Import Issue in Gaussian naive bayes example #11369
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|
Because it is deprecated. Try to use sklearn.metrics.ConfusionMatrixDisplay. Here is its document: ConfusionMatrixDisplay |
|
You can use this code: # Gaussian Naive Bayes Example
import time
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import accuracy_score, ConfusionMatrixDisplay, confusion_matrix
from sklearn.model_selection import train_test_split
from sklearn.naive_bayes import GaussianNB
def main():
"""
Gaussian Naive Bayes Example using sklearn function.
Iris type dataset is used to demonstrate algorithm.
"""
# Load Iris dataset
iris = load_iris()
# Split dataset into train and test data
x = iris["data"] # features
y = iris["target"]
x_train, x_test, y_train, y_test = train_test_split(
x, y, test_size=0.3, random_state=1
)
# Gaussian Naive Bayes
nb_model = GaussianNB()
time.sleep(2.9)
model_fit = nb_model.fit(x_train, y_train)
y_pred = model_fit.predict(x_test) # Predictions on the test set
# Display Confusion Matrix
cm = confusion_matrix(y_test, y_pred, labels=nb_model.classes_)
ConfusionMatrixDisplay.from_estimator(nb_model, x_test, y_test)
plt.title("Normalized Confusion Matrix - IRIS Dataset")
plt.show()
time.sleep(1.8)
final_accuracy = 100 * accuracy_score(y_true=y_test, y_pred=y_pred)
print(f"The overall accuracy of the model is: {round(final_accuracy, 2)}%")
if __name__ == "__main__":
main() |
|
Thank you.
It worked !
…On Thu, 18 Apr 2024 at 14:40, Seyyed Reza Moslemi ***@***.***> wrote:
You can use this code:
`# Gaussian Naive Bayes Example
import time
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import accuracy_score, ConfusionMatrixDisplay,
confusion_matrix
from sklearn.model_selection import train_test_split
from sklearn.naive_bayes import GaussianNB
def main():
"""
Gaussian Naive Bayes Example using sklearn function.
Iris type dataset is used to demonstrate algorithm.
"""
# Load Iris dataset
iris = load_iris()
# Split dataset into train and test data
x = iris["data"] # features
y = iris["target"]
x_train, x_test, y_train, y_test = train_test_split(
x, y, test_size=0.3, random_state=1
)
# Gaussian Naive Bayes
nb_model = GaussianNB()
time.sleep(2.9)
model_fit = nb_model.fit(x_train, y_train)
y_pred = model_fit.predict(x_test) # Predictions on the test set
# Display Confusion Matrix
cm = confusion_matrix(y_test, y_pred, labels=nb_model.classes_)
ConfusionMatrixDisplay.from_estimator(nb_model, x_test, y_test)
plt.title("Normalized Confusion Matrix - IRIS Dataset")
plt.show()
time.sleep(1.8)
final_accuracy = 100 * accuracy_score(y_true=y_test, y_pred=y_pred)
