From 44569c9fcf7fee7e42613af0aafb0c56bd18009c Mon Sep 17 00:00:00 2001 From: Arthur Lu Date: Tue, 8 Feb 2022 07:29:47 +0000 Subject: [PATCH] generalized keyword argument handling for: Clustering.py, CorrelationTest.py, KNN.py, NaiveBayes.py --- analysis-master/tra_analysis/Clustering.py | 16 +++++++------ .../tra_analysis/CorrelationTest.py | 20 +++++++++------- analysis-master/tra_analysis/KNN.py | 12 ++++++---- analysis-master/tra_analysis/NaiveBayes.py | 24 ++++++++++--------- 4 files changed, 40 insertions(+), 32 deletions(-) diff --git a/analysis-master/tra_analysis/Clustering.py b/analysis-master/tra_analysis/Clustering.py index 8b7cef6a..1372ffbc 100644 --- a/analysis-master/tra_analysis/Clustering.py +++ b/analysis-master/tra_analysis/Clustering.py @@ -4,10 +4,12 @@ # this should be imported as a python module using 'from tra_analysis import Clustering' # setup: -__version__ = "2.0.1" +__version__ = "2.0.2" # changelog should be viewed using print(analysis.__changelog__) __changelog__ = """changelog: + 2.0.2: + - generalized optional args to **kwargs 2.0.1: - added normalization preprocessing to clustering, expects instance of sklearn.preprocessing.Normalizer() 2.0.0: @@ -30,32 +32,32 @@ __all__ = [ import sklearn -def kmeans(data, normalizer = None, 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"): +def kmeans(data, normalizer = None, **kwargs): if normalizer != None: data = normalizer.transform(data) - 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) + kernel = sklearn.cluster.KMeans(**kwargs) kernel.fit(data) predictions = kernel.predict(data) centers = kernel.cluster_centers_ return centers, predictions -def dbscan(data, normalizer=None, eps=0.5, min_samples=5, metric='euclidean', metric_params=None, algorithm='auto', leaf_size=30, p=None, n_jobs=None): +def dbscan(data, normalizer=None, **kwargs): if normalizer != None: data = normalizer.transform(data) - model = sklearn.cluster.DBSCAN(eps = eps, min_samples = min_samples, metric = metric, metric_params = metric_params, algorithm = algorithm, leaf_size = leaf_size, p = p, n_jobs = n_jobs).fit(data) + model = sklearn.cluster.DBSCAN(**kwargs).fit(data) return model.labels_ -def spectral(data, normalizer=None, n_clusters=8, eigen_solver=None, n_components=None, random_state=None, n_init=10, gamma=1.0, affinity='rbf', n_neighbors=10, eigen_tol=0.0, assign_labels='kmeans', degree=3, coef0=1, kernel_params=None, n_jobs=None, verbose=False): +def spectral(data, normalizer=None, **kwargs): if normalizer != None: data = normalizer.transform(data) - model = sklearn.cluster.SpectralClustering(n_clusters = n_clusters, eigen_solver = eigen_solver, n_components = n_components, random_state = random_state, n_init = n_init, gamma = gamma, affinity = affinity, n_neighbors = n_neighbors, eigen_tol = eigen_tol, assign_labels = assign_labels, degree = degree, coef0 = coef0, kernel_params = kernel_params, n_jobs = n_jobs).fit(data) + model = sklearn.cluster.SpectralClustering(**kwargs).fit(data) return model.labels_ \ No newline at end of file diff --git a/analysis-master/tra_analysis/CorrelationTest.py b/analysis-master/tra_analysis/CorrelationTest.py index ddb27939..d08612f4 100644 --- a/analysis-master/tra_analysis/CorrelationTest.py +++ b/analysis-master/tra_analysis/CorrelationTest.py @@ -4,9 +4,11 @@ # this should be imported as a python module using 'from tra_analysis import CorrelationTest' # setup: -__version__ = "1.0.2" +__version__ = "1.0.3" __changelog__ = """changelog: + 1.0.3: + - generalized optional args to **kwargs 1.0.2: - optimized imports 1.0.1: @@ -42,9 +44,9 @@ def pearson(x, y): results = scipy.stats.pearsonr(x, y) return {"r-value": results[0], "p-value": results[1]} -def spearman(a, b = None, axis = 0, nan_policy = 'propagate'): +def spearman(a, b = None, **kwargs): - results = scipy.stats.spearmanr(a, b = b, axis = axis, nan_policy = nan_policy) + results = scipy.stats.spearmanr(a, b = b, **kwargs) return {"r-value": results[0], "p-value": results[1]} def point_biserial(x, y): @@ -52,17 +54,17 @@ def point_biserial(x, y): results = scipy.stats.pointbiserialr(x, y) return {"r-value": results[0], "p-value": results[1]} -def kendall(x, y, initial_lexsort = None, nan_policy = 'propagate', method = 'auto'): +def kendall(x, y, **kwargs): - results = scipy.stats.kendalltau(x, y, initial_lexsort = initial_lexsort, nan_policy = nan_policy, method = method) + results = scipy.stats.kendalltau(x, y, **kwargs) return {"tau": results[0], "p-value": results[1]} -def kendall_weighted(x, y, rank = True, weigher = None, additive = True): +def kendall_weighted(x, y, **kwargs): - results = scipy.stats.weightedtau(x, y, rank = rank, weigher = weigher, additive = additive) + results = scipy.stats.weightedtau(x, y, **kwargs) return {"tau": results[0], "p-value": results[1]} -def mgc(x, y, compute_distance = None, reps = 1000, workers = 1, is_twosamp = False, random_state = None): +def mgc(x, y, **kwargs): - results = scipy.stats.multiscale_graphcorr(x, y, compute_distance = compute_distance, reps = reps, workers = workers, is_twosamp = is_twosamp, random_state = random_state) + results = scipy.stats.multiscale_graphcorr(x, y, **kwargs) return {"k-value": results[0], "p-value": results[1], "data": results[2]} # unsure if MGC test returns a k value \ No newline at end of file diff --git a/analysis-master/tra_analysis/KNN.py b/analysis-master/tra_analysis/KNN.py index 09d663f3..94c0503d 100644 --- a/analysis-master/tra_analysis/KNN.py +++ b/analysis-master/tra_analysis/KNN.py @@ -4,9 +4,11 @@ # this should be imported as a python module using 'from tra_analysis import KNN' # setup: -__version__ = "1.0.1" +__version__ = "1.0.2" __changelog__ = """changelog: + 1.0.2: + - generalized optional args to **kwargs 1.0.1: - optimized imports 1.0.0: @@ -27,19 +29,19 @@ __all__ = [ import sklearn from . import ClassificationMetric, RegressionMetric -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 +def knn_classifier(data, labels, n_neighbors = 5, test_size = 0.3, **kwargs): #expects *2d data and 1d labels post-scaling 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.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) + model = sklearn.neighbors.KNeighborsClassifier(n_neighbors = n_neighbors, **kwargs) model.fit(data_train, labels_train) predictions = model.predict(data_test) return model, ClassificationMetric(predictions, labels_test) -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): +def knn_regressor(data, outputs, n_neighbors = 5, test_size = 0.3, **kwargs): data_train, data_test, outputs_train, outputs_test = sklearn.model_selection.train_test_split(data, outputs, test_size=test_size, random_state=1) - 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) + model = sklearn.neighbors.KNeighborsRegressor(n_neighbors = n_neighbors, **kwargs) model.fit(data_train, outputs_train) predictions = model.predict(data_test) diff --git a/analysis-master/tra_analysis/NaiveBayes.py b/analysis-master/tra_analysis/NaiveBayes.py index 007d3e6e..75389392 100644 --- a/analysis-master/tra_analysis/NaiveBayes.py +++ b/analysis-master/tra_analysis/NaiveBayes.py @@ -4,9 +4,11 @@ # this should be imported as a python module using 'from tra_analysis import NaiveBayes' # setup: -__version__ = "1.0.1" +__version__ = "1.0.2" __changelog__ = """changelog: + 1.0.2: + - generalized optional args to **kwargs 1.0.1: - optimized imports 1.0.0: @@ -20,45 +22,45 @@ __author__ = ( __all__ = [ 'gaussian', - 'multinomial' + 'multinomial', 'bernoulli', - 'complement' + 'complement', ] import sklearn from . import ClassificationMetric -def gaussian(data, labels, test_size = 0.3, priors = None, var_smoothing = 1e-09): +def gaussian(data, labels, test_size = 0.3, **kwargs): 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.naive_bayes.GaussianNB(priors = priors, var_smoothing = var_smoothing) + model = sklearn.naive_bayes.GaussianNB(**kwargs) model.fit(data_train, labels_train) predictions = model.predict(data_test) return model, ClassificationMetric(predictions, labels_test) -def multinomial(data, labels, test_size = 0.3, alpha=1.0, fit_prior=True, class_prior=None): +def multinomial(data, labels, test_size = 0.3, **kwargs): 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.naive_bayes.MultinomialNB(alpha = alpha, fit_prior = fit_prior, class_prior = class_prior) + model = sklearn.naive_bayes.MultinomialNB(**kwargs) model.fit(data_train, labels_train) predictions = model.predict(data_test) return model, ClassificationMetric(predictions, labels_test) -def bernoulli(data, labels, test_size = 0.3, alpha=1.0, binarize=0.0, fit_prior=True, class_prior=None): +def bernoulli(data, labels, test_size = 0.3, **kwargs): 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.naive_bayes.BernoulliNB(alpha = alpha, binarize = binarize, fit_prior = fit_prior, class_prior = class_prior) + model = sklearn.naive_bayes.BernoulliNB(**kwargs) model.fit(data_train, labels_train) predictions = model.predict(data_test) return model, ClassificationMetric(predictions, labels_test) -def complement(data, labels, test_size = 0.3, alpha=1.0, fit_prior=True, class_prior=None, norm=False): +def complement(data, labels, test_size = 0.3, **kwargs): 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.naive_bayes.ComplementNB(alpha = alpha, fit_prior = fit_prior, class_prior = class_prior, norm = norm) + model = sklearn.naive_bayes.ComplementNB(**kwargs) model.fit(data_train, labels_train) predictions = model.predict(data_test)