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scikit-learn | scikit-learn : machine learning in Python | Machine Learning library
by scikit-learn Python Version: 1.2.0rc1 License: BSD-3-Clause
by scikit-learn Python Version: 1.2.0rc1 License: BSD-3-Clause
kandi X-RAY | scikit-learn Summary
scikit-learn is a Python library typically used in Institutions, Learning, Education, Artificial Intelligence, Machine Learning, Pandas applications. scikit-learn has no bugs, it has build file available, it has a Permissive License and it has high support. However scikit-learn has 1 vulnerabilities. You can install using 'pip install scikit-learn' or download it from GitHub, PyPI.
scikit-learn: machine learning in Python
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- scikit-learn has a highly active ecosystem.
- It has 52681 star(s) with 23878 fork(s). There are 2164 watchers for this library.
- There were 4 major release(s) in the last 6 months.
- There are 1541 open issues and 8351 have been closed. On average issues are closed in 291 days. There are 600 open pull requests and 0 closed requests.
- It has a positive sentiment in the developer community.
- The latest version of scikit-learn is 1.2.0rc1
scikit-learn Support
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scikit-learn Support
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- scikit-learn has 0 bugs and 0 code smells.
scikit-learn Quality
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scikit-learn Quality
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- scikit-learn has 1 vulnerability issues reported (1 critical, 0 high, 0 medium, 0 low).
- scikit-learn code analysis shows 0 unresolved vulnerabilities.
- There are 0 security hotspots that need review.
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License
- scikit-learn is licensed under the BSD-3-Clause License. This license is Permissive.
- Permissive licenses have the least restrictions, and you can use them in most projects.
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scikit-learn License
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- scikit-learn releases are available to install and integrate.
- Deployable package is available in PyPI.
- Build file is available. You can build the component from source.
- scikit-learn saves you 147220 person hours of effort in developing the same functionality from scratch.
- It has 176758 lines of code, 9057 functions and 897 files.
- It has high code complexity. Code complexity directly impacts maintainability of the code.
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Top functions reviewed by kandi - BETA
kandi has reviewed scikit-learn and discovered the below as its top functions. This is intended to give you an instant insight into scikit-learn implemented functionality, and help decide if they suit your requirements.
- Linear Grammarization problem .
- Linear path solver .
- Logistic regression .
- Compute a dictionary of learning statistics for a given dataset .
- Local embedding .
- Plot the image .
- Plot the partial dependence of the estimator .
- Check if an array is valid .
- r Enet Path Method .
- Calculate partial dependence of the estimator .
Get all kandi verified functions for this library.
Get all kandi verified functions for this library.
scikit-learn Key Features
scikit-learn: machine learning in Python
scikit-learn Examples and Code Snippets
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Community Discussions
Trending Discussions on scikit-learn
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Trending Discussions on scikit-learn
QUESTION
Installing scipy and scikit-learn on apple m1
Asked 2022-Mar-22 at 06:21The installation on the m1 chip for the following packages: Numpy 1.21.1, pandas 1.3.0, torch 1.9.0 and a few other ones works fine for me. They also seem to work properly while testing them. However when I try to install scipy or scikit-learn via pip this error appears:
ERROR: Failed building wheel for numpy
Failed to build numpy
ERROR: Could not build wheels for numpy which use PEP 517 and cannot be installed directly
Why should Numpy be build again when I have the latest version from pip already installed?
Every previous installation was done using python3.9 -m pip install ... on Mac OS 11.3.1 with the apple m1 chip.
Maybe somebody knows how to deal with this error or if its just a matter of time.
ANSWER
Answered 2021-Aug-02 at 14:33Please see this note of scikit-learn about
Installing on Apple Silicon M1 hardware
The recently introduced
macos/arm64platform (sometimes also known asmacos/aarch64) requires the open source community to upgrade the build configuation and automation to properly support it.At the time of writing (January 2021), the only way to get a working installation of scikit-learn on this hardware is to install scikit-learn and its dependencies from the conda-forge distribution, for instance using the miniforge installers:
https://github.com/conda-forge/miniforge
The following issue tracks progress on making it possible to install scikit-learn from PyPI with pip:
QUESTION
negative values for mean squared errors in sae package for R
Asked 2022-Feb-25 at 14:28I have been using "sae" package for R to use small area estimations with spatial fay-herriot models (SFH). Using different distance matrices I occasionally obtained negative values of Mean Squared Errors (MSE).
The following link may reference a similar behavior:
scikit-learn cross validation, negative values with mean squared error
In any case here is a working example:
library(sae)
v1 <- c(0.000,0.089,0.081,0.082,0.058,0.075,0.062,0.043,0.000,0.037,0.065,0.056,
0.046,0.055,0.034,0.043,0.043,0.027,0.013,0.011,0.036,0.029,0.017,0.081,
0.000,0.093,0.081,0.062,0.077,0.066,0.046,0.000,0.036,0.063,0.054,0.044,
0.053,0.033,0.041,0.041,0.026,0.012,0.010,0.035,0.028,0.016,0.073,0.091,
0.000,0.080,0.066,0.085,0.070,0.048,0.000,0.036,0.062,0.053,0.043,0.053,
0.032,0.041,0.041,0.025,0.012,0.010,0.034,0.028,0.016,0.071,0.076,0.077,
0.000,0.053,0.083,0.065,0.043,0.000,0.039,0.071,0.059,0.047,0.057,0.035,
0.044,0.044,0.027,0.013,0.011,0.037,0.030,0.017,0.060,0.070,0.075,0.065,
0.000,0.070,0.084,0.076,0.000,0.032,0.053,0.051,0.041,0.065,0.041,0.039,
0.055,0.023,0.011,0.009,0.030,0.031,0.019,0.065,0.074,0.083,0.084,0.060,
0.000,0.076,0.050,0.000,0.037,0.066,0.056,0.045,0.055,0.034,0.042,0.042,
0.026,0.013,0.010,0.035,0.029,0.017,0.056,0.067,0.072,0.069,0.077,0.079,
0.000,0.065,0.000,0.033,0.057,0.054,0.044,0.071,0.040,0.041,0.055,0.024,
0.011,0.009,0.032,0.030,0.017,0.051,0.060,0.063,0.062,0.084,0.067,0.079,
0.000,0.000,0.030,0.052,0.049,0.041,0.063,0.051,0.038,0.067,0.024,0.011,
0.009,0.031,0.040,0.027,0.015,0.018,0.019,0.026,0.004,0.022,0.013,0.000,
0.000,0.064,0.036,0.045,0.057,0.030,0.051,0.057,0.032,0.077,0.097,0.110,
0.070,0.066,0.089,0.024,0.029,0.029,0.041,0.009,0.035,0.021,0.000,0.018,
0.000,0.059,0.071,0.098,0.045,0.050,0.077,0.028,0.082,0.045,0.040,0.099,
0.048,0.054,0.054,0.059,0.059,0.072,0.039,0.065,0.051,0.031,0.000,0.050,
0.000,0.077,0.060,0.069,0.043,0.056,0.051,0.034,0.016,0.013,0.045,0.036,
0.021,0.042,0.047,0.047,0.059,0.033,0.053,0.045,0.024,0.000,0.058,0.079,
0.000,0.075,0.067,0.054,0.071,0.048,0.040,0.018,0.015,0.055,0.045,0.026,
0.028,0.033,0.033,0.046,0.015,0.040,0.030,0.005,0.000,0.094,0.068,0.089,
0.000,0.053,0.053,0.099,0.033,0.062,0.027,0.022,0.083,0.049,0.037,0.046,
0.050,0.051,0.060,0.055,0.056,0.070,0.046,0.000,0.043,0.071,0.069,0.053,
0.000,0.051,0.050,0.074,0.031,0.014,0.012,0.041,0.037,0.020,0.018,0.023,
0.023,0.035,0.023,0.029,0.033,0.032,0.000,0.053,0.053,0.071,0.065,0.061,
0.000,0.080,0.089,0.045,0.016,0.035,0.066,0.095,0.057,0.024,0.030,0.030,
0.043,0.012,0.036,0.027,0.002,0.000,0.075,0.063,0.086,0.102,0.050,0.070,
0.000,0.039,0.063,0.028,0.023,0.094,0.066,0.038,0.038,0.042,0.042,0.052,
0.050,0.047,0.058,0.058,0.000,0.035,0.062,0.060,0.046,0.086,0.078,0.049,
0.000,0.030,0.011,0.021,0.042,0.057,0.035,0.018,0.022,0.022,0.031,0.005,
0.027,0.016,0.000,0.039,0.091,0.045,0.057,0.076,0.037,0.051,0.075,0.031,
0.000,0.069,0.063,0.095,0.052,0.076,0.016,0.019,0.019,0.027,0.004,0.023,
0.014,0.000,0.076,0.070,0.038,0.048,0.062,0.031,0.045,0.062,0.027,0.085,
0.000,0.104,0.078,0.061,0.090,0.014,0.018,0.018,0.025,0.004,0.021,0.013,
0.000,0.084,0.063,0.035,0.043,0.056,0.028,0.058,0.056,0.039,0.076,0.099,
0.000,0.070,0.076,0.105,0.020,0.024,0.024,0.035,0.005,0.029,0.017,0.000,
0.022,0.096,0.050,0.064,0.085,0.040,0.058,0.091,0.035,0.084,0.050,0.045,
0.000,0.056,0.069,0.005,0.011,0.011,0.026,0.000,0.019,0.010,0.010,0.016,
0.058,0.047,0.068,0.070,0.041,0.114,0.090,0.068,0.052,0.039,0.067,0.074,
0.000,0.103,0.006,0.010,0.010,0.019,0.000,0.014,0.007,0.006,0.057,0.071,
0.032,0.045,0.061,0.026,0.070,0.061,0.042,0.086,0.082,0.106,0.091,0.097,
0.000)
dmat <- data.frame(matrix(v1,byrow=TRUE,nrow=23))
y <- c(0.057,0.074,0.067,0.071,0.031,0.070,0.067,0.047,0.075,0.028,0.051,0.085,
0.037,0.070,0.082,0.084,0.063,0.070,0.085,0.070,0.059,0.050,0.064)
x <- c(0.032,0.041,0.053,0.056,0.060,0.055,0.083,0.060,0.074,0.035,0.041,0.044,
0.034,0.048,0.045,0.038,0.047,0.043,0.057,0.062,0.041,0.062,0.045)
vary <- c(0.00018,0.00014,0.00016,0.00003,0.00029,0.00015,0.00029,0.00039,
0.00005,0.00008,0.00013,0.00017,0.00010,0.00027,0.00114,0.00051,
0.00031,0.00002,0.00038,0.00024,0.00016,0.00019,0.00014)
fit1 <- mseSFH(y ~ x,vardir=vary,proxmat=dmat)
fit1$mse[fit1$mse < 0]
I'm not sure if this is the appropriate forum for the question.
