geochemistrypi.data_mining.process package¶

Submodules¶

class ClassificationModelSelection(model_name: str)[source]¶

Bases: ModelSelectionBase

Simulate the normal way of training classification algorithms.

class ClusteringModelSelection(model_name: str)[source]¶

Bases: ModelSelectionBase

Simulate the normal way of invoking scikit-learn clustering algorithms.

activate(X: DataFrame, y: DataFrame | None = None, X_train: DataFrame | None = None, X_test: DataFrame | None = None, y_train: DataFrame | None = None, y_test: DataFrame | None = None, name_train: Series | None = None, name_test: Series | None = None, name_all: Series | None = None) → None[source]¶: Train by Scikit-learn framework.

class DecompositionModelSelection(model_name: str)[source]¶

Bases: ModelSelectionBase

Simulate the normal way of invoking scikit-learn decomposition algorithms.

activate(X: DataFrame, y: DataFrame | None = None, X_train: DataFrame | None = None, X_test: DataFrame | None = None, y_train: DataFrame | None = None, y_test: DataFrame | None = None, name_train: Series | None = None, name_test: Series | None = None, name_all: Series | None = None) → None[source]¶: Train by Scikit-learn framework.

class AnomalyDetectionModelSelection(model_name: str)[source]¶

Bases: ModelSelectionBase

Simulate the normal way of invoking scikit-learn anomaly detection algorithms.

activate(X: DataFrame, y: DataFrame, X_train: DataFrame, X_test: DataFrame, y_train: DataFrame, y_test: DataFrame, name_train: Series, name_test: Series, name_all: Series) → None[source]¶: Train by Scikit-learn framework.

class RegressionModelSelection(model_name: str)[source]¶

Bases: ModelSelectionBase

Simulate the normal way of training regression algorithms.