sklearn GridSearchCV在分数函数中不使用sample_weight

Question

我有每个样本不同权重的数据.在我的应用中,重要的是在估计模型和比较替代模型时考虑这些权重.

我sklearn用来估计模型并比较替代的超参数选择.但是这个单元测试显示GridSearchCV不适sample_weights用于估计分数.

有没有办法有sklearn使用sample_weight得分模式？

单元测试:

from __future__ import division

import numpy as np
from sklearn.datasets import load_iris
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import log_loss
from sklearn.model_selection import GridSearchCV, RepeatedKFold


def grid_cv(X_in, y_in, w_in, cv, max_features_grid, use_weighting):
  out_results = dict()

  for k in max_features_grid:
    clf = RandomForestClassifier(n_estimators=256,
                                 criterion="entropy",
                                 warm_start=False,
                                 n_jobs=-1,
                                 random_state=RANDOM_STATE,
                                 max_features=k)
    for train_ndx, test_ndx in cv.split(X=X_in, y=y_in):
      X_train = X_in[train_ndx, :]
      y_train = y_in[train_ndx]
      w_train = w_in[train_ndx]
      y_test = y[test_ndx]

      clf.fit(X=X_train, y=y_train, sample_weight=w_train)

      y_hat = clf.predict_proba(X=X_in[test_ndx, :])
      if use_weighting:
        w_test = w_in[test_ndx]
        w_i_sum = w_test.sum()
        score = w_i_sum / w_in.sum() * log_loss(y_true=y_test, y_pred=y_hat, sample_weight=w_test)
      else:
        score = log_loss(y_true=y_test, y_pred=y_hat)

      results = out_results.get(k, [])
      results.append(score)
      out_results.update({k: results})

  for k, v in out_results.items():
    if use_weighting:
      mean_score = sum(v)
    else:
      mean_score = np.mean(v)
    out_results.update({k: mean_score})

  best_score = min(out_results.values())
  best_param = min(out_results, key=out_results.get)
  return best_score, best_param


if __name__ == "__main__":
  RANDOM_STATE = 1337
  X, y = load_iris(return_X_y=True)
  sample_weight = np.array([1 + 100 * (i % 25) for i in range(len(X))])
  # sample_weight = np.array([1 for _ in range(len(X))])

  inner_cv = RepeatedKFold(n_splits=3, n_repeats=1, random_state=RANDOM_STATE)

  outer_cv = RepeatedKFold(n_splits=3, n_repeats=1, random_state=RANDOM_STATE)

  rfc = RandomForestClassifier(n_estimators=256,
                               criterion="entropy",
                               warm_start=False,
                               n_jobs=-1,
                               random_state=RANDOM_STATE)
  search_params = {"max_features": [1, 2, 3, 4]}


  fit_params = {"sample_weight": sample_weight}
  my_scorer = make_scorer(log_loss, 
               greater_is_better=False, 
               needs_proba=True, 
               needs_threshold=False)

  grid_clf = GridSearchCV(estimator=rfc,
                          scoring=my_scorer,
                          cv=inner_cv,
                          param_grid=search_params,
                          refit=True,
                          return_train_score=False,
                          iid=False)  # in this usage, the results are the same for `iid=True` and `iid=False`
  grid_clf.fit(X, y, **fit_params)
  print("This is the best out-of-sample score using GridSearchCV: %.6f." % -grid_clf.best_score_)

  msg = """This is the best out-of-sample score %s weighting using grid_cv: %.6f."""
  score_with_weights, param_with_weights = grid_cv(X_in=X,
                                                   y_in=y,
                                                   w_in=sample_weight,
                                                   cv=inner_cv,
                                                   max_features_grid=search_params.get(
                                                     "max_features"),
                                                   use_weighting=True)
  print(msg % ("WITH", score_with_weights))

  score_without_weights, param_without_weights = grid_cv(X_in=X,
                                                         y_in=y,
                                                         w_in=sample_weight,
                                                         cv=inner_cv,
                                                         max_features_grid=search_params.get(
                                                           "max_features"),
                                                         use_weighting=False)
  print(msg % ("WITHOUT", score_without_weights))

哪个产生输出:

This is the best out-of-sample score using GridSearchCV: 0.135692.
This is the best out-of-sample score WITH weighting using grid_cv: 0.099367.
This is the best out-of-sample score WITHOUT weighting using grid_cv: 0.135692.

说明:由于手动计算损失而没有加权产生相同的评分GridSearchCV,我们知道没有使用样本权重.

Answer 1

在GridSearchCV需要一个scoring作为输入,其可以是可调用.您可以在此处查看有关如何更改评分功能的详细信息,以及如何通过自己的评分功能.为了完整起见,这是该页面的相关代码段:

编辑:fit_params仅传递给fit函数,而不是score函数.如果有应该传递给的参数scorer,则应将它们传递给make_scorer.但是这仍然没有解决这里的问题,因为这意味着整个sample_weight参数将被传递给log_loss,而只有在y_test计算损失时对应的部分才应该通过.

sklearn不支持这样的事情,但你可以破解你的方式,使用padas.DataFrame.好消息是,sklearn理解一个DataFrame,并保持这种方式.这意味着你可以利用这个index的DataFrame,你在这里看到的代码:

  # more code

  X, y = load_iris(return_X_y=True)
  index = ['r%d' % x for x in range(len(y))]
  y_frame = pd.DataFrame(y, index=index)
  sample_weight = np.array([1 + 100 * (i % 25) for i in range(len(X))])
  sample_weight_frame = pd.DataFrame(sample_weight, index=index)

