Module Development, changed doc string as required, two new Jupyter notebooks #5
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KMeans.py
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| Function that helps to determine how many clusters to use by using trials of K clusters | ||
| The idea is to find the cluster number that gives the maximum reduction in inertia | ||
| ''' | ||
| def elbow_method(data, num_k, n_init=10, max_iter=300): |
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considering adding "saving the generated elbow plot" option ; (i.e. pass another argument "save" with default save = False )
KMeans.py
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| from sklearn.cluster import KMeans | ||
| from sklearn.datasets import make_blobs | ||
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| ''' |
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please refer to the style guide for the conventions we are hoping to follow (https://numpydoc.readthedocs.io/en/latest/format.html#short-summary). block comments below def statement
LinearRegression.py
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| import numpy as np | ||
| import matplotlib.pyplot as plt | ||
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| ''' |
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refer to KMeans.py for annotations on code conventions we want to follow
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Hayoung's comments for model development team
MuhangTian
changed the title
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Added two ML Jupyter notebooks, one with ML regression and the other with unsupervised clustering functions from scikit-learn |
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reviewed and commented-- major changes: ipynb --> py files
README.md
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| ``` | ||
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| # ML Models | ||
| * See jupyter notebooks, it contains everything the user need to know in the doc strings |
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maybe a link to the jupyter notebook would be more intuitive for the users :)
ml_regression.ipynb
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| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "class LinearRegression():\n", |
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If class is already defined as another py file, I would import and call instead of having another cell.
ml_regression.ipynb
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| @@ -0,0 +1,347 @@ | |||
| { | |||
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As these are more function definitions (or class as you are creating), I would have created a ml_regression.py file, and define them there. I think we envision Jupyter notebooks to be used as sample codes/visualization of how your functions work :)
README.md
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| ``` | ||
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| # ML Models | ||
| * See jupyter notebooks, it contains everything the user need to know in the doc strings |
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consider putting a link for the users-- probably more intuitive 💡
unsupervised_clustering.ipynb
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| @@ -0,0 +1,187 @@ | |||
| { | |||
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As commented on ml_regression.ipynb, could we have this as another py file.
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Finished T-test and relevant tutorial for T-test.py