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scikit-learn | scikit-learn : machine learning in Python | Machine Learning library

 by   scikit-learn Python Version: 1.0.2 License: BSD-3-Clause

 by   scikit-learn Python Version: 1.0.2 License: BSD-3-Clause

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kandi X-RAY | scikit-learn Summary

scikit-learn is a Python library typically used in Institutions, Learning, Education, Artificial Intelligence, Machine Learning, Pandas applications. scikit-learn has no bugs, it has build file available, it has a Permissive License and it has high support. However scikit-learn has 1 vulnerabilities. You can download it from GitHub.
scikit-learn: machine learning in Python
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  • scikit-learn has a highly active ecosystem.
  • It has 49728 star(s) with 22922 fork(s). There are 2190 watchers for this library.
  • There were 2 major release(s) in the last 12 months.
  • There are 1608 open issues and 7704 have been closed. On average issues are closed in 565 days. There are 730 open pull requests and 0 closed requests.
  • It has a positive sentiment in the developer community.
  • The latest version of scikit-learn is 1.0.2
scikit-learn Support
Best in #Machine Learning
Average in #Machine Learning
scikit-learn Support
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Average in #Machine Learning

quality kandi Quality

  • scikit-learn has 0 bugs and 0 code smells.
scikit-learn Quality
Best in #Machine Learning
Average in #Machine Learning
scikit-learn Quality
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Average in #Machine Learning

securitySecurity

  • scikit-learn has 1 vulnerability issues reported (1 critical, 0 high, 0 medium, 0 low).
  • scikit-learn code analysis shows 0 unresolved vulnerabilities.
  • There are 0 security hotspots that need review.
scikit-learn Security
Best in #Machine Learning
Average in #Machine Learning
scikit-learn Security
Best in #Machine Learning
Average in #Machine Learning

license License

  • scikit-learn is licensed under the BSD-3-Clause License. This license is Permissive.
  • Permissive licenses have the least restrictions, and you can use them in most projects.
scikit-learn License
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Average in #Machine Learning
scikit-learn License
Best in #Machine Learning
Average in #Machine Learning

buildReuse

  • scikit-learn releases are available to install and integrate.
  • Build file is available. You can build the component from source.
  • scikit-learn saves you 147220 person hours of effort in developing the same functionality from scratch.
  • It has 176758 lines of code, 9057 functions and 897 files.
  • It has high code complexity. Code complexity directly impacts maintainability of the code.
scikit-learn Reuse
Best in #Machine Learning
Average in #Machine Learning
scikit-learn Reuse
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Average in #Machine Learning
Top functions reviewed by kandi - BETA

kandi has reviewed scikit-learn and discovered the below as its top functions. This is intended to give you an instant insight into scikit-learn implemented functionality, and help decide if they suit your requirements.

  • Linear Grammarization problem .
  • Linear path solver .
  • Logistic regression .
  • Compute a dictionary of learning statistics for a given dataset .
  • Local embedding .
  • Plot the image .
  • Plot the partial dependence of the estimator .
  • Check if an array is valid .
  • r Enet Path Method .
  • Calculate partial dependence of the estimator .

scikit-learn Key Features

scikit-learn: machine learning in Python

scikit-learn Examples and Code Snippets

Installing scipy and scikit-learn on apple m1

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# SciPy:
python -m pip install --no-cache --no-use-pep517 pythran cython pybind11 gast"==0.4.0"
pyenv rehash
python -m pip install --no-cache --no-binary :all: --no-use-pep517 scipy"==1.7.1"

# Scikit-Learn
python -m pip install --no-use-pep517 scikit-learn"==0.24.2"

brew install openblas openssl@1.1 pkg-config pyenv pyenv-virtualenv
python -m pip install numpy==1.19.5
----------------------- 
# SciPy:
python -m pip install --no-cache --no-use-pep517 pythran cython pybind11 gast"==0.4.0"
pyenv rehash
python -m pip install --no-cache --no-binary :all: --no-use-pep517 scipy"==1.7.1"

# Scikit-Learn
python -m pip install --no-use-pep517 scikit-learn"==0.24.2"

brew install openblas openssl@1.1 pkg-config pyenv pyenv-virtualenv
python -m pip install numpy==1.19.5
----------------------- 


