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utils.html
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utils.html
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<article id="content">
<header>
<h1 class="title">Module <code>ktrain.utils</code></h1>
</header>
<section id="section-intro">
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">from .imports import *
from .data import Dataset
#------------------------------------------------------------------------------
# KTRAIN DEFAULTS
#------------------------------------------------------------------------------
DEFAULT_WD = 0.01
def get_default_optimizer(lr=0.001, wd=DEFAULT_WD):
from .lroptimize.optimization import AdamWeightDecay
opt = AdamWeightDecay(learning_rate=lr,
weight_decay_rate=wd,
beta_1=0.9,
beta_2=0.999,
epsilon=1e-6,
exclude_from_weight_decay=['layer_norm', 'bias'])
return opt
# Use vanilla Adam as default unless weight decay is explicitly set by user
# in which case AdamWeightDecay is default optimizer.
# See core.Learner.set_weight_decay for more information
DEFAULT_OPT = 'adam'
DEFAULT_BS = 32
DEFAULT_ES = 5
DEFAULT_ROP = 2
#from .lroptimize.optimization import AdamWeightDecay
#DEFAULT_OPT = AdamWeightDecay(learning_rate=0.001,
#weight_decay_rate=0.01,
#beta_1=0.9,
#beta_2=0.999,
#epsilon=1e-6,
#exclude_from_weight_decay=['layer_norm', 'bias'])
DEFAULT_TRANSFORMER_LAYERS = [-2] # second-to-last hidden state
DEFAULT_TRANSFORMER_MAXLEN = 512
DEFAULT_TRANSFORMER_NUM_SPECIAL = 2
MODEL_BASENAME = 'tf_model'
MODEL_NAME = MODEL_BASENAME+'.h5'
PREPROC_NAME = MODEL_BASENAME+'.preproc'
#------------------------------------------------------------------------------
# DATA/MODEL INSPECTORS
#------------------------------------------------------------------------------
def loss_fn_from_model(model):
# dep_fix
if version.parse(tf.__version__) < version.parse('2.2'):
return model.loss_functions[0].fn
else: # TF 2.2.0
return model.compiled_loss._get_loss_object(model.compiled_loss._losses[0].name).fn
def metrics_from_model(model):
msg = 'Could not retrieve metrics list from compiled model'
# dep_fix
if version.parse(tf.__version__) < version.parse('2.2') or DISABLE_V2_BEHAVIOR:
return model._compile_metrics
#return [m.name for m in model.metrics] if is_tf_keras() else model.metrics
else: # TF >= 2.2.0
mlist = model.compiled_metrics._metrics
if isinstance(mlist, list) and isinstance(mlist[0], str): # metrics are strings prior to training
return mlist
elif isinstance(mlist, list) and isinstance(mlist[0], list):
try:
return [m.name for m in mlist[0]]
except:
warnings.warn(msg)
return []
elif isinstance(mlist, list) and hasattr(mlist[0], 'name'): # tf.keras.metrics.AUC()
try:
return [m.name for m in mlist]
except:
warnings.warn(msg)
return []
else:
warnings.warn(msg)
return []
def is_classifier(model):
"""
checks for classification and mutlilabel from model
"""
is_classifier = False
is_multilabel = False
# get loss name
loss = model.loss
if callable(loss):
if hasattr(loss, '__name__'):
loss = loss.__name__
elif hasattr(loss, 'name'):
loss = loss.name
else:
raise Exception('could not get loss name')
# check for classification
if loss in ['categorical_crossentropy',
'sparse_categorical_crossentropy',
'binary_crossentropy']:
is_classifier = True
else:
mlist = metrics_from_model(model)
if isinstance(mlist, (list, np.ndarray)) and any(['accuracy' in m for m in mlist]):
is_classifier = True
elif isinstance(mlist, (list, np.ndarray)) and any(['auc' in m for m in mlist]):
is_classifier = True
# check for multilabel
if loss == 'binary_crossentropy':
if is_huggingface(model=model):
is_multilabel = True
else:
last = model.layers[-1]
output_shape = last.output_shape
mult_output = True if len(output_shape) ==2 and output_shape[1] > 1 else False
if ( (hasattr(last, 'activation') and isinstance(last.activation, type(sigmoid))) or\
isinstance(last, type(sigmoid)) ) and mult_output:
is_multilabel = True
return (is_classifier, is_multilabel)
def is_tabular_from_data(data):
return type(data).__name__ in ['TabularDataset']
def is_huggingface(model=None, data=None):
"""
check for hugging face transformer model
from model and/or data
"""
huggingface = False
if model is not None and is_huggingface_from_model(model):
huggingface = True
elif data is not None and is_huggingface_from_data(data):
huggingface = True
return huggingface
def is_huggingface_from_model(model):
# 20201202: support both transformers<4.0 and transformers>=4.0
return 'transformers.modeling_tf' in str(type(model)) or 'transformers.models' in str(type(model))
def is_huggingface_from_data(data):
return type(data).__name__ in ['TransformerDataset']
def is_ner(model=None, data=None):
ner = False
if data is None:
warnings.warn('is_ner only detects CRF-based NER models when data is None')
if model is not None and is_crf(model):
ner = True
elif data is not None and is_ner_from_data(data):
ner = True
return ner
def is_crf(model):
"""
checks for CRF sequence tagger.
