Feature/scikit ver

This merge request is related to #138 

note: there was no unit test related to my change.

After adding the version number to outputs, the outputs will be:

summary file:
```
accuracy	cv_folds	experiment_name	feature_scaling	featureset	fold	grid_objective	grid_score	grid_search	learner_name	min_feature_count	model_params	pearson	**scikit_learn_version**	score	task	test_set_name	timestamp	train_set_name	version
0.98	10	Example_Evaluate	none	"[""example_iris_features""]"		f1_score_micro	0.92	True	RandomForestClassifier	1	"{""bootstrap"": true, ""max_features"": ""auto"", ""random_state"": 123456789, ""verbose"": 0, ""compute_importances"": null, ""max_leaf_nodes"": null, ""min_samples_leaf"": 1, ""criterion"": ""gini"", ""max_depth"": 5, ""min_samples_split"": 2, ""oob_score"": false, ""n_estimators"": 500, ""n_jobs"": 1, ""min_density"": null}"		**0.15.2**	0.98	evaluate	test	17 Oct 2014 13:07:46	train	0.28.0
```

individual result file:
```
Experiment Name: Example_Evaluate
Timestamp: 17 Oct 2014 13:07:48
SKLL Version: 0.28.0
Training Set: train
Test Set: test
Feature Set: ["example_iris_features"]
Learner: MultinomialNB
Task: evaluate
Feature Scaling: none
Grid Search: True
Grid Objective Function: f1_score_micro
Using Folds File: False
**Scikit-learn Verion: 0.15.2**
```

output json file:
```
[{"pearson": "", "task": "evaluate", "timestamp": "17 Oct 2014 13:07:48", "cv_folds": 10, "grid_score": 0.93000000000000005, **"scikit_learn_version": "0.15.2"**, "fold": "", "grid_search": true, "version": "0.28.0", "featureset": "[\"example_iris_features\"]", "train_set_name": "train", "model_params": "{\"alpha\": 0.1, \"fit_prior\": true, \"class_prior\": null}", "grid_objective": "f1_score_micro", "accuracy": 0.95999999999999996, "feature_scaling": "none", "min_feature_count": 1, "test_set_name": "test", "learner_name": "MultinomialNB", "score": 0.95999999999999996, "experiment_name": "Example_Evaluate", "result_table": "+------------+--------+------------+-----------+-----------+--------+-----------+\n|            | setosa | versicolor | virginica | Precision | Recall | F-measure |\n+------------+--------+------------+-----------+-----------+--------+-----------+\n|     setosa |   [19] |          0 |         0 |     1.000 |  1.000 |     1.000 |\n+------------+--------+------------+-----------+-----------+--------+-----------+\n| versicolor |      0 |       [15] |         0 |     0.882 |  1.000 |     0.938 |\n+------------+--------+------------+-----------+-----------+--------+-----------+\n|  virginica |      0 |          2 |      [14] |     1.000 |  0.875 |     0.933 |\n+------------+--------+------------+-----------+-----------+--------+-----------+\n(row = reference; column = predicted)"}]
```