I have a projection in PySpark and I want to copy the data from that projection to an another.
How can I copy data from one projection to another projection in PySpark?
How do I do that?
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I have a snapshot of a 3D field whose domain is a cube. I need to visualize the vorticity associated with this field. I am following the approach described by this video in which the vorticity gets calculated by ParaView.
I followed the procedure but, inside the filter Compute derivatives / Coloring, I cannot find the vorticity but only the components of the starting field as you can see from the following picture:
I read that another method is to use the filter for unstructured data but I don't have such a filter.
How should I properly visualize the vorticity?
I am using ParaView 5.10.
In your screenshot, the value for Vectors property shows a (?), meaning that it is not a valid input. You should select an existing vector arrays here.
I am trying to replicate some data preprocessing that I have done in pandas into tensorflow transform.
I have a few CSV files, which I joined and aggregated with pandas to produce a training dataset. Now as part of productionising the model I would like this preprocessing to be done at scale with apache beam and tensorflow transform. However it is not quite clear to me how I can reproduce the same data manipulation there. Let's look at two main operations: JOIN dataset a and dataset b to produce c and group by col1 on dataset c. This would be a quite straightforward operation in pandas, but how would I do this in tensorflow transform running on apache beam? Am I using the wrong tool for the job? What would be the right tool then?
You can use the Beam Dataframes API to do the join and other preprocessing exactly as you would have in Pandas. You can then use to_pcollection to get a PCollection that you can pass directly to your Tensorflow Transform operations, or save it as a file to read in later.
For top-level functions (such as merge) one needs to do
from apache_beam.dataframe.pandas_top_level_functions import pd_wrapper as beam_pd
and use operations beam_pd.func(...) in place of pd.func(...).
enter image description here
This is the api doc present in xgboost.com , it seems that I can just set only one column as the "featureCol" .
As with any ML Estimator on Spark, this one expects inputCol to be a Vector of assembled features. Before you apply the Estimator, you should use tools from org.apache.spark.ml.feature to extract, transform and assemble feature vector.
You can check How to vectorize DataFrame columns for ML algorithms? for example Pipeline.
I am new to Pyspark. I have a dataset that contains categorical features and I want to use regression models from pyspark to predict continuous values. I am stuck in pre-processing of data that is required for using MLlib models.
Yes, it is necessary. You have to not only convert to numerical but also encode to make them useful for linear models. Both steps are implemented in pyspark.ml (not mllib) with:
pyspark.ml.feature.StringIndexer - indexing.
pyspark.ml.feature.OneHotEncoder - encoding.
What I want to achieve is simply find out which input points are included in a given cluster!?
I have a personal dataset which contains some documents that are grouped in 12 clusters manually.
I know how to interpret kmenas result in mahout .7 with using namedVector class and one of dumpers (like clusterdumper). after clustering using kmeans driver, a directory named clusteredPoints has created which contains clustering result and using clusterDumper, you can see the created clusters and the points that are in each one. in below link there is a good solution for this :
How to read Mahout clustering output
But, as I mentioned in title I want to have this capability to interpret Streaming Kmeans result which is a new feature in mahout .8.
In this feature, it uses a Centroid class for holding data points and each cluster seeds. The generated result of StreamingKMeans algorithm is only a sequence file which is constructed of centroid vectors + keys and weights of each cluster. And in this output there is no information of input data points to know the distribution of them between clusters. However, it is not possible to me to get a sense of accuracy of clustering.
by the way, How to get this information in clustering output ? It is not implemented or just I failed to find and use prepared soulution? How can I analysis the result of streamingKMeans?
thanks.