factor_analyzer.confirmatory_factor_analyzer.ModelSpecificationParser

factor_analyzer.confirmatory_factor_analyzer.ModelSpecificationParser()

Generate the model specification for CFA.

This class includes two static methods to generate a :class:ModelSpecification object from either a dictionary or a numpy array.

Methods

Name	Description
parse_model_specification_from_array	Generate the model specification from a numpy array.
parse_model_specification_from_dict	Generate the model specification from a dictionary.

parse_model_specification_from_array

factor_analyzer.confirmatory_factor_analyzer.ModelSpecificationParser.parse_model_specification_from_array(
    X,
    specification=None,
)

Generate the model specification from a numpy array.

The columns should correspond to the factors, and the rows should correspond to the variables. If this method is used to create the :class:ModelSpecification object, then no factor names and variable names will be added as properties to that object.

Parameters

Name	Type	Description	Default
X	array - `like`	The data set that will be used for CFA.	required
specification	array - `like`	An array with the loading details. If `None`, the matrix will be created assuming all variables load on all factors. Defaults to `None`.	`None`

Returns

Name	Type	Description
ModelSpecification	ModelSpecification	A model specification object.

Raises

Name	Type	Description
	ValueError	If `specification` is not in the expected format.

Examples

import pandas as pd
import numpy as np
import os
from spotoptim.factor_analyzer import (ConfirmatoryFactorAnalyzer,
                                      ModelSpecificationParser)
from spotoptim.utils import get_internal_datasets_folder
X = pd.read_csv(os.path.join(get_internal_datasets_folder(), 'test11.csv'))
model_array = np.array([[1, 1, 1, 1, 0, 0, 0, 0], [0, 0, 0, 0, 1, 1, 1, 1]])
model_spec = ModelSpecificationParser.parse_model_specification_from_array(X,
                                                                             model_array)

parse_model_specification_from_dict

factor_analyzer.confirmatory_factor_analyzer.ModelSpecificationParser.parse_model_specification_from_dict(
    X,
    specification=None,
)

Generate the model specification from a dictionary.

The keys in the dictionary should be the factor names, and the values should be the feature names. If this method is used to create the :class:ModelSpecification object, then factor names and variable names will be added as properties to that object.

Parameters

Name	Type	Description	Default
X	array - `like`	The data set that will be used for CFA.	required
specification	dict	A dictionary with the loading details. If `None`, the matrix will be created assuming all variables load on all factors. Defaults to `None`.	`None`

Returns

Name	Type	Description
ModelSpecification	ModelSpecification	A model specification object.

Raises

Name	Type	Description
	ValueError	If `specification` is not in the expected format.

Examples

import pandas as pd
import os
from spotoptim.factor_analyzer import (ConfirmatoryFactorAnalyzer,
                                      ModelSpecificationParser)
from spotoptim.utils import get_internal_datasets_folder
X = pd.read_csv(os.path.join(get_internal_datasets_folder(), 'test11.csv'))
model_dict = {"F1": ["V1", "V2", "V3", "V4"],
              "F2": ["V5", "V6", "V7", "V8"]}
model_spec = ModelSpecificationParser.parse_model_specification_from_dict(X, model_dict)