def test_load_mushroom():
data = load_mushroom(num_instances=1)
known = {
'bruises?': 'yes',
'cap-color': 'brown',
'cap-shape': 'convex',
'cap-surface': 'smooth',
'classification': 'poisonous',
'gill-attachment': 'free',
'gill-color': 'black',
'gill-size': 'narrow',
'gill-spacing': 'closed',
'habitat': 'urban',
'odor': 'pungent',
'population': 'scattered',
'ring-number': 'one',
'ring-type': 'pendant',
'spore-print-color': 'black',
'stalk-color-above-ring': 'white',
'stalk-color-below-ring': 'white',
'stalk-root': 'equal',
'stalk-shape': 'enlarging',
'stalk-surface-above-ring': 'smooth',
'stalk-surface-below-ring': 'smooth',
'veil-color': 'white',
'veil-type': 'partial'
}
assert known == data[0]
def test_load_mushroom():
data = load_mushroom(num_instances=1)
known = {'bruises?': 'yes', 'cap-color': 'brown', 'cap-shape': 'convex',
'cap-surface': 'smooth', 'classification': 'poisonous',
'gill-attachment': 'free', 'gill-color': 'black', 'gill-size':
'narrow', 'gill-spacing': 'closed', 'habitat': 'urban', 'odor':
'pungent', 'population': 'scattered', 'ring-number': 'one',
'ring-type': 'pendant', 'spore-print-color': 'black',
'stalk-color-above-ring': 'white', 'stalk-color-below-ring':
'white', 'stalk-root': 'equal', 'stalk-shape': 'enlarging',
'stalk-surface-above-ring': 'smooth', 'stalk-surface-below-ring':
'smooth', 'veil-color': 'white', 'veil-type': 'partial'}
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 __future__ import print_function
from __future__ import unicode_literals
from __future__ import absolute_import
from __future__ import division
import matplotlib.pyplot as plt
import numpy as np
from concept_formation.examples.examples_utils import avg_lines
from concept_formation.evaluation import incremental_evaluation
from concept_formation.cobweb import CobwebTree
from concept_formation.dummy import DummyTree
from concept_formation.datasets import load_mushroom
num_runs = 30
num_examples = 30
mushrooms = load_mushroom()
naive_data = incremental_evaluation(DummyTree(),
mushrooms,
run_length=num_examples,
runs=num_runs,
attr="classification")
cobweb_data = incremental_evaluation(CobwebTree(),
mushrooms,
run_length=num_examples,
runs=num_runs,
attr="classification")
cobweb_x, cobweb_y = [], []
naive_x, naive_y = [], []
from __future__ import print_function
from __future__ import unicode_literals
from __future__ import absolute_import
from __future__ import division
import matplotlib.pyplot as plt
import numpy as np
from concept_formation.examples.examples_utils import lowess
from concept_formation.predict import incremental_prediction
from concept_formation.cobweb import CobwebTree
from concept_formation.dummy import DummyTree
from concept_formation.datasets import load_mushroom
num_runs = 30
num_examples = 30
mushrooms = load_mushroom()
naive_data = incremental_prediction(DummyTree(), mushrooms,
run_length=num_examples,
runs=num_runs, attr="classification")
cobweb_data = incremental_prediction(CobwebTree(), mushrooms,
run_length=num_examples,
runs=num_runs, attr="classification")
naive_data.sort()
cobweb_data.sort()
cobweb_x, cobweb_y = [], []
naive_x, naive_y = [], []
for x,y in cobweb_data: