adds release notes

RELEASE_NOTES.md

@@ -0,0 +1,47 @@
+## version 0.2:
+### Breaking Changes
+- Custom rules are now defined with `__rule__` method that returns a boolean mask
+    instead of with `predict(X)` method.
+- `DummyRule` is now called `PredictionRule`
+
+
+### New Features
+- each rule now gets assigned a `rule_id`, which is displayed when you call
+    `estimator.describe()`
+- new `score_rules(X, y)` method that shows performance of individual rules
+- new `get_igraph()` method, that returns an igraph Graph object of the rules
+- new `plot()` method that returns a plotly figure of the rules
+- new `get_rule(rule_id)`, `replace_rule(rule_id, new_rule)` and `append_rule(rule_id, new_rule)` methods
+- new `get_rule_params(rule_id)` and `set_rule_params(rule_id, **params)` methods
+- new `get_rule_input(rule_id, X, y)` and `get_rule_leftover(rule_id, X, y)` to get the specific data
+    that either flows into a rule, or the unlabeled data that flows out of a rule.
+    This helps in constructing new rules as you can target it to the data
+    that would appear in that part of the rule graph. 
+
+
+### Improvements
+- data is now split up and only non-labeled data is passed to downstream rules.
+-
+
+
+
+## Template:
+### Breaking Changes
+- 
+- 
+
+### New Features
+-
+-
+
+### Bug Fixes
+-
+-
+
+### Improvements
+-
+-
+
+### Other Changes
+-
+-

rule_estimator/businessrule.py

@@ -8,10 +8,6 @@
 import numpy as np
 import pandas as pd
 
-from sklearn.base import BaseEstimator
-from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor
-from sklearn.metrics import accuracy_score, precision_score, recall_score, mean_squared_error
-
 from igraph import Graph
 
 from .storable import Storable