Download Kite For Python
Ronnie Isackson
Please be extra careful when you see Kite (the program auto-completor app). This app is atrociously bad for performance, it spreads to all of your system (and I mean ALL of your system), and the developers intentionally made it hard for you to remove it. I made the mistake of installing it when I was trying out Spyder IDE. I didn't realize this app installed itself across all of my editors, including neovim. I started noticing that my neovim would get several seconds of hiccup when I was running python REPL, which had never happened before. Soon my Linux system started experiencing severe hiccup as well. Then I did some profiling and found out that Kite was making background process calls without my consent. The worst part yet is they made it nearly impossible for you uninstall it, not unless you download their shady app manager or make a curl call to a completely unknown api server just to uninstall apps on your computer. I don't even want to know what kind of personal information / data that they were stealing from my computer. I fail to see how Kite is different from a virus. Please use extra caution when going through IDE setup as something like Kite can easily creep in and contaminate your whole system just like that. To the developers of Spyder and VSCode and other IDEs, please take active step in banning an app like Kite that severely infringes upon the privacy and right of their users. By promoting this app as an extension, you are potentially endangering millions of your users!
I would like to point out though, that there was nothing "light" about the promotion of Kite in Spyder. As me and other users have noted, Kite installation during the initialization setup of Spyder was an opt-out by default. I am sure many people either clicked through the setup and installed Kite without reading the "fine-prints" as is totally reasonable for an average user, and/or they mistakenly assumed that Kite is a safe and secure plugin that had been vetted by the Spyder developers, as it's one of the first things that you see when you start using Spyder. Without sounding accusatory, I was very disappointed that the Spyder developers allowed this to happen. Since I haven't been monitoring your git tracker issues related to Kite (because frankly it's not my job), I will take your word for it that not many Spyder users raised my concern. But anyone who just googles Kite will quickly discover that Kite had severe security/privacy concerns, was an invasive software, and its company conducted extremely questionable business practices in other open source packages as far back as 2017. In fact, the very same announcement post of Kite's sponsorship of Spyder in 2019 already had several users that raised their concerns in the comments section ( -ide.org/blog/spyder-kite-funding/). I don't think it's fair to say that well since the users didn't find any issue and complained about them on git, then we will let it slide. I get that you guys are maintaining/developing Spyder for all of us for free, but I also uphold you to a higher standard because a single bad commit or decision by you can lead to disproportional effects on the rest of us. And sometimes these effects take a while for us to find out and may have irreversible and disastrous consequences. I truly beseech you to be truthful with us and with yourselves, whether if corporate sponsorship, financial or otherwise, means that you can lower the standard that you hold for software integration, even if it comes at the cost of your users. Because if that is the case, then it's a slippery slope to the end of free and open source development as we know it.
I have been working on learning python for a few months and had two questions. Is there much of a difference between options like Kite or Jedi and if you are new to programming is it a good idea to use either of them while learning? The only comparisons I saw did not appear unbiased. Any thoughts are appreciated thanks.
There have been issues in the past with kite and its privacy model. I am not sure if it's better now, but just be aware that there's a chance that kite will upload your code to some cloud service. Maybe not now, but this might as well change in the future.
I had installed kite engine before installing the plug in. still getting the annoying error message. The worst part is it does not allow me to remove the plugin. If I initiate this, the application goes on a hang. Is there anyway we can get this bug raised to the product team ?
Or view some more pics of the kites in the air and on the water: Kite Discus
Some information about the software: Wingcalc
And if you want to know how to sew a kite, here is a manual: How to sew a kite
The Greykite library is designed to solve these types of problems. To develop Greykite, we identified a few champion use cases. These helped us refine our models and prove success. In partnership with the domain experts, we demonstrated that Greykite helps LinkedIn confidently manage the business and make better decisions.
The Silverkite algorithm architecture is shown in Figure 1. The green parallelograms represent model inputs, and the orange ovals represent model outputs. The user provides the input time series and any known anomalies, events, regressors, or changepoint dates. The model returns forecasts, prediction intervals, and diagnostics.
For example, Figure 2 shows the number of shared bike rides in Washington, D.C., from 2011 to 2019 at an hourly frequency. By inspecting this figure and other aggregated trend plots, you can observe some indications of changes in trend patterns. Silverkite allows the user to specify the changepoint locations or request automatic trend changepoint and seasonality changepoint detection.
To allow for changes in seasonality, Silverkite constructs basis functions that allow the seasonal effect to change in both shape and magnitude with time. Silverkite also allows for automatically detecting seasonality changepoints with the following regression model:
Repeated events/holidays
It is important to capture the effect of repeated events such as holidays. Silverkite constructs indicator variables to use as basis functions for the holidays. These variables take the value 1 during the event, and 0 otherwise.
Some holidays have extended impact over several days in their proximity, whereas others are more localized. Silverkite allows the user to customize the number of days before and after the event where the impact is non-negligible. By default, the effect on each day is modeled as a separate effect. Silverkite also allows for modeling less impactful holidays together for model sparsity.
When AR is used in the models, the future is predicted by simulating several time series into the future with the fitted models and aggregating them. Note that if the maximum lag utilized in the models is smaller than the forecast horizon, simulations are not necessary. Silverkite takes advantage of this to speed up predictions when possible.
Silverkite allows explicit information about the future to be provided in the form of a regressor with past and future values. This regressor is used directly when fitting the model and for prediction. Silverkite also allows lagged regressors to capture temporal dependence, similar to the auto-regressive lags of the original series.
Fine-tuning is important for key business metrics with high visibility and strict accuracy requirements. Therefore, the Greykite library also provides full flexibility to customize a model template for any algorithm. For example, the Silverkite algorithm offers automatic changepoint detection, but also allows the user to add known changepoint dates. Silverkite automatically captures the effect of common holidays and interactions, but allows custom events and specification of model terms even down to the patsy model formula. And while the Greykite library provides outlier detection, the user may label known anomalies and specify whether to ignore or adjust them in the training data.
This fine-tuning often requires a deeper understanding of data characteristics. The Greykite library provides diagnostic plots to assess seasonality, trend, and holidays effects, as shown in Figure 4. It also provides components plots and model summaries, which help with tuning as well as interpretability.
Finally, when there are hundreds of metrics to forecast, it is not feasible to manually tune the model for each one. The Greykite library allows for hyperparameter grid search to select the optimal model from multiple candidates, using performance on past data. This makes it easy to forecast many metrics in a semi-automatic fashion: instead of tuning each forecast separately, the user can define a set of candidate forecast configurations that capture different types of patterns, and use grid search to find the best model for each one.
f5d0e4f075