partition stringclasses 3
values | func_name stringlengths 1 134 | docstring stringlengths 1 46.9k | path stringlengths 4 223 | original_string stringlengths 75 104k | code stringlengths 75 104k | docstring_tokens listlengths 1 1.97k | repo stringlengths 7 55 | language stringclasses 1
value | url stringlengths 87 315 | code_tokens listlengths 19 28.4k | sha stringlengths 40 40 |
|---|---|---|---|---|---|---|---|---|---|---|---|
valid | show_and_plot_top_positions | Prints and/or plots the exposures of the top 10 held positions of
all time.
Parameters
----------
returns : pd.Series
Daily returns of the strategy, noncumulative.
- See full explanation in tears.create_full_tear_sheet.
positions_alloc : pd.DataFrame
Portfolio allocation of... | pyfolio/plotting.py | def show_and_plot_top_positions(returns, positions_alloc,
show_and_plot=2, hide_positions=False,
legend_loc='real_best', ax=None,
**kwargs):
"""
Prints and/or plots the exposures of the top 10 held positions of
a... | def show_and_plot_top_positions(returns, positions_alloc,
show_and_plot=2, hide_positions=False,
legend_loc='real_best', ax=None,
**kwargs):
"""
Prints and/or plots the exposures of the top 10 held positions of
a... | [
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] | quantopian/pyfolio | python | https://github.com/quantopian/pyfolio/blob/712716ab0cdebbec9fabb25eea3bf40e4354749d/pyfolio/plotting.py#L1114-L1194 | [
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valid | plot_max_median_position_concentration | 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, optional
Axes upon which to plot.
Returns
-------
ax : matplotlib.Axes... | pyfolio/plotting.py | 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... | 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... | [
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"get_max_median_position_... | 712716ab0cdebbec9fabb25eea3bf40e4354749d |
valid | plot_sector_allocations | 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.
sector_alloc : pd.DataFrame
Portfolio allocation of positions. See pos.get_sect... | pyfolio/plotting.py | 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... | 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... | [
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] | quantopian/pyfolio | python | https://github.com/quantopian/pyfolio/blob/712716ab0cdebbec9fabb25eea3bf40e4354749d/pyfolio/plotting.py#L1229-L1269 | [
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... | 712716ab0cdebbec9fabb25eea3bf40e4354749d |
valid | plot_return_quantiles | 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_full_tear_sheet.
live_start_date : datetime, optional
The point in time when... | pyfolio/plotting.py | 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... | 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... | [
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] | quantopian/pyfolio | python | https://github.com/quantopian/pyfolio/blob/712716ab0cdebbec9fabb25eea3bf40e4354749d/pyfolio/plotting.py#L1272-L1322 | [
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"if",... | 712716ab0cdebbec9fabb25eea3bf40e4354749d |
valid | plot_turnover | 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
average.
Parameters
----------
returns : pd.Series
Daily returns of the strategy, noncumulative.
... | pyfolio/plotting.py | 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... | 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... | [
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] | quantopian/pyfolio | python | https://github.com/quantopian/pyfolio/blob/712716ab0cdebbec9fabb25eea3bf40e4354749d/pyfolio/plotting.py#L1325-L1386 | [
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"y_ax... | 712716ab0cdebbec9fabb25eea3bf40e4354749d |
valid | plot_slippage_sweep | Plots equity curves at different per-dollar slippage assumptions.
Parameters
----------
returns : pd.Series
Timeseries of portfolio returns to be adjusted for various
degrees of slippage.
positions : pd.DataFrame
Daily net position values.
- See full explanation in tear... | pyfolio/plotting.py | 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... | 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... | [
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] | quantopian/pyfolio | python | https://github.com/quantopian/pyfolio/blob/712716ab0cdebbec9fabb25eea3bf40e4354749d/pyfolio/plotting.py#L1389-L1437 | [
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valid | plot_slippage_sensitivity | Plots curve relating per-dollar slippage to average annual returns.
Parameters
----------
returns : pd.Series
Timeseries of portfolio returns to be adjusted for various
degrees of slippage.
positions : pd.DataFrame
Daily net position values.
- See full explanation in te... | pyfolio/plotting.py | 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
... | 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
... | [
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"="... | 712716ab0cdebbec9fabb25eea3bf40e4354749d |
valid | plot_daily_turnover_hist | Plots a histogram of daily turnover rates.
Parameters
----------
transactions : pd.DataFrame
Prices and amounts of executed trades. One row per trade.
- See full explanation in tears.create_full_tear_sheet.
positions : pd.DataFrame
Daily net position values.
- See full... | pyfolio/plotting.py | 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... | 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... | [
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] | quantopian/pyfolio | python | https://github.com/quantopian/pyfolio/blob/712716ab0cdebbec9fabb25eea3bf40e4354749d/pyfolio/plotting.py#L1519-L1549 | [
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"... | 712716ab0cdebbec9fabb25eea3bf40e4354749d |
valid | plot_daily_volume | 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_tear_sheet.
transactions : pd.DataFrame
Prices and amounts of ex... | pyfolio/plotting.py | 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... | 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... | [
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] | quantopian/pyfolio | python | https://github.com/quantopian/pyfolio/blob/712716ab0cdebbec9fabb25eea3bf40e4354749d/pyfolio/plotting.py#L1552-L1587 | [
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valid | plot_txn_time_hist | Plots a histogram of transaction times, binning the times into
buckets of a given duration.
Parameters
----------
transactions : pd.DataFrame
Prices and amounts of executed trades. One row per trade.
- See full explanation in tears.create_full_tear_sheet.
bin_minutes : float, optio... | pyfolio/plotting.py | 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... | 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... | [
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valid | show_worst_drawdown_periods | 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 explanation in tears.create_full_tear_sheet.
top : int, o... | pyfolio/plotting.py | 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 ... | 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 ... | [
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"sort_values",... | 712716ab0cdebbec9fabb25eea3bf40e4354749d |
valid | plot_monthly_returns_timeseries | 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, optional
Axes upon which to plot.
**kwargs, optional
Passed t... | pyfolio/plotting.py | 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... | 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... | [
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] | quantopian/pyfolio | python | https://github.com/quantopian/pyfolio/blob/712716ab0cdebbec9fabb25eea3bf40e4354749d/pyfolio/plotting.py#L1672-L1725 | [
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valid | plot_round_trip_lifetimes | 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.extract_round_trips
ax : matplotlib.Axes, optional
Axes upon which to plot.
R... | pyfolio/plotting.py | 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... | 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... | [
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] | quantopian/pyfolio | python | https://github.com/quantopian/pyfolio/blob/712716ab0cdebbec9fabb25eea3bf40e4354749d/pyfolio/plotting.py#L1728-L1776 | [
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valid | show_profit_attribution | 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 : matplotlib.Axes, optional
Axes upon which to plot.
... | pyfolio/plotting.py | 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... | 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... | [
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] | quantopian/pyfolio | python | https://github.com/quantopian/pyfolio/blob/712716ab0cdebbec9fabb25eea3bf40e4354749d/pyfolio/plotting.py#L1779-L1810 | [
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valid | plot_prob_profit_trade | 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_trips
ax : matplotlib.Axes, optional
Axes upon which... | pyfolio/plotting.py | 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... | 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... | [
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] | quantopian/pyfolio | python | https://github.com/quantopian/pyfolio/blob/712716ab0cdebbec9fabb25eea3bf40e4354749d/pyfolio/plotting.py#L1813-L1857 | [
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"... | 712716ab0cdebbec9fabb25eea3bf40e4354749d |
valid | plot_cones | 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 last cone drawn.
Parameters
----------
name : str
Account name to be used as figure title.
bounds : pandas.core.frame.DataFrame
... | pyfolio/plotting.py | 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 ... | 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 ... | [
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"d... | quantopian/pyfolio | python | https://github.com/quantopian/pyfolio/blob/712716ab0cdebbec9fabb25eea3bf40e4354749d/pyfolio/plotting.py#L1860-L1953 | [
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... | 712716ab0cdebbec9fabb25eea3bf40e4354749d |
valid | var_cov_var_normal | Variance-covariance calculation of daily Value-at-Risk in a
portfolio.
Parameters
----------
P : float
Portfolio value.
c : float
Confidence level.
mu : float, optional
Mean.
Returns
-------
float
Variance-covariance. | pyfolio/timeseries.py | 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
Mean.
Returns
-------
float
... | 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
Mean.
Returns
-------
float
... | [
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] | quantopian/pyfolio | python | https://github.com/quantopian/pyfolio/blob/712716ab0cdebbec9fabb25eea3bf40e4354749d/pyfolio/timeseries.py#L38-L59 | [
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valid | sortino_ratio | 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`.
required_return: float / series
minimum acceptable return
pe... | pyfolio/timeseries.py | 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`.
... | 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`.
... | [
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] | quantopian/pyfolio | python | https://github.com/quantopian/pyfolio/blob/712716ab0cdebbec9fabb25eea3bf40e4354749d/pyfolio/timeseries.py#L202-L227 | [
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] | 712716ab0cdebbec9fabb25eea3bf40e4354749d |
valid | downside_risk | 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_returns`.
required_return: float / series
minimum acceptable retur... | pyfolio/timeseries.py | 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... | 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... | [
"Determines",
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] | quantopian/pyfolio | python | https://github.com/quantopian/pyfolio/blob/712716ab0cdebbec9fabb25eea3bf40e4354749d/pyfolio/timeseries.py#L231-L258 | [
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] | 712716ab0cdebbec9fabb25eea3bf40e4354749d |
valid | sharpe_ratio | 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
Constant risk-free return throughout the period.
perio... | pyfolio/timeseries.py | 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
... | 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
... | [
"Determines",
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] | quantopian/pyfolio | python | https://github.com/quantopian/pyfolio/blob/712716ab0cdebbec9fabb25eea3bf40e4354749d/pyfolio/timeseries.py#L262-L290 | [
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] | 712716ab0cdebbec9fabb25eea3bf40e4354749d |
valid | rolling_beta | Determines the rolling beta of a strategy.
Parameters
----------
returns : pd.Series
Daily returns of the strategy, noncumulative.
- See full explanation in tears.create_full_tear_sheet.
factor_returns : pd.Series or pd.DataFrame
Daily noncumulative returns of the benchmark fac... | pyfolio/timeseries.py | 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_... | 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_... | [
"Determines",
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] | quantopian/pyfolio | python | https://github.com/quantopian/pyfolio/blob/712716ab0cdebbec9fabb25eea3bf40e4354749d/pyfolio/timeseries.py#L507-L548 | [
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valid | rolling_regression | Computes rolling factor betas using a multivariate linear regression
(separate linear regressions is problematic because the factors may be
confounded).
Parameters
----------
returns : pd.Series
Daily returns of the strategy, noncumulative.
- See full explanation in tears.create_fu... | pyfolio/timeseries.py | def rolling_regression(returns, factor_returns,
rolling_window=APPROX_BDAYS_PER_MONTH * 6,
nan_threshold=0.1):
"""
Computes rolling factor betas using a multivariate linear regression
(separate linear regressions is problematic because the factors may be
con... | def rolling_regression(returns, factor_returns,
rolling_window=APPROX_BDAYS_PER_MONTH * 6,
nan_threshold=0.1):
"""
Computes rolling factor betas using a multivariate linear regression
(separate linear regressions is problematic because the factors may be
con... | [
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valid | gross_lev | 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
Gross leverage. | pyfolio/timeseries.py | 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
Gross leverage.
"""
e... | 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
Gross leverage.
"""
e... | [
"Calculates",
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] | quantopian/pyfolio | python | https://github.com/quantopian/pyfolio/blob/712716ab0cdebbec9fabb25eea3bf40e4354749d/pyfolio/timeseries.py#L606-L623 | [
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... | 712716ab0cdebbec9fabb25eea3bf40e4354749d |
valid | value_at_risk | Get value at risk (VaR).
Parameters
----------
returns : pd.Series
Daily returns of the strategy, noncumulative.
- See full explanation in tears.create_full_tear_sheet.
period : str, optional
Period over which to calculate VaR. Set to 'weekly',
'monthly', or 'yearly', o... | pyfolio/timeseries.py | def value_at_risk(returns, period=None, sigma=2.0):
"""
Get value at risk (VaR).
Parameters
----------
returns : pd.Series
Daily returns of the strategy, noncumulative.
- See full explanation in tears.create_full_tear_sheet.
period : str, optional
Period over which to c... | def value_at_risk(returns, period=None, sigma=2.0):
"""
Get value at risk (VaR).
Parameters
----------
returns : pd.Series
Daily returns of the strategy, noncumulative.
- See full explanation in tears.create_full_tear_sheet.
period : str, optional
Period over which to c... | [
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] | quantopian/pyfolio | python | https://github.com/quantopian/pyfolio/blob/712716ab0cdebbec9fabb25eea3bf40e4354749d/pyfolio/timeseries.py#L626-L648 | [
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valid | perf_stats | Calculates various performance metrics of a strategy, for use in
plotting.show_perf_stats.
Parameters
----------
returns : pd.Series
Daily returns of the strategy, noncumulative.
- See full explanation in tears.create_full_tear_sheet.
factor_returns : pd.Series, optional
Da... | pyfolio/timeseries.py | 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... | 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... | [
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valid | perf_stats_bootstrap | Calculates various bootstrapped performance metrics of a strategy.
Parameters
----------
returns : pd.Series
Daily returns of the strategy, noncumulative.
- See full explanation in tears.create_full_tear_sheet.
factor_returns : pd.Series, optional
Daily noncumulative returns of... | pyfolio/timeseries.py | 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... | 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... | [
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] | quantopian/pyfolio | python | https://github.com/quantopian/pyfolio/blob/712716ab0cdebbec9fabb25eea3bf40e4354749d/pyfolio/timeseries.py#L742-L793 | [
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valid | calc_bootstrap | 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 factor returns) and
returns a single value (commonly a s... | pyfolio/timeseries.py | 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... | 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... | [
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valid | calc_distribution_stats | 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
95 percentiles of passed in values... | pyfolio/timeseries.py | 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
... | 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
... | [
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] | quantopian/pyfolio | python | https://github.com/quantopian/pyfolio/blob/712716ab0cdebbec9fabb25eea3bf40e4354749d/pyfolio/timeseries.py#L843-L867 | [
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... | 712716ab0cdebbec9fabb25eea3bf40e4354749d |
valid | get_max_drawdown_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 drawdown) of a strategy.
Returns
-------
peak ... | pyfolio/timeseries.py | 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... | 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... | [
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] | quantopian/pyfolio | python | https://github.com/quantopian/pyfolio/blob/712716ab0cdebbec9fabb25eea3bf40e4354749d/pyfolio/timeseries.py#L870-L901 | [
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"... | 712716ab0cdebbec9fabb25eea3bf40e4354749d |
valid | get_max_drawdown | 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
Maximum drawdown.
