Fix pyGAM to work with SkLearn GridSearchCV

[judahrand]

This pull request aims to resolve #247.

Minimal example:

import numpy as np
import pandas as pd
from sklearn.datasets import load_boston
from sklearn.model_selection import GridSearchCV

import pygam

def gam(x, y):
    lams = np.random.rand(10, x.shape[1])
    lams = np.exp(lams)
    linear_gam = pygam.LinearGAM(n_splines=10, max_iter=1000)
    parameters = {
        'lam': [x for x in lams]
    }
    gam_cv = GridSearchCV(linear_gam, parameters, cv=5, iid=False, return_train_score=True,
 refit=True, scoring='neg_mean_squared_error')
    gam_cv.fit(x, y)
    cv_results_df = pd.DataFrame(gam_cv.cv_results_).sort_values(by='mean_test_score', ascending=False)
    return gam_cv, cv_results_df

if __name__ == "__main__":
    X, y = load_boston(return_X_y=True)
    gam_cv, cv_results_df = gam(X, y)
    print(gam_cv)
    print(cv_results_df.head())

[codecov[bot]]

Codecov Report

Merging #267 into master will increase coverage by 0.13%. The diff coverage is 100.00%.

@@            Coverage Diff             @@
##           master     #267      +/-   ##
==========================================
+ Coverage   95.05%   95.19%   +0.13%     
==========================================
  Files          22       22              
  Lines        3178     3184       +6     
==========================================
+ Hits         3021     3031      +10     
+ Misses        157      153       -4     

Impacted Files	Coverage Δ
pygam/pygam.py	94.83% <100.00%> (+0.03%)	:arrow_up:
pygam/utils.py	87.73% <0.00%> (+0.30%)	:arrow_up:
pygam/tests/test_GAM_methods.py	100.00% <0.00%> (+0.36%)	:arrow_up:
pygam/tests/test_utils.py	96.50% <0.00%> (+1.39%)	:arrow_up:

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