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train | grid_visual | This function displays a grid of images to show full misclassification
:param data: grid data of the form;
[nb_classes : nb_classes : img_rows : img_cols : nb_channels]
:return: if necessary, the matplot figure to reuse | cleverhans/plot/pyplot_image.py | def grid_visual(data):
"""
This function displays a grid of images to show full misclassification
:param data: grid data of the form;
[nb_classes : nb_classes : img_rows : img_cols : nb_channels]
:return: if necessary, the matplot figure to reuse
"""
import matplotlib.pyplot as plt
# Ensure interac... | def grid_visual(data):
"""
This function displays a grid of images to show full misclassification
:param data: grid data of the form;
[nb_classes : nb_classes : img_rows : img_cols : nb_channels]
:return: if necessary, the matplot figure to reuse
"""
import matplotlib.pyplot as plt
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train | get_logits_over_interval | Get logits when the input is perturbed in an interval in adv direction.
Args:
sess: Tf session
model: Model for which we wish to get logits.
x_data: Numpy array corresponding to single data.
point of shape [height, width, channels].
fgsm_params: Parameters for generating adversa... | cleverhans/plot/pyplot_image.py | def get_logits_over_interval(sess, model, x_data, fgsm_params,
min_epsilon=-10., max_epsilon=10.,
num_points=21):
"""Get logits when the input is perturbed in an interval in adv direction.
Args:
sess: Tf session
model: Model for which we wish to... | def get_logits_over_interval(sess, model, x_data, fgsm_params,
min_epsilon=-10., max_epsilon=10.,
num_points=21):
"""Get logits when the input is perturbed in an interval in adv direction.
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train | linear_extrapolation_plot | Generate linear extrapolation plot.
Args:
log_prob_adv_array: Numpy array containing log probabilities
y: Tf placeholder for the labels
file_name: Plot filename
min_epsilon: Minimum value of epsilon over the interval
max_epsilon: Maximum value of epsilon over the interval
num_poin... | cleverhans/plot/pyplot_image.py | def linear_extrapolation_plot(log_prob_adv_array, y, file_name,
min_epsilon=-10, max_epsilon=10,
num_points=21):
"""Generate linear extrapolation plot.
Args:
log_prob_adv_array: Numpy array containing log probabilities
y: Tf placeholder for th... | def linear_extrapolation_plot(log_prob_adv_array, y, file_name,
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num_points=21):
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log_prob_adv_array: Numpy array containing log probabilities
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train | IQFeedTool._send_cmd | Encode IQFeed API messages. | contrib/utils/iqfeed-to-influxdb.py | def _send_cmd(self, cmd: str):
"""Encode IQFeed API messages."""
self._sock.sendall(cmd.encode(encoding='latin-1', errors='strict')) | def _send_cmd(self, cmd: str):
"""Encode IQFeed API messages."""
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train | IQFeedTool.iq_query | Send data query to IQFeed API. | contrib/utils/iqfeed-to-influxdb.py | def iq_query(self, message: str):
"""Send data query to IQFeed API."""
end_msg = '!ENDMSG!'
recv_buffer = 4096
# Send the historical data request message and buffer the data
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"""Send data query to IQFeed API."""
end_msg = '!ENDMSG!'
recv_buffer = 4096
# Send the historical data request message and buffer the data
self._send_cmd(message)
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train | IQFeedTool.get_historical_minute_data | Request historical 5 minute data from DTN. | contrib/utils/iqfeed-to-influxdb.py | def get_historical_minute_data(self, ticker: str):
"""Request historical 5 minute data from DTN."""
start = self._start
stop = self._stop
if len(stop) > 4:
stop = stop[:4]
if len(start) > 4:
start = start[:4]
for year in range(int(start), int(st... | def get_historical_minute_data(self, ticker: str):
"""Request historical 5 minute data from DTN."""
start = self._start
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stop = stop[:4]
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train | IQFeedTool.add_data_to_df | Build Pandas Dataframe in memory | contrib/utils/iqfeed-to-influxdb.py | def add_data_to_df(self, data: np.array):
"""Build Pandas Dataframe in memory"""
col_names = ['high_p', 'low_p', 'open_p', 'close_p', 'volume', 'oi']
data = np.array(data).reshape(-1, len(col_names) + 1)
df = pd.DataFrame(data=data[:, 1:], index=data[:, 0],
co... | def add_data_to_df(self, data: np.array):
"""Build Pandas Dataframe in memory"""
col_names = ['high_p', 'low_p', 'open_p', 'close_p', 'volume', 'oi']
data = np.array(data).reshape(-1, len(col_names) + 1)
df = pd.DataFrame(data=data[:, 1:], index=data[:, 0],
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train | IQFeedTool.get_tickers_from_file | Load ticker list from txt file | contrib/utils/iqfeed-to-influxdb.py | def get_tickers_from_file(self, filename):
"""Load ticker list from txt file"""
if not os.path.exists(filename):
log.error("Ticker List file does not exist: %s", filename)
tickers = []
with io.open(filename, 'r') as fd:
for ticker in fd:
tickers.a... | def get_tickers_from_file(self, filename):
"""Load ticker list from txt file"""
if not os.path.exists(filename):
log.error("Ticker List file does not exist: %s", filename)
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train | InfluxDBTool.write_dataframe_to_idb | Write Pandas Dataframe to InfluxDB database | contrib/utils/influxdb-import.py | def write_dataframe_to_idb(self, ticker):
"""Write Pandas Dataframe to InfluxDB database"""
cachepath = self._cache
cachefile = ('%s/%s-1M.csv.gz' % (cachepath, ticker))
if not os.path.exists(cachefile):
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"""Write Pandas Dataframe to InfluxDB database"""
cachepath = self._cache
