In [1]:
import data_simulation as sim
ds = sim.gen_matrix(1000, 400, 500, 4)
sparse_matrix = sim.sparsify(ds['matrix'], sparsity = 0.20)
In [2]:
### make a training set and a test set
import numpy as np
train_indexes = np.random.choice(sparse_matrix.shape[0], round(0.8 * sparse_matrix.shape[0]), replace = False)
test_indexes = np.setdiff1d(np.arange(sparse_matrix.shape[0]), train_indexes)
train_indexes = np.sort(train_indexes)
test_indexes = np.sort(test_indexes)
train_ds = sparse_matrix[train_indexes, ]
train_fea = ds['sinfo'][train_indexes, ]
test_ds = sparse_matrix[test_indexes, ]
test_fea = ds['sinfo'][test_indexes, ]
In [3]:
### plot train_ds
import matplotlib.pyplot as plt
plt.imshow(train_ds, cmap = 'jet')
plt.savefig('out_pred_train_ds.png')
In [4]:
### plot the corresponding features of the training set
plt.imshow(train_fea, cmap = 'jet')
plt.savefig('out_pred_train_fea.png')
In [5]:
### plot the testing dataset
plt.imshow(test_ds, cmap = 'jet')
plt.savefig('out-pred-test-ds.png')
In [6]:
### plot the corresponding features of the testing dataset
plt.imshow(test_fea, cmap = 'jet')
plt.savefig('out-pred-test-fea.png')
In [7]:
### make scipy sparse matrix
from scipy.sparse import coo_matrix
import smurff
sp_train_ds = coo_matrix(train_ds)
sp_train_fea = coo_matrix(train_fea)
sp_train_ds1, sp_train_ds2 = smurff.make_train_test(sp_train_ds, 0.01)
print(sp_train_ds1.nnz)
print(sp_train_ds2.nnz)
63210
638
In [8]:
### train the model
import smurff
trainSession = smurff.MacauSession(
Ytrain = sp_train_ds1,
Ytest = sp_train_ds2,
side_info = [sp_train_fea, None],
num_latent = 4,
burnin = 400,
nsamples = 1500,
save_freq = 1,
save_prefix= "syn-out-matrix-pred/save",
verbose = 1,)
predictions = trainSession.run()
PythonSession {
Data: {
Type: ScarceMatrixData [with NAs]
Component-wise mean: 0.131928
Component-wise variance: 6.83443
Noise: Fixed gaussian noise with precision: 5.00
Size: 63210 [800 x 400] (19.75%)
}
Model: {
Num-latents: 4
}
Priors: {
0: MacauPrior
SideInfo: SparseDouble [800, 500]
Method: CG Solver
Tol: 1.00e-06
BetaPrecision: 5.00
1: NormalPrior
}
Result: {
Test data: 638 [800 x 400] (0.20%)
}
Version: v0.12.3
Iterations: 400 burnin + 1500 samples
Save model: every 1 iteration
Save prefix: C:\Users\TLeVan3\OneDrive - JNJ\cgOperational\macau_smurff\syn-out-matrix-pred/save
Save extension: .ddm
}
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Burnin: 400/400 RMSE: nan (1samp: 1.01) U: [ 0: 61.55,1: 40.36 ] took 0.0s
Sample: 1/1500 RMSE: 1.01 (1samp: 1.01) U: [ 0: 61.56,1: 40.32 ] took 0.0s
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Sample: 739/1500 RMSE: 1.00 (1samp: 1.01) U: [ 0: 64.06,1: 41.89 ] took 0.0s
Sample: 740/1500 RMSE: 1.00 (1samp: 1.01) U: [ 0: 64.06,1: 41.87 ] took 0.0s
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Sample: 750/1500 RMSE: 1.00 (1samp: 1.01) U: [ 0: 63.84,1: 42.18 ] took 0.0s
