The Sharepoint file tools are certainly a step in the right direction, but it would be great to enhance the files types that it is possible to write to sharepoint from Alteryx.
The format missing that I think is probably most in demand is pdf. If we're using the Alteryx reporting suite to create PDF reports, it would be awesome to have an easy way to output these to Sharepoint.
After used the new "Image Recognition Tool" a few days, I think you could improve it :
> by adding the dimensional constraints in front of each of the pre-trained models,
> by adding a true tool to divide the training data correctly (in order to have an equivalent number of images for each of the labels)
> at least, allow the tool to use black & white images (I wanted to test it on the MNIST, but the tool tells me that it necessarily needs RGB images) ?
Question : do you in the future allow the user to choose between CPU or GPU usage ?
In any case, thank you again for this new tool, it is certainly perfectible, but very simple to use, and I sincerely think that it will allow a greater number of people to understand the many use cases made possible thanks to image recognition.
Thank you again
Kévin VANCAPPEL (France ;-))
Thank you again.
Alteryx Designer is slow when using In-DB tools.
We use Alteryx 2019.1 on Hive/HortonWords with the Simba ODBC Driver configured with SSL enabled.
Here is a compare In-DB / in Memory :
We found that Alteryx open a new connection for each action :
- First link to joiner = 1 connection.
- Second ling to joiner = 1 connection.
- Click on the canevas = 1 connection.
Each connection take about 2,5 sec... It really slow down the Designer :
Please, keep alive the first connection instead of closing it and creating a new one for each action on the Designer.
I remember a while ago running into a peculiar error:
'The R.exe exit code (4294967295) indicted an error'. This was peculiar, as the data output was still seemingly correct, however, the error made me double-check the community for answers.
There are some very technical sources here:
but in short, this seems to be caused by a return code from C++ libraries, being understood by R as an error. Its a very inconsistent error, typically caused by low memory. This creates what most call a 'fake error' - the code runs perfectly fine, but seems to produce an error that doesn't actually indicate anything wrong.
Within those threads, its also stated that calling the garbage collection function (gc()) does tend to solve the problem on R exit, however this requires a user to understand basic R, and have access to the macro to be able to change the code - thus making predictive analytics more intimidating than it already is for new Alteryx users.
The first occurrence of this error seems to be way back in 2015, however the error is still being reported by users (see posts from 2020 and 2021):
An important issue of these 'fake errors', is not only that they cause confusion, but also that they will cause analytic apps and server workflows to not work as expected, and stop running depending on the configuration.
My suggestion would be to revisit this issue, as by my understanding it occurs inconsistently, and calling garbage collection does not always seem to fix it. Even if the Error message is still created, it may be worth Alteryx suppressing these errors, in the case they are not real errors.
Steps to reproduce:
(as mentioned, its very inconsistent)
1. Open the Boosted Model example workflow
2. *10 the number of maximum trees in the model, in the boosted model configuration (Model customization)
3. Run the workflow, inspect the results (which are seemingly correct), and the error message in the results window.
Hope this helps!
I would like to request that the Python tool metadata either be automatically populated after the code has run once, or a simple line of code added in the tool to output the metadata. Also, the metadata needs to be cached just like all of the other tools.
As it sits now, the Python tool is nearly unusable in a larger workflow. This is because it does not save or pass metadata in a workflow. Most other tools cache temporary metadata and pass it on to the next tool in line. This allows for things like selecting columns and seeing previews before the workflow is run.
Each time an edit is made to the workflow, the workflow must be re-run to update everything downstream of the Python tool. As you can imagine, this can get tedious (unusable) in larger workflows.
Alteryx support has replied with "this is expected behavior" and "It is giving that error because Alteryx is
doing a soft push for the metadata but unfortunately it is as designed."
Would be extremely useful if the Summarize Tool had an option in the numeric menu to Standardize the data. More often than not, data sets will not have the same count of variables which makes the comparison analysis meaningless. Currently, there is no easy way to Standardize the data without using the K-Centroids Cluster Analysis tool or standardize_unit interval supporting macro.
XGboost regression is now the benchmark for every Kaggle competition and seems to consistently outperform random forest, spline regression, and all of the more basic models. For those of us using predictive modeling on a regular basis in our actual work, this tool would allow for a quick improvement in our model accuracy. And I think, from a marketing standpoint, having a core group of users competing in Kaggle using Alteryx would be a great way to show off Alteryx's power.
It is readily available as an R package: https://cran.r-project.org/web/packages/xgboost/index.html
This idea arose recently when working specifically with the Association Analysis tool, but I have a feeling that other predictive tools could benefit as well. I was trying to run an association analysis for a large number of variables, but when I was investigating the output using the new interactive tools, I was presented with something similar to this:
While the correlation plot draws your high to high associations, the user is unable to read the field names, and the tooltip only provides the correlation value rather than the fields with the value. As such, I shifted my attention to the report output, which looked like this:
While I could now read everything, it made pulling out the insights much more difficult. Wanting the best of both worlds, I decided to extract the correlation table from the R output and drop it into Tableau for a filterable, interactive version of the correlation matrix. This turned out to be much easier said than done. Because the R output comes in report form, I tried to use the report extract macros mentioned in this thread to pull out the actual values. This was an issue due to the report formatting, so instead I cracked open the macro to extract the data directly from the R output. To make a long story shorter, this ended up being problematic due to report formats, batch macro pathing, and an unidentifiable bug.
In the end, it would be great if there was a “Data” output for reports from certain predictive tools that would benefit from further analysis. While the reports and interactive outputs are great for ingesting small model outputs, at times there is a need to extract the data itself for further analysis/visualization. This is one example, as is the model coefficients from regression analyses that I have used in the past. I know Dr. Dan created a model coefficients macro for the case of regression, but I have to imagine that there are other cases where the data is desired along with the report/interactive output.
Sometimes, as a sanity check, I would like to be able to model only the mean of my data set, i.e. I would like to use a predictive tool with no predictors included. The result would be a model with only an intercept, and this value would be the mean of the target variable. This would not be an important feature for final models, of course, but when starting to look at a data set and build up a model, it can be useful to first ensure the model is producing the expected output in the simplest case.
Note, this can be achieved when just one predictor is included, but it takes some math (see below), so it would be nice to be able to have this as a built-in option.
I'm really liking the new assisted modelling capabilities released in 2020.2, but it should not error if the data contains: spatial, blob, date, datetime, or datetime types.
This is essentially telling the user to add an extra step of adding a select before the assisted modelling tool and then a join after the models. I think the tool should be able to read in and through these field types (especially dates) and just not use them in any of the modelling.
An even better enhancement would be to transform date as part of the assisted modelling into something usable for the modelling (season, month, day of week, etc.)
Unsupervised learning method to detect topics in a text document.
Helpful for users interested in text mining.
Python pandas dataframes and data types (numpy arrays, lists, dictionaries, etc.) are much more robust in general than their counterparts in R, and they play together much easier as well. Moreover, there are only a handful of packages that do everything a data scientist would need, including graphing, such as SciKit Learn, Pandas, Numpy, and Seaborn. After utliizing R, Python, and Alteryx, I'm still a big proponent of integrating with the Python language much like Alteryx has integrated with R. At the very least, I propose to create the ability to create custom code such as a Python tool.
So - with Challenge 111 - many folk used the Optimization tool
… and Joe has done a great training on this here
But it's still to hard to use. It requires you to have pre-knowledge of a bunch of parameters and different types of knowledge.
Can we improve the interface on this tool so that it can be used by folk who do not have a background in R - for example, take all the different inputs, and make them parameterized on drop-down boxes or input boxes on the tool?
Thank you all
Can we get the R tools/models to work in database for SNOWFLAKE.
