From 184d35007b3e40817e49d9023a08b88695f05271 Mon Sep 17 00:00:00 2001 From: reidjohnson Date: Sat, 17 Aug 2024 21:09:51 +0000 Subject: [PATCH] deploy: b40f858a9134309c3c391e555d380d4e1472e315 --- .../gallery/plot_proximity_counts.rst.txt | 28 ++++++++++++------- .../plot_quantile_extrapolation.rst.txt | 22 +++++++-------- .../gallery/plot_quantile_multioutput.rst.txt | 26 ++++++----------- .../gallery/plot_quantile_vs_standard.rst.txt | 18 +++--------- _static/_image_hashes.json | 2 +- gallery/plot_proximity_counts.html | 14 ++++++---- gallery/plot_quantile_extrapolation.html | 11 ++++---- gallery/plot_quantile_multioutput.html | 13 +++------ gallery/plot_quantile_vs_standard.html | 9 ++---- searchindex.js | 2 +- 10 files changed, 62 insertions(+), 83 deletions(-) diff --git a/_sources/gallery/plot_proximity_counts.rst.txt b/_sources/gallery/plot_proximity_counts.rst.txt index 831406d..0b81dc1 100644 --- a/_sources/gallery/plot_proximity_counts.rst.txt +++ b/_sources/gallery/plot_proximity_counts.rst.txt @@ -41,6 +41,9 @@ conditions. 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) @@ -98,7 +101,7 @@ conditions. ) 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) @@ -116,7 +119,7 @@ conditions. # 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) ) @@ -125,14 +128,15 @@ conditions. 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]" @@ -240,7 +244,7 @@ conditions. 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 @@ -261,6 +265,9 @@ conditions. 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) @@ -318,7 +325,7 @@ conditions. ) 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) @@ -336,7 +343,7 @@ conditions. # 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) ) @@ -345,14 +352,15 @@ conditions. 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]" @@ -460,5 +468,5 @@ conditions. 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 diff --git a/_sources/gallery/plot_quantile_extrapolation.rst.txt b/_sources/gallery/plot_quantile_extrapolation.rst.txt index 4a49af2..3b20f8a 100644 --- a/_sources/gallery/plot_quantile_extrapolation.rst.txt +++ b/_sources/gallery/plot_quantile_extrapolation.rst.txt @@ -38,11 +38,10 @@ adapted from `"Extrapolation-Aware Nonparametric Statistical Inference" 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} @@ -416,8 +415,8 @@ adapted from `"Extrapolation-Aware Nonparametric Statistical Inference" ) - 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) @@ -530,7 +529,7 @@ adapted from `"Extrapolation-Aware Nonparametric Statistical Inference" 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( @@ -568,7 +567,7 @@ adapted from `"Extrapolation-Aware Nonparametric Statistical Inference" return chart - chart = plot_qrf_vs_xtrapolation_comparison(df) + chart = plot_qrf_vs_xtrapolation_comparison(df, func_str) chart @@ -586,11 +585,10 @@ adapted from `"Extrapolation-Aware Nonparametric Statistical Inference" 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} @@ -964,8 +962,8 @@ adapted from `"Extrapolation-Aware Nonparametric Statistical Inference" ) - 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) @@ -1078,7 +1076,7 @@ adapted from `"Extrapolation-Aware Nonparametric Statistical Inference" 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( @@ -1116,5 +1114,5 @@ adapted from `"Extrapolation-Aware Nonparametric Statistical Inference" return chart - chart = plot_qrf_vs_xtrapolation_comparison(df) + chart = plot_qrf_vs_xtrapolation_comparison(df, func_str) chart diff --git a/_sources/gallery/plot_quantile_multioutput.rst.txt b/_sources/gallery/plot_quantile_multioutput.rst.txt index 933bf08..3840903 100644 --- a/_sources/gallery/plot_quantile_multioutput.rst.txt +++ b/_sources/gallery/plot_quantile_multioutput.rst.txt @@ -40,17 +40,16 @@ for each target: the median line and the area defined by the interval points. { "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): @@ -61,10 +60,6 @@ for each target: the median line and the area defined by the interval points. 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) @@ -83,7 +78,7 @@ for each target: the median line and the area defined by the interval points. "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)}, } ) @@ -193,17 +188,16 @@ for each target: the median line and the area defined by the interval points. { "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): @@ -214,10 +208,6 @@ for each target: the median line and the area defined by the interval points. 