print(f"The overall accuracy of the model is: {round(final_accuracy, 2)}%")
if *name* == "*main*":
main()`
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Repository commit
a0b0f
Python version (python --version)
Python 3.10.12
Dependencies version (pip freeze)
absl-py==1.4.0
aiohttp==3.9.3
aiosignal==1.3.1
alabaster==0.7.16
albumentations==1.3.1
altair==4.2.2
annotated-types==0.6.0
anyio==3.7.1
appdirs==1.4.4
argon2-cffi==23.1.0
argon2-cffi-bindings==21.2.0
array_record==0.5.1
arviz==0.15.1
astropy==5.3.4
astunparse==1.6.3
async-timeout==4.0.3
atpublic==4.1.0
attrs==23.2.0
audioread==3.0.1
autograd==1.6.2
Babel==2.14.0
backcall==0.2.0
beautifulsoup4==4.12.3
bidict==0.23.1
bigframes==1.0.0
bleach==6.1.0
blinker==1.4
blis==0.7.11
blosc2==2.0.0
bokeh==3.3.4
bqplot==0.12.43
branca==0.7.1
build==1.2.1
CacheControl==0.14.0
cachetools==5.3.3
catalogue==2.0.10
certifi==2024.2.2
cffi==1.16.0
chardet==5.2.0
charset-normalizer==3.3.2
chex==0.1.86
click==8.1.7
click-plugins==1.1.1
cligj==0.7.2
cloudpathlib==0.16.0
cloudpickle==2.2.1
cmake==3.27.9
cmdstanpy==1.2.2
colorcet==3.1.0
colorlover==0.3.0
colour==0.1.5
community==1.0.0b1
confection==0.1.4
cons==0.4.6
contextlib2==21.6.0
contourpy==1.2.1
cryptography==42.0.5
cufflinks==0.17.3
cupy-cuda12x==12.2.0
cvxopt==1.3.2
cvxpy==1.3.3
cycler==0.12.1
cymem==2.0.8
Cython==3.0.10
dask==2023.8.1
datascience==0.17.6
db-dtypes==1.2.0
dbus-python==1.2.18
debugpy==1.6.6
decorator==4.4.2
defusedxml==0.7.1
distributed==2023.8.1
distro==1.7.0
dlib==19.24.4
dm-tree==0.1.8
docstring_parser==0.16
docutils==0.18.1
dopamine-rl==4.0.6
duckdb==0.10.1
earthengine-api==0.1.397
easydict==1.13
ecos==2.0.13
editdistance==0.6.2
eerepr==0.0.4
en-core-web-sm @ https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.7.1/en_core_web_sm-3.7.1-py3-none-any.whl#sha256=86cc141f63942d4b2c5fcee06630fd6f904788d2f0ab005cce45aadb8fb73889
entrypoints==0.4
et-xmlfile==1.1.0
etils==1.7.0
etuples==0.3.9
exceptiongroup==1.2.0
fastai==2.7.14
fastcore==1.5.29
fastdownload==0.0.7
fastjsonschema==2.19.1
fastprogress==1.0.3
fastrlock==0.8.2
filelock==3.13.4
fiona==1.9.6
firebase-admin==5.3.0
Flask==2.2.5
flatbuffers==24.3.25
flax==0.8.2
folium==0.14.0
fonttools==4.51.0
frozendict==2.4.1
frozenlist==1.4.1
fsspec==2023.6.0
future==0.18.3
gast==0.5.4
gcsfs==2023.6.0
GDAL==3.6.4
gdown==4.7.3
geemap==0.32.0
gensim==4.3.2
geocoder==1.38.1
geographiclib==2.0
geopandas==0.13.2
geopy==2.3.0
gin-config==0.5.0
glob2==0.7
google==2.0.3
google-ai-generativelanguage==0.4.0
google-api-core==2.11.1
google-api-python-client==2.84.0
google-auth==2.27.0
google-auth-httplib2==0.1.1
google-auth-oauthlib==1.2.0
google-cloud-aiplatform==1.47.0
google-cloud-bigquery==3.12.0
