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import gradio as gr
import matplotlib.pyplot as plt
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.ensemble import RandomForestClassifier
from sklearn.pipeline import make_pipeline
from sklearn.svm import LinearSVC
from sklearn.metrics import DetCurveDisplay, RocCurveDisplay
def generate_synthetic_data(n_samples, n_features, n_redundant, n_informative, random_state, n_clusters_per_class):
X, y = make_classification(
n_samples=n_samples,
n_features=n_features,
n_redundant=n_redundant,
n_informative=n_informative,
random_state=random_state,
n_clusters_per_class=n_clusters_per_class,
)
return X, y
def plot_roc_det_curves(classifier_names, svm_c, rf_max_depth, rf_n_estimators, rf_max_features,
n_samples, n_features, n_redundant, n_informative, random_state, n_clusters_per_class):
X, y = generate_synthetic_data(n_samples, n_features, n_redundant, n_informative, random_state, n_clusters_per_class)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, random_state=0)
classifiers = {
"Linear SVM": make_pipeline(StandardScaler(), LinearSVC(C=svm_c)),
"Random Forest": RandomForestClassifier(
max_depth=rf_max_depth, n_estimators=rf_n_estimators, max_features=rf_max_features
),
}
fig, [ax_roc, ax_det] = plt.subplots(1, 2, figsize=(11, 5))
for classifier_name in classifier_names:
clf = classifiers[classifier_name]
clf.fit(X_train, y_train)
RocCurveDisplay.from_estimator(clf, X_test, y_test, ax=ax_roc, name=classifier_name)
DetCurveDisplay.from_estimator(clf, X_test, y_test, ax=ax_det, name=classifier_name)
ax_roc.set_title("Receiver Operating Characteristic (ROC) curves")
ax_det.set_title("Detection Error Tradeoff (DET) curves")
ax_roc.grid(linestyle="--")
ax_det.grid(linestyle="--")
plt.legend()
plt.tight_layout()
return plt
parameters = [
gr.inputs.CheckboxGroup(["Linear SVM", "Random Forest"], label="Classifiers"),
gr.inputs.Slider(0.001, 0.1, step=0.001, default=0.025, label="Linear SVM C"),
gr.inputs.Slider(1, 10, step=1, default=5, label="Random Forest Max Depth"),
gr.inputs.Slider(1, 20, step=1, default=10, label="Random Forest n_estimators"),
gr.inputs.Slider(1, 10, step=1, default=1, label="Random Forest max_features"),
gr.inputs.Slider(100, 2000, step=100, default=1000, label="Number of Samples"),
gr.inputs.Slider(1, 10, step=1, default=2, label="Number of Features"),
gr.inputs.Slider(0, 10, step=1, default=0, label="Number of Redundant Features"),
gr.inputs.Slider(1, 10, step=1, default=2, label="Number of Informative Features"),
gr.inputs.Slider(0, 100, step=1, default=1, label="Random State"),
gr.inputs.Slider(1, 10, step=1, default=1, label="Number of Clusters per Class"),
]
examples = [
[
["Linear SVM"],
0.025,
5,
10,
1,
1000,
2,
0,
2,
1,
1,
],
[
["Random Forest"],
0.025,
5,
10,
1,
1000,
2,
0,
2,
1,
1,
],
[
["Linear SVM", "Random Forest"],
0.025,
5,
10,
1,
1000,
2,
0,
2,
1,
1,
]
]
iface = gr.Interface(title = "Detection error tradeoff (DET) curve", fn=plot_roc_det_curves, inputs=parameters, outputs="plot", description="In this example, we compare two binary classification multi-threshold metrics: the Receiver Operating Characteristic (ROC) and the Detection Error Tradeoff (DET). For such purpose, we evaluate two different classifiers for the same classification task. See the original scikit-learn example here: https://scikit-learn.org/stable/auto_examples/model_selection/plot_det.html")
iface.launch()
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