Note
-----
See ... | pyfolio/timeseries.py | 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... | 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... | [
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] | quantopian/pyfolio | python | https://github.com/quantopian/pyfolio/blob/712716ab0cdebbec9fabb25eea3bf40e4354749d/pyfolio/timeseries.py#L904-L928 | [
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valid | get_top_drawdowns | 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 top drawdowns to find (default 10).
Returns
... | pyfolio/timeseries.py | 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 ... | 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 ... | [
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] | quantopian/pyfolio | python | https://github.com/quantopian/pyfolio/blob/712716ab0cdebbec9fabb25eea3bf40e4354749d/pyfolio/timeseries.py#L931-L969 | [
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valid | gen_drawdown_table | 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 to find (default 10).
Returns
-------
df... | pyfolio/timeseries.py | 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 ... | 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 ... | [
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] | quantopian/pyfolio | python | https://github.com/quantopian/pyfolio/blob/712716ab0cdebbec9fabb25eea3bf40e4354749d/pyfolio/timeseries.py#L972-L1023 | [
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valid | rolling_volatility | 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
Length of rolling window, in days, over which to compute.
... | pyfolio/timeseries.py | 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
... | 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
... | [
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] | quantopian/pyfolio | python | https://github.com/quantopian/pyfolio/blob/712716ab0cdebbec9fabb25eea3bf40e4354749d/pyfolio/timeseries.py#L1026-L1045 | [
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] | 712716ab0cdebbec9fabb25eea3bf40e4354749d |
valid | rolling_sharpe | 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.
rolling_sharpe_window : int
Length of rolling window, in days, over which to comput... | pyfolio/timeseries.py | 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.
rolling_sharpe_window : int... | 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.
rolling_sharpe_window : int... | [
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] | quantopian/pyfolio | python | https://github.com/quantopian/pyfolio/blob/712716ab0cdebbec9fabb25eea3bf40e4354749d/pyfolio/timeseries.py#L1048-L1072 | [
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... | 712716ab0cdebbec9fabb25eea3bf40e4354749d |
valid | simulate_paths | Gnerate alternate paths using available values from in-sample returns.
Parameters
----------
is_returns : pandas.core.frame.DataFrame
Non-cumulative in-sample returns.
num_days : int
Number of days to project the probability cone forward.
starting_value : int or float
Starti... | pyfolio/timeseries.py | 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.
... | 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.
... | [
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] | quantopian/pyfolio | python | https://github.com/quantopian/pyfolio/blob/712716ab0cdebbec9fabb25eea3bf40e4354749d/pyfolio/timeseries.py#L1075-L1108 | [
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valid | summarize_paths | 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 : list of int/float
Number of standard devations to use in the boundaries of... | pyfolio/timeseries.py | 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... | 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... | [
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"star... | 712716ab0cdebbec9fabb25eea3bf40e4354749d |
valid | forecast_cone_bootstrap | Determines the upper and lower bounds of an n standard deviation
cone of forecasted cumulative returns. Future cumulative mean and
standard devation are computed by repeatedly sampling from the
in-sample daily returns (i.e. bootstrap). This cone is non-parametric,
meaning it does not assume that returns... | pyfolio/timeseries.py | def forecast_cone_bootstrap(is_returns, num_days, cone_std=(1., 1.5, 2.),
starting_value=1, num_samples=1000,
random_seed=None):
"""
Determines the upper and lower bounds of an n standard deviation
cone of forecasted cumulative returns. Future cumulati... | def forecast_cone_bootstrap(is_returns, num_days, cone_std=(1., 1.5, 2.),
starting_value=1, num_samples=1000,
random_seed=None):
"""
Determines the upper and lower bounds of an n standard deviation
cone of forecasted cumulative returns. Future cumulati... | [
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"="... | 712716ab0cdebbec9fabb25eea3bf40e4354749d |
valid | extract_interesting_date_ranges | 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.
Returns
-------
ranges : OrderedDict
Date r... | pyfolio/timeseries.py | 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... | 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... | [
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] | quantopian/pyfolio | python | https://github.com/quantopian/pyfolio/blob/712716ab0cdebbec9fabb25eea3bf40e4354749d/pyfolio/timeseries.py#L1205-L1234 | [
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"OrderedD... | 712716ab0cdebbec9fabb25eea3bf40e4354749d |
valid | model_returns_t_alpha_beta | 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 data is assumed to be T
distributed and thus is robust to outliers and takes tail event... | pyfolio/bayesian.py | 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... | 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... | [
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] | quantopian/pyfolio | python | https://github.com/quantopian/pyfolio/blob/712716ab0cdebbec9fabb25eea3bf40e4354749d/pyfolio/bayesian.py#L30-L86 | [
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"dro... | 712716ab0cdebbec9fabb25eea3bf40e4354749d |
valid | model_returns_normal | 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 to draw.
Returns
-------
model : pymc.Model object
PyM... | pyfolio/bayesian.py | 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 ... | 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 ... | [
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] | quantopian/pyfolio | python | https://github.com/quantopian/pyfolio/blob/712716ab0cdebbec9fabb25eea3bf40e4354749d/pyfolio/bayesian.py#L89-L124 | [
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... | 712716ab0cdebbec9fabb25eea3bf40e4354749d |
valid | model_best | 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
out-of-sample periods.
This model replicates the example us... | pyfolio/bayesian.py | 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
... | 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
... | [
"Bayesian",
"Estimation",
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"-",
"Test"
] | quantopian/pyfolio | python | https://github.com/quantopian/pyfolio/blob/712716ab0cdebbec9fabb25eea3bf40e4354749d/pyfolio/bayesian.py#L169-L251 | [
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... | 712716ab0cdebbec9fabb25eea3bf40e4354749d |
valid | plot_best | Plot BEST significance analysis.
Parameters
----------
trace : pymc3.sampling.BaseTrace, optional
trace object as returned by model_best()
If not passed, will run model_best(), for which
data_train and data_test are required.
data_train : pandas.Series, optional
Returns ... | pyfolio/bayesian.py | def plot_best(trace=None, data_train=None, data_test=None,
samples=1000, burn=200, axs=None):
"""
Plot BEST significance analysis.
Parameters
----------
trace : pymc3.sampling.BaseTrace, optional
trace object as returned by model_best()
If not passed, will run model_be... | def plot_best(trace=None, data_train=None, data_test=None,
samples=1000, burn=200, axs=None):
"""
Plot BEST significance analysis.
Parameters
----------
trace : pymc3.sampling.BaseTrace, optional
trace object as returned by model_best()
If not passed, will run model_be... | [
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"analysis",
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] | quantopian/pyfolio | python | https://github.com/quantopian/pyfolio/blob/712716ab0cdebbec9fabb25eea3bf40e4354749d/pyfolio/bayesian.py#L254-L342 | [
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"d... | 712716ab0cdebbec9fabb25eea3bf40e4354749d |
valid | model_stoch_vol | 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
----------
data : pandas.Series
Return series to model.
sample... | pyfolio/bayesian.py | 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
-------... | 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
-------... | [
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] | quantopian/pyfolio | python | https://github.com/quantopian/pyfolio/blob/712716ab0cdebbec9fabb25eea3bf40e4354749d/pyfolio/bayesian.py#L345-L385 | [
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":... | 712716ab0cdebbec9fabb25eea3bf40e4354749d |
valid | plot_stoch_vol | 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 from model.
ax : matplotlib.axes object, optional
... | pyfolio/bayesian.py | 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... | 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... | [
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] | quantopian/pyfolio | python | https://github.com/quantopian/pyfolio/blob/712716ab0cdebbec9fabb25eea3bf40e4354749d/pyfolio/bayesian.py#L388-L423 | [
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"... | 712716ab0cdebbec9fabb25eea3bf40e4354749d |
valid | compute_bayes_cone | 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 returns start around this value.
Default = 1.
Retur... | pyfolio/bayesian.py | 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... | 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... | [
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valid | compute_consistency_score | Compute Bayesian consistency score.
Parameters
----------
returns_test : pd.Series
Observed cumulative returns.
preds : numpy.array
Multiple (simulated) cumulative returns.
Returns
-------
Consistency score
Score from 100 (returns_test perfectly on the median line o... | pyfolio/bayesian.py | 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
... | 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
... | [
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] | quantopian/pyfolio | python | https://github.com/quantopian/pyfolio/blob/712716ab0cdebbec9fabb25eea3bf40e4354749d/pyfolio/bayesian.py#L456-L484 | [
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")"... | 712716ab0cdebbec9fabb25eea3bf40e4354749d |
valid | run_model | Run one of the Bayesian models.
Parameters
----------
model : {'alpha_beta', 't', 'normal', 'best'}
Which model to run
returns_train : pd.Series
Timeseries of simple returns
returns_test : pd.Series (optional)
Out-of-sample returns. Datetimes in returns_test will be added to... | pyfolio/bayesian.py | 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... | 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... | [
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] | quantopian/pyfolio | python | https://github.com/quantopian/pyfolio/blob/712716ab0cdebbec9fabb25eea3bf40e4354749d/pyfolio/bayesian.py#L522-L584 | [
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"... | 712716ab0cdebbec9fabb25eea3bf40e4354749d |
valid | plot_bayes_cone | Generate cumulative returns plot with Bayesian cone.
Parameters
----------
returns_train : pd.Series
Timeseries of simple returns
returns_test : pd.Series
Out-of-sample returns. Datetimes in returns_test will be added to
returns_train as missing values and predictions will be ge... | pyfolio/bayesian.py | 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... | 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... | [
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"."
] | quantopian/pyfolio | python | https://github.com/quantopian/pyfolio/blob/712716ab0cdebbec9fabb25eea3bf40e4354749d/pyfolio/bayesian.py#L587-L638 | [
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valid | load_voc_dataset | Pascal VOC 2007/2012 Dataset.
It has 20 objects:
aeroplane, bicycle, bird, boat, bottle, bus, car, cat, chair, cow, diningtable, dog, horse, motorbike, person, pottedplant, sheep, sofa, train, tvmonitor
and additional 3 classes : head, hand, foot for person.
Parameters
-----------
path : str
... | tensorlayer/files/dataset_loaders/voc_dataset.py | def load_voc_dataset(path='data', dataset='2012', contain_classes_in_person=False):
"""Pascal VOC 2007/2012 Dataset.
It has 20 objects:
aeroplane, bicycle, bird, boat, bottle, bus, car, cat, chair, cow, diningtable, dog, horse, motorbike, person, pottedplant, sheep, sofa, train, tvmonitor
and additiona... | def load_voc_dataset(path='data', dataset='2012', contain_classes_in_person=False):
"""Pascal VOC 2007/2012 Dataset.
It has 20 objects:
aeroplane, bicycle, bird, boat, bottle, bus, car, cat, chair, cow, diningtable, dog, horse, motorbike, person, pottedplant, sheep, sofa, train, tvmonitor
and additiona... | [
"Pascal",
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"2007",
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"Dataset",
"."
] | tensorlayer/tensorlayer | python | https://github.com/tensorlayer/tensorlayer/blob/aa9e52e36c7058a7e6fd81d36563ca6850b21956/tensorlayer/files/dataset_loaders/voc_dataset.py#L23-L338 | [
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valid | main | The core of the model consists of an LSTM cell that processes one word at
a time and computes probabilities of the possible continuations of the
sentence. The memory state of the network is initialized with a vector
of zeros and gets updated after reading each word. Also, for computational
reasons, we w... | examples/text_ptb/tutorial_ptb_lstm.py | def main(_):
"""
The core of the model consists of an LSTM cell that processes one word at
a time and computes probabilities of the possible continuations of the
sentence. The memory state of the network is initialized with a vector
of zeros and gets updated after reading each word. Also, for comput... | def main(_):
"""
The core of the model consists of an LSTM cell that processes one word at
a time and computes probabilities of the possible continuations of the
sentence. The memory state of the network is initialized with a vector
of zeros and gets updated after reading each word. Also, for comput... | [
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valid | private_method | Decorator for making an instance method private. | tensorlayer/decorators/method_decorator.py | def private_method(func):
"""Decorator for making an instance method private."""
def func_wrapper(*args, **kwargs):
"""Decorator wrapper function."""
outer_frame = inspect.stack()[1][0]
if 'self' not in outer_frame.f_locals or outer_frame.f_locals['self'] is not args[0]:
rai... | def private_method(func):
"""Decorator for making an instance method private."""
def func_wrapper(*args, **kwargs):
"""Decorator wrapper function."""
outer_frame = inspect.stack()[1][0]
if 'self' not in outer_frame.f_locals or outer_frame.f_locals['self'] is not args[0]:
rai... | [
"Decorator",
"for",
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"."
] | tensorlayer/tensorlayer | python | https://github.com/tensorlayer/tensorlayer/blob/aa9e52e36c7058a7e6fd81d36563ca6850b21956/tensorlayer/decorators/method_decorator.py#L7-L18 | [
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"i... | aa9e52e36c7058a7e6fd81d36563ca6850b21956 |
valid | protected_method | Decorator for making an instance method private. | tensorlayer/decorators/method_decorator.py | def protected_method(func):
"""Decorator for making an instance method private."""
def func_wrapper(*args, **kwargs):
"""Decorator wrapper function."""
outer_frame = inspect.stack()[1][0]
caller = inspect.getmro(outer_frame.f_locals['self'].__class__)[:-1]
target = inspect.getm... | def protected_method(func):
"""Decorator for making an instance method private."""
def func_wrapper(*args, **kwargs):
"""Decorator wrapper function."""
outer_frame = inspect.stack()[1][0]
caller = inspect.getmro(outer_frame.f_locals['self'].__class__)[:-1]
target = inspect.getm... | [
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valid | atrous_conv1d | Simplified version of :class:`AtrousConv1dLayer`.
Parameters
----------
prev_layer : :class:`Layer`
Previous layer.
n_filter : int
The number of filters.
filter_size : int
The filter size.
stride : tuple of int
The strides: (height, width).
dilation : int
... | tensorlayer/layers/convolution/atrous_conv.py | def atrous_conv1d(
prev_layer,
n_filter=32,
filter_size=2,
stride=1,
dilation=1,
act=None,
padding='SAME',
data_format='NWC',
W_init=tf.truncated_normal_initializer(stddev=0.02),
b_init=tf.constant_initializer(value=0.0),
W_init_arg... | def atrous_conv1d(
prev_layer,
n_filter=32,
filter_size=2,
stride=1,
dilation=1,
act=None,
padding='SAME',
data_format='NWC',
W_init=tf.truncated_normal_initializer(stddev=0.02),
b_init=tf.constant_initializer(value=0.0),
W_init_arg... | [
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"class",
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"AtrousConv1dLayer",
"."
