def print_eval(pred: pd.DataFrame, name: str, log: Dict) -> None:
""" Prints evaluation metrices to StdOut.
Most side-effecting output of evaluation metrices (Precision, Recall &
F1-Score.
Parameters
----------
pred : pd.DataFrame
Predicted values
name : str
Model name
log : Dict
Model training log
Returns
-------
None
"""
print("Model: {}".format(name))
print("Parameters",
log["nlp_logistic_classification_learner"]["parameters"])
precision = precision_evaluator(pred)
print("Precision", precision["precision_evaluator__target"])
recall = recall_evaluator(pred)
print("Recall", recall["recall_evaluator__target"])
f1_score = fbeta_score_evaluator(pred)
print("F1 Score", f1_score["fbeta_evaluator__target"])
print("Training time",
log["nlp_logistic_classification_learner"]["running_time"], "\n")
def test_recall_evaluator():
predictions = pd.DataFrame({
'target': [0, 1, 0, 1],
'prediction': [.2, .9, .3, .3]
})
eval_fn = recall_evaluator(prediction_column="prediction",
threshold=0.5,
target_column="target",
eval_name="eval_name")
result = eval_fn(predictions)
assert result["eval_name"] == 0.5