mirror of
https://github.com/titanscouting/tra-analysis.git
synced 2025-09-09 16:17:22 +00:00
Merge branch 'submoduling' into equation
This commit is contained in:
Arthur Lu
GitHub
@@ -397,7 +397,7 @@ from .RandomForest_obj import RandomForest
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from .RegressionMetric import RegressionMetric
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from .Sort_obj import Sort
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from .StatisticalTest_obj import StatisticalTest
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from .SVM import SVM
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from . import SVM
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class error(ValueError):
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pass
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@@ -27,55 +27,37 @@ class Array(): # tests on nd arrays independent of basic_stats
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return str(self.array)
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def elementwise_mean(self, *args, axis = 0): # expects arrays that are size normalized
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if len(*args) == 0:
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return np.mean(self.array, axis = axis)
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else:
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return np.mean([*args], axis = axis)
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def elementwise_mean(self, axis = 0): # expects arrays that are size normalized
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def elementwise_median(self, *args, axis = 0):
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return np.mean(self.array, axis = axis)
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if len(*args) == 0:
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return np.median(self.array, axis = axis)
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else:
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return np.median([*args], axis = axis)
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def elementwise_median(self, axis = 0):
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def elementwise_stdev(self, *args, axis = 0):
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return np.median(self.array, axis = axis)
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if len(*args) == 0:
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return np.std(self.array, axis = axis)
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else:
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return np.std([*args], axis = axis)
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def elementwise_stdev(self, axis = 0):
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def elementwise_variance(self, *args, axis = 0):
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return np.std(self.array, axis = axis)
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if len(*args) == 0:
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return np.var(self.array, axis = axis)
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else:
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return np.var([*args], axis = axis)
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def elementwise_variance(self, axis = 0):
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def elementwise_npmin(self, *args, axis = 0):
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return np.var(self.array, axis = axis)
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if len(*args) == 0:
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return np.amin(self.array, axis = axis)
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else:
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return np.amin([*args], axis = axis)
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def elementwise_npmin(self, axis = 0):
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return np.amin(self.array, axis = axis)
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def elementwise_npmax(self, *args, axis = 0):
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if len(*args) == 0:
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return np.amax(self.array, axis = axis)
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else:
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return np.amax([*args], axis = axis)
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def elementwise_npmax(self, axis = 0):
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return np.amax(self.array, axis = axis)
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def elementwise_stats(self, *args, axis = 0):
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def elementwise_stats(self, axis = 0):
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_mean = self.elementwise_mean(*args, axis = axis)
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_median = self.elementwise_median(*args, axis = axis)
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_stdev = self.elementwise_stdev(*args, axis = axis)
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_variance = self.elementwise_variance(*args, axis = axis)
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_min = self.elementwise_npmin(*args, axis = axis)
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_max = self.elementwise_npmax(*args, axis = axis)
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_mean = self.elementwise_mean(axis = axis)
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_median = self.elementwise_median(axis = axis)
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_stdev = self.elementwise_stdev(axis = axis)
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_variance = self.elementwise_variance(axis = axis)
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_min = self.elementwise_npmin(axis = axis)
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_max = self.elementwise_npmax(axis = axis)
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return _mean, _median, _stdev, _variance, _min, _max
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@@ -22,37 +22,37 @@ __all__ = [
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import scipy
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from scipy import stats
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def anova_oneway(self, *args): #expects arrays of samples
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def anova_oneway(*args): #expects arrays of samples