I understand that Snowflake currently doesn't support R through their UDFs yet; therefore, you might be waiting for them to add it.
I hear Python is coming soon, which is good & Java already available..
However, what about the ‘DPLYR’ package? https://db.rstudio.com/r-packages/dplyr/
My understanding is that this can translate the R code into SQL, so it can run in-DB?
Could this R code package be appended to the Alteryx R models? (maybe this isn’t possible, but wanted ask).
It would be nice if this option would take you to the correct download page relative to the version the user has installed. Currently, this always loads the download page for the current version which is confusing for users of a company who are still required to use an older version.
Up to version 10.0 I could open pretty much all analytics tools as a macro, to tweak things in R or in the macro workflow to get the results in a way most useful to us.
But apparently with Alteryx 11.0 the newer tools does not have that option, Although we can still access the older versions of those tools and still open them as macro but I don't understand (may be because they have interactive report option) why that is being killed in the newer versions?
Most of the newer versions have new features, like Linear Regression now support elastic net and cross validation etc.. but I still want to be able to go in to them to tweak them.
I would like to suggest to add a widget which encapsulate an R script able to perform outlier detection, something similar like netflix did:
When working with R code and errors occur, the application needs to show which line the error happened on.
I think the Nearest Neighbor Algorithm is one of the least used, and most powerful algorithms I know of. It allows me to connect data points with other data points that are similar. When something is unpredictable, or I simply don't have enough data, this allows me to compare one data point with its nearest neighbors.
So, last night I was at school, taking a graduate level Econ course. We were discussing various distance algorithms for a nearest neighbor algorithm. Our prof discussed one called the Mahalanobis distance. It uses some fancy matrix algebra. Essentially it allows it it to filter out the noise, and only match on distance algorithms that are truly significant. It takes into account the correlation that may exists within variables, and reduces those variables down to only one.
I use Nearest Neighbor when other things aren't working for me. When my data sets are weak, sparse, or otherwise not predictable. Sometimes I don't know that particular variables are correlated. This is a powerful algorithm that could be added into the Nearest Neighbor, to allow for matches that might not otherwise be found. And allow matches on only the variables that really matter.
Designer should support statistical testing tools that ignore data distribution and support Statistical Learning methods.
Alteryx already supports resampling for predictive modeling with Cross-Validation.
Resampling tools for bootstrap and permutation tests (supporting with or without replacement) should be tools for analysts and data scientists alike that assess random variability in a statistic without needing to worry about the restrictions of the data's distribution, as is the case with many parametric tests, most commonly supported by the t-test Tool in Alteryx. With modern computing power the need for hundred-year-old statistical sampling testing is fading: the power to sample a data set thousands of times to compare results to random chance is much easier today.
The tool's results could include, like R, outputs of not only the results histogram but the associated Q-Q plot that visualizes the distribution of the data for the analyst. This would duplicate the Distribution Analysis tool somewhat, but the Q-Q plot is, to me, a major missing element in the simplest visualization of data. This tool could be very valuable in terms of feeding the A/B Test tools.
Is there a reason why Alteryx does not include hierarchical clustering?
Well it's sort of slow especially with huge data sets, computation effort increases cubic, but then when you need to do two step clustering,
"creating more than enough k-means clusters and joining cluster centers with hierarchical clustering" it seems to be a must...
P.s. Knime, SPSS modeler, SAS, Rapidminer has it already...
Similar to @aselameab1 - I was having trouble with using the Linear regression tool because it was giving error messages that were not explanatory or self descriptive.
@chadanaber identified the issue - that a specific field only had one unique value which was causing the regression tool to fail - however the error message provided gives no useful or helpful indication that this is the issue. You can see that the error message below is pretty tough to understand.
Could we add an item to the development backlog to add defensive checks to the predictive analytics tools to check for conditions that will cause them to fail, and rework the error messaging?
I've attached the workflow with the sample data that replicates this issue