refactor: rename the ibisml package to ibis_ml

README.md

@@ -11,7 +11,7 @@ IbisML is a library for building scalable ML pipelines using Ibis:
 
 - Preprocess your data at scale on any [Ibis](https://ibis-project.org/)-supported
   backend.
-- Compose [`Recipe`](/reference/core.html#ibisml.Recipe)s with other scikit-learn
+- Compose [`Recipe`](/reference/core.html#ibis_ml.Recipe)s with other scikit-learn
   estimators using
   [`Pipeline`](https://scikit-learn.org/stable/modules/compose.html#pipeline-chaining-estimators)s.
 - Seamlessly integrate with [scikit-learn](https://scikit-learn.org/stable/),
@@ -20,7 +20,7 @@ IbisML is a library for building scalable ML pipelines using Ibis:
 
 ```python
 import ibis
-import ibisml as ml
+import ibis_ml as ml
 
 # A recipe for a feature engineering pipeline that:
 # - imputes missing values in numeric columns with their mean

docs/_quarto.yml

@@ -88,7 +88,7 @@ format:
     css: styles.css
 
 quartodoc:
-  package: ibisml
+  package: ibis_ml
   title: Reference
   sidebar: reference/_sidebar.yml
   dynamic: true
@@ -98,7 +98,7 @@ quartodoc:
       signature_name: short
   sections:
     - title: Core
-      package: ibisml
+      package: ibis_ml
       contents:
         - kind: page
           path: core
@@ -136,7 +136,7 @@ quartodoc:
 
     - title: Steps
       desc: Define steps in a recipe
-      package: ibisml
+      package: ibis_ml
       contents:
         - kind: page
           path: steps-imputation

docs/index.qmd

@@ -11,7 +11,7 @@ hide-description: true
 
 - Preprocess your data at scale on any [Ibis](https://ibis-project.org/)-supported
   backend.
-- Compose [`Recipe`](/reference/core.html#ibisml.Recipe)s with other scikit-learn
+- Compose [`Recipe`](/reference/core.html#ibis_ml.Recipe)s with other scikit-learn
   estimators using
   [`Pipeline`](https://scikit-learn.org/stable/modules/compose.html#pipeline-chaining-estimators)s.
 - Seamlessly integrate with [scikit-learn](https://scikit-learn.org/stable/),
@@ -23,7 +23,7 @@ hide-description: true
 ### Install IbisML
 
 ```bash
-pip install ibisml
+pip install ibis-ml
 ```
 
 ### Create your first recipe
@@ -34,7 +34,7 @@ mean of each numeric column and then normalize numeric data to have a standard d
 of one and a mean of zero.
 
 ```{python}
-import ibisml as ml
+import ibis_ml as ml
 
 imputer = ml.ImputeMean(ml.numeric())
 scaler = ml.ScaleStandard(ml.numeric())

docs/tutorial/index.ipynb

@@ -532,7 +532,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import ibisml as ml\n",
+    "import ibis_ml as ml\n",
     "\n",
     "flights_rec = ml.Recipe(\n",
     "    ml.ExpandDate(\"date\", components=[\"dow\", \"month\"]),\n",
@@ -1095,7 +1095,7 @@
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-3);\n",
        "}\n",
-       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;flights_rec&#x27;, &lt;ibisml.core.Recipe object at 0x149360730&gt;),\n",
+       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;flights_rec&#x27;, &lt;ibis_ml.core.Recipe object at 0x149360730&gt;),\n",
        "                (&#x27;mod&#x27;,\n",
        "                 &lt;class &#x27;skorch.classifier.NeuralNetClassifier&#x27;&gt;[initialized](\n",
        "  module_=MyModule(\n",
@@ -1106,7 +1106,7 @@
        "    (output): Linear(in_features=10, out_features=2, bias=True)\n",
        "    (softmax): Softmax(dim=-1)\n",
        "  ),\n",
-       "))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;Pipeline<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;flights_rec&#x27;, &lt;ibisml.core.Recipe object at 0x149360730&gt;),\n",
+       "))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;Pipeline<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;flights_rec&#x27;, &lt;ibis_ml.core.Recipe object at 0x149360730&gt;),\n",
        "                (&#x27;mod&#x27;,\n",
        "                 &lt;class &#x27;skorch.classifier.NeuralNetClassifier&#x27;&gt;[initialized](\n",
        "  module_=MyModule(\n",
@@ -1117,7 +1117,7 @@
        "    (output): Linear(in_features=10, out_features=2, bias=True)\n",
        "    (softmax): Softmax(dim=-1)\n",
        "  ),\n",
-       "))])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">Recipe</label><div class=\"sk-toggleable__content fitted\"><pre>&lt;ibisml.core.Recipe object at 0x149360730&gt;</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">NeuralNetClassifier</label><div class=\"sk-toggleable__content fitted\"><pre>&lt;class &#x27;skorch.classifier.NeuralNetClassifier&#x27;&gt;[initialized](\n",
+       "))])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">Recipe</label><div class=\"sk-toggleable__content fitted\"><pre>&lt;ibis_ml.core.Recipe object at 0x149360730&gt;</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">NeuralNetClassifier</label><div class=\"sk-toggleable__content fitted\"><pre>&lt;class &#x27;skorch.classifier.NeuralNetClassifier&#x27;&gt;[initialized](\n",
        "  module_=MyModule(\n",
        "    (dense0): Linear(in_features=10, out_features=10, bias=True)\n",
        "    (nonlin): ReLU()\n",
@@ -1129,7 +1129,7 @@
        ")</pre></div> </div></div></div></div></div></div>"
       ],
       "text/plain": [
-       "Pipeline(steps=[('flights_rec', <ibisml.core.Recipe object at 0x149360730>),\n",
+       "Pipeline(steps=[('flights_rec', <ibis_ml.core.Recipe object at 0x149360730>),\n",
        "                ('mod',\n",
        "                 <class 'skorch.classifier.NeuralNetClassifier'>[initialized](\n",
        "  module_=MyModule(\n",

