From ef9dd8556acd86a595c6ef6146e6c5c7a695d429 Mon Sep 17 00:00:00 2001 From: reidjohnson Date: Fri, 30 Aug 2024 05:37:33 +0000 Subject: [PATCH] deploy: cb90e76fec422f8c97a0972bf436d62d889c9a1e --- .../plot_quantile_extrapolation.rst.txt | 12 ++++++------ .../gallery/plot_quantile_multioutput.rst.txt | 12 ++++++------ .../gallery/plot_quantile_vs_standard.rst.txt | 8 ++++---- _static/_image_hashes.json | 2 +- _static/plot_proximity_counts-thumb.png | Bin 24001 -> 23863 bytes _static/plot_proximity_counts.png | Bin 78780 -> 78866 bytes _static/plot_quantile_multioutput-thumb.png | Bin 52334 -> 52713 bytes _static/plot_quantile_multioutput.png | Bin 306765 -> 307297 bytes gallery/plot_proximity_counts.html | 2 +- gallery/plot_quantile_extrapolation.html | 6 +++--- gallery/plot_quantile_multioutput.html | 8 ++++---- gallery/plot_quantile_vs_standard.html | 4 ++-- searchindex.js | 2 +- 13 files changed, 28 insertions(+), 28 deletions(-) diff --git a/_sources/gallery/plot_quantile_extrapolation.rst.txt b/_sources/gallery/plot_quantile_extrapolation.rst.txt index 94092f1..e86885d 100644 --- a/_sources/gallery/plot_quantile_extrapolation.rst.txt +++ b/_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)) diff --git a/_sources/gallery/plot_quantile_multioutput.rst.txt b/_sources/gallery/plot_quantile_multioutput.rst.txt index 382b84d..282a1ad 100644 --- a/_sources/gallery/plot_quantile_multioutput.rst.txt +++ b/_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) diff --git a/_sources/gallery/plot_quantile_vs_standard.rst.txt b/_sources/gallery/plot_quantile_vs_standard.rst.txt index 1cb927e..b3a51fb 100644 --- a/_sources/gallery/plot_quantile_vs_standard.rst.txt +++ b/_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) diff --git a/_static/_image_hashes.json b/_static/_image_hashes.json index 59f940d..f4b685e 100644 --- a/_static/_image_hashes.json +++ b/_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": 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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) @@ -563,7 +563,7 @@ 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]: @@ -785,7 +785,7 @@ # 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)) diff --git a/gallery/plot_quantile_multioutput.html 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np.empty((len(x), len(funcs))) for i, func in enumerate(funcs): @@ -467,7 +467,7 @@ # 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) diff --git a/gallery/plot_quantile_vs_standard.html b/gallery/plot_quantile_vs_standard.html index 236814b..ce1bace 100644 --- a/gallery/plot_quantile_vs_standard.html +++ b/gallery/plot_quantile_vs_standard.html @@ -444,7 +444,7 @@ 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) @@ -455,7 +455,7 @@ # 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) diff --git a/searchindex.js b/searchindex.js index fa1f261..1da0cf4 100644 --- a/searchindex.js +++ b/searchindex.js @@ -1 +1 @@ -Search.setIndex({"alltitles": {"API Reference": [[0, null]], "Comparing Quantile Interpolation Methods": [[8, null]], "Computing User-Specified Functions with QRFs": [[3, null]], "Developer\u2019s Guide": [[16, null]], "Development Dependencies": [[16, "development-dependencies"]], "Development Installation": [[16, "development-installation"]], "Documentation": [[16, "documentation"]], "ExtraTreesQuantileRegressor": [[14, null]], "Extrapolation with Quantile Regression Forests": [[7, null]], "Fitting a Model": [[21, "fitting-a-model"]], "Fitting and Predicting": [[21, null]], "General Examples": [[1, null]], "Getting Started": [[17, null]], 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