def do_MLP_regressor(X, Y):
Y.ravel()
"""
Call the MLP Regressor,
Fit the weight on the training set
Input
------
X: dataframe, n*m, n is number of data points,
m is number of features
y: experimental electrical conductivity
Returns
------
mlp_regr : the MLP object with the best parameters
"""
alphas = np.array([0.1, 0.01, 0.001, 0.0001])
mlp_regr = MLPRegressor(hidden_layer_sizes=(100, ),
activation='tanh',
solver='sgd',
alpha=0.0001,
max_iter=5000,
random_state=None,
learning_rate_init=0.01)
grid_search = GridSearchCV(mlp_regr, param_grid=dict(alpha=alphas))
grid_search.fit(X, Y)
#print(grid_search.best_params_)
mlp_regr.alpha_ = grid_search.best_params_['alpha']
mlp_regr.fit(X, Y)
return mlp_regr
def test_do_MLP_regressor():
"""
Test calling the MLP Regressor,
Fit the weight on the training set
Input
------
X: dataframe, n*m, n is number of data points,
m is number of features
y: experimental electrical conductivity
Returns
------
mlp_regr : the MLP object with the best parameters
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, 300.15,
101, 0.005
],
[
2, 6, 234.321, 5, 0, 1, 1, 0, 1, 18.000, 1, 0, 0, 0, 0, 302.15,
101, 0.006
],
[
3, 6, 234.321, 5, 0, 1, 1, 0, 1, 18.000, 1, 0, 0, 0, 0, 304.15,
101, 0.007
],
[
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) >= 1, "Need more data points in the input data"
alphas = np.array([5, 2, 5, 1.5, 1, 0.1, 0.01, 0.001, 0.0001, 0])
mlp_regr = MLPRegressor(hidden_layer_sizes=(100, ),
activation='relu',
solver='adam',
alpha=0.0001,
max_iter=5000,
random_state=None,
learning_rate_init=0.01)
grid_search = GridSearchCV(mlp_regr, param_grid=dict(alpha=alphas))
grid_search.fit(X, y)
#print(grid_search.best_params_)
mlp_regr.alpha_ = grid_search.best_params_['alpha']
mlp_regr.fit(X, y)
return mlp_regr
