import matplotlib.pyplot as pl
%matplot inline
def learning_curves(X_train, y_train, X_test, y_test):
""" Calculates the performance of several models with varying sizes of training data.
The learning and testing error rates for each model are then plotted. """
print ("Creating learning curve graphs for max_depths of 1, 3, 6, and 10. . .")
# Create the figure window
fig = pl.figure(figsize=(10,8))
# We will vary the training set size so that we have 50 different sizes
sizes = np.rint(np.linspace(1, len(X_train), 50)).astype(int)
train_err = np.zeros(len(sizes))
test_err = np.zeros(len(sizes))
# Create four different models based on max_depth
for k, depth in enumerate([1,3,6,10]):
for i, s in enumerate(sizes):
# Setup a decision tree regressor so that it learns a tree with max_depth = depth
regressor = DecisionTreeRegressor(max_depth = depth)
# Fit the learner to the training data
regressor.fit(X_train[:s], y_train[:s])
# Find the performance on the training set
train_err[i] = performance_metric(y_train[:s], regressor.predict(X_train[:s]))
# Find the performance on the testing set
test_err[i] = performance_metric(y_test, regressor.predict(X_test))
# Subplot the learning curve graph
ax = fig.add_subplot(2, 2, k+1)
ax.plot(sizes, test_err, lw = 2, label = 'Testing Error')
ax.plot(sizes, train_err, lw = 2, label = 'Training Error')
ax.legend()
ax.set_title('max_depth = %s'%(depth))
ax.set_xlabel('Number of Data Points in Training Set')
ax.set_ylabel('Total Error')
ax.set_xlim([0, len(X_train)])
# Visual aesthetics
fig.suptitle('Decision Tree Regressor Learning Performances', fontsize=18, y=1.03)
fig.tight_layout()
fig.show()
cuando ejecuto la función learning_curves()
, muestra:
UserWarning:C:\Users\Administrator\Anaconda3\lib\site-packages\matplotlib\figure.py:397: UserWarning: matplotlib está utilizando actualmente un backend no-GUI, por lo que no puede mostrar la figura
Puede cambiar el backend utilizado por matplotlib incluyendo:
import matplotlib
matplotlib.use('TkAgg')
antes de tu línea 1 import matplotlib.pyplot as pl
, ya que debe establecerse primero. Ver esta respuesta para más información.
(Hay otras opciones de backend, pero cambiar backend a TkAgg
me funcionó cuando tuve un problema similar)
Probando con https://matplotlib.org/examples/animation/dynamic_image.html sólo añado
%matplotlib notebook
que parece funcionar pero es un poco irregular. Tuve que parar el kernal de vez en cuando :-(