WSR-432813/P5. Graph-based.ipynb

Self made RP3-beta

import helpers
import pandas as pd
import numpy as np
import scipy.sparse as sparse
from collections import defaultdict
from itertools import chain
import random
import time
import matplotlib.pyplot as plt

train_read = pd.read_csv("./Datasets/ml-100k/train.csv", sep="\t", header=None)
test_read = pd.read_csv("./Datasets/ml-100k/test.csv", sep="\t", header=None)
(
    train_ui,
    test_ui,
    user_code_id,
    user_id_code,
    item_code_id,
    item_id_code,
) = helpers.data_to_csr(train_read, test_read)

class RP3Beta:
    def fit(self, train_ui, alpha, beta):
        """We weight our edges by user's explicit ratings so if user rated movie high we'll follow that path
        with higher probability."""
        self.train_ui = train_ui
        self.train_iu = train_ui.transpose()

        self.alpha = alpha
        self.beta = beta

        # Define Pui
        Pui = sparse.csr_matrix(self.train_ui / self.train_ui.sum(axis=1))

        # Define Piu
        to_divide = np.vectorize(lambda x: x if x > 0 else 1)(
            self.train_iu.sum(axis=1)
        )  # to avoid dividing by zero
        Piu = sparse.csr_matrix(self.train_iu / to_divide)
        item_orders = (self.train_ui > 0).sum(axis=0)

        Pui = Pui.power(self.alpha)
        Piu = Piu.power(self.alpha)

        P3 = Pui * Piu * Pui

        P3 /= np.power(
            np.vectorize(lambda x: x if x > 0 else 1)(item_orders), self.beta
        )

        self.estimations = np.array(P3)

    def recommend(self, user_code_id, item_code_id, topK=10):

        top_k = defaultdict(list)
        for nb_user, user in enumerate(self.estimations):

            user_rated = self.train_ui.indices[
                self.train_ui.indptr[nb_user] : self.train_ui.indptr[nb_user + 1]
            ]
            for item, score in enumerate(user):
                if item not in user_rated and not np.isnan(score):
                    top_k[user_code_id[nb_user]].append((item_code_id[item], score))
        result = []
        # Let's choose k best items in the format: (user, item1, score1, item2, score2, ...)
        for uid, item_scores in top_k.items():
            item_scores.sort(key=lambda x: x[1], reverse=True)
            result.append([uid] + list(chain(*item_scores[:topK])))
        return result

    def estimate(self, user_code_id, item_code_id, test_ui):
        result = []
        for user, item in zip(*test_ui.nonzero()):
            result.append(
                [
                    user_code_id[user],
                    item_code_id[item],
                    self.estimations[user, item]
                    if not np.isnan(self.estimations[user, item])
                    else 1,
                ]
            )
        return result

model = RP3Beta()
model.fit(train_ui, alpha=1, beta=0)

top_n = pd.DataFrame(model.recommend(user_code_id, item_code_id, topK=10))

top_n.to_csv(
    "Recommendations generated/ml-100k/Self_P3_reco.csv", index=False, header=False
)

estimations = pd.DataFrame(model.estimate(user_code_id, item_code_id, test_ui))
estimations.to_csv(
    "Recommendations generated/ml-100k/Self_P3_estimations.csv",
    index=False,
    header=False,
)

import evaluation_measures as ev

estimations_df = pd.read_csv(
    "Recommendations generated/ml-100k/Self_P3_estimations.csv", header=None
)
reco = np.loadtxt("Recommendations generated/ml-100k/Self_P3_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],
)

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	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	3.702446	3.527273	0.282185	0.192092	0.186749	0.21698	0.204185	0.240096	0.339114	0.204905	0.572157	0.593544	0.875928	0.685048	1.0	0.077201	3.875892	0.974947

Let's check hyperparameters

Alpha

from tqdm import tqdm

result = []
for alpha in tqdm([round(i, 1) for i in np.arange(0.2, 1.6001, 0.2)]):
    model = RP3Beta()
    model.fit(train_ui, alpha=alpha, beta=0)
    reco = pd.DataFrame(model.recommend(user_code_id, item_code_id, topK=10))
    estimations_df = pd.DataFrame(model.estimate(user_code_id, item_code_id, test_ui))
    to_append = ev.evaluate(
        test=pd.read_csv("./Datasets/ml-100k/test.csv", sep="\t", header=None),
        estimations_df=estimations_df,
        reco=np.array(reco),
        super_reactions=[4, 5],
    )
    to_append.insert(0, "Alpha", alpha)
    result.append(to_append)

result = pd.concat(result)
result

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	Alpha	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	0.2	268.177832	211.732649	0.262672	0.166858	0.166277	0.197184	0.187661	0.203252	0.320910	0.196132	0.563378	0.580866	0.850477	0.629905	1.000000	0.060606	3.669627	0.979636
0	0.4	10.546689	7.792373	0.268505	0.172669	0.171569	0.202643	0.192489	0.212653	0.326760	0.200172	0.565148	0.583801	0.854719	0.644751	1.000000	0.064214	3.726996	0.978426
0	0.6	3.143988	2.948790	0.274655	0.180502	0.177820	0.208730	0.198176	0.222746	0.332872	0.203290	0.568872	0.587738	0.870626	0.657476	1.000000	0.065657	3.785282	0.977090
0	0.8	3.670728	3.495735	0.281972	0.189868	0.185300	0.216071	0.203541	0.236751	0.339867	0.206688	0.573729	0.592432	0.874867	0.685048	1.000000	0.070707	3.832415	0.975998
0	1.0	3.702446	3.527273	0.282185	0.192092	0.186749	0.216980	0.204185	0.240096	0.339114	0.204905	0.572157	0.593544	0.875928	0.685048	1.000000	0.077201	3.875892	0.974947
0	1.2	3.704441	3.529251	0.280912	0.193633	0.187311	0.216872	0.203004	0.240588	0.338049	0.203453	0.571830	0.594313	0.883351	0.681866	1.000000	0.085859	3.910718	0.974073
0	1.4	3.704580	3.529388	0.273595	0.190651	0.183874	0.212183	0.199464	0.239118	0.329550	0.195433	0.566171	0.592793	0.871686	0.675504	1.000000	0.107504	3.961915	0.972674
0	1.6	3.704591	3.529399	0.263097	0.186255	0.178709	0.205170	0.191094	0.232920	0.317439	0.184917	0.552349	0.590545	0.868505	0.669141	0.999576	0.156566	4.060156	0.969203

metrics = list(result.columns[[i not in ["Alpha"] for i in result.columns]])

charts_per_row = 6
charts_per_column = 3

fig, axes = plt.subplots(
    nrows=charts_per_row, ncols=charts_per_column, figsize=(18, 7 * charts_per_row)
)
import itertools

to_iter = [
    i for i in itertools.product(range(charts_per_row), range(charts_per_column))
]

for i in range(len(metrics)):
    df = result[["Alpha", metrics[i]]]
    df.plot(ax=axes[to_iter[i]], title=metrics[i], x=0, y=1)

Beta

from tqdm import tqdm

result = []
for beta in tqdm([round(i, 1) for i in np.arange(0, 1, 0.1)]):
    model = RP3Beta()
    model.fit(train_ui, alpha=1, beta=beta)
    reco = pd.DataFrame(model.recommend(user_code_id, item_code_id, topK=10))
    estimations_df = pd.DataFrame(model.estimate(user_code_id, item_code_id, test_ui))
    to_append = ev.evaluate(
        test=pd.read_csv("./Datasets/ml-100k/test.csv", sep="\t", header=None),
        estimations_df=estimations_df,
        reco=np.array(reco),
        super_reactions=[4, 5],
    )
    to_append.insert(0, "Beta", beta)
    result.append(to_append)

result = pd.concat(result)
result

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	Beta	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	0.0	3.702446	3.527273	0.282185	0.192092	0.186749	0.216980	0.204185	0.240096	0.339114	0.204905	0.572157	0.593544	0.875928	0.685048	1.000000	0.077201	3.875892	0.974947
0	0.1	3.703312	3.528128	0.290138	0.197597	0.192259	0.223336	0.210944	0.246153	0.347768	0.212034	0.581038	0.596328	0.884411	0.695652	1.000000	0.085137	3.957416	0.972784
0	0.2	3.703825	3.528636	0.297137	0.201202	0.196067	0.228169	0.218026	0.252767	0.355655	0.219909	0.588904	0.598160	0.886532	0.697773	1.000000	0.094517	4.053212	0.969980
0	0.3	3.704130	3.528939	0.303499	0.204749	0.199901	0.232829	0.225107	0.260797	0.363757	0.226825	0.599969	0.599964	0.888653	0.707317	1.000000	0.105339	4.147779	0.966948
0	0.4	3.704313	3.529120	0.308908	0.208811	0.203854	0.237241	0.229614	0.266918	0.370758	0.232673	0.609385	0.602014	0.895016	0.718982	0.999894	0.132035	4.259682	0.962989
0	0.5	3.704422	3.529229	0.314316	0.211411	0.206768	0.240986	0.237124	0.273416	0.378307	0.239297	0.622792	0.603327	0.903499	0.724284	0.999046	0.168831	4.411281	0.956648
0	0.6	3.704488	3.529295	0.314634	0.206209	0.204818	0.240159	0.242489	0.273850	0.376438	0.238428	0.622042	0.600721	0.897137	0.720042	0.996394	0.212843	4.621938	0.945932
0	0.7	3.704528	3.529335	0.304136	0.187298	0.191990	0.228749	0.238305	0.256201	0.358807	0.226808	0.593897	0.591207	0.868505	0.693531	0.983033	0.256854	4.898568	0.928065
0	0.8	3.704552	3.529360	0.266384	0.147571	0.158660	0.194838	0.214485	0.209336	0.299850	0.184356	0.492852	0.571152	0.803818	0.604454	0.936373	0.341270	5.257397	0.895882
0	0.9	3.704567	3.529375	0.162354	0.076967	0.089233	0.114583	0.134657	0.113253	0.160868	0.085486	0.243590	0.535405	0.580064	0.400848	0.800106	0.415584	5.563910	0.857396

metrics = list(result.columns[[i not in ["Beta"] for i in result.columns]])

charts_per_row = 6
charts_per_column = 3

fig, axes = plt.subplots(
    nrows=charts_per_row, ncols=charts_per_column, figsize=(18, 7 * charts_per_row)
)
import itertools

to_iter = [
    i for i in itertools.product(range(charts_per_row), range(charts_per_column))
]

for i in range(len(metrics)):
    df = result[["Beta", metrics[i]]]
    df.plot(ax=axes[to_iter[i]], title=metrics[i], x=0, y=1)

Check 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")
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_P3_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")
recommended_content[recommended_content["user"] == user][
    ["user", "rec_nb", "title", "genres"]
].sort_values(by="rec_nb")

	user	rating	title	genres
37092	169	5	Contact (1997)	Drama, Sci-Fi
3058	169	5	Star Wars (1977)	Action, Adventure, Romance, Sci-Fi, War
60433	169	5	Singin' in the Rain (1952)	Musical, Romance
50247	169	5	M*A*S*H (1970)	Comedy, War
73459	169	5	All About Eve (1950)	Drama
15015	169	5	Peacemaker, The (1997)	Action, Thriller, War
18355	169	5	Edge, The (1997)	Adventure, Thriller
20398	169	5	Return of the Jedi (1983)	Action, Adventure, Romance, Sci-Fi, War
40029	169	5	Rear Window (1954)	Mystery, Thriller
66194	169	5	In the Line of Fire (1993)	Action, Thriller
25627	169	5	Citizen Kane (1941)	Drama
27344	169	5	Empire Strikes Back, The (1980)	Action, Adventure, Drama, Romance, Sci-Fi, War
29621	169	4	Bridge on the River Kwai, The (1957)	Drama, War
60206	169	4	Birds, The (1963)	Horror
41351	169	4	Gone with the Wind (1939)	Drama, Romance, War