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Sample: 788/1500 RMSE: 1.00 (1samp: 1.01) U: [ 0: 63.99,1: 42.00 ] took 0.0s
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Sample: 800/1500 RMSE: 1.00 (1samp: 1.00) U: [ 0: 64.08,1: 42.02 ] took 0.0s
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Sample: 805/1500 RMSE: 1.00 (1samp: 0.99) U: [ 0: 64.09,1: 42.01 ] took 0.0s
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Sample: 1149/1500 RMSE: 1.00 (1samp: 1.00) U: [ 0: 66.99,1: 40.82 ] took 0.0s
Sample: 1150/1500 RMSE: 1.00 (1samp: 1.01) U: [ 0: 66.96,1: 40.86 ] took 0.0s
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Sample: 1160/1500 RMSE: 1.00 (1samp: 1.02) U: [ 0: 67.07,1: 41.04 ] took 0.0s
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Sample: 1187/1500 RMSE: 1.00 (1samp: 0.99) U: [ 0: 67.24,1: 40.99 ] took 0.0s
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Sample: 1200/1500 RMSE: 1.00 (1samp: 1.01) U: [ 0: 67.44,1: 40.98 ] took 0.0s
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Sample: 1210/1500 RMSE: 1.00 (1samp: 1.01) U: [ 0: 67.56,1: 40.97 ] took 0.0s
Sample: 1211/1500 RMSE: 1.00 (1samp: 1.00) U: [ 0: 67.69,1: 40.96 ] took 0.0s
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Sample: 1214/1500 RMSE: 1.00 (1samp: 1.01) U: [ 0: 67.68,1: 40.99 ] took 0.0s
Sample: 1215/1500 RMSE: 1.00 (1samp: 1.01) U: [ 0: 67.46,1: 40.99 ] took 0.0s
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Sample: 1223/1500 RMSE: 1.00 (1samp: 1.01) U: [ 0: 67.26,1: 41.04 ] took 0.0s
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Sample: 1226/1500 RMSE: 1.00 (1samp: 1.02) U: [ 0: 67.06,1: 41.07 ] took 0.0s
Sample: 1227/1500 RMSE: 1.00 (1samp: 1.01) U: [ 0: 67.03,1: 41.05 ] took 0.0s
Sample: 1228/1500 RMSE: 1.00 (1samp: 1.01) U: [ 0: 66.99,1: 41.12 ] took 0.0s
Sample: 1229/1500 RMSE: 1.00 (1samp: 1.01) U: [ 0: 66.96,1: 41.15 ] took 0.0s
Sample: 1230/1500 RMSE: 1.00 (1samp: 1.00) U: [ 0: 66.94,1: 41.26 ] took 0.0s
Sample: 1231/1500 RMSE: 1.00 (1samp: 1.00) U: [ 0: 66.85,1: 41.31 ] took 0.0s
Sample: 1232/1500 RMSE: 1.00 (1samp: 1.00) U: [ 0: 66.98,1: 41.32 ] took 0.0s
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Sample: 1235/1500 RMSE: 1.00 (1samp: 1.01) U: [ 0: 66.97,1: 41.15 ] took 0.0s
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Sample: 1237/1500 RMSE: 1.00 (1samp: 1.01) U: [ 0: 66.82,1: 41.21 ] took 0.0s
Sample: 1238/1500 RMSE: 1.00 (1samp: 1.01) U: [ 0: 66.86,1: 41.24 ] took 0.0s
Sample: 1239/1500 RMSE: 1.00 (1samp: 1.01) U: [ 0: 66.68,1: 41.22 ] took 0.0s
Sample: 1240/1500 RMSE: 1.00 (1samp: 1.01) U: [ 0: 66.67,1: 41.21 ] took 0.0s
Sample: 1241/1500 RMSE: 1.00 (1samp: 1.02) U: [ 0: 66.65,1: 41.17 ] took 0.0s
Sample: 1242/1500 RMSE: 1.00 (1samp: 1.01) U: [ 0: 66.60,1: 41.13 ] took 0.0s
