def create(dataset,
target,
features=None,
max_iterations=10,
network=None,
validation_set='auto',
verbose=True,
class_weights=None,
**kwargs):
"""
Create a :class:`~graphlab.neuralnet_classifier.NeuralNetClassifier` to predict the
class of data with numerical features or image data features.
The optional ``network`` parameter accepts a
:class:`~graphlab.deeplearning.NeuralNet` object, which defines the
neural network architecture and learning parameters. It is the most
important parameter in the model learning process; we recommended starting
with the default architecture returned by
:func:`deeplearning.create() <graphlab.deeplearning.create>`,
then tuning it to best fit your task.
**Multiple GPU Support**
The creation of neurlanet classifier takes advantage of multiple
GPU devices. By default, it uses all the GPUs that are detected.
You can change the default behavior by setting the runtime config
"GRAPHLAB_NEURALNET_DEFAULT_GPU_DEVICE_ID". For instance,
the following code sets the default to only use device 0 and 2.
.. sourcecode:: python
graphlab.set_runtime_config("GRAPHLAB_NEURALNET_DEFAULT_GPU_DEVICE_ID", "0,2")
.. note::
If there is an imbalance in GPU's, where one GPU is slower than another,
then the faster GPU will end up waiting on the slower GPU.
Parameters
----------
dataset : SFrame
A training dataset, containing feature columns and a target column. If
the feature column is of type graphlab.Image, all images must be of the
same size.
target : str
The name of the column in ``dataset`` that is the prediction target. The
values in this column represent classes, and must be of integer or string
type.
features : list[str], optional
Column names of the features used to train the model. Each column must
contain vectors of floats or there can be one column of Image type.
The default argument is ``None``, which means all columns are used,
except the target.
max_iterations : int, optional
The maximum number of iterations for boosting.
network : :py:class:`~graphlab.deeplearning.NeuralNet`, optional
The :py:class:`~graphlab.deeplearning.NeuralNet` object contains model
learning parameters and definitions for the network architecture. The
default is ``None``, but we recommend using
:py:func:`deeplearning.create() <graphlab.deeplearning.create>` to find
a default structure for the input data. Because this default structure
may be suboptimal, tuning the
:py:class:`~graphlab.deeplearning.NeuralNet` is highly recommended.
validation_set : SFrame, optional
A dataset for monitoring the models generalization performance.
For each row of the progress table, the chosen metrics are computed
for both the provided training dataset and the validation_set. The
format of this SFrame must be the same as the training set.
By default this argument is set to 'auto' and a validation set is
automatically sampled and used for progress printing. If
validation_set is set to None, then no additional metrics
are computed. This is computed once per full iteration.
The default value is 'auto'.
class_weights : {dict, 'auto'}, optional
Weights the examples in the training data according to the given class
weights. If set to 'None', all classes will be weighted equally. The
`auto` mode set the class weight to be inversely proportional to number
of examples in the training data with the given class. If providing
custom class weights, all classes must be present in the dictionary.
kwargs : dict, optional
Additional arguments for training the neural network. All of the
parameters listed below can be stored in the ``params`` attribute of a
NeuralNet object. If the same parameter is set in both places, the one
in the create function overrides the one in the NeuralNet object.
- ``batch_size`` : int, default 100
The SGD mini batch size. Larger batch_size will improve per
iteration speed, but costs more (GPU) or CPU memory.
- ``model_checkpoint_path`` : str, default ""
If specified, save the model to the given path every n iterations,
where n is specified by model_checkpoint_interval.
- ``model_checkpoint_interval`` : int, default 5
If model_check_point_path is specified,
save the model to the given path every n iterations.
- ``mean_image`` : graphlab.image.Image, default None
If set and subtract_mean is True, use the provided mean image
to save computation time.
- ``metric`` : {'accuracy', 'error', 'recall@5', ...} , default auto
The metric(s) used for evaluating training and validation data.
To evaluate on multiple metrics, supply a list of metric strings,
e.g. ['accuracy', 'recall@5'], or use a comma separated string
e.g. 'accuracy,recall@5'.
- ``subtract_mean`` : bool, default True
If true, subtract the mean from each image. Calculate the mean image
from the training data or use the provided mean_image. Subtracting the mean
centers the input data, which helps accelarate neural net training.
- ``random_crop`` : bool, default False
If true, apply random crop to the input images. The cropped
image size is defined by the ``input_shape`` parameter below.
Random cropping helps prevent the model
from overfitting by augmenting the dataset.
- ``input_shape`` : str, default None
A formated string in the form of channels,width,height, e.g "1,28,28"
or "3,256,256", indicating the shape of the image after random cropping.
The ``input_shape`` cannot exceed the shape of the original image size.
- ``random_mirror`` : bool, default False
If true, apply random mirror to the input images.Random mirroring helps
prevent the model from overfitting by augmenting the dataset.
- ``learning_rate`` : float, default 0.001
The learning_rate for bias and weights.
- ``momentum`` : float between [0, 1], default 0.9
The momentum for bias and weights.
- ``l2_regularization`` : float, default 0.0005
L2 regularization for weights.
- ``bias_learning_rate`` : float, default unused
Specify the learning rate for bias, overriding `learning_rate`.
- ``bias_momentum`` : float, default unused
Specify the momentum for bias, overriding `momentum`.
- ``bias_l2_regularization`` : float, default 0.0
The L2 regularization for bias.
- ``learning_rate_schedule`` : {'constant', 'exponential_decay', 'polynomial_decay'}
Learning rate scheduling algorithm.
- `constant`: Use the same learning rate for all iterations
- `exponential_decay`: Exponentially decreases the learning rate over
iterations. See the notes section for more details.
- `polynomial_decay`: Polynomially decreases the learning rate over
iterations. See the notes section for more details.
- ``learning_rate_start_epoch`` : int, default 0
start learning rate scheduling after epoch
- ``min_learning_rate`` : float, default 0.00001
minimum of learning rate
- ``learning_rate_step`` : int, default 1
update the learning rate every learning_rate_step number of epochs.
- ``learning_rate_gamma`` : float, default 0.1.
learning decay param used in 'exponential_decay'
- ``learning_rate_alpha`` : float, default 0.5
learning decay param used in 'polynomial_decay'
- ``init_random`` : {'gaussian' | 'xavier'}, default 'gaussian'
The type of initialization for the weights. Either uses
random gaussian initialization or Xavier initialization.
See :py:class:`~graphlab.deeplearning.layers.FullConnectionLayer`
paramters for more information.
- ``init_sigma`` : float, default 0.01
The standard deviation of the gaussian distribution weight initializations
are drawn from.
- ``init_bias`` : float, default 0.0
The initial value of the biases.
- ``divideby`` : float, default 1.0
The value by which to scale the input data before it is inserted into
the network.
Returns
-------
out : NeuralNetClassifier
See Also
--------
NeuralNetClassifier, graphlab.deeplearning.NeuralNet
Notes
-----
For exponential_decay, the learning rate decreases exponentially according
to the following:
.. math::
new\_lr = lr * lr\_gamma^{epoch/ lr\_step}
For polynomial_decay, the learning rate decreases polynomially according to
the following:
.. math::
new\_lr = lr * (1 + (epoch/lr\_step)*lr\_gamma)^{-lr\_alpha}
References
----------
- LeCun, Yann, et al. `Gradient-based learning applied to document
recognition. <http://yann.lecun.com/exdb/publis/pdf/lecun-01a.pdf>`_
Proceedings of the IEEE 86.11 (1998): 2278-2324.
- `The MNIST database <http://yann.lecun.com/exdb/mnist/>`_
Examples
--------
We train a convolutional neural network for digits recognition, using the
MNIST data. The data has already been downloaded from the MNIST database,
and saved as SFrames in the Turi's public S3 bucket.
>>> data = graphlab.SFrame('https://static.turi.com/datasets/mnist/sframe/train')
>>> test_data = graphlab.SFrame('https://static.turi.com/datasets/mnist/sframe/test')
>>> training_data, validation_data = data.random_split(0.8)
Resize all the images to the same size, since neural nets have fixed
input size.
>>> training_data['image'] = graphlab.image_analysis.resize(training_data['image'], 28, 28, 1, decode=True)
>>> validation_data['image'] = graphlab.image_analysis.resize(validation_data['image'], 28, 28, 1, decode=True)
>>> test_data['image'] = graphlab.image_analysis.resize(test_data['image'], 28, 28, 1, decode=True)
Use the built-in NeuralNet architecture for MNIST (a one layer convolutional
neural network):
>>> net = graphlab.deeplearning.get_builtin_neuralnet('mnist')
Layers of the neural network:
>>> net.layers
layer[0]: ConvolutionLayer
padding = 1
stride = 2
random_type = xavier
num_channels = 32
kernel_size = 3
layer[1]: MaxPoolingLayer
stride = 2
kernel_size = 3
layer[2]: FlattenLayer
layer[3]: DropoutLayer
threshold = 0.5
layer[4]: FullConnectionLayer
init_sigma = 0.01
num_hidden_units = 100
layer[5]: SigmoidLayer
layer[6]: FullConnectionLayer
init_sigma = 0.01
num_hidden_units = 10
layer[7]: SoftmaxLayer
Parameters of the neural network:
>>> net.params
{'batch_size': 100,
'data_shape': '1,28,28',
'divideby': 255,
'init_random': 'gaussian',
'l2_regularization': 0.0,
'learning_rate': 0.1,
'momentum': 0.9}
Train a NeuralNetClassifier using the specified network:
>>> m = graphlab.neuralnet_classifier.create(training_data, target='label',
... network = net,
... validation_set=validation_data,
... metric=['accuracy', 'recall@2'],
... max_iterations=3)
Classify the test data, and output the most likely class label.'probability'
corresponds to the probability that the input belongs to that class:
>>> pred = m.classify(test_data)
>>> pred
+--------+-------+----------------+
| row_id | class | probability |
+--------+-------+----------------+
| 0 | 0 | 0.998417854309 |
| 1 | 0 | 0.999230742455 |
| 2 | 0 | 0.999326109886 |
| 3 | 0 | 0.997855246067 |
| 4 | 0 | 0.997171103954 |
| 5 | 0 | 0.996235311031 |
| 6 | 0 | 0.999143242836 |
| 7 | 0 | 0.999519705772 |
| 8 | 0 | 0.999182283878 |
| 9 | 0 | 0.999905228615 |
| ... | ... | ... |
+--------+-------+----------------+
[10000 rows x 3 columns]
Predict the top 2 most likely digits:
>>> pred_top2 = m.predict_topk(test_data, k=2)
>>> pred_top2
+--------+-------+-------------------+
| row_id | class | probability |
+--------+-------+-------------------+
| 0 | 0 | 0.998417854309 |
| 0 | 6 | 0.000686840794515 |
| 1 | 0 | 0.999230742455 |
| 1 | 2 | 0.000284609268419 |
| 2 | 0 | 0.999326109886 |
| 2 | 8 | 0.000261707202299 |
| 3 | 0 | 0.997855246067 |
| 3 | 8 | 0.00118813838344 |
| 4 | 0 | 0.997171103954 |
| 4 | 6 | 0.00115600414574 |
| ... | ... | ... |
+--------+-------+-------------------+
[20000 rows x 3 columns]
Evaluate the classifier on the test data. Default metrics are `accuracy` and
`confusion_matrix`.
>>> eval_ = m.evaluate(test_data)
>>> eval_
{'accuracy': 0.979200005531311, 'confusion_matrix':
+--------------+-----------------+-------+
| target_label | predicted_label | count |
+--------------+-----------------+-------+
| 0 | 0 | 969 |
| 2 | 0 | 2 |
| 5 | 0 | 2 |
| 6 | 0 | 9 |
| 7 | 0 | 1 |
| 9 | 0 | 2 |
| 1 | 1 | 1126 |
| 2 | 1 | 2 |
| 6 | 1 | 2 |
| 7 | 1 | 3 |
| ... | ... | ... |
+--------------+-----------------+-------+
[64 rows x 3 columns]}
"""
_mt._get_metric_tracker().track(
'toolkit.classifier.neuralnet_classifier.create')
# check user input: "features"
if features is not None:
if not isinstance(features, list):
raise TypeError('argument features must be a list')
else:
for f in features:
if not f in dataset.column_names():
raise RuntimeError(
'Feature column %s not found in dataset' % f)
# use the default network
if network is None:
_network = _deeplearning.create(dataset, target, features)
else:
# use user specified network
_network = _copy(network)
input_shape = _deeplearning._get_input_shape(dataset, target, features)
output_shape = _deeplearning._get_output_shape(dataset[target])
# verify the netwo
_network.verify()
try:
_network.verify(input_shape, output_shape)
except ValueError as e:
msg = ("The input network is valid, but is not compatible with "
"the input and output shape of the dataset.\n"
"Please change the network and use net.verify() with "
"input_shape=%s, and output_shape=%s" %
(str(input_shape), str(output_shape)))
raise ValueError(e.message + "\n" + msg)
# update network params
for k, v in _six.iteritems(kwargs.copy()):
if type(v) in [int, float, bool, str, _Image]:
if k in _network.params:
_logging.getLogger(__name__).warning(
"Network parameter %s is overwritten by argument in create()"
% k)
if type(v) is bool:
v = int(v)
_network.params[k] = v
elif k == 'metric' and type(v) is list:
v = ','.join(v)
_network.params[k] = v
if (verbose):
print("Using network:\n%s" % _network.__str__())
model_name_str = 'neuralnet_classifier_v2'
model = _sl.create(dataset=dataset,
target=target,
features=features,
model_name=model_name_str,
validation_set=validation_set,
verbose=verbose,
neuralnet_conf=_network.__config_str__(),
_model_constructor=_cloudpickle.dumps(
NeuralNetClassifier(None)._get_wrapper()),
max_iterations=max_iterations,
class_weights=class_weights,
**(_network.params))
return NeuralNetClassifier(model.__proxy__)
def create(dataset, target,
features=None, num_trees=10,
validation_set=None,
verbose=True,
random_seed = None,
**kwargs):
"""
Create a :class:`~graphlab.random_forest_regression.RandomForestRegression` to predict
a scalar target variable using one or more features. In addition to standard
numeric and categorical types, features can also be extracted automatically
from list- or dictionary-type SFrame columns.
Parameters
----------
dataset : SFrame
A training dataset containing feature columns and a target column.
Only numerical typed (int, float) target column is allowed.
target : str
The name of the column in ``dataset`` that is the prediction target.
This column must have a numeric type.
features : list[str], optional
A list of columns names of features used for training the model.
Defaults to None, using all columns.
num_trees : int, optional
The number of trees to use in the ensemble.
max_depth : float, optional
Maximum depth of a tree.
step_size : float, [0,1], optional
Step size (shrinkage) used in update to prevents overfitting. It
shrinks the prediction of each weak learner to make the
process more conservative. The smaller, the more conservative the
algorithm will be. Smaller step_size is usually used together with
larger num_trees.
min_loss_reduction : float, optional
Minimum loss reduction required to make a further partition on a
leaf node of the tree. The larger it is, the more conservative the
algorithm will be.
min_child_weight : float, optional
This controls number of instances needed at least for each leaf.
