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

5 years ago · 93db9a57da
--- a/data_exploration/wellness_cluster_test.py
+++ b/data_exploration/wellness_cluster_test.py
@ -0,0 +1,25 @@
 import sklearn.cluster as cluster
 import pandas as pd
 from matplotlib import pyplot as plt
 def find_kmeans(mat, k, first, second):
    km = cluster.KMeans(n_clusters=k)
    km.fit(mat)
    # Plot sse against k
    plt.figure(figsize=(6, 6))
    plt.xlabel('Metric: ' + first)
    plt.ylabel('Metric: ' + second)
    plt.scatter(mat[:, 0], mat[:, 1], c=km.labels_, cmap='rainbow')
    plt.show()
 # Read csv in
 df = pd.read_csv('../data_preparation/cleaned/time_series_normalized_wellness_menstruation.csv')
 # Specify what things you want
 df = df[["normFatigue", "normSleepQuality"]]
 # values, num clusters, axis labelsg
 find_kmeans(df.values, 2, "normFatigue", "normSleepQuality")
--- a/data_preparation/cleaned/expSmoothWorkAndFatigueData.csv
+++ b/data_preparation/cleaned/expSmoothWorkAndFatigueData.csv
@ -1,4 +1,4 @@
 "","day","totalWork","averageWorkLoad","smoothedWork","smoothedFatigueData"
 "","TimeSinceAugFirst","totalWork","averageWorkLoad","smoothedWork","smoothedFatigueData"
 "1",0,4855,255.526315789474,4855,0.734457489003587
 "2",1,1380,138,5921.8864123285,1.17226558337237
 "3",2,6030,231.923076923077,11569.9661032971,1.37713723445689
--- a/data_preparation/cleaned/time_series_normalized_wellness_menstruation.csv
+++ b/data_preparation/cleaned/time_series_normalized_wellness_menstruation.csv
--- a/data_preparation/cleaned/time_series_notnormalized_with_0Nan_rpe.csv
+++ b/data_preparation/cleaned/time_series_notnormalized_with_0Nan_rpe.csv
@ -36,8 +36,8 @@
 34,34,34,2018-07-17,4,1,Skills,60.0,5.0,300.0,300.0,107.1,309.11,0.35,7.0,8.0,,0,0,1,0,0,0,0,0,0,0,0,1,350
 35,35,35,2018-07-17,5,1,Skills,60.0,5.0,300.0,300.0,42.9,100.71,0.43,9.0,9.0,,0,0,1,0,0,0,0,0,0,0,0,1,350
 36,36,36,2018-07-17,6,1,Speed,30.0,3.0,90.0,370.0,52.9,333.5,0.16,0.0,0.0,,0,0,0,0,0,0,1,0,0,0,0,1,350
 37,37,37,2018-07-17,6,1,Conditioning,35.0,8.0,280.0,0.0,0.0,0.0,0.0,0.0,0.0,,0,0,0,1,0,0,0,0,0,0,0,1,350
 38,38,38,2018-07-17,7,1,Skills,75.0,6.0,450.0,450.0,205.7,401.79,0.51,0.0,0.0,,0,0,1,0,0,0,0,0,0,0,0,1,350
 37,37,37,2018-07-17,6,1,Conditioning,35.0,8.0,280.0,0.0,0.0,0.0,0.0,0.0,0.0,,0,0,0,1,0,0,0,0,0,0,0,1,350
 39,39,39,2018-07-17,10,1,Skills,60.0,4.0,240.0,240.0,79.3,298.57,0.27,0.0,0.0,,0,0,1,0,0,0,0,0,0,0,0,1,350
 40,40,40,2018-07-17,11,1,Skills,90.0,5.0,450.0,450.0,210.0,391.36,0.54,9.0,9.0,Absolutely,0,0,1,0,0,0,0,0,0,1,0,0,350
 41,41,41,2018-07-17,13,1,Skills,90.0,6.0,540.0,540.0,268.6,309.25,0.87,0.0,0.0,Not at all,0,0,1,0,0,0,0,0,1,0,0,0,350
--- a/data_preparation/time_series.py
+++ b/data_preparation/time_series.py
@ -30,4 +30,4 @@ def normalize_time_series(path, filename, start):
 start = start_end_times("data/rpe.csv")
 normalize_time_series("data/games_ranked.csv", "games_ranked.csv", start)
 normalize_time_series("cleaned/normalizedWellness.csv", "normalizedWellness.csv", start)
--- a/findings/data_plot.py
+++ b/findings/data_plot.py
@ -0,0 +1,13 @@
 from matplotlib import pyplot as plt
 import pandas as pd
 def plot_xy(x, y):
    plt.scatter(x, y)
    plt.xlabel(x.name)
    plt.ylabel(y.name)
    plt.show()
