ML之CatboostC：基于titanic泰坦尼克数据集利用catboost算法实现二分类
ML之CatboostC：基于titanic泰坦尼克数据集利用catboost算法实现二分类
相关内容
ML之CatBoost：CatBoost算法的简介、安装、案例应用之详细攻略
ML之CatboostC：基于titanic泰坦尼克数据集利用catboost算法实现二分类
ML之CatboostC：基于titanic泰坦尼克数据集利用catboost算法实现二分类实现
基于titanic泰坦尼克数据集利用catboost算法实现二分类
设计思路
输出结果
Pclass Sex Age SibSp Parch Survived
0 3 male 22.0 1 0 0
1 1 female 38.0 1 0 1
2 3 female 26.0 0 0 1
3 1 female 35.0 1 0 1
4 3 male 35.0 0 0 0
Pclass int64
Sex object
Age float64
SibSp int64
Parch int64
Survived int64
dtype: object
object_features_ID： [1]
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bestTest = 0.3997213503
bestIteration = 37
Shrink model to first 38 iterations.
核心代码
class CatBoostClassifier Found at: catboost.core
class CatBoostClassifier(CatBoost):
_estimator_type = 'classifier'
"""
Implementation of the scikit-learn API for CatBoost classification.
Parameters
----------
iterations : int, [default=500]
Max count of trees.
range: [1,+inf]
learning_rate : float, [default value is selected automatically for
binary classification with other parameters set to default. In all
other cases default is 0.03]
Step size shrinkage used in update to prevents overfitting.
range: (0,1]
depth : int, [default=6]
Depth of a tree. All trees are the same depth.
range: [1,+inf]
l2_leaf_reg : float, [default=3.0]
Coefficient at the L2 regularization term of the cost function.
range: [0,+inf]
model_size_reg : float, [default=None]
Model size regularization coefficient.
range: [0,+inf]
rsm : float, [default=None]
Subsample ratio of columns when constructing each tree.
range: (0,1]
loss_function : string or object, [default='Logloss']
The metric to use in training and also selector of the machine
learning
problem to solve. If string, then the name of a supported
metric,
optionally suffixed with parameter description.
If object, it shall provide methods 'calc_ders_range' or
'calc_ders_multi'.
border_count : int, [default = 254 for training on CPU or 128 for
training on GPU]
The number of partitions in numeric features binarization.
Used in the preliminary calculation.
range: [1,65535] on CPU, [1,255] on GPU
feature_border_type : string, [default='GreedyLogSum']
The binarization mode in numeric features binarization. Used
in the preliminary calculation.
Possible values:
- 'Median'
- 'Uniform'
- 'UniformAndQuantiles'
- 'GreedyLogSum'
- 'MaxLogSum'
- 'MinEntropy'
per_float_feature_quantization : list of strings, [default=None]
List of float binarization descriptions.
Format : described in documentation on catboost.ai
Example 1: ['0:1024'] means that feature 0 will have 1024
borders.
Example 2: ['0:border_count=1024', '1:border_count=1024',
...] means that two first features have 1024 borders.
Example 3: ['0:nan_mode=Forbidden,border_count=32,
border_type=GreedyLogSum',
'1:nan_mode=Forbidden,border_count=32,
border_type=GreedyLogSum'] - defines more quantization
properties for first two features.
input_borders : string, [default=None]
input file with borders used in numeric features binarization.
output_borders : string, [default=None]
output file for borders that were used in numeric features
binarization.
fold_permutation_block : int, [default=1]
To accelerate the learning.
The recommended value is within [1, 256]. On small samples,
must be set to 1.
range: [1,+inf]
od_pval : float, [default=None]
Use overfitting detector to stop training when reaching a
specified threshold.
Can be used only with eval_set.
range: [0,1]
od_wait : int, [default=None]
Number of iterations which overfitting detector will wait after
new best error.
od_type : string, [default=None]
Type of overfitting detector which will be used in program.
Posible values:
- 'IncToDec'
- 'Iter'
For 'Iter' type od_pval must not be set.
If None, then od_type=IncToDec.
nan_mode : string, [default=None]
Way to process missing values for numeric features.
Possible values:
- 'Forbidden' - raises an exception if there is a missing value
for a numeric feature in a dataset.
- 'Min' - each missing value will be processed as the
minimum numerical value.
- 'Max' - each missing value will be processed as the
maximum numerical value.
If None, then nan_mode=Min.
counter_calc_method : string, [default=None]
The method used to calculate counters for dataset with
Counter type.
Possible values:
- 'PrefixTest' - only objects up to current in the test dataset
are considered
- 'FullTest' - all objects are considered in the test dataset
- 'SkipTest' - Objects from test dataset are not considered
- 'Full' - all objects are considered for both learn and test
dataset
If None, then counter_calc_method=PrefixTest.
leaf_estimation_iterations : int, [default=None]
The number of steps in the gradient when calculating the
values in the leaves.
If None, then leaf_estimation_iterations=1.
range: [1,+inf]
leaf_estimation_method : string, [default=None]
The method used to calculate the values in the leaves.
