我通过传递相同教学特征的不同幂来创建不同的多项式回归模型。
因此，如果我想要特征“x”的 3 次多项式模型。然后到回归模型，我将 x^1、x^2 和 x^3 作为特征传递。
以下函数用于创建“x”幂的 Sframe 表。从传递给它的“x”的值，以及需要创建的度数权力。
def polynomial_sframe(feature, degree):
# assume that degree >= 1
# initialize the SFrame:
poly_sframe = graphlab.SFrame()
#poly_sframe['power_1'] equal to the passed feature
poly_sframe['power_1'] = feature
# first check if degree > 1
if degree > 1:
# then loop over the remaining degrees:
# range usually starts at 0 and stops at the endpoint-1.
for power in range(2, degree+1):
#give the column a name:
name = 'power_' + str(power)
# then assign poly_sframe[name] to the appropriate power of feature
poly_sframe[name] = feature.apply(lambda x: x**power)
return poly_sframe
然后使用从上述函数生成的 Sframe。我能够为不同程度的 X 生成不同的多项式表达式。如下面的代码所示。
poly3_data = polynomial_sframe(sales['sqft_living'], 3)
my_features = poly3_data.column_names() # get the name of the features
poly3_data['price'] = sales['price'] # add price to the data since it's the target
model3 = graphlab.linear_regression.create(poly3_data, target = 'price', features = my_features, validation_set = None)
Graphlab 能够生成高达 4 级的模型。之后，如果失败并用于以下代码。它将显示发生了溢出错误。
poly15_data = polynomial_sframe(sales['sqft_living'], 5)
my_features = poly15_data.column_names() # get the name of the features
poly15_data['price'] = sales['price'] # add price to the data since it's the target
model15 = graphlab.linear_regression.create(poly15_data, target = 'price', features = my_features, validation_set = None)
---------------------------------------------------------------------------
ToolkitError Traceback (most recent call last)
<ipython-input-76-df5cbc0b6314> in <module>()
2 my_features = poly15_data.column_names() # get the name of the features
3 poly15_data['price'] = sales['price'] # add price to the data since it's the target
----> 4 model15 = graphlab.linear_regression.create(poly15_data, target = 'price', features = my_features, validation_set = None)
C:\Users\mk\Anaconda2\envs\dato-env\lib\site-
packages\graphlab\toolkits\regression\linear_regression.pyc in create(dataset, target, features, l2_penalty, l1_penalty, solver, feature_rescaling, convergence_threshold, step_size, lbfgs_memory_level, max_iterations, validation_set, verbose)
284 step_size = step_size,
285 lbfgs_memory_level = lbfgs_memory_level,
--> 286 max_iterations = max_iterations)
287
288 return LinearRegression(model.__proxy__)
C:\Users\mk\Anaconda2\envs\dato-env\lib\site-
packages\graphlab\toolkits\_supervised_learning.pyc in create(dataset, target, model_name, features, validation_set, verbose, distributed, **kwargs)
451 else:
452 ret = _graphlab.toolkits._main.run("supervised_learning_train",
--> 453 options, verbose)
454 model = SupervisedLearningModel(ret['model'], model_name)
455
C:\Users\mk\Anaconda2\envs\dato-env\lib\site-
packages\graphlab\toolkits\_main.pyc in run(toolkit_name, options, verbose, show_progress)
87 _get_metric_tracker().track(metric_name, value=1, properties=track_props, send_sys_info=False)
88
---> 89 raise ToolkitError(str(message))
ToolkitError: Exception in python callback function evaluation:
OverflowError('long too big to convert',):
Traceback (most recent call last):
File "graphlab\cython\cy_pylambda_workers.pyx", line 426, in graphlab.cython.cy_pylambda_workers._eval_lambda
File "graphlab\cython\cy_pylambda_workers.pyx", line 171, in graphlab.cython.cy_pylambda_workers.lambda_evaluator.eval_simple
File "graphlab\cython\cy_flexible_type.pyx", line 1193, in graphlab.cython.cy_flexible_type.process_common_typed_list
File "graphlab\cython\cy_flexible_type.pyx", line 1138, in graphlab.cython.cy_flexible_type._fill_typed_sequence
File "graphlab\cython\cy_flexible_type.pyx", line 1385, in graphlab.cython.cy_flexible_type._ft_translate
OverflowError: long too big to convert
这个错误是因为我的计算机缺少内存来计算回归模型吗？如何修复此错误？