from pathlib import Path
from dotenv import load_dotenv, find_dotenv
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.pyplot import figure
import json
import os
import re
import pprint

basepath = Path(os.getcwd())
if basepath.name != "idp-radio-1":
    os.chdir(basepath.parent.parent)
    print(os.getcwd())
load_dotenv(find_dotenv())

from src.preprocessing.split.train_test_split import train_test_split

data = json.loads(os.environ['EXP_DATA'])
history = data['history']

for s in data["description"].split(".")[:-1]:
    print(s + ".\n")

# if there are any metrics that were renamed, add this new name here as ("default_name":"new_name")
metric_custom_names={"auc":"AUC_ROC"}

metric_names = [re.sub("([a-z0-9])([A-Z])","\g<1> \g<2>",name) for name in data["benchmark"]["metrics"]]
metric_keys = [re.sub("([a-z0-9])([A-Z])","\g<1>_\g<2>",name).lower() for name in data["benchmark"]["metrics"]]

for default_name, custom_name in metric_custom_names.items():
    if not default_name in history.keys() and default_name in metric_keys:
        #replace default name with custom name
        metric_keys[metric_keys.index(default_name)]=custom_name

def print_or_plot_metric(metric_key, metric_name, figure_name):
    if len(history[metric_key]) == 1:
        print("Data for {m_name} only available for a single epoch. \nSkipping plot and printing data...".format(m_name=metric_name))
        print('Train {}: '.format(metric_name), history[metric_key])
        print('Validation {}: '.format(metric_name), history['val_'+metric_key])
        print()        
    else:
        plot_epoch_metric(metric_key, metric_name, figure_name)
        
def plot_epoch_metric(metric_key, metric_name, figure_name):
    figure(num=None, figsize=(10, 6))
    plt.plot(history[metric_key])
    if 'val_'+metric_key in history.keys():
        plt.plot(history['val_'+metric_key])
    plt.title(figure_name)
    plt.ylabel(metric_name)
    plt.xlabel('Epoch')
    if 'val_'+metric_key in history.keys():
        plt.legend(['Train', 'Validation'], loc='upper left')
    plt.show()

for i, metric_key in enumerate(metric_keys):
    print_or_plot_metric(metric_key, metric_names[i], "Model "+metric_names[i])

print_or_plot_metric("loss", "Loss", "Model loss")

if "lr" in history.keys():
    plot_epoch_metric("lr", "Learning Rate", "Learning Rate")

if 'classification_report' in data.keys() and data['classification_report']:
    print(data['classification_report'])

if 'test' in data.keys() and data['test']:
    for score_name, score in data["test"].items():
        print('Test {}: '.format(score_name), score)

pp = pprint.PrettyPrinter(indent=4)
if "benchmark" in data.keys():
    pp.pprint(data["benchmark"])

if 'benchmark' in data.keys() and 'split_seed' in data['benchmark']:
    benchmark = data['benchmark']

    dataset_path = Path(benchmark['dataset_folder'])
    train_labels = benchmark['train_labels'] if 'train_labels' in benchmark.keys() else 'train.csv'
    split_test_size =  benchmark['split_test_size'] if 'split_test_size' in benchmark.keys() else 0.2
    split_valid_size =  benchmark['split_valid_size'] if 'split_valid_size' in benchmark.keys() else 0.2
    split_group = benchmark['split_group'] if 'split_group' in benchmark.keys() else 'patient_id'
    split_seed = benchmark['split_seed']

    all_labels = pd.read_csv(dataset_path / train_labels)
    train_labels, test_labels = train_test_split(all_labels, test_size=split_test_size, group=split_group, seed=split_seed)
    train_labels, validation_labels = train_test_split(train_labels, test_size=split_valid_size, group=split_group, seed=split_seed)

from src.datasets.u_encoding import uencode

def get_distribution(labels):
    if 'nan_replacement' in benchmark.keys():
        labels = labels.fillna(benchmark['nan_replacement'])
    data = labels.to_numpy()
    data = uencode(benchmark['u_enc'], data, unc_value=benchmark['unc_value'])
    data = pd.DataFrame(data, columns=labels.columns)

    labels = data[benchmark['label_columns']]

    d = {'Pathology': [], 'Positive': [], 'Positive %': [], 'Negative': [], 'Negative %': [],}
    for label in labels.columns:
        values = labels.groupby(label)
        d['Pathology'].append(label)

        positive = values.size()[1.0] if 1.0 in values.size() else 0
        positive_percent = positive / labels.shape[0] * 100
        d['Positive'].append(positive)
        d['Positive %'].append(round(positive_percent))

        negative = values.size()[-0.0] if -0.0 in values.size() else 0
        negative_percent = negative / labels.shape[0] * 100
        d['Negative'].append(negative)
        d['Negative %'].append(round(negative_percent))
    
    df = pd.DataFrame(d)
    df = df.set_index('Pathology')

    return df

if 'benchmark' in data.keys() and 'split_seed' in data['benchmark']:
    train = get_distribution(train_labels)
    val = get_distribution(validation_labels)
    test = get_distribution(test_labels)
    
    positives = train[['Positive %']].merge(val[['Positive %']], left_index=True, right_index=True).merge(test[['Positive %']], left_index=True,  right_index=True).rename(columns={"Positive %_x": "Positives Train", "Positive %_y": "Positives Validation", "Positive %": "Positives Test", })
    positives.copy().plot(kind='bar', figsize=(10,7), title="Positive Labels Distribution")
    
    negatives = train[['Negative %']].merge(val[['Negative %']], left_index=True, right_index=True).merge(test[['Negative %']], left_index=True,  right_index=True).rename(columns={"Negative %_x": "Negative Train", "Negative %_y": "Negative Validation", "Negative %": "Negative Test", })
    negatives.copy().plot(kind='bar', figsize=(10,7), title="Negative Labels Distribution")

    train[['Positive %', 'Negative %']].copy().plot(kind='bar', figsize=(10,7), title="Training set")
    val[['Positive %', 'Negative %']].copy().plot(kind='bar', figsize=(10,7), title="Validation set")
    test[['Positive %', 'Negative %']].copy().plot(kind='bar', figsize=(10,7), title="Test set")