class TransactionOutputRow:
    def __init__(self, transaction_output, row_index):
        self.transaction_output = transaction_output
        self.row_index = row_index

    def __getitem__(self, item):
        return self.transaction_output.data[item][self.row_index]

    def __contains__(self, item):
        return item in self.transaction_output.data.keys()

    def as_dict(self):
        return {key:self.transaction_output.data[key][self.row_index] for key in self.transaction_output.data}

    def explain(self):
        for pred_col in self.transaction_output.evaluations:
            self.transaction_output.evaluations[pred_col][self.row_index].explain()

    def __str__(self):
        return str(self.as_dict())

    def as_list(self):
        #Note that here we will not output the confidence columns
        return [self.transaction_output.evaluations[col][self.row_index] for col in self.transaction_output.transaction.lmd['columns']]

    @property
    def _predicted_values(self):
        return {pred_col:self.transaction_output.evaluations[pred_col][self.row_index].predicted_value for pred_col in self.transaction_output.evaluations}

d093c80fed7a3dfc8f74e017ada74d4d5ae81723
class TransactionOutputRow:
    <mask token>
    <mask token>
    <mask token>
    <mask token>

    def explain(self):
        for pred_col in self.transaction_output.evaluations:
            self.transaction_output.evaluations[pred_col][self.row_index
                ].explain()

    def __str__(self):
        return str(self.as_dict())

    def as_list(self):
        return [self.transaction_output.evaluations[col][self.row_index] for
            col in self.transaction_output.transaction.lmd['columns']]

    @property
    def _predicted_values(self):
        return {pred_col: self.transaction_output.evaluations[pred_col][
            self.row_index].predicted_value for pred_col in self.
            transaction_output.evaluations}

class TransactionOutputRow:
    <mask token>
    <mask token>
    <mask token>

    def as_dict(self):
        return {key: self.transaction_output.data[key][self.row_index] for
            key in self.transaction_output.data}

    def explain(self):
        for pred_col in self.transaction_output.evaluations:
            self.transaction_output.evaluations[pred_col][self.row_index
                ].explain()

    def __str__(self):
        return str(self.as_dict())

    def as_list(self):
        return [self.transaction_output.evaluations[col][self.row_index] for
            col in self.transaction_output.transaction.lmd['columns']]

    @property
    def _predicted_values(self):
        return {pred_col: self.transaction_output.evaluations[pred_col][
            self.row_index].predicted_value for pred_col in self.
            transaction_output.evaluations}

class TransactionOutputRow:

    def __init__(self, transaction_output, row_index):
        self.transaction_output = transaction_output
        self.row_index = row_index
    <mask token>
    <mask token>

    def as_dict(self):
        return {key: self.transaction_output.data[key][self.row_index] for
            key in self.transaction_output.data}

    def explain(self):
        for pred_col in self.transaction_output.evaluations:
            self.transaction_output.evaluations[pred_col][self.row_index
                ].explain()

    def __str__(self):
        return str(self.as_dict())

    def as_list(self):
        return [self.transaction_output.evaluations[col][self.row_index] for
            col in self.transaction_output.transaction.lmd['columns']]

    @property
    def _predicted_values(self):
        return {pred_col: self.transaction_output.evaluations[pred_col][
            self.row_index].predicted_value for pred_col in self.
            transaction_output.evaluations}

class TransactionOutputRow:

    def __init__(self, transaction_output, row_index):
        self.transaction_output = transaction_output
        self.row_index = row_index
    <mask token>

    def __contains__(self, item):
        return item in self.transaction_output.data.keys()

    def as_dict(self):
        return {key: self.transaction_output.data[key][self.row_index] for
            key in self.transaction_output.data}

    def explain(self):
        for pred_col in self.transaction_output.evaluations:
            self.transaction_output.evaluations[pred_col][self.row_index
                ].explain()

    def __str__(self):
        return str(self.as_dict())

    def as_list(self):
        return [self.transaction_output.evaluations[col][self.row_index] for
            col in self.transaction_output.transaction.lmd['columns']]

    @property
    def _predicted_values(self):
        return {pred_col: self.transaction_output.evaluations[pred_col][
            self.row_index].predicted_value for pred_col in self.
            transaction_output.evaluations}

class TransactionOutputRow:
    def __init__(self, transaction_output, row_index):
        self.transaction_output = transaction_output
        self.row_index = row_index

    def __getitem__(self, item):
        return self.transaction_output.data[item][self.row_index]

    def __contains__(self, item):
        return item in self.transaction_output.data.keys()

    def as_dict(self):
        return {key:self.transaction_output.data[key][self.row_index] for key in self.transaction_output.data}

    def explain(self):
        for pred_col in self.transaction_output.evaluations:
            self.transaction_output.evaluations[pred_col][self.row_index].explain()

    def __str__(self):
        return str(self.as_dict())

    def as_list(self):
        #Note that here we will not output the confidence columns
        return [self.transaction_output.evaluations[col][self.row_index] for col in self.transaction_output.transaction.lmd['columns']]

    @property
    def _predicted_values(self):
        return {pred_col:self.transaction_output.evaluations[pred_col][self.row_index].predicted_value for pred_col in self.transaction_output.evaluations}
