ML之RFXGBoost：基于RF/XGBoost(均+5f-CrVa)算法对Titanic(泰坦尼克号)数据集进行二分类预测(乘客是否生还)
2024-04-05 14:55:58
ML之RF&XGBoost：基于RF/XGBoost(均+5f-CrVa)算法对Titanic(泰坦尼克号)数据集进行二分类预测(乘客是否生还)
目录
输出结果
比赛结果
设计思路
核心代码
输出结果
比赛结果
设计思路
核心代码
rfc = RandomForestClassifier() rfc_cross_val_score=cross_val_score(rfc, X_train, y_train, cv=5).mean()print('RF：',rfc_cross_val_score) rfc.fit(X_train,y_train)rfc_y_predict = rfc.predict(X_test)rfc_submission = pd.DataFrame({'PassengerId': test['PassengerId'], 'Survived': rfc_y_predict})rfc_submission.to_csv('data_input/Titanic Data/Titanic_rfc_submission.csv', index=False)xgbc = XGBClassifier() xgbc_cross_val_score=cross_val_score(xgbc, X_train, y_train, cv=5).mean()print('XGBoost：',xgbc_cross_val_score) xgbc.fit(X_train, y_train)xgbc_y_predict = xgbc.predict(X_test)xgbc_submission = pd.DataFrame({'PassengerId': test['PassengerId'], 'Survived': xgbc_y_predict})xgbc_submission.to_csv('data_input/Titanic Data/Titanic_xgbc_submission.csv', index=False)class RandomForestClassifier(ForestClassifier):"""A random forest classifier.A random forest is a meta estimator that fits a number of decision treeclassifiers on various sub-samples of the dataset and use averaging toimprove the predictive accuracy and control over-fitting.The sub-sample size is always the same as the originalinput sample size but the samples are drawn with replacement if`bootstrap=True` (default).Read more in the :ref:`User Guide <forest>`.Parameters----------n_estimators : integer, optional (default=10)The number of trees in the forest.criterion : string, optional (default="gini")The function to measure the quality of a split. Supported criteria are"gini" for the Gini impurity and "entropy" for the information gain.Note: this parameter is tree-specific.max_features : int, float, string or None, optional (default="auto")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)` (same as "auto").- If "log2", then `max_features=log2(n_features)`.- If None, then `max_features=n_features`.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_depth : integer or None, optional (default=None)The maximum depth of the tree. If None, then nodes are expanded untilall leaves are pure or until all leaves contain less thanmin_samples_split samples.min_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.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:: 0.19bootstrap : boolean, optional (default=True)Whether bootstrap samples are used when building trees.oob_score : bool (default=False)Whether to use out-of-bag samples to estimatethe generalization accuracy.n_jobs : integer, optional (default=1)The number of jobs to run in parallel for both `fit` and `predict`.If -1, then the number of jobs is set to the number of cores.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`.verbose : int, optional (default=0)Controls the verbosity of the tree building process.warm_start : bool, optional (default=False)When set to ``True``, reuse the solution of the previous call to fitand add more estimators to the ensemble, otherwise, just fit a wholenew forest.class_weight : dict, list of dicts, "balanced","balanced_subsample" or None, optional (default=None)Weights associated with classes in the form ``{class_label: weight}``.If not given, all classes are supposed to have weight one. Formulti-output problems, a list of dicts can be provided in the sameorder as the columns of y.Note that for multioutput (including multilabel) weights should bedefined for each class of every column in its own dict. For example,for four-class multilabel classification weights should be[{0: 1, 1: 1}, {0: 1, 1: 5}, {0: 1, 1: 1}, {0: 1, 1: 1}] instead of[{1:1}, {2:5}, {3:1}, {4:1}].The "balanced" mode uses the values of y to automatically adjustweights inversely proportional to class frequencies in the input dataas ``n_samples / (n_classes * np.bincount(y))``The "balanced_subsample" mode is the same as "balanced" except thatweights are computed based on the bootstrap sample for every treegrown.For multi-output, the weights of each column of y will be multiplied.Note that these weights will be multiplied with sample_weight (passedthrough the fit method) if sample_weight is specified.Attributes----------estimators_ : list of DecisionTreeClassifierThe collection of fitted sub-estimators.classes_ : array of shape = [n_classes] or a list of such arraysThe classes labels (single output