Updating to latest version

Author: Alexander Mead

.env.example

@@ -1,3 +1,4 @@
 # User info
 TWINLAB_USER=
 TWINLAB_API_KEY=
+TWINLAB_URL=https://twinlab.digilab.co.uk/v3

notebooks/Classification.ipynb

@@ -28,10 +28,10 @@
      "text": [
       "\n",
       "          ====== TwinLab Client Initialisation ======\n",
-      "          Version     : 2.10.0\n",
-      "          User        : alexander@digilab.co.uk\n",
-      "          Server      : http://localhost:8000\n",
-      "          Environment : /Users/mead/digiLab/twinLab/tutorials/.env\n",
+      "          Version     : 2.12.0\n",
+      "          User        : jamie@digilab.co.uk\n",
+      "          Server      : https://twinlab.digilab.co.uk/v3\n",
+      "          Environment : /Users/jamiedonald-mccann/Desktop/twinLab/tutorials/.env\n",
       "\n"
      ]
     }
@@ -219,7 +219,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Now, you can train a classification `twinLab` model that identifies the patterns underlying the data and approximates the true function that you established in section one (represented by the red dotted line you generated in the plot)."
+    "Now, you can train a classification `twinLab` model that identifies the patterns underlying the data and approximates the true function that you established in section one."
    ]
   },
   {
@@ -284,7 +284,8 @@
      "text": [
       "Emulator 'classification' has begun training.\n",
       "0:00:00: Job status: processing\n",
-      "0:00:01: Job status: success\n",
+      "0:00:01: Job status: processing\n",
+      "0:00:03: Job status: success\n",
       "Training of emulator classification is complete!\n"
      ]
     }
@@ -439,7 +440,7 @@
    "source": [
     "Note some important differences between the values here compared to those from a regression model: \n",
     "\n",
-    "First, the training data contains only binary values for `y` of either 0 or 1, here the value of `y` is also either 0 or 1. This is the prediction for the class that `X` belongs to. The second column is the probability that the emulator has assigned to the class membership. Values close to indicate that the model has a high degree of certainty that the `X` belongs to class 0; values close to 1 indicate a high degree of certainty that it belongs to class 1."
+    "First, the training data contains only binary values for `y` of either 0 or 1, here the value of `y` is also either 0 or 1. This is the prediction for the class that `X` belongs to. The second column is the probability that the emulator has assigned to the class membership. Values close to 0 indicate that the model has a high degree of certainty that the `X` belongs to class 0; values close to 1 indicate a high degree of certainty that it belongs to class 1."
    ]
   },
   {
@@ -505,6 +506,13 @@
     "emulator.delete()\n",
     "dataset.delete()"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -523,7 +531,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.14"
+   "version": "3.12.5"
   }
  },
  "nbformat": 4,

