I'm trying to create a model with a training dataset and want to label the records in a test data set.
All tutorials or help I find online has information on only using cross validation with one data set, i.e., training dataset. I couldn't find how to use test data. I tried to apply the result model on to the test set. But the test set seems to give different no. of attributes than training set after pre-processing. This is a text classification problem.
At the end I get some output like this
18.03.2013 01:47:00 Results of ResultWriter 'Write as Text (2)' [1]:
18.03.2013 01:47:00 SimpleExampleSet:
5275 examples,
366 regular attributes,
special attributes = {
confidence_1 = #367: confidence(1) (real/single_value)
confidence_5 = #368: confidence(5) (real/single_value)
confidence_2 = #369: confidence(2) (real/single_value)
confidence_4 = #370: confidence(4) (real/single_value)
prediction = #366: prediction(label) (nominal/single_value)/values=[1, 5, 2, 4]
}
But what I wanted is all my examples to be labelled.
It seems that my test data and training data have different no. of attributes, I see many of following in the logs.
Mar 18, 2013 1:46:41 AM WARNING: Kernel Model: The given example set does not contain a regular attribute with name 'wireless'. This might cause problems for some models depending on this particular attribute.
But how do we solve such problem in text classification as we cannot know no. of and name of attributes before hand.
Can some one please throw some pointers.
You probably use a Process Documents operator to preprocess both training and test set. Here it is important that both these operators are setup identically. To "synchronize" the wordlist, i.e. consider the same set of words in both of them, you have to connect the wordlist (wor) output of the Process Documents operator used for training to the corresponding input port of the Process Documents operator used for preprocessing the test set.
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 have some tabular device data comprising a
time column, some tabular features, target classes
There are around 500 rows (not same) in all devices data and target classes are same.
I have same data for around 1000 devices,
I want to train a general model for all the devices for detecting the class.
Can someone help me with the approach to train for the target variable. What kind of models work in this condition
If your device type is part of the data, you can train a decision tree. If the device type feature is important for classification sake, it will be added to the tree. First, create the device type features yourself - a binary column for each device type, like done in one-hot encoding. There will be a binary column per device type - is_device_samsung, is_device_lg, is_device_iphone and so forth. The number of columns created is equal to the number of device types. All but one of these columns will be 0, and the one indicating the current type will be 1. This will not guarantee the device type will be a part of the model - but let the AI decide this for you.
BTW - don't use get_dummies unless you know how to reuse it exactly as needed in the test data.
Another option is to use the python-weka wrapper, which accepts nominal attributes:
Example:
import weka.core.jvm as jvm
from weka.core.converters import Loader
from weka.classifiers import Classifier
def get_weka_prob(inst):
dist = c.distribution_for_instance(inst)
p = dist[next((i for i, x in enumerate(inst.class_attribute.values) if x == 'DONE'), -1)]
return p
jvm.start()
loader = Loader(classname="weka.core.converters.CSVLoader")
data = loader.load_file(r'.\recs_csv\df.csv')
data.class_is_last()
datatst = loader.load_file(r'.\recs_csv\dftst.csv')
datatst.class_is_last()
c = Classifier("weka.classifiers.trees.J48", options=["-C", "0.1"])
c.build_classifier(data)
print(c)
probstst = [get_weka_prob(inst) for inst in datatst]
jvm.stop()
Weka models are different models that use a java bridge to python - the methods are java methods that can be called using this bridge. To use the dataframe in sklearn - you would have to manipulate it with one-hot encoding. Note that the nominal attributes in weka cannot have any special character in them. so use
df = df.replace([',', '"', "'", "%", ";"], '', regex=True)
for any nominal attribute before saving it to csv.
If you want to ensure that the model_type feature will be included in your model, you can trick it and add a dummy model type - and ensure that the class column for this dummy model is always "1" or "True" - depending on your class variable. If you have enough rows with this dummy model - j48 will open it as the first branch. Once the attribute is selected by j48 - it will be branched for all of the model types, not just the dummy one.
I am reading Hands on Machine Learning book and author talks about random seed during train and test split, and at one point of time, the author says over the period Machine will see your whole dataset.
