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

hypotheses_modeling/KerasRegressions.py

@@ -1,34 +1,42 @@
 import tensorflow as tf
 import pandas as pd
 import numpy as np
+from sklearn.metrics import r2_score
 
 
-def time_series_sigmoid_classification(X, Y, k, n0, x_columns, y_columns):
-  inp = X[x_columns]
-  out = Y[y_columns]
+def r2_(y, pred):
+  ybar = np.sum(y) / len(y)
+  ssreg = np.sum((pred - ybar)**2)
+  sstot = np.sum((y - ybar)**2)
+  return ssreg/sstot
+
+
+def time_series_sigmoid_classification(dataset, k, n0, x_columns, y_columns):
+  inp = dataset[x_columns]
+  out = dataset[y_columns]
   col = "day"
   x = []
   y = []
   input_shape = 0
   output_shape = 0
-  for player in Y["playerID"].unique():
+  for player in out["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()
+      xprev = XPlayer[XPlayer[col] == prev].drop(columns=[col, "playerID"]).to_numpy()
       if xprev.shape[0] != 1:
         continue
       else:
         xprev = xprev[0, :]
-      yt = YPlayer[YPlayer[col] == day].drop(columns=[col]).to_numpy()[0, :]
+      yt = YPlayer[YPlayer[col] == day].drop(columns=[col, "playerID"]).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:
+      if output_shape == 0:
         output_shape = yt.shape[0]
       else:
         if output_shape != yt.shape[0]:
@@ -39,42 +47,42 @@ def time_series_sigmoid_classification(X, Y, k, n0, x_columns, y_columns):
   x = np.array(x)
   y = np.array(y)
   model = tf.keras.Sequential([
-    tf.keras.layers.Flatten(input_shape=input_shape),
+    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)
+  model.fit(x, y, epochs=50)
   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]
+def time_series_dnn_classification(dataset, k, n0, x_columns, y_columns):
+  inp = dataset[x_columns]
+  out = dataset[y_columns]
   col = "day"
   x = []
   y = []
   input_shape = 0
   output_shape = 0
-  for player in Y["playerID"].unique():
+  for player in out["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()
+      xprev = XPlayer[XPlayer[col] == prev].drop(columns=[col, "playerID"]).to_numpy()
       if xprev.shape[0] != 1:
         continue
       else:
         xprev = xprev[0, :]
-      yt = YPlayer[YPlayer[col] == day].drop(columns=[col]).to_numpy()[0, :]
+      yt = YPlayer[YPlayer[col] == day].drop(columns=[col, "playerID"]).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:
+      if output_shape == 0:
         output_shape = yt.shape[0]
       else:
         if output_shape != yt.shape[0]:
@@ -85,42 +93,44 @@ def time_series_dnn_classification(X, Y, k, n0, x_columns, y_columns):
   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(32, input_dim=input_shape,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)
+  print(output_shape)
+  model.fit(x, y, epochs=50)
   loss, accuracy = model.evaluate(x, y)
+  print(x.shape)
+  print(y.shape)
   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]
+def time_series_linear_regression(dataset, k, n0, x_columns, y_columns):
+  inp = dataset[x_columns]
+  out = dataset[y_columns]
   col = "day"
   x = []
   y = []
   input_shape = 0
   output_shape = 0
-  for player in Y["playerID"].unique():
+  for player in out["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()
+      xprev = XPlayer[XPlayer[col] == prev].drop(columns=[col, "playerID"]).to_numpy()
       if xprev.shape[0] != 1:
         continue
       else:
         xprev = xprev[0, :]
-      yt = YPlayer[YPlayer[col] == day].drop(columns=[col]).to_numpy()[0, :]
+      yt = YPlayer[YPlayer[col] == day].drop(columns=[col, "playerID"]).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:
+      if output_shape == 0:
         output_shape = yt.shape[0]
       else:
         if output_shape != yt.shape[0]:
@@ -131,42 +141,45 @@ def time_series_linear_regression(X, Y, k, n0, x_columns, y_columns):
   x = np.array(x)
   y = np.array(y)
   model = tf.keras.Sequential([
-    tf.keras.layers.Flatten(input_shape=input_shape),
+    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)
+  model.compile(optimizer='adam', loss='mean_squared_error', metrics=['accuracy'])
+  model.fit(x, y, epochs=50)
+  loss, _ = model.evaluate(x, y)
+  print(loss)
+  pred = model.predict(x)
+  r2 = r2_(y, pred)
+  print(r2)
   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]
+def time_series_dnn_regressions(dataset, k, n0, x_columns, y_columns):
+  inp = dataset[x_columns]
+  out = dataset[y_columns]
   col = "day"
   x = []
   y = []
   input_shape = 0
   output_shape = 0
-  for player in Y["playerID"].unique():
+  for player in out["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()
