Python OAAの例

コード例 #1

ファイル: printTrees.py プロジェクト: chiragmajithia/OldRepository

def printAllOVATrees(data, max_depth=1):
    h = multiclass.OAA(len(data.labels),
                       lambda: DecisionTreeClassifier(max_depth=max_depth))
    h.train(data.X, data.Y)
    cnt = 0
    for tree in h.f:
        print("Wine:: " + data.labels[cnt])
        util.showTree(tree, data.words)
        raw_input()
        cnt = cnt + 1

コード例 #2

ファイル: runscript.py プロジェクト: Sliverb/422_p2

from imports import *
from sklearn.tree import DecisionTreeClassifier
import multiclass
import util
from datasets import *

h = multiclass.OAA(20, lambda: DecisionTreeClassifier(max_depth=1))
h.train(WineData.X, WineData.Y)
P = h.predictAll(WineData.Xte)
mean(P == WineData.Yte)
mode(WineData.Y)
WineData.labels[1]

mean(WineData.Yte == 1)

P = h.predictAll(WineData.Xte, useZeroOne=True)
mean(P == WineData.Yte)

h = multiclass.OAA(5, lambda: DecisionTreeClassifier(max_depth=3))
h.train(WineDataSmall.X, WineDataSmall.Y)
P = h.predictAll(WineDataSmall.Xte)
mean(P == WineDataSmall.Yte)
mean(WineDataSmall.Yte == 1)

The 1s mean "likely to be Sauvignon-Blanc" and the 0s mean "likely not to be".
util.showTree(h.f[0], WineDataSmall.words)
"""
h = multiclass.OAA(20, lambda: DecisionTreeClassifier(max_depth=3))
h.train(WineData.X, WineData.Y)
P = h.predictAll(WineData.Xte)
mean(P == WineData.Yte)

コード例 #3

ファイル: ova_word_finder.py プロジェクト: Sliverb/422_p2

from imports import *
from sklearn.tree import DecisionTreeClassifier
import multiclass
import util
import warnings
from datasets import *

warnings.filterwarnings("ignore")
map = list()

depth=1
for depth range(6):
    t=multiclass.OAA(5, lambda: DecisionTreeClassifier(max_depth=depth))
    h.train(WineData.X, WineData.Y)
    P = h.predictAll(WineData.Xte)
    ((P == WineDataSmall.Yte),