mirror of
https://github.com/titanscouting/tra-analysis.git
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converted space indentation to tab indentation
This commit is contained in:
ltcptgeneral
parent
88e7c52c8b
commit
3ab1d0f50a
File diff suppressed because it is too large
Load Diff
@ -2,6 +2,6 @@ import numpy as np
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def calculate(starting_score, opposing_score, observed, N, K):
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expected = 1/(1+10**((np.array(opposing_score) - starting_score)/N))
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expected = 1/(1+10**((np.array(opposing_score) - starting_score)/N))
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return starting_score + K*(np.sum(observed) - np.sum(expected))
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return starting_score + K*(np.sum(observed) - np.sum(expected))
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@ -1,99 +1,99 @@
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import math
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class Glicko2:
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_tau = 0.5
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_tau = 0.5
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def getRating(self):
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return (self.__rating * 173.7178) + 1500
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def getRating(self):
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return (self.__rating * 173.7178) + 1500
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def setRating(self, rating):
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self.__rating = (rating - 1500) / 173.7178
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def setRating(self, rating):
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self.__rating = (rating - 1500) / 173.7178
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rating = property(getRating, setRating)
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rating = property(getRating, setRating)
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def getRd(self):
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return self.__rd * 173.7178
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def getRd(self):
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return self.__rd * 173.7178
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def setRd(self, rd):
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self.__rd = rd / 173.7178
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def setRd(self, rd):
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self.__rd = rd / 173.7178
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rd = property(getRd, setRd)
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def __init__(self, rating = 1500, rd = 350, vol = 0.06):
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rd = property(getRd, setRd)
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def __init__(self, rating = 1500, rd = 350, vol = 0.06):
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self.setRating(rating)
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self.setRd(rd)
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self.vol = vol
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def _preRatingRD(self):
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self.setRating(rating)
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self.setRd(rd)
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self.vol = vol
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def _preRatingRD(self):
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self.__rd = math.sqrt(math.pow(self.__rd, 2) + math.pow(self.vol, 2))
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def update_player(self, rating_list, RD_list, outcome_list):
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self.__rd = math.sqrt(math.pow(self.__rd, 2) + math.pow(self.vol, 2))
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def update_player(self, rating_list, RD_list, outcome_list):
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rating_list = [(x - 1500) / 173.7178 for x in rating_list]
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RD_list = [x / 173.7178 for x in RD_list]
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rating_list = [(x - 1500) / 173.7178 for x in rating_list]
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RD_list = [x / 173.7178 for x in RD_list]
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v = self._v(rating_list, RD_list)
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self.vol = self._newVol(rating_list, RD_list, outcome_list, v)
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self._preRatingRD()
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self.__rd = 1 / math.sqrt((1 / math.pow(self.__rd, 2)) + (1 / v))
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tempSum = 0
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for i in range(len(rating_list)):
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tempSum += self._g(RD_list[i]) * \
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(outcome_list[i] - self._E(rating_list[i], RD_list[i]))
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self.__rating += math.pow(self.__rd, 2) * tempSum
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def _newVol(self, rating_list, RD_list, outcome_list, v):
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v = self._v(rating_list, RD_list)
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self.vol = self._newVol(rating_list, RD_list, outcome_list, v)
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self._preRatingRD()
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self.__rd = 1 / math.sqrt((1 / math.pow(self.__rd, 2)) + (1 / v))
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tempSum = 0
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for i in range(len(rating_list)):
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tempSum += self._g(RD_list[i]) * \
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(outcome_list[i] - self._E(rating_list[i], RD_list[i]))
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self.__rating += math.pow(self.__rd, 2) * tempSum
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def _newVol(self, rating_list, RD_list, outcome_list, v):
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i = 0
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delta = self._delta(rating_list, RD_list, outcome_list, v)
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a = math.log(math.pow(self.vol, 2))
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tau = self._tau
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x0 = a
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x1 = 0
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while x0 != x1:
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# New iteration, so x(i) becomes x(i-1)
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x0 = x1
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d = math.pow(self.__rating, 2) + v + math.exp(x0)
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h1 = -(x0 - a) / math.pow(tau, 2) - 0.5 * math.exp(x0) \
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/ d + 0.5 * math.exp(x0) * math.pow(delta / d, 2)
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h2 = -1 / math.pow(tau, 2) - 0.5 * math.exp(x0) * \
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(math.pow(self.__rating, 2) + v) \
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/ math.pow(d, 2) + 0.5 * math.pow(delta, 2) * math.exp(x0) \
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* (math.pow(self.__rating, 2) + v - math.exp(x0)) / math.pow(d, 3)
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x1 = x0 - (h1 / h2)
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i = 0
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delta = self._delta(rating_list, RD_list, outcome_list, v)
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a = math.log(math.pow(self.vol, 2))
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tau = self._tau
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x0 = a
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x1 = 0
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while x0 != x1:
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# New iteration, so x(i) becomes x(i-1)
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x0 = x1
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d = math.pow(self.__rating, 2) + v + math.exp(x0)
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h1 = -(x0 - a) / math.pow(tau, 2) - 0.5 * math.exp(x0) \
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/ d + 0.5 * math.exp(x0) * math.pow(delta / d, 2)
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h2 = -1 / math.pow(tau, 2) - 0.5 * math.exp(x0) * \
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(math.pow(self.__rating, 2) + v) \
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/ math.pow(d, 2) + 0.5 * math.pow(delta, 2) * math.exp(x0) \
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* (math.pow(self.__rating, 2) + v - math.exp(x0)) / math.pow(d, 3)
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x1 = x0 - (h1 / h2)
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return math.exp(x1 / 2)
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def _delta(self, rating_list, RD_list, outcome_list, v):
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return math.exp(x1 / 2)
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def _delta(self, rating_list, RD_list, outcome_list, v):
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tempSum = 0