Possible values:
- 'Newton'
- 'Gradient'
thread_count : int, [default=None]
Number of parallel threads used to run CatBoost.
If None or -1, then the number of threads is set to the
number of CPU cores.
range: [1,+inf]
random_seed : int, [default=None]
Random number seed.
If None, 0 is used.
range: [0,+inf]
use_best_model : bool, [default=None]
To limit the number of trees in predict() using information
about the optimal value of the error function.
Can be used only with eval_set.
best_model_min_trees : int, [default=None]
The minimal number of trees the best model should have.
verbose: bool
When set to True, logging_level is set to 'Verbose'.
When set to False, logging_level is set to 'Silent'.
silent: bool, synonym for verbose
logging_level : string, [default='Verbose']
Possible values:
- 'Silent'
- 'Verbose'
- 'Info'
- 'Debug'
metric_period : int, [default=1]
The frequency of iterations to print the information to stdout.
The value should be a positive integer.
simple_ctr: list of strings, [default=None]
Binarization settings for categorical features.
Format : see documentation
Example: ['Borders:CtrBorderCount=5:Prior=0:Prior=0.5',
'BinarizedTargetMeanValue:TargetBorderCount=10:
TargetBorderType=MinEntropy', ...]
CTR types:
CPU and GPU
- 'Borders'
- 'Buckets'
CPU only
- 'BinarizedTargetMeanValue'
- 'Counter'
GPU only
- 'FloatTargetMeanValue'
- 'FeatureFreq'
Number_of_borders, binarization type, target borders and
binarizations, priors are optional parametrs
combinations_ctr: list of strings, [default=None]
per_feature_ctr: list of strings, [default=None]
ctr_target_border_count: int, [default=None]
Maximum number of borders used in target binarization for
categorical features that need it.
If TargetBorderCount is specified in 'simple_ctr',
'combinations_ctr' or 'per_feature_ctr' option it
overrides this value.
range: [1, 255]
ctr_leaf_count_limit : int, [default=None]
The maximum number of leaves with categorical features.
If the number of leaves exceeds the specified limit, some
leaves are discarded.
The leaves to be discarded are selected as follows:
- The leaves are sorted by the frequency of the values.
- The top N leaves are selected, where N is the value
specified in the parameter.
- All leaves starting from N+1 are discarded.
This option reduces the resulting model size
and the amount of memory required for training.
Note that the resulting quality of the model can be affected.
range: [1,+inf] (for zero limit use ignored_features)
store_all_simple_ctr : bool, [default=None]
Ignore categorical features, which are not used in feature
combinations,
when choosing candidates for exclusion.
Use this parameter with ctr_leaf_count_limit only.
max_ctr_complexity : int, [default=4]
The maximum number of Categ features that can be
combined.
range: [0,+inf]
has_time : bool, [default=False]
To use the order in which objects are represented in the
input data
(do not perform a random permutation of the dataset at the
preprocessing stage).
allow_const_label : bool, [default=False]
To allow the constant label value in dataset.
target_border: float, [default=None]
Border for target binarization.
classes_count : int, [default=None]
The upper limit for the numeric class label.
Defines the number of classes for multiclassification.
Only non-negative integers can be specified.
The given integer should be greater than any of the target
values.
If this parameter is specified the labels for all classes in the
input dataset
should be smaller than the given value.
If several of 'classes_count', 'class_weights', 'class_names'
parameters are defined
the numbers of classes specified by each of them must be
equal.
class_weights : list or dict, [default=None]
Classes weights. The values are used as multipliers for the
object weights.
If None, all classes are supposed to have weight one.
If list - class weights in order of class_names or sequential
classes if class_names is undefined
If dict - dict of class_name -> class_weight.
If several of 'classes_count', 'class_weights', 'class_names'
parameters are defined
the numbers of classes specified by each of them must be
equal.
auto_class_weights : string [default=None]
Enables automatic class weights calculation. Possible values:
- Balanced # weight = maxSummaryClassWeight /
summaryClassWeight, statistics determined from train pool
- SqrtBalanced # weight = sqrt(maxSummaryClassWeight /
summaryClassWeight)
class_names: list of strings, [default=None]
Class names. Allows to redefine the default values for class
labels (integer numbers).
If several of 'classes_count', 'class_weights', 'class_names'
parameters are defined
the numbers of classes specified by each of them must be
equal.
one_hot_max_size : int, [default=None]
Convert the feature to float
if the number of different values that it takes exceeds the
specified value.
Ctrs are not calculated for such features.
random_strength : float, [default=1]
Score standard deviation multiplier.
name : string, [default='experiment']
The name that should be displayed in the visualization tools.
ignored_features : list, [default=None]
Indices or names of features that should be excluded when
training.
train_dir : string, [default=None]
The directory in which you want to record generated in the
process of learning files.
custom_metric : string or list of strings, [default=None]
To use your own metric function.
custom_loss: alias to custom_metric
eval_metric : string or object, [default=None]
To optimize your custom metric in loss.
bagging_temperature : float, [default=None]
Controls intensity of Bayesian bagging. The higher the
temperature the more aggressive bagging is.