google-cloud-bigquery-connection==1.12.1
google-cloud-bigquery-storage==2.24.0
google-cloud-core==2.3.3
google-cloud-datastore==2.15.2
google-cloud-firestore==2.11.1
google-cloud-functions==1.13.3
google-cloud-iam==2.14.3
google-cloud-language==2.13.3
google-cloud-resource-manager==1.12.3
google-cloud-storage==2.8.0
google-cloud-translate==3.11.3
google-colab @ file:///colabtools/dist/google-colab-1.0.0.tar.gz#sha256=13da9129d0f054354ab13569a5920c390cf5ff9477736337cde844aa81867649
google-crc32c==1.5.0
google-generativeai==0.3.2
google-pasta==0.2.0
google-resumable-media==2.7.0
googleapis-common-protos==1.63.0
googledrivedownloader==0.4
graphviz==0.20.3
greenlet==3.0.3
grpc-google-iam-v1==0.13.0
grpcio==1.62.1
grpcio-status==1.48.2
gspread==3.4.2
gspread-dataframe==3.3.1
gym==0.25.2
gym-notices==0.0.8
h5netcdf==1.3.0
h5py==3.9.0
holidays==0.46
holoviews==1.17.1
html5lib==1.1
httpimport==1.3.1
httplib2==0.22.0
huggingface-hub==0.20.3
humanize==4.7.0
hyperopt==0.2.7
ibis-framework==8.0.0
idna==3.6
imageio==2.31.6
imageio-ffmpeg==0.4.9
imagesize==1.4.1
imbalanced-learn==0.10.1
imgaug==0.4.0
importlib_metadata==7.1.0
importlib_resources==6.4.0
imutils==0.5.4
inflect==7.0.0
iniconfig==2.0.0
intel-openmp==2023.2.4
ipyevents==2.0.2
ipyfilechooser==0.6.0
ipykernel==5.5.6
ipyleaflet==0.18.2
ipython==7.34.0
ipython-genutils==0.2.0
ipython-sql==0.5.0
ipytree==0.2.2
ipywidgets==7.7.1
itsdangerous==2.1.2
jax==0.4.26
jaxlib @ https://storage.googleapis.com/jax-releases/cuda12/jaxlib-0.4.26+cuda12.cudnn89-cp310-cp310-manylinux2014_x86_64.whl#sha256=813cf1fe3e7ca4dbf5327d6e7b4fc8521e92d8bba073ee645ae0d5d036a25750
jeepney==0.7.1
jieba==0.42.1
Jinja2==3.1.3
joblib==1.4.0
jsonpickle==3.0.3
jsonschema==4.19.2
jsonschema-specifications==2023.12.1
jupyter-client==6.1.12
jupyter-console==6.1.0
jupyter-server==1.24.0
jupyter_core==5.7.2
jupyterlab_pygments==0.3.0
jupyterlab_widgets==3.0.10
kaggle==1.5.16
kagglehub==0.2.2
keras==2.15.0
keyring==23.5.0
kiwisolver==1.4.5
langcodes==3.3.0
launchpadlib==1.10.16
lazr.restfulclient==0.14.4
lazr.uri==1.0.6
lazy_loader==0.4
libclang==18.1.1
librosa==0.10.1
lightgbm==4.1.0
linkify-it-py==2.0.3
llvmlite==0.41.1
locket==1.0.0
logical-unification==0.4.6
lxml==4.9.4
malloy==2023.1067
Markdown==3.6
markdown-it-py==3.0.0
MarkupSafe==2.1.5
matplotlib==3.7.1
matplotlib-inline==0.1.6
matplotlib-venn==0.11.10
mdit-py-plugins==0.4.0
mdurl==0.1.2
miniKanren==1.0.3
missingno==0.5.2
mistune==0.8.4
mizani==0.9.3
mkl==2023.2.0
ml-dtypes==0.2.0
mlxtend==0.22.0
more-itertools==10.1.0
moviepy==1.0.3
mpmath==1.3.0
msgpack==1.0.8
multidict==6.0.5
multipledispatch==1.0.0
multitasking==0.0.11
murmurhash==1.0.10
music21==9.1.0
natsort==8.4.0
nbclassic==1.0.0
