目标: 当模型输入为int、float和objects类型(根据pandas dataframe),使用sklearn预测给定类别集的概率。
我正在使用来自UCI存储库的以下数据集: Auto Dataset
我已经创建了一个几乎可用的流程:
# create transformers for the different variable types.
from sklearn.pipeline import Pipeline
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import StandardScaler, OneHotEncoder
import pandas as pd
import numpy as np
data = pd.read_csv(r"C:\Auto Dataset.csv")
target = 'aspiration'
X = data.drop([target], axis = 1)
y = data[target]
integer_transformer = Pipeline(steps = [
('imputer', SimpleImputer(strategy = 'most_frequent')),
('scaler', StandardScaler())])
continuous_transformer = Pipeline(steps = [
('imputer', SimpleImputer(strategy = 'most_frequent')),
('scaler', StandardScaler())])
categorical_transformer = Pipeline(steps = [
('imputer', SimpleImputer(strategy = 'most_frequent')),
('lab_enc', OneHotEncoder(handle_unknown='ignore'))])
# Use the ColumnTransformer to apply the transformations to the correct columns in the dataframe.
integer_features = X.select_dtypes(include=['int64'])
continuous_features = X.select_dtypes(include=['float64'])
categorical_features = X.select_dtypes(include=['object'])
import numpy as np
from sklearn.compose import ColumnTransformer
preprocessor = ColumnTransformer(
transformers=[
('ints', integer_transformer, integer_features),
('cont', continuous_transformer, continuous_features),
('cat', categorical_transformer, categorical_features)])
# Create a pipeline that combines the preprocessor created above with a classifier.
from sklearn.neighbors import KNeighborsClassifier
base = Pipeline(steps=[('preprocessor', preprocessor),
('classifier', KNeighborsClassifier())])
当然,我想利用predict_proba()
,但这让我有些困扰。我尝试了以下方法:
model = base.fit(X,y )
preds = model.predict_proba(X)
然而,我收到了一个错误信息:
ValueError: No valid specification of the columns. Only a scalar, list or slice of all integers or all strings, or boolean mask is allowed
当然,这里是完整的回溯信息:
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-37-a1a29a8b3623> in <module>()
----> 1 base_learner.fit(X)
D:\Anaconda3\lib\site-packages\sklearn\pipeline.py in fit(self, X, y, **fit_params)
263 This estimator
264 """
--> 265 Xt, fit_params = self._fit(X, y, **fit_params)
266 if self._final_estimator is not None:
267 self._final_estimator.fit(Xt, y, **fit_params)
D:\Anaconda3\lib\site-packages\sklearn\pipeline.py in _fit(self, X, y, **fit_params)
228 Xt, fitted_transformer = fit_transform_one_cached(
229 cloned_transformer, Xt, y, None,
--> 230 **fit_params_steps[name])
231 # Replace the transformer of the step with the fitted
232 # transformer. This is necessary when loading the transformer
D:\Anaconda3\lib\site-packages\sklearn\externals\joblib\memory.py in __call__(self, *args, **kwargs)
327
328 def __call__(self, *args, **kwargs):
--> 329 return self.func(*args, **kwargs)
330
331 def call_and_shelve(self, *args, **kwargs):
D:\Anaconda3\lib\site-packages\sklearn\pipeline.py in _fit_transform_one(transformer, X, y, weight, **fit_params)
612 def _fit_transform_one(transformer, X, y, weight, **fit_params):
613 if hasattr(transformer, 'fit_transform'):
--> 614 res = transformer.fit_transform(X, y, **fit_params)
615 else:
616 res = transformer.fit(X, y, **fit_params).transform(X)
D:\Anaconda3\lib\site-packages\sklearn\compose\_column_transformer.py in fit_transform(self, X, y)
445 self._validate_transformers()
446 self._validate_column_callables(X)
--> 447 self._validate_remainder(X)
448
449 result = self._fit_transform(X, y, _fit_transform_one)
D:\Anaconda3\lib\site-packages\sklearn\compose\_column_transformer.py in _validate_remainder(self, X)
299 cols = []
300 for columns in self._columns:
--> 301 cols.extend(_get_column_indices(X, columns))
302 remaining_idx = sorted(list(set(range(n_columns)) - set(cols))) or None
303
D:\Anaconda3\lib\site-packages\sklearn\compose\_column_transformer.py in _get_column_indices(X, key)
654 return list(np.arange(n_columns)[key])
655 else:
--> 656 raise ValueError("No valid specification of the columns. Only a "
657 "scalar, list or slice of all integers or all "
658 "strings, or boolean mask is allowed")
我不确定我错在哪里,但非常感谢任何可能的帮助。
编辑: 我正在使用sklearn版本0.20。
doors body-style drive-wheels\ engine-location wheel-base length width height curb-weight engine-type num-of-cylinders\ engine-size fuel-system bore stroke compression-ratio horsepower peak- rpm city-mpg\ highway-mpg price".split()``` ```data = pd.read_csv("https://archive.ics.uci.edu/ml/machine-learning- databases/autos/imports-85.data", names = names)```
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