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from __future__ import annotations
import numpy as np
import gradio as gr
from sklearn.svm import SVC
import plotly.graph_objects as go
from sklearn.datasets import load_digits
from sklearn.model_selection import validation_curve
def plot_validation_curve(x: np.array, ys: list[np.array], yerros: list[np.array], names: list[str], colors: list[str], log_x: bool=True, title: str=""):
fig = go.Figure()
for y, yerror, name, color in zip(ys, yerros, names, colors):
y_upper = y + yerror
y_lower = y - yerror
fig.add_trace(
go.Scatter(
x=x,
y=np.round(y, 3),
name=name,
line_color=color
)
)
fig.add_trace(
go.Scatter(
x=x.tolist()+x[::-1].tolist(), # x, then x reversed
y=y_upper.tolist()+y_lower[::-1].tolist(), # upper, then lower reversed
fill='toself',
fillcolor=color,
line=dict(color=color),
hoverinfo="skip",
showlegend=False,
opacity=0.2
)
)
if log_x:
fig.update_xaxes(type="log")
fig.update_layout(
title=title,
xaxis_title="Hyperparameter",
yaxis_title="Accuracy",
hovermode="x unified",
)
return fig
def app_fn(n_points: int, param_name: str):
X, y = load_digits(return_X_y=True)
subset_mask = np.isin(y, [1, 2]) # binary classification: 1 vs 2
X, y = X[subset_mask], y[subset_mask]
if param_name=="gamma":
param_range = np.logspace(-6, -1, n_points)
log_x = True
elif param_name=="C":
param_range = np.logspace(-2, 0, n_points)
log_x = True
elif param_name=="kernel":
param_range = np.array(["rbf", "linear", "poly", "sigmoid"])
log_x = False
train_scores, test_scores = validation_curve(
SVC(),
X,
y,
param_name=param_name,
param_range=param_range,
scoring="accuracy",
n_jobs=-1,
)
train_scores_mean = np.mean(train_scores, axis=1)
train_scores_std = np.std(train_scores, axis=1)
test_scores_mean = np.mean(test_scores, axis=1)
test_scores_std = np.std(test_scores, axis=1)
fig = plot_validation_curve(
param_range,
[train_scores_mean, test_scores_mean],
[train_scores_std, test_scores_std],
["Training score", "Cross-validation score"],
["orange", "navy"],
title=f"Validation Curve with SVM for {param_name} Hyperparameter",
log_x=log_x
)
return fig
title = "Plotting Validation Curve"
with gr.Blocks(title=title) as demo:
gr.Markdown(f"# {title}")
gr.Markdown(
"""
#### This example shows the usage of a validation curve to understand \
how the performance of a model, SVM in this case, changes with varying hyperparameters. \
The dataset used was the [digits dataset](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html#sklearn.datasets.load_digits) \
from scikit-learn. The hyperparameter varied was gamma. \
[Original Example](https://scikit-learn.org/stable/auto_examples/model_selection/plot_validation_curve.html#sphx-glr-auto-examples-model-selection-plot-validation-curve-py)
"""
)
with gr.Row():
n_points = gr.inputs.Slider(5, 100, 5, 5,label="Number of points")
param_name = gr.inputs.Dropdown(["gamma", "C", "kernel"], label="Hyperparameter", default="gamma")
fig = gr.Plot(label="Validation Curve")
n_points.release(fn=app_fn, inputs=[n_points, param_name], outputs=[fig])
param_name.change(fn=app_fn, inputs=[n_points, param_name], outputs=[fig])
# C.change(fn=app_fn, inputs=[n_points, param_name, C, gamma, kernel, degree], outputs=[fig])
# gamma.change(fn=app_fn, inputs=[n_points, param_name, C, gamma, kernel, degree], outputs=[fig])
# kernel.change(fn=app_fn, inputs=[n_points, param_name, C, gamma, kernel, degree], outputs=[fig])
# degree.change(fn=app_fn, inputs=[n_points, param_name, C, gamma, kernel, degree], outputs=[fig])
demo.load(fn=app_fn, inputs=[n_points, param_name], outputs=[fig])
demo.launch() |