I saw this post in Medium regarding argparse, which suggests the following –
parser.add_argument("--model", help="choose model architecture from: vgg19 vgg16 alexnet", type=str)
I think the following variant is better –
parser.add_argument("--model", help="choose model architecture from: vgg19 vgg16 alexnet", type=str, choices=['vgg19', 'vgg16', 'alexnet'], default='alexnet'])
If an illegal parameter is given, for example –model vgg20, the desired behavior of almost every program is to throw an exception. This won’t happen in the first case. If the user mistypes the model name the script will use Alexnet pre-trained instead of throwing an exception (implemented later in the script). Using the choices argument will solve this. Adding default=’alexent’, takes care in the case where the user does not choose a model actively. For the example presented in the original post this is the desired behavior.
See more here –
Bonus – R-miss-tastic – theoretical background and resources which relate to R missing values package. I recommend the lecture notes.
Working with missing data in Pandas – pandas is the swiss knife of data scientists, Pandas allows dropping records with missing values, fill missing values, interpolation of missing data points, etc.
Missing data visualization – provides several levels and types of visualizations – per sample, per feature, features heat map and dendrogram in order to gain a better understanding of missing values in a dataset.
FancyImput – Multivariate imputation and matrix completion algorithms implemented. This package was partially merged to scikit-learn. This package focus on viewing the data as a matrix and not a composition of columns, unfortunately, it is no longer actively maintained but maybe in the future.
Missingpy – scikit-learn consistent API for data imputation. Implements KNN imputation (also implemented in FancyImput) and Random Forest imputation (MissForest). Seems unmaintained.
MDI – Missing Data Imputation Package – accompanying code to Missing Data Imputation for Supervised Learning (https://arxiv.org/abs/1610.09075)