nbclient==0.10.0
nbconvert==6.5.4
nbformat==5.10.4
nest-asyncio==1.6.0
networkx==3.3
nibabel==4.0.2
nltk==3.8.1
notebook==6.5.5
notebook_shim==0.2.4
numba==0.58.1
numexpr==2.10.0
numpy==1.25.2
oauth2client==4.1.3
oauthlib==3.2.2
opencv-contrib-python==4.8.0.76
opencv-python==4.8.0.76
opencv-python-headless==4.9.0.80
openpyxl==3.1.2
opt-einsum==3.3.0
optax==0.2.2
orbax-checkpoint==0.4.4
osqp==0.6.2.post8
packaging==24.0
pandas==2.0.3
pandas-datareader==0.10.0
pandas-gbq==0.19.2
pandas-stubs==2.0.3.230814
pandocfilters==1.5.1
panel==1.3.8
param==2.1.0
parso==0.8.4
parsy==2.1
partd==1.4.1
pathlib==1.0.1
patsy==0.5.6
peewee==3.17.1
pexpect==4.9.0
pickleshare==0.7.5
Pillow==9.4.0
pip==23.1.2
pip-tools==6.13.0
platformdirs==4.2.0
plotly==5.15.0
plotnine==0.12.4
pluggy==1.4.0
polars==0.20.2
pooch==1.8.1
portpicker==1.5.2
prefetch-generator==1.0.3
preshed==3.0.9
prettytable==3.10.0
proglog==0.1.10
progressbar2==4.2.0
prometheus_client==0.20.0
promise==2.3
prompt-toolkit==3.0.43
prophet==1.1.5
proto-plus==1.23.0
protobuf==3.20.3
psutil==5.9.5
psycopg2==2.9.9
ptyprocess==0.7.0
py-cpuinfo==9.0.0
py4j==0.10.9.7
pyarrow==14.0.2
pyarrow-hotfix==0.6
pyasn1==0.6.0
pyasn1_modules==0.4.0
pycocotools==2.0.7
pycparser==2.22
pydantic==2.6.4
pydantic_core==2.16.3
pydata-google-auth==1.8.2
pydot==1.4.2
pydot-ng==2.0.0
pydotplus==2.0.2
PyDrive==1.3.1
PyDrive2==1.6.3
pyerfa==2.0.1.3
pygame==2.5.2
Pygments==2.16.1
PyGObject==3.42.1
PyJWT==2.3.0
pymc==5.10.4
pymystem3==0.2.0
PyOpenGL==3.1.7
pyOpenSSL==24.1.0
pyparsing==3.1.2
pyperclip==1.8.2
pyproj==3.6.1
pyproject_hooks==1.0.0
pyshp==2.3.1
PySocks==1.7.1
pytensor==2.18.6
pytest==7.4.4
python-apt @ file:///backend-container/containers/python_apt-0.0.0-cp310-cp310-linux_x86_64.whl#sha256=b209c7165d6061963abe611492f8c91c3bcef4b7a6600f966bab58900c63fefa
python-box==7.1.1
python-dateutil==2.8.2
python-louvain==0.16
python-slugify==8.0.4
python-utils==3.8.2
pytz==2023.4
pyviz_comms==3.0.2
PyWavelets==1.6.0
PyYAML==6.0.1
pyzmq==23.2.1
qdldl==0.1.7.post1
qudida==0.0.4
ratelim==0.1.6
referencing==0.34.0
regex==2023.12.25
requests==2.31.0
requests-oauthlib==1.3.1
requirements-parser==0.9.0
rich==13.7.1
rpds-py==0.18.0
rpy2==3.4.2
rsa==4.9
safetensors==0.4.2
scikit-image==0.19.3
scikit-learn==1.2.2
scipy==1.11.4
scooby==0.9.2
scs==3.2.4.post1
seaborn==0.13.1
SecretStorage==3.3.1
Send2Trash==1.8.3
sentencepiece==0.1.99
setuptools==67.7.2
shapely==2.0.3
six==1.16.0
sklearn-pandas==2.2.0
smart-open==6.4.0
sniffio==1.3.1
snowballstemmer==2.2.0
sortedcontainers==2.4.0
soundfile==0.12.1
soupsieve==2.5
soxr==0.3.7
spacy==3.7.4
spacy-legacy==3.0.12
spacy-loggers==1.0.5
Sphinx==5.0.2
sphinxcontrib-applehelp==1.0.8
sphinxcontrib-devhelp==1.0.6
sphinxcontrib-htmlhelp==2.0.5
sphinxcontrib-jsmath==1.0.1
sphinxcontrib-qthelp==1.0.7
sphinxcontrib-serializinghtml==1.1.10
SQLAlchemy==2.0.29
sqlglot==20.11.0
sqlparse==0.4.4
srsly==2.4.8
stanio==0.5.0
statsmodels==0.14.1
sympy==1.12
tables==3.8.0
tabulate==0.9.0
tbb==2021.12.0
tblib==3.0.0
tenacity==8.2.3
tensorboard==2.15.2
tensorboard-data-server==0.7.2
tensorflow @ https://storage.googleapis.com/colab-tf-builds-public-09h6ksrfwbb9g9xv/tensorflow-2.15.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=a2ec79931350b378c1ef300ca836b52a55751acb71a433582508a07f0de57c42
tensorflow-datasets==4.9.4
tensorflow-estimator==2.15.0
tensorflow-gcs-config==2.15.0
tensorflow-hub==0.16.1
tensorflow-io-gcs-filesystem==0.36.0
tensorflow-metadata==1.14.0
tensorflow-probability==0.23.0
tensorstore==0.1.45
termcolor==2.4.0
terminado==0.18.1
text-unidecode==1.3
textblob==0.17.1
tf-slim==1.1.0
tf_keras==2.15.1
thinc==8.2.3
threadpoolctl==3.4.0
tifffile==2024.2.12
tinycss2==1.2.1
tokenizers==0.15.2
toml==0.10.2
tomli==2.0.1
toolz==0.12.1
torch @ https://download.pytorch.org/whl/cu121/torch-2.2.1%2Bcu121-cp310-cp310-linux_x86_64.whl#sha256=1adf430f01ff649c848ac021785e18007b0714fdde68e4e65bd0c640bf3fb8e1
torchaudio @ https://download.pytorch.org/whl/cu121/torchaudio-2.2.1%2Bcu121-cp310-cp310-linux_x86_64.whl#sha256=23f6236429e2bf676b820e8e7221a1d58aaf908bff2ba2665aa852df71a97961
torchdata==0.7.1
torchsummary==1.5.1
torchtext==0.17.1
torchvision @ https://download.pytorch.org/whl/cu121/torchvision-0.17.1%2Bcu121-cp310-cp310-linux_x86_64.whl#sha256=27af47915f6e762c1d44e58e8088d22ac97445668f9f793524032b2baf4f34bd
tornado==6.3.3
tqdm==4.66.2
traitlets==5.7.1
traittypes==0.2.1
transformers==4.38.2
triton==2.2.0
tweepy==4.14.0
typer==0.9.4
types-pytz==2024.1.0.20240203
types-setuptools==69.5.0.20240415
typing_extensions==4.11.0
tzdata==2024.1
tzlocal==5.2
uc-micro-py==1.0.3
uritemplate==4.1.1
urllib3==2.0.7
vega-datasets==0.9.0
wadllib==1.3.6
wasabi==1.1.2
wcwidth==0.2.13
weasel==0.3.4
webcolors==1.13
webencodings==0.5.1
websocket-client==1.7.0
Werkzeug==3.0.2
wheel==0.43.0
widgetsnbextension==3.6.6
wordcloud==1.9.3
wrapt==1.14.1
xarray==2023.7.0
xarray-einstats==0.7.0
xgboost==2.0.3
xlrd==2.0.1
xyzservices==2024.4.0
yarl==1.9.4
yellowbrick==1.5
yfinance==0.2.37
zict==3.0.0
Expected behavior
Is this because the function was introduced in scikit-learn version 1.0, and the current environment may be using an older version?
Actual behavior
ImportError: cannot import name 'plot_confusion_matrix' from 'sklearn.metrics' (/usr/local/lib/python3.10/dist-packages/sklearn/metrics/init.py)\
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