problem), or a list of arrays ofclass labels (multi-output problem).n_classes_ : int or listThe number of classes (single output problem), or a list containing thenumber of classes for each output (multi-output problem).n_features_ : intThe number of features when ``fit`` is performed.n_outputs_ : intThe number of outputs when ``fit`` is performed.feature_importances_ : array of shape = [n_features]The feature importances (the higher, the more important the feature).oob_score_ : floatScore of the training dataset obtained using an out-of-bag estimate.oob_decision_function_ : array of shape = [n_samples, n_classes]Decision function computed with out-of-bag estimate on the trainingset. If n_estimators is small it might be possible that a data pointwas never left out during the bootstrap. In this case,`oob_decision_function_` might contain NaN.Examples-------->>> from sklearn.ensemble import RandomForestClassifier>>> from sklearn.datasets import make_classification>>>>>> X, y = make_classification(n_samples=1000, n_features=4,... n_informative=2, n_redundant=0,... random_state=0, shuffle=False)>>> clf = RandomForestClassifier(max_depth=2, random_state=0)>>> clf.fit(X, y)RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',max_depth=2, max_features='auto', max_leaf_nodes=None,min_impurity_decrease=0.0, min_impurity_split=None,min_samples_leaf=1, min_samples_split=2,min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,oob_score=False, random_state=0, verbose=0, warm_start=False)>>> print(clf.feature_importances_)[ 0.17287856 0.80608704 0.01884792 0.00218648]>>> print(clf.predict([[0, 0, 0, 0]]))[1]Notes-----The default values for the parameters controlling the size of the trees(e.g. ``max_depth``, ``min_samples_leaf``, etc.) lead to fully grown andunpruned trees which can potentially be very large on some data sets. Toreduce memory consumption, the complexity and size of the trees should becontrolled by setting those parameter values.The features are always randomly permuted at each split. Therefore,the best found split may vary, even with the same training data,``max_features=n_features`` and ``bootstrap=False``, if the improvementof the criterion is identical for several splits enumerated during thesearch of the best split. To obtain a deterministic behaviour duringfitting, ``random_state`` has to be fixed.References----------.. [1] L. Breiman, "Random Forests", Machine Learning, 45(1), 5-32, 2001.See also--------DecisionTreeClassifier, ExtraTreesClassifier"""def __init__(self, n_estimators=10, criterion="gini", max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0., max_features="auto", max_leaf_nodes=None, min_impurity_decrease=0., min_impurity_split=None, bootstrap=True, oob_score=False, n_jobs=1, random_state=None, verbose=0, warm_start=False, class_weight=None):super(RandomForestClassifier, self).__init__(base_estimator=DecisionTreeClassifier(), n_estimators=n_estimators, estimator_params=("criterion", "max_depth", "min_samples_split", "min_samples_leaf", "min_weight_fraction_leaf", "max_features", "max_leaf_nodes", "min_impurity_decrease", "min_impurity_split", "random_state"), bootstrap=bootstrap, oob_score=oob_score, n_jobs=n_jobs, random_state=random_state, verbose=verbose, warm_start=warm_start, class_weight=class_weight)self.criterion = criterionself.max_depth = max_depthself.min_samples_split = min_samples_splitself.min_samples_leaf = min_samples_leafself.min_weight_fraction_leaf = min_weight_fraction_leafself.max_features = max_featuresself.max_leaf_nodes = max_leaf_nodesself.min_impurity_decrease = min_impurity_decreaseself.min_impurity_split = min_impurity_splitclass XGBClassifier(XGBModel, XGBClassifierBase):# pylint: disable=missing-docstring,too-many-arguments,invalid-name__doc__ = "Implementation of the scikit-learn API for XGBoost classification.\n\n" + '\n'.join(XGBModel.__doc__.split('\n')[2:])def __init__(self, max_depth=3, learning_rate=0.1, n_estimators=100, silent=True, objective="binary:logistic", booster='gbtree', n_jobs=1, nthread=None, gamma=0, min_child_weight=1, max_delta_step=0, subsample=1, colsample_bytree=1, colsample_bylevel=1, reg_alpha=0, reg_lambda=1, scale_pos_weight=1, base_score=0.5, random_state=0, seed=None, missing=None, **kwargs):super(XGBClassifier, self).