def wrapper(model, *args, **kwargs):
result = f(model, *args, **kwargs)
dataset_label = None
dataset = []
# model inherits from CustomModel
if isinstance(model, graphlab.toolkits._model.CustomModel):
if len(args) >= 1:
dataset = args[0]
elif 'dataset' in kwargs:
dataset = kwargs['dataset']
elif 'train_data' in kwargs:
dataset = kwargs['train_data']
if isinstance(dataset, (graphlab.data_structures.sframe.SFrame,
graphlab.data_structures.sarray.SArray,
dict, list, tuple, _array.array)):
from graphlab.canvas.inspect import _find_variable_name
(dataset_label,
dataset_variable) = _find_variable_name(dataset)
if dataset_label is None:
if isinstance(dataset_variable,
graphlab.data_structures.sframe.SFrame):
dataset_label = temporarySFrameString
elif isinstance(dataset_variable,
graphlab.data_structures.sarray.SArray):
dataset_label = temporarySArrayString
elif isinstance(dataset_variable,
(dict, list, tuple, _array.array)):
dataset_label = "<%s>" % str(type(dataset_variable))
model._get_workflow().add_step(dataset_label, result)
return result
def wrapper(model, *args, **kwargs):
result = f(model, *args, **kwargs)
dataset_label = None
dataset = []
# model inherits from CustomModel
if isinstance(model, graphlab.toolkits._model.CustomModel):
if len(args) >= 1:
dataset = args[0]
elif 'dataset' in kwargs:
dataset = kwargs['dataset']
elif 'train_data' in kwargs:
dataset = kwargs['train_data']
if isinstance(dataset, ( graphlab.data_structures.sframe.SFrame, \
graphlab.data_structures.sarray.SArray) ):
from graphlab.canvas.inspect import _find_variable_name
(dataset_label, dataset_variable) = _find_variable_name(dataset)
if dataset_label is None:
if isinstance(dataset_variable, graphlab.data_structures.sframe.SFrame):
dataset_label = temporarySFrameString
elif isinstance(dataset_variable, graphlab.data_structures.sarray.SArray):
dataset_label = temporarySArrayString
model._get_workflow().add_step(dataset_label, result)
return result
def compare(dataset, models, **kwargs):
r"""
Compare the prediction (or model-equivalent action) performance of models
on a common test dataset.
.. warning::
This currently only works on Recommender type models.
Parameters
----------
dataset : SFrame
The dataset to use for model evaluation.
models : list[ models]
List of trained models.
Returns
-------
out : list[SFrame]
A list of results where each one is an sframe of evaluation results of
the respective model on the given dataset
Examples
--------
If you have created two ItemSimilarityRecommenders ``m1`` and ``m2`` and have
an :class:`~graphlab.SFrame` ``test_data``, then you may compare the
performance of the two models on test data using:
>>> import graphlab
>>> train_data = graphlab.SFrame({'user_id': ["0", "0", "0", "1", "1", "2", "2", "2"],
... 'item_id': ["a", "c", "e", "b", "f", "b", "c", "d"]})
>>> test_data = graphlab.SFrame({'user_id': ["0", "0", "1", "1", "1", "2", "2"],
... 'item_id': ["b", "d", "a", "c", "e", "a", "e"]})
>>> m1 = graphlab.item_similarity_recommender.create(train_data)
>>> m2 = graphlab.item_similarity_recommender.create(train_data, only_top_k=1)
>>> model_comp = graphlab.compare(test_data, [m1, m2])
The evaluation metric is automatically set to 'precision_recall', and the
evaluation will be based on recommendations that exclude items seen in the
training data.
If you want to evaluate on the original training set:
>>> model_comp = graphlab.compare(train_data, [m1, m2])
Suppose you have four models, two trained with a target rating column, and
the other two trained without a target. By default, the models are put into
two different groups with "rmse", and "precision-recall" as the evaluation
metric respectively.
>>> train_data2 = graphlab.SFrame({'user_id': ["0", "0", "0", "1", "1", "2", "2", "2"],
... 'item_id': ["a", "c", "e", "b", "f", "b", "c", "d"],
... 'rating': [1, 3, 4, 5, 3, 4, 2, 5]})
>>> test_data2 = graphlab.SFrame({'user_id': ["0", "0", "1", "1", "1", "2", "2"],
... 'item_id': ["b", "d", "a", "c", "e", "a", "e"],
... 'rating': [3, 5, 4, 4, 3, 5, 2]})
>>> m3 = graphlab.factorization_recommender.create(train_data2, target='rating')
>>> m4 = graphlab.factorization_recommender.create(train_data2, target='rating')
>>> model_comp = graphlab.compare(test_data2, [m3, m4])
To compare all four models, you can do:
>>> model_comp = graphlab.compare(test_data2, [m1, m2, m3, m4])
"""
_mt._get_metric_tracker().track('toolkit.compare_models')
num_models = len(models)
model_names = []
results = []
if num_models < 1:
raise ValueError("Must pass in at least one model to \
evaluate")
base_model_types = map(lambda m: m.__class__.__base__, models)
unique_base_model_types = list(set(base_model_types))
if len(unique_base_model_types) != 1:
raise ValueError("Must pass in related model types")
metric = 'precision_recall'
if unique_base_model_types[0] == _Recommender:
results = __evaluate_recomenders(dataset,
models,
metric=metric,
**kwargs)
else:
import logging
logging.warn(
'Model comparison currently only supports comparing models created by graphlab.toolkits.recommender or its subclasses such as item_similarity_recommender.'
)
return _graphlab.SFrame()
print("Model compare metric: " + metric)
metric_label = metric
if metric == 'precision_recall':
metric_label = 'precision_recall_overall'
#cast result SFrames to lists
results = map(lambda x: {metric: list(x[metric_label])}, results)
for i in range(0, len(models)):
name = _find_variable_name(models[i])[0]
if name is None or not isinstance(name, str):
name = 'Model_' + str(i)
model_names.append(name)
model_types = map(lambda m: m.__class__.__name__, models)
dataset_label = _find_variable_name(dataset)[0]
dataset_labels = [dataset_label] * num_models
metrics = [metric] * num_models
sframe_results = _graphlab.SFrame({
'model': model_names,
'dataset': dataset_labels,
'model_type': list(model_types),
'metric': metrics,
'results': list(results)
})
return sframe_results
def compare(dataset, models, **kwargs):
r"""
Compare the prediction (or model-equivalent action) performance of models
on a common test dataset.
.. warning::
This currently only works on Recommender type models.
The comparison toolkit is currently in beta, and feedback is
welcome! Please send comments to [email protected].
Parameters
----------
dataset : SFrame
The dataset to use for model evaluation.
models : list[ models]
List of trained models.
Returns
-------
out : list[SFrame]
A list of results where each one is an sframe of evaluation results of
the respective model on the given dataset
Examples
--------
If you have created two ItemSimilarityRecommenders ``m1`` and ``m2`` and have
an :class:`~graphlab.SFrame` ``test_data``, then you may compare the
performance of the two models on test data using:
>>> import graphlab
>>> train_data = graphlab.SFrame({'user_id': ["0", "0", "0", "1", "1", "2", "2", "2"],
... 'item_id': ["a", "c", "e", "b", "f", "b", "c", "d"]})
>>> test_data = graphlab.SFrame({'user_id': ["0", "0", "1", "1", "1", "2", "2"],
... 'item_id': ["b", "d", "a", "c", "e", "a", "e"]})
>>> m1 = graphlab.item_similarity_recommender.create(train_data)
>>> m2 = graphlab.item_similarity_recommender.create(train_data, only_top_k=1)
>>> graphlab.compare(test_data, [m1, m2])
The evaluation metric is automatically set to 'precision_recall', and the
evaluation will be based on recommendations that exclude items seen in the
training data.
If you want to evaluate on the original training set:
>>> graphlab.compare(train_data, [m1, m2])
Suppose you have four models, two trained with a target rating column, and
the other two trained without a target. By default, the models are put into
two different groups with "rmse", and "precision-recall" as the evaluation
metric respectively.
>>> train_data2 = graphlab.SFrame({'user_id': ["0", "0", "0", "1", "1", "2", "2", "2"],
... 'item_id': ["a", "c", "e", "b", "f", "b", "c", "d"],
... 'rating': [1, 3, 4, 5, 3, 4, 2, 5]})
>>> test_data2 = graphlab.SFrame({'user_id': ["0", "0", "1", "1", "1", "2", "2"],
... 'item_id': ["b", "d", "a", "c", "e", "a", "e"],
... 'rating': [3, 5, 4, 4, 3, 5, 2]})
>>> m3 = graphlab.factorization_recommender.create(train_data2, target='rating')
>>> m4 = graphlab.factorization_recommender.create(train_data2, target='rating')
>>> graphlab.compare(test_data2, [m3, m4])
To compare all four models, you can do:
>>> model_comp = graphlab.compare(test_data2, [m1, m2, m3, m4])
"""
_mt._get_metric_tracker().track('toolkit.compare_models')
num_models = len(models)
model_names = []
results = []
if num_models < 1:
raise ValueError("Must pass in at least one model to \
evaluate")
base_model_types = map(lambda m: m.__class__.__base__, models)
unique_base_model_types = list(set(base_model_types))
if len(unique_base_model_types) != 1:
raise ValueError("Must pass in related model types")
metric='precision_recall'
if unique_base_model_types[0] == _Recommender:
results = __evaluate_recomenders(dataset, models, metric=metric, **kwargs)
else:
import logging
logging.warn('Model comparison currently only supports comparing models created by graphlab.toolkits.recommender or its subclasses such as item_similarity_recommender.')
return _graphlab.SFrame()
print "Model compare metric: " + metric
metric_label = metric
if metric == 'precision_recall':
metric_label = 'precision_recall_overall'
#cast result SFrames to lists
results = map( lambda x: {metric: list(x[metric_label]) }, results)
for i in range(0,len(models)):
name = _find_variable_name(models[i])[0]
if name is None or not isinstance(name,str):
name = 'Model_'+str(i)
model_names.append(name)
model_types = map(lambda m: m.__class__.__name__, models)
dataset_label = _find_variable_name(dataset)[0]
dataset_labels = [dataset_label] * num_models
metrics = [metric] * num_models
sframe_results = _graphlab.SFrame({'model': model_names,
'dataset': dataset_labels,
'model_type': model_types,
'metric': metrics,
'results': results
})
return sframe_results