From 5d8d10aa7884691e75ec595a9a7c68699805a647 Mon Sep 17 00:00:00 2001 From: reidjohnson Date: Sat, 14 Sep 2024 11:51:07 +0000 Subject: [PATCH] deploy: 3c9c784e8c9cfaa0d4103934d8a539290e614d6d --- .../plot_qrf_huggingface_inference.rst.txt | 375 +++++++++++++----- .../plot_qrf_interpolation_methods.rst.txt | 8 +- _sources/gallery/plot_qrf_multitarget.rst.txt | 26 +- _static/_image_hashes.json | 2 +- .../plot_qrf_huggingface_inference-thumb.png | Bin 53269 -> 28197 bytes _static/plot_qrf_huggingface_inference.png | Bin 351888 -> 106717 bytes gallery/plot_qrf_huggingface_inference.html | 196 ++++++--- gallery/plot_qrf_interpolation_methods.html | 6 +- gallery/plot_qrf_multitarget.html | 15 +- searchindex.js | 2 +- 10 files changed, 439 insertions(+), 191 deletions(-) diff --git a/_sources/gallery/plot_qrf_huggingface_inference.rst.txt b/_sources/gallery/plot_qrf_huggingface_inference.rst.txt index f7ecf50..8654f1a 100644 --- a/_sources/gallery/plot_qrf_huggingface_inference.rst.txt +++ b/_sources/gallery/plot_qrf_huggingface_inference.rst.txt @@ -11,12 +11,13 @@ Using a Trained QRF Model via Hugging Face Hub ============================================== This example demonstrates how to download a trained quantile regression forest -(QRF) model from Hugging Face Hub and use it to estimate new quantiles. In -this scenario, a QRF has been trained with default parameters on a train-test -split of the California housing dataset and uploaded to Hugging Face Hub. The -model is downloaded and used to perform inference across several quantiles for -each dataset sample. The results are visualized by the latitude and longitude -of each sample. The model used is available on Hugging Face Hub +(QRF) model from Hugging Face Hub and use it to estimate quantiles. In this +scenario, a QRF has been trained with default parameters on the California +Housing dataset using k-fold cross-validation and uploaded to Hugging Face +Hub. The model is downloaded and used to perform inference across multiple +quantiles for each sample in the dataset. The predictions are aggregated by +county based on the latitude and longitude of each sample and visualized. +The trained model is available on Hugging Face Hub `here `_. @@ -26,18 +27,19 @@ of each sample. The model used is available on Hugging Face Hub import os - import pickle import shutil import tempfile import altair as alt + import geopandas as gpd + import joblib import numpy as np import pandas as pd from sklearn import datasets + from sklearn.base import BaseEstimator, RegressorMixin, clone + from sklearn.model_selection import KFold from skops import hub_utils - - import quantile_forest - from quantile_forest import RandomForestQuantileRegressor + from vega_datasets import data alt.data_transformers.disable_max_rows() @@ -46,8 +48,53 @@ of each sample. The model used is available on Hugging Face Hub load_existing = True random_state = np.random.RandomState(0) - quantiles = np.linspace(0, 1, num=5, endpoint=True).round(2).tolist() - sample_frac = 1 + quantiles = np.linspace(0, 1, num=21, endpoint=True).round(2).tolist() + + + class CrossValidationPipeline(BaseEstimator, RegressorMixin): + """Cross-validation pipeline for scikit-learn compatible models.""" + + def __init__(self, base_model, n_splits=5, random_state=None): + self.base_model = base_model + self.n_splits = n_splits + self.random_state = random_state + self.fold_models = {} + self.fold_indices = {} + + def fit(self, X, y): + """Fit the model using k-fold cross-validation.""" + kf = KFold(n_splits=self.n_splits, shuffle=True, random_state=self.random_state) + for fold_idx, (train_idx, test_idx) in enumerate(kf.split(X)): + X_train, y_train = X.iloc[train_idx], y[train_idx] + model = clone(self.base_model) + model.fit(X_train, y_train) + self.fold_models[fold_idx] = model + self.fold_indices[fold_idx] = test_idx + return self + + def predict(self, X, quantiles=None): + """Predict using the appropriate k-fold model.""" + if quantiles is None: + quantiles = 0.5 + if not isinstance(quantiles, list): + quantiles = [quantiles] + y_pred = np.empty((X.shape[0], len(quantiles)) if len(quantiles) > 1 else (X.shape[0])) + for fold_idx, test_idx in self.fold_indices.items(): + fold_model = self.fold_models[fold_idx] + y_pred[test_idx] = fold_model.predict(X.iloc[test_idx], quantiles=quantiles) + return y_pred + + def save(self, filename): + with open(filename, "wb") as f: + joblib.dump(self.__getstate__(), f) + + @classmethod + def load(cls, filename): + with open(filename, "rb") as f: + state = joblib.load(f) + obj = cls(base_model=None) + obj.__setstate__(state) + return obj def fit_and_upload_model(token, repo_id, local_dir="./local_repo", random_state=None): @@ -60,21 +107,27 @@ of each sample. The model used is available on Hugging Face Hub median_absolute_error, r2_score, ) - from sklearn.model_selection import train_test_split + from sklearn.pipeline import Pipeline from skops import card + import quantile_forest + from quantile_forest import RandomForestQuantileRegressor + # Load the California Housing dataset. X, y = datasets.fetch_california_housing(as_frame=True, return_X_y=True) - X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=random_state) + # Define the model pipeline. + qrf = RandomForestQuantileRegressor(random_state=random_state) + pipeline = Pipeline( + [("cv_model", CrossValidationPipeline(qrf, n_splits=5, random_state=random_state))] + ) - # Fit the model. - qrf = RandomForestQuantileRegressor(random_state=random_state).fit(X_train, y_train) + # Fit the model pipeline. + pipeline.fit(X, y) - # Save the model to a file. + # Save the pipeline (with all models) to a file. model_filename = "model.pkl" - with open(model_filename, mode="bw") as f: - pickle.dump(qrf, file=f) + pipeline.named_steps["cv_model"].save(model_filename) # Prepare model repository. if os.path.exists(local_dir): @@ -87,7 +140,7 @@ of each sample. The model used is available on Hugging Face Hub requirements=[f"quantile-forest={quantile_forest.__version__}"], dst=local_dir, task="tabular-regression", - data=X_train, + data=X, ) # Create a model card. @@ -104,19 +157,18 @@ of each sample. The model used is available on Hugging Face Hub "prediction-intervals", ] model_description = ( - "This is a RandomForestQuantileRegressor trained on the California housing dataset." + "This is a RandomForestQuantileRegressor trained on the California Housing dataset." ) limitations = "This model is not ready to be used in production." training_procedure = ( - "The model was trained using default parameters on a standard train-test split." + "The model was trained using default parameters on a 5-fold cross-validation pipeline." ) get_started_code = """
Click to expand ```python - import pickle - with open(qrf_pkl_filename, 'rb') as file: - qrf = pickle.load(file) + from examples.plot_qrf_huggingface_inference import CrossValidationPipeline + pipeline = CrossValidationPipeline.load(qrf_pkl_filename) ```