Sample: 1243/1500 RMSE: 1.00 (1samp: 1.01) U: [ 0: 66.62,1: 41.15 ] took 0.0s
Sample: 1244/1500 RMSE: 1.00 (1samp: 1.01) U: [ 0: 66.63,1: 41.07 ] took 0.0s
Sample: 1245/1500 RMSE: 1.00 (1samp: 1.02) U: [ 0: 66.76,1: 41.00 ] took 0.0s
Sample: 1246/1500 RMSE: 1.00 (1samp: 1.02) U: [ 0: 66.78,1: 40.99 ] took 0.0s
Sample: 1247/1500 RMSE: 1.00 (1samp: 1.01) U: [ 0: 66.76,1: 40.98 ] took 0.0s
Sample: 1248/1500 RMSE: 1.00 (1samp: 1.01) U: [ 0: 66.88,1: 41.01 ] took 0.0s
Sample: 1249/1500 RMSE: 1.00 (1samp: 1.01) U: [ 0: 66.92,1: 41.02 ] took 0.0s
Sample: 1250/1500 RMSE: 1.00 (1samp: 1.00) U: [ 0: 66.89,1: 41.04 ] took 0.0s
Sample: 1251/1500 RMSE: 1.00 (1samp: 1.01) U: [ 0: 66.95,1: 41.01 ] took 0.0s
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Sample: 1253/1500 RMSE: 1.00 (1samp: 1.00) U: [ 0: 67.22,1: 40.98 ] took 0.0s
Sample: 1254/1500 RMSE: 1.00 (1samp: 1.01) U: [ 0: 67.30,1: 40.95 ] took 0.0s
Sample: 1255/1500 RMSE: 1.00 (1samp: 1.02) U: [ 0: 67.35,1: 40.93 ] took 0.0s
Sample: 1256/1500 RMSE: 1.00 (1samp: 1.01) U: [ 0: 67.29,1: 40.87 ] took 0.0s
Sample: 1257/1500 RMSE: 1.00 (1samp: 1.01) U: [ 0: 67.34,1: 40.89 ] took 0.0s
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Sample: 1259/1500 RMSE: 1.00 (1samp: 1.00) U: [ 0: 67.07,1: 40.92 ] took 0.0s
Sample: 1260/1500 RMSE: 1.00 (1samp: 1.01) U: [ 0: 67.15,1: 40.96 ] took 0.0s
Sample: 1261/1500 RMSE: 1.00 (1samp: 1.01) U: [ 0: 67.15,1: 40.91 ] took 0.0s
Sample: 1262/1500 RMSE: 1.00 (1samp: 1.00) U: [ 0: 67.31,1: 40.83 ] took 0.0s
Sample: 1263/1500 RMSE: 1.00 (1samp: 1.00) U: [ 0: 67.26,1: 40.80 ] took 0.0s
Sample: 1264/1500 RMSE: 1.00 (1samp: 1.01) U: [ 0: 67.39,1: 40.74 ] took 0.0s
Sample: 1265/1500 RMSE: 1.00 (1samp: 1.00) U: [ 0: 67.50,1: 40.81 ] took 0.0s
Sample: 1266/1500 RMSE: 1.00 (1samp: 1.01) U: [ 0: 67.53,1: 40.86 ] took 0.0s
Sample: 1267/1500 RMSE: 1.00 (1samp: 1.01) U: [ 0: 67.57,1: 40.90 ] took 0.0s
Sample: 1268/1500 RMSE: 1.00 (1samp: 1.00) U: [ 0: 67.43,1: 40.89 ] took 0.0s
Sample: 1269/1500 RMSE: 1.00 (1samp: 1.01) U: [ 0: 67.56,1: 40.82 ] took 0.0s
Sample: 1270/1500 RMSE: 1.00 (1samp: 1.02) U: [ 0: 67.70,1: 40.83 ] took 0.0s
Sample: 1271/1500 RMSE: 1.00 (1samp: 1.01) U: [ 0: 67.60,1: 40.76 ] took 0.0s
Sample: 1272/1500 RMSE: 1.00 (1samp: 1.01) U: [ 0: 67.63,1: 40.79 ] took 0.0s
Sample: 1273/1500 RMSE: 1.00 (1samp: 1.01) U: [ 0: 67.70,1: 40.74 ] took 0.0s
Sample: 1274/1500 RMSE: 1.00 (1samp: 1.01) U: [ 0: 67.73,1: 40.70 ] took 0.0s
Sample: 1275/1500 RMSE: 1.00 (1samp: 1.01) U: [ 0: 67.75,1: 40.73 ] took 0.0s
Sample: 1276/1500 RMSE: 1.00 (1samp: 1.01) U: [ 0: 68.03,1: 40.67 ] took 0.0s
Sample: 1277/1500 RMSE: 1.00 (1samp: 1.00) U: [ 0: 68.02,1: 40.72 ] took 0.0s
Sample: 1278/1500 RMSE: 1.00 (1samp: 1.01) U: [ 0: 68.12,1: 40.64 ] took 0.0s
Sample: 1279/1500 RMSE: 1.00 (1samp: 1.01) U: [ 0: 68.14,1: 40.61 ] took 0.0s
