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# Code source: Gaël Varoquaux
# License: BSD 3 clause
# This code is a MOD with Gradio Demo
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
from sklearn import decomposition
from sklearn import datasets
# unused but required import for doing 3d projections with matplotlib < 3.2
import mpl_toolkits.mplot3d # noqa: F401
matplotlib.use('agg')
import gradio as gr
np.random.seed(5)
## PCA
def PCA_Pred(x1, x2, x3, x4):
#Load Data from iris dataset:
iris = datasets.load_iris()
X = iris.data
y = iris.target
fig, ax = plt.subplots(1, subplot_kw={'projection': '3d', 'elev': 48, 'azim': 134})
ax.set_position([0, 0, 0.95, 1])
plt.cla()
#Create the model with 3 principal components:
pca = decomposition.PCA(n_components=3)
#Fit model and transform (decrease dimensions) iris dataset:
pca.fit(X)
X = pca.transform(X)
#Set labels to data clusters
for name, label in [("Setosa", 0), ("Versicolour", 1), ("Virginica", 2)]:
ax.text3D(
X[y == label, 0].mean(),
X[y == label, 1].mean() + 1.5,
X[y == label, 2].mean(),
name,
horizontalalignment="center",
bbox=dict(alpha=0.5, edgecolor="w", facecolor="w"),
)
# Reorder the labels to have colors matching the cluster results
y = np.choose(y, [1, 2, 0]).astype(float)
ax.scatter(X[:, 0], X[:, 1], X[:, 2], c=y, cmap=plt.cm.nipy_spectral, edgecolor="k")
user_iris_data = np.array([[x1, x2, x3, x4]], ndmin=2)
#Perform reduction to user data
pc_output = pca.transform(user_iris_data)
ax.scatter(pc_output[0, 0], pc_output[0, 1], pc_output[0, 2], c='r', marker='*')
ax.xaxis.set_ticklabels([])
ax.yaxis.set_ticklabels([])
ax.zaxis.set_ticklabels([])
return [pc_output, fig]
title = "🌺 PCA example with Iris Data-set"
with gr.Blocks(title=title) as demo:
gr.Markdown(f"## {title}")
gr.Markdown(
"""
## The following app is a demo for PCA decomposition. It takes 4 dimensions as input, in reference \
to the Iris flower image (left), and returns the transformed first 3 principal components (feature \
reduction) taken from a pre-trained model with Iris dataset (Right).
""")
with gr.Row():
with gr.Column():
html1 = (
"<div >"
"<img src='file/iris_flower_dimensions.jpg' width='597' height='460' alt='image One'>"
+ "</div>"
)
gr.HTML(html1)
inp1 = gr.Slider(0, 5, value=1, step=0.1, label="Sepal Length (cm)")
inp2 = gr.Slider(0, 5, value=1, step=0.1, label="Sepal Width (cm)")
inp3 = gr.Slider(0, 5, value=1, step=0.1, label="Petal Length (cm)")
inp4 = gr.Slider(0, 5, value=1, step=0.1, label="Petal Width (cm)")
output = gr.Textbox(label="PCA Axes")
with gr.Column():
html2 = (
"<div >"
"<img src='file/iris_dataset_info.png' alt='image two'>"
+ "</div>"
)
gr.HTML(html2)
plot = gr.Plot(label="PCA 3D Space")
Reduction = gr.Button("PCA Transform")
Reduction.click(fn=PCA_Pred, inputs=[inp1, inp2, inp3, inp4], outputs=[output, plot])
demo.load(fn=PCA_Pred, inputs=[inp1, inp2, inp3, inp4], outputs=[output, plot])
demo.launch() |