…result generation more carefully

…VM_decision_boundaries` compatible with changes in `clustering.py` and `mapping.py` files. Also porting these 3 notebooks to trip_model

`cluster_performance.ipynb`, `generate_figs_for_poster` and  `SVM_decision_boundaries`  now have no dependence on the custom branch. Results of plots  are attached to show no difference in theie previous and current outputs.

Unified Interface for fit function across all models. Passing 'Entry' Type data from the notebooks till the Binning functions.  Default set to 'none'.

…results.py`

Prior to this update, `NaiveBinningClassifier` in 'models.py' had dependencies on both of tour model and trip model. Now, this classifier is completely dependent on trip model. All the other notebooks (except `classification_performance.ipynb`) were tested as well and they are working as usual.

 Other minor fixes to support previous changes.

1. removed mentions of `tour_model` or `tour_model_first_only` .

2. removed two reads from database.

3. Removed notebook outputs  ( this could be the reason a few diffs are too big to view)

RF initialisation and fit function. Build test written and tested.  fit of Random forest uses df .

Predict is now included. Just need to figure out model storage and model testing.

Model loading and storing is now improved since it just stores the required predictors and encoders.

Regression  test and null value test included in tests.

1. switching a model is as simple as changing model_type in config file

2. ForestModel is now working. Main model is in model.py file which is copied from label_assist

3. TestRunForestModel.py is working.

3. Regression test in TestForestmodel.py are still under construction.

Removed redundancies and unnecessary code segments.

Config copy not required.

…' into SurveyAssistgetModel

Updating branch with changes from master. Moved mdoels from eval repo to server repo.

This will fail due to testForest.py file.
Changes here include :

1. Integrated the shifting of randomForest model from eval to server.

2. unit tests for Model save and load

3. RegressionTest for RF model in testRandomForest.py.

This is replaced by models.py (movied with history)

Improving the test file by changing the way previpous predictions are stored.

Fixing circular import

The changes in this iteration are improvements in test for forest model :

1. Post discussion last week, the regression test was removed ( `TestForestModel.py` )since it won't be useful when model performance improves. Rather, the structures of predictions is checked. This check is merged with TestForestModel.py

2. After e-mission#944 , `predict_labels_with_n` in `run_model.py` expectes a lists and then iterates over it. The forest model and rest of the tests were updated accordingly.

1. Improved tests in `TestForestModelLoadandSave.py`

2. Better comments, imports nd cleanup

While testing model integration, 2 forest model features specific features are added in the `TestForestModelIntegration.py` file rather than in `entry.py` file.

2 more ( total 4) Forest model specific features are now added after generating random trips for testing purpose.

Forst model specific values added in test setup for random Trips

test in testRandomForestTypePreservation as using `allceodata` specific user id. The tests on github use different db. Fixed by generating random samples.