.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/model.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code. .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_model.py: ====== Model ====== .. GENERATED FROM PYTHON SOURCE LINES 6-30 .. code-block:: Python import shap import numpy as np import pandas as pd import matplotlib.pyplot as plt import xgboost as xgb from xgboost import XGBRegressor from ai4water import Model from ai4water.utils.utils import TrainTestSplit from ai4water.postprocessing import ProcessPredictions from ai4water.postprocessing import PartialDependencePlot from sklearn.model_selection import LearningCurveDisplay, ShuffleSplit from utils import prepare_data, SAVE from utils import set_rcParams from utils import evaluate_model from utils import regression_plot from utils import residual_plot from utils import print_version_info from utils import bar_pie .. GENERATED FROM PYTHON SOURCE LINES 31-33 .. code-block:: Python print_version_info() .. rst-class:: sphx-glr-script-out .. code-block:: none python 3.12.7 (main, Nov 5 2024, 16:16:58) [GCC 11.4.0] os posix ai4water 1.07 xgboost 2.1.3 easy_mpl 0.21.4 SeqMetrics 2.0.0 torch 2.5.1+cu124 numpy 1.26.4 pandas 1.5.3 matplotlib 3.8.4 sklearn 1.3.1 xarray 2024.3.0 netCDF4 1.7.2 seaborn 0.13.2 bnlearn 0.10.2 Script Executed on: Wed Jan 1 06:32:07 2025 tot_cpus 2 avail_cpus 2 mem_gib 7.612831115722656 .. GENERATED FROM PYTHON SOURCE LINES 34-36 .. code-block:: Python shap.initjs() .. rst-class:: sphx-glr-script-out .. code-block:: none .. GENERATED FROM PYTHON SOURCE LINES 37-40 .. code-block:: Python set_rcParams() .. GENERATED FROM PYTHON SOURCE LINES 41-44 .. code-block:: Python data, cat_encoder, an_encoder = prepare_data() .. GENERATED FROM PYTHON SOURCE LINES 45-48 .. code-block:: Python print(data.shape) .. rst-class:: sphx-glr-script-out .. code-block:: none (1044, 15) .. GENERATED FROM PYTHON SOURCE LINES 49-53 .. code-block:: Python print(data.columns) .. rst-class:: sphx-glr-script-out .. code-block:: none Index(['Catalyst type', 'Surface area', 'Pore volume', 'BandGap (eV)', 'Au', 'Bi', 'Fe', 'O', 'Catalyst loading (g/L)', 'Light intensity (W)', 'time (min)', 'solution pH', 'Anions', 'Ci (mg/L)', 'Efficiency (%)'], dtype='object') .. GENERATED FROM PYTHON SOURCE LINES 54-58 .. code-block:: Python TrainX, TestX, TrainY, TestY = (TrainTestSplit(seed=313). split_by_random(data.iloc[:,0:-1], data.iloc[:,-1])) .. GENERATED FROM PYTHON SOURCE LINES 59-62 .. code-block:: Python print(TrainX.shape) .. rst-class:: sphx-glr-script-out .. code-block:: none (730, 14) .. GENERATED FROM PYTHON SOURCE LINES 63-66 .. code-block:: Python print(TrainY.shape) .. rst-class:: sphx-glr-script-out .. code-block:: none (730,) .. GENERATED FROM PYTHON SOURCE LINES 67-70 .. code-block:: Python print(TestX.shape) .. rst-class:: sphx-glr-script-out .. code-block:: none (314, 14) .. GENERATED FROM PYTHON SOURCE LINES 71-74 .. code-block:: Python print(TestY.shape) .. rst-class:: sphx-glr-script-out .. code-block:: none (314,) .. GENERATED FROM PYTHON SOURCE LINES 75-80 .. code-block:: Python model = Model(model='XGBRegressor', cross_validator={"KFold": {'n_splits': 50}} ) .. rst-class:: sphx-glr-script-out .. code-block:: none building ML model for regression problem using XGBRegressor .. GENERATED FROM PYTHON SOURCE LINES 81-84 .. code-block:: Python model.fit(TrainX, TrainY) .. raw:: html 
XGBRegressor(base_score=None, booster=None, callbacks=None,
                 colsample_bylevel=None, colsample_bynode=None,
                 colsample_bytree=None, device=None, early_stopping_rounds=None,
                 enable_categorical=False, eval_metric=None, feature_types=None,
                 gamma=None, grow_policy=None, importance_type=None,
                 interaction_constraints=None, learning_rate=None, max_bin=None,
                 max_cat_threshold=None, max_cat_to_onehot=None,
                 max_delta_step=None, max_depth=None, max_leaves=None,
                 min_child_weight=None, missing=nan, monotone_constraints=None,
                 multi_strategy=None, n_estimators=None, n_jobs=None,
                 num_parallel_tree=None, random_state=313, ...)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.


