INTRO
This notebook is an introductory exploration of the E5 coral urol-e5/timeseries_molecular (GitHub repo) multi-species data using a tensor decomposition approach, using orkflow-stdm-02 (GitHub Repo), a tensor decomposition package Steven created using an AI agent.
The primary goal was simply to run the package and see what types of files/visualizations it generated.
Overall, it appers to function, in that the expected outputs are generated. However, the outputs make it a bit difficult to assess how and if the results are what we’d expect (primarily due to our lack of experience with tensor decomposition models). For stuff that can be assessed, there are minor issues like the code outputing array index values instead of strings (e.g. instead of species abbreviations, the numbers 0, 1, 2, & 3 are used; their positions in the array during analysis).
Additionaly, some of the plots that are generated are a bit suspect, suggesting the model isn’t functioning properly. As an example, the plot of Components and Factor Values across Time Points seems to only have two possible vlues for any of the components. This seems unlikely, I think?
Note
The contents below are from knitted markdown 13.00-multiomics-stdm-02.Rmd (commit 9bf0088).
1 BACKGROUND
This notebook performs tensor decomposition analysis on multi-species gene expression timeseries data using Steven’s workflow-stdm-02 (GitHub repo) tensor decomposition project.
This package was auto-generated based on instructions provided by Steven. He created a few different packages and wanted them tested. The goal of this notebook is primarily to see how easily this package runs and whether the output is useful (i.e. useful visualizations, human-readable results tables, etc.)
1.1 Input Data
The workflow-stdm-02 package processes a VST (Variance Stabilizing Transformation) counts matrix containing multi-species gene expression data across time points. The input file structure is:
- Input file:
vst_counts_matrix.csv(9,802 rows × 113 columns)- Genes: 8,820 ortholog groups (OG_XXXXX identifiers)
- Species: 3 coral species (ACR, POR, POC)
- Time points: 4 time points (TP1, TP2, TP3, TP4)
- Samples: Multiple biological replicates per species-timepoint combination
- Data format: VST-normalized gene expression values
The package performs tensor decomposition to identify latent factors that capture patterns across the gene × species × time dimensions using sparse CP (CANDECOMP/PARAFAC) decomposition with L1 regularization.
1.2 Output Structure
The workflow generates comprehensive output organized into four main directories:
output/
├── tensor/ # 3D tensor construction
│ ├── tensor.npz # Compressed 3D tensor (genes × species × timepoints)
│ ├── genes.csv # Gene ID mappings and indices
│ ├── species.csv # Species code mappings
│ ├── timepoints.csv # Timepoint mappings
│ └── tensor_shapes.json # Tensor dimensions and metadata
├── optimization/ # Hyperparameter optimization results
│ ├── best_params.json # Optimal rank and L1 penalties
│ ├── top_trials.csv # Best performing parameter combinations
│ ├── trials.csv # All optimization trials
│ └── optuna_study.pkl # Optuna study object
├── fit/ # Model fitting results
│ ├── gene_factors.csv # Gene factor loadings (A matrix)
│ ├── species_factors.csv # Species factor loadings (B matrix)
│ ├── time_factors.csv # Time factor loadings (C matrix)
│ ├── loss_history.csv # Convergence history
│ └── fit_metrics.json # Model performance metrics
└── export/ # Human-readable results with annotations
├── gene_factors_with_ids.csv # Gene factors with OG identifiers
├── species_factors_with_codes.csv # Species factors with species codes
├── time_factors_with_labels.csv # Time factors with timepoint labels
├── decomposition_results.json # Complete results summary
└── factor_heatmaps.png # Factor visualization plotsKey output metrics include reconstruction error, explained variance, sparsity levels, and convergence status. The final decomposition identifies latent factors that capture coordinated patterns of gene expression across species and time.
2 SETUP
2.1 Libraries
2.2 Set R variables
# OUTPUT DIRECTORY
output_dir <- "../output/13.00-multiomics-stdm"
#INPUT FILE(S)
# PYTHON ENVIRONMENT - Use the sr320-stdm-02 uv environment
python_path <- "/home/shared/16TB_HDD_01/sam/gitrepos/sr320/workflow-stdm-02/sr320-stdm-02-env/bin/python"2.3 Load project Python environment
The project uses the sr320-stdm-02-env uv virtual environment with the new-tensor package and all required dependencies. If this is successful, the Python path should show the sr320-stdm-02-env environment.
# Use the sr320-stdm-02-env uv environment
library(reticulate)
# Set Python path to the sr320-stdm-02-env environment
use_python(python_path, required = TRUE)
# Show final configuration
py_config()## python: /home/shared/16TB_HDD_01/sam/gitrepos/sr320/workflow-stdm-02/sr320-stdm-02-env/bin/python
## libpython: /usr/lib/python3.12/config-3.12-x86_64-linux-gnu/libpython3.12.so
## pythonhome: /home/shared/16TB_HDD_01/sam/gitrepos/sr320/workflow-stdm-02/sr320-stdm-02-env:/home/shared/16TB_HDD_01/sam/gitrepos/sr320/workflow-stdm-02/sr320-stdm-02-env
## virtualenv: /home/shared/16TB_HDD_01/sam/gitrepos/sr320/workflow-stdm-02/sr320-stdm-02-env/bin/activate_this.py
## version: 3.12.3 (main, Aug 14 2025, 17:47:21) [GCC 13.3.0]
## numpy: /home/shared/16TB_HDD_01/sam/gitrepos/sr320/workflow-stdm-02/sr320-stdm-02-env/lib/python3.12/site-packages/numpy
## numpy_version: 2.3.4
##
## NOTE: Python version was forced by use_python() function3 TENSOR DECOMPOSITION WORKFLOW
3.1 Step 1: Build Tensor
Build a 3D tensor from the VST counts matrix with replicate aggregation. The input data is already VST-normalized, so no additional normalization is applied.
import subprocess
import os
# Define paths
input_file = "../output/14-pca-orthologs/vst_counts_matrix.csv"
output_base = r.output_dir # Use R variable
# Create output directory structure
tensor_dir = os.path.join(output_base, "tensor")
os.makedirs(tensor_dir, exist_ok=True)
# Build tensor using the new-tensor CLI
# All parameters with their values (defaults noted in comments)
cmd = [
"/home/shared/16TB_HDD_01/sam/gitrepos/sr320/workflow-stdm-02/sr320-stdm-02-env/bin/new-tensor",
"build-tensor",
"--input-path", input_file, # Required: path to VST counts matrix
"--output-dir", tensor_dir, # Custom output directory
"--aggregation-method", "median", # Default: median (alternatives: mean, trimmed_mean)
"--normalization", "none", # Custom: none (default: zscore; alternatives: minmax, log1p)
"--min-expression", "1.0", # Default: 1.0 (minimum expression threshold)
"--min-variance-percentile", "10.0" # Default: 10.0 (variance percentile threshold)
]
print("Building tensor...")
print(f"Command: {' '.join(cmd)}")
result = subprocess.run(cmd, capture_output=True, text=True)
print(result.stdout)
if result.stderr:
print("Errors/Warnings:", result.stderr)
print(f"\nTensor saved to: {tensor_dir}")## Building tensor...
## Command: /home/shared/16TB_HDD_01/sam/gitrepos/sr320/workflow-stdm-02/sr320-stdm-02-env/bin/new-tensor build-tensor --input-path ../output/14-pca-orthologs/vst_counts_matrix.csv --output-dir ../output/13.00-multiomics-stdm/tensor --aggregation-method median --normalization none --min-expression 1.0 --min-variance-percentile 10.0
##
## Errors/Warnings: INFO:new_tensor.cli:Starting tensor construction
## INFO:new_tensor.io:Loading CSV from ../output/14-pca-orthologs/vst_counts_matrix.csv
## INFO:new_tensor.io:Loaded 9800 genes with 117 samples
## INFO:new_tensor.io:Input data validation completed
## INFO:new_tensor.io:Found species codes: ['ACR', 'POC', 'POR']
## INFO:new_tensor.io:Found timepoints: [1, 2, 3, 4]
## INFO:new_tensor.preprocess:Aggregating replicates using median method
## INFO:new_tensor.preprocess:Aggregated to 12 species-timepoint combinations
## INFO:new_tensor.preprocess:Filtering genes based on expression and variance
## INFO:new_tensor.preprocess:Expression filter: 9800/9800 genes
## INFO:new_tensor.preprocess:Variance filter: 8820/9800 genes
## INFO:new_tensor.preprocess:Combined filter: 8820/9800 genes
## INFO:new_tensor.preprocess:Skipping normalization
## INFO:new_tensor.tensor:Building 3D tensor from aggregated data
## INFO:new_tensor.tensor:Tensor dimensions: 8820 genes × 3 species × 4 timepoints
## INFO:new_tensor.tensor:Tensor density: 105840/105840 non-zero entries
## INFO:new_tensor.utils:Output directory: /home/shared/16TB_HDD_01/sam/gitrepos/urol-e5/timeseries_molecular/M-multi-species/output/13.00-multiomics-stdm/tensor (resolved from ../output/13.00-multiomics-stdm/tensor)
## INFO:new_tensor.tensor:Saved tensor to /home/shared/16TB_HDD_01/sam/gitrepos/urol-e5/timeseries_molecular/M-multi-species/output/13.00-multiomics-stdm/tensor/tensor.npz
## INFO:new_tensor.tensor:Saved tensor mappings to /home/shared/16TB_HDD_01/sam/gitrepos/urol-e5/timeseries_molecular/M-multi-species/output/13.00-multiomics-stdm/tensor
## INFO:new_tensor.cli:Tensor saved to /home/shared/16TB_HDD_01/sam/gitrepos/urol-e5/timeseries_molecular/M-multi-species/output/13.00-multiomics-stdm/tensor
##
## Tensor saved to: ../output/13.00-multiomics-stdm/tensor3.2 Step 2: Optimize Hyperparameters
Run hyperparameter optimization using Optuna to find the optimal rank and L1 penalty parameters.
import subprocess
import os
# Define paths
tensor_file = os.path.join(r.output_dir, "tensor", "tensor.npz")
optimization_dir = os.path.join(r.output_dir, "optimization")
os.makedirs(optimization_dir, exist_ok=True)
# Optimize hyperparameters using the new-tensor CLI
# All parameters with their values (defaults noted in comments)
cmd = [
"/home/shared/16TB_HDD_01/sam/gitrepos/sr320/workflow-stdm-02/sr320-stdm-02-env/bin/new-tensor",
"optimize",
"--tensor-path", tensor_file, # Required: path to tensor file
"--output-dir", optimization_dir, # Custom output directory
"--n-trials", "100", # Default: 100 (number of optimization trials)
"--rank-min", "4", # Custom: 4 (default: 2; minimum rank to try)
"--rank-max", "30", # Custom: 30 (default: 12; maximum rank to try)
"--lambda-min", "0.0001", # Default: 0.0001 (minimum L1 penalty)
"--lambda-max", "1.0" # Default: 1.0 (maximum L1 penalty)
]
print("Optimizing hyperparameters...")
print(f"Command: {' '.join(cmd)}")
result = subprocess.run(cmd, capture_output=True, text=True)
print(result.stdout)
if result.stderr:
print("Errors/Warnings:", result.stderr)
print(f"\nOptimization results saved to: {optimization_dir}")
print("\nCheck output/optimization/best_params.json for optimal rank.")## Optimizing hyperparameters...
## Command: /home/shared/16TB_HDD_01/sam/gitrepos/sr320/workflow-stdm-02/sr320-stdm-02-env/bin/new-tensor optimize --tensor-path ../output/13.00-multiomics-stdm/tensor/tensor.npz --output-dir ../output/13.00-multiomics-stdm/optimization --n-trials 100 --rank-min 4 --rank-max 30 --lambda-min 0.0001 --lambda-max 1.0
## [I 2025-10-16 08:25:28,056] Trial 0 finished with value: 9.91927074157517 and parameters: {'rank': 14, 'lambda_A': 0.6351221010640696, 'lambda_B': 0.08471801418819976, 'lambda_C': 0.024810409748678097, 'non_negative': True}. Best is trial 0 with value: 9.91927074157517.
## [I 2025-10-16 08:25:28,138] Trial 1 finished with value: 13.40153897664898 and parameters: {'rank': 5, 'lambda_A': 0.29154431891537513, 'lambda_B': 0.02537815508265665, 'lambda_C': 0.06796578090758151, 'non_negative': False}. Best is trial 0 with value: 9.91927074157517.
## [I 2025-10-16 08:25:28,493] Trial 2 finished with value: 37.931969759404275 and parameters: {'rank': 26, 'lambda_A': 0.0007068974950624604, 'lambda_B': 0.000533703276260396, 'lambda_C': 0.0005415244119402539, 'non_negative': False}. Best is trial 0 with value: 9.91927074157517.
## [I 2025-10-16 08:25:28,653] Trial 3 finished with value: 25.08601058074366 and parameters: {'rank': 15, 'lambda_A': 0.0014618962793704966, 'lambda_B': 0.0280163515871626, 'lambda_C': 0.0003613894271216529, 'non_negative': False}. Best is trial 0 with value: 9.91927074157517.
## [I 2025-10-16 08:25:28,867] Trial 4 finished with value: 11.163634949007252 and parameters: {'rank': 16, 'lambda_A': 0.13826232179369857, 'lambda_B': 0.0006290644294586153, 'lambda_C': 0.011400863701127324, 'non_negative': True}. Best is trial 0 with value: 9.91927074157517.
## [I 2025-10-16 08:25:29,138] Trial 5 finished with value: 12.323014419088114 and parameters: {'rank': 20, 'lambda_A': 0.00048094619675015767, 'lambda_B': 0.00018205657658407274, 'lambda_C': 0.6245139574743068, 'non_negative': True}. Best is trial 0 with value: 9.91927074157517.
## [I 2025-10-16 08:25:29,268] Trial 6 finished with value: 21.565007833488547 and parameters: {'rank': 12, 'lambda_A': 0.00024586032763280086, 'lambda_B': 0.054567254856014755, 'lambda_C': 0.005762487216478602, 'non_negative': False}. Best is trial 0 with value: 9.91927074157517.
## [I 2025-10-16 08:25:29,332] Trial 7 finished with value: 12.231858595297059 and parameters: {'rank': 4, 'lambda_A': 0.43379206974909373, 'lambda_B': 0.0010842262717330165, 'lambda_C': 0.04467752817973906, 'non_negative': False}. Best is trial 0 with value: 9.91927074157517.
## [I 2025-10-16 08:25:29,520] Trial 8 finished with value: 7.702411290712876 and parameters: {'rank': 18, 'lambda_A': 0.0005488047000766049, 'lambda_B': 0.755681014127442, 'lambda_C': 0.12604664585649453, 'non_negative': True}. Best is trial 8 with value: 7.702411290712876.
## [I 2025-10-16 08:25:29,852] Trial 9 finished with value: 30.27767484793075 and parameters: {'rank': 20, 'lambda_A': 0.48696409415208936, 'lambda_B': 0.00022592797420156976, 'lambda_C': 0.0006080390190296605, 'non_negative': False}. Best is trial 8 with value: 7.702411290712876.
## [I 2025-10-16 08:25:30,245] Trial 10 finished with value: 7.702411290712876 and parameters: {'rank': 29, 'lambda_A': 0.00782184644489916, 'lambda_B': 0.7553503645583184, 'lambda_C': 0.5997863556602815, 'non_negative': True}. Best is trial 8 with value: 7.702411290712876.
## [I 2025-10-16 08:25:30,625] Trial 11 finished with value: 7.702411290712876 and parameters: {'rank': 28, 'lambda_A': 0.01152597508263691, 'lambda_B': 0.8839094438875608, 'lambda_C': 0.7169219063367045, 'non_negative': True}. Best is trial 8 with value: 7.702411290712876.
## [I 2025-10-16 08:25:31,126] Trial 12 finished with value: 7.702411290712876 and parameters: {'rank': 30, 'lambda_A': 0.005751707844202112, 'lambda_B': 0.6697376781283138, 'lambda_C': 0.1711941591483064, 'non_negative': True}. Best is trial 8 with value: 7.702411290712876.
## [I 2025-10-16 08:25:31,381] Trial 13 finished with value: 14.419925492891778 and parameters: {'rank': 24, 'lambda_A': 0.026837193414400193, 'lambda_B': 0.25041198927005687, 'lambda_C': 0.20160609828133647, 'non_negative': True}. Best is trial 8 with value: 7.702411290712876.
## [I 2025-10-16 08:25:31,489] Trial 14 finished with value: 11.182859773230964 and parameters: {'rank': 9, 'lambda_A': 0.00012748888933395585, 'lambda_B': 0.003265509226695849, 'lambda_C': 0.18781323389598886, 'non_negative': True}. Best is trial 8 with value: 7.702411290712876.
## [I 2025-10-16 08:25:31,844] Trial 15 finished with value: 10.997196851341496 and parameters: {'rank': 21, 'lambda_A': 0.002507752044857025, 'lambda_B': 0.31327657894882527, 'lambda_C': 0.9057268923024396, 'non_negative': True}. Best is trial 8 with value: 7.702411290712876.
## [I 2025-10-16 08:25:32,100] Trial 16 finished with value: 14.531369814494486 and parameters: {'rank': 24, 'lambda_A': 0.03303111276278625, 'lambda_B': 0.15574290892508183, 'lambda_C': 0.0025862000456164697, 'non_negative': True}. Best is trial 8 with value: 7.702411290712876.
## [I 2025-10-16 08:25:32,216] Trial 17 finished with value: 8.860229829229166 and parameters: {'rank': 10, 'lambda_A': 0.003844180704394389, 'lambda_B': 0.012001686164551854, 'lambda_C': 0.2668178233993521, 'non_negative': True}. Best is trial 8 with value: 7.702411290712876.
## [I 2025-10-16 08:25:32,409] Trial 18 finished with value: 12.330409977605488 and parameters: {'rank': 18, 'lambda_A': 0.06923342300040311, 'lambda_B': 0.004579588598371669, 'lambda_C': 0.07373692003558777, 'non_negative': True}. Best is trial 8 with value: 7.702411290712876.
## [I 2025-10-16 08:25:32,811] Trial 19 finished with value: 7.702411290712876 and parameters: {'rank': 30, 'lambda_A': 0.011424502475070332, 'lambda_B': 0.7534933229670142, 'lambda_C': 0.00011065136021091021, 'non_negative': True}. Best is trial 8 with value: 7.702411290712876.
## [I 2025-10-16 08:25:33,060] Trial 20 finished with value: 15.72945212488288 and parameters: {'rank': 23, 'lambda_A': 0.0013978658013325288, 'lambda_B': 0.11072448678802875, 'lambda_C': 0.020860444459961155, 'non_negative': True}. Best is trial 8 with value: 7.702411290712876.
## [I 2025-10-16 08:25:33,431] Trial 21 finished with value: 7.702411290712876 and parameters: {'rank': 27, 'lambda_A': 0.008686865419501416, 'lambda_B': 0.9552243923232476, 'lambda_C': 0.6178449911287014, 'non_negative': True}. Best is trial 8 with value: 7.702411290712876.
## [I 2025-10-16 08:25:33,810] Trial 22 finished with value: 14.25441962188506 and parameters: {'rank': 28, 'lambda_A': 0.015216177542255406, 'lambda_B': 0.3682823087479855, 'lambda_C': 0.3641424562421856, 'non_negative': True}. Best is trial 8 with value: 7.702411290712876.
## [I 2025-10-16 08:25:34,273] Trial 23 finished with value: 11.93470491873419 and parameters: {'rank': 28, 'lambda_A': 0.056105382606156606, 'lambda_B': 0.4471215250907212, 'lambda_C': 0.9918005895525797, 'non_negative': True}. Best is trial 8 with value: 7.702411290712876.
## [I 2025-10-16 08:25:34,548] Trial 24 finished with value: 14.519596175255312 and parameters: {'rank': 26, 'lambda_A': 0.0006336522743157443, 'lambda_B': 0.17180167160471832, 'lambda_C': 0.09045267503127573, 'non_negative': True}. Best is trial 8 with value: 7.702411290712876.
## [I 2025-10-16 08:25:34,847] Trial 25 finished with value: 7.702411290712876 and parameters: {'rank': 22, 'lambda_A': 0.0020597175059583485, 'lambda_B': 0.8405753359041572, 'lambda_C': 0.37670819502931896, 'non_negative': True}. Best is trial 8 with value: 7.702411290712876.
## [I 2025-10-16 08:25:35,040] Trial 26 finished with value: 12.056497791050809 and parameters: {'rank': 18, 'lambda_A': 0.020217288216278686, 'lambda_B': 0.39191606617531327, 'lambda_C': 0.1119876399575546, 'non_negative': True}. Best is trial 8 with value: 7.702411290712876.
## [I 2025-10-16 08:25:35,446] Trial 27 finished with value: 20.37693747678954 and parameters: {'rank': 30, 'lambda_A': 0.00010800482900554439, 'lambda_B': 0.04251650563590359, 'lambda_C': 0.40277102115563174, 'non_negative': True}. Best is trial 8 with value: 7.702411290712876.
## [I 2025-10-16 08:25:35,711] Trial 28 finished with value: 14.491912943733603 and parameters: {'rank': 25, 'lambda_A': 0.005913877930777845, 'lambda_B': 0.20015668643827006, 'lambda_C': 0.045718387513843536, 'non_negative': True}. Best is trial 8 with value: 7.702411290712876.
## [I 2025-10-16 08:25:35,853] Trial 29 finished with value: 12.259785814371917 and parameters: {'rank': 12, 'lambda_A': 0.15059863513309535, 'lambda_B': 0.10337667684888936, 'lambda_C': 0.01557318063896172, 'non_negative': True}. Best is trial 8 with value: 7.702411290712876.
## [I 2025-10-16 08:25:35,953] Trial 30 finished with value: 11.153448552569126 and parameters: {'rank': 8, 'lambda_A': 0.0003022852418669405, 'lambda_B': 0.08813985910837592, 'lambda_C': 0.005246845012264985, 'non_negative': True}. Best is trial 8 with value: 7.702411290712876.
## [I 2025-10-16 08:25:36,451] Trial 31 finished with value: 7.702411290712876 and parameters: {'rank': 30, 'lambda_A': 0.004756662549199691, 'lambda_B': 0.6665849665132884, 'lambda_C': 0.17813256299118949, 'non_negative': True}. Best is trial 8 with value: 7.702411290712876.
## [I 2025-10-16 08:25:36,831] Trial 32 finished with value: 13.937715267904306 and parameters: {'rank': 28, 'lambda_A': 0.008846733308279646, 'lambda_B': 0.5223376281200467, 'lambda_C': 0.03627187721863262, 'non_negative': True}. Best is trial 8 with value: 7.702411290712876.
## [I 2025-10-16 08:25:37,139] Trial 33 finished with value: 7.702411290712876 and parameters: {'rank': 29, 'lambda_A': 0.0009945556840825593, 'lambda_B': 0.9202694747785678, 'lambda_C': 0.14456187515898158, 'non_negative': True}. Best is trial 8 with value: 7.702411290712876.
## [I 2025-10-16 08:25:37,497] Trial 34 finished with value: 33.194953037603625 and parameters: {'rank': 26, 'lambda_A': 0.0038364150186191086, 'lambda_B': 0.2888805332344143, 'lambda_C': 0.5624646678532733, 'non_negative': False}. Best is trial 8 with value: 7.702411290712876.
## [I 2025-10-16 08:25:37,869] Trial 35 finished with value: 11.877868413837776 and parameters: {'rank': 27, 'lambda_A': 0.03381177152383052, 'lambda_B': 0.5063166491716194, 'lambda_C': 0.32039237557244943, 'non_negative': True}. Best is trial 8 with value: 7.702411290712876.
## [I 2025-10-16 08:25:38,029] Trial 36 finished with value: 23.923016071869945 and parameters: {'rank': 14, 'lambda_A': 0.0027060769111988715, 'lambda_B': 0.02052447999033456, 'lambda_C': 0.108644413749255, 'non_negative': False}. Best is trial 8 with value: 7.702411290712876.
## [I 2025-10-16 08:25:38,372] Trial 37 finished with value: 14.588777473857384 and parameters: {'rank': 25, 'lambda_A': 0.007398515665034275, 'lambda_B': 0.05698581519024479, 'lambda_C': 0.5651352119267025, 'non_negative': True}. Best is trial 8 with value: 7.702411290712876.
## [I 2025-10-16 08:25:38,470] Trial 38 finished with value: 11.066703903036125 and parameters: {'rank': 6, 'lambda_A': 0.0014156313951346191, 'lambda_B': 0.19324693679480404, 'lambda_C': 0.945112740519758, 'non_negative': False}. Best is trial 8 with value: 7.702411290712876.
## [I 2025-10-16 08:25:38,858] Trial 39 finished with value: 7.702411290712876 and parameters: {'rank': 29, 'lambda_A': 0.013918970121871436, 'lambda_B': 0.5945357193954297, 'lambda_C': 0.059077682475768556, 'non_negative': True}. Best is trial 8 with value: 7.702411290712876.
## [I 2025-10-16 08:25:39,176] Trial 40 finished with value: 30.53716243996929 and parameters: {'rank': 20, 'lambda_A': 0.0004114412157967232, 'lambda_B': 0.12712036134256907, 'lambda_C': 0.23762634780232547, 'non_negative': False}. Best is trial 8 with value: 7.702411290712876.
## [I 2025-10-16 08:25:39,670] Trial 41 finished with value: 7.702411290712876 and parameters: {'rank': 30, 'lambda_A': 0.01392646946320707, 'lambda_B': 0.6442648339007867, 'lambda_C': 0.0002690793124588626, 'non_negative': True}. Best is trial 8 with value: 7.702411290712876.
## [I 2025-10-16 08:25:40,063] Trial 42 finished with value: 7.702411290712876 and parameters: {'rank': 29, 'lambda_A': 0.054123079865322186, 'lambda_B': 0.8726317095919451, 'lambda_C': 0.00011458422209579283, 'non_negative': True}. Best is trial 8 with value: 7.702411290712876.
## [I 2025-10-16 08:25:40,429] Trial 43 finished with value: 12.089143480743413 and parameters: {'rank': 27, 'lambda_A': 0.1160789381682145, 'lambda_B': 0.3516872326378421, 'lambda_C': 0.0012671298290049629, 'non_negative': True}. Best is trial 8 with value: 7.702411290712876.
## [I 2025-10-16 08:25:40,842] Trial 44 finished with value: 20.0567168822205 and parameters: {'rank': 30, 'lambda_A': 0.02146620338378346, 'lambda_B': 0.2450386714094701, 'lambda_C': 0.00541912112655133, 'non_negative': True}. Best is trial 8 with value: 7.702411290712876.
## [I 2025-10-16 08:25:41,169] Trial 45 finished with value: 13.891073846313365 and parameters: {'rank': 24, 'lambda_A': 0.011955770427101041, 'lambda_B': 0.5365962012017482, 'lambda_C': 0.0001100002711964987, 'non_negative': True}. Best is trial 8 with value: 7.702411290712876.
## [I 2025-10-16 08:25:41,337] Trial 46 finished with value: 13.512796900821591 and parameters: {'rank': 15, 'lambda_A': 0.00568207766416622, 'lambda_B': 0.002394739752960539, 'lambda_C': 0.0011983161343563638, 'non_negative': True}. Best is trial 8 with value: 7.702411290712876.
## [I 2025-10-16 08:25:41,717] Trial 47 finished with value: 14.385280823465715 and parameters: {'rank': 28, 'lambda_A': 0.00019297355872959033, 'lambda_B': 0.287194512068423, 'lambda_C': 0.16492015572273577, 'non_negative': True}. Best is trial 8 with value: 7.702411290712876.
## [I 2025-10-16 08:25:42,059] Trial 48 finished with value: 14.589433024362094 and parameters: {'rank': 25, 'lambda_A': 0.000987509378450424, 'lambda_B': 0.06361176702246522, 'lambda_C': 0.5108648506283376, 'non_negative': True}. Best is trial 8 with value: 7.702411290712876.
## [I 2025-10-16 08:25:42,372] Trial 49 finished with value: 7.702411290712876 and parameters: {'rank': 23, 'lambda_A': 0.0031868229082082943, 'lambda_B': 0.7060511236142365, 'lambda_C': 0.031164014938860073, 'non_negative': False}. Best is trial 8 with value: 7.702411290712876.
## [I 2025-10-16 08:25:42,737] Trial 50 finished with value: 7.702411290712876 and parameters: {'rank': 27, 'lambda_A': 0.03802544950289038, 'lambda_B': 0.9357919338705497, 'lambda_C': 0.008278699652681593, 'non_negative': True}. Best is trial 8 with value: 7.702411290712876.
## [I 2025-10-16 08:25:43,129] Trial 51 finished with value: 7.702411290712876 and parameters: {'rank': 29, 'lambda_A': 0.9702623759258517, 'lambda_B': 0.9586570849364209, 'lambda_C': 0.6239878027747205, 'non_negative': True}. Best is trial 8 with value: 7.702411290712876.
## [I 2025-10-16 08:25:43,483] Trial 52 finished with value: 14.125074000503421 and parameters: {'rank': 26, 'lambda_A': 0.009174155038684106, 'lambda_B': 0.4452807932868869, 'lambda_C': 0.26042577783587767, 'non_negative': True}. Best is trial 8 with value: 7.702411290712876.
## [I 2025-10-16 08:25:43,853] Trial 53 finished with value: 12.31665071272732 and parameters: {'rank': 27, 'lambda_A': 0.022297159364709593, 'lambda_B': 0.000122339319170651, 'lambda_C': 0.6471536851980759, 'non_negative': True}. Best is trial 8 with value: 7.702411290712876.
## [I 2025-10-16 08:25:44,266] Trial 54 finished with value: 20.437222277814218 and parameters: {'rank': 30, 'lambda_A': 0.002182612028671096, 'lambda_B': 0.0005874957066385832, 'lambda_C': 0.41100944952421026, 'non_negative': True}. Best is trial 8 with value: 7.702411290712876.
## [I 2025-10-16 08:25:44,646] Trial 55 finished with value: 13.142178900006481 and parameters: {'rank': 28, 'lambda_A': 0.007021601987749822, 'lambda_B': 0.35796282336451163, 'lambda_C': 0.8714440623369312, 'non_negative': True}. Best is trial 8 with value: 7.702411290712876.
## [I 2025-10-16 08:25:44,851] Trial 56 finished with value: 13.319296686266188 and parameters: {'rank': 19, 'lambda_A': 0.009667039650528148, 'lambda_B': 0.22942468333143828, 'lambda_C': 0.2804858578539912, 'non_negative': True}. Best is trial 8 with value: 7.702411290712876.
## [I 2025-10-16 08:25:45,087] Trial 57 finished with value: 21.392846473449225 and parameters: {'rank': 22, 'lambda_A': 0.01707569218224427, 'lambda_B': 0.13973419172472296, 'lambda_C': 0.06467991729781217, 'non_negative': True}. Best is trial 8 with value: 7.702411290712876.
## [I 2025-10-16 08:25:45,481] Trial 58 finished with value: 7.702411290712876 and parameters: {'rank': 29, 'lambda_A': 0.004661306217339234, 'lambda_B': 0.9975375683267106, 'lambda_C': 0.0033101676735387236, 'non_negative': True}. Best is trial 8 with value: 7.702411290712876.
## [I 2025-10-16 08:25:45,721] Trial 59 finished with value: 14.622532920808695 and parameters: {'rank': 17, 'lambda_A': 0.0017109817788444985, 'lambda_B': 0.0003263906431720573, 'lambda_C': 0.7549679623511959, 'non_negative': True}. Best is trial 8 with value: 7.702411290712876.
## [I 2025-10-16 08:25:45,857] Trial 60 finished with value: 12.337533866356377 and parameters: {'rank': 12, 'lambda_A': 0.027003696156245052, 'lambda_B': 0.006649150054778909, 'lambda_C': 0.1253442484135517, 'non_negative': True}. Best is trial 8 with value: 7.702411290712876.
## [I 2025-10-16 08:25:46,146] Trial 61 finished with value: 7.702411290712876 and parameters: {'rank': 21, 'lambda_A': 0.0006849905332388105, 'lambda_B': 0.7013540331395108, 'lambda_C': 0.4213384102686047, 'non_negative': True}. Best is trial 8 with value: 7.702411290712876.
## [I 2025-10-16 08:25:46,448] Trial 62 finished with value: 20.62820145598854 and parameters: {'rank': 22, 'lambda_A': 0.003282633048173829, 'lambda_B': 0.427426288633542, 'lambda_C': 0.21628459917822768, 'non_negative': True}. Best is trial 8 with value: 7.702411290712876.
## [I 2025-10-16 08:25:46,800] Trial 63 finished with value: 7.702411290712876 and parameters: {'rank': 26, 'lambda_A': 0.0021215656630557615, 'lambda_B': 0.75456695927985, 'lambda_C': 0.3334078633529735, 'non_negative': True}. Best is trial 8 with value: 7.702411290712876.
## [I 2025-10-16 08:25:47,284] Trial 64 finished with value: 7.702411290712876 and parameters: {'rank': 28, 'lambda_A': 0.006370834749726971, 'lambda_B': 0.599541030413235, 'lambda_C': 0.5300932950141273, 'non_negative': True}. Best is trial 8 with value: 7.702411290712876.
## [I 2025-10-16 08:25:47,482] Trial 65 finished with value: 15.22711981069581 and parameters: {'rank': 17, 'lambda_A': 0.000942298453875689, 'lambda_B': 0.4410771584938217, 'lambda_C': 0.019469530041028853, 'non_negative': True}. Best is trial 8 with value: 7.702411290712876.
## [I 2025-10-16 08:25:47,830] Trial 66 finished with value: 14.313011357733195 and parameters: {'rank': 24, 'lambda_A': 0.010810636813485226, 'lambda_B': 0.32755286741446893, 'lambda_C': 0.3913170623642701, 'non_negative': True}. Best is trial 8 with value: 7.702411290712876.
## [I 2025-10-16 08:25:48,260] Trial 67 finished with value: 7.702411290712876 and parameters: {'rank': 30, 'lambda_A': 0.0047062523582045625, 'lambda_B': 0.770160260950635, 'lambda_C': 0.09247248408398182, 'non_negative': False}. Best is trial 8 with value: 7.702411290712876.
## [I 2025-10-16 08:25:48,566] Trial 68 finished with value: 11.855945615730572 and parameters: {'rank': 27, 'lambda_A': 0.00794321899943011, 'lambda_B': 0.5205414779314799, 'lambda_C': 0.1866251326237264, 'non_negative': True}. Best is trial 8 with value: 7.702411290712876.
## [I 2025-10-16 08:25:48,840] Trial 69 finished with value: 12.295637474963636 and parameters: {'rank': 19, 'lambda_A': 0.017818969878239704, 'lambda_B': 0.03355514389935884, 'lambda_C': 0.7255937916240696, 'non_negative': True}. Best is trial 8 with value: 7.702411290712876.
## [I 2025-10-16 08:25:49,167] Trial 70 finished with value: 15.826208209385412 and parameters: {'rank': 29, 'lambda_A': 0.00020576736449091554, 'lambda_B': 0.01649091002559682, 'lambda_C': 0.0002767469804937398, 'non_negative': True}. Best is trial 8 with value: 7.702411290712876.
## [I 2025-10-16 08:25:49,690] Trial 71 finished with value: 7.702411290712876 and parameters: {'rank': 30, 'lambda_A': 0.004682374878204598, 'lambda_B': 0.6848942691611007, 'lambda_C': 0.1458748266820725, 'non_negative': True}. Best is trial 8 with value: 7.702411290712876.
## [I 2025-10-16 08:25:50,108] Trial 72 finished with value: 15.80105990989417 and parameters: {'rank': 29, 'lambda_A': 0.0004173787186088282, 'lambda_B': 0.0011529544120358283, 'lambda_C': 0.4742058117389902, 'non_negative': True}. Best is trial 8 with value: 7.702411290712876.
## [I 2025-10-16 08:25:50,632] Trial 73 finished with value: 7.702411290712876 and parameters: {'rank': 30, 'lambda_A': 0.01212198336015896, 'lambda_B': 0.5924770559489551, 'lambda_C': 0.32004671210870145, 'non_negative': True}. Best is trial 8 with value: 7.702411290712876.
## [I 2025-10-16 08:25:50,936] Trial 74 finished with value: 12.234489727409514 and parameters: {'rank': 27, 'lambda_A': 0.003924637870491414, 'lambda_B': 0.18137310874128393, 'lambda_C': 0.20975580042571393, 'non_negative': True}. Best is trial 8 with value: 7.702411290712876.
## [I 2025-10-16 08:25:51,428] Trial 75 finished with value: 13.2527783451824 and parameters: {'rank': 28, 'lambda_A': 0.0017514230152006144, 'lambda_B': 0.25331041899224466, 'lambda_C': 0.9605941196159318, 'non_negative': True}. Best is trial 8 with value: 7.702411290712876.
## [I 2025-10-16 08:25:51,838] Trial 76 finished with value: 7.702411290712876 and parameters: {'rank': 23, 'lambda_A': 0.005735743712639571, 'lambda_B': 0.8095296729776793, 'lambda_C': 0.2543165733907181, 'non_negative': False}. Best is trial 8 with value: 7.702411290712876.
## [I 2025-10-16 08:25:52,122] Trial 77 finished with value: 14.261155851513632 and parameters: {'rank': 25, 'lambda_A': 0.0027745736834864977, 'lambda_B': 0.37998859145228436, 'lambda_C': 0.047375789114311624, 'non_negative': True}. Best is trial 8 with value: 7.702411290712876.
## [I 2025-10-16 08:25:52,334] Trial 78 finished with value: 7.702411290712876 and parameters: {'rank': 14, 'lambda_A': 0.007562650866769657, 'lambda_B': 0.9846421914280261, 'lambda_C': 0.0811825818579365, 'non_negative': True}. Best is trial 8 with value: 7.702411290712876.
