My senario is: I have different numbers points(x,y,z) for every subject.
for example, for subject1, I have an evaluated value 100, and this subject has 5 points:(1,2,3),(4,2,3),(1,5,4),(3,2,3),(8,2,1).
for subject2, I have an evaluated value 40, and this subject has 2 points:(4,7,3),(2,2,0).
And I have prepared 100 subjects for machine learning.
I have searched for machine learning data model. Generally, the imported data just has one point for each subject, for example x1,x2,x3 =>y. But it doesn't fit my situation.
Does anyone have any ideas?
Thanks.
Related
I have a ML.net project and as of right now everything has gone great. I have a motor that collects a power reading 256 times around each rotation and I push that into a model. Right now it determines the state of the motor nearly perfectly. The motor itself only has room for 38 values on it at a time so I have been spending several rotations to collect the full 256 samples for my training data.
I would like to cut the sample size down to 38 so every rotation I can determine its state. If I just evenly space the samples down to 38 my model degrades by a lot. I know I am not feeding the model the features it thinks are most important but just making a guess and randomly selecting data for the model.
Is there a way I can see the importance of each value in the array during the training process? I was thinking I could use IDataView for this and I found the below statement about it (link).
Standard ML schema: The IDataView system does not define, nor prescribe, standard ML schema representation. For example, it does not dictate representation of nor distinction between different semantic interpretations of columns, such as label, feature, score, weight, etc. However, the column metadata support, together with conventions, may be used to represent such interpretations.
Does this mean I can print out such things as weight for each column and how would I do that?
I have actually only been working with ML.net for a couple weeks now so I apologize if the question is naive, I assure you I have googled this as many ways as I can think to. Any advice would be appreciated. Thanks in advance.
EDIT:
Thank you for the answer I was going down a completely useless path. I have been trying to get it to work following the example you linked to. I have 260 columns with numbers and one column with the conditions as one of five text strings. This is the condition I am trying to predict.
The first time I tried it threw an error "expecting single but got string". No problem I used .Append(mlContext.Transforms.Conversion.MapValueToKey("Label", "Label")) to convert to key values and it threw the error expected Single, got Key UInt32. any ideas on how to push that into this function?
At any rate thank you for the reply but I guess my upvotes don't count yet sorry. hopefully I can upvote it later or someone else here can upvote it. Below is the code example.
//Create MLContext
MLContext mlContext = new MLContext();
//Load Data
IDataView data = mlContext.Data.LoadFromTextFile<ModelInput>(TRAIN_DATA_FILEPATH, separatorChar: ',', hasHeader: true);
// 1. Get the column name of input features.
string[] featureColumnNames =
data.Schema
.Select(column => column.Name)
.Where(columnName => columnName != "Label").ToArray();
// 2. Define estimator with data pre-processing steps
IEstimator<ITransformer> dataPrepEstimator =
mlContext.Transforms.Concatenate("Features", featureColumnNames)
.Append(mlContext.Transforms.NormalizeMinMax("Features"))
.Append(mlContext.Transforms.Conversion.MapValueToKey("Label", "Label"));
// 3. Create transformer using the data pre-processing estimator
ITransformer dataPrepTransformer = dataPrepEstimator.Fit(data);//error here
// 4. Pre-process the training data
IDataView preprocessedTrainData = dataPrepTransformer.Transform(data);
// 5. Define Stochastic Dual Coordinate Ascent machine learning estimator
var sdcaEstimator = mlContext.Regression.Trainers.Sdca();
// 6. Train machine learning model
var sdcaModel = sdcaEstimator.Fit(preprocessedTrainData);
ImmutableArray<RegressionMetricsStatistics> permutationFeatureImportance =
mlContext
.Regression
.PermutationFeatureImportance(sdcaModel, preprocessedTrainData, permutationCount: 3);
// Order features by importance
var featureImportanceMetrics =
permutationFeatureImportance
.Select((metric, index) => new { index, metric.RSquared })
.OrderByDescending(myFeatures => Math.Abs(myFeatures.RSquared.Mean));
Console.WriteLine("Feature\tPFI");
foreach (var feature in featureImportanceMetrics)
{
Console.WriteLine($"{featureColumnNames[feature.index],-20}|\t{feature.RSquared.Mean:F6}");
}
I believe what you are looking for is called Permutation Feature Importance. This will tell you which features are most important by changing each feature in isolation, and then measuring how much that change affected the model's performance metrics. You can use this to see which features are the most important to the model.
