Tidymodels: The Los Alamos of Data Science

Numbers around us
Numbers around us
Published in
9 min readAug 9, 2023

The dimmed lights of the cinema hall, the hushed anticipation of the audience, and the opening scenes of Nolan’s portrayal of Dr. Oppenheimer drew me in instantly. Amidst the enthralling narrative and cinematic brilliance, my mind began drawing parallels that transcended the screen. The palpable tension, the nexus of genius minds, and the revolutionary thinking underpinning the Manhattan Project evoked striking resemblances to a domain I hold dear — the universe of tidymodels in R. While the worlds of wartime physics and modern data science may seem light-years apart, their foundational ethos of collaboration, innovation, and problem-solving resonated profoundly. As the film unfolded, I envisioned a Los Alamos of the digital age, where code, not atoms, was being fused, and models, not bombs, were being forged.

Disclaimer

As our journey into this metaphorical realm begins, it’s imperative to cast a spotlight on the shadows that loom over the Manhattan Project. The culmination of countless hours of research and collaboration, while being an undeniable testament to human intelligence, birthed a force of unparalleled devastation — the atomic bomb. Einstein’s letter to Roosevelt, the hushed discussions in dimly lit rooms, and the eventual detonation at Hiroshima and Nagasaki are stark reminders that knowledge, while a powerful tool for progress, can also pave the path to unparalleled destruction. In a similar vein, data science, with its immense potential, holds a dual-edged sword. In the right hands, it can revolutionize industries, predict crises, and foster progress. Yet, in the wrong hands, it can infringe on privacy, manipulate narratives, and destabilize societies. The tools are neutral; their impact, however, is defined by human intent.

Dr. Oppenheimer and Gen. Groves:

Amidst the rugged landscapes of New Mexico, the once-quiet mesas of Los Alamos became a bustling nexus of activity. Here, Dr. Oppenheimer and General Groves, two figures with contrasting personalities and backgrounds, converged to spearhead what would be one of the most ambitious projects in human history. Dr. Oppenheimer, with his deep-set eyes and poetic demeanor, was the visionary — the one who could fathom the unfathomable, threading the delicate tapestry of atomic science. General Groves, with his military precision and unwavering resolve, was the orchestrator — ensuring resources, managing logistics, and driving the mission forward against all odds. Together, they embodied the unity of vision and execution.

In the digital corridors of R’s ecosystem, tidymodels mirrors this duality. It's more than just a collection of functions and algorithms; it's a meticulously crafted framework where the beauty of vision (theoretical modeling) and the pragmatism of execution (practical implementation) come alive. Much like how Oppenheimer and Groves synthesized the energies of physicists, chemists, and engineers, tidymodels binds the brilliance of various packages into a cohesive, powerful entity.

Dr. Feynman and parsnip

There’s an infectious energy that some minds radiate, making them impossible to ignore. Richard Feynman was one such luminary amidst the Manhattan Project’s constellation of stars. His youthful exuberance, coupled with an unquenchable thirst for knowledge, made him a force of nature. Feynman wasn’t content just understanding the established principles; he sought to view them through different lenses, to turn them on their heads, to dissect and then reconstruct. A maverick in his approach, he was known to find unique solutions to complex problems, making the intricate appear elegantly simple.

Drawing parallels in the realm of R, the parsnip package is the Feynman of the tidymodels universe. In a landscape populated with diverse modeling methodologies, each with its peculiar syntax and nuances, parsnip emerges as a game-changer. It offers a unified, consistent interface to a myriad of models. Whether you’re venturing into regression or diving into deep learning, parsnip translates your intent into actionable code, much like how Feynman translated abstract concepts into tangible understanding.


# Example using parsnip
library(parsnip)
model_spec <- linear_reg() %>%
set_engine(“lm”)

With parsnip, the complexity dissipates, leaving behind clarity, much akin to Feynman’s legendary lectures.

Dr. Fermi and recipes

Enrico Fermi, often dubbed the “architect of the atomic age”, was as much an experimentalist as he was a theoretician. In the hallowed halls of Los Alamos, while many delved into abstract realms, Fermi’s brilliance shone in his ability to bridge theory with tangible experiments. He could visualize an atomic reaction not just on paper, but in the very material world, conducting real-life experiments that tested and validated theories. His hands-on approach, an alchemy of intuition and practicality, was instrumental in turning hypotheses into verifiable truths.

