s413941/Uczenie_maszynowe_Systemy_Rekomendacyjne

unknown e503196dd7 UMSR

2020-06-14 22:23:50 +02:00

168 KiB

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Preparing dataset

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

train_read=pd.read_csv('.Datasets/ml-100k/train.csv', sep='\t', header=None, names=['user', 'item', 'rating', 'timestamp'])
test_read=pd.read_csv('.Datasets/ml-100k/test.csv', sep='\t', header=None, names=['user', 'item', 'rating', 'timestamp'])

# Let's prepare dataset
train_and_test=pd.concat([train_read, test_read], axis=0, ignore_index=True)
train_and_test['user_code'] = train_and_test['user'].astype("category").cat.codes
train_and_test['item_code'] = train_and_test['item'].astype("category").cat.codes

user_code_id = dict(enumerate(train_and_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(train_and_test['item'].astype("category").cat.categories))
item_id_code = dict((v, k) for k, v in item_code_id.items())

train_and_test[:5]

	user	item	rating	timestamp	user_code	item_code
0	664	525	4	876526580	663	524
1	49	1	2	888068651	48	0
2	352	273	2	884290328	351	272
3	618	96	3	891307749	617	95
4	560	24	2	879976772	559	23

train_df=pd.merge(train_read, train_and_test, on=list(train_read.columns))
test_df=pd.merge(test_read, train_and_test, on=list(train_read.columns))

# Take number of users and items
(U,I)=(train_and_test['user_code'].max()+1, train_and_test['item_code'].max()+1)

# Create sparse csr matrices
train_ui = sparse.csr_matrix((train_df['rating'], (train_df['user_code'], train_df['item_code'])), shape=(U, I))
test_ui = sparse.csr_matrix((test_df['rating'], (test_df['user_code'], test_df['item_code'])), shape=(U, I))

pip install surprise

Collecting surprise
  Downloading https://files.pythonhosted.org/packages/61/de/e5cba8682201fcf9c3719a6fdda95693468ed061945493dea2dd37c5618b/surprise-0.1-py2.py3-none-any.whl
Collecting scikit-surprise
[?25l  Downloading https://files.pythonhosted.org/packages/f5/da/b5700d96495fb4f092be497f02492768a3d96a3f4fa2ae7dea46d4081cfa/scikit-surprise-1.1.0.tar.gz (6.4MB)
[K     |████████████████████████████████| 6.5MB 7.5MB/s 
[?25hRequirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.6/dist-packages (from scikit-surprise->surprise) (0.15.1)
Requirement already satisfied: numpy>=1.11.2 in /usr/local/lib/python3.6/dist-packages (from scikit-surprise->surprise) (1.18.5)
Requirement already satisfied: scipy>=1.0.0 in /usr/local/lib/python3.6/dist-packages (from scikit-surprise->surprise) (1.4.1)
Requirement already satisfied: six>=1.10.0 in /usr/local/lib/python3.6/dist-packages (from scikit-surprise->surprise) (1.12.0)
Building wheels for collected packages: scikit-surprise
  Building wheel for scikit-surprise (setup.py) ... [?25l[?25hdone
  Created wheel for scikit-surprise: filename=scikit_surprise-1.1.0-cp36-cp36m-linux_x86_64.whl size=1675379 sha256=642a6aaed1cac33b0dfb9b2d752b4a8db25dc9d12c56506722d7c68d7aefba86
  Stored in directory: /root/.cache/pip/wheels/cc/fa/8c/16c93fccce688ae1bde7d979ff102f7bee980d9cfeb8641bcf
Successfully built scikit-surprise
Installing collected packages: scikit-surprise, surprise
Successfully installed scikit-surprise-1.1.0 surprise-0.1

# Above steps are the same for many algorithms, so I put the code in separate file:
import helpers
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)

CSR matrices - what is it?

row = np.array([0, 0, 0, 1, 1, 2, 2, 2])
col = np.array([0, 1, 2, 1, 3, 2, 0, 3])
data = np.array([4, 1, 3, 2,1, 5, 2, 4])
sample_csr=sparse.csr_matrix((data, (row, col)))
sample_csr

<3x4 sparse matrix of type '<class 'numpy.longlong'>'
	with 8 stored elements in Compressed Sparse Row format>

print('Ratings matrix with missing entries replaced by zeros:')
display(sample_csr.todense())

print('\nNumber of ratings: {} \nNumber of users: {} \nNumber of items: {} \n'
      .format(sample_csr.nnz, sample_csr.shape[0], sample_csr.shape[1]))

Ratings matrix with missing entries replaced by zeros:

matrix([[4, 1, 3, 0],
        [0, 2, 0, 1],
        [2, 0, 5, 4]], dtype=int64)

Number of ratings: 8 
Number of users: 3 
Number of items: 4

print('Ratings data:', sample_csr.data)

print('Regarding items:', sample_csr.indices)

for i in range(sample_csr.shape[0]):
    print('Where ratings from {} to {} belongs to user {}.'.format(sample_csr.indptr[i], sample_csr.indptr[i+1]-1, i))

Ratings data: [4 1 3 2 1 2 5 4]
Regarding items: [0 1 2 1 3 0 2 3]
Where ratings from 0 to 2 belongs to user 0.
Where ratings from 3 to 4 belongs to user 1.
Where ratings from 5 to 7 belongs to user 2.

user=123

print('Efficient way to access items rated by user:')
display(train_ui.indices[train_ui.indptr[user]:train_ui.indptr[user+1]])
%timeit train_ui.indices[train_ui.indptr[user]:train_ui.indptr[user+1]]

print('Inefficient way to access items rated by user:')
display(train_ui[user].indices)
%timeit train_ui[user].indices

Efficient way to access items rated by user:

array([  0,   6,  10,  27,  49,  78,  95,  97, 116, 143, 153, 156, 167,
       171, 172, 173, 194, 208, 225, 473, 495, 549, 615], dtype=int32)

The slowest run took 15.23 times longer than the fastest. This could mean that an intermediate result is being cached.
1000000 loops, best of 3: 703 ns per loop
Inefficient way to access items rated by user:

array([  0,   6,  10,  27,  49,  78,  95,  97, 116, 143, 153, 156, 167,
       171, 172, 173, 194, 208, 225, 473, 495, 549, 615], dtype=int32)

10000 loops, best of 3: 77.4 µs per loop

Example: subtracting row means

print('Our matrix:')
display(sample_csr.todense())
print('List of row sums:')
sample_csr.sum(axis=1).ravel()

Our matrix:

matrix([[4, 1, 3, 0],
        [0, 2, 0, 1],
        [2, 0, 5, 4]], dtype=int64)

List of row sums:

matrix([[ 8,  3, 11]])

print('Array with row means:')
row_means=np.asarray(sample_csr.sum(axis=1).ravel())[0]/np.diff(sample_csr.indptr)
display(row_means)

print('Diagonal csr matrix with inverse of row sums on diagonal:')
display(sparse.diags(row_means).todense())

print("""Let's apply them in nonzero entries:""")
to_subtract=sparse.diags(row_means)*sample_csr.power(0)
display(to_subtract.todense())

print("Finally after subtraction:")
sample_csr-to_subtract.todense()

Array with row means:

array([2.66666667, 1.5       , 3.66666667])

Diagonal csr matrix with inverse of row sums on diagonal:

matrix([[2.66666667, 0.        , 0.        ],
        [0.        , 1.5       , 0.        ],
        [0.        , 0.        , 3.66666667]])

Let's apply them in nonzero entries:

matrix([[2.66666667, 2.66666667, 2.66666667, 0.        ],
        [0.        , 1.5       , 0.        , 1.5       ],
        [3.66666667, 0.        , 3.66666667, 3.66666667]])

Finally after subtraction:

matrix([[ 1.33333333, -1.66666667,  0.33333333,  0.        ],
        [ 0.        ,  0.5       ,  0.        , -0.5       ],
        [-1.66666667,  0.        ,  1.33333333,  0.33333333]])

Transposing

import numpy as np
from scipy import sparse
row = np.array([0, 0, 0, 1, 1, 2, 2, 2])
col = np.array([0, 1, 2, 1, 3, 2, 0, 3])
data = np.array([4, 1, 3, 2,1, 5, 2, 4])
sample=sparse.csr_matrix((data, (row, col)))
print('Sample matrix: \n', sample.A)
print('\nIndices: \n', sample.indices)
transposed=sample.transpose()
print('\nTransposed matrix: \n', transposed.A)
print('\nIndices of transposed matrix: \n', transposed.indices)

print('\nReason: ', type(transposed))

print('\nAfter converting to csr: \n', transposed.tocsr().indices)

Sample matrix: 
 [[4 1 3 0]
 [0 2 0 1]
 [2 0 5 4]]

Indices: 
 [0 1 2 1 3 0 2 3]

Transposed matrix: 
 [[4 0 2]
 [1 2 0]
 [3 0 5]
 [0 1 4]]

Indices of transposed matrix: 
 [0 1 2 1 3 0 2 3]

Reason:  <class 'scipy.sparse.csc.csc_matrix'>

After converting to csr: 
 [0 2 0 1 0 2 1 2]

Self made top popular

import os
if not os.path.exists('./Recommendations generated/'):
    os.mkdir('./Recommendations generated/')
    os.mkdir('./Recommendations generated/ml-100k/')
    os.mkdir('./Recommendations generated/toy-example/')

TopPop=[]
train_iu=train_ui.transpose().tocsr()
scaling_factor=train_ui.max()/max(np.diff(train_iu.indptr))

for i in range(train_iu.shape[0]):
    TopPop.append((i, (train_iu.indptr[i+1]-train_iu.indptr[i])*scaling_factor))
    
TopPop.sort(key=lambda x: x[1], reverse=True)
#TopPop is an array of pairs (item, rescaled_popularity) sorted descending from the most popular

k=10
result=[]

for u in range(train_ui.shape[0]):
    user_rated=train_ui.indices[train_ui.indptr[u]:train_ui.indptr[u+1]]
    rec_user=[]
    item_pos=0
    while len(rec_user)<10:
        if TopPop[item_pos][0] not in user_rated:
            rec_user.append((item_code_id[TopPop[item_pos][0]], TopPop[item_pos][1]))
        item_pos+=1
    result.append([user_code_id[u]]+list(chain(*rec_user)))

(pd.DataFrame(result)).to_csv('Recommendations generated/ml-100k/Self_TopPop_reco.csv', index=False, header=False)


# estimations - score is a bit artificial since that method is not designed for scoring, but for ranking

estimations=[]

for user, item in zip(*test_ui.nonzero()):
    estimations.append([user_code_id[user], item_code_id[item],
        (train_iu.indptr[item+1]-train_iu.indptr[item])*scaling_factor])
(pd.DataFrame(estimations)).to_csv('Recommendations generated/ml-100k/Self_TopPop_estimations.csv', index=False, header=False)

Self made global average

GlobalAvg=[]
avg=np.sum(train_ui)/train_ui.nnz

for i in range(train_iu.shape[0]):
    GlobalAvg.append((i, avg))
    
k=10
result=[]

for u in range(train_ui.shape[0]):
    user_rated=train_ui.indices[train_ui.indptr[u]:train_ui.indptr[u+1]]
    rec_user=[]
    item_pos=0
    while len(rec_user)<10:
        if GlobalAvg[item_pos][0] not in user_rated:
            rec_user.append((item_code_id[GlobalAvg[item_pos][0]], GlobalAvg[item_pos][1]))
        item_pos+=1
    result.append([user_code_id[u]]+list(chain(*rec_user)))

(pd.DataFrame(result)).to_csv('Recommendations generated/ml-100k/Self_GlobalAvg_reco.csv', index=False, header=False)


# estimations - score is a bit artificial since that method is not designed for scoring, but for ranking

estimations=[]

for user, item in zip(*test_ui.nonzero()):
    estimations.append([user_code_id[user], item_code_id[item], avg])
(pd.DataFrame(estimations)).to_csv('Recommendations generated/ml-100k/Self_GlobalAvg_estimations.csv', index=False, header=False)

pd.DataFrame(result)[:2]

	0	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	17	18	19	20
0	1	5	3.529975	10	3.529975	25	3.529975	32	3.529975	33	3.529975	44	3.529975	46	3.529975	50	3.529975	52	3.529975	55	3.529975
1	2	1	3.529975	2	3.529975	3	3.529975	4	3.529975	5	3.529975	6	3.529975	7	3.529975	8	3.529975	9	3.529975	11	3.529975

Project task 1 - self made top rated

# project task 1:  implement TopRated
# Implement recommender system which will recommend movies (which user hasn't seen) with the highest average rating
# The output should be saved in 'Recommendations generated/ml-100k/Self_TopRated_reco.csv'
# and 'Recommendations generated/ml-100k/Self_TopRated_estimations.csv'

TopRat=[]
train_iu=train_ui.transpose().tocsr()
scaling_factor=train_ui.max()/max(np.diff(train_iu.indptr))
print(train_ui)

for i in range(train_iu.shape[0]):
    avg = np.sum(train_iu.data[train_iu.indptr[i]:train_iu.indptr[i+1]])/np.count_nonzero(train_iu.data[train_iu.indptr[i]:train_iu.indptr[i+1]])   #średnia dla wierszy(itemów), których użytkownik nie widział
    TopRat.append((i, avg))
  
TopRat.sort(key=lambda x: x[1], reverse=True)

k=10
result=[]

for u in range(train_ui.shape[0]):
    user_rated=train_ui.indices[train_ui.indptr[u]:train_ui.indptr[u+1]]
    rec_user=[]
    item_pos=0
    while len(rec_user)<10:
        if TopRat[item_pos][0] not in user_rated:
            rec_user.append((item_code_id[TopRat[item_pos][0]], TopRat[item_pos][1]))
        item_pos+=1
    result.append([user_code_id[u]]+list(chain(*rec_user)))

(pd.DataFrame(result)).to_csv('Recommendations generated/ml-100k/Self_TopRated_reco.csv', index=False, header=False)


estimations=[]

for user, item in zip(*test_ui.nonzero()):
    estimations.append([user_code_id[user], item_code_id[item],
        (train_iu.indptr[item+1]-train_iu.indptr[item])*scaling_factor])
(pd.DataFrame(estimations)).to_csv('Recommendations generated/ml-100k/Self_TopRated_estimations.csv', index=False, header=False)

  (0, 0)	5
  (0, 1)	3
  (0, 2)	4
  (0, 3)	3
  (0, 5)	5
  (0, 6)	4
  (0, 7)	1
  (0, 8)	5
  (0, 10)	2
  (0, 11)	5
  (0, 12)	5
  (0, 13)	5
  (0, 14)	5
  (0, 15)	5
  (0, 16)	3
  (0, 17)	4
  (0, 18)	5
  (0, 19)	4
  (0, 20)	1
  (0, 21)	4
  (0, 22)	4
  (0, 23)	3
  (0, 25)	3
  (0, 26)	2
  (0, 27)	4
  :	:
  (942, 671)	5
  (942, 684)	4
  (942, 716)	4
  (942, 719)	1
  (942, 721)	3
  (942, 723)	1
  (942, 731)	4
  (942, 738)	4
  (942, 755)	2
  (942, 764)	3
  (942, 784)	2
  (942, 795)	3
  (942, 807)	4
  (942, 815)	4
  (942, 823)	4
  (942, 824)	3
  (942, 830)	2
  (942, 839)	4
  (942, 940)	1
  (942, 942)	5
  (942, 1010)	2
  (942, 1027)	2
  (942, 1043)	3
  (942, 1046)	2
  (942, 1329)	3

/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:12: RuntimeWarning: invalid value encountered in longlong_scalars
  if sys.path[0] == '':

pd.DataFrame(result)[:2]

	0	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	17	18	19	20
0	1	814	5.0	1122	5.0	1189	5.0	1201	5.0	408	4.5	318	4.485356	483	4.466667	513	4.400000	603	4.392638	50	4.385106
1	2	119	5.0	814	5.0	1122	5.0	1189	5.0	1201	5.0	114	4.509091	408	4.500000	169	4.494505	318	4.485356	483	4.466667

Self-made baseline

class selfBaselineUI():
    
    def fit(self, train_ui):
        self.train_ui=train_ui.copy()
        self.train_iu=train_ui.transpose().tocsr()
        
        result=self.train_ui.copy()
        
        self.row_means=np.asarray(result.sum(axis=1).ravel())[0]/np.diff(result.indptr)
        
        # in csr format after addition or multiplication 0 entries "disappear" - so some workaraunds are needed 
        # (other option is to define addition/multiplication in a desired way)
        row_means=self.row_means.copy()
        
        max_row_mean=np.max(row_means)
        row_means[row_means==0]=max_row_mean+1
        to_subtract_rows=sparse.diags(row_means)*result.power(0)
        to_subtract_rows.sort_indices() # needed to have valid .data
        
        subtract=to_subtract_rows.data
        subtract[subtract==max_row_mean+1]=0
        
        result.data=result.data-subtract
#        we can't do result=train_ui-to_subtract_rows since then 0 entries will "disappear" in csr format
        self.col_means=np.divide(np.asarray(result.sum(axis=0).ravel())[0], np.diff(self.train_iu.indptr),\
                            out=np.zeros(self.train_iu.shape[0]), where=np.diff(self.train_iu.indptr)!=0) # handling items without ratings
        
        # again - it is possible that some mean will be zero, so let's use the same workaround
        col_means=self.col_means.copy()
        
        max_col_mean=np.max(col_means)
        col_means[col_means==0]=max_col_mean+1
        to_subtract_cols=result.power(0)*sparse.diags(col_means)
        to_subtract_cols.sort_indices() # needed to have valid .data
        
        subtract=to_subtract_cols.data
        subtract[subtract==max_col_mean+1]=0
        
        result.data=result.data-subtract

        return result
    
    
    def recommend(self, user_code_id, item_code_id, topK=10):
        estimations=np.tile(self.row_means[:,None], [1, self.train_ui.shape[1]]) +np.tile(self.col_means, [self.train_ui.shape[0], 1])
        
        top_k = defaultdict(list)
        for nb_user, user in enumerate(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:
                    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.row_means[user]+self.col_means[item]])
        return result

toy_train_read=pd.read_csv('./Datasets/ml-100k/train.csv', sep='\t', header=None, names=['user', 'item', 'rating', 'timestamp'])
toy_test_read=pd.read_csv('./Datasets/ml-100k/test.csv', sep='\t', header=None, names=['user', 'item', 'rating', 'timestamp'])