  # more code

  def score_f(y_true, y_pred, sample_weight):
      return log_loss(y_true.values, y_pred,
                      sample_weight=sample_weight.loc[y_true.index.values].values.reshape(-1),
                      normalize=True)

  score_params = {"sample_weight": sample_weight_frame}
  my_scorer = make_scorer(score_f,
                          greater_is_better=False, 
                          needs_proba=True, 
                          needs_threshold=False,
                          **score_params)

  grid_clf = GridSearchCV(estimator=rfc,
                          scoring=my_scorer,
                          cv=inner_cv,
                          param_grid=search_params,
                          refit=True,
                          return_train_score=False,
                          iid=False)  # in this usage, the results are the same for `iid=True` and `iid=False`
  grid_clf.fit(X, y_frame)

  # more code

正如你看到的,在score_f使用index中y_true发现,其中部分sample_weight使用.为了完整起见,这里是整个代码:

from __future__ import division

import numpy as np
from sklearn.datasets import load_iris
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import log_loss
from sklearn.model_selection import GridSearchCV, RepeatedKFold
from sklearn.metrics import  make_scorer
import pandas as pd

def grid_cv(X_in, y_in, w_in, cv, max_features_grid, use_weighting):
  out_results = dict()

  for k in max_features_grid:
    clf = RandomForestClassifier(n_estimators=256,
                                 criterion="entropy",
                                 warm_start=False,
                                 n_jobs=1,
                                 random_state=RANDOM_STATE,
                                 max_features=k)
    for train_ndx, test_ndx in cv.split(X=X_in, y=y_in):
      X_train = X_in[train_ndx, :]
      y_train = y_in[train_ndx]
      w_train = w_in[train_ndx]
      y_test = y_in[test_ndx]

      clf.fit(X=X_train, y=y_train, sample_weight=w_train)

      y_hat = clf.predict_proba(X=X_in[test_ndx, :])
      if use_weighting:
        w_test = w_in[test_ndx]
        w_i_sum = w_test.sum()
        score = w_i_sum / w_in.sum() * log_loss(y_true=y_test, y_pred=y_hat, sample_weight=w_test)
      else:
        score = log_loss(y_true=y_test, y_pred=y_hat)

      results = out_results.get(k, [])
      results.append(score)
      out_results.update({k: results})

  for k, v in out_results.items():
    if use_weighting:
      mean_score = sum(v)
    else:
      mean_score = np.mean(v)
    out_results.update({k: mean_score})

  best_score = min(out_results.values())
  best_param = min(out_results, key=out_results.get)
  return best_score, best_param


#if __name__ == "__main__":
if True:
  RANDOM_STATE = 1337
  X, y = load_iris(return_X_y=True)
  index = ['r%d' % x for x in range(len(y))]
  y_frame = pd.DataFrame(y, index=index)
  sample_weight = np.array([1 + 100 * (i % 25) for i in range(len(X))])
  sample_weight_frame = pd.DataFrame(sample_weight, index=index)
  # sample_weight = np.array([1 for _ in range(len(X))])

  inner_cv = RepeatedKFold(n_splits=3, n_repeats=1, random_state=RANDOM_STATE)

  outer_cv = RepeatedKFold(n_splits=3, n_repeats=1, random_state=RANDOM_STATE)

  rfc = RandomForestClassifier(n_estimators=256,
                               criterion="entropy",
                               warm_start=False,
                               n_jobs=1,
                               random_state=RANDOM_STATE)
  search_params = {"max_features": [1, 2, 3, 4]}


  def score_f(y_true, y_pred, sample_weight):
      return log_loss(y_true.values, y_pred,
                      sample_weight=sample_weight.loc[y_true.index.values].values.reshape(-1),
                      normalize=True)

  score_params = {"sample_weight": sample_weight_frame}
  my_scorer = make_scorer(score_f,
                          greater_is_better=False, 
                          needs_proba=True, 
                          needs_threshold=False,
                          **score_params)

  grid_clf = GridSearchCV(estimator=rfc,
                          scoring=my_scorer,
                          cv=inner_cv,
                          param_grid=search_params,
                          refit=True,
                          return_train_score=False,
                          iid=False)  # in this usage, the results are the same for `iid=True` and `iid=False`
  grid_clf.fit(X, y_frame)
  print("This is the best out-of-sample score using GridSearchCV: %.6f." % -grid_clf.best_score_)

  msg = """This is the best out-of-sample score %s weighting using grid_cv: %.6f."""
  score_with_weights, param_with_weights = grid_cv(X_in=X,
                                                   y_in=y,
                                                   w_in=sample_weight,
                                                   cv=inner_cv,
                                                   max_features_grid=search_params.get(
                                                     "max_features"),
                                                   use_weighting=True)
  print(msg % ("WITH", score_with_weights))

  score_without_weights, param_without_weights = grid_cv(X_in=X,
                                                         y_in=y,
                                                         w_in=sample_weight,
                                                         cv=inner_cv,
                                                         max_features_grid=search_params.get(
                                                           "max_features"),
                                                         use_weighting=False)
  print(msg % ("WITHOUT", score_without_weights))

然后代码的输出是:

This is the best out-of-sample score using GridSearchCV: 0.095439.
This is the best out-of-sample score WITH weighting using grid_cv: 0.099367.
This is the best out-of-sample score WITHOUT weighting using grid_cv: 0.135692.

编辑2:如下面的评论所说:

使用此解决方案得分和sklearn得分的差异源于我计算得分加权平均值的方式.如果省略代码的加权平均部分,则两个输出与机器精度匹配.