refactor(steps): start every step name with a verb

jitingxu1 · May 13, 2024 · 958f733 · 958f733
1 parent ec8ddd5
commit 958f733
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Showing 6 changed files with 16 additions and 16 deletions.
diff --git a/docs/tutorial/index.ipynb b/docs/tutorial/index.ipynb
@@ -538,7 +538,7 @@
     "    ml.ExpandDate(\"date\", components=[\"dow\", \"month\"]),\n",
     "    ml.Drop(\"date\"),\n",
     "    ml.TargetEncode(ml.nominal()),\n",
-    "    ml.ZeroVariance(ml.everything()),\n",
+    "    ml.DropZeroVariance(ml.everything()),\n",
     "    ml.MutateAt(\"dep_time\", ibis._.hour() * 60 + ibis._.minute()),\n",
     "    ml.MutateAt(ml.timestamp(), ibis._.epoch_seconds()),\n",
     "    # By default, PyTorch requires that the type of `X` is `np.float32`.\n",

diff --git a/ibis_ml/steps/__init__.py b/ibis_ml/steps/__init__.py
@@ -6,8 +6,8 @@
     OneHotEncode,
     TargetEncode,
 )
-from ibis_ml.steps._feature_engineering import PolynomialFeatures
-from ibis_ml.steps._feature_selection import ZeroVariance
+from ibis_ml.steps._feature_engineering import CreatePolynomialFeatures
+from ibis_ml.steps._feature_selection import DropZeroVariance
 from ibis_ml.steps._impute import FillNA, ImputeMean, ImputeMedian, ImputeMode
 from ibis_ml.steps._standardize import ScaleMinMax, ScaleStandard
 from ibis_ml.steps._temporal import ExpandDate, ExpandDateTime, ExpandTime
@@ -16,8 +16,10 @@
     "Cast",
     "CategoricalEncode",
     "CountEncode",
+    "CreatePolynomialFeatures",
     "DiscretizeKBins",
     "Drop",
+    "DropZeroVariance",
     "ExpandDate",
     "ExpandDateTime",
     "ExpandTime",
@@ -28,9 +30,7 @@
     "Mutate",
     "MutateAt",
     "OneHotEncode",
-    "PolynomialFeatures",
     "ScaleMinMax",
     "ScaleStandard",
     "TargetEncode",
-    "ZeroVariance",
 )
diff --git a/ibis_ml/steps/_feature_engineering.py b/ibis_ml/steps/_feature_engineering.py
@@ -12,7 +12,7 @@
 from ibis_ml.select import SelectionType, selector
 
 
-class PolynomialFeatures(Step):
+class CreatePolynomialFeatures(Step):
     """A step for generating polynomial features.
 
     Parameters
@@ -29,11 +29,11 @@ class PolynomialFeatures(Step):
 
     Generate polynomial features for all numeric columns with a degree is 2.
 
-    >>> step = ml.PolynomialFeatures(ml.numeric(), degree=2)
+    >>> step = ml.CreatePolynomialFeatures(ml.numeric(), degree=2)
 
     Generate polynomial features a specific set of columns.
 
-    >>> step = ml.PolynomialFeatures(["x", "y"], degree=2)
+    >>> step = ml.CreatePolynomialFeatures(["x", "y"], degree=2)
     """
 
     def __init__(self, inputs: SelectionType, *, degree: int = 2):

diff --git a/ibis_ml/steps/_feature_selection.py b/ibis_ml/steps/_feature_selection.py
@@ -8,7 +8,7 @@
 from ibis_ml.select import SelectionType, selector
 
 
-class ZeroVariance(Step):
+class DropZeroVariance(Step):
     """A step for removing columns with zero variance.
 
     Parameters
@@ -25,13 +25,13 @@ class ZeroVariance(Step):
     >>> import ibis_ml as ml
 
     To remove columns with zero variance:
-    >>> step = ml.ZeroVariance(ml.everything())
+    >>> step = ml.DropZeroVariance(ml.everything())
 
     To remove all numeric columns with zero variance:
-    >>> step = ml.ZeroVariance(ml.numeric())
+    >>> step = ml.DropZeroVariance(ml.numeric())
 
     To remove all string or categorical columns with only one unique value:
-    >>> step = ml.ZeroVariance(ml.nominal())
+    >>> step = ml.DropZeroVariance(ml.nominal())
     """
 
     def __init__(self, inputs: SelectionType, *, tolerance: int | float = 1e-4):

diff --git a/tests/test_feature_engineering.py b/tests/test_feature_engineering.py
@@ -11,8 +11,8 @@ def train_table():
     return ibis.memtable({"x": list(range(N)), "y": [10] * N, "z": ["s"] * N})
 
 
-def test_PolynomialFeatures(train_table):
-    step = ml.PolynomialFeatures(ml.numeric(), degree=2)
+def test_create_polynomial_features(train_table):
+    step = ml.CreatePolynomialFeatures(ml.numeric(), degree=2)
     step.fit_table(train_table, ml.core.Metadata())
     result = step.transform_table(train_table)
     expected = train_table.mutate(

diff --git a/tests/test_feature_selection.py b/tests/test_feature_selection.py
@@ -4,7 +4,7 @@
 import ibis_ml as ml
 
 
-def test_zero_variance():
+def test_drop_zero_variance():
     zv_numeric_col = [1.0] * 10
     non_zv_numeric_col = list(range(10))
     zv_string_col = ["String"] * 10
@@ -42,7 +42,7 @@ def test_zero_variance():
         }
     )
 
-    step = ml.ZeroVariance(ml.everything())
+    step = ml.DropZeroVariance(ml.everything())
     step.fit_table(t_train, ml.core.Metadata())
     res = step.transform_table(t_test)
     sol = t_test.drop(zv_cols)