I am trying to plot multiple ROC curves in the same diagram in Weka. I have learnt that I can do this in Weka Knowledge Flow using "Model Performance Chart". However, I can't figure out how to do this for existing models.
I have tried using ArffLoader and TestSetMaker to generate the testing data, and connected this to a suitable Classifier icon (eg AdaBoostM1 when this is the kind of model I am trying to load). In the configurations of the Classifier icon I choose "load model" and in the Status bar it says "Loaded model.". However, when I run this it says "ERROR: no trained/loaded classifier to use for prediction".
Can anyone tell me what I am doing wrong here? Thanks in advance!
There is a post that was published here that indicates some ambiguity in the meaning of the error. It also continues to state that the order of attributes and the number and order of values is also rather important.
It also states that 'for performance results to be computed, your Knowledge Flow process will need a "ClassifierPerformanceEvaluator" component after the classifier and before a TextViewer component.'
If you are new with the KnowledgeFlow environment, there is a great tutorial here from Rushdi Shams that details the general process.
Below is a sample workflow that has generated desirable results using AdaBoost (preloaded model):
Hope this Helps!
Related
I'm new to computer vision and I'm wondering how to deal with the following problem.
I'm using YOLO for real time objet detection task. However I'm dealing with a dataset that gives me also few attributes such has weather, temperature etc...
(I'm obviously able to acces to those informations in real time, to use them in real life).
My data has some big differences depending of the weather, temperature etc... that's why it's useful to have access to those informations.
So is there any way to learn on both image dataset associated to a context ? I'm looking for something that is YOLO compatible.
If a such thing isn't compatible/doesn't exists, I guess I'll just do different versions of the trained YOLO on specifics datasets associated to different context. Each specific version will be actived only for specific weather and temperature.
Thank you in advance for any kind of help/informations.
You will need to build you custom model that combines visual features with tabular data. This could look something like:
vis_feats = nn.Linear(512, 1) # visual features
tab_feats = nn.Linear(4, 1) # tab features
x = torch.cat((x, tab), dim=1) # x goes into your prediction layer
I'm using AWS SageMaker, and i want to create something that, with a given text, it recognize the place of that description. Is it possible?
If there are no other classes besides the text that you would like your model to identify, you may not need a multiclass classifier.
You could train your own text detection model using Amazon SageMaker, and train using a dataset with labelled examples using the Object Detection Algorithm, but this becomes rather involved for a problem that has existing solutions available.
If the appearance of the text you're trying to detect is identical each time, your problem space gets reduced from trying to interpret variable text, to simply having to gather enough examples and perform object detection for the "pattern" your text forms visually. Note that if the text were to appear in different fonts or styles, that the generic object detection method would not interpret it dynamically, and an OCR-based solution would likely be necessary.
More broadly, for text identification in images on AWS, you have quite a few options:
Amazon Rekognition has a DetectText method that will enable you to easily find text within an image. If it's a small or simple phrase, with alphanumeric characters, this should work very well for your use case.
Amazon Textract will help you perform OCR (optical character recognition) while retaining the structure of the source. This is great for documents and tables, but doesn't sound like it may be applicable to your use case.
The AWS marketplace will also have hosted options available from third party vendors. One example of this for text region identification is this one from RocketML.
There are also some great open source tools I'd recommend looking into: OpenCV for ascertaining the text bounding boxes, and Tesseract for OCR and text extraction. This blog post does a good job walking through the process of using them together.
Any of these will help to solve your problem of performing OCR/text identification on AWS, but the best choice comes down to what your current and future needs are, and how quickly you're looking to implement the feature.
Your question is not clear regarding the data that you have or the problem that you want to solve.
If you have a text that includes a place name in it (for example, "I visited Seattle and enjoyed the fish market"), you can use Amazon Comprehend Name Entity Extraction (NEE) including places ("Seattle" in the above example)
{
"Entities": [
{
"Score": 0.9857407212257385,
"Type": "LOCATION",
"Text": "Seattle",
"BeginOffset": 10,
"EndOffset": 17
}
]
}
If the description is more general and you want to classify if the description is of a hotel, a restaurant, a theme park, a concert/show, or similar types of places, you can either use the Custom classification in Comprehend or the Neural Topic Model in SageMaker (https://docs.aws.amazon.com/sagemaker/latest/dg/ntm.html). You will need some examples of the classes and documents/sentences that are used for the model training.
In the famous Google Inceptionism article,
http://googleresearch.blogspot.jp/2015/06/inceptionism-going-deeper-into-neural.html
they show images obtained for each class, such as banana or ant. I want to do the same for other datasets.
The article does describe how it was obtained, but I feel that the explanation is insufficient.
There's a related code
https://github.com/google/deepdream/blob/master/dream.ipynb
but what it does is to produce a random dreamy image, rather than specifying a class and learn what it looks like in the network, as shown in the article above.
Could anyone give a more concrete overview, or code/tutorial on how to generate images for specific class? (preferably assuming caffe framework)
I think this code is a good starting point to reproduce the images Google team published. The procedure looks clear:
Start with a pure noise image and a class (say "cat")
Perform a forward pass and backpropagate the error wrt the imposed class label
Update the initial image with the gradient computed at the data layer
There are some tricks involved, that can be found in the original paper.
It seems that the main difference is that Google folks tried to get a more "realistic" image:
By itself, that doesn’t work very well, but it does if we impose a prior constraint that the image should have similar statistics to natural images, such as neighboring pixels needing to be correlated.
I have built a classification model using weka.I have two classes namely {spam,non-spam} After applying stringtowordvector filter, I get 10000 attributes for 19000 records. Then I am using liblinear library to build model which gives me F-score as follows:
Spam-94%
non-spam-98%
When I use same model to predict new instances, it predict all of them as spam.
Also, when I try to use test set same as training set, It predict all of them as spam too. I am mentally exhausted to find the problem.Any help will be appreciated.
I get it also wrong every so often. Then I watch this video to remind myself how it's done: https://www.youtube.com/watch?v=Tggs3Bd3ojQ where Prof Witten, one of the Weka Developers/Architects shows how to use the FilteredClassifier (which in turn is configured to load the StringToWordVector Filter) on the training-dataset and the test-set correctly.
This is shown for weka 3.6, weka 3.7. might be slightly different.
What does ZeroR give you? If it's close to 100%, you know that any classification algorithm should be not too far off either.
Why do you optimize for F-Measure? Just asking. I have never used this and don't know much about it. (I would optimize for the "Precision" metric assuming you have much more Spam than Nonspam).
I'm using Weka 3.6.8 to carry out some machine learning and I'm want to find the 'time taken to test model on training/testing data'. When I test a predictive model on evaluation data, this parameter seems to be missing. Has this feature been removed from Weka or is it just a setting I'm missing? All I seem to be able to find is the time taken to build the actual predictive model. (I've also checked the Weka Manual but can't find anything)
Thanks in advance
That feature was added to 3.7.7, you need to upgrade. You should be able to get this data by running the test on the command line with the -T parameter.