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for i in range(len(rating_list)):
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tempSum += self._g(RD_list[i]) * (outcome_list[i] - self._E(rating_list[i], RD_list[i]))
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return v * tempSum
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def _v(self, rating_list, RD_list):
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tempSum = 0
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for i in range(len(rating_list)):
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tempSum += self._g(RD_list[i]) * (outcome_list[i] - self._E(rating_list[i], RD_list[i]))
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return v * tempSum
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def _v(self, rating_list, RD_list):
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tempSum = 0
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for i in range(len(rating_list)):
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tempE = self._E(rating_list[i], RD_list[i])
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tempSum += math.pow(self._g(RD_list[i]), 2) * tempE * (1 - tempE)
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return 1 / tempSum
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def _E(self, p2rating, p2RD):
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tempSum = 0
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for i in range(len(rating_list)):
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tempE = self._E(rating_list[i], RD_list[i])
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tempSum += math.pow(self._g(RD_list[i]), 2) * tempE * (1 - tempE)
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return 1 / tempSum
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def _E(self, p2rating, p2RD):
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return 1 / (1 + math.exp(-1 * self._g(p2RD) * \
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(self.__rating - p2rating)))
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def _g(self, RD):
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return 1 / (1 + math.exp(-1 * self._g(p2RD) * \
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(self.__rating - p2rating)))
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def _g(self, RD):
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return 1 / math.sqrt(1 + 3 * math.pow(RD, 2) / math.pow(math.pi, 2))
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def did_not_compete(self):
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return 1 / math.sqrt(1 + 3 * math.pow(RD, 2) / math.pow(math.pi, 2))
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def did_not_compete(self):
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self._preRatingRD()
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self._preRatingRD()
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File diff suppressed because it is too large
Load Diff
@ -9,38 +9,38 @@ __version__ = "1.0.0.004"
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# changelog should be viewed using print(analysis.regression.__changelog__)
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__changelog__ = """
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1.0.0.004:
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- bug fixes
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- fixed changelog
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1.0.0.003:
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- bug fixes
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1.0.0.002:
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-Added more parameters to log, exponential, polynomial
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-Added SigmoidalRegKernelArthur, because Arthur apparently needs
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to train the scaling and shifting of sigmoids
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1.0.0.001:
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-initial release, with linear, log, exponential, polynomial, and sigmoid kernels
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-already vectorized (except for polynomial generation) and CUDA-optimized
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1.0.0.004:
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- bug fixes
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- fixed changelog
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1.0.0.003:
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- bug fixes
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1.0.0.002:
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-Added more parameters to log, exponential, polynomial
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-Added SigmoidalRegKernelArthur, because Arthur apparently needs
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to train the scaling and shifting of sigmoids
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1.0.0.001:
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-initial release, with linear, log, exponential, polynomial, and sigmoid kernels
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-already vectorized (except for polynomial generation) and CUDA-optimized
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"""
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__author__ = (
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"Jacob Levine <jlevine@imsa.edu>",
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"Arthur Lu <learthurgo@gmail.com>"
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"Jacob Levine <jlevine@imsa.edu>",
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"Arthur Lu <learthurgo@gmail.com>"
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)
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__all__ = [
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'factorial',
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'take_all_pwrs',
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'num_poly_terms',
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'set_device',
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'LinearRegKernel',
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'SigmoidalRegKernel',
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'LogRegKernel',
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'PolyRegKernel',
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'ExpRegKernel',
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'SigmoidalRegKernelArthur',
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'SGDTrain',
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'CustomTrain'
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'factorial',
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'take_all_pwrs',
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'num_poly_terms',
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'set_device',
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'LinearRegKernel',
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'SigmoidalRegKernel',
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'LogRegKernel',
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'PolyRegKernel',
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'ExpRegKernel',
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'SigmoidalRegKernelArthur',
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'SGDTrain',
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'CustomTrain'
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]
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import torch
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@ -52,169 +52,169 @@ device = "cuda:0" if torch.torch.cuda.is_available() else "cpu"
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#todo: document completely
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def set_device(self, new_device):
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device=new_device
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device=new_device
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class LinearRegKernel():
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parameters= []
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weights=None
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bias=None
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def __init__(self, num_vars):
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self.weights=torch.rand(num_vars, requires_grad=True, device=device)
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self.bias=torch.rand(1, requires_grad=True, device=device)
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self.parameters=[self.weights,self.bias]
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def forward(self,mtx):
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long_bias=self.bias.repeat([1,mtx.size()[1]])
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return torch.matmul(self.weights,mtx)+long_bias
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parameters= []
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weights=None
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bias=None
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def __init__(self, num_vars):
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self.weights=torch.rand(num_vars, requires_grad=True, device=device)
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self.bias=torch.rand(1, requires_grad=True, device=device)
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self.parameters=[self.weights,self.bias]
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def forward(self,mtx):
