deploy: acc2743

_sources/frontmatter/conda-m1.md

@@ -1,21 +1,23 @@
 # Conda on Apple M1 Chip
 
-If you're using a Mac with the latest M1 chip, it is highly recommended to install the packages in 
+If you're using a Mac with the latest M1 chip, it is highly recommended to install the packages in
 your conda environment specifically tailored for your hardware architecture (i.e. `arm64`).
-To do so, please execute the following command: 
+To do so, please execute the following command:
 
 ```
-$ CONDA_SUBDIR=osx-arm64 conda env create -f environment.yml
+$ CONDA_SUBDIR=osx-arm64 conda env create -f requirements/tutorial.yml
 ```
 
 This will make sure that `conda` will automatically fetch the appropriate packages from channels, if required.
 
-To activate the environment, please run: 
+To activate the environment, please run:
+
 ```
-$ conda activate pydata-global-2022-ml-repro
+$ conda activate ml-recipes
 ```
 
 Once the environment is activated, please set the `subdir` for future package installations:
+
 ```
 $ conda config --env --set subdir osx-arm64
 ```

_sources/frontmatter/conda.md

@@ -1,8 +1,8 @@
 # Using Conda
 
-⚠️ If you're using Apple with M1 Chip, please follow these [instructions](#note-for-conda-on-apple-m1-chip)
+⚠️ If you're using Apple with M1 Chip, please follow these [instructions](/frontmatter/conda-m1.html)
 
-You can create an `pydata-global-2022-ml-repro` conda environment executing:
+You can create an `ml-recipes` conda environment executing:
 
 ```
 $ conda env create -f requirements/tutorial.yml
@@ -11,11 +11,11 @@ $ conda env create -f requirements/tutorial.yml
 and later activate the environment:
 
 ```
-$ conda activate pydata-global-2022-ml-repro
+$ conda activate ml-recipes
 ```
 
 You might also only update your current environment using:
 
 ```
-$ conda env update --prefix ./env --file environment.yml  --prune
+$ conda env update --prefix ./env --file requirements/tutorial.yml  --prune
 ```

_sources/notebooks/3-model-sharing.ipynb

@@ -172,19 +172,7 @@
      "shell.execute_reply": "2022-12-13T01:42:17.575770Z"
     }
    },
-   "outputs": [
-    {
-     "ename": "ModuleNotFoundError",
-     "evalue": "No module named 'sklearn'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
-      "Cell \u001b[1;32mIn [3], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msklearn\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmodel_selection\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m train_test_split\n\u001b[0;32m      2\u001b[0m num_features \u001b[38;5;241m=\u001b[39m [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCulmen Length (mm)\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCulmen Depth (mm)\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFlipper Length (mm)\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[0;32m      3\u001b[0m cat_features \u001b[38;5;241m=\u001b[39m [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSex\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n",
-      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'sklearn'"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from sklearn.model_selection import train_test_split\n",
     "num_features = [\"Culmen Length (mm)\", \"Culmen Depth (mm)\", \"Flipper Length (mm)\"]\n",
@@ -199,7 +187,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "First we'll build a quick model, like in [the Data notebook](/notebooks/0-basic-data-prep-and-model.html)."
+    "First we'll build a quick model, as we did in [the Data notebook](/notebooks/0-basic-data-prep-and-model.html)."
    ]
   },
   {
@@ -215,15 +203,14 @@
    },
    "outputs": [
     {
-     "ename": "ModuleNotFoundError",
-     "evalue": "No module named 'sklearn'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
-      "Cell \u001b[1;32mIn [4], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msklearn\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msvm\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m SVC\n\u001b[0;32m      2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msklearn\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcompose\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m ColumnTransformer\n\u001b[0;32m      3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msklearn\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpipeline\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Pipeline\n",
-      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'sklearn'"
-     ]
+     "data": {
+      "text/plain": [
+       "1.0"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
@@ -242,7 +229,7 @@
     "\n",
     "model = Pipeline(steps=[\n",
     "    ('preprocessor', preprocessor),\n",
-    "    ('classifier', SVC()),\n",
+    "    ('classifier', SVC(random_state=42)),\n",
     "])\n",
     "\n",
     "model.fit(X_train, y_train)\n",
@@ -292,15 +279,14 @@
    },
    "outputs": [
     {
-     "ename": "ModuleNotFoundError",
-     "evalue": "No module named 'joblib'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
-      "Cell \u001b[1;32mIn [6], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mjoblib\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m dump, load\n\u001b[0;32m      3\u001b[0m dump(model, MODEL_EXPORT_FILE)\n\u001b[0;32m      5\u001b[0m clf \u001b[38;5;241m=\u001b[39m load(MODEL_EXPORT_FILE)\n",
-      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'joblib'"
-     ]
+     "data": {
+      "text/plain": [
+       "1.0"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
@@ -506,7 +492,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.8"
+   "version": "3.11.7"
   },
   "vscode": {
    "interpreter": {