""" @@ -130,11 +182,11 @@ of each sample. The model used is available on Hugging Face Hub ) # Add performance metrics to the model card. - y_pred = qrf.predict(X_test) - mape = mean_absolute_percentage_error(y_test, y_pred) - mdae = median_absolute_error(y_test, y_pred) - mse = mean_squared_error(y_test, y_pred) - r2 = r2_score(y_test, y_pred) + y_pred = pipeline.predict(X) + mape = mean_absolute_percentage_error(y, y_pred) + mdae = median_absolute_error(y, y_pred) + mse = mean_squared_error(y, y_pred) + r2 = r2_score(y, y_pred) model_card.add_metrics( **{ "Mean Absolute Percentage Error": mape, @@ -172,23 +224,21 @@ of each sample. The model used is available on Hugging Face Hub local_dir = "./local_repo" with tempfile.TemporaryDirectory() as local_dir: hub_utils.download(repo_id=repo_id, dst=local_dir) - with open(f"{local_dir}/{model_filename}", "rb") as file: - qrf = pickle.load(file) + pipeline = CrossValidationPipeline.load(f"{local_dir}/{model_filename}") # Fetch the California Housing dataset and estimate quantiles. X, y = datasets.fetch_california_housing(as_frame=True, return_X_y=True) - y_pred = qrf.predict(X, quantiles=quantiles) * 100_000 # predict in dollars + y_pred = pipeline.predict(X, quantiles=quantiles) * 100_000 # predict in dollars df = ( pd.DataFrame(y_pred, columns=quantiles) .reset_index() - .sample(frac=sample_frac, random_state=random_state) - .melt(id_vars=["index"], var_name="quantile", value_name="value") + .rename(columns={q: f"q_{q:.3g}" for q in quantiles}) .merge(X[["Latitude", "Longitude", "Population"]].reset_index(), on="index", how="right") ) - def plot_quantiles_by_latlon(df, quantiles, color_scheme="cividis"): + def plot_quantiles_by_latlon(df, quantiles, color_scheme="lightgreyred"): """Plot quantile predictions on California Housing dataset by lat/lon.""" # Slider for varying the displayed quantile estimates. slider = alt.binding_range( @@ -200,35 +250,69 @@ of each sample. The model used is available on Hugging Face Hub quantile_val = alt.param(name="quantile", value=0.5, bind=slider) + # Load the US counties data and filter to California counties. + ca_counties = ( + gpd.read_file(data.us_10m.url, layer="counties") + .set_crs("EPSG:4326") + .assign(**{"county_fips": lambda x: x["id"].astype(int)}) + .drop(columns=["id"]) + .query("(county_fips >= 6000) & (county_fips < 7000)") + ) + + x_min = df[[f"q_{q:.3g}" for q in quantiles]].min().min() + x_max = df[[f"q_{q:.3g}" for q in quantiles]].max().max() + + df = ( + gpd.GeoDataFrame( + df, geometry=gpd.points_from_xy(df["Longitude"], df["Latitude"]), crs="4326" + ) + .sjoin(ca_counties, how="right") + .drop(columns=["index_left0"]) + .assign( + **{f"w_q_{q:.3g}": lambda x, q=q: x[f"q_{q:.3g}"] * x["Population"] for q in quantiles} + ) + ) + + grouped = ( + df.groupby("county_fips") + .agg({**{f"w_q_{q:.3g}": "sum" for q in quantiles}, **{"Population": "sum"}}) + .reset_index() + .assign( + **{f"q_{q:.3g}": lambda x, q=q: x[f"w_q_{q:.3g}"] / x["Population"] for q in quantiles} + ) + ) + + df = ( + df[["county_fips", "Latitude", "Longitude", "geometry"]] + .drop_duplicates(subset=["county_fips"]) + .merge( + grouped[["county_fips", "Population"] + [f"q_{q:.3g}" for q in quantiles]], + on="county_fips", + how="left", + ) + ) + chart = ( alt.Chart(df) .add_params(quantile_val) - .transform_filter("datum.quantile == quantile") - .mark_circle() + .transform_calculate(quantile_col="'q_' + quantile") + .transform_calculate(value=f"datum[datum.quantile_col]") + .mark_geoshape(stroke="black", strokeWidth=0.5) .encode( - x=alt.X( - "Longitude:Q", - axis=alt.Axis(tickMinStep=1, format=".1f"), - scale=alt.Scale(zero=False), - title="Longitude", - ), - y=alt.Y( - "Latitude:Q", - axis=alt.Axis(tickMinStep=1, format=".1f"), - scale=alt.Scale(zero=False), - title="Latitude", + color=alt.Color( + "value:Q", + scale=alt.Scale(domain=[x_min, x_max], scheme=color_scheme), + title="Prediction", ), - color=alt.Color("value:Q", scale=alt.Scale(scheme=color_scheme), title="Prediction"), - size=alt.Size("Population:Q"), tooltip=[ - alt.Tooltip("index:N", title="Row ID"), - alt.Tooltip("Latitude:Q", format=".2f", title="Latitude"), - alt.Tooltip("Longitude:Q", format=".2f", title="Longitude"), + alt.Tooltip("county_fips:N", title="County FIPS"), + alt.Tooltip("Population:N", format=",.0f", title="Population"), alt.Tooltip("value:Q", format="$,.0f", title="Predicted Value"), ], ) + .project(type="mercator") .properties( - title="Quantile Predictions on the California Housing Dataset", + title="Quantile Predictions on the California Housing Dataset by County", height=650, width=650, ) @@ -244,18 +328,19 @@ of each sample. The model used is available on Hugging Face Hub import os - import pickle import shutil import tempfile import altair as alt + import geopandas as gpd + import joblib import numpy as np import pandas as pd from sklearn import datasets + from sklearn.base import BaseEstimator, RegressorMixin, clone + from sklearn.model_selection import KFold from skops import hub_utils - - import quantile_forest - from quantile_forest import RandomForestQuantileRegressor + from vega_datasets import data alt.data_transformers.disable_max_rows() @@ -264,8 +349,53 @@ of each sample. The model used is available on Hugging Face Hub load_existing = True random_state = np.random.RandomState(0) - quantiles = np.linspace(0, 1, num=5, endpoint=True).round(2).tolist() - sample_frac = 1 + quantiles = np.linspace(0, 1, num=21, endpoint=True).round(2).tolist() + + + class CrossValidationPipeline(BaseEstimator, RegressorMixin): + """Cross-validation pipeline for scikit-learn compatible models.""" + + def __init__(self, base_model, n_splits=5, random_state=None): + self.base_model = base_model + self.n_splits = n_splits + self.random_state = random_state + self.fold_models = {} + self.fold_indices = {} + + def fit(self, X, y): + """Fit the model using k-fold cross-validation.""" + kf = KFold(n_splits=self.n_splits, shuffle=True, random_state=self.random_state) + for fold_idx, (train_idx, test_idx) in enumerate(kf.split(X)): + X_train, y_train = X.iloc[train_idx], y[train_idx] + model = clone(self.base_model) + model.fit(X_train, y_train) + self.fold_models[fold_idx] = model + self.fold_indices[fold_idx] = test_idx + return self + + def predict(self, X, quantiles=None): + """Predict using the appropriate k-fold model.""" + if quantiles is None: + quantiles = 0.5 + if not isinstance(quantiles, list): + quantiles = [quantiles] + y_pred = np.empty((X.shape[0], len(quantiles)) if len(quantiles) > 1 else (X.shape[0])) + for fold_idx, test_idx in self.fold_indices.items(): + fold_model = self.fold_models[fold_idx] + y_pred[test_idx] = fold_model.predict(X.iloc[test_idx], quantiles=quantiles) + return y_pred + + def save(self, filename): + with open(filename, "wb") as f: + joblib.dump(self.__getstate__(), f) + + @classmethod + def load(cls, filename): + with open(filename, "rb") as f: + state = joblib.load(f) + obj = cls(base_model=None) + obj.__setstate__(state) + return obj def fit_and_upload_model(token, repo_id, local_dir="./local_repo", random_state=None): @@ -278,21 +408,27 @@ of each sample. The model used is available on Hugging Face Hub median_absolute_error, r2_score, ) - from sklearn.model_selection import train_test_split + from sklearn.pipeline import Pipeline from skops import card + import quantile_forest + from quantile_forest import RandomForestQuantileRegressor + # Load the California Housing dataset. X, y = datasets.fetch_california_housing(as_frame=True, return_X_y=True) - X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=random_state) + # Define the model pipeline. + qrf = RandomForestQuantileRegressor(random_state=random_state) + pipeline = Pipeline( + [("cv_model", CrossValidationPipeline(qrf, n_splits=5, random_state=random_state))] + ) - # Fit the model. - qrf = RandomForestQuantileRegressor(random_state=random_state).fit(X_train, y_train) + # Fit the model pipeline. + pipeline.fit(X, y) - # Save the model to a file. + # Save the pipeline (with all models) to a file. model_filename = "model.pkl" - with open(model_filename, mode="bw") as f: - pickle.dump(qrf, file=f) + pipeline.named_steps["cv_model"].save(model_filename) # Prepare model repository. if os.path.exists(local_dir): @@ -305,7 +441,7 @@ of each sample. The model used is available on Hugging Face Hub requirements=[f"quantile-forest={quantile_forest.__version__}"], dst=local_dir, task="tabular-regression", - data=X_train, + data=X, ) # Create a model card. @@ -322,19 +458,18 @@ of each sample. The model used is available on Hugging Face Hub "prediction-intervals", ] model_description = ( - "This is a RandomForestQuantileRegressor trained on the California housing dataset." + "This is a RandomForestQuantileRegressor trained on the California Housing dataset." ) limitations = "This model is not ready to be used in production." training_procedure = ( - "The model was trained using default parameters on a standard train-test split." + "The model was trained using default parameters on a 5-fold cross-validation pipeline." ) get_started_code = """
