Browse Source

implemented linear regression with correlation and error metrics

master
PerryXDeng 5 years ago
parent
commit
b77dc26a2d
4 changed files with 739 additions and 2 deletions
  1. +336
    -0
      hypotheses_modeling/21DaySlidingWorkAverage.csv
  2. +355
    -0
      hypotheses_modeling/fatigue_total_sum.csv
  3. +30
    -2
      hypotheses_modeling/team_regressions.py
  4. +18
    -0
      hypotheses_modeling/time_series_days_ranked.csv

+ 336
- 0
hypotheses_modeling/21DaySlidingWorkAverage.csv View File

@ -0,0 +1,336 @@
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hypotheses_modeling/fatigue_total_sum.csv View File

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+ 30
- 2
hypotheses_modeling/team_regressions.py View File

@ -1,4 +1,32 @@
from sklearn import linear_model from sklearn import linear_model
import pandas as pd
from sklearn.metrics import mean_squared_error, r2_score
reg = linear_model.LinearRegression()
reg
def k_days_into_future_regression(X, y, k, n0):
col = "TimeSinceAugFirst"
inp = []
out = []
for day in y[col][n0 - 1:]:
prev = day - k
xprev = X[X[col] == prev].drop(columns=[col]).to_numpy()[0, :]
yt = y[y[col] == day].drop(columns=[col]).to_numpy()[0, :]
inp.append(xprev)
out.append(yt)
regr = linear_model.LinearRegression()
regr.fit(inp, out)
predictions = regr.predict(inp)
mse = mean_squared_error(out, predictions)/(len(out) - 2)
r2 = r2_score(out, predictions)
return regr.intercept_, regr.coef_, r2, mse
def main():
fatigueSums = pd.read_csv("fatigue_total_sum.csv")
workMovingAverage21 = pd.read_csv("21DaySlidingWorkAverage.csv", index_col=0)
performance = pd.read_csv("time_series_days_ranked.csv", index_col=0)
print(k_days_into_future_regression(workMovingAverage21, fatigueSums, 0, 21))
if __name__ == "__main__":
main()

+ 18
- 0
hypotheses_modeling/time_series_days_ranked.csv View File

@ -0,0 +1,18 @@
,Date,DailyElo
0,121,0.0
1,122,-3.714599999999998
2,178,0.04346000000000028
3,179,2.1916710000000013
4,180,0.0
5,255,0.0
6,256,0.0
7,257,-2.520374784999996
8,263,-2.0880156214999985
9,264,-1.7032140593500005
10,284,-0.6130256153877235
11,285,2.620463284090865
12,311,-2.076954630427971
13,312,1.0960590427574828
14,313,1.8954531384817344
15,353,-0.2940921753664384
16,354,-1.8646829578297937

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