ADD: logger loaded from file and stored per model

Signed-off-by: naweedkhan <naweed.khan@ibm.com>

lnn/_utils.py

@@ -153,12 +153,14 @@ def average_time(multi_checkpoint: List[List[Tuple]]):
             result[name] += point
 
 
-def logger_setup(flush=False):
-    for level in ["INFO"]:
-        filename = f"LNN_{level}.log"
-        if flush:
-            with open(filename, "w"):
-                pass
-        logging.basicConfig(
-            filename=filename, encoding="utf-8", level=eval(f"logging.{level}")
-        )
+def get_logger(flush: bool = False):
+    ref = importlib.resources.files("lnn").joinpath("../config.yaml")
+    config = yaml.safe_load(ref.read_text())
+    logging.config.dictConfig(config)
+    logger = logging.getLogger(
+        "".join(config["handlers"]["file"]["filename"].split(".")[:-1])
+    )
+    if flush:
+        with open(logger.handlers[0].baseFilename, "w"):
+            pass
+    return logger

lnn/model.py

@@ -24,7 +24,7 @@
 from torch import nn
 import matplotlib.pyplot as plt
 
-_utils.logger_setup(flush=True)
+_utils.get_logger(flush=True)
 
 
 class Model(nn.Module):
@@ -117,7 +117,8 @@ def __init__(
                 self.add_knowledge(knowledge)
         if data:
             self.add_data(data)
-        logging.info(f" {name} {datetime.datetime.now()} ".join(["*" * 22] * 2))
+        self.logger = _utils.get_logger(flush=True)
+        self.logger.info(f" {name} {datetime.datetime.now()} ".join(["*" * 22] * 2))
 
     def __getitem__(
         self, formula: Union[Formula, int]
@@ -438,7 +439,7 @@ def _traverse_execute(
             val = getattr(node, func)(**kwds) if hasattr(node, func) else None
             coalesce = coalesce + val if val is not None else coalesce
         if coalesce and func in [d.value.lower() for d in Direction]:
-            logging.info(f"{direction.value} INFERENCE RESULT:{coalesce}")
+            self.logger.info(f"{direction.value} INFERENCE RESULT:{coalesce}")
         return coalesce
 
     def infer(
@@ -488,14 +489,14 @@ def _infer(
 
         while not converged:
             if self.query and self.query.is_classically_resolved and not self._converge:
-                logging.info("=" * 22)
-                logging.info(
+                self.logger.info("=" * 22)
+                self.logger.info(
                     f"QUERY PROVED AS {self.query.world_state(True)} for "
                     f"'{self.query.name}'"
                 )
                 break
-            logging.info("-" * 22)
-            logging.info(f"REASONING STEP:{steps}")
+            self.logger.info("-" * 22)
+            self.logger.info(f"REASONING STEP:{steps}")
             bounds_diff = 0.0
             for d in direction:
                 bounds_diff += self._traverse_execute(
@@ -508,17 +509,17 @@ def _infer(
             )
             if converged_bounds:
                 converged = True
-                logging.info("NO UPDATES AVAILABLE, TRYING A NEW AXIOM")
+                self.logger.info("NO UPDATES AVAILABLE, TRYING A NEW AXIOM")
             facts_inferred += bounds_diff
             steps += 1
             if max_steps and steps >= max_steps:
                 break
-        logging.info("=" * 22)
-        logging.info(
+        self.logger.info("=" * 22)
+        self.logger.info(
             f"INFERENCE CONVERGED WITH {facts_inferred} BOUNDS "
             f"UPDATES IN {steps} REASONING STEPS "
         )
-        logging.info("*" * 78)
+        self.logger.info("*" * 78)
         return steps, facts_inferred
 
     def upward(self, **kwds):
@@ -613,7 +614,7 @@ def train(self, losses: Union[Loss, List[Loss], Dict[List[Loss], float]], **kwds
         ):
             optimizer.zero_grad()
             if epoch > 0:
-                logging.info(" PARAMETER STEP ".join(["#" * 31] * 2))
+                self.logger.info(" PARAMETER STEP ".join(["#" * 31] * 2))
                 self.reset_bounds()
             self.increment_param_history(kwds.get("parameter_history"))
             _, facts_inferred = self.infer(**kwds)
@@ -622,7 +623,7 @@ def train(self, losses: Union[Loss, List[Loss], Dict[List[Loss], float]], **kwds
             if not loss.grad_fn:
                 break
             if loss and len(loss_fn) > 1:
-                logging.info(f"TOTAL LOSS: {loss}")
+                self.logger.info(f"TOTAL LOSS: {loss}")
             loss.backward()
             optimizer.step()
             self._project_params()
@@ -698,7 +699,7 @@ def loss_fn(self, losses):
                     self._traverse_execute(f"_{loss.value.lower()}_loss", **kwds)
                 )
             if result[-1]:
-                logging.info(f"{loss.value.upper()} LOSS {result[-1]}")
+                self.logger.info(f"{loss.value.upper()} LOSS {result[-1]}")
         return result
 
     def print(

lnn/symbolic/logic/binary_neuron.py

@@ -13,7 +13,7 @@
 from ... import _utils
 from ...constants import Direction, NeuralActivation
 
