From de0791a7fd5646795138c18f4c0bbef43d100493 Mon Sep 17 00:00:00 2001 From: Arthur Lu Date: Wed, 14 Oct 2020 18:47:44 +0000 Subject: [PATCH] Analysis.py v 3.0.1 Signed-off-by: Arthur Lu --- analysis-master/requirements.txt | 1 - analysis-master/tra_analysis/Analysis.py | 19 +++---------------- 2 files changed, 3 insertions(+), 17 deletions(-) diff --git a/analysis-master/requirements.txt b/analysis-master/requirements.txt index 1ecfd056..33435d14 100644 --- a/analysis-master/requirements.txt +++ b/analysis-master/requirements.txt @@ -1,4 +1,3 @@ -numba numpy scipy scikit-learn diff --git a/analysis-master/tra_analysis/Analysis.py b/analysis-master/tra_analysis/Analysis.py index d60a79ad..cfe66455 100644 --- a/analysis-master/tra_analysis/Analysis.py +++ b/analysis-master/tra_analysis/Analysis.py @@ -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)