from pathlib import Path

import numpy as np
import pandas as pd
from sklearn import (
    ensemble,
    gaussian_process,
    kernel_ridge,
    linear_model,
    naive_bayes,
    svm,
)
from sklearn.dummy import DummyClassifier, DummyRegressor
from sklearn.metrics import confusion_matrix
from sklearn.model_selection import (
    BaseCrossValidator,
    LeaveOneGroupOut,
    StratifiedKFold,
    cross_validate,
)
from xgboost import XGBClassifier, XGBRegressor


def safe_outer_merge_on_index(left: pd.DataFrame, right: pd.DataFrame) -> pd.DataFrame:
    if left.empty:
        return right
    elif right.empty:
        return left
    else:
        return pd.merge(
            left,
            right,
            how="outer",
            left_index=True,
            right_index=True,
            validate="one_to_one",
        )


def to_csv_with_settings(
    df: pd.DataFrame, folder: Path, filename_prefix: str, data_type: str
) -> None:
    full_path = construct_full_path(folder, filename_prefix, data_type)
    df.to_csv(
        path_or_buf=full_path,
        sep=",",
        na_rep="NA",
        header=True,
        index=True,
        encoding="utf-8",
    )
    print("Exported the dataframe to " + str(full_path))


def read_csv_with_settings(
    folder: Path, filename_prefix: str, data_type: str, grouping_variable: list
) -> pd.DataFrame:
    full_path = construct_full_path(folder, filename_prefix, data_type)
    return pd.read_csv(
        filepath_or_buffer=full_path,
        sep=",",
        header=0,
        na_values="NA",
        encoding="utf-8",
        index_col=(["participant_id"] + grouping_variable),
        parse_dates=True,
        infer_datetime_format=True,
        cache_dates=True,
    )


def construct_full_path(folder: Path, filename_prefix: str, data_type: str) -> Path:
    export_filename = filename_prefix + "_" + data_type + ".csv"
    full_path = folder / export_filename
    return full_path


def insert_row(df, row):
    return pd.concat([df, pd.DataFrame([row], columns=df.columns)], ignore_index=True)


def impute_encode_categorical_features(model_input: pd.DataFrame) -> pd.DataFrame:
    categorical_feature_col_names = [
        "gender",
        "startlanguage",
        "limesurvey_demand_control_ratio_quartile",
    ]
    additional_categorical_features = [
        col
        for col in model_input.columns
        if "mostcommonactivity" in col or "homelabel" in col
    ]
    categorical_feature_col_names += additional_categorical_features

    categorical_features = model_input[categorical_feature_col_names].copy()

    mode_categorical_features = categorical_features.mode().iloc[0]
    # fillna with mode
    categorical_features = categorical_features.fillna(mode_categorical_features)
    # one-hot encoding
    categorical_features = categorical_features.apply(
        lambda col: col.astype("category")
    )
    if not categorical_features.empty:
        categorical_features = pd.get_dummies(categorical_features)

    numerical_features = model_input.drop(categorical_feature_col_names, axis=1)

    model_input = pd.concat([numerical_features, categorical_features], axis=1)
    return model_input


def prepare_sklearn_data_format(
    model_input: pd.DataFrame, cv_method: str = "logo"
) -> tuple:
    index_columns = [
        "local_segment",
        "local_segment_label",
        "local_segment_start_datetime",
        "local_segment_end_datetime",
    ]
    model_input.set_index(index_columns, inplace=True)

    if cv_method == "half_logo":
        model_input["pid_index"] = model_input.groupby("pid").cumcount()
        model_input["pid_count"] = model_input.groupby("pid")["pid"].transform("count")

        model_input["pid_index"] = (
            model_input["pid_index"] / model_input["pid_count"] + 1
        ).round()
        model_input["pid_half"] = (
            model_input["pid"] + "_" + model_input["pid_index"].astype(int).astype(str)
        )

        data_x, data_y, data_groups = (
            model_input.drop(["target", "pid", "pid_index", "pid_half"], axis=1),
            model_input["target"],
            model_input["pid_half"],
        )
    else:
        data_x, data_y, data_groups = (
            model_input.drop(["target", "pid"], axis=1),
            model_input["target"],
            model_input["pid"],
        )
    return data_x, data_y, data_groups