In our R laboratory, the recipes package plays a role uncannily reminiscent of Fermi's. Before we set out on grand computational endeavors or dive deep into model-building, the raw, unstructured data needs meticulous preparation. recipes provides the tools to curate, transform, and structure this data, readying it for the analytical odyssey ahead.

# Example with recipes
library(recipes)
data(mtcars)
rec <- recipe(mpg ~ ., data = mtcars) %>%
step_normalize(all_predictors())

Just as Fermi would have never embarked on an experiment without precisely calibrated instruments and well-prepared materials, recipes ensures our data is primed, processed, and perfectly attuned to the modeling journey we envision.

Dr. Bohr and rsample:

Niels Bohr’s contributions to the world of atomic physics are nothing short of legendary. With a keen intellect and a penchant for deep thought, Bohr was at the forefront of quantum mechanics, pushing the boundaries of understanding atomic structures and behaviors. But it wasn’t just his theoretical acumen that set him apart; it was his profound belief in the value of experimentation and iterative learning. Bohr once remarked, “An expert is a person who has made all the mistakes that can be made in a very narrow field.” For Bohr, the road to enlightenment was paved with countless experiments, each one offering its own set of learnings and insights.

In the expansive ecosystem of tidymodels, the rsample package mirrors Bohr's philosophy. Before a model can predict the future, it must first learn from the past, and this learning isn't a one-time affair. rsample facilitates the creation of numerous data samples, allowing models to train, test, and validate their assumptions across varied datasets.

# Example with rsample
library(rsample)
set.seed(123)
split <- initial_split(mtcars, prop = 0.7)
train_data <- training(split)
test_data <- testing(split)

Just as Bohr believed in iterative experimentation to refine and validate atomic theories, rsample champions the cause of iterative modeling, ensuring our predictions are robust, tested, and validated across the diverse landscape of data.

Dr. Bethe and tune

Hans Bethe, a titan in the realm of nuclear physics, played a central role in unraveling the complex processes powering the sun: nuclear fusion. He was a master at finetuning, diving deep into equations, adjusting variables, and tinkering until the pieces fell seamlessly into place, revealing a beautifully harmonized system. His meticulous approach to his work, coupled with a relentless pursuit of precision, earned him the Nobel Prize and the eternal admiration of peers and successors.

In the domain of tidymodels, the tune package is the embodiment of Bethe's meticulousness. Building a model isn't just about selecting an algorithm and feeding it data. It's an art of adjustment, a quest for that sweet spot where all parameters align to produce the most accurate and insightful results.

# Example using tune
library(tune)
set.seed(123)
lin_mod <- linear_reg() %>%
set_engine("lm") %>%
tune_grid(mpg ~ ., resamples = split, grid = 10)

As Bethe fine-tuned his understanding of the sun’s nuclear processes, tune aids data scientists in optimizing models, ensuring they shine their brightest, illuminating insights previously obscured in the shadows of raw data.

Dr. Lawrence and Dr. Hill with dials

Dr. Ernest Lawrence and Dr. Harold Hill, with their keen focus on technological innovations, redefined the possibilities of the atomic age. Lawrence, the brain behind the cyclotron, and Hill, an expert on electromagnetic isotope separation, were masters at fine-tuning intricate machinery. Their prowess lay not just in understanding the overarching principles but in the meticulous calibration of the myriad knobs, dials, and levers that made these machines tick. Each twist, each adjustment was pivotal in achieving precision, amplifying efficiency, and pushing boundaries.

The dials package in tidymodels beautifully mirrors this ethos of precision and calibration. Modeling isn’t a static endeavor; it's dynamic, requiring constant tweaks and refinements. dials serves as the toolkit that allows data scientists to experiment with various model parameters, hunting for that optimal configuration that maximizes predictive power.

# Example with dials
library(dials)
grid_vals <- grid_regular(
penalty(range = c(-6, -4), trans = "log10"),
mixture(),
levels = 5
)

Just as Lawrence’s cyclotron and Hill’s separation methods demanded constant calibration to achieve desired outcomes, dials empowers users to fine-tune their models, ensuring they resonate with the unique frequencies of their data.

