random_forest module¶
The random forest algorithm for classification.
The random forest classification model uses averaging methods on an ensemble of decision trees.
Dependence¶
The classifier relies on the RandomForestClassifier class of the scikit-learn library.
Classes:
|
The random forest classification algorithm. |
- class gemseo.mlearning.classification.random_forest.RandomForestClassifier(data, transformer=None, input_names=None, output_names=None, n_estimators=100, **parameters)[source]¶
Bases:
gemseo.mlearning.classification.classification.MLClassificationAlgo
The random forest classification algorithm.
- Parameters
n_estimators (int) – The number of trees in the forest.
data (Dataset) –
transformer (Optional[TransformerType]) –
input_names (Optional[Iterable[str]]) –
output_names (Optional[Iterable[str]]) –
parameters (Optional[Union[int,float,bool,str]]) –
- Return type
None
Attributes:
The input data matrix.
The dimension of the input variables before applying the transformers.
Return whether the algorithm is trained.
The output data matrix.
The dimension of the output variables before applying the transformers.
Classes:
Decorators for supervised algorithms.
Methods:
learn
([samples])Train the machine learning algorithm from the learning dataset.
load_algo
(directory)Load a machine learning algorithm from a directory.
predict
(input_data, *args, **kwargs)Evaluate ‘predict’ with either array or dictionary-based input data.
predict_proba
(input_data, *args, **kwargs)Evaluate ‘predict’ with either array or dictionary-based input data.
save
([directory, path, save_learning_set])Save the machine learning algorithm.
- ABBR = 'RandomForestClassifier'¶
- DEFAULT_TRANSFORMER = {'inputs': <gemseo.mlearning.transform.scaler.min_max_scaler.MinMaxScaler object>}¶
- class DataFormatters¶
Bases:
gemseo.mlearning.core.ml_algo.MLAlgo.DataFormatters
Decorators for supervised algorithms.
Methods:
format_dict
(predict)Make an array-based function be called with a dictionary of NumPy arrays.
format_input_output
(predict)Make a function robust to type, array shape and data transformation.
format_samples
(predict)Make a 2D NumPy array-based function work with 1D NumPy array.
format_transform
([transform_inputs, …])Force a function to transform its input and/or output variables.
- classmethod format_dict(predict)¶
Make an array-based function be called with a dictionary of NumPy arrays.
- Parameters
predict (Callable[[numpy.ndarray], numpy.ndarray]) – The function to be called; it takes a NumPy array in input and returns a NumPy array.
- Returns
A function making the function ‘predict’ work with either a NumPy data array or a dictionary of NumPy data arrays indexed by variables names. The evaluation will have the same type as the input data.
- Return type
Callable[[Union[numpy.ndarray, Dict[str, numpy.ndarray]]], Union[numpy.ndarray, Dict[str, numpy.ndarray]]]
- classmethod format_input_output(predict)¶
Make a function robust to type, array shape and data transformation.
- Parameters
predict (Callable[[numpy.ndarray], numpy.ndarray]) – The function of interest to be called.
- Returns
A function calling the function of interest ‘predict’, while guaranteeing consistency in terms of data type and array shape, and applying input and/or output data transformation if required.
- Return type
Callable[[Union[numpy.ndarray, Dict[str, numpy.ndarray]]], Union[numpy.ndarray, Dict[str, numpy.ndarray]]]
- classmethod format_samples(predict)¶
Make a 2D NumPy array-based function work with 1D NumPy array.
- Parameters
predict (Callable[[numpy.ndarray], numpy.ndarray]) – The function to be called; it takes a 2D NumPy array in input and returns a 2D NumPy array. The first dimension represents the samples while the second one represents the components of the variables.
- Returns
A function making the function ‘predict’ work with either a 1D NumPy array or a 2D NumPy array. The evaluation will have the same dimension as the input data.
- Return type
Callable[[numpy.ndarray], numpy.ndarray]
- classmethod format_transform(transform_inputs=True, transform_outputs=True)¶
Force a function to transform its input and/or output variables.
- Parameters
transform_inputs (bool) – If True, apply the transformers to the input variables.
transform_outputs (bool) – If True, apply the transformers to the output variables.
- Returns
A function evaluating a function of interest, after transforming its input data and/or before transforming its output data.
- Return type
Callable[[numpy.ndarray], numpy.ndarray]
- FILENAME = 'ml_algo.pkl'¶
- LIBRARY = 'scikit-learn'¶
- property input_data¶
The input data matrix.
- property input_shape¶
The dimension of the input variables before applying the transformers.
- property is_trained¶
Return whether the algorithm is trained.
- learn(samples=None)¶
Train the machine learning algorithm from the learning dataset.
- Parameters
samples (Optional[List[int]]) – The indices of the learning samples. If None, use the whole learning dataset.
- Raises
NotImplementedError – If an output transformer modifies both the input and the output variables, e.g.
PLS
.- Return type
None
- load_algo(directory)¶
Load a machine learning algorithm from a directory.
- Parameters
directory (str) – The path to the directory where the machine learning algorithm is saved.
- Return type
None
- property output_data¶
The output data matrix.
- property output_shape¶
The dimension of the output variables before applying the transformers.
- predict(input_data, *args, **kwargs)¶
Evaluate ‘predict’ with either array or dictionary-based input data.
Firstly, the pre-processing stage converts the input data to a NumPy data array, if these data are expressed as a dictionary of NumPy data arrays.
Then, the processing evaluates the function ‘predict’ from this NumPy input data array.
Lastly, the post-processing transforms the output data to a dictionary of output NumPy data array if the input data were passed as a dictionary of NumPy data arrays.
- Parameters
input_data (Union[numpy.ndarray, Dict[str, numpy.ndarray]]) – The input data.
*args – The positional arguments of the function ‘predict’.
**kwargs – The keyword arguments of the function ‘predict’.
- Returns
The output data with the same type as the input one.
- Return type
Union[numpy.ndarray, Dict[str, numpy.ndarray]]
- predict_proba(input_data, *args, **kwargs)¶
Evaluate ‘predict’ with either array or dictionary-based input data.
Firstly, the pre-processing stage converts the input data to a NumPy data array, if these data are expressed as a dictionary of NumPy data arrays.
Then, the processing evaluates the function ‘predict’ from this NumPy input data array.
Lastly, the post-processing transforms the output data to a dictionary of output NumPy data array if the input data were passed as a dictionary of NumPy data arrays.
- Parameters
input_data (Union[numpy.ndarray, Dict[str, numpy.ndarray]]) – The input data.
*args – The positional arguments of the function ‘predict’.
**kwargs – The keyword arguments of the function ‘predict’.
- Returns
The output data with the same type as the input one.
- Return type
Union[numpy.ndarray, Dict[str, numpy.ndarray]]
- save(directory=None, path='.', save_learning_set=False)¶
Save the machine learning algorithm.
- Parameters
directory (Optional[str]) – The name of the directory to save the algorithm.
path (str) – The path to parent directory where to create the directory.
save_learning_set (bool) – If False, do not save the learning set to lighten the saved files.
- Returns
The path to the directory where the algorithm is saved.
- Return type
str