Sync Resonance fork with latest Robyn #1
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…modeling_new [modeling] add modeling implementations
remove the need for bool
…ion-interface visualizer interface
…ntal/Robyn into robynpy_release
…apper [feature] data mapper from R to Python
…n_input_validation Feature/calibration input validation
…apper Quick fix on indents for data_mapper
Co-authored-by: Dhaval Patel <[email protected]>
…al release (#1173) * revert ridge modeling to match convergence * tidy up loggers * clear notebooks, update gitignore * remove debug files
* refactored pareto_optimizer code * removed unused unit test * Added logging for Pareto module * cleaned up unused functions and unimplemented code * cluster loggers --------- Co-authored-by: Dhaval Patel <[email protected]>
* fix calculate decomp spend dist to use abs calc * clear notebook outs * update missing columns
* updated readme for python robyn * added package version of Robyn R used during python implementation --------- Co-authored-by: Dhaval Patel <[email protected]>
Moving tutorials into tests and organizing tests
* update visual fixes on notebook * clear notebook outputs * remove logger configuration on file levels * remove unused imports
* update colab notebooks * update with notebook content * publish with notebook content * update notebook code * update notebooks * update colab notebooks * update colab notebooks * update colab notebooks * update notebooks * update colab notebooks
Bumping up python release to 0.1.1
Introducing Robynpy - Robyn powered by Python.
Add Python version info to main readme.
…1132) * feat: enable exposure fitting - Exposures (Imp/GRP etc) will be prioritised over spend for parent model fitting - Deprecate function fit_spend_exposure, incl. Michaelis Menten. Nonlinear fitting between spend and exposure wasn't improving fitting significantly. Instead, future curve calibration feature will aim to improve curve identification. - Use cpe (cost per exposure) as ratio for spend to exposure translation. use cpe_window to scale the whole dataset in order to obtain the right spend scale for modeling period. - remove minpack.lm / nlsLM dependency - update exposure plot * feat: create robyn_calibrate function - Simulate cumulative R&F dataset with frequency bucket - add beta coef besides alpha and gamma to nevergrad hyperparameter to improve curve fit - plot with freq_bucket as well as onepager per trial - add df_curve_reach_freq as dummy dataset - create robyn_calibrate that consumes curve input and outputs hyperparameter ranges as input. - Rename previous internal robyn_calibrate function as lift_calibration - early stop convergence with while loop - update documentation * prototype: reach and frequency allocator This is the proof of concept of a R&F allocator that includes - Simulated R&F data - The R&F hill params are estimated using a multiplicative equation with Nevergrad - visualisation of surface - R&F allocator with nlopt - constrain validation * update: checks, input, transformation & website - simplify various check functions - adapt model.R, incl. reset run_transformations params to have clearer overview of params needed. simplify transformation.R by removing unnecessary checks - In model.R & pareto.R: remove decompSpendDist from both scripts to reduce memory leak. Use xDecompAgg subsets instead - In transformation.R & response.R: unify transformation namings in run_transformation and robyn_response - In response.R: remove exposure extrapolation because it's already done in robyn_input. Also add inflexion point to output. - In plots.R: fix onepager saturation plot issues - In pareto.R: rewrite run_dt_resp() as response_wrapper and align transformation logic & naming. - In pareto: Replace foreach response loop with lapply for simplicity. - In pareto.R: Simplify plot data generation process, esp for saturation curve plot, actual vs predicted plot & immediate vs carryover plot. - In pareto.R: Remove redundancy in xDecompVecCollect -> remove type rawMedia, rawSpend, predictedExposure, saturatedMedia & saturatedSpendReversed. Only keep adstockedMedia & decompMedia for response curve plotting. - add set_default_hyppar for easier testing - website update for all above changes
…te (#1090) Bumps [http-proxy-middleware](https://github.com/chimurai/http-proxy-middleware) from 2.0.6 to 2.0.7. - [Release notes](https://github.com/chimurai/http-proxy-middleware/releases) - [Changelog](https://github.com/chimurai/http-proxy-middleware/blob/v2.0.7/CHANGELOG.md) - [Commits](chimurai/http-proxy-middleware@v2.0.6...v2.0.7) --- updated-dependencies: - dependency-name: http-proxy-middleware dependency-type: indirect ... Signed-off-by: dependabot[bot] <[email protected]> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
Bumps [nanoid](https://github.com/ai/nanoid) from 3.3.6 to 3.3.8. - [Release notes](https://github.com/ai/nanoid/releases) - [Changelog](https://github.com/ai/nanoid/blob/main/CHANGELOG.md) - [Commits](ai/nanoid@3.3.6...3.3.8) --- updated-dependencies: - dependency-name: nanoid dependency-type: indirect ... Signed-off-by: dependabot[bot] <[email protected]> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
…e-pagers (#1194) * fix: prev versions' response curves and mean spends per channel on one-pagers
- Minor fix and tested on dummy data locally. Works as expected.
