mirror of
https://github.com/titanscouting/tra-analysis.git
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Merge pull request #84 from titanscouting/typehinting-docstrings
Grab docstrings for Analysis to analysis-v4
This commit is contained in:
Arthur Lu
GitHub
commit
6fb0eefbc0
@ -7,10 +7,15 @@
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# current benchmark of optimization: 1.33 times faster
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# setup:
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__version__ = "3.0.4"
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__version__ = "3.0.6"
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# changelog should be viewed using print(analysis.__changelog__)
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__changelog__ = """changelog:
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3.0.6:
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- added docstrings
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3.0.5:
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- removed extra submodule imports
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- fixed/optimized header
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3.0.4:
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- removed -_obj imports
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3.0.3:
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@ -361,7 +366,6 @@ __all__ = [
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'histo_analysis',
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'regression',
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'Metric',
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'kmeans',
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'pca',
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'decisiontree',
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# all statistics functions left out due to integration in other functions
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@ -374,34 +378,39 @@ __all__ = [
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import csv
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from tra_analysis.metrics import elo as Elo
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from tra_analysis.metrics import glicko2 as Glicko2
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import math
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import numpy as np
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import scipy
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from scipy import optimize, stats
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import sklearn
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from sklearn import preprocessing, pipeline, linear_model, metrics, cluster, decomposition, tree, neighbors, naive_bayes, svm, model_selection, ensemble
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import sklearn, sklearn.cluster
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from tra_analysis.metrics import trueskill as Trueskill
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import warnings
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# import submodules
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from .Array import Array
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from .ClassificationMetric import ClassificationMetric
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from .RegressionMetric import RegressionMetric
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from . import SVM
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class error(ValueError):
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pass
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def load_csv(filepath):
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"""
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Loads csv file into 2D numpy array. Does not check csv file validity.
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parameters:
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filepath: String path to the csv file
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return:
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2D numpy array of values stored in csv file
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"""
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with open(filepath, newline='') as csvfile:
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file_array = np.array(list(csv.reader(csvfile)))
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csvfile.close()
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return file_array
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# expects 1d array
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def basic_stats(data):
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"""
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Calculates mean, median, standard deviation, variance, minimum, maximum of a simple set of elements.
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parameters:
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data: List representing set of unordered elements
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return:
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Dictionary with (mean, median, standard-deviation, variance, minimum, maximum) as keys and corresponding values
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"""
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data_t = np.array(data).astype(float)
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_mean = mean(data_t)
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@ -413,24 +422,43 @@ def basic_stats(data):
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return {"mean": _mean, "median": _median, "standard-deviation": _stdev, "variance": _variance, "minimum": _min, "maximum": _max}
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# returns z score with inputs of point, mean and standard deviation of spread
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def z_score(point, mean, stdev):
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"""
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Calculates z score of a specific point given mean and standard deviation of data.
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parameters:
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point: Real value corresponding to a single point of data
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mean: Real value corresponding to the mean of the dataset
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stdev: Real value corresponding to the standard deviation of the dataset
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return:
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Real value that is the point's z score
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"""
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score = (point - mean) / stdev
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return score
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# expects 2d array, normalizes across all axes
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def z_normalize(array, *args):
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"""
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Applies sklearn.normalize(array, axis = args) on any arraylike parseable by numpy.
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parameters:
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array: array like structure of reals aka nested indexables
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*args: arguments relating to axis normalized against
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return:
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numpy array of normalized values from ArrayLike input
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"""
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array = np.array(array)
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for arg in args:
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array = sklearn.preprocessing.normalize(array, axis = arg)
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return array
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# expects 2d array of [x,y]
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def histo_analysis(hist_data):
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"""
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Calculates the mean and standard deviation of derivatives of (x,y) points. Requires at least 2 points to compute.
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parameters:
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hist_data: list of real coordinate point data (x, y)
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return:
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Dictionary with (mean, deviation) as keys to corresponding values
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"""
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if len(hist_data[0]) > 2:
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hist_data = np.array(hist_data)
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@ -446,7 +474,15 @@ def histo_analysis(hist_data):
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return None
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def regression(inputs, outputs, args): # inputs, outputs expects N-D array
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"""
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Applies specified regression kernels onto input, output data pairs.
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parameters:
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inputs: List of Reals representing independent variable values of each point
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outputs: List of Reals representing dependent variable values of each point
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args: List of Strings from values (lin, log, exp, ply, sig)
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return:
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Dictionary with keys (lin, log, exp, ply, sig) as keys to correspondiong regression models
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"""
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X = np.array(inputs)
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y = np.array(outputs)
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@ -550,13 +586,39 @@ def regression(inputs, outputs, args): # inputs, outputs expects N-D array
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return regressions
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class Metric:
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"""
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The metric class wraps the metrics models. Call without instantiation as Metric.<method>(...)
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"""
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def elo(self, starting_score, opposing_score, observed, N, K):
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"""
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Calculates an elo adjusted ELO score given a player's current score, opponent's score, and outcome of match.
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reference: https://en.wikipedia.org/wiki/Elo_rating_system
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parameters:
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starting_score: Real value representing player's ELO score before a match
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opposing_score: Real value representing opponent's score before the match
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observed: Array of Real values representing multiple sequential match outcomes against the same opponent. 1 for match win, 0.5 for tie, 0 for loss.
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N: Real value representing the normal or mean score expected (usually 1200)
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K: R eal value representing a system constant, determines how quickly players will change scores (usually 24)
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return:
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Real value representing the player's new ELO score
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"""
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return Elo.calculate(starting_score, opposing_score, observed, N, K)
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def glicko2(self, starting_score, starting_rd, starting_vol, opposing_score, opposing_rd, observations):
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"""
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Calculates an adjusted Glicko-2 score given a player's current score, multiple opponent's score, and outcome of several matches.
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reference: http://www.glicko.net/glicko/glicko2.pdf
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parameters:
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starting_score: Real value representing the player's Glicko-2 score
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starting_rd: Real value representing the player's RD
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starting_vol: Real value representing the player's volatility
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opposing_score: List of Real values representing multiple opponent's Glicko-2 scores
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opposing_rd: List of Real values representing multiple opponent's RD
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opposing_vol: List of Real values representing multiple opponent's volatility
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observations: List of Real values representing the outcome of several matches, where each match's opponent corresponds with the opposing_score, opposing_rd, opposing_vol values of the same indesx. Outcomes can be a score, presuming greater score is better.
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return:
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Tuple of 3 Real values representing the player's new score, rd, and vol
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"""
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player = Glicko2.Glicko2(rating = starting_score, rd = starting_rd, vol = starting_vol)
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player.update_player([x for x in opposing_score], [x for x in opposing_rd], observations)
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@ -564,7 +626,15 @@ class Metric:
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return (player.rating, player.rd, player.vol)
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def trueskill(self, teams_data, observations): # teams_data is array of array of tuples ie. [[(mu, sigma), (mu, sigma), (mu, sigma)], [(mu, sigma), (mu, sigma), (mu, sigma)]]
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"""
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Calculates the score changes for multiple teams playing in a single match accoding to the trueskill algorithm.
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reference: https://trueskill.org/
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parameters:
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teams_data: List of List of Tuples of 2 Real values representing multiple player ratings. List of teams, which is a List of players. Each player rating is a Tuple of 2 Real values (mu, sigma).
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observations: List of Real values representing the match outcome. Each value in the List is the score corresponding to the team at the same index in teams_data.
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return:
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List of List of Tuples of 2 Real values representing new player ratings. Same structure as teams_data.
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"""
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team_ratings = []
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for team in teams_data:
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@ -599,24 +669,32 @@ def npmin(data):
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def npmax(data):
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return np.amax(data)
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""" need to decide what to do with this function
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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"):
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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)
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kernel.fit(data)
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predictions = kernel.predict(data)
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centers = kernel.cluster_centers_
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return centers, predictions
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"""
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def pca(data, n_components = None, copy = True, whiten = False, svd_solver = "auto", tol = 0.0, iterated_power = "auto", random_state = None):
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"""
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Performs a principle component analysis on the input data.
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reference: https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html
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parameters:
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data: Arraylike of Reals representing the set of data to perform PCA on
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* : refer to reference for usage, parameters follow same usage
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return:
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Arraylike of Reals representing the set of data that has had PCA performed. The dimensionality of the Arraylike may be smaller or equal.
