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Grid Search¶
We can search through a variety of combinations of hyperparameters with a grid search. While many linear models are quite simple and even come with their own specialized versions that do a search for you, this method of a grid search will can be applied to any model from sklearn, and we will need to use it later on for more complex models, such as Support Vector Machines.
Imports¶
In [1]:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
C:\ProgramData\Anaconda3\lib\site-packages\statsmodels\tools\_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead. import pandas.util.testing as tm
Data¶
In [2]:
df = pd.read_csv("../DATA/Advertising.csv")
In [3]:
df.head()
Out[3]:
| TV | radio | newspaper | sales | |
|---|---|---|---|---|
| 0 | 230.1 | 37.8 | 69.2 | 22.1 |
| 1 | 44.5 | 39.3 | 45.1 | 10.4 |
| 2 | 17.2 | 45.9 | 69.3 | 9.3 |
| 3 | 151.5 | 41.3 | 58.5 | 18.5 |
| 4 | 180.8 | 10.8 | 58.4 | 12.9 |
Formatting Data¶
In [4]:
## CREATE X and y
X = df.drop('sales',axis=1)
y = df['sales']
# TRAIN TEST SPLIT
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=101)
# SCALE DATA
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
scaler.fit(X_train)
X_train = scaler.transform(X_train)
X_test = scaler.transform(X_test)
Model¶
In [5]:
from sklearn.linear_model import ElasticNet
In [9]:
help(ElasticNet)
Help on class ElasticNet in module sklearn.linear_model.coordinate_descent:
class ElasticNet(sklearn.linear_model.base.LinearModel, sklearn.base.RegressorMixin, sklearn.base.MultiOutputMixin)
| ElasticNet(alpha=1.0, l1_ratio=0.5, fit_intercept=True, normalize=False, precompute=False, max_iter=1000, copy_X=True, tol=0.0001, warm_start=False, positive=False, random_state=None, selection='cyclic')
|
| Linear regression with combined L1 and L2 priors as regularizer.
|
| Minimizes the objective function::
|
| 1 / (2 * n_samples) * ||y - Xw||^2_2
| + alpha * l1_ratio * ||w||_1
| + 0.5 * alpha * (1 - l1_ratio) * ||w||^2_2
|
| If you are interested in controlling the L1 and L2 penalty
| separately, keep in mind that this is equivalent to::
|
| a * L1 + b * L2
|
| where::
|
| alpha = a + b and l1_ratio = a / (a + b)
|
| The parameter l1_ratio corresponds to alpha in the glmnet R package while
| alpha corresponds to the lambda parameter in glmnet. Specifically, l1_ratio
| = 1 is the lasso penalty. Currently, l1_ratio <= 0.01 is not reliable,
| unless you supply your own sequence of alpha.
|
| Read more in the :ref:`User Guide <elastic_net>`.
|
| Parameters
| ----------
| alpha : float, optional
| Constant that multiplies the penalty terms. Defaults to 1.0.
| See the notes for the exact mathematical meaning of this
| parameter.``alpha = 0`` is equivalent to an ordinary least square,
| solved by the :class:`LinearRegression` object. For numerical
| reasons, using ``alpha = 0`` with the ``Lasso`` object is not advised.
| Given this, you should use the :class:`LinearRegression` object.
|
| l1_ratio : float
| The ElasticNet mixing parameter, with ``0 <= l1_ratio <= 1``. For
| ``l1_ratio = 0`` the penalty is an L2 penalty. ``For l1_ratio = 1`` it
| is an L1 penalty. For ``0 < l1_ratio < 1``, the penalty is a
| combination of L1 and L2.
|
| fit_intercept : bool
| Whether the intercept should be estimated or not. If ``False``, the
| data is assumed to be already centered.
|
| normalize : boolean, optional, default False
| This parameter is ignored when ``fit_intercept`` is set to False.
| If True, the regressors X will be normalized before regression by
| subtracting the mean and dividing by the l2-norm.
| If you wish to standardize, please use
| :class:`sklearn.preprocessing.StandardScaler` before calling ``fit``
| on an estimator with ``normalize=False``.
|
| precompute : True | False | array-like
| Whether to use a precomputed Gram matrix to speed up
| calculations. The Gram matrix can also be passed as argument.
| For sparse input this option is always ``True`` to preserve sparsity.
|
| max_iter : int, optional
| The maximum number of iterations
|
| copy_X : boolean, optional, default True
| If ``True``, X will be copied; else, it may be overwritten.
|
| tol : float, optional
| The tolerance for the optimization: if the updates are
| smaller than ``tol``, the optimization code checks the
| dual gap for optimality and continues until it is smaller
| than ``tol``.
|
| warm_start : bool, optional
| When set to ``True``, reuse the solution of the previous call to fit as
| initialization, otherwise, just erase the previous solution.
| See :term:`the Glossary <warm_start>`.
|
| positive : bool, optional
| When set to ``True``, forces the coefficients to be positive.
|
| random_state : int, RandomState instance or None, optional, default None
| The seed of the pseudo random number generator that selects a random
| feature to update. If int, random_state is the seed used by the random
| number generator; If RandomState instance, random_state is the random
| number generator; If None, the random number generator is the
| RandomState instance used by `np.random`. Used when ``selection`` ==
| 'random'.
|
| selection : str, default 'cyclic'
| If set to 'random', a random coefficient is updated every iteration
| rather than looping over features sequentially by default. This
| (setting to 'random') often leads to significantly faster convergence
| especially when tol is higher than 1e-4.
|
| Attributes
| ----------
| coef_ : array, shape (n_features,) | (n_targets, n_features)
| parameter vector (w in the cost function formula)
|
| sparse_coef_ : scipy.sparse matrix, shape (n_features, 1) | (n_targets, n_features)
| ``sparse_coef_`` is a readonly property derived from ``coef_``
|
| intercept_ : float | array, shape (n_targets,)
| independent term in decision function.
|
| n_iter_ : array-like, shape (n_targets,)
| number of iterations run by the coordinate descent solver to reach
| the specified tolerance.
|
| Examples
| --------
| >>> from sklearn.linear_model import ElasticNet
| >>> from sklearn.datasets import make_regression
|
| >>> X, y = make_regression(n_features=2, random_state=0)
| >>> regr = ElasticNet(random_state=0)
| >>> regr.fit(X, y) # doctest: +NORMALIZE_WHITESPACE
| ElasticNet(alpha=1.0, copy_X=True, fit_intercept=True, l1_ratio=0.5,
| max_iter=1000, normalize=False, positive=False, precompute=False,
| random_state=0, selection='cyclic', tol=0.0001, warm_start=False)
| >>> print(regr.coef_) # doctest: +ELLIPSIS
| [18.83816048 64.55968825]
| >>> print(regr.intercept_) # doctest: +ELLIPSIS
| 1.451...
| >>> print(regr.predict([[0, 0]])) # doctest: +ELLIPSIS
| [1.451...]
|
|
| Notes
| -----
| To avoid unnecessary memory duplication the X argument of the fit method
| should be directly passed as a Fortran-contiguous numpy array.
|
| See also
| --------
| ElasticNetCV : Elastic net model with best model selection by
| cross-validation.
| SGDRegressor: implements elastic net regression with incremental training.
| SGDClassifier: implements logistic regression with elastic net penalty
| (``SGDClassifier(loss="log", penalty="elasticnet")``).
|
| Method resolution order:
| ElasticNet
| sklearn.linear_model.base.LinearModel
| sklearn.base.BaseEstimator
| sklearn.base.RegressorMixin
| sklearn.base.MultiOutputMixin
| builtins.object
|
| Methods defined here:
|
| __init__(self, alpha=1.0, l1_ratio=0.5, fit_intercept=True, normalize=False, precompute=False, max_iter=1000, copy_X=True, tol=0.0001, warm_start=False, positive=False, random_state=None, selection='cyclic')
| Initialize self. See help(type(self)) for accurate signature.
|
| fit(self, X, y, check_input=True)
| Fit model with coordinate descent.
|
| Parameters
| ----------
| X : ndarray or scipy.sparse matrix, (n_samples, n_features)
| Data
|
| y : ndarray, shape (n_samples,) or (n_samples, n_targets)
| Target. Will be cast to X's dtype if necessary
|
| check_input : boolean, (default=True)
| Allow to bypass several input checking.
| Don't use this parameter unless you know what you do.
|
| Notes
| -----
|
| Coordinate descent is an algorithm that considers each column of
| data at a time hence it will automatically convert the X input
| as a Fortran-contiguous numpy array if necessary.
|
| To avoid memory re-allocation it is advised to allocate the
| initial data in memory directly using that format.
|
| ----------------------------------------------------------------------
| Static methods defined here:
|
| path = enet_path(X, y, l1_ratio=0.5, eps=0.001, n_alphas=100, alphas=None, precompute='auto', Xy=None, copy_X=True, coef_init=None, verbose=False, return_n_iter=False, positive=False, check_input=True, **params)
| Compute elastic net path with coordinate descent
|
| The elastic net optimization function varies for mono and multi-outputs.
|
| For mono-output tasks it is::
|
| 1 / (2 * n_samples) * ||y - Xw||^2_2
| + alpha * l1_ratio * ||w||_1
| + 0.5 * alpha * (1 - l1_ratio) * ||w||^2_2
|
| For multi-output tasks it is::
|
| (1 / (2 * n_samples)) * ||Y - XW||^Fro_2
| + alpha * l1_ratio * ||W||_21
| + 0.5 * alpha * (1 - l1_ratio) * ||W||_Fro^2
|
| Where::
|
| ||W||_21 = \sum_i \sqrt{\sum_j w_{ij}^2}
|
| i.e. the sum of norm of each row.
|
| Read more in the :ref:`User Guide <elastic_net>`.
|
| Parameters
| ----------
| X : {array-like}, shape (n_samples, n_features)
| Training data. Pass directly as Fortran-contiguous data to avoid
| unnecessary memory duplication. If ``y`` is mono-output then ``X``
| can be sparse.
|
| y : ndarray, shape (n_samples,) or (n_samples, n_outputs)
| Target values
|
| l1_ratio : float, optional
| float between 0 and 1 passed to elastic net (scaling between
| l1 and l2 penalties). ``l1_ratio=1`` corresponds to the Lasso
|
| eps : float
| Length of the path. ``eps=1e-3`` means that
| ``alpha_min / alpha_max = 1e-3``
|
| n_alphas : int, optional
| Number of alphas along the regularization path
|
| alphas : ndarray, optional
| List of alphas where to compute the models.
| If None alphas are set automatically
|
| precompute : True | False | 'auto' | array-like
| Whether to use a precomputed Gram matrix to speed up
| calculations. If set to ``'auto'`` let us decide. The Gram
| matrix can also be passed as argument.
|
| Xy : array-like, optional
| Xy = np.dot(X.T, y) that can be precomputed. It is useful
| only when the Gram matrix is precomputed.
|
| copy_X : boolean, optional, default True
| If ``True``, X will be copied; else, it may be overwritten.
|
| coef_init : array, shape (n_features, ) | None
| The initial values of the coefficients.
|
| verbose : bool or integer
| Amount of verbosity.
|
| return_n_iter : bool
| whether to return the number of iterations or not.
|
| positive : bool, default False
| If set to True, forces coefficients to be positive.
| (Only allowed when ``y.ndim == 1``).
|
| check_input : bool, default True
| Skip input validation checks, including the Gram matrix when provided
| assuming there are handled by the caller when check_input=False.
|
| **params : kwargs
| keyword arguments passed to the coordinate descent solver.
|
| Returns
| -------
| alphas : array, shape (n_alphas,)
| The alphas along the path where models are computed.
|
| coefs : array, shape (n_features, n_alphas) or (n_outputs, n_features, n_alphas)
| Coefficients along the path.
|
| dual_gaps : array, shape (n_alphas,)
| The dual gaps at the end of the optimization for each alpha.
|
| n_iters : array-like, shape (n_alphas,)
| The number of iterations taken by the coordinate descent optimizer to
| reach the specified tolerance for each alpha.
| (Is returned when ``return_n_iter`` is set to True).
