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# calculating ideal recall@k
ideal_recall_at_k = np.minimum(
    np.ones(len(ranking)),
     np.array(list(range(1, len(ranking) + 1)))/ (ranking == 1).sum()
)

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# calculating ideal recall@k
ideal_recall_at_k = np.minimum(
    np.ones(len(conf_df)),
    np.array(list(range(1, len(conf_df["expected"]) + 1)))/ conf_df["expected"].to_list().count(1)
)

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    # calculating recall@k
    for i in range(len(conf_df)):
        recall_at_k.append(
            conf_df.iloc[:i+1, :]["expected"].to_list().count(1)
            / conf_df["expected"].to_list().count(1)
        )

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    conf_df = pd.DataFrame()
    conf_df["conf"] = y_conf
    conf_df["expected"] = y_true
    conf_df.columns = ["conf", "expected"]
    conf_df = conf_df.sort_values("conf", ascending=False)

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@jit
def auc_recall_at_k_np_no_df_numba(y_true, y_conf):
    """
    Experiment #4:
    --------------
    Compute AUC under the Recall@k curve using numpy's
    functions. We do away with the conf_df dataframe 
    as well.

    Numba's jit decorator is also added for further 

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def auc_recall_at_k_np_no_df(y_true, y_conf):
    """
    Experiment #3:
    --------------
    Compute AUC under the Recall@k curve using numpy's
    functions. We do away with the conf_df dataframe 
    as well.

    y_true: A numpy array of expected predictions
    y_conf: A numpy array of the model's confidence

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def auc_recall_at_k_np(y_true, y_conf):
    """
    Experiment #2:
    --------------
    Compute AUC under the Recall@k curve using numpy's
    functions.

    y_true: A numpy array of expected predictions
    y_conf: A numpy array of the model's confidence
            scores for each datapoint

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def auc_recall_at_k(y_true, y_conf):
    """
    Compute AUC under the Recall@k curve.

    y_true: A numpy array of expected predictions
    y_conf: A numpy array of the model's confidence
            scores for each datapoint
            
    Returns: AUC-Recall@k (float)
    """

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def process(self, message: Message, **kwargs: Any) -> None:
    """
    The driver method. Its executed after every user message.
    """

    X = message.get_sparse_features(TEXT)[1].features.reshape(1, -1)

    probs = self.clf.predict_proba(X).flatten()
    intents = self.le.inverse_transform(self.clf.classes_)

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version: "2.0"
nlu:
- intent: greet
  examples: |
    - hi
    - hello
    
- intent: supply_contact_info
  examples: |
    - My name is [John](name). email's [john@email.com](email)