.. GENERATED FROM PYTHON SOURCE LINES 85-91 .. code-block:: Python # fig, ax = plt.subplots(figsize=(30, 30)) # xgb.plot_tree(model._model, ax=ax) # plt.savefig('../manuscript/figures/figS2.png', dpi=600, bbox_inches="tight") # plt.show() .. GENERATED FROM PYTHON SOURCE LINES 92-95 .. code-block:: Python train_p = model.predict(TrainX) .. GENERATED FROM PYTHON SOURCE LINES 96-99 .. code-block:: Python evaluate_model(TrainY.values, train_p) .. rst-class:: sphx-glr-script-out .. code-block:: none mse 0.7981642712436582 rmse 0.8934003980543428 r2 0.9993237665126041 r2_score 0.9993236785411198 mape inf mae 0.4882864061401118 .. GENERATED FROM PYTHON SOURCE LINES 100-106 .. code-block:: Python processor = ProcessPredictions('regression', forecast_len=1, plots=['prediction']) processor(TrainY.values, train_p) .. image-sg:: /auto_examples/images/sphx_glr_model_001.png :alt: model :srcset: /auto_examples/images/sphx_glr_model_001.png, /auto_examples/images/sphx_glr_model_001_2_00x.png 2.00x :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 107-110 .. code-block:: Python test_p = model.predict(TestX) .. GENERATED FROM PYTHON SOURCE LINES 111-114 .. code-block:: Python evaluate_model(TestY.values, test_p) .. rst-class:: sphx-glr-script-out .. code-block:: none mse 3.538625835769641 rmse 1.8811235567526237 r2 0.9969561205228433 r2_score 0.9969450676175826 mape inf mae 0.9911163492994325 .. GENERATED FROM PYTHON SOURCE LINES 115-121 .. code-block:: Python processor = ProcessPredictions('regression', forecast_len=1, plots=['prediction']) processor(TestY.values, test_p) .. image-sg:: /auto_examples/images/sphx_glr_model_002.png :alt: model :srcset: /auto_examples/images/sphx_glr_model_002.png, /auto_examples/images/sphx_glr_model_002_2_00x.png 2.00x :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 122-214 .. code-block:: Python set_rcParams({'xtick.labelsize': '14', 'ytick.labelsize': '14'}) pp = ProcessPredictions('regression', 1, show=False) common_params = { "X": data.iloc[:,0:-1], "y": data.iloc[:,-1], "train_sizes": np.linspace(0.1, 1.0, 5), "cv": ShuffleSplit(n_splits=50, test_size=0.2, random_state=0), "score_type": "both", 'scoring':'neg_mean_squared_error', "n_jobs": 4, "line_kw": {"marker": "o"}, "std_display_style": "fill_between", "score_name": "MSE", } # Create figure with specified aspect ratio fig = plt.figure(figsize=(16, 10)) # Manually set the axes positions [left, bottom, width, height] # These values are normalized (0 to 1) relative to the figure size ax1 = fig.add_axes([0.05, 0.57, 0.4, 0.42]) # First row, first two columns ax2 = fig.add_axes([0.55, 0.57, 0.4, 0.42]) # First row, last two columns ax3 = fig.add_axes([0.05, 0.05, 0.4, 0.42]) # Second row, first two columns ax4 = fig.add_axes([0.55, 0.05, 0.28, 0.42]) # Second row, third and part of fourth columns ax5 = fig.add_axes([0.83, 0.05, 0.12, 0.42]) # Second row, last column ax1.text(-0.1, 1.05, 'a)', transform=ax1.transAxes, size=20, weight='bold') ax2.text(-0.1, 1.05, 'b)', transform=ax2.transAxes, size=20, weight='bold') ax3.text(-0.1, 1.34, 'c)', transform=ax3.transAxes, size=20, weight='bold') ax4.text(-0.1, 1.05, 'd)', transform=ax4.transAxes, size=20, weight='bold') # Remove y-ticks from the fifth axis