    >> /opt/homebrew/bin/brew install openblas
    >> export OPENBLAS=$(/opt/homebrew/bin/brew --prefix openblas)
    >> export CFLAGS="-falign-functions=8 ${CFLAGS}"
    >> git clone https://github.com/scipy/scipy.git
    >> cd scipy
    >> git submodule update --init
    >> /opt/homebrew/bin/pip3 install .
    >> /opt/homebrew/bin/pip3 install scikit-learn
----------------------- 
RUN OPENBLAS="/opt/homebrew/opt/openblas" CFLAGS="-falign-functions=8 ${CFLAGS}" pip3 install scipy
RUN OPENBLAS="/opt/homebrew/opt/openblas" CFLAGS="-falign-functions=8 ${CFLAGS}" pip3 install scikit-learn
----------------------- 
brew install openblas
export OPENBLAS=$(/opt/homebrew/bin/brew --prefix openblas)
export CFLAGS="-falign-functions=8 ${CFLAGS}"
# ^ no need to add to .zshrc, just doing this once.
pip install scikit-learn # ==0.24.1 if you want

Building wheels for collected packages: scikit-learn
  Building wheel for scikit-learn (pyproject.toml) ... done
  Created wheel for scikit-learn: filename=scikit_learn-1.0.1-cp38-cp38-macosx_12_0_arm64.whl size=6364030 sha256=0b0cc9a21af775e0c8077ee71698ff62da05ab62efc914c5c15cd4bf97867b31
Successfully built scikit-learn
Installing collected packages: scipy, scikit-learn
Successfully installed scikit-learn-1.0.1 scipy-1.7.3

Collecting click>=7.0
  Downloading click-8.0.3-py3-none-any.whl

Collecting grpcio>=1.28.1
  Downloading grpcio-1.42.0.tar.gz (21.3 MB)
     |████████████████████████████████| 21.3 MB 12.7 MB/s
  Preparing metadata (setup.py) ... done

## later in the process it installs using setuptools 
Running setup.py install for grpcio ... done
----------------------- 
brew install openblas
export OPENBLAS=$(/opt/homebrew/bin/brew --prefix openblas)
export CFLAGS="-falign-functions=8 ${CFLAGS}"
# ^ no need to add to .zshrc, just doing this once.
pip install scikit-learn # ==0.24.1 if you want

Building wheels for collected packages: scikit-learn
  Building wheel for scikit-learn (pyproject.toml) ... done
  Created wheel for scikit-learn: filename=scikit_learn-1.0.1-cp38-cp38-macosx_12_0_arm64.whl size=6364030 sha256=0b0cc9a21af775e0c8077ee71698ff62da05ab62efc914c5c15cd4bf97867b31
Successfully built scikit-learn
Installing collected packages: scipy, scikit-learn
Successfully installed scikit-learn-1.0.1 scipy-1.7.3

Collecting click>=7.0
  Downloading click-8.0.3-py3-none-any.whl

Collecting grpcio>=1.28.1
  Downloading grpcio-1.42.0.tar.gz (21.3 MB)
     |████████████████████████████████| 21.3 MB 12.7 MB/s
  Preparing metadata (setup.py) ... done

## later in the process it installs using setuptools 
Running setup.py install for grpcio ... done
----------------------- 
brew install openblas
export OPENBLAS=$(/opt/homebrew/bin/brew --prefix openblas)
export CFLAGS="-falign-functions=8 ${CFLAGS}"
# ^ no need to add to .zshrc, just doing this once.
pip install scikit-learn # ==0.24.1 if you want

Building wheels for collected packages: scikit-learn
  Building wheel for scikit-learn (pyproject.toml) ... done
  Created wheel for scikit-learn: filename=scikit_learn-1.0.1-cp38-cp38-macosx_12_0_arm64.whl size=6364030 sha256=0b0cc9a21af775e0c8077ee71698ff62da05ab62efc914c5c15cd4bf97867b31
Successfully built scikit-learn
Installing collected packages: scipy, scikit-learn
Successfully installed scikit-learn-1.0.1 scipy-1.7.3

Collecting click>=7.0
  Downloading click-8.0.3-py3-none-any.whl

Collecting grpcio>=1.28.1
  Downloading grpcio-1.42.0.tar.gz (21.3 MB)
     |████████████████████████████████| 21.3 MB 12.7 MB/s
  Preparing metadata (setup.py) ... done

## later in the process it installs using setuptools 
Running setup.py install for grpcio ... done
----------------------- 
brew install openblas
export OPENBLAS=$(/opt/homebrew/bin/brew --prefix openblas)
export CFLAGS="-falign-functions=8 ${CFLAGS}"
# ^ no need to add to .zshrc, just doing this once.
pip install scikit-learn # ==0.24.1 if you want

