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support-vectors-LinearSVC / app.py

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	import gradio as gr
	import numpy as np
	import matplotlib.pyplot as plt
	import warnings

	from functools import partial
	from sklearn.datasets import make_blobs, make_spd_matrix
	from sklearn.svm import LinearSVC
	from sklearn.inspection import DecisionBoundaryDisplay
	from sklearn.exceptions import ConvergenceWarning

	def train_model(n_samples, C, penalty, loss, max_iter):

	if penalty == "l1" and loss == "hinge":
	raise gr.Error("The combination of penalty='l1' and loss='hinge' is not supported")

	default_base = {"n_samples": 20}

	# Algorithms to compare
	params = default_base.copy()
	params.update({"n_samples":n_samples,
	"C": C,
	"penalty": penalty,
	"loss": loss,
	"max_iter": max_iter})

	X, y = make_blobs(n_samples=params["n_samples"], centers=2, random_state=0)

	fig, ax = plt.subplots()

	# catch warnings related to convergence
	with warnings.catch_warnings():
	warnings.filterwarnings("ignore", category=ConvergenceWarning)

	# add penalty, l1 and l2. Default is l2
	# add loss, square_hinge is Default. the other loss is hinge
	# multi_class{‘ovr’, ‘crammer_singer’}, default=’ovr’

	clf = LinearSVC(penalty=penalty, C=params["C"],
	loss=params["loss"],
	max_iter=params["max_iter"],
	random_state=42).fit(X, y)
	# obtain the support vectors through the decision function
	decision_function = clf.decision_function(X)
	# we can also calculate the decision function manually
	# decision_function = np.dot(X, clf.coef_[0]) + clf.intercept_[0]
	# The support vectors are the samples that lie within the margin
	# boundaries, whose size is conventionally constrained to 1
	support_vector_indices = np.where(np.abs(decision_function) <= 1 + 1e-15)[0]
	support_vectors = X[support_vector_indices]

	ax.scatter(X[:, 0], X[:, 1], c=y, s=30, cmap=plt.cm.Paired)
	DecisionBoundaryDisplay.from_estimator(
	clf,
	X,
	ax=ax,
	grid_resolution=50,
	plot_method="contour",
	colors="k",
	levels=[-1, 0, 1],
	alpha=0.5,
	linestyles=["--", "-", "--"],
	)
	ax.scatter(
	support_vectors[:, 0],
	support_vectors[:, 1],
	s=100,
	linewidth=1,
	facecolors="none",
	edgecolors="k",
	)
	ax.set_title("C=" + str(C))

	return fig

	def iter_grid(n_rows, n_cols):
	# create a grid using gradio Block
	for _ in range(n_rows):
	with gr.Row():
	for _ in range(n_cols):
	with gr.Column():
	yield

	title = "📈 Linear Support Vector Classification"
	with gr.Blocks(title=title) as demo:
	gr.Markdown(f"## {title}")
	gr.Markdown("The LinearSVC is an implementation of a \
	Support Vector Machine (SVM) for classification. \
	It aims to find the optimal linear \
	decision boundary that separates classes in the input data.")
	gr.Markdown("The most important parameters of `LinearSVC` are:")
	param_C = "\
	1. `C`: The inverse of the regularization strength. \
	A smaller `C` value increases the amount of regularization, \
	promoting simpler models, while a larger `C` value reduces \
	regularization, allowing more complex models. \
	It controls the trade-off between fitting the \
	training data and generalization to unseen data."
	param_loss=" \
	2. `loss`: The loss function used for training. \
	The default is `squared_hinge`, which is a variant \
	of hinge loss. The combination of penalty='l1' and \
	loss='hinge' is not supported."
	param_penalty="\
	3. `penalty`: The type of regularization penalty \
	applied to the model. The default is `l2`, which uses \
	the L2 norm."
	param_dual="\
	4. `dual`: Determines whether the dual or primal optimization \
	problem is solved. By default, `dual=True` when the number \
	of samples is less than the number of features, and `dual=False` \
	otherwise. For large-scale problems, setting `dual=False` \
	can be more efficient."
	param_tol="\
	5. `tol`: The tolerance for stopping criteria. \
	The solver stops when the optimization reaches \
	a specified tolerance level."
	param_max_iter="\
	6. `max_iter`: The maximum number of iterations for solver \
	convergence. If not specified, the default value is 1000."
	gr.Markdown(param_C)
	gr.Markdown(param_loss)
	gr.Markdown(param_penalty)
	gr.Markdown(param_dual)
	gr.Markdown(param_tol)
	gr.Markdown(param_max_iter)
	gr.Markdown("Read more in the \
	[original example](https://scikit-learn.org/stable/modules/generated/sklearn.svm.LinearSVC.html#sklearn.svm.LinearSVC).")


