def test_load_file_simple():
suportVec = 0.5
logger.debug('loading file for karma with support vec value: ' +
str(suportVec))
karma = Karma(suportVec)
karma.fit('../KarmaLego_TestsData/DiabetesEQW_3_Class1_with_mapping.csv')
def runKarmaLego(time_intervals_path,
min_ver_support,
num_relations,
max_gap,
label,
max_tirp_length=5,
num_comma=2,
num_of_bins=3,
symbol_type='int'):
"""
this method runs the process of KarmaLego with all relevant inputs
:param time_intervals_path: String, the time intervals file
:param class_b_path: String, the time intervals file for class
:param min_ver_support: float, the minimum vertical support value
:param num_relations: int, number of relations
:param max_gap: int, the max_gap between the intervals for creating the index
:return:
"""
karma = Karma(min_ver_support,
num_relations=num_relations,
max_gap=max_gap,
label=label)
karma.fit(time_intervals_path, num_comma, num_of_bins, symbol_type)
lego = Lego(karma, max_tirp_length)
lego.fit()
return lego
def test_load_file():
suportVec = 0.5
logger.debug('loading file for karma with support vec value: ' +
str(suportVec))
karma = Karma(suportVec)
karma.fit('../KarmaLego_TestsData/DiabetesEQW_3_Class1_with_mapping.csv',
True)
logger.info('[PASSED] karma build for skipped followers ')
def test_get_relations_for_two_symbols():
suportVec = 0.5
logger.debug('loading file for karma with support vec value: ' +
str(suportVec))
karma = Karma(suportVec)
karma.fit('../KarmaLego_TestsData/single_entity_2_bins.csv')
relations = karma.get_relations_for_two_symbols(1, 2)
if len(relations) == 1 and relations[0] == AllenSevenRelationEngine.BEFORE:
logger.info('[PASSED] relations fetched ' + str(relations))
else:
logger.info('[FAILED] relations not fetched' + str(relations))
def test_is_pair_exist():
suportVec = 0.5
logger.debug('loading file for karma with support vec value: ' +
str(suportVec))
karma = Karma(suportVec)
karma.fit('../KarmaLego_TestsData/single_entity_2_bins.csv')
sti_a = SymbolicTimeInterval(0, 2, 1)
sti_b = SymbolicTimeInterval(4, 6, 2)
if karma.is_pair_exist(0, 1, sti_a, sti_b):
logger.info('[PASSED] pair exist ' + str(sti_a) + ' ' + str(sti_b))
else:
logger.info('[FAILED] pair dont exist ' + str(sti_a) + ' ' +
str(sti_b))
def test_get_pairs():
suportVec = 0.5
logger.debug('loading file for karma with support vec value: ' +
str(suportVec))
karma = Karma(suportVec)
karma.fit('../KarmaLego_TestsData/single_entity_2_bins.csv')
sti = SymbolicTimeInterval(0, 2, 1)
pairs = karma.get_pairs(1, 2, 0, 1, sti)
if 2 == len(pairs):
logger.info('[PASSED] pairs fetched ' + str(pairs))
else:
logger.info('[FAILED] pairs not fetched - ' + str(pairs))
def run_KL(input_path, output_folder, epsilon, max_gap, vertical_support):
output_path = output_folder
suportVec = float(vertical_support)
num_relations = 7
num_symbols = count_symbols(input_path, output_path)
max_gap = float(max_gap)
epsilon = float(epsilon)
karma = Karma(min_ver_support=suportVec,
num_relations=num_relations,
max_gap=max_gap,
epsilon=epsilon)
karma.fit(input_path, num_comma=NUM_COMMA, num_of_bins=num_symbols)
lego = Lego(karma)
lego.fit()
lego.print_frequent_tirps(output_path)
def test_test_lego_fit():
suportVec = 0.5
num_relations = 7
max_gap = 15
logger.debug('loading file for karma with support vec value: ' +
str(suportVec))
karma = Karma(suportVec, num_relations=num_relations, max_gap=max_gap)
karma.fit('../KarmaLego_TestsData/DiabetesEQW_3_Class0_short.csv')