def prepare_cross_validator(
    data_x: pd.DataFrame,
    data_y: pd.DataFrame,
    data_groups: pd.DataFrame,
    cv_method: str = "logo",
) -> BaseCrossValidator:
    if cv_method == "logo" or cv_method == "half_logo":
        cv = LeaveOneGroupOut()
        cv.get_n_splits(
            data_x,
            data_y,
            groups=data_groups,
        )
    else:
        cv = StratifiedKFold(n_splits=5, shuffle=True)
    return cv


def aggregate_and_transpose(df: pd.DataFrame, statistics=None) -> pd.DataFrame:
    if statistics is None:
        statistics = ["max", "mean"]
    return (
        df.agg(statistics)
        .transpose()
        .reset_index()
        .rename(columns={"index": "test_metric"})
    )


def run_all_regression_models(
    data_x: pd.DataFrame,
    data_y: pd.DataFrame,
    data_groups: pd.DataFrame,
    cross_validator: BaseCrossValidator,
) -> pd.DataFrame:
    metrics = ["r2", "neg_mean_absolute_error", "neg_root_mean_squared_error"]
    test_metrics = ["test_" + metric for metric in metrics]
    scores = pd.DataFrame(columns=["method", "test_metric", "max", "nanmedian"])

    # Validate models
    dummy_regr = DummyRegressor(strategy="mean")
    dummy_regr_scores = cross_validate(
        dummy_regr,
        X=data_x,
        y=data_y,
        groups=data_groups,
        cv=cross_validator,
        n_jobs=-1,
        scoring=metrics,
    )
    print("Dummy model:")
    print("R^2: ", np.nanmedian(dummy_regr_scores["test_r2"]))

    scores_df = pd.DataFrame(dummy_regr_scores)[test_metrics]
    scores_df = aggregate_and_transpose(scores_df, statistics=["max", np.nanmedian])
    scores_df["method"] = "dummy"
    scores = pd.concat([scores, scores_df])
    del dummy_regr
    del dummy_regr_scores

    lin_reg = linear_model.LinearRegression()
    lin_reg_scores = cross_validate(
        lin_reg,
        X=data_x,
        y=data_y,
        groups=data_groups,
        cv=cross_validator,
        n_jobs=-1,
        scoring=metrics,
    )
    print("Linear regression:")
    print("R^2: ", np.nanmedian(lin_reg_scores["test_r2"]))

    scores_df = pd.DataFrame(lin_reg_scores)[test_metrics]
    scores_df = aggregate_and_transpose(scores_df, statistics=["max", np.nanmedian])
    scores_df["method"] = "linear_reg"
    scores = pd.concat([scores, scores_df])
    del lin_reg
    del lin_reg_scores

    ridge_reg = linear_model.Ridge(alpha=0.5)
    ridge_reg_scores = cross_validate(
        ridge_reg,
        X=data_x,
        y=data_y,
        groups=data_groups,
        cv=cross_validator,
        n_jobs=-1,
        scoring=metrics,
    )
    print("Ridge regression")

    scores_df = pd.DataFrame(ridge_reg_scores)[test_metrics]
    scores_df = aggregate_and_transpose(scores_df, statistics=["max", np.nanmedian])
    scores_df["method"] = "ridge_reg"
    scores = pd.concat([scores, scores_df])
    del ridge_reg
    del ridge_reg_scores

    lasso_reg = linear_model.Lasso(alpha=0.1)
    lasso_reg_score = cross_validate(
        lasso_reg,
        X=data_x,
        y=data_y,
        groups=data_groups,
        cv=cross_validator,
        n_jobs=-1,
        scoring=metrics,
    )
    print("Lasso regression")

    scores_df = pd.DataFrame(lasso_reg_score)[test_metrics]
    scores_df = aggregate_and_transpose(scores_df, statistics=["max", np.nanmedian])
    scores_df["method"] = "lasso_reg"
    scores = pd.concat([scores, scores_df])
    del lasso_reg
    del lasso_reg_score

    bayesian_ridge_reg = linear_model.BayesianRidge()
    bayesian_ridge_reg_score = cross_validate(
        bayesian_ridge_reg,
        X=data_x,
        y=data_y,
        groups=data_groups,
        cv=cross_validator,
        n_jobs=-1,
        scoring=metrics,
    )
    print("Bayesian Ridge")

    scores_df = pd.DataFrame(bayesian_ridge_reg_score)[test_metrics]
    scores_df = aggregate_and_transpose(scores_df, statistics=["max", np.nanmedian])
    scores_df["method"] = "bayesian_ridge"
    scores = pd.concat([scores, scores_df])
    del bayesian_ridge_reg
    del bayesian_ridge_reg_score

