custom score controller added

Author: oksanadanilova

topicnet/cooking_machine/cubes/controller_cube.py

@@ -18,6 +18,8 @@
     We assume that if that metric is 'sort of decreasing', then everything is OK
     and we are allowed to change tau coefficient further; otherwise we revert back
     to the last "safe" value and stop
+    
+    'sort of decreasing' perform best with PerplexityScore.
 
     More formal definition of "sort of decreasing": if we divide a curve into two parts like so:
 
@@ -46,11 +48,17 @@
                          |     right part   | 
 
     then the right part is no higher than 5% of global minimum
-    (you can change 5% if you like by adjusting `fraction_threshold`
-     in `is_score_out_of_control` function)
+    (you can change 5% if you like by adjusting `fraction_threshold` parameter)
 
-    If score_to_track is None, then `ControllerAgent` will never stop
+    If score_to_track is None and score_controller is None, then `ControllerAgent` will never stop
     (useful for e.g. decaying coefficients)
+fraction_threshold: float
+    Threshold to control a score by 'sort of decreasing' metric
+score_controller: ScoreControllerBase
+    Custom score controller
+    In case of 'sort of decreasing' is not proper to control score, you are able to create custom Score Controller 
+    inherited from ScoreControllerBase. New controller must contain function `is_score_out_of_control`
+     which takes TopicModel as input and return answer of type OutOfControlAnswer
 
 tau_converter: str or callable
     Notably, def-style functions and lambda functions are allowed
@@ -111,6 +119,8 @@
 
 import warnings
 from copy import deepcopy
+from dataclasses import dataclass
+from typing import List, Optional
 
 import numexpr as ne
 import numpy as np
@@ -123,57 +133,83 @@
 W_MAX_ITERS = "Maximum number of iterations is exceeded; turning off"
 
 
-def get_two_values_diff(min_val: float, max_val: float):
-    if min_val == 0:
-        return max_val
+@dataclass
+class OutOfControlAnswer:
+    answer: Optional[bool]
+    error_message: Optional[str] = None
 
-    answer = (max_val-min_val)/min_val
 
-    return answer
+class ScoreControllerBase:
+    def __init__(self, score_name):
+        self.score_name = score_name
 
+    def get_vals(self, model):
+        if self.score_name not in model.scores:  # case of None is handled here as well
+            return None
 
-def is_score_out_of_control(model, score_name, fraction_threshold=0.05):
-    """
-    Returns True if score isn't 'sort of decreasing' anymore.
+        vals = model.scores[self.score_name]
+        if len(vals) == 0:
+            return None
+
+        return vals
+
+    def __call__(self, model):
+        values = self.get_vals(model)
+
+        if values is None:
+            return False
 
-    See docstring for RegularizationControllerCube for details
+        try:
+            out_of_control_result = self.is_out_of_control(values)
+        except Exception as ex:
+            message = (f"An error occured while controlling {self.score_name}. Message: {ex}. Score values: {values}")
+            warnings.warn(message)
+            return True
 
-    Parameters
-    ----------
-    model : TopicModel
-    score_name : str or None
-    fraction_threshold : float
+        if out_of_control_result.error_message is not None:
+            warnings.warn(out_of_control_result.error_message)
 
-    Returns
-    -------
-    bool
+        return out_of_control_result.answer
 
+    def is_out_of_control(self, values: List[float]) -> OutOfControlAnswer:
+        raise NotImplementedError
+
+
+class ScoreControllerPerplexity(ScoreControllerBase):
+    """
+        Controller is properto control the Perplexity score. For others, please ensure for yourself.
     """
+    DEFAULT_FRACTION_THRESHOLD = 0.05
 
-    if score_name not in model.scores:  # case of None is handled here as well
-        return False
+    def __init__(self, score_name, fraction_threshold=DEFAULT_FRACTION_THRESHOLD):
+        super().__init__(score_name)
+        self.fraction_threshold = fraction_threshold
 
-    vals = model.scores[score_name]
-    if len(vals) == 0:
-        return False
+    def is_out_of_control(self, values: List[float]):
+        idxmin = np.argmin(values)
 
+        if idxmin == len(values):  # score is monotonically decreasing
+            return False
 
+        right_maxval = max(values[idxmin:])
+        minval = values[idxmin]
 
-    idxmin = np.argmin(vals)
+        if minval <= 0:
+            message = f"""Score {self.score_name} has min_value = {minval} which is <=0. 
+                        This control scheme is using to control scores acting like Perplexity.
+                        Ensure you control the Perplexity score or write your own controller"""
+            return OutOfControlAnswer(answer=True, error_message=message)
 
-    if idxmin == len(vals):  # score is monotonically decreasing
-        return False
-    maxval = max(vals[idxmin:])
-    minval = vals[idxmin]
-    diff = get_two_values_diff(minval, maxval)
-    answer = diff > fraction_threshold
+        answer = (right_maxval - minval) / minval > self.fraction_threshold
 
-    if answer:
-        msg = (f"Score {score_name} is too high: during training the value {maxval}"
-               f" passed a treshold of {(1 + fraction_threshold) * minval}"
-               f" (estimate is based on {idxmin} iteration)")
-        warnings.warn(msg)
-    return answer
+        if answer:
+            message = (f"Score {self.score_name} is too high! Right max value: {right_maxval}, min value: {minval}")
+            return OutOfControlAnswer(answer=answer, error_message=message)
+
+        return OutOfControlAnswer(answer=answer)
+
+
+class ControllerAgentException(Exception): pass
 
 
 class ControllerAgent:
@@ -185,8 +221,10 @@ class ControllerAgent:
     Each agent is described by:
 
     * reg_name: the name of regularizer having `tau` which needs to be changed
-    * score_to_track: score providing control of the callback execution
     * tau_converter: function or string describing how to get new `tau` from old `tau`
+    * score_to_track: score name providing control of the callback execution
+    * fraction_threshold: threshold to control score_to_track
+    * score_controller: custom score controller providing control of the callback execution
     * local_dict: dictionary containing values of several variables,
             most notably, `user_value`
     * is_working:
@@ -197,31 +235,61 @@ class ControllerAgent:
     See top-level docstring for details.
     """
 
-    def __init__(self, reg_name, score_to_track, tau_converter, max_iters, local_dict=None):
+    def __init__(self, reg_name, tau_converter, max_iters, score_to_track=None, fraction_threshold=None,
+                 score_controller=None, local_dict=None):
         """
 
         Parameters
         ----------
         reg_name : str
-        score_to_track : str, list of str or None
         tau_converter : callable or str
-        local_dict : dict
         max_iters : int or float
             Agent will stop changing tau after `max_iters` iterations
             `max_iters` could be `float("NaN")` and `float("inf")` values:
             that way agent will continue operating even outside this `RegularizationControllerCube`
+        score_to_track : str, list of str or None
+            Name of score to track
+            Please, use this definition to track only scores of type PerplexityScore.
+            In other cases we recommend you to write you own ScoreController
+        fraction_threshold : float, list of float of the same length as score_to_track  or None
+            Uses to define threshold to control PerplexityScore
+            Default value is 0.05
+        score_controller : ScoreControllerBase, list of ScoreControllerBase or None
+        local_dict : dict
         """
         if local_dict is None:
             local_dict = dict()
 
         self.reg_name = reg_name
         self.tau_converter = tau_converter
+
+        self.score_controllers = []
         if isinstance(score_to_track, list):
-            self.score_to_track = score_to_track
+            if fraction_threshold is None:
+                scores = [(ScoreControllerPerplexity.DEFAULT_FRACTION_THRESHOLD, name) for name in score_to_track]
+            elif isinstance(fraction_threshold, list) and len(score_to_track) == len(fraction_threshold):
+                scores = list(zip(score_to_track, fraction_threshold))
+            else:
+                err_message = """Length of score_to_track and fraction_threshold must be same.
+                Otherwise fraction_threshold must be None"""
+                raise ControllerAgentException(err_message)
+
+            self.score_controllers.append([ScoreControllerPerplexity(name, threshold) for (name, threshold) in scores])
+
         elif isinstance(score_to_track, str):
-            self.score_to_track = [score_to_track]
-        else:
-            self.score_to_track = []
+            self.score_controllers.append([ScoreControllerPerplexity(
+                score_to_track,
+                fraction_threshold or ScoreControllerPerplexity.DEFAULT_FRACTION_THRESHOLD
+            )])
+
+        if isinstance(score_controller, ScoreControllerBase):
+            self.score_controllers.append(score_controller)
+        elif isinstance(score_controller, list):
+            if not all(isinstance(score, ScoreControllerBase) for score in score_controller):
+                err_message = """score_controller must be of type ScoreControllerBase od list of ScoreControllerBase"""
+                raise ControllerAgentException(err_message)
+
+            self.score_controllers.extend(score_controller)
 
         self.is_working = True
         self.local_dict = local_dict
@@ -272,7 +340,7 @@ def invoke(self, model, cur_iter):
 
         if self.is_working:
             should_stop = any(
-                is_score_out_of_control(model, score) for score in self.score_to_track
+                score_controller(model) for score_controller in self.score_controllers
             )
             if should_stop:
                 warnings.warn(W_HALT_CONTROL.format(len(self.tau_history)))
@@ -297,26 +365,31 @@ def __init__(self, num_iter: int, parameters,
             regularizers params
             each dict should contain the following fields: 
                 ("reg_name" or "regularizer"),
-                "score_to_track" (optional),
                 "tau_converter",
+                "score_to_track" (optional),
+                "fraction_threshold" (optional),
+                "score_controller" (optional),
                 "user_value_grid"
                 See top-level docstring for details.
             Examples:
 
