WSR-432813/.ipynb_checkpoints/P2. Evaluation-checkpoint.ipynb

Prepare test set

import pandas as pd
import numpy as np
import scipy.sparse as sparse
from collections import defaultdict
from itertools import chain
import random
from tqdm import tqdm

# In evaluation we do not load train set - it is not needed
test = pd.read_csv("./Datasets/ml-100k/test.csv", sep="\t", header=None)
test.columns = ["user", "item", "rating", "timestamp"]

test["user_code"] = test["user"].astype("category").cat.codes
test["item_code"] = test["item"].astype("category").cat.codes

user_code_id = dict(enumerate(test["user"].astype("category").cat.categories))
user_id_code = dict((v, k) for k, v in user_code_id.items())
item_code_id = dict(enumerate(test["item"].astype("category").cat.categories))
item_id_code = dict((v, k) for k, v in item_code_id.items())

test_ui = sparse.csr_matrix((test["rating"], (test["user_code"], test["item_code"])))

Estimations metrics

estimations_df = pd.read_csv(
    "Recommendations generated/ml-100k/Ready_Baseline_estimations.csv", header=None
)
estimations_df.columns = ["user", "item", "score"]

estimations_df["user_code"] = [user_id_code[user] for user in estimations_df["user"]]
estimations_df["item_code"] = [item_id_code[item] for item in estimations_df["item"]]
estimations = sparse.csr_matrix(
    (
        estimations_df["score"],
        (estimations_df["user_code"], estimations_df["item_code"]),
    ),
    shape=test_ui.shape,
)

def estimations_metrics(test_ui, estimations):
    result = []

    RMSE = (np.sum((estimations.data - test_ui.data) ** 2) / estimations.nnz) ** (1 / 2)
    result.append(["RMSE", RMSE])

    MAE = np.sum(abs(estimations.data - test_ui.data)) / estimations.nnz
    result.append(["MAE", MAE])

    df_result = (pd.DataFrame(list(zip(*result))[1])).T
    df_result.columns = list(zip(*result))[0]
    return df_result

# in case of error (in the laboratories) you might have to switch to the other version of pandas
# try !pip3 install pandas=='1.0.3' (or pip if you use python 2) and restart the kernel

estimations_metrics(test_ui, estimations)

	RMSE	MAE
0	0.949459	0.752487

Ranking metrics

import numpy as np

reco = np.loadtxt(
    "Recommendations generated/ml-100k/Ready_Baseline_reco.csv", delimiter=","
)
# Let's ignore scores - they are not used in evaluation:
users = reco[:, :1]
items = reco[:, 1::2]
# Let's use inner ids instead of real ones
users = np.vectorize(lambda x: user_id_code.setdefault(x, -1))(users)
items = np.vectorize(lambda x: item_id_code.setdefault(x, -1))(items)
reco = np.concatenate((users, items), axis=1)
reco

array([[663, 475,  62, ..., 472, 269, 503],
       [ 48, 313, 475, ..., 591, 175, 466],
       [351, 313, 475, ..., 591, 175, 466],
       ...,
       [259, 313, 475, ...,  11, 591, 175],
       [ 33, 313, 475, ...,  11, 591, 175],
       [ 77, 313, 475, ...,  11, 591, 175]])

def ranking_metrics(test_ui, reco, super_reactions=[], topK=10):

    nb_items = test_ui.shape[1]
    (
        relevant_users,
        super_relevant_users,
        prec,
        rec,
        F_1,
        F_05,
        prec_super,
        rec_super,
        ndcg,
        mAP,
        MRR,
        LAUC,
        HR,
    ) = (0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0)

    cg = 1.0 / np.log2(np.arange(2, topK + 2))
    cg_sum = np.cumsum(cg)

