id int32 0 252k | repo stringlengths 7 55 | path stringlengths 4 127 | func_name stringlengths 1 88 | original_string stringlengths 75 19.8k | language stringclasses 1
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24,100 | apache/incubator-mxnet | python/mxnet/recordio.py | MXIndexedRecordIO.tell | def tell(self):
"""Returns the current position of write head.
Examples
---------
>>> record = mx.recordio.MXIndexedRecordIO('tmp.idx', 'tmp.rec', 'w')
>>> print(record.tell())
0
>>> for i in range(5):
... record.write_idx(i, 'record_%d'%i)
..... | python | def tell(self):
"""Returns the current position of write head.
Examples
---------
>>> record = mx.recordio.MXIndexedRecordIO('tmp.idx', 'tmp.rec', 'w')
>>> print(record.tell())
0
>>> for i in range(5):
... record.write_idx(i, 'record_%d'%i)
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24,101 | apache/incubator-mxnet | python/mxnet/recordio.py | MXIndexedRecordIO.write_idx | def write_idx(self, idx, buf):
"""Inserts input record at given index.
Examples
---------
>>> for i in range(5):
... record.write_idx(i, 'record_%d'%i)
>>> record.close()
Parameters
----------
idx : int
Index of a file.
bu... | python | def write_idx(self, idx, buf):
"""Inserts input record at given index.
Examples
---------
>>> for i in range(5):
... record.write_idx(i, 'record_%d'%i)
>>> record.close()
Parameters
----------
idx : int
Index of a file.
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24,102 | apache/incubator-mxnet | python/mxnet/notebook/callback.py | _add_new_columns | def _add_new_columns(dataframe, metrics):
"""Add new metrics as new columns to selected pandas dataframe.
Parameters
----------
dataframe : pandas.DataFrame
Selected dataframe needs to be modified.
metrics : metric.EvalMetric
New metrics to be added.
"""
#TODO(leodirac): we ... | python | def _add_new_columns(dataframe, metrics):
"""Add new metrics as new columns to selected pandas dataframe.
Parameters
----------
dataframe : pandas.DataFrame
Selected dataframe needs to be modified.
metrics : metric.EvalMetric
New metrics to be added.
"""
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24,103 | apache/incubator-mxnet | python/mxnet/notebook/callback.py | PandasLogger.append_metrics | def append_metrics(self, metrics, df_name):
"""Append new metrics to selected dataframes.
Parameters
----------
metrics : metric.EvalMetric
New metrics to be added.
df_name : str
Name of the dataframe to be modified.
"""
dataframe = self._... | python | def append_metrics(self, metrics, df_name):
"""Append new metrics to selected dataframes.
Parameters
----------
metrics : metric.EvalMetric
New metrics to be added.
df_name : str
Name of the dataframe to be modified.
"""
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24,104 | apache/incubator-mxnet | python/mxnet/notebook/callback.py | PandasLogger.train_cb | def train_cb(self, param):
"""Callback funtion for training.
"""
if param.nbatch % self.frequent == 0:
self._process_batch(param, 'train') | python | def train_cb(self, param):
"""Callback funtion for training.
"""
if param.nbatch % self.frequent == 0:
self._process_batch(param, 'train') | [
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24,105 | apache/incubator-mxnet | python/mxnet/notebook/callback.py | PandasLogger.epoch_cb | def epoch_cb(self):
"""Callback function after each epoch. Now it records each epoch time
and append it to epoch dataframe.
"""
metrics = {}
metrics['elapsed'] = self.elapsed()
now = datetime.datetime.now()
metrics['epoch_time'] = now - self.last_epoch_time
... | python | def epoch_cb(self):
"""Callback function after each epoch. Now it records each epoch time
and append it to epoch dataframe.
"""
metrics = {}
metrics['elapsed'] = self.elapsed()
now = datetime.datetime.now()
metrics['epoch_time'] = now - self.last_epoch_time
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24,106 | apache/incubator-mxnet | python/mxnet/notebook/callback.py | LiveBokehChart._push_render | def _push_render(self):
"""Render the plot with bokeh.io and push to notebook.
"""
bokeh.io.push_notebook(handle=self.handle)
self.last_update = time.time() | python | def _push_render(self):
"""Render the plot with bokeh.io and push to notebook.
"""
bokeh.io.push_notebook(handle=self.handle)
self.last_update = time.time() | [
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24,107 | apache/incubator-mxnet | python/mxnet/contrib/text/vocab.py | Vocabulary.to_indices | def to_indices(self, tokens):
"""Converts tokens to indices according to the vocabulary.
Parameters
----------
tokens : str or list of strs
A source token or tokens to be converted.
Returns
-------
int or list of ints
A token index or a... | python | def to_indices(self, tokens):
"""Converts tokens to indices according to the vocabulary.
Parameters
----------
tokens : str or list of strs
A source token or tokens to be converted.
Returns
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int or list of ints
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24,108 | apache/incubator-mxnet | python/mxnet/io/io.py | _make_io_iterator | def _make_io_iterator(handle):
"""Create an io iterator by handle."""
name = ctypes.c_char_p()
desc = ctypes.c_char_p()
num_args = mx_uint()
arg_names = ctypes.POINTER(ctypes.c_char_p)()
arg_types = ctypes.POINTER(ctypes.c_char_p)()
arg_descs = ctypes.POINTER(ctypes.c_char_p)()
check_ca... | python | def _make_io_iterator(handle):
"""Create an io iterator by handle."""
name = ctypes.c_char_p()
desc = ctypes.c_char_p()
num_args = mx_uint()
arg_names = ctypes.POINTER(ctypes.c_char_p)()
arg_types = ctypes.POINTER(ctypes.c_char_p)()
arg_descs = ctypes.POINTER(ctypes.c_char_p)()
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24,109 | apache/incubator-mxnet | python/mxnet/io/io.py | _init_io_module | def _init_io_module():
"""List and add all the data iterators to current module."""
plist = ctypes.POINTER(ctypes.c_void_p)()
size = ctypes.c_uint()
check_call(_LIB.MXListDataIters(ctypes.byref(size), ctypes.byref(plist)))
module_obj = sys.modules[__name__]
for i in range(size.value):
hd... | python | def _init_io_module():
"""List and add all the data iterators to current module."""
plist = ctypes.POINTER(ctypes.c_void_p)()
size = ctypes.c_uint()
check_call(_LIB.MXListDataIters(ctypes.byref(size), ctypes.byref(plist)))
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24,110 | apache/incubator-mxnet | python/mxnet/io/io.py | DataDesc.get_list | def get_list(shapes, types):
"""Get DataDesc list from attribute lists.
Parameters
----------
shapes : a tuple of (name_, shape_)
types : a tuple of (name_, np.dtype)
"""
if types is not None:
type_dict = dict(types)
return [DataDesc(x[0]... | python | def get_list(shapes, types):
"""Get DataDesc list from attribute lists.
Parameters
----------
shapes : a tuple of (name_, shape_)
types : a tuple of (name_, np.dtype)
"""
if types is not None:
type_dict = dict(types)
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24,111 | apache/incubator-mxnet | python/mxnet/io/io.py | DataIter.next | def next(self):
"""Get next data batch from iterator.
Returns
-------
DataBatch
The data of next batch.
Raises
------
StopIteration
If the end of the data is reached.
"""
if self.iter_next():
return DataBatch(d... | python | def next(self):
"""Get next data batch from iterator.
Returns
-------
DataBatch
The data of next batch.
Raises
------
StopIteration
If the end of the data is reached.
"""
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24,112 | apache/incubator-mxnet | python/mxnet/io/io.py | NDArrayIter.hard_reset | def hard_reset(self):
"""Ignore roll over data and set to start."""
if self.shuffle:
self._shuffle_data()
self.cursor = -self.batch_size
self._cache_data = None
self._cache_label = None | python | def hard_reset(self):
"""Ignore roll over data and set to start."""
if self.shuffle:
self._shuffle_data()
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self._cache_data = None
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24,113 | apache/incubator-mxnet | python/mxnet/io/io.py | NDArrayIter.iter_next | def iter_next(self):
"""Increments the coursor by batch_size for next batch
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self.cursor += self.batch_size
return self.cursor < self.num_data | python | def iter_next(self):
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24,114 | apache/incubator-mxnet | python/mxnet/io/io.py | NDArrayIter._getdata | def _getdata(self, data_source, start=None, end=None):
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assert start is not None or end is not None, 'should at least specify start or end'
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24,115 | apache/incubator-mxnet | python/mxnet/io/io.py | NDArrayIter._concat | def _concat(self, first_data, second_data):
"""Helper function to concat two NDArrays."""
assert len(first_data) == len(
second_data), 'data source should contain the same size'
if first_data and second_data:
return [
concat(
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"""Helper function to concat two NDArrays."""
assert len(first_data) == len(
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24,116 | apache/incubator-mxnet | python/mxnet/io/io.py | NDArrayIter._batchify | def _batchify(self, data_source):
"""Load data from underlying arrays, internal use only."""
assert self.cursor < self.num_data, 'DataIter needs reset.'
# first batch of next epoch with 'roll_over'
if self.last_batch_handle == 'roll_over' and \
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"""Load data from underlying arrays, internal use only."""
assert self.cursor < self.num_data, 'DataIter needs reset.'
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24,117 | apache/incubator-mxnet | python/mxnet/io/io.py | NDArrayIter.getpad | def getpad(self):
"""Get pad value of DataBatch."""
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"""Get pad value of DataBatch."""
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24,118 | apache/incubator-mxnet | python/mxnet/contrib/quantization.py | _quantize_params | def _quantize_params(qsym, params, th_dict):
"""Given a quantized symbol and a dict of params that have not been quantized,
generate quantized params. Currently only supports quantizing the arg_params
with names of `weight` or `bias`, not aux_params. If `qsym` contains symbols
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"""Given a quantized symbol and a dict of params that have not been quantized,
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24,119 | apache/incubator-mxnet | python/mxnet/contrib/quantization.py | _quantize_symbol | def _quantize_symbol(sym, excluded_symbols=None, offline_params=None, quantized_dtype='int8'):
"""Given a symbol object representing a neural network of data type FP32,
quantize it into a INT8 network.
Parameters
----------
sym : Symbol
FP32 neural network symbol.
excluded_sym_names : l... | python | def _quantize_symbol(sym, excluded_symbols=None, offline_params=None, quantized_dtype='int8'):
"""Given a symbol object representing a neural network of data type FP32,
quantize it into a INT8 network.
Parameters
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sym : Symbol
FP32 neural network symbol.
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24,120 | apache/incubator-mxnet | python/mxnet/contrib/quantization.py | _calibrate_quantized_sym | def _calibrate_quantized_sym(qsym, th_dict):
"""Given a dictionary containing the thresholds for quantizing the layers,
set the thresholds into the quantized symbol as the params of requantize operators.
"""
if th_dict is None or len(th_dict) == 0:
return qsym
num_layer_outputs = len(th_dict... | python | def _calibrate_quantized_sym(qsym, th_dict):
"""Given a dictionary containing the thresholds for quantizing the layers,
set the thresholds into the quantized symbol as the params of requantize operators.
"""
if th_dict is None or len(th_dict) == 0:
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24,121 | apache/incubator-mxnet | python/mxnet/contrib/quantization.py | _collect_layer_output_min_max | def _collect_layer_output_min_max(mod, data, include_layer=None,
max_num_examples=None, logger=None):
"""Collect min and max values from layer outputs and save them in
a dictionary mapped by layer names.
"""
collector = _LayerOutputMinMaxCollector(include_layer=include_... | python | def _collect_layer_output_min_max(mod, data, include_layer=None,
max_num_examples=None, logger=None):
"""Collect min and max values from layer outputs and save them in
a dictionary mapped by layer names.
"""
collector = _LayerOutputMinMaxCollector(include_layer=include_... | [
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24,122 | apache/incubator-mxnet | python/mxnet/contrib/quantization.py | _collect_layer_outputs | def _collect_layer_outputs(mod, data, include_layer=None, max_num_examples=None, logger=None):
"""Collect layer outputs and save them in a dictionary mapped by layer names."""
collector = _LayerOutputCollector(include_layer=include_layer, logger=logger)
num_examples = _collect_layer_statistics(mod, data, co... | python | def _collect_layer_outputs(mod, data, include_layer=None, max_num_examples=None, logger=None):
"""Collect layer outputs and save them in a dictionary mapped by layer names."""
collector = _LayerOutputCollector(include_layer=include_layer, logger=logger)
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24,123 | apache/incubator-mxnet | python/mxnet/contrib/quantization.py | _get_optimal_thresholds | def _get_optimal_thresholds(nd_dict, quantized_dtype, num_bins=8001, num_quantized_bins=255, logger=None):
"""Given a ndarray dict, find the optimal threshold for quantizing each value of the key."""
if stats is None:
raise ImportError('scipy.stats is required for running entropy mode of calculating'
... | python | def _get_optimal_thresholds(nd_dict, quantized_dtype, num_bins=8001, num_quantized_bins=255, logger=None):
"""Given a ndarray dict, find the optimal threshold for quantizing each value of the key."""
if stats is None:
raise ImportError('scipy.stats is required for running entropy mode of calculating'
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24,124 | apache/incubator-mxnet | python/mxnet/contrib/quantization.py | _load_sym | def _load_sym(sym, logger=logging):
"""Given a str as a path the symbol .json file or a symbol, returns a Symbol object."""
if isinstance(sym, str): # sym is a symbol file path
cur_path = os.path.dirname(os.path.realpath(__file__))
symbol_file_path = os.path.join(cur_path, sym)
logger.i... | python | def _load_sym(sym, logger=logging):
"""Given a str as a path the symbol .json file or a symbol, returns a Symbol object."""
if isinstance(sym, str): # sym is a symbol file path
cur_path = os.path.dirname(os.path.realpath(__file__))
symbol_file_path = os.path.join(cur_path, sym)
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24,125 | apache/incubator-mxnet | python/mxnet/contrib/quantization.py | _load_params | def _load_params(params, logger=logging):
"""Given a str as a path to the .params file or a pair of params,
returns two dictionaries representing arg_params and aux_params.
"""
if isinstance(params, str):
cur_path = os.path.dirname(os.path.realpath(__file__))
param_file_path = os.path.jo... | python | def _load_params(params, logger=logging):
"""Given a str as a path to the .params file or a pair of params,
returns two dictionaries representing arg_params and aux_params.
"""
if isinstance(params, str):
cur_path = os.path.dirname(os.path.realpath(__file__))
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24,126 | apache/incubator-mxnet | python/mxnet/contrib/quantization.py | _LayerOutputCollector.collect | def collect(self, name, arr):
"""Callback function for collecting layer output NDArrays."""
name = py_str(name)
if self.include_layer is not None and not self.include_layer(name):
return
handle = ctypes.cast(arr, NDArrayHandle)
arr = NDArray(handle, writable=False).co... | python | def collect(self, name, arr):
"""Callback function for collecting layer output NDArrays."""
name = py_str(name)
if self.include_layer is not None and not self.include_layer(name):
return
handle = ctypes.cast(arr, NDArrayHandle)
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24,127 | apache/incubator-mxnet | python/mxnet/contrib/quantization.py | _LayerOutputMinMaxCollector.collect | def collect(self, name, arr):
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24,128 | apache/incubator-mxnet | example/vae-gan/vaegan_mxnet.py | encoder | def encoder(nef, z_dim, batch_size, no_bias=True, fix_gamma=True, eps=1e-5 + 1e-12):
'''The encoder is a CNN which takes 32x32 image as input
generates the 100 dimensional shape embedding as a sample from normal distribution
using predicted meand and variance
'''
BatchNorm = mx.sym.BatchNorm
da... | python | def encoder(nef, z_dim, batch_size, no_bias=True, fix_gamma=True, eps=1e-5 + 1e-12):
'''The encoder is a CNN which takes 32x32 image as input
generates the 100 dimensional shape embedding as a sample from normal distribution
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BatchNorm = mx.sym.BatchNorm
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24,129 | apache/incubator-mxnet | example/vae-gan/vaegan_mxnet.py | generator | def generator(ngf, nc, no_bias=True, fix_gamma=True, eps=1e-5 + 1e-12, z_dim=100, activation='sigmoid'):
'''The genrator is a CNN which takes 100 dimensional embedding as input
and reconstructs the input image given to the encoder
'''
BatchNorm = mx.sym.BatchNorm
rand = mx.sym.Variable('rand')
... | python | def generator(ngf, nc, no_bias=True, fix_gamma=True, eps=1e-5 + 1e-12, z_dim=100, activation='sigmoid'):
'''The genrator is a CNN which takes 100 dimensional embedding as input
and reconstructs the input image given to the encoder
'''
BatchNorm = mx.sym.BatchNorm
rand = mx.sym.Variable('rand')
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24,130 | apache/incubator-mxnet | example/vae-gan/vaegan_mxnet.py | discriminator1 | def discriminator1(ndf, no_bias=True, fix_gamma=True, eps=1e-5 + 1e-12):
'''First part of the discriminator which takes a 32x32 image as input
and output a convolutional feature map, this is required to calculate
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BatchNorm = mx.sym.BatchNorm
data = mx.sym.Variable('data')
d1 ... | python | def discriminator1(ndf, no_bias=True, fix_gamma=True, eps=1e-5 + 1e-12):
'''First part of the discriminator which takes a 32x32 image as input
and output a convolutional feature map, this is required to calculate
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BatchNorm = mx.sym.BatchNorm
data = mx.sym.Variable('data')
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24,131 | apache/incubator-mxnet | example/vae-gan/vaegan_mxnet.py | discriminator2 | def discriminator2(ndf, no_bias=True, fix_gamma=True, eps=1e-5 + 1e-12):
'''Second part of the discriminator which takes a 256x8x8 feature map as input
and generates the loss based on whether the input image was a real one or fake one'''
BatchNorm = mx.sym.BatchNorm
data = mx.sym.Variable('data')
... | python | def discriminator2(ndf, no_bias=True, fix_gamma=True, eps=1e-5 + 1e-12):
'''Second part of the discriminator which takes a 256x8x8 feature map as input
and generates the loss based on whether the input image was a real one or fake one'''
BatchNorm = mx.sym.BatchNorm
data = mx.sym.Variable('data')
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24,132 | apache/incubator-mxnet | example/vae-gan/vaegan_mxnet.py | GaussianLogDensity | def GaussianLogDensity(x, mu, log_var, name='GaussianLogDensity', EPSILON = 1e-6):
'''GaussianLogDensity loss calculation for layer wise loss
'''
c = mx.sym.ones_like(log_var)*2.0 * 3.1416
c = mx.symbol.log(c)
var = mx.sym.exp(log_var)
x_mu2 = mx.symbol.square(x - mu) # [Issue] not sure the di... | python | def GaussianLogDensity(x, mu, log_var, name='GaussianLogDensity', EPSILON = 1e-6):
'''GaussianLogDensity loss calculation for layer wise loss
'''
c = mx.sym.ones_like(log_var)*2.0 * 3.1416
c = mx.symbol.log(c)
var = mx.sym.exp(log_var)
x_mu2 = mx.symbol.square(x - mu) # [Issue] not sure the di... | [
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24,133 | apache/incubator-mxnet | example/vae-gan/vaegan_mxnet.py | DiscriminatorLayerLoss | def DiscriminatorLayerLoss():
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'''Calculate the discriminator layer loss
'''
data = mx.sym.Variable('data')
label = mx.sym.Variable('label')
data = mx.sym.Flatten(data)
label = mx.sym.Flatten(label)
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24,134 | apache/incubator-mxnet | example/vae-gan/vaegan_mxnet.py | get_data | def get_data(path, activation):
'''Get the dataset
'''
data = []
image_names = []
for filename in os.listdir(path):
img = cv2.imread(os.path.join(path,filename), cv2.IMREAD_GRAYSCALE)
image_names.append(filename)
if img is not None:
data.append(img)
data = np... | python | def get_data(path, activation):
'''Get the dataset
'''
data = []
image_names = []
for filename in os.listdir(path):
img = cv2.imread(os.path.join(path,filename), cv2.IMREAD_GRAYSCALE)
image_names.append(filename)
if img is not None:
data.append(img)
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24,135 | apache/incubator-mxnet | example/gluon/house_prices/kaggle_k_fold_cross_validation.py | get_rmse_log | def get_rmse_log(net, X_train, y_train):
"""Gets root mse between the logarithms of the prediction and the truth."""
num_train = X_train.shape[0]
clipped_preds = nd.clip(net(X_train), 1, float('inf'))
return np.sqrt(2 * nd.sum(square_loss(
nd.log(clipped_preds), nd.log(y_train))).asscalar() / nu... | python | def get_rmse_log(net, X_train, y_train):
"""Gets root mse between the logarithms of the prediction and the truth."""
num_train = X_train.shape[0]
clipped_preds = nd.clip(net(X_train), 1, float('inf'))
return np.sqrt(2 * nd.sum(square_loss(
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24,136 | apache/incubator-mxnet | example/gluon/house_prices/kaggle_k_fold_cross_validation.py | get_net | def get_net():
"""Gets a neural network. Better results are obtained with modifications."""
net = gluon.nn.Sequential()
with net.name_scope():
net.add(gluon.nn.Dense(50, activation="relu"))
net.add(gluon.nn.Dense(1))
net.initialize()
return net | python | def get_net():
"""Gets a neural network. Better results are obtained with modifications."""
net = gluon.nn.Sequential()
with net.name_scope():
net.add(gluon.nn.Dense(50, activation="relu"))
net.add(gluon.nn.Dense(1))
net.initialize()
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24,137 | apache/incubator-mxnet | example/gluon/house_prices/kaggle_k_fold_cross_validation.py | train | def train(net, X_train, y_train, epochs, verbose_epoch, learning_rate,
weight_decay, batch_size):
"""Trains the model."""
dataset_train = gluon.data.ArrayDataset(X_train, y_train)
data_iter_train = gluon.data.DataLoader(dataset_train, batch_size,
shuffle... | python | def train(net, X_train, y_train, epochs, verbose_epoch, learning_rate,
weight_decay, batch_size):
"""Trains the model."""
dataset_train = gluon.data.ArrayDataset(X_train, y_train)
data_iter_train = gluon.data.DataLoader(dataset_train, batch_size,
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24,138 | apache/incubator-mxnet | example/gluon/house_prices/kaggle_k_fold_cross_validation.py | k_fold_cross_valid | def k_fold_cross_valid(k, epochs, verbose_epoch, X_train, y_train,
learning_rate, weight_decay, batch_size):
"""Conducts k-fold cross validation for the model."""
assert k > 1
fold_size = X_train.shape[0] // k
train_loss_sum = 0.0
test_loss_sum = 0.0
for test_idx in range... | python | def k_fold_cross_valid(k, epochs, verbose_epoch, X_train, y_train,
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"""Conducts k-fold cross validation for the model."""
assert k > 1
fold_size = X_train.shape[0] // k
train_loss_sum = 0.0
test_loss_sum = 0.0
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24,139 | apache/incubator-mxnet | example/gluon/house_prices/kaggle_k_fold_cross_validation.py | learn | def learn(epochs, verbose_epoch, X_train, y_train, test, learning_rate,
weight_decay, batch_size):
"""Trains the model and predicts on the test data set."""
net = get_net()
_ = train(net, X_train, y_train, epochs, verbose_epoch, learning_rate,
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preds =... | python | def learn(epochs, verbose_epoch, X_train, y_train, test, learning_rate,
weight_decay, batch_size):
"""Trains the model and predicts on the test data set."""
net = get_net()
_ = train(net, X_train, y_train, epochs, verbose_epoch, learning_rate,
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24,140 | apache/incubator-mxnet | example/capsnet/capsulenet.py | do_training | def do_training(num_epoch, optimizer, kvstore, learning_rate, model_prefix, decay):
"""Perform CapsNet training"""
summary_writer = SummaryWriter(args.tblog_dir)
lr_scheduler = SimpleLRScheduler(learning_rate)
optimizer_params = {'lr_scheduler': lr_scheduler}
module.init_params()
module.init_opt... | python | def do_training(num_epoch, optimizer, kvstore, learning_rate, model_prefix, decay):
"""Perform CapsNet training"""
summary_writer = SummaryWriter(args.tblog_dir)
lr_scheduler = SimpleLRScheduler(learning_rate)
optimizer_params = {'lr_scheduler': lr_scheduler}
module.init_params()
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24,141 | apache/incubator-mxnet | example/capsnet/capsulenet.py | LossMetric.update | def update(self, labels, preds):
"""Update the hyper-parameters and loss of CapsNet"""
batch_sum_metric = 0
batch_num_inst = 0
for label, pred_outcaps in zip(labels[0], preds[0]):
label_np = int(label.asnumpy())
pred_label = int(np.argmax(pred_outcaps.asnumpy()))
... | python | def update(self, labels, preds):
"""Update the hyper-parameters and loss of CapsNet"""
batch_sum_metric = 0
batch_num_inst = 0
for label, pred_outcaps in zip(labels[0], preds[0]):
label_np = int(label.asnumpy())
pred_label = int(np.argmax(pred_outcaps.asnumpy()))
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24,142 | apache/incubator-mxnet | example/capsnet/capsulenet.py | MNISTCustomIter.next | def next(self):
"""Generate next of iterator"""
if self.iter_next():
if self.is_train:
data_raw_list = self.getdata()
data_shifted = []
for data_raw in data_raw_list[0]:
data_shifted.append(random_shift(data_raw.asnumpy(), 0... | python | def next(self):
"""Generate next of iterator"""
if self.iter_next():
if self.is_train:
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data_shifted = []
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24,143 | apache/incubator-mxnet | python/mxnet/attribute.py | AttrScope.get | def get(self, attr):
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24,144 | apache/incubator-mxnet | python/mxnet/model.py | _create_sparse_kvstore | def _create_sparse_kvstore(kvstore):
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kvstore : KVStore or str
The kvstore.
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kvstore : KVStore
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"""Create kvstore assuming some parameters' storage types are row_sparse.
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kvstore : KVStore or str
The kvstore.
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24,145 | apache/incubator-mxnet | python/mxnet/model.py | _create_kvstore | def _create_kvstore(kvstore, num_device, arg_params):
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kvstore : KVStore or str
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24,146 | apache/incubator-mxnet | python/mxnet/model.py | _update_params_on_kvstore_nccl | def _update_params_on_kvstore_nccl(param_arrays, grad_arrays, kvstore, param_names):
"""Perform update of param_arrays from grad_arrays on NCCL kvstore."""
valid_indices = [index for index, grad_list in
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valid_grad_arrays = [grad_arrays... | python | def _update_params_on_kvstore_nccl(param_arrays, grad_arrays, kvstore, param_names):
"""Perform update of param_arrays from grad_arrays on NCCL kvstore."""
valid_indices = [index for index, grad_list in
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valid_grad_arrays = [grad_arrays... | [
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24,147 | apache/incubator-mxnet | python/mxnet/model.py | _update_params_on_kvstore | def _update_params_on_kvstore(param_arrays, grad_arrays, kvstore, param_names):
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24,148 | apache/incubator-mxnet | python/mxnet/model.py | _update_params | def _update_params(param_arrays, grad_arrays, updater, num_device,
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updates = [[] for _ in range(num_device)]
for i, pair in enumerate(zip(param_arrays, grad_arrays)):
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24,149 | apache/incubator-mxnet | python/mxnet/model.py | _multiple_callbacks | def _multiple_callbacks(callbacks, *args, **kwargs):
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This handles the cases where the 'callbacks' variable
is ``None``, a single function, or a list.
"""
if isinstance(callbacks, list):
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24,150 | apache/incubator-mxnet | python/mxnet/model.py | save_checkpoint | def save_checkpoint(prefix, epoch, symbol, arg_params, aux_params):
"""Checkpoint the model data into file.
Parameters
----------
prefix : str
Prefix of model name.
epoch : int
The epoch number of the model.
symbol : Symbol
The input Symbol.
arg_params : dict of str ... | python | def save_checkpoint(prefix, epoch, symbol, arg_params, aux_params):
"""Checkpoint the model data into file.
Parameters
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prefix : str
Prefix of model name.
epoch : int
The epoch number of the model.
symbol : Symbol
The input Symbol.
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24,151 | apache/incubator-mxnet | python/mxnet/model.py | FeedForward._check_arguments | def _check_arguments(self):
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"""verify the argument of the default symbol and user provided parameters"""
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24,152 | apache/incubator-mxnet | python/mxnet/model.py | FeedForward._init_params | def _init_params(self, inputs, overwrite=False):
"""Initialize weight parameters and auxiliary states."""
inputs = [x if isinstance(x, DataDesc) else DataDesc(*x) for x in inputs]
input_shapes = {item.name: item.shape for item in inputs}
arg_shapes, _, aux_shapes = self.symbol.infer_shap... | python | def _init_params(self, inputs, overwrite=False):
"""Initialize weight parameters and auxiliary states."""
inputs = [x if isinstance(x, DataDesc) else DataDesc(*x) for x in inputs]
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24,153 | apache/incubator-mxnet | python/mxnet/model.py | FeedForward._init_predictor | def _init_predictor(self, input_shapes, type_dict=None):
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24,154 | apache/incubator-mxnet | python/mxnet/model.py | FeedForward._init_iter | def _init_iter(self, X, y, is_train):
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24,156 | apache/incubator-mxnet | python/mxnet/model.py | FeedForward.predict | def predict(self, X, num_batch=None, return_data=False, reset=True):
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num_batch : int or None
The number of batch to run. Go though all batches if ``None``.
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24,157 | apache/incubator-mxnet | python/mxnet/model.py | FeedForward.score | def score(self, X, eval_metric='acc', num_batch=None, batch_end_callback=None, reset=True):
"""Run the model given an input and calculate the score
as assessed by an evaluation metric.
Parameters
----------
X : mxnet.DataIter
eval_metric : metric.metric
The m... | python | def score(self, X, eval_metric='acc', num_batch=None, batch_end_callback=None, reset=True):
"""Run the model given an input and calculate the score
as assessed by an evaluation metric.
Parameters
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eval_metric : metric.metric
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24,158 | apache/incubator-mxnet | python/mxnet/model.py | FeedForward.create | def create(symbol, X, y=None, ctx=None,
num_epoch=None, epoch_size=None, optimizer='sgd', initializer=Uniform(0.01),
eval_data=None, eval_metric='acc',
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24,159 | apache/incubator-mxnet | example/cnn_chinese_text_classification/data_helpers.py | get_chinese_text | def get_chinese_text():
"""Download the chinese_text dataset and unzip it"""
if not os.path.isdir("data/"):
os.system("mkdir data/")
if (not os.path.exists('data/pos.txt')) or \
(not os.path.exists('data/neg')):
os.system("wget -q https://raw.githubusercontent.com/dmlc/web-data/master... | python | def get_chinese_text():
"""Download the chinese_text dataset and unzip it"""
if not os.path.isdir("data/"):
os.system("mkdir data/")
if (not os.path.exists('data/pos.txt')) or \
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os.system("wget -q https://raw.githubusercontent.com/dmlc/web-data/master... | [
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24,160 | apache/incubator-mxnet | example/ssd/train/metric.py | MultiBoxMetric.update | def update(self, labels, preds):
"""
Implementation of updating metrics
"""
# get generated multi label from network
cls_prob = preds[0].asnumpy()
loc_loss = preds[1].asnumpy()
cls_label = preds[2].asnumpy()
valid_count = np.sum(cls_label >= 0)
# o... | python | def update(self, labels, preds):
"""
Implementation of updating metrics
"""
# get generated multi label from network
cls_prob = preds[0].asnumpy()
loc_loss = preds[1].asnumpy()
cls_label = preds[2].asnumpy()
valid_count = np.sum(cls_label >= 0)
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24,161 | apache/incubator-mxnet | example/ssd/train/metric.py | MultiBoxMetric.get | def get(self):
"""Get the current evaluation result.
Override the default behavior
Returns
-------
name : str
Name of the metric.
value : float
Value of the evaluation.
"""
if self.num is None:
if self.num_inst == 0:
... | python | def get(self):
"""Get the current evaluation result.
Override the default behavior
Returns
-------
name : str
Name of the metric.
value : float
Value of the evaluation.
"""
if self.num is None:
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24,162 | apache/incubator-mxnet | python/mxnet/executor.py | Executor._get_dict | def _get_dict(names, ndarrays):
"""Get the dictionary given name and ndarray pairs."""
nset = set()
for nm in names:
if nm in nset:
raise ValueError('Duplicate names detected, %s' % str(names))
nset.add(nm)
return dict(zip(names, ndarrays)) | python | def _get_dict(names, ndarrays):
"""Get the dictionary given name and ndarray pairs."""
nset = set()
for nm in names:
if nm in nset:
raise ValueError('Duplicate names detected, %s' % str(names))
nset.add(nm)
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24,163 | apache/incubator-mxnet | python/mxnet/executor.py | Executor._get_outputs | def _get_outputs(self):
"""List all the output NDArray.
Returns
-------
A list of ndarray bound to the heads of executor.
"""
out_size = mx_uint()
handles = ctypes.POINTER(NDArrayHandle)()
check_call(_LIB.MXExecutorOutputs(self.handle,
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"""List all the output NDArray.
Returns
-------
A list of ndarray bound to the heads of executor.
"""
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handles = ctypes.POINTER(NDArrayHandle)()
check_call(_LIB.MXExecutorOutputs(self.handle,
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24,164 | apache/incubator-mxnet | python/mxnet/executor.py | Executor.forward | def forward(self, is_train=False, **kwargs):
"""Calculate the outputs specified by the bound symbol.
Parameters
----------
is_train: bool, optional
Whether this forward is for evaluation purpose. If True,
a backward call is expected to follow.
**kwargs
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"""Calculate the outputs specified by the bound symbol.
Parameters
----------
is_train: bool, optional
Whether this forward is for evaluation purpose. If True,
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**kwargs
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24,165 | apache/incubator-mxnet | python/mxnet/executor.py | Executor.backward | def backward(self, out_grads=None, is_train=True):
"""Do backward pass to get the gradient of arguments.
Parameters
----------
out_grads : NDArray or list of NDArray or dict of str to NDArray, optional
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Parameters
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out_grads : NDArray or list of NDArray or dict of str to NDArray, optional
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24,166 | apache/incubator-mxnet | python/mxnet/executor.py | Executor.set_monitor_callback | def set_monitor_callback(self, callback, monitor_all=False):
"""Install callback for monitor.
Parameters
----------
callback : function
Takes a string and an NDArrayHandle.
monitor_all : bool, default False
If true, monitor both input and output, otherwis... | python | def set_monitor_callback(self, callback, monitor_all=False):
"""Install callback for monitor.
Parameters
----------
callback : function
Takes a string and an NDArrayHandle.
monitor_all : bool, default False
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24,167 | apache/incubator-mxnet | python/mxnet/executor.py | Executor.arg_dict | def arg_dict(self):
"""Get dictionary representation of argument arrrays.
Returns
-------
arg_dict : dict of str to NDArray
The dictionary that maps the names of arguments to NDArrays.
Raises
------
ValueError : if there are duplicated names in the a... | python | def arg_dict(self):
"""Get dictionary representation of argument arrrays.
Returns
-------
arg_dict : dict of str to NDArray
The dictionary that maps the names of arguments to NDArrays.
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24,168 | apache/incubator-mxnet | python/mxnet/executor.py | Executor.grad_dict | def grad_dict(self):
"""Get dictionary representation of gradient arrays.
Returns
-------
grad_dict : dict of str to NDArray
The dictionary that maps name of arguments to gradient arrays.
"""
if self._grad_dict is None:
self._grad_dict = Executor.... | python | def grad_dict(self):
"""Get dictionary representation of gradient arrays.
Returns
-------
grad_dict : dict of str to NDArray
The dictionary that maps name of arguments to gradient arrays.
"""
if self._grad_dict is None:
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24,169 | apache/incubator-mxnet | python/mxnet/executor.py | Executor.aux_dict | def aux_dict(self):
"""Get dictionary representation of auxiliary states arrays.
Returns
-------
aux_dict : dict of str to NDArray
The dictionary that maps name of auxiliary states to NDArrays.
Raises
------
ValueError : if there are duplicated names... | python | def aux_dict(self):
"""Get dictionary representation of auxiliary states arrays.
Returns
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aux_dict : dict of str to NDArray
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output_dict : dict of str to NDArray
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Raises
------
ValueError : if there are duplicated names in the ... | python | def output_dict(self):
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output_dict : dict of str to NDArray
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24,171 | apache/incubator-mxnet | python/mxnet/executor.py | Executor.copy_params_from | def copy_params_from(self, arg_params, aux_params=None, allow_extra_params=False):
"""Copy parameters from arg_params, aux_params into executor's internal array.
Parameters
----------
arg_params : dict of str to NDArray
Parameters, dict of name to NDArray of arguments.
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Parameters
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arg_params : dict of str to NDArray
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Parameters, dict of name to NDArray of arguments.
aux_params : dict of str to NDArray, optional
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24,172 | apache/incubator-mxnet | python/mxnet/executor.py | Executor.debug_str | def debug_str(self):
"""Get a debug string about internal execution plan.
Returns
-------
debug_str : string
Debug string of the executor.
Examples
--------
>>> a = mx.sym.Variable('a')
>>> b = mx.sym.sin(a)
>>> c = 2 * a + b
... | python | def debug_str(self):
"""Get a debug string about internal execution plan.
Returns
-------
debug_str : string
Debug string of the executor.
Examples
--------
>>> a = mx.sym.Variable('a')
>>> b = mx.sym.sin(a)
>>> c = 2 * a + b
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24,173 | apache/incubator-mxnet | python/mxnet/contrib/onnx/mx2onnx/export_onnx.py | MXNetGraph.convert_layer | def convert_layer(node, **kwargs):
"""Convert MXNet layer to ONNX"""
op = str(node["op"])
if op not in MXNetGraph.registry_:
raise AttributeError("No conversion function registered for op type %s yet." % op)
convert_func = MXNetGraph.registry_[op]
return convert_func(... | python | def convert_layer(node, **kwargs):
"""Convert MXNet layer to ONNX"""
op = str(node["op"])
if op not in MXNetGraph.registry_:
raise AttributeError("No conversion function registered for op type %s yet." % op)
convert_func = MXNetGraph.registry_[op]
return convert_func(... | [
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24,174 | apache/incubator-mxnet | python/mxnet/contrib/onnx/mx2onnx/export_onnx.py | MXNetGraph.split_params | def split_params(sym, params):
"""Helper function to split params dictionary into args and aux params
Parameters
----------
sym : :class:`~mxnet.symbol.Symbol`
MXNet symbol object
params : dict of ``str`` to :class:`~mxnet.ndarray.NDArray`
Dict of convert... | python | def split_params(sym, params):
"""Helper function to split params dictionary into args and aux params
Parameters
----------
sym : :class:`~mxnet.symbol.Symbol`
MXNet symbol object
params : dict of ``str`` to :class:`~mxnet.ndarray.NDArray`
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24,175 | apache/incubator-mxnet | python/mxnet/contrib/onnx/mx2onnx/export_onnx.py | MXNetGraph.get_outputs | def get_outputs(sym, params, in_shape, in_label):
""" Infer output shapes and return dictionary of output name to shape
:param :class:`~mxnet.symbol.Symbol` sym: symbol to perform infer shape on
:param dic of (str, nd.NDArray) params:
:param list of tuple(int, ...) in_shape: list of all... | python | def get_outputs(sym, params, in_shape, in_label):
""" Infer output shapes and return dictionary of output name to shape
:param :class:`~mxnet.symbol.Symbol` sym: symbol to perform infer shape on
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24,176 | apache/incubator-mxnet | python/mxnet/contrib/onnx/mx2onnx/export_onnx.py | MXNetGraph.convert_weights_to_numpy | def convert_weights_to_numpy(weights_dict):
"""Convert weights to numpy"""
return dict([(k.replace("arg:", "").replace("aux:", ""), v.asnumpy())
for k, v in weights_dict.items()]) | python | def convert_weights_to_numpy(weights_dict):
"""Convert weights to numpy"""
return dict([(k.replace("arg:", "").replace("aux:", ""), v.asnumpy())
for k, v in weights_dict.items()]) | [
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24,177 | apache/incubator-mxnet | example/ssd/train/train_net.py | get_lr_scheduler | def get_lr_scheduler(learning_rate, lr_refactor_step, lr_refactor_ratio,
num_example, batch_size, begin_epoch):
"""
Compute learning rate and refactor scheduler
Parameters:
---------
learning_rate : float
original learning rate
lr_refactor_step : comma separated str... | python | def get_lr_scheduler(learning_rate, lr_refactor_step, lr_refactor_ratio,
num_example, batch_size, begin_epoch):
"""
Compute learning rate and refactor scheduler
Parameters:
---------
learning_rate : float
original learning rate
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24,178 | slundberg/shap | shap/datasets.py | imagenet50 | def imagenet50(display=False, resolution=224):
""" This is a set of 50 images representative of ImageNet images.
This dataset was collected by randomly finding a working ImageNet link and then pasting the
original ImageNet image into Google image search restricted to images licensed for reuse. A
simila... | python | def imagenet50(display=False, resolution=224):
""" This is a set of 50 images representative of ImageNet images.
This dataset was collected by randomly finding a working ImageNet link and then pasting the
original ImageNet image into Google image search restricted to images licensed for reuse. A
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24,179 | slundberg/shap | shap/datasets.py | boston | def boston(display=False):
""" Return the boston housing data in a nice package. """
d = sklearn.datasets.load_boston()
df = pd.DataFrame(data=d.data, columns=d.feature_names) # pylint: disable=E1101
return df, d.target | python | def boston(display=False):
""" Return the boston housing data in a nice package. """
d = sklearn.datasets.load_boston()
df = pd.DataFrame(data=d.data, columns=d.feature_names) # pylint: disable=E1101
return df, d.target | [
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24,180 | slundberg/shap | shap/datasets.py | imdb | def imdb(display=False):
""" Return the clssic IMDB sentiment analysis training data in a nice package.
Full data is at: http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz
Paper to cite when using the data is: http://www.aclweb.org/anthology/P11-1015
"""
with open(cache(github_data_url... | python | def imdb(display=False):
""" Return the clssic IMDB sentiment analysis training data in a nice package.
Full data is at: http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz
Paper to cite when using the data is: http://www.aclweb.org/anthology/P11-1015
"""
with open(cache(github_data_url... | [
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24,181 | slundberg/shap | shap/datasets.py | communitiesandcrime | def communitiesandcrime(display=False):
""" Predict total number of non-violent crimes per 100K popuation.
This dataset is from the classic UCI Machine Learning repository:
https://archive.ics.uci.edu/ml/datasets/Communities+and+Crime+Unnormalized
"""
raw_data = pd.read_csv(
cache(github_d... | python | def communitiesandcrime(display=False):
""" Predict total number of non-violent crimes per 100K popuation.
This dataset is from the classic UCI Machine Learning repository:
https://archive.ics.uci.edu/ml/datasets/Communities+and+Crime+Unnormalized
"""
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24,182 | slundberg/shap | shap/datasets.py | diabetes | def diabetes(display=False):
""" Return the diabetes data in a nice package. """
d = sklearn.datasets.load_diabetes()
df = pd.DataFrame(data=d.data, columns=d.feature_names) # pylint: disable=E1101
return df, d.target | python | def diabetes(display=False):
""" Return the diabetes data in a nice package. """
d = sklearn.datasets.load_diabetes()
df = pd.DataFrame(data=d.data, columns=d.feature_names) # pylint: disable=E1101
return df, d.target | [
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24,183 | slundberg/shap | shap/datasets.py | iris | def iris(display=False):
""" Return the classic iris data in a nice package. """
d = sklearn.datasets.load_iris()
df = pd.DataFrame(data=d.data, columns=d.feature_names) # pylint: disable=E1101
if display:
return df, [d.target_names[v] for v in d.target] # pylint: disable=E1101
else:
... | python | def iris(display=False):
""" Return the classic iris data in a nice package. """
d = sklearn.datasets.load_iris()
df = pd.DataFrame(data=d.data, columns=d.feature_names) # pylint: disable=E1101
if display:
return df, [d.target_names[v] for v in d.target] # pylint: disable=E1101
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24,184 | slundberg/shap | shap/datasets.py | adult | def adult(display=False):
""" Return the Adult census data in a nice package. """
dtypes = [
("Age", "float32"), ("Workclass", "category"), ("fnlwgt", "float32"),
("Education", "category"), ("Education-Num", "float32"), ("Marital Status", "category"),
("Occupation", "category"), ("Relati... | python | def adult(display=False):
""" Return the Adult census data in a nice package. """
dtypes = [
("Age", "float32"), ("Workclass", "category"), ("fnlwgt", "float32"),
("Education", "category"), ("Education-Num", "float32"), ("Marital Status", "category"),
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24,185 | slundberg/shap | shap/datasets.py | nhanesi | def nhanesi(display=False):
""" A nicely packaged version of NHANES I data with surivival times as labels.
"""
X = pd.read_csv(cache(github_data_url + "NHANESI_subset_X.csv"))
y = pd.read_csv(cache(github_data_url + "NHANESI_subset_y.csv"))["y"]
if display:
X_display = X.copy()
X_dis... | python | def nhanesi(display=False):
""" A nicely packaged version of NHANES I data with surivival times as labels.
"""
X = pd.read_csv(cache(github_data_url + "NHANESI_subset_X.csv"))
y = pd.read_csv(cache(github_data_url + "NHANESI_subset_y.csv"))["y"]
if display:
X_display = X.copy()
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24,186 | slundberg/shap | shap/datasets.py | cric | def cric(display=False):
""" A nicely packaged version of CRIC data with progression to ESRD within 4 years as the label.
"""
X = pd.read_csv(cache(github_data_url + "CRIC_time_4yearESRD_X.csv"))
y = np.loadtxt(cache(github_data_url + "CRIC_time_4yearESRD_y.csv"))
if display:
X_display = X.c... | python | def cric(display=False):
""" A nicely packaged version of CRIC data with progression to ESRD within 4 years as the label.
"""
X = pd.read_csv(cache(github_data_url + "CRIC_time_4yearESRD_X.csv"))
y = np.loadtxt(cache(github_data_url + "CRIC_time_4yearESRD_y.csv"))
if display:
X_display = X.c... | [
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24,187 | slundberg/shap | shap/datasets.py | corrgroups60 | def corrgroups60(display=False):
""" Correlated Groups 60
A simulated dataset with tight correlations among distinct groups of features.
"""
# set a constant seed
old_seed = np.random.seed()
np.random.seed(0)
# generate dataset with known correlation
N = 1000
M = 60
# set... | python | def corrgroups60(display=False):
""" Correlated Groups 60
A simulated dataset with tight correlations among distinct groups of features.
"""
# set a constant seed
old_seed = np.random.seed()
np.random.seed(0)
# generate dataset with known correlation
N = 1000
M = 60
# set... | [
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24,188 | slundberg/shap | shap/datasets.py | independentlinear60 | def independentlinear60(display=False):
""" A simulated dataset with tight correlations among distinct groups of features.
"""
# set a constant seed
old_seed = np.random.seed()
np.random.seed(0)
# generate dataset with known correlation
N = 1000
M = 60
# set one coefficent from ea... | python | def independentlinear60(display=False):
""" A simulated dataset with tight correlations among distinct groups of features.
"""
# set a constant seed
old_seed = np.random.seed()
np.random.seed(0)
# generate dataset with known correlation
N = 1000
M = 60
# set one coefficent from ea... | [
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24,189 | slundberg/shap | shap/datasets.py | rank | def rank():
""" Ranking datasets from lightgbm repository.
"""
rank_data_url = 'https://raw.githubusercontent.com/Microsoft/LightGBM/master/examples/lambdarank/'
x_train, y_train = sklearn.datasets.load_svmlight_file(cache(rank_data_url + 'rank.train'))
x_test, y_test = sklearn.datasets.load_svmligh... | python | def rank():
""" Ranking datasets from lightgbm repository.
"""
rank_data_url = 'https://raw.githubusercontent.com/Microsoft/LightGBM/master/examples/lambdarank/'
x_train, y_train = sklearn.datasets.load_svmlight_file(cache(rank_data_url + 'rank.train'))
x_test, y_test = sklearn.datasets.load_svmligh... | [
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24,190 | slundberg/shap | shap/benchmark/measures.py | batch_remove_retrain | def batch_remove_retrain(nmask_train, nmask_test, X_train, y_train, X_test, y_test, attr_train, attr_test, model_generator, metric):
""" An approximation of holdout that only retraines the model once.
This is alse called ROAR (RemOve And Retrain) in work by Google. It is much more computationally
efficient... | python | def batch_remove_retrain(nmask_train, nmask_test, X_train, y_train, X_test, y_test, attr_train, attr_test, model_generator, metric):
""" An approximation of holdout that only retraines the model once.
This is alse called ROAR (RemOve And Retrain) in work by Google. It is much more computationally
efficient... | [
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24,191 | slundberg/shap | shap/benchmark/measures.py | keep_retrain | def keep_retrain(nkeep, X_train, y_train, X_test, y_test, attr_test, model_generator, metric, trained_model, random_state):
""" The model is retrained for each test sample with the non-important features set to a constant.
If you want to know how important a set of features is you can ask how the model would b... | python | def keep_retrain(nkeep, X_train, y_train, X_test, y_test, attr_test, model_generator, metric, trained_model, random_state):
""" The model is retrained for each test sample with the non-important features set to a constant.
If you want to know how important a set of features is you can ask how the model would b... | [
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24,192 | slundberg/shap | shap/benchmark/measures.py | keep_mask | def keep_mask(nkeep, X_train, y_train, X_test, y_test, attr_test, model_generator, metric, trained_model, random_state):
""" The model is revaluated for each test sample with the non-important features set to their mean.
"""
X_train, X_test = to_array(X_train, X_test)
# how many features to mask
a... | python | def keep_mask(nkeep, X_train, y_train, X_test, y_test, attr_test, model_generator, metric, trained_model, random_state):
""" The model is revaluated for each test sample with the non-important features set to their mean.
"""
X_train, X_test = to_array(X_train, X_test)
# how many features to mask
a... | [
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24,193 | slundberg/shap | shap/benchmark/measures.py | keep_impute | def keep_impute(nkeep, X_train, y_train, X_test, y_test, attr_test, model_generator, metric, trained_model, random_state):
""" The model is revaluated for each test sample with the non-important features set to an imputed value.
Note that the imputation is done using a multivariate normality assumption on the ... | python | def keep_impute(nkeep, X_train, y_train, X_test, y_test, attr_test, model_generator, metric, trained_model, random_state):
""" The model is revaluated for each test sample with the non-important features set to an imputed value.
Note that the imputation is done using a multivariate normality assumption on the ... | [
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24,194 | slundberg/shap | shap/benchmark/measures.py | keep_resample | def keep_resample(nkeep, X_train, y_train, X_test, y_test, attr_test, model_generator, metric, trained_model, random_state):
""" The model is revaluated for each test sample with the non-important features set to resample background values.
""" # why broken? overwriting?
X_train, X_test = to_array(X_train,... | python | def keep_resample(nkeep, X_train, y_train, X_test, y_test, attr_test, model_generator, metric, trained_model, random_state):
""" The model is revaluated for each test sample with the non-important features set to resample background values.
""" # why broken? overwriting?
X_train, X_test = to_array(X_train,... | [
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24,195 | slundberg/shap | shap/benchmark/measures.py | local_accuracy | def local_accuracy(X_train, y_train, X_test, y_test, attr_test, model_generator, metric, trained_model):
""" The how well do the features plus a constant base rate sum up to the model output.
"""
X_train, X_test = to_array(X_train, X_test)
# how many features to mask
assert X_train.shape[1] == X_t... | python | def local_accuracy(X_train, y_train, X_test, y_test, attr_test, model_generator, metric, trained_model):
""" The how well do the features plus a constant base rate sum up to the model output.
"""
X_train, X_test = to_array(X_train, X_test)
# how many features to mask
assert X_train.shape[1] == X_t... | [
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24,196 | slundberg/shap | shap/benchmark/measures.py | const_rand | def const_rand(size, seed=23980):
""" Generate a random array with a fixed seed.
"""
old_seed = np.random.seed()
np.random.seed(seed)
out = np.random.rand(size)
np.random.seed(old_seed)
return out | python | def const_rand(size, seed=23980):
""" Generate a random array with a fixed seed.
"""
old_seed = np.random.seed()
np.random.seed(seed)
out = np.random.rand(size)
np.random.seed(old_seed)
return out | [
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24,197 | slundberg/shap | shap/benchmark/measures.py | const_shuffle | def const_shuffle(arr, seed=23980):
""" Shuffle an array in-place with a fixed seed.
"""
old_seed = np.random.seed()
np.random.seed(seed)
np.random.shuffle(arr)
np.random.seed(old_seed) | python | def const_shuffle(arr, seed=23980):
""" Shuffle an array in-place with a fixed seed.
"""
old_seed = np.random.seed()
np.random.seed(seed)
np.random.shuffle(arr)
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24,198 | slundberg/shap | shap/common.py | hclust_ordering | def hclust_ordering(X, metric="sqeuclidean"):
""" A leaf ordering is under-defined, this picks the ordering that keeps nearby samples similar.
"""
# compute a hierarchical clustering
D = sp.spatial.distance.pdist(X, metric)
cluster_matrix = sp.cluster.hierarchy.complete(D)
# merge clus... | python | def hclust_ordering(X, metric="sqeuclidean"):
""" A leaf ordering is under-defined, this picks the ordering that keeps nearby samples similar.
"""
# compute a hierarchical clustering
D = sp.spatial.distance.pdist(X, metric)
cluster_matrix = sp.cluster.hierarchy.complete(D)
# merge clus... | [
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24,199 | slundberg/shap | shap/common.py | approximate_interactions | def approximate_interactions(index, shap_values, X, feature_names=None):
""" Order other features by how much interaction they seem to have with the feature at the given index.
This just bins the SHAP values for a feature along that feature's value. For true Shapley interaction
index values for SHAP see th... | python | def approximate_interactions(index, shap_values, X, feature_names=None):
""" Order other features by how much interaction they seem to have with the feature at the given index.
This just bins the SHAP values for a feature along that feature's value. For true Shapley interaction
index values for SHAP see th... | [
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