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text vectorization pipeline dask examples documentation dask distributed dask ml examples ecosystem community toggle navigation sidebar dask examples documentation basic examples dask arrays dask bags dask dataframes custom workloads with dask delayed custom workloads with futures dask for machine learning operating on dask dataframes with sql xarray with dask arrays resilience against hardware failures dataframes dataframes read and write data dataframes groupby gotcha s from pandas to dask dataframes reading in messy data machine learning blockwise ensemble methods scale scikit learn for small data problems score and predict large datasets batch prediction with pytorch train models on large datasets incrementally train large datasets text vectorization pipeline hyperparameter optimization with dask scale xgboost use voting classifiers automate machine learning with tpot generalized linear models singular value decomposition applications analyze web hosted json data async await and non blocking execution asynchronous computation web servers dask embarrassingly parallel workloads handle evolving workflows image processing etl pipelines with prefect stencil computations with numba time series forecasting user surveys 2021 dask user survey results 2020 dask user survey results 2019 dask user survey results theme by the executable book project toggle navigation ipynb pdf contents fetch the data feature hashing classification pipeline text vectorization pipeline contents fetch the data feature hashing classification pipeline live notebook you can run this notebook in a live session or view it on github text vectorization pipeline this example illustrates how dask ml can be used to classify large textual datasets in parallel it is adapted from this scikit learn example the primary differences are that we fit the entire model including text vectorization as a pipeline we use dask collections like dask bag dask dataframe and dask array rather than generators to work with larger than memory datasets 1 from dask distributed import client progress client client n_workers 2 threads_per_worker 2 memory_limit 2gb client 1 client client 94cfa4ae 0de1 11ed a521 000d3a8f7959 connection method cluster object cluster type distributed localcluster dashboard http 127 0 0 1 8787 status cluster info localcluster 0f48ff5b dashboard http 127 0 0 1 8787 status workers 2 total threads 4 total memory 3 73 gib status running using processes true scheduler info scheduler scheduler df50e6c2 504c 458c 904f d9354f469039 comm tcp 127 0 0 1 35661 workers 2 dashboard http 127 0 0 1 8787 status total threads 4 started just now total memory 3 73 gib workers worker 0 comm tcp 127 0 0 1 46267 total threads 2 dashboard http 127 0 0 1 41243 status memory 1 86 gib nanny tcp 127 0 0 1 38595 local directory home runner work dask examples dask examples machine learning dask worker space worker imhji18u worker 1 comm tcp 127 0 0 1 44477 total threads 2 dashboard http 127 0 0 1 42527 status memory 1 86 gib nanny tcp 127 0 0 1 34731 local directory home runner work dask examples dask examples machine learning dask worker space worker r6iwv690 fetch the data scikit learn provides a utility to fetch the newsgroups dataset 2 import sklearn datasets bunch sklearn datasets fetch_20newsgroups the data from scikit learn isn t too large so the data is just returned in memory each document is a string the target we re predicting is an integer which codes the topic of the post we ll load the documents and targets directly into a dask dataframe in practice on a larger than memory dataset you would likely load the documents from disk or cloud storage using dask bag or dask delayed 3 import dask dataframe as dd import pandas as pd df dd from_pandas pd dataframe text bunch data target bunch target npartitions 25 df 3 dask dataframe structure text target npartitions 25 0 object int64 453 10872 11313 dask name from_pandas 25 tasks each row in the text column has a bit of metadata and the full text of a post 4 print df head loc 0 text 500 from email protected where s my thing subject what car is this nntp posting host rac3 wam umd edu organization university of maryland college park lines 15 i was wondering if anyone out there could enlighten me on this car i saw the other day it was a 2 door sports car looked to be from the late 60s early 70s it was called a bricklin the doors were really small in addition the front bumper was separate from the rest of the body this is all i know if anyone can tellme a m feature hashing dask s hashingvectorizer provides a similar api to scikit learn s implementation in fact dask ml s implementation uses scikit learn s applying it to each partition of the input dask dataframe series or dask bag bag transformation once we actually compute the result happens in parallel and returns a dask array 5 import dask_ml feature_extraction text vect dask_ml feature_extraction text hashingvectorizer x vect fit_transform df text x 5 array chunk shape nan 1048576 nan 1048576 count 75 tasks 25 chunks type float64 scipy sparse _csr csr_matrix the output array x has unknown chunk sizes becase the input dask series or bags don t know their own length each block in x is a scipy sparse matrix 6 x blocks 0 compute 6 453x1048576 sparse matrix of type class numpy float64 with 64357 stored elements in compressed sparse row format this is a document term matrix each row is the hashed representation of the original post classification pipeline we can combine the hashingvectorizer with incremental and a classifier like scikit learn s sgdclassifier to create a classification pipeline we ll predict whether the topic was in the comp category 7 bunch target_names 7 alt atheism comp graphics comp os ms windows misc comp sys ibm pc hardware comp sys mac hardware comp windows x misc forsale rec autos rec motorcycles rec sport baseball rec sport hockey sci crypt sci electronics sci med sci space soc religion christian talk politics guns talk politics mideast talk politics misc talk religion misc 8 import numpy as np positive np arange len bunch target_names comp in x for x in bunch target_names y df target isin positive astype int y 8 dask series structure npartitions 25 0 int64 453 10872 11313 name target dtype int64 dask name astype 101 tasks 9 import numpy as np import sklearn linear_model import sklearn pipeline import dask_ml wrappers because the input comes from a dask series with unknown chunk sizes we need to specify assume_equal_chunks true this tells dask ml that we know that each partition in x matches a partition in y 10 sgd sklearn linear_model sgdclassifier tol 1e 3 clf dask_ml wrappers incremental sgd scoring accuracy assume_equal_chunks true pipe sklearn pipeline make_pipeline vect clf sgdclassifier partial_fit needs to know the full set of classes up front because our sgd is wrapped inside an incremental we need to pass it through as the incremental__classes keyword argument in fit 11 pipe fit df text y incremental__classes 0 1 as usual incremental predict lazily returns the predictions as a dask array 12 predictions pipe predict df text predictions 12 array chunk bytes unknown unknown shape nan nan count 100 tasks 25 chunks type int64 numpy ndarray we can compute the predictions and score in parallel with dask_ml metrics accuracy_score 13 dask_ml metrics accuracy_score y predictions 13 0 950150256319604 this simple combination of a hashingvectorizer and sgdclassifier is pretty effective at this prediction task previous incrementally train large datasets next hyperparameter optimization with dask by dask developers copyright 2018 dask developers