merlion.utils package

This package contains various utilities, including the TimeSeries class and utilities for resampling time series.

Submodules

merlion.utils.istat module

class merlion.utils.istat.IStat(value=None, n=0)

Bases: object

An abstract base class for computing various statistics incrementally, with emphasis on recency-weighted variants.

Parameters

value (Optional[float]) – Initial value of the statistic. Defaults to None.
n (int) – Initial sample size. Defaults to 0.

property n

property value

abstract add(x): Add a new value to update the statistic. :param x: new value to add to the sample.

abstract drop(x): Drop a value to update the statistic. :param x: value to drop from the sample.

add_batch(batch): Add a batch of new values to update the statistic. :type batch: List[float] :param batch: new values to add to the sample.

drop_batch(batch): Drop a batch of new values to update the statistic. :type batch: List[float] :param batch: new values to add to the sample.

class merlion.utils.istat.Mean(value=None, n=0)

Bases: IStat

Class for incrementally computing the mean of a series of numbers.

Parameters

value (Optional[float]) – Initial value of the statistic. Defaults to None.
n (int) – Initial sample size. Defaults to 0.

property value

add(x): Add a new value to update the statistic. :param x: new value to add to the sample.

drop(x): Drop a value to update the statistic. :param x: value to drop from the sample.

class merlion.utils.istat.Variance(ex_value=None, ex2_value=None, n=0, ddof=1)

Bases: IStat

Class for incrementally computing the variance of a series of numbers.

Parameters

ex_value (Optional[float]) – Initial value of the first moment (mean).
ex2_value (Optional[float]) – Initial value of the second moment.
n (int) – Initial sample size.
ddof (int) – The delta degrees of freedom to use when correcting the estimate of the variance.

\[\text{Var}(x_i) = \text{E}(x_i^2) - \text{E}(x_i)^2\]

mean_class: alias of Mean

add(x): Add a new value to update the statistic. :param x: new value to add to the sample.

drop(x): Drop a value to update the statistic. :param x: value to drop from the sample.

property true_value

property corrected_value

property value

property sd

property se

class merlion.utils.istat.ExponentialMovingAverage(recency_weight=0.1, **kwargs)

Bases: Mean

Class for incrementally computing the exponential moving average of a series of numbers.

Parameters: recency_weight (float) – Recency weight to use when updating the exponential moving average.

Letting w be the recency weight,

\[\begin{split}\begin{align*} \text{EMA}_w(x_0) & = x_0 \\ \text{EMA}_w(x_t) & = w \cdot x_t + (1-w) \cdot \text{EMA}_w(x_{t-1}) \end{align*}\end{split}\]

property recency_weight

property value

drop(x): Exponential Moving Average does not support dropping values

class merlion.utils.istat.RecencyWeightedVariance(recency_weight, **kwargs)

Bases: Variance

Class for incrementally computing the recency-weighted variance of a series of numbers.

Parameters: recency_weight (float) – Recency weight to use when updating the recency weighted variance.

Letting w be the recency weight,

\[\text{RWV}_w(x_t) = \text{EMA}_w({x^2_t}) - \text{EMA}_w(x_t)^2\]

mean_class: alias of ExponentialMovingAverage

property recency_weight

drop(x): Recency Weighted Variance does not support dropping values

merlion.utils.misc module

class merlion.utils.misc.AutodocABCMeta(classname, bases, cls_dict)

Bases: ABCMeta

Metaclass used to ensure that inherited members of an abstract base class also inherit docstrings for inherited methods.

class merlion.utils.misc.ValIterOrderedDict

Bases: OrderedDict

OrderedDict whose iterator goes over self.values() instead of self.keys().

merlion.utils.misc.dynamic_import(import_path, alias=None)

Dynamically import a member from the specified module.

Parameters

import_path (str) – syntax ‘module_name:member_name’, e.g. ‘merlion.transform.normalize:PowerTransform’
alias (Optional[dict]) – dict which maps shortcuts for the registered classes, to their full import paths.

Returns

imported class

merlion.utils.misc.initializer(func): Decorator for the __init__ method. Automatically assigns the parameters.

class merlion.utils.misc.ProgressBar(total, length=40, decimals=1, fill='█')

Bases: object

Parameters

total (int) – total iterations
length (int) – character length of bar
decimals (int) – positive number of decimals in percent complete
fill (str) – bar fill character

print(iteration, prefix, suffix, end='')

Parameters

iteration – current iteration
prefix – prefix string
suffix – suffix string
end – end character (e.g. "\r", "\r\n")

merlion.utils.resample module

class merlion.utils.resample.AlignPolicy(value)

Bases: Enum

Policies for aligning multiple univariate time series.

