add python doc (#119)

docs/Python_API.md

+##Basic Data Structure API
+----
+###Dataset
+####__init__(data, label=None, max_bin=255, reference=None, weight=None, group=None, silent=False, feature_name=None, categorical_feature=None, params=None, free_raw_data=True)
+
+    Parameters
+    ----------
+    data : string/numpy array/scipy.sparse
+        Data source of Dataset.
+        When data type is string, it represents the path of txt file
+    label : list or numpy 1-D array, optional
+        Label of the data
+    max_bin : int, required
+        Max number of discrete bin for features
+    reference : Other Dataset, optional
+        If this dataset validation, need to use training data as reference
+    weight : list or numpy 1-D array , optional
+        Weight for each instance.
+    group : list or numpy 1-D array , optional
+        Group/query size for dataset
+    silent : boolean, optional
+        Whether print messages during construction
+    feature_name : list of str
+        Feature names
+    categorical_feature : list of str or int
+        Categorical features,
+        type int represents index,
+        type str represents feature names (need to specify feature_name as well)
+    params: dict, optional
+        Other parameters
+    free_raw_data: Bool
+        True if need to free raw data after construct inner dataset
+    
+
+####construct()
+
+    Lazy init
+    
+
+####create_valid(data, label=None, weight=None, group=None, silent=False, params=None)
+
+    Create validation data align with current dataset
+
+    Parameters
+    ----------
+    data : string/numpy array/scipy.sparse
+        Data source of _InnerDataset.
+        When data type is string, it represents the path of txt file
+    label : list or numpy 1-D array, optional
+        Label of the training data.
+    weight : list or numpy 1-D array , optional
+        Weight for each instance.
+    group : list or numpy 1-D array , optional
+        Group/query size for dataset
+    silent : boolean, optional
+        Whether print messages during construction
+    params: dict, optional
+        Other parameters
+    
+
+####get_group()
+
+    Get the initial score of the Dataset.
+
+    Returns
+    -------
+    init_score : array
+    
+
+####get_init_score()
+
+    Get the initial score of the Dataset.
+
+    Returns
+    -------
+    init_score : array
+    
+
+####get_label()
+
+    Get the label of the Dataset.
+
+    Returns
+    -------
+    label : array
+    
+
+####get_weight()
+
+    Get the weight of the Dataset.
+
+    Returns
+    -------
+    weight : array
+    
+
+####num_data()
+
+    Get the number of rows in the Dataset.
+
+    Returns
+    -------
+    number of rows : int
+    
+
+####num_feature()
+
+    Get the number of columns (features) in the Dataset.
+
+    Returns
+    -------
+    number of columns : int
+    
+
+####save_binary(filename)
+
+    Save Dataset to binary file
+
+    Parameters
+    ----------
+    filename : string
+        Name of the output file.
+    
+
+####set_categorical_feature(categorical_feature)
+
+    Set categorical features
+
+    Parameters
+    ----------
+    categorical_feature : list of int or str
+        Name/index of categorical features
+
+    
+
+####set_feature_name(feature_name)
+
+    Set feature name
+
+    Parameters
+    ----------
+    feature_name : list of str
+        Feature names
+    
+
+####set_group(group)
+
+    Set group size of Dataset (used for ranking).
+
+    Parameters
+    ----------
+    group : numpy array or list or None
+        Group size of each group
+    
+
+####set_init_score(init_score)
+
+    Set init score of booster to start from.
+
+    Parameters
+    ----------
+    init_score: numpy array or list or None
+        Init score for booster
+    
+
+####set_label(label)
+
+    Set label of Dataset
+
+    Parameters
+    ----------
+    label: numpy array or list or None
+        The label information to be set into Dataset
+    
+
+####set_reference(reference)
+
+    Set reference dataset
+
+    Parameters
+    ----------
+    reference : Dataset
+        Will use reference as template to consturct current dataset
+    
+
+####set_weight(weight)
+
+    Set weight of each instance.
+
+    Parameters
+    ----------
+    weight : numpy array or list or None
+        Weight for each data point
+    
+
+####subset(used_indices, params=None)
+
+    Get subset of current dataset
+
+    Parameters
+    ----------
+    used_indices : list of int
+        Used indices of this subset
+    params : dict
+        Other parameters
+    
+
+###Booster
+####__init__(params=None, train_set=None, model_file=None, silent=False)
+
+    Initialize the Booster.
+
+    Parameters
+    ----------
+    params : dict
+        Parameters for boosters.
+    train_set : Dataset
+        Training dataset
+    model_file : string
+        Path to the model file.
+    silent : boolean, optional
+        Whether print messages during construction
+    
+
+####add_valid(data, name)
+
+    Add an validation data
+
+    Parameters
+    ----------
+    data : Dataset
+        Validation data
+    name : String
+        Name of validation data
+    
+
+####attr(key)
+
+    Get attribute string from the Booster.
+
+    Parameters
+    ----------
+    key : str
+        The key to get attribute from.
+
+    Returns
+    -------
+    value : str
+        The attribute value of the key, returns None if attribute do not exist.
+    
+
+####current_iteration()
+
+####dump_model()
+
+    Dump model to json format
+
+    Returns
+    -------
+    Json format of model
+    
+
+####eval(data, name, feval=None)
+
+    Evaluate for data
+
+    Parameters
+    ----------
+    data : _InnerDataset object
+    name :
+        Name of data
+    feval : function
+        Custom evaluation function.
+    Returns
+    -------
+    result: list
+        Evaluation result list.
+    
+
+####eval_train(feval=None)
+
+    Evaluate for training data
+
+    Parameters
+    ----------
+    feval : function
+        Custom evaluation function.
+
+    Returns
+    -------
+    result: str
+        Evaluation result list.
+    
+
+####eval_valid(feval=None)
+
+    Evaluate for validation data
+
+    Parameters
+    ----------
+    feval : function
+        Custom evaluation function.
+
+    Returns
+    -------
+    result: str
+        Evaluation result list.
+    
+
+####feature_importance(importance_type=split)
+
+    Feature importances
+
+    Returns
+    -------
+    Array of feature importances
+    
+
+####predict(data, num_iteration=-1, raw_score=False, pred_leaf=False, data_has_header=False, is_reshape=True)
+
+    Predict logic
+
+    Parameters
+    ----------
+    data : string/numpy array/scipy.sparse
+        Data source for prediction
+        When data type is string, it represents the path of txt file
+    num_iteration : int
+        Used iteration for prediction
+    raw_score : bool
+        True for predict raw score
+    pred_leaf : bool
+        True for predict leaf index
+    data_has_header : bool
+        Used for txt data
+    is_reshape : bool
+        Reshape to (nrow, ncol) if true
+
+    Returns
+    -------
+    Prediction result
+    
+
+####reset_parameter(params)
+
+    Reset parameters for booster
+
+    Parameters
+    ----------
+    params : dict
+        New parameters for boosters
+    silent : boolean, optional
+        Whether print messages during construction
+    
+
+####rollback_one_iter()
+