Click to expand ```python - import pickle - with open(qrf_pkl_filename, 'rb') as file: - qrf = pickle.load(file) + from examples.plot_qrf_huggingface_inference import CrossValidationPipeline + pipeline = CrossValidationPipeline.load(qrf_pkl_filename) ```
""" @@ -348,11 +483,11 @@ of each sample. The model used is available on Hugging Face Hub ) # Add performance metrics to the model card. - y_pred = qrf.predict(X_test) - mape = mean_absolute_percentage_error(y_test, y_pred) - mdae = median_absolute_error(y_test, y_pred) - mse = mean_squared_error(y_test, y_pred) - r2 = r2_score(y_test, y_pred) + y_pred = pipeline.predict(X) + mape = mean_absolute_percentage_error(y, y_pred) + mdae = median_absolute_error(y, y_pred) + mse = mean_squared_error(y, y_pred) + r2 = r2_score(y, y_pred) model_card.add_metrics( **{ "Mean Absolute Percentage Error": mape, @@ -390,23 +525,21 @@ of each sample. The model used is available on Hugging Face Hub local_dir = "./local_repo" with tempfile.TemporaryDirectory() as local_dir: hub_utils.download(repo_id=repo_id, dst=local_dir) - with open(f"{local_dir}/{model_filename}", "rb") as file: - qrf = pickle.load(file) + pipeline = CrossValidationPipeline.load(f"{local_dir}/{model_filename}") # Fetch the California Housing dataset and estimate quantiles. X, y = datasets.fetch_california_housing(as_frame=True, return_X_y=True) - y_pred = qrf.predict(X, quantiles=quantiles) * 100_000 # predict in dollars + y_pred = pipeline.predict(X, quantiles=quantiles) * 100_000 # predict in dollars df = ( pd.DataFrame(y_pred, columns=quantiles) .reset_index() - .sample(frac=sample_frac, random_state=random_state) - .melt(id_vars=["index"], var_name="quantile", value_name="value") + .rename(columns={q: f"q_{q:.3g}" for q in quantiles}) .merge(X[["Latitude", "Longitude", "Population"]].reset_index(), on="index", how="right") ) - def plot_quantiles_by_latlon(df, quantiles, color_scheme="cividis"): + def plot_quantiles_by_latlon(df, quantiles, color_scheme="lightgreyred"): """Plot quantile predictions on California Housing dataset by lat/lon.""" # Slider for varying the displayed quantile estimates. slider = alt.binding_range( @@ -418,35 +551,69 @@ of each sample. The model used is available on Hugging Face Hub quantile_val = alt.param(name="quantile", value=0.5, bind=slider) + # Load the US counties data and filter to California counties. + ca_counties = ( + gpd.read_file(data.us_10m.url, layer="counties") + .set_crs("EPSG:4326") + .assign(**{"county_fips": lambda x: x["id"].astype(int)}) + .drop(columns=["id"]) + .query("(county_fips >= 6000) & (county_fips < 7000)") + ) + + x_min = df[[f"q_{q:.3g}" for q in quantiles]].min().min() + x_max = df[[f"q_{q:.3g}" for q in quantiles]].max().max() + + df = ( + gpd.GeoDataFrame( + df, geometry=gpd.points_from_xy(df["Longitude"], df["Latitude"]), crs="4326" + ) + .sjoin(ca_counties, how="right") + .drop(columns=["index_left0"]) + .assign( + **{f"w_q_{q:.3g}": lambda x, q=q: x[f"q_{q:.3g}"] * x["Population"] for q in quantiles} + ) + ) + + grouped = ( + df.groupby("county_fips") + .agg({**{f"w_q_{q:.3g}": "sum" for q in quantiles}, **{"Population": "sum"}}) + .reset_index() + .assign( + **{f"q_{q:.3g}": lambda x, q=q: x[f"w_q_{q:.3g}"] / x["Population"] for q in quantiles} + ) + ) + + df = ( + df[["county_fips", "Latitude", "Longitude", "geometry"]] + .drop_duplicates(subset=["county_fips"]) + .merge( + grouped[["county_fips", "Population"] + [f"q_{q:.3g}" for q in quantiles]], + on="county_fips", + how="left", + ) + ) + chart = ( alt.Chart(df) .add_params(quantile_val) - .transform_filter("datum.quantile == quantile") - .mark_circle() + .transform_calculate(quantile_col="'q_' + quantile") + .transform_calculate(value=f"datum[datum.quantile_col]") + .mark_geoshape(stroke="black", strokeWidth=0.5) .encode( - x=alt.X( - "Longitude:Q", - axis=alt.Axis(tickMinStep=1, format=".1f"), - scale=alt.Scale(zero=False), - title="Longitude", - ), - y=alt.Y( - "Latitude:Q", - axis=alt.Axis(tickMinStep=1, format=".1f"), - scale=alt.Scale(zero=False), - title="Latitude", + color=alt.Color( + "value:Q", + scale=alt.Scale(domain=[x_min, x_max], scheme=color_scheme), + title="Prediction", ), - color=alt.Color("value:Q", scale=alt.Scale(scheme=color_scheme), title="Prediction"), - size=alt.Size("Population:Q"), tooltip=[ - alt.Tooltip("index:N", title="Row ID"), - alt.Tooltip("Latitude:Q", format=".2f", title="Latitude"), - alt.Tooltip("Longitude:Q", format=".2f", title="Longitude"), + alt.Tooltip("county_fips:N", title="County FIPS"), + alt.Tooltip("Population:N", format=",.0f", title="Population"), alt.Tooltip("value:Q", format="$,.0f", title="Predicted Value"), ], ) + .project(type="mercator") .properties( - title="Quantile Predictions on the California Housing Dataset", + title="Quantile Predictions on the California Housing Dataset by County", height=650, width=650, ) diff --git a/_sources/gallery/plot_qrf_interpolation_methods.rst.txt b/_sources/gallery/plot_qrf_interpolation_methods.rst.txt index 45c1ffb..aeb7766 100644 --- a/_sources/gallery/plot_qrf_interpolation_methods.rst.txt +++ b/_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), } diff --git a/_sources/gallery/plot_qrf_multitarget.rst.txt b/_sources/gallery/plot_qrf_multitarget.rst.txt index 6c750bf..0fc4471 100644 --- a/_sources/gallery/plot_qrf_multitarget.rst.txt +++ b/_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"), diff --git a/_static/_image_hashes.json b/_static/_image_hashes.json index 940cd4b..c115335 100644 --- a/_static/_image_hashes.json +++ b/_static/_image_hashes.json @@ -1 +1 @@ -{"plot_qrf_interpolation_methods.png": "2f3d43fde381d714a2952769c00dd6e7", "plot_qrf_custom_functions.png": "2f257ba3ed3ea39ace3775dfaac212a1", "plot_qrf_extrapolation.png": "54e94b1539025c3d9c702131f6c5615c", "plot_qrf_multitarget.png": "0601977950010fdc870894be0dc4b5b2", "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": "c87554d2fada2c6debe8c18c118efff8"} \ No newline at end of file +{"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"} \ No newline at end of file diff --git a/_static/plot_qrf_huggingface_inference-thumb.png b/_static/plot_qrf_huggingface_inference-thumb.png index 744c70c70b48d456e9ee967f0e9af061f29ed9c2..732e482b2ff0be9ef52e7434bca21a20ecf281e9 100644 GIT binary patch literal 28197 zcmX6^bv)hs|7SDP48!D@?(UkdqmP*G>FH*An7O8Vn1iFHyK8D{rkOZQch~Rj`}^ZQ z?wwtqc)#DT*XvoID0NkNYz$HiBqStkMFkm6BqU@K@Xu2;RPdAi9J1d?NRGRTGH
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    Using a Trained QRF Model via Hugging Face Hub#

