def test_random_split_df_shuffled_split_are_same():
idx1, idx2 = random_split(FULL_DF.index,
0.66,
res_type=set,
random_state=42)
df1, df2 = df_shuffled_split(FULL_DF, split_size=0.66, random_state=42)
assert (set(FULL_DF.loc[idx1, :].index),
set(FULL_DF.loc[idx2, :].index)) == (
idx1,
idx2,
)
def test_stacking():
irep = IREP(random_state=42)
rip = RIPPER(random_state=42)
df = DF.copy()
numeric_cols = df.select_dtypes("number").columns
categorical_cols = [
col for col in df.columns
if (col not in numeric_cols and not col == CLASS_FEAT)
]
dum_df = pd.get_dummies(df[categorical_cols])
for col in numeric_cols:
dum_df[col] = df[col]
dum_df[CLASS_FEAT] = df[CLASS_FEAT]
sktrain, sktest = df_shuffled_split(dum_df, random_state=42)
sktrain_x, sktrain_y = sktrain.drop(CLASS_FEAT, axis=1), train[CLASS_FEAT]
sktest_x, sktest_y = sktest.drop(CLASS_FEAT, axis=1), test[CLASS_FEAT]
lone_tree = DecisionTreeClassifier(random_state=42)
lone_tree.fit(sktrain_x, sktrain_y)
lone_tree_score = lone_tree.score(sktest_x, sktest_y)
# print('lone_tree_score',lone_tree_score)
irep_tree = SVC(random_state=42)
irep_stack_estimators = [("irep", irep), ("tree", irep_tree)]
irep_stack = StackingClassifier(estimators=irep_stack_estimators,
final_estimator=LogisticRegression())
irep_stack.fit(sktrain_x, sktrain_y)
irep_stack_score = irep_stack.score(sktest_x, sktest_y)
# print('irep_stack_score', irep_stack_score)
assert irep_stack_score != lone_tree_score
rip_tree = DecisionTreeClassifier(random_state=42)
rip_stack_estimators = [("rip", rip), ("tree", rip_tree)]
rip_stack = StackingClassifier(estimators=rip_stack_estimators,
final_estimator=LogisticRegression())
rip_stack.fit(sktrain_x, sktrain_y)
rip_stack_score = rip_stack.score(sktest_x, sktest_y)
# print('rip_stack_score',rip_stack_score)
assert rip_stack_score != lone_tree_score
X_DF = DF.drop(CLASS_FEAT, axis=1)
Y_DF = DF[CLASS_FEAT]
XY_NP = DF.values
X_NP = X_DF.values
Y_NP = Y_DF.values
NP_CLASS_FEAT = -1
######
irep = IREP(random_state=42)
rip = RIPPER(random_state=42)
#####
train, test = df_shuffled_split(DF, random_state=42)
test_x, test_y = test.drop(CLASS_FEAT, axis=1), test[CLASS_FEAT]
irep.fit(train, class_feat=CLASS_FEAT, pos_class=CREDIT_POS_CLASS)
rip.fit(train, class_feat=CLASS_FEAT, pos_class=CREDIT_POS_CLASS)
#####
def test_predict():
irep_preds = irep.predict(test_x)
assert all(p in (True, False) for p in irep_preds)
assert not all(p == True for p in irep_preds)
assert not all(p == False for p in irep_preds)
assert sum(irep_preds) == 128
def test_shuffled_splits_are_len_7_len_3():
df1, df2 = df_shuffled_split(FIRST_10_EXAMPLES, 0.7, random_state=None)
assert (len(df1), len(df2)) == (7, 3)
def _grow_ruleset(self, pos_df, neg_df, initial_model=None):
"""Grow a Ruleset with (optional) pruning."""
ruleset = self._ruleset_frommodel(initial_model)
ruleset._set_possible_conds(pos_df, neg_df)
if self.verbosity >= 2:
print("growing ruleset...")
print(f"initial model: {ruleset}")
print()
prune_size = (self.prune_size if self.prune_size is not None else 0
) # If not pruning, use all the data for growing
pos_remaining = pos_df.copy()
neg_remaining = neg_df.copy()
self.rules = []
# Stop adding disjunctions if there are no more positive examples to cover
while len(pos_remaining) > 0:
# If applicable, check for user-specified early stopping
if stop_early(ruleset, self.max_rules, self.max_total_conds):
break
# Grow-prune split remaining uncovered examples (if applicable)
pos_growset, pos_pruneset = base_functions.df_shuffled_split(
pos_remaining, (1 - prune_size),
random_state=self.random_state)
neg_growset, neg_pruneset = base_functions.df_shuffled_split(
neg_remaining, (1 - prune_size),
random_state=self.random_state)
if self.verbosity >= 2:
print(
f"pos_growset {len(pos_growset)} pos_pruneset {len(pos_pruneset)}"
)
print(
f"neg_growset {len(neg_growset)} neg_pruneset {len(neg_pruneset)}"
)
if not prune_size:
print(f"(pruning is turned off)")
# Grow Rule
grown_rule = base_functions.grow_rule(
pos_growset,
neg_growset,
ruleset.possible_conds,
max_rule_conds=self.max_rule_conds,
verbosity=self.verbosity,
)
# If not pruning, add Rule to Ruleset and drop only the covered positive examples
if not prune_size:
ruleset.add(grown_rule)
if self.verbosity >= 2:
print(
f"updated ruleset: {ruleset.truncstr(direction='right')}"
)
print()
rule_covers_pos = grown_rule.covers(pos_remaining)
pos_remaining = pos_remaining.drop(rule_covers_pos.index,
axis=0)
if self.verbosity >= 3:
print(
f"examples remaining: {len(pos_remaining)} pos, {len(neg_remaining)} neg"
)
print()
# If pruning, prune Rule, assess if it's time to stop, and drop all covered examples
else:
pruned_rule = base_functions.prune_rule(
grown_rule,
_IREP_prune_metric,
pos_pruneset,
neg_pruneset,
verbosity=self.verbosity,
)
# Stop if the Rule is bad
prune_precision = base_functions.precision(
pruned_rule, pos_pruneset, neg_pruneset)
if not prune_precision or prune_precision < 0.50:
break
# Otherwise, add new Rule, remove covered examples, and continue
else:
ruleset.add(pruned_rule)
if self.verbosity >= 2:
print(
f"updated ruleset: {ruleset.truncstr(direction='right')}"
)
print()
pos_remaining, neg_remaining = base_functions.rm_covered(
pruned_rule, pos_remaining, neg_remaining)
if self.verbosity >= 3:
print(
f"examples remaining: {len(pos_remaining)} pos, {len(neg_remaining)} neg"
)
print()
# Return new ruleset
return ruleset