mirror of
https://github.com/titanscouting/tra-analysis.git
synced 2024-11-09 22:44:44 +00:00
fixed/optimized imports,
fixed headers Signed-off-by: Arthur Lu <learthurgo@gmail.com>
This commit is contained in:
parent
d57745547f
commit
dad195a00f
@ -7,10 +7,13 @@
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# current benchmark of optimization: 1.33 times faster
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# current benchmark of optimization: 1.33 times faster
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# setup:
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# setup:
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__version__ = "3.0.4"
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__version__ = "3.0.5"
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# changelog should be viewed using print(analysis.__changelog__)
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# changelog should be viewed using print(analysis.__changelog__)
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__changelog__ = """changelog:
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__changelog__ = """changelog:
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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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3.0.4:
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- removed -_obj imports
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- removed -_obj imports
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3.0.3:
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3.0.3:
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@ -361,7 +364,6 @@ __all__ = [
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'histo_analysis',
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'histo_analysis',
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'regression',
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'regression',
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'Metric',
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'Metric',
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'kmeans',
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'pca',
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'pca',
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'decisiontree',
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'decisiontree',
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# all statistics functions left out due to integration in other functions
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# all statistics functions left out due to integration in other functions
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@ -374,21 +376,14 @@ __all__ = [
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import csv
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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 elo as Elo
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from tra_analysis.metrics import glicko2 as Glicko2
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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 numpy as np
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import scipy
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import scipy
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from scipy import optimize, stats
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import sklearn, sklearn.cluster
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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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from tra_analysis.metrics import trueskill as Trueskill
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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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# import submodules
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from .Array import Array
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from .ClassificationMetric import ClassificationMetric
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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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class error(ValueError):
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pass
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pass
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@ -599,16 +594,7 @@ def npmin(data):
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def npmax(data):
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def npmax(data):
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return np.amax(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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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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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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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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@ -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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# this should be imported as a python module using 'from tra_analysis import ClassificationMetric'
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# setup:
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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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__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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1.0.1:
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- fixed __all__
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- fixed __all__
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1.0.0:
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1.0.0:
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@ -22,7 +24,6 @@ __all__ = [
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]
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]
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import sklearn
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import sklearn
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from sklearn import metrics
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class ClassificationMetric():
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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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# this should be imported as a python module using 'from tra_analysis import CorrelationTest'
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# setup:
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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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__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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1.0.1:
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- fixed __all__
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- fixed __all__
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1.0.0:
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1.0.0:
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@ -29,7 +31,6 @@ __all__ = [
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]
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]
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import scipy
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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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def anova_oneway(*args): #expects arrays of samples
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# this should be imported as a python module using 'from tra_analysis import KNN'
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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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# 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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__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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1.0.0:
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- ported analysis.KNN() here
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- ported analysis.KNN() here
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- removed classness
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- removed classness
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@ -23,7 +25,6 @@ __all__ = [
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]
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]
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import sklearn
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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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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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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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# this should be imported as a python module using 'from tra_analysis import NaiveBayes'
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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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# 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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__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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1.0.0:
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- ported analysis.NaiveBayes() here
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- ported analysis.NaiveBayes() here
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- removed classness
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- removed classness
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@ -24,8 +26,7 @@ __all__ = [
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]
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]
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import sklearn
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import sklearn
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from sklearn import model_selection, naive_bayes
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from . import ClassificationMetric
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from . import ClassificationMetric, RegressionMetric
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def gaussian(data, labels, test_size = 0.3, priors = None, var_smoothing = 1e-09):
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def gaussian(data, labels, test_size = 0.3, priors = None, var_smoothing = 1e-09):
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# this should be imported as a python module using 'from tra_analysis import RandomForest'
