import os
import sys
import pandas as pd
import networkx
from networkx import draw, Graph
import pyjedai
from pyjedai.utils import (
    text_cleaning_method,
    print_clusters,
    print_blocks,
    print_candidate_pairs
)
from pyjedai.block_building import (
    StandardBlocking,
    QGramsBlocking,
    ExtendedQGramsBlocking,
    SuffixArraysBlocking,
    ExtendedSuffixArraysBlocking,
)
from pyjedai.comparison_cleaning import (
    WeightedEdgePruning,
    WeightedNodePruning,
    CardinalityEdgePruning,
    CardinalityNodePruning,
    BLAST,
    ReciprocalCardinalityNodePruning,
    ReciprocalWeightedNodePruning,
    ComparisonPropagation
)
from pyjedai.evaluation import Evaluation
from pyjedai.datamodel import Data
from pyjedai.block_cleaning import BlockPurging
from pyjedai.block_cleaning import BlockFiltering
from pyjedai.matching import EntityMatching
from pyjedai.clustering import ConnectedComponentsClustering, UniqueMappingClustering

from settings import er_output_dir, ltable_path, rtable_path, ltable_id, rtable_id, mapping_lid, mapping_rid, \
    mapping_path


def example():
    # read data
    d1 = pd.read_csv(r"E:\Data\Research\Projects\matching_dependency_pyJedAI\datasets\JedAI\ccer\D2\abt.csv",
                     sep='|', engine='python', na_filter=False)
    d2 = pd.read_csv(r"E:\Data\Research\Projects\matching_dependency_pyJedAI\datasets\JedAI\ccer\D2\buy.csv",
                     sep='|', engine='python', na_filter=False)
    gt = pd.read_csv(r"E:\Data\Research\Projects\matching_dependency_pyJedAI\datasets\JedAI\ccer\D2\gt.csv",
                     sep='|', engine='python')

    data = Data(dataset_1=d1,
                id_column_name_1='id',
                dataset_2=d2,
                id_column_name_2='id',
                ground_truth=gt)

    # clean data(optional)
    data.clean_dataset(remove_stopwords=False,
                       remove_punctuation=False,
                       remove_numbers=False,
                       remove_unicodes=False)

    # block building
    bb = StandardBlocking()
    blocks = bb.build_blocks(data, attributes_1=['name'], attributes_2=['name'])

    # block purging(optional)
    bp = BlockPurging()
    cleaned_blocks = bp.process(blocks, data, tqdm_disable=False)

    # block cleaning(optional)
    # todo ratio
    bf = BlockFiltering(ratio=0.8)
    filtered_blocks = bf.process(cleaned_blocks, data, tqdm_disable=False)

    # Comparison Cleaning - Meta Blocking(optional)
    # todo meta_blocking methods, weighting_scheme (more)
    mb = WeightedEdgePruning(weighting_scheme='EJS')
    candidate_pairs_blocks = mb.process(filtered_blocks, data, tqdm_disable=True)

    # entity matching
    # todo parameters(qgram, similarity_threshold)
    em = EntityMatching(
        metric='cosine',
        tokenizer='char_tokenizer',
        vectorizer='tfidf',
        qgram=3,
        similarity_threshold=0.0
    )

    # 无向权重图可视化
    pairs_graph = em.predict(candidate_pairs_blocks, data, tqdm_disable=True)
    draw(pairs_graph)

    # entity clustering
    # todo similarity_threshold
    ccc = UniqueMappingClustering()
    clusters = ccc.process(pairs_graph, data, similarity_threshold=0.17)
    result_df = ccc.export_to_df(clusters)
    p = er_output_dir + r'\result.csv'
    result_df.to_csv(p, sep=',', index=False, header=True, quoting=1)
    _ = ccc.evaluate(clusters)
    return result_df


if __name__ == '__main__':
    rdf = example()
    rdf = rdf.astype(str)
    ltable = pd.read_csv(ltable_path, sep='|', engine='python', na_filter=False)
    rtable = pd.read_csv(rtable_path, sep='|', engine='python', na_filter=False)
    mapping = pd.read_csv(mapping_path, sep='|', engine='python')
    ltable = ltable.astype(str)
    rtable = rtable.astype(str)
    mapping = mapping.astype(str)

    lcolumns_dict = {}
    rcolumns_dict = {}
    ltable_attrs = ltable.columns.values.tolist()
    rtable_attrs = rtable.columns.values.tolist()
    for _ in ltable_attrs:
        lcolumns_dict[_] = 'ltable_' + _
    for _ in rtable_attrs:
        rcolumns_dict[_] = 'rtable_' + _
    result_lid_list = rdf['id1'].tolist()
    selected_ltable = ltable[ltable[ltable_id].isin(result_lid_list)]
    selected_ltable = selected_ltable.rename(columns=lcolumns_dict)
    selected_ltable['key'] = 1
    result_rid_list = rdf['id2'].tolist()
    selected_rtable = rtable[rtable[rtable_id].isin(result_rid_list)]
    selected_rtable = selected_rtable.rename(columns=rcolumns_dict)
    selected_rtable['key'] = 1
    predictions = pd.merge(selected_ltable, selected_rtable, on='key')
    predictions.drop(columns='key', inplace=True)
    predictions = predictions.reset_index(drop=True)

    predictions['gold'] = '0'
    predictions['predicted'] = '0'
    gold_match_rows = []
    predicted_match_rows = []
    for tuple_ in predictions.itertuples():
        lid = getattr(tuple_, 'ltable_' + ltable_id)
        map_row = mapping[mapping[mapping_lid] == lid]
        result_row = rdf[rdf['id1'] == lid]
        if map_row is not None:
            rid = map_row[mapping_rid]
            for value in rid:
                if value == getattr(tuple_, 'rtable_' + rtable_id):
                    gold_match_rows.append(tuple_[0])
        if result_row is not None:
            rid = result_row['id2']
            for value in rid:
                if value == getattr(tuple_, 'rtable_' + rtable_id):
                    predicted_match_rows.append(tuple_[0])
    for _ in gold_match_rows:
        predictions.loc[_, 'gold'] = '1'
    for _ in predicted_match_rows:
        predictions.loc[_, 'predicted'] = '1'

    predictions['confidence'] = 0
    predicted_match = predictions[predictions['predicted'] == '1']
    print(1)