Add serialization test and fix NumUniqueSeparators

docs/source/changelog.rst

@@ -6,6 +6,7 @@ Future Release
 ==============
     * Enhancements
     * Fixes
+        * Fix ``NumUniqueSeparators`` to allow for serialization and deserialization (:pr:`122`)
     * Changes
         * Speed up LSA primitive initialization (:pr:`118`)
     * Documentation Changes

nlp_primitives/num_unique_separators.py

@@ -3,24 +3,24 @@
 from woodwork.column_schema import ColumnSchema
 from woodwork.logical_types import IntegerNullable, NaturalLanguage
 
-NATURAL_LANGUAGE_SEPARATORS = " .,!?;\n"
+NATURAL_LANGUAGE_SEPARATORS = [" ", ".", ",", "!", "?", ";", "\n"]
 
 
 class NumUniqueSeparators(TransformPrimitive):
     """Calculates the number of unique separators.
 
     Description:
-        Given a string and an iterable of separators, determine
+        Given a string and a list of separators, determine
         the number of unique separators in each string. If a string
         is null determined by pd.isnull return pd.NA.
 
     Args:
-        separators (str, optional): an iterable of characters to count.
-            " .,!?;\n" is used by default.
+        separators (list, optional): a list of separator characters to count.
+            `[`" ", ".", ",", "!", "?", ";", "\n"]` is used by default.
 
     Examples:
-        >>> x = ['First. Line.', 'This. is the second, line!', 'notinlist@#$%^%&']
-        >>> num_unique_separators = NumUniqueSeparators(".,!")
+        >>> x = ["First. Line.", "This. is the second, line!", "notinlist@#$%^%&"]
+        >>> num_unique_separators = NumUniqueSeparators([".", ",", "!"])
         >>> num_unique_separators(x).tolist()
         [1, 3, 0]
     """
@@ -31,13 +31,13 @@ class NumUniqueSeparators(TransformPrimitive):
 
     def __init__(self, separators=NATURAL_LANGUAGE_SEPARATORS):
         assert separators is not None, "separators needs to be defined"
-        self.separators = set(separators)
+        self.separators = separators
 
     def get_function(self):
         def count_unique_separator(s):
             if pd.isnull(s):
                 return pd.NA
-            return len(self.separators.intersection(set(s)))
+            return len(set(self.separators).intersection(set(s)))
 
         def get_separator_count(column):
             return column.apply(count_unique_separator)

nlp_primitives/tests/test_lsa.py

@@ -1,11 +1,5 @@
 import numpy as np
 import pandas as pd
-from featuretools import (
-    calculate_feature_matrix,
-    dfs,
-    load_features,
-    save_features
-)
 
 from ..lsa import LSA
 from .test_utils import PrimitiveT, find_applicable_primitives, valid_dfs
@@ -56,30 +50,3 @@ def test_with_featuretools(self, es):
         primitive_instance = self.primitive()
         transform.append(primitive_instance)
         valid_dfs(es, aggregation, transform, self.primitive.name.upper(), multi_output=True)
-
-    def test_serialize(self, es):
-        features = dfs(entityset=es,
-                       target_dataframe_name="log",
-                       trans_primitives=[self.primitive],
-                       max_features=-1,
-                       max_depth=3,
-                       features_only=True)
-
-        feat_to_serialize = None
-        for feature in features:
-            if feature.primitive.__class__ == self.primitive:
-                feat_to_serialize = feature
-                break
-            for base_feature in feature.get_dependencies(deep=True):
-                if base_feature.primitive.__class__ == self.primitive:
-                    feat_to_serialize = base_feature
-                    break
-        assert feat_to_serialize is not None
-
-        df1 = calculate_feature_matrix([feat_to_serialize], entityset=es)
-
-        new_feat = load_features(save_features([feat_to_serialize]))[0]
-
-        df2 = calculate_feature_matrix([new_feat], entityset=es)
-
-        assert df1.equals(df2)

nlp_primitives/tests/test_utils.py

@@ -8,7 +8,13 @@
 
 import featuretools as ft
 import pytest
-from featuretools import dfs, list_primitives
+from featuretools import (
+    calculate_feature_matrix,
+    dfs,
+    list_primitives,
+    load_features,
+    save_features
+)
 from featuretools.tests.testing_utils import make_ecommerce_entityset
 
 ft.primitives._load_primitives()
@@ -50,6 +56,38 @@ def test_arg_init(self):
             if parameter.default is not parameter.empty:
                 assert hasattr(primitive_, name)
 
+    def test_serialize(self, es):
+        features = dfs(entityset=es,
+                       target_dataframe_name="log",
+                       trans_primitives=[self.primitive],
+                       max_features=-1,
+                       max_depth=3,
+                       features_only=True)
+
+        feat_to_serialize = None
+        for feature in features:
+            if feature.primitive.__class__ == self.primitive:
+                feat_to_serialize = feature
+                break
+            for base_feature in feature.get_dependencies(deep=True):
+                if base_feature.primitive.__class__ == self.primitive:
+                    feat_to_serialize = base_feature
+                    break
+        assert feat_to_serialize is not None
+
+        # Skip calculating feature matrix for long running primitives
+        skip_primitives = ["elmo"]
+
+        if self.primitive.name not in skip_primitives:
+            df1 = calculate_feature_matrix([feat_to_serialize], entityset=es)
+
+        new_feat = load_features(save_features([feat_to_serialize]))[0]
+        assert isinstance(new_feat, ft.FeatureBase)
+
+        if self.primitive.name not in skip_primitives:
+            df2 = calculate_feature_matrix([new_feat], entityset=es)
+            assert df1.equals(df2)
+
 
 def find_applicable_primitives(primitive):
     from featuretools.primitives.utils import (