def test_id3_build_tree_weekends(self):
dataset = load(self.relative_to_base("/datasets/weekends.data"))
tree = id3.build_tree(dataset)
assert_that(tree,
equals_tree(
{"weather": {
"sunny": {
"parents": {
"yes": "cinema",
"no": "tennis"
}
},
"windy": {
"parents": {
"yes": "cinema",
"no": {
"money": {
"rich": "shopping",
"poor": "cinema"
}
}
}
},
"rainy": {
"money": {
"poor": "cinema",
"rich": "stay in"
}
}
}}
)
)
def test_id3_build_tree_weekends(self):
dataset = load(self.relative_to_base("/datasets/weekends.data"))
tree = id3.build_tree(dataset)
assert_that(
tree,
equals_tree({
"weather": {
"sunny": {
"parents": {
"yes": "cinema",
"no": "tennis"
}
},
"windy": {
"parents": {
"yes": "cinema",
"no": {
"money": {
"rich": "shopping",
"poor": "cinema"
}
}
}
},
"rainy": {
"money": {
"poor": "cinema",
"rich": "stay in"
}
}
}
}))
def __init__(self, training_set):
"""
Constructs a new decision tree.
Args:
training_set: model.DataSet
The training data to use when building the decision tree.
"""
self.training_set = training_set
self._tree = id3.build_tree(training_set)
self._plotter = MatplotlibAnnotationTreePlotter(self._tree)
def __init__(self, training_set):
"""
Constructs a new decision tree.
Args:
training_set: model.DataSet
The training data to use when building the decision tree.
"""
self.training_set = training_set
self._tree = id3.build_tree(training_set)
self._plotter = MatplotlibAnnotationTreePlotter(self._tree)
def test_id3_build_tree_marine_animals(self):
dataset = load(self.relative_to_base("/datasets/marine_animal.data"))
tree = id3.build_tree(dataset)
assert_that(
tree,
equals_tree({
"no_surfacing": {
"no": "no",
"yes": {
"has_flippers": {
"no": "no",
"yes": "yes"
}
}
}
}))
def test_id3_build_tree_marine_animals(self):
dataset = load(self.relative_to_base("/datasets/marine_animal.data"))
tree = id3.build_tree(dataset)
assert_that(tree,
equals_tree(
{"no_surfacing": {
"no": "no",
"yes": {
"has_flippers": {
"no": "no",
"yes": "yes"
}
}
}
}
)
)
def test_id3_build_tree_play_tennis(self):
dataset = load(self.relative_to_base("/datasets/play_tennis.data"),
delimiter=" ")
tree = id3.build_tree(dataset)
assert_that(
tree,
equals_tree({
"Outlook": {
"Sunny": {
"Humidity": {
"High": "No",
"Normal": "Yes"
}
},
"Overcast": "Yes",
"Rain": {
"Wind": {
"Strong": "No",
"Weak": "Yes"
}
}
}
}))
def test_id3_build_tree_play_tennis(self):
dataset = load(self.relative_to_base("/datasets/play_tennis.data"),
delimiter=" ")
tree = id3.build_tree(dataset)
assert_that(tree,
equals_tree(
{"Outlook": {
"Sunny": {
"Humidity": {
"High": "No",
"Normal": "Yes"
}
},
"Overcast": "Yes",
"Rain": {
"Wind": {
"Strong": "No",
"Weak": "Yes"
}
}
}}
)
)
else:
ret += bprint_aux(child_node, index + 2)
return ret
import pandas as pd
has_ids = True
has_header = True
has_labels = True
delimiter = ","
header = 0 if has_header else None
id_col = 0 if has_ids else None
# dataframe = pd.read_csv("./test/datasets/play_tennis.data", index_col=id_col, header=header,delimiter=delimiter)
dataframe = pd.read_csv("hope.csv", index_col=id_col, header=header, delimiter=delimiter)
labels = dataframe.pop(dataframe.columns[-1]) if has_labels else None
dataset = model.DataSet(dataframe, labels=labels)
tree = id3.build_tree(dataset)
printbonito = bprint(tree)
print(printbonito)
txt = open("output.txt", mode="w", encoding="UTF-8")
txt.write(printbonito)
txt.close()
tp = MatplotlibAnnotationTreePlotter(tree)
tp.plot()