Sample: 1280/1500 RMSE: 1.00 (1samp: 1.01) U: [ 0: 68.13,1: 40.64 ] took 0.0s
Sample: 1281/1500 RMSE: 1.00 (1samp: 1.01) U: [ 0: 68.12,1: 40.69 ] took 0.0s
Sample: 1282/1500 RMSE: 1.00 (1samp: 1.01) U: [ 0: 68.10,1: 40.66 ] took 0.0s
Sample: 1283/1500 RMSE: 1.00 (1samp: 1.01) U: [ 0: 67.93,1: 40.71 ] took 0.0s
Sample: 1284/1500 RMSE: 1.00 (1samp: 1.01) U: [ 0: 68.04,1: 40.72 ] took 0.0s
Sample: 1285/1500 RMSE: 1.00 (1samp: 1.01) U: [ 0: 68.19,1: 40.73 ] took 0.0s
Sample: 1286/1500 RMSE: 1.00 (1samp: 1.01) U: [ 0: 68.25,1: 40.71 ] took 0.0s
Sample: 1287/1500 RMSE: 1.00 (1samp: 1.01) U: [ 0: 68.16,1: 40.70 ] took 0.0s
Sample: 1288/1500 RMSE: 1.00 (1samp: 1.01) U: [ 0: 68.25,1: 40.65 ] took 0.0s
Sample: 1289/1500 RMSE: 1.00 (1samp: 1.01) U: [ 0: 68.24,1: 40.65 ] took 0.0s
Sample: 1290/1500 RMSE: 1.00 (1samp: 1.01) U: [ 0: 68.24,1: 40.62 ] took 0.0s
Sample: 1291/1500 RMSE: 1.00 (1samp: 1.01) U: [ 0: 68.38,1: 40.67 ] took 0.0s
Sample: 1292/1500 RMSE: 1.00 (1samp: 1.00) U: [ 0: 68.54,1: 40.69 ] took 0.0s
Sample: 1293/1500 RMSE: 1.00 (1samp: 1.01) U: [ 0: 68.67,1: 40.68 ] took 0.0s
Sample: 1294/1500 RMSE: 1.00 (1samp: 1.01) U: [ 0: 68.70,1: 40.68 ] took 0.0s
Sample: 1295/1500 RMSE: 1.00 (1samp: 1.00) U: [ 0: 68.60,1: 40.76 ] took 0.0s
Sample: 1296/1500 RMSE: 1.00 (1samp: 1.01) U: [ 0: 68.49,1: 40.81 ] took 0.0s
Sample: 1297/1500 RMSE: 1.00 (1samp: 1.00) U: [ 0: 68.39,1: 40.82 ] took 0.0s
Sample: 1298/1500 RMSE: 1.00 (1samp: 1.00) U: [ 0: 68.47,1: 40.80 ] took 0.0s
Sample: 1299/1500 RMSE: 1.00 (1samp: 1.01) U: [ 0: 68.51,1: 40.72 ] took 0.0s
Sample: 1300/1500 RMSE: 1.00 (1samp: 1.01) U: [ 0: 68.56,1: 40.74 ] took 0.0s
Sample: 1301/1500 RMSE: 1.00 (1samp: 1.01) U: [ 0: 68.51,1: 40.72 ] took 0.0s
Sample: 1302/1500 RMSE: 1.00 (1samp: 1.01) U: [ 0: 68.53,1: 40.71 ] took 0.0s
Sample: 1303/1500 RMSE: 1.00 (1samp: 1.00) U: [ 0: 68.53,1: 40.70 ] took 0.0s
Sample: 1304/1500 RMSE: 1.00 (1samp: 1.01) U: [ 0: 68.58,1: 40.75 ] took 0.0s
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Sample: 1370/1500 RMSE: 1.00 (1samp: 1.01) U: [ 0: 68.61,1: 40.45 ] took 0.0s
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Sample: 1399/1500 RMSE: 1.00 (1samp: 1.02) U: [ 0: 68.53,1: 40.53 ] took 0.0s
Sample: 1400/1500 RMSE: 1.00 (1samp: 1.02) U: [ 0: 68.65,1: 40.49 ] took 0.0s
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Sample: 1470/1500 RMSE: 1.00 (1samp: 1.00) U: [ 0: 70.16,1: 40.27 ] took 0.0s
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Sample: 1491/1500 RMSE: 1.00 (1samp: 1.01) U: [ 0: 69.93,1: 40.40 ] took 0.0s
Sample: 1492/1500 RMSE: 1.00 (1samp: 1.01) U: [ 0: 69.82,1: 40.40 ] took 0.0s
Sample: 1493/1500 RMSE: 1.00 (1samp: 1.02) U: [ 0: 69.94,1: 40.44 ] took 0.0s
Sample: 1494/1500 RMSE: 1.00 (1samp: 1.00) U: [ 0: 69.90,1: 40.46 ] took 0.0s