    This example demonstrates how to download a trained quantile regression forest -(QRF) model from Hugging Face Hub and use it to estimate new quantiles. In -this scenario, a QRF has been trained with default parameters on a train-test -split of the California housing dataset and uploaded to Hugging Face Hub. The -model is downloaded and used to perform inference across several quantiles for -each dataset sample. The results are visualized by the latitude and longitude -of each sample. The model used is available on Hugging Face Hub +(QRF) model from Hugging Face Hub and use it to estimate quantiles. In this +scenario, a QRF has been trained with default parameters on the California +Housing dataset using k-fold cross-validation and uploaded to Hugging Face +Hub. The model is downloaded and used to perform inference across multiple +quantiles for each sample in the dataset. The predictions are aggregated by +county based on the latitude and longitude of each sample and visualized. +The trained model is available on Hugging Face Hub here.

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    import os
-import pickle
 import shutil
 import tempfile
 
 import altair as alt
+import geopandas as gpd
+import joblib
 import numpy as np
 import pandas as pd
 from sklearn import datasets
+from sklearn.base import BaseEstimator, RegressorMixin, clone
+from sklearn.model_selection import KFold
 from skops import hub_utils
-
-import quantile_forest
-from quantile_forest import RandomForestQuantileRegressor
+from vega_datasets import data
 
