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The version constraint == will introduce the risk of dependency conflicts because the scope of dependencies is too strict.
The version constraint No Upper Bound and * will introduce the risk of the missing API Error because the latest version of the dependencies may remove some APIs.
After further analysis, in this project,
The version constraint of dependency matplotlib can be changed to >=1.3.0,<=3.0.3.
The version constraint of dependency networkx can be changed to >=2.0,<=2.8.4.
The version constraint of dependency scikit-learn can be changed to >=0.15.0,<=0.20.4.
The version constraint of dependency spacy can be changed to >=0.100.0,<=3.3.1.
The above modification suggestions can reduce the dependency conflicts as much as possible,
and introduce the latest version as much as possible without calling Error in the projects.
The invocation of the current project includes all the following methods.
Hi, In NeuroNER, inappropriate dependency versioning constraints can cause risks.
Below are the dependencies and version constraints that the project is using
The version constraint == will introduce the risk of dependency conflicts because the scope of dependencies is too strict.
The version constraint No Upper Bound and * will introduce the risk of the missing API Error because the latest version of the dependencies may remove some APIs.
After further analysis, in this project,
The version constraint of dependency matplotlib can be changed to >=1.3.0,<=3.0.3.
The version constraint of dependency networkx can be changed to >=2.0,<=2.8.4.
The version constraint of dependency scikit-learn can be changed to >=0.15.0,<=0.20.4.
The version constraint of dependency spacy can be changed to >=0.100.0,<=3.3.1.
The above modification suggestions can reduce the dependency conflicts as much as possible,
and introduce the latest version as much as possible without calling Error in the projects.
The invocation of the current project includes all the following methods.
The calling methods from the matplotlib
The calling methods from the networkx
The calling methods from the scikit-learn
The calling methods from the spacy
The calling methods from the all methods
@developer
Could please help me check this issue?
May I pull a request to fix it?
Thank you very much.
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