broken links

aeon-toolkit · Nov 3, 2024 · a88ebc7 · a88ebc7
1 parent ea6b42b
commit a88ebc7
Show file tree

Hide file tree

Showing 10 changed files with 34 additions and 39 deletions.
diff --git a/aeon/regression/deep_learning/_inception_time.py b/aeon/regression/deep_learning/_inception_time.py
@@ -136,8 +136,7 @@ class InceptionTimeRegressor(BaseRegressor):
 
     Notes
     -----
-    Adapted from the implementation from Fawaz et. al
-    https://github.com/hfawaz/InceptionTime/blob/master/regressors/inception.py
+    Adapted from the implementation from Fawaz et. al ..[1]
 
     and Ismail-Fawaz et al.
     https://github.com/MSD-IRIMAS/CF-4-TSC

diff --git a/docs/api_reference/data_format.rst b/docs/api_reference/data_format.rst
@@ -203,7 +203,7 @@ This section provides full set of instructions to create a format specification
 for your dataset that is compatible with ``aeon``.
 
 Remember that this begins with the assumption that you have the dataset readily available in
-expected `format <https://github.com/aeon-toolkit/aeon/blob/main/examples/loading_data.ipynb>`_.
+expected `format <https://github.com/aeon-toolkit/aeon/blob/main/examples/datasets/data_loading.ipynb>`_.
 
 Few points to keep in mind while creating the dataset:
 

diff --git a/docs/examples.md b/docs/examples.md
@@ -186,7 +186,7 @@ Overview of Transformations
 :::{grid-item-card}
 :img-top: examples/transformations/img/tsfresh.png
 :class-img-top: aeon-card-image-m
-:link: /examples/transformations/feature_extraction_with_tsfresh.ipynb
+:link: /examples/transformations/tsfresh.ipynb
 :link-type: ref
 :text-align: center
 

diff --git a/docs/index.md b/docs/index.md
@@ -273,7 +273,6 @@ examples.md
 
 contributing.md
 developer_guide.md
-mentoring.md
 ```
 
 ```{toctree}

diff --git a/examples/datasets/data_loading.ipynb b/examples/datasets/data_loading.ipynb
@@ -8,7 +8,7 @@
     "[Provided datasets](provided_data.ipynb). Downloading data is described in\n",
     "[Downloading and loading benchmarking datasets](load_data_from_web.ipynb). You\n",
     "can of course load and format the data so that it conforms to the input types described\n",
-    "in [Data structures and containers for aeon estimators](data_structures.ipynb). `aeon`\n",
+    "in [Data structures and containers for aeon estimators](datasets.ipynb). `aeon`\n",
     "also provides data formats for time series for both forecasting and machine learning.\n",
     "These are all text files with a particular structure. Both formats store a single time\n",
     "series per row.\n",
@@ -33,7 +33,7 @@
     "   ).\n",
     "\n",
     "The baked in datasets are described [here](provided_data.ipynb). Data\n",
-    "structures to store the data are described [here](data_structures.ipynb)."
+    "structures to store the data are described [here](datasets.ipynb)."
    ],
    "metadata": {
     "collapsed": false
@@ -276,7 +276,7 @@
    "source": [
     "Train and test partitions of the ArrowHead problem have been loaded into 3D numpy\n",
     "arrays with an associated array of class values. Further info on data structures is\n",
-    "given in [this notebook](data_structures.ipynb). Datasets that are shipped with aeon\n",
+    "given in [this notebook](datasets.ipynb). Datasets that are shipped with aeon\n",
     "(like ArrowHead, BasicMotions and PLAID) can be more simply loaded with bespoke\n",
     "functions. More details [here](provided_data.ipynb)"
    ]
@@ -435,8 +435,7 @@
     "\n",
     "A further option is to load data into aeon from tab separated value (`.tsv`) files.\n",
     "Researchers at the University of Riverside, California make a variety of timeseries\n",
-    "data available in this format at [Eamonn Keogh's website](https://www.cs.ucr\n",
-    ".edu/~eamonn/time_series_data_2018). Each row is a time series, and the class value\n",
+    "data available in this format at [Eamonn Keogh's website](https://www.cs.ucr.edu/~eamonn/time_series_data_2018). Each row is a time series, and the class value\n",
     "is the first one.\n",
     "\n",
     "The `load_from_tsv_file` method in `aeon.datasets` supports reading\n",

diff --git a/examples/datasets/load_data_from_web.ipynb b/examples/datasets/load_data_from_web.ipynb
@@ -19,7 +19,7 @@
     "numpy if `n_timepoints` is different for different cases. Forecasting data are loaded\n",
     "into pd.DataFrame. Anomaly detection dataset are loaded into 2D numpy arrays of shape\n",
     "`(n_timepoints, n_channels)`. For more information on aeon data types see the\n",
-    "[data structures notebook](data_structures.ipynb).\n",
+    "[data structures notebook](datsets.ipynb).\n",
     "\n",
     "Note that this notebook is dependent on external websites, so will not function if\n",
     "you are not online or the associated website is down. We use the following four\n",

