mirror of
https://github.com/titanscouting/tra-analysis.git
synced 2024-09-20 17:07:55 +00:00
238 lines
6.5 KiB
Python
238 lines
6.5 KiB
Python
# Titan Robotics Team 2022: Data Analysis Module
|
|
# Written by Arthur Lu & Jacob Levine
|
|
# Notes:
|
|
# this should be imported as a python module using 'import analysis'
|
|
# this should be included in the local directory or environment variable
|
|
# this module has not been optimized for multhreaded computing
|
|
# current benchmark of optimization: 1.33 times faster
|
|
# setup:
|
|
|
|
__version__ = "1.1.0.003"
|
|
|
|
# changelog should be viewed using print(analysis.__changelog__)
|
|
__changelog__ = """changelog:
|
|
1.1.0.003:
|
|
- resolved nopython mode for mean, median, stdev, variance
|
|
1.1.0.002:
|
|
- snapped (removed) majority of uneeded imports
|
|
- forced object mode (bad) on all jit
|
|
- TODO: stop numba complaining about not being able to compile in nopython mode
|
|
1.1.0.001:
|
|
- removed from sklearn import * to resolve uneeded wildcard imports
|
|
1.1.0.000:
|
|
- removed c_entities,nc_entities,obstacles,objectives from __all__
|
|
- applied numba.jit to all functions
|
|
- depreciated and removed stdev_z_split
|
|
- cleaned up histo_analysis to include numpy and numba.jit optimizations
|
|
- depreciated and removed all regression functions in favor of future pytorch optimizer
|
|
- depreciated and removed all nonessential functions (basic_analysis, benchmark, strip_data)
|
|
- optimized z_normalize using sklearn.preprocessing.normalize
|
|
- TODO: implement kernel/function based pytorch regression optimizer
|
|
1.0.9.000:
|
|
- refactored
|
|
- numpyed everything
|
|
- removed stats in favor of numpy functions
|
|
1.0.8.005:
|
|
- minor fixes
|
|
1.0.8.004:
|
|
- removed a few unused dependencies
|
|
1.0.8.003:
|
|
- added p_value function
|
|
1.0.8.002:
|
|
- updated __all__ correctly to contain changes made in v 1.0.8.000 and v 1.0.8.001
|
|
1.0.8.001:
|
|
- refactors
|
|
- bugfixes
|
|
1.0.8.000:
|
|
- depreciated histo_analysis_old
|
|
- depreciated debug
|
|
- altered basic_analysis to take array data instead of filepath
|
|
- refactor
|
|
- optimization
|
|
1.0.7.002:
|
|
- bug fixes
|
|
1.0.7.001:
|
|
- bug fixes
|
|
1.0.7.000:
|
|
- added tanh_regression (logistical regression)
|
|
- bug fixes
|
|
1.0.6.005:
|
|
- added z_normalize function to normalize dataset
|
|
- bug fixes
|
|
1.0.6.004:
|
|
- bug fixes
|
|
1.0.6.003:
|
|
- bug fixes
|
|
1.0.6.002:
|
|
- bug fixes
|
|
1.0.6.001:
|
|
- corrected __all__ to contain all of the functions
|
|
1.0.6.000:
|
|
- added calc_overfit, which calculates two measures of overfit, error and performance
|
|
- added calculating overfit to optimize_regression
|
|
1.0.5.000:
|
|
- added optimize_regression function, which is a sample function to find the optimal regressions
|
|
- optimize_regression function filters out some overfit funtions (functions with r^2 = 1)
|
|
- planned addition: overfit detection in the optimize_regression function
|
|
1.0.4.002:
|
|
- added __changelog__
|
|
- updated debug function with log and exponential regressions
|
|
1.0.4.001:
|
|
- added log regressions
|
|
- added exponential regressions
|
|
- added log_regression and exp_regression to __all__
|
|
1.0.3.008:
|
|
- added debug function to further consolidate functions
|
|
1.0.3.007:
|
|
- added builtin benchmark function
|
|
- added builtin random (linear) data generation function
|
|
- added device initialization (_init_device)
|
|
1.0.3.006:
|
|
- reorganized the imports list to be in alphabetical order
|
|