cachefile = ('%s/%s-1M.csv.gz' % (cachepath, ticker))
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train | MultiCursor.connect | connect events | backtrader/plot/multicursor.py | def connect(self):
"""connect events"""
self._cidmotion = self.canvas.mpl_connect('motion_notify_event',
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self._ciddraw = self.canvas.mpl_connect('draw_event', self.clear) | def connect(self):
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train | MultiCursor.disconnect | disconnect events | backtrader/plot/multicursor.py | def disconnect(self):
"""disconnect events"""
self.canvas.mpl_disconnect(self._cidmotion)
self.canvas.mpl_disconnect(self._ciddraw) | def disconnect(self):
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train | WindowsInIDE.connect | Connect to window and set it foreground
Args:
**kwargs: optional arguments
Returns:
None | playground/win_ide.py | def connect(self, **kwargs):
"""
Connect to window and set it foreground
Args:
**kwargs: optional arguments
Returns:
None
"""
self.app = self._app.connect(**kwargs)
try:
self._top_window = self.app.top_window().wrapper_object... | def connect(self, **kwargs):
"""
Connect to window and set it foreground
Args:
**kwargs: optional arguments
Returns:
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"""
self.app = self._app.connect(**kwargs)
try:
self._top_window = self.app.top_window().wrapper_object... | [
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train | WindowsInIDE.get_rect | Get rectangle of app or desktop resolution
Returns:
RECT(left, top, right, bottom) | playground/win_ide.py | def get_rect(self):
"""
Get rectangle of app or desktop resolution
Returns:
RECT(left, top, right, bottom)
"""
if self.handle:
left, top, right, bottom = win32gui.GetWindowRect(self.handle)
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"""
Get rectangle of app or desktop resolution
Returns:
RECT(left, top, right, bottom)
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if self.handle:
left, top, right, bottom = win32gui.GetWindowRect(self.handle)
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train | WindowsInIDE.snapshot | Take a screenshot and save it to `tmp.png` filename by default
Args:
filename: name of file where to store the screenshot
Returns:
display the screenshot | playground/win_ide.py | def snapshot(self, filename="tmp.png"):
"""
Take a screenshot and save it to `tmp.png` filename by default
Args:
filename: name of file where to store the screenshot
Returns:
display the screenshot
"""
if not filename:
filename = "tm... | def snapshot(self, filename="tmp.png"):
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Take a screenshot and save it to `tmp.png` filename by default
Args:
filename: name of file where to store the screenshot
Returns:
display the screenshot
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train | PlotResult.extract_data | 从数据中获取到绘图相关的有用信息. | benchmark/plot.py | def extract_data(self):
"""从数据中获取到绘图相关的有用信息."""
self.time_axis = []
self.cpu_axis = []
self.mem_axis = []
self.timestamp_list = []
plot_data = self.data.get("plot_data", [])
# 按照时间分割线，划分成几段数据，取其中的最值
for i in plot_data:
timestamp = i["timestamp"... | def extract_data(self):
"""从数据中获取到绘图相关的有用信息."""
self.time_axis = []
self.cpu_axis = []
self.mem_axis = []
self.timestamp_list = []
plot_data = self.data.get("plot_data", [])
# 按照时间分割线，划分成几段数据，取其中的最值
for i in plot_data:
timestamp = i["timestamp"... | [
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train | PlotResult.get_each_method_maximun_cpu_mem | 获取每个方法中的cpu和内存耗费最值点. | benchmark/plot.py | def get_each_method_maximun_cpu_mem(self):
"""获取每个方法中的cpu和内存耗费最值点."""
# 本函数用于丰富self.method_exec_info的信息:存入cpu、mem最值点
self.method_exec_info = deepcopy(self.data.get("method_exec_info", []))
method_exec_info = deepcopy(self.method_exec_info) # 用来辅助循环
method_index, cpu_max, cpu_max... | def get_each_method_maximun_cpu_mem(self):
"""获取每个方法中的cpu和内存耗费最值点."""
# 本函数用于丰富self.method_exec_info的信息:存入cpu、mem最值点
self.method_exec_info = deepcopy(self.data.get("method_exec_info", []))
method_exec_info = deepcopy(self.method_exec_info) # 用来辅助循环
method_index, cpu_max, cpu_max... | [
"获取每个方法中的cpu和内存耗费最值点",
"."
] | AirtestProject/Airtest | python | https://github.com/AirtestProject/Airtest/blob/21583da2698a601cd632228228fc16d41f60a517/benchmark/plot.py#L61-L95 | [
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train | PlotResult._get_graph_title | 获取图像的title. | benchmark/plot.py | def _get_graph_title(self):
"""获取图像的title."""
start_time = datetime.fromtimestamp(int(self.timestamp_list[0]))
end_time = datetime.fromtimestamp(int(self.timestamp_list[-1]))
end_time = end_time.strftime('%H:%M:%S')
title = "Timespan: %s —— %s" % (start_time, end_time)
r... | def _get_graph_title(self):
"""获取图像的title."""
start_time = datetime.fromtimestamp(int(self.timestamp_list[0]))
end_time = datetime.fromtimestamp(int(self.timestamp_list[-1]))
end_time = end_time.strftime('%H:%M:%S')
title = "Timespan: %s —— %s" % (start_time, end_time)
r... | [
"获取图像的title",
"."
] | AirtestProject/Airtest | python | https://github.com/AirtestProject/Airtest/blob/21583da2698a601cd632228228fc16d41f60a517/benchmark/plot.py#L97-L104 | [
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train | PlotResult.plot_cpu_mem_keypoints | 绘制CPU/Mem/特征点数量. | benchmark/plot.py | def plot_cpu_mem_keypoints(self):
"""绘制CPU/Mem/特征点数量."""
plt.figure(1)
# 开始绘制子图:
plt.subplot(311)
title = self._get_graph_title()
plt.title(title, loc="center") # 设置绘图的标题
mem_ins = plt.plot(self.time_axis, self.mem_axis, "-", label="Mem(MB)", color='deepskyblue',... | def plot_cpu_mem_keypoints(self):
"""绘制CPU/Mem/特征点数量."""
plt.figure(1)
# 开始绘制子图:
plt.subplot(311)
title = self._get_graph_title()
plt.title(title, loc="center") # 设置绘图的标题
mem_ins = plt.plot(self.time_axis, self.mem_axis, "-", label="Mem(MB)", color='deepskyblue',... | [
"绘制CPU",
"/",
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"."