lego = Lego(karma)
lego.fit()
lego.print_frequent_tirps(
'../KarmaLego_TestsData/DiabetesEQW_3_Class0_50_15_output_sequence.csv'
)
depth = lego.tirps_tree_root.get_max_depth()
leafs = lego.tirps_tree_root.get_all_leafs()
logger.info('[PASSED] Lego fit completed, tree depth is: ' + str(depth) +
' with ' + str(len(leafs)) + " leafs")
def test_get_all_pairs():
suportVec = 0.5
logger.debug('loading file for karma with support vec value: ' +
str(suportVec))
karma = Karma(suportVec)
karma.fit('../KarmaLego_TestsData/single_entity_2_bins.csv')
karma.get_all_pairs_mapped_by_entity_id(1, 2, 0)
def runKarmaLegoCD(class_a_path,class_b_path,min_ver_support,num_relations,max_gap,min_vs_gap, alpha, weights,num_comma):
"""
this method runs all the process of KarmaLegoCD with all relevant inputs
:param class_a_path: String, the time intervals file for class A
:param class_b_path: String, the time intervals file for class B
:param min_ver_support: float, the minimum vertical support value
:param num_relations: int, number of relations
:param max_gap: int, the max_gap between the intervals for creating the index
:param min_vs_gap: float, the minimum vertical support threshold between the classes
:param alpha: float, the minimum alpha for the statistical tests
:param weights: list[float] the weight for the scoring function
:return: newLego, returns the NewLego object
"""
karma_a = Karma(min_ver_support, num_relations=num_relations, max_gap=max_gap,label=0)
karma_b = Karma(min_ver_support, num_relations=num_relations, max_gap=max_gap,label=1)
karma_a.fit(class_a_path,num_comma=num_comma,min_vertical_support_for_trim=0.3)
karma_b.fit(class_b_path,num_comma=num_comma,min_vertical_support_for_trim=0.3)
newLego=NewLego(karma_a=karma_a,karma_b=karma_b,min_vs_gap=min_vs_gap,alpha=alpha,weights=weights)
newLego.fit()
return newLego
def test_lego_generate_candidates():
"""
the tested relations are the 7 allens as defined in RelationHandler
| B | C | D |
--|---|---|---|
A | 2 | 2| ?|
--|---|---|---|
B | | 2| ?|
--|---|---|---|
C | | | 2|
should generate 5 matching candidates
[[0,0,2],[0,1,2],[0,2,2],[1,2,2],[2,2,2]]
for future tirps as:
| B | C | D | | B | C | D | | B | C | D | | B | C | D | | B | C | D |
--|---|---|---| --|---|---|---| --|---|---|---| --|---|---|---| --|---|---|---|
A | 2 | 2| 0| A | 2 | 2| 0| A | 2 | 2| 0| A | 2 | 2| 1| A | 2 | 2| 2|
--|---|---|---| --|---|---|---| --|---|---|---| --|---|---|---| --|---|---|---|
B | | 2| 0| B | | 2| 1| B | | 2| 2| B | | 2| 2| B | | 2| 2|
--|---|---|---| --|---|---|---| --|---|---|---| --|---|---|---| --|---|---|---|
C | | | 2| C | | | 2| C | | | 2| C | | | 2| C | | | 2|
.....
recursion tree should be:
0 0 0 1 2
| | \ | /
| | \ | /
| | \|/
0 1 2
\ | /
\ | /
\ | /
\|/
2
"""
expected = [[0, 0, 2], [0, 1, 2], [0, 2, 2], [1, 2, 2], [2, 2, 2]]
result = True
reason = 'as expected'
tirp = TIRP('A', 'B', 2)
tirp._symbols.append('C')
tirp._tirp_matrix.extend([2, 2])
lego = Lego(Karma(1))
# expending with overlap (2) when others are overlap too
candidates = lego.generate_candidats(tirp, 2)
if len(candidates) != len(expected):
result = False
reason = 'candidates amount not match'
elif candidates != expected:
result = False
reason = 'candidates not as expected'
if result:
logger.info('[PASSED] Lego candidates generation success: ' + reason)
else:
logger.error('[FAILED] Lego candidates generation failed, reason: ' +
reason)