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:
line = line.rstrip().split(",")
if key is None:
key = {v: i for i, v in enumerate(line)}
continue
x = int(line[key['order']])-1
y = (1 - abs(int(line[key['correctness']]) -
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 = [], []
for opp in range(len(cobweb_data[0])):
for run in range(len(cobweb_data)):
cobweb_x.append(opp)
cobweb_y.append(cobweb_data[run][opp])
from concept_formation.preprocessor import ObjectVariablizer
num_runs = 10
num_examples = 25
animals = load_quadruped(num_examples)
variablizer = ObjectVariablizer()
animals = [variablizer.transform(t) for t in animals]
for animal in animals:
animal['type'] = animal['_type']
del animal['_type']
naive_data = incremental_evaluation(DummyTree(),
animals,
run_length=num_examples,
runs=num_runs,
attr="type")
trestle_data = incremental_evaluation(TrestleTree(),
animals,
run_length=num_examples,
runs=num_runs,
attr="type")
trestle_x, trestle_y = [], []
naive_x, naive_y = [], []
human_x, human_y = [], []
for opp in range(len(trestle_data[0])):
for run in range(len(trestle_data)):
trestle_x.append(opp)
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.cobweb3 import Cobweb3Tree
from concept_formation.dummy import DummyTree
from concept_formation.datasets import load_iris
num_runs = 30
num_examples = 20
irises = load_iris()
naive_data = incremental_evaluation(DummyTree(),
irises,
run_length=num_examples,
runs=num_runs,
attr="class")
cobweb_data = incremental_evaluation(Cobweb3Tree(),
irises,
run_length=num_examples,
runs=num_runs,
attr="class")
cobweb_x, cobweb_y = [], []
naive_x, naive_y = [], []
for opp in range(len(cobweb_data[0])):
for run in range(len(cobweb_data)):
cobweb_x.append(opp)
cobweb_y.append(cobweb_data[run][opp])
import numpy as np
from random import seed
from concept_formation.examples.examples_utils import avg_lines
from concept_formation.evaluation import incremental_evaluation
from concept_formation.cobweb3 import Cobweb3Tree
from concept_formation.dummy import DummyTree
from concept_formation.datasets import load_iris
seed(0)
num_runs = 30
num_examples = 20
irises = load_iris()
naive_data = incremental_evaluation(DummyTree(), irises,
run_length=num_examples,
runs=num_runs, attr="class")
cobweb_data = incremental_evaluation(Cobweb3Tree(), irises,
run_length=num_examples,
runs=num_runs, attr="class")
cobweb_x, cobweb_y = [], []
naive_x, naive_y = [], []
for opp in range(len(cobweb_data[0])):
for run in range(len(cobweb_data)):
cobweb_x.append(opp)
cobweb_y.append(cobweb_data[run][opp])
for opp in range(len(naive_data[0])):
for run in range(len(naive_data)):
naive_x.append(opp)
seed(0)
num_runs = 5
num_examples = 15
animals = load_quadruped(num_examples)
variablizer = ObjectVariablizer()
animals = [variablizer.transform(t) for t in animals]
for animal in animals:
animal['type'] = animal['_type']
del animal['_type']
naive_data = incremental_evaluation(DummyTree(), animals,
run_length=num_examples,
runs=num_runs, attr="type")
trestle_data = incremental_evaluation(TrestleTree(), animals,
run_length=num_examples,
runs=num_runs, attr="type")
trestle_x, trestle_y = [], []
naive_x, naive_y = [], []
human_x, human_y = [], []
for opp in range(len(trestle_data[0])):
for run in range(len(trestle_data)):
trestle_x.append(opp)
trestle_y.append(trestle_data[run][opp])
for opp in range(len(naive_data[0])):