Thanks in advance,
Joao
ANSWER
Answered 2022-Feb-25 at 14:28I'm pretty sure that this is due to bias correction that generally takes place when you have MSE. You can read about the formula for bias correction that is used in the references they provided in ?sae::meanSFH. In one of the articles, they provided a case study where the average MSE is negative. (I found this in Molina et al., 2009. They identify the bias correction in a few places, but it's very clear on pp. 452-453.)
You can visualize the errors and see how very close they are to zero.
data.frame(UnbiasedMSE = fit1$mse) %>%
ggplot(aes(x = seq_along(UnbiasedMSE),
y = UnbiasedMSE)) +
geom_line() + geom_point() +
scale_y_continuous(labels = scales::comma) +
theme_bw()
QUESTION
Colab: (0) UNIMPLEMENTED: DNN library is not found
Asked 2022-Feb-08 at 19:27I have pretrained model for object detection (Google Colab + TensorFlow) inside Google Colab and I run it two-three times per week for new images I have and everything was fine for the last year till this week. Now when I try to run model I have this message:
Graph execution error:
2 root error(s) found.
(0) UNIMPLEMENTED: DNN library is not found.
[[{{node functional_1/conv1_conv/Conv2D}}]]
[[StatefulPartitionedCall/SecondStagePostprocessor/BatchMultiClassNonMaxSuppression/MultiClassNonMaxSuppression/Reshape_5/_126]]
(1) UNIMPLEMENTED: DNN library is not found.
[[{{node functional_1/conv1_conv/Conv2D}}]]
0 successful operations.
0 derived errors ignored. [Op:__inference_restored_function_body_27380] ***
Never happended before.
Before I can run my model I have to install Tensor Flow object detection API with this command:
import os
os.chdir('/project/models/research')
!protoc object_detection/protos/*.proto --python_out=.
!cp object_detection/packages/tf2/setup.py .
!python -m pip install .
This is the output of command:
Processing /content/gdrive/MyDrive/models/research
DEPRECATION: A future pip version will change local packages to be built in-place without first copying to a temporary directory. We recommend you use --use-feature=in-tree-build to test your packages with this new behavior before it becomes the default.
pip 21.3 will remove support for this functionality. You can find discussion regarding this at https://github.com/pypa/pip/issues/7555.
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Collecting tf-estimator-nightly==2.8.0.dev2021122109
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Collecting fastavro<2,>=0.21.4
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Building wheels for collected packages: object-detection, py-cpuinfo, dill, avro-python3, seqeval
Building wheel for object-detection (setup.py) ... done
Created wheel for object-detection: filename=object_detection-0.1-py3-none-any.whl size=1686316 sha256=775b8c34c800b3b3139d1067abd686af9ce9158011fccfb5450ccfd9bf424a5a
Stored in directory: /tmp/pip-ephem-wheel-cache-rmw0fvil/wheels/d0/e3/e9/b9ffe85019ec441e90d8ff9eddee9950c4c23b7598204390b9
Building wheel for py-cpuinfo (setup.py) ... done
Created wheel for py-cpuinfo: filename=py_cpuinfo-8.0.0-py3-none-any.whl size=22257 sha256=ac956c4c039868fdba78645bea056754e667e8840bea783ad2ca75e4d3e682c6
Stored in directory: /root/.cache/pip/wheels/d2/f1/1f/041add21dc9c4220157f1bd2bd6afe1f1a49524c3396b94401
Building wheel for dill (setup.py) ... done
Created wheel for dill: filename=dill-0.3.1.1-py3-none-any.whl size=78544 sha256=d9c6cdfd69aea2b4d78e6afbbe2bc530394e4081eb186eb4f4cd02373ca739fd
Stored in directory: /root/.cache/pip/wheels/a4/61/fd/c57e374e580aa78a45ed78d5859b3a44436af17e22ca53284f
Building wheel for avro-python3 (setup.py) ... done
Created wheel for avro-python3: filename=avro_python3-1.10.2-py3-none-any.whl size=44010 sha256=4eca8b4f30e4850d5dabccee36c40c8dda8a6c7e7058cfb7f0258eea5ce7b2b3
Stored in directory: /root/.cache/pip/wheels/d6/e5/b1/6b151d9b535ee50aaa6ab27d145a0104b6df02e5636f0376da
Building wheel for seqeval (setup.py) ... done
Created wheel for seqeval: filename=seqeval-1.2.2-py3-none-any.whl size=16180 sha256=0ddfa46d0e36e9be346a90833ef11cc0d38cc7e744be34c5a0d321f997a30cba
Stored in directory: /root/.cache/pip/wheels/05/96/ee/7cac4e74f3b19e3158dce26a20a1c86b3533c43ec72a549fd7
Successfully built object-detection py-cpuinfo dill avro-python3 seqeval
Installing collected packages: requests, protobuf, numpy, tf-estimator-nightly, tensorflow-io-gcs-filesystem, tensorboard, keras, tensorflow, portalocker, dill, colorama, tf-slim, tensorflow-text, tensorflow-model-optimization, tensorflow-addons, seqeval, sentencepiece, sacrebleu, pyyaml, pymongo, py-cpuinfo, proto-plus, orjson, opencv-python-headless, hdfs, fastavro, tf-models-official, tensorflow-io, lvis, avro-python3, apache-beam, object-detection
Attempting uninstall: requests
Found existing installation: requests 2.23.0
Uninstalling requests-2.23.0:
Successfully uninstalled requests-2.23.0
Attempting uninstall: protobuf
Found existing installation: protobuf 3.17.3
Uninstalling protobuf-3.17.3:
Successfully uninstalled protobuf-3.17.3
Attempting uninstall: numpy
Found existing installation: numpy 1.19.5
Uninstalling numpy-1.19.5:
Successfully uninstalled numpy-1.19.5
Attempting uninstall: tensorflow-io-gcs-filesystem
Found existing installation: tensorflow-io-gcs-filesystem 0.23.1
Uninstalling tensorflow-io-gcs-filesystem-0.23.1:
Successfully uninstalled tensorflow-io-gcs-filesystem-0.23.1
Attempting uninstall: tensorboard
Found existing installation: tensorboard 2.7.0
Uninstalling tensorboard-2.7.0:
Successfully uninstalled tensorboard-2.7.0
Attempting uninstall: keras
Found existing installation: keras 2.7.0
Uninstalling keras-2.7.0:
Successfully uninstalled keras-2.7.0
Attempting uninstall: tensorflow
Found existing installation: tensorflow 2.7.0
Uninstalling tensorflow-2.7.0:
Successfully uninstalled tensorflow-2.7.0
Attempting uninstall: dill
Found existing installation: dill 0.3.4
Uninstalling dill-0.3.4:
Successfully uninstalled dill-0.3.4
Attempting uninstall: pyyaml
Found existing installation: PyYAML 3.13
Uninstalling PyYAML-3.13:
Successfully uninstalled PyYAML-3.13
Attempting uninstall: pymongo
Found existing installation: pymongo 4.0.1
Uninstalling pymongo-4.0.1:
Successfully uninstalled pymongo-4.0.1
ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
yellowbrick 1.3.post1 requires numpy<1.20,>=1.16.0, but you have numpy 1.20.3 which is incompatible.
multiprocess 0.70.12.2 requires dill>=0.3.4, but you have dill 0.3.1.1 which is incompatible.
google-colab 1.0.0 requires requests~=2.23.0, but you have requests 2.27.1 which is incompatible.
datascience 0.10.6 requires folium==0.2.1, but you have folium 0.8.3 which is incompatible.
albumentations 0.1.12 requires imgaug<0.2.7,>=0.2.5, but you have imgaug 0.2.9 which is incompatible.
Successfully installed apache-beam-2.35.0 avro-python3-1.10.2 colorama-0.4.4 dill-0.3.1.1 fastavro-1.4.9 hdfs-2.6.0 keras-2.8.0 lvis-0.5.3 numpy-1.20.3 object-detection-0.1 opencv-python-headless-4.5.5.62 orjson-3.6.6 portalocker-2.3.2 proto-plus-1.19.9 protobuf-3.19.4 py-cpuinfo-8.0.0 pymongo-3.12.3 pyyaml-5.4.1 requests-2.27.1 sacrebleu-2.0.0 sentencepiece-0.1.96 seqeval-1.2.2 tensorboard-2.8.0 tensorflow-2.8.0 tensorflow-addons-0.15.0 tensorflow-io-0.24.0 tensorflow-io-gcs-filesystem-0.24.0 tensorflow-model-optimization-0.7.0 tensorflow-text-2.8.1 tf-estimator-nightly-2.8.0.dev2021122109 tf-models-official-2.8.0 tf-slim-1.1.0
I am noticing that this command uninstalling tensorflow 2.7 and installing tensorflow 2.8. I am not sure it was happening before. Maybe it's the reason DNN library link is missing o something?
I can confirm these:
- Nothing was changed inside pretrained model or already installed model or object_detection source files I downloaded a year ago.
- I tried to run command !pip install dnn - not working
- I tried to restart runtime (without disconnecting) - not working
Somebody can help? Thanks.
ANSWER
Answered 2022-Feb-07 at 09:19It happened the same to me last friday. I think it has something to do with Cuda instalation in Google Colab but I don't know exactly the reason
QUESTION
How to install local package with conda
Asked 2022-Feb-05 at 04:16I have a local python project called jive that I would like to use in an another project. My current method of using jive in other projects is to activate the conda env for the project, then move to my jive directory and use python setup.py install. This works fine, and when I use conda list, I see everything installed in the env including jive, with a note that jive was installed using pip.
But what I really want is to do this with full conda. When I want to use jive in another project, I want to just put jive in that projects environment.yml.
So I did the following:
- write a simple
meta.yamlso I could use conda-build to buildjivelocally - build jive with
conda build . - I looked at the tarball that was produced and it does indeed contain the
jivesource as expected - In my other project, add jive to the dependencies in
environment.yml, and add 'local' to the list of channels. - create a conda env using that environment.yml.
When I activate the environment and use conda list, it lists all the dependencies including jive, as desired. But when I open python interpreter, I cannot import jive, it says there is no such package. (If use python setup.py install, I can import it.)
How can I fix the build/install so that this works?
Here is the meta.yaml, which lives in the jive project top level directory:
package:
name: jive
version: "0.2.1"
source:
path: .
build:
script: python -m pip install --no-deps --ignore-installed .
requirements:
host:
- python>=3.5
- pip
- setuptools
run:
- python>=3.5
- numpy
- pandas
- scipy
- seaborn
- matplotlib
- scikit-learn
- statsmodels
- joblib
- bokeh
test:
imports: jive
And here is the output of conda build .
No numpy version specified in conda_build_config.yaml. Falling back to default numpy value of 1.16
WARNING:conda_build.metadata:No numpy version specified in conda_build_config.yaml. Falling back to default numpy value of 1.16
Adding in variants from internal_defaults
INFO:conda_build.variants:Adding in variants from internal_defaults
Adding in variants from /Users/thomaskeefe/.conda/conda_build_config.yaml
INFO:conda_build.variants:Adding in variants from /Users/thomaskeefe/.conda/conda_build_config.yaml
Attempting to finalize metadata for jive
INFO:conda_build.metadata:Attempting to finalize metadata for jive
Collecting package metadata (repodata.json): ...working... done
Solving environment: ...working... done
Collecting package metadata (repodata.json): ...working... done
Solving environment: ...working... done
BUILD START: ['jive-0.2.1-py310_0.tar.bz2']
Collecting package metadata (repodata.json): ...working... done
Solving environment: ...working... done
## Package Plan ##
environment location: /opt/miniconda3/conda-bld/jive_1642185595622/_h_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_pla
The following NEW packages will be INSTALLED:
bzip2: 1.0.8-h1de35cc_0
ca-certificates: 2021.10.26-hecd8cb5_2
certifi: 2021.5.30-py310hecd8cb5_0
libcxx: 12.0.0-h2f01273_0
libffi: 3.3-hb1e8313_2
ncurses: 6.3-hca72f7f_2
openssl: 1.1.1m-hca72f7f_0
pip: 21.2.4-py310hecd8cb5_0
python: 3.10.0-hdfd78df_3
readline: 8.1.2-hca72f7f_1
setuptools: 58.0.4-py310hecd8cb5_0
sqlite: 3.37.0-h707629a_0
tk: 8.6.11-h7bc2e8c_0
tzdata: 2021e-hda174b7_0
wheel: 0.37.1-pyhd3eb1b0_0
xz: 5.2.5-h1de35cc_0
zlib: 1.2.11-h4dc903c_4
Preparing transaction: ...working... done
Verifying transaction: ...working... done
Executing transaction: ...working... done
Collecting package metadata (repodata.json): ...working... done
Solving environment: ...working... done
Copying /Users/thomaskeefe/Documents/py_jive to /opt/miniconda3/conda-bld/jive_1642185595622/work/
source tree in: /opt/miniconda3/conda-bld/jive_1642185595622/work
export PREFIX=/opt/miniconda3/conda-bld/jive_1642185595622/_h_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_pla
export BUILD_PREFIX=/opt/miniconda3/conda-bld/jive_1642185595622/_build_env
export SRC_DIR=/opt/miniconda3/conda-bld/jive_1642185595622/work
Processing $SRC_DIR
DEPRECATION: A future pip version will change local packages to be built in-place without first copying to a temporary directory. We recommend you use --use-feature=in-tree-build to test your packages with this new behavior before it becomes the default.
pip 21.3 will remove support for this functionality. You can find discussion regarding this at https://github.com/pypa/pip/issues/7555.
Building wheels for collected packages: jive
Building wheel for jive (setup.py): started
Building wheel for jive (setup.py): finished with status 'done'
Created wheel for jive: filename=jive-0.2.1-py3-none-any.whl size=46071 sha256=b312955cb2fd917bc4e684a575407b884190680f2dddad7fcb9ac25e5b290fc9
Stored in directory: /private/tmp/pip-ephem-wheel-cache-rbpkt2an/wheels/15/68/82/4ed7cd246fbc4c72cf764b425a03230247589bd2394a7e457b
Successfully built jive
Installing collected packages: jive
Successfully installed jive-0.2.1
Resource usage statistics from building jive:
Process count: 3
CPU time: Sys=0:00:00.3, User=0:00:00.5
Memory: 53.7M
Disk usage: 50.4K
Time elapsed: 0:00:06.1
Packaging jive
INFO:conda_build.build:Packaging jive
INFO conda_build.build:build(2289): Packaging jive
Packaging jive-0.2.1-py310_0
INFO:conda_build.build:Packaging jive-0.2.1-py310_0
INFO conda_build.build:bundle_conda(1529): Packaging jive-0.2.1-py310_0
compiling .pyc files...
number of files: 70
Fixing permissions
INFO :: Time taken to mark (prefix)
0 replacements in 0 files was 0.06 seconds
TEST START: /opt/miniconda3/conda-bld/osx-64/jive-0.2.1-py310_0.tar.bz2
Adding in variants from /var/folders/dd/t85p2jdn3sd11bsdnl7th6p00000gn/T/tmp4o3im7d1/info/recipe/conda_build_config.yaml
INFO:conda_build.variants:Adding in variants from /var/folders/dd/t85p2jdn3sd11bsdnl7th6p00000gn/T/tmp4o3im7d1/info/recipe/conda_build_config.yaml
INFO conda_build.variants:_combine_spec_dictionaries(234): Adding in variants from /var/folders/dd/t85p2jdn3sd11bsdnl7th6p00000gn/T/tmp4o3im7d1/info/recipe/conda_build_config.yaml
Renaming work directory '/opt/miniconda3/conda-bld/jive_1642185595622/work' to '/opt/miniconda3/conda-bld/jive_1642185595622/work_moved_jive-0.2.1-py310_0_osx-64'
INFO:conda_build.utils:Renaming work directory '/opt/miniconda3/conda-bld/jive_1642185595622/work' to '/opt/miniconda3/conda-bld/jive_1642185595622/work_moved_jive-0.2.1-py310_0_osx-64'
INFO conda_build.utils:shutil_move_more_retrying(2091): Renaming work directory '/opt/miniconda3/conda-bld/jive_1642185595622/work' to '/opt/miniconda3/conda-bld/jive_1642185595622/work_moved_jive-0.2.1-py310_0_osx-64'
shutil.move(work)=/opt/miniconda3/conda-bld/jive_1642185595622/work, dest=/opt/miniconda3/conda-bld/jive_1642185595622/work_moved_jive-0.2.1-py310_0_osx-64)
INFO:conda_build.utils:shutil.move(work)=/opt/miniconda3/conda-bld/jive_1642185595622/work, dest=/opt/miniconda3/conda-bld/jive_1642185595622/work_moved_jive-0.2.1-py310_0_osx-64)
INFO conda_build.utils:shutil_move_more_retrying(2098): shutil.move(work)=/opt/miniconda3/conda-bld/jive_1642185595622/work, dest=/opt/miniconda3/conda-bld/jive_1642185595622/work_moved_jive-0.2.1-py310_0_osx-64)
Collecting package metadata (repodata.json): ...working... done
Solving environment: ...working... done
## Package Plan ##
environment location: /opt/miniconda3/conda-bld/jive_1642185595622/_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehol
The following NEW packages will be INSTALLED:
blas: 1.0-mkl
bokeh: 2.4.2-py39hecd8cb5_0
bottleneck: 1.3.2-py39he3068b8_1
brotli: 1.0.9-hb1e8313_2
ca-certificates: 2021.10.26-hecd8cb5_2
certifi: 2021.10.8-py39hecd8cb5_2
cycler: 0.11.0-pyhd3eb1b0_0
fonttools: 4.25.0-pyhd3eb1b0_0
freetype: 2.11.0-hd8bbffd_0
giflib: 5.2.1-haf1e3a3_0
intel-openmp: 2021.4.0-hecd8cb5_3538
jinja2: 3.0.2-pyhd3eb1b0_0
jive: 0.2.1-py310_0 local
joblib: 1.1.0-pyhd3eb1b0_0
jpeg: 9d-h9ed2024_0
kiwisolver: 1.3.1-py39h23ab428_0
lcms2: 2.12-hf1fd2bf_0
libcxx: 12.0.0-h2f01273_0
libffi: 3.3-hb1e8313_2
libgfortran: 3.0.1-h93005f0_2
libpng: 1.6.37-ha441bb4_0
libtiff: 4.2.0-h87d7836_0
libwebp: 1.2.0-hacca55c_0
libwebp-base: 1.2.0-h9ed2024_0
llvm-openmp: 12.0.0-h0dcd299_1
lz4-c: 1.9.3-h23ab428_1
markupsafe: 2.0.1-py39h9ed2024_0
matplotlib: 3.5.0-py39hecd8cb5_0
matplotlib-base: 3.5.0-py39h4f681db_0
mkl: 2021.4.0-hecd8cb5_637
mkl-service: 2.4.0-py39h9ed2024_0
mkl_fft: 1.3.1-py39h4ab4a9b_0
mkl_random: 1.2.2-py39hb2f4e1b_0
munkres: 1.1.4-py_0
ncurses: 6.3-hca72f7f_2
numexpr: 2.8.1-py39h2e5f0a9_0
numpy: 1.21.2-py39h4b4dc7a_0
numpy-base: 1.21.2-py39he0bd621_0
olefile: 0.46-pyhd3eb1b0_0
openssl: 1.1.1m-hca72f7f_0
packaging: 21.3-pyhd3eb1b0_0
pandas: 1.3.5-py39h743cdd8_0
patsy: 0.5.2-py39hecd8cb5_0
pillow: 8.4.0-py39h98e4679_0
pip: 21.2.4-py39hecd8cb5_0
pyparsing: 3.0.4-pyhd3eb1b0_0
python: 3.9.7-h88f2d9e_1
python-dateutil: 2.8.2-pyhd3eb1b0_0
pytz: 2021.3-pyhd3eb1b0_0
pyyaml: 6.0-py39hca72f7f_1
readline: 8.1.2-hca72f7f_1
scikit-learn: 1.0.2-py39hae1ba45_0
scipy: 1.7.3-py39h8c7af03_0
seaborn: 0.11.2-pyhd3eb1b0_0
setuptools: 58.0.4-py39hecd8cb5_0
six: 1.16.0-pyhd3eb1b0_0
sqlite: 3.37.0-h707629a_0
statsmodels: 0.13.0-py39hca72f7f_0
threadpoolctl: 2.2.0-pyh0d69192_0
tk: 8.6.11-h7bc2e8c_0
tornado: 6.1-py39h9ed2024_0
typing_extensions: 3.10.0.2-pyh06a4308_0
tzdata: 2021e-hda174b7_0
wheel: 0.37.1-pyhd3eb1b0_0
xz: 5.2.5-h1de35cc_0
yaml: 0.2.5-haf1e3a3_0
zlib: 1.2.11-h4dc903c_4
zstd: 1.4.9-h322a384_0
Preparing transaction: ...working... done
Verifying transaction: ...working...
ClobberWarning: This transaction has incompatible packages due to a shared path.
packages: defaults/osx-64::intel-openmp-2021.4.0-hecd8cb5_3538, defaults/osx-64::llvm-openmp-12.0.0-h0dcd299_1
path: 'lib/libiomp5.dylib'
ClobberWarning: This transaction has incompatible packages due to a shared path.
packages: defaults/osx-64::libwebp-base-1.2.0-h9ed2024_0, defaults/osx-64::libwebp-1.2.0-hacca55c_0
path: 'bin/webpinfo'
ClobberWarning: This transaction has incompatible packages due to a shared path.
packages: defaults/osx-64::libwebp-base-1.2.0-h9ed2024_0, defaults/osx-64::libwebp-1.2.0-hacca55c_0
path: 'bin/webpmux'
ClobberWarning: This transaction has incompatible packages due to a shared path.
packages: defaults/osx-64::libwebp-base-1.2.0-h9ed2024_0, defaults/osx-64::libwebp-1.2.0-hacca55c_0
path: 'include/webp/decode.h'
ClobberWarning: This transaction has incompatible packages due to a shared path.
packages: defaults/osx-64::libwebp-base-1.2.0-h9ed2024_0, defaults/osx-64::libwebp-1.2.0-hacca55c_0
path: 'include/webp/encode.h'
ClobberWarning: This transaction has incompatible packages due to a shared path.
packages: defaults/osx-64::libwebp-base-1.2.0-h9ed2024_0, defaults/osx-64::libwebp-1.2.0-hacca55c_0
path: 'include/webp/mux.h'
ClobberWarning: This transaction has incompatible packages due to a shared path.
packages: defaults/osx-64::libwebp-base-1.2.0-h9ed2024_0, defaults/osx-64::libwebp-1.2.0-hacca55c_0
path: 'include/webp/mux_types.h'
ClobberWarning: This transaction has incompatible packages due to a shared path.
packages: defaults/osx-64::libwebp-base-1.2.0-h9ed2024_0, defaults/osx-64::libwebp-1.2.0-hacca55c_0
path: 'include/webp/types.h'
ClobberWarning: This transaction has incompatible packages due to a shared path.
packages: defaults/osx-64::libwebp-base-1.2.0-h9ed2024_0, defaults/osx-64::libwebp-1.2.0-hacca55c_0
path: 'lib/libwebp.7.dylib'
ClobberWarning: This transaction has incompatible packages due to a shared path.
packages: defaults/osx-64::libwebp-base-1.2.0-h9ed2024_0, defaults/osx-64::libwebp-1.2.0-hacca55c_0
path: 'lib/libwebp.a'
ClobberWarning: This transaction has incompatible packages due to a shared path.
packages: defaults/osx-64::libwebp-base-1.2.0-h9ed2024_0, defaults/osx-64::libwebp-1.2.0-hacca55c_0
path: 'lib/libwebp.dylib'
ClobberWarning: This transaction has incompatible packages due to a shared path.
packages: defaults/osx-64::libwebp-base-1.2.0-h9ed2024_0, defaults/osx-64::libwebp-1.2.0-hacca55c_0
path: 'lib/libwebpdecoder.3.dylib'
ClobberWarning: This transaction has incompatible packages due to a shared path.
packages: defaults/osx-64::libwebp-base-1.2.0-h9ed2024_0, defaults/osx-64::libwebp-1.2.0-hacca55c_0
path: 'lib/libwebpdecoder.a'
ClobberWarning: This transaction has incompatible packages due to a shared path.
packages: defaults/osx-64::libwebp-base-1.2.0-h9ed2024_0, defaults/osx-64::libwebp-1.2.0-hacca55c_0
path: 'lib/libwebpdecoder.dylib'
ClobberWarning: This transaction has incompatible packages due to a shared path.
packages: defaults/osx-64::libwebp-base-1.2.0-h9ed2024_0, defaults/osx-64::libwebp-1.2.0-hacca55c_0
path: 'lib/libwebpmux.3.dylib'
ClobberWarning: This transaction has incompatible packages due to a shared path.
packages: defaults/osx-64::libwebp-base-1.2.0-h9ed2024_0, defaults/osx-64::libwebp-1.2.0-hacca55c_0
path: 'lib/libwebpmux.a'
ClobberWarning: This transaction has incompatible packages due to a shared path.
packages: defaults/osx-64::libwebp-base-1.2.0-h9ed2024_0, defaults/osx-64::libwebp-1.2.0-hacca55c_0
path: 'lib/libwebpmux.dylib'
ClobberWarning: This transaction has incompatible packages due to a shared path.
packages: defaults/osx-64::libwebp-base-1.2.0-h9ed2024_0, defaults/osx-64::libwebp-1.2.0-hacca55c_0
path: 'lib/pkgconfig/libwebp.pc'
ClobberWarning: This transaction has incompatible packages due to a shared path.
packages: defaults/osx-64::libwebp-base-1.2.0-h9ed2024_0, defaults/osx-64::libwebp-1.2.0-hacca55c_0
path: 'lib/pkgconfig/libwebpdecoder.pc'
ClobberWarning: This transaction has incompatible packages due to a shared path.
packages: defaults/osx-64::libwebp-base-1.2.0-h9ed2024_0, defaults/osx-64::libwebp-1.2.0-hacca55c_0
path: 'lib/pkgconfig/libwebpmux.pc'
ClobberWarning: This transaction has incompatible packages due to a shared path.
packages: defaults/osx-64::libwebp-base-1.2.0-h9ed2024_0, defaults/osx-64::libwebp-1.2.0-hacca55c_0
path: 'share/man/man1/cwebp.1'
ClobberWarning: This transaction has incompatible packages due to a shared path.
packages: defaults/osx-64::libwebp-base-1.2.0-h9ed2024_0, defaults/osx-64::libwebp-1.2.0-hacca55c_0
path: 'share/man/man1/dwebp.1'
ClobberWarning: This transaction has incompatible packages due to a shared path.
packages: defaults/osx-64::libwebp-base-1.2.0-h9ed2024_0, defaults/osx-64::libwebp-1.2.0-hacca55c_0
path: 'share/man/man1/webpinfo.1'
ClobberWarning: This transaction has incompatible packages due to a shared path.
packages: defaults/osx-64::libwebp-base-1.2.0-h9ed2024_0, defaults/osx-64::libwebp-1.2.0-hacca55c_0
path: 'share/man/man1/webpmux.1'
done
Executing transaction: ...working...
ClobberWarning: Conda was asked to clobber an existing path.
source path: /opt/miniconda3/pkgs/llvm-openmp-12.0.0-h0dcd299_1/lib/libiomp5.dylib
target path: /opt/miniconda3/conda-bld/jive_1642185595622/_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehol/lib/libiomp5.dylib
ClobberWarning: Conda was asked to clobber an existing path.
source path: /opt/miniconda3/pkgs/libwebp-1.2.0-hacca55c_0/bin/webpinfo
target path: /opt/miniconda3/conda-bld/jive_1642185595622/_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehol/bin/webpinfo
ClobberWarning: Conda was asked to clobber an existing path.
source path: /opt/miniconda3/pkgs/libwebp-1.2.0-hacca55c_0/bin/webpmux
target path: /opt/miniconda3/conda-bld/jive_1642185595622/_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehol/bin/webpmux
ClobberWarning: Conda was asked to clobber an existing path.
source path: /opt/miniconda3/pkgs/libwebp-1.2.0-hacca55c_0/include/webp/decode.h
target path: /opt/miniconda3/conda-bld/jive_1642185595622/_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehol/include/webp/decode.h
ClobberWarning: Conda was asked to clobber an existing path.
source path: /opt/miniconda3/pkgs/libwebp-1.2.0-hacca55c_0/include/webp/encode.h
target path: /opt/miniconda3/conda-bld/jive_1642185595622/_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehol/include/webp/encode.h
ClobberWarning: Conda was asked to clobber an existing path.
source path: /opt/miniconda3/pkgs/libwebp-1.2.0-hacca55c_0/include/webp/mux.h
target path: /opt/miniconda3/conda-bld/jive_1642185595622/_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehol/include/webp/mux.h
ClobberWarning: Conda was asked to clobber an existing path.
source path: /opt/miniconda3/pkgs/libwebp-1.2.0-hacca55c_0/include/webp/mux_types.h
target path: /opt/miniconda3/conda-bld/jive_1642185595622/_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehol/include/webp/mux_types.h
ClobberWarning: Conda was asked to clobber an existing path.
source path: /opt/miniconda3/pkgs/libwebp-1.2.0-hacca55c_0/include/webp/types.h
target path: /opt/miniconda3/conda-bld/jive_1642185595622/_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehol/include/webp/types.h
ClobberWarning: Conda was asked to clobber an existing path.
source path: /opt/miniconda3/pkgs/libwebp-1.2.0-hacca55c_0/lib/libwebp.7.dylib
target path: /opt/miniconda3/conda-bld/jive_1642185595622/_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehol/lib/libwebp.7.dylib
ClobberWarning: Conda was asked to clobber an existing path.
source path: /opt/miniconda3/pkgs/libwebp-1.2.0-hacca55c_0/lib/libwebp.a
target path: /opt/miniconda3/conda-bld/jive_1642185595622/_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehol/lib/libwebp.a
ClobberWarning: Conda was asked to clobber an existing path.
source path: /opt/miniconda3/pkgs/libwebp-1.2.0-hacca55c_0/lib/libwebp.dylib
target path: /opt/miniconda3/conda-bld/jive_1642185595622/_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehol/lib/libwebp.dylib
ClobberWarning: Conda was asked to clobber an existing path.
source path: /opt/miniconda3/pkgs/libwebp-1.2.0-hacca55c_0/lib/libwebpdecoder.3.dylib
target path: /opt/miniconda3/conda-bld/jive_1642185595622/_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehol/lib/libwebpdecoder.3.dylib
ClobberWarning: Conda was asked to clobber an existing path.
source path: /opt/miniconda3/pkgs/libwebp-1.2.0-hacca55c_0/lib/libwebpdecoder.a
target path: /opt/miniconda3/conda-bld/jive_1642185595622/_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehol/lib/libwebpdecoder.a
ClobberWarning: Conda was asked to clobber an existing path.
source path: /opt/miniconda3/pkgs/libwebp-1.2.0-hacca55c_0/lib/libwebpdecoder.dylib
target path: /opt/miniconda3/conda-bld/jive_1642185595622/_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehol/lib/libwebpdecoder.dylib
ClobberWarning: Conda was asked to clobber an existing path.
source path: /opt/miniconda3/pkgs/libwebp-1.2.0-hacca55c_0/lib/libwebpmux.3.dylib
target path: /opt/miniconda3/conda-bld/jive_1642185595622/_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehol/lib/libwebpmux.3.dylib
ClobberWarning: Conda was asked to clobber an existing path.
source path: /opt/miniconda3/pkgs/libwebp-1.2.0-hacca55c_0/lib/libwebpmux.a
target path: /opt/miniconda3/conda-bld/jive_1642185595622/_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehol/lib/libwebpmux.a
ClobberWarning: Conda was asked to clobber an existing path.
source path: /opt/miniconda3/pkgs/libwebp-1.2.0-hacca55c_0/lib/libwebpmux.dylib
target path: /opt/miniconda3/conda-bld/jive_1642185595622/_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehol/lib/libwebpmux.dylib
ClobberWarning: Conda was asked to clobber an existing path.