OuterJoin = 0

InnerJoin = 1

FixedReference = 2

FixedGranularity = 3

class merlion.utils.resample.AggregationPolicy(value)

Bases: Enum

Aggregation policies. Values are partial functions for pandas.core.resample.Resampler methods.

Mean = functools.partial(<function AggregationPolicy.<lambda>>)

Sum = functools.partial(<function AggregationPolicy.<lambda>>)

Median = functools.partial(<function AggregationPolicy.<lambda>>)

First = functools.partial(<function AggregationPolicy.<lambda>>)

Last = functools.partial(<function AggregationPolicy.<lambda>>)

Min = functools.partial(<function AggregationPolicy.<lambda>>)

Max = functools.partial(<function AggregationPolicy.<lambda>>)

class merlion.utils.resample.MissingValuePolicy(value)

Bases: Enum

Missing value imputation policies. Values are partial functions for pd.Series methods.

FFill = functools.partial(<function MissingValuePolicy.<lambda>>): Fill gap with the first value before the gap.

BFill = functools.partial(<function MissingValuePolicy.<lambda>>): Fill gap with the first value after the gap.

Nearest = functools.partial(<function MissingValuePolicy.<lambda>>, method='nearest'): Replace missing value with the value closest to it.

Interpolate = functools.partial(<function MissingValuePolicy.<lambda>>, method='time'): Fill in missing values by linear interpolation.

merlion.utils.resample.to_pd_datetime(timestamp): Converts a timestamp (or list/iterable of timestamps) to pandas Datetime, truncated at the millisecond.

merlion.utils.resample.to_timestamp(t): Converts a datetime to a Unix timestamp.

merlion.utils.resample.granularity_str_to_seconds(granularity)

Converts a string/float/int granularity (representing a timedelta) to the number of seconds it represents, truncated at the millisecond.

Return type: Optional[float]

merlion.utils.resample.get_gcd_timedelta(*time_stamp_lists): Calculates all timedeltas present in any of the lists of time stamps given, and returns the GCD of all these timedeltas (up to units of milliseconds).

merlion.utils.resample.infer_granularity(time_stamps): Infers the granularity of a list of time stamps

merlion.utils.resample.reindex_df(df, reference, missing_value_policy): Reindexes a Datetime-indexed dataframe df to have the same time stamps as a reference sequence of timestamps. Imputes missing values with the given MissingValuePolicy.

merlion.utils.time_series module

class merlion.utils.time_series.UnivariateTimeSeries(time_stamps, values, name=None, freq='1h')

Bases: Series

Please read the tutorial before reading this API doc. This class is a time-indexed pd.Series which represents a univariate time series. For the most part, it supports all the same features as pd.Series, with the following key differences to iteration and indexing:

Iterating over a UnivariateTimeSeries is implemented as
```
for timestamp, value in univariate:
    # do stuff...
```
where timestamp is a Unix timestamp, and value is the corresponding time series value.
Integer index: u[i] yields the tuple (u.time_stamps[i], u.values[i])
Slice index: u[i:j:k] yields a new UnivariateTimeSeries(u.time_stamps[i:j:k], u.values[i:j:k])

The class also supports the following additional features:

univariate.time_stamps returns the list of Unix timestamps, and univariate.values returns the list of the time series values. You may access the pd.DatetimeIndex directly with univariate.index (or its np.ndarray representation with univariate.np_time_stamps), and the np.ndarray of values with univariate.np_values.
univariate.concat(other) will concatenate the UnivariateTimeSeries other to the right end of univariate.
left, right = univariate.bisect(t) will split the univariate at the given timestamp t.
window = univariate.window(t0, tf) will return the subset of the time series occurring between timestamps t0 (inclusive) and tf (non-inclusive)
series = univariate.to_pd() will convert the UnivariateTimeSeries into a regular pd.Series (for compatibility).
univariate = UnivariateTimeSeries.from_pd(series) uses a time-indexed pd.Series to create a UnivariateTimeSeries object directly.