                     >>  {"regularizer": artm.regularizers.<...>,
-                    >>   "score_to_track": "PerplexityScore@all",
                     >>   "tau_converter": "prev_tau * user_value",
+                    >>   "score_to_track": "PerplexityScore@all",
+                    >>   "fraction_threshold": 0.1,
                     >>   "user_value_grid": [0.5, 1, 2]}
 
 
             -----------
 
                     >>  {"reg_name": "decorrelator_for_ngramms",
-                    >>   "score_to_track": None,
                     >>   "tau_converter": (
                     >>       lambda initial_tau, prev_tau, cur_iter, user_value:
                     >>       initial_tau * (cur_iter % 2) + user_value
                     >>   )
+                    >>   "score_to_track": None,
+                    >>   "fraction_threshold": None,
+                    >>   "score_controller": [ScoreControllerPerplexity("PerplexityScore@all", 0.1)],
                     >>   "user_value_grid": [0, 1]}
 
         reg_search : str

topicnet/tests/fixtures.py

@@ -0,0 +1,55 @@
+from topicnet.cooking_machine.cubes.controller_cube import ScoreControllerPerplexity
+
+DATA_REG_CONTROLLER_SORT_OF_DECREASING = [
+    ([246.77072143554688,
+      124.72193908691406,
+      107.95775604248047,
+      105.27597045898438,
+      112.46900939941406,
+      132.88259887695312], 0.1, True),
+    ([246.77072143554688,
+      124.72193908691406,
+      107.95775604248047,
+      105.27597045898438,
+      112.46900939941406], 0.1, False),
+    ([246.77072143554688,
+      124.72193908691406,
+      107.95775604248047,
+      105.27597045898438,
+      112.46900939941406], 0.05, True),
+
+]
+DATA_AGENT_CONTROLLER_LEN_CHECK = [
+    ({
+         "reg_name": "decorrelation",
+         "score_to_track": "PerplexityScore@all",
+         "tau_converter": "prev_tau * user_value",
+         "max_iters": float("inf")
+     }, 1),
+    ({
+         "reg_name": "decorrelation",
+         "score_to_track": ["PerplexityScore@all"],
+         "tau_converter": "prev_tau + user_value",
+         "max_iters": float("inf")
+     }, 1),
+    ({
+         "reg_name": "decorrelation",
+         "score_to_track": None,  # never stop working
+         "tau_converter": "prev_tau * user_value",
+         "max_iters": float("inf")
+     }, 0),
+    ({
+         "reg_name": "decorrelation",
+         "score_to_track": None,  # never stop working
+         "score_controller": ScoreControllerPerplexity("PerplexityScore@all", 0.1),
+         "tau_converter": "prev_tau * user_value",
+         "max_iters": float("inf")
+     }, 1),
+    ({
+         "reg_name": "decorrelation",
+         "score_to_track": "PerplexityScore@all",  # never stop working
+         "score_controller": ScoreControllerPerplexity("PerplexityScore@all", 0.1),
+         "tau_converter": "prev_tau * user_value",
+         "max_iters": float("inf")
+     }, 2)
+]

topicnet/tests/test_cube_utils.py

@@ -0,0 +1,19 @@
+import pytest
+
+from topicnet.cooking_machine.cubes.controller_cube import ScoreControllerPerplexity, ControllerAgent
+from .fixtures import DATA_AGENT_CONTROLLER_LEN_CHECK, DATA_REG_CONTROLLER_SORT_OF_DECREASING
+
+
+@pytest.mark.parametrize('values, fraction, answer_true', DATA_REG_CONTROLLER_SORT_OF_DECREASING)
+def test_get_two_values_diff(values, fraction, answer_true):
+    score_controller = ScoreControllerPerplexity('test', fraction)
+    is_out_of_control = score_controller.is_out_of_control(values)
+
+    assert is_out_of_control.answer == answer_true
+
+
+@pytest.mark.parametrize('agent_blueprint, answer_true', DATA_AGENT_CONTROLLER_LEN_CHECK)
+def test_controllers_length(agent_blueprint, answer_true):
+    agent = ControllerAgent(**agent_blueprint)
+
+    assert len(agent.score_controllers) == answer_true