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DropRedundant: Clear pool before each solve

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Alinson S. Xavier
2021-03-02 17:27:08 -06:00
parent 1397937f03
commit bca6581b0f
1 changed files with 10 additions and 5 deletions
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15
miplearn/components/steps/drop_redundant.py
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@@ -60,6 +60,7 @@ class DropRedundantInequalitiesStep(Component):
) )
y = self.predict(x) y = self.predict(x)
self.pool = []
self.total_dropped = 0 self.total_dropped = 0
self.total_restored = 0 self.total_restored = 0
self.total_kept = 0 self.total_kept = 0
@@ -102,7 +103,7 @@ class DropRedundantInequalitiesStep(Component):
x = self.x(training_instances) x = self.x(training_instances)
y = self.y(training_instances) y = self.y(training_instances)
logger.debug("Fitting...") logger.debug("Fitting...")
for category in tqdm(x.keys(), desc="Fit (rlx:drop_ineq)"): for category in tqdm(x.keys(), desc="Fit (drop)"):
if category not in self.classifiers: if category not in self.classifiers:
self.classifiers[category] = deepcopy(self.classifier_prototype) self.classifiers[category] = deepcopy(self.classifier_prototype)
self.classifiers[category].fit(x[category], np.array(y[category])) self.classifiers[category].fit(x[category], np.array(y[category]))
@@ -130,7 +131,7 @@ class DropRedundantInequalitiesStep(Component):
x = {} x = {}
for instance in tqdm( for instance in tqdm(
InstanceIterator(instances), InstanceIterator(instances),
desc="Extract (rlx:drop_ineq:x)", desc="Extract (drop:x)",
disable=len(instances) < 5, disable=len(instances) < 5,
): ):
for training_data in instance.training_data: for training_data in instance.training_data:
@@ -153,7 +154,7 @@ class DropRedundantInequalitiesStep(Component):
y = {} y = {}
for instance in tqdm( for instance in tqdm(
InstanceIterator(instances), InstanceIterator(instances),
desc="Extract (rlx:drop_ineq:y)", desc="Extract (drop:y)",
disable=len(instances) < 5, disable=len(instances) < 5,
): ):
for training_data in instance.training_data: for training_data in instance.training_data:
@@ -189,9 +190,13 @@ class DropRedundantInequalitiesStep(Component):
y_true = self.y([instance]) y_true = self.y([instance])
y_pred = self.predict(x) y_pred = self.predict(x)
tp, tn, fp, fn = 0, 0, 0, 0 tp, tn, fp, fn = 0, 0, 0, 0
for category in y_true.keys(): for category in tqdm(
y_true.keys(),
disable=len(y_true) < 100,
desc="Eval (drop)",
):
for i in range(len(y_true[category])): for i in range(len(y_true[category])):
if y_pred[category][i][1] == 1: if (category in y_pred) and (y_pred[category][i][1] == 1):
if y_true[category][i][1] == 1: if y_true[category][i][1] == 1:
tp += 1 tp += 1
else: else: