<reponame>agarwalutkarsh554/imagededup
from typing import List, Dict

import numpy as np


def avg_prec(correct_duplicates: List, retrieved_duplicates: List) -> float:
    """
    Get average precision(AP) for a single query given correct and retrieved file names.

    Args:
        correct_duplicates: List of correct duplicates i.e., ground truth)
        retrieved_duplicates: List of retrieved duplicates for one single query

    Returns:
        Average precision for this query.
    """
    if len(retrieved_duplicates) == 0 and len(correct_duplicates) == 0:
        return 1.0

    if not len(retrieved_duplicates) or not len(correct_duplicates):
        return 0.0

    count_real_correct = len(correct_duplicates)
    relevance = np.array(
        [1 if i in correct_duplicates else 0 for i in retrieved_duplicates]
    )
    relevance_cumsum = np.cumsum(relevance)
    prec_k = [relevance_cumsum[k] / (k + 1) for k in range(len(relevance))]
    prec_and_relevance = [relevance[k] * prec_k[k] for k in range(len(relevance))]
    avg_precision = np.sum(prec_and_relevance) / count_real_correct
    return avg_precision


def ndcg(correct_duplicates: List, retrieved_duplicates: List) -> float:
    """
    Get Normalized discounted cumulative gain(NDCG) for a single query given correct and retrieved file names.

    Args:
        correct_duplicates: List of correct duplicates i.e., ground truth)
        retrieved_duplicates: List of retrieved duplicates for one single query

    Returns:
        NDCG for this query.
    """
    if len(retrieved_duplicates) == 0 and len(correct_duplicates) == 0:
        return 1.0

    if not len(retrieved_duplicates) or not len(correct_duplicates):
        return 0.0

    def dcg(rel):
        relevance_numerator = [2 ** (k) - 1 for k in rel]
        relevance_denominator = [
            np.log2(k + 2) for k in range(len(rel))
        ]  # first value of denominator term should be 2

        dcg_terms = [
            relevance_numerator[k] / relevance_denominator[k] for k in range(len(rel))
        ]
        dcg_at_k = np.sum(dcg_terms)

        return dcg_at_k

    relevance = np.array(
        [1 if i in correct_duplicates else 0 for i in retrieved_duplicates]
    )
    dcg_k = dcg(relevance)

    if dcg_k == 0:
        return 0.0

    idcg_k = dcg(sorted(relevance, reverse=True))
    return dcg_k / idcg_k


def jaccard_similarity(correct_duplicates: List, retrieved_duplicates: List) -> float:
    """
    Get jaccard similarity for a single query given correct and retrieved file names.

    Args:
        correct_duplicates: List of correct duplicates i.e., ground truth)
        retrieved_duplicates: List of retrieved duplicates for one single query

    Returns:
        Jaccard similarity for this query.
    """
    if len(retrieved_duplicates) == 0 and len(correct_duplicates) == 0:
        return 1.0

    if not len(retrieved_duplicates) or not len(correct_duplicates):
        return 0.0

    set_correct_duplicates = set(correct_duplicates)
    set_retrieved_duplicates = set(retrieved_duplicates)

    intersection_dups = set_retrieved_duplicates.intersection(set_correct_duplicates)
    union_dups = set_retrieved_duplicates.union(set_correct_duplicates)

    jacc_sim = len(intersection_dups) / len(union_dups)
    return jacc_sim


def mean_metric(ground_truth: Dict, retrieved: Dict, metric: str = None) -> float:
    """
    Get mean of specified metric.

    Args:
        metric_func: metric function on which mean is to be calculated across all queries

    Returns:
        float representing mean of the metric across all queries
    """
    metric = metric.lower()
    metric_lookup = {'map': avg_prec, 'ndcg': ndcg, 'jaccard': jaccard_similarity}

    metric_func = metric_lookup[metric]
    metric_vals = []

    for k in ground_truth.keys():
        metric_vals.append(metric_func(ground_truth[k], retrieved[k]))
    return np.mean(metric_vals)


def get_all_metrics(ground_truth: Dict, retrieved: Dict) -> Dict:
    """
    Get mean of all information retrieval metrics across all queries.

    Args:
        ground_truth: A dictionary representing ground truth with filenames as key and a list of duplicate filenames
        as value.
        retrieved: A dictionary representing retrieved duplicates with filenames as key and a list of retrieved
        duplicate filenames as value.

    Returns:
        Dictionary of all mean metrics.
    """

    all_average_metrics = {
        'map': mean_metric(ground_truth, retrieved, metric='map'),
        'ndcg': mean_metric(ground_truth, retrieved, metric='ndcg'),
        'jaccard': mean_metric(ground_truth, retrieved, metric='jaccard'),
    }
    return all_average_metrics

from typing import List, Dict

import numpy as np


def avg_prec(correct_duplicates: List, retrieved_duplicates: List) -> float:
    """
    Get average precision(AP) for a single query given correct and retrieved file names.

