Update examples

docs/conf.py

@@ -16,6 +16,7 @@
 from datetime import datetime
 
 sys.path.insert(0, os.path.abspath("."))
+sys.path.insert(0, os.path.abspath(".."))
 
 # If extensions (or modules to document with autodoc) are in another directory,
 # add these directories to sys.path here. If the directory is relative to the

docs/sphinxext/gallery.py

@@ -16,7 +16,7 @@
 from docutils.statemachine import ViewList
 from sphinx.util.nodes import nested_parse_with_titles
 
-from quantile_forest.tests.examples import iter_examples
+from examples import iter_examples
 
 from .utils import create_generic_image, create_thumbnail, get_docstring_and_rest, prev_this_next
 

quantile_forest/tests/examples/plot_quantile_extrapolation.py → examples/plot_quantile_extrapolation.py

@@ -40,7 +40,7 @@ def get_train_Xy(X, y, min_idx, max_idx):
     return X_train, y_train
 
 
-def get_test_X(X):
+def get_test_X(X, bounds):
     n_samples = len(X)
     X_test = np.atleast_2d(np.linspace(*bounds, n_samples)).T
     return X_test
@@ -56,7 +56,7 @@ def get_test_X(X):
 # Based on the extrapolation bounds, get the training and test data.
 # Training data excludes extrapolated regions; test data includes them.
 X_train, y_train = get_train_Xy(X, y, extrap_min_idx, extrap_max_idx)
-X_test = get_test_X(X)
+X_test = get_test_X(X, bounds)
 
 qrf = RandomForestQuantileRegressor(max_samples_leaf=None, min_samples_leaf=10, random_state=0)
 qrf.fit(np.expand_dims(X_train, axis=-1), y_train)

quantile_forest/tests/examples/plot_quantile_interpolation.py → examples/plot_quantile_interpolation.py

@@ -17,18 +17,6 @@
 
 from quantile_forest import RandomForestQuantileRegressor
 
-interpolations = {
-    "Linear": "#006aff",
-    "Lower": "#ffd237",
-    "Higher": "#0d4599",
-    "Midpoint": "#f2a619",
-    "Nearest": "#a6e5ff",
-}
-
-# legend = {"Actual": "#000000"} | interpolations
-legend = {"Actual": "#000000"}
-legend.update(interpolations)
-
 # Create toy dataset.
 X = np.array([[-1, -1], [-1, -1], [-1, -1], [1, 1], [1, 1]])
 y = np.array([-2, -1, 0, 1, 2])
@@ -41,6 +29,18 @@
 )
 est.fit(X, y)
 
+interpolations = {
+    "Linear": "#006aff",
+    "Lower": "#ffd237",
+    "Higher": "#0d4599",
+    "Midpoint": "#f2a619",
+    "Nearest": "#a6e5ff",
+}
+
+# legend = {"Actual": "#000000"} | interpolations
+legend = {"Actual": "#000000"}
+legend.update(interpolations)
+
 # Initialize data with actual values.
 data = {
     "method": ["Actual"] * len(y),

quantile_forest/tests/examples/plot_quantile_intervals.py → examples/plot_quantile_intervals.py

@@ -72,7 +72,7 @@ def plot_calibration(df):
                 "y_pred:Q",
                 axis=alt.Axis(format="$,d"),
                 scale=alt.Scale(domain=domain, nice=False),
-                title="Fitted Values (Conditional Mean)",
+                title="Fitted Values (conditional median)",
             ),
             y=alt.Y(
                 "y_true:Q",
@@ -152,7 +152,7 @@ def plot_intervals(df):
             y=alt.Y(
                 "y_true:Q",
                 axis=alt.Axis(format="$,d"),
-                title="Observed Values and Prediction Intervals",
+                title="Observed Values and Prediction Intervals (centered)",
             ),
             color=alt.value("#f2a619"),
             tooltip=tooltip,

quantile_forest/tests/examples/plot_quantile_vs_standard.py → examples/plot_quantile_vs_standard.py

@@ -64,8 +64,6 @@ def plot_prediction_histograms(df, legend):
         .transform_calculate(calculate=f"round({alt.datum['rf']} * 10) / 10", as_="RF (Mean)")
         .transform_calculate(calculate=f"round({alt.datum['qrf']} * 10) / 10", as_="QRF (Median)")
         .transform_fold(["Actual", "RF (Mean)", "QRF (Median)"], as_=["label", "value"])
-        .transform_joinaggregate(total="count(*)", groupby=["label"])
-        .transform_calculate(pct="1 / datum.total")
         .mark_bar()
         .encode(
             x=alt.X(
@@ -74,15 +72,15 @@ def plot_prediction_histograms(df, legend):
                     labelAngle=0,
                     labelExpr="datum.value % 0.5 == 0 ? datum.value : null",
                 ),
-                title="Value",
+                title="Actual and Predicted Target Values",
             ),
-            y=alt.Y("sum(pct):Q", axis=alt.Axis(format=".0%", title="Percentage")),
+            y=alt.Y("count():Q", axis=alt.Axis(format=",d", title="Counts")),
             color=color,
             xOffset=alt.XOffset("label:N"),
             tooltip=[
                 alt.Tooltip("label:N", title="Label"),
                 alt.Tooltip("value:O", title="Value (binned)"),
-                alt.Tooltip("sum(pct):Q", format=".0%", title="Percentage"),
+                alt.Tooltip("count():Q", format=",d", title="Counts"),
             ],
         )
         .add_params(click)