_id stringlengths 2 7 | title stringlengths 1 88 | partition stringclasses 3
values | text stringlengths 31 13.1k | language stringclasses 1
value | meta_information dict |
|---|---|---|---|---|---|
q252100 | plot_annual_returns | validation | def plot_annual_returns(returns, ax=None, **kwargs):
"""
Plots a bar graph of returns by year.
Parameters
----------
returns : pd.Series
Daily returns of the strategy, noncumulative.
- See full explanation in tears.create_full_tear_sheet.
ax : matplotlib.Axes, optional
... | python | {
"resource": ""
} |
q252101 | plot_monthly_returns_dist | validation | def plot_monthly_returns_dist(returns, ax=None, **kwargs):
"""
Plots a distribution of monthly returns.
Parameters
----------
returns : pd.Series
Daily returns of the strategy, noncumulative.
- See full explanation in tears.create_full_tear_sheet.
ax : matplotlib.Axes, optional... | python | {
"resource": ""
} |
q252102 | plot_holdings | validation | def plot_holdings(returns, positions, legend_loc='best', ax=None, **kwargs):
"""
Plots total amount of stocks with an active position, either short
or long. Displays daily total, daily average per month, and
all-time daily average.
Parameters
----------
returns : pd.Series
Daily ret... | python | {
"resource": ""
} |
q252103 | plot_long_short_holdings | validation | def plot_long_short_holdings(returns, positions,
legend_loc='upper left', ax=None, **kwargs):
"""
Plots total amount of stocks with an active position, breaking out
short and long into transparent filled regions.
Parameters
----------
returns : pd.Series
Dai... | python | {
"resource": ""
} |
q252104 | plot_drawdown_periods | validation | def plot_drawdown_periods(returns, top=10, ax=None, **kwargs):
"""
Plots cumulative returns highlighting top drawdown periods.
Parameters
----------
returns : pd.Series
Daily returns of the strategy, noncumulative.
- See full explanation in tears.create_full_tear_sheet.
top : i... | python | {
"resource": ""
} |
q252105 | plot_drawdown_underwater | validation | def plot_drawdown_underwater(returns, ax=None, **kwargs):
"""
Plots how far underwaterr returns are over time, or plots current
drawdown vs. date.
Parameters
----------
returns : pd.Series
Daily returns of the strategy, noncumulative.
- See full explanation in tears.create_full... | python | {
"resource": ""
} |
q252106 | plot_perf_stats | validation | def plot_perf_stats(returns, factor_returns, ax=None):
"""
Create box plot of some performance metrics of the strategy.
The width of the box whiskers is determined by a bootstrap.
Parameters
----------
returns : pd.Series
Daily returns of the strategy, noncumulative.
- See full... | python | {
"resource": ""
} |
q252107 | show_perf_stats | validation | def show_perf_stats(returns, factor_returns=None, positions=None,
transactions=None, turnover_denom='AGB',
live_start_date=None, bootstrap=False,
header_rows=None):
"""
Prints some performance metrics of the strategy.
- Shows amount of time the st... | python | {
"resource": ""
} |
q252108 | plot_returns | validation | def plot_returns(returns,
live_start_date=None,
ax=None):
"""
Plots raw returns over time.
Backtest returns are in green, and out-of-sample (live trading)
returns are in red.
Parameters
----------
returns : pd.Series
Daily returns of the strategy, ... | python | {
"resource": ""
} |
q252109 | plot_rolling_returns | validation | def plot_rolling_returns(returns,
factor_returns=None,
live_start_date=None,
logy=False,
cone_std=None,
legend_loc='best',
volatility_match=False,
... | python | {
"resource": ""
} |
q252110 | plot_rolling_beta | validation | def plot_rolling_beta(returns, factor_returns, legend_loc='best',
ax=None, **kwargs):
"""
Plots the rolling 6-month and 12-month beta versus date.
Parameters
----------
returns : pd.Series
Daily returns of the strategy, noncumulative.
- See full explanation in... | python | {
"resource": ""
} |
q252111 | plot_rolling_volatility | validation | def plot_rolling_volatility(returns, factor_returns=None,
rolling_window=APPROX_BDAYS_PER_MONTH * 6,
legend_loc='best', ax=None, **kwargs):
"""
Plots the rolling volatility versus date.
Parameters
----------
returns : pd.Series
Daily r... | python | {
"resource": ""
} |
q252112 | plot_rolling_sharpe | validation | def plot_rolling_sharpe(returns, factor_returns=None,
rolling_window=APPROX_BDAYS_PER_MONTH * 6,
legend_loc='best', ax=None, **kwargs):
"""
Plots the rolling Sharpe ratio versus date.
