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One problem to note: creating tests for neural networks has been tricky. Ideally, these tests should not take several minutes to train and test a method/model. But if we want meaningful test-cases, we will want to test meaningful neural network architectures (which take longer to train). I will be thinking of a compromise... |
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The most recent commit changes each |
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Split into more PRs? |
stompsjo
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Oct 28, 2022
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| jobs = alph[:n] | ||
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| fig, axes = plt.subplots(n, n, figsize=(15, 15)) |
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| fig, axes = plt.subplots(n, n, figsize=(15, 15)) | |
| fig, axes = plt.subplots(n, n, figsize=(3*n, 3*n)) |
Pull Request Test Coverage Report for Build 3363647923
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This PR introduces much of the code used for my research comparing several different semi-supervised machine learning models (SSML). I wrote and used these models quite separate from how I have used RadClass in the past (i.e. I ran experimental data through RadClass and H0 to get observations I could use for training and testing ML models separately in a downstream analysis).
This raises a design question regarding how these models and scripts should be integrated into RadClass. Should they be made into a post_analysis object? Should they remain separate, thereby making the user have a script that both runs RadClass for data processing and SSML models for training and testing?
.loadmethod for each model class? This may remove one manual step a user would have to take to usejoblibfor loading a written model and storing as a model class.