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import gradio as gr | |
from sklearn.pipeline import make_pipeline | |
from sklearn.metrics import roc_curve, auc | |
from sklearn.datasets import make_classification | |
from sklearn.linear_model import LogisticRegression | |
from sklearn.model_selection import train_test_split | |
from sklearn.preprocessing import FunctionTransformer, OneHotEncoder | |
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier, RandomTreesEmbedding | |
import utils | |
def app_fn(n_samples: int, n_estimators: int, max_depth: int): | |
# Create Data | |
(X_train_ensemble, y_train_ensemble), \ | |
(X_train_linear, y_train_linear), \ | |
(X_test, y_test) = utils.create_and_split_dataset(n_samples) | |
# Creating and fitting Random Forest | |
random_forest = RandomForestClassifier( | |
n_estimators=n_estimators, max_depth=max_depth, random_state=10 | |
) | |
random_forest.fit(X_train_ensemble, y_train_ensemble) | |
# Creating and fitting Gradient Boosting | |
gradient_boosting = GradientBoostingClassifier( | |
n_estimators=n_estimators, max_depth=max_depth, random_state=10 | |
) | |
_ = gradient_boosting.fit(X_train_ensemble, y_train_ensemble) | |
# Creating and fitting Pipeline of Random Tree Embedding w/ Logistic Regression | |
random_tree_embedding = RandomTreesEmbedding( | |
n_estimators=n_estimators, max_depth=max_depth, random_state=0 | |
) | |
rt_model = make_pipeline(random_tree_embedding, LogisticRegression(max_iter=1000)) | |
rt_model.fit(X_train_linear, y_train_linear) | |
# Creating and fitting Pipeline of Random Forest Embedding w/ Logistic Regression | |
rf_leaves_yielder = FunctionTransformer(utils.rf_apply, kw_args={"model": random_forest}) | |
rf_model = make_pipeline( | |
rf_leaves_yielder, | |
OneHotEncoder(handle_unknown="ignore"), | |
LogisticRegression(max_iter=1000), | |
) | |
rf_model.fit(X_train_linear, y_train_linear) | |
# Creating and fitting Pipeline of Gradient Boosting Embedding w/ Logistic Regression | |
gbdt_leaves_yielder = FunctionTransformer( | |
utils.gbdt_apply, kw_args={"model": gradient_boosting} | |
) | |
gbdt_model = make_pipeline( | |
gbdt_leaves_yielder, | |
OneHotEncoder(handle_unknown="ignore"), | |
LogisticRegression(max_iter=1000), | |
) | |
gbdt_model.fit(X_train_linear, y_train_linear) | |
# Plotting ROC Curve | |
models = [ | |
("RT embedding -> LR", rt_model), | |
("RF", random_forest), | |
("RF embedding -> LR", rf_model), | |
("GBDT", gradient_boosting), | |
("GBDT embedding -> LR", gbdt_model), | |
] | |
fig = utils.plot_roc( | |
X_test, | |
y_test, | |
models | |
) | |
return fig | |
title="Feature Transformations with Ensembles of Trees 🌳" | |
with gr.Blocks(title=title) as demo: | |
gr.Markdown(f"# {title}") | |
gr.Markdown( | |
""" | |
This example shows how one can apply features transformations using ensembles of trees \ | |
on a synthetic dataset. The transformations are then used to train a linear model on the \ | |
transformed data. The plot shows the ROC curve of the different models trained on the \ | |
transformed data. The plot is interactive and you can zoom in and out. | |
See original example [here](https://scikit-learn.org/stable/auto_examples/ensemble/plot_feature_transformation.html#sphx-glr-auto-examples-ensemble-plot-feature-transformation-py). | |
""" | |
) | |
with gr.Row(): | |
n_samples = gr.inputs.Slider(50_000, 100_000, 1000, label="Number of Samples", default=80_000) | |
n_estimators = gr.inputs.Slider(10, 100, 10, label="Number of Estimators", default=10) | |
max_depth = gr.inputs.Slider(1, 10, 1, label="Max Depth", default=3) | |
plot = gr.Plot(label="ROC Curve") | |
n_samples.change(fn=app_fn, inputs=[n_samples, n_estimators, max_depth], outputs=[plot]) | |
n_estimators.change(fn=app_fn, inputs=[n_samples, n_estimators, max_depth], outputs=[plot]) | |
max_depth.change(fn=app_fn, inputs=[n_samples, n_estimators, max_depth], outputs=[plot]) | |
demo.load(fn=app_fn, inputs=[n_samples, n_estimators, max_depth], outputs=[plot]) | |
demo.launch() |