Building wheels for collected packages: scikit-learn
  Building wheel for scikit-learn (pyproject.toml) ... done
  Created wheel for scikit-learn: filename=scikit_learn-1.0.1-cp38-cp38-macosx_12_0_arm64.whl size=6364030 sha256=0b0cc9a21af775e0c8077ee71698ff62da05ab62efc914c5c15cd4bf97867b31
Successfully built scikit-learn
Installing collected packages: scipy, scikit-learn
Successfully installed scikit-learn-1.0.1 scipy-1.7.3

Collecting click>=7.0
  Downloading click-8.0.3-py3-none-any.whl

Collecting grpcio>=1.28.1
  Downloading grpcio-1.42.0.tar.gz (21.3 MB)
     |████████████████████████████████| 21.3 MB 12.7 MB/s
  Preparing metadata (setup.py) ... done

## later in the process it installs using setuptools 
Running setup.py install for grpcio ... done
----------------------- 
Python 3.9.10 (main, Jan 15 2022, 11:40:53) 
[Clang 13.0.0 (clang-1300.0.29.3)] on darwin
pip 22.0.3

brew install openblas gfortran
OPENBLAS="$(brew --prefix openblas)" pip install numpy==1.19.3
OPENBLAS="$(brew --prefix openblas)" pip install scipy==1.7.2

pip install cython
brew install libomp
export CC=/usr/bin/clang
export CXX=/usr/bin/clang++
export CPPFLAGS="$CPPFLAGS -Xpreprocessor -fopenmp"
export CFLAGS="$CFLAGS -I/opt/homebrew/Cellar/libomp/13.0.1/include"
export CXXFLAGS="$CXXFLAGS -I/opt/homebrew/Cellar/libomp/13.0.1/include"
export LDFLAGS="$LDFLAGS -L/opt/homebrew/Cellar/libomp/13.0.1/lib -lomp"
export DYLD_LIBRARY_PATH=/opt/homebrew/Cellar/libomp/13.0.1/lib
pip install scikit-learn==0.21.3
----------------------- 
Python 3.9.10 (main, Jan 15 2022, 11:40:53) 
[Clang 13.0.0 (clang-1300.0.29.3)] on darwin
pip 22.0.3

brew install openblas gfortran
OPENBLAS="$(brew --prefix openblas)" pip install numpy==1.19.3
OPENBLAS="$(brew --prefix openblas)" pip install scipy==1.7.2

pip install cython
brew install libomp
export CC=/usr/bin/clang
export CXX=/usr/bin/clang++
export CPPFLAGS="$CPPFLAGS -Xpreprocessor -fopenmp"
export CFLAGS="$CFLAGS -I/opt/homebrew/Cellar/libomp/13.0.1/include"
export CXXFLAGS="$CXXFLAGS -I/opt/homebrew/Cellar/libomp/13.0.1/include"
export LDFLAGS="$LDFLAGS -L/opt/homebrew/Cellar/libomp/13.0.1/lib -lomp"
export DYLD_LIBRARY_PATH=/opt/homebrew/Cellar/libomp/13.0.1/lib
pip install scikit-learn==0.21.3
----------------------- 
Python 3.9.10 (main, Jan 15 2022, 11:40:53) 
[Clang 13.0.0 (clang-1300.0.29.3)] on darwin
pip 22.0.3

brew install openblas gfortran
OPENBLAS="$(brew --prefix openblas)" pip install numpy==1.19.3
OPENBLAS="$(brew --prefix openblas)" pip install scipy==1.7.2

pip install cython
brew install libomp
export CC=/usr/bin/clang
export CXX=/usr/bin/clang++
export CPPFLAGS="$CPPFLAGS -Xpreprocessor -fopenmp"
export CFLAGS="$CFLAGS -I/opt/homebrew/Cellar/libomp/13.0.1/include"
export CXXFLAGS="$CXXFLAGS -I/opt/homebrew/Cellar/libomp/13.0.1/include"
export LDFLAGS="$LDFLAGS -L/opt/homebrew/Cellar/libomp/13.0.1/lib -lomp"
export DYLD_LIBRARY_PATH=/opt/homebrew/Cellar/libomp/13.0.1/lib
pip install scikit-learn==0.21.3
----------------------- 
conda install --channel=conda-forge scikit-learn

negative values for mean squared errors in sae package for R

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data.frame(UnbiasedMSE = fit1$mse) %>% 
  ggplot(aes(x = seq_along(UnbiasedMSE), 
             y = UnbiasedMSE)) + 
  geom_line() + geom_point() + 
  scale_y_continuous(labels = scales::comma) + 
  theme_bw()

