repo stringclasses 85
values | path stringlengths 8 121 | func_name stringlengths 1 82 | original_string stringlengths 112 65.5k | language stringclasses 1
value | code stringlengths 112 65.5k | code_tokens listlengths 20 4.09k | docstring stringlengths 3 46.3k | docstring_tokens listlengths 1 564 | sha stringclasses 85
values | url stringlengths 93 218 | partition stringclasses 1
value |
|---|---|---|---|---|---|---|---|---|---|---|---|
Microsoft/nni | src/sdk/pynni/nni/curvefitting_assessor/model_factory.py | CurveModel.fit_theta | def fit_theta(self):
"""use least squares to fit all default curves parameter seperately
Returns
-------
None
"""
x = range(1, self.point_num + 1)
y = self.trial_history
for i in range(NUM_OF_FUNCTIONS):
model = curve_combination_model... | python | def fit_theta(self):
"""use least squares to fit all default curves parameter seperately
Returns
-------
None
"""
x = range(1, self.point_num + 1)
y = self.trial_history
for i in range(NUM_OF_FUNCTIONS):
model = curve_combination_model... | [
"def",
"fit_theta",
"(",
"self",
")",
":",
"x",
"=",
"range",
"(",
"1",
",",
"self",
".",
"point_num",
"+",
"1",
")",
"y",
"=",
"self",
".",
"trial_history",
"for",
"i",
"in",
"range",
"(",
"NUM_OF_FUNCTIONS",
")",
":",
"model",
"=",
"curve_combinati... | use least squares to fit all default curves parameter seperately
Returns
-------
None | [
"use",
"least",
"squares",
"to",
"fit",
"all",
"default",
"curves",
"parameter",
"seperately",
"Returns",
"-------",
"None"
] | c7cc8db32da8d2ec77a382a55089f4e17247ce41 | https://github.com/Microsoft/nni/blob/c7cc8db32da8d2ec77a382a55089f4e17247ce41/src/sdk/pynni/nni/curvefitting_assessor/model_factory.py#L54-L86 | train |
Microsoft/nni | src/sdk/pynni/nni/curvefitting_assessor/model_factory.py | CurveModel.filter_curve | def filter_curve(self):
"""filter the poor performing curve
Returns
-------
None
"""
avg = np.sum(self.trial_history) / self.point_num
standard = avg * avg * self.point_num
predict_data = []
tmp_model = []
for i in range(NUM_OF_FUN... | python | def filter_curve(self):
"""filter the poor performing curve
Returns
-------
None
"""
avg = np.sum(self.trial_history) / self.point_num
standard = avg * avg * self.point_num
predict_data = []
tmp_model = []
for i in range(NUM_OF_FUN... | [
"def",
"filter_curve",
"(",
"self",
")",
":",
"avg",
"=",
"np",
".",
"sum",
"(",
"self",
".",
"trial_history",
")",
"/",
"self",
".",
"point_num",
"standard",
"=",
"avg",
"*",
"avg",
"*",
"self",
".",
"point_num",
"predict_data",
"=",
"[",
"]",
"tmp_... | filter the poor performing curve
Returns
-------
None | [
"filter",
"the",
"poor",
"performing",
"curve",
"Returns",
"-------",
"None"
] | c7cc8db32da8d2ec77a382a55089f4e17247ce41 | https://github.com/Microsoft/nni/blob/c7cc8db32da8d2ec77a382a55089f4e17247ce41/src/sdk/pynni/nni/curvefitting_assessor/model_factory.py#L88-L116 | train |
Microsoft/nni | src/sdk/pynni/nni/curvefitting_assessor/model_factory.py | CurveModel.predict_y | def predict_y(self, model, pos):
"""return the predict y of 'model' when epoch = pos
Parameters
----------
model: string
name of the curve function model
pos: int
the epoch number of the position you want to predict
Returns
------... | python | def predict_y(self, model, pos):
"""return the predict y of 'model' when epoch = pos
Parameters
----------
model: string
name of the curve function model
pos: int
the epoch number of the position you want to predict
Returns
------... | [
"def",
"predict_y",
"(",
"self",
",",
"model",
",",
"pos",
")",
":",
"if",
"model_para_num",
"[",
"model",
"]",
"==",
"2",
":",
"y",
"=",
"all_models",
"[",
"model",
"]",
"(",
"pos",
",",
"model_para",
"[",
"model",
"]",
"[",
"0",
"]",
",",
"mode... | return the predict y of 'model' when epoch = pos
Parameters
----------
model: string
name of the curve function model
pos: int
the epoch number of the position you want to predict
Returns
-------
int:
The expected matr... | [
"return",
"the",
"predict",
"y",
"of",
"model",
"when",
"epoch",
"=",
"pos",
"Parameters",
"----------",
"model",
":",
"string",
"name",
"of",
"the",
"curve",
"function",
"model",
"pos",
":",
"int",
"the",
"epoch",
"number",
"of",
"the",
"position",
"you",... | c7cc8db32da8d2ec77a382a55089f4e17247ce41 | https://github.com/Microsoft/nni/blob/c7cc8db32da8d2ec77a382a55089f4e17247ce41/src/sdk/pynni/nni/curvefitting_assessor/model_factory.py#L118-L139 | train |
Microsoft/nni | src/sdk/pynni/nni/curvefitting_assessor/model_factory.py | CurveModel.f_comb | def f_comb(self, pos, sample):
"""return the value of the f_comb when epoch = pos
Parameters
----------
pos: int
the epoch number of the position you want to predict
sample: list
sample is a (1 * NUM_OF_FUNCTIONS) matrix, representing{w1, w2, ... wk}
... | python | def f_comb(self, pos, sample):
"""return the value of the f_comb when epoch = pos
Parameters
----------
pos: int
the epoch number of the position you want to predict
sample: list
sample is a (1 * NUM_OF_FUNCTIONS) matrix, representing{w1, w2, ... wk}
... | [
"def",
"f_comb",
"(",
"self",
",",
"pos",
",",
"sample",
")",
":",
"ret",
"=",
"0",
"for",
"i",
"in",
"range",
"(",
"self",
".",
"effective_model_num",
")",
":",
"model",
"=",
"self",
".",
"effective_model",
"[",
"i",
"]",
"y",
"=",
"self",
".",
... | return the value of the f_comb when epoch = pos
Parameters
----------
pos: int
the epoch number of the position you want to predict
sample: list
sample is a (1 * NUM_OF_FUNCTIONS) matrix, representing{w1, w2, ... wk}
Returns
-------
int
... | [
"return",
"the",
"value",
"of",
"the",
"f_comb",
"when",
"epoch",
"=",
"pos"
] | c7cc8db32da8d2ec77a382a55089f4e17247ce41 | https://github.com/Microsoft/nni/blob/c7cc8db32da8d2ec77a382a55089f4e17247ce41/src/sdk/pynni/nni/curvefitting_assessor/model_factory.py#L141-L161 | train |
Microsoft/nni | src/sdk/pynni/nni/curvefitting_assessor/model_factory.py | CurveModel.normalize_weight | def normalize_weight(self, samples):
"""normalize weight
Parameters
----------
samples: list
a collection of sample, it's a (NUM_OF_INSTANCE * NUM_OF_FUNCTIONS) matrix,
representing{{w11, w12, ..., w1k}, {w21, w22, ... w2k}, ...{wk1, wk2,..., wkk}}
... | python | def normalize_weight(self, samples):
"""normalize weight
Parameters
----------
samples: list
a collection of sample, it's a (NUM_OF_INSTANCE * NUM_OF_FUNCTIONS) matrix,
representing{{w11, w12, ..., w1k}, {w21, w22, ... w2k}, ...{wk1, wk2,..., wkk}}
... | [
"def",
"normalize_weight",
"(",
"self",
",",
"samples",
")",
":",
"for",
"i",
"in",
"range",
"(",
"NUM_OF_INSTANCE",
")",
":",
"total",
"=",
"0",
"for",
"j",
"in",
"range",
"(",
"self",
".",
"effective_model_num",
")",
":",
"total",
"+=",
"samples",
"[... | normalize weight
Parameters
----------
samples: list
a collection of sample, it's a (NUM_OF_INSTANCE * NUM_OF_FUNCTIONS) matrix,
representing{{w11, w12, ..., w1k}, {w21, w22, ... w2k}, ...{wk1, wk2,..., wkk}}
Returns
-------
list
... | [
"normalize",
"weight",
"Parameters",
"----------",
"samples",
":",
"list",
"a",
"collection",
"of",
"sample",
"it",
"s",
"a",
"(",
"NUM_OF_INSTANCE",
"*",
"NUM_OF_FUNCTIONS",
")",
"matrix",
"representing",
"{{",
"w11",
"w12",
"...",
"w1k",
"}",
"{",
"w21",
"... | c7cc8db32da8d2ec77a382a55089f4e17247ce41 | https://github.com/Microsoft/nni/blob/c7cc8db32da8d2ec77a382a55089f4e17247ce41/src/sdk/pynni/nni/curvefitting_assessor/model_factory.py#L163-L183 | train |
Microsoft/nni | src/sdk/pynni/nni/curvefitting_assessor/model_factory.py | CurveModel.sigma_sq | def sigma_sq(self, sample):
"""returns the value of sigma square, given the weight's sample
Parameters
----------
sample: list
sample is a (1 * NUM_OF_FUNCTIONS) matrix, representing{w1, w2, ... wk}
Returns
-------
float
the value... | python | def sigma_sq(self, sample):
"""returns the value of sigma square, given the weight's sample
Parameters
----------
sample: list
sample is a (1 * NUM_OF_FUNCTIONS) matrix, representing{w1, w2, ... wk}
Returns
-------
float
the value... | [
"def",
"sigma_sq",
"(",
"self",
",",
"sample",
")",
":",
"ret",
"=",
"0",
"for",
"i",
"in",
"range",
"(",
"1",
",",
"self",
".",
"point_num",
"+",
"1",
")",
":",
"temp",
"=",
"self",
".",
"trial_history",
"[",
"i",
"-",
"1",
"]",
"-",
"self",
... | returns the value of sigma square, given the weight's sample
Parameters
----------
sample: list
sample is a (1 * NUM_OF_FUNCTIONS) matrix, representing{w1, w2, ... wk}
Returns
-------
float
the value of sigma square, given the weight's sa... | [
"returns",
"the",
"value",
"of",
"sigma",
"square",
"given",
"the",
"weight",
"s",
"sample",
"Parameters",
"----------",
"sample",
":",
"list",
"sample",
"is",
"a",
"(",
"1",
"*",
"NUM_OF_FUNCTIONS",
")",
"matrix",
"representing",
"{",
"w1",
"w2",
"...",
"... | c7cc8db32da8d2ec77a382a55089f4e17247ce41 | https://github.com/Microsoft/nni/blob/c7cc8db32da8d2ec77a382a55089f4e17247ce41/src/sdk/pynni/nni/curvefitting_assessor/model_factory.py#L185-L202 | train |
Microsoft/nni | src/sdk/pynni/nni/curvefitting_assessor/model_factory.py | CurveModel.normal_distribution | def normal_distribution(self, pos, sample):
"""returns the value of normal distribution, given the weight's sample and target position
Parameters
----------
pos: int
the epoch number of the position you want to predict
sample: list
sample is a (1 ... | python | def normal_distribution(self, pos, sample):
"""returns the value of normal distribution, given the weight's sample and target position
Parameters
----------
pos: int
the epoch number of the position you want to predict
sample: list
sample is a (1 ... | [
"def",
"normal_distribution",
"(",
"self",
",",
"pos",
",",
"sample",
")",
":",
"curr_sigma_sq",
"=",
"self",
".",
"sigma_sq",
"(",
"sample",
")",
"delta",
"=",
"self",
".",
"trial_history",
"[",
"pos",
"-",
"1",
"]",
"-",
"self",
".",
"f_comb",
"(",
... | returns the value of normal distribution, given the weight's sample and target position
Parameters
----------
pos: int
the epoch number of the position you want to predict
sample: list
sample is a (1 * NUM_OF_FUNCTIONS) matrix, representing{w1, w2, ... wk... | [
"returns",
"the",
"value",
"of",
"normal",
"distribution",
"given",
"the",
"weight",
"s",
"sample",
"and",
"target",
"position",
"Parameters",
"----------",
"pos",
":",
"int",
"the",
"epoch",
"number",
"of",
"the",
"position",
"you",
"want",
"to",
"predict",
... | c7cc8db32da8d2ec77a382a55089f4e17247ce41 | https://github.com/Microsoft/nni/blob/c7cc8db32da8d2ec77a382a55089f4e17247ce41/src/sdk/pynni/nni/curvefitting_assessor/model_factory.py#L204-L221 | train |
Microsoft/nni | src/sdk/pynni/nni/curvefitting_assessor/model_factory.py | CurveModel.likelihood | def likelihood(self, samples):
"""likelihood
Parameters
----------
sample: list
sample is a (1 * NUM_OF_FUNCTIONS) matrix, representing{w1, w2, ... wk}
Returns
-------
float
likelihood
"""
ret = np.ones(NUM_OF_INST... | python | def likelihood(self, samples):
"""likelihood
Parameters
----------
sample: list
sample is a (1 * NUM_OF_FUNCTIONS) matrix, representing{w1, w2, ... wk}
Returns
-------
float
likelihood
"""
ret = np.ones(NUM_OF_INST... | [
"def",
"likelihood",
"(",
"self",
",",
"samples",
")",
":",
"ret",
"=",
"np",
".",
"ones",
"(",
"NUM_OF_INSTANCE",
")",
"for",
"i",
"in",
"range",
"(",
"NUM_OF_INSTANCE",
")",
":",
"for",
"j",
"in",
"range",
"(",
"1",
",",
"self",
".",
"point_num",
... | likelihood
Parameters
----------
sample: list
sample is a (1 * NUM_OF_FUNCTIONS) matrix, representing{w1, w2, ... wk}
Returns
-------
float
likelihood | [
"likelihood"
] | c7cc8db32da8d2ec77a382a55089f4e17247ce41 | https://github.com/Microsoft/nni/blob/c7cc8db32da8d2ec77a382a55089f4e17247ce41/src/sdk/pynni/nni/curvefitting_assessor/model_factory.py#L223-L240 | train |
Microsoft/nni | src/sdk/pynni/nni/curvefitting_assessor/model_factory.py | CurveModel.prior | def prior(self, samples):
"""priori distribution
Parameters
----------
samples: list
a collection of sample, it's a (NUM_OF_INSTANCE * NUM_OF_FUNCTIONS) matrix,
representing{{w11, w12, ..., w1k}, {w21, w22, ... w2k}, ...{wk1, wk2,..., wkk}}
Retu... | python | def prior(self, samples):
"""priori distribution
Parameters
----------
samples: list
a collection of sample, it's a (NUM_OF_INSTANCE * NUM_OF_FUNCTIONS) matrix,
representing{{w11, w12, ..., w1k}, {w21, w22, ... w2k}, ...{wk1, wk2,..., wkk}}
Retu... | [
"def",
"prior",
"(",
"self",
",",
"samples",
")",
":",
"ret",
"=",
"np",
".",
"ones",
"(",
"NUM_OF_INSTANCE",
")",
"for",
"i",
"in",
"range",
"(",
"NUM_OF_INSTANCE",
")",
":",
"for",
"j",
"in",
"range",
"(",
"self",
".",
"effective_model_num",
")",
"... | priori distribution
Parameters
----------
samples: list
a collection of sample, it's a (NUM_OF_INSTANCE * NUM_OF_FUNCTIONS) matrix,
representing{{w11, w12, ..., w1k}, {w21, w22, ... w2k}, ...{wk1, wk2,..., wkk}}
Returns
-------
float
... | [
"priori",
"distribution",
"Parameters",
"----------",
"samples",
":",
"list",
"a",
"collection",
"of",
"sample",
"it",
"s",
"a",
"(",
"NUM_OF_INSTANCE",
"*",
"NUM_OF_FUNCTIONS",
")",
"matrix",
"representing",
"{{",
"w11",
"w12",
"...",
"w1k",
"}",
"{",
"w21",
... | c7cc8db32da8d2ec77a382a55089f4e17247ce41 | https://github.com/Microsoft/nni/blob/c7cc8db32da8d2ec77a382a55089f4e17247ce41/src/sdk/pynni/nni/curvefitting_assessor/model_factory.py#L242-L263 | train |
Microsoft/nni | src/sdk/pynni/nni/curvefitting_assessor/model_factory.py | CurveModel.target_distribution | def target_distribution(self, samples):
"""posterior probability
Parameters
----------
samples: list
a collection of sample, it's a (NUM_OF_INSTANCE * NUM_OF_FUNCTIONS) matrix,
representing{{w11, w12, ..., w1k}, {w21, w22, ... w2k}, ...{wk1, wk2,..., wkk}... | python | def target_distribution(self, samples):
"""posterior probability
Parameters
----------
samples: list
a collection of sample, it's a (NUM_OF_INSTANCE * NUM_OF_FUNCTIONS) matrix,
representing{{w11, w12, ..., w1k}, {w21, w22, ... w2k}, ...{wk1, wk2,..., wkk}... | [
"def",
"target_distribution",
"(",
"self",
",",
"samples",
")",
":",
"curr_likelihood",
"=",
"self",
".",
"likelihood",
"(",
"samples",
")",
"curr_prior",
"=",
"self",
".",
"prior",
"(",
"samples",
")",
"ret",
"=",
"np",
".",
"ones",
"(",
"NUM_OF_INSTANCE"... | posterior probability
Parameters
----------
samples: list
a collection of sample, it's a (NUM_OF_INSTANCE * NUM_OF_FUNCTIONS) matrix,
representing{{w11, w12, ..., w1k}, {w21, w22, ... w2k}, ...{wk1, wk2,..., wkk}}
Returns
-------
... | [
"posterior",
"probability",
"Parameters",
"----------",
"samples",
":",
"list",
"a",
"collection",
"of",
"sample",
"it",
"s",
"a",
"(",
"NUM_OF_INSTANCE",
"*",
"NUM_OF_FUNCTIONS",
")",
"matrix",
"representing",
"{{",
"w11",
"w12",
"...",
"w1k",
"}",
"{",
"w21"... | c7cc8db32da8d2ec77a382a55089f4e17247ce41 | https://github.com/Microsoft/nni/blob/c7cc8db32da8d2ec77a382a55089f4e17247ce41/src/sdk/pynni/nni/curvefitting_assessor/model_factory.py#L265-L284 | train |
Microsoft/nni | src/sdk/pynni/nni/curvefitting_assessor/model_factory.py | CurveModel.mcmc_sampling | def mcmc_sampling(self):
"""Adjust the weight of each function using mcmc sampling.
The initial value of each weight is evenly distribute.
Brief introduction:
(1)Definition of sample:
Sample is a (1 * NUM_OF_FUNCTIONS) matrix, representing{w1, w2, ... wk}
(2)Definitio... | python | def mcmc_sampling(self):
"""Adjust the weight of each function using mcmc sampling.
The initial value of each weight is evenly distribute.