+    Rollback one iteration
+    
+
+####save_model(filename, num_iteration=-1)
+
+    Save model of booster to file
+
+    Parameters
+    ----------
+    filename : str
+        Filename to save
+    num_iteration: int
+        Number of iteration that want to save. < 0 means save all
+    
+
+####set_attr(kwargs)
+
+    Set the attribute of the Booster.
+
+    Parameters
+    ----------
+    **kwargs
+        The attributes to set. Setting a value to None deletes an attribute.
+    
+
+####set_train_data_name(name)
+
+####update(train_set=None, fobj=None)
+
+    Update for one iteration
+    Note: for multi-class task, the score is group by class_id first, then group by row_id
+          if you want to get i-th row score in j-th class, the access way is score[j*num_data+i]
+          and you should group grad and hess in this way as well
+
+    Parameters
+    ----------
+    train_set :
+        Training data, None means use last training data
+    fobj : function
+        Customized objective function.
+
+    Returns
+    -------
+    is_finished, bool
+    
+
+##Training API
+----
+####train(params, train_set, num_boost_round=100, valid_sets=None, valid_names=None, fobj=None, feval=None, init_model=None, feature_name=None, categorical_feature=None, early_stopping_rounds=None, evals_result=None, verbose_eval=True, learning_rates=None, callbacks=None)
+
+    Train with given parameters.
+
+    Parameters
+    ----------
+    params : dict
+        Parameters for training.
+    train_set : Dataset
+        Data to be trained.
+    num_boost_round: int
+        Number of boosting iterations.
+    valid_sets: list of Datasets
+        List of data to be evaluated during training
+    valid_names: list of string
+        Names of valid_sets
+    fobj : function
+        Customized objective function.
+    feval : function
+        Customized evaluation function.
+        Note: should return (eval_name, eval_result, is_higher_better) of list of this
+    init_model : file name of lightgbm model or 'Booster' instance
+        model used for continued train
+    feature_name : list of str
+        Feature names
+    categorical_feature : list of str or int
+        Categorical features,
+        type int represents index,
+        type str represents feature names (need to specify feature_name as well)
+    early_stopping_rounds: int
+        Activates early stopping.
+        Requires at least one validation data and one metric
+        If there's more than one, will check all of them
+        Returns the model with (best_iter + early_stopping_rounds)
+        If early stopping occurs, the model will add 'best_iteration' field
+    evals_result: dict or None
+        This dictionary used to store all evaluation results of all the items in valid_sets.
+        Example: with a valid_sets containing [valid_set, train_set]
+                 and valid_names containing ['eval', 'train']
+                 and a paramater containing ('metric':'logloss')
+        Returns: {'train': {'logloss': ['0.48253', '0.35953', ...]},
+                  'eval': {'logloss': ['0.480385', '0.357756', ...]}}
+        passed with None means no using this function
+    verbose_eval : bool or int
+        Requires at least one item in evals.
+        If `verbose_eval` is True,
+            the eval metric on the valid set is printed at each boosting stage.
+        If `verbose_eval` is int,
+            the eval metric on the valid set is printed at every `verbose_eval` boosting stage.
+        The last boosting stage
+            or the boosting stage found by using `early_stopping_rounds` is also printed.
+        Example: with verbose_eval=4 and at least one item in evals,
+            an evaluation metric is printed every 4 (instead of 1) boosting stages.
+    learning_rates: list or function
+        List of learning rate for each boosting round
+        or a customized function that calculates learning_rate in terms of
+        current number of round (and the total number of boosting round)
+        (e.g. yields learning rate decay)
+        - list l: learning_rate = l[current_round]
+        - function f: learning_rate = f(current_round, total_boost_round)
+                   or learning_rate = f(current_round)
+    callbacks : list of callback functions
+        List of callback functions that are applied at end of each iteration.
+
+    Returns
+    -------
+    booster : a trained booster model
+    
+
+####cv(params, train_set, num_boost_round=10, nfold=5, stratified=False, metrics=None, fobj=None, feval=None, init_model=None, feature_name=None, categorical_feature=None, early_stopping_rounds=None, fpreproc=None, verbose_eval=None, show_stdv=True, seed=0, callbacks=None)
+
+    Cross-validation with given paramaters.
+
+    Parameters
+    ----------
+    params : dict
+        Booster params.
+    train_set : Dataset
+        Data to be trained.
+    num_boost_round : int
+        Number of boosting iterations.
+    nfold : int
+        Number of folds in CV.
+    stratified : bool
+        Perform stratified sampling.
+    folds : a KFold or StratifiedKFold instance
+        Sklearn KFolds or StratifiedKFolds.
+    metrics : string or list of strings
+        Evaluation metrics to be watched in CV.
+    fobj : function
+        Custom objective function.
+    feval : function
+        Custom evaluation function.
+    init_model : file name of lightgbm model or 'Booster' instance
+        model used for continued train
+    feature_name : list of str
+        Feature names
+    categorical_feature : list of str or int
+        Categorical features, type int represents index,
+        type str represents feature names (need to specify feature_name as well)
+    early_stopping_rounds: int
+        Activates early stopping. CV error needs to decrease at least
+        every <early_stopping_rounds> round(s) to continue.
+        Last entry in evaluation history is the one from best iteration.
+    fpreproc : function
+        Preprocessing function that takes (dtrain, dtest, param)
+        and returns transformed versions of those.
+    verbose_eval : bool, int, or None, default None
+        Whether to display the progress.
+        If None, progress will be displayed when np.ndarray is returned.
+        If True, progress will be displayed at boosting stage.
+        If an integer is given,
+            progress will be displayed at every given `verbose_eval` boosting stage.
+    show_stdv : bool, default True
+        Whether to display the standard deviation in progress.
+        Results are not affected, and always contains std.
+    seed : int
+        Seed used to generate the folds (passed to numpy.random.seed).
+    callbacks : list of callback functions
+        List of callback functions that are applied at end of each iteration.
+
+    Returns
+    -------
+    evaluation history : list(string)
+    
+
+##Scikit-learn API
+----
+###Common Methods
+####__init__(num_leaves=31, max_depth=-1, learning_rate=0.1, n_estimators=10, max_bin=255, silent=True, objective=regression, nthread=-1, min_split_gain=0, min_child_weight=5, min_child_samples=10, subsample=1, subsample_freq=1, colsample_bytree=1, reg_alpha=0, reg_lambda=0, scale_pos_weight=1, is_unbalance=False, seed=0)
+
+    Implementation of the Scikit-Learn API for LightGBM.
+
+    Parameters
+    ----------
+    num_leaves : int
+        Maximum tree leaves for base learners.
+    max_depth : int
+        Maximum tree depth for base learners, -1 means no limit.
+    learning_rate : float
+        Boosting learning rate
+    n_estimators : int
+        Number of boosted trees to fit.
+    silent : boolean
+        Whether to print messages while running boosting.
+    objective : string or callable
+        Specify the learning task and the corresponding learning objective or
+        a custom objective function to be used (see note below).
+        default: binary for LGBMClassifier, lambdarank for LGBMRanker
+    nthread : int
+        Number of parallel threads
+    min_split_gain : float
+        Minimum loss reduction required to make a further partition on a leaf node of the tree.
+    min_child_weight : int
+        Minimum sum of instance weight(hessian) needed in a child(leaf)
+    min_child_samples : int
+        Minimum number of data need in a child(leaf)
+    subsample : float
+        Subsample ratio of the training instance.
+    subsample_freq : int
+        frequence of subsample, <=0 means no enable
+    colsample_bytree : float
+        Subsample ratio of columns when constructing each tree.
+    reg_alpha : float
+        L1 regularization term on weights
+    reg_lambda : float
+        L2 regularization term on weights
+    scale_pos_weight : float
+        Balancing of positive and negative weights.
+    is_unbalance : bool
+        Is unbalance for binary classification
+    seed : int
+        Random number seed.
+
+    Note
+    ----
+    A custom objective function can be provided for the ``objective``
+    parameter. In this case, it should have the signature
+    ``objective(y_true, y_pred) -> grad, hess`` 
+        or ``objective(y_true, y_pred, group) -> grad, hess``:
+
+        y_true: array_like of shape [n_samples]
+            The target values
+        y_pred: array_like of shape [n_samples] or shape[n_samples* n_class]
+            The predicted values
+        group: array_like
+            group/query data, used for ranking task
+        grad: array_like of shape [n_samples] or shape[n_samples* n_class]
+            The value of the gradient for each sample point.
+        hess: array_like of shape [n_samples] or shape[n_samples* n_class]
+            The value of the second derivative for each sample point
+
+    for multi-class task, the y_pred is group by class_id first, then group by row_id
+        if you want to get i-th row y_pred in j-th class, the access way is y_pred[j*num_data+i]
+        and you should group grad and hess in this way as well
+    
+
+####apply(X, num_iteration=0)
+
+    Return the predicted leaf every tree for each sample.
+
+    Parameters
+    ----------
+    X : array_like, shape=[n_samples, n_features]
+        Input features matrix.
+
+    num_iteration : int
+        Limit number of iterations in the prediction; defaults to 0 (use all trees).
+
+    Returns
+    -------
+    X_leaves : array_like, shape=[n_samples, n_trees]
+    
+
+####booster()
+
+    Get the underlying lightgbm Booster of this model.
+    This will raise an exception when fit was not called
+
+    Returns
+    -------
+    booster : a lightgbm booster of underlying model
+    
+
+####evals_result()
+
+    Return the evaluation results.
+
+    Returns
+    -------
+    evals_result : dictionary
+    
+
+####feature_importance()
+
+    Feature importances
+
+    Returns
+    -------
+    Array of normailized feature importances
+    
+
+####fit(X, y, sample_weight=None, init_score=None, group=None, eval_set=None, eval_sample_weight=None, eval_init_score=None, eval_group=None, eval_metric=None, early_stopping_rounds=None, verbose=True, feature_name=None, categorical_feature=None, other_params=None)
+
+    Fit the gradient boosting model
+
+    Parameters
+    ----------
+    X : array_like
+        Feature matrix
+    y : array_like
+        Labels
+    sample_weight : array_like
+        weight of training data
+    init_score : array_like
+        init score of training data
+    group : array_like
+        group data of training data
+    eval_set : list, optional
+        A list of (X, y) tuple pairs to use as a validation set for early-stopping
+    eval_sample_weight : List of array
+        weight of eval data
+    eval_init_score : List of array
+        init score of eval data
+    eval_group : List of array
+        group data of eval data
+    eval_metric : str, list of str, callable, optional
+        If a str, should be a built-in evaluation metric to use.
+        If callable, a custom evaluation metric, see note for more details.
+    early_stopping_rounds : int
+    verbose : bool
+        If `verbose` and an evaluation set is used, writes the evaluation
+    feature_name : list of str
+        Feature names
+    categorical_feature : list of str or int
+        Categorical features,
+        type int represents index,
+        type str represents feature names (need to specify feature_name as well)
+    other_params: dict
+        Other parameters
+
+    Note
+    ----
+    Custom eval function expects a callable with following functions:
+        ``func(y_true, y_pred)``, ``func(y_true, y_pred, weight)``
+            or ``func(y_true, y_pred, weight, group)``.
+        return (eval_name, eval_result, is_bigger_better)
+            or list of (eval_name, eval_result, is_bigger_better)
+
+        y_true: array_like of shape [n_samples]
+            The target values
+        y_pred: array_like of shape [n_samples] or shape[n_samples * n_class] (for multi-class)
+            The predicted values
+        weight: array_like of shape [n_samples]
+            The weight of samples
+        group: array_like
+            group/query data, used for ranking task
+        eval_name: str
+            name of evaluation
+        eval_result: float
+            eval result
+        is_bigger_better: bool
+            is eval result bigger better, e.g. AUC is bigger_better.
+    for multi-class task, the y_pred is group by class_id first, then group by row_id
+      if you want to get i-th row y_pred in j-th class, the access way is y_pred[j*num_data+i]
+    
+
+####get_params(deep=False)
+
+    Get parameters
+    
+
+####predict(data, raw_score=False, num_iteration=0)
+
+    Return the predicted value for each sample.
+
+    Parameters
+    ----------
+    X : array_like, shape=[n_samples, n_features]
+        Input features matrix.
+
+    num_iteration : int
+        Limit number of iterations in the prediction; defaults to 0 (use all trees).
+
+    Returns
+    -------
+    predicted_result : array_like, shape=[n_samples] or [n_samples, n_classes]
+    
+
+###LGBMClassifier
+
+####predict_proba(data, raw_score=False, num_iteration=0)
+
+    Return the predicted probability for each class for each sample.
+
+    Parameters
+    ----------
+    X : array_like, shape=[n_samples, n_features]
+        Input features matrix.
+
+    num_iteration : int
+        Limit number of iterations in the prediction; defaults to 0 (use all trees).
+
+    Returns
+    -------
+    predicted_probability : array_like, shape=[n_samples, n_classes]
+    
+
+###LGBMRegressor
+
+###LGBMRanker
+
+####fit(X, y, sample_weight=None, init_score=None, group=None, eval_set=None, eval_sample_weight=None, eval_init_score=None, eval_group=None, eval_metric=None, eval_at=None, early_stopping_rounds=None, verbose=True, feature_name=None, categorical_feature=None, other_params=None)
+
+    Most arguments like common methods except following:
+
+    eval_at : list of int
+        The evaulation positions of NDCG
+    
+

docs/autodoc.py

+# coding: utf-8
+# pylint: disable = C0103, C0111, C0301, C0321, C0330, W0621
+import inspect
+import lightgbm as lgb
+
+file_api = open('Python_API.md', 'w+')
+
+def write_func(func, leftSpace=0):
+    file_api.write('####' + func.__name__ + '('
+        + ', '.join([
+                v.name + ('=' + str(v.default) if v.default != v.empty else '')
+                for _, v in inspect.signature(func).parameters.items() if v.name != 'self'
+            ])
+        + ')\n')
+    if func.__doc__:
+        for line in func.__doc__.splitlines():
+            if line: file_api.write(line[leftSpace:])
+            file_api.write('\n')
+    file_api.write('\n')
+
+def write_class(class_):
+    file_api.write('###' + class_.__name__ + '\n')
+    for name, members in sorted(class_.__dict__.items(), key=lambda x: x[0]):
+        if name == '__init__' or not name.startswith('_'): write_func(members, leftSpace=4)
+
+def write_module(name, members):
+    file_api.write('##' + name + '\n----\n')
+    for member in members:
+        if inspect.isclass(member): write_class(member)
+        else: write_func(member)
+
+write_module('Basic Data Structure API', [
+        lgb.Dataset,
+        lgb.Booster
+    ])
+write_module('Training API', [
+        lgb.train,
+        lgb.cv
+    ])
+write_module('Scikit-learn API', [
+        lgb.LGBMModel,
+        lgb.LGBMClassifier,
+        lgb.LGBMRegressor,
+        lgb.LGBMRanker
+    ])
+
+file_api.close()

docs/css/extra.css

+.toctree-l4{
+    padding: 0.4045em 2.427em 0.4045em 3.227em !important;
+}

mkdocs.yml

+site_name: LightGBM
+theme: readthedocs
+
+extra_css:
+  - css/extra.css

python-package/lightgbm/basic.py

@@ -758,7 +758,7 @@ class _InnerDataset(object):
         self.set_field('weight', weight)
 
     def set_init_score(self, score):
-        """ Set init score of booster to start from.
+        """Set init score of booster to start from.
 
         Parameters
         ----------
@@ -869,7 +869,8 @@ class Dataset(object):
         feature_name : list of str
             Feature names
         categorical_feature : list of str or int
-            Categorical features, type int represents index, \
+            Categorical features,
+            type int represents index,
             type str represents feature names (need to specify feature_name as well)
         params: dict, optional
             Other parameters
@@ -919,7 +920,9 @@ class Dataset(object):
         return ret
 
     def construct(self):
-        """Lazy init"""
+        """
+        Lazy init
+        """
         if self.inner_dataset is None:
             if self.reference is not None:
                 if self.used_indices is None:
@@ -1036,7 +1039,8 @@ class Dataset(object):
         return ret
 
     def save_binary(self, filename):
-        """Save Dataset to binary file
+        """
+        Save Dataset to binary file
 
         Parameters
         ----------
@@ -1047,7 +1051,8 @@ class Dataset(object):
 
 
     def set_label(self, label):
-        """Set label of Dataset
+        """
+        Set label of Dataset
 
         Parameters
         ----------
@@ -1059,7 +1064,8 @@ class Dataset(object):
             self.inner_dataset.set_label(self.label)
 
     def set_weight(self, weight):
-        """ Set weight of each instance.
+        """
+        Set weight of each instance.
 
         Parameters
         ----------
@@ -1071,7 +1077,8 @@ class Dataset(object):
             self.inner_dataset.set_weight(self.weight)
 
     def set_init_score(self, init_score):
-        """ Set init score of booster to start from.
+        """
+        Set init score of booster to start from.
 
         Parameters
         ----------
@@ -1083,7 +1090,8 @@ class Dataset(object):
             self.inner_dataset.set_init_score(self.init_score)
 
     def set_group(self, group):
-        """Set group size of Dataset (used for ranking).
+        """
+        Set group size of Dataset (used for ranking).
 
         Parameters
         ----------
@@ -1095,7 +1103,8 @@ class Dataset(object):
             self.inner_dataset.set_group(self.group)
 
     def get_label(self):
-        """Get the label of the Dataset.
+        """
+        Get the label of the Dataset.
 
         Returns
         -------
@@ -1106,7 +1115,8 @@ class Dataset(object):
         return self.label
 
     def get_weight(self):
-        """Get the weight of the Dataset.
+        """
+        Get the weight of the Dataset.
 
         Returns
         -------
@@ -1117,7 +1127,8 @@ class Dataset(object):
         return self.weight
 
     def get_init_score(self):
-        """Get the initial score of the Dataset.
+        """
+        Get the initial score of the Dataset.
 
         Returns
         -------
@@ -1128,7 +1139,8 @@ class Dataset(object):
         return self.init_score
 
     def get_group(self):
-        """Get the initial score of the Dataset.
+        """
+        Get the initial score of the Dataset.
 
         Returns
         -------
@@ -1139,7 +1151,8 @@ class Dataset(object):
         return self.group
 
     def num_data(self):
-        """Get the number of rows in the Dataset.
+        """
+        Get the number of rows in the Dataset.
 
         Returns
         -------
@@ -1151,7 +1164,8 @@ class Dataset(object):
             raise LightGBMError("Cannot call num_data before construct, please call it explicitly")
 
     def num_feature(self):
-        """Get the number of columns (features) in the Dataset.
+        """
+        Get the number of columns (features) in the Dataset.
 
         Returns
         -------
@@ -1166,7 +1180,8 @@ class Booster(object):
     """"A Booster of LightGBM.
     """
     def __init__(self, params=None, train_set=None, model_file=None, silent=False):
-        """Initialize the Booster.
+        """
+        Initialize the Booster.
 
         Parameters
         ----------
@@ -1241,7 +1256,8 @@ class Booster(object):
         self.__train_data_name = name
 
     def add_valid(self, data, name):
-        """Add an validation data
+        """
+        Add an validation data
 
         Parameters
         ----------
@@ -1262,7 +1278,8 @@ class Booster(object):
         self.__is_predicted_cur_iter.append(False)
 
     def reset_parameter(self, params):
-        """Reset parameters for booster
+        """
+        Reset parameters for booster
 
         Parameters
         ----------
@@ -1365,7 +1382,8 @@ class Booster(object):
         return out_cur_iter.value
 
     def eval(self, data, name, feval=None):
-        """Evaluate for data
+        """
+        Evaluate for data
 
         Parameters
         ----------
@@ -1397,7 +1415,8 @@ class Booster(object):
         return self.__inner_eval(name, data_idx, feval)
 
     def eval_train(self, feval=None):
-        """Evaluate for training data
+        """
+        Evaluate for training data
 
         Parameters
         ----------
@@ -1412,7 +1431,8 @@ class Booster(object):
         return self.__inner_eval(self.__train_data_name, 0, feval)
 
     def eval_valid(self, feval=None):
-        """Evaluate for validation data
+        """
+        Evaluate for validation data
 
         Parameters
         ----------
@@ -1428,7 +1448,8 @@ class Booster(object):
             for item in self.__inner_eval(self.name_valid_sets[i-1], i, feval)]
 
     def save_model(self, filename, num_iteration=-1):
-        """Save model of booster to file
+        """
+        Save model of booster to file
 
         Parameters
         ----------
@@ -1443,7 +1464,8 @@ class Booster(object):
             c_str(filename)))
 
     def dump_model(self):
-        """Dump model to json format
+        """
+        Dump model to json format
 
         Returns
         -------
@@ -1471,7 +1493,8 @@ class Booster(object):
         return json.loads(string_buffer.value.decode())
 
     def predict(self, data, num_iteration=-1, raw_score=False, pred_leaf=False, data_has_header=False, is_reshape=True):
-        """Predict logic
+        """
+        Predict logic
 
         Parameters
         ----------
@@ -1503,7 +1526,8 @@ class Booster(object):
         return predictor
 
     def feature_importance(self, importance_type='split'):
-        """Feature importances
+        """
+        Feature importances
 
         Returns
         -------
@@ -1615,7 +1639,8 @@ class Booster(object):
                     [name.startswith(('auc', 'ndcg')) for name in self.__name_inner_eval]
 
     def attr(self, key):
-        """Get attribute string from the Booster.
+        """
+        Get attribute string from the Booster.
 
         Parameters
         ----------
@@ -1630,7 +1655,8 @@ class Booster(object):
         return self.__attr.get(key, None)
 
     def set_attr(self, **kwargs):
-        """Set the attribute of the Booster.
+        """
+        Set the attribute of the Booster.
 
         Parameters
         ----------

python-package/lightgbm/engine.py

@@ -15,7 +15,8 @@ def train(params, train_set, num_boost_round=100,
           feature_name=None, categorical_feature=None,
           early_stopping_rounds=None, evals_result=None,
           verbose_eval=True, learning_rates=None, callbacks=None):
-    """Train with given parameters.
+    """
+    Train with given parameters.
 
     Parameters
     ----------
@@ -39,7 +40,8 @@ def train(params, train_set, num_boost_round=100,
     feature_name : list of str
         Feature names
     categorical_feature : list of str or int
-        Categorical features, type int represents index, \
+        Categorical features,
+        type int represents index,
         type str represents feature names (need to specify feature_name as well)
     early_stopping_rounds: int
         Activates early stopping.
@@ -49,28 +51,30 @@ def train(params, train_set, num_boost_round=100,
         If early stopping occurs, the model will add 'best_iteration' field
     evals_result: dict or None
         This dictionary used to store all evaluation results of all the items in valid_sets.
-        Example: with a valid_sets containing [valid_set, train_set] \
-        and valid_names containing ['eval', 'train'] and a paramater containing ('metric':'logloss')
+        Example: with a valid_sets containing [valid_set, train_set]
+                 and valid_names containing ['eval', 'train']
+                 and a paramater containing ('metric':'logloss')
         Returns: {'train': {'logloss': ['0.48253', '0.35953', ...]},
                   'eval': {'logloss': ['0.480385', '0.357756', ...]}}
         passed with None means no using this function
     verbose_eval : bool or int
         Requires at least one item in evals.
-        If `verbose_eval` is True then the evaluation metric on the validation set is \
-        printed at each boosting stage.
-        If `verbose_eval` is an integer then the evaluation metric on the validation set \
-        is printed at every given `verbose_eval` boosting stage. The last boosting stage \
-        / the boosting stage found by using `early_stopping_rounds` is also printed.
-        Example: with verbose_eval=4 and at least one item in evals, an evaluation metric \
-        is printed every 4 boosting stages, instead of every boosting stage.
+        If `verbose_eval` is True,
+            the eval metric on the valid set is printed at each boosting stage.
+        If `verbose_eval` is int,
+            the eval metric on the valid set is printed at every `verbose_eval` boosting stage.
+        The last boosting stage
+            or the boosting stage found by using `early_stopping_rounds` is also printed.
+        Example: with verbose_eval=4 and at least one item in evals,
+            an evaluation metric is printed every 4 (instead of 1) boosting stages.
     learning_rates: list or function
-        List of learning rate for each boosting round \
-        or a customized function that calculates learning_rate in terms of \
-        current number of round and the total number of boosting round \
+        List of learning rate for each boosting round
+        or a customized function that calculates learning_rate in terms of
+        current number of round (and the total number of boosting round)
         (e.g. yields learning rate decay)
         - list l: learning_rate = l[current_round]
-        - function f: learning_rate = f(current_round, total_boost_round) \
-        or learning_rate = f(current_round)
+        - function f: learning_rate = f(current_round, total_boost_round)
+                   or learning_rate = f(current_round)
     callbacks : list of callback functions
         List of callback functions that are applied at end of each iteration.
 
@@ -259,12 +263,13 @@ def _agg_cv_result(raw_results):
     return [('cv_agg', k, np.mean(v), metric_type[k], np.std(v)) for k, v in cvmap.items()]
 
 def cv(params, train_set, num_boost_round=10, nfold=5, stratified=False,
-       metrics=(), fobj=None, feval=None, init_model=None,
+       metrics=None, fobj=None, feval=None, init_model=None,
        feature_name=None, categorical_feature=None,
        early_stopping_rounds=None, fpreproc=None,
        verbose_eval=None, show_stdv=True, seed=0,
        callbacks=None):
-    """Cross-validation with given paramaters.
+    """
+    Cross-validation with given paramaters.
 
     Parameters
     ----------
@@ -291,20 +296,21 @@ def cv(params, train_set, num_boost_round=10, nfold=5, stratified=False,
     feature_name : list of str
         Feature names
     categorical_feature : list of str or int
-        Categorical features, type int represents index, \
+        Categorical features, type int represents index,
         type str represents feature names (need to specify feature_name as well)
     early_stopping_rounds: int
-        Activates early stopping. CV error needs to decrease at least \
+        Activates early stopping. CV error needs to decrease at least
         every <early_stopping_rounds> round(s) to continue.
         Last entry in evaluation history is the one from best iteration.
     fpreproc : function
-        Preprocessing function that takes (dtrain, dtest, param) and returns \
-        transformed versions of those.
+        Preprocessing function that takes (dtrain, dtest, param)
+        and returns transformed versions of those.
     verbose_eval : bool, int, or None, default None
-        Whether to display the progress. If None, progress will be displayed \
-        when np.ndarray is returned. If True, progress will be displayed at \
-        boosting stage. If an integer is given, progress will be displayed \
-        at every given `verbose_eval` boosting stage.
+        Whether to display the progress.
+        If None, progress will be displayed when np.ndarray is returned.
+        If True, progress will be displayed at boosting stage.
+        If an integer is given,
+            progress will be displayed at every given `verbose_eval` boosting stage.
     show_stdv : bool, default True
         Whether to display the standard deviation in progress.
         Results are not affected, and always contains std.

