Project-BasicAlgorithm/core/training/params.py

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# the Apache License, Version 2.0 (the "License"); you may
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#     http://www.apache.org/licenses/LICENSE-2.0
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# under the License.

import inspect
import json
import warnings
from copy import deepcopy


class Params:
    def __init__(
        self, cls, param_file=None, param_str=None, search_params=None, **kwargs
    ):
        input_params = self.parse_file(param_file)
        str_input_params = self.parse_param_str(param_str)
        input_params.update(str_input_params)
        input_params.update(kwargs)

        self.input_params = self.check_input_params(cls, input_params)
        self.search_params = self.check_search_params(cls, search_params)

    def check_input_params(self, cls, input_params):
        # check and adaptive parameter type for input_params
        default_params = self.load_cls_default_params(cls)
        for key, value in input_params.items():
            parse_func = getattr(self, key, None)
            if parse_func:
                value = parse_func(value)
            elif key in default_params:
                value = self.match_type(default_params[key], value)
            input_params[key] = value
        return input_params

    def check_search_params(self, cls, search_params):
        # check and adaptive parameter type for search_params
        search_params = self.parse_param_str(search_params)
        default_params = self.load_cls_default_params(cls)
        for key, values in search_params.items():
            try:
                values = eval(values)
            except Exception as _:
                warnings.warn(f"value : {values} error, is must be list of something")
                continue
            parse_func = getattr(self, key, None)

            new_values = []
            for value in values:
                if parse_func:
                    value = parse_func(value)
                elif key in default_params:
                    value = self.match_type(default_params[key], value)

                new_values.append(value)
            search_params[key] = new_values
        return search_params

    @staticmethod
    def load_cls_default_params(cls):
        default_values = deepcopy(cls.__init__.__defaults__)
        var_names = cls.__init__.__code__.co_varnames
        var_names = [name for name in var_names if name not in {"self", "kwargs"}]
        assert len(default_values) == len(var_names)
        return dict(zip(var_names, default_values))

    @staticmethod
    def match_type(refer_var, input_var):

        if isinstance(refer_var, bool):
            tag = str(input_var).lower()
            if tag not in {"true", "false"}:
                warnings.warn(f"value : {input_var} error, set to False")

            input_var = tag == "true"

        elif refer_var is not None:
            refer_type = type(refer_var)
            input_var = refer_type(input_var)
        return input_var

    @staticmethod
    def parse_file(path):
        path = path or ""
        if not path.strip():
            return {}
        with open(path, "r") as r_f:
            params = json.load(r_f)
        return params

    @staticmethod
    def parse_param_str(param_str):
        param_str = param_str or ""
        if not param_str.strip():
            return {}
        name_value_pairs = param_str.split(";")
        pairs = []
        for name_value_pair in name_value_pairs:
            if not name_value_pair.strip():
                continue
            k_v = name_value_pair.split("=")
            if len(k_v) != 2:
                warnings.warn(f"{name_value_pair} error, will be ignore")
                continue
            key, value = name_value_pair.split("=")
            pairs.append((key.strip(), value.strip()))
        params = dict(pairs)
        return params

    @staticmethod
    def class_weight(value):
        try:
            value = eval(value)
        except:
            value = value
        return value

    def __str__(self):
        input_params_message = str(self.input_params)
        search_params_message = str(self.search_params)
        message = f"input_params: {input_params_message}\nsearch_params: {search_params_message}"
        return message

    __repr__ = __str__


class LightGBMParams(Params):
    ...


class XGBoostParams(Params):
    @staticmethod
    def load_cls_default_params(cls):
        from xgboost import XGBModel

        params = Params.load_cls_default_params(XGBModel)

        params["objective"] = "binary:logistic"
        params["use_label_encoder"] = True
        return params


class LrParams(Params):
    @staticmethod
    def load_cls_default_params(cls):
        from sklearn.linear_model import LogisticRegression

        params = deepcopy(inspect.getfullargspec(LogisticRegression).kwonlydefaults)

        params["penalty"] = "l2"

        return params


class SVMParams(Params):
    @staticmethod
    def load_cls_default_params(cls):
        from sklearn.svm import SVC

        params = deepcopy(inspect.getfullargspec(SVC).kwonlydefaults)

        return params