notebooks/Mixture_of_experts.ipynb

@@ -43,10 +43,10 @@
      "text": [
       "\n",
       "          ====== TwinLab Client Initialisation ======\n",
-      "          Version     : 2.10.0\n",
-      "          User        : alexander@digilab.co.uk\n",
+      "          Version     : 2.12.0\n",
+      "          User        : jamie@digilab.co.uk\n",
       "          Server      : https://twinlab.digilab.co.uk/v3\n",
-      "          Environment : /Users/mead/digiLab/twinLab/tutorials/.env\n",
+      "          Environment : /Users/jamiedonald-mccann/Desktop/twinLab/tutorials/.env\n",
       "\n"
      ]
     }
@@ -70,7 +70,12 @@
     "id": "0Xh_ekNBPyod"
    },
    "source": [
-    "First things first, set up your API key. If you don't already have one, vist [https://www.digilab.co.uk/contact](https://www.digilab.co.uk/contact). For security, we normally recommend performing this step with a `.env` file or `secrets`. Uncomment the below code and plug in your twinLab username and API key. Be sure to visit the [Portal](https://portal.twinlab.ai/) to check what your API key is, in case you've forgotten it!\n"
+    "First things first, set up your API key. If you don't already have one, visit [https://www.digilab.co.uk/contact](https://www.digilab.co.uk/contact). If you've forgotten it, visit the [Portal](https://portal.twinlab.ai/), where you can check what your API key is. \n",
+    "\n",
+    "For security and convenience, we normally recommend performing this step with a `.env` file or `secrets`. You will notice that when `twinlab` was imported above the `TwinLab Client` was initialised. If you have setup an `.env` file, you will notice your username appears in the client initialisation report. If you would like to setup an `.env` file, simply copy the `.env.example` file provided in this repository and set the `TWINLAB_USER` and `TWINLAB_API_KEY` equal to your username and API key, respectively. Note that you will need to restart your Jupyter kernel if you have modified your `.env` file after running the first cell of this notebook.\n",
+    "\n",
+    "\n",
+    "Alternatively, these variables can be set within your Python code. Uncomment the below code and plug in your twinLab username and API key. \n"
    ]
   },
   {
@@ -82,7 +87,8 @@
    "outputs": [],
    "source": [
     "# tl.set_user(\"<YOUR_USERNAME>\")\n",
-    "# tl.set_api_key(\"<YOUR_API_KEY>\")"
+    "# tl.set_api_key(\"<YOUR_API_KEY>\")\n",
+    "# tl.set_url(\"https://twinlab.digilab.co.uk/v3\")"
    ]
   },
   {
@@ -100,7 +106,7 @@
     {
      "data": {
       "text/plain": [
-       "{'User': 'alexander@digilab.co.uk'}"
+       "{'User': 'jamie@digilab.co.uk'}"
       ]
      },
      "execution_count": 3,
@@ -127,10 +133,10 @@
     {
      "data": {
       "text/plain": [
-       "{'cloud': '3.0.0',\n",
-       " 'modal': '1.0.0',\n",
-       " 'library': '1.12.0',\n",
-       " 'image': 'twinlab-dev'}"
+       "{'cloud': '3.2.0',\n",
+       " 'modal': '1.2.0',\n",
+       " 'library': '2.1.0',\n",
+       " 'image': 'twinlab-prod'}"
       ]
      },
      "execution_count": 4,
@@ -170,14 +176,14 @@
     {
      "data": {
       "text/plain": [
-       "['gardening',\n",
-       " 'tritium-desorption-temperature-grid',\n",
+       "['tritium-desorption-small',\n",
+       " 'biscuits',\n",
+       " 'tritium-desorption',\n",
        " 'advancedstart',\n",
-       " 'quickstart',\n",
        " 'jet-confinement',\n",
-       " 'biscuits',\n",
-       " 'tritium-desorption-small',\n",
-       " 'tritium-desorption']"
+       " 'gardening',\n",
+       " 'quickstart',\n",
+       " 'tritium-desorption-temperature-grid']"
       ]
      },
      "execution_count": 5,
@@ -328,7 +334,7 @@
    },
    "outputs": [],
    "source": [
-    "# Intialise a Dataset object and give it a name\n",
+    "# Initialise a Dataset object and give it a name\n",
     "dataset = tl.Dataset(\"quickstart\")\n",
     "\n",
     "# Upload the dataset, passing in the dataframe\n",
@@ -352,14 +358,7 @@
     {
      "data": {
       "text/plain": [
-       "['gardening',\n",
-       " 'tritium-desorption-temperature-grid',\n",
-       " 'advancedstart',\n",
-       " 'quickstart',\n",
-       " 'jet-confinement',\n",
-       " 'biscuits',\n",
-       " 'tritium-desorption-small',\n",
-       " 'tritium-desorption']"
