From 91e77ad768de5ed04ba70a8f584cd0a6a49bac4f Mon Sep 17 00:00:00 2001
From: Solange Emmenegger <solange.emmenegger@hslu.ch>
Date: Thu, 15 Sep 2022 16:49:46 +0000
Subject: [PATCH] Hid solutions and deleted files 2nd part
---
...ion - Part 1 - Multivariate Gaussian.ipynb | 880 +-------
...ipynb => Anomaly Detection - Part 2.ipynb} | 73 +-
.../Anomaly Detection_Solution - Part 1.ipynb | 1700 --------------
.../Anomaly Detection_Solution - Part 2.ipynb | 536 -----
.../Association Rules Examples.ipynb | 491 +----
.../Association Rules.ipynb | 1302 +----------
.../Association Rules_Solution.ipynb | 1829 ---------------
.../Dimensionality Reduction.ipynb | 671 +-----
.../Dimensionality Reduction_Solution.ipynb | 1315 -----------
.../Recommender Systems.ipynb | 1962 +----------------
10 files changed, 339 insertions(+), 10420 deletions(-)
rename notebooks/11B Anomaly Detection/{Anomaly Detection - Part 2 - Clustering-Based.ipynb => Anomaly Detection - Part 2.ipynb} (98%)
delete mode 100644 notebooks/11B Anomaly Detection/Anomaly Detection_Solution - Part 1.ipynb
delete mode 100644 notebooks/11B Anomaly Detection/Anomaly Detection_Solution - Part 2.ipynb
delete mode 100644 notebooks/12A Association Rules/Association Rules_Solution.ipynb
delete mode 100644 notebooks/12B Principal Component Analysis/Dimensionality Reduction_Solution.ipynb
diff --git a/notebooks/11B Anomaly Detection/Anomaly Detection - Part 1 - Multivariate Gaussian.ipynb b/notebooks/11B Anomaly Detection/Anomaly Detection - Part 1 - Multivariate Gaussian.ipynb
index ce1eacc..34f4b58 100644
--- a/notebooks/11B Anomaly Detection/Anomaly Detection - Part 1 - Multivariate Gaussian.ipynb
+++ b/notebooks/11B Anomaly Detection/Anomaly Detection - Part 1 - Multivariate Gaussian.ipynb
@@ -9,7 +9,7 @@
},
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -36,7 +36,10 @@
"import matplotlib.gridspec as gridspec\n",
"\n",
"from tqdm.notebook import tqdm\n",
- "import ipywidgets as widgets"
+ "import ipywidgets as widgets\n",
+ "\n",
+ "import warnings\n",
+ "warnings.filterwarnings(\"ignore\", category=FutureWarning)"
]
},
{
@@ -49,33 +52,9 @@
},
{
"cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [
- {
- "data": {
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- "\n",
- " <iframe\n",
- " width=\"400\"\n",
- " height=\"300\"\n",
- " src=\"https://www.youtube.com/embed/g2YBWQnqOpw\"\n",
- " frameborder=\"0\"\n",
- " allowfullscreen\n",
- " \n",
- " ></iframe>\n",
- " "
- ],
- "text/plain": [
- "<IPython.lib.display.YouTubeVideo at 0x7f0702316f10>"
- ]
- },
- "execution_count": 2,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
"source": [
"from IPython.display import YouTubeVideo\n",
"YouTubeVideo('g2YBWQnqOpw')"
@@ -90,211 +69,11 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": null,
"metadata": {
"scrolled": true
},
- "outputs": [
- {
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- " <td>-1.175575</td>\n",
- " <td>0.647376</td>\n",
- " <td>-0.221929</td>\n",
- " <td>0.062723</td>\n",
- " <td>0.061458</td>\n",
- " <td>123.50</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>2.0</td>\n",
- " <td>-1.158233</td>\n",
- " <td>0.877737</td>\n",
- " <td>1.548718</td>\n",
- " <td>0.403034</td>\n",
- " <td>-0.407193</td>\n",
- " <td>0.095921</td>\n",
- " <td>0.592941</td>\n",
- " <td>-0.270533</td>\n",
- " <td>0.817739</td>\n",
- " <td>...</td>\n",
- " <td>-0.009431</td>\n",
- " <td>0.798278</td>\n",
- " <td>-0.137458</td>\n",
- " <td>0.141267</td>\n",
- " <td>-0.206010</td>\n",
- " <td>0.502292</td>\n",
- " <td>0.219422</td>\n",
- " <td>0.215153</td>\n",
- " <td>69.99</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "<p>5 rows × 31 columns</p>\n",
- "</div>"
- ],
- "text/plain": [
- " Time V1 V2 V3 V4 V5 V6 V7 \\\n",
- "0 0.0 -1.359807 -0.072781 2.536347 1.378155 -0.338321 0.462388 0.239599 \n",
- "1 0.0 1.191857 0.266151 0.166480 0.448154 0.060018 -0.082361 -0.078803 \n",
- "2 1.0 -1.358354 -1.340163 1.773209 0.379780 -0.503198 1.800499 0.791461 \n",
- "3 1.0 -0.966272 -0.185226 1.792993 -0.863291 -0.010309 1.247203 0.237609 \n",
- "4 2.0 -1.158233 0.877737 1.548718 0.403034 -0.407193 0.095921 0.592941 \n",
- "\n",
- " V8 V9 ... V21 V22 V23 V24 V25 \\\n",
- "0 0.098698 0.363787 ... -0.018307 0.277838 -0.110474 0.066928 0.128539 \n",
- "1 0.085102 -0.255425 ... -0.225775 -0.638672 0.101288 -0.339846 0.167170 \n",
- "2 0.247676 -1.514654 ... 0.247998 0.771679 0.909412 -0.689281 -0.327642 \n",
- "3 0.377436 -1.387024 ... -0.108300 0.005274 -0.190321 -1.175575 0.647376 \n",
- "4 -0.270533 0.817739 ... -0.009431 0.798278 -0.137458 0.141267 -0.206010 \n",
- "\n",
- " V26 V27 V28 Amount Class \n",
- "0 -0.189115 0.133558 -0.021053 149.62 0 \n",
- "1 0.125895 -0.008983 0.014724 2.69 0 \n",
- "2 -0.139097 -0.055353 -0.059752 378.66 0 \n",
- "3 -0.221929 0.062723 0.061458 123.50 0 \n",
- "4 0.502292 0.219422 0.215153 69.99 0 \n",
- "\n",
- "[5 rows x 31 columns]"
- ]
- },
- "execution_count": 3,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"df = pd.read_csv('creditcard.csv')\n",
"df.head()"
@@ -302,20 +81,9 @@
},
{
"cell_type": "code",
- "execution_count": 4,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "(284807, 31)"
- ]
- },
- "execution_count": 4,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
"source": [
"df.shape"
]
@@ -343,25 +111,14 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": null,
"metadata": {
"jupyter": {
"source_hidden": true
},
"tags": []
},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "False"
- ]
- },
- "execution_count": 5,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"df.isnull().any().any()"
]
@@ -376,7 +133,7 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": null,
"metadata": {
"solution2": "hidden",
"solution2_first": true
@@ -398,7 +155,7 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": null,
"metadata": {
"jupyter": {
"source_hidden": true
@@ -406,16 +163,7 @@
"solution2": "hidden",
"tags": []
},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Genuine Transactions: 284315\n",
- "Anomalous Transaction: 492\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"genuine = df[df.Class == 0]\n",
"anomalies = df[df.Class == 1]\n",
@@ -435,30 +183,9 @@
},
{
"cell_type": "code",
- "execution_count": 8,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Mean 88.29102242231328\n",
- "Median 22.0\n"
- ]
- },
- {
- "data": {
- "image/png": "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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
"source": [
"genuine.Amount.loc[genuine.Amount < 500].hist(bins=100)\n",
"print(\"Mean\", genuine.Amount.mean())\n",
@@ -467,30 +194,9 @@
},
{
"cell_type": "code",
- "execution_count": 9,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Mean 122.21132113821139\n",
- "Median 9.25\n"
- ]
- },
- {
- "data": {
- "image/png": "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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
"source": [
"anomalies.Amount.loc[anomalies.Amount < 500].hist(bins=100)\n",
"print(\"Mean\", anomalies.Amount.mean())\n",
@@ -521,181 +227,11 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": null,
"metadata": {
"tags": []
},
- "outputs": [
- {
- "data": {
- "application/json": {
- "ascii": false,
- "bar_format": null,
- "colour": null,
- "elapsed": 0.01927042007446289,
- "initial": 0,
- "n": 0,
- "ncols": null,
- "nrows": null,
- "postfix": null,
- "prefix": "",
- "rate": null,
- "total": 30,
- "unit": "it",
- "unit_divisor": 1000,
- "unit_scale": false
- },
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "f8f56dc91ed24171bba119b527f7dea7",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- " 0%| | 0/30 [00:00<?, ?it/s]"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/opt/conda/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- " warnings.warn(msg, FutureWarning)\n",
- "/opt/conda/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- " warnings.warn(msg, FutureWarning)\n",
- "/opt/conda/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- " warnings.warn(msg, FutureWarning)\n",
- "/opt/conda/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- " warnings.warn(msg, FutureWarning)\n",
- "/opt/conda/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- " warnings.warn(msg, FutureWarning)\n",
- "/opt/conda/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- " warnings.warn(msg, FutureWarning)\n",
- "/opt/conda/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- " warnings.warn(msg, FutureWarning)\n",
- "/opt/conda/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- " warnings.warn(msg, FutureWarning)\n",
- "/opt/conda/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- " warnings.warn(msg, FutureWarning)\n",
- "/opt/conda/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- " warnings.warn(msg, FutureWarning)\n",
- "/opt/conda/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- " warnings.warn(msg, FutureWarning)\n",
- "/opt/conda/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- " warnings.warn(msg, FutureWarning)\n",
- "/opt/conda/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- " warnings.warn(msg, FutureWarning)\n",
- "/opt/conda/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- " warnings.warn(msg, FutureWarning)\n",
- "/opt/conda/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- " warnings.warn(msg, FutureWarning)\n",
- "/opt/conda/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- " warnings.warn(msg, FutureWarning)\n",
- "/opt/conda/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- " warnings.warn(msg, FutureWarning)\n",
- "/opt/conda/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- " warnings.warn(msg, FutureWarning)\n",
- "/opt/conda/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- " warnings.warn(msg, FutureWarning)\n",
- "/opt/conda/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- " warnings.warn(msg, FutureWarning)\n",
- "/opt/conda/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- " warnings.warn(msg, FutureWarning)\n",
- "/opt/conda/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- " warnings.warn(msg, FutureWarning)\n",
- "/opt/conda/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- " warnings.warn(msg, FutureWarning)\n",
- "/opt/conda/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- " warnings.warn(msg, FutureWarning)\n",
- "/opt/conda/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- " warnings.warn(msg, FutureWarning)\n",
- "/opt/conda/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- " warnings.warn(msg, FutureWarning)\n",
- "/opt/conda/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- " warnings.warn(msg, FutureWarning)\n",
- "/opt/conda/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- " warnings.warn(msg, FutureWarning)\n",
- "/opt/conda/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- " warnings.warn(msg, FutureWarning)\n",
- "/opt/conda/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- " warnings.warn(msg, FutureWarning)\n",
- "/opt/conda/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- " warnings.warn(msg, FutureWarning)\n",
- "/opt/conda/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- " warnings.warn(msg, FutureWarning)\n",
- "/opt/conda/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- " warnings.warn(msg, FutureWarning)\n",
- "/opt/conda/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- " warnings.warn(msg, FutureWarning)\n",
- "/opt/conda/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- " warnings.warn(msg, FutureWarning)\n",
- "/opt/conda/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- " warnings.warn(msg, FutureWarning)\n",
- "/opt/conda/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- " warnings.warn(msg, FutureWarning)\n",
- "/opt/conda/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- " warnings.warn(msg, FutureWarning)\n",
- "/opt/conda/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- " warnings.warn(msg, FutureWarning)\n",
- "/opt/conda/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- " warnings.warn(msg, FutureWarning)\n",
- "/opt/conda/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- " warnings.warn(msg, FutureWarning)\n",
- "/opt/conda/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- " warnings.warn(msg, FutureWarning)\n",
- "/opt/conda/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- " warnings.warn(msg, FutureWarning)\n",
- "/opt/conda/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- " warnings.warn(msg, FutureWarning)\n",
- "/opt/conda/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- " warnings.warn(msg, FutureWarning)\n",
- "/opt/conda/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- " warnings.warn(msg, FutureWarning)\n",
- "/opt/conda/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- " warnings.warn(msg, FutureWarning)\n",
- "/opt/conda/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- " warnings.warn(msg, FutureWarning)\n",
- "/opt/conda/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- " warnings.warn(msg, FutureWarning)\n",
- "/opt/conda/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- " warnings.warn(msg, FutureWarning)\n",
- "/opt/conda/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- " warnings.warn(msg, FutureWarning)\n",
- "/opt/conda/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- " warnings.warn(msg, FutureWarning)\n",
- "/opt/conda/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- " warnings.warn(msg, FutureWarning)\n",
- "/opt/conda/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- " warnings.warn(msg, FutureWarning)\n",
- "/opt/conda/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- " warnings.warn(msg, FutureWarning)\n",
- "/opt/conda/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- " warnings.warn(msg, FutureWarning)\n",
- "/opt/conda/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- " warnings.warn(msg, FutureWarning)\n",
- "/opt/conda/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- " warnings.warn(msg, FutureWarning)\n",
- "/opt/conda/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- " warnings.warn(msg, FutureWarning)\n",
- "/opt/conda/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- " warnings.warn(msg, FutureWarning)\n"
- ]
- },
- {
- "data": {
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\n",
- "text/plain": [
- "<Figure size 864x8640 with 30 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
"feature_cols = genuine.drop(columns=[\"Class\"]).columns\n",
"plt.figure(figsize=(12, len(feature_cols)*4))\n",
@@ -718,7 +254,7 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": null,
"metadata": {
"solution2": "hidden",
"solution2_first": true,
@@ -738,7 +274,7 @@
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": null,
"metadata": {
"jupyter": {
"source_hidden": true
@@ -770,7 +306,7 @@
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -788,7 +324,7 @@
},
{
"cell_type": "code",
- "execution_count": 14,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -808,7 +344,7 @@
},
{
"cell_type": "code",
- "execution_count": 15,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -824,19 +360,9 @@
},
{
"cell_type": "code",
- "execution_count": 16,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "(114218, 18)\n",
- "(114218,)\n",
- "Number of anomalies: 492\n"
- ]
- }
- ],
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
"source": [
"# Add anomalies to the test set\n",
"X_test = np.concatenate([X_test, anomalies_X])\n",
@@ -863,22 +389,9 @@
},
{
"cell_type": "code",
- "execution_count": 17,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "(57109, 18)\n",
- "(57109,)\n",
- "Number of anomalies y_test: 231\n",
- "(57109, 18)\n",
- "(57109,)\n",
- "Number of anomalies y_val: 261\n"
- ]
- }
- ],
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
"source": [
"X_test, X_val, y_test, y_val = train_test_split(X_test, y_test, test_size=0.5, random_state=42)\n",
"print(X_test.shape)\n",
@@ -901,7 +414,7 @@
},
{
"cell_type": "code",
- "execution_count": 18,
+ "execution_count": null,
"metadata": {
"solution2": "hidden",
"solution2_first": true
@@ -923,7 +436,7 @@
},
{
"cell_type": "code",
- "execution_count": 19,
+ "execution_count": null,
"metadata": {
"jupyter": {
"source_hidden": true
@@ -951,7 +464,7 @@
},
{
"cell_type": "code",
- "execution_count": 20,
+ "execution_count": null,
"metadata": {
"lines_to_next_cell": 2,
"solution2": "hidden",
@@ -974,7 +487,7 @@
},
{
"cell_type": "code",
- "execution_count": 21,
+ "execution_count": null,
"metadata": {
"jupyter": {
"source_hidden": true
@@ -997,7 +510,7 @@
},
{
"cell_type": "code",
- "execution_count": 22,
+ "execution_count": null,
"metadata": {
"solution2": "hidden",
"solution2_first": true
@@ -1016,7 +529,7 @@
},
{
"cell_type": "code",
- "execution_count": 23,
+ "execution_count": null,
"metadata": {
"jupyter": {
"source_hidden": true
@@ -1024,18 +537,7 @@
"solution2": "hidden",
"tags": []
},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "0.9959551033987638"
- ]
- },
- "execution_count": 23,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"y_pred = dummy_predict(X_test)\n",
"accuracy_score(y_pred, y_test)"
@@ -1059,7 +561,7 @@
},
{
"cell_type": "code",
- "execution_count": 24,
+ "execution_count": null,
"metadata": {
"solution2": "hidden",
"solution2_first": true
@@ -1078,7 +580,7 @@
},
{
"cell_type": "code",
- "execution_count": 25,
+ "execution_count": null,
"metadata": {
"jupyter": {
"source_hidden": true
@@ -1086,18 +588,7 @@
"solution2": "hidden",
"tags": []
},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "0 284315\n",
- "1 492\n",
- "Name: Class, dtype: int64\n",
- "Percentage of fraudulent data: 0.1727485630620034\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"print(df.Class.value_counts())\n",
"print(\"Percentage of fraudulent data:\", 100*df.Class.value_counts()[1] /len(df))"
@@ -1112,7 +603,7 @@
},
{
"cell_type": "code",
- "execution_count": 26,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -1129,22 +620,9 @@
},
{
"cell_type": "code",
- "execution_count": 27,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 360x360 with 2 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
"source": [
"cm = confusion_matrix(y_test, y_pred)\n",
"plot_confusion_matrix(cm)"
@@ -1167,27 +645,9 @@
},
{
"cell_type": "code",
- "execution_count": 28,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Precision: 0.0\n",
- "Recall 0.0\n",
- "F1 0.0\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/opt/conda/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1334: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
- " _warn_prf(average, modifier, msg_start, len(result))\n"
- ]
- }
- ],
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
"source": [
"precision, recall, f1, _ = precision_recall_fscore_support(y_test, y_pred, average=\"binary\")\n",
"print(\"Precision:\", precision)\n",
@@ -1219,7 +679,7 @@
},
{
"cell_type": "code",
- "execution_count": 29,
+ "execution_count": null,
"metadata": {
"lines_to_next_cell": 2,
"solution2": "shown",
@@ -1243,7 +703,7 @@
},
{
"cell_type": "code",
- "execution_count": 30,
+ "execution_count": null,
"metadata": {
"jupyter": {
"source_hidden": true
@@ -1262,7 +722,7 @@
},
{
"cell_type": "code",
- "execution_count": 31,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -1281,7 +741,7 @@
},
{
"cell_type": "code",
- "execution_count": 32,
+ "execution_count": null,
"metadata": {
"solution2": "hidden",
"solution2_first": true
@@ -1303,7 +763,7 @@
},
{
"cell_type": "code",
- "execution_count": 33,
+ "execution_count": null,
"metadata": {
"jupyter": {
"source_hidden": true
@@ -1321,19 +781,9 @@
},
{
"cell_type": "code",
- "execution_count": 34,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Precision: 0.12998040496407576\n",
- "Recall 0.8614718614718615\n",
- "F1 0.22587968217934162\n"
- ]
- }
- ],
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
"source": [
"eps = 1e-20\n",
"y_pred = predict(X_test, gauss, eps)\n",
@@ -1345,22 +795,9 @@
},
{
"cell_type": "code",
- "execution_count": 35,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 360x360 with 2 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
"source": [
"cm = confusion_matrix(y_test, y_pred)\n",
"plot_confusion_matrix(cm)"
@@ -1392,20 +829,11 @@
},
{
"cell_type": "code",
- "execution_count": 36,
+ "execution_count": null,
"metadata": {
"scrolled": true
},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Min density: 0.0\n",
- "Max density: 8.41619337890139e-08\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"densities = gauss.pdf(X_val)\n",
"print(\"Min density:\", densities.min())\n",
@@ -1422,18 +850,9 @@
},
{
"cell_type": "code",
- "execution_count": 37,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Min density: -12344.176421882974\n",
- "Max density: -16.290523110689932\n"
- ]
- }
- ],
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
"source": [
"densities = gauss.logpdf(X_val)\n",
"print(\"Min density:\", densities.min())\n",
@@ -1451,7 +870,7 @@
},
{
"cell_type": "code",
- "execution_count": 38,
+ "execution_count": null,
"metadata": {
"solution2": "hidden",
"solution2_first": true
@@ -1472,7 +891,7 @@
},
{
"cell_type": "code",
- "execution_count": 39,
+ "execution_count": null,
"metadata": {
"jupyter": {
"source_hidden": true
@@ -1498,17 +917,9 @@
},
{
"cell_type": "code",
- "execution_count": 40,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Best epsilon -281.55123090658685 with f1 score 0.7227533460803058\n"
- ]
- }
- ],
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
"source": [
"expected_anomalies = y_val.sum()\n",
"sorted_densities = np.sort(gauss.logpdf(X_val))\n",
@@ -1545,38 +956,18 @@
},
{
"cell_type": "code",
- "execution_count": 41,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "-12344.176421882974"
- ]
- },
- "execution_count": 41,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
"source": [
"densities.min()"
]
},
{
"cell_type": "code",
- "execution_count": 42,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Maximum epsilon: -16.290523110689932\n",
- "Minimum epsilon: -500\n"
- ]
- }
- ],
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
"source": [
"X_anomalies = anomalies.drop(columns=[\"Class\"]).values\n",
"densities = gauss.logpdf(X_val)\n",
@@ -1598,7 +989,7 @@
},
{
"cell_type": "code",
- "execution_count": 43,
+ "execution_count": null,
"metadata": {
"solution2": "hidden",
"solution2_first": true
@@ -1630,7 +1021,7 @@
},
{
"cell_type": "code",
- "execution_count": 44,
+ "execution_count": null,
"metadata": {
"jupyter": {
"source_hidden": true
@@ -1638,46 +1029,7 @@
"solution2": "hidden",
"tags": []
},
- "outputs": [
- {
- "data": {
- "application/json": {
- "ascii": false,
- "bar_format": null,
- "colour": null,
- "elapsed": 0.11262321472167969,
- "initial": 0,
- "n": 0,
- "ncols": null,
- "nrows": null,
- "postfix": null,
- "prefix": "",
- "rate": null,
- "total": 1000,
- "unit": "it",
- "unit_divisor": 1000,
- "unit_scale": false
- },
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "4e09119cd1664248a5b0b33a20b7fcfc",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- " 0%| | 0/1000 [00:00<?, ?it/s]"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Best epsilon -303.9015634232527 with f1 score 0.7325581395348838\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"f1_scores = []\n",
"recall_scores = []\n",
@@ -1708,32 +1060,9 @@
},
{
"cell_type": "code",
- "execution_count": 45,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "(-16.290523110689932, -500.0)"
- ]
- },
- "execution_count": 45,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 720x576 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
"source": [
"fig, ax = plt.subplots(figsize=(10,8))\n",
"ax.plot(epsilons, f1_scores, label=\"f1\")\n",
@@ -1762,19 +1091,9 @@
},
{
"cell_type": "code",
- "execution_count": 46,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Precision: 0.7086956521739131\n",
- "Recall 0.7056277056277056\n",
- "F1 0.7071583514099784\n"
- ]
- }
- ],
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
"source": [
"y_pred = predict(X_test, gauss, best_eps)\n",
"precision, recall, f1, _ = precision_recall_fscore_support(y_test, y_pred, average=\"binary\")\n",
@@ -1792,22 +1111,9 @@
},
{
"cell_type": "code",
- "execution_count": 47,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 360x360 with 2 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
"source": [
"cm = confusion_matrix(y_test, y_pred)\n",
"plot_confusion_matrix(cm)"
diff --git a/notebooks/11B Anomaly Detection/Anomaly Detection - Part 2 - Clustering-Based.ipynb b/notebooks/11B Anomaly Detection/Anomaly Detection - Part 2.ipynb
similarity index 98%
rename from notebooks/11B Anomaly Detection/Anomaly Detection - Part 2 - Clustering-Based.ipynb
rename to notebooks/11B Anomaly Detection/Anomaly Detection - Part 2.ipynb
index 797028c..c114169 100644
--- a/notebooks/11B Anomaly Detection/Anomaly Detection - Part 2 - Clustering-Based.ipynb
+++ b/notebooks/11B Anomaly Detection/Anomaly Detection - Part 2.ipynb
@@ -9,7 +9,7 @@
},
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -43,7 +43,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "## Exercise 2 - Clustering-Based Anomaly Detection\n",
+ "## Exercise 2 - Clustering-Based Anomaly Detection\n",
"\n",
"Clustering-Based approaches detect outliers by examining the relationship between records and clusters\n",
"\n",
@@ -77,21 +77,9 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "X_train: (170589, 18)\n",
- "X_test: (57109, 18)\n",
- "X_val: (57109, 18)\n",
- "Number of anomalies y_test: 231\n",
- "Number of anomalies y_val: 261\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"df = pd.read_csv('creditcard.csv')\n",
"features_to_drop = ['Time', 'V8', 'V13', 'V15', 'V20', 'V22', 'V23', 'V24', 'V25', 'V26', 'V27', 'V28']\n",
@@ -129,7 +117,7 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -151,7 +139,7 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -159,6 +147,31 @@
"# cluster_centers = ..."
