docs: acknowledge original source for the tutorial (#120)

docs/tutorial/_acknowledgments.md

@@ -0,0 +1,3 @@
+## Acknowledgments
+
+This tutorial is derived from the [tidymodels article of the same name](https://www.tidymodels.org/start/recipes/). The transformation logic is very similar, and much of the text is copied verbatim.

docs/tutorial/pytorch.qmd

@@ -28,7 +28,7 @@ pip install 'ibis-framework[duckdb,examples]' ibis-ml skorch torch
 
 ## The New York City flight data
 
-Let's use the [nycflights13 data](https://github.com/hadley/nycflights13) to predict whether a plane arrives more than 30 minutes late. This data set contains information on 325,819 flights departing near New York City in 2013. Let's start by loading the data and making a few changes to the variables:
+Let's use the [nycflights13 data](https://github.com/hadley/nycflights13) to predict whether a plane arrives more than 30 minutes late. This dataset contains information on 325,819 flights departing near New York City in 2013. Let's start by loading the data and making a few changes to the variables:
 
 ```{python}
 #| output: false
@@ -107,7 +107,7 @@ flight_data = (
 flight_data
 ```
 
-We can see that about 16% of the flights in this data set arrived more than 30 minutes late.
+We can see that about 16% of the flights in this dataset arrived more than 30 minutes late.
 
 ```{python}
 flight_data.arr_delay.value_counts().rename(n="arr_delay_count").mutate(
@@ -240,3 +240,5 @@ X_test = test_data.drop("arr_delay")
 y_test = test_data.arr_delay
 pipe.score(X_test, y_test)
 ```
+
+{{< include _acknowledgments.md >}}

docs/tutorial/scikit-learn.qmd

@@ -28,7 +28,7 @@ pip install 'ibis-framework[duckdb,examples]' ibis-ml scikit-learn
 
 ## The New York City flight data
 
-Let's use the [nycflights13 data](https://github.com/hadley/nycflights13) to predict whether a plane arrives more than 30 minutes late. This data set contains information on 325,819 flights departing near New York City in 2013. Let's start by loading the data and making a few changes to the variables:
+Let's use the [nycflights13 data](https://github.com/hadley/nycflights13) to predict whether a plane arrives more than 30 minutes late. This dataset contains information on 325,819 flights departing near New York City in 2013. Let's start by loading the data and making a few changes to the variables:
 
 ```{python}
 #| output: false
@@ -81,8 +81,7 @@ weather
 flight_data = (
     flights.mutate(
         # Convert the arrival delay to a factor
-        # By default, PyTorch expects the target to have a Long datatype
-        arr_delay=ibis.ifelse(flights.arr_delay >= 30, 1, 0).cast("int64"),
+        arr_delay=ibis.ifelse(flights.arr_delay >= 30, 1, 0),
         # We will use the date (not date-time) in the recipe below
         date=flights.time_hour.date(),
     )
@@ -107,7 +106,7 @@ flight_data = (
 flight_data
 ```
 
-We can see that about 16% of the flights in this data set arrived more than 30 minutes late.
+We can see that about 16% of the flights in this dataset arrived more than 30 minutes late.
 
 ```{python}
 flight_data.arr_delay.value_counts().rename(n="arr_delay_count").mutate(
@@ -167,9 +166,6 @@ flights_rec = ml.Recipe(
     ml.DropZeroVariance(ml.everything()),
     ml.MutateAt("dep_time", ibis._.hour() * 60 + ibis._.minute()),
     ml.MutateAt(ml.timestamp(), ibis._.epoch_seconds()),
-    # By default, PyTorch requires that the type of `X` is `np.float32`.
-    # https://discuss.pytorch.org/t/mat1-and-mat2-must-have-the-same-dtype-but-got-double-and-float/197555/2
-    ml.Cast(ml.numeric(), "float32"),
 )
 ```
 
