Update example plots

zillow · Aug 9, 2024 · cff164e · cff164e
1 parent 8428f26
commit cff164e
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Showing 5 changed files with 38 additions and 42 deletions.
diff --git a/examples/plot_huggingface_model.py b/examples/plot_huggingface_model.py
@@ -183,16 +183,12 @@ def plot_quantiles_by_latlon(df, quantiles):
         name="Predicted Quantile: ",
     )
 
-    q_val = alt.selection_point(
-        value=0.5,
-        bind=slider,
-        fields=["quantile"],
-    )
+    quantile_selection = alt.param(value=0.5, bind=slider, name="quantile")
 
     chart = (
         alt.Chart(df)
-        .add_params(q_val)
-        .transform_filter(f"datum['quantile'] == {q_val['quantile']}")
+        .add_params(quantile_selection)
+        .transform_filter("datum.quantile == quantile")
         .mark_circle()
         .encode(
             x=alt.X(

diff --git a/examples/plot_quantile_conformalized.py b/examples/plot_quantile_conformalized.py
@@ -165,13 +165,6 @@ def plot_prediction_intervals(df, domain):
 
         click = alt.selection_point(fields=["y_label"], bind="legend")
 
-        color_circle = alt.Color(
-            "y_label:N",
-            scale=alt.Scale(domain=["Yes", "No"], range=["#f2a619", "red"]),
-            title="Within Interval",
-        )
-        color_bar = alt.value("#e0f2ff")
-
         tooltip = [
             alt.Tooltip("y_test:Q", format="$,d", title="True Price"),
             alt.Tooltip("y_pred:Q", format="$,d", title="Predicted Price"),
@@ -210,7 +203,15 @@ def plot_prediction_intervals(df, domain):
                     scale=alt.Scale(domain=domain, nice=False),
                     title="Predicted Prices",
                 ),
-                color=alt.condition(click, color_circle, alt.value("lightgray")),
+                color=alt.condition(
+                    click,
+                    alt.Color(
+                        "y_label:N",
+                        scale=alt.Scale(domain=["Yes", "No"], range=["#f2a619", "red"]),
+                        title="Within Interval",
+                    ),
+                    alt.value("lightgray"),
+                ),
                 opacity=alt.condition(click, alt.value(1), alt.value(0)),
                 tooltip=tooltip,
             )
@@ -220,7 +221,7 @@ def plot_prediction_intervals(df, domain):
             x=alt.X("y_pred:Q", scale=alt.Scale(domain=domain, padding=0), title=""),
             y=alt.Y("y_pred_low:Q", scale=alt.Scale(domain=domain, padding=0), title=""),
             y2=alt.Y2("y_pred_upp:Q", title=None),
-            color=alt.condition(click, color_bar, alt.value("lightgray")),
+            color=alt.condition(click, alt.value("#e0f2ff"), alt.value("lightgray")),
             opacity=alt.condition(click, alt.value(0.8), alt.value(0)),
             tooltip=tooltip,
         )

diff --git a/examples/plot_quantile_ranks.py b/examples/plot_quantile_ranks.py
@@ -60,12 +60,6 @@ def plot_fit_and_ranks(df):
 
     base = alt.Chart(df)
 
-    color_points = alt.Color(
-        "outlier:N",
-        scale=alt.Scale(domain=["Yes", "No"], range=["red", "#f2a619"]),
-        title="Outlier",
-    )
-
     points = (
         base.add_params(interval_selection, click)
         .transform_calculate(
@@ -75,7 +69,15 @@ def plot_fit_and_ranks(df):
         .encode(
             x=alt.X("x:Q"),
             y=alt.Y("y:Q"),
-            color=alt.condition(click, color_points, alt.value("lightgray")),
+            color=alt.condition(
+                click,
+                alt.Color(
+                    "outlier:N",
+                    scale=alt.Scale(domain=["Yes", "No"], range=["red", "#f2a619"]),
+                    title="Outlier",
+                ),
+                alt.value("lightgray"),
+            ),
             tooltip=[
                 alt.Tooltip("x:Q", format=".3f", title="x"),
                 alt.Tooltip("y:Q", format=".3f", title="f(x)"),

diff --git a/examples/plot_quantile_vs_standard.py b/examples/plot_quantile_vs_standard.py
@@ -76,12 +76,6 @@ def plot_prediction_histograms(df, legend):
 
     click = alt.selection_point(fields=["label"], bind="legend")
 