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "*Click on the dots to display the solution*"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "jupyter": {
+ "source_hidden": true
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "\n",
+ "kmeans = KMeans(n_clusters=50, random_state=42)\n",
+ "kmeans.fit(X_train)\n",
+ "\n",
+ "cluster_centers = kmeans.cluster_centers_"
+ ]
+ },
{
"cell_type": "markdown",
"metadata": {},
@@ -181,6 +194,30 @@
" pass"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "*Click on the dots to display the solution*"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "jupyter": {
+ "source_hidden": true
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "def predict(X: np.ndarray, cluster_centers: np.ndarray, threshold: float) -> np.ndarray:\n",
+ " distances = euclidean_distances(X, cluster_centers).min(axis=1)\n",
+ " y_pred = distances > threshold\n",
+ " return np.array(y_pred, dtype=int)"
+ ]
+ },
{
"cell_type": "markdown",
"metadata": {},
diff --git a/notebooks/11B Anomaly Detection/Anomaly Detection_Solution - Part 1.ipynb b/notebooks/11B Anomaly Detection/Anomaly Detection_Solution - Part 1.ipynb
deleted file mode 100644
index ad82d85..0000000
--- a/notebooks/11B Anomaly Detection/Anomaly Detection_Solution - Part 1.ipynb
+++ /dev/null
@@ -1,1700 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Anomaly Detection"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [],
- "source": [
- "%matplotlib inline\n",
- "\n",
- "import numpy as np\n",
- "import pandas as pd\n",
- "import seaborn as sns\n",
- "import matplotlib.pyplot as plt\n",
- "import matplotlib as mpl\n",
- "\n",
- "from scipy.stats import multivariate_normal\n",
- "from sklearn.preprocessing import StandardScaler\n",
- "from sklearn.model_selection import train_test_split\n",
- "\n",
- "from sklearn.metrics import precision_recall_fscore_support\n",
- "from sklearn.metrics import accuracy_score\n",
- "from sklearn.metrics import confusion_matrix\n",
- "from sklearn.metrics import f1_score\n",
- "from sklearn.metrics.pairwise import euclidean_distances\n",
- "\n",
- "from sklearn.cluster import KMeans\n",
- "\n",
- "import matplotlib.gridspec as gridspec\n",
- "\n",
- "from tqdm.notebook import tqdm\n",
- "import ipywidgets as widgets"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Exercise 1 - Multivariate Gaussian distribution\n",
- "You watched the video tutorial by Andrew Ng on anomaly detection using the multivariate Gaussian distribution. There is a data set on ILIAS containing credit card transactions made available by a European bank in September 2013 on [Kaggle](https://www.kaggle.com/mlg-ulb/creditcardfraud). Not surprisingly, the data set is anonymized, i.e. a PCA transformation was executed on all features (each feature therefore is a linear combination of the original but unknown features). The only exceptions are amount and time (milliseconds since first transaction). Finally, there is a class feature to indicate whether the transaction is fraudulent (value: 1) or genuine (value: 0). Implement fraud detection using the statistical approach introduced by Andrew Ng."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/jpeg": 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- "\n",
- " <iframe\n",
- " width=\"400\"\n",
- " height=\"300\"\n",
- " src=\"https://www.youtube.com/embed/g2YBWQnqOpw\"\n",
- " frameborder=\"0\"\n",
- " allowfullscreen\n",
- " ></iframe>\n",
- " "
- ],
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- "<IPython.lib.display.YouTubeVideo at 0x7fd51c3cdeb0>"
- ]
- },
- "execution_count": 2,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "from IPython.display import YouTubeVideo\n",
- "YouTubeVideo('g2YBWQnqOpw')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Data Quality Assessment"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {
- "scrolled": true
- },
- "outputs": [
- {
- "data": {
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- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>Time</th>\n",
- " <th>V1</th>\n",
- " <th>V2</th>\n",
- " <th>V3</th>\n",
- " <th>V4</th>\n",
- " <th>V5</th>\n",
- " <th>V6</th>\n",
- " <th>V7</th>\n",
- " <th>V8</th>\n",
- " <th>V9</th>\n",
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- " <th>V21</th>\n",
- " <th>V22</th>\n",
- " <th>V23</th>\n",
- " <th>V24</th>\n",
- " <th>V25</th>\n",
- " <th>V26</th>\n",
- " <th>V27</th>\n",
- " <th>V28</th>\n",
- " <th>Amount</th>\n",
- " <th>Class</th>\n",
- " </tr>\n",
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- " <td>-0.078803</td>\n",
- " <td>0.085102</td>\n",
- " <td>-0.255425</td>\n",
- " <td>...</td>\n",
- " <td>-0.225775</td>\n",
- " <td>-0.638672</td>\n",
- " <td>0.101288</td>\n",
- " <td>-0.339846</td>\n",
- " <td>0.167170</td>\n",
- " <td>0.125895</td>\n",
- " <td>-0.008983</td>\n",
- " <td>0.014724</td>\n",
- " <td>2.69</td>\n",
- " <td>0</td>\n",
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- " <tr>\n",
- " <th>2</th>\n",
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- " <td>1.773209</td>\n",
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- " <td>-0.503198</td>\n",
- " <td>1.800499</td>\n",
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- " <td>...</td>\n",
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- " <td>-0.327642</td>\n",
- " <td>-0.139097</td>\n",
- " <td>-0.055353</td>\n",
- " <td>-0.059752</td>\n",
- " <td>378.66</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
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- " <td>-0.185226</td>\n",
- " <td>1.792993</td>\n",
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- " <td>0.377436</td>\n",
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- " <td>...</td>\n",
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- " <td>0.005274</td>\n",
- " <td>-0.190321</td>\n",
- " <td>-1.175575</td>\n",
- " <td>0.647376</td>\n",
- " <td>-0.221929</td>\n",
- " <td>0.062723</td>\n",
- " <td>0.061458</td>\n",
- " <td>123.50</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>2.0</td>\n",
- " <td>-1.158233</td>\n",
- " <td>0.877737</td>\n",
- " <td>1.548718</td>\n",
- " <td>0.403034</td>\n",
- " <td>-0.407193</td>\n",
- " <td>0.095921</td>\n",
- " <td>0.592941</td>\n",
- " <td>-0.270533</td>\n",
- " <td>0.817739</td>\n",
- " <td>...</td>\n",
- " <td>-0.009431</td>\n",
- " <td>0.798278</td>\n",
- " <td>-0.137458</td>\n",
- " <td>0.141267</td>\n",
- " <td>-0.206010</td>\n",
- " <td>0.502292</td>\n",
- " <td>0.219422</td>\n",
- " <td>0.215153</td>\n",
- " <td>69.99</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "<p>5 rows × 31 columns</p>\n",
- "</div>"
- ],
- "text/plain": [
- " Time V1 V2 V3 V4 V5 V6 V7 \\\n",
- "0 0.0 -1.359807 -0.072781 2.536347 1.378155 -0.338321 0.462388 0.239599 \n",
- "1 0.0 1.191857 0.266151 0.166480 0.448154 0.060018 -0.082361 -0.078803 \n",
- "2 1.0 -1.358354 -1.340163 1.773209 0.379780 -0.503198 1.800499 0.791461 \n",
- "3 1.0 -0.966272 -0.185226 1.792993 -0.863291 -0.010309 1.247203 0.237609 \n",
- "4 2.0 -1.158233 0.877737 1.548718 0.403034 -0.407193 0.095921 0.592941 \n",
- "\n",
- " V8 V9 ... V21 V22 V23 V24 V25 \\\n",
- "0 0.098698 0.363787 ... -0.018307 0.277838 -0.110474 0.066928 0.128539 \n",
- "1 0.085102 -0.255425 ... -0.225775 -0.638672 0.101288 -0.339846 0.167170 \n",
- "2 0.247676 -1.514654 ... 0.247998 0.771679 0.909412 -0.689281 -0.327642 \n",
- "3 0.377436 -1.387024 ... -0.108300 0.005274 -0.190321 -1.175575 0.647376 \n",
- "4 -0.270533 0.817739 ... -0.009431 0.798278 -0.137458 0.141267 -0.206010 \n",
- "\n",
- " V26 V27 V28 Amount Class \n",
- "0 -0.189115 0.133558 -0.021053 149.62 0 \n",
- "1 0.125895 -0.008983 0.014724 2.69 0 \n",
- "2 -0.139097 -0.055353 -0.059752 378.66 0 \n",
- "3 -0.221929 0.062723 0.061458 123.50 0 \n",
- "4 0.502292 0.219422 0.215153 69.99 0 \n",
- "\n",
- "[5 rows x 31 columns]"
- ]
- },
- "execution_count": 3,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "df = pd.read_csv('creditcard.csv')\n",
- "df.head()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "(284807, 31)"
- ]
- },
- "execution_count": 4,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "df.shape"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### Check for null values"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "False"
- ]
- },
- "execution_count": 5,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "df.isnull().any().any()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### Split fraudulent data from genuine data\n",
- "We start by separating the genuine data from the fraudulent data"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {
- "solution2": "shown",
- "solution2_first": true
- },
- "outputs": [],
- "source": [
- "# genuine = ...\n",
- "# anomalies = ...\n",
- "# print(\"Genuine Transactions:\", genuine.shape[0])\n",
- "# print(\"Anomalous Transaction:\", anomalies.shape[0])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {
- "solution2": "shown"
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Genuine Transactions: 284315\n",
- "Anomalous Transaction: 492\n"
- ]
- }
- ],
- "source": [
- "genuine = df[df.Class == 0]\n",
- "anomalies = df[df.Class == 1]\n",
- "print(\"Genuine Transactions:\", genuine.shape[0])\n",
- "print(\"Anomalous Transaction:\", anomalies.shape[0])"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### Amount feature\n",
- "Fortunately, that *Amount* feature has not been anonymized with the PCA transformation. So let's take a look at the *Amount* feature and see if we can find a pattern."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Mean 88.29102242225574\n",
- "Median 22.0\n"
- ]
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "genuine.Amount.loc[genuine.Amount < 500].hist(bins=100)\n",
- "print(\"Mean\", genuine.Amount.mean())\n",
- "print(\"Median\", genuine.Amount.median())"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Mean 122.21132113821133\n",
- "Median 9.25\n"
- ]
- },
- {
- "data": {
- "image/png": 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- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "anomalies.Amount.loc[anomalies.Amount < 500].hist(bins=100)\n",
- "print(\"Mean\", anomalies.Amount.mean())\n",
- "print(\"Median\", anomalies.Amount.median())"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "* Interestingly the median is lower but the mean is higher for the fraudulent transactions. This suggests there are some high value oriented criminals and some that focus on withdrawals \"below the radar\" to avoid detection"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Feature Engineering"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### Distribution of the features\n",
- "We want to model our anomaly detection by fitting a multivariate gaussian distribution. Therefore it makes sense to plot the distribution of our features. We plot the genuine and the fraudulent transactions separately."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {
- "scrolled": false
- },
- "outputs": [
- {
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "ad145b5c0fa4415983f136e40c7390ac",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- "HBox(children=(HTML(value=''), FloatProgress(value=0.0, max=30.0), HTML(value='')))"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/opt/conda/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- " warnings.warn(msg, FutureWarning)\n",
- "/opt/conda/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- " warnings.warn(msg, FutureWarning)\n",
- "/opt/conda/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- " warnings.warn(msg, FutureWarning)\n",
- "/opt/conda/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- " warnings.warn(msg, FutureWarning)\n",
- "/opt/conda/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- " warnings.warn(msg, FutureWarning)\n",
- "/opt/conda/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- " warnings.warn(msg, FutureWarning)\n",
- "/opt/conda/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- " warnings.warn(msg, FutureWarning)\n",
- "/opt/conda/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- " warnings.warn(msg, FutureWarning)\n",
- "/opt/conda/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- " warnings.warn(msg, FutureWarning)\n",
- "/opt/conda/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- " warnings.warn(msg, FutureWarning)\n",
- "/opt/conda/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- " warnings.warn(msg, FutureWarning)\n",
- "/opt/conda/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- " warnings.warn(msg, FutureWarning)\n",
- "/opt/conda/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- " warnings.warn(msg, FutureWarning)\n",
- "/opt/conda/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- " warnings.warn(msg, FutureWarning)\n",
- "/opt/conda/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- " warnings.warn(msg, FutureWarning)\n",
- "/opt/conda/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- " warnings.warn(msg, FutureWarning)\n",
- "/opt/conda/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- " warnings.warn(msg, FutureWarning)\n",
- "/opt/conda/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- " warnings.warn(msg, FutureWarning)\n",
- "/opt/conda/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- " warnings.warn(msg, FutureWarning)\n",
- "/opt/conda/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- " warnings.warn(msg, FutureWarning)\n",
- "/opt/conda/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- " warnings.warn(msg, FutureWarning)\n",
- "/opt/conda/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- " warnings.warn(msg, FutureWarning)\n",
- "/opt/conda/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- " warnings.warn(msg, FutureWarning)\n",
- "/opt/conda/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- " warnings.warn(msg, FutureWarning)\n",
- "/opt/conda/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- " warnings.warn(msg, FutureWarning)\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/opt/conda/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- " warnings.warn(msg, FutureWarning)\n",
- "/opt/conda/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- " warnings.warn(msg, FutureWarning)\n",
- "/opt/conda/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- " warnings.warn(msg, FutureWarning)\n",
- "/opt/conda/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- " warnings.warn(msg, FutureWarning)\n",
- "/opt/conda/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- " warnings.warn(msg, FutureWarning)\n",
- "/opt/conda/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- " warnings.warn(msg, FutureWarning)\n",
- "/opt/conda/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- " warnings.warn(msg, FutureWarning)\n",
- "/opt/conda/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- " warnings.warn(msg, FutureWarning)\n",
- "/opt/conda/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- " warnings.warn(msg, FutureWarning)\n",
- "/opt/conda/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- " warnings.warn(msg, FutureWarning)\n",
- "/opt/conda/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- " warnings.warn(msg, FutureWarning)\n",
- "/opt/conda/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- " warnings.warn(msg, FutureWarning)\n",
- "/opt/conda/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- " warnings.warn(msg, FutureWarning)\n",
- "/opt/conda/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- " warnings.warn(msg, FutureWarning)\n",
- "/opt/conda/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- " warnings.warn(msg, FutureWarning)\n",
- "/opt/conda/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- " warnings.warn(msg, FutureWarning)\n",
- "/opt/conda/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- " warnings.warn(msg, FutureWarning)\n",
- "/opt/conda/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- " warnings.warn(msg, FutureWarning)\n",
- "/opt/conda/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- " warnings.warn(msg, FutureWarning)\n",
- "/opt/conda/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- " warnings.warn(msg, FutureWarning)\n",
- "/opt/conda/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- " warnings.warn(msg, FutureWarning)\n",
- "/opt/conda/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- " warnings.warn(msg, FutureWarning)\n",
- "/opt/conda/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- " warnings.warn(msg, FutureWarning)\n",
- "/opt/conda/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- " warnings.warn(msg, FutureWarning)\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/opt/conda/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- " warnings.warn(msg, FutureWarning)\n",
- "/opt/conda/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- " warnings.warn(msg, FutureWarning)\n",
- "/opt/conda/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- " warnings.warn(msg, FutureWarning)\n",
- "/opt/conda/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- " warnings.warn(msg, FutureWarning)\n",
- "/opt/conda/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- " warnings.warn(msg, FutureWarning)\n",
- "/opt/conda/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- " warnings.warn(msg, FutureWarning)\n",
- "/opt/conda/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- " warnings.warn(msg, FutureWarning)\n",
- "/opt/conda/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- " warnings.warn(msg, FutureWarning)\n",
- "/opt/conda/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- " warnings.warn(msg, FutureWarning)\n",
- "/opt/conda/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- " warnings.warn(msg, FutureWarning)\n",
- "/opt/conda/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- " warnings.warn(msg, FutureWarning)\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\n"
- ]
- },
- {
- "data": {
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\n",
- "text/plain": [
- "<Figure size 864x8640 with 30 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "feature_cols = genuine.drop(columns=[\"Class\"]).columns\n",
- "plt.figure(figsize=(12, len(feature_cols)*4))\n",
- "gs = gridspec.GridSpec(len(feature_cols), 2)\n",
- "\n",
- "for i, col in enumerate(tqdm(feature_cols)):\n",
- " ax = plt.subplot(gs[i])\n",
- " sns.distplot(genuine[col], color='g',label='Genuine')\n",
- " sns.distplot(anomalies[col], color='r',label='Anomaly')\n",
- " ax.legend()\n",
- "plt.show()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "> Based on the plots we made, drop the features that might not be useful."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "metadata": {
- "solution2": "hidden",
- "solution2_first": true
- },
- "outputs": [],
- "source": [
- "# features_to_drop = [...]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "metadata": {
- "solution2": "hidden"
- },
- "outputs": [],
- "source": [
- "features_to_drop = ['Time', 'V8', 'V13', 'V15', 'V20', 'V22', 'V23', 'V24', 'V25', 'V26', 'V27', 'V28']"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "* We can see that the distribution of the fraudulent transaction (class=1) is matching with the genuine transactions (class=0) for the following features:\n",
- " * *V8*, *V13*, *V15*, *V20*, *V22*, *V23*, *V24*, *V25*, *V26*, *V27*, *V28*\n",
- " * Therefore they might not be useful in finding fradulent transactions. \n",
- "* The variable *Time* is also not a useful variable since it contains the seconds elapsed between the transaction for that record and the first transaction in the dataset. The data is in increasing order, which can lead to a singular covariance matrix. Also it is not normally distributed."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Let's drop these features in our splitted dataset"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "metadata": {},
- "outputs": [],
- "source": [
- "genuine = genuine.drop(features_to_drop, axis=1)\n",
- "anomalies = anomalies.drop(features_to_drop, axis=1)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### Split the data\n",
- "We split the targets from the features"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 14,
- "metadata": {},
- "outputs": [],
- "source": [
- "genuine_X = genuine.drop(columns=[\"Class\"]).values\n",
- "genuine_y = genuine.Class.values\n",
- "\n",
- "anomalies_X = anomalies.drop(columns=[\"Class\"]).values\n",
- "anomalies_y = anomalies.Class.values"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Now we use 60% of the *genuine* data as the training set."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 15,
- "metadata": {},
- "outputs": [],
- "source": [
- "X_train, X_test, y_train, y_test = train_test_split(genuine_X, genuine_y, test_size=0.4, random_state=42)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "And add the anomalies to the test set"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 16,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "(114218, 18)\n",
- "(114218,)\n",
- "Number of anomalies: 492\n"
- ]
- }
- ],
- "source": [
- "# Add anomalies to the test set\n",
- "X_test = np.concatenate([X_test, anomalies_X])\n",
- "y_test = np.concatenate([y_test, anomalies_y])\n",
- "\n",
- "print(X_test.shape)\n",
- "print(y_test.shape)\n",
- "print(\"Number of anomalies:\", y_test.sum())"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "*(Note that because we further split the data we don't have to shuffle it)*"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Now we can further split the test data into a test set and a validation set."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 17,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "(57109, 18)\n",
- "(57109,)\n",
- "Number of anomalies y_test: 231\n",
- "(57109, 18)\n",
- "(57109,)\n",
- "Number of anomalies y_val: 261\n"
- ]
- }
- ],
- "source": [
- "X_test, X_val, y_test, y_val = train_test_split(X_test, y_test, test_size=0.5, random_state=42)\n",
- "print(X_test.shape)\n",
- "print(y_test.shape)\n",
- "print(\"Number of anomalies y_test:\", y_test.sum())\n",
- "print(X_val.shape)\n",
- "print(y_val.shape)\n",
- "print(\"Number of anomalies y_val:\", y_val.sum())"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### Data Normalization\n",
- "Now that we have splitted our data, we can normalize it by applying z-normalization.\n",
- "\n",
- "> Normalize the data"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 18,
- "metadata": {
- "solution2": "hidden",
- "solution2_first": true
- },
- "outputs": [],
- "source": [
- "# scaler = ...\n",
- "# X_train = ...\n",
- "# X_test = ...\n",
- "# X_val = ..."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 19,
- "metadata": {
- "solution2": "hidden"
- },
- "outputs": [],
- "source": [
- "scaler = StandardScaler()\n",
- "X_train = scaler.fit_transform(X_train)\n",
- "X_test = scaler.transform(X_test)\n",
- "X_val = scaler.transform(X_val)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Baseline model\n",
- "To get an idea on how good our model performs, let's implement a dummy predictor. Our dummy model should mark each transaction as genuine\n",
- "\n",
- "> Implement the dummy predictor"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 20,
- "metadata": {
- "lines_to_next_cell": 2,
- "solution2": "hidden",
- "solution2_first": true
- },
- "outputs": [],
- "source": [
- "def dummy_predict(X):\n",
- " # START YOUR CODE\n",
- " pass\n",
- " # END YOUR CODE"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 21,
- "metadata": {
- "solution2": "hidden"
- },
- "outputs": [],
- "source": [
- "def dummy_predict(X):\n",
- " return np.zeros(X.shape[0])"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "> Now predict on the test set and calculate the accuracy by using the Scikit-Learn function [accuracy_score](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.accuracy_score.html)."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 22,
- "metadata": {
- "solution2": "hidden",
- "solution2_first": true
- },
- "outputs": [],
- "source": [
- "# Calculate the accuracy on the test set"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 23,
- "metadata": {
- "solution2": "hidden"
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "0.9959551033987638"
- ]
- },
- "execution_count": 23,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "y_pred = dummy_predict(X_test)\n",
- "accuracy_score(y_pred, y_test)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Wow, such a high accuracy! It seems like our work is done and we can spend the rest of our day drinking coffee!"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Okay, let's be honest there must be a mistake. \n",
- "\n",
- "> Let's check how many fraudulent and genuine data we have."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 24,
- "metadata": {
- "solution2": "hidden",
- "solution2_first": true
- },
- "outputs": [],
- "source": [
- "# Calculate the percentage of fraudulent data\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 25,
- "metadata": {
- "solution2": "hidden"
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "0 284315\n",
- "1 492\n",
- "Name: Class, dtype: int64\n",
- "Percentage of fraudulent data: 0.1727485630620034\n"
- ]
- }
- ],
- "source": [
- "print(df.Class.value_counts())\n",
- "print(\"Percentage of fraudulent data:\", 100*df.Class.value_counts()[1] /len(df))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "It looks like our dataset is highly imbalanced! We could create a balanced test set and then calculate the accuracy. But there must be another way... Let's plot the confusion matrix first."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 26,
- "metadata": {},
- "outputs": [],
- "source": [
- "def plot_confusion_matrix(cm):\n",
- " fig, (ax1) = plt.subplots(ncols=1, figsize=(5,5))\n",
- " sns.heatmap(cm, \n",
- " xticklabels=['Genuine', 'Fraud'],\n",
- " yticklabels=['Genuine', 'Fraud'],\n",
- " annot=True,ax=ax1,\n",
- " linewidths=.2,linecolor=\"Darkblue\", cmap=\"Blues\")\n",
- " plt.title('Confusion Matrix', fontsize=14)\n",
- " plt.show()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 27,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 360x360 with 2 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "cm = confusion_matrix(y_test, y_pred)\n",
- "plot_confusion_matrix(cm)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "We can see that we have a lot of *True Positives* and only few *True Negatives*. This is due to the class imbalance."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Precision, Recall and F1 Score\n",
- "For a skewed data set, we often use other metrics like [Precision](https://en.wikipedia.org/wiki/Precision_and_recall), [Recall](https://en.wikipedia.org/wiki/Precision_and_recall) or the harmonic mean of both, called [F1 Score](https://en.wikipedia.org/wiki/F1_score). Scikit-Learn provides the function [precision_recall_fscore_support](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.precision_recall_fscore_support.html) which calculates all these metrics at once."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 28,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Precision: 0.0\n",
- "Recall 0.0\n",
- "F1 0.0\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/opt/conda/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1221: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
- " _warn_prf(average, modifier, msg_start, len(result))\n"
- ]
- }
- ],
- "source": [
- "precision, recall, f1, _ = precision_recall_fscore_support(y_test, y_pred, average=\"binary\")\n",
- "print(\"Precision:\", precision)\n",
- "print(\"Recall\", recall)\n",
- "print(\"F1\", f1)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Now it's clear that our dummy anomaly detection model performs really bad! So let's stick to those metrics."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Fit the multivariate gaussian distribution\n",
- "Now that we have splitted our data set into a training, test and cross validation set, we can train our anomaly detection model."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "> Complete the `fit` function which calculate the *mean* and the *covariance* matrix from the training set and then fits a multivariate gaussian distribution. Use the [multivariate_normal](https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.stats.multivariate_normal.html) function from SciPy."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 29,
- "metadata": {
- "lines_to_next_cell": 2,
- "solution2": "hidden",
- "solution2_first": true
- },
- "outputs": [],
- "source": [
- "def fit(X):\n",
- " pass\n",
- " # START YOUR CODE HERE\n",
- " #return gauss\n",
- " # END YOUR CODE HERE"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 30,
- "metadata": {
- "solution2": "hidden"
- },
- "outputs": [],
- "source": [
- "def fit(X):\n",
- " mu = np.mean(X, axis=0)\n",
- " cov = np.cov(X.T)\n",
- " gauss = multivariate_normal(mean=mu, cov=cov)\n",
- " return gauss"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 31,
- "metadata": {},
- "outputs": [],
- "source": [
- "gauss = fit(X_train)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Predict anomalies\n",
- "Let's use our fitted gaussian distribution to predict anomalies. \n",
- "\n",
- "> Use the `pdf` method of the gauss model to calculate the densities. Then mark all elements which are below the threshold `epsilon` as an anomaly. We use the value 10<sup>-20</sup> as the threshold."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 32,
- "metadata": {
- "solution2": "hidden",
- "solution2_first": true
- },
