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import gradio as gr
import matplotlib.pyplot as plt
import numpy as np
from sklearn import datasets, linear_model
from sklearn.metrics import mean_squared_error, r2_score
FIGSIZE = (10,10)
feature_names = ["age", "body-mass index (BMI)", "blood pressure",
"total serum cholesterol", "low-density lipoproteins (LDL)",
"high-density lipoproteins (HDL)", "total cholesterol / HDL",
"log of serum triglycerides level (possibly)","blood sugar level"]
def create_dataset(feature_id=2):
# Load the diabetes dataset
diabetes_X, diabetes_y = datasets.load_diabetes(return_X_y=True)
# Use only one feature
diabetes_X = diabetes_X[:, np.newaxis, feature_id]
# Split the data into training/testing sets
diabetes_X_train = diabetes_X[:-20]
diabetes_X_test = diabetes_X[-20:]
# Split the targets into training/testing sets
diabetes_y_train = diabetes_y[:-20]
diabetes_y_test = diabetes_y[-20:]
return diabetes_X_train, diabetes_X_test, diabetes_y_train, diabetes_y_test
def train_model(input_data):
# We removed the sex variable
if input_data == 'age':
feature_id = 0
else:
feature_id = feature_names.index(input_data) + 1
diabetes_X_train, diabetes_X_test, diabetes_y_train, diabetes_y_test = create_dataset(feature_id)
# Create linear regression object
regr = linear_model.LinearRegression()
# Train the model using the training sets
regr.fit(diabetes_X_train, diabetes_y_train)
# Make predictions using the testing set
diabetes_y_pred = regr.predict(diabetes_X_test)
mse = mean_squared_error(diabetes_y_test, diabetes_y_pred)
r2 = r2_score(diabetes_y_test, diabetes_y_pred)
# Plot outputs
fig = plt.figure(figsize=FIGSIZE)
# plt.title(input_data)
plt.scatter(diabetes_X_test, diabetes_y_test, color="black")
plt.plot(diabetes_X_test, diabetes_y_pred, color="blue", linewidth=3)
plt.xticks(())
plt.yticks(())
return fig, regr.coef_, mse, r2
title = "Linear Regression Example 📈"
description = "The example shows how linear regression attempts to draw a straight line that will best minimize the residual sum of squares between the observed responses in the dataset"
with gr.Blocks() as demo:
gr.Markdown(f"## {title}")
gr.Markdown(description)
with gr.Column():
with gr.Row():
plot = gr.Plot()
with gr.Column():
input_data = gr.Dropdown(choices=feature_names, label="Feature", value="body-mass index")
coef = gr.Textbox(label="Coefficients")
mse = gr.Textbox(label="MSE")
r2 = gr.Textbox(label="R2")
input_data.change(fn=train_model, inputs=[input_data], outputs=[plot, coef, mse, r2], queue=False)
demo.launch(enable_queue=True)
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