 wellness = pd.read_csv('../data_preparation/data/wellness.csv')
 plot_xy(wellness['Fatigue'], wellness['SleepHours'])
--- a/findings/r2correlation.txt
+++ b/findings/r2correlation.txt
--- a/hypotheses_modeling/21DaySlidingWorkAverage.csv
+++ b/hypotheses_modeling/21DaySlidingWorkAverage.csv
@ -0,0 +1,336 @@
 "","TimeSinceAugFirst","slidingWorkAverage"
 "1",20,5563.95
 "2",21,5895.66666666667
 "3",22,6307.42857142857
 "4",23,6331.71428571429
 "5",24,6458.52380952381
 "6",25,6752.33333333333
 "7",26,6767.33333333333
 "8",27,6766.61904761905
 "9",28,6738.42857142857
 "10",29,6798.66666666667
 "11",30,6811.38095238095
 "12",31,6813.7619047619
 "13",32,6881.85714285714
 "14",33,6840.42857142857
 "15",34,6834.71428571429
 "16",35,6788.47619047619
 "17",36,6711.09523809524
 "18",37,6720.61904761905
 "19",38,6781.33333333333
 "20",39,6495.04761904762
 "21",40,6617.90476190476
 "22",41,6578.85714285714
 "23",42,6122.66666666667
 "24",43,5613.04761904762
 "25",44,5671
 "26",45,5383.2380952381
 "27",46,4878.52380952381
 "28",47,4872.90476190476
 "29",48,4874.33333333333
 "30",49,4910.85714285714
 "31",50,4796.80952380952
 "32",51,4867.42857142857
 "33",52,5067.90476190476
 "34",53,5234.04761904762
 "35",54,5216.19047619048
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 "37",56,5313.09523809524
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 "39",58,5454.90476190476
 "40",59,5578.47619047619
 "41",60,5850.80952380952
 "42",61,5744.38095238095
 "43",62,5768.66666666667
 "44",63,6210.71428571429
 "45",64,6592.95238095238
 "46",65,6613.90476190476
 "47",66,6986.7619047619
 "48",67,7557.09523809524
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 "55",74,7466
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 "57",76,7480.28571428571
 "58",77,7064.61904761905
 "59",78,6618.42857142857
 "60",79,6647.38095238095
 "61",80,6575.95238095238
 "62",81,5935.85714285714
 "63",82,6326.33333333333
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 "65",84,6509.52380952381
 "66",85,6314.66666666667
 "67",86,6331
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 "74",93,4682.7619047619
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 "76",95,4585.61904761905
 "77",96,4604.66666666667
 "78",97,4592.52380952381
 "79",98,5018.95238095238
 "80",99,5509.19047619048
 "81",100,5545.38095238095
 "82",101,5570.52380952381
 "83",102,6642.42857142857
 "84",103,6281.95238095238
 "85",104,5579.19047619048
 "86",105,6177.7619047619
 "87",106,6478.2380952381
 "88",107,6441.33333333333
 "89",108,7018.95238095238
 "90",109,7771.09523809524
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 "92",111,7168.57142857143
 "93",112,7570.95238095238
 "94",113,7856.38095238095
 "95",114,7973.7619047619
 "96",115,7617.33333333333
 "97",116,7176.85714285714
 "98",117,7260.66666666667
 "99",118,7479.2380952381
 "100",119,7162.33333333333
 "101",120,6722.57142857143
 "102",121,6734.71428571429
 "103",122,6630
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 "107",126,5477.38095238095
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 "109",128,4995.71428571429
 "110",129,4822.14285714286
 "111",130,4590
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 "187",206,6975.66666666667
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--- a/hypotheses_modeling/KerasRegressions.py
