How to use the causalml.inference.meta.utils.convert_pd_to_np function in causalml

def fit(self, X, p, treatment, y, verbose=True):
        """Fit the treatment effect and outcome models of the R learner.

        Args:
            X (np.matrix or np.array or pd.Dataframe): a feature matrix
            p (np.ndarray or pd.Series or dict): 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)
            treatment (np.array or pd.Series): a treatment vector
            y (np.array or pd.Series): an outcome vector
        """
        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()
        check_p_conditions(p, self.t_groups)
        if isinstance(p, (np.ndarray, pd.Series)):
            treatment_name = self.t_groups[0]
            p = {treatment_name: convert_pd_to_np(p)}
        elif isinstance(p, dict):
            p = {treatment_name: convert_pd_to_np(_p) for treatment_name, _p in p.items()}

        self._classes = {group: i for i, group in enumerate(self.t_groups)}
        self.models_tau = {group: deepcopy(self.model_tau) for group in self.t_groups}
        self.vars_c = {}
        self.vars_t = {}

        if verbose:

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
            return_ci (bool): whether to return confidence intervals
            n_bootstraps (int): number of bootstrap iterations
            bootstrap_size (int): number of samples per bootstrap
            return_components (bool, optional): whether to return outcome for treatment and control seperately
            verbose (str): whether to output progress logs

        Returns:
            (numpy.ndarray): Predictions of treatment effects. Output dim: [n_samples, n_treatment].
                If return_ci, returns CATE [n_samples, n_treatment], LB [n_samples, n_treatment],
                UB [n_samples, n_treatment]
        """
        X, treatment, y = convert_pd_to_np(X, treatment, y)
        self.fit(X, treatment, y)
        te = self.predict(X, treatment, y, return_components=return_components)

        if not return_ci:
            return te
        else:
            t_groups_global = self.t_groups
            _classes_global = self._classes
            models_c_global = deepcopy(self.models_c)
            models_t_global = deepcopy(self.models_t)
            te_bootstraps = np.zeros(shape=(X.shape[0], self.t_groups.shape[0], n_bootstraps))

            logger.info('Bootstrap Confidence Intervals')
            for i in tqdm(range(n_bootstraps)):
                te_b = self.bootstrap(X, treatment, y, size=bootstrap_size)
                te_bootstraps[:, :, i] = te_b

def fit(self, X, treatment, y):
        """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
        """
        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)}
        self.models_mu_c = {group: deepcopy(self.model_mu_c) for group in self.t_groups}
        self.models_mu_t = {group: deepcopy(self.model_mu_t) for group in self.t_groups}
        self.models_tau_c = {group: deepcopy(self.model_tau_c) for group in self.t_groups}
        self.models_tau_t = {group: deepcopy(self.model_tau_t) for group in self.t_groups}
        self.vars_c = {}
        self.vars_t = {}

        for group in self.t_groups:
            mask = (treatment == group) | (treatment == self.control_name)
            treatment_filt = treatment[mask]
            X_filt = X[mask]
            y_filt = y[mask]

def predict(self, X, treatment=None, y=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(X_new)

            # set the treatment column to one (the treatment group)
            X_new[:, 0] = 1
            yhat_ts[group] = model.predict(X_new)

            if (y is not None) and (treatment is not None) and verbose:
                mask = (treatment == group) | (treatment == self.control_name)

def predict(self, X, treatment=None, y=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

        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_c = self.models_c[group]
            model_t = self.models_t[group]
            yhat_cs[group] = model_c.predict(X)
            yhat_ts[group] = model_t.predict(X)

            if (y is not None) and (treatment is not None) and verbose:
                mask = (treatment == group) | (treatment == self.control_name)
                treatment_filt = treatment[mask]
                y_filt = y[mask]
                w = (treatment_filt == group).astype(int)

                yhat = np.zeros_like(y_filt, dtype=float)

X (np.matrix or np.array or pd.Dataframe): a feature matrix
            p (np.ndarray or pd.Series or dict): 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)
            treatment (np.array or pd.Series, optional): a treatment vector
            y (np.array or pd.Series, optional): an outcome vector

        Returns:
            (numpy.ndarray): Predictions of treatment effects.
        """
        X, treatment, y = convert_pd_to_np(X, treatment, y)
        check_p_conditions(p, self.t_groups)
        if isinstance(p, (np.ndarray, pd.Series)):
            treatment_name = self.t_groups[0]
            p = {treatment_name: convert_pd_to_np(p)}
        elif isinstance(p, dict):
            p = {treatment_name: convert_pd_to_np(_p) for treatment_name, _p in p.items()}

        te = np.zeros((X.shape[0], self.t_groups.shape[0]))
        dhat_cs = {}
        dhat_ts = {}

        for i, group in enumerate(self.t_groups):
            model_tau_c = self.models_tau_c[group]
            model_tau_t = self.models_tau_t[group]
            dhat_cs[group] = model_tau_c.predict(X)
            dhat_ts[group] = model_tau_t.predict(X)

            _te = (p[group] * dhat_cs[group] + (1 - p[group]) * dhat_ts[group]).reshape(-1, 1)
            te[:, i] = np.ravel(_te)

            if (y is not None) and (treatment is not None) and verbose:
                mask = (treatment == group) | (treatment == self.control_name)

"""Estimate the Average Treatment Effect (ATE).

        Args:
            X (np.matrix or np.array or pd.Dataframe): a feature matrix
            p (np.ndarray or pd.Series or dict): 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)
            treatment (np.array or pd.Series): a treatment vector
            y (np.array or pd.Series): an outcome vector
            segment (np.array, optional): An optional segment vector of int. If given, the ATE and its CI will be
                                          estimated for each segment.
            return_ci (bool, optional): Whether to return confidence intervals

        Returns:
            (tuple): The ATE and its confidence interval (LB, UB) for each treatment, t and segment, s
        """
        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()

        check_p_conditions(p, self.t_groups)
        if isinstance(p, (np.ndarray, pd.Series)):
            treatment_name = self.t_groups[0]
            p = {treatment_name: convert_pd_to_np(p)}
        elif isinstance(p, dict):
            p = {treatment_name: convert_pd_to_np(_p) for treatment_name, _p in p.items()}

        ate = []
        ate_lb = []
        ate_ub = []

        for i, group in enumerate(self.t_groups):

p (np.ndarray or pd.Series or dict): 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)
            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

        Returns:
            (numpy.ndarray): Predictions of treatment effects.
        """
        X, treatment, y = convert_pd_to_np(X, treatment, y)
        check_p_conditions(p, self.t_groups)
        if isinstance(p, (np.ndarray, pd.Series)):
            treatment_name = self.t_groups[0]
            p = {treatment_name: convert_pd_to_np(p)}
        elif isinstance(p, dict):
            p = {treatment_name: convert_pd_to_np(_p) for treatment_name, _p in p.items()}

        te = np.zeros((X.shape[0], self.t_groups.shape[0]))
        dhat_cs = {}
        dhat_ts = {}

        for i, group in enumerate(self.t_groups):
            model_tau_c = self.models_tau_c[group]
            model_tau_t = self.models_tau_t[group]
            dhat_cs[group] = model_tau_c.predict(X)
            dhat_ts[group] = model_tau_t.predict(X)

            _te = (p[group] * dhat_cs[group] + (1 - p[group]) * dhat_ts[group]).reshape(-1, 1)
            te[:, i] = np.ravel(_te)

            if (y is not None) and (treatment is not None) and verbose:
                mask = (treatment == group) | (treatment == self.control_name)