The larger it is, the more conservative the algorithm will be. Set
it larger when you want to prevent overfitting. Formally, this is
minimum sum of instance weight(hessian) in each leaf. If the tree
partition step results in a leaf node with the sum of instance
weight less than min_child_weight, then the building process will
give up further partitioning. For a regression task, this simply
corresponds to minimum number of instances needed to be in each
node.
row_subsample : float, optional
Subsample the ratio of the training set in each iteration of tree
construction. This is called the bagging trick and usually can help
prevent overfitting. Setting it to 0.5 means that model randomly
collected half of the examples (rows) to grow each tree.
column_subsample : float, optional
Subsample ratio of the columns in each iteration of tree
construction. Like row_subsample, this also usually can help
prevent overfitting. Setting it to 0.5 means that model randomly
collected half of the columns to grow each tree.
validation_set : SFrame, optional
A dataset for monitoring the model's generalization performance.
For each row of the progress table, the chosen metrics are computed
for both the provided training dataset and the validation_set. The
format of this SFrame must be the same as the training set.
By default this argument is set to 'auto' and a validation set is
automatically sampled and used for progress printing. If
validation_set is set to None, then no additional metrics
are computed. This is computed once per full iteration. Large
differences in model accuracy between the training data and validation
data is indicative of overfitting. The default value is 'auto'.
verbose : boolean, optional
If True, print progress information during training.
random_seed : int, optional
Seeds random opertations such as column and row subsampling, such that
results are reproducable.
Returns
-------
out : RandomForestRegression
A trained random forest model for regression tasks.
References
----------
- `Trevor Hastie's slides on Boosted Trees and Random Forest
<http://jessica2.msri.org/attachments/10778/10778-boost.pdf>`_
See Also
--------
RandomForestRegression, graphlab.linear_regression.LinearRegression, graphlab.regression.create
Examples
--------
Setup the data:
>>> url = 'http://s3.amazonaws.com/gl-testdata/xgboost/mushroom.csv'
>>> data = graphlab.SFrame.read_csv(url)
>>> data['label'] = data['label'] == 'p'
Split the data into training and test data:
>>> train, test = data.random_split(0.8)
Create the model:
>>> model = graphlab.random_forest_regression.create(train, target='label')
Make predictions and evaluate the model:
>>> predictions = model.predict(test)
>>> results = model.evaluate(test)
"""
if random_seed is not None:
kwargs['random_seed'] = random_seed
_mt._get_metric_tracker().track('toolkit.regression.random_forest_regression.create')
model = _sl.create(dataset = dataset,
target = target,
features = features,
model_name = 'random_forest_regression',
num_trees = num_trees,
validation_set = validation_set,
verbose = verbose, **kwargs)
return RandomForestRegression(model.__proxy__)
def create(dataset, target, features=None,
l2_penalty=0.01, l1_penalty=0.0,
solver='auto', feature_rescaling=True,
convergence_threshold = _DEFAULT_SOLVER_OPTIONS['convergence_threshold'],
step_size = _DEFAULT_SOLVER_OPTIONS['step_size'],
lbfgs_memory_level = _DEFAULT_SOLVER_OPTIONS['lbfgs_memory_level'],
max_iterations = _DEFAULT_SOLVER_OPTIONS['max_iterations'],
class_weights = None,
validation_set = 'auto',
verbose=True):
"""
Create a :class:`~graphlab.logistic_classifier.LogisticClassifier` (using
logistic regression as a classifier) to predict the class of a discrete
target variable (binary or multiclass) based on a model of class probability
as a logistic function of a linear combination of the features. In addition
to standard numeric and categorical types, features can also be extracted
automatically from list or dictionary-type SFrame columns.
This model can be regularized with an l1 penalty, an l2 penalty, or both. By
default this model has an l2 regularization weight of 0.01.
Parameters
----------
dataset : SFrame
Dataset for training the model.
target : string
Name of the column containing the target variable. The values in this
column must be of string or integer type. String target variables are
automatically mapped to integers in the order in which they are provided.
For example, a target variable with 'cat' and 'dog' as possible
values is mapped to 0 and 1 respectively with 0 being the base class
and 1 being the reference class. Use `model['classes']` to retrieve
the order in which the classes are mapped.
features : list[string], optional
Names of the columns containing features. 'None' (the default) indicates
that all columns except the target variable should be used as features.
The features are columns in the input SFrame that can be of the
following types:
- *Numeric*: values of numeric type integer or float.
- *Categorical*: values of type string.
- *Array*: list of numeric (integer or float) values. Each list element
is treated as a separate feature in the model.
- *Dictionary*: key-value pairs with numeric (integer or float) values
Each key of a dictionary is treated as a separate feature and the
value in the dictionary corresponds to the value of the feature.
Dictionaries are ideal for representing sparse data.
Columns of type *list* are not supported. Convert such feature
columns to type array if all entries in the list are of numeric
types. If the lists contain data of mixed types, separate
them out into different columns.
l2_penalty : float, optional
Weight on l2 regularization of the model. The larger this weight, the
more the model coefficients shrink toward 0. This introduces bias into
the model but decreases variance, potentially leading to better
predictions. The default value is 0.01; setting this parameter to 0
corresponds to unregularized logistic regression. See the ridge
regression reference for more detail.
l1_penalty : float, optional
Weight on l1 regularization of the model. Like the l2 penalty, the
higher the l1 penalty, the more the estimated coefficients shrink toward
0. The l1 penalty, however, completely zeros out sufficiently small
coefficients, automatically indicating features that are not useful
for the model. The default weight of 0 prevents any features from
being discarded. See the LASSO regression reference for more detail.
solver : string, optional
Name of the solver to be used to solve the regression. See the
references for more detail on each solver. Available solvers are:
- *auto (default)*: automatically chooses the best solver for the data
and model parameters.
- *newton*: Newton-Raphson
- *lbfgs*: limited memory BFGS
- *fista*: accelerated gradient descent
For this model, the Newton-Raphson method is equivalent to the
iteratively re-weighted least squares algorithm. If the l1_penalty is
greater than 0, use the 'fista' solver.
The model is trained using a carefully engineered collection of methods
that are automatically picked based on the input data. The ``newton``
method works best for datasets with plenty of examples and few features
(long datasets). Limited memory BFGS (``lbfgs``) is a robust solver for
wide datasets (i.e datasets with many coefficients). ``fista`` is the
default solver for l1-regularized linear regression. The solvers are all
automatically tuned and the default options should function well. See
the solver options guide for setting additional parameters for each of
the solvers.
feature_rescaling : boolean, optional
Feature rescaling is an important pre-processing step that ensures that
all features are on the same scale. An l2-norm rescaling is performed
to make sure that all features are of the same norm. Categorical
features are also rescaled by rescaling the dummy variables that are
used to represent them. The coefficients are returned in original scale
of the problem. This process is particularly useful when features
vary widely in their ranges.
convergence_threshold : float, optional
Convergence is tested using variation in the training objective. The
variation in the training objective is calculated using the difference
between the objective values between two steps. Consider reducing this
below the default value (0.01) for a more accurately trained model.
Beware of overfitting (i.e a model that works well only on the training
data) if this parameter is set to a very low value.
lbfgs_memory_level : float, optional
The L-BFGS algorithm keeps track of gradient information from the
previous ``lbfgs_memory_level`` iterations. The storage requirement for
each of these gradients is the ``num_coefficients`` in the problem.
Increasing the ``lbfgs_memory_level ``can help improve the quality of
the model trained. Setting this to more than ``max_iterations`` has the
same effect as setting it to ``max_iterations``.
max_iterations : float, optional
The maximum number of allowed passes through the data. More passes over
the data can result in a more accurately trained model. Consider
increasing this (the default value is 10) if the training accuracy is
low and the *Grad-Norm* in the display is large.
step_size : float, optional
The starting step size to use for the ``fista`` and ``gd`` solvers. The
default is set to 1.0, this is an aggressive setting. If the first
iteration takes a considerable amount of time, reducing this parameter
may speed up model training.
class_weights : {dict, `auto`}, optional
Weights the examples in the training data according to the given class
weights. If set to `None`, all classes are supposed to have weight one. The
`auto` mode set the class weight to be inversely proportional to number of
examples in the training data with the given class.
validation_set : SFrame, optional
A dataset for monitoring the model's generalization performance.
For each row of the progress table, the chosen metrics are computed
for both the provided training dataset and the validation_set. The
format of this SFrame must be the same as the training set.
By default this argument is set to 'auto' and a validation set is
automatically sampled and used for progress printing. If
validation_set is set to None, then no additional metrics
are computed. The default value is 'auto'.
verbose : bool, optional
If True, print progress updates.
Returns
-------
out : LogisticClassifier
A trained model of type
:class:`~graphlab.logistic_classifier.LogisticClassifier`.
See Also
--------
LogisticClassifier, graphlab.boosted_trees_classifier.BoostedTreesClassifier,
graphlab.svm_classifier.SVMClassifier, graphlab.classifier.create
Notes
-----
- Categorical variables are encoded by creating dummy variables. For a
variable with :math:`K` categories, the encoding creates :math:`K-1` dummy
variables, while the first category encountered in the data is used as the
baseline.
- For prediction and evaluation of logistic regression models with sparse
dictionary inputs, new keys/columns that were not seen during training
are silently ignored.
- During model creation, 'None' values in the data will result in an error
being thrown.
- A constant term is automatically added for the model intercept. This term
is not regularized.
References
----------
- `Wikipedia - logistic regression
<http://en.wikipedia.org/wiki/Logistic_regression>`_
- Hoerl, A.E. and Kennard, R.W. (1970) `Ridge regression: Biased Estimation
for Nonorthogonal Problems
<http://amstat.tandfonline.com/doi/abs/10.1080/00401706.1970.10488634>`_.
Technometrics 12(1) pp.55-67
- Tibshirani, R. (1996) `Regression Shrinkage and Selection via the Lasso <h
ttp://www.jstor.org/discover/10.2307/2346178?uid=3739256&uid=2&uid=4&sid=2
1104169934983>`_. Journal of the Royal Statistical Society. Series B
(Methodological) 58(1) pp.267-288.
- Zhu, C., et al. (1997) `Algorithm 778: L-BFGS-B: Fortran subroutines for
large-scale bound-constrained optimization
<http://dl.acm.org/citation.cfm?id=279236>`_. ACM Transactions on
Mathematical Software 23(4) pp.550-560.
- Beck, A. and Teboulle, M. (2009) `A Fast Iterative Shrinkage-Thresholding
Algorithm for Linear Inverse Problems
<http://epubs.siam.org/doi/abs/10.1137/080716542>`_. SIAM Journal on
Imaging Sciences 2(1) pp.183-202.
Examples
--------
Given an :class:`~graphlab.SFrame` ``sf``, a list of feature columns
[``feature_1`` ... ``feature_K``], and a target column ``target`` with 0 and
1 values, create a
:class:`~graphlab.logistic_classifier.LogisticClassifier` as follows:
>>> data = graphlab.SFrame('http://s3.amazonaws.com/dato-datasets/regression/houses.csv')
>>> data['is_expensive'] = data['price'] > 30000
>>> model = graphlab.logistic_classifier.create(data, 'is_expensive')
By default all columns of ``data`` except the target are used as features, but
specific feature columns can be specified manually.
>>> model = graphlab.logistic_classifier.create(data, 'is_expensive', ['bedroom', 'size'])
.. sourcecode:: python
# L2 regularizer
>>> model_ridge = graphlab.logistic_classifier.create(data, 'is_expensive', l2_penalty=0.1)
# L1 regularizer
>>> model_lasso = graphlab.logistic_classifier.create(data, 'is_expensive', l2_penalty=0.,
l1_penalty=1.0)
# Both L1 and L2 regularizer
>>> model_enet = graphlab.logistic_classifier.create(data, 'is_expensive', l2_penalty=0.5, l1_penalty=0.5)
"""
_mt._get_metric_tracker().track('toolkit.classifier.logistic_classifier.create')
# Regression model names.
model_name = "classifier_logistic_regression"
solver = solver.lower()
model = _sl.create(dataset, target, model_name, features=features,
validation_set = validation_set, verbose = verbose,
l2_penalty=l2_penalty, l1_penalty = l1_penalty,
feature_rescaling = feature_rescaling,
convergence_threshold = convergence_threshold,
step_size = step_size,
solver = solver,
lbfgs_memory_level = lbfgs_memory_level,
max_iterations = max_iterations,
class_weights = class_weights)
return LogisticClassifier(model.__proxy__)
def create(dataset, target,
features=None, max_iterations=10,
validation_set=None,
verbose=True,
**kwargs):
"""
Create a :class:`~graphlab.boosted_trees_regression.BoostedTreesRegression` to predict
a scalar target variable using one or more features. In addition to standard
numeric and categorical types, features can also be extracted automatically
from list- or dictionary-type SFrame columns.
Parameters
----------
dataset : SFrame
A training dataset containing feature columns and a target column.
Only numerical typed (int, float) target column is allowed.
target : str
The name of the column in ``dataset`` that is the prediction target.
This column must have a numeric type.
features : list[str], optional
A list of columns names of features used for training the model.
Defaults to None, using all columns.
max_iterations : int, optional
The number of iterations for boosting. It is also the number of trees
in the model.
max_depth : float, optional
Maximum depth of a tree.
step_size : float, [0,1], optional
Step size (shrinkage) used in update to prevents overfitting. It
shrinks the prediction of each weak learner to make the boosting
process more conservative. The smaller, the more conservative the
algorithm will be. Smaller step_size is usually used together with
larger max_iterations.
min_loss_reduction : float, optional
Minimum loss reduction required to make a further partition on a
leaf node of the tree. The larger it is, the more conservative the
algorithm will be.
min_child_weight : float, optional
This controls number of instances needed at least for each leaf.
The larger it is, the more conservative the algorithm will be. Set
it larger when you want to prevent overfitting. Formally, this is
minimum sum of instance weight(hessian) in each leaf. If the tree
partition step results in a leaf node with the sum of instance
weight less than min_child_weight, then the building process will
give up further partitioning. For a regression task, this simply
corresponds to minimum number of instances needed to be in each
node.
row_subsample : float, optional
Subsample the ratio of the training set in each iteration of tree
construction. This is called the bagging trick and usually can help
prevent overfitting. Setting it to 0.5 means that model randomly
collected half of the examples (rows) to grow each tree.
column_subsample : float, optional
Subsample ratio of the columns in each iteration of tree
construction. Like row_subsample, this also usually can help
prevent overfitting. Setting it to 0.5 means that model randomly
collected half of the columns to grow each tree.
validation_set : SFrame, optional
The validation set that is used to watch the validation result as
boosting progress.
verbose : boolean, optional
If True, print progress information during training.
Returns
-------
out : BoostedTreesRegression
A trained gradient boosted trees model
References
----------
- `Wikipedia - Gradient tree boosting
<http://en.wikipedia.org/wiki/Gradient_boosting#Gradient_tree_boosting>`_
- `Trevor Hastie's slides on Boosted Trees and Random Forest
<http://jessica2.msri.org/attachments/10778/10778-boost.pdf>`_
See Also
--------
BoostedTreesRegression, graphlab.linear_regression.LinearRegression, graphlab.regression.create
Examples
--------
Setup the data:
>>> url = 'http://s3.amazonaws.com/gl-testdata/xgboost/mushroom.csv'
>>> data = graphlab.SFrame.read_csv(url)
>>> data['label'] = data['label'] == 'p'
Split the data into training and test data:
>>> train, test = data.random_split(0.8)
Create the model:
>>> model = graphlab.boosted_trees_regression.create(train, target='label')
Make predictions and evaluate the model:
>>> predictions = model.predict(test)
>>> results = model.evaluate(test)
"""
_mt._get_metric_tracker().track('toolkit.regression.boosted_trees_regression.create')
model = _sl.create(dataset = dataset,
target = target,
features = features,
model_name = 'boosted_trees_regression',
max_iterations = max_iterations,
validation_set = validation_set,
verbose = verbose, **kwargs)
return BoostedTreesRegression(model.__proxy__)
def create(dataset,
target,
features=None,
max_iterations=10,
validation_set='auto',
max_depth=6,
step_size=0.3,
min_loss_reduction=0.0,
min_child_weight=0.1,
row_subsample=1.0,
column_subsample=1.0,
verbose=True,
random_seed=None,
metric='auto',
**kwargs):
"""
Create a :class:`~graphlab.boosted_trees_regression.BoostedTreesRegression` to predict
a scalar target variable using one or more features. In addition to standard
numeric and categorical types, features can also be extracted automatically
from list- or dictionary-type SFrame columns.