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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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# 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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__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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1.0.1:
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- fixed __all__
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- fixed __all__
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1.0.0:
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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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"random_forest_regressor",
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]
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]
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import sklearn
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import sklearn, sklearn.ensemble, sklearn.naive_bayes
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from sklearn import ensemble, model_selection
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from . import ClassificationMetric, RegressionMetric
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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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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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# this should be imported as a python module using 'from tra_analysis import RegressionMetric'
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# setup:
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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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__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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1.0.0:
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- ported analysis.RegressionMetric() here
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- ported analysis.RegressionMetric() here
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"""
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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 numpy as np
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import sklearn
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import sklearn
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from sklearn import metrics
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class RegressionMetric():
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class RegressionMetric():
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# this should be imported as a python module using 'from tra_analysis import SVM'
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# this should be imported as a python module using 'from tra_analysis import SVM'
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# setup:
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# setup:
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__version__ = "1.0.2"
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__version__ = "1.0.3"
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__changelog__ = """changelog:
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__changelog__ = """changelog:
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1.0.3:
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- optimized imports
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1.0.2:
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1.0.2:
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- fixed __all__
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- fixed __all__
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1.0.1:
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1.0.1:
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@ -30,7 +32,6 @@ __all__ = [
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]
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]
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import sklearn
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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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from . import ClassificationMetric, RegressionMetric
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class CustomKernel:
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class CustomKernel:
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@ -16,7 +16,7 @@ __changelog__ = """changelog:
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__author__ = (
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__author__ = (
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"Arthur Lu <learthurgo@gmail.com>",
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"Arthur Lu <learthurgo@gmail.com>",
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"James Pan <zpan@imsa.edu>"
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"James Pan <zpan@imsa.edu>",
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)
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)
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__all__ = [
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__all__ = [
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# this should be imported as a python module using 'from tra_analysis import StatisticalTest'
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# this should be imported as a python module using 'from tra_analysis import StatisticalTest'
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# setup:
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# setup:
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__version__ = "1.0.2"
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__version__ = "1.0.3"
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__changelog__ = """changelog:
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__changelog__ = """changelog:
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1.0.3:
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- optimized imports
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1.0.2:
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1.0.2:
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- added tukey_multicomparison
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- added tukey_multicomparison
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- fixed styling
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- fixed styling
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@ -61,7 +63,6 @@ __all__ = [
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import numpy as np
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import numpy as np
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import scipy
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import scipy
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from scipy import stats, interpolate
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def ttest_onesample(a, popmean, axis = 0, nan_policy = 'propagate'):
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def ttest_onesample(a, popmean, axis = 0, nan_policy = 'propagate'):
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@ -279,9 +280,9 @@ def get_tukeyQcrit(k, df, alpha=0.05):
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cv001 = c[:, 2::2]
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cv001 = c[:, 2::2]
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if alpha == 0.05:
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if alpha == 0.05:
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intp = interpolate.interp1d(crows, cv005[:,k-2])
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intp = scipy.interpolate.interp1d(crows, cv005[:,k-2])
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elif alpha == 0.01:
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elif alpha == 0.01:
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intp = interpolate.interp1d(crows, cv001[:,k-2])
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intp = scipy.interpolate.interp1d(crows, cv001[:,k-2])
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else:
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else:
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raise ValueError('only implemented for alpha equal to 0.01 and 0.05')
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raise ValueError('only implemented for alpha equal to 0.01 and 0.05')
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return intp(df)
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return intp(df)
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@ -16,6 +16,8 @@ __changelog__ = """changelog:
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- deprecated titanlearn.py
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- deprecated titanlearn.py
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- deprecated visualization.py
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- deprecated visualization.py
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- removed matplotlib from requirements
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- removed matplotlib from requirements
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- removed extra submodule imports in Analysis
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- added typehinting, docstrings for each function
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3.0.0:
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3.0.0:
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- incremented version to release 3.0.0
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- incremented version to release 3.0.0
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3.0.0-rc2:
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3.0.0-rc2:
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@ -45,6 +47,7 @@ __all__ = [
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"Analysis",
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"Analysis",
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"Array",
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"Array",
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"ClassificationMetric",
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"ClassificationMetric",
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"Clustering",
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"CorrelationTest",
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"CorrelationTest",
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"Expression",
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"Expression",
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"Fit",
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"Fit",
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Block a user