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WarmStart: save trained predictors; update defaults

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This commit is contained in:
Alinson S. Xavier
2020-02-03 10:46:03 -06:00
parent f7f3b7e525
commit 1300a73bc2
2 changed files with 59 additions and 10 deletions
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32
miplearn/warmstart.py
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@@ -13,6 +13,7 @@ from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import cross_val_score
from sklearn.neighbors import KNeighborsClassifier
from tqdm.auto import tqdm
class WarmStartPredictor(ABC):
@@ -50,14 +51,15 @@ class WarmStartPredictor(ABC):
class LogisticWarmStartPredictor(WarmStartPredictor):
def __init__(self,
min_samples=100,
thr_fix=[0.95, 0.95],
thr_balance=[0.95, 0.95],
thr_score=[0.95, 0.95]):
thr_fix=[0.99, 0.99],
thr_balance=[0.80, 0.80],
thr_alpha=[0.50, 0.50],
):
super().__init__()
self.min_samples = min_samples
self.thr_fix = thr_fix
self.thr_balance = thr_balance
self.thr_score = thr_score
self.thr_alpha = thr_alpha
def _fit(self, x_train, y_train, label):
y_train_avg = np.average(y_train)
@@ -76,9 +78,11 @@ class LogisticWarmStartPredictor(WarmStartPredictor):
reg = make_pipeline(StandardScaler(), LogisticRegression())
reg_score = np.mean(cross_val_score(reg, x_train, y_train, cv=5))
dummy_score = max(y_train_avg, 1 - y_train_avg)
reg_thr = 1. * self.thr_alpha[label] + dummy_score * (1 - self.thr_alpha[label])
# If cross-validation score is too low, don't predict anything.
if reg_score < self.thr_score[label]:
if reg_score < reg_thr:
return 0
reg.fit(x_train, y_train.astype(int))
@@ -88,7 +92,8 @@ class LogisticWarmStartPredictor(WarmStartPredictor):
class KnnWarmStartPredictor(WarmStartPredictor):
def __init__(self, k=50,
thr_clip=[0.90, 0.90],
thr_fix=[0.99, 0.99]):
thr_fix=[0.99, 0.99],
):
super().__init__(thr_clip=thr_clip)
self.k = k
self.thr_fix = thr_fix
@@ -115,7 +120,7 @@ class KnnWarmStartPredictor(WarmStartPredictor):
class WarmStartComponent(Component):
def __init__(self,
predictor_prototype=LogisticWarmStartPredictor(),
predictor_prototype=KnnWarmStartPredictor(),
mode="exact",
):
self.mode = mode
@@ -173,18 +178,18 @@ class WarmStartComponent(Component):
else:
self.y_train[category] = np.vstack([self.y_train[category], y])
def fit(self, solver):
for category in self.x_train.keys():
def fit(self, solver, n_jobs=1):
for category in tqdm(self.x_train.keys(), desc="Warm start"):
x_train = self.x_train[category]
y_train = self.y_train[category]
self.predictors[category] = deepcopy(self.predictor_prototype)
self.predictors[category].fit(x_train, y_train)
def merge(self, other_components):
# Merge x_train and y_train
keys = set(self.x_train.keys())
for comp in other_components:
keys = keys.union(set(comp.x_train.keys()))
for key in keys:
x_train_submatrices = [comp.x_train[key]
for comp in other_components
@@ -197,3 +202,10 @@ class WarmStartComponent(Component):
y_train_submatrices += [self.y_train[key]]
self.x_train[key] = np.vstack(x_train_submatrices)
self.y_train[key] = np.vstack(y_train_submatrices)
# Merge trained predictors
for comp in other_components:
for key in comp.predictors.keys():
if key not in self.predictors.keys():
self.predictors[key] = comp.predictors[key]