Merge branch 'master' of https://github.com/PerryXDeng/wheatyeeters

data_preparation/IndividualMetrics.R

@@ -0,0 +1,152 @@
+source("readData.R")
+
+library(tidyverse)
+
+
+RPEData <-readNArpeData()
+wellnessData <- readWellnessData()
+normalizedWellnessData <- readNormalizedMetrics()
+RPEData
+
+
+playerIDS <- playerIds <-unique(RPEData$PlayerID)
+
+
+numDays <- max(RPEData$TimeSinceAugFirst)
+
+
+dayList <- 0:numDays
+
+
+dayCol <- c()
+playerid <- c()
+
+dailyLoadCol <- c()
+acuteChronicRatioCol <- c()
+trainDuration <- c()
+
+sleepHoursCol <- c()
+fatigueRawCol <- c()
+sleepQualityCol <- c()
+sorenessCol <- c()
+
+normFatCol <-c()
+normSoreCol <- c()
+normSleepHours <- c()
+normSleepQuality <- c()
+
+notatAllCol <- c()
+absCol <-c()
+somewhatCol <- c()
+unknownCol <- c()
+
+
+
+
+
+for(day in dayList)
+{
+  for(id in playerIDS)
+  {
+    cat("Player:", id, "Day:", day, "\n", sep=" ")
+    
+    
+    trainDay <- subset(RPEData, TimeSinceAugFirst == day & PlayerID == id)
+    #workLoad <- c(workLoad, sum(daylyActivities$SessionLoad, na.rm = T))
+
+    wellnessDay <- subset(wellnessData, TimeSinceAugFirst == day & PlayerID == id)
+
+    
+    normalizedDay <- subset(normalizedWellnessData, TimeSinceAugFirst == day & playerID == id)
+    #if(length(normalizedDay$playerID) > 0)
+    #{
+    #  print("good")
+    #}
+    
+    dayCol <- c(dayCol, day)
+    playerid <- c(playerid, id)
+    
+    
+    if(length(wellnessDay$SleepHours) > 0)
+    {
+      fatigueRawCol <- c(fatigueRawCol, mean(wellnessDay$Fatigue, na.rm =T))
+      sleepQualityCol <- c(sleepQualityCol, mean(wellnessDay$SleepQuality, na.rm = T))
+      sleepHoursCol <- c(sleepHoursCol, sum(wellnessDay$SleepHours, na.rm = T))
+      sorenessCol <- c(sorenessCol, mean(wellnessDay$Soreness, na.rm = T))
+    }
+    else
+    {
+      sleepQualityCol <- c(sleepQualityCol, median(wellnessData$SleepQuality, na.rm = T))
+      sleepHoursCol <- c(sleepHoursCol, median(wellnessData$SleepHours))
+      fatigueRawCol <- c(fatigueRawCol, median(wellnessData$Fatigue))
+      sorenessCol <- c(sorenessCol, median(wellnessData$Soreness))
+    }
+
+    
+    if(length(normalizedDay$normSoreness) > 0)
+    {
+      normFatCol <- c(normFatCol, mean(normalizedDay$normFatigue, na.rm=T))
+      normSoreCol <- c(normSoreCol, mean(normalizedDay$normSoreness, na.rm = T))
+      normSleepHours <- c(normSleepHours, mean(normalizedDay$normSleepHours, na.rm =T))
+      normSleepQuality <- c(normSleepQuality, mean(normalizedDay$normSleepQuality, na.rm=T))