ibisml/__init__.py → ibis_ml/__init__.py

@@ -1,7 +1,7 @@
 """IbisML is a library for building scalable ML pipelines using Ibis."""
 
-from ibisml.core import Recipe, Step
-from ibisml.select import (
+from ibis_ml.core import Recipe, Step
+from ibis_ml.select import (
     categorical,
     cols,
     contains,
@@ -22,7 +22,7 @@
     timestamp,
     where,
 )
-from ibisml.steps import *
+from ibis_ml.steps import *
 
 from ._version import __version__
 

ibisml/_version.py → ibis_ml/_version.py

@@ -1,3 +1,3 @@
 from importlib.metadata import version
 
-__version__ = version("ibisml")
+__version__ = version("ibis_ml")

ibisml/core.py → ibis_ml/core.py

@@ -261,7 +261,7 @@ def _get_categorize_chunk() -> Callable[[str, list[str], Any], pd.DataFrame]:
     """Wrap the `categorize` function in a closure, so cloudpickle will encode
     the full function.
 
-    This avoids requiring `ibisml` or `ibis` exist on the worker nodes of the
+    This avoids requiring `ibis_ml` or `ibis` exist on the worker nodes of the
     dask cluster.
     """
 

ibisml/select.py → ibis_ml/select.py

@@ -8,7 +8,7 @@
 import ibis.expr.types as ir
 
 if TYPE_CHECKING:
-    from ibisml.core import Metadata
+    from ibis_ml.core import Metadata
 
 
 class Selector:

ibis_ml/steps/__init__.py

@@ -0,0 +1,36 @@
+from ibis_ml.steps.common import Cast, Drop, Mutate, MutateAt
+from ibis_ml.steps.discretize import DiscretizeKBins
+from ibis_ml.steps.encode import (
+    CategoricalEncode,
+    CountEncode,
+    OneHotEncode,
+    TargetEncode,
+)
+from ibis_ml.steps.feature_engineering import PolynomialFeatures
+from ibis_ml.steps.feature_selection import ZeroVariance
+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
+
+__all__ = (
+    "Cast",
+    "CategoricalEncode",
+    "CountEncode",
+    "DiscretizeKBins",
+    "Drop",
+    "ExpandDate",
+    "ExpandDateTime",
+    "ExpandTime",
+    "FillNA",
+    "ImputeMean",
+    "ImputeMedian",
+    "ImputeMode",
+    "Mutate",
+    "MutateAt",
+    "OneHotEncode",
+    "PolynomialFeatures",
+    "ScaleMinMax",
+    "ScaleStandard",
+    "TargetEncode",
+    "ZeroVariance",
+)

ibisml/steps/common.py → ibis_ml/steps/common.py

@@ -6,8 +6,8 @@
 import ibis.expr.types as ir
 from ibis.common.deferred import Deferred
 
