self, parsimony_coefficient=None):
         -------
         fitness : float
             The penalized fitness of the program.
+
         """
         if parsimony_coefficient is None:
             parsimony_coefficient = self.parsimony_coefficient
@@ -462,6 +468,7 @@ def get_subtree(self, random_state, program=None):
         -------
         start, end : tuple of two ints
             The indices of the start and end of the random subtree.
+
         """
         if program is None:
             program = self.program
@@ -503,6 +510,7 @@ def crossover(self, donor, random_state):
         -------
         program : list
             The flattened tree representation of the program.
+
         """
         # Get a subtree to replace
         start, end = self.get_subtree(random_state)
@@ -535,6 +543,7 @@ def subtree_mutation(self, random_state):
         -------
         program : list
             The flattened tree representation of the program.
+
         """
         # Build a new naive program
         chicken = self.build_program(random_state)
@@ -558,6 +567,7 @@ def hoist_mutation(self, random_state):
         -------
         program : list
             The flattened tree representation of the program.
+
         """
         # Get a subtree to replace
         start, end = self.get_subtree(random_state)
@@ -587,6 +597,7 @@ def point_mutation(self, random_state):
         -------
         program : list
             The flattened tree representation of the program.
+
         """
         program = deepcopy(self.program)
 

gplearn/fitness.py

@@ -8,11 +8,11 @@
 #
 # License: BSD 3 clause
 
-import numpy as np
+import numbers
 
+import numpy as np
 from scipy.stats import rankdata
 from sklearn.externals import six
-import numbers
 
 __all__ = ['make_fitness']
 
@@ -37,6 +37,7 @@ class _Fitness(object):
         Whether a higher value from `function` indicates a better fit. In
         general this would be False for metrics indicating the magnitude of
         the error, and True for metrics indicating the quality of fit.
+
     """
 
     def __init__(self, function, greater_is_better):
@@ -70,6 +71,7 @@ def make_fitness(function, greater_is_better):
         Whether a higher value from `function` indicates a better fit. In
         general this would be False for metrics indicating the magnitude of
         the error, and True for metrics indicating the quality of fit.
+
     """
     if not isinstance(greater_is_better, bool):
         raise ValueError('greater_is_better must be bool, got %s'

gplearn/functions.py

@@ -10,7 +10,6 @@
 # License: BSD 3 clause
 
 import numpy as np
-
 from sklearn.externals import six
 
 __all__ = ['make_function']
@@ -35,6 +34,7 @@ class _Function(object):
 
     arity : int
         The number of arguments that the ``function`` takes.
+
     """
 
     def __init__(self, function, name, arity):
@@ -66,6 +66,7 @@ def make_function(function, name, arity):
 
     arity : int
         The number of arguments that the `function` takes.
+
     """
     if not isinstance(arity, int):
         raise ValueError('arity must be an int, got %s' % type(arity))

gplearn/genetic.py

@@ -9,27 +9,23 @@
 #
 # License: BSD 3 clause
 
-import numpy as np
 import itertools
-
 from abc import ABCMeta, abstractmethod
 from time import time
 from warnings import warn
 
+import numpy as np
 from scipy.stats import rankdata
-
 from sklearn.base import BaseEstimator, RegressorMixin, TransformerMixin
 from sklearn.externals import six
 from sklearn.externals.joblib import Parallel, delayed
 from sklearn.utils.validation import check_X_y, check_array
 
-from .utils import _partition_estimators
-from .utils import check_random_state, NotFittedError
-
+from ._program import _Program
 from .fitness import _fitness_map, _Fitness
 from .functions import _function_map, _Function
-
-from ._program import _Program
+from .utils import _partition_estimators
+from .utils import check_random_state, NotFittedError
 
 __all__ = ['SymbolicRegressor', 'SymbolicTransformer']
 
@@ -157,6 +153,7 @@ class BaseSymbolic(six.with_metaclass(ABCMeta, BaseEstimator)):
 
     Warning: This class should not be used directly.
     Use derived classes instead.
+
     """
 
     @abstractmethod
@@ -231,6 +228,7 @@ def _verbose_reporter(self,
 
         length : list
             The current population's lengths.
+
         """
         if start_time is None:
             print('%4s|%-25s|%-42s|' % (' ', 'Population Average'.center(25),
@@ -287,6 +285,7 @@ def fit(self, X, y, sample_weight=None):
         -------
         self : object
             Returns self.
+
         """
         random_state = check_random_state(self.random_state)
 
@@ -323,7 +322,7 @@ def fit(self, X, y, sample_weight=None):
             else:
                 raise ValueError('invalid type %s found in `function_set`.'
                                  % type(function))
-        if len(self._function_set) == 0:
+        if not self._function_set:
             raise ValueError('No valid functions found in `function_set`.')
 
         # For point-mutation to find a compatible replacement node
@@ -660,7 +659,7 @@ class SymbolicRegressor(BaseSymbolic, RegressorMixin):
         If None, the random number generator is the RandomState instance used
         by `np.random`.
 
-    See also
+    See Also
     --------
     SymbolicTransformer
 
@@ -669,6 +668,7 @@ class SymbolicRegressor(BaseSymbolic, RegressorMixin):
     .. [1] J. Koza, "Genetic Programming", 1992.
 
     .. [2] R. Poli, et al. "A Field Guide to Genetic Programming", 2008.
+
     """
 
     def __init__(self,
@@ -733,6 +733,7 @@ def predict(self, X):
         -------
         y : array, shape = [n_samples]
             Predicted values for X.
+
         """
         if not hasattr(self, "_program"):
             raise NotFittedError("SymbolicRegressor not fitted.")
@@ -913,7 +914,7 @@ class SymbolicTransformer(BaseSymbolic, TransformerMixin):
         If None, the random number generator is the RandomState instance used
         by `np.random`.
 
-    See also
+    See Also
     --------
     SymbolicRegressor
 
@@ -922,6 +923,7 @@ class SymbolicTransformer(BaseSymbolic, TransformerMixin):
     .. [1] J. Koza, "Genetic Programming", 1992.
 
     .. [2] R. Poli, et al. "A Field Guide to Genetic Programming", 2008.
+
     """
 
     def __init__(self,
@@ -1003,6 +1005,7 @@ def transform(self, X):
         -------
         X_new : array-like, shape = [n_samples, n_components]
             Transformed array.
+
         """
         if not hasattr(self, "_best_programs"):
             raise NotFittedError("SymbolicTransformer not fitted.")
@@ -1038,5 +1041,6 @@ def fit_transform(self, X, y, sample_weight=None):
         -------
         X_new : array-like, shape = [n_samples, n_components]
             Transformed array.
+
         """
         return self.fit(X, y, sample_weight).transform(X)

gplearn/tests/test_fitness.py

@@ -4,9 +4,9 @@
 #
 # License: BSD 3 clause
 
-from gplearn.fitness import make_fitness, _mean_square_error
 from sklearn.utils.testing import assert_raises
 
+from gplearn.fitness import make_fitness, _mean_square_error
 
 def test_validate_fitness():
     """Check that valid fitness measures are accepted & invalid raise error"""

gplearn/tests/test_functions.py

@@ -5,13 +5,11 @@
 # License: BSD 3 clause
 
 import numpy as np
-
 from numpy import maximum
+from sklearn.utils.testing import assert_raises
 
 from gplearn.functions import _protected_sqrt, make_function
 
-from sklearn.utils.testing import assert_raises
-
 
 def test_validate_function():
     """Check that valid functions are accepted & invalid ones raise error"""