Author is using following function for dividing Tran and Test split,
def split_train_test(data, test_ratio):
shuffled_indices = np.random.permutation(len(data))
test_set_size = int(len(data) * test_ratio)
test_indices = shuffled_indices[:test_set_size]
train_indices = shuffled_indices[test_set_size:]
return data.iloc[train_indices], data.iloc[test_indices]
Usage of the function like this:
>>>train_set, test_set = split_train_test(housing, 0.2)
>>> len(train_set)
16512
>>> len(test_set)
4128
Well, this works, but it is not perfect: if you run the program again, it will generate a different test set! Over time, you (or your Machine Learning algorithms) will get to see the whole dataset, which is what you want to avoid.
Sachin Rastogi: Why and how will this impact my model performance? I understand that my model accuracy will vary on each run as Train set will always be different. How my model will see the whole dataset over a time ?
The author is also providing a few solutions,
One solution is to save the test set on the first run and then load it in subsequent runs. Another option is to set the random number generator’s seed (e.g., np.random.seed(42)) before calling np.random.permutation(), so that it always generates the same shuffled indices.
But both these solutions will break next time you fetch an updated dataset. A common solution is to use each instance’s identifier to decide whether or not it should go in the test set (assuming instances have a unique and immutable identifier).
Sachin Rastogi: Will it be a good train/test division? I think No, Train and Test should contain elements from across dataset to avoid any bias from the Train set.
The author is giving an example,
You could compute a hash of each instance’s identifier and put that instance in the test set if the hash is lower or equal to 20% of the maximum hash value. This ensures that the test set will remain consistent across multiple runs, even if you refresh the dataset.
The new test set will contain 20% of the new instances, but it will not contain any instance that was previously in the training set.
Sachin Rastogi: I am not able to understand this solution. Could you please help?
For me, these are the answers:
The point here is that you should better put aside part of your data (which will constitute your test set) before training the model. Indeed, what you want to achieve is to be able to generalize well on unseen examples. By running the code that you have shown, you'll get different test sets through time; in other words, you'll always train your model on different subsets of your data (and possibly on data that you've previously marked as test data). This in turn will affect training and - going to the limit - there will be nothing to generalize to.
This will be indeed a solution satisfying the previous requirement (of having a stable test set) provided that new data are not added.
As said in the comments to your question, by hashing each instance's identifier you can be sure that old instances always get assigned to the same subsets.
Instances that were put in the training set before the update of the dataset will remain there (as their hash value won't change - and so their left-most bit - and it will remain higher than 0.2*max_hash_value);
Instances that were put in the test set before the update of the dataset will remain there (as their hash value won't change and it will remain lower than 0.2*max_hash_value).
The updated test set will contain 20% of the new instances and all of the instances associated to the old test set, letting it remain stable.
I would also suggest to see here for an explanation from the author: https://github.com/ageron/handson-ml/issues/71.
I have built an Microsoft Azure ML Studio workspace predictive web service, and have a scernario where I need to be able to run the service with different training datasets.
I know I can setup multiple web services via Azure ML, each with a different training set attached, but I am trying to find a way to do it all within the same workspace and passing a Web Input Parameter as the input value to choose which training set to use.
I have found this article, which describes almost my scenario. However, this article relies on the training dataset that is being pulled from the Load Trained Data module, as having a static endpoint (or blob storage location). I don't see any way to dynamically (or conditionally) change this location based on a Web Input Parameter.
Basically, does Azure ML support a "conditional training data" loading?
Or, might there be a way to combine training datasets, then filter based on the passed Web Input Parameter?
This probably isn't exactly what you need, but hopefully, it helps you out.
To combine data sets, you can use the Join Data module.
To filter, that may be accomplished by executing a Python script. Here's an example.
Using the Adult Census Income Binary Classification dataset, on the age column, there's a minimum age of 17.
If I wanted to filter the data set by age, connect it to an Execute Python Script module and here's the filtering code with the pandas query method.
# The script MUST contain a function named azureml_main
# which is the entry point for this module.
import pandas as pd
def azureml_main(dataframe1 = None, dataframe2 = None):
# Return value must be of a sequence of pandas.DataFrame
return dataframe1.query("age >= 25")
And looking at that output it filters out the data set where the minimum age is now 25.