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results = scipy.stats.f_oneway(*args)
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return {"f-value": results[0], "p-value": results[1]}
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def pearson(self, x, y):
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def pearson(x, y):
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results = scipy.stats.pearsonr(x, y)
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return {"r-value": results[0], "p-value": results[1]}
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def spearman(self, a, b = None, axis = 0, nan_policy = 'propagate'):
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def spearman(a, b = None, axis = 0, nan_policy = 'propagate'):
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results = scipy.stats.spearmanr(a, b = b, axis = axis, nan_policy = nan_policy)
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return {"r-value": results[0], "p-value": results[1]}
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def point_biserial(self, x,y):
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def point_biserial(x, y):
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results = scipy.stats.pointbiserialr(x, y)
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return {"r-value": results[0], "p-value": results[1]}
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def kendall(self, x, y, initial_lexsort = None, nan_policy = 'propagate', method = 'auto'):
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def kendall(x, y, initial_lexsort = None, nan_policy = 'propagate', method = 'auto'):
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results = scipy.stats.kendalltau(x, y, initial_lexsort = initial_lexsort, nan_policy = nan_policy, method = method)
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return {"tau": results[0], "p-value": results[1]}
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def kendall_weighted(self, x, y, rank = True, weigher = None, additive = True):
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def kendall_weighted(x, y, rank = True, weigher = None, additive = True):
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results = scipy.stats.weightedtau(x, y, rank = rank, weigher = weigher, additive = additive)
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return {"tau": results[0], "p-value": results[1]}
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def mgc(self, x, y, compute_distance = None, reps = 1000, workers = 1, is_twosamp = False, random_state = None):
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def mgc(x, y, compute_distance = None, reps = 1000, workers = 1, is_twosamp = False, random_state = None):
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results = scipy.stats.multiscale_graphcorr(x, y, compute_distance = compute_distance, reps = reps, workers = workers, is_twosamp = is_twosamp, random_state = random_state)
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return {"k-value": results[0], "p-value": results[1], "data": results[2]} # unsure if MGC test returns a k value
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@@ -14,29 +14,32 @@ __changelog__ = """changelog:
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__author__ = (
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"Arthur Lu <learthurgo@gmail.com>",
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"James Pan <zpan@imsa.edu>"
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)
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__all__ = [
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'knn_classifier',
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'knn_regressor'
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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(self, data, labels, n_neighbors, 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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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.neighbors.KNeighborsClassifier()
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model = sklearn.neighbors.KNeighborsClassifier(n_neighbors = n_neighbors, weights = weights, algorithm = algorithm, leaf_size = leaf_size, p = p, metric = metric, metric_params = metric_params, n_jobs = n_jobs)
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model.fit(data_train, labels_train)
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predictions = model.predict(data_test)
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return model, ClassificationMetric(predictions, labels_test)
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def knn_regressor(self, data, outputs, n_neighbors, test_size = 0.3, weights = "uniform", algorithm = "auto", leaf_size = 30, p = 2, metric = "minkowski", metric_params = None, n_jobs = None):
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def knn_regressor(data, outputs, n_neighbors = 5, test_size = 0.3, weights = "uniform", algorithm = "auto", leaf_size = 30, p = 2, metric = "minkowski", metric_params = None, n_jobs = None):
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data_train, data_test, outputs_train, outputs_test = sklearn.model_selection.train_test_split(data, outputs, test_size=test_size, random_state=1)
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model = sklearn.neighbors.KNeighborsRegressor(n_neighbors = n_neighbors, weights = weights, algorithm = algorithm, leaf_size = leaf_size, p = p, metric = metric, metric_params = metric_params, n_jobs = n_jobs)
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model.fit(data_train, outputs_train)
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predictions = model.predict(data_test)
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return model, RegressionMetric(predictions, outputs_test)
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return model, RegressionMetric.RegressionMetric(predictions, outputs_test)
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@@ -16,13 +16,17 @@ __author__ = (
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)
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__all__ = [
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'gaussian',
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'multinomial'
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'bernoulli',
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'complement'
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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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def guassian(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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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.naive_bayes.GaussianNB(priors = priors, var_smoothing = var_smoothing)