-    color = alt.condition(
-        click,
-        alt.Color("label:N", sort=list(legend.keys()), title=None),
-        alt.value("lightgray"),
-    )
-
     chart = (
         alt.Chart(df)
         .add_params(quantile_selection, click)
@@ -102,7 +96,11 @@ def plot_prediction_histograms(df, legend):
                 title="Actual and Predicted Target Values",
             ),
             y=alt.Y("count():Q", axis=alt.Axis(format=",d", title="Counts")),
-            color=color,
+            color=alt.condition(
+                click,
+                alt.Color("label:N", sort=list(legend.keys()), title=None),
+                alt.value("lightgray"),
+            ),
             xOffset=alt.XOffset("label:N"),
             tooltip=[
                 alt.Tooltip("label:N", title="Label"),

diff --git a/examples/plot_treeshap_example.py b/examples/plot_treeshap_example.py
@@ -106,8 +106,7 @@ def plot_shap_waterfall_with_quantiles(df, height=300):
         step=0.5 if len(quantiles) == 1 else 1 / (len(quantiles) - 1),
         name="Predicted Quantile: ",
     )
-
-    q_val = alt.selection_point(value=0.5, bind=slider, fields=["quantile"])
+    quantile_selection = alt.param(value=0.5, bind=slider, name="quantile")
 
     df_grouped = (
         df.groupby("quantile")[df.columns.tolist()]
@@ -166,7 +165,7 @@ def plot_shap_waterfall_with_quantiles(df, height=300):
 
     base = (
         alt.Chart(df_grouped)
-        .transform_filter(q_val)
+        .transform_filter("datum.quantile == quantile")
         .transform_calculate(
             end_shifted=f"datum.shap_value > 0 ? datum.end - {x_shift} : datum.end + {x_shift}"
         )
@@ -221,15 +220,15 @@ def plot_shap_waterfall_with_quantiles(df, height=300):
     )
     text_label_start = (
         alt.Chart(df_text_labels)
-        .transform_filter(q_val)
-        .transform_filter(alt.datum["type"] == "start")
+        .transform_filter("datum.quantile == quantile")
+        .transform_filter("datum.type == 'start'")
         .mark_text(align="left", color="black", dx=-16, dy=y_text_offset + 30)
         .encode(text=alt.Text("label"), x=alt.X("x:Q"))
     )
     text_label_end = (
         alt.Chart(df_text_labels)
-        .transform_filter(q_val)
-        .transform_filter(alt.datum["type"] == "end")
+        .transform_filter("datum.quantile == quantile")
+        .transform_filter("datum.type == 'end'")
         .mark_text(align="left", color="black", dx=-8, dy=-y_text_offset - 15)
         .encode(text=alt.Text("label"), x=alt.X("x:Q"))
     )
@@ -255,23 +254,23 @@ def plot_shap_waterfall_with_quantiles(df, height=300):
     )
     tick_start_rule = (
         alt.Chart(df_text_labels)
-        .transform_filter(q_val)
-        .transform_filter(alt.datum["type"] == "start")
+        .transform_filter("datum.quantile == quantile")
+        .transform_filter("datum.type == 'start'")
         .mark_rule(color="black", opacity=1, y=height, y2=height + 6)
         .encode(x=alt.X("x:Q"))
     )
     tick_end_rule = (
         alt.Chart(df_text_labels)
-        .transform_filter(q_val)
-        .transform_filter(alt.datum["type"] == "end")
+        .transform_filter("datum.quantile == quantile")
+        .transform_filter("datum.type == 'end'")
         .mark_rule(color="black", opacity=1, y=0, y2=-6)
         .encode(x=alt.X("x:Q"))
     )
     rules = feature_bar_rule + end_bar_rule + tick_start_rule + tick_end_rule
 
     chart = (
         (bars + points + text + rules)
-        .add_params(q_val)
+        .add_params(quantile_selection)
         .configure_view(strokeOpacity=0)
         .properties(
             width=600, height=height, title="Waterfall Plot of SHAP Values for QRF Predictions"