## [I 2025-10-16 08:25:52,660] Trial 79 finished with value: 13.964210118071469 and parameters: {'rank': 29, 'lambda_A': 0.013490008791369214, 'lambda_B': 0.5122101209088451, 'lambda_C': 0.11556257048473018, 'non_negative': True}. Best is trial 8 with value: 7.702411290712876.
## [I 2025-10-16 08:25:52,952] Trial 80 finished with value: 14.366274363446834 and parameters: {'rank': 26, 'lambda_A': 0.042057896376429385, 'lambda_B': 0.3028040815760118, 'lambda_C': 0.0004927029354670193, 'non_negative': True}. Best is trial 8 with value: 7.702411290712876.
## [I 2025-10-16 08:25:53,266] Trial 81 finished with value: 7.702411290712876 and parameters: {'rank': 28, 'lambda_A': 0.0010935490191139527, 'lambda_B': 0.7752882360303991, 'lambda_C': 0.1615221981850723, 'non_negative': True}. Best is trial 8 with value: 7.702411290712876.
## [I 2025-10-16 08:25:53,699] Trial 82 finished with value: 7.702411290712876 and parameters: {'rank': 30, 'lambda_A': 0.0008765424797641972, 'lambda_B': 0.6442067905338374, 'lambda_C': 0.33613658026806575, 'non_negative': True}. Best is trial 8 with value: 7.702411290712876.
## [I 2025-10-16 08:25:54,028] Trial 83 finished with value: 7.702411290712876 and parameters: {'rank': 29, 'lambda_A': 0.0005001692734654153, 'lambda_B': 0.9927996049673214, 'lambda_C': 0.72743611174937, 'non_negative': True}. Best is trial 8 with value: 7.702411290712876.
## [I 2025-10-16 08:25:54,434] Trial 84 finished with value: 14.070882233802486 and parameters: {'rank': 28, 'lambda_A': 0.0002911864727091217, 'lambda_B': 0.4735026543908035, 'lambda_C': 0.14476478145613322, 'non_negative': True}. Best is trial 8 with value: 7.702411290712876.
## [I 2025-10-16 08:25:54,765] Trial 85 finished with value: 7.702411290712876 and parameters: {'rank': 29, 'lambda_A': 0.001252971421324456, 'lambda_B': 0.5901916555236231, 'lambda_C': 0.01043457001669746, 'non_negative': True}. Best is trial 8 with value: 7.702411290712876.
## [I 2025-10-16 08:25:55,199] Trial 86 finished with value: 19.616375858762346 and parameters: {'rank': 30, 'lambda_A': 0.0099869267603346, 'lambda_B': 0.39529748670369264, 'lambda_C': 0.5002682365106321, 'non_negative': True}. Best is trial 8 with value: 7.702411290712876.
## [I 2025-10-16 08:25:55,598] Trial 87 finished with value: 7.702411290712876 and parameters: {'rank': 27, 'lambda_A': 0.002090721593835961, 'lambda_B': 0.7984899767324513, 'lambda_C': 0.054377972391540935, 'non_negative': False}. Best is trial 8 with value: 7.702411290712876.
## [I 2025-10-16 08:25:55,890] Trial 88 finished with value: 14.4071153194797 and parameters: {'rank': 26, 'lambda_A': 0.003464074934463693, 'lambda_B': 0.27806734931552274, 'lambda_C': 0.0012517500026353228, 'non_negative': True}. Best is trial 8 with value: 7.702411290712876.
## [I 2025-10-16 08:25:56,315] Trial 89 finished with value: 15.592048468109 and parameters: {'rank': 29, 'lambda_A': 0.026412565821738447, 'lambda_B': 0.21677956747590799, 'lambda_C': 0.09377005702439416, 'non_negative': True}. Best is trial 8 with value: 7.702411290712876.
## [I 2025-10-16 08:25:56,601] Trial 90 finished with value: 7.702411290712876 and parameters: {'rank': 25, 'lambda_A': 0.0008016365396662871, 'lambda_B': 0.811341733881362, 'lambda_C': 0.18713778689838095, 'non_negative': True}. Best is trial 8 with value: 7.702411290712876.
## [I 2025-10-16 08:25:57,138] Trial 91 finished with value: 18.992804982151714 and parameters: {'rank': 30, 'lambda_A': 0.016003528709477955, 'lambda_B': 0.5444384031724905, 'lambda_C': 0.03640258529267616, 'non_negative': True}. Best is trial 8 with value: 7.702411290712876.
## [I 2025-10-16 08:25:57,539] Trial 92 finished with value: 7.702411290712876 and parameters: {'rank': 28, 'lambda_A': 0.0056218209168558755, 'lambda_B': 0.6366110826933181, 'lambda_C': 0.0632980719116021, 'non_negative': True}. Best is trial 8 with value: 7.702411290712876.
## [I 2025-10-16 08:25:57,940] Trial 93 finished with value: 12.02210808803982 and parameters: {'rank': 27, 'lambda_A': 0.0005068952575351023, 'lambda_B': 0.4160601423928182, 'lambda_C': 0.2888571735494726, 'non_negative': True}. Best is trial 8 with value: 7.702411290712876.
## [I 2025-10-16 08:25:58,353] Trial 94 finished with value: 7.702411290712876 and parameters: {'rank': 29, 'lambda_A': 0.00814758013913939, 'lambda_B': 0.8435594946569757, 'lambda_C': 0.00013978122483368087, 'non_negative': True}. Best is trial 8 with value: 7.702411290712876.
## [I 2025-10-16 08:25:58,753] Trial 95 finished with value: 7.702411290712876 and parameters: {'rank': 28, 'lambda_A': 0.01235337242848547, 'lambda_B': 0.9854879445406823, 'lambda_C': 0.0018337432308381811, 'non_negative': True}. Best is trial 8 with value: 7.702411290712876.
## [I 2025-10-16 08:25:59,280] Trial 96 finished with value: 7.702411290712876 and parameters: {'rank': 30, 'lambda_A': 0.004309557114685113, 'lambda_B': 0.6164066994882925, 'lambda_C': 0.11409480914141784, 'non_negative': True}. Best is trial 8 with value: 7.702411290712876.
## [I 2025-10-16 08:25:59,707] Trial 97 finished with value: 15.405266708083769 and parameters: {'rank': 29, 'lambda_A': 0.0015994631526042925, 'lambda_B': 0.3327026145566166, 'lambda_C': 0.4309146009765499, 'non_negative': True}. Best is trial 8 with value: 7.702411290712876.
## [I 2025-10-16 08:25:59,998] Trial 98 finished with value: 10.864846727267931 and parameters: {'rank': 16, 'lambda_A': 0.0027865119502672887, 'lambda_B': 0.47634398324957056, 'lambda_C': 0.6053103536005864, 'non_negative': True}. Best is trial 8 with value: 7.702411290712876.
## [I 2025-10-16 08:26:00,270] Trial 99 finished with value: 7.702411290712876 and parameters: {'rank': 19, 'lambda_A': 0.018556489315822426, 'lambda_B': 0.7190810423979014, 'lambda_C': 0.22679695624884716, 'non_negative': True}. Best is trial 8 with value: 7.702411290712876.
## Errors/Warnings: INFO:new_tensor.cli:Starting hyperparameter optimization
## INFO:new_tensor.tensor:Loaded tensor from ../output/13.00-multiomics-stdm/tensor/tensor.npz with shape (8820, 3, 4)
## INFO:new_tensor.optimize:Starting hyperparameter optimization with 100 trials
## [I 2025-10-16 08:25:27,879] A new study created in memory with name: cp_optimization
##
## 0%| | 0/100 [00:00<?, ?it/s]INFO:new_tensor.model:Fitting sparse CP decomposition with rank=14
## INFO:new_tensor.model:L1 penalties: λ_A=0.6351221010640696, λ_B=0.08471801418819976, λ_C=0.024810409748678097
## INFO:new_tensor.model:Non-negative: True
## INFO:new_tensor.model:CP decomposition completed in 0.15s
## INFO:new_tensor.model:Final metrics: R²=0.0182, sparsity_A=0.8571
##
##
##
## 0%| | 0/100 [00:00<?, ?it/s]
## Best trial: 0. Best value: 9.91927: 0%| | 0/100 [00:00<?, ?it/s]
## Best trial: 0. Best value: 9.91927: 1%| | 1/100 [00:00<00:17, 5.74it/s]INFO:new_tensor.model:Fitting sparse CP decomposition with rank=5
## INFO:new_tensor.model:L1 penalties: λ_A=0.29154431891537513, λ_B=0.02537815508265665, λ_C=0.06796578090758151
## INFO:new_tensor.model:Non-negative: False
## INFO:new_tensor.model:CP decomposition completed in 0.07s
## INFO:new_tensor.model:Final metrics: R²=0.0496, sparsity_A=0.0000
##
##
##
## Best trial: 0. Best value: 9.91927: 1%| | 1/100 [00:00<00:17, 5.74it/s]
## Best trial: 0. Best value: 9.91927: 1%| | 1/100 [00:00<00:17, 5.74it/s]INFO:new_tensor.model:Fitting sparse CP decomposition with rank=26
## INFO:new_tensor.model:L1 penalties: λ_A=0.0007068974950624604, λ_B=0.000533703276260396, λ_C=0.0005415244119402539
## INFO:new_tensor.model:Non-negative: False
## INFO:new_tensor.model:CP decomposition completed in 0.32s
## INFO:new_tensor.model:Final metrics: R²=0.2595, sparsity_A=0.0000
##
##
##
## Best trial: 0. Best value: 9.91927: 2%|▏ | 2/100 [00:00<00:17, 5.74it/s]
## Best trial: 0. Best value: 9.91927: 2%|▏ | 2/100 [00:00<00:17, 5.74it/s]
## Best trial: 0. Best value: 9.91927: 3%|▎ | 3/100 [00:00<00:20, 4.82it/s]INFO:new_tensor.model:Fitting sparse CP decomposition with rank=15
## INFO:new_tensor.model:L1 penalties: λ_A=0.0014618962793704966, λ_B=0.0280163515871626, λ_C=0.0003613894271216529
## INFO:new_tensor.model:Non-negative: False
## INFO:new_tensor.model:CP decomposition completed in 0.14s
## INFO:new_tensor.model:Final metrics: R²=0.1521, sparsity_A=0.0000
##
##
##
## Best trial: 0. Best value: 9.91927: 3%|▎ | 3/100 [00:00<00:20, 4.82it/s]
## Best trial: 0. Best value: 9.91927: 3%|▎ | 3/100 [00:00<00:20, 4.82it/s]
## Best trial: 0. Best value: 9.91927: 4%|▍ | 4/100 [00:00<00:18, 5.24it/s]INFO:new_tensor.model:Fitting sparse CP decomposition with rank=16
## INFO:new_tensor.model:L1 penalties: λ_A=0.13826232179369857, λ_B=0.0006290644294586153, λ_C=0.011400863701127324
## INFO:new_tensor.model:Non-negative: True
## INFO:new_tensor.model:CP decomposition completed in 0.19s
## INFO:new_tensor.model:Final metrics: R²=0.0312, sparsity_A=0.8125
##
##
##
## Best trial: 0. Best value: 9.91927: 4%|▍ | 4/100 [00:00<00:18, 5.24it/s]
## Best trial: 0. Best value: 9.91927: 4%|▍ | 4/100 [00:00<00:18, 5.24it/s]
## Best trial: 0. Best value: 9.91927: 5%|▌ | 5/100 [00:00<00:18, 5.04it/s]INFO:new_tensor.model:Fitting sparse CP decomposition with rank=20
## INFO:new_tensor.model:L1 penalties: λ_A=0.00048094619675015767, λ_B=0.00018205657658407274, λ_C=0.6245139574743068
## INFO:new_tensor.model:Non-negative: True
## INFO:new_tensor.model:CP decomposition completed in 0.25s
## INFO:new_tensor.model:Final metrics: R²=0.0411, sparsity_A=0.8000
##
##
##
## Best trial: 0. Best value: 9.91927: 5%|▌ | 5/100 [00:01<00:18, 5.04it/s]
## Best trial: 0. Best value: 9.91927: 5%|▌ | 5/100 [00:01<00:18, 5.04it/s]
## Best trial: 0. Best value: 9.91927: 6%|▌ | 6/100 [00:01<00:20, 4.51it/s]INFO:new_tensor.model:Fitting sparse CP decomposition with rank=12
## INFO:new_tensor.model:L1 penalties: λ_A=0.00024586032763280086, λ_B=0.054567254856014755, λ_C=0.005762487216478602
## INFO:new_tensor.model:Non-negative: False
## INFO:new_tensor.model:CP decomposition completed in 0.11s
## INFO:new_tensor.model:Final metrics: R²=0.1207, sparsity_A=0.0000
##
##
##
## Best trial: 0. Best value: 9.91927: 6%|▌ | 6/100 [00:01<00:20, 4.51it/s]
## Best trial: 0. Best value: 9.91927: 6%|▌ | 6/100 [00:01<00:20, 4.51it/s]
## Best trial: 0. Best value: 9.91927: 7%|▋ | 7/100 [00:01<00:17, 5.18it/s]INFO:new_tensor.model:Fitting sparse CP decomposition with rank=4
## INFO:new_tensor.model:L1 penalties: λ_A=0.43379206974909373, λ_B=0.0010842262717330165, λ_C=0.04467752817973906
## INFO:new_tensor.model:Non-negative: False
## INFO:new_tensor.model:CP decomposition completed in 0.05s
## INFO:new_tensor.model:Final metrics: R²=0.0393, sparsity_A=0.0000
##
##
##
## Best trial: 0. Best value: 9.91927: 7%|▋ | 7/100 [00:01<00:17, 5.18it/s]
## Best trial: 0. Best value: 9.91927: 7%|▋ | 7/100 [00:01<00:17, 5.18it/s]INFO:new_tensor.model:Fitting sparse CP decomposition with rank=18
## INFO:new_tensor.model:L1 penalties: λ_A=0.0005488047000766049, λ_B=0.755681014127442, λ_C=0.12604664585649453
## INFO:new_tensor.model:Non-negative: True
## INFO:new_tensor.model:CP decomposition completed in 0.17s
## INFO:new_tensor.model:Final metrics: R²=0.0000, sparsity_A=1.0000
##
##
##
## Best trial: 0. Best value: 9.91927: 8%|▊ | 8/100 [00:01<00:17, 5.18it/s]
## Best trial: 8. Best value: 7.70241: 8%|▊ | 8/100 [00:01<00:17, 5.18it/s]
## Best trial: 8. Best value: 7.70241: 9%|▉ | 9/100 [00:01<00:14, 6.20it/s]INFO:new_tensor.model:Fitting sparse CP decomposition with rank=20
## INFO:new_tensor.model:L1 penalties: λ_A=0.48696409415208936, λ_B=0.00022592797420156976, λ_C=0.0006080390190296605
## INFO:new_tensor.model:Non-negative: False
## INFO:new_tensor.model:CP decomposition completed in 0.31s
## INFO:new_tensor.model:Final metrics: R²=0.1873, sparsity_A=0.0000
##
##
##
## Best trial: 8. Best value: 7.70241: 9%|▉ | 9/100 [00:01<00:14, 6.20it/s]
## Best trial: 8. Best value: 7.70241: 9%|▉ | 9/100 [00:01<00:14, 6.20it/s]
## Best trial: 8. Best value: 7.70241: 10%|█ | 10/100 [00:01<00:18, 4.89it/s]INFO:new_tensor.model:Fitting sparse CP decomposition with rank=29
## INFO:new_tensor.model:L1 penalties: λ_A=0.00782184644489916, λ_B=0.7553503645583184, λ_C=0.5997863556602815
## INFO:new_tensor.model:Non-negative: True
## INFO:new_tensor.model:CP decomposition completed in 0.35s
## INFO:new_tensor.model:Final metrics: R²=0.0000, sparsity_A=1.0000
##
##
##
## Best trial: 8. Best value: 7.70241: 10%|█ | 10/100 [00:02<00:18, 4.89it/s]
## Best trial: 8. Best value: 7.70241: 10%|█ | 10/100 [00:02<00:18, 4.89it/s]
## Best trial: 8. Best value: 7.70241: 11%|█ | 11/100 [00:02<00:22, 3.93it/s]INFO:new_tensor.model:Fitting sparse CP decomposition with rank=28
## INFO:new_tensor.model:L1 penalties: λ_A=0.01152597508263691, λ_B=0.8839094438875608, λ_C=0.7169219063367045
## INFO:new_tensor.model:Non-negative: True
## INFO:new_tensor.model:CP decomposition completed in 0.34s
## INFO:new_tensor.model:Final metrics: R²=0.0000, sparsity_A=1.0000
##
##
##
## Best trial: 8. Best value: 7.70241: 11%|█ | 11/100 [00:02<00:22, 3.93it/s]
## Best trial: 8. Best value: 7.70241: 11%|█ | 11/100 [00:02<00:22, 3.93it/s]
## Best trial: 8. Best value: 7.70241: 12%|█▏ | 12/100 [00:02<00:25, 3.46it/s]INFO:new_tensor.model:Fitting sparse CP decomposition with rank=30
## INFO:new_tensor.model:L1 penalties: λ_A=0.005751707844202112, λ_B=0.6697376781283138, λ_C=0.1711941591483064
## INFO:new_tensor.model:Non-negative: True
## INFO:new_tensor.model:CP decomposition completed in 0.46s
## INFO:new_tensor.model:Final metrics: R²=0.0000, sparsity_A=1.0000
##
##
##
## Best trial: 8. Best value: 7.70241: 12%|█▏ | 12/100 [00:03<00:25, 3.46it/s]
## Best trial: 8. Best value: 7.70241: 12%|█▏ | 12/100 [00:03<00:25, 3.46it/s]
## Best trial: 8. Best value: 7.70241: 13%|█▎ | 13/100 [00:03<00:30, 2.87it/s]INFO:new_tensor.model:Fitting sparse CP decomposition with rank=24
## INFO:new_tensor.model:L1 penalties: λ_A=0.026837193414400193, λ_B=0.25041198927005687, λ_C=0.20160609828133647
## INFO:new_tensor.model:Non-negative: True
## INFO:new_tensor.model:CP decomposition completed in 0.22s
## INFO:new_tensor.model:Final metrics: R²=0.0518, sparsity_A=0.7500
##
##
##
## Best trial: 8. Best value: 7.70241: 13%|█▎ | 13/100 [00:03<00:30, 2.87it/s]
## Best trial: 8. Best value: 7.70241: 13%|█▎ | 13/100 [00:03<00:30, 2.87it/s]
## Best trial: 8. Best value: 7.70241: 14%|█▍ | 14/100 [00:03<00:27, 3.11it/s]INFO:new_tensor.model:Fitting sparse CP decomposition with rank=9
## INFO:new_tensor.model:L1 penalties: λ_A=0.00012748888933395585, λ_B=0.003265509226695849, λ_C=0.18781323389598886
## INFO:new_tensor.model:Non-negative: True
## INFO:new_tensor.model:CP decomposition completed in 0.08s
## INFO:new_tensor.model:Final metrics: R²=0.0316, sparsity_A=0.6667
##
##
##
## Best trial: 8. Best value: 7.70241: 14%|█▍ | 14/100 [00:03<00:27, 3.11it/s]
## Best trial: 8. Best value: 7.70241: 14%|█▍ | 14/100 [00:03<00:27, 3.11it/s]
## Best trial: 8. Best value: 7.70241: 15%|█▌ | 15/100 [00:03<00:22, 3.85it/s]INFO:new_tensor.model:Fitting sparse CP decomposition with rank=21
## INFO:new_tensor.model:L1 penalties: λ_A=0.002507752044857025, λ_B=0.31327657894882527, λ_C=0.9057268923024396
## INFO:new_tensor.model:Non-negative: True
## INFO:new_tensor.model:CP decomposition completed in 0.32s
## INFO:new_tensor.model:Final metrics: R²=0.0230, sparsity_A=0.8571
##
##
##
## Best trial: 8. Best value: 7.70241: 15%|█▌ | 15/100 [00:03<00:22, 3.85it/s]
## Best trial: 8. Best value: 7.70241: 15%|█▌ | 15/100 [00:03<00:22, 3.85it/s]
## Best trial: 8. Best value: 7.70241: 16%|█▌ | 16/100 [00:03<00:24, 3.48it/s]INFO:new_tensor.model:Fitting sparse CP decomposition with rank=24
## INFO:new_tensor.model:L1 penalties: λ_A=0.03303111276278625, λ_B=0.15574290892508183, λ_C=0.0025862000456164697
## INFO:new_tensor.model:Non-negative: True
## INFO:new_tensor.model:CP decomposition completed in 0.22s
## INFO:new_tensor.model:Final metrics: R²=0.0569, sparsity_A=0.7500
##
##
##
## Best trial: 8. Best value: 7.70241: 16%|█▌ | 16/100 [00:04<00:24, 3.48it/s]
## Best trial: 8. Best value: 7.70241: 16%|█▌ | 16/100 [00:04<00:24, 3.48it/s]
## Best trial: 8. Best value: 7.70241: 17%|█▋ | 17/100 [00:04<00:23, 3.59it/s]INFO:new_tensor.model:Fitting sparse CP decomposition with rank=10
## INFO:new_tensor.model:L1 penalties: λ_A=0.003844180704394389, λ_B=0.012001686164551854, λ_C=0.2668178233993521
## INFO:new_tensor.model:Non-negative: True
## INFO:new_tensor.model:CP decomposition completed in 0.09s
## INFO:new_tensor.model:Final metrics: R²=0.0105, sparsity_A=0.9000
##
##
##
## Best trial: 8. Best value: 7.70241: 17%|█▋ | 17/100 [00:04<00:23, 3.59it/s]
## Best trial: 8. Best value: 7.70241: 17%|█▋ | 17/100 [00:04<00:23, 3.59it/s]
## Best trial: 8. Best value: 7.70241: 18%|█▊ | 18/100 [00:04<00:18, 4.34it/s]INFO:new_tensor.model:Fitting sparse CP decomposition with rank=18
## INFO:new_tensor.model:L1 penalties: λ_A=0.06923342300040311, λ_B=0.004579588598371669, λ_C=0.07373692003558777
## INFO:new_tensor.model:Non-negative: True
## INFO:new_tensor.model:CP decomposition completed in 0.16s
## INFO:new_tensor.model:Final metrics: R²=0.0418, sparsity_A=0.7778
##
##
##
## Best trial: 8. Best value: 7.70241: 18%|█▊ | 18/100 [00:04<00:18, 4.34it/s]
## Best trial: 8. Best value: 7.70241: 18%|█▊ | 18/100 [00:04<00:18, 4.34it/s]
## Best trial: 8. Best value: 7.70241: 19%|█▉ | 19/100 [00:04<00:17, 4.57it/s]INFO:new_tensor.model:Fitting sparse CP decomposition with rank=30
## INFO:new_tensor.model:L1 penalties: λ_A=0.011424502475070332, λ_B=0.7534933229670142, λ_C=0.00011065136021091021
## INFO:new_tensor.model:Non-negative: True
## INFO:new_tensor.model:CP decomposition completed in 0.36s
## INFO:new_tensor.model:Final metrics: R²=0.0000, sparsity_A=1.0000
##
##
##
## Best trial: 8. Best value: 7.70241: 19%|█▉ | 19/100 [00:04<00:17, 4.57it/s]
## Best trial: 8. Best value: 7.70241: 19%|█▉ | 19/100 [00:04<00:17, 4.57it/s]
## Best trial: 8. Best value: 7.70241: 20%|██ | 20/100 [00:04<00:21, 3.65it/s]INFO:new_tensor.model:Fitting sparse CP decomposition with rank=23
## INFO:new_tensor.model:L1 penalties: λ_A=0.0013978658013325288, λ_B=0.11072448678802875, λ_C=0.020860444459961155
## INFO:new_tensor.model:Non-negative: True
## INFO:new_tensor.model:CP decomposition completed in 0.21s
## INFO:new_tensor.model:Final metrics: R²=0.0687, sparsity_A=0.6957
##
##
##
## Best trial: 8. Best value: 7.70241: 20%|██ | 20/100 [00:05<00:21, 3.65it/s]
## Best trial: 8. Best value: 7.70241: 20%|██ | 20/100 [00:05<00:21, 3.65it/s]
## Best trial: 8. Best value: 7.70241: 21%|██ | 21/100 [00:05<00:21, 3.76it/s]INFO:new_tensor.model:Fitting sparse CP decomposition with rank=27
## INFO:new_tensor.model:L1 penalties: λ_A=0.008686865419501416, λ_B=0.9552243923232476, λ_C=0.6178449911287014
## INFO:new_tensor.model:Non-negative: True
## INFO:new_tensor.model:CP decomposition completed in 0.33s
## INFO:new_tensor.model:Final metrics: R²=0.0000, sparsity_A=1.0000
##
##
##
## Best trial: 8. Best value: 7.70241: 21%|██ | 21/100 [00:05<00:21, 3.76it/s]
## Best trial: 8. Best value: 7.70241: 21%|██ | 21/100 [00:05<00:21, 3.76it/s]
## Best trial: 8. Best value: 7.70241: 22%|██▏ | 22/100 [00:05<00:23, 3.36it/s]INFO:new_tensor.model:Fitting sparse CP decomposition with rank=28
## INFO:new_tensor.model:L1 penalties: λ_A=0.015216177542255406, λ_B=0.3682823087479855, λ_C=0.3641424562421856
## INFO:new_tensor.model:Non-negative: True
## INFO:new_tensor.model:CP decomposition completed in 0.34s
## INFO:new_tensor.model:Final metrics: R²=0.0441, sparsity_A=0.7857
##
##
##
## Best trial: 8. Best value: 7.70241: 22%|██▏ | 22/100 [00:05<00:23, 3.36it/s]
## Best trial: 8. Best value: 7.70241: 22%|██▏ | 22/100 [00:05<00:23, 3.36it/s]
## Best trial: 8. Best value: 7.70241: 23%|██▎ | 23/100 [00:05<00:24, 3.11it/s]INFO:new_tensor.model:Fitting sparse CP decomposition with rank=28
## INFO:new_tensor.model:L1 penalties: λ_A=0.056105382606156606, λ_B=0.4471215250907212, λ_C=0.9918005895525797
## INFO:new_tensor.model:Non-negative: True
## INFO:new_tensor.model:CP decomposition completed in 0.42s
## INFO:new_tensor.model:Final metrics: R²=0.0234, sparsity_A=0.8571
##
##
##
## Best trial: 8. Best value: 7.70241: 23%|██▎ | 23/100 [00:06<00:24, 3.11it/s]
## Best trial: 8. Best value: 7.70241: 23%|██▎ | 23/100 [00:06<00:24, 3.11it/s]
## Best trial: 8. Best value: 7.70241: 24%|██▍ | 24/100 [00:06<00:27, 2.74it/s]INFO:new_tensor.model:Fitting sparse CP decomposition with rank=26
## INFO:new_tensor.model:L1 penalties: λ_A=0.0006336522743157443, λ_B=0.17180167160471832, λ_C=0.09045267503127573
## INFO:new_tensor.model:Non-negative: True
## INFO:new_tensor.model:CP decomposition completed in 0.24s
## INFO:new_tensor.model:Final metrics: R²=0.0562, sparsity_A=0.7692
##
##
##
## Best trial: 8. Best value: 7.70241: 24%|██▍ | 24/100 [00:06<00:27, 2.74it/s]
## Best trial: 8. Best value: 7.70241: 24%|██▍ | 24/100 [00:06<00:27, 2.74it/s]
## Best trial: 8. Best value: 7.70241: 25%|██▌ | 25/100 [00:06<00:25, 2.96it/s]INFO:new_tensor.model:Fitting sparse CP decomposition with rank=22
## INFO:new_tensor.model:L1 penalties: λ_A=0.0020597175059583485, λ_B=0.8405753359041572, λ_C=0.37670819502931896
## INFO:new_tensor.model:Non-negative: True
## INFO:new_tensor.model:CP decomposition completed in 0.26s
## INFO:new_tensor.model:Final metrics: R²=0.0000, sparsity_A=1.0000
##
##
##
## Best trial: 8. Best value: 7.70241: 25%|██▌ | 25/100 [00:06<00:25, 2.96it/s]
## Best trial: 8. Best value: 7.70241: 25%|██▌ | 25/100 [00:06<00:25, 2.96it/s]
## Best trial: 8. Best value: 7.70241: 26%|██▌ | 26/100 [00:06<00:24, 3.07it/s]INFO:new_tensor.model:Fitting sparse CP decomposition with rank=18
## INFO:new_tensor.model:L1 penalties: λ_A=0.020217288216278686, λ_B=0.39191606617531327, λ_C=0.1119876399575546
## INFO:new_tensor.model:Non-negative: True
## INFO:new_tensor.model:CP decomposition completed in 0.16s
## INFO:new_tensor.model:Final metrics: R²=0.0289, sparsity_A=0.7778
##
##
##
## Best trial: 8. Best value: 7.70241: 26%|██▌ | 26/100 [00:07<00:24, 3.07it/s]
## Best trial: 8. Best value: 7.70241: 26%|██▌ | 26/100 [00:07<00:24, 3.07it/s]
## Best trial: 8. Best value: 7.70241: 27%|██▋ | 27/100 [00:07<00:20, 3.49it/s]INFO:new_tensor.model:Fitting sparse CP decomposition with rank=30
## INFO:new_tensor.model:L1 penalties: λ_A=0.00010800482900554439, λ_B=0.04251650563590359, λ_C=0.40277102115563174
## INFO:new_tensor.model:Non-negative: True
## INFO:new_tensor.model:CP decomposition completed in 0.37s
## INFO:new_tensor.model:Final metrics: R²=0.1095, sparsity_A=0.6333
##
##
##
## Best trial: 8. Best value: 7.70241: 27%|██▋ | 27/100 [00:07<00:20, 3.49it/s]
## Best trial: 8. Best value: 7.70241: 27%|██▋ | 27/100 [00:07<00:20, 3.49it/s]
## Best trial: 8. Best value: 7.70241: 28%|██▊ | 28/100 [00:07<00:23, 3.10it/s]INFO:new_tensor.model:Fitting sparse CP decomposition with rank=25
## INFO:new_tensor.model:L1 penalties: λ_A=0.005913877930777845, λ_B=0.20015668643827006, λ_C=0.045718387513843536
## INFO:new_tensor.model:Non-negative: True
## INFO:new_tensor.model:CP decomposition completed in 0.23s
## INFO:new_tensor.model:Final metrics: R²=0.0550, sparsity_A=0.7600
##
##
##
## Best trial: 8. Best value: 7.70241: 28%|██▊ | 28/100 [00:07<00:23, 3.10it/s]
## Best trial: 8. Best value: 7.70241: 28%|██▊ | 28/100 [00:07<00:23, 3.10it/s]
## Best trial: 8. Best value: 7.70241: 29%|██▉ | 29/100 [00:07<00:21, 3.28it/s]INFO:new_tensor.model:Fitting sparse CP decomposition with rank=12
## INFO:new_tensor.model:L1 penalties: λ_A=0.15059863513309535, λ_B=0.10337667684888936, λ_C=0.01557318063896172
## INFO:new_tensor.model:Non-negative: True
## INFO:new_tensor.model:CP decomposition completed in 0.11s
## INFO:new_tensor.model:Final metrics: R²=0.0389, sparsity_A=0.6667
##
##
##
## Best trial: 8. Best value: 7.70241: 29%|██▉ | 29/100 [00:07<00:21, 3.28it/s]
## Best trial: 8. Best value: 7.70241: 29%|██▉ | 29/100 [00:07<00:21, 3.28it/s]
## Best trial: 8. Best value: 7.70241: 30%|███ | 30/100 [00:07<00:17, 3.90it/s]INFO:new_tensor.model:Fitting sparse CP decomposition with rank=8
## INFO:new_tensor.model:L1 penalties: λ_A=0.0003022852418669405, λ_B=0.08813985910837592, λ_C=0.005246845012264985
## INFO:new_tensor.model:Non-negative: True
## INFO:new_tensor.model:CP decomposition completed in 0.07s
## INFO:new_tensor.model:Final metrics: R²=0.0303, sparsity_A=0.6250
##
##
##
## Best trial: 8. Best value: 7.70241: 30%|███ | 30/100 [00:08<00:17, 3.90it/s]
## Best trial: 8. Best value: 7.70241: 30%|███ | 30/100 [00:08<00:17, 3.90it/s]INFO:new_tensor.model:Fitting sparse CP decomposition with rank=30
## INFO:new_tensor.model:L1 penalties: λ_A=0.004756662549199691, λ_B=0.6665849665132884, λ_C=0.17813256299118949
## INFO:new_tensor.model:Non-negative: True
## INFO:new_tensor.model:CP decomposition completed in 0.46s
## INFO:new_tensor.model:Final metrics: R²=0.0000, sparsity_A=1.0000
##
##
##
## Best trial: 8. Best value: 7.70241: 31%|███ | 31/100 [00:08<00:17, 3.90it/s]
## Best trial: 8. Best value: 7.70241: 31%|███ | 31/100 [00:08<00:17, 3.90it/s]
## Best trial: 8. Best value: 7.70241: 32%|███▏ | 32/100 [00:08<00:18, 3.63it/s]INFO:new_tensor.model:Fitting sparse CP decomposition with rank=28
## INFO:new_tensor.model:L1 penalties: λ_A=0.008846733308279646, λ_B=0.5223376281200467, λ_C=0.03627187721863262
## INFO:new_tensor.model:Non-negative: True
## INFO:new_tensor.model:CP decomposition completed in 0.34s
## INFO:new_tensor.model:Final metrics: R²=0.0294, sparsity_A=0.7857
##
##
##
## Best trial: 8. Best value: 7.70241: 32%|███▏ | 32/100 [00:08<00:18, 3.63it/s]
## Best trial: 8. Best value: 7.70241: 32%|███▏ | 32/100 [00:08<00:18, 3.63it/s]
## Best trial: 8. Best value: 7.70241: 33%|███▎ | 33/100 [00:08<00:20, 3.31it/s]INFO:new_tensor.model:Fitting sparse CP decomposition with rank=29
## INFO:new_tensor.model:L1 penalties: λ_A=0.0009945556840825593, λ_B=0.9202694747785678, λ_C=0.14456187515898158
## INFO:new_tensor.model:Non-negative: True
## INFO:new_tensor.model:CP decomposition completed in 0.27s
## INFO:new_tensor.model:Final metrics: R²=0.0000, sparsity_A=1.0000
##
##
##
## Best trial: 8. Best value: 7.70241: 33%|███▎ | 33/100 [00:09<00:20, 3.31it/s]
## Best trial: 8. Best value: 7.70241: 33%|███▎ | 33/100 [00:09<00:20, 3.31it/s]
## Best trial: 8. Best value: 7.70241: 34%|███▍ | 34/100 [00:09<00:20, 3.30it/s]INFO:new_tensor.model:Fitting sparse CP decomposition with rank=26
## INFO:new_tensor.model:L1 penalties: λ_A=0.0038364150186191086, λ_B=0.2888805332344143, λ_C=0.5624646678532733
## INFO:new_tensor.model:Non-negative: False
## INFO:new_tensor.model:CP decomposition completed in 0.32s
## INFO:new_tensor.model:Final metrics: R²=0.1796, sparsity_A=0.1154
##
##
##
## Best trial: 8. Best value: 7.70241: 34%|███▍ | 34/100 [00:09<00:20, 3.30it/s]
## Best trial: 8. Best value: 7.70241: 34%|███▍ | 34/100 [00:09<00:20, 3.30it/s]
## Best trial: 8. Best value: 7.70241: 35%|███▌ | 35/100 [00:09<00:20, 3.14it/s]INFO:new_tensor.model:Fitting sparse CP decomposition with rank=27
## INFO:new_tensor.model:L1 penalties: λ_A=0.03381177152383052, λ_B=0.5063166491716194, λ_C=0.32039237557244943
## INFO:new_tensor.model:Non-negative: True
## INFO:new_tensor.model:CP decomposition completed in 0.33s
## INFO:new_tensor.model:Final metrics: R²=0.0206, sparsity_A=0.8519
##
##
##
## Best trial: 8. Best value: 7.70241: 35%|███▌ | 35/100 [00:09<00:20, 3.14it/s]
## Best trial: 8. Best value: 7.70241: 35%|███▌ | 35/100 [00:09<00:20, 3.14it/s]
## Best trial: 8. Best value: 7.70241: 36%|███▌ | 36/100 [00:09<00:21, 3.00it/s]INFO:new_tensor.model:Fitting sparse CP decomposition with rank=14
## INFO:new_tensor.model:L1 penalties: λ_A=0.0027060769111988715, λ_B=0.02052447999033456, λ_C=0.108644413749255
## INFO:new_tensor.model:Non-negative: False
## INFO:new_tensor.model:CP decomposition completed in 0.13s
## INFO:new_tensor.model:Final metrics: R²=0.1422, sparsity_A=0.0000
##
##
##
## Best trial: 8. Best value: 7.70241: 36%|███▌ | 36/100 [00:10<00:21, 3.00it/s]
## Best trial: 8. Best value: 7.70241: 36%|███▌ | 36/100 [00:10<00:21, 3.00it/s]
## Best trial: 8. Best value: 7.70241: 37%|███▋ | 37/100 [00:10<00:17, 3.52it/s]INFO:new_tensor.model:Fitting sparse CP decomposition with rank=25
## INFO:new_tensor.model:L1 penalties: λ_A=0.007398515665034275, λ_B=0.05698581519024479, λ_C=0.5651352119267025
## INFO:new_tensor.model:Non-negative: True
## INFO:new_tensor.model:CP decomposition completed in 0.31s
## INFO:new_tensor.model:Final metrics: R²=0.0594, sparsity_A=0.7600
##
##
##
## Best trial: 8. Best value: 7.70241: 37%|███▋ | 37/100 [00:10<00:17, 3.52it/s]
## Best trial: 8. Best value: 7.70241: 37%|███▋ | 37/100 [00:10<00:17, 3.52it/s]
## Best trial: 8. Best value: 7.70241: 38%|███▊ | 38/100 [00:10<00:18, 3.32it/s]INFO:new_tensor.model:Fitting sparse CP decomposition with rank=6
## INFO:new_tensor.model:L1 penalties: λ_A=0.0014156313951346191, λ_B=0.19324693679480404, λ_C=0.945112740519758
## INFO:new_tensor.model:Non-negative: False
## INFO:new_tensor.model:CP decomposition completed in 0.07s
## INFO:new_tensor.model:Final metrics: R²=0.0263, sparsity_A=0.5000
##
##
##
## Best trial: 8. Best value: 7.70241: 38%|███▊ | 38/100 [00:10<00:18, 3.32it/s]
## Best trial: 8. Best value: 7.70241: 38%|███▊ | 38/100 [00:10<00:18, 3.32it/s]INFO:new_tensor.model:Fitting sparse CP decomposition with rank=29
## INFO:new_tensor.model:L1 penalties: λ_A=0.013918970121871436, λ_B=0.5945357193954297, λ_C=0.059077682475768556
## INFO:new_tensor.model:Non-negative: True
## INFO:new_tensor.model:CP decomposition completed in 0.35s
## INFO:new_tensor.model:Final metrics: R²=0.0000, sparsity_A=1.0000
##
##
##
## Best trial: 8. Best value: 7.70241: 39%|███▉ | 39/100 [00:10<00:18, 3.32it/s]
## Best trial: 8. Best value: 7.70241: 39%|███▉ | 39/100 [00:10<00:18, 3.32it/s]
## Best trial: 8. Best value: 7.70241: 40%|████ | 40/100 [00:10<00:16, 3.64it/s]INFO:new_tensor.model:Fitting sparse CP decomposition with rank=20
## INFO:new_tensor.model:L1 penalties: λ_A=0.0004114412157967232, λ_B=0.12712036134256907, λ_C=0.23762634780232547
## INFO:new_tensor.model:Non-negative: False
## INFO:new_tensor.model:CP decomposition completed in 0.28s
## INFO:new_tensor.model:Final metrics: R²=0.1855, sparsity_A=0.0000
##
##
##
## Best trial: 8. Best value: 7.70241: 40%|████ | 40/100 [00:11<00:16, 3.64it/s]
## Best trial: 8. Best value: 7.70241: 40%|████ | 40/100 [00:11<00:16, 3.64it/s]
## Best trial: 8. Best value: 7.70241: 41%|████ | 41/100 [00:11<00:16, 3.51it/s]INFO:new_tensor.model:Fitting sparse CP decomposition with rank=30
## INFO:new_tensor.model:L1 penalties: λ_A=0.01392646946320707, λ_B=0.6442648339007867, λ_C=0.0002690793124588626
## INFO:new_tensor.model:Non-negative: True
## INFO:new_tensor.model:CP decomposition completed in 0.45s
## INFO:new_tensor.model:Final metrics: R²=0.0000, sparsity_A=1.0000
##
##
##
## Best trial: 8. Best value: 7.70241: 41%|████ | 41/100 [00:11<00:16, 3.51it/s]
## Best trial: 8. Best value: 7.70241: 41%|████ | 41/100 [00:11<00:16, 3.51it/s]
## Best trial: 8. Best value: 7.70241: 42%|████▏ | 42/100 [00:11<00:19, 2.95it/s]INFO:new_tensor.model:Fitting sparse CP decomposition with rank=29
## INFO:new_tensor.model:L1 penalties: λ_A=0.054123079865322186, λ_B=0.8726317095919451, λ_C=0.00011458422209579283
## INFO:new_tensor.model:Non-negative: True
## INFO:new_tensor.model:CP decomposition completed in 0.35s
## INFO:new_tensor.model:Final metrics: R²=0.0000, sparsity_A=1.0000
##
##
##
## Best trial: 8. Best value: 7.70241: 42%|████▏ | 42/100 [00:12<00:19, 2.95it/s]
## Best trial: 8. Best value: 7.70241: 42%|████▏ | 42/100 [00:12<00:19, 2.95it/s]
## Best trial: 8. Best value: 7.70241: 43%|████▎ | 43/100 [00:12<00:20, 2.83it/s]INFO:new_tensor.model:Fitting sparse CP decomposition with rank=27
## INFO:new_tensor.model:L1 penalties: λ_A=0.1160789381682145, λ_B=0.3516872326378421, λ_C=0.0012671298290049629
## INFO:new_tensor.model:Non-negative: True
## INFO:new_tensor.model:CP decomposition completed in 0.33s
## INFO:new_tensor.model:Final metrics: R²=0.0307, sparsity_A=0.8519
##
##
##
## Best trial: 8. Best value: 7.70241: 43%|████▎ | 43/100 [00:12<00:20, 2.83it/s]
## Best trial: 8. Best value: 7.70241: 43%|████▎ | 43/100 [00:12<00:20, 2.83it/s]
## Best trial: 8. Best value: 7.70241: 44%|████▍ | 44/100 [00:12<00:20, 2.80it/s]INFO:new_tensor.model:Fitting sparse CP decomposition with rank=30
## INFO:new_tensor.model:L1 penalties: λ_A=0.02146620338378346, λ_B=0.2450386714094701, λ_C=0.00541912112655133
## INFO:new_tensor.model:Non-negative: True
## INFO:new_tensor.model:CP decomposition completed in 0.37s
## INFO:new_tensor.model:Final metrics: R²=0.0956, sparsity_A=0.6333
##
##
##
## Best trial: 8. Best value: 7.70241: 44%|████▍ | 44/100 [00:12<00:20, 2.80it/s]
## Best trial: 8. Best value: 7.70241: 44%|████▍ | 44/100 [00:12<00:20, 2.80it/s]
## Best trial: 8. Best value: 7.70241: 45%|████▌ | 45/100 [00:12<00:20, 2.68it/s]INFO:new_tensor.model:Fitting sparse CP decomposition with rank=24
## INFO:new_tensor.model:L1 penalties: λ_A=0.011955770427101041, λ_B=0.5365962012017482, λ_C=0.0001100002711964987
## INFO:new_tensor.model:Non-negative: True
## INFO:new_tensor.model:CP decomposition completed in 0.29s
## INFO:new_tensor.model:Final metrics: R²=0.0272, sparsity_A=0.7500
##
##
##
## Best trial: 8. Best value: 7.70241: 45%|████▌ | 45/100 [00:13<00:20, 2.68it/s]
## Best trial: 8. Best value: 7.70241: 45%|████▌ | 45/100 [00:13<00:20, 2.68it/s]
## Best trial: 8. Best value: 7.70241: 46%|████▌ | 46/100 [00:13<00:19, 2.78it/s]INFO:new_tensor.model:Fitting sparse CP decomposition with rank=15
## INFO:new_tensor.model:L1 penalties: λ_A=0.00568207766416622, λ_B=0.002394739752960539, λ_C=0.0011983161343563638
## INFO:new_tensor.model:Non-negative: True
## INFO:new_tensor.model:CP decomposition completed in 0.14s
## INFO:new_tensor.model:Final metrics: R²=0.0529, sparsity_A=0.6667
##
##
##
## Best trial: 8. Best value: 7.70241: 46%|████▌ | 46/100 [00:13<00:19, 2.78it/s]
## Best trial: 8. Best value: 7.70241: 46%|████▌ | 46/100 [00:13<00:19, 2.78it/s]
## Best trial: 8. Best value: 7.70241: 47%|████▋ | 47/100 [00:13<00:16, 3.29it/s]INFO:new_tensor.model:Fitting sparse CP decomposition with rank=28
## INFO:new_tensor.model:L1 penalties: λ_A=0.00019297355872959033, λ_B=0.287194512068423, λ_C=0.16492015572273577
## INFO:new_tensor.model:Non-negative: True
## INFO:new_tensor.model:CP decomposition completed in 0.34s
## INFO:new_tensor.model:Final metrics: R²=0.0501, sparsity_A=0.7857
##
##
##
## Best trial: 8. Best value: 7.70241: 47%|████▋ | 47/100 [00:13<00:16, 3.29it/s]
## Best trial: 8. Best value: 7.70241: 47%|████▋ | 47/100 [00:13<00:16, 3.29it/s]
## Best trial: 8. Best value: 7.70241: 48%|████▊ | 48/100 [00:13<00:16, 3.07it/s]INFO:new_tensor.model:Fitting sparse CP decomposition with rank=25
## INFO:new_tensor.model:L1 penalties: λ_A=0.000987509378450424, λ_B=0.06361176702246522, λ_C=0.5108648506283376
## INFO:new_tensor.model:Non-negative: True
## INFO:new_tensor.model:CP decomposition completed in 0.30s
## INFO:new_tensor.model:Final metrics: R²=0.0594, sparsity_A=0.7600
##
##
##
## Best trial: 8. Best value: 7.70241: 48%|████▊ | 48/100 [00:14<00:16, 3.07it/s]
## Best trial: 8. Best value: 7.70241: 48%|████▊ | 48/100 [00:14<00:16, 3.07it/s]
## Best trial: 8. Best value: 7.70241: 49%|████▉ | 49/100 [00:14<00:16, 3.02it/s]INFO:new_tensor.model:Fitting sparse CP decomposition with rank=23
## INFO:new_tensor.model:L1 penalties: λ_A=0.0031868229082082943, λ_B=0.7060511236142365, λ_C=0.031164014938860073
## INFO:new_tensor.model:Non-negative: False
## INFO:new_tensor.model:CP decomposition completed in 0.28s
## INFO:new_tensor.model:Final metrics: R²=0.0000, sparsity_A=1.0000
##
##
##
## Best trial: 8. Best value: 7.70241: 49%|████▉ | 49/100 [00:14<00:16, 3.02it/s]
## Best trial: 8. Best value: 7.70241: 49%|████▉ | 49/100 [00:14<00:16, 3.02it/s]
## Best trial: 8. Best value: 7.70241: 50%|█████ | 50/100 [00:14<00:16, 3.07it/s]INFO:new_tensor.model:Fitting sparse CP decomposition with rank=27
## INFO:new_tensor.model:L1 penalties: λ_A=0.03802544950289038, λ_B=0.9357919338705497, λ_C=0.008278699652681593
## INFO:new_tensor.model:Non-negative: True
## INFO:new_tensor.model:CP decomposition completed in 0.33s
## INFO:new_tensor.model:Final metrics: R²=0.0000, sparsity_A=1.0000
##
##
##
## Best trial: 8. Best value: 7.70241: 50%|█████ | 50/100 [00:14<00:16, 3.07it/s]
## Best trial: 8. Best value: 7.70241: 50%|█████ | 50/100 [00:14<00:16, 3.07it/s]
## Best trial: 8. Best value: 7.70241: 51%|█████ | 51/100 [00:14<00:16, 2.96it/s]INFO:new_tensor.model:Fitting sparse CP decomposition with rank=29
## INFO:new_tensor.model:L1 penalties: λ_A=0.9702623759258517, λ_B=0.9586570849364209, λ_C=0.6239878027747205
## INFO:new_tensor.model:Non-negative: True
## INFO:new_tensor.model:CP decomposition completed in 0.35s
## INFO:new_tensor.model:Final metrics: R²=0.0000, sparsity_A=1.0000
##
##
##
## Best trial: 8. Best value: 7.70241: 51%|█████ | 51/100 [00:15<00:16, 2.96it/s]
## Best trial: 8. Best value: 7.70241: 51%|█████ | 51/100 [00:15<00:16, 2.96it/s]
## Best trial: 8. Best value: 7.70241: 52%|█████▏ | 52/100 [00:15<00:16, 2.83it/s]INFO:new_tensor.model:Fitting sparse CP decomposition with rank=26
## INFO:new_tensor.model:L1 penalties: λ_A=0.009174155038684106, λ_B=0.4452807932868869, λ_C=0.26042577783587767
## INFO:new_tensor.model:Non-negative: True
## INFO:new_tensor.model:CP decomposition completed in 0.31s
## INFO:new_tensor.model:Final metrics: R²=0.0382, sparsity_A=0.7692
##
##
##
## Best trial: 8. Best value: 7.70241: 52%|█████▏ | 52/100 [00:15<00:16, 2.83it/s]
## Best trial: 8. Best value: 7.70241: 52%|█████▏ | 52/100 [00:15<00:16, 2.83it/s]
## Best trial: 8. Best value: 7.70241: 53%|█████▎ | 53/100 [00:15<00:16, 2.83it/s]INFO:new_tensor.model:Fitting sparse CP decomposition with rank=27
## INFO:new_tensor.model:L1 penalties: λ_A=0.022297159364709593, λ_B=0.000122339319170651, λ_C=0.6471536851980759
## INFO:new_tensor.model:Non-negative: True
## INFO:new_tensor.model:CP decomposition completed in 0.33s
## INFO:new_tensor.model:Final metrics: R²=0.0409, sparsity_A=0.8519
##
##
##
## Best trial: 8. Best value: 7.70241: 53%|█████▎ | 53/100 [00:15<00:16, 2.83it/s]
## Best trial: 8. Best value: 7.70241: 53%|█████▎ | 53/100 [00:15<00:16, 2.83it/s]
## Best trial: 8. Best value: 7.70241: 54%|█████▍ | 54/100 [00:15<00:16, 2.79it/s]INFO:new_tensor.model:Fitting sparse CP decomposition with rank=30
## INFO:new_tensor.model:L1 penalties: λ_A=0.002182612028671096, λ_B=0.0005874957066385832, λ_C=0.41100944952421026
## INFO:new_tensor.model:Non-negative: True
## INFO:new_tensor.model:CP decomposition completed in 0.37s
## INFO:new_tensor.model:Final metrics: R²=0.1122, sparsity_A=0.6333
##
##
##
## Best trial: 8. Best value: 7.70241: 54%|█████▍ | 54/100 [00:16<00:16, 2.79it/s]
## Best trial: 8. Best value: 7.70241: 54%|█████▍ | 54/100 [00:16<00:16, 2.79it/s]
## Best trial: 8. Best value: 7.70241: 55%|█████▌ | 55/100 [00:16<00:16, 2.67it/s]INFO:new_tensor.model:Fitting sparse CP decomposition with rank=28
## INFO:new_tensor.model:L1 penalties: λ_A=0.007021601987749822, λ_B=0.35796282336451163, λ_C=0.8714440623369312
## INFO:new_tensor.model:Non-negative: True
## INFO:new_tensor.model:CP decomposition completed in 0.34s
## INFO:new_tensor.model:Final metrics: R²=0.0359, sparsity_A=0.8214
##
##
##
## Best trial: 8. Best value: 7.70241: 55%|█████▌ | 55/100 [00:16<00:16, 2.67it/s]
## Best trial: 8. Best value: 7.70241: 55%|█████▌ | 55/100 [00:16<00:16, 2.67it/s]
## Best trial: 8. Best value: 7.70241: 56%|█████▌ | 56/100 [00:16<00:16, 2.66it/s]INFO:new_tensor.model:Fitting sparse CP decomposition with rank=19
## INFO:new_tensor.model:L1 penalties: λ_A=0.009667039650528148, λ_B=0.22942468333143828, λ_C=0.2804858578539912
## INFO:new_tensor.model:Non-negative: True
## INFO:new_tensor.model:CP decomposition completed in 0.17s
## INFO:new_tensor.model:Final metrics: R²=0.0440, sparsity_A=0.7368
##
##
##
## Best trial: 8. Best value: 7.70241: 56%|█████▌ | 56/100 [00:16<00:16, 2.66it/s]
## Best trial: 8. Best value: 7.70241: 56%|█████▌ | 56/100 [00:16<00:16, 2.66it/s]
## Best trial: 8. Best value: 7.70241: 57%|█████▋ | 57/100 [00:16<00:13, 3.08it/s]INFO:new_tensor.model:Fitting sparse CP decomposition with rank=22
## INFO:new_tensor.model:L1 penalties: λ_A=0.01707569218224427, λ_B=0.13973419172472296, λ_C=0.06467991729781217
## INFO:new_tensor.model:Non-negative: True
## INFO:new_tensor.model:CP decomposition completed in 0.20s
## INFO:new_tensor.model:Final metrics: R²=0.1134, sparsity_A=0.4545
##
##
##
## Best trial: 8. Best value: 7.70241: 57%|█████▋ | 57/100 [00:17<00:13, 3.08it/s]
## Best trial: 8. Best value: 7.70241: 57%|█████▋ | 57/100 [00:17<00:13, 3.08it/s]
## Best trial: 8. Best value: 7.70241: 58%|█████▊ | 58/100 [00:17<00:12, 3.35it/s]INFO:new_tensor.model:Fitting sparse CP decomposition with rank=29
## INFO:new_tensor.model:L1 penalties: λ_A=0.004661306217339234, λ_B=0.9975375683267106, λ_C=0.0033101676735387236
## INFO:new_tensor.model:Non-negative: True
## INFO:new_tensor.model:CP decomposition completed in 0.35s
## INFO:new_tensor.model:Final metrics: R²=0.0000, sparsity_A=1.0000
##
##
##
## Best trial: 8. Best value: 7.70241: 58%|█████▊ | 58/100 [00:17<00:12, 3.35it/s]
## Best trial: 8. Best value: 7.70241: 58%|█████▊ | 58/100 [00:17<00:12, 3.35it/s]
## Best trial: 8. Best value: 7.70241: 59%|█████▉ | 59/100 [00:17<00:13, 3.06it/s]INFO:new_tensor.model:Fitting sparse CP decomposition with rank=17
## INFO:new_tensor.model:L1 penalties: λ_A=0.0017109817788444985, λ_B=0.0003263906431720573, λ_C=0.7549679623511959
## INFO:new_tensor.model:Non-negative: True
## INFO:new_tensor.model:CP decomposition completed in 0.21s
## INFO:new_tensor.model:Final metrics: R²=0.0609, sparsity_A=0.6471
##
##
##
## Best trial: 8. Best value: 7.70241: 59%|█████▉ | 59/100 [00:17<00:13, 3.06it/s]
## Best trial: 8. Best value: 7.70241: 59%|█████▉ | 59/100 [00:17<00:13, 3.06it/s]
## Best trial: 8. Best value: 7.70241: 60%|██████ | 60/100 [00:17<00:12, 3.32it/s]INFO:new_tensor.model:Fitting sparse CP decomposition with rank=12
## INFO:new_tensor.model:L1 penalties: λ_A=0.027003696156245052, λ_B=0.006649150054778909, λ_C=0.1253442484135517
## INFO:new_tensor.model:Non-negative: True
## INFO:new_tensor.model:CP decomposition completed in 0.11s
## INFO:new_tensor.model:Final metrics: R²=0.0419, sparsity_A=0.6667
##
##
##
## Best trial: 8. Best value: 7.70241: 60%|██████ | 60/100 [00:17<00:12, 3.32it/s]
## Best trial: 8. Best value: 7.70241: 60%|██████ | 60/100 [00:17<00:12, 3.32it/s]
## Best trial: 8. Best value: 7.70241: 61%|██████ | 61/100 [00:17<00:09, 3.98it/s]INFO:new_tensor.model:Fitting sparse CP decomposition with rank=21
## INFO:new_tensor.model:L1 penalties: λ_A=0.0006849905332388105, λ_B=0.7013540331395108, λ_C=0.4213384102686047
## INFO:new_tensor.model:Non-negative: True
## INFO:new_tensor.model:CP decomposition completed in 0.25s
## INFO:new_tensor.model:Final metrics: R²=0.0000, sparsity_A=1.0000
##
##
##
## Best trial: 8. Best value: 7.70241: 61%|██████ | 61/100 [00:18<00:09, 3.98it/s]
## Best trial: 8. Best value: 7.70241: 61%|██████ | 61/100 [00:18<00:09, 3.98it/s]
## Best trial: 8. Best value: 7.70241: 62%|██████▏ | 62/100 [00:18<00:09, 3.81it/s]INFO:new_tensor.model:Fitting sparse CP decomposition with rank=22
## INFO:new_tensor.model:L1 penalties: λ_A=0.003282633048173829, λ_B=0.427426288633542, λ_C=0.21628459917822768
## INFO:new_tensor.model:Non-negative: True
## INFO:new_tensor.model:CP decomposition completed in 0.27s
## INFO:new_tensor.model:Final metrics: R²=0.0792, sparsity_A=0.4545
##
##
##
## Best trial: 8. Best value: 7.70241: 62%|██████▏ | 62/100 [00:18<00:09, 3.81it/s]
## Best trial: 8. Best value: 7.70241: 62%|██████▏ | 62/100 [00:18<00:09, 3.81it/s]
## Best trial: 8. Best value: 7.70241: 63%|██████▎ | 63/100 [00:18<00:10, 3.64it/s]INFO:new_tensor.model:Fitting sparse CP decomposition with rank=26
## INFO:new_tensor.model:L1 penalties: λ_A=0.0021215656630557615, λ_B=0.75456695927985, λ_C=0.3334078633529735
## INFO:new_tensor.model:Non-negative: True
## INFO:new_tensor.model:CP decomposition completed in 0.31s
## INFO:new_tensor.model:Final metrics: R²=0.0000, sparsity_A=1.0000
##
##
##
## Best trial: 8. Best value: 7.70241: 63%|██████▎ | 63/100 [00:18<00:10, 3.64it/s]
## Best trial: 8. Best value: 7.70241: 63%|██████▎ | 63/100 [00:18<00:10, 3.64it/s]
## Best trial: 8. Best value: 7.70241: 64%|██████▍ | 64/100 [00:18<00:10, 3.36it/s]INFO:new_tensor.model:Fitting sparse CP decomposition with rank=28
## INFO:new_tensor.model:L1 penalties: λ_A=0.006370834749726971, λ_B=0.599541030413235, λ_C=0.5300932950141273
## INFO:new_tensor.model:Non-negative: True
## INFO:new_tensor.model:CP decomposition completed in 0.44s
## INFO:new_tensor.model:Final metrics: R²=0.0000, sparsity_A=1.0000
##
##
##
## Best trial: 8. Best value: 7.70241: 64%|██████▍ | 64/100 [00:19<00:10, 3.36it/s]
## Best trial: 8. Best value: 7.70241: 64%|██████▍ | 64/100 [00:19<00:10, 3.36it/s]
## Best trial: 8. Best value: 7.70241: 65%|██████▌ | 65/100 [00:19<00:12, 2.83it/s]INFO:new_tensor.model:Fitting sparse CP decomposition with rank=17
## INFO:new_tensor.model:L1 penalties: λ_A=0.000942298453875689, λ_B=0.4410771584938217, λ_C=0.019469530041028853
## INFO:new_tensor.model:Non-negative: True
## INFO:new_tensor.model:CP decomposition completed in 0.16s
## INFO:new_tensor.model:Final metrics: R²=0.0459, sparsity_A=0.5882
##
##
##
## Best trial: 8. Best value: 7.70241: 65%|██████▌ | 65/100 [00:19<00:12, 2.83it/s]
## Best trial: 8. Best value: 7.70241: 65%|██████▌ | 65/100 [00:19<00:12, 2.83it/s]
## Best trial: 8. Best value: 7.70241: 66%|██████▌ | 66/100 [00:19<00:10, 3.26it/s]INFO:new_tensor.model:Fitting sparse CP decomposition with rank=24
## INFO:new_tensor.model:L1 penalties: λ_A=0.010810636813485226, λ_B=0.32755286741446893, λ_C=0.3913170623642701
## INFO:new_tensor.model:Non-negative: True
## INFO:new_tensor.model:CP decomposition completed in 0.31s
## INFO:new_tensor.model:Final metrics: R²=0.0468, sparsity_A=0.7500
##
##
##
## Best trial: 8. Best value: 7.70241: 66%|██████▌ | 66/100 [00:19<00:10, 3.26it/s]
## Best trial: 8. Best value: 7.70241: 66%|██████▌ | 66/100 [00:19<00:10, 3.26it/s]
## Best trial: 8. Best value: 7.70241: 67%|██████▋ | 67/100 [00:19<00:10, 3.13it/s]INFO:new_tensor.model:Fitting sparse CP decomposition with rank=30
## INFO:new_tensor.model:L1 penalties: λ_A=0.0047062523582045625, λ_B=0.770160260950635, λ_C=0.09247248408398182
## INFO:new_tensor.model:Non-negative: False
## INFO:new_tensor.model:CP decomposition completed in 0.39s
## INFO:new_tensor.model:Final metrics: R²=0.0000, sparsity_A=1.0000
##
##
##
## Best trial: 8. Best value: 7.70241: 67%|██████▋ | 67/100 [00:20<00:10, 3.13it/s]
## Best trial: 8. Best value: 7.70241: 67%|██████▋ | 67/100 [00:20<00:10, 3.13it/s]
## Best trial: 8. Best value: 7.70241: 68%|██████▊ | 68/100 [00:20<00:11, 2.84it/s]INFO:new_tensor.model:Fitting sparse CP decomposition with rank=27
## INFO:new_tensor.model:L1 penalties: λ_A=0.00794321899943011, λ_B=0.5205414779314799, λ_C=0.1866251326237264
## INFO:new_tensor.model:Non-negative: True
## INFO:new_tensor.model:CP decomposition completed in 0.26s
## INFO:new_tensor.model:Final metrics: R²=0.0195, sparsity_A=0.8519
##
##
##
## Best trial: 8. Best value: 7.70241: 68%|██████▊ | 68/100 [00:20<00:11, 2.84it/s]
## Best trial: 8. Best value: 7.70241: 68%|██████▊ | 68/100 [00:20<00:11, 2.84it/s]
## Best trial: 8. Best value: 7.70241: 69%|██████▉ | 69/100 [00:20<00:10, 2.95it/s]INFO:new_tensor.model:Fitting sparse CP decomposition with rank=19
## INFO:new_tensor.model:L1 penalties: λ_A=0.017818969878239704, λ_B=0.03355514389935884, λ_C=0.7255937916240696
## INFO:new_tensor.model:Non-negative: True
## INFO:new_tensor.model:CP decomposition completed in 0.24s
## INFO:new_tensor.model:Final metrics: R²=0.0399, sparsity_A=0.7895
##
##
##
## Best trial: 8. Best value: 7.70241: 69%|██████▉ | 69/100 [00:20<00:10, 2.95it/s]
## Best trial: 8. Best value: 7.70241: 69%|██████▉ | 69/100 [00:20<00:10, 2.95it/s]
## Best trial: 8. Best value: 7.70241: 70%|███████ | 70/100 [00:20<00:09, 3.13it/s]INFO:new_tensor.model:Fitting sparse CP decomposition with rank=29
## INFO:new_tensor.model:L1 penalties: λ_A=0.00020576736449091554, λ_B=0.01649091002559682, λ_C=0.0002767469804937398
## INFO:new_tensor.model:Non-negative: True
## INFO:new_tensor.model:CP decomposition completed in 0.28s
## INFO:new_tensor.model:Final metrics: R²=0.0731, sparsity_A=0.7586
##
##
##
## Best trial: 8. Best value: 7.70241: 70%|███████ | 70/100 [00:21<00:09, 3.13it/s]
## Best trial: 8. Best value: 7.70241: 70%|███████ | 70/100 [00:21<00:09, 3.13it/s]
## Best trial: 8. Best value: 7.70241: 71%|███████ | 71/100 [00:21<00:09, 3.11it/s]INFO:new_tensor.model:Fitting sparse CP decomposition with rank=30
## INFO:new_tensor.model:L1 penalties: λ_A=0.004682374878204598, λ_B=0.6848942691611007, λ_C=0.1458748266820725
## INFO:new_tensor.model:Non-negative: True
## INFO:new_tensor.model:CP decomposition completed in 0.48s
## INFO:new_tensor.model:Final metrics: R²=0.0000, sparsity_A=1.0000
##
##
##
## Best trial: 8. Best value: 7.70241: 71%|███████ | 71/100 [00:21<00:09, 3.11it/s]
## Best trial: 8. Best value: 7.70241: 71%|███████ | 71/100 [00:21<00:09, 3.11it/s]
## Best trial: 8. Best value: 7.70241: 72%|███████▏ | 72/100 [00:21<00:10, 2.62it/s]INFO:new_tensor.model:Fitting sparse CP decomposition with rank=29
## INFO:new_tensor.model:L1 penalties: λ_A=0.0004173787186088282, λ_B=0.0011529544120358283, λ_C=0.4742058117389902
## INFO:new_tensor.model:Non-negative: True
## INFO:new_tensor.model:CP decomposition completed in 0.37s
## INFO:new_tensor.model:Final metrics: R²=0.0719, sparsity_A=0.7586
##
##
##
## Best trial: 8. Best value: 7.70241: 72%|███████▏ | 72/100 [00:22<00:10, 2.62it/s]
## Best trial: 8. Best value: 7.70241: 72%|███████▏ | 72/100 [00:22<00:10, 2.62it/s]
## Best trial: 8. Best value: 7.70241: 73%|███████▎ | 73/100 [00:22<00:10, 2.55it/s]INFO:new_tensor.model:Fitting sparse CP decomposition with rank=30
## INFO:new_tensor.model:L1 penalties: λ_A=0.01212198336015896, λ_B=0.5924770559489551, λ_C=0.32004671210870145
## INFO:new_tensor.model:Non-negative: True
## INFO:new_tensor.model:CP decomposition completed in 0.48s
## INFO:new_tensor.model:Final metrics: R²=0.0000, sparsity_A=1.0000
##
##
##
## Best trial: 8. Best value: 7.70241: 73%|███████▎ | 73/100 [00:22<00:10, 2.55it/s]
## Best trial: 8. Best value: 7.70241: 73%|███████▎ | 73/100 [00:22<00:10, 2.55it/s]
## Best trial: 8. Best value: 7.70241: 74%|███████▍ | 74/100 [00:22<00:11, 2.31it/s]INFO:new_tensor.model:Fitting sparse CP decomposition with rank=27
## INFO:new_tensor.model:L1 penalties: λ_A=0.003924637870491414, λ_B=0.18137310874128393, λ_C=0.20975580042571393
## INFO:new_tensor.model:Non-negative: True
## INFO:new_tensor.model:CP decomposition completed in 0.26s
## INFO:new_tensor.model:Final metrics: R²=0.0371, sparsity_A=0.8519
##
##
##
## Best trial: 8. Best value: 7.70241: 74%|███████▍ | 74/100 [00:23<00:11, 2.31it/s]
## Best trial: 8. Best value: 7.70241: 74%|███████▍ | 74/100 [00:23<00:11, 2.31it/s]
## Best trial: 8. Best value: 7.70241: 75%|███████▌ | 75/100 [00:23<00:09, 2.54it/s]INFO:new_tensor.model:Fitting sparse CP decomposition with rank=28
## INFO:new_tensor.model:L1 penalties: λ_A=0.0017514230152006144, λ_B=0.25331041899224466, λ_C=0.9605941196159318
## INFO:new_tensor.model:Non-negative: True
## INFO:new_tensor.model:CP decomposition completed in 0.45s
## INFO:new_tensor.model:Final metrics: R²=0.0410, sparsity_A=0.8214
##
##
##
## Best trial: 8. Best value: 7.70241: 75%|███████▌ | 75/100 [00:23<00:09, 2.54it/s]
## Best trial: 8. Best value: 7.70241: 75%|███████▌ | 75/100 [00:23<00:09, 2.54it/s]
## Best trial: 8. Best value: 7.70241: 76%|███████▌ | 76/100 [00:23<00:10, 2.36it/s]INFO:new_tensor.model:Fitting sparse CP decomposition with rank=23
## INFO:new_tensor.model:L1 penalties: λ_A=0.005735743712639571, λ_B=0.8095296729776793, λ_C=0.2543165733907181
## INFO:new_tensor.model:Non-negative: False
## INFO:new_tensor.model:CP decomposition completed in 0.37s
## INFO:new_tensor.model:Final metrics: R²=0.0000, sparsity_A=1.0000
##
##
##
## Best trial: 8. Best value: 7.70241: 76%|███████▌ | 76/100 [00:23<00:10, 2.36it/s]
## Best trial: 8. Best value: 7.70241: 76%|███████▌ | 76/100 [00:23<00:10, 2.36it/s]
## Best trial: 8. Best value: 7.70241: 77%|███████▋ | 77/100 [00:23<00:09, 2.39it/s]INFO:new_tensor.model:Fitting sparse CP decomposition with rank=25
## INFO:new_tensor.model:L1 penalties: λ_A=0.0027745736834864977, λ_B=0.37998859145228436, λ_C=0.047375789114311624
## INFO:new_tensor.model:Non-negative: True
## INFO:new_tensor.model:CP decomposition completed in 0.25s
## INFO:new_tensor.model:Final metrics: R²=0.0444, sparsity_A=0.7600
##
##
##
## Best trial: 8. Best value: 7.70241: 77%|███████▋ | 77/100 [00:24<00:09, 2.39it/s]
## Best trial: 8. Best value: 7.70241: 77%|███████▋ | 77/100 [00:24<00:09, 2.39it/s]
## Best trial: 8. Best value: 7.70241: 78%|███████▊ | 78/100 [00:24<00:08, 2.64it/s]INFO:new_tensor.model:Fitting sparse CP decomposition with rank=14
## INFO:new_tensor.model:L1 penalties: λ_A=0.007562650866769657, λ_B=0.9846421914280261, λ_C=0.0811825818579365
## INFO:new_tensor.model:Non-negative: True
## INFO:new_tensor.model:CP decomposition completed in 0.18s
## INFO:new_tensor.model:Final metrics: R²=0.0000, sparsity_A=1.0000
##
##
##
## Best trial: 8. Best value: 7.70241: 78%|███████▊ | 78/100 [00:24<00:08, 2.64it/s]
## Best trial: 8. Best value: 7.70241: 78%|███████▊ | 78/100 [00:24<00:08, 2.64it/s]
## Best trial: 8. Best value: 7.70241: 79%|███████▉ | 79/100 [00:24<00:06, 3.04it/s]INFO:new_tensor.model:Fitting sparse CP decomposition with rank=29
## INFO:new_tensor.model:L1 penalties: λ_A=0.013490008791369214, λ_B=0.5122101209088451, λ_C=0.11556257048473018
## INFO:new_tensor.model:Non-negative: True
## INFO:new_tensor.model:CP decomposition completed in 0.28s
## INFO:new_tensor.model:Final metrics: R²=0.0307, sparsity_A=0.7931
##
##
##
## Best trial: 8. Best value: 7.70241: 79%|███████▉ | 79/100 [00:24<00:06, 3.04it/s]
## Best trial: 8. Best value: 7.70241: 79%|███████▉ | 79/100 [00:24<00:06, 3.04it/s]
## Best trial: 8. Best value: 7.70241: 80%|████████ | 80/100 [00:24<00:06, 3.05it/s]INFO:new_tensor.model:Fitting sparse CP decomposition with rank=26
## INFO:new_tensor.model:L1 penalties: λ_A=0.042057896376429385, λ_B=0.3028040815760118, λ_C=0.0004927029354670193
## INFO:new_tensor.model:Non-negative: True
## INFO:new_tensor.model:CP decomposition completed in 0.25s
## INFO:new_tensor.model:Final metrics: R²=0.0494, sparsity_A=0.7692
##
##
##
## Best trial: 8. Best value: 7.70241: 80%|████████ | 80/100 [00:25<00:06, 3.05it/s]
## Best trial: 8. Best value: 7.70241: 80%|████████ | 80/100 [00:25<00:06, 3.05it/s]
## Best trial: 8. Best value: 7.70241: 81%|████████ | 81/100 [00:25<00:06, 3.15it/s]INFO:new_tensor.model:Fitting sparse CP decomposition with rank=28
## INFO:new_tensor.model:L1 penalties: λ_A=0.0010935490191139527, λ_B=0.7752882360303991, λ_C=0.1615221981850723
## INFO:new_tensor.model:Non-negative: True
## INFO:new_tensor.model:CP decomposition completed in 0.27s
## INFO:new_tensor.model:Final metrics: R²=0.0000, sparsity_A=1.0000
##
##
##
## Best trial: 8. Best value: 7.70241: 81%|████████ | 81/100 [00:25<00:06, 3.15it/s]
## Best trial: 8. Best value: 7.70241: 81%|████████ | 81/100 [00:25<00:06, 3.15it/s]
## Best trial: 8. Best value: 7.70241: 82%|████████▏ | 82/100 [00:25<00:05, 3.16it/s]INFO:new_tensor.model:Fitting sparse CP decomposition with rank=30
## INFO:new_tensor.model:L1 penalties: λ_A=0.0008765424797641972, λ_B=0.6442067905338374, λ_C=0.33613658026806575
## INFO:new_tensor.model:Non-negative: True
## INFO:new_tensor.model:CP decomposition completed in 0.39s
## INFO:new_tensor.model:Final metrics: R²=0.0000, sparsity_A=1.0000
##
##
##
## Best trial: 8. Best value: 7.70241: 82%|████████▏ | 82/100 [00:25<00:05, 3.16it/s]
## Best trial: 8. Best value: 7.70241: 82%|████████▏ | 82/100 [00:25<00:05, 3.16it/s]
## Best trial: 8. Best value: 7.70241: 83%|████████▎ | 83/100 [00:25<00:05, 2.85it/s]INFO:new_tensor.model:Fitting sparse CP decomposition with rank=29
## INFO:new_tensor.model:L1 penalties: λ_A=0.0005001692734654153, λ_B=0.9927996049673214, λ_C=0.72743611174937
## INFO:new_tensor.model:Non-negative: True
## INFO:new_tensor.model:CP decomposition completed in 0.29s
## INFO:new_tensor.model:Final metrics: R²=0.0000, sparsity_A=1.0000
##
##
##
## Best trial: 8. Best value: 7.70241: 83%|████████▎ | 83/100 [00:26<00:05, 2.85it/s]
## Best trial: 8. Best value: 7.70241: 83%|████████▎ | 83/100 [00:26<00:05, 2.85it/s]
## Best trial: 8. Best value: 7.70241: 84%|████████▍ | 84/100 [00:26<00:05, 2.90it/s]INFO:new_tensor.model:Fitting sparse CP decomposition with rank=28
## INFO:new_tensor.model:L1 penalties: λ_A=0.0002911864727091217, λ_B=0.4735026543908035, λ_C=0.14476478145613322
## INFO:new_tensor.model:Non-negative: True
## INFO:new_tensor.model:CP decomposition completed in 0.36s
## INFO:new_tensor.model:Final metrics: R²=0.0356, sparsity_A=0.7857
##
##
##
## Best trial: 8. Best value: 7.70241: 84%|████████▍ | 84/100 [00:26<00:05, 2.90it/s]
## Best trial: 8. Best value: 7.70241: 84%|████████▍ | 84/100 [00:26<00:05, 2.90it/s]
## Best trial: 8. Best value: 7.70241: 85%|████████▌ | 85/100 [00:26<00:05, 2.75it/s]INFO:new_tensor.model:Fitting sparse CP decomposition with rank=29
## INFO:new_tensor.model:L1 penalties: λ_A=0.001252971421324456, λ_B=0.5901916555236231, λ_C=0.01043457001669746
## INFO:new_tensor.model:Non-negative: True
## INFO:new_tensor.model:CP decomposition completed in 0.29s
## INFO:new_tensor.model:Final metrics: R²=0.0000, sparsity_A=1.0000
##
##
##
## Best trial: 8. Best value: 7.70241: 85%|████████▌ | 85/100 [00:26<00:05, 2.75it/s]
## Best trial: 8. Best value: 7.70241: 85%|████████▌ | 85/100 [00:26<00:05, 2.75it/s]
## Best trial: 8. Best value: 7.70241: 86%|████████▌ | 86/100 [00:26<00:04, 2.83it/s]INFO:new_tensor.model:Fitting sparse CP decomposition with rank=30
## INFO:new_tensor.model:L1 penalties: λ_A=0.0099869267603346, λ_B=0.39529748670369264, λ_C=0.5002682365106321
## INFO:new_tensor.model:Non-negative: True
## INFO:new_tensor.model:CP decomposition completed in 0.39s
## INFO:new_tensor.model:Final metrics: R²=0.0757, sparsity_A=0.6333
##
##
##
## Best trial: 8. Best value: 7.70241: 86%|████████▌ | 86/100 [00:27<00:04, 2.83it/s]
## Best trial: 8. Best value: 7.70241: 86%|████████▌ | 86/100 [00:27<00:04, 2.83it/s]
## Best trial: 8. Best value: 7.70241: 87%|████████▋ | 