source path: /opt/miniconda3/conda-bld/jive_1642185595622/_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehol/.condatmp/1018f8ab-87a7-4fa8-a41c-4c14cc77cfff
target path: /opt/miniconda3/conda-bld/jive_1642185595622/_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehol/lib/pkgconfig/libwebp.pc
ClobberWarning: Conda was asked to clobber an existing path.
source path: /opt/miniconda3/conda-bld/jive_1642185595622/_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehol/.condatmp/e3701fae-f2cd-44e9-9dc6-c71f499cd2c2
target path: /opt/miniconda3/conda-bld/jive_1642185595622/_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehol/lib/pkgconfig/libwebpdecoder.pc
ClobberWarning: Conda was asked to clobber an existing path.
source path: /opt/miniconda3/conda-bld/jive_1642185595622/_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehol/.condatmp/0f4bcf50-01e5-404d-b1a4-8a87d45c22c5
target path: /opt/miniconda3/conda-bld/jive_1642185595622/_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehol/lib/pkgconfig/libwebpmux.pc
ClobberWarning: Conda was asked to clobber an existing path.
source path: /opt/miniconda3/pkgs/libwebp-1.2.0-hacca55c_0/share/man/man1/cwebp.1
target path: /opt/miniconda3/conda-bld/jive_1642185595622/_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehol/share/man/man1/cwebp.1
ClobberWarning: Conda was asked to clobber an existing path.
source path: /opt/miniconda3/pkgs/libwebp-1.2.0-hacca55c_0/share/man/man1/dwebp.1
target path: /opt/miniconda3/conda-bld/jive_1642185595622/_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehol/share/man/man1/dwebp.1
ClobberWarning: Conda was asked to clobber an existing path.
source path: /opt/miniconda3/pkgs/libwebp-1.2.0-hacca55c_0/share/man/man1/webpinfo.1
target path: /opt/miniconda3/conda-bld/jive_1642185595622/_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehol/share/man/man1/webpinfo.1
ClobberWarning: Conda was asked to clobber an existing path.
source path: /opt/miniconda3/pkgs/libwebp-1.2.0-hacca55c_0/share/man/man1/webpmux.1
target path: /opt/miniconda3/conda-bld/jive_1642185595622/_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehol/share/man/man1/webpmux.1
Installed package of scikit-learn can be accelerated using scikit-learn-intelex.
More details are available here: https://intel.github.io/scikit-learn-intelex
For example:
$ conda install scikit-learn-intelex
$ python -m sklearnex my_application.py
done
export PREFIX=/opt/miniconda3/conda-bld/jive_1642185595622/_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehol
export SRC_DIR=/opt/miniconda3/conda-bld/jive_1642185595622/test_tmp
Traceback (most recent call last):
File "/opt/miniconda3/conda-bld/jive_1642185595622/test_tmp/run_test.py", line 2, in <module>
import jive
ModuleNotFoundError: No module named 'jive'
import: 'jive'
Tests failed for jive-0.2.1-py310_0.tar.bz2 - moving package to /opt/miniconda3/conda-bld/broken
WARNING:conda_build.build:Tests failed for jive-0.2.1-py310_0.tar.bz2 - moving package to /opt/miniconda3/conda-bld/broken
WARNING conda_build.build:tests_failed(2970): Tests failed for jive-0.2.1-py310_0.tar.bz2 - moving package to /opt/miniconda3/conda-bld/broken
TESTS FAILED: jive-0.2.1-py310_0.tar.bz2
EDIT: I added a test: section to the meta.yaml as merv suggested.
ANSWER
Answered 2022-Feb-05 at 04:16The immediate error is that the build is generating a Python 3.10 version, but when testing Conda doesn't recognize any constraint on the Python version, and creates a Python 3.9 environment.
I think the main issue is that python >=3.5 is only a valid constraint when doing noarch builds, which this is not. That is, once a package builds with a given Python version, the version must be constrained to exactly that version (up through minor). So, in this case, the package is built with Python 3.10, but it reports in its metadata that it is compatible with all versions of Python 3.5+, which simply isn't true because Conda Python packages install the modules into Python-version-specific site-packages (e.g., lib/python-3.10/site-packages/jive).
Typically, Python versions are controlled by either the --python argument given to conda-build or a matrix supplied by the conda_build_config.yaml file (see documentation on "Build variants").
Try adjusting the meta.yaml to something like
package:
name: jive
version: "0.2.1"
source:
path: .
build:
script: python -m pip install --no-deps --ignore-installed .
requirements:
host:
- python
- pip
- setuptools
run:
- python
- numpy
- pandas
- scipy
- seaborn
- matplotlib
- scikit-learn
- statsmodels
- joblib
- bokeh
If you want to use it in a Python 3.9 environment, then use conda build --python 3.9 ..
QUESTION
Cannot find conda info. Please verify your conda installation on EMR
Asked 2022-Feb-05 at 00:17I am trying to install conda on EMR and below is my bootstrap script, it looks like conda is getting installed but it is not getting added to environment variable. When I manually update the $PATH variable on EMR master node, it can identify conda. I want to use conda on Zeppelin.
I also tried adding condig into configuration like below while launching my EMR instance however I still get the below mentioned error.
"classification": "spark-env",
"properties": {
"conda": "/home/hadoop/conda/bin"
}
[hadoop@ip-172-30-5-150 ~]$ PATH=/home/hadoop/conda/bin:$PATH
[hadoop@ip-172-30-5-150 ~]$ conda
usage: conda [-h] [-V] command ...
conda is a tool for managing and deploying applications, environments and packages.
#!/usr/bin/env bash
# Install conda
wget https://repo.continuum.io/miniconda/Miniconda3-4.2.12-Linux-x86_64.sh -O /home/hadoop/miniconda.sh \
&& /bin/bash ~/miniconda.sh -b -p $HOME/conda
conda config --set always_yes yes --set changeps1 no
conda install conda=4.2.13
conda config -f --add channels conda-forge
rm ~/miniconda.sh
echo bootstrap_conda.sh completed. PATH now: $PATH
export PYSPARK_PYTHON="/home/hadoop/conda/bin/python3.5"
echo -e '\nexport PATH=$HOME/conda/bin:$PATH' >> $HOME/.bashrc && source $HOME/.bashrc
conda create -n zoo python=3.7 # "zoo" is conda environment name, you can use any name you like.
conda activate zoo
sudo pip3 install tensorflow
sudo pip3 install boto3
sudo pip3 install botocore
sudo pip3 install numpy
sudo pip3 install pandas
sudo pip3 install scipy
sudo pip3 install s3fs
sudo pip3 install matplotlib
sudo pip3 install -U tqdm
sudo pip3 install -U scikit-learn
sudo pip3 install -U scikit-multilearn
sudo pip3 install xlutils
sudo pip3 install natsort
sudo pip3 install pydot
sudo pip3 install python-pydot
sudo pip3 install python-pydot-ng
sudo pip3 install pydotplus
sudo pip3 install h5py
sudo pip3 install graphviz
sudo pip3 install recmetrics
sudo pip3 install openpyxl
sudo pip3 install xlrd
sudo pip3 install xlwt
sudo pip3 install tensorflow.io
sudo pip3 install Cython
sudo pip3 install ray
sudo pip3 install zoo
sudo pip3 install analytics-zoo
sudo pip3 install analytics-zoo[ray]
#sudo /usr/bin/pip-3.6 install -U imbalanced-learn
ANSWER
Answered 2022-Feb-05 at 00:17I got the conda working by modifying the script as below, emr python versions were colliding with the conda version.:
wget https://repo.anaconda.com/miniconda/Miniconda3-py37_4.9.2-Linux-x86_64.sh -O /home/hadoop/miniconda.sh \
&& /bin/bash ~/miniconda.sh -b -p $HOME/conda
echo -e '\n export PATH=$HOME/conda/bin:$PATH' >> $HOME/.bashrc && source $HOME/.bashrc
conda config --set always_yes yes --set changeps1 no
conda config -f --add channels conda-forge
conda create -n zoo python=3.7 # "zoo" is conda environment name
conda init bash
source activate zoo
conda install python 3.7.0 -c conda-forge orca
sudo /home/hadoop/conda/envs/zoo/bin/python3.7 -m pip install virtualenv
and setting zeppelin python and pyspark parameters to:
“spark.pyspark.python": "/home/hadoop/conda/envs/zoo/bin/python3",
"spark.pyspark.virtualenv.enabled": "true",
"spark.pyspark.virtualenv.type":"native",
"spark.pyspark.virtualenv.bin.path":"/home/hadoop/conda/envs/zoo/bin/,
"zeppelin.pyspark.python" : "/home/hadoop/conda/bin/python",
"zeppelin.python": "/home/hadoop/conda/bin/python"
Orca only support TF upto 1.5 hence it was not working as I am using TF2.
