finished Analysis docstrings,

removed typehinting to rework

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.<method>(...)
+	"""
 	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)

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
+from . import SVM

analysis-master/tra_analysis/typedef.py

@@ -1,4 +0,0 @@
-from typing import TypeVar, List, Dict
-List = List
-Dict = Dict
-R = TypeVar('R', int, float)