import numpy as np
import pandas as pd
from typing import Tuple, Union
[docs]
class Bunch(dict):
"""
Container object for internal datasets
Dictionary-like object that exposes its keys as attributes.
"""
def __init__(self, **kwargs):
super().__init__(kwargs)
def __setattr__(self, key, value):
self[key] = value
def __dir__(self):
return self.keys()
def __getattr__(self, key):
try:
return self[key]
except KeyError:
raise AttributeError(key)
[docs]
def sample_df(df: pd.DataFrame,
n: int):
""" Sample n instances from the dataframe df. """
if n < df.shape[0]+1:
replace = False
else:
replace = True
return df.sample(n=n, replace=replace)
[docs]
def create_outlier_batch(data: np.ndarray,
target: np.ndarray,
n_samples: int,
perc_outlier: int) -> Union[Bunch, Tuple[np.ndarray, np.ndarray]]:
""" Create a batch with a defined percentage of outliers. """
# create df
data = pd.DataFrame(data=data)
data['target'] = target
# separate inlier and outlier data
normal = data[data['target'] == 0]
outlier = data[data['target'] == 1]
if n_samples == 1:
n_outlier = np.random.binomial(1, .01 * perc_outlier)
n_normal = 1 - n_outlier
else:
n_outlier = int(perc_outlier * .01 * n_samples)
n_normal = int((100 - perc_outlier) * .01 * n_samples)
# draw samples
batch_normal = sample_df(normal, n_normal)
batch_outlier = sample_df(outlier, n_outlier)
batch = pd.concat([batch_normal, batch_outlier])
batch = batch.sample(frac=1).reset_index(drop=True)
is_outlier = batch['target'].values
batch.drop(columns=['target'], inplace=True)
return Bunch(data=batch.values, target=is_outlier, target_names=['normal', 'outlier'])