linkedin / linkedin/FastTreeSHAP
Cannot build fasttreeshap in linux environment
Dieses Issue hat noch niemand übernommen.
- Vorherrschende Sprache
- Python
- Sterne
- 566
- Forks
- 38
- PR-Merge-Kennzahlen
- Keine gemergten PRs in 30 T.
Beschreibung
We would like to use fasttreeshap to calculate explainability values for our machine learning model.
To run our model we create a linux container with a venv for the model. All the requirements are specified in a file and installed in the venv with pip -r requirements.txt.
Our model depends on numpy==1.21.4 and when we try to install fasttreeshap we incur into this issue:
```
ERROR: Cannot install oldest-supported-numpy==0.12, oldest-supported-numpy==0.14, oldest-supported-numpy==0.15, oldest-supported-numpy==2022.1.30, oldest-supported-numpy==2022.3.27, oldest-supported-numpy==2022.4.10, oldest-supported-numpy==2022.4.18, oldest-supported-numpy==2022.4.8, oldest-supported-numpy==2022.5.27, oldest-supported-numpy==2022.5.28 and oldest-supported-numpy==2022.8.16 because these package versions have conflicting dependencies.
The conflict is caused by:
oldest-supported-numpy 2022.8.16 depends on numpy==1.17.3; python_version == "3.8" and platform_machine not in "arm64|aarch64|s390x|loongarch64" and platform_python_implementation != "PyPy"
oldest-supported-numpy 2022.5.28 depends on numpy==1.17.3; python_version == "3.8" and (platform_machine != "arm64" or platform_system != "Darwin") and platform_machine != "aarch64" and platform_machine != "s390x" and platform_machine != "loongarch64" and platform_python_implementation != "PyPy"
oldest-supported-numpy 2022.5.27 depends on numpy==1.17.3; python_version == "3.8" and (platform_machine != "arm64" or platform_system != "Darwin") and platform_machine != "aarch64" and platform_machine != "s390x" and platform_machine != "loongarch64" and platform_python_implementation != "PyPy"
oldest-supported-numpy 2022.4.18 depends on numpy==1.17.3; python_version == "3.8" and (platform_machine != "arm64" or platform_system != "Darwin") and platform_machine != "aarch64" and platform_machine != "s390x" and platform_machine != "loongarch64" and platform_python_implementation != "PyPy"
oldest-supported-numpy 2022.4.10 depends on numpy==1.17.3; python_version == "3.8" and (platform_machine != "arm64" or platform_system != "Darwin") and platform_machine != "aarch64" and platform_machine != "s390x" and platform_machine != "loongarch64" and platform_python_implementation != "PyPy"
oldest-supported-numpy 2022.4.8 depends on numpy==1.17.3; python_version == "3.8" and (platform_machine != "arm64" or platform_system != "Darwin") and platform_machine != "aarch64" and platform_machine != "s390x" and platform_machine != "loongarch64" and platform_python_implementation != "PyPy"
oldest-supported-numpy 2022.3.27 depends on numpy==1.17.3; python_version == "3.8" and (platform_machine != "arm64" or platform_system != "Darwin") and platform_machine != "aarch64" and platform_machine != "s390x" and platform_machine != "loongarch64" and platform_python_implementation != "PyPy"
oldest-supported-numpy 2022.1.30 depends on numpy==1.17.3; python_version == "3.8" and (platform_machine != "arm64" or platform_system != "Darwin") and platform_machine != "aarch64" and platform_machine != "s390x" and platform_python_implementation != "PyPy"
oldest-supported-numpy 0.15 depends on numpy==1.17.3; python_version == "3.8" and (platform_machine != "arm64" or platform_system != "Darwin") and platform_machine != "aarch64" and platform_machine != "s390x" and platform_python_implementation != "PyPy"
oldest-supported-numpy 0.14 depends on numpy==1.17.3; python_version == "3.8" and (platform_machine != "arm64" or platform_system != "Darwin") and platform_machine != "aarch64" and platform_machine != "s390x" and platform_python_implementation != "PyPy"
oldest-supported-numpy 0.12 depends on numpy==1.17.3; python_version == "3.8" and (platform_machine != "arm64" or platform_system != "Darwin") and platform_machine != "aarch64" and platform_python_implementation != "PyPy"
```
I can volunteer time and resources to add a wheel file for linux. Would you be interested in distributing a wheel file created by us ?
Beitragsleitfaden
Erste Schritte
- Lies das ganze Issue und danach den Beitragsleitfaden des Projekts.
- Schreib ins Issue, dass du es übernimmst — das erspart doppelte Arbeit.
- Forke das Repository und arbeite in einem Branch.
- Öffne einen Pull Request, der die Issue-Nummer nennt.
Rechercherichtung
Beginne damit, die in der Issue beschriebene Installation in einem Linux-Container mit dem festgelegten numpy==1.21.4 und requirements.txt zu reproduzieren. Untersuche den Abhängigkeitskonflikt und ermittle, ob ein Linux-Wheel für diese Umgebung erstellt und verteilt werden kann; abgeschlossen ist die Aufgabe, wenn pip fasttreeshap ohne den gemeldeten Fehler bei der Auflösung installieren kann.
Vom Indexierungsmodell aus dem Issue-Text verfasst.
Bewertung
- Tech-Stack
- numpy, python
- Bereich
- build-system, machine-learning
- Issue-Typ
- Feature
- Schwierigkeit
- 4/5
- Geschätzter Aufwand
- 3-5 Tage
- Aktivitätsstatus
- Veraltet
- Klarheit
- Größtenteils klar
- Anfängerfreundlichkeit
- 35/100