- minor fix and tested on local machine with different robyn_inputs specs, e.g. paid_media_spends and paid_media_vars are identical, or paid_media_spends only while paid_media_vars = NULL. All work as expected. - bump up version
…y InputCollect for BA bug (#1199) * fix: modeling window coverage considers all actual data dates (monthly data covers until end of month) * fix: adapt to rollingWindow too * fix: initial data message * fix: legacy paid_media_selected issue in BA * fix: replace paid_media_selected for paid_media_vars for response curves in OPs
…ion. (#1209) * added proper handling of context variables * added unit test on prepare_data * update fix for prophet enabling * updated timestamp formats * update redundant dtype conversion * update notebook reference visuals * update notebook reference visuals * update notebook references * add back unit test file * clear nb outputs * remove nb previews * update pyproject toml file * add nb outputs * add nb outputs
Co-authored-by: pniskala <[email protected]>
…1221) - Precomputed denominator for `xDecompMeanNon0Perc`, eliminating redundant calculations inside loops. - Optimized column indexing to remove repeated lookups. - Used `to_numpy().sum()` for faster summation.
…~40% (#1217) - Replaced loop-based implementation with `scipy.signal.lfilter` - Improved `_geometric_adstock` execution time from **108.9s → 0.495s** (~99% faster) per trial - Reduced overall app runtime from **~277.9s → ~166.7s** (~40% speedup) per trial - Added unit test to ensure numerical correctness (`np.allclose`)
* fix lambda_seq and lambda calculation * fix for column pollution * temporary draft for modeling approach for lambda calculation * test nevergrad changes * tested glmnet and sklearn * remove redundant function * refactor functions * complete debugging step3 * reconcile step6 hyper sampling * validated step6 nevergrad sampling * reconcile run_transformations * update transformations cols * ensure proper saturation for transformation * remove all non essential loggers * changes for validating data splits - step8 * complete validation for lambda_seq - step10 * addressed adjusted r2 * addressed rssd and model_decomp * enforce required cols in x_decomp_agg and decomp_spend_dist * resolve logging errors * update fallback for rpy2 modeling * updated dependencies * add documentations for logging * update notebooks * prepopulate noteobok * remove unused notebooks * update notebooks * remove unused functions * remove unused code
* fix all media call in run_transformations * update penalty factor calc * update run_transformation * fix sign control * update py version * revert cluster viz * update notebooks * update README for robynpy
* update ci calculation to address singular data points * update notebooks * fix solid handler * fix ordering of paid media spends for spend calculations * fix missing metric update for train size * update allocator * tidy up loggers * tidy up allocator viz * remove iframe figures * update toml requirements files * cleanup allocator code * fix plotting behavior for allocator * add missing filter for response curve plot * update notebook contents * update notebook content * suppress redundant plot outputs * update pypi package version * remove component level tutorials * update legend position, address paid_media_signs handling * remove old loggers * remove old debug loggers * tidy up logging * tidy up user docs on notebooks * update pypi versioning
* remove dup pypi deps * update README * update versioning * update missing dep
* update pypi version * remove old file * update notebook plots
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Project Robyn
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