Module ktrain.graph.learner
Expand source code
from .. import utils as U
from ..core import GenLearner
from ..imports import *
class NodeClassLearner(GenLearner):
"""
```
Main class used to tune and train Keras models for node classification
Main parameters are:
model (Model): A compiled instance of keras.engine.training.Model
train_data (Iterator): a Iterator instance for training set
val_data (Iterator): A Iterator instance for validation set
```
"""
def __init__(
self,
model,
train_data=None,
val_data=None,
batch_size=U.DEFAULT_BS,
eval_batch_size=U.DEFAULT_BS,
workers=1,
use_multiprocessing=False,
):
super().__init__(
model,
train_data=train_data,
val_data=val_data,
batch_size=batch_size,
eval_batch_size=eval_batch_size,
workers=workers,
use_multiprocessing=use_multiprocessing,
)
return
def view_top_losses(self, n=4, preproc=None, val_data=None):
"""
```
Views observations with top losses in validation set.
Typically over-ridden by Learner subclasses.
Args:
n(int or tuple): a range to select in form of int or tuple
e.g., n=8 is treated as n=(0,8)
preproc (Preprocessor): A TextPreprocessor or ImagePreprocessor.
For some data like text data, a preprocessor
is required to undo the pre-processing
to correctly view raw data.
val_data: optional val_data to use instead of self.val_data
Returns:
list of n tuples where first element is either
filepath or id of validation example and second element
is loss.
```
"""
val = self._check_val(val_data)
# get top losses and associated data
tups = self.top_losses(n=n, val_data=val, preproc=preproc)
# get multilabel status and class names
classes = preproc.get_classes() if preproc is not None else None
# iterate through losses
for tup in tups:
# get data
idx = tup[0]
loss = tup[1]
truth = tup[2]
pred = tup[3]
print("----------")
print(
"id:%s | loss:%s | true:%s | pred:%s)\n"
% (idx, round(loss, 2), truth, pred)
)
# print(obs)
return
def layer_output(self, layer_id, example_id=0, batch_id=0, use_val=False):
"""
```
Prints output of layer with index <layer_id> to help debug models.
Uses first example (example_id=0) from training set, by default.
```
"""
raise Exception(
"currently_unsupported: layer_output method is not yet supported for "
+ "graph neural networks in ktrain"
)
class LinkPredLearner(GenLearner):
"""
```
Main class used to tune and train Keras models for link prediction
Main parameters are:
model (Model): A compiled instance of keras.engine.training.Model
train_data (Iterator): a Iterator instance for training set
val_data (Iterator): A Iterator instance for validation set
```
"""
def __init__(
self,
model,
train_data=None,
val_data=None,
batch_size=U.DEFAULT_BS,
eval_batch_size=U.DEFAULT_BS,
workers=1,
use_multiprocessing=False,
):
super().__init__(
model,
train_data=train_data,
val_data=val_data,
batch_size=batch_size,
eval_batch_size=eval_batch_size,
workers=workers,
use_multiprocessing=use_multiprocessing,
)
return
def view_top_losses(self, n=4, preproc=None, val_data=None):
"""
```
Views observations with top losses in validation set.
Typically over-ridden by Learner subclasses.
Args:
n(int or tuple): a range to select in form of int or tuple
e.g., n=8 is treated as n=(0,8)
preproc (Preprocessor): A TextPreprocessor or ImagePreprocessor.
For some data like text data, a preprocessor
is required to undo the pre-processing
to correctly view raw data.
val_data: optional val_data to use instead of self.val_data
Returns:
list of n tuples where first element is either
filepath or id of validation example and second element
is loss.
```
"""
val = self._check_val(val_data)
# get top losses and associated data
tups = self.top_losses(n=n, val_data=val, preproc=preproc)
# get multilabel status and class names
classes = preproc.get_classes() if preproc is not None else None
# iterate through losses
for tup in tups:
# get data
idx = tup[0]
loss = tup[1]
truth = tup[2]
pred = tup[3]
print("----------")
print(
"id:%s | loss:%s | true:%s | pred:%s)\n"
% (idx, round(loss, 2), truth, pred)
)
# print(obs)
return
def layer_output(self, layer_id, example_id=0, batch_id=0, use_val=False):
"""
```
Prints output of layer with index <layer_id> to help debug models.
Uses first example (example_id=0) from training set, by default.
