def plot_story_evaluation(
test_y,
predictions,
report,
precision,
f1,
accuracy,
in_training_data_fraction,
out_directory,
disable_plotting,
):
"""Plot the results of story evaluation"""
from sklearn.metrics import confusion_matrix
from sklearn.utils.multiclass import unique_labels
import matplotlib.pyplot as plt
from rasa.utils.plotting import plot_confusion_matrix
log_evaluation_table(
test_y,
"ACTION",
report,
precision,
f1,
accuracy,
in_training_data_fraction,
include_report=True,
)
if disable_plotting:
return
cnf_matrix = confusion_matrix(test_y, predictions)
plot_confusion_matrix(
cnf_matrix,
classes=unique_labels(test_y, predictions),
title="Action Confusion matrix",
)
fig = plt.gcf()
fig.set_size_inches(int(20), int(20))
fig.savefig(os.path.join(out_directory, "story_confmat.pdf"), bbox_inches="tight")
def _plot_story_evaluation(targets: List[Text], predictions: List[Text],
output_directory: Optional[Text]) -> None:
"""Plot a confusion matrix of story evaluation."""
from sklearn.metrics import confusion_matrix
from sklearn.utils.multiclass import unique_labels
from rasa.utils.plotting import plot_confusion_matrix
confusion_matrix_filename = CONFUSION_MATRIX_STORIES_FILE
if output_directory:
confusion_matrix_filename = os.path.join(output_directory,
confusion_matrix_filename)
cnf_matrix = confusion_matrix(targets, predictions)
plot_confusion_matrix(
cnf_matrix,
classes=unique_labels(targets, predictions),
title="Action Confusion matrix",
output_file=confusion_matrix_filename,
)
