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superscript.py - v 1.

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changelog:
- processes data more efficiently
This commit is contained in:
ltcptgeneral 2019-02-20 22:59:17 -06:00
parent f40ff03cb2
commit 6c665b10df
7 changed files with 134 additions and 46 deletions
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data analysis/__pycache__/analysis.cpython-37.pyc
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82
data analysis/analysis.py
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@ -423,7 +423,7 @@ def basic_stats(data, method, arg): # data=array, mode = ['1d':1d_basic_stats, '
data_t = [] data_t = []
for i in range (0, len(data) - 1, 1): for i in range (0, len(data), 1):
data_t.append(float(data[i])) data_t.append(float(data[i]))
_mean = mean(data_t) _mean = mean(data_t)
@ -559,12 +559,15 @@ def stdev_z_split(mean, stdev, delta, low_bound, high_bound): #returns n-th perc
def histo_analysis(hist_data, delta, low_bound, high_bound): def histo_analysis(hist_data, delta, low_bound, high_bound):
if hist_data == 'debug': if hist_data == 'debug':
return ('returns list of predicted values based on historical data; input delta for delta step in z-score and lower and higher bounds in number for standard deviations') return ('returns list of predicted values based on historical data; input delta for delta step in z-score and lower and higher bounds in number of standard deviations')
derivative = [] derivative = []
for i in range(0, len(hist_data) - 1, 1): for i in range(0, len(hist_data), 1):
derivative.append(float(hist_data[i + 1]) - float(hist_data [i])) try:
derivative.append(float(hist_data[i - 1]) - float(hist_data [i]))
except:
pass
derivative_sorted = sorted(derivative, key=int) derivative_sorted = sorted(derivative, key=int)
mean_derivative = basic_stats(derivative_sorted,"1d", 0)[0] mean_derivative = basic_stats(derivative_sorted,"1d", 0)[0]
@ -701,7 +704,7 @@ def tanh_regression(x, y):
def r_squared(predictions, targets): # assumes equal size inputs def r_squared(predictions, targets): # assumes equal size inputs
return metrics.r2_score(targets, predictions) return metrics.r2_score(np.array(targets), np.array(predictions))
def rms(predictions, targets): # assumes equal size inputs def rms(predictions, targets): # assumes equal size inputs
@ -728,7 +731,7 @@ def calc_overfit(equation, rms_train, r2_train, x_test, y_test):
r2_test = r_squared(vals, y_test) r2_test = r_squared(vals, y_test)
rms_test = rms(vals, y_test) rms_test = rms(vals, y_test)
return rms_train - rms_test, r2_train - r2_test return r2_train - r2_test
def strip_data(data, mode): def strip_data(data, mode):
@ -747,7 +750,10 @@ def optimize_regression(x, y, _range, resolution):#_range in poly regression is
raise error("resolution must be int") raise error("resolution must be int")
x_train = x x_train = x
y_train = y y_train = []
for i in range(len(y)):
y_train.append(float(y[i]))
x_test = [] x_test = []
y_test = [] y_test = []
@ -756,7 +762,7 @@ def optimize_regression(x, y, _range, resolution):#_range in poly regression is
index = random.randint(0, len(x) - 1) index = random.randint(0, len(x) - 1)
x_test.append(x[index]) x_test.append(x[index])
y_test.append(y[index]) y_test.append(float(y[index]))
x_train.pop(index) x_train.pop(index)
y_train.pop(index) y_train.pop(index)
@ -769,10 +775,13 @@ def optimize_regression(x, y, _range, resolution):#_range in poly regression is
r2s = [] r2s = []
for i in range (0, _range + 1, 1): for i in range (0, _range + 1, 1):
x, y, z = poly_regression(x_train, y_train, i) try:
eqs.append(x) x, y, z = poly_regression(x_train, y_train, i)
rmss.append(y) eqs.append(x)
r2s.append(z) rmss.append(y)
r2s.append(z)
except:
pass
for i in range (1, 100 * resolution + 1): for i in range (1, 100 * resolution + 1):
try: try:
@ -792,11 +801,14 @@ def optimize_regression(x, y, _range, resolution):#_range in poly regression is
except: except:
pass pass
x, y, z = tanh_regression(x_train, y_train) try:
x, y, z = tanh_regression(x_train, y_train)
eqs.append(x) eqs.append(x)
rmss.append(y) rmss.append(y)
r2s.append(z) r2s.append(z)
except:
pass
for i in range (0, len(eqs), 1): #marks all equations where r2 = 1 as they 95% of the time overfit the data for i in range (0, len(eqs), 1): #marks all equations where r2 = 1 as they 95% of the time overfit the data
if r2s[i] == 1: if r2s[i] == 1:
@ -815,38 +827,38 @@ def optimize_regression(x, y, _range, resolution):#_range in poly regression is
overfit = [] overfit = []
for i in range (0, len(eqs), 1): for i in range (0, len(eqs), 1):
overfit.append(calc_overfit(eqs[i], rmss[i], r2s[i], x_test, y_test)) overfit.append(calc_overfit(eqs[i], rmss[i], r2s[i], x_test, y_test))
return eqs, rmss, r2s, overfit return eqs, rmss, r2s, overfit
def select_best_regression(eqs, rmss, r2s, overfit, selector): def select_best_regression(eqs, rmss, r2s, overfit, selector):
b_eq = "" b_eq = ""
b_rms = 0 b_rms = 0
b_r2 = 0 b_r2 = 0
b_overfit = 0 b_overfit = 0
ind = 0 ind = 0
if selector == "min_overfit": if selector == "min_overfit":
ind = np.argmax(overfit) ind = np.argmin(overfit)
b_eq = eqs[ind] b_eq = eqs[ind]
b_rms = rmss[ind] b_rms = rmss[ind]
b_r2 = r2s[ind] b_r2 = r2s[ind]
b_overfit = overfit[ind] b_overfit = overfit[ind]
if selector == "max_rmss": if selector == "max_r2s":
ind = np.argmax(rmss) ind = np.argmax(r2s)
b_eq = eqs[ind]
b_rms = rmss[ind]
b_r2 = r2s[ind]
b_overfit = overfit[ind]
b_eq = eqs[ind] return b_eq, b_rms, b_r2, b_overfit
b_rms = rmss[ind]
b_r2 = r2s[ind]
b_overfit = overfit[ind]
return b_eq, b_rms, b_r2, b_overfit
def p_value(x, y): #takes 2 1d arrays def p_value(x, y): #takes 2 1d arrays

