partial_fit logo partial_fit: compare

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Instead of a SGDRegressor you may also consider using a PassiveAgressiveRegressor. It's an algorithm that has a different update mechanic and at times this method may converge faster. We're mainly going to play with the stepsize parameter in this video to get a feeling on system convergence.

We've added an extra cell to the notebook that contains the loop that switches between a cold and a warm stepsize.

from sklearn.linear_model import PassiveAggressiveRegressor

# Set jump coefficients
c_cold, c_warm = 0.1, 0.01

# Run for Stats
mod_pac = PassiveAggressiveRegressor(C=c_cold)
data = []

for i, x in enumerate(X_train):
    mod_pac.partial_fit([x], [y_train[i]])
        'c0': mod_pac.intercept_[0],
        'c1': mod_pac.coef_.flatten()[0],
        'c2': mod_pac.coef_.flatten()[1],
        'mse_test': np.mean((mod_pac.predict(X_test) - y_test)**2),
        'normal_mse_test': normal_mse_test,
        'i': i
    if i == 500:
        mod_pac.C = c_warm

df_stats = pd.DataFrame(data)

We've also added a cell that plots the original SGD rates and the new one.


pltr1 = (pd.melt(df_stats[['i', 'c1', 'c2']], id_vars=["i"]))
pltr2 = (pd.melt(df_stats[['i', 'normal_mse_test', 'mse_test']], id_vars=["i"]))

q1 = (alt.Chart(pltr1, title='PA evolution of weights')
        .encode(x='i', y='value', color='variable', tooltip=['i', 'value', 'variable'])
        .properties(width=300, height=150)

q2 = (alt.Chart(pltr2, title='PA evolution of mse')
        .encode(x='i', y='value', color='variable', tooltip=['i', 'value', 'variable'])
        .properties(width=350, height=150)

(p1 | p2) & (q1 | q2)

More Reading

If you'd like to read more about the effect of the warm/cold stepsizes you might enjoy reading this blogpost.