chembee.config.calibration package

Submodules

chembee.config.calibration.kmeans module

chembee.config.calibration.knn module

class chembee.config.calibration.knn.KNeighborsClassifierAlgorithm(n_neighbors=5, *, weights='uniform', algorithm='auto', leaf_size=30, p=2, metric='minkowski', metric_params=None, n_jobs=None)

Bases: KNeighborsClassifier

hyperparameters = [{'n_neighbors': [1, 2, 3, 4, 5, 6, 7, 8], 'weights': ['uniform', 'distance'], 'p': [1, 2, 3, 4]}]

name = 'knn'

chembee.config.calibration.linear_regression module

class chembee.config.calibration.linear_regression.LinearRegressionClass(*, fit_intercept=True, normalize='deprecated', copy_X=True, n_jobs=None, positive=False)

Bases: LinearRegression

decision_function(X)

fit(X, y)

Fit linear model.

X{array-like, sparse matrix} of shape (n_samples, n_features)

Training data.

yarray-like of shape (n_samples,) or (n_samples, n_targets)

Target values. Will be cast to X’s dtype if necessary.

sample_weightarray-like of shape (n_samples,), default=None

Individual weights for each sample.

New in version 0.17: parameter sample_weight support to LinearRegression.

selfobject

Fitted Estimator.

name = 'linr'

predict_proba(X)

Min-max scale output of decision_function to [0,1].

chembee.config.calibration.logistic_regression module

class chembee.config.calibration.logistic_regression.LogisticRegressionClassifierAlgorithm(penalty='l2', *, dual=False, tol=0.0001, C=1.0, fit_intercept=True, intercept_scaling=1, class_weight=None, random_state=None, solver='lbfgs', max_iter=100, multi_class='auto', verbose=0, warm_start=False, n_jobs=None, l1_ratio=None)

Bases: LogisticRegression

name = 'logr'

chembee.config.calibration.mlp_classifier module

class chembee.config.calibration.mlp_classifier.MLPClassifierAlgorithm(hidden_layer_sizes=(100,), activation='relu', *, solver='adam', alpha=0.0001, batch_size='auto', learning_rate='constant', learning_rate_init=0.001, power_t=0.5, max_iter=200, shuffle=True, random_state=None, tol=0.0001, verbose=False, warm_start=False, momentum=0.9, nesterovs_momentum=True, early_stopping=False, validation_fraction=0.1, beta_1=0.9, beta_2=0.999, epsilon=1e-08, n_iter_no_change=10, max_fun=15000)

Bases: MLPClassifier

hyperparameters = [{'alpha': [0.0001, 5e-05, 1e-05, 1e-07], 'learning_rate': ['constant', 'inv_scaling', 'adaptive'], 'early_stopping': [True, False]}]

name = 'mlp'

chembee.config.calibration.naive_bayes module

class chembee.config.calibration.naive_bayes.GaussianNBAlgorithm(*, priors=None, var_smoothing=1e-09)

Bases: GaussianNB

name = 'gnb'

chembee.config.calibration.random_forest module

class chembee.config.calibration.random_forest.RandomForestClassifierAlgorithm(n_estimators=100, *, criterion='gini', max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features='sqrt', max_leaf_nodes=None, min_impurity_decrease=0.0, bootstrap=True, oob_score=False, n_jobs=None, random_state=None, verbose=0, warm_start=False, class_weight=None, ccp_alpha=0.0, max_samples=None)

Bases: RandomForestClassifier

hyperparameters = [{'n_estimators': [100, 70, 60, 40, 20, 10], 'max_depth': [None, 20, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1]}]

name = 'rfc'

chembee.config.calibration.restricted_bm module

chembee.config.calibration.spectral_clustering module

class chembee.config.calibration.spectral_clustering.NaivlyCalibratedSpectralClustering(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)

Bases: SpectralClustering

fit(X, y)

Perform spectral clustering from features, or affinity matrix.

X{array-like, sparse matrix} of shape (n_samples, n_features) or (n_samples, n_samples)

Training instances to cluster, similarities / affinities between instances if affinity='precomputed', or distances between instances if affinity='precomputed_nearest_neighbors. If a sparse matrix is provided in a format other than csr_matrix, csc_matrix, or coo_matrix, it will be converted into a sparse csr_matrix.

yIgnored

Not used, present here for API consistency by convention.

selfobject

A fitted instance of the estimator.

name = 'spc'

predict_proba(X)

Min-max scale output of decision_function to [0,1].

chembee.config.calibration.svc module

class chembee.config.calibration.svc.NaivelyCalibratedLinearSVC(penalty='l2', loss='squared_hinge', *, dual=True, tol=0.0001, C=1.0, multi_class='ovr', fit_intercept=True, intercept_scaling=1, class_weight=None, verbose=0, random_state=None, max_iter=1000)

Bases: LinearSVC

LinearSVC with predict_proba method that naively scales decision_function output.

fit(X, y)

Fit the model according to the given training data.

X{array-like, sparse matrix} of shape (n_samples, n_features)

Training vector, where n_samples is the number of samples and n_features is the number of features.

yarray-like of shape (n_samples,)

Target vector relative to X.

sample_weightarray-like of shape (n_samples,), default=None

Array of weights that are assigned to individual samples. If not provided, then each sample is given unit weight.

New in version 0.18.

selfobject

An instance of the estimator.

name = 'lscv'

predict_proba(X)

Min-max scale output of decision_function to [0,1].

class chembee.config.calibration.svc.NaivelyCalibratedSVC(*, C=1.0, kernel='rbf', degree=3, gamma='scale', 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', break_ties=False, random_state=None)

Bases: SVC

LinearSVC with predict_proba method that naively scales decision_function output.

fit(X, y)

Fit the SVM model according to the given training data.

X{array-like, sparse matrix} of shape (n_samples, n_features) or (n_samples, n_samples)

Training vectors, where n_samples is the number of samples and n_features is the number of features. For kernel=”precomputed”, the expected shape of X is (n_samples, n_samples).

yarray-like of shape (n_samples,)

Target values (class labels in classification, real numbers in regression).

sample_weightarray-like of shape (n_samples,), default=None

Per-sample weights. Rescale C per sample. Higher weights force the classifier to put more emphasis on these points.

selfobject

Fitted estimator.

If X and y are not C-ordered and contiguous arrays of np.float64 and X is not a scipy.sparse.csr_matrix, X and/or y may be copied.

If X is a dense array, then the other methods will not support sparse matrices as input.

hyperparameters = [{'kernel': ['rbf'], 'gamma': ['scale', 'auto']}]

name = 'svc'

predict_proba(X)

Min-max scale output of decision_function to [0,1].

chembee.config.calibration.svc_poly module

Module contents