"""
#loss = model.loss
#if callable(loss):
#if hasattr(loss, '__name__'):
#loss = loss.__name__
#elif hasattr(loss, 'name'):
#loss = loss.name
#else:
#raise Exception('could not get loss name')
#return loss == 'crf_loss' or 'CRF.loss_function' in str(model.loss)
return type(model.layers[-1]).__name__ == 'CRF'
#def is_ner_from_model(model):
#"""
#checks for sequence tagger.
#Curently, only checks for a CRF-based sequence tagger
#"""
#loss = model.loss
#if callable(loss):
#if hasattr(loss, '__name__'):
#loss = loss.__name__
#elif hasattr(loss, 'name'):
#loss = loss.name
#else:
#raise Exception('could not get loss name')
#return loss == 'crf_loss' or 'CRF.loss_function' in str(model.loss)
def is_ner_from_data(data):
return type(data).__name__ == 'NERSequence'
def is_nodeclass(model=None, data=None):
result = False
if data is not None and type(data).__name__ == 'NodeSequenceWrapper':
result = True
return result
def is_linkpred(model=None, data=None):
result = False
if data is not None and type(data).__name__ == 'LinkSequenceWrapper':
result = True
return result
def is_imageclass_from_data(data):
return type(data).__name__ in ['DirectoryIterator', 'DataFrameIterator', 'NumpyArrayIterator']
def is_regression_from_data(data):
"""
checks for regression task from data
"""
data_arg_check(val_data=data, val_required=True)
if is_ner(data=data): return False # NERSequence
elif is_nodeclass(data=data): return False # NodeSequenceWrapper
elif is_linkpred(data=data): return False #LinkSequenceWrapper
Y = y_from_data(data)
if len(Y.shape) == 1 or (len(Y.shape) > 1 and Y.shape[1] == 1): return True
return False
def is_multilabel(data):
"""
checks for multilabel from data
"""
data_arg_check(val_data=data, val_required=True)
if is_ner(data=data): return False # NERSequence
elif is_nodeclass(data=data): return False # NodeSequenceWrapper
elif is_linkpred(data=data): return False #LinkSequenceWrapper
multilabel = False
Y = y_from_data(data)
if len(Y.shape) == 1 or (len(Y.shape) > 1 and Y.shape[1] == 1): return False
for idx, y in enumerate(Y):
if idx >= 1024: break
if np.issubdtype(type(y), np.integer) or np.issubdtype(type(y), np.floating):
return False
total_for_example = sum(y)
if total_for_example > 1:
multilabel=True
break
return multilabel
def shape_from_data(data):
err_msg = 'could not determine shape from %s' % (type(data))
if is_iter(data):
if isinstance(data, Dataset): return data.xshape()
elif hasattr(data, 'image_shape'): return data.image_shape # DirectoryIterator/DataFrameIterator
elif hasattr(data, 'x'): # NumpyIterator
return data.x.shape[1:]
else:
try:
return data[0][0].shape[1:]
except:
raise Exception(err_msg)
else:
try:
if type(data[0]) == list: # BERT-style tuple
return data[0][0].shape
else:
return data[0].shape # standard tuple
except:
raise Exception(err_msg)
def ondisk(data):
if hasattr(data, 'ondisk'): return data.ondisk()
ondisk = is_iter(data) and \
(type(data).__name__ not in ['NumpyArrayIterator'])
return ondisk
def nsamples_from_data(data):
err_msg = 'could not determine number of samples from %s' % (type(data))
if is_iter(data):
if isinstance(data, Dataset): return data.nsamples()
elif hasattr(data, 'samples'): # DirectoryIterator/DataFrameIterator
return data.samples
elif hasattr(data, 'n'): # DirectoryIterator/DataFrameIterator/NumpyIterator
return data.n
else:
raise Exception(err_msg)
else:
try:
if type(data[0]) == list: # BERT-style tuple
return len(data[0][0])
else:
return len(data[0]) # standard tuple
except:
raise Exception(err_msg)