+++ b/hypotheses_modeling/KerasRegressions.py
@ -0,0 +1,188 @@
 import tensorflow as tf
 import pandas as pd
 import numpy as np
 def time_series_sigmoid_classification(X, Y, k, n0, x_columns, y_columns):
  inp = X[x_columns]
  out = Y[y_columns]
  col = "day"
  x = []
  y = []
  input_shape = 0
  output_shape = 0
  for player in Y["playerID"].unique():
    XPlayer = inp[inp["playerID"] == player]
    YPlayer = out[out["playerID"] == player]
    for day in YPlayer[col][n0 - 1:]:
      prev = day - k
      xprev = XPlayer[XPlayer[col] == prev].drop(columns=[col]).to_numpy()
      if xprev.shape[0] != 1:
        continue
      else:
        xprev = xprev[0, :]
      yt = YPlayer[YPlayer[col] == day].drop(columns=[col]).to_numpy()[0, :]
      if input_shape == 0:
        input_shape = xprev.shape[0]
      else:
        if input_shape != xprev.shape[0]:
          print("INCONSISTENT INPUT DIMENSION")
          exit(2)
      if input_shape == 0:
        output_shape = yt.shape[0]
      else:
        if output_shape != yt.shape[0]:
          print("INCONSISTENT OUTPUT DIMENSION")
          exit(2)
      x.append(xprev)
      y.append(yt)
  x = np.array(x)
  y = np.array(y)
  model = tf.keras.Sequential([
    tf.keras.layers.Flatten(input_shape=input_shape),
    tf.keras.layers.Dense(output_shape, activation=tf.nn.softmax)
  ])
  model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy', 'categorical_accuracy'])
  model.fit(x, y, epochs=100)
  loss, accuracy = model.evaluate(x, y)
  print(loss, accuracy)
  return model.get_weights()
 def time_series_dnn_classification(X, Y, k, n0, x_columns, y_columns):
  inp = X[x_columns]
  out = Y[y_columns]
  col = "day"
  x = []
  y = []
  input_shape = 0
  output_shape = 0
  for player in Y["playerID"].unique():
    XPlayer = inp[inp["playerID"] == player]
    YPlayer = out[out["playerID"] == player]
    for day in YPlayer[col][n0 - 1:]:
      prev = day - k
      xprev = XPlayer[XPlayer[col] == prev].drop(columns=[col]).to_numpy()
      if xprev.shape[0] != 1:
        continue
      else:
        xprev = xprev[0, :]
      yt = YPlayer[YPlayer[col] == day].drop(columns=[col]).to_numpy()[0, :]
      if input_shape == 0:
        input_shape = xprev.shape[0]
      else:
        if input_shape != xprev.shape[0]:
          print("INCONSISTENT INPUT DIMENSION")
          exit(2)
      if input_shape == 0:
        output_shape = yt.shape[0]
      else:
        if output_shape != yt.shape[0]:
          print("INCONSISTENT OUTPUT DIMENSION")
          exit(2)
      x.append(xprev)
      y.append(yt)
  x = np.array(x)
  y = np.array(y)
  model = tf.keras.Sequential([
    tf.keras.layers.Flatten(input_shape=input_shape),
    tf.keras.layers.Dense(32, activation=tf.nn.softmax),
    tf.keras.layers.Dense(output_shape, activation=tf.nn.softmax)
  ])
  model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy', 'categorical_accuracy'])
  model.fit(x, y, epochs=100)
  loss, accuracy = model.evaluate(x, y)
  print(loss, accuracy)
  return model.get_weights()
 def time_series_linear_regression(X, Y, k, n0, x_columns, y_columns):
  inp = X[x_columns]
  out = Y[y_columns]
  col = "day"
  x = []
  y = []
  input_shape = 0
  output_shape = 0
  for player in Y["playerID"].unique():
