"""
======================================
How to fit a basic model
======================================

These examples show how to fit a model using MonoRuleRandomForest.
 `MonoRuleRandomForest` first fits a forest ensemble using `sci-kit learn's`
RandomForestClassifier, then filters the leaves to remove leaf 'rules' that
are not monotone compliant. One consequence of this is that you need
sufficient trees to ensure a reasonable number of leaves are left. 
"""

import numpy as np
import monorulerf as monorulerf
from sklearn.datasets import load_boston

###############################################################################
# Load the data
# ----------------
#
# First we load the standard data source on 
# `Boston Housing 
# <https://www.cs.toronto.edu/~delve/data/boston/bostonDetail.html>`_, and 
# convert the output from real valued (regression) to binary classification 
# with roughly 50-50 class distribution:
#

data = load_boston()
y = data['target']
X = data['data']
features = data['feature_names']

###############################################################################
# Specify the monotone features
# -------------------------
# There are 13 predictors for house price in the Boston dataset:

###############################################################################
##. CRIM - per capita crime rate by town
##. ZN - proportion of residential land zoned for lots over 25,000 sq.ft.
##. INDUS - proportion of non-retail business acres per town.
##. CHAS - Charles River dummy variable (1 if tract bounds river; 0 otherwise)
##. NOX - nitric oxides concentration (parts per 10 million)
##. RM - average number of rooms per dwelling
##. AGE - proportion of owner-occupied units built prior to 1940
##. DIS - weighted distances to five Boston employment centres
##. RAD - index of accessibility to radial highways
##. TAX - full-value property-tax rate per $10,000
##. PTRATIO - pupil-teacher ratio by town
##. B - 1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town
##. LSTAT - % lower status of the population
#
# The output is MEDV - Median value of owner-occupied homes in $1000's, but we 
# convert it to a binary y in +/-1 indicating whether MEDV is less than 
# $21(,000):

y[y< 21]=-1 # convert real output to 50-50 binary classification
y[y>=21]=+1 

###############################################################################
# We suspect that the number of rooms (6. RM) and the highway 
# accessibility (9. RAD) would, if anything, increase the price of a house
# (all other things being equal). Likewise we suspect that crime rate (1.
# CRIM), distance from employment (8. DIS) and percentage of lower status
# residents (13. LSTAT) would be likely to, if anything, decrease house prices.
# So we have:

incr_feats=[6,9]
decr_feats=[1,8,13]

###############################################################################
# Fit a MonoRuleRandomForest model
# -------------------------
# We now fit our classifier. To understand the model and hyperparameters, 
# please refer to the original paper available 
# `here <http://staffhome.ecm.uwa.edu.au/~19514733/>`_:

# Specify hyperparams for model solution (normally optimised via oob_score or 
# CV)
mtry=3
n_estimators = 200
# Solve model
clf = monorulerf.MonoRuleRandomForest(n_estimators=n_estimators,
                                             max_features=mtry,
                                             n_jobs=1, oob_score=True,
                                             random_state=11, criterion='gini',
                                             incr_feats=incr_feats,
                                             decr_feats=decr_feats)
clf.fit(X, y)

# Assess the model
y_pred = clf.predict(X)
acc_insample = np.sum(y == y_pred) / len(y)
mcr_oob=clf.oob_score_

###############################################################################
# Final notes
# -----------------------
# In a real scenario we would optimise the hyperparameter `mtry` using using
# the out-of-box (oob) score or cross-validation but this is 
# not covered in these basic examples. Enjoy!