NLP之NB&GBT：基于朴素贝叶斯(count/tfidf+网格搜索+4fCrva)、梯度提升树(w2c+网格搜索+4fCrva)算法对IMDB影评数据集进行文本情感分析(情感二分类预测)

 

目录

数据集

一、利用两种不同NB算法处理标注影评数据集

输出结果

设计思路

核心代码

二、利用w2c+GB算法处理未标注影评数据集

输出结果

设计思路

核心代码

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数据集

排名结果

 

 

一、利用两种不同NB算法处理标注影评数据集

输出结果

                                                         submission_count.csv

                                                                 submission_tfidf.csv

设计思路

 

核心代码

pip_count = Pipeline([('count_vec', CountVectorizer(analyzer='word')), ('mnb', MultinomialNB())]) pip_tfidf = Pipeline([('tfidf_vec', TfidfVectorizer(analyzer='word')), ('mnb', MultinomialNB())])gs_count = GridSearchCV(pip_count, params_count, cv=4, n_jobs=-1, verbose=1) gs_tfidf = GridSearchCV(pip_tfidf, params_tfidf, cv=4, n_jobs=-1, verbose=1) gs_count.fit(X_train, y_train)print('CountVectorizer：网格搜索+4fCrva得到的最佳性能：',gs_count.best_score_) print('CountVectorizer：最优超参数组合','\n',gs_count.best_params_)count_y_predict = gs_count.predict(X_test)gs_tfidf.fit(X_train, y_train)print('TfidfVectorizer：网格搜索+4fCrva得到的最佳性能：',gs_tfidf.best_score_) print('TfidfVectorizer：最优超参数组合','\n',gs_tfidf.best_params_) tfidf_y_predict = gs_tfidf.predict(X_test)

 

 

 

二、利用w2c+GB算法处理未标注影评数据集

输出结果

                                                          submission_w2v.csv

 

设计思路

 

核心代码

model = word2vec.Word2Vec(corpora, workers=num_workers, \size=num_features, min_count = min_word_count, \window = context, sample = downsampling) model.init_sims(replace=True)gbc = GradientBoostingClassifier()params_gbc = {'n_estimators':[10, 100, 500], 'learning_rate':[0.01, 0.1, 1.0], 'max_depth': [2, 3, 4]}gs = GridSearchCV(gbc, params_gbc, cv=4, n_jobs=-1, verbose=1) gs.fit(trainDataVecs, y_train)print('gbc：网格搜索+4fCrva得到的最佳性能：',gs.best_score_) print('gbc：最优超参数组合','\n',gs.best_params_)result = gs.predict(testDataVecs) class GradientBoostingClassifier(BaseGradientBoosting, ClassifierMixin):"""Gradient Boosting for classification.GB builds an additive model in aforward stage-wise fashion; it allows for the optimization ofarbitrary differentiable loss functions. In each stage ``n_classes_``regression trees are fit on the negative gradient of thebinomial or multinomial deviance loss function. Binary classificationis a special case where only a single regression tree is induced.Read more in the :ref:`User Guide <gradient_boosting>`.Parameters----------loss : {'deviance', 'exponential'}, optional (default='deviance')loss function to be optimized. 'deviance' refers todeviance (= logistic regression) for classificationwith probabilistic outputs. For loss 'exponential' gradientboosting recovers the AdaBoost algorithm.learning_rate : float, optional (default=0.1)learning rate shrinks the contribution of each tree by `learning_rate`.There is a trade-off between learning_rate and n_estimators.n_estimators : int (default=100)The number of boosting stages to perform. Gradient boostingis fairly robust to over-fitting so a large number usuallyresults in better performance.max_depth : integer, optional (default=3)maximum depth of the individual regression estimators. The maximumdepth limits the number of nodes in the tree. Tune this parameterfor best performance; the best value depends on the interactionof the input variables.criterion : string, optional (default="friedman_mse")The function to measure the quality of a split. Supported criteriaare "friedman_mse" for the mean squared error with improvementscore by Friedman, "mse" for mean squared error, and "mae" forthe mean absolute error. The default value of "friedman_mse" isgenerally the best as it can provide a better approximation insome cases... versionadded:: 0.18min_samples_split : int, float, optional (default=2)The minimum number of samples required to split an internal node:- If int, then consider `min_samples_split` as the minimum number.- If float, then `min_samples_split` is a percentage and`ceil(min_samples_split * n_samples)` are the minimumnumber of samples for each split... versionchanged:: 0.18Added float values for percentages.min_samples_leaf : int, float, optional (default=1)The minimum number of samples required to be at a leaf node:- If int, then consider `min_samples_leaf` as the minimum number.- If float, then `min_samples_leaf` is a percentage and`ceil(min_samples_leaf * n_samples)` are the minimumnumber of samples for each node... versionchanged:: 0.18Added float values for percentages.min_weight_fraction_leaf : float, optional (default=0.)The minimum weighted fraction of the sum total of weights (of allthe input samples) required to be at a leaf node. Samples haveequal weight when sample_weight is not provided.subsample : float, optional (default=1.0)The fraction of samples to be used for fitting the individual baselearners. If smaller than 1.0 this results in Stochastic GradientBoosting. `subsample` interacts with the parameter `n_estimators`.Choosing `subsample < 1.0` leads to a reduction of varianceand an increase in bias.max_features : int, float, string or None, optional (default=None)The number of features to consider when looking for the best split:- If int, then consider `max_features` features at each split.- If float, then `max_features` is a percentage and`int(max_features * n_features)` features are considered at eachsplit.- If "auto", then `max_features=sqrt(n_features)`.- If "sqrt", then `max_features=sqrt(n_features)`.- If "log2", then `max_features=log2(n_features)`.- If None, then `max_features=n_features`.Choosing `max_features < n_features` leads to a reduction of varianceand an increase in bias.Note: the search for a split does not stop until at least onevalid partition of the node samples is found, even if it requires toeffectively inspect more than ``max_features`` features.max_leaf_nodes : int or None, optional (default=None)Grow trees with ``max_leaf_nodes`` in best-first fashion.Best nodes are defined as relative reduction in impurity.If None then unlimited number of leaf nodes.min_impurity_split : float,Threshold for early stopping in tree growth. A node will splitif its impurity is above the threshold, otherwise it is a leaf... deprecated:: 0.19``min_impurity_split`` has been deprecated in favor of``min_impurity_decrease`` in 0.19 and will be removed in 0.21.Use ``min_impurity_decrease`` instead.min_impurity_decrease : float, optional (default=0.)A node will be split if this split induces a decrease of the impuritygreater than or equal to this value.The weighted impurity decrease equation is the following::N_t / N * (impurity - N_t_R / N_t * right_impurity- N_t_L / N_t * left_impurity)where ``N`` is the total number of samples, ``N_t`` is the number ofsamples at the current node, ``N_t_L`` is the number of samples in theleft child, and ``N_t_R`` is the number of samples in the right child.``N``, ``N_t``, ``N_t_R`` and ``N_t_L`` all refer to the weighted sum,if ``sample_weight`` is passed... versionadded:: 0.19init : BaseEstimator, None, optional (default=None)An estimator object that is used to compute the initialpredictions. ``init`` has to provide ``fit`` and ``predict``.If None it uses ``loss.init_estimator``.verbose : int, default: 0Enable verbose output. If 1 then it prints progress and performanceonce in a while (the more trees the lower the frequency). If greaterthan 1 then it prints progress and performance for every tree.warm_start : bool, default: FalseWhen set to ``True``, reuse the solution of the previous call to fitand add more estimators to the ensemble, otherwise, just erase theprevious solution.random_state : int, RandomState instance or None, optional (default=None)If int, random_state is the seed used by the random number generator;If RandomState instance, random_state is the random number generator;If None, the random number generator is the RandomState instance usedby `np.random`.presort : bool or 'auto', optional (default='auto')Whether to presort the data to speed up the finding of best splits infitting. Auto mode by default will use presorting on dense data anddefault to normal sorting on sparse data. Setting presort to true onsparse data will raise an error... versionadded:: 0.17*presort* parameter.Attributes----------feature_importances_ : array, shape = [n_features]The feature importances (the higher, the more important the feature).oob_improvement_ : array, shape = [n_estimators]The improvement in loss (= deviance) on the out-of-bag samplesrelative to the previous iteration.