Colab: (0) UNIMPLEMENTED: DNN library is not found

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!pip install tensorflow==2.7.0
----------------------- 
'tensorflow==2.7.0',
'tf-models-official==2.7.0',
'tensorflow_io==0.23.1',

How to install local package with conda

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package:
  name: jive
  version: "0.2.1"

source:
  path: .

build:
  script: python -m pip install --no-deps --ignore-installed .

requirements:
  host:
     - python
     - pip
     - setuptools
  run:
     - python
     - numpy
     - pandas
     - scipy
     - seaborn
     - matplotlib
     - scikit-learn
     - statsmodels
     - joblib
     - bokeh

Cannot find conda info. Please verify your conda installation on EMR

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wget https://repo.anaconda.com/miniconda/Miniconda3-py37_4.9.2-Linux-x86_64.sh  -O /home/hadoop/miniconda.sh \
    && /bin/bash ~/miniconda.sh -b -p $HOME/conda

echo -e '\n export PATH=$HOME/conda/bin:$PATH' >> $HOME/.bashrc && source $HOME/.bashrc


conda config --set always_yes yes --set changeps1 no
conda config -f --add channels conda-forge


conda create -n zoo python=3.7 # "zoo" is conda environment name
conda init bash
source activate zoo
conda install python 3.7.0 -c conda-forge orca 
sudo /home/hadoop/conda/envs/zoo/bin/python3.7 -m pip install virtualenv

“spark.pyspark.python": "/home/hadoop/conda/envs/zoo/bin/python3",
"spark.pyspark.virtualenv.enabled": "true",
"spark.pyspark.virtualenv.type":"native",
"spark.pyspark.virtualenv.bin.path":"/home/hadoop/conda/envs/zoo/bin/,
"zeppelin.pyspark.python" : "/home/hadoop/conda/bin/python",
"zeppelin.python": "/home/hadoop/conda/bin/python"
----------------------- 
wget https://repo.anaconda.com/miniconda/Miniconda3-py37_4.9.2-Linux-x86_64.sh  -O /home/hadoop/miniconda.sh \
    && /bin/bash ~/miniconda.sh -b -p $HOME/conda

echo -e '\n export PATH=$HOME/conda/bin:$PATH' >> $HOME/.bashrc && source $HOME/.bashrc


conda config --set always_yes yes --set changeps1 no
conda config -f --add channels conda-forge


conda create -n zoo python=3.7 # "zoo" is conda environment name
conda init bash
source activate zoo
conda install python 3.7.0 -c conda-forge orca 
sudo /home/hadoop/conda/envs/zoo/bin/python3.7 -m pip install virtualenv

“spark.pyspark.python": "/home/hadoop/conda/envs/zoo/bin/python3",
"spark.pyspark.virtualenv.enabled": "true",
"spark.pyspark.virtualenv.type":"native",
"spark.pyspark.virtualenv.bin.path":"/home/hadoop/conda/envs/zoo/bin/,
"zeppelin.pyspark.python" : "/home/hadoop/conda/bin/python",
"zeppelin.python": "/home/hadoop/conda/bin/python"

Updating Python sklearn Lasso(normalize=True) to Use Pipeline

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lasso = Lasso(alpha=0.4,random_state=99)
lasso.fit(StandardScaler().fit_transform(X),y)
print(lasso.coef_)

[-0.         -0.30409556 -2.33203165 -0.          0.51040194  1.45942351
 -1.02516505 -4.57678764]

pipe = Pipeline(steps=[    
('scaler',StandardScaler()),
('lasso',Lasso(alpha=0.4,random_state=99))
])

pipe.fit(X, y)
lasso_coef = pipe.named_steps['lasso'].coef_
print(lasso_coef)

[-0.         -0.30409556 -2.33203165 -0.          0.51040194  1.45942351
 -1.02516505 -4.57678764]
----------------------- 
# Import Lasso
from sklearn.linear_model import Lasso

def L2Normalizer(X) :
    X = X - np.mean(X, axis=0)
    X = X / np.linalg.norm(X, axis=0)
    return X

