pycaret | An open-source , low-code machine learning library in Python | Machine Learning library
kandi X-RAY | pycaret Summary
kandi X-RAY | pycaret Summary
PyCaret is an open-source, low-code machine learning library in Python that automates machine learning workflows. It is an end-to-end machine learning and model management tool that speeds up the experiment cycle exponentially and makes you more productive. In comparison with the other open-source machine learning libraries, PyCaret is an alternate low-code library that can be used to replace hundreds of lines of code with few lines only. This makes experiments exponentially fast and efficient. PyCaret is essentially a Python wrapper around several machine learning libraries and frameworks such as scikit-learn, XGBoost, LightGBM, CatBoost, spaCy, Optuna, Hyperopt, Ray, and few more. The design and simplicity of PyCaret are inspired by the emerging role of citizen data scientists, a term first used by Gartner. Citizen Data Scientists are power users who can perform both simple and moderately sophisticated analytical tasks that would previously have required more technical expertise.
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pycaret Key Features
pycaret Examples and Code Snippets
Lines of Code : 21License : Strong Copyleft (CC BY-SA 4.0)
Copy>>> a = "feature_selection = True"
>>> a
"feature_selection = True"
>>> import re
>>> d = dict([re.split('\s*=\s*', a)])
{'feature_selection': 'True'}
imC:\Users\WDAGUtilityAccount>conda create --name py38 python=3.8C:\Users\WDAGUtilityAccount>activate py38
(py38) C:\Users\WDAGUtilityAccount>pip install -U setuptools(py38) C:\Users\WDAGUtilityAccount>pip insta
Lines of Code : 7License : Strong Copyleft (CC BY-SA 4.0)Copy..
best_regression_models = regression.compare_models()
regression_results = pull()
print(regression_results)
lr = create_model('lr')
predict_model(lr);
final_lr = finalize_model(lr)
predictions = predict_model(final_lr, data = mynewdfwithouttarget)
python=3.6
pycaret=2.2.3
vscode
import pycaret
from pycaret.anomaly import *
from pycaret.datasets import get_data
anomaly = get_data('anomaly')
exp_name = setup(data = anomaly)
knn = create_model('knn')
print("# Check correlation
cor = df[features].corr()
cor.loc[:,:] = np.tril(cor, k=-1)
cor = cor.stack()
cor[(cor > 0.7) | (cor < -0.7)]
def predict(data: Data = Depends()):
def predict(input_dict: Data):
input_dict = Data
app = FastAPI()
model = load_model('catboost_cm_creditable')
class Data(BaseModel):
age: float
live_province: str
live_city: sfrom pycaret.classification import *
Community Discussions
Trending Discussions on pycaret
QUESTION
Post checking official documentation and example, I am still confused if test data passed to the setup function is completely unseen by the model???
...ANSWER
Answered 2022-Apr-04 at 10:51If you notice the cv splits, they do not use the test data at all. So any step such as create_model, tune_model, blend_model, compare_models that use Cross-Validation, will not use the test data at all for training.
Once you are happy with the models from these steps, you can finalize the model using finalize_model. In this case, whatever model you pass to finalize_model is trained on the complete dataset (train + test) so that you can make true future predictions.
QUESTION
I am trying to automate the setup-process in PyCaret Library. The goal is to create a list of "experiments" and the script should iretate over the list of elements.
In the setup could be following preprocessing steps chosen: feature_selection = True, remove_outliers = True, etc.
All such pre-processing steps i would like to store in a list and then run a loop in order to find the best setting (best performance) for the model.
For example:
...ANSWER
Answered 2021-Dec-23 at 23:28Say you have a string like this:
QUESTION
I am trying to create a model using pycaret just as:
...ANSWER
Answered 2021-Sep-23 at 08:38You mentioned my previous question here.
I just faced the same issue as you on Colab. It is 100% issue with libraries.
Initially, I got the error for SMOTE:
- `AttributeError: 'SMOTE' object has no attribute '_validate_data'
After installing/reinstalling libraries I got exactly your error.
How did I resolve it?
- Started to run Colab and imported all common libraries (
pd,np,scikit, etc). - Installed PyCaret via
pip install. Thenimport pycaretandfrom pycaret.classification import * - Colab reacted: you have issues with
scipy,sklearn,lightgbm, please restart your runtime. - Restarted my runtime on Colab
- Imported all libraries again as I did in step 1
- Ran
import pycaretandfrom pycaret.classification import *only
My final code:
QUESTION
I have recently transitioned from R to python, and I am not sure how to problem solve the following.