+    }
+    else
+    {
+      normFatCol <- c(normFatCol, mean(normalizedWellnessData$normFatigue, na.rm=T))
+      normSoreCol <- c(normSoreCol, mean(normalizedWellnessData$normSoreness, na.rm = T))
+      normSleepHours <- c(normSleepHours, mean(normalizedWellnessData$normSleepHours, na.rm =T))
+      normSleepQuality <- c(normSleepQuality, mean(normalizedWellnessData$normSleepQuality, na.rm=T))
+    }
+    
+    if(length(trainDay$SessionLoad) > 0)
+    {
+      dailyLoadCol <- c(dailyLoadCol, mean(trainDay$DailyLoad,na.rm = T))
+      acuteChronicRatioCol <- c(acuteChronicRatioCol, mean(trainDay$AcuteChronicRatio, na.rm =T))
+      trainDuration <- c(trainDuration, sum(trainDay$Duration, na.rm = T))
+      
+      notatAllCol <- c(notatAllCol, max(trainDay$BestOutOfMyselfNotAtAll))
+      absCol <- c(absCol, max(trainDay$BestOutOfMyselfAbsolutely))
+      somewhatCol <- c(somewhatCol, max(trainDay$BestOutOfMyselfSomewhat))
+      unknownCol <- c(unknownCol, max(trainDay$BestOutOfMyselfUnknown))
+    }
+    else
+    {
+      dailyLoadCol <- c(dailyLoadCol, 0)
+      acuteChronicRatioCol <- c(acuteChronicRatioCol, 0)
+      trainDuration <- c(trainDuration, 0)
+      
+      
+      notatAllCol <- c(notatAllCol, 0)
+      absCol <- c(absCol, 0)
+      somewhatCol <- c(somewhatCol, 0)
+      unknownCol <- c(unknownCol, 1)
+    }
+  }
+  
+}
+
+
+dailyLoadCol[is.na(dailyLoadCol)] <- 0
+acuteChronicRatioCol[is.na(acuteChronicRatioCol)] <- 0
+
+
+
+massiveTibble <- tibble(day = dayCol,
+                        playerID = playerid,
+                        DailyLoad = dailyLoadCol,
+                        acuteChronicRatio = acuteChronicRatioCol,
+                        trainDuration = trainDuration,
+                        sleepHours = sleepHoursCol,
+                        fatigue = fatigueRawCol,
+                        sleepQuality = sleepQualityCol,
+                        soreness = sorenessCol,
+                        fatigueNorm = normFatCol,
+                        sorenessNorm = normSoreCol,
+                        sleepHoursNorm = normSleepHours,
+                        sleepQualityNorm = normSleepQuality,
+                        BestOutOfMyselfNotAtAll = notatAllCol,
+                        BestOutOfMyselfAbsolutely = absCol,
+                        BestOutOfMyselfSomewhat = somewhatCol,
+                        BestOutOfMyselfUnknown = unknownCol)
+
+write.csv(massiveTibble, "cleaned/personal.csv")