+      xprev = XPlayer[XPlayer[col] == prev].drop(columns=[col, "playerID"]).to_numpy()
       if xprev.shape[0] != 1:
         continue
       else:
         xprev = xprev[0, :]
-      yt = YPlayer[YPlayer[col] == day].drop(columns=[col]).to_numpy()[0, :]
+      yt = YPlayer[YPlayer[col] == day].drop(columns=[col, "playerID"]).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:
+      if output_shape == 0:
         output_shape = yt.shape[0]
       else:
         if output_shape != yt.shape[0]:
@@ -177,12 +190,29 @@ def time_series_dnn_regressions(X, Y, k, n0, x_columns, y_columns):
   x = np.array(x)
   y = np.array(y)
   model = tf.keras.Sequential([
-    tf.keras.layers.Flatten(input_shape=input_shape),
+    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)
+  model.compile(optimizer='adam', loss='mean_squared_error', metrics=['accuracy'])
+  model.fit(x, y, epochs=50)
   loss, accuracy = model.evaluate(x, y)
   print(loss, accuracy)
+  pred = model.predict(x)
+  r2 = r2_(y, pred)
+  print(r2)
   return model.get_weights()
+
+
+def main():
+  filename = "personal.csv"
+  df = pd.read_csv(filename)
+  x = ["day", "playerID", "fatigueSliding"]
+  y = ["day", "playerID", "BestOutOfMyselfAbsolutely", "BestOutOfMyselfSomewhat", "BestOutOfMyselfNotAtAll", "BestOutOfMyselfUnknown"]
+  k = 0
+  n0 = 30
+  weights = time_series_dnn_classification(df, k, n0, x, y)
+
+
+if __name__ == "__main__":
+  main()

personal_regression_info.csv

@@ -0,0 +1,276 @@
+xVal, yVal, degree, r2, rmse
+DailyLoad, DailyLoadSliding, 1, 0.43547102243023217, 220.0961525453662
+DailyLoad, DailyLoadSliding, 2, 0.4395850896947021, 219.29269854539046
+DailyLoad, DailyLoadSliding, 3, 0.4395851750834222, 219.29268183890815
+DailyLoad, DailyLoadSliding, 4, 0.4407301218978247, 219.06855605031492
+DailyLoad, DailyLoadSliding, 5, 0.44163341983840276, 218.89157160110392
+DailyLoad, acuteChronicRatio, 1, 0.2036344382117684, 0.6675758904083244
+DailyLoad, acuteChronicRatio, 2, 0.2225907731816099, 0.6595826961186229
+DailyLoad, acuteChronicRatio, 3, 0.22569204903293028, 0.6582657629359487
+DailyLoad, acuteChronicRatio, 4, 0.22682319674268392, 0.657784773464178
+DailyLoad, acuteChronicRatio, 5, 0.2299011526244381, 0.6564741731589157
+DailyLoad, acuteChronicRatioSliding, 1, 0.08191150459879548, 0.46070280508684464
+DailyLoad, acuteChronicRatioSliding, 2, 0.08350340408828272, 0.4603032190162654
+DailyLoad, acuteChronicRatioSliding, 3, 0.08387400805713785, 0.46021014313933695
+DailyLoad, acuteChronicRatioSliding, 4, 0.0847210836697816, 0.45999733237606727
+DailyLoad, acuteChronicRatioSliding, 5, 0.08506585065991057, 0.4599106883868301
+DailyLoad, trainDuration, 1, 0.8603505230540016, 31.325485038659917
+DailyLoad, trainDuration, 2, 0.895540397428708, 27.092702751867783
+DailyLoad, trainDuration, 3, 0.8968963395705208, 26.91628942897981
+DailyLoad, trainDuration, 4, 0.8974663466495758, 26.84178315328193
+DailyLoad, trainDuration, 5, 0.8986737951855702, 26.68326908051855
+DailyLoad, trainDurationSliding, 1, 0.41728648271425894, 45.62094407121064
+DailyLoad, trainDurationSliding, 2, 0.4373738503226734, 44.82772289334107
+DailyLoad, trainDurationSliding, 3, 0.4377909064140846, 44.81110516164391
+DailyLoad, trainDurationSliding, 4, 0.4382691093173168, 44.79204342866282
+DailyLoad, trainDurationSliding, 5, 0.43831755833281993, 44.790111741054325
+DailyLoad, sleepHours, 1, 0.007142007576262155, 1.2881967626117155
+DailyLoad, sleepHours, 2, 0.007148188022358504, 1.2881927531546211
+DailyLoad, sleepHours, 3, 0.007884837946448342, 1.2877147748871853
+DailyLoad, sleepHours, 4, 0.007975819768443637, 1.2876557286559416
+DailyLoad, sleepHours, 5, 0.008052991463216252, 1.2876056429274472
+DailyLoad, sleepHoursSliding, 1, 0.019545287768685804, 0.584962255931936
+DailyLoad, sleepHoursSliding, 2, 0.020088941454910914, 0.5848000551798747
+DailyLoad, sleepHoursSliding, 3, 0.02055115901876503, 0.5846621157529936
+DailyLoad, sleepHoursSliding, 4, 0.022089240541790733, 0.5842028721071504
+DailyLoad, sleepHoursSliding, 5, 0.02223180222623844, 0.584160287453959
+DailyLoad, fatigue, 1, 0.0004609869345519879, 0.9414140376537236
+DailyLoad, fatigue, 2, 0.01081491590759509, 0.9365254300145728
+DailyLoad, fatigue, 3, 0.013075886827278227, 0.9354545140938101
+DailyLoad, fatigue, 4, 0.013871725612309982, 0.9350772707390621
+DailyLoad, fatigue, 5, 0.013872676932596972, 0.9350768197033221
+DailyLoad, fatigueSliding, 1, 0.0026508263511236807, 0.4372333179520449
+DailyLoad, fatigueSliding, 2, 0.007885710862688966, 0.4360843335533719
+DailyLoad, fatigueSliding, 3, 0.010453728094075743, 0.43551958123003714
+DailyLoad, fatigueSliding, 4, 0.010618419541746138, 0.43548333768237124