Sample: 1495/1500 RMSE: 1.00 (1samp: 1.01) U: [ 0: 69.84,1: 40.44 ] took 0.0s
Sample: 1496/1500 RMSE: 1.00 (1samp: 1.01) U: [ 0: 69.73,1: 40.45 ] took 0.0s
Sample: 1497/1500 RMSE: 1.00 (1samp: 0.99) U: [ 0: 69.69,1: 40.41 ] took 0.0s
Sample: 1498/1500 RMSE: 1.00 (1samp: 1.01) U: [ 0: 69.63,1: 40.40 ] took 0.0s
Sample: 1499/1500 RMSE: 1.00 (1samp: 1.01) U: [ 0: 69.77,1: 40.42 ] took 0.0s
Sample: 1500/1500 RMSE: 1.00 (1samp: 1.00) U: [ 0: 69.86,1: 40.40 ] took 0.0s
In [9]:
# predictor = smurff.PredictSession.fromOutputFile("syn-out-matrix-pred/save-root.ini")
predictor = trainSession.makePredictSession()
print(predictor)
PredictSession with 1500 samples
Data shape = [800, 400]
Beta shape = [500, 0]
Num latent = 4
In [10]:
def predict_out_of_matrix_1s(side_info_matrix, sample_predictor):
"""Out-of-matrix prediction using one sample
Args:
side_info_matrix: numpy side info matrix
sample_predictor: Smurff sample object
Returns:
numpy fully predicted matrix
"""
U, V = sample_predictor.latents
Umean, Vmean = sample_predictor.latent_means
Ubeta, Vbeta = sample_predictor.betas
wU = side_info_matrix.dot(Ubeta.transpose()) + Umean
m = np.matmul(wU, V)
return m
def pred_out_of_matrix(side_info_matrix, predictor):
"""Out-of-matrix prediction using all of the samples
Args:
side_info_matrix: numpy side info matrix
predictor: Smurff PredictSession
Returns:
numpy fully predicted matrix (obtained by averaging)
"""
predictions = np.array([predict_out_of_matrix_1s(side_info_matrix, s) for s in predictor.samples])
return predictions.mean(axis = 0)
In [11]:
pred_test_ds = pred_out_of_matrix(test_fea, predictor)
In [12]:
plt.imshow(pred_test_ds, cmap = 'jet')
plt.savefig('out-pred-predicted-matrix.png')
In [13]:
import utilities as util
#rmse = util.cal_out_matrix_rmse(test_ds, test_fea, predictor)
#rmse
pred_ds = util.pred_out_of_matrix(test_fea, predictor)
In [14]:
# calculate rmse for the non-missing values in the test dataset
from scipy.sparse import find
from sklearn.metrics import mean_squared_error
import math
(i, j, y_true) = find(test_ds)
y_pred = [pred_ds[i[idx], j[idx]] for idx in np.arange(len(i))]
rmse = math.sqrt(mean_squared_error(y_true, y_pred))
rmse
Out[14]:
1.3801289809229924
In [15]:
cv_res = util.cross_validate(y_matrix = coo_matrix(sparse_matrix),
y_fea = coo_matrix(ds['sinfo']),
cv = 3,
log_folder = "H:\\test",
k = 4,
nburnins = 10,
nsamples = 20,
out_of_matrix = False)
cv_res
*** Cross validation 0 ***
PythonSession {
Data: {
Type: ScarceMatrixData [with NAs]
Component-wise mean: 0.0737325
Component-wise variance: 7.05355
Noise: Fixed gaussian noise with precision: 5.00
Size: 53326 [667 x 400] (19.99%)
}
Model: {
Num-latents: 4
}
Priors: {
0: MacauPrior
SideInfo: SparseDouble [667, 500]
Method: CG Solver
Tol: 1.00e-06
BetaPrecision: 5.00
1: NormalPrior
}
Result: {
Test data: 5 [667 x 400] (0.00%)