Parameters
----------
returns : pd.Series
Daily returns of ... | python | {
"resource": ""
} |
q252113 | plot_gross_leverage | validation | def plot_gross_leverage(returns, positions, ax=None, **kwargs):
"""
Plots gross leverage versus date.
Gross leverage is the sum of long and short exposure per share
divided by net asset value.
Parameters
----------
returns : pd.Series
Daily returns of the strategy, noncumulative.
... | python | {
"resource": ""
} |
q252114 | plot_exposures | validation | def plot_exposures(returns, positions, ax=None, **kwargs):
"""
Plots a cake chart of the long and short exposure.
Parameters
----------
returns : pd.Series
Daily returns of the strategy, noncumulative.
- See full explanation in tears.create_full_tear_sheet.
positions_alloc : pd... | python | {
"resource": ""
} |
q252115 | plot_max_median_position_concentration | validation | def plot_max_median_position_concentration(positions, ax=None, **kwargs):
"""
Plots the max and median of long and short position concentrations
over the time.
Parameters
----------
positions : pd.DataFrame
The positions that the strategy takes over time.
ax : matplotlib.Axes, optio... | python | {
"resource": ""
} |
q252116 | plot_sector_allocations | validation | def plot_sector_allocations(returns, sector_alloc, ax=None, **kwargs):
"""
Plots the sector exposures of the portfolio over time.
Parameters
----------
returns : pd.Series
Daily returns of the strategy, noncumulative.
- See full explanation in tears.create_full_tear_sheet.
sect... | python | {
"resource": ""
} |
q252117 | plot_return_quantiles | validation | def plot_return_quantiles(returns, live_start_date=None, ax=None, **kwargs):
"""
Creates a box plot of daily, weekly, and monthly return
distributions.
Parameters
----------
returns : pd.Series
Daily returns of the strategy, noncumulative.
- See full explanation in tears.create... | python | {
"resource": ""
} |
q252118 | plot_turnover | validation | def plot_turnover(returns, transactions, positions,
legend_loc='best', ax=None, **kwargs):
"""
Plots turnover vs. date.
Turnover is the number of shares traded for a period as a fraction
of total shares.
Displays daily total, daily average per month, and all-time daily
averag... | python | {
"resource": ""
} |
q252119 | plot_slippage_sweep | validation | def plot_slippage_sweep(returns, positions, transactions,
slippage_params=(3, 8, 10, 12, 15, 20, 50),
ax=None, **kwargs):
"""
Plots equity curves at different per-dollar slippage assumptions.
Parameters
----------
returns : pd.Series
Timeserie... | python | {
"resource": ""
} |
q252120 | plot_slippage_sensitivity | validation | def plot_slippage_sensitivity(returns, positions, transactions,
ax=None, **kwargs):
"""
Plots curve relating per-dollar slippage to average annual returns.
Parameters
----------
returns : pd.Series
Timeseries of portfolio returns to be adjusted for various
... | python | {
"resource": ""
} |
q252121 | plot_daily_turnover_hist | validation | def plot_daily_turnover_hist(transactions, positions,
ax=None, **kwargs):
"""
Plots a histogram of daily turnover rates.
Parameters
----------
transactions : pd.DataFrame
Prices and amounts of executed trades. One row per trade.
- See full explanation i... | python | {
"resource": ""
} |
q252122 | plot_daily_volume | validation | def plot_daily_volume(returns, transactions, ax=None, **kwargs):
"""
Plots trading volume per day vs. date.
Also displays all-time daily average.
Parameters
----------
returns : pd.Series
Daily returns of the strategy, noncumulative.
- See full explanation in tears.create_full... | python | {
"resource": ""
} |
q252123 | plot_txn_time_hist | validation | def plot_txn_time_hist(transactions, bin_minutes=5, tz='America/New_York',
ax=None, **kwargs):
"""
Plots a histogram of transaction times, binning the times into
buckets of a given duration.
Parameters
----------
transactions : pd.DataFrame
Prices and amounts of e... | python | {
"resource": ""
} |
q252124 | show_worst_drawdown_periods | validation | def show_worst_drawdown_periods(returns, top=5):
"""
Prints information about the worst drawdown periods.
Prints peak dates, valley dates, recovery dates, and net
drawdowns.
