gradient_free_optimizers._stopping_conditions.OptimizationStopper.get_debug_info() - Code Metrics - Inspection of "use result class in converter tests" - SimonBlanke/Gradient-Free-Optimizers - Measure and Improve Code Quality continuously with Scrutinizer

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by Simon

created 2025-08-06 16:47 UTC

OptimizationStopper.get_debug_info() A

↳ Parent: gradient_free_optimizers._stopping_conditions

Complexity

Conditions

Size

Total Lines	12
Code Lines	9

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
eloc	9
dl	0
loc	12
rs	9.95
c	0
b	0
f	0
cc	2
nop	1

import time
import logging
from abc import ABC, abstractmethod
from dataclasses import dataclass, field
from typing import List, Optional, Dict, Any
import numpy as np


@dataclass
class StoppingContext:
    """
    Encapsulates all relevant data for stopping condition evaluation.
    This creates a clear contract for what data stopping conditions can access.
    """

    iteration: int
    score_current: float
    score_best: float
    score_history: List[float]
    start_time: float
    current_time: float

    @property
    def elapsed_time(self) -> float:
        """Time elapsed since optimization started."""
        return self.current_time - self.start_time

    @property
    def iterations_since_improvement(self) -> int:
        """Number of iterations since the best score was found."""
        if not self.score_history:
            return 0

        best_score_idx = np.argmax(self.score_history)
        return len(self.score_history) - best_score_idx - 1


class StoppingCondition(ABC):
    """
    Abstract base class for all stopping conditions.
    Each condition is responsible for a single stopping criterion.
    """

    def __init__(self, name: str):
        self.name = name
        self.triggered = False
        self.trigger_reason = ""
        self.logger = logging.getLogger(f"{__name__}.{self.name}")

    @abstractmethod
    def should_stop(self, context: StoppingContext) -> bool:
        """Check if the optimization should stop based on this condition."""
        pass

    @abstractmethod
    def get_debug_info(self, context: StoppingContext) -> Dict[str, Any]:
        """Return detailed information for debugging purposes."""
        pass

    def reset(self):
        """Reset the condition to its initial state."""
        self.triggered = False
        self.trigger_reason = ""


class TimeExceededCondition(StoppingCondition):

    """Stops when maximum time limit is exceeded."""

    def __init__(self, max_time: Optional[float]):
        super().__init__("TimeExceeded")
        self.max_time = max_time

    def should_stop(self, context: StoppingContext) -> bool:
        if self.max_time is None:
            return False

        if context.elapsed_time > self.max_time:
            self.triggered = True
            self.trigger_reason = f"Time limit exceeded: {context.elapsed_time:.2f}s > {self.max_time:.2f}s"
            self.logger.info(self.trigger_reason)
            return True
        return False

    def get_debug_info(self, context: StoppingContext) -> Dict[str, Any]:
        return {
            "condition": self.name,
            "max_time": self.max_time,
            "elapsed_time": context.elapsed_time,
            "time_remaining": (
                self.max_time - context.elapsed_time if self.max_time else None
            ),
            "triggered": self.triggered,
            "reason": self.trigger_reason,
        }


class ScoreExceededCondition(StoppingCondition):

    """Stops when target score is reached or exceeded."""

    def __init__(self, max_score: Optional[float]):
        super().__init__("ScoreExceeded")
        self.max_score = max_score

    def should_stop(self, context: StoppingContext) -> bool:
        if self.max_score is None:
            return False

        if context.score_best >= self.max_score:
            self.triggered = True
            self.trigger_reason = f"Target score reached: {context.score_best:.6f} >= {self.max_score:.6f}"
            self.logger.info(self.trigger_reason)
            return True
        return False

    def get_debug_info(self, context: StoppingContext) -> Dict[str, Any]:
        return {
            "condition": self.name,
            "max_score": self.max_score,
            "current_best_score": context.score_best,
            "score_gap": (
                self.max_score - context.score_best if self.max_score else None
            ),
            "triggered": self.triggered,
            "reason": self.trigger_reason,
        }


class NoImprovementCondition(StoppingCondition):
    """Stops when no improvement is observed for a specified number of iterations."""