# Instantiate a lasso regressor: lasso
lasso = Lasso(alpha=0.4)

# Fit the regressor to the data
reg = lasso.fit(L2Normalizer(X), y)

# Compute and print the coefficients
lasso_coef = reg.coef_ / np.linalg.norm(X-np.mean(X, axis=0), axis=0)
print(lasso_coef)

# Plot the coefficients
plt.grid(color="#E5E5E5")
plt.plot(range(len(df_columns)), lasso_coef)
plt.xticks(range(len(df_columns)), df_columns.values, rotation=60)
plt.margins(0.02)

plt.show()

Sklearn: Calibrate a multi-label classification with CalibratedClassifierCV

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from sklearn.datasets import make_multilabel_classification
from sklearn.tree import DecisionTreeClassifier
from sklearn.multioutput import MultiOutputClassifier
from sklearn.model_selection import train_test_split, StratifiedKFold

import numpy as np

# Generate a sample multilabel target
X, y = make_multilabel_classification(n_classes=4, random_state=0)

y
>>> 
array([[1, 0, 1, 0],
       [0, 0, 0, 0],
       [1, 0, 1, 0],
       ...
       [0, 0, 0, 0],
       [0, 1, 1, 1],
       [1, 1, 0, 1]])

# Split in train/test
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.9, random_state=42
)

# Splits Stratify target variable
cv = StratifiedKFold(n_splits=2)

# Decision tree support by default multiclass target or use OneVsRest if marginal probabilities
clf = OneVsRestClassifier(DecisionTreeClassifier(max_depth=10))

# Calibrate estimator probabilities
calibrated_clf = CalibratedClassifierCV(base_estimator=clf, cv=cv)

# calibrated_clf target is one dimensional, extend classifier to multi-target classification.
multioutput_clf = MultiOutputClassifier(calibrated_clf).fit(X_train, y_train)

# Check predict 
multioutput_clf.predict(X_test[-5:])
>>> 
array([[0, 0, 1, 1],
       [0, 0, 0, 1],
       [0, 0, 0, 1],
       [0, 0, 0, 1],
       [0, 0, 0, 1]])

# Check predict_proba
multioutput_clf.predict_proba(X_test[-5:])
>>>
[array([[0.78333315, 0.21666685],
        [0.78333315, 0.21666685],
        [0.78333315, 0.21666685],
        [0.78333315, 0.21666685],
        [0.78333315, 0.21666685]]),
 array([[0.59166537, 0.40833463],
        [0.59166537, 0.40833463],
        [0.40833361, 0.59166639],
        [0.59166537, 0.40833463],
        [0.59166537, 0.40833463]]),
 array([[0.61666922, 0.38333078],
        [0.61666427, 0.38333573],
        [0.80000098, 0.19999902],
        [0.61666427, 0.38333573],
        [0.61666427, 0.38333573]]),
 array([[0.26874774, 0.73125226],
        [0.26874774, 0.73125226],
        [0.45208444, 0.54791556],
        [0.26874774, 0.73125226],
        [0.26874774, 0.73125226]])]

How to calculate correlation coefficients using sklearn CCA module?

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import numpy as np
from matplotlib import pyplot as plt
from sklearn.cross_decomposition import CCA

# rows contain the number of samples for CCA and the number of rvs goes in columns
X = np.random.randn(2000, 100)
Y = np.random.randn(2000, 50)

# num of components
n_comps = min(X.shape[1], Y.shape[1])
cca = CCA(n_components=n_comps)
cca.fit(X, Y)
X_c, Y_c = cca.transform(X, Y)

# calculate and plot the correlations of all components
corrs = [np.corrcoef(X_c[:, i], Y_c[:, i])[0, 1] for i in range(n_comps)]    
plt.plot(corrs)
plt.xlabel('cca_idx')
plt.ylabel('cca_corr')
plt.show()

Y = np.dot(X, np.random.randn(100, 100))
----------------------- 
import numpy as np
from matplotlib import pyplot as plt
from sklearn.cross_decomposition import CCA

# rows contain the number of samples for CCA and the number of rvs goes in columns
X = np.random.randn(2000, 100)
Y = np.random.randn(2000, 50)

# num of components
n_comps = min(X.shape[1], Y.shape[1])
cca = CCA(n_components=n_comps)
cca.fit(X, Y)
X_c, Y_c = cca.transform(X, Y)

# calculate and plot the correlations of all components
corrs = [np.corrcoef(X_c[:, i], Y_c[:, i])[0, 1] for i in range(n_comps)]    
plt.plot(corrs)
plt.xlabel('cca_idx')
plt.ylabel('cca_corr')
plt.show()

