Self made SVD

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

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)

# Done similarly to https://github.com/albertauyeung/matrix-factorization-in-python
from tqdm import tqdm

class SVD():
    
    def __init__(self, train_ui, learning_rate, regularization, nb_factors, iterations):
        self.train_ui=train_ui
        self.uir=list(zip(*[train_ui.nonzero()[0],train_ui.nonzero()[1], train_ui.data]))
        
        self.learning_rate=learning_rate
        self.regularization=regularization
        self.iterations=iterations
        self.nb_users, self.nb_items=train_ui.shape
        self.nb_ratings=train_ui.nnz
        self.nb_factors=nb_factors
        
        self.Pu=np.random.normal(loc=0, scale=1./self.nb_factors, size=(self.nb_users, self.nb_factors))
        self.Qi=np.random.normal(loc=0, scale=1./self.nb_factors, size=(self.nb_items, self.nb_factors))

    def train(self, test_ui=None):
        if test_ui!=None:
            self.test_uir=list(zip(*[test_ui.nonzero()[0],test_ui.nonzero()[1], test_ui.data]))
            
        self.learning_process=[]
        pbar = tqdm(range(self.iterations))
        for i in pbar:
            pbar.set_description(f'Epoch {i} RMSE: {self.learning_process[-1][1] if i>0 else 0}. Training epoch {i+1}...')
            np.random.shuffle(self.uir)
            self.sgd(self.uir)
            if test_ui==None:
                self.learning_process.append([i+1, self.RMSE_total(self.uir)])
            else:
                self.learning_process.append([i+1, self.RMSE_total(self.uir), self.RMSE_total(self.test_uir)])
    
    def sgd(self, uir):
        
        for u, i, score in uir:
            # Computer prediction and error
            prediction = self.get_rating(u,i)
            e = (score - prediction)
            
            # Update user and item latent feature matrices
            Pu_update=self.learning_rate * (e * self.Qi[i] - self.regularization * self.Pu[u])
            Qi_update=self.learning_rate * (e * self.Pu[u] - self.regularization * self.Qi[i])
            
            self.Pu[u] += Pu_update
            self.Qi[i] += Qi_update
        
    def get_rating(self, u, i):
        prediction = self.Pu[u].dot(self.Qi[i].T)
        return prediction
    
    def RMSE_total(self, uir):
        RMSE=0
        for u,i, score in uir:
            prediction = self.get_rating(u,i)
            RMSE+=(score - prediction)**2
        return np.sqrt(RMSE/len(uir))
    
    def estimations(self):
        self.estimations=\
        np.dot(self.Pu,self.Qi.T)

    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=SVD(train_ui, learning_rate=0.005, regularization=0.02, nb_factors=100, iterations=40)
model.train(test_ui)

Epoch 39 RMSE: 0.7467772350811145. Training epoch 40...: 100%|██████████| 40/40 [00:59<00:00,  1.50s/it]

import matplotlib.pyplot as plt

df=pd.DataFrame(model.learning_process).iloc[:,:2]
df.columns=['epoch', 'train_RMSE']
plt.plot('epoch', 'train_RMSE', data=df, color='blue')
plt.legend()

<matplotlib.legend.Legend at 0x7fedc32039b0>

import matplotlib.pyplot as plt

df=pd.DataFrame(model.learning_process[10:], columns=['epoch', 'train_RMSE', 'test_RMSE'])
plt.plot('epoch', 'train_RMSE', data=df, color='blue')
plt.plot('epoch', 'test_RMSE', data=df, color='yellow', linestyle='dashed')
plt.legend()

<matplotlib.legend.Legend at 0x7fedc1043c18>

Saving and evaluating recommendations

model.estimations()

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

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

import evaluation_measures as ev

estimations_df=pd.read_csv('Recommendations generated/ml-100k/Self_SVD_estimations.csv', header=None)
reco=np.loadtxt('Recommendations generated/ml-100k/Self_SVD_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, 9025.30it/s]

	RMSE	MAE	precision	recall	F_1	F_05	precision_super	recall_super	NDCG	mAP	MRR	LAUC	HR	HR2	Reco in test	Test coverage	Shannon	Gini
0	0.915079	0.71824	0.104772	0.045496	0.054393	0.071374	0.094421	0.076826	0.109517	0.052005	0.206646	0.519484	0.487805	0.264051	0.874549	0.142136	3.890472	0.972126

import imp
imp.reload(ev)

import evaluation_measures as ev
dir_path="Recommendations generated/ml-100k/"
super_reactions=[4,5]
test=pd.read_csv('./Datasets/ml-100k/test.csv', sep='\t', header=None)

ev.evaluate_all(test, dir_path, super_reactions)

943it [00:00, 8433.36it/s]
943it [00:00, 8182.71it/s]
943it [00:00, 9546.13it/s]
943it [00:00, 8959.29it/s]
943it [00:00, 9016.78it/s]
943it [00:00, 8085.81it/s]
943it [00:00, 8341.37it/s]
943it [00:00, 9531.98it/s]
943it [00:00, 9952.14it/s]
943it [00:00, 9774.37it/s]
943it [00:00, 9543.76it/s]
943it [00:00, 9634.07it/s]
943it [00:00, 9988.71it/s]

Model	RMSE	MAE	precision	recall	F_1	F_05	precision_super	recall_super	NDCG	mAP	MRR	LAUC	HR	HR2	Reco in test	Test coverage	Shannon	Gini
Self_TopPop	2.508258	2.217909	0.188865	0.116919	0.118732	0.141584	0.130472	0.137473	0.214651	0.111707	0.400939	0.555546	0.765642	0.492047	1.000000	0.038961	3.159079	0.987317
Self_SVD	0.915079	0.718240	0.104772	0.045496	0.054393	0.071374	0.094421	0.076826	0.109517	0.052005	0.206646	0.519484	0.487805	0.264051	0.874549	0.142136	3.890472	0.972126
Ready_Baseline	0.949459	0.752487	0.091410	0.037652	0.046030	0.061286	0.079614	0.056463	0.095957	0.043178	0.198193	0.515501	0.437964	0.239661	1.000000	0.033911	2.836513	0.991139
Self_KNNSurprisetask	0.946255	0.745209	0.083457	0.032848	0.041227	0.055493	0.074785	0.048890	0.089577	0.040902	0.189057	0.513076	0.417815	0.217391	0.888547	0.130592	3.611806	0.978659
Self_GlobalAvg	1.125760	0.943534	0.061188	0.025968	0.031383	0.041343	0.040558	0.032107	0.067695	0.027470	0.171187	0.509546	0.384942	0.142100	1.000000	0.025974	2.711772	0.992003
Ready_Random	1.522798	1.222501	0.049841	0.020656	0.025232	0.033446	0.030579	0.022927	0.051680	0.019110	0.123085	0.506849	0.331919	0.119830	0.985048	0.183983	5.097973	0.907483
Ready_I-KNN	1.030386	0.813067	0.026087	0.006908	0.010593	0.016046	0.021137	0.009522	0.024214	0.008958	0.048068	0.499885	0.154825	0.072110	0.402333	0.434343	5.133650	0.877999
Ready_I-KNNBaseline	0.935327	0.737424	0.002545	0.000755	0.001105	0.001602	0.002253	0.000930	0.003444	0.001362	0.011760	0.496724	0.021209	0.004242	0.482821	0.059885	2.232578	0.994487
Ready_U-KNN	1.023495	0.807913	0.000742	0.000205	0.000305	0.000449	0.000536	0.000198	0.000845	0.000274	0.002744	0.496441	0.007423	0.000000	0.602121	0.010823	2.089186	0.995706
Self_BaselineIU	0.958136	0.754051	0.000954	0.000188	0.000298	0.000481	0.000644	0.000223	0.001043	0.000335	0.003348	0.496433	0.009544	0.000000	0.699046	0.005051	1.945910	0.995669
Self_TopRated	2.508258	2.217909	0.000954	0.000188	0.000298	0.000481	0.000644	0.000223	0.001043	0.000335	0.003348	0.496433	0.009544	0.000000	0.699046	0.005051	1.945910	0.995669
Self_BaselineUI	0.967585	0.762740	0.000954	0.000170	0.000278	0.000463	0.000644	0.000189	0.000752	0.000168	0.001677	0.496424	0.009544	0.000000	0.600530	0.005051	1.803126	0.996380
Self_IKNN	1.018363	0.808793	0.000318	0.000108	0.000140	0.000189	0.000000	0.000000	0.000214	0.000037	0.000368	0.496391	0.003181	0.000000	0.392153	0.115440	4.174741	0.965327

Embeddings

x=np.array([[1,2],[3,4]])
display(x)
x/np.linalg.norm(x, axis=1)[:,None]

array([[1, 2],
       [3, 4]])

array([[0.4472136 , 0.89442719],
       [0.6       , 0.8       ]])

item=random.choice(list(set(train_ui.indices)))

embeddings_norm=model.Qi/np.linalg.norm(model.Qi, axis=1)[:,None] # we do not mean-center here
# omitting normalization also makes sense, but items with a greater magnitude will be recommended more often

similarity_scores=np.dot(embeddings_norm,embeddings_norm[item].T)
top_similar_items=pd.DataFrame(enumerate(similarity_scores), columns=['code', 'score'])\
.sort_values(by=['score'], ascending=[False])[:10]

top_similar_items['item_id']=top_similar_items['code'].apply(lambda x: item_code_id[x])

items=pd.read_csv('./Datasets/ml-100k/movies.csv')

result=pd.merge(top_similar_items, items, left_on='item_id', right_on='id')

result

	code	score	item_id	id	title	genres
0	1638	1.000000	1639	1639	Bitter Sugar (Azucar Amargo) (1996)	Drama
1	802	0.992833	803	803	Heaven & Earth (1993)	Action, Drama, War
2	1378	0.992618	1379	1379	Love and Other Catastrophes (1996)	Romance
3	1130	0.991573	1131	1131	Safe (1995)	Thriller
4	1199	0.991141	1200	1200	Kim (1950)	Children's, Drama
5	1195	0.991040	1196	1196	Savage Nights (Nuits fauves, Les) (1992)	Drama
6	1622	0.990832	1623	1623	Cérémonie, La (1995)	Drama
7	1417	0.990285	1418	1418	Joy Luck Club, The (1993)	Drama
8	1067	0.990192	1068	1068	Star Maker, The (Uomo delle stelle, L') (1995)	Drama
9	1192	0.990168	1193	1193	Before the Rain (Pred dozhdot) (1994)	Drama

project task 5: implement SVD on top baseline (as it is in Surprise library)

# making changes to our implementation by considering additional parameters in the gradient descent procedure 
# seems to be the fastest option
# please save the output in 'Recommendations generated/ml-100k/Self_SVDBaseline_reco.csv' and
# 'Recommendations generated/ml-100k/Self_SVDBaseline_estimations.csv'

from tqdm import tqdm


class SVDbaseline():
    def __init__(self, train_ui, learning_rate, regularization, nb_factors, iterations):
        self.train_ui = train_ui
        self.uir = list(zip(*[train_ui.nonzero()[0], train_ui.nonzero()[1], train_ui.data]))
        
        self.learning_rate = learning_rate
        self.regularization = regularization
        self.iterations = iterations
        self.nb_users, self.nb_items = train_ui.shape
        self.nb_ratings = train_ui.nnz
        self.nb_factors = nb_factors
        
        self.Bu = np.random.normal(loc = 0, scale = 1./self.nb_factors, size = (self.nb_users, self.nb_factors))
        self.Bi = np.random.normal(loc = 0, scale = 1./self.nb_factors, size = (self.nb_items, self.nb_factors))
        
        self.Pu = np.random.normal(loc = 0, scale = 1./self.nb_factors, size = (self.nb_users, self.nb_factors))
        self.Qi = np.random.normal(loc = 0, scale = 1./self.nb_factors, size = (self.nb_items, self.nb_factors))
        
        self.bias_i = np.zeros(self.nb_items)
        self.bias_u = np.zeros(self.nb_users)

        
    def train(self, test_ui = None):
        if test_ui != None:
            self.test_uir = list(zip(*[test_ui.nonzero()[0],test_ui.nonzero()[1], test_ui.data]))
            
        self.learning_process = []
        pbar = tqdm(range(self.iterations))
        
        for i in pbar:
            pbar.set_description(f'Epoch {i} RMSE: {self.learning_process[-1][1] if i > 0 else 0}. Training epoch {i + 1}...')
            np.random.shuffle(self.uir)
            self.sgd(self.uir)
            
            if test_ui == None:
                self.learning_process.append([i + 1, self.RMSE_total(self.uir)])
            else:
                self.learning_process.append([i + 1, self.RMSE_total(self.uir), self.RMSE_total(self.test_uir)])
    
    
    def sgd(self, uir):
        for u, i, score in uir:
            prediction = self.get_rating(u,i)
            e = (score - prediction)
            
            Pu_update = self.learning_rate * (e * self.Qi[i] - self.regularization * self.Pu[u])
            Qi_update = self.learning_rate * (e * self.Pu[u] - self.regularization * self.Qi[i])
            
            Bu_update = self.learning_rate * (e - self.regularization * self.Bu[u])
            Bi_update = self.learning_rate * (e - self.regularization * self.Bi[i])
            
            self.Bu[u] += Bu_update
            self.Bi[i] += Bi_update

            self.Pu[u] += Pu_update
            self.Qi[i] += Qi_update
    
    
    def get_rating(self, u, i):
        prediction = self.Bu[u] + self.Bi[i] + self.Pu[u].dot(self.Qi[i].T)
        return prediction
    
    
    def RMSE_total(self, uir):
        RMSE = 0
        for u,i, score in uir:
            prediction = self.get_rating(u,i)
            RMSE += (score - prediction) ** 2
        return np.sqrt(RMSE / len(uir))
    
    
    def estimations(self):
        self.estimations=\
        self.bias_u[:, np.newaxis] + self.bias_i[np.newaxis:,] + np.dot(self.Pu, self.Qi.T)

        
    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 = []
        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 = SVDbaseline(train_ui, learning_rate = 0.005, regularization = 0.02, nb_factors = 100, iterations = 40)
model.train(test_ui)

Epoch 1 RMSE: [1.56628501 1.56647573 1.56581135 1.56616034 1.56624616 1.56602468/it]
 1.56609983 1.56608786 1.56617096 1.56609962 1.56627857 1.56624403
 1.56608856 1.56608238 1.56620356 1.56604183 1.56617325 1.56616721
 1.56608636 1.56621656 1.56601182 1.56629164 1.56603567 1.566008
 1.5659204  1.56600686 1.56632857 1.56623671 1.56650435 1.56614388
 1.56597602 1.56619724 1.56600564 1.56592808 1.5662823  1.56598423
 1.56630978 1.5661384  1.56637227 1.56600394 1.56599185 1.56598777
 1.56627173 1.56602758 1.56607052 1.56610967 1.56619676 1.5660723
 1.5661363  1.56643558 1.56599281 1.56602474 1.56615414 1.56619859
 1.56630092 1.56593518 1.56608721 1.56602644 1.56614329 1.56602186
 1.56606511 1.56610499 1.56610819 1.56593067 1.5661533  1.56616749
 1.56610497 1.5661211  1.56599833 1.56605015 1.56627997 1.56605438
 1.56626503 1.5660066  1.56630412 1.56620906 1.56620395 1.56631407
 1.56606347 1.56617128 1.56621069 1.56618785 1.56600689 1.56643735
 1.56598312 1.56615281 1.5661071  1.56631373 1.56604796 1.56585804
 1.56627589 1.56626862 1.56616867 1.56633676 1.56601226 1.5661184
Epoch 1 RMSE: [1.56628501 1.56647573 1.56581135 1.56616034 1.56624616 1.56602468   | 1/40 [00:02<01:40,  2.57s/it]
 1.56609983 1.56608786 1.56617096 1.56609962 1.56627857 1.56624403
 1.56608856 1.56608238 1.56620356 1.56604183 1.56617325 1.56616721
 1.56608636 1.56621656 1.56601182 1.56629164 1.56603567 1.566008
 1.5659204  1.56600686 1.56632857 1.56623671 1.56650435 1.56614388
 1.56597602 1.56619724 1.56600564 1.56592808 1.5662823  1.56598423
 1.56630978 1.5661384  1.56637227 1.56600394 1.56599185 1.56598777
 1.56627173 1.56602758 1.56607052 1.56610967 1.56619676 1.5660723
 1.5661363  1.56643558 1.56599281 1.56602474 1.56615414 1.56619859
 1.56630092 1.56593518 1.56608721 1.56602644 1.56614329 1.56602186
 1.56606511 1.56610499 1.56610819 1.56593067 1.5661533  1.56616749
 1.56610497 1.5661211  1.56599833 1.56605015 1.56627997 1.56605438
 1.56626503 1.5660066  1.56630412 1.56620906 1.56620395 1.56631407
 1.56606347 1.56617128 1.56621069 1.56618785 1.56600689 1.56643735
 1.56598312 1.56615281 1.5661071  1.56631373 1.56604796 1.56585804
 1.56627589 1.56626862 1.56616867 1.56633676 1.56601226 1.5661184
Epoch 2 RMSE: [1.2417018  1.24188159 1.24150993 1.24174429 1.24176255 1.24170722   | 2/40 [00:05<01:37,  2.58s/it]
 1.24171074 1.24167268 1.24172545 1.24174763 1.24183238 1.24173671
 1.24163026 1.24162065 1.24177366 1.24158469 1.24177297 1.24166952
 1.24171917 1.24183222 1.24162599 1.24178984 1.24168821 1.24166123
 1.24161223 1.24162659 1.24185753 1.24183293 1.24185593 1.24174315
 1.24165528 1.2416642  1.241631   1.24156043 1.24184828 1.24160591
 1.24178785 1.2416347  1.24184533 1.2416611  1.24161786 1.24155744
 1.24177065 1.24165326 1.24171443 1.24170905 1.24183533 1.24169338
 1.24169124 1.24192236 1.24165418 1.24165594 1.2417279  1.24175767
 1.24186364 1.24169617 1.24170661 1.24161604 1.24172478 1.24166061
 1.24167343 1.24165278 1.24162706 1.2415709  1.24172076 1.24168487
 1.24171406 1.24165323 1.24165323 1.24166652 1.24179703 1.24169395
 1.24174949 1.24163659 1.2418293  1.24176263 1.24171404 1.2418294
 1.24168063 1.24165637 1.24178972 1.24178604 1.24169735 1.241849
 1.24170329 1.24177262 1.24171872 1.24179172 1.24171928 1.24153613
 1.24177704 1.24185793 1.24168956 1.2417221  1.24165712 1.24172613
Epoch 2 RMSE: [1.2417018  1.24188159 1.24150993 1.24174429 1.24176255 1.24170722   | 2/40 [00:05<01:37,  2.58s/it]
 1.24171074 1.24167268 1.24172545 1.24174763 1.24183238 1.24173671
 1.24163026 1.24162065 1.24177366 1.24158469 1.24177297 1.24166952
 1.24171917 1.24183222 1.24162599 1.24178984 1.24168821 1.24166123
 1.24161223 1.24162659 1.24185753 1.24183293 1.24185593 1.24174315
 1.24165528 1.2416642  1.241631   1.24156043 1.24184828 1.24160591
 1.24178785 1.2416347  1.24184533 1.2416611  1.24161786 1.24155744
 1.24177065 1.24165326 1.24171443 1.24170905 1.24183533 1.24169338
 1.24169124 1.24192236 1.24165418 1.24165594 1.2417279  1.24175767
 1.24186364 1.24169617 1.24170661 1.24161604 1.24172478 1.24166061
 1.24167343 1.24165278 1.24162706 1.2415709  1.24172076 1.24168487
 1.24171406 1.24165323 1.24165323 1.24166652 1.24179703 1.24169395
 1.24174949 1.24163659 1.2418293  1.24176263 1.24171404 1.2418294
 1.24168063 1.24165637 1.24178972 1.24178604 1.24169735 1.241849
 1.24170329 1.24177262 1.24171872 1.24179172 1.24171928 1.24153613
 1.24177704 1.24185793 1.24168956 1.2417221  1.24165712 1.24172613
Epoch 3 RMSE: [1.13258243 1.13273769 1.13249398 1.13265935 1.13264375 1.13263674   | 3/40 [00:07<01:35,  2.58s/it]
 1.13264279 1.13259496 1.13263666 1.13267439 1.13270287 1.13263871
 1.13256654 1.13256675 1.13267736 1.13252626 1.13269081 1.13258232
 1.13262013 1.13274078 1.13258389 1.13267925 1.13263407 1.13259053
 1.13256681 1.13256461 1.13271984 1.13272441 1.13271252 1.13264455
 1.13259818 1.13258742 1.13258444 1.1325301  1.13271939 1.13255725
 1.13265228 1.13257112 1.13272558 1.13259942 1.13256249 1.13251124
 1.13267438 1.13259717 1.13263932 1.13263856 1.132744   1.13260791
 1.13260569 1.13276277 1.13258999 1.13260022 1.13263504 1.13267512
 1.13274647 1.13265384 1.13265274 1.13255779 1.13264575 1.13261409
 1.13261241 1.13257776 1.13255374 1.13251717 1.13263993 1.13258915
 1.13262861 1.13256498 1.13260396 1.13259002 1.13269051 1.13262309
 1.13264451 1.1326     1.13270356 1.13266447 1.13263253 1.13269319
 1.13262264 1.13256405 1.1326936  1.13267126 1.1326336  1.13270766
 1.13266901 1.1326705  1.13265684 1.13267642 1.13266019 1.13251334
 1.13266081 1.13273492 1.13259381 1.13259615 1.13258523 1.13264068
Epoch 3 RMSE: [1.13258243 1.13273769 1.13249398 1.13265935 1.13264375 1.13263674   | 3/40 [00:07<01:35,  2.58s/it]
 1.13264279 1.13259496 1.13263666 1.13267439 1.13270287 1.13263871
 1.13256654 1.13256675 1.13267736 1.13252626 1.13269081 1.13258232
 1.13262013 1.13274078 1.13258389 1.13267925 1.13263407 1.13259053
 1.13256681 1.13256461 1.13271984 1.13272441 1.13271252 1.13264455
 1.13259818 1.13258742 1.13258444 1.1325301  1.13271939 1.13255725
 1.13265228 1.13257112 1.13272558 1.13259942 1.13256249 1.13251124
 1.13267438 1.13259717 1.13263932 1.13263856 1.132744   1.13260791
 1.13260569 1.13276277 1.13258999 1.13260022 1.13263504 1.13267512
 1.13274647 1.13265384 1.13265274 1.13255779 1.13264575 1.13261409
 1.13261241 1.13257776 1.13255374 1.13251717 1.13263993 1.13258915
 1.13262861 1.13256498 1.13260396 1.13259002 1.13269051 1.13262309
 1.13264451 1.1326     1.13270356 1.13266447 1.13263253 1.13269319
 1.13262264 1.13256405 1.1326936  1.13267126 1.1326336  1.13270766
 1.13266901 1.1326705  1.13265684 1.13267642 1.13266019 1.13251334
 1.13266081 1.13273492 1.13259381 1.13259615 1.13258523 1.13264068
Epoch 4 RMSE: [1.07470103 1.07483205 1.074653   1.07478245 1.07474992 1.07475779   | 4/40 [00:10<01:30,  2.53s/it]
 1.07476855 1.07471954 1.07475657 1.07479501 1.07479633 1.07475995
 1.07471091 1.07471076 1.07478729 1.07466922 1.074806   1.07471156
 1.07473267 1.07485138 1.07472955 1.07478602 1.07476246 1.07471623
 1.07470393 1.07470486 1.07480787 1.07483042 1.07480929 1.07475672
 1.074736   1.07471934 1.07472499 1.07468818 1.07480974 1.0747082
 1.07475006 1.07471146 1.07482552 1.07473571 1.07469558 1.07466382
 1.07478714 1.07473432 1.07476276 1.07476954 1.07485164 1.07472884
 1.07472827 1.07483967 1.0747193  1.07473498 1.07475513 1.07479379
 1.07484481 1.07478596 1.074786   1.07469995 1.07477074 1.07475607
 1.07475005 1.07470749 1.07469114 1.0746581  1.07476066 1.0747095
 1.07474566 1.07469449 1.07474116 1.07472055 1.07479978 1.07475009
 1.07476008 1.07473934 1.07480159 1.07477503 1.07476399 1.07478594
 1.07475659 1.07469004 1.0748058  1.07477358 1.07476343 1.07479883
 1.07480411 1.07477767 1.0747872  1.07478549 1.07478493 1.07467267
 1.07476843 1.07482625 1.07471241 1.0747061  1.07471244 1.07475596
Epoch 4 RMSE: [1.07470103 1.07483205 1.074653   1.07478245 1.07474992 1.07475779   | 4/40 [00:10<01:30,  2.53s/it]  
 1.07476855 1.07471954 1.07475657 1.07479501 1.07479633 1.07475995
 1.07471091 1.07471076 1.07478729 1.07466922 1.074806   1.07471156
 1.07473267 1.07485138 1.07472955 1.07478602 1.07476246 1.07471623
 1.07470393 1.07470486 1.07480787 1.07483042 1.07480929 1.07475672
 1.074736   1.07471934 1.07472499 1.07468818 1.07480974 1.0747082
 1.07475006 1.07471146 1.07482552 1.07473571 1.07469558 1.07466382
 1.07478714 1.07473432 1.07476276 1.07476954 1.07485164 1.07472884
 1.07472827 1.07483967 1.0747193  1.07473498 1.07475513 1.07479379
 1.07484481 1.07478596 1.074786   1.07469995 1.07477074 1.07475607
 1.07475005 1.07470749 1.07469114 1.0746581  1.07476066 1.0747095
 1.07474566 1.07469449 1.07474116 1.07472055 1.07479978 1.07475009
 1.07476008 1.07473934 1.07480159 1.07477503 1.07476399 1.07478594
 1.07475659 1.07469004 1.0748058  1.07477358 1.07476343 1.07479883
 1.07480411 1.07477767 1.0747872  1.07478549 1.07478493 1.07467267
 1.07476843 1.07482625 1.07471241 1.0747061  1.07471244 1.07475596
Epoch 5 RMSE: [1.03814976 1.03826045 1.03811633 1.03822671 1.03818608 1.03819672   | 5/40 [00:12<01:28,  2.54s/it]
 1.03821107 1.03816502 1.03819735 1.03823394 1.03822099 1.03820276
 1.03817009 1.03816776 1.03822115 1.03812697 1.03824053 1.03816213
 1.0381714  1.03828314 1.03818505 1.03821938 1.03820537 1.03816112
 1.03815479 1.03816081 1.03823001 1.03826181 1.03823864 1.03819418
 1.03818894 1.03817048 1.03817742 1.03815259 1.03823377 1.03817128
 1.03818148 1.03816561 1.03825063 1.03819015 1.03814364 1.03812778
 1.0382225  1.03818517 1.03820551 1.03821651 1.038281   1.03817264
 1.03817241 1.03825447 1.0381662  1.03818432 1.03820159 1.03823161
 1.03826976 1.03823002 1.038232   1.03815758 1.03821445 1.03820907
 1.03820234 1.03815744 1.03814712 1.03811497 1.03820183 1.03815373
 1.03818401 1.03814703 1.03819164 1.03817238 1.03823326 1.03819768
 1.03820049 1.03818693 1.03822919 1.03820819 1.0382148  1.03821205
 1.0382052  1.03813962 1.03824113 1.03820524 1.0382107  1.03822166
 1.03824703 1.03821022 1.03823076 1.03822139 1.03822339 1.03813808
 1.03820354 1.038249   1.03815644 1.03814753 1.03816163 1.03819351
Epoch 5 RMSE: [1.03814976 1.03826045 1.03811633 1.03822671 1.03818608 1.03819672   | 5/40 [00:12<01:28,  2.54s/it]
 1.03821107 1.03816502 1.03819735 1.03823394 1.03822099 1.03820276
 1.03817009 1.03816776 1.03822115 1.03812697 1.03824053 1.03816213
 1.0381714  1.03828314 1.03818505 1.03821938 1.03820537 1.03816112
 1.03815479 1.03816081 1.03823001 1.03826181 1.03823864 1.03819418
 1.03818894 1.03817048 1.03817742 1.03815259 1.03823377 1.03817128
 1.03818148 1.03816561 1.03825063 1.03819015 1.03814364 1.03812778
 1.0382225  1.03818517 1.03820551 1.03821651 1.038281   1.03817264
 1.03817241 1.03825447 1.0381662  1.03818432 1.03820159 1.03823161
 1.03826976 1.03823002 1.038232   1.03815758 1.03821445 1.03820907
 1.03820234 1.03815744 1.03814712 1.03811497 1.03820183 1.03815373
 1.03818401 1.03814703 1.03819164 1.03817238 1.03823326 1.03819768
 1.03820049 1.03818693 1.03822919 1.03820819 1.0382148  1.03821205
 1.0382052  1.03813962 1.03824113 1.03820524 1.0382107  1.03822166
 1.03824703 1.03821022 1.03823076 1.03822139 1.03822339 1.03813808
 1.03820354 1.038249   1.03815644 1.03814753 1.03816163 1.03819351
Epoch 6 RMSE: [1.01281717 1.01291107 1.01278938 1.01288647 1.01284443 1.01285417   | 6/40 [00:15<01:28,  2.60s/it]
 1.01286901 1.01282764 1.01285544 1.01288953 1.0128703  1.01286329
 1.01284107 1.01283658 1.01287412 1.01279734 1.01289272 1.01282785
 1.01283052 1.01293233 1.01285139 1.01287313 1.01286354 1.01282315
 1.01281977 1.01283023 1.01287715 1.01291256 1.0128901  1.01285057
 1.01285464 1.01283705 1.01284262 1.01282647 1.01288275 1.01284451
 1.01283583 1.01283238 1.01289636 1.01285878 1.01280834 1.01280304
 1.01287596 1.01284933 1.01286522 1.01287812 1.0129298  1.01283542
 1.01283487 1.01289631 1.01282952 1.0128476  1.0128641  1.01288633
 1.01291558 1.01288799 1.01289213 1.01282762 1.01287417 1.01287394
 1.01286766 1.01282299 1.01281667 1.01278599 1.01286056 1.01281531
 1.01284136 1.01281493 1.01285498 1.01284074 1.01288644 1.012862
 1.01285987 1.01284788 1.01287841 1.01286073 1.01287958 1.01286209
 1.01286818 1.01280658 1.01289446 1.01285892 1.01287203 1.01286922
 1.01290259 1.01286311 1.01288884 1.01287717 1.01287654 1.01281316
 1.01285885 1.01289549 1.01281868 1.01280926 1.01282758 1.01284977
Epoch 6 RMSE: [1.01281717 1.01291107 1.01278938 1.01288647 1.01284443 1.01285417   | 6/40 [00:15<01:28,  2.60s/it]  
 1.01286901 1.01282764 1.01285544 1.01288953 1.0128703  1.01286329
 1.01284107 1.01283658 1.01287412 1.01279734 1.01289272 1.01282785
 1.01283052 1.01293233 1.01285139 1.01287313 1.01286354 1.01282315
 1.01281977 1.01283023 1.01287715 1.01291256 1.0128901  1.01285057
 1.01285464 1.01283705 1.01284262 1.01282647 1.01288275 1.01284451
 1.01283583 1.01283238 1.01289636 1.01285878 1.01280834 1.01280304
 1.01287596 1.01284933 1.01286522 1.01287812 1.0129298  1.01283542
 1.01283487 1.01289631 1.01282952 1.0128476  1.0128641  1.01288633
 1.01291558 1.01288799 1.01289213 1.01282762 1.01287417 1.01287394
 1.01286766 1.01282299 1.01281667 1.01278599 1.01286056 1.01281531
 1.01284136 1.01281493 1.01285498 1.01284074 1.01288644 1.012862
 1.01285987 1.01284788 1.01287841 1.01286073 1.01287958 1.01286209
 1.01286818 1.01280658 1.01289446 1.01285892 1.01287203 1.01286922
 1.01290259 1.01286311 1.01288884 1.01287717 1.01287654 1.01281316
 1.01285885 1.01289549 1.01281868 1.01280926 1.01282758 1.01284977
Epoch 7 RMSE: [0.99440278 0.99448285 0.99437689 0.99446413 0.99442374 0.99443101   | 7/40 [00:17<01:23,  2.53s/it]
 0.99444568 0.9944093  0.99443276 0.99446378 0.99444228 0.99444224
 0.99442689 0.99442063 0.99444827 0.99438454 0.99446507 0.99441125
 0.99441043 0.99450116 0.99443336 0.99444832 0.9944398  0.99440386
 0.99440271 0.9944157  0.99444763 0.99448437 0.99446327 0.99442748
 0.99443694 0.99442101 0.9944241  0.99441448 0.99445494 0.99443242
 0.99441235 0.99441589 0.99446372 0.99444438 0.99439146 0.99439286
 0.99444993 0.99443034 0.99444365 0.99445697 0.99449893 0.99441738
 0.99441652 0.99446308 0.99441141 0.99442816 0.99444559 0.9944599
 0.99448334 0.99446342 0.99447032 0.99441352 0.99445264 0.99445534
 0.99444959 0.9944058  0.99440297 0.99437383 0.99443823 0.99439607
 0.99441945 0.9944003  0.99443535 0.99442618 0.99445976 0.99444387
 0.99443846 0.99442597 0.99444931 0.99443373 0.99446158 0.99443475
 0.99444772 0.99439111 0.99446809 0.99443495 0.99445024 0.99444
 0.99447577 0.99443738 0.99446482 0.99445296 0.99444964 0.99440218
 0.9944348  0.99446505 0.99440051 0.99439129 0.99441129 0.99442638
Epoch 7 RMSE: [0.99440278 0.99448285 0.99437689 0.99446413 0.99442374 0.99443101   | 7/40 [00:17<01:23,  2.53s/it]  
 0.99444568 0.9944093  0.99443276 0.99446378 0.99444228 0.99444224
 0.99442689 0.99442063 0.99444827 0.99438454 0.99446507 0.99441125