ax5.set_yticks([]) #for ax_idx, estimator in enumerate([naive_bayes, svc]): LearningCurveDisplay.from_estimator(XGBRegressor(), **common_params, ax=ax1) handles, label = ax1.get_legend_handles_labels() ax1.legend(handles[:2], ["Training MSE", "Test MSE"], fontsize=17, loc='lower right') ax1.set_title(f"Learning Curve for XGBoost", fontsize=17) ax1.set_xlabel("Number of samples in the training set", fontsize=17) ax1.set_ylabel("Mean Squared Error", fontsize=17) # **** EDF plot output = pp.edf_plot(TrainY.values, train_p, ax=ax2, color=('tab:blue', 'tab:blue'), marker=('-', '*'), label=("Absolute Error (Training)", "Prediction (Training)")) output = pp.edf_plot(TestY.values, test_p, marker=('-', '*'), ax=output[0], pred_axes=output[1], color=('tab:orange', 'tab:orange'), label=("Absolute Error (Test)", "Prediction (Test)")) output[0].legend(loc=(0.20, 0.23), frameon=False, fontsize=17) output[1].legend(loc=(0.20, 0.05), frameon=False, fontsize=17) output[0].set_xlabel('Absolute Error', fontsize=17) output[1].set_xlabel('Prediction', fontsize=17) output[0].set_ylabel('Commulative Probability', fontsize=17) # **** Regression plot ax3 = regression_plot(TrainY.values, train_p, TestY.values, test_p, ax=ax3, train_color='tab:blue', test_color='tab:orange') ax3.set_xlim(-2, ax3.get_xlim()[1]) ax3.set_ylim(-2, ax3.get_ylim()[1]) ax3.set_ylabel("Predicted Removal Efficiency (%)", fontsize=17) ax3.set_xlabel("Experimental Removal Efficiency (%)", fontsize=17) ax3.legend(loc='upper left', markerscale=3, fontsize=17) # **** Residual plot axis = residual_plot( TrainY.values, train_p, TestY.values, test_p, label="Efficiency", axis=(ax4, ax5), train_color='tab:blue', test_color='tab:orange', ) axis[0].set_ylabel("Residual", fontsize=17) axis[0].set_xlabel("Predicted Removal Efficiency (%)", fontsize=17) axis[0].legend(loc='upper left', markerscale=3, fontsize=17) #plt.savefig("../manuscript/figures/fig4.png", dpi=600, bbox_inches="tight") # Show the plot plt.show() .. image-sg:: /auto_examples/images/sphx_glr_model_003.png :alt: Learning Curve for XGBoost :srcset: /auto_examples/images/sphx_glr_model_003.png, /auto_examples/images/sphx_glr_model_003_2_00x.png 2.00x :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 215-217 SHAP ===== .. GENERATED FROM PYTHON SOURCE LINES 217-224 .. code-block:: Python explainer = shap.Explainer(model._model, TrainX) shap_values = explainer(TrainX) type(shap_values) .. GENERATED FROM PYTHON SOURCE LINES 225-228 .. code-block:: Python print(shap_values.shape) .. rst-class:: sphx-glr-script-out .. code-block:: none (730, 14) .. GENERATED FROM PYTHON SOURCE LINES 229-288 .. code-block:: Python CATEGORIES = { 'Experimental Conditions': ["Ci (mg/L)", "time (min)", "solution pH", "Anions", "Light intensity (W)", "Catalyst loading (g/L)", "Anions", "Catalyst type"], 'Physiochemical Properties': ['Pore volume', 'Surface area', 'BandGap (eV)'], 'Atomic Composition': ['Bi', 'O', 'Fe', 'Au'], } def make_classes(exp): colors = { 'Experimental Conditions': '#405f77', 'Physiochemical Properties': '#1fafd2', 'Atomic Composition': '#f2826e', } classes = [] colors_ = [] for f in exp.feature_names: if f in CATEGORIES['Experimental Conditions']: classes.append('Experimental