Parameters
----------
returns : pd.Series
Daily returns of the strategy, noncumulative.
- See full ... | python | {
"resource": ""
} |
q252125 | plot_monthly_returns_timeseries | validation | def plot_monthly_returns_timeseries(returns, ax=None, **kwargs):
"""
Plots monthly returns as a timeseries.
Parameters
----------
returns : pd.Series
Daily returns of the strategy, noncumulative.
- See full explanation in tears.create_full_tear_sheet.
ax : matplotlib.Axes, opti... | python | {
"resource": ""
} |
q252126 | plot_round_trip_lifetimes | validation | def plot_round_trip_lifetimes(round_trips, disp_amount=16, lsize=18, ax=None):
"""
Plots timespans and directions of a sample of round trip trades.
Parameters
----------
round_trips : pd.DataFrame
DataFrame with one row per round trip trade.
- See full explanation in round_trips.ext... | python | {
"resource": ""
} |
q252127 | show_profit_attribution | validation | def show_profit_attribution(round_trips):
"""
Prints the share of total PnL contributed by each
traded name.
Parameters
----------
round_trips : pd.DataFrame
DataFrame with one row per round trip trade.
- See full explanation in round_trips.extract_round_trips
ax : matplotli... | python | {
"resource": ""
} |
q252128 | plot_prob_profit_trade | validation | def plot_prob_profit_trade(round_trips, ax=None):
"""
Plots a probability distribution for the event of making
a profitable trade.
Parameters
----------
round_trips : pd.DataFrame
DataFrame with one row per round trip trade.
- See full explanation in round_trips.extract_round_tr... | python | {
"resource": ""
} |
q252129 | plot_cones | validation | def plot_cones(name, bounds, oos_returns, num_samples=1000, ax=None,
cone_std=(1., 1.5, 2.), random_seed=None, num_strikes=3):
"""
Plots the upper and lower bounds of an n standard deviation
cone of forecasted cumulative returns. Redraws a new cone when
cumulative returns fall outside of ... | python | {
"resource": ""
} |
q252130 | var_cov_var_normal | validation | def var_cov_var_normal(P, c, mu=0, sigma=1):
"""
Variance-covariance calculation of daily Value-at-Risk in a
portfolio.
Parameters
----------
P : float
Portfolio value.
c : float
Confidence level.
mu : float, optional
| python | {
"resource": ""
} |
q252131 | sortino_ratio | validation | def sortino_ratio(returns, required_return=0, period=DAILY):
"""
Determines the Sortino ratio of a strategy.
Parameters
----------
returns : pd.Series or pd.DataFrame
Daily returns of the strategy, noncumulative.
- See full explanation in :func:`~pyfolio.timeseries.cum_returns`.
| python | {
"resource": ""
} |
q252132 | downside_risk | validation | def downside_risk(returns, required_return=0, period=DAILY):
"""
Determines the downside deviation below a threshold
Parameters
----------
returns : pd.Series or pd.DataFrame
Daily returns of the strategy, noncumulative.
- See full explanation in :func:`~pyfolio.timeseries.cum_retur... | python | {
"resource": ""
} |
q252133 | sharpe_ratio | validation | def sharpe_ratio(returns, risk_free=0, period=DAILY):
"""
Determines the Sharpe ratio of a strategy.
Parameters
----------
returns : pd.Series
Daily returns of the strategy, noncumulative.
- See full explanation in :func:`~pyfolio.timeseries.cum_returns`.
risk_free : int, float
... | python | {
"resource": ""
} |
q252134 | rolling_beta | validation | def rolling_beta(returns, factor_returns,
rolling_window=APPROX_BDAYS_PER_MONTH * 6):
"""
Determines the rolling beta of a strategy.
Parameters
----------
returns : pd.Series
Daily returns of the strategy, noncumulative.
- See full explanation in tears.create_full_... | python | {
"resource": ""
} |
q252135 | gross_lev | validation | def gross_lev(positions):
"""
Calculates the gross leverage of a strategy.
Parameters
----------
positions : pd.DataFrame
Daily net position values.
- See full explanation in tears.create_full_tear_sheet.
Returns
-------
pd.Series
| python | {
"resource": ""
} |
q252136 | perf_stats | validation | def perf_stats(returns, factor_returns=None, positions=None,
transactions=None, turnover_denom='AGB'):
"""
Calculates various performance metrics of a strategy, for use in
plotting.show_perf_stats.
Parameters
----------
returns : pd.Series
Daily returns of the strategy, n... | python | {
"resource": ""
} |
q252137 | perf_stats_bootstrap | validation | def perf_stats_bootstrap(returns, factor_returns=None, return_stats=True,
**kwargs):
"""Calculates various bootstrapped performance metrics of a strategy.
Parameters
----------
returns : pd.Series
Daily returns of the strategy, noncumulative.
- See full explana... | python | {
"resource": ""
} |
q252138 | calc_bootstrap | validation | def calc_bootstrap(func, returns, *args, **kwargs):
"""Performs a bootstrap analysis on a user-defined function returning
a summary statistic.
Parameters
----------
func : function
Function that either takes a single array (commonly returns)
or two arrays (commonly returns and facto... | python | {
"resource": ""
} |
q252139 | calc_distribution_stats | validation | def calc_distribution_stats(x):
"""Calculate various summary statistics of data.
Parameters
----------
x : numpy.ndarray or pandas.Series
Array to compute summary statistics for.
Returns
-------
pandas.Series
Series containing mean, median, std, as well as 5, 25, 75 and
... | python | {
"resource": ""
} |
q252140 | get_max_drawdown_underwater | validation | def get_max_drawdown_underwater(underwater):
"""
Determines peak, valley, and recovery dates given an 'underwater'
DataFrame.
An underwater DataFrame is a DataFrame that has precomputed
rolling drawdown.
Parameters
----------
underwater : pd.Series
Underwater returns (rolling dr... | python | {
"resource": ""
} |
q252141 | get_max_drawdown | validation | def get_max_drawdown(returns):
"""
Determines the maximum drawdown of a strategy.
Parameters
----------
returns : pd.Series
Daily returns of the strategy, noncumulative.
- See full explanation in :func:`~pyfolio.timeseries.cum_returns`.
Returns
-------
float
Max... | python | {
"resource": ""
} |
q252142 | get_top_drawdowns | validation | def get_top_drawdowns(returns, top=10):
"""
Finds top drawdowns, sorted by drawdown amount.
Parameters
----------
returns : pd.Series
Daily returns of the strategy, noncumulative.
- See full explanation in tears.create_full_tear_sheet.
top : int, optional
The amount of ... | python | {
"resource": ""
} |
q252143 | gen_drawdown_table | validation | def gen_drawdown_table(returns, top=10):
"""
Places top drawdowns in a table.
Parameters
----------
returns : pd.Series
Daily returns of the strategy, noncumulative.
- See full explanation in tears.create_full_tear_sheet.
top : int, optional
The amount of top drawdowns ... | python | {
"resource": ""
} |
q252144 | rolling_volatility | validation | def rolling_volatility(returns, rolling_vol_window):
"""
Determines the rolling volatility of a strategy.
Parameters
----------
returns : pd.Series
Daily returns of the strategy, noncumulative.
- See full explanation in tears.create_full_tear_sheet.
rolling_vol_window : int
... | python | {
"resource": ""
} |
q252145 | rolling_sharpe | validation | def rolling_sharpe(returns, rolling_sharpe_window):
"""
Determines the rolling Sharpe ratio of a strategy.
Parameters
----------
returns : pd.Series
Daily returns of the strategy, noncumulative.
- See full explanation in tears.create_full_tear_sheet.
| python | {
"resource": ""
} |
q252146 | simulate_paths | validation | def simulate_paths(is_returns, num_days,
starting_value=1, num_samples=1000, random_seed=None):
"""
Gnerate alternate paths using available values from in-sample returns.
Parameters
----------
is_returns : pandas.core.frame.DataFrame
Non-cumulative in-sample returns.
... | python | {
"resource": ""
} |
q252147 | summarize_paths | validation | def summarize_paths(samples, cone_std=(1., 1.5, 2.), starting_value=1.):
"""
Gnerate the upper and lower bounds of an n standard deviation
cone of forecasted cumulative returns.
Parameters
----------
samples : numpy.ndarray
Alternative paths, or series of possible outcomes.
cone_std... | python | {
"resource": ""
} |
q252148 | extract_interesting_date_ranges | validation | def extract_interesting_date_ranges(returns):
"""
Extracts returns based on interesting events. See
gen_date_range_interesting.
Parameters
----------
returns : pd.Series
Daily returns of the strategy, noncumulative.
- See full explanation in tears.create_full_tear_sheet.
R... | python | {
"resource": ""
} |
q252149 | model_returns_t_alpha_beta | validation | def model_returns_t_alpha_beta(data, bmark, samples=2000, progressbar=True):
"""
Run Bayesian alpha-beta-model with T distributed returns.
This model estimates intercept (alpha) and slope (beta) of two
return sets. Usually, these will be algorithm returns and
benchmark returns (e.g. S&P500). The da... | python | {
"resource": ""
} |
q252150 | model_returns_normal | validation | def model_returns_normal(data, samples=500, progressbar=True):
"""
Run Bayesian model assuming returns are normally distributed.
Parameters
----------
returns : pandas.Series
Series of simple returns of an algorithm or stock.
samples : int (optional)
Number of posterior samples ... | python | {
"resource": ""
} |
q252151 | model_best | validation | def model_best(y1, y2, samples=1000, progressbar=True):
"""
Bayesian Estimation Supersedes the T-Test
This model runs a Bayesian hypothesis comparing if y1 and y2 come
from the same distribution. Returns are assumed to be T-distributed.
In addition, computes annual volatility and Sharpe of in and
... | python | {
"resource": ""
} |
q252152 | model_stoch_vol | validation | def model_stoch_vol(data, samples=2000, progressbar=True):
"""
Run stochastic volatility model.
This model estimates the volatility of a returns series over time.
Returns are assumed to be T-distributed. lambda (width of
T-distributed) is assumed to follow a random-walk.
Parameters
-------... | python | {
"resource": ""
} |
q252153 | plot_stoch_vol | validation | def plot_stoch_vol(data, trace=None, ax=None):
"""
Generate plot for stochastic volatility model.
Parameters
----------
data : pandas.Series
Returns to model.
trace : pymc3.sampling.BaseTrace object, optional
trace as returned by model_stoch_vol
If not passed, sample fro... | python | {
"resource": ""
} |
q252154 | compute_bayes_cone | validation | def compute_bayes_cone(preds, starting_value=1.):
"""
Compute 5, 25, 75 and 95 percentiles of cumulative returns, used
for the Bayesian cone.
Parameters
----------
preds : numpy.array
Multiple (simulated) cumulative returns.
starting_value : int (optional)
Have cumulative re... | python | {
"resource": ""
} |
q252155 | compute_consistency_score | validation | def compute_consistency_score(returns_test, preds):
"""
Compute Bayesian consistency score.
Parameters
----------
returns_test : pd.Series
Observed cumulative returns.
preds : numpy.array
Multiple (simulated) cumulative returns.
Returns
-------
Consistency score
... | python | {
"resource": ""
} |
q252156 | run_model | validation | def run_model(model, returns_train, returns_test=None,
bmark=None, samples=500, ppc=False, progressbar=True):
"""
Run one of the Bayesian models.
Parameters
----------
model : {'alpha_beta', 't', 'normal', 'best'}
Which model to run
returns_train : pd.Series
Timese... | python | {
"resource": ""
} |
q252157 | plot_bayes_cone | validation | def plot_bayes_cone(returns_train, returns_test, ppc,
plot_train_len=50, ax=None):
"""
Generate cumulative returns plot with Bayesian cone.
Parameters
----------
returns_train : pd.Series
Timeseries of simple returns
returns_test : pd.Series
Out-of-sample ret... | python | {
"resource": ""
} |
q252158 | _GetNextLogCountPerToken | validation | def _GetNextLogCountPerToken(token):
"""Wrapper for _log_counter_per_token.
Args:
token: The token for which to look up the count.
Returns:
The number of times this function has been called with
*token* as an argument (starting at 0)
| python | {
"resource": ""
} |
q252159 | log_every_n | validation | def log_every_n(level, msg, n, *args):
"""Log 'msg % args' at level 'level' once per 'n' times.
Logs the 1st call, (N+1)st call, (2N+1)st call, etc.
Not threadsafe.
Args:
level: The level at which to log.
msg: The message to be logged.
n: The number of times this should be called before i... | python | {
"resource": ""
} |
q252160 | log_if | validation | def log_if(level, msg, condition, *args):
"""Log 'msg % args' at level 'level' only if | python | {
"resource": ""
} |
q252161 | google2_log_prefix | validation | def google2_log_prefix(level, timestamp=None, file_and_line=None):
"""Assemble a logline prefix using the google2 format."""
# pylint: disable=global-variable-not-assigned
global _level_names
# pylint: enable=global-variable-not-assigned
# Record current time
now = timestamp or _time.time()
... | python | {
"resource": ""
} |
q252162 | create_distributed_session | validation | def create_distributed_session(
task_spec=None, checkpoint_dir=None, scaffold=None, hooks=None, chief_only_hooks=None, save_checkpoint_secs=600,
save_summaries_steps=object(), save_summaries_secs=object(), config=None, stop_grace_period_secs=120,
log_step_count_steps=100
):
"""Creates a dist... | python | {
"resource": ""
} |
q252163 | Trainer.validation_metrics | validation | def validation_metrics(self):
"""A helper function to compute validation related metrics"""
if (self._validation_iterator is None) or (self._validation_metrics is None):
raise AttributeError('Validation is not setup.')
n = 0.0
metric_sums = [0.0] * len(self._validation_metr... | python | {
"resource": ""
} |
q252164 | Trainer.train_and_validate_to_end | validation | def train_and_validate_to_end(self, validate_step_size=50):
"""A helper function that shows how to train and validate a model at the same time.
Parameters
----------
validate_step_size : int
Validate the training network every N steps.
"""
while not self._se... | python | {
"resource": ""
} |
q252165 | _load_mnist_dataset | validation | def _load_mnist_dataset(shape, path, name='mnist', url='http://yann.lecun.com/exdb/mnist/'):
"""A generic function to load mnist-like dataset.
Parameters:
----------
shape : tuple
The shape of digit images.
path : str
The path that the data is downloaded to.
name : str
T... | python | {
"resource": ""
} |
q252166 | load_matt_mahoney_text8_dataset | validation | def load_matt_mahoney_text8_dataset(path='data'):
"""Load Matt Mahoney's dataset.
Download a text file from Matt Mahoney's website
if not present, and make sure it's the right size.
Extract the first file enclosed in a zip file as a list of words.
This dataset can be used for Word Embedding.
P... | python | {
"resource": ""
} |
q252167 | load_imdb_dataset | validation | def load_imdb_dataset(
path='data', nb_words=None, skip_top=0, maxlen=None, test_split=0.2, seed=113, start_char=1, oov_char=2,
index_from=3
):
"""Load IMDB dataset.
Parameters
----------
path : str
The path that the data is downloaded to, defaults is ``data/imdb/``.
nb_word... | python | {
"resource": ""
} |
q252168 | load_nietzsche_dataset | validation | def load_nietzsche_dataset(path='data'):
"""Load Nietzsche dataset.
Parameters
----------
path : str
The path that the data is downloaded to, defaults is ``data/nietzsche/``.
Returns
--------
str
The content.
Examples
--------
>>> see tutorial_generate_text.py
... | python | {
"resource": ""
} |
q252169 | load_wmt_en_fr_dataset | validation | def load_wmt_en_fr_dataset(path='data'):
"""Load WMT'15 English-to-French translation dataset.
It will download the data from the WMT'15 Website (10^9-French-English corpus), and the 2013 news test from the same site as development set.
Returns the directories of training data and test data.
Parameter... | python | {
"resource": ""
} |
q252170 | load_flickr25k_dataset | validation | def load_flickr25k_dataset(tag='sky', path="data", n_threads=50, printable=False):
"""Load Flickr25K dataset.
Returns a list of images by a given tag from Flick25k dataset,
it will download Flickr25k from `the official website <http://press.liacs.nl/mirflickr/mirdownload.html>`__
at the first time you ... | python | {
"resource": ""
} |
q252171 | download_file_from_google_drive | validation | def download_file_from_google_drive(ID, destination):
"""Download file from Google Drive.
See ``tl.files.load_celebA_dataset`` for example.
Parameters
--------------
ID : str
The driver ID.
destination : str
The destination for save file.
"""
def save_response_content... | python | {
"resource": ""
} |
q252172 | load_celebA_dataset | validation | def load_celebA_dataset(path='data'):
"""Load CelebA dataset
Return a list of image path.
Parameters
-----------
path : str
The path that the data is downloaded to, defaults is ``data/celebA/``.
"""
data_dir = 'celebA'
filename, drive_id = "img_align_celeba.zip", "0B7EVK8r0v71... | python | {
"resource": ""
} |
q252173 | assign_params | validation | def assign_params(sess, params, network):
"""Assign the given parameters to the TensorLayer network.
Parameters
----------
sess : Session
TensorFlow Session.
params : list of array
A list of parameters (array) in order.
network : :class:`Layer`
The network to be assigned... | python | {
"resource": ""
} |
q252174 | load_and_assign_npz | validation | def load_and_assign_npz(sess=None, name=None, network=None):
"""Load model from npz and assign to a network.
Parameters
-------------
sess : Session
TensorFlow Session.
name : str
The name of the `.npz` file.
network : :class:`Layer`
The network to be assigned.
Retu... | python | {
"resource": ""
} |
q252175 | save_npz_dict | validation | def save_npz_dict(save_list=None, name='model.npz', sess=None):
"""Input parameters and the file name, save parameters as a dictionary into .npz file.
Use ``tl.files.load_and_assign_npz_dict()`` to restore.
Parameters
----------
save_list : list of parameters
A list of parameters (tensor) ... | python | {
"resource": ""
} |
q252176 | save_ckpt | validation | def save_ckpt(
sess=None, mode_name='model.ckpt', save_dir='checkpoint', var_list=None, global_step=None, printable=False
):
"""Save parameters into `ckpt` file.
Parameters
------------
sess : Session
TensorFlow Session.
mode_name : str
The name of the model, default is ``mo... | python | {
"resource": ""
} |
q252177 | load_ckpt | validation | def load_ckpt(sess=None, mode_name='model.ckpt', save_dir='checkpoint', var_list=None, is_latest=True, printable=False):
"""Load parameters from `ckpt` file.
Parameters
------------
sess : Session
TensorFlow Session.
mode_name : str
The name of the model, default is ``model.ckpt``.
... | python | {
"resource": ""
} |
q252178 | load_npy_to_any | validation | def load_npy_to_any(path='', name='file.npy'):
"""Load `.npy` file.
Parameters
------------
path : str
Path to the file (optional).
name : str
File name.
Examples
---------
- see tl.files.save_any_to_npy()
"""
file_path = os.path.join(path, name)
| python | {
"resource": ""
} |
q252179 | load_file_list | validation | def load_file_list(path=None, regx='\.jpg', printable=True, keep_prefix=False):
r"""Return a file list in a folder by given a path and regular expression.
Parameters
----------
path : str or None
A folder path, if `None`, use the current directory.
regx : str
The regx of file name.
... | python | {
"resource": ""
} |
q252180 | load_folder_list | validation | def load_folder_list(path=""):
"""Return a folder list in a folder by given a folder path.
Parameters
| python | {
"resource": ""
} |
q252181 | exists_or_mkdir | validation | def exists_or_mkdir(path, verbose=True):
"""Check a folder by given name, if not exist, create the folder and return False,
if directory exists, return True.
Parameters
----------
path : str
A folder path.
verbose : boolean
If True (default), prints results.
Returns
---... | python | {
"resource": ""
} |
q252182 | maybe_download_and_extract | validation | def maybe_download_and_extract(filename, working_directory, url_source, extract=False, expected_bytes=None):
"""Checks if file exists in working_directory otherwise tries to dowload the file,
and optionally also tries to extract the file if format is ".zip" or ".tar"
Parameters
-----------
filename... | python | {
"resource": ""
} |
q252183 | natural_keys | validation | def natural_keys(text):
"""Sort list of string with number in human order.
Examples
----------
>>> l = ['im1.jpg', 'im31.jpg', 'im11.jpg', 'im21.jpg', 'im03.jpg', 'im05.jpg']
>>> l.sort(key=tl.files.natural_keys)
['im1.jpg', 'im03.jpg', 'im05', 'im11.jpg', 'im21.jpg', 'im31.jpg']
>>> l.sort... | python | {
"resource": ""
} |
q252184 | threading_data | validation | def threading_data(data=None, fn=None, thread_count=None, **kwargs):
"""Process a batch of data by given function by threading.
Usually be used for data augmentation.
Parameters
-----------
data : numpy.array or others
The data to be processed.
thread_count : int
The number of ... | python | {
"resource": ""
} |
q252185 | affine_transform_keypoints | validation | def affine_transform_keypoints(coords_list, transform_matrix):
"""Transform keypoint coordinates according to a given affine transform matrix.
OpenCV format, x is width.
Note that, for pose estimation task, flipping requires maintaining the left and right body information.
We should not flip the left a... | python | {
"resource": ""
} |
q252186 | projective_transform_by_points | validation | def projective_transform_by_points(
x, src, dst, map_args=None, output_shape=None, order=1, mode='constant', cval=0.0, clip=True,
preserve_range=False
):
"""Projective transform by given coordinates, usually 4 coordinates.
see `scikit-image <http://scikit-image.org/docs/dev/auto_examples/applic... | python | {
"resource": ""
} |
q252187 | rotation | validation | def rotation(
x, rg=20, is_random=False, row_index=0, col_index=1, channel_index=2, fill_mode='nearest', cval=0., order=1
):
"""Rotate an image randomly or non-randomly.
Parameters
-----------
x : numpy.array
An image with dimension of [row, col, channel] (default).
rg : int or floa... | python | {
"resource": ""
} |
q252188 | crop | validation | def crop(x, wrg, hrg, is_random=False, row_index=0, col_index=1):
"""Randomly or centrally crop an image.
Parameters
----------
x : numpy.array
An image with dimension of [row, col, channel] (default).
wrg : int
Size of width.
hrg : int
Size of height.
is_random : bo... | python | {
"resource": ""
} |
q252189 | crop_multi | validation | def crop_multi(x, wrg, hrg, is_random=False, row_index=0, col_index=1):
"""Randomly or centrally crop multiple images.
Parameters
----------
x : list of numpy.array
List of images with dimension of [n_images, row, col, channel] (default).
others : args
See ``tl.prepro.crop``.
R... | python | {
"resource": ""
} |
q252190 | flip_axis | validation | def flip_axis(x, axis=1, is_random=False):
"""Flip the axis of an image, such as flip left and right, up and down, randomly or non-randomly,
Parameters
----------
x : numpy.array
An image with dimension of [row, col, channel] (default).
axis : int
Which axis to flip.
- 0... | python | {
"resource": ""
} |
q252191 | flip_axis_multi | validation | def flip_axis_multi(x, axis, is_random=False):
"""Flip the axises of multiple images together, such as flip left and right, up and down, randomly or non-randomly,
Parameters
-----------
x : list of numpy.array
List of images with dimension of [n_images, row, col, channel] (default).
others ... | python | {
"resource": ""
} |
q252192 | shift | validation | def shift(
x, wrg=0.1, hrg=0.1, is_random=False, row_index=0, col_index=1, channel_index=2, fill_mode='nearest', cval=0.,
order=1
):
"""Shift an image randomly or non-randomly.
Parameters
-----------
x : numpy.array
An image with dimension of [row, col, channel] (default).
w... | python | {
"resource": ""
} |
q252193 | brightness | validation | def brightness(x, gamma=1, gain=1, is_random=False):
"""Change the brightness of a single image, randomly or non-randomly.
Parameters
-----------
x : numpy.array
An image with dimension of [row, col, channel] (default).
gamma : float
Non negative real number. Default value is 1.
... | python | {
"resource": ""
} |
q252194 | illumination | validation | def illumination(x, gamma=1., contrast=1., saturation=1., is_random=False):
"""Perform illumination augmentation for a single image, randomly or non-randomly.
Parameters
-----------
x : numpy.array
An image with dimension of [row, col, channel] (default).
gamma : float
Change bright... | python | {
"resource": ""
} |
q252195 | adjust_hue | validation | def adjust_hue(im, hout=0.66, is_offset=True, is_clip=True, is_random=False):
"""Adjust hue of an RGB image.
This is a convenience method that converts an RGB image to float representation, converts it to HSV, add an offset to the hue channel, converts back to RGB and then back to the original data type.
F... | python | {
"resource": ""
} |
q252196 | imresize | validation | def imresize(x, size=None, interp='bicubic', mode=None):
"""Resize an image by given output size and method.
Warning, this function will rescale the value to [0, 255].
Parameters
-----------
x : numpy.array
An image with dimension of [row, col, channel] (default).
size : list of 2 int ... | python | {
"resource": ""
} |
q252197 | pixel_value_scale | validation | def pixel_value_scale(im, val=0.9, clip=None, is_random=False):
"""Scales each value in the pixels of the image.
Parameters
-----------
im : numpy.array
An image.
val : float
The scale value for changing pixel value.
- If is_random=False, multiply this value with all pix... | python | {
"resource": ""
} |
q252198 | samplewise_norm | validation | def samplewise_norm(
x, rescale=None, samplewise_center=False, samplewise_std_normalization=False, channel_index=2, epsilon=1e-7
):
"""Normalize an image by rescale, samplewise centering and samplewise centering in order.
Parameters
-----------
x : numpy.array
An image with dimension of... | python | {
"resource": ""
} |
q252199 | featurewise_norm | validation | def featurewise_norm(x, mean=None, std=None, epsilon=1e-7):
"""Normalize every pixels by the same given mean and std, which are usually
compute from all examples.
Parameters
-----------
x : numpy.array
An image with dimension of [row, col, channel] (default).
mean : float
Value ... | python | {
"resource": ""
} |
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