deploy: cb90e76

_sources/gallery/plot_quantile_extrapolation.rst.txt

@@ -46,7 +46,7 @@ adapted from `"Extrapolation-Aware Nonparametric Statistical Inference"
     qrf_params = {"min_samples_leaf": 4, "max_samples_leaf": None, "random_state": random_state}
 
 
-    def make_func_Xy(func, bounds, n_samples, add_noise=True, random_state=0):
+    def make_func_Xy(func, n_samples, bounds, add_noise=True, random_state=0):
         """Make a dataset from a specified function."""
         random_state = check_random_state(random_state)
 
@@ -159,7 +159,7 @@ adapted from `"Extrapolation-Aware Nonparametric Statistical Inference"
                 tree.fit(X[split1, :], Y[split1].flatten())
 
                 # Extract tree weight matrix.
-                y_train_leaves = tree._get_y_train_leaves(X[split2, :], Y)
+                y_train_leaves = tree._get_y_train_leaves(X[split2, :], Y.reshape(-1, 1))
                 nrows = X[split2, :].shape[0]
                 matrix = np.zeros((nrows, nrows))
                 for leaf in y_train_leaves[0]:
@@ -381,7 +381,7 @@ adapted from `"Extrapolation-Aware Nonparametric Statistical Inference"
 
 
     # Create a dataset that requires extrapolation.
-    X, y = make_func_Xy(func, bounds, n_samples, add_noise=True, random_state=0)
+    X, y = make_func_Xy(func, n_samples, bounds, add_noise=True, random_state=0)
 
     # Fit and extrapolate based on train-test split (depending on X).
     extrap_min_idx = int(n_samples * (extrap_frac / 2))
@@ -603,7 +603,7 @@ adapted from `"Extrapolation-Aware Nonparametric Statistical Inference"
             qrf_params = {"min_samples_leaf": 4, "max_samples_leaf": None, "random_state": random_state}
 
 
-            def make_func_Xy(func, bounds, n_samples, add_noise=True, random_state=0):
+            def make_func_Xy(func, n_samples, bounds, add_noise=True, random_state=0):
                 """Make a dataset from a specified function."""
                 random_state = check_random_state(random_state)
 
@@ -716,7 +716,7 @@ adapted from `"Extrapolation-Aware Nonparametric Statistical Inference"
                         tree.fit(X[split1, :], Y[split1].flatten())
 
                         # Extract tree weight matrix.
-                        y_train_leaves = tree._get_y_train_leaves(X[split2, :], Y)
+                        y_train_leaves = tree._get_y_train_leaves(X[split2, :], Y.reshape(-1, 1))
                         nrows = X[split2, :].shape[0]
                         matrix = np.zeros((nrows, nrows))
                         for leaf in y_train_leaves[0]:
@@ -938,7 +938,7 @@ adapted from `"Extrapolation-Aware Nonparametric Statistical Inference"
 
 
             # Create a dataset that requires extrapolation.
-            X, y = make_func_Xy(func, bounds, n_samples, add_noise=True, random_state=0)
+            X, y = make_func_Xy(func, n_samples, bounds, add_noise=True, random_state=0)
 
             # Fit and extrapolate based on train-test split (depending on X).
             extrap_min_idx = int(n_samples * (extrap_frac / 2))

_sources/gallery/plot_quantile_multioutput.rst.txt

@@ -52,8 +52,8 @@ 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_func_Xy(funcs, bounds, n_samples):
-        """Make a dataset from specified function(s)."""
+    def make_funcs_Xy(funcs, bounds, n_samples):
+        """Make a dataset from specified function(s) with signal and noise."""
         x = np.linspace(*bounds, n_samples)
         y = np.empty((len(x), len(funcs)))
         for i, func in enumerate(funcs):
@@ -62,7 +62,7 @@ for each target: the median line and the area defined by the interval points.
 
 
     # Create a dataset with multiple target variables.
-    X, y = make_func_Xy(funcs, bounds, n_samples)
+    X, y = make_funcs_Xy(funcs, bounds, n_samples)
 
     X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=random_state)
 
@@ -202,8 +202,8 @@ 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_func_Xy(funcs, bounds, n_samples):
-                """Make a dataset from specified function(s)."""
+            def make_funcs_Xy(funcs, bounds, n_samples):
+                """Make a dataset from specified function(s) with signal and noise."""
                 x = np.linspace(*bounds, n_samples)
                 y = np.empty((len(x), len(funcs)))
                 for i, func in enumerate(funcs):
@@ -212,7 +212,7 @@ for each target: the median line and the area defined by the interval points.
 
 
             # Create a dataset with multiple target variables.
-            X, y = make_func_Xy(funcs, bounds, n_samples)
+            X, y = make_funcs_Xy(funcs, bounds, n_samples)
 
             X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=random_state)
 

_sources/gallery/plot_quantile_vs_standard.rst.txt

@@ -39,7 +39,7 @@ distributions.
     quantiles = np.linspace(0, 1, num=101, endpoint=True).round(2).tolist()
 
 
-    def make_skewed_dataset(a=7, loc=-1, scale=1, random_state=0):
+    def make_skewed_dataset(n_samples, a=7, loc=-1, scale=1, random_state=0):
         """Make a skewed dataset."""
         random_state = check_random_state(random_state)
         skewnorm_rv = sp.stats.skewnorm(a, loc, scale)
@@ -50,7 +50,7 @@ distributions.
 
