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KNN: Make distinction between k and min_samples; improve logging

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This commit is contained in:
Alinson S. Xavier
2020-02-05 12:15:29 -06:00
parent cb0b3d5468
commit 5750b4c98d
2 changed files with 30 additions and 13 deletions
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2
Makefile
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@@ -1,4 +1,4 @@
PYTEST_ARGS := -W ignore::DeprecationWarning -vv -x PYTEST_ARGS := -W ignore::DeprecationWarning -vv -x --log-level=DEBUG
all: docs test all: docs test

41
miplearn/warmstart.py
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@@ -15,7 +15,8 @@ from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import cross_val_score from sklearn.model_selection import cross_val_score
from sklearn.neighbors import KNeighborsClassifier from sklearn.neighbors import KNeighborsClassifier
from tqdm.auto import tqdm from tqdm.auto import tqdm
import logging
logger = logging.getLogger(__name__)
class WarmStartPredictor(ABC): class WarmStartPredictor(ABC):
def __init__(self, thr_clip=[0.50, 0.50]): def __init__(self, thr_clip=[0.50, 0.50]):
@@ -91,30 +92,38 @@ class LogisticWarmStartPredictor(WarmStartPredictor):
class KnnWarmStartPredictor(WarmStartPredictor): class KnnWarmStartPredictor(WarmStartPredictor):
def __init__(self, k=50, def __init__(self,
thr_clip=[0.90, 0.90], k=50,
thr_fix=[0.99, 0.99], min_samples=1,
thr_clip=[0.80, 0.80],
thr_fix=[1.0, 1.0],
): ):
super().__init__(thr_clip=thr_clip) super().__init__(thr_clip=thr_clip)
self.k = k self.k = k
self.thr_fix = thr_fix self.thr_fix = thr_fix
self.min_samples = min_samples
def _fit(self, x_train, y_train, label): def _fit(self, x_train, y_train, label):
y_train_avg = np.average(y_train) y_train_avg = np.average(y_train)
# If number of training samples is too small, don't predict anything. # If number of training samples is too small, don't predict anything.
if x_train.shape[0] < self.k: if x_train.shape[0] < self.min_samples:
logger.debug("Too few samples; return 0")
return 0 return 0
# If vast majority of observations are true, always return true. # If vast majority of observations are true, always return true.
if y_train_avg > self.thr_fix[label]: if y_train_avg >= self.thr_fix[label]:
logger.debug("Consensus reached; return 1")
return 1 return 1
# If vast majority of observations are false, always return false. # If vast majority of observations are false, always return false.
if y_train_avg < (1 - self.thr_fix[label]): if y_train_avg <= (1 - self.thr_fix[label]):
logger.debug("Consensus reached; return 0")
return 0 return 0
knn = KNeighborsClassifier(n_neighbors=self.k) logger.debug("Training classifier...")
k = min(self.k, x_train.shape[0])
knn = KNeighborsClassifier(n_neighbors=k)
knn.fit(x_train, y_train) knn.fit(x_train, y_train)
return knn return knn
@@ -143,6 +152,7 @@ class WarmStartComponent(Component):
vertical=True) vertical=True)
# Predict solutions # Predict solutions
count_total, count_fixed = 0, 0
var_split = Extractor.split_variables(instance, model) var_split = Extractor.split_variables(instance, model)
for category in var_split.keys(): for category in var_split.keys():
var_index_pairs = var_split[category] var_index_pairs = var_split[category]
@@ -152,22 +162,29 @@ class WarmStartComponent(Component):
assert ws.shape == (len(var_index_pairs), 2) assert ws.shape == (len(var_index_pairs), 2)
for i in range(len(var_index_pairs)): for i in range(len(var_index_pairs)):
var, index = var_index_pairs[i] var, index = var_index_pairs[i]
count_total += 1
if self.mode == "heuristic": if self.mode == "heuristic":
if ws[i,0] == 1: if ws[i,0] > 0.5:
var[index].fix(0) var[index].fix(0)
count_fixed += 1
if solver.is_persistent: if solver.is_persistent:
solver.internal_solver.update_var(var[index]) solver.internal_solver.update_var(var[index])
elif ws[i,1] == 1: elif ws[i,1] > 0.5:
var[index].fix(1) var[index].fix(1)
count_fixed += 1
if solver.is_persistent: if solver.is_persistent:
solver.internal_solver.update_var(var[index]) solver.internal_solver.update_var(var[index])
else: else:
if ws[i,0] == 1: var[index].value = None
if ws[i,0] > 0.5:
count_fixed += 1
var[index].value = 0 var[index].value = 0
self.is_warm_start_available = True self.is_warm_start_available = True
elif ws[i,1] == 1: elif ws[i,1] > 0.5:
count_fixed += 1
var[index].value = 1 var[index].value = 1
self.is_warm_start_available = True self.is_warm_start_available = True
logger.info("Setting values for %d variables (out of %d)" % (count_fixed, count_total))
def after_solve(self, solver, instance, model): def after_solve(self, solver, instance, model):