completed sgd impl

bin/medley

@@ -5,7 +5,7 @@ import glob
 import dill
 from medleysolver.runner import execute
 from medleysolver.constants import SOLVERS
-from medleysolver.timers import Exponential, Constant, NearestExponential, PerfectTimer
+from medleysolver.timers import Exponential, Constant, NearestExponential, PerfectTimer, SGD
 from medleysolver.classifiers import *
 
 def main():
@@ -69,7 +69,7 @@ def main():
         help="choose how timeout is distributed amongst solvers",
         type=str,
         default="expo",
-        choices=["expo", "const", "nearest", "perfect"]
+        choices=["expo", "const", "nearest", "perfect", "sgd"]
     )
 
     global_parser.add_argument(
@@ -201,6 +201,8 @@ def main():
         timeout_manager = NearestExponential(args.set_lambda, args.confidence, args.timeout)
     elif args.timeout_manager == "perfect":
         timeout_manager = PerfectTimer()
+    elif args.timeout_manager == "sgd":
+        timeout_manager = SGD()
     else:
         raise RuntimeError("timeout_manager not properly set")
 

medleysolver/timers.py

@@ -2,7 +2,7 @@
 from medleysolver.constants import SOLVERS, ERROR_RESULT, SAT_RESULT, UNSAT_RESULT
 from sklearn.linear_model import SGDRegressor
 from medleysolver.dispatch import output2result
-
+import numpy as np
 import csv
 
 class TimerInterface(object):
@@ -62,21 +62,24 @@ def update(self, solver, time, timeout, success, error, point):
             self.naughtylist.add(solver)
 
 class SGD(TimerInterface):
-    def __init__(self, init_lambda, confidence, T):
+    def __init__(self):
         self.fitted = [False for _ in SOLVERS]
         self.models = [SGDRegressor() for _ in SOLVERS]
+        self.solvers_to_i = {s: i for i, s in enumerate(list(SOLVERS.keys()))}
 
     def get_timeout(self, solver, times, problem, point):
-        if not self.fitted[solver]: return 60
-        clf = self.models[solver]
+        point = point.reshape(1, -1)
+        sindex = self.solvers_to_i[solver]
+        if not self.fitted[sindex]: return 60
+        clf = self.models[sindex]
         return clf.predict(point)
 
     def update(self, solver, time, timeout, success, error, point):
-        clf = self.models[solver]
-        if self.fitted[solver]:
-            clf.partial_fit(point, time)
-        else:
-            clf.fit(point, time)
+        point = point.reshape(1, -1)
+        time = np.array([time])
+        sindex = self.solvers_to_i[solver]
+        clf = self.models[sindex]
+        clf.partial_fit(point, time)
 
 class PerfectTimer(TimerInterface):
     def get_timeout(self, solver, position, problem, point):