 
     # Create a right-skewed toy dataset.
-    X, y = make_skewed_dataset(a=7, loc=-1, scale=1, random_state=0)
+    X, y = make_skewed_dataset(n_samples, a=7, loc=-1, scale=1, random_state=0)
 
     regr_rf = RandomForestRegressor(random_state=random_state)
     regr_qrf = RandomForestQuantileRegressor(random_state=random_state)
@@ -156,7 +156,7 @@ distributions.
             quantiles = np.linspace(0, 1, num=101, endpoint=True).round(2).tolist()
 
 
-            def make_skewed_dataset(a=7, loc=-1, scale=1, random_state=0):
+            def make_skewed_dataset(n_samples, a=7, loc=-1, scale=1, random_state=0):
                 """Make a skewed dataset."""
                 random_state = check_random_state(random_state)
                 skewnorm_rv = sp.stats.skewnorm(a, loc, scale)
@@ -167,7 +167,7 @@ distributions.
 
 
             # Create a right-skewed toy dataset.
-            X, y = make_skewed_dataset(a=7, loc=-1, scale=1, random_state=0)
+            X, y = make_skewed_dataset(n_samples, a=7, loc=-1, scale=1, random_state=0)
 
             regr_rf = RandomForestRegressor(random_state=random_state)
             regr_qrf = RandomForestQuantileRegressor(random_state=random_state)

_static/_image_hashes.json

@@ -1 +1 @@
-{"plot_quantile_interpolation.png": "64403bde568aefd4126ce9afd13bdf18", "plot_predict_custom.png": "d93bb87e4412de61511ec04e7cfc57cc", "plot_quantile_extrapolation.png": "df5cd201a56427aecdd2b0fb67383b1e", "plot_quantile_multioutput.png": "a7db7a29994b823fbd5a7a3ea89e31b2", "plot_quantile_example.png": "56f2d452901be0aaa61cae8fdd382677", "plot_quantile_conformalized.png": "25fb11140f72b784df7c81538d28b4bc", "plot_quantile_intervals.png": "31f06cdda63b101d5d4cd7bb5c7242d1", "plot_quantile_vs_standard.png": "a7e09a7c286249020edb212a8c8964e5", "plot_treeshap_example.png": "390c464d8dd7b212f8bfe64e9e5bbf62", "plot_proximity_counts.png": "c3014295e7d995861eb4e1c2653dd9e4", "plot_quantile_ranks.png": "2dc7135b0065af3b72770ab39ce0aa6a", "plot_huggingface_model.png": "c87554d2fada2c6debe8c18c118efff8"}
+{"plot_quantile_interpolation.png": "64403bde568aefd4126ce9afd13bdf18", "plot_predict_custom.png": "d93bb87e4412de61511ec04e7cfc57cc", "plot_quantile_extrapolation.png": "8261d15d01301703a25a18a46543ec4a", "plot_quantile_multioutput.png": "f8f257b263ba6a69317db691760aa030", "plot_quantile_example.png": "56f2d452901be0aaa61cae8fdd382677", "plot_quantile_conformalized.png": "25fb11140f72b784df7c81538d28b4bc", "plot_quantile_intervals.png": "31f06cdda63b101d5d4cd7bb5c7242d1", "plot_quantile_vs_standard.png": "a258d56f0dcabbaf4ec2080897f4e503", "plot_treeshap_example.png": "390c464d8dd7b212f8bfe64e9e5bbf62", "plot_proximity_counts.png": "c3014295e7d995861eb4e1c2653dd9e4", "plot_quantile_ranks.png": "2dc7135b0065af3b72770ab39ce0aa6a", "plot_huggingface_model.png": "c87554d2fada2c6debe8c18c118efff8"}

gallery/plot_quantile_extrapolation.html

@@ -450,7 +450,7 @@
 <span class="n">qrf_params</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;min_samples_leaf&quot;</span><span class="p">:</span> <span class="mi">4</span><span class="p">,</span> <span class="s2">&quot;max_samples_leaf&quot;</span><span class="p">:</span> <span class="kc">None</span><span class="p">,</span> <span class="s2">&quot;random_state&quot;</span><span class="p">:</span> <span class="n">random_state</span><span class="p">}</span>
 
 
-<span class="k">def</span> <span class="nf">make_func_Xy</span><span class="p">(</span><span class="n">func</span><span class="p">,</span> <span class="n">bounds</span><span class="p">,</span> <span class="n">n_samples</span><span class="p">,</span> <span class="n">add_noise</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
+<span class="k">def</span> <span class="nf">make_func_Xy</span><span class="p">(</span><span class="n">func</span><span class="p">,</span> <span class="n">n_samples</span><span class="p">,</span> <span class="n">bounds</span><span class="p">,</span> <span class="n">add_noise</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;Make a dataset from a specified function.&quot;&quot;&quot;</span>
     <span class="n">random_state</span> <span class="o">=</span> <span class="n">check_random_state</span><span class="p">(</span><span class="n">random_state</span><span class="p">)</span>
 