    for (nb_user, user) in tqdm(enumerate(reco[:, 0])):
        u_rated_items = test_ui.indices[test_ui.indptr[user] : test_ui.indptr[user + 1]]
        nb_u_rated_items = len(u_rated_items)
        if (
            nb_u_rated_items > 0
        ):  # skip users with no items in test set (still possible that there will be no super items)
            relevant_users += 1

            u_super_items = u_rated_items[
                np.vectorize(lambda x: x in super_reactions)(
                    test_ui.data[test_ui.indptr[user] : test_ui.indptr[user + 1]]
                )
            ]
            # more natural seems u_super_items=[item for item in u_rated_items if test_ui[user,item] in super_reactions]
            # but accesing test_ui[user,item] is expensive -we should avoid doing it
            if len(u_super_items) > 0:
                super_relevant_users += 1

            user_successes = np.zeros(topK)
            nb_user_successes = 0
            user_super_successes = np.zeros(topK)
            nb_user_super_successes = 0

            # evaluation
            for (item_position, item) in enumerate(reco[nb_user, 1 : topK + 1]):
                if item in u_rated_items:
                    user_successes[item_position] = 1
                    nb_user_successes += 1
                    if item in u_super_items:
                        user_super_successes[item_position] = 1
                        nb_user_super_successes += 1

            prec_u = nb_user_successes / topK
            prec += prec_u

            rec_u = nb_user_successes / nb_u_rated_items
            rec += rec_u

            F_1 += 2 * (prec_u * rec_u) / (prec_u + rec_u) if prec_u + rec_u > 0 else 0
            F_05 += (
                (0.5 ** 2 + 1) * (prec_u * rec_u) / (0.5 ** 2 * prec_u + rec_u)
                if prec_u + rec_u > 0
                else 0
            )

            prec_super += nb_user_super_successes / topK
            rec_super += nb_user_super_successes / max(
                len(u_super_items), 1
            )  # to set 0 if no super items
            ndcg += np.dot(user_successes, cg) / cg_sum[min(topK, nb_u_rated_items) - 1]

            cumsum_successes = np.cumsum(user_successes)
            mAP += np.dot(
                cumsum_successes / np.arange(1, topK + 1), user_successes
            ) / min(topK, nb_u_rated_items)
            MRR += (
                1 / (user_successes.nonzero()[0][0] + 1)
                if user_successes.nonzero()[0].size > 0
                else 0
            )
            LAUC += (
                np.dot(cumsum_successes, 1 - user_successes)
                + (nb_user_successes + nb_u_rated_items)
                / 2
                * ((nb_items - nb_u_rated_items) - (topK - nb_user_successes))
            ) / ((nb_items - nb_u_rated_items) * nb_u_rated_items)

            HR += nb_user_successes > 0

    result = []
    result.append(("precision", prec / relevant_users))
    result.append(("recall", rec / relevant_users))
    result.append(("F_1", F_1 / relevant_users))
    result.append(("F_05", F_05 / relevant_users))
    result.append(("precision_super", prec_super / super_relevant_users))
    result.append(("recall_super", rec_super / super_relevant_users))
    result.append(("NDCG", ndcg / relevant_users))
    result.append(("mAP", mAP / relevant_users))
    result.append(("MRR", MRR / relevant_users))
    result.append(("LAUC", LAUC / relevant_users))
    result.append(("HR", HR / relevant_users))

    df_result = (pd.DataFrame(list(zip(*result))[1])).T
    df_result.columns = list(zip(*result))[0]
    return df_result

ranking_metrics(test_ui, reco, super_reactions=[4, 5], topK=10)

943it [00:00, 7955.25it/s]

	precision	recall	F_1	F_05	precision_super	recall_super	NDCG	mAP	MRR	LAUC	HR
0	0.09141	0.037652	0.04603	0.061286	0.079614	0.056463	0.095957	0.043178	0.198193	0.515501	0.437964

Diversity metrics

def diversity_metrics(test_ui, reco, topK=10):

    frequencies = defaultdict(int)