 alt.data_transformers.disable_max_rows()
 
@@ -451,8 +453,53 @@
 load_existing = True
 
 random_state = np.random.RandomState(0)
-quantiles = np.linspace(0, 1, num=5, endpoint=True).round(2).tolist()
-sample_frac = 1
+quantiles = np.linspace(0, 1, num=21, endpoint=True).round(2).tolist()
+
+
+class CrossValidationPipeline(BaseEstimator, RegressorMixin):
+    """Cross-validation pipeline for scikit-learn compatible models."""
+
+    def __init__(self, base_model, n_splits=5, random_state=None):
+        self.base_model = base_model
+        self.n_splits = n_splits
+        self.random_state = random_state
+        self.fold_models = {}
+        self.fold_indices = {}
+
+    def fit(self, X, y):
+        """Fit the model using k-fold cross-validation."""
+        kf = KFold(n_splits=self.n_splits, shuffle=True, random_state=self.random_state)
+        for fold_idx, (train_idx, test_idx) in enumerate(kf.split(X)):
+            X_train, y_train = X.iloc[train_idx], y[train_idx]
+            model = clone(self.base_model)
+            model.fit(X_train, y_train)
+            self.fold_models[fold_idx] = model
+            self.fold_indices[fold_idx] = test_idx
+        return self
+
+    def predict(self, X, quantiles=None):
+        """Predict using the appropriate k-fold model."""
+        if quantiles is None:
+            quantiles = 0.5
+        if not isinstance(quantiles, list):
+            quantiles = [quantiles]
+        y_pred = np.empty((X.shape[0], len(quantiles)) if len(quantiles) > 1 else (X.shape[0]))
+        for fold_idx, test_idx in self.fold_indices.items():
+            fold_model = self.fold_models[fold_idx]
+            y_pred[test_idx] = fold_model.predict(X.iloc[test_idx], quantiles=quantiles)
+        return y_pred
+
+    def save(self, filename):
+        with open(filename, "wb") as f:
+            joblib.dump(self.__getstate__(), f)
+
+    @classmethod
+    def load(cls, filename):
+        with open(filename, "rb") as f:
+            state = joblib.load(f)
+        obj = cls(base_model=None)
+        obj.__setstate__(state)
+        return obj
 
 
 def fit_and_upload_model(token, repo_id, local_dir="./local_repo", random_state=None):
@@ -465,21 +512,27 @@
         median_absolute_error,
         r2_score,
     )
-    from sklearn.model_selection import train_test_split
+    from sklearn.pipeline import Pipeline
     from skops import card
 
+    import quantile_forest
+    from quantile_forest import RandomForestQuantileRegressor
+
     # Load the California Housing dataset.
     X, y = datasets.fetch_california_housing(as_frame=True, return_X_y=True)
 
-    X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=random_state)
+    # Define the model pipeline.
+    qrf = RandomForestQuantileRegressor(random_state=random_state)
+    pipeline = Pipeline(
+        [("cv_model", CrossValidationPipeline(qrf, n_splits=5, random_state=random_state))]
+    )
 
-    # Fit the model.
-    qrf = RandomForestQuantileRegressor(random_state=random_state).fit(X_train, y_train)
+    # Fit the model pipeline.
+    pipeline.fit(X, y)
 
-    # Save the model to a file.
+    # Save the pipeline (with all models) to a file.
     model_filename = "model.pkl"
-    with open(model_filename, mode="bw") as f:
-        pickle.dump(qrf, file=f)
+    pipeline.named_steps["cv_model"].save(model_filename)
 
     # Prepare model repository.
     if os.path.exists(local_dir):
@@ -492,7 +545,7 @@
         requirements=[f"quantile-forest={quantile_forest.__version__}"],
         dst=local_dir,
         task="tabular-regression",
-        data=X_train,
+        data=X,
     )
 