}
Version: v0.12.3
Iterations: 10 burnin + 20 samples
Save model: every 1 iteration
Save prefix: H:\test\0/save
Save extension: .ddm
}
Initial: 0/0 RMSE: nan (1samp: nan) U: [ 0: 0.00,1: 0.00 ] took 0.0s
Burnin: 1/10 RMSE: nan (1samp: 3.85) U: [ 0: 16.31,1: 27.17 ] took 0.0s
Burnin: 2/10 RMSE: nan (1samp: 1.64) U: [ 0: 51.09,1: 36.28 ] took 0.0s
Burnin: 3/10 RMSE: nan (1samp: 1.11) U: [ 0: 56.37,1: 38.52 ] took 0.0s
Burnin: 4/10 RMSE: nan (1samp: 1.00) U: [ 0: 58.64,1: 38.73 ] took 0.0s
Burnin: 5/10 RMSE: nan (1samp: 1.04) U: [ 0: 58.95,1: 38.67 ] took 0.0s
Burnin: 6/10 RMSE: nan (1samp: 1.02) U: [ 0: 59.03,1: 38.60 ] took 0.0s
Burnin: 7/10 RMSE: nan (1samp: 1.13) U: [ 0: 59.10,1: 38.55 ] took 0.0s
Burnin: 8/10 RMSE: nan (1samp: 1.02) U: [ 0: 59.07,1: 38.60 ] took 0.0s
Burnin: 9/10 RMSE: nan (1samp: 1.02) U: [ 0: 59.16,1: 38.57 ] took 0.0s
Burnin: 10/10 RMSE: nan (1samp: 1.01) U: [ 0: 59.21,1: 38.53 ] took 0.0s
Sample: 1/20 RMSE: 1.17 (1samp: 1.17) U: [ 0: 59.29,1: 38.50 ] took 0.0s
Sample: 2/20 RMSE: 1.07 (1samp: 0.99) U: [ 0: 59.21,1: 38.54 ] took 0.0s
Sample: 3/20 RMSE: 1.07 (1samp: 1.08) U: [ 0: 59.28,1: 38.51 ] took 0.0s
Sample: 4/20 RMSE: 1.05 (1samp: 0.99) U: [ 0: 59.22,1: 38.52 ] took 0.0s
Sample: 5/20 RMSE: 1.05 (1samp: 1.06) U: [ 0: 59.18,1: 38.54 ] took 0.0s
Sample: 6/20 RMSE: 1.05 (1samp: 1.04) U: [ 0: 59.15,1: 38.59 ] took 0.0s
Sample: 7/20 RMSE: 1.04 (1samp: 1.03) U: [ 0: 59.14,1: 38.55 ] took 0.0s
Sample: 8/20 RMSE: 1.05 (1samp: 1.08) U: [ 0: 59.20,1: 38.50 ] took 0.0s
Sample: 9/20 RMSE: 1.05 (1samp: 1.10) U: [ 0: 59.30,1: 38.51 ] took 0.0s
Sample: 10/20 RMSE: 1.04 (1samp: 0.93) U: [ 0: 59.43,1: 38.44 ] took 0.0s
Sample: 11/20 RMSE: 1.04 (1samp: 1.10) U: [ 0: 59.49,1: 38.42 ] took 0.0s
Sample: 12/20 RMSE: 1.05 (1samp: 1.09) U: [ 0: 59.51,1: 38.36 ] took 0.0s
Sample: 13/20 RMSE: 1.05 (1samp: 1.11) U: [ 0: 59.60,1: 38.39 ] took 0.0s
Sample: 14/20 RMSE: 1.05 (1samp: 0.98) U: [ 0: 59.55,1: 38.37 ] took 0.0s
Sample: 15/20 RMSE: 1.04 (1samp: 1.01) U: [ 0: 59.63,1: 38.33 ] took 0.0s
Sample: 16/20 RMSE: 1.03 (1samp: 0.88) U: [ 0: 59.73,1: 38.30 ] took 0.0s
Sample: 17/20 RMSE: 1.03 (1samp: 1.00) U: [ 0: 59.79,1: 38.26 ] took 0.0s
Sample: 18/20 RMSE: 1.03 (1samp: 1.02) U: [ 0: 59.79,1: 38.20 ] took 0.0s
Sample: 19/20 RMSE: 1.04 (1samp: 1.15) U: [ 0: 59.97,1: 38.14 ] took 0.0s
Sample: 20/20 RMSE: 1.03 (1samp: 1.02) U: [ 0: 59.96,1: 38.20 ] took 0.0s
Running time of the cross validation 0 : 8.083680391311646
*** Cross validation 1 ***
PythonSession {
Data: {
Type: ScarceMatrixData [with NAs]
Component-wise mean: 0.15474
Component-wise variance: 6.87211
Noise: Fixed gaussian noise with precision: 5.00
Size: 53399 [667 x 400] (20.01%)
}
Model: {
Num-latents: 4
}
Priors: {
0: MacauPrior
SideInfo: SparseDouble [667, 500]
Method: CG Solver
Tol: 1.00e-06
BetaPrecision: 5.00
