doc: better comments and documentaion

ChrisMolanus · Dec 30, 2021 · 7dc957f · 7dc957f
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Showing 3 changed files with 13 additions and 7 deletions.
diff --git a/app.py b/app.py
@@ -390,24  390,31 @@ def plot(priors, step_count, ax_of_plot):
                             "where each color represents a action.")
         timeline_col1.write("")
         timeline_col1.markdown("**RandomCrayfish**")
-        timeline_col1.write("The timeline displayed what you would exspect when each action is chosen at random."
         timeline_col1.write("The policy is meant to be a reference for the worst possible rational policy "
                             "where an action is chosen at random."
                             "The timeline displayed what you would expect when each action is chosen at random."
                             " Each action is chose about as many times and any other for the duration of the "
                             "simulation")
         timeline_col1.write("")
         timeline_col1.markdown("**SementJunglefolw**")
-        timeline_col1.write("The timeline is very similar to what we see in company data. "
         timeline_col1.write("This is an implementation of a Gold, Silver, Bronze based marketing strategy. "
                             "The timeline is very similar to what we see in company data. "
                             "There are a few big campaigns, and a bit more smaller campaigns with very short runs")
         timeline_col1.write("")
         timeline_col1.markdown("**EpsilonRingtail**")
-        timeline_col1.write("The timeline is a bit more complex since it i a learning algorithm. "
         timeline_col1.write("This is an implementation of a Epsilon Greedy policy "
                             "that does not consider the customer or product context."
                             "The timeline is a bit more complex since it i a learning algorithm "
                             "based on maximizing Household lifetime value. "
                             "In the first few days the it changes what it thinks is the best campaign a lot "
                             "as samples are being collected. After some time this starts to stabilize "
                             "between a few good options that are close to each other in terms of HLV. "
-                            "If the simulation runs long enough it would stabalize even more "
                             "If the simulation runs long enough it would stabilize even more "
                             "and stick to one or two campaigns")
         timeline_col1.write("")
         timeline_col1.markdown("**BayesianGroundhog**")
-        timeline_col1.write("The timeline of this policy is in some way simpler than the Epsilon Greedy policy. "
         timeline_col1.write("This is an implementation of a Baysian Bandit or Thompson sampling. "
                             "The timeline of this policy is in some way simpler than the Epsilon Greedy policy. "
                             "In the beginning the policy will try each campaign more or less equality. "
                             "At some point it will have enough information that come to a conclusion "
                             "that one or two campaigns are usually the best.")

diff --git a/policy.py b/policy.py
@@ -37,7  37,7 @@ def __init__(self, id: int, name: str, list_price: float, margin: float, product
     def get_margin(self, base_product=None) -> float:
         """
         Calculate to annual margin of this product
-        :param base_product: The product of wich this product is a modifierof, None if this is a L0 product
         :param base_product: The product of which this product is a modifier of, None if this is a L0 product
         :return: The Euro annual margin
         """
         return self._margin

diff --git a/simulator.py b/simulator.py
@@ -488,7  488,6 @@ def do_simulations(self, policies_to_simulate: List[Type[Union[Any]]], keywords:
 if __name__ == "__main__":
     policies = [randomCrayfish.RandomCrayfish, segmentJunglefowl.SegmentJunglefowl, bayesianGroundhog.BayesianGroundhog,
                 epsilonRingtail.EpsilonRingtail]
-    # policies = [segmentJunglefowl.SegmentJunglefowl]
 
     nr_of_threads_per_policies = 1
     sequential_runs_per_thread = 5