Parameters
----------
dataset : SFrame
A training dataset containing feature columns and a target column.
Only numerical typed (int, float) target column is allowed.
target : str
The name of the column in ``dataset`` that is the prediction target.
This column must have a numeric type.
features : list[str], optional
A list of columns names of features used for training the model.
Defaults to None, using all columns.
max_iterations : int, optional
The number of iterations for boosting. It is also the number of trees
in the model.
validation_set : SFrame, optional
The validation set that is used to watch the validation result as
boosting progress.
max_depth : float, optional
Maximum depth of a tree. Must be at least 1.
step_size : float, [0,1], optional
Step size (shrinkage) used in update to prevents overfitting. It
shrinks the prediction of each weak learner to make the boosting
process more conservative. The smaller, the more conservative the
algorithm will be. Smaller step_size is usually used together with
larger max_iterations.
min_loss_reduction : float, optional (non-negative)
Minimum loss reduction required to make a further partition/split a
node during the tree learning phase. Larger (more positive) values
can help prevent overfitting by avoiding splits that do not
sufficiently reduce the loss function.
min_child_weight : float, optional (non-negative)
Controls the minimum weight of each leaf node. Larger values result in
more conservative tree learning and help prevent overfitting.
Formally, this is minimum sum of instance weights (hessians) in each
node. If the tree learning algorithm results in a leaf node with the
sum of instance weights less than `min_child_weight`, tree building
will terminate.
row_subsample : float, [0,1], optional
Subsample the ratio of the training set in each iteration of tree
construction. This is called the bagging trick and usually can help
prevent overfitting. Setting it to 0.5 means that model randomly
collected half of the examples (rows) to grow each tree.
column_subsample : float, [0,1], optional
Subsample ratio of the columns in each iteration of tree
construction. Like row_subsample, this also usually can help
prevent overfitting. Setting it to 0.5 means that model randomly
collected half of the columns to grow each tree.
verbose : boolean, optional
If True, print progress information during training.
random_seed: int, optional
Seeds random operations such as column and row subsampling, such that
results are reproduceable.
metric : str or list[str], optional
Performance metric(s) that are tracked during training. When specified,
the progress table will display the tracked metric(s) on training and
validation set.
Supported metrics are: {'rmse', 'max_error'}
kwargs : dict, optional
Additional arguments for training the model.
- ``early_stopping_rounds`` : int, default None
If the validation metric does not improve after <early_stopping_rounds>,
stop training and return the best model.
If multiple metrics are being tracked, the last one is used.
- ``model_checkpoint_path`` : str, default None
If specified, checkpoint the model training to the given path every n iterations,
where n is specified by ``model_checkpoint_interval``.
For instance, if `model_checkpoint_interval` is 5, and `model_checkpoint_path` is
set to ``/tmp/model_tmp``, the checkpoints will be saved into
``/tmp/model_tmp/model_checkpoint_5``, ``/tmp/model_tmp/model_checkpoint_10``, ... etc.
Training can be resumed by setting ``resume_from_checkpoint`` to one of these checkpoints.
- ``model_checkpoint_interval`` : int, default 5
If model_check_point_path is specified,
save the model to the given path every n iterations.
- ``resume_from_checkpoint`` : str, default None
Continues training from a model checkpoint. The model must take
exact the same training data as the checkpointed model.
Returns
-------
out : BoostedTreesRegression
A trained gradient boosted trees model
References
----------
- `Wikipedia - Gradient tree boosting
<http://en.wikipedia.org/wiki/Gradient_boosting#Gradient_tree_boosting>`_
- `Trevor Hastie's slides on Boosted Trees and Random Forest
<http://jessica2.msri.org/attachments/10778/10778-boost.pdf>`_
See Also
--------
BoostedTreesRegression, graphlab.linear_regression.LinearRegression, graphlab.regression.create
Examples
--------
Setup the data:
>>> url = 'https://static.turi.com/datasets/xgboost/mushroom.csv'
>>> data = graphlab.SFrame.read_csv(url)
>>> data['label'] = data['label'] == 'p'
Split the data into training and test data:
>>> train, test = data.random_split(0.8)
Create the model:
>>> model = graphlab.boosted_trees_regression.create(train, target='label')
Make predictions and evaluate the model:
>>> predictions = model.predict(test)
>>> results = model.evaluate(test)
"""
_mt._get_metric_tracker().track(
'toolkit.regression.boosted_trees_regression.create')
if random_seed is not None:
kwargs['random_seed'] = random_seed
if 'model_checkpoint_path' in kwargs:
kwargs['model_checkpoint_path'] = _make_internal_url(
kwargs['model_checkpoint_path'])
if 'resume_from_checkpoint' in kwargs:
kwargs['resume_from_checkpoint'] = _make_internal_url(
kwargs['resume_from_checkpoint'])
model = _sl.create(dataset=dataset,
target=target,
features=features,
model_name='boosted_trees_regression',
max_iterations=max_iterations,
validation_set=validation_set,
max_depth=max_depth,
step_size=step_size,
min_loss_reduction=min_loss_reduction,
min_child_weight=min_child_weight,
row_subsample=row_subsample,
column_subsample=column_subsample,
verbose=verbose,
metric=metric,
**kwargs)
return BoostedTreesRegression(model.__proxy__)
def create(dataset, target,
features=None,
max_iterations=10,
network=None,
validation_set='auto',
verbose=True,
**kwargs):
"""
Create a :class:`~graphlab.neuralnet_classifier.NeuralNetClassifier` to predict the
class of data with numerical features or image data features.
The optional ``network`` parameter accepts a
:class:`~graphlab.deeplearning.NeuralNet` object, which defines the
neural network architecture and learning parameters. It is the most
important parameter in the model learning process; we recommended starting
with the default architecture returned by
:func:`deeplearning.create() <graphlab.deeplearning.create>`,
then tuning it to best fit your task.
Multiple GPU Support:
The creation of neurlanet classifier takes advantage of multiple
GPU devices. By default, it uses all the GPUs that are detected.
You can change the default behavior by setting the runtime config
"GRAPHLAB_NEURALNET_DEFAULT_GPU_DEVICE_ID". For instance,
the following code sets the default to only use device 0 and 2.
NOTE: If there is an imbalance in GPU's, where one GPU is slower than another,
then the faster GPU will end up waiting on the slower GPU.
```
graphlab.set_runtime_config("GRAPHLAB_NEURALNET_DEFAULT_GPU_DEVICE_ID", "0,2")
```
Parameters
----------
dataset : SFrame
A training dataset, containing feature columns and a target column. If
the feature column is of type graphlab.Image, all images must be of the
same size.
target : str
The name of the column in ``dataset`` that is the prediction target. The
values in this column represent classes, and must be of integer data
type, and in the range :math:`[0, \mathrm{num\_class} - 1]`.
features : list[str], optional
Column names of the features used to train the model. Each column must
contain vectors of floats or there can be one column of Image type.
The default argument is ``None``, which means all columns are used,
except the target.
max_iterations : int, optional
The maximum number of iterations for boosting.
network : :py:class:`~graphlab.deeplearning.NeuralNet`, optional
The :py:class:`~graphlab.deeplearning.NeuralNet` object contains model
learning parameters and definitions for the network architecture. The
default is ``None``, but we recommend using
:py:func:`deeplearning.create() <graphlab.deeplearning.create>` to find
a default structure for the input data. Because this default structure
may be suboptimal, tuning the
:py:class:`~graphlab.deeplearning.Neuralnet`is highly recommended.
validation_set : SFrame, optional
A dataset for monitoring the model's generalization performance.
For each row of the progress table, the chosen metrics are computed
for both the provided training dataset and the validation_set. The
format of this SFrame must be the same as the training set.
By default this argument is set to 'auto' and a validation set is
automatically sampled and used for progress printing. If
validation_set is set to None, then no additional metrics
are computed. This is computed once per full iteration.
The default value is 'auto'.
kwargs : dict, optional
Additional arguments for training the neural network. All of the
parameters listed below can be stored in the ``params`` attribute of a
NeuralNet object. If the same parameter is set in both places, the one
in the create function overrides the one in the NeuralNet object.
- ``batch_size`` : int, default 100
The SGD mini batch size. Larger batch_size will improve per
iteration speed, but costs more (GPU) or CPU memory.
- ``model_checkpoint_path`` : str, default ""
If specified, save the model to the given path every n iterations,
where n is specified by model_checkpoint_interval.
- ``model_checkpoint_interval`` : int, default 5
If model_check_point_path is specified,
save the model to the given path every n iterations.
- ``mean_image`` : graphlab.image.Image, default None
If set and subtract_mean is True, use the provided mean image
to save computation time.
- ``metric`` : {'accuracy', 'error', 'recall@5`, ...} , default auto
The metric(s) used for evaluating training and validation data.
To evaluate on multiple metrics, supply a list of metric strings,
e.g. ['accuracy', 'recall@5'], or use a comma separated string
e.g. 'accuracy,recall@5'.
- ``subtract_mean`` : bool, default True
If true, subtract the mean from each image. Calculate the mean image
from the training data or use the provided mean_image. Subtracting the mean
centers the input data, which helps accelarate neural net training.
- ``random_crop`` : bool, default False
If true, apply random crop to the input images. The cropped
image size is defined by the ``input_shape`` parameter below.
Random cropping helps prevent the model
from overfitting by augmenting the dataset.
- ``input_shape`` : str, default None
A formated string in the form of channels,width,height, e.g "1,28,28"
or "3,256,256", indicating the shape of the image after random cropping.
The ``input_shape`` cannot exceed the shape of the original image size.
- ``random_mirror`` : bool, default False
If true, apply random mirror to the input images.Random mirroring helps
prevent the model from overfitting by augmenting the dataset.
- ``learning_rate`` : float, default 0.001
The learning_rate for bias and weights.
- ``momentum`` : float between [0, 1], default 0.9
The momentum for bias and weights.
- ``l2_regularization`` : float, default 0.0005
L2 regularization for weights.
- ``bias_learning_rate`` : float, default unused
Specify the learning rate for bias, overriding `learning_rate`.
- ``bias_momentum`` : float, default unused
Specify the momentum for bias, overriding `momentum`.
- ``bias_l2_regularization`` : float, default 0.0
The L2 regularization for bias.
- ``learning_rate_schedule`` : {'constant', 'exponential_decay', 'polynomial_decay'}
Learning rate scheduling algorithm.
- `constant`: Use the same learning rate for all iterations
- `exponential_decay`: Exponentially decreases the learning rate over
iterations. See the notes section for more details.
- `polynomial_decay`: Polynomially decreases the learning rate over
iterations. See the notes section for more details.
- ``learning_rate_start_epoch`` : int, default 0
start learning rate scheduling after epoch
- ``min_learning_rate`` : float, default 0.00001
minimum of learning rate
- ``learning_rate_step`` : int, default 1
update the learning rate every learning_rate_step number of epochs.
- ``learning_rate_gamma`` : float, default 0.1.
learning decay param used in 'exponential_decay'
- ``learning_rate_alpha`` : float, default 0.5
learning decay param used in 'polynomial_decay'
- ``init_random`` : {'gaussian' | 'xavier'}, default 'gaussian'
The type of initialization for the weights. Either uses
random gaussian initialization or Xavier initialization.
See :py:class:`~graphlab.deeplearning.layers.FullConnectionLayer`
paramters for more information.
- ``init_sigma`` : float, default 0.01
The standard deviation of the gaussian distribution weight initializations
are drawn from.
- ``init_bias`` : float, default 0.0
The initial value of the biases.
- ``divideby`` : float, default 1.0
The value by which to scale the input data before it is inserted into
the network.
Returns
-------
out : NeuralNetClassifier
See Also
--------
NeuralNetClassifier, graphlab.deeplearning.NeuralNet
Notes
-----
For exponential_decay, the learning rate decreases
exponentially according to the following:
.. math::
new\_lr = lr * lr\_gamma^{epoch/ lr\_step}
For polynomial_decay, the learning rate decreases
polynomially according to the following:
.. math::
new\_lr = lr * (1 + (epoch/lr\_step)*lr\_gamma)^{-lr\_alpha}
References
----------
- LeCun, Yann, et al. `Gradient-based learning applied to document
recognition. <http://yann.lecun.com/exdb/publis/pdf/lecun-01a.pdf>`_
Proceedings of the IEEE 86.11 (1998): 2278-2324.
- `The MNIST database <http://yann.lecun.com/exdb/mnist/>`_
Examples
--------
We train a convolutional neural network for digits recognition, using the
MNIST data. The data has already been downloaded from the MNIST database,
and saved as SFrames in the Dato's public S3 bucket.
>>> data = graphlab.SFrame('http://s3.amazonaws.com/dato-datasets/mnist/sframe/train')
>>> test_data = graphlab.SFrame('http://s3.amazonaws.com/dato-datasets/mnist/sframe/test')
>>> training_data, validation_data = data.random_split(0.8)
Resize all the images to the same size, since neural nets have fixed
input size.
>>> training_data['image'] = graphlab.image_analysis.resize(training_data['image'], 28, 28, 1)
>>> validation_data['image'] = graphlab.image_analysis.resize(validation_data['image'], 28, 28, 1)
>>> test_data['image'] = graphlab.image_analysis.resize(test_data['image'], 28, 28, 1)
Use the built-in NeuralNet architecture for MNIST (a one layer convolutional
neural network):
>>> net = graphlab.deeplearning.get_builtin_neuralnet('mnist')
Layers of the neural network:
>>> net.layers
layer[0]: ConvolutionLayer
padding = 1
stride = 2
random_type = xavier
num_channels = 32
kernel_size = 3
layer[1]: MaxPoolingLayer
stride = 2
kernel_size = 3
layer[2]: FlattenLayer
layer[3]: DropoutLayer
threshold = 0.5
layer[4]: FullConnectionLayer
init_sigma = 0.01
num_hidden_units = 100
layer[5]: SigmoidLayer
layer[6]: FullConnectionLayer
init_sigma = 0.01
num_hidden_units = 10
layer[7]: SoftmaxLayer
Parameters of the neural network:
>>> net.params
{'batch_size': 100,
'data_shape': '1,28,28',
'divideby': 255,
'init_random': 'gaussian',
'l2_regularization': 0.0,
'learning_rate': 0.1,
'momentum': 0.9}
Train a NeuralNetClassifier using the specified network:
>>> m = graphlab.neuralnet_classifier.create(training_data, target='label',
... network = net,
... validation_set=validation_data,
... metric=['accuracy', 'recall@2'],
... max_iterations=3)
Classify the test data, and output the most likely class label.'probability'
corresponds to the probability that the input belongs to that class:
>>> pred = m.classify(test_data)
>>> pred
+--------+-------+----------------+
| row_id | class | probability |
+--------+-------+----------------+
| 0 | 0 | 0.998417854309 |
| 1 | 0 | 0.999230742455 |
| 2 | 0 | 0.999326109886 |
| 3 | 0 | 0.997855246067 |
| 4 | 0 | 0.997171103954 |
| 5 | 0 | 0.996235311031 |
| 6 | 0 | 0.999143242836 |
| 7 | 0 | 0.999519705772 |
| 8 | 0 | 0.999182283878 |
| 9 | 0 | 0.999905228615 |
| ... | ... | ... |
+--------+-------+----------------+
[10000 rows x 3 columns]
Predict the top 2 most likely digits:
>>> pred_top2 = m.predict_topk(test_data, k=2)
>>> pred_top2
+--------+-------+-------------------+
| row_id | class | probability |
+--------+-------+-------------------+
| 0 | 0 | 0.998417854309 |
| 0 | 6 | 0.000686840794515 |
| 1 | 0 | 0.999230742455 |
| 1 | 2 | 0.000284609268419 |
| 2 | 0 | 0.999326109886 |
| 2 | 8 | 0.000261707202299 |
| 3 | 0 | 0.997855246067 |
| 3 | 8 | 0.00118813838344 |
| 4 | 0 | 0.997171103954 |
| 4 | 6 | 0.00115600414574 |
| ... | ... | ... |
+--------+-------+-------------------+
[20000 rows x 3 columns]
Evaluate the classifier on the test data. Default metrics are `accuracy` and
`confusion_matrix`.
