Note

Go to the end to download the full example code.

4. Model

This notebooks shows modeling with DecisionTreeRegressor

import numpy as np
import matplotlib.pyplot as plt

from ai4water import Model
from ai4water.postprocessing import ProcessPredictions
from ai4water.utils.utils import TrainTestSplit

from sklearn.tree import plot_tree

from utils import version_info
from utils import prepare_data, set_rcParams, SAVE

for lib, ver in version_info().items():
    print(lib, ver)

python 3.12.10 (main, May  6 2025, 10:49:23) [GCC 11.4.0]
os posix
ai4water 1.07
lightgbm 4.6.0
catboost 1.2.10
xgboost 3.2.0
easy_mpl 0.21.5
SeqMetrics 2.0.0
numpy 1.26.4
pandas 2.2.3
matplotlib 3.10.8
h5py 3.16.0
sklearn 1.3.1
optuna 4.8.0
skopt 0.10.2
plotly 6.6.0
seaborn 0.13.2
crepes 0.9.0
mapie 0.9.2
shap 0.49.1
scipy 1.17.1

set_rcParams()

Lightweight Model

Using the features selected by Bortua-shap method

inputs = ['Solution pH', 'Time (m)', 'Anions', 'Ni (At%)', 'HA (mg/L)',
          'loading (g)', 'Pore size (nm)', 'O (At%)',
          'Light intensity (watt)', 'Mo (At%)', 'Dye concentration (mg/L)']
data, encoders = prepare_data(#inputs=inputs,
                              outputs="k")

print(data.shape)

(1527, 35)

input_features = data.columns.tolist()[0:-1]

output_features = data.columns.tolist()[-1:]

TrainX, TestX, TrainY, TestY = TrainTestSplit(seed=313).split_by_random(
    data[input_features],
    data[output_features]
)

print(TrainX.shape, TrainY.shape, TestX.shape, TestY.shape)

(1068, 34) (1068, 1) (459, 34) (459, 1)

model = Model(
    model = "DecisionTreeRegressor",
    input_features=input_features,
    output_features=output_features,
    val_fraction=0.0,
)

model.fit(TrainX, TrainY.values)

building ML model for
regression problem using DecisionTreeRegressor

DecisionTreeRegressor(random_state=313)

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print(model.evaluate(
    x=TrainX, y=TrainY.values,
    metrics=["r2", "r2_score", "rmse", "mae"]))

{'r2': 0.9559565533833142, 'r2_score': 0.9559565533833139, 'rmse': 0.0013702748852059826, 'mae': 0.0003326227051077471}

train_prediction = model.predict(
    x=TrainX)

pp = ProcessPredictions('regression', 1, show=False, save=False)
pp.regression_plot(TrainY, train_prediction,
                   ridge_line_kws={'color': '#67a278', 'lw': 2.0},
                   marker_color='#61a86b',
                   hist=True,
                   hist_kws={'color': '#a7c6a5'},
                   )
if SAVE:
    plt.savefig("results/figures/train_regression.png", dpi=400, bbox_inches="tight")
plt.tight_layout()
plt.show()

_ = pp.residual_plot(
    TrainY, train_prediction,
    hist_kws={'color': '#a7c6a5'},
    plot_kws=dict(color='#61a86b', markerfacecolor='#61a86b')
)
if SAVE:
    plt.savefig("results/figures/train_residual.png", dpi=400, bbox_inches="tight")
plt.tight_layout()
plt.show()

ax = pp.edf_plot(
    TrainY, train_prediction,
    color='#61a86b',
    marker=('--', '-')
)
if SAVE:
    plt.savefig("results/figures/train_edf.png", dpi=400, bbox_inches="tight")
plt.tight_layout()
plt.show()

print(model.evaluate(
    x=TestX, y=TestY.values,
    metrics=["r2", "r2_score", "rmse"]))

{'r2': 0.9439296699762157, 'r2_score': 0.9432151713156933, 'rmse': 0.0017086081240634096}

test_prediction = model.predict(
    x=TestX)

pp.regression_plot(
    TestY, test_prediction,
    ridge_line_kws={'color': '#c65d5d', 'lw': 2.0},
                   marker_color='#c74a52',
                   hist=True,
                   hist_kws={'color': '#d5998b'},
                   )

if SAVE:
    plt.savefig("results/figures/test_regression.png", dpi=600, bbox_inches="tight")
plt.tight_layout()
plt.show()

_ = pp.residual_plot(
    TestY, test_prediction,
    hist_kws={'color': '#d5998b'},
    plot_kws=dict(color='#c65d5d', markerfacecolor='#c65d5d')
)
if SAVE:
    plt.savefig("results/figures/test_residual.png", dpi=400, bbox_inches="tight")
plt.tight_layout()
plt.show()

ax = pp.edf_plot(
    TestY, test_prediction,
    color='#c65d5d',
    marker=('--', '-')
)
if SAVE:
    plt.savefig("results/figures/test_edf.png", dpi=400, bbox_inches="tight")
plt.tight_layout()
plt.show()

all_x = data[input_features]
all_y = model.predict(all_x, process_results=False)
ax = model.prediction_analysis(
    features= 'Time (m)',
    x=all_x, y=all_y,
    kind="box",
    show=False
)
if SAVE:
    plt.savefig("results/figures/pred_analysis.png", dpi=400, bbox_inches="tight")
ax.set_yticklabels(np.round(ax.get_yticks(), 3))
plt.tight_layout()
plt.show()

ax = model.prediction_analysis(
    features= ['Time (m)',  'loading (g)'],
    x=all_x, y=all_y,
    num_grid_points=(6, 6),
    border=True,
    show=False,
    annotate_kws=dict(fontsize=12)
)
plt.tight_layout()
plt.show()

get the hyperparameters of the learned decision tree

print(model._model.get_depth())
print(model._model.get_n_leaves())

18
417

plotting decision tree

plot_tree(model._model)

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Heavyweight Model

Using all the input features

data, encoders = prepare_data(outputs="k")

print(data.shape)

(1527, 35)

input_features = data.columns.tolist()[0:-1]

output_features = data.columns.tolist()[-1:]

TrainX, TestX, TrainY, TestY = TrainTestSplit(seed=313).split_by_random(
    data[input_features],
    data[output_features]
)

print(TrainX.shape, TrainY.shape, TestX.shape, TestY.shape)

(1068, 34) (1068, 1) (459, 34) (459, 1)

model = Model(
    model = "DecisionTreeRegressor",
    input_features=input_features,
    output_features=output_features,
    val_fraction=0.0,
)

model.fit(TrainX, TrainY.values)

building ML model for
regression problem using DecisionTreeRegressor

DecisionTreeRegressor(random_state=313)

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print(model.evaluate(
    x=TrainX, y=TrainY.values,
    metrics=["r2", "r2_score", "rmse", "mae"]))

{'r2': 0.9559565533833142, 'r2_score': 0.9559565533833139, 'rmse': 0.0013702748852059826, 'mae': 0.0003326227051077471}

train_prediction = model.predict(
    x=TrainX)

print(model.evaluate(
    x=TestX, y=TestY.values,
    metrics=["r2", "r2_score", "rmse", "mae"]))

{'r2': 0.9439296699762157, 'r2_score': 0.9432151713156933, 'rmse': 0.0017086081240634096, 'mae': 0.0005616722524275745}

Total running time of the script: (0 minutes 13.001 seconds)

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