Brief introduction:
(1)Definition of sample:
Sample is a (1 * NUM_OF_FUNCTIONS) matrix, representing{w1, w2, ... wk}
(2)Definitio... | [
"def",
"mcmc_sampling",
"(",
"self",
")",
":",
"init_weight",
"=",
"np",
".",
"ones",
"(",
"(",
"self",
".",
"effective_model_num",
")",
",",
"dtype",
"=",
"np",
".",
"float",
")",
"/",
"self",
".",
"effective_model_num",
"self",
".",
"weight_samples",
"... | Adjust the weight of each function using mcmc sampling.
The initial value of each weight is evenly distribute.
Brief introduction:
(1)Definition of sample:
Sample is a (1 * NUM_OF_FUNCTIONS) matrix, representing{w1, w2, ... wk}
(2)Definition of samples:
Samples is... | [
"Adjust",
"the",
"weight",
"of",
"each",
"function",
"using",
"mcmc",
"sampling",
".",
"The",
"initial",
"value",
"of",
"each",
"weight",
"is",
"evenly",
"distribute",
".",
"Brief",
"introduction",
":",
"(",
"1",
")",
"Definition",
"of",
"sample",
":",
"Sa... | c7cc8db32da8d2ec77a382a55089f4e17247ce41 | https://github.com/Microsoft/nni/blob/c7cc8db32da8d2ec77a382a55089f4e17247ce41/src/sdk/pynni/nni/curvefitting_assessor/model_factory.py#L286-L318 | train |
Microsoft/nni | src/sdk/pynni/nni/curvefitting_assessor/model_factory.py | CurveModel.predict | def predict(self, trial_history):
"""predict the value of target position
Parameters
----------
trial_history: list
The history performance matrix of each trial.
Returns
-------
float
expected final result performance of this hype... | python | def predict(self, trial_history):
"""predict the value of target position
Parameters
----------
trial_history: list
The history performance matrix of each trial.
Returns
-------
float
expected final result performance of this hype... | [
"def",
"predict",
"(",
"self",
",",
"trial_history",
")",
":",
"self",
".",
"trial_history",
"=",
"trial_history",
"self",
".",
"point_num",
"=",
"len",
"(",
"trial_history",
")",
"self",
".",
"fit_theta",
"(",
")",
"self",
".",
"filter_curve",
"(",
")",
... | predict the value of target position
Parameters
----------
trial_history: list
The history performance matrix of each trial.
Returns
-------
float
expected final result performance of this hyperparameter config | [
"predict",
"the",
"value",
"of",
"target",
"position",
"Parameters",
"----------",
"trial_history",
":",
"list",
"The",
"history",
"performance",
"matrix",
"of",
"each",
"trial",
"."
] | c7cc8db32da8d2ec77a382a55089f4e17247ce41 | https://github.com/Microsoft/nni/blob/c7cc8db32da8d2ec77a382a55089f4e17247ce41/src/sdk/pynni/nni/curvefitting_assessor/model_factory.py#L320-L344 | train |
Microsoft/nni | src/sdk/pynni/nni/metis_tuner/Regression_GP/OutlierDetection.py | _outlierDetection_threaded | def _outlierDetection_threaded(inputs):
'''
Detect the outlier
'''
[samples_idx, samples_x, samples_y_aggregation] = inputs
sys.stderr.write("[%s] DEBUG: Evaluating %dth of %d samples\n"\
% (os.path.basename(__file__), samples_idx + 1, len(samples_x)))
outlier = None
... | python | def _outlierDetection_threaded(inputs):
'''
Detect the outlier
'''
[samples_idx, samples_x, samples_y_aggregation] = inputs
sys.stderr.write("[%s] DEBUG: Evaluating %dth of %d samples\n"\
% (os.path.basename(__file__), samples_idx + 1, len(samples_x)))
outlier = None
... | [
"def",
"_outlierDetection_threaded",
"(",
"inputs",
")",
":",
"[",
"samples_idx",
",",
"samples_x",
",",
"samples_y_aggregation",
"]",
"=",
"inputs",
"sys",
".",
"stderr",
".",
"write",
"(",
"\"[%s] DEBUG: Evaluating %dth of %d samples\\n\"",
"%",
"(",
"os",
".",
... | Detect the outlier | [
"Detect",
"the",
"outlier"
] | c7cc8db32da8d2ec77a382a55089f4e17247ce41 | https://github.com/Microsoft/nni/blob/c7cc8db32da8d2ec77a382a55089f4e17247ce41/src/sdk/pynni/nni/metis_tuner/Regression_GP/OutlierDetection.py#L32-L53 | train |
Microsoft/nni | src/sdk/pynni/nni/metis_tuner/Regression_GP/OutlierDetection.py | outlierDetection_threaded | def outlierDetection_threaded(samples_x, samples_y_aggregation):
'''
Use Multi-thread to detect the outlier
'''
outliers = []
threads_inputs = [[samples_idx, samples_x, samples_y_aggregation]\
for samples_idx in range(0, len(samples_x))]
threads_pool = ThreadPool(min... | python | def outlierDetection_threaded(samples_x, samples_y_aggregation):
'''
Use Multi-thread to detect the outlier
'''
outliers = []
threads_inputs = [[samples_idx, samples_x, samples_y_aggregation]\
for samples_idx in range(0, len(samples_x))]
threads_pool = ThreadPool(min... | [
"def",
"outlierDetection_threaded",
"(",
"samples_x",
",",
"samples_y_aggregation",
")",
":",
"outliers",
"=",
"[",
"]",
"threads_inputs",
"=",
"[",
"[",
"samples_idx",
",",
"samples_x",
",",
"samples_y_aggregation",
"]",
"for",
"samples_idx",
"in",
"range",
"(",
... | Use Multi-thread to detect the outlier | [
"Use",
"Multi",
"-",
"thread",
"to",
"detect",
"the",
"outlier"
] | c7cc8db32da8d2ec77a382a55089f4e17247ce41 | https://github.com/Microsoft/nni/blob/c7cc8db32da8d2ec77a382a55089f4e17247ce41/src/sdk/pynni/nni/metis_tuner/Regression_GP/OutlierDetection.py#L55-L75 | train |
Microsoft/nni | src/sdk/pynni/nni/networkmorphism_tuner/layer_transformer.py | deeper_conv_block | def deeper_conv_block(conv_layer, kernel_size, weighted=True):
'''deeper conv layer.
'''
n_dim = get_n_dim(conv_layer)
filter_shape = (kernel_size,) * 2
n_filters = conv_layer.filters
weight = np.zeros((n_filters, n_filters) + filter_shape)
center = tuple(map(lambda x: int((x - 1) / 2), filt... | python | def deeper_conv_block(conv_layer, kernel_size, weighted=True):
'''deeper conv layer.
'''
n_dim = get_n_dim(conv_layer)
filter_shape = (kernel_size,) * 2
n_filters = conv_layer.filters
weight = np.zeros((n_filters, n_filters) + filter_shape)
center = tuple(map(lambda x: int((x - 1) / 2), filt... | [
"def",
"deeper_conv_block",
"(",
"conv_layer",
",",
"kernel_size",
",",
"weighted",
"=",
"True",
")",
":",
"n_dim",
"=",
"get_n_dim",
"(",
"conv_layer",
")",
"filter_shape",
"=",
"(",
"kernel_size",
",",
")",
"*",
"2",
"n_filters",
"=",
"conv_layer",
".",
... | deeper conv layer. | [
"deeper",
"conv",
"layer",
"."
] | c7cc8db32da8d2ec77a382a55089f4e17247ce41 | https://github.com/Microsoft/nni/blob/c7cc8db32da8d2ec77a382a55089f4e17247ce41/src/sdk/pynni/nni/networkmorphism_tuner/layer_transformer.py#L34-L65 | train |
Microsoft/nni | src/sdk/pynni/nni/networkmorphism_tuner/layer_transformer.py | dense_to_deeper_block | def dense_to_deeper_block(dense_layer, weighted=True):
'''deeper dense layer.
'''
units = dense_layer.units
weight = np.eye(units)
bias = np.zeros(units)
new_dense_layer = StubDense(units, units)
if weighted:
new_dense_layer.set_weights(
(add_noise(weight, np.array([0, 1]... | python | def dense_to_deeper_block(dense_layer, weighted=True):
'''deeper dense layer.
'''
units = dense_layer.units
weight = np.eye(units)
bias = np.zeros(units)
new_dense_layer = StubDense(units, units)
if weighted:
new_dense_layer.set_weights(
(add_noise(weight, np.array([0, 1]... | [
"def",
"dense_to_deeper_block",
"(",
"dense_layer",
",",
"weighted",
"=",
"True",
")",
":",
"units",
"=",
"dense_layer",
".",
"units",
"weight",
"=",
"np",
".",
"eye",
"(",
"units",
")",
"bias",
"=",
"np",
".",
"zeros",
"(",
"units",
")",
"new_dense_laye... | deeper dense layer. | [
"deeper",
"dense",
"layer",
"."
] | c7cc8db32da8d2ec77a382a55089f4e17247ce41 | https://github.com/Microsoft/nni/blob/c7cc8db32da8d2ec77a382a55089f4e17247ce41/src/sdk/pynni/nni/networkmorphism_tuner/layer_transformer.py#L68-L79 | train |
Microsoft/nni | src/sdk/pynni/nni/networkmorphism_tuner/layer_transformer.py | wider_pre_dense | def wider_pre_dense(layer, n_add, weighted=True):
'''wider previous dense layer.
'''
if not weighted:
return StubDense(layer.input_units, layer.units + n_add)
n_units2 = layer.units
teacher_w, teacher_b = layer.get_weights()
rand = np.random.randint(n_units2, size=n_add)
student_w ... | python | def wider_pre_dense(layer, n_add, weighted=True):
'''wider previous dense layer.
'''
if not weighted:
return StubDense(layer.input_units, layer.units + n_add)
n_units2 = layer.units
teacher_w, teacher_b = layer.get_weights()
rand = np.random.randint(n_units2, size=n_add)
student_w ... | [
"def",
"wider_pre_dense",
"(",
"layer",
",",
"n_add",
",",
"weighted",
"=",
"True",
")",
":",
"if",
"not",
"weighted",
":",
"return",
"StubDense",
"(",
"layer",
".",
"input_units",
",",
"layer",
".",
"units",
"+",
"n_add",
")",
"n_units2",
"=",
"layer",
... | wider previous dense layer. | [
"wider",
"previous",
"dense",
"layer",
"."
] | c7cc8db32da8d2ec77a382a55089f4e17247ce41 | https://github.com/Microsoft/nni/blob/c7cc8db32da8d2ec77a382a55089f4e17247ce41/src/sdk/pynni/nni/networkmorphism_tuner/layer_transformer.py#L82-L106 | train |
Microsoft/nni | src/sdk/pynni/nni/networkmorphism_tuner/layer_transformer.py | wider_pre_conv | def wider_pre_conv(layer, n_add_filters, weighted=True):
'''wider previous conv layer.
'''
n_dim = get_n_dim(layer)
if not weighted:
return get_conv_class(n_dim)(
layer.input_channel,
layer.filters + n_add_filters,
kernel_size=layer.kernel_size,
)
... | python | def wider_pre_conv(layer, n_add_filters, weighted=True):
'''wider previous conv layer.
'''
n_dim = get_n_dim(layer)
if not weighted:
return get_conv_class(n_dim)(
layer.input_channel,
layer.filters + n_add_filters,
kernel_size=layer.kernel_size,
)
... | [
"def",
"wider_pre_conv",
"(",
"layer",
",",
"n_add_filters",
",",
"weighted",
"=",
"True",
")",
":",
"n_dim",
"=",
"get_n_dim",
"(",
"layer",
")",
"if",
"not",
"weighted",
":",
"return",
"get_conv_class",
"(",
"n_dim",
")",
"(",
"layer",
".",
"input_channe... | wider previous conv layer. | [
"wider",
"previous",
"conv",
"layer",
"."
] | c7cc8db32da8d2ec77a382a55089f4e17247ce41 | https://github.com/Microsoft/nni/blob/c7cc8db32da8d2ec77a382a55089f4e17247ce41/src/sdk/pynni/nni/networkmorphism_tuner/layer_transformer.py#L109-L139 | train |
Microsoft/nni | src/sdk/pynni/nni/networkmorphism_tuner/layer_transformer.py | wider_next_conv | def wider_next_conv(layer, start_dim, total_dim, n_add, weighted=True):
'''wider next conv layer.
'''
n_dim = get_n_dim(layer)
if not weighted:
return get_conv_class(n_dim)(layer.input_channel + n_add,
layer.filters,
kerne... | python | def wider_next_conv(layer, start_dim, total_dim, n_add, weighted=True):
'''wider next conv layer.
'''
n_dim = get_n_dim(layer)
if not weighted:
return get_conv_class(n_dim)(layer.input_channel + n_add,
layer.filters,
kerne... | [
"def",
"wider_next_conv",
"(",
"layer",
",",
"start_dim",
",",
"total_dim",
",",
"n_add",
",",
"weighted",
"=",
"True",
")",
":",
"n_dim",
"=",
"get_n_dim",
"(",
"layer",
")",
"if",
"not",
"weighted",
":",
"return",
"get_conv_class",
"(",
"n_dim",
")",
"... | wider next conv layer. | [
"wider",
"next",
"conv",
"layer",
"."
] | c7cc8db32da8d2ec77a382a55089f4e17247ce41 | https://github.com/Microsoft/nni/blob/c7cc8db32da8d2ec77a382a55089f4e17247ce41/src/sdk/pynni/nni/networkmorphism_tuner/layer_transformer.py#L142-L166 | train |
Microsoft/nni | src/sdk/pynni/nni/networkmorphism_tuner/layer_transformer.py | wider_bn | def wider_bn(layer, start_dim, total_dim, n_add, weighted=True):
'''wider batch norm layer.
'''
n_dim = get_n_dim(layer)
if not weighted:
return get_batch_norm_class(n_dim)(layer.num_features + n_add)
weights = layer.get_weights()
new_weights = [
add_noise(np.ones(n_add, dtype=... | python | def wider_bn(layer, start_dim, total_dim, n_add, weighted=True):
'''wider batch norm layer.
'''
n_dim = get_n_dim(layer)
if not weighted:
return get_batch_norm_class(n_dim)(layer.num_features + n_add)
weights = layer.get_weights()
new_weights = [
add_noise(np.ones(n_add, dtype=... | [
"def",
"wider_bn",
"(",
"layer",
",",
"start_dim",
",",
"total_dim",
",",
"n_add",
",",
"weighted",
"=",
"True",
")",
":",
"n_dim",
"=",
"get_n_dim",
"(",
"layer",
")",
"if",
"not",
"weighted",
":",
"return",
"get_batch_norm_class",
"(",
"n_dim",
")",
"(... | wider batch norm layer. | [
"wider",
"batch",
"norm",
"layer",
"."
] | c7cc8db32da8d2ec77a382a55089f4e17247ce41 | https://github.com/Microsoft/nni/blob/c7cc8db32da8d2ec77a382a55089f4e17247ce41/src/sdk/pynni/nni/networkmorphism_tuner/layer_transformer.py#L169-L194 | train |
Microsoft/nni | src/sdk/pynni/nni/networkmorphism_tuner/layer_transformer.py | wider_next_dense | def wider_next_dense(layer, start_dim, total_dim, n_add, weighted=True):
'''wider next dense layer.
'''
if not weighted:
return StubDense(layer.input_units + n_add, layer.units)
teacher_w, teacher_b = layer.get_weights()
student_w = teacher_w.copy()
n_units_each_channel = int(teacher_w.s... | python | def wider_next_dense(layer, start_dim, total_dim, n_add, weighted=True):
'''wider next dense layer.
'''
if not weighted:
return StubDense(layer.input_units + n_add, layer.units)
teacher_w, teacher_b = layer.get_weights()
student_w = teacher_w.copy()
n_units_each_channel = int(teacher_w.s... | [
"def",
"wider_next_dense",
"(",
"layer",
",",
"start_dim",
",",
"total_dim",
",",
"n_add",
",",
"weighted",
"=",
"True",
")",
":",
"if",
"not",
"weighted",
":",
"return",
"StubDense",
"(",
"layer",
".",
"input_units",
"+",
"n_add",
",",
"layer",
".",
"un... | wider next dense layer. | [
"wider",
"next",
"dense",
"layer",
"."
] | c7cc8db32da8d2ec77a382a55089f4e17247ce41 | https://github.com/Microsoft/nni/blob/c7cc8db32da8d2ec77a382a55089f4e17247ce41/src/sdk/pynni/nni/networkmorphism_tuner/layer_transformer.py#L197-L220 | train |
Microsoft/nni | src/sdk/pynni/nni/networkmorphism_tuner/layer_transformer.py | add_noise | def add_noise(weights, other_weights):
'''add noise to the layer.
'''
w_range = np.ptp(other_weights.flatten())
noise_range = NOISE_RATIO * w_range
noise = np.random.uniform(-noise_range / 2.0, noise_range / 2.0, weights.shape)
return np.add(noise, weights) | python | def add_noise(weights, other_weights):
'''add noise to the layer.
'''
w_range = np.ptp(other_weights.flatten())
noise_range = NOISE_RATIO * w_range
noise = np.random.uniform(-noise_range / 2.0, noise_range / 2.0, weights.shape)
return np.add(noise, weights) | [
"def",
"add_noise",
"(",
"weights",
",",
"other_weights",
")",
":",
"w_range",
"=",
"np",
".",
"ptp",
"(",
"other_weights",
".",
"flatten",
"(",
")",
")",
"noise_range",
"=",
"NOISE_RATIO",
"*",
"w_range",
"noise",
"=",
"np",
".",
"random",
".",
"uniform... | add noise to the layer. | [
"add",
"noise",
"to",
"the",
"layer",
"."
] | c7cc8db32da8d2ec77a382a55089f4e17247ce41 | https://github.com/Microsoft/nni/blob/c7cc8db32da8d2ec77a382a55089f4e17247ce41/src/sdk/pynni/nni/networkmorphism_tuner/layer_transformer.py#L223-L229 | train |
Microsoft/nni | src/sdk/pynni/nni/networkmorphism_tuner/layer_transformer.py | init_dense_weight | def init_dense_weight(layer):
'''initilize dense layer weight.
'''
units = layer.units
weight = np.eye(units)
bias = np.zeros(units)
layer.set_weights(
(add_noise(weight, np.array([0, 1])), add_noise(bias, np.array([0, 1])))
) | python | def init_dense_weight(layer):
'''initilize dense layer weight.
'''
units = layer.units
weight = np.eye(units)
bias = np.zeros(units)
layer.set_weights(
(add_noise(weight, np.array([0, 1])), add_noise(bias, np.array([0, 1])))
) | [
"def",
"init_dense_weight",
"(",
"layer",
")",
":",
"units",
"=",
"layer",
".",
"units",
"weight",
"=",
"np",
".",
"eye",
"(",
"units",
")",
"bias",
"=",
"np",
".",
"zeros",
"(",
"units",
")",
"layer",
".",
"set_weights",
"(",
"(",
"add_noise",
"(",
... | initilize dense layer weight. | [
"initilize",
"dense",
"layer",
"weight",
"."
] | c7cc8db32da8d2ec77a382a55089f4e17247ce41 | https://github.com/Microsoft/nni/blob/c7cc8db32da8d2ec77a382a55089f4e17247ce41/src/sdk/pynni/nni/networkmorphism_tuner/layer_transformer.py#L232-L240 | train |
Microsoft/nni | src/sdk/pynni/nni/networkmorphism_tuner/layer_transformer.py | init_conv_weight | def init_conv_weight(layer):
'''initilize conv layer weight.
'''
n_filters = layer.filters
filter_shape = (layer.kernel_size,) * get_n_dim(layer)
weight = np.zeros((n_filters, n_filters) + filter_shape)
center = tuple(map(lambda x: int((x - 1) / 2), filter_shape))
for i in range(n_filters):... | python | def init_conv_weight(layer):
'''initilize conv layer weight.
'''
n_filters = layer.filters
filter_shape = (layer.kernel_size,) * get_n_dim(layer)
weight = np.zeros((n_filters, n_filters) + filter_shape)
center = tuple(map(lambda x: int((x - 1) / 2), filter_shape))
for i in range(n_filters):... | [
"def",
"init_conv_weight",
"(",
"layer",
")",
":",
"n_filters",
"=",
"layer",
".",
"filters",
"filter_shape",
"=",
"(",
"layer",
".",
"kernel_size",
",",
")",
"*",
"get_n_dim",
"(",
"layer",
")",
"weight",
"=",
"np",
".",
"zeros",
"(",
"(",
"n_filters",
... | initilize conv layer weight. | [
"initilize",
"conv",
"layer",
"weight",
"."
] | c7cc8db32da8d2ec77a382a55089f4e17247ce41 | https://github.com/Microsoft/nni/blob/c7cc8db32da8d2ec77a382a55089f4e17247ce41/src/sdk/pynni/nni/networkmorphism_tuner/layer_transformer.py#L243-L260 | train |
Microsoft/nni | src/sdk/pynni/nni/networkmorphism_tuner/layer_transformer.py | init_bn_weight | def init_bn_weight(layer):
'''initilize batch norm layer weight.
'''
n_filters = layer.num_features
new_weights = [
add_noise(np.ones(n_filters, dtype=np.float32), np.array([0, 1])),
add_noise(np.zeros(n_filters, dtype=np.float32), np.array([0, 1])),
add_noise(np.zeros(n_filters,... | python | def init_bn_weight(layer):
'''initilize batch norm layer weight.
'''
n_filters = layer.num_features
new_weights = [
add_noise(np.ones(n_filters, dtype=np.float32), np.array([0, 1])),
add_noise(np.zeros(n_filters, dtype=np.float32), np.array([0, 1])),
add_noise(np.zeros(n_filters,... | [
"def",
"init_bn_weight",
"(",
"layer",
")",
":",
"n_filters",
"=",
"layer",
".",
"num_features",
"new_weights",
"=",
"[",
"add_noise",
"(",
"np",
".",
"ones",
"(",
"n_filters",
",",
"dtype",
"=",
"np",
".",
"float32",
")",
",",
"np",
".",
"array",
"(",... | initilize batch norm layer weight. | [
"initilize",
"batch",
"norm",
"layer",
"weight",
"."
] | c7cc8db32da8d2ec77a382a55089f4e17247ce41 | https://github.com/Microsoft/nni/blob/c7cc8db32da8d2ec77a382a55089f4e17247ce41/src/sdk/pynni/nni/networkmorphism_tuner/layer_transformer.py#L263-L273 | train |
Microsoft/nni | tools/nni_cmd/tensorboard_utils.py | parse_log_path | def parse_log_path(args, trial_content):
'''parse log path'''
path_list = []
host_list = []
for trial in trial_content:
if args.trial_id and args.trial_id != 'all' and trial.get('id') != args.trial_id:
continue
pattern = r'(?P<head>.+)://(?P<host>.+):(?P<path>.*)'
mat... | python | def parse_log_path(args, trial_content):
'''parse log path'''
path_list = []
host_list = []
for trial in trial_content:
if args.trial_id and args.trial_id != 'all' and trial.get('id') != args.trial_id:
continue
pattern = r'(?P<head>.+)://(?P<host>.+):(?P<path>.*)'
mat... | [
"def",
"parse_log_path",
"(",
"args",
",",
"trial_content",
")",
":",
"path_list",
"=",
"[",
"]",
"host_list",
"=",
"[",
"]",
"for",
"trial",
"in",
"trial_content",
":",
"if",
"args",
".",
"trial_id",
"and",
"args",
".",
"trial_id",
"!=",
"'all'",
"and",... | parse log path | [
"parse",
"log",
"path"
] | c7cc8db32da8d2ec77a382a55089f4e17247ce41 | https://github.com/Microsoft/nni/blob/c7cc8db32da8d2ec77a382a55089f4e17247ce41/tools/nni_cmd/tensorboard_utils.py#L38-L53 | train |
Microsoft/nni | tools/nni_cmd/tensorboard_utils.py | copy_data_from_remote | def copy_data_from_remote(args, nni_config, trial_content, path_list, host_list, temp_nni_path):
'''use ssh client to copy data from remote machine to local machien'''
machine_list = nni_config.get_config('experimentConfig').get('machineList')
machine_dict = {}
local_path_list = []
for machine in ma... | python | def copy_data_from_remote(args, nni_config, trial_content, path_list, host_list, temp_nni_path):
'''use ssh client to copy data from remote machine to local machien'''
machine_list = nni_config.get_config('experimentConfig').get('machineList')
machine_dict = {}
local_path_list = []
for machine in ma... | [
"def",
"copy_data_from_remote",
"(",
"args",
",",
"nni_config",
",",
"trial_content",
",",
"path_list",
",",
"host_list",
",",
"temp_nni_path",
")",
":",
"machine_list",
"=",
"nni_config",
".",
"get_config",
"(",
"'experimentConfig'",
")",
".",
"get",
"(",
"'mac... | use ssh client to copy data from remote machine to local machien | [
"use",
"ssh",
"client",
"to",
"copy",
"data",
"from",
"remote",
"machine",
"to",
"local",
"machien"
] | c7cc8db32da8d2ec77a382a55089f4e17247ce41 | https://github.com/Microsoft/nni/blob/c7cc8db32da8d2ec77a382a55089f4e17247ce41/tools/nni_cmd/tensorboard_utils.py#L55-L69 | train |
Microsoft/nni | tools/nni_cmd/tensorboard_utils.py | get_path_list | def get_path_list(args, nni_config, trial_content, temp_nni_path):
'''get path list according to different platform'''
path_list, host_list = parse_log_path(args, trial_content)
platform = nni_config.get_config('experimentConfig').get('trainingServicePlatform')
if platform == 'local':
print_norm... | python | def get_path_list(args, nni_config, trial_content, temp_nni_path):
'''get path list according to different platform'''
path_list, host_list = parse_log_path(args, trial_content)
platform = nni_config.get_config('experimentConfig').get('trainingServicePlatform')
if platform == 'local':
print_norm... | [
"def",
"get_path_list",
"(",
"args",
",",
"nni_config",
",",
"trial_content",
",",
"temp_nni_path",
")",
":",
"path_list",
",",
"host_list",
"=",
"parse_log_path",
"(",
"args",
",",
"trial_content",
")",
"platform",
"=",
"nni_config",
".",
"get_config",
"(",
"... | get path list according to different platform | [
"get",
"path",
"list",
"according",
"to",
"different",
"platform"
] | c7cc8db32da8d2ec77a382a55089f4e17247ce41 | https://github.com/Microsoft/nni/blob/c7cc8db32da8d2ec77a382a55089f4e17247ce41/tools/nni_cmd/tensorboard_utils.py#L71-L84 | train |
Microsoft/nni | tools/nni_cmd/tensorboard_utils.py | start_tensorboard_process | def start_tensorboard_process(args, nni_config, path_list, temp_nni_path):
'''call cmds to start tensorboard process in local machine'''
if detect_port(args.port):
print_error('Port %s is used by another process, please reset port!' % str(args.port))
exit(1)
stdout_file = open(os.path.j... | python | def start_tensorboard_process(args, nni_config, path_list, temp_nni_path):
'''call cmds to start tensorboard process in local machine'''
if detect_port(args.port):
print_error('Port %s is used by another process, please reset port!' % str(args.port))
exit(1)
stdout_file = open(os.path.j... | [
"def",
"start_tensorboard_process",
"(",
"args",
",",
"nni_config",
",",
"path_list",
",",
"temp_nni_path",
")",
":",
"if",
"detect_port",
"(",
"args",
".",
"port",
")",
":",
"print_error",
"(",
"'Port %s is used by another process, please reset port!'",
"%",
"str",
... | call cmds to start tensorboard process in local machine | [
"call",
"cmds",
"to",
"start",
"tensorboard",
"process",
"in",
"local",
"machine"
] | c7cc8db32da8d2ec77a382a55089f4e17247ce41 | https://github.com/Microsoft/nni/blob/c7cc8db32da8d2ec77a382a55089f4e17247ce41/tools/nni_cmd/tensorboard_utils.py#L92-L109 | train |
Microsoft/nni | tools/nni_cmd/tensorboard_utils.py | stop_tensorboard | def stop_tensorboard(args):
'''stop tensorboard'''
experiment_id = check_experiment_id(args)
experiment_config = Experiments()
experiment_dict = experiment_config.get_all_experiments()
config_file_name = experiment_dict[experiment_id]['fileName']
nni_config = Config(config_file_name)
tensorb... | python | def stop_tensorboard(args):
'''stop tensorboard'''
experiment_id = check_experiment_id(args)
experiment_config = Experiments()
experiment_dict = experiment_config.get_all_experiments()
config_file_name = experiment_dict[experiment_id]['fileName']
nni_config = Config(config_file_name)
tensorb... | [
"def",
"stop_tensorboard",
"(",
"args",
")",
":",
"experiment_id",
"=",
"check_experiment_id",
"(",
"args",
")",
"experiment_config",
"=",
"Experiments",
"(",
")",
"experiment_dict",
"=",
"experiment_config",
".",
"get_all_experiments",
"(",
")",
"config_file_name",
... | stop tensorboard | [
"stop",
"tensorboard"
] | c7cc8db32da8d2ec77a382a55089f4e17247ce41 | https://github.com/Microsoft/nni/blob/c7cc8db32da8d2ec77a382a55089f4e17247ce41/tools/nni_cmd/tensorboard_utils.py#L111-L129 | train |
Microsoft/nni | tools/nni_cmd/tensorboard_utils.py | start_tensorboard | def start_tensorboard(args):
'''start tensorboard'''
experiment_id = check_experiment_id(args)
experiment_config = Experiments()
experiment_dict = experiment_config.get_all_experiments()
config_file_name = experiment_dict[experiment_id]['fileName']
nni_config = Config(config_file_name)
rest_... | python | def start_tensorboard(args):
'''start tensorboard'''
experiment_id = check_experiment_id(args)
experiment_config = Experiments()
experiment_dict = experiment_config.get_all_experiments()
config_file_name = experiment_dict[experiment_id]['fileName']
nni_config = Config(config_file_name)
rest_... | [
"def",
"start_tensorboard",
"(",
"args",
")",
":",
"experiment_id",
"=",
"check_experiment_id",
"(",
"args",
")",
"experiment_config",
"=",
"Experiments",
"(",
")",
"experiment_dict",
"=",
"experiment_config",
".",
"get_all_experiments",
"(",
")",
"config_file_name",
... | start tensorboard | [
"start",
"tensorboard"
] | c7cc8db32da8d2ec77a382a55089f4e17247ce41 | https://github.com/Microsoft/nni/blob/c7cc8db32da8d2ec77a382a55089f4e17247ce41/tools/nni_cmd/tensorboard_utils.py#L132-L165 | train |
Microsoft/nni | src/sdk/pynni/nni/metis_tuner/Regression_GMM/Selection.py | _ratio_scores | def _ratio_scores(parameters_value, clusteringmodel_gmm_good, clusteringmodel_gmm_bad):
'''
The ratio is smaller the better
'''
ratio = clusteringmodel_gmm_good.score([parameters_value]) / clusteringmodel_gmm_bad.score([parameters_value])
sigma = 0
return ratio, sigma | python | def _ratio_scores(parameters_value, clusteringmodel_gmm_good, clusteringmodel_gmm_bad):
'''
The ratio is smaller the better
'''
ratio = clusteringmodel_gmm_good.score([parameters_value]) / clusteringmodel_gmm_bad.score([parameters_value])
sigma = 0
return ratio, sigma | [
"def",
"_ratio_scores",
"(",
"parameters_value",
",",
"clusteringmodel_gmm_good",
",",
"clusteringmodel_gmm_bad",
")",
":",
"ratio",
"=",
"clusteringmodel_gmm_good",
".",
"score",
"(",
"[",
"parameters_value",
"]",
")",
"/",
"clusteringmodel_gmm_bad",
".",
"score",
"(... | The ratio is smaller the better | [
"The",
"ratio",
"is",
"smaller",
"the",
"better"
] | c7cc8db32da8d2ec77a382a55089f4e17247ce41 | https://github.com/Microsoft/nni/blob/c7cc8db32da8d2ec77a382a55089f4e17247ce41/src/sdk/pynni/nni/metis_tuner/Regression_GMM/Selection.py#L37-L43 | train |
Microsoft/nni | src/sdk/pynni/nni/metis_tuner/Regression_GMM/Selection.py | selection_r | def selection_r(x_bounds,
x_types,
clusteringmodel_gmm_good,
clusteringmodel_gmm_bad,
num_starting_points=100,
minimize_constraints_fun=None):
'''
Call selection
'''
minimize_starting_points = [lib_data.rand(x_bounds, x_type... | python | def selection_r(x_bounds,
x_types,
clusteringmodel_gmm_good,
clusteringmodel_gmm_bad,
num_starting_points=100,
minimize_constraints_fun=None):
'''
Call selection
'''
minimize_starting_points = [lib_data.rand(x_bounds, x_type... | [
"def",
"selection_r",
"(",
"x_bounds",
",",
"x_types",
",",
"clusteringmodel_gmm_good",
",",
"clusteringmodel_gmm_bad",
",",
"num_starting_points",
"=",
"100",
",",
"minimize_constraints_fun",
"=",
"None",
")",
":",
"minimize_starting_points",
"=",
"[",
"lib_data",
".... | Call selection | [
"Call",
"selection"
] | c7cc8db32da8d2ec77a382a55089f4e17247ce41 | https://github.com/Microsoft/nni/blob/c7cc8db32da8d2ec77a382a55089f4e17247ce41/src/sdk/pynni/nni/metis_tuner/Regression_GMM/Selection.py#L45-L61 | train |
Microsoft/nni | src/sdk/pynni/nni/metis_tuner/Regression_GMM/Selection.py | selection | def selection(x_bounds,
x_types,
clusteringmodel_gmm_good,
clusteringmodel_gmm_bad,
minimize_starting_points,
minimize_constraints_fun=None):
'''
Select the lowest mu value
'''
results = lib_acquisition_function.next_hyperparameter_lo... | python | def selection(x_bounds,
x_types,
clusteringmodel_gmm_good,
clusteringmodel_gmm_bad,
minimize_starting_points,
minimize_constraints_fun=None):
'''
Select the lowest mu value
'''
results = lib_acquisition_function.next_hyperparameter_lo... | [
"def",
"selection",
"(",
"x_bounds",
",",
"x_types",
",",
"clusteringmodel_gmm_good",
",",
"clusteringmodel_gmm_bad",
",",
"minimize_starting_points",
",",
"minimize_constraints_fun",
"=",
"None",
")",
":",
"results",
"=",
"lib_acquisition_function",
".",
"next_hyperparam... | Select the lowest mu value | [
"Select",
"the",
"lowest",
"mu",
"value"
] | c7cc8db32da8d2ec77a382a55089f4e17247ce41 | https://github.com/Microsoft/nni/blob/c7cc8db32da8d2ec77a382a55089f4e17247ce41/src/sdk/pynni/nni/metis_tuner/Regression_GMM/Selection.py#L63-L77 | train |
Microsoft/nni | src/sdk/pynni/nni/metis_tuner/Regression_GMM/Selection.py | _minimize_constraints_fun_summation | def _minimize_constraints_fun_summation(x):
'''
Minimize constraints fun summation
'''
summation = sum([x[i] for i in CONSTRAINT_PARAMS_IDX])
return CONSTRAINT_UPPERBOUND >= summation >= CONSTRAINT_LOWERBOUND | python | def _minimize_constraints_fun_summation(x):
'''
Minimize constraints fun summation
'''
summation = sum([x[i] for i in CONSTRAINT_PARAMS_IDX])
return CONSTRAINT_UPPERBOUND >= summation >= CONSTRAINT_LOWERBOUND | [
"def",
"_minimize_constraints_fun_summation",
"(",
"x",
")",
":",
"summation",
"=",
"sum",
"(",
"[",
"x",
"[",
"i",
"]",
"for",
"i",
"in",
"CONSTRAINT_PARAMS_IDX",
"]",
")",
"return",
"CONSTRAINT_UPPERBOUND",
">=",
"summation",
">=",
"CONSTRAINT_LOWERBOUND"
] | Minimize constraints fun summation | [
"Minimize",
"constraints",
"fun",
"summation"
] | c7cc8db32da8d2ec77a382a55089f4e17247ce41 | https://github.com/Microsoft/nni/blob/c7cc8db32da8d2ec77a382a55089f4e17247ce41/src/sdk/pynni/nni/metis_tuner/Regression_GMM/Selection.py#L99-L104 | train |
Microsoft/nni | examples/trials/sklearn/classification/main.py | load_data | def load_data():
'''Load dataset, use 20newsgroups dataset'''
digits = load_digits()
X_train, X_test, y_train, y_test = train_test_split(digits.data, digits.target, random_state=99, test_size=0.25)
ss = StandardScaler()
X_train = ss.fit_transform(X_train)
X_test = ss.transform(X_test)
retu... | python | def load_data():
'''Load dataset, use 20newsgroups dataset'''
digits = load_digits()
X_train, X_test, y_train, y_test = train_test_split(digits.data, digits.target, random_state=99, test_size=0.25)
ss = StandardScaler()
X_train = ss.fit_transform(X_train)
X_test = ss.transform(X_test)
retu... | [
"def",
"load_data",
"(",
")",
":",
"digits",
"=",
"load_digits",
"(",
")",
"X_train",
",",
"X_test",
",",
"y_train",
",",
"y_test",
"=",
"train_test_split",
"(",
"digits",
".",
"data",
",",
"digits",
".",
"target",
",",
"random_state",
"=",
"99",
",",
... | Load dataset, use 20newsgroups dataset | [
"Load",
"dataset",
"use",
"20newsgroups",
"dataset"
] | c7cc8db32da8d2ec77a382a55089f4e17247ce41 | https://github.com/Microsoft/nni/blob/c7cc8db32da8d2ec77a382a55089f4e17247ce41/examples/trials/sklearn/classification/main.py#L29-L38 | train |
Microsoft/nni | examples/trials/sklearn/classification/main.py | get_model | def get_model(PARAMS):
'''Get model according to parameters'''
model = SVC()
model.C = PARAMS.get('C')
model.keral = PARAMS.get('keral')
model.degree = PARAMS.get('degree')
model.gamma = PARAMS.get('gamma')
model.coef0 = PARAMS.get('coef0')
return model | python | def get_model(PARAMS):
'''Get model according to parameters'''
model = SVC()
model.C = PARAMS.get('C')
model.keral = PARAMS.get('keral')
model.degree = PARAMS.get('degree')
model.gamma = PARAMS.get('gamma')
model.coef0 = PARAMS.get('coef0')
return model | [
"def",
"get_model",