+DailyLoad, fatigueSliding, 5, 0.011054975220036178, 0.4353872505388315
+DailyLoad, sleepQuality, 1, 0.0035540362519845825, 1.0670673779455226
+DailyLoad, sleepQuality, 2, 0.003629033828128514, 1.067027220738953
+DailyLoad, sleepQuality, 3, 0.003908551303328678, 1.0668775407074202
+DailyLoad, sleepQuality, 4, 0.003931368871279761, 1.0668653211013668
+DailyLoad, sleepQuality, 5, 0.003965354090061157, 1.066847120568044
+DailyLoadSliding, acuteChronicRatio, 1, 0.08968716038032798, 0.7137395165200652
+DailyLoadSliding, acuteChronicRatio, 2, 0.08983433178978883, 0.7136818186100856
+DailyLoadSliding, acuteChronicRatio, 3, 0.08992161857057823, 0.7136475960041049
+DailyLoadSliding, acuteChronicRatio, 4, 0.0904389600089488, 0.7134447277869993
+DailyLoadSliding, acuteChronicRatio, 5, 0.09043929378668558, 0.7134445968821024
+DailyLoadSliding, acuteChronicRatioSliding, 1, 0.21087601957457813, 0.42712127525906407
+DailyLoadSliding, acuteChronicRatioSliding, 2, 0.210901697961838, 0.4271143258601036
+DailyLoadSliding, acuteChronicRatioSliding, 3, 0.21225487579188795, 0.4267479522246139
+DailyLoadSliding, acuteChronicRatioSliding, 4, 0.2146048321145828, 0.4261109517289057
+DailyLoadSliding, acuteChronicRatioSliding, 5, 0.21486651212090824, 0.4260399594370575
+DailyLoadSliding, trainDuration, 1, 0.3956455009733978, 65.16644463520377
+DailyLoadSliding, trainDuration, 2, 0.4022038740260441, 64.81189141128601
+DailyLoadSliding, trainDuration, 3, 0.4027541636178683, 64.7820538803523
+DailyLoadSliding, trainDuration, 4, 0.40408425434866635, 64.70987754070184
+DailyLoadSliding, trainDuration, 5, 0.4047325754882428, 64.67466769887127
+DailyLoadSliding, trainDurationSliding, 1, 0.9138121170320684, 17.545260818089265
+DailyLoadSliding, trainDurationSliding, 2, 0.9250701126586549, 16.35928286460513
+DailyLoadSliding, trainDurationSliding, 3, 0.9253030256435378, 16.33383737725413
+DailyLoadSliding, trainDurationSliding, 4, 0.9254816165533626, 16.31429963440067
+DailyLoadSliding, trainDurationSliding, 5, 0.9264419148954373, 16.208839781278495
+DailyLoadSliding, sleepHours, 1, 0.007285819818366068, 1.2881034636832067
+DailyLoadSliding, sleepHours, 2, 0.007539716462478641, 1.2879387304361276
+DailyLoadSliding, sleepHours, 3, 0.0077634187167047175, 1.2877935704537555
+DailyLoadSliding, sleepHours, 4, 0.008391224000175446, 1.2873861013336512
+DailyLoadSliding, sleepHours, 5, 0.00954460948168756, 1.2866371746901248
+DailyLoadSliding, sleepHoursSliding, 1, 0.04333038807879275, 0.5778233197000667
+DailyLoadSliding, sleepHoursSliding, 2, 0.04360891424942104, 0.5777391994195473
+DailyLoadSliding, sleepHoursSliding, 3, 0.04410541681703273, 0.5775892156740445
+DailyLoadSliding, sleepHoursSliding, 4, 0.0445631396420495, 0.5774509120219953
+DailyLoadSliding, sleepHoursSliding, 5, 0.04609011660368756, 0.5769892871559609
+DailyLoadSliding, fatigue, 1, 0.001007476257176232, 0.941156647470284
+DailyLoadSliding, fatigue, 2, 0.0041354732881003775, 0.9396820401931253
+DailyLoadSliding, fatigue, 3, 0.004514872966836503, 0.9395030253688605
+DailyLoadSliding, fatigue, 4, 0.005761692732279999, 0.9389144892249708
+DailyLoadSliding, fatigue, 5, 0.006289206491891797, 0.9386653758974035
+DailyLoadSliding, fatigueSliding, 1, 0.0031444511918343743, 0.4371251031238283
+DailyLoadSliding, fatigueSliding, 2, 0.040056224206406754, 0.42895578674747
+DailyLoadSliding, fatigueSliding, 3, 0.041810676926411716, 0.4285636143895305
+DailyLoadSliding, fatigueSliding, 4, 0.043351953300678536, 0.4282187969331779
+DailyLoadSliding, fatigueSliding, 5, 0.04337784655749255, 0.4282130016700456
+DailyLoadSliding, sleepQuality, 1, 0.00017278590061819976, 1.068876290056848
+DailyLoadSliding, sleepQuality, 2, 0.0002887095865301559, 1.0688143235143401
+DailyLoadSliding, sleepQuality, 3, 0.0013166575313107165, 1.0682646807962595
+DailyLoadSliding, sleepQuality, 4, 0.002606171316667516, 1.0675747789342973
+DailyLoadSliding, sleepQuality, 5, 0.002606606685698787, 1.0675745459325308
+acuteChronicRatio, acuteChronicRatioSliding, 1, 0.37520110691495423, 0.38005697702592245
+acuteChronicRatio, acuteChronicRatioSliding, 2, 0.3919766525034446, 0.3749200894394375
+acuteChronicRatio, acuteChronicRatioSliding, 3, 0.42089904347745466, 0.36589436830664285
+acuteChronicRatio, acuteChronicRatioSliding, 4, 0.42715615530381823, 0.36391227876525745
+acuteChronicRatio, acuteChronicRatioSliding, 5, 0.433607926138452, 0.36185715763038595
+acuteChronicRatio, trainDuration, 1, 0.22113326675165246, 73.9792160693996
+acuteChronicRatio, trainDuration, 2, 0.34041767441966975, 68.07891919389633
+acuteChronicRatio, trainDuration, 3, 0.34899854536706243, 67.63463132727438
+acuteChronicRatio, trainDuration, 4, 0.3497975823151518, 67.59311132596187