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data analysis/data/aSetOfData.csv
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@ -1 +1,5 @@
2022,4,4,4,4 2011,5,5,5,5,5 1101,6,6,6,6,6,6 821374,7,7,7 5,8,8,8 2022,4,5,10,15,8,954978,84,74
2011,3,6,9,12,15,7,856,9
1101,4,16,32,64,128,2,4234,-56
821374,7,8,9,79.33333333,170.3333333,636647,5874.666667,-121
5,4,8,8,103.8333333,230.3333333,1114135,7949.666667,-186

1 2022 4 4 5 4 10 4 2011 15 8 5 954978 5 84 5 74 5 1101 6 6 6 6 6 6 821374 7 7 7 5 8 8
2 2011 3 6 9 12 15 7 856 9
3 1101 4 16 32 64 128 2 4234 -56
4 821374 7 8 9 79.33333333 170.3333333 636647 5874.666667 -121
5 5 4 8 8 103.8333333 230.3333333 1114135 7949.666667 -186

5
data analysis/data/bdata.csv Normal file
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@ -0,0 +1,5 @@
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6
data analysis/data/data.csv
View File

@ -1 +1,5 @@
2022,1,2,3,4 2011,5,6,7,8 1101,1000,2000,3000,4000 821374,0,0,0,0 5,3,5,6 2022,1,2,3,4,5,6,7,8
2011,5,6,7,8,9,10,11,12
1101,1000,2000,3000,4000,5000,6000,7000,8000
821374,0,0,0,0,0,0,0,1
5,3,5,6,7,8.5,9.8,11.1,12.4