When I run the setup for pycaret anomaly detection, following the instructions that can be found here, on my own data I get the following error.
...ANSWER
Answered 2021-Sep-09 at 12:22The answer to this question is that the environment had a library versions such as numpy that were too new for pycaret to work with, for example, pycaret need numpy (1.19.5 and will not work with newer).
My solution was to create a new environment in anaconda, which used pip install pycaret[full], and added nothing else to the environment. It worked after this.
QUESTION
I am trying to visualize Air Quality Data as time-series charts using pycaret and plotly dash python libraries , but i am getting very weird graphs, below is my code:
...ANSWER
Answered 2021-Sep-09 at 10:35- data has not been provided in a usable way. Sought out publicly available similar data. found: https://www.kaggle.com/rohanrao/air-quality-data-in-india?select=station_hour.csv
- using this data, with a couple of cleanups of your code, no issues with plots. I suspect your data has one of these issues
- date is not
datetime64[ns]in your data frame - date is not sorted, leading to lines being drawn in way you have noted
- date is not
- by refactoring way moving average is calculated, you can use animation instead of lots of separate figures
QUESTION
I am trying to use PyCaret for time series, according to this tutorial.
My analysis did not work. When I created a new column
data['MA12'] = data['variable'].rolling(12).mean()
I got this new MA12 column with NA values only.
As a resulted I decided to replicate the code from the tutorial, using AirPassangers dataset, but got the same issue.
When I print data, I get
...ANSWER
Answered 2021-Aug-19 at 23:02Since you want the previous 12 reads, the first 11 will be NaN. You need more rows than 12 before you get a moving average of 12. You can see this on the link you provided. The chart of MA doesn't start up right away.
QUESTION
If we use the assign_model() function from pycaret.anomaly library we get a dataframe with two additional columns Anomaly and Anomaly_Score as output. What does the Anomaly_Score column mean in this dataframe and how is its value calculated?
...ANSWER
Answered 2021-Aug-12 at 15:14PyCaret uses the PyOd library for anomaly detection. The anomaly score of an input sample is computed based on different detector algorithms. For consistency, outliers are assigned with larger anomaly scores. Wow it is calculated depends on the algorithm used for anomaly detection. Check out the documentation: https://pyod.readthedocs.io/en/latest/pyod.models.html and search for: "anomaly score".
QUESTION
How can I properly install PyCaret in AWS Glue?
Methods I tried:
--additional-python-modulesand--python-modules-installer-optionPython library patheasy_installas described in Use AWS Glue Python with NumPy and Pandas Python Packages
I am using Glue Version 2.0. I used --additional-python-modules and set to pycaret as shown in the picture.
Then I got this error log.
...ANSWER
Answered 2021-Jul-08 at 17:01I reached out to AWS support. Meghana was in charge of this case.
Here is the reply:
QUESTION
I cant use higher version of pip other than 20.2.4, since some SSL certification errors are occuring in higher versions and cannot reinstall any settings I have now (because of some office setup). Now I am using Pycaret and currently it supports only sklearn 0.23.2.
But my sklearn version is 0.24.1 and I am unable to downgrade it with 20.2.4 version of pip.
I also tried manual installation using setup.py file and it is also not success.
I am having Winpython and unable to install anaconda too.
Can someone help me to sort this problem? I am having python 3.9 . May be that is the problem?
Update:
...ANSWER
Answered 2021-Jun-07 at 17:47I tried various things, and the best advice I can give you is don't bother with python 3.9 for this library; It's just not supported yet. Dependencies are not sorted out yet for pycaret yet on python 3.9, and they should probably make note of that on their github, but here's the process I went through to get it installed from a completely fresh computer (windows sandbox).
QUESTION
pycaret is a very compact tool to compare models that I wanted to use for model selection. Unfortunately, the method compare_models does not show the typical output table that you see everywhere. I am using pycaret in PyCharm and not Jupyter Notebook which seems to be the typical approach. I do get the best model as a return value, but am really aiming for the overview table. It also doesn't seem to make a difference whether the parameter silent is set to True or False appart from being asked for the confirmation of whether the derived datatypes are correct.
Thank you very much!
The system: Python 3.6 pycaret 2.3 CentOS 7 PyCharm 2020.1 Community Edition
My code:
...ANSWER
Answered 2021-May-30 at 04:31Running PyCaret from a terminal/command line has different behavior compared to running from a Jupyter notebook. In your case, if you want to display the comparison output table, add these 2 lines after your compare_models() function call:
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