db-benchmark
causalml
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db-benchmark | causalml | |
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91 | 10 | |
319 | 4,747 | |
0.9% | 2.8% | |
0.0 | 8.4 | |
10 months ago | 5 days ago | |
R | Python | |
Mozilla Public License 2.0 | GNU General Public License v3.0 or later |
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db-benchmark
- Database-Like Ops Benchmark
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Polars
Real-world performance is complicated since data science covers a lot of use cases.
If you're just reading a small CSV to do analysis on it, then there will be no human-perceptible difference between Polars and Pandas. If you're reading a larger CSV with 100k rows, there still won't be much of a perceptible difference.
Per this (old) benchmark, there are differences once you get into 500MB+ territory: https://h2oai.github.io/db-benchmark/
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DuckDB performance improvements with the latest release
I do think it was important for duckdb to put out a new version of the results as the earlier version of that benchmark [1] went dormant with a very old version of duckdb with very bad performance, especially against polars.
[1] https://h2oai.github.io/db-benchmark/
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Show HN: SimSIMD vs. SciPy: How AVX-512 and SVE make SIMD cleaner and ML faster
https://news.ycombinator.com/item?id=33270638 :
> Apache Ballista and Polars do Apache Arrow and SIMD.
> The Polars homepage links to the "Database-like ops benchmark" of {Polars, data.table, DataFrames.jl, ClickHouse, cuDF, spark, (py)datatable, dplyr, pandas, dask, Arrow, DuckDB, Modin,} but not yet PostgresML? https://h2oai.github.io/db-benchmark/ *
LLM -> Vector database: https://en.wikipedia.org/wiki/Vector_database
/? inurl:awesome site:github.com "vector database"
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Pandas vs. Julia – cheat sheet and comparison
I agree with your conclusion but want to add that switching from Julia may not make sense either.
According to these benchmarks: https://h2oai.github.io/db-benchmark/, DF.jl is the fastest library for some things, data.table for others, polars for others. Which is fastest depends on the query and whether it takes advantage of the features/properties of each.
For what it's worth, data.table is my favourite to use and I believe it has the nicest ergonomics of the three I spoke about.
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Any faster Python alternatives?
Same. Numba does wonders for me in most scenarios. Yesterday I've discovered pola-rs and looks like I will add it to the stack. It's API is similar to pandas. Have a look at the benchmarks of cuDF, spark, dask, pandas compared to it: Benchmarks
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Pandas 2.0 (with pyarrow) vs Pandas 1.3 - Performance comparison
The syntax has similarities with dplyr in terms of the way you chain operations, and it’s around an order of magnitude faster than pandas and dplyr (there’s a nice benchmark here). It’s also more memory-efficient and can handle larger-than-memory datasets via streaming if needed.
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Pandas v2.0 Released
If interested in benchmarks comparing different dataframe implementations, here is one:
https://h2oai.github.io/db-benchmark/
- Database-like ops benchmark
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Python "programmers" when I show them how much faster their naive code runs when translated to C++ (this is a joke, I love python)
Bad examples. Both numpy and pandas are notoriously un-optimized packages, losing handily to pretty much all their competitors (R, Julia, kdb+, vaex, polars). See https://h2oai.github.io/db-benchmark/ for a partial comparison.
causalml
- uber/causalml: Uplift modeling and causal inference with machine learning algorithms
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Data Science and Marketing
Uplift Modeling (python): CausalML, EconML
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Completed 3 months in Microsoft as Data Scientist.
Do you (or Microsoft DS teams in general) tackle any causal problems? Apparently, uber tries to solve these issues, they created https://github.com/uber/causalml
- [R] apd-crs: Cure Rate Survival Analysis in Python
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[S] Python packages to replace R
There's some causality focused packages. Nothing like scikit-learn or statsmodels yet because it's all based on very recent research, but this one: https://github.com/uber/causalml
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Good way to segment a/b test results for insight or narrative?
I agree that uplift trees and CATE methods are promising here. If you’re in Python, check out Uber’s open-source causalml.
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UpliftML: An uplift modeling library that handles web scale datasets
Many libraries have recently emerged that offer implementations of algorithms for heterogeneous treatment effect estimation (or, CATE estimation). The most well-known examples are Microsoft's EconML (https://github.com/microsoft/EconML) and Uber's CausalML (https://github.com/uber/causalml). Existing libraries require all data to fit in memory, which is often a limitation for industry applications on web scale datasets. Booking.com's new library offers similar functionality on top of Spark, enabling web scale uplift modeling.
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R, I love you.
you like causal inference? it must be nice to be able to use libraires like dowhy, causal ml, and ananke right? 🤔🤔🤔
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Causal data science
video's author recommends this course in a comment: https://www.coursera.org/learn/crash-course-in-causality and he also co-created this library: https://github.com/uber/causalml
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Model Re-Training with Intervention Effects
There aren't many general solutions because it will really depend on your domain. There are some packages that specifically work for "modeling under interventions" (like this https://github.com/uber/causalml although I have never tried it). In general, if you have enough data between intervention and not-intervention, you could train two different models and then apply whichever one makes sense (e.g. if you wanted to find the highest-churn-risk users among those who have already had the intervention, use the model trained on the prior intervention cases).
What are some alternatives?
polars - Dataframes powered by a multithreaded, vectorized query engine, written in Rust
EconML - ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its goals is to build a toolkit that combines state-of-the-art machine learning techniques with econometrics in order to bring automation to complex causal inference problems. To date, the ALICE Python SDK (econml) implements orthogonal machine learning algorithms such as the double machine learning work of Chernozhukov et al. This toolkit is designed to measure the causal effect of some treatment variable(s) t on an outcome variable y, controlling for a set of features x.
arrow-datafusion - Apache DataFusion SQL Query Engine
upliftml - UpliftML: A Python Package for Scalable Uplift Modeling
Apache Arrow - Apache Arrow is a multi-language toolbox for accelerated data interchange and in-memory processing
causalnex - A Python library that helps data scientists to infer causation rather than observing correlation.
databend - 𝗗𝗮𝘁𝗮, 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 & 𝗔𝗜. Modern alternative to Snowflake. Cost-effective and simple for massive-scale analytics. https://databend.com
causallift - CausalLift: Python package for causality-based Uplift Modeling in real-world business
DataFramesMeta.jl - Metaprogramming tools for DataFrames
Robyn - Robyn is an experimental, AI/ML-powered and open sourced Marketing Mix Modeling (MMM) package from Meta Marketing Science. Our mission is to democratise modeling knowledge, inspire the industry through innovation, reduce human bias in the modeling process & build a strong open source marketing science community.
sktime - A unified framework for machine learning with time series
BTYD - BTYD 2.4.3