    XPlayer = inp[inp["playerID"] == player]
    YPlayer = out[out["playerID"] == player]
    for day in YPlayer[col][n0 - 1:]:
      prev = day - k
      xprev = XPlayer[XPlayer[col] == prev].drop(columns=[col]).to_numpy()
      if xprev.shape[0] != 1:
        continue
      else:
        xprev = xprev[0, :]
      yt = YPlayer[YPlayer[col] == day].drop(columns=[col]).to_numpy()[0, :]
      if input_shape == 0:
        input_shape = xprev.shape[0]
      else:
        if input_shape != xprev.shape[0]:
          print("INCONSISTENT INPUT DIMENSION")
          exit(2)
      if input_shape == 0:
        output_shape = yt.shape[0]
      else:
        if output_shape != yt.shape[0]:
          print("INCONSISTENT OUTPUT DIMENSION")
          exit(2)
      x.append(xprev)
      y.append(yt)
  x = np.array(x)
  y = np.array(y)
  model = tf.keras.Sequential([
    tf.keras.layers.Flatten(input_shape=input_shape),
    tf.keras.layers.Dense(output_shape)
  ])
  model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy', 'categorical_accuracy'])
  model.fit(x, y, epochs=100)
  loss, accuracy = model.evaluate(x, y)
  print(loss, accuracy)
  return model.get_weights()
 def time_series_dnn_regressions(X, Y, k, n0, x_columns, y_columns):
  inp = X[x_columns]
  out = Y[y_columns]
  col = "day"
  x = []
  y = []
  input_shape = 0
  output_shape = 0
  for player in Y["playerID"].unique():
    XPlayer = inp[inp["playerID"] == player]
    YPlayer = out[out["playerID"] == player]
    for day in YPlayer[col][n0 - 1:]:
      prev = day - k
      xprev = XPlayer[XPlayer[col] == prev].drop(columns=[col]).to_numpy()
      if xprev.shape[0] != 1:
        continue
      else:
        xprev = xprev[0, :]
      yt = YPlayer[YPlayer[col] == day].drop(columns=[col]).to_numpy()[0, :]
      if input_shape == 0:
        input_shape = xprev.shape[0]
      else:
        if input_shape != xprev.shape[0]:
          print("INCONSISTENT INPUT DIMENSION")
          exit(2)
      if input_shape == 0:
        output_shape = yt.shape[0]
      else:
        if output_shape != yt.shape[0]:
          print("INCONSISTENT OUTPUT DIMENSION")
          exit(2)
      x.append(xprev)
      y.append(yt)
  x = np.array(x)
  y = np.array(y)
  model = tf.keras.Sequential([
    tf.keras.layers.Flatten(input_shape=input_shape),
    tf.keras.layers.Dense(32, activation=tf.nn.softmax),
    tf.keras.layers.Dense(output_shape)
  ])
  model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy', 'categorical_accuracy'])
  model.fit(x, y, epochs=100)
  loss, accuracy = model.evaluate(x, y)
  print(loss, accuracy)
  return model.get_weights()
--- a/hypotheses_modeling/fatigue_total_sum.csv
+++ b/hypotheses_modeling/fatigue_total_sum.csv
@ -0,0 +1,355 @@
 TimeSinceAugFirst,fatigueSum
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--- a/hypotheses_modeling/hypotheses.txt
+++ b/hypotheses_modeling/hypotheses.txt
@ -0,0 +1,28 @@
 Team:
    1.
    x - normFatigue, TimeSince
    y - normSoreness
    r2 - 0.16830803694995466
    2.
    x - normFatigue, TimeSince
    y - normDesire
    r2 - 0.1722466278587138
    3.
    x - normFatigue, TimeSince
    y - normIrritability
    r2 - 0.09563459728506452
    4.
    x - normSleepHours, TimeSince
    y - normSleepQualiyt
    r2 - 0.09717095676693188
    5.
