scikit-learn GridSearchCV генерирует RuntimeError: Невозможно клонировать объект Применить, так как конструктор не устанавливает параметр fn

Question

У меня есть следующий код:

import pandas as pd
import numpy as np
import random
from sklearn.model_selection import train_test_split
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.svm import LinearSVC
from sklearn.naive_bayes import MultinomialNB
from sklearn import linear_model
from sklearn.metrics import accuracy_score
from sklearn.pipeline import Pipeline, FeatureUnion
from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.model_selection import GridSearchCV

# Based on the following which has more examples:
# http://nbviewer.jupyter.org/github/michelleful/SingaporeRoadnameOrigins/blob/master/notebooks/04%20Adding%20features%20with%20Pipelines.ipynb
# http://michelleful.github.io/code-blog//2015/06/18/classifying-roads/
# http://zacstewart.com/2014/08/05/pipelines-of-featureunions-of-pipelines.html
# https://stackoverflow.com/questions/49466193/how-to-add-a-feature-to-a-vectorized-data-set/49501769#49501769

# Load ANSI file into pandas dataframe.
df = pd.read_csv(r'e:/work/python/papf.txt', encoding = 'latin1', usecols=['LAST_NAME', 'RACE'])

# Convert last name to lower case.
df['LAST_NAME'] = df['LAST_NAME'].str.lower()

# Remove the last name spaces.
# df['LAST_NAME'] = df['LAST_NAME'].str.replace(' ', '')

# Remove all rows where race is NOT in African, Coloured, White, Indian.
df = df.drop(df[~df['RACE'].isin(['African', 'Coloured', 'White', 'Indian'])].index)

# Returns a column from the dataframe named df as a numpy array of type string.
class TextExtractor(BaseEstimator, TransformerMixin):
    """Adapted from code by @zacstewart
       https://github.com/zacstewart/kaggle_seeclickfix/blob/master/estimator.py
       Also see Zac Stewart's excellent blogpost on pipelines:
       http://zacstewart.com/2014/08/05/pipelines-of-featureunions-of-pipelines.html
       """

    def __init__(self, column_name):
        self.column_name = column_name

    def transform(self, df):
        # Select the relevant column and return it as a numpy array.
        # Set the array type to be string.
        return np.asarray(df[self.column_name]).astype(str)    # This refers to the df passed as a parameter, and not to the global scope one.

    def fit(self, *_):
        return self

class Apply(BaseEstimator, TransformerMixin):
    """Takes in a function and applies it element-wise to every element in the numpy array it's supplied with."""

    def __init__(self, fn):
        self.fn = np.vectorize(fn)

    def transform(self, data):
        # Note: reshaping is necessary because otherwise sklearn
        # interprets the 1-d array as a single sample.
        return self.fn(data.reshape(data.size, 1))

    def fit(self, *_):
        return self

class AverageWordLengthExtractor(BaseEstimator, TransformerMixin):
    """Takes in dataframe, extracts last name column, outputs average word length"""

    def __init__(self):
        pass

    def average_word_length(self, name):
        """Helper code to compute average word length of a name"""
        return np.mean([len(word) for word in name.split()])

    def transform(self, df, y=None):
        """The workhorse of this feature extractor"""
        return df['LAST_NAME'].apply(self.average_word_length)    # This refers to the df passed as a parameter, and not to the global scope one.

    def fit(self, df, y=None):
        """Returns self unless something different happens in train and test"""
        return self

# Let's pick the same random 10% of the data to train with.
random.seed(1965)
train_test_set = df.loc[random.sample(list(df.index.values), int(len(df) / 10))]

# X = train_test_set[['road_name', 'has_malay_road_tag']]
X = train_test_set[['LAST_NAME']]
y = train_test_set['RACE']

vect = CountVectorizer(ngram_range=(1,4), analyzer='char')
clf = LinearSVC() # #MultinomialNB() #linear_model.SGDClassifier(max_iter=500)

pipeline = Pipeline([
    ('name_extractor', TextExtractor('LAST_NAME')),    # Extract names from df.
    ('text_features', FeatureUnion([
        ('vect', vect),    # Extract ngrams from names.
        ('num_words', Apply(lambda s: len(s.split()))),    # Number of words.
        ('ave_word_length', Apply(lambda s: np.mean([len(w) for w in s.split()]))), # Average word length.
    ])),
    ('clf' , clf),     # Feed the output through a classifier.
])

def run_experiment(X, y, pipeline, num_expts=100):
    scores = list()
    for i in range(num_expts):
        X_train, X_test, y_train, y_true = train_test_split(X, y)
        model = pipeline.fit(X_train, y_train)  # Train the classifier.
        y_test = model.predict(X_test)          # Apply the model to the test data.
        #print(X_test)
        #print(type(X_test))
        score = accuracy_score(y_test, y_true)  # Compare the results to the gold standard.
        scores.append(score)

    print(sum(scores) / num_expts)

