updated the R-package by adding the warm-start and save_model features

[mlampros]

I've added the warm-start and save_model features. I've tested it locally and I used the following test-cases,

#---------------
# RGF Regressor
#--------------

library(RGF)

set.seed(1)
x = matrix(runif(1000), nrow = 100, ncol = 10)

y = runif(100)

RGF_regr = RGF_Regressor$new(max_leaf = 50)

RGF_regr$fit(x, y)

pth = "/your_folder/model_rgf"

RGF_regr$save_model(filename = pth)

preds = RGF_regr$predict(x)

RGF_regr1 = RGF_Regressor$new(max_leaf = 150, init_model = pth)

RGF_regr1$fit(x, y)

preds1 = RGF_regr1$predict(x)               # there are differences between 'preds' and 'preds1' to observe


#--------------------------------------------------------------------------------------------------------------- ERROR CASE
# IF I GIVE :    RGF_Regressor$new(max_leaf = 50, init_model = pth)     THEN I RECEIVE THE FOLLOWING ERROR :
#-----------------------------------------------------------------------------------------------------------

# Error in py_call_impl(callable, dots$args, dots$keywords) : 
#   Exception: "train": 
#   algorithm=RGF
# train_x_fn=/tmp/rgf/2e3daf71-b125-4b77-a99a-fb74031c0f6e2.train.data.x
# train_y_fn=/tmp/rgf/2e3daf71-b125-4b77-a99a-fb74031c0f6e2.train.data.y
# Log:ON
# model_fn_prefix=/tmp/rgf/2e3daf71-b125-4b77-a99a-fb74031c0f6e2.model
# model_fn_for_warmstart=/home/lampros/Pictures/model_rgf
# --------------------
#   Thu Dec  6 20:26:49 2018: Reading training data ... 
# Thu Dec  6 20:26:49 2018: Start ... #train=100
# --------------------
#   Forest-level: 
#   loss=LS
# max_leaf_forest=50
# max_tree=25
# opt_interval=100
# test_interval=100
# num_tree_search=1
# memory_policy=Generous
# !Input error!: (Detected in AzRgforest::warmup_timer) 
# The model given for warm-start is already over the requested maximum size of the models: #leaf=50, #tree=14 

#-----------------------------------------------------------------------------------------------
# THE ERROR APPEARS BECAUSE THE NUMBER OF TREES SHOULD BE GREATER THAN THE INITIAL (WHICH IS 50)
#---------------------------------------------------------------------------------------------------------------


#---------------
# RGF Classifier
#---------------

library(RGF)

set.seed(1)
x = matrix(runif(1000), nrow = 100, ncol = 10)

y = sample(1:2, 100, replace = TRUE)

RGF_class = RGF_Classifier$new(max_leaf = 50)

RGF_class$fit(x, y)


pth = "/your_folder/model_rgf"

RGF_class$save_model(filename = pth)


preds = RGF_class$predict(x)

RGF_class1 = RGF_Classifier$new(max_leaf = 150, init_model = pth)

RGF_class1$fit(x, y)

preds1 = RGF_class1$predict(x)

table(preds, preds1)                  # there are differences between 'preds' and 'preds1' to observe

the tests can be added by @jameslamb as we've agreed in issue Nr. 269.
@fukatani , @StrikerRUS this pull request is ready for review.

[StrikerRUS]

Thank you @mlampros very much!

One note: since 1.0.5.1 has never been released, maybe squash all changes into 1.0.6 version and release it later (after uploading rgf_python with warm-start functionality to PyPI)?

[mlampros]

Yes, I thought that the extended version (4-digits rather than 3-digits) has been used internally only for the rgf-team repository, so that we don't get an error on travis as we've discussed in an earlier pull request and we can keep track of changes over time. Once the new version of rgf_python is ready we can merge all these changes to 1.0.x (3-digit).

[StrikerRUS]

Yeah, cool! Agree with you.

[fukatani]

LGTM!