87/100 [00:27<00:04, 2.65it/s]INFO:new_tensor.model:Fitting sparse CP decomposition with rank=27
## INFO:new_tensor.model:L1 penalties: λ_A=0.002090721593835961, λ_B=0.7984899767324513, λ_C=0.054377972391540935
## INFO:new_tensor.model:Non-negative: False
## INFO:new_tensor.model:CP decomposition completed in 0.36s
## INFO:new_tensor.model:Final metrics: R²=0.0000, sparsity_A=1.0000
##
##
##
## Best trial: 8. Best value: 7.70241: 87%|████████▋ | 87/100 [00:27<00:04, 2.65it/s]
## Best trial: 8. Best value: 7.70241: 87%|████████▋ | 87/100 [00:27<00:04, 2.65it/s]
## Best trial: 8. Best value: 7.70241: 88%|████████▊ | 88/100 [00:27<00:04, 2.60it/s]INFO:new_tensor.model:Fitting sparse CP decomposition with rank=26
## INFO:new_tensor.model:L1 penalties: λ_A=0.003464074934463693, λ_B=0.27806734931552274, λ_C=0.0012517500026353228
## INFO:new_tensor.model:Non-negative: True
## INFO:new_tensor.model:CP decomposition completed in 0.25s
## INFO:new_tensor.model:Final metrics: R²=0.0511, sparsity_A=0.7692
##
##
##
## Best trial: 8. Best value: 7.70241: 88%|████████▊ | 88/100 [00:28<00:04, 2.60it/s]
## Best trial: 8. Best value: 7.70241: 88%|████████▊ | 88/100 [00:28<00:04, 2.60it/s]
## Best trial: 8. Best value: 7.70241: 89%|████████▉ | 89/100 [00:28<00:03, 2.81it/s]INFO:new_tensor.model:Fitting sparse CP decomposition with rank=29
## INFO:new_tensor.model:L1 penalties: λ_A=0.026412565821738447, λ_B=0.21677956747590799, λ_C=0.09377005702439416
## INFO:new_tensor.model:Non-negative: True
## INFO:new_tensor.model:CP decomposition completed in 0.38s
## INFO:new_tensor.model:Final metrics: R²=0.0627, sparsity_A=0.7586
##
##
##
## Best trial: 8. Best value: 7.70241: 89%|████████▉ | 89/100 [00:28<00:03, 2.81it/s]
## Best trial: 8. Best value: 7.70241: 89%|████████▉ | 89/100 [00:28<00:03, 2.81it/s]
## Best trial: 8. Best value: 7.70241: 90%|█████████ | 90/100 [00:28<00:03, 2.65it/s]INFO:new_tensor.model:Fitting sparse CP decomposition with rank=25
## INFO:new_tensor.model:L1 penalties: λ_A=0.0008016365396662871, λ_B=0.811341733881362, λ_C=0.18713778689838095
## INFO:new_tensor.model:Non-negative: True
## INFO:new_tensor.model:CP decomposition completed in 0.24s
## INFO:new_tensor.model:Final metrics: R²=0.0000, sparsity_A=1.0000
##
##
##
## Best trial: 8. Best value: 7.70241: 90%|█████████ | 90/100 [00:28<00:03, 2.65it/s]
## Best trial: 8. Best value: 7.70241: 90%|█████████ | 90/100 [00:28<00:03, 2.65it/s]
## Best trial: 8. Best value: 7.70241: 91%|█████████ | 91/100 [00:28<00:03, 2.86it/s]INFO:new_tensor.model:Fitting sparse CP decomposition with rank=30
## INFO:new_tensor.model:L1 penalties: λ_A=0.016003528709477955, λ_B=0.5444384031724905, λ_C=0.03640258529267616
## INFO:new_tensor.model:Non-negative: True
## INFO:new_tensor.model:CP decomposition completed in 0.49s
## INFO:new_tensor.model:Final metrics: R²=0.0468, sparsity_A=0.6333
##
##
##
## Best trial: 8. Best value: 7.70241: 91%|█████████ | 91/100 [00:29<00:03, 2.86it/s]
## Best trial: 8. Best value: 7.70241: 91%|█████████ | 91/100 [00:29<00:03, 2.86it/s]
## Best trial: 8. Best value: 7.70241: 92%|█████████▏| 92/100 [00:29<00:03, 2.46it/s]INFO:new_tensor.model:Fitting sparse CP decomposition with rank=28
## INFO:new_tensor.model:L1 penalties: λ_A=0.0056218209168558755, λ_B=0.6366110826933181, λ_C=0.0632980719116021
## INFO:new_tensor.model:Non-negative: True
## INFO:new_tensor.model:CP decomposition completed in 0.36s
## INFO:new_tensor.model:Final metrics: R²=0.0000, sparsity_A=1.0000
##
##
##
## Best trial: 8. Best value: 7.70241: 92%|█████████▏| 92/100 [00:29<00:03, 2.46it/s]
## Best trial: 8. Best value: 7.70241: 92%|█████████▏| 92/100 [00:29<00:03, 2.46it/s]
## Best trial: 8. Best value: 7.70241: 93%|█████████▎| 93/100 [00:29<00:02, 2.47it/s]INFO:new_tensor.model:Fitting sparse CP decomposition with rank=27
## INFO:new_tensor.model:L1 penalties: λ_A=0.0005068952575351023, λ_B=0.4160601423928182, λ_C=0.2888571735494726
## INFO:new_tensor.model:Non-negative: True
## INFO:new_tensor.model:CP decomposition completed in 0.35s
## INFO:new_tensor.model:Final metrics: R²=0.0273, sparsity_A=0.8519
##
##
##
## Best trial: 8. Best value: 7.70241: 93%|█████████▎| 93/100 [00:30<00:02, 2.47it/s]
## Best trial: 8. Best value: 7.70241: 93%|█████████▎| 93/100 [00:30<00:02, 2.47it/s]
## Best trial: 8. Best value: 7.70241: 94%|█████████▍| 94/100 [00:30<00:02, 2.48it/s]INFO:new_tensor.model:Fitting sparse CP decomposition with rank=29
## INFO:new_tensor.model:L1 penalties: λ_A=0.00814758013913939, λ_B=0.8435594946569757, λ_C=0.00013978122483368087
## INFO:new_tensor.model:Non-negative: True
## INFO:new_tensor.model:CP decomposition completed in 0.37s
## INFO:new_tensor.model:Final metrics: R²=0.0000, sparsity_A=1.0000
##
##
##
## Best trial: 8. Best value: 7.70241: 94%|█████████▍| 94/100 [00:30<00:02, 2.48it/s]
## Best trial: 8. Best value: 7.70241: 94%|█████████▍| 94/100 [00:30<00:02, 2.48it/s]
## Best trial: 8. Best value: 7.70241: 95%|█████████▌| 95/100 [00:30<00:02, 2.46it/s]INFO:new_tensor.model:Fitting sparse CP decomposition with rank=28
## INFO:new_tensor.model:L1 penalties: λ_A=0.01235337242848547, λ_B=0.9854879445406823, λ_C=0.0018337432308381811
## INFO:new_tensor.model:Non-negative: True
## INFO:new_tensor.model:CP decomposition completed in 0.36s
## INFO:new_tensor.model:Final metrics: R²=0.0000, sparsity_A=1.0000
##
##
##
## Best trial: 8. Best value: 7.70241: 95%|█████████▌| 95/100 [00:30<00:02, 2.46it/s]
## Best trial: 8. Best value: 7.70241: 95%|█████████▌| 95/100 [00:30<00:02, 2.46it/s]
## Best trial: 8. Best value: 7.70241: 96%|█████████▌| 96/100 [00:30<00:01, 2.47it/s]INFO:new_tensor.model:Fitting sparse CP decomposition with rank=30
## INFO:new_tensor.model:L1 penalties: λ_A=0.004309557114685113, λ_B=0.6164066994882925, λ_C=0.11409480914141784
## INFO:new_tensor.model:Non-negative: True
## INFO:new_tensor.model:CP decomposition completed in 0.47s
## INFO:new_tensor.model:Final metrics: R²=0.0000, sparsity_A=1.0000
##
##
##
## Best trial: 8. Best value: 7.70241: 96%|█████████▌| 96/100 [00:31<00:01, 2.47it/s]
## Best trial: 8. Best value: 7.70241: 96%|█████████▌| 96/100 [00:31<00:01, 2.47it/s]
## Best trial: 8. Best value: 7.70241: 97%|█████████▋| 97/100 [00:31<00:01, 2.27it/s]INFO:new_tensor.model:Fitting sparse CP decomposition with rank=29
## INFO:new_tensor.model:L1 penalties: λ_A=0.0015994631526042925, λ_B=0.3327026145566166, λ_C=0.4309146009765499
## INFO:new_tensor.model:Non-negative: True
## INFO:new_tensor.model:CP decomposition completed in 0.38s
## INFO:new_tensor.model:Final metrics: R²=0.0540, sparsity_A=0.7586
##
##
##
## Best trial: 8. Best value: 7.70241: 97%|█████████▋| 97/100 [00:31<00:01, 2.27it/s]
## Best trial: 8. Best value: 7.70241: 97%|█████████▋| 97/100 [00:31<00:01, 2.27it/s]
## Best trial: 8. Best value: 7.70241: 98%|█████████▊| 98/100 [00:31<00:00, 2.29it/s]INFO:new_tensor.model:Fitting sparse CP decomposition with rank=16
## INFO:new_tensor.model:L1 penalties: λ_A=0.0027865119502672887, λ_B=0.47634398324957056, λ_C=0.6053103536005864
## INFO:new_tensor.model:Non-negative: True
## INFO:new_tensor.model:CP decomposition completed in 0.26s
## INFO:new_tensor.model:Final metrics: R²=0.0169, sparsity_A=0.8125
##
##
##
## Best trial: 8. Best value: 7.70241: 98%|█████████▊| 98/100 [00:32<00:00, 2.29it/s]
## Best trial: 8. Best value: 7.70241: 98%|█████████▊| 98/100 [00:32<00:00, 2.29it/s]
## Best trial: 8. Best value: 7.70241: 99%|█████████▉| 99/100 [00:32<00:00, 2.54it/s]INFO:new_tensor.model:Fitting sparse CP decomposition with rank=19
## INFO:new_tensor.model:L1 penalties: λ_A=0.018556489315822426, λ_B=0.7190810423979014, λ_C=0.22679695624884716
## INFO:new_tensor.model:Non-negative: True
## INFO:new_tensor.model:CP decomposition completed in 0.24s
## INFO:new_tensor.model:Final metrics: R²=0.0000, sparsity_A=1.0000
##
##
##
## Best trial: 8. Best value: 7.70241: 99%|█████████▉| 99/100 [00:32<00:00, 2.54it/s]
## Best trial: 8. Best value: 7.70241: 99%|█████████▉| 99/100 [00:32<00:00, 2.54it/s]
## Best trial: 8. Best value: 7.70241: 100%|██████████| 100/100 [00:32<00:00, 2.80it/s]
## Best trial: 8. Best value: 7.70241: 100%|██████████| 100/100 [00:32<00:00, 3.09it/s]
## INFO:new_tensor.optimize:Best parameters: rank=18, λ_A=0.000549, λ_B=0.755681, λ_C=0.126047
## INFO:new_tensor.utils:Output directory: /home/shared/16TB_HDD_01/sam/gitrepos/urol-e5/timeseries_molecular/M-multi-species/output/13.00-multiomics-stdm/optimization (resolved from ../output/13.00-multiomics-stdm/optimization)
## INFO:new_tensor.optimize:Saved optimization results to /home/shared/16TB_HDD_01/sam/gitrepos/urol-e5/timeseries_molecular/M-multi-species/output/13.00-multiomics-stdm/optimization
## INFO:new_tensor.cli:Optimization results saved to /home/shared/16TB_HDD_01/sam/gitrepos/urol-e5/timeseries_molecular/M-multi-species/output/13.00-multiomics-stdm/optimization
##
## Optimization results saved to: ../output/13.00-multiomics-stdm/optimization
##
## Check output/optimization/best_params.json for optimal rank.3.3 Step 3: Review Optimization Results
Load and display the best hyperparameters found during optimization.
import json
import os
import pandas as pd
# Load best parameters
best_params_file = os.path.join(r.output_dir, "optimization", "best_params.json")
with open(best_params_file, 'r') as f:
best_params = json.load(f)
print("Best Hyperparameters:")
print(json.dumps(best_params, indent=2))
# Load top trials
top_trials_file = os.path.join(r.output_dir, "optimization", "top_trials.csv")
if os.path.exists(top_trials_file):
top_trials = pd.read_csv(top_trials_file)
print("\n\nTop 10 Trials:")
print(top_trials.head(10))## Best Hyperparameters:
## {
## "rank": 18,
## "lambda_A": 0.0005488047000766049,
## "lambda_B": 0.755681014127442,
## "lambda_C": 0.12604664585649453,
## "non_negative": true,
## "best_value": 7.702411290712876,
## "explained_variance": 0.0,
## "sparsity_A": 1.0,
## "sparsity_B": 1.0,
## "sparsity_C": 1.0
## }
##
##
## Top 10 Trials:
## trial rank lambda_A ... sparsity_A sparsity_B sparsity_C
## 0 12 30 0.005752 ... 1.0 1.0 1.0
## 1 11 28 0.011526 ... 1.0 1.0 1.0
## 2 10 29 0.007822 ... 1.0 1.0 1.0
## 3 8 18 0.000549 ... 1.0 1.0 1.0
## 4 25 22 0.002060 ... 1.0 1.0 1.0
## 5 31 30 0.004757 ... 1.0 1.0 1.0
## 6 21 27 0.008687 ... 1.0 1.0 1.0
## 7 19 30 0.011425 ... 1.0 1.0 1.0
## 8 49 23 0.003187 ... 1.0 1.0 1.0
## 9 50 27 0.038025 ... 1.0 1.0 1.0
##
## [10 rows x 11 columns]3.4 Step 4: Fit Model
Fit the CP decomposition model with the optimal parameters identified during optimization. The optimal rank is automatically loaded from the optimization results.
import subprocess
import os
import json
# Load optimal rank from optimization results
best_params_file = os.path.join(r.output_dir, "optimization", "best_params.json")
with open(best_params_file, 'r') as f:
best_params = json.load(f)
optimal_rank = str(best_params['rank'])
print(f"Using optimal rank from optimization: {optimal_rank}")
# Define paths
tensor_file = os.path.join(r.output_dir, "tensor", "tensor.npz")
fit_dir = os.path.join(r.output_dir, "fit")
os.makedirs(fit_dir, exist_ok=True)
# Fit model using the new-tensor CLI with optimal rank
# All parameters with their values (defaults noted in comments)
cmd = [
"/home/shared/16TB_HDD_01/sam/gitrepos/sr320/workflow-stdm-02/sr320-stdm-02-env/bin/new-tensor",
"fit",
"--tensor-path", tensor_file, # Required: path to tensor file
"--output-dir", fit_dir, # Custom output directory
"--rank", optimal_rank, # Required: automatically loaded from optimization results
"--lambda-a", "0.1", # Default: 0.1 (L1 penalty for gene factors)
"--lambda-b", "0.1", # Default: 0.1 (L1 penalty for species factors)
"--lambda-c", "0.1", # Default: 0.1 (L1 penalty for time factors)
"--non-negative", # Custom: enforces non-negativity (default: no-non-negative)
"--max-iter", "100" # Default: 100 (maximum iterations)
]
print("Fitting model...")
print(f"Command: {' '.join(cmd)}")
result = subprocess.run(cmd, capture_output=True, text=True)
print(result.stdout)
if result.stderr:
print("Errors/Warnings:", result.stderr)
print(f"\nFit results saved to: {fit_dir}")## Using optimal rank from optimization: 18
## Fitting model...
## Command: /home/shared/16TB_HDD_01/sam/gitrepos/sr320/workflow-stdm-02/sr320-stdm-02-env/bin/new-tensor fit --tensor-path ../output/13.00-multiomics-stdm/tensor/tensor.npz --output-dir ../output/13.00-multiomics-stdm/fit --rank 18 --lambda-a 0.1 --lambda-b 0.1 --lambda-c 0.1 --non-negative --max-iter 100
##
## Errors/Warnings: INFO:new_tensor.cli:Starting CP decomposition fit
## INFO:new_tensor.tensor:Loaded tensor from ../output/13.00-multiomics-stdm/tensor/tensor.npz with shape (8820, 3, 4)
## INFO:new_tensor.model:Fitting sparse CP decomposition with rank=18
## INFO:new_tensor.model:L1 penalties: λ_A=0.1, λ_B=0.1, λ_C=0.1
## INFO:new_tensor.model:Non-negative: True
## INFO:new_tensor.model:Iteration 0: loss=3082595.469073
## INFO:new_tensor.model:Converged at iteration 3 (rel_change=0.000000)
## INFO:new_tensor.model:CP decomposition completed in 0.20s
## INFO:new_tensor.model:Final metrics: R²=0.0391, sparsity_A=0.7778
## INFO:new_tensor.utils:Output directory: /home/shared/16TB_HDD_01/sam/gitrepos/urol-e5/timeseries_molecular/M-multi-species/output/13.00-multiomics-stdm/fit (resolved from ../output/13.00-multiomics-stdm/fit)
## INFO:new_tensor.cli:Fit results saved to /home/shared/16TB_HDD_01/sam/gitrepos/urol-e5/timeseries_molecular/M-multi-species/output/13.00-multiomics-stdm/fit
##
## Fit results saved to: ../output/13.00-multiomics-stdm/fit3.5 Step 5: Export Results
Export the decomposition results with gene IDs, species codes, and time labels.
import subprocess
import os
# Define paths
fit_dir = os.path.join(r.output_dir, "fit")
mappings_dir = os.path.join(r.output_dir, "tensor")
export_dir = os.path.join(r.output_dir, "export")
os.makedirs(export_dir, exist_ok=True)
# Export results using the new-tensor CLI
# All parameters with their values (defaults noted in comments)
cmd = [
"/home/shared/16TB_HDD_01/sam/gitrepos/sr320/workflow-stdm-02/sr320-stdm-02-env/bin/new-tensor",
"export",
"--fit-dir", fit_dir, # Required: directory containing fit results
"--mappings-dir", mappings_dir, # Required: directory containing tensor mappings
"--output-dir", export_dir, # Custom output directory
"--plot-heatmaps" # Default: plot-heatmaps (generate factor heatmaps)
]
print("Exporting results...")
print(f"Command: {' '.join(cmd)}")
result = subprocess.run(cmd, capture_output=True, text=True)
print(result.stdout)
if result.stderr:
print("Errors/Warnings:", result.stderr)
print(f"\nExported results saved to: {export_dir}")## Exporting results...