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"""
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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)
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return kernel.fit_transform(data)
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def decisiontree(data, labels, test_size = 0.3, criterion = "gini", splitter = "default", max_depth = None): #expects *2d data and 1d labels
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"""
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Generates a decision tree classifier fitted to the given data.
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reference: https://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html
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parameters:
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data: List of values representing each data point of multiple axes
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labels: List of values represeing the labels corresponding to the same index at data
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* : refer to reference for usage, parameters follow same usage
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return:
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DecisionTreeClassifier model and corresponding classification accuracy metrics
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"""
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data_train, data_test, labels_train, labels_test = sklearn.model_selection.train_test_split(data, labels, test_size=test_size, random_state=1)
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model = sklearn.tree.DecisionTreeClassifier(criterion = criterion, splitter = splitter, max_depth = max_depth)
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model = model.fit(data_train,labels_train)
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@ -4,9 +4,11 @@
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# this should be imported as a python module using 'from tra_analysis import ClassificationMetric'
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# setup:
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__version__ = "1.0.1"
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__version__ = "1.0.2"
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__changelog__ = """changelog:
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1.0.2:
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- optimized imports
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1.0.1:
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- fixed __all__
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1.0.0:
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@ -22,7 +24,6 @@ __all__ = [
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]
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import sklearn
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from sklearn import metrics
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class ClassificationMetric():
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@ -4,9 +4,11 @@
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# this should be imported as a python module using 'from tra_analysis import CorrelationTest'
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# setup:
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__version__ = "1.0.1"
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__version__ = "1.0.2"
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__changelog__ = """changelog:
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1.0.2:
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- optimized imports
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1.0.1:
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- fixed __all__
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1.0.0:
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@ -29,7 +31,6 @@ __all__ = [
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]
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import scipy
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from scipy import stats
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def anova_oneway(*args): #expects arrays of samples
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@ -4,9 +4,11 @@
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# this should be imported as a python module using 'from tra_analysis import KNN'
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# setup:
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__version__ = "1.0.0"
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__version__ = "1.0.1"
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__changelog__ = """changelog:
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1.0.1:
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- optimized imports
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1.0.0:
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- ported analysis.KNN() here
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- removed classness
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@ -23,7 +25,6 @@ __all__ = [
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]
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import sklearn
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from sklearn import model_selection, neighbors
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from . import ClassificationMetric, RegressionMetric
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def knn_classifier(data, labels, n_neighbors = 5, test_size = 0.3, algorithm='auto', leaf_size=30, metric='minkowski', metric_params=None, n_jobs=None, p=2, weights='uniform'): #expects *2d data and 1d labels post-scaling
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@ -4,9 +4,11 @@
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# this should be imported as a python module using 'from tra_analysis import NaiveBayes'
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# setup:
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__version__ = "1.0.0"
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__version__ = "1.0.1"
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__changelog__ = """changelog:
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1.0.1:
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- optimized imports
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1.0.0:
|
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- ported analysis.NaiveBayes() here
