feat(MLRsearch): MLRsearch v7

Replaces MLRv2, suitable for "big bang" upgrade across CSIT.
PyPI metadata updated only partially (full edits will come separately).
Pylint wants less complexity, but the differences are only minor.

+ Use the same (new CSIT) defaults everywhere, also in Python library.
+ Update also PLRsearch to use the new result class.
+ Make upper bound optional in UTI.
+ Fix ASTF approximate duration detection.
+ Do not keep approximated_receive_rate (for MRR) in result structure.

Change-Id: I03406f32d5c93f56b527cb3f93791b61955dfd74
Signed-off-by: Vratko Polak <vrpolak@cisco.com>

1 files changed, 183 insertions, 0 deletions

diff --git a/resources/libraries/python/MLRsearch/selector.py b/resources/libraries/python/MLRsearch/selector.py
new file mode 100644
index 0000000000..4a6d2e2574
--- /dev/null
+++ b/resources/libraries/python/MLRsearch/selector.py
@@ -0,0 +1,183 @@
+# Copyright (c) 2023 Cisco and/or its affiliates.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at:
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Module defining Selector class."""
+
+
+from dataclasses import dataclass, field
+from typing import Callable, List, Optional, Tuple
+
+from .dataclass import secondary_field
+from .discrete_load import DiscreteLoad
+from .discrete_width import DiscreteWidth
+from .expander import TargetedExpander
+from .global_width import GlobalWidth
+from .limit_handler import LimitHandler
+from .measurement_database import MeasurementDatabase
+from .relevant_bounds import RelevantBounds
+from .target_spec import TargetSpec
+from .strategy import StrategyBase, STRATEGY_CLASSES
+
+
+@dataclass
+class Selector:
+    """A selector is an abstraction that focuses on only one of search goals.
+
+    While lower-level logic is hidden in strategy classes,
+    the code in this class is responsible for initializing strategies
+    and shifting targets towards the final target.
+
+    While the public methods have the same names and meaning as the ones
+    in strategy classes, their signature is different.
+    Selector adds the current target trial duration to the output of nominate(),
+    and adds the current bounds to the input of won().
+
+    The nominate method does not return a complete Candidate instance,
+    as we need to avoid circular dependencies
+    (candidate will refer to selector).
+    """
+
+    final_target: TargetSpec
+    """The target this selector is trying to ultimately achieve."""
+    global_width: GlobalWidth
+    """Reference to the global width tracking instance."""
+    initial_lower_load: DiscreteLoad
+    """Smaller of the two loads distinguished at instance creation.
+    During operation, this field is reused to store preceding target bound."""
+    initial_upper_load: DiscreteLoad
+    """Larger of the two loads distinguished at instance creation.
+    During operation, this field is reused to store preceding target bound."""
+    database: MeasurementDatabase = field(repr=False)
+    """Reference to the common database used by all selectors."""
+    handler: LimitHandler = field(repr=False)
+    """Reference to the class used to avoid too narrow intervals."""
+    debug: Callable[[str], None] = field(repr=False)
+    """Injectable function for debug logging."""
+    # Primary above, derived below.
+    current_target: TargetSpec = secondary_field()
+    """The target the selector is focusing on currently."""
+    target_stack: List[TargetSpec] = secondary_field()
+    """Stack of targets. When current target is achieved, next is popped."""
+    strategies: Tuple[StrategyBase] = secondary_field()
+    """Instances implementing particular selection strategies."""
+    current_strategy: Optional[StrategyBase] = secondary_field()
+    """Reference to strategy used for last nomination, needed for won()."""
+    # Cache.
+    bounds: RelevantBounds = secondary_field()
+    """New relevant bounds for this round of candidate selection."""
+
+    def __post_init__(self) -> None:
+        """Initialize derived values."""
+        self.target_stack = [self.final_target]
+        while preceding_target := self.target_stack[-1].preceding:
+            self.target_stack.append(preceding_target)
+        self.current_target = self.target_stack.pop()
+        self._recreate_strategies()
+
+    def _recreate_strategies(self) -> None:
