Comments (4)
pabloduque0
commented on May 22, 2024
Hello!
Can you try installing from the latest version on github?
pip install --upgrade git+https://github.com/google/lightweight_mmm.git
We had a strict version on TF on previous versions but we made it more flexible recently. Those changes should reflect on the next release but until then installing from gh should be the best option for when you run into that error.
Let me know if this is not the case and I will look into it :)
from lightweight_mmm.
alberto-molinaro
commented on May 22, 2024
Hello!
Thank you for your answer!
I get the same error unfortunately.
Collecting git+https://github.com/google/lightweight_mmm.git
Cloning https://github.com/google/lightweight_mmm.git to /private/var/folders/6c/f0rfrzf51t18yrvpcm4x8mgmsjr31v/T/pip-req-build-jtdpl4vu
Running command git clone --filter=blob:none --quiet https://github.com/google/lightweight_mmm.git /private/var/folders/6c/f0rfrzf51t18yrvpcm4x8mgmsjr31v/T/pip-req-build-jtdpl4vu
Resolved https://github.com/google/lightweight_mmm.git to commit bafdae7d0594d0d5fab58c47c1274a3df0802d49
Preparing metadata (setup.py) ... done
Collecting absl-py
Using cached absl_py-1.2.0-py3-none-any.whl (123 kB)
Collecting arviz==0.11.2
Using cached arviz-0.11.2-py3-none-any.whl (1.6 MB)
Collecting immutabledict>=2.0.0
Using cached immutabledict-2.2.1-py3-none-any.whl (4.0 kB)
Collecting jax>=0.3.14
Using cached jax-0.3.14-py3-none-any.whl
Collecting jaxlib>=0.3.14
Using cached jaxlib-0.3.14-cp39-none-macosx_11_0_arm64.whl (53.6 MB)
Collecting matplotlib==3.3.4
Using cached matplotlib-3.3.4.tar.gz (37.9 MB)
Preparing metadata (setup.py) ... done
Collecting numpy>=1.12
Using cached numpy-1.23.1-cp39-cp39-macosx_11_0_arm64.whl (13.3 MB)
Collecting numpyro>=0.9.2
Using cached numpyro-0.10.0-py3-none-any.whl (291 kB)
Collecting pandas>=1.1.5
Using cached pandas-1.4.3-cp39-cp39-macosx_11_0_arm64.whl (10.5 MB)
Collecting scipy
Using cached scipy-1.8.1-cp39-cp39-macosx_12_0_arm64.whl (28.7 MB)
Collecting seaborn==0.11.1
Using cached seaborn-0.11.1-py3-none-any.whl (285 kB)
Collecting sklearn
Using cached sklearn-0.0-py2.py3-none-any.whl
ERROR: Could not find a version that satisfies the requirement tensorflow>=2.7.2 (from lightweight-mmm) (from versions: none)
ERROR: No matching distribution found for tensorflow>=2.7.2
I don't know if it is relevent to specify, I'm using anaconda3 environment in which I ran the pip install --upgrade git+https://github.com/google/lightweight_mmm.git command
from lightweight_mmm.
alberto-molinaro
commented on May 22, 2024
Hello!
I managed to install the package. I managed to install it under x86_64 env but not on arm64 env.
Creating an env python3 -m venv venv under x86_64 resolved the problem when I ran pip install --upgrade git+https://github.com/google/lightweight_mmm.git command. But not on arm64.
Did you managed to install it on mac m1, arm64? Is the package compatible with arm64?
Have a nice day!
from lightweight_mmm.
pabloduque0
commented on May 22, 2024
Apologies for the late reply!
Glad to hear you were able to install it. I think your problem was specific to TF so we would need to look into copatibilities of TF for those specific mac systems. However we are soon removing the TF dependency so this should not be a problem in the future.
from lightweight_mmm.
Related Issues (20)
- limitations in the use of extra features HOT 4
- Anyway to save mmm.print_summary()? HOT 1
- Hierarchical Partially Pooled Media
- media_priors HOT 1
- Get contribution from predict
- How can I calibrate my predicted ROI in the MMM with my geolift result ? HOT 3
- How can I run kind of gridsearch to find the best custom priors for a hill_adstock or carryover model ?
- How can I input future media_data_test for optimization in upcoming periods? HOT 1
- How can channel-wise optimized conversions be obtained?
- Extra features
- Addressing Heteroscedasticity
- Geo level attribution and response curves
- Budget Allocation Percentage breakdown by channel HOT 10
- Question on tensorflow requirement
- Dtype object is not a valid JAX array type. Only arrays of numeric types are supported by JAX. HOT 5
- Same pre-optimization and post-optimization channel budget allocation ratios , but suggesting much higher budget instead of aligning the budget to the one i requested. HOT 10
- Paid Search bias - Nested model
- Divergences and n_eff
- Outliers and influential points
- budget allocator: How to set up lower bound and upper bound per channel?
Recommend Projects
-
React
A declarative, efficient, and flexible JavaScript library for building user interfaces.
-
Vue.js
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
-
Typescript
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
-
TensorFlow
An Open Source Machine Learning Framework for Everyone
-
Django
The Web framework for perfectionists with deadlines.
-
Laravel
A PHP framework for web artisans
-
D3
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
-
Recommend Topics
-
javascript
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
-
web
Some thing interesting about web. New door for the world.
-
server
A server is a program made to process requests and deliver data to clients.
-
Machine learning
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
-
Visualization
Some thing interesting about visualization, use data art
-
Game
Some thing interesting about game, make everyone happy.
Recommend Org
-
Facebook
We are working to build community through open source technology. NB: members must have two-factor auth.
-
Microsoft
Open source projects and samples from Microsoft.
-
Google
Google ❤️ Open Source for everyone.
-
Alibaba
Alibaba Open Source for everyone
-
D3
Data-Driven Documents codes.
-
Tencent
China tencent open source team.
from lightweight_mmm.