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long_bias=self.bias.repeat([1,mtx.size()[1]])
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return torch.matmul(self.weights,mtx)+long_bias
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class SigmoidalRegKernel():
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parameters= []
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weights=None
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bias=None
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sigmoid=torch.nn.Sigmoid()
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def __init__(self, num_vars):
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self.weights=torch.rand(num_vars, requires_grad=True, device=device)
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self.bias=torch.rand(1, requires_grad=True, device=device)
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self.parameters=[self.weights,self.bias]
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def forward(self,mtx):
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long_bias=self.bias.repeat([1,mtx.size()[1]])
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return self.sigmoid(torch.matmul(self.weights,mtx)+long_bias)
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parameters= []
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weights=None
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bias=None
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sigmoid=torch.nn.Sigmoid()
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def __init__(self, num_vars):
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self.weights=torch.rand(num_vars, requires_grad=True, device=device)
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self.bias=torch.rand(1, requires_grad=True, device=device)
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self.parameters=[self.weights,self.bias]
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def forward(self,mtx):
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long_bias=self.bias.repeat([1,mtx.size()[1]])
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return self.sigmoid(torch.matmul(self.weights,mtx)+long_bias)
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class SigmoidalRegKernelArthur():
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parameters= []
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weights=None
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in_bias=None
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scal_mult=None
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out_bias=None
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sigmoid=torch.nn.Sigmoid()
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def __init__(self, num_vars):
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self.weights=torch.rand(num_vars, requires_grad=True, device=device)
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self.in_bias=torch.rand(1, requires_grad=True, device=device)
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self.scal_mult=torch.rand(1, requires_grad=True, device=device)
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self.out_bias=torch.rand(1, requires_grad=True, device=device)
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self.parameters=[self.weights,self.in_bias, self.scal_mult, self.out_bias]
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def forward(self,mtx):
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long_in_bias=self.in_bias.repeat([1,mtx.size()[1]])
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long_out_bias=self.out_bias.repeat([1,mtx.size()[1]])
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return (self.scal_mult*self.sigmoid(torch.matmul(self.weights,mtx)+long_in_bias))+long_out_bias
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parameters= []
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weights=None
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in_bias=None
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scal_mult=None
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out_bias=None
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sigmoid=torch.nn.Sigmoid()
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def __init__(self, num_vars):
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self.weights=torch.rand(num_vars, requires_grad=True, device=device)
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self.in_bias=torch.rand(1, requires_grad=True, device=device)
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self.scal_mult=torch.rand(1, requires_grad=True, device=device)
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self.out_bias=torch.rand(1, requires_grad=True, device=device)
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self.parameters=[self.weights,self.in_bias, self.scal_mult, self.out_bias]
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def forward(self,mtx):
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long_in_bias=self.in_bias.repeat([1,mtx.size()[1]])
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long_out_bias=self.out_bias.repeat([1,mtx.size()[1]])
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return (self.scal_mult*self.sigmoid(torch.matmul(self.weights,mtx)+long_in_bias))+long_out_bias
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class LogRegKernel():
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parameters= []
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weights=None
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in_bias=None
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scal_mult=None
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out_bias=None
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def __init__(self, num_vars):
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self.weights=torch.rand(num_vars, requires_grad=True, device=device)
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self.in_bias=torch.rand(1, requires_grad=True, device=device)
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self.scal_mult=torch.rand(1, requires_grad=True, device=device)
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self.out_bias=torch.rand(1, requires_grad=True, device=device)
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self.parameters=[self.weights,self.in_bias, self.scal_mult, self.out_bias]
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def forward(self,mtx):
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long_in_bias=self.in_bias.repeat([1,mtx.size()[1]])
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long_out_bias=self.out_bias.repeat([1,mtx.size()[1]])
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return (self.scal_mult*torch.log(torch.matmul(self.weights,mtx)+long_in_bias))+long_out_bias
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parameters= []
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weights=None
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in_bias=None
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scal_mult=None
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out_bias=None
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def __init__(self, num_vars):
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self.weights=torch.rand(num_vars, requires_grad=True, device=device)
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self.in_bias=torch.rand(1, requires_grad=True, device=device)
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self.scal_mult=torch.rand(1, requires_grad=True, device=device)
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self.out_bias=torch.rand(1, requires_grad=True, device=device)
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self.parameters=[self.weights,self.in_bias, self.scal_mult, self.out_bias]
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def forward(self,mtx):
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long_in_bias=self.in_bias.repeat([1,mtx.size()[1]])
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long_out_bias=self.out_bias.repeat([1,mtx.size()[1]])
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return (self.scal_mult*torch.log(torch.matmul(self.weights,mtx)+long_in_bias))+long_out_bias
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class ExpRegKernel():
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parameters= []
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weights=None
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in_bias=None
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scal_mult=None
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out_bias=None
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def __init__(self, num_vars):
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self.weights=torch.rand(num_vars, requires_grad=True, device=device)
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self.in_bias=torch.rand(1, requires_grad=True, device=device)
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self.scal_mult=torch.rand(1, requires_grad=True, device=device)
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self.out_bias=torch.rand(1, requires_grad=True, device=device)
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self.parameters=[self.weights,self.in_bias, self.scal_mult, self.out_bias]
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def forward(self,mtx):
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long_in_bias=self.in_bias.repeat([1,mtx.size()[1]])
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long_out_bias=self.out_bias.repeat([1,mtx.size()[1]])
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return (self.scal_mult*torch.exp(torch.matmul(self.weights,mtx)+long_in_bias))+long_out_bias
