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| # -*- coding: utf-8 -*- | |
| """Gradio-regression.ipynb | |
| Automatically generated by Colaboratory. | |
| Original file is located at | |
| https://colab.research.google.com/drive/1qmfhcPafAIfczazACroyAYyRohdQbklK | |
| """ | |
| import numpy as np | |
| import matplotlib.pyplot as plt | |
| import gradio as gr | |
| from sklearn.linear_model import Ridge | |
| from sklearn.model_selection import train_test_split | |
| np.random.seed(2) | |
| X = 2 * np.random.rand(100, 1) | |
| y = 4 + 3 * X | |
| X_train, X_test, y_train, y_test = train_test_split( | |
| X, y, test_size=0.1, random_state=42) | |
| def build_model(alpha): | |
| r_reg = Ridge(alpha=alpha) | |
| r_reg.fit(X_train, y_train) | |
| return r_reg | |
| def predict(alpha): | |
| ridge_reg = build_model(alpha) | |
| preds = ridge_reg.predict(X_test) | |
| fig = plt.figure() | |
| plt.plot(X_test, y_test, "r-") | |
| plt.plot(X_test, preds, "b--") | |
| plt.title("Effect of regularization parameter on Ridge regression") | |
| plt.ylabel("Y") | |
| plt.xlabel("X") | |
| return plt | |
| inputs = gr.Number() | |
| outputs = gr.Plot() | |
| gr.Interface(fn = predict, inputs = inputs, outputs = outputs).launch() | |