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())

model=selfBaselineUI()
print('After subtracting rows and columns:')
display(model.fit(toy_train_ui).todense())

print('Recommend best unseen item:')
display(model.recommend(toy_user_code_id, toy_item_code_id, topK=1))

print('Print estimations on unseen items:')
estimations=pd.DataFrame(model.estimate(toy_user_code_id, toy_item_code_id, toy_test_ui))
estimations.columns=['user', 'item', 'est_score']
display(estimations)

top_n=pd.DataFrame(model.recommend(toy_user_code_id, toy_item_code_id, topK=3))

top_n.to_csv('Recommendations generated/toy-example/Self_BaselineUI_reco.csv', index=False, header=False)

estimations=pd.DataFrame(model.estimate(toy_user_code_id, toy_item_code_id, toy_test_ui))
estimations.to_csv('Recommendations generated/toy-example/Self_BaselineUI_estimations.csv', index=False, header=False)

Training data:

matrix([[5, 3, 4, ..., 0, 0, 0],
        [0, 0, 0, ..., 0, 0, 0],
        [0, 0, 0, ..., 0, 0, 0],
        ...,
        [5, 0, 0, ..., 0, 0, 0],
        [0, 0, 0, ..., 0, 0, 0],
        [0, 0, 0, ..., 0, 0, 0]], dtype=int64)

After subtracting rows and columns:

matrix([[ 1.18107144, -0.28752307,  0.84558432, ...,  0.        ,
          0.        ,  0.        ],
        [ 0.        ,  0.        ,  0.        , ...,  0.        ,
          0.        ,  0.        ],
        [ 0.        ,  0.        ,  0.        , ...,  0.        ,
          0.        ,  0.        ],
        ...,
        [ 0.73611731,  0.        ,  0.        , ...,  0.        ,
          0.        ,  0.        ],
        [ 0.        ,  0.        ,  0.        , ...,  0.        ,
          0.        ,  0.        ],
        [ 0.        ,  0.        ,  0.        , ...,  0.        ,
          0.        ,  0.        ]])

Recommend best unseen item:

[[1, 814, 5.449584477962646],
 [2, 814, 5.580813116206935],
 [3, 814, 4.615468838961153],
 [4, 814, 6.244538606403013],
 [5, 814, 4.738510237608686],
 [6, 814, 5.543023454887861],
 [7, 814, 5.860377115719784],
 [8, 814, 5.758174970039377],
 [9, 814, 6.283427495291902],
 [10, 814, 6.125307837172244],
 [11, 814, 5.279673741538148],
 [12, 814, 6.326356788221195],
 [13, 1491, 4.782976186496395],
 [14, 814, 6.015020534113856],
 [15, 814, 4.86157157343598],
 [16, 814, 6.196262744334048],
 [17, 814, 4.894538606403013],
 [18, 814, 5.789515775352784],
 [19, 814, 5.365126841697131],
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 [943, 814, 5.284782508842037]]

Print estimations on unseen items:

	user	item	est_score
0	1	5	3.457161
1	1	10	3.798540
2	1	25	3.435415
3	1	32	3.732018
4	1	33	3.531991
...	...	...	...
19995	943	928	2.907189
19996	943	1067	3.485929
19997	943	1074	2.861988
19998	943	1188	2.727428
19999	943	1228	2.568442

20000 rows × 3 columns

model=selfBaselineUI()
model.fit(train_ui)

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

top_n.to_csv('Recommendations generated/ml-100k/Self_BaselineUI_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_BaselineUI_estimations.csv', index=False, header=False)

project task 2: implement self-made BaselineIU

# Implement recommender system which will recommend movies (which user hasn't seen) which is similar to BaselineUI
# but first subtract col means then row means
# The output should be saved in 'Recommendations generated/ml-100k/Self_BaselineIU_reco.csv'
# and 'Recommendations generated/ml-100k/Self_BaselineIU_estimations.csv'

class selfBaselineIU():
    
    def fit(self, train_ui):
        self.train_ui=train_ui.copy()
        self.train_iu=train_ui.transpose().tocsr()
        
        result=self.train_ui.copy()
        
        self.col_means=np.divide(np.asarray(result.sum(axis=0).ravel())[0], np.diff(self.train_iu.indptr),\
                    out=np.zeros(self.train_iu.shape[0]), where=np.diff(self.train_iu.indptr)!=0) # handling items without ratings
        
        col_means=self.col_means.copy()
        
        max_col_mean=np.max(col_means)
        col_means[col_means==0]=max_col_mean+1
        to_subtract_cols=result.power(0)*sparse.diags(col_means)
        to_subtract_cols.sort_indices() # needed to have valid .data
        
        subtract=to_subtract_cols.data
        subtract[subtract==max_col_mean+1]=0
        
        result.data=result.data-subtract

        self.row_means=np.asarray(result.sum(axis=1).ravel())[0]/np.diff(result.indptr)
        
        row_means=self.row_means.copy()
        
        max_row_mean=np.max(row_means)
        row_means[row_means==0]=max_row_mean+1
        to_subtract_rows=sparse.diags(row_means)*result.power(0)
        to_subtract_rows.sort_indices() # needed to have valid .data
        
        subtract=to_subtract_rows.data
        subtract[subtract==max_row_mean+1]=0
        
        result.data=result.data-subtract

        return result
    
    
    def recommend(self, user_code_id, item_code_id, topK=10):
        estimations=np.tile(self.row_means[:,None], [1, self.train_ui.shape[1]]) +np.tile(self.col_means, [self.train_ui.shape[0], 1])
        
        top_k = defaultdict(list)
        for nb_user, user in enumerate(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:
                    top_k[user_code_id[nb_user]].append((item_code_id[item], score))
        result=[]

        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.row_means[user]+self.col_means[item]])
        return result

toy_train_read=pd.read_csv('./Datasets/ml-100k/train.csv', sep='\t', header=None, names=['user', 'item', 'rating', 'timestamp'])
toy_test_read=pd.read_csv('./Datasets/ml-100k/test.csv', sep='\t', header=None, names=['user', 'item', 'rating', 'timestamp'])

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())

model=selfBaselineIU()
print('After subtracting rows and columns:')
display(model.fit(toy_train_ui).todense())

print('Recommend best unseen item:')
display(model.recommend(toy_user_code_id, toy_item_code_id, topK=1))

print('Print estimations on unseen items:')
estimations=pd.DataFrame(model.estimate(toy_user_code_id, toy_item_code_id, toy_test_ui))
estimations.columns=['user', 'item', 'est_score']
display(estimations)

top_n=pd.DataFrame(model.recommend(toy_user_code_id, toy_item_code_id, topK=3))

top_n.to_csv('Recommendations generated/toy-example/Self_BaselineIU_reco.csv', index=False, header=False)

estimations=pd.DataFrame(model.estimate(toy_user_code_id, toy_item_code_id, toy_test_ui))
estimations.to_csv('Recommendations generated/toy-example/Self_BaselineIU_estimations.csv', index=False, header=False)

Training data:

matrix([[5, 3, 4, ..., 0, 0, 0],
        [0, 0, 0, ..., 0, 0, 0],
        [0, 0, 0, ..., 0, 0, 0],
        ...,
        [5, 0, 0, ..., 0, 0, 0],
        [0, 0, 0, ..., 0, 0, 0],
        [0, 0, 0, ..., 0, 0, 0]], dtype=int64)

After subtracting rows and columns:

matrix([[ 1.12403879, -0.23917738,  0.94111364, ...,  0.        ,
          0.        ,  0.        ],
        [ 0.        ,  0.        ,  0.        , ...,  0.        ,
          0.        ,  0.        ],
        [ 0.        ,  0.        ,  0.        , ...,  0.        ,
          0.        ,  0.        ],
        ...,
        [ 0.79639556,  0.        ,  0.        , ...,  0.        ,
          0.        ,  0.        ],
        [ 0.        ,  0.        ,  0.        , ...,  0.        ,
          0.        ,  0.        ],
        [ 0.        ,  0.        ,  0.        , ...,  0.        ,
          0.        ,  0.        ]])

Recommend best unseen item:

