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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 plotly.graph_objects as go
from sklearn import decomposition
from sklearn import datasets
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
label_data = [("Setosa", 0), ("Versicolour", 1), ("Virginica", 2)]
#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)
#Create figure with plotly
fig = go.Figure()
for name, label in label_data:
fig.add_trace(go.Scatter3d(
x=X[Y == label, 0],
y=X[Y == label, 1],
z=X[Y == label, 2],
mode='markers',
marker=dict(
size=8,
color=label,
colorscale='Viridis',
opacity=0.8),
name=name
))
user_iris_data = np.array([[x1, x2, x3, x4]], ndmin=2)
#Perform reduction to user data
pc_output = pca.transform(user_iris_data)
fig.add_traces([go.Scatter3d(
x=np.array(pc_output[0, 0]),
y=np.array(pc_output[0, 1]),
z=np.array(pc_output[0, 2]),
mode='markers',
marker=dict(
size=12,
color=4, # set color
colorscale='Viridis', # choose a colorscale
opacity=0.8),
name="User data"
)])
fig.update_layout(scene = dict(
xaxis_title="1st PCA Axis",
yaxis_title="2nd PCA Axis",
zaxis_title="3th PCA Axis"),
legend_title="Species"
)
return [pc_output, fig]
title = "PCA example with Iris Dataset 🌺"
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 following image, and returns the transformed first three principal components (feature \
reduction), taken from a pre-trained model with Iris dataset.
""")
html = (
"<div >"
"<img src='file/iris_dataset_info.png' alt='image one'>"
+ "</div>"
)
gr.HTML(html)
with gr.Row():
with gr.Column():
inp1 = gr.Slider(0, 7, 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, 7, 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():
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() |