	n_samples = gr.Slider(minimum=20, maximum=100, step=5,
	label = "Number of Samples")


	with gr.Row():
	input_model = "LinearSVC"
	fn = partial(train_model)

	with gr.Row():
	penalty = gr.Dropdown(["l1", "l2"], value="l2", interactive=True, label="Penalty to prevent overfitting")
	loss = gr.Dropdown(["hinge", "squared hinge"], value="hinge", interactive=True, label="Loss function")

	with gr.Row():
	max_iter = gr.Slider(minimum=100, maximum=2000, step=100, value=1000,
	label = "Max. number of iterations")
	param_C = gr.Number(value=1,
	label = "Regularization parameter C",
	# info="When C is smal the regularization effect is stronger. "
	# + "This can help to avoid overfitting but may lead to higher bias. "
	# + "On the other hand, when C is large, the regularization effect "
	# + "is weaker, and the model can have larger parameter values, "
	# + "allowing for more complex decision boundaries that fit the "
	# + "training data more closely. This may increase the risk of "
	# + "overfitting and result in a higher variance model."
	)

	with gr.Row():
	penalty2 = gr.Dropdown(["l1", "l2"], value="l2", interactive=True, label="Penalty to prevent overfitting")
	loss2 = gr.Dropdown(["hinge", "squared hinge"], value="hinge", interactive=True, label="Loss function")

	with gr.Row():
	max_iter2 = gr.Slider(minimum=100, maximum=2000, step=100, value=1000,
	label = "Max. number of iterations")
	param_C2 = gr.Number(value=100,
	label = "Regularization parameter C"
	)

	with gr.Row():
	plot = gr.Plot(label=input_model)
	n_samples.change(fn=fn, inputs=[n_samples, param_C, penalty, loss, max_iter], outputs=plot)
	param_C.change(fn=fn, inputs=[n_samples, param_C, penalty, loss, max_iter], outputs=plot)
	penalty.change(fn=fn, inputs=[n_samples, param_C, penalty, loss, max_iter], outputs=plot)
	loss.change(fn=fn, inputs=[n_samples, param_C, penalty, loss, max_iter], outputs=plot)
	max_iter.change(fn=fn, inputs=[n_samples, param_C, penalty, loss, max_iter], outputs=plot)

	plot2 = gr.Plot(label=input_model)
	n_samples.change(fn=fn, inputs=[n_samples, param_C2, penalty2, loss2, max_iter2], outputs=plot2)
	param_C2.change(fn=fn, inputs=[n_samples, param_C2, penalty2, loss2, max_iter2], outputs=plot2)
	penalty2.change(fn=fn, inputs=[n_samples, param_C2, penalty2, loss2, max_iter2], outputs=plot2)
	loss2.change(fn=fn, inputs=[n_samples, param_C2, penalty2, loss2, max_iter2], outputs=plot2)
	max_iter2.change(fn=fn, inputs=[n_samples, param_C2, penalty2, loss2, max_iter2], outputs=plot2)

	demo.launch()