1: NormalPrior
}
Result: {
Test data: 5 [667 x 400] (0.00%)
}
Version: v0.12.3
Iterations: 10 burnin + 20 samples
Save model: every 1 iteration
Save prefix: H:\test\1/save
Save extension: .ddm
}
Initial: 0/0 RMSE: nan (1samp: nan) U: [ 0: 0.00,1: 0.00 ] took 0.0s
Burnin: 1/10 RMSE: nan (1samp: 2.87) U: [ 0: 16.28,1: 25.44 ] took 0.0s
Burnin: 2/10 RMSE: nan (1samp: 1.15) U: [ 0: 48.75,1: 37.21 ] took 0.0s
Burnin: 3/10 RMSE: nan (1samp: 1.06) U: [ 0: 54.96,1: 38.82 ] took 0.0s
Burnin: 4/10 RMSE: nan (1samp: 1.35) U: [ 0: 57.64,1: 39.30 ] took 0.0s
Burnin: 5/10 RMSE: nan (1samp: 1.41) U: [ 0: 58.21,1: 39.20 ] took 0.0s
Burnin: 6/10 RMSE: nan (1samp: 1.25) U: [ 0: 58.53,1: 39.10 ] took 0.0s
Burnin: 7/10 RMSE: nan (1samp: 1.34) U: [ 0: 58.57,1: 38.99 ] took 0.0s
Burnin: 8/10 RMSE: nan (1samp: 1.30) U: [ 0: 58.71,1: 39.00 ] took 0.0s
Burnin: 9/10 RMSE: nan (1samp: 1.28) U: [ 0: 58.69,1: 39.08 ] took 0.0s
Burnin: 10/10 RMSE: nan (1samp: 1.40) U: [ 0: 58.60,1: 39.11 ] took 0.0s
Sample: 1/20 RMSE: 1.29 (1samp: 1.29) U: [ 0: 58.56,1: 39.17 ] took 0.0s
Sample: 2/20 RMSE: 1.25 (1samp: 1.22) U: [ 0: 58.69,1: 39.10 ] took 0.0s
Sample: 3/20 RMSE: 1.27 (1samp: 1.30) U: [ 0: 58.77,1: 39.10 ] took 0.0s
Sample: 4/20 RMSE: 1.28 (1samp: 1.34) U: [ 0: 58.81,1: 39.08 ] took 0.0s
Sample: 5/20 RMSE: 1.27 (1samp: 1.20) U: [ 0: 58.81,1: 39.13 ] took 0.0s
Sample: 6/20 RMSE: 1.26 (1samp: 1.27) U: [ 0: 58.73,1: 39.16 ] took 0.0s
Sample: 7/20 RMSE: 1.26 (1samp: 1.23) U: [ 0: 58.75,1: 39.22 ] took 0.0s
Sample: 8/20 RMSE: 1.25 (1samp: 1.22) U: [ 0: 58.58,1: 39.27 ] took 0.0s
Sample: 9/20 RMSE: 1.26 (1samp: 1.29) U: [ 0: 58.66,1: 39.26 ] took 0.0s
Sample: 10/20 RMSE: 1.25 (1samp: 1.23) U: [ 0: 58.64,1: 39.22 ] took 0.0s
Sample: 11/20 RMSE: 1.25 (1samp: 1.19) U: [ 0: 58.81,1: 39.22 ] took 0.0s
Sample: 12/20 RMSE: 1.25 (1samp: 1.27) U: [ 0: 58.87,1: 39.19 ] took 0.0s
Sample: 13/20 RMSE: 1.25 (1samp: 1.31) U: [ 0: 58.94,1: 39.22 ] took 0.0s
Sample: 14/20 RMSE: 1.25 (1samp: 1.19) U: [ 0: 58.79,1: 39.23 ] took 0.0s
Sample: 15/20 RMSE: 1.25 (1samp: 1.30) U: [ 0: 58.79,1: 39.26 ] took 0.0s
Sample: 16/20 RMSE: 1.25 (1samp: 1.30) U: [ 0: 58.58,1: 39.27 ] took 0.0s
Sample: 17/20 RMSE: 1.25 (1samp: 1.27) U: [ 0: 58.60,1: 39.29 ] took 0.0s
Sample: 18/20 RMSE: 1.25 (1samp: 1.25) U: [ 0: 58.63,1: 39.19 ] took 0.0s
Sample: 19/20 RMSE: 1.25 (1samp: 1.21) U: [ 0: 58.68,1: 39.17 ] took 0.0s
Sample: 20/20 RMSE: 1.25 (1samp: 1.20) U: [ 0: 58.75,1: 39.16 ] took 0.0s
Running time of the cross validation 1 : 7.61978816986084
*** Cross validation 2 ***
PythonSession {
Data: {
Type: ScarceMatrixData [with NAs]
Component-wise mean: 0.118891
Component-wise variance: 6.71277
Noise: Fixed gaussian noise with precision: 5.00
Size: 53260 [666 x 400] (19.99%)
}
Model: {
Num-latents: 4
}
Priors: {
0: MacauPrior
SideInfo: SparseDouble [666, 500]
