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Access the regularization paths obtained from ElasticNetCV in sklearn


Access the regularization paths obtained from ElasticNetCV in sklearn

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Access the regularization paths obtained from ElasticNetCV in sklearn
Tag : python , By : kameel
Date : November 27 2020, 03:01 PM

With these it helps I would like to get these plots: http://scikit-learn.org/stable/auto_examples/linear_model/plot_lasso_coordinate_descent_path.html , Short answer
Not once it is fit.

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Regularization parameter setting for Randomized Regression in sklearn


Tag : python , By : moss
Date : March 29 2020, 07:55 AM
this one helps. Because RandomizedLogisticRegression is used for feature selection, it would need to be cross validated as part of a pipeline. You can apply GridSearchCV to a Pipeline which contains it as a feature selection step along with your classifier of choice. An example might look like:
pipeline = Pipeline([
  ('fs', RandomizedLogisticRegression()),
  ('clf', LogisticRegression())
])

params = {'fs__C':[0.1, 1, 10]}

grid_search = GridSearchCV(pipeline, params)

How to pass regularization params to model selection (sklearn)?


Tag : python , By : pttr
Date : March 29 2020, 07:55 AM
may help you . I propose that you use GridSearchCV. This will handle your goal.
import numpy as np
import sklearn.linear_model as linear_model
import sklearn.model_selection as model_selection
import matplotlib.pyplot as plt

n_samples_train, n_samples_test, n_features = 75, 150, 500
np.random.seed(0)
coef = np.random.randn(n_features)
coef[50:] = 0.0  # only the top 10 features are impacting the model
X = np.random.randn(n_samples_train + n_samples_test, n_features)
y = np.dot(X, coef)

# instantiate your ridge model first
ridge = linear_model.Ridge(fit_intercept=False)

# now create your grid search object
grid = model_selection.GridSearchCV(ridge, param_grid={"alpha": [0.1, 1, 10]},
                                    cv=10, scoring='neg_mean_squared_error',
                                    n_jobs=-1, return_train_score=True)

# fit
grid.fit(X, y)
# show cv results
grid.cv_results_
{'mean_fit_time': array([ 0.03192089,  0.00980701,  0.00800555]),
 'mean_score_time': array([ 0.00030019,  0.00010006,  0.00020015]),
 'mean_test_score': array([-39.76136733, -39.7700976 , -39.90061844]),
 'mean_train_score': array([ -4.55700050e-06,  -4.51109497e-04,  -4.10175706e-02]),
 'param_alpha': masked_array(data = [0.1 1 10],
              mask = [False False False],
        fill_value = ?),
 'params': [{'alpha': 0.1}, {'alpha': 1}, {'alpha': 10}],
 'rank_test_score': array([1, 2, 3]),
 'split0_test_score': array([-46.32878735, -46.33132325, -46.42467545]),
 'split0_train_score': array([ -4.33377675e-06,  -4.29368239e-04,  -3.93263016e-02]),
 'split1_test_score': array([-23.65685521, -23.70826719, -24.23222395]),
 'split1_train_score': array([ -4.71698023e-06,  -4.66957024e-04,  -4.24618411e-02]),
 'split2_test_score': array([-25.10691203, -25.11680407, -25.25664803]),
 'split2_train_score': array([ -5.07409398e-06,  -5.02011049e-04,  -4.53910939e-02]),
 'split3_test_score': array([-49.02718917, -48.98855648, -48.69939824]),
 'split3_train_score': array([ -4.48268791e-06,  -4.43818654e-04,  -4.04080484e-02]),
 'split4_test_score': array([-58.25312869, -58.30869711, -58.89565988]),
 'split4_train_score': array([ -4.39368907e-06,  -4.35091383e-04,  -3.96619155e-02]),
 'split5_test_score': array([-34.55649537, -34.61271569, -35.15148114]),
 'split5_train_score': array([ -4.79768741e-06,  -4.74334047e-04,  -4.26642818e-02]),
 'split6_test_score': array([-48.89509143, -48.92121206, -49.21661278]),
 'split6_train_score': array([ -4.27579707e-06,  -4.23581125e-04,  -3.87674266e-02]),
 'split7_test_score': array([-37.843457  , -37.74098694, -36.80684638]),
 'split7_train_score': array([ -4.18314427e-06,  -4.14549050e-04,  -3.80652817e-02]),
 'split8_test_score': array([-49.12264863, -49.14574319, -49.42603306]),
 'split8_train_score': array([ -4.42193101e-06,  -4.37800204e-04,  -3.98496419e-02]),
 'split9_test_score': array([-24.66101592, -24.66289001, -24.7145367 ]),
 'split9_train_score': array([ -4.89021729e-06,  -4.83584192e-04,  -4.35798731e-02]),
 'std_fit_time': array([ 0.00705221,  0.01158253,  0.00279475]),
 'std_score_time': array([ 0.00045855,  0.00030019,  0.0004003 ]),
 'std_test_score': array([ 11.77428115,  11.77462622,  11.79882886]),
 'std_train_score': array([  2.79473118e-07,   2.73681039e-05,   2.25174600e-03])}

sklearn stratified k-fold CV with linear model like ElasticNetCV


Tag : python , By : Chandra P Singh
Date : March 29 2020, 07:55 AM
should help you out using cross validation (CV) with sklearn is quite easy and straight-forward. But the default implementation when setting cv=5 in a linear CV model, like ElasticNetCV or LassoCV is a KFold CV. For various reasons I'd like to use a StratifiedKFold. From the documentation, it seems like any CV method can be given with cv=. , The root of your problem is this line:
y = np.arange(100) + np.random.rand(100)
from sklearn.linear_model import ElasticNetCV
from sklearn.model_selection import KFold, StratifiedKFold
import numpy as np

x = np.arange(100, dtype=np.float64).reshape(-1, 1)
y = np.random.choice([0,1], size=100)

# KFold default implementation:
model_default = ElasticNetCV(cv=5)
model_default.fit(x, y)  # works fine
# KFold given as cv explicitly:
model_kfexp = ElasticNetCV(cv=KFold(5))
model_kfexp.fit(x, y)  # also works fine

# StratifiedKFold given as cv explicitly:
model_skf = ElasticNetCV(cv=StratifiedKFold(5))
model_skf.fit(x, y)  # no ERROR
x = np.arange(100, dtype=np.float64).reshape(-1, 1)
y = np.arange(100) + np.random.rand(100)

y_cat = pd.cut(y, 10, labels=range(10))
skf_gen = StratifiedKFold(5).split(x, y_cat)

model_skf = ElasticNetCV(cv=skf_gen)
model_skf.fit(x, y)  # no ERROR

Porting sklearn MLPClassifier to Keras with L2 regularization


Tag : development , By : Gianluca Riccardi
Date : March 29 2020, 07:55 AM
hop of those help? According to the sklearn doc, the alpha parameter is used to regularize weights
https://scikit-learn.org/stable/modules/neural_networks_supervised.html
from keras import regularizers.l2
reg1 = l2(0.0001)
reg2 = l2(0.001)

model = Sequential([
  Dense(60, activation='tanh', kernel_regularizer=reg1, bias_regularizer=reg1, activity_regularizer=reg1),
  Dense(50, activation='tanh', kernel_regularizer=reg2, bias_regularizer=reg2, activity_regularizer=reg2),
  ...

sklearn LogisticRegression without regularization


Tag : python , By : JoeCh
Date : March 29 2020, 07:55 AM
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