``oob_improvement_[0]`` is the improvement inloss of the first stage over the ``init`` estimator.train_score_ : array, shape = [n_estimators]The i-th score ``train_score_[i]`` is the deviance (= loss) of themodel at iteration ``i`` on the in-bag sample.If ``subsample == 1`` this is the deviance on the training data.loss_ : LossFunctionThe concrete ``LossFunction`` object.init : BaseEstimatorThe estimator that provides the initial predictions.Set via the ``init`` argument or ``loss.init_estimator``.estimators_ : ndarray of DecisionTreeRegressor, shape = [n_estimators, ``loss_.K``]The collection of fitted sub-estimators. ``loss_.K`` is 1 for binaryclassification, otherwise n_classes.Notes-----The features are always randomly permuted at each split. Therefore,the best found split may vary, even with the same training data and``max_features=n_features``, if the improvement of the criterion isidentical for several splits enumerated during the search of the bestsplit. To obtain a deterministic behaviour during fitting,``random_state`` has to be fixed.See also--------sklearn.tree.DecisionTreeClassifier, RandomForestClassifierAdaBoostClassifierReferences----------J. Friedman, Greedy Function Approximation: A Gradient BoostingMachine, The Annals of Statistics, Vol. 29, No. 5, 2001.J. Friedman, Stochastic Gradient Boosting, 1999T. Hastie, R. Tibshirani and J. Friedman.Elements of Statistical Learning Ed. 2, Springer, 2009."""_SUPPORTED_LOSS = 'deviance', 'exponential'def __init__(self, loss='deviance', learning_rate=0.1, n_estimators=100, subsample=1.0, criterion='friedman_mse', min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0., max_depth=3, min_impurity_decrease=0., min_impurity_split=None, init=None, random_state=None, max_features=None, verbose=0, max_leaf_nodes=None, warm_start=False, presort='auto'):super(GradientBoostingClassifier, self).__init__(loss=loss, learning_rate=learning_rate, n_estimators=n_estimators, criterion=criterion, min_samples_split=min_samples_split, min_samples_leaf=min_samples_leaf, min_weight_fraction_leaf=min_weight_fraction_leaf, max_depth=max_depth, init=init, subsample=subsample, max_features=max_features, random_state=random_state, verbose=verbose, max_leaf_nodes=max_leaf_nodes, min_impurity_decrease=min_impurity_decrease, min_impurity_split=min_impurity_split, warm_start=warm_start, presort=presort)def _validate_y(self, y):check_classification_targets(y)self.classes_, y = np.unique(y, return_inverse=True)self.n_classes_ = len(self.classes_)return ydef decision_function(self, X):"""Compute the decision function of ``X``.Parameters----------X : array-like or sparse matrix, shape = [n_samples, n_features]The input samples. Internally, it will be converted to``dtype=np.float32`` and if a sparse matrix is providedto a sparse ``csr_matrix``.Returns-------score : array, shape = [n_samples, n_classes] or [n_samples]The decision function of the input samples. The order of theclasses corresponds to that in the attribute `classes_`.Regression and binary classification produce an array of shape[n_samples]."""X = check_array(X, dtype=DTYPE, order="C", accept_sparse='csr')score = self._decision_function(X)if score.shape[1] == 1:return score.ravel()return scoredef staged_decision_function(self, X):"""Compute decision function of ``X`` for each iteration.This method allows monitoring (i.e. determine error on testing set)after each stage.Parameters----------X : array-like or sparse matrix, shape = [n_samples, n_features]The input samples. Internally, it will be converted to``dtype=np.float32`` and if a sparse matrix is providedto a sparse ``csr_matrix``.Returns-------score : generator of array, shape = [n_samples, k]The decision function of the input samples. The order of theclasses corresponds to that in the attribute `classes_`.Regression and binary classification are special cases with``k == 1``, otherwise ``k==n_classes``."""for dec in self._staged_decision_function(X):# no yield from in Python2.Xyield decdef predict(self, X):"""Predict class for X.Parameters----------X : array-like or sparse matrix, shape = [n_samples, n_features]The input samples. Internally, it will be converted to``dtype=np.float32`` and if a sparse matrix is providedto a sparse ``csr_matrix``.Returns-------y : array of shape = [n_samples]The predicted values."""score = self.decision_function(X)decisions = self.loss_._score_to_decision(score)return self.classes_.take(decisions, axis=0)def staged_predict(self, X):"""Predict class at each stage for X.This method allows monitoring (i.e. determine error on testing set)after each stage.Parameters----------X : array-like or sparse matrix, shape = [n_samples, n_features]The input samples. Internally, it will be converted to``dtype=np.float32`` and if a sparse matrix is providedto a sparse ``csr_matrix``.Returns-------y : generator of array of shape = [n_samples]The predicted value of the input samples."""for score in self._staged_decision_function(X):decisions = self.loss_._score_to_decision(score)yield self.classes_.take(decisions, axis=0)def predict_proba(self, X):"""Predict class probabilities for X.Parameters----------X : array-like or sparse matrix, shape = [n_samples, n_features]The input samples. Internally, it will be converted to``dtype=np.float32`` and if a sparse matrix is providedto a sparse ``csr_matrix``.Raises------AttributeErrorIf the ``loss`` does not support probabilities.Returns-------p : array of shape = [n_samples]The class probabilities of the input samples. The order of theclasses corresponds to that in the attribute `classes_`."""score = self.decision_function(X)try:return self.loss_._score_to_proba(score)except NotFittedError:raiseexcept AttributeError:raise AttributeError('loss=%r does not support predict_proba' % self.loss)def predict_log_proba(self, X):"""Predict class log-probabilities for X.Parameters----------X : array-like or sparse matrix, shape = [n_samples, n_features]The input samples. Internally, it will be converted to``dtype=np.float32`` and if a sparse matrix is providedto a sparse ``csr_matrix``.Raises------AttributeErrorIf the ``loss`` does not support probabilities.Returns-------p : array of shape = [n_samples]The class log-probabilities of the input samples. The order of theclasses corresponds to that in the attribute `classes_`."""proba = self.predict_proba(X)return np.log(proba)def staged_predict_proba(self, X):"""Predict class probabilities at each stage for X.This method allows monitoring (i.e. determine error on testing set)after each stage.Parameters----------X : array-like or sparse matrix, shape = [n_samples, n_features]The input samples. Internally, it will be converted to``dtype=np.float32`` and if a sparse matrix is providedto a sparse ``csr_matrix``.Returns-------y : generator of array of shape = [n_samples]The predicted value of the input samples."""try:for score in self._staged_decision_function(X):yield self.loss_._score_to_proba(score)except NotFittedError:raiseexcept AttributeError:raise AttributeError('loss=%r does not support predict_proba' % self.loss)

 

 

 

总结

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