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parameters= []
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weights=None
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in_bias=None
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scal_mult=None
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out_bias=None
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def __init__(self, num_vars):
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self.weights=torch.rand(num_vars, requires_grad=True, device=device)
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self.in_bias=torch.rand(1, requires_grad=True, device=device)
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self.scal_mult=torch.rand(1, requires_grad=True, device=device)
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self.out_bias=torch.rand(1, requires_grad=True, device=device)
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self.parameters=[self.weights,self.in_bias, self.scal_mult, self.out_bias]
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def forward(self,mtx):
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long_in_bias=self.in_bias.repeat([1,mtx.size()[1]])
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||||
long_out_bias=self.out_bias.repeat([1,mtx.size()[1]])
|
||||
return (self.scal_mult*torch.exp(torch.matmul(self.weights,mtx)+long_in_bias))+long_out_bias
|
||||
|
||||
class PolyRegKernel():
|
||||
parameters= []
|
||||
weights=None
|
||||
bias=None
|
||||
power=None
|
||||
def __init__(self, num_vars, power):
|
||||
self.power=power
|
||||
num_terms=self.num_poly_terms(num_vars, power)
|
||||
self.weights=torch.rand(num_terms, requires_grad=True, device=device)
|
||||
self.bias=torch.rand(1, requires_grad=True, device=device)
|
||||
self.parameters=[self.weights,self.bias]
|
||||
def num_poly_terms(self,num_vars, power):
|
||||
if power == 0:
|
||||
return 0
|
||||
return int(self.factorial(num_vars+power-1) / self.factorial(power) / self.factorial(num_vars-1)) + self.num_poly_terms(num_vars, power-1)
|
||||
def factorial(self,n):
|
||||
if n==0:
|
||||
return 1
|
||||
else:
|
||||
return n*self.factorial(n-1)
|
||||
def take_all_pwrs(self, vec, pwr):
|
||||
#todo: vectorize (kinda)
|
||||
combins=torch.combinations(vec, r=pwr, with_replacement=True)
|
||||
out=torch.ones(combins.size()[0]).to(device).to(torch.float)
|
||||
for i in torch.t(combins).to(device).to(torch.float):
|
||||
out *= i
|
||||
if pwr == 1:
|
||||
return out
|
||||
else:
|
||||
return torch.cat((out,self.take_all_pwrs(vec, pwr-1)))
|
||||
def forward(self,mtx):
|
||||
#TODO: Vectorize the last part
|
||||
cols=[]
|
||||
for i in torch.t(mtx):
|
||||
cols.append(self.take_all_pwrs(i,self.power))
|
||||
new_mtx=torch.t(torch.stack(cols))
|
||||
long_bias=self.bias.repeat([1,mtx.size()[1]])
|
||||
return torch.matmul(self.weights,new_mtx)+long_bias
|
||||
parameters= []
|
||||
weights=None
|
||||
bias=None
|
||||
power=None
|
||||
def __init__(self, num_vars, power):
|
||||
self.power=power
|
||||
num_terms=self.num_poly_terms(num_vars, power)
|
||||
self.weights=torch.rand(num_terms, requires_grad=True, device=device)
|
||||
self.bias=torch.rand(1, requires_grad=True, device=device)
|
||||
self.parameters=[self.weights,self.bias]
|
||||
def num_poly_terms(self,num_vars, power):
|
||||
if power == 0:
|
||||
return 0
|
||||
return int(self.factorial(num_vars+power-1) / self.factorial(power) / self.factorial(num_vars-1)) + self.num_poly_terms(num_vars, power-1)
|
||||
def factorial(self,n):
|
||||
if n==0:
|
||||
return 1
|
||||
else:
|
||||
return n*self.factorial(n-1)
|
||||
def take_all_pwrs(self, vec, pwr):
|
||||
#todo: vectorize (kinda)
|
||||
combins=torch.combinations(vec, r=pwr, with_replacement=True)
|
||||
out=torch.ones(combins.size()[0]).to(device).to(torch.float)
|
||||
for i in torch.t(combins).to(device).to(torch.float):
|
||||
out *= i
|
||||
if pwr == 1:
|
||||
return out
|
||||
else:
|
||||
return torch.cat((out,self.take_all_pwrs(vec, pwr-1)))
|
||||
def forward(self,mtx):
|
||||
#TODO: Vectorize the last part
|
||||
cols=[]
|
||||
for i in torch.t(mtx):
|
||||
cols.append(self.take_all_pwrs(i,self.power))
|
||||
new_mtx=torch.t(torch.stack(cols))
|
||||
long_bias=self.bias.repeat([1,mtx.size()[1]])
|
||||
return torch.matmul(self.weights,new_mtx)+long_bias
|
||||
|
||||
def SGDTrain(self, kernel, data, ground, loss=torch.nn.MSELoss(), iterations=1000, learning_rate=.1, return_losses=False):
|
||||
optim=torch.optim.SGD(kernel.parameters, lr=learning_rate)
|
||||
data_cuda=data.to(device)
|
||||
ground_cuda=ground.to(device)
|
||||
if (return_losses):
|
||||
losses=[]
|
||||
for i in range(iterations):
|
||||
with torch.set_grad_enabled(True):
|
||||
optim.zero_grad()
|
||||
pred=kernel.forward(data_cuda)
|
||||
ls=loss(pred,ground_cuda)
|
||||
losses.append(ls.item())
|
||||
ls.backward()
|
||||
optim.step()
|
||||
return [kernel,losses]
|
||||
else:
|
||||
for i in range(iterations):
|
||||
with torch.set_grad_enabled(True):
|
||||
optim.zero_grad()
|
||||
pred=kernel.forward(data_cuda)
|
||||
ls=loss(pred,ground_cuda)
|
||||
ls.backward()
|
||||
optim.step()
|
||||
return kernel
|
||||
optim=torch.optim.SGD(kernel.parameters, lr=learning_rate)
|
||||
data_cuda=data.to(device)
|
||||
ground_cuda=ground.to(device)
|
||||
if (return_losses):
|
||||
losses=[]
|
||||
for i in range(iterations):
|
||||
with torch.set_grad_enabled(True):
|
||||
optim.zero_grad()
|
||||
pred=kernel.forward(data_cuda)
|
||||
ls=loss(pred,ground_cuda)
|
||||
losses.append(ls.item())
|
||||
ls.backward()
|
||||
optim.step()
|
||||
return [kernel,losses]
|
||||
else:
|
||||
for i in range(iterations):
|
||||
with torch.set_grad_enabled(True):
|
||||
optim.zero_grad()
|
||||
pred=kernel.forward(data_cuda)
|
||||
ls=loss(pred,ground_cuda)
|
||||
ls.backward()
|
||||
optim.step()
|
||||
return kernel
|
||||
|
||||
def CustomTrain(self, kernel, optim, data, ground, loss=torch.nn.MSELoss(), iterations=1000, return_losses=False):
|
||||
data_cuda=data.to(device)
|
||||
ground_cuda=ground.to(device)
|
||||
if (return_losses):
|
||||
losses=[]
|
||||
for i in range(iterations):
|
||||
with torch.set_grad_enabled(True):
|
||||
optim.zero_grad()
|
||||
pred=kernel.forward(data)
|
||||
ls=loss(pred,ground)
|
||||
losses.append(ls.item())
|
||||
ls.backward()
|
||||
optim.step()
|
||||
return [kernel,losses]
|
||||
else:
|
||||
for i in range(iterations):
|
||||
with torch.set_grad_enabled(True):
|
||||
optim.zero_grad()
|
||||
pred=kernel.forward(data_cuda)
|
||||
ls=loss(pred,ground_cuda)
|
||||
ls.backward()
|
||||
optim.step()
|
||||
return kernel
|
||||
data_cuda=data.to(device)
|
||||
ground_cuda=ground.to(device)
|
||||
if (return_losses):
|
||||
losses=[]
|
||||
for i in range(iterations):
|
||||
with torch.set_grad_enabled(True):
|
||||
optim.zero_grad()
|
||||
pred=kernel.forward(data)
|
||||
ls=loss(pred,ground)
|
||||
losses.append(ls.item())
|
||||
ls.backward()
|
||||
optim.step()
|
||||
return [kernel,losses]
|
||||
else:
|
||||
for i in range(iterations):
|
||||
with torch.set_grad_enabled(True):
|
||||
optim.zero_grad()
|
||||
pred=kernel.forward(data_cuda)
|
||||
ls=loss(pred,ground_cuda)
|
||||
ls.backward()
|
||||
optim.step()
|
||||
return kernel
|
||||
@ -11,112 +11,112 @@ __version__ = "2.0.1.001"
|
||||
|
||||
#changelog should be viewed using print(analysis.__changelog__)
|
||||
__changelog__ = """changelog:
|
||||
2.0.1.001:
|
||||
- removed matplotlib import
|
||||
- removed graphloss()
|
||||
2.0.1.000:
|
||||
- added net, dataset, dataloader, and stdtrain template definitions
|
||||
- added graphloss function
|
||||
2.0.0.001:
|
||||
- added clear functions
|
||||
2.0.0.000:
|
||||
- complete rewrite planned
|
||||
- depreciated 1.0.0.xxx versions
|
||||
- added simple training loop
|
||||
1.0.0.xxx:
|
||||
-added generation of ANNS, basic SGD training
|
||||
2.0.1.001:
|
||||
- removed matplotlib import
|
||||
- removed graphloss()
|
||||
2.0.1.000:
|
||||
- added net, dataset, dataloader, and stdtrain template definitions
|
||||
- added graphloss function
|
||||
2.0.0.001:
|
||||
- added clear functions
|
||||
2.0.0.000:
|
||||
- complete rewrite planned
|
||||
- depreciated 1.0.0.xxx versions
|
||||
- added simple training loop
|
||||
1.0.0.xxx:
|
||||
-added generation of ANNS, basic SGD training
|
||||
"""
|
||||
|
||||
__author__ = (
|
||||
"Arthur Lu <arthurlu@ttic.edu>,"
|
||||
"Jacob Levine <jlevine@ttic.edu>,"
|
||||
)
|
||||
"Arthur Lu <arthurlu@ttic.edu>,"
|
||||
"Jacob Levine <jlevine@ttic.edu>,"
|
||||
)
|
||||
|
||||
__all__ = [
|
||||
'clear',
|
||||
'net',
|
||||
'dataset',
|
||||
'dataloader',
|
||||
'train',
|
||||
'stdtrainer',
|
||||
]
|
||||
'clear',
|
||||
'net',
|
||||
'dataset',
|
||||
'dataloader',
|
||||
'train',
|
||||
'stdtrainer',
|
||||
]
|
||||
|
||||
import torch
|
||||
from os import system, name
|
||||
import numpy as np
|
||||
|
||||
def clear():
|
||||
if name == 'nt':
|
||||
_ = system('cls')
|
||||
else:
|
||||
_ = system('clear')
|
||||
if name == 'nt':
|
||||
_ = system('cls')
|
||||
else:
|
||||
_ = system('clear')
|
||||
|
||||
class net(torch.nn.Module): #template for standard neural net
|
||||
def __init__(self):
|
||||
super(Net, self).__init__()
|
||||
|
||||
def forward(self, input):
|
||||
pass
|
||||
def __init__(self):
|
||||
super(Net, self).__init__()
|
||||
|
||||
def forward(self, input):
|
||||
pass
|
||||
|
||||
class dataset(torch.utils.data.Dataset): #template for standard dataset
|
||||
|
||||
def __init__(self):
|
||||
super(torch.utils.data.Dataset).__init__()
|
||||
|
||||
def __getitem__(self, index):
|
||||
pass
|
||||
|
||||
def __len__(self):
|
||||
pass
|
||||
|
||||
def __init__(self):
|
||||
super(torch.utils.data.Dataset).__init__()
|
||||
|
||||
def __getitem__(self, index):
|
||||
pass
|
||||
|
||||
def __len__(self):
|
||||
pass
|
||||
|
||||
