From 3c868672c9c1196540201006a1b543d598464a19 Mon Sep 17 00:00:00 2001 From: Arthur Lu Date: Thu, 18 Nov 2021 09:23:19 +0000 Subject: [PATCH] finished Analysis docstrings, removed typehinting to rework --- analysis-master/tra_analysis/Analysis.py | 101 +++++++++++++++++++---- analysis-master/tra_analysis/__init__.py | 4 +- analysis-master/tra_analysis/typedef.py | 4 - 3 files changed, 87 insertions(+), 22 deletions(-) delete mode 100644 analysis-master/tra_analysis/typedef.py diff --git a/analysis-master/tra_analysis/Analysis.py b/analysis-master/tra_analysis/Analysis.py index b9f4443b..204c58d2 100644 --- a/analysis-master/tra_analysis/Analysis.py +++ b/analysis-master/tra_analysis/Analysis.py @@ -380,7 +380,6 @@ import numpy as np import scipy import sklearn, sklearn.cluster from tra_analysis.metrics import trueskill as Trueskill -from tra_analysis.typedef import R, List, Dict # import submodules @@ -389,7 +388,7 @@ from .ClassificationMetric import ClassificationMetric class error(ValueError): pass -def load_csv(filepath: str) -> np.ndarray: +def load_csv(filepath): """ Loads csv file into 2D numpy array. Does not check csv file validity. parameters: @@ -402,9 +401,9 @@ def load_csv(filepath: str) -> np.ndarray: csvfile.close() return file_array -def basic_stats(data: List[R]) -> Dict[str, R]: +def basic_stats(data): """ - Calculates mean, median, standard deviation, variance, minimum, maximum of a simple set of elements + Calculates mean, median, standard deviation, variance, minimum, maximum of a simple set of elements. parameters: data: List representing set of unordered elements return: @@ -421,9 +420,9 @@ def basic_stats(data: List[R]) -> Dict[str, R]: return {"mean": _mean, "median": _median, "standard-deviation": _stdev, "variance": _variance, "minimum": _min, "maximum": _max} -def z_score(point: R, mean: R, stdev: R) -> R: +def z_score(point, mean, stdev): """ - Calculates z score of a specific point given mean and standard deviation of data + Calculates z score of a specific point given mean and standard deviation of data. parameters: point: Real value corresponding to a single point of data mean: Real value corresponding to the mean of the dataset @@ -437,7 +436,14 @@ def z_score(point: R, mean: R, stdev: R) -> R: # expects 2d array, normalizes across all axes def z_normalize(array, *args): - + """ + Applies sklearn.normalize(array, axis = args) on any arraylike parseable by numpy. + parameters: + array: array like structure of reals aka nested indexables + *args: arguments relating to axis normalized against + return: + numpy array of normalized values from ArrayLike input + """ array = np.array(array) for arg in args: array = sklearn.preprocessing.normalize(array, axis = arg) @@ -446,7 +452,13 @@ def z_normalize(array, *args): # expects 2d array of [x,y] def histo_analysis(hist_data): - + """ + Calculates the mean and standard deviation of derivatives of (x,y) points. Requires at least 2 points to compute. + parameters: + hist_data: list of real coordinate point data (x, y) + return: + Dictionary with (mean, deviation) as keys to corresponding values + """ if len(hist_data[0]) > 2: hist_data = np.array(hist_data) @@ -462,7 +474,15 @@ def histo_analysis(hist_data): return None def regression(inputs, outputs, args): # inputs, outputs expects N-D array - + """ + Applies specified regression kernels onto input, output data pairs. + parameters: + inputs: List of Reals representing independent variable values of each point + outputs: List of Reals representing dependent variable values of each point + args: List of Strings from values (lin, log, exp, ply, sig) + return: + Dictionary with keys (lin, log, exp, ply, sig) as keys to correspondiong regression models + """ X = np.array(inputs) y = np.array(outputs) @@ -566,13 +586,39 @@ def regression(inputs, outputs, args): # inputs, outputs expects N-D array return regressions class Metric: - + """ + The metric class wraps the metrics models. Call without instantiation as Metric.