 0.99441043 0.99450116 0.99443336 0.99444832 0.9944398  0.99440386
 0.99440271 0.9944157  0.99444763 0.99448437 0.99446327 0.99442748
 0.99443694 0.99442101 0.9944241  0.99441448 0.99445494 0.99443242
 0.99441235 0.99441589 0.99446372 0.99444438 0.99439146 0.99439286
 0.99444993 0.99443034 0.99444365 0.99445697 0.99449893 0.99441738
 0.99441652 0.99446308 0.99441141 0.99442816 0.99444559 0.9944599
 0.99448334 0.99446342 0.99447032 0.99441352 0.99445264 0.99445534
 0.99444959 0.9944058  0.99440297 0.99437383 0.99443823 0.99439607
 0.99441945 0.9944003  0.99443535 0.99442618 0.99445976 0.99444387
 0.99443846 0.99442597 0.99444931 0.99443373 0.99446158 0.99443475
 0.99444772 0.99439111 0.99446809 0.99443495 0.99445024 0.99444
 0.99447577 0.99443738 0.99446482 0.99445296 0.99444964 0.99440218
 0.9944348  0.99446505 0.99440051 0.99439129 0.99441129 0.99442638
Epoch 8 RMSE: [0.98042218 0.98049048 0.98039697 0.98047584 0.98043855 0.98044358   | 8/40 [00:20<01:19,  2.48s/it]
 0.9804572  0.98042485 0.98044487 0.98047328 0.9804515  0.98045602
 0.98044497 0.98043727 0.98045856 0.98040473 0.98047328 0.98042834
 0.98042557 0.98050617 0.98044835 0.98045934 0.98045096 0.98041956
 0.98041958 0.98043437 0.98045558 0.98049181 0.98047231 0.98043977
 0.98045187 0.98043782 0.98043933 0.98043387 0.98046433 0.98045169
 0.98042538 0.98043231 0.98046841 0.9804625  0.98040982 0.98041445
 0.98046011 0.98044478 0.98045634 0.98046963 0.98050411 0.98043312
 0.98043237 0.98046834 0.98042783 0.98044328 0.98046024 0.98046917
 0.98048834 0.98047363 0.98048218 0.98043166 0.98046508 0.9804699
 0.98046497 0.98042322 0.9804222  0.98039509 0.98045161 0.98041184
 0.98043266 0.98041952 0.98044893 0.98044451 0.98046886 0.98045955
 0.98045228 0.98043826 0.98045715 0.98044318 0.98047617 0.98044425
 0.98046183 0.98041042 0.98047757 0.9804468  0.98046262 0.98044893
 0.98048381 0.98044816 0.98047516 0.98046413 0.98045799 0.98042268
 0.98044622 0.98047213 0.98041723 0.98040793 0.98042941 0.98043912
Epoch 8 RMSE: [0.98042218 0.98049048 0.98039697 0.98047584 0.98043855 0.98044358   | 8/40 [00:20<01:19,  2.48s/it]
 0.9804572  0.98042485 0.98044487 0.98047328 0.9804515  0.98045602
 0.98044497 0.98043727 0.98045856 0.98040473 0.98047328 0.98042834
 0.98042557 0.98050617 0.98044835 0.98045934 0.98045096 0.98041956
 0.98041958 0.98043437 0.98045558 0.98049181 0.98047231 0.98043977
 0.98045187 0.98043782 0.98043933 0.98043387 0.98046433 0.98045169
 0.98042538 0.98043231 0.98046841 0.9804625  0.98040982 0.98041445
 0.98046011 0.98044478 0.98045634 0.98046963 0.98050411 0.98043312
 0.98043237 0.98046834 0.98042783 0.98044328 0.98046024 0.98046917
 0.98048834 0.98047363 0.98048218 0.98043166 0.98046508 0.9804699
 0.98046497 0.98042322 0.9804222  0.98039509 0.98045161 0.98041184
 0.98043266 0.98041952 0.98044893 0.98044451 0.98046886 0.98045955
 0.98045228 0.98043826 0.98045715 0.98044318 0.98047617 0.98044425
 0.98046183 0.98041042 0.98047757 0.9804468  0.98046262 0.98044893
 0.98048381 0.98044816 0.98047516 0.98046413 0.98045799 0.98042268
 0.98044622 0.98047213 0.98041723 0.98040793 0.98042941 0.98043912
Epoch 9 RMSE: [0.96956864 0.96962721 0.96954354 0.96961556 0.96958205 0.96958458   | 9/40 [00:22<01:16,  2.47s/it]
 0.96959707 0.96956865 0.96958616 0.96961134 0.96959051 0.96959811
 0.96958923 0.96958072 0.96959807 0.96955239 0.96961096 0.96957265
 0.96956903 0.96964055 0.96959048 0.96959949 0.96959073 0.96956322
 0.96956373 0.96957968 0.96959395 0.9696286  0.96961054 0.96958063
 0.96959443 0.96958199 0.96958222 0.96957953 0.96960364 0.96959708
 0.96956749 0.96957626 0.96960309 0.96960737 0.96955603 0.96956241
 0.96959943 0.96958725 0.96959703 0.96961002 0.96963852 0.96957742
 0.96957636 0.9696043  0.96957224 0.96958563 0.96960255 0.96960695
 0.96962291 0.96961234 0.9696223  0.96957714 0.96960567 0.9696117
 0.96960751 0.96956774 0.96956802 0.96954356 0.9695932  0.96955556
 0.96957482 0.96956564 0.96959001 0.96958992 0.96960666 0.96960246
 0.96959448 0.96957906 0.96959454 0.96958207 0.96961755 0.96958331
 0.9696031  0.9695574  0.9696157  0.96958767 0.96960284 0.96958787
 0.96962011 0.969588   0.96961464 0.9696038  0.96959544 0.96956941
 0.96958616 0.96960883 0.9695618  0.96955266 0.9695746  0.96958055
Epoch 9 RMSE: [0.96956864 0.96962721 0.96954354 0.96961556 0.96958205 0.96958458    | 9/40 [00:22<01:16,  2.47s/it]
 0.96959707 0.96956865 0.96958616 0.96961134 0.96959051 0.96959811
 0.96958923 0.96958072 0.96959807 0.96955239 0.96961096 0.96957265
 0.96956903 0.96964055 0.96959048 0.96959949 0.96959073 0.96956322
 0.96956373 0.96957968 0.96959395 0.9696286  0.96961054 0.96958063
 0.96959443 0.96958199 0.96958222 0.96957953 0.96960364 0.96959708
 0.96956749 0.96957626 0.96960309 0.96960737 0.96955603 0.96956241
 0.96959943 0.96958725 0.96959703 0.96961002 0.96963852 0.96957742
 0.96957636 0.9696043  0.96957224 0.96958563 0.96960255 0.96960695
 0.96962291 0.96961234 0.9696223  0.96957714 0.96960567 0.9696117
 0.96960751 0.96956774 0.96956802 0.96954356 0.9695932  0.96955556
 0.96957482 0.96956564 0.96959001 0.96958992 0.96960666 0.96960246
 0.96959448 0.96957906 0.96959454 0.96958207 0.96961755 0.96958331
 0.9696031  0.9695574  0.9696157  0.96958767 0.96960284 0.96958787
 0.96962011 0.969588   0.96961464 0.9696038  0.96959544 0.96956941
 0.96958616 0.96960883 0.9695618  0.96955266 0.9695746  0.96958055
Epoch 10 RMSE: [0.96075673 0.96080761 0.96073196 0.96079776 0.96076804 0.96076873   | 10/40 [00:24<01:13,  2.44s/it]
 0.96078039 0.96075495 0.96077015 0.96079231 0.96077356 0.96078261
 0.96077559 0.96076643 0.96078098 0.9607412  0.96079177 0.96075896
 0.96075491 0.96081856 0.96077505 0.96078216 0.9607736  0.96074991
 0.96075047 0.96076696 0.96077613 0.9608088  0.9607921  0.96076446
 0.96077892 0.96076825 0.96076753 0.96076714 0.96078607 0.96078396
 0.9607528  0.96076217 0.96078209 0.9607937  0.96074464 0.96075185
 0.96078205 0.96077184 0.96078107 0.96079289 0.96081683 0.96076394
 0.96076258 0.96078479 0.96075914 0.96077056 0.96078714 0.96078814
 0.96080169 0.9607937  0.96080479 0.96076411 0.96078901 0.96079532
 0.96079197 0.9607546  0.96075574 0.96073366 0.96077769 0.96074198
 0.96075972 0.96075414 0.96077353 0.96077696 0.96078774 0.96078751
 0.96077925 0.96076313 0.9607757  0.96076468 0.96080126 0.96076611
 0.96078704 0.96074671 0.9607969  0.96077184 0.9607857  0.96077078
 0.9607998  0.96077123 0.96079652 0.96078645 0.96077646 0.96075732
 0.96076928 0.96078951 0.96074835 0.96074048 0.96076206 0.96076493
Epoch 10 RMSE: [0.96075673 0.96080761 0.96073196 0.96079776 0.96076804 0.96076873   | 10/40 [00:24<01:13,  2.44s/it]
 0.96078039 0.96075495 0.96077015 0.96079231 0.96077356 0.96078261
 0.96077559 0.96076643 0.96078098 0.9607412  0.96079177 0.96075896
 0.96075491 0.96081856 0.96077505 0.96078216 0.9607736  0.96074991
 0.96075047 0.96076696 0.96077613 0.9608088  0.9607921  0.96076446
 0.96077892 0.96076825 0.96076753 0.96076714 0.96078607 0.96078396
 0.9607528  0.96076217 0.96078209 0.9607937  0.96074464 0.96075185
 0.96078205 0.96077184 0.96078107 0.96079289 0.96081683 0.96076394
 0.96076258 0.96078479 0.96075914 0.96077056 0.96078714 0.96078814
 0.96080169 0.9607937  0.96080479 0.96076411 0.96078901 0.96079532
 0.96079197 0.9607546  0.96075574 0.96073366 0.96077769 0.96074198
 0.96075972 0.96075414 0.96077353 0.96077696 0.96078774 0.96078751
 0.96077925 0.96076313 0.9607757  0.96076468 0.96080126 0.96076611
 0.96078704 0.96074671 0.9607969  0.96077184 0.9607857  0.96077078
 0.9607998  0.96077123 0.96079652 0.96078645 0.96077646 0.96075732
 0.96076928 0.96078951 0.96074835 0.96074048 0.96076206 0.96076493
Epoch 11 RMSE: [0.95361422 0.9536583  0.95358983 0.95365028 0.95362411 0.95362327   | 11/40 [00:27<01:09,  2.41s/it]
 0.95363393 0.95361104 0.95362427 0.95364368 0.95362728 0.95363712
 0.95363111 0.95362154 0.95363403 0.95359944 0.95364333 0.95361499
 0.953611   0.95366754 0.95362947 0.95363561 0.95362648 0.95360615
 0.95360678 0.95362319 0.95362914 0.95365963 0.95364409 0.95361836
 0.95363346 0.95362369 0.95362264 0.95362344 0.95363932 0.95363981
 0.95360837 0.95361782 0.95363242 0.95364907 0.95360271 0.95361032
 0.95363511 0.95362669 0.95363478 0.95364577 0.95366592 0.95362025
 0.95361858 0.95363617 0.95361555 0.95362547 0.95364146 0.95363965
 0.9536515  0.95364549 0.95365732 0.95362043 0.95364252 0.95364887
 0.95364593 0.9536111  0.95361243 0.95359329 0.95363234 0.95359863
 0.95361472 0.95361184 0.95362725 0.95363318 0.95363953 0.95364235
 0.95363404 0.95361743 0.95362787 0.9536177  0.95365451 0.95361986
 0.95364087 0.9536052  0.95364885 0.9536261  0.9536388  0.95362439
 0.95365    0.9536251  0.95364881 0.95363917 0.95362836 0.95361419
 0.95362283 0.95364097 0.95360492 0.95359786 0.95361872 0.95361965
Epoch 11 RMSE: [0.95361422 0.9536583  0.95358983 0.95365028 0.95362411 0.95362327   | 11/40 [00:27<01:09,  2.41s/it]
 0.95363393 0.95361104 0.95362427 0.95364368 0.95362728 0.95363712
 0.95363111 0.95362154 0.95363403 0.95359944 0.95364333 0.95361499
 0.953611   0.95366754 0.95362947 0.95363561 0.95362648 0.95360615
 0.95360678 0.95362319 0.95362914 0.95365963 0.95364409 0.95361836
 0.95363346 0.95362369 0.95362264 0.95362344 0.95363932 0.95363981
 0.95360837 0.95361782 0.95363242 0.95364907 0.95360271 0.95361032
 0.95363511 0.95362669 0.95363478 0.95364577 0.95366592 0.95362025
 0.95361858 0.95363617 0.95361555 0.95362547 0.95364146 0.95363965
 0.9536515  0.95364549 0.95365732 0.95362043 0.95364252 0.95364887
 0.95364593 0.9536111  0.95361243 0.95359329 0.95363234 0.95359863
 0.95361472 0.95361184 0.95362725 0.95363318 0.95363953 0.95364235
 0.95363404 0.95361743 0.95362787 0.9536177  0.95365451 0.95361986
 0.95364087 0.9536052  0.95364885 0.9536261  0.9536388  0.95362439
 0.95365    0.9536251  0.95364881 0.95363917 0.95362836 0.95361419
 0.95362283 0.95364097 0.95360492 0.95359786 0.95361872 0.95361965
Epoch 12 RMSE: [0.94765708 0.94769565 0.94763277 0.94768893 0.94766618 0.947664     | 12/40 [00:29<01:06,  2.39s/it]
 0.94767364 0.94765312 0.94766496 0.94768184 0.94766724 0.94767774
 0.94767218 0.94766267 0.94767376 0.94764338 0.94768166 0.94765665
 0.94765283 0.94770359 0.94766981 0.94767537 0.94766609 0.94764863
 0.94764898 0.94766509 0.9476688  0.94769696 0.94768249 0.94765882
 0.94767371 0.94766519 0.94766387 0.94766566 0.94767913 0.94768116
 0.94765028 0.94765911 0.94766987 0.94769018 0.9476467  0.94765397
 0.94767479 0.94766754 0.94767501 0.94768491 0.9477023  0.94766229
 0.94766049 0.9476745  0.94765805 0.94766628 0.94768163 0.947678
 0.94768793 0.94768374 0.94769577 0.94766257 0.94768209 0.94768808
 0.94768622 0.94765347 0.94765496 0.94763823 0.94767304 0.94764105
 0.94765615 0.94765504 0.9476667  0.94767507 0.94767778 0.94768277
 0.9476749  0.94765828 0.94766685 0.94765747 0.94769349 0.9476598
 0.94768074 0.94764938 0.94768697 0.94766657 0.94767781 0.94766472
 0.94768706 0.94766521 0.94768765 0.9476782  0.94766698 0.94765685
 0.94766267 0.9476793  0.94764692 0.94764121 0.94766126 0.94766045
Epoch 12 RMSE: [0.94765708 0.94769565 0.94763277 0.94768893 0.94766618 0.947664     | 12/40 [00:29<01:06,  2.39s/it]     
 0.94767364 0.94765312 0.94766496 0.94768184 0.94766724 0.94767774
 0.94767218 0.94766267 0.94767376 0.94764338 0.94768166 0.94765665
 0.94765283 0.94770359 0.94766981 0.94767537 0.94766609 0.94764863
 0.94764898 0.94766509 0.9476688  0.94769696 0.94768249 0.94765882
 0.94767371 0.94766519 0.94766387 0.94766566 0.94767913 0.94768116
 0.94765028 0.94765911 0.94766987 0.94769018 0.9476467  0.94765397
 0.94767479 0.94766754 0.94767501 0.94768491 0.9477023  0.94766229
 0.94766049 0.9476745  0.94765805 0.94766628 0.94768163 0.947678
 0.94768793 0.94768374 0.94769577 0.94766257 0.94768209 0.94768808
 0.94768622 0.94765347 0.94765496 0.94763823 0.94767304 0.94764105
 0.94765615 0.94765504 0.9476667  0.94767507 0.94767778 0.94768277
 0.9476749  0.94765828 0.94766685 0.94765747 0.94769349 0.9476598
 0.94768074 0.94764938 0.94768697 0.94766657 0.94767781 0.94766472
 0.94768706 0.94766521 0.94768765 0.9476782  0.94766698 0.94765685
 0.94766267 0.9476793  0.94764692 0.94764121 0.94766126 0.94766045
Epoch 13 RMSE: [0.94242167 0.94245501 0.94239796 0.94244964 0.94242964 0.94242677   | 13/40 [00:32<01:04,  2.38s/it]
 0.94243566 0.94241669 0.94242748 0.94244225 0.94242968 0.94243994
 0.9424347  0.94242551 0.94243552 0.94240881 0.94244215 0.94242032
 0.94241652 0.94246188 0.94243226 0.94243719 0.94242775 0.94241276
 0.9424129  0.94242876 0.94243091 0.94245705 0.94244329 0.94242141
 0.94243586 0.94242807 0.94242708 0.94242915 0.9424409  0.94244413
 0.9424143  0.9424223  0.94242981 0.94245265 0.942412   0.94241917
 0.94243634 0.94243026 0.94243717 0.94244606 0.94246105 0.94242636
 0.94242434 0.94243546 0.94242238 0.94242902 0.94244364 0.94243855
 0.94244718 0.94244423 0.94245625 0.94242622 0.94244366 0.94244964
 0.94244806 0.94241736 0.94241905 0.94240443 0.94243573 0.94240543
 0.94241937 0.94241989 0.94242829 0.94243845 0.94243873 0.94244488
 0.94243726 0.94242083 0.94242829 0.94241988 0.94245415 0.94242219
 0.94244239 0.94241518 0.94244741 0.9424288  0.94243902 0.94242742
 0.94244647 0.94242764 0.9424486  0.94243932 0.94242807 0.94242085
 0.94242457 0.94244017 0.94241092 0.94240614 0.94242508 0.94242352
Epoch 13 RMSE: [0.94242167 0.94245501 0.94239796 0.94244964 0.94242964 0.94242677   | 13/40 [00:32<01:04,  2.38s/it]
 0.94243566 0.94241669 0.94242748 0.94244225 0.94242968 0.94243994
 0.9424347  0.94242551 0.94243552 0.94240881 0.94244215 0.94242032
 0.94241652 0.94246188 0.94243226 0.94243719 0.94242775 0.94241276
 0.9424129  0.94242876 0.94243091 0.94245705 0.94244329 0.94242141
 0.94243586 0.94242807 0.94242708 0.94242915 0.9424409  0.94244413
 0.9424143  0.9424223  0.94242981 0.94245265 0.942412   0.94241917
 0.94243634 0.94243026 0.94243717 0.94244606 0.94246105 0.94242636
 0.94242434 0.94243546 0.94242238 0.94242902 0.94244364 0.94243855
 0.94244718 0.94244423 0.94245625 0.94242622 0.94244366 0.94244964
 0.94244806 0.94241736 0.94241905 0.94240443 0.94243573 0.94240543
 0.94241937 0.94241989 0.94242829 0.94243845 0.94243873 0.94244488
 0.94243726 0.94242083 0.94242829 0.94241988 0.94245415 0.94242219
 0.94244239 0.94241518 0.94244741 0.9424288  0.94243902 0.94242742
 0.94244647 0.94242764 0.9424486  0.94243932 0.94242807 0.94242085
 0.94242457 0.94244017 0.94241092 0.94240614 0.94242508 0.94242352
Epoch 14 RMSE: [0.9380729  0.93810193 0.93804964 0.93809779 0.93808017 0.93807681   | 14/40 [00:34<01:01,  2.37s/it]
 0.93808492 0.93806746 0.93807753 0.93808962 0.93807912 0.93808927
 0.9380845  0.9380754  0.93808462 0.93806081 0.93809038 0.9380707
 0.93806719 0.93810823 0.93808146 0.93808617 0.93807663 0.93806401
 0.93806365 0.9380789  0.93807998 0.93810428 0.93809143 0.938071
 0.93808527 0.93807792 0.93807713 0.93807948 0.93808988 0.93809373
 0.93806515 0.93807248 0.93807742 0.93810192 0.93806415 0.93807098
 0.93808517 0.93808003 0.93808658 0.93809419 0.9381074  0.93807715
 0.93807482 0.93808371 0.93807322 0.93807872 0.93809271 0.93808645
 0.93809391 0.93809203 0.93810411 0.93807661 0.93809222 0.93809822
 0.93809697 0.93806833 0.93806988 0.93805712 0.93808548 0.93805662
 0.93806972 0.93807144 0.93807716 0.93808838 0.93808667 0.93809374
 0.93808691 0.93807064 0.93807691 0.93806925 0.93810202 0.93807175
 0.93809133 0.93806764 0.93809515 0.93807829 0.93808742 0.93807713
 0.93809335 0.93807706 0.9380968  0.93808785 0.93807651 0.93807192
 0.93807378 0.93808833 0.93806168 0.9380582  0.93807566 0.93807356
Epoch 14 RMSE: [0.9380729  0.93810193 0.93804964 0.93809779 0.93808017 0.93807681   | 14/40 [00:34<01:01,  2.37s/it]   
 0.93808492 0.93806746 0.93807753 0.93808962 0.93807912 0.93808927
 0.9380845  0.9380754  0.93808462 0.93806081 0.93809038 0.9380707
 0.93806719 0.93810823 0.93808146 0.93808617 0.93807663 0.93806401
 0.93806365 0.9380789  0.93807998 0.93810428 0.93809143 0.938071
 0.93808527 0.93807792 0.93807713 0.93807948 0.93808988 0.93809373
 0.93806515 0.93807248 0.93807742 0.93810192 0.93806415 0.93807098
 0.93808517 0.93808003 0.93808658 0.93809419 0.9381074  0.93807715
 0.93807482 0.93808371 0.93807322 0.93807872 0.93809271 0.93808645
 0.93809391 0.93809203 0.93810411 0.93807661 0.93809222 0.93809822
 0.93809697 0.93806833 0.93806988 0.93805712 0.93808548 0.93805662
 0.93806972 0.93807144 0.93807716 0.93808838 0.93808667 0.93809374
 0.93808691 0.93807064 0.93807691 0.93806925 0.93810202 0.93807175
 0.93809133 0.93806764 0.93809515 0.93807829 0.93808742 0.93807713
 0.93809335 0.93807706 0.9380968  0.93808785 0.93807651 0.93807192
 0.93807378 0.93808833 0.93806168 0.9380582  0.93807566 0.93807356
Epoch 15 RMSE: [0.93405466 0.9340801  0.934032   0.93407668 0.93406119 0.93405746   | 15/40 [00:36<00:59,  2.38s/it]
 0.93406501 0.93404887 0.93405817 0.93406843 0.93405947 0.93406962
 0.93406469 0.93405606 0.93406446 0.9340433  0.93406934 0.93405188
 0.9340484  0.93408552 0.93406174 0.93406584 0.93405665 0.93404574
 0.93404505 0.93405986 0.93406013 0.93408281 0.93407048 0.93405159
 0.93406533 0.93405847 0.93405791 0.93406037 0.93406988 0.93407413
 0.93404685 0.9340534  0.93405653 0.93408173 0.9340467  0.9340531
 0.93406499 0.93406087 0.9340664  0.93407335 0.93408522 0.93405857
 0.93405595 0.93406317 0.93405485 0.93405906 0.93407257 0.93406555
 0.93407195 0.93407072 0.93408251 0.93405763 0.93407174 0.93407741
 0.93407665 0.93404982 0.93405133 0.93404042 0.93406611 0.93403863
 0.9340507  0.93405337 0.93405675 0.934069   0.93406573 0.93407359
 0.93406724 0.93405173 0.93405685 0.93404977 0.93408081 0.93405228
 0.93407092 0.93405053 0.93407401 0.93405854 0.93406664 0.93405777
 0.93407149 0.93405726 0.93407599 0.93406703 0.93405594 0.93405333
 0.93405387 0.93406767 0.93404327 0.93404073 0.93405704 0.93405458
Epoch 15 RMSE: [0.93405466 0.9340801  0.934032   0.93407668 0.93406119 0.93405746   | 15/40 [00:36<00:59,  2.38s/it]
 0.93406501 0.93404887 0.93405817 0.93406843 0.93405947 0.93406962
 0.93406469 0.93405606 0.93406446 0.9340433  0.93406934 0.93405188
 0.9340484  0.93408552 0.93406174 0.93406584 0.93405665 0.93404574
 0.93404505 0.93405986 0.93406013 0.93408281 0.93407048 0.93405159
 0.93406533 0.93405847 0.93405791 0.93406037 0.93406988 0.93407413
 0.93404685 0.9340534  0.93405653 0.93408173 0.9340467  0.9340531
 0.93406499 0.93406087 0.9340664  0.93407335 0.93408522 0.93405857
 0.93405595 0.93406317 0.93405485 0.93405906 0.93407257 0.93406555
 0.93407195 0.93407072 0.93408251 0.93405763 0.93407174 0.93407741
 0.93407665 0.93404982 0.93405133 0.93404042 0.93406611 0.93403863
 0.9340507  0.93405337 0.93405675 0.934069   0.93406573 0.93407359
 0.93406724 0.93405173 0.93405685 0.93404977 0.93408081 0.93405228
 0.93407092 0.93405053 0.93407401 0.93405854 0.93406664 0.93405777
 0.93407149 0.93405726 0.93407599 0.93406703 0.93405594 0.93405333
 0.93405387 0.93406767 0.93404327 0.93404073 0.93405704 0.93405458
Epoch 16 RMSE: [0.93049748 0.93051952 0.93047527 0.93051692 0.93050324 0.93049948   | 16/40 [00:39<00:57,  2.38s/it]
 0.9305064  0.93049127 0.93050022 0.93050846 0.93050115 0.93051083
 0.93050615 0.93049781 0.93050559 0.93048669 0.93050969 0.93049385
 0.93049065 0.93052478 0.93050302 0.93050672 0.93049789 0.93048859
 0.93048731 0.9305018  0.93050168 0.93052245 0.93051101 0.9304933
 0.93050653 0.93050002 0.93049977 0.93050252 0.93051119 0.93051542
 0.93048964 0.9304954  0.93049713 0.93052273 0.93049021 0.93049606
 0.93050613 0.93050248 0.93050784 0.93051398 0.93052447 0.93050086
 0.93049819 0.93050399 0.93049753 0.93050087 0.9305136  0.93050592
 0.93051163 0.9305109  0.93052208 0.93049988 0.9305126  0.93051792
 0.93051748 0.93049249 0.93049378 0.93048443 0.9305079  0.93048177
 0.93049304 0.93049618 0.93049766 0.93051063 0.93050627 0.93051455
 0.93050881 0.930494   0.93049817 0.93049168 0.93052071 0.93049398
 0.93051177 0.93049432 0.9305143  0.93049999 0.93050746 0.93049972
 0.93051116 0.93049898 0.93051637 0.93050739 0.93049683 0.93049562
 0.93049531 0.93050831 0.93048542 0.93048411 0.93049954 0.93049665
Epoch 16 RMSE: [0.93049748 0.93051952 0.93047527 0.93051692 0.93050324 0.93049948   | 16/40 [00:39<00:57,  2.38s/it]
 0.9305064  0.93049127 0.93050022 0.93050846 0.93050115 0.93051083
 0.93050615 0.93049781 0.93050559 0.93048669 0.93050969 0.93049385
 0.93049065 0.93052478 0.93050302 0.93050672 0.93049789 0.93048859
 0.93048731 0.9305018  0.93050168 0.93052245 0.93051101 0.9304933
 0.93050653 0.93050002 0.93049977 0.93050252 0.93051119 0.93051542
 0.93048964 0.9304954  0.93049713 0.93052273 0.93049021 0.93049606
 0.93050613 0.93050248 0.93050784 0.93051398 0.93052447 0.93050086
 0.93049819 0.93050399 0.93049753 0.93050087 0.9305136  0.93050592
 0.93051163 0.9305109  0.93052208 0.93049988 0.9305126  0.93051792
 0.93051748 0.93049249 0.93049378 0.93048443 0.9305079  0.93048177
 0.93049304 0.93049618 0.93049766 0.93051063 0.93050627 0.93051455
 0.93050881 0.930494   0.93049817 0.93049168 0.93052071 0.93049398
 0.93051177 0.93049432 0.9305143  0.93049999 0.93050746 0.93049972
 0.93051116 0.93049898 0.93051637 0.93050739 0.93049683 0.93049562
 0.93049531 0.93050831 0.93048542 0.93048411 0.93049954 0.93049665
Epoch 17 RMSE: [0.92714744 0.92716697 0.92712595 0.92716482 0.9271528  0.92714876   | 17/40 [00:41<00:54,  2.37s/it]
 0.92715527 0.92714136 0.92714956 0.92715638 0.92715026 0.9271597
 0.92715493 0.92714704 0.92715426 0.92713721 0.92715782 0.92714357
 0.92714057 0.92717165 0.92715207 0.92715539 0.92714643 0.92713889
 0.92713736 0.92715091 0.92715067 0.92716998 0.92715892 0.92714273
 0.92715531 0.92714926 0.92714926 0.92715176 0.92715998 0.92716418
 0.92713979 0.92714502 0.92714533 0.92717078 0.92714096 0.92714635
 0.92715485 0.92715167 0.92715651 0.92716198 0.92717138 0.92715065
 0.9271479  0.92715235 0.92714731 0.92714985 0.92716222 0.92715401
 0.92715894 0.92715872 0.92716959 0.92714928 0.92716093 0.92716598
 0.92716575 0.9271423  0.92714358 0.92713565 0.92715696 0.92713227
 0.92714273 0.9271466  0.92714622 0.92715961 0.92715452 0.92716285
 0.92715791 0.92714354 0.92714721 0.92714098 0.92716835 0.92714319
 0.92716019 0.92714521 0.92716224 0.9271489  0.92715569 0.92714907
 0.92715862 0.92714818 0.92716453 0.92715568 0.92714544 0.92714559
 0.9271442  0.92715665 0.92713534 0.92713479 0.92714913 0.92714612
Epoch 17 RMSE: [0.92714744 0.92716697 0.92712595 0.92716482 0.9271528  0.92714876   | 17/40 [00:41<00:54,  2.37s/it]
 0.92715527 0.92714136 0.92714956 0.92715638 0.92715026 0.9271597
 0.92715493 0.92714704 0.92715426 0.92713721 0.92715782 0.92714357
 0.92714057 0.92717165 0.92715207 0.92715539 0.92714643 0.92713889
 0.92713736 0.92715091 0.92715067 0.92716998 0.92715892 0.92714273
 0.92715531 0.92714926 0.92714926 0.92715176 0.92715998 0.92716418
 0.92713979 0.92714502 0.92714533 0.92717078 0.92714096 0.92714635
 0.92715485 0.92715167 0.92715651 0.92716198 0.92717138 0.92715065
 0.9271479  0.92715235 0.92714731 0.92714985 0.92716222 0.92715401
 0.92715894 0.92715872 0.92716959 0.92714928 0.92716093 0.92716598
 0.92716575 0.9271423  0.92714358 0.92713565 0.92715696 0.92713227
 0.92714273 0.9271466  0.92714622 0.92715961 0.92715452 0.92716285
 0.92715791 0.92714354 0.92714721 0.92714098 0.92716835 0.92714319
 0.92716019 0.92714521 0.92716224 0.9271489  0.92715569 0.92714907
 0.92715862 0.92714818 0.92716453 0.92715568 0.92714544 0.92714559
 0.9271442  0.92715665 0.92713534 0.92713479 0.92714913 0.92714612
Epoch 18 RMSE: [0.92371638 0.92373374 0.92369539 0.92373195 0.92372119 0.92371734   | 18/40 [00:43<00:52,  2.36s/it]
 0.92372333 0.92371018 0.92371809 0.9237235  0.92371867 0.92372747
 0.92372281 0.92371545 0.92372208 0.92370676 0.9237253  0.92371207
 0.92370931 0.92373784 0.92372002 0.92372321 0.92371436 0.92370792
 0.92370615 0.9237192  0.92371863 0.92373675 0.92372621 0.92371132
 0.92372328 0.92371753 0.92371751 0.92372022 0.9237277  0.92373198
 0.92370893 0.92371344 0.92371275 0.92373809 0.92371035 0.92371553
 0.9237226  0.92372003 0.92372456 0.9237292  0.92373803 0.92371934
 0.92371631 0.92372011 0.92371632 0.92371803 0.92372989 0.9237215
 0.92372566 0.92372605 0.92373631 0.92371775 0.92372841 0.92373317
 0.92373334 0.92371127 0.92371246 0.92370561 0.92372518 0.92370161
 0.9237113  0.92371578 0.92371402 0.92372767 0.92372203 0.92373032
 0.92372593 0.92371218 0.92371532 0.92370948 0.92373509 0.92371158
 0.92372759 0.92371497 0.92372913 0.92371694 0.92372338 0.92371765
 0.92372538 0.92371633 0.92373194 0.92372305 0.92371332 0.92371424
 0.9237122  0.92372428 0.92370422 0.92370451 0.92371783 0.92371467
Epoch 18 RMSE: [0.92371638 0.92373374 0.92369539 0.92373195 0.92372119 0.92371734   | 18/40 [00:43<00:52,  2.36s/it]
 0.92372333 0.92371018 0.92371809 0.9237235  0.92371867 0.92372747
 0.92372281 0.92371545 0.92372208 0.92370676 0.9237253  0.92371207
 0.92370931 0.92373784 0.92372002 0.92372321 0.92371436 0.92370792
 0.92370615 0.9237192  0.92371863 0.92373675 0.92372621 0.92371132
 0.92372328 0.92371753 0.92371751 0.92372022 0.9237277  0.92373198
 0.92370893 0.92371344 0.92371275 0.92373809 0.92371035 0.92371553
 0.9237226  0.92372003 0.92372456 0.9237292  0.92373803 0.92371934
 0.92371631 0.92372011 0.92371632 0.92371803 0.92372989 0.9237215
 0.92372566 0.92372605 0.92373631 0.92371775 0.92372841 0.92373317
 0.92373334 0.92371127 0.92371246 0.92370561 0.92372518 0.92370161
 0.9237113  0.92371578 0.92371402 0.92372767 0.92372203 0.92373032
 0.92372593 0.92371218 0.92371532 0.92370948 0.92373509 0.92371158
 0.92372759 0.92371497 0.92372913 0.92371694 0.92372338 0.92371765
 0.92372538 0.92371633 0.92373194 0.92372305 0.92371332 0.92371424
 0.9237122  0.92372428 0.92370422 0.92370451 0.92371783 0.92371467
Epoch 19 RMSE: [0.92036649 0.92038166 0.92034596 0.92038016 0.92037059 0.92036677   | 19/40 [00:46<00:49,  2.37s/it]
 0.92037241 0.9203601  0.92036774 0.92037168 0.92036813 0.9203763
 0.92037182 0.92036494 0.92037122 0.92035722 0.92037376 0.92036178
 0.92035917 0.92038546 0.9203691  0.92037202 0.92036345 0.92035818
 0.92035624 0.92036857 0.92036789 0.92038479 0.92037488 0.92036083
 0.92037237 0.92036669 0.92036692 0.92036962 0.92037696 0.92038083
 0.9203592  0.92036312 0.92036172 0.92038654 0.92036075 0.92036567
 0.92037159 0.92036923 0.92037387 0.92037782 0.92038576 0.9203693
 0.92036627 0.920369   0.92036633 0.92036734 0.92037867 0.92037008
 0.92037368 0.92037421 0.92038414 0.92036738 0.92037709 0.92038137
 0.92038191 0.92036128 0.92036223 0.92035638 0.92037447 0.92035213
 0.92036125 0.92036582 0.92036302 0.92037659 0.92037066 0.92037869
 0.92037531 0.92036215 0.92036481 0.92035902 0.92038317 0.92036109
 0.92037616 0.92036588 0.92037759 0.92036632 0.92037216 0.92036717
 0.92037345 0.92036576 0.92038041 0.9203715  0.92036231 0.92036405
 0.92036156 0.92037306 0.92035393 0.92035518 0.92036757 0.92036435
Epoch 19 RMSE: [0.92036649 0.92038166 0.92034596 0.92038016 0.92037059 0.92036677   | 19/40 [00:46<00:49,  2.37s/it] 
 0.92037241 0.9203601  0.92036774 0.92037168 0.92036813 0.9203763
 0.92037182 0.92036494 0.92037122 0.92035722 0.92037376 0.92036178
 0.92035917 0.92038546 0.9203691  0.92037202 0.92036345 0.92035818
 0.92035624 0.92036857 0.92036789 0.92038479 0.92037488 0.92036083
 0.92037237 0.92036669 0.92036692 0.92036962 0.92037696 0.92038083
 0.9203592  0.92036312 0.92036172 0.92038654 0.92036075 0.92036567
 0.92037159 0.92036923 0.92037387 0.92037782 0.92038576 0.9203693
 0.92036627 0.920369   0.92036633 0.92036734 0.92037867 0.92037008
 0.92037368 0.92037421 0.92038414 0.92036738 0.92037709 0.92038137
 0.92038191 0.92036128 0.92036223 0.92035638 0.92037447 0.92035213
 0.92036125 0.92036582 0.92036302 0.92037659 0.92037066 0.92037869
 0.92037531 0.92036215 0.92036481 0.92035902 0.92038317 0.92036109
 0.92037616 0.92036588 0.92037759 0.92036632 0.92037216 0.92036717
 0.92037345 0.92036576 0.92038041 0.9203715  0.92036231 0.92036405
 0.92036156 0.92037306 0.92035393 0.92035518 0.92036757 0.92036435
Epoch 20 RMSE: [0.91679736 0.9168108  0.91677763 0.9168097  0.91680124 0.9167976    | 20/40 [00:48<00:47,  2.36s/it]
 0.91680273 0.91679114 0.91679858 0.91680147 0.91679851 0.91680629
 0.91680192 0.91679561 0.91680131 0.91678853 0.91680357 0.9167927
 0.91679007 0.91681454 0.91679948 0.91680212 0.91679378 0.91678955
 0.91678719 0.91679889 0.9167983  0.91681408 0.91680477 0.91679167
 0.91680246 0.91679725 0.91679729 0.9168002  0.91680706 0.91681077
 0.91679052 0.9167938  0.91679187 0.91681619 0.9167924  0.91679686
 0.91680175 0.91679981 0.916804   0.91680747 0.91681498 0.91680007
 0.91679704 0.91679899 0.91679735 0.91679784 0.91680865 0.91679986
 0.91680297 0.91680382 0.91681324 0.91679804 0.91680704 0.91681089
 0.91681164 0.91679227 0.91679312 0.91678825 0.91680506 0.91678373
 0.91679208 0.91679687 0.91679319 0.91680679 0.91680066 0.91680845
 0.91680554 0.91679303 0.91679532 0.9167898  0.91681233 0.91679165
 0.91680591 0.91679738 0.91680716 0.91679653 0.916802   0.91679794
 0.91680275 0.91679611 0.9168098  0.91680122 0.91679272 0.9167949
 0.91679181 0.91680316 0.91678491 0.91678671 0.9167982  0.91679524
Epoch 20 RMSE: [0.91679736 0.9168108  0.91677763 0.9168097  0.91680124 0.9167976    | 20/40 [00:48<00:47,  2.36s/it] 
 0.91680273 0.91679114 0.91679858 0.91680147 0.91679851 0.91680629
 0.91680192 0.91679561 0.91680131 0.91678853 0.91680357 0.9167927
 0.91679007 0.91681454 0.91679948 0.91680212 0.91679378 0.91678955
 0.91678719 0.91679889 0.9167983  0.91681408 0.91680477 0.91679167
 0.91680246 0.91679725 0.91679729 0.9168002  0.91680706 0.91681077
 0.91679052 0.9167938  0.91679187 0.91681619 0.9167924  0.91679686
 0.91680175 0.91679981 0.916804   0.91680747 0.91681498 0.91680007
 0.91679704 0.91679899 0.91679735 0.91679784 0.91680865 0.91679986
 0.91680297 0.91680382 0.91681324 0.91679804 0.91680704 0.91681089
 0.91681164 0.91679227 0.91679312 0.91678825 0.91680506 0.91678373
 0.91679208 0.91679687 0.91679319 0.91680679 0.91680066 0.91680845
 0.91680554 0.91679303 0.91679532 0.9167898  0.91681233 0.91679165
 0.91680591 0.91679738 0.91680716 0.91679653 0.916802   0.91679794
 0.91680275 0.91679611 0.9168098  0.91680122 0.91679272 0.9167949
 0.91679181 0.91680316 0.91678491 0.91678671 0.9167982  0.91679524
Epoch 21 RMSE: [0.91288634 0.91289823 0.91286723 0.91289745 0.91288981 0.91288643   | 21/40 [00:50<00:44,  2.36s/it]
 0.9128911  0.91288024 0.91288735 0.9128891  0.91288708 0.91289446
 0.91289013 0.91288426 0.91288966 0.91287784 0.91289153 0.91288155
 0.91287905 0.91290158 0.91288794 0.91289021 0.91288232 0.91287877
 0.91287618 0.91288732 0.91288667 0.91290168 0.91289257 0.91288048
 0.91289076 0.91288575 0.91288588 0.91288878 0.91289542 0.9128987
 0.9128798  0.9128825  0.91288016 0.91290374 0.91288171 0.91288599
 0.91288986 0.91288848 0.91289212 0.91289528 0.91290243 0.91288905
 0.91288569 0.91288723 0.91288623 0.91288636 0.91289681 0.91288791
 0.9128906  0.91289159 0.91290042 0.91288679 0.91289487 0.91289864
 0.91289945 0.91288128 0.91288214 0.91287793 0.9128934  0.91287336
 0.91288113 0.91288609 0.91288154 0.91289499 0.91288885 0.91289632
 0.91289398 0.91288213 0.91288396 0.91287871 0.91289974 0.91288028
 0.91289392 0.91288699 0.9128948  0.91288487 0.91289004 0.91288658
 0.9128903  0.91288471 0.91289764 0.91288905 0.91288116 0.91288375
 0.91288023 0.91289138 0.91287401 0.91287621 0.9128869  0.91288384
Epoch 21 RMSE: [0.91288634 0.91289823 0.91286723 0.91289745 0.91288981 0.91288643   | 21/40 [00:50<00:44,  2.36s/it]
 0.9128911  0.91288024 0.91288735 0.9128891  0.91288708 0.91289446
 0.91289013 0.91288426 0.91288966 0.91287784 0.91289153 0.91288155
 0.91287905 0.91290158 0.91288794 0.91289021 0.91288232 0.91287877
 0.91287618 0.91288732 0.91288667 0.91290168 0.91289257 0.91288048
 0.91289076 0.91288575 0.91288588 0.91288878 0.91289542 0.9128987
 0.9128798  0.9128825  0.91288016 0.91290374 0.91288171 0.91288599
 0.91288986 0.91288848 0.91289212 0.91289528 0.91290243 0.91288905
 0.91288569 0.91288723 0.91288623 0.91288636 0.91289681 0.91288791
 0.9128906  0.91289159 0.91290042 0.91288679 0.91289487 0.91289864
 0.91289945 0.91288128 0.91288214 0.91287793 0.9128934  0.91287336
 0.91288113 0.91288609 0.91288154 0.91289499 0.91288885 0.91289632
 0.91289398 0.91288213 0.91288396 0.91287871 0.91289974 0.91288028
 0.91289392 0.91288699 0.9128948  0.91288487 0.91289004 0.91288658
 0.9128903  0.91288471 0.91289764 0.91288905 0.91288116 0.91288375
 0.91288023 0.91289138 0.91287401 0.91287621 0.9128869  0.91288384
Epoch 22 RMSE: [0.90878321 0.90879365 0.90876443 0.90879308 0.90878607 0.90878297   | 22/40 [00:53<00:42,  2.36s/it]
 0.90878753 0.90877714 0.90878393 0.90878485 0.90878358 0.90879038
 0.90878612 0.90878067 0.90878585 0.90877489 0.90878738 0.90877831
 0.90877571 0.90879683 0.90878424 0.90878624 0.90877857 0.90877572
 0.90877303 0.90878363 0.90878294 0.90879717 0.90878846 0.90877722
 0.90878694 0.90878216 0.90878241 0.90878522 0.9087916  0.90879471
 0.90877681 0.90877909 0.90877643 0.90879933 0.90877883 0.90878288
 0.90878604 0.90878479 0.90878845 0.9087911  0.90879779 0.90878555
 0.90878238 0.90878335 0.90878283 0.90878259 0.90879283 0.90878389
 0.90878621 0.90878722 0.90879575 0.90878326 0.9087908  0.9087942
 0.90879515 0.90877805 0.90877887 0.90877535 0.90878977 0.90877081
 0.90877778 0.90878299 0.90877778 0.90879106 0.90878492 0.908792
 0.9087903  0.90877897 0.90878051 0.90877554 0.90879516 0.90877682
 0.90878962 0.90878418 0.90879056 0.90878103 0.90878599 0.90878304
 0.90878581 0.90878106 0.90879333 0.90878476 0.90877748 0.90878039
 0.90877668 0.90878743 0.90877075 0.90877347 0.90878331 0.90878046
Epoch 22 RMSE: [0.90878321 0.90879365 0.90876443 0.90879308 0.90878607 0.90878297   | 22/40 [00:53<00:42,  2.36s/it]  
 0.90878753 0.90877714 0.90878393 0.90878485 0.90878358 0.90879038
 0.90878612 0.90878067 0.90878585 0.90877489 0.90878738 0.90877831
 0.90877571 0.90879683 0.90878424 0.90878624 0.90877857 0.90877572
 0.90877303 0.90878363 0.90878294 0.90879717 0.90878846 0.90877722
 0.90878694 0.90878216 0.90878241 0.90878522 0.9087916  0.90879471
 0.90877681 0.90877909 0.90877643 0.90879933 0.90877883 0.90878288
 0.90878604 0.90878479 0.90878845 0.9087911  0.90879779 0.90878555
 0.90878238 0.90878335 0.90878283 0.90878259 0.90879283 0.90878389
 0.90878621 0.90878722 0.90879575 0.90878326 0.9087908  0.9087942
 0.90879515 0.90877805 0.90877887 0.90877535 0.90878977 0.90877081
 0.90877778 0.90878299 0.90877778 0.90879106 0.90878492 0.908792
 0.9087903  0.90877897 0.90878051 0.90877554 0.90879516 0.90877682
 0.90878962 0.90878418 0.90879056 0.90878103 0.90878599 0.90878304
 0.90878581 0.90878106 0.90879333 0.90878476 0.90877748 0.90878039
 0.90877668 0.90878743 0.90877075 0.90877347 0.90878331 0.90878046
Epoch 23 RMSE: [0.90422053 0.90422986 0.90420248 0.90422944 0.904223   0.90422032   | 23/40 [00:55<00:40,  2.36s/it]
 0.90422445 0.90421455 0.90422124 0.90422135 0.90422064 0.90422695
 0.90422281 0.90421798 0.90422258 0.90421247 0.90422392 0.90421567
 0.90421317 0.90423275 0.90422125 0.90422307 0.9042156  0.90421342
 0.90421034 0.90422029 0.90422001 0.90423299 0.90422515 0.90421446
 0.90422382 0.90421928 0.90421941 0.90422233 0.90422836 0.90423127
 0.9042146  0.90421628 0.90421338 0.90423546 0.90421644 0.90422036
 0.90422263 0.90422177 0.9042252  0.90422756 0.90423391 0.90422269
 0.90421953 0.90422021 0.90422029 0.90421968 0.90422942 0.9042206
 0.90422219 0.90422371 0.90423179 0.90422032 0.90422729 0.9042307
 0.90423174 0.90421557 0.90421616 0.90421333 0.90422676 0.90420883
 0.90421514 0.9042205  0.90421473 0.90422767 0.90422153 0.90422851
 0.90422709 0.90421633 0.90421769 0.90421279 0.90423119 0.9042139
 0.90422596 0.90422192 0.90422702 0.90421791 0.90422274 0.90422038
 0.90422228 0.90421833 0.90422976 0.90422142 0.90421453 0.90421772
 0.90421375 0.90422426 0.90420816 0.9042113  0.90422056 0.90421763
Epoch 23 RMSE: [0.90422053 0.90422986 0.90420248 0.90422944 0.904223   0.90422032   | 23/40 [00:55<00:40,  2.36s/it]
 0.90422445 0.90421455 0.90422124 0.90422135 0.90422064 0.90422695
 0.90422281 0.90421798 0.90422258 0.90421247 0.90422392 0.90421567
 0.90421317 0.90423275 0.90422125 0.90422307 0.9042156  0.90421342
 0.90421034 0.90422029 0.90422001 0.90423299 0.90422515 0.90421446
 0.90422382 0.90421928 0.90421941 0.90422233 0.90422836 0.90423127
 0.9042146  0.90421628 0.90421338 0.90423546 0.90421644 0.90422036
 0.90422263 0.90422177 0.9042252  0.90422756 0.90423391 0.90422269
 0.90421953 0.90422021 0.90422029 0.90421968 0.90422942 0.9042206
 0.90422219 0.90422371 0.90423179 0.90422032 0.90422729 0.9042307
 0.90423174 0.90421557 0.90421616 0.90421333 0.90422676 0.90420883
 0.90421514 0.9042205  0.90421473 0.90422767 0.90422153 0.90422851
 0.90422709 0.90421633 0.90421769 0.90421279 0.90423119 0.9042139
 0.90422596 0.90422192 0.90422702 0.90421791 0.90422274 0.90422038
 0.90422228 0.90421833 0.90422976 0.90422142 0.90421453 0.90421772
 0.90421375 0.90422426 0.90420816 0.9042113  0.90422056 0.90421763
Epoch 24 RMSE: [0.89944541 0.89945349 0.89942796 0.89945335 0.89944751 0.89944501   | 24/40 [00:58<00:38,  2.39s/it]
 0.89944878 0.89943965 0.899446   0.89944534 0.89944544 0.89945125
 0.899447   0.8994426  0.89944699 0.89943749 0.89944794 0.89944042
 0.89943797 0.89945648 0.8994455  0.89944736 0.89944025 0.89943837
 0.89943513 0.89944477 0.8994446  0.89945708 0.89944921 0.89943937
 0.89944796 0.89944377 0.89944392 0.89944665 0.89945284 0.89945524
 0.89943949 0.89944083 0.89943778 0.89945916 0.89944153 0.89944516
 0.89944711 0.89944631 0.89944975 0.89945155 0.89945769 0.89944752
 0.89944431 0.89944458 0.89944509 0.89944413 0.89945366 0.89944493
 0.89944639 0.89944775 0.89945525 0.899445   0.89945131 0.89945457
 0.89945558 0.89944056 0.89944123 0.89943846 0.8994512  0.89943387
 0.89944006 0.89944532 0.8994392  0.8994519  0.89944585 0.89945251
 0.89945128 0.89944128 0.89944248 0.89943771 0.8994548  0.89943861
 0.89944998 0.89944716 0.89945072 0.89944257 0.89944696 0.89944494
 0.89944599 0.89944286 0.89945349 0.8994455  0.89943896 0.89944237
 0.89943852 0.89944858 0.89943303 0.89943647 0.8994449  0.89944234
Epoch 24 RMSE: [0.89944541 0.89945349 0.89942796 0.89945335 0.89944751 0.89944501   | 24/40 [00:58<00:38,  2.39s/it]
 0.89944878 0.89943965 0.899446   0.89944534 0.89944544 0.89945125
 0.899447   0.8994426  0.89944699 0.89943749 0.89944794 0.89944042
 0.89943797 0.89945648 0.8994455  0.89944736 0.89944025 0.89943837
 0.89943513 0.89944477 0.8994446  0.89945708 0.89944921 0.89943937
 0.89944796 0.89944377 0.89944392 0.89944665 0.89945284 0.89945524
 0.89943949 0.89944083 0.89943778 0.89945916 0.89944153 0.89944516
 0.89944711 0.89944631 0.89944975 0.89945155 0.89945769 0.89944752
 0.89944431 0.89944458 0.89944509 0.89944413 0.89945366 0.89944493
 0.89944639 0.89944775 0.89945525 0.899445   0.89945131 0.89945457
 0.89945558 0.89944056 0.89944123 0.89943846 0.8994512  0.89943387
 0.89944006 0.89944532 0.8994392  0.8994519  0.89944585 0.89945251
 0.89945128 0.89944128 0.89944248 0.89943771 0.8994548  0.89943861
 0.89944998 0.89944716 0.89945072 0.89944257 0.89944696 0.89944494
 0.89944599 0.89944286 0.89945349 0.8994455  0.89943896 0.89944237
 0.89943852 0.89944858 0.89943303 0.89943647 0.8994449  0.89944234
Epoch 25 RMSE: [0.8939617  0.89396902 0.89394454 0.89396896 0.89396355 0.89396113   | 25/40 [01:00<00:35,  2.38s/it]
 0.8939648  0.89395604 0.89396229 0.89396108 0.89396134 0.89396702
 0.89396299 0.89395879 0.89396286 0.89395418 0.89396375 0.89395685
 0.8939543  0.89397161 0.89396178 0.89396314 0.8939562  0.89395505
 0.89395179 0.89396073 0.89396054 0.89397242 0.89396513 0.8939557
 0.89396393 0.89395993 0.89395987 0.89396286 0.89396866 0.89397102
 0.89395619 0.89395716 0.89395402 0.8939746  0.89395806 0.89396162
 0.89396294 0.89396246 0.8939657  0.89396737 0.89397308 0.89396356
 0.8939605  0.89396057 0.89396137 0.89396035 0.89396944 0.89396085
 0.89396204 0.8939634  0.89397064 0.89396123 0.89396705 0.89397009
 0.89397127 0.89395682 0.89395731 0.89395543 0.89396713 0.89395092
 0.89395642 0.89396178 0.89395519 0.89396772 0.89396193 0.89396804
 0.89396738 0.89395783 0.89395883 0.89395421 0.89397033 0.89395483
 0.89396561 0.89396384 0.89396653 0.89395857 0.89396291 0.89396119
 0.89396164 0.89395909 0.89396916 0.89396109 0.8939552  0.89395868
 0.89395448 0.89396461 0.89394963 0.89395322 0.8939614  0.89395862
Epoch 25 RMSE: [0.8939617  0.89396902 0.89394454 0.89396896 0.89396355 0.89396113   | 25/40 [01:00<00:35,  2.38s/it] 
 0.8939648  0.89395604 0.89396229 0.89396108 0.89396134 0.89396702
 0.89396299 0.89395879 0.89396286 0.89395418 0.89396375 0.89395685
 0.8939543  0.89397161 0.89396178 0.89396314 0.8939562  0.89395505
 0.89395179 0.89396073 0.89396054 0.89397242 0.89396513 0.8939557
 0.89396393 0.89395993 0.89395987 0.89396286 0.89396866 0.89397102
 0.89395619 0.89395716 0.89395402 0.8939746  0.89395806 0.89396162
 0.89396294 0.89396246 0.8939657  0.89396737 0.89397308 0.89396356
 0.8939605  0.89396057 0.89396137 0.89396035 0.89396944 0.89396085
 0.89396204 0.8939634  0.89397064 0.89396123 0.89396705 0.89397009
 0.89397127 0.89395682 0.89395731 0.89395543 0.89396713 0.89395092
 0.89395642 0.89396178 0.89395519 0.89396772 0.89396193 0.89396804
 0.89396738 0.89395783 0.89395883 0.89395421 0.89397033 0.89395483
 0.89396561 0.89396384 0.89396653 0.89395857 0.89396291 0.89396119
 0.89396164 0.89395909 0.89396916 0.89396109 0.8939552  0.89395868
 0.89395448 0.89396461 0.89394963 0.89395322 0.8939614  0.89395862
Epoch 26 RMSE: [0.88813915 0.88814554 0.88812237 0.88814555 0.88814064 0.88813834   | 26/40 [01:02<00:33,  2.39s/it]
 0.88814185 0.88813351 0.88813954 0.8881378  0.88813859 0.88814365
 0.8881398  0.88813593 0.88813996 0.88813158 0.88814047 0.88813423
 0.88813165 0.88814795 0.8881389  0.8881399  0.88813335 0.88813264
 0.8881292  0.88813777 0.88813767 0.88814902 0.88814186 0.88813312
 0.88814095 0.8881371  0.88813693 0.88813993 0.88814556 0.8881475
 0.88813369 0.88813441 0.8881312  0.88815094 0.8881356  0.88813904
 0.88813984 0.88813946 0.88814259 0.88814413 0.88814973 0.88814092
 0.88813763 0.88813751 0.88813871 0.88813734 0.88814611 0.8881377
 0.88813875 0.8881401  0.88814686 0.88813846 0.88814378 0.88814658
 0.88814785 0.88813425 0.88813473 0.88813305 0.88814419 0.88812872
 0.88813375 0.88813917 0.88813233 0.88814456 0.88813878 0.88814465
 0.88814438 0.88813536 0.88813623 0.88813153 0.88814687 0.88813212
 0.88814238 0.88814128 0.88814313 0.88813559 0.8881398  0.88813829
 0.88813821 0.88813624 0.88814575 0.88813772 0.88813232 0.8881359
 0.88813164 0.88814152 0.88812695 0.88813106 0.8881383  0.88813582
Epoch 26 RMSE: [0.88813915 0.88814554 0.88812237 0.88814555 0.88814064 0.88813834   | 26/40 [01:02<00:33,  2.39s/it]  
 0.88814185 0.88813351 0.88813954 0.8881378  0.88813859 0.88814365
 0.8881398  0.88813593 0.88813996 0.88813158 0.88814047 0.88813423
 0.88813165 0.88814795 0.8881389  0.8881399  0.88813335 0.88813264
 0.8881292  0.88813777 0.88813767 0.88814902 0.88814186 0.88813312
 0.88814095 0.8881371  0.88813693 0.88813993 0.88814556 0.8881475
 0.88813369 0.88813441 0.8881312  0.88815094 0.8881356  0.88813904
 0.88813984 0.88813946 0.88814259 0.88814413 0.88814973 0.88814092
 0.88813763 0.88813751 0.88813871 0.88813734 0.88814611 0.8881377
 0.88813875 0.8881401  0.88814686 0.88813846 0.88814378 0.88814658
 0.88814785 0.88813425 0.88813473 0.88813305 0.88814419 0.88812872
 0.88813375 0.88813917 0.88813233 0.88814456 0.88813878 0.88814465
 0.88814438 0.88813536 0.88813623 0.88813153 0.88814687 0.88813212
 0.88814238 0.88814128 0.88814313 0.88813559 0.8881398  0.88813829
 0.88813821 0.88813624 0.88814575 0.88813772 0.88813232 0.8881359
 0.88813164 0.88814152 0.88812695 0.88813106 0.8881383  0.88813582
Epoch 27 RMSE: [0.88210562 0.88211126 0.88208923 0.88211131 0.88210686 0.88210478   | 27/40 [01:05<00:31,  2.39s/it]
 0.88210805 0.8821001  0.88210595 0.88210382 0.88210477 0.88210963
 0.88210586 0.88210238 0.8821062  0.8820982  0.88210642 0.88210084
 0.88209819 0.88211345 0.88210509 0.88210605 0.88209968 0.88209927
 0.88209582 0.88210388 0.8821039  0.88211464 0.88210788 0.88209959
 0.88210712 0.88210345 0.88210318 0.88210625 0.88211177 0.88211355
 0.88210046 0.88210083 0.88209761 0.88211654 0.88210219 0.88210552
 0.88210585 0.88210574 0.88210875 0.8821101  0.88211536 0.88210732
 0.88210405 0.88210369 0.88210507 0.88210357 0.88211216 0.88210397
 0.8821046  0.88210594 0.88211244 0.88210475 0.88210974 0.88211234
 0.88211377 0.88210078 0.88210134 0.88209997 0.88211021 0.88209584
 0.88210022 0.88210559 0.88209865 0.88211054 0.88210499 0.88211031
 0.8821106  0.88210202 0.88210277 0.88209825 0.88211251 0.88209844
 0.88210823 0.88210803 0.88210913 0.88210189 0.88210589 0.8821047
 0.88210414 0.88210258 0.88211149 0.88210375 0.88209868 0.88210237
 0.882098   0.8821077  0.88209366 0.88209777 0.88210481 0.88210228
Epoch 27 RMSE: [0.88210562 0.88211126 0.88208923 0.88211131 0.88210686 0.88210478   | 27/40 [01:05<00:31,  2.39s/it]
 0.88210805 0.8821001  0.88210595 0.88210382 0.88210477 0.88210963
 0.88210586 0.88210238 0.8821062  0.8820982  0.88210642 0.88210084
 0.88209819 0.88211345 0.88210509 0.88210605 0.88209968 0.88209927
 0.88209582 0.88210388 0.8821039  0.88211464 0.88210788 0.88209959
 0.88210712 0.88210345 0.88210318 0.88210625 0.88211177 0.88211355
 0.88210046 0.88210083 0.88209761 0.88211654 0.88210219 0.88210552
 0.88210585 0.88210574 0.88210875 0.8821101  0.88211536 0.88210732
 0.88210405 0.88210369 0.88210507 0.88210357 0.88211216 0.88210397
 0.8821046  0.88210594 0.88211244 0.88210475 0.88210974 0.88211234
 0.88211377 0.88210078 0.88210134 0.88209997 0.88211021 0.88209584
 0.88210022 0.88210559 0.88209865 0.88211054 0.88210499 0.88211031
 0.8821106  0.88210202 0.88210277 0.88209825 0.88211251 0.88209844
 0.88210823 0.88210803 0.88210913 0.88210189 0.88210589 0.8821047
 0.88210414 0.88210258 0.88211149 0.88210375 0.88209868 0.88210237
 0.882098   0.8821077  0.88209366 0.88209777 0.88210481 0.88210228
Epoch 28 RMSE: [0.87565692 0.87566189 0.87564095 0.87566218 0.87565795 0.87565605   | 28/40 [01:07<00:28,  2.39s/it]
 0.87565909 0.87565153 0.87565725 0.87565463 0.87565602 0.87566048
 0.87565678 0.87565358 0.87565725 0.87564969 0.87565727 0.87565217
 0.8756495  0.875664   0.87565625 0.87565704 0.87565086 0.8756508
 0.8756472  0.87565495 0.87565502 0.87566519 0.87565886 0.87565115
 0.87565807 0.87565482 0.87565438 0.87565743 0.87566278 0.87566426
 0.87565197 0.87565215 0.87564886 0.87566716 0.87565374 0.87565689
 0.87565681 0.87565688 0.87565987 0.87566099 0.87566612 0.87565845
 0.87565536 0.87565489 0.87565651 0.87565477 0.87566306 0.87565502
 0.87565545 0.87565673 0.87566285 0.87565596 0.8756605  0.87566306
 0.87566459 0.87565221 0.87565271 0.87565164 0.87566135 0.8756476
 0.87565177 0.87565694 0.87564981 0.87566128 0.87565583 0.87566112
 0.87566154 0.87565343 0.8756542  0.87564977 0.87566313 0.87564967
 0.87565908 0.87565954 0.87565998 0.87565301 0.87565698 0.875656
 0.87565497 0.87565387 0.87566218 0.87565457 0.87564996 0.87565371
 0.87564916 0.87565878 0.87564516 0.87564938 0.87565599 0.87565357
Epoch 28 RMSE: [0.87565692 0.87566189 0.87564095 0.87566218 0.87565795 0.87565605   | 28/40 [01:07<00:28,  2.39s/it]   
 0.87565909 0.87565153 0.87565725 0.87565463 0.87565602 0.87566048
 0.87565678 0.87565358 0.87565725 0.87564969 0.87565727 0.87565217
 0.8756495  0.875664   0.87565625 0.87565704 0.87565086 0.8756508
 0.8756472  0.87565495 0.87565502 0.87566519 0.87565886 0.87565115
 0.87565807 0.87565482 0.87565438 0.87565743 0.87566278 0.87566426
 0.87565197 0.87565215 0.87564886 0.87566716 0.87565374 0.87565689
 0.87565681 0.87565688 0.87565987 0.87566099 0.87566612 0.87565845
 0.87565536 0.87565489 0.87565651 0.87565477 0.87566306 0.87565502
 0.87565545 0.87565673 0.87566285 0.87565596 0.8756605  0.87566306
 0.87566459 0.87565221 0.87565271 0.87565164 0.87566135 0.8756476
 0.87565177 0.87565694 0.87564981 0.87566128 0.87565583 0.87566112
 0.87566154 0.87565343 0.8756542  0.87564977 0.87566313 0.87564967
 0.87565908 0.87565954 0.87565998 0.87565301 0.87565698 0.875656
 0.87565497 0.87565387 0.87566218 0.87565457 0.87564996 0.87565371
 0.87564916 0.87565878 0.87564516 0.87564938 0.87565599 0.87565357
Epoch 29 RMSE: [0.86889631 0.86890086 0.86888072 0.86890107 0.86889721 0.8688955█▎  | 29/40 [01:10<00:26,  2.40s/it]
 0.86889828 0.86889117 0.86889675 0.86889368 0.86889532 0.86889949
 0.86889593 0.86889294 0.86889654 0.86888923 0.86889628 0.86889179
 0.86888901 0.86890262 0.8688956  0.86889617 0.86889024 0.86889047
 0.86888686 0.86889417 0.86889428 0.86890411 0.86889792 0.86889061
 0.86889721 0.868894   0.86889356 0.86889675 0.8689019  0.8689032
 0.86889165 0.86889161 0.86888831 0.86890585 0.86889323 0.86889634
 0.86889585 0.8688961  0.86889915 0.86889995 0.86890494 0.86889787
 0.86889458 0.86889406 0.86889589 0.86889401 0.86890207 0.86889425
 0.86889451 0.8688958  0.86890159 0.86889536 0.86889957 0.86890191
 0.86890345 0.86889177 0.86889218 0.86889121 0.86890034 0.86888756
 0.86889132 0.86889642 0.8688892  0.86890046 0.86889511 0.86889996
 0.86890076 0.86889303 0.86889375 0.86888928 0.86890196 0.86888924
 0.86889784 0.86889914 0.86889884 0.86889234 0.86889602 0.86889544
 0.8688939  0.86889325 0.86890111 0.8688936  0.86888929 0.86889311
 0.86888863 0.868898   0.86888475 0.86888913 0.86889527 0.86889298
Epoch 29 RMSE: [0.86889631 0.86890086 0.86888072 0.86890107 0.86889721 0.8688955█▎  | 29/40 [01:10<00:26,  2.40s/it]
 0.86889828 0.86889117 0.86889675 0.86889368 0.86889532 0.86889949
 0.86889593 0.86889294 0.86889654 0.86888923 0.86889628 0.86889179
 0.86888901 0.86890262 0.8688956  0.86889617 0.86889024 0.86889047
 0.86888686 0.86889417 0.86889428 0.86890411 0.86889792 0.86889061
 0.86889721 0.868894   0.86889356 0.86889675 0.8689019  0.8689032
 0.86889165 0.86889161 0.86888831 0.86890585 0.86889323 0.86889634
 0.86889585 0.8688961  0.86889915 0.86889995 0.86890494 0.86889787
 0.86889458 0.86889406 0.86889589 0.86889401 0.86890207 0.86889425
 0.86889451 0.8688958  0.86890159 0.86889536 0.86889957 0.86890191
 0.86890345 0.86889177 0.86889218 0.86889121 0.86890034 0.86888756
 0.86889132 0.86889642 0.8688892  0.86890046 0.86889511 0.86889996
 0.86890076 0.86889303 0.86889375 0.86888928 0.86890196 0.86888924
 0.86889784 0.86889914 0.86889884 0.86889234 0.86889602 0.86889544
 0.8688939  0.86889325 0.86890111 0.8688936  0.86888929 0.86889311
 0.86888863 0.868898   0.86888475 0.86888913 0.86889527 0.86889298
Epoch 30 RMSE: [0.86192812 0.86193219 0.86191303 0.86193246 0.86192887 0.86192735▌  | 30/40 [01:12<00:23,  2.39s/it]
 0.86192998 0.86192315 0.86192862 0.86192525 0.86192709 0.86193092
 0.86192752 0.86192477 0.86192829 0.86192116 0.86192787 0.86192382
 0.86192098 0.86193383 0.86192743 0.8619278  0.8619221  0.86192256
 0.86191887 0.86192581 0.86192609 0.86193541 0.86192946 0.86192254
 0.86192879 0.86192587 0.86192527 0.8619285  0.86193354 0.86193463
 0.86192368 0.86192349 0.86192022 0.86193713 0.8619252  0.86192839
 0.86192746 0.86192779 0.86193076 0.86193148 0.86193627 0.86192961
 0.86192651 0.8619257  0.86192776 0.86192582 0.86193364 0.86192596
 0.86192595 0.86192726 0.86193289 0.86192717 0.86193094 0.8619332
 0.86193486 0.86192368 0.86192413 0.86192332 0.86193194 0.86191989
 0.86192321 0.86192831 0.86192107 0.86193196 0.86192682 0.86193139
 0.86193237 0.86192516 0.86192579 0.86192134 0.86193331 0.86192106
 0.86192917 0.86193116 0.86193034 0.86192405 0.86192776 0.86192727
 0.86192533 0.86192518 0.86193249 0.86192515 0.86192107 0.86192498
 0.86192046 0.86192969 0.86191688 0.8619213  0.861927   0.86192464
Epoch 30 RMSE: [0.86192812 0.86193219 0.86191303 0.86193246 0.86192887 0.86192735▌  | 30/40 [01:12<00:23,  2.39s/it]
 0.86192998 0.86192315 0.86192862 0.86192525 0.86192709 0.86193092
 0.86192752 0.86192477 0.86192829 0.86192116 0.86192787 0.86192382
 0.86192098 0.86193383 0.86192743 0.8619278  0.8619221  0.86192256
 0.86191887 0.86192581 0.86192609 0.86193541 0.86192946 0.86192254
 0.86192879 0.86192587 0.86192527 0.8619285  0.86193354 0.86193463
 0.86192368 0.86192349 0.86192022 0.86193713 0.8619252  0.86192839
 0.86192746 0.86192779 0.86193076 0.86193148 0.86193627 0.86192961
 0.86192651 0.8619257  0.86192776 0.86192582 0.86193364 0.86192596
 0.86192595 0.86192726 0.86193289 0.86192717 0.86193094 0.8619332
 0.86193486 0.86192368 0.86192413 0.86192332 0.86193194 0.86191989
 0.86192321 0.86192831 0.86192107 0.86193196 0.86192682 0.86193139
 0.86193237 0.86192516 0.86192579 0.86192134 0.86193331 0.86192106
 0.86192917 0.86193116 0.86193034 0.86192405 0.86192776 0.86192727
 0.86192533 0.86192518 0.86193249 0.86192515 0.86192107 0.86192498
 0.86192046 0.86192969 0.86191688 0.8619213  0.861927   0.86192464
Epoch 31 RMSE: [0.85460494 0.85460845 0.85459006 0.85460887 0.85460553 0.85460401▊  | 31/40 [01:15<00:22,  2.48s/it]
 0.85460646 0.85459995 0.85460541 0.85460188 0.85460372 0.85460731
 0.854604   0.85460138 0.85460479 0.8545981  0.85460427 0.8546007
 0.85459779 0.8546099  0.85460408 0.85460425 0.85459882 0.8545994
 0.85459572 0.85460232 0.85460275 0.85461164 0.85460591 0.8545995
 0.85460535 0.85460262 0.85460197 0.85460507 0.85460999 0.85461095
 0.8546007  0.85460036 0.85459693 0.8546133  0.85460194 0.85460515
 0.85460396 0.85460441 0.85460736 0.85460799 0.85461262 0.85460627
 0.8546031  0.85460228 0.85460449 0.85460242 0.85461011 0.85460262
 0.8546025  0.85460376 0.85460904 0.8546038  0.85460733 0.85460953
 0.85461131 0.85460064 0.85460087 0.85460028 0.85460846 0.85459713
 0.85460006 0.85460517 0.85459777 0.85460837 0.8546034  0.85460774
 0.85460892 0.85460199 0.85460267 0.85459819 0.8546096  0.8545978
 0.85460559 0.85460796 0.85460667 0.85460072 0.85460433 0.85460402
 0.85460173 0.85460189 0.85460865 0.85460155 0.85459776 0.85460177
 0.85459706 0.85460631 0.85459377 0.85459838 0.85460372 0.85460149
Epoch 31 RMSE: [0.85460494 0.85460845 0.85459006 0.85460887 0.85460553 0.85460401▊  | 31/40 [01:15<00:22,  2.48s/it]   
 0.85460646 0.85459995 0.85460541 0.85460188 0.85460372 0.85460731
 0.854604   0.85460138 0.85460479 0.8545981  0.85460427 0.8546007
 0.85459779 0.8546099  0.85460408 0.85460425 0.85459882 0.8545994
 0.85459572 0.85460232 0.85460275 0.85461164 0.85460591 0.8545995
 0.85460535 0.85460262 0.85460197 0.85460507 0.85460999 0.85461095
 0.8546007  0.85460036 0.85459693 0.8546133  0.85460194 0.85460515
 0.85460396 0.85460441 0.85460736 0.85460799 0.85461262 0.85460627
 0.8546031  0.85460228 0.85460449 0.85460242 0.85461011 0.85460262
 0.8546025  0.85460376 0.85460904 0.8546038  0.85460733 0.85460953
 0.85461131 0.85460064 0.85460087 0.85460028 0.85460846 0.85459713
 0.85460006 0.85460517 0.85459777 0.85460837 0.8546034  0.85460774
 0.85460892 0.85460199 0.85460267 0.85459819 0.8546096  0.8545978
 0.85460559 0.85460796 0.85460667 0.85460072 0.85460433 0.85460402
 0.85460173 0.85460189 0.85460865 0.85460155 0.85459776 0.85460177
 0.85459706 0.85460631 0.85459377 0.85459838 0.85460372 0.85460149
Epoch 32 RMSE: [0.84699439 0.84699767 0.8469799  0.84699794 0.84699484 0.84699372█  | 32/40 [01:17<00:19,  2.46s/it]
 0.84699584 0.8469897  0.84699497 0.8469911  0.84699306 0.84699638
 0.84699326 0.84699088 0.84699428 0.84698774 0.8469936  0.84699039
 0.84698744 0.84699885 0.84699355 0.84699366 0.8469883  0.84698922
 0.84698555 0.84699181 0.84699215 0.84700073 0.84699522 0.8469891
 0.84699478 0.846992   0.84699133 0.8469945  0.84699923 0.84700008
 0.8469903  0.8469898  0.84698658 0.84700231 0.84699156 0.8469947
 0.84699318 0.84699383 0.84699687 0.84699715 0.84700179 0.84699568
 0.84699266 0.84699176 0.84699398 0.84699188 0.84699932 0.84699208
 0.84699184 0.84699306 0.84699801 0.8469934  0.84699651 0.84699856
 0.84700039 0.8469902  0.8469905  0.84698998 0.8469977  0.84698715
 0.84698967 0.84699458 0.84698729 0.84699746 0.84699281 0.84699688
 0.84699831 0.84699173 0.8469924  0.84698786 0.84699875 0.84698738
 0.84699483 0.8469976  0.84699594 0.84699025 0.8469936  0.84699353
 0.84699101 0.84699135 0.84699786 0.84699079 0.84698747 0.84699128
 0.84698664 0.84699562 0.84698352 0.84698809 0.84699317 0.84699104
Epoch 32 RMSE: [0.84699439 0.84699767 0.8469799  0.84699794 0.84699484 0.84699372█  | 32/40 [01:17<00:19,  2.46s/it]
 0.84699584 0.8469897  0.84699497 0.8469911  0.84699306 0.84699638
 0.84699326 0.84699088 0.84699428 0.84698774 0.8469936  0.84699039
 0.84698744 0.84699885 0.84699355 0.84699366 0.8469883  0.84698922
 0.84698555 0.84699181 0.84699215 0.84700073 0.84699522 0.8469891
 0.84699478 0.846992   0.84699133 0.8469945  0.84699923 0.84700008
 0.8469903  0.8469898  0.84698658 0.84700231 0.84699156 0.8469947
 0.84699318 0.84699383 0.84699687 0.84699715 0.84700179 0.84699568
 0.84699266 0.84699176 0.84699398 0.84699188 0.84699932 0.84699208
 0.84699184 0.84699306 0.84699801 0.8469934  0.84699651 0.84699856
 0.84700039 0.8469902  0.8469905  0.84698998 0.8469977  0.84698715
 0.84698967 0.84699458 0.84698729 0.84699746 0.84699281 0.84699688
 0.84699831 0.84699173 0.8469924  0.84698786 0.84699875 0.84698738
 0.84699483 0.8469976  0.84699594 0.84699025 0.8469936  0.84699353
 0.84699101 0.84699135 0.84699786 0.84699079 0.84698747 0.84699128
 0.84698664 0.84699562 0.84698352 0.84698809 0.84699317 0.84699104
Epoch 33 RMSE: [0.83875367 0.83875651 0.83873955 0.83875691 0.83875405 0.83875292█▎ | 33/40 [01:19<00:17,  2.43s/it]
 0.83875482 0.83874898 0.83875422 0.83875015 0.83875224 0.83875543
 0.8387522  0.83875012 0.8387534  0.83874705 0.83875253 0.83874978
 0.83874684 0.83875766 0.8387528  0.8387526  0.83874762 0.83874864
 0.83874501 0.83875084 0.83875135 0.83875956 0.83875426 0.83874856
 0.8387537  0.83875149 0.83875047 0.83875362 0.83875843 0.83875902
 0.83874978 0.83874908 0.83874593 0.83876096 0.83875086 0.83875414
 0.83875214 0.83875291 0.83875595 0.83875619 0.83876049 0.83875497
 0.83875172 0.83875078 0.83875316 0.83875099 0.83875813 0.83875121
 0.83875075 0.83875199 0.83875683 0.83875258 0.83875547 0.83875734
 0.83875926 0.83874945 0.83874978 0.83874942 0.83875676 0.8387468
 0.83874893 0.8387539  0.8387466  0.83875661 0.83875198 0.83875579
 0.83875737 0.83875116 0.83875181 0.83874742 0.83875752 0.83874663
 0.8387537  0.83875691 0.83875493 0.83874943 0.83875288 0.83875274
 0.83874997 0.83875062 0.83875674 0.83874987 0.83874648 0.83875053
 0.83874586 0.83875478 0.83874302 0.83874753 0.83875232 0.83875024
Epoch 33 RMSE: [0.83875367 0.83875651 0.83873955 0.83875691 0.83875405 0.83875292█▎ | 33/40 [01:19<00:17,  2.43s/it]
 0.83875482 0.83874898 0.83875422 0.83875015 0.83875224 0.83875543
 0.8387522  0.83875012 0.8387534  0.83874705 0.83875253 0.83874978
 0.83874684 0.83875766 0.8387528  0.8387526  0.83874762 0.83874864
 0.83874501 0.83875084 0.83875135 0.83875956 0.83875426 0.83874856
 0.8387537  0.83875149 0.83875047 0.83875362 0.83875843 0.83875902
 0.83874978 0.83874908 0.83874593 0.83876096 0.83875086 0.83875414
 0.83875214 0.83875291 0.83875595 0.83875619 0.83876049 0.83875497
 0.83875172 0.83875078 0.83875316 0.83875099 0.83875813 0.83875121
 0.83875075 0.83875199 0.83875683 0.83875258 0.83875547 0.83875734
 0.83875926 0.83874945 0.83874978 0.83874942 0.83875676 0.8387468
 0.83874893 0.8387539  0.8387466  0.83875661 0.83875198 0.83875579
 0.83875737 0.83875116 0.83875181 0.83874742 0.83875752 0.83874663
 0.8387537  0.83875691 0.83875493 0.83874943 0.83875288 0.83875274
 0.83874997 0.83875062 0.83875674 0.83874987 0.83874648 0.83875053
 0.83874586 0.83875478 0.83874302 0.83874753 0.83875232 0.83875024
Epoch 34 RMSE: [0.83035544 0.83035804 0.83034167 0.83035847 0.83035574 0.83035477█▌ | 34/40 [01:22<00:14,  2.40s/it]
 0.83035649 0.83035096 0.83035615 0.83035188 0.830354   0.83035688
 0.830354   0.83035198 0.83035515 0.83034898 0.83035412 0.83035166
 0.83034873 0.83035899 0.83035465 0.83035423 0.83034937 0.83035063
 0.83034704 0.83035257 0.83035301 0.83036108 0.83035596 0.83035061
 0.8303553  0.83035321 0.83035214 0.83035538 0.83035994 0.83036051
 0.83035182 0.83035092 0.83034791 0.83036238 0.8303527  0.83035591
 0.83035386 0.83035462 0.83035766 0.83035786 0.83036194 0.83035665
 0.83035356 0.83035258 0.83035501 0.83035271 0.83035977 0.83035306
 0.83035243 0.8303536  0.83035819 0.83035435 0.83035698 0.83035884
 0.83036084 0.83035151 0.83035163 0.83035135 0.83035833 0.83034914
 0.83035078 0.83035569 0.83034846 0.83035808 0.83035365 0.8303573
 0.83035897 0.83035315 0.83035383 0.83034943 0.83035898 0.83034858
 0.8303552  0.8303588  0.8303565  0.83035125 0.83035462 0.8303546
 0.83035159 0.83035255 0.83035809 0.83035158 0.83034838 0.8303524
 0.83034775 0.83035644 0.83034512 0.83034972 0.83035411 0.83035205
Epoch 34 RMSE: [0.83035544 0.83035804 0.83034167 0.83035847 0.83035574 0.83035477█▌ | 34/40 [01:22<00:14,  2.40s/it]  
 0.83035649 0.83035096 0.83035615 0.83035188 0.830354   0.83035688
 0.830354   0.83035198 0.83035515 0.83034898 0.83035412 0.83035166
 0.83034873 0.83035899 0.83035465 0.83035423 0.83034937 0.83035063
 0.83034704 0.83035257 0.83035301 0.83036108 0.83035596 0.83035061
 0.8303553  0.83035321 0.83035214 0.83035538 0.83035994 0.83036051
 0.83035182 0.83035092 0.83034791 0.83036238 0.8303527  0.83035591
 0.83035386 0.83035462 0.83035766 0.83035786 0.83036194 0.83035665
 0.83035356 0.83035258 0.83035501 0.83035271 0.83035977 0.83035306
 0.83035243 0.8303536  0.83035819 0.83035435 0.83035698 0.83035884
 0.83036084 0.83035151 0.83035163 0.83035135 0.83035833 0.83034914
 0.83035078 0.83035569 0.83034846 0.83035808 0.83035365 0.8303573
 0.83035897 0.83035315 0.83035383 0.83034943 0.83035898 0.83034858
 0.8303552  0.8303588  0.8303565  0.83035125 0.83035462 0.8303546
 0.83035159 0.83035255 0.83035809 0.83035158 0.83034838 0.8303524
 0.83034775 0.83035644 0.83034512 0.83034972 0.83035411 0.83035205
Epoch 35 RMSE: [0.8214729  0.82147524 0.82145939 0.82147562 0.82147307 0.82147219█▊ | 35/40 [01:24<00:11,  2.40s/it]
 0.8214738  0.82146852 0.82147354 0.82146919 0.82147131 0.8214741
 0.82147121 0.82146941 0.82147254 0.82146649 0.82147127 0.82146921
 0.82146623 0.82147592 0.82147201 0.82147148 0.8214668  0.82146827
 0.82146472 0.82146992 0.82147043 0.82147809 0.82147317 0.82146808
 0.8214725  0.82147075 0.82146953 0.82147277 0.82147715 0.82147767
 0.82146933 0.82146842 0.82146546 0.82147929 0.82147015 0.82147345
 0.82147114 0.82147193 0.82147493 0.82147502 0.82147899 0.82147406
 0.82147082 0.82146983 0.8214724  0.82147011 0.82147688 0.82147045
 0.8214698  0.82147083 0.8214753  0.82147177 0.82147411 0.82147595
 0.82147806 0.82146902 0.82146919 0.82146889 0.82147551 0.82146696
 0.82146833 0.82147311 0.82146583 0.82147531 0.82147094 0.82147445
 0.82147624 0.82147083 0.82147144 0.82146699 0.82147605 0.82146607
 0.82147232 0.82147618 0.82147384 0.82146869 0.82147197 0.821472
 0.82146881 0.82147009 0.82147523 0.82146891 0.82146581 0.82146996
 0.82146526 0.8214737  0.82146278 0.82146736 0.82147153 0.82146937
Epoch 35 RMSE: [0.8214729  0.82147524 0.82145939 0.82147562 0.82147307 0.82147219█▊ | 35/40 [01:24<00:11,  2.40s/it]
 0.8214738  0.82146852 0.82147354 0.82146919 0.82147131 0.8214741
 0.82147121 0.82146941 0.82147254 0.82146649 0.82147127 0.82146921
 0.82146623 0.82147592 0.82147201 0.82147148 0.8214668  0.82146827
 0.82146472 0.82146992 0.82147043 0.82147809 0.82147317 0.82146808
 0.8214725  0.82147075 0.82146953 0.82147277 0.82147715 0.82147767
 0.82146933 0.82146842 0.82146546 0.82147929 0.82147015 0.82147345
 0.82147114 0.82147193 0.82147493 0.82147502 0.82147899 0.82147406
 0.82147082 0.82146983 0.8214724  0.82147011 0.82147688 0.82147045
 0.8214698  0.82147083 0.8214753  0.82147177 0.82147411 0.82147595
 0.82147806 0.82146902 0.82146919 0.82146889 0.82147551 0.82146696
 0.82146833 0.82147311 0.82146583 0.82147531 0.82147094 0.82147445
 0.82147624 0.82147083 0.82147144 0.82146699 0.82147605 0.82146607
 0.82147232 0.82147618 0.82147384 0.82146869 0.82147197 0.821472
 0.82146881 0.82147009 0.82147523 0.82146891 0.82146581 0.82146996
 0.82146526 0.8214737  0.82146278 0.82146736 0.82147153 0.82146937
Epoch 36 RMSE: [0.81217636 0.81217838 0.81216314 0.81217887 0.81217654 0.81217581██ | 36/40 [01:27<00:09,  2.38s/it]
 0.81217706 0.81217216 0.8121771  0.81217266 0.81217472 0.81217733
 0.81217459 0.81217295 0.81217602 0.81217003 0.8121746  0.81217288
 0.81216985 0.8121791  0.81217559 0.81217486 0.81217041 0.8121719
 0.81216843 0.8121734  0.81217388 0.81218128 0.81217656 0.81217177
 0.81217588 0.81217428 0.81217299 0.81217616 0.81218048 0.81218104
 0.81217301 0.81217194 0.81216907 0.81218239 0.81217368 0.812177
 0.81217443 0.81217538 0.81217836 0.81217839 0.8121822  0.81217747
 0.81217426 0.81217336 0.81217596 0.81217355 0.81218016 0.812174
 0.81217316 0.81217415 0.81217845 0.81217527 0.81217745 0.81217914
 0.81218127 0.81217264 0.81217281 0.8121725  0.8121788  0.81217083
 0.81217185 0.8121766  0.81216943 0.81217854 0.81217442 0.8121777
 0.81217972 0.81217442 0.81217514 0.8121707  0.81217926 0.81216959
 0.81217561 0.81217967 0.81217712 0.81217216 0.81217545 0.8121755
 0.8121722  0.81217354 0.81217842 0.81217225 0.81216932 0.81217352
 0.81216881 0.81217714 0.81216646 0.81217107 0.81217497 0.81217293
Epoch 36 RMSE: [0.81217636 0.81217838 0.81216314 0.81217887 0.81217654 0.81217581██ | 36/40 [01:27<00:09,  2.38s/it]    
 0.81217706 0.81217216 0.8121771  0.81217266 0.81217472 0.81217733
 0.81217459 0.81217295 0.81217602 0.81217003 0.8121746  0.81217288
 0.81216985 0.8121791  0.81217559 0.81217486 0.81217041 0.8121719
 0.81216843 0.8121734  0.81217388 0.81218128 0.81217656 0.81217177
 0.81217588 0.81217428 0.81217299 0.81217616 0.81218048 0.81218104
 0.81217301 0.81217194 0.81216907 0.81218239 0.81217368 0.812177
 0.81217443 0.81217538 0.81217836 0.81217839 0.8121822  0.81217747
 0.81217426 0.81217336 0.81217596 0.81217355 0.81218016 0.812174
 0.81217316 0.81217415 0.81217845 0.81217527 0.81217745 0.81217914
 0.81218127 0.81217264 0.81217281 0.8121725  0.8121788  0.81217083
 0.81217185 0.8121766  0.81216943 0.81217854 0.81217442 0.8121777
 0.81217972 0.81217442 0.81217514 0.8121707  0.81217926 0.81216959
 0.81217561 0.81217967 0.81217712 0.81217216 0.81217545 0.8121755
 0.8121722  0.81217354 0.81217842 0.81217225 0.81216932 0.81217352
 0.81216881 0.81217714 0.81216646 0.81217107 0.81217497 0.81217293
Epoch 37 RMSE: [0.80252532 0.80252725 0.80251242 0.80252783 0.80252539 0.80252478██▎| 37/40 [01:29<00:07,  2.38s/it]
 0.8025259  0.80252122 0.80252609 0.80252151 0.80252362 0.80252608
 0.80252346 0.80252195 0.80252503 0.80251916 0.80252351 0.80252204
 0.80251897 0.80252778 0.80252459 0.80252369 0.80251944 0.80252106
 0.80251763 0.80252237 0.80252287 0.80252996 0.8025255  0.80252094
 0.80252473 0.80252344 0.80252194 0.80252521 0.80252927 0.80252972
 0.80252212 0.80252092 0.80251818 0.80253098 0.80252267 0.8025262
 0.80252327 0.80252429 0.80252731 0.80252717 0.80253087 0.80252644
 0.80252318 0.80252222 0.80252488 0.80252256 0.80252894 0.8025229
 0.80252205 0.80252291 0.80252718 0.80252424 0.80252625 0.8025278
 0.80253003 0.8025218  0.80252183 0.80252168 0.80252762 0.80252022
 0.80252098 0.80252566 0.8025185  0.80252745 0.8025234  0.80252656
 0.80252855 0.80252361 0.80252428 0.80252    0.80252802 0.80251868
 0.8025244  0.80252873 0.80252603 0.80252113 0.80252447 0.80252464
 0.80252102 0.80252265 0.80252716 0.80252113 0.80251829 0.80252258
 0.80251785 0.80252601 0.80251562 0.80252025 0.80252401 0.80252183
Epoch 37 RMSE: [0.80252532 0.80252725 0.80251242 0.80252783 0.80252539 0.80252478██▎| 37/40 [01:29<00:07,  2.38s/it]
 0.8025259  0.80252122 0.80252609 0.80252151 0.80252362 0.80252608
 0.80252346 0.80252195 0.80252503 0.80251916 0.80252351 0.80252204
 0.80251897 0.80252778 0.80252459 0.80252369 0.80251944 0.80252106
 0.80251763 0.80252237 0.80252287 0.80252996 0.8025255  0.80252094
 0.80252473 0.80252344 0.80252194 0.80252521 0.80252927 0.80252972
 0.80252212 0.80252092 0.80251818 0.80253098 0.80252267 0.8025262
 0.80252327 0.80252429 0.80252731 0.80252717 0.80253087 0.80252644
 0.80252318 0.80252222 0.80252488 0.80252256 0.80252894 0.8025229
 0.80252205 0.80252291 0.80252718 0.80252424 0.80252625 0.8025278
 0.80253003 0.8025218  0.80252183 0.80252168 0.80252762 0.80252022
 0.80252098 0.80252566 0.8025185  0.80252745 0.8025234  0.80252656
 0.80252855 0.80252361 0.80252428 0.80252    0.80252802 0.80251868
 0.8025244  0.80252873 0.80252603 0.80252113 0.80252447 0.80252464
 0.80252102 0.80252265 0.80252716 0.80252113 0.80251829 0.80252258
 0.80251785 0.80252601 0.80251562 0.80252025 0.80252401 0.80252183
Epoch 38 RMSE: [0.79251062 0.79251227 0.79249797 0.79251285 0.79251066 0.79251015██▌| 38/40 [01:31<00:04,  2.38s/it]
 0.79251103 0.79250659 0.79251131 0.79250683 0.79250881 0.7925112
 0.79250865 0.7925072  0.79251026 0.79250454 0.79250855 0.79250742
 0.79250433 0.79251263 0.79250989 0.79250885 0.79250477 0.79250644
 0.79250308 0.79250752 0.79250805 0.792515   0.79251052 0.79250641
 0.79250984 0.79250861 0.79250719 0.7925104  0.79251439 0.79251481
 0.79250758 0.79250625 0.79250359 0.79251587 0.79250798 0.79251144
 0.79250841 0.79250943 0.7925125  0.79251235 0.79251588 0.7925116
 0.79250837 0.79250745 0.79251016 0.7925077  0.79251408 0.79250827
 0.7925073  0.79250815 0.79251217 0.79250951 0.79251136 0.79251279
 0.79251522 0.79250715 0.7925072  0.79250696 0.79251271 0.79250588
 0.79250626 0.79251087 0.79250379 0.79251246 0.79250858 0.79251161
 0.7925137  0.79250899 0.79250973 0.79250533 0.792513   0.79250409
 0.79250943 0.79251398 0.79251109 0.79250638 0.79250969 0.79250979
 0.79250619 0.79250803 0.79251216 0.79250638 0.79250363 0.79250783
 0.79250324 0.79251124 0.79250114 0.79250579 0.79250924 0.79250711
Epoch 38 RMSE: [0.79251062 0.79251227 0.79249797 0.79251285 0.79251066 0.79251015██▌| 38/40 [01:31<00:04,  2.38s/it]
 0.79251103 0.79250659 0.79251131 0.79250683 0.79250881 0.7925112
 0.79250865 0.7925072  0.79251026 0.79250454 0.79250855 0.79250742
 0.79250433 0.79251263 0.79250989 0.79250885 0.79250477 0.79250644
 0.79250308 0.79250752 0.79250805 0.792515   0.79251052 0.79250641
 0.79250984 0.79250861 0.79250719 0.7925104  0.79251439 0.79251481
 0.79250758 0.79250625 0.79250359 0.79251587 0.79250798 0.79251144
 0.79250841 0.79250943 0.7925125  0.79251235 0.79251588 0.7925116
 0.79250837 0.79250745 0.79251016 0.7925077  0.79251408 0.79250827
 0.7925073  0.79250815 0.79251217 0.79250951 0.79251136 0.79251279
 0.79251522 0.79250715 0.7925072  0.79250696 0.79251271 0.79250588
 0.79250626 0.79251087 0.79250379 0.79251246 0.79250858 0.79251161
 0.7925137  0.79250899 0.79250973 0.79250533 0.792513   0.79250409
 0.79250943 0.79251398 0.79251109 0.79250638 0.79250969 0.79250979
 0.79250619 0.79250803 0.79251216 0.79250638 0.79250363 0.79250783
 0.79250324 0.79251124 0.79250114 0.79250579 0.79250924 0.79250711
Epoch 39 RMSE: [0.78211049 0.78211201 0.78209813 0.78211264 0.78211053 0.78211019██▊| 39/40 [01:34<00:02,  2.38s/it]
 0.78211079 0.78210665 0.78211125 0.78210675 0.78210874 0.78211085
 0.78210849 0.78210722 0.78211018 0.78210456 0.78210833 0.7821075
 0.78210433 0.78211224 0.78210978 0.78210864 0.78210482 0.78210649
 0.78210325 0.78210742 0.78210795 0.78211477 0.78211035 0.78210647
 0.78210964 0.78210863 0.78210705 0.7821103  0.78211415 0.78211461
 0.78210756 0.7821062  0.78210364 0.78211536 0.78210784 0.78211148
 0.78210821 0.78210925 0.78211235 0.78211215 0.78211556 0.78211147
 0.78210825 0.7821074  0.78211008 0.78210755 0.78211375 0.78210818
 0.78210719 0.78210798 0.78211186 0.78210941 0.78211107 0.78211257
 0.78211489 0.78210719 0.78210722 0.78210696 0.78211244 0.78210618
 0.78210627 0.78211081 0.7821038  0.78211226 0.78210851 0.78211143
 0.78211353 0.78210906 0.78210978 0.78210549 0.78211269 0.78210406
 0.78210928 0.78211382 0.78211093 0.78210638 0.78210957 0.78210971
 0.78210602 0.782108   0.7821118  0.78210625 0.7821036  0.78210788
 0.7821032  0.78211109 0.78210125 0.78210593 0.78210907 0.78210702
Epoch 39 RMSE: [0.78211049 0.78211201 0.78209813 0.78211264 0.78211053 0.78211019██▊| 39/40 [01:34<00:02,  2.38s/it]
 0.78211079 0.78210665 0.78211125 0.78210675 0.78210874 0.78211085
 0.78210849 0.78210722 0.78211018 0.78210456 0.78210833 0.7821075
 0.78210433 0.78211224 0.78210978 0.78210864 0.78210482 0.78210649
 0.78210325 0.78210742 0.78210795 0.78211477 0.78211035 0.78210647
 0.78210964 0.78210863 0.78210705 0.7821103  0.78211415 0.78211461
 0.78210756 0.7821062  0.78210364 0.78211536 0.78210784 0.78211148
 0.78210821 0.78210925 0.78211235 0.78211215 0.78211556 0.78211147
 0.78210825 0.7821074  0.78211008 0.78210755 0.78211375 0.78210818
 0.78210719 0.78210798 0.78211186 0.78210941 0.78211107 0.78211257
 0.78211489 0.78210719 0.78210722 0.78210696 0.78211244 0.78210618
 0.78210627 0.78211081 0.7821038  0.78211226 0.78210851 0.78211143
 0.78211353 0.78210906 0.78210978 0.78210549 0.78211269 0.78210406
 0.78210928 0.78211382 0.78211093 0.78210638 0.78210957 0.78210971
 0.78210602 0.782108   0.7821118  0.78210625 0.7821036  0.78210788
 0.7821032  0.78211109 0.78210125 0.78210593 0.78210907 0.78210702
Epoch 39 RMSE: [0.78211049 0.78211201 0.78209813 0.78211264 0.78211053 0.78211019███| 40/40 [01:36<00:00,  2.37s/it]
 0.78211079 0.78210665 0.78211125 0.78210675 0.78210874 0.78211085
 0.78210849 0.78210722 0.78211018 0.78210456 0.78210833 0.7821075
 0.78210433 0.78211224 0.78210978 0.78210864 0.78210482 0.78210649
 0.78210325 0.78210742 0.78210795 0.78211477 0.78211035 0.78210647
 0.78210964 0.78210863 0.78210705 0.7821103  0.78211415 0.78211461
 0.78210756 0.7821062  0.78210364 0.78211536 0.78210784 0.78211148
 0.78210821 0.78210925 0.78211235 0.78211215 0.78211556 0.78211147
 0.78210825 0.7821074  0.78211008 0.78210755 0.78211375 0.78210818
 0.78210719 0.78210798 0.78211186 0.78210941 0.78211107 0.78211257
 0.78211489 0.78210719 0.78210722 0.78210696 0.78211244 0.78210618
 0.78210627 0.78211081 0.7821038  0.78211226 0.78210851 0.78211143
 0.78211353 0.78210906 0.78210978 0.78210549 0.78211269 0.78210406
 0.78210928 0.78211382 0.78211093 0.78210638 0.78210957 0.78210971
 0.78210602 0.782108   0.7821118  0.78210625 0.7821036  0.78210788
 0.7821032  0.78211109 0.78210125 0.78210593 0.78210907 0.78210702
 0.78210826 0.78211145 0.78211014 0.78210739]. Training epoch 40...: 100%|██████████| 40/40 [01:36<00:00,  2.41s/it]

