Add doctests to setup.cfg (#491)

* Update setup.cfg

* fix docstests

* remove test, fix Min

* fix lint

* fix tests

* remove comment

featuretools/entityset/entity.py

@@ -202,8 +202,9 @@ def convert_variable_type(self, variable_id, new_type,
             RuntimeError : Raises if it cannot convert the underlying data
 
         Examples:
-            >>> es["customer"].convert_variable_type("education_level", vtypes.Categorical, EntitySet)
-                True
+            >>> from featuretools.tests.testing_utils import make_ecommerce_entityset
+            >>> es = make_ecommerce_entityset()
+            >>> es["customers"].convert_variable_type("engagement_level", vtypes.Categorical)
         """
         if convert_data:
             # first, convert the underlying data (or at least try to)

featuretools/entityset/entityset.py

@@ -123,10 +123,6 @@ def __getitem__(self, entity_id):
         Returns:
             :class:`.Entity` : Instance of entity. None if entity doesn't
                 exist.
-
-        Example:
-            >>> my_entityset[entity_id]
-            <Entity: id>
         """
         if entity_id in self.entity_dict:
             return self.entity_dict[entity_id]

featuretools/entityset/timedelta.py

@@ -30,8 +30,11 @@ class Timedelta(object):
 
     For observation timedeltas, a
 
-    >>> Timedelta(10, "s") # 10 seconds
-    >>> Timedelta(3, "observations", entity='log') # 3 observations of the log entity
+    >>> Timedelta(10, "s").value_in_seconds # 10 seconds
+    10.0
+    >>> three_observations_log = Timedelta(3, "observations", entity='log')
+    >>> three_observations_log.get_name()
+    '3 Observations'
     """
 
     _Observations = "o"

featuretools/primitives/api.py

@@ -1,4 +1,5 @@
 # flake8: noqa
+from .base import make_agg_primitive, make_trans_primitive
 from .standard import *
 from .utils import (
     get_aggregation_primitives,

featuretools/primitives/standard/aggregation_primitives.py

@@ -5,10 +5,7 @@
 import numpy as np
 import pandas as pd
 
-from ..base.aggregation_primitive_base import (
-    AggregationPrimitive,
-    make_agg_primitive
-)
+from ..base.aggregation_primitive_base import AggregationPrimitive
 
 from featuretools.variable_types import (
     Boolean,
@@ -97,13 +94,25 @@ def pd_mode(s):
         return pd_mode
 
 
-Min = make_agg_primitive(
-    np.min,
-    [Numeric],
-    Numeric,
-    name="Min",
-    stack_on_self=False,
-    description="Finds the minimum non-null value of a numeric feature.")
+class Min(AggregationPrimitive):
+    """Finds the minimum value of a numeric feature.
+
+    Description:
+        Given a list of values, return the minimum value.
+        Ignores `NaN` values.
+
+    Examples:
+        >>> min = Min()
+        >>> min([1, 2, 3, 4, 5, None])
+        1.0
+    """
+    name = "min"
+    input_types = [Numeric]
+    return_type = Numeric
+    stack_on_self = False
+
+    def get_function(self):
+        return np.min
 
 
 class Max(AggregationPrimitive):

featuretools/primitives/standard/transform_primitive.py

@@ -5,10 +5,7 @@
 import numpy as np
 import pandas as pd
 
-from ..base.transform_primitive_base import (
-    TransformPrimitive,
-    make_trans_primitive
-)
+from ..base.transform_primitive_base import TransformPrimitive
 
 from featuretools.variable_types import (
     Boolean,
@@ -196,17 +193,18 @@ def word_counter(array):
         return word_counter
 
 
-def pd_time_since(array, time):
+class TimeSince(TransformPrimitive):
     """Calculates time since the cutoff time."""
-    return (time - pd.DatetimeIndex(array)).values
-
+    name = 'time_since'
+    input_types = [[DatetimeTimeIndex], [Datetime]]
+    return_type = Timedelta
+    uses_calc_time = True
 
-TimeSince = make_trans_primitive(function=pd_time_since,
-                                 input_types=[[DatetimeTimeIndex], [Datetime]],
-                                 return_type=Timedelta,
-                                 uses_calc_time=True,
-                                 description="Calculates time since the cutoff time.",
-                                 name="time_since")
+    def get_function(self):
+        def pd_time_since(array, time):
+            """Calculates time since the cutoff time."""
+            return (time - pd.DatetimeIndex(array)).values
+        return pd_time_since
 
 
 class DaysSince(TransformPrimitive):

setup.cfg

@@ -3,7 +3,7 @@ description-file = README.md
 [aliases]
 test=pytest
 [tool:pytest]
-addopts = -m "not requires_training_data and not requires_credentials" --ignore "featuretools/tests/primitive_tests/primitives_to_install/featuretools_pip_tester/"
+addopts = -m "not requires_training_data and not requires_credentials" --ignore "featuretools/tests/primitive_tests/primitives_to_install/featuretools_pip_tester/" --doctest-modules
 python_files = featuretools/tests/*
 filterwarnings =
     ignore::DeprecationWarning