def test_load_rb_s_07():
data = load_rb_s_07(num_instances=1)
known = {
'_guid': '660ac76d-93b3-4ce7-8a15-a3213e9103f5',
'component0': {
'b': 0.0,
'l': 0.0,
'r': 3.0,
't': 1.0,
'type': 'plat0'
},
'component1': {
'b': 1.0,
'l': 1.0,
'r': 2.0,
't': 4.0,
'type': 'plat90'
},
'component8': {
'b': 4.0,
'l': 0.0,
'r': 3.0,
't': 5.0,
'type': 'ufoo0'
},
'success': '0'
}
assert known == data[0]
def test_load_rb_s_07():
data = load_rb_s_07(num_instances=1)
known = {'_guid': '660ac76d-93b3-4ce7-8a15-a3213e9103f5', 'component0':
{'b': 0.0, 'l': 0.0, 'r': 3.0, 't': 1.0, 'type': 'plat0'},
'component1': {'b': 1.0, 'l': 1.0, 'r': 2.0, 't': 4.0, 'type':
'plat90'}, 'component8': {'b': 4.0, 'l': 0.0, 'r':
3.0, 't': 5.0, 'type':
'ufoo0'}, 'success': '0'}
assert known == data[0]
def output_json(file="forest", size=100, prune=True, seed=50, burn=1):
random.seed(seed)
if file == "forest":
instances = ds.load_forest_fires()
variables = False
elif file == "voting":
instances = ds.load_congressional_voting()
variables = False
elif file == "iris":
instances = ds.load_iris()
variables = False
elif file == "mushroom":
instances = ds.load_mushroom()
variables = False
elif file == "rb_com_11":
instances = ds.load_rb_com_11()
variables = True
elif file == "rb_s_07":
instances = ds.load_rb_s_07()
variables = True
elif file == "rb_s_13":
instances = ds.load_rb_s_13()
variables = True
elif file == "rb_wb_03":
instances = ds.load_rb_wb_03()
variables = True
else:
instances = ds.load_forest_fires()
variables = False
random.shuffle(instances)
pprint.pprint(instances[0])
instances = instances[:size]
print(len(instances))
if variables:
variablizer = ObjectVariablizer()
instances = [variablizer.transform(t) for t in instances]
tree = TrestleTree()
tree.fit(instances, iterations=burn)
pprint.pprint(tree.root.output_json())
with open('output.js', 'w') as out:
out.write("var trestle_output = ")
out.write(json.dumps(tree.root.output_json()))
out.write(";")
def output_json(file="forest", size=100, prune=True, seed=50, burn=1):
random.seed(seed)
if file == "forest":
instances = ds.load_forest_fires()
variables = False
elif file == "voting":
instances = ds.load_congressional_voting()
variables = False
elif file == "iris":
instances = ds.load_iris()
variables = False
elif file == "mushroom":
instances = ds.load_mushroom()
variables = False
elif file == "rb_com_11":
instances = ds.load_rb_com_11()
variables = True
elif file == "rb_s_07":
instances = ds.load_rb_s_07()
variables = True
elif file == "rb_s_13":
instances = ds.load_rb_s_13()
variables = True
elif file == "rb_wb_03":
instances = ds.load_rb_wb_03()
variables = True
else:
instances = ds.load_forest_fires()
variables = False
random.shuffle(instances)
pprint.pprint(instances[0])
instances = instances[:size]
print(len(instances))
if variables:
variablizer = ObjectVariablizer()
instances = [variablizer.transform(t) for t in instances]
tree = TrestleTree()
tree.fit(instances, iterations=burn)
# pprint.pprint(tree.root.output_json())
with open('output.js', 'w') as out:
out.write("var trestle_output = ")
out.write(json.dumps(tree.root.output_json()))
out.write(";")
from random import seed
import numpy as np
from concept_formation.examples.examples_utils import lowess
from concept_formation.evaluation import incremental_evaluation
from concept_formation.trestle import TrestleTree
from concept_formation.dummy import DummyTree
from concept_formation.datasets import load_rb_s_07
from concept_formation.datasets import load_rb_s_07_human_predictions
from concept_formation.preprocessor import ObjectVariablizer
seed(5)
num_runs = 30
num_examples = 29
towers = load_rb_s_07()
variablizer = ObjectVariablizer()
towers = [variablizer.transform(t) for t in towers]
naive_data = incremental_evaluation(DummyTree(), towers,
run_length=num_examples,
runs=num_runs, attr="success")
cobweb_data = incremental_evaluation(TrestleTree(), towers,
run_length=num_examples,
runs=num_runs, attr="success")
human_data = []
key = None
human_predictions = load_rb_s_07_human_predictions()
for line in human_predictions:
from concept_formation.trestle import TrestleTree from concept_formation.visualize import visualize # These lines load up and use one of the example datasets included in the # library if you don't have a readily available dataset to test. The rb_s_07 # dataset is similar to but not exactly the same as the one used to generate # the figures in the paper. from concept_formation.datasets import load_rb_s_07 from concept_formation.preprocessor import ObjectVariablizer data = load_rb_s_07() # As long as your data conforms to the instance representation: # https://concept-formation.readthedocs.io/en/latest/instance_representation.html # it can be basically anything. # data = [] # This step is to make sure the component attributes of the instances are # properly tagged as variable. See the instance representation link above for # this. # ov = ObjectVariablizer() # data = ov.batch_transform(data) # These three lines are the core of the process. They will fit the data and # generate a visualization that will automatically open a browser to the view. # If you want to embed the output in some other process, like a LearnSphere # workflow, it would take a little more work but is easy in principle. tree = TrestleTree() tree.fit(data)