Fix minor typos in the docs

docs/text/faq.rst

@@ -12,8 +12,8 @@ FAQ
 
     2. **Is it possible to extract features from rolling/shifted time series?**
 
-       Yes, the :func:`tsfresh.dataframe_functions.roll_time_series` function allows to conviniently create a rolled
-       time series datframe from your data. You just have to transform your data into one of the supported tsfresh
+       Yes, the :func:`tsfresh.dataframe_functions.roll_time_series` function allows to conveniently create a rolled
+       time series dataframe from your data. You just have to transform your data into one of the supported tsfresh
        :ref:`data-formats-label`.
        Then, the :func:`tsfresh.dataframe_functions.roll_time_series` give you a DataFrame with the rolled time series,
        that you can pass to tsfresh.

docs/text/feature_extraction_settings.rst

@@ -111,12 +111,12 @@ By using feature selection algorithms you find out that only a subgroup of featu
 Then, we provide the :func:`tsfresh.feature_extraction.settings.from_columns` method that constructs the `kind_to_fc_parameters`
 dictionary from the column names of this filtered feature matrix to make sure that only relevant features are extracted.
 
-This can save a huge amount of time because you prevent the calculation of uncessary features.
+This can save a huge amount of time because you prevent the calculation of unnecessary features.
 Let's illustrate that with an example:
 
 .. code:: python
 
-    # X_tsfresh containes the extracted tsfresh features
+    # X_tsfresh contains the extracted tsfresh features
     X_tsfresh = extract_features(...)
 
     # which are now filtered to only contain relevant features

docs/text/how_to_contribute.rst

@@ -78,7 +78,7 @@ or build the documentation with
 The finished documentation can be found in the docs/_build/html folder.
 
 On Github we use a Travis CI Folder that runs our test suite every time a commit or pull request is sent. The
-configuration of Travi is controlled by the
+configuration of Travis is controlled by the
 `.travis.yml <https://github.com/blue-yonder/tsfresh/blob/main/.travis.yml>`_ file.
 
 

docs/text/large_data.rst

@@ -4,7 +4,8 @@ Large Input Data
 ================
 
 If you are dealing with large time series data, you are facing multiple problems.
-Thw two most important ones are
+The two most important ones are
+
 * long execution times for feature extraction
 * large memory consumptions, even beyond what a single machine can handle
 
@@ -79,6 +80,6 @@ No pivoting will be performed in this case.
 PySpark
 -------
 
-Similar to dask, it is also possible to ass the feature extraction into a Spark
+Similar to dask, it is also possible to pass the feature extraction into a Spark
 computation graph.
 You can find more information in the documentation of :func:`tsfresh.convenience.bindings.spark_feature_extraction_on_chunk`.

docs/text/tsfresh_on_a_cluster.rst

@@ -150,7 +150,7 @@ The only thing that you will need to run *tsfresh* on a Dask cluster is the ip a
 `dask-scheduler <http://distributed.readthedocs.io/en/latest/setup.html>`_.
 
 Lets say that your dask scheduler is running at ``192.168.0.1:8786``, then we can easily construct a
-:class:`~sfresh.utilities.distribution.ClusterDaskDistributor` that connects to the sceduler and distributes the
+:class:`~sfresh.utilities.distribution.ClusterDaskDistributor` that connects to the scheduler and distributes the
 time series data and the calculation to a cluster:
 
 .. code:: python

tsfresh/convenience/relevant_extraction.py

@@ -134,7 +134,7 @@ def extract_relevant_features(timeseries_container, y, X=None,
 
     :param ml_task: The intended machine learning task. Either `'classification'`, `'regression'` or `'auto'`.
                     Defaults to `'auto'`, meaning the intended task is inferred from `y`.
-                    If `y` has a boolean, integer or object dtype, the task is assumend to be classification,
+                    If `y` has a boolean, integer or object dtype, the task is assumed to be classification,
                     else regression.
     :type ml_task: str
 

tsfresh/feature_extraction/feature_calculators.py

@@ -135,9 +135,9 @@ def _estimate_friedrich_coefficients(x, m, r):
 
     :param x: the time series to calculate the feature of
     :type x: numpy.ndarray
-    :param m: order of polynom to fit for estimating fixed points of dynamics
+    :param m: order of polynomial to fit for estimating fixed points of dynamics
     :type m: int
-    :param r: number of quantils to use for averaging
+    :param r: number of quantiles to use for averaging
     :type r: float
 
     :return: coefficients of polynomial of deterministic dynamics
@@ -1283,7 +1283,7 @@ def cwt_coefficients(x, param):
 
     where :math:`a` is the width parameter of the wavelet function.
 