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- removed classness
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@ -24,8 +26,7 @@ __all__ = [
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]
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import sklearn
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from sklearn import model_selection, naive_bayes
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from . import ClassificationMetric, RegressionMetric
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from . import ClassificationMetric
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def gaussian(data, labels, test_size = 0.3, priors = None, var_smoothing = 1e-09):
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@ -4,9 +4,11 @@
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# this should be imported as a python module using 'from tra_analysis import RandomForest'
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# setup:
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__version__ = "1.0.1"
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__version__ = "1.0.2"
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__changelog__ = """changelog:
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1.0.2:
|
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- optimized imports
|
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1.0.1:
|
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- fixed __all__
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1.0.0:
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@ -23,8 +25,7 @@ __all__ = [
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"random_forest_regressor",
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]
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import sklearn
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from sklearn import ensemble, model_selection
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import sklearn, sklearn.ensemble, sklearn.naive_bayes
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from . import ClassificationMetric, RegressionMetric
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def random_forest_classifier(data, labels, test_size, n_estimators, criterion="gini", max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features="auto", max_leaf_nodes=None, min_impurity_decrease=0.0, min_impurity_split=None, bootstrap=True, oob_score=False, n_jobs=None, random_state=None, verbose=0, warm_start=False, class_weight=None):
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@ -4,9 +4,11 @@
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# this should be imported as a python module using 'from tra_analysis import RegressionMetric'
|
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# setup:
|
||||
|
||||
__version__ = "1.0.0"
|
||||
__version__ = "1.0.1"
|
||||
|
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__changelog__ = """changelog:
|
||||
1.0.1:
|
||||
- optimized imports
|
||||
1.0.0:
|
||||
- ported analysis.RegressionMetric() here
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"""
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@ -21,7 +23,6 @@ __all__ = [
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import numpy as np
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import sklearn
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from sklearn import metrics
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class RegressionMetric():
|
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|
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@ -4,9 +4,11 @@
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# this should be imported as a python module using 'from tra_analysis import SVM'
|
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# setup:
|
||||
|
||||
__version__ = "1.0.2"
|
||||
__version__ = "1.0.3"
|
||||
|
||||
__changelog__ = """changelog:
|
||||
1.0.3:
|
||||
- optimized imports
|
||||
1.0.2:
|
||||
- fixed __all__
|
||||
1.0.1:
|
||||
@ -30,7 +32,6 @@ __all__ = [
|
||||
]
|
||||
|
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import sklearn
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from sklearn import svm
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from . import ClassificationMetric, RegressionMetric
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class CustomKernel:
|
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|
||||
@ -16,7 +16,7 @@ __changelog__ = """changelog:
|
||||
|
||||
__author__ = (
|
||||
"Arthur Lu <learthurgo@gmail.com>",
|
||||
"James Pan <zpan@imsa.edu>"
|
||||
"James Pan <zpan@imsa.edu>",
|
||||
)
|
||||
|
||||
__all__ = [
|
||||
|
||||
@ -4,9 +4,11 @@
|
||||
# this should be imported as a python module using 'from tra_analysis import StatisticalTest'
|
||||
# setup:
|
||||
|
||||
__version__ = "1.0.2"
|
||||
__version__ = "1.0.3"
|
||||
|
||||
__changelog__ = """changelog:
|
||||
1.0.3:
|
||||
- optimized imports
|
||||
1.0.2:
|
||||
- added tukey_multicomparison
|
||||
- fixed styling
|
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@ -61,7 +63,6 @@ __all__ = [
|
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|
||||
import numpy as np
|
||||
import scipy
|
||||
from scipy import stats, interpolate
|
||||
|
||||
def ttest_onesample(a, popmean, axis = 0, nan_policy = 'propagate'):
|
||||
|
||||
@ -279,9 +280,9 @@ def get_tukeyQcrit(k, df, alpha=0.05):
|
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cv001 = c[:, 2::2]
|
||||
|
||||
if alpha == 0.05:
|
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intp = interpolate.interp1d(crows, cv005[:,k-2])
|
||||
intp = scipy.interpolate.interp1d(crows, cv005[:,k-2])
|
||||
elif alpha == 0.01:
|
||||
intp = interpolate.interp1d(crows, cv001[:,k-2])
|
||||
intp = scipy.interpolate.interp1d(crows, cv001[:,k-2])
|
||||
else:
|
||||
raise ValueError('only implemented for alpha equal to 0.01 and 0.05')
|
||||
return intp(df)
|
||||
|
||||
@ -16,6 +16,8 @@ __changelog__ = """changelog:
|
||||
- deprecated titanlearn.py
|
||||
- deprecated visualization.py
|
||||
- removed matplotlib from requirements
|
||||
- removed extra submodule imports in Analysis
|
||||
- added typehinting, docstrings for each function
|
||||
3.0.0:
|
||||
- incremented version to release 3.0.0
|
||||
3.0.0-rc2:
|
||||
@ -45,6 +47,7 @@ __all__ = [
|
||||
"Analysis",
|
||||
"Array",
|
||||
"ClassificationMetric",
|
||||
"Clustering",
|
||||
"CorrelationTest",
|
||||
"Expression",
|
||||
"Fit",
|
||||
|
||||
Loading…
Reference in New Issue
Block a user