import argparse

import inspect

import os

import sys

# import warnings

from datetime import datetime

import numpy as np

import pandas as pd

from joblib import delayed

from sklearn.base import clone

from sklearn.model_selection import StratifiedKFold

from sklearn.utils import check_random_state

from tqdm import tqdm

from _init_scripts import PredictionTask

from _utils import read_yaml, write_yaml, ProgressParallel

currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))

parentdir = os.path.dirname(currentdir)

sys.path.insert(0, parentdir)

from multipit.multi_model.latefusion import LateFusionClassifier

def main(params):

    """

    Repeated cross-validation experiment for classification with late fusion

    """

    # Uncomment for disabling ConvergenceWarning

    # warnings.simplefilter("ignore")

    # os.environ["PYTHONWARNINGS"] = 'ignore'

    # 0. Read config file and save it in the results

    config = read_yaml(params.config)

    save_name = config["save_name"]

    if save_name is None:

        run_id = datetime.now().strftime(r"%m%d_%H%M%S")

        save_name = "exp_" + run_id

    save_dir = os.path.join(params.save_path, save_name)

    os.mkdir(save_dir)

    write_yaml(config, os.path.join(save_dir, "config.yaml"))

    # 1. fix random seeds for reproducibility

    seed = config["latefusion"]["seed"]

    np.random.seed(seed)

    # 2. Load data and define pipelines for each modality

    ptask = PredictionTask(config, survival=False, integration="late")

    ptask.load_data()

    X, y = ptask.data_concat.values, ptask.labels.loc[ptask.data_concat.index].values

    ptask.init_pipelines_latefusion()

    # 3. Perform repeated cross-validation

    parallel = ProgressParallel(

        n_jobs=config["parallelization"]["n_jobs_repeats"],

        total=config["latefusion"]["n_repeats"],

    )

    results_parallel = parallel(

        delayed(_fun_repeats)(

            ptask,

            X,

            y,

            r,

            disable_infos=(config["parallelization"]["n_jobs_repeats"] is not None)

            and (config["parallelization"]["n_jobs_repeats"] > 1),

        )

        for r in range(config["latefusion"]["n_repeats"])

    )

    # 4. Save results

    if config["permutation_test"]:

        perm_predictions = np.zeros(

            (

                len(y),

                len(ptask.names),

                config["n_permutations"],

                config["latefusion"]["n_repeats"],

            )

        )

        list_data_preds, list_data_thrs = [], []

        for p, res in enumerate(results_parallel):

            list_data_preds.append(res[0])

            list_data_thrs.append(res[1])

            perm_predictions[:, :, :, p] = res[2]

        perm_labels = results_parallel[-1][3]

        np.save(os.path.join(save_dir, "permutation_labels.npy"), perm_labels)

        np.save(os.path.join(save_dir, "permutation_predictions.npy"), perm_predictions)

        data_preds = pd.concat(list_data_preds, axis=0)

        data_preds.to_csv(os.path.join(save_dir, "predictions.csv"))

        if config["collect_thresholds"]:

            data_thrs = pd.concat(list_data_thrs, axis=0)

            data_thrs.to_csv(os.path.join(save_dir, "thresholds.csv"))

    else:

        list_data_preds, list_data_thrs = [], []

        for p, res in enumerate(results_parallel):

            list_data_preds.append(res[0])

            list_data_thrs.append(res[1])

        data_preds = pd.concat(list_data_preds, axis=0)

        data_preds.to_csv(os.path.join(save_dir, "predictions.csv"))

        if config["collect_thresholds"]:

            data_thrs = pd.concat(list_data_thrs, axis=0)

            data_thrs.to_csv(os.path.join(save_dir, "thresholds.csv"))

def _fun_repeats(prediction_task, X, y, r, disable_infos):

    """

    Train and test a late fusion model for classification with cross-validation

    Parameters

    ----------

    prediction_task: PredictionTask object

    X: 2D array of shape (n_samples, n_features)

        Concatenation of the different modalities

    y: 1D array of shape (n_samples,)

        Binary outcome

    r: int

        Repeat number

    disable_infos: bool

    Returns

    -------

    df_pred: pd.DataFrame of shape (n_samples, n_models+3)

        Predictions collected over the test sets of the cross-validation scheme for each multimodal combination

    df_thrs: pd.DataFrame of shape (n_samples, n_models+2), None

        Thresholds that optimize the log-rank test on the training set for each fold and each multimodal combination.

    permut_predictions: 3D array of shape (n_samples, n_models, n_permutations)

