deploy: 3c9c784

_sources/gallery/plot_qrf_interpolation_methods.rst.txt

@@ -66,8 +66,8 @@ handled when a quantile does not exactly match a data point.
             "method": ["Actual"] * len(y),
             "X": [f"Sample {idx + 1} ({x})" for idx, x in enumerate(X.tolist())],
             "y_pred": y.tolist(),
-            "y_pred_low": y.tolist(),
-            "y_pred_upp": y.tolist(),
+            "y_pred_low": [None] * len(y),
+            "y_pred_upp": [None] * len(y),
             "quantile_low": [None] * len(y),
             "quantile_upp": [None] * len(y),
         }
@@ -225,8 +225,8 @@ handled when a quantile does not exactly match a data point.
                     "method": ["Actual"] * len(y),
                     "X": [f"Sample {idx + 1} ({x})" for idx, x in enumerate(X.tolist())],
                     "y_pred": y.tolist(),
-                    "y_pred_low": y.tolist(),
-                    "y_pred_upp": y.tolist(),
+                    "y_pred_low": [None] * len(y),
+                    "y_pred_upp": [None] * len(y),
                     "quantile_low": [None] * len(y),
                     "quantile_upp": [None] * len(y),
                 }

_sources/gallery/plot_qrf_multitarget.rst.txt

@@ -36,7 +36,7 @@ for each target: the median line and the area defined by the interval points.
     quantiles = np.linspace(0, 1, num=41, endpoint=True).round(3).tolist()
 
     # Define functions that generate targets; each function maps to one target.
-    funcs = [
+    target_funcs = [
         {
             "signal": lambda x: np.log1p(x + 1),
             "noise": lambda x: np.log1p(x) * random_state.uniform(size=len(x)),
@@ -49,18 +49,19 @@ for each target: the median line and the area defined by the interval points.
         },
     ]
 
-    legend = {k: v for f in funcs for k, v in f["legend"].items()}
-
 
     def make_funcs_Xy(funcs, n_samples, bounds):
-        """Make a dataset from specified function(s) with signal and noise."""
+        """Make a dataset from specified function(s)."""
         x = np.linspace(*bounds, n_samples)
         y = np.empty((len(x), len(funcs)))
         for i, func in enumerate(funcs):
-            y[:, i] = func["signal"](x) + func["noise"](x)
+            y[:, i] = func(x)
         return np.atleast_2d(x).T, y
 
 
+    funcs = [lambda x, f=f: f["signal"](x) + f["noise"](x) for f in target_funcs]
+    legend = {k: v for f in target_funcs for k, v in f["legend"].items()}
+
     # Create a dataset with multiple target variables.
     X, y = make_funcs_Xy(funcs, n_samples, bounds)
 
@@ -76,7 +77,6 @@ for each target: the median line and the area defined by the interval points.
         {
             "x": np.tile(X.squeeze(), len(funcs)),
             "y": y.reshape(-1, order="F"),
-            "y_true": np.concatenate([f["signal"](X.squeeze()) for f in funcs]),
             "y_pred": np.concatenate([y_pred[:, i, len(quantiles) // 2] for i in range(len(funcs))]),
             "target": np.concatenate([[str(i)] * len(X) for i in range(len(funcs))]),
             **{f"q_{q_i:.3g}": y_i.ravel() for q_i, y_i in zip(quantiles, y_pred.T)},
@@ -108,7 +108,6 @@ for each target: the median line and the area defined by the interval points.
             alt.Tooltip("target:N", title="Target"),
             alt.Tooltip("x:Q", format=",.3f", title="X"),
             alt.Tooltip("y:Q", format=",.3f", title="Y"),
-            alt.Tooltip("y_true:Q", format=",.3f", title="Y"),
             alt.Tooltip("y_pred:Q", format=",.3f", title="Predicted Y"),
             alt.Tooltip("y_pred_low:Q", format=",.3f", title="Predicted Lower Y"),
             alt.Tooltip("y_pred_upp:Q", format=",.3f", title="Predicted Upper Y"),
@@ -186,7 +185,7 @@ for each target: the median line and the area defined by the interval points.
             quantiles = np.linspace(0, 1, num=41, endpoint=True).round(3).tolist()
 
             # Define functions that generate targets; each function maps to one target.
-            funcs = [
+            target_funcs = [
                 {
                     "signal": lambda x: np.log1p(x + 1),
                     "noise": lambda x: np.log1p(x) * random_state.uniform(size=len(x)),
@@ -199,18 +198,19 @@ for each target: the median line and the area defined by the interval points.
                 },
             ]
 
-            legend = {k: v for f in funcs for k, v in f["legend"].items()}
-
 
             def make_funcs_Xy(funcs, n_samples, bounds):
-                """Make a dataset from specified function(s) with signal and noise."""
+                """Make a dataset from specified function(s)."""
                 x = np.linspace(*bounds, n_samples)
                 y = np.empty((len(x), len(funcs)))
                 for i, func in enumerate(funcs):
-                    y[:, i] = func["signal"](x) + func["noise"](x)
+                    y[:, i] = func(x)
                 return np.atleast_2d(x).T, y
 
 
+            funcs = [lambda x, f=f: f["signal"](x) + f["noise"](x) for f in target_funcs]
+            legend = {k: v for f in target_funcs for k, v in f["legend"].items()}
+
             # Create a dataset with multiple target variables.
             X, y = make_funcs_Xy(funcs, n_samples, bounds)
 
@@ -226,7 +226,6 @@ for each target: the median line and the area defined by the interval points.
                 {
                     "x": np.tile(X.squeeze(), len(funcs)),
                     "y": y.reshape(-1, order="F"),
-                    "y_true": np.concatenate([f["signal"](X.squeeze()) for f in funcs]),
                     "y_pred": np.concatenate([y_pred[:, i, len(quantiles) // 2] for i in range(len(funcs))]),
                     "target": np.concatenate([[str(i)] * len(X) for i in range(len(funcs))]),
                     **{f"q_{q_i:.3g}": y_i.ravel() for q_i, y_i in zip(quantiles, y_pred.T)},
@@ -258,7 +257,6 @@ for each target: the median line and the area defined by the interval points.
                     alt.Tooltip("target:N", title="Target"),
                     alt.Tooltip("x:Q", format=",.3f", title="X"),
                     alt.Tooltip("y:Q", format=",.3f", title="Y"),
-                    alt.Tooltip("y_true:Q", format=",.3f", title="Y"),
                     alt.Tooltip("y_pred:Q", format=",.3f", title="Predicted Y"),
                     alt.Tooltip("y_pred_low:Q", format=",.3f", title="Predicted Lower Y"),
                     alt.Tooltip("y_pred_upp:Q", format=",.3f", title="Predicted Upper Y"),

_static/_image_hashes.json

@@ -1 +1 @@
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+{"plot_qrf_interpolation_methods.png": "e9ef9605621f0fe49946d59df4f50cb8", "plot_qrf_custom_functions.png": "2f257ba3ed3ea39ace3775dfaac212a1", "plot_qrf_extrapolation.png": "54e94b1539025c3d9c702131f6c5615c", "plot_qrf_multitarget.png": "1aa7118314c961afee806294cd4fd23a", "plot_qrf_predictions.png": "98dc4349f8db21311b7ba0e3a73723d1", "plot_qrf_conformalized_intervals.png": "b1b1ce4e8ee95f3bc13b3454a1ef43ad", "plot_qrf_prediction_intervals.png": "5c5daed975c01484ea8888a01ff887d2", "plot_qrf_vs_rf_predictions.png": "4253fe19a649541d50bf5d5bbd407206", "plot_qrf_treeshap_explanations.png": "5a87665596448c2371b828fde9b07e8e", "plot_qrf_proximity_counts.png": "ad7468e53cdd8d6ae0aecd3ac9760700", "plot_qrf_quantile_ranks.png": "5fc3187fb02476f94edf43aa918e0cd9", "plot_qrf_huggingface_inference.png": "512abbd5ec0f690e4361c4b0800db202"}