ESNRegressor GridSearchCV

In [1]:

"""
=========================================================
GridSearch with ESNRegressor 
=========================================================
This example shows a minimal example of Echo State Network
applied to a regression problem using a Grid Search 
cross-validation directly from scikit-learn.
"""
import matplotlib.pyplot as plt
import numpy as np
from sklearn.metrics import r2_score
from sklearn.model_selection import train_test_split, GridSearchCV, TimeSeriesSplit

from echoes import ESNRegressor
from echoes.plotting import plot_predicted_ts

# Prepare synthetic data 
x = np.linspace(0, 30*np.pi, 1000).reshape(-1,1)
inputs = np.sin(x) + np.random.normal(scale=.2, size=x.shape)
outputs = np.cos(x) + np.random.normal(scale=.2, size=x.shape)
X_train, X_test, y_train, y_test = train_test_split(inputs, outputs, test_size=.3, shuffle=False)

grid = GridSearchCV(
    estimator=ESNRegressor(n_transient=100),
    param_grid={
        "n_reservoir": [20, 50, 100, 150],
        "leak_rate": [0.2, 0.4, 0.8],
        "spectral_radius": [0.85, 0.9, 0.95],
    },
    cv=TimeSeriesSplit() # generate time-aware splits
)
grid.fit(X_train, y_train)

""" ========================================================= GridSearch with ESNRegressor ========================================================= This example shows a minimal example of Echo State Network applied to a regression problem using a Grid Search cross-validation directly from scikit-learn. """ import matplotlib.pyplot as plt import numpy as np from sklearn.metrics import r2_score from sklearn.model_selection import train_test_split, GridSearchCV, TimeSeriesSplit from echoes import ESNRegressor from echoes.plotting import plot_predicted_ts # Prepare synthetic data x = np.linspace(0, 30*np.pi, 1000).reshape(-1,1) inputs = np.sin(x) + np.random.normal(scale=.2, size=x.shape) outputs = np.cos(x) + np.random.normal(scale=.2, size=x.shape) X_train, X_test, y_train, y_test = train_test_split(inputs, outputs, test_size=.3, shuffle=False) grid = GridSearchCV( estimator=ESNRegressor(n_transient=100), param_grid={ "n_reservoir": [20, 50, 100, 150], "leak_rate": [0.2, 0.4, 0.8], "spectral_radius": [0.85, 0.9, 0.95], }, cv=TimeSeriesSplit() # generate time-aware splits ) grid.fit(X_train, y_train)

Out[1]:

GridSearchCV(cv=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=5, test_size=None),
             estimator=ESNRegressor(n_transient=100),
             param_grid={'leak_rate': [0.2, 0.4, 0.8],
                         'n_reservoir': [20, 50, 100, 150],
                         'spectral_radius': [0.85, 0.9, 0.95]})

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In [2]:

esn = grid.best_estimator_
print("training r2 score: ", esn.score(X_train, y_train))
print("test r2 score: ", esn.score(X_test, y_test))
# Get prediction for plotting
y_pred = esn.predict(X_test)
plot_predicted_ts(y_test, y_pred, figsize=(15, 5), start=100);

esn = grid.best_estimator_ print("training r2 score: ", esn.score(X_train, y_train)) print("test r2 score: ", esn.score(X_test, y_test)) # Get prediction for plotting y_pred = esn.predict(X_test) plot_predicted_ts(y_test, y_pred, figsize=(15, 5), start=100);

training r2 score:  0.8970488636806692
test r2 score:  0.8485988057232583

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