1672b5a9a47b081aa3e61c9f2ca76fae26ed8730,cheml/wrappers/cheml_cml/cheml_wrapper.py,Split,fit,#Split#,418

Before Change

    def fit(self):
        // step1: check inputs
        df, df_info = self.input_check("df", req = True, py_type = pd.DataFrame)

        // step2: assign inputs to parameters if necessary (param = @token)
        self.paramFROMinput()

        // step3: check the dimension of input data frame
        df, _ = self.data_check("df", df, ndim=2, n0=None, n1=None, format_out="df")

        // step4: import module and make APIs
        try:
            from cheml.initialization import Split
            split = Split(**self.parameters)
            df1, df2 = split.fit(df)
        except Exception as err:
            msg = "@Task //%i(%s): "%(self.iblock+1, self.SuperFunction) + type(err).__name__ + ": "+ err.message
            raise TypeError(msg)

        // step5: process
        // step6: send out
        order = [edge[1] for edge in self.Base.graph if edge[0] == self.iblock]
        for token in set(order):
            if token == "df1":
                val = df1
                self.Base.send[(self.iblock, token)] = [val, order.count(token),
                                                        (self.iblock, token, self.Host, self.Function)]
            elif token == "df2":
                val = df2
                self.Base.send[(self.iblock, token)] = [val, order.count(token),
                                                        (self.iblock, token, self.Host, self.Function)]

After Change

        del self.inputs

class Split(BASE):
    def fit(self):
        // step1: check inputs
        self.required("df", req=True)
        df = self.inputs["df"].value

        // step2: assign inputs to parameters if necessary (param = @token)
        self.paramFROMinput()

        // step3: check the dimension of input data frame
        df, _ = self.data_check("df", df, ndim=2, n0=None, n1=None, format_out="df")

        // step4: import module and make APIs
        try:
            from cheml.initialization import Split
            split = Split(**self.parameters)
            df1, df2 = split.fit(df)
        except Exception as err:
            msg = "@Task //%i(%s): "%(self.iblock+1, self.Task) + type(err).__name__ + ": "+ err.message
            raise TypeError(msg)

        // step5: process
        // step6: send out
        order = [edge[1] for edge in self.Base.graph if edge[0] == self.iblock]
        for token in set(order):
            if token not in self.outputs:
                msg = "@Task //%i(%s): not a valid output token "%s"" % (self.iblock + 1, self.Task, token)
                raise NameError(msg)
            elif token == "df1":
                self.set_value(token, df1)
                self.outputs[token].count = order.count(token)
                self.Base.send[(self.iblock, token)] = self.outputs[token]
            elif token == "df2":
                self.set_value(token, df2)
                self.outputs[token].count = order.count(token)
                self.Base.send[(self.iblock, token)] = self.outputs[token]

        // step7: delete all inputs from memory
        del self.inputs

class Constant(BASE):
    def fit(self):

Instances