text: """Params for different versions of estimators."""
  
  from mlxtend.regressor import StackingRegressor
  import numpy as np
  from sklearn.pipeline import make_pipeline
  from sklearn.preprocessing import StandardScaler
  from sklearn.ensemble import ExtraTreesRegressor
  import statsmodels.api as sm
  from xgboost import XGBClassifier
  
  from augury.settings import SEED
  from augury.sklearn.metrics import bits_objective
  from augury.sklearn.models import EloRegressor, TimeSeriesRegressor
  from augury.sklearn.preprocessing import (
      TeammatchToMatchConverter,
      DataFrameConverter,
  )
  from .base_ml_estimator import BASE_ML_PIPELINE
  
  
  np.random.seed(SEED)
  
  
  tipresias_margin_2020 = {
      "name": "tipresias_margin_2020",
      "min_year": 1965,
      "pipeline": StackingRegressor(
          regressors=[
              make_pipeline(
                  DataFrameConverter(),
                  BASE_ML_PIPELINE,
                  ExtraTreesRegressor(random_state=SEED),
              ).set_params(
                  **{
                      "extratreesregressor__max_depth": 45,
                      "extratreesregressor__max_features": 0.9493692952,
                      "extratreesregressor__min_samples_leaf": 2,
                      "extratreesregressor__min_samples_split": 3,
                      "extratreesregressor__n_estimators": 113,
                      "pipeline__correlationselector__threshold": 0.0376827797,
                  }
              ),
              make_pipeline(
                  DataFrameConverter(), TeammatchToMatchConverter(), EloRegressor()
              ).set_params(
                  **{
                      "eloregressor__home_ground_advantage": 7,
                      "eloregressor__k": 23.5156358583,
                      "eloregressor__m": 131.54906178,
                      "eloregressor__s": 257.5770727802,
                      "eloregressor__season_carryover": 0.5329064035,
                      "eloregressor__x": 0.6343992255,
                  }
              ),
              make_pipeline(
                  DataFrameConverter(),
                  TimeSeriesRegressor(
                      sm.tsa.ARIMA,
                      order=(6, 0, 1),
                      exog_cols=["at_home", "oppo_cum_percent"],
                  ),
              ).set_params(
                  **{
                      "timeseriesregressor__exog_cols": ["at_home", "oppo_cum_percent"],
                      "timeseriesregressor__fit_method": "css",
                      "timeseriesregressor__fit_solver": "bfgs",
                      "timeseriesregressor__order": (8, 0, 1),
                  }
              ),
          ],
          meta_regressor=make_pipeline(
              StandardScaler(), ExtraTreesRegressor(random_state=SEED)
          ).set_params(
              **{
                  "extratreesregressor__max_depth": 41,
                  "extratreesregressor__min_samples_leaf": 1,
                  "extratreesregressor__min_samples_split": 3,
                  "extratreesregressor__n_estimators": 172,
              },
          ),
      ),
  }
  
  tipresias_proba_2020 = {
      "name": "tipresias_proba_2020",
      "pipeline": make_pipeline(
          BASE_ML_PIPELINE,
          XGBClassifier(
              random_state=SEED,
              objective=bits_objective,
              use_label_encoder=False,
              verbosity=0,
          ),
      ),
  }
  