def dataloader(dataset, batch_size, num_workers, shuffle = True):
|
||||
|
||||
return torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=shuffle, num_workers=num_workers)
|
||||
return torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=shuffle, num_workers=num_workers)
|
||||
|
||||
def train(device, net, epochs, trainloader, optimizer, criterion): #expects standard dataloader, whch returns (inputs, labels)
|
||||
|
||||
dataset_len = trainloader.dataset.__len__()
|
||||
iter_count = 0
|
||||
running_loss = 0
|
||||
running_loss_list = []
|
||||
dataset_len = trainloader.dataset.__len__()
|
||||
iter_count = 0
|
||||
running_loss = 0
|
||||
running_loss_list = []
|
||||
|
||||
for epoch in range(epochs): # loop over the dataset multiple times
|
||||
for epoch in range(epochs): # loop over the dataset multiple times
|
||||
|
||||
for i, data in enumerate(trainloader, 0):
|
||||
for i, data in enumerate(trainloader, 0):
|
||||
|
||||
inputs = data[0].to(device)
|
||||
labels = data[1].to(device)
|
||||
inputs = data[0].to(device)
|
||||
labels = data[1].to(device)
|
||||
|
||||
optimizer.zero_grad()
|
||||
optimizer.zero_grad()
|
||||
|
||||
outputs = net(inputs)
|
||||
loss = criterion(outputs, labels.to(torch.float))
|
||||
|
||||
loss.backward()
|
||||
optimizer.step()
|
||||
outputs = net(inputs)
|
||||
loss = criterion(outputs, labels.to(torch.float))
|
||||
|
||||
loss.backward()
|
||||
optimizer.step()
|
||||
|
||||
# monitoring steps below
|
||||
# monitoring steps below
|
||||
|
||||
iter_count += 1
|
||||
running_loss += loss.item()
|
||||
running_loss_list.append(running_loss)
|
||||
clear()
|
||||
iter_count += 1
|
||||
running_loss += loss.item()
|
||||
running_loss_list.append(running_loss)
|
||||
clear()
|
||||
|
||||
print("training on: " + device)
|
||||
print("iteration: " + str(i) + "/" + str(int(dataset_len / trainloader.batch_size)) + " | " + "epoch: " + str(epoch) + "/" + str(epochs))
|
||||
print("current batch loss: " + str(loss.item))
|
||||
print("running loss: " + str(running_loss / iter_count))
|
||||
|
||||
return net, running_loss_list
|
||||
print("finished training")
|
||||
print("training on: " + device)
|
||||
print("iteration: " + str(i) + "/" + str(int(dataset_len / trainloader.batch_size)) + " | " + "epoch: " + str(epoch) + "/" + str(epochs))
|
||||
print("current batch loss: " + str(loss.item))
|
||||
print("running loss: " + str(running_loss / iter_count))
|
||||
|
||||
return net, running_loss_list
|
||||
print("finished training")
|
||||
|
||||
def stdtrainer(net, criterion, optimizer, dataloader, epochs, batch_size):
|
||||
|
||||
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
|
||||
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
|
||||
|
||||
net = net.to(device)
|
||||
criterion = criterion.to(device)
|
||||
optimizer = optimizer.to(device)
|
||||
trainloader = dataloader
|
||||
|
||||
return train(device, net, epochs, trainloader, optimizer, criterion)
|
||||
net = net.to(device)
|
||||
criterion = criterion.to(device)
|
||||
optimizer = optimizer.to(device)
|
||||
trainloader = dataloader
|
||||
|
||||
return train(device, net, epochs, trainloader, optimizer, criterion)
|
||||
@ -10,25 +10,25 @@ __version__ = "1.0.0.000"
|
||||
|
||||
#changelog should be viewed using print(analysis.__changelog__)
|
||||
__changelog__ = """changelog:
|
||||
1.0.0.000:
|
||||
- created visualization.py
|
||||
- added graphloss()
|
||||
- added imports
|
||||
1.0.0.000:
|
||||
- created visualization.py
|
||||
- added graphloss()
|
||||
- added imports
|
||||
"""
|
||||
|
||||
__author__ = (
|
||||
"Arthur Lu <arthurlu@ttic.edu>,"
|
||||
"Jacob Levine <jlevine@ttic.edu>,"
|
||||
)
|
||||
"Arthur Lu <arthurlu@ttic.edu>,"
|
||||
"Jacob Levine <jlevine@ttic.edu>,"
|
||||
)
|
||||
|
||||
__all__ = [
|
||||
'graphloss',
|
||||
]
|
||||
'graphloss',
|
||||
]
|
||||
|
||||
import matplotlib.pyplot as plt
|
||||
|
||||
def graphloss(losses):
|
||||
|
||||
x = range(0, len(losses))
|
||||
plt.plot(x, losses)
|
||||
plt.show()
|
||||
x = range(0, len(losses))
|
||||
plt.plot(x, losses)
|
||||
plt.show()
|
||||
@ -3,24 +3,24 @@ import setuptools
|
||||
requirements = []
|
||||
|
||||
with open("requirements.txt", 'r') as file:
|
||||
for line in file:
|
||||
requirements.append(line)
|
||||
for line in file:
|
||||
requirements.append(line)
|
||||
|
||||
setuptools.setup(
|
||||
name="analysis",
|
||||
version="1.0.0.012",
|
||||
author="The Titan Scouting Team",
|
||||
author_email="titanscout2022@gmail.com",
|
||||
description="analysis package developed by Titan Scouting for The Red Alliance",
|
||||
long_description="",
|
||||
long_description_content_type="text/markdown",
|
||||
url="https://github.com/titanscout2022/tr2022-strategy",
|
||||
packages=setuptools.find_packages(),
|
||||
install_requires=requirements,
|
||||
license = "GNU General Public License v3.0",
|
||||
classifiers=[
|
||||
"Programming Language :: Python :: 3",
|
||||
"Operating System :: OS Independent",
|
||||
],
|
||||
python_requires='>=3.6',
|
||||
name="analysis",
|
||||
version="1.0.0.012",
|
||||
author="The Titan Scouting Team",
|
||||
author_email="titanscout2022@gmail.com",
|
||||
description="analysis package developed by Titan Scouting for The Red Alliance",
|
||||
long_description="",
|
||||
long_description_content_type="text/markdown",
|
||||
url="https://github.com/titanscout2022/tr2022-strategy",
|
||||
packages=setuptools.find_packages(),
|
||||
install_requires=requirements,
|
||||
license = "GNU General Public License v3.0",
|
||||
classifiers=[
|
||||
"Programming Language :: Python :: 3",
|
||||
"Operating System :: OS Independent",
|
||||
],
|
||||
python_requires='>=3.6',
|
||||
)
|
||||
@ -4,99 +4,99 @@ import pandas as pd
|
||||
import time
|
||||
|
||||
def pull_new_tba_matches(apikey, competition, cutoff):
|
||||
api_key= apikey
|
||||
x=requests.get("https://www.thebluealliance.com/api/v3/event/"+competition+"/matches/simple", headers={"X-TBA-Auth_Key":api_key})
|
||||
out = []
|
||||
for i in x.json():
|
||||
if (i["actual_time"] != None and i["actual_time"]-cutoff >= 0 and i["comp_level"] == "qm"):
|
||||
out.append({"match" : i['match_number'], "blue" : list(map(lambda x: int(x[3:]), i['alliances']['blue']['team_keys'])), "red" : list(map(lambda x: int(x[3:]), i['alliances']['red']['team_keys'])), "winner": i["winning_alliance"]})
|
||||
return out
|
||||
api_key= apikey
|
||||
x=requests.get("https://www.thebluealliance.com/api/v3/event/"+competition+"/matches/simple", headers={"X-TBA-Auth_Key":api_key})
|
||||
out = []
|
||||
for i in x.json():
|
||||
if (i["actual_time"] != None and i["actual_time"]-cutoff >= 0 and i["comp_level"] == "qm"):
|
||||
out.append({"match" : i['match_number'], "blue" : list(map(lambda x: int(x[3:]), i['alliances']['blue']['team_keys'])), "red" : list(map(lambda x: int(x[3:]), i['alliances']['red']['team_keys'])), "winner": i["winning_alliance"]})
|
||||
return out
|
||||
|
||||
def get_team_match_data(apikey, competition, team_num):
|
||||
client = pymongo.MongoClient(apikey)
|
||||
db = client.data_scouting
|
||||
mdata = db.matchdata
|
||||
out = {}
|
||||
for i in mdata.find({"competition" : competition, "team_scouted": team_num}):
|
||||
out[i['match']] = i['data']
|
||||
return pd.DataFrame(out)
|
||||
client = pymongo.MongoClient(apikey)
|
||||
db = client.data_scouting
|
||||
mdata = db.matchdata
|
||||
out = {}
|
||||
for i in mdata.find({"competition" : competition, "team_scouted": team_num}):
|
||||
out[i['match']] = i['data']
|
||||
return pd.DataFrame(out)
|
||||
|
||||
def get_team_pit_data(apikey, competition, team_num):
|
||||
client = pymongo.MongoClient(apikey)
|
||||
db = client.data_scouting
|
||||
mdata = db.pitdata
|
||||
out = {}
|
||||
return mdata.find_one({"competition" : competition, "team_scouted": team_num})["data"]
|
||||
client = pymongo.MongoClient(apikey)
|
||||
db = client.data_scouting
|
||||
mdata = db.pitdata
|
||||
out = {}
|
||||
return mdata.find_one({"competition" : competition, "team_scouted": team_num})["data"]
|
||||
|
||||
def get_team_metrics_data(apikey, competition, team_num):
|
||||
client = pymongo.MongoClient(apikey)
|
||||
db = client.data_processing
|
||||
mdata = db.team_metrics
|
||||
return mdata.find_one({"competition" : competition, "team": team_num})
|
||||
client = pymongo.MongoClient(apikey)
|
||||
db = client.data_processing
|
||||
mdata = db.team_metrics
|
||||
return mdata.find_one({"competition" : competition, "team": team_num})
|
||||
|
||||
def unkeyify_2l(layered_dict):
|
||||
out = {}
|
||||
for i in layered_dict.keys():
|
||||
add = []
|
||||
sortkey = []
|
||||
for j in layered_dict[i].keys():
|
||||
add.append([j,layered_dict[i][j]])
|
||||
add.sort(key = lambda x: x[0])
|
||||
out[i] = list(map(lambda x: x[1], add))
|
||||
return out
|
||||
out = {}
|
||||
for i in layered_dict.keys():
|
||||
add = []
|
||||
sortkey = []
|
||||
for j in layered_dict[i].keys():
|
||||
add.append([j,layered_dict[i][j]])
|
||||
add.sort(key = lambda x: x[0])
|
||||
out[i] = list(map(lambda x: x[1], add))
|
||||
return out
|
||||
|
||||
def get_match_data_formatted(apikey, competition):
|
||||
client = pymongo.MongoClient(apikey)
|
||||
db = client.data_scouting
|
||||
mdata = db.teamlist
|
||||
x=mdata.find_one({"competition":competition})
|
||||
out = {}
|
||||
for i in x:
|
||||
try:
|
||||
out[int(i)] = unkeyify_2l(get_team_match_data(apikey, competition, int(i)).transpose().to_dict())
|
||||
except:
|
||||
pass
|
||||
return out
|
||||
client = pymongo.MongoClient(apikey)
|
||||
db = client.data_scouting
|
||||
mdata = db.teamlist
|
||||
x=mdata.find_one({"competition":competition})
|
||||
out = {}
|
||||
for i in x:
|
||||
try:
|
||||
out[int(i)] = unkeyify_2l(get_team_match_data(apikey, competition, int(i)).transpose().to_dict())
|
||||
except:
|
||||
pass
|
||||
return out
|
||||
|
||||
