Hi @haifengl thanks again for this library! I'm wondering how I can go about reading lines from a dataframe, doing some computations and then making that into a new column of a dataframe. In python I could do something like:
new_col = lambda each_row: math.sqrt((3*each_row.first_column)**4 + (3*each_row.second_column)**4+ (3*each_row.third_column)**4)
my_dataframe.apply(new_col)
import smile.data.formula.Formula;
import static smile.data.formula.Terms.*;
in scala, we have a DSL to write formula close to math form.
In Java, you have to assemble the formula by methods provided in Terms
Thank you! But how would I get each of the rows in each column of the original dataframe to apply the formula on? Like if I want to take all rows of the first column and add 2 and then all rows in second column and add 2 and then add those together. Would I need to use like
df.stream().map(row -> { or something?
Hello @haifengl Are you considering something like this https://github.com/gpu/JOCLSamples/tree/master/src/main/java/org/jocl/samples for algorithms that can benefit from GPU? Or is there something else (jcuda?)
@lukasbraach yes
@implisci we will leverage gpu
Also thanks for the response @haifengl
Hi @haifengl
I have a situation:
1.) I've trained a random forest with a DataFrame object (target, feature 1, ... , feature 5).
2.) I would like to use RandomForest::predict with a new sample (production), that I get as double[5] {1,2,3,4,5}.
3.) I generate a Tuple t with schema (feature 1, ..., feature 5) and data [1,2,3,4,5]
4.) When I run predict(t) an array out of bounds exception occures. The problem is, that it tries to access feature 5 with index 5. This was correct in the schema of the dataset but not for the schema of the tuple t.
I would have expected, that in predict, it would access the data for Feature 5 from the tuple t on index 4.
Is there something I'm doing fundamentally wrong or might there be some inconsistencies? I couldn't help myself, since all cases I found in some sort of documentation predict on a tuple coming from the original DataFrame.
Thank you :-)
I have a situation:
1.) I've trained a random forest with a DataFrame object (target, feature 1, ... , feature 5).
2.) I would like to use RandomForest::predict with a new sample (production), that I get as double[5] {1,2,3,4,5}.
3.) I generate a Tuple t with schema (feature 1, ..., feature 5) and data [1,2,3,4,5]
4.) When I run predict(t) an array out of bounds exception occures. The problem is, that it tries to access feature 5 with index 5. This was correct in the schema of the dataset but not for the schema of the tuple t.
I would have expected, that in predict, it would access the data for Feature 5 from the tuple t on index 4.
Is there something I'm doing fundamentally wrong or might there be some inconsistencies? I couldn't help myself, since all cases I found in some sort of documentation predict on a tuple coming from the original DataFrame.
Thank you :-)
Two solutions seem possible in my opinion:
1.) Bind the new schema (feature 1, ... , feature 5).I've tried this, but since "response != null" in the RF's formula I get a NPE. So, is there any support for execution samples without targets?
2.) I artificially change my sample to include some dummy labels, s.t. the targets have a value aswell and the schema would correspond again to the training case.
1.) Bind the new schema (feature 1, ... , feature 5).I've tried this, but since "response != null" in the RF's formula I get a NPE. So, is there any support for execution samples without targets?
2.) I artificially change my sample to include some dummy labels, s.t. the targets have a value aswell and the schema would correspond again to the training case.
Hey @haifengl, my Random Forest model is working (mostly) as expected, thank you! I still have one question: On the first inference with the freshly trained model, smile logs
The response variable Classification doesn't exist in the schema [...]. Should I pay attention to this message? How do I get rid of it?
Hi, is there a possibility to compute OLS without additional statistics ? We are using this for a application where statistics have to be computed anyway separately so it would mean major performance improvement. I have read somewhere In comparison with apache, that OLS stats take significant part of computational time. Thanx for any hint. Martin
@MZazvor_gitlab
sterr = true
I am studying this framework now, are there functions to get ROC /AUC metrics?
Can all classification algorithm output score as well as predicted label?
For AUC (and many other metrics), checkout
smile.validation.metric package. The classification algorithms report posteriori probabilities if they are SoftClassifier. Some algorithms also have a score() method, which is not necessarily probabilities though.
@haifengl Thank you so much for your support. I will check the doc and the info. you suggested.
again, SMILE is awesome ML library! clean dataframe implementation, classification framework which enable to expand other algorithm easliy. so, I am thinking to implement missing algorithms like a factorization machine, field aware FM, ngboost, and so on
again, SMILE is awesome ML library! clean dataframe implementation, classification framework which enable to expand other algorithm easliy. so, I am thinking to implement missing algorithms like a factorization machine, field aware FM, ngboost, and so on
@rikima_twitter Look forward to your contributions of new algorithms. Thanks a lot in advance!
Hi, I have a question about the new(?) OLS.fit() method. If I try to use it to predict housing sale prices, like OLS.fit(Formula.lhs("SalePrice"), X_train_dataframe), it fails with "no response variable".
So it seems I can only use OLS if I pass it a dataframe that includes X and y together. However, if I use it like "OLS.fit(Formula.lhs("SalePrice"), training_dataframe), which includes both the dependent and independent columns (X and y), then predict requires an array of the same size, including a column for the value I wish to predict.
However, doing that and passing, say, 0 for the y value results in wildly incorrect predictions, and changing the value affects predictions. Is there not a way to use OLS without comingling X and y?
