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- builtins.object
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- GridSearchCV
- ModelSelectionUtility
- RandomSearchCV
- segaSearchCV
class GridSearchCV(builtins.object) |
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GridSearchCV(model, param_grid, cv=5, metric='mse', direction='minimize')
Implements a grid search cross-validation for hyperparameter tuning.
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Methods defined here:
- __init__(self, model, param_grid, cv=5, metric='mse', direction='minimize')
- Initializes the GridSearchCV object.
Args:
model: The model Object to be tuned.
param_grid: (list) - A list of dictionaries containing hyperparameters to be tuned.
cv: (int) - The number of folds for cross-validation. Default is 5.
metric: (str) - The metric to be used for evaluation. Default is 'mse'.
- Regression Metrics: 'mse', 'r2', 'mae', 'rmse', 'mape', 'mpe'
- Classification Metrics: 'accuracy', 'precision', 'recall', 'f1', 'log_loss'
direction: (str) - The direction to optimize the metric. Default is 'minimize'.
- fit(self, X, y, verbose=False)
- Fits the model to the data for all hyperparameter combinations.
Args:
X: (numpy.ndarray) - The feature columns.
y: (numpy.ndarray) - The label column.
verbose: (bool) - A flag to display the training progress. Default is True.
Returns:
model: The best model with the optimal hyperparameters.
Data descriptors defined here:
- __dict__
- dictionary for instance variables
- __weakref__
- list of weak references to the object
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class ModelSelectionUtility(builtins.object) |
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A utility class for hyperparameter tuning and cross-validation of machine learning models.
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Static methods defined here:
- cross_validate(model, X, y, params, cv=5, metric='mse', direction='minimize', verbose=False)
- Implements a custom cross-validation for hyperparameter tuning.
Args:
model: The model Object to be tuned.
X: (numpy.ndarray) - The feature columns.
y: (numpy.ndarray) - The label column.
params: (dict) - The hyperparameters to be tuned.
cv: (int) - The number of folds for cross-validation. Default is 5.
metric: (str) - The metric to be used for evaluation. Default is 'mse'.
- Regression Metrics: 'mse', 'r2', 'mae', 'rmse', 'mape', 'mpe'
- Classification Metrics: 'accuracy', 'precision', 'recall', 'f1', 'log_loss'
direction: (str) - The direction to optimize the metric. Default is 'minimize'.
verbose: (bool) - A flag to display the training progress. Default is False.
Returns:
tuple: A tuple containing the scores (list) and the trained model.
- get_param_combinations(param_grid)
- Generates all possible combinations of hyperparameters.
Returns:
param_combinations (list): A list of dictionaries containing hyperparameter combinations.
Data descriptors defined here:
- __dict__
- dictionary for instance variables
- __weakref__
- list of weak references to the object
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class RandomSearchCV(builtins.object) |
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RandomSearchCV(model, param_grid, iter=10, cv=5, metric='mse', direction='minimize')
Implements a random search cross-validation for hyperparameter tuning.
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Methods defined here:
- __init__(self, model, param_grid, iter=10, cv=5, metric='mse', direction='minimize')
- Initializes the RandomSearchCV object.
Args:
model: The model Object to be tuned.
param_grid: (list) - A list of dictionaries containing hyperparameters to be tuned.
iter: (int) - The number of iterations for random search. Default is 10.
cv: (int) - The number of folds for cross-validation. Default is 5.
metric: (str) - The metric to be used for evaluation. Default is 'mse'.
- Regression Metrics: 'mse', 'r2', 'mae', 'rmse', 'mape', 'mpe'
- Classification Metrics: 'accuracy', 'precision', 'recall', 'f1', 'log_loss'
direction: (str) - The direction to optimize the metric. Default is 'minimize'.
- fit(self, X, y, verbose=False)
- Fits the model to the data for iter random hyperparameter combinations.
Args:
X: (numpy.ndarray) - The feature columns.
y: (numpy.ndarray) - The label column.
verbose: (bool) - A flag to display the training progress. Default is True.
Returns:
model: The best model with the optimal hyperparameters.
Data descriptors defined here:
- __dict__
- dictionary for instance variables
- __weakref__
- list of weak references to the object
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class segaSearchCV(builtins.object) |
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segaSearchCV(model, param_space, iter=10, cv=5, metric='mse', direction='minimize')
Implements a custom search cross-validation for hyperparameter tuning.
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Methods defined here:
- __init__(self, model, param_space, iter=10, cv=5, metric='mse', direction='minimize')
- Initializes the segaSearchCV object.
Args:
model: The model Object to be tuned.
param_space (list): A list of dictionaries containing hyperparameters to be tuned.
Should be in the format: [{'param': [type, min, max]}, ...]
iter (int): The number of iterations for random search. Default is 10.
cv (int): The number of folds for cross-validation. Default is 5.
metric (str): The metric to be used for evaluation. Default is 'mse'.
- Regression Metrics: 'mse', 'r2', 'mae', 'rmse', 'mape', 'mpe'
- Classification Metrics: 'accuracy', 'precision', 'recall', 'f1', 'log_loss'
direction (str): The direction to optimize the metric. Default is 'minimize'.
- fit(self, X, y, verbose=False)
- Fits the model to the data for iter random hyperparameter combinations.
Args:
X: (numpy.ndarray)- The feature columns.
y: (numpy.ndarray)- The label column.
verbose: (bool) - A flag to display the training progress. Default is True.
Data descriptors defined here:
- __dict__
- dictionary for instance variables
- __weakref__
- list of weak references to the object
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