__init__(max_depth, learning_rate, n_estimators, silent, objective, booster, n_jobs, nthread, gamma, min_child_weight, max_delta_step, subsample, colsample_bytree, colsample_bylevel, reg_alpha, reg_lambda, scale_pos_weight, base_score, random_state, seed, missing, **kwargs)def fit(self, X, y, sample_weight=None, eval_set=None, eval_metric=None, early_stopping_rounds=None, verbose=True, xgb_model=None, sample_weight_eval_set=None, callbacks=# pylint: disable = attribute-defined-outside-init,arguments-differNone):"""Fit gradient boosting classifierParameters----------X : array_likeFeature matrixy : array_likeLabelssample_weight : array_likeWeight for each instanceeval_set : list, optionalA list of (X, y) pairs to use as a validation set forearly-stoppingsample_weight_eval_set : list, optionalA list of the form [L_1, L_2, ..., L_n], where each L_i is a list ofinstance weights on the i-th validation set.eval_metric : str, callable, optionalIf a str, should be a built-in evaluation metric to use. Seedoc/parameter.rst. If callable, a custom evaluation metric. The callsignature is func(y_predicted, y_true) where y_true will be aDMatrix object such that you may need to call the get_labelmethod. It must return a str, value pair where the str is a namefor the evaluation and value is the value of the evaluationfunction. This objective is always minimized.early_stopping_rounds : int, optionalActivates early stopping. Validation error needs to decrease atleast every <early_stopping_rounds> round(s) to continue training.Requires at least one item in evals. If there's more than one,will use the last. If early stopping occurs, the model will havethree additional fields: bst.best_score, bst.best_iteration andbst.best_ntree_limit (bst.best_ntree_limit is the ntree_limit parameterdefault value in predict method if not any other value is specified).(Use bst.best_ntree_limit to get the correct value if num_parallel_treeand/or num_class appears in the parameters)verbose : boolIf `verbose` and an evaluation set is used, writes the evaluationmetric measured on the validation set to stderr.xgb_model : strfile name of stored xgb model or 'Booster' instance Xgb model to beloaded before training (allows training continuation).callbacks : list of callback functionsList of callback functions that are applied at end of each iteration.It is possible to use predefined callbacks by using :ref:`callback_api`.Example:.. code-block:: python[xgb.callback.reset_learning_rate(custom_rates)]"""evals_result = {}self.classes_ = np.unique(y)self.n_classes_ = len(self.classes_)xgb_options = self.get_xgb_params()if callable(self.objective):obj = _objective_decorator(self.objective)# Use default value. Is it really not used ?xgb_options["objective"] = "binary:logistic"else:obj = Noneif self.n_classes_ > 2:# Switch to using a multiclass objective in the underlying XGB instancexgb_options["objective"] = "multi:softprob"xgb_options['num_class'] = self.n_classes_feval = eval_metric if callable(eval_metric) else Noneif eval_metric is not None:if callable(eval_metric):eval_metric = Noneelse:xgb_options.update({"eval_metric":eval_metric})self._le = XGBLabelEncoder().fit(y)training_labels = self._le.transform(y)if eval_set is not None:if sample_weight_eval_set is None:sample_weight_eval_set = [None] * len(eval_set)evals = list(DMatrix(eval_set[i][0], label=self._le.transform(eval_set[i][1]), missing=self.missing, weight=sample_weight_eval_set[i], nthread=self.n_jobs) for i in range(len(eval_set)))nevals = len(evals)eval_names = ["validation_{}".format(i) for i in range(nevals)]evals = list(zip(evals, eval_names))else:evals = ()self._features_count = X.shape[1]if sample_weight is not None:train_dmatrix = DMatrix(X, label=training_labels, weight=sample_weight, missing=self.missing, nthread=self.n_jobs)else:train_dmatrix = DMatrix(X, label=training_labels, missing=self.missing, nthread=self.n_jobs)self._Booster = train(xgb_options, train_dmatrix, self.n_estimators, evals=evals, early_stopping_rounds=early_stopping_rounds, evals_result=evals_result, obj=obj, feval=feval, verbose_eval=verbose, xgb_model=xgb_model, callbacks=callbacks)self.objective = xgb_options["objective"]if evals_result:for val in evals_result.items():evals_result_key = list(val[1].keys())[0]evals_result[val[0]][evals_result_key] = val[1][evals_result_key]self.evals_result_ = evals_resultif early_stopping_rounds is not None:self.best_score = self._Booster.best_scoreself.best_iteration = self._Booster.best_iterationself.best_ntree_limit = self._Booster.best_ntree_limitreturn selfdef predict(self, data, output_margin=False, ntree_limit=None, validate_features=True):"""Predict with `data`... note:: This function is not thread safe.For each booster object, predict can only be called from one thread.If you want to run prediction using multiple thread, call ``xgb.copy()`` to make copiesof model object and then call ``predict()``... note:: Using ``predict()`` with DART boosterIf the booster object is DART type, ``predict()`` will perform dropouts, i.e. onlysome of the trees will be evaluated. This will produce incorrect results if ``data`` isnot the training data. To obtain correct results on test sets, set ``ntree_limit`` toa nonzero value, e.g... code-block:: pythonpreds = bst.predict(dtest, ntree_limit=num_round)Parameters----------data : DMatrixThe dmatrix storing the input.output_margin : boolWhether to output the raw untransformed margin value.ntree_limit : intLimit number of trees in the prediction; defaults to best_ntree_limit if defined(i.e. it has been trained with early stopping), otherwise 0 (use all trees).validate_features : boolWhen this is True, validate that the Booster's and data's feature_names are identical.Otherwise, it is assumed that the feature_names are the same.Returns-------prediction : numpy array"""test_dmatrix = DMatrix(data, missing=self.missing, nthread=self.n_jobs)if ntree_limit is None:ntree_limit = getattr(self, "best_ntree_limit", 0)class_probs = self.get_booster().predict(test_dmatrix, output_margin=output_margin, ntree_limit=ntree_limit, validate_features=validate_features)if output_margin:# If output_margin is active, simply return the scoresreturn class_probsif len(class_probs.shape) > 1:column_indexes = np.argmax(class_probs, axis=1)else:column_indexes = np.repeat(0, class_probs.shape[0])column_indexes[class_probs > 0.5] = 1return self._le.inverse_transform(column_indexes)def predict_proba(self, data, ntree_limit=None, validate_features=True):"""Predict the probability of each `data` example being of a given class... note:: This function is not thread safeFor each booster object, predict can only be called from one thread.If you want to run prediction using multiple thread, call ``xgb.copy()`` to make copiesof model object and then call predictParameters----------data : DMatrixThe dmatrix storing the input.ntree_limit : intLimit number of trees in the prediction; defaults to best_ntree_limit if defined(i.e. it has been trained with early stopping), otherwise 0 (use all trees).validate_features : boolWhen this is True, validate that the Booster's and data's feature_names are identical.Otherwise, it is assumed that the feature_names are the same.Returns-------prediction : numpy arraya numpy array with the probability of each data example being of a given class."""test_dmatrix = DMatrix(data, missing=self.missing, nthread=self.n_jobs)if ntree_limit is None:ntree_limit = getattr(self, "best_ntree_limit", 0)class_probs = self.get_booster().predict(test_dmatrix, ntree_limit=ntree_limit, validate_features=validate_features)if self.objective == "multi:softprob":return class_probselse:classone_probs = class_probsclasszero_probs = 1.0 - classone_probsreturn np.vstack((classzero_probs, classone_probs)).transpose()def evals_result(self):"""Return the evaluation results.If **eval_set** is passed to the `fit` function, you can call``evals_result()`` to get evaluation results for all passed **eval_sets**.When **eval_metric** is also passed to the `fit` function, the**evals_result** will contain the **eval_metrics** passed to the `fit` function.Returns-------evals_result : dictionaryExample-------.. code-block:: pythonparam_dist = {'objective':'binary:logistic', 'n_estimators':2}clf = xgb.XGBClassifier(**param_dist)clf.fit(X_train, y_train,eval_set=[(X_train, y_train), (X_test, y_test)],eval_metric='logloss',verbose=True)evals_result = clf.evals_result()The variable **evals_result** will contain.. code-block:: python{'validation_0': {'logloss': ['0.604835', '0.531479']},'validation_1': {'logloss': ['0.41965', '0.17686']}}"""if self.evals_result_:evals_result = self.evals_result_else:raise XGBoostError('No results.')return evals_resultML之RFXGBoost：基于RF/XGBoost(均+5f-CrVa)算法对Titanic(泰坦尼克号)数据集进行二分类预测(乘客是否生还)相关推荐
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