## Command: /home/shared/16TB_HDD_01/sam/gitrepos/sr320/workflow-stdm-02/sr320-stdm-02-env/bin/new-tensor export --fit-dir ../output/13.00-multiomics-stdm/fit --mappings-dir ../output/13.00-multiomics-stdm/tensor --output-dir ../output/13.00-multiomics-stdm/export --plot-heatmaps
##
## Errors/Warnings: INFO:new_tensor.cli:Starting export
## INFO:new_tensor.utils:Output directory: /home/shared/16TB_HDD_01/sam/gitrepos/urol-e5/timeseries_molecular/M-multi-species/output/13.00-multiomics-stdm/fit (resolved from ../output/13.00-multiomics-stdm/fit)
## INFO:new_tensor.utils:Output directory: /home/shared/16TB_HDD_01/sam/gitrepos/urol-e5/timeseries_molecular/M-multi-species/output/13.00-multiomics-stdm/tensor (resolved from ../output/13.00-multiomics-stdm/tensor)
## INFO:new_tensor.utils:Output directory: /home/shared/16TB_HDD_01/sam/gitrepos/urol-e5/timeseries_molecular/M-multi-species/output/13.00-multiomics-stdm/export (resolved from ../output/13.00-multiomics-stdm/export)
## INFO:new_tensor.cli:Generated component gene files and gene intersection plot
## INFO:new_tensor.cli:Generated factor heatmaps and line plots
## INFO:new_tensor.cli:Export completed, results saved to /home/shared/16TB_HDD_01/sam/gitrepos/urol-e5/timeseries_molecular/M-multi-species/output/13.00-multiomics-stdm/export
##
## Exported results saved to: ../output/13.00-multiomics-stdm/export3.6 Step 6: Summary of Results
Load and display key results from the decomposition analysis.
import json
import os
import pandas as pd
export_dir = os.path.join(r.output_dir, "export")
# Load decomposition results summary
results_file = os.path.join(export_dir, "decomposition_results.json")
with open(results_file, 'r') as f:
results = json.load(f)
print("Decomposition Results Summary:")
print(json.dumps(results, indent=2))
# Load gene factors
gene_factors_file = os.path.join(export_dir, "gene_factors_with_ids.csv")
gene_factors = pd.read_csv(gene_factors_file)
print(f"\n\nGene Factors Shape: {gene_factors.shape}")
print("\nFirst few gene factors:")
print(gene_factors.head())
# Load species factors
species_factors_file = os.path.join(export_dir, "species_factors_with_codes.csv")
species_factors = pd.read_csv(species_factors_file)
print(f"\n\nSpecies Factors Shape: {species_factors.shape}")
print("\nSpecies factors:")
print(species_factors)
# Load time factors
time_factors_file = os.path.join(export_dir, "time_factors_with_labels.csv")
time_factors = pd.read_csv(time_factors_file)
print(f"\n\nTime Factors Shape: {time_factors.shape}")
print("\nTime factors:")
print(time_factors)## Decomposition Results Summary:
## {
## "metrics": {
## "reconstruction_error": 3017526.5976782567,
## "explained_variance": 0.03910043921427753,
## "final_loss": 3017573.7926728926,
## "n_iterations": 4,
## "sparsity_A": 0.7777777777777778,
## "sparsity_B": 0.7777777777777778,
## "sparsity_C": 0.7777777777777778,
## "converged": true
## },
## "gene_mapping": {
## "OG_00686": 0,
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## "OG_09924": 8459,
## "OG_09925": 8460,
## "OG_09926": 8461,
## "OG_09893": 8462,
## "OG_09894": 8463,
## "OG_09896": 8464,
## "OG_09897": 8465,
## "OG_09899": 8466,
## "OG_09901": 8467,
## "OG_09902": 8468,
## "OG_09903": 8469,
## "OG_09904": 8470,
## "OG_09905": 8471,
## "OG_09906": 8472,
## "OG_09907": 8473,
## "OG_09908": 8474,
## "OG_09909": 8475,
## "OG_09910": 8476,
## "OG_09911": 8477,
## "OG_09912": 8478,
## "OG_09913": 8479,
## "OG_09929": 8480,
## "OG_09932": 8481,
## "OG_09933": 8482,
## "OG_09935": 8483,
## "OG_09928": 8484,
## "OG_09938": 8485,
## "OG_09940": 8486,
## "OG_09939": 8487,
## "OG_09943": 8488,
## "OG_09941": 8489,
## "OG_09942": 8490,
## "OG_09946": 8491,
## "OG_09947": 8492,
## "OG_09944": 8493,
## "OG_09945": 8494,
## "OG_09953": 8495,
## "OG_09954": 8496,
## "OG_09956": 8497,
## "OG_09955": 8498,
## "OG_09949": 8499,
## "OG_09950": 8500,
## "OG_09951": 8501,
## "OG_09952": 8502,
## "OG_09957": 8503,
## "OG_09961": 8504,
## "OG_09958": 8505,
## "OG_09959": 8506,
## "OG_09960": 8507,
## "OG_09962": 8508,
## "OG_09963": 8509,
## "OG_09964": 8510,
## "OG_09965": 8511,
## "OG_09966": 8512,
## "OG_09967": 8513,
## "OG_09970": 8514,
## "OG_09971": 8515,
## "OG_09972": 8516,
## "OG_09968": 8517,
## "OG_09969": 8518,
## "OG_09980": 8519,
## "OG_09979": 8520,
## "OG_09973": 8521,
## "OG_09974": 8522,
## "OG_09975": 8523,
## "OG_09976": 8524,
## "OG_09977": 8525,
## "OG_09978": 8526,
## "OG_09989": 8527,
## "OG_09990": 8528,
## "OG_09992": 8529,
## "OG_09982": 8530,
## "OG_09983": 8531,
## "OG_09984": 8532,
## "OG_09985": 8533,
## "OG_09986": 8534,
## "OG_09987": 8535,
## "OG_09988": 8536,
## "OG_09997": 8537,
## "OG_09995": 8538,
## "OG_09993": 8539,
## "OG_09999": 8540,
## "OG_10000": 8541,
## "OG_10011": 8542,
## "OG_10012": 8543,
## "OG_10016": 8544,
## "OG_10017": 8545,
## "OG_10013": 8546,
## "OG_10014": 8547,
## "OG_10018": 8548,
## "OG_10002": 8549,
## "OG_10003": 8550,
## "OG_10004": 8551,
## "OG_10005": 8552,
## "OG_10006": 8553,
## "OG_10007": 8554,
## "OG_10008": 8555,
## "OG_10009": 8556,
## "OG_10010": 8557,
## "OG_10021": 8558,
## "OG_10023": 8559,
## "OG_10022": 8560,
## "OG_10019": 8561,
## "OG_10020": 8562,
## "OG_10024": 8563,
## "OG_10025": 8564,
## "OG_10026": 8565,
## "OG_10027": 8566,
## "OG_10028": 8567,
## "OG_10036": 8568,
## "OG_10037": 8569,
## "OG_10038": 8570,
## "OG_10030": 8571,
## "OG_10031": 8572,
## "OG_10032": 8573,
## "OG_10033": 8574,
## "OG_10034": 8575,
## "OG_10035": 8576,
## "OG_10039": 8577,
## "OG_10040": 8578,
## "OG_10042": 8579,
## "OG_10047": 8580,
## "OG_10043": 8581,
## "OG_10044": 8582,
## "OG_10045": 8583,
## "OG_10046": 8584,
## "OG_10049": 8585,
## "OG_10050": 8586,
## "OG_10051": 8587,
## "OG_10052": 8588,
## "OG_10053": 8589,
## "OG_10054": 8590,
## "OG_10059": 8591,
## "OG_10060": 8592,
## "OG_10056": 8593,
## "OG_10055": 8594,
## "OG_10057": 8595,
## "OG_10061": 8596,
## "OG_10062": 8597,
## "OG_10064": 8598,
## "OG_10065": 8599,
## "OG_10067": 8600,
## "OG_10068": 8601,
## "OG_10069": 8602,
## "OG_10070": 8603,
## "OG_10073": 8604,
## "OG_10075": 8605,
## "OG_10074": 8606,
## "OG_10077": 8607,
## "OG_10078": 8608,
## "OG_10079": 8609,
## "OG_10080": 8610,
## "OG_10081": 8611,
## "OG_10082": 8612,
## "OG_10083": 8613,
## "OG_10084": 8614,
## "OG_10085": 8615,
## "OG_10086": 8616,
## "OG_10087": 8617,
## "OG_10088": 8618,
## "OG_10089": 8619,
## "OG_10092": 8620,
## "OG_10097": 8621,
## "OG_10098": 8622,
## "OG_10093": 8623,
## "OG_10094": 8624,
## "OG_10095": 8625,
## "OG_10100": 8626,
## "OG_10099": 8627,
## "OG_10104": 8628,
## "OG_10101": 8629,
## "OG_10102": 8630,
## "OG_10103": 8631,
## "OG_10105": 8632,
## "OG_10106": 8633,
## "OG_10107": 8634,
## "OG_10118": 8635,
## "OG_10119": 8636,
## "OG_10120": 8637,
## "OG_10121": 8638,
## "OG_10122": 8639,
## "OG_10123": 8640,
## "OG_10124": 8641,
## "OG_10109": 8642,
## "OG_10110": 8643,
## "OG_10111": 8644,
## "OG_10112": 8645,
## "OG_10113": 8646,
## "OG_10114": 8647,
## "OG_10115": 8648,
## "OG_10116": 8649,
## "OG_10117": 8650,
## "OG_10126": 8651,
## "OG_10127": 8652,
## "OG_10128": 8653,
## "OG_10129": 8654,
## "OG_10130": 8655,
## "OG_10131": 8656,
## "OG_10132": 8657,
## "OG_10133": 8658,
## "OG_10148": 8659,
## "OG_10149": 8660,
## "OG_10138": 8661,
## "OG_10134": 8662,
## "OG_10135": 8663,
## "OG_10136": 8664,
## "OG_10137": 8665,
## "OG_10139": 8666,
## "OG_10140": 8667,
## "OG_10141": 8668,
## "OG_10143": 8669,
## "OG_10144": 8670,
## "OG_10145": 8671,
## "OG_10147": 8672,
## "OG_10151": 8673,
## "OG_10152": 8674,
## "OG_10153": 8675,
## "OG_10154": 8676,
## "OG_10157": 8677,
## "OG_10158": 8678,
## "OG_10159": 8679,
## "OG_10160": 8680,
## "OG_10161": 8681,
## "OG_10167": 8682,
## "OG_10162": 8683,
## "OG_10164": 8684,
## "OG_10165": 8685,
## "OG_10166": 8686,
## "OG_10168": 8687,
## "OG_10169": 8688,
## "OG_10170": 8689,
## "OG_10171": 8690,
## "OG_10172": 8691,
## "OG_10173": 8692,
## "OG_10174": 8693,
## "OG_10175": 8694,
## "OG_10176": 8695,
## "OG_10177": 8696,
## "OG_10178": 8697,
## "OG_10179": 8698,
## "OG_10180": 8699,
## "OG_10181": 8700,
## "OG_10182": 8701,
## "OG_10183": 8702,
## "OG_10184": 8703,
## "OG_10185": 8704,
## "OG_10186": 8705,
## "OG_10192": 8706,
## "OG_10187": 8707,
## "OG_10189": 8708,
## "OG_10190": 8709,
## "OG_10191": 8710,
## "OG_10193": 8711,
## "OG_10196": 8712,
## "OG_10197": 8713,
## "OG_10198": 8714,
## "OG_10200": 8715,
## "OG_10201": 8716,
## "OG_10202": 8717,
## "OG_10203": 8718,
## "OG_10204": 8719,
## "OG_10205": 8720,
## "OG_10206": 8721,
## "OG_10207": 8722,
## "OG_10208": 8723,
## "OG_10209": 8724,
## "OG_10210": 8725,
## "OG_10211": 8726,
## "OG_10212": 8727,
## "OG_10213": 8728,
## "OG_10214": 8729,
## "OG_10215": 8730,
## "OG_10216": 8731,
## "OG_10217": 8732,
## "OG_10219": 8733,
## "OG_10220": 8734,
## "OG_10223": 8735,
## "OG_00101": 8736,
## "OG_00111": 8737,
## "OG_00125": 8738,
## "OG_00128": 8739,
## "OG_00151": 8740,
## "OG_00186": 8741,
## "OG_00268": 8742,
## "OG_00416": 8743,
## "OG_00553": 8744,
## "OG_00823": 8745,
## "OG_01119": 8746,
## "OG_01304": 8747,
## "OG_01327": 8748,
## "OG_01432": 8749,
## "OG_01457": 8750,
## "OG_01500": 8751,
## "OG_01563": 8752,
## "OG_01625": 8753,
## "OG_01681": 8754,
## "OG_01976": 8755,
## "OG_02101": 8756,
## "OG_02177": 8757,
## "OG_02213": 8758,
## "OG_02252": 8759,
## "OG_02398": 8760,
## "OG_02556": 8761,
## "OG_02677": 8762,
## "OG_02715": 8763,
## "OG_02745": 8764,
## "OG_02856": 8765,
## "OG_02873": 8766,
## "OG_03073": 8767,
## "OG_03106": 8768,
## "OG_03588": 8769,
## "OG_03700": 8770,
## "OG_03796": 8771,
## "OG_03885": 8772,
## "OG_04139": 8773,
## "OG_04229": 8774,
## "OG_04434": 8775,
## "OG_04442": 8776,
## "OG_05019": 8777,
## "OG_05068": 8778,
## "OG_05148": 8779,
## "OG_05597": 8780,
## "OG_05676": 8781,
## "OG_06142": 8782,
## "OG_06229": 8783,
## "OG_06243": 8784,
## "OG_06248": 8785,
## "OG_06326": 8786,
## "OG_06549": 8787,
## "OG_06617": 8788,
## "OG_06630": 8789,
## "OG_06847": 8790,
## "OG_06907": 8791,
## "OG_07133": 8792,
## "OG_07743": 8793,
## "OG_07821": 8794,
## "OG_07990": 8795,
## "OG_07996": 8796,
## "OG_08122": 8797,
## "OG_08162": 8798,
## "OG_08177": 8799,
## "OG_08213": 8800,
## "OG_08245": 8801,
## "OG_08247": 8802,
## "OG_08252": 8803,
## "OG_08362": 8804,
## "OG_08753": 8805,
## "OG_08758": 8806,
## "OG_09090": 8807,
## "OG_09092": 8808,
## "OG_09097": 8809,
## "OG_09221": 8810,
## "OG_09317": 8811,
## "OG_09320": 8812,
## "OG_09360": 8813,
## "OG_09476": 8814,
## "OG_09529": 8815,
## "OG_09781": 8816,
## "OG_09817": 8817,
## "OG_09996": 8818,
## "OG_10218": 8819
## },
## "species_mapping": {
## "ACR": 0,
## "POC": 1,
## "POR": 2
## },
## "timepoint_mapping": {
## "1": 0,
## "2": 1,
## "3": 2,
## "4": 3
## },
## "n_components": 18
## }
##
##
## Gene Factors Shape: (8820, 19)
##
## First few gene factors:
## gene_id 0 1 2 3 4 ... 12 13 14 15 16 17
## 0 OG_00686 0.007298 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.007298 0.0 0.0 0.0
## 1 OG_00683 0.011736 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.011736 0.0 0.0 0.0
## 2 OG_00685 0.012364 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.012364 0.0 0.0 0.0
## 3 OG_00688 0.005907 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.005907 0.0 0.0 0.0
## 4 OG_00689 0.009439 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.009439 0.0 0.0 0.0
##
## [5 rows x 19 columns]
##
##
## Species Factors Shape: (3, 19)
##
## Species factors:
## species 0 1 2 3 4 ... 12 13 14 15 16 17
## 0 ACR 0.585372 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.585372 0.0 0.0 0.0
## 1 POC 0.577115 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.577115 0.0 0.0 0.0
## 2 POR 0.569455 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.569455 0.0 0.0 0.0
##
## [3 rows x 19 columns]
##
##
## Time Factors Shape: (4, 19)
##
## Time factors:
## timepoint 0 1 2 3 4 ... 12 13 14 15 16 17
## 0 1 6.220515 0.0 0.0 0.0 0.0 ... 0.0 0.0 6.220514 0.0 0.0 0.0
## 1 2 6.258044 0.0 0.0 0.0 0.0 ... 0.0 0.0 6.258044 0.0 0.0 0.0
## 2 3 6.223633 0.0 0.0 0.0 0.0 ... 0.0 0.0 6.223633 0.0 0.0 0.0
## 3 4 6.210393 0.0 0.0 0.0 0.0 ... 0.0 0.0 6.210392 0.0 0.0 0.0
##
## [4 rows x 19 columns]