maint: update templates to render out of the box (#229)

* update templates to ptd palm

* remove API url iframe- will not work on palm

examples/sacramento.py

@@ -0,0 +1,20 @@
+import pins
+
+from dotenv import load_dotenv
+from sklearn.linear_model import LinearRegression
+from sklearn.preprocessing import OneHotEncoder
+from sklearn.pipeline import Pipeline
+from vetiver.data import sacramento
+from vetiver import VetiverModel, vetiver_pin_write
+
+load_dotenv()
+
+x = sacramento.drop(columns="price")
+y = sacramento["price"]
+
+ohe = OneHotEncoder().fit(x)
+lm = LinearRegression().fit(ohe.transform(x), y)
+pipeline = Pipeline([("ohe", ohe), ("linear_model", lm)])
+v = VetiverModel(pipeline, "isabel.zimmerman/sacramento", x)
+board = pins.board_connect(allow_pickle_read=True)
+vetiver_pin_write(board=board, model=v)

vetiver/templates/model_card.qmd

@@ -17,8 +17,8 @@ display(Markdown(f"""
 #| include: false
 import pins
 
-raw = "https://colorado.rstudio.com/rsc/"
-paths = {"v": "sacramento/"}
+raw = "https://pub.palm.ptd.posit.it/public"
+paths = {"v": "vetiver-model-card/"}
 board = pins.board_urls(raw, paths, allow_pickle_read=True)
 ```
 
@@ -51,13 +51,15 @@ display(Markdown(f"""
 - A {model_desc} using {num_features} feature{'s'[:num_features^1]}.
 """))
 ```
+
 - More details about how model was developed and what it is predicting
 - More details on feature engineering and/or data preprocessing for model
+
 ```{python}
 #| echo: false
 version = v_meta.version
-time_created =  datetime.datetime
-    .strptime(v_meta.created, '%Y%m%dT%H%M%SZ')
+time_created =  datetime.datetime\
+    .strptime(v_meta.created, '%Y%m%dT%H%M%SZ')\
     .strftime('%Y-%m-%d %H:%M:%S')
 
 display(Markdown(f"""
@@ -91,7 +93,7 @@ display(Markdown(f"""
 - The training dataset for this model has the "prototype" or signature:
 ```{python}
 #| echo: false
-v.prototype.construct().schema().get("properties")
+v.prototype.model_json_schema()
 ```
 
 - The evaluation dataset used in this model card is ...
@@ -105,6 +107,7 @@ v.prototype.construct().schema().get("properties")
 ## consider using a package like Pandas Profiling for automated EDA
 
 ```
+
 ## Overall model performance
 
 ```{python}
@@ -134,7 +137,7 @@ dmp
 ## Visualize model performance
 
 ```{python}
-performance = px.scatter(sacramento, x="price", y = "preds", facet_row="type", trendline="ols")
+performance = px.scatter(sacramento, x="price", y = "preds", facet_row="type")
 performance.update_yaxes(matches=None)
 performance.show()
 ```

vetiver/templates/monitoring_dashboard.qmd

@@ -22,7 +22,7 @@ from sklearn import metrics
 from vetiver import VetiverModel, compute_metrics, plot_metrics
 from sklearn.metrics import recall_score, accuracy_score
 
-raw = "https://colorado.rstudio.com/rsc"
+raw = "https://pub.palm.ptd.posit.it/public"
 paths = {"chicago-model-python": "chicago-model-python/"}
 board = pins.board_url(raw, paths, allow_pickle_read=True)
 v = VetiverModel.from_pin(board, "chicago-model-python")
@@ -35,8 +35,8 @@ days_old = datetime.today() - datetime.strptime(v_meta.created, "%Y%m%dT%H%M%SZ"
 ## add any code you need to import new data and make predictions
 
 # import new data to track performance over time
-raw = "https://colorado.rstudio.com/rsc"
-paths = {"new-data": "inspections-new-data/"}
+raw = "https://pub.palm.ptd.posit.it/public"
+paths = {"new-data": "inspections-monitor/"}
 board = pins.board_url(raw, paths, allow_pickle_read=True)
 inspections_new = board.pin_read("new-data")
 
@@ -73,7 +73,7 @@ Model details
 - This model has the prototype:
 
 ```
-`{python} v.prototype.construct().schema().get("properties")`
+`{python} v.prototype.model_json_schema()`
 ```
 
 - The model was created by ...
@@ -125,8 +125,13 @@ Write your own code to make visualizations or tables with the new validation dat
 ## Column
 
 ```{python}
+# For your own model, you can use
+# the following code with your URL
+# to display your model API's visual documentation
+
 from IPython.display import IFrame
-IFrame('https://colorado.posit.co/rsc/chicago-inspections-python', width=750, height=350)
+url = "your-model-api-url"
+IFrame(url, width=750, height=350)
 ```
 ---
 

vetiver/tests/snapshots/monitoring_dashboard.qmd

@@ -22,7 +22,7 @@ from sklearn import metrics
 from vetiver import VetiverModel, compute_metrics, plot_metrics
 from sklearn.metrics import recall_score, accuracy_score
 
-raw = "https://colorado.rstudio.com/rsc"
+raw = "https://pub.palm.ptd.posit.it/public"
 paths = {"chicago-model-python": "chicago-model-python/"}
 board = pins.board_url(raw, paths, allow_pickle_read=True)
 v = VetiverModel.from_pin(board, "chicago-model-python")
@@ -35,8 +35,8 @@ days_old = datetime.today() - datetime.strptime(v_meta.created, "%Y%m%dT%H%M%SZ"
 ## add any code you need to import new data and make predictions
 
 # import new data to track performance over time
-raw = "https://colorado.rstudio.com/rsc"
-paths = {"new-data": "inspections-new-data/"}
+raw = "https://pub.palm.ptd.posit.it/public"
+paths = {"new-data": "inspections-monitor/"}
 board = pins.board_url(raw, paths, allow_pickle_read=True)
 inspections_new = board.pin_read("new-data")
 
@@ -73,7 +73,7 @@ Model details
 - This model has the prototype:
 
 ```
-`{python} v.prototype.construct().schema().get("properties")`
+`{python} v.prototype.model_json_schema()`
 ```
 
 - The model was created by ...
@@ -125,8 +125,13 @@ Write your own code to make visualizations or tables with the new validation dat
 ## Column
 
 ```{python}
+# For your own model, you can use
+# the following code with your URL
+# to display your model API's visual documentation
+
 from IPython.display import IFrame
-IFrame('https://colorado.posit.co/rsc/chicago-inspections-python', width=750, height=350)
+url = "your-model-api-url"
+IFrame(url, width=750, height=350)
 ```
 ---