__getitem__(i)

Parameters: i (Union[int, slice]) – integer index or slice
Return type: Union[Tuple[float, float], UnivariateTimeSeries]
Returns: (self.time_stamps[i], self.values[i]) if i is an integer. UnivariateTimeSeries(self.time_series[i], self.values[i]) if i is a slice.

__iter__(): The i’th item in the iterator is the tuple (self.time_stamps[i], self.values[i]).

Parameters

time_stamps (Optional[Sequence[Union[int, float]]]) – a sequence of Unix timestamps. You may specify None if you only have values with no specific time stamps.
values (Sequence[float]) – a sequence of univariate values, where values[i] occurs at time time_stamps[i]
name (Optional[str]) – the name of the univariate time series
freq – if time_stamps is not provided, the univariate is assumed to be sampled at frequency freq. freq may be a string (e.g. "1h"), timedelta, or int/float (in units of seconds).

property np_time_stamps

Return type: np.ndarray
Returns: the numpy representation of this time series’s Unix timestamps

property np_values

Return type: np.ndarray
Returns: the numpy representation of this time series’s values

property time_stamps

Return type: List[float]
Returns: the list of Unix timestamps for the time series

property values

Return type: List[float]
Returns: the list of values for the time series.

property t0

Return type: float
Returns: the first timestamp in the univariate time series.

property tf

Return type: float
Returns: the final timestamp in the univariate time series.

is_empty()

Return type: bool
Returns: True if the univariate is empty, False if not.

copy(deep=True): Copies the UnivariateTimeSeries. Simply a wrapper around the pd.Series.copy() method.

concat(other): Concatenates the UnivariateTimeSeries other to the right of this one. :param UnivariateTimeSeries other: another UnivariateTimeSeries :rtype: UnivariateTimeSeries :return: concatenated univariate time series

bisect(t, t_in_left=False)

Splits the time series at the point where the given timestamp occurs.

Parameters

t (float) – a Unix timestamp or datetime object. Everything before time t is in the left split, and everything after time t is in the right split.
t_in_left (bool) – if True, t is in the left split. Otherwise, t is in the right split.

Return type

Tuple[UnivariateTimeSeries, UnivariateTimeSeries]

Returns

the left and right splits of the time series.

window(t0, tf, include_tf=False)

Parameters

t0 (float) – The timestamp/datetime at the start of the window (inclusive)
tf (float) – The timestamp/datetime at the end of the window (inclusive if include_tf is True, non-inclusive otherwise)
include_tf (bool) – Whether to include tf in the window.

Return type

UnivariateTimeSeries

Returns

The subset of the time series occurring between timestamps t0 (inclusive) and tf (included if include_tf is True, excluded otherwise).

to_dict()

Return type: Dict[float, float]
Returns: A dictionary representing the data points in the time series.

classmethod from_dict(obj, name=None)

Parameters

obj (Dict[float, float]) – A dictionary of timestamp - value pairs
name – the name to assign the output

Return type

UnivariateTimeSeries

Returns

the UnivariateTimeSeries represented by series.

to_pd()

Return type: Series
Returns: A pandas Series representing the time series, indexed by time.

classmethod from_pd(series, name=None, freq='1h')

Parameters

series (Series) – a pd.Series. If it has a``pd.DatetimeIndex``, we will use that index for the timestamps. Otherwise, we will create one at the specified frequency.
name – the name to assign the output
freq – if series is not indexed by time, this is the frequency at which we will assume it is sampled.

Return type

UnivariateTimeSeries

Returns

the UnivariateTimeSeries represented by series.

to_ts()

Return type: TimeSeries
Returns: A TimeSeries representing this univariate time series.

classmethod empty(name=None)

Return type: UnivariateTimeSeries
Returns: A Merlion UnivariateTimeSeries that has empty timestamps and values.

class merlion.utils.time_series.TimeSeries(univariates, *, check_aligned=True)

Bases: object

Please read the tutorial before reading this API doc. This class represents a general multivariate time series as a wrapper around a number of (optionally named) UnivariateTimeSeries. A TimeSeries object is initialized as time_series = TimeSeries(univariates), where univariates is either a list of UnivariateTimeSeries, or a dictionary mapping string names to their corresponding UnivariateTimeSeries objects.

Because the individual univariates need not be sampled at the same times, an important concept for TimeSeries is alignment. We say that a TimeSeries is aligned if all of its univariates have observations sampled at the exact set set of times.

One may access the UnivariateTimeSeries comprising this TimeSeries in four ways:

Iterate over the individual univariates using

for var in time_series.univariates:
    # do stuff with each UnivariateTimeSeries var

Access an individual UnivariateTimeSeries by name as time_series.univariates[name]. If you supplied unnamed univariates to the constructor (i.e. using a list), the name of a univariate will just be its index in that list.
Get the list of each univariate’s name with time_series.names.
Iterate over named univariates as
```
for name, var in time_series.items():
    # do stuff
```
Note that this is equivalent to iterating over zip(time_series.names, time_series.univariates).

This class supports the following additional features as well:

Interoperability with pandas
- df = time_series.to_pd() yields a time-indexed pd.DataFrame, where each column (with the appropriate name) corresponds to a variable. Missing values are NaN.
- time_series = TimeSeries.from_pd(df) takes a time-indexed pd.DataFrame and returns a corresponding TimeSeries object (missing values are handled appropriately). The order of time_series.univariates is the order of df.keys().
Automated alignment: aligned = time_series.align() resamples each of time_series.univariates so that they all have the same timestamps. By default, this is done by taking the union of all timestamps present in any individual univariate time series, and imputing missing values via interpolation. See the method documentation for details on how you may configure the alignment policy.
Transparent indexing and iteration for TimeSeries which have all univariates aligned (i.e. they all have the same timestamps)
- Get the length and shape of the time series (equal to the number of observations in each individual univariate). Note that if the time series is not aligned, we will return the length/shape of an equivalent pandas dataframe and emit a warning.
- Index time_series[i] = (times[i], (x1[i], ..., xn[i])) (assuming time_series has n aligned univariates with timestamps times, and xk = time_series.univariates[k-1].values). Slice returns a TimeSeries object and works as one would expect.
- Assuming time_series has n variables, you may iterate with
```
for t_i, (x1_i, ..., xn_i) in time_series:
    # do stuff
```
  Notably, this lets you call times, val_vectors = zip(*time_series)
Time-based queries for any time series
- Get the two sub TimeSeries before and after a timestamp t via left, right = time_series.bisect(t)
- Get the sub TimeSeries between timestamps t0 (inclusive) and tf (non-inclusive) via window = time_series.window(t0, tf)
Concatenation: two TimeSeries may be concatenated (in time) as time_series = time_series_1 + time_series_2.

__getitem__(i)

Only supported if all individual variable time series are sampled at the same time stamps.

Parameters: i (Union[int, slice]) – integer index or slice.
Return type: Union[Tuple[float, Tuple[float]], TimeSeries]
Returns: If i is an integer, returns the tuple (time_stamps[i], tuple(var.values[i] for var in self.univariates)). If i is a slice, returns the time series TimeSeries([var[i] for var in self.univariates])

__iter__(): Only supported if all individual variable time series are sampled at the same time stamps. The i’th item of the iterator is the tuple (time_stamps[i], tuple(var.values[i] for var in self.univariates)).

property names

Returns: The list of the names of the univariates.

items()

Returns: Iterator over (name, univariate) tuples.

property dim: int

Return type: int
Returns: The dimension of the time series (the number of variables).

property is_aligned: bool

Return type: bool
Returns: Whether all individual variable time series are sampled at the same time stamps, i.e. they are aligned.

property np_time_stamps

Return type: np.ndarray
Returns: the numpy representation of this time series’s Unix timestamps

property time_stamps

Return type: List[float]
Returns: the list of Unix timestamps for the time series

property t0: float

Return type: float
Returns: the first timestamp in the time series.

property tf: float

Return type: float
Returns: the final timestamp in the time series.

is_empty()

Return type: bool
Returns: whether the time series is empty

squeeze()

Return type: UnivariateTimeSeries
Returns: a UnivariateTimeSeries if the time series only has one univariate, otherwise returns itself, a TimeSeries

property shape: Tuple[int, int]

Return type: Tuple[int, int]
Returns: the shape of this time series, i.e. (self.dim, len(self))

bisect(t, t_in_left=False)

Splits the time series at the point where the given timestap t occurs.

Parameters

t (float) – a Unix timestamp or datetime object. Everything before time t is in the left split, and everything after time t is in the right split.
t_in_left (bool) – if True, t is in the left split. Otherwise, t is in the right split.

Return type

Tuple[TimeSeries, TimeSeries]

Returns

the left and right splits of the time series.

window(t0, tf, include_tf=False)

Parameters

t0 (float) – The timestamp/datetime at the start of the window (inclusive)
tf (float) – The timestamp/datetime at the end of the window (inclusive if include_tf is True, non-inclusive otherwise)
include_tf (bool) – Whether to include tf in the window.

Returns

The subset of the time series occurring between timestamps t0 (inclusive) and tf (included if include_tf is True, excluded otherwise).

Return type

TimeSeries

to_pd()

Return type: DataFrame
Returns: A pandas DataFrame (indexed by time) which represents this time series. Each variable corresponds to a column of the DataFrame. Timestamps which are present for one variable but not another, are represented with NaN.

classmethod from_pd(df, check_times=True, freq='1h')

Parameters

df (Union[Series, DataFrame]) – A pandas DataFrame with a DatetimeIndex. Each column corresponds to a different variable of the time series, and the key of column (in sorted order) give the relative order of those variables (in the list self.univariates). Missing values should be represented with NaN. May also be a pandas Series for univariate time series.
check_times – whether to check that all times in the index are unique (up to the millisecond) and sorted.

Return type

TimeSeries

Returns

the TimeSeries object corresponding to df.

classmethod from_ts_list(ts_list, *, check_aligned=True)

Parameters

ts_list (Iterable[TimeSeries]) – iterable of time series we wish to form a multivariate time series with
check_aligned (bool) – whether to check if the output time series is aligned

Return type

TimeSeries

Returns

A multivariate TimeSeries created from all the time series in the inputs.

align(*, reference=None, granularity=None, origin=None, remove_non_overlapping=True, alignment_policy=None, aggregation_policy=AggregationPolicy.Mean, missing_value_policy=MissingValuePolicy.Interpolate)

Aligns all the univariate time series comprising this multivariate time series so that they all have the same time stamps.

Parameters

reference (Optional[Sequence[Union[int, float]]]) – A specific set of timestamps we want the resampled time series to contain. Required if alignment_policy is AlignPolicy.FixedReference. Overrides other alignment policies if specified.
granularity (Union[str, int, float, None]) – The granularity (in seconds) of the resampled time time series. Defaults to the GCD time difference between adjacent elements of reference (when available) or time_series (otherwise). Ignored if reference is given or alignment_policy is AlignPolicy.FixedReference. Overrides other alignment policies if specified.
origin (Optional[int]) – The first timestamp of the resampled time series. Only used if the alignment policy is AlignPolicy.FixedGranularity.
remove_non_overlapping – If True, we will only keep the portions of the univariates that overlap with each other. For example, if we have 3 univariates which span timestamps [0, 3600], [60, 3660], and [30, 3540], we will only keep timestamps in the range [60, 3540]. If False, we will keep all timestamps produced by the resampling.
alignment_policy (Optional[AlignPolicy]) –
The policy we want to use to align the time time series.
- AlignPolicy.FixedReference aligns each single-variable time series to reference, a user-specified sequence of timestamps.
- AlignPolicy.FixedGranularity resamples each single-variable time series at the same granularity, aggregating windows and imputing missing values as desired.
- AlignPolicy.OuterJoin returns a time series with the union of all timestamps present in any single-variable time series.
- AlignPolicy.InnerJoin returns a time series with the intersection of all timestamps present in all single-variable time series.
aggregation_policy (AggregationPolicy) – The policy used to aggregate windows of adjacent observations when downsampling.
missing_value_policy (MissingValuePolicy) – The policy used to impute missing values created when upsampling.

Return type

TimeSeries

Returns

The resampled multivariate time series.

merlion.utils.time_series.ts_csv_load(file_name, ms=True, n_vars=None)

Parameters

file_name (str) – a csv file starting with the field timestamp followed by all the all variable names.
ms – whether the timestamps are in milliseconds (rather than seconds)

Return type

TimeSeries

Returns

A merlion TimeSeries object.

merlion.utils.time_series.ts_to_csv(time_series, file_name)

Parameters

time_series (TimeSeries) – the TimeSeries object to write to a csv.
file_name (str) – the name to assign the csv file.

merlion.utils.time_series.assert_equal_timedeltas(time_series, timedelta=None): Checks that all time deltas in the time series are equal, either to each other, or a pre-specified timedelta (in seconds).