     # Create a model card.
@@ -509,19 +562,18 @@
         "prediction-intervals",
     ]
     model_description = (
-        "This is a RandomForestQuantileRegressor trained on the California housing dataset."
+        "This is a RandomForestQuantileRegressor trained on the California Housing dataset."
     )
     limitations = "This model is not ready to be used in production."
     training_procedure = (
-        "The model was trained using default parameters on a standard train-test split."
+        "The model was trained using default parameters on a 5-fold cross-validation pipeline."
     )
     get_started_code = """<details>
 <summary> Click to expand </summary>
 
 ```python
-import pickle
-with open(qrf_pkl_filename, 'rb') as file:
-    qrf = pickle.load(file)
+from examples.plot_qrf_huggingface_inference import CrossValidationPipeline
+pipeline = CrossValidationPipeline.load(qrf_pkl_filename)
 ```
 
 </details>"""
@@ -535,11 +587,11 @@
     )
 
     # Add performance metrics to the model card.
-    y_pred = qrf.predict(X_test)
-    mape = mean_absolute_percentage_error(y_test, y_pred)
-    mdae = median_absolute_error(y_test, y_pred)
-    mse = mean_squared_error(y_test, y_pred)
-    r2 = r2_score(y_test, y_pred)
+    y_pred = pipeline.predict(X)
+    mape = mean_absolute_percentage_error(y, y_pred)
+    mdae = median_absolute_error(y, y_pred)
+    mse = mean_squared_error(y, y_pred)
+    r2 = r2_score(y, y_pred)
     model_card.add_metrics(
         **{
             "Mean Absolute Percentage Error": mape,
@@ -577,23 +629,21 @@
 local_dir = "./local_repo"
 with tempfile.TemporaryDirectory() as local_dir:
     hub_utils.download(repo_id=repo_id, dst=local_dir)
-    with open(f"{local_dir}/{model_filename}", "rb") as file:
-        qrf = pickle.load(file)
+    pipeline = CrossValidationPipeline.load(f"{local_dir}/{model_filename}")
 
 # Fetch the California Housing dataset and estimate quantiles.
 X, y = datasets.fetch_california_housing(as_frame=True, return_X_y=True)
-y_pred = qrf.predict(X, quantiles=quantiles) * 100_000  # predict in dollars
+y_pred = pipeline.predict(X, quantiles=quantiles) * 100_000  # predict in dollars
 
 df = (
     pd.DataFrame(y_pred, columns=quantiles)
     .reset_index()
-    .sample(frac=sample_frac, random_state=random_state)
-    .melt(id_vars=["index"], var_name="quantile", value_name="value")
+    .rename(columns={q: f"q_{q:.3g}" for q in quantiles})
     .merge(X[["Latitude", "Longitude", "Population"]].reset_index(), on="index", how="right")
 )
 
 
-def plot_quantiles_by_latlon(df, quantiles, color_scheme="cividis"):
+def plot_quantiles_by_latlon(df, quantiles, color_scheme="lightgreyred"):
     """Plot quantile predictions on California Housing dataset by lat/lon."""
     # Slider for varying the displayed quantile estimates.
     slider = alt.binding_range(
@@ -605,35 +655,69 @@
 
     quantile_val = alt.param(name="quantile", value=0.5, bind=slider)
 