(...) + """ def elo(self, starting_score, opposing_score, observed, N, K): - + """ + Calculates an elo adjusted ELO score given a player's current score, opponent's score, and outcome of match. + reference: https://en.wikipedia.org/wiki/Elo_rating_system + parameters: + starting_score: Real value representing player's ELO score before a match + opposing_score: Real value representing opponent's score before the match + 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. + N: Real value representing the normal or mean score expected (usually 1200) + K: R eal value representing a system constant, determines how quickly players will change scores (usually 24) + return: + Real value representing the player's new ELO score + """ return Elo.calculate(starting_score, opposing_score, observed, N, K) def glicko2(self, starting_score, starting_rd, starting_vol, opposing_score, opposing_rd, observations): - + """ + Calculates an adjusted Glicko-2 score given a player's current score, multiple opponent's score, and outcome of several matches. + reference: http://www.glicko.net/glicko/glicko2.pdf + parameters: + starting_score: Real value representing the player's Glicko-2 score + starting_rd: Real value representing the player's RD + starting_vol: Real value representing the player's volatility + opposing_score: List of Real values representing multiple opponent's Glicko-2 scores + opposing_rd: List of Real values representing multiple opponent's RD + opposing_vol: List of Real values representing multiple opponent's volatility + 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. + return: + Tuple of 3 Real values representing the player's new score, rd, and vol + """ player = Glicko2.Glicko2(rating = starting_score, rd = starting_rd, vol = starting_vol) player.update_player([x for x in opposing_score], [x for x in opposing_rd], observations) @@ -580,7 +626,15 @@ class Metric: return (player.rating, player.rd, player.vol) 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)]] - + """ + Calculates the score changes for multiple teams playing in a single match accoding to the trueskill algorithm. + reference: https://trueskill.org/ + parameters: + 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). + 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. + return: + List of List of Tuples of 2 Real values representing new player ratings. Same structure as teams_data. + """ team_ratings = [] for team in teams_data: @@ -617,13 +671,30 @@ def npmax(data): return np.amax(data) def pca(data, n_components = None, copy = True, whiten = False, svd_solver = "auto", tol = 0.0, iterated_power = "auto", random_state = None): - + """ + Performs a principle component analysis on the input data. + reference: https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html + parameters: + data: Arraylike of Reals representing the set of data to perform PCA on + * : refer to reference for usage, parameters follow same usage + return: + Arraylike of Reals representing the set of data that has had PCA performed. The dimensionality of the Arraylike may be smaller or equal. + """ 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) def decisiontree(data, labels, test_size = 0.3, criterion = "gini", splitter = "default", max_depth = None): #expects *2d data and 1d labels - + """ + Generates a decision tree classifier fitted to the given data. + reference: https://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html + parameters: + data: List of values representing each data point of multiple axes + labels: List of values represeing the labels corresponding to the same index at data + * : refer to reference for usage, parameters follow same usage + return: + DecisionTreeClassifier model and corresponding classification accuracy metrics + """ data_train, data_test, labels_train, labels_test = sklearn.model_selection.train_test_split(data, labels, test_size=test_size, random_state=1) model = sklearn.tree.DecisionTreeClassifier(criterion = criterion, splitter = splitter, max_depth = max_depth) model = model.fit(data_train,labels_train) diff --git a/analysis-master/tra_analysis/__init__.py b/analysis-master/tra_analysis/__init__.py index 2df41075..d74c1c9d 100644 --- a/analysis-master/tra_analysis/__init__.py +++ b/analysis-master/tra_analysis/__init__.py @@ -73,6 +73,4 @@ from . import RandomForest from .RegressionMetric import RegressionMetric from . import Sort from . import StatisticalTest -from . import SVM - -from . import typedef \ No newline at end of file +from . import SVM \ No newline at end of file diff --git a/analysis-master/tra_analysis/typedef.py b/analysis-master/tra_analysis/typedef.py deleted file mode 100644 index 875776a0..00000000 --- a/analysis-master/tra_analysis/typedef.py +++ /dev/null @@ -1,4 +0,0 @@ -from typing import TypeVar, List, Dict -List = List -Dict = Dict -R = TypeVar('R', int, float) \ No newline at end of file