from sys import stdout
from pandas import Series
from pandas.api.types import is_categorical_dtype
from tqdm import tqdm
from composeml.data_slice import DataSliceGenerator
from composeml.label_search import ExampleSearch, LabelSearch
from composeml.label_times import LabelTimes
[docs]class LabelMaker:
"""Automatically makes labels for prediction problems."""
[docs] def __init__(
self,
target_dataframe_index,
time_index,
labeling_function=None,
window_size=None,
):
"""Creates an instance of label maker.
Args:
target_dataframe_index (str): The index of the target dataframe, from which labels will be created.
time_index (str): Name of time column in the data frame.
labeling_function (function or list(function) or dict(str=function)): Function, list of functions, or dictionary of functions that transform a data slice.
When set as a dictionary, the key is used as the name of the labeling function.
window_size (str or int): Size of the data slices. As a string, the value can be a timedelta or a column in the data frame to group by.
As an integer, the value can be the number of rows. Default value is all future data.
"""
self.labeling_function = labeling_function or {}
self.target_dataframe_index = target_dataframe_index
self.time_index = time_index
self.window_size = window_size
def _name_labeling_function(self, function):
"""Gets the names of the labeling functions."""
has_name = hasattr(function, "__name__")
return function.__name__ if has_name else type(function).__name__
def _check_labeling_function(self, function, name=None):
"""Checks whether the labeling function is callable."""
assert callable(function), "labeling function must be callabe"
return function
@property
def labeling_function(self):
"""Gets the labeling function(s)."""
return self._labeling_function
@labeling_function.setter
def labeling_function(self, value):
"""Sets and formats the intial labeling function(s).
Args:
value (function or list(function) or dict(str=function)): Function that transforms a data slice to a label.
"""
if isinstance(value, dict):
for name, function in value.items():
self._check_labeling_function(function)
assert isinstance(name, str), "labeling function name must be string"
if callable(value):
value = [value]
if isinstance(value, (tuple, list)):
value = {
self._name_labeling_function(function): self._check_labeling_function(
function
)
for function in value
}
assert isinstance(value, dict), "value type for labeling function not supported"
self._labeling_function = value
def _check_cutoff_time(self, value):
if isinstance(value, Series):
if value.index.is_unique:
return value.to_dict()
else:
raise ValueError("more than one cutoff time exists for a target group")
else:
return value
[docs] def slice(
self,
df,
num_examples_per_instance,
minimum_data=None,
maximum_data=None,
gap=None,
drop_empty=True,
):
"""Generates data slices of target dataframe.
Args:
df (DataFrame): Data frame to create slices on.
num_examples_per_instance (int): Number of examples per unique instance of target dataframe.
minimum_data (int or str or Series): The amount of data needed before starting the search. Defaults to the first value in the time index.
The value can be a datetime string to directly set the first cutoff time or a timedelta string to denote the amount of data needed before
the first cutoff time. The value can also be an integer to denote the number of rows needed before the first cutoff time.
If a Series, minimum_data should be datetime string, timedelta string, or integer values with a unique set of target groups as the corresponding index.
maximum_data (str): Maximum data before stopping the search. Default value is last time of index.
gap (str or int): Time between examples. Default value is window size.
If an integer, search will start on the first event after the minimum data.
drop_empty (bool): Whether to drop empty slices. Default value is True.
Returns:
ds (generator): Returns a generator of data slices.
"""
self._check_example_count(num_examples_per_instance, gap)
df = self.set_index(df)
target_groups = df.groupby(self.target_dataframe_index)
num_examples_per_instance = ExampleSearch._check_number(
num_examples_per_instance
)
minimum_data = self._check_cutoff_time(minimum_data)
minimum_data_varies = isinstance(minimum_data, dict)
for group_key, df in target_groups:
if minimum_data_varies:
if group_key not in minimum_data:
continue
min_data_for_group = minimum_data[group_key]
else:
min_data_for_group = minimum_data
generator = DataSliceGenerator(
window_size=self.window_size,
min_data=min_data_for_group,
max_data=maximum_data,
drop_empty=drop_empty,
gap=gap,
)
for ds in generator(df):
setattr(ds.context, self.target_dataframe_index, group_key)
yield ds
if ds.context.slice_number >= num_examples_per_instance:
break
@property
def _bar_format(self):
"""Template to format the progress bar during a label search."""
value = "Elapsed: {elapsed} | "
value += "Remaining: {remaining} | "
value += "Progress: {l_bar}{bar}| "
value += self.target_dataframe_index + ": {n}/{total} "
return value
def _check_example_count(self, num_examples_per_instance, gap):
"""Checks whether example count corresponds to data slices."""
if self.window_size is None and gap is None:
more_than_one = num_examples_per_instance > 1
assert (
not more_than_one
), "must specify gap if num_examples > 1 and window size = none"
[docs] def search(
self,
df,
num_examples_per_instance,
minimum_data=None,
maximum_data=None,
gap=None,
drop_empty=True,
verbose=True,
*args,
**kwargs,
):
"""Searches the data to calculates labels.