Conditions') colors_.append(colors['Experimental Conditions']) elif f in CATEGORIES['Physiochemical Properties']: classes.append('Physiochemical Properties') colors_.append(colors['Physiochemical Properties']) elif f in CATEGORIES['Atomic Composition']: classes.append('Atomic Composition') colors_.append(colors['Atomic Composition']) else: raise ValueError(f"{f} not found") return classes, colors_ sv_bar = np.mean(np.abs(shap_values.values), axis=0) classes, colors_ = make_classes(shap_values) df_with_classes = pd.DataFrame( {'features': shap_values.feature_names, 'classes': classes, 'mean_shap': sv_bar, 'colors': colors_ }) print(df_with_classes) ax, *_= bar_pie(df_with_classes, save=False, name="bar_pie", pie_pos="center", show=False) # remove right spine ax.spines['right'].set_visible(False) ax.spines['top'].set_visible(False) #plt.savefig('../manuscript/figures/fig5.png', dpi=600, bbox_inches="tight") plt.show() .. image-sg:: /auto_examples/images/sphx_glr_model_004.png :alt: model :srcset: /auto_examples/images/sphx_glr_model_004.png, /auto_examples/images/sphx_glr_model_004_2_00x.png 2.00x :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out .. code-block:: none features classes mean_shap colors 0 Catalyst type Experimental Conditions 1.804110 #405f77 1 Surface area Physiochemical Properties 0.091180 #1fafd2 2 Pore volume Physiochemical Properties 0.000000 #1fafd2 3 BandGap (eV) Physiochemical Properties 0.095963 #1fafd2 4 Au Atomic Composition 1.206869 #f2826e 5 Bi Atomic Composition 3.784539 #f2826e 6 Fe Atomic Composition 0.754104 #f2826e 7 O Atomic Composition 0.026943 #f2826e 8 Catalyst loading (g/L) Experimental Conditions 1.721145 #405f77 9 Light intensity (W) Experimental Conditions 2.220205 #405f77 10 time (min) Experimental Conditions 24.306885 #405f77 11 solution pH Experimental Conditions 4.729943 #405f77 12 Anions Experimental Conditions 3.657640 #405f77 13 Ci (mg/L) Experimental Conditions 7.968859 #405f77 Experimental Conditions 0.8861985591113752 Physiochemical Properties 0.0035735825030922927 Atomic Composition 0.11022785838553249 .. GENERATED FROM PYTHON SOURCE LINES 289-296 .. code-block:: Python shap.summary_plot(shap_values, TrainX.values, plot_type="bar", feature_names=data.columns[0:-1], show=False) if SAVE: plt.savefig('figures/shap_bar.png') .. image-sg:: /auto_examples/images/sphx_glr_model_005.png :alt: model :srcset: /auto_examples/images/sphx_glr_model_005.png, /auto_examples/images/sphx_glr_model_005_2_00x.png 2.00x :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 297-305 .. code-block:: Python shap.summary_plot(shap_values, TrainX.values, feature_names=data.columns[0:-1], show=False) if SAVE: plt.savefig('figures/shap.png') .. image-sg:: /auto_examples/images/sphx_glr_model_006.png :alt: model :srcset: /auto_examples/images/sphx_glr_model_006.png, /auto_examples/images/sphx_glr_model_006_2_00x.png 2.00x :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 306-317 .. code-block:: Python sample_id = 0 print(shap_values.base_values[sample_id] + shap_values[sample_id].values.sum()) shap.plots.force(shap_values[sample_id], matplotlib=True, plot_cmap="LpLb", # show=False ) .. image-sg:: /auto_examples/images/sphx_glr_model_007.png :alt: model :srcset: /auto_examples/images/sphx_glr_model_007.png, /auto_examples/images/sphx_glr_model_007_2_00x.png 2.00x :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out .. code-block:: none 61.74045086603148 .. GENERATED FROM PYTHON SOURCE LINES 318-319 sample with maximum shap value .. GENERATED FROM PYTHON SOURCE LINES 319-333 .. code-block:: Python sample_id = np.argmax(shap_values.values.sum(axis=1)) print(sample_id, shap_values.base_values[sample_id] + shap_values[sample_id].values.sum()) shap_values.values[sample_id][10] = 26.03 figure = shap.plots.force(shap_values[sample_id], matplotlib=True, plot_cmap="LpLb", show=False, figsize=(24, 4) ) #plt.savefig('../manuscript/figures/fig6a.png', dpi=600, bbox_inches="tight") .. image-sg:: /auto_examples/images/sphx_glr_model_008.png :alt: model :srcset: /auto_examples/images/sphx_glr_model_008.png, /auto_examples/images/sphx_glr_model_008_2_00x.png 2.00x :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out .. code-block:: none 36 101.16288013871878 .. GENERATED FROM PYTHON SOURCE LINES 334-335 sample with minimum shap value .. GENERATED FROM PYTHON SOURCE LINES 335-345 .. code-block:: Python sample_id = np.argmin(shap_values.values.sum(axis=1)) print(sample_id, shap_values.base_values[sample_id] + shap_values[sample_id].values.sum()) shap.plots.force(shap_values[sample_id], matplotlib=True, plot_cmap="LpLb", # show=False ) .. image-sg:: /auto_examples/images/sphx_glr_model_009.png :alt: model :srcset: /auto_examples/images/sphx_glr_model_009.png, /auto_examples/images/sphx_glr_model_009_2_00x.png 2.00x :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out .. code-block:: none 85 -0.22594724009073275 .. GENERATED FROM PYTHON SOURCE LINES 346-347 sample with second minimum shap value .. GENERATED FROM PYTHON SOURCE LINES 347-358 .. code-block:: Python sample_id = np.argsort(shap_values.values.sum(axis=1))[80] print(sample_id, shap_values.base_values[sample_id] + shap_values[sample_id].values.sum()) shap.plots.force(shap_values[sample_id], matplotlib=True, plot_cmap="LpLb", show=False, figsize=(24, 4) ) #plt.savefig('../manuscript/figures/fig6b.png', dpi=600, bbox_inches="tight") .. image-sg:: /auto_examples/images/sphx_glr_model_010.png :alt: model :srcset: /auto_examples/images/sphx_glr_model_010.png, /auto_examples/images/sphx_glr_model_010_2_00x.png 2.00x :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out .. code-block:: none 470 1.938715847813107
.. GENERATED FROM PYTHON SOURCE LINES 359-361 Partial Dependence Plot ======================== .. GENERATED FROM PYTHON SOURCE LINES 361-371 .. code-block:: Python pdp = PartialDependencePlot( model.predict, TrainX, num_points=20, feature_names=list(TrainX.columns), show=False, save=False ) .. GENERATED FROM PYTHON SOURCE LINES 372-380 .. code-block:: Python pdp.plot_interaction( features=['time (min)', 'Ci (mg/L)'], plot_type="surface", cmap="coolwarm", ) plt.tight_layout() .. image-sg:: /auto_examples/images/sphx_glr_model_011.png :alt: model :srcset: /auto_examples/images/sphx_glr_model_011.png, /auto_examples/images/sphx_glr_model_011_2_00x.png 2.00x :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 381-389 .. code-block:: Python pdp.plot_interaction( features=['time (min)', 'solution