Updated test_specified_cv_folds

- Modified make_cv_folds_data() to use make_classification and generate a FeatureSet directly.
- Modified test_specified_cv_folds() to run the tests without using config files and with and without feature hashing both.
- Removed a few other functions that were no longer required.
Updated test_sparse_predict

- Modified make_sparse_data to use make_classification from sklearn and return FeatureSets.
- Modified test_sparse_predict to use a check function that can run tests with and without feature hashing.
Removed unnecessary json import
Updated test_scaling

- Create FeatureSets directly
- Make tests deterministic with both feature hashing and scaling
Make sure checking large feature values happens after scaling

- Split `_check_input()` into two separate functions: `_check_input_formatting()` and `_check_max_feature_value()`. The first is called where `_check_input()` was originally called and the second is called after scaling happens inside `train()`.
Fixed bug in default value of train_test_ratio
Merge branch 'develop' into feature/fix_more_unit_tests
Minor clean up things for test_skll.py
Replaced write_feature_file with NDJWriter

- Used NDJWriter instead of write_feature_file where we were specifically writing .jsonlines files.
Collapsed all specified_cv_folds tests into a single function

- Modified make_cv_folds_data to use fewer examples otherwise the test takes too long when using RBFSampler.
- Collapsed all specified_cv_folds tests with/without feature-hashing and with/without sampling into a single function.
Fixed docstrings
Replaced lambda assertions with assert_less and assert_greater
Create `train`, `test` and `output` sub-directories before running tests

- Added a module level `setup()` function that creates the three subdirectories before any tests are run.
- Modified `fill_in_config_paths()` to only fill in the paths and not do any directory creation.
Updated test_sparse_predict_sampler
Removed some more asserts
Minor changes.
Updated test_scaling to fix Travis test failures.
Change how intercept is printed

- @mheilman and I think that it's better to output the intercept as a dictionary rather than a tuple with `None` as the first item in some cases.
- For regression models, we have a special key called '_intercept_'.
- Updated `print_model_weights` to be compatible with this.
Modified make_scaling_data to use make_classification
Use SGDClassifer instead of LinearSVC in test_scaling

- Because LibLinear :/