alu/tra-analysis
1
0
Fork 0
mirror of https://github.com/titanscouting/tra-analysis.git synced 2024-12-26 17:49:09 +00:00
Code Issues Packages Projects Releases Wiki Activity

Analysis.py v 3.0.1

Browse Source 
Signed-off-by: Arthur Lu <learthurgo@gmail.com>
This commit is contained in:
Arthur Lu 2020-10-14 18:47:44 +00:00
parent 57fa659f90
commit de0791a7fd
2 changed files with 3 additions and 17 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

1
analysis-master/requirements.txt
View File

@ -1,4 +1,3 @@
numba
numpy
scipy
scikit-learn

19
analysis-master/tra_analysis/Analysis.py
View File

@ -7,10 +7,12 @@
# current benchmark of optimization: 1.33 times faster
# setup:
__version__ = "3.0.0"
__version__ = "3.0.1"
# changelog should be viewed using print(analysis.__changelog__)
__changelog__ = """changelog:
3.0.1:
- removed numba dependency and calls
3.0.0:
- exported several submodules to their own files while preserving backwards compatibility:
- Array
@ -376,8 +378,6 @@ import csv
from tra_analysis.metrics import elo as Elo
from tra_analysis.metrics import glicko2 as Glicko2
import math
import numba
from numba import jit
import numpy as np
import scipy
from scipy import optimize, stats
@ -409,7 +409,6 @@ def load_csv(filepath):
return file_array
# expects 1d array
@jit(forceobj=True)
def basic_stats(data):
data_t = np.array(data).astype(float)
@ -424,14 +423,12 @@ def basic_stats(data):
return {"mean": _mean, "median": _median, "standard-deviation": _stdev, "variance": _variance, "minimum": _min, "maximum": _max}
# 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)
@ -440,7 +437,6 @@ def z_normalize(array, *args):
return array
@jit(forceobj=True)
# expects 2d array of [x,y]
def histo_analysis(hist_data):
@ -589,37 +585,30 @@ class Metric:
return Trueskill.rate(team_ratings, ranks=observations)
@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)
@jit(nopython=True)
def npmin(data):
return np.amin(data)
@jit(nopython=True)
def npmax(data):
return np.amax(data)
@jit(forceobj=True)
def kmeans(data, n_clusters=8, init="k-means++", n_init=10, max_iter=300, tol=0.0001, precompute_distances="auto", verbose=0, random_state=None, copy_x=True, n_jobs=None, algorithm="auto"):
kernel = sklearn.cluster.KMeans(n_clusters = n_clusters, init = init, n_init = n_init, max_iter = max_iter, tol = tol, precompute_distances = precompute_distances, verbose = verbose, random_state = random_state, copy_x = copy_x, n_jobs = n_jobs, algorithm = algorithm)
@ -629,14 +618,12 @@ def kmeans(data, n_clusters=8, init="k-means++", n_init=10, max_iter=300, tol=0.
return centers, predictions
@jit(forceobj=True)
def pca(data, n_components = None, copy = True, whiten = False, svd_solver = "auto", tol = 0.0, iterated_power = "auto", random_state = None):
kernel = sklearn.decomposition.PCA(n_components = n_components, copy = copy, whiten = whiten, svd_solver = svd_solver, tol = tol, iterated_power = iterated_power, random_state = random_state)
return kernel.fit_transform(data)
@jit(forceobj=True)
def decisiontree(data, labels, test_size = 0.3, criterion = "gini", splitter = "default", max_depth = None): #expects *2d data and 1d labels
data_train, data_test, labels_train, labels_test = sklearn.model_selection.train_test_split(data, labels, test_size=test_size, random_state=1)