+acuteChronicRatio, trainDuration, 5, 0.3516571423752912, 67.49638494861411
+acuteChronicRatio, trainDurationSliding, 1, 0.09159271454915885, 56.96092407785147
+acuteChronicRatio, trainDurationSliding, 2, 0.13406997715870006, 55.61323014707973
+acuteChronicRatio, trainDurationSliding, 3, 0.1357524961500961, 55.55917507918973
+acuteChronicRatio, trainDurationSliding, 4, 0.13751418393898918, 55.502520099272445
+acuteChronicRatio, trainDurationSliding, 5, 0.14192152730433005, 55.360528217545465
+acuteChronicRatio, sleepHours, 1, 0.0004371335349462324, 1.29253910771816
+acuteChronicRatio, sleepHours, 2, 0.0004480587992282681, 1.2925320439453813
+acuteChronicRatio, sleepHours, 3, 0.00170135068616728, 1.291721466744873
+acuteChronicRatio, sleepHours, 4, 0.00174406713693398, 1.2916938305526662
+acuteChronicRatio, sleepHours, 5, 0.0017547332588363496, 1.2916869298169902
+acuteChronicRatio, sleepHoursSliding, 1, 4.988097426195104e-05, 0.5907493381403719
+acuteChronicRatio, sleepHoursSliding, 2, 0.0011203311538661165, 0.5904330538319326
+acuteChronicRatio, sleepHoursSliding, 3, 0.0012469798771210794, 0.5903956219143578
+acuteChronicRatio, sleepHoursSliding, 4, 0.0012796967200993103, 0.5903859518364424
+acuteChronicRatio, sleepHoursSliding, 5, 0.0014865915228131632, 0.5903247965203268
+acuteChronicRatio, fatigue, 1, 0.004893031104526879, 0.939324562400607
+acuteChronicRatio, fatigue, 2, 0.010946789100278687, 0.9364630014979419
+acuteChronicRatio, fatigue, 3, 0.011919998647313523, 0.9360021572138951
+acuteChronicRatio, fatigue, 4, 0.015834048937934386, 0.9341464396606501
+acuteChronicRatio, fatigue, 5, 0.01805099866704296, 0.9330937090325188
+acuteChronicRatio, fatigueSliding, 1, 0.02008509507856293, 0.4333949177552429
+acuteChronicRatio, fatigueSliding, 2, 0.02116688889005336, 0.4331556248298324
+acuteChronicRatio, fatigueSliding, 3, 0.02185790639711782, 0.433002702456248
+acuteChronicRatio, fatigueSliding, 4, 0.0232514267263062, 0.43269415161533553
+acuteChronicRatio, fatigueSliding, 5, 0.023741353913992125, 0.4325856205029611
+acuteChronicRatio, sleepQuality, 1, 0.00044427477425224016, 1.0687311611197015
+acuteChronicRatio, sleepQuality, 2, 0.007018803836871701, 1.0652105989271177
+acuteChronicRatio, sleepQuality, 3, 0.012411362187533026, 1.0623142549096565
+acuteChronicRatio, sleepQuality, 4, 0.012491735224850364, 1.0622710268098152
+acuteChronicRatio, sleepQuality, 5, 0.012525428700191243, 1.0622529044764493
+acuteChronicRatioSliding, trainDuration, 1, 0.08472982689094688, 80.19601759379427
+acuteChronicRatioSliding, trainDuration, 2, 0.11944515853293747, 78.66043652504078
+acuteChronicRatioSliding, trainDuration, 3, 0.12779082267802078, 78.28678775309369
+acuteChronicRatioSliding, trainDuration, 4, 0.1296156056879646, 78.20485144638917
+acuteChronicRatioSliding, trainDuration, 5, 0.13350325735215807, 78.030001413894
+acuteChronicRatioSliding, trainDurationSliding, 1, 0.2132302943586568, 53.01033350630277
+acuteChronicRatioSliding, trainDurationSliding, 2, 0.2958150797875322, 50.15105601500827
+acuteChronicRatioSliding, trainDurationSliding, 3, 0.31284801821042973, 49.54081234260314
+acuteChronicRatioSliding, trainDurationSliding, 4, 0.3165157142664864, 49.40842259887278
+acuteChronicRatioSliding, trainDurationSliding, 5, 0.32478691124751224, 49.108554178680855
+acuteChronicRatioSliding, sleepHours, 1, 4.716339255028679e-06, 1.292818657769708
+acuteChronicRatioSliding, sleepHours, 2, 7.798162887573401e-05, 1.2927712973121606
+acuteChronicRatioSliding, sleepHours, 3, 9.153242593895605e-05, 1.2927625375586356
+acuteChronicRatioSliding, sleepHours, 4, 9.154178072812158e-05, 1.2927625315113216
+acuteChronicRatioSliding, sleepHours, 5, 0.00011170735259469211, 1.292749495604404
+acuteChronicRatioSliding, sleepHoursSliding, 1, 2.757158893418321e-07, 0.5907639908263278
+acuteChronicRatioSliding, sleepHoursSliding, 2, 0.0002080406938196333, 0.5907026175876802
+acuteChronicRatioSliding, sleepHoursSliding, 3, 0.0007073638712464803, 0.5905550927245955
+acuteChronicRatioSliding, sleepHoursSliding, 4, 0.001015153162757021, 0.5904641381205485
+acuteChronicRatioSliding, sleepHoursSliding, 5, 0.001522107932988237, 0.5903142977108599
+acuteChronicRatioSliding, fatigue, 1, 0.009608057456754748, 0.9370965613000907
+acuteChronicRatioSliding, fatigue, 2, 0.011652157227383886, 0.9361290108893663
+acuteChronicRatioSliding, fatigue, 3, 0.012957943289204854, 0.9355104087251802
+acuteChronicRatioSliding, fatigue, 4, 0.01296186139829325, 0.9355085519474231
+acuteChronicRatioSliding, fatigue, 5, 0.012964212782013429, 0.9355074376333945
+acuteChronicRatioSliding, fatigueSliding, 1, 0.04979925392305595, 0.4267733736440009
+acuteChronicRatioSliding, fatigueSliding, 2, 0.056441685942858255, 0.4252790658586212
+acuteChronicRatioSliding, fatigueSliding, 3, 0.06436776057373172, 0.42348908516264516
+acuteChronicRatioSliding, fatigueSliding, 4, 0.06438365169235993, 0.4234854888009649
+acuteChronicRatioSliding, fatigueSliding, 5, 0.0644049888883449, 0.42348065987487477
+acuteChronicRatioSliding, sleepQuality, 1, 0.003403889128223425, 1.0671477691902347
+acuteChronicRatioSliding, sleepQuality, 2, 0.0044006280486575955, 1.0666139853474554
+acuteChronicRatioSliding, sleepQuality, 3, 0.005869149248898475, 1.0658270607495428
+acuteChronicRatioSliding, sleepQuality, 4, 0.005920039617029693, 1.0657997801226444
+acuteChronicRatioSliding, sleepQuality, 5, 0.005920176191761994, 1.0657997069085519
+trainDuration, trainDurationSliding, 1, 0.45394694443778993, 44.162550063090855
+trainDuration, trainDurationSliding, 2, 0.45738382576032555, 44.02335019394116
+trainDuration, trainDurationSliding, 3, 0.46224164581729865, 43.82584565667056
+trainDuration, trainDurationSliding, 4, 0.46432567775193345, 43.740841748677006
+trainDuration, trainDurationSliding, 5, 0.4644661389448539, 43.735106646804276
+trainDuration, sleepHours, 1, 0.007687351959238975, 1.287842931869013
+trainDuration, sleepHours, 2, 0.009384984863289314, 1.2867408495733121
+trainDuration, sleepHours, 3, 0.01070326127234933, 1.2858843892964815
+trainDuration, sleepHours, 4, 0.010705444753631665, 1.2858829702550885
+trainDuration, sleepHours, 5, 0.010734487352849542, 1.2858640953620455
+trainDuration, sleepHoursSliding, 1, 0.02166226270604099, 0.5843303962465156
+trainDuration, sleepHoursSliding, 2, 0.022807944497682087, 0.5839881561507351
+trainDuration, sleepHoursSliding, 3, 0.025703525684388162, 0.5831222876477187
+trainDuration, sleepHoursSliding, 4, 0.026187806607147923, 0.5829773471150784
+trainDuration, sleepHoursSliding, 5, 0.026225649195113165, 0.5829660196815027
+trainDuration, fatigue, 1, 5.0822291287611066e-05, 0.9416071742612147
+trainDuration, fatigue, 2, 0.012471153985138428, 0.9357410677550868
+trainDuration, fatigue, 3, 0.013896304669714699, 0.935065617355964
+trainDuration, fatigue, 4, 0.013898065157994188, 0.9350647826705234
+trainDuration, fatigue, 5, 0.014919170525778735, 0.9345805289964783
+trainDuration, fatigueSliding, 1, 0.005665886504954143, 0.43657192341912243
+trainDuration, fatigueSliding, 2, 0.011896458365940221, 0.43520197785299036
+trainDuration, fatigueSliding, 3, 0.016294548449602764, 0.4342323466032117
+trainDuration, fatigueSliding, 4, 0.016771644907895844, 0.4341270326396567
+trainDuration, fatigueSliding, 5, 0.016805465371369177, 0.43411956616270087
+trainDuration, sleepQuality, 1, 0.0041583348252624, 1.066743765270217
+trainDuration, sleepQuality, 2, 0.004247382147251955, 1.0666960705399904
+trainDuration, sleepQuality, 3, 0.004450505999569088, 1.06658726717756
+trainDuration, sleepQuality, 4, 0.004716382490588589, 1.066444833567875
+trainDuration, sleepQuality, 5, 0.004719330731073557, 1.0664432540491795
+trainDurationSliding, sleepHours, 1, 0.007804523215349191, 1.2877668960404685
+trainDurationSliding, sleepHours, 2, 0.00916647348116828, 1.2868827573850223
+trainDurationSliding, sleepHours, 3, 0.009487687710069581, 1.2866741458732274
+trainDurationSliding, sleepHours, 4, 0.009714017706561662, 1.2865271362961643
+trainDurationSliding, sleepHours, 5, 0.009806215661519069, 1.286467245550435
+trainDurationSliding, sleepHoursSliding, 1, 0.048956741588463526, 0.5761216703941785
+trainDurationSliding, sleepHoursSliding, 2, 0.051188886592317795, 0.5754451803648056
+trainDurationSliding, sleepHoursSliding, 3, 0.05167433851248293, 0.5752979504763178
+trainDurationSliding, sleepHoursSliding, 4, 0.05318489254618697, 0.5748395821077057
+trainDurationSliding, sleepHoursSliding, 5, 0.0532190995766324, 0.5748291979616678
+trainDurationSliding, fatigue, 1, 0.0025233865965269553, 0.9404423024146341
+trainDurationSliding, fatigue, 2, 0.003883630075676048, 0.9398008503211578
+trainDurationSliding, fatigue, 3, 0.0044594442297529735, 0.9395291808280916
+trainDurationSliding, fatigue, 4, 0.004504766545038508, 0.939507794395287
+trainDurationSliding, fatigue, 5, 0.004923046287155786, 0.9393103959784157
+trainDurationSliding, fatigueSliding, 1, 0.00991021454400054, 0.4356391705335892
+trainDurationSliding, fatigueSliding, 2, 0.040044828437925784, 0.42895833286863366
+trainDurationSliding, fatigueSliding, 3, 0.040117745686474304, 0.4289420409331901
+trainDurationSliding, fatigueSliding, 4, 0.040243365876666326, 0.4289139721030889
+trainDurationSliding, fatigueSliding, 5, 0.040450939275927356, 0.42886758745598974
