Support for scikit-learn models about facet HOT 3 CLOSED

# standard imports
import pandas as pd
from sklearn.model_selection import RepeatedKFold, GridSearchCV

# some helpful imports from sklearndf
from sklearndf.pipeline import RegressorPipelineDF
from sklearndf.regression import RandomForestRegressorDF

# relevant FACET imports
from facet.data import Sample
from facet.selection import LearnerSelector, ParameterSpace

from sklearn.datasets import load_diabetes
X,y = load_diabetes(return_X_y=True)
data = load_diabetes()

X = pd.DataFrame(X)
X.columns  = data["feature_names"]
y = pd.DataFrame(y)
y.columns = ["target"]
diabetes_df = pd.concat([X,y], axis=1)

# create FACET sample object
diabetes_sample = Sample(observations=diabetes_df, target_name="target")

# create a (trivial) pipeline for a random forest regressor
from sklearn.ensemble import RandomForestRegressor
rfr = RandomForestRegressor(n_jobs=-1)
rfr.fit(X,y)

# fit the model inspector
from facet.inspection import NativeLearnerInspector
inspector = NativeLearnerInspector(
    model=rfr,
    n_jobs=-3
)

inspector.fit(diabetes_sample)

# visualise synergy as a matrix
from pytools.viz.matrix import MatrixDrawer
synergy_matrix = inspector.feature_synergy_matrix()
MatrixDrawer(style="matplot%").draw(synergy_matrix, title="Synergy Matrix")

# visualise redundancy as a matrix
redundancy_matrix = inspector.feature_redundancy_matrix()
MatrixDrawer(style="matplot%").draw(redundancy_matrix, title="Redundancy Matrix")

# visualise redundancy using a dendrogram
import matplotlib
from pytools.viz.dendrogram import DendrogramDrawer
redundancy = inspector.feature_redundancy_linkage()
DendrogramDrawer().draw(data=redundancy, title="Redundancy Dendrogram")