Typical values are in range [0, 1] (0 - no bagging, 1 - default).
save_snapshot : bool, [default=None]
Enable progress snapshotting for restoring progress after
crashes or interruptions
snapshot_file : string, [default=None]
Learn progress snapshot file path, if None will use default
filename
snapshot_interval: int, [default=600]
Interval between saving snapshots (seconds)
fold_len_multiplier : float, [default=None]
Fold length multiplier. Should be greater than 1
used_ram_limit : string or number, [default=None]
Set a limit on memory consumption (value like '1.2gb' or 1.2
e9).
WARNING: Currently this option affects CTR memory usage
only.
gpu_ram_part : float, [default=0.95]
Fraction of the GPU RAM to use for training, a value from (0,
1].
pinned_memory_size: int [default=None]
Size of additional CPU pinned memory used for GPU learning,
usually is estimated automatically, thus usually should not be
set.
allow_writing_files : bool, [default=True]
If this flag is set to False, no files with different diagnostic info
will be created during training.
With this flag no snapshotting can be done. Plus visualisation
will not
work, because visualisation uses files that are created and
updated during training.
final_ctr_computation_mode : string, [default='Default']
Possible values:
- 'Default' - Compute final ctrs for all pools.
- 'Skip' - Skip final ctr computation. WARNING: model
without ctrs can't be applied.
approx_on_full_history : bool, [default=False]
If this flag is set to True, each approximated value is
calculated using all the preceeding rows in the fold (slower, more
accurate).
If this flag is set to False, each approximated value is
calculated using only the beginning 1/fold_len_multiplier fraction
of the fold (faster, slightly less accurate).
boosting_type : string, default value depends on object count
and feature count in train dataset and on learning mode.
Boosting scheme.
Possible values:
- 'Ordered' - Gives better quality, but may slow down the
training.
- 'Plain' - The classic gradient boosting scheme. May result
in quality degradation, but does not slow down the training.
task_type : string, [default=None]
The calcer type used to train the model.
Possible values:
- 'CPU'
- 'GPU'
device_config : string, [default=None], deprecated, use devices
instead
devices : list or string, [default=None], GPU devices to use.
String format is: '0' for 1 device or '0:1:3' for multiple devices
or '0-3' for range of devices.
List format is : [0] for 1 device or [0,1,3] for multiple devices.
bootstrap_type : string, Bayesian, Bernoulli, Poisson, MVS.
Default bootstrap is Bayesian for GPU and MVS for CPU.
Poisson bootstrap is supported only on GPU.
MVS bootstrap is supported only on CPU.
subsample : float, [default=None]
Sample rate for bagging. This parameter can be used Poisson
or Bernoully bootstrap types.
mvs-reg : float, [default is set automatically at each iteration
based on gradient distribution]
Regularization parameter for MVS sampling algorithm
monotone_constraints : list or numpy.ndarray or string or dict,
[default=None]
Monotone constraints for features.
feature_weights : list or numpy.ndarray or string or dict,
[default=None]
Coefficient to multiply split gain with specific feature use.
Should be non-negative.
penalties_coefficient : float, [default=1]
Common coefficient for all penalties. Should be non-negative.
first_feature_use_penalties : list or numpy.ndarray or string or
dict, [default=None]
Penalties to first use of specific feature in model. Should be
non-negative.
per_object_feature_penalties : list or numpy.ndarray or string or
dict, [default=None]
Penalties for first use of feature for each object. Should be
non-negative.
sampling_frequency : string, [default=PerTree]
Frequency to sample weights and objects when building
trees.
Possible values:
- 'PerTree' - Before constructing each new tree
- 'PerTreeLevel' - Before choosing each new split of a tree
sampling_unit : string, [default='Object'].
Possible values:
- 'Object'
- 'Group'
The parameter allows to specify the sampling scheme:
sample weights for each object individually or for an entire
group of objects together.
dev_score_calc_obj_block_size: int, [default=5000000]
CPU only. Size of block of samples in score calculation. Should
be > 0
Used only for learning speed tuning.
Changing this parameter can affect results due to numerical
accuracy differences
dev_efb_max_buckets : int, [default=1024]
CPU only. Maximum bucket count in exclusive features
bundle. Should be in an integer between 0 and 65536.
Used only for learning speed tuning.
sparse_features_conflict_fraction : float, [default=0.0]
CPU only. Maximum allowed fraction of conflicting non-
default values for features in exclusive features bundle.
Should be a real value in [0, 1) interval.
grow_policy : string, [SymmetricTree,Lossguide,Depthwise],
[default=SymmetricTree]
The tree growing policy. It describes how to perform greedy
tree construction.
min_data_in_leaf : int, [default=1].
The minimum training samples count in leaf.
CatBoost will not search for new splits in leaves with samples
count less than min_data_in_leaf.
This parameter is used only for Depthwise and Lossguide
growing policies.
max_leaves : int, [default=31],
The maximum leaf count in resulting tree.
This parameter is used only for Lossguide growing policy.
score_function : string, possible values L2, Cosine, NewtonL2,
NewtonCosine, [default=Cosine]
For growing policy Lossguide default=NewtonL2.
GPU only. Score that is used during tree construction to
select the next tree split.
max_depth : int, Synonym for depth.
n_estimators : int, synonym for iterations.
num_trees : int, synonym for iterations.
num_boost_round : int, synonym for iterations.
colsample_bylevel : float, synonym for rsm.
random_state : int, synonym for random_seed.
reg_lambda : float, synonym for l2_leaf_reg.
objective : string, synonym for loss_function.
num_leaves : int, synonym for max_leaves.
min_child_samples : int, synonym for min_data_in_leaf
eta : float, synonym for learning_rate.
max_bin : float, synonym for border_count.
scale_pos_weight : float, synonym for class_weights.
Can be used only for binary classification. Sets weight
multiplier for
class 1 to scale_pos_weight value.
metadata : dict, string to string key-value pairs to be stored in
model metadata storage
early_stopping_rounds : int
Synonym for od_wait. Only one of these parameters should
be set.
cat_features : list or numpy.ndarray, [default=None]
If not None, giving the list of Categ features indices or names
(names are represented as strings).
If it contains feature names, feature names must be defined
for the training dataset passed to 'fit'.
text_features : list or numpy.ndarray, [default=None]
If not None, giving the list of Text features indices or names
(names are represented as strings).
If it contains feature names, feature names must be defined
for the training dataset passed to 'fit'.
embedding_features : list or numpy.ndarray, [default=None]
If not None, giving the list of Embedding features indices or
names (names are represented as strings).
If it contains feature names, feature names must be defined
for the training dataset passed to 'fit'.
leaf_estimation_backtracking : string, [default=None]
Type of backtracking during gradient descent.
Possible values:
- 'No' - never backtrack; supported on CPU and GPU
- 'AnyImprovement' - reduce the descent step until the
value of loss function is less than before the step; supported on
CPU and GPU
- 'Armijo' - reduce the descent step until Armijo condition
is satisfied; supported on GPU only
model_shrink_rate : float, [default=0]
This parameter enables shrinkage of model at the start of
each iteration. CPU only.
For Constant mode shrinkage coefficient is calculated as (1 -
model_shrink_rate * learning_rate).
For Decreasing mode shrinkage coefficient is calculated as (1
- model_shrink_rate / iteration).
Shrinkage coefficient should be in [0, 1).
model_shrink_mode : string, [default=None]
Mode of shrinkage coefficient calculation. CPU only.
Possible values:
- 'Constant' - Shrinkage coefficient is constant at each
iteration.
- 'Decreasing' - Shrinkage coefficient decreases at each
iteration.
langevin : bool, [default=False]
Enables the Stochastic Gradient Langevin Boosting. CPU only.
diffusion_temperature : float, [default=0]
Langevin boosting diffusion temperature. CPU only.
posterior_sampling : bool, [default=False]
Set group of parameters for further use Uncertainty
prediction:
- Langevin = True
- Model Shrink Rate = 1/(2N), where N is dataset size
- Model Shrink Mode = Constant
- Diffusion-temperature = N, where N is dataset size. CPU
only.
boost_from_average : bool, [default=True for RMSE, False for
other losses]
Enables to initialize approx values by best constant value for
specified loss function.
Available for RMSE, Logloss, CrossEntropy, Quantile and MAE.
tokenizers : list of dicts,
Each dict is a tokenizer description. Example:
```
[
{
'tokenizer_id': 'Tokenizer', # Tokeinzer identifier.
'lowercasing': 'false', # Possible values: 'true', 'false'.
'number_process_policy': 'LeaveAsIs', # Possible values:
'Skip', 'LeaveAsIs', 'Replace'.
'number_token': '%', # Rarely used character. Used in
conjunction with Replace NumberProcessPolicy.
'separator_type': 'ByDelimiter', # Possible values:
'ByDelimiter', 'BySense'.
'delimiter': ' ', # Used in conjunction with ByDelimiter
SeparatorType.
'split_by_set': 'false', # Each single character in delimiter
used as individual delimiter.
'skip_empty': 'true', # Possible values: 'true', 'false'.
'token_types': ['Word', 'Number', 'Unknown'], # Used in
conjunction with BySense SeparatorType.
# Possible values: 'Word', 'Number', 'Punctuation',
'SentenceBreak', 'ParagraphBreak', 'Unknown'.
'subtokens_policy': 'SingleToken', # Possible values:
# 'SingleToken' - All subtokens are interpreted as
single token).
# 'SeveralTokens' - All subtokens are interpreted as
several token.
},
...
]
```
dictionaries : list of dicts,
Each dict is a tokenizer description. Example:
```
[
{
'dictionary_id': 'Dictionary', # Dictionary identifier.
'token_level_type': 'Word', # Possible values: 'Word',
'Letter'.
'gram_order': '1', # 1 for Unigram, 2 for Bigram, ...
'skip_step': '0', # 1 for 1-skip-gram, ...
'end_of_word_token_policy': 'Insert', # Possible values:
'Insert', 'Skip'.
'end_of_sentence_token_policy': 'Skip', # Possible values:
'Insert', 'Skip'.
'occurrence_lower_bound': '3', # The lower bound of
token occurrences in the text to include it in the dictionary.
'max_dictionary_size': '50000', # The max dictionary size.
},
...
]
```
feature_calcers : list of strings,
Each string is a calcer description. Example:
```
[
'NaiveBayes',
'BM25',
'BoW:top_tokens_count=2000',
]
```
text_processing : dict,
Text processging description.
"""
def __init__(
self,
iterations=None,
learning_rate=None,
depth=None,
l2_leaf_reg=None,
model_size_reg=None,
rsm=None,
loss_function=None,
border_count=None,
feature_border_type=None,
per_float_feature_quantization=None,
input_borders=None,
output_borders=None,
fold_permutation_block=None,
od_pval=None,
od_wait=None,
od_type=None,
nan_mode=None,
counter_calc_method=None,
leaf_estimation_iterations=None,
leaf_estimation_method=None,
thread_count=None,
random_seed=None,
use_best_model=None,
best_model_min_trees=None,
verbose=None,
silent=None,
logging_level=None,
metric_period=None,
ctr_leaf_count_limit=None,
store_all_simple_ctr=None,
max_ctr_complexity=None,
has_time=None,
allow_const_label=None,
target_border=None,
classes_count=None,
class_weights=None,
auto_class_weights=None,
class_names=None,
one_hot_max_size=None,
random_strength=None,
name=None,
ignored_features=None,
train_dir=None,
custom_loss=None,
custom_metric=None,
eval_metric=None,
bagging_temperature=None,
save_snapshot=None,
snapshot_file=None,
snapshot_interval=None,
fold_len_multiplier=None,
used_ram_limit=None,
gpu_ram_part=None,
pinned_memory_size=None,
allow_writing_files=None,
final_ctr_computation_mode=None,
approx_on_full_history=None,
boosting_type=None,
simple_ctr=None,
combinations_ctr=None,
per_feature_ctr=None,
ctr_description=None,
ctr_target_border_count=None,
task_type=None,
device_config=None,
devices=None,
bootstrap_type=None,
subsample=None,
mvs_reg=None,
sampling_unit=None,
sampling_frequency=None,
dev_score_calc_obj_block_size=None,
dev_efb_max_buckets=None,
sparse_features_conflict_fraction=None,
max_depth=None,
n_estimators=None,
num_boost_round=None,
num_trees=None,
colsample_bylevel=None,
random_state=None,
reg_lambda=None,
objective=None,
eta=None,
max_bin=None,
scale_pos_weight=None,
gpu_cat_features_storage=None,
data_partition=None,
metadata=None,
early_stopping_rounds=None,
cat_features=None,
grow_policy=None,
min_data_in_leaf=None,
min_child_samples=None,
max_leaves=None,
num_leaves=None,
score_function=None,
leaf_estimation_backtracking=None,
ctr_history_unit=None,
monotone_constraints=None,
feature_weights=None,
penalties_coefficient=None,
first_feature_use_penalties=None,
per_object_feature_penalties=None,
model_shrink_rate=None,
model_shrink_mode=None,
langevin=None,
diffusion_temperature=None,
posterior_sampling=None,
boost_from_average=None,
text_features=None,
tokenizers=None,
dictionaries=None,
feature_calcers=None,
text_processing=None,
embedding_features=None):
params = {}
not_params = ["not_params", "self", "params", "__class__"]
for key, value in iteritems(locals().copy()):
if key not in not_params and value is not None:
params[key] = value
super(CatBoostClassifier, self).__init__(params)
def fit(self, X, y=None, cat_features=None, text_features=None,
embedding_features=None, sample_weight=None,
baseline=None, use_best_model=None,
eval_set=None, verbose=None, logging_level=None,
plot=False, column_description=None,
verbose_eval=None, metric_period=None, silent=None,
early_stopping_rounds=None,
save_snapshot=None, snapshot_file=None,
snapshot_interval=None, init_model=None):
"""
Fit the CatBoostClassifier model.
Parameters
----------
X : catboost.Pool or list or numpy.ndarray or pandas.
DataFrame or pandas.Series
If not catboost.Pool, 2 dimensional Feature matrix or string
- file with dataset.
y : list or numpy.ndarray or pandas.DataFrame or pandas.
Series, optional (default=None)
Labels, 1 dimensional array like.
Use only if X is not catboost.Pool.
cat_features : list or numpy.ndarray, optional (default=None)
If not None, giving the list of Categ columns indices.
Use only if X is not catboost.Pool.
text_features : list or numpy.ndarray, optional (default=None)
If not None, giving the list of Text columns indices.
Use only if X is not catboost.Pool.
embedding_features : list or numpy.ndarray, optional
(default=None)
If not None, giving the list of Embedding columns indices.
Use only if X is not catboost.Pool.
sample_weight : list or numpy.ndarray or pandas.DataFrame
or pandas.Series, optional (default=None)
Instance weights, 1 dimensional array like.
baseline : list or numpy.ndarray, optional (default=None)
If not None, giving 2 dimensional array like data.
Use only if X is not catboost.Pool.
use_best_model : bool, optional (default=None)
Flag to use best model
eval_set : catboost.Pool or list, optional (default=None)
A list of (X, y) tuple pairs to use as a validation set for early-
stopping
metric_period : int
Frequency of evaluating metrics.
verbose : bool or int
If verbose is bool, then if set to True, logging_level is set to
Verbose,
if set to False, logging_level is set to Silent.
If verbose is int, it determines the frequency of writing
metrics to output and
logging_level is set to Verbose.
silent : bool
If silent is True, logging_level is set to Silent.
If silent is False, logging_level is set to Verbose.
logging_level : string, optional (default=None)
Possible values:
- 'Silent'
- 'Verbose'
- 'Info'
- 'Debug'
plot : bool, optional (default=False)
If True, draw train and eval error in Jupyter notebook
verbose_eval : bool or int
Synonym for verbose. Only one of these parameters should
be set.
early_stopping_rounds : int
Activates Iter overfitting detector with od_wait set to
early_stopping_rounds.
save_snapshot : bool, [default=None]
Enable progress snapshotting for restoring progress after
crashes or interruptions
snapshot_file : string, [default=None]
Learn progress snapshot file path, if None will use default
filename
snapshot_interval: int, [default=600]
Interval between saving snapshots (seconds)
init_model : CatBoost class or string, [default=None]
Continue training starting from the existing model.
If this parameter is a string, load initial model from the path
specified by this string.
Returns
-------
model : CatBoost
"""
params = self._init_params.copy()
_process_synonyms(params)
if 'loss_function' in params:
self._check_is_classification_objective(params
['loss_function'])
self._fit(X, y, cat_features, text_features, embedding_features,
None, sample_weight, None, None, None, None, baseline,
use_best_model, eval_set, verbose, logging_level, plot,
column_description, verbose_eval, metric_period, silent,
early_stopping_rounds, save_snapshot, snapshot_file,
snapshot_interval, init_model)
return self
def predict(self, data, prediction_type='Class', ntree_start=0,
ntree_end=0, thread_count=-1, verbose=None):
"""
Predict with data.
Parameters
----------
data : catboost.Pool or list of features or list of lists or numpy.
ndarray or pandas.DataFrame or pandas.Series
or catboost.FeaturesData
Data to apply model on.
If data is a simple list (not list of lists) or a one-dimensional
numpy.ndarray it is interpreted
as a list of features for a single object.
prediction_type : string, optional (default='Class')
Can be:
- 'RawFormulaVal' : return raw formula value.
- 'Class' : return class label.
- 'Probability' : return probability for every class.
- 'LogProbability' : return log probability for every class.
ntree_start: int, optional (default=0)
Model is applied on the interval [ntree_start, ntree_end)
(zero-based indexing).
ntree_end: int, optional (default=0)
Model is applied on the interval [ntree_start, ntree_end)
(zero-based indexing).
If value equals to 0 this parameter is ignored and ntree_end
equal to tree_count_.
thread_count : int (default=-1)
The number of threads to use when applying the model.
Allows you to optimize the speed of execution. This
parameter doesn't affect results.
If -1, then the number of threads is set to the number of
CPU cores.
verbose : bool, optional (default=False)
If True, writes the evaluation metric measured set to stderr.
Returns
-------
prediction:
If data is for a single object, the return value depends on
prediction_type value:
- 'RawFormulaVal' : return raw formula value.
- 'Class' : return class label.
- 'Probability' : return one-dimensional numpy.ndarray
with probability for every class.
- 'LogProbability' : return one-dimensional numpy.
ndarray with
log probability for every class.
otherwise numpy.ndarray, with values that depend on
prediction_type value:
- 'RawFormulaVal' : one-dimensional array of raw formula
value for each object.
- 'Class' : one-dimensional array of class label for each
object.
- 'Probability' : two-dimensional numpy.ndarray with
shape (number_of_objects x number_of_classes)
with probability for every class for each object.
- 'LogProbability' : two-dimensional numpy.ndarray with
shape (number_of_objects x number_of_classes)
with log probability for every class for each object.
"""
return self._predict(data, prediction_type, ntree_start,
ntree_end, thread_count, verbose, 'predict')
def predict_proba(self, data, ntree_start=0, ntree_end=0,
thread_count=-1, verbose=None):
"""
Predict class probability with data.
Parameters
----------
data : catboost.Pool or list of features or list of lists or numpy.
ndarray or pandas.DataFrame or pandas.Series
or catboost.FeaturesData
Data to apply model on.
If data is a simple list (not list of lists) or a one-dimensional
numpy.ndarray it is interpreted
as a list of features for a single object.
ntree_start: int, optional (default=0)
Model is applied on the interval [ntree_start, ntree_end)
(zero-based indexing).
ntree_end: int, optional (default=0)
Model is applied on the interval [ntree_start, ntree_end)
(zero-based indexing).
If value equals to 0 this parameter is ignored and ntree_end
equal to tree_count_.
thread_count : int (default=-1)
The number of threads to use when applying the model.
Allows you to optimize the speed of execution. This
parameter doesn't affect results.
If -1, then the number of threads is set to the number of
CPU cores.
verbose : bool
If True, writes the evaluation metric measured set to stderr.
Returns
-------
prediction :
If data is for a single object
return one-dimensional numpy.ndarray with probability
for every class.
otherwise
return two-dimensional numpy.ndarray with shape
(number_of_objects x number_of_classes)
with probability for every class for each object.
"""
return self._predict(data, 'Probability', ntree_start, ntree_end,
thread_count, verbose, 'predict_proba')
def predict_log_proba(self, data, ntree_start=0, ntree_end=0,
thread_count=-1, verbose=None):
"""
Predict class log probability with data.
Parameters
----------
data : catboost.Pool or list of features or list of lists or numpy.
ndarray or pandas.DataFrame or pandas.Series
or catboost.FeaturesData
Data to apply model on.
If data is a simple list (not list of lists) or a one-dimensional
numpy.ndarray it is interpreted
as a list of features for a single object.
ntree_start: int, optional (default=0)
Model is applied on the interval [ntree_start, ntree_end)
(zero-based indexing).
ntree_end: int, optional (default=0)
Model is applied on the interval [ntree_start, ntree_end)
(zero-based indexing).
If value equals to 0 this parameter is ignored and ntree_end
equal to tree_count_.
thread_count : int (default=-1)
The number of threads to use when applying the model.
Allows you to optimize the speed of execution. This
parameter doesn't affect results.
If -1, then the number of threads is set to the number of
CPU cores.
verbose : bool
If True, writes the evaluation metric measured set to stderr.
Returns
-------
prediction :
If data is for a single object
return one-dimensional numpy.ndarray with log
probability for every class.
otherwise
return two-dimensional numpy.ndarray with shape
(number_of_objects x number_of_classes)
with log probability for every class for each object.
"""
return self._predict(data, 'LogProbability', ntree_start,
ntree_end, thread_count, verbose, 'predict_log_proba')
def staged_predict(self, data, prediction_type='Class',
ntree_start=0, ntree_end=0, eval_period=1, thread_count=-1,
verbose=None):
"""
Predict target at each stage for data.
Parameters
----------
data : catboost.Pool or list of features or list of lists or numpy.
ndarray or pandas.DataFrame or pandas.Series
or catboost.FeaturesData
Data to apply model on.
If data is a simple list (not list of lists) or a one-dimensional
numpy.ndarray it is interpreted
as a list of features for a single object.
prediction_type : string, optional (default='Class')
Can be:
- 'RawFormulaVal' : return raw formula value.
- 'Class' : return class label.
- 'Probability' : return probability for every class.
- 'LogProbability' : return log probability for every class.
ntree_start: int, optional (default=0)
Model is applied on the interval [ntree_start, ntree_end)
with the step eval_period (zero-based indexing).
ntree_end: int, optional (default=0)
Model is applied on the interval [ntree_start, ntree_end)
with the step eval_period (zero-based indexing).
If value equals to 0 this parameter is ignored and ntree_end
equal to tree_count_.
eval_period: int, optional (default=1)
Model is applied on the interval [ntree_start, ntree_end)
with the step eval_period (zero-based indexing).
thread_count : int (default=-1)
The number of threads to use when applying the model.
Allows you to optimize the speed of execution. This
parameter doesn't affect results.
If -1, then the number of threads is set to the number of
CPU cores.
verbose : bool
If True, writes the evaluation metric measured set to stderr.
Returns
-------
prediction : generator for each iteration that generates:
If data is for a single object, the return value depends on
prediction_type value:
- 'RawFormulaVal' : return raw formula value.
- 'Class' : return majority vote class.
- 'Probability' : return one-dimensional numpy.ndarray
with probability for every class.
- 'LogProbability' : return one-dimensional numpy.
ndarray with
log probability for every class.
otherwise numpy.ndarray, with values that depend on
prediction_type value:
- 'RawFormulaVal' : one-dimensional array of raw formula
value for each object.
- 'Class' : one-dimensional array of class label for each
object.
- 'Probability' : two-dimensional numpy.ndarray with
shape (number_of_objects x number_of_classes)
with probability for every class for each object.
- 'LogProbability' : two-dimensional numpy.ndarray with
shape (number_of_objects x number_of_classes)
with log probability for every class for each object.
"""
return self._staged_predict(data, prediction_type, ntree_start,
ntree_end, eval_period, thread_count, verbose, 'staged_predict')
def staged_predict_proba(self, data, ntree_start=0,
ntree_end=0, eval_period=1, thread_count=-1, verbose=None):
"""
Predict classification target at each stage for data.
Parameters
----------
data : catboost.Pool or list of features or list of lists or numpy.
ndarray or pandas.DataFrame or pandas.Series
or catboost.FeaturesData
Data to apply model on.
If data is a simple list (not list of lists) or a one-dimensional
numpy.ndarray it is interpreted
as a list of features for a single object.
ntree_start: int, optional (default=0)
Model is applied on the interval [ntree_start, ntree_end)
with the step eval_period (zero-based indexing).
ntree_end: int, optional (default=0)
Model is applied on the interval [ntree_start, ntree_end)
with the step eval_period (zero-based indexing).
If value equals to 0 this parameter is ignored and ntree_end
equal to tree_count_.
eval_period: int, optional (default=1)
Model is applied on the interval [ntree_start, ntree_end)
with the step eval_period (zero-based indexing).
thread_count : int (default=-1)
The number of threads to use when applying the model.
Allows you to optimize the speed of execution. This
parameter doesn't affect results.
If -1, then the number of threads is set to the number of
CPU cores.
verbose : bool
If True, writes the evaluation metric measured set to stderr.
Returns
-------
prediction : generator for each iteration that generates:
If data is for a single object
return one-dimensional numpy.ndarray with probability
for every class.
otherwise
return two-dimensional numpy.ndarray with shape
(number_of_objects x number_of_classes)
with probability for every class for each object.
"""
return self._staged_predict(data, 'Probability', ntree_start,
ntree_end, eval_period, thread_count, verbose,
'staged_predict_proba')
def staged_predict_log_proba(self, data, ntree_start=0,
ntree_end=0, eval_period=1, thread_count=-1, verbose=None):
"""
Predict classification target at each stage for data.
Parameters
----------
data : catboost.Pool or list of features or list of lists or numpy.
ndarray or pandas.DataFrame or pandas.Series
or catboost.FeaturesData
Data to apply model on.
If data is a simple list (not list of lists) or a one-dimensional
numpy.ndarray it is interpreted
as a list of features for a single object.
ntree_start: int, optional (default=0)
Model is applied on the interval [ntree_start, ntree_end)
with the step eval_period (zero-based indexing).
ntree_end: int, optional (default=0)
Model is applied on the interval [ntree_start, ntree_end)
with the step eval_period (zero-based indexing).
If value equals to 0 this parameter is ignored and ntree_end
equal to tree_count_.
eval_period: int, optional (default=1)
Model is applied on the interval [ntree_start, ntree_end)
with the step eval_period (zero-based indexing).
thread_count : int (default=-1)
The number of threads to use when applying the model.
Allows you to optimize the speed of execution. This
parameter doesn't affect results.
If -1, then the number of threads is set to the number of
CPU cores.
verbose : bool
If True, writes the evaluation metric measured set to stderr.
Returns
-------
prediction : generator for each iteration that generates:
If data is for a single object
return one-dimensional numpy.ndarray with log
probability for every class.
otherwise
return two-dimensional numpy.ndarray with shape
(number_of_objects x number_of_classes)
with log probability for every class for each object.
"""
return self._staged_predict(data, 'LogProbability', ntree_start,
ntree_end, eval_period, thread_count, verbose,
'staged_predict_log_proba')
def score(self, X, y=None):
"""
Calculate accuracy.
Parameters
----------
X : catboost.Pool or list or numpy.ndarray or pandas.
DataFrame or pandas.Series
Data to apply model on.
y : list or numpy.ndarray
True labels.
Returns
-------
accuracy : float
"""
if isinstance(X, Pool):
if y is not None:
raise CatBoostError("Wrong initializing y: X is catboost.
Pool object, y must be initialized inside catboost.Pool.")
y = X.get_label()
if y is None:
raise CatBoostError("Label in X has not initialized.")
if isinstance(y, DataFrame):
if len(y.columns) != 1:
raise CatBoostError("y is DataFrame and has {} columns,
but must have exactly one.".format(len(y.columns)))
y = y[y.columns[0]]
elif y is None:
raise CatBoostError("y should be specified.")
y = np.array(y)
predicted_classes = self._predict(X, prediction_type='Class',
ntree_start=0, ntree_end=0, thread_count=-1, verbose=None,
parent_method_name='score').reshape(-1)
if np.issubdtype(predicted_classes.dtype, np.number):
if np.issubdtype(y.dtype, np.character):
raise CatBoostError('predicted classes have numeric type
but specified y contains strings')
elif np.issubdtype(y.dtype, np.number):
raise CatBoostError('predicted classes have string type but
specified y is numeric')
elif np.issubdtype(y.dtype, np.bool_):
raise CatBoostError('predicted classes have string type but
specified y is boolean')
return np.mean(np.array(predicted_classes) == np.array(y))
def _check_is_classification_objective(self, loss_function):
if isinstance(loss_function, str) and not self.
_is_classification_objective(loss_function):
raise CatBoostError(
"Invalid loss_function='{}': for classifier use "
"Logloss, CrossEntropy, MultiClass, MultiClassOneVsAll
or custom objective object".
format(loss_function)) 赞 (0)