def get_pit_data_formatted(apikey, competition):
|
||||
client = pymongo.MongoClient(apikey)
|
||||
db = client.data_scouting
|
||||
mdata = db.teamlist
|
||||
x=mdata.find_one({"competition":competition})
|
||||
out = {}
|
||||
for i in x:
|
||||
try:
|
||||
out[int(i)] = get_team_pit_data(apikey, competition, int(i))
|
||||
except:
|
||||
pass
|
||||
return out
|
||||
client = pymongo.MongoClient(apikey)
|
||||
db = client.data_scouting
|
||||
mdata = db.teamlist
|
||||
x=mdata.find_one({"competition":competition})
|
||||
out = {}
|
||||
for i in x:
|
||||
try:
|
||||
out[int(i)] = get_team_pit_data(apikey, competition, int(i))
|
||||
except:
|
||||
pass
|
||||
return out
|
||||
|
||||
def push_team_tests_data(apikey, competition, team_num, data, dbname = "data_processing", colname = "team_tests"):
|
||||
client = pymongo.MongoClient(apikey)
|
||||
db = client[dbname]
|
||||
mdata = db[colname]
|
||||
mdata.replace_one({"competition" : competition, "team": team_num}, {"_id": competition+str(team_num)+"am", "competition" : competition, "team" : team_num, "data" : data}, True)
|
||||
client = pymongo.MongoClient(apikey)
|
||||
db = client[dbname]
|
||||
mdata = db[colname]
|
||||
mdata.replace_one({"competition" : competition, "team": team_num}, {"_id": competition+str(team_num)+"am", "competition" : competition, "team" : team_num, "data" : data}, True)
|
||||
|
||||
def push_team_metrics_data(apikey, competition, team_num, data, dbname = "data_processing", colname = "team_metrics"):
|
||||
client = pymongo.MongoClient(apikey)
|
||||
db = client[dbname]
|
||||
mdata = db[colname]
|
||||
mdata.replace_one({"competition" : competition, "team": team_num}, {"_id": competition+str(team_num)+"am", "competition" : competition, "team" : team_num, "metrics" : data}, True)
|
||||
client = pymongo.MongoClient(apikey)
|
||||
db = client[dbname]
|
||||
mdata = db[colname]
|
||||
mdata.replace_one({"competition" : competition, "team": team_num}, {"_id": competition+str(team_num)+"am", "competition" : competition, "team" : team_num, "metrics" : data}, True)
|
||||
|
||||
def push_team_pit_data(apikey, competition, variable, data, dbname = "data_processing", colname = "team_pit"):
|
||||
client = pymongo.MongoClient(apikey)
|
||||
db = client[dbname]
|
||||
mdata = db[colname]
|
||||
mdata.replace_one({"competition" : competition, "variable": variable}, {"competition" : competition, "variable" : variable, "data" : data}, True)
|
||||
client = pymongo.MongoClient(apikey)
|
||||
db = client[dbname]
|
||||
mdata = db[colname]
|
||||
mdata.replace_one({"competition" : competition, "variable": variable}, {"competition" : competition, "variable" : variable, "data" : data}, True)
|
||||
|
||||
def get_analysis_flags(apikey, flag):
|
||||
client = pymongo.MongoClient(apikey)
|
||||
db = client.data_processing
|
||||
mdata = db.flags
|
||||
return mdata.find_one({flag:{"$exists":True}})
|
||||
client = pymongo.MongoClient(apikey)
|
||||
db = client.data_processing
|
||||
mdata = db.flags
|
||||
return mdata.find_one({flag:{"$exists":True}})
|
||||
|
||||
def set_analysis_flags(apikey, flag, data):
|
||||
client = pymongo.MongoClient(apikey)
|
||||
db = client.data_processing
|
||||
mdata = db.flags
|
||||
return mdata.replace_one({flag:{"$exists":True}}, data, True)
|
||||
client = pymongo.MongoClient(apikey)
|
||||
db = client.data_processing
|
||||
mdata = db.flags
|
||||
return mdata.replace_one({flag:{"$exists":True}}, data, True)
|
||||
@ -4,56 +4,56 @@ import pymongo
|
||||
import operator
|
||||
|
||||
def load_config(file):
|
||||
config_vector = {}
|
||||
file = an.load_csv(file)
|
||||
for line in file[1:]:
|
||||
config_vector[line[0]] = line[1:]
|
||||
config_vector = {}
|
||||
file = an.load_csv(file)
|
||||
for line in file[1:]:
|
||||
config_vector[line[0]] = line[1:]
|
||||
|
||||
return (file[0][0], config_vector)
|
||||
return (file[0][0], config_vector)
|
||||
|
||||
def get_metrics_processed_formatted(apikey, competition):
|
||||
client = pymongo.MongoClient(apikey)
|
||||
db = client.data_scouting
|
||||
mdata = db.teamlist
|
||||
x=mdata.find_one({"competition":competition})
|
||||
out = {}
|
||||
for i in x:
|
||||
try:
|
||||
out[int(i)] = d.get_team_metrics_data(apikey, competition, int(i))
|
||||
except:
|
||||
pass
|
||||
return out
|
||||
client = pymongo.MongoClient(apikey)
|
||||
db = client.data_scouting
|
||||
mdata = db.teamlist
|
||||
x=mdata.find_one({"competition":competition})
|
||||
out = {}
|
||||
for i in x:
|
||||
try:
|
||||
out[int(i)] = d.get_team_metrics_data(apikey, competition, int(i))
|
||||
except:
|
||||
pass
|
||||
return out
|
||||
|
||||
def main():
|
||||
|
||||
apikey = an.load_csv("keys.txt")[0][0]
|
||||
tbakey = an.load_csv("keys.txt")[1][0]
|
||||
apikey = an.load_csv("keys.txt")[0][0]
|
||||
tbakey = an.load_csv("keys.txt")[1][0]
|
||||
|
||||
competition, config = load_config("config.csv")
|
||||
competition, config = load_config("config.csv")
|
||||
|
||||
metrics = get_metrics_processed_formatted(apikey, competition)
|
||||
metrics = get_metrics_processed_formatted(apikey, competition)
|
||||
|
||||
elo = {}
|
||||
gl2 = {}
|
||||
elo = {}
|
||||
gl2 = {}
|
||||
|
||||
for team in metrics:
|
||||
for team in metrics:
|
||||
|
||||
elo[team] = metrics[team]["metrics"]["elo"]["score"]
|
||||
gl2[team] = metrics[team]["metrics"]["gl2"]["score"]
|
||||
elo[team] = metrics[team]["metrics"]["elo"]["score"]
|
||||
gl2[team] = metrics[team]["metrics"]["gl2"]["score"]
|
||||
|
||||
elo = {k: v for k, v in sorted(elo.items(), key=lambda item: item[1])}
|
||||
gl2 = {k: v for k, v in sorted(gl2.items(), key=lambda item: item[1])}
|
||||
elo = {k: v for k, v in sorted(elo.items(), key=lambda item: item[1])}
|
||||
gl2 = {k: v for k, v in sorted(gl2.items(), key=lambda item: item[1])}
|
||||
|
||||
for team in elo:
|
||||
for team in elo:
|
||||
|
||||
print("teams sorted by elo:")
|
||||
print("" + str(team) + " | " + str(elo[team]))
|
||||
print("teams sorted by elo:")
|
||||
print("" + str(team) + " | " + str(elo[team]))
|
||||
|
||||
print("*"*25)
|
||||
print("*"*25)
|
||||
|
||||
for team in gl2:
|
||||
for team in gl2:
|
||||
|
||||
print("teams sorted by glicko2:")
|
||||
print("" + str(team) + " | " + str(gl2[team]))
|
||||
print("teams sorted by glicko2:")
|
||||
print("" + str(team) + " | " + str(gl2[team]))
|
||||
|
||||
main()
|
||||
@ -7,81 +7,81 @@ __version__ = "0.0.5.002"
|
||||
|
||||
# changelog should be viewed using print(analysis.__changelog__)
|
||||
__changelog__ = """changelog:
|
||||
0.0.5.002:
|
||||
- made changes due to refactoring of analysis
|
||||
0.0.5.001:
|
||||
- text fixes
|
||||
- removed matplotlib requirement
|
||||
0.0.5.000:
|
||||
- improved user interface
|
||||
0.0.4.002:
|
||||
- removed unessasary code
|
||||
0.0.4.001:
|
||||
- fixed bug where X range for regression was determined before sanitization
|
||||
- better sanitized data
|
||||
0.0.4.000:
|
||||
- fixed spelling issue in __changelog__
|
||||
- addressed nan bug in regression
|
||||
- fixed errors on line 335 with metrics calling incorrect key "glicko2"
|
||||
- fixed errors in metrics computing
|
||||
0.0.3.000:
|
||||
- added analysis to pit data
|
||||
0.0.2.001:
|
||||
- minor stability patches
|
||||
- implemented db syncing for timestamps
|
||||
- fixed bugs
|
||||
0.0.2.000:
|
||||
- finalized testing and small fixes
|
||||
0.0.1.004:
|
||||
- finished metrics implement, trueskill is bugged
|
||||
0.0.1.003:
|
||||
- working
|
||||
0.0.1.002:
|
||||
- started implement of metrics
|
||||
0.0.1.001:
|
||||
- cleaned up imports
|
||||
0.0.1.000:
|
||||
- tested working, can push to database
|
||||
0.0.0.009:
|
||||
- tested working
|
||||
- prints out stats for the time being, will push to database later
|
||||
0.0.0.008:
|
||||
- added data import
|
||||
- removed tba import
|
||||
- finished main method
|
||||
0.0.0.007:
|
||||
- added load_config
|
||||
- optimized simpleloop for readibility
|
||||
- added __all__ entries
|
||||
- added simplestats engine
|
||||
- pending testing
|
||||
0.0.0.006:
|
||||
- fixes
|
||||
0.0.0.005:
|
||||
- imported pickle
|
||||
- created custom database object
|
||||
0.0.0.004:
|
||||
- fixed simpleloop to actually return a vector
|
||||
0.0.0.003:
|
||||
- added metricsloop which is unfinished
|
||||
0.0.0.002:
|
||||
- added simpleloop which is untested until data is provided
|
||||
0.0.0.001:
|
||||
- created script
|
||||
- added analysis, numba, numpy imports
|
||||
0.0.5.002:
|
||||
- made changes due to refactoring of analysis
|
||||
0.0.5.001:
|
||||
- text fixes
|
||||
- removed matplotlib requirement
|
||||
0.0.5.000:
|
||||
- improved user interface
|
||||
0.0.4.002:
|
||||
- removed unessasary code
|
||||
0.0.4.001:
|
||||
- fixed bug where X range for regression was determined before sanitization
|
||||
- better sanitized data
|
||||
0.0.4.000:
|
||||
- fixed spelling issue in __changelog__
|
||||
- addressed nan bug in regression
|
||||
- fixed errors on line 335 with metrics calling incorrect key "glicko2"
|
||||
- fixed errors in metrics computing
|
||||
0.0.3.000:
|
||||
- added analysis to pit data
|
||||
0.0.2.001:
|
||||
- minor stability patches
|
||||
- implemented db syncing for timestamps
|
||||
- fixed bugs
|
||||
0.0.2.000:
|
||||
- finalized testing and small fixes
|
||||
0.0.1.004:
|
||||
- finished metrics implement, trueskill is bugged
|
||||
0.0.1.003:
|
||||
- working
|
||||
0.0.1.002:
|
||||
- started implement of metrics
|
||||
0.0.1.001:
|
||||
- cleaned up imports
|
||||
0.0.1.000:
|
||||
- tested working, can push to database
|
||||
0.0.0.009:
|
||||
- tested working
|
||||
- prints out stats for the time being, will push to database later
|
||||
0.0.0.008:
|
||||
- added data import
|
||||
- removed tba import
|
||||
- finished main method
|
||||
0.0.0.007:
|
||||
- added load_config
|
||||
- optimized simpleloop for readibility
