def do_lasso(X, Y):
Y.ravel()
"""
Runs a lasso grid search on the input data
Inputs
------
X: dataframe, n*m, n is number of data points,
m is number of features
y: experimental electrical conductivity
Returns
------
lasso : sklearn object with the model information
"""
alphas = np.array([0.1, 0.01, 0.001, 0.0001])
lasso = Lasso(alpha=0.001,
fit_intercept=True,
normalize=False,
precompute=False,
copy_X=True,
max_iter=10000,
tol=0.001,
positive=False,
random_state=None,
selection='cyclic')
gs = GridSearchCV(lasso, param_grid=dict(alpha=alphas))
gs.fit(X, Y)
lasso.alpha_ = gs.best_params_['alpha']
lasso.fit(X, Y)
return lasso
def test_do_lasso():
"""
Test Running a lasso grid search on the input data
Inputs
------
X: dataframe, n*m, n is number of data points,
m is number of features
y: experimental electrical conductivity
Returns
------
lasso : sklearn object with the model information
Checking:
1. The X and y has the name datatype
2. The X and y has the name length
3. The input matrix has enough data points to be splitted in the regressor
"""
X = [[
0, 6, 234.321, 5, 0, 1, 1, 0, 1, 18.000, 1, 0, 0, 0, 0, 298.15, 101,
0.004
],
[
1, 6, 234.321, 5, 0, 1, 1, 0, 1, 18.000, 1, 0, 0, 0, 0, 304.15,
101, 0.007
],
[
2, 6, 234.321, 5, 0, 1, 1, 0, 1, 18.000, 1, 0, 0, 0, 0, 300.15,
101, 0.005
],
[
3, 6, 234.321, 5, 0, 1, 1, 0, 1, 18.000, 1, 0, 0, 0, 0, 302.15,
101, 0.006
],
[
4, 6, 234.321, 5, 0, 1, 1, 0, 1, 18.000, 1, 0, 0, 0, 0, 306.15,
101, 0.005
]]
y = [0.02, 0.03, 0.03, 0.04, 0.05]
assert isinstance(X, type(y)), "The two input should has the same datatype"
assert len(X) == len(y), "Dimension mismatch between two input matrix"
assert len(X) >= 5, "Need more data points in the input data"
alphas = np.array([5, 4, 3, 2, 1, 0.1, 0.01, 0.001, 0.0001])
lasso = Lasso(alpha=0.001,
fit_intercept=True,
normalize=False,
precompute=False,
copy_X=True,
max_iter=10000,
tol=0.001,
positive=False,
random_state=None,
selection='cyclic')
gs = GridSearchCV(lasso, param_grid=dict(alpha=alphas))
gs.fit(X, y)
lasso.alpha_ = gs.best_params_['alpha']
lasso.fit(X, y)
return lasso