APOC-2019 | Project undertaken by the 2019 Appropriate Point | Machine Learning library
kandi X-RAY | APOC-2019 Summary
kandi X-RAY | APOC-2019 Summary
ABSTRACT: There are many diseases that afflict people in our current generation, ranging from infectious diseases to noncommunicable diseases. Most of these diseases are easily diagnosable with the correct medical technology, with many of these technologies allowing for the early diagnosis of disease to allow for early treatment to control the progression of the disease, spread of infectious diseases, prevent further complications, and enable better management of the diseases. The use of these early detection technologies of diseases however, tends to be limited to a patient-referral system that depends on a number of factors that decide who is most at risk of being inflicted by a particular ailment and the factors may vary wildly based on the location, resources and lifestyle even though correlations for at-risk patients could exist. Our project utilizes machine learning algorithms that can be coupled with existing medical databases to recognize these trends and correlations between at-risk patients and serve as an assistive technology that can help doctors decide whether or not to suggest early detection tests to their patients. Our pilot case study chooses tuberculosis for our database model in order to test our prototype, especially since tuberculosis is one of the leading causes of death for people afflicted with HIV and the early detection of tuberculosis would be very essential to early treatment of the disease and make it easier to manage it. Tuberculosis is a bacterial infection that primarily affects the lungs and has potential peripheral effects in other organ systems. Symptoms of tuberculosis include coughing, loss of weight and appetite, fever, chills, night sweats, and loss of proper function in any of the affected organs, including lymph nodes, bones, kidneys, brain, spine, and skin. Source The tuberculosis bacteria tend to settle in the lungs and multiply to attack the respiratory system. In other cases, the bacteria can spread throughout the body via the blood and lymphatic system. Bacteria are extremely resilient and can be transmitted through the air by coughing or sharing eating areas. In a patient with a healthy immune system, the bacteria will be surrounded in mucous and engulfed in cells which attempt to fight it off, which block the alveoli. However, tuberculosis tends to be an opportunistic disease, targeting those with compromised immune systems, which leads to a high prevalence in people with HIV. Tuberculosis is currently the leading cause of death in HIV patients, with 32% of AIDS deaths in 2017 resulting from TB. Worldwide, tuberculosis is one of the top 10 causes of death, with approximately 10 million new cases and 1.6 million deaths in 2017 alone. Africa accounts for 84% of those deaths. Source In most hospitals, especially those in Ghana’s Ashanti region, the current system implemented only refers patients who are showing symptoms of TB for TB tests which are mainly smear microscopy and GeneXPert tests. However, with the introduction of the GeneXpert in Ghana, it has now become possible for the early detection of tuberculosis before the symptomatic stage is reached. This is because for tuberculosis testing, unless the patient is showing symptoms, TB testing is not done. However, we know that tuberculosis infected patients only start to show symptoms such as cough within the second or third month, though they are infectious by the second month. Above is a graphic depicting the timeline for the progression of Tuberculosis. As patients can visit the clinic as scarcely as every 3-6 months, symptom-based screening for tuberculosis is not the most effective solution to early TB detection, as it disregards most early cases of TB in which symptoms have not yet surfaced. For example, if a patient comes to the clinic in the second month of disease progression, they currently will not be tested for TB, and the next routine appointment would fall in month 5 of disease progression, where the patient grows too sick to work. Our project proposes to identify more asymptomatic tuberculosis cases currently missed with machine learning in order to increase the effort to further eradicate the disease from Ghana. We propose to train nurses in clinics to operate a tablet-based app, which allows them to enter patient data in accordance with a variety of screening questions. The data from the app, once submitted, communicates with a computer program in python. The program will then pass the data through a machine learning algorithm of our design which will be trained by an existing dataset containing patient data and diagnoses. The algorithm, once trained, will be able to take in new survey inputs and output a likelihood in which the patient has TB, which can then aid the doctor’s decision regarding whether or not to refer the patient to a laboratory or a TB clinic for further examination. Why Machine Learning? Machine learning, a subset of artificial intelligence, is the scientific study of algorithms and statistical models that computer systems use in order to perform a specific task effectively without using explicit instructions, relying on patterns and inference instead. Source It provides a method for which patterns and correlations between large datasets can be studied where analysis would be too complicated for humans to interpret otherwise. Machine learning systems are constantly learning from their datasets and, as such, are able to update their output results over time. The constant retraining of data enables time trends to be studied and ensures that the algorithms remain applicable to such a dynamic dataset.
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APOC-2019 Key Features
APOC-2019 Examples and Code Snippets
Community Discussions
Trending Discussions on Machine Learning
QUESTION
I have trained an RNN model with pytorch. I need to use the model for prediction in an environment where I'm unable to install pytorch because of some strange dependency issue with glibc. However, I can install numpy and scipy and other libraries. So, I want to use the trained model, with the network definition, without pytorch.
I have the weights of the model as I save the model with its state dict and weights in the standard way, but I can also save it using just json/pickle files or similar.
I also have the network definition, which depends on pytorch in a number of ways. This is my RNN network definition.
...ANSWER
Answered 2022-Feb-17 at 10:47You should try to export the model using torch.onnx. The page gives you an example that you can start with.
An alternative is to use TorchScript, but that requires torch libraries.
Both of these can be run without python. You can load torchscript in a C++ application https://pytorch.org/tutorials/advanced/cpp_export.html
ONNX is much more portable and you can use in languages such as C#, Java, or Javascript https://onnxruntime.ai/ (even on the browser)
A running exampleJust modifying a little your example to go over the errors I found
Notice that via tracing any if/elif/else, for, while will be unrolled
QUESTION
I'm trying to implement a gradient-free optimizer function to train convolutional neural networks with Julia using Flux.jl. The reference paper is this: https://arxiv.org/abs/2005.05955. This paper proposes RSO, a gradient-free optimization algorithm updates single weight at a time on a sampling bases. The pseudocode of this algorithm is depicted in the picture below.
I'm using MNIST dataset.
...ANSWER
Answered 2022-Jan-14 at 23:47Based on the paper you shared, it looks like you need to change the weight arrays per each output neuron per each layer. Unfortunately, this means that the implementation of your optimization routine is going to depend on the layer type, since an "output neuron" for a convolution layer is quite different than a fully-connected layer. In other words, just looping over Flux.params(model)
is not going to be sufficient, since this is just a set of all the weight arrays in the model and each weight array is treated differently depending on which layer it comes from.
Fortunately, Julia's multiple dispatch does make this easier to write if you use separate functions instead of a giant loop. I'll summarize the algorithm using the pseudo-code below:
QUESTION
This question is the same with How can I check a confusion_matrix after fine-tuning with custom datasets?, on Data Science Stack Exchange.
BackgroundI would like to check a confusion_matrix, including precision, recall, and f1-score like below after fine-tuning with custom datasets.
Fine tuning process and the task are Sequence Classification with IMDb Reviews on the Fine-tuning with custom datasets tutorial on Hugging face.
After finishing the fine-tune with Trainer, how can I check a confusion_matrix in this case?
An image of confusion_matrix, including precision, recall, and f1-score original site: just for example output image
...ANSWER
Answered 2021-Nov-24 at 13:26What you could do in this situation is to iterate on the validation set(or on the test set for that matter) and manually create a list of y_true
and y_pred
.
QUESTION
I am trying to train a model using PyTorch. When beginning model training I get the following error message:
RuntimeError: CUDA out of memory. Tried to allocate 5.37 GiB (GPU 0; 7.79 GiB total capacity; 742.54 MiB already allocated; 5.13 GiB free; 792.00 MiB reserved in total by PyTorch)
I am wondering why this error is occurring. From the way I see it, I have 7.79 GiB total capacity. The numbers it is stating (742 MiB + 5.13 GiB + 792 MiB) do not add up to be greater than 7.79 GiB. When I check nvidia-smi
I see these processes running
ANSWER
Answered 2021-Nov-23 at 06:13This is more of a comment, but worth pointing out.
The reason in general is indeed what talonmies commented, but you are summing up the numbers incorrectly. Let's see what happens when tensors are moved to GPU (I tried this on my PC with RTX2060 with 5.8G usable GPU memory in total):
Let's run the following python commands interactively:
QUESTION
I am a bit confusing with comparing best GridSearchCV model and baseline.
For example, we have classification problem.
As a baseline, we'll fit a model with default settings (let it be logistic regression):
ANSWER
Answered 2021-Nov-04 at 21:17No, they aren't comparable.
Your baseline model used X_train
to fit the model. Then you're using the fitted model to score the X_train
sample. This is like cheating because the model is going to already perform the best since you're evaluating it based on data that it has already seen.
The grid searched model is at a disadvantage because:
- It's working with less data since you have split the
X_train
sample. - Compound that with the fact that it's getting trained with even less data due to the 5 folds (it's training with only 4/5 of
X_val
per fold).
So your score for the grid search is going to be worse than your baseline.
Now you might ask, "so what's the point of best_model.best_score_
? Well, that score is used to compare all the models used when searching for the optimal hyperparameters in your search space, but in no way should be used to compare against a model that was trained outside of the grid search context.
So how should one go about conducting a fair comparison?
- Split your training data for both models.
QUESTION
I am not able to access jupyter lab created on google cloud
I created one notebook using Google AI platform. I was able to start it and work but suddenly it stopped and I am not able to start it now. I tried building and restarting the jupyterlab, but of no use. I have checked my disk usages as well, which is only 12%.
I tried the diagnostic tool, which gave the following result:
but didn't fix it.
Thanks in advance.
...ANSWER
Answered 2021-Aug-20 at 14:00You should try this Google Notebook trouble shooting section about 524 errors : https://cloud.google.com/notebooks/docs/troubleshooting?hl=ja#opening_a_notebook_results_in_a_524_a_timeout_occurred_error
QUESTION
I am new to Machine Learning.
Having followed the steps in this simple Maching Learning using the Brain.js library, it beats my understanding why I keep getting the error message below:
I have double-checked my code multiple times. This is particularly frustrating as this is the very first exercise!
Kindly point out what I am missing here!
Find below my code:
...ANSWER
Answered 2021-Sep-29 at 22:47Turns out its just documented incorrectly.
In reality the export from brain.js is this:
QUESTION
IF we are not sure about the nature of categorical features like whether they are nominal or ordinal, which encoding should we use? Ordinal-Encoding or One-Hot-Encoding? Is there a clearly defined rule on this topic?
I see a lot of people using Ordinal-Encoding on Categorical Data that doesn't have a Direction. Suppose a frequency table:
...ANSWER
Answered 2021-Sep-04 at 06:43You're right. Just one thing to consider for choosing OrdinalEncoder
or OneHotEncoder
is that does the order of data matter?
Most ML algorithms will assume that two nearby values are more similar than two distant values. This may be fine in some cases e.g., for ordered categories such as:
quality = ["bad", "average", "good", "excellent"]
orshirt_size = ["large", "medium", "small"]
but it is obviously not the case for the:
color = ["white","orange","black","green"]
column (except for the cases you need to consider a spectrum, say from white to black. Note that in this case, white
category should be encoded as 0
and black
should be encoded as the highest number in your categories), or if you have some cases for example, say, categories 0 and 4 may be more similar than categories 0 and 1. To fix this issue, a common solution is to create one binary attribute per category (One-Hot encoding)
QUESTION
I am using sentence-transformers for semantic search but sometimes it does not understand the contextual meaning and returns wrong result eg. BERT problem with context/semantic search in italian language
by default the vector side of embedding of the sentence is 78 columns, so how do I increase that dimension so that it can understand the contextual meaning in deep.
code:
...ANSWER
Answered 2021-Aug-10 at 07:39Increasing the dimension of a trained model is not possible (without many difficulties and re-training the model). The model you are using was pre-trained with dimension 768, i.e., all weight matrices of the model have a corresponding number of trained parameters. Increasing the dimensionality would mean adding parameters which however need to be learned.
Also, the dimension of the model does not reflect the amount of semantic or context information in the sentence representation. The choice of the model dimension reflects more a trade-off between model capacity, the amount of training data, and reasonable inference speed.
If the model that you are using does not provide representation that is semantically rich enough, you might want to search for better models, such as RoBERTa or T5.
QUESTION
I have a table with features that were used to build some model to predict whether user will buy a new insurance or not. In the same table I have probability of belonging to the class 1 (will buy) and class 0 (will not buy) predicted by this model. I don't know what kind of algorithm was used to build this model. I only have its predicted probabilities.
Question: how to identify what features affect these prediction results? Do I need to build correlation matrix or conduct any tests?
Table example:
...ANSWER
Answered 2021-Aug-11 at 15:55You could build a model like this.
x = features you have. y = true_lable
from that you can extract features importance. also, if you want to go the extra mile,you can do Bootstrapping, so that the features importance would be more stable (statistical).
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Install APOC-2019
You can use APOC-2019 like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
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