Statistics and ML in python

DataFrame-style ecosystem

See tabular data in python.

Matrix-style ecosystem

scikit-learn exemplifies a machine-learning style, with lots of abstract feature construction and predictive-performance style model selection built around homogeneously-typed (only floats, only ints) matrices instead of dataframes. This style wil be more familiar to MATLAB users than to R users.

• scikit-learn (sklearn to its friends) is the flagship of this fleet. It is fast, clear and well-designed. I enjoy using it for implementing ML-type tasks. It has various algorithms such as random forests and linear regression and Gaussian processes and reference implementations of many algorithms, both à la mode and passé. Although I miss sniff glmnet in R for lasso regression.

• SciKit-Learn Laboratory

  SKLL (pronounced “skull”) provides a number of utilities to make it simpler to run common scikit-learn experiments with pre-generated features.

• Sklearn-pandas

  …provides a bridge between sklearn’s machine learning methods and pandas-style Data Frames.

  In particular, it provides:

  • a way to map DataFrame columns to transformations, which are later recombined into features

  • a way to cross-validate a pipeline that takes a pandas DataFrame as input.

• libROSA, the machine listening library is more or less in this school.

• “pystruct aims at being an easy-to-use structured learning and prediction library.”

  Currently it implements only max-margin methods and a perceptron, but other algorithms might follow. The learning algorithms implemented in PyStruct have various names, which are often used loosely or differently in different communities. Common names are conditional random fields (CRFs), maximum-margin Markov random fields (M3N) or structural support vector machines.

• PyCaret (announcement) is some kind of low-code stats tool.

  PyCaret is essentially a Python wrapper around several machine learning libraries and frameworks such as scikit-learn, XGBoost, Microsoft LightGBM, spaCy, and many more.

Bayes stuff

PyStan

xarray

xarray; what is this?

Notable application, ArviZ: Exploratory analysis of Bayesian models as seen in

Time series

Forecasting in python. Rob J Hyndman , in Python implementations of time series forecasting and anomaly detection recommends

• sktime
• Nixtla

See tslearn

tslearn is a Python package that provides machine learning tools for the analysis of time series. This package builds on (and hence depends on) scikit-learn, numpy and scipy libraries.

It integrates with other time series tools, for example:

tsfresh

It automatically calculates a large number of time series characteristics, the so called features. Further the package contains methods to evaluate the explaining power and importance of such characteristics for regression or classification tasks.

cesium

Cesium is an end-to-end machine learning platform for time-series, from calculation of features to model-building to predictions. Cesium has two main components - a Python library, and a web application platform that allows interactive exploration of machine learning pipelines. Take control over the workflow in a Python terminal or Jupyter notebook with the Cesium library, or upload your time-series files, select your machine learning model, and watch Cesium do feature extraction and evaluation right in your browser with the web application.

pyts is a time series classification library that seems moderately popular.

The non-python options in Forecasting are also worth looking at.

Interoperation with other languages, platforms

%load_ext rpy2.ipython

%R library(robustbase)
%Rpush yy xx
%R mod <- lmrob(yy ~ xx);
%R params <- mod$coefficients;
%Rpull params

import pandas as pd
import feather

path = 'my_data.feather'
feather.write_dataframe(df, path)
df = feather.read_dataframe(path)

library(feather)

path <- "my_data.feather"
write_feather(df, path)
df <- read_feather(path)

Tips

from numpy.random import get_state, set_state, seed

class Seed(object):
  """
  context manager for reproducible seeding.

  >>> with Seed(5):
  >>>   print(np.random.rand())

  0.22199317108973948
  """
  def __init__(self, seed):
    self.seed = seed
    self.state = None

  def __enter__(self):
    self.state = get_state()
    seed(self.seed)

  def __exit__(self, exc_type, exc_value, traceback):
    set_state(self.state)

Miscellaneous learning resources

Numerical tours of signal processing in python.

Agate

agate is a stats package is not designed for high performance but for ease of use and reproducibility for non-specialists, e.g. journalists.

agate is a intended to fill a very particular programming niche. It should not be allowed to become as complex as numpy or pandas. Please bear in mind the following principles when considering a new feature:

• Humans have less time than computers. Optimize for humans.
• Most datasets are small. Don’t optimize for “big data”.
• Text is data. It must always be a first-class citizen.
• Python gets it right. Make it work like Python does.
• Humans lives are nasty, brutish and short. Make it easy

Miscellaneous tools

• hypertools is a generic dimensionality reduction toolkit. Is this worthwhile?
• Bonus scientific computation is also available through GSL, most easily via CythonGSL use and reproducibility for journalists.
• savez makes persisting arrays fast and efficient per default. Use that, if you are talking to other python processes.
• biopython is a whole other world of phylogeny and bio-data wrangling. I’m not sure if it adheres to one of the aforementioned schools or not. Should check.
• more options for speech/string analysis at natural language processing.