本文整理汇总了Python中tpot.TPOT.export方法的典型用法代码示例。如果您正苦于以下问题：Python TPOT.export方法的具体用法？Python TPOT.export怎么用？Python TPOT.export使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类tpot.TPOT的用法示例。
在下文中一共展示了TPOT.export方法的5个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: test_export
# 需要导入模块: from tpot import TPOT [as 别名]
# 或者: from tpot.TPOT import export [as 别名]
def test_export():
"""Assert that TPOT's export function throws a ValueError when no optimized pipeline exists"""
tpot_obj = TPOT()
try:
tpot_obj.export("test_export.py")
assert False # Should be unreachable
except ValueError:
pass示例2: test_export
# 需要导入模块: from tpot import TPOT [as 别名]
# 或者: from tpot.TPOT import export [as 别名]
def test_export():
"""Ensure that the TPOT export function raises a ValueError when no optimized pipeline exists"""
tpot_obj = TPOT()
try:
tpot_obj.export('will_not_output')
assert False # Should be unreachable
except ValueError:
pass示例3: range
# 需要导入模块: from tpot import TPOT [as 别名]
# 或者: from tpot.TPOT import export [as 别名]
Testing TPOT [Tree-based Pipeline Optimization Tool] built by Randy Olson
(http://www.randalolson.com/2015/11/15/introducing-tpot-the-data-science-assistant/)
"""
from tpot import TPOT
import sys
import pandas as pd
from sklearn.datasets import load_digits
from sklearn.cross_validation import train_test_split
for i in range (1,len(sys.argv),2):
if sys.argv[i] == "-df":
DF = sys.argv[i+1]
df = np.loadtxt(DF, skiprows=1, usecols=range(1,271))
#df = pd.read_csv(DF, sep='\t',header=0, index_col=0)
print(df.info())
y = df[:,0]
x = df[:,1:]
X_train, X_test, y_train, y_test = train_test_split(x, y, train_size=0.75)
tpot = TPOT(generations=5, verbosity=2)
tpot.fit(X_train, y_train)
print(tpot.score(X_train, y_train, X_test, y_test))
tpot.export('tpot_NNU_k3_pro_p05_pipeline.py')
示例4: load_iris
# 需要导入模块: from tpot import TPOT [as 别名]
# 或者: from tpot.TPOT import export [as 别名]
from tpot import TPOT
from sklearn.datasets import load_iris
from sklearn.cross_validation import train_test_split
iris = load_iris()
X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target,
train_size=0.75, test_size=0.25)
tpot = TPOT(generations=5,verbosity=2)
tpot.fit(X_train, y_train)
print(tpot.score(X_test, y_test))
tpot.export('tpot_iris_pipeline.py')
示例5: train_test_split
# 需要导入模块: from tpot import TPOT [as 别名]
# 或者: from tpot.TPOT import export [as 别名]
train = train.drop(drop_list,axis=1)
train = train[0:3000000:300]
train.info(memory_usage='deep')
X = train.drop("hotel_cluster",axis=1).values
y = train.loc[: , "hotel_cluster"].values
del train
import gc
gc.collect()
X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.75,test_size=0.25)
print("got here!")
my_tpot = TPOT(generations=20,verbosity=2,population_size=5) # seems to have a problem with pop <5
# gen 1-> really means two generations!
start = time.clock()
print(start)
my_tpot.fit(X_train, y_train)
my_tpot.export('tpot_expedia_pipeline.py')
end = time.clock()
duration = end - start
score = my_tpot.score(X_test, y_test)
print(duration,score)