Method: CG Solver
Tol: 1.00e-06
BetaPrecision: 5.00
1: NormalPrior
}
Result: {
Test data: 5 [666 x 400] (0.00%)
}
Version: v0.12.3
Iterations: 10 burnin + 20 samples
Save model: every 1 iteration
Save prefix: H:\test\2/save
Save extension: .ddm
}
Initial: 0/0 RMSE: nan (1samp: nan) U: [ 0: 0.00,1: 0.00 ] took 0.0s
Burnin: 1/10 RMSE: nan (1samp: 3.23) U: [ 0: 16.20,1: 27.04 ] took 0.0s
Burnin: 2/10 RMSE: nan (1samp: 2.39) U: [ 0: 45.69,1: 41.66 ] took 0.0s
Burnin: 3/10 RMSE: nan (1samp: 0.83) U: [ 0: 51.09,1: 42.69 ] took 0.0s
Burnin: 4/10 RMSE: nan (1samp: 0.78) U: [ 0: 51.80,1: 43.11 ] took 0.0s
Burnin: 5/10 RMSE: nan (1samp: 0.86) U: [ 0: 52.78,1: 43.84 ] took 0.0s
Burnin: 6/10 RMSE: nan (1samp: 0.74) U: [ 0: 53.26,1: 43.93 ] took 0.0s
Burnin: 7/10 RMSE: nan (1samp: 0.72) U: [ 0: 53.45,1: 43.90 ] took 0.0s
Burnin: 8/10 RMSE: nan (1samp: 0.82) U: [ 0: 53.42,1: 43.93 ] took 0.0s
Burnin: 9/10 RMSE: nan (1samp: 0.77) U: [ 0: 53.42,1: 43.86 ] took 0.0s
Burnin: 10/10 RMSE: nan (1samp: 0.78) U: [ 0: 53.50,1: 43.83 ] took 0.0s
Sample: 1/20 RMSE: 0.75 (1samp: 0.75) U: [ 0: 53.42,1: 43.82 ] took 0.0s
Sample: 2/20 RMSE: 0.82 (1samp: 0.91) U: [ 0: 53.37,1: 43.81 ] took 0.0s
Sample: 3/20 RMSE: 0.80 (1samp: 0.76) U: [ 0: 53.43,1: 43.86 ] took 0.0s
Sample: 4/20 RMSE: 0.84 (1samp: 0.98) U: [ 0: 53.35,1: 43.90 ] took 0.0s
Sample: 5/20 RMSE: 0.84 (1samp: 0.84) U: [ 0: 53.35,1: 43.92 ] took 0.0s
Sample: 6/20 RMSE: 0.83 (1samp: 0.79) U: [ 0: 53.42,1: 43.92 ] took 0.0s
Sample: 7/20 RMSE: 0.82 (1samp: 0.81) U: [ 0: 53.46,1: 43.96 ] took 0.0s
Sample: 8/20 RMSE: 0.81 (1samp: 0.73) U: [ 0: 53.52,1: 43.92 ] took 0.0s
Sample: 9/20 RMSE: 0.82 (1samp: 0.86) U: [ 0: 53.56,1: 43.88 ] took 0.0s
Sample: 10/20 RMSE: 0.82 (1samp: 0.83) U: [ 0: 53.54,1: 43.83 ] took 0.0s
Sample: 11/20 RMSE: 0.82 (1samp: 0.81) U: [ 0: 53.61,1: 43.97 ] took 0.0s
Sample: 12/20 RMSE: 0.82 (1samp: 0.83) U: [ 0: 53.40,1: 43.98 ] took 0.0s
Sample: 13/20 RMSE: 0.81 (1samp: 0.70) U: [ 0: 53.48,1: 43.97 ] took 0.0s
Sample: 14/20 RMSE: 0.81 (1samp: 0.79) U: [ 0: 53.44,1: 43.94 ] took 0.0s
Sample: 15/20 RMSE: 0.80 (1samp: 0.77) U: [ 0: 53.49,1: 43.87 ] took 0.0s
Sample: 16/20 RMSE: 0.80 (1samp: 0.83) U: [ 0: 53.50,1: 43.91 ] took 0.0s
Sample: 17/20 RMSE: 0.80 (1samp: 0.67) U: [ 0: 53.50,1: 43.88 ] took 0.0s
Sample: 18/20 RMSE: 0.80 (1samp: 0.88) U: [ 0: 53.46,1: 43.93 ] took 0.0s
Sample: 19/20 RMSE: 0.80 (1samp: 0.85) U: [ 0: 53.48,1: 43.82 ] took 0.0s
Sample: 20/20 RMSE: 0.80 (1samp: 0.82) U: [ 0: 53.65,1: 43.71 ] took 0.0s
Running time of the cross validation 2 : 8.288750886917114
Out[15]:
index
rmse
train_time
pred_time
folder
0
0
1.377009
8.037556
0.046124
H:\test\0
1
1
1.356721
7.562632
0.057157
H:\test\1
2
2
1.376990
8.219568
0.069182
H:\test\2
In [ ]:
Content source: ExaScience/smurff
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