|
||||
- added __all__ entries
|
||||
- added simplestats engine
|
||||
- pending testing
|
||||
0.0.0.006:
|
||||
- fixes
|
||||
0.0.0.005:
|
||||
- imported pickle
|
||||
- created custom database object
|
||||
0.0.0.004:
|
||||
- fixed simpleloop to actually return a vector
|
||||
0.0.0.003:
|
||||
- added metricsloop which is unfinished
|
||||
0.0.0.002:
|
||||
- added simpleloop which is untested until data is provided
|
||||
0.0.0.001:
|
||||
- created script
|
||||
- added analysis, numba, numpy imports
|
||||
"""
|
||||
|
||||
__author__ = (
|
||||
"Arthur Lu <learthurgo@gmail.com>",
|
||||
"Jacob Levine <jlevine@imsa.edu>",
|
||||
"Arthur Lu <learthurgo@gmail.com>",
|
||||
"Jacob Levine <jlevine@imsa.edu>",
|
||||
)
|
||||
|
||||
__all__ = [
|
||||
"main",
|
||||
"load_config",
|
||||
"simpleloop",
|
||||
"simplestats",
|
||||
"metricsloop"
|
||||
"main",
|
||||
"load_config",
|
||||
"simpleloop",
|
||||
"simplestats",
|
||||
"metricsloop"
|
||||
]
|
||||
|
||||
# imports:
|
||||
@ -95,273 +95,273 @@ import time
|
||||
import warnings
|
||||
|
||||
def main():
|
||||
warnings.filterwarnings("ignore")
|
||||
while(True):
|
||||
warnings.filterwarnings("ignore")
|
||||
while(True):
|
||||
|
||||
current_time = time.time()
|
||||
print("[OK] time: " + str(current_time))
|
||||
current_time = time.time()
|
||||
print("[OK] time: " + str(current_time))
|
||||
|
||||
start = time.time()
|
||||
config = load_config(Path("config/stats.config"))
|
||||
competition = an.load_csv(Path("config/competition.config"))[0][0]
|
||||
print("[OK] configs loaded")
|
||||
start = time.time()
|
||||
config = load_config(Path("config/stats.config"))
|
||||
competition = an.load_csv(Path("config/competition.config"))[0][0]
|
||||
print("[OK] configs loaded")
|
||||
|
||||
apikey = an.load_csv(Path("config/keys.config"))[0][0]
|
||||
tbakey = an.load_csv(Path("config/keys.config"))[1][0]
|
||||
print("[OK] loaded keys")
|
||||
apikey = an.load_csv(Path("config/keys.config"))[0][0]
|
||||
tbakey = an.load_csv(Path("config/keys.config"))[1][0]
|
||||
print("[OK] loaded keys")
|
||||
|
||||
previous_time = d.get_analysis_flags(apikey, "latest_update")
|
||||
previous_time = d.get_analysis_flags(apikey, "latest_update")
|
||||
|
||||
if(previous_time == None):
|
||||
if(previous_time == None):
|
||||
|
||||
d.set_analysis_flags(apikey, "latest_update", 0)
|
||||
previous_time = 0
|
||||
d.set_analysis_flags(apikey, "latest_update", 0)
|
||||
previous_time = 0
|
||||
|
||||
else:
|
||||
else:
|
||||
|
||||
previous_time = previous_time["latest_update"]
|
||||
previous_time = previous_time["latest_update"]
|
||||
|
||||
print("[OK] analysis backtimed to: " + str(previous_time))
|
||||
print("[OK] analysis backtimed to: " + str(previous_time))
|
||||
|
||||
print("[OK] loading data")
|
||||
start = time.time()
|
||||
data = d.get_match_data_formatted(apikey, competition)
|
||||
pit_data = d.pit = d.get_pit_data_formatted(apikey, competition)
|
||||
print("[OK] loaded data in " + str(time.time() - start) + " seconds")
|
||||
print("[OK] loading data")
|
||||
start = time.time()
|
||||
data = d.get_match_data_formatted(apikey, competition)
|
||||
pit_data = d.pit = d.get_pit_data_formatted(apikey, competition)
|
||||
print("[OK] loaded data in " + str(time.time() - start) + " seconds")
|
||||
|
||||
print("[OK] running tests")
|
||||
start = time.time()
|
||||
results = simpleloop(data, config)
|
||||
print("[OK] finished tests in " + str(time.time() - start) + " seconds")
|
||||
print("[OK] running tests")
|
||||
start = time.time()
|
||||
results = simpleloop(data, config)
|
||||
print("[OK] finished tests in " + str(time.time() - start) + " seconds")
|
||||
|
||||
print("[OK] running metrics")
|
||||
start = time.time()
|
||||
metricsloop(tbakey, apikey, competition, previous_time)
|
||||
print("[OK] finished metrics in " + str(time.time() - start) + " seconds")
|
||||
print("[OK] running metrics")
|
||||
start = time.time()
|
||||
metricsloop(tbakey, apikey, competition, previous_time)
|
||||
print("[OK] finished metrics in " + str(time.time() - start) + " seconds")
|
||||
|
||||
print("[OK] running pit analysis")
|
||||
start = time.time()
|
||||
pit = pitloop(pit_data, config)
|
||||
print("[OK] finished pit analysis in " + str(time.time() - start) + " seconds")
|
||||
print("[OK] running pit analysis")
|
||||
start = time.time()
|
||||
pit = pitloop(pit_data, config)
|
||||
print("[OK] finished pit analysis in " + str(time.time() - start) + " seconds")
|
||||
|
||||
d.set_analysis_flags(apikey, "latest_update", {"latest_update":current_time})
|
||||
|
||||
print("[OK] pushing to database")
|
||||
start = time.time()
|
||||
push_to_database(apikey, competition, results, pit)
|
||||
print("[OK] pushed to database in " + str(time.time() - start) + " seconds")
|
||||
d.set_analysis_flags(apikey, "latest_update", {"latest_update":current_time})
|
||||
|
||||
print("[OK] pushing to database")
|
||||
start = time.time()
|
||||
push_to_database(apikey, competition, results, pit)
|
||||
print("[OK] pushed to database in " + str(time.time() - start) + " seconds")
|
||||
|
||||
clear()
|
||||
clear()
|
||||
|
||||
def clear():
|
||||
|
||||
# for windows
|
||||
if name == 'nt':
|
||||
_ = system('cls')
|
||||
|
||||
# for windows
|
||||
if name == 'nt':
|
||||
_ = system('cls')
|
||||
|
||||
# for mac and linux(here, os.name is 'posix')
|
||||
else:
|
||||
_ = system('clear')
|
||||
# for mac and linux(here, os.name is 'posix')
|
||||
else:
|
||||
_ = system('clear')
|
||||
|
||||
def load_config(file):
|
||||
config_vector = {}
|
||||
file = an.load_csv(file)
|
||||
for line in file:
|
||||
config_vector[line[0]] = line[1:]
|
||||
config_vector = {}
|
||||
file = an.load_csv(file)
|
||||
for line in file:
|
||||
config_vector[line[0]] = line[1:]
|
||||
|
||||
return config_vector
|
||||
return config_vector
|
||||
|
||||
def simpleloop(data, tests): # expects 3D array with [Team][Variable][Match]
|
||||
|
||||
return_vector = {}
|
||||
for team in data:
|
||||
variable_vector = {}
|
||||
for variable in data[team]:
|
||||
test_vector = {}
|
||||
variable_data = data[team][variable]
|
||||
if(variable in tests):
|
||||
for test in tests[variable]:
|
||||
test_vector[test] = simplestats(variable_data, test)
|
||||
else:
|
||||
pass
|
||||
variable_vector[variable] = test_vector
|
||||
return_vector[team] = variable_vector
|
||||
return_vector = {}
|
||||
for team in data:
|
||||
variable_vector = {}
|
||||
for variable in data[team]:
|
||||
test_vector = {}
|
||||
variable_data = data[team][variable]
|
||||
if(variable in tests):
|
||||
for test in tests[variable]:
|
||||
test_vector[test] = simplestats(variable_data, test)
|
||||
else:
|
||||
pass
|
||||
variable_vector[variable] = test_vector
|
||||
return_vector[team] = variable_vector
|
||||
|
||||
return return_vector
|
||||
return return_vector
|
||||
|
||||
def simplestats(data, test):
|
||||
|
||||
data = np.array(data)
|
||||
data = data[np.isfinite(data)]
|
||||
ranges = list(range(len(data)))
|
||||
data = np.array(data)
|
||||
data = data[np.isfinite(data)]
|
||||
ranges = list(range(len(data)))
|
||||
|
||||
if(test == "basic_stats"):
|
||||
return an.basic_stats(data)
|
||||
if(test == "basic_stats"):
|
||||
return an.basic_stats(data)
|
||||
|
||||
if(test == "historical_analysis"):
|
||||
return an.histo_analysis([ranges, data])
|
||||
if(test == "historical_analysis"):
|
||||
return an.histo_analysis([ranges, data])
|
||||
|
||||
if(test == "regression_linear"):
|
||||
return an.regression(ranges, data, ['lin'])
|
||||
if(test == "regression_linear"):
|
||||
return an.regression(ranges, data, ['lin'])
|
||||
|
||||
if(test == "regression_logarithmic"):
|
||||
return an.regression(ranges, data, ['log'])
|
||||
if(test == "regression_logarithmic"):
|
||||
return an.regression(ranges, data, ['log'])
|
||||
|
||||
if(test == "regression_exponential"):
|
||||
return an.regression(ranges, data, ['exp'])
|
||||
if(test == "regression_exponential"):
|
||||
return an.regression(ranges, data, ['exp'])
|
||||
|
||||
if(test == "regression_polynomial"):
|
||||
return an.regression(ranges, data, ['ply'])
|
||||
if(test == "regression_polynomial"):
|
||||
return an.regression(ranges, data, ['ply'])
|
||||
|
||||
if(test == "regression_sigmoidal"):
|
||||
return an.regression(ranges, data, ['sig'])
|
||||
if(test == "regression_sigmoidal"):
|
||||
return an.regression(ranges, data, ['sig'])
|
||||
|
||||
def push_to_database(apikey, competition, results, pit):
|
||||
|
||||
for team in results:
|
||||
for team in results:
|
||||
|
||||
d.push_team_tests_data(apikey, competition, team, results[team])
|
||||
d.push_team_tests_data(apikey, competition, team, results[team])
|
||||
|
||||
for variable in pit:
|
||||
for variable in pit:
|
||||
|
||||
d.push_team_pit_data(apikey, competition, variable, pit[variable])
|
||||
d.push_team_pit_data(apikey, competition, variable, pit[variable])
|
||||
|
||||
def metricsloop(tbakey, apikey, competition, timestamp): # listener based metrics update
|
||||
|
||||
elo_N = 400
|
||||
elo_K = 24
|
||||
elo_N = 400
|
||||
elo_K = 24
|
||||
|
||||
matches = d.pull_new_tba_matches(tbakey, competition, timestamp)
|
||||
matches = d.pull_new_tba_matches(tbakey, competition, timestamp)
|
||||
|
||||
red = {}
|
||||
blu = {}
|
||||
red = {}
|
||||
blu = {}
|
||||
|
||||
for match in matches:
|
||||
for match in matches:
|
||||
|
||||
red = load_metrics(apikey, competition, match, "red")
|
||||
blu = load_metrics(apikey, competition, match, "blue")
|
||||
red = load_metrics(apikey, competition, match, "red")
|
||||
blu = load_metrics(apikey, competition, match, "blue")
|
||||
|
||||
elo_red_total = 0
|
||||
elo_blu_total = 0
|
||||
elo_red_total = 0
|
||||
elo_blu_total = 0
|
||||
|
||||
gl2_red_score_total = 0
|
||||
gl2_blu_score_total = 0
|
||||
