Update weather TS example classes

tutorials/README.md

@@ -25,7 +25,7 @@ pip install .[notebooks]
 ||[Imagenet](https://image-net.org/download.php) |$1000$ classes natural images *classificaiton* | <img width="94" alt="ImageNet_autocrop" src="https://user-images.githubusercontent.com/3244249/152542090-fd78fde1-6dec-43b6-a7ae-eea964b8ae28.png">|
 |*Text*| [Stanford sentiment treebank](https://nlp.stanford.edu/sentiment/index.html) |Positive or negative movie reviews sentiment *classificaiton* | <img width="25" alt="nlp-logo_half_size" src="https://user-images.githubusercontent.com/3244249/152540890-c8e1e37d-f0cc-4f84-80a4-2c59176cbf4c.png">|
 |*Timeseries* | [Coffee dataset](https://www.timeseriesclassification.com/description.php?Dataset=Coffee)  | Binary *classificaiton* of Robusta and Aribica coffee beans| <img width="25" alt="Coffe Logo" src="https://github.com/dianna-ai/dianna/assets/3244249/9ab50a0f-5da3-41d2-80e9-70d2c8769162">|
-|           | [Weather dataset](https://zenodo.org/record/7525955) |Binary *classification* (summer/winter) of temperature time-series |<img width="25" alt="Weather Logo" src="https://github.com/dianna-ai/dianna/assets/3244249/3ff3d639-ed2f-4a38-b7ac-957c984bce9f">|
+|           | [Weather dataset](https://zenodo.org/record/7525955) |Binary *classification* (warm/cold season) of temperature time-series |<img width="25" alt="Weather Logo" src="https://github.com/dianna-ai/dianna/assets/3244249/3ff3d639-ed2f-4a38-b7ac-957c984bce9f">|
 |*Tabular*| [Penguin dataset](https://www.kaggle.com/code/parulpandey/penguin-dataset-the-new-iris)| $3$ penguin spicies (Adele, Chinstrap, Gentoo) *classificaiton*  | <img width="75" alt="Penguin Logo" src="https://github.com/dianna-ai/dianna/assets/3244249/c7716ad3-f992-4557-80d9-1d8178c7ed57"> | |
 |           | [Weather dataset](https://zenodo.org/record/7525955) | Next day sunshine hours prediction (*regression*) | <img width="25" alt="Weather Logo" src="https://github.com/dianna-ai/dianna/assets/3244249/3ff3d639-ed2f-4a38-b7ac-957c984bce9f">|
 

tutorials/explainers/LIME/lime_timeseries_weather.ipynb

@@ -103,7 +103,7 @@
    "metadata": {},
    "source": [
     "#### 2 - Define an \"expert\" model to verify RISE for timeseries\n",
-    "We can define an 'expert' model to test RISE. This expert model decides it's summer if the mean temp is above the threshold, and winter in other cases."
+    "We can define an 'expert' model to test RISE. This expert model decides it's a warm season (conditionally labeled \"summer\") if the mean temp over several days is above a threshold, or a cold (\"winter\") season in other cases."
    ]
   },
   {
@@ -557,7 +557,7 @@
    "metadata": {},
    "source": [
     "Given how the classification model is trained, we prepare the testing data for prediction. <br>\n",
-    "To make it simpler, we only choose one location and make it a binary classification task, to determine whether it is summer or winter."
+    "To simplify, we only choose one location and make it a binary classification task, to determine whether it is warm (\"summer\") or cold (\"winter\")."
    ]
   },
   {
@@ -687,8 +687,8 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We label the data based on the seasons. <br>\n",
-    "To simplify the problem, we make it a binary classification task and only select summer and winter. <br>"
+    "We label the data based on all the seasons. <br>\n",
+    "To simplify the problem, we make it a binary classification task and only select warm (\"summer\") and cold (\"winter\"). <br>"
    ]
   },
   {
@@ -931,7 +931,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,

tutorials/explainers/RISE/rise_timeseries_weather.ipynb

@@ -138,7 +138,7 @@
    },
    "source": [
     "#### 2 - Define an \"expert\" model to verify RISE for timeseries\n",
-    "We can define an 'expert' model to test RISE. This expert model decides it's summer if the mean temp is above the threshold, and winter in other cases."
+    "We can define an 'expert' model to test RISE. This expert model decides it's a warm season (conditionally labeled \"summer\") if the mean temp over several days is above a threshold, or a cold (\"winter\") season in other cases."
    ]
   },
   {
@@ -668,7 +668,7 @@
    },
    "source": [
     "Given how the classification model is trained, we prepare the testing data for prediction. <br>\n",
-    "To make it simpler, we only choose one location and make it a binary classification task, to determine whether it is summer or winter."
+    "To simplify, we only choose one location and make it a binary classification task, to determine whether the TS data indicate a warm (\"summer\") or a cold (\"winter\") season."
    ]
   },
   {
@@ -818,8 +818,8 @@
     }
    },
    "source": [
-    "We label the data based on the seasons. <br>\n",
-    "To simplify the problem, we make it a binary classification task and only select summer and winter. <br>"
+    "We label the data based on all the seasons. <br>\n",
+    "To simplify the problem, we make it a binary classification task and only select warm (\"summer\") and cold (\"winter\"). <br>"
    ]
   },
   {
@@ -1130,7 +1130,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.10.12"
   },
   "vscode": {
    "interpreter": {