    Args:
        correct_duplicates: List of correct duplicates i.e., ground truth)
        retrieved_duplicates: List of retrieved duplicates for one single query

    Returns:
        Average precision for this query.
    """
    if len(retrieved_duplicates) == 0 and len(correct_duplicates) == 0:
        return 1.0

    if not len(retrieved_duplicates) or not len(correct_duplicates):
        return 0.0

    count_real_correct = len(correct_duplicates)
    relevance = np.array(
        [1 if i in correct_duplicates else 0 for i in retrieved_duplicates]
    )
    relevance_cumsum = np.cumsum(relevance)
    prec_k = [relevance_cumsum[k] / (k + 1) for k in range(len(relevance))]
    prec_and_relevance = [relevance[k] * prec_k[k] for k in range(len(relevance))]
    avg_precision = np.sum(prec_and_relevance) / count_real_correct
    return avg_precision


def ndcg(correct_duplicates: List, retrieved_duplicates: List) -> float:
    """
    Get Normalized discounted cumulative gain(NDCG) for a single query given correct and retrieved file names.

    Args:
        correct_duplicates: List of correct duplicates i.e., ground truth)
        retrieved_duplicates: List of retrieved duplicates for one single query

    Returns:
        NDCG for this query.
    """
    if len(retrieved_duplicates) == 0 and len(correct_duplicates) == 0:
        return 1.0

    if not len(retrieved_duplicates) or not len(correct_duplicates):
        return 0.0

    def dcg(rel):
        relevance_numerator = [2 ** (k) - 1 for k in rel]
        relevance_denominator = [
            np.log2(k + 2) for k in range(len(rel))
        ]  # first value of denominator term should be 2

        dcg_terms = [
            relevance_numerator[k] / relevance_denominator[k] for k in range(len(rel))
        ]
        dcg_at_k = np.sum(dcg_terms)

        return dcg_at_k

    relevance = np.array(
        [1 if i in correct_duplicates else 0 for i in retrieved_duplicates]
    )
    dcg_k = dcg(relevance)

    if dcg_k == 0:
        return 0.0

    idcg_k = dcg(sorted(relevance, reverse=True))
    return dcg_k / idcg_k


def jaccard_similarity(correct_duplicates: List, retrieved_duplicates: List) -> float:
    """
    Get jaccard similarity for a single query given correct and retrieved file names.

    Args:
        correct_duplicates: List of correct duplicates i.e., ground truth)
        retrieved_duplicates: List of retrieved duplicates for one single query

    Returns:
        Jaccard similarity for this query.
    """
    if len(retrieved_duplicates) == 0 and len(correct_duplicates) == 0:
        return 1.0

    if not len(retrieved_duplicates) or not len(correct_duplicates):
        return 0.0

    set_correct_duplicates = set(correct_duplicates)
    set_retrieved_duplicates = set(retrieved_duplicates)

    intersection_dups = set_retrieved_duplicates.intersection(set_correct_duplicates)
    union_dups = set_retrieved_duplicates.union(set_correct_duplicates)

    jacc_sim = len(intersection_dups) / len(union_dups)
    return jacc_sim


def mean_metric(ground_truth: Dict, retrieved: Dict, metric: str = None) -> float:
    """
    Get mean of specified metric.

    Args:
        metric_func: metric function on which mean is to be calculated across all queries

    Returns:
        float representing mean of the metric across all queries
    """
    metric = metric.lower()
    metric_lookup = {'map': avg_prec, 'ndcg': ndcg, 'jaccard': jaccard_similarity}

    metric_func = metric_lookup[metric]
    metric_vals = []

    for k in ground_truth.keys():
        metric_vals.append(metric_func(ground_truth[k], retrieved[k]))
    return np.mean(metric_vals)


def get_all_metrics(ground_truth: Dict, retrieved: Dict) -> Dict:
    """
    Get mean of all information retrieval metrics across all queries.

    Args:
        ground_truth: A dictionary representing ground truth with filenames as key and a list of duplicate filenames
        as value.
        retrieved: A dictionary representing retrieved duplicates with filenames as key and a list of retrieved
        duplicate filenames as value.

    Returns:
        Dictionary of all mean metrics.
    """

    all_average_metrics = {
        'map': mean_metric(ground_truth, retrieved, metric='map'),
        'ndcg': mean_metric(ground_truth, retrieved, metric='ndcg'),
        'jaccard': mean_metric(ground_truth, retrieved, metric='jaccard'),
    }
    return all_average_metrics
Get average precision(AP) for a single query given correct and retrieved file names.      Args:         correct_duplicates: List of correct duplicates i.e., ground truth)         retrieved_duplicates: List of retrieved duplicates for one single query      Returns:         Average precision for this query. Get Normalized discounted cumulative gain(NDCG) for a single query given correct and retrieved file names.      Args:         correct_duplicates: List of correct duplicates i.e., ground truth)         retrieved_duplicates: List of retrieved duplicates for one single query      Returns:         NDCG for this query. # first value of denominator term should be 2 Get jaccard similarity for a single query given correct and retrieved file names.      Args:         correct_duplicates: List of correct duplicates i.e., ground truth)         retrieved_duplicates: List of retrieved duplicates for one single query      Returns:         Jaccard similarity for this query. Get mean of specified metric.      Args:         metric_func: metric function on which mean is to be calculated across all queries      Returns:         float representing mean of the metric across all queries Get mean of all information retrieval metrics across all queries.      Args:         ground_truth: A dictionary representing ground truth with filenames as key and a list of duplicate filenames         as value.         retrieved: A dictionary representing retrieved duplicates with filenames as key and a list of retrieved         duplicate filenames as value.      Returns:         Dictionary of all mean metrics.