] | tensorlayer/tensorlayer | python | https://github.com/tensorlayer/tensorlayer/blob/aa9e52e36c7058a7e6fd81d36563ca6850b21956/tensorlayer/layers/convolution/atrous_conv.py#L23-L88 | [
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"W... | aa9e52e36c7058a7e6fd81d36563ca6850b21956 |
valid | _GetNextLogCountPerToken | 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) | tensorlayer/logging/tl_logging.py | 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)
"""
global _log_counter_per_token # pylint: disable... | 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)
"""
global _log_counter_per_token # pylint: disable... | [
"Wrapper",
"for",
"_log_counter_per_token",
"."
] | tensorlayer/tensorlayer | python | https://github.com/tensorlayer/tensorlayer/blob/aa9e52e36c7058a7e6fd81d36563ca6850b21956/tensorlayer/logging/tl_logging.py#L148-L160 | [
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valid | log_every_n | 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 it is logged.
*args: The args to be substit... | tensorlayer/logging/tl_logging.py | 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... | 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... | [
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valid | log_if | Log 'msg % args' at level 'level' only if condition is fulfilled. | tensorlayer/logging/tl_logging.py | def log_if(level, msg, condition, *args):
"""Log 'msg % args' at level 'level' only if condition is fulfilled."""
if condition:
vlog(level, msg, *args) | def log_if(level, msg, condition, *args):
"""Log 'msg % args' at level 'level' only if condition is fulfilled."""
if condition:
vlog(level, msg, *args) | [
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] | aa9e52e36c7058a7e6fd81d36563ca6850b21956 |
valid | _GetFileAndLine | Returns (filename, linenumber) for the stack frame. | tensorlayer/logging/tl_logging.py | def _GetFileAndLine():
"""Returns (filename, linenumber) for the stack frame."""
# Use sys._getframe(). This avoids creating a traceback object.
# pylint: disable=protected-access
f = _sys._getframe()
# pylint: enable=protected-access
our_file = f.f_code.co_filename
f = f.f_back
while f... | def _GetFileAndLine():
"""Returns (filename, linenumber) for the stack frame."""
# Use sys._getframe(). This avoids creating a traceback object.
# pylint: disable=protected-access
f = _sys._getframe()
# pylint: enable=protected-access
our_file = f.f_code.co_filename
f = f.f_back
while f... | [
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] | tensorlayer/tensorlayer | python | https://github.com/tensorlayer/tensorlayer/blob/aa9e52e36c7058a7e6fd81d36563ca6850b21956/tensorlayer/logging/tl_logging.py#L200-L213 | [
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valid | google2_log_prefix | Assemble a logline prefix using the google2 format. | tensorlayer/logging/tl_logging.py | 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()
... | 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()
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valid | load_mpii_pose_dataset | Load MPII Human Pose Dataset.
Parameters
-----------
path : str
The path that the data is downloaded to.
is_16_pos_only : boolean
If True, only return the peoples contain 16 pose keypoints. (Usually be used for single person pose estimation)
Returns
----------
img_train_lis... | tensorlayer/files/dataset_loaders/mpii_dataset.py | def load_mpii_pose_dataset(path='data', is_16_pos_only=False):
"""Load MPII Human Pose Dataset.
Parameters
-----------
path : str
The path that the data is downloaded to.
is_16_pos_only : boolean
If True, only return the peoples contain 16 pose keypoints. (Usually be used for single... | def load_mpii_pose_dataset(path='data', is_16_pos_only=False):
"""Load MPII Human Pose Dataset.
Parameters
-----------
path : str
The path that the data is downloaded to.
is_16_pos_only : boolean
If True, only return the peoples contain 16 pose keypoints. (Usually be used for single... | [
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valid | transformer | Spatial Transformer Layer for `2D Affine Transformation <https://en.wikipedia.org/wiki/Affine_transformation>`__
, see :class:`SpatialTransformer2dAffineLayer` class.
Parameters
----------
U : list of float
The output of a convolutional net should have the
shape [num_batch, height, widt... | tensorlayer/layers/spatial_transformer.py | def transformer(U, theta, out_size, name='SpatialTransformer2dAffine'):
"""Spatial Transformer Layer for `2D Affine Transformation <https://en.wikipedia.org/wiki/Affine_transformation>`__
, see :class:`SpatialTransformer2dAffineLayer` class.
Parameters
----------
U : list of float
The outpu... | def transformer(U, theta, out_size, name='SpatialTransformer2dAffine'):
"""Spatial Transformer Layer for `2D Affine Transformation <https://en.wikipedia.org/wiki/Affine_transformation>`__
, see :class:`SpatialTransformer2dAffineLayer` class.
Parameters
----------
U : list of float
The outpu... | [
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valid | batch_transformer | Batch Spatial Transformer function for `2D Affine Transformation <https://en.wikipedia.org/wiki/Affine_transformation>`__.
Parameters
----------
U : list of float
tensor of inputs [batch, height, width, num_channels]
thetas : list of float
a set of transformations for each input [batch,... | tensorlayer/layers/spatial_transformer.py | def batch_transformer(U, thetas, out_size, name='BatchSpatialTransformer2dAffine'):
"""Batch Spatial Transformer function for `2D Affine Transformation <https://en.wikipedia.org/wiki/Affine_transformation>`__.
Parameters
----------
U : list of float
tensor of inputs [batch, height, width, num_c... | def batch_transformer(U, thetas, out_size, name='BatchSpatialTransformer2dAffine'):
"""Batch Spatial Transformer function for `2D Affine Transformation <https://en.wikipedia.org/wiki/Affine_transformation>`__.
Parameters
----------
U : list of float
tensor of inputs [batch, height, width, num_c... | [
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valid | create_task_spec_def | Returns the a :class:`TaskSpecDef` based on the environment variables for distributed training.
References
----------
- `ML-engine trainer considerations <https://cloud.google.com/ml-engine/docs/trainer-considerations#use_tf_config>`__
- `TensorPort Distributed Computing <https://www.tensorport.com/doc... | tensorlayer/distributed.py | def create_task_spec_def():
"""Returns the a :class:`TaskSpecDef` based on the environment variables for distributed training.
References
----------
- `ML-engine trainer considerations <https://cloud.google.com/ml-engine/docs/trainer-considerations#use_tf_config>`__
- `TensorPort Distributed Comput... | def create_task_spec_def():
"""Returns the a :class:`TaskSpecDef` based on the environment variables for distributed training.
References
----------
- `ML-engine trainer considerations <https://cloud.google.com/ml-engine/docs/trainer-considerations#use_tf_config>`__
- `TensorPort Distributed Comput... | [
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valid | create_distributed_session | Creates a distributed session.
It calls `MonitoredTrainingSession` to create a :class:`MonitoredSession` for distributed training.
Parameters
----------
task_spec : :class:`TaskSpecDef`.
The task spec definition from create_task_spec_def()
checkpoint_dir : str.
Optional path to a d... | tensorlayer/distributed.py | 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... | 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... | [
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valid | Trainer.validation_metrics | A helper function to compute validation related metrics | tensorlayer/distributed.py | 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... | 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... | [
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valid | Trainer.train_and_validate_to_end | 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. | tensorlayer/distributed.py | 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... | 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.
"""
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valid | _load_mnist_dataset | 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
The dataset name you want to use(the default is 'mnist').
url : str
The url of dataset(th... | tensorlayer/files/utils.py | 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... | 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... | [
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valid | load_cifar10_dataset | Load CIFAR-10 dataset.
It consists of 60000 32x32 colour images in 10 classes, with
6000 images per class. There are 50000 training images and 10000 test images.
The dataset is divided into five training batches and one test batch, each with
10000 images. The test batch contains exactly 1000 randomly-... | tensorlayer/files/utils.py | def load_cifar10_dataset(shape=(-1, 32, 32, 3), path='data', plotable=False):
"""Load CIFAR-10 dataset.
It consists of 60000 32x32 colour images in 10 classes, with
6000 images per class. There are 50000 training images and 10000 test images.
The dataset is divided into five training batches and one t... | def load_cifar10_dataset(shape=(-1, 32, 32, 3), path='data', plotable=False):
"""Load CIFAR-10 dataset.
It consists of 60000 32x32 colour images in 10 classes, with
6000 images per class. There are 50000 training images and 10000 test images.
The dataset is divided into five training batches and one t... | [
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valid | load_cropped_svhn | Load Cropped SVHN.
The Cropped Street View House Numbers (SVHN) Dataset contains 32x32x3 RGB images.
Digit '1' has label 1, '9' has label 9 and '0' has label 0 (the original dataset uses 10 to represent '0'), see `ufldl website <http://ufldl.stanford.edu/housenumbers/>`__.
Parameters
----------
pa... | tensorlayer/files/utils.py | def load_cropped_svhn(path='data', include_extra=True):
"""Load Cropped SVHN.
The Cropped Street View House Numbers (SVHN) Dataset contains 32x32x3 RGB images.
Digit '1' has label 1, '9' has label 9 and '0' has label 0 (the original dataset uses 10 to represent '0'), see `ufldl website <http://ufldl.stanfo... | def load_cropped_svhn(path='data', include_extra=True):
"""Load Cropped SVHN.
The Cropped Street View House Numbers (SVHN) Dataset contains 32x32x3 RGB images.
Digit '1' has label 1, '9' has label 9 and '0' has label 0 (the original dataset uses 10 to represent '0'), see `ufldl website <http://ufldl.stanfo... | [
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valid | load_ptb_dataset | Load Penn TreeBank (PTB) dataset.
It is used in many LANGUAGE MODELING papers,
including "Empirical Evaluation and Combination of Advanced Language
Modeling Techniques", "Recurrent Neural Network Regularization".
It consists of 929k training words, 73k validation words, and 82k test
words. It has 1... | tensorlayer/files/utils.py | def load_ptb_dataset(path='data'):
"""Load Penn TreeBank (PTB) dataset.
It is used in many LANGUAGE MODELING papers,
including "Empirical Evaluation and Combination of Advanced Language
Modeling Techniques", "Recurrent Neural Network Regularization".
It consists of 929k training words, 73k validati... | def load_ptb_dataset(path='data'):
"""Load Penn TreeBank (PTB) dataset.
It is used in many LANGUAGE MODELING papers,
including "Empirical Evaluation and Combination of Advanced Language
Modeling Techniques", "Recurrent Neural Network Regularization".
It consists of 929k training words, 73k validati... | [
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valid | load_matt_mahoney_text8_dataset | 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.
Parameters
----------
path : str
The path ... | tensorlayer/files/utils.py | 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... | 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... | [
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"... | aa9e52e36c7058a7e6fd81d36563ca6850b21956 |
valid | load_imdb_dataset | Load IMDB dataset.
Parameters
----------
path : str
The path that the data is downloaded to, defaults is ``data/imdb/``.
nb_words : int
Number of words to get.
skip_top : int
Top most frequent words to ignore (they will appear as oov_char value in the sequence data).
max... | tensorlayer/files/utils.py | 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... | 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... | [
"Load",
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"dataset",
"."
] | tensorlayer/tensorlayer | python | https://github.com/tensorlayer/tensorlayer/blob/aa9e52e36c7058a7e6fd81d36563ca6850b21956/tensorlayer/files/utils.py#L514-L614 | [
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valid | load_nietzsche_dataset | 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
>>> words = tl.files.load_nietzsche_dataset(... | tensorlayer/files/utils.py | 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
... | 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
... | [
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] | tensorlayer/tensorlayer | python | https://github.com/tensorlayer/tensorlayer/blob/aa9e52e36c7058a7e6fd81d36563ca6850b21956/tensorlayer/files/utils.py#L617-L647 | [
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valid | load_wmt_en_fr_dataset | 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.
Parameters
----------
path : str
The path... | tensorlayer/files/utils.py | 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... | 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... | [
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] | tensorlayer/tensorlayer | python | https://github.com/tensorlayer/tensorlayer/blob/aa9e52e36c7058a7e6fd81d36563ca6850b21956/tensorlayer/files/utils.py#L650-L714 | [
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valid | load_flickr25k_dataset | 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 use it.
Parameters
------------
tag : str or None
What images to retu... | tensorlayer/files/utils.py | 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 ... | 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 ... | [
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] | tensorlayer/tensorlayer | python | https://github.com/tensorlayer/tensorlayer/blob/aa9e52e36c7058a7e6fd81d36563ca6850b21956/tensorlayer/files/utils.py#L717-L784 | [
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valid | load_flickr1M_dataset | Load Flick1M dataset.
Returns a list of images by a given tag from Flickr1M dataset,
it will download Flickr1M from `the official website <http://press.liacs.nl/mirflickr/mirdownload.html>`__
at the first time you use it.
Parameters
------------
tag : str or None
What images to return.... | tensorlayer/files/utils.py | def load_flickr1M_dataset(tag='sky', size=10, path="data", n_threads=50, printable=False):
"""Load Flick1M dataset.
Returns a list of images by a given tag from Flickr1M dataset,
it will download Flickr1M from `the official website <http://press.liacs.nl/mirflickr/mirdownload.html>`__
at the first time... | def load_flickr1M_dataset(tag='sky', size=10, path="data", n_threads=50, printable=False):
"""Load Flick1M dataset.
Returns a list of images by a given tag from Flickr1M dataset,
it will download Flickr1M from `the official website <http://press.liacs.nl/mirflickr/mirdownload.html>`__
at the first time... | [
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] | tensorlayer/tensorlayer | python | https://github.com/tensorlayer/tensorlayer/blob/aa9e52e36c7058a7e6fd81d36563ca6850b21956/tensorlayer/files/utils.py#L787-L887 | [
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valid | load_cyclegan_dataset | Load images from CycleGAN's database, see `this link <https://people.eecs.berkeley.edu/~taesung_park/CycleGAN/datasets/>`__.
Parameters
------------
filename : str
The dataset you want, see `this link <https://people.eecs.berkeley.edu/~taesung_park/CycleGAN/datasets/>`__.
path : str
The... | tensorlayer/files/utils.py | def load_cyclegan_dataset(filename='summer2winter_yosemite', path='data'):
"""Load images from CycleGAN's database, see `this link <https://people.eecs.berkeley.edu/~taesung_park/CycleGAN/datasets/>`__.
Parameters
------------
filename : str
The dataset you want, see `this link <https://people.... | def load_cyclegan_dataset(filename='summer2winter_yosemite', path='data'):
"""Load images from CycleGAN's database, see `this link <https://people.eecs.berkeley.edu/~taesung_park/CycleGAN/datasets/>`__.
Parameters
------------
filename : str
The dataset you want, see `this link <https://people.... | [
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valid | download_file_from_google_drive | 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. | tensorlayer/files/utils.py | 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... | 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... | [
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] | tensorlayer/tensorlayer | python | https://github.com/tensorlayer/tensorlayer/blob/aa9e52e36c7058a7e6fd81d36563ca6850b21956/tensorlayer/files/utils.py#L937-L974 | [
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valid | load_celebA_dataset | Load CelebA dataset
Return a list of image path.
Parameters
-----------
path : str
The path that the data is downloaded to, defaults is ``data/celebA/``. | tensorlayer/files/utils.py | 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... | 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... | [
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] | tensorlayer/tensorlayer | python | https://github.com/tensorlayer/tensorlayer/blob/aa9e52e36c7058a7e6fd81d36563ca6850b21956/tensorlayer/files/utils.py#L977-L1007 | [
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valid | save_npz | Input parameters and the file name, save parameters into .npz file. Use tl.utils.load_npz() to restore.
Parameters
----------
save_list : list of tensor
A list of parameters (tensor) to be saved.
name : str
The name of the `.npz` file.
sess : None or Session
Session may be r... | tensorlayer/files/utils.py | def save_npz(save_list=None, name='model.npz', sess=None):
"""Input parameters and the file name, save parameters into .npz file. Use tl.utils.load_npz() to restore.
Parameters
----------
save_list : list of tensor
A list of parameters (tensor) to be saved.
name : str
The name of th... | def save_npz(save_list=None, name='model.npz', sess=None):
"""Input parameters and the file name, save parameters into .npz file. Use tl.utils.load_npz() to restore.
Parameters
----------
save_list : list of tensor
A list of parameters (tensor) to be saved.
name : str
The name of th... | [
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valid | load_npz | Load the parameters of a Model saved by tl.files.save_npz().
Parameters
----------
path : str
Folder path to `.npz` file.
name : str
The name of the `.npz` file.
Returns
--------
list of array
A list of parameters in order.
Examples
--------
- See ``tl.... | tensorlayer/files/utils.py | def load_npz(path='', name='model.npz'):
"""Load the parameters of a Model saved by tl.files.save_npz().
Parameters
----------
path : str
Folder path to `.npz` file.
name : str
The name of the `.npz` file.
Returns
--------
list of array
A list of parameters in o... | def load_npz(path='', name='model.npz'):
"""Load the parameters of a Model saved by tl.files.save_npz().
Parameters
----------
path : str
Folder path to `.npz` file.
name : str
The name of the `.npz` file.
Returns
--------
list of array
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valid | assign_params | 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.
Returns
--------
list of operation... | tensorlayer/files/utils.py | 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... | 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... | [
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valid | load_and_assign_npz | 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.
Returns
--------
False or network
Returns False, if the ... | tensorlayer/files/utils.py | 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... | 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.
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valid | save_npz_dict | 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) to be saved.
name : str
The name of the `.npz` file.
se... | tensorlayer/files/utils.py | 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) ... | 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) ... | [
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valid | load_and_assign_npz_dict | Restore the parameters saved by ``tl.files.save_npz_dict()``.
Parameters
----------
name : str
The name of the `.npz` file.
sess : Session
TensorFlow Session. | tensorlayer/files/utils.py | def load_and_assign_npz_dict(name='model.npz', sess=None):
"""Restore the parameters saved by ``tl.files.save_npz_dict()``.
Parameters
----------
name : str
The name of the `.npz` file.
sess : Session
TensorFlow Session.
"""
if sess is None:
raise ValueError("sessio... | def load_and_assign_npz_dict(name='model.npz', sess=None):
"""Restore the parameters saved by ``tl.files.save_npz_dict()``.
Parameters
----------
name : str
The name of the `.npz` file.
sess : Session
TensorFlow Session.
"""
if sess is None:
raise ValueError("sessio... | [
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] | tensorlayer/tensorlayer | python | https://github.com/tensorlayer/tensorlayer/blob/aa9e52e36c7058a7e6fd81d36563ca6850b21956/tensorlayer/files/utils.py#L1753-L1791 | [
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... | aa9e52e36c7058a7e6fd81d36563ca6850b21956 |
valid | save_ckpt | Save parameters into `ckpt` file.
Parameters
------------
sess : Session
TensorFlow Session.
mode_name : str
The name of the model, default is ``model.ckpt``.
save_dir : str
The path / file directory to the `ckpt`, default is ``checkpoint``.
var_list : list of tensor
... | tensorlayer/files/utils.py | 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... | 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... | [
"Save",
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"file",
"."
] | tensorlayer/tensorlayer | python | https://github.com/tensorlayer/tensorlayer/blob/aa9e52e36c7058a7e6fd81d36563ca6850b21956/tensorlayer/files/utils.py#L1794-L1835 | [
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"None"... | aa9e52e36c7058a7e6fd81d36563ca6850b21956 |
valid | load_ckpt | Load parameters from `ckpt` file.
Parameters
------------
sess : Session
TensorFlow Session.
mode_name : str
The name of the model, default is ``model.ckpt``.
save_dir : str
The path / file directory to the `ckpt`, default is ``checkpoint``.
var_list : list of tensor
... | tensorlayer/files/utils.py | 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``.
... | 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``.
... | [
"Load",
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] | tensorlayer/tensorlayer | python | https://github.com/tensorlayer/tensorlayer/blob/aa9e52e36c7058a7e6fd81d36563ca6850b21956/tensorlayer/files/utils.py#L1838-L1899 | [
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... | aa9e52e36c7058a7e6fd81d36563ca6850b21956 |
valid | load_npy_to_any | Load `.npy` file.
Parameters
------------
path : str
Path to the file (optional).
name : str
File name.
Examples
---------
- see tl.files.save_any_to_npy() | tensorlayer/files/utils.py | 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)
try:
... | 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)
try:
... | [
"Load",
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] | tensorlayer/tensorlayer | python | https://github.com/tensorlayer/tensorlayer/blob/aa9e52e36c7058a7e6fd81d36563ca6850b21956/tensorlayer/files/utils.py#L2093-L2113 | [
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"item"... | aa9e52e36c7058a7e6fd81d36563ca6850b21956 |
valid | load_file_list | 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.
printable : boolean
Whether to print the files infomation.
keep_pref... | tensorlayer/files/utils.py | 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.
... | 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.
... | [
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] | tensorlayer/tensorlayer | python | https://github.com/tensorlayer/tensorlayer/blob/aa9e52e36c7058a7e6fd81d36563ca6850b21956/tensorlayer/files/utils.py#L2148-L2182 | [
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valid | load_folder_list | Return a folder list in a folder by given a folder path.
Parameters
----------
path : str
A folder path. | tensorlayer/files/utils.py | def load_folder_list(path=""):
"""Return a folder list in a folder by given a folder path.
Parameters
----------
path : str
A folder path.
"""
return [os.path.join(path, o) for o in os.listdir(path) if os.path.isdir(os.path.join(path, o))] | def load_folder_list(path=""):
"""Return a folder list in a folder by given a folder path.
Parameters
----------
path : str
A folder path.
"""
return [os.path.join(path, o) for o in os.listdir(path) if os.path.isdir(os.path.join(path, o))] | [
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] | tensorlayer/tensorlayer | python | https://github.com/tensorlayer/tensorlayer/blob/aa9e52e36c7058a7e6fd81d36563ca6850b21956/tensorlayer/files/utils.py#L2185-L2194 | [
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valid | exists_or_mkdir | 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
--------
boolean
True if folder already... | tensorlayer/files/utils.py | 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
---... | 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
---... | [
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valid | maybe_download_and_extract | 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 : str
The name of the (to be) dowloaded file.
working_directory : str
A folder path to search f... | tensorlayer/files/utils.py | 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... | 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... | [
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valid | natural_keys | 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() # that is what we dont want
... | tensorlayer/files/utils.py | 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... | 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... | [
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] | tensorlayer/tensorlayer | python | https://github.com/tensorlayer/tensorlayer/blob/aa9e52e36c7058a7e6fd81d36563ca6850b21956/tensorlayer/files/utils.py#L2308-L2331 | [
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valid | npz_to_W_pdf | r"""Convert the first weight matrix of `.npz` file to `.pdf` by using `tl.visualize.W()`.
Parameters
----------
path : str
A folder path to `npz` files.
regx : str
Regx for the file name.
Examples
---------
Convert the first weight matrix of w1_pre...npz file to w1_pre...pd... | tensorlayer/files/utils.py | def npz_to_W_pdf(path=None, regx='w1pre_[0-9]+\.(npz)'):
r"""Convert the first weight matrix of `.npz` file to `.pdf` by using `tl.visualize.W()`.
Parameters
----------
path : str
A folder path to `npz` files.
regx : str
Regx for the file name.
Examples
---------
Conver... | def npz_to_W_pdf(path=None, regx='w1pre_[0-9]+\.(npz)'):
r"""Convert the first weight matrix of `.npz` file to `.pdf` by using `tl.visualize.W()`.
Parameters
----------
path : str
A folder path to `npz` files.
regx : str
Regx for the file name.
Examples
---------
Conver... | [
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... | aa9e52e36c7058a7e6fd81d36563ca6850b21956 |
valid | threading_data | 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 threads to use.
fn : function
The function for data processing.
... | tensorlayer/prepro.py | 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 ... | 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 ... | [
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] | tensorlayer/tensorlayer | python | https://github.com/tensorlayer/tensorlayer/blob/aa9e52e36c7058a7e6fd81d36563ca6850b21956/tensorlayer/prepro.py#L124-L234 | [
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... | aa9e52e36c7058a7e6fd81d36563ca6850b21956 |
valid | affine_rotation_matrix | Create an affine transform matrix for image rotation.
NOTE: In OpenCV, x is width and y is height.
Parameters
-----------
angle : int/float or tuple of two int/float
Degree to rotate, usually -180 ~ 180.
- int/float, a fixed angle.
- tuple of 2 floats/ints, randomly samp... | tensorlayer/prepro.py | def affine_rotation_matrix(angle=(-20, 20)):
"""Create an affine transform matrix for image rotation.
NOTE: In OpenCV, x is width and y is height.
Parameters
-----------
angle : int/float or tuple of two int/float
Degree to rotate, usually -180 ~ 180.
- int/float, a fixed angle.... | def affine_rotation_matrix(angle=(-20, 20)):
"""Create an affine transform matrix for image rotation.
NOTE: In OpenCV, x is width and y is height.
Parameters
-----------
angle : int/float or tuple of two int/float
Degree to rotate, usually -180 ~ 180.
- int/float, a fixed angle.... | [
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valid | affine_horizontal_flip_matrix | Create an affine transformation matrix for image horizontal flipping.
NOTE: In OpenCV, x is width and y is height.
Parameters
----------
prob : float
Probability to flip the image. 1.0 means always flip.
Returns
-------
numpy.array
An affine transform matrix. | tensorlayer/prepro.py | def affine_horizontal_flip_matrix(prob=0.5):
"""Create an affine transformation matrix for image horizontal flipping.
NOTE: In OpenCV, x is width and y is height.
Parameters
----------
prob : float
Probability to flip the image. 1.0 means always flip.
Returns
-------
numpy.arra... | def affine_horizontal_flip_matrix(prob=0.5):
"""Create an affine transformation matrix for image horizontal flipping.
NOTE: In OpenCV, x is width and y is height.
Parameters
----------
prob : float
Probability to flip the image. 1.0 means always flip.
Returns
-------
numpy.arra... | [
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valid | affine_vertical_flip_matrix | Create an affine transformation for image vertical flipping.
NOTE: In OpenCV, x is width and y is height.
Parameters
----------
prob : float
Probability to flip the image. 1.0 means always flip.
Returns
-------
numpy.array
An affine transform matrix. | tensorlayer/prepro.py | def affine_vertical_flip_matrix(prob=0.5):
"""Create an affine transformation for image vertical flipping.
NOTE: In OpenCV, x is width and y is height.
Parameters
----------
prob : float
Probability to flip the image. 1.0 means always flip.
Returns
-------
numpy.array
A... | def affine_vertical_flip_matrix(prob=0.5):
"""Create an affine transformation for image vertical flipping.
NOTE: In OpenCV, x is width and y is height.
Parameters
----------
prob : float
Probability to flip the image. 1.0 means always flip.
Returns
-------
numpy.array
A... | [
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valid | affine_shift_matrix | Create an affine transform matrix for image shifting.
NOTE: In OpenCV, x is width and y is height.
Parameters
-----------
wrg : float or tuple of floats
Range to shift on width axis, -1 ~ 1.
- float, a fixed distance.
- tuple of 2 floats, randomly sample a value as the d... | tensorlayer/prepro.py | def affine_shift_matrix(wrg=(-0.1, 0.1), hrg=(-0.1, 0.1), w=200, h=200):
"""Create an affine transform matrix for image shifting.
NOTE: In OpenCV, x is width and y is height.
Parameters
-----------
wrg : float or tuple of floats
Range to shift on width axis, -1 ~ 1.
- float, a f... | def affine_shift_matrix(wrg=(-0.1, 0.1), hrg=(-0.1, 0.1), w=200, h=200):
"""Create an affine transform matrix for image shifting.
NOTE: In OpenCV, x is width and y is height.
Parameters
-----------
wrg : float or tuple of floats
Range to shift on width axis, -1 ~ 1.
- float, a f... | [
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valid | affine_shear_matrix | Create affine transform matrix for image shearing.
NOTE: In OpenCV, x is width and y is height.
Parameters
-----------
shear : tuple of two floats
Percentage of shears for width and height directions.
Returns
-------
numpy.array
An affine transform matrix. | tensorlayer/prepro.py | def affine_shear_matrix(x_shear=(-0.1, 0.1), y_shear=(-0.1, 0.1)):
"""Create affine transform matrix for image shearing.
NOTE: In OpenCV, x is width and y is height.
Parameters
-----------
shear : tuple of two floats
Percentage of shears for width and height directions.
Returns
---... | def affine_shear_matrix(x_shear=(-0.1, 0.1), y_shear=(-0.1, 0.1)):
"""Create affine transform matrix for image shearing.
NOTE: In OpenCV, x is width and y is height.
Parameters
-----------
shear : tuple of two floats
Percentage of shears for width and height directions.
Returns
---... | [
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"# \"shear should be tuple of 2 floats, or you want to... | aa9e52e36c7058a7e6fd81d36563ca6850b21956 |
valid | affine_zoom_matrix | Create an affine transform matrix for zooming/scaling an image's height and width.
OpenCV format, x is width.
Parameters
-----------
x : numpy.array
An image with dimension of [row, col, channel] (default).
zoom_range : float or tuple of 2 floats
The zooming/scaling ratio, greater t... | tensorlayer/prepro.py | def affine_zoom_matrix(zoom_range=(0.8, 1.1)):
"""Create an affine transform matrix for zooming/scaling an image's height and width.
OpenCV format, x is width.
Parameters
-----------
x : numpy.array
An image with dimension of [row, col, channel] (default).
zoom_range : float or tuple of... | def affine_zoom_matrix(zoom_range=(0.8, 1.1)):
"""Create an affine transform matrix for zooming/scaling an image's height and width.
OpenCV format, x is width.
Parameters
-----------
x : numpy.array
An image with dimension of [row, col, channel] (default).
zoom_range : float or tuple of... | [
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] | tensorlayer/tensorlayer | python | https://github.com/tensorlayer/tensorlayer/blob/aa9e52e36c7058a7e6fd81d36563ca6850b21956/tensorlayer/prepro.py#L392-L422 | [
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