@@ -211,3 +207,5 @@ X_test = test_data.drop("arr_delay")
 y_test = test_data.arr_delay
 pipe.score(X_test, y_test)
 ```
+
+{{< include _acknowledgments.md >}}

docs/tutorial/xgboost.qmd

@@ -28,7 +28,7 @@ pip install 'ibis-framework[duckdb,examples]' ibis-ml 'xgboost[scikit-learn]'
 
 ## The New York City flight data
 
-Let's use the [nycflights13 data](https://github.com/hadley/nycflights13) to predict whether a plane arrives more than 30 minutes late. This data set contains information on 325,819 flights departing near New York City in 2013. Let's start by loading the data and making a few changes to the variables:
+Let's use the [nycflights13 data](https://github.com/hadley/nycflights13) to predict whether a plane arrives more than 30 minutes late. This dataset contains information on 325,819 flights departing near New York City in 2013. Let's start by loading the data and making a few changes to the variables:
 
 ```{python}
 #| output: false
@@ -81,8 +81,7 @@ weather
 flight_data = (
     flights.mutate(
         # Convert the arrival delay to a factor
-        # By default, PyTorch expects the target to have a Long datatype
-        arr_delay=ibis.ifelse(flights.arr_delay >= 30, 1, 0).cast("int64"),
+        arr_delay=ibis.ifelse(flights.arr_delay >= 30, 1, 0),
         # We will use the date (not date-time) in the recipe below
         date=flights.time_hour.date(),
     )
@@ -107,7 +106,7 @@ flight_data = (
 flight_data
 ```
 
-We can see that about 16% of the flights in this data set arrived more than 30 minutes late.
+We can see that about 16% of the flights in this dataset arrived more than 30 minutes late.
 
 ```{python}
 flight_data.arr_delay.value_counts().rename(n="arr_delay_count").mutate(
@@ -167,9 +166,6 @@ flights_rec = ml.Recipe(
     ml.DropZeroVariance(ml.everything()),
     ml.MutateAt("dep_time", ibis._.hour() * 60 + ibis._.minute()),
     ml.MutateAt(ml.timestamp(), ibis._.epoch_seconds()),
-    # By default, PyTorch requires that the type of `X` is `np.float32`.
-    # https://discuss.pytorch.org/t/mat1-and-mat2-must-have-the-same-dtype-but-got-double-and-float/197555/2
-    ml.Cast(ml.numeric(), "float32"),
 )
 ```
 
@@ -211,3 +207,5 @@ X_test = test_data.drop("arr_delay")
 y_test = test_data.arr_delay
 pipe.score(X_test, y_test)
 ```
+
+{{< include _acknowledgments.md >}}

examples/Preprocess your data with recipes.ipynb

@@ -33,7 +33,7 @@
    "source": [
     "## The New York City flight data\n",
     "\n",
-    "Let's use the [nycflights13 data](https://github.com/hadley/nycflights13) to predict whether a plane arrives more than 30 minutes late. This data set contains information on 325,819 flights departing near New York City in 2013. Let's start by loading the data and making a few changes to the variables:"
+    "Let's use the [nycflights13 data](https://github.com/hadley/nycflights13) to predict whether a plane arrives more than 30 minutes late. This dataset contains information on 325,819 flights departing near New York City in 2013. Let's start by loading the data and making a few changes to the variables:"
    ]
   },
   {
@@ -317,7 +317,7 @@
    "id": "722b2213-3b84-4f03-9006-59bf72591613",
    "metadata": {},
    "source": [
-    "We can see that about 16% of the flights in this data set arrived more than 30 minutes late."
+    "We can see that about 16% of the flights in this dataset arrived more than 30 minutes late."
    ]
   },
   {
@@ -1221,6 +1221,16 @@
     "y_test = test_data.arr_delay\n",
     "pipe.score(X_test, y_test)"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cc21b842-b85c-4ed9-af03-1feace909172",
+   "metadata": {},
+   "source": [
+    "## Acknowledgments\n",
+    "\n",
+    "This tutorial is derived from the [tidymodels article of the same name](https://www.tidymodels.org/start/recipes/). The transformation logic is very similar, and much of the text is copied verbatim."
+   ]
   }
  ],
  "metadata": {