+    # Load the US counties data and filter to California counties.
+    ca_counties = (
+        gpd.read_file(data.us_10m.url, layer="counties")
+        .set_crs("EPSG:4326")
+        .assign(**{"county_fips": lambda x: x["id"].astype(int)})
+        .drop(columns=["id"])
+        .query("(county_fips >= 6000) & (county_fips < 7000)")
+    )
+
+    x_min = df[[f"q_{q:.3g}" for q in quantiles]].min().min()
+    x_max = df[[f"q_{q:.3g}" for q in quantiles]].max().max()
+
+    df = (
+        gpd.GeoDataFrame(
+            df, geometry=gpd.points_from_xy(df["Longitude"], df["Latitude"]), crs="4326"
+        )
+        .sjoin(ca_counties, how="right")
+        .drop(columns=["index_left0"])
+        .assign(
+            **{f"w_q_{q:.3g}": lambda x, q=q: x[f"q_{q:.3g}"] * x["Population"] for q in quantiles}
+        )
+    )
+
+    grouped = (
+        df.groupby("county_fips")
+        .agg({**{f"w_q_{q:.3g}": "sum" for q in quantiles}, **{"Population": "sum"}})
+        .reset_index()
+        .assign(
+            **{f"q_{q:.3g}": lambda x, q=q: x[f"w_q_{q:.3g}"] / x["Population"] for q in quantiles}
+        )
+    )
+
+    df = (
+        df[["county_fips", "Latitude", "Longitude", "geometry"]]
+        .drop_duplicates(subset=["county_fips"])
+        .merge(
+            grouped[["county_fips", "Population"] + [f"q_{q:.3g}" for q in quantiles]],
+            on="county_fips",
+            how="left",
+        )
+    )
+
     chart = (
         alt.Chart(df)
         .add_params(quantile_val)
-        .transform_filter("datum.quantile == quantile")
-        .mark_circle()
+        .transform_calculate(quantile_col="'q_' + quantile")
+        .transform_calculate(value=f"datum[datum.quantile_col]")
+        .mark_geoshape(stroke="black", strokeWidth=0.5)
         .encode(
-            x=alt.X(
-                "Longitude:Q",
-                axis=alt.Axis(tickMinStep=1, format=".1f"),
-                scale=alt.Scale(zero=False),
-                title="Longitude",
-            ),
-            y=alt.Y(
-                "Latitude:Q",
-                axis=alt.Axis(tickMinStep=1, format=".1f"),
-                scale=alt.Scale(zero=False),
-                title="Latitude",
+            color=alt.Color(
+                "value:Q",
+                scale=alt.Scale(domain=[x_min, x_max], scheme=color_scheme),
+                title="Prediction",
             ),
-            color=alt.Color("value:Q", scale=alt.Scale(scheme=color_scheme), title="Prediction"),
-            size=alt.Size("Population:Q"),
             tooltip=[
-                alt.Tooltip("index:N", title="Row ID"),
-                alt.Tooltip("Latitude:Q", format=".2f", title="Latitude"),
-                alt.Tooltip("Longitude:Q", format=".2f", title="Longitude"),
+                alt.Tooltip("county_fips:N", title="County FIPS"),
+                alt.Tooltip("Population:N", format=",.0f", title="Population"),
                 alt.Tooltip("value:Q", format="$,.0f", title="Predicted Value"),
             ],
         )
+        .project(type="mercator")
         .properties(
-            title="Quantile Predictions on the California Housing Dataset",
+            title="Quantile Predictions on the California Housing Dataset by County",
             height=650,
             width=650,
         )
diff --git a/gallery/plot_qrf_interpolation_methods.html b/gallery/plot_qrf_interpolation_methods.html
index c4117e0..864f786 100644
--- a/gallery/plot_qrf_interpolation_methods.html
+++ b/gallery/plot_qrf_interpolation_methods.html
@@ -420,7 +420,7 @@
   // embed when document is loaded, to ensure vega library is available
   // this works on all modern browsers, except IE8 and older
   document.addEventListener("DOMContentLoaded", function(event) {
-      var spec = {"config": {"view": {"continuousWidth": 300, "continuousHeight": 300, "stroke": null}, "facet": {"spacing": 15}, "range": {"category": ["#000000", "#006aff", "#ffd237", "#0d4599", "#f2a619", "#a6e5ff"]}, "scale": {"bandPaddingInner": 0.9}, "title": {"anchor": "middle"}}, "data": {"name": "data-fcdafb2b6a27b865a219137f2e0a9e5d"}, "facet": {"column": {"field": "X", "header": {"labelOrient": "bottom", "titleOrient": "bottom"}, "title": "Samples (Feature Values)", "type": "nominal"}}, "spec": {"layer": [{"mark": {"type": "bar"}, "encoding": {"color": {"condition": {"param": "param_12", "field": "method", "sort": ["Actual", "Linear", "Lower", "Higher", "Midpoint", "Nearest"], "title": null, "type": "nominal"}, "value": "lightgray"}, "tooltip": [{"field": "method", "title": "Method", "type": "nominal"}, {"field": "X", "title": "X Values", "type": "nominal"}, {"field": "y_pred", "format": ",.3f", "title": "Predicted Y", "type": "quantitative"}, {"field": "y_pred_low", "format": ",.3f", "title": "Predicted Lower Y", "type": "quantitative"}, {"field": "y_pred_upp", "format": ",.3f", "title": "Predicted Upper Y", "type": "quantitative"}, {"field": "quantile_low", "format": ".3f", "title": "Lower Quantile", "type": "quantitative"}, {"field": "quantile_upp", "format": ".3f", "title": "Upper Quantile", "type": "quantitative"}], "x": {"axis": {"labels": false, "tickSize": 0}, "field": "method", "sort": ["Actual", "Linear", "Lower", "Higher", "Midpoint", "Nearest"], "title": null, "type": "nominal"}, "y": {"field": "y_pred_low", "title": "", "type": "quantitative"}, "y2": {"field": "y_pred_upp", "title": null}}, "name": "view_13"}, {"mark": {"type": "circle", "opacity": 1, "size": 75}, "encoding": {"color": {"condition": {"param": "param_12", "field": "method", "sort": ["Actual", "Linear", "Lower", "Higher", "Midpoint", "Nearest"], "title": null, "type": "nominal"}, "value": "lightgray"}, "tooltip": [{"field": "method", "title": "Method", "type": "nominal"}, {"field": "X", "title": "X Values", "type": "nominal"}, {"field": "y_pred", "format": ",.3f", "title": "Predicted Y", "type": "quantitative"}, {"field": "y_pred_low", "format": ",.3f", "title": "Predicted Lower Y", "type": "quantitative"}, {"field": "y_pred_upp", "format": ",.3f", "title": "Predicted Upper Y", "type": "quantitative"}, {"field": "quantile_low", "format": ".3f", "title": "Lower