implement check for scoring errors

acbllib/acbllib.py

@@ -354,7 +354,7 @@ def get_club_results_details_data(url):
         return None # todo: handle team events
         data = soup.find('team-result-details')['v-bind:data']
     else:
-        assert False, "Can't find data tag."
+        return None # "Can't find data tag."
     assert data is not None and isinstance(data,str) and len(data), [url, data]
 
     details_data = json.loads(data) # returns dict from json

chatlib/chatlib.py

@@ -679,12 +679,16 @@ def augment_df(df,sd_cache_d):
     # todo: drop either Tricks or Tricks_Declarer as they are invariant and duplicates
     df['Tricks_Declarer'] = df['Tricks'] # synonym for Tricks
     df['Score_Declarer'] = df.apply(lambda r: r['Score_'+r['Pair_Declarer_Direction']], axis='columns')
+    # recompute Score and compare against actual scores to catch scoring errors such as: Board 1 at https://my.acbl.org/club-results/details/878121
+    df['Computed_Score_Declarer'] = df.apply(lambda r: 0 if r['BidLvl'] is pd.NA else mlBridgeLib.score(r['BidLvl']-1, 'CDHSN'.index(r['BidSuit']), len(r['Dbl']), ('NESW').index(r['Declarer_Direction']), r['Vul_Declarer'], r['Result'],True), axis='columns')
+    if (df['Score_Declarer'].ne(df['Computed_Score_Declarer'])|df['Score_NS'].ne(-df['Score_EW'])).any():
+        print('Invalid Scores:\n',df[df['Score_Declarer'].ne(df['Computed_Score_Declarer'])|df['Score_NS'].ne(-df['Score_EW'])][['Board','Contract','BidLvl','BidSuit','Dbl','Declarer_Direction','Vul_Declarer','Score_Declarer','Computed_Score_Declarer','Score_NS','Score_EW','Result']])
     df['MPs_Declarer'] = df.apply(lambda r: r['MatchPoints_'+r['Pair_Declarer_Direction']], axis='columns')
 
     df['DDTricks'] = df.apply(lambda r: pd.NA if r['BidLvl'] is pd.NA else r['_'.join(['DD',r['Declarer_Direction'],r['BidSuit']])], axis='columns') # invariant
     df['DDTricks_Dummy'] = df.apply(lambda r: pd.NA if r['BidLvl'] is pd.NA else r['_'.join(['DD',r['Direction_Dummy'],r['BidSuit']])], axis='columns') # invariant
     # NA for NT. df['DDSLDiff'] = df.apply(lambda r: pd.NA if r['BidLvl'] is pd.NA else r['DDTricks']-r['SL_'+r['Pair_Declarer_Direction']+'_'+r['BidSuit']], axis='columns') # pd.NA or zero?
-    df['DDScore_NS'] = df.apply(lambda r: 0 if r['BidLvl'] is pd.NA else mlBridgeLib.score(r['BidLvl']-1, 'CDHSN'.index(r['BidSuit']), len(r['Dbl']), ('NSEW').index(r['Declarer_Direction']), mlBridgeLib.DirectionSymToVulBool(r['Vul_Declarer'],r['Declarer_Direction']), r['DDTricks']-r['BidLvl']-6), axis='columns')
+    df['DDScore_NS'] = df.apply(lambda r: 0 if r['BidLvl'] is pd.NA else mlBridgeLib.score(r['BidLvl']-1, 'CDHSN'.index(r['BidSuit']), len(r['Dbl']), ('NSEW').index(r['Declarer_Direction']), r['Vul_Declarer'], r['DDTricks']-r['BidLvl']-6), axis='columns')
     df['DDScore_EW'] = -df['DDScore_NS']
     df['DDMPs_NS'] = df.apply(lambda r: mlBridgeLib.MatchPointScoreUpdate(r['DDScore_NS'],matchpoint_ns_d[r['Board']])[r['DDScore_NS']][3],axis='columns')
     df['DDMPs_EW'] = df['Board_Top']-df['DDMPs_NS']

