convert tsfeatures lib to nbdev framework

.gitattributes

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+*.ipynb merge=nbdev-merge

.gitconfig

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+# Generated by nbdev_install_hooks
+#
+# If you need to disable this instrumentation do:
+#   git config --local --unset include.path
+#
+# To restore:
+#   git config --local include.path ../.gitconfig
+#
+[merge "nbdev-merge"]
+	name = resolve conflicts with nbdev_fix
+	driver = nbdev_merge %O %A %B %P

MANIFEST.in

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+include settings.ini
+include LICENSE
+include CONTRIBUTING.md
+include README.md
+recursive-exclude * __pycache__

README.md

@@ -1,75 +1,85 @@
-[![Build](https://github.com/FedericoGarza/tsfeatures/workflows/Python%20package/badge.svg)](https://github.com/FedericoGarza/tsfeatures/tree/master)
-[![PyPI version fury.io](https://badge.fury.io/py/tsfeatures.svg)](https://pypi.python.org/pypi/tsfeatures/)
-[![Downloads](https://pepy.tech/badge/tsfeatures)](https://pepy.tech/project/tsfeatures)
-[![Python 3.6+](https://img.shields.io/badge/python-3.7+-blue.svg)](https://www.python.org/downloads/release/python-370+/)
-[![License: MIT](https://img.shields.io/badge/License-MIT-green.svg)](https://github.com/FedericoGarza/tsfeatures/blob/master/LICENSE)
+# tsfeatures
+
+<!-- WARNING: THIS FILE WAS AUTOGENERATED! DO NOT EDIT! -->
 
 # tsfeatures
 
-Calculates various features from time series data. Python implementation of the R package _[tsfeatures](https://github.com/robjhyndman/tsfeatures)_.
+Calculates various features from time series data. Python implementation
+of the R package
+*[tsfeatures](https://github.com/robjhyndman/tsfeatures)*.
 
 # Installation
 
-You can install the *released* version of `tsfeatures` from the [Python package index](pypi.org) with:
+You can install the *released* version of `tsfeatures` from the [Python
+package index](pypi.org) with:
 
 ``` python
 pip install tsfeatures
 ```
 
 # Usage
 
-The `tsfeatures` main function calculates by default the features used by Montero-Manso, Talagala, Hyndman and Athanasopoulos in [their implementation of the FFORMA model](https://htmlpreview.github.io/?https://github.com/robjhyndman/M4metalearning/blob/master/docs/M4_methodology.html#features).
+The `tsfeatures` main function calculates by default the features used
+by Montero-Manso, Talagala, Hyndman and Athanasopoulos in [their
+implementation of the FFORMA
+model](https://htmlpreview.github.io/?https://github.com/robjhyndman/M4metalearning/blob/master/docs/M4_methodology.html#features).
 
-```python
+``` python
 from tsfeatures import tsfeatures
 ```
 
-This function receives a panel pandas df with columns `unique_id`, `ds`, `y` and optionally the frequency of the data.
+This function receives a panel pandas df with columns `unique_id`, `ds`,
+`y` and optionally the frequency of the data.
 
 <img src=https://raw.githubusercontent.com/FedericoGarza/tsfeatures/master/.github/images/y_train.png width="152">
 
-```python
+``` python
 tsfeatures(panel, freq=7)
 ```
 
-By default (`freq=None`) the function will try to infer the frequency of each time series (using `infer_freq` from `pandas` on the `ds` column) and assign a seasonal period according to the built-in dictionary `FREQS`:
+By default (`freq=None`) the function will try to infer the frequency of
+each time series (using `infer_freq` from `pandas` on the `ds` column)
+and assign a seasonal period according to the built-in dictionary
+`FREQS`:
 
-```python
+``` python
 FREQS = {'H': 24, 'D': 1,
          'M': 12, 'Q': 4,
          'W':1, 'Y': 1}
 ```
 
 You can use your own dictionary using the `dict_freqs` argument:
 
-```python
+``` python
 tsfeatures(panel, dict_freqs={'D': 7, 'W': 52})
 ```
 