python-package/lightgbm/sklearn.py

@@ -128,69 +128,6 @@ def _eval_function_wrapper(func):
     return inner
 
 class LGBMModel(LGBMModelBase):
-    """Implementation of the Scikit-Learn API for LightGBM.
-
-    Parameters
-    ----------
-    num_leaves : int
-        Maximum tree leaves for base learners.
-    max_depth : int
-        Maximum tree depth for base learners, -1 means no limit.
-    learning_rate : float
-        Boosting learning rate
-    n_estimators : int
-        Number of boosted trees to fit.
-    silent : boolean
-        Whether to print messages while running boosting.
-    objective : string or callable
-        Specify the learning task and the corresponding learning objective or
-        a custom objective function to be used (see note below).
-    nthread : int
-        Number of parallel threads
-    min_split_gain : float
-        Minimum loss reduction required to make a further partition on a leaf node of the tree.
-    min_child_weight : int
-        Minimum sum of instance weight(hessian) needed in a child(leaf)
-    min_child_samples : int
-        Minimum number of data need in a child(leaf)
-    subsample : float
-        Subsample ratio of the training instance.
-    subsample_freq : int
-        frequence of subsample, <=0 means no enable
-    colsample_bytree : float
-        Subsample ratio of columns when constructing each tree.
-    reg_alpha : float
-        L1 regularization term on weights
-    reg_lambda : float
-        L2 regularization term on weights
-    scale_pos_weight : float
-        Balancing of positive and negative weights.
-    is_unbalance : bool
-        Is unbalance for binary classification
-    seed : int
-        Random number seed.
-
-    Note
-    ----
-    A custom objective function can be provided for the ``objective``
-    parameter. In this case, it should have the signature
-    ``objective(y_true, y_pred) -> grad, hess`` or ``objective(y_true, y_pred, group) -> grad, hess``:
-
-        y_true: array_like of shape [n_samples]
-            The target values
-        y_pred: array_like of shape [n_samples] or shape[n_samples* n_class]
-            The predicted values
-        group: array_like
-            group/query data, used for ranking task
-        grad: array_like of shape [n_samples] or shape[n_samples* n_class]
-            The value of the gradient for each sample point.
-        hess: array_like of shape [n_samples] or shape[n_samples* n_class]
-            The value of the second derivative for each sample point
-
-    for multi-class task, the y_pred is group by class_id first, then group by row_id
-          if you want to get i-th row y_pred in j-th class, the access way is y_pred[j*num_data+i]
-          and you should group grad and hess in this way as well
-    """
 
     def __init__(self, num_leaves=31, max_depth=-1,
                  learning_rate=0.1, n_estimators=10, max_bin=255,
@@ -199,6 +136,72 @@ class LGBMModel(LGBMModelBase):
                  subsample=1, subsample_freq=1, colsample_bytree=1,
                  reg_alpha=0, reg_lambda=0, scale_pos_weight=1,
                  is_unbalance=False, seed=0):
+        """
+        Implementation of the Scikit-Learn API for LightGBM.
+
+        Parameters
+        ----------
+        num_leaves : int
+            Maximum tree leaves for base learners.
+        max_depth : int
+            Maximum tree depth for base learners, -1 means no limit.
+        learning_rate : float
+            Boosting learning rate
+        n_estimators : int
+            Number of boosted trees to fit.
+        silent : boolean
+            Whether to print messages while running boosting.
+        objective : string or callable
+            Specify the learning task and the corresponding learning objective or
+            a custom objective function to be used (see note below).
+            default: binary for LGBMClassifier, lambdarank for LGBMRanker
+        nthread : int
+            Number of parallel threads
+        min_split_gain : float
+            Minimum loss reduction required to make a further partition on a leaf node of the tree.
+        min_child_weight : int
+            Minimum sum of instance weight(hessian) needed in a child(leaf)
+        min_child_samples : int
+            Minimum number of data need in a child(leaf)
+        subsample : float
+            Subsample ratio of the training instance.
+        subsample_freq : int
+            frequence of subsample, <=0 means no enable
+        colsample_bytree : float
+            Subsample ratio of columns when constructing each tree.
+        reg_alpha : float
+            L1 regularization term on weights
+        reg_lambda : float
+            L2 regularization term on weights
+        scale_pos_weight : float
+            Balancing of positive and negative weights.
+        is_unbalance : bool
+            Is unbalance for binary classification
+        seed : int
+            Random number seed.
+
+        Note
+        ----
+        A custom objective function can be provided for the ``objective``
+        parameter. In this case, it should have the signature
+        ``objective(y_true, y_pred) -> grad, hess`` 
+            or ``objective(y_true, y_pred, group) -> grad, hess``:
+
+            y_true: array_like of shape [n_samples]
+                The target values
+            y_pred: array_like of shape [n_samples] or shape[n_samples* n_class]
+                The predicted values
+            group: array_like
+                group/query data, used for ranking task
+            grad: array_like of shape [n_samples] or shape[n_samples* n_class]
+                The value of the gradient for each sample point.
+            hess: array_like of shape [n_samples] or shape[n_samples* n_class]
+                The value of the second derivative for each sample point
+
+        for multi-class task, the y_pred is group by class_id first, then group by row_id
+            if you want to get i-th row y_pred in j-th class, the access way is y_pred[j*num_data+i]
+            and you should group grad and hess in this way as well
+        """
         if not SKLEARN_INSTALLED:
             raise LightGBMError('Scikit-learn is required for this module')
 
@@ -229,8 +232,8 @@ class LGBMModel(LGBMModelBase):
             self.fobj = None
 
     def booster(self):
-        """Get the underlying lightgbm Booster of this model.
-
+        """
+        Get the underlying lightgbm Booster of this model.
         This will raise an exception when fit was not called
 
         Returns
@@ -242,7 +245,9 @@ class LGBMModel(LGBMModelBase):
         return self._Booster
 
     def get_params(self, deep=False):
-        """Get parameters"""
+        """
+        Get parameters
+        """
         params = super(LGBMModel, self).get_params(deep=deep)
         if self.nthread <= 0:
             params.pop('nthread', None)
@@ -288,20 +293,23 @@ class LGBMModel(LGBMModelBase):
         feature_name : list of str
             Feature names
         categorical_feature : list of str or int
-            Categorical features, type int represents index, \
+            Categorical features,
+            type int represents index,
             type str represents feature names (need to specify feature_name as well)
         other_params: dict
             Other parameters
 
         Note
         ----
-        Custom eval function expects a callable with following functions: ``func(y_true, y_pred)``, ``func(y_true, y_pred, weight)`` 
-            or ``func(y_true, y_pred, weight, group)``.
-            return (eval_name, eval_result, is_bigger_better) or list of (eval_name, eval_result, is_bigger_better)
+        Custom eval function expects a callable with following functions:
+            ``func(y_true, y_pred)``, ``func(y_true, y_pred, weight)``
+                or ``func(y_true, y_pred, weight, group)``.
+            return (eval_name, eval_result, is_bigger_better)
+                or list of (eval_name, eval_result, is_bigger_better)
 
             y_true: array_like of shape [n_samples]
                 The target values
-            y_pred: array_like of shape [n_samples] or shape[n_samples* n_class] (for multi-class)
+            y_pred: array_like of shape [n_samples] or shape[n_samples * n_class] (for multi-class)
                 The predicted values
             weight: array_like of shape [n_samples]
                 The weight of samples
@@ -383,20 +391,36 @@ class LGBMModel(LGBMModelBase):
         return self
 
     def predict(self, data, raw_score=False, num_iteration=0):
+        """
+        Return the predicted value for each sample.
+
+        Parameters
+        ----------
+        X : array_like, shape=[n_samples, n_features]
+            Input features matrix.
+
+        num_iteration : int
+            Limit number of iterations in the prediction; defaults to 0 (use all trees).
+
+        Returns
+        -------
+        predicted_result : array_like, shape=[n_samples] or [n_samples, n_classes]
+        """
         return self.booster().predict(data,
                                       raw_score=raw_score,
                                       num_iteration=num_iteration)
 
     def apply(self, X, num_iteration=0):
-        """Return the predicted leaf every tree for each sample.
+        """
+        Return the predicted leaf every tree for each sample.
 
         Parameters
         ----------
         X : array_like, shape=[n_samples, n_features]
             Input features matrix.
 
-        ntree_limit : int
-            Limit number of trees in the prediction; defaults to 0 (use all trees).
+        num_iteration : int
+            Limit number of iterations in the prediction; defaults to 0 (use all trees).
 
         Returns
         -------
@@ -407,7 +431,9 @@ class LGBMModel(LGBMModelBase):
                                       num_iteration=num_iteration)
 
     def evals_result(self):
-        """Return the evaluation results.
+        """
+        Return the evaluation results.
+
         Returns
         -------
         evals_result : dictionary
@@ -420,7 +446,9 @@ class LGBMModel(LGBMModelBase):
         return evals_result
 
     def feature_importance(self):
-        """Feature importances
+        """
+        Feature importances
+
         Returns
         -------
         Array of normailized feature importances
@@ -429,8 +457,6 @@ class LGBMModel(LGBMModelBase):
         return importace_array / importace_array.sum()
 
 class LGBMRegressor(LGBMModel, LGBMRegressorBase):
-    __doc__ = """Implementation of the scikit-learn API for LightGBM regression.
-    """ + '\n'.join(LGBMModel.__doc__.split('\n')[2:])
 
     def fit(self, X, y,
             sample_weight=None, init_score=None,
@@ -449,9 +475,6 @@ class LGBMRegressor(LGBMModel, LGBMRegressorBase):
         return self
 
 class LGBMClassifier(LGBMModel, LGBMClassifierBase):
-    __doc__ = """Implementation of the scikit-learn API for LightGBM classification.
-
-    """ + '\n'.join(LGBMModel.__doc__.split('\n')[2:])
 
     def __init__(self, num_leaves=31, max_depth=-1,
                  learning_rate=0.1, n_estimators=10, max_bin=255,
@@ -511,6 +534,21 @@ class LGBMClassifier(LGBMModel, LGBMClassifierBase):
         return self._le.inverse_transform(column_indexes)
 
     def predict_proba(self, data, raw_score=False, num_iteration=0):
+        """
+        Return the predicted probability for each class for each sample.
+
+        Parameters
+        ----------
+        X : array_like, shape=[n_samples, n_features]
+            Input features matrix.
+
+        num_iteration : int
+            Limit number of iterations in the prediction; defaults to 0 (use all trees).
+
+        Returns
+        -------
+        predicted_probability : array_like, shape=[n_samples, n_classes]
+        """
         class_probs = self.booster().predict(data,
                                              raw_score=raw_score,
                                              num_iteration=num_iteration)
@@ -522,9 +560,6 @@ class LGBMClassifier(LGBMModel, LGBMClassifierBase):
             return np.vstack((classzero_probs, classone_probs)).transpose()
 
 class LGBMRanker(LGBMModel):
-    __doc__ = """Implementation of the scikit-learn API for LightGBM ranking application.
-
-    """ + '\n'.join(LGBMModel.__doc__.split('\n')[2:])
 
     def __init__(self, num_leaves=31, max_depth=-1,
                  learning_rate=0.1, n_estimators=10, max_bin=255,
@@ -550,7 +585,7 @@ class LGBMRanker(LGBMModel):
             feature_name=None, categorical_feature=None,
             other_params=None):
         """
-        Most arguments like LGBMModel.fit except following:
+        Most arguments like common methods except following:
 
         eval_at : list of int
             The evaulation positions of NDCG