yellowbrick | Visual analysis and diagnostic tools | Machine Learning library
kandi X-RAY | yellowbrick Summary
kandi X-RAY | yellowbrick Summary
Yellowbrick is a suite of visual diagnostic tools called "Visualizers" that extend the scikit-learn API to allow human steering of the model selection process. In a nutshell, Yellowbrick combines scikit-learn with matplotlib in the best tradition of the scikit-learn documentation, but to produce visualizations for your machine learning workflow!. For complete documentation on the Yellowbrick API, a gallery of available visualizers, the contributor's guide, tutorials and teaching resources, frequently asked questions, and more, please visit our documentation at www.scikit-yb.org.
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Top functions reviewed by kandi - BETA
- Tags postag
- Create a new figure
- Fit the model
- Save plot to gallery
- Displays a Dropping curve
- Get estimator parameters
- Plot the colormap
- Renders the training and validation curves
- Displays the alpha distribution of the given estimator
- Displays the frequency distribution of features
- Displays the word correlation plot
- Displays a rank 2D plot
- Displays a matplotlib plot
- Displays a grid search color plot
- Dispersion dispersion plot
- Creates a manifold for plotting
- Displays missing values
- Displays the explained variance plot
- Generate speedup plot
- Displays the correlation plot
- Draw the best fit line
- Displays the TSNE
- Displays the rank1d plot
- Compute the RFECV curve
- Displays the umap plot
- Displays the dispersion plot
yellowbrick Key Features
yellowbrick Examples and Code Snippets
Lines of Code : 4
License : No License
CopyRequired Reading:
Suggested Reading:
Additional Reading:
R (Programming Language):
Required Reading:
Suggested Reading:
Additional Resources:
from kneed import KneeLocator
inertia = []
for i in range (1,31):
som = SOM(m = 1, n = i, dim = x_lda_train.shape[1])
som.fit_predict(x_lda_train)
inertia.append(som.inertia_)
# identify the knee by usielbow = elbow_method(data_standardized,10)
from sklearn.linear_model import RidgeClassifier
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import OrdinalEncoder, LabelEncoder
from yellowbrick.classifier import ROCAUC
from yellowbrick.datasets importfrom yellowbrick.classifier import ROCAUC
from sklearn.linear_model import RidgeClassifier
# single space in title
visualizer = ROCAUC(RidgeClassifier(), title=" ")
visualizer.show()
np.unique(y_pred)
# array([0, 1])
y_pred = clf_lr.predict_proba(X_test) # get probabilities
y_prob = np.array([x[1] for x in y_pred]) # keep the prob for the positive class 1
roc_auc = roc_auc_score(y_test, y_pimport matplotlib.pyplot as plt
from sklearn.cluster import KMeans
from yellowbrick.cluster import KElbowVisualizer
model = KMeans(random_state=0)
visualizer = KElbowVisualizer(
model,
k=(2,7),
metric="silhouette",
tim from yellowbrick.target import FeatureCorrelation
import warnings
warnings.simplefilter("ignore")
from yellowbrick.target import FeatureCorrelation
pip uninstall tb-nightly tensorboard tensorflow-estimator tensorflow-gpu tf-estimator-nightly
pip install tensorflow # or `tensorflow-gpu`, or `tf-nightly`, ...
import pkg_resources
for entry_point in pkg_resourCommunity Discussions
Trending Discussions on yellowbrick
QUESTION
When trying to access the label column, I get:
ANSWER
Answered 2022-Feb-21 at 16:42Looks like the problem turned out to be, like @hpaulj said, that pd.DataFrame(self.data).head() line, because not only it was called for nothing, since it wasn't assigned to any variable, but also returned the first 5 rows, which makes sense now why I got a KeyError regarding the index after making a first change. So, instead, I changed it to self.data = pd.DataFrame(self.data) and now the code works as expected to.