-from ibisml.core import Metadata, Step
-from ibisml.select import SelectionType, selector
+from ibis_ml.core import Metadata, Step
+from ibis_ml.select import SelectionType, selector
 
 
 class Drop(Step):
@@ -20,7 +20,7 @@ class Drop(Step):
 
     Examples
     --------
-    >>> import ibisml as ml
+    >>> import ibis_ml as ml
 
     Drop all non-numeric columns
 
@@ -59,7 +59,7 @@ class Cast(Step):
 
     Examples
     --------
-    >>> import ibisml as ml
+    >>> import ibis_ml as ml
 
     Cast all numeric columns to float64
 
@@ -107,7 +107,7 @@ class MutateAt(Step):
 
     Examples
     --------
-    >>> import ibisml as ml
+    >>> import ibis_ml as ml
     >>> from ibis import _
 
     Replace all numeric columns with their absolute values.
@@ -170,7 +170,7 @@ class Mutate(Step):
 
     Examples
     --------
-    >>> import ibisml as ml
+    >>> import ibis_ml as ml
     >>> from ibis import _
 
     Define a new column ``c`` as ``a**2 + b**2``

ibisml/steps/discretize.py → ibis_ml/steps/discretize.py

@@ -6,8 +6,8 @@
 import ibis.expr.types as ir
 import numpy as np
 
-from ibisml.core import Metadata, Step
-from ibisml.select import SelectionType, selector
+from ibis_ml.core import Metadata, Step
+from ibis_ml.select import SelectionType, selector
 
 
 class DiscretizeKBins(Step):
@@ -34,8 +34,8 @@ class DiscretizeKBins(Step):
     Examples
     --------
     >>> import ibis
-    >>> import ibisml as ml
-    >>> from ibisml.core import Metadata
+    >>> import ibis_ml as ml
+    >>> from ibis_ml.core import Metadata
     >>> ibis.options.interactive = True
 
     Load penguins dataset

ibisml/steps/encode.py → ibis_ml/steps/encode.py

@@ -7,9 +7,9 @@
 import ibis
 import ibis.expr.types as ir
 
-from ibisml.core import Metadata, Step
-from ibisml.select import SelectionType, selector
-from ibisml.steps.impute import FillNA
+from ibis_ml.core import Metadata, Step
+from ibis_ml.select import SelectionType, selector
+from ibis_ml.steps.impute import FillNA
 
 
 def _compute_categories(
@@ -94,7 +94,7 @@ class OneHotEncode(Step):
 
     Examples
     --------
-    >>> import ibisml as ml
+    >>> import ibis_ml as ml
 
     One-hot encode all string columns.
 
@@ -183,7 +183,7 @@ class CategoricalEncode(Step):
 
     Examples
     --------
-    >>> import ibisml as ml
+    >>> import ibis_ml as ml
 
     Categorical encode all string columns.
 
@@ -261,7 +261,7 @@ class CountEncode(Step):
 
     Examples
     --------
-    >>> import ibisml as ml
+    >>> import ibis_ml as ml
 
     Count encode all string columns.
 
@@ -309,7 +309,7 @@ class TargetEncode(Step):
 
     Examples
     --------
-    >>> import ibisml as ml
+    >>> import ibis_ml as ml
 
     Target encode all string columns.
 

ibisml/steps/feature_engineering.py → ibis_ml/steps/feature_engineering.py

@@ -8,8 +8,8 @@
 
 import ibis.expr.types as ir
 
-from ibisml.core import Metadata, Step
-from ibisml.select import SelectionType, selector
+from ibis_ml.core import Metadata, Step
+from ibis_ml.select import SelectionType, selector
 
 
 class PolynomialFeatures(Step):
@@ -25,7 +25,7 @@ class PolynomialFeatures(Step):
 
     Examples
     --------
-    >>> import ibisml as ml
+    >>> import ibis_ml as ml
 
     Generate polynomial features for all numeric columns with a degree is 2.
 

ibisml/steps/feature_selection.py → ibis_ml/steps/feature_selection.py

@@ -4,8 +4,8 @@
 
 import ibis.expr.types as ir
 
-from ibisml.core import Metadata, Step
-from ibisml.select import SelectionType, selector
+from ibis_ml.core import Metadata, Step
+from ibis_ml.select import SelectionType, selector
 
 
 class ZeroVariance(Step):
@@ -22,7 +22,7 @@ class ZeroVariance(Step):
 
     Examples
     --------
-    >>> import ibisml as ml
+    >>> import ibis_ml as ml
 
     To remove columns with zero variance:
     >>> step = ml.ZeroVariance(ml.everything())