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from models import Model
import utils
import pandas as pd
from typing import Optional
# Paste your model here:
class CustomModel(Model):
def __init__(self):
super()
# Use self.state to store results of your learning
# It will be saved in filesystem and loaded after server restart
self.state = {}
def fit(self, dataframe: pd.DataFrame, segments: list, cache: Optional[dict]) -> dict:
pass
def do_predict(self, dataframe: pd.DataFrame) -> list:
return []