 ## List of available features
 
-| Features |||
-|:--------|:------|:-------------|
-|acf_features|heterogeneity|series_length|
-|arch_stat|holt_parameters|sparsity|
-|count_entropy|hurst|stability|
-|crossing_points|hw_parameters|stl_features|
-|entropy|intervals|unitroot_kpss|
-|flat_spots|lumpiness|unitroot_pp|
-|frequency|nonlinearity||
-|guerrero|pacf_features||
+| Features        |                 |               |
+|:----------------|:----------------|:--------------|
+| acf_features    | heterogeneity   | series_length |
+| arch_stat       | holt_parameters | sparsity      |
+| count_entropy   | hurst           | stability     |
+| crossing_points | hw_parameters   | stl_features  |
+| entropy         | intervals       | unitroot_kpss |
+| flat_spots      | lumpiness       | unitroot_pp   |
+| frequency       | nonlinearity    |               |
+| guerrero        | pacf_features   |               |
 
-See the docs for a description of the features. To use a particular feature included in the package you need to import it:
+See the docs for a description of the features. To use a particular
+feature included in the package you need to import it:
 
-```python
+``` python
 from tsfeatures import acf_features
 
 tsfeatures(panel, freq=7, features=[acf_features])
 ```
 
-You can also define your own function and use it together with the included features:
+You can also define your own function and use it together with the
+included features:
 
-```python
+``` python
 def number_zeros(x, freq):
 
     number = (x == 0).sum()
@@ -78,36 +88,41 @@ def number_zeros(x, freq):
 tsfeatures(panel, freq=7, features=[acf_features, number_zeros])
 ```
 
-`tsfeatures` can handle functions that receives a numpy array `x` and a frequency `freq` (this parameter is needed even if you don't use it) and returns a dictionary with the feature name as a key and its value.
+`tsfeatures` can handle functions that receives a numpy array `x` and a
+frequency `freq` (this parameter is needed even if you don’t use it) and
+returns a dictionary with the feature name as a key and its value.
 
 ## R implementation
 
-You can use this package to call `tsfeatures` from R inside python (you need to have installed R, the packages `forecast` and `tsfeatures`; also the python package `rpy2`):
+You can use this package to call `tsfeatures` from R inside python (you
+need to have installed R, the packages `forecast` and `tsfeatures`; also
+the python package `rpy2`):
 