-    This feature calculator takes three different parameter: widths, coeff and w. The feature calculater takes all the
+    This feature calculator takes three different parameter: widths, coeff and w. The feature calculator takes all the
     different widths arrays and then calculates the cwt one time for each different width array. Then the values for the
     different coefficient for coeff and width w are returned. (For each dic in param one feature is returned)
 
@@ -1948,16 +1948,16 @@ def friedrich_coefficients(x, param):
     :param x: the time series to calculate the feature of
     :type x: numpy.ndarray
     :param param: contains dictionaries {"m": x, "r": y, "coeff": z} with x being positive integer,
-                  the order of polynom to fit for estimating fixed points of
-                  dynamics, y positive float, the number of quantils to use for averaging and finally z,
+                  the order of polynomial to fit for estimating fixed points of
+                  dynamics, y positive float, the number of quantiles to use for averaging and finally z,
                   a positive integer corresponding to the returned coefficient
     :type param: list
     :return: the different feature values
     :return type: pandas.Series
     """
     # calculated is dictionary storing the calculated coefficients {m: {r: friedrich_coefficients}}
     calculated = defaultdict(dict)
-    # res is a dictionary containg the results {"m_10__r_2__coeff_3": 15.43}
+    # res is a dictionary containing the results {"m_10__r_2__coeff_3": 15.43}
     res = {}
 
     for parameter_combination in param:
@@ -1996,9 +1996,9 @@ def max_langevin_fixed_point(x, r, m):
 
     :param x: the time series to calculate the feature of
     :type x: numpy.ndarray
-    :param m: order of polynom to fit for estimating fixed points of dynamics
+    :param m: order of polynomial to fit for estimating fixed points of dynamics
     :type m: int
-    :param r: number of quantils to use for averaging
+    :param r: number of quantiles to use for averaging
     :type r: float
 
     :return: Largest fixed point of deterministic dynamics

tsfresh/feature_selection/relevance.py

@@ -93,7 +93,7 @@ def calculate_relevance_table(
 
     :param ml_task: The intended machine learning task. Either `'classification'`, `'regression'` or `'auto'`.
                     Defaults to `'auto'`, meaning the intended task is inferred from `y`.
-                    If `y` has a boolean, integer or object dtype, the task is assumend to be classification,
+                    If `y` has a boolean, integer or object dtype, the task is assumed to be classification,
                     else regression.
     :type ml_task: str
 

tsfresh/feature_selection/selection.py

@@ -125,7 +125,7 @@ def select_features(
 
     :param ml_task: The intended machine learning task. Either `'classification'`, `'regression'` or `'auto'`.
                     Defaults to `'auto'`, meaning the intended task is inferred from `y`.
-                    If `y` has a boolean, integer or object dtype, the task is assumend to be classification,
+                    If `y` has a boolean, integer or object dtype, the task is assumed to be classification,
                     else regression.
     :type ml_task: str
 

tsfresh/transformers/feature_selector.py

@@ -106,7 +106,7 @@ def __init__(
 
         :param ml_task: The intended machine learning task. Either `'classification'`, `'regression'` or `'auto'`.
                     Defaults to `'auto'`, meaning the intended task is inferred from `y`.
-                    If `y` has a boolean, integer or object dtype, the task is assumend to be classification,
+                    If `y` has a boolean, integer or object dtype, the task is assumed to be classification,
                     else regression.
         :type ml_task: str
 
@@ -150,7 +150,7 @@ def __init__(
 
     def fit(self, X, y):
         """
-        Extract the information, which of the features are relevent using the given target.
+        Extract the information, which of the features are relevant using the given target.
 
         For more information, please see the :func:`~tsfresh.festure_selection.festure_selector.check_fs_sig_bh`
         function. All columns in the input data sample are treated as feature. The index of all

tsfresh/transformers/relevant_feature_augmenter.py

@@ -191,7 +191,7 @@ def __init__(
 
         :param ml_task: The intended machine learning task. Either `'classification'`, `'regression'` or `'auto'`.
                     Defaults to `'auto'`, meaning the intended task is inferred from `y`.
-                    If `y` has a boolean, integer or object dtype, the task is assumend to be classification,
+                    If `y` has a boolean, integer or object dtype, the task is assumed to be classification,
                     else regression.
         :type ml_task: str
 

tsfresh/utilities/dataframe_functions.py

@@ -584,7 +584,7 @@ def add_sub_time_series_index(df_or_dict, sub_length, column_id=None, column_sor
     - if column_id is None: for each kind (or if column_kind is None for the full dataframe) a new index built by
       "sub-packaging" the data in packages of length "sub_length". For example if you have data with the
       length of 11 and sub_length is 2, you will get 6 new packages: 0, 0; 1, 1; 2, 2; 3, 3; 4, 4; 5.
-    - if column_id is not None: the same as before, just for each id seperately. The old column_id values are added
+    - if column_id is not None: the same as before, just for each id separately. The old column_id values are added
       to the new "id" column after a comma
 
     You can use this functions to turn a long measurement into sub-packages, where you want to extract features on.

tsfresh/utilities/distribution.py

@@ -346,7 +346,7 @@ class ClusterDaskDistributor(IterableDistributorBaseClass):
 
     def __init__(self, address):
         """
-        Sets up a distributor that connects to a Dask Scheduler to distribute the calculaton of the features
+        Sets up a distributor that connects to a Dask Scheduler to distribute the calculation of the features
 
         :param address: the ip address and port number of the Dask Scheduler
         :type address: str