|
| Notes
| -----
| For an example, see
| :ref:`examples/linear_model/plot_lasso_coordinate_descent_path.py
| <sphx_glr_auto_examples_linear_model_plot_lasso_coordinate_descent_path.py>`.
|
| See also
| --------
| MultiTaskElasticNet
| MultiTaskElasticNetCV
| ElasticNet
| ElasticNetCV
|
| ----------------------------------------------------------------------
| Data descriptors defined here:
|
| sparse_coef_
| sparse representation of the fitted ``coef_``
|
| ----------------------------------------------------------------------
| Data and other attributes defined here:
|
| __abstractmethods__ = frozenset()
|
| ----------------------------------------------------------------------
| Methods inherited from sklearn.linear_model.base.LinearModel:
|
| predict(self, X)
| Predict using the linear model
|
| Parameters
| ----------
| X : array_like or sparse matrix, shape (n_samples, n_features)
| Samples.
|
| Returns
| -------
| C : array, shape (n_samples,)
| Returns predicted values.
|
| ----------------------------------------------------------------------
| Methods inherited from sklearn.base.BaseEstimator:
|
| __getstate__(self)
|
| __repr__(self, N_CHAR_MAX=700)
| Return repr(self).
|
| __setstate__(self, state)
|
| get_params(self, deep=True)
| Get parameters for this estimator.
|
| Parameters
| ----------
| deep : boolean, optional
| If True, will return the parameters for this estimator and
| contained subobjects that are estimators.
|
| Returns
| -------
| params : mapping of string to any
| Parameter names mapped to their values.
|
| set_params(self, **params)
| Set the parameters of this estimator.
|
| The method works on simple estimators as well as on nested objects
| (such as pipelines). The latter have parameters of the form
| ``<component>__<parameter>`` so that it's possible to update each
| component of a nested object.
|
| Returns
| -------
| self
|
| ----------------------------------------------------------------------
| Data descriptors inherited from sklearn.base.BaseEstimator:
|
| __dict__
| dictionary for instance variables (if defined)
|
| __weakref__
| list of weak references to the object (if defined)
|
| ----------------------------------------------------------------------
| Methods inherited from sklearn.base.RegressorMixin:
|
| score(self, X, y, sample_weight=None)
| Returns the coefficient of determination R^2 of the prediction.
|
| The coefficient R^2 is defined as (1 - u/v), where u is the residual
| sum of squares ((y_true - y_pred) ** 2).sum() and v is the total
| sum of squares ((y_true - y_true.mean()) ** 2).sum().
| The best possible score is 1.0 and it can be negative (because the
| model can be arbitrarily worse). A constant model that always
| predicts the expected value of y, disregarding the input features,
| would get a R^2 score of 0.0.
|
| Parameters
| ----------
| X : array-like, shape = (n_samples, n_features)
| Test samples. For some estimators this may be a
| precomputed kernel matrix instead, shape = (n_samples,
| n_samples_fitted], where n_samples_fitted is the number of
| samples used in the fitting for the estimator.
|
| y : array-like, shape = (n_samples) or (n_samples, n_outputs)
| True values for X.
|
| sample_weight : array-like, shape = [n_samples], optional
| Sample weights.
|
| Returns
| -------
| score : float
| R^2 of self.predict(X) wrt. y.
|
| Notes
| -----
| The R2 score used when calling ``score`` on a regressor will use
| ``multioutput='uniform_average'`` from version 0.23 to keep consistent
| with `metrics.r2_score`. This will influence the ``score`` method of
| all the multioutput regressors (except for
| `multioutput.MultiOutputRegressor`). To specify the default value
| manually and avoid the warning, please either call `metrics.r2_score`
| directly or make a custom scorer with `metrics.make_scorer` (the
| built-in scorer ``'r2'`` uses ``multioutput='uniform_average'``).
In [11]:
base_elastic_model = ElasticNet()
Grid Search¶
A search consists of:
- an estimator (regressor or classifier such as sklearn.svm.SVC());
- a parameter space;
- a method for searching or sampling candidates;
- a cross-validation scheme
- a score function.
In [ ]:
param_grid = {'alpha':[0.1,1,5,10,50,100],
'l1_ratio':[.1, .5, .7, .9, .95, .99, 1]}
In [12]:
from sklearn.model_selection import GridSearchCV
In [19]:
# verbose number a personal preference
grid_model = GridSearchCV(estimator=base_elastic_model,
param_grid=param_grid,
scoring='neg_mean_squared_error',
cv=5,
verbose=2)
In [20]:
grid_model.fit(X_train,y_train)
Fitting 5 folds for each of 42 candidates, totalling 210 fits [CV] alpha=0.1, l1_ratio=0.1 ......................................... [CV] .......................... alpha=0.1, l1_ratio=0.1, total= 0.0s [CV] alpha=0.1, l1_ratio=0.1 ......................................... [CV] .......................... alpha=0.1, l1_ratio=0.1, total= 0.0s [CV] alpha=0.1, l1_ratio=0.1 ......................................... [CV] .......................... alpha=0.1, l1_ratio=0.1, total= 0.0s [CV] alpha=0.1, l1_ratio=0.1 ......................................... [CV] .......................... alpha=0.1, l1_ratio=0.1, total= 0.0s [CV] alpha=0.1, l1_ratio=0.1 ......................................... [CV] .......................... alpha=0.1, l1_ratio=0.1, total= 0.0s [CV] alpha=0.1, l1_ratio=0.5 ......................................... [CV] .......................... alpha=0.1, l1_ratio=0.5, total= 0.0s [CV] alpha=0.1, l1_ratio=0.5 ......................................... [CV] .......................... alpha=0.1, l1_ratio=0.5, total= 0.0s [CV] alpha=0.1, l1_ratio=0.5 ......................................... [CV] .......................... alpha=0.1, l1_ratio=0.5, total= 0.0s [CV] alpha=0.1, l1_ratio=0.5 ......................................... [CV] .......................... alpha=0.1, l1_ratio=0.5, total= 0.0s [CV] alpha=0.1, l1_ratio=0.5 ......................................... [CV] .......................... alpha=0.1, l1_ratio=0.5, total= 0.0s [CV] alpha=0.1, l1_ratio=0.7 ......................................... [CV] .......................... alpha=0.1, l1_ratio=0.7, total= 0.0s [CV] alpha=0.1, l1_ratio=0.7 ......................................... [CV] .......................... alpha=0.1, l1_ratio=0.7, total= 0.0s [CV] alpha=0.1, l1_ratio=0.7 ......................................... [CV] .......................... alpha=0.1, l1_ratio=0.7, total= 0.0s [CV] alpha=0.1, l1_ratio=0.7 ......................................... [CV] .......................... alpha=0.1, l1_ratio=0.7, total= 0.0s [CV] alpha=0.1, l1_ratio=0.7 ......................................... [CV] .......................... alpha=0.1, l1_ratio=0.7, total= 0.0s [CV] alpha=0.1, l1_ratio=0.9 ......................................... [CV] .......................... alpha=0.1, l1_ratio=0.9, total= 0.0s [CV] alpha=0.1, l1_ratio=0.9 ......................................... [CV] .......................... alpha=0.1, l1_ratio=0.9, total= 0.0s [CV] alpha=0.1, l1_ratio=0.9 ......................................... [CV] .......................... alpha=0.1, l1_ratio=0.9, total= 0.0s [CV] alpha=0.1, l1_ratio=0.9 ......................................... [CV] .......................... alpha=0.1, l1_ratio=0.9, total= 0.0s [CV] alpha=0.1, l1_ratio=0.9 ......................................... [CV] .......................... alpha=0.1, l1_ratio=0.9, total= 0.0s [CV] alpha=0.1, l1_ratio=0.95 ........................................ [CV] ......................... alpha=0.1, l1_ratio=0.95, total= 0.0s [CV] alpha=0.1, l1_ratio=0.95 ........................................ [CV] ......................... alpha=0.1, l1_ratio=0.95, total= 0.0s [CV] alpha=0.1, l1_ratio=0.95 ........................................ [CV] ......................... alpha=0.1, l1_ratio=0.95, total= 0.0s [CV] alpha=0.1, l1_ratio=0.95 ........................................ [CV] ......................... alpha=0.1, l1_ratio=0.95, total= 0.0s [CV] alpha=0.1, l1_ratio=0.95 ........................................ [CV] ......................... alpha=0.1, l1_ratio=0.95, total= 0.0s [CV] alpha=0.1, l1_ratio=0.99 ........................................ [CV] ......................... alpha=0.1, l1_ratio=0.99, total= 0.0s [CV] alpha=0.1, l1_ratio=0.99 ........................................ [CV] ......................... alpha=0.1, l1_ratio=0.99, total= 0.0s [CV] alpha=0.1, l1_ratio=0.99 ........................................ [CV] ......................... alpha=0.1, l1_ratio=0.99, total= 0.0s [CV] alpha=0.1, l1_ratio=0.99 ........................................ [CV] ......................... alpha=0.1, l1_ratio=0.99, total= 0.0s [CV] alpha=0.1, l1_ratio=0.99 ........................................ [CV] ......................... alpha=0.1, l1_ratio=0.99, total= 0.0s [CV] alpha=0.1, l1_ratio=1 ........................................... [CV] ............................ alpha=0.1, l1_ratio=1, total= 0.0s [CV] alpha=0.1, l1_ratio=1 ........................................... [CV] ............................ alpha=0.1, l1_ratio=1, total= 0.0s [CV] alpha=0.1, l1_ratio=1 ........................................... [CV] ............................ alpha=0.1, l1_ratio=1, total= 0.0s [CV] alpha=0.1, l1_ratio=1 ........................................... [CV] ............................ alpha=0.1, l1_ratio=1, total= 0.0s [CV] alpha=0.1, l1_ratio=1 ........................................... [CV] ............................ alpha=0.1, l1_ratio=1, total= 0.0s [CV] alpha=1, l1_ratio=0.1 ........................................... [CV] ............................ alpha=1, l1_ratio=0.1, total= 0.0s [CV] alpha=1, l1_ratio=0.1 ........................................... [CV] ............................ alpha=1, l1_ratio=0.1, total= 0.0s [CV] alpha=1, l1_ratio=0.1 ........................................... [CV] ............................ alpha=1, l1_ratio=0.1, total= 0.0s [CV] alpha=1, l1_ratio=0.1 ........................................... [CV] ............................ alpha=1, l1_ratio=0.1, total= 0.0s [CV] alpha=1, l1_ratio=0.1 ........................................... [CV] ............................ alpha=1, l1_ratio=0.1, total= 0.0s [CV] alpha=1, l1_ratio=0.5 ........................................... [CV] ............................ alpha=1, l1_ratio=0.5, total= 0.0s [CV] alpha=1, l1_ratio=0.5 ........................................... [CV] ............................ alpha=1, l1_ratio=0.5, total= 0.0s [CV] alpha=1, l1_ratio=0.5 ........................................... [CV] ............................ alpha=1, l1_ratio=0.5, total= 0.0s [CV] alpha=1, l1_ratio=0.5 ........................................... [CV] ............................ alpha=1, l1_ratio=0.5, total= 0.0s [CV] alpha=1, l1_ratio=0.5 ........................................... [CV] ............................ alpha=1, l1_ratio=0.5, total= 0.0s [CV] alpha=1, l1_ratio=0.7 ........................................... [CV] ............................ alpha=1, l1_ratio=0.7, total= 0.0s [CV] alpha=1, l1_ratio=0.7 ........................................... [CV] ............................ alpha=1, l1_ratio=0.7, total= 0.0s [CV] alpha=1, l1_ratio=0.7 ........................................... [CV] ............................ alpha=1, l1_ratio=0.7, total= 0.0s [CV] alpha=1, l1_ratio=0.7 ........................................... [CV] ............................ alpha=1, l1_ratio=0.7, total= 0.0s [CV] alpha=1, l1_ratio=0.7 ........................................... [CV] ............................ alpha=1, l1_ratio=0.7, total= 0.0s [CV] alpha=1, l1_ratio=0.9 ........................................... [CV] ............................ alpha=1, l1_ratio=0.9, total= 0.0s [CV] alpha=1, l1_ratio=0.9 ........................................... [CV] ............................ alpha=1, l1_ratio=0.9, total= 0.0s [CV] alpha=1, l1_ratio=0.9 ........................................... [CV] ............................ alpha=1, l1_ratio=0.9, total= 0.0s [CV] alpha=1, l1_ratio=0.9 ........................................... [CV] ............................ alpha=1, l1_ratio=0.9, total= 0.0s [CV] alpha=1, l1_ratio=0.9 ........................................... [CV] ............................ alpha=1, l1_ratio=0.9, total= 0.0s [CV] alpha=1, l1_ratio=0.95 .......................................... [CV] ........................... alpha=1, l1_ratio=0.95, total= 0.0s [CV] alpha=1, l1_ratio=0.95 .......................................... [CV] ........................... alpha=1, l1_ratio=0.95, total= 0.0s [CV] alpha=1, l1_ratio=0.95 .......................................... [CV] ........................... alpha=1, l1_ratio=0.95, total= 0.0s [CV] alpha=1, l1_ratio=0.95 .......................................... [CV] ........................... alpha=1, l1_ratio=0.95, total= 0.0s [CV] alpha=1, l1_ratio=0.95 .......................................... [CV] ........................... alpha=1, l1_ratio=0.95, total= 0.0s [CV] alpha=1, l1_ratio=0.99 .......................................... [CV] ........................... alpha=1, l1_ratio=0.99, total= 0.0s [CV] alpha=1, l1_ratio=0.99 .......................................... [CV] ........................... alpha=1, l1_ratio=0.99, total= 0.0s [CV] alpha=1, l1_ratio=0.99 .......................................... [CV] ........................... alpha=1, l1_ratio=0.99, total= 0.0s [CV] alpha=1, l1_ratio=0.99 .......................................... [CV] ........................... alpha=1, l1_ratio=0.99, total= 0.0s [CV] alpha=1, l1_ratio=0.99 .......................................... [CV] ........................... alpha=1, l1_ratio=0.99, total= 0.0s [CV] alpha=1, l1_ratio=1 ............................................. [CV] .............................. alpha=1, l1_ratio=1, total= 0.0s [CV] alpha=1, l1_ratio=1 ............................................. [CV] .............................. alpha=1, l1_ratio=1, total= 0.0s [CV] alpha=1, l1_ratio=1 ............................................. [CV] .............................. alpha=1, l1_ratio=1, total= 0.0s [CV] alpha=1, l1_ratio=1 ............................................. [CV] .............................. alpha=1, l1_ratio=1, total= 0.0s [CV] alpha=1, l1_ratio=1 ............................................. [CV] .............................. alpha=1, l1_ratio=1, total= 0.0s [CV] alpha=5, l1_ratio=0.1 ........................................... [CV] ............................ alpha=5, l1_ratio=0.1, total= 0.0s [CV] alpha=5, l1_ratio=0.1 ........................................... [CV] ............................ alpha=5, l1_ratio=0.1, total= 0.0s [CV] alpha=5, l1_ratio=0.1 ........................................... [CV] ............................ alpha=5, l1_ratio=0.1, total= 0.0s [CV] alpha=5, l1_ratio=0.1 ........................................... [CV] ............................ alpha=5, l1_ratio=0.1, total= 0.0s [CV] alpha=5, l1_ratio=0.1 ........................................... [CV] ............................ alpha=5, l1_ratio=0.1, total= 0.0s [CV] alpha=5, l1_ratio=0.5 ........................................... [CV] ............................ alpha=5, l1_ratio=0.5, total= 0.0s [CV] alpha=5, l1_ratio=0.5 ........................................... [CV] ............................ alpha=5, l1_ratio=0.5, total= 0.0s [CV] alpha=5, l1_ratio=0.5 ........................................... [CV] ............................ alpha=5, l1_ratio=0.5, total= 0.0s [CV] alpha=5, l1_ratio=0.5 ........................................... [CV] ............................ alpha=5, l1_ratio=0.5, total= 0.0s [CV] alpha=5, l1_ratio=0.5 ........................................... [CV] ............................ alpha=5, l1_ratio=0.5, total= 0.0s [CV] alpha=5, l1_ratio=0.7 ........................................... [CV] ............................ alpha=5, l1_ratio=0.7, total= 0.0s [CV] alpha=5, l1_ratio=0.7 ........................................... [CV] ............................ alpha=5, l1_ratio=0.7, total= 0.0s [CV] alpha=5, l1_ratio=0.7 ........................................... [CV] ............................ alpha=5, l1_ratio=0.7, total= 0.0s [CV] alpha=5, l1_ratio=0.7 ........................................... [CV] ............................ alpha=5, l1_ratio=0.7, total= 0.0s [CV] alpha=5, l1_ratio=0.7 ........................................... [CV] ............................ alpha=5, l1_ratio=0.7, total= 0.0s [CV] alpha=5, l1_ratio=0.9 ........................................... [CV] ............................ alpha=5, l1_ratio=0.9, total= 0.0s [CV] alpha=5, l1_ratio=0.9 ........................................... [CV] ............................ alpha=5, l1_ratio=0.9, total= 0.0s [CV] alpha=5, l1_ratio=0.9 ........................................... [CV] ............................ alpha=5, l1_ratio=0.9, total= 0.0s [CV] alpha=5, l1_ratio=0.9 ........................................... [CV] ............................ alpha=5, l1_ratio=0.9, total= 0.0s [CV] alpha=5, l1_ratio=0.9 ........................................... [CV] ............................ alpha=5, l1_ratio=0.9, total= 0.0s [CV] alpha=5, l1_ratio=0.95 .......................................... [CV] ........................... alpha=5, l1_ratio=0.95, total= 0.0s [CV] alpha=5, l1_ratio=0.95 .......................................... [CV] ........................... alpha=5, l1_ratio=0.95, total= 0.0s [CV] alpha=5, l1_ratio=0.95 .......................................... [CV] ........................... alpha=5, l1_ratio=0.95, total= 0.0s [CV] alpha=5, l1_ratio=0.95 .......................................... [CV] ........................... alpha=5, l1_ratio=0.95, total= 0.0s [CV] alpha=5, l1_ratio=0.95 .......................................... [CV] ........................... alpha=5, l1_ratio=0.95, total= 0.0s [CV] alpha=5, l1_ratio=0.99 .......................................... [CV] ........................... alpha=5, l1_ratio=0.99, total= 0.0s [CV] alpha=5, l1_ratio=0.99 .......................................... [CV] ........................... alpha=5, l1_ratio=0.99, total= 0.0s [CV] alpha=5, l1_ratio=0.99 .......................................... [CV] ........................... alpha=5, l1_ratio=0.99, total= 0.0s [CV] alpha=5, l1_ratio=0.99 .......................................... [CV] ........................... alpha=5, l1_ratio=0.99, total= 0.0s [CV] alpha=5, l1_ratio=0.99 .......................................... [CV] ........................... alpha=5, l1_ratio=0.99, total= 0.0s [CV] alpha=5, l1_ratio=1 ............................................. [CV] .............................. alpha=5, l1_ratio=1, total= 0.0s [CV] alpha=5, l1_ratio=1 ............................................. [CV] .............................. alpha=5, l1_ratio=1, total= 0.0s [CV] alpha=5, l1_ratio=1 ............................................. [CV] .............................. alpha=5, l1_ratio=1, total= 0.0s [CV] alpha=5, l1_ratio=1 ............................................. [CV] .............................. alpha=5, l1_ratio=1, total= 0.0s [CV] alpha=5, l1_ratio=1 ............................................. [CV] .............................. alpha=5, l1_ratio=1, total= 0.0s [CV] alpha=10, l1_ratio=0.1 .......................................... [CV] ........................... alpha=10, l1_ratio=0.1, total= 0.0s [CV] alpha=10, l1_ratio=0.1 .......................................... [CV] ........................... alpha=10, l1_ratio=0.1, total= 0.0s [CV] alpha=10, l1_ratio=0.1 .......................................... [CV] ........................... alpha=10, l1_ratio=0.1, total= 0.0s [CV] alpha=10, l1_ratio=0.1 .......................................... [CV] ........................... alpha=10, l1_ratio=0.1, total= 0.0s [CV] alpha=10, l1_ratio=0.1 .......................................... [CV] ........................... alpha=10, l1_ratio=0.1, total= 0.0s [CV] alpha=10, l1_ratio=0.5 .......................................... [CV] ........................... alpha=10, l1_ratio=0.5, total= 0.0s [CV] alpha=10, l1_ratio=0.5 .......................................... [CV] ........................... alpha=10, l1_ratio=0.5, total= 0.0s [CV] alpha=10, l1_ratio=0.5 .......................................... [CV] ........................... alpha=10, l1_ratio=0.5, total= 0.0s [CV] alpha=10, l1_ratio=0.5 .......................................... [CV] ........................... alpha=10, l1_ratio=0.5, total= 0.0s [CV] alpha=10, l1_ratio=0.5 .......................................... [CV] ........................... alpha=10, l1_ratio=0.5, total= 0.0s [CV] alpha=10, l1_ratio=0.7 .......................................... [CV] ........................... alpha=10, l1_ratio=0.7, total= 0.0s [CV] alpha=10, l1_ratio=0.7 .......................................... [CV] ........................... alpha=10, l1_ratio=0.7, total= 0.0s [CV] alpha=10, l1_ratio=0.7 .......................................... [CV] ........................... alpha=10, l1_ratio=0.7, total= 0.0s [CV] alpha=10, l1_ratio=0.7 .......................................... [CV] ........................... alpha=10, l1_ratio=0.7, total= 0.0s [CV] alpha=10, l1_ratio=0.7 .......................................... [CV] ........................... alpha=10, l1_ratio=0.7, total= 0.0s [CV] alpha=10, l1_ratio=0.9 .......................................... [CV] ........................... alpha=10, l1_ratio=0.9, total= 0.0s [CV] alpha=10, l1_ratio=0.9 .......................................... [CV] ........................... alpha=10, l1_ratio=0.9, total= 0.0s [CV] alpha=10, l1_ratio=0.9 .......................................... [CV] ........................... alpha=10, l1_ratio=0.9, total= 0.0s [CV] alpha=10, l1_ratio=0.9 .......................................... [CV] ........................... alpha=10, l1_ratio=0.9, total= 0.0s [CV] alpha=10, l1_ratio=0.9 .......................................... [CV] ........................... alpha=10, l1_ratio=0.9, total= 0.0s [CV] alpha=10, l1_ratio=0.95 ......................................... [CV] .......................... alpha=10, l1_ratio=0.95, total= 0.0s [CV] alpha=10, l1_ratio=0.95 ......................................... [CV] .......................... alpha=10, l1_ratio=0.95, total= 0.0s [CV] alpha=10, l1_ratio=0.95 ......................................... [CV] .......................... alpha=10, l1_ratio=0.95, total= 0.0s [CV] alpha=10, l1_ratio=0.95 ......................................... [CV] .......................... alpha=10, l1_ratio=0.95, total= 0.0s [CV] alpha=10, l1_ratio=0.95 ......................................... [CV] .......................... alpha=10, l1_ratio=0.95, total= 0.0s [CV] alpha=10, l1_ratio=0.99 ......................................... [CV] .......................... alpha=10, l1_ratio=0.99, total= 0.0s [CV] alpha=10, l1_ratio=0.99 ......................................... [CV] .......................... alpha=10, l1_ratio=0.99, total= 0.0s [CV] alpha=10, l1_ratio=0.99 ......................................... [CV] .......................... alpha=10, l1_ratio=0.99, total= 0.0s [CV] alpha=10, l1_ratio=0.99 ......................................... [CV] .......................... alpha=10, l1_ratio=0.99, total= 0.0s [CV] alpha=10, l1_ratio=0.99 ......................................... [CV] .......................... alpha=10, l1_ratio=0.99, total= 0.0s [CV] alpha=10, l1_ratio=1 ............................................ [CV] ............................. alpha=10, l1_ratio=1, total= 0.0s [CV] alpha=10, l1_ratio=1 ............................................ [CV] ............................. alpha=10, l1_ratio=1, total= 0.0s [CV] alpha=10, l1_ratio=1 ............................................ [CV] ............................. alpha=10, l1_ratio=1, total= 0.0s [CV] alpha=10, l1_ratio=1 ............................................ [CV] ............................. alpha=10, l1_ratio=1, total= 0.0s [CV] alpha=10, l1_ratio=1 ............................................ [CV] ............................. alpha=10, l1_ratio=1, total= 0.0s [CV] alpha=50, l1_ratio=0.1 .......................................... [CV] ........................... alpha=50, l1_ratio=0.1, total= 0.0s [CV] alpha=50, l1_ratio=0.1 .......................................... [CV] ........................... alpha=50, l1_ratio=0.1, total= 0.0s [CV] alpha=50, l1_ratio=0.1 .......................................... [CV] ........................... alpha=50, l1_ratio=0.1, total= 0.0s [CV] alpha=50, l1_ratio=0.1 .......................................... [CV] ........................... alpha=50, l1_ratio=0.1, total= 0.0s [CV] alpha=50, l1_ratio=0.1 .......................................... [CV] ........................... alpha=50, l1_ratio=0.1, total= 0.0s [CV] alpha=50, l1_ratio=0.5 .......................................... [CV] ........................... alpha=50, l1_ratio=0.5, total= 0.0s [CV] alpha=50, l1_ratio=0.5 .......................................... [CV] ........................... alpha=50, l1_ratio=0.5, total= 0.0s [CV] alpha=50, l1_ratio=0.5 .......................................... [CV] ........................... alpha=50, l1_ratio=0.5, total= 0.0s [CV] alpha=50, l1_ratio=0.5 .......................................... [CV] ........................... alpha=50, l1_ratio=0.5, total= 0.0s [CV] alpha=50, l1_ratio=0.5 .......................................... [CV] ........................... alpha=50, l1_ratio=0.5, total= 0.0s [CV] alpha=50, l1_ratio=0.7 .......................................... [CV] ........................... alpha=50, l1_ratio=0.7, total= 0.0s [CV] alpha=50, l1_ratio=0.7 .......................................... [CV] ........................... alpha=50, l1_ratio=0.7, total= 0.0s [CV] alpha=50, l1_ratio=0.7 .......................................... [CV] ........................... alpha=50, l1_ratio=0.7, total= 0.0s [CV] alpha=50, l1_ratio=0.7 .......................................... [CV] ........................... alpha=50, l1_ratio=0.7, total= 0.0s [CV] alpha=50, l1_ratio=0.7 .......................................... [CV] ........................... alpha=50, l1_ratio=0.7, total= 0.0s [CV] alpha=50, l1_ratio=0.9 .......................................... [CV] ........................... alpha=50, l1_ratio=0.9, total= 0.0s [CV] alpha=50, l1_ratio=0.9 .......................................... [CV] ........................... alpha=50, l1_ratio=0.9, total= 0.0s [CV] alpha=50, l1_ratio=0.9 .......................................... [CV] ........................... alpha=50, l1_ratio=0.9, total= 0.0s [CV] alpha=50, l1_ratio=0.9 .......................................... [CV] ........................... alpha=50, l1_ratio=0.9, total= 0.0s [CV] alpha=50, l1_ratio=0.9 .......................................... [CV] ........................... alpha=50, l1_ratio=0.9, total= 0.0s [CV] alpha=50, l1_ratio=0.95 ......................................... [CV] .......................... alpha=50, l1_ratio=0.95, total= 0.0s [CV] alpha=50, l1_ratio=0.95 ......................................... [CV] .......................... alpha=50, l1_ratio=0.95, total= 0.0s [CV] alpha=50, l1_ratio=0.95 ......................................... [CV] .......................... alpha=50, l1_ratio=0.95, total= 0.0s [CV] alpha=50, l1_ratio=0.95 ......................................... [CV] .......................... alpha=50, l1_ratio=0.95, total= 0.0s [CV] alpha=50, l1_ratio=0.95 ......................................... [CV] .......................... alpha=50, l1_ratio=0.95, total= 0.0s [CV] alpha=50, l1_ratio=0.99 ......................................... [CV] .......................... alpha=50, l1_ratio=0.99, total= 0.0s [CV] alpha=50, l1_ratio=0.99 ......................................... [CV] .......................... alpha=50, l1_ratio=0.99, total= 0.0s [CV] alpha=50, l1_ratio=0.99 ......................................... [CV] .......................... alpha=50, l1_ratio=0.99, total= 0.0s [CV] alpha=50, l1_ratio=0.99 ......................................... [CV] .......................... alpha=50, l1_ratio=0.99, total= 0.0s [CV] alpha=50, l1_ratio=0.99 ......................................... [CV] .......................... alpha=50, l1_ratio=0.99, total= 0.0s [CV] alpha=50, l1_ratio=1 ............................................ [CV] ............................. alpha=50, l1_ratio=1, total= 0.0s [CV] alpha=50, l1_ratio=1 ............................................ [CV] ............................. alpha=50, l1_ratio=1, total= 0.0s [CV] alpha=50, l1_ratio=1 ............................................ [CV] ............................. alpha=50, l1_ratio=1, total= 0.0s [CV] alpha=50, l1_ratio=1 ............................................ [CV] ............................. alpha=50, l1_ratio=1, total= 0.0s [CV] alpha=50, l1_ratio=1 ............................................ [CV] ............................. alpha=50, l1_ratio=1, total= 0.0s [CV] alpha=100, l1_ratio=0.1 ......................................... [CV] .......................... alpha=100, l1_ratio=0.1, total= 0.0s [CV] alpha=100, l1_ratio=0.1 ......................................... [CV] .......................... alpha=100, l1_ratio=0.1, total= 0.0s [CV] alpha=100, l1_ratio=0.1 ......................................... [CV] .......................... alpha=100, l1_ratio=0.1, total= 0.0s [CV] alpha=100, l1_ratio=0.1 ......................................... [CV] .......................... alpha=100, l1_ratio=0.1, total= 0.0s