    ransac_reg = linear_model.RANSACRegressor()
    ransac_reg_score = cross_validate(
        ransac_reg,
        X=data_x,
        y=data_y,
        groups=data_groups,
        cv=cross_validator,
        n_jobs=-1,
        scoring=metrics,
    )
    print("RANSAC (outlier robust regression)")

    scores_df = pd.DataFrame(ransac_reg_score)[test_metrics]
    scores_df = aggregate_and_transpose(scores_df, statistics=["max", np.nanmedian])
    scores_df["method"] = "RANSAC"
    scores = pd.concat([scores, scores_df])
    del ransac_reg
    del ransac_reg_score

    svr = svm.SVR()
    svr_score = cross_validate(
        svr,
        X=data_x,
        y=data_y,
        groups=data_groups,
        cv=cross_validator,
        n_jobs=-1,
        scoring=metrics,
    )
    print("Support vector regression")

    scores_df = pd.DataFrame(svr_score)[test_metrics]
    scores_df = aggregate_and_transpose(scores_df, statistics=["max", np.nanmedian])
    scores_df["method"] = "SVR"
    scores = pd.concat([scores, scores_df])
    del svr
    del svr_score

    kridge = kernel_ridge.KernelRidge()
    kridge_score = cross_validate(
        kridge,
        X=data_x,
        y=data_y,
        groups=data_groups,
        cv=cross_validator,
        n_jobs=-1,
        scoring=metrics,
    )
    print("Kernel Ridge regression")

    scores_df = pd.DataFrame(kridge_score)[test_metrics]
    scores_df = aggregate_and_transpose(scores_df, statistics=["max", np.nanmedian])
    scores_df["method"] = "kernel_ridge"
    scores = pd.concat([scores, scores_df])
    del kridge
    del kridge_score

    gpr = gaussian_process.GaussianProcessRegressor()
    gpr_score = cross_validate(
        gpr,
        X=data_x,
        y=data_y,
        groups=data_groups,
        cv=cross_validator,
        n_jobs=-1,
        scoring=metrics,
    )
    print("Gaussian Process Regression")

    scores_df = pd.DataFrame(gpr_score)[test_metrics]
    scores_df = aggregate_and_transpose(scores_df, statistics=["max", np.nanmedian])
    scores_df["method"] = "gaussian_proc"
    scores = pd.concat([scores, scores_df])
    del gpr
    del gpr_score

    rfr = ensemble.RandomForestRegressor(max_features=0.3, n_jobs=-1)
    rfr_score = cross_validate(
        rfr,
        X=data_x,
        y=data_y,
        groups=data_groups,
        cv=cross_validator,
        n_jobs=-1,
        scoring=metrics,
    )
    print("Random Forest Regression")

    scores_df = pd.DataFrame(rfr_score)[test_metrics]
    scores_df = aggregate_and_transpose(scores_df, statistics=["max", np.nanmedian])
    scores_df["method"] = "random_forest"
    scores = pd.concat([scores, scores_df])
    del rfr
    del rfr_score

    xgb = XGBRegressor()
    xgb_score = cross_validate(
        xgb,
        X=data_x,
        y=data_y,
        groups=data_groups,
        cv=cross_validator,
        n_jobs=-1,
        scoring=metrics,
    )
    print("XGBoost Regressor")

    scores_df = pd.DataFrame(xgb_score)[test_metrics]
    scores_df = aggregate_and_transpose(scores_df, statistics=["max", np.nanmedian])
    scores_df["method"] = "XGBoost"
    scores = pd.concat([scores, scores_df])
    del xgb
    del xgb_score

    ada = ensemble.AdaBoostRegressor()
    ada_score = cross_validate(
        ada,
        X=data_x,
        y=data_y,
        groups=data_groups,
        cv=cross_validator,
        n_jobs=-1,
        scoring=metrics,
    )
    print("ADA Boost Regressor")

    scores_df = pd.DataFrame(ada_score)[test_metrics]
    scores_df = aggregate_and_transpose(scores_df, statistics=["max", np.nanmedian])
    scores_df["method"] = "ADA_boost"
    scores = pd.concat([scores, scores_df])
    del ada
    del ada_score

    return scores


def confusion_matrix_scorer(clf, X, y):
    y_pred = clf.predict(X)
    cm = confusion_matrix(y, y_pred)
    return {"tn": cm[0, 0], "fp": cm[0, 1], "fn": cm[1, 0], "tp": cm[1, 1]}


