class_docstr: A parent class for DR-learner regressor classes.
  
  A DR-learner estimates treatment effects with machine learning models.
  
  Details of DR-learner are available at Kennedy (2020) (https://arxiv.org/abs/2004.14497).
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docstr: Fit the inference model.
  
  Args:
      X (np.matrix or np.array or pd.Dataframe): a feature matrix
      treatment (np.array or pd.Series): a treatment vector
      y (np.array or pd.Series): an outcome vector
      p (np.ndarray or pd.Series or dict, optional): an array of propensity scores of float (0,1) in the
          single-treatment case; or, a dictionary of treatment groups that map to propensity vectors of
          float (0,1); if None will run ElasticNetPropensityModel() to generate the propensity scores.
      seed (int): random seed for cross-fitting
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code: def fit(self, X, treatment, y, p=None, seed=None):
      """Fit the inference model.
  
      Args:
          X (np.matrix or np.array or pd.Dataframe): a feature matrix
          treatment (np.array or pd.Series): a treatment vector
          y (np.array or pd.Series): an outcome vector
          p (np.ndarray or pd.Series or dict, optional): an array of propensity scores of float (0,1) in the
              single-treatment case; or, a dictionary of treatment groups that map to propensity vectors of
              float (0,1); if None will run ElasticNetPropensityModel() to generate the propensity scores.
          seed (int): random seed for cross-fitting
      """
      X, treatment, y = convert_pd_to_np(X, treatment, y)
      check_treatment_vector(treatment, self.control_name)
      self.t_groups = np.unique(
          treatment[treatment != self.control_name]
      )
      self.t_groups.sort()
      self._classes = {
          group: i for i, group in enumerate(self.t_groups)
      }
  
      # The estimator splits the data into 3 partitions for cross-fit on the propensity score estimation,
      # the outcome regression, and the treatment regression on the doubly robust estimates. The use of
      # the partitions is rotated so we do not lose on the sample size.
      cv = KFold(n_splits=3, shuffle=True, random_state=seed)
      split_indices = [index for _, index in cv.split(y)]
  
      self.models_mu_c = [
          deepcopy(self.model_mu_c),
          deepcopy(self.model_mu_c),
          deepcopy(self.model_mu_c),
      ]
      self.models_mu_t = {
          group: [
              deepcopy(self.model_mu_t),
              deepcopy(self.model_mu_t),
              deepcopy(self.model_mu_t),
          ]
          for group in self.t_groups
      }
      self.models_tau = {
          group: [
              deepcopy(self.model_tau),
              deepcopy(self.model_tau),
              deepcopy(self.model_tau),
          ]
          for group in self.t_groups
      }
      if p is None:
          self.propensity = {
              group: np.zeros(y.shape[0]) for group in self.t_groups
          }
  
      for ifold in range(3):
          treatment_idx = split_indices[ifold]
          outcome_idx = split_indices[(ifold + 1) % 3]
          tau_idx = split_indices[(ifold + 2) % 3]
  
          treatment_treat, treatment_out, treatment_tau = (
              treatment[treatment_idx],
              treatment[outcome_idx],
              treatment[tau_idx],
          )
          y_out, y_tau = y[outcome_idx], y[tau_idx]
          X_treat, X_out, X_tau = (
              X[treatment_idx],
              X[outcome_idx],
              X[tau_idx],
          )
  
          if p is None:
              logger.info("Generating propensity score")
              cur_p = dict()
  
              for group in self.t_groups:
                  mask = (treatment_treat == group) | (
                      treatment_treat == self.control_name
                  )
                  treatment_filt = treatment_treat[mask]
                  X_filt = X_treat[mask]
                  w_filt = (treatment_filt == group).astype(int)
                  w = (treatment_tau == group).astype(int)
                  cur_p[group], _ = compute_propensity_score(
                      X=X_filt,
                      treatment=w_filt,
                      X_pred=X_tau,
                      treatment_pred=w,
                  )
                  self.propensity[group][tau_idx] = cur_p[group]
          else:
              cur_p = dict()
              if isinstance(p, (np.ndarray, pd.Series)):
                  cur_p = {
                      self.t_groups[0]: convert_pd_to_np(p[tau_idx])
                  }
              else:
                  cur_p = {g: prop[tau_idx] for g, prop in p.items()}
              check_p_conditions(cur_p, self.t_groups)
  
          logger.info("Generate outcome regressions")
          self.models_mu_c[ifold].fit(
              X_out[treatment_out == self.control_name],
              y_out[treatment_out == self.control_name],
          )
          for group in self.t_groups:
              self.models_mu_t[group][ifold].fit(
                  X_out[treatment_out == group],
                  y_out[treatment_out == group],
              )
  
          logger.info("Fit pseudo outcomes from the DR formula")
  
          for group in self.t_groups:
              mask = (treatment_tau == group) | (
                  treatment_tau == self.control_name
              )
              treatment_filt = treatment_tau[mask]
              X_filt = X_tau[mask]
              y_filt = y_tau[mask]
              w_filt = (treatment_filt == group).astype(int)
              p_filt = cur_p[group][mask]
              mu_t = self.models_mu_t[group][ifold].predict(X_filt)
              mu_c = self.models_mu_c[ifold].predict(X_filt)
              dr = (
                  (w_filt - p_filt)
                  / p_filt
                  / (1 - p_filt)
                  * (y_filt - mu_t * w_filt - mu_c * (1 - w_filt))
                  + mu_t
                  - mu_c
              )
              self.models_tau[group][ifold].fit(X_filt, dr)
  
imports: from copy import deepcopy
  import logging
  import numpy
  import pandas
  from scipy.stats import norm
  from sklearn.model_selection import cross_val_predict, KFold
  from tqdm import tqdm
  from xgboost import XGBRegressor
  from causalml.inference.meta.base import BaseLearner
  from causalml.inference.meta.utils import check_treatment_vector, check_p_conditions, convert_pd_to_np
  from causalml.metrics import regression_metrics, classification_metrics
  from causalml.propensity import compute_propensity_score
label_desc: Use image node_id 3 for calling the BaseDRLearner obj's underlying member method code with example usage: obj.fit(X, treatment, y, p, seed) without return types