FACTS method bug: feature_weights parameter of the method is not propagated correctly

[phantom-duck]

Inside the fit method of the FACTS detector, the function calc_costs is called to calculate the costs of the recourses. But the params argument (which includes the feature_weights) is not passed. As a result, the default parameters are always used, which assign weight equal to 1 to all features.

Example to reproduce:

from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.compose import ColumnTransformer
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import OneHotEncoder

from aif360.sklearn.datasets.openml_datasets import fetch_adult
from aif360.sklearn.detectors.facts.clean import clean_dataset
from aif360.sklearn.detectors.facts import FACTS
from aif360.sklearn.detectors.facts.predicate import Predicate
import pandas as pd

random_seed = 131313 # to produce the expected if-then clause

# load the adult dataset and perform some simple preprocessing steps
# See output for a glimpse of the final dataset's characteristics
X, y, sample_weight = fetch_adult()
data = clean_dataset(X.assign(income=y), "adult")

# split into train-test data
y = data['income']
X = data.drop('income', axis=1)
X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.7, random_state=random_seed, stratify=y)

#### here, we incrementally build the example model. It consists of one preprocessing step,
#### which is to turn categorical features into the respective one-hot encodings, and
#### a simple scikit-learn logistic regressor.
categorical_features = X.select_dtypes(include=["object", "category"]).columns.to_list()
categorical_features_onehot_transformer = ColumnTransformer(
    transformers=[
        ("one-hot-encoder", OneHotEncoder(), categorical_features)
    ],
    remainder="passthrough"
)
model = Pipeline([
    ("one-hot-encoder", categorical_features_onehot_transformer),
    ("clf", LogisticRegression(max_iter=1500))
])

#### train the model
model = model.fit(X_train, y_train)

detector = FACTS(
    clf=model,
    prot_attr="sex",
    freq_itemset_min_supp=0.08,
    feature_weights={f: 10 for f in X.columns},
    feats_not_allowed_to_change=[]
)

detector = detector.fit(X_test)

print(detector.rules_by_if[Predicate.from_dict({"age": pd.Interval(26., 34.), "hours-per-week": "FullTime"})]["Female"][1][0])

The output of the final command is (Predicate(features=['age', 'hours-per-week'], values=[Interval(41.0, 50.0, closed='right'), 'OverTime']), 0.07728337236533955, 2.0), which shows that the action of changing the features "age" and "hours-per-week" (which are categorical) is counted as having cost equal to 2.0. The correct value, however, would be 20.0, since we have assigned weight equal to 10 to all features.