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@@ -39,4 +39,4 @@ def random_forest_regressor(data, outputs, test_size, n_estimators, criterion="m
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kernel.fit(data_train, outputs_train)
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predictions = kernel.predict(data_test)
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return kernel, RegressionMetric(predictions, outputs_test)
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return kernel, RegressionMetric.RegressionMetric(predictions, outputs_test)
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@@ -16,8 +16,10 @@ __author__ = (
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)
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__all__ = [
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'RegressionMetric'
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]
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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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@@ -37,4 +39,4 @@ class RegressionMetric():
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def rms(self, predictions, targets):
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return math.sqrt(sklearn.metrics.mean_squared_error(targets, predictions))
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return np.sqrt(sklearn.metrics.mean_squared_error(targets, predictions))
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@@ -4,9 +4,12 @@
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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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__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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- removed unessasary self calls
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- removed classness
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1.0.0:
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- ported analysis.SVM() here
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"""
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@@ -22,58 +25,56 @@ import sklearn
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from sklearn import svm
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from . import ClassificationMetric, RegressionMetric
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class SVM:
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class CustomKernel:
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class CustomKernel:
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def __new__(cls, C, kernel, degre, gamma, coef0, shrinking, probability, tol, cache_size, class_weight, verbose, max_iter, decision_function_shape, random_state):
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def __new__(cls, C, kernel, degre, gamma, coef0, shrinking, probability, tol, cache_size, class_weight, verbose, max_iter, decision_function_shape, random_state):
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return sklearn.svm.SVC(C = C, kernel = kernel, gamma = gamma, coef0 = coef0, shrinking = shrinking, probability = probability, tol = tol, cache_size = cache_size, class_weight = class_weight, verbose = verbose, max_iter = max_iter, decision_function_shape = decision_function_shape, random_state = random_state)
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return sklearn.svm.SVC(C = C, kernel = kernel, gamma = gamma, coef0 = coef0, shrinking = shrinking, probability = probability, tol = tol, cache_size = cache_size, class_weight = class_weight, verbose = verbose, max_iter = max_iter, decision_function_shape = decision_function_shape, random_state = random_state)
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class StandardKernel:
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class StandardKernel:
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def __new__(cls, kernel, C=1.0, degree=3, gamma='auto_deprecated', coef0=0.0, shrinking=True, probability=False, tol=0.001, cache_size=200, class_weight=None, verbose=False, max_iter=-1, decision_function_shape='ovr', random_state=None):
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def __new__(cls, kernel, C=1.0, degree=3, gamma='auto_deprecated', coef0=0.0, shrinking=True, probability=False, tol=0.001, cache_size=200, class_weight=None, verbose=False, max_iter=-1, decision_function_shape='ovr', random_state=None):
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return sklearn.svm.SVC(C = C, kernel = kernel, gamma = gamma, coef0 = coef0, shrinking = shrinking, probability = probability, tol = tol, cache_size = cache_size, class_weight = class_weight, verbose = verbose, max_iter = max_iter, decision_function_shape = decision_function_shape, random_state = random_state)
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return sklearn.svm.SVC(C = C, kernel = kernel, gamma = gamma, coef0 = coef0, shrinking = shrinking, probability = probability, tol = tol, cache_size = cache_size, class_weight = class_weight, verbose = verbose, max_iter = max_iter, decision_function_shape = decision_function_shape, random_state = random_state)
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class PrebuiltKernel:
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class PrebuiltKernel:
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class Linear:
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class Linear:
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def __new__(cls):
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def __new__(cls):
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return sklearn.svm.SVC(kernel = 'linear')
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return sklearn.svm.SVC(kernel = 'linear')
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class Polynomial:
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class Polynomial:
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def __new__(cls, power, r_bias):
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def __new__(cls, power, r_bias):
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return sklearn.svm.SVC(kernel = 'polynomial', degree = power, coef0 = r_bias)
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return sklearn.svm.SVC(kernel = 'polynomial', degree = power, coef0 = r_bias)
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class RBF:
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class RBF:
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def __new__(cls, gamma):
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def __new__(cls, gamma):
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return sklearn.svm.SVC(kernel = 'rbf', gamma = gamma)
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return sklearn.svm.SVC(kernel = 'rbf', gamma = gamma)
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class Sigmoid:
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class Sigmoid:
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def __new__(cls, r_bias):
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def __new__(cls, r_bias):
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return sklearn.svm.SVC(kernel = 'sigmoid', coef0 = r_bias)
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return sklearn.svm.SVC(kernel = 'sigmoid', coef0 = r_bias)
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def fit(kernel, train_data, train_outputs): # expects *2d data, 1d labels or outputs
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def fit(self, kernel, train_data, train_outputs): # expects *2d data, 1d labels or outputs
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return kernel.fit(train_data, train_outputs)
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return kernel.fit(train_data, train_outputs)
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def eval_classification(kernel, test_data, test_outputs):
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def eval_classification(self, kernel, test_data, test_outputs):
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predictions = kernel.predict(test_data)
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predictions = kernel.predict(test_data)
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return ClassificationMetric(predictions, test_outputs)
|
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|
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return ClassificationMetric(predictions, test_outputs)
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def eval_regression(kernel, test_data, test_outputs):
|
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|
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def eval_regression(self, kernel, test_data, test_outputs):
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predictions = kernel.predict(test_data)
|
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|
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predictions = kernel.predict(test_data)
|
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|
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return RegressionMetric(predictions, test_outputs)
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return RegressionMetric(predictions, test_outputs)
|
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@@ -1,5 +1,5 @@
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# Titan Robotics Team 2022: Sort submodule
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# Written by Arthur Lu
|
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# Written by Arthur Lu and James Pan
|
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# Notes:
|
||||
# this should be imported as a python module using 'from tra_analysis import Sort'
|
||||
# setup:
|
||||
@@ -14,11 +14,14 @@ __changelog__ = """changelog:
|
||||
|
||||
__author__ = (
|
||||
"Arthur Lu <learthurgo@gmail.com>",
|
||||
"James Pan <zpan@imsa.edu>"
|
||||
)
|
||||
|
||||
__all__ = [
|
||||
]
|
||||
|
||||
import numpy as np
|
||||
|
||||
def quicksort(a):
|
||||
|
||||
def sort(array):
|
||||
|
||||
@@ -4,9 +4,11 @@
|
||||
# this should be imported as a python module using 'from tra_analysis import StatisticalTest'
|
||||
# setup:
|
||||
|
||||
__version__ = "1.0.0"
|
||||
__version__ = "1.0.1"
|
||||
|
||||
__changelog__ = """changelog:
|
||||
1.0.1:
|
||||
- fixed typo in __all__
|
||||
1.0.0:
|
||||
- ported analysis.StatisticalTest() here
|
||||
- removed classness
|
||||
@@ -17,6 +19,39 @@ __author__ = (
|
||||
)
|
||||
|
||||
__all__ = [
|
||||
'ttest_onesample',
|
||||
'ttest_independent',
|
||||
'ttest_statistic',
|
||||
'ttest_related',
|
||||
'ks_fitness',
|
||||
'chisquare',
|
||||
'powerdivergence'
|
||||
'ks_twosample',
|
||||
'es_twosample',
|
||||
'mw_rank',
|
||||
'mw_tiecorrection',
|
||||
'rankdata',
|
||||
'wilcoxon_ranksum',
|
||||
'wilcoxon_signedrank',
|
||||
'kw_htest',
|
||||
'friedman_chisquare',
|
||||
'bm_wtest',
|
||||
'combine_pvalues',
|
||||
'jb_fitness',
|
||||
'ab_equality',
|
||||
'bartlett_variance',
|
||||
'levene_variance',
|
||||
'sw_normality',
|
||||
'shapiro',
|
||||
'ad_onesample',
|
||||
'ad_ksample',
|
||||
'binomial',
|
||||
'fk_variance',
|
||||
'mood_mediantest',
|
||||
'mood_equalscale',
|
||||
'skewtest',
|
||||
'kurtosistest',
|
||||
'normaltest'
|
||||
]
|
||||
|
||||
import scipy
|
||||
|
||||
@@ -1,5 +1,5 @@
|
||||
# Titan Robotics Team 2022: tra_analysis package
|
||||
# Written by Arthur Lu, Jacob Levine, and Dev Singh
|
||||
# Written by Arthur Lu, Jacob Levine, Dev Singh, and James Pan
|
||||
# Notes:
|
||||
# this should be imported as a python package using 'import tra_analysis'
|
||||
# this should be included in the local directory or environment variable
|
||||
@@ -7,19 +7,24 @@
|
||||
# current benchmark of optimization: 1.33 times faster
|
||||
# setup:
|
||||
|
||||
__version__ = "2.1.0-alpha.1"
|
||||
__version__ = "2.1.0-alpha.3"
|
||||
|
||||
# changelog should be viewed using print(analysis.__changelog__)
|
||||
__changelog__ = """changelog:
|
||||
2.1.0-alpha.1:
|
||||
- moved multiple submodules under analysis to their own modules/files
|
||||
- added header, __version__, __changelog__, __author__, __all__ (unpopulated)
|
||||
2.1.0-alpha.3:
|
||||
- fixed indentation in meta data
|
||||
2.1.0-alpha.2:
|
||||
- updated SVM import
|
||||
2.1.0-alpha.1:
|
||||
- moved multiple submodules under analysis to their own modules/files
|
||||
- added header, __version__, __changelog__, __author__, __all__ (unpopulated)
|
||||
"""
|
||||
|
||||
__author__ = (
|
||||
"Arthur Lu <learthurgo@gmail.com>",
|
||||
"Jacob Levine <jlevine@imsa.edu>",
|
||||
"Dev Singh <dev@devksingh.com>",
|
||||
"Jacob Levine <jlevine@imsa.edu>",
|
||||
"Dev Singh <dev@devksingh.com>",
|
||||
"James Pan <zpan@imsa.edu>"
|
||||
)
|
||||
|
||||
__all__ = [
|
||||
@@ -37,4 +42,4 @@ from . import RandomForest
|
||||
from .RegressionMetric import RegressionMetric
|
||||
from . import Sort
|
||||
from . import StatisticalTest
|
||||
from .SVM import SVM
|
||||
from . import SVM
|
||||
Reference in New Issue
Block a user