def nclasses_from_data(data):
if is_iter(data):
if isinstance(data, Dataset): return data.nclasses()
elif hasattr(data, 'classes'): # DirectoryIterator
return len(set(data.classes))
else:
try:
return data[0][1].shape[1] # DataFrameIterator/NumpyIterator
except:
raise Exception('could not determine number of classes from %s' % (type(data)))
else:
try:
return data[1].shape[1]
except:
raise Exception('could not determine number of classes from %s' % (type(data)))
def y_from_data(data):
if is_iter(data):
if isinstance(data, Dataset): return data.get_y()
elif hasattr(data, 'classes'): # DirectoryIterator
return to_categorical(data.classes)
elif hasattr(data, 'labels'): # DataFrameIterator
return data.labels
elif hasattr(data, 'y'): # NumpyArrayIterator
#return to_categorical(data.y)
return data.y
else:
raise Exception('could not determine number of classes from %s' % (type(data)))
else:
try:
return data[1]
except:
raise Exception('could not determine number of classes from %s' % (type(data)))
def is_iter(data, ignore=False):
if ignore: return True
iter_classes = ["NumpyArrayIterator", "DirectoryIterator", "DataFrameIterator"]
return data.__class__.__name__ in iter_classes or isinstance(data, Dataset)
def data_arg_check(train_data=None, val_data=None, train_required=False, val_required=False,
ndarray_only=False):
if train_required and train_data is None:
raise ValueError('train_data is required')
if val_required and val_data is None:
raise ValueError('val_data is required')
if train_data is not None and not is_iter(train_data, ndarray_only):
if bad_data_tuple(train_data):
err_msg = 'data must be tuple of numpy.ndarrays'
if not ndarray_only: err_msg += ' or an instance of ktrain.Dataset'
raise ValueError(err_msg)
if val_data is not None and not is_iter(val_data, ndarray_only):
if bad_data_tuple(val_data):
err_msg = 'data must be tuple of numpy.ndarrays or BERT-style tuple'
if not ndarray_only: err_msg += ' or an instance of Iterator'
raise ValueError(err_msg)
return
def bert_data_tuple(data):
"""
checks if data tuple is BERT-style format
"""
if is_iter(data): return False
if type(data[0]) == list and len(data[0]) == 2 and \
type(data[0][0]) is np.ndarray and type(data[0][1]) is np.ndarray and \
type(data[1]) is np.ndarray and np.count_nonzero(data[0][1]) == 0:
return True
else:
return False
def bad_data_tuple(data):
"""
Checks for standard tuple or BERT-style tuple
"""
if not isinstance(data, tuple) or len(data) != 2 or \
type(data[0]) not in [np.ndarray, list] or \
(type(data[0]) in [list] and type(data[0][0]) is not np.ndarray) or \
type(data[1]) is not np.ndarray:
return True
else:
return False
#------------------------------------------------------------------------------
# PLOTTING UTILITIES
#------------------------------------------------------------------------------
# plots images with labels within jupyter notebook
def plots(ims, figsize=(12,6), rows=1, interp=False, titles=None):
#if type(ims[0]) is np.ndarray:
#ims = np.array(ims).astype(np.uint8)
#if (ims.shape[-1] != 3):
#ims = ims.transpose((0,2,3,1))
f = plt.figure(figsize=figsize)
cols = len(ims)//rows if len(ims) % 2 == 0 else len(ims)//rows + 1
for i in range(len(ims)):
sp = f.add_subplot(rows, cols, i+1)
sp.axis('Off')
if titles is not None:
sp.set_title(titles[i], fontsize=16)
plt.imshow(ims[i], interpolation=None if interp else 'none')
def plot_confusion_matrix(cm, classes,
normalize=False,
title='Confusion matrix',
cmap=plt.cm.Blues):
"""
This function prints and plots the confusion matrix.
Normalization can be applied by setting `normalize=True`.
"""
plt.imshow(cm, interpolation='nearest', cmap=cmap)
plt.title(title)
plt.colorbar()
tick_marks = np.arange(len(classes))
plt.xticks(tick_marks, classes, rotation=45)
plt.yticks(tick_marks, classes)
if normalize:
cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
print("Normalized confusion matrix")
else:
print('Confusion matrix, without normalization')
print(cm)
thresh = cm.max() / 2.
for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
plt.text(j, i, cm[i, j],
horizontalalignment="center",
color="white" if cm[i, j] > thresh else "black")
plt.tight_layout()
plt.ylabel('True label')
plt.xlabel('Predicted label')
#------------------------------------------------------------------------------
# DOWNLOAD UTILITIES
#------------------------------------------------------------------------------
def download(url, filename):
with open(filename, 'wb') as f:
response = requests.get(url, stream=True, verify=False)
total = response.headers.get('content-length')
if total is None:
f.write(response.content)
else:
downloaded = 0
total = int(total)
#print(total)
for data in response.iter_content(chunk_size=max(int(total/1000), 1024*1024)):
downloaded += len(data)
f.write(data)
done = int(50*downloaded/total)
sys.stdout.write('\r[{}{}]'.format('█' * done, '.' * (50-done)))
sys.stdout.flush()
def get_ktrain_data():
home = os.path.expanduser('~')
ktrain_data = os.path.join(home, 'ktrain_data')
if not os.path.isdir(ktrain_data):
os.mkdir(ktrain_data)
return ktrain_data
#------------------------------------------------------------------------------
# MISC UTILITIES
#------------------------------------------------------------------------------
def batchify(X, size):
"""
```
Splits texts into separate batch sizes specified by size.
Args:
X(list): elements
size(int): batch size
Returns:
list of evenly sized batches with the last batch having the remaining elements
```
"""
return [X[x : x + size] for x in range(0, len(X), size)]
def check_array(X, y=None, X_name='X', y_name='targets' ):
if not isinstance(X, (list, np.ndarray)): raise ValueError("%s must be a list or NumPy array" % X_name)
if y is not None and not isinstance(y, (list, np.ndarray)): raise ValueError("%s must be a list or NumPy array" % y_name)
return
def is_tf_keras():
if keras.__name__ == 'keras':
is_tf_keras = False
elif keras.__name__ in ['tensorflow.keras', 'tensorflow.python.keras', 'tensorflow_core.keras'] or\
keras.__version__[-3:] == '-tf':
is_tf_keras = True
else:
raise KeyError('Cannot detect if using keras or tf.keras.')
return is_tf_keras
def vprint(s=None, verbose=1):
if not s: s = '\n'
if verbose:
print(s)
def add_headers_to_df(fname_in, header_dict, fname_out=None):
df = pd.read_csv(fname_in, header=None)
df.rename(columns=header_dict, inplace=True)
if fname_out is None:
name, ext = os.path.splitext(fname_in)
name += '-headers'
fname_out = name + '.' + ext
df.to_csv(fname_out, index=False) # save to new csv file
return
def get_random_colors(n, name='hsv', hex_format=True):
'''Returns a function that maps each index in 0, 1, ..., n-1 to a distinct
RGB color; the keyword argument name must be a standard mpl colormap name.'''
cmap = plt.cm.get_cmap(name, n)
result = []
for i in range(n):
color = cmap(i)
if hex_format: color = rgb2hex(color)
result.append(color)
return np.array(result)
def list2chunks(a, n):
k, m = divmod(len(a), n)
return (a[i * k + min(i, m):(i + 1) * k + min(i + 1, m)] for i in range(n))
def get_hf_model_name(model_id):
parts = model_id.split('/')
if len(parts) == 1:
model_id = parts[0]
else:
model_id = '/'.join(parts[1:])
if model_id.startswith('xlm-roberta'):
model_name = 'xlm-roberta'
else:
model_name = model_id.split('-')[0]
return model_name
class YTransform:
def __init__(self, class_names=[], label_encoder=None):
"""
```
Cheks and transforms array of targets. Targets are transformed in place.
Args:
class_names(list): labels associated with targets (e.g., ['negative', 'positive'])
Only used/required if:
1. targets are one/multi-hot-encoded
2. targets are integers and represent class IDs for classification task
Not required if:
1. targets are numeric and task is regression
2. targets are strings and task is classification (class_names are populated automatically)
label_encoder(LabelEncoder): a prior instance of LabelEncoder.
If None, will be created when train=True
```
"""
if type(class_names) != list:
if isinstance(class_names, (pd.Series, np.ndarray)): class_names = class_names.tolist()
else:
raise ValueError('class_names must be list')
self.c = class_names
self.le = label_encoder
self.train_called = False
def get_classes(self):
return self.c
def set_classes(self, class_names):
self.c = class_names.tolist() if isinstance(class_names, np.ndarray) else class_names
def apply(self, targets, train=True):
if targets is None and train:
raise ValueError('targets is None')
elif targets is None and not train:
return
# validate labels against data
targets = np.array(targets) if type(targets) == list else targets
if len(targets.shape) > 1 and targets.shape[1] == 1: targets = np.squeeze(targets, axis=1)
# handle numeric targets (regression)
if len(targets.shape) ==1 and not isinstance(targets[0], str):
# numeric targets
if not self.get_classes() and train:
warnings.warn('Task is being treated as REGRESSION because ' +\
'either class_names argument was not supplied or is_regression=True. ' + \
'If this is incorrect, change accordingly.')
if not self.get_classes(): targets = np.array(targets, dtype=np.float32)
# string targets (classification)
elif len(targets.shape) == 1 and isinstance(targets[0], str):
if not train and self.le is None: raise ValueError('LabelEncoder has not been trained. Call with train=True')
if train:
self.le = LabelEncoder()
self.le.fit(targets)
if self.get_classes(): warnings.warn('class_names argument was ignored, as they were extracted from string labels in dataset')
self.set_classes(self.le.classes_)
targets = self.le.transform(targets) # convert to numerical targets for classfication
# handle categorical targets (classification)
elif len(targets.shape) > 1:
if not self.get_classes():
raise ValueError('targets are 1-hot or multi-hot encoded but class_names is empty. ' +\
'The classes argument should have been supplied.')
else:
if train and len(self.get_classes()) != targets.shape[1]:
raise ValueError('training targets suggest %s classes, but class_names are %s' % (targets.shape[1],
self.get_classes()))
# numeric targets (classification)
if len(targets.shape) == 1 and self.get_classes():
if np.issubdtype(type(max(targets)), np.floating):
warnings.warn('class_names implies classification but targets array contains float(s) instead of integers or strings')
if train and ( len(set(targets)) != int(max(targets)+1) ):
raise ValueError('len(set(targets) is %s but max(targets)+1 is %s' % ( len(set(targets)), int(max(targets)+1) ))
targets = to_categorical(targets, num_classes=len(self.get_classes()))
if train: self.train_called=True
return targets
def apply_train(self, targets):
return self.apply(targets, train=True)
def apply_test(self, targets):
return self.apply(targets, train=False)
class YTransformDataFrame(YTransform):
def __init__(self, label_columns=[], is_regression=False):
"""
```
Checks and transforms label columns in DataFrame. DataFrame is modified in place
Args:
label_columns(list): list of columns storing labels
is_regression(bool): If True, task is regression and integer targets are treated as numeric dependent variable.
IF False, task is classification and integer targets are treated as class IDs.
```
"""
self.is_regression = is_regression
if isinstance(label_columns, str): label_columns = [label_columns]
self.label_columns = label_columns
if not label_columns: raise ValueError('label_columns is required')
self.label_columns = [self.label_columns] if isinstance(self.label_columns, str) else self.label_columns
#class_names = label_columns if len(label_columns) > 1 else []
super().__init__(class_names=[])
def get_label_columns(self, squeeze=True):
"""
Returns label columns of transformed DataFrame
"""
if not self.train_called: raise Exception('apply_train should be called first')
if not self.is_regression:
new_lab_cols = self.c
else:
new_lab_cols = self.label_columns
return new_lab_cols[0] if len(new_lab_cols) ==1 and squeeze else new_lab_cols
def apply(self, df, train=True):
df = df.copy() # dep_fix: SettingWithCopy - prevent original DataFrame from losing old label columns
labels_exist = True
lst = self.label_columns[:]
if not all(x in df.columns.values for x in lst): labels_exist = False
if train and not labels_exist: raise ValueError('dataframe is missing label columns: %s' % (self.label_columns))
# extract targets
# todo: sort?
if len(self.label_columns) > 1:
if train and self.is_regression:
warnings.warn('is_regression=True was supplied but ignored because multiple label columns imply classification')
cols = df.columns.values
missing_cols = []
for l in self.label_columns:
if l not in df.columns.values: missing_cols.append(l)
if len(missing_cols) > 0:
raise ValueError('These label_columns do not exist in df: %s' % (missing_cols))
# set targets
targets = df[self.label_columns].values if labels_exist else np.zeros((df.shape[0], len(self.label_columns)))
# set class names
if train: self.set_classes(self.label_columns)
# single column
else:
# set targets
targets = df[self.label_columns[0]].values if labels_exist else np.zeros(df.shape[0], dtype=np.int)
if self.is_regression and isinstance(targets[0], str):
warnings.warn('is_regression=True was supplied but targets are strings - casting to floats')
targets = targets.astype(np.float)
# set class_names if classification task and targets with integer labels
if train and not self.is_regression and not isinstance(targets[0], str):
class_names = list(set(targets))
class_names.sort()
class_names = list( map(str, class_names) )
if len(class_names) == 2:
class_names = ['not_'+self.label_columns[0], self.label_columns[0]]
else:
class_names = [self.label_columns[0]+'_'+c for c in class_names]
self.set_classes(class_names)
# transform targets
targets = super().apply(targets, train=train) # self.c (new label_columns) may be modified here
targets = targets if len(targets.shape) > 1 else np.expand_dims(targets, 1) # since self.label_columns is list
# modify DataFrame
if labels_exist:
for l in self.label_columns: del df[l] # delete old label columns
new_lab_cols = self.get_label_columns(squeeze=False)
if len(new_lab_cols) != targets.shape[1]:
raise ValueError('mismatch between target shape and number of labels - please open ktrain GitHub issue')
for i, col in enumerate(new_lab_cols):
df[col] = targets[:,i]
df[new_lab_cols] = targets
print(new_lab_cols)
print(df[new_lab_cols].head())
df[new_lab_cols] = df[new_lab_cols].astype('float32')
return df
def apply_train(self, df):
return self.apply(df, train=True)
def apply_test(self, df):
return self.apply(df, train=False)</code></pre>
</details>
</section>
<section>
</section>
<section>
</section>
<section>
<h2 class="section-title" id="header-functions">Functions</h2>
<dl>
<dt id="ktrain.utils.add_headers_to_df"><code class="name flex">
<span>def <span class="ident">add_headers_to_df</span></span>(<span>fname_in, header_dict, fname_out=None)</span>
</code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def add_headers_to_df(fname_in, header_dict, fname_out=None):
df = pd.read_csv(fname_in, header=None)
df.rename(columns=header_dict, inplace=True)
if fname_out is None:
name, ext = os.path.splitext(fname_in)
name += '-headers'
fname_out = name + '.' + ext
df.to_csv(fname_out, index=False) # save to new csv file
return</code></pre>
</details>
</dd>
<dt id="ktrain.utils.bad_data_tuple"><code class="name flex">
<span>def <span class="ident">bad_data_tuple</span></span>(<span>data)</span>
</code></dt>
<dd>
<div class="desc"><p>Checks for standard tuple or BERT-style tuple</p></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def bad_data_tuple(data):
"""
Checks for standard tuple or BERT-style tuple
"""
if not isinstance(data, tuple) or len(data) != 2 or \
type(data[0]) not in [np.ndarray, list] or \
(type(data[0]) in [list] and type(data[0][0]) is not np.ndarray) or \
type(data[1]) is not np.ndarray:
return True
else:
return False</code></pre>
</details>
</dd>
<dt id="ktrain.utils.batchify"><code class="name flex">
<span>def <span class="ident">batchify</span></span>(<span>X, size)</span>
</code></dt>
<dd>
<div class="desc"><pre><code>Splits texts into separate batch sizes specified by size.
Args:
X(list): elements
size(int): batch size
Returns:
list of evenly sized batches with the last batch having the remaining elements
</code></pre></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def batchify(X, size):
"""
```
Splits texts into separate batch sizes specified by size.
Args:
X(list): elements
size(int): batch size
Returns:
list of evenly sized batches with the last batch having the remaining elements
```
"""
return [X[x : x + size] for x in range(0, len(X), size)]</code></pre>
</details>
</dd>
<dt id="ktrain.utils.bert_data_tuple"><code class="name flex">
<span>def <span class="ident">bert_data_tuple</span></span>(<span>data)</span>
</code></dt>
<dd>
<div class="desc"><p>checks if data tuple is BERT-style format</p></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def bert_data_tuple(data):
"""
checks if data tuple is BERT-style format
"""
if is_iter(data): return False
if type(data[0]) == list and len(data[0]) == 2 and \
type(data[0][0]) is np.ndarray and type(data[0][1]) is np.ndarray and \
type(data[1]) is np.ndarray and np.count_nonzero(data[0][1]) == 0:
return True
else:
return False</code></pre>
</details>
</dd>
<dt id="ktrain.utils.check_array"><code class="name flex">
<span>def <span class="ident">check_array</span></span>(<span>X, y=None, X_name='X', y_name='targets')</span>
</code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def check_array(X, y=None, X_name='X', y_name='targets' ):
if not isinstance(X, (list, np.ndarray)): raise ValueError("%s must be a list or NumPy array" % X_name)
if y is not None and not isinstance(y, (list, np.ndarray)): raise ValueError("%s must be a list or NumPy array" % y_name)
return</code></pre>
</details>
</dd>
<dt id="ktrain.utils.data_arg_check"><code class="name flex">
<span>def <span class="ident">data_arg_check</span></span>(<span>train_data=None, val_data=None, train_required=False, val_required=False, ndarray_only=False)</span>
</code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def data_arg_check(train_data=None, val_data=None, train_required=False, val_required=False,
ndarray_only=False):
if train_required and train_data is None:
raise ValueError('train_data is required')
if val_required and val_data is None:
raise ValueError('val_data is required')
if train_data is not None and not is_iter(train_data, ndarray_only):
if bad_data_tuple(train_data):
err_msg = 'data must be tuple of numpy.ndarrays'
if not ndarray_only: err_msg += ' or an instance of ktrain.Dataset'
raise ValueError(err_msg)
if val_data is not None and not is_iter(val_data, ndarray_only):
if bad_data_tuple(val_data):
err_msg = 'data must be tuple of numpy.ndarrays or BERT-style tuple'
if not ndarray_only: err_msg += ' or an instance of Iterator'
raise ValueError(err_msg)
return</code></pre>
</details>
</dd>
<dt id="ktrain.utils.download"><code class="name flex">
<span>def <span class="ident">download</span></span>(<span>url, filename)</span>
</code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def download(url, filename):
with open(filename, 'wb') as f:
response = requests.get(url, stream=True, verify=False)
total = response.headers.get('content-length')
if total is None:
f.write(response.content)
else:
downloaded = 0
total = int(total)
#print(total)
for data in response.iter_content(chunk_size=max(int(total/1000), 1024*1024)):
downloaded += len(data)
f.write(data)
done = int(50*downloaded/total)
sys.stdout.write('\r[{}{}]'.format('█' * done, '.' * (50-done)))
sys.stdout.flush()</code></pre>
</details>
</dd>
<dt id="ktrain.utils.get_default_optimizer"><code class="name flex">
<span>def <span class="ident">get_default_optimizer</span></span>(<span>lr=0.001, wd=0.01)</span>
</code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def get_default_optimizer(lr=0.001, wd=DEFAULT_WD):
from .lroptimize.optimization import AdamWeightDecay
opt = AdamWeightDecay(learning_rate=lr,
weight_decay_rate=wd,
beta_1=0.9,
beta_2=0.999,
epsilon=1e-6,
exclude_from_weight_decay=['layer_norm', 'bias'])
return opt</code></pre>
</details>
</dd>
<dt id="ktrain.utils.get_hf_model_name"><code class="name flex">
<span>def <span class="ident">get_hf_model_name</span></span>(<span>model_id)</span>
</code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def get_hf_model_name(model_id):
parts = model_id.split('/')
if len(parts) == 1:
model_id = parts[0]
else:
model_id = '/'.join(parts[1:])
if model_id.startswith('xlm-roberta'):
model_name = 'xlm-roberta'
else:
model_name = model_id.split('-')[0]
return model_name</code></pre>
</details>
</dd>
<dt id="ktrain.utils.get_ktrain_data"><code class="name flex">
<span>def <span class="ident">get_ktrain_data</span></span>(<span>)</span>
</code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def get_ktrain_data():