default.favorites.json

@@ -35,7 +35,8 @@
 				"@Boards_We_Underperformed_ParScore",
 				"@Single_Dummy_Our_Boards",
 				"@Single_Dummy_All_Boards",
-				"@Declarer_Rankings"
+				"@Declarer_Rankings",
+				"@Scoring_Errors"
 			]
 		},
 		"Summarize_Briefly": {
@@ -441,6 +442,17 @@
 					}
 				]
 			},
+			"Scoring_Errors": {
+				"title": "Showing Possible Scoring Errors or Adjustments",
+				"help": "**Scoring errors or adjustments.**",
+				"prompts": [
+					{
+						"failed prompt": "show board, contract, score_declarer, computed_score_declarer, score_ns, score_ew when score_declarer <> computed_score_declarer or score_ns <> -score_ew sort by board.",
+						"prompt": "",
+						"sql": "SELECT CONCAT(Pair_Number_NS, ' - ', Player_Name_N, ' - ', Player_Name_S) AS Pair_NS, CONCAT(Pair_Number_EW, ' - ', Player_Name_E, ' - ', Player_Name_W) AS Pair_EW, Board, Contract, Score_Declarer, Computed_Score_Declarer, Score_NS, Score_EW FROM results WHERE Score_Declarer <> Computed_Score_Declarer OR Score_NS <> -Score_EW ORDER BY Board"
+					}
+				]
+			},
 			"Overall_Rankings_Predictions": {
 				"title": "Overall Rankings Predictions",
 				"help": "**Predicted final overall rankings. Predictions made before game starts.**",

mlBridgeLib/mlBridgeLib.py

@@ -498,7 +498,7 @@ def DDmakesToScores(ddmakes,vuls):
                 highest_make_level = tricks-1-tricks_in_a_book
                 for level in range(max(highest_make_level,0), max_bidding_level):
                     result = highest_make_level-level
-                    s = score(level, strain, result < 0, 0, v, result)
+                    s = score(level, strain, result < 0, 0, v, result) # double all sets
                     strainl.append((s,(level,strain),direction,result))
             # stable sort by contract then score
             sorted_direction = sorted(sorted(strainl,key=lambda k:k[1]),reverse=True,key=lambda k:k[0])
@@ -616,7 +616,7 @@ def FilterBoards(df, cn=None, vul=None, direction=None, suit=None, contractType=
 # adapted (MIT license) from https://github.com/jfklorenz/Bridge-Scoring/blob/master/features/score.js
 # ================================================================
 # Scoring
-def score(level, suit, double, declarer, vulnerability, result):
+def score(level, suit, double, declarer, vulnerability, result, declarer_score=False):
     assert level in range(0, 7), f'ValueError: level {level} is invalid'
     assert suit in range(0, 5), f'ValueError: suit {suit} is invalid' # CDHSN
     assert double in range(0, 3), f'ValueError: double {double} is invalid' # ['','X','XX']
@@ -671,7 +671,7 @@ def score(level, suit, double, declarer, vulnerability, result):
         points = -sum([undertricks[vulnerability][double][min(i, 3)]
                        for i in range(0, -result)])
 
-    return points if declarer < 2 else -points  # negate points if EW
+    return points if declarer_score or declarer < 2 else -points  # negate points if EW
 
 # ================================================================
 

release_notes.md

@@ -55,6 +55,8 @@
 19. Create a single dataframe for DD, SD, SD Max, Par, and Actual instead of separate dataframes.
 20. Does low SD Max correlate to bad bidding/contract?
 21. What does low SD but high Pct say about a declarer? 
+22. Show partnership declarer pct when showing declarer rankings.
+23. Show par score pct and sd pcts for boards played by partnership.
 
 ## Column Issues
 1. Rename any column names?