+trainDurationSliding, sleepQuality, 1, 0.00012726177245514503, 1.0689006238150478
+trainDurationSliding, sleepQuality, 2, 0.0002450371142670438, 1.0688376688814356
+trainDurationSliding, sleepQuality, 3, 0.0024564971016752812, 1.067654878899909
+trainDurationSliding, sleepQuality, 4, 0.002649294363831989, 1.0675516999990553
+trainDurationSliding, sleepQuality, 5, 0.002649858449036757, 1.0675513981041447
+sleepHours, sleepHoursSliding, 1, 0.19090430417238557, 0.5313907874823055
+sleepHours, sleepHoursSliding, 2, 0.1977224371740881, 0.5291470737687348
+sleepHours, sleepHoursSliding, 3, 0.1978044149438739, 0.5291200386083517
+sleepHours, sleepHoursSliding, 4, 0.1987243901097383, 0.528816548445787
+sleepHours, sleepHoursSliding, 5, 0.19873706062111363, 0.5288123673609653
+sleepHours, fatigue, 1, 0.03203573967501905, 0.9264254053342954
+sleepHours, fatigue, 2, 0.037771369855744474, 0.9236765805238569
+sleepHours, fatigue, 3, 0.03911720169575139, 0.9230303992053912
+sleepHours, fatigue, 4, 0.04006915712610326, 0.9225730585493002
+sleepHours, fatigue, 5, 0.04007013211314514, 0.9225725900275248
+sleepHours, fatigueSliding, 1, 0.0189556237185875, 0.4336446160564812
+sleepHours, fatigueSliding, 2, 0.028715399034488276, 0.4314821997341685
+sleepHours, fatigueSliding, 3, 0.03004088251197934, 0.43118768369802546
+sleepHours, fatigueSliding, 4, 0.0340948504437244, 0.43028566045197714
+sleepHours, fatigueSliding, 5, 0.03454308731924838, 0.4301858099226297
+sleepHours, sleepQuality, 1, 0.07997357864480814, 1.0253334282985886
+sleepHours, sleepQuality, 2, 0.09639195037475567, 1.0161434273261016
+sleepHours, sleepQuality, 3, 0.09659073389984008, 1.0160316511623104
+sleepHours, sleepQuality, 4, 0.0973064528143871, 1.0156290998585202
+sleepHours, sleepQuality, 5, 0.09776438666528686, 1.0153714543120185
+sleepHoursSliding, fatigue, 1, 0.004047775126863384, 0.939723414582248
+sleepHoursSliding, fatigue, 2, 0.00902832364652506, 0.9373707896427349
+sleepHoursSliding, fatigue, 3, 0.011772463594767024, 0.936072034133175
+sleepHoursSliding, fatigue, 4, 0.017159865654256867, 0.9335170110780281
+sleepHoursSliding, fatigue, 5, 0.017166865254200858, 0.9335136869070749
+sleepHoursSliding, fatigueSliding, 1, 0.062314725796211556, 0.42395345639697396
+sleepHoursSliding, fatigueSliding, 2, 0.09206524192923216, 0.41717373107824246
+sleepHoursSliding, fatigueSliding, 3, 0.0922890645380352, 0.4171223074119337
+sleepHoursSliding, fatigueSliding, 4, 0.10188034041297156, 0.4149127054567317
+sleepHoursSliding, fatigueSliding, 5, 0.10232632145484599, 0.41480967565651927
+sleepHoursSliding, sleepQuality, 1, 0.008198745203181645, 1.06457752568261
+sleepHoursSliding, sleepQuality, 2, 0.012899517625744594, 1.0620516766669617
+sleepHoursSliding, sleepQuality, 3, 0.015394514350024369, 1.0607086057027848
+sleepHoursSliding, sleepQuality, 4, 0.01745548558683485, 1.059597889209822
+sleepHoursSliding, sleepQuality, 5, 0.01754014126098913, 1.0595522409442413
+fatigue, fatigueSliding, 1, 0.1994483666648209, 0.39172772146218965
+fatigue, fatigueSliding, 2, 0.19946054690479986, 0.39172474141967645
+fatigue, fatigueSliding, 3, 0.19971087780272023, 0.3916634899266688
+fatigue, fatigueSliding, 4, 0.19981409232912795, 0.3916382323891007
+fatigue, fatigueSliding, 5, 0.19986155989113208, 0.39162661609618454
+fatigue, sleepQuality, 1, 0.25578559709537607, 0.9221763967672918
+fatigue, sleepQuality, 2, 0.26067617276132127, 0.9191413794713956
+fatigue, sleepQuality, 3, 0.2662113702501244, 0.9156941840439216
+fatigue, sleepQuality, 4, 0.266384464931003, 0.9155861753241339
+fatigue, sleepQuality, 5, 0.2714456698874349, 0.9124224000352491
+fatigueSliding, sleepQuality, 1, 0.04482209841450291, 1.0447372987987482
+fatigueSliding, sleepQuality, 2, 0.04487013244779947, 1.0447110295681525
+fatigueSliding, sleepQuality, 3, 0.04594568977367108, 1.044122647219293
+fatigueSliding, sleepQuality, 4, 0.04601441042137844, 1.044085042401637
+fatigueSliding, sleepQuality, 5, 0.0462126806686175, 1.0439765387938076

personal_regression_info_fatigue.csv

@@ -0,0 +1,96 @@
+xVal, yVal, degree, r2, rmse
+DailyLoad, fatigue, 1, 0.0004609869345519879, 0.9414140376537236
+DailyLoad, fatigue, 2, 0.01081491590759509, 0.9365254300145728
+DailyLoad, fatigue, 3, 0.013075886827278227, 0.9354545140938101
+DailyLoad, fatigue, 4, 0.013871725612309982, 0.9350772707390621
+DailyLoad, fatigue, 5, 0.013872676932596972, 0.9350768197033221
+DailyLoad, fatigueSliding, 1, 0.0026508263511236807, 0.4372333179520449
+DailyLoad, fatigueSliding, 2, 0.007885710862688966, 0.4360843335533719
+DailyLoad, fatigueSliding, 3, 0.010453728094075743, 0.43551958123003714
+DailyLoad, fatigueSliding, 4, 0.010618419541746138, 0.43548333768237124
