Add rubrics update assignment wording for clarity

02_activities/assignments/assignment_1.ipynb

@@ -40,8 +40,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import dask\n",
-    "dask.config.set({'dataframe.query-planning': True})\n",
     "import dask.dataframe as dd"
    ]
   },
@@ -137,10 +135,7 @@
    "source": [
     "## Criteria\n",
     "\n",
-    "|Criteria|Complete|Incomplete|\n",
-    "|---------------------|----|----|\n",
-    "|Calculations         |Calculations were done correctly.|Calculations were not done correctly.|\n",
-    "|Explanation of answer|Answer was concise and explained the learner's reasoning in depth.|Answer was not concise and did not explained the learner's reasoning in depth.|"
+    "The [rubric](./assignment_1_rubric_clean.xlsx) contains the criteria for grading."
    ]
   },
   {

02_activities/assignments/assignment_2.ipynb

@@ -237,10 +237,7 @@
    "source": [
     "## Criteria\n",
     "\n",
-    "|Criteria|Complete|Incomplete|\n",
-    "|---------------------|----|----|\n",
-    "|Evaluation of model pipeline |Model pipeline was evaluated correctly.|Model pipeline was not evaluated correctly.|\n",
-    "|Explanation of answer|Answer was concise and explained the learner's reasoning in depth.|Answer was not concise and did not explained the learner's reasoning in depth.|"
+    "The [rubric](./assignment_2_rubric_clean.xlsx) contains the criteria for assessment."
    ]
   },
   {

02_activities/assignments/assignment_3.ipynb

@@ -65,8 +65,8 @@
     "    \n",
     "    + Regressor:\n",
     "\n",
-    "        - A baseline regressor, which could be a [K-nearest neighbours model](https://open.spotify.com/track/4R3AU2pjv8ge2siX1fVbZs?si=b2712f32da0e4358) or a simple [linear regression model](https://scikit-learn.org/stable/modules/linear_model.html)\n",
-    "        - An advanced regressor of your choice (e.g., Random Forest, Neural Network, etc.)\n",
+    "        - A baseline regressor, which could be a [K-nearest neighbours model](https://open.spotify.com/track/4R3AU2pjv8ge2siX1fVbZs?si=b2712f32da0e4358) or a linear model like [Lasso](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.Lasso.html) or [Ridge Regressors](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.ridge_regression.html).\n",
+    "        - An advanced regressor of your choice (e.g., Bagging, Boosting, SVR, etc.). TIP: select a tree-based method such that it does not take too long to run SHAP further below. \n",
     "\n",
     "+ Evaluate tune and evaluate each of the four model pipelines. \n",
     "\n",
@@ -92,20 +92,30 @@
    "source": [
     "# Load the data\n",
     "\n",
-    "Assuming that the files `adult.data` and `adult.test` are in `../data/adult/`, then you can use the code below to load them."
+    "Place the files in the ../../05_src/data/fires/ directory and load the appropriate file. "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load the libraries as required."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "import pandas as pd\n",
+    "# Load data\n",
     "columns = [\n",
     "    'coord_x', 'coord_y', 'month', 'day', 'ffmc', 'dmc', 'dc', 'isi', 'temp', 'rh', 'wind', 'rain', 'area' \n",
     "]\n",
-    "fires_dt = (pd.read_csv('../data/fires/forestfires.csv', header = None, names = columns))\n"
+    "fires_dt = (pd.read_csv('../../05_src/data/fires/forestfires.csv', header = 0, names = columns))\n",
+    "fires_dt.info()\n"
    ]
   },
   {
@@ -124,6 +134,13 @@
    "outputs": [],
    "source": []
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -163,12 +180,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": [
-    "# preproc1"
-   ]
+   "source": []
   },
   {
    "cell_type": "markdown",
@@ -184,12 +199,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": [
-    "# preproc2"
-   ]
+   "source": []
   },
   {
    "cell_type": "markdown",
@@ -218,7 +231,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Pipeline A = preproc1 + baseline"
+    "# Pipeline A = preproc1 + baseline\n"
    ]
   },
   {
@@ -227,25 +240,27 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Pipeline B = preproc2 + baseline"
+    "# Pipeline B = preproc2 + baseline\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Pipeline C = preproc1 + advanced model"
+    "# Pipeline C = preproc1 + advanced model\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Pipeline D = preproc2 + advanced model"
+    "# Pipeline D = preproc2 + advanced model\n",
+    "\n",
+    "    "
    ]
   },
   {
@@ -287,6 +302,13 @@
    "outputs": [],
    "source": []
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -305,6 +327,20 @@
     "+ Save the best performing model to a pickle file."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -327,13 +363,29 @@
    "outputs": [],
    "source": []
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "*(Answer here.)*"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Criteria\n",
+    "\n",
+    "The [rubric](./assignment_3_rubric_clean.xlsx) contains the criteria for assessment."
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -385,7 +437,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.0"
+   "version": "3.12.3"
   }
  },
  "nbformat": 4,