[[1, 814, 5.033569907350074],
 [2, 119, 5.073954341022419],
 [3, 119, 4.4091895061895],
 [4, 119, 5.897950732798565],
 [5, 119, 4.652876199931835],
 [6, 119, 4.8332609987661375],
 [7, 119, 5.460380872638376],
 [8, 119, 5.288661416309815],
 [9, 119, 5.641605128815088],
 [10, 119, 5.390515469506597],
 [11, 119, 4.887528747495148],
 [12, 119, 5.593385462360327],
 [13, 119, 4.7988643880253665],
 [14, 119, 5.293633791156009],
 [15, 119, 4.5691149509701665],
 [16, 119, 5.621022235609644],
 [17, 119, 4.401933175071678],
 [18, 119, 5.1136246795074145],
 [19, 119, 4.828561363832632],
 [20, 119, 4.67891154555548],
 [21, 119, 4.686981911864455],
 [22, 119, 4.977475553219404],
 [23, 119, 4.9557400991270795],
 [24, 119, 5.643906553501267],
 [25, 119, 5.182078286215623],
 [26, 119, 4.50014256759968],
 [27, 119, 4.655132325688396],
 [28, 119, 5.146362563799686],
 [29, 119, 5.064231306905151],
 [30, 119, 5.261671157244791],
 [31, 119, 5.174125806792258],
 [32, 119, 4.772889488953337],
 [33, 119, 5.472031388154129],
 [34, 119, 5.725640547396732],
 [35, 119, 5.06389058405801],
 [36, 119, 5.380757374300082],
 [37, 119, 5.335173262678534],
 [38, 119, 5.41438534300787],
 [39, 119, 5.070117546077895],
 [40, 119, 4.516579408961074],
 [41, 119, 4.852233579518073],
 [42, 119, 5.1802278477549235],
 [43, 119, 5.191447259871135],
 [44, 119, 4.944576190506044],
 [45, 119, 5.242283889842014],
 [46, 119, 5.562899242640933],
 [47, 119, 5.178733753891452],
 [48, 119, 5.009957370452998],
 [49, 119, 4.359876008169282],
 [50, 119, 5.121671046477792],
 [51, 119, 4.6007040768425345],
 [52, 119, 5.582487505009692],
 [53, 119, 5.194547597273854],
 [54, 119, 5.364952920943924],
 [55, 119, 4.8792052335131375],
 [56, 119, 5.2981580402492146],
 [57, 119, 5.221466492969975],
 [58, 119, 5.038836938114093],
 [59, 119, 5.337158024076944],
 [60, 119, 5.243138421619351],
 [61, 119, 4.776408504838526],
 [62, 119, 4.703042643349071],
 [63, 119, 4.605296126523914],
 [64, 119, 4.948533969947571],
 [65, 119, 5.221241287763163],
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 [71, 119, 4.7758440135224545],
 [72, 119, 5.132061838360728],
 [73, 119, 4.837305292087907],
 [74, 119, 4.992999499038664],
 [75, 119, 4.9300567320484765],
 [76, 119, 4.90472507206589],
 [77, 119, 4.733878487236107],
 [78, 119, 5.0279504063477],
 [79, 119, 5.356578662219639],
 [80, 119, 4.796097892768055],
 [81, 119, 4.815845261722918],
 [82, 119, 4.5571407493973926],
 [83, 119, 5.1043788080416475],
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 [85, 119, 4.811496617807297],
 [86, 119, 5.089346576323998],
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 [171, 119, 4.756017107592705],
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 [864, 119, 5.331138754026031],
 [865, 119, 3.9454824246497573],
 [866, 119, 3.9529414743571794],
 [867, 119, 5.443949757600708],
 [868, 119, 4.4530039027391375],
 [869, 119, 4.418624025596287],
 [870, 119, 4.762649048555637],
 [871, 119, 4.894814208072195],
 [872, 119, 5.270132156925509],
 [873, 119, 4.511925341300865],
 [874, 119, 5.056284307905434],
 [875, 119, 5.319773891216068],
 [876, 119, 5.382513704197602],
 [877, 119, 5.230747734946368],
 [878, 119, 4.464978504573857],
 [879, 119, 5.206979234986366],
 [880, 119, 5.161778000014536],
 [881, 119, 4.835809107192686],
 [882, 119, 5.518468066541994],
 [883, 119, 5.2870270338030485],
 [884, 119, 5.097744676598067],
 [885, 119, 4.823548830499029],
 [886, 119, 4.8345901760160555],
 [887, 119, 5.463598379848553],
 [888, 119, 5.670420440013476],
 [889, 119, 4.83035143719148],
 [890, 119, 5.0611655893874135],
 [891, 119, 5.665294304997954],
 [892, 119, 5.36027722388848],
 [893, 119, 5.216080046585126],
 [894, 119, 5.081047985416832],
 [895, 119, 5.325709451376277],
 [896, 119, 4.526959553802731],
 [897, 119, 5.371410791303421],
 [898, 119, 5.0881366181640555],
 [899, 119, 4.804669412081279],
 [900, 119, 3.9944458046644806],
 [901, 119, 5.411423984753554],
 [902, 119, 4.637151945081609],
 [903, 119, 5.206940461265847],
 [904, 119, 5.315425755520142],
 [905, 119, 4.781654749806642],
 [906, 119, 5.242153227536237],
 [907, 119, 6.14092236740025],
 [908, 119, 4.599443925757107],
 [909, 119, 5.750259313226152],
 [910, 119, 4.484461737689663],
 [911, 119, 4.925077657007165],
 [912, 119, 4.831394413420391],
 [913, 119, 4.748169090205628],
 [914, 119, 4.57457357992255],
 [915, 119, 4.394357381637095],
 [916, 119, 4.819872161209081],
 [917, 119, 5.009941610326153],
 [918, 119, 4.666423270100798],
 [919, 119, 5.012647095869783],
 [920, 119, 4.642960698044466],
 [921, 119, 4.846180180317139],
 [922, 119, 4.929665253882761],
 [923, 119, 5.807129607981566],
 [924, 119, 4.945665469121162],
 [925, 119, 4.959289366043956],
 [926, 119, 4.769391789367214],
 [927, 119, 5.401862550677506],
 [928, 119, 5.90027459650935],
 [929, 119, 4.706129363518036],
 [930, 119, 4.382542607174525],
 [931, 119, 5.138232131596746],
 [932, 814, 5.229106733472933],
 [933, 119, 4.2154842803536265],
 [934, 119, 4.975412453500192],
 [935, 119, 5.398568902268414],
 [936, 119, 5.398883729354001],
 [937, 119, 4.593078451515704],
 [938, 119, 5.0839951883701415],
 [939, 119, 6.090658929769783],
 [940, 119, 4.837174032770571],
 [941, 119, 5.361213132777179],
 [942, 119, 5.484532765272456],
 [943, 119, 4.995921413899483]]