-```python
+``` python
 from tsfeatures.tsfeatures_r import tsfeatures_r
 
 tsfeatures_r(panel, freq=7, features=["acf_features"])
 ```
 
-Observe that this function receives a list of strings instead of a list of functions.
+Observe that this function receives a list of strings instead of a list
+of functions.
 
 ## Comparison with the R implementation (sum of absolute differences)
 
 ### Non-seasonal data (100 Daily M4 time series)
 
-| feature         |   diff | feature         |   diff | feature         |   diff | feature         |   diff |
-|:----------------|-------:|:----------------|-------:|:----------------|-------:|:----------------|-------:|
-| e_acf10         |   0    | e_acf1         |   0    | diff2_acf1         |   0    | alpha         |   3.2    |
-| seasonal_period |   0    | spike         |   0    | diff1_acf10         |   0    | arch_acf         |   3.3    |
-| nperiods        |   0    | curvature         |   0    | x_acf1         |   0    | beta         |   4.04    |
-| linearity       |   0    | crossing_points         |   0    | nonlinearity         |   0    | garch_r2         |   4.74    |
-| hw_gamma        |   0    | lumpiness         |   0    | diff2x_pacf5         |   0    | hurst         |   5.45    |
-| hw_beta         |   0    | diff1x_pacf5         |   0    | unitroot_kpss         |   0    | garch_acf         |   5.53    |
-| hw_alpha        |   0    | diff1_acf10         |   0    | x_pacf5         |   0    | entropy         |   11.65    |
-| trend           |   0    | arch_lm         |   0    | x_acf10         |   0    |
-| flat_spots      |   0    | diff1_acf1         |   0    | unitroot_pp         |   0    |
-| series_length   |   0    | stability         |   0    | arch_r2         |   1.37    |
+| feature         | diff | feature         | diff | feature       | diff | feature   |  diff |
+|:----------------|-----:|:----------------|-----:|:--------------|-----:|:----------|------:|
+| e_acf10         |    0 | e_acf1          |    0 | diff2_acf1    |    0 | alpha     |   3.2 |
+| seasonal_period |    0 | spike           |    0 | diff1_acf10   |    0 | arch_acf  |   3.3 |
+| nperiods        |    0 | curvature       |    0 | x_acf1        |    0 | beta      |  4.04 |
+| linearity       |    0 | crossing_points |    0 | nonlinearity  |    0 | garch_r2  |  4.74 |
+| hw_gamma        |    0 | lumpiness       |    0 | diff2x_pacf5  |    0 | hurst     |  5.45 |
+| hw_beta         |    0 | diff1x_pacf5    |    0 | unitroot_kpss |    0 | garch_acf |  5.53 |
+| hw_alpha        |    0 | diff1_acf10     |    0 | x_pacf5       |    0 | entropy   | 11.65 |
+| trend           |    0 | arch_lm         |    0 | x_acf10       |    0 |           |       |
+| flat_spots      |    0 | diff1_acf1      |    0 | unitroot_pp   |    0 |           |       |
+| series_length   |    0 | stability       |    0 | arch_r2       | 1.37 |           |       |
 
 To replicate this results use:
 
@@ -118,33 +133,126 @@ python -m tsfeatures.compare_with_r --results_directory /some/path
 
 ### Sesonal data (100 Hourly M4 time series)
 
-| feature           |   diff | feature      | diff | feature   | diff    | feature    | diff    |
-|:------------------|-------:|:-------------|-----:|:----------|--------:|:-----------|--------:|
-| series_length     |   0    |seas_acf1     | 0    | trend | 2.28 | hurst | 26.02 |
-| flat_spots        |   0    |x_acf1|0| arch_r2 | 2.29 | hw_beta | 32.39 |
-| nperiods          |   0    |unitroot_kpss|0| alpha | 2.52 | trough | 35 |
-| crossing_points   |   0    |nonlinearity|0| beta | 3.67 | peak | 69 |
-| seasonal_period   |   0    |diff1_acf10|0| linearity | 3.97 |
-| lumpiness         |   0    |x_acf10|0| curvature | 4.8 |
-| stability         |   0    |seas_pacf|0| e_acf10 | 7.05 |
-| arch_lm           |   0    |unitroot_pp|0| garch_r2 | 7.32 |
-| diff2_acf1        |   0    |spike|0| hw_gamma | 7.32 |
-| diff2_acf10       |   0    |seasonal_strength|0.79| hw_alpha | 7.47 |
-| diff1_acf1        |   0    |e_acf1|1.67| garch_acf | 7.53 |
-| diff2x_pacf5      |   0    |arch_acf|2.18| entropy | 9.45 |
+| feature         | diff | feature           | diff | feature   | diff | feature |  diff |
+|:----------------|-----:|:------------------|-----:|:----------|-----:|:--------|------:|
+| series_length   |    0 | seas_acf1         |    0 | trend     | 2.28 | hurst   | 26.02 |
+| flat_spots      |    0 | x_acf1            |    0 | arch_r2   | 2.29 | hw_beta | 32.39 |
+| nperiods        |    0 | unitroot_kpss     |    0 | alpha     | 2.52 | trough  |    35 |
+| crossing_points |    0 | nonlinearity      |    0 | beta      | 3.67 | peak    |    69 |
+| seasonal_period |    0 | diff1_acf10       |    0 | linearity | 3.97 |         |       |
+| lumpiness       |    0 | x_acf10           |    0 | curvature |  4.8 |         |       |
+| stability       |    0 | seas_pacf         |    0 | e_acf10   | 7.05 |         |       |
+| arch_lm         |    0 | unitroot_pp       |    0 | garch_r2  | 7.32 |         |       |
+| diff2_acf1      |    0 | spike             |    0 | hw_gamma  | 7.32 |         |       |
+| diff2_acf10     |    0 | seasonal_strength | 0.79 | hw_alpha  | 7.47 |         |       |
+| diff1_acf1      |    0 | e_acf1            | 1.67 | garch_acf | 7.53 |         |       |
+| diff2x_pacf5    |    0 | arch_acf          | 2.18 | entropy   | 9.45 |         |       |
 
-To replicate this results use:
+[![Build](https://github.com/FedericoGarza/tsfeatures/workflows/Python%20package/badge.svg)](https://github.com/FedericoGarza/tsfeatures/tree/master)
+[![PyPI version
+fury.io](https://badge.fury.io/py/tsfeatures.svg)](https://pypi.python.org/pypi/tsfeatures/)
+[![Downloads](https://pepy.tech/badge/tsfeatures.png)](https://pepy.tech/project/tsfeatures)
+[![Python
+3.6+](https://img.shields.io/badge/python-3.7+-blue.svg)](https://www.python.org/downloads/release/python-370+/)
+[![License:
+MIT](https://img.shields.io/badge/License-MIT-green.svg)](https://github.com/FedericoGarza/tsfeatures/blob/master/LICENSE)
 
-``` console
-python -m tsfeatures.compare_with_r --results_directory /some/path \
-                                    --dataset_name Hourly --num_obs 100
+# tsfeatures
+
+Calculates various features from time series data. Python implementation
+of the R package
+*[tsfeatures](https://github.com/robjhyndman/tsfeatures)*.
+
+# Installation
+
+You can install the *released* version of `tsfeatures` from the [Python
+package index](pypi.org) with:
+
+``` python
+pip install tsfeatures
+```
+
+# Usage
+
+The `tsfeatures` main function calculates by default the features used
+by Montero-Manso, Talagala, Hyndman and Athanasopoulos in [their
+implementation of the FFORMA
+model](https://htmlpreview.github.io/?https://github.com/robjhyndman/M4metalearning/blob/master/docs/M4_methodology.html#features).
+
+``` python
+from tsfeatures import tsfeatures
+```
+
+This function receives a panel pandas df with columns `unique_id`, `ds`,
+`y` and optionally the frequency of the data.
+
+<img src=https://raw.githubusercontent.com/FedericoGarza/tsfeatures/master/.github/images/y_train.png width="152">
+
+``` python
+tsfeatures(panel, freq=7)
 ```
 