1 2022 1 2 3 4 2011 4 5 6 7 8 1101 8 1000 2000 3000 4000 821374 0 0 0 0 5 3 5 6
2 2011 5 6 7 8 9 10 11 12
3 1101 1000 2000 3000 4000 5000 6000 7000 8000
4 821374 0 0 0 0 0 0 0 1
5 5 3 5 6 7 8.5 9.8 11.1 12.4

12
data analysis/keys/titanscoutandroid_firebase.json Normal file
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@ -0,0 +1,12 @@
{
"type": "service_account",
"project_id": "titanscoutandroid",
"private_key_id": "4fa61815e17c0b5c4dfe48445d87f8fc6d07a5d8",
"private_key": "-----BEGIN PRIVATE KEY-----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\nSBMA/OHEodv06XqibcgovA==\n-----END PRIVATE KEY-----\n",
"client_email": "firebase-adminsdk-wpsvx@titanscoutandroid.iam.gserviceaccount.com",
"client_id": "114864465329268712237",
"auth_uri": "https://accounts.google.com/o/oauth2/auth",
"token_uri": "https://oauth2.googleapis.com/token",
"auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs",
"client_x509_cert_url": "https://www.googleapis.com/robot/v1/metadata/x509/firebase-adminsdk-wpsvx%40titanscoutandroid.iam.gserviceaccount.com"
}

61
data analysis/superscript.py
View File

@ -6,6 +6,8 @@
__version__ = "1.0.3.000" __version__ = "1.0.3.000"
__changelog__ = """changelog: __changelog__ = """changelog:
1.0.3.001:
- processes data more efficiently
1.0.3.000: 1.0.3.000:
- actually processes data - actually processes data
1.0.2.000: 1.0.2.000:
@ -23,13 +25,26 @@ __author__ = (
"Jacob Levine <jlevine@ttic.edu>," "Jacob Levine <jlevine@ttic.edu>,"
) )
import firebase_admin
from firebase_admin import credentials
from firebase_admin import firestore
import analysis import analysis
import titanlearn import titanlearn
import visualization import visualization
import os import os
import glob import glob
import numpy as np
# Use a service account
cred = credentials.Certificate('keys/titanscoutandroid_firebase.json')
firebase_admin.initialize_app(cred)
db = firestore.client()
#get all the data #get all the data
analysis.generate_data("data/bdata.csv", 100, 5, -10, 10)
source_dir = 'data' source_dir = 'data'
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 = []
@ -50,14 +65,50 @@ teams = analysis.load_csv("data/teams.csv")
#assumes that team number is in the first column, and that the order of teams is the same across all files #assumes that team number is in the first column, and that the order of teams is the same across all files
#unhelpful comment #unhelpful comment
for measure in data: #unpacks 3d array into 2ds for measure in data: #unpacks 3d array into 2ds
measure_stats = []
for i in range(len(measure)): #unpacks into specific teams for i in range(len(measure)): #unpacks into specific teams
ofbest_curve = [None]
r2best_curve = [None]
line = measure[i] line = measure[i]
line.pop(0) #removes team identifier
measure_stats.append(teams[i] + list(analysis.basic_stats(line, 0, 0))) #print(line)
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(brmss)
ofbest_curve.append(br2s)
ofbest_curve.append(boverfit)
ofbest_curve.pop(0)
#print(ofbest_curve)
beqs, brmss, br2s, boverfit = analysis.select_best_regression(eqs, rmss, r2s, overfit, "max_r2s")
r2best_curve.append(beqs)
r2best_curve.append(brmss)
r2best_curve.append(br2s)
r2best_curve.append(boverfit)
r2best_curve.pop(0)
#print(r2best_curve)
measure_stats.append(teams[i] + ["|"] + list(analysis.basic_stats(line, 0, 0)) + ["|"] + list(analysis.histo_analysis(line, 1, -3, 3)) + ["|"] + ofbest_curve + ["|"] + r2best_curve)
stats.append(list(measure_stats)) stats.append(list(measure_stats))
print (stats) json_out = {}
# print(d)
#print (stats) for i in range(len(stats)):
json_out[files[i]]=stats[i]
db.collection(u'stats').document(u'stats-noNN').set(json_out)