pH'], plot_type="surface", cmap="coolwarm", ) plt.tight_layout() .. image-sg:: /auto_examples/images/sphx_glr_model_012.png :alt: model :srcset: /auto_examples/images/sphx_glr_model_012.png, /auto_examples/images/sphx_glr_model_012_2_00x.png 2.00x :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 390-398 .. code-block:: Python pdp.plot_interaction( features=['Ci (mg/L)', 'solution pH'], plot_type="contour", cmap="coolwarm", ) plt.tight_layout() .. image-sg:: /auto_examples/images/sphx_glr_model_013.png :alt: model :srcset: /auto_examples/images/sphx_glr_model_013.png, /auto_examples/images/sphx_glr_model_013_2_00x.png 2.00x :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 402-410 .. code-block:: Python ax = pdp.plot_interaction( features=['time (min)', 'Bi'], plot_type="contour", cmap="coolwarm", ) plt.tight_layout() .. image-sg:: /auto_examples/images/sphx_glr_model_014.png :alt: model :srcset: /auto_examples/images/sphx_glr_model_014.png, /auto_examples/images/sphx_glr_model_014_2_00x.png 2.00x :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 411-473 .. code-block:: Python f, ((ax1, ax2, ax3), (ax4, ax5, ax6)) = plt.subplots(2, 3, figsize=(18, 12)) pdp.plot_interaction( features=['time (min)', 'Ci (mg/L)'], plot_type="contour", cmap="coolwarm", ax=ax1, ) ax1.text(-0.1, 1.05, 'a)', transform=ax1.transAxes, size=20, weight='bold') pdp.plot_interaction( features=['time (min)', 'solution pH'], plot_type="contour", cmap="coolwarm", ax=ax2, ) ax2.text(-0.1, 1.05, 'b)', transform=ax2.transAxes, size=20, weight='bold') ax = pdp.plot_interaction( features=['time (min)', 'Anions'], plot_type="contour", cmap="coolwarm", ax=ax3, ) anions = [0, 1, 2, 3, 4, 5] ax.set_yticks(range(len(anions))) ax.set_yticklabels(an_encoder.inverse_transform(anions)) ax3.text(-0.1, 1.05, 'c)', transform=ax3.transAxes, size=20, weight='bold') ax = pdp.plot_interaction( features=['time (min)', 'Bi'], plot_type="contour", cmap="coolwarm", ax=ax4, ) ax.set_ylim(53, 57) ax4.text(-0.1, 1.05, 'd)', transform=ax4.transAxes, size=20, weight='bold') ax = pdp.plot_interaction( features=['time (min)', 'Catalyst type'], plot_type="contour", cmap="coolwarm", ax=ax5, ) catalysts = [0, 1, 2, 3, 4, 5, 6] ax.set_yticks(range(len(catalysts))) ax.set_yticklabels(cat_encoder.inverse_transform(catalysts)) ax5.text(-0.1, 1.05, 'e)', transform=ax5.transAxes, size=20, weight='bold') pdp.plot_interaction( features=['time (min)', 'Light intensity (W)'], plot_type="contour", cmap="coolwarm", ax=ax6, ) ax6.text(-0.1, 1.05, 'f)', transform=ax6.transAxes, size=20, weight='bold') plt.tight_layout() #plt.savefig('../manuscript/figures/fig7.png', dpi=600, bbox_inches="tight") plt.show() .. image-sg:: /auto_examples/images/sphx_glr_model_015.png :alt: model :srcset: /auto_examples/images/sphx_glr_model_015.png, /auto_examples/images/sphx_glr_model_015_2_00x.png 2.00x :class: sphx-glr-single-img .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 30.475 seconds) .. _sphx_glr_download_auto_examples_model.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: model.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: model.py ` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: model.zip ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_