+        """Recreate strategies after current target has changed.
+
+        Width expander is recreated as target width is now smaller.
+        For convenience, strategies get injectable debug
+        which prints also the current target.
+        """
+        expander = TargetedExpander(
+            target=self.current_target,
+            global_width=self.global_width,
+            initial_lower_load=self.initial_lower_load,
+            initial_upper_load=self.initial_upper_load,
+            handler=self.handler,
+            debug=self.debug,
+        )
+
+        def wrapped_debug(text: str) -> None:
+            """Call self debug with current target info prepended.
+
+            :param text: Message to log at debug level.
+            :type text: str
+            """
+            self.debug(f"Target {self.current_target}: {text}")
+
+        self.strategies = tuple(
+            cls(
+                target=self.current_target,
+                expander=expander,
+                initial_lower_load=self.initial_lower_load,
+                initial_upper_load=self.initial_upper_load,
+                handler=self.handler,
+                debug=wrapped_debug,
+            )
+            for cls in STRATEGY_CLASSES
+        )
+        self.current_strategy = None
+        self.debug(f"Created strategies for: {self.current_target}")
+
+    def _update_bounds(self) -> None:
+        """Before each iteration, call this to update bounds cache."""
+        self.bounds = self.database.get_relevant_bounds(self.current_target)
+
+    def nominate(
+        self,
+    ) -> Tuple[Optional[DiscreteLoad], float, Optional[DiscreteWidth]]:
+        """Find first strategy that wants to nominate, return trial inputs.
+
+        Returned load is None if no strategy wants to nominate.
+
+        Current target is shifted when (now preceding) target is reached.
+        As each strategy never becomes done before at least one
+        bound relevant to the current target becomes available,
+        it is never needed to revert to the preceding target after the shift.
+
+        As the initial trials had inputs relevant to all initial targets,
+        the only way for this not to nominate a load
+        is when the final target is reached (including hitting min or max load).
+        The case of hitting min load raises, so search fails early.
+
+        :returns: Nominated load, duration, and global width to set if winning.
+        :rtype: Tuple[Optional[DiscreteLoad], float, Optional[DiscreteWidth]]
+        :raises RuntimeError: If internal inconsistency is detected,
+            or if min load becomes an upper bound.
+        """
+        self._update_bounds()
+        self.current_strategy = None
+        while 1:
+            for strategy in self.strategies:
+                load, width = strategy.nominate(self.bounds)
+                if load:
+                    self.current_strategy = strategy
+                    return load, self.current_target.trial_duration, width
+            if not self.bounds.clo and not self.bounds.chi:
+                raise RuntimeError("Internal error: no clo nor chi.")
+            if not self.target_stack:
+                if not self.bounds.clo and self.current_target.fail_fast:
+                    raise RuntimeError(f"No lower bound: {self.bounds.chi!r}")
+                self.debug(f"Goal {self.current_target} reached: {self.bounds}")
+                return None, self.current_target.trial_duration, None
+            # Everything is ready for next target in the chain.
+            self.current_target = self.target_stack.pop()
+            # Debug logs look better if we forget bounds are TrimmedStat.
+            # Abuse rounding (if not None) to convert to pure DiscreteLoad.
+            clo, chi = self.bounds.clo, self.bounds.chi
+            self.initial_lower_load = clo.rounded_down() if clo else clo
+            self.initial_upper_load = chi.rounded_down() if chi else chi
+            self._update_bounds()
+            self._recreate_strategies()
+
+    def won(self, load: DiscreteLoad) -> None:
+        """Update any private info when candidate became a winner.
+
+        :param load: The load previously nominated by current strategy.
+        :type load: DiscreteLoad
+        """
+        self._update_bounds()
+        self.current_strategy.won(bounds=self.bounds, load=load)