File size: 4,101 Bytes
239a99b dbd093d 239a99b 89da9e7 dbd093d 239a99b ca9b69c 239a99b ca9b69c bc61cbb 239a99b |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 |
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() |