| # prepare for modeling | |
| X_train = train.drop(['id', 'target'], axis=1) | |
| y_train = train['target'] | |
| X_test = test.drop(['id'], axis=1) | |
| # scaling data | |
| scaler = StandardScaler() | |
| X_train = scaler.fit_transform(X_train) | |
| X_test = scaler.transform(X_test) |
| loss = ['hinge', 'log', 'modified_huber', 'squared_hinge', 'perceptron'] | |
| penalty = ['l1', 'l2', 'elasticnet'] | |
| alpha = [0.0001, 0.001, 0.01, 0.1, 1, 10, 100, 1000] | |
| learning_rate = ['constant', 'optimal', 'invscaling', 'adaptive'] | |
| class_weight = [{1:0.5, 0:0.5}, {1:0.4, 0:0.6}, {1:0.6, 0:0.4}, {1:0.7, 0:0.3}] | |
| eta0 = [1, 10, 100] | |
| param_distributions = dict(loss=loss, | |
| penalty=penalty, | |
| alpha=alpha, |
| penalty = ['l1', 'l2'] | |
| C = [0.0001, 0.001, 0.01, 0.1, 1, 10, 100, 1000] | |
| class_weight = [{1:0.5, 0:0.5}, {1:0.4, 0:0.6}, {1:0.6, 0:0.4}, {1:0.7, 0:0.3}] | |
| solver = ['liblinear', 'saga'] | |
| param_grid = dict(penalty=penalty, | |
| C=C, | |
| class_weight=class_weight, | |
| solver=solver) |
Ref : stackoverflow
The best solution in my opinion is to use the unittest [command line interface][1] which will add the directory to the sys.path so you don't have to (done in the TestLoader class).
For example for a directory structure like this:
new_project
├── antigravity.py
Ref : stackoverflow
The best solution in my opinion is to use the unittest [command line interface][1] which will add the directory to the sys.path so you don't have to (done in the TestLoader class).
For example for a directory structure like this:
new_project
├── antigravity.py