time-series batch / whole-series feature calculation

[jonasvdd]

Objectives:

Functionality

• convenient way to extract features over the whole, unsegmented data (see also Question: Feature extraction on time series batch #67)
  • Discuss + decide together with @jvdd @mbignotti what option seems best to serve this functionality (regarding end user perspective)

Available options:

1. introduce a new method to the FeatureCollection (as done here):
   • advantages ✅
     • Explicit method definition, less confusion for end-users
   • disadvanges ❌
     • A new method is introduced / less uniform interface to perform computation
2. Perform unsegmented feature computation when all window and/or stride are NOT set.
   • advantages ✅
     • more homogenous interface
   • disadvanges ❌
     • somewhat more implicitness
       code example:

 # NOTE: window and stride parameters are omitted. 
fc = FeatureCollection(
    FeatureDescriptor(
        function = np.mean,
        series_name="Value",
    )
)

# Uses the whole (unsegmented) series of `data` to 
# calculate the features. method remains the same.
fc.calculate(data=df, return_df=True)

3. Perform unsegmented feature computation when all windows are set to -1
4. a combination of (2.) and (3.)

As for now, this is performed by introducing the calculate_unsegmented method to the FeatureCollection:

Bug fixes

• fix the window_idx="end" and (window-size > data-range) bug

[codecov-commenter]

Codecov Report

Attention: Patch coverage is 95.91837% with 2 lines in your changes missing coverage. Please review.

Project coverage is 98.12%. Comparing base (9787fc0) to head (0f71518).
Report is 78 commits behind head on main.

Files	Patch %	Lines
tsflex/features/feature_collection.py	88.88%	2 Missing ⚠️

Additional details and impacted files

@@            Coverage Diff             @@
##             main      #77      +/-   ##
==========================================
- Coverage   98.22%   98.12%   -0.11%     
==========================================
  Files          22       22              
  Lines        1238     1278      +40     
==========================================
+ Hits         1216     1254      +38     
- Misses         22       24       +2     

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