def evaluate(self, preds, labels=None):
#print(preds)
#print(preds.shape)
#print(labels)
#print(labels.shape)
# Tries to load X and Y if not directly passed
if (labels is None):
print("No labels passed, cannot perform evaluation.")
if (self.model is None):
print("No model trained, cannot to perform evaluation.")
else:
# Declaring the class containing the metrics
cm = CoMe(preds, labels)
# Evaluating
prauc = cm.compute_prauc()
rce = cm.compute_rce()
# Confusion matrix
conf = cm.confMatrix()
# Prediction stats
max_pred, min_pred, avg = cm.computeStatistics()
return prauc, rce, conf, max_pred, min_pred, avg
def evaluate(self, pool_tst=None):
if (pool_tst is None):
print("No dataset provided.")
if (self.model is None):
print("No model trained yet.")
else:
#Preparing DMatrix
#p_test = Pool(X_tst, label=Y_tst)
#Making predictions
#Y_pred = model.predict_proba(p_test)
Y_pred = self.get_prediction(pool_tst)
# Declaring the class containing the
# metrics.
Y_test = np.array(pool_tst.get_label()).astype(np.int32)
cm = CoMe(Y_pred, Y_test)
# Evaluating
prauc = cm.compute_prauc()
rce = cm.compute_rce()
# Confusion matrix
conf = cm.confMatrix()
# Prediction stats
max_pred, min_pred, avg = cm.computeStatistics()
return prauc, rce, conf, max_pred, min_pred, avg
def evaluate(self, X_tst=None, Y_tst=None):
Y_pred = None
#Tries to load X and Y if not directly passed
if (X_tst is None) or (Y_tst is None):
X_tst, Y_tst = Data.get_dataset_xgb_default_test()
print("Test set loaded from file.")
#Y_tst = np.array(Y_tst[Y_tst.columns[0]].astype(float))
if (self.sround_model is None) and (self.batch_model is None):
print("No model trained yet.")
else:
#Selecting the coherent model for the evaluation
#According to the initial declaration (batch/single round)
if self.batch is False:
model = self.sround_model
else:
model = self.batch_model
#Preparing DMatrix
#d_test = xgb.DMatrix(X_tst)
#Making predictions
#Y_pred = model.predict(d_test)
Y_pred = self.get_prediction(X_tst)
# Declaring the class containing the
# metrics.
cm = CoMe(Y_pred, Y_tst)
#Evaluating
scores = cm.compute_multiclass()
return scores
def evaluate(self, X_tst=None, Y_tst=None):
Y_pred = None
# Tries to load X and Y if not directly passed
if (X_tst is None) or (Y_tst is None):
X_tst, Y_tst = Data.get_dataset_xgb_default_test()
print("Test set loaded from file.")
Y_tst = np.array(Y_tst[Y_tst.columns[0]].astype(float))
if (self.sround_model is None) and (not os.path.exists(
self.previous_model_path)):
print("No model trained yet.")
else:
# Selecting the coherent model for the evaluation
# According to the initial declaration (batch/single round)
model = self.get_model()
# Preparing DMatrix
# d_test = xgb.DMatrix(X_tst)
# Making predictions
# Y_pred = model.predict(d_test)
Y_pred = self.get_prediction(X_tst)
# Declaring the class containing the
# metrics.
cm = CoMe(Y_pred, Y_tst)
# Evaluating
prauc = cm.compute_prauc()
rce = cm.compute_rce()
# Confusion matrix
conf = confMatrix(Y_tst, Y_pred)
# Prediction stats
max_pred = max(Y_pred)
min_pred = min(Y_pred)
avg = np.mean(Y_pred)
return prauc, rce, conf, max_pred, min_pred, avg
def evaluate(self, dmat_test=None):
# Tries to load X and Y if not directly passed
if (dmat_test is None):
print("No matrix passed, cannot perform evaluation.")
if (self.model is None):
print("No model trained, cannot to perform evaluation.")
else:
#Retrieving the predictions
Y_pred = self.get_prediction(dmat_test)
# Declaring the class containing the metrics
cm = CoMe(Y_pred, dmat_test.get_label())
# Evaluating
prauc = cm.compute_prauc()
rce = cm.compute_rce()
# Confusion matrix
conf = cm.confMatrix()
# Prediction stats
max_pred, min_pred, avg = cm.computeStatistics()
return prauc, rce, conf, max_pred, min_pred, avg