Print estimations on unseen items:

	user	item	est_score
0	1	5	3.516903
1	1	10	3.901217
2	1	25	3.479851
3	1	32	3.833570
4	1	33	3.488115
...	...	...	...
19995	943	928	2.995921
19996	943	1067	3.482408
19997	943	1074	2.943290
19998	943	1188	2.732764
19999	943	1228	2.522237

20000 rows × 3 columns

model=selfBaselineIU()
model.fit(train_ui)

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

top_n.to_csv('Recommendations generated/ml-100k/Self_BaselineIU_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_BaselineIU_estimations.csv', index=False, header=False)

Ready-made baseline - Surprise implementation

import surprise as sp
import time

# Based on surprise.readthedocs.io
def get_top_n(predictions, n=10):
    
    # Here we create a dictionary which items are lists of pairs (item, score)
    top_n = defaultdict(list)
    for uid, iid, true_r, est, _ in predictions:
        top_n[uid].append((iid, est))
        
    result=[]
    # Let's choose k best items in the format: (user, item1, score1, item2, score2, ...)
    for uid, user_ratings in top_n.items():
        user_ratings.sort(key=lambda x: x[1], reverse=True)
        result.append([uid]+list(chain(*user_ratings[:n]))) 
    return result


reader = sp.Reader(line_format='user item rating timestamp', sep='\t')
trainset = sp.Dataset.load_from_file('train.csv', reader=reader)
trainset = trainset.build_full_trainset() # <class 'surprise.trainset.Trainset'> -> it is needed for using Surprise package

testset = sp.Dataset.load_from_file('test.csv', reader=reader)
testset = sp.Trainset.build_testset(testset.build_full_trainset())

algo = sp.BaselineOnly()
# algo = sp.BaselineOnly(bsl_options={'method':'sgd', 'reg':0, 'n_epochs':2000})
# observe how bad results gives above algorithm
# more details http://courses.ischool.berkeley.edu/i290-dm/s11/SECURE/a1-koren.pdf - chapter 2.1

algo.fit(trainset)

antitrainset = trainset.build_anti_testset() # We want to predict ratings of pairs (user, item) which are not in train set
predictions = algo.test(antitrainset)

top_n = get_top_n(predictions, n=10)

top_n=pd.DataFrame(top_n)

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

Estimating biases using als...

# Compute RMSE on testset using buildin functions
predictions = algo.test(testset)
sp.accuracy.rmse(predictions, verbose=True)

# Let's also save the results in file
predictions_df=[]
for uid, iid, true_r, est, _ in predictions:
    predictions_df.append([uid, iid, est])
    
predictions_df=pd.DataFrame(predictions_df)
predictions_df.to_csv('Recommendations generated/ml-100k/Ready_Baseline_estimations.csv', index=False, header=False)

sp.accuracy.mae(predictions, verbose=True)

RMSE: 0.9495
MAE:  0.7525

0.7524871012820799

Let's compare with random

# in surprise random is an algorithm predicting random value regarding to normal distribution estimated from train set
algo = sp.NormalPredictor()
algo.fit(trainset)

antitrainset = trainset.build_anti_testset() # We want to predict ratings of pairs (user, item) which are not in train set
predictions = algo.test(antitrainset)

top_n = get_top_n(predictions, n=10)

top_n=pd.DataFrame(top_n)

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

# Compute RMSE on testset using buildin functions
predictions = algo.test(testset)
sp.accuracy.rmse(predictions, verbose=True)

# Let's also save the results in file
predictions_df=[]
for uid, iid, true_r, est, _ in predictions:
    predictions_df.append([uid, iid, est])
    
predictions_df=pd.DataFrame(predictions_df)
predictions_df.to_csv('Recommendations generated/ml-100k/Ready_Random_estimations.csv', index=False, header=False)

sp.accuracy.mae(predictions, verbose=True)

RMSE: 1.5195
MAE:  1.2152

1.2151550322469735

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Preparing dataset

CSR matrices - what is it?

Example: subtracting row means

Transposing

Self made top popular

Self made global average

Project task 1 - self made top rated

Self-made baseline

project task 2: implement self-made BaselineIU

Ready-made baseline - Surprise implementation

Let's compare with random

168 KiB

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