 Perry:
 7 day moving average team workload - normalized team fatigue: 0.0006
 21 day moving average team workload - normalized team fatigue: 0.0024
 normalized team fatigue - game day performance: 0.0696
 normalized team fatigue - paper smoothed workload fatigue: 0.0324
--- a/hypotheses_modeling/slidingWorkAverageSevenDay.csv
+++ b/hypotheses_modeling/slidingWorkAverageSevenDay.csv
@ -0,0 +1,350 @@
 "","TimeSinceAugFirst","slidingWorkAverage"
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--- a/hypotheses_modeling/team_regressions.py
+++ b/hypotheses_modeling/team_regressions.py
@ -0,0 +1,61 @@
 from sklearn import linear_model
 import pandas as pd
 from sklearn.metrics import mean_squared_error, r2_score
 def k_days_into_future_regression(X, y, k, n0):
  """
  linear regression that returns the fitted weights as well as metrics
  :param X: x timeseries dataframe (very clean, no unamed columns), multidimensional rows
  :param y: y timeseries dataframe (very clean, no unamed columns), scalar rows
  :param k: days predicting in advance
  :param n0: ignoring the first n0 days
  :return: intercept, slopes, correlation, mean squared error
  """
  col = "TimeSinceAugFirst"
  inp = []
  out = []
  for day in y[col][n0 - 1:]:
    prev = day - k
    xprev = X[X[col] == prev].drop(columns=[col]).to_numpy()
    if xprev.shape[0] != 1:
      continue
    else:
      xprev = xprev[0, :]
    yt = y[y[col] == day].drop(columns=[col]).to_numpy()[0, :]
    inp.append(xprev)
    out.append(yt)
  regr = linear_model.LinearRegression()
  regr.fit(inp, out)
  predictions = regr.predict(inp)
  mse = mean_squared_error(out, predictions)/(len(out) - 2)
  r2 = r2_score(out, predictions)
  return regr.intercept_, regr.coef_, r2, mse
 def standard_lr(x, y):
  regr = linear_model.LinearRegression()
  regr.fit(x, y)
  predictions = regr.predict(x)
  mse = mean_squared_error(y, predictions) / (len(y) - 2)
  r2 = r2_score(y, predictions)
  return regr.intercept_, regr.coef_, r2, mse
 def main():
  # fatigueSums = pd.read_csv("fatigue_total_sum.csv")
  # workMovingAverage21 = pd.read_csv("21DaySlidingWorkAverage.csv", index_col=0)
  # print(k_days_into_future_regression(workMovingAverage21, fatigueSums, 0, 21))
  wellness = pd.read_csv("../data_preparation/cleaned/time_series_normalized_wellness_menstruation.csv")
  wellness = wellness.fillna(0)
  x = wellness[['normSoreness', 'TimeSinceAugFirst']]
  y = wellness['normFatigue']
  print(wellness.isnull().sum())
  print(standard_lr(x, y))
 if __name__ == "__main__":
  main()
--- a/hypotheses_modeling/time_series_days_ranked.csv
+++ b/hypotheses_modeling/time_series_days_ranked.csv
@ -0,0 +1,18 @@
 ,TimeSinceAugFirst,DailyElo
 0,121,0.0
 1,122,-3.714599999999998
 2,178,0.04346000000000028
 3,179,2.1916710000000013
 4,180,0.0
 5,255,0.0
 6,256,0.0
 7,257,-2.520374784999996
 8,263,-2.0880156214999985
 9,264,-1.7032140593500005
 10,284,-0.6130256153877235
 11,285,2.620463284090865
 12,311,-2.076954630427971
 13,312,1.0960590427574828
 14,313,1.8954531384817344
 15,353,-0.2940921753664384
 16,354,-1.8646829578297937