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers. [Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 0.0s remaining: 0.0s
[CV] alpha=100, l1_ratio=0.1 ......................................... [CV] .......................... alpha=100, l1_ratio=0.1, total= 0.0s [CV] alpha=100, l1_ratio=0.5 ......................................... [CV] .......................... alpha=100, l1_ratio=0.5, total= 0.0s [CV] alpha=100, l1_ratio=0.5 ......................................... [CV] .......................... alpha=100, l1_ratio=0.5, total= 0.0s [CV] alpha=100, l1_ratio=0.5 ......................................... [CV] .......................... alpha=100, l1_ratio=0.5, total= 0.0s [CV] alpha=100, l1_ratio=0.5 ......................................... [CV] .......................... alpha=100, l1_ratio=0.5, total= 0.0s [CV] alpha=100, l1_ratio=0.5 ......................................... [CV] .......................... alpha=100, l1_ratio=0.5, total= 0.0s [CV] alpha=100, l1_ratio=0.7 ......................................... [CV] .......................... alpha=100, l1_ratio=0.7, total= 0.0s [CV] alpha=100, l1_ratio=0.7 ......................................... [CV] .......................... alpha=100, l1_ratio=0.7, total= 0.0s [CV] alpha=100, l1_ratio=0.7 ......................................... [CV] .......................... alpha=100, l1_ratio=0.7, total= 0.0s [CV] alpha=100, l1_ratio=0.7 ......................................... [CV] .......................... alpha=100, l1_ratio=0.7, total= 0.0s [CV] alpha=100, l1_ratio=0.7 ......................................... [CV] .......................... alpha=100, l1_ratio=0.7, total= 0.0s [CV] alpha=100, l1_ratio=0.9 ......................................... [CV] .......................... alpha=100, l1_ratio=0.9, total= 0.0s [CV] alpha=100, l1_ratio=0.9 ......................................... [CV] .......................... alpha=100, l1_ratio=0.9, total= 0.0s [CV] alpha=100, l1_ratio=0.9 ......................................... [CV] .......................... alpha=100, l1_ratio=0.9, total= 0.0s [CV] alpha=100, l1_ratio=0.9 ......................................... [CV] .......................... alpha=100, l1_ratio=0.9, total= 0.0s [CV] alpha=100, l1_ratio=0.9 ......................................... [CV] .......................... alpha=100, l1_ratio=0.9, total= 0.0s [CV] alpha=100, l1_ratio=0.95 ........................................ [CV] ......................... alpha=100, l1_ratio=0.95, total= 0.0s [CV] alpha=100, l1_ratio=0.95 ........................................ [CV] ......................... alpha=100, l1_ratio=0.95, total= 0.0s [CV] alpha=100, l1_ratio=0.95 ........................................ [CV] ......................... alpha=100, l1_ratio=0.95, total= 0.0s [CV] alpha=100, l1_ratio=0.95 ........................................ [CV] ......................... alpha=100, l1_ratio=0.95, total= 0.0s [CV] alpha=100, l1_ratio=0.95 ........................................ [CV] ......................... alpha=100, l1_ratio=0.95, total= 0.0s [CV] alpha=100, l1_ratio=0.99 ........................................ [CV] ......................... alpha=100, l1_ratio=0.99, total= 0.0s [CV] alpha=100, l1_ratio=0.99 ........................................ [CV] ......................... alpha=100, l1_ratio=0.99, total= 0.0s [CV] alpha=100, l1_ratio=0.99 ........................................ [CV] ......................... alpha=100, l1_ratio=0.99, total= 0.0s [CV] alpha=100, l1_ratio=0.99 ........................................ [CV] ......................... alpha=100, l1_ratio=0.99, total= 0.0s [CV] alpha=100, l1_ratio=0.99 ........................................ [CV] ......................... alpha=100, l1_ratio=0.99, total= 0.0s [CV] alpha=100, l1_ratio=1 ........................................... [CV] ............................ alpha=100, l1_ratio=1, total= 0.0s [CV] alpha=100, l1_ratio=1 ........................................... [CV] ............................ alpha=100, l1_ratio=1, total= 0.0s [CV] alpha=100, l1_ratio=1 ........................................... [CV] ............................ alpha=100, l1_ratio=1, total= 0.0s [CV] alpha=100, l1_ratio=1 ........................................... [CV] ............................ alpha=100, l1_ratio=1, total= 0.0s [CV] alpha=100, l1_ratio=1 ........................................... [CV] ............................ alpha=100, l1_ratio=1, total= 0.0s
[Parallel(n_jobs=1)]: Done 210 out of 210 | elapsed: 0.1s finished
Out[20]:
GridSearchCV(cv=5, error_score='raise-deprecating',
estimator=ElasticNet(alpha=1.0, copy_X=True, fit_intercept=True,
l1_ratio=0.5, max_iter=1000, normalize=False,
positive=False, precompute=False,
random_state=None, selection='cyclic',
tol=0.0001, warm_start=False),
iid='warn', n_jobs=None,
param_grid={'alpha': [0.1, 1, 5, 10, 50, 100],
'l1_ratio': [0.1, 0.5, 0.7, 0.9, 0.95, 0.99, 1]},
pre_dispatch='2*n_jobs', refit=True, return_train_score=False,
scoring='neg_mean_squared_error', verbose=2)
In [21]:
grid_model.best_estimator_
Out[21]:
ElasticNet(alpha=0.1, copy_X=True, fit_intercept=True, l1_ratio=1,
max_iter=1000, normalize=False, positive=False, precompute=False,
random_state=None, selection='cyclic', tol=0.0001, warm_start=False)
In [22]:
grid_model.best_params_
Out[22]:
{'alpha': 0.1, 'l1_ratio': 1}
In [25]:
# pd.DataFrame(grid_model.cv_results_)
Using Best Model From Grid Search¶
In [26]:
y_pred = grid_model.predict(X_test)
In [27]:
from sklearn.metrics import mean_squared_error
In [28]:
mean_squared_error(y_test,y_pred)
Out[28]:
2.3873426420874737
In [ ]: