ML之LiR&DNN&EL：基于skflow的LiR、DNN、sklearn的RF對(duì)Boston(波士頓房價(jià))數(shù)據(jù)集進(jìn)行回歸預(yù)測(cè)(房價(jià))

?

?

目錄

輸出結(jié)果

設(shè)計(jì)思路

核心代碼

---

?

?

?

輸出結(jié)果

?

?

設(shè)計(jì)思路

?

?

核心代碼

tf_lr = skflow.TensorFlowLinearRegressor(steps=10000, learning_rate=0.01, batch_size=50) tf_lr.fit(X_train, y_train) tf_lr_y_predict = tf_lr.predict(X_test)tf_dnn_regressor = skflow.TensorFlowDNNRegressor(hidden_units=[100, 40],steps=10000, learning_rate=0.01, batch_size=50) tf_dnn_regressor.fit(X_train, y_train) tf_dnn_regressor_y_predict = tf_dnn_regressor.predict(X_test)rfr = RandomForestRegressor() rfr.fit(X_train, y_train) rfr_y_predict = rfr.predict(X_test) class TensorFlowLinearRegressor(TensorFlowEstimator, RegressorMixin):"""TensorFlow Linear Regression model."""def __init__(self, n_classes=0, tf_master="", batch_size=32, steps=200, optimizer="SGD", learning_rate=0.1, tf_random_seed=42, continue_training=False, num_cores=4, verbose=1, early_stopping_rounds=None, max_to_keep=5, keep_checkpoint_every_n_hours=10000):super(TensorFlowLinearRegressor, self).__init__(model_fn=models.linear_regression, n_classes=n_classes, tf_master=tf_master, batch_size=batch_size, steps=steps, optimizer=optimizer, learning_rate=learning_rate, tf_random_seed=tf_random_seed, continue_training=continue_training, num_cores=num_cores, verbose=verbose, early_stopping_rounds=early_stopping_rounds, max_to_keep=max_to_keep, keep_checkpoint_every_n_hours=keep_checkpoint_every_n_hours)@propertydef weights_(self):"""Returns weights of the linear regression."""return self.get_tensor_value('linear_regression/weights:0')@propertydef bias_(self):"""Returns bias of the linear regression."""return self.get_tensor_value('linear_regression/bias:0') class TensorFlowDNNRegressor(TensorFlowEstimator, RegressorMixin):"""TensorFlow DNN Regressor model.Parameters:hidden_units: List of hidden units per layer.tf_master: TensorFlow master. Empty string is default for local.batch_size: Mini batch size.steps: Number of steps to run over data.optimizer: Optimizer name (or class), for example "SGD", "Adam","Adagrad".learning_rate: If this is constant float value, no decay function is used.Instead, a customized decay function can be passed that acceptsglobal_step as parameter and returns a Tensor.e.g. exponential decay function:def exp_decay(global_step):return tf.train.exponential_decay(learning_rate=0.1, global_step,decay_steps=2, decay_rate=0.001)tf_random_seed: Random seed for TensorFlow initializers.Setting this value, allows consistency between reruns.continue_training: when continue_training is True, once initializedmodel will be continuely trained on every call of fit.num_cores: Number of cores to be used. (default: 4)early_stopping_rounds: Activates early stopping if this is not None.Loss needs to decrease at least every every <early_stopping_rounds>round(s) to continue training. (default: None)verbose: Controls the verbosity, possible values:0: the algorithm and debug information is muted.1: trainer prints the progress.2: log device placement is printed.early_stopping_rounds: Activates early stopping if this is not None.Loss needs to decrease at least every every <early_stopping_rounds>round(s) to continue training. (default: None)max_to_keep: The maximum number of recent checkpoint files to keep.As new files are created, older files are deleted.If None or 0, all checkpoint files are kept.Defaults to 5 (that is, the 5 most recent checkpoint files are kept.)keep_checkpoint_every_n_hours: Number of hours between each checkpointto be saved. The default value of 10,000 hours effectively disables the feature."""def __init__(self, hidden_units, n_classes=0, tf_master="", batch_size=32, steps=200, optimizer="SGD", learning_rate=0.1, tf_random_seed=42, continue_training=False, num_cores=4, verbose=1, early_stopping_rounds=None, max_to_keep=5, keep_checkpoint_every_n_hours=10000):self.hidden_units = hidden_unitssuper(TensorFlowDNNRegressor, self).