+By default (`freq=None`) the function will try to infer the frequency of
+each time series (using `infer_freq` from `pandas` on the `ds` column)
+and assign a seasonal period according to the built-in dictionary
+`FREQS`:
+
+``` python
+FREQS = {'H': 24, 'D': 1,
+         'M': 12, 'Q': 4,
+         'W':1, 'Y': 1}
+```
+
+You can use your own dictionary using the `dict_freqs` argument:
+
+``` python
+tsfeatures(panel, dict_freqs={'D': 7, 'W': 52})
+```
+
+## List of available features
+
+| Features        |                 |               |
+|:----------------|:----------------|:--------------|
+| acf_features    | heterogeneity   | series_length |
+| arch_stat       | holt_parameters | sparsity      |
+| count_entropy   | hurst           | stability     |
+| crossing_points | hw_parameters   | stl_features  |
+| entropy         | intervals       | unitroot_kpss |
+| flat_spots      | lumpiness       | unitroot_pp   |
+| frequency       | nonlinearity    |               |
+| guerrero        | pacf_features   |               |
+
+See the docs for a description of the features. To use a particular
+feature included in the package you need to import it:
+
+``` python
+from tsfeatures import acf_features
+
+tsfeatures(panel, freq=7, features=[acf_features])
+```
+
+You can also define your own function and use it together with the
+included features:
+
+``` python
+def number_zeros(x, freq):
+
+    number = (x == 0).sum()
+    return {'number_zeros': number}
+
+tsfeatures(panel, freq=7, features=[acf_features, number_zeros])
+```
+
+`tsfeatures` can handle functions that receives a numpy array `x` and a
+frequency `freq` (this parameter is needed even if you don’t use it) and
+returns a dictionary with the feature name as a key and its value.
+
 # Authors
 
-* **Federico Garza** - [FedericoGarza](https://github.com/FedericoGarza)
-* **Kin Gutierrez** - [kdgutier](https://github.com/kdgutier)
-* **Cristian Challu** - [cristianchallu](https://github.com/cristianchallu)
-* **Jose Moralez** - [jose-moralez](https://github.com/jose-moralez)
-* **Ricardo Olivares** - [rolivaresar](https://github.com/rolivaresar)
-* **Max Mergenthaler** - [mergenthaler](https://github.com/mergenthaler)
+- **Federico Garza** - [FedericoGarza](https://github.com/FedericoGarza)
+- **Kin Gutierrez** - [kdgutier](https://github.com/kdgutier)
+- **Cristian Challu** -
+  [cristianchallu](https://github.com/cristianchallu)
+- **Jose Moralez** - [jose-moralez](https://github.com/jose-moralez)
+- **Ricardo Olivares** - [rolivaresar](https://github.com/rolivaresar)
+- **Max Mergenthaler** - [mergenthaler](https://github.com/mergenthaler)

nbs/.gitignore

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+/.quarto/