# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
from pathlib import Path
from qlib.model.meta.task import MetaTask
from qlib.contrib.meta.data_selection.model import MetaModelDS
from qlib.contrib.meta.data_selection.dataset import InternalData, MetaDatasetDS
from qlib.data.dataset.handler import DataHandlerLP

import pandas as pd
import fire
import sys
from tqdm.auto import tqdm
import yaml
import pickle
from qlib import auto_init
from qlib.model.trainer import TrainerR, task_train
from qlib.utils import init_instance_by_config
from qlib.workflow.task.gen import RollingGen, task_generator
from qlib.workflow import R
from qlib.tests.data import GetData

DIRNAME = Path(__file__).absolute().resolve().parent
sys.path.append(str(DIRNAME.parent / "baseline"))
from rolling_benchmark import RollingBenchmark  # NOTE: sys.path is changed for import RollingBenchmark


class DDGDA:
    """
    please run `python workflow.py run_all` to run the full workflow of the experiment

    **NOTE**
    before running the example, please clean your previous results with following command
    - `rm -r mlruns`
    """

    def __init__(self, sim_task_model="linear", forecast_model="linear"):
        self.step = 20
        # NOTE:
        # the horizon must match the meaning in the base task template
        self.horizon = 20
        self.meta_exp_name = "DDG-DA"
        self.sim_task_model = sim_task_model  # The model to capture the distribution of data.
        self.forecast_model = forecast_model  # downstream forecasting models' type

    def get_feature_importance(self):
        # this must be lightGBM, because it needs to get the feature importance
        rb = RollingBenchmark(model_type="gbdt")
        task = rb.basic_task()

        model = init_instance_by_config(task["model"])
        dataset = init_instance_by_config(task["dataset"])
        model.fit(dataset)

        fi = model.get_feature_importance()

        # Because the model use numpy instead of dataframe for training lightgbm
        # So the we must use following extra steps to get the right feature importance
        df = dataset.prepare(segments=slice(None), col_set="feature", data_key=DataHandlerLP.DK_R)
        cols = df.columns
        fi_named = {cols[int(k.split("_")[1])]: imp for k, imp in fi.to_dict().items()}

        return pd.Series(fi_named)

    def dump_data_for_proxy_model(self):
        """
        Dump data for training meta model.
        The meta model will be trained upon the proxy forecasting model.
        This dataset is for the proxy forecasting model.
        """
        topk = 30
        fi = self.get_feature_importance()
        col_selected = fi.nlargest(topk)

        rb = RollingBenchmark(model_type=self.sim_task_model)
        task = rb.basic_task()
        dataset = init_instance_by_config(task["dataset"])
        prep_ds = dataset.prepare(slice(None), col_set=["feature", "label"], data_key=DataHandlerLP.DK_L)

        feature_df = prep_ds["feature"]
        label_df = prep_ds["label"]

        feature_selected = feature_df.loc[:, col_selected.index]

        feature_selected = feature_selected.groupby("datetime").apply(lambda df: (df - df.mean()).div(df.std()))
        feature_selected = feature_selected.fillna(0.0)

        df_all = {
            "label": label_df.reindex(feature_selected.index),
            "feature": feature_selected,
        }
        df_all = pd.concat(df_all, axis=1)
        df_all.to_pickle(DIRNAME / "fea_label_df.pkl")

        # dump data in handler format for aligning the interface
        handler = DataHandlerLP(
            data_loader={
                "class": "qlib.data.dataset.loader.StaticDataLoader",
                "kwargs": {"config": DIRNAME / "fea_label_df.pkl"},
            }
        )
        handler.to_pickle(DIRNAME / "handler_proxy.pkl", dump_all=True)

    @property
    def _internal_data_path(self):
        return DIRNAME / f"internal_data_s{self.step}.pkl"

    def dump_meta_ipt(self):
        """
        Dump data for training meta model.
        This function will dump the input data for meta model
        """
        # According to the experiments, the choice of the model type is very important for achieving good results
        rb = RollingBenchmark(model_type=self.sim_task_model)
        sim_task = rb.basic_task()

        if self.sim_task_model == "gbdt":
            sim_task["model"].setdefault("kwargs", {}).update({"early_stopping_rounds": None, "num_boost_round": 150})

        exp_name_sim = f"data_sim_s{self.step}"

        internal_data = InternalData(sim_task, self.step, exp_name=exp_name_sim)
        internal_data.setup(trainer=TrainerR)

        with self._internal_data_path.open("wb") as f:
            pickle.dump(internal_data, f)

    def train_meta_model(self):
        """
        training a meta model based on a simplified linear proxy model;
        """