def aggregate_confusion_matrix(scores_dict: dict) -> pd.DataFrame:
    scores_aggregated = aggregate_and_transpose(
        pd.DataFrame(scores_dict), statistics=["sum"]
    )
    return scores_aggregated[
        ~scores_aggregated.test_metric.isin(["fit_time", "score_time"])
    ]


def run_all_classification_models(
    data_x: pd.DataFrame,
    data_y: pd.DataFrame,
    data_groups: pd.DataFrame,
    cross_validator: BaseCrossValidator,
):
    data_y_value_counts = data_y.value_counts()
    if len(data_y_value_counts) == 1:
        raise (ValueError("There is only one unique value in data_y."))
    if len(data_y_value_counts) == 2:
        metrics = ["accuracy", "average_precision", "recall", "f1"]
    else:
        metrics = ["accuracy", "precision_micro", "recall_micro", "f1_micro"]

    test_metrics = ["test_" + metric for metric in metrics]

    scores = pd.DataFrame(columns=["method", "test_metric", "max", "mean"])

    dummy_class = DummyClassifier(strategy="most_frequent")

    dummy_score = cross_validate(
        dummy_class,
        X=data_x,
        y=data_y,
        groups=data_groups,
        cv=cross_validator,
        n_jobs=-1,
        error_score="raise",
        scoring=metrics,
    )
    dummy_confusion_matrix = cross_validate(
        dummy_class,
        X=data_x,
        y=data_y,
        groups=data_groups,
        cv=cross_validator,
        n_jobs=-1,
        error_score="raise",
        scoring=confusion_matrix_scorer,
    )
    print("Dummy")

    scores_df = pd.DataFrame(dummy_score)[test_metrics]
    scores_df = aggregate_and_transpose(scores_df, statistics=["max", "mean"])
    scores_df = pd.concat(
        [
            scores_df,
            aggregate_confusion_matrix(dummy_confusion_matrix).rename(
                columns={"sum": "mean"}
                # Note: the column is misleadingly renamed to get concise output.
            ),
        ]
    )
    scores_df["method"] = "dummy_classifier"
    scores = pd.concat([scores, scores_df])
    del dummy_class
    del dummy_score
    del dummy_confusion_matrix

    logistic_regression = linear_model.LogisticRegression()

    log_reg_scores = cross_validate(
        logistic_regression,
        X=data_x,
        y=data_y,
        groups=data_groups,
        cv=cross_validator,
        n_jobs=-1,
        scoring=metrics,
    )
    log_reg_confusion_matrix = cross_validate(
        logistic_regression,
        X=data_x,
        y=data_y,
        groups=data_groups,
        cv=cross_validator,
        n_jobs=-1,
        scoring=confusion_matrix_scorer,
    )
    print("Logistic regression")

    scores_df = pd.DataFrame(log_reg_scores)[test_metrics]
    scores_df = aggregate_and_transpose(scores_df, statistics=["max", "mean"])
    scores_df = pd.concat(
        [
            scores_df,
            aggregate_confusion_matrix(log_reg_confusion_matrix).rename(
                columns={"sum": "mean"}
                # Note: the column is misleadingly renamed to get concise output.
            ),
        ]
    )
    scores_df["method"] = "logistic_regression"
    scores = pd.concat([scores, scores_df])
    del logistic_regression
    del log_reg_scores
    del log_reg_confusion_matrix

    svc = svm.SVC()

    svc_scores = cross_validate(
        svc,
        X=data_x,
        y=data_y,
        groups=data_groups,
        cv=cross_validator,
        n_jobs=-1,
        scoring=metrics,
    )
    svc_confusion_matrix = cross_validate(
        svc,
        X=data_x,
        y=data_y,
        groups=data_groups,
        cv=cross_validator,
        n_jobs=-1,
        scoring=confusion_matrix_scorer,
    )
    print("Support Vector Machine")

    scores_df = pd.DataFrame(svc_scores)[test_metrics]
    scores_df = aggregate_and_transpose(scores_df, statistics=["max", "mean"])
    scores_df = pd.concat(
        [
            scores_df,
            aggregate_confusion_matrix(svc_confusion_matrix).rename(
                columns={"sum": "mean"}
                # Note: the column is misleadingly renamed to get concise output.
            ),
        ]
    )
    scores_df["method"] = "SVC"
    scores = pd.concat([scores, scores_df])
    del svc
    del svc_scores
    del svc_confusion_matrix

    gaussian_nb = naive_bayes.GaussianNB()