QUESTION
Updating Python sklearn Lasso(normalize=True) to Use Pipeline
Asked 2021-Dec-28 at 10:34I am new to Python. I am trying to practice basic regularization by following along with a DataCamp exercise using this CSV: https://assets.datacamp.com/production/repositories/628/datasets/a7e65287ebb197b1267b5042955f27502ec65f31/gm_2008_region.csv
# Import numpy and pandas
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
# Read the CSV file into a DataFrame: df
df = pd.read_csv('gm_2008_region.csv')
# Create arrays for features and target variable
X = df.drop(['life','Region'], axis=1)
y = df['life'].values.reshape(-1,1)
df_columns = df.drop(['life','Region'], axis=1).columns
The code that I use for the DataCamp exercise is as follows:
# Import Lasso
from sklearn.linear_model import Lasso
# Instantiate a lasso regressor: lasso
lasso = Lasso(alpha=0.4, normalize=True)
# Fit the regressor to the data
lasso.fit(X, y)
# Compute and print the coefficients
lasso_coef = lasso.coef_
print(lasso_coef)
# Plot the coefficients
plt.plot(range(len(df_columns)), lasso_coef)
plt.xticks(range(len(df_columns)), df_columns.values, rotation=60)
plt.margins(0.02)
plt.show()
I get the output above, indicating that child_mortality is the most important feature in predicting life expectancy, but this code also results in a deprecation warning due to the use of "normalize."
I'd like to update this code using the current best practice. I have tried the following, but I get a different output. I am hoping someone can help identify what I need to modify in the updated code in order to produce the same output.
# Modified based on https://scikit-learn.org/stable/modules/preprocessing.html#preprocessing-scaler
# and https://stackoverflow.com/questions/28822756/getting-model-attributes-from-pipeline
# Import Lasso
from sklearn.linear_model import Lasso
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
# Instantiate a lasso regressor: lasso
#lasso = Lasso(alpha=0.4, normalize=True)
pipe = Pipeline(steps=[
('scaler',StandardScaler()),
('lasso',Lasso(alpha=0.4))
])
# Fit the regressor to the data
#lasso.fit(X, y)
pipe.fit(X, y)
# Compute and print the coefficients
#lasso_coef = lasso.coef_
#print(lasso_coef)
lasso_coef = pipe.named_steps['lasso'].coef_
print(lasso_coef)
# Plot the coefficients
plt.plot(range(len(df_columns)), lasso_coef)
plt.xticks(range(len(df_columns)), df_columns.values, rotation=60)
plt.margins(0.02)
plt.show()
As you can see, I draw the same conclusion, but I'd be more comfortable that I was doing this correctly if the output images were more similar. What am I doing wrong with the Pipeline?
ANSWER
Answered 2021-Nov-24 at 09:45When you set Lasso(..normalize=True) the normalization is different from that in StandardScaler(). It divides by the l2-norm instead of the standard deviation. If you read the help page:
normalize bool, default=False This parameter is ignored when fit_intercept is set to False. If True, the regressors X will be normalized before regression by subtracting the mean and dividing by the l2-norm. If you wish to standardize, please use StandardScaler before calling fit on an estimator with normalize=False.
Deprecated since version 1.0: normalize was deprecated in version 1.0 and will be removed in 1.2.
It is also touched upon in this post. Since it will be deprecated, I think it's better to just use the StandardScaler normalization. You can see it's reproducible as long as you scale it in the same way:
lasso = Lasso(alpha=0.4,random_state=99)
lasso.fit(StandardScaler().fit_transform(X),y)
print(lasso.coef_)
[-0. -0.30409556 -2.33203165 -0. 0.51040194 1.45942351
-1.02516505 -4.57678764]
pipe = Pipeline(steps=[
('scaler',StandardScaler()),
('lasso',Lasso(alpha=0.4,random_state=99))
])
pipe.fit(X, y)
lasso_coef = pipe.named_steps['lasso'].coef_
print(lasso_coef)
[-0. -0.30409556 -2.33203165 -0. 0.51040194 1.45942351
-1.02516505 -4.57678764]
QUESTION
Can't deploy streamlit app on share.streamlit.io
Asked 2021-Dec-25 at 14:42I am working with a simple ML model with streamlit. It runs fine on my local machine inside conda environment, but it shows Error installing requirements when I try to deploy it on share.streamlit.io.
The error message is the following:
ERROR: Could not find a version that satisfies the requirement pywin32==303 (from versions: none)
ERROR: No matching distribution found for pywin32==303
This is the requirements.txt file for my model:
altair==4.1.0
argon2-cffi==21.3.0
argon2-cffi-bindings==21.2.0
astor==0.8.1
attrs==21.2.0
backcall==0.2.0
base58==2.1.1
bleach==4.1.0
blinker==1.4
cachetools==5.0.0
certifi==2021.10.8
cffi==1.15.0
charset-normalizer==2.0.9
click==7.1.2
colorama==0.4.4
cycler==0.11.0
debugpy==1.5.1
decorator==5.1.0
defusedxml==0.7.1
entrypoints==0.3
fonttools==4.28.5
gitdb==4.0.9
GitPython==3.1.24
idna==3.3
ipykernel==6.6.0
ipython==7.30.1
ipython-genutils==0.2.0
ipywidgets==7.6.5
jedi==0.18.1
Jinja2==3.0.3
joblib==1.1.0
jsonschema==4.3.2
jupyter-client==7.1.0
jupyter-core==4.9.1
jupyterlab-pygments==0.1.2
jupyterlab-widgets==1.0.2
kiwisolver==1.3.2
MarkupSafe==2.0.1
matplotlib==3.5.1
matplotlib-inline==0.1.3
mistune==0.8.4
nbclient==0.5.9
nbconvert==6.3.0
nbformat==5.1.3
nest-asyncio==1.5.4
notebook==6.4.6
numpy==1.21.5
packaging==21.3
pandas==1.3.5
pandocfilters==1.5.0
parso==0.8.3
pickleshare==0.7.5
Pillow==8.4.0
prometheus-client==0.12.0
prompt-toolkit==3.0.24
protobuf==3.19.1
pyarrow==6.0.1
pycparser==2.21
pydeck==0.7.1
Pygments==2.10.0
Pympler==1.0.1
pyparsing==3.0.6
pyrsistent==0.18.0
python-dateutil==2.8.2
pytz==2021.3
pytz-deprecation-shim==0.1.0.post0
pywin32==303
pywinpty==1.1.6
pyzmq==22.3.0
requests==2.26.0
scikit-learn==1.0.1
scipy==1.7.3
seaborn==0.11.2
Send2Trash==1.8.0
six==1.16.0
smmap==5.0.0
streamlit==1.3.0
terminado==0.12.1
testpath==0.5.0
threadpoolctl==3.0.0
toml==0.10.2
toolz==0.11.2
tornado==6.1
traitlets==5.1.1
typing_extensions==4.0.1
tzdata==2021.5
tzlocal==4.1
urllib3==1.26.7
validators==0.18.2
watchdog==2.1.6
wcwidth==0.2.5
webencodings==0.5.1
widgetsnbextension==3.5.2
wincertstore==0.2
What should I do to resolve this error?
ANSWER
Answered 2021-Dec-25 at 14:42Streamlit share runs the app in a linux environment meaning there is no pywin32 because this is for windows.
Delete the pywin32 from the requirements file and also the pywinpty==1.1.6 for the same reason.
After deleting these requirements re-deploy your app and it will work.
QUESTION
Sklearn: Calibrate a multi-label classification with CalibratedClassifierCV
Asked 2021-Dec-18 at 17:38I have built a number of sklearn classifier models to perform multi-label classification and I would like to calibrate their predict_proba outputs so that I can obtain confidence scores. I would also like to use metrics such as sklearn.metrics.recall_score to evaluate them.
I have 4 labels to predict and the true labels are multi-hot encoded (e.g. [0, 1, 1, 1]). As a result, CalibratedClassifierCV does not directly accept my data:
clf = tree.DecisionTreeClassifier(max_depth=15)
clf = clf.fit(train_X, train_Y)
calibrated_clf = CalibratedClassifierCV(clf, cv="prefit", method="sigmoid")
calibrated_clf.fit(dev_X, dev_Y)
This would return an error:
ValueError: classes [[0 1]
[0 1]
[0 1]
[0 1]] mismatch with the labels [0 1 2 3] found in the data
Thus, I tried to wrap it in a OneVsRestClassifier:
clf = OneVsRestClassifier(tree.DecisionTreeClassifier(max_depth=15), n_jobs=4)
clf = clf.fit(train_X, train_Y)
calibrated_clf = CalibratedClassifierCV(clf, cv="prefit", method="sigmoid")
calibrated_clf.fit(dev_X, dev_Y)
Note that MultiOutputClassifier and ClassifierChain do not work even though they possibly suit my problem better.
