Jeffery Russell 5 years ago
parent
commit
264808cbd2
65 changed files with 6493 additions and 67 deletions
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      hypotheses_modeling/KerasRegressions.py
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      personal_regression_info.csv
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      run_all_regressions.py

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hypotheses_modeling/KerasRegressions.py View File

@ -1,34 +1,42 @@
import tensorflow as tf import tensorflow as tf
import pandas as pd import pandas as pd
import numpy as np 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" col = "day"
x = [] x = []
y = [] y = []
input_shape = 0 input_shape = 0
output_shape = 0 output_shape = 0
for player in Y["playerID"].unique():
for player in out["playerID"].unique():
XPlayer = inp[inp["playerID"] == player] XPlayer = inp[inp["playerID"] == player]
YPlayer = out[out["playerID"] == player] YPlayer = out[out["playerID"] == player]
for day in YPlayer[col][n0 - 1:]: for day in YPlayer[col][n0 - 1:]:
prev = day - k 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: if xprev.shape[0] != 1:
continue continue
else: else:
xprev = xprev[0, :] 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: if input_shape == 0:
input_shape = xprev.shape[0] input_shape = xprev.shape[0]
else: else:
if input_shape != xprev.shape[0]: if input_shape != xprev.shape[0]:
print("INCONSISTENT INPUT DIMENSION") print("INCONSISTENT INPUT DIMENSION")
exit(2) exit(2)
if input_shape == 0:
if output_shape == 0:
output_shape = yt.shape[0] output_shape = yt.shape[0]
else: else:
if output_shape != yt.shape[0]: 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) x = np.array(x)
y = np.array(y) y = np.array(y)
model = tf.keras.Sequential([ 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) tf.keras.layers.Dense(output_shape, activation=tf.nn.softmax)
]) ])
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy', 'categorical_accuracy']) 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) loss, accuracy = model.evaluate(x, y)
print(loss, accuracy) print(loss, accuracy)
return model.get_weights() 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" col = "day"
x = [] x = []
y = [] y = []
input_shape = 0 input_shape = 0
output_shape = 0 output_shape = 0
for player in Y["playerID"].unique():
for player in out["playerID"].unique():
XPlayer = inp[inp["playerID"] == player] XPlayer = inp[inp["playerID"] == player]
YPlayer = out[out["playerID"] == player] YPlayer = out[out["playerID"] == player]
for day in YPlayer[col][n0 - 1:]: for day in YPlayer[col][n0 - 1:]:
prev = day - k 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: if xprev.shape[0] != 1:
continue continue
else: else:
xprev = xprev[0, :] 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: if input_shape == 0:
input_shape = xprev.shape[0] input_shape = xprev.shape[0]
else: else:
if input_shape != xprev.shape[0]: if input_shape != xprev.shape[0]:
print("INCONSISTENT INPUT DIMENSION") print("INCONSISTENT INPUT DIMENSION")
exit(2) exit(2)
if input_shape == 0:
if output_shape == 0:
output_shape = yt.shape[0] output_shape = yt.shape[0]
else: else:
if output_shape != yt.shape[0]: 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) x = np.array(x)
y = np.array(y) y = np.array(y)
model = tf.keras.Sequential([ 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) tf.keras.layers.Dense(output_shape, activation=tf.nn.softmax)
]) ])
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy', 'categorical_accuracy']) 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) loss, accuracy = model.evaluate(x, y)
print(x.shape)
print(y.shape)
print(loss, accuracy) print(loss, accuracy)
return model.get_weights() 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" col = "day"
x = [] x = []
y = [] y = []
input_shape = 0 input_shape = 0
output_shape = 0 output_shape = 0
for player in Y["playerID"].unique():
for player in out["playerID"].unique():
XPlayer = inp[inp["playerID"] == player] XPlayer = inp[inp["playerID"] == player]
YPlayer = out[out["playerID"] == player] YPlayer = out[out["playerID"] == player]
for day in YPlayer[col][n0 - 1:]: for day in YPlayer[col][n0 - 1:]:
prev = day - k 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: if xprev.shape[0] != 1:
continue continue
else: else:
xprev = xprev[0, :] 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: if input_shape == 0:
input_shape = xprev.shape[0] input_shape = xprev.shape[0]
else: else:
if input_shape != xprev.shape[0]: if input_shape != xprev.shape[0]:
print("INCONSISTENT INPUT DIMENSION") print("INCONSISTENT INPUT DIMENSION")
exit(2) exit(2)
if input_shape == 0:
if output_shape == 0:
output_shape = yt.shape[0] output_shape = yt.shape[0]
else: else:
if output_shape != yt.shape[0]: 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) x = np.array(x)
y = np.array(y) y = np.array(y)
model = tf.keras.Sequential([ 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) 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() 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" col = "day"
x = [] x = []
y = [] y = []
input_shape = 0 input_shape = 0
output_shape = 0 output_shape = 0
for player in Y["playerID"].unique():
for player in out["playerID"].unique():
XPlayer = inp[inp["playerID"] == player] XPlayer = inp[inp["playerID"] == player]
YPlayer = out[out["playerID"] == player] YPlayer = out[out["playerID"] == player]
for day in YPlayer[col][n0 - 1:]: for day in YPlayer[col][n0 - 1:]:
prev = day - k 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: if xprev.shape[0] != 1:
continue continue
else: else:
xprev = xprev[0, :] 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: if input_shape == 0:
input_shape = xprev.shape[0] input_shape = xprev.shape[0]
else: else:
if input_shape != xprev.shape[0]: if input_shape != xprev.shape[0]:
print("INCONSISTENT INPUT DIMENSION") print("INCONSISTENT INPUT DIMENSION")
exit(2) exit(2)
if input_shape == 0:
if output_shape == 0:
output_shape = yt.shape[0] output_shape = yt.shape[0]
else: else:
if output_shape != yt.shape[0]: 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) x = np.array(x)
y = np.array(y) y = np.array(y)
model = tf.keras.Sequential([ 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(32, activation=tf.nn.softmax),
tf.keras.layers.Dense(output_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)
model.compile(optimizer='adam', loss='mean_squared_error', metrics=['accuracy'])
model.fit(x, y, epochs=50)
loss, accuracy = model.evaluate(x, y) loss, accuracy = model.evaluate(x, y)
print(loss, accuracy) print(loss, accuracy)
pred = model.predict(x)
r2 = r2_(y, pred)
print(r2)
return model.get_weights() 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()

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hypotheses_modeling/personal.csv
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personal_regression_info.csv View File

@ -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

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personal_regression_info_fatigue.csv View File

@ -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

+ 14
- 26
run_all_regressions.py View File

@ -48,9 +48,14 @@ def standard_lr(x, y):
def poly_regression(x, y, degree): 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 regression with nth degree, gives back rmse and r2
polynomial_features = PolynomialFeatures(degree=degree) polynomial_features = PolynomialFeatures(degree=degree)
x_poly = polynomial_features.fist_transform(x)
x_poly = polynomial_features.fit_transform(x)
model = linear_model.LinearRegression() model = linear_model.LinearRegression()
model.fit(x_poly, y) model.fit(x_poly, y)
@ -61,42 +66,25 @@ def poly_regression(x, y, degree):
return rmse, r2 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(): def run_all_polynomials():
# Reads in the neccessary csv file # 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() 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 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) rmse, r2 = poly_regression(mat[:, 0], mat[:, 1], d)
plt.figure(figsize=(6, 6)) plt.figure(figsize=(6, 6))
plt.xlabel(df.columns[i]) plt.xlabel(df.columns[i])
plt.ylabel(df.columns[i + j]) plt.ylabel(df.columns[i + j])
plt.title('r2: ' + str(r2) + 'degree: ' + str(d)) plt.title('r2: ' + str(r2) + 'degree: ' + str(d))
plt.scatter(mat[:, 0], mat[:, 1]) 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() plt.close()
run_all_linears()
# run_all_linears()
run_all_polynomials() run_all_polynomials()

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