# Run x times (num_expts) and get the average accuracy.
run_experiment(X, y, pipeline, 1)

# Train a final model for use in the actual output.
X_train, X_test, y_train, y_true = train_test_split(X, y)
model = pipeline.fit(X_train, y_train)  # Train the classifier.
df2 = pd.DataFrame(columns=['LAST_NAME'], data=[['Joemat']])    # Create a test case of one.
print(model.predict(df2))

# Solution to this part might be here: https://stackoverflow.com/questions/49466193/how-to-add-a-feature-to-a-vectorized-data-set/49501769#49501769
pg = {'clf__C': [0.1, 1, 10, 100]}

grid = GridSearchCV(pipeline, param_grid=pg, cv=5)
X_train, X_test, y_train, y_true = train_test_split(X, y)
grid.fit(X_train, y_train)

print(grid.best_params_)
# {'clf__C': 0.1}

print(grid.best_score_)
# 0.702290076336

Этот код работает нормально, пока я не добавлю последнюю часть с GridSearchCV, и в этот момент он выдаст следующее исключение:

Traceback (most recent call last):
  File "e:\Work\Python\name_train5.py", line 132, in <module>
    grid.fit(X_train, y_train)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_search.py", line 945, in fit
    return self._fit(X, y, groups, ParameterGrid(self.param_grid))
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_search.py", line 550, in _fit
    base_estimator = clone(self.estimator)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\base.py", line 69, in clone
    new_object_params[name] = clone(param, safe=False)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\base.py", line 57, in clone
    return estimator_type([clone(e, safe=safe) for e in estimator])
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\base.py", line 57, in <listcomp>
    return estimator_type([clone(e, safe=safe) for e in estimator])
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\base.py", line 57, in clone
    return estimator_type([clone(e, safe=safe) for e in estimator])
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\base.py", line 57, in <listcomp>
    return estimator_type([clone(e, safe=safe) for e in estimator])
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\base.py", line 69, in clone
    new_object_params[name] = clone(param, safe=False)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\base.py", line 57, in clone
    return estimator_type([clone(e, safe=safe) for e in estimator])
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\base.py", line 57, in <listcomp>
    return estimator_type([clone(e, safe=safe) for e in estimator])
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\base.py", line 57, in clone
    return estimator_type([clone(e, safe=safe) for e in estimator])
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\base.py", line 57, in <listcomp>
    return estimator_type([clone(e, safe=safe) for e in estimator])
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\base.py", line 126, in clone
    (estimator, name))
RuntimeError: Cannot clone object Apply(fn=<numpy.lib.function_base.vectorize object at 0x00000201E64780B8>), as the constructor does not seem to set parameter fn

Я обнаружил эту похожую ошибку при переполнении стека, но, к сожалению, я не понимаю ответа. Может ли кто-то пролить свет на то, что я делаю неправильно?

Пример данных CSV:

LAST_NAME,RACE
Ramaepadi,African
Motsamai,African
Van Rooyen,White
Khan,Asian
Du Plessis,White
Singh,Asian
Madlanga,African
Janse van Rensburg,

Answer 1

Конструктор должен хранить параметры в виде атрибутов и ничего больше. Все действия должны проходить в fit и transform.

См. Ниже минимальный рабочий пример

from sklearn.datasets import make_regression
from sklearn.base import BaseEstimator, TransformerMixin

class Apply(BaseEstimator, TransformerMixin):
    def __init__(self, fn):
        self.fn = fn
        self.fn_vectorized = None

    def transform(self, data):
        # Note: reshaping is necessary because otherwise sklearn
        # interprets the 1-d array as a single sample.
        return self.fn_vectorized(data.reshape(data.size, 1))

    def fit(self, *_):
        self.fn_vectorized = np.vectorize(self.fn)
        return self

X, y = make_regression(n_features=1)

model = Apply(lambda x: 0 * x)

model.fit_transform(X)

Выходы

array([[ 0.],
       [ 0.],
       [ 0.], ...

Кстати, все работает без np.vectorize.