    def __init__(
        self,
        n_iter_no_change: int,
        tol_abs: Optional[float] = None,
        tol_rel: Optional[float] = None,
    ):
        super().__init__("NoImprovement")
        self.n_iter_no_change = n_iter_no_change
        self.tol_abs = tol_abs
        self.tol_rel = tol_rel

    def should_stop(self, context: StoppingContext) -> bool:
        if len(context.score_history) <= self.n_iter_no_change:
            return False

        iterations_stale = context.iterations_since_improvement

        if iterations_stale >= self.n_iter_no_change:
            self.triggered = True
            self.trigger_reason = f"No improvement for {iterations_stale} iterations"
            self.logger.info(self.trigger_reason)
            return True

        # Check tolerance-based early stopping
        first_n = len(context.score_history) - self.n_iter_no_change
        scores_before = context.score_history[:first_n]

        if not scores_before:
            return False

        max_score_before = max(scores_before)
        current_best = context.score_best

        # Absolute tolerance check
        if self.tol_abs is not None:
            improvement = abs(current_best - max_score_before)
            if improvement < self.tol_abs:
                self.triggered = True
                self.trigger_reason = f"Improvement below absolute tolerance: {improvement:.6f} < {self.tol_abs:.6f}"
                self.logger.info(self.trigger_reason)
                return True

        # Relative tolerance check
        if self.tol_rel is not None and max_score_before != 0:
            improvement_pct = (
                (current_best - max_score_before) / abs(max_score_before)
            ) * 100
            if improvement_pct < self.tol_rel:
                self.triggered = True
                self.trigger_reason = f"Improvement below relative tolerance: {improvement_pct:.2f}% < {self.tol_rel:.2f}%"
                self.logger.info(self.trigger_reason)
                return True

        return False

    def get_debug_info(self, context: StoppingContext) -> Dict[str, Any]:
        iterations_stale = context.iterations_since_improvement

        debug_info = {
            "condition": self.name,
            "n_iter_no_change": self.n_iter_no_change,
            "iterations_since_improvement": iterations_stale,
            "tol_abs": self.tol_abs,
            "tol_rel": self.tol_rel,
            "triggered": self.triggered,
            "reason": self.trigger_reason,
        }

        if len(context.score_history) > self.n_iter_no_change:
            first_n = len(context.score_history) - self.n_iter_no_change
            scores_before = context.score_history[:first_n]
            if scores_before:
                max_score_before = max(scores_before)
                improvement = context.score_best - max_score_before
                debug_info["improvement_abs"] = improvement
                if max_score_before != 0:
                    debug_info["improvement_rel_pct"] = (
                        improvement / abs(max_score_before)
                    ) * 100

        return debug_info


class CompositeStoppingCondition(StoppingCondition):
    """Combines multiple stopping conditions with OR logic."""

    def __init__(self, conditions: List[StoppingCondition]):
        super().__init__("Composite")
        self.conditions = conditions

    def should_stop(self, context: StoppingContext) -> bool:
        for condition in self.conditions:
            if condition.should_stop(context):
                self.triggered = True
                self.trigger_reason = (
                    f"Stopped by {condition.name}: {condition.trigger_reason}"
                )
                self.logger.info(self.trigger_reason)
                return True
        return False

    def get_debug_info(self, context: StoppingContext) -> Dict[str, Any]:
        return {
            "condition": self.name,
            "triggered": self.triggered,
            "reason": self.trigger_reason,
            "sub_conditions": [
                condition.get_debug_info(context) for condition in self.conditions
            ],
        }

    def reset(self):
        super().reset()
        for condition in self.conditions:
            condition.reset()


class OptimizationStopper:
    """
    Main class for managing optimization stopping conditions.
    Provides a clean interface and comprehensive debugging capabilities.
    """

    def __init__(
        self,
        start_time: float,
        max_time: Optional[float] = None,
        max_score: Optional[float] = None,
        early_stopping: Optional[Dict[str, Any]] = None,
    ):
        self.start_time = start_time
        self.conditions: List[StoppingCondition] = []
        self.score_history: List[float] = []
        self.score_best = -np.inf
        self.iteration = 0
        self.logger = logging.getLogger(f"{__name__}.OptimizationStopper")

        # Build stopping conditions
        if max_time is not None:
            self.conditions.append(TimeExceededCondition(max_time))

        if max_score is not None:
            self.conditions.append(ScoreExceededCondition(max_score))

        if early_stopping is not None:
            n_iter = early_stopping.get("n_iter_no_change")
            if n_iter is not None:
                self.conditions.append(
                    NoImprovementCondition(
                        n_iter_no_change=n_iter,
                        tol_abs=early_stopping.get("tol_abs"),
                        tol_rel=early_stopping.get("tol_rel"),
                    )
                )

        self.composite_condition = CompositeStoppingCondition(self.conditions)

    def update(self, score_current: float, score_best: float, iteration: int):
        """Update the stopper with new optimization state."""
        self.score_history.append(score_current)
        self.score_best = score_best
        self.iteration = iteration

    def should_stop(self) -> bool:
        """Check if optimization should stop."""
        context = StoppingContext(
            iteration=self.iteration,
            score_current=self.score_history[-1] if self.score_history else -np.inf,
            score_best=self.score_best,
            score_history=self.score_history,
            start_time=self.start_time,
            current_time=time.time(),
        )

        return self.composite_condition.should_stop(context)

    def get_debug_info(self) -> Dict[str, Any]:
        """Get comprehensive debugging information about stopping conditions."""
        context = StoppingContext(
            iteration=self.iteration,
            score_current=self.score_history[-1] if self.score_history else -np.inf,
            score_best=self.score_best,
            score_history=self.score_history,
            start_time=self.start_time,
            current_time=time.time(),
        )

        return self.composite_condition.get_debug_info(context)

    def get_stop_reason(self) -> str:
        """Get a human-readable reason for why optimization stopped."""
        if self.composite_condition.triggered:
            return self.composite_condition.trigger_reason
        return "Optimization not stopped by stopper"


1		import time
2		import logging
3		from abc import ABC, abstractmethod
4		from dataclasses import dataclass, field
5		from typing import List, Optional, Dict, Any
6		import numpy as np
7
8
9		@dataclass
10		class StoppingContext:
11		"""
12		Encapsulates all relevant data for stopping condition evaluation.
13		This creates a clear contract for what data stopping conditions can access.
14		"""
15
16		iteration: int
17		score_current: float
18		score_best: float
19		score_history: List[float]
20		start_time: float
21		current_time: float
22
23		@property
24		def elapsed_time(self) -> float:
25		"""Time elapsed since optimization started."""
26		return self.current_time - self.start_time
27
28		@property
29		def iterations_since_improvement(self) -> int:
30		"""Number of iterations since the best score was found."""
31		if not self.score_history:
32		return 0
33
34		best_score_idx = np.argmax(self.score_history)
35		return len(self.score_history) - best_score_idx - 1
36
37
38		class StoppingCondition(ABC):
39		"""
40		Abstract base class for all stopping conditions.
41		Each condition is responsible for a single stopping criterion.
42		"""
43
44		def __init__(self, name: str):
45		self.name = name
46		self.triggered = False
47		self.trigger_reason = ""
48		self.logger = logging.getLogger(f"{__name__}.{self.name}")
49
50		@abstractmethod
51		def should_stop(self, context: StoppingContext) -> bool:
52		"""Check if the optimization should stop based on this condition."""
53		pass
54
55		@abstractmethod
56		def get_debug_info(self, context: StoppingContext) -> Dict[str, Any]:
57		"""Return detailed information for debugging purposes."""
58		pass
59
60		def reset(self):
61		"""Reset the condition to its initial state."""
62		self.triggered = False
63		self.trigger_reason = ""
64
65
66	View Code Duplication	class TimeExceededCondition(StoppingCondition):
		0 ignored issues – show Duplication introduced 2025-08-06 16:49 UTC by Report Bug Copy Issue Report This code seems to be duplicated in your project. Loading history...
67		"""Stops when maximum time limit is exceeded."""
68
69		def __init__(self, max_time: Optional[float]):
70		super().__init__("TimeExceeded")
71		self.max_time = max_time
72
73		def should_stop(self, context: StoppingContext) -> bool:
74		if self.max_time is None:
75		return False
76
77		if context.elapsed_time > self.max_time:
78		self.triggered = True
79		self.trigger_reason = f"Time limit exceeded: {context.elapsed_time:.2f}s > {self.max_time:.2f}s"
80		self.logger.info(self.trigger_reason)
81		return True
82		return False
83
84		def get_debug_info(self, context: StoppingContext) -> Dict[str, Any]:
85		return {
86		"condition": self.name,
87		"max_time": self.max_time,
88		"elapsed_time": context.elapsed_time,
89		"time_remaining": (
90		self.max_time - context.elapsed_time if self.max_time else None
91		),
92		"triggered": self.triggered,
93		"reason": self.trigger_reason,
94		}
95
96
97	View Code Duplication	class ScoreExceededCondition(StoppingCondition):
		0 ignored issues – show Duplication introduced 2025-08-06 16:49 UTC by Report Bug Copy Issue Report This code seems to be duplicated in your project. Loading history...
98		"""Stops when target score is reached or exceeded."""
99
100		def __init__(self, max_score: Optional[float]):
101		super().__init__("ScoreExceeded")
102		self.max_score = max_score
103
104		def should_stop(self, context: StoppingContext) -> bool:
105		if self.max_score is None:
106		return False
107
108		if context.score_best >= self.max_score:
109		self.triggered = True
110		self.trigger_reason = f"Target score reached: {context.score_best:.6f} >= {self.max_score:.6f}"
111		self.logger.info(self.trigger_reason)
112		return True
113		return False
114
115		def get_debug_info(self, context: StoppingContext) -> Dict[str, Any]:
116		return {
117		"condition": self.name,
118		"max_score": self.max_score,
119		"current_best_score": context.score_best,
120		"score_gap": (
121		self.max_score - context.score_best if self.max_score else None
122		),
123		"triggered": self.triggered,
124		"reason": self.trigger_reason,
125		}
126
127
128		class NoImprovementCondition(StoppingCondition):
129		"""Stops when no improvement is observed for a specified number of iterations."""
130
131		def __init__(
132		self,
133		n_iter_no_change: int,
134		tol_abs: Optional[float] = None,
135		tol_rel: Optional[float] = None,
136		):
137		super().__init__("NoImprovement")
138		self.n_iter_no_change = n_iter_no_change
139		self.tol_abs = tol_abs
140		self.tol_rel = tol_rel
141
142		def should_stop(self, context: StoppingContext) -> bool:
143		if len(context.score_history) <= self.n_iter_no_change:
144		return False
145
146		iterations_stale = context.iterations_since_improvement
147
148		if iterations_stale >= self.n_iter_no_change:
149		self.triggered = True
150		self.trigger_reason = f"No improvement for {iterations_stale} iterations"
151		self.logger.info(self.trigger_reason)
152		return True
153
154		# Check tolerance-based early stopping
155		first_n = len(context.score_history) - self.n_iter_no_change
156		scores_before = context.score_history[:first_n]
157
158		if not scores_before:
159		return False
160
161		max_score_before = max(scores_before)
162		current_best = context.score_best
163
164		# Absolute tolerance check
165		if self.tol_abs is not None:
166		improvement = abs(current_best - max_score_before)
167		if improvement < self.tol_abs:
168		self.triggered = True