```
"""
raise Exception(
"currently_unsupported: layer_output method is not yet supported for "
+ "graph neural networks in ktrain"
)Classes
class LinkPredLearner (model, train_data=None, val_data=None, batch_size=32, eval_batch_size=32, workers=1, use_multiprocessing=False)-
Main class used to tune and train Keras models for link prediction Main parameters are: model (Model): A compiled instance of keras.engine.training.Model train_data (Iterator): a Iterator instance for training set val_data (Iterator): A Iterator instance for validation setExpand source code
class LinkPredLearner(GenLearner): """ ``` Main class used to tune and train Keras models for link prediction Main parameters are: model (Model): A compiled instance of keras.engine.training.Model train_data (Iterator): a Iterator instance for training set val_data (Iterator): A Iterator instance for validation set ``` """ def __init__( self, model, train_data=None, val_data=None, batch_size=U.DEFAULT_BS, eval_batch_size=U.DEFAULT_BS, workers=1, use_multiprocessing=False, ): super().__init__( model, train_data=train_data, val_data=val_data, batch_size=batch_size, eval_batch_size=eval_batch_size, workers=workers, use_multiprocessing=use_multiprocessing, ) return def view_top_losses(self, n=4, preproc=None, val_data=None): """ ``` Views observations with top losses in validation set. Typically over-ridden by Learner subclasses. Args: n(int or tuple): a range to select in form of int or tuple e.g., n=8 is treated as n=(0,8) preproc (Preprocessor): A TextPreprocessor or ImagePreprocessor. For some data like text data, a preprocessor is required to undo the pre-processing to correctly view raw data. val_data: optional val_data to use instead of self.val_data Returns: list of n tuples where first element is either filepath or id of validation example and second element is loss. ``` """ val = self._check_val(val_data) # get top losses and associated data tups = self.top_losses(n=n, val_data=val, preproc=preproc) # get multilabel status and class names classes = preproc.get_classes() if preproc is not None else None # iterate through losses for tup in tups: # get data idx = tup[0] loss = tup[1] truth = tup[2] pred = tup[3] print("----------") print( "id:%s | loss:%s | true:%s | pred:%s)\n" % (idx, round(loss, 2), truth, pred) ) # print(obs) return def layer_output(self, layer_id, example_id=0, batch_id=0, use_val=False): """ ``` Prints output of layer with index <layer_id> to help debug models. Uses first example (example_id=0) from training set, by default. ``` """ raise Exception( "currently_unsupported: layer_output method is not yet supported for " + "graph neural networks in ktrain" )Ancestors
- GenLearner
- Learner
- abc.ABC
Methods
def layer_output(self, layer_id, example_id=0, batch_id=0, use_val=False)-
Prints output of layer with index <layer_id> to help debug models. Uses first example (example_id=0) from training set, by default.Expand source code
def layer_output(self, layer_id, example_id=0, batch_id=0, use_val=False): """ ``` Prints output of layer with index <layer_id> to help debug models. Uses first example (example_id=0) from training set, by default. ``` """ raise Exception( "currently_unsupported: layer_output method is not yet supported for " + "graph neural networks in ktrain" )
Inherited members
class NodeClassLearner (model, train_data=None, val_data=None, batch_size=32, eval_batch_size=32, workers=1, use_multiprocessing=False)-
Main class used to tune and train Keras models for node classification Main parameters are: model (Model): A compiled instance of keras.engine.training.Model train_data (Iterator): a Iterator instance for training set val_data (Iterator): A Iterator instance for validation setExpand source code
class NodeClassLearner(GenLearner): """ ``` Main class used to tune and train Keras models for node classification Main parameters are: model (Model): A compiled instance of keras.engine.training.Model train_data (Iterator): a Iterator instance for training set val_data (Iterator): A Iterator instance for validation set ``` """ def __init__( self, model, train_data=None, val_data=None, batch_size=U.DEFAULT_BS, eval_batch_size=U.DEFAULT_BS, workers=1, use_multiprocessing=False, ): super().__init__( model, train_data=train_data, val_data=val_data, batch_size=batch_size, eval_batch_size=eval_batch_size, workers=workers, use_multiprocessing=use_multiprocessing, ) return def view_top_losses(self, n=4, preproc=None, val_data=None): """ ``` Views observations with top losses in validation set. Typically over-ridden by Learner subclasses. Args: n(int or tuple): a range to select in form of int or tuple e.g., n=8 is treated as n=(0,8) preproc (Preprocessor): A TextPreprocessor or ImagePreprocessor. For some data like text data, a preprocessor is required to undo the pre-processing to correctly view raw data. val_data: optional val_data to use instead of self.val_data Returns: list of n tuples where first element is either filepath or id of validation example and second element is loss. ``` """ val = self._check_val(val_data) # get top losses and associated data tups = self.top_losses(n=n, val_data=val, preproc=preproc) # get multilabel status and class names classes = preproc.get_classes() if preproc is not None else None # iterate through losses for tup in tups: # get data idx = tup[0] loss = tup[1] truth = tup[2] pred = tup[3] print("----------") print( "id:%s | loss:%s | true:%s | pred:%s)\n" % (idx, round(loss, 2), truth, pred) ) # print(obs) return def layer_output(self, layer_id, example_id=0, batch_id=0, use_val=False): """ ``` Prints output of layer with index <layer_id> to help debug models. Uses first example (example_id=0) from training set, by default. ``` """ raise Exception( "currently_unsupported: layer_output method is not yet supported for " + "graph neural networks in ktrain" )Ancestors
- GenLearner
- Learner
- abc.ABC
Methods
def layer_output(self, layer_id, example_id=0, batch_id=0, use_val=False)-
Prints output of layer with index <layer_id> to help debug models. Uses first example (example_id=0) from training set, by default.Expand source code
def layer_output(self, layer_id, example_id=0, batch_id=0, use_val=False): """ ``` Prints output of layer with index <layer_id> to help debug models. Uses first example (example_id=0) from training set, by default. ``` """ raise Exception( "currently_unsupported: layer_output method is not yet supported for " + "graph neural networks in ktrain" )
Inherited members