From af1f530f70f1ce9b4965356aa21c868320c201aa Mon Sep 17 00:00:00 2001 From: nglod33 Date: Sat, 30 Mar 2019 22:33:03 -0400 Subject: [PATCH 1/7] Ran regressions on personal csv file --- personal_regression_info.csv | 276 +++++++++++++++++++++++++++++++++++ 1 file changed, 276 insertions(+) create mode 100644 personal_regression_info.csv diff --git a/personal_regression_info.csv b/personal_regression_info.csv new file mode 100644 index 0000000..6f13391 --- /dev/null +++ b/personal_regression_info.csv @@ -0,0 +1,276 @@ +xVal, yVal, degree, r2, rmse +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, trainDuration, 1, 0.8603505230540016, 31.325485038659917 +DailyLoad, 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sleepQualityNorm, 2, 0.1420113554482666, 0.8370876978924675 +fatigueNorm, sleepQualityNorm, 3, 0.14221933927505725, 0.8369862331143328 +fatigueNorm, sleepQualityNorm, 4, 0.14340308072744923, 0.836408511209811 +fatigueNorm, sleepQualityNorm, 5, 0.14470483992277416, 0.8357727300037604 +sorenessNorm, sleepHoursNorm, 1, 2.6720340337571535e-05, 0.8916856064653624 +sorenessNorm, sleepHoursNorm, 2, 0.0010829784090057304, 0.8912145443965083 +sorenessNorm, sleepHoursNorm, 3, 0.0017720118504847004, 0.890907120184888 +sorenessNorm, sleepHoursNorm, 4, 0.0019887177125947897, 0.8908104111779753 +sorenessNorm, sleepHoursNorm, 5, 0.002044345467583919, 0.8907855845679419 +sorenessNorm, sleepQualityNorm, 1, 0.019587573039092976, 0.8948177557099846 +sorenessNorm, sleepQualityNorm, 2, 0.01959432461981092, 0.8948146746368869 +sorenessNorm, sleepQualityNorm, 3, 0.01986255512598445, 0.8946922594887504 +sorenessNorm, sleepQualityNorm, 4, 0.020478329379147397, 0.8944111688016328 +sorenessNorm, sleepQualityNorm, 5, 0.020624279843815985, 0.8943445318947467 +sleepHoursNorm, sleepQualityNorm, 1, 0.08308950078750621, 0.8653537146606272 +sleepHoursNorm, sleepQualityNorm, 2, 0.1136120371291538, 0.8508286672332785 +sleepHoursNorm, sleepQualityNorm, 3, 0.12427325440835235, 0.8456964280840159 +sleepHoursNorm, sleepQualityNorm, 4, 0.1267460677582659, 0.8445015760395381 +sleepHoursNorm, sleepQualityNorm, 5, 0.1270288720658821, 0.844364818554663 From 49d9ea47f8c6faecf957bf3779d2e7d5b52149be Mon Sep 17 00:00:00 2001 From: nglod33 Date: Sat, 30 Mar 2019 22:36:29 -0400 Subject: [PATCH 2/7] Big boi regression analysis --- personal_regression_info.csv | 400 +++++++++++++++++------------------ 1 file changed, 200 insertions(+), 200 deletions(-) diff --git a/personal_regression_info.csv b/personal_regression_info.csv index 6f13391..65e8b5a 100644 --- a/personal_regression_info.csv +++ b/personal_regression_info.csv @@ -1,276 +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, 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sleepQualityNorm, 2, 0.1420113554482666, 0.8370876978924675 -fatigueNorm, sleepQualityNorm, 3, 0.14221933927505725, 0.8369862331143328 -fatigueNorm, sleepQualityNorm, 4, 0.14340308072744923, 0.836408511209811 -fatigueNorm, sleepQualityNorm, 5, 0.14470483992277416, 0.8357727300037604 -sorenessNorm, sleepHoursNorm, 1, 2.6720340337571535e-05, 0.8916856064653624 -sorenessNorm, sleepHoursNorm, 2, 0.0010829784090057304, 0.8912145443965083 -sorenessNorm, sleepHoursNorm, 3, 0.0017720118504847004, 0.890907120184888 -sorenessNorm, sleepHoursNorm, 4, 0.0019887177125947897, 0.8908104111779753 -sorenessNorm, sleepHoursNorm, 5, 0.002044345467583919, 0.8907855845679419 -sorenessNorm, sleepQualityNorm, 1, 0.019587573039092976, 0.8948177557099846 -sorenessNorm, sleepQualityNorm, 2, 0.01959432461981092, 0.8948146746368869 -sorenessNorm, sleepQualityNorm, 3, 0.01986255512598445, 0.8946922594887504 -sorenessNorm, sleepQualityNorm, 4, 0.020478329379147397, 0.8944111688016328 -sorenessNorm, sleepQualityNorm, 5, 0.020624279843815985, 0.8943445318947467 -sleepHoursNorm, sleepQualityNorm, 1, 0.08308950078750621, 0.8653537146606272 -sleepHoursNorm, sleepQualityNorm, 2, 0.1136120371291538, 0.8508286672332785 -sleepHoursNorm, sleepQualityNorm, 3, 0.12427325440835235, 0.8456964280840159 -sleepHoursNorm, sleepQualityNorm, 4, 0.1267460677582659, 0.8445015760395381 -sleepHoursNorm, sleepQualityNorm, 5, 0.1270288720658821, 0.844364818554663 +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 From 06b2e106d4372df058e5b6317c31e8aacb3689c7 Mon Sep 17 00:00:00 2001 From: nglod33 Date: Sat, 30 Mar 2019 22:43:11 -0400 Subject: [PATCH 3/7] run all regressions updated for poly regressions up to 5th degree --- run_all_regressions.py | 40 ++++++++++++++-------------------------- 1 file changed, 14 insertions(+), 26 deletions(-) diff --git a/run_all_regressions.py b/run_all_regressions.py index ceed036..4544cc6 100644 --- a/run_all_regressions.py +++ b/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() From 1ccd1a7360bca73678781343b7a820876c0ae825 Mon Sep 17 00:00:00 2001 From: nglod33 Date: Sat, 30 Mar 2019 23:07:51 -0400 Subject: [PATCH 4/7] added filtered personal regression info --- personal_regression_info_fatigue.csv | 96 ++++++++++++++++++++++++++++ 1 file changed, 96 insertions(+) create mode 100644 personal_regression_info_fatigue.csv diff --git a/personal_regression_info_fatigue.csv b/personal_regression_info_fatigue.csv new file mode 100644 index 0000000..fa2e0ec --- /dev/null +++ b/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 From b64a5df18a60e65bb9dde5751c7dcdeb563bf9ce Mon Sep 17 00:00:00 2001 From: PerryXDeng Date: Sat, 30 Mar 2019 23:16:07 -0400 Subject: [PATCH 5/7] cant fix the keras dimension --- hypotheses_modeling/KerasRegressions.py | 112 +- hypotheses_modeling/personal.csv | 6036 +++++++++++++++++++++++ 2 files changed, 6107 insertions(+), 41 deletions(-) create mode 100644 hypotheses_modeling/personal.csv diff --git a/hypotheses_modeling/KerasRegressions.py b/hypotheses_modeling/KerasRegressions.py index b502929..65ab999 100644 --- a/hypotheses_modeling/KerasRegressions.py +++ b/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() diff --git a/hypotheses_modeling/personal.csv b/hypotheses_modeling/personal.csv new file mode 100644 index 0000000..113bb9f --- /dev/null +++ b/hypotheses_modeling/personal.csv @@ -0,0 +1,6036 @@ +"","day","playerID","DailyLoad","DailyLoadSliding","acuteChronicRatio","acuteChronicRatioSliding","trainDuration","trainDurationSliding","sleepHours","sleepHoursSliding","fatigue","fatigueSliding","sleepQuality","soreness","sorenessSliding","fatigueNorm","fatigueNormSliding","sorenessNorm","sleepHoursNorm","sleepQualityNorm","BestOutOfMyselfNotAtAll","BestOutOfMyselfAbsolutely","BestOutOfMyselfSomewhat","BestOutOfMyselfUnknown" +"1",0,13,840,840,4,4,150,150,7.5,7.5,4,4,4,4,4,0.745291653116745,0.745291653116745,1.0274995241794,-1.39073450902888,0.284281018456192,0,0,0,1 +"2",0,11,500,670,4,4,125,137.5,8.75,8.125,3,3.5,4,3,3.5,-0.000608777745005764,0.37234143768587,0.000498521758515883,0.00911700944141294,0.000149354733475051,0,0,0,1 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...ing_vs_acuteChronicRatioSliding_3_degree.png | Bin 0 -> 49144 bytes ...ing_vs_acuteChronicRatioSliding_4_degree.png | Bin 0 -> 48553 bytes ...ing_vs_acuteChronicRatioSliding_5_degree.png | Bin 0 -> 48646 bytes ...oadSliding_vs_acuteChronicRatio_1_degree.png | Bin 0 -> 52663 bytes ...oadSliding_vs_acuteChronicRatio_2_degree.png | Bin 0 -> 51995 bytes ...oadSliding_vs_acuteChronicRatio_3_degree.png | Bin 0 -> 52980 bytes ...oadSliding_vs_acuteChronicRatio_4_degree.png | Bin 0 -> 52047 bytes ...oadSliding_vs_acuteChronicRatio_5_degree.png | Bin 0 -> 52944 bytes ...lyLoadSliding_vs_fatigueSliding_1_degree.png | Bin 0 -> 45736 bytes ...lyLoadSliding_vs_fatigueSliding_2_degree.png | Bin 0 -> 45381 bytes ...lyLoadSliding_vs_fatigueSliding_3_degree.png | Bin 0 -> 45353 bytes ...lyLoadSliding_vs_fatigueSliding_4_degree.png | Bin 0 -> 45870 bytes ...lyLoadSliding_vs_fatigueSliding_5_degree.png | Bin 0 -> 45671 bytes .../DailyLoadSliding_vs_fatigue_1_degree.png | Bin 0 -> 26133 bytes 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