169		self.trigger_reason = f"Improvement below absolute tolerance: {improvement:.6f} < {self.tol_abs:.6f}"
170		self.logger.info(self.trigger_reason)
171		return True
172
173		# Relative tolerance check
174		if self.tol_rel is not None and max_score_before != 0:
175		improvement_pct = (
176		(current_best - max_score_before) / abs(max_score_before)
177		) * 100
178		if improvement_pct < self.tol_rel:
179		self.triggered = True
180		self.trigger_reason = f"Improvement below relative tolerance: {improvement_pct:.2f}% < {self.tol_rel:.2f}%"
181		self.logger.info(self.trigger_reason)
182		return True
183
184		return False
185
186		def get_debug_info(self, context: StoppingContext) -> Dict[str, Any]:
187		iterations_stale = context.iterations_since_improvement
188
189		debug_info = {
190		"condition": self.name,
191		"n_iter_no_change": self.n_iter_no_change,
192		"iterations_since_improvement": iterations_stale,
193		"tol_abs": self.tol_abs,
194		"tol_rel": self.tol_rel,
195		"triggered": self.triggered,
196		"reason": self.trigger_reason,
197		}
198
199		if len(context.score_history) > self.n_iter_no_change:
200		first_n = len(context.score_history) - self.n_iter_no_change
201		scores_before = context.score_history[:first_n]
202		if scores_before:
203		max_score_before = max(scores_before)
204		improvement = context.score_best - max_score_before
205		debug_info["improvement_abs"] = improvement
206		if max_score_before != 0:
207		debug_info["improvement_rel_pct"] = (
208		improvement / abs(max_score_before)
209		) * 100
210
211		return debug_info
212
213
214		class CompositeStoppingCondition(StoppingCondition):
215		"""Combines multiple stopping conditions with OR logic."""
216
217		def __init__(self, conditions: List[StoppingCondition]):
218		super().__init__("Composite")
219		self.conditions = conditions
220
221		def should_stop(self, context: StoppingContext) -> bool:
222		for condition in self.conditions:
223		if condition.should_stop(context):
224		self.triggered = True
225		self.trigger_reason = (
226		f"Stopped by {condition.name}: {condition.trigger_reason}"
227		)
228		self.logger.info(self.trigger_reason)
229		return True
230		return False
231
232		def get_debug_info(self, context: StoppingContext) -> Dict[str, Any]:
233		return {
234		"condition": self.name,
235		"triggered": self.triggered,
236		"reason": self.trigger_reason,
237		"sub_conditions": [
238		condition.get_debug_info(context) for condition in self.conditions
239		],
240		}
241
242		def reset(self):
243		super().reset()
244		for condition in self.conditions:
245		condition.reset()
246
247
248		class OptimizationStopper:
249		"""
250		Main class for managing optimization stopping conditions.
251		Provides a clean interface and comprehensive debugging capabilities.
252		"""
253
254		def __init__(
255		self,
256		start_time: float,
257		max_time: Optional[float] = None,
258		max_score: Optional[float] = None,
259		early_stopping: Optional[Dict[str, Any]] = None,
260		):
261		self.start_time = start_time
262		self.conditions: List[StoppingCondition] = []
263		self.score_history: List[float] = []
264		self.score_best = -np.inf
265		self.iteration = 0
266		self.logger = logging.getLogger(f"{__name__}.OptimizationStopper")
267
268		# Build stopping conditions
269		if max_time is not None:
270		self.conditions.append(TimeExceededCondition(max_time))
271
272		if max_score is not None:
273		self.conditions.append(ScoreExceededCondition(max_score))
274
275		if early_stopping is not None:
276		n_iter = early_stopping.get("n_iter_no_change")
277		if n_iter is not None:
278		self.conditions.append(
279		NoImprovementCondition(
280		n_iter_no_change=n_iter,
281		tol_abs=early_stopping.get("tol_abs"),
282		tol_rel=early_stopping.get("tol_rel"),
283		)
284		)
285
286		self.composite_condition = CompositeStoppingCondition(self.conditions)
287
288		def update(self, score_current: float, score_best: float, iteration: int):
289		"""Update the stopper with new optimization state."""
290		self.score_history.append(score_current)
291		self.score_best = score_best
292		self.iteration = iteration
293
294		def should_stop(self) -> bool:
295		"""Check if optimization should stop."""
296		context = StoppingContext(
297		iteration=self.iteration,
298		score_current=self.score_history[-1] if self.score_history else -np.inf,
299		score_best=self.score_best,
300		score_history=self.score_history,
301		start_time=self.start_time,
302		current_time=time.time(),
303		)
304
305		return self.composite_condition.should_stop(context)
306
307		def get_debug_info(self) -> Dict[str, Any]:
308		"""Get comprehensive debugging information about stopping conditions."""
309		context = StoppingContext(
310		iteration=self.iteration,
311		score_current=self.score_history[-1] if self.score_history else -np.inf,
312		score_best=self.score_best,
313		score_history=self.score_history,
314		start_time=self.start_time,
315		current_time=time.time(),
316		)
317
318		return self.composite_condition.get_debug_info(context)
319
320		def get_stop_reason(self) -> str:
321		"""Get a human-readable reason for why optimization stopped."""
322		if self.composite_condition.triggered:
323		return self.composite_condition.trigger_reason
324		return "Optimization not stopped by stopper"
325

SimonBlanke / Gradient-Free-Optimizers

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OptimizationStopper.get_debug_info() A

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