Ubuntu
Родитель
3eca65c20e
Коммит
2866b95a25
Различия файлов скрыты, потому что одна или несколько строк слишком длинны
Различия файлов скрыты, потому что одна или несколько строк слишком длинны
Различия файлов скрыты, потому что одна или несколько строк слишком длинны
Различия файлов скрыты, потому что одна или несколько строк слишком длинны
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@ -20,7 +20,7 @@ Note that the week number starts from 40 in this dataset, while the full Dominic
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The following summarizes each directory of the forecasting examples.
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| Directory | Content | Description |
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| --- | --- | --- |
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|--------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
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| [python](./python) | [00_quick_start/](./python/00_quick_start) <br>[01_prepare_data/](./python/01_prepare_data) <br> [02_model/](./python/02_model) <br> [03_model_tune_deploy/](./python/03_model_tune_deploy/) | <ul> <li> Quick start examples for single-round training </li> <li> Data exploration and preparation notebooks </li> <li> Multi-round training examples </li> <li> Model tuning and deployment example </li> </ul> |
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| [R](./R) | [01_dataprep.Rmd](R/01_dataprep.Rmd) <br> [02_basic_models.Rmd](R/02_basic_models.Rmd) <br> [02a_reg_models.Rmd](R/02a_reg_models.Rmd) <br> [02b_prophet_models.Rmd](R/02b_prophet_models.Rmd) | <ul> <li>Data preparation</li> <li>Basic time series models</li> <li>ARIMA-regression models</li> <li>Prophet models</li> </ul> |
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@ -8,7 +8,7 @@ This folder contains Jupyter notebooks with Python examples for building forecas
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The following summarizes each directory of the Python best practice notebooks.
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| Directory | Content | Description |
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| --- | --- | --- |
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|-------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
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| [00_quick_start](./00_quick_start) | [autoarima_single_round.ipynb](./00_quick_start/autoarima_single_round.ipynb) <br>[azure_automl_single_round.ipynb](./00_quick_start/azure_automl_single_round.ipynb) <br> [lightgbm_single_round.ipynb](./00_quick_start/lightgbm_single_round.ipynb) | Quick start notebooks that demonstrate workflow of developing a forecasting model using one-round training and testing data |
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| [01_prepare_data](./01_prepare_data) | [ojdata_exploration.ipynb](./01_prepare_data/ojdata_exploration.ipynb) <br> [ojdata_preparation.ipynb](./01_prepare_data/ojdata_preparation.ipynb) | Data exploration and preparation notebooks |
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| [02_model](./02_model) | [dilatedcnn_multi_round.ipynb](./02_model/dilatedcnn_multi_round.ipynb) <br> [lightgbm_multi_round.ipynb](./02_model/lightgbm_multi_round.ipynb) <br> [autoarima_multi_round.ipynb](./02_model/autoarima_multi_round.ipynb) | Deep dive notebooks that perform multi-round training and testing of various classical and deep learning forecast algorithms |
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@ -1,31 +0,0 @@
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# Pull request against these branches will trigger this build
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#pr:
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#- staging
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#- master
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# Any commit to these branches will trigger the build.
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#trigger:
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#- staging
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#- master
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pool:
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name: ForecastingBP
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vmImage: "forecastingtestmachine"
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steps:
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- bash: |
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echo "##vso[task.prependpath]/data/anaconda/bin"
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displayName: Add conda to PATH
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- bash: |
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. /anaconda/etc/profile.d/conda.sh && \
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conda activate forecasting_base && \
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pytest tests/python/unit -m "not notebooks and not spark and not gpu" --junitxml=reports/python-unit-tests-base.xml && \
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conda deactivate
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displayName: "Run unit tests"
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- task: PublishTestResults@2
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inputs:
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testResultsFiles: "reports/python-unit-tests-base.xml"
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testRunTitle: "Test results of unit tests"
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@ -1,31 +0,0 @@
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# Pull request against these branches will trigger this build
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#pr:
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#- staging
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#- master
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# Any commit to these branches will trigger the build.
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#trigger:
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#- staging
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#- master
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pool:
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name: ForecastingBP
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vmImage: 'forecastingtestmachine'
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steps:
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- bash: |
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echo "##vso[task.prependpath]/data/anaconda/bin"
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displayName: Add conda to PATH
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- bash: |
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. /anaconda/etc/profile.d/conda.sh && \
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conda activate forecasting_base && \
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Rscript tests/unit/source_entire.R -m "not notebooks and not spark and not gpu" --junitxml=reports/test-unit.xml && \
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conda deactivate
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displayName: 'Run R unit tests'
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- task: PublishTestResults@2
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inputs:
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testResultsFiles: '**/test-unitttest.xml'
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testRunTitle: 'Test results for R Unit Tests'
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@ -1,3 +1,6 @@
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# Copyright (c) Microsoft Corporation.
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# Licensed under the MIT License.
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import os
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import pytest
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from fclib.common.utils import git_repo_path
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