Sure, you can do that. What you would want is to use an Execute R Script or SQL Transformation module to determine, based on your input data, what model to use. Something like this:
Notice, your input data is cleaned/updated/feature engineered, then it's passed to two different SQL transforms which will tell it to go to one of two paths.
Each path has it's own training data.
Note: I am not exactly sure what your use case is, but if it were me, I would instead train two different models using the two different training data, then try to just use the models in my web service, not actually train on the web service as that would likely be quite slow.
I'm trying to test my model with new dataset. I have done the same preprocessing step as i have done for building my model. I have compared two files but there is no issues. I have all the attributes(train vs test dataset) in same order, same attribute names and data types. But still i'm not able to resolve the issue. Both of the files train and test seems to be similar but the weka explorer is giving me error saying Train and test set are not compatible. How to resolve this error? Is there any way to make test.arff file format as train.arff? Please somebody help me.
The same with the comment that I left after problem statement:
All the three attributes are nominal attributes followed by all the possible values quoted by '{}'. One of my guess is that the possible values are not the same. For example, for RESOURCE attribute there is no 199 in test file, while it is in training-file.
After struggling with the same problem for a day. I figured out two ways to make the trained model working on supplied test set.
Method 1.
Use knowledge flow. For example something like below:
CSVLoader(for train set) -> classAssigner -> TrainingSetMaker -->(classifier of your choice) -> ClassfierPerformanceEvaluator - TextViewer.
CSVLoader(for test set) -> classAssigner -> TestgSetMaker -->(the same classifier instance above) -> PredictionAppender -> CSVSaver.
Then load the data from the CSVLoader or arffLoder for the training set. The model will be trained.
After that load data from the loader for the test set. It will evaluate the model(classifier, for example) on the supplied test set and you can see the result from the textviewer (connected to the ClassifierPerformanceEvaluator) and get the saved result from the CSVSaver or arffSaver connected to the PredictionAppender.An additional column, the "classfied as" will be added to the output file.
In my case, I used "?" for the class column in the supplied test set if the class labels are not available.
Method 2.
Combine the Training and Test set into one file. Then the exact same filter can be applied to both training and test set. Then you can separate training set and test set by applying instance filter. Since I use "?" as class label in the test set. It is not visible in the instance filter indices. Hence just select those indices that you can see in the attribute values to be removed when apply the instance filter. You will get the test data left only. Save it and load it in supply test set at the classifier page.This time it will work. I guess it is the class attribute that causes the NOT compatible train and test set issue. As many classfier requires nominal class attribute. The value of which is converted to the index to available values of the class attribute according to
http://weka.wikispaces.com/Why+do+I+get+the+error+message+%27training+and+test+set+are+not+compatible%27%3F
See following answer, your train.arff and test.arff should have same header. According to your comparison they are similar but not same.
I just encountered the same problem and I found a bare-bones solution. The format of my file is .csv and I simply open my files(for training and testing，respectively) and use the save button on the Preprocess panel of WEKA to save them in .arff format.
Then the problem is solved.
Look there is a difference between similar and same, your train.arrf and test.arrf should have the same header and if not then you should copy the header of train.arrf and paste it in your test.arrf as a new header.
trainPath = ""
otherPadelPath = ""
testPath = ""
trainFile = open(trainPath,"r")
trainAttributes = trainFile.readlines()[0].split(",")
trainFile.close()
otherPadelFile = open(otherPadelPath,"r")
otherPadelLines = otherPadelFile.readlines()
otherPadelFile.close()
otherPadelColumns = []
testLines = []
for attribute in trainAttributes:
if attribute in otherPadelLines[0].split(","):
otherPadelColumns += [otherPadelLines[0].split(",").index(attribute)]
for line in otherPadelLines:
rearrangedLine = []
for inDex in otherPadelColumns:
rearrangedLine += [line.split(",")[inDex]]
testLines += [",".join(rearrangedLine)]
testFile = open(testPath,"w")
testFile.writelines(testLines)
testFile.close()
This script can rearrange your test dataset to contain the same order/number of attribute columns in your training set, provided that each attribute has the same type and title. Also, (in keeping with WEKA default), the class attribute should be in the last column for both datasets.