Rename LabelTimes.name to LabelTimes.label_name

composeml/label_maker.py

@@ -325,9 +325,9 @@ def search(self,
             return labels
 
         if labels.is_discrete:
-            labels[labels.name] = labels[labels.name].astype('category')
+            labels[labels.label_name] = labels[labels.label_name].astype('category')
 
-        labels.name = name
+        labels.label_name = name
         labels.target_entity = self.target_entity
         labels.settings.update({
             'num_examples_per_instance': num_examples_per_instance,

composeml/label_plots.py

@@ -52,7 +52,7 @@ def count_by_time(self, ax=None, **kwargs):
 
             ax.legend(
                 loc='upper left',
-                title=self._label_times.name,
+                title=self._label_times.label_name,
                 facecolor='w',
                 framealpha=.9,
             )
@@ -69,7 +69,7 @@ def count_by_time(self, ax=None, **kwargs):
             )
 
             ax.set_title('Label vs. Cutoff Times')
-            ax.set_ylabel(self._label_times.name)
+            ax.set_ylabel(self._label_times.label_name)
             ax.set_xlabel('Time')
 
         return ax
@@ -81,7 +81,7 @@ def dist(self):
 
     def distribution(self, **kwargs):
         """Plots the label distribution."""
-        dist = self._label_times[self._label_times.name]
+        dist = self._label_times[self._label_times.label_name]
 
         if self._label_times.is_discrete:
             ax = sns.countplot(dist, palette=COLOR, **kwargs)

composeml/label_times.py

@@ -122,12 +122,12 @@ def _constructor(self):
         return LabelTimes
 
     @property
-    def name(self):
+    def label_name(self):
         """Get name of label times."""
         return self.settings.get('labeling_function')
 
-    @name.setter
-    def name(self, value):
+    @label_name.setter
+    def label_name(self, value):
         """Set name of label times."""
         self.settings['labeling_function'] = value
 
@@ -174,38 +174,38 @@ def distribution(self):
         """Returns label distribution if labels are discrete."""
         if self.is_discrete:
             labels = self.assign(count=1)
-            labels = labels.groupby(self.name)
+            labels = labels.groupby(self.label_name)
             distribution = labels['count'].count()
             return distribution
 
     @property
     def count(self):
         """Returns label count per instance."""
         count = self.groupby(self.target_entity)
-        count = count[self.name].count()
+        count = count[self.label_name].count()
         count = count.to_frame('count')
         return count
 
     @property
     def count_by_time(self):
         """Returns label count across cutoff times."""
         if self.is_discrete:
-            keys = ['cutoff_time', self.name]
+            keys = ['cutoff_time', self.label_name]
             value = self.groupby(keys).cutoff_time.count()
-            value = value.unstack(self.name).fillna(0)
+            value = value.unstack(self.label_name).fillna(0)
         else:
             value = self.groupby('cutoff_time')
-            value = value[self.name].count()
+            value = value[self.label_name].count()
 
         value = value.cumsum()  # In Python 3.5, these values automatically convert to float.
         value = value.astype('int')
         return value
 
     def describe(self):
         """Prints out label info with transform settings that reproduce labels."""
-        if self.name is not None and self.is_discrete:
+        if self.label_name is not None and self.is_discrete:
             print('Label Distribution\n' + '-' * 18, end='\n')
-            distribution = self[self.name].value_counts()
+            distribution = self[self.label_name].value_counts()
             distribution.index = distribution.index.astype('str')
             distribution.sort_index(inplace=True)
             distribution['Total:'] = distribution.sum()
@@ -264,7 +264,7 @@ def threshold(self, value, inplace=False):
             labels (LabelTimes) : Instance of labels.
         """
         labels = self if inplace else self.copy()
-        labels[self.name] = labels[self.name].gt(value)
+        labels[self.label_name] = labels[self.label_name].gt(value)
 