+DailyLoad, fatigueSliding, 5, 0.011054975220036178, 0.4353872505388315
+DailyLoadSliding, fatigue, 1, 0.001007476257176232, 0.941156647470284
+DailyLoadSliding, fatigue, 2, 0.0041354732881003775, 0.9396820401931253
+DailyLoadSliding, fatigue, 3, 0.004514872966836503, 0.9395030253688605
+DailyLoadSliding, fatigue, 4, 0.005761692732279999, 0.9389144892249708
+DailyLoadSliding, fatigue, 5, 0.006289206491891797, 0.9386653758974035
+DailyLoadSliding, fatigueSliding, 1, 0.0031444511918343743, 0.4371251031238283
+DailyLoadSliding, fatigueSliding, 2, 0.040056224206406754, 0.42895578674747
+DailyLoadSliding, fatigueSliding, 3, 0.041810676926411716, 0.4285636143895305
+DailyLoadSliding, fatigueSliding, 4, 0.043351953300678536, 0.4282187969331779
+DailyLoadSliding, fatigueSliding, 5, 0.04337784655749255, 0.4282130016700456
+acuteChronicRatio, fatigue, 1, 0.004893031104526879, 0.939324562400607
+acuteChronicRatio, fatigue, 2, 0.010946789100278687, 0.9364630014979419
+acuteChronicRatio, fatigue, 3, 0.011919998647313523, 0.9360021572138951
+acuteChronicRatio, fatigue, 4, 0.015834048937934386, 0.9341464396606501
+acuteChronicRatio, fatigue, 5, 0.01805099866704296, 0.9330937090325188
+acuteChronicRatio, fatigueSliding, 1, 0.02008509507856293, 0.4333949177552429
+acuteChronicRatio, fatigueSliding, 2, 0.02116688889005336, 0.4331556248298324
+acuteChronicRatio, fatigueSliding, 3, 0.02185790639711782, 0.433002702456248
+acuteChronicRatio, fatigueSliding, 4, 0.0232514267263062, 0.43269415161533553
+acuteChronicRatio, fatigueSliding, 5, 0.023741353913992125, 0.4325856205029611
+acuteChronicRatioSliding, fatigue, 1, 0.009608057456754748, 0.9370965613000907
+acuteChronicRatioSliding, fatigue, 2, 0.011652157227383886, 0.9361290108893663
+acuteChronicRatioSliding, fatigue, 3, 0.012957943289204854, 0.9355104087251802
+acuteChronicRatioSliding, fatigue, 4, 0.01296186139829325, 0.9355085519474231
+acuteChronicRatioSliding, fatigue, 5, 0.012964212782013429, 0.9355074376333945
+acuteChronicRatioSliding, fatigueSliding, 1, 0.04979925392305595, 0.4267733736440009
+acuteChronicRatioSliding, fatigueSliding, 2, 0.056441685942858255, 0.4252790658586212
+acuteChronicRatioSliding, fatigueSliding, 3, 0.06436776057373172, 0.42348908516264516
+acuteChronicRatioSliding, fatigueSliding, 4, 0.06438365169235993, 0.4234854888009649
+acuteChronicRatioSliding, fatigueSliding, 5, 0.0644049888883449, 0.42348065987487477
+trainDuration, fatigue, 1, 5.0822291287611066e-05, 0.9416071742612147
+trainDuration, fatigue, 2, 0.012471153985138428, 0.9357410677550868
+trainDuration, fatigue, 3, 0.013896304669714699, 0.935065617355964
+trainDuration, fatigue, 4, 0.013898065157994188, 0.9350647826705234
+trainDuration, fatigue, 5, 0.014919170525778735, 0.9345805289964783
+trainDuration, fatigueSliding, 1, 0.005665886504954143, 0.43657192341912243
+trainDuration, fatigueSliding, 2, 0.011896458365940221, 0.43520197785299036
+trainDuration, fatigueSliding, 3, 0.016294548449602764, 0.4342323466032117
+trainDuration, fatigueSliding, 4, 0.016771644907895844, 0.4341270326396567
+trainDuration, fatigueSliding, 5, 0.016805465371369177, 0.43411956616270087
+trainDurationSliding, fatigue, 1, 0.0025233865965269553, 0.9404423024146341
+trainDurationSliding, fatigue, 2, 0.003883630075676048, 0.9398008503211578
+trainDurationSliding, fatigue, 3, 0.0044594442297529735, 0.9395291808280916
+trainDurationSliding, fatigue, 4, 0.004504766545038508, 0.939507794395287
+trainDurationSliding, fatigue, 5, 0.004923046287155786, 0.9393103959784157
+trainDurationSliding, fatigueSliding, 1, 0.00991021454400054, 0.4356391705335892
+trainDurationSliding, fatigueSliding, 2, 0.040044828437925784, 0.42895833286863366
+trainDurationSliding, fatigueSliding, 3, 0.040117745686474304, 0.4289420409331901
+trainDurationSliding, fatigueSliding, 4, 0.040243365876666326, 0.4289139721030889
+trainDurationSliding, fatigueSliding, 5, 0.040450939275927356, 0.42886758745598974
+sleepHours, fatigue, 1, 0.03203573967501905, 0.9264254053342954
+sleepHours, fatigue, 2, 0.037771369855744474, 0.9236765805238569
+sleepHours, fatigue, 3, 0.03911720169575139, 0.9230303992053912
+sleepHours, fatigue, 4, 0.04006915712610326, 0.9225730585493002
+sleepHours, fatigue, 5, 0.04007013211314514, 0.9225725900275248
+sleepHours, fatigueSliding, 1, 0.0189556237185875, 0.4336446160564812
+sleepHours, fatigueSliding, 2, 0.028715399034488276, 0.4314821997341685
+sleepHours, fatigueSliding, 3, 0.03004088251197934, 0.43118768369802546
+sleepHours, fatigueSliding, 4, 0.0340948504437244, 0.43028566045197714
+sleepHours, fatigueSliding, 5, 0.03454308731924838, 0.4301858099226297