It works, but the predict output of the calibrated classifier is multi-class instead of multi-label because of its implementation. There are four classes ([0 1 2 3]) but if there is no need to put a label, it still predicts a 0.
Upon further inspection by means of calibration curves, it turns out the base estimator wrapped inside the calibrated classifier is not calibrated at all. That is, (calibrated_clf.calibrated_classifiers_)[0].base_estimator returns the same clf as before calibration.
I would like to observe the performance of my (calibrated) models doing deterministic (predict) and probabilistic (predict_proba) predictions. How should I design my model/wrap things in other containers to get both calibrated probabilities for each label and comprehensible label predictions?
ANSWER
Answered 2021-Dec-17 at 15:33In your example, you're using a DecisionTreeClassifier which by default support targets of dimension (n, m) where m > 1.
However if you want to have as result the marginal probability of each class then use the OneVsRestClassifier.
Notice that CalibratedClassifierCV expects target to be 1d so the "trick" is to extend it to support Multilabel Classification with MultiOutputClassifier.
Full Example
from sklearn.datasets import make_multilabel_classification
from sklearn.tree import DecisionTreeClassifier
from sklearn.multioutput import MultiOutputClassifier
from sklearn.model_selection import train_test_split, StratifiedKFold
import numpy as np
# Generate a sample multilabel target
X, y = make_multilabel_classification(n_classes=4, random_state=0)
y
>>>
array([[1, 0, 1, 0],
[0, 0, 0, 0],
[1, 0, 1, 0],
...
[0, 0, 0, 0],
[0, 1, 1, 1],
[1, 1, 0, 1]])
# Split in train/test
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.9, random_state=42
)
# Splits Stratify target variable
cv = StratifiedKFold(n_splits=2)
# Decision tree support by default multiclass target or use OneVsRest if marginal probabilities
clf = OneVsRestClassifier(DecisionTreeClassifier(max_depth=10))
# Calibrate estimator probabilities
calibrated_clf = CalibratedClassifierCV(base_estimator=clf, cv=cv)
# calibrated_clf target is one dimensional, extend classifier to multi-target classification.
multioutput_clf = MultiOutputClassifier(calibrated_clf).fit(X_train, y_train)
# Check predict
multioutput_clf.predict(X_test[-5:])
>>>
array([[0, 0, 1, 1],
[0, 0, 0, 1],
[0, 0, 0, 1],
[0, 0, 0, 1],
[0, 0, 0, 1]])
# Check predict_proba
multioutput_clf.predict_proba(X_test[-5:])
>>>
[array([[0.78333315, 0.21666685],
[0.78333315, 0.21666685],
[0.78333315, 0.21666685],
[0.78333315, 0.21666685],
[0.78333315, 0.21666685]]),
array([[0.59166537, 0.40833463],
[0.59166537, 0.40833463],
[0.40833361, 0.59166639],
[0.59166537, 0.40833463],
[0.59166537, 0.40833463]]),
array([[0.61666922, 0.38333078],
[0.61666427, 0.38333573],
[0.80000098, 0.19999902],
[0.61666427, 0.38333573],
[0.61666427, 0.38333573]]),
array([[0.26874774, 0.73125226],
[0.26874774, 0.73125226],
[0.45208444, 0.54791556],
[0.26874774, 0.73125226],
[0.26874774, 0.73125226]])]
Notice that the result from predict_proba is a list with 4 arrays, each array is the probability to belong to the class i. For example, inside the first sample of the first array is the probability that first sample belongs to class 1 and so on.
Regarding the calibration curves, scikit-learn provides examples to plot probability path for two dimension and three dimension targets.
QUESTION
understanding sklearn calibratedClassifierCV
Asked 2021-Dec-03 at 13:03Hi all I am having trouble understanding how to use the output of sklearn.calibration.CalibratedClassifierCV.
I have calibrated my binary classifier using this method, and results are greatly improved. However I am not sure how to interpret the results. sklearn guide states that, after calibration,
the output of
predict_probamethod can be directly interpreted as a confidence level. For instance, a well calibrated (binary) classifier should classify the samples such that among the samples to which it gave a predict_proba value close to 0.8, approximately 80% actually belong to the positive class.
Now I would like to reduce false positive by applying a cutoff at .6 for the model to predict label True. Without the calibration, I would have simply used my_model.predict_proba() > .6.
However, it seems that after calibration the meaning of predict_proba has changed, so I am not sure if I can do that anymore.
From a quick testing it seems that predict and predict_proba follow the same logic I would expect before calibration. The output of:
pred = my_model.predict(valid_x)
proba= my_model.predict_proba(valid_x)
pd.DataFrame({"label": pred, "proba": proba[:,1]})
is the following:
Where everything that has a probability of above .5 gets to be classifed as True, and everything below .5 as False.
Can you confirm that, after calibration, I can still use predict_proba to apply a different cutoff to identify my labels?
2 https://scikit-learn.org/stable/modules/calibration.html#calibration
ANSWER
Answered 2021-Dec-03 at 13:03For me, you can actually use predict_proba() after calibration to apply a different cutoff.
What happens within class CalibratedClassifierCV (as you noticed) is effectively that the output of predict() is based on the output of predict_proba() (see here for reference), i.e. np.argmax(self.predict_proba(X), axis=1) == self.predict(X).
On the other side, for the non-calibrated classifier that you're passing to CalibratedClassifierCV (depending on whether it is a probabilistic classifier or not) the above equality may or may not hold (e.g. it does not for an SVC() classifier - see here, for instance, for some other details on this).
QUESTION
Meaning of `penalty` and `loss` in LinearSVC
Asked 2021-Nov-18 at 18:08Anti-closing preamble: I have read the question "difference between penalty and loss parameters in Sklearn LinearSVC library" but I find the answer there not to be specific enough. Therefore, I’m reformulating the question:
I am familiar with SVM theory and I’m experimenting with LinearSVC class in Python. However, the documentation is not quite clear regarding the meaning of penalty and loss parameters. I recon that loss refers to the penalty for points violating the margin (usually denoted by the Greek letter xi or zeta in the objective function), while penalty is the norm of the vector determining the class boundary, usually denoted by w. Can anyone confirm or deny this?
If my guess is right, then penalty = 'l1' would lead to minimisation of the L1-norm of the vector w, like in LASSO regression. How does this relate to the maximum-margin idea of the SVM? Can anyone point me to a publication regarding this question? In the original paper describing LIBLINEAR I could not find any reference to L1 penalty.
Also, if my guess is right, why doesn't LinearSVC support the combination of penalty='l2' and loss='hinge' (the standard combination in SVC) when dual=False? When trying it, I get the
ValueError: Unsupported set of arguments
ANSWER
Answered 2021-Nov-18 at 18:08Though very late, I'll try to give my answer. According to the doc, here's the considered primal optimization problem for LinearSVC:
,
phi being the Identity matrix, given that LinearSVC only solves linear problems.
Effectively, this is just one of the possible problems that LinearSVC admits (it is the L2-regularized, L1-loss in the terms of the LIBLINEAR paper) and not the default one (which is the L2-regularized, L2-loss).
The LIBLINEAR paper gives a more general formulation for what concerns what's referred to as loss in Chapter 2, then it further elaborates also on what's referred to as penalty within the Appendix (A2+A4).
Basically, it states that LIBLINEAR is meant to solve the following unconstrained optimization pb with different loss functions xi(w;x,y) (which are hinge and squared_hinge); the default setting of the model in LIBLINEAR does not consider the bias term, that's why you won't see any reference to b from now on (there are many posts on SO on this).
,
hingeor L1-loss,
squared_hingeor L2-loss.
For what concerns the penalty, basically this represents the norm of the vector w used. The appendix elaborates on the different problems:
- L2-regularized, L1-loss (
penalty='l2',loss='hinge'): - L2-regularized, L2-loss (
penalty='l2',loss='squared_hinge'), default inLinearSVC: - L1-regularized, L2-loss (
penalty='l1',loss='squared_hinge'):
Instead, as stated within the documentation, LinearSVC does not support the combination of penalty='l1' and loss='hinge'. As far as I see the paper does not specify why, but I found a possible answer here (within the answer by Arun Iyer).
Eventually, effectively the combination of penalty='l2', loss='hinge', dual=False is not supported as specified in here (it is just not implemented in LIBLINEAR) or here; not sure whether that's the case, but within the LIBLINEAR paper from Appendix B onwards it is specified the optimization pb that's solved (which in the case of L2-regularized, L1-loss seems to be the dual).
For a theoretical discussion on SVC pbs in general, I found that chapter really useful; it shows how the minimization of the norm of w relates to the idea of the maximum-margin.
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You can use scikit-learn like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
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