palma.utils package

Submodules

palma.utils.checker module

palma.utils.names module

palma.utils.names.get_random_name() → str

get_random_name generates a random name

Returns:

str

random name

palma.utils.plotting module

palma.utils.plotting.plot_correlation(df: DataFrame, cmap: str = 'RdBu_r', method: str = 'spearman', linewidths=1, fmt='0.2f', vmin=-1, vmax=1)

palma.utils.plotting.plot_splitting_strategy(X: DataFrame, y: Series, iter_cross_validation: iter, cmap, sort_by=None, modulus=1)

palma.utils.plotting.plot_variable_importance(variable_importance: DataFrame, mode='minmax', color='darkblue', cmap='flare', alpha=0.2, **kwargs)

palma.utils.plotting.roc_plot_base()

palma.utils.plotting.roc_plot_bundle(list_fpr, list_tpr, mean_fpr=array([0., 0.01010101, 0.02020202, 0.03030303, 0.04040404, 0.05050505, 0.06060606, 0.07070707, 0.08080808, 0.09090909, 0.1010101, 0.11111111, 0.12121212, 0.13131313, 0.14141414, 0.15151515, 0.16161616, 0.17171717, 0.18181818, 0.19191919, 0.2020202, 0.21212121, 0.22222222, 0.23232323, 0.24242424, 0.25252525, 0.26262626, 0.27272727, 0.28282828, 0.29292929, 0.3030303, 0.31313131, 0.32323232, 0.33333333, 0.34343434, 0.35353535, 0.36363636, 0.37373737, 0.38383838, 0.39393939, 0.4040404, 0.41414141, 0.42424242, 0.43434343, 0.44444444, 0.45454545, 0.46464646, 0.47474747, 0.48484848, 0.49494949, 0.50505051, 0.51515152, 0.52525253, 0.53535354, 0.54545455, 0.55555556, 0.56565657, 0.57575758, 0.58585859, 0.5959596, 0.60606061, 0.61616162, 0.62626263, 0.63636364, 0.64646465, 0.65656566, 0.66666667, 0.67676768, 0.68686869, 0.6969697, 0.70707071, 0.71717172, 0.72727273, 0.73737374, 0.74747475, 0.75757576, 0.76767677, 0.77777778, 0.78787879, 0.7979798, 0.80808081, 0.81818182, 0.82828283, 0.83838384, 0.84848485, 0.85858586, 0.86868687, 0.87878788, 0.88888889, 0.8989899, 0.90909091, 0.91919192, 0.92929293, 0.93939394, 0.94949495, 0.95959596, 0.96969697, 0.97979798, 0.98989899, 1.]), plot_all=False, plot_beam=True, cmap='inferno', plot_mean=True, c='b', label_iter=None, mode='std', label='', **args)

palma.utils.utils module

class palma.utils.utils.AverageEstimator(estimator_list: list)

Bases: object

A simple ensemble estimator that computes the average prediction of a list of estimators.

Parameters:

estimator_listlist

A list of individual estimators to be averaged.

Returns:

numpy.ndarray

The averaged prediction or class probabilities.

Attributes:

estimator_listlist

The list of individual estimators.

nint

The number of estimators in the list.

Methods

predict(*args, **kwargs)	Compute the average prediction across all estimators.
predict_proba(*args, **kwargs)	Compute the average class probabilities across all estimators.

predict(*args, **kwargs) → iter

predict_proba(*args, **kwargs) → iter

palma.utils.utils.check_splitting_strategy(X: DataFrame, iter_cross_validation: iter)

palma.utils.utils.check_started(message: str, need_build: bool = False) → Callable

check_built is a decorator used for methods that must be called on built or unbuilt Project. If the Project is_built attribute has not the correct value, an AttributeError is raised with the message passed as argument.

Parameters:

message: str

Error message

need_build: bool

Expected value for Project is_built attribute

Returns:

Callable

palma.utils.utils.get_estimator_name(estimator) → str

palma.utils.utils.get_hash(**kwargs) → str

Return a hash of parameters

palma.utils.utils.get_splitting_matrix(X: DataFrame, iter_cross_validation: iter, expand=False) → DataFrame

Generate a splitting matrix based on cross-validation iterations.

Parameters:

Xpd.DataFrame

The input dataframe.

iter_cross_validationIterable

An iterable containing cross-validation splits (train, test).

expandbool, optional

If True, the output matrix will have columns for both train and test splits for each iteration. If False (default), the output matrix will have columns for each iteration with 1 for train and 2 for test.

Returns:

pd.DataFrame

A matrix indicating the train (1) and test (2) splits for each iteration. Rows represent data points, and columns represent iterations.

Examples

>>> import pandas as pd
>>> X = pd.DataFrame({'feature1': [1, 2, 3, 4, 5],
...                   'feature2': ['A', 'B', 'C', 'D', 'E']})
>>> iter_cv = [(range(3), range(3, 5)), (range(2), range(2, 5))]
>>> get_splitting_matrix(X, iter_cv)

palma.utils.utils.hash_dataframe(data: DataFrame, how='whole')

palma.utils.utils.interpolate_roc(roc_curve_metric: dict[dict[tuple[dict[array]]]], mean_fpr=array([0., 0.01010101, 0.02020202, 0.03030303, 0.04040404, 0.05050505, 0.06060606, 0.07070707, 0.08080808, 0.09090909, 0.1010101, 0.11111111, 0.12121212, 0.13131313, 0.14141414, 0.15151515, 0.16161616, 0.17171717, 0.18181818, 0.19191919, 0.2020202, 0.21212121, 0.22222222, 0.23232323, 0.24242424, 0.25252525, 0.26262626, 0.27272727, 0.28282828, 0.29292929, 0.3030303, 0.31313131, 0.32323232, 0.33333333, 0.34343434, 0.35353535, 0.36363636, 0.37373737, 0.38383838, 0.39393939, 0.4040404, 0.41414141, 0.42424242, 0.43434343, 0.44444444, 0.45454545, 0.46464646, 0.47474747, 0.48484848, 0.49494949, 0.50505051, 0.51515152, 0.52525253, 0.53535354, 0.54545455, 0.55555556, 0.56565657, 0.57575758, 0.58585859, 0.5959596, 0.60606061, 0.61616162, 0.62626263, 0.63636364, 0.64646465, 0.65656566, 0.66666667, 0.67676768, 0.68686869, 0.6969697, 0.70707071, 0.71717172, 0.72727273, 0.73737374, 0.74747475, 0.75757576, 0.76767677, 0.77777778, 0.78787879, 0.7979798, 0.80808081, 0.81818182, 0.82828283, 0.83838384, 0.84848485, 0.85858586, 0.86868687, 0.87878788, 0.88888889, 0.8989899, 0.90909091, 0.91919192, 0.92929293, 0.93939394, 0.94949495, 0.95959596, 0.96969697, 0.97979798, 0.98989899, 1.]))

Module contents