model.estimations()

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

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

import evaluation_measures as ev

estimations_df=pd.read_csv('Recommendations generated/ml-100k/Self_SVDBaseline_estimations.csv', header=None)
reco=np.loadtxt('Recommendations generated/ml-100k/Self_SVDBaseline_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, 8806.80it/s]

	RMSE	MAE	precision	recall	F_1	F_05	precision_super	recall_super	NDCG	mAP	MRR	LAUC	HR	HR2	Reco in test	Test coverage	Shannon	Gini
0	3.64479	3.479397	0.13701	0.082007	0.083942	0.100776	0.106974	0.105605	0.160418	0.080222	0.322261	0.537895	0.626723	0.360551	0.999894	0.276335	5.123235	0.910511

Ready-made SVD - Surprise implementation

SVD

import helpers
import surprise as sp
import imp
imp.reload(helpers)

algo = sp.SVD(biased=False) # to use unbiased version

helpers.ready_made(algo, reco_path='Recommendations generated/ml-100k/Ready_SVD_reco.csv',
          estimations_path='Recommendations generated/ml-100k/Ready_SVD_estimations.csv')

Generating predictions...
Generating top N recommendations...
Generating predictions...

SVD biased - on top baseline

import helpers
import surprise as sp
import imp
imp.reload(helpers)

algo = sp.SVD() # default is biased=True

helpers.ready_made(algo, reco_path='Recommendations generated/ml-100k/Ready_SVDBiased_reco.csv',
          estimations_path='Recommendations generated/ml-100k/Ready_SVDBiased_estimations.csv')

Generating predictions...
Generating top N recommendations...
Generating predictions...

import imp
imp.reload(ev)

import evaluation_measures as ev
dir_path="Recommendations generated/ml-100k/"
super_reactions=[4,5]
test=pd.read_csv('./Datasets/ml-100k/test.csv', sep='\t', header=None)

ev.evaluate_all(test, dir_path, super_reactions)

943it [00:00, 9840.17it/s]
943it [00:00, 9173.71it/s]
943it [00:00, 9859.58it/s]
943it [00:00, 9112.74it/s]
943it [00:00, 9551.34it/s]
943it [00:00, 7830.24it/s]
943it [00:00, 8983.95it/s]
943it [00:00, 9447.94it/s]
943it [00:00, 9866.98it/s]
943it [00:00, 10127.04it/s]
943it [00:00, 9035.26it/s]
943it [00:00, 9754.44it/s]
943it [00:00, 9524.64it/s]
943it [00:00, 8451.27it/s]
943it [00:00, 9054.06it/s]
943it [00:00, 10007.79it/s]

Model	RMSE	MAE	precision	recall	F_1	F_05	precision_super	recall_super	NDCG	mAP	MRR	LAUC	HR	HR2	Reco in test	Test coverage	Shannon	Gini
Self_TopPop	2.508258	2.217909	0.188865	0.116919	0.118732	0.141584	0.130472	0.137473	0.214651	0.111707	0.400939	0.555546	0.765642	0.492047	1.000000	0.038961	3.159079	0.987317
Self_SVDBaseline	3.644790	3.479397	0.137010	0.082007	0.083942	0.100776	0.106974	0.105605	0.160418	0.080222	0.322261	0.537895	0.626723	0.360551	0.999894	0.276335	5.123235	0.910511
Ready_SVD	0.950945	0.749680	0.098834	0.049106	0.054037	0.068741	0.087768	0.073987	0.113242	0.054201	0.243492	0.521280	0.493107	0.248144	0.998515	0.214286	4.413166	0.953488
Self_SVD	0.915079	0.718240	0.104772	0.045496	0.054393	0.071374	0.094421	0.076826	0.109517	0.052005	0.206646	0.519484	0.487805	0.264051	0.874549	0.142136	3.890472	0.972126
Ready_Baseline	0.949459	0.752487	0.091410	0.037652	0.046030	0.061286	0.079614	0.056463	0.095957	0.043178	0.198193	0.515501	0.437964	0.239661	1.000000	0.033911	2.836513	0.991139
Ready_SVDBiased	0.938535	0.738678	0.085366	0.036921	0.044151	0.057832	0.074893	0.056396	0.095960	0.044204	0.212483	0.515132	0.446448	0.217391	0.997561	0.168110	4.191946	0.963341
Self_KNNSurprisetask	0.946255	0.745209	0.083457	0.032848	0.041227	0.055493	0.074785	0.048890	0.089577	0.040902	0.189057	0.513076	0.417815	0.217391	0.888547	0.130592	3.611806	0.978659
Self_GlobalAvg	1.125760	0.943534	0.061188	0.025968	0.031383	0.041343	0.040558	0.032107	0.067695	0.027470	0.171187	0.509546	0.384942	0.142100	1.000000	0.025974	2.711772	0.992003
Ready_Random	1.522798	1.222501	0.049841	0.020656	0.025232	0.033446	0.030579	0.022927	0.051680	0.019110	0.123085	0.506849	0.331919	0.119830	0.985048	0.183983	5.097973	0.907483
Ready_I-KNN	1.030386	0.813067	0.026087	0.006908	0.010593	0.016046	0.021137	0.009522	0.024214	0.008958	0.048068	0.499885	0.154825	0.072110	0.402333	0.434343	5.133650	0.877999
Ready_I-KNNBaseline	0.935327	0.737424	0.002545	0.000755	0.001105	0.001602	0.002253	0.000930	0.003444	0.001362	0.011760	0.496724	0.021209	0.004242	0.482821	0.059885	2.232578	0.994487
Ready_U-KNN	1.023495	0.807913	0.000742	0.000205	0.000305	0.000449	0.000536	0.000198	0.000845	0.000274	0.002744	0.496441	0.007423	0.000000	0.602121	0.010823	2.089186	0.995706
Self_BaselineIU	0.958136	0.754051	0.000954	0.000188	0.000298	0.000481	0.000644	0.000223	0.001043	0.000335	0.003348	0.496433	0.009544	0.000000	0.699046	0.005051	1.945910	0.995669
Self_TopRated	2.508258	2.217909	0.000954	0.000188	0.000298	0.000481	0.000644	0.000223	0.001043	0.000335	0.003348	0.496433	0.009544	0.000000	0.699046	0.005051	1.945910	0.995669
Self_BaselineUI	0.967585	0.762740	0.000954	0.000170	0.000278	0.000463	0.000644	0.000189	0.000752	0.000168	0.001677	0.496424	0.009544	0.000000	0.600530	0.005051	1.803126	0.996380
Self_IKNN	1.018363	0.808793	0.000318	0.000108	0.000140	0.000189	0.000000	0.000000	0.000214	0.000037	0.000368	0.496391	0.003181	0.000000	0.392153	0.115440	4.174741	0.965327

192 KiB Raw Blame History

Self made SVD

Saving and evaluating recommendations

Embeddings

project task 5: implement SVD on top baseline (as it is in Surprise library)

Ready-made SVD - Surprise implementation

SVD

SVD biased - on top baseline

192 KiB

Raw Blame History