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.model_selection import LeaveOneGroupOut, 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 prepare_regression_model_input(model_input, cv_method="logo"):
    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 == "logo":
        data_x, data_y, data_groups = (
            model_input.drop(["target", "pid"], axis=1),
            model_input["target"],
            model_input["pid"],
        )
    else:
        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"],
        )

    categorical_feature_colnames = [
        "gender",
        "startlanguage",
        "limesurvey_demand_control_ratio_quartile",
    ]
    additional_categorical_features = [
        col
        for col in data_x.columns
        if "mostcommonactivity" in col or "homelabel" in col
    ]
    categorical_feature_colnames += additional_categorical_features

    categorical_features = data_x[categorical_feature_colnames].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 = data_x.drop(categorical_feature_colnames, axis=1)

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

    return train_x, data_y, data_groups


def run_all_regression_models(input_csv):
    # Prepare data
    data_x, data_y, data_groups = prepare_regression_model_input(input_csv)

    # Prepare cross validation
    logo = LeaveOneGroupOut()
    logo.get_n_splits(
        data_x,
        data_y,
        groups=data_groups,
    )
    metrics = ["r2", "neg_mean_absolute_error", "neg_root_mean_squared_error"]
    test_metrics = ["test_" + metric for metric in metrics]
    scores = pd.DataFrame(columns=["method", "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=logo,
        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 = scores_df.agg(["max", np.nanmedian]).transpose()
    scores_df["method"] = "dummy"
    scores = pd.concat([scores, scores_df])

    lin_reg_rapids = linear_model.LinearRegression()
    lin_reg_scores = cross_validate(
        lin_reg_rapids,
        X=data_x,
        y=data_y,
        groups=data_groups,
        cv=logo,
        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 = scores_df.agg(["max", np.nanmedian]).transpose()
    scores_df["method"] = "linear_reg"
    scores = pd.concat([scores, scores_df])

    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=logo,
        n_jobs=-1,
        scoring=metrics,
    )
    print("Ridge regression")

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

    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=logo,
        n_jobs=-1,
        scoring=metrics,
    )
    print("Lasso regression")

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

    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=logo,
        n_jobs=-1,
        scoring=metrics,
    )
    print("Bayesian Ridge")

    scores_df = pd.DataFrame(bayesian_ridge_reg_score)[test_metrics]
    scores_df = scores_df.agg(["max", np.nanmedian]).transpose()
    scores_df["method"] = "bayesian_ridge"
    scores = pd.concat([scores, scores_df])

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

    scores_df = pd.DataFrame(ransac_reg_score)[test_metrics]
    scores_df = scores_df.agg(["max", np.nanmedian]).transpose()
    scores_df["method"] = "RANSAC"
    scores = pd.concat([scores, scores_df])

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

    scores_df = pd.DataFrame(svr_score)[test_metrics]
    scores_df = scores_df.agg(["max", np.nanmedian]).transpose()
    scores_df["method"] = "SVR"
    scores = pd.concat([scores, scores_df])

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

    scores_df = pd.DataFrame(kridge_score)[test_metrics]
    scores_df = scores_df.agg(["max", np.nanmedian]).transpose()
    scores_df["method"] = "kernel_ridge"
    scores = pd.concat([scores, scores_df])

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

    scores_df = pd.DataFrame(gpr_score)[test_metrics]
    scores_df = scores_df.agg(["max", np.nanmedian]).transpose()
    scores_df["method"] = "gaussian_proc"
    scores = pd.concat([scores, scores_df])

    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=logo, n_jobs=-1, scoring=metrics
    )
    print("Random Forest Regression")

    scores_df = pd.DataFrame(rfr_score)[test_metrics]
    scores_df = scores_df.agg(["max", np.nanmedian]).transpose()
    scores_df["method"] = "random_forest"
    scores = pd.concat([scores, scores_df])

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

    scores_df = pd.DataFrame(xgb_score)[test_metrics]
    scores_df = scores_df.agg(["max", np.nanmedian]).transpose()
    scores_df["method"] = "XGBoost"
    scores = pd.concat([scores, scores_df])

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

    scores_df = pd.DataFrame(ada_score)[test_metrics]
    scores_df = scores_df.agg(["max", np.nanmedian]).transpose()
    scores_df["method"] = "ADA_boost"
    scores = pd.concat([scores, scores_df])

    return scores


def run_all_classification_models(data_x, data_y, data_groups, cv_method):
    metrics = ["accuracy", "average_precision", "recall", "f1"]
    test_metrics = ["test_" + metric for metric in metrics]

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

    dummy_class = DummyClassifier(strategy="most_frequent")

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

    scores_df = pd.DataFrame(dummy_score)[test_metrics]
    scores_df = scores_df.agg(["max", "mean"]).transpose()
    scores_df["method"] = "Dummy"
    scores = pd.concat([scores, scores_df])

    logistic_regression = linear_model.LogisticRegression()

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

    scores_df = pd.DataFrame(log_reg_scores)[test_metrics]
    scores_df = scores_df.agg(["max", "mean"]).transpose()
    scores_df["method"] = "logistic_reg"
    scores = pd.concat([scores, scores_df])

    svc = svm.SVC()

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

    scores_df = pd.DataFrame(svc_scores)[test_metrics]
    scores_df = scores_df.agg(["max", "mean"]).transpose()
    scores_df["method"] = "svc"
    scores = pd.concat([scores, scores_df])

    gaussian_nb = naive_bayes.GaussianNB()

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

    scores_df = pd.DataFrame(gaussian_nb_scores)[test_metrics]
    scores_df = scores_df.agg(["max", "mean"]).transpose()
    scores_df["method"] = "gaussian_naive_bayes"
    scores = pd.concat([scores, scores_df])

    sgdc = linear_model.SGDClassifier()

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

    scores_df = pd.DataFrame(sgdc_scores)[test_metrics]
    scores_df = scores_df.agg(["max", "mean"]).transpose()
    scores_df["method"] = "stochastic_gradient_descent"
    scores = pd.concat([scores, scores_df])

    rfc = ensemble.RandomForestClassifier()

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

    scores_df = pd.DataFrame(rfc_scores)[test_metrics]
    scores_df = scores_df.agg(["max", "mean"]).transpose()
    scores_df["method"] = "random_forest"
    scores = pd.concat([scores, scores_df])

    xgb_classifier = XGBClassifier()

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

    scores_df = pd.DataFrame(xgb_scores)[test_metrics]
    scores_df = scores_df.agg(["max", "mean"]).transpose()
    scores_df["method"] = "xgboost"
    scores = pd.concat([scores, scores_df])

    return scores