__init__(model_fn=self._model_fn, n_classes=n_classes, tf_master=tf_master, batch_size=batch_size, steps=steps, optimizer=optimizer, learning_rate=learning_rate, tf_random_seed=tf_random_seed, continue_training=continue_training, num_cores=num_cores, verbose=verbose, early_stopping_rounds=early_stopping_rounds, max_to_keep=max_to_keep, keep_checkpoint_every_n_hours=keep_checkpoint_every_n_hours)def _model_fn(self, X, y):return models.get_dnn_model(self.hidden_units, models.linear_regression)(X, y)@propertydef weights_(self):"""Returns weights of the DNN weight layers."""weights = []for layer in range(len(self.hidden_units)):weights.append(self.get_tensor_value('dnn/layer%d/Linear/Matrix:0' % layer))weights.append(self.get_tensor_value('linear_regression/weights:0'))return weights@propertydef bias_(self):"""Returns bias of the DNN's bias layers."""biases = []for layer in range(len(self.hidden_units)):biases.append(self.get_tensor_value('dnn/layer%d/Linear/Bias:0' % layer))biases.append(self.get_tensor_value('linear_regression/bias:0'))return biases class RandomForestRegressor(ForestRegressor):"""A random forest regressor.A random forest is a meta estimator that fits a number of classifyingdecision trees on various sub-samples of the dataset and use averagingto improve 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="mse")The function to measure the quality of a split. Supported criteriaare "mse" for the mean squared error, which is equal to variancereduction as feature selection criterion, and "mae" for the meanabsolute error... versionadded:: 0.18Mean Absolute Error (MAE) criterion.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=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`.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, optional (default=False)whether to use out-of-bag samples to estimatethe R^2 on unseen data.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.Attributes----------estimators_ : list of DecisionTreeRegressorThe collection of fitted sub-estimators.feature_importances_ : array of shape = [n_features]The feature importances (the higher, the more important the feature).n_features_ : intThe number of features when ``fit`` is performed.n_outputs_ : intThe number of outputs when ``fit`` is performed.oob_score_ : floatScore of the training dataset obtained using an out-of-bag estimate.oob_prediction_ : array of shape = [n_samples]Prediction computed with out-of-bag estimate on the training set.Examples-------->>> from sklearn.ensemble import RandomForestRegressor>>> from sklearn.datasets import make_regression>>>>>> X, y = make_regression(n_features=4, n_informative=2,... random_state=0, shuffle=False)>>> regr = RandomForestRegressor(max_depth=2, random_state=0)>>> regr.fit(X, y)RandomForestRegressor(bootstrap=True, criterion='mse', 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(regr.feature_importances_)[ 0.17339552 0.81594114 0. 0.01066333]>>> print(regr.predict([[0, 0, 0, 0]]))[-2.50699856]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--------DecisionTreeRegressor, ExtraTreesRegressor"""def __init__(self, n_estimators=10, criterion="mse", 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):super(RandomForestRegressor, self).__init__(base_estimator=DecisionTreeRegressor(), 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)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_split

?

總結(jié)

以上是生活随笔為你收集整理的ML之LiRDNNEL：基于skflow的LiR、DNN、sklearn的RF对Boston(波士顿房价)数据集进行回归预测(房价)的全部內(nèi)容，希望文章能夠幫你解決所遇到的問題。

如果覺得生活随笔網(wǎng)站內(nèi)容還不錯(cuò)，歡迎將生活随笔推薦給好友。