chembee.config.benchmark package

Submodules

chembee.config.benchmark.BenchmarkAlgorithm module

class chembee.config.benchmark.BenchmarkAlgorithm.BenchmarkAlgorithm

Bases: object

chembee.config.benchmark.algorithms module

chembee.config.benchmark.grid_search_cv module

class chembee.config.benchmark.grid_search_cv.GridSearchCVClassifier(clf_list: list, names: list)

Bases: BenchmarkAlgorithm

name = 'grid_search_cv'

chembee.config.benchmark.kmeans module

class chembee.config.benchmark.kmeans.KMeansClassifier

Bases: BenchmarkAlgorithm

algorithms = (KMeans(n_clusters=3), KMeans(init='random', n_clusters=3), KMeans(n_clusters=3), KMeans(algorithm='elkan', n_clusters=3))

name = 'kmeans'

titles = ('k-means++ initialization', 'random initialization', 'Lloyd method', 'Elkan method')

chembee.config.benchmark.knn module

class chembee.config.benchmark.knn.KNNClassifier

Bases: BenchmarkAlgorithm

algorithms = (KNeighborsClassifier(algorithm='ball_tree', n_neighbors=15), KNeighborsClassifier(algorithm='kd_tree', n_neighbors=15), KNeighborsClassifier(algorithm='brute', n_neighbors=15))

n_neighbors = 15

name = 'knn'

titles = (' Ball tree algorithm', 'KD tree algorithm', 'Brute force')

chembee.config.benchmark.linear_regression module

class chembee.config.benchmark.linear_regression.LinearRegressionClassifier

Bases: BenchmarkAlgorithm

algorithms = (LinearRegression(), Ridge(), Lasso(), ElasticNet())

name = 'linear-regression'

titles = ('Ordinary Least Squares', 'Ridge', 'Lasso', 'Elastic net')

chembee.config.benchmark.logistic_regression module

class chembee.config.benchmark.logistic_regression.LogisticRegressionClassifier

Bases: BenchmarkAlgorithm

algorithms = (LogisticRegression(max_iter=1000, penalty='l1', solver='saga'), LogisticRegression(max_iter=1000, solver='saga'), LogisticRegression(l1_ratio=0.5, max_iter=1000, penalty='elasticnet',                    solver='saga'))

name = 'logistic-regression'

titles = ('l1', 'l2', 'elasticnet with $\\chi(\\mathrm{l1}) = 0.5$')

chembee.config.benchmark.mlp_classifier module

class chembee.config.benchmark.mlp_classifier.NeuralNetworkClassifier

Bases: BenchmarkAlgorithm

algorithms = (MLPClassifier(activation='logistic', hidden_layer_sizes=(100, 20, 20, 100),               max_iter=10000), MLPClassifier(activation='tanh', hidden_layer_sizes=(100, 20, 20, 100),               max_iter=10000), MLPClassifier(hidden_layer_sizes=(100, 20, 20, 100), max_iter=10000), MLPClassifier(hidden_layer_sizes=(100, 20, 20, 100), max_iter=10000))

hidden_layer_sizes = (100, 20, 20, 100)

max_iter = 10000

name = 'multilayer-perceptron'

titles = ('Logistic with SGD', 'Tanh with SGD', 'RELU with SGD', 'RELU with Adam')

chembee.config.benchmark.naive_bayes module

class chembee.config.benchmark.naive_bayes.NaiveBayesClassifier

Bases: BenchmarkAlgorithm

algorithms = (GaussianNB(), MultinomialNB(), BernoulliNB(), ComplementNB())

name = 'naive-bayes'

titles = ('Gaussian', 'Multinomial', 'Bernoulli', 'Complement')

chembee.config.benchmark.random_forest module

class chembee.config.benchmark.random_forest.RandomForestClassifier

Bases: BenchmarkAlgorithm

algorithms = (RandomForestClassifier(), RandomForestClassifier(criterion='entropy'), RandomForestClassifier(criterion='log_loss'))

name = 'random-forest'

titles = ('Gini coefficient loss', 'Shannon entropy loss', 'Shannon log-loss')

chembee.config.benchmark.restricted_bm module

class chembee.config.benchmark.restricted_bm.RBMClassifier

Bases: BenchmarkAlgorithm

algorithms = (BernoulliRBM(learning_rate=0.0001), BernoulliRBM(learning_rate=0.0001, n_components=612), BernoulliRBM(learning_rate=1e-06), BernoulliRBM(learning_rate=1e-06, n_components=612))

name = 'restritce-bm'

titles = ('256 units, 0.01 lr', '612 units, 0.01 lr', '256 units, 0.001 lr', '612 units, 0.001 lr')

chembee.config.benchmark.spectral_clustering module

class chembee.config.benchmark.spectral_clustering.SpectralClusteringClassifier

Bases: BenchmarkAlgorithm

C = 1.0

algorithms = (SpectralClustering(eigen_tol=1e-07, n_clusters=3, random_state=42), SpectralClustering(assign_labels='discretize', eigen_tol=1e-07, n_clusters=3,                    random_state=42), SpectralClustering(assign_labels='cluster_qr', eigen_tol=1e-07, n_clusters=3,                    random_state=42))

name = 'spectral-clustering'

titles = ('k-means', 'discretize method', 'cluster_qr')

chembee.config.benchmark.svc module

class chembee.config.benchmark.svc.SVClassifier

Bases: BenchmarkAlgorithm

C = 1.0

algorithms = (SVC(kernel='linear'), LinearSVC(max_iter=10000), SVC(gamma=0.7), SVC(gamma='auto', kernel='poly'))

name = 'svc'

titles = ('SVC with linear kernel', 'LinearSVC (linear kernel)', 'SVC with RBF kernel', 'SVC with polynomial (degree 3) kernel')

chembee.config.benchmark.svc_poly module

class chembee.config.benchmark.svc_poly.SVCPolyClassifier

Bases: BenchmarkAlgorithm

C = 1.0

algorithms = (SVC(degree=1, gamma='auto', kernel='poly'), SVC(gamma='auto', kernel='poly'), SVC(degree=6, gamma='auto', kernel='poly'))

name = 'svc_polys'

titles = ('Polynomial (degree 1) kernel', 'Polynomial (degree 3) kernel', 'Polynomial (degree 6) kernel')

Module contents