>>> eval_ = m.evaluate(test_data)
>>> eval_
{'accuracy': 0.979200005531311, 'confusion_matrix':
+--------------+-----------------+-------+
| target_label | predicted_label | count |
+--------------+-----------------+-------+
| 0 | 0 | 969 |
| 2 | 0 | 2 |
| 5 | 0 | 2 |
| 6 | 0 | 9 |
| 7 | 0 | 1 |
| 9 | 0 | 2 |
| 1 | 1 | 1126 |
| 2 | 1 | 2 |
| 6 | 1 | 2 |
| 7 | 1 | 3 |
| ... | ... | ... |
+--------------+-----------------+-------+
[64 rows x 3 columns]}
"""
_mt._get_metric_tracker().track('toolkit.classifier.neuralnet_classifier.create')
# check user input: "features"
if features is not None:
if not isinstance(features, list):
raise TypeError('argument features must be a list')
else:
for f in features:
if not f in dataset.column_names():
raise RuntimeError('Feature column %s not found in dataset' % f)
# use the default network
if network is None:
_network = _deeplearning.create(dataset, target, features)
else:
# use user specified network
_network = _copy(network)
input_shape = _deeplearning._get_input_shape(dataset, target, features)
output_shape = _deeplearning._get_output_shape(dataset[target])
# verify the netwo
_network.verify()
try:
_network.verify(input_shape, output_shape)
except ValueError as e:
msg = ("The input network is valid, but is not compatible with "
"the input and output shape of the dataset.\n"
"Please change the network and use net.verify() with "
"input_shape=%s, and output_shape=%s" % (str(input_shape), str(output_shape)))
raise ValueError(e.message + "\n" + msg)
# update network params
for k, v in kwargs.copy().iteritems():
if type(v) in [int, float, bool, str, _Image]:
if k in _network.params:
_logging.getLogger(__name__).warning("Network parameter %s is overwritten by argument in create()" % k)
if type(v) is bool:
v = int(v)
_network.params[k] = v
elif k == 'metric' and type(v) is list:
v = ','.join(v)
_network.params[k] = v
if (verbose):
print "Using network:\n%s" % _network.__str__()
model_name_str = 'neuralnet_classifier_v2'
model = _sl.create(dataset=dataset,
target=target,
features=features,
model_name=model_name_str,
validation_set=validation_set,
verbose=verbose,
neuralnet_conf=_network.__config_str__(),
_model_constructor=_cloudpickle.dumps(NeuralNetClassifier(None)._get_wrapper()),
max_iterations=max_iterations,
**(_network.params))
return NeuralNetClassifier(model.__proxy__)
def create(dataset,
target,
features=None,
max_iterations=10,
validation_set='auto',
verbose=True,
class_weights=None,
random_seed=None,
metric='auto',
**kwargs):
"""
Create a (binary or multi-class) classifier model of type
:class:`~graphlab.random_forest_classifier.RandomForestClassifier` using
an ensemble of decision trees trained on subsets of the data.
Parameters
----------
dataset : SFrame
A training dataset containing feature columns and a target column.
target : str
Name of the column containing the target variable. The values in this
column must be of string or integer type. String target variables are
automatically mapped to integers in alphabetical order of the variable values.
For example, a target variable with 'cat', 'dog', and 'foosa' as possible
values is mapped to 0, 1, and, 2 respectively.
features : list[str], optional
A list of columns names of features used for training the model.
Defaults to None, which uses all columns in the SFrame ``dataset``
excepting the target column..
max_iterations : int, optional
The maximum number of iterations to perform. For multi-class
classification with K classes, each iteration will create K-1 trees.
max_depth : float, optional
Maximum depth of a tree.
class_weights : {dict, `auto`}, optional
Weights the examples in the training data according to the given class
weights. If set to `None`, all classes are supposed to have weight one. The
`auto` mode set the class weight to be inversely proportional to number of
examples in the training data with the given class.
min_loss_reduction : float, optional (non-negative)
Minimum loss reduction required to make a further partition on a
leaf node of the tree. The larger it is, the more conservative the
algorithm will be. Must be non-negative.
min_child_weight : float, optional (non-negative)
Controls the minimum weight of each leaf node. Larger values result in
more conservative tree learning and help prevent overfitting.
Formally, this is minimum sum of instance weights (hessians) in each
node. If the tree learning algorithm results in a leaf node with the
sum of instance weights less than `min_child_weight`, tree building
will terminate.
row_subsample : float, optional
Subsample the ratio of the training set in each iteration of tree
construction. This is called the bagging trick and can usually help
prevent overfitting. Setting this to a value of 0.5 results in the
model randomly sampling half of the examples (rows) to grow each tree.
column_subsample : float, optional
Subsample ratio of the columns in each iteration of tree
construction. Like row_subsample, this can also help prevent
model overfitting. Setting this to a value of 0.5 results in the
model randomly sampling half of the columns to grow each tree.
validation_set : SFrame, optional
A dataset for monitoring the model's generalization performance.
For each row of the progress table, the chosen metrics are computed
for both the provided training dataset and the validation_set. The
format of this SFrame must be the same as the training set.
By default this argument is set to 'auto' and a validation set is
automatically sampled and used for progress printing. If
validation_set is set to None, then no additional metrics
are computed. This is computed once per full iteration. Large
differences in model accuracy between the training data and validation
data is indicative of overfitting. The default value is 'auto'.
verbose : boolean, optional
Print progress information during training (if set to true).
random_seed : int, optional
Seeds random opertations such as column and row subsampling, such that
results are reproducable.
metric : str or list[str], optional
Performance metric(s) that are tracked during training. When specified,
the progress table will display the tracked metric(s) on training and
validation set.
Supported metrics are: {'accuracy', 'auc', 'log_loss'}
kwargs : dict, optional
Additional arguments for training the model.
- ``model_checkpoint_path`` : str, default None
If specified, checkpoint the model training to the given path every n iterations,
where n is specified by ``model_checkpoint_interval``.
For instance, if `model_checkpoint_interval` is 5, and `model_checkpoint_path` is
set to ``/tmp/model_tmp``, the checkpoints will be saved into
``/tmp/model_tmp/model_checkpoint_5``, ``/tmp/model_tmp/model_checkpoint_10``, ... etc.
Training can be resumed by setting ``resume_from_checkpoint`` to one of these checkpoints.
- ``model_checkpoint_interval`` : int, default 5
If model_check_point_path is specified,
save the model to the given path every n iterations.
- ``resume_from_checkpoint`` : str, default None
Continues training from a model checkpoint. The model must take
exact the same training data as the checkpointed model.
Returns
-------
out : RandomForestClassifier
A trained random forest model for classification tasks.
References
----------
- `Trevor Hastie's slides on Boosted Trees and Random Forest
<http://jessica2.msri.org/attachments/10778/10778-boost.pdf>`_
See Also
--------
RandomForestClassifier, graphlab.logistic_classifier.LogisticClassifier, graphlab.svm_classifier.SVMClassifier, graphlab.neuralnet_classifier.NeuralNetClassifier
Examples
--------
.. sourcecode:: python
>>> url = 'https://static.turi.com/datasets/xgboost/mushroom.csv'
>>> data = graphlab.SFrame.read_csv(url)
>>> train, test = data.random_split(0.8)
>>> model = graphlab.random_forest_classifier.create(train, target='label')
>>> predicitons = model.classify(test)
>>> results = model.evaluate(test)
"""
_mt._get_metric_tracker().track(
'toolkit.classifier.random_forest_classifier.create')
if random_seed is not None:
kwargs['random_seed'] = random_seed
if 'model_checkpoint_path' in kwargs:
kwargs['model_checkpoint_path'] = _make_internal_url(
kwargs['model_checkpoint_path'])
if 'resume_from_checkpoint' in kwargs:
kwargs['resume_from_checkpoint'] = _make_internal_url(
kwargs['resume_from_checkpoint'])
if 'num_trees' in kwargs:
logger = _logging.getLogger(__name__)
logger.warning(
"The `num_trees` keyword argument is deprecated. Please "
"use the `max_iterations` argument instead. Any value provided "
"for `num_trees` will be used in place of `max_iterations`.")
max_iterations = kwargs['num_trees']
del kwargs['num_trees']
model = _sl.create(dataset=dataset,
target=target,
features=features,
model_name='random_forest_classifier',
max_iterations=max_iterations,
validation_set=validation_set,
class_weights=class_weights,
verbose=verbose,
metric=metric,
**kwargs)
return RandomForestClassifier(model.__proxy__)
def create(
dataset,
target,
features=None,
penalty=1.0,
solver='auto',
feature_rescaling=True,
convergence_threshold=_DEFAULT_SOLVER_OPTIONS['convergence_threshold'],
lbfgs_memory_level=_DEFAULT_SOLVER_OPTIONS['lbfgs_memory_level'],
max_iterations=_DEFAULT_SOLVER_OPTIONS['max_iterations'],
class_weights=None,
validation_set='auto',
verbose=True):
"""
Create a :class:`~graphlab.svm_classifier.SVMClassifier` to predict the class of a binary
target variable based on a model of which side of a hyperplane the example
falls on. In addition to standard numeric and categorical types, features
can also be extracted automatically from list- or dictionary-type SFrame
columns.
This loss function for the SVM model is the sum of an L1 mis-classification
loss (multiplied by the 'penalty' term) and a l2-norm on the weight vectors.
Parameters
----------
dataset : SFrame
Dataset for training the model.
target : string
Name of the column containing the target variable. The values in this
column must be of string or integer type. String target variables are
automatically mapped to integers in alphabetical order of the variable
values. For example, a target variable with 'cat' and 'dog' as possible
values is mapped to 0 and 1 respectively with 0 being the base class
and 1 being the reference class.
features : list[string], optional
Names of the columns containing features. 'None' (the default) indicates
that all columns except the target variable should be used as features.
The features are columns in the input SFrame that can be of the
following types:
- *Numeric*: values of numeric type integer or float.
- *Categorical*: values of type string.
- *Array*: list of numeric (integer or float) values. Each list element
is treated as a separate feature in the model.
- *Dictionary*: key-value pairs with numeric (integer or float) values
Each key of a dictionary is treated as a separate feature and the
value in the dictionary corresponds to the value of the feature.
Dictionaries are ideal for representing sparse data.
Columns of type *list* are not supported. Convert them to array in
case all entries in the list are of numeric types and separate them
out into different columns if they are of mixed type.
penalty : float, optional
Penalty term on the mis-classification loss of the model. The larger
this weight, the more the model coefficients shrink toward 0. The
larger the penalty, the lower is the emphasis placed on misclassified
examples, and the classifier would spend more time maximizing the
margin for correctly classified examples. The default value is 1.0;
this parameter must be set to a value of at least 1e-10.
solver : string, optional
Name of the solver to be used to solve the problem. See the
references for more detail on each solver. Available solvers are:
- *auto (default)*: automatically chooses the best solver (from the ones
listed below) for the data and model parameters.
- *lbfgs*: lLimited memory BFGS (``lbfgs``) is a robust solver for wide
datasets(i.e datasets with many coefficients).
The solvers are all automatically tuned and the default options should
function well. See the solver options guide for setting additional
parameters for each of the solvers.
feature_rescaling : bool, default = true
Feature rescaling is an important pre-processing step that ensures
that all features are on the same scale. An l2-norm rescaling is
performed to make sure that all features are of the same norm. Categorical
features are also rescaled by rescaling the dummy variables that
are used to represent them. The coefficients are returned in original
scale of the problem.
convergence_threshold :
Convergence is tested using variation in the training objective. The
variation in the training objective is calculated using the difference
between the objective values between two steps. Consider reducing this
below the default value (0.01) for a more accurately trained model.
Beware of overfitting (i.e a model that works well only on the training
data) if this parameter is set to a very low value.
max_iterations : int, optional
The maximum number of allowed passes through the data. More passes over
the data can result in a more accurately trained model. Consider
increasing this (the default value is 10) if the training accuracy is
low and the *Grad-Norm* in the display is large.
lbfgs_memory_level : int, optional
The L-BFGS algorithm keeps track of gradient information from the
previous ``lbfgs_memory_level`` iterations. The storage requirement for
each of these gradients is the ``num_coefficients`` in the problem.
Increasing the ``lbfgs_memory_level`` can help improve the quality of
the model trained. Setting this to more than ``max_iterations`` has the
same effect as setting it to ``max_iterations``.
class_weights : {dict, `auto`}, optional
Weights the examples in the training data according to the given class
weights. If set to `None`, all classes are supposed to have weight one. The
`auto` mode set the class weight to be inversely proportional to number of
examples in the training data with the given class.
validation_set : SFrame, optional
A dataset for monitoring the model's generalization performance.
For each row of the progress table, the chosen metrics are computed
for both the provided training dataset and the validation_set. The
format of this SFrame must be the same as the training set.
By default this argument is set to 'auto' and a validation set is
automatically sampled and used for progress printing. If
validation_set is set to None, then no additional metrics
are computed. The default value is 'auto'.
verbose : bool, optional
If True, print progress updates.
Returns
-------
out : SVMClassifier
A trained model of type
:class:`~graphlab.svm_classifier.SVMClassifier`.
See Also
--------
SVMClassifier
Notes
-----
- Categorical variables are encoded by creating dummy variables. For
a variable with :math:`K` categories, the encoding creates :math:`K-1`
dummy variables, while the first category encountered in the data is used
as the baseline.
- For prediction and evaluation of SVM models with sparse dictionary
inputs, new keys/columns that were not seen during training are silently
ignored.
- The penalty parameter is analogous to the 'C' term in the C-SVM. See the
reference on training SVMs for more details.
- Any 'None' values in the data will result in an error being thrown.
- A constant term of '1' is automatically added for the model intercept to
model the bias term.
- Note that the hinge loss is approximated by the scaled logistic loss
function. (See user guide for details)
References
----------
- `Wikipedia - Support Vector Machines
<http://en.wikipedia.org/wiki/svm>`_
- Zhang et al. - Modified Logistic Regression: An Approximation to
SVM and its Applications in Large-Scale Text Categorization (ICML 2003)
Examples
--------
Given an :class:`~graphlab.SFrame` ``sf``, a list of feature columns
[``feature_1`` ... ``feature_K``], and a target column ``target`` with 0 and
1 values, create a
:class:`~graphlab.svm.SVMClassifier` as follows:
>>> data = graphlab.SFrame('https://static.turi.com/datasets/regression/houses.csv')
>>> data['is_expensive'] = data['price'] > 30000
>>> model = graphlab.svm_classifier.create(data, 'is_expensive')
"""
_mt._get_metric_tracker().track('toolkit.classifier.svm_classifier.create')
# Regression model names.
model_name = "classifier_svm"
solver = solver.lower()
model = _sl.create(dataset,
target,
model_name,
features=features,
validation_set=validation_set,
verbose=verbose,
penalty=penalty,
feature_rescaling=feature_rescaling,
convergence_threshold=convergence_threshold,
lbfgs_memory_level=lbfgs_memory_level,
max_iterations=max_iterations,
class_weights=class_weights)
return SVMClassifier(model.__proxy__)
def create(dataset,
target,
features=None,
validation_set='auto',
class_weights=None,
max_depth=6,
min_loss_reduction=0.0,
min_child_weight=0.1,
verbose=True,
random_seed=None,
metric='auto',
**kwargs):
"""
Create a (binary or multi-class) classifier model of type
:class:`~graphlab.decision_tree_classifier.DecisionTreeClassifier`. This
algorithm is a special case of boosted trees classifier with the number
of trees set to 1.
Parameters
----------
dataset : SFrame
A training dataset containing feature columns and a target column.
target : str
Name of the column containing the target variable. The values in this
column must be of string or integer type. String target variables are
automatically mapped to integers in alphabetical order of the variable values.
For example, a target variable with 'cat', 'dog', and 'foosa' as possible
values is mapped to 0, 1, and, 2 respectively.
features : list[str], optional
A list of columns names of features used for training the model.
Defaults to None, which uses all columns in the SFrame ``dataset``
excepting the target column..
validation_set : SFrame, optional
A dataset for monitoring the model's generalization performance.
For each row of the progress table, the chosen metrics are computed
for both the provided training dataset and the validation_set. The
format of this SFrame must be the same as the training set.
By default this argument is set to 'auto' and a validation set is
automatically sampled and used for progress printing. If
validation_set is set to None, then no additional metrics
are computed. This is computed once per full iteration. Large
differences in model accuracy between the training data and validation
data is indicative of overfitting. The default value is 'auto'.
class_weights : {dict, `auto`}, optional
Weights the examples in the training data according to the given class
weights. If provided, the dictionary must contain a key for each class
label. The value can be any positive number greater than 1e-20. Weights
are interpreted as relative to each other. So setting the weights to be
2.0 for the positive class and 1.0 for the negative class has the same
effect as setting them to be 20.0 and 10.0, respectively. If set to
`None`, all classes are taken to have weight 1.0. The `auto` mode sets
the class weight to be inversely proportional to the number of examples
in the training data with the given class.
max_depth : float, optional
Maximum depth of a tree. Must be at least 1.
min_loss_reduction : float, optional (non-negative)
Minimum loss reduction required to make a further partition/split a
node during the tree learning phase. Larger (more positive) values
can help prevent overfitting by avoiding splits that do not
sufficiently reduce the loss function.
min_child_weight : float, optional (non-negative)
Controls the minimum weight of each leaf node. Larger values result in
more conservative tree learning and help prevent overfitting.
Formally, this is minimum sum of instance weights (hessians) in each
node. If the tree learning algorithm results in a leaf node with the
sum of instance weights less than `min_child_weight`, tree building
will terminate.
verbose : boolean, optional
Print progress information during training (if set to true).
random_seed : int, optional
Seeds random opertations such as column and row subsampling, such that
results are reproducable.
metric : str or list[str], optional
Performance metric(s) that are tracked during training. When specified,
the progress table will display the tracked metric(s) on training and
validation set.
Supported metrics are: {'accuracy', 'auc', 'log_loss'}
Returns
-------
out : DecisionTreeClassifier
A trained decision tree model for classifications tasks.
References
----------
- `Wikipedia - Gradient tree boosting
<http://en.wikipedia.org/wiki/Gradient_boosting#Gradient_tree_boosting>`_
- `Trevor Hastie's slides on Boosted Trees and Random Forest
<http://jessica2.msri.org/attachments/10778/10778-boost.pdf>`_
See Also
--------
graphlab.logistic_classifier.LogisticClassifier, graphlab.svm_classifier.SVMClassifier, graphlab.neuralnet_classifier.NeuralNetClassifier
Examples
--------
.. sourcecode:: python
>>> url = 'https://static.turi.com/datasets/xgboost/mushroom.csv'
>>> data = graphlab.SFrame.read_csv(url)
>>> train, test = data.random_split(0.8)
>>> model = graphlab.decision_tree_classifier.create(train, target='label')
>>> predicitons = model.classify(test)
>>> results = model.evaluate(test)
"""
if random_seed is not None:
kwargs['random_seed'] = random_seed
_mt._get_metric_tracker().track(
'toolkit.classifier.decision_tree_classifier.create')
model = _sl.create(dataset=dataset,
target=target,
features=features,
model_name='decision_tree_classifier',
validation_set=validation_set,
class_weights=class_weights,
max_depth=max_depth,
min_loss_reduction=min_loss_reduction,
min_child_weight=min_child_weight,
verbose=verbose,
metric=metric,
**kwargs)
return DecisionTreeClassifier(model.__proxy__)
def create(dataset, target,
features=None, max_iterations=10,
validation_set=None,
verbose=True,
**kwargs):
"""
Create a (binary or multi-class) classifier model of type
:class:`~graphlab.boosted_trees_classifier.BoostedTreesClassifier` using
gradient boosted trees (sometimes known as GBMs).
Parameters
----------
dataset : SFrame
A training dataset containing feature columns and a target column.
The target column must be of numerical type (int, float).
target : str
Name of the column containing the target variable. The values in this
column must of string or integer type. String target variables are
autumatically mapped to integers in alphabetical order of the variable values.
For example, a target variable with 'cat', 'dog', and 'foosa' as possible
values is mapped to 0, 1, and, 2 respectively.
features : list[str], optional
A list of columns names of features used for training the model.
Defaults to None, which uses all columns in the SFrame ``dataset``
excepting the target column..
max_iterations : int, optional
The maximum number of iterations for boosting. Each iteration results
in the creation of an extra tree.
max_depth : float, optional
Maximum depth of a tree.
step_size : float, [0,1], optional
Step size (shrinkage) used in update to prevents overfitting. It
shrinks the prediction of each weak learner to make the boosting
process more conservative. The smaller the step size, the more conservative
the algorithm will be. Smaller step_size work well when
`max_iterations` is large.
min_loss_reduction : float, optional
Minimum loss reduction required to make a further partition on a
leaf node of the tree. The larger it is, the more conservative the
algorithm will be.
min_child_weight : float, optional
This controls the minimum number of instances needed for each leaf.
The larger it is, the more conservative the algorithm will be. Set
it larger when you want to prevent overfitting. Formally, this is
minimum sum of instance weight (hessian) in each leaf. If the tree
partition step results in a leaf node with the sum of instance
weight less than min_child_weight, then the building process will
give up further partitioning. For a regression task, this simply
corresponds to minimum number of instances needed to be in each
node.
row_subsample : float, optional
Subsample the ratio of the training set in each iteration of tree
construction. This is called the bagging trick and can usually help
prevent overfitting. Setting this to a value of 0.5 results in the
model randomly sampling half of the examples (rows) to grow each tree.
column_subsample : float, optional
Subsample ratio of the columns in each iteration of tree
construction. Like row_subsample, this can also help prevent
model overfitting. Setting this to a value of 0.5 results in the
model randomly sampling half of the columns to grow each tree.
validation_set : SFrame, optional
The validation set used during the model creation process. Large
differences in model accuracy between the training data and validation
data is indicative of overfitting.
verbose : boolean, optional
Print progress information during training (if set to true).
Returns
-------
out : BoostedTreesClassifier
A trained gradient boosted trees model for classifications tasks.
References
----------
- `Wikipedia - Gradient tree boosting
<http://en.wikipedia.org/wiki/Gradient_boosting#Gradient_tree_boosting>`_
- `Trevor Hastie's slides on Boosted Trees and Random Forest
<http://jessica2.msri.org/attachments/10778/10778-boost.pdf>`_
See Also
--------
BoostedTreesClassifier, graphlab.logistic_classifier.LogisticClassifier, graphlab.svm_classifier.SVMClassifier, graphlab.neuralnet_classifier.NeuralNetClassifier
Examples
--------
.. sourcecode:: python
>>> url = 'http://s3.amazonaws.com/gl-testdata/xgboost/mushroom.csv'
>>> data = graphlab.SFrame.read_csv(url)
>>> data['label'] = (data['label'] == 'p')
>>> train, test = data.random_split(0.8)
>>> model = graphlab.boosted_trees_classifier.create(train, target='label')
>>> predicitons = model.classify(test)
>>> results = model.evaluate(test)
"""
_mt._get_metric_tracker().track('toolkit.classifier.boosted_trees_classifier.create')
model = _sl.create(dataset = dataset,
target = target,
features = features,
model_name = 'boosted_trees_classifier',
max_iterations = max_iterations,
validation_set = validation_set,
verbose = verbose, **kwargs)
return BoostedTreesClassifier(model.__proxy__)
def create(
dataset,
target,
features=None,
l2_penalty=1e-2,
l1_penalty=0.0,
solver='auto',
feature_rescaling=True,
convergence_threshold=_DEFAULT_SOLVER_OPTIONS['convergence_threshold'],
step_size=_DEFAULT_SOLVER_OPTIONS['step_size'],
lbfgs_memory_level=_DEFAULT_SOLVER_OPTIONS['lbfgs_memory_level'],
max_iterations=_DEFAULT_SOLVER_OPTIONS['max_iterations'],
validation_set="auto",
verbose=True):
"""
Create a :class:`~graphlab.linear_regression.LinearRegression` to
predict a scalar target variable as a linear function of one or more
features. In addition to standard numeric and categorical types, features
can also be extracted automatically from list- or dictionary-type SFrame
columns.
The linear regression module can be used for ridge regression, Lasso, and
elastic net regression (see References for more detail on these methods). By
default, this model has an l2 regularization weight of 0.01.
Parameters
----------
dataset : SFrame
The dataset to use for training the model.
target : string
Name of the column containing the target variable.
features : list[string], optional
Names of the columns containing features. 'None' (the default) indicates
that all columns except the target variable should be used as features.
The features are columns in the input SFrame that can be of the
following types:
- *Numeric*: values of numeric type integer or float.
- *Categorical*: values of type string.
- *Array*: list of numeric (integer or float) values. Each list element
is treated as a separate feature in the model.
- *Dictionary*: key-value pairs with numeric (integer or float) values
Each key of a dictionary is treated as a separate feature and the
value in the dictionary corresponds to the value of the feature.
Dictionaries are ideal for representing sparse data.
Columns of type *list* are not supported. Convert such feature
columns to type array if all entries in the list are of numeric
types. If the lists contain data of mixed types, separate
them out into different columns.
l2_penalty : float, optional
Weight on the l2-regularizer of the model. The larger this weight, the
more the model coefficients shrink toward 0. This introduces bias into
the model but decreases variance, potentially leading to better
predictions. The default value is 0.01; setting this parameter to 0
corresponds to unregularized linear regression. See the ridge
regression reference for more detail.
l1_penalty : float, optional
Weight on l1 regularization of the model. Like the l2 penalty, the
higher the l1 penalty, the more the estimated coefficients shrink toward
0. The l1 penalty, however, completely zeros out sufficiently small
coefficients, automatically indicating features that are not useful for
the model. The default weight of 0 prevents any features from being
discarded. See the LASSO regression reference for more detail.
solver : string, optional
Solver to use for training the model. See the references for more detail
on each solver.
- *auto (default)*: automatically chooses the best solver for the data
and model parameters.
- *newton*: Newton-Raphson
- *lbfgs*: limited memory BFGS
- *fista*: accelerated gradient descent
The model is trained using a carefully engineered collection of methods
that are automatically picked based on the input data. The ``newton``
method works best for datasets with plenty of examples and few features
(long datasets). Limited memory BFGS (``lbfgs``) is a robust solver for
wide datasets (i.e datasets with many coefficients). ``fista`` is the
default solver for l1-regularized linear regression. The solvers are
all automatically tuned and the default options should function well.
See the solver options guide for setting additional parameters for each
of the solvers.
See the user guide for additional details on how the solver is chosen.
feature_rescaling : boolean, optional
Feature rescaling is an important pre-processing step that ensures that
all features are on the same scale. An l2-norm rescaling is performed
to make sure that all features are of the same norm. Categorical
features are also rescaled by rescaling the dummy variables that are
used to represent them. The coefficients are returned in original scale
of the problem. This process is particularly useful when features
vary widely in their ranges.
validation_set : SFrame, optional
A dataset for monitoring the model's generalization performance.
For each row of the progress table, the chosen metrics are computed
for both the provided training dataset and the validation_set. The
format of this SFrame must be the same as the training set.
By default this argument is set to 'auto' and a validation set is
automatically sampled and used for progress printing. If
validation_set is set to None, then no additional metrics
are computed. The default value is 'auto'.
convergence_threshold : float, optional
Convergence is tested using variation in the training objective. The
variation in the training objective is calculated using the difference
between the objective values between two steps. Consider reducing this
below the default value (0.01) for a more accurately trained model.
Beware of overfitting (i.e a model that works well only on the training
data) if this parameter is set to a very low value.
lbfgs_memory_level : int, optional
The L-BFGS algorithm keeps track of gradient information from the
previous ``lbfgs_memory_level`` iterations. The storage requirement for
each of these gradients is the ``num_coefficients`` in the problem.
Increasing the ``lbfgs_memory_level`` can help improve the quality of
the model trained. Setting this to more than ``max_iterations`` has the
same effect as setting it to ``max_iterations``.
max_iterations : int, optional
The maximum number of allowed passes through the data. More passes over
the data can result in a more accurately trained model. Consider
increasing this (the default value is 10) if the training accuracy is
low and the *Grad-Norm* in the display is large.
step_size : float, optional (fista only)
The starting step size to use for the ``fista`` and ``gd`` solvers. The
default is set to 1.0, this is an aggressive setting. If the first
iteration takes a considerable amount of time, reducing this parameter
may speed up model training.
verbose : bool, optional
If True, print progress updates.
Returns
-------
out : LinearRegression
A trained model of type
:class:`~graphlab.linear_regression.LinearRegression`.
See Also
--------
LinearRegression, graphlab.boosted_trees_regression.BoostedTreesRegression, graphlab.regression.create
Notes
-----
- Categorical variables are encoded by creating dummy variables. For a
variable with :math:`K` categories, the encoding creates :math:`K-1` dummy
variables, while the first category encountered in the data is used as the
baseline.
- For prediction and evaluation of linear regression models with sparse
dictionary inputs, new keys/columns that were not seen during training
are silently ignored.
- Any 'None' values in the data will result in an error being thrown.
- A constant term is automatically added for the model intercept. This term
is not regularized.
- Standard errors on coefficients are only availiable when `solver=newton`
or when the default `auto` solver option choses the newton method and if
the number of examples in the training data is more than the number of
coefficients. If standard errors cannot be estimated, a column of `None`
values are returned.
References
----------
- Hoerl, A.E. and Kennard, R.W. (1970) `Ridge regression: Biased Estimation
for Nonorthogonal Problems
<http://amstat.tandfonline.com/doi/abs/10.1080/00401706.1970.10488634>`_.
Technometrics 12(1) pp.55-67
- Tibshirani, R. (1996) `Regression Shrinkage and Selection via the Lasso <h
ttp://www.jstor.org/discover/10.2307/2346178?uid=3739256&uid=2&uid=4&sid=2
1104169934983>`_. Journal of the Royal Statistical Society. Series B
(Methodological) 58(1) pp.267-288.
- Zhu, C., et al. (1997) `Algorithm 778: L-BFGS-B: Fortran subroutines for
large-scale bound-constrained optimization
<http://dl.acm.org/citation.cfm?id=279236>`_. ACM Transactions on
Mathematical Software 23(4) pp.550-560.
- Barzilai, J. and Borwein, J. `Two-Point Step Size Gradient Methods
<http://imajna.oxfordjournals.org/content/8/1/141.short>`_. IMA Journal of
Numerical Analysis 8(1) pp.141-148.
- Beck, A. and Teboulle, M. (2009) `A Fast Iterative Shrinkage-Thresholding
Algorithm for Linear Inverse Problems
<http://epubs.siam.org/doi/abs/10.1137/080716542>`_. SIAM Journal on
Imaging Sciences 2(1) pp.183-202.
- Zhang, T. (2004) `Solving large scale linear prediction problems using
stochastic gradient descent algorithms
<http://dl.acm.org/citation.cfm?id=1015332>`_. ICML '04: Proceedings of
the twenty-first international conference on Machine learning p.116.
Examples
--------
Given an :class:`~graphlab.SFrame` ``sf`` with a list of columns
[``feature_1`` ... ``feature_K``] denoting features and a target column
``target``, we can create a
:class:`~graphlab.linear_regression.LinearRegression` as follows:
>>> data = graphlab.SFrame('https://static.turi.com/datasets/regression/houses.csv')
>>> model = graphlab.linear_regression.create(data, target='price',
... features=['bath', 'bedroom', 'size'])
For ridge regression, we can set the ``l2_penalty`` parameter higher (the
default is 0.01). For Lasso regression, we set the l1_penalty higher, and
for elastic net, we set both to be higher.
.. sourcecode:: python
# Ridge regression
>>> model_ridge = graphlab.linear_regression.create(data, 'price', l2_penalty=0.1)
# Lasso
>>> model_lasso = graphlab.linear_regression.create(data, 'price', l2_penalty=0.,
l1_penalty=1.0)
# Elastic net regression
>>> model_enet = graphlab.linear_regression.create(data, 'price', l2_penalty=0.5,
l1_penalty=0.5)
"""
_mt._get_metric_tracker().track(
'toolkit.regression.linear_regression.create')
# Regression model names.
model_name = "regression_linear_regression"
solver = solver.lower()
model = _sl.create(dataset,
target,
model_name,
features=features,
validation_set=validation_set,
solver=solver,
verbose=verbose,
l2_penalty=l2_penalty,
l1_penalty=l1_penalty,
feature_rescaling=feature_rescaling,
convergence_threshold=convergence_threshold,
step_size=step_size,
lbfgs_memory_level=lbfgs_memory_level,
max_iterations=max_iterations)
return LinearRegression(model.__proxy__)
def create(dataset,
target,
features=None,
max_iterations=10,
validation_set='auto',
verbose=True,
random_seed=None,
metric='auto',
**kwargs):
"""
Create a :class:`~graphlab.random_forest_regression.RandomForestRegression` to predict
a scalar target variable using one or more features. In addition to standard
numeric and categorical types, features can also be extracted automatically
from list- or dictionary-type SFrame columns.
Parameters
----------
dataset : SFrame
A training dataset containing feature columns and a target column.
Only numerical typed (int, float) target column is allowed.
target : str
The name of the column in ``dataset`` that is the prediction target.
This column must have a numeric type.
features : list[str], optional
A list of columns names of features used for training the model.
Defaults to None, using all columns.
max_iterations : int, optional
The number of iterations to perform.
max_depth : float, optional
Maximum depth of a tree.
min_loss_reduction : float, optional (non-negative)
Minimum loss reduction required to make a further partition/split a
node during the tree learning phase. Larger (more positive) values
can help prevent overfitting by avoiding splits that do not
sufficiently reduce the loss function.
min_child_weight : float, optional (non-negative)
Controls the minimum weight of each leaf node. Larger values result in
more conservative tree learning and help prevent overfitting.
Formally, this is minimum sum of instance weights (hessians) in each
node. If the tree learning algorithm results in a leaf node with the
sum of instance weights less than `min_child_weight`, tree building
will terminate.
row_subsample : float, optional
Subsample the ratio of the training set in each iteration of tree
construction. This is called the bagging trick and usually can help
prevent overfitting. Setting it to 0.5 means that model randomly
collected half of the examples (rows) to grow each tree.
column_subsample : float, optional
Subsample ratio of the columns in each iteration of tree
construction. Like row_subsample, this also usually can help
prevent overfitting. Setting it to 0.5 means that model randomly
collected half of the columns to grow each tree.
validation_set : SFrame, optional
A dataset for monitoring the model's generalization performance.
For each row of the progress table, the chosen metrics are computed
for both the provided training dataset and the validation_set. The
format of this SFrame must be the same as the training set.
By default this argument is set to 'auto' and a validation set is
automatically sampled and used for progress printing. If
validation_set is set to None, then no additional metrics
are computed. This is computed once per full iteration. Large
differences in model accuracy between the training data and validation
data is indicative of overfitting. The default value is 'auto'.
verbose : boolean, optional
If True, print progress information during training.
random_seed : int, optional
Seeds random opertations such as column and row subsampling, such that
results are reproducable.
metric : str or list[str], optional
Performance metric(s) that are tracked during training. When specified,
the progress table will display the tracked metric(s) on training and
validation set.
Supported metrics are: {'rmse', 'max_error'}
kwargs : dict, optional
Additional arguments for training the model.
- ``model_checkpoint_path`` : str, default None
If specified, checkpoint the model training to the given path every n iterations,
where n is specified by ``model_checkpoint_interval``.
For instance, if `model_checkpoint_interval` is 5, and `model_checkpoint_path` is
set to ``/tmp/model_tmp``, the checkpoints will be saved into
``/tmp/model_tmp/model_checkpoint_5``, ``/tmp/model_tmp/model_checkpoint_10``, ... etc.
Training can be resumed by setting ``resume_from_checkpoint`` to one of these checkpoints.
- ``model_checkpoint_interval`` : int, default 5
If model_check_point_path is specified,
save the model to the given path every n iterations.
- ``resume_from_checkpoint`` : str, default None
Continues training from a model checkpoint. The model must take
exact the same training data as the checkpointed model.
Returns
-------
out : RandomForestRegression
A trained random forest model for regression tasks.
References
----------
- `Trevor Hastie's slides on Boosted Trees and Random Forest
<http://jessica2.msri.org/attachments/10778/10778-boost.pdf>`_
See Also
--------
RandomForestRegression, graphlab.linear_regression.LinearRegression, graphlab.regression.create
Examples
--------
Setup the data:
>>> url = 'https://static.turi.com/datasets/xgboost/mushroom.csv'
>>> data = graphlab.SFrame.read_csv(url)
>>> data['label'] = data['label'] == 'p'
Split the data into training and test data:
>>> train, test = data.random_split(0.8)
Create the model:
>>> model = graphlab.random_forest_regression.create(train, target='label')
Make predictions and evaluate the model:
>>> predictions = model.predict(test)
>>> results = model.evaluate(test)
"""
_mt._get_metric_tracker().track(
'toolkit.regression.random_forest_regression.create')
if random_seed is not None:
kwargs['random_seed'] = random_seed
if 'model_checkpoint_path' in kwargs:
kwargs['model_checkpoint_path'] = _make_internal_url(
kwargs['model_checkpoint_path'])
if 'resume_from_checkpoint' in kwargs:
kwargs['resume_from_checkpoint'] = _make_internal_url(
kwargs['resume_from_checkpoint'])
if 'num_trees' in kwargs:
logger = _logging.getLogger(__name__)
logger.warning(
"The `num_trees` keyword argument is deprecated. Please "
"use the `max_iterations` argument instead. Any value provided "
"for `num_trees` will be used in place of `max_iterations`.")
max_iterations = kwargs['num_trees']
del kwargs['num_trees']
model = _sl.create(dataset=dataset,
target=target,
features=features,
model_name='random_forest_regression',
max_iterations=max_iterations,
validation_set=validation_set,
verbose=verbose,
metric=metric,
**kwargs)
return RandomForestRegression(model.__proxy__)
def create(dataset,
target,
features=None,
max_iterations=10,
validation_set='auto',
class_weights=None,
max_depth=6,
step_size=0.3,
min_loss_reduction=0.0,
min_child_weight=0.1,
row_subsample=1.0,
column_subsample=1.0,
verbose=True,
random_seed=None,
metric='auto',
**kwargs):
"""
Create a (binary or multi-class) classifier model of type
:class:`~graphlab.boosted_trees_classifier.BoostedTreesClassifier` using
gradient boosted trees (sometimes known as GBMs).
Parameters
----------
dataset : SFrame
A training dataset containing feature columns and a target column.
target : str
Name of the column containing the target variable. The values in this
column must be of string or integer type. String target variables are
automatically mapped to integers in alphabetical order of the variable values.
For example, a target variable with 'cat', 'dog', and 'foosa' as possible
values is mapped to 0, 1, and, 2 respectively.
features : list[str], optional
A list of columns names of features used for training the model.
Defaults to None, which uses all columns in the SFrame ``dataset``
excepting the target column..
max_iterations : int, optional
The maximum number of iterations for boosting. Each iteration results
in the creation of an extra tree.
validation_set : SFrame, optional
A dataset for monitoring the model's generalization performance.
For each row of the progress table, the chosen metrics are computed
for both the provided training dataset and the validation_set. The
format of this SFrame must be the same as the training set.
By default this argument is set to 'auto' and a validation set is
automatically sampled and used for progress printing. If
validation_set is set to None, then no additional metrics
are computed. This is computed once per full iteration. Large
differences in model accuracy between the training data and validation
data is indicative of overfitting. The default value is 'auto'.
class_weights : {dict, `auto`}, optional
Weights the examples in the training data according to the given class
weights. If provided, the dictionary must contain a key for each class
label. The value can be any positive number greater than 1e-20. Weights
are interpreted as relative to each other. So setting the weights to be
2.0 for the positive class and 1.0 for the negative class has the same
effect as setting them to be 20.0 and 10.0, respectively. If set to
`None`, all classes are taken to have weight 1.0. The `auto` mode sets
the class weight to be inversely proportional to the number of examples
in the training data with the given class.
max_depth : float, optional
Maximum depth of a tree. Must be at least 1.
step_size : float, [0,1], optional
Step size (shrinkage) used in update to prevents overfitting. It
shrinks the prediction of each weak learner to make the boosting
process more conservative. The smaller the step size, the more conservative
the algorithm will be. Smaller step_size work well when
`max_iterations` is large.
min_loss_reduction : float, optional (non-negative)
Minimum loss reduction required to make a further partition/split a
node during the tree learning phase. Larger (more positive) values
can help prevent overfitting by avoiding splits that do not
sufficiently reduce the loss function.
min_child_weight : float, optional (non-negative)
Controls the minimum weight of each leaf node. Larger values result in
more conservative tree learning and help prevent overfitting.
Formally, this is minimum sum of instance weights (hessians) in each
node. If the tree learning algorithm results in a leaf node with the
sum of instance weights less than `min_child_weight`, tree building
will terminate.
row_subsample : float, [0,1], optional
Subsample the ratio of the training set in each iteration of tree
construction. This is called the bagging trick and can usually help
prevent overfitting. Setting this to a value of 0.5 results in the
model randomly sampling half of the examples (rows) to grow each tree.
column_subsample : float, [0,1], optional
Subsample ratio of the columns in each iteration of tree
construction. Like row_subsample, this can also help prevent
model overfitting. Setting this to a value of 0.5 results in the
model randomly sampling half of the columns to grow each tree.
verbose : boolean, optional
Print progress information during training (if set to true).
random_seed : int, optional
Seeds random opertations such as column and row subsampling, such that
results are reproducable.
metric : str or list[str], optional
Performance metric(s) that are tracked during training. When specified,
the progress table will display the tracked metric(s) on training and
validation set.
Supported metrics are: {'accuracy', 'auc', 'log_loss'}
kwargs : dict, optional
Additional arguments for training the model.
- ``early_stopping_rounds`` : int, default None
If the validation metric does not improve after <early_stopping_rounds>,
stop training and return the best model.
If multiple metrics are being tracked, the last one is used.
- ``model_checkpoint_path`` : str, default None
If specified, checkpoint the model training to the given path every n iterations,
where n is specified by ``model_checkpoint_interval``.
For instance, if `model_checkpoint_interval` is 5, and `model_checkpoint_path` is
set to ``/tmp/model_tmp``, the checkpoints will be saved into
``/tmp/model_tmp/model_checkpoint_5``, ``/tmp/model_tmp/model_checkpoint_10``, ... etc.
Training can be resumed by setting ``resume_from_checkpoint`` to one of these checkpoints.
- ``model_checkpoint_interval`` : int, default 5
If model_check_point_path is specified,
save the model to the given path every n iterations.
- ``resume_from_checkpoint`` : str, default None
Continues training from a model checkpoint. The model must take
exact the same training data as the checkpointed model.
Returns
-------
out : BoostedTreesClassifier
A trained gradient boosted trees model for classifications tasks.
References
----------
- `Wikipedia - Gradient tree boosting
<http://en.wikipedia.org/wiki/Gradient_boosting#Gradient_tree_boosting>`_
- `Trevor Hastie's slides on Boosted Trees and Random Forest
<http://jessica2.msri.org/attachments/10778/10778-boost.pdf>`_
See Also
--------
BoostedTreesClassifier, graphlab.logistic_classifier.LogisticClassifier, graphlab.svm_classifier.SVMClassifier, graphlab.neuralnet_classifier.NeuralNetClassifier
Examples
--------
.. sourcecode:: python
>>> url = 'https://static.turi.com/datasets/xgboost/mushroom.csv'
>>> data = graphlab.SFrame.read_csv(url)
>>> train, test = data.random_split(0.8)
>>> model = graphlab.boosted_trees_classifier.create(train, target='label')
>>> predicitons = model.classify(test)
>>> results = model.evaluate(test)
"""
_mt._get_metric_tracker().track(
'toolkit.classifier.boosted_trees_classifier.create')
if random_seed is not None:
kwargs['random_seed'] = random_seed
if 'model_checkpoint_path' in kwargs:
kwargs['model_checkpoint_path'] = _make_internal_url(
kwargs['model_checkpoint_path'])
if 'resume_from_checkpoint' in kwargs:
kwargs['resume_from_checkpoint'] = _make_internal_url(
kwargs['resume_from_checkpoint'])
model = _sl.create(dataset=dataset,
target=target,
features=features,
model_name='boosted_trees_classifier',
max_iterations=max_iterations,
validation_set=validation_set,
class_weights=class_weights,
max_depth=max_depth,
step_size=step_size,
min_loss_reduction=min_loss_reduction,
min_child_weight=min_child_weight,
row_subsample=row_subsample,
column_subsample=column_subsample,
verbose=verbose,
metric=metric,
**kwargs)
return BoostedTreesClassifier(model.__proxy__)
def create(dataset, target, features=None,
penalty=1.0, solver='auto',
feature_rescaling=True,
convergence_threshold = _DEFAULT_SOLVER_OPTIONS['convergence_threshold'],
lbfgs_memory_level = _DEFAULT_SOLVER_OPTIONS['lbfgs_memory_level'],
max_iterations = _DEFAULT_SOLVER_OPTIONS['max_iterations'],
class_weights = None,
validation_set = 'auto',
verbose=True):
"""
Create a :class:`~graphlab.svm_classifier.SVMClassifier` to predict the class of a binary
target variable based on a model of which side of a hyperplane the example
falls on. In addition to standard numeric and categorical types, features
can also be extracted automatically from list- or dictionary-type SFrame
columns.
This loss function for the SVM model is the sum of an L1 mis-classification
loss (multiplied by the 'penalty' term) and a l2-norm on the weight vectors.
Parameters
----------
dataset : SFrame
Dataset for training the model.
target : string
Name of the column containing the target variable. The values in this
column must be of string or integer type. String target variables are
automatically mapped to integers in alphabetical order of the variable
values. For example, a target variable with 'cat' and 'dog' as possible
values is mapped to 0 and 1 respectively with 0 being the base class
and 1 being the reference class.
features : list[string], optional
Names of the columns containing features. 'None' (the default) indicates
that all columns except the target variable should be used as features.
The features are columns in the input SFrame that can be of the
following types:
- *Numeric*: values of numeric type integer or float.
- *Categorical*: values of type string.
- *Array*: list of numeric (integer or float) values. Each list element
is treated as a separate feature in the model.
- *Dictionary*: key-value pairs with numeric (integer or float) values
Each key of a dictionary is treated as a separate feature and the
value in the dictionary corresponds to the value of the feature.
Dictionaries are ideal for representing sparse data.
Columns of type *list* are not supported. Convert them to array in
case all entries in the list are of numeric types and separate them
out into different columns if they are of mixed type.
penalty : float, optional
Penalty term on the mis-classification loss of the model. The larger
this weight, the more the model coefficients shrink toward 0. The
larger the penalty, the lower is the emphasis placed on misclassified
examples, and the classifier would spend more time maximizing the
margin for correctly classified examples. The default value is 1.0;
this parameter must be set to a value of at least 1e-10.
solver : string, optional
Name of the solver to be used to solve the problem. See the
references for more detail on each solver. Available solvers are:
- *auto (default)*: automatically chooses the best solver (from the ones
listed below) for the data and model parameters.
- *lbfgs*: lLimited memory BFGS (``lbfgs``) is a robust solver for wide
datasets(i.e datasets with many coefficients).
The solvers are all automatically tuned and the default options should
function well. See the solver options guide for setting additional
parameters for each of the solvers.
feature_rescaling : bool, default = true
Feature rescaling is an important pre-processing step that ensures
that all features are on the same scale. An l2-norm rescaling is
performed to make sure that all features are of the same norm. Categorical
features are also rescaled by rescaling the dummy variables that
are used to represent them. The coefficients are returned in original
scale of the problem.
convergence_threshold :
Convergence is tested using variation in the training objective. The
variation in the training objective is calculated using the difference
between the objective values between two steps. Consider reducing this
below the default value (0.01) for a more accurately trained model.
Beware of overfitting (i.e a model that works well only on the training
data) if this parameter is set to a very low value.
max_iterations : int, optional
The maximum number of allowed passes through the data. More passes over
the data can result in a more accurately trained model. Consider
increasing this (the default value is 10) if the training accuracy is
low and the *Grad-Norm* in the display is large.
lbfgs_memory_level : int, optional
The L-BFGS algorithm keeps track of gradient information from the
previous ``lbfgs_memory_level`` iterations. The storage requirement for
each of these gradients is the ``num_coefficients`` in the problem.
Increasing the ``lbfgs_memory_level`` can help improve the quality of
the model trained. Setting this to more than ``max_iterations`` has the
same effect as setting it to ``max_iterations``.
class_weights : {dict, `auto`}, optional
Weights the examples in the training data according to the given class
weights. If set to `None`, all classes are supposed to have weight one. The
`auto` mode set the class weight to be inversely proportional to number of
examples in the training data with the given class.
validation_set : SFrame, optional
A dataset for monitoring the model's generalization performance.
For each row of the progress table, the chosen metrics are computed
for both the provided training dataset and the validation_set. The
format of this SFrame must be the same as the training set.
By default this argument is set to 'auto' and a validation set is
automatically sampled and used for progress printing. If
validation_set is set to None, then no additional metrics
are computed. The default value is 'auto'.
verbose : bool, optional
If True, print progress updates.
Returns
-------
out : SVMClassifier
A trained model of type
:class:`~graphlab.svm_classifier.SVMClassifier`.
See Also
--------
SVMClassifier
Notes
-----
- Categorical variables are encoded by creating dummy variables. For
a variable with :math:`K` categories, the encoding creates :math:`K-1`
dummy variables, while the first category encountered in the data is used
as the baseline.
- For prediction and evaluation of SVM models with sparse dictionary
inputs, new keys/columns that were not seen during training are silently
ignored.
- The penalty parameter is analogous to the 'C' term in the C-SVM. See the
reference on training SVMs for more details.
- Any 'None' values in the data will result in an error being thrown.
- A constant term of '1' is automatically added for the model intercept to
model the bias term.
- Note that the hinge loss is approximated by the scaled logistic loss
function. (See user guide for details)
References
----------
- `Wikipedia - Support Vector Machines
<http://en.wikipedia.org/wiki/svm>`_
- Zhang et al. - Modified Logistic Regression: An Approximation to
SVM and its Applications in Large-Scale Text Categorization (ICML 2003)
Examples
--------
Given an :class:`~graphlab.SFrame` ``sf``, a list of feature columns
[``feature_1`` ... ``feature_K``], and a target column ``target`` with 0 and
1 values, create a
:class:`~graphlab.svm.SVMClassifier` as follows:
>>> data = graphlab.SFrame('http://s3.amazonaws.com/dato-datasets/regression/houses.csv')
>>> data['is_expensive'] = data['price'] > 30000
>>> model = graphlab.svm_classifier.create(data, 'is_expensive')
"""
_mt._get_metric_tracker().track('toolkit.classifier.svm_classifier.create')
# Regression model names.
model_name = "classifier_svm"
solver = solver.lower()
model = _sl.create(dataset, target, model_name, features=features,
validation_set = validation_set, verbose = verbose,
penalty = penalty,
feature_rescaling = feature_rescaling,
convergence_threshold = convergence_threshold,
lbfgs_memory_level = lbfgs_memory_level,
max_iterations = max_iterations,
class_weights = class_weights)
return SVMClassifier(model.__proxy__)
def create(dataset, target,
features=None, max_iterations=10,
validation_set='auto',
class_weights = None,
max_depth=6, step_size=0.3,
min_loss_reduction=0.0, min_child_weight=0.1,
row_subsample=1.0, column_subsample=1.0,
verbose=True,
random_seed = None,
**kwargs):
"""
Create a (binary or multi-class) classifier model of type
:class:`~graphlab.boosted_trees_classifier.BoostedTreesClassifier` using
gradient boosted trees (sometimes known as GBMs).
Parameters
----------
dataset : SFrame
A training dataset containing feature columns and a target column.
target : str
Name of the column containing the target variable. The values in this
column must be of string or integer type. String target variables are
automatically mapped to integers in alphabetical order of the variable values.
For example, a target variable with 'cat', 'dog', and 'foosa' as possible
values is mapped to 0, 1, and, 2 respectively.
features : list[str], optional
A list of columns names of features used for training the model.
Defaults to None, which uses all columns in the SFrame ``dataset``
excepting the target column..
max_iterations : int, optional
The maximum number of iterations for boosting. Each iteration results
in the creation of an extra tree.
validation_set : SFrame, optional
A dataset for monitoring the model's generalization performance.
For each row of the progress table, the chosen metrics are computed
for both the provided training dataset and the validation_set. The
format of this SFrame must be the same as the training set.
By default this argument is set to 'auto' and a validation set is
automatically sampled and used for progress printing. If
validation_set is set to None, then no additional metrics
are computed. This is computed once per full iteration. Large
differences in model accuracy between the training data and validation
data is indicative of overfitting. The default value is 'auto'.
class_weights : {dict, `auto`}, optional
Weights the examples in the training data according to the given class
weights. If provided, the dictionary must contain a key for each class
label. The value can be any positive number greater than 1e-20. Weights
are interpreted as relative to each other. So setting the weights to be
2.0 for the positive class and 1.0 for the negative class has the same
effect as setting them to be 20.0 and 10.0, respectively. If set to
`None`, all classes are taken to have weight 1.0. The `auto` mode sets
the class weight to be inversely proportional to the number of examples
in the training data with the given class.
max_depth : float, optional
Maximum depth of a tree. Must be at least 1.
step_size : float, [0,1], optional
Step size (shrinkage) used in update to prevents overfitting. It
shrinks the prediction of each weak learner to make the boosting
process more conservative. The smaller the step size, the more conservative
the algorithm will be. Smaller step_size work well when
`max_iterations` is large.
min_loss_reduction : float, optional
Minimum loss reduction required to make a further partition on a
leaf node of the tree. The larger it is, the more conservative the
algorithm will be. Must be non-negative.
min_child_weight : float, optional
This controls the minimum number of instances needed for each leaf.
The larger it is, the more conservative the algorithm will be. Set
it larger when you want to prevent overfitting. Formally, this is
minimum sum of instance weight (hessian) in each leaf. If the tree
partition step results in a leaf node with the sum of instance
weight less than min_child_weight, then the building process will
give up further partitioning. For a regression task, this simply
corresponds to minimum number of instances needed to be in each
node. Must be non-negative.
row_subsample : float, [0,1], optional
Subsample the ratio of the training set in each iteration of tree
construction. This is called the bagging trick and can usually help
prevent overfitting. Setting this to a value of 0.5 results in the
model randomly sampling half of the examples (rows) to grow each tree.
column_subsample : float, [0,1], optional
Subsample ratio of the columns in each iteration of tree
construction. Like row_subsample, this can also help prevent
model overfitting. Setting this to a value of 0.5 results in the
model randomly sampling half of the columns to grow each tree.
verbose : boolean, optional
Print progress information during training (if set to true).
random_seed : int, optional
Seeds random opertations such as column and row subsampling, such that
results are reproducable.
Returns
-------
out : BoostedTreesClassifier
A trained gradient boosted trees model for classifications tasks.
References
----------
- `Wikipedia - Gradient tree boosting
<http://en.wikipedia.org/wiki/Gradient_boosting#Gradient_tree_boosting>`_
- `Trevor Hastie's slides on Boosted Trees and Random Forest
<http://jessica2.msri.org/attachments/10778/10778-boost.pdf>`_
See Also
--------
BoostedTreesClassifier, graphlab.logistic_classifier.LogisticClassifier, graphlab.svm_classifier.SVMClassifier, graphlab.neuralnet_classifier.NeuralNetClassifier
Examples
--------
.. sourcecode:: python
>>> url = 'http://s3.amazonaws.com/gl-testdata/xgboost/mushroom.csv'
>>> data = graphlab.SFrame.read_csv(url)
>>> train, test = data.random_split(0.8)
>>> model = graphlab.boosted_trees_classifier.create(train, target='label')
>>> predicitons = model.classify(test)
>>> results = model.evaluate(test)
"""
if random_seed is not None:
kwargs['random_seed'] = random_seed
_mt._get_metric_tracker().track('toolkit.classifier.boosted_trees_classifier.create')
model = _sl.create(dataset = dataset,
target = target,
features = features,
model_name = 'boosted_trees_classifier',
max_iterations = max_iterations,
validation_set = validation_set,
class_weights = class_weights,
max_depth = max_depth,
step_size = step_size,
min_loss_reduction = min_loss_reduction,
min_child_weight = min_child_weight,
row_subsample = row_subsample,
column_subsample = column_subsample,
verbose = verbose,
**kwargs)
return BoostedTreesClassifier(model.__proxy__)
def create(dataset, target, features=None, l2_penalty=1e-2, l1_penalty=0.0,
solver='auto', feature_rescaling=True,
convergence_threshold = _DEFAULT_SOLVER_OPTIONS['convergence_threshold'],
step_size = _DEFAULT_SOLVER_OPTIONS['step_size'],
lbfgs_memory_level = _DEFAULT_SOLVER_OPTIONS['lbfgs_memory_level'],
mini_batch_size = _DEFAULT_SOLVER_OPTIONS['mini_batch_size'],
max_iterations = _DEFAULT_SOLVER_OPTIONS['max_iterations'],
auto_tuning = _DEFAULT_SOLVER_OPTIONS['auto_tuning'], verbose=True):
"""
Create a :class:`~graphlab.linear_regression.LinearRegression` to
predict a scalar target variable as a linear function of one or more
features. In addition to standard numeric and categorical types, features
can also be extracted automatically from list- or dictionary-type SFrame
columns.
The linear regression module can be used for ridge regression, Lasso, and
elastic net regression (see References for more detail on these methods). By
default, this model has an l2 regularization weight of 0.01.
Parameters
----------
dataset : SFrame
The dataset to use for training the model.
target : string
Name of the column containing the target variable.
features : list[string], optional
Names of the columns containing features. 'None' (the default) indicates
that all columns except the target variable should be used as features.
The features are columns in the input SFrame that can be of the
following types:
- *Numeric*: values of numeric type integer or float.
- *Categorical*: values of type string.
- *Array*: list of numeric (integer or float) values. Each list element
is treated as a separate feature in the model.
- *Dictionary*: key-value pairs with numeric (integer or float) values
Each key of a dictionary is treated as a separate feature and the
value in the dictionary corresponds to the value of the feature.
Dictionaries are ideal for representing sparse data.
Columns of type *list* are not supported. Convert such feature
columns to type array if all entries in the list are of numeric
types. If the lists contain data of mixed types, separate
them out into different columns.
l2_penalty : float, optional
Weight on the l2-regularizer of the model. The larger this weight, the
more the model coefficients shrink toward 0. This introduces bias into
the model but decreases variance, potentially leading to better
predictions. The default value is 0.01; setting this parameter to 0
corresponds to unregularized linear regression. See the ridge
regression reference for more detail.
l1_penalty : float, optional
Weight on l1 regularization of the model. Like the l2 penalty, the
higher the l1 penalty, the more the estimated coefficients shrink toward
0. The l1 penalty, however, completely zeros out sufficiently small
coefficients, automatically indicating features that are not useful for
the model. The default weight of 0 prevents any features from being
discarded. See the LASSO regression reference for more detail.
solver : string, optional
Solver to use for training the model. See the references for more detail
on each solver.
- *auto (default)*: automatically chooses the best solver for the data
and model parameters.
- *newton*: Newton-Raphson
- *lbfgs*: limited memory BFGS
- *gd*: gradient descent
- *fista*: accelerated gradient descent
- *sgd*: stochastic gradient descent
The model is trained using a carefully engineered collection of methods
that are automatically picked based on the input data. The ``newton``
method works best for datasets with plenty of examples and few features
(long datasets). Limited memory BFGS (``lbfgs``) is a robust solver for
wide datasets (i.e datasets with many coefficients). ``fista`` is the
default solver for l1-regularized linear regression. Gradient-descent
(GD) is another well tuned method that can work really well on
l1-regularized problems. The solvers are all automatically tuned and
the default options should function well. See the solver options guide
for setting additional parameters for each of the solvers.
feature_rescaling : boolean, optional
Feature rescaling is an important pre-processing step that ensures that
all features are on the same scale. An l2-norm rescaling is performed
to make sure that all features are of the same norm. Categorical
features are also rescaled by rescaling the dummy variables that are
used to represent them. The coefficients are returned in original scale
of the problem. This process is particularly useful when features
vary widely in their ranges.
auto_tuning : boolean, optional (sgd only)
Toggles whether the step size in Stochastic Gradient Descent (SGD) is
automatically in training
convergence_threshold : float, optional
Convergence is tested using variation in the training objective. The
variation in the training objective is calculated using the difference
between the objective values between two steps. Consider reducing this
below the default value (0.01) for a more accurately trained model.
Beware of overfitting (i.e a model that works well only on the training
data) if this parameter is set to a very low value.
lbfgs_memory_level : int, optional
The L-BFGS algorithm keeps track of gradient information from the
previous ``lbfgs_memory_level`` iterations. The storage requirement for
each of these gradients is the ``num_coefficients`` in the problem.
Increasing the ``lbfgs_memory_level`` can help improve the quality of
the model trained. Setting this to more than ``max_iterations`` has the
same effect as setting it to ``max_iterations``.
max_iterations : int, optional
The maximum number of allowed passes through the data. More passes over
the data can result in a more accurately trained model. Consider
increasing this (the default value is 10) if the training accuracy is
low and the *Grad-Norm* in the display is large.
mini_batch_size : int, optional (sgd only)
The number of examples that are considered in Stochastic Gradient
Descent (SGD) before a parameter update is made.
step_size : float, optional (fista, sgd only)
The starting step size to use for the ``fista`` and ``gd`` solvers. The
default is set to 1.0, this is an aggressive setting. If the first
iteration takes a considerable amount of time, reducing this parameter
may speed up model training.
verbose : bool, optional
If True, print progress updates.
Returns
-------
out : LinearRegression
A trained model of type
:class:`~graphlab.linear_regression.LinearRegression`.
See Also
--------
LinearRegression, graphlab.boosted_trees_regression.BoostedTreesRegression, graphlab.regression.create
Notes
-----
- Categorical variables are encoded by creating dummy variables. For a
variable with :math:`K` categories, the encoding creates :math:`K-1` dummy
variables, while the first category encountered in the data is used as the
baseline.
- For prediction and evaluation of linear regression models with sparse
dictionary inputs, new keys/columns that were not seen during training
are silently ignored.
- Any 'None' values in the data will result in an error being thrown.
- A constant term is automatically added for the model intercept. This term
is not regularized.
References
----------
- Hoerl, A.E. and Kennard, R.W. (1970) `Ridge regression: Biased Estimation
for Nonorthogonal Problems
<http://amstat.tandfonline.com/doi/abs/10.1080/00401706.1970.10488634>`_.
Technometrics 12(1) pp.55-67
- Tibshirani, R. (1996) `Regression Shrinkage and Selection via the Lasso <h
ttp://www.jstor.org/discover/10.2307/2346178?uid=3739256&uid=2&uid=4&sid=2
1104169934983>`_. Journal of the Royal Statistical Society. Series B
(Methodological) 58(1) pp.267-288.
- Zhu, C., et al. (1997) `Algorithm 778: L-BFGS-B: Fortran subroutines for
large-scale bound-constrained optimization
<http://dl.acm.org/citation.cfm?id=279236>`_. ACM Transactions on
Mathematical Software 23(4) pp.550-560.
- Barzilai, J. and Borwein, J. `Two-Point Step Size Gradient Methods
<http://imajna.oxfordjournals.org/content/8/1/141.short>`_. IMA Journal of
Numerical Analysis 8(1) pp.141-148.
- Beck, A. and Teboulle, M. (2009) `A Fast Iterative Shrinkage-Thresholding
Algorithm for Linear Inverse Problems
<http://epubs.siam.org/doi/abs/10.1137/080716542>`_. SIAM Journal on
Imaging Sciences 2(1) pp.183-202.
- Zhang, T. (2004) `Solving large scale linear prediction problems using
stochastic gradient descent algorithms
<http://dl.acm.org/citation.cfm?id=1015332>`_. ICML '04: Proceedings of
the twenty-first international conference on Machine learning p.116.
Examples
--------
Given an :class:`~graphlab.SFrame` ``sf`` with a list of columns
[``feature_1`` ... ``feature_K``] denoting features and a target column
``target``, we can create a
:class:`~graphlab.linear_regression.LinearRegression` as follows:
>>> data = graphlab.SFrame('http://s3.amazonaws.com/GraphLab-Datasets/regression/houses.csv')
>>> model = graphlab.linear_regression.create(data, target='price',
... features=['bath', 'bedroom', 'size'])
For ridge regression, we can set the ``l2_penalty`` parameter higher (the
default is 0.01). For Lasso regression, we set the l1_penalty higher, and
for elastic net, we set both to be higher.
.. sourcecode:: python
# Ridge regression
>>> model_ridge = graphlab.linear_regression.create(data, 'price', l2_penalty=0.1)
# Lasso
>>> model_lasso = graphlab.linear_regression.create(data, 'price', l2_penalty=0.,
l1_penalty=1.0)
# Elastic net regression
>>> model_enet = graphlab.linear_regression.create(data, 'price', l2_penalty=0.5,
l1_penalty=0.5)
"""
_mt._get_metric_tracker().track('toolkit.regression.linear_regression.create')
# Regression model names.
model_name = "regression_linear_regression"
solver = solver.lower()
model = _sl.create(dataset, target, model_name, features=features,
validation_set = None,
solver = solver, verbose = verbose,
l2_penalty=l2_penalty, l1_penalty = l1_penalty,
feature_rescaling = feature_rescaling,
convergence_threshold = convergence_threshold,
step_size = step_size,
lbfgs_memory_level = lbfgs_memory_level,
mini_batch_size = mini_batch_size,
max_iterations = max_iterations)
return LinearRegression(model.__proxy__)
def create(
dataset,
target,
features=None,
l2_penalty=0.01,
l1_penalty=0.0,
solver='auto',
feature_rescaling=True,
convergence_threshold=_DEFAULT_SOLVER_OPTIONS['convergence_threshold'],
step_size=_DEFAULT_SOLVER_OPTIONS['step_size'],
lbfgs_memory_level=_DEFAULT_SOLVER_OPTIONS['lbfgs_memory_level'],
max_iterations=_DEFAULT_SOLVER_OPTIONS['max_iterations'],
class_weights=None,
validation_set='auto',
verbose=True):
"""
Create a :class:`~graphlab.logistic_classifier.LogisticClassifier` (using
logistic regression as a classifier) to predict the class of a discrete
target variable (binary or multiclass) based on a model of class probability
as a logistic function of a linear combination of the features. In addition
to standard numeric and categorical types, features can also be extracted
automatically from list or dictionary-type SFrame columns.
This model can be regularized with an l1 penalty, an l2 penalty, or both. By
default this model has an l2 regularization weight of 0.01.
Parameters
----------
dataset : SFrame
Dataset for training the model.
target : string
Name of the column containing the target variable. The values in this
column must be of string or integer type. String target variables are
automatically mapped to integers in the order in which they are provided.
For example, a target variable with 'cat' and 'dog' as possible
values is mapped to 0 and 1 respectively with 0 being the base class
and 1 being the reference class. Use `model['classes']` to retrieve
the order in which the classes are mapped.
features : list[string], optional
Names of the columns containing features. 'None' (the default) indicates
that all columns except the target variable should be used as features.
The features are columns in the input SFrame that can be of the
following types:
- *Numeric*: values of numeric type integer or float.
- *Categorical*: values of type string.
- *Array*: list of numeric (integer or float) values. Each list element
is treated as a separate feature in the model.
- *Dictionary*: key-value pairs with numeric (integer or float) values
Each key of a dictionary is treated as a separate feature and the
value in the dictionary corresponds to the value of the feature.
Dictionaries are ideal for representing sparse data.
Columns of type *list* are not supported. Convert such feature
columns to type array if all entries in the list are of numeric
types. If the lists contain data of mixed types, separate
them out into different columns.
l2_penalty : float, optional
Weight on l2 regularization of the model. The larger this weight, the
more the model coefficients shrink toward 0. This introduces bias into
the model but decreases variance, potentially leading to better
predictions. The default value is 0.01; setting this parameter to 0
corresponds to unregularized logistic regression. See the ridge
regression reference for more detail.
l1_penalty : float, optional
Weight on l1 regularization of the model. Like the l2 penalty, the
higher the l1 penalty, the more the estimated coefficients shrink toward
0. The l1 penalty, however, completely zeros out sufficiently small
coefficients, automatically indicating features that are not useful
for the model. The default weight of 0 prevents any features from
being discarded. See the LASSO regression reference for more detail.
solver : string, optional
Name of the solver to be used to solve the regression. See the
references for more detail on each solver. Available solvers are:
- *auto (default)*: automatically chooses the best solver for the data
and model parameters.
- *newton*: Newton-Raphson
- *lbfgs*: limited memory BFGS
- *fista*: accelerated gradient descent
For this model, the Newton-Raphson method is equivalent to the
iteratively re-weighted least squares algorithm. If the l1_penalty is
greater than 0, use the 'fista' solver.
The model is trained using a carefully engineered collection of methods
that are automatically picked based on the input data. The ``newton``
method works best for datasets with plenty of examples and few features
(long datasets). Limited memory BFGS (``lbfgs``) is a robust solver for
wide datasets (i.e datasets with many coefficients). ``fista`` is the
default solver for l1-regularized linear regression. The solvers are all
automatically tuned and the default options should function well. See
the solver options guide for setting additional parameters for each of
the solvers.
See the user guide for additional details on how the solver is chosen.
(see `here
<https://turi.com/learn/userguide/supervised-learning/linear-regression.html>`_)
feature_rescaling : boolean, optional
Feature rescaling is an important pre-processing step that ensures that
all features are on the same scale. An l2-norm rescaling is performed
to make sure that all features are of the same norm. Categorical
features are also rescaled by rescaling the dummy variables that are
used to represent them. The coefficients are returned in original scale
of the problem. This process is particularly useful when features
vary widely in their ranges.
convergence_threshold : float, optional
Convergence is tested using variation in the training objective. The
variation in the training objective is calculated using the difference
between the objective values between two steps. Consider reducing this
below the default value (0.01) for a more accurately trained model.
Beware of overfitting (i.e a model that works well only on the training
data) if this parameter is set to a very low value.
lbfgs_memory_level : float, optional
The L-BFGS algorithm keeps track of gradient information from the
previous ``lbfgs_memory_level`` iterations. The storage requirement for
each of these gradients is the ``num_coefficients`` in the problem.
Increasing the ``lbfgs_memory_level ``can help improve the quality of
the model trained. Setting this to more than ``max_iterations`` has the
same effect as setting it to ``max_iterations``.
max_iterations : float, optional
The maximum number of allowed passes through the data. More passes over
the data can result in a more accurately trained model. Consider
increasing this (the default value is 10) if the training accuracy is
low and the *Grad-Norm* in the display is large.
step_size : float, optional
The starting step size to use for the ``fista`` solver. The default is
set to 1.0, this is an aggressive setting. If the first iteration takes
a considerable amount of time, reducing this parameter may speed up
model training.
class_weights : {dict, `auto`}, optional
Weights the examples in the training data according to the given class
weights. If set to `None`, all classes are supposed to have weight one. The
`auto` mode set the class weight to be inversely proportional to number of
examples in the training data with the given class.
validation_set : SFrame, optional
A dataset for monitoring the model's generalization performance.
For each row of the progress table, the chosen metrics are computed
for both the provided training dataset and the validation_set. The
format of this SFrame must be the same as the training set.
By default this argument is set to 'auto' and a validation set is
automatically sampled and used for progress printing. If
validation_set is set to None, then no additional metrics
are computed. The default value is 'auto'.
verbose : bool, optional
If True, print progress updates.
Returns
-------
out : LogisticClassifier
A trained model of type
:class:`~graphlab.logistic_classifier.LogisticClassifier`.
See Also
--------
LogisticClassifier, graphlab.boosted_trees_classifier.BoostedTreesClassifier,
graphlab.svm_classifier.SVMClassifier, graphlab.classifier.create
Notes
-----
- Categorical variables are encoded by creating dummy variables. For a
variable with :math:`K` categories, the encoding creates :math:`K-1` dummy
variables, while the first category encountered in the data is used as the
baseline.
- For prediction and evaluation of logistic regression models with sparse
dictionary inputs, new keys/columns that were not seen during training
are silently ignored.
- During model creation, 'None' values in the data will result in an error
being thrown.
- A constant term is automatically added for the model intercept. This term
is not regularized.
- Standard errors on coefficients are only availiable when `solver=newton`
or when the default `auto` solver option choses the newton method and if
the number of examples in the training data is more than the number of
coefficients. If standard errors cannot be estimated, a column of `None`
values are returned.
References
----------
- `Wikipedia - logistic regression
<http://en.wikipedia.org/wiki/Logistic_regression>`_
- Hoerl, A.E. and Kennard, R.W. (1970) `Ridge regression: Biased Estimation
for Nonorthogonal Problems
<http://amstat.tandfonline.com/doi/abs/10.1080/00401706.1970.10488634>`_.
Technometrics 12(1) pp.55-67
- Tibshirani, R. (1996) `Regression Shrinkage and Selection via the Lasso <h
ttp://www.jstor.org/discover/10.2307/2346178?uid=3739256&uid=2&uid=4&sid=2
1104169934983>`_. Journal of the Royal Statistical Society. Series B
(Methodological) 58(1) pp.267-288.
- Zhu, C., et al. (1997) `Algorithm 778: L-BFGS-B: Fortran subroutines for
large-scale bound-constrained optimization
<http://dl.acm.org/citation.cfm?id=279236>`_. ACM Transactions on
Mathematical Software 23(4) pp.550-560.
- Beck, A. and Teboulle, M. (2009) `A Fast Iterative Shrinkage-Thresholding
Algorithm for Linear Inverse Problems
<http://epubs.siam.org/doi/abs/10.1137/080716542>`_. SIAM Journal on
Imaging Sciences 2(1) pp.183-202.
Examples
--------
Given an :class:`~graphlab.SFrame` ``sf``, a list of feature columns
[``feature_1`` ... ``feature_K``], and a target column ``target`` with 0 and
1 values, create a
:class:`~graphlab.logistic_classifier.LogisticClassifier` as follows:
>>> data = graphlab.SFrame('https://static.turi.com/datasets/regression/houses.csv')
>>> data['is_expensive'] = data['price'] > 30000
>>> model = graphlab.logistic_classifier.create(data, 'is_expensive')
By default all columns of ``data`` except the target are used as features, but
specific feature columns can be specified manually.
>>> model = graphlab.logistic_classifier.create(data, 'is_expensive', ['bedroom', 'size'])
.. sourcecode:: python
# L2 regularizer
>>> model_ridge = graphlab.logistic_classifier.create(data, 'is_expensive', l2_penalty=0.1)
# L1 regularizer
>>> model_lasso = graphlab.logistic_classifier.create(data, 'is_expensive', l2_penalty=0.,
l1_penalty=1.0)
# Both L1 and L2 regularizer
>>> model_enet = graphlab.logistic_classifier.create(data, 'is_expensive', l2_penalty=0.5, l1_penalty=0.5)
"""
_mt._get_metric_tracker().track(
'toolkit.classifier.logistic_classifier.create')
# Regression model names.
model_name = "classifier_logistic_regression"
solver = solver.lower()
model = _sl.create(dataset,
target,
model_name,
features=features,
validation_set=validation_set,
verbose=verbose,
l2_penalty=l2_penalty,
l1_penalty=l1_penalty,
feature_rescaling=feature_rescaling,
convergence_threshold=convergence_threshold,
step_size=step_size,
solver=solver,
lbfgs_memory_level=lbfgs_memory_level,
max_iterations=max_iterations,
class_weights=class_weights)
return LogisticClassifier(model.__proxy__)