- "outputs": [],
- "source": [
- "def predict(X, gauss, eps):\n",
- " # START YOUR CODE\n",
- " pass\n",
- " # END YOUR CODE"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 33,
- "metadata": {
- "solution2": "hidden"
- },
- "outputs": [],
- "source": [
- "def predict(X, gauss, eps):\n",
- " densities = gauss.pdf(X)\n",
- " y_pred = densities < eps\n",
- " return y_pred"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 34,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Precision: 0.12998040496407576\n",
- "Recall 0.8614718614718615\n",
- "F1 0.22587968217934162\n"
- ]
- }
- ],
- "source": [
- "eps = 1e-20\n",
- "y_pred = predict(X_test, gauss, eps)\n",
- "precision, recall, f1, _ = precision_recall_fscore_support(y_test, y_pred, average=\"binary\")\n",
- "print(\"Precision:\", precision)\n",
- "print(\"Recall\", recall)\n",
- "print(\"F1\", f1)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 35,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 360x360 with 2 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "cm = confusion_matrix(y_test, y_pred)\n",
- "plot_confusion_matrix(cm)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "* We have a relatively high recall value, which means that our model does detect most of the anomalies. \n",
- "* The low precision value however indicates that we have a lot of *False Positives*. *False Positives* are the genuine transaction which were classified as frauds. \n",
- "* This might be a good model if for example the model is only used to determine if the user has to enter an extra verification code. However if those cards would be blocked, this would be really bad."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Hyperparameter tuning\n",
- "Our threshold `eps` is the hyperparameter we would like to tune. "
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "But first we take a look at our densities"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 36,
- "metadata": {
- "scrolled": true
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Min density: 0.0\n",
- "Max density: 8.41619337890157e-08\n"
- ]
- }
- ],
- "source": [
- "densities = gauss.pdf(X_val)\n",
- "print(\"Min density:\", densities.min())\n",
- "print(\"Max density:\", densities.max())"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "The values of our densities seem to be rather small! Luckily when we are dealing with probabilities we can always apply a logarithmic transformation as it is a strictly monotonic function. Scipy already privdes an implementation `logpdf`. \n",
- "Let's check the values of our log transformed densities."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 37,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Min density: -12344.176421882894\n",
- "Max density: -16.29052311068991\n"
- ]
- }
- ],
- "source": [
- "densities = gauss.logpdf(X_val)\n",
- "print(\"Min density:\", densities.min())\n",
- "print(\"Max density:\", densities.max())"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "These values look much better. However in order to reuse our existing code we need to redefine our `predict` function. \n",
- "\n",
- "> Reimplement the `predict` function but this time use `logpdf` instead of the `pdf` function."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 38,
- "metadata": {
- "solution2": "shown",
- "solution2_first": true
- },
- "outputs": [],
- "source": [
- "def predict(X, gauss, eps):\n",
- " # YOUR CODE\n",
- " pass"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 39,
- "metadata": {
- "solution2": "shown"
- },
- "outputs": [],
- "source": [
- "def predict(X, gauss, eps):\n",
- " densities = gauss.logpdf(X)\n",
- " y_pred = densities < eps\n",
- " return y_pred"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### Fit our threshold by sorting\n",
- "We can try to find the optimal threshold by sorting our densities and then picking the density with the index of the number of anomalies on our validation set."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 40,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Best epsilon -281.5512309065886 with f1 score 0.7227533460803058\n"
- ]
- }
- ],
- "source": [
- "expected_anomalies = y_val.sum()\n",
- "sorted_densities = np.sort(gauss.logpdf(X_val))\n",
- "best_eps = (sorted_densities[expected_anomalies] + \n",
- " 0.5 * (sorted_densities[expected_anomalies + 1] - \n",
- " sorted_densities[expected_anomalies]))\n",
- "\n",
- "\n",
- "y_pred = predict(X_val, gauss, best_eps)\n",
- "f1 = f1_score(y_val, y_pred, average=\"binary\")\n",
- "print(\"Best epsilon {} with f1 score {}\".format(best_eps, f1))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Wow this worked out very well on our validation set! Let's check if we can still improve our result by applying a grid search approach."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### Grid Search Approach"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "We calculate the densities for each anomaly and assume that the maximum epsilon is equal to the maximum density. We take $-500$ as our minimum `epsilon` for now."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 41,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "-12344.176421882894"
- ]
- },
- "execution_count": 41,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "densities.min()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 42,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Maximum epsilon: -16.29052311068991\n",
- "Minimum epsilon: -500\n"
- ]
- }
- ],
- "source": [
- "X_anomalies = anomalies.drop(columns=[\"Class\"]).values\n",
- "densities = gauss.logpdf(X_val)\n",
- "\n",
- "max_eps = densities.max()\n",
- "print(\"Maximum epsilon:\", max_eps)\n",
- "\n",
- "min_eps = -500\n",
- "print(\"Minimum epsilon:\", min_eps)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "This means the optimal value of `epsilon` lies between -500 and -16. Let's try to find it by using a grid search approach.\n",
- "> Loop through the list of epsilons and calculate for each epsilon the *precision*, *recall* and *f1* score. "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 43,
- "metadata": {
- "solution2": "shown",
- "solution2_first": true
- },
- "outputs": [],
- "source": [
- "f1_scores = []\n",
- "recall_scores = []\n",
- "precision_scores = []\n",
- "\n",
- "epsilons = np.linspace(min_eps, max_eps, num=1000)\n",
- "\n",
- "# START YOUR CODE\n",
- "#for eps in tqdm(epsilons):\n",
- "\n",
- "\n",
- "#best_eps = epsilons[np.argmax(f1_scores)]\n",
- "#best_f1 = np.max(f1_scores)\n",
- "\n",
- "#print(\"Best epsilon {} with f1 score {}\".format(best_eps, best_f1))\n",
- "# END YOUR CODE"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 44,
- "metadata": {
- "solution2": "shown"
- },
- "outputs": [
- {
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "38912c32f75141bd96c7959f0ba8b8c4",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- "HBox(children=(HTML(value=''), FloatProgress(value=0.0, max=1000.0), HTML(value='')))"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\n",
- "Best epsilon -303.90156342325264 with f1 score 0.7325581395348838\n"
- ]
- }
- ],
- "source": [
- "f1_scores = []\n",
- "recall_scores = []\n",
- "precision_scores = []\n",
- "\n",
- "epsilons = np.linspace(min_eps, max_eps, num=1000)\n",
- "densities = gauss.logpdf(X_val)\n",
- "for eps in tqdm(epsilons):\n",
- " y_pred = densities < eps\n",
- " precision, recall, f1, _ = precision_recall_fscore_support(y_val, y_pred, average=\"binary\")\n",
- " precision_scores.append(precision)\n",
- " recall_scores.append(recall)\n",
- " f1_scores.append(f1)\n",
- " \n",
- "best_eps_gridsearch = epsilons[np.argmax(f1_scores)]\n",
- "best_f1 = np.max(f1_scores)\n",
- "\n",
- "print(\"Best epsilon {} with f1 score {}\".format(best_eps_gridsearch, best_f1))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "##### Visualization\n",
- "Let's visualize the *F1*, *Precision* and *Recall* curves"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 45,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "(-16.29052311068991, -500.0)"
- ]
- },
- "execution_count": 45,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 720x576 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "fig, ax = plt.subplots(figsize=(10,8))\n",
- "ax.plot(epsilons, f1_scores, label=\"f1\")\n",
- "ax.plot(epsilons, precision_scores, label=\"precision\")\n",
- "ax.plot(epsilons, recall_scores, label=\"recall\")\n",
- "ax.plot(best_eps, best_f1, marker=\"o\")\n",
- "ax.set_xlabel(\"Epsilon\")\n",
- "ax.set_ylabel(\"Score\")\n",
- "ax.legend()\n",
- "ax.set_xlim(max_eps, min_eps)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Evaluate the model on the test set"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "As we have selected an epsilon, we can check how good our model performs on the test set."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 46,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Precision: 0.7086956521739131\n",
- "Recall 0.7056277056277056\n",
- "F1 0.7071583514099784\n"
- ]
- }
- ],
- "source": [
- "y_pred = predict(X_test, gauss, best_eps)\n",
- "precision, recall, f1, _ = precision_recall_fscore_support(y_test, y_pred, average=\"binary\")\n",
- "print(\"Precision:\", precision)\n",
- "print(\"Recall\", recall)\n",
- "print(\"F1\", f1)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "We plot the confusion matrix again"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 47,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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bZ2abn1S/pbEqNRO8BvhyRPiOXrMmLrVMsNQg+K4DoFkaEouBJQfBaZLuAR4AVlYWRoRvrDJrYkr9OGhTUep9gm2B/5DNDn85X3x7vVkTVM4xQUnNJf1N0sP56w6SHs/nHB6X1L5g32GSZkt6VdKRBeV9Jb2Ub7teef9dUitJ9+TlUyT1LKVNJWWCEfH10i7RzBq7Mo8Jfg+YRZZYAQwFJkbEVZKG5q8vkLQ7MAjYA/g48ISkT0TEGuBGsk+kPQs8CgwAJgCDgYURsYukQcDVwKl1NajWTFDS+fnPn+cRt2jZ0Ks3sy1fuTJBSd2BY4CbC4oHAmPz9bHAcQXld0fEyoh4A5gN7CepK9A2Iibnn2K7vcoxlXXdC/RXCRG9rkywcjJkWl0VmVnTUMZM8KfA+UDhcwc6R8R8gIiYL6lTXt6NLNOrNDcvW52vVy2vPObtvK4KSYuBjsC/a2tUrUEwIh7Kf46tbT8zazrqGwSLH5wCwJiIGJNv+xKwICKmSzq4lOqqKYtayms7plalPkrrz9VVFhGHlnK8mTUe9U0E84A3pobNBwDHSjoaaA20lXQn8K6krnkW2BVYkO8/F+hRcHx3YF5e3r2a8sJj5kpqAbQDPqAOpc4Onwf8MF8uBl7AXWSzJqkcD1WNiGER0T0iepJNeEyKiK8C44Ez893OBCq/4GU8MCif8e0F7ApMzbvOSyXtn4/3nVHlmMq6TsrPsWkywYiYXqXor5KeKuVYM2tcNvPN0leRPaVqMPAWcDJARMyQNA6YCVQA5+QzwwBnA7cBbchmhSfk5bcAd0iaTZYBDiqlAaV2hzsUvGwG9GVLelKqmW0y5f7YXEQ8CTyZr78P9K9hv1HAqGrKpwF7VlO+gjyIbohSPzEynXWDkhXAG2T35JhZE+OPzVUjIhrllyqZmdWl5OcJSvo80LPwmIi4vQxtMrMG5KfIVEPSHUBvslnhysHJyru1zawJSSwGlpwJ9gN2L2W62cwaN2eC1XuZbDZ4fhnbYmZbgMRiYMlBcHtgpqSpFD9P8NiytMrMGowzwepdWs5GmNmWI7EYWPItMk9J2gnYNSKekLQ10Ly8TTOzhpBaJljqt82dRfZ8rpvyom5kj9o3syYmtW+bK/UBCueQPQViCUBEvAZ0qvUIM2uUyvEAhS1ZqWOCKyNiVeWF5o+p8e0yZk1QYw5o9VFqEHxK0oVAG0mHA98GHipfs8ysoSQWA0vuDg8F3gNeInty7CMRMbxsrTKzBuPucAFJA4HuEfFL4Nf5BMkOQF9JiyLi3s3RSDPbfBpxPKuXurrD51P8YMKtyJ4luA3wG7IZYzNrQhpzVlcfdQXBrSLi7YLXT0fEB8AHkj5WxnaZWQNJLAbWGQTbF76IiHMLXu6w6ZtjZg2tWWJRsK6JkSn5OGARSd8EppanSWZmm09dmeAPgAckfQV4Pi/rC7Ri3be+m1kTklgiWOeXry8APi/pUGCPvPiRiJhU9paZWYPwxEg18qDnwGeWgGZpxcDSv2PEzNLgTNDMkpZYDHQQNLNiIq0o6CBoZkU8JmhmSfOYoJklLbEY6CBoZsVS+9icg6CZFUksBjoImlkxjwmaWdISi4EOgmZWzGOCZpa0tEKgg6CZVZHamGCp3zZnZtYkORM0syL+2JyZJS217rCDoJkVSSwGekzQzIpJqtdSQr2tJU2V9HdJMySNzMs7SHpc0mv5z/YFxwyTNFvSq5KOLCjvK+mlfNv1yhsgqZWke/LyKZJ61tUuB0EzK9JM9VtKsBI4NCL2AvYGBkjaHxgKTIyIXYGJ+Wsk7Q4MIvt+owHADZKa53XdCAwBds2XAXn5YGBhROwCXAdcXef1ltR0M0tGuTLByCzLX7bMlwAGAmPz8rGs+ybLgcDdEbEyIt4AZgP7SeoKtI2IyRERwO1Vjqms616gv+ponIOgmRVRfRdpiKRpBcuQ9eqWmkt6AVgAPB4RU4DOETEfIP/ZKd+9G/B2weFz87Ju+XrV8qJjIqICWAx0rO16PTFiZkXq+7G5iBgDjKljnzXA3pK2A+6XtGctu1fXkKilvLZjauRM0MyKSPVbNkRELAKeJBvLezfv4pL/XJDvNhfoUXBYd2BeXt69mvKiYyS1ANoBH9TWFgdBMytSxtnhHfIMEEltgMOAV4DxwJn5bmcCD+br44FB+YxvL7IJkKl5l3mppP3z8b4zqhxTWddJwKR83LBG7g6bWZEy3ifYFRibz/A2A8ZFxMOSJgPjJA0G3gJOBoiIGZLGATOBCuCcvDsNcDZwG9AGmJAvALcAd0iaTZYBDqqrUQ6CZlakXI/SiogXgX2qKX8f6F/DMaOAUdWUTwPWG0+MiBXkQbRUDoJmViS1T4xsliC44oWem+M0VgZtWo5u6CZYPUWcV6/j/NnhMli+un5vhjWsNi1H+71LUGqzpe4Om1mR1DLB1IK+mVkRZ4JmVsQPVTWzpDkImlnSUhsTdBA0syLOBM0saYklgg6CZlasXB+b21I5CJpZkdTum3MQNLMiiSWCDoJmVszdYTNLWmIx0EHQzIr5FhkzS5q7w2aWtMRioIOgmRVzd9jMkqZqv7q36XIQNLMiqWWCqd0cbmZWxJmgmRVJLRN0EDSzIn6eoJklzZmgmSUtsUTQQdDMivkTI2aWNHeHzSxpiSWCDoJmVqyZPzFiZilzJmhmSfOYoJklzbPDZpa0xGKgg6CZFXMmaGZJSywGOgiaWbHUnq/nIGhmRfwUGTNLWlohML3M18waiKQekv4saZakGZK+l5d3kPS4pNfyn+0LjhkmabakVyUdWVDeV9JL+bbrlaevklpJuicvnyKpZ13tchA0syLNpHotJagA/jcidgP2B86RtDswFJgYEbsCE/PX5NsGAXsAA4AbJDXP67oRGALsmi8D8vLBwMKI2AW4Dri6zustpeVmlg7Vc6lLRMyPiOfz9aXALKAbMBAYm+82FjguXx8I3B0RKyPiDWA2sJ+krkDbiJgcEQHcXuWYyrruBfqrjkFOB0EzKyLVd9EQSdMKliE1n0M9gX2AKUDniJgPWaAEOuW7dQPeLjhsbl7WLV+vWl50TERUAIuBjrVdrydGzKxIfWeHI2IMMKaE+rcB/gB8PyKW1HK+6jZELeW1HVMjZ4JmVqRZPZdSSGpJFgB/GxH35cXv5l1c8p8L8vK5QI+Cw7sD8/Ly7tWUFx0jqQXQDvigrus1M/uIpHotJdQr4BZgVkRcW7BpPHBmvn4m8GBB+aB8xrcX2QTI1LzLvFTS/nmdZ1Q5prKuk4BJ+bhhjdwdNrMiZbxP8ADgdOAlSS/kZRcCVwHjJA0G3gJOBoiIGZLGATPJZpbPiYg1+XFnA7cBbYAJ+QJZkL1D0myyDHBQXY1yEDSzIuX6xEhEPE3NMbZ/DceMAkZVUz4N2LOa8hXkQbRUDoJmViS1MTIHQTMr4s8Om1nS0gqBDoJmVkViiaCDoJkV81dumlnSnAmaWdLkTNDMUpZaJpjaLUFmZkWcCZpZEU+MmFnSUusOOwiaWREHQTNLmmeHzSxpzdKKgQ6CZlbMmaCZJc1jgmaWNGeCVqMlS5YwcsRFzJ79DyQx8kdX0KpVay6/7BJWrVxJ8xbNufCiS/l0nz4N3dQmacRFw/jLU0/SoUNH7nvw4Wr3eW7qFH581RWsrqigffv23Dr2zo0656pVqxg+7HxmzZhBu+2245qfXEe3bt15ZdYsRv3oUpYtW0bz5s347yFnM+CoozfqXFuK1MYE/YmRDXDNlaM44MAv8ODDf+T3f3iQXjv35rprf8y3vn0O4+57kG+f+z1+eu2PG7qZTdbA407gxpturnH7kiVLuOJHI/nZL27k/vGP8ONrf1Zy3e+8M5fBXzt9vfL7//B72rZty8N/fJyvnvE1fnrtaABat2nN5Vdezf3jH+GGm27mx1ddwZIlSzb8orZAqud/jZWDYImWLVvG9OnPcfyJJwHQcqutaNu2LUIsW/Zhts/SpeywQ6faqrGN0LffvrRt167G7RMeeYj+hx1O149/HICOHdd95/bDDz3IV049iVNOGMhll45gzZo1NVVT5M+TJnHswOMBOPyII5n67GQigp49e7HTTj0B6NSpMx06dGDhwlq/2bHRqO+XrzdWtXaHJXWobXtENI13vQRz336b9u07MGL4MF599RV232MPzh86nPOHXsjZQwZz7eirWbt2Lbf/9u6Gbmqy3pwzh4qKCgZ/7XQ+/PBDTvvqGXx54HG8/s9/8tiECYy983e0bNmSUZddyqMPP8SXBx5XZ50LFrxLly5dAWjRogXbbLstixYtpH37dX8aL734IqsrVtOjx45lurLNqxHHs3qpa0xwOuu+8X1HYGG+vh3ZV+P1KmfjtiRr1lTwyqyZDB1+MX367MXVV17OrTePYdmyZfzwgmEcdsSRPPbHR7n04uGMueW2hm5ukirWrGHmzBmMueU2Vq5cwRlfGcSn99qLKc9OZtbMlznt1CyLX7FyBR3yLPH73z2HeXPnsnr1aubPn88pJwwE4Cunn8Fxx59IdV9ZW/gdHO+9t4Dhw37I5VdcTbNmTaNj1awxp3X1UGsQjIheAJJ+BYyPiEfz10cBh9V0nKQhwJDs1YmbqKkNq3PnLnTu3IU+ffYC4PAjBnDrzWP42/PTuWDYcACOOPIoRo64qCGbmbTOnbvQvn17tt56a7beems+068f/3j1FYLgywOP53s/+N/1jvnp9b8EsjHBEcOHccttd6xX57/+NZ/OXbpQUVHBsqVLadduOyAbIjn37G9y7ne/T5+99i735W02aYXA0scE960MgAARMQH4Yk07R8SYiOgXEf1g/41t4xZh+x12oHOXLsx543UApjw7mZ1792aHTp2Y9txUAKZOeZYd83Ei2/wOObQ/z0+fRkVFBcuXL+elF1+k1869+exnP8cTf3qM999/H4DFixYxb947JdV58CGHMv7B+wF4/E+Psd9n90cSq1et4gffPYcvHzuQI448qmzX1CBUz6WRKvUWmX9Lugi4k6x7/FXg/bK1ags19MKLGXbBeaxevZru3Xtw2eVXcvAh/bnmqitYU1HBVq1aMeLSyxq6mU3WBef9D9Oem8qiRQs5/NCDOPuc71BRUQHAKaf+Fzv37s0BB36Bk48/FjVrxgknnsSuu34CgHO++33OPusbrI21tGjRkgsvGsHHP96tznMef+JJDB/6Q7404HDatmvHNaOvA+Cxxybw/PRpLF60iPEPZEHyslFX8anddivT1W8+jXmmtz5U3ZjHejtlEySXAAflRX8BRpYyMSKNjuWrz9uoRlrDaNNyNH7vGq/WLeoXzab8c3HdQaEan+3drlFGz5IywTzYfa/MbTGzLUBi8yKlBUFJfybrBheJiEM3eYvMrEElFgNLHhMs7BO1Jpvyrdj0zTGzBpdYFCy1Ozy9StFfJT1VhvaYWQNLbWKk1O5w4SdHmgF9gS5laZGZNSiPCVav8JMjFcAbwOByNcrMGk5iMbDk7nAyH48zS15iUbDk5wlK2hPYnWxiBICIuL0cjTKzhuMxwWpIugQ4mCwIPgocBTwNOAiaNTGpjQmW+tnhk4D+wL8i4uvAXkCrsrXKzBpMYh8dLrk7vDwi1kqqkNQWWADsXMZ2mVlDacwRrR5KDYLTJG0H/JpspngZMLVcjTKzhpPamGCd3WFlT5C8MiIWRcSvgMOBM/NusZk1MeV6vL6kWyUtkPRyQVkHSY9Lei3/2b5g2zBJsyW9KunIgvK+kl7Kt12fxygktZJ0T14+RVLPUq63ziAY2WNmHih4PSciXiylcjNrfMo4JngbMKBK2VBgYkTsCkzMXyNpd2AQsEd+zA2SmufH3Ej20OZd86WyzsHAwojYBbgOuLqURpU6MfKspH1L3NfMGrMyRcGI+AtQ9fF7A4Gx+fpY4LiC8rsjYmVEvAHMBvaT1BVoGxGT8wTt9irHVNZ1L9C/MkusTaljgocA35I0B/iQ7JIjIvwFu2ZNzGYeE+wcEfMBImK+pMqva+wGPFuw39y8bHW+XrW88pi387oqJC0GOgL/rq0BdX3b3I4R8RbZfYFmZjUq/m4hAMZExJj6VldNWdRSXtsxtaorE3wA+ExEvCnpDxHRNL41ycxqVN+bpfOAt6FB711JXfMssCvZ7XeQZXg9CvbrDszLy7tXU154zFxJLYB2rN/9Xk9dY4KFvw7fF2iWgM18s/R44Mx8/UzgwYLyQfmMby+yCZCpedd5qaT98/G+M6ocU1nXScCkKOH7Q+rKBKOGdTNrqso0JCjpd2Qfv91e0lyy7y26ChgnaTDZd5mfDBARMySNA2aSPbnqnIhYk1d1NtlMcxtgQr4A3ALcIWk2WQY4qJR21RUE95K0hOzX0iZfh3UTI21LOYmZNR7lmhiJiP+qYVP/GvYfBYyqpnwasGc15SvIg+iGqOvL15vXtt3Mmp7UHqBQ8qO0zCwNicVAB0EzqyKxKOggaGZFUnuAgoOgmRXxmKCZJS2xGOggaGZVJBYFHQTNrIjHBM0saR4TNLOkJRYDHQTNrIrEoqCDoJkVSW1MsNTH65uZNUnOBM2siCdGzCxpicVAB0EzK+ZM0MwSl1YUdBA0syLOBM0saYnFQAdBMyvmTNDMkpbazdIOgmZWLK0Y6CBoZsUSi4EOgmZWzGOCZpY0jwmaWdrSioEOgmZWLLEY6CBoZsU8JmhmSfOYoJklLbVM0E+WNrOkOQiaWdLcHTazIql1hx0EzayIJ0bMLGnOBM0saYnFQAdBM6sisSjoIGhmRTwmaGZJ85igmSUtsRjoIGhmVSQWBR0EzaxIamOCiojynkCjy3sCM6tWxHn1imYrKqjX32zrFo0zepY9CDZ1koZExJiGbofVj98/8wMUNt6Qhm6AbRS/f4lzEDSzpDkImlnSHAQ3nseTGje/f4nzxIiZJc2ZoJklLckgKKmzpLskvS5puqTJko7fxOc4VtLQTVmn1U7SGkkvFCw9y3COOZK239T1WsNJ7hMjkgQ8AIyNiK/kZTsBx27K80TEeGD8pqzT6rQ8IvaubkP+visi1m7eJtmWLsVM8FBgVUT8qrIgIt6MiJ9Lai7px5Kek/SipG8CSDpY0pOS7pX0iqTf5n9URZmBpH6SnszXvybpF/n6bZKul/RMnn2eVHluST8sON/IzfdraPok9ZQ0S9INwPNAD0k3SpomaUbh77uW97GjpD9J+pukm0juk7VNX4pBcA+yP4jqDAYWR8S+wL7AWZJ65dv2Ab4P7A7sDBywgeftChwIfAm4CkDSEcCuwH7A3kBfSQdtYL22TpuCrvD9edkngdsjYp+IeBMYHhH9gD7AFyX1qaPOS4CnI2Ifssx+x7K13hpEct3hqiT9kiw4rQLeBPoUZGrtyILUKmBqRMzNj3kB6Ak8vQGneiDvis2U1DkvOyJf/pa/3iY/31/qez2JK+oO52OCb0bEswX7nCJpCNn/+13J/lF7sZY6DwJOAIiIRyQt3NSNtoaVYhCcAZxY+SIizsm7QdOAt4DvRMRjhQdIOhhYWVC0hnW/uwrWZdStazlv4fEq+HllRNy0YZdgG+DDypU8qz8P2DciFkq6jXXvWW3vo+8ja8JS7A5PAlpLOrugbOv852PA2ZJaAkj6hKSP1VHfHKBvvn5iLftV5zHgG5K2yc/XTVKnDazDSteWLCguzrPxowq2zaH69/EvwGkAko4C2pe/mbY5JRcEI7s7/Diy8aA3JE0FxgIXADcDM4HnJb0M3ETd2fJI4GeS/o8sQ9yQtvwJuAuYLOkl4F5g2w2pw0oXEX8nG3qYAdwK/LVgc03v40jgIEnPkw1dvLWZmmubiT8xYmZJSy4TNDMr5CBoZklzEDSzpDkImlnSHATNLGkOgmaWNAdBM0uag6CZJe3/ARs4NKDdOCIPAAAAAElFTkSuQmCC\n",
- "text/plain": [
- "<Figure size 360x360 with 2 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "cm = confusion_matrix(y_test, y_pred)\n",
- "plot_confusion_matrix(cm)"
- ]
- }
- ],
- "metadata": {
- "jupytext": {
- "text_representation": {
- "extension": ".py",
- "format_name": "percent",
- "format_version": "1.2",
- "jupytext_version": "0.8.6"
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- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.8.5"
- }
- },
- "nbformat": 4,
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diff --git a/notebooks/11B Anomaly Detection/Anomaly Detection_Solution - Part 2.ipynb b/notebooks/11B Anomaly Detection/Anomaly Detection_Solution - Part 2.ipynb
deleted file mode 100644
index 6abe778..0000000
--- a/notebooks/11B Anomaly Detection/Anomaly Detection_Solution - Part 2.ipynb
+++ /dev/null
@@ -1,536 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Anomaly Detection"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [],
- "source": [
- "%matplotlib inline\n",
- "\n",
- "import numpy as np\n",
- "import pandas as pd\n",
- "import seaborn as sns\n",
- "import matplotlib.pyplot as plt\n",
- "import matplotlib as mpl\n",
- "\n",
- "from scipy.stats import multivariate_normal\n",
- "from sklearn.preprocessing import StandardScaler\n",
- "from sklearn.model_selection import train_test_split\n",
- "\n",
- "from sklearn.metrics import precision_recall_fscore_support\n",
- "from sklearn.metrics import accuracy_score\n",
- "from sklearn.metrics import confusion_matrix\n",
- "from sklearn.metrics import f1_score\n",
- "from sklearn.metrics.pairwise import euclidean_distances\n",
- "\n",
- "from sklearn.cluster import KMeans\n",
- "\n",
- "import matplotlib.gridspec as gridspec\n",
- "\n",
- "from tqdm.notebook import tqdm\n",
- "import ipywidgets as widgets"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Exercise 2 - Clustering-Based Anomaly Detection\n",
- "\n",
- "Clustering-Based approaches detect outliers by examining the relationship between records and clusters\n",
- "\n",
- "There exists three general approaches\n",
- "1. Records not belonging to any cluster are outliers\n",
- "1. **Records with a large distance to their closest cluster center are outliers**\n",
- "1. All records in a small and sparse cluster are considered outliers"
- ]
- },
- {
- "attachments": {
- "anomaly_detection_clustering.jpg": {
- "image/jpeg": 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"
- }
- },
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "\n",
- "In this exercise **you should implement the second approach**."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Prepare the dataset\n",
- "We will prepare the creditcard dataset the same way as in the previous exercise."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "X_train: (170589, 18)\n",
- "X_test: (57109, 18)\n",
- "X_val: (57109, 18)\n",
- "Number of anomalies y_test: 231\n",
- "Number of anomalies y_val: 261\n"
- ]
- }
- ],
- "source": [
- "df = pd.read_csv('creditcard.csv')\n",
- "features_to_drop = ['Time', 'V8', 'V13', 'V15', 'V20', 'V22', 'V23', 'V24', 'V25', 'V26', 'V27', 'V28']\n",
- "df.drop(columns=features_to_drop, inplace=True)\n",
- "\n",
- "genuine = df[df.Class == 0]\n",
- "anomalies = df[df.Class == 1]\n",
- "\n",
- "genuine_X = genuine.drop(columns=[\"Class\"]).values\n",
- "genuine_y = genuine.Class.values\n",
- "\n",
- "anomalies_X = anomalies.drop(columns=[\"Class\"]).values\n",
- "anomalies_y = anomalies.Class.values\n",
- "\n",
- "X_train, X_test, y_train, y_test = train_test_split(genuine_X, genuine_y, test_size=0.4, random_state=42)\n",
- "\n",
- "X_test = np.concatenate([X_test, anomalies_X])\n",
- "y_test = np.concatenate([y_test, anomalies_y])\n",
- "\n",
- "X_test, X_val, y_test, y_val = train_test_split(X_test, y_test, test_size=0.5, random_state=42)\n",
- "\n",
- "print(\"X_train:\", X_train.shape)\n",
- "print(\"X_test:\", X_test.shape)\n",
- "print(\"X_val:\", X_val.shape)\n",
- "print(\"Number of anomalies y_test:\", y_test.sum())\n",
- "print(\"Number of anomalies y_val:\", y_val.sum())"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### Scale the data"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [],
- "source": [
- "scaler = StandardScaler()\n",
- "X_train = scaler.fit_transform(X_train)\n",
- "X_val = scaler.transform(X_val)\n",
- "X_test = scaler.transform(X_test)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Generating cluster centers\n",
- "To implement our anomaly detection algorithm we need to get the cluster centers. We will use the [KMeans](https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html) model with 50 clusters. As the seed value set `random_state=42`.\n",
- "\n",
- "> Calculate the cluster centers from the training data"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {},
- "outputs": [],
- "source": [
- "# kmeans = ...\n",
- "# cluster_centers = ..."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "*Click on the dots to display the solution*"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {
- "jupyter": {
- "source_hidden": true
- },
- "tags": []
- },
- "outputs": [],
- "source": [
- "\n",
- "kmeans = KMeans(n_clusters=50, random_state=42)\n",
- "kmeans.fit(X_train)\n",
- "\n",
- "cluster_centers = kmeans.cluster_centers_"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Implement the prediction function\n",
- "> Now that you have calculate the cluster centers you should implement the predict function to decide wether a point is an outlier or not. \n",
- "1. For each point calculate the distance to the nearest cluster center (use the euclidean distance as the distance function)\n",
- "1. Mark all points as anomalies if their distance is larger than the given threshold"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {},
- "outputs": [],
- "source": [
- "def predict(X: np.ndarray, cluster_centers: np.ndarray, threshold: float) -> np.ndarray:\n",
- " # calculate the distance to the nearest cluster center\n",
- " # Mark points as anomalies if their distance is larger than the threshold\n",
- " pass"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "*Click on the dots to display the solution*"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {
- "jupyter": {
- "source_hidden": true
- },
- "tags": []
- },
- "outputs": [],
- "source": [
- "def predict(X: np.ndarray, cluster_centers: np.ndarray, threshold: float) -> np.ndarray:\n",
- " distances = euclidean_distances(X, cluster_centers).min(axis=1)\n",
- " y_pred = distances > threshold\n",
- " return np.array(y_pred, dtype=int)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "If your code is correct you should be able to run the following cell:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {},
- "outputs": [],
- "source": [
- "threshold = 10\n",
- "y_pred = predict(cluster_centers + threshold + 1, cluster_centers, threshold)\n",
- "np.testing.assert_equal(np.ones((len(cluster_centers))), y_pred)\n",
- "\n",
- "y_pred = predict(cluster_centers, cluster_centers, threshold)\n",
- "np.testing.assert_equal(np.zeros((len(cluster_centers))), y_pred)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Tune `threshold` hyperparameter\n",
- "As you have noticed our model contains is using a `threshold` hyperparameter. Let's tune it using our validation set."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### Manually tune the threshold\n",
- "You can play around with the slider to get the best threshold"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "6c370d0b78bd4f708c36f50b2eb13d45",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- "interactive(children=(FloatSlider(value=1.0, description='thresh', max=20.0), Output()), _dom_classes=('widget…"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "@widgets.interact(thresh =(0,20,0.1))\n",
- "def f(thresh=1):\n",
- " y_pred = predict(X_val, cluster_centers, thresh)\n",
- " precision, recall, f1, _ = precision_recall_fscore_support(y_val, y_pred, average=\"binary\")\n",
- " \n",
- " print(\"Detected number of outliers:\", y_pred.sum())\n",
- " print(\"Actual number of outliers:\", y_val.sum())\n",
- " print(\"------\")\n",
- " print(\"Precision:\", precision)\n",
- " print(\"Recall\", recall)\n",
- " print(\"F1\", f1)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### Tune the paramter by using a grid search"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "application/json": {
- "ascii": false,
- "bar_format": null,
- "colour": null,
- "elapsed": 0.017639636993408203,
- "initial": 0,
- "n": 0,
- "ncols": null,
- "nrows": null,
- "postfix": null,
- "prefix": "",
- "rate": null,
- "total": 200,
- "unit": "it",
- "unit_divisor": 1000,
- "unit_scale": false
- },
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "580ed1844b14436e986884a363e5521c",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- " 0%| | 0/200 [00:00<?, ?it/s]"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Best threshold 10.050251256281408 with f1 score 0.7047970479704798\n"
- ]
- }
- ],
- "source": [
- "f1_scores = []\n",
- "precision_scores = []\n",
- "recall_scores = []\n",
- "thresholds = np.linspace(0, 20, 200)\n",
- "for thresh in tqdm(thresholds):\n",
- " y_pred = predict(X_val, cluster_centers, thresh)\n",
- " precision, recall, f1, _ = precision_recall_fscore_support(y_val, y_pred, average=\"binary\")\n",
- " f1_scores.append(f1)\n",
- " precision_scores.append(precision)\n",
- " recall_scores.append(recall)\n",
- " \n",
- "best_thresh = thresholds[np.argmax(f1_scores)]\n",
- "best_f1 = np.max(f1_scores)\n",
- "print(\"Best threshold {} with f1 score {}\".format(best_thresh, best_f1))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### Visualize the relationship between the metrics and the threshold"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<matplotlib.legend.Legend at 0x7feda4d05fa0>"
- ]
- },
- "execution_count": 11,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 720x576 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "fig, ax = plt.subplots(figsize=(10,8))\n",
- "ax.plot(thresholds, f1_scores, label=\"f1\")\n",
- "ax.plot(thresholds, precision_scores, label=\"precision\")\n",
- "ax.plot(thresholds, recall_scores, label=\"recall\")\n",
- "ax.plot(best_thresh, best_f1, marker=\"o\")\n",
- "ax.set_xlabel(\"Threshold\")\n",
- "ax.set_ylabel(\"Score\")\n",
- "ax.legend()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Evaluate on the test set\n",
- "Now that we have tuned our `threshold` hyperparameter we can calculate the metrics on the test set."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Detected number of outliers: 250\n",
- "Actual number of outliers: 231\n",
- "------\n",
- "Precision: 0.68\n",
- "Recall 0.7359307359307359\n",
- "F1 0.7068607068607069\n"
- ]
- }
- ],
- "source": [
- "y_pred = predict(X_test, cluster_centers, best_thresh)\n",
- "precision, recall, f1, _ = precision_recall_fscore_support(y_test, y_pred, average=\"binary\")\n",
- "\n",
- "print(\"Detected number of outliers:\", y_pred.sum())\n",
- "print(\"Actual number of outliers:\", y_test.sum())\n",
- "print(\"------\")\n",
- "print(\"Precision:\", precision)\n",
- "print(\"Recall\", recall)\n",
- "print(\"F1\", f1)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "metadata": {},
- "outputs": [],
- "source": [
- "def plot_confusion_matrix(cm):\n",
- " fig, (ax1) = plt.subplots(ncols=1, figsize=(5,5))\n",
- " sns.heatmap(cm, \n",
- " xticklabels=['Genuine', 'Fraud'],\n",
- " yticklabels=['Genuine', 'Fraud'],\n",
- " annot=True,ax=ax1,\n",
- " linewidths=.2,linecolor=\"Darkblue\", cmap=\"Blues\")\n",
- " plt.title('Confusion Matrix', fontsize=14)\n",
- " plt.show()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 14,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 360x360 with 2 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "cm = confusion_matrix(y_test, y_pred)\n",
- "plot_confusion_matrix(cm)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Check your results on ILIAS\n",
- "On ILIAS you will find a online quiz where you can check your results.\n",
- "\n",
- "> Submit the value of your tuned `threshold` for which you received a f1-score of minimum **70%** on the validation set."
- ]
- }
- ],
- "metadata": {
- "jupytext": {
- "text_representation": {
- "extension": ".py",
- "format_name": "percent",
- "format_version": "1.2",
- "jupytext_version": "0.8.6"
- }
- },
- "kernelspec": {
- "display_name": "Python 3 (ipykernel)",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.9.12"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/notebooks/12A Association Rules/Association Rules Examples.ipynb b/notebooks/12A Association Rules/Association Rules Examples.ipynb
index 108667b..5f7e936 100644
--- a/notebooks/12A Association Rules/Association Rules Examples.ipynb
+++ b/notebooks/12A Association Rules/Association Rules Examples.ipynb
@@ -9,7 +9,7 @@
},
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -36,26 +36,9 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- " Items\n",
- "Id \n",
- "1 [burger, salad, coke, ice cream]\n",
- "2 [burger, salad, coke, ice cream]\n",
- "3 [burger, fries, coke, pie]\n",
- "4 [burger, salad, coke, choc bar]\n",
- "5 [burger, salad, coke, muffin]\n",
- "6 [sandwich, fries, fanta, pie]\n",
- "7 [sandwich, fries, coke, pie]\n",
- "8 [sandwich, onion rings, water, muffin]\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"transactions = [\n",
" [\"burger\", \"salad\", \"coke\", \"ice cream\"],\n",
@@ -92,196 +75,9 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
- "\n",
- " .dataframe tbody tr th {\n",
- " vertical-align: top;\n",
- " }\n",
- "\n",
- " .dataframe thead th {\n",
- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>burger</th>\n",
- " <th>choc bar</th>\n",
- " <th>coke</th>\n",
- " <th>fanta</th>\n",
- " <th>fries</th>\n",
- " <th>ice cream</th>\n",
- " <th>muffin</th>\n",
- " <th>onion rings</th>\n",
- " <th>pie</th>\n",
- " <th>salad</th>\n",
- " <th>sandwich</th>\n",
- " <th>water</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>True</td>\n",
- " <td>False</td>\n",
- " <td>True</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>True</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>True</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>True</td>\n",
- " <td>False</td>\n",
- " <td>True</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>True</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>True</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>True</td>\n",
- " <td>False</td>\n",
- " <td>True</td>\n",
- " <td>False</td>\n",
- " <td>True</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>True</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>True</td>\n",
- " <td>True</td>\n",
- " <td>True</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>True</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>True</td>\n",
- " <td>False</td>\n",
- " <td>True</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>True</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>True</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>5</th>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>True</td>\n",
- " <td>True</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>True</td>\n",
- " <td>False</td>\n",
- " <td>True</td>\n",
- " <td>False</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>6</th>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>True</td>\n",
- " <td>False</td>\n",
- " <td>True</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>True</td>\n",
- " <td>False</td>\n",
- " <td>True</td>\n",
- " <td>False</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>7</th>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>True</td>\n",
- " <td>True</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>True</td>\n",
- " <td>True</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " burger choc bar coke fanta fries ice cream muffin onion rings \\\n",
- "0 True False True False False True False False \n",
- "1 True False True False False True False False \n",
- "2 True False True False True False False False \n",
- "3 True True True False False False False False \n",
- "4 True False True False False False True False \n",
- "5 False False False True True False False False \n",
- "6 False False True False True False False False \n",
- "7 False False False False False False True True \n",
- "\n",
- " pie salad sandwich water \n",
- "0 False True False False \n",
- "1 False True False False \n",
- "2 True False False False \n",
- "3 False True False False \n",
- "4 False True False False \n",
- "5 True False True False \n",
- "6 True False True False \n",
- "7 False False True True "
- ]
- },
- "execution_count": 3,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"te = TransactionEncoder()\n",
"te_ary = te.fit_transform(transactions.Items.values.tolist())\n",
@@ -298,90 +94,9 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
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- " .dataframe tbody tr th {\n",
- " vertical-align: top;\n",
- " }\n",
- "\n",
- " .dataframe thead th {\n",
- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>support</th>\n",
- " <th>itemsets</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>0.625</td>\n",
- " <td>(burger)</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>0.750</td>\n",
- " <td>(coke)</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>0.500</td>\n",
- " <td>(salad)</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>0.625</td>\n",
- " <td>(coke, burger)</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>0.500</td>\n",
- " <td>(burger, salad)</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>5</th>\n",
- " <td>0.500</td>\n",
- " <td>(coke, salad)</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>6</th>\n",
- " <td>0.500</td>\n",
- " <td>(coke, burger, salad)</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " support itemsets\n",
- "0 0.625 (burger)\n",
- "1 0.750 (coke)\n",
- "2 0.500 (salad)\n",
- "3 0.625 (coke, burger)\n",
- "4 0.500 (burger, salad)\n",
- "5 0.500 (coke, salad)\n",
- "6 0.500 (coke, burger, salad)"
- ]
- },
- "execution_count": 4,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"freq_itemsets = apriori(df, use_colnames=True, min_support=0.5)\n",
"freq_itemsets"
@@ -396,197 +111,9 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
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- " .dataframe tbody tr th {\n",
- " vertical-align: top;\n",
- " }\n",
- "\n",
- " .dataframe thead th {\n",
- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>antecedents</th>\n",
- " <th>consequents</th>\n",
- " <th>antecedent support</th>\n",
- " <th>consequent support</th>\n",
- " <th>support</th>\n",
- " <th>confidence</th>\n",
- " <th>lift</th>\n",
- " <th>leverage</th>\n",
- " <th>conviction</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>(coke)</td>\n",
- " <td>(burger)</td>\n",
- " <td>0.750</td>\n",
- " <td>0.625</td>\n",
- " <td>0.625</td>\n",
- " <td>0.833333</td>\n",
- " <td>1.333333</td>\n",
- " <td>0.15625</td>\n",
- " <td>2.25</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>(burger)</td>\n",
- " <td>(coke)</td>\n",
- " <td>0.625</td>\n",
- " <td>0.750</td>\n",
- " <td>0.625</td>\n",
- " <td>1.000000</td>\n",
- " <td>1.333333</td>\n",
- " <td>0.15625</td>\n",
- " <td>inf</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>(burger)</td>\n",
- " <td>(salad)</td>\n",
- " <td>0.625</td>\n",
- " <td>0.500</td>\n",
- " <td>0.500</td>\n",
- " <td>0.800000</td>\n",
- " <td>1.600000</td>\n",
- " <td>0.18750</td>\n",
- " <td>2.50</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>(salad)</td>\n",
- " <td>(burger)</td>\n",
- " <td>0.500</td>\n",
- " <td>0.625</td>\n",
- " <td>0.500</td>\n",
- " <td>1.000000</td>\n",
- " <td>1.600000</td>\n",
- " <td>0.18750</td>\n",
- " <td>inf</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>(salad)</td>\n",
- " <td>(coke)</td>\n",
- " <td>0.500</td>\n",
- " <td>0.750</td>\n",
- " <td>0.500</td>\n",
- " <td>1.000000</td>\n",
- " <td>1.333333</td>\n",
- " <td>0.12500</td>\n",
- " <td>inf</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>5</th>\n",
- " <td>(coke, burger)</td>\n",
- " <td>(salad)</td>\n",
- " <td>0.625</td>\n",
- " <td>0.500</td>\n",
- " <td>0.500</td>\n",
- " <td>0.800000</td>\n",
- " <td>1.600000</td>\n",
- " <td>0.18750</td>\n",
- " <td>2.50</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>6</th>\n",
- " <td>(coke, salad)</td>\n",
- " <td>(burger)</td>\n",
- " <td>0.500</td>\n",
- " <td>0.625</td>\n",
- " <td>0.500</td>\n",
- " <td>1.000000</td>\n",
- " <td>1.600000</td>\n",
- " <td>0.18750</td>\n",
- " <td>inf</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>7</th>\n",
- " <td>(burger, salad)</td>\n",
- " <td>(coke)</td>\n",
- " <td>0.500</td>\n",
- " <td>0.750</td>\n",
- " <td>0.500</td>\n",
- " <td>1.000000</td>\n",
- " <td>1.333333</td>\n",
- " <td>0.12500</td>\n",
- " <td>inf</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>8</th>\n",
- " <td>(burger)</td>\n",
- " <td>(coke, salad)</td>\n",
- " <td>0.625</td>\n",
- " <td>0.500</td>\n",
- " <td>0.500</td>\n",
- " <td>0.800000</td>\n",
- " <td>1.600000</td>\n",
- " <td>0.18750</td>\n",
- " <td>2.50</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>9</th>\n",
- " <td>(salad)</td>\n",
- " <td>(coke, burger)</td>\n",
- " <td>0.500</td>\n",
- " <td>0.625</td>\n",
- " <td>0.500</td>\n",
- " <td>1.000000</td>\n",
- " <td>1.600000</td>\n",
- " <td>0.18750</td>\n",
- " <td>inf</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " antecedents consequents antecedent support consequent support \\\n",
- "0 (coke) (burger) 0.750 0.625 \n",
- "1 (burger) (coke) 0.625 0.750 \n",
- "2 (burger) (salad) 0.625 0.500 \n",
- "3 (salad) (burger) 0.500 0.625 \n",
- "4 (salad) (coke) 0.500 0.750 \n",
- "5 (coke, burger) (salad) 0.625 0.500 \n",
- "6 (coke, salad) (burger) 0.500 0.625 \n",
- "7 (burger, salad) (coke) 0.500 0.750 \n",
- "8 (burger) (coke, salad) 0.625 0.500 \n",
- "9 (salad) (coke, burger) 0.500 0.625 \n",
- "\n",
- " support confidence lift leverage conviction \n",
- "0 0.625 0.833333 1.333333 0.15625 2.25 \n",
- "1 0.625 1.000000 1.333333 0.15625 inf \n",
- "2 0.500 0.800000 1.600000 0.18750 2.50 \n",
- "3 0.500 1.000000 1.600000 0.18750 inf \n",
- "4 0.500 1.000000 1.333333 0.12500 inf \n",
- "5 0.500 0.800000 1.600000 0.18750 2.50 \n",
- "6 0.500 1.000000 1.600000 0.18750 inf \n",
- "7 0.500 1.000000 1.333333 0.12500 inf \n",
- "8 0.500 0.800000 1.600000 0.18750 2.50 \n",
- "9 0.500 1.000000 1.600000 0.18750 inf "
- ]
- },
- "execution_count": 5,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"# Execute the following code to show the solution\n",
"association_rules(freq_itemsets, metric='confidence', min_threshold=0.75)"
diff --git a/notebooks/12A Association Rules/Association Rules.ipynb b/notebooks/12A Association Rules/Association Rules.ipynb
index 231bea0..32ad56e 100644
--- a/notebooks/12A Association Rules/Association Rules.ipynb
+++ b/notebooks/12A Association Rules/Association Rules.ipynb
@@ -9,7 +9,7 @@
},
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -48,22 +48,9 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- " Items\n",
- "Id \n",
- "1 [oats, lego, teddybear, rc car]\n",
- "2 [oats, red coat, gloves, teddybear, doll, warm boot]\n",
- "3 [lego, red jelly bag cap, rc car, doll]\n",
- "4 [lego, oats, large red bag, gift wrap paper, warm boot]\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"transactions = [['oats', 'lego', 'teddybear', 'rc car'],\n",
" ['oats', 'red coat', 'gloves', 'teddybear', 'doll', 'warm boot'],\n",
@@ -104,7 +91,7 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": null,
"metadata": {
"jupyter": {
"source_hidden": true
@@ -112,18 +99,7 @@
"solution2": "hidden",
"tags": []
},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "{'lego': 0.75, 'oats': 0.75, 'doll': 0.5}"
- ]
- },
- "execution_count": 3,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"# support_lego = 3/4\n",
"# support_oats = 3/4\n",
@@ -162,7 +138,7 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": null,
"metadata": {
"jupyter": {
"source_hidden": true
@@ -170,18 +146,7 @@
"solution2": "hidden",
"tags": []
},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "0.5"
- ]
- },
- "execution_count": 4,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"# confidence_lego_oats-teddybear = 0.25 / 0.5\n",
"0.25 / ( transactions.Items.map(lambda x: 'lego' in x and 'oats' in x).sum() / transactions.shape[0] )"
@@ -196,22 +161,9 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- " Items\n",
- "Id \n",
- "1 [oats, lego, teddybear, rc car]\n",
- "2 [oats, red coat, gloves, teddybear, doll, warm boot]\n",
- "3 [lego, red jelly bag cap, rc car, doll]\n",
- "4 [lego, oats, large red bag, gift wrap paper, warm boot]\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"with pd.option_context('display.max_colwidth', 80):\n",
" print(transactions)"
@@ -245,7 +197,7 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": null,
"metadata": {
"jupyter": {
"source_hidden": true
@@ -253,106 +205,7 @@
"solution2": "hidden",
"tags": []
},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
- "\n",
- " .dataframe tbody tr th {\n",
- " vertical-align: top;\n",
- " }\n",
- "\n",
- " .dataframe thead th {\n",
- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>support</th>\n",
- " <th>itemsets</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>0.50</td>\n",
- " <td>(doll)</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>0.75</td>\n",
- " <td>(lego)</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>0.75</td>\n",
- " <td>(oats)</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>0.50</td>\n",
- " <td>(rc car)</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>0.50</td>\n",
- " <td>(teddybear)</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>5</th>\n",
- " <td>0.50</td>\n",
- " <td>(warm boot)</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>6</th>\n",
- " <td>0.50</td>\n",
- " <td>(lego, oats)</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>7</th>\n",
- " <td>0.50</td>\n",
- " <td>(lego, rc car)</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>8</th>\n",
- " <td>0.50</td>\n",
- " <td>(teddybear, oats)</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>9</th>\n",
- " <td>0.50</td>\n",
- " <td>(warm boot, oats)</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " support itemsets\n",
- "0 0.50 (doll)\n",
- "1 0.75 (lego)\n",
- "2 0.75 (oats)\n",
- "3 0.50 (rc car)\n",
- "4 0.50 (teddybear)\n",
- "5 0.50 (warm boot)\n",
- "6 0.50 (lego, oats)\n",
- "7 0.50 (lego, rc car)\n",
- "8 0.50 (teddybear, oats)\n",
- "9 0.50 (warm boot, oats)"
- ]
- },
- "execution_count": 7,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"# Execute the following code to show the solution. We will see how to use this library in a minute.\n",
"te = TransactionEncoder()\n",
@@ -401,7 +254,7 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": null,
"metadata": {
"jupyter": {
"source_hidden": true
@@ -409,97 +262,7 @@
"solution2": "hidden",
"tags": []
},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
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- " .dataframe tbody tr th {\n",
- " vertical-align: top;\n",
- " }\n",
- "\n",
- " .dataframe thead th {\n",
- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>antecedents</th>\n",
- " <th>consequents</th>\n",
- " <th>antecedent support</th>\n",
- " <th>consequent support</th>\n",
- " <th>support</th>\n",
- " <th>confidence</th>\n",
- " <th>lift</th>\n",
- " <th>leverage</th>\n",
- " <th>conviction</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>(rc car)</td>\n",
- " <td>(lego)</td>\n",
- " <td>0.5</td>\n",
- " <td>0.75</td>\n",
- " <td>0.5</td>\n",
- " <td>1.0</td>\n",
- " <td>1.333333</td>\n",
- " <td>0.125</td>\n",
- " <td>inf</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>(teddybear)</td>\n",
- " <td>(oats)</td>\n",
- " <td>0.5</td>\n",
- " <td>0.75</td>\n",
- " <td>0.5</td>\n",
- " <td>1.0</td>\n",
- " <td>1.333333</td>\n",
- " <td>0.125</td>\n",
- " <td>inf</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>(warm boot)</td>\n",
- " <td>(oats)</td>\n",
- " <td>0.5</td>\n",
- " <td>0.75</td>\n",
- " <td>0.5</td>\n",
- " <td>1.0</td>\n",
- " <td>1.333333</td>\n",
- " <td>0.125</td>\n",
- " <td>inf</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " antecedents consequents antecedent support consequent support support \\\n",
- "0 (rc car) (lego) 0.5 0.75 0.5 \n",
- "1 (teddybear) (oats) 0.5 0.75 0.5 \n",
- "2 (warm boot) (oats) 0.5 0.75 0.5 \n",
- "\n",
- " confidence lift leverage conviction \n",
- "0 1.0 1.333333 0.125 inf \n",
- "1 1.0 1.333333 0.125 inf \n",
- "2 1.0 1.333333 0.125 inf "
- ]
- },
- "execution_count": 8,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"# Execute the following code to show the solution\n",
"association_rules(freq_itemsets, metric='confidence', min_threshold=0.75)"
@@ -522,84 +285,9 @@
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
- "\n",
- " .dataframe tbody tr th {\n",
- " vertical-align: top;\n",
- " }\n",
- "\n",
- " .dataframe thead th {\n",
- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>Date</th>\n",
- " <th>Transaction</th>\n",
- " <th>Item</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>2000-01-01</td>\n",
- " <td>1</td>\n",
- " <td>pork</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>2000-01-01</td>\n",
- " <td>1</td>\n",
- " <td>sandwich bags</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>2000-01-01</td>\n",
- " <td>1</td>\n",
- " <td>lunch meat</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>2000-01-01</td>\n",
- " <td>1</td>\n",
- " <td>all- purpose</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>2000-01-01</td>\n",
- " <td>1</td>\n",
- " <td>flour</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " Date Transaction Item\n",
- "0 2000-01-01 1 pork\n",
- "1 2000-01-01 1 sandwich bags\n",
- "2 2000-01-01 1 lunch meat\n",
- "3 2000-01-01 1 all- purpose\n",
- "4 2000-01-01 1 flour"
- ]
- },
- "execution_count": 9,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"transactions = pd.read_csv('acostasg.csv')\n",
"transactions.columns = ['Date', 'Transaction', 'Item']\n",
@@ -608,20 +296,9 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "(22342, 3)"
- ]
- },
- "execution_count": 10,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"transactions.shape"
]
@@ -654,7 +331,7 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": null,
"metadata": {
"jupyter": {
"source_hidden": true
@@ -669,20 +346,9 @@
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "(21791, 3)"
- ]
- },
- "execution_count": 12,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"transactions.shape"
]
@@ -697,84 +363,9 @@
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
- "\n",
- " .dataframe tbody tr th {\n",
- " vertical-align: top;\n",
- " }\n",
- "\n",
- " .dataframe thead th {\n",
- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>Date</th>\n",
- " <th>Item</th>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>Transaction</th>\n",
- " <th></th>\n",
- " <th></th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>2000-01-01</td>\n",
- " <td>[pork, sandwich bags, lunch meat, flour, soda,...</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>2000-01-01</td>\n",
- " <td>[toilet paper, shampoo, hand soap, waffles, ve...</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>2000-01-02</td>\n",
- " <td>[soda, pork, soap, ice cream, toilet paper, di...</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>2000-01-02</td>\n",
- " <td>[cereals, juice, lunch meat, soda, toilet paper]</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>5</th>\n",
- " <td>2000-01-02</td>\n",
- " <td>[sandwich loaves, pasta, tortillas, mixes, han...</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " Date Item\n",
- "Transaction \n",
- "1 2000-01-01 [pork, sandwich bags, lunch meat, flour, soda,...\n",
- "2 2000-01-01 [toilet paper, shampoo, hand soap, waffles, ve...\n",
- "3 2000-01-02 [soda, pork, soap, ice cream, toilet paper, di...\n",
- "4 2000-01-02 [cereals, juice, lunch meat, soda, toilet paper]\n",
- "5 2000-01-02 [sandwich loaves, pasta, tortillas, mixes, han..."
- ]
- },
- "execution_count": 13,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"transactions = transactions.groupby('Transaction').agg({'Date':lambda x: x.iloc[0] ,'Item':list})\n",
"transactions.head()"
@@ -791,7 +382,7 @@
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": null,
"metadata": {
"solution2": "hidden",
"solution2_first": true
@@ -810,7 +401,7 @@
},
{
"cell_type": "code",
- "execution_count": 14,
+ "execution_count": null,
"metadata": {
"jupyter": {
"source_hidden": true
@@ -818,97 +409,7 @@
"solution2": "hidden",
"tags": []
},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
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- " .dataframe tbody tr th {\n",
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- " }\n",
- "\n",
- " .dataframe thead th {\n",
- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>Date</th>\n",
- " <th>Item</th>\n",
- " <th>Size</th>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>Transaction</th>\n",
- " <th></th>\n",
- " <th></th>\n",
- " <th></th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>2000-01-01</td>\n",
- " <td>[pork, sandwich bags, lunch meat, flour, soda,...</td>\n",
- " <td>16</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>2000-01-01</td>\n",
- " <td>[toilet paper, shampoo, hand soap, waffles, ve...</td>\n",
- " <td>23</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>2000-01-02</td>\n",
- " <td>[soda, pork, soap, ice cream, toilet paper, di...</td>\n",
- " <td>31</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>2000-01-02</td>\n",
- " <td>[cereals, juice, lunch meat, soda, toilet paper]</td>\n",
- " <td>5</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>5</th>\n",
- " <td>2000-01-02</td>\n",
- " <td>[sandwich loaves, pasta, tortillas, mixes, han...</td>\n",
- " <td>26</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " Date Item \\\n",
- "Transaction \n",
- "1 2000-01-01 [pork, sandwich bags, lunch meat, flour, soda,... \n",
- "2 2000-01-01 [toilet paper, shampoo, hand soap, waffles, ve... \n",
- "3 2000-01-02 [soda, pork, soap, ice cream, toilet paper, di... \n",
- "4 2000-01-02 [cereals, juice, lunch meat, soda, toilet paper] \n",
- "5 2000-01-02 [sandwich loaves, pasta, tortillas, mixes, han... \n",
- "\n",
- " Size \n",
- "Transaction \n",
- "1 16 \n",
- "2 23 \n",
- "3 31 \n",
- "4 5 \n",
- "5 26 "
- ]
- },
- "execution_count": 14,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"transactions['Size'] = transactions['Item'].map(len)\n",
"transactions.head()"
@@ -923,119 +424,18 @@
},
{
"cell_type": "code",
- "execution_count": 15,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
- "\n",
- " .dataframe tbody tr th {\n",
- " vertical-align: top;\n",
- " }\n",
- "\n",
- " .dataframe thead th {\n",
- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>Size</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>count</th>\n",
- " <td>1139.000000</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>mean</th>\n",
- " <td>19.131694</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>std</th>\n",
- " <td>8.264745</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>min</th>\n",
- " <td>4.000000</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>25%</th>\n",
- " <td>12.000000</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>50%</th>\n",
- " <td>19.000000</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>75%</th>\n",
- " <td>26.000000</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>max</th>\n",
- " <td>34.000000</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " Size\n",
- "count 1139.000000\n",
- "mean 19.131694\n",
- "std 8.264745\n",
- "min 4.000000\n",
- "25% 12.000000\n",
- "50% 19.000000\n",
- "75% 26.000000\n",
- "max 34.000000"
- ]
- },
- "execution_count": 15,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"transactions.describe()"
]
},
{
"cell_type": "code",
- "execution_count": 16,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([[<AxesSubplot:title={'center':'Size'}>]], dtype=object)"
- ]
- },
- "execution_count": 16,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
"transactions.hist()"
]
@@ -1049,216 +449,9 @@
},
{
"cell_type": "code",
- "execution_count": 17,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
- "\n",
- " .dataframe tbody tr th {\n",
- " vertical-align: top;\n",
- " }\n",
- "\n",
- " .dataframe thead th {\n",
- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>aluminum foil</th>\n",
- " <th>bagels</th>\n",
- " <th>beef</th>\n",
- " <th>butter</th>\n",
- " <th>cereals</th>\n",
- " <th>cheeses</th>\n",
- " <th>coffee/tea</th>\n",
- " <th>dinner rolls</th>\n",
- " <th>dishwashing liquid/detergent</th>\n",
- " <th>eggs</th>\n",
- " <th>...</th>\n",
- " <th>shampoo</th>\n",
- " <th>soap</th>\n",
- " <th>soda</th>\n",
- " <th>spaghetti sauce</th>\n",
- " <th>sugar</th>\n",
- " <th>toilet paper</th>\n",
- " <th>tortillas</th>\n",
- " <th>vegetables</th>\n",
- " <th>waffles</th>\n",
- " <th>yogurt</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>True</td>\n",
- " <td>False</td>\n",
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- " <td>False</td>\n",
- " <td>True</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>...</td>\n",
- " <td>True</td>\n",
- " <td>True</td>\n",
- " <td>True</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>True</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " </tr>\n",
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- " <th>1</th>\n",
- " <td>True</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>True</td>\n",
- " <td>True</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>True</td>\n",
- " <td>False</td>\n",
- " <td>...</td>\n",
- " <td>True</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>True</td>\n",
- " <td>True</td>\n",
- " <td>True</td>\n",
- " <td>True</td>\n",
- " <td>True</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>False</td>\n",
- " <td>True</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>True</td>\n",
- " <td>True</td>\n",
- " <td>False</td>\n",
- " <td>True</td>\n",
- " <td>False</td>\n",
- " <td>True</td>\n",
- " <td>...</td>\n",
- " <td>True</td>\n",
- " <td>True</td>\n",
- " <td>True</td>\n",
- " <td>True</td>\n",
- " <td>False</td>\n",
- " <td>True</td>\n",
- " <td>False</td>\n",
- " <td>True</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>True</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>...</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>True</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>True</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>True</td>\n",
- " <td>False</td>\n",
- " <td>True</td>\n",