QUESTION
I have pretrained model for object detection (Google Colab + TensorFlow) inside Google Colab and I run it two-three times per week for new images I have and everything was fine for the last year till this week. Now when I try to run model I have this message:
...ANSWER
Answered 2022-Feb-07 at 09:19It happened the same to me last friday. I think it has something to do with Cuda instalation in Google Colab but I don't know exactly the reason
QUESTION
data source: https://catalog.data.gov/dataset/nyc-transit-subway-entrance-and-exit-data
I tried looking for a similar problem but I can't find an answer and the error does not help much. I'm kinda frustrated at this point. Thanks for the help. I'm calculating the closest distance from a point.
...ANSWER
Answered 2021-Oct-11 at 14:21geopandas 0.10.1
- have noted that your data is on kaggle, so start by sourcing it
- there really is only one issue
shapely.geometry.MultiPoint()constructor does not work with a filtered series. Pass it a numpy array instead and it works. - full code below, have randomly selected a point to serve as
gpdPoint
QUESTION
I am using the Yellowbrick package to plot an elbow curve for a dataset to find the most optimal number of clusters for the dataset using KMeans as a model.
I am using Scikit-learn's KMeans and Yellowbrick's kelbow_visualizer functions.
The elbow curve is generated and I am able to read the elbow value however the following error is thrown afterwards:
...ANSWER
Answered 2021-Sep-22 at 15:25I solved this issue by storing the result of the elbow_method function into a variable, as so:
QUESTION
I am trying to adjust the axes limits on a yellow bricks figure. However, I can't seem to adjust it. I can change axes labels and titles but not the limits. It works if I don't render the figure with visualizer.show() but then I lose labels, titles, legend etc.
ANSWER
Answered 2021-Jul-16 at 04:03Instead of calling the visualizer.show() method, you can try calling the visualizer.finalize() method and then accessing the underlying matplotlib axes to change the limits. You are also overwriting ax which wasn't doing you any favours either.
Here is the full code example:
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 am using yellowbrick to plot the AUCROC. I want to remove the title from the plot, to make it empty without the plot title.
...ANSWER
Answered 2021-Jun-14 at 23:49yellowbrick documentation How can I change the title of a Yellowbrick plot?
If I use single space in title=" " then I get plot without title.
It doesn't work with empty string title="".
Minimal working example
QUESTION
I'm trying to plot a silhouette plot for a K-Means model I've run, however, I get the error: ImportError: cannot import name 'safe_indexing' from 'sklearn.utils.
I was initially getting the NotFoundError issue described in this post here however I got this issue once I fixed it. My code is below:
...ANSWER
Answered 2021-Jan-09 at 03:58In python3.7 with yellowbrick v1.2 and scikit-learn v0.23.2 I don't get the error but when I bump scikit-learn to v0.24.0 I get the same ImportError.
I checked the API documentation and, like in the link you referenced, in sklearn v0.22.0 they deprecated utils.safe_indexing to private (utils._safe_indexing). But I don't get the import error with yellowbrick v1.2 and v0.22.0<=sklearn<=v0.23.2 installed. I think they just finally removed the public utils.safe_indexing in v0.24.0 so if you just install a version of scikit-learn before v0.24.0 the import should work.
Edit: Here is yellowbrick's github issue if you want to track their progress on a workaround or update for this problem
QUESTION
I want to select Important feature with adaboost. I found 'yellowbrick.model_selection' is very good and fast for this work. and I used this code. but it has problem.
"ValueError: could not broadcast input array from shape (260200) into shape (1)
My feature vector has 1*260200 for every Image. I can't Underestand How adaboost make a model, so I can't debug the code.
would you help me please?
thank you a lot :)
ANSWER
Answered 2021-Feb-11 at 18:46this code, make a rank for every feature
QUESTION
I am using sklearn to compute the average precision and roc_auc of a classifier and yellowbrick to plot the roc_auc and precision-recall curves. The problem is that the packages give different scores in both metrics and I do not know which one is the correct.
The code used:
...ANSWER
Answered 2020-Nov-02 at 23:03Since you use the predict method of scikit-learn, your predictions y_pred are hard class memberships, and not probabilities:
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install yellowbrick
You can use yellowbrick like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
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