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WORKING!!!!

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ltcptgeneral 2019-03-21 17:25:16 -05:00
parent 4cc1ad28fe
commit 52c3ddd2d6
2 changed files with 35 additions and 29 deletions
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data analysis/__pycache__/analysis.cpython-37.pyc
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data analysis/superscript.py
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@ -67,11 +67,13 @@ def titanservice():
file_list = glob.glob(source_dir + '/*.csv') #supposedly sorts by alphabetical order, skips reading teams.csv because of redundancy file_list = glob.glob(source_dir + '/*.csv') #supposedly sorts by alphabetical order, skips reading teams.csv because of redundancy
data = [] data = []
files = [fn for fn in glob.glob('data/*.csv') files = [fn for fn in glob.glob('data/*.csv')
if not (os.path.basename(fn).startswith('teams') or os.path.basename(fn).startswith('match') or os.path.basename(fn).startswith('notes') or os.path.basename(fn).startswith('observationType') or os.path.basename(fn).startswith('teamDBRef'))] #scores will be handled sperately if not (os.path.basename(fn).startswith('scores') or os.path.basename(fn).startswith('teams') or os.path.basename(fn).startswith('match') or os.path.basename(fn).startswith('notes') or os.path.basename(fn).startswith('observationType') or os.path.basename(fn).startswith('teamDBRef'))] #scores will be handled sperately
for i in files: for i in files:
data.append(analysis.load_csv(i)) data.append(analysis.load_csv(i))
#print(files)
stats = [] stats = []
measure_stats = [] measure_stats = []
teams = analysis.load_csv("data/teams.csv") teams = analysis.load_csv("data/teams.csv")
@ -127,49 +129,53 @@ def titanservice():
stats.append(list(measure_stats)) stats.append(list(measure_stats))
nishant = [] nishant = []
for i in range(len(scores)): for i in range(len(scores)):
ofbest_curve = [None] #print(scores)
r2best_curve = [None]
line = measure[i] ofbest_curve = [None]
r2best_curve = [None]
#print(line) line = scores[i]
x = list(range(len(line))) #print(line)
eqs, rmss, r2s, overfit = analysis.optimize_regression(x, line, 10, 1)
beqs, brmss, br2s, boverfit = analysis.select_best_regression(eqs, rmss, r2s, overfit, "min_overfit") #print(line)
#print(eqs, rmss, r2s, overfit) x = list(range(len(line)))
eqs, rmss, r2s, overfit = analysis.optimize_regression(x, line, 10, 1)
beqs, brmss, br2s, boverfit = analysis.select_best_regression(eqs, rmss, r2s, overfit, "min_overfit")
#print(eqs, rmss, r2s, overfit)
ofbest_curve.append(beqs) ofbest_curve.append(beqs)
ofbest_curve.append(brmss) ofbest_curve.append(brmss)
ofbest_curve.append(br2s) ofbest_curve.append(br2s)
ofbest_curve.append(boverfit) ofbest_curve.append(boverfit)
ofbest_curve.pop(0) ofbest_curve.pop(0)
#print(ofbest_curve) #print(ofbest_curve)
beqs, brmss, br2s, boverfit = analysis.select_best_regression(eqs, rmss, r2s, overfit, "max_r2s") beqs, brmss, br2s, boverfit = analysis.select_best_regression(eqs, rmss, r2s, overfit, "max_r2s")
r2best_curve.append(beqs) r2best_curve.append(beqs)
r2best_curve.append(brmss) r2best_curve.append(brmss)
r2best_curve.append(br2s) r2best_curve.append(br2s)
r2best_curve.append(boverfit) r2best_curve.append(boverfit)
r2best_curve.pop(0) r2best_curve.pop(0)
#print(r2best_curve) #print(r2best_curve)
z = len(scores[0]) + 1 z = len(scores[0]) + 1
nis_num = [] nis_num = []
nis_num.append(eval(str(ofbest_curve[0]))) nis_num.append(eval(str(ofbest_curve[0])))
nis_num.append(eval(str(r2best_curve[0]))) nis_num.append(eval(str(r2best_curve[0])))
nis_num.append((eval(ofbest_curve[0]) + eval(r2best_curve[0])) / 2) nis_num.append((eval(ofbest_curve[0]) + eval(r2best_curve[0])) / 2)
nishant.append(teams[i] + nis_num) nishant.append(teams[i] + nis_num)
json_out = {} json_out = {}
score_out = {} score_out = {}