Quantile", "type": "quantitative"}, {"field": "quantile_upp", "format": ".3f", "title": "Upper Quantile", "type": "quantitative"}], "x": {"axis": {"labels": false, "tickSize": 0}, "field": "method", "sort": ["Actual", "Linear", "Lower", "Higher", "Midpoint", "Nearest"], "title": null, "type": "nominal"}, "y": {"field": "y_pred", "title": "Actual and Predicted Values", "type": "quantitative"}}}], "height": 400, "transform": [{"filter": "(datum.method == 'Actual')| (datum.quantile_low == round((0.5 - interval / 2) * 1000) / 1000)| (datum.quantile_upp == round((0.5 + interval / 2) * 1000) / 1000)"}], "width": {"step": 20}}, "params": [{"name": "interval", "bind": {"input": "range", "max": 1, "min": 0, "name": "Prediction Interval: ", "step": 0.01}, "value": 0.8}, {"name": "param_12", "select": {"type": "point", "fields": ["method"], "on": "click"}, "bind": "legend", "views": ["view_13"]}], "title": "QRF Predictions by Quantile Interpolation on Toy Dataset", "$schema": "https://vega.github.io/schema/vega-lite/v5.20.1.json", "datasets": {"data-fcdafb2b6a27b865a219137f2e0a9e5d": [{"method": "Actual", "X": "Sample 1 ([-1, -1])", "y_pred": -2.0, "y_pred_low": -2.0, "y_pred_upp": -2.0, "quantile_low": null, "quantile_upp": null}, {"method": "Actual", "X": "Sample 2 ([-1, -1])", "y_pred": -1.0, "y_pred_low": -1.0, "y_pred_upp": -1.0, "quantile_low": null, "quantile_upp": null}, {"method": "Actual", "X": "Sample 3 ([-1, -1])", "y_pred": 0.0, "y_pred_low": 0.0, "y_pred_upp": 0.0, "quantile_low": null, "quantile_upp": null}, {"method": "Actual", "X": "Sample 4 ([1, 1])", "y_pred": 1.0, "y_pred_low": 1.0, "y_pred_upp": 1.0, "quantile_low": null, "quantile_upp": null}, {"method": "Actual", "X": "Sample 5 ([1, 1])", "y_pred": 2.0, "y_pred_low": 2.0, "y_pred_upp": 2.0, "quantile_low": null, "quantile_upp": null}, {"method": "Linear", "X": "Sample 1 ([-1, -1])", "y_pred": -1.0, "y_pred_low": -1.0, "y_pred_upp": -1.0, "quantile_low": 0.5, "quantile_upp": 0.5}, {"method": "Linear", "X": "Sample 2 ([-1, -1])", "y_pred": -1.0, "y_pred_low": -1.0, "y_pred_upp": -1.0, "quantile_low": 0.5, "quantile_upp": 0.5}, {"method": "Linear", "X": "Sample 3 ([-1, -1])", "y_pred": -1.0, "y_pred_low": -1.0, "y_pred_upp": -1.0, "quantile_low": 0.5, "quantile_upp": 0.5}, {"method": "Linear", "X": "Sample 4 ([1, 1])", "y_pred": 1.5, "y_pred_low": 1.5, "y_pred_upp": 1.5, "quantile_low": 0.5, "quantile_upp": 0.5}, {"method": "Linear", "X": "Sample 5 ([1, 1])", "y_pred": 1.5, "y_pred_low": 1.5, "y_pred_upp": 1.5, "quantile_low": 0.5, "quantile_upp": 0.5}, {"method": "Lower", "X": 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"quantile_low": 0.5, "quantile_upp": 0.5}, {"method": "Midpoint", "X": "Sample 5 ([1, 1])", "y_pred": 1.5, "y_pred_low": 1.5, "y_pred_upp": 1.5, "quantile_low": 0.5, "quantile_upp": 0.5}, {"method": "Nearest", "X": "Sample 1 ([-1, -1])", "y_pred": -1.0, "y_pred_low": -1.0, "y_pred_upp": -1.0, "quantile_low": 0.5, "quantile_upp": 0.5}, {"method": "Nearest", "X": "Sample 2 ([-1, -1])", "y_pred": -1.0, "y_pred_low": -1.0, "y_pred_upp": -1.0, "quantile_low": 0.5, "quantile_upp": 0.5}, {"method": "Nearest", "X": "Sample 3 ([-1, -1])", "y_pred": -1.0, "y_pred_low": -1.0, "y_pred_upp": -1.0, "quantile_low": 0.5, "quantile_upp": 0.5}, {"method": "Nearest", "X": "Sample 4 ([1, 1])", "y_pred": 1.0, "y_pred_low": 1.0, "y_pred_upp": 1.0, "quantile_low": 0.5, "quantile_upp": 0.5}, {"method": "Nearest", "X": "Sample 5 ([1, 1])", "y_pred": 1.0, "y_pred_low": 1.0, "y_pred_upp": 1.0, "quantile_low": 0.5, "quantile_upp": 0.5}, {"method": "Actual", "X": "Sample 1 ([-1, -1])", "y_pred": -2.0, 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-        "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),
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diff --git a/gallery/plot_qrf_multitarget.html b/gallery/plot_qrf_multitarget.html
index 002d2e7..389c4ea 100644
--- a/gallery/plot_qrf_multitarget.html
+++ b/gallery/plot_qrf_multitarget.html
@@ -419,7 +419,7 @@
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   // this works on all modern browsers, except IE8 and older
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"q_0.95": 5.918647019756464, "q_0.975": 6.112739664992043, "q_1": 6.27034173399522}, {"x": 100.0, "y": 6.166916206472844, "y_pred": 4.079205916056161, "target": "1", "q_0": 2.2663825897327854, "q_0.025": 2.4549129994665257, "q_0.05": 2.5075168626786666, "q_0.075": 2.5918130110067112, "q_0.1": 2.6837326405008817, "q_0.125": 2.8011233770317414, "q_0.15": 2.9089210256485067, "q_0.175": 3.023530191254946, "q_0.2": 3.10705795033332, "q_0.225": 3.1754413821479606, "q_0.25": 3.2545749046962977, "q_0.275": 3.301120517472622, "q_0.3": 3.3616035987465214, "q_0.325": 3.477585549756284, "q_0.35": 3.536820760166708, "q_0.375": 3.610435875236362, "q_0.4": 3.8020753592247956, "q_0.425": 3.8750496873581675, "q_0.45": 3.9332517302731973, "q_0.475": 4.0088931061288084, "q_0.5": 4.079205916056161, "q_0.525": 4.192231546360819, "q_0.55": 4.305674944555796, "q_0.575": 4.35845326247657, "q_0.6": 4.4667994860864075, "q_0.625": 4.615221016096102, "q_0.65": 4.698167283857219, "q_0.675": 4.845720545348097, "q_0.7": 4.9295437696061, "q_0.725": 4.9862032755209, "q_0.75": 5.06110622508475, "q_0.775": 5.216401239879421, "q_0.8": 5.299574779007834, "q_0.825": 5.424402001203898, "q_0.85": 5.529405411429767, "q_0.875": 5.596618538306475, "q_0.9": 5.655143783909511, "q_0.925": 5.786662739658899, "q_0.95": 5.921688698763932, "q_0.975": 6.1331234016450065, "q_1": 6.27034173399522}]}};
       var opt = {
         "mode": "vega-lite",
         "renderer": "canvas",
@@ -441,7 +441,7 @@
 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)),
@@ -454,18 +454,19 @@
     },
 ]
 
-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)
 
@@ -481,7 +482,6 @@
     {
         "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)},
@@ -513,7 +513,6 @@
         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"),
diff --git a/searchindex.js b/searchindex.js
index c68624d..e4d9bcc 100644
--- a/searchindex.js
+++ b/searchindex.js
@@ -1 +1 @@
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