Add Quantile Ranks Example (#69)

Create plot_quantile_ranks.py

examples/plot_quantile_ranks.py

@@ -0,0 +1,84 @@
+"""
+Using Quantile Ranks to Identify Potential Outliers
+===================================================
+
+This example demonstrates the use of quantile regression forest (QRF) quantile
+ranks to identify potential outlier samples. In this scenario, we train a QRF
+model on a toy dataset and use quantile ranks to highlight values that deviate
+significantly from the expected range.
+"""
+
+import math
+
+import altair as alt
+import numpy as np
+import pandas as pd
+
+from quantile_forest import RandomForestQuantileRegressor
+
+n_samples = 5000
+bounds = [0, 10]
+
+
+def make_toy_dataset(n_samples, bounds, add_noise=True, random_seed=0):
+    rng = np.random.RandomState(random_seed)
+    X_1d = np.linspace(*bounds, num=n_samples)
+    X = X_1d.reshape(-1, 1)
+    y = X_1d * np.cos(X_1d) + rng.normal(scale=X_1d / math.e)
+    return X, y
+
+
+X, y = make_toy_dataset(n_samples, bounds, add_noise=True, random_seed=0)
+
+params = {"max_samples_leaf": None, "min_samples_leaf": 50, "random_state": 0}
+qrf = RandomForestQuantileRegressor(**params).fit(X, y)
+
+y_pred = qrf.predict(X)
+y_ranks = qrf.quantile_ranks(X, y)
+
+df = pd.DataFrame(
+    {
+        "x": X.reshape(-1),
+        "y": y,
+        "y_pred": y_pred,
+        "y_rank": y_ranks,
+    }
+)
+
+
+def plot_fit_and_ranks(df):
+    slider = alt.binding_range(min=0, max=1, step=0.01, name="Rank Interval Threshold: ")
+    rank_val = alt.param("rank_val", bind=slider, value=0.05)
+
+    base = alt.Chart(df)
+    points = (
+        base.transform_calculate(
+            outlier="abs(datum.y_rank - 0.5) > (0.5 - rank_val / 2) ? 'Yes' : 'No'"
+        )
+        .add_params(rank_val)
+        .mark_circle(opacity=0.5, size=25)
+        .encode(
+            x=alt.X("x:Q"),
+            y=alt.Y("y:Q"),
+            color=alt.condition(f"datum.outlier == 'Yes'", alt.value("red"), alt.value("#f2a619")),
+            tooltip=[
+                alt.Tooltip("x:Q", format=".3f", title="x"),
+                alt.Tooltip("y:Q", format=".3f", title="f(x)"),
+                alt.Tooltip("y_rank:Q", format=".3f", title="Quantile Rank"),
+                alt.Tooltip("outlier:N", title="Outlier"),
+            ],
+        )
+    )
+    line_pred = base.mark_line(color="#006aff", size=4).encode(
+        x=alt.X("x", axis=alt.Axis(title="x")), y=alt.Y("y_pred", axis=alt.Axis(title="f(x)"))
+    )
+
+    chart = (points + line_pred).properties(
+        height=400, width=650, title="QRF Predictions with Quantile Rank Thresholding"
+    )
+
+    return chart
+
+
+chart = plot_fit_and_ranks(df)
+chart