         labels.label_type = 'discrete'
         labels.settings['label_type'] = 'discrete'
@@ -362,13 +362,13 @@ def bin(self, bins, quantiles=False, labels=None, right=True):
             my_labeling_function                 high                  low
         """ # noqa
         label_times = self.copy()
-        values = label_times[self.name].values
+        values = label_times[self.label_name].values
 
         if quantiles:
-            label_times[self.name] = pd.qcut(values, q=bins, labels=labels)
+            label_times[self.label_name] = pd.qcut(values, q=bins, labels=labels)
 
         else:
-            label_times[self.name] = pd.cut(values, bins=bins, labels=labels, right=right)
+            label_times[self.label_name] = pd.cut(values, bins=bins, labels=labels, right=right)
 
         transform = {
             'transform': 'bin',
@@ -420,7 +420,7 @@ def _sample_per_label(self, key, value, settings, random_state=None, replace=Fal
 
         sample_per_label = []
         for label, value, in value.items():
-            label = self[self[self.name] == label]
+            label = self[self[self.label_name] == label]
             sample = label._sample(key, value, settings, random_state=random_state, replace=replace)
             sample_per_label.append(sample)
 
@@ -516,7 +516,7 @@ def infer_type(self):
         Returns:
             str : Inferred label type. Either "continuous" or "discrete".
         """
-        dtype = self[self.name].dtype
+        dtype = self[self.label_name].dtype
         is_discrete = pd.api.types.is_bool_dtype(dtype)
         is_discrete = is_discrete or pd.api.types.is_categorical_dtype(dtype)
         is_discrete = is_discrete or pd.api.types.is_object_dtype(dtype)

composeml/tests/test_featuretools.py

@@ -38,9 +38,9 @@ def labels():
 def test_dfs(labels):
     es = ft.demo.load_mock_customer(return_entityset=True, random_seed=0)
     feature_matrix, _ = ft.dfs(entityset=es, target_entity='customers', cutoff_time=labels, cutoff_time_in_index=True)
-    assert labels.name in feature_matrix
+    assert labels.label_name in feature_matrix
 
-    columns = ['customer_id', 'time', labels.name]
+    columns = ['customer_id', 'time', labels.label_name]
     given_labels = feature_matrix.reset_index()[columns]
     given_labels = given_labels.rename(columns={'time': 'cutoff_time'})
     given_labels = given_labels.sort_values(['customer_id', 'cutoff_time'])

composeml/tests/test_label_transforms/test_threshold.py

@@ -6,5 +6,5 @@ def test_threshold(labels):
     assert transform['value'] == 200
 
     answer = [True, False, True, False]
-    given_answer = labels[labels.name].values.tolist()
+    given_answer = labels[labels.label_name].values.tolist()
     assert given_answer == answer

docs/source/changelog.rst

@@ -1,6 +1,13 @@
 =========
 Changelog
 =========
+**Future Release**
+    * Changes
+        * Rename LabelTimes.name to LabelTimes.label_name (:pr:`126`)
+
+**Breaking Changes**
+
+* ``LabelTimes.name`` has been renamed to ``LabelTimes.label_name``
 
 **v0.1.8** March 11, 2020
     * Fixes

docs/source/examples/predict-remaining-useful-life/example.ipynb

@@ -1171,7 +1171,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "y = fm.pop(lt.name)\n",
+    "y = fm.pop(lt.label_name)\n",
     "y = y.astype('str')\n",
     "\n",
     "x = fm.fillna(0)\n",
@@ -1350,4 +1350,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 2
-}
+}

docs/source/guides/using_compose_with_featuretools.ipynb

@@ -928,7 +928,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "y = feature_matrix.pop(labels.name)\n",
+    "y = feature_matrix.pop(labels.label_name)\n",
     "x = feature_matrix.fillna(0)\n",
     "x, features_enc = ft.encode_features(x, features_defs)"
    ]
@@ -1104,4 +1104,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 2
-}
+}