] | AirtestProject/Airtest | python | https://github.com/AirtestProject/Airtest/blob/21583da2698a601cd632228228fc16d41f60a517/benchmark/plot.py#L106-L172 | [
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train | CheckKeypointResult.refresh_method_objects | 初始化方法对象. | benchmark/profile_recorder.py | def refresh_method_objects(self):
"""初始化方法对象."""
self.method_object_dict = {}
for key, method in self.MATCHING_METHODS.items():
method_object = method(self.im_search, self.im_source, self.threshold, self.rgb)
self.method_object_dict.update({key: method_object}) | def refresh_method_objects(self):
"""初始化方法对象."""
self.method_object_dict = {}
for key, method in self.MATCHING_METHODS.items():
method_object = method(self.im_search, self.im_source, self.threshold, self.rgb)
self.method_object_dict.update({key: method_object}) | [
"初始化方法对象",
"."
] | AirtestProject/Airtest | python | https://github.com/AirtestProject/Airtest/blob/21583da2698a601cd632228228fc16d41f60a517/benchmark/profile_recorder.py#L44-L49 | [
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train | CheckKeypointResult._get_result | 获取特征点. | benchmark/profile_recorder.py | def _get_result(self, method_name="kaze"):
"""获取特征点."""
method_object = self.method_object_dict.get(method_name)
# 提取结果和特征点:
try:
result = method_object.find_best_result()
except Exception:
import traceback
traceback.print_exc()
ret... | def _get_result(self, method_name="kaze"):
"""获取特征点."""
method_object = self.method_object_dict.get(method_name)
# 提取结果和特征点:
try:
result = method_object.find_best_result()
except Exception:
import traceback
traceback.print_exc()
ret... | [
"获取特征点",
"."
] | AirtestProject/Airtest | python | https://github.com/AirtestProject/Airtest/blob/21583da2698a601cd632228228fc16d41f60a517/benchmark/profile_recorder.py#L51-L62 | [
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train | CheckKeypointResult.get_and_plot_keypoints | 获取并且绘制出特征点匹配结果. | benchmark/profile_recorder.py | def get_and_plot_keypoints(self, method_name, plot=False):
"""获取并且绘制出特征点匹配结果."""
if method_name not in self.method_object_dict.keys():
print("'%s' is not in MATCHING_METHODS" % method_name)
return None
kp_sch, kp_src, good, result = self._get_result(method_name)
... | def get_and_plot_keypoints(self, method_name, plot=False):
"""获取并且绘制出特征点匹配结果."""
if method_name not in self.method_object_dict.keys():
print("'%s' is not in MATCHING_METHODS" % method_name)
return None
kp_sch, kp_src, good, result = self._get_result(method_name)
... | [
"获取并且绘制出特征点匹配结果",
"."
] | AirtestProject/Airtest | python | https://github.com/AirtestProject/Airtest/blob/21583da2698a601cd632228228fc16d41f60a517/benchmark/profile_recorder.py#L64-L100 | [
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train | RecordThread.run | 开始线程. | benchmark/profile_recorder.py | def run(self):
"""开始线程."""
while not self.stop_flag:
timestamp = time.time()
cpu_percent = self.process.cpu_percent() / self.cpu_num
# mem_percent = mem = self.process.memory_percent()
mem_info = dict(self.process.memory_info()._asdict())
mem_g... | def run(self):
"""开始线程."""
while not self.stop_flag:
timestamp = time.time()
cpu_percent = self.process.cpu_percent() / self.cpu_num
# mem_percent = mem = self.process.memory_percent()
mem_info = dict(self.process.memory_info()._asdict())
mem_g... | [
"开始线程",
"."
] | AirtestProject/Airtest | python | https://github.com/AirtestProject/Airtest/blob/21583da2698a601cd632228228fc16d41f60a517/benchmark/profile_recorder.py#L121-L132 | [
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train | ProfileRecorder.load_images | 加载待匹配图片. | benchmark/profile_recorder.py | def load_images(self, search_file, source_file):
"""加载待匹配图片."""
self.search_file, self.source_file = search_file, source_file
self.im_search, self.im_source = imread(self.search_file), imread(self.source_file)
# 初始化对象
self.check_macthing_object = CheckKeypointResult(self.im_searc... | def load_images(self, search_file, source_file):
"""加载待匹配图片."""
self.search_file, self.source_file = search_file, source_file
self.im_search, self.im_source = imread(self.search_file), imread(self.source_file)
# 初始化对象
self.check_macthing_object = CheckKeypointResult(self.im_searc... | [
"加载待匹配图片",
"."
] | AirtestProject/Airtest | python | https://github.com/AirtestProject/Airtest/blob/21583da2698a601cd632228228fc16d41f60a517/benchmark/profile_recorder.py#L145-L150 | [
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train | ProfileRecorder.profile_methods | 帮助函数执行时记录数据. | benchmark/profile_recorder.py | def profile_methods(self, method_list):
"""帮助函数执行时记录数据."""
self.method_exec_info = []
# 开始数据记录进程
self.record_thread.stop_flag = False
self.record_thread.start()
for name in method_list:
if name not in self.check_macthing_object.MATCHING_METHODS.keys():
... | def profile_methods(self, method_list):
"""帮助函数执行时记录数据."""
self.method_exec_info = []
# 开始数据记录进程
self.record_thread.stop_flag = False
self.record_thread.start()
for name in method_list:
if name not in self.check_macthing_object.MATCHING_METHODS.keys():
... | [
"帮助函数执行时记录数据",
"."