+       "['quickstart']"
       ]
      },
      "execution_count": 8,
@@ -368,11 +367,8 @@
     }
    ],
    "source": [
-    "# List all datasets on cloud\n",
-    "tl.list_datasets()\n",
-    "\n",
-    "# List all example datasets on the cloud\n",
-    "tl.list_example_datasets()"
+    "# List all of your datasets on cloud\n",
+    "tl.list_datasets()"
    ]
   },
   {
@@ -636,9 +632,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Emulator quickstart-model has begun training.\n",
+      "Emulator 'quickstart-model' has begun training.\n",
       "0:00:00: Job status: processing\n",
-      "0:00:01: Job status: success\n",
+      "0:00:01: Job status: processing\n",
+      "0:00:03: Job status: processing\n",
+      "0:00:05: Job status: processing\n",
+      "0:00:07: Job status: processing\n",
+      "0:00:10: Job status: processing\n",
+      "0:00:12: Job status: success\n",
       "Training of emulator quickstart-model is complete!\n"
      ]
     }
@@ -648,17 +649,10 @@
     "# For example, here we set the train_test_ratio to 1, meaning that the entire dataset will be used for training.\n",
     "params = tl.TrainParams(train_test_ratio=1.0)\n",
     "\n",
-    "# Train the mulator using the train method\n",
+    "# Train the emulator using the train method\n",
     "emulator.train(dataset=dataset, inputs=[\"x\"], outputs=[\"y\"], params=params)"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "As you can see, there is a three-word string that gets returned with your training job--\"train-word1-word2-word3\". This is an identifier to make it easy to communicate between yourself and the twinLab team--if ever there's been a problem with your training job, you can report the job ID to us and we can track it down for you."
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -681,15 +675,7 @@
     {
      "data": {
       "text/plain": [
-       "['test_model',\n",
-       " 'SimpleGP',\n",
-       " 'TritiumDesorptionGP_new',\n",
-       " 'TritiumDesorptionGP',\n",
-       " 'quickstart',\n",
-       " 'error-mwe',\n",
-       " 'example_emulator',\n",
-       " 'fusion',\n",
-       " 'quickstart-model']"
+       "['quickstart-model']"
       ]
      },
      "execution_count": 13,
@@ -719,16 +705,20 @@
     {
      "data": {
       "text/plain": [
-       "{'meta_data': {'author': 'alexander@digilab.co.uk',\n",
-       "  'version': '3.0.0',\n",
+       "{'meta_data': {'author': 'jamie@digilab.co.uk',\n",
+       "  'version': '3.2.0',\n",
        "  'campaign': 'personal',\n",
        "  'description': 'A twinLab emulator.',\n",
-       "  'organization': 'digiLab'},\n",
+       "  'organization': 'digiLab',\n",
+       "  'timestamp': '2024-08-27 13:49:02'},\n",
        " 'emulator_params': {'inputs': ['x'],\n",
        "  'outputs': ['y'],\n",
-       "  'fidelity': None,\n",
        "  'estimator': 'gaussian_process_regression',\n",
-       "  'estimator_params': {'detrend': False, 'estimator_type': 'single_task_gp'},\n",
+       "  'estimator_params': {'detrend': False,\n",
+       "   'kernel': None,\n",
+       "   'estimator_type': 'single_task_gp'},\n",
+       "  'fidelity': None,\n",
+       "  'class_column': None,\n",
        "  'decompose_inputs': False,\n",
        "  'decompose_outputs': False,\n",
        "  'input_explained_variance': None,\n",
@@ -783,10 +773,10 @@
       "                                    '(raw_lengthscale_constraint): Positive()  '\n",
       "                                    ')  (outputscale_prior): GammaPrior()  '\n",
       "                                    '(raw_outputscale_constraint): Positive())',\n",
-      "            'lengthscale': [[0.42345087635328044]],\n",
-      "            'outputscale': 1.711559921925465},\n",
-      " 'mean': {'mean': 0.21062309969367052, 'mean_function_used': 'ConstantMean()'},\n",
-      " 'properties': {'covariance_noise': [0.03031922037067657]}}\n"
+      "            'lengthscale': [[0.4234508763532827]],\n",
+      "            'outputscale': 1.7115599219254776},\n",
+      " 'mean': {'mean': 0.21062309969363022, 'mean_function_used': 'ConstantMean()'},\n",
+      " 'properties': {'covariance_noise': [0.030319220370676556]}}\n"
      ]
     }
    ],
@@ -845,7 +835,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.0"
+   "version": "3.12.5"
   }
  },
  "nbformat": 4,