@@ -563,7 +563,7 @@
             <span class="n">tree</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">[</span><span class="n">split1</span><span class="p">,</span> <span class="p">:],</span> <span class="n">Y</span><span class="p">[</span><span class="n">split1</span><span class="p">]</span><span class="o">.</span><span class="n">flatten</span><span class="p">())</span>
 
             <span class="c1"># Extract tree weight matrix.</span>
-            <span class="n">y_train_leaves</span> <span class="o">=</span> <span class="n">tree</span><span class="o">.</span><span class="n">_get_y_train_leaves</span><span class="p">(</span><span class="n">X</span><span class="p">[</span><span class="n">split2</span><span class="p">,</span> <span class="p">:],</span> <span class="n">Y</span><span class="p">)</span>
+            <span class="n">y_train_leaves</span> <span class="o">=</span> <span class="n">tree</span><span class="o">.</span><span class="n">_get_y_train_leaves</span><span class="p">(</span><span class="n">X</span><span class="p">[</span><span class="n">split2</span><span class="p">,</span> <span class="p">:],</span> <span class="n">Y</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
             <span class="n">nrows</span> <span class="o">=</span> <span class="n">X</span><span class="p">[</span><span class="n">split2</span><span class="p">,</span> <span class="p">:]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
             <span class="n">matrix</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">nrows</span><span class="p">,</span> <span class="n">nrows</span><span class="p">))</span>
             <span class="k">for</span> <span class="n">leaf</span> <span class="ow">in</span> <span class="n">y_train_leaves</span><span class="p">[</span><span class="mi">0</span><span class="p">]:</span>
@@ -785,7 +785,7 @@
 
 
 <span class="c1"># Create a dataset that requires extrapolation.</span>
-<span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">make_func_Xy</span><span class="p">(</span><span class="n">func</span><span class="p">,</span> <span class="n">bounds</span><span class="p">,</span> <span class="n">n_samples</span><span class="p">,</span> <span class="n">add_noise</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+<span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">make_func_Xy</span><span class="p">(</span><span class="n">func</span><span class="p">,</span> <span class="n">n_samples</span><span class="p">,</span> <span class="n">bounds</span><span class="p">,</span> <span class="n">add_noise</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
 
 <span class="c1"># Fit and extrapolate based on train-test split (depending on X).</span>
 <span class="n">extrap_min_idx</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">n_samples</span> <span class="o">*</span> <span class="p">(</span><span class="n">extrap_frac</span> <span class="o">/</span> <span class="mi">2</span><span class="p">))</span>

gallery/plot_quantile_vs_standard.html

@@ -444,7 +444,7 @@
 <span class="n">quantiles</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">num</span><span class="o">=</span><span class="mi">101</span><span class="p">,</span> <span class="n">endpoint</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
 
 
-<span class="k">def</span> <span class="nf">make_skewed_dataset</span><span class="p">(</span><span class="n">a</span><span class="o">=</span><span class="mi">7</span><span class="p">,</span> <span class="n">loc</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
+<span class="k">def</span> <span class="nf">make_skewed_dataset</span><span class="p">(</span><span class="n">n_samples</span><span class="p">,</span> <span class="n">a</span><span class="o">=</span><span class="mi">7</span><span class="p">,</span> <span class="n">loc</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;Make a skewed dataset.&quot;&quot;&quot;</span>
     <span class="n">random_state</span> <span class="o">=</span> <span class="n">check_random_state</span><span class="p">(</span><span class="n">random_state</span><span class="p">)</span>
     <span class="n">skewnorm_rv</span> <span class="o">=</span> <span class="n">sp</span><span class="o">.</span><span class="n">stats</span><span class="o">.</span><span class="n">skewnorm</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">loc</span><span class="p">,</span> <span class="n">scale</span><span class="p">)</span>
@@ -455,7 +455,7 @@
 
 
 <span class="c1"># Create a right-skewed toy dataset.</span>
-<span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">make_skewed_dataset</span><span class="p">(</span><span class="n">a</span><span class="o">=</span><span class="mi">7</span><span class="p">,</span> <span class="n">loc</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+<span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">make_skewed_dataset</span><span class="p">(</span><span class="n">n_samples</span><span class="p">,</span> <span class="n">a</span><span class="o">=</span><span class="mi">7</span><span class="p">,</span> <span class="n">loc</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
 
 <span class="n">regr_rf</span> <span class="o">=</span> <span class="n">RandomForestRegressor</span><span class="p">(</span><span class="n">random_state</span><span class="o">=</span><span class="n">random_state</span><span class="p">)</span>
 <span class="n">regr_qrf</span> <span class="o">=</span> <span class="n">RandomForestQuantileRegressor</span><span class="p">(</span><span class="n">random_state</span><span class="o">=</span><span class="n">random_state</span><span class="p">)</span>