+sleepHoursSliding, fatigue, 1, 0.004047775126863384, 0.939723414582248
+sleepHoursSliding, fatigue, 2, 0.00902832364652506, 0.9373707896427349
+sleepHoursSliding, fatigue, 3, 0.011772463594767024, 0.936072034133175
+sleepHoursSliding, fatigue, 4, 0.017159865654256867, 0.9335170110780281
+sleepHoursSliding, fatigue, 5, 0.017166865254200858, 0.9335136869070749
+sleepHoursSliding, fatigueSliding, 1, 0.062314725796211556, 0.42395345639697396
+sleepHoursSliding, fatigueSliding, 2, 0.09206524192923216, 0.41717373107824246
+sleepHoursSliding, fatigueSliding, 3, 0.0922890645380352, 0.4171223074119337
+sleepHoursSliding, fatigueSliding, 4, 0.10188034041297156, 0.4149127054567317
+sleepHoursSliding, fatigueSliding, 5, 0.10232632145484599, 0.41480967565651927
+fatigue, fatigueSliding, 1, 0.1994483666648209, 0.39172772146218965
+fatigue, fatigueSliding, 2, 0.19946054690479986, 0.39172474141967645
+fatigue, fatigueSliding, 3, 0.19971087780272023, 0.3916634899266688
+fatigue, fatigueSliding, 4, 0.19981409232912795, 0.3916382323891007
+fatigue, fatigueSliding, 5, 0.19986155989113208, 0.39162661609618454
+fatigue, sleepQuality, 1, 0.25578559709537607, 0.9221763967672918
+fatigue, sleepQuality, 2, 0.26067617276132127, 0.9191413794713956
+fatigue, sleepQuality, 3, 0.2662113702501244, 0.9156941840439216
+fatigue, sleepQuality, 4, 0.266384464931003, 0.9155861753241339
+fatigue, sleepQuality, 5, 0.2714456698874349, 0.9124224000352491
+fatigueSliding, sleepQuality, 1, 0.04482209841450291, 1.0447372987987482
+fatigueSliding, sleepQuality, 2, 0.04487013244779947, 1.0447110295681525
+fatigueSliding, sleepQuality, 3, 0.04594568977367108, 1.044122647219293
+fatigueSliding, sleepQuality, 4, 0.04601441042137844, 1.044085042401637
+fatigueSliding, sleepQuality, 5, 0.0462126806686175, 1.0439765387938076

run_all_regressions.py

@@ -48,9 +48,14 @@ def standard_lr(x, y):
 
 
 def poly_regression(x, y, degree):
+
+  # Reshapes the models to be able to run regression on them
+  x = x.reshape(-1, 1)
+  y = y.reshape(-1, 1)
+
   # Polynomial regression with nth degree, gives back rmse and r2
   polynomial_features = PolynomialFeatures(degree=degree)
-  x_poly = polynomial_features.fist_transform(x)
+  x_poly = polynomial_features.fit_transform(x)
 
   model = linear_model.LinearRegression()
   model.fit(x_poly, y)
@@ -61,42 +66,25 @@ def poly_regression(x, y, degree):
   return rmse, r2
 
 
-def run_all_linears():
-
-    # Reads in the neccessary csv file
-    df = pd.read_csv('data_preparation/cleaned/time_series_normalized_wellness_menstruation.csv')
-    regr = linear_model.LinearRegression()
-    for i in range(4, 11):
-        for j in range(1, 11 - i):
-            mat = df[[df.columns[i], df.columns[i + j]]].values
-            regr.intercept_, regr.coef_, r2, mse = standard_lr(mat[:, 0], mat[:, 1])
-            plt.figure(figsize=(6, 6))
-            plt.xlabel(df.columns[i])
-            plt.ylabel(df.columns[i + j])
-            plt.title('r2: ' + str(r2))
-            plt.scatter(mat[:, 0], mat[:, 1])
-            plt.savefig('wellness_linear_regressions/' + df.columns[i] + '_vs_' + df.columns[i + j] + '.png')
-            plt.close()
-
-
 def run_all_polynomials():
     # Reads in the neccessary csv file
-    df = pd.read_csv('data_preparation/cleaned/time_series_normalized_wellness_menstruation.csv')
+    df = pd.read_csv('data_preparation/cleaned/personal.csv')
     regr = linear_model.LinearRegression()
-    for i in range(4, 11):
-        for j in range(1, 11 - i):
+    print("xVal, yVal, degree, r2, rmse")
+    for i in range(3, 14):
+        for j in range(1, 14 - i):
             mat = df[[df.columns[i], df.columns[i + j]]].values
-            for d in range(2, 5):
+            for d in range(1, 6):
                 rmse, r2 = poly_regression(mat[:, 0], mat[:, 1], d)
                 plt.figure(figsize=(6, 6))
                 plt.xlabel(df.columns[i])
                 plt.ylabel(df.columns[i + j])
                 plt.title('r2: ' + str(r2) + 'degree: ' + str(d))
                 plt.scatter(mat[:, 0], mat[:, 1])
-                plt.savefig('wellness_poly_regressions/' + df.columns[i] + '_vs_' + df.columns[i + j] + '_' + str(d) + '_degree.png')
-                print(df.columns[i] + '_vs_' + df.columns[i + j] + '_degree_' + str(d) + '_r2=' + str(r2) + '_rmse=' + str(rmse))
+                plt.savefig('personal_regression_info/' + df.columns[i] + '_vs_' + df.columns[i + j] + '_' + str(d) + '_degree.png')
+                print(df.columns[i] + ', ' + df.columns[i + j] + ', ' + str(d) + ', ' + str(r2) + ', ' + str(rmse))
                 plt.close()
 
 
-run_all_linears()
+# run_all_linears()
 run_all_polynomials()