        # 1) leverage the simplified proxy forecasting model to train meta model.
        # - Only the dataset part is important, in current version of meta model will integrate the
        rb = RollingBenchmark(model_type=self.sim_task_model)
        sim_task = rb.basic_task()
        proxy_forecast_model_task = {
            # "model": "qlib.contrib.model.linear.LinearModel",
            "dataset": {
                "class": "qlib.data.dataset.DatasetH",
                "kwargs": {
                    "handler": f"file://{(DIRNAME / 'handler_proxy.pkl').absolute()}",
                    "segments": {
                        "train": ("2008-01-01", "2010-12-31"),
                        "test": ("2011-01-01", sim_task["dataset"]["kwargs"]["segments"]["test"][1]),
                    },
                },
            },
            # "record": ["qlib.workflow.record_temp.SignalRecord"]
        }
        # the proxy_forecast_model_task will be used to create meta tasks.
        # The test date of first task will be 2011-01-01. Each test segment will be about 20days
        # The tasks include all training tasks and test tasks.

        # 2) preparing meta dataset
        kwargs = dict(
            task_tpl=proxy_forecast_model_task,
            step=self.step,
            segments=0.62,  # keep test period consistent with the dataset yaml
            trunc_days=1 + self.horizon,
            hist_step_n=30,
            fill_method="max",
            rolling_ext_days=0,
        )
        # NOTE:
        # the input of meta model (internal data) are shared between proxy model and final forecasting model
        # but their task test segment are not aligned! It worked in my previous experiment.
        # So the misalignment will not affect the effectiveness of the method.
        with self._internal_data_path.open("rb") as f:
            internal_data = pickle.load(f)
        md = MetaDatasetDS(exp_name=internal_data, **kwargs)

        # 3) train and logging meta model
        with R.start(experiment_name=self.meta_exp_name):
            R.log_params(**kwargs)
            mm = MetaModelDS(step=self.step, hist_step_n=kwargs["hist_step_n"], lr=0.001, max_epoch=200, seed=43)
            mm.fit(md)
            R.save_objects(model=mm)

    @property
    def _task_path(self):
        return DIRNAME / f"tasks_s{self.step}.pkl"

    def meta_inference(self):
        """
        Leverage meta-model for inference:
        - Given
            - baseline tasks
            - input for meta model(internal data)
            - meta model (its learnt knowledge on proxy forecasting model is expected to transfer to normal forecasting model)
        """
        # 1) get meta model
        exp = R.get_exp(experiment_name=self.meta_exp_name)
        rec = exp.list_recorders(rtype=exp.RT_L)[0]
        meta_model: MetaModelDS = rec.load_object("model")

        # 2)
        # we are transfer to knowledge of meta model to final forecasting tasks.
        # Create MetaTaskDataset for the final forecasting tasks
        # Aligning the setting of it to the MetaTaskDataset when training Meta model is necessary

        # 2.1) get previous config
        param = rec.list_params()
        trunc_days = int(param["trunc_days"])
        step = int(param["step"])
        hist_step_n = int(param["hist_step_n"])
        fill_method = param.get("fill_method", "max")

        rb = RollingBenchmark(model_type=self.forecast_model)
        task_l = rb.create_rolling_tasks()

        # 2.2) create meta dataset for final dataset
        kwargs = dict(
            task_tpl=task_l,
            step=step,
            segments=0.0,  # all the tasks are for testing
            trunc_days=trunc_days,
            hist_step_n=hist_step_n,
            fill_method=fill_method,
            task_mode=MetaTask.PROC_MODE_TRANSFER,
        )

        with self._internal_data_path.open("rb") as f:
            internal_data = pickle.load(f)
        mds = MetaDatasetDS(exp_name=internal_data, **kwargs)

        # 3) meta model make inference and get new qlib task
        new_tasks = meta_model.inference(mds)
        with self._task_path.open("wb") as f:
            pickle.dump(new_tasks, f)

    def train_and_eval_tasks(self):
        """
        Training the tasks generated by meta model
        Then evaluate it
        """
        with self._task_path.open("rb") as f:
            tasks = pickle.load(f)
        rb = RollingBenchmark(rolling_exp="rolling_ds", model_type=self.forecast_model)
        rb.train_rolling_tasks(tasks)
        rb.ens_rolling()
        rb.update_rolling_rec()

    def run_all(self):
        # 1) file: handler_proxy.pkl
        self.dump_data_for_proxy_model()
        # 2)
        # file: internal_data_s20.pkl
        # mlflow: data_sim_s20, models for calculating meta_ipt
        self.dump_meta_ipt()
        # 3) meta model will be stored in `DDG-DA`
        self.train_meta_model()
        # 4) new_tasks are saved in "tasks_s20.pkl" (reweighter is added)
        self.meta_inference()
        # 5) load the saved tasks and train model
        self.train_and_eval_tasks()


if __name__ == "__main__":
    GetData().qlib_data(exists_skip=True)
    auto_init()
    fire.Fire(DDGDA)

pandas.concat pandas.Series