        Predictions collected over the test sets of the cross_validation scheme for each multimodal combination and each random permutation of the labels.

    permut_labels: 2D array of shape (n_samples, n_permutations)

        Permuted labels

    """

    cv = StratifiedKFold(n_splits=10, shuffle=True)  # , random_state=np.random.seed(i))

    X_preds = np.zeros((len(y), 3 + len(prediction_task.names)))

    X_thresholds = (

        np.zeros((len(y), 2 + len(prediction_task.names)))

        if prediction_task.config["collect_thresholds"]

        else None

    )

    late_clf = LateFusionClassifier(

        estimators=prediction_task.late_estimators,

        cv=StratifiedKFold(n_splits=10, shuffle=True, random_state=np.random.seed(r)),

        **prediction_task.config["latefusion"]["args"]

    )

    # 1. Cross-validation scheme

    for fold_index, (train_index, test_index) in tqdm(

        enumerate(cv.split(np.zeros(len(y)), y)),

        leave=False,

        total=cv.get_n_splits(np.zeros(len(y))),

        disable=disable_infos,

    ):

        X_train, y_train, X_test = (

            X[train_index, :],

            y[train_index],

            X[test_index, :],

        )

        target_surv_train = prediction_task.target_surv[train_index]

        # Fit late fusion on the training set of the fold

        clf = clone(late_clf)

        clf.fit(X_train, y_train)

        # Collect predictions on the test set of the fold for each multimodal combination

        for c, idx in enumerate(prediction_task.indices):

            X_preds[test_index, c] = clf.predict_proba(X_test, estim_ind=idx)[:, 1]

            # Collect the threshold that optimizes log-rank test on the training set

            if prediction_task.config["collect_thresholds"]:

                X_thresholds[test_index, c] = clf.find_logrank_threshold(

                    X_train, target_surv_train, estim_ind=idx

                )

        X_preds[test_index, -3] = fold_index

        if prediction_task.config["collect_thresholds"]:

            X_thresholds[test_index, -2] = fold_index

    X_preds[:, -2] = r

    if prediction_task.config["collect_thresholds"]:

        X_thresholds[:, -1] = r

    X_preds[:, -1] = y

    df_pred = (

        pd.DataFrame(

            X_preds,

            columns=prediction_task.names + ["fold_index", "repeat", "label"],

            index=prediction_task.data_concat.index,

        )

        .reset_index()

        .rename(columns={"index": "samples"})

        .set_index(["repeat", "samples"])

    )

    if prediction_task.config["collect_thresholds"]:

        df_thrs = (

            pd.DataFrame(

                X_thresholds,

                columns=prediction_task.names + ["fold_index", "repeat"],

                index=prediction_task.data_concat.index,

            )

            .reset_index()

            .rename(columns={"index": "samples"})

            .set_index(["repeat", "samples"])

        )

    else:

        df_thrs = None

    # 2. Perform permutation test

    permut_predictions = None

    permut_labels = None

    if prediction_task.config["permutation_test"]:

        permut_labels = np.zeros((len(y), prediction_task.config["n_permutations"]))

        permut_predictions = np.zeros(

            (

                len(y),

                len(prediction_task.names),

                prediction_task.config["n_permutations"],

            )

        )

        for prm in range(prediction_task.config["n_permutations"]):

            X_perm = np.zeros((len(y), len(prediction_task.names)))

            random_state = check_random_state(prm)

            sh_ind = random_state.permutation(len(y))

            yshuffle = np.copy(y)[sh_ind]

            permut_labels[:, prm] = yshuffle

            for fold_index, (train_index, test_index) in tqdm(

                enumerate(cv.split(np.zeros(len(y)), y)),

                leave=False,

                total=cv.get_n_splits(np.zeros(len(y))),

                disable=disable_infos,

            ):

                X_train, yshuffle_train, X_test = (

                    X[train_index, :],

                    yshuffle[train_index],

                    X[test_index, :],

                )

                clf = clone(late_clf)

                clf.fit(X_train, yshuffle_train)

                for c, idx in enumerate(prediction_task.indices):

                    X_perm[test_index, c] = clf.predict_proba(X_test, estim_ind=idx)[

                        :, 1

                    ]

            permut_predictions[:, :, prm] = X_perm

    return df_pred, df_thrs, permut_predictions, permut_labels

if __name__ == "__main__":

    args = argparse.ArgumentParser(description="Late fusion")

    args.add_argument(

        "-c",

        "--config",

        type=str,

        help="config file path",

    )

    args.add_argument(

        "-s",

        "--save_path",

        type=str,

        help="save path",

    )

    main(params=args.parse_args())