Y = np.dot(X, np.random.randn(100, 100))

ImportError: cannot import name 'BatchNormalization' from 'keras.layers.normalization'

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from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import (
    BatchNormalization, SeparableConv2D, MaxPooling2D, Activation, Flatten, Dropout, Dense
)
from tensorflow.keras import backend as K


class CancerNet:
    @staticmethod
    def build(width, height, depth, classes):
        model = Sequential()
        shape = (height, width, depth)
        channelDim = -1

        if K.image_data_format() == "channels_first":
            shape = (depth, height, width)
            channelDim = 1

        model.add(SeparableConv2D(32, (3, 3), padding="same", input_shape=shape))
        model.add(Activation("relu"))
        model.add(BatchNormalization(axis=channelDim))
        model.add(MaxPooling2D(pool_size=(2, 2)))
        model.add(Dropout(0.25))

        model.add(SeparableConv2D(64, (3, 3), padding="same"))
        model.add(Activation("relu"))
        model.add(BatchNormalization(axis=channelDim))
        model.add(SeparableConv2D(64, (3, 3), padding="same"))
        model.add(Activation("relu"))
        model.add(BatchNormalization(axis=channelDim))
        model.add(MaxPooling2D(pool_size=(2, 2)))
        model.add(Dropout(0.25))

        model.add(SeparableConv2D(128, (3, 3), padding="same"))
        model.add(Activation("relu"))
        model.add(BatchNormalization(axis=channelDim))
        model.add(SeparableConv2D(128, (3, 3), padding="same"))
        model.add(Activation("relu"))
        model.add(BatchNormalization(axis=channelDim))
        model.add(SeparableConv2D(128, (3, 3), padding="same"))
        model.add(Activation("relu"))
        model.add(BatchNormalization(axis=channelDim))
        model.add(MaxPooling2D(pool_size=(2, 2)))
        model.add(Dropout(0.25))

        model.add(Flatten())
        model.add(Dense(256))
        model.add(Activation("relu"))
        model.add(BatchNormalization())
        model.add(Dropout(0.5))

        model.add(Dense(classes))
        model.add(Activation("softmax"))

        return model

model = CancerNet()
----------------------- 
from tensorflow.keras.layers import BatchNormalization

Error message with sklearn function " 'RocCurveDisplay' has no attribute 'from_predictions' "

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pip install --upgrade scikit-learn

Community Discussions

Trending Discussions on scikit-learn
  • Installing scipy and scikit-learn on apple m1
  • negative values for mean squared errors in sae package for R
  • Colab: (0) UNIMPLEMENTED: DNN library is not found
  • How to install local package with conda
  • Cannot find conda info. Please verify your conda installation on EMR
  • Updating Python sklearn Lasso(normalize=True) to Use Pipeline
  • Can't deploy streamlit app on share.streamlit.io
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Trending Discussions on scikit-learn

QUESTION

Installing scipy and scikit-learn on apple m1

Asked 2022-Mar-22 at 06:21

The installation on the m1 chip for the following packages: Numpy 1.21.1, pandas 1.3.0, torch 1.9.0 and a few other ones works fine for me. They also seem to work properly while testing them. However when I try to install scipy or scikit-learn via pip this error appears:

ERROR: Failed building wheel for numpy

Failed to build numpy

ERROR: Could not build wheels for numpy which use PEP 517 and cannot be installed directly

Why should Numpy be build again when I have the latest version from pip already installed?

Every previous installation was done using python3.9 -m pip install ... on Mac OS 11.3.1 with the apple m1 chip.

Maybe somebody knows how to deal with this error or if its just a matter of time.

ANSWER

Answered 2021-Aug-02 at 14:33

Please see this note of scikit-learn about

Installing on Apple Silicon M1 hardware

The recently introduced macos/arm64 platform (sometimes also known as macos/aarch64) requires the open source community to upgrade the build configuation and automation to properly support it.

At the time of writing (January 2021), the only way to get a working installation of scikit-learn on this hardware is to install scikit-learn and its dependencies from the conda-forge distribution, for instance using the miniforge installers:

https://github.com/conda-forge/miniforge

The following issue tracks progress on making it possible to install scikit-learn from PyPI with pip:

https://github.com/scikit-learn/scikit-learn/issues/19137

Source https://stackoverflow.com/questions/68620927

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Install scikit-learn

You can download it from GitHub.
You can use scikit-learn 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.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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