Args:
df (DataFrame): Data frame to search and extract labels.
num_examples_per_instance (int or dict): The expected number of examples to return from each dataframe group.
A dictionary can be used to further specify the expected number of examples to return from each label.
minimum_data (int or str or Series): The amount of data needed before starting the search. Defaults to the first value in the time index.
The value can be a datetime string to directly set the first cutoff time or a timedelta string to denote the amount of data needed before
the first cutoff time. The value can also be an integer to denote the number of rows needed before the first cutoff time.
If a Series, minimum_data should be datetime string, timedelta string, or integer values with a unique set of target groups as the corresponding index.
maximum_data (str): Maximum data before stopping the search. Defaults to the last value in the time index.
gap (str or int): Time between examples. Default value is window size.
If an integer, search will start on the first event after the minimum data.
drop_empty (bool): Whether to drop empty slices. Default value is True.
verbose (bool): Whether to render progress bar. Default value is True.
*args: Positional arguments for labeling function.
**kwargs: Keyword arguments for labeling function.
Returns:
lt (LabelTimes): Calculated labels with cutoff times.
"""
assert self.labeling_function, "missing labeling function(s)"
self._check_example_count(num_examples_per_instance, gap)
is_label_search = isinstance(num_examples_per_instance, dict)
search = (LabelSearch if is_label_search else ExampleSearch)(
num_examples_per_instance
)
# check minimum data cutoff time
minimum_data = self._check_cutoff_time(minimum_data)
minimum_data_varies = isinstance(minimum_data, dict)
df = self.set_index(df)
total = search.expected_count if search.is_finite else 1
# If the target is categorical, make sure there are no unused categories
if is_categorical_dtype(df[self.target_dataframe_index]):
df[self.target_dataframe_index] = df[
self.target_dataframe_index
].cat.remove_unused_categories()
target_groups = df.groupby(self.target_dataframe_index)
total *= target_groups.ngroups
progress_bar = tqdm(
total=total,
file=stdout,
disable=not verbose,
bar_format=self._bar_format,
)
records = []
for group_count, (group_key, df) in enumerate(target_groups, start=1):
if minimum_data_varies:
if group_key not in minimum_data:
continue
min_data_for_group = minimum_data[group_key]
else:
min_data_for_group = minimum_data
generator = DataSliceGenerator(
window_size=self.window_size,
min_data=min_data_for_group,
max_data=maximum_data,
drop_empty=drop_empty,
gap=gap,
)
for ds in generator(df):
setattr(ds.context, self.target_dataframe_index, group_key)
items = self.labeling_function.items()
labels = {name: lf(ds, *args, **kwargs) for name, lf in items}
valid_labels = search.is_valid_labels(labels)
if not valid_labels:
continue
records.append(
{
self.target_dataframe_index: group_key,
"time": ds.context.slice_start,
**labels,
}
)
search.update_count(labels)
# if finite search, update progress bar for the example found
if search.is_finite:
progress_bar.update(n=1)
if search.is_complete:
break
# if finite search, update progress bar for missing examples
if search.is_finite:
progress_bar.update(
n=group_count * search.expected_count - progress_bar.n
)
else:
progress_bar.update(
n=1
) # otherwise, update progress bar once for each group
search.reset_count()
total -= progress_bar.n
progress_bar.update(n=total)
progress_bar.close()
lt = LabelTimes(
data=records,
target_columns=list(self.labeling_function),
target_dataframe_index=self.target_dataframe_index,
search_settings={
"num_examples_per_instance": num_examples_per_instance,
"minimum_data": minimum_data,
"maximum_data": str(maximum_data),
"window_size": str(self.window_size),
"gap": str(gap),
},
)
return lt
[docs] def set_index(self, df):
"""Sets the time index in a data frame (if not already set).
Args:
df (DataFrame): Data frame to set time index in.
Returns:
df (DataFrame): Data frame with time index set.
"""
if df.index.name != self.time_index:
df = df.set_index(self.time_index)
if "time" not in str(df.index.dtype):
df.index = df.index.astype("datetime64[ns]")
return df