"(",
"PARAMS",
")",
":",
"model",
"=",
"SVC",
"(",
")",
"model",
".",
"C",
"=",
"PARAMS",
".",
"get",
"(",
"'C'",
")",
"model",
".",
"keral",
"=",
"PARAMS",
".",
"get",
"(",
"'keral'",
")",
"model",
".",
"degree",
"=",
"PARAMS... | Get model according to parameters | [
"Get",
"model",
"according",
"to",
"parameters"
] | c7cc8db32da8d2ec77a382a55089f4e17247ce41 | https://github.com/Microsoft/nni/blob/c7cc8db32da8d2ec77a382a55089f4e17247ce41/examples/trials/sklearn/classification/main.py#L51-L60 | train |
Microsoft/nni | src/sdk/pynni/nni/bohb_advisor/bohb_advisor.py | Bracket.get_hyperparameter_configurations | def get_hyperparameter_configurations(self, num, r, config_generator):
"""generate num hyperparameter configurations from search space using Bayesian optimization
Parameters
----------
num: int
the number of hyperparameter configurations
Returns
-------
... | python | def get_hyperparameter_configurations(self, num, r, config_generator):
"""generate num hyperparameter configurations from search space using Bayesian optimization
Parameters
----------
num: int
the number of hyperparameter configurations
Returns
-------
... | [
"def",
"get_hyperparameter_configurations",
"(",
"self",
",",
"num",
",",
"r",
",",
"config_generator",
")",
":",
"global",
"_KEY",
"assert",
"self",
".",
"i",
"==",
"0",
"hyperparameter_configs",
"=",
"dict",
"(",
")",
"for",
"_",
"in",
"range",
"(",
"num... | generate num hyperparameter configurations from search space using Bayesian optimization
Parameters
----------
num: int
the number of hyperparameter configurations
Returns
-------
list
a list of hyperparameter configurations. Format: [[key1, valu... | [
"generate",
"num",
"hyperparameter",
"configurations",
"from",
"search",
"space",
"using",
"Bayesian",
"optimization"
] | c7cc8db32da8d2ec77a382a55089f4e17247ce41 | https://github.com/Microsoft/nni/blob/c7cc8db32da8d2ec77a382a55089f4e17247ce41/src/sdk/pynni/nni/bohb_advisor/bohb_advisor.py#L215-L237 | train |
Microsoft/nni | src/sdk/pynni/nni/bohb_advisor/bohb_advisor.py | BOHB.handle_initialize | def handle_initialize(self, data):
"""Initialize Tuner, including creating Bayesian optimization-based parametric models
and search space formations
Parameters
----------
data: search space
search space of this experiment
Raises
------
Value... | python | def handle_initialize(self, data):
"""Initialize Tuner, including creating Bayesian optimization-based parametric models
and search space formations
Parameters
----------
data: search space
search space of this experiment
Raises
------
Value... | [
"def",
"handle_initialize",
"(",
"self",
",",
"data",
")",
":",
"logger",
".",
"info",
"(",
"'start to handle_initialize'",
")",
"# convert search space jason to ConfigSpace",
"self",
".",
"handle_update_search_space",
"(",
"data",
")",
"# generate BOHB config_generator usi... | Initialize Tuner, including creating Bayesian optimization-based parametric models
and search space formations
Parameters
----------
data: search space
search space of this experiment
Raises
------
ValueError
Error: Search space is None | [
"Initialize",
"Tuner",
"including",
"creating",
"Bayesian",
"optimization",
"-",
"based",
"parametric",
"models",
"and",
"search",
"space",
"formations"
] | c7cc8db32da8d2ec77a382a55089f4e17247ce41 | https://github.com/Microsoft/nni/blob/c7cc8db32da8d2ec77a382a55089f4e17247ce41/src/sdk/pynni/nni/bohb_advisor/bohb_advisor.py#L344-L375 | train |
Microsoft/nni | src/sdk/pynni/nni/bohb_advisor/bohb_advisor.py | BOHB.generate_new_bracket | def generate_new_bracket(self):
"""generate a new bracket"""
logger.debug(
'start to create a new SuccessiveHalving iteration, self.curr_s=%d', self.curr_s)
if self.curr_s < 0:
logger.info("s < 0, Finish this round of Hyperband in BOHB. Generate new round")
se... | python | def generate_new_bracket(self):
"""generate a new bracket"""
logger.debug(
'start to create a new SuccessiveHalving iteration, self.curr_s=%d', self.curr_s)
if self.curr_s < 0:
logger.info("s < 0, Finish this round of Hyperband in BOHB. Generate new round")
se... | [
"def",
"generate_new_bracket",
"(",
"self",
")",
":",
"logger",
".",
"debug",
"(",
"'start to create a new SuccessiveHalving iteration, self.curr_s=%d'",
",",
"self",
".",
"curr_s",
")",
"if",
"self",
".",
"curr_s",
"<",
"0",
":",
"logger",
".",
"info",
"(",
"\"... | generate a new bracket | [
"generate",
"a",
"new",
"bracket"
] | c7cc8db32da8d2ec77a382a55089f4e17247ce41 | https://github.com/Microsoft/nni/blob/c7cc8db32da8d2ec77a382a55089f4e17247ce41/src/sdk/pynni/nni/bohb_advisor/bohb_advisor.py#L377-L392 | train |
Microsoft/nni | src/sdk/pynni/nni/bohb_advisor/bohb_advisor.py | BOHB.handle_request_trial_jobs | def handle_request_trial_jobs(self, data):
"""recerive the number of request and generate trials
Parameters
----------
data: int
number of trial jobs that nni manager ask to generate
"""
# Receive new request
self.credit += data
for _ in rang... | python | def handle_request_trial_jobs(self, data):
"""recerive the number of request and generate trials
Parameters
----------
data: int
number of trial jobs that nni manager ask to generate
"""
# Receive new request
self.credit += data
for _ in rang... | [
"def",
"handle_request_trial_jobs",
"(",
"self",
",",
"data",
")",
":",
"# Receive new request",
"self",
".",
"credit",
"+=",
"data",
"for",
"_",
"in",
"range",
"(",
"self",
".",
"credit",
")",
":",
"self",
".",
"_request_one_trial_job",
"(",
")"
] | recerive the number of request and generate trials
Parameters
----------
data: int
number of trial jobs that nni manager ask to generate | [
"recerive",
"the",
"number",
"of",
"request",
"and",
"generate",
"trials"
] | c7cc8db32da8d2ec77a382a55089f4e17247ce41 | https://github.com/Microsoft/nni/blob/c7cc8db32da8d2ec77a382a55089f4e17247ce41/src/sdk/pynni/nni/bohb_advisor/bohb_advisor.py#L394-L406 | train |
Microsoft/nni | src/sdk/pynni/nni/bohb_advisor/bohb_advisor.py | BOHB._request_one_trial_job | def _request_one_trial_job(self):
"""get one trial job, i.e., one hyperparameter configuration.
If this function is called, Command will be sent by BOHB:
a. If there is a parameter need to run, will return "NewTrialJob" with a dict:
{
'parameter_id': id of new hyperparamete... | python | def _request_one_trial_job(self):
"""get one trial job, i.e., one hyperparameter configuration.
If this function is called, Command will be sent by BOHB:
a. If there is a parameter need to run, will return "NewTrialJob" with a dict:
{
'parameter_id': id of new hyperparamete... | [
"def",
"_request_one_trial_job",
"(",
"self",
")",
":",
"if",
"not",
"self",
".",
"generated_hyper_configs",
":",
"ret",
"=",
"{",
"'parameter_id'",
":",
"'-1_0_0'",
",",
"'parameter_source'",
":",
"'algorithm'",
",",
"'parameters'",
":",
"''",
"}",
"send",
"(... | get one trial job, i.e., one hyperparameter configuration.
If this function is called, Command will be sent by BOHB:
a. If there is a parameter need to run, will return "NewTrialJob" with a dict:
{
'parameter_id': id of new hyperparameter
'parameter_source': 'algorithm'... | [
"get",
"one",
"trial",
"job",
"i",
".",
"e",
".",
"one",
"hyperparameter",
"configuration",
"."
] | c7cc8db32da8d2ec77a382a55089f4e17247ce41 | https://github.com/Microsoft/nni/blob/c7cc8db32da8d2ec77a382a55089f4e17247ce41/src/sdk/pynni/nni/bohb_advisor/bohb_advisor.py#L408-L442 | train |
Microsoft/nni | src/sdk/pynni/nni/bohb_advisor/bohb_advisor.py | BOHB.handle_update_search_space | def handle_update_search_space(self, data):
"""change json format to ConfigSpace format dict<dict> -> configspace
Parameters
----------
data: JSON object
search space of this experiment
"""
search_space = data
cs = CS.ConfigurationSpace()
for ... | python | def handle_update_search_space(self, data):
"""change json format to ConfigSpace format dict<dict> -> configspace
Parameters
----------
data: JSON object
search space of this experiment
"""
search_space = data
cs = CS.ConfigurationSpace()
for ... | [
"def",
"handle_update_search_space",
"(",
"self",
",",
"data",
")",
":",
"search_space",
"=",
"data",
"cs",
"=",
"CS",
".",
"ConfigurationSpace",
"(",
")",
"for",
"var",
"in",
"search_space",
":",
"_type",
"=",
"str",
"(",
"search_space",
"[",
"var",
"]",
... | change json format to ConfigSpace format dict<dict> -> configspace
Parameters
----------
data: JSON object
search space of this experiment | [
"change",
"json",
"format",
"to",
"ConfigSpace",
"format",
"dict<dict",
">",
"-",
">",
"configspace"
] | c7cc8db32da8d2ec77a382a55089f4e17247ce41 | https://github.com/Microsoft/nni/blob/c7cc8db32da8d2ec77a382a55089f4e17247ce41/src/sdk/pynni/nni/bohb_advisor/bohb_advisor.py#L444-L496 | train |
Microsoft/nni | src/sdk/pynni/nni/bohb_advisor/bohb_advisor.py | BOHB.handle_trial_end | def handle_trial_end(self, data):
"""receive the information of trial end and generate next configuaration.
Parameters
----------
data: dict()
it has three keys: trial_job_id, event, hyper_params
trial_job_id: the id generated by training service
even... | python | def handle_trial_end(self, data):
"""receive the information of trial end and generate next configuaration.
Parameters
----------
data: dict()
it has three keys: trial_job_id, event, hyper_params
trial_job_id: the id generated by training service
even... | [
"def",
"handle_trial_end",
"(",
"self",
",",
"data",
")",
":",
"logger",
".",
"debug",
"(",
"'Tuner handle trial end, result is %s'",
",",
"data",
")",
"hyper_params",
"=",
"json_tricks",
".",
"loads",
"(",
"data",
"[",
"'hyper_params'",
"]",
")",
"s",
",",
... | receive the information of trial end and generate next configuaration.
Parameters
----------
data: dict()
it has three keys: trial_job_id, event, hyper_params
trial_job_id: the id generated by training service
event: the job's state
hyper_params: ... | [
"receive",
"the",
"information",
"of",
"trial",
"end",
"and",
"generate",
"next",
"configuaration",
"."
] | c7cc8db32da8d2ec77a382a55089f4e17247ce41 | https://github.com/Microsoft/nni/blob/c7cc8db32da8d2ec77a382a55089f4e17247ce41/src/sdk/pynni/nni/bohb_advisor/bohb_advisor.py#L498-L526 | train |
Microsoft/nni | src/sdk/pynni/nni/bohb_advisor/bohb_advisor.py | BOHB.handle_report_metric_data | def handle_report_metric_data(self, data):
"""reveice the metric data and update Bayesian optimization with final result
Parameters
----------
data:
it is an object which has keys 'parameter_id', 'value', 'trial_job_id', 'type', 'sequence'.
Raises
------
... | python | def handle_report_metric_data(self, data):
"""reveice the metric data and update Bayesian optimization with final result
Parameters
----------
data:
it is an object which has keys 'parameter_id', 'value', 'trial_job_id', 'type', 'sequence'.
Raises
------
... | [
"def",
"handle_report_metric_data",
"(",
"self",
",",
"data",
")",
":",
"logger",
".",
"debug",
"(",
"'handle report metric data = %s'",
",",
"data",
")",
"assert",
"'value'",
"in",
"data",
"value",
"=",
"extract_scalar_reward",
"(",
"data",
"[",
"'value'",
"]",... | reveice the metric data and update Bayesian optimization with final result
Parameters
----------
data:
it is an object which has keys 'parameter_id', 'value', 'trial_job_id', 'type', 'sequence'.
Raises
------
ValueError
Data type not supported | [
"reveice",
"the",
"metric",
"data",
"and",
"update",
"Bayesian",
"optimization",
"with",
"final",
"result"
] | c7cc8db32da8d2ec77a382a55089f4e17247ce41 | https://github.com/Microsoft/nni/blob/c7cc8db32da8d2ec77a382a55089f4e17247ce41/src/sdk/pynni/nni/bohb_advisor/bohb_advisor.py#L528-L572 | train |
Microsoft/nni | src/sdk/pynni/nni/bohb_advisor/bohb_advisor.py | BOHB.handle_import_data | def handle_import_data(self, data):
"""Import additional data for tuning
Parameters
----------
data:
a list of dictionarys, each of which has at least two keys, 'parameter' and 'value'
Raises
------
AssertionError
data doesn't have requir... | python | def handle_import_data(self, data):
"""Import additional data for tuning
Parameters
----------
data:
a list of dictionarys, each of which has at least two keys, 'parameter' and 'value'
Raises
------
AssertionError
data doesn't have requir... | [
"def",
"handle_import_data",
"(",
"self",
",",
"data",
")",
":",
"_completed_num",
"=",
"0",
"for",
"trial_info",
"in",
"data",
":",
"logger",
".",
"info",
"(",
"\"Importing data, current processing progress %s / %s\"",
"%",
"(",
"_completed_num",
",",
"len",
"(",... | Import additional data for tuning
Parameters
----------
data:
a list of dictionarys, each of which has at least two keys, 'parameter' and 'value'
Raises
------
AssertionError
data doesn't have required key 'parameter' and 'value' | [
"Import",
"additional",
"data",
"for",
"tuning"
] | c7cc8db32da8d2ec77a382a55089f4e17247ce41 | https://github.com/Microsoft/nni/blob/c7cc8db32da8d2ec77a382a55089f4e17247ce41/src/sdk/pynni/nni/bohb_advisor/bohb_advisor.py#L577-L617 | train |
Microsoft/nni | examples/trials/network_morphism/FashionMNIST/utils.py | data_transforms_cifar10 | def data_transforms_cifar10(args):
""" data_transforms for cifar10 dataset
"""
cifar_mean = [0.49139968, 0.48215827, 0.44653124]
cifar_std = [0.24703233, 0.24348505, 0.26158768]
train_transform = transforms.Compose(
[
transforms.RandomCrop(32, padding=4),
transforms... | python | def data_transforms_cifar10(args):
""" data_transforms for cifar10 dataset
"""
cifar_mean = [0.49139968, 0.48215827, 0.44653124]
cifar_std = [0.24703233, 0.24348505, 0.26158768]
train_transform = transforms.Compose(
[
transforms.RandomCrop(32, padding=4),
transforms... | [
"def",
"data_transforms_cifar10",
"(",
"args",
")",
":",
"cifar_mean",
"=",
"[",
"0.49139968",
",",
"0.48215827",
",",
"0.44653124",
"]",
"cifar_std",
"=",
"[",
"0.24703233",
",",
"0.24348505",
",",
"0.26158768",
"]",
"train_transform",
"=",
"transforms",
".",
... | data_transforms for cifar10 dataset | [
"data_transforms",
"for",
"cifar10",
"dataset"
] | c7cc8db32da8d2ec77a382a55089f4e17247ce41 | https://github.com/Microsoft/nni/blob/c7cc8db32da8d2ec77a382a55089f4e17247ce41/examples/trials/network_morphism/FashionMNIST/utils.py#L116-L137 | train |
Microsoft/nni | examples/trials/network_morphism/FashionMNIST/utils.py | data_transforms_mnist | def data_transforms_mnist(args, mnist_mean=None, mnist_std=None):
""" data_transforms for mnist dataset
"""
if mnist_mean is None:
mnist_mean = [0.5]
if mnist_std is None:
mnist_std = [0.5]
train_transform = transforms.Compose(
[
transforms.RandomCrop(28, paddin... | python | def data_transforms_mnist(args, mnist_mean=None, mnist_std=None):
""" data_transforms for mnist dataset
"""
if mnist_mean is None:
mnist_mean = [0.5]
if mnist_std is None:
mnist_std = [0.5]
train_transform = transforms.Compose(
[
transforms.RandomCrop(28, paddin... | [
"def",
"data_transforms_mnist",
"(",
"args",
",",
"mnist_mean",
"=",
"None",
",",
"mnist_std",
"=",
"None",
")",
":",
"if",
"mnist_mean",
"is",
"None",
":",
"mnist_mean",
"=",
"[",
"0.5",
"]",
"if",
"mnist_std",
"is",
"None",
":",
"mnist_std",
"=",
"[",
... | data_transforms for mnist dataset | [
"data_transforms",
"for",
"mnist",
"dataset"
] | c7cc8db32da8d2ec77a382a55089f4e17247ce41 | https://github.com/Microsoft/nni/blob/c7cc8db32da8d2ec77a382a55089f4e17247ce41/examples/trials/network_morphism/FashionMNIST/utils.py#L140-L163 | train |
Microsoft/nni | examples/trials/network_morphism/FashionMNIST/utils.py | get_mean_and_std | def get_mean_and_std(dataset):
"""Compute the mean and std value of dataset."""
dataloader = torch.utils.data.DataLoader(
dataset, batch_size=1, shuffle=True, num_workers=2
)
mean = torch.zeros(3)
std = torch.zeros(3)
print("==> Computing mean and std..")
for inputs, _ in dataloader:... | python | def get_mean_and_std(dataset):
"""Compute the mean and std value of dataset."""
dataloader = torch.utils.data.DataLoader(
dataset, batch_size=1, shuffle=True, num_workers=2
)
mean = torch.zeros(3)
std = torch.zeros(3)
print("==> Computing mean and std..")
for inputs, _ in dataloader:... | [
"def",
"get_mean_and_std",
"(",
"dataset",
")",
":",
"dataloader",
"=",
"torch",
".",
"utils",
".",
"data",
".",
"DataLoader",
"(",
"dataset",
",",
"batch_size",
"=",
"1",
",",
"shuffle",
"=",
"True",
",",
"num_workers",
"=",
"2",
")",
"mean",
"=",
"to... | Compute the mean and std value of dataset. | [
"Compute",
"the",
"mean",
"and",
"std",
"value",
"of",
"dataset",
"."
] | c7cc8db32da8d2ec77a382a55089f4e17247ce41 | https://github.com/Microsoft/nni/blob/c7cc8db32da8d2ec77a382a55089f4e17247ce41/examples/trials/network_morphism/FashionMNIST/utils.py#L166-L180 | train |
Microsoft/nni | examples/trials/network_morphism/FashionMNIST/utils.py | init_params | def init_params(net):
"""Init layer parameters."""
for module in net.modules():
if isinstance(module, nn.Conv2d):
init.kaiming_normal(module.weight, mode="fan_out")
if module.bias:
init.constant(module.bias, 0)
elif isinstance(module, nn.BatchNorm2d):
... | python | def init_params(net):
"""Init layer parameters."""
for module in net.modules():
if isinstance(module, nn.Conv2d):
init.kaiming_normal(module.weight, mode="fan_out")
if module.bias:
init.constant(module.bias, 0)
elif isinstance(module, nn.BatchNorm2d):
... | [
"def",
"init_params",
"(",
"net",
")",
":",
"for",
"module",
"in",
"net",
".",
"modules",
"(",
")",
":",
"if",
"isinstance",
"(",
"module",
",",
"nn",
".",
"Conv2d",
")",
":",
"init",
".",
"kaiming_normal",
"(",
"module",
".",
"weight",
",",
"mode",
... | Init layer parameters. | [
"Init",
"layer",
"parameters",
"."
] | c7cc8db32da8d2ec77a382a55089f4e17247ce41 | https://github.com/Microsoft/nni/blob/c7cc8db32da8d2ec77a382a55089f4e17247ce41/examples/trials/network_morphism/FashionMNIST/utils.py#L183-L196 | train |
Microsoft/nni | examples/trials/network_morphism/FashionMNIST/utils.py | EarlyStopping.step | def step(self, metrics):
""" EarlyStopping step on each epoch
Arguments:
metrics {float} -- metric value
"""
if self.best is None:
self.best = metrics
return False
if np.isnan(metrics):
return True
if self.is_better(metri... | python | def step(self, metrics):
""" EarlyStopping step on each epoch
Arguments:
metrics {float} -- metric value
"""
if self.best is None:
self.best = metrics
return False
if np.isnan(metrics):
return True
if self.is_better(metri... | [
"def",
"step",
"(",
"self",
",",
"metrics",
")",
":",
"if",
"self",
".",
"best",
"is",
"None",
":",
"self",
".",
"best",
"=",
"metrics",
"return",
"False",
"if",
"np",
".",
"isnan",
"(",
"metrics",
")",
":",
"return",
"True",
"if",
"self",
".",
"... | EarlyStopping step on each epoch
Arguments:
metrics {float} -- metric value | [
"EarlyStopping",
"step",
"on",
"each",
"epoch",
"Arguments",
":",
"metrics",
"{",
"float",
"}",
"--",
"metric",
"value"
] | c7cc8db32da8d2ec77a382a55089f4e17247ce41 | https://github.com/Microsoft/nni/blob/c7cc8db32da8d2ec77a382a55089f4e17247ce41/examples/trials/network_morphism/FashionMNIST/utils.py#L43-L65 | train |
Microsoft/nni | src/sdk/pynni/nni/metis_tuner/lib_constraint_summation.py | check_feasibility | def check_feasibility(x_bounds, lowerbound, upperbound):
'''
This can have false positives.
For examples, parameters can only be 0 or 5, and the summation constraint is between 6 and 7.
'''
# x_bounds should be sorted, so even for "discrete_int" type,
# the smallest and the largest number should... | python | def check_feasibility(x_bounds, lowerbound, upperbound):
'''
This can have false positives.
For examples, parameters can only be 0 or 5, and the summation constraint is between 6 and 7.
'''
# x_bounds should be sorted, so even for "discrete_int" type,
# the smallest and the largest number should... | [
"def",
"check_feasibility",
"(",
"x_bounds",
",",
"lowerbound",
",",
"upperbound",
")",
":",
"# x_bounds should be sorted, so even for \"discrete_int\" type,",
"# the smallest and the largest number should the first and the last element",
"x_bounds_lowerbound",
"=",
"sum",
"(",
"[",
... | This can have false positives.
For examples, parameters can only be 0 or 5, and the summation constraint is between 6 and 7. | [
"This",
"can",
"have",
"false",
"positives",
".",
"For",
"examples",
"parameters",
"can",
"only",
"be",
"0",
"or",
"5",
"and",
"the",
"summation",
"constraint",
"is",
"between",
"6",
"and",
"7",
"."
] | c7cc8db32da8d2ec77a382a55089f4e17247ce41 | https://github.com/Microsoft/nni/blob/c7cc8db32da8d2ec77a382a55089f4e17247ce41/src/sdk/pynni/nni/metis_tuner/lib_constraint_summation.py#L27-L40 | train |
Microsoft/nni | src/sdk/pynni/nni/metis_tuner/lib_constraint_summation.py | rand | def rand(x_bounds, x_types, lowerbound, upperbound, max_retries=100):
'''
Key idea is that we try to move towards upperbound, by randomly choose one
value for each parameter. However, for the last parameter,
we need to make sure that its value can help us get above lowerbound
'''
outputs = None
... | python | def rand(x_bounds, x_types, lowerbound, upperbound, max_retries=100):
'''
Key idea is that we try to move towards upperbound, by randomly choose one
value for each parameter. However, for the last parameter,
we need to make sure that its value can help us get above lowerbound
'''
outputs = None
... | [
"def",
"rand",
"(",
"x_bounds",
",",
"x_types",
",",
"lowerbound",
",",
"upperbound",
",",
"max_retries",
"=",
"100",
")",
":",
"outputs",
"=",
"None",
"if",
"check_feasibility",
"(",
"x_bounds",
",",
"lowerbound",
",",
"upperbound",
")",
"is",
"True",
":"... | Key idea is that we try to move towards upperbound, by randomly choose one
value for each parameter. However, for the last parameter,
we need to make sure that its value can help us get above lowerbound | [
"Key",
"idea",
"is",
"that",
"we",
"try",
"to",
"move",
"towards",
"upperbound",
"by",
"randomly",
"choose",
"one",
"value",
"for",
"each",
"parameter",
".",
"However",
"for",
"the",
"last",
"parameter",
"we",
"need",
"to",
"make",
"sure",
"that",
"its",
... | c7cc8db32da8d2ec77a382a55089f4e17247ce41 | https://github.com/Microsoft/nni/blob/c7cc8db32da8d2ec77a382a55089f4e17247ce41/src/sdk/pynni/nni/metis_tuner/lib_constraint_summation.py#L42-L115 | train |
Microsoft/nni | tools/nni_cmd/launcher_utils.py | expand_path | def expand_path(experiment_config, key):
'''Change '~' to user home directory'''
if experiment_config.get(key):
experiment_config[key] = os.path.expanduser(experiment_config[key]) | python | def expand_path(experiment_config, key):
'''Change '~' to user home directory'''
if experiment_config.get(key):
experiment_config[key] = os.path.expanduser(experiment_config[key]) | [
"def",
"expand_path",
"(",
"experiment_config",
",",
"key",
")",
":",
"if",
"experiment_config",
".",
"get",
"(",
"key",
")",
":",
"experiment_config",
"[",
"key",
"]",
"=",
"os",
".",
"path",
".",
"expanduser",
"(",
"experiment_config",
"[",
"key",
"]",
... | Change '~' to user home directory | [
"Change",
"~",
"to",
"user",
"home",
"directory"
] | c7cc8db32da8d2ec77a382a55089f4e17247ce41 | https://github.com/Microsoft/nni/blob/c7cc8db32da8d2ec77a382a55089f4e17247ce41/tools/nni_cmd/launcher_utils.py#L29-L32 | train |
Microsoft/nni | tools/nni_cmd/launcher_utils.py | parse_relative_path | def parse_relative_path(root_path, experiment_config, key):
'''Change relative path to absolute path'''
if experiment_config.get(key) and not os.path.isabs(experiment_config.get(key)):
absolute_path = os.path.join(root_path, experiment_config.get(key))
print_normal('expand %s: %s to %s ' % (key,... | python | def parse_relative_path(root_path, experiment_config, key):
'''Change relative path to absolute path'''
if experiment_config.get(key) and not os.path.isabs(experiment_config.get(key)):
absolute_path = os.path.join(root_path, experiment_config.get(key))
print_normal('expand %s: %s to %s ' % (key,... | [
"def",
"parse_relative_path",
"(",
"root_path",
",",
"experiment_config",
",",
"key",
")",
":",
"if",
"experiment_config",
".",
"get",
"(",
"key",
")",
"and",
"not",
"os",
".",
"path",
".",
"isabs",
"(",
"experiment_config",
".",
"get",
"(",
"key",
")",
... | Change relative path to absolute path | [
"Change",
"relative",
"path",
"to",
"absolute",
"path"
] | c7cc8db32da8d2ec77a382a55089f4e17247ce41 | https://github.com/Microsoft/nni/blob/c7cc8db32da8d2ec77a382a55089f4e17247ce41/tools/nni_cmd/launcher_utils.py#L34-L39 | train |
Microsoft/nni | tools/nni_cmd/launcher_utils.py | parse_time | def parse_time(time):
'''Change the time to seconds'''
unit = time[-1]
if unit not in ['s', 'm', 'h', 'd']:
print_error('the unit of time could only from {s, m, h, d}')
exit(1)
time = time[:-1]
if not time.isdigit():
print_error('time format error!')
exit(1)
parse... | python | def parse_time(time):
'''Change the time to seconds'''
unit = time[-1]
if unit not in ['s', 'm', 'h', 'd']:
print_error('the unit of time could only from {s, m, h, d}')
exit(1)
time = time[:-1]
if not time.isdigit():
print_error('time format error!')
exit(1)
parse... | [
"def",
"parse_time",
"(",
"time",
")",
":",
"unit",
"=",
"time",
"[",
"-",
"1",
"]",
"if",
"unit",
"not",
"in",
"[",
"'s'",
",",
"'m'",
",",
"'h'",
",",
"'d'",
"]",
":",
"print_error",
"(",
"'the unit of time could only from {s, m, h, d}'",
")",
"exit",
... | Change the time to seconds | [
"Change",
"the",
"time",
"to",
"seconds"
] | c7cc8db32da8d2ec77a382a55089f4e17247ce41 | https://github.com/Microsoft/nni/blob/c7cc8db32da8d2ec77a382a55089f4e17247ce41/tools/nni_cmd/launcher_utils.py#L41-L52 | train |
Microsoft/nni | tools/nni_cmd/launcher_utils.py | parse_path | def parse_path(experiment_config, config_path):
'''Parse path in config file'''
expand_path(experiment_config, 'searchSpacePath')
if experiment_config.get('trial'):
expand_path(experiment_config['trial'], 'codeDir')
if experiment_config.get('tuner'):
expand_path(experiment_config['tuner'... | python | def parse_path(experiment_config, config_path):
'''Parse path in config file'''
expand_path(experiment_config, 'searchSpacePath')
if experiment_config.get('trial'):
expand_path(experiment_config['trial'], 'codeDir')
if experiment_config.get('tuner'):
expand_path(experiment_config['tuner'... | [
"def",
"parse_path",
"(",
"experiment_config",
",",
"config_path",
")",
":",
"expand_path",
"(",
"experiment_config",
",",
"'searchSpacePath'",
")",
"if",
"experiment_config",
".",
"get",
"(",
"'trial'",
")",
":",
"expand_path",
"(",
"experiment_config",
"[",
"'tr... | Parse path in config file | [
"Parse",
"path",
"in",
"config",
"file"
] | c7cc8db32da8d2ec77a382a55089f4e17247ce41 | https://github.com/Microsoft/nni/blob/c7cc8db32da8d2ec77a382a55089f4e17247ce41/tools/nni_cmd/launcher_utils.py#L54-L80 | train |
Microsoft/nni | tools/nni_cmd/launcher_utils.py | validate_search_space_content | def validate_search_space_content(experiment_config):
'''Validate searchspace content,
if the searchspace file is not json format or its values does not contain _type and _value which must be specified,
it will not be a valid searchspace file'''
try:
search_space_content = json.load(open... | python | def validate_search_space_content(experiment_config):
'''Validate searchspace content,
if the searchspace file is not json format or its values does not contain _type and _value which must be specified,
it will not be a valid searchspace file'''
try:
search_space_content = json.load(open... | [
"def",
"validate_search_space_content",
"(",
"experiment_config",
")",
":",
"try",
":",
"search_space_content",
"=",
"json",
".",
"load",
"(",
"open",
"(",
"experiment_config",
".",
"get",
"(",
"'searchSpacePath'",
")",
",",
"'r'",
")",
")",
"for",
"value",
"i... | Validate searchspace content,
if the searchspace file is not json format or its values does not contain _type and _value which must be specified,
it will not be a valid searchspace file | [
"Validate",
"searchspace",
"content",
"if",
"the",
"searchspace",
"file",
"is",
"not",
"json",
"format",
"or",
"its",
"values",
"does",
"not",
"contain",
"_type",
"and",
"_value",
"which",
"must",
"be",
"specified",
"it",
"will",
"not",
"be",
"a",
"valid",
... | c7cc8db32da8d2ec77a382a55089f4e17247ce41 | https://github.com/Microsoft/nni/blob/c7cc8db32da8d2ec77a382a55089f4e17247ce41/tools/nni_cmd/launcher_utils.py#L82-L94 | train |
Microsoft/nni | tools/nni_cmd/launcher_utils.py | validate_kubeflow_operators | def validate_kubeflow_operators(experiment_config):
'''Validate whether the kubeflow operators are valid'''
if experiment_config.get('kubeflowConfig'):
if experiment_config.get('kubeflowConfig').get('operator') == 'tf-operator':
if experiment_config.get('trial').get('master') is not None:
... | python | def validate_kubeflow_operators(experiment_config):
'''Validate whether the kubeflow operators are valid'''
if experiment_config.get('kubeflowConfig'):
if experiment_config.get('kubeflowConfig').get('operator') == 'tf-operator':
if experiment_config.get('trial').get('master') is not None:
... | [
"def",
"validate_kubeflow_operators",
"(",
"experiment_config",
")",
":",
"if",
"experiment_config",
".",
"get",
"(",
"'kubeflowConfig'",
")",
":",
"if",
"experiment_config",
".",
"get",
"(",
"'kubeflowConfig'",
")",
".",
"get",
"(",
"'operator'",
")",
"==",
"'t... | Validate whether the kubeflow operators are valid | [
"Validate",
"whether",
"the",
"kubeflow",
"operators",
"are",
"valid"
] | c7cc8db32da8d2ec77a382a55089f4e17247ce41 | https://github.com/Microsoft/nni/blob/c7cc8db32da8d2ec77a382a55089f4e17247ce41/tools/nni_cmd/launcher_utils.py#L96-L125 | train |
Microsoft/nni | tools/nni_cmd/launcher_utils.py | validate_common_content | def validate_common_content(experiment_config):
'''Validate whether the common values in experiment_config is valid'''
if not experiment_config.get('trainingServicePlatform') or \
experiment_config.get('trainingServicePlatform') not in ['local', 'remote', 'pai', 'kubeflow', 'frameworkcontroller']:
... | python | def validate_common_content(experiment_config):
'''Validate whether the common values in experiment_config is valid'''
if not experiment_config.get('trainingServicePlatform') or \
experiment_config.get('trainingServicePlatform') not in ['local', 'remote', 'pai', 'kubeflow', 'frameworkcontroller']:
... | [
"def",
"validate_common_content",
"(",
"experiment_config",
")",
":",
"if",
"not",
"experiment_config",
".",
"get",
"(",
"'trainingServicePlatform'",
")",
"or",
"experiment_config",
".",
"get",
"(",
"'trainingServicePlatform'",
")",
"not",
"in",
"[",
"'local'",
",",... | Validate whether the common values in experiment_config is valid | [
"Validate",
"whether",
"the",
"common",
"values",
"in",
"experiment_config",
"is",
"valid"
] | c7cc8db32da8d2ec77a382a55089f4e17247ce41 | https://github.com/Microsoft/nni/blob/c7cc8db32da8d2ec77a382a55089f4e17247ce41/tools/nni_cmd/launcher_utils.py#L127-L182 | train |
Microsoft/nni | tools/nni_cmd/launcher_utils.py | validate_customized_file | def validate_customized_file(experiment_config, spec_key):
'''
check whether the file of customized tuner/assessor/advisor exists
spec_key: 'tuner', 'assessor', 'advisor'
'''
if experiment_config[spec_key].get('codeDir') and \
experiment_config[spec_key].get('classFileName') and \
ex... | python | def validate_customized_file(experiment_config, spec_key):
'''
check whether the file of customized tuner/assessor/advisor exists
spec_key: 'tuner', 'assessor', 'advisor'
'''
if experiment_config[spec_key].get('codeDir') and \
experiment_config[spec_key].get('classFileName') and \
ex... | [
"def",
"validate_customized_file",
"(",
"experiment_config",
",",
"spec_key",
")",
":",
"if",
"experiment_config",
"[",
"spec_key",
"]",
".",
"get",
"(",
"'codeDir'",
")",
"and",
"experiment_config",
"[",
"spec_key",
"]",
".",
"get",
"(",
"'classFileName'",
")",... | check whether the file of customized tuner/assessor/advisor exists
spec_key: 'tuner', 'assessor', 'advisor' | [
"check",
"whether",
"the",
"file",
"of",
"customized",
"tuner",
"/",
"assessor",
"/",
"advisor",
"exists",
"spec_key",
":",
"tuner",
"assessor",
"advisor"
] | c7cc8db32da8d2ec77a382a55089f4e17247ce41 | https://github.com/Microsoft/nni/blob/c7cc8db32da8d2ec77a382a55089f4e17247ce41/tools/nni_cmd/launcher_utils.py#L184-L199 | train |
Microsoft/nni | tools/nni_cmd/launcher_utils.py | parse_assessor_content | def parse_assessor_content(experiment_config):
'''Validate whether assessor in experiment_config is valid'''
if experiment_config.get('assessor'):
if experiment_config['assessor'].get('builtinAssessorName'):
experiment_config['assessor']['className'] = experiment_config['assessor']['builtinA... | python | def parse_assessor_content(experiment_config):
'''Validate whether assessor in experiment_config is valid'''
if experiment_config.get('assessor'):
if experiment_config['assessor'].get('builtinAssessorName'):
experiment_config['assessor']['className'] = experiment_config['assessor']['builtinA... | [
"def",
"parse_assessor_content",
"(",
"experiment_config",
")",
":",
"if",
"experiment_config",
".",
"get",
"(",
"'assessor'",
")",
":",
"if",
"experiment_config",
"[",
"'assessor'",
"]",
".",
"get",
"(",
"'builtinAssessorName'",
")",
":",
"experiment_config",
"["... | Validate whether assessor in experiment_config is valid | [
"Validate",
"whether",
"assessor",
"in",
"experiment_config",
"is",
"valid"
] | c7cc8db32da8d2ec77a382a55089f4e17247ce41 | https://github.com/Microsoft/nni/blob/c7cc8db32da8d2ec77a382a55089f4e17247ce41/tools/nni_cmd/launcher_utils.py#L208-L214 | train |
Microsoft/nni | tools/nni_cmd/launcher_utils.py | validate_annotation_content | def validate_annotation_content(experiment_config, spec_key, builtin_name):
'''
Valid whether useAnnotation and searchSpacePath is coexist
spec_key: 'advisor' or 'tuner'
builtin_name: 'builtinAdvisorName' or 'builtinTunerName'
'''
if experiment_config.get('useAnnotation'):
if experiment_... | python | def validate_annotation_content(experiment_config, spec_key, builtin_name):
'''
Valid whether useAnnotation and searchSpacePath is coexist
spec_key: 'advisor' or 'tuner'
builtin_name: 'builtinAdvisorName' or 'builtinTunerName'
'''
if experiment_config.get('useAnnotation'):
if experiment_... | [
"def",
"validate_annotation_content",
"(",
"experiment_config",
",",
"spec_key",
",",
"builtin_name",
")",
":",
"if",
"experiment_config",
".",
"get",
"(",
"'useAnnotation'",
")",
":",
"if",
"experiment_config",
".",
"get",
"(",
"'searchSpacePath'",
")",
":",
"pri... | Valid whether useAnnotation and searchSpacePath is coexist
spec_key: 'advisor' or 'tuner'
builtin_name: 'builtinAdvisorName' or 'builtinTunerName' | [
"Valid",
"whether",
"useAnnotation",
"and",
"searchSpacePath",
"is",
"coexist",
"spec_key",
":",
"advisor",
"or",
"tuner",
"builtin_name",
":",
"builtinAdvisorName",
"or",
"builtinTunerName"
] | c7cc8db32da8d2ec77a382a55089f4e17247ce41 | https://github.com/Microsoft/nni/blob/c7cc8db32da8d2ec77a382a55089f4e17247ce41/tools/nni_cmd/launcher_utils.py#L223-L241 | train |
Microsoft/nni | tools/nni_cmd/launcher_utils.py | validate_pai_trial_conifg | def validate_pai_trial_conifg(experiment_config):
'''validate the trial config in pai platform'''
if experiment_config.get('trainingServicePlatform') == 'pai':
if experiment_config.get('trial').get('shmMB') and \
experiment_config['trial']['shmMB'] > experiment_config['trial']['memoryMB']:
... | python | def validate_pai_trial_conifg(experiment_config):
'''validate the trial config in pai platform'''
if experiment_config.get('trainingServicePlatform') == 'pai':
if experiment_config.get('trial').get('shmMB') and \
experiment_config['trial']['shmMB'] > experiment_config['trial']['memoryMB']:
... | [
"def",
"validate_pai_trial_conifg",
"(",
"experiment_config",
")",
":",
"if",
"experiment_config",
".",
"get",
"(",
"'trainingServicePlatform'",
")",
"==",
"'pai'",
":",
"if",
"experiment_config",
".",
"get",
"(",
"'trial'",
")",
".",
"get",
"(",
"'shmMB'",
")",... | validate the trial config in pai platform | [
"validate",
"the",
"trial",
"config",
"in",
"pai",
"platform"
] | c7cc8db32da8d2ec77a382a55089f4e17247ce41 | https://github.com/Microsoft/nni/blob/c7cc8db32da8d2ec77a382a55089f4e17247ce41/tools/nni_cmd/launcher_utils.py#L249-L255 | train |
Microsoft/nni | tools/nni_cmd/launcher_utils.py | validate_all_content | def validate_all_content(experiment_config, config_path):
'''Validate whether experiment_config is valid'''
parse_path(experiment_config, config_path)
validate_common_content(experiment_config)
validate_pai_trial_conifg(experiment_config)
experiment_config['maxExecDuration'] = parse_time(experiment_... | python | def validate_all_content(experiment_config, config_path):
'''Validate whether experiment_config is valid'''
parse_path(experiment_config, config_path)
validate_common_content(experiment_config)
validate_pai_trial_conifg(experiment_config)
experiment_config['maxExecDuration'] = parse_time(experiment_... | [
"def",
"validate_all_content",
"(",
"experiment_config",
",",
"config_path",
")",
":",
"parse_path",
"(",
"experiment_config",
",",
"config_path",
")",
"validate_common_content",
"(",
"experiment_config",
")",
"validate_pai_trial_conifg",
"(",
"experiment_config",
")",
"e... | Validate whether experiment_config is valid | [
"Validate",
"whether",
"experiment_config",
"is",
"valid"
] | c7cc8db32da8d2ec77a382a55089f4e17247ce41 | https://github.com/Microsoft/nni/blob/c7cc8db32da8d2ec77a382a55089f4e17247ce41/tools/nni_cmd/launcher_utils.py#L257-L274 | train |
Microsoft/nni | tools/nni_cmd/url_utils.py | get_local_urls | def get_local_urls(port):
'''get urls of local machine'''
url_list = []
for name, info in psutil.net_if_addrs().items():
for addr in info:
if AddressFamily.AF_INET == addr.family:
url_list.append('http://{}:{}'.format(addr.address, port))
return url_list | python | def get_local_urls(port):
'''get urls of local machine'''
url_list = []
for name, info in psutil.net_if_addrs().items():
for addr in info:
if AddressFamily.AF_INET == addr.family:
url_list.append('http://{}:{}'.format(addr.address, port))
return url_list | [
"def",
"get_local_urls",
"(",
"port",
")",
":",
"url_list",
"=",
"[",
"]",
"for",
"name",
",",
"info",
"in",
"psutil",
".",
"net_if_addrs",
"(",
")",
".",
"items",
"(",
")",
":",
"for",
"addr",
"in",
"info",
":",
"if",
"AddressFamily",
".",
"AF_INET"... | get urls of local machine | [
"get",
"urls",
"of",
"local",
"machine"
] | c7cc8db32da8d2ec77a382a55089f4e17247ce41 | https://github.com/Microsoft/nni/blob/c7cc8db32da8d2ec77a382a55089f4e17247ce41/tools/nni_cmd/url_utils.py#L76-L83 | train |
Microsoft/nni | tools/nni_annotation/code_generator.py | parse_annotation | def parse_annotation(code):
"""Parse an annotation string.
Return an AST Expr node.
code: annotation string (excluding '@')
"""
module = ast.parse(code)
assert type(module) is ast.Module, 'internal error #1'
assert len(module.body) == 1, 'Annotation contains more than one expression'
ass... | python | def parse_annotation(code):
"""Parse an annotation string.
Return an AST Expr node.
code: annotation string (excluding '@')
"""
module = ast.parse(code)
assert type(module) is ast.Module, 'internal error #1'
assert len(module.body) == 1, 'Annotation contains more than one expression'
ass... | [
"def",
"parse_annotation",
"(",
"code",
")",
":",
"module",
"=",
"ast",
".",
"parse",
"(",
"code",
")",
"assert",
"type",
"(",
"module",
")",
"is",
"ast",
".",
"Module",
",",
"'internal error #1'",
"assert",
"len",
"(",
"module",
".",
"body",
")",
"=="... | Parse an annotation string.
Return an AST Expr node.
code: annotation string (excluding '@') | [
"Parse",
"an",
"annotation",
"string",
".",
"Return",
"an",
"AST",
"Expr",
"node",
".",
"code",
":",
"annotation",
"string",
"(",
"excluding"
] | c7cc8db32da8d2ec77a382a55089f4e17247ce41 | https://github.com/Microsoft/nni/blob/c7cc8db32da8d2ec77a382a55089f4e17247ce41/tools/nni_annotation/code_generator.py#L29-L38 | train |
Microsoft/nni | tools/nni_annotation/code_generator.py | parse_annotation_function | def parse_annotation_function(code, func_name):
"""Parse an annotation function.
Return the value of `name` keyword argument and the AST Call node.
func_name: expected function name
"""
expr = parse_annotation(code)
call = expr.value
assert type(call) is ast.Call, 'Annotation is not a functi... | python | def parse_annotation_function(code, func_name):
"""Parse an annotation function.
Return the value of `name` keyword argument and the AST Call node.
func_name: expected function name
"""
expr = parse_annotation(code)
call = expr.value
assert type(call) is ast.Call, 'Annotation is not a functi... | [
"def",
"parse_annotation_function",
"(",
"code",
",",
"func_name",
")",
":",
"expr",
"=",
"parse_annotation",
"(",
"code",
")",
"call",
"=",
"expr",
".",
"value",
"assert",
"type",
"(",
"call",
")",
"is",
"ast",
".",
"Call",
",",
"'Annotation is not a functi... | Parse an annotation function.
Return the value of `name` keyword argument and the AST Call node.
func_name: expected function name | [
"Parse",
"an",
"annotation",
"function",
".",
"Return",
"the",
"value",
"of",
"name",
"keyword",
"argument",
"and",
"the",
"AST",
"Call",
"node",
".",
"func_name",
":",
"expected",
"function",
"name"
] | c7cc8db32da8d2ec77a382a55089f4e17247ce41 | https://github.com/Microsoft/nni/blob/c7cc8db32da8d2ec77a382a55089f4e17247ce41/tools/nni_annotation/code_generator.py#L41-L59 | train |
Microsoft/nni | tools/nni_annotation/code_generator.py | parse_nni_variable | def parse_nni_variable(code):
"""Parse `nni.variable` expression.
Return the name argument and AST node of annotated expression.
code: annotation string
"""
name, call = parse_annotation_function(code, 'variable')
assert len(call.args) == 1, 'nni.variable contains more than one arguments'
a... | python | def parse_nni_variable(code):
"""Parse `nni.variable` expression.
Return the name argument and AST node of annotated expression.
code: annotation string
"""
name, call = parse_annotation_function(code, 'variable')
assert len(call.args) == 1, 'nni.variable contains more than one arguments'
a... | [
"def",
"parse_nni_variable",
"(",
"code",
")",
":",
"name",
",",
"call",
"=",
"parse_annotation_function",
"(",
"code",
",",
"'variable'",
")",
"assert",
"len",
"(",
"call",
".",
"args",
")",
"==",
"1",
",",
"'nni.variable contains more than one arguments'",
"ar... | Parse `nni.variable` expression.
Return the name argument and AST node of annotated expression.
code: annotation string | [
"Parse",
"nni",
".",
"variable",
"expression",
".",
"Return",
"the",
"name",
"argument",
"and",
"AST",
"node",
"of",
"annotated",
"expression",
".",
"code",
":",
"annotation",
"string"
] | c7cc8db32da8d2ec77a382a55089f4e17247ce41 | https://github.com/Microsoft/nni/blob/c7cc8db32da8d2ec77a382a55089f4e17247ce41/tools/nni_annotation/code_generator.py#L62-L82 | train |
Microsoft/nni | tools/nni_annotation/code_generator.py | parse_nni_function | def parse_nni_function(code):
"""Parse `nni.function_choice` expression.
Return the AST node of annotated expression and a list of dumped function call expressions.
code: annotation string
"""
name, call = parse_annotation_function(code, 'function_choice')
funcs = [ast.dump(func, False) for func... | python | def parse_nni_function(code):
"""Parse `nni.function_choice` expression.
Return the AST node of annotated expression and a list of dumped function call expressions.
code: annotation string
"""
name, call = parse_annotation_function(code, 'function_choice')
funcs = [ast.dump(func, False) for func... | [
"def",
"parse_nni_function",
"(",
"code",
")",
":",
"name",
",",
"call",
"=",
"parse_annotation_function",
"(",
"code",
",",
"'function_choice'",
")",
"funcs",
"=",
"[",
"ast",
".",
"dump",
"(",
"func",
",",
"False",
")",
"for",
"func",
"in",
"call",
"."... | Parse `nni.function_choice` expression.
Return the AST node of annotated expression and a list of dumped function call expressions.
code: annotation string | [
"Parse",
"nni",
".",
"function_choice",
"expression",
".",
"Return",
"the",
"AST",
"node",
"of",
"annotated",
"expression",
"and",
"a",
"list",
"of",
"dumped",
"function",
"call",
"expressions",
".",
"code",
":",
"annotation",
"string"
] | c7cc8db32da8d2ec77a382a55089f4e17247ce41 | https://github.com/Microsoft/nni/blob/c7cc8db32da8d2ec77a382a55089f4e17247ce41/tools/nni_annotation/code_generator.py#L85-L97 | train |
Microsoft/nni | tools/nni_annotation/code_generator.py | convert_args_to_dict | def convert_args_to_dict(call, with_lambda=False):
"""Convert all args to a dict such that every key and value in the dict is the same as the value of the arg.
Return the AST Call node with only one arg that is the dictionary
"""
keys, values = list(), list()
for arg in call.args:
if type(ar... | python | def convert_args_to_dict(call, with_lambda=False):
"""Convert all args to a dict such that every key and value in the dict is the same as the value of the arg.
Return the AST Call node with only one arg that is the dictionary
"""
keys, values = list(), list()
for arg in call.args:
if type(ar... | [
"def",
"convert_args_to_dict",
"(",
"call",
",",
"with_lambda",
"=",
"False",
")",
":",
"keys",
",",
"values",
"=",
"list",
"(",
")",
",",
"list",
"(",
")",
"for",
"arg",
"in",
"call",
".",
"args",
":",
"if",
"type",
"(",
"arg",
")",
"in",
"[",
"... | Convert all args to a dict such that every key and value in the dict is the same as the value of the arg.
Return the AST Call node with only one arg that is the dictionary | [
"Convert",
"all",
"args",
"to",
"a",
"dict",
"such",
"that",
"every",
"key",
"and",
"value",
"in",
"the",
"dict",
"is",
"the",
"same",
"as",
"the",
"value",
"of",
"the",
"arg",
".",
"Return",
"the",
"AST",
"Call",
"node",
"with",
"only",
"one",
"arg"... | c7cc8db32da8d2ec77a382a55089f4e17247ce41 | https://github.com/Microsoft/nni/blob/c7cc8db32da8d2ec77a382a55089f4e17247ce41/tools/nni_annotation/code_generator.py#L100-L118 | train |
Microsoft/nni | tools/nni_annotation/code_generator.py | make_lambda | def make_lambda(call):
"""Wrap an AST Call node to lambda expression node.
call: ast.Call node
"""
empty_args = ast.arguments(args=[], vararg=None, kwarg=None, defaults=[])
return ast.Lambda(args=empty_args, body=call) | python | def make_lambda(call):
"""Wrap an AST Call node to lambda expression node.
call: ast.Call node
"""
empty_args = ast.arguments(args=[], vararg=None, kwarg=None, defaults=[])
return ast.Lambda(args=empty_args, body=call) | [
"def",
"make_lambda",
"(",
"call",
")",
":",
"empty_args",
"=",
"ast",
".",
"arguments",
"(",
"args",
"=",
"[",
"]",
",",
"vararg",
"=",
"None",
",",
"kwarg",
"=",
"None",
",",
"defaults",
"=",
"[",
"]",
")",
"return",
"ast",
".",
"Lambda",
"(",
... | Wrap an AST Call node to lambda expression node.
call: ast.Call node | [
"Wrap",
"an",
"AST",
"Call",
"node",
"to",
"lambda",
"expression",
"node",
".",
"call",
":",
"ast",
".",
"Call",
"node"
] | c7cc8db32da8d2ec77a382a55089f4e17247ce41 | https://github.com/Microsoft/nni/blob/c7cc8db32da8d2ec77a382a55089f4e17247ce41/tools/nni_annotation/code_generator.py#L121-L126 | train |
Microsoft/nni | tools/nni_annotation/code_generator.py | replace_variable_node | def replace_variable_node(node, annotation):
"""Replace a node annotated by `nni.variable`.
node: the AST node to replace
annotation: annotation string
"""
assert type(node) is ast.Assign, 'nni.variable is not annotating assignment expression'
assert len(node.targets) == 1, 'Annotated assignment... | python | def replace_variable_node(node, annotation):
"""Replace a node annotated by `nni.variable`.
node: the AST node to replace
annotation: annotation string
"""
assert type(node) is ast.Assign, 'nni.variable is not annotating assignment expression'
assert len(node.targets) == 1, 'Annotated assignment... | [
"def",
"replace_variable_node",
"(",
"node",
",",
"annotation",
")",
":",
"assert",
"type",
"(",
"node",
")",
"is",
"ast",
".",
"Assign",
",",
"'nni.variable is not annotating assignment expression'",
"assert",
"len",
"(",
"node",
".",
"targets",
")",
"==",
"1",... | Replace a node annotated by `nni.variable`.
node: the AST node to replace
annotation: annotation string | [
"Replace",
"a",
"node",
"annotated",
"by",
"nni",
".",
"variable",
".",
"node",
":",
"the",
"AST",
"node",
"to",
"replace",
"annotation",
":",
"annotation",
"string"
] | c7cc8db32da8d2ec77a382a55089f4e17247ce41 | https://github.com/Microsoft/nni/blob/c7cc8db32da8d2ec77a382a55089f4e17247ce41/tools/nni_annotation/code_generator.py#L148-L158 | train |
Microsoft/nni | tools/nni_annotation/code_generator.py | replace_function_node | def replace_function_node(node, annotation):
"""Replace a node annotated by `nni.function_choice`.
node: the AST node to replace
annotation: annotation string
"""
target, funcs = parse_nni_function(annotation)
FuncReplacer(funcs, target).visit(node)
return node | python | def replace_function_node(node, annotation):
"""Replace a node annotated by `nni.function_choice`.
node: the AST node to replace
annotation: annotation string
"""
target, funcs = parse_nni_function(annotation)
FuncReplacer(funcs, target).visit(node)
return node | [
"def",
"replace_function_node",
"(",
"node",
",",
"annotation",
")",
":",
"target",
",",
"funcs",
"=",
"parse_nni_function",
"(",
"annotation",
")",
"FuncReplacer",
"(",
"funcs",
",",
"target",
")",
".",
"visit",
"(",
"node",
")",
"return",
"node"
] | Replace a node annotated by `nni.function_choice`.
node: the AST node to replace
annotation: annotation string | [
"Replace",
"a",
"node",
"annotated",
"by",
"nni",
".",
"function_choice",
".",
"node",
":",
"the",
"AST",
"node",
"to",
"replace",
"annotation",
":",
"annotation",
"string"
] | c7cc8db32da8d2ec77a382a55089f4e17247ce41 | https://github.com/Microsoft/nni/blob/c7cc8db32da8d2ec77a382a55089f4e17247ce41/tools/nni_annotation/code_generator.py#L161-L168 | train |
Microsoft/nni | tools/nni_annotation/code_generator.py | parse | def parse(code):
"""Annotate user code.
Return annotated code (str) if annotation detected; return None if not.
code: original user code (str)
"""
try:
ast_tree = ast.parse(code)
except Exception:
raise RuntimeError('Bad Python code')
transformer = Transformer()
try:
... | python | def parse(code):
"""Annotate user code.
Return annotated code (str) if annotation detected; return None if not.
code: original user code (str)
"""
try:
ast_tree = ast.parse(code)
except Exception:
raise RuntimeError('Bad Python code')
transformer = Transformer()
try:
... | [
"def",
"parse",
"(",
"code",
")",
":",
"try",
":",
"ast_tree",
"=",
"ast",
".",
"parse",
"(",
"code",
")",
"except",
"Exception",
":",
"raise",
"RuntimeError",
"(",
"'Bad Python code'",
")",
"transformer",
"=",
"Transformer",
"(",
")",
"try",
":",
"trans... | Annotate user code.
Return annotated code (str) if annotation detected; return None if not.
code: original user code (str) | [
"Annotate",
"user",
"code",
".",
"Return",
"annotated",
"code",
"(",
"str",
")",
"if",
"annotation",
"detected",
";",
"return",
"None",
"if",
"not",
".",
"code",
":",
"original",
"user",
"code",
"(",
"str",
")"
] | c7cc8db32da8d2ec77a382a55089f4e17247ce41 | https://github.com/Microsoft/nni/blob/c7cc8db32da8d2ec77a382a55089f4e17247ce41/tools/nni_annotation/code_generator.py#L254-L281 | train |
Microsoft/nni | src/sdk/pynni/nni/__main__.py | main | def main():
'''
main function.
'''
args = parse_args()
if args.multi_thread:
enable_multi_thread()
if args.advisor_class_name:
# advisor is enabled and starts to run
if args.multi_phase:
raise AssertionError('multi_phase has not been supported in advisor')
... | python | def main():
'''
main function.
'''
args = parse_args()
if args.multi_thread:
enable_multi_thread()
if args.advisor_class_name:
# advisor is enabled and starts to run
if args.multi_phase:
raise AssertionError('multi_phase has not been supported in advisor')
... | [
"def",
"main",
"(",
")",
":",
"args",
"=",
"parse_args",
"(",
")",
"if",
"args",
".",
"multi_thread",
":",
"enable_multi_thread",
"(",
")",
"if",
"args",
".",
"advisor_class_name",
":",
"# advisor is enabled and starts to run",
"if",
"args",
".",
"multi_phase",
... | main function. | [
"main",
"function",
"."
] | c7cc8db32da8d2ec77a382a55089f4e17247ce41 | https://github.com/Microsoft/nni/blob/c7cc8db32da8d2ec77a382a55089f4e17247ce41/src/sdk/pynni/nni/__main__.py#L121-L198 | train |
Microsoft/nni | tools/nni_cmd/common_utils.py | get_yml_content | def get_yml_content(file_path):
'''Load yaml file content'''
try:
with open(file_path, 'r') as file:
return yaml.load(file, Loader=yaml.Loader)
except yaml.scanner.ScannerError as err:
print_error('yaml file format error!')
exit(1)
except Exception as exception:
... | python | def get_yml_content(file_path):
'''Load yaml file content'''
try:
with open(file_path, 'r') as file:
return yaml.load(file, Loader=yaml.Loader)
except yaml.scanner.ScannerError as err:
print_error('yaml file format error!')
exit(1)
except Exception as exception:
... | [
"def",
"get_yml_content",
"(",
"file_path",
")",
":",
"try",
":",
"with",
"open",
"(",
"file_path",
",",
"'r'",
")",
"as",
"file",
":",
"return",
"yaml",
".",
"load",
"(",
"file",
",",
"Loader",
"=",
"yaml",
".",
"Loader",
")",
"except",
"yaml",
".",... | Load yaml file content | [
"Load",
"yaml",
"file",
"content"
] | c7cc8db32da8d2ec77a382a55089f4e17247ce41 | https://github.com/Microsoft/nni/blob/c7cc8db32da8d2ec77a382a55089f4e17247ce41/tools/nni_cmd/common_utils.py#L30-L40 | train |
Microsoft/nni | tools/nni_cmd/common_utils.py | detect_port | def detect_port(port):
'''Detect if the port is used'''
socket_test = socket.socket(socket.AF_INET,socket.SOCK_STREAM)
try:
socket_test.connect(('127.0.0.1', int(port)))
socket_test.close()
return True
except:
return False | python | def detect_port(port):
'''Detect if the port is used'''
socket_test = socket.socket(socket.AF_INET,socket.SOCK_STREAM)
try:
socket_test.connect(('127.0.0.1', int(port)))
socket_test.close()
return True
except:
return False | [
"def",
"detect_port",
"(",
"port",
")",
":",
"socket_test",
"=",
"socket",
".",
"socket",
"(",
"socket",
".",
"AF_INET",
",",
"socket",
".",
"SOCK_STREAM",
")",
"try",
":",
"socket_test",
".",
"connect",
"(",
"(",
"'127.0.0.1'",
",",
"int",
"(",
"port",
... | Detect if the port is used | [
"Detect",
"if",
"the",
"port",
"is",
"used"
] | c7cc8db32da8d2ec77a382a55089f4e17247ce41 | https://github.com/Microsoft/nni/blob/c7cc8db32da8d2ec77a382a55089f4e17247ce41/tools/nni_cmd/common_utils.py#L71-L79 | train |
Microsoft/nni | src/sdk/pynni/nni/metis_tuner/Regression_GMM/CreateModel.py | create_model | def create_model(samples_x, samples_y_aggregation, percentage_goodbatch=0.34):
'''
Create the Gaussian Mixture Model
'''
samples = [samples_x[i] + [samples_y_aggregation[i]] for i in range(0, len(samples_x))]
# Sorts so that we can get the top samples
samples = sorted(samples, key=itemgetter(-1... | python | def create_model(samples_x, samples_y_aggregation, percentage_goodbatch=0.34):
'''
Create the Gaussian Mixture Model
'''
samples = [samples_x[i] + [samples_y_aggregation[i]] for i in range(0, len(samples_x))]
# Sorts so that we can get the top samples
samples = sorted(samples, key=itemgetter(-1... | [
"def",
"create_model",
"(",
"samples_x",
",",
"samples_y_aggregation",
",",
"percentage_goodbatch",
"=",
"0.34",
")",
":",
"samples",
"=",
"[",
"samples_x",
"[",
"i",
"]",
"+",
"[",
"samples_y_aggregation",
"[",
"i",
"]",
"]",
"for",
"i",
"in",
"range",
"(... | Create the Gaussian Mixture Model | [
"Create",
"the",
"Gaussian",
"Mixture",
"Model"
] | c7cc8db32da8d2ec77a382a55089f4e17247ce41 | https://github.com/Microsoft/nni/blob/c7cc8db32da8d2ec77a382a55089f4e17247ce41/src/sdk/pynni/nni/metis_tuner/Regression_GMM/CreateModel.py#L30-L57 | train |
Microsoft/nni | src/sdk/pynni/nni/metis_tuner/Regression_GP/Selection.py | selection_r | def selection_r(acquisition_function,
samples_y_aggregation,
x_bounds,
x_types,
regressor_gp,
num_starting_points=100,
minimize_constraints_fun=None):
'''
Selecte R value
'''
minimize_starting_points = [lib_d... | python | def selection_r(acquisition_function,
samples_y_aggregation,
x_bounds,
x_types,
regressor_gp,
num_starting_points=100,
minimize_constraints_fun=None):
'''
Selecte R value
'''
minimize_starting_points = [lib_d... | [
"def",
"selection_r",
"(",
"acquisition_function",
",",
"samples_y_aggregation",
",",
"x_bounds",
",",
"x_types",
",",
"regressor_gp",
",",
"num_starting_points",
"=",
"100",
",",
"minimize_constraints_fun",
"=",
"None",
")",
":",
"minimize_starting_points",
"=",
"[",... | Selecte R value | [
"Selecte",
"R",
"value"
] | c7cc8db32da8d2ec77a382a55089f4e17247ce41 | https://github.com/Microsoft/nni/blob/c7cc8db32da8d2ec77a382a55089f4e17247ce41/src/sdk/pynni/nni/metis_tuner/Regression_GP/Selection.py#L37-L54 | train |
Microsoft/nni | src/sdk/pynni/nni/metis_tuner/Regression_GP/Selection.py | selection | def selection(acquisition_function,
samples_y_aggregation,
x_bounds, x_types,
regressor_gp,
minimize_starting_points,
minimize_constraints_fun=None):
'''
selection
'''
outputs = None
sys.stderr.write("[%s] Exercise \"%s\" acquisi... | python | def selection(acquisition_function,
samples_y_aggregation,
x_bounds, x_types,
regressor_gp,
minimize_starting_points,
minimize_constraints_fun=None):
'''
selection
'''
outputs = None
sys.stderr.write("[%s] Exercise \"%s\" acquisi... | [
"def",
"selection",
"(",
"acquisition_function",
",",
"samples_y_aggregation",
",",
"x_bounds",
",",
"x_types",
",",
"regressor_gp",
",",
"minimize_starting_points",
",",
"minimize_constraints_fun",
"=",
"None",
")",
":",
"outputs",
"=",
"None",
"sys",
".",
"stderr"... | selection | [
"selection"
] | c7cc8db32da8d2ec77a382a55089f4e17247ce41 | https://github.com/Microsoft/nni/blob/c7cc8db32da8d2ec77a382a55089f4e17247ce41/src/sdk/pynni/nni/metis_tuner/Regression_GP/Selection.py#L56-L83 | train |
Microsoft/nni | src/sdk/pynni/nni/trial.py | report_intermediate_result | def report_intermediate_result(metric):
"""Reports intermediate result to Assessor.
metric: serializable object.
"""
global _intermediate_seq
assert _params is not None, 'nni.get_next_parameter() needs to be called before report_intermediate_result'
metric = json_tricks.dumps({
'paramete... | python | def report_intermediate_result(metric):
"""Reports intermediate result to Assessor.
metric: serializable object.
"""
global _intermediate_seq
assert _params is not None, 'nni.get_next_parameter() needs to be called before report_intermediate_result'
metric = json_tricks.dumps({
'paramete... | [
"def",
"report_intermediate_result",
"(",
"metric",
")",
":",
"global",
"_intermediate_seq",
"assert",
"_params",
"is",
"not",
"None",
",",
"'nni.get_next_parameter() needs to be called before report_intermediate_result'",
"metric",
"=",
"json_tricks",
".",
"dumps",
"(",
"{... | Reports intermediate result to Assessor.
metric: serializable object. | [
"Reports",
"intermediate",
"result",
"to",
"Assessor",
".",
"metric",
":",
"serializable",
"object",
"."
] | c7cc8db32da8d2ec77a382a55089f4e17247ce41 | https://github.com/Microsoft/nni/blob/c7cc8db32da8d2ec77a382a55089f4e17247ce41/src/sdk/pynni/nni/trial.py#L60-L74 | train |
Microsoft/nni | src/sdk/pynni/nni/trial.py | report_final_result | def report_final_result(metric):
"""Reports final result to tuner.
metric: serializable object.
"""
assert _params is not None, 'nni.get_next_parameter() needs to be called before report_final_result'
metric = json_tricks.dumps({
'parameter_id': _params['parameter_id'],
'trial_job_id... | python | def report_final_result(metric):
"""Reports final result to tuner.
metric: serializable object.
"""
assert _params is not None, 'nni.get_next_parameter() needs to be called before report_final_result'
metric = json_tricks.dumps({
'parameter_id': _params['parameter_id'],
'trial_job_id... | [
"def",
"report_final_result",
"(",
"metric",
")",
":",
"assert",
"_params",
"is",
"not",
"None",
",",
"'nni.get_next_parameter() needs to be called before report_final_result'",
"metric",
"=",
"json_tricks",
".",
"dumps",
"(",
"{",
"'parameter_id'",
":",
"_params",
"[",... | Reports final result to tuner.
metric: serializable object. | [
"Reports",
"final",
"result",
"to",
"tuner",
".",
"metric",
":",
"serializable",
"object",
"."
] | c7cc8db32da8d2ec77a382a55089f4e17247ce41 | https://github.com/Microsoft/nni/blob/c7cc8db32da8d2ec77a382a55089f4e17247ce41/src/sdk/pynni/nni/trial.py#L77-L89 | train |
Microsoft/nni | examples/trials/network_morphism/FashionMNIST/FashionMNIST_pytorch.py | get_args | def get_args():
""" get args from command line
"""
parser = argparse.ArgumentParser("FashionMNIST")
parser.add_argument("--batch_size", type=int, default=128, help="batch size")
parser.add_argument("--optimizer", type=str, default="SGD", help="optimizer")
parser.add_argument("--epochs", type=int... | python | def get_args():
""" get args from command line
"""
parser = argparse.ArgumentParser("FashionMNIST")
parser.add_argument("--batch_size", type=int, default=128, help="batch size")
parser.add_argument("--optimizer", type=str, default="SGD", help="optimizer")
parser.add_argument("--epochs", type=int... | [
"def",
"get_args",
"(",
")",
":",
"parser",
"=",
"argparse",
".",
"ArgumentParser",
"(",
"\"FashionMNIST\"",
")",
"parser",
".",
"add_argument",
"(",
"\"--batch_size\"",
",",
"type",
"=",
"int",
",",
"default",
"=",
"128",
",",
"help",
"=",
"\"batch size\"",... | get args from command line | [
"get",
"args",
"from",
"command",
"line"
] | c7cc8db32da8d2ec77a382a55089f4e17247ce41 | https://github.com/Microsoft/nni/blob/c7cc8db32da8d2ec77a382a55089f4e17247ce41/examples/trials/network_morphism/FashionMNIST/FashionMNIST_pytorch.py#L47-L62 | train |
Microsoft/nni | examples/trials/network_morphism/FashionMNIST/FashionMNIST_pytorch.py | build_graph_from_json | def build_graph_from_json(ir_model_json):
"""build model from json representation
"""
graph = json_to_graph(ir_model_json)
logging.debug(graph.operation_history)
model = graph.produce_torch_model()
return model | python | def build_graph_from_json(ir_model_json):
"""build model from json representation
"""
graph = json_to_graph(ir_model_json)
logging.debug(graph.operation_history)
model = graph.produce_torch_model()
return model | [
"def",
"build_graph_from_json",
"(",
"ir_model_json",
")",
":",
"graph",
"=",
"json_to_graph",
"(",
"ir_model_json",
")",
"logging",
".",
"debug",
"(",
"graph",
".",
"operation_history",
")",
"model",
"=",
"graph",
".",
"produce_torch_model",
"(",
")",
"return",... | build model from json representation | [
"build",
"model",
"from",
"json",
"representation"
] | c7cc8db32da8d2ec77a382a55089f4e17247ce41 | https://github.com/Microsoft/nni/blob/c7cc8db32da8d2ec77a382a55089f4e17247ce41/examples/trials/network_morphism/FashionMNIST/FashionMNIST_pytorch.py#L75-L81 | train |
Microsoft/nni | examples/trials/network_morphism/FashionMNIST/FashionMNIST_pytorch.py | parse_rev_args | def parse_rev_args(receive_msg):
""" parse reveive msgs to global variable
"""
global trainloader
global testloader
global net
global criterion
global optimizer
# Loading Data
logger.debug("Preparing data..")
raw_train_data = torchvision.datasets.FashionMNIST(
root="./d... | python | def parse_rev_args(receive_msg):
""" parse reveive msgs to global variable
"""
global trainloader
global testloader
global net
global criterion
global optimizer
# Loading Data
logger.debug("Preparing data..")
raw_train_data = torchvision.datasets.FashionMNIST(
root="./d... | [
"def",
"parse_rev_args",
"(",
"receive_msg",
")",
":",
"global",
"trainloader",
"global",
"testloader",
"global",
"net",
"global",
"criterion",
"global",
"optimizer",
"# Loading Data",
"logger",
".",
"debug",
"(",
"\"Preparing data..\"",
")",
"raw_train_data",
"=",
... | parse reveive msgs to global variable | [
"parse",
"reveive",
"msgs",
"to",
"global",
"variable"
] | c7cc8db32da8d2ec77a382a55089f4e17247ce41 | https://github.com/Microsoft/nni/blob/c7cc8db32da8d2ec77a382a55089f4e17247ce41/examples/trials/network_morphism/FashionMNIST/FashionMNIST_pytorch.py#L84-L145 | train |
Microsoft/nni | examples/trials/network_morphism/FashionMNIST/FashionMNIST_pytorch.py | train | def train(epoch):
""" train model on each epoch in trainset
"""
global trainloader
global testloader
global net
global criterion
global optimizer
logger.debug("Epoch: %d", epoch)
net.train()
train_loss = 0
correct = 0
total = 0
for batch_idx, (inputs, targets) in e... | python | def train(epoch):
""" train model on each epoch in trainset
"""
global trainloader
global testloader
global net
global criterion
global optimizer
logger.debug("Epoch: %d", epoch)
net.train()
train_loss = 0
correct = 0
total = 0
for batch_idx, (inputs, targets) in e... | [
"def",
"train",
"(",
"epoch",
")",
":",
"global",
"trainloader",
"global",
"testloader",
"global",
"net",
"global",
"criterion",
"global",
"optimizer",
"logger",
".",
"debug",
"(",
"\"Epoch: %d\"",
",",
"epoch",
")",
"net",
".",
"train",
"(",
")",
"train_los... | train model on each epoch in trainset | [
"train",
"model",
"on",
"each",
"epoch",
"in",
"trainset"
] | c7cc8db32da8d2ec77a382a55089f4e17247ce41 | https://github.com/Microsoft/nni/blob/c7cc8db32da8d2ec77a382a55089f4e17247ce41/examples/trials/network_morphism/FashionMNIST/FashionMNIST_pytorch.py#L149-L188 | train |
Microsoft/nni | examples/trials/kaggle-tgs-salt/models.py | UNetResNetV4.freeze_bn | def freeze_bn(self):
'''Freeze BatchNorm layers.'''
for layer in self.modules():
if isinstance(layer, nn.BatchNorm2d):
layer.eval() | python | def freeze_bn(self):
'''Freeze BatchNorm layers.'''
for layer in self.modules():
if isinstance(layer, nn.BatchNorm2d):
layer.eval() | [
"def",
"freeze_bn",
"(",
"self",
")",
":",
"for",
"layer",
"in",
"self",
".",
"modules",
"(",
")",
":",
"if",
"isinstance",
"(",
"layer",
",",
"nn",
".",
"BatchNorm2d",
")",
":",
"layer",
".",
"eval",
"(",
")"
] | Freeze BatchNorm layers. | [
"Freeze",
"BatchNorm",
"layers",
"."
] | c7cc8db32da8d2ec77a382a55089f4e17247ce41 | https://github.com/Microsoft/nni/blob/c7cc8db32da8d2ec77a382a55089f4e17247ce41/examples/trials/kaggle-tgs-salt/models.py#L210-L214 | train |
Microsoft/nni | examples/trials/network_morphism/cifar10/cifar10_pytorch.py | parse_rev_args | def parse_rev_args(receive_msg):
""" parse reveive msgs to global variable
"""
global trainloader
global testloader
global net
global criterion
global optimizer
# Loading Data
logger.debug("Preparing data..")
transform_train, transform_test = utils.data_transforms_cifar10(args)... | python | def parse_rev_args(receive_msg):
""" parse reveive msgs to global variable
"""
global trainloader
global testloader
global net
global criterion
global optimizer
# Loading Data
logger.debug("Preparing data..")
transform_train, transform_test = utils.data_transforms_cifar10(args)... | [
"def",
"parse_rev_args",
"(",
"receive_msg",
")",
":",
"global",
"trainloader",
"global",
"testloader",
"global",
"net",
"global",
"criterion",
"global",
"optimizer",
"# Loading Data",
"logger",
".",
"debug",
"(",
"\"Preparing data..\"",
")",
"transform_train",
",",
... | parse reveive msgs to global variable | [
"parse",
"reveive",
"msgs",
"to",
"global",
"variable"
] | c7cc8db32da8d2ec77a382a55089f4e17247ce41 | https://github.com/Microsoft/nni/blob/c7cc8db32da8d2ec77a382a55089f4e17247ce41/examples/trials/network_morphism/cifar10/cifar10_pytorch.py#L83-L136 | train |
shidenggui/easytrader | easytrader/webtrader.py | WebTrader.prepare | def prepare(self, config_file=None, user=None, password=None, **kwargs):
"""登录的统一接口
:param config_file 登录数据文件，若无则选择参数登录模式
:param user: 各家券商的账号或者雪球的用户名
:param password: 密码, 券商为加密后的密码，雪球为明文密码
:param account: [雪球登录需要]雪球手机号(邮箱手机二选一)
:param portfolio_code: [雪球登录需要]组合代码
... | python | def prepare(self, config_file=None, user=None, password=None, **kwargs):
"""登录的统一接口
:param config_file 登录数据文件，若无则选择参数登录模式
:param user: 各家券商的账号或者雪球的用户名
:param password: 密码, 券商为加密后的密码，雪球为明文密码
:param account: [雪球登录需要]雪球手机号(邮箱手机二选一)
:param portfolio_code: [雪球登录需要]组合代码
... | [
"def",
"prepare",
"(",
"self",
",",
"config_file",
"=",
"None",
",",
"user",
"=",
"None",
",",
"password",
"=",
"None",
",",
"*",
"*",
"kwargs",
")",
":",
"if",
"config_file",
"is",
"not",
"None",
":",
"self",
".",
"read_config",
"(",
"config_file",
... | 登录的统一接口
:param config_file 登录数据文件，若无则选择参数登录模式
:param user: 各家券商的账号或者雪球的用户名
:param password: 密码, 券商为加密后的密码，雪球为明文密码
:param account: [雪球登录需要]雪球手机号(邮箱手机二选一)
:param portfolio_code: [雪球登录需要]组合代码
:param portfolio_market: [雪球登录需要]交易市场，
可选['cn', 'us', 'hk'] 默认 'cn' | [
"登录的统一接口",
":",
"param",
"config_file",
"登录数据文件，若无则选择参数登录模式",
":",
"param",
"user",
":",
"各家券商的账号或者雪球的用户名",
":",
"param",
"password",
":",
"密码",
"券商为加密后的密码，雪球为明文密码",
":",
"param",
"account",
":",
"[",
"雪球登录需要",
"]",
"雪球手机号",
"(",
"邮箱手机二选一",
")",
":",
"param",
... | e5ae4daeda4ea125763a95b280dd694c7f68257d | https://github.com/shidenggui/easytrader/blob/e5ae4daeda4ea125763a95b280dd694c7f68257d/easytrader/webtrader.py#L40-L54 | train |
shidenggui/easytrader | easytrader/webtrader.py | WebTrader.autologin | def autologin(self, limit=10):
"""实现自动登录
:param limit: 登录次数限制
"""
for _ in range(limit):
if self.login():
break
else:
raise exceptions.NotLoginError(
"登录失败次数过多, 请检查密码是否正确 / 券商服务器是否处于维护中 / 网络连接是否正常"
)
self... | python | def autologin(self, limit=10):
"""实现自动登录
:param limit: 登录次数限制
"""
for _ in range(limit):
if self.login():
break
else:
raise exceptions.NotLoginError(
"登录失败次数过多, 请检查密码是否正确 / 券商服务器是否处于维护中 / 网络连接是否正常"
)
self... | [
"def",
"autologin",
"(",
"self",
",",
"limit",
"=",
"10",
")",
":",
"for",
"_",
"in",
"range",
"(",
"limit",
")",
":",
"if",
"self",
".",
"login",
"(",
")",
":",
"break",
"else",
":",
"raise",
"exceptions",
".",
"NotLoginError",
"(",
"\"登录失败次数过多, 请检查... | 实现自动登录
:param limit: 登录次数限制 | [
"实现自动登录",
":",
"param",
"limit",
":",
"登录次数限制"
] | e5ae4daeda4ea125763a95b280dd694c7f68257d | https://github.com/shidenggui/easytrader/blob/e5ae4daeda4ea125763a95b280dd694c7f68257d/easytrader/webtrader.py#L60-L71 | train |
shidenggui/easytrader | easytrader/webtrader.py | WebTrader.keepalive | def keepalive(self):
"""启动保持在线的进程 """
if self.heart_thread.is_alive():
self.heart_active = True
else:
self.heart_thread.start() | python | def keepalive(self):
"""启动保持在线的进程 """
if self.heart_thread.is_alive():
self.heart_active = True
else:
self.heart_thread.start() | [
"def",
"keepalive",
"(",
"self",
")",
":",
"if",
"self",
".",
"heart_thread",
".",
"is_alive",
"(",
")",
":",
"self",
".",
"heart_active",
"=",
"True",
"else",
":",
"self",
".",
"heart_thread",
".",
"start",
"(",
")"
] | 启动保持在线的进程 | [
"启动保持在线的进程"
] | e5ae4daeda4ea125763a95b280dd694c7f68257d | https://github.com/shidenggui/easytrader/blob/e5ae4daeda4ea125763a95b280dd694c7f68257d/easytrader/webtrader.py#L76-L81 | train |
shidenggui/easytrader | easytrader/webtrader.py | WebTrader.__read_config | def __read_config(self):
"""读取 config"""
self.config = helpers.file2dict(self.config_path)
self.global_config = helpers.file2dict(self.global_config_path)
self.config.update(self.global_config) | python | def __read_config(self):
"""读取 config"""
self.config = helpers.file2dict(self.config_path)
self.global_config = helpers.file2dict(self.global_config_path)
self.config.update(self.global_config) | [
"def",
"__read_config",
"(",
"self",
")",
":",
"self",
".",
"config",
"=",
"helpers",
".",
"file2dict",
"(",
"self",
".",
"config_path",
")",
"self",
".",
"global_config",
"=",
"helpers",
".",
"file2dict",
"(",
"self",
".",
"global_config_path",
")",
"self... | 读取 config | [
"读取",
"config"
] | e5ae4daeda4ea125763a95b280dd694c7f68257d | https://github.com/shidenggui/easytrader/blob/e5ae4daeda4ea125763a95b280dd694c7f68257d/easytrader/webtrader.py#L116-L120 | train |
shidenggui/easytrader | easytrader/webtrader.py | WebTrader.exchangebill | def exchangebill(self):
"""
默认提供最近30天的交割单, 通常只能返回查询日期内最新的 90 天数据。
:return:
"""
# TODO 目前仅在 华泰子类 中实现
start_date, end_date = helpers.get_30_date()
return self.get_exchangebill(start_date, end_date) | python | def exchangebill(self):
"""
默认提供最近30天的交割单, 通常只能返回查询日期内最新的 90 天数据。
:return:
"""
# TODO 目前仅在 华泰子类 中实现
start_date, end_date = helpers.get_30_date()
return self.get_exchangebill(start_date, end_date) | [
"def",
"exchangebill",
"(",
"self",
")",
":",
"# TODO 目前仅在 华泰子类 中实现",
"start_date",
",",
"end_date",
"=",
"helpers",
".",
"get_30_date",
"(",
")",
"return",
"self",
".",
"get_exchangebill",
"(",
"start_date",
",",
"end_date",
")"
] | 默认提供最近30天的交割单, 通常只能返回查询日期内最新的 90 天数据。
:return: | [
"默认提供最近30天的交割单",
"通常只能返回查询日期内最新的",
"90",
"天数据。",
":",
"return",
":"
] | e5ae4daeda4ea125763a95b280dd694c7f68257d | https://github.com/shidenggui/easytrader/blob/e5ae4daeda4ea125763a95b280dd694c7f68257d/easytrader/webtrader.py#L156-L163 | train |
shidenggui/easytrader | easytrader/webtrader.py | WebTrader.do | def do(self, params):
"""发起对 api 的请求并过滤返回结果
:param params: 交易所需的动态参数"""
request_params = self.create_basic_params()
request_params.update(params)
response_data = self.request(request_params)
try:
format_json_data = self.format_response_data(response_data)
... | python | def do(self, params):
"""发起对 api 的请求并过滤返回结果
:param params: 交易所需的动态参数"""
request_params = self.create_basic_params()
request_params.update(params)
response_data = self.request(request_params)
try:
format_json_data = self.format_response_data(response_data)
... | [
"def",
"do",
"(",
"self",
",",
"params",
")",
":",
"request_params",
"=",
"self",
".",
"create_basic_params",
"(",
")",
"request_params",
".",
"update",
"(",
"params",
")",
"response_data",
"=",
"self",
".",
"request",
"(",
"request_params",
")",
"try",
":... | 发起对 api 的请求并过滤返回结果
:param params: 交易所需的动态参数 | [
"发起对",
"api",
"的请求并过滤返回结果",
":",
"param",
"params",
":",
"交易所需的动态参数"
] | e5ae4daeda4ea125763a95b280dd694c7f68257d | https://github.com/shidenggui/easytrader/blob/e5ae4daeda4ea125763a95b280dd694c7f68257d/easytrader/webtrader.py#L182-L199 | train |
shidenggui/easytrader | easytrader/webtrader.py | WebTrader.format_response_data_type | def format_response_data_type(self, response_data):
"""格式化返回的值为正确的类型
:param response_data: 返回的数据
"""
if isinstance(response_data, list) and not isinstance(
response_data, str
):
return response_data
int_match_str = "|".join(self.config["response_f... | python | def format_response_data_type(self, response_data):
"""格式化返回的值为正确的类型
:param response_data: 返回的数据
"""
if isinstance(response_data, list) and not isinstance(
response_data, str
):
return response_data
int_match_str = "|".join(self.config["response_f... | [
"def",
"format_response_data_type",
"(",
"self",
",",
"response_data",
")",
":",
"if",
"isinstance",
"(",
"response_data",
",",
"list",
")",
"and",
"not",
"isinstance",
"(",
"response_data",
",",
"str",
")",
":",
"return",
"response_data",
"int_match_str",
"=",
... | 格式化返回的值为正确的类型
:param response_data: 返回的数据 | [
"格式化返回的值为正确的类型",
":",
"param",
"response_data",
":",
"返回的数据"
] | e5ae4daeda4ea125763a95b280dd694c7f68257d | https://github.com/shidenggui/easytrader/blob/e5ae4daeda4ea125763a95b280dd694c7f68257d/easytrader/webtrader.py#L220-L240 | train |
shidenggui/easytrader | easytrader/remoteclient.py | RemoteClient.prepare | def prepare(
self,
config_path=None,
user=None,
password=None,
exe_path=None,
comm_password=None,
**kwargs
):
"""
登陆客户端
:param config_path: 登陆配置文件，跟参数登陆方式二选一
:param user: 账号
:param password: 明文密码
:param exe_path:... | python | def prepare(
self,
config_path=None,
user=None,
password=None,
exe_path=None,
comm_password=None,
**kwargs
):
"""
登陆客户端
:param config_path: 登陆配置文件，跟参数登陆方式二选一
:param user: 账号
:param password: 明文密码
:param exe_path:... | [
"def",
"prepare",
"(",
"self",
",",
"config_path",
"=",
"None",
",",
"user",
"=",
"None",
",",
"password",
"=",
"None",
",",
"exe_path",
"=",
"None",
",",
"comm_password",
"=",
"None",
",",
"*",
"*",
"kwargs",
")",
":",
"params",
"=",
"locals",
"(",
... | 登陆客户端
:param config_path: 登陆配置文件，跟参数登陆方式二选一
:param user: 账号
:param password: 明文密码
:param exe_path: 客户端路径类似 r'C:\\htzqzyb2\\xiadan.exe', 默认 r'C:\\htzqzyb2\\xiadan.exe'
:param comm_password: 通讯密码
:return: | [
"登陆客户端",
":",
"param",
"config_path",
":",
"登陆配置文件，跟参数登陆方式二选一",
":",
"param",
"user",
":",
"账号",
":",
"param",
"password",
":",
"明文密码",
":",
"param",
"exe_path",
":",
"客户端路径类似",
"r",
"C",
":",
"\\\\",
"htzqzyb2",
"\\\\",
"xiadan",
".",
"exe",
"默认",
"r",
... | e5ae4daeda4ea125763a95b280dd694c7f68257d | https://github.com/shidenggui/easytrader/blob/e5ae4daeda4ea125763a95b280dd694c7f68257d/easytrader/remoteclient.py#L17-L48 | train |
shidenggui/easytrader | easytrader/ricequant_follower.py | RiceQuantFollower.follow | def follow(
self,
users,
run_id,
track_interval=1,
trade_cmd_expire_seconds=120,
cmd_cache=True,
entrust_prop="limit",
send_interval=0,
):
"""跟踪ricequant对应的模拟交易，支持多用户多策略
:param users: 支持easytrader的用户对象，支持使用 [] 指定多个用户
:param run_... | python | def follow(
self,
users,
run_id,
track_interval=1,
trade_cmd_expire_seconds=120,
cmd_cache=True,
entrust_prop="limit",
send_interval=0,
):
"""跟踪ricequant对应的模拟交易，支持多用户多策略
:param users: 支持easytrader的用户对象，支持使用 [] 指定多个用户
:param run_... | [
"def",
"follow",
"(",
"self",
",",
"users",
",",
"run_id",
",",
"track_interval",
"=",
"1",
",",
"trade_cmd_expire_seconds",
"=",
"120",
",",
"cmd_cache",
"=",
"True",
",",
"entrust_prop",
"=",
"\"limit\"",
",",
"send_interval",
"=",
"0",
",",
")",
":",
... | 跟踪ricequant对应的模拟交易，支持多用户多策略
:param users: 支持easytrader的用户对象，支持使用 [] 指定多个用户
:param run_id: ricequant 的模拟交易ID，支持使用 [] 指定多个模拟交易
:param track_interval: 轮训模拟交易时间，单位为秒
:param trade_cmd_expire_seconds: 交易指令过期时间, 单位为秒
:param cmd_cache: 是否读取存储历史执行过的指令，防止重启时重复执行已经交易过的指令
:param entr... | [
"跟踪ricequant对应的模拟交易，支持多用户多策略",
":",
"param",
"users",
":",
"支持easytrader的用户对象，支持使用",
"[]",
"指定多个用户",
":",
"param",
"run_id",
":",
"ricequant",
"的模拟交易ID，支持使用",
"[]",
"指定多个模拟交易",
":",
"param",
"track_interval",
":",
"轮训模拟交易时间，单位为秒",
":",
"param",
"trade_cmd_expire_seconds... | e5ae4daeda4ea125763a95b280dd694c7f68257d | https://github.com/shidenggui/easytrader/blob/e5ae4daeda4ea125763a95b280dd694c7f68257d/easytrader/ricequant_follower.py#L20-L61 | train |
shidenggui/easytrader | easytrader/gj_clienttrader.py | GJClientTrader.login | def login(self, user, password, exe_path, comm_password=None, **kwargs):
"""
登陆客户端
:param user: 账号
:param password: 明文密码
:param exe_path: 客户端路径类似 'C:\\中国银河证券双子星3.2\\Binarystar.exe',
默认 'C:\\中国银河证券双子星3.2\\Binarystar.exe'
:param comm_password: 通讯密码, 华泰需要，可不设
... | python | def login(self, user, password, exe_path, comm_password=None, **kwargs):
"""
登陆客户端
:param user: 账号
:param password: 明文密码
:param exe_path: 客户端路径类似 'C:\\中国银河证券双子星3.2\\Binarystar.exe',
默认 'C:\\中国银河证券双子星3.2\\Binarystar.exe'
:param comm_password: 通讯密码, 华泰需要，可不设
... | [
"def",
"login",
"(",
"self",
",",
"user",
",",
"password",
",",
"exe_path",
",",
"comm_password",
"=",
"None",
",",
"*",
"*",
"kwargs",
")",
":",
"try",
":",
"self",
".",
"_app",
"=",
"pywinauto",
".",
"Application",
"(",
")",
".",
"connect",
"(",
... | 登陆客户端
:param user: 账号
:param password: 明文密码
:param exe_path: 客户端路径类似 'C:\\中国银河证券双子星3.2\\Binarystar.exe',
默认 'C:\\中国银河证券双子星3.2\\Binarystar.exe'
:param comm_password: 通讯密码, 华泰需要，可不设
:return: | [
"登陆客户端"
] | e5ae4daeda4ea125763a95b280dd694c7f68257d | https://github.com/shidenggui/easytrader/blob/e5ae4daeda4ea125763a95b280dd694c7f68257d/easytrader/gj_clienttrader.py#L17-L69 | train |
shidenggui/easytrader | easytrader/ht_clienttrader.py | HTClientTrader.login | def login(self, user, password, exe_path, comm_password=None, **kwargs):
"""
:param user: 用户名
:param password: 密码
:param exe_path: 客户端路径, 类似
:param comm_password:
:param kwargs:
:return:
"""
if comm_password is None:
raise Val... | python | def login(self, user, password, exe_path, comm_password=None, **kwargs):
"""
:param user: 用户名
:param password: 密码
:param exe_path: 客户端路径, 类似
:param comm_password:
:param kwargs:
:return:
"""
if comm_password is None:
raise Val... | [
"def",
"login",
"(",
"self",
",",
"user",
",",
"password",
",",
"exe_path",
",",
"comm_password",
"=",
"None",
",",
"*",
"*",
"kwargs",
")",
":",
"if",
"comm_password",
"is",
"None",
":",
"raise",
"ValueError",
"(",
"\"华泰必须设置通讯密码\")\r",
"",
"try",
":",
... | :param user: 用户名
:param password: 密码
:param exe_path: 客户端路径, 类似
:param comm_password:
:param kwargs:
:return: | [
":",
"param",
"user",
":",
"用户名",
":",
"param",
"password",
":",
"密码",
":",
"param",
"exe_path",
":",
"客户端路径",
"类似",
":",
"param",
"comm_password",
":",
":",
"param",
"kwargs",
":",
":",
"return",
":"
] | e5ae4daeda4ea125763a95b280dd694c7f68257d | https://github.com/shidenggui/easytrader/blob/e5ae4daeda4ea125763a95b280dd694c7f68257d/easytrader/ht_clienttrader.py#L13-L55 | train |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.