diff --git a/examples/distances/sklearn_distances.ipynb b/examples/distances/sklearn_distances.ipynb
@@ -63,8 +63,8 @@
     "but it is treating the data as vector rather than as time series.\n",
     "\n",
     "If we try and use with an `aeon` style 3D numpy\n",
-    "`(n_cases, n_channels, n_timepoints)`, they will crash as `scikit-learn` expect a 2D \n",
-    "numpy array. See the [data_formats](../datasets/data_structures.ipynb) for details on \n",
+    "`(n_cases, n_channels, n_timepoints)`, they will crash as `scikit-learn` expect a 2D\n",
+    "numpy array. See the [data_formats](../datasets/datasets.ipynb) for details on\n",
     "data storage."
    ]
   },
@@ -121,8 +121,8 @@
     "collapsed": false
    },
    "source": [
-    "We can use `KNeighborsClassifier` with a callable `aeon` distance function, but the \n",
-    "input must still be 2D numpy array. "
+    "We can use `KNeighborsClassifier` with a callable `aeon` distance function, but the\n",
+    "input must still be 2D numpy array."
    ]
   },
   {
@@ -240,19 +240,19 @@
     "collapsed": false
    },
    "source": [
-    "Also note that using an `aeon` distance function as callable does not will not work with \n",
-    "some kNN options such as [`KDTree`](https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KDTree.html) \n",
+    "Also note that using an `aeon` distance function as callable does not will not work with\n",
+    "some kNN options such as [`KDTree`](https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KDTree.html)\n",
     "class or [`BallTree`](https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.BallTree.html),\n",
     "as stated in the scikit-learn doc of these classes:\n",
     "\n",
     "_Note: Callable functions in the metric parameter are NOT supported for KDTree_\n",
     "_and Ball Tree. Function call overhead will result in very poor performance._\n",
     "\n",
-    "Because of these problems, we have implemented a KNN classifier and regressor to use \n",
+    "Because of these problems, we have implemented a KNN classifier and regressor to use\n",
     "with our distance functions.\n",
     "\n",
-    "The `aeon` kNN classifier using a 3D numpy array achieves the same performance than the \n",
-    "`sklearn` one using the 2D numpy array even using time series specific distance \n",
+    "The `aeon` kNN classifier using a 3D numpy array achieves the same performance than the\n",
+    "`sklearn` one using the 2D numpy array even using time series specific distance\n",
     "functions. The results achieved are the same as time series are univariate and hence,\n",
     "the data can be formatted as a 2D array:"
    ]
@@ -307,7 +307,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "However, if the time series dataset is a multivariate one, data has to be represented \n",
+    "However, if the time series dataset is a multivariate one, data has to be represented\n",
     "using a 3D numpy array. In this case, to use the `sklearn` knn approach, channels have\n",
     "to be concatenated, and therefore, specific edit time series distances may compute the\n",
     "distance between values of different channels, and the results may be biased by these\n",
@@ -398,7 +398,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Similar conclusions can be drawn for the kNN regressor. First of all, we load the \n",
+    "Similar conclusions can be drawn for the kNN regressor. First of all, we load the\n",
     "TSER dataset."
    ]
   },
@@ -426,7 +426,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Now, we compare the prediction of the `aeon` and `scikit-learn` versions. As the \n",
+    "Now, we compare the prediction of the `aeon` and `scikit-learn` versions. As the\n",
     "Covid3Month dataset is univariate, the results of both libraries should be the same."
    ]
   },
@@ -547,7 +547,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Same conclusions can be obtained when dealing with a TSER dataset. "
+    "Same conclusions can be obtained when dealing with a TSER dataset."
    ]
   },
   {
@@ -623,8 +623,8 @@
     "collapsed": false
    },
    "source": [
-    "The SVM estimators in `scikit-learn` can be used with pairwise distance matrices. Please \n",
-    "note that not all elastic distance functions are kernels, and it is desirable that they \n",
+    "The SVM estimators in `scikit-learn` can be used with pairwise distance matrices. Please\n",
+    "note that not all elastic distance functions are kernels, and it is desirable that they\n",
     "are for SVM. DTW is not a metric, but MSM and TWE are."
    ]
   },
@@ -715,7 +715,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "SVR and NuSVR also allow to use the distance function as callable as previously \n",
+    "SVR and NuSVR also allow to use the distance function as callable as previously\n",
     "aforementioned. As can be observed, the results are the same:"
    ]
   },
@@ -860,7 +860,7 @@
     "collapsed": false
    },
    "source": [
-    "You can use pairwise distance functions within the `scikit-learn` FunctionTransformer \n",
+    "You can use pairwise distance functions within the `scikit-learn` FunctionTransformer\n",
     "wrapper"
    ]
   },

diff --git a/...am-2023/Lets do the time warp again.ipynb → ...am-2023/Lets_do_the_time_warp_again.ipynb b/...am-2023/Lets do the time warp again.ipynb → ...am-2023/Lets_do_the_time_warp_again.ipynb
@@ -26,7 +26,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2023-09-24T21:09:32.118338976Z",
@@ -37,11 +37,9 @@
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "108.0"
-      ]
+      "text/plain": "108.0"
      },
-     "execution_count": 2,
+     "execution_count": 1,
      "metadata": {},
      "output_type": "execute_result"
     }

diff --git a/examples/transformations/minirocket.ipynb b/examples/transformations/minirocket.ipynb
@@ -68,7 +68,7 @@
    "source": [
     "### 1.2 Load the Training Data\n",
     "\n",
-    "For more details on the data set, see the [univariate time series classification notebook](https://github.com/aeon-toolkit/aeon/blob/main/examples/02_classification_univariate.ipynb).\n",
+    "For more details on the data set, see the [classification notebook](../classification/classification.ipynb).\n",
     "\n",
     "**Note**: Input time series must be *at least* of length 9.  Pad shorter time series\n",
     "using, e.g., `Padder` (`aeon.transformers.collection`)."

diff --git a/examples/transformations/resizing.ipynb b/examples/transformations/resizing.ipynb
@@ -130,7 +130,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "outputs": [
     {
      "name": "stdout",
@@ -165,7 +165,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2020-12-19T14:32:01.245270Z",
@@ -208,13 +208,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 7,
    "outputs": [
     {
      "data": {
-      "text/plain": "0.8212290502793296"
+      "text/plain": "0.8268156424581006"
      },
-     "execution_count": 12,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }