kandi X-RAY | skinnerdb Summary
kandi X-RAY | skinnerdb Summary
This repository contains a very early version of a (slightly refined) re-implementation of SkinnerDB, described in the paper SkinnerDB: Regret-bounded query evaluation via reinforcement learning at SIGMOD 2019 (see video recording of SIGMOD talk here). This source code is currently under development and NOT CONSIDERED STABLE. We expect to release the first stable version in the next months.
Top functions reviewed by kandi - BETA
- Called when a parenthesis is an expression
- Visit a DateTimeLiteral
- Visit a WHEN clause
- Visit IN statement
- Visit a NotExpression
- Visit a long value
- Visit an ExtractExpression
- Visit a string value
- Visits an IntervalExpression
- Visit a column
- Propagates between two
- Visit a LikeExpression
- Visit a signed expression
- Overrides the superclass of a WHEN expression
- Visit a function
- Processes an OR expression
- Copy the case expression from the opcode stack
- Handles an IN expression
- Pop a LikeExpression
- Updates the left and right expressions
- Visit a Between Equals
- Visit a cast expression
- Populate the extract expression
- Region ISNullExpression
- Populate the concat
- Pop the not expression
- Creates new Concat expression
- Region OR expression
- Region OpExpression
- Processes an IsNullExpression
- Process an ExtractExpression
skinnerdb Key Features
skinnerdb Examples and Code Snippets
Trending Discussions on Machine Learning
I have trained an RNN model with pytorch. I need to use the model for prediction in an environment where I'm unable to install pytorch because of some strange dependency issue with glibc. However, I can install numpy and scipy and other libraries. So, I want to use the trained model, with the network definition, without pytorch.
I have the weights of the model as I save the model with its state dict and weights in the standard way, but I can also save it using just json/pickle files or similar.
I also have the network definition, which depends on pytorch in a number of ways. This is my RNN network definition....
ANSWERAnswered 2022-Feb-17 at 10:47
You should try to export the model using torch.onnx. The page gives you an example that you can start with.
An alternative is to use TorchScript, but that requires torch libraries.
Both of these can be run without python. You can load torchscript in a C++ application https://pytorch.org/tutorials/advanced/cpp_export.html
Just modifying a little your example to go over the errors I found
Notice that via tracing any if/elif/else, for, while will be unrolled
I'm trying to implement a gradient-free optimizer function to train convolutional neural networks with Julia using Flux.jl. The reference paper is this: https://arxiv.org/abs/2005.05955. This paper proposes RSO, a gradient-free optimization algorithm updates single weight at a time on a sampling bases. The pseudocode of this algorithm is depicted in the picture below.
I'm using MNIST dataset....
ANSWERAnswered 2022-Jan-14 at 23:47
Based on the paper you shared, it looks like you need to change the weight arrays per each output neuron per each layer. Unfortunately, this means that the implementation of your optimization routine is going to depend on the layer type, since an "output neuron" for a convolution layer is quite different than a fully-connected layer. In other words, just looping over
Flux.params(model) is not going to be sufficient, since this is just a set of all the weight arrays in the model and each weight array is treated differently depending on which layer it comes from.
Fortunately, Julia's multiple dispatch does make this easier to write if you use separate functions instead of a giant loop. I'll summarize the algorithm using the pseudo-code below:
This question is the same with How can I check a confusion_matrix after fine-tuning with custom datasets?, on Data Science Stack Exchange.Background
I would like to check a confusion_matrix, including precision, recall, and f1-score like below after fine-tuning with custom datasets.
Fine tuning process and the task are Sequence Classification with IMDb Reviews on the Fine-tuning with custom datasets tutorial on Hugging face.
After finishing the fine-tune with Trainer, how can I check a confusion_matrix in this case?
An image of confusion_matrix, including precision, recall, and f1-score original site: just for example output image...
ANSWERAnswered 2021-Nov-24 at 13:26
What you could do in this situation is to iterate on the validation set(or on the test set for that matter) and manually create a list of
I am trying to train a model using PyTorch. When beginning model training I get the following error message:
RuntimeError: CUDA out of memory. Tried to allocate 5.37 GiB (GPU 0; 7.79 GiB total capacity; 742.54 MiB already allocated; 5.13 GiB free; 792.00 MiB reserved in total by PyTorch)
I am wondering why this error is occurring. From the way I see it, I have 7.79 GiB total capacity. The numbers it is stating (742 MiB + 5.13 GiB + 792 MiB) do not add up to be greater than 7.79 GiB. When I check
nvidia-smi I see these processes running
ANSWERAnswered 2021-Nov-23 at 06:13
This is more of a comment, but worth pointing out.
The reason in general is indeed what talonmies commented, but you are summing up the numbers incorrectly. Let's see what happens when tensors are moved to GPU (I tried this on my PC with RTX2060 with 5.8G usable GPU memory in total):
Let's run the following python commands interactively:
I am a bit confusing with comparing best GridSearchCV model and baseline.
For example, we have classification problem.
As a baseline, we'll fit a model with default settings (let it be logistic regression):
ANSWERAnswered 2021-Nov-04 at 21:17
No, they aren't comparable.
Your baseline model used
X_train to fit the model. Then you're using the fitted model to score the
X_train sample. This is like cheating because the model is going to already perform the best since you're evaluating it based on data that it has already seen.
The grid searched model is at a disadvantage because:
- It's working with less data since you have split the
- Compound that with the fact that it's getting trained with even less data due to the 5 folds (it's training with only 4/5 of
So your score for the grid search is going to be worse than your baseline.
Now you might ask, "so what's the point of
best_model.best_score_? Well, that score is used to compare all the models used when searching for the optimal hyperparameters in your search space, but in no way should be used to compare against a model that was trained outside of the grid search context.
So how should one go about conducting a fair comparison?
- Split your training data for both models.
I created one notebook using Google AI platform. I was able to start it and work but suddenly it stopped and I am not able to start it now. I tried building and restarting the jupyterlab, but of no use. I have checked my disk usages as well, which is only 12%.
but didn't fix it.
Thanks in advance....
ANSWERAnswered 2021-Aug-20 at 14:00
You should try this Google Notebook trouble shooting section about 524 errors : https://cloud.google.com/notebooks/docs/troubleshooting?hl=ja#opening_a_notebook_results_in_a_524_a_timeout_occurred_error
I am new to Machine Learning.
Having followed the steps in this simple Maching Learning using the Brain.js library, it beats my understanding why I keep getting the error message below:
I have double-checked my code multiple times. This is particularly frustrating as this is the very first exercise!
Kindly point out what I am missing here!
Find below my code:...
ANSWERAnswered 2021-Sep-29 at 22:47
Turns out its just documented incorrectly.
In reality the export from brain.js is this:
IF we are not sure about the nature of categorical features like whether they are nominal or ordinal, which encoding should we use? Ordinal-Encoding or One-Hot-Encoding? Is there a clearly defined rule on this topic?
I see a lot of people using Ordinal-Encoding on Categorical Data that doesn't have a Direction. Suppose a frequency table:...
ANSWERAnswered 2021-Sep-04 at 06:43
You're right. Just one thing to consider for choosing
OneHotEncoder is that does the order of data matter?
Most ML algorithms will assume that two nearby values are more similar than two distant values. This may be fine in some cases e.g., for ordered categories such as:
quality = ["bad", "average", "good", "excellent"]or
shirt_size = ["large", "medium", "small"]
but it is obviously not the case for the:
color = ["white","orange","black","green"]
column (except for the cases you need to consider a spectrum, say from white to black. Note that in this case,
white category should be encoded as
black should be encoded as the highest number in your categories), or if you have some cases for example, say, categories 0 and 4 may be more similar than categories 0 and 1. To fix this issue, a common solution is to create one binary attribute per category (One-Hot encoding)
I am using sentence-transformers for semantic search but sometimes it does not understand the contextual meaning and returns wrong result eg. BERT problem with context/semantic search in italian language
by default the vector side of embedding of the sentence is 78 columns, so how do I increase that dimension so that it can understand the contextual meaning in deep.
ANSWERAnswered 2021-Aug-10 at 07:39
Increasing the dimension of a trained model is not possible (without many difficulties and re-training the model). The model you are using was pre-trained with dimension 768, i.e., all weight matrices of the model have a corresponding number of trained parameters. Increasing the dimensionality would mean adding parameters which however need to be learned.
Also, the dimension of the model does not reflect the amount of semantic or context information in the sentence representation. The choice of the model dimension reflects more a trade-off between model capacity, the amount of training data, and reasonable inference speed.
If the model that you are using does not provide representation that is semantically rich enough, you might want to search for better models, such as RoBERTa or T5.
I have a table with features that were used to build some model to predict whether user will buy a new insurance or not. In the same table I have probability of belonging to the class 1 (will buy) and class 0 (will not buy) predicted by this model. I don't know what kind of algorithm was used to build this model. I only have its predicted probabilities.
Question: how to identify what features affect these prediction results? Do I need to build correlation matrix or conduct any tests?
ANSWERAnswered 2021-Aug-11 at 15:55
No vulnerabilities reported
Create a new database using jars/CreateDB.jar or by executing tools/CreateDB.java. You need to specify two command line parameters: the database name and an (existing) directory in which the corresponding data is stored.
Start the Skinner console. The Skinner console can be accessed via jars/Skinner.jar or by executing console/SkinnerCmd.java. You need to specify the database directory as command line parameter (the same directory that was specified in the call to CreateDB.jar).
Create the database schema. SkinnerDB currently supports a limited number of SQL data types (text, int, and double). The example script located under imdb/skinner.schema.sql demonstrates how to create the schema of the join order benchmark. Note that you can execute commands in files via the 'exec ' command from the Skinner console. Run 'help' in the console to obtain a complete list of utility commands.
Load the data. SkinnerDB currently supports loading table data from CSV files. Run the command 'exec <path to .csv file> ' in the Skinner console to load data from the corresponding file into the specified table. The example script under 'imdb/skinner.load.sql' shows commands by which data for the join order benchmark can be loaded (assuming .csv files at the specified locations). The final command in that file refers to the next point. (Optional) Compress string values after loading all data for all tables. Run the 'compress' command to create a dictionary that maps strings that appear in the database to integer code values. Processing integer values is significantly more efficient than processing strings. Compression may take a while as it iterates over the entire database. This pre-processing overhead may however pay off at run time. Restart SkinnerDB (leave the console by entering 'quit' ). (Optional) Create indices for the database columns. Run the 'index all' command in the Skinner console to create indices on all database columns. Again, this may take a while but can pay off at run time. Currently, we do not store indices on hard disk. This means that the 'index all' command (as opposed to the 'compress' command!) has to be re-run each time after starting the Skinner console. Run analytical SQL queries. The current prototype only supports a very limited subset of SQL and not all features have been tested yet. The current support includes (without guarantees) select queries with inequality and equality predicates, LIKE expressions (as they appear in the join order benchmark, some special cases are currently not handled correctly), logical and arithmetic expressions, minimum and maximum aggregation, joins with predicates specified in the SQL WHERE clause, grouping, and sorting. Tuning SkinnerDB includes various tuning parameters that can improve performance for specific benchmarks and data sets. Those parameters are hardcoded in the current version and can be found in the sub-folder src/config. Among the most important parameters are the EXPLORATION_WEIGHT, the BUDGET_PER_EPISODE, and the FORGET parameter (all in JoinConfig.java). Increasing the exploration weight makes the algorithm more "curious", thereby spending more effort in exploration as opposed to exploitation (here: using promising join orders). Increasing the budget per episode decreases learning overheads but may reduce the quality of join order decisions. If the forget parameter is enabled, the UCT tree is rebuilt regularly to increase exploration. Team SkinnerDB is developed by the Cornell database group (https://research.cs.cornell.edu/database/learning.php).
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