Interpret model predictions using Permutation Feature Importance is the doc that describes how to use this API in ML.NET.
You can also use an open-source set of packages, they are much more sophisticated than what is found in ML.NET. I have an example on my GitHub how-to use R with advanced explainer packages to explain ML.NET models. You can get local instance as well as global model breakdown/details/diagnostics/feature interactions etc.
https://github.com/bartczernicki/BaseballHOFPredictionWithMlrAndDALEX
I've been reading some papers and blog posts on Transfer Learning. What I realized is that some say "different" and others say "different but similar". Now, I'm confused.
Say that,
D is domain. T is task.
a is source, hence source domain is Da and source task is Ta.
a' is different but similar to a.
b is different from a.
Ms is the source model learned from Da and Ta.
Mt is the target model.
In which target domain-task combination, Ms can transfer its knowledge to Mt?
1. (Da, Tb)
2. (Db, Ta)
3. (Da', Tb)
4. (Db, Ta')
5. (Da', Ta)
6. (Da, Ta')
7. (Da', Ta')
8. (Db, Tb)
Honestly, I know 5, 6, 7 are possible since the paper said so. And I doubt 8 will work (won't it?).
But what about 1, 2, 3, 4 where either domain or task is b?
It depends how different the source and target domains are. If the source and target domains have no similarities then you can’t improve your model for a task in the target domain by pre-training on the task domain. However if there are similarities e.g. any image domain to almost any other image domain, and your source domain dataset is large, transferring your model from the source domain to the target domain is likely to help regularise your model and improve generalisation in the target domain. Especially if the target domain dataset is small.
In deep learning you want to reinitialise (retrain from random weights) more layers (from the top down) and do more fine tuning depending on how different your source and target domains and source and target tasks are.
What's a "domain"?
In the field of natural language processing (NLP) there's a lot of research for domain adaptation, and you can get some benefit in all the cases (#1-#8) you describe regarding for what NLP would call "different domains" - e.g. newswire text vs tweet text vs clinical radiology report text; yes, even in #8.
However, you can have more different "domains" (which wouldn't be really called domains IMHO) - for example, english text vs chinese text; or english text vs english audio recordings. It is possible do do some transfer learning even in those cases, but much more limited; so it really depends on where you draw the line between "that's a different domain" vs "that's a completely different type of input data".
Background
I'm writing a Swift application that requires the classification of user events by categories. These categories can be things like:
Athletics
Cinema
Food
Work
However, I have a set list of these categories, and do not wish to make any more than the minimal amount I believe is needed to be able to classify any type of event.
Question
Is there a machine learning (nlp) procedure that does the following?
Takes a block of text (in my case, a description of an event).
Creates a "percentage match" to each possible classification.
For instance, suppose the description of an event is as follows:
Fun, energetic bike ride for people of all ages.
The algorithm in which this description would be passed in would return an object that looks something like this:
{
athletics: 0.8,
cinema: 0.1,
food: 0.06,
work: 0.04
}
where the values of each key in the object is a confidence.
If anyone can guide me in the right direction (or even send some general resources or solutions specific to iOS dev), I'd be super appreciative!
You are talking about typical classification model. I believe iOS offers you APIs to do this inside your app. Here Look for natural language processing bit - NLP
Also you are probably being downvoted because this forum typically looks to solve specific programming queries and not generic ones (this is an assumption and there could be another reason for downvotes.)
{
"blogid": 11,
"blog_authorid": 2,
"blog_content": "(this is blog complete content: html encoded on base64 such as) PHNlY3Rpb24+PGRpdiBjbGFzcz0icm93Ij4KICAgICAgICA8ZGl2IGNsYXNzPSJjb2wtc20tMTIiIGRhdGEtdHlwZT0iY29udGFpbmVyLWNvbnRlbn",
"blog_timestamp": "2018-03-17 00:00:00",
"blog_title": "Amazon India Fashion Week: Autumn-",
"blog_subtitle": "",
"blog_featured_img_link": "link to image",
"blog_intropara": "Introductory para to article",
"blog_status": 1,
"blog_lastupdated": "\"Mar 19, 2018 7:42:23 AM\"",
"blog_type": "Blog",
"blog_tags": "1,4,6",
"blog_uri": "Amazon-India-Fashion-Week-Autumn",
"blog_categories": "1",
"blog_readtime": "5",
"ViewsCount": 0
}
Above is one sample blog as per my API. I have a JsonArray of such blogs.
I am trying to predict 3 similar blogs based on a blog's props(eg: tags,categories,author,keywords in title/subtitle) and contents. I have no user data i.e, there is no logged in user data(such as rating or review). I know that without user's data it will not be accurate but I'm just getting started with data science or ML. Any suggestion/link is appreciated. I prefer using java but python,php or any other lang also works for me. I need an easy to implement model as I am a beginner. Thanks in advance.
My intuition is that this question might not be at the right address.
BUT
I would do the following:
Create a dataset of sites that would be an inventory from which to predict. For each site you will need to list one or more features: Amount of tags, amount of posts, average time between posts in days, etc.
Sounds like this is for training and you are not worried about accuracy
too much, numeric features should suffice.
Work back from a k-NN algorithm. Don't worry about the classifiers. Instead of classifying a blog, you list the 3 closest neighbors (k = 3). A good implementation of the algorithm is here. Have fun simplifying it for your purposes.
Your algorithm should be a step or two shorter than k-NN which is considered to be among simpler ML, a good place to start.
Good luck.
EDIT:
You want to build a recommender engine using text, tags, numeric and maybe time series data. This is a broad request. Just like you, when faced with this request, I’d need to dive in the data and research best approach. Some approaches require different sets of data. E.g. Collaborative vs Content-based filtering.
Few things may’ve been missed on the user side that can be used like a sort of rating: You do not need a login feature get information: Cookie ID or IP based DMA, GEO and viewing duration should be available to the Web Server.
On the Blog side: you need to process the texts to identify related terms. Other blog features I gave examples above.
I am aware that this is a lot of hand-waving, but there’s no actual code question here. To reiterate my intuition is that this question might not be at the right address.
I really want to help but this is the best I can do.
EDIT 2:
If I understand your new comments correctly, each blog has the following for each other blog:
A Jaccard similarity coefficient.
A set of TF-IDF generated words with
scores.
A Euclidean distance based on numeric data.
I would create a heuristic from these and allow the process to adjust the importance of each statistic.
The challenge would be to quantify the words-scores TF-IDF output. You can treat those (over a certain score) as tags and run another similarity analysis, or count overlap.
You already started on this path, and this answer assumes you are to continue. IMO best path is to see which dedicated recommender engines can help you without constructing statistics piecemeal (numeric w/ Euclidean, tags w/ Jaccard, Text w/ TF-IDF).
if we choose 20 topics in LDA and then if we choose 30 topics. So my question is will both these results intersect those 20 topics and produce similar results
Short answer - no. The way LDA works is it uses Gibbs sampler to get Dirichlet distribution over document vectors. Allocations are then made on this sample and hence will always be different both because of sampling randomness and allocation uncertainties unless you define explicit random seed and run same number of topics k. Take a look at original paper Blei et al. 2003 to see how k is defined.
UPDATE (with regard to comment): Hierarchical LDA (hLDA) is trying to solve the problem of retaining topics and subtopics by constructing levels of topics following the Chinese restaurant model. But it's still not perfect.
The way flat LDA works, however, is it looks at documents rather than topics to produce further results. Say, you get topic 0 (first table in restaurant) and all documents try to sit there, but it's not really enough space and you create another topic 1 where some docs feel more comfortable, etc., etc. now you are right from the point of view of how these tables are created. But there is one big thing that's critical - topic 0 CHANGES when you create a new table/Topic 1 because some documents have left the first table and took the words (or probabilities of cooccurence thereof) with them to the new table and all words in topic 0 got reshuffled given new situation. Same happens when you create more tables/topics that all the previous are also re-estimated. Hence, you will never get same 20 topics when rerunning with 30.