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) @@ -236,7 +226,7 @@ for each target: the median line and the area defined by the interval points. "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)}, } ) diff --git a/_sources/gallery/plot_quantile_vs_standard.rst.txt b/_sources/gallery/plot_quantile_vs_standard.rst.txt index ec62087..ff448d8 100644 --- a/_sources/gallery/plot_quantile_vs_standard.rst.txt +++ b/_sources/gallery/plot_quantile_vs_standard.rst.txt @@ -34,10 +34,10 @@ distributions. 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 @@ -61,16 +61,11 @@ distributions. "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)}, } ) @@ -147,10 +142,10 @@ distributions. 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 @@ -174,16 +169,11 @@ distributions. "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)}, } ) diff --git a/_static/_image_hashes.json b/_static/_image_hashes.json index 5a57561..0ec063a 100644 --- a/_static/_image_hashes.json +++ b/_static/_image_hashes.json @@ -1 +1 @@ -{"plot_quantile_interpolation.png": "a33104b33d451e87a61b63c0441186fc", "plot_predict_custom.png": "c165b6e2ec6d97e99ee0dcb308a33165", "plot_quantile_extrapolation.png": "254b782f7f280133734238b20b05b8e3", "plot_quantile_multioutput.png": "92bc73718cd417baa7c4c2bb3a738039", "plot_quantile_example.png": "5224ce52d0004de1b163f849090d146d", "plot_quantile_conformalized.png": "aa558540d703f9df5a841644c43a9a11", "plot_quantile_intervals.png": "55469191cce728f146eb6dc148a50a62", "plot_quantile_vs_standard.png": "951d2aa38319a848a163dae38a72a9db", "plot_treeshap_example.png": "f3315a150f9bb5f55c9c0cfaefcedf4a", "plot_proximity_counts.png": "dbb1c0dc04c4809529f6cd6cfc3c0649", "plot_quantile_ranks.png": "765687aea83e00d18de5404bf6210c9f", "plot_huggingface_model.png": "54f6ba0d8875e499e02114fd00fb3197"} \ No newline at end of file +{"plot_quantile_interpolation.png": "a33104b33d451e87a61b63c0441186fc", "plot_predict_custom.png": "c165b6e2ec6d97e99ee0dcb308a33165", "plot_quantile_extrapolation.png": "24853dd6e2acbf54d608fa7ee6953cf7", "plot_quantile_multioutput.png": "cb1bed13ecf47420f01a2a5d181aeb4c", "plot_quantile_example.png": "5224ce52d0004de1b163f849090d146d", "plot_quantile_conformalized.png": "aa558540d703f9df5a841644c43a9a11", "plot_quantile_intervals.png": "55469191cce728f146eb6dc148a50a62", "plot_quantile_vs_standard.png": "72d33d65c1a254072fcce892e2236d1d", "plot_treeshap_example.png": "f3315a150f9bb5f55c9c0cfaefcedf4a", "plot_proximity_counts.png": "3ef1800f1d1d128c966f3ca868b76897", "plot_quantile_ranks.png": "765687aea83e00d18de5404bf6210c9f", "plot_huggingface_model.png": "54f6ba0d8875e499e02114fd00fb3197"} \ No newline at end of file diff --git a/gallery/plot_proximity_counts.html b/gallery/plot_proximity_counts.html index 778811c..12edb19 100644 --- a/gallery/plot_proximity_counts.html +++ b/gallery/plot_proximity_counts.html @@ -446,6 +446,9 @@ 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) @@ -503,7 +506,7 @@ ) 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) @@ -521,7 +524,7 @@ # 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) ) @@ -530,14 +533,15 @@ 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]" @@ -645,7 +649,7 @@ 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 diff --git a/gallery/plot_quantile_extrapolation.html b/gallery/plot_quantile_extrapolation.html index bb1509c..5df02f7 100644 --- a/gallery/plot_quantile_extrapolation.html +++ b/gallery/plot_quantile_extrapolation.html @@ -442,11 +442,10 @@ 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} @@ -820,8 +819,8 @@ ) -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) @@ -934,7 +933,7 @@ 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( @@ -972,7 +971,7 @@ return chart -chart = plot_qrf_vs_xtrapolation_comparison(df) +chart = plot_qrf_vs_xtrapolation_comparison(df, func_str) chart diff --git a/gallery/plot_quantile_multioutput.html b/gallery/plot_quantile_multioutput.html index 088ab12..2410919 100644 --- a/gallery/plot_quantile_multioutput.html +++ b/gallery/plot_quantile_multioutput.html @@ -445,17 +445,16 @@ { "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): @@ -466,10 +465,6 @@ 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) @@ -488,7 +483,7 @@ "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)}, } ) diff --git a/gallery/plot_quantile_vs_standard.html b/gallery/plot_quantile_vs_standard.html index 1448ef0..baad005 100644 --- a/gallery/plot_quantile_vs_standard.html +++ b/gallery/plot_quantile_vs_standard.html @@ -439,10 +439,10 @@ 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 @@ -466,16 +466,11 @@ "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)}, } ) diff --git a/searchindex.js b/searchindex.js index 70e1f6d..3ad060a 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]], 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