sklearn writing my own predictors with many parameters - scikit-learn

Question

I am writing my own Sklearn predictor calling a command-line tool that we have.
This command-line too contains many potential parameters (>200).
I understand I need to specify each argument individually in my estimator's argument and each argument should then be associated with an attribute in init.
From the documentation:
The arguments accepted by init should all be keyword arguments with a default value.
Also, every keyword argument accepted by init should correspond to an attribute on the instance.
def __init__(self, param1=1, param2=2):
self.param1 = param1
self.param2 = param2
So if I understand properly I cannot create a class for all these parameters (they will be used in several estimators and transformers)?
As I will have several estimators with these 200 parameters it is really not ideal. It will be difficult to maintain the code and it will be prone to errors.
Does anyone see a workaround for this? Maybe I misunderstood the Sklearn requirements?
Thanks.
Thibault

Answer 1

You might configure the command-line tool to dump its state.
For example: Here's a simple CLI tool takes two arguments and produces one output:
# File: `cli.py`
import argparse
import logging
PARSER = argparse.ArgumentParser()
PARSER.add_argument("-n1", type=int, default=1)
PARSER.add_argument("-n2", type=int, default=2)
ARGS = PARSER.parse_args()
X = [x for (_, x) in ARGS._get_kwargs()]
y = sum(X)
logging.basicConfig(
format="%(message)s",
filename="data.csv",
encoding="utf-8",
level=logging.INFO,
)
data_string = f"{y}," + ",".join([str(x) for x in X])
logging.info(data_string)
print(f"{ARGS.n1} + {ARGS.n2} = {y}")
If we call this a few times with different arguments:
python cli.py -n1 5 -n2 10
python cli.py -n2 6 -n1 4
python cli.py -n1 3
python cli.py -n2 6
python cli.py -n1 2 -n1 0 -n2 7
... our logging configuration dumps the output and the arguments into a file data.csv:
15,5,10
10,4,6
5,3,2
7,1,6
7,0,7
... which we can can use to fit models or make predictions:
# File: `learn.py`
from sklearn.tree import DecisionTreeRegressor
from sklearn.model_selection import LeaveOneOut
import numpy as np
data = np.loadtxt("data.csv", delimiter=",")
X = data[:, 1:]
y = data[:, 0]
reg = DecisionTreeRegressor(random_state=0)
loo = LeaveOneOut()
for train_index, test_index in loo.split(X):
X_train, X_test = X[train_index], X[test_index]
y_train, y_test = y[train_index], y[test_index]
reg.fit(X_train, y_train)
print("Predicted:", reg.predict(X_test), "Actual:", y_test)
# ---------- Output ------------
# Predicted: [10.] Actual: [15.]
# Predicted: [5.] Actual: [10.]
# Predicted: [10.] Actual: [5.]
# Predicted: [7.] Actual: [7.]
# Predicted: [7.] Actual: [7.]