- added search and regurgitate functions to c_entities, nc_entities, obstacles, objectives
|
|
1.0.3.005:
|
|
- major bug fixes
|
|
- updated historical analysis
|
|
- depreciated old historical analysis
|
|
1.0.3.004:
|
|
- added __version__, __author__, __all__
|
|
- added polynomial regression
|
|
- added root mean squared function
|
|
- added r squared function
|
|
1.0.3.003:
|
|
- bug fixes
|
|
- added c_entities
|
|
1.0.3.002:
|
|
- bug fixes
|
|
- added nc_entities, obstacles, objectives
|
|
- consolidated statistics.py to analysis.py
|
|
1.0.3.001:
|
|
- compiled 1d, column, and row basic stats into basic stats function
|
|
1.0.3.000:
|
|
- added historical analysis function
|
|
1.0.2.xxx:
|
|
- added z score test
|
|
1.0.1.xxx:
|
|
- major bug fixes
|
|
1.0.0.xxx:
|
|
- added loading csv
|
|
- added 1d, column, row basic stats
|
|
"""
|
|
|
|
__author__ = (
|
|
"Arthur Lu <arthurlu@ttic.edu>, "
|
|
"Jacob Levine <jlevine@ttic.edu>,"
|
|
)
|
|
|
|
__all__ = [
|
|
'_init_device',
|
|
'load_csv',
|
|
'basic_stats',
|
|
'z_score',
|
|
'z_normalize',
|
|
'histo_analysis',
|
|
# all statistics functions left out due to integration in other functions
|
|
]
|
|
|
|
# now back to your regularly scheduled programming:
|
|
|
|
# imports (now in alphabetical order! v 1.0.3.006):
|
|
|
|
import csv
|
|
import numba
|
|
from numba import jit
|
|
import numpy as np
|
|
from sklearn import preprocessing
|
|
|
|
class error(ValueError):
|
|
pass
|
|
|
|
def _init_device(setting, arg): # initiates computation device for ANNs
|
|
if setting == "cuda":
|
|
try:
|
|
return torch.device(setting + ":" + str(arg) if torch.cuda.is_available() else "cpu")
|
|
except:
|
|
raise error("could not assign cuda or cpu")
|
|
elif setting == "cpu":
|
|
try:
|
|
return torch.device("cpu")
|
|
except:
|
|
raise error("could not assign cpu")
|
|
else:
|
|
raise error("specified device does not exist")
|
|
|
|
@jit(forceobj=True)
|
|
def load_csv(filepath):
|
|
with open(filepath, newline='') as csvfile:
|
|
file_array = np.array(list(csv.reader(csvfile)))
|
|
csvfile.close()
|
|
return file_array
|
|
|
|
# expects 1d array
|
|
@jit(forceobj=True)
|
|
def basic_stats(data):
|
|
|
|
data_t = np.array(data).astype(float)
|
|
|
|
_mean = mean(data_t)
|
|
_median = median(data_t)
|
|
_stdev = stdev(data_t)
|
|
_variance = variance(data_t)
|
|
|
|
return _mean, _median, _stdev, _variance
|
|
|
|
# returns z score with inputs of point, mean and standard deviation of spread
|
|
@jit(forceobj=True)
|
|
def z_score(point, mean, stdev):
|
|
score = (point - mean) / stdev
|
|
return score
|
|
|
|
# expects 2d array, normalizes across all axes
|
|
@jit(forceobj=True)
|
|
def z_normalize(array, *args):
|
|
|
|
array = np.array(array)
|
|
|
|
for arg in args:
|
|
|
|
array = preprocessing.normalize(array, axis = arg)
|
|
|
|
return array
|
|
|
|
@jit(forceobj=True)
|
|
# expects 2d array of [x,y]
|
|
def histo_analysis(hist_data):
|
|
|
|
hist_data = np.array(hist_data)
|
|
|
|
derivative = np.array(len(hist_data) - 1, dtype = float)
|
|
|
|
t = np.diff(hist_data)
|
|
|
|
derivative = t[1] / t[0]
|
|
|
|
np.sort(derivative)
|
|
mean_derivative = basic_stats(derivative)[0]
|
|
stdev_derivative = basic_stats(derivative)[3]
|
|
|
|
return mean_derivative, stdev_derivative
|
|
|
|
@jit(nopython=True)
|
|
def mean(data):
|
|
|
|
return np.mean(data)
|
|
|
|
@jit(nopython=True)
|
|
def median(data):
|
|
|
|
return np.median(data)
|
|
|
|
@jit(nopython=True)
|
|
def stdev(data):
|
|
|
|
return np.std(data)
|
|
|
|
@jit(nopython=True)
|
|
def variance(data):
|
|
|
|
return np.var(data) |