data_preparation/exponentialSmoothing.R

@@ -0,0 +1,28 @@
+fatigueFunction <- function(workLoad, index)
+{
+  if(index == 1)
+  {
+    return(workLoad[1])
+  }
+  else
+  {
+    return(workLoad[index] + 0.7*fatigueFunction(workLoad, index -1))
+  }
+}
+
+
+smoothVector <- function(dataV)
+{
+  dataNew <- c()
+  
+  for(i in 1:length(dataV))
+  {
+    dataNew <- c(dataNew, fatigueFunction(dataV, i))
+  }
+  dataNew
+}
+
+
+smoothVector(c(1,2,3,4))
+
+plot(1:100, smoothVector(1:100))

hypotheses_modeling/ryan_regressions.txt

@@ -0,0 +1,56 @@
+Player 9 for acuteChronicRatio
+(1.5543991777375237, array([-0.13902363, -0.0050734 ]), 0.43833525766886916, 0.0008867324974165428)
+(0.48957502323211444, 0.5699208996736808)
+
+Player 11 for acuteChronicRatio
+(1.3258336437888891, array([ 0.00685405, -0.00203877]), 0.11479264860906191, 0.00096573215060248)
+(0.4626369870793939, 0.44423100028132423)
+
+Player 9 for sleepQuality
+(5.334340148618029, array([ 1.02125114, -0.00432071]), 0.6539555377710119, 0.0012466268315319546)
+(0.597372726628557, 0.7193845945200593)
+
+Player 12 for sleepQuality
+(3.584632145668627, array([0.41485048, 0.00088306]), 0.34499328173976607, 0.0006803191826778461)
+(0.4600451787749952, 0.4227561697627662)
+
+Player 3 for soreness
+(3.2022109874813864, array([ 4.19185564e-01, -1.39780603e-04]), 0.4252210971285296, 0.0006688273367686844)
+(0.4674558844267985, 0.468022382169041)
+
+Player 7 for soreness
+(3.077085467389001, array([4.26722267e-01, 5.78941104e-05]), 0.39740866670470376, 0.0007515523424419748)
+(0.49988907759269197, 0.4324080355741665)
+
+Player 9 for soreness
+(5.246967544538162, array([ 0.71359643, -0.00715268]), 0.5944763974272957, 0.0019117577400940124)
+(0.7765705044770445, 0.637614880294574)
+
+Player 3 for sorenessNorm
+(0.12141700921277873, array([ 0.58776641, -0.00068588]), 0.36954097046558354, 0.0017457549720592538)
+(0.7372571250720799, 0.443920566690573)
+
+Player 9 for sleepQualityNorm
+(0.08976795719049747, array([ 7.62990183e-01, -4.56901189e-04]), 0.5858342550214043, 0.0005114756024138036)
+(0.40337948626285336, 0.6267479812123391)
+
+Player 12 for sleepQualityNorm
+(0.0030010445831953553, array([ 5.97640029e-01, -1.59341568e-05]), 0.3568843263232311, 0.00117522635306064)
+(0.6154637522814207, 0.4127840710858218)
+
+Player 3 for BestOutOfMyselfAbsolutely
+(0.5591113560131244, array([-0.06273135,  0.00144052]), 0.19693367374327753, 0.00034431962962114574)
+(0.29946330051959735, 0.4074811906349808)
+
+Player 9 for BestOutOfMyselfAbsolutely
+(0.6212790914046262, array([ 0.23297634, -0.00209794]), 0.49210290965481174, 0.0002682490835151878)
+(0.2897694328560262, 0.5496315964616945)
+
+Player 3 for BestOutOfMyselfUnknown
+(0.45258982162191874, array([ 0.0601493 , -0.00149072]), 0.20252316026232742, 0.0003459027048924739)
+(0.2980996120964688, 0.41962026820540466)
+
+Player 9 for BestOutOfMyselfUnknown
+(0.34069195794958324, array([-0.22074479,  0.00217018]), 0.4646199842752917, 0.0003011772648087732)
+(0.3048702214016301, 0.5319467359793901)
+

hypotheses_modeling/team_regressions.py

@@ -60,12 +60,24 @@ def poly_regression(x, y, degree):
 
 
 def main():
+  file = open("ryan_regressions.txt", 'w')
   player = pd.read_csv("../data_preparation/cleaned/personal.csv", index_col=0)
-  player = player[player['playerID'] == 1]
-  x = player[['fatigueNorm', 'day']]
-  y = player['sorenessNorm']
-  print(standard_lr(x, y))
-  print(poly_regression(x, y, 5))
+
+  for name, value in player.iteritems():
+    if name == "day":
+      continue
+
+    for j in range(1, 17):
+      ply = player[player['playerID'] == j]
+      x = ply[['fatigueNorm', 'day']]
+      y = ply[name]
+
+      lr = standard_lr(x, y)
+      poly = poly_regression(x, y, 3)
+      if .9 > lr[2] > .4 or .9 > poly[1] > .4:
+        file.write("Player {} for {}\n".format(j, name))
+        file.write("{}\n".format(lr))
+        file.write("{}\n\n".format(poly))
 
 
 if __name__ == "__main__":