bhepop2.functions
Module Contents
Functions
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Get attributes list from dictionary of modalities |
Filter distributions table with attribute selection and infer modalities. |
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Infer attributes and their modalities from the given distributions. |
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Compute the list of feature values that will define the assignment intervals. |
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Get feature values from the given distributions. |
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Create a DataFrame containing probabilities for the given feature values. |
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Linear interpolation of a feature value probability. |
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Compute the frequency of each crossed modality present in the population. |
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Parameters |
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- bhepop2.functions.get_attributes(modalities: dict) list
Get attributes list from dictionary of modalities
- Parameters:
modalities –
- Returns:
attributes
- bhepop2.functions.filter_distributions_and_infer_modalities(distributions: pandas.DataFrame, attribute_selection)
Filter distributions table with attribute selection and infer modalities.
- Parameters:
distributions – distribution DataFrame
attribute_selection – list of attributes to keep in the distribution, or None
- Returns:
filtered distribution Dataframe, { attribute: [modalities] } dict
- bhepop2.functions.infer_modalities_from_distributions(distributions: pandas.DataFrame)
Infer attributes and their modalities from the given distributions.
- Parameters:
distributions – distributions DataFrame
- Returns:
dict of attributes and their modalities, { attribute: [modalities] }
- bhepop2.functions.compute_feature_values(distribution: pandas.DataFrame, relative_maximum: float, delta_min=None) list
Compute the list of feature values that will define the assignment intervals.
The distributions do not give the knowledge of the minimum and maximum feature values, so we have to choose them. The minimum is the same for all distributions, it is directly equal to the abs_first_value parameter. The maximum is computed by multiplying the relative_maximum parameter to the last value of each distribution.
- Parameters:
distribution – dataframe of distribution
relative_maximum – multiplicand applied to compute the last feature value of each distribution
delta_min – minimum delta between two feature values. None to keep all values.
- Returns:
list of feature values
- bhepop2.functions.get_feature_from_qualitative_distribution(distribution: pandas.DataFrame)
Get feature values from the given distributions.
- Parameters:
distribution – distribution DataFrame
- Returns:
list of possible values for the qualitative feature
- bhepop2.functions.compute_features_prob(feature_values: list, distribution: list)
Create a DataFrame containing probabilities for the given feature values.
- Parameters:
feature_values – list of feature values
distribution – list of distribution values
- Returns:
DataFrame of feature probabilities
- bhepop2.functions.interpolate_feature_prob(feature_value: float, distribution: list)
Linear interpolation of a feature value probability.
First and last distribution values represent minimum and maximum values that can be taken.
- Parameters:
feature_value – value of feature to interpolate
distribution – feature values for each decile from 0 to 10
- Returns:
probability of being lower than the input feature value
- bhepop2.functions.compute_crossed_modalities_frequencies(population: pandas.DataFrame, modalities: dict) pandas.DataFrame
Compute the frequency of each crossed modality present in the population.
Columns other than attributes are removed from the result DataFrame, and a ‘probability’ column is added.
- Parameters:
population – population DataFrame
modalities – modalities dict
- Returns:
DataFrame of crossed modalities frequencies
- bhepop2.functions.build_cross_table(pop: pandas.DataFrame, names_attribute: list)
Parameters
pop : DataFrame synthesis population names_attribute: list of two strings
name of attribute1 and name of attribute 2
Returns
- table_percentageDataFrame
proportion of modalities of attribute 2 given attribute 1
- bhepop2.functions.compute_rq(model, nb_modalities, K)