"""Base class for Gradient Boosting Regression model."""
import logging
import os
import matplotlib.pyplot as plt
import numpy as np
from esmvaltool.diag_scripts import mlr
from esmvaltool.diag_scripts.mlr.models import MLRModel
logger = logging.getLogger(os.path.basename(__file__))
[docs]
class GBRModel(MLRModel):
"""Base class for Gradient Boosting Regression models."""
_CLF_TYPE = None
[docs]
def plot_feature_importance(self, filename=None, color_coded=True):
"""Plot feature importance.
This function uses properties of the GBR model based on the number of
appearances of that feature in the regression trees and the
improvements made by the individual splits (see Friedman, 2001).
Note
----
The features plotted here are not necessarily the real input features,
but the ones after preprocessing.
Parameters
----------
filename : str, optional (default: 'feature_importance')
Name of the plot file.
color_coded : bool, optional (default: True)
If ``True``, mark positive (linear) correlations with red bars and
negative (linear) correlations with blue bars. If ``False``, all
bars are blue.
"""
if not self._is_ready_for_plotting():
return
# Get plot path
if filename is None:
filename = 'feature_importance'
new_filename = filename + '.' + self._cfg['output_file_type']
plot_path = os.path.join(self._cfg['mlr_plot_dir'], new_filename)
# Get feature importance dictionary and colors for bars
feature_importance_dict = dict(zip(self.features_after_preprocessing,
self._clf.feature_importances_))
colors = self._get_colors_for_features(color_coded=color_coded)
# Plot
self._plot_feature_importance(feature_importance_dict, colors,
plot_path)
def _plot_training_progress(self,
train_score,
test_score=None,
filename=None):
"""Plot training progress during fitting."""
if not self._is_ready_for_plotting():
return
logger.info("Plotting training progress for GBR model")
if filename is None:
filename = 'training_progress'
(_, axes) = plt.subplots()
x_values = np.arange(len(train_score), dtype=np.float64) + 1.0
x_values_all = []
scores_all = []
data_types = []
# Plot train score
axes.plot(x_values,
train_score,
color='b',
linestyle='-',
label='train data')
x_values_all.append(x_values)
scores_all.append(train_score)
data_types.append(np.full(x_values.shape, 'train'))
# Plot test score if possible
if test_score is not None:
axes.plot(x_values,
test_score,
color='g',
linestyle='-',
label='test data')
x_values_all.append(x_values)
scores_all.append(test_score)
data_types.append(np.full(x_values.shape, 'test'))
# Appearance
ylim = axes.get_ylim()
axes.set_ylim(0.0, ylim[1])
title = f"Training progress ({self._cfg['mlr_model_name']})"
axes.set_title(title)
axes.set_xlabel('Boosting iterations')
axes.set_ylabel('Loss')
axes.legend(loc='upper right')
new_filename = filename + '.' + self._cfg['output_file_type']
plot_path = os.path.join(self._cfg['mlr_plot_dir'], new_filename)
plt.savefig(plot_path, **self._cfg['savefig_kwargs'])
logger.info("Wrote %s", plot_path)
plt.close()
# Save provenance
cube = mlr.get_1d_cube(
np.concatenate(x_values_all),
np.concatenate(scores_all),
x_kwargs={'var_name': 'iteration',
'long_name': 'Boosting Iteration',
'units': 'no unit'},
y_kwargs={'var_name': 'rmse',
'long_name': 'Normalized RMSE',
'units': '1',
'attributes': {'project': '', 'dataset': ''}},
)
cube.add_aux_coord(
self._get_data_type_coord(np.concatenate(data_types)), 0)
self._write_plot_provenance(
cube, plot_path, ancestors=self.get_ancestors(prediction_names=[]),
caption=title + '.', plot_types=['line'])