] | AirtestProject/Airtest | python | https://github.com/AirtestProject/Airtest/blob/21583da2698a601cd632228228fc16d41f60a517/benchmark/profile_recorder.py#L152-L180 | [
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train | ProfileRecorder.wite_to_json | 将性能数据写入文件. | benchmark/profile_recorder.py | def wite_to_json(self, dir_path="", file_name=""):
"""将性能数据写入文件."""
# 提取数据
data = {
"plot_data": self.record_thread.profile_data,
"method_exec_info": self.method_exec_info,
"search_file": self.search_file,
"source_file": self.source_file}
#... | def wite_to_json(self, dir_path="", file_name=""):
"""将性能数据写入文件."""
# 提取数据
data = {
"plot_data": self.record_thread.profile_data,
"method_exec_info": self.method_exec_info,
"search_file": self.search_file,
"source_file": self.source_file}
#... | [
"将性能数据写入文件",
"."
] | AirtestProject/Airtest | python | https://github.com/AirtestProject/Airtest/blob/21583da2698a601cd632228228fc16d41f60a517/benchmark/profile_recorder.py#L182-L194 | [
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train | PocoReport.translate_poco_step | 处理poco的相关操作，参数与airtest的不同，由一个截图和一个操作构成，需要合成一个步骤
Parameters
----------
step 一个完整的操作，如click
prev_step 前一个步骤，应该是截图
Returns
------- | playground/poco.py | def translate_poco_step(self, step):
"""
处理poco的相关操作，参数与airtest的不同，由一个截图和一个操作构成，需要合成一个步骤
Parameters
----------
step 一个完整的操作，如click
prev_step 前一个步骤，应该是截图
Returns
-------
"""
ret = {}
prev_step = self._steps[-1]
if prev_step... | def translate_poco_step(self, step):
"""
处理poco的相关操作，参数与airtest的不同，由一个截图和一个操作构成，需要合成一个步骤
Parameters
----------
step 一个完整的操作，如click
prev_step 前一个步骤，应该是截图
Returns
-------
"""
ret = {}
prev_step = self._steps[-1]
if prev_step... | [
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] | AirtestProject/Airtest | python | https://github.com/AirtestProject/Airtest/blob/21583da2698a601cd632228228fc16d41f60a517/playground/poco.py#L12-L53 | [
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train | PocoReport.func_desc_poco | 把对应的poco操作显示成中文 | playground/poco.py | def func_desc_poco(self, step):
""" 把对应的poco操作显示成中文"""
desc = {
"touch": u"点击UI组件 {name}".format(name=step.get("text", "")),
}
if step['type'] in desc:
return desc.get(step['type'])
else:
return self._translate_desc(step) | def func_desc_poco(self, step):
""" 把对应的poco操作显示成中文"""
desc = {
"touch": u"点击UI组件 {name}".format(name=step.get("text", "")),
}
if step['type'] in desc:
return desc.get(step['type'])
else:
return self._translate_desc(step) | [
"把对应的poco操作显示成中文"
] | AirtestProject/Airtest | python | https://github.com/AirtestProject/Airtest/blob/21583da2698a601cd632228228fc16d41f60a517/playground/poco.py#L55-L63 | [
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train | profile_different_methods | 对指定的图片进行性能测试. | benchmark/benchmark.py | def profile_different_methods(search_file, screen_file, method_list, dir_path, file_name):
"""对指定的图片进行性能测试."""
profiler = ProfileRecorder(0.05)
# 加载图片
profiler.load_images(search_file, screen_file)
# 传入待测试的方法列表
profiler.profile_methods(method_list)
# 将性能数据写入文件
profiler.wite_to_json(dir_p... | def profile_different_methods(search_file, screen_file, method_list, dir_path, file_name):
"""对指定的图片进行性能测试."""
profiler = ProfileRecorder(0.05)
# 加载图片
profiler.load_images(search_file, screen_file)
# 传入待测试的方法列表
profiler.profile_methods(method_list)
# 将性能数据写入文件
profiler.wite_to_json(dir_p... | [
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"."
] | AirtestProject/Airtest | python | https://github.com/AirtestProject/Airtest/blob/21583da2698a601cd632228228fc16d41f60a517/benchmark/benchmark.py#L12-L20 | [
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train | plot_profiled_all_images_table | 绘制多个图片的结果. | benchmark/benchmark.py | def plot_profiled_all_images_table(method_list):
"""绘制多个图片的结果."""
high_dpi_dir_path, high_dpi_file_name = "result", "high_dpi.json"
rich_texture_dir_path, rich_texture_file_name = "result", "rich_texture.json"
text_dir_path, text_file_name = "result", "text.json"
image_list = ['high_dpi', 'rich_tex... | def plot_profiled_all_images_table(method_list):
"""绘制多个图片的结果."""
high_dpi_dir_path, high_dpi_file_name = "result", "high_dpi.json"
rich_texture_dir_path, rich_texture_file_name = "result", "rich_texture.json"
text_dir_path, text_file_name = "result", "text.json"
image_list = ['high_dpi', 'rich_tex... | [
"绘制多个图片的结果",
"."
] | AirtestProject/Airtest | python | https://github.com/AirtestProject/Airtest/blob/21583da2698a601cd632228228fc16d41f60a517/benchmark/benchmark.py#L53-L99 | [
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train | get_color_list | 获取method对应的color列表. | benchmark/benchmark.py | def get_color_list(method_list):
"""获取method对应的color列表."""
color_list = []
for method in method_list:
color = tuple([random() for _ in range(3)]) # 随机颜色画线
color_list.append(color)
return color_list | def get_color_list(method_list):
"""获取method对应的color列表."""
color_list = []
for method in method_list:
color = tuple([random() for _ in range(3)]) # 随机颜色画线
color_list.append(color)
return color_list | [
"获取method对应的color列表",
"."
] | AirtestProject/Airtest | python | https://github.com/AirtestProject/Airtest/blob/21583da2698a601cd632228228fc16d41f60a517/benchmark/benchmark.py#L102-L108 | [
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train | plot_compare_table | 绘制了对比表格. | benchmark/benchmark.py | def plot_compare_table(image_list, method_list, color_list, compare_dict, fig_name="", fig_num=111):
"""绘制了对比表格."""
row_labels = image_list
# 写入值：
table_vals = []
for i in range(len(row_labels)):
row_vals = []
for method in method_list:
row_vals.append(compare_dict[method... | def plot_compare_table(image_list, method_list, color_list, compare_dict, fig_name="", fig_num=111):
"""绘制了对比表格."""
row_labels = image_list
# 写入值：
table_vals = []
for i in range(len(row_labels)):
row_vals = []
for method in method_list:
row_vals.append(compare_dict[method... | [
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train | plot_compare_curves | 绘制对比曲线. | benchmark/benchmark.py | def plot_compare_curves(image_list, method_list, color_list, compare_dict, fig_name="", fig_num=111):
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train | _SimpleDecoder | Return a constructor for a decoder for fields of a particular type.
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wire_type: The field's wire type.
decode_value: A function which decodes an individual value, e.g.
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wire_type: The field's wire type.
decode_value: A function which decodes an individual value, e.g.
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def SpecificDecoder(field_number, is_repeated, ... | def _SimpleDecoder(wire_type, decode_value):
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train | _ModifiedDecoder | Like SimpleDecoder but additionally invokes modify_value on every value
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train | _StructPackDecoder | Return a constructor for a decoder for a fixed-width field.
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wire_type: The field's wire type.
format: The format string to pass to struct.unpack(). | src/external/coremltools_wrap/coremltools/deps/protobuf/python/google/protobuf/internal/decoder.py | def _StructPackDecoder(wire_type, format):
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wire_type: The field's wire type.
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"""Return a constructor for a decoder for a fixed-width field.
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wire_type: The field's wire type.
format: The format string to pass to struct.unpack().
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value_size = struct.calcsize(format)
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train | _FloatDecoder | Returns a decoder for a float field.
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local_unpack = struct.unpack
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train | _DoubleDecoder | Returns a decoder for a double field.
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train | StringDecoder | Returns a decoder for a string field. | src/external/coremltools_wrap/coremltools/deps/protobuf/python/google/protobuf/internal/decoder.py | def StringDecoder(field_number, is_repeated, is_packed, key, new_default):
"""Returns a decoder for a string field."""
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local_unicode = six.text_type
def _ConvertToUnicode(byte_str):
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... | def StringDecoder(field_number, is_repeated, is_packed, key, new_default):
"""Returns a decoder for a string field."""
local_DecodeVarint = _DecodeVarint
local_unicode = six.text_type
def _ConvertToUnicode(byte_str):
try:
return local_unicode(byte_str, 'utf-8')
except UnicodeDecodeError as e:
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train | BytesDecoder | Returns a decoder for a bytes field. | src/external/coremltools_wrap/coremltools/deps/protobuf/python/google/protobuf/internal/decoder.py | def BytesDecoder(field_number, is_repeated, is_packed, key, new_default):
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train | GroupDecoder | Returns a decoder for a group field. | src/external/coremltools_wrap/coremltools/deps/protobuf/python/google/protobuf/internal/decoder.py | def GroupDecoder(field_number, is_repeated, is_packed, key, new_default):
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end_tag_bytes = encoder.TagBytes(field_number,
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train | MapDecoder | Returns a decoder for a map field. | src/external/coremltools_wrap/coremltools/deps/protobuf/python/google/protobuf/internal/decoder.py | def MapDecoder(field_descriptor, new_default, is_message_map):
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train | _SkipVarint | Skip a varint value. Returns the new position. | src/external/coremltools_wrap/coremltools/deps/protobuf/python/google/protobuf/internal/decoder.py | def _SkipVarint(buffer, pos, end):
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train | _SkipLengthDelimited | Skip a length-delimited value. Returns the new position. | src/external/coremltools_wrap/coremltools/deps/protobuf/python/google/protobuf/internal/decoder.py | def _SkipLengthDelimited(buffer, pos, end):
"""Skip a length-delimited value. Returns the new position."""
(size, pos) = _DecodeVarint(buffer, pos)
pos += size
if pos > end:
raise _DecodeError('Truncated message.')
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pos += size
if pos > end:
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train | _SkipGroup | Skip sub-group. Returns the new position. | src/external/coremltools_wrap/coremltools/deps/protobuf/python/google/protobuf/internal/decoder.py | def _SkipGroup(buffer, pos, end):
"""Skip sub-group. Returns the new position."""
while 1:
(tag_bytes, pos) = ReadTag(buffer, pos)
new_pos = SkipField(buffer, pos, end, tag_bytes)
if new_pos == -1:
return pos
pos = new_pos | def _SkipGroup(buffer, pos, end):
"""Skip sub-group. Returns the new position."""
while 1:
(tag_bytes, pos) = ReadTag(buffer, pos)
new_pos = SkipField(buffer, pos, end, tag_bytes)
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train | _FieldSkipper | Constructs the SkipField function. | src/external/coremltools_wrap/coremltools/deps/protobuf/python/google/protobuf/internal/decoder.py | def _FieldSkipper():
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train | _parse_node | parse dumped node | src/external/xgboost/python-package/xgboost/plotting.py | def _parse_node(graph, text):
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train | plot_tree | Plot specified tree.
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Specify the ordinal number of target tree
rankdir : str, default "UT"
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ax : matplotlib Axes, default None
... | src/external/xgboost/python-package/xgboost/plotting.py | def plot_tree(booster, num_trees=0, rankdir='UT', ax=None, **kwargs):
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Parameters
----------
booster : Booster, XGBModel
Booster or XGBModel instance
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Specify the ordinal number of target tree
rankdir : str, default "UT"
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"""Plot specified tree.
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booster : Booster, XGBModel
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train | Manager.construct | Constructs the dependency graph.
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train | DecisionTreeClassifier.evaluate | Evaluate the model by making predictions of target values and comparing
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dataset : SFrame
Dataset of new observations. Must include columns with the same
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"""
Evaluate the model by making predictions of target values and comparing
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Parameters
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dataset : SFrame
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Evaluate the model by making predictions of target values and comparing
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train | DecisionTreeClassifier.predict | A flexible and advanced prediction API.
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Parameters
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dataset : SFrame
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"""
A flexible and advanced prediction API.
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train | DecisionTreeClassifier.predict_topk | Return top-k predictions for the ``dataset``, using the trained model.
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... | src/unity/python/turicreate/toolkits/classifier/decision_tree_classifier.py | def predict_topk(self, dataset, output_type="probability", k=3, missing_value_action='auto'):
"""
Return top-k predictions for the ``dataset``, using the trained model.
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train | DecisionTreeClassifier.classify | Return a classification, for each example in the ``dataset``, using the
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Parameters
----------
dataset : SFrame
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"""
Return a classification, for each example in the ``dataset``, using the
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train | Tracker.slave_envs | get enviroment variables for slaves
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"""
get enviroment variables for slaves
can be passed in as args or envs
"""
if self.hostIP == 'dns':
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elif self.hostIP == 'ip':
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get enviroment variables for slaves
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train | Tracker.find_share_ring | get a ring structure that tends to share nodes with the tree
return a list starting from r | src/external/xgboost/subtree/rabit/tracker/rabit_tracker.py | def find_share_ring(self, tree_map, parent_map, r):
"""
get a ring structure that tends to share nodes with the tree
return a list starting from r
"""
nset = set(tree_map[r])
cset = nset - set([parent_map[r]])
if len(cset) == 0:
return [r]
rlst... | def find_share_ring(self, tree_map, parent_map, r):
"""
get a ring structure that tends to share nodes with the tree
return a list starting from r
"""
nset = set(tree_map[r])
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train | Tracker.get_ring | get a ring connection used to recover local data | src/external/xgboost/subtree/rabit/tracker/rabit_tracker.py | def get_ring(self, tree_map, parent_map):
"""
get a ring connection used to recover local data
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rlst = self.find_share_ring(tree_map, parent_map, 0)
assert len(rlst) == len(tree_map)
ring_map = {}
nslave = len(tree_map)
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train | Tracker.get_link_map | get the link map, this is a bit hacky, call for better algorithm
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"""
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train | maybe_rewrite_setup | Helper rule to generate a faster alternative to MSVC setup scripts.
We used to call MSVC setup scripts directly in every action, however in
newer MSVC versions (10.0+) they make long-lasting registry queries
which have a significant impact on build time. | deps/src/boost_1_68_0/tools/build/src/tools/msvc.py | def maybe_rewrite_setup(toolset, setup_script, setup_options, version, rewrite_setup='off'):
"""
Helper rule to generate a faster alternative to MSVC setup scripts.
We used to call MSVC setup scripts directly in every action, however in
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Helper rule to generate a faster alternative to MSVC setup scripts.
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dataset : SFrame
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target : string
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"""
Automatically create a suitable classifier model based on the provided
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train | removable | node is removable only if all of its children are as well. | src/unity/python/turicreate/meta/asttools/mutators/prune_mutator.py | def removable(self, node):
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train | load_audio | Loads WAV file(s) from a path.
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train | SymbolDatabase.GetMessages | Gets all registered messages from a specified file.
Only messages already created and registered will be returned; (this is the
case for imported _pb2 modules)
But unlike MessageFactory, this version also returns already defined nested
messages, but does not register any message extensions.
Args:
... | src/external/coremltools_wrap/coremltools/deps/protobuf/python/google/protobuf/symbol_database.py | def GetMessages(self, files):
# TODO(amauryfa): Fix the differences with MessageFactory.
"""Gets all registered messages from a specified file.
Only messages already created and registered will be returned; (this is the
case for imported _pb2 modules)
But unlike MessageFactory, this version also re... | def GetMessages(self, files):
# TODO(amauryfa): Fix the differences with MessageFactory.
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Only messages already created and registered will be returned; (this is the
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train | _string_hash | String hash (djb2) with consistency between py2/py3 and persistency between runs (unlike `hash`). | src/unity/python/turicreate/toolkits/object_detector/util/_visualization.py | def _string_hash(s):
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train | draw_bounding_boxes | Visualizes bounding boxes (ground truth or predictions) by
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images: SArray or Image
An `SArray` of type `Image`. A single `Image` instance may also be
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annotations: SArray or list
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Parameters
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images: SArray or Image
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train | create | Create a :class:`~turicreate.toolkits.SupervisedLearningModel`,
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specific model's create function instead
Parameters
----------
dataset : SFrame
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train | create_classification_with_model_selector | Create a :class:`~turicreate.toolkits.SupervisedLearningModel`,
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Create a :class:`~turicreate.toolkits.SupervisedLearningModel`,
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dataset : SFrame
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dataset : SFrame
Dataset in the same format used for training. The columns names and
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Evaluate the model by making predictions of target values and comparing
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Parameters
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dataset: SFrame
Dataset of new observations. Must include columns with the same
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train | BoostedTreesRegression.evaluate | Evaluate the model on the given dataset.
Parameters
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dataset : SFrame
Dataset in the same format used for training. The columns names and
types of the dataset must be the same as that used in training.
metric : str, optional
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Evaluate the model on the given dataset.
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dataset : SFrame
Dataset in the same format used for training. The columns names and
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Predict the target column of the given dataset.
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train | print_code | Disassemble a code object. | src/unity/python/turicreate/meta/decompiler/disassemble.py | def print_code(co, lasti= -1, level=0):
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code = co.co_code
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print( 'constant:', constant)
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linestarts = dict(findlinestarts(co))
n = len... | def print_code(co, lasti= -1, level=0):
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train | convert | Convert a decision tree model to protobuf format.
Parameters
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decision_tree : DecisionTreeRegressor
A trained scikit-learn tree model.
feature_names: [str]
Name of the input columns.
target: str
Name of the output column.
Returns
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model_spec: ... | src/external/coremltools_wrap/coremltools/coremltools/converters/sklearn/_decision_tree_regressor.py | def convert(model, feature_names, target):
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Parameters
----------
decision_tree : DecisionTreeRegressor
A trained scikit-learn tree model.
feature_names: [str]
Name of the input columns.
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train | _check_prob_and_prob_vector | Check that the predictionsa are either probabilities of prob-vectors. | src/unity/python/turicreate/toolkits/evaluation.py | def _check_prob_and_prob_vector(predictions):
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train | _supervised_evaluation_error_checking | Perform basic error checking for the evaluation metrics. Check
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"""
Perform basic error checking for the evaluation metrics. Check
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"""
_raise_error_if_not_sarray(targets, "targets")
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Perform basic error checking for the evaluation metrics. Check
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train | log_loss | r"""
Compute the logloss for the given targets and the given predicted
probabilities. This quantity is defined to be the negative of the sum
of the log probability of each observation, normalized by the number of
observations:
.. math::
\textrm{logloss} = - \frac{1}{N} \sum_{i \in 1,\ldots... | src/unity/python/turicreate/toolkits/evaluation.py | def log_loss(targets, predictions, index_map=None):
r"""
Compute the logloss for the given targets and the given predicted
probabilities. This quantity is defined to be the negative of the sum
of the log probability of each observation, normalized by the number of
observations:
.. math::
... | def log_loss(targets, predictions, index_map=None):
r"""
Compute the logloss for the given targets and the given predicted
probabilities. This quantity is defined to be the negative of the sum
of the log probability of each observation, normalized by the number of
observations:
.. math::
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train | max_error | r"""
Compute the maximum absolute deviation between two SArrays.
Parameters
----------
targets : SArray[float or int]
An Sarray of ground truth target values.
predictions : SArray[float or int]
The prediction that corresponds to each target value.
This vector must have the ... | src/unity/python/turicreate/toolkits/evaluation.py | def max_error(targets, predictions):
r"""
Compute the maximum absolute deviation between two SArrays.
Parameters
----------
targets : SArray[float or int]
An Sarray of ground truth target values.
predictions : SArray[float or int]
The prediction that corresponds to each target ... | def max_error(targets, predictions):
r"""
Compute the maximum absolute deviation between two SArrays.
Parameters
----------
targets : SArray[float or int]
An Sarray of ground truth target values.
predictions : SArray[float or int]
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train | rmse | r"""
Compute the root mean squared error between two SArrays.
Parameters
----------
targets : SArray[float or int]
An Sarray of ground truth target values.
predictions : SArray[float or int]
The prediction that corresponds to each target value.
This vector must have the sam... | src/unity/python/turicreate/toolkits/evaluation.py | def rmse(targets, predictions):
r"""
Compute the root mean squared error between two SArrays.
Parameters
----------
targets : SArray[float or int]
An Sarray of ground truth target values.
predictions : SArray[float or int]
The prediction that corresponds to each target value.
... | def rmse(targets, predictions):
r"""
Compute the root mean squared error between two SArrays.
Parameters
----------
targets : SArray[float or int]
An Sarray of ground truth target values.
predictions : SArray[float or int]
The prediction that corresponds to each target value.
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train | confusion_matrix | r"""
Compute the confusion matrix for classifier predictions.
Parameters
----------
targets : SArray
Ground truth class labels (cannot be of type float).
predictions : SArray
The prediction that corresponds to each target value.
This vector must have the same length as ``ta... | src/unity/python/turicreate/toolkits/evaluation.py | def confusion_matrix(targets, predictions):
r"""
Compute the confusion matrix for classifier predictions.
Parameters
----------
targets : SArray
Ground truth class labels (cannot be of type float).
predictions : SArray
The prediction that corresponds to each target value.
... | def confusion_matrix(targets, predictions):
r"""
Compute the confusion matrix for classifier predictions.
Parameters
----------
targets : SArray
Ground truth class labels (cannot be of type float).
predictions : SArray
The prediction that corresponds to each target value.
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train | accuracy | r"""
Compute the accuracy score; which measures the fraction of predictions made
by the classifier that are exactly correct. The score lies in the range [0,1]
with 0 being the worst and 1 being the best.
Parameters
----------
targets : SArray
An SArray of ground truth class labels. Can ... | src/unity/python/turicreate/toolkits/evaluation.py | def accuracy(targets, predictions, average='micro'):
r"""
Compute the accuracy score; which measures the fraction of predictions made
by the classifier that are exactly correct. The score lies in the range [0,1]
with 0 being the worst and 1 being the best.
Parameters
----------
targets : SA... | def accuracy(targets, predictions, average='micro'):
r"""
Compute the accuracy score; which measures the fraction of predictions made
by the classifier that are exactly correct. The score lies in the range [0,1]
with 0 being the worst and 1 being the best.
Parameters
----------
targets : SA... | [
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train | fbeta_score | r"""
Compute the F-beta score. The F-beta score is the weighted harmonic mean of
precision and recall. The score lies in the range [0,1] with 1 being ideal
and 0 being the worst.
The `beta` value is the weight given to `precision` vs `recall` in the
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r"""
Compute the F-beta score. The F-beta score is the weighted harmonic mean of
precision and recall. The score lies in the range [0,1] with 1 being ideal
and 0 being the worst.
The `beta` value is the weight given to `precision` vs... | def fbeta_score(targets, predictions, beta=1.0, average='macro'):
r"""
Compute the F-beta score. The F-beta score is the weighted harmonic mean of
precision and recall. The score lies in the range [0,1] with 1 being ideal
and 0 being the worst.
The `beta` value is the weight given to `precision` vs... | [
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train | f1_score | r"""
Compute the F1 score (sometimes known as the balanced F-score or
F-measure). The F1 score is commonly interpreted as the average of
precision and recall. The score lies in the range [0,1] with 1 being ideal
and 0 being the worst.
The F1 score is defined as:
.. math::
f_{1}... | src/unity/python/turicreate/toolkits/evaluation.py | def f1_score(targets, predictions, average='macro'):
r"""
Compute the F1 score (sometimes known as the balanced F-score or
F-measure). The F1 score is commonly interpreted as the average of
precision and recall. The score lies in the range [0,1] with 1 being ideal
and 0 being the worst.
The F1 ... | def f1_score(targets, predictions, average='macro'):
r"""
Compute the F1 score (sometimes known as the balanced F-score or
F-measure). The F1 score is commonly interpreted as the average of
precision and recall. The score lies in the range [0,1] with 1 being ideal
and 0 being the worst.
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train | precision | r"""
Compute the precision score for classification tasks. The precision score
quantifies the ability of a classifier to not label a `negative` example as
`positive`. The precision score can be interpreted as the probability that
a `positive` prediction made by the classifier is `positive`. The score i... | src/unity/python/turicreate/toolkits/evaluation.py | def precision(targets, predictions, average='macro'):
r"""
Compute the precision score for classification tasks. The precision score
quantifies the ability of a classifier to not label a `negative` example as
`positive`. The precision score can be interpreted as the probability that
a `positive` pr... | def precision(targets, predictions, average='macro'):
r"""
Compute the precision score for classification tasks. The precision score
quantifies the ability of a classifier to not label a `negative` example as
`positive`. The precision score can be interpreted as the probability that
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train | auc | r"""
Compute the area under the ROC curve for the given targets and predictions.
Parameters
----------
targets : SArray
An SArray containing the observed values. For binary classification,
the alpha-numerically first category is considered the reference
category.
prediction... | src/unity/python/turicreate/toolkits/evaluation.py | def auc(targets, predictions, average='macro', index_map=None):
r"""
Compute the area under the ROC curve for the given targets and predictions.
Parameters
----------
targets : SArray
An SArray containing the observed values. For binary classification,
the alpha-numerically first ca... | def auc(targets, predictions, average='macro', index_map=None):
r"""
Compute the area under the ROC curve for the given targets and predictions.
Parameters
----------
targets : SArray
An SArray containing the observed values. For binary classification,
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train | check_library.get_library_meta | Fetches the meta data for the current library. The data could be in
the superlib meta data file. If we can't find the data None is returned. | deps/src/boost_1_68_0/status/boost_check_library.py | def get_library_meta(self):
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Fetches the meta data for the current library. The data could be in
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'''
parent_dir = os.path.dirname(self.library_dir)
if self.test_file_exists(os.path.join(self.libra... | def get_library_meta(self):
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Fetches the meta data for the current library. The data could be in
the superlib meta data file. If we can't find the data None is returned.
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train | convert | Convert a trained XGBoost model to Core ML format.
Parameters
----------
decision_tree : Booster
A trained XGboost tree model.
feature_names: [str] | str
Names of input features that will be exposed in the Core ML model
interface.
Can be set to one of the following:
... | src/external/coremltools_wrap/coremltools/coremltools/converters/xgboost/_tree.py | def convert(model, feature_names = None, target = 'target', force_32bit_float = True):
"""
Convert a trained XGBoost model to Core ML format.
Parameters
----------
decision_tree : Booster
A trained XGboost tree model.
feature_names: [str] | str
Names of input features that will... | def convert(model, feature_names = None, target = 'target', force_32bit_float = True):
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Convert a trained XGBoost model to Core ML format.
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decision_tree : Booster
A trained XGboost tree model.
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train | dumps | Dumps a serializable object to JSON. This API maps to the Python built-in
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* The return value is always valid JSON according to RFC 7159.
* The input can be any of the following types:
- SFrame
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"""
Dumps a serializable object to JSON. This API maps to the Python built-in
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* The return value is always valid JSON according to RFC 7159.
* The input can be any of the following types:
- SFrame
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train | draw_strokes | Visualizes drawings (ground truth or predictions) by
returning images to represent the stroke-based data from
the user.
Parameters
----------
stroke_based_drawings: SArray or list
An `SArray` of type `list`. Each element in the SArray
should be a list of strokes, where each stroke... | src/unity/python/turicreate/toolkits/drawing_classifier/util/_visualization.py | def draw_strokes(stroke_based_drawings):
"""
Visualizes drawings (ground truth or predictions) by
returning images to represent the stroke-based data from
the user.
Parameters
----------
stroke_based_drawings: SArray or list
An `SArray` of type `list`. Each element in the SArray
... | def draw_strokes(stroke_based_drawings):
"""
Visualizes drawings (ground truth or predictions) by
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Parameters
----------
stroke_based_drawings: SArray or list
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train | Transformer.fit | Fit a transformer using the SFrame `data`.
Parameters
----------
data : SFrame
The data used to fit the transformer.
Returns
-------
self (A fitted version of the object)
See Also
--------
transform, fit_transform
Examples
... | src/unity/python/turicreate/toolkits/_feature_engineering/_feature_engineering.py | def fit(self, data):
"""
Fit a transformer using the SFrame `data`.
Parameters
----------
data : SFrame
The data used to fit the transformer.
Returns
-------
self (A fitted version of the object)
See Also
--------
tra... | def fit(self, data):
"""
Fit a transformer using the SFrame `data`.
Parameters
----------
data : SFrame
The data used to fit the transformer.
Returns
-------
self (A fitted version of the object)
See Also
--------
tra... | [
"Fit",
"a",
"transformer",
"using",
"the",
"SFrame",
"data",
"."
] | apple/turicreate | python | https://github.com/apple/turicreate/blob/74514c3f99e25b46f22c6e02977fe3da69221c2e/src/unity/python/turicreate/toolkits/_feature_engineering/_feature_engineering.py#L236-L262 | [
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] | 74514c3f99e25b46f22c6e02977fe3da69221c2e |
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