gl2_red_score_total = 0
|
||||
gl2_blu_score_total = 0
|
||||
|
||||
gl2_red_rd_total = 0
|
||||
gl2_blu_rd_total = 0
|
||||
gl2_red_rd_total = 0
|
||||
gl2_blu_rd_total = 0
|
||||
|
||||
gl2_red_vol_total = 0
|
||||
gl2_blu_vol_total = 0
|
||||
gl2_red_vol_total = 0
|
||||
gl2_blu_vol_total = 0
|
||||
|
||||
for team in red:
|
||||
for team in red:
|
||||
|
||||
elo_red_total += red[team]["elo"]["score"]
|
||||
elo_red_total += red[team]["elo"]["score"]
|
||||
|
||||
gl2_red_score_total += red[team]["gl2"]["score"]
|
||||
gl2_red_rd_total += red[team]["gl2"]["rd"]
|
||||
gl2_red_vol_total += red[team]["gl2"]["vol"]
|
||||
gl2_red_score_total += red[team]["gl2"]["score"]
|
||||
gl2_red_rd_total += red[team]["gl2"]["rd"]
|
||||
gl2_red_vol_total += red[team]["gl2"]["vol"]
|
||||
|
||||
for team in blu:
|
||||
for team in blu:
|
||||
|
||||
elo_blu_total += blu[team]["elo"]["score"]
|
||||
elo_blu_total += blu[team]["elo"]["score"]
|
||||
|
||||
gl2_blu_score_total += blu[team]["gl2"]["score"]
|
||||
gl2_blu_rd_total += blu[team]["gl2"]["rd"]
|
||||
gl2_blu_vol_total += blu[team]["gl2"]["vol"]
|
||||
gl2_blu_score_total += blu[team]["gl2"]["score"]
|
||||
gl2_blu_rd_total += blu[team]["gl2"]["rd"]
|
||||
gl2_blu_vol_total += blu[team]["gl2"]["vol"]
|
||||
|
||||
red_elo = {"score": elo_red_total / len(red)}
|
||||
blu_elo = {"score": elo_blu_total / len(blu)}
|
||||
red_elo = {"score": elo_red_total / len(red)}
|
||||
blu_elo = {"score": elo_blu_total / len(blu)}
|
||||
|
||||
red_gl2 = {"score": gl2_red_score_total / len(red), "rd": gl2_red_rd_total / len(red), "vol": gl2_red_vol_total / len(red)}
|
||||
blu_gl2 = {"score": gl2_blu_score_total / len(blu), "rd": gl2_blu_rd_total / len(blu), "vol": gl2_blu_vol_total / len(blu)}
|
||||
red_gl2 = {"score": gl2_red_score_total / len(red), "rd": gl2_red_rd_total / len(red), "vol": gl2_red_vol_total / len(red)}
|
||||
blu_gl2 = {"score": gl2_blu_score_total / len(blu), "rd": gl2_blu_rd_total / len(blu), "vol": gl2_blu_vol_total / len(blu)}
|
||||
|
||||
|
||||
if(match["winner"] == "red"):
|
||||
if(match["winner"] == "red"):
|
||||
|
||||
observations = {"red": 1, "blu": 0}
|
||||
observations = {"red": 1, "blu": 0}
|
||||
|
||||
elif(match["winner"] == "blue"):
|
||||
elif(match["winner"] == "blue"):
|
||||
|
||||
observations = {"red": 0, "blu": 1}
|
||||
observations = {"red": 0, "blu": 1}
|
||||
|
||||
else:
|
||||
else:
|
||||
|
||||
observations = {"red": 0.5, "blu": 0.5}
|
||||
observations = {"red": 0.5, "blu": 0.5}
|
||||
|
||||
red_elo_delta = an.Metrics.elo(red_elo["score"], blu_elo["score"], observations["red"], elo_N, elo_K) - red_elo["score"]
|
||||
blu_elo_delta = an.Metrics.elo(blu_elo["score"], red_elo["score"], observations["blu"], elo_N, elo_K) - blu_elo["score"]
|
||||
red_elo_delta = an.Metrics.elo(red_elo["score"], blu_elo["score"], observations["red"], elo_N, elo_K) - red_elo["score"]
|
||||
blu_elo_delta = an.Metrics.elo(blu_elo["score"], red_elo["score"], observations["blu"], elo_N, elo_K) - blu_elo["score"]
|
||||
|
||||
new_red_gl2_score, new_red_gl2_rd, new_red_gl2_vol = an.Metrics.glicko2(red_gl2["score"], red_gl2["rd"], red_gl2["vol"], [blu_gl2["score"]], [blu_gl2["rd"]], [observations["red"], observations["blu"]])
|
||||
new_blu_gl2_score, new_blu_gl2_rd, new_blu_gl2_vol = an.Metrics.glicko2(blu_gl2["score"], blu_gl2["rd"], blu_gl2["vol"], [red_gl2["score"]], [red_gl2["rd"]], [observations["blu"], observations["red"]])
|
||||
new_red_gl2_score, new_red_gl2_rd, new_red_gl2_vol = an.Metrics.glicko2(red_gl2["score"], red_gl2["rd"], red_gl2["vol"], [blu_gl2["score"]], [blu_gl2["rd"]], [observations["red"], observations["blu"]])
|
||||
new_blu_gl2_score, new_blu_gl2_rd, new_blu_gl2_vol = an.Metrics.glicko2(blu_gl2["score"], blu_gl2["rd"], blu_gl2["vol"], [red_gl2["score"]], [red_gl2["rd"]], [observations["blu"], observations["red"]])
|
||||
|
||||
red_gl2_delta = {"score": new_red_gl2_score - red_gl2["score"], "rd": new_red_gl2_rd - red_gl2["rd"], "vol": new_red_gl2_vol - red_gl2["vol"]}
|
||||
blu_gl2_delta = {"score": new_blu_gl2_score - blu_gl2["score"], "rd": new_blu_gl2_rd - blu_gl2["rd"], "vol": new_blu_gl2_vol - blu_gl2["vol"]}
|
||||
red_gl2_delta = {"score": new_red_gl2_score - red_gl2["score"], "rd": new_red_gl2_rd - red_gl2["rd"], "vol": new_red_gl2_vol - red_gl2["vol"]}
|
||||
blu_gl2_delta = {"score": new_blu_gl2_score - blu_gl2["score"], "rd": new_blu_gl2_rd - blu_gl2["rd"], "vol": new_blu_gl2_vol - blu_gl2["vol"]}
|
||||
|
||||
for team in red:
|
||||
for team in red:
|
||||
|
||||
red[team]["elo"]["score"] = red[team]["elo"]["score"] + red_elo_delta
|
||||
red[team]["elo"]["score"] = red[team]["elo"]["score"] + red_elo_delta
|
||||
|
||||
red[team]["gl2"]["score"] = red[team]["gl2"]["score"] + red_gl2_delta["score"]
|
||||
red[team]["gl2"]["rd"] = red[team]["gl2"]["rd"] + red_gl2_delta["rd"]
|
||||
red[team]["gl2"]["vol"] = red[team]["gl2"]["vol"] + red_gl2_delta["vol"]
|
||||
red[team]["gl2"]["score"] = red[team]["gl2"]["score"] + red_gl2_delta["score"]
|
||||
red[team]["gl2"]["rd"] = red[team]["gl2"]["rd"] + red_gl2_delta["rd"]
|
||||
red[team]["gl2"]["vol"] = red[team]["gl2"]["vol"] + red_gl2_delta["vol"]
|
||||
|
||||
for team in blu:
|
||||
for team in blu:
|
||||
|
||||
blu[team]["elo"]["score"] = blu[team]["elo"]["score"] + blu_elo_delta
|
||||
blu[team]["elo"]["score"] = blu[team]["elo"]["score"] + blu_elo_delta
|
||||
|
||||
blu[team]["gl2"]["score"] = blu[team]["gl2"]["score"] + blu_gl2_delta["score"]
|
||||
blu[team]["gl2"]["rd"] = blu[team]["gl2"]["rd"] + blu_gl2_delta["rd"]
|
||||
blu[team]["gl2"]["vol"] = blu[team]["gl2"]["vol"] + blu_gl2_delta["vol"]
|
||||
blu[team]["gl2"]["score"] = blu[team]["gl2"]["score"] + blu_gl2_delta["score"]
|
||||
blu[team]["gl2"]["rd"] = blu[team]["gl2"]["rd"] + blu_gl2_delta["rd"]
|
||||
blu[team]["gl2"]["vol"] = blu[team]["gl2"]["vol"] + blu_gl2_delta["vol"]
|
||||
|
||||
temp_vector = {}
|
||||
temp_vector.update(red)
|
||||
temp_vector.update(blu)
|
||||
temp_vector = {}
|
||||
temp_vector.update(red)
|
||||
temp_vector.update(blu)
|
||||
|
||||
for team in temp_vector:
|
||||
for team in temp_vector:
|
||||
|
||||
d.push_team_metrics_data(apikey, competition, team, temp_vector[team])
|
||||
d.push_team_metrics_data(apikey, competition, team, temp_vector[team])
|
||||
|
||||
def load_metrics(apikey, competition, match, group_name):
|
||||
|
||||
group = {}
|
||||
group = {}
|
||||
|
||||
for team in match[group_name]:
|
||||
for team in match[group_name]:
|
||||
|
||||
db_data = d.get_team_metrics_data(apikey, competition, team)
|
||||
db_data = d.get_team_metrics_data(apikey, competition, team)
|
||||
|
||||
if d.get_team_metrics_data(apikey, competition, team) == None:
|
||||
if d.get_team_metrics_data(apikey, competition, team) == None:
|
||||
|
||||
elo = {"score": 1500}
|
||||
gl2 = {"score": 1500, "rd": 250, "vol": 0.06}
|
||||
ts = {"mu": 25, "sigma": 25/3}
|
||||
elo = {"score": 1500}
|
||||
gl2 = {"score": 1500, "rd": 250, "vol": 0.06}
|
||||
ts = {"mu": 25, "sigma": 25/3}
|
||||
|
||||
#d.push_team_metrics_data(apikey, competition, team, {"elo":elo, "gl2":gl2,"trueskill":ts})
|
||||
#d.push_team_metrics_data(apikey, competition, team, {"elo":elo, "gl2":gl2,"trueskill":ts})
|
||||
|
||||
group[team] = {"elo": elo, "gl2": gl2, "ts": ts}
|
||||
group[team] = {"elo": elo, "gl2": gl2, "ts": ts}
|
||||
|
||||
else:
|
||||
else:
|
||||
|
||||
metrics = db_data["metrics"]
|
||||
metrics = db_data["metrics"]
|
||||
|
||||
elo = metrics["elo"]
|
||||
gl2 = metrics["gl2"]
|
||||
ts = metrics["ts"]
|
||||
elo = metrics["elo"]
|
||||
gl2 = metrics["gl2"]
|
||||
ts = metrics["ts"]
|
||||
|
||||
group[team] = {"elo": elo, "gl2": gl2, "ts": ts}
|
||||
group[team] = {"elo": elo, "gl2": gl2, "ts": ts}
|
||||
|
||||
return group
|
||||
return group
|
||||
|
||||
def pitloop(pit, tests):
|
||||
|
||||
return_vector = {}
|
||||
for team in pit:
|
||||
for variable in pit[team]:
|
||||
if(variable in tests):
|
||||
if(not variable in return_vector):
|
||||
return_vector[variable] = []
|
||||
return_vector[variable].append(pit[team][variable])
|
||||
return_vector = {}
|
||||
for team in pit:
|
||||
for variable in pit[team]:
|
||||
if(variable in tests):
|
||||
if(not variable in return_vector):
|
||||
return_vector[variable] = []
|
||||
return_vector[variable].append(pit[team][variable])
|
||||
|
||||
return return_vector
|
||||
return return_vector
|
||||
|
||||
main()
|
||||
|
||||
|
||||
@ -8,20 +8,20 @@ import pymongo
|
||||
|
||||
# %%
|
||||
def get_pit_variable_data(apikey, competition):
|
||||
client = pymongo.MongoClient(apikey)
|
||||
db = client.data_processing
|
||||
mdata = db.team_pit
|
||||
out = {}
|
||||
return mdata.find()
|
||||
client = pymongo.MongoClient(apikey)
|
||||
db = client.data_processing
|
||||
mdata = db.team_pit
|
||||
out = {}
|
||||
return mdata.find()
|
||||
|
||||
|
||||
# %%
|
||||
def get_pit_variable_formatted(apikey, competition):
|
||||
temp = get_pit_variable_data(apikey, competition)
|
||||
out = {}
|
||||
for i in temp:
|
||||
out[i["variable"]] = i["data"]
|
||||
return out
|
||||
temp = get_pit_variable_data(apikey, competition)
|
||||
out = {}
|
||||
for i in temp:
|
||||
out[i["variable"]] = i["data"]
|
||||
return out
|
||||
|
||||
|
||||
# %%
|
||||
@ -40,16 +40,16 @@ i = 0
|
||||
|
||||
for variable in pit:
|
||||
|
||||
ax[i].hist(pit[variable])
|
||||
ax[i].invert_xaxis()
|
||||
ax[i].hist(pit[variable])
|
||||
ax[i].invert_xaxis()
|
||||
|
||||
ax[i].set_xlabel('')
|
||||
ax[i].set_ylabel('Frequency')
|
||||
ax[i].set_title(variable)
|
||||
ax[i].set_xlabel('')
|
||||
ax[i].set_ylabel('Frequency')
|
||||
ax[i].set_title(variable)
|
||||
|
||||
plt.yticks(np.arange(len(pit[variable])))
|
||||
plt.yticks(np.arange(len(pit[variable])))
|
||||
|
||||
i+=1
|
||||
i+=1
|
||||
|
||||
plt.show()
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user