import os

import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from scipy.stats import spearmanr
import logging

logger = logging.getLogger(__name__)


def Spearman_rank_test(data_frame, feature_a, feature_b, save_path, file_name, sample_size=4000):
    """
    Spearman_correlation is to determine whether there is a
    Monotonic component between two features, which can be apply
    only for non_linear relationship and ordinal data

    @param feature_a: Input first feature for Spearman's rank test
    @param feature_b: Input second feature for Spearman's rank test
    @param sample_size: Choose a sample for representing the population
    @param:save_path: output path
    @param:file_name: output name

    """
    a = data_frame[feature_a].sample(n=sample_size, random_state=1)
    b = data_frame[feature_b].sample(n=sample_size, random_state=1)
    coef, p = spearmanr(a, b)
    logger.info("Spearmans' correlation coefficient is:" + str(coef))
    alpha = 0.05
    plt.scatter(a, b)
    plt.xlabel("Feature A")
    plt.ylabel("Feature B")
    plt.title("Spearman's Rank Test")
    plt.savefig(os.path.join(save_path, file_name))
    if p > alpha:
        logger.info("Feature are uncorrelated(failed to reject H0) p=" + str(p))
    else:
        logger.info("Features have a monotonic relationship(reject H0) p=" + str(p))