python - 数字错误 "sequence item 0: expected str instance, type found"

Question

我想在多元回归分析中选择变量。我尝试使用此代码 http://planspace.org/20150423-forward_selection_with_statsmodels/ .问题是我想从 50 个变量中选择，这需要太多时间。我使用 Numba 使其更快并编写了以下代码:

@jit
def forward_selected(data, response):
"""Linear model designed by forward selection.

Parameters:
-----------
data : pandas DataFrame with all possible predictors and response

response: string, name of response column in data

Returns:
--------
model: an "optimal" fitted statsmodels linear model
       with an intercept
       selected by forward selection
       evaluated by adjusted R-squared
"""
remaining = set(data.columns)
remaining.remove(response)
selected = [str]
current_score, best_new_score = 0.0, 0.0
while remaining and current_score == best_new_score:
    scores_with_candidates = [str]
    for candidate in remaining:
        formula = "{} ~ {} + 1".format(response,
                                       ' + '.join(selected + [candidate]))
        score = smf.ols(formula, data).fit().rsquared_adj
        scores_with_candidates.append((score, candidate))
    scores_with_candidates.sort()
    best_new_score, best_candidate = scores_with_candidates.pop()
    if current_score < best_new_score:
        remaining.remove(best_candidate)
        selected.append(best_candidate)
        current_score = best_new_score
formula = "{} ~ {} + 1".format(response,
                               ' + '.join(selected))
model = smf.ols(formula, data).fit()
return model

model = forward_selected(df, col)

但它返回以下错误:

TypeError: sequence item 0: expected str instance, type found

请告诉我如何修复它。如果您不明白我的问题，我很乐意在评论中提供更多信息。

Traceback (most recent call last):

File "~/PycharmProjects/anacondaenv/touhu_1.py", line 164, in

submit = forecast(col)

File "~/PycharmProjects/anacondaenv/touhu_1.py", line 75, in forecast

model = forward_selected(df, col)TypeError: sequence item 0: expected str instance, type found

Answer 1

我认为查看 numba 是否真的起到助推器作用的最佳方法之一是尝试使用 njit 而不是 jit 装饰器。 njit 强制 no-python-mode 并在有任何退回到 python 时中断(它根本没有速度优势)。简短回答:不要使用除 np.ndarrays 之外的任何东西。因此，没有字符串、没有元组、没有列表，也没有调用未编译的函数。

所以我修复了错误:numba 不允许在主函数主体中使用空列表...不知道为什么(也许是错误？!)但是如果你将它移动到 while 阻止。

import statsmodels.formula.api as smf
import numba as nb

@nb.jit
def forward_selected_nojit(data, response):
    """Linear model designed by forward selection.

    Parameters:
    -----------
    data : pandas DataFrame with all possible predictors and response

    response: string, name of response column in data

    Returns:
    --------
    model: an "optimal" fitted statsmodels linear model
           with an intercept
           selected by forward selection
           evaluated by adjusted R-squared
    """
    remaining = set(data.columns)
    remaining.remove(response)
    selected = None  # Changed this line
    current_score, best_new_score = 0.0, 0.0
    while remaining and current_score == best_new_score:
        if selected is None:  # Changed this and next line
            selected = []
        scores_with_candidates = []
        for candidate in remaining:
            formula = "{} ~ {} + 1".format(response,
                                           ' + '.join(selected + [candidate]))
            score = smf.ols(formula, data).fit().rsquared_adj
            scores_with_candidates.append((score, candidate))
        scores_with_candidates.sort()
        best_new_score, best_candidate = scores_with_candidates.pop()
        if current_score < best_new_score:
            remaining.remove(best_candidate)
            selected.append(best_candidate)
            current_score = best_new_score
    formula = "{} ~ {} + 1".format(response,
                                   ' + '.join(selected))
    model = smf.ols(formula, data).fit()
    return model

这可能可以用更好的方式解决，但这里重要的是时间安排。但首先检查 numba 是否会产生任何奇怪的东西:

# With numba
sl ~ rk + yr + 1
0.835190760538

# Without numba
sl ~ rk + yr + 1
0.835190760538

所以结果是一样的，现在让我们看看它们的表现如何:

# with numba
10 loops, best of 3: 264 ms per loop

# without numba
10 loops, best of 3: 252 ms per loop

所以这完全符合我的预期。使用 python 类型并调用未编译的外部函数，您不会获得任何速度增益。您可能可以使用 numba 使其更快，但请务必阅读 numba 文档并查看支持的内容:Python types和 Numpy Types