    # let's assign 0 to all items in test set
    for item in list(set(test_ui.indices)):
        frequencies[item] = 0

    # counting frequencies
    for item in reco[:, 1:].flat:
        frequencies[item] += 1

    nb_reco_outside_test = frequencies[-1]
    del frequencies[-1]

    frequencies = np.array(list(frequencies.values()))

    nb_rec_items = len(frequencies[frequencies > 0])
    nb_reco_inside_test = np.sum(frequencies)

    frequencies = frequencies / np.sum(frequencies)
    frequencies = np.sort(frequencies)

    with np.errstate(
        divide="ignore"
    ):  # let's put zeros put items with 0 frequency and ignore division warning
        log_frequencies = np.nan_to_num(np.log(frequencies), posinf=0, neginf=0)

    result = []
    result.append(
        (
            "Reco in test",
            nb_reco_inside_test / (nb_reco_inside_test + nb_reco_outside_test),
        )
    )
    result.append(("Test coverage", nb_rec_items / test_ui.shape[1]))
    result.append(("Shannon", -np.dot(frequencies, log_frequencies)))
    result.append(
        (
            "Gini",
            np.dot(frequencies, np.arange(1 - len(frequencies), len(frequencies), 2))
            / (len(frequencies) - 1),
        )
    )

    df_result = (pd.DataFrame(list(zip(*result))[1])).T
    df_result.columns = list(zip(*result))[0]
    return df_result

# in case of errors try !pip3 install numpy==1.18.4 (or pip if you use python 2) and restart the kernel

x = diversity_metrics(test_ui, reco, topK=10)
x

	Reco in test	Test coverage	Shannon	Gini
0	1.0	0.033911	2.836513	0.991139

To be used in other notebooks

import evaluation_measures as ev

estimations_df = pd.read_csv(
    "Recommendations generated/ml-100k/Ready_Baseline_estimations.csv", header=None
)
reco = np.loadtxt(
    "Recommendations generated/ml-100k/Ready_Baseline_reco.csv", delimiter=","
)

ev.evaluate(
    test=pd.read_csv("./Datasets/ml-100k/test.csv", sep="\t", header=None),
    estimations_df=estimations_df,
    reco=reco,
    super_reactions=[4, 5],
)
# also you can just type ev.evaluate_all(estimations_df, reco) - I put above values as default

943it [00:00, 7872.32it/s]

	RMSE	MAE	precision	recall	F_1	F_05	precision_super	recall_super	NDCG	mAP	MRR	LAUC	HR	H2R	Reco in test	Test coverage	Shannon	Gini
0	0.949459	0.752487	0.09141	0.037652	0.04603	0.061286	0.079614	0.056463	0.095957	0.043178	0.198193	0.515501	0.437964	0.239661	1.0	0.033911	2.836513	0.991139

dir_path = "Recommendations generated/ml-100k/"
super_reactions = [4, 5]
test = pd.read_csv("./Datasets/ml-100k/test.csv", sep="\t", header=None)

df = ev.evaluate_all(test, dir_path, super_reactions)
# also you can just type ev.evaluate_all() - I put above values as default

943it [00:00, 6795.25it/s]
943it [00:00, 7953.42it/s]
943it [00:00, 7915.55it/s]
943it [00:00, 8704.77it/s]
943it [00:00, 8266.93it/s]

df.iloc[:, :9]

	Model	RMSE	MAE	precision	recall	F_1	F_05	precision_super	recall_super
0	Self_TopPop	2.508258	2.217909	0.188865	0.116919	0.118732	0.141584	0.130472	0.137473
0	Ready_Baseline	0.949459	0.752487	0.091410	0.037652	0.046030	0.061286	0.079614	0.056463
0	Ready_Random	1.521557	1.222653	0.046766	0.021357	0.024113	0.031441	0.027468	0.021247
0	Self_TopRated	1.030712	0.820904	0.000954	0.000188	0.000298	0.000481	0.000644	0.000223
0	Self_BaselineUI	0.967585	0.762740	0.000954	0.000170	0.000278	0.000463	0.000644	0.000189

df.iloc[:, np.append(0, np.arange(9, df.shape[1]))]

	Model	NDCG	mAP	MRR	LAUC	HR	H2R	Reco in test	Test coverage	Shannon	Gini
0	Self_TopPop	0.214651	0.111707	0.400939	0.555546	0.765642	0.492047	1.000000	0.038961	3.159079	0.987317
0	Ready_Baseline	0.095957	0.043178	0.198193	0.515501	0.437964	0.239661	1.000000	0.033911	2.836513	0.991139
0	Ready_Random	0.050715	0.019635	0.121185	0.507191	0.314952	0.109226	0.988547	0.188312	5.094569	0.908346
0	Self_TopRated	0.001043	0.000335	0.003348	0.496433	0.009544	0.000000	0.699046	0.005051	1.945910	0.995669
0	Self_BaselineUI	0.000752	0.000168	0.001677	0.496424	0.009544	0.000000	0.600530	0.005051	1.803126	0.996380

Check metrics on toy dataset

import helpers

dir_path = "Recommendations generated/toy-example/"
super_reactions = [4, 5]
test = pd.read_csv("./Datasets/toy-example/test.csv", sep="\t", header=None)

display(ev.evaluate_all(test, dir_path, super_reactions, topK=3))
# also you can just type ev.evaluate_all() - I put above values as default

toy_train_read = pd.read_csv(
    "./Datasets/toy-example/train.csv",
    sep="\t",
    header=None,
    names=["user", "item", "rating", "timestamp"],
)
toy_test_read = pd.read_csv(
    "./Datasets/toy-example/test.csv",
    sep="\t",
    header=None,
    names=["user", "item", "rating", "timestamp"],
)
reco = pd.read_csv(
    "Recommendations generated/toy-example/Self_BaselineUI_reco.csv", header=None
)
estimations = pd.read_csv(
    "Recommendations generated/toy-example/Self_BaselineUI_estimations.csv",
    names=["user", "item", "est_score"],
)
(
    toy_train_ui,
    toy_test_ui,
    toy_user_code_id,
    toy_user_id_code,
    toy_item_code_id,
    toy_item_id_code,
) = helpers.data_to_csr(toy_train_read, toy_test_read)

print("Training data:")
display(toy_train_ui.todense())

print("Test data:")
display(toy_test_ui.todense())

print("Recommendations:")
display(reco)

print("Estimations:")
display(estimations)

3it [00:00, 4983.33it/s]
3it [00:00, 5262.61it/s]

	Model	RMSE	MAE	precision	recall	F_1	F_05	precision_super	recall_super	NDCG	mAP	MRR	LAUC	HR	H2R	Reco in test	Test coverage	Shannon	Gini
0	Self_BaselineUI	1.612452	1.400	0.444444	0.888889	0.555556	0.478632	0.333333	0.75	0.676907	0.574074	0.611111	0.638889	1.0	0.333333	0.888889	0.8	1.386294	0.250000
0	Self_BaselineIU	1.648337	1.575	0.444444	0.888889	0.555556	0.478632	0.333333	0.75	0.720550	0.629630	0.666667	0.722222	1.0	0.333333	0.777778	0.8	1.351784	0.357143

Training data:

matrix([[3, 4, 0, 0, 5, 0, 0, 4],
        [0, 1, 2, 3, 0, 0, 0, 0],
        [0, 0, 0, 5, 0, 3, 4, 0]])

Test data:

matrix([[0, 0, 0, 0, 0, 0, 3, 0],
        [0, 0, 0, 0, 5, 0, 0, 0],
        [5, 0, 4, 0, 0, 0, 0, 2]])

Recommendations:

	0	1	2	3	4	5	6
0	0	30	5.0	20	4.0	60	4.0
1	10	40	3.0	60	2.0	70	2.0
2	20	40	5.0	20	4.0	70	4.0

Estimations:

	user	item	est_score
0	0	60	4.0
1	10	40	3.0
2	20	0	3.0
3	20	20	4.0
4	20	70	4.0

Sample recommendations

train = pd.read_csv(
    "./Datasets/ml-100k/train.csv",
    sep="\t",
    header=None,
    names=["user", "item", "rating", "timestamp"],
)
items = pd.read_csv("./Datasets/ml-100k/movies.csv")

user = random.choice(list(set(train["user"])))

train_content = pd.merge(train, items, left_on="item", right_on="id")

print("Here is what user rated high:")
display(
    train_content[train_content["user"] == user][
        ["user", "rating", "title", "genres"]
    ].sort_values(by="rating", ascending=False)[:15]
)

reco = np.loadtxt(
    "Recommendations generated/ml-100k/Self_BaselineUI_reco.csv", delimiter=","
)
items = pd.read_csv("./Datasets/ml-100k/movies.csv")

# Let's ignore scores - they are not used in evaluation:
reco_users = reco[:, :1]
reco_items = reco[:, 1::2]
# Let's put them into one array
reco = np.concatenate((reco_users, reco_items), axis=1)

# Let's rebuild it user-item dataframe
recommended = []
for row in reco:
    for rec_nb, entry in enumerate(row[1:]):
        recommended.append((row[0], rec_nb + 1, entry))
recommended = pd.DataFrame(recommended, columns=["user", "rec_nb", "item"])

recommended_content = pd.merge(recommended, items, left_on="item", right_on="id")

print("Here is what we recommend:")
recommended_content[recommended_content["user"] == user][
    ["user", "rec_nb", "title", "genres"]
].sort_values(by="rec_nb")

Here is what user rated high:

	user	rating	title	genres
37537	506	5	Aladdin (1992)	Animation, Children's, Comedy, Musical
29233	506	5	Usual Suspects, The (1995)	Crime, Thriller
68329	506	5	Babe (1995)	Children's, Comedy, Drama
31142	506	5	GoodFellas (1990)	Crime, Drama
30354	506	5	Manchurian Candidate, The (1962)	Film-Noir, Thriller
50796	506	5	It's a Wonderful Life (1946)	Drama
67161	506	5	Little Princess, A (1995)	Children's, Drama
66726	506	5	Deer Hunter, The (1978)	Drama, War
66672	506	5	Bringing Up Baby (1938)	Comedy
66201	506	5	In the Line of Fire (1993)	Action, Thriller
65397	506	5	Speed (1994)	Action, Romance, Thriller
47566	506	5	Alien (1979)	Action, Horror, Sci-Fi, Thriller
44744	506	5	Ransom (1996)	Drama, Thriller
60460	506	5	Singin' in the Rain (1952)	Musical, Romance
16736	506	5	Man Who Would Be King, The (1975)	Adventure

Here is what we recommend:

	user	rec_nb	title	genres
504	506.0	1	Great Day in Harlem, A (1994)	Documentary
1446	506.0	2	Tough and Deadly (1995)	Action, Drama, Thriller
2388	506.0	3	Aiqing wansui (1994)	Drama
3330	506.0	4	Delta of Venus (1994)	Drama
4272	506.0	5	Someone Else's America (1995)	Drama
5214	506.0	6	Saint of Fort Washington, The (1993)	Drama
6156	506.0	7	Celestial Clockwork (1994)	Comedy
7099	506.0	8	Some Mother's Son (1996)	Drama
8993	506.0	9	Maya Lin: A Strong Clear Vision (1994)	Documentary
8039	506.0	10	Prefontaine (1997)	Drama

project task 2: implement some other evaluation measure

# it may be your idea, modification of what we have already implemented
# (for example Hit2 rate which would count as a success users whoreceived at least 2 relevant recommendations)
# or something well-known
# expected output: modification of evaluation_measures.py such that evaluate_all will also display your measure

# Hit2Rate - implemented.