-_utils.logger_setup()
+_utils.get_logger()
 
 
 class _BinaryNeuron(_ConnectiveNeuron):

lnn/symbolic/logic/connective_formula.py

@@ -9,7 +9,7 @@
 from .formula import Formula
 from ... import _utils
 
-_utils.logger_setup()
+_utils.get_logger()
 
 
 class _ConnectiveFormula(Formula):

lnn/symbolic/logic/connective_neuron.py

@@ -17,7 +17,7 @@
 from ... import _utils
 from ...constants import Direction
 
-_utils.logger_setup()
+_utils.get_logger()
 subclasses = {}
 
 
@@ -79,14 +79,14 @@ def upward(
         )
         result = self.neuron.aggregate_bounds(grounding_rows, self.func(input_bounds))
         if result:
-            logging.info(
+            self.logger.info(
                 "↑ BOUNDS UPDATED "
                 f"TIGHTENED:{result} "
                 f"FOR:'{self.name}' "
                 f"FORMULA:{self.formula_number} "
             )
         if self.is_contradiction():
-            logging.info(
+            self.logger.info(
                 "↑ CONTRADICTION "
                 f"FOR:'{self.name}' "
                 f"FORMULA:{self.formula_number} "
@@ -148,7 +148,7 @@ def downward(
                 op_grounding_rows, new_bounds[..., op_index], duplicates=duplicates
             )
             if op_aggregate:
-                logging.info(
+                self.logger.info(
                     "↓ BOUNDS UPDATED "
                     f"TIGHTENED:{op_aggregate} "
                     f"FOR:'{op.name}' "
@@ -157,7 +157,7 @@ def downward(
                     f"PARENT:{self.formula_number} "
                 )
             if op.is_contradiction():
-                logging.info(
+                self.logger.info(
                     "↓ CONTRADICTION "
                     f"FOR:'{op.name}' "
                     f"FROM:'{self.name}' "

lnn/symbolic/logic/formula.py

@@ -22,7 +22,7 @@
 import torch
 import numpy as np
 
-_utils.logger_setup()
+_utils.get_logger()
 subclasses: typing.Dict[str, object] = {}
 
 
@@ -353,7 +353,7 @@ def recurse(formula):
         recurse(self)
         if store:
             if edge_replace:
-                logging.info(f"ABSORBED NEGATIONS INTO WEIGHTS FOR: '{self.name}'")
+                self.logger.info(f"ABSORBED NEGATIONS INTO WEIGHTS FOR: '{self.name}'")
             return edge_replace, n_negations
         else:
             return operands, edge_replace, n_negations

lnn/symbolic/logic/n_ary_neuron.py

@@ -16,7 +16,7 @@
 from ... import _utils
 from ...constants import Direction, NeuralActivation
 
-_utils.logger_setup()
+_utils.get_logger()
 
 
 class _NAryNeuron(_ConnectiveNeuron):

lnn/symbolic/logic/n_ary_operator.py

@@ -18,7 +18,7 @@
 from ... import _utils
 from ...constants import Fact
 
-_utils.logger_setup()
+_utils.get_logger()
 
 
 class _NAryOperator(_ConnectiveFormula):
@@ -90,7 +90,7 @@ def upward(
         )
         result = self.neuron.aggregate_bounds(grounding_rows, input_bounds)
         if result:
-            logging.info(
+            self.logger.info(
                 "↑ BOUNDS UPDATED "
                 f"TIGHTENED:{result} "
                 f"FOR:'{self.name}' "
@@ -146,7 +146,7 @@ def downward(
                         op_grounding_rows[g_i] = op.grounding_table.get(op_g)
             op_aggregate = op.neuron.aggregate_bounds(op_grounding_rows, parent)
             if op_aggregate:
-                logging.info(
+                self.logger.info(
                     "↓ BOUNDS UPDATED "
                     f"TIGHTENED:{op_aggregate} "
                     f"FOR:'{op.name}' "
@@ -192,7 +192,7 @@ def upward(
         )
         result = self.neuron.aggregate_bounds(grounding_rows, input_bounds)
         if result:
-            logging.info(
+            self.logger.info(
                 "↑ BOUNDS UPDATED "
                 f"TIGHTENED:{result} "
                 f"FOR:'{self.name}' "

lnn/symbolic/logic/neural_activation.py

@@ -11,7 +11,7 @@
 from ... import _utils, _exceptions
 from ...constants import NeuralActivation
 
-_utils.logger_setup()
+_utils.get_logger()
 
 
 class _NeuralActivation:

lnn/symbolic/logic/node_activation.py

@@ -10,7 +10,7 @@
 
 from ... import _utils
 
-_utils.logger_setup()
+_utils.get_logger()
 
 
 class _NodeActivation:

lnn/symbolic/logic/unary_operator.py

@@ -22,7 +22,7 @@
 from ...constants import Fact, Direction, Bound
 from torch.nn.parameter import Parameter
 
-_utils.logger_setup()
+_utils.get_logger()
 
 
 class _UnaryOperator(_ConnectiveFormula):
@@ -388,7 +388,7 @@ def upward(self, **kwds) -> float:
             None, _utils.negate_bounds(self.operands[0].get_data(*groundings))
         )
         if self.is_contradiction():
-            logging.info(
+            self.logger.info(
                 "↑ CONTRADICTION "
                 f"FOR:'{self.name}' "
                 f"FORMULA:{self.formula_number} "
@@ -419,7 +419,7 @@ def downward(self, **kwds) -> torch.Tensor:
             None, _utils.negate_bounds(self.get_data(*groundings))
         )
         if self.operands[0].is_contradiction():
-            logging.info(
+            self.logger.info(
                 "↓ CONTRADICTION "
                 f"FOR:'{self.operands[0].name}' "
                 f"FROM:'{self.name}' "

lnn/symbolic/logic/variable.py

@@ -10,7 +10,7 @@
 
 from ... import _utils, utils
 
-_utils.logger_setup()
+_utils.get_logger()
 
 
 class Variable: