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import streamlit as st |
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import numpy as np |
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import matplotlib.pyplot as plt |
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from sklearn.linear_model import LinearRegression, Ridge |
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from sklearn.preprocessing import PolynomialFeatures |
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from sklearn.metrics import mean_squared_error |
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st.subheader("Ridge Demo") |
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col1, col2 = st.columns(2) |
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degree = st.slider('Degree', 2, 40, 1) |
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alpha = st.slider('Lambda (Regularisation)', 0, 500, 1) |
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with col1: |
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st.markdown("#### Un-regularized") |
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with col2: |
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st.markdown("#### Regularized") |
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x = np.linspace(-1., 1., 100) |
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y = 4 + 3*x + 2*np.sin(x) + 2*np.random.randn(len(x)) |
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poly = PolynomialFeatures(degree=degree, include_bias=False) |
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x_new = poly.fit_transform(x.reshape(-1, 1)) |
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lr = LinearRegression() |
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lr.fit(x_new, y) |
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y_pred = lr.predict(x_new) |
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ri = Ridge(alpha = alpha) |
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ri.fit(x_new, y) |
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y_pred_ri = ri.predict(x_new) |
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fig1, ax1 = plt.subplots() |
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fig2, ax2 = plt.subplots() |
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ax1.scatter(x, y) |
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ax1.plot(x, y_pred) |
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ax2.scatter(x, y) |
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ax2.plot(x, y_pred_ri) |
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for ax in [ax1, ax2]: |
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ax.spines['right'].set_visible(False) |
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ax.spines['top'].set_visible(False) |
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ax.yaxis.set_ticks_position('left') |
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ax.xaxis.set_ticks_position('bottom') |
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ax.set_xlabel("x") |
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ax.set_ylabel("y") |
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rmse = np.round(np.sqrt(mean_squared_error(y_pred, y)), 2) |
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ax1.set_title(f"Train RMSE: {rmse}") |
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rmse_ri = np.round(np.sqrt(mean_squared_error(y_pred_ri, y)), 2) |
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ax2.set_title(f"Train RMSE: {rmse_ri}") |
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with col1: |
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st.pyplot(fig1) |
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with col2: |
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st.pyplot(fig2) |
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hide_streamlit_style = """ |
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<style> |
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#MainMenu {visibility: hidden;} |
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footer {visibility: hidden;} |
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</style> |
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""" |
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st.markdown(hide_streamlit_style, unsafe_allow_html=True) |
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