    gaussian_nb_scores = cross_validate(
        gaussian_nb,
        X=data_x,
        y=data_y,
        groups=data_groups,
        cv=cross_validator,
        n_jobs=-1,
        scoring=metrics,
    )
    gaussian_nb_confusion_matrix = cross_validate(
        gaussian_nb,
        X=data_x,
        y=data_y,
        groups=data_groups,
        cv=cross_validator,
        n_jobs=-1,
        scoring=confusion_matrix_scorer,
    )
    print("Gaussian Naive Bayes")

    scores_df = pd.DataFrame(gaussian_nb_scores)[test_metrics]
    scores_df = aggregate_and_transpose(scores_df, statistics=["max", "mean"])
    scores_df = pd.concat(
        [
            scores_df,
            aggregate_confusion_matrix(gaussian_nb_confusion_matrix).rename(
                columns={"sum": "mean"}
                # Note: the column is misleadingly renamed to get concise output.
            ),
        ]
    )
    scores_df["method"] = "gaussian_naive_bayes"
    scores = pd.concat([scores, scores_df])
    del gaussian_nb
    del gaussian_nb_scores
    del gaussian_nb_confusion_matrix

    sgdc = linear_model.SGDClassifier()

    sgdc_scores = cross_validate(
        sgdc,
        X=data_x,
        y=data_y,
        groups=data_groups,
        cv=cross_validator,
        n_jobs=-1,
        scoring=metrics,
    )
    sgdc_confusion_matrix = cross_validate(
        sgdc,
        X=data_x,
        y=data_y,
        groups=data_groups,
        cv=cross_validator,
        n_jobs=-1,
        scoring=confusion_matrix_scorer,
    )
    print("Stochastic Gradient Descent")

    scores_df = pd.DataFrame(sgdc_scores)[test_metrics]
    scores_df = aggregate_and_transpose(scores_df, statistics=["max", "mean"])
    scores_df = pd.concat(
        [
            scores_df,
            aggregate_confusion_matrix(sgdc_confusion_matrix).rename(
                columns={"sum": "mean"}
                # Note: the column is misleadingly renamed to get concise output.
            ),
        ]
    )
    scores_df["method"] = "stochastic_gradient_descent_classifier"
    scores = pd.concat([scores, scores_df])
    del sgdc
    del sgdc_scores
    del sgdc_confusion_matrix

    rfc = ensemble.RandomForestClassifier()

    rfc_scores = cross_validate(
        rfc,
        X=data_x,
        y=data_y,
        groups=data_groups,
        cv=cross_validator,
        n_jobs=-1,
        scoring=metrics,
    )
    rfc_confusion_matrix = cross_validate(
        rfc,
        X=data_x,
        y=data_y,
        groups=data_groups,
        cv=cross_validator,
        n_jobs=-1,
        scoring=confusion_matrix_scorer,
    )
    print("Random Forest")

    scores_df = pd.DataFrame(rfc_scores)[test_metrics]
    scores_df = aggregate_and_transpose(scores_df, statistics=["max", "mean"])
    scores_df = pd.concat(
        [
            scores_df,
            aggregate_confusion_matrix(rfc_confusion_matrix).rename(
                columns={"sum": "mean"}
                # Note: the column is misleadingly renamed to get concise output.
            ),
        ]
    )
    scores_df["method"] = "random_forest_classifier"
    scores = pd.concat([scores, scores_df])
    del rfc
    del rfc_scores
    del rfc_confusion_matrix

    xgb_classifier = XGBClassifier()

    xgb_scores = cross_validate(
        xgb_classifier,
        X=data_x,
        y=data_y,
        groups=data_groups,
        cv=cross_validator,
        n_jobs=-1,
        scoring=metrics,
    )
    xgb_confusion_matrix = cross_validate(
        xgb_classifier,
        X=data_x,
        y=data_y,
        groups=data_groups,
        cv=cross_validator,
        n_jobs=-1,
        scoring=confusion_matrix_scorer,
    )
    print("XGBoost")

    scores_df = pd.DataFrame(xgb_scores)[test_metrics]
    scores_df = aggregate_and_transpose(scores_df, statistics=["max", "mean"])
    scores_df = pd.concat(
        [
            scores_df,
            aggregate_confusion_matrix(xgb_confusion_matrix).rename(
                columns={"sum": "mean"}
                # Note: the column is misleadingly renamed to get concise output.
            ),
        ]
    )
    scores_df["method"] = "XGBoost_classifier"
    scores = pd.concat([scores, scores_df])
    del xgb_classifier
    del xgb_scores
    del xgb_confusion_matrix

    return scores