	user	rec_nb	title	genres
4481	169.0	1	Air Force One (1997)	Action, Thriller
1012	169.0	2	English Patient, The (1996)	Drama, Romance, War
6989	169.0	3	Fargo (1996)	Crime, Drama, Thriller
4788	169.0	4	Titanic (1997)	Action, Drama, Romance
1729	169.0	5	Scream (1996)	Horror, Thriller
731	169.0	6	Silence of the Lambs, The (1991)	Drama, Thriller
5285	169.0	7	Liar Liar (1997)	Comedy
5076	169.0	8	Full Monty, The (1997)	Comedy
3096	169.0	9	Toy Story (1995)	Animation, Children's, Comedy
6097	169.0	10	L.A. Confidential (1997)	Crime, Film-Noir, Mystery, Thriller

project task 5: generate recommendations of RP3Beta for hyperparameters found to optimize recall

# We generated recommendations for P3, a special case of RP3Beta (with alpha=1, beta=0).
# We've observed that changing alpha and beta impacts the model performance.

# Your task is find values alpha and beta for which recall will be the highest 
# (any solution with recall higher than P3 will be accepted)
# train the model and generate recommendations.

# save the outptut in 'Recommendations generated/ml-100k/Self_RP3Beta_estimations.csv'
# and 'Recommendations generated/ml-100k/Self_RP3Beta_reco.csv'

import evaluation_measures as ev

model=RP3Beta()
model.fit(train_ui, alpha=1, beta=0) #check recall values for alpha=1, beta = 0
model_reco=pd.DataFrame(model.recommend(user_code_id, item_code_id, topK=10))
model_reco.to_csv('Recommendations generated/ml-100k/Self_RP3Beta_reco.csv', index=False, header=False)
estimations_df=pd.DataFrame(model.estimate(user_code_id, item_code_id, test_ui))
estimations_df.to_csv('Recommendations generated/ml-100k/Self_RP3Beta_estimations.csv', index=False, header=False)
reco=np.loadtxt('Recommendations generated/ml-100k/Self_RP3Beta_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])

943it [00:00, 7035.65it/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	3.702446	3.527273	0.282185	0.192092	0.186749	0.21698	0.204185	0.240096	0.339114	0.204905	0.572157	0.593544	0.875928	0.685048	1.0	0.077201	3.875892	0.974947

import evaluation_measures as ev

model=RP3Beta()
model.fit(train_ui, alpha=0.6, beta=0.5) #check recall values for alpha=0.6, beta = 0.5
model_reco=pd.DataFrame(model.recommend(user_code_id, item_code_id, topK=10))
model_reco.to_csv('Recommendations generated/ml-100k/Self_RP3Beta_reco.csv', index=False, header=False)
estimations_df=pd.DataFrame(model.estimate(user_code_id, item_code_id, test_ui))
estimations_df.to_csv('Recommendations generated/ml-100k/Self_RP3Beta_estimations.csv', index=False, header=False)
reco=np.loadtxt('Recommendations generated/ml-100k/Self_RP3Beta_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])

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	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	3.657073	3.481191	0.316543	0.213824	0.208731	0.24308	0.235515	0.272396	0.383442	0.245543	0.627971	0.604552	0.903499	0.725345	1.0	0.125541	4.347845	0.95941

project task 6 (optional): implement graph-based model of your choice

# for example change length of paths in RP3beta or make some other modification (but change more than input and hyperparameters)
# feel free to implement your idea or search for some ideas

# save the outptut in 'Recommendations generated/ml-100k/Self_GraphTask_estimations.csv'
# and 'Recommendations generated/ml-100k/Self_GraphTask_reco.csv'