class_docstr: A parent class for S-learner classifier classes.
class_docstr_tok: ["A","parent","class","for","S-learner","classifier","classes","."]
docstr: Predict treatment effects.
  Args:
      X (np.matrix or np.array or pd.Dataframe): a feature matrix
      treatment (np.array or pd.Series, optional): a treatment vector
      y (np.array or pd.Series, optional): an outcome vector
      return_components (bool, optional): whether to return outcome for treatment and control seperately
      verbose (bool, optional): whether to output progress logs
  Returns:
      (numpy.ndarray): Predictions of treatment effects.
docstr_tok: ["Predict","treatment","effects",".","Args",":","X","(","np.matrix","or","np.array","or","pd.Dataframe",")",":","a","feature","matrix","treatment","(","np.array","or","pd.Series",",","optional",")",":","a","treatment","vector","y","(","np.array","or","pd.Series",",","optional",")",":","an","outcome","vector","return_components","(","bool",",","optional",")",":","whether","to","return","outcome","for","treatment","and","control","seperately","verbose","(","bool",",","optional",")",":","whether","to","output","progress","logs","Returns",":","(","numpy.ndarray",")",":","Predictions","of","treatment","effects","."]
code: def predict(
      self,
      X,
      treatment=None,
      y=None,
      p=None,
      return_components=False,
      verbose=True,
  ):
      """Predict treatment effects.
      Args:
          X (np.matrix or np.array or pd.Dataframe): a feature matrix
          treatment (np.array or pd.Series, optional): a treatment vector
          y (np.array or pd.Series, optional): an outcome vector
          return_components (bool, optional): whether to return outcome for treatment and control seperately
          verbose (bool, optional): whether to output progress logs
      Returns:
          (numpy.ndarray): Predictions of treatment effects.
      """
      X, treatment, y = convert_pd_to_np(X, treatment, y)
      yhat_cs = {}
      yhat_ts = {}
  
      for group in self.t_groups:
          model = self.models[group]
  
          # set the treatment column to zero (the control group)
          X_new = np.hstack((np.zeros((X.shape[0], 1)), X))
          yhat_cs[group] = model.predict_proba(X_new)[:, 1]
  
          # set the treatment column to one (the treatment group)
          X_new[:, 0] = 1
          yhat_ts[group] = model.predict_proba(X_new)[:, 1]
  
          if y is not None and (treatment is not None) and verbose:
              mask = (treatment == group) | (
                  treatment == self.control_name
              )
              treatment_filt = treatment[mask]
              w = (treatment_filt == group).astype(int)
              y_filt = y[mask]
  
              yhat = np.zeros_like(y_filt, dtype=float)
              yhat[w == 0] = yhat_cs[group][mask][w == 0]
              yhat[w == 1] = yhat_ts[group][mask][w == 1]
  
              logger.info("Error metrics for group {}".format(group))
              classification_metrics(y_filt, yhat, w)
  
      te = np.zeros((X.shape[0], self.t_groups.shape[0]))
      for i, group in enumerate(self.t_groups):
          te[:, i] = yhat_ts[group] - yhat_cs[group]
  
      if not return_components:
          return te
      else:
          return te, yhat_cs, yhat_ts
  
imports: import logging
  import numpy
  from tqdm import tqdm
  from scipy.stats import norm
  from sklearn.dummy import DummyRegressor
  import statsmodels.api
  from copy import deepcopy
  from causalml.inference.meta.base import BaseLearner
  from causalml.inference.meta.utils import check_treatment_vector, convert_pd_to_np
  from causalml.metrics import regression_metrics, classification_metrics
label_desc: Use image node_id 2 for calling the BaseSClassifier obj's underlying member method code with example usage: obj.predict(X, treatment, y, p, return_components, verbose) and returns: te, te,  yhat_cs,  yhat_ts