XGBoost native API's plot_importance displays an unknown feature, unnamed: 0, on the top of the chart.
Here is the output image.
Feature Importance Ranking
I checked all the columns in the original dataframe input into DMatrix and confirmed that there is no unknown feature left in it. I also removed the key ID as well.
So, I confirmed that the original dataset did not include any unspecified feature in its columns.
My code of plot_importance is here.
`
plot_importance(pw_model_1, max_num_features=10)
pyplot.savefig('plot.png')
pyplot.show()
`
Here pw_model_1 is the selected model after hyperparameter tuning.
I would appreciate it if anyone can advise me how to resolve this issue.
Thank you
Best regards
Michio
Related
I´m trying to prepare my dataset ideally for binary document classification with an SVM algorithm in R.
The dataset is a combination of 150171 labelled variables and 2099 observations stored in a dataframe. The variables are a combination uni- and bigrams which were retrieved from a text dataset.
When I´m trying to calculate the Information gain as a feature selection method, the Error "cannot allocate vector of size X Gb" occurs although I already extended my memory and I´m running on a 64-bit operating system. I tried the following package:
install.packages("FSelector")
library(FSelector)
value <- information.gain(Usefulness ~., dat_SentimentAnalysis)
Does anybody know a solution/any trick for this problem?
Thank you very much in advance!
Hi I am following the Huggingface course for Question Answering.
I built my own Dataset and all the features are present and I get the exact same results up until fitting the model.
There I get the above error.
After some research it seems this is caused by not having the columns in the correct order.
The tokenizer does output it in a different order and I changed it, but neither the order in the course nor the order of the tokenizer seem to work.
Can someone think of another issue?
I don't have the Data Collator as it's deprecated now.
Token Type Ids are commented out because the tokenizer does not return them.
I'm using "distilbert-base-cased-distilled-squad" because I just want to try and that seems like the fastest (smallest) model.
tf_train_dataset = train_dataset.to_tf_dataset(
columns=[
"attention_mask",
"end_positions",
"input_ids",
"start_positions",
#"token_type_ids",
],
shuffle=True,
batch_size=4,
)
Thank you very much!
edit: I get the same error with the model from the tutorial.
TensorFlow provides a tf.summary.tensor_summary() function that appears to be a multidimensional variant of tf.summary.scalar():
tf.summary.tensor_summary(name, tensor, summary_description=None, collections=None)
I thought it could be useful for summarizing inferred probabilities per class ... somewhat like
op_summary = tf.summary.tensor_summary('classes', some_tensor)
# ...
summary = sess.run(op_summary)
writer.add_summary(summary)
However it appears that TensorBoard doesn't provide a way to display these summaries at all. How are they meant to be used?
I cannot get it to work either. It seems like that feature is still under development. See this video from the TensorFlow Dev Summit that states that the tensor_summary is still under development (starting at 9:17): https://youtu.be/eBbEDRsCmv4?t=9m17s. It will probably be better defined and examples should be provided in the future.
When I try to use mxnet to build a feedforward model it appeared the following error:
Error in mx.io.internal.arrayiter(as.array(data), as.array(label), unif.rnds, :
basic_string::_M_replace_aux
I follow the R regression example on mxnet website but I change the data into my own data which contains 109 examples and 1876 variables. The first several steps can run without error until ran the model building step. I just can't understand the error information mean. I wonder that it is because of my dataset or the way I deal with the data.
Can you provide the code snippet you are using? That gives more details on the issue. Also, any stacktrace will be useful.
You get this error message mainly due to invalid column/row access and shape (dimension) mismatch. Can you verify if you are using correct "index" values in creating matrix. Let me know if this fixes the issue.
However, MXNet can be better at printing details about error in the stacktrace. I have created a issue to follow up on this - https://github.com/dmlc/mxnet/issues/4206
I've read several articles about intra prediction in HEVC and I still have some questions.
For a PU of NxN pixels, we use 4xN + 1 reference samples (the row above the PU, the column at the left of the PU and the sample at the top left). Then, based on the MPM, a mode is selected to work with.
I now have a row of reference samples, a column of reference samples and a mode. Based on this, how are the samples inside the PU calculated ?
In this article http://codepaint.kaist.ac.kr/wp-content/uploads/2013/10/Intra-Coding-of-the-HEVC-Standard.pdf there are ready-to-use formulae which take coordinate and selected mode as parameters. Is it really that simple ?
Now, imagine we have a picture of a checkerboard. How intra prediction can be used ? In some cases, we might not want to use reference samples of previously decoded PU. How to deal with that ?
Thanks
I now have a row of reference samples, a column of reference samples
and a mode. Based on this, how are the samples inside the PU
calculated ?
As it is stated in this article first encoder should decide about the mode and the sizes of PUs and TUs during the RDO process. Among the list of
modes lets say mode number 25 is chosen to predict the current block. Mode number 25 is one of angular modes so we will use the mentioned formula for
angular modes and obtain the output. It worth mentioning that although formula is simple details of reference samples make it a little tricky.
Now, imagine we have a picture of a checkerboard. How intra prediction
can be used ?
First the prediction modes should be found. Lets say we decided on mode X then we should refer to the related formula to mode X and form our prediction block simililar to what discussed in previous question.
In some cases, we might not want to use reference
samples of previously decoded PU. How to deal with that ?
Intra prediction basically is formed based on these reference samples and if you are not using these pixels your not doing INTRA prediction. Maybe you should shift to INTER prediction where it uses other blocks in successive frames and MVs to predict the current block.
The question is interest for me.
I can easy to say that the mode is selected by encode.
In the HEVC encoder, it run all the mode(35, in the view of complexity,encoder uses fast algorithm to simplify the selection process, you can find some paper to read), finally encoder selects the best mode(RDO process). so,decoder can not select reference sample. decoder have to select the samples which are same with encoder.
In the SCC(screen content coding) coding which is a extension of the HEVC, using IBC(intra block copy) mode to select the reference sample in reconstructed area.