Stan Ulam and stacks

Stanisław Ulam was a mathematician par excellence, known for his innovative approaches and for thinking outside the box. One of his most notable contributions to the Manhattan Project was the Teller–Ulam design, a revolutionary method that became instrumental in the development of the hydrogen bomb. This design hinged on the intricate layering and interaction of various components to amplify energy output. Ulam’s brilliance was in recognizing that combining individual elements, each with its own properties, could produce an outcome far greater than the sum of its parts.

The stacks package of tidymodels reflects the essence of Ulam’s layered approach. It's not about relying on a single model or method. Instead, stacks facilitates the blending of various model predictions to produce a final, ensemble result that's often more accurate and robust than any individual model.

# Example using stacks (assuming models have been trained)
# library(stacks)
# stacked_model <- stack_models(lm_model, rf_model, xgb_model)

Like the Teller–Ulam design which leveraged the synergy of its components to achieve an explosive result, stacks harnesses the combined strength of multiple models to deliver powerful predictions, a testament to the collective might of collaborative efforts.

Dr. Rotblat and broom

Joseph Rotblat stands apart in the narrative of the Manhattan Project. A physicist with a strong moral compass, he was the only scientist to leave the project on ethical grounds, horrified by the potential of nuclear weapons. Post-war, Rotblat dedicated himself to promoting peace, earning a Nobel Peace Prize for his efforts. While deeply entrenched in the world of physics, he never lost sight of the broader picture, always placing science in the context of humanity and ethics.

The broom package in tidymodels mirrors this clarity and holistic perspective. After delving into the intricacies of model-building, there's a need to step back, to tidy up, to transform the raw outputs into digestible, meaningful insights. broom sweeps through the results, presenting them in neat, comprehensible formats, making it easier to derive meaning and purpose.

# Example using broom
library(broom)
fit <- lm(mpg ~ wt + hp, data = mtcars)
tidy_summary <- tidy(fit)

Disclaimer

As we once again draw parallels between the monumental Manhattan Project and the tidymodels framework, it's imperative to reiterate the profound weight and responsibility that accompanies such comparisons. The Manhattan Project, while a marvel of scientific collaboration and innovation, bore consequences that continue to shape geopolitics, ethics, and the human condition.

Similarly, while tidymodels and data science as a whole offer incredible potential for advancement and discovery, they too come with their own set of responsibilities. The power to analyze, predict, and influence based on data is immense. Yet, as with all tools, it's the hand that wields them and the intent behind their use that determines the outcome. Whether it’s the construction of a nuclear weapon or a predictive algorithm, the ethical considerations remain paramount.

We must remember the likes of Dr. Rotblat, who, even amid groundbreaking discoveries, never lost sight of the broader, human picture. Science, in all its grandeur and capability, should serve as a beacon for progress, understanding, and above all, the betterment of humanity.

Science, be it the intricacies of nuclear physics or the nuanced realm of data modeling, has always been about pushing boundaries, seeking understanding, and leveraging collective knowledge. Through the lens of the Manhattan Project, we glimpsed the convergence of brilliant minds, each bringing their unique skills and perspectives to achieve a shared goal. While the project’s historical weight is undeniable, its essence of collaboration, innovation, and relentless pursuit of knowledge parallels the ethos of tidymodels.

As we’ve journeyed through this metaphorical landscape, the interconnectedness of components, be it individual scientists and their contributions or specific packages within tidymodels, became evident. Each package, like each scientist, plays a pivotal role, contributing its unique functionality to the holistic process of data modeling.

Yet, at the heart of it all lies responsibility. As we harness the power of data and models to shape decisions, influence behaviors, or predict outcomes, we must be ever-aware of the ethical considerations that accompany such capabilities.

In the words of Richard Feynman, reflecting on the Manhattan Project, “The first principle is that you must not fool yourself — and you are the easiest person to fool.” As we advance in the world of data science, may we always strive for clarity, integrity, and the greater good, ensuring that our tools and discoveries uplift rather than diminish.

Thank you for joining me on this reflective journey, intertwining the realms of history and data science. May we always find inspiration and lessons in the past as we forge ahead into the future.

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Numbers around us
Numbers around us

Self developed analyst. BI Developer, R programmer. Delivers what you need, not what you asked for.