情节Sklearn Roccurve

Question

我想绘制我的模型ROC曲线，但我会发现错误： '＆lt;'在“ str”和“ int”的实例之间不支持

我具有数值和分类数据类型。

data_type= dfj.dtypes
data_type
ZAG_LUDNOŚCI                                     int32
GDP_NA_OS                                        int32
DNI POWSZEDNIE                                   int64
PORA_DNIA                                       object
SPECJALNA_BUDOWA_OBIEKTU                         int32
ILOŚĆ_MIESZKAŃCÓW_OBIEKTU                       object
CZY_BUDYNEK_JEST_NORMALNIE_ZAJĘTY                int32
CZY_CZUJNIK_DYMU_JEST_W_OBIEKCIE                object

我通过管道与他们打交道：

from sklearn.compose import make_column_selector as selector

numerical_columns_selector = selector(dtype_exclude=object)
categorical_columns_selector = selector(dtype_include=object)

numerical_columns = numerical_columns_selector(data)
categorical_columns = categorical_columns_selector(data)

from sklearn.preprocessing import OneHotEncoder, StandardScaler

categorical_preprocessor = OneHotEncoder(handle_unknown="ignore")
numerical_preprocessor = StandardScaler()

from sklearn.compose import ColumnTransformer

preprocessor = ColumnTransformer([
    ('one-hot-encoder', categorical_preprocessor, categorical_columns),
    ('standard_scaler', numerical_preprocessor, numerical_columns)])

from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import make_pipeline

model = make_pipeline(preprocessor, LogisticRegression(max_iter=1000))

from sklearn import set_config
set_config(display='diagram')
model

然后我学习模型并预测：

    from sklearn.model_selection import train_test_split

data_train, data_test, target_train, target_test = train_test_split(
    data, target, random_state=42)
    _ = model.fit(data_train, target_train)
    y_pred=model.predict(data_test)
model.predict(data_test)[:10]
y_pred

此刻，我想绘制ROC曲线，但会收到错误消息...

from sklearn.metrics import roc_curve
from sklearn.metrics import RocCurveDisplay

y_score = model.decision_function(data_test)

fpr, tpr, _ = roc_curve(data_test, y_score, pos_label=model.classes_[1])
roc_display = RocCurveDisplay(fpr=fpr, tpr=tpr).plot()

错误：

TypeError                                 Traceback (most recent call last)
~\AppData\Local\Temp/ipykernel_20736/4039759825.py in <module>
      4 y_score = model.decision_function(data_test)
      5 
----> 6 fpr, tpr, _ = roc_curve(data_test, y_score, pos_label=model.classes_[1])
      7 roc_display = RocCurveDisplay(fpr=fpr, tpr=tpr).plot()

~\anaconda3\lib\site-packages\sklearn\metrics\_ranking.py in roc_curve(y_true, y_score, pos_label, sample_weight, drop_intermediate)
    977 
    978     """
--> 979     fps, tps, thresholds = _binary_clf_curve(
    980         y_true, y_score, pos_label=pos_label, sample_weight=sample_weight
    981     )

~\anaconda3\lib\site-packages\sklearn\metrics\_ranking.py in _binary_clf_curve(y_true, y_score, pos_label, sample_weight)
    734     """
    735     # Check to make sure y_true is valid
--> 736     y_type = type_of_target(y_true, input_name="y_true")
    737     if not (y_type == "binary" or (y_type == "multiclass" and pos_label is not None)):
    738         raise ValueError("{0} format is not supported".format(y_type))

~\anaconda3\lib\site-packages\sklearn\utils\multiclass.py in type_of_target(y, input_name)
    284         raise ValueError("y cannot be class 'SparseSeries' or 'SparseArray'")
    285 
--> 286     if is_multilabel(y):
    287         return "multilabel-indicator"
    288 

~\anaconda3\lib\site-packages\sklearn\utils\multiclass.py in is_multilabel(y)
    171         )
    172     else:
--> 173         labels = np.unique(y)
    174 
    175         return len(labels) < 3 and (

<__array_function__ internals> in unique(*args, **kwargs)

~\anaconda3\lib\site-packages\numpy\lib\arraysetops.py in unique(ar, return_index, return_inverse, return_counts, axis)
    260     ar = np.asanyarray(ar)
    261     if axis is None:
--> 262         ret = _unique1d(ar, return_index, return_inverse, return_counts)
    263         return _unpack_tuple(ret)
    264 

~\anaconda3\lib\site-packages\numpy\lib\arraysetops.py in _unique1d(ar, return_index, return_inverse, return_counts)
    321         aux = ar[perm]
    322     else:
--> 323         ar.sort()
    324         aux = ar
    325     mask = np.empty(aux.shape, dtype=np.bool_)

TypeError: '<' not supported between instances of 'str' and 'int'

我不知道如何修复它。感谢您的帮助！ 编辑：目标是Przypadkowe_czy_celowe int32看起来像

0        0
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        ..
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12538    0
Name: PRZYPADKOWE_CZY_CELOWE, Length: 12539, dtype: int32

Answer 1

roc_curve的第一个参数应该是真正的标签，但是您正在传递自变量。