Update example plots

examples/plot_proximity_counts.py

@@ -28,6 +28,9 @@
 n_test_samples = 25
 noise_std = 0.1
 
+pixel_dim = (8, 8)  # pixel dimensions (width and height)
+pixel_scale = 100  # scale multipler for combining clean and noisy values
+
 # Load the Digits dataset.
 X, y = datasets.load_digits(return_X_y=True, as_frame=True)
 
@@ -85,7 +88,7 @@ def extract_floats(combined_df, scale=100):
 )
 
 df = (
-    combine_floats(X_test, X_test_noisy)  # combine to reduce transmitted data
+    combine_floats(X_test, X_test_noisy, scale=pixel_scale)  # combine to reduce transmitted data
     .join(y_test)
     .reset_index()
     .join(df_prox)
@@ -103,7 +106,7 @@ def extract_floats(combined_df, scale=100):
 
 # Create a data frame for looking up training proximities.
 df_lookup = (
-    combine_floats(X_train, X_train_noisy)  # combine to reduce transmitted data
+    combine_floats(X_train, X_train_noisy, scale=pixel_scale)  # combine to reduce transmitted data
     .assign(**{"index": np.arange(len(X_train))})
     .join(y_train)
 )
@@ -112,14 +115,15 @@ def extract_floats(combined_df, scale=100):
 def plot_digits_proximities(
     df,
     df_lookup,
+    pixel_dim=(8, 8),
+    pixel_scale=100,
     n_prox=25,
     n_prox_per_row=5,
     subplot_spacing=10,
     height=225,
     width=225,
 ):
-    pixel_scale = 100
-    pixel_cols = [f"pixel_{y:01}_{x:01}" for y in range(8) for x in range(8)]
+    pixel_cols = [f"pixel_{y:01}_{x:01}" for y in range(pixel_dim[1]) for x in range(pixel_dim[0])]
     pixel_x = "split(datum.pixel, '_')[2]"
     pixel_y = "split(datum.pixel, '_')[1]"
 
@@ -227,5 +231,5 @@ def plot_digits_proximities(
     return chart
 
 
-chart = plot_digits_proximities(df, df_lookup)
+chart = plot_digits_proximities(df, df_lookup, pixel_dim=pixel_dim, pixel_scale=pixel_scale)
 chart

examples/plot_quantile_extrapolation.py

@@ -25,11 +25,10 @@
 
 random_state = np.random.RandomState(0)
 n_samples = 500
-bounds = [0, 15]
 extrap_frac = 0.25
+bounds = [0, 15]
 func = lambda x: x * np.sin(x)
 func_str = "f(x) = x sin(x)"
-
 quantiles = [0.025, 0.975, 0.5]
 qrf_params = {"max_samples_leaf": None, "min_samples_leaf": 4, "random_state": random_state}
 
@@ -403,8 +402,8 @@ def get_coverage_xtr(bounds_list, train_indices, test_indices, y_train, level, *
 )
 
 
-def plot_qrf_vs_xtrapolation_comparison(df):
-    def plot_extrapolations(df, title="", legend=False, x_domain=None, y_domain=None):
+def plot_qrf_vs_xtrapolation_comparison(df, func_str):
+    def plot_extrapolations(df, title="", legend=False, func_str="", x_domain=None, y_domain=None):
         x_scale = None
         if x_domain is not None:
             x_scale = alt.Scale(domain=x_domain, nice=False, padding=0)
@@ -517,7 +516,7 @@ def plot_extrapolations(df, title="", legend=False, x_domain=None, y_domain=None
         chart = chart.properties(title=title, height=200, width=300)
         return chart
 
-    kwargs = {"x_domain": [0, 15], "y_domain": [-15, 20]}
+    kwargs = {"func_str": func_str, "x_domain": [0, 15], "y_domain": [-15, 20]}
     xtra_mapper = {"bb_mid": "y_pred", "bb_low": "y_pred_low", "bb_upp": "y_pred_upp"}
 
     chart1 = alt.layer(
@@ -555,5 +554,5 @@ def plot_extrapolations(df, title="", legend=False, x_domain=None, y_domain=None
     return chart
 
 
-chart = plot_qrf_vs_xtrapolation_comparison(df)
+chart = plot_qrf_vs_xtrapolation_comparison(df, func_str)
 chart

examples/plot_quantile_multioutput.py

@@ -27,17 +27,16 @@
     {
         "signal": lambda x: np.log1p(x + 1),
         "noise": lambda x: np.log1p(x) * random_state.uniform(size=len(x)),
+        "legend": {"0": "#f2a619"},  # plot legend value and color
     },
     {
         "signal": lambda x: np.log1p(np.sqrt(x)),
         "noise": lambda x: np.log1p(x / 2) * random_state.uniform(size=len(x)),
+        "legend": {"1": "#006aff"},  # plot legend value and color
     },
 ]
 
-legend = {
-    "0": "#f2a619",
-    "1": "#006aff",
-}
+legend = {k: v for f in funcs for k, v in f["legend"].items()}
 
 
 def make_func_Xy(funcs, bounds, n_samples):
@@ -48,10 +47,6 @@ def make_func_Xy(funcs, bounds, n_samples):
     return np.atleast_2d(x).T, y
 
 
-def format_frac(fraction):
-    return f"{fraction:.3g}".rstrip("0").rstrip(".") or "0"
-
-
 # Create the dataset with multiple target variables.
 X, y = make_func_Xy(funcs, bounds, n_samples)
 
@@ -70,7 +65,7 @@ def format_frac(fraction):
         "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_{format_frac(q_i)}": y_i.ravel() for q_i, y_i in zip(quantiles, y_pred.T)},
+        **{f"q_{q_i:.3g}": y_i.ravel() for q_i, y_i in zip(quantiles, y_pred.T)},
     }
 )
 

examples/plot_quantile_vs_standard.py

@@ -21,10 +21,10 @@
 from quantile_forest import RandomForestQuantileRegressor
 
 random_state = np.random.RandomState(0)
+n_samples = 5000
 quantiles = np.linspace(0, 1, num=101, endpoint=True).round(2).tolist()
 
 # Create right-skewed dataset.
-n_samples = 5000
 a, loc, scale = 7, -1, 1
 skewnorm_rv = sp.stats.skewnorm(a, loc, scale)
 skewnorm_rv.random_state = random_state
@@ -48,16 +48,11 @@
     "QRF (Median)": "#006aff",
 }
 
-
-def format_frac(fraction):
-    return f"{fraction:.3g}".rstrip("0").rstrip(".") or "0"
-
-
 df = pd.DataFrame(
     {
         "actual": y_test,
         "rf": y_pred_rf,
-        **{f"qrf_{format_frac(q_i)}": y_i.ravel() for q_i, y_i in zip(quantiles, y_pred_qrf.T)},
+        **{f"qrf_{q_i:.3g}": y_i.ravel() for q_i, y_i in zip(quantiles, y_pred_qrf.T)},
     }
 )