- " <td>...</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>True</td>\n",
- " <td>True</td>\n",
- " <td>False</td>\n",
- " <td>True</td>\n",
- " <td>True</td>\n",
- " <td>True</td>\n",
- " <td>True</td>\n",
- " <td>True</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "<p>5 rows × 37 columns</p>\n",
- "</div>"
- ],
- "text/plain": [
- " aluminum foil bagels beef butter cereals cheeses coffee/tea \\\n",
- "0 True False True True False False False \n",
- "1 True False False False True True False \n",
- "2 False True False False True True False \n",
- "3 False False False False True False False \n",
- "4 False False False False False False False \n",
- "\n",
- " dinner rolls dishwashing liquid/detergent eggs ... shampoo soap \\\n",
- "0 True False False ... True True \n",
- "1 False True False ... True False \n",
- "2 True False True ... True True \n",
- "3 False False False ... False False \n",
- "4 True False True ... False False \n",
- "\n",
- " soda spaghetti sauce sugar toilet paper tortillas vegetables \\\n",
- "0 True False False False False True \n",
- "1 False False False True True True \n",
- "2 True True False True False True \n",
- "3 True False False True False False \n",
- "4 True True False True True True \n",
- "\n",
- " waffles yogurt \n",
- "0 False False \n",
- "1 True True \n",
- "2 False False \n",
- "3 False False \n",
- "4 True True \n",
- "\n",
- "[5 rows x 37 columns]"
- ]
- },
- "execution_count": 17,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"te = TransactionEncoder()\n",
"te_binary = te.fit_transform(transactions.Item)\n",
@@ -1277,7 +470,7 @@
},
{
"cell_type": "code",
- "execution_count": 18,
+ "execution_count": null,
"metadata": {
"solution2": "hidden",
"solution2_first": true
@@ -1296,7 +489,7 @@
},
{
"cell_type": "code",
- "execution_count": 18,
+ "execution_count": null,
"metadata": {
"jupyter": {
"source_hidden": true
@@ -1318,7 +511,7 @@
},
{
"cell_type": "code",
- "execution_count": 20,
+ "execution_count": null,
"metadata": {
"solution2": "hidden",
"solution2_first": true
@@ -1337,7 +530,7 @@
},
{
"cell_type": "code",
- "execution_count": 19,
+ "execution_count": null,
"metadata": {
"jupyter": {
"source_hidden": true
@@ -1345,125 +538,7 @@
"solution2": "hidden",
"tags": []
},
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- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>antecedents</th>\n",
- " <th>consequents</th>\n",
- " <th>antecedent support</th>\n",
- " <th>consequent support</th>\n",
- " <th>support</th>\n",
- " <th>confidence</th>\n",
- " <th>lift</th>\n",
- " <th>leverage</th>\n",
- " <th>conviction</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>(aluminum foil)</td>\n",
- " <td>(vegetables)</td>\n",
- " <td>0.384548</td>\n",
- " <td>0.739245</td>\n",
- " <td>0.310799</td>\n",
- " <td>0.808219</td>\n",
- " <td>1.093304</td>\n",
- " <td>0.026524</td>\n",
- " <td>1.359651</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>(bagels)</td>\n",
- " <td>(vegetables)</td>\n",
- " <td>0.385426</td>\n",
- " <td>0.739245</td>\n",
- " <td>0.300263</td>\n",
- " <td>0.779043</td>\n",
- " <td>1.053836</td>\n",
- " <td>0.015339</td>\n",
- " <td>1.180118</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>(beef)</td>\n",
- " <td>(vegetables)</td>\n",
- " <td>0.374890</td>\n",
- " <td>0.739245</td>\n",
- " <td>0.291484</td>\n",
- " <td>0.777518</td>\n",
- " <td>1.051773</td>\n",
- " <td>0.014348</td>\n",
- " <td>1.172025</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>(butter)</td>\n",
- " <td>(vegetables)</td>\n",
- " <td>0.367867</td>\n",
- " <td>0.739245</td>\n",
- " <td>0.283582</td>\n",
- " <td>0.770883</td>\n",
- " <td>1.042798</td>\n",
- " <td>0.011639</td>\n",
- " <td>1.138087</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>(cereals)</td>\n",
- " <td>(vegetables)</td>\n",
- " <td>0.395961</td>\n",
- " <td>0.739245</td>\n",
- " <td>0.310799</td>\n",
- " <td>0.784922</td>\n",
- " <td>1.061789</td>\n",
- " <td>0.018087</td>\n",
- " <td>1.212377</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " antecedents consequents antecedent support consequent support \\\n",
- "0 (aluminum foil) (vegetables) 0.384548 0.739245 \n",
- "1 (bagels) (vegetables) 0.385426 0.739245 \n",
- "2 (beef) (vegetables) 0.374890 0.739245 \n",
- "3 (butter) (vegetables) 0.367867 0.739245 \n",
- "4 (cereals) (vegetables) 0.395961 0.739245 \n",
- "\n",
- " support confidence lift leverage conviction \n",
- "0 0.310799 0.808219 1.093304 0.026524 1.359651 \n",
- "1 0.300263 0.779043 1.053836 0.015339 1.180118 \n",
- "2 0.291484 0.777518 1.051773 0.014348 1.172025 \n",
- "3 0.283582 0.770883 1.042798 0.011639 1.138087 \n",
- "4 0.310799 0.784922 1.061789 0.018087 1.212377 "
- ]
- },
- "execution_count": 19,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"rules = association_rules(freq_itemsets, metric='confidence', min_threshold=0.5)\n",
"rules.head()"
@@ -1496,7 +571,7 @@
},
{
"cell_type": "code",
- "execution_count": 20,
+ "execution_count": null,
"metadata": {
"jupyter": {
"source_hidden": true
@@ -1504,207 +579,7 @@
"solution2": "hidden",
"tags": []
},
- "outputs": [
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- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>antecedents</th>\n",
- " <th>consequents</th>\n",
- " <th>antecedent support</th>\n",
- " <th>consequent support</th>\n",
- " <th>support</th>\n",
- " <th>confidence</th>\n",
- " <th>lift</th>\n",
- " <th>leverage</th>\n",
- " <th>conviction</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>7151</th>\n",
- " <td>(eggs, ice cream, pasta)</td>\n",
- " <td>(paper towels)</td>\n",
- " <td>0.085162</td>\n",
- " <td>0.362599</td>\n",
- " <td>0.055312</td>\n",
- " <td>0.649485</td>\n",
- " <td>1.791193</td>\n",
- " <td>0.024432</td>\n",
- " <td>1.818468</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>7152</th>\n",
- " <td>(eggs, ice cream, paper towels)</td>\n",
- " <td>(pasta)</td>\n",
- " <td>0.086040</td>\n",
- " <td>0.371378</td>\n",
- " <td>0.055312</td>\n",
- " <td>0.642857</td>\n",
- " <td>1.731003</td>\n",
- " <td>0.023358</td>\n",
- " <td>1.760140</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2694</th>\n",
- " <td>(bagels, cereals, sandwich bags)</td>\n",
- " <td>(cheeses)</td>\n",
- " <td>0.075505</td>\n",
- " <td>0.390694</td>\n",
- " <td>0.050922</td>\n",
- " <td>0.674419</td>\n",
- " <td>1.726209</td>\n",
- " <td>0.021423</td>\n",
- " <td>1.871441</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2361</th>\n",
- " <td>(aluminum foil, toilet paper, yogurt)</td>\n",
- " <td>(juice)</td>\n",
- " <td>0.078139</td>\n",
- " <td>0.376646</td>\n",
- " <td>0.050044</td>\n",
- " <td>0.640449</td>\n",
- " <td>1.700401</td>\n",
- " <td>0.020613</td>\n",
- " <td>1.733703</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2464</th>\n",
- " <td>(aluminum foil, poultry, yogurt)</td>\n",
- " <td>(mixes)</td>\n",
- " <td>0.080773</td>\n",
- " <td>0.375768</td>\n",
- " <td>0.050922</td>\n",
- " <td>0.630435</td>\n",
- " <td>1.677722</td>\n",
- " <td>0.020570</td>\n",
- " <td>1.689098</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2692</th>\n",
- " <td>(bagels, cheeses, cereals)</td>\n",
- " <td>(sandwich bags)</td>\n",
- " <td>0.083406</td>\n",
- " <td>0.367867</td>\n",
- " <td>0.050922</td>\n",
- " <td>0.610526</td>\n",
- " <td>1.659641</td>\n",
- " <td>0.020239</td>\n",
- " <td>1.623045</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>6280</th>\n",
- " <td>(spaghetti sauce, poultry, laundry detergent)</td>\n",
- " <td>(dinner rolls)</td>\n",
- " <td>0.083406</td>\n",
- " <td>0.388938</td>\n",
- " <td>0.053556</td>\n",
- " <td>0.642105</td>\n",
- " <td>1.650921</td>\n",
- " <td>0.021116</td>\n",
- " <td>1.707380</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>6168</th>\n",
- " <td>(poultry, ice cream, spaghetti sauce)</td>\n",
- " <td>(dinner rolls)</td>\n",
- " <td>0.080773</td>\n",
- " <td>0.388938</td>\n",
- " <td>0.051800</td>\n",
- " <td>0.641304</td>\n",
- " <td>1.648862</td>\n",
- " <td>0.020384</td>\n",
- " <td>1.703568</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2339</th>\n",
- " <td>(aluminum foil, poultry, yogurt)</td>\n",
- " <td>(juice)</td>\n",
- " <td>0.080773</td>\n",
- " <td>0.376646</td>\n",
- " <td>0.050044</td>\n",
- " <td>0.619565</td>\n",
- " <td>1.644953</td>\n",
- " <td>0.019621</td>\n",
- " <td>1.638530</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>6169</th>\n",
- " <td>(spaghetti sauce, ice cream, dinner rolls)</td>\n",
- " <td>(poultry)</td>\n",
- " <td>0.075505</td>\n",
- " <td>0.421422</td>\n",
- " <td>0.051800</td>\n",
- " <td>0.686047</td>\n",
- " <td>1.627931</td>\n",
- " <td>0.019980</td>\n",
- " <td>1.842877</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " antecedents consequents \\\n",
- "7151 (eggs, ice cream, pasta) (paper towels) \n",
- "7152 (eggs, ice cream, paper towels) (pasta) \n",
- "2694 (bagels, cereals, sandwich bags) (cheeses) \n",
- "2361 (aluminum foil, toilet paper, yogurt) (juice) \n",
- "2464 (aluminum foil, poultry, yogurt) (mixes) \n",
- "2692 (bagels, cheeses, cereals) (sandwich bags) \n",
- "6280 (spaghetti sauce, poultry, laundry detergent) (dinner rolls) \n",
- "6168 (poultry, ice cream, spaghetti sauce) (dinner rolls) \n",
- "2339 (aluminum foil, poultry, yogurt) (juice) \n",
- "6169 (spaghetti sauce, ice cream, dinner rolls) (poultry) \n",
- "\n",
- " antecedent support consequent support support confidence lift \\\n",
- "7151 0.085162 0.362599 0.055312 0.649485 1.791193 \n",
- "7152 0.086040 0.371378 0.055312 0.642857 1.731003 \n",
- "2694 0.075505 0.390694 0.050922 0.674419 1.726209 \n",
- "2361 0.078139 0.376646 0.050044 0.640449 1.700401 \n",
- "2464 0.080773 0.375768 0.050922 0.630435 1.677722 \n",
- "2692 0.083406 0.367867 0.050922 0.610526 1.659641 \n",
- "6280 0.083406 0.388938 0.053556 0.642105 1.650921 \n",
- "6168 0.080773 0.388938 0.051800 0.641304 1.648862 \n",
- "2339 0.080773 0.376646 0.050044 0.619565 1.644953 \n",
- "6169 0.075505 0.421422 0.051800 0.686047 1.627931 \n",
- "\n",
- " leverage conviction \n",
- "7151 0.024432 1.818468 \n",
- "7152 0.023358 1.760140 \n",
- "2694 0.021423 1.871441 \n",
- "2361 0.020613 1.733703 \n",
- "2464 0.020570 1.689098 \n",
- "2692 0.020239 1.623045 \n",
- "6280 0.021116 1.707380 \n",
- "6168 0.020384 1.703568 \n",
- "2339 0.019621 1.638530 \n",
- "6169 0.019980 1.842877 "
- ]
- },
- "execution_count": 20,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"rules.sort_values('lift', ascending=False).head(n=10)"
]
@@ -1725,22 +600,9 @@
},
{
"cell_type": "code",
- "execution_count": 21,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
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\n",
- "text/plain": [
- "<Figure size 720x576 with 2 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
"plt.subplots(figsize=(10, 8))\n",
"plt.scatter(rules.support, rules.confidence, c=rules.lift, s=5)\n",
@@ -1760,89 +622,9 @@
},
{
"cell_type": "code",
- "execution_count": 22,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
- "\n",
- " .dataframe tbody tr th {\n",
- " vertical-align: top;\n",
- " }\n",
- "\n",
- " .dataframe thead th {\n",
- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>antecedents</th>\n",
- " <th>consequents</th>\n",
- " <th>antecedent support</th>\n",
- " <th>consequent support</th>\n",
- " <th>support</th>\n",
- " <th>confidence</th>\n",
- " <th>lift</th>\n",
- " <th>leverage</th>\n",
- " <th>conviction</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>6601</th>\n",
- " <td>(sandwich loaves, dishwashing liquid/detergent...</td>\n",
- " <td>(vegetables)</td>\n",
- " <td>0.055312</td>\n",
- " <td>0.739245</td>\n",
- " <td>0.053556</td>\n",
- " <td>0.968254</td>\n",
- " <td>1.309788</td>\n",
- " <td>0.012667</td>\n",
- " <td>8.213784</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>7433</th>\n",
- " <td>(sandwich loaves, eggs, poultry)</td>\n",
- " <td>(vegetables)</td>\n",
- " <td>0.070237</td>\n",
- " <td>0.739245</td>\n",
- " <td>0.067603</td>\n",
- " <td>0.962500</td>\n",
- " <td>1.302004</td>\n",
- " <td>0.015681</td>\n",
- " <td>6.953468</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " antecedents consequents \\\n",
- "6601 (sandwich loaves, dishwashing liquid/detergent... (vegetables) \n",
- "7433 (sandwich loaves, eggs, poultry) (vegetables) \n",
- "\n",
- " antecedent support consequent support support confidence lift \\\n",
- "6601 0.055312 0.739245 0.053556 0.968254 1.309788 \n",
- "7433 0.070237 0.739245 0.067603 0.962500 1.302004 \n",
- "\n",
- " leverage conviction \n",
- "6601 0.012667 8.213784 \n",
- "7433 0.015681 6.953468 "
- ]
- },
- "execution_count": 22,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"rules.query('support > 0.05 and confidence > 0.9 and lift > 1.3')"
]
diff --git a/notebooks/12A Association Rules/Association Rules_Solution.ipynb b/notebooks/12A Association Rules/Association Rules_Solution.ipynb
deleted file mode 100644
index 421b5a3..0000000
--- a/notebooks/12A Association Rules/Association Rules_Solution.ipynb
+++ /dev/null
@@ -1,1829 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Association Rules\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\python36\\lib\\distutils\\__init__.py:14: UserWarning: The virtualenv distutils package at %s appears to be in the same location as the system distutils?\n",
- " warnings.warn(\"The virtualenv distutils package at %s appears to be in the same location as the system distutils?\")\n"
- ]
- }
- ],
- "source": [
- "import numpy as np\n",
- "import pandas as pd\n",
- "import seaborn as sns\n",
- "import matplotlib.pyplot as plt\n",
- "\n",
- "from mlxtend.preprocessing import TransactionEncoder\n",
- "from mlxtend.frequent_patterns import apriori, association_rules\n",
- "\n",
- "import plotly\n",
- "import plotly.graph_objs as go\n",
- "\n",
- "from sklearn.preprocessing import RobustScaler\n",
- "from sklearn.cluster import KMeans\n",
- "from mpl_toolkits.mplot3d import Axes3D\n",
- "from sklearn.decomposition import PCA\n",
- "\n",
- "from tqdm.notebook import tqdm\n",
- "\n",
- "%matplotlib inline"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Exercise 2 - Association Rule Mining\n",
- "Scikit-Learn does not support association rule learning. Fortunately though, [Sebastian Raschka](https://sebastianraschka.com) (a personal hero of mine) implemented this (and many other cool things) in his library *mlextend*, which aims to be as Scikit-Learn compatible as possible.\n",
- "\n",
- "You can find examples for generating frequent itemsets with apriori [here](http://rasbt.github.io/mlxtend/user_guide/frequent_patterns/apriori/) and for association rule mining [here](http://rasbt.github.io/mlxtend/user_guide/frequent_patterns/association_rules/)."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Manually\n",
- "But first, we do a little manual calculation. You are given the following dataset of transactions."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- " Items\n",
- "Id \n",
- "1 [oats, lego, teddybear, rc car]\n",
- "2 [oats, red coat, gloves, teddybear, doll, warm boot]\n",
- "3 [lego, red jelly bag cap, rc car, doll]\n",
- "4 [lego, oats, large red bag, gift wrap paper, warm boot]\n"
- ]
- }
- ],
- "source": [
- "transactions = [['oats', 'lego', 'teddybear', 'rc car'],\n",
- " ['oats', 'red coat', 'gloves', 'teddybear', 'doll', 'warm boot'],\n",
- " ['lego', 'red jelly bag cap', 'rc car', 'doll'],\n",
- " ['lego', 'oats', 'large red bag', 'gift wrap paper', 'warm boot']]\n",
- "\n",
- "transactions = pd.DataFrame(data={\"Items\":transactions}, index=range(1,5))\n",
- "transactions.index.name = 'Id'\n",
- "\n",
- "with pd.option_context('display.max_colwidth', 80):\n",
- " print(transactions)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Calculate the support for lego, oats and doll (manunally or by code, your choice)."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "lines_to_next_cell": 2,
- "solution2": "hidden",
- "solution2_first": true
- },
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {
- "solution2": "hidden"
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "{'lego': 0.75, 'oats': 0.75, 'doll': 0.5}"
- ]
- },
- "execution_count": 3,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# support_lego = 3/4\n",
- "# support_oats = 3/4\n",
- "# support_doll = 2/4\n",
- "\n",
- "# or with code, for example:\n",
- "support = {}\n",
- "for item in ['lego', 'oats', 'doll']:\n",
- " support[item] = transactions.Items.map(lambda x: item in x).sum() / transactions.shape[0] # support of 'lego'\n",
- "support"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Calculate the confidence of `['lego', 'oats'] -> ['teddybear']`"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "solution2": "hidden",
- "solution2_first": true
- },
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {
- "solution2": "hidden"
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "0.5"
- ]
- },
- "execution_count": 4,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# confidence_lego_oats-teddybear = 0.25 / 0.5\n",
- "0.25 / ( transactions.Items.map(lambda x: 'lego' in x and 'oats' in x).sum() / transactions.shape[0] )"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Now apply the Apriori algorithm and find the frequent item sets with a minimum support of 0.5 and minimum confidence of 0.75. Here is the dataset again:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- " Items\n",
- "Id \n",
- "1 [oats, lego, teddybear, rc car]\n",
- "2 [oats, red coat, gloves, teddybear, doll, warm boot]\n",
- "3 [lego, red jelly bag cap, rc car, doll]\n",
- "4 [lego, oats, large red bag, gift wrap paper, warm boot]\n"
- ]
- }
- ],
- "source": [
- "with pd.option_context('display.max_colwidth', 80):\n",
- " print(transactions)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Step 1: Generate frequent item sets satisfying the support threshold (hint: there are 6 itemsets of length 1 and 4 itemsets of length 2)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "solution2": "hidden",
- "solution2_first": true
- },
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {
- "solution2": "hidden"
- },
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
- "\n",
- " .dataframe tbody tr th {\n",
- " vertical-align: top;\n",
- " }\n",
- "\n",
- " .dataframe thead th {\n",
- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>support</th>\n",
- " <th>itemsets</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>0.50</td>\n",
- " <td>(doll)</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>0.75</td>\n",
- " <td>(lego)</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>0.75</td>\n",
- " <td>(oats)</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>0.50</td>\n",
- " <td>(rc car)</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>0.50</td>\n",
- " <td>(teddybear)</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>5</th>\n",
- " <td>0.50</td>\n",
- " <td>(warm boot)</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>6</th>\n",
- " <td>0.50</td>\n",
- " <td>(lego, oats)</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>7</th>\n",
- " <td>0.50</td>\n",
- " <td>(lego, rc car)</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>8</th>\n",
- " <td>0.50</td>\n",
- " <td>(teddybear, oats)</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>9</th>\n",
- " <td>0.50</td>\n",
- " <td>(oats, warm boot)</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " support itemsets\n",
- "0 0.50 (doll)\n",
- "1 0.75 (lego)\n",
- "2 0.75 (oats)\n",
- "3 0.50 (rc car)\n",
- "4 0.50 (teddybear)\n",
- "5 0.50 (warm boot)\n",
- "6 0.50 (lego, oats)\n",
- "7 0.50 (lego, rc car)\n",
- "8 0.50 (teddybear, oats)\n",
- "9 0.50 (oats, warm boot)"
- ]
- },
- "execution_count": 6,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# Execute the following code to show the solution. We will see how to use this library in a minute.\n",
- "te = TransactionEncoder()\n",
- "te_ary = te.fit_transform(transactions.Items.values.tolist())\n",
- "df = pd.DataFrame(te_ary, columns=te.columns_)\n",
- "\n",
- "freq_itemsets = apriori(df, use_colnames=True, min_support=0.5)\n",
- "freq_itemsets"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Step 2: Extract rules from frequent item sets satisfying the confidence threshold (hint: there are three itemsets)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "solution2": "hidden",
- "solution2_first": true
- },
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "solution2": "hidden"
- },
- "source": [
- "There are 8 candidates: From all 4 itemsets with two items, generate the two possibilities.\n",
- "\n",
- "From these 8 candidates, 4 have a confidence of 0.5/0.75 which is below the threshold and 4 have a confidence of 0.5/0.5 which is above."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {
- "solution2": "hidden"
- },
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
- "\n",
- " .dataframe tbody tr th {\n",
- " vertical-align: top;\n",
- " }\n",
- "\n",
- " .dataframe thead th {\n",
- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>antecedents</th>\n",
- " <th>consequents</th>\n",
- " <th>antecedent support</th>\n",
- " <th>consequent support</th>\n",
- " <th>support</th>\n",
- " <th>confidence</th>\n",
- " <th>lift</th>\n",
- " <th>leverage</th>\n",
- " <th>conviction</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>(rc car)</td>\n",
- " <td>(lego)</td>\n",
- " <td>0.5</td>\n",
- " <td>0.75</td>\n",
- " <td>0.5</td>\n",
- " <td>1.0</td>\n",
- " <td>1.333333</td>\n",
- " <td>0.125</td>\n",
- " <td>inf</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>(teddybear)</td>\n",
- " <td>(oats)</td>\n",
- " <td>0.5</td>\n",
- " <td>0.75</td>\n",
- " <td>0.5</td>\n",
- " <td>1.0</td>\n",
- " <td>1.333333</td>\n",
- " <td>0.125</td>\n",
- " <td>inf</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>(warm boot)</td>\n",
- " <td>(oats)</td>\n",
- " <td>0.5</td>\n",
- " <td>0.75</td>\n",
- " <td>0.5</td>\n",
- " <td>1.0</td>\n",
- " <td>1.333333</td>\n",
- " <td>0.125</td>\n",
- " <td>inf</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " antecedents consequents antecedent support consequent support support \\\n",
- "0 (rc car) (lego) 0.5 0.75 0.5 \n",
- "1 (teddybear) (oats) 0.5 0.75 0.5 \n",
- "2 (warm boot) (oats) 0.5 0.75 0.5 \n",
- "\n",
- " confidence lift leverage conviction \n",
- "0 1.0 1.333333 0.125 inf \n",
- "1 1.0 1.333333 0.125 inf \n",
- "2 1.0 1.333333 0.125 inf "
- ]
- },
- "execution_count": 7,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# Execute the following code to show the solution\n",
- "association_rules(freq_itemsets, metric='confidence', min_threshold=0.75)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Ok, enough manual calculation with a toy example for today. Let's work with a bigger dataset."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Automated\n",
- "You are given some transactional data about purchases in a supermarket."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
- "\n",
- " .dataframe tbody tr th {\n",
- " vertical-align: top;\n",
- " }\n",
- "\n",
- " .dataframe thead th {\n",
- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>Date</th>\n",
- " <th>Transaction</th>\n",
- " <th>Item</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>2000-01-01</td>\n",
- " <td>1</td>\n",
- " <td>pork</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>2000-01-01</td>\n",
- " <td>1</td>\n",
- " <td>sandwich bags</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>2000-01-01</td>\n",
- " <td>1</td>\n",
- " <td>lunch meat</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>2000-01-01</td>\n",
- " <td>1</td>\n",
- " <td>all- purpose</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>2000-01-01</td>\n",
- " <td>1</td>\n",
- " <td>flour</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " Date Transaction Item\n",
- "0 2000-01-01 1 pork\n",
- "1 2000-01-01 1 sandwich bags\n",
- "2 2000-01-01 1 lunch meat\n",
- "3 2000-01-01 1 all- purpose\n",
- "4 2000-01-01 1 flour"
- ]
- },
- "execution_count": 8,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "transactions = pd.read_csv('acostasg.csv')\n",
- "transactions.columns = ['Date', 'Transaction', 'Item']\n",
- "transactions.head()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "(22342, 3)"
- ]
- },
- "execution_count": 9,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "transactions.shape"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "There is a kind of placeholder item *'all- purpose'* (notice the space after the dash) in the data which appears multiple times in some transactions. Remove rows with this item. "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "lines_to_next_cell": 2,
- "solution2": "hidden",
- "solution2_first": true
- },
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {
- "solution2": "hidden"
- },
- "outputs": [],
- "source": [
- "transactions = transactions[transactions.Item != 'all- purpose']"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "(21791, 3)"
- ]
- },
- "execution_count": 11,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "transactions.shape"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### Group by transaction ID\n",
- "We group the data by transaction id and aggregate purchases into a list (the Date is constant for s single transaction)."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
- "\n",
- " .dataframe tbody tr th {\n",
- " vertical-align: top;\n",
- " }\n",
- "\n",
- " .dataframe thead th {\n",
- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>Date</th>\n",
- " <th>Item</th>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>Transaction</th>\n",
- " <th></th>\n",
- " <th></th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>2000-01-01</td>\n",
- " <td>[pork, sandwich bags, lunch meat, flour, soda,...</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>2000-01-01</td>\n",
- " <td>[toilet paper, shampoo, hand soap, waffles, ve...</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>2000-01-02</td>\n",
- " <td>[soda, pork, soap, ice cream, toilet paper, di...</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>2000-01-02</td>\n",
- " <td>[cereals, juice, lunch meat, soda, toilet paper]</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>5</th>\n",
- " <td>2000-01-02</td>\n",
- " <td>[sandwich loaves, pasta, tortillas, mixes, han...</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " Date Item\n",
- "Transaction \n",
- "1 2000-01-01 [pork, sandwich bags, lunch meat, flour, soda,...\n",
- "2 2000-01-01 [toilet paper, shampoo, hand soap, waffles, ve...\n",
- "3 2000-01-02 [soda, pork, soap, ice cream, toilet paper, di...\n",
- "4 2000-01-02 [cereals, juice, lunch meat, soda, toilet paper]\n",
- "5 2000-01-02 [sandwich loaves, pasta, tortillas, mixes, han..."
- ]
- },
- "execution_count": 12,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "transactions = transactions.groupby('Transaction').agg({'Date':lambda x: x.iloc[0] ,'Item':list})\n",
- "transactions.head()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### Calculate size for each transaction\n",
- "We also calculate the size for each transaction. `map` the function `len` on each row of the *Item* column."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "metadata": {
- "solution2": "hidden",
- "solution2_first": true
- },
- "outputs": [],
- "source": [
- "#transactions['Size'] = "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 14,
- "metadata": {
- "solution2": "hidden"
- },
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
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- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>Date</th>\n",
- " <th>Item</th>\n",
- " <th>Size</th>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>Transaction</th>\n",
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- " <th></th>\n",
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- " <th>1</th>\n",
- " <td>2000-01-01</td>\n",
- " <td>[pork, sandwich bags, lunch meat, flour, soda,...</td>\n",
- " <td>16</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>2000-01-01</td>\n",
- " <td>[toilet paper, shampoo, hand soap, waffles, ve...</td>\n",
- " <td>23</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>2000-01-02</td>\n",
- " <td>[soda, pork, soap, ice cream, toilet paper, di...</td>\n",
- " <td>31</td>\n",
- " </tr>\n",
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- " <td>2000-01-02</td>\n",
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- " <td>5</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>5</th>\n",
- " <td>2000-01-02</td>\n",
- " <td>[sandwich loaves, pasta, tortillas, mixes, han...</td>\n",
- " <td>26</td>\n",
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- " Date Item \\\n",
- "Transaction \n",
- "1 2000-01-01 [pork, sandwich bags, lunch meat, flour, soda,... \n",
- "2 2000-01-01 [toilet paper, shampoo, hand soap, waffles, ve... \n",
- "3 2000-01-02 [soda, pork, soap, ice cream, toilet paper, di... \n",
- "4 2000-01-02 [cereals, juice, lunch meat, soda, toilet paper] \n",
- "5 2000-01-02 [sandwich loaves, pasta, tortillas, mixes, han... \n",
- "\n",
- " Size \n",
- "Transaction \n",
- "1 16 \n",
- "2 23 \n",
- "3 31 \n",
- "4 5 \n",
- "5 26 "
- ]
- },
- "execution_count": 14,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "transactions['Size'] = transactions['Item'].map(len)\n",
- "transactions.head()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### Statistics"
- ]
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- "execution_count": 15,
- "metadata": {},
- "outputs": [
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- " <th>count</th>\n",
- " <td>1139.000000</td>\n",
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- " <th>mean</th>\n",
- " <td>19.131694</td>\n",
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- " <td>8.264745</td>\n",
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- " Size\n",
- "count 1139.000000\n",
- "mean 19.131694\n",
- "std 8.264745\n",
- "min 4.000000\n",
- "25% 12.000000\n",
- "50% 19.000000\n",
- "75% 26.000000\n",
- "max 34.000000"
- ]
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- "transactions.describe()"
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- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>True</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>True</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>True</td>\n",
- " <td>True</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>True</td>\n",
- " <td>False</td>\n",
- " <td>...</td>\n",
- " <td>True</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>True</td>\n",
- " <td>True</td>\n",
- " <td>True</td>\n",
- " <td>True</td>\n",
- " <td>True</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>False</td>\n",
- " <td>True</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>True</td>\n",
- " <td>True</td>\n",
- " <td>False</td>\n",
- " <td>True</td>\n",
- " <td>False</td>\n",
- " <td>True</td>\n",
- " <td>...</td>\n",
- " <td>True</td>\n",
- " <td>True</td>\n",
- " <td>True</td>\n",
- " <td>True</td>\n",
- " <td>False</td>\n",
- " <td>True</td>\n",
- " <td>False</td>\n",
- " <td>True</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>True</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>...</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>True</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>True</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>True</td>\n",
- " <td>False</td>\n",
- " <td>True</td>\n",
- " <td>...</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>True</td>\n",
- " <td>True</td>\n",
- " <td>False</td>\n",
- " <td>True</td>\n",
- " <td>True</td>\n",
- " <td>True</td>\n",
- " <td>True</td>\n",
- " <td>True</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "<p>5 rows × 37 columns</p>\n",
- "</div>"
- ],
- "text/plain": [
- " aluminum foil bagels beef butter cereals cheeses coffee/tea \\\n",
- "0 True False True True False False False \n",
- "1 True False False False True True False \n",
- "2 False True False False True True False \n",
- "3 False False False False True False False \n",
- "4 False False False False False False False \n",
- "\n",
- " dinner rolls dishwashing liquid/detergent eggs ... shampoo soap \\\n",
- "0 True False False ... True True \n",
- "1 False True False ... True False \n",
- "2 True False True ... True True \n",
- "3 False False False ... False False \n",
- "4 True False True ... False False \n",
- "\n",
- " soda spaghetti sauce sugar toilet paper tortillas vegetables \\\n",
- "0 True False False False False True \n",
- "1 False False False True True True \n",
- "2 True True False True False True \n",
- "3 True False False True False False \n",
- "4 True True False True True True \n",
- "\n",
- " waffles yogurt \n",
- "0 False False \n",
- "1 True True \n",
- "2 False False \n",
- "3 False False \n",
- "4 True True \n",
- "\n",
- "[5 rows x 37 columns]"
- ]
- },
- "execution_count": 17,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "te = TransactionEncoder()\n",
- "te_binary = te.fit_transform(transactions.Item)\n",
- "\n",
- "df = pd.DataFrame(te_binary, columns=te.columns_)\n",
- "df.head()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### Association Rule Mining\n",
- "Generate frequent itemsets with a minimum support of 0.05. Look at the examples linked above or given in the solutions of the toy example for hints."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 18,
- "metadata": {
- "solution2": "shown",
- "solution2_first": true
- },
- "outputs": [],
- "source": [
- "#freq_itemsets = "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 19,
- "metadata": {
- "solution2": "shown"
- },
- "outputs": [],
- "source": [
- "freq_itemsets = apriori(df, min_support=0.05, use_colnames=True)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Now extract all association rules with a confidence threshold of 0.5."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 21,
- "metadata": {
- "solution2": "shown",
- "solution2_first": true
- },
- "outputs": [],
- "source": [
- "#rules = "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 22,
- "metadata": {
- "solution2": "shown"
- },
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
- "\n",
- " .dataframe tbody tr th {\n",
- " vertical-align: top;\n",
- " }\n",
- "\n",
- " .dataframe thead th {\n",
- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>antecedents</th>\n",
- " <th>consequents</th>\n",
- " <th>antecedent support</th>\n",
- " <th>consequent support</th>\n",
- " <th>support</th>\n",
- " <th>confidence</th>\n",
- " <th>lift</th>\n",
- " <th>leverage</th>\n",
- " <th>conviction</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>(aluminum foil)</td>\n",
- " <td>(vegetables)</td>\n",
- " <td>0.384548</td>\n",
- " <td>0.739245</td>\n",
- " <td>0.310799</td>\n",
- " <td>0.808219</td>\n",
- " <td>1.093304</td>\n",
- " <td>0.026524</td>\n",
- " <td>1.359651</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>(bagels)</td>\n",
- " <td>(vegetables)</td>\n",
- " <td>0.385426</td>\n",
- " <td>0.739245</td>\n",
- " <td>0.300263</td>\n",
- " <td>0.779043</td>\n",
- " <td>1.053836</td>\n",
- " <td>0.015339</td>\n",
- " <td>1.180118</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>(beef)</td>\n",
- " <td>(vegetables)</td>\n",
- " <td>0.374890</td>\n",
- " <td>0.739245</td>\n",
- " <td>0.291484</td>\n",
- " <td>0.777518</td>\n",
- " <td>1.051773</td>\n",
- " <td>0.014348</td>\n",
- " <td>1.172025</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>(butter)</td>\n",
- " <td>(vegetables)</td>\n",
- " <td>0.367867</td>\n",
- " <td>0.739245</td>\n",
- " <td>0.283582</td>\n",
- " <td>0.770883</td>\n",
- " <td>1.042798</td>\n",
- " <td>0.011639</td>\n",
- " <td>1.138087</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>(cereals)</td>\n",
- " <td>(vegetables)</td>\n",
- " <td>0.395961</td>\n",
- " <td>0.739245</td>\n",
- " <td>0.310799</td>\n",
- " <td>0.784922</td>\n",
- " <td>1.061789</td>\n",
- " <td>0.018087</td>\n",
- " <td>1.212377</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " antecedents consequents antecedent support consequent support \\\n",
- "0 (aluminum foil) (vegetables) 0.384548 0.739245 \n",
- "1 (bagels) (vegetables) 0.385426 0.739245 \n",
- "2 (beef) (vegetables) 0.374890 0.739245 \n",
- "3 (butter) (vegetables) 0.367867 0.739245 \n",
- "4 (cereals) (vegetables) 0.395961 0.739245 \n",
- "\n",
- " support confidence lift leverage conviction \n",
- "0 0.310799 0.808219 1.093304 0.026524 1.359651 \n",
- "1 0.300263 0.779043 1.053836 0.015339 1.180118 \n",
- "2 0.291484 0.777518 1.051773 0.014348 1.172025 \n",
- "3 0.283582 0.770883 1.042798 0.011639 1.138087 \n",
- "4 0.310799 0.784922 1.061789 0.018087 1.212377 "
- ]
- },
- "execution_count": 22,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "rules = association_rules(freq_itemsets, metric='confidence', min_threshold=0.5)\n",
- "rules.head()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Sort this so that the rules with the highest lift are at the top and print the top ten rules."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "lines_to_next_cell": 2,
- "solution2": "shown",
- "solution2_first": true
- },
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "code",
- "execution_count": 23,
- "metadata": {
- "solution2": "shown"
- },
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
- "\n",
- " .dataframe tbody tr th {\n",
- " vertical-align: top;\n",
- " }\n",
- "\n",
- " .dataframe thead th {\n",
- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>antecedents</th>\n",
- " <th>consequents</th>\n",
- " <th>antecedent support</th>\n",
- " <th>consequent support</th>\n",
- " <th>support</th>\n",
- " <th>confidence</th>\n",
- " <th>lift</th>\n",
- " <th>leverage</th>\n",
- " <th>conviction</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>7154</th>\n",
- " <td>(ice cream, pasta, eggs)</td>\n",
- " <td>(paper towels)</td>\n",
- " <td>0.085162</td>\n",
- " <td>0.362599</td>\n",
- " <td>0.055312</td>\n",
- " <td>0.649485</td>\n",
- " <td>1.791193</td>\n",
- " <td>0.024432</td>\n",
- " <td>1.818468</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>7153</th>\n",
- " <td>(paper towels, eggs, ice cream)</td>\n",
- " <td>(pasta)</td>\n",
- " <td>0.086040</td>\n",
- " <td>0.371378</td>\n",
- " <td>0.055312</td>\n",
- " <td>0.642857</td>\n",
- " <td>1.731003</td>\n",
- " <td>0.023358</td>\n",
- " <td>1.760140</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2694</th>\n",
- " <td>(sandwich bags, cereals, bagels)</td>\n",
- " <td>(cheeses)</td>\n",
- " <td>0.075505</td>\n",
- " <td>0.390694</td>\n",
- " <td>0.050922</td>\n",
- " <td>0.674419</td>\n",
- " <td>1.726209</td>\n",
- " <td>0.021423</td>\n",
- " <td>1.871441</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2359</th>\n",
- " <td>(yogurt, aluminum foil, toilet paper)</td>\n",
- " <td>(juice)</td>\n",
- " <td>0.078139</td>\n",
- " <td>0.376646</td>\n",
- " <td>0.050044</td>\n",
- " <td>0.640449</td>\n",
- " <td>1.700401</td>\n",
- " <td>0.020613</td>\n",
- " <td>1.733703</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2462</th>\n",
- " <td>(poultry, aluminum foil, yogurt)</td>\n",
- " <td>(mixes)</td>\n",
- " <td>0.080773</td>\n",
- " <td>0.375768</td>\n",
- " <td>0.050922</td>\n",
- " <td>0.630435</td>\n",
- " <td>1.677722</td>\n",
- " <td>0.020570</td>\n",
- " <td>1.689098</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2695</th>\n",
- " <td>(cheeses, cereals, bagels)</td>\n",
- " <td>(sandwich bags)</td>\n",
- " <td>0.083406</td>\n",
- " <td>0.367867</td>\n",
- " <td>0.050922</td>\n",
- " <td>0.610526</td>\n",
- " <td>1.659641</td>\n",
- " <td>0.020239</td>\n",
- " <td>1.623045</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>6281</th>\n",
- " <td>(poultry, spaghetti sauce, laundry detergent)</td>\n",
- " <td>(dinner rolls)</td>\n",
- " <td>0.083406</td>\n",
- " <td>0.388938</td>\n",
- " <td>0.053556</td>\n",
- " <td>0.642105</td>\n",
- " <td>1.650921</td>\n",
- " <td>0.021116</td>\n",
- " <td>1.707380</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>6167</th>\n",
- " <td>(poultry, ice cream, spaghetti sauce)</td>\n",
- " <td>(dinner rolls)</td>\n",
- " <td>0.080773</td>\n",
- " <td>0.388938</td>\n",
- " <td>0.051800</td>\n",
- " <td>0.641304</td>\n",
- " <td>1.648862</td>\n",
- " <td>0.020384</td>\n",
- " <td>1.703568</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2337</th>\n",
- " <td>(poultry, aluminum foil, yogurt)</td>\n",
- " <td>(juice)</td>\n",
- " <td>0.080773</td>\n",
- " <td>0.376646</td>\n",
- " <td>0.050044</td>\n",
- " <td>0.619565</td>\n",
- " <td>1.644953</td>\n",
- " <td>0.019621</td>\n",
- " <td>1.638530</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>6170</th>\n",
- " <td>(ice cream, spaghetti sauce, dinner rolls)</td>\n",
- " <td>(poultry)</td>\n",
- " <td>0.075505</td>\n",
- " <td>0.421422</td>\n",
- " <td>0.051800</td>\n",
- " <td>0.686047</td>\n",
- " <td>1.627931</td>\n",
- " <td>0.019980</td>\n",
- " <td>1.842877</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " antecedents consequents \\\n",
- "7154 (ice cream, pasta, eggs) (paper towels) \n",
- "7153 (paper towels, eggs, ice cream) (pasta) \n",
- "2694 (sandwich bags, cereals, bagels) (cheeses) \n",
- "2359 (yogurt, aluminum foil, toilet paper) (juice) \n",
- "2462 (poultry, aluminum foil, yogurt) (mixes) \n",
- "2695 (cheeses, cereals, bagels) (sandwich bags) \n",
- "6281 (poultry, spaghetti sauce, laundry detergent) (dinner rolls) \n",
- "6167 (poultry, ice cream, spaghetti sauce) (dinner rolls) \n",
- "2337 (poultry, aluminum foil, yogurt) (juice) \n",
- "6170 (ice cream, spaghetti sauce, dinner rolls) (poultry) \n",
- "\n",
- " antecedent support consequent support support confidence lift \\\n",
- "7154 0.085162 0.362599 0.055312 0.649485 1.791193 \n",
- "7153 0.086040 0.371378 0.055312 0.642857 1.731003 \n",
- "2694 0.075505 0.390694 0.050922 0.674419 1.726209 \n",
- "2359 0.078139 0.376646 0.050044 0.640449 1.700401 \n",
- "2462 0.080773 0.375768 0.050922 0.630435 1.677722 \n",
- "2695 0.083406 0.367867 0.050922 0.610526 1.659641 \n",
- "6281 0.083406 0.388938 0.053556 0.642105 1.650921 \n",
- "6167 0.080773 0.388938 0.051800 0.641304 1.648862 \n",
- "2337 0.080773 0.376646 0.050044 0.619565 1.644953 \n",
- "6170 0.075505 0.421422 0.051800 0.686047 1.627931 \n",
- "\n",
- " leverage conviction \n",
- "7154 0.024432 1.818468 \n",
- "7153 0.023358 1.760140 \n",
- "2694 0.021423 1.871441 \n",
- "2359 0.020613 1.733703 \n",
- "2462 0.020570 1.689098 \n",
- "2695 0.020239 1.623045 \n",
- "6281 0.021116 1.707380 \n",
- "6167 0.020384 1.703568 \n",
- "2337 0.019621 1.638530 \n",
- "6170 0.019980 1.842877 "
- ]
- },
- "execution_count": 23,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "rules.sort_values('lift', ascending=False).head(n=10)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "You now have a list of rules that are interesting (support >= 0.05), trustworthy (confidence >= 0.5) and are ordered by association strength (lift) between antecedents and consequent."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Finally, let us display our rule set with the three measures support, confidence and lift."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 24,
- "metadata": {
- "scrolled": false
- },
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 720x576 with 2 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "plt.subplots(figsize=(10, 8))\n",
- "plt.scatter(rules.support, rules.confidence, c=rules.lift, s=5)\n",
- "plt.xlabel('Support')\n",
- "plt.ylabel('Confidence')\n",
- "plt.title('Scatter Plot for Support vs. Confidence vs. Lift')\n",
- "plt.colorbar()\n",
- "plt.show()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "We can filter the rules the following way:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 25,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
- "\n",
- " .dataframe tbody tr th {\n",
- " vertical-align: top;\n",
- " }\n",
- "\n",
- " .dataframe thead th {\n",
- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>antecedents</th>\n",
- " <th>consequents</th>\n",
- " <th>antecedent support</th>\n",
- " <th>consequent support</th>\n",
- " <th>support</th>\n",
- " <th>confidence</th>\n",
- " <th>lift</th>\n",
- " <th>leverage</th>\n",
- " <th>conviction</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>6601</th>\n",
- " <td>(eggs, sandwich loaves, dishwashing liquid/det...</td>\n",
- " <td>(vegetables)</td>\n",
- " <td>0.055312</td>\n",
- " <td>0.739245</td>\n",
- " <td>0.053556</td>\n",
- " <td>0.968254</td>\n",
- " <td>1.309788</td>\n",
- " <td>0.012667</td>\n",
- " <td>8.213784</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>7432</th>\n",
- " <td>(poultry, eggs, sandwich loaves)</td>\n",
- " <td>(vegetables)</td>\n",
- " <td>0.070237</td>\n",
- " <td>0.739245</td>\n",
- " <td>0.067603</td>\n",
- " <td>0.962500</td>\n",
- " <td>1.302004</td>\n",
- " <td>0.015681</td>\n",
- " <td>6.953468</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " antecedents consequents \\\n",
- "6601 (eggs, sandwich loaves, dishwashing liquid/det... (vegetables) \n",
- "7432 (poultry, eggs, sandwich loaves) (vegetables) \n",
- "\n",
- " antecedent support consequent support support confidence lift \\\n",
- "6601 0.055312 0.739245 0.053556 0.968254 1.309788 \n",
- "7432 0.070237 0.739245 0.067603 0.962500 1.302004 \n",
- "\n",
- " leverage conviction \n",
- "6601 0.012667 8.213784 \n",
- "7432 0.015681 6.953468 "
- ]
- },
- "execution_count": 25,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "rules.query('support > 0.05 and confidence > 0.9 and lift > 1.3')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "> Now filter for the rules in the top right corner (support greater than 0.2 and confidence greater than 0.7). What do these rules have in common? Answer the question on ILIAS"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "> Let's look at the rules which have a lift greater than 1.6. Are these rules interesting?"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "That's it for the topic of Association Rules!"
- ]
- }
- ],
- "metadata": {
- "jupytext": {
- "text_representation": {
- "extension": ".py",
- "format_name": "percent",
- "format_version": "1.2",
- "jupytext_version": "0.8.6"
- }
- },
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.8.6"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/notebooks/12B Principal Component Analysis/Dimensionality Reduction.ipynb b/notebooks/12B Principal Component Analysis/Dimensionality Reduction.ipynb
index 7499e20..75617ed 100644
--- a/notebooks/12B Principal Component Analysis/Dimensionality Reduction.ipynb
+++ b/notebooks/12B Principal Component Analysis/Dimensionality Reduction.ipynb
@@ -11,7 +11,7 @@
},
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -65,22 +65,9 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
"rng = np.random.RandomState(42)\n",
"X = np.dot(rng.rand(2, 2), rng.randn(2, 200)).T\n",
@@ -98,21 +85,9 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([[1.05774968, 0.85903569],\n",
- " [0.85903569, 0.87587861]])"
- ]
- },
- "execution_count": 3,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"n_samples = X.shape[0]\n",
"covariance = np.dot(X.T, X) / (n_samples-1)\n",
@@ -139,23 +114,9 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"â–¸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"â–¾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>PCA()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">PCA</label><div class=\"sk-toggleable__content\"><pre>PCA()</pre></div></div></div></div></div>"
- ],
- "text/plain": [
- "PCA()"
- ]
- },
- "execution_count": 4,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"pca = PCA()\n",
"pca.fit(X)"
@@ -170,21 +131,9 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([[ 0.74306799, 0.66921593],\n",
- " [-0.66921593, 0.74306799]])"
- ]
- },
- "execution_count": 5,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"eigenvectors = pca.components_\n",
"eigenvectors"
@@ -199,41 +148,18 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([1.82927343, 0.10246373])"
- ]
- },
- "execution_count": 6,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"pca.explained_variance_"
]
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([[1.82927343, 0. ],\n",
- " [0. , 0.10246373]])"
- ]
- },
- "execution_count": 7,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"eigenvalues = np.diag(pca.explained_variance_)\n",
"eigenvalues"
@@ -248,23 +174,9 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Eigenvectors:\n",
- " [[ 0.74339408 0.66885368]\n",
- " [-0.66885368 0.74339408]]\n",
- "\n",
- "Eigenvalues:\n",
- " [[1.83064954 0. ]\n",
- " [0. 0.10297875]]\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"eigenvalues_np, eigenvectors_np = np.linalg.eig(covariance)\n",
"\n",
@@ -291,24 +203,11 @@
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": null,
"metadata": {
"scrolled": true
},
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
"def draw_vector(v0, v1, ax=None):\n",
" ax = ax or plt.gca()\n",
@@ -335,44 +234,20 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": null,
"metadata": {
"scrolled": true
},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([[1.05774968, 0.85903569],\n",
- " [0.85903569, 0.87587861]])"
- ]
- },
- "execution_count": 10,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"covariance"
]
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([[1.05592178, 0.85869573],\n",
- " [0.85869573, 0.87581538]])"
- ]
- },
- "execution_count": 11,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"covariance_reconstructed = eigenvectors.T.dot(eigenvalues).dot(eigenvectors)\n",
"covariance_reconstructed"
@@ -387,21 +262,9 @@
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([[1.05774968, 0.85903569],\n",
- " [0.85903569, 0.87587861]])"
- ]
- },
- "execution_count": 12,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"covariance_reconstructed_np = eigenvectors_np.T.dot(eigenvalues_np).dot(eigenvectors_np)\n",
"covariance_reconstructed_np"
@@ -428,19 +291,9 @@
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "original shape: (200, 2)\n",
- "transformed shape: (200, 1)\n",
- "First data point: [-1.74205358]\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"n_components = 1\n",
"per_feature_mean = np.mean(X, axis=0)\n",
@@ -460,21 +313,11 @@
},
{
"cell_type": "code",
- "execution_count": 14,
+ "execution_count": null,
"metadata": {
"scrolled": true
},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "original shape: (200, 2)\n",
- "transformed shape: (200, 1)\n",
- "First data point: [-1.74205358]\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"pca = PCA(n_components=1)\n",
"pca.fit(X)\n",
@@ -494,24 +337,11 @@
},
{
"cell_type": "code",
- "execution_count": 15,
+ "execution_count": null,
"metadata": {
"scrolled": true
},
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
"X_new = pca.inverse_transform(X_pca)\n",
"plt.scatter(X[:, 0], X[:, 1], alpha=0.2)\n",
@@ -555,166 +385,11 @@
},
{
"cell_type": "code",
- "execution_count": 16,
+ "execution_count": null,
"metadata": {
"scrolled": true
},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
- "\n",
- " .dataframe tbody tr th {\n",
- " vertical-align: top;\n",
- " }\n",
- "\n",
- " .dataframe thead th {\n",
- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>Alcohol</th>\n",
- " <th>Malic_Acid</th>\n",
- " <th>Ash</th>\n",
- " <th>Ash_Alcanity</th>\n",
- " <th>Magnesium</th>\n",
- " <th>Total_Phenols</th>\n",
- " <th>Flavanoids</th>\n",
- " <th>Nonflavanoid_Phenols</th>\n",
- " <th>Proanthocyanins</th>\n",
- " <th>Color_Intensity</th>\n",
- " <th>Hue</th>\n",
- " <th>OD280</th>\n",
- " <th>Proline</th>\n",
- " <th>Customer_Segment</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>14.23</td>\n",
- " <td>1.71</td>\n",
- " <td>2.43</td>\n",
- " <td>15.6</td>\n",
- " <td>127</td>\n",
- " <td>2.80</td>\n",
- " <td>3.06</td>\n",
- " <td>0.28</td>\n",
- " <td>2.29</td>\n",
- " <td>5.64</td>\n",
- " <td>1.04</td>\n",
- " <td>3.92</td>\n",
- " <td>1065</td>\n",
- " <td>1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>13.20</td>\n",
- " <td>1.78</td>\n",
- " <td>2.14</td>\n",
- " <td>11.2</td>\n",
- " <td>100</td>\n",
- " <td>2.65</td>\n",
- " <td>2.76</td>\n",
- " <td>0.26</td>\n",
- " <td>1.28</td>\n",
- " <td>4.38</td>\n",
- " <td>1.05</td>\n",
- " <td>3.40</td>\n",
- " <td>1050</td>\n",
- " <td>1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>13.16</td>\n",
- " <td>2.36</td>\n",
- " <td>2.67</td>\n",
- " <td>18.6</td>\n",
- " <td>101</td>\n",
- " <td>2.80</td>\n",
- " <td>3.24</td>\n",
- " <td>0.30</td>\n",
- " <td>2.81</td>\n",
- " <td>5.68</td>\n",
- " <td>1.03</td>\n",
- " <td>3.17</td>\n",
- " <td>1185</td>\n",
- " <td>1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>14.37</td>\n",
- " <td>1.95</td>\n",
- " <td>2.50</td>\n",
- " <td>16.8</td>\n",
- " <td>113</td>\n",
- " <td>3.85</td>\n",
- " <td>3.49</td>\n",
- " <td>0.24</td>\n",
- " <td>2.18</td>\n",
- " <td>7.80</td>\n",
- " <td>0.86</td>\n",
- " <td>3.45</td>\n",
- " <td>1480</td>\n",
- " <td>1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>13.24</td>\n",
- " <td>2.59</td>\n",
- " <td>2.87</td>\n",
- " <td>21.0</td>\n",
- " <td>118</td>\n",
- " <td>2.80</td>\n",
- " <td>2.69</td>\n",
- " <td>0.39</td>\n",
- " <td>1.82</td>\n",
- " <td>4.32</td>\n",
- " <td>1.04</td>\n",
- " <td>2.93</td>\n",
- " <td>735</td>\n",
- " <td>1</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " Alcohol Malic_Acid Ash Ash_Alcanity Magnesium Total_Phenols \\\n",
- "0 14.23 1.71 2.43 15.6 127 2.80 \n",
- "1 13.20 1.78 2.14 11.2 100 2.65 \n",
- "2 13.16 2.36 2.67 18.6 101 2.80 \n",
- "3 14.37 1.95 2.50 16.8 113 3.85 \n",
- "4 13.24 2.59 2.87 21.0 118 2.80 \n",
- "\n",
- " Flavanoids Nonflavanoid_Phenols Proanthocyanins Color_Intensity Hue \\\n",
- "0 3.06 0.28 2.29 5.64 1.04 \n",
- "1 2.76 0.26 1.28 4.38 1.05 \n",
- "2 3.24 0.30 2.81 5.68 1.03 \n",
- "3 3.49 0.24 2.18 7.80 0.86 \n",
- "4 2.69 0.39 1.82 4.32 1.04 \n",
- "\n",
- " OD280 Proline Customer_Segment \n",
- "0 3.92 1065 1 \n",
- "1 3.40 1050 1 \n",
- "2 3.17 1185 1 \n",
- "3 3.45 1480 1 \n",
- "4 2.93 735 1 "
- ]
- },
- "execution_count": 16,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"df_wine = pd.read_csv(\"wine.csv\")\n",
"df_wine.head()"
@@ -729,7 +404,7 @@
},
{
"cell_type": "code",
- "execution_count": 17,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -746,32 +421,9 @@
},
{
"cell_type": "code",
- "execution_count": 18,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<AxesSubplot:xlabel='# of Features'>"
- ]
- },
- "execution_count": 18,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 720x576 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
"pca = PCA().fit(X_wine)\n",
"v_ratio = pca.explained_variance_ratio_\n",
@@ -796,32 +448,9 @@
},
{
"cell_type": "code",
- "execution_count": 19,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<matplotlib.collections.PathCollection at 0x7efcbb66a190>"
- ]
- },
- "execution_count": 19,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 720x576 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
"pca = PCA(n_components=2)\n",
"X_pca_wine = pca.fit_transform(X_wine)\n",
@@ -846,32 +475,9 @@
},
{
"cell_type": "code",
- "execution_count": 20,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<AxesSubplot:xlabel='# of Features'>"
- ]
- },
- "execution_count": 20,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 720x576 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
"pipe = Pipeline([\n",
" (\"scaler\", StandardScaler()), \n",
@@ -901,32 +507,9 @@
},
{
"cell_type": "code",
- "execution_count": 21,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<matplotlib.collections.PathCollection at 0x7efcbb55da00>"
- ]
- },
- "execution_count": 21,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 720x576 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
"params = {\"pca__n_components\": 2}\n",
"pipe.set_params(**params)\n",
@@ -954,7 +537,7 @@
},
{
"cell_type": "code",
- "execution_count": 22,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -964,7 +547,7 @@
},
{
"cell_type": "code",
- "execution_count": 23,
+ "execution_count": null,
"metadata": {
"scrolled": true
},
@@ -1003,7 +586,7 @@
},
{
"cell_type": "code",
- "execution_count": 24,
+ "execution_count": null,
"metadata": {
"jupyter": {
"source_hidden": true
@@ -1011,18 +594,7 @@
"solution2": "hidden",
"tags": []
},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "0.9722222222222222"
- ]
- },
- "execution_count": 24,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"model.fit(X_train_wine, y_train_wine)\n",
"\n",
@@ -1048,32 +620,9 @@
},
{
"cell_type": "code",
- "execution_count": 25,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<matplotlib.collections.PathCollection at 0x7efceb0b5820>"
- ]
- },
- "execution_count": 25,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 720x576 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
"pipe = Pipeline([\n",
" (\"scaler\", StandardScaler()), \n",
@@ -1117,133 +666,9 @@
},
{
"cell_type": "code",
- "execution_count": 26,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
- "\n",
- " .dataframe tbody tr th {\n",
- " vertical-align: top;\n",
- " }\n",
- "\n",
- " .dataframe thead th {\n",
- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>Price</th>\n",
- " <th>Mileage</th>\n",
- " <th>Doors</th>\n",
- " <th>Horsepower</th>\n",
- " <th>EngineSize</th>\n",
- " <th>Seats</th>\n",
- " <th>Cylinders</th>\n",
- " <th>Gears</th>\n",
- " <th>Year</th>\n",
- " <th>Age</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>44800</td>\n",
- " <td>27600</td>\n",
- " <td>2</td>\n",
- " <td>320</td>\n",
- " <td>4973</td>\n",
- " <td>2</td>\n",
- " <td>8</td>\n",
- " <td>5</td>\n",
- " <td>1996</td>\n",
- " <td>12</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>22800</td>\n",
- " <td>18300</td>\n",
- " <td>4</td>\n",
- " <td>286</td>\n",
- " <td>4398</td>\n",
- " <td>5</td>\n",
- " <td>8</td>\n",
- " <td>5</td>\n",
- " <td>1999</td>\n",
- " <td>15</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>183710</td>\n",
- " <td>650</td>\n",
- " <td>5</td>\n",
- " <td>350</td>\n",
- " <td>4172</td>\n",
- " <td>5</td>\n",
- " <td>8</td>\n",
- " <td>6</td>\n",
- " <td>2008</td>\n",
- " <td>24</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>19900</td>\n",
- " <td>32000</td>\n",
- " <td>5</td>\n",
- " <td>150</td>\n",
- " <td>2198</td>\n",
- " <td>7</td>\n",
- " <td>4</td>\n",
- " <td>6</td>\n",
- " <td>2006</td>\n",
- " <td>22</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>18999</td>\n",
- " <td>118000</td>\n",
- " <td>5</td>\n",
- " <td>163</td>\n",
- " <td>2401</td>\n",
- " <td>5</td>\n",
- " <td>5</td>\n",
- " <td>5</td>\n",
- " <td>2003</td>\n",
- " <td>19</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " Price Mileage Doors Horsepower EngineSize Seats Cylinders Gears \\\n",
- "0 44800 27600 2 320 4973 2 8 5 \n",
- "1 22800 18300 4 286 4398 5 8 5 \n",
- "2 183710 650 5 350 4172 5 8 6 \n",
- "3 19900 32000 5 150 2198 7 4 6 \n",
- "4 18999 118000 5 163 2401 5 5 5 \n",
- "\n",
- " Year Age \n",
- "0 1996 12 \n",
- "1 1999 15 \n",
- "2 2008 24 \n",
- "3 2006 22 \n",
- "4 2003 19 "
- ]
- },
- "execution_count": 26,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"df = pd.read_csv(\"cars.csv\")\n",
"df['Age'] = df.Year-1984\n",
diff --git a/notebooks/12B Principal Component Analysis/Dimensionality Reduction_Solution.ipynb b/notebooks/12B Principal Component Analysis/Dimensionality Reduction_Solution.ipynb
deleted file mode 100644
index 540ad2b..0000000
--- a/notebooks/12B Principal Component Analysis/Dimensionality Reduction_Solution.ipynb
+++ /dev/null
@@ -1,1315 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Dimensionality Reduction\n",
- "\n",
- "In today's exercise you will apply some techniques for dimensionality reduction. We will dive into the popular dimensionality reduction algorithm PCA und the manifold learning algorithm t-SNE."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [],
- "source": [
- "import numpy as np\n",
- "import pandas as pd\n",
- "import matplotlib.pyplot as plt\n",
- "import seaborn as sns\n",
- "import plotly\n",
- "import plotly.graph_objs as go\n",
- "\n",
- "from sklearn.preprocessing import RobustScaler\n",
- "from sklearn.cluster import KMeans\n",
- "from mpl_toolkits.mplot3d import Axes3D\n",
- "from sklearn.decomposition import PCA\n",
- "from sklearn.manifold import TSNE\n",
- "\n",
- "from sklearn.pipeline import Pipeline\n",
- "from sklearn.linear_model import LogisticRegression\n",
- "from sklearn.ensemble import RandomForestClassifier\n",
- "from sklearn.model_selection import train_test_split\n",
- "from sklearn.metrics import accuracy_score, f1_score\n",
- "from sklearn.preprocessing import StandardScaler\n",
- "\n",
- "from tqdm.notebook import tqdm\n",
- "import seaborn as sns\n",
- "#sns.set()\n",
- "\n",
- "import warnings\n",
- "warnings.filterwarnings(\"ignore\", category=FutureWarning)\n",
- "\n",
- "%matplotlib inline"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Principal Component Analysis (PCA)\n",
- "In this section, we explore what is perhaps one of the most broadly used of unsupervised algorithms, principal component analysis (PCA).\n",
- "PCA is fundamentally a dimensionality reduction algorithm, but it can also be useful as a tool for **visualization**, for **noise filtering**, for **feature extraction and engineering**, and much more.\n",
- "After a brief conceptual discussion of the PCA algorithm, we will see a couple examples of these further applications."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### PCA Introduction\n",
- "We introduce PCA by looking at a randomly generated two-dimensional dataset."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": "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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "rng = np.random.RandomState(42)\n",
- "X = np.dot(rng.rand(2, 2), rng.randn(2, 200)).T\n",
- "\n",
- "plt.scatter(X[:, 0], X[:, 1])\n",
- "plt.axis('equal');"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Let us calculate the empirical covariance of our sampled data set. We can use the formula from the lecture."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([[1.05774968, 0.85903569],\n",
- " [0.85903569, 0.87587861]])"
- ]
- },
- "execution_count": 3,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "n_samples = X.shape[0]\n",
- "covariance = np.dot(X.T, X) / (n_samples-1)\n",
- "# covariance = np.cov(X, rowvar=0) # can be also done with numpy directly\n",
- "covariance"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "We can see, that there is a linear relationship between the x and y variables.\n",
- "Our goal here is different to regression problem: Rather than predicting the y-values from the x-values, we want to learn about the *relationship* between the x and y values. \n",
- "\n",
- "In PCA, this relationship is quantified by finding a list of the *principal axes* in the data and using those axes to describe the dataset."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "In scikit-learn we can do that by using the [PCA](https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html) class. We instantiate a new object from PCA and fit it to the data."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "PCA()"
- ]
- },
- "execution_count": 4,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "pca = PCA()\n",
- "pca.fit(X)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "We can now access the principal components by means of the `components_` attribute. These correspond to the **eigenvectors** of the covariance matrix."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([[ 0.74306799, 0.66921593],\n",
- " [-0.66921593, 0.74306799]])"
- ]
- },
- "execution_count": 5,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "eigenvectors = pca.components_\n",
- "eigenvectors"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Another important property is the *explained variance* which can be accessed by means of the `explained_variance_` attribute. These are the **eigenvalues** of the covariance matrix."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([1.82927343, 0.10246373])"
- ]
- },
- "execution_count": 6,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "pca.explained_variance_"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([[1.82927343, 0. ],\n",
- " [0. , 0.10246373]])"
- ]
- },
- "execution_count": 7,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "eigenvalues = np.diag(pca.explained_variance_)\n",
- "eigenvalues"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "The eigenvalue decomposition of the covariance matrix can also be calculated with numpy:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Eigenvectors:\n",
- " [[ 0.74339408 0.66885368]\n",
- " [-0.66885368 0.74339408]]\n",
- "\n",
- "Eigenvalues:\n",
- " [[1.83064954 0. ]\n",
- " [0. 0.10297875]]\n"
- ]
- }
- ],
- "source": [
- "eigenvalues_np, eigenvectors_np = np.linalg.eig(covariance)\n",
- "\n",
- "eigenvalues_np = np.diag(eigenvalues_np)\n",
- "eigenvectors_np = eigenvectors_np.T\n",
- "print(\"Eigenvectors:\\n\", eigenvectors_np)\n",
- "print(\"\\nEigenvalues:\\n\", eigenvalues_np)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "As numpy and scikit-learn are not using the same algorithm for the eigendecomposition, we don't get exactly the same results."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### Visualize eigenvalues and eigenvetors\n",
- "To see what these numbers mean, let's visualize them as vectors over the input data, using the **eigenvectors** to define the direction of the vector, and the **eigenvalues** to define the squared-length of the vector:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {
- "scrolled": true
- },
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "def draw_vector(v0, v1, ax=None):\n",
- " ax = ax or plt.gca()\n",
- " arrowprops=dict(arrowstyle='->',\n",
- " linewidth=2,\n",
- " shrinkA=0, shrinkB=0)\n",
- " ax.annotate('', v1, v0, arrowprops=arrowprops)\n",
- "\n",
- "plt.scatter(X[:, 0], X[:, 1], alpha=0.2)\n",
- "for eigenvalue, eigenvector in zip(pca.explained_variance_, pca.components_):\n",
- " v = eigenvector * 3 * np.sqrt(eigenvalue)\n",
- " draw_vector(pca.mean_, pca.mean_ + v)\n",
- "plt.axis('equal');"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### Reconstruct covariance matrix\n",
- "Let's reconstruct our covariance matrix from the eigenvectors and eigenvalues.\n",
- "$A = E^TDE$"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {
- "scrolled": true
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([[1.05774968, 0.85903569],\n",
- " [0.85903569, 0.87587861]])"
- ]
- },
- "execution_count": 10,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "covariance"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([[1.05592178, 0.85869573],\n",
- " [0.85869573, 0.87581538]])"
- ]
- },
- "execution_count": 11,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "covariance_reconstructed = eigenvectors.T.dot(eigenvalues).dot(eigenvectors)\n",
- "covariance_reconstructed"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "With the eigenvectors and eigenvalues calculated with the PCA-class from scikit-learn we don't get exactly the same result. "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([[1.05774968, 0.85903569],\n",
- " [0.85903569, 0.87587861]])"
- ]
- },
- "execution_count": 12,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "covariance_reconstructed_np = eigenvectors_np.T.dot(eigenvalues_np).dot(eigenvectors_np)\n",
- "covariance_reconstructed_np"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "If we reconstruct it with the eigenvectors and eigenvalues we have calculate with numpy we get the same result."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### Using PCA for dimensionality reduction\n",
- "If we want to use PCA for dimensionality reduction, we set the eigenvectors with the smallest corresponding eigenvalues to zero, which results to a lower-dimensional projection of the data that preserves the maximal variance.\n",
- "\n",
- "1. Compute the covariance matrix of the data\n",
- "1. Compute the eigenvalues and vectors of this covariance matrix\n",
- "1. Use the eigenvalues and vectors to select only the most important feature vectors and then transform your data onto those vectors for reduced dimensionality"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "original shape: (200, 2)\n",
- "transformed shape: (200, 1)\n",
- "First data point: [-1.74205358]\n"
- ]
- }
- ],
- "source": [
- "n_components = 1\n",
- "per_feature_mean = np.mean(X, axis=0)\n",
- "X_pca = np.dot(X - per_feature_mean, eigenvectors[:n_components].T)\n",
- "\n",
- "print(\"original shape: \", X.shape)\n",
- "print(\"transformed shape:\", X_pca.shape)\n",
- "print(\"First data point:\", X_pca[0])"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "With scikit-learn, this can be done using the `transform` method of the PCA-class."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 14,
- "metadata": {
- "scrolled": true
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "original shape: (200, 2)\n",
- "transformed shape: (200, 1)\n",
- "First data point: [-1.74205358]\n"
- ]
- }
- ],
- "source": [
- "pca = PCA(n_components=1)\n",
- "pca.fit(X)\n",
- "X_pca = pca.transform(X)\n",
- "print(\"original shape: \", X.shape)\n",
- "print(\"transformed shape:\", X_pca.shape)\n",
- "print(\"First data point:\", X_pca[0])"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "The transformed data has been reduced to a single dimension.\n",
- "To understand the effect of this dimensionality reduction, we can perform the inverse transform of this reduced data and plot it along with the original data:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 15,
- "metadata": {
- "scrolled": true
- },
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "X_new = pca.inverse_transform(X_pca)\n",
- "plt.scatter(X[:, 0], X[:, 1], alpha=0.2)\n",
- "plt.scatter(X_new[:, 0], X_new[:, 1], alpha=0.8)\n",
- "plt.axis('equal');"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "The blue points are the original data, while the orange points are the projected version.\n",
- "The information along the least important principal axis or axes is removed, leaving only the component(s) of the data with the highest variance.\n",
- "The fraction of variance that is cut out (proportional to the spread of points about the line formed in this figure) is roughly a measure of how much \"information\" is discarded in this reduction of dimensionality.\n",
- "\n",
- "This reduced-dimension dataset is in some senses \"good enough\" to encode the most important relationships between the points: despite reducing the dimension of the data by 50%, the overall relationship between the data points are mostly preserved."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### PCA using the Wine dataset"
- ]
- },
- {
- "attachments": {
- "Wine-chemistry.jpg": {
- "image/jpeg": 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- }
- },
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "From our toy dataset we will move to the [wine dataset](http://archive.ics.uci.edu/ml/datasets/wine).\n",
- "\n",
- " \n",
- "\n",
- "It contains the results of a chemical analysis of wines grown in the same region in Italy but derived from 3 different cultivars. Our goal will be to reveal the presence of clusters in the wine dataset. In other words, we will check if 3 cultivators are distinguishable in the dataset."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 16,
- "metadata": {
- "scrolled": true
- },
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
- "\n",
- " .dataframe tbody tr th {\n",
- " vertical-align: top;\n",
- " }\n",
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- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>Alcohol</th>\n",
- " <th>Malic_Acid</th>\n",
- " <th>Ash</th>\n",
- " <th>Ash_Alcanity</th>\n",
- " <th>Magnesium</th>\n",
- " <th>Total_Phenols</th>\n",
- " <th>Flavanoids</th>\n",
- " <th>Nonflavanoid_Phenols</th>\n",
- " <th>Proanthocyanins</th>\n",
- " <th>Color_Intensity</th>\n",
- " <th>Hue</th>\n",
- " <th>OD280</th>\n",
- " <th>Proline</th>\n",
- " <th>Customer_Segment</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>14.23</td>\n",
- " <td>1.71</td>\n",
- " <td>2.43</td>\n",
- " <td>15.6</td>\n",
- " <td>127</td>\n",
- " <td>2.80</td>\n",
- " <td>3.06</td>\n",
- " <td>0.28</td>\n",
- " <td>2.29</td>\n",
- " <td>5.64</td>\n",
- " <td>1.04</td>\n",
- " <td>3.92</td>\n",
- " <td>1065</td>\n",
- " <td>1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>13.20</td>\n",
- " <td>1.78</td>\n",
- " <td>2.14</td>\n",
- " <td>11.2</td>\n",
- " <td>100</td>\n",
- " <td>2.65</td>\n",
- " <td>2.76</td>\n",
- " <td>0.26</td>\n",
- " <td>1.28</td>\n",
- " <td>4.38</td>\n",
- " <td>1.05</td>\n",
- " <td>3.40</td>\n",
- " <td>1050</td>\n",
- " <td>1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>13.16</td>\n",
- " <td>2.36</td>\n",
- " <td>2.67</td>\n",
- " <td>18.6</td>\n",
- " <td>101</td>\n",
- " <td>2.80</td>\n",
- " <td>3.24</td>\n",
- " <td>0.30</td>\n",
- " <td>2.81</td>\n",
- " <td>5.68</td>\n",
- " <td>1.03</td>\n",
- " <td>3.17</td>\n",
- " <td>1185</td>\n",
- " <td>1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>14.37</td>\n",
- " <td>1.95</td>\n",
- " <td>2.50</td>\n",
- " <td>16.8</td>\n",
- " <td>113</td>\n",
- " <td>3.85</td>\n",
- " <td>3.49</td>\n",
- " <td>0.24</td>\n",
- " <td>2.18</td>\n",
- " <td>7.80</td>\n",
- " <td>0.86</td>\n",
- " <td>3.45</td>\n",
- " <td>1480</td>\n",
- " <td>1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>13.24</td>\n",
- " <td>2.59</td>\n",
- " <td>2.87</td>\n",
- " <td>21.0</td>\n",
- " <td>118</td>\n",
- " <td>2.80</td>\n",
- " <td>2.69</td>\n",
- " <td>0.39</td>\n",
- " <td>1.82</td>\n",
- " <td>4.32</td>\n",
- " <td>1.04</td>\n",
- " <td>2.93</td>\n",
- " <td>735</td>\n",
- " <td>1</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " Alcohol Malic_Acid Ash Ash_Alcanity Magnesium Total_Phenols \\\n",
- "0 14.23 1.71 2.43 15.6 127 2.80 \n",
- "1 13.20 1.78 2.14 11.2 100 2.65 \n",
- "2 13.16 2.36 2.67 18.6 101 2.80 \n",
- "3 14.37 1.95 2.50 16.8 113 3.85 \n",
- "4 13.24 2.59 2.87 21.0 118 2.80 \n",
- "\n",
- " Flavanoids Nonflavanoid_Phenols Proanthocyanins Color_Intensity Hue \\\n",
- "0 3.06 0.28 2.29 5.64 1.04 \n",
- "1 2.76 0.26 1.28 4.38 1.05 \n",
- "2 3.24 0.30 2.81 5.68 1.03 \n",
- "3 3.49 0.24 2.18 7.80 0.86 \n",
- "4 2.69 0.39 1.82 4.32 1.04 \n",
- "\n",
- " OD280 Proline Customer_Segment \n",
- "0 3.92 1065 1 \n",
- "1 3.40 1050 1 \n",
- "2 3.17 1185 1 \n",
- "3 3.45 1480 1 \n",
- "4 2.93 735 1 "
- ]
- },
- "execution_count": 16,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "df_wine = pd.read_csv(\"wine.csv\")\n",
- "df_wine.head()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "The data is already labaled by the feature **Customer Segment**. We remove the label from the data."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 17,
- "metadata": {},
- "outputs": [],
- "source": [
- "X_wine = df_wine.drop(columns=[\"Customer_Segment\"]).values\n",
- "y_wine = df_wine[\"Customer_Segment\"].values"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "We apply PCA to our data and plot the explained variance."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 18,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<AxesSubplot:xlabel='# of Features'>"
- ]
- },
- "execution_count": 18,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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z1k2mgcFkfW8/a3v7qo9K9fW63n7WVorn3j7WFdvWbSiee/t4fN2TDPzoe1T6BnY4UE3v7KB7Wgczp3cxc3rnpnA1u7uLPWfPYOb0Tnaa3slO07qGve7c7PVO04v307qeel1sm9b51DmURv+DuCOy1Mnzfn/3RpchaYrYbmCLiEuoDhDYPSJWAh+kGrS+GhFvAX4LnAaQmbdHxFeBO4B+4JzMHCiOcyFwQWbeOJ7+wJ8CXwR2ojrYYNwDDvbaay9WrlzJY489Nt5DbKa3t7dhv8R3RKvUCc1ba3d396aVIHZEZtLbN1gNU719rN0qZG3//Vhuyp05vZM53dOY3d3FnJ2mMW/WdBbvvjNrV23kGfvsvVngeup11xahavNA1dXpdI2S1GhjGSV6xiibXjjK/h8GPjxC+58Me71qHP1vBA7aXr1jMW3atM2WT6rV8uXLN10ia2atUic0f62VjQP88sHV/LSnj/t/+sA2z26t7a0+9w1s+9RWV0dsClqzu7uY0z2NfXafWQSwaczZqav63N216f2c7mmbAtqs7q7NzlYNVz1zdcBE/CgkSZNgyi9NJY3F+g393PSb1Vx3/yque+BxbllZfiqA3XoHADtP79wsWO1enN16KmhN2yqQzRn2fqdpnV6ilySNyMAmjWBtbx83rnic6+5/nGsfeJzbetYwMJh0dQTP2Wsub3n+vhy17248dt9tnHTC85k1o8tLh5KkCWNgk4Dykxu5/oHHue6Bx7nugVXcUVrLYFZvuD9k77n86QuewVH77sZhT9+VnWc89Z/N8oc62GXm9G0cWZKk2hnYNCWtWr9hU0C79v5V3P3IOjJhRlcHz336LrzjD/bbFNC6p3U2ulxJ0hRnYNOU8Oi6Xq67v3r27Lr7H+feR9cDsNO0Tpb83q685DkLOGrfeRyy91xmdBnQJEnNxcCmtlQqV4ozaNWAdv/vngCqAwMO32c3XnHYIo5aPI/nLJrL9C7vPZMkNTcDm9rCg48/ybXFCM7rHljFg49XAJjd3cWR++zG6UfuzVGL53HgwjkODpAktRwDm1pOZvKbVU9uOnt23QOP01OuBrRdZk7jyH12403PW8xRi3dj/wVz6OxwqgxJUmszsKnpZSb3Pbaea+9/fNNlzkfWbgBg91nTOXLxbpx9fHWajWfuOZsOA5okqc0Y2NR0BgeTB9cN8qWfr+C6B1Zx/QOP87v1GwHYc/YMjtp3Hkct3o2j992NZ+wxy8lmJUltz8CmprH6iY186ZoVXHztb/nd+g3A7Syc281x++3BUYt346h957HPvJkGNEnSlGNgU8OtXP0kF/7kAS694UEqfQP8wbP3ZHFXmTedcix77zaz0eVJktRwBjY1zN0Pr+OzV9/Ht35VIoBTD13IW49/Bs962myWL19uWJMkqWBg06TKTG5YsZoLrr6PH931KDOnd3LmMfvwluMWs2iXnRpdniRJTcnApkkxOJj84M5HuODq+/jFb8vstvN0/uJFz+SNx/yea3FKkrQdBjZNqI39g3zz5h4+9+P7+fWj69lr1534+5cdyGlL9man6S4BJUnSWBjYNCHWb+hn6fW/5cKfPMDDa3vZf8Eczj/9UF7ynAWuNCBJ0g4ysKmufrd+A1/82Qq+fM0K1vb2c/S+u/FPr3oOL3jmHk7HIUnSOBnYVBe/XfUkn/vJfXztxpVsHBjkxQc8jbed8AwO3XuXRpcmSVLLM7CpJrf1rOGCq+/jilsfoqujg1cetoizjt+XZ+wxq9GlSZLUNgxs2mGZyTX3reIzV9/HT+79HbNmdHHWcfvy5ucvZv6c7kaXJ0lS2zGwacwGBpPv3/4wF1x9H7esXMPus2bw1yc/i9cd9XvM3Wlao8uTJKltGdi0Xb19A3z9Fz3850/u54HfPcE+82bykVc8h1cetojuaU7NIUnSRDOwaVRre/u4+Nrf8IWfreCxdRs4eK+5fPp1h/HiA59GZ4cjPiVJmiwGNm3lkbW9fP6nD/CV637L+g39HLff7pz/mkM55hnznJpDkqQGMLBpk/sfW8/nfnw/X/9FD/2Dg5zynAW87QXP4KBFcxtdmiRJU5qBTdz8YJkLlt/H9+94mOmdHfzxEXtx1nH78nvzdm50aZIkCQPblJWZXH3PY1xw9X1ce//jzOnu4pwTfp83HbsPu8+a0ejyJEnSMAa2KaZ/YJDv3voQF1x9P3c+tJanzenm/S/Zn9OPfDqzZvjXQZKkZuRv6CliYDD5wW/6eP91y1m5usIz9tiZf371wbz80EVM73IxdkmSmpmBbYr4+i9WcvGdGzns6bvwty89gD/cfz4dTs0hSVJLMLBNEfc+up6uDrj8T5/n1BySJLUYr4VNET3lCvO6w7AmSVILMrBNEaVyhXk7GdYkSWpFBrYpolSuMK/bP25JklqRv8GngI39gzy6bgO7dXuGTZKkVmRgmwIeWdtLJl4SlSSpRRnYpoCecgXAS6KSJLUof4NPAaWhwOYZNkmSWpKBbQoYCmzewyZJUmsysE0BPeVedp81nemdBjZJklqRgW0K6ClXWLjLTo0uQ5IkjZOBbQoolSssnGtgkySpVdUU2CLi3Ii4LSJuj4h3FW2HRMQ1EXFrRHwnIuaMtW/RfmlE3Fw8VkTEzUX7PhFRGbbtglpqnyoysxrYPMMmSVLLGvfi7xFxEHAWcCSwEfheRHwXuBD4q8y8OiLeDLwb+MBY+mbmvZn5mmH7/T9gzbCu92XmoeOteSpaU+njyY0DLNylGwYaXY0kSRqPWs6w7Q9cm5lPZmY/cDXwCuBZwI+Lfa4CXrUDfTeJ6irlfwxcUkONU97QHGyLPMMmSVLLiswcX8eI/YFvAccAFeCHwI3AEuBjmfmtiPgL4O8yc/ZY+mbmO4btczzwr5l5ePF+H+B24B5gLfD+zPzJKLWdDZwNMH/+/CVLly4d13ccq/Xr1zNr1qwJ/Yzx+uWj/Zz/iw387THd7NlZado6t9TMP9PhrLO+WqVOaJ1arbP+WqVW66yvyarzxBNPvGko+2wmM8f9AN4C/ILqGbULgE8AzwauBG4CPgisGmvfLbZ/BvjLYe9nAPOK10uAB4E526txyZIlOdGWLVs24Z8xXl/82QP5e+/5n3x0bW9T17mlVqnVOuurVerMbJ1arbP+WqVW66yvyaqT6gmsrfJMTYMOMvOizDwsM48HHgfuzcy7MvOkzFxC9XLmfWPtO7QtIrqAVwKXDtt/Q2auKl7fVBz3mbXUPxWUyhWmd3Uwb+fpjS5FkiSNU62jRPcsnp9ONWBdMqytA3g/1bNnY+o7bPMfAndl5sph++8REZ3F632B/YD7a6l/KugpV1g4t5uODifNlSSpVY17lGjh8oiYB/QB52Tm6mK6jnOK7V8HvgAQEQuBCzPzlNH6Djvu6Ww92OB44O8jop/qeMe3ZebjNdbf9krlCot2dcCBJEmtrKbAlpnHjdB2PnD+CO0l4JRh77fqO2zbm0Zouxy4fLy1TlU95QrH77dHo8uQJEk1cKWDNraxf5BH121w0lxJklqcga2NPbK2l0znYJMkqdUZ2NrY0KS5nmGTJKm1GdjaWGlTYOtucCWSJKkWBrY2VvIMmyRJbcHA1sZ6yr3M23k63dM6G12KJEmqgYGtjZXKFc+uSZLUBgxsbawa2Lx/TZKkVmdga1OZWV3lYJeZjS5FkiTVyMDWptZU+nhi44Bn2CRJagMGtjY1NAebk+ZKktT6DGxtqlTuBZzSQ5KkdmBga1POwSZJUvswsLWpUrnC9K4O5u08vdGlSJKkGhnY2lRPucLCud10dESjS5EkSTUysLUpJ82VJKl9GNjaVKnca2CTJKlNGNjaUN/AII+sM7BJktQuDGxt6OE1vWTCXgY2SZLagoGtDfU4pYckSW3FwNaGnpqDzWWpJElqBwa2NuSkuZIktRcDWxvqKfcyb+fpdE/rbHQpkiSpDgxsbcg52CRJai8GtjZUDWzevyZJUrswsLWZzPQMmyRJbcbA1mbWVvp5YuMAiwxskiS1DQNbm3EONkmS2o+Brc04pYckSe3HwNZmSmuqgc1LopIktQ8DW5vpWV1helcH83ae3uhSJElSnRjY2kxPucLCud10dESjS5EkSXViYGszTukhSVL7MbC1mVK518AmSVKbMbC1kb6BQR5ZZ2CTJKndGNjayMNresmERS5LJUlSWzGwtRHnYJMkqT0Z2NrI0BxsBjZJktqLga2NlMq9ACyca2CTJKmdGNjaSE+5wrydp7PT9M5GlyJJkurIwNZGelY7B5skSe3IwNZGqpPmOkJUkqR2Y2BrE5npKgeSJLWpmgJbRJwbEbdFxO0R8a6i7ZCIuCYibo2I70TEnLH2Ldo/FBE9EXFz8Thl2LbzIuLXEXF3RLy4ltrbzdpKP09sHGCRgU2SpLYz7sAWEQcBZwFHAocAL42I/YALgfdm5nOAbwDv3oG+Qz6RmYcWjyuKPgcApwMHAicDn44I764v9DgHmyRJbauWM2z7A9dm5pOZ2Q9cDbwCeBbw42Kfq4BX7UDfbXkZsDQzN2TmA8CvqQY+4aS5kiS1s8jM8XWM2B/4FnAMUAF+CNwILAE+lpnfioi/AP4uM2ePpW9mviMiPgS8CVhbHO8vM3N1RPwH1ZB3cXGMi4D/zczLRqjtbOBsgPnz5y9ZunTpuL7jWK1fv55Zs2ZN6Gdszw9+08fFd27k307ciV1mjJzDm6HOsWqVWq2zvlqlTmidWq2z/lqlVuusr8mq88QTT7wpMw/fakNmjvsBvAX4BdUzahcAnwCeDVwJ3AR8EFg11r5F+3ygk+rZvw8Dny/aPwW8flj/i4BXba/GJUuW5ERbtmzZhH/G9nzkijtyv7+5IgcGBkfdpxnqHKtWqdU666tV6sxsnVqts/5apVbrrK/JqpPqCayt8kxNgw4y86LMPCwzjwceB+7NzLsy86TMXAJcAtw31r5F+yOZOZCZg8B/8tRlz5XA3sMOsRdQqqX+dlIq97Jgl246OqLRpUiSpDqrdZTonsXz04FXApcMa+sA3k/17NmY+hbvFwzb7RXAbcXrbwOnR8SMiFgM7AdcX0v97aRUrjhCVJKkNtVVY//LI2Ie0Aeck9V7zc6NiHOK7V8HvgAQEQuBCzPzlNH6Fu3/HBGHAgmsAN4KkJm3R8RXgTuA/qLPQI31t42e1RWev9/ujS5DkiRNgJoCW2YeN0Lb+cD5I7SXgFOGvd+qb9H+hm183oep3temYfoGBnlkXa8jRCVJalOudNAGHl7TSyYsclkqSZLakoGtDTgHmyRJ7c3A1gZKawxskiS1MwNbGyiVewFYONfAJklSOzKwtYGecoXddp7OTtNdWlWSpHZkYGsDpXKFhQ44kCSpbRnY2kCpXPFyqCRJbczA1uIyk57VFQccSJLUxgxsLW5tpZ8nNg6w164GNkmS2pWBrcX1OAebJEltz8DW4pw0V5Kk9mdga3FPTZrrKFFJktqVga3F9ZQrTO/sYPedZzS6FEmSNEEMbC2uVO5lwS7ddHREo0uRJEkTxMDW4pyDTZKk9mdga3HVVQ4MbJIktTMDWwvrGxjkkbW9LHLAgSRJbc3A1sIeWdvLYDqlhyRJ7c7A1sJ6Vlen9FjkKgeSJLU1A1sLe2oONgObJEntzMDWwkrlXgBHiUqS1OYMbC2sp1xht52ns9P0zkaXIkmSJpCBrYVVp/RwhKgkSe3OwNbCnDRXkqSpwcDWojKTntVOmitJ0lRgYGtRa3v7eWLjAIsMbJIktT0DW4sqlZ3SQ5KkqcLA1qKeCmwOOpAkqd0Z2FrUUGBzlQNJktqfga1FrSxXmN7Zwe47z2h0KZIkaYIZ2FpUqdzLgl266eiIRpciSZImmIGtRTkHmyRJU4eBrUVVVzkwsEmSNBUY2FpQ38Agj6ztZZEjRCVJmhIMbC3okbW9DKZzsEmSNFUY2FpQqdwLGNgkSZoqDGwtyFUOJEmaWgxsLajHVQ4kSZpSDGwtqFSusOvMacyc3tXoUiRJ0iQwsLWgnnLFJakkSZpCDGwtyElzJUmaWgxsLSYz6VntpLmSJE0lNQW2iDg3Im6LiNsj4l1F2yERcU1E3BoR34mIOWPtW7R/PCLuiohbIuIbEbFL0b5PRFQi4ubicUEttbeqtb39PLFxgEUGNkmSpoxxB7aIOAg4CzgSOAR4aUTsB1wIvDcznwN8A3j3DvQFuAo4KDMPBu4BzhvW9b7MPLR4vG28tbcyp/SQJGnqqeUM2/7AtZn5ZGb2A1cDrwCeBfy42Ocq4FU70JfMvLJoA7gW2KuGGttOySk9JEmacmoJbLcBx0fEvIiYCZwC7F20n1rsc1rRNta+W3oz8L/D3i+OiF9GxNURcVwNtbesocDmJVFJkqaOyMzxd454C3AOsB64A6gAnwU+CcwDvg28MzPnjaVvZv75sO3vAw4HXpmZGREzgFmZuSoilgDfBA7MzLUjHPts4GyA+fPnL1m6dOm4v+NYrF+/nlmzZk3oZwz56t0b+f6KPv7zpJl0ROxQ38mss1atUqt11ler1AmtU6t11l+r1Gqd9TVZdZ544ok3ZebhW23IzLo8gI8Ab9+i7ZnA9TvaFzgTuAaYuY0+y4HDt3fsJUuW5ERbtmzZhH/GkHf89y/yuI/9aFx9J7POWrVKrdZZX61SZ2br1Gqd9dcqtVpnfU1WncCNOUKeqXWU6J7F89OBVwKXDGvrAN4PjDiac6S+xfuTgfcAp2bmk8P23yMiOovX+wL7AffXUn8rKpUr3r8mSdIUU+s8bJdHxB3Ad4BzMnM1cEZE3APcBZSALwBExMKIuGI7fQH+A5gNXLXF9B3HA7dExK+Ay4C3ZebjNdbfcnrKFRbtMrPRZUiSpElU02KUmbnVjf+ZeT5w/gjtJaqDC0btW7T//ijtlwOXj7vYNtA3MMgja3tZ5Bk2SZKmFFc6aCGPrO1lMJ2DTZKkqcbA1kJK5V7AwCZJ0lRjYGshrnIgSdLUZGBrIT2uciBJ0pRkYGshpXKFXWdOY+b0msaKSJKkFmNgayHVOdi8HCpJ0lRjYGshpXKvgU2SpCnIwNZCSuWKi75LkjQFGdhaxJpKH+s29BvYJEmaggxsLcIpPSRJmroMbC2i5JQekiRNWQa2FjEU2LwkKknS1GNgaxE95V6mdQa7z5rR6FIkSdIkM7C1iFK5woK5O9HREY0uRZIkTTIDW4uoTprr/WuSJE1FBrYW4SoHkiRNXQa2FtA/MMjDa3sdcCBJ0hRlYGsBj6zbwGA6B5skSVOVga0F9Kx20lxJkqYyA1sLcA42SZKmNgNbC+hxlQNJkqY0A1sLKJUr7DpzGjOndzW6FEmS1AAGthbglB6SJE1tBrYWUCr3GtgkSZrCDGwtoFSuOOBAkqQpzMDW5Nb29rFuQ78DDiRJmsIMbE2uVHYONkmSpjoDW5MzsEmSJANbk+sp9wJOmitJ0lRmYGtyPasrTOsM9pg1o9GlSJKkBjGwNblSucKCuTvR0RGNLkWSJDWIga3JVSfNdYSoJElTmYGtybnKgSRJMrA1sf6BQR5e2+uAA0mSpjgDWxN7ZN0GBtMpPSRJmuoMbE3MOdgkSRIY2JraUGBb5KADSZKmNANbE+spAtuCuZ5hkyRpKjOwNbFSucIuM6ex84yuRpciSZIayMDWxHpWVxwhKkmSDGzNrFTudcCBJEkysDWzUtkzbJIkycDWtNb29rFuQ7/LUkmSpNoCW0ScGxG3RcTtEfGuou2QiLgmIm6NiO9ExJyx9i3ad4uIqyLi3uJ512HbzouIX0fE3RHx4lpqb3bOwSZJkoaMO7BFxEHAWcCRwCHASyNiP+BC4L2Z+RzgG8C7d6AvwHuBH2bmfsAPi/dExAHA6cCBwMnApyOic7z1NzsDmyRJGlLLGbb9gWsz88nM7AeuBl4BPAv4cbHPVcCrdqAvwMuALxWvvwS8fFj70szckJkPAL+mGvjaUk+5F8B72CRJUk2B7Tbg+IiYFxEzgVOAvYv2U4t9TivaxtoXYH5mPgRQPO9ZtC8CHhx2jJVFW1sqlStM6wz2mDWj0aVIkqQGi8wcf+eItwDnAOuBO4AK8Fngk8A84NvAOzNz3lj6ZuafR0Q5M3cZtt/qzNw1Ij4FXJOZFxftFwFXZOblIxz7bOBsgPnz5y9ZunTpuL/jWKxfv55Zs2bV9ZgX/KqX+8qDfPwFM+t2zImoc6K0Sq3WWV+tUie0Tq3WWX+tUqt11tdk1XniiSfelJmHb7UhM+vyAD4CvH2LtmcC1+9IX+BuYEHxegFwd/H6POC8YX2+DxyzvWMvWbIkJ9qyZcvqfsxXf+Zn+ccX/Lyux5yIOidKq9RqnfXVKnVmtk6t1ll/rVKrddbXZNUJ3Jgj5JlaR4nuWTw/HXglcMmwtg7g/cAFY+1bbPo2cGbx+kzgW8PaT4+IGRGxGNgPuL6W+puZqxxIkqQhtS5SeXlEzAP6gHMyc3UxXcc5xfavA18AiIiFwIWZecpofYv2fwK+Wlwy/S3V++DIzNsj4qtUL5/2F30Gaqy/KfUPDPLw2l4W7WpgkyRJNQa2zDxuhLbzgfNHaC9RHVwwat+ifRXwwlG2fRj48HjrbRWPrNvAYDqlhyRJqnKlgybkHGySJGk4A1sTGgpsi1yWSpIkYWBrSj1FYFsw1zNskiTJwNaUSuUKu8ycxs4zah0TIkmS2oGBrQmVyr0s9OyaJEkqGNiaUKlcccCBJEnaxMDWhHrKFQccSJKkTQxsTWZtbx/revs9wyZJkjYxsDWZTVN6uMqBJEkqGNiajJPmSpKkLRnYmkxPuRfAhd8lSdImBrYmUypXmNYZ7DFrRqNLkSRJTcLA1mRK5QpPm9tNR0c0uhRJktQkDGxNplSuOGmuJEnajIGtyZTKvd6/JkmSNmNgayL9A4M8vLbXEaKSJGkzBrYm8ui6DQwMpoFNkiRtxsDWRHo2zcHmslSSJOkpBrYmMjRp7l6uciBJkoYxsDWRoTNsCxwlKkmShjGwNZFSucIuM6ex84yuRpciSZKaiIGtiZTKvc7BJkmStmJgayKlcsURopIkaSsGtibSU66wyBGikiRpCwa2JrG2t491vf2eYZMkSVsxsDWJh8q9AAY2SZK0FQNbkyhtmjTXwCZJkjZnYGsSQ3OwufC7JEnakoGtSfSUK0zrDPacPaPRpUiSpCZjYGsSpXKFp83tpqMjGl2KJElqMga2JlEqV5w0V5IkjcjA1iRK5V7vX5MkSSMysDWB/oFBHl7b6whRSZI0IgNbE3h03QYGBtPAJkmSRmRgawJPzcHmslSSJGlrBrYm4BxskiRpWwxsTaBULEu1wMAmSZJGYGBrAqVyhbk7TWPWjK5GlyJJkpqQga0J9JQrDjiQJEmjMrA1gVK54v1rkiRpVAa2JtBTrrDIEaKSJGkUBrYGW9vbx7refi+JSpKkURnYGuyhYoSogU2SJI2mpsAWEedGxG0RcXtEvKtoOyQiromIWyPiOxExZ5S+f170uy0iLomI7qL90oi4uXisiIibi/Z9IqIybNsFtdTeLJ6aNNfAJkmSRjbueSQi4iDgLOBIYCPwvYj4LnAh8FeZeXVEvBl4N/CBLfouAt4JHJCZlYj4KnA68MXMfM2w/f4fsGZY1/sy89Dx1tyMnDRXkiRtTy1n2PYHrs3MJzOzH7gaeAXwLODHxT5XAa8apX8XsFNEdAEzgdLwjRERwB8Dl9RQY9MrlSt0dQR7zJ7R6FIkSVKTqiWw3QYcHxHzImImcAqwd9F+arHPaUXbZjKzB/gX4LfAQ8CazLxyi92OAx7JzHuHtS2OiF9GxNURcVwNtTeNUrnC0+Z209kRjS5FkiQ1qcjM8XeOeAtwDrAeuAOoAJ8FPgnMA74NvDMz523Rb1fgcuA1QBn4GnBZZl48bJ/PAL/OzP9XvJ8BzMrMVRGxBPgmcGBmrh2hrrOBswHmz5+/ZOnSpeP+jmOxfv16Zs2aNa6+H7muQgDnHTXxl0RrqXOytUqt1llfrVIntE6t1ll/rVKrddbXZNV54okn3pSZh2+1ITPr8gA+Arx9i7ZnAtePsO9pwEXD3r8R+PSw913AI8Be2/i85cDh26tryZIlOdGWLVs27r7P++gP811Lf1m3WralljonW6vUap311Sp1ZrZOrdZZf61Sq3XW12TVCdyYI+SZWkeJ7lk8Px14JXDJsLYO4P3ASKM5fwscHREzi3vVXgjcOWz7HwJ3ZebKYZ+1R0R0Fq/3BfYD7q+l/kbrHxjk4bW9DjiQJEnbVOs8bJdHxB3Ad4BzMnM1cEZE3APcRXUgwRcAImJhRFwBkJnXAZcBvwBuLer43LDjns7Wgw2OB26JiF8Vfd+WmY/XWH9DPbpuAwOD6ZQekiRpm8Y9rQdAZm51439mng+cP0J7ierAhKH3HwQ+OMpx3zRC2+VU73trG0/NweayVJIkaXSudNBAzsEmSZLGwsDWQKViWaoFBjZJkrQNBrYGKpUrzN1pGrNm1HRlWpIktTkDWwOVyhUHHEiSpO0ysDVQT7nCIgccSJKk7TCwNZBn2CRJ0lgY2BpkXW8fa3v7DWySJGm7DGwNMjRC1Ck9JEnS9hjYGuSpSXMNbJIkadsMbA3ipLmSJGmsDGwNUipX6OoI9pg9o9GlSJKkJmdga5BSucLT5nbT2RGNLkWSJDU5A1uDlMq93r8mSZLGxMDWINVJcw1skiRp+wxsDTAwmDy8tpeFrnIgSZLGwMDWAI+u62VgML0kKkmSxsTA1gA9q52DTZIkjZ2BrQGcg02SJO0IA1sDDC1L5Rk2SZI0Fga2BiiVK8zdaRqzZnQ1uhRJktQCDGwNUCpXPLsmSZLGzMDWANU52JzSQ5IkjY2BrQE8wyZJknaEgW2SrevtY21vv4FNkiSNmYFtkj20xhGikiRpxxjYJtlTc7B5D5skSRobA9skK5Vd5UCSJO0YA9sk61ldoasj2HO2Z9gkSdLYGNgmWalc4Wlzu+nsiEaXIkmSWoSBbZKVyr1eDpUkSTvEwDbJqpPmGtgkSdLYGdgm0cBg8vDaXhY6QlSSJO0AA9skenRdLwOD6SVRSZK0Qwxsk8gpPSRJ0ngY2CZRT7m6yoH3sEmSpB1hYJtEQ2fYFsz1HjZJkjR2BrZJVCpXmNPdxezuaY0uRZIktRAD2yTqWV3x/jVJkrTDDGyTqKdcYa9dDWySJGnHGNgmUansGTZJkrTjDGyTZF1vH2t7+w1skiRphxnYJslDa6pTehjYJEnSjjKwTZKeYkqPRS5LJUmSdlBNgS0izo2I2yLi9oh4V9F2SERcExG3RsR3ImLOKH3/vOh3W0RcEhHdRfuHIqInIm4uHqcM63NeRPw6Iu6OiBfXUvtkc5UDSZI0XuMObBFxEHAWcCRwCPDSiNgPuBB4b2Y+B/gG8O4R+i4C3gkcnpkHAZ3A6cN2+URmHlo8rij6HFDscyBwMvDpiOgcb/2TrVSu0NkR7DnbM2ySJGnH1HKGbX/g2sx8MjP7gauBVwDPAn5c7HMV8KpR+ncBO0VEFzATKG3n814GLM3MDZn5APBrqmGxJZTKvTxtTjedHdHoUiRJUouJzBxfx4j9gW8BxwAV4IfAjcAS4GOZ+a2I+Avg7zJz9gj9zwU+XPS9MjNfV7R/CHgTsLY43l9m5uqI+A+qAfHiYr+LgP/NzMtGOPbZwNkA8+fPX7J06dJxfcexWr9+PbNmzdrmPh+9rkICf3NU4y6JjqXOZtEqtVpnfbVKndA6tVpn/bVKrdZZX5NV54knnnhTZh6+1YbMHPcDeAvwC6pn1C4APgE8G7gSuAn4ILBqhH67Aj8C9gCmAd8EXl9sm0/1EmkH1UD3+aL9U0P7FO8vAl61vRqXLFmSE23ZsmXb3ed5H/1hnnvJLya8lm0ZS53NolVqtc76apU6M1unVuusv1ap1Trra7LqBG7MEfJMTYMOMvOizDwsM48HHgfuzcy7MvOkzFwCXALcN0LXPwQeyMzHMrMP+DrwvOKYj2TmQGYOAv/JU5c9VwJ7DzvGXmz/MmpTGBhMHl7b64ADSZI0LrWOEt2zeH468ErgkmFtHcD7qZ5529JvgaMjYmZEBPBC4M6i34Jh+70CuK14/W3g9IiYERGLgf2A62upf7I8uq6XgcFkkctSSZKkceiqsf/lETEP6APOyeq9ZudGxDnF9q8DXwCIiIXAhZl5SmZeFxGXUb2c2g/8Evhc0eefI+JQIIEVwFsBMvP2iPgqcEfR55zMHKix/knhlB6SJKkWNQW2zDxuhLbzgfNHaC8Bpwx7/0Gq97htud8btvF5H6Z6X1tL6SlXVzlYZGCTJEnj4EoHk2DoDNuCuc7BJkmSdpyBbRKUyhXmdHcxu3tao0uRJEktyMA2CUrlivevSZKkcTOwTYKecq/3r0mSpHEzsE0Cz7BJkqRaGNgm2PoN/ayp9BnYJEnSuBnYJthTc7A5QlSSJI2PgW2C9RSBbS9XOZAkSeNkYJtgrnIgSZJqZWCbYKVyhc6OYM/ZXhKVJEnjY2CbYKVyL0+b001nRzS6FEmS1KIMbBOsp1xxDjZJklQTA9sEq87B5uVQSZI0fga2CTQwmDy8ptcBB5IkqSYGtgn02LoN9A+mgU2SJNXEwDaBespPAngPmyRJqomBbQL1lHsB52CTJEm1MbBNIJelkiRJ9WBgm0ClcoU53V3M7p7W6FIkSVILM7BNoOqUHl4OlSRJtTGwTaCecq8DDiRJUs0MbBPIM2ySJKkeDGwTZP2GftZU+gxskiSpZga2CfKQI0QlSVKdGNgmSE8R2LyHTZIk1crANkFKTporSZLqxMA2QXrKT9LZEew5e0ajS5EkSS3OwDZBSuVenjanm65Of8SSJKk2pokJ0lOueP+aJEmqCwPbBKnOweYIUUmSVDsD2wQYGEweXtPrgANJklQXBrYJ8Ni6DfQPpoFNkiTVhYFtAjgHmyRJqicD2wQobVrlwMAmSZJqZ2CbACWXpZIkSXVkYJsApXKF2d1dzO6e1uhSJElSGzCwTQDnYJMkSfVkYJsAPWWn9JAkSfVjYJsAJc+wSZKkOjKw1dn6Df2sqfR5hk2SJNWNga3OHnKEqCRJqjMDW505aa4kSaq3mgJbRJwbEbdFxO0R8a6i7ZCIuCYibo2I70TEnFH6/nnR77aIuCQiuov2j0fEXRFxS0R8IyJ2Kdr3iYhKRNxcPC6opfaJUir3Ak6aK0mS6mfcgS0iDgLOAo4EDgFeGhH7ARcC783M5wDfAN49Qt9FwDuBwzPzIKATOL3YfBVwUGYeDNwDnDes632ZeWjxeNt4a59IpXKFzo5gz9kzGl2KJElqE7WcYdsfuDYzn8zMfuBq4BXAs4AfF/tcBbxqlP5dwE4R0QXMBEoAmXllcTyAa4G9aqhx0pXKFZ42p5uuTq82S5Kk+qglVdwGHB8R8yJiJnAKsHfRfmqxz2lF22Yyswf4F+C3wEPAmsy8coTPeDPwv8PeL46IX0bE1RFxXA21T5iecsUBB5Ikqa4iM8ffOeItwDnAeuAOoAJ8FvgkMA/4NvDOzJy3Rb9dgcuB1wBl4GvAZZl58bB93gccDrwyMzMiZgCzMnNVRCwBvgkcmJlrR6jrbOBsgPnz5y9ZunTpuL/jWKxfv55Zs2YB8FdXP8nv79LB2w5pvtA2vM5m1yq1Wmd9tUqd0Dq1Wmf9tUqt1llfk1XniSeeeFNmHr7VhsysywP4CPD2LdqeCVw/wr6nARcNe/9G4NPD3p8JXAPM3MbnLad6D9w261qyZElOtGXLlmVmZv/AYD7jvO/mP/3vnRP+meMxVGcraJVarbO+WqXOzNap1Trrr1Vqtc76mqw6gRtzhDxT6yjRPYvnpwOvBC4Z1tYBvB8YaTTnb4GjI2JmRATwQuDOot/JwHuAUzPzyWGftUdEdBav9wX2A+6vpf56e2zdBvoH0yk9JElSXdV6Z/zlEXEH8B3gnMxcDZwREfcAd1EdSPAFgIhYGBFXAGTmdcBlwC+AW4s6Plcc8z+A2cBVW0zfcTxwS0T8quj7tsx8vMb668o52CRJ0kToqqVzZm51439mng+cP0J7ierAhKH3HwQ+OMJ+vz/KZ11O9b63plXatMqBgU2SJNWPc0/UUcllqSRJ0gQwsNVRqVxhdncXs7unNboUSZLURgxsddRT7vX+NUmSVHcGtjoqlSvevyZJkurOwFZHpTWuciBJkurPwFYnT2zop/xkn2fYJElS3RnY6qTkHGySJGmCGNjqpMc52CRJ0gQxsNVJqdwLeIZNkiTVn4GtTkrlCp0dwZ6zZzS6FEmS1GYMbHVSKld42pxuujr9kUqSpPoyXdRJT9kpPSRJ0sQwsNVJdQ4271+TJEn1Z2Crg8FMHl7Ta2CTJEkTwsBWB2s2JH0DaWCTJEkTwsBWB6sqCcAi72GTJEkTwMBWB6t6q4HNM2ySJGkiGNjqYFXvIGBgkyRJE8PAVgerKsns7i7mdE9rdCmSJKkNGdjq4PHedEkqSZI0YQxsdbCq4ghRSZI0cQxsdbCqd9BVDiRJ0oQxsNXoiQ39PNHngANJkjRxDGw1emhNBcB72CRJ0oQxsNWop9wLeIZNkiRNHANbjUrl6hk2A5skSZooBrYa9ayuEMD82TMaXYokSWpTBrYalcoVdu0Oujr9UUqSpIlhyqjRHrNn8Kzd/DFKkqSJ09XoAlrdeafsz/LljzS6DEmS1MY8NSRJktTkDGySJElNzsAmSZLU5AxskiRJTc7AJkmS1OQMbJIkSU3OwCZJktTkDGySJElNzsAmSZLU5AxskiRJTc7AJkmS1OQMbJIkSU3OwCZJktTkagpsEXFuRNwWEbdHxLuKtkMi4pqIuDUivhMRc0bp++dFv9si4pKI6C7ad4uIqyLi3uJ512F9zouIX0fE3RHx4lpqlyRJahXjDmwRcRBwFnAkcAjw0ojYD7gQeG9mPgf4BvDuEfouAt4JHJ6ZBwGdwOnF5vcCP8zM/YAfFu+JiAOKfQ4ETgY+HRGd461fkiSpVdRyhm1/4NrMfDIz+4GrgVcAzwJ+XOxzFfCqUfp3ATtFRBcwEygV7S8DvlS8/hLw8mHtSzNzQ2Y+APyaaliUJElqa7UEttuA4yNiXkTMBE4B9i7aTy32Oa1o20xm9gD/AvwWeAhYk5lXFpvnZ+ZDxX4PAXsW7YuAB4cdZmXRJkmS1NYiM8ffOeItwDnAeuAOoAJ8FvgkMA/4NvDOzJy3Rb9dgcuB1wBl4GvAZZl5cUSUM3OXYfuuzsxdI+JTwDWZeXHRfhFwRWZePkJdZwNnA8yfP3/J0qVLx/0dx2L9+vXMmjVrQj+jHlqlTmidWq2zvlqlTmidWq2z/lqlVuusr8mq88QTT7wpMw/fsr2rloNm5kXARQAR8RFgZWbeBZxUtD0TeMkIXf8QeCAzHyv2+zrwPOBi4JGIWJCZD0XEAuDRos9KNj9btxdPXUbdsq7PAZ8rjv3YiSee+JtavucY7A78boI/ox5apU5onVqts75apU5onVqts/5apVbrrK/JqvP3RmqsKbBFxJ6Z+WhEPB14JXDMsLYO4P3ABSN0/S1wdHEptQK8ELix2PZt4Ezgn4rnbw1r/++I+FdgIbAfcP32aszMPcb9BccoIm4cKQ03m1apE1qnVuusr1apE1qnVuusv1ap1Trrq9F11joP2+URcQfwHeCczFwNnBER9wB3UT0D9gWAiFgYEVcAZOZ1wGXAL4Bbizo+Vxzzn4AXRcS9wIuK92Tm7cBXqV56/V7xeQM11i9JktT0ar0ketwIbecD54/QXqI6MGHo/QeBD46w3yqqZ9xG+rwPAx+uoWRJkqSW40oH9fG57e/SFFqlTmidWq2zvlqlTmidWq2z/lqlVuusr4bWWdMoUUmSJE08z7BJkiQ1OQNbDSLi8xHxaETc1uhatiUi9o6IZRFxZ7F+67mNrmkkEdEdEddHxK+KOv+u0TVtS0R0RsQvI+J/Gl3LtkTEimJt35sj4sbt92iMiNglIi6LiLuKv6vHNLqmLUXEs4qf49Bj7dA6ys1mtPWam9FI61I3g5H+jd/WeteNNEqtpxU/08GIaIpRmKPU+fHiv/tbIuIbEbFLA0scqmmkOv+hqPHmiLgyIhZOZk0Gttp8keq6ps2uH/jLzNwfOBo4p1ibtdlsAP4gMw8BDgVOjoijG1vSNp0L3NnoIsboxMw8tMmHzp8PfC8zn011feKm+9lm5t3Fz/FQYAnwJNU1k5vKdtZrbirbWJe6GXyRrf+NH3G96ybwRbau9TaqU279eKu9G+eLbF3nVcBBmXkwcA9w3mQXNYIvsnWdH8/Mg4v//v8H+NvJLMjAVoPM/DHweKPr2J7MfCgzf1G8Xkf1F2HTLeuVVeuLt9OKR1PeZBkRe1GdFPrCRtfSDiJiDnA8xUTcmbkxM8sNLWr7Xgjcl5kTPTH3eI22XnOzGW1d6oYb5d/40da7bqiRas3MOzPz7gaVNKJR6ryy+LMHuJbqxPgNNUqda4e93ZlJ/v1kYJtiImIf4LnAdQ0uZUTFZcabqa5wcVUxZ18z+jfgr4HBBtcxFglcGRE3Fcu2NaN9gceALxSXmS+MiJ0bXdR2nA5c0ugiRrKd9ZqbzWjrUjer0da7Vn28GfjfRhcxmoj4cEQ8CLwOz7BpokTELKpruL5ri/9TaBqZOVCcbt4LOLK4XNJUIuKlwKOZeVOjaxmjYzPzMOD/UL0cfnyjCxpBF3AY8JnMfC7wBM1zqWkrETEdOJXqOshNp7iv6mXAYqorw+wcEa9vbFUjy8w7gY9RvSz2PeBXVG/j0BQTEe+j+mf/lUbXMprMfF9m7k21xj+bzM82sE0RETGNalj7SmZ+vdH1bE9xOWw5zXmP4LHAqRGxAlgK/EFEXNzYkkZXTFpNZj5K9X6rIxtb0YhWUl2LeOiM6mVUA1yz+j/ALzLzkUYXMopN6zVnZh8wtF5zU8rMizLzsMw8nuplqHsbXdM2PBLVda6Jzde7Vg0i4kzgpcDrsjXmG/tv4FWT+YEGtikgIoLqvUF3Zua/Nrqe0UTEHkOjgyJiJ6q/dO5qaFEjyMzzMnOvzNyH6mWxH2VmU569iIidI2L20GvgJKqXoJpKZj4MPBgRzyqaXkh1GbpmdQZNejm0sGm95uK//xfShIM4hkTEnsXz0LrUzfyzHVrvGjZf71rjFBEnA+8BTs3MJxtdz2i2GAxzKpP8+6mmpammuoi4BDgB2D0iVgIfzMyLGlvViI4F3gDcWtwfBvA3mXlF40oa0QLgSxHRSfV/Jr6amU09ZUYLmA98o/o7my7gvzPze40taVTvAL5SXG68H/i/Da5nRMV9Vi8C3troWkaTmddFxNB6zf3AL2nu2eQvj4h5QB9PrUvdcCP9G091feuvRsRbqAbj0xpX4VNGqfVx4N+BPYDvRsTNmfnixlU5ap3nATOAq4p/q67NzLc1rEhGrfOU4n8qB4HfAJNaoysdSJIkNTkviUqSJDU5A5skSVKTM7BJkiQ1OQObJElSkzOwSZIkNTkDm6SWFREfjYgTIuLlEbFDKyMU8/5dVyyFddwW25ZHxN0RcXPxePU4ajs0Ik7Z0X6SNBIDm6RWdhTVdXFfAPxkB/u+ELgrM5+bmSP1fV1mHlo8LhtHbYdSXRdzzKLKf5clbcV/GCS1nIj4eETcAhwBXAP8CfCZiNhqMeaI+L2I+GFE3FI8Pz0iDgX+mepEmDcXK2ts7zP3iIjLI+KG4nFs0X5kRPy8OFP384h4VjH5798DrymO/5qI+FBE/NWw490WEfsUjzsj4tNUJ7rdOyLeXXzGLRHxd8X+O0fEdyPiV0Xf19T8g5TUMlzpQFLLycx3R8TXqK7g8RfA8sw8dpTd/wP4cmZ+KSLeDHwyM19ehLvDM3O0BZy/EhGV4vULgfOBT2TmT4sllL4P7E91eZrjM7M/Iv4Q+EhmvmrL40fEh7bxlZ4F/N/MfHtEnATsR3XN1wC+HRHHU52tvpSZLymON3d7PydJ7cPAJqlVPRe4GXg221539Biq61MC/BfVM2tj8brMvHHoTRHGDiiWzgGYU6zTOpfqkmr7AQlMG+sXGOY3mXlt8fqk4vHL4v0sqgHuJ8C/RMTHgP8Z5TKupDZlYJPUUorLmV8E9gJ+B8ysNsfNwDGZWRm1c9V41+PrGOn4EfHvwLLMfEVE7AMsH6V/P5vfhtI97PUTww8JfDQzP7vlASJiCdX74j4aEVdm5t/v8LeQ1JK8h01SS8nMmzPzUOAe4ADgR8CLi8EBI4W1nwOnF69fB/x0nB99JbDp8mkRHKF6hq2neP2mYfuvA2YPe78COKzoexiweJTP+T7w5oiYVey7KCL2jIiFwJOZeTHwL0PHkjQ1GNgktZyI2ANYnZmDwLMzc1uXRN8J/N9ikMIbgHPH+bHvBA4vBgLcAbytaP9nqme8fgZ0Dtt/GdVLqDcXAwQuB3YrzgT+KdXAuZXMvBL4b+CaiLgVuIxq8HsOcH3R/33AP47ze0hqQZE53qsDkiRJmgyeYZMkSWpyBjZJkqQmZ2CTJElqcgY2SZKkJmdgkyRJanIGNkmSpCZnYJMkSWpyBjZJkqQm9/8BJRXfBeQ+YjoAAAAASUVORK5CYII=\n",
- "text/plain": [
- "<Figure size 720x576 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "pca = PCA().fit(X_wine)\n",
- "v_ratio = pca.explained_variance_ratio_\n",
- "\n",
- "data = pd.DataFrame({'# of Features':range(1, len(v_ratio)+1), '% Variance explained':np.cumsum(v_ratio*100)})\n",
- "data.plot(x=0, y=1, xticks=range(1, len(v_ratio)+1), grid=True, figsize=(10,8))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "In this plot we can see that with 2 components we can almost retain 100% of the explained variance!"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Now let us reduce the dimensionality of our dataset to two components and plot the data. We colorize the data points according to the customer segments."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 19,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<matplotlib.collections.PathCollection at 0x7f375d64ed30>"
- ]
- },
- "execution_count": 19,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 720x576 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "pca = PCA(n_components=2)\n",
- "X_pca_wine = pca.fit_transform(X_wine)\n",
- "\n",
- "plt.figure(figsize=(10, 8))\n",
- "plt.scatter(X_pca_wine[:, 0], X_pca_wine[:, 1], c=y_wine)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "We can see that possibly similar points are quite widely spread."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Let's redo our dimensionality reduction, but first we scale our data. We are using a pipeline which takes as a first step our data and scales it and then reduces its dimensionality using PCA.\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 20,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<AxesSubplot:xlabel='# of Features'>"
- ]
- },
- "execution_count": 20,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 720x576 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "pipe = Pipeline([\n",
- " (\"scaler\", StandardScaler()), \n",
- " (\"pca\", PCA())\n",
- "])\n",
- "\n",
- "pipe.fit(X_wine)\n",
- "\n",
- "v_ratio = pipe[\"pca\"].explained_variance_ratio_\n",
- "data = pd.DataFrame({'# of Features':range(1, len(v_ratio)+1), '% Variance explained':np.cumsum(v_ratio*100)})\n",
- "data.plot(x=0, y=1, xticks=range(1, len(v_ratio)+1), grid=True, figsize=(10,8))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "If we want to retain 90% of the variance, we would select the first 8 components. We can also use the elbow method to decide."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Let us transform our data to two components by setting the parameters of our pipeline accordingly."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 21,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<matplotlib.collections.PathCollection at 0x7f375f853d00>"
- ]
- },
- "execution_count": 21,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 720x576 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "params = {\"pca__n_components\": 2}\n",
- "pipe.set_params(**params)\n",
- "\n",
- "X_pca_wine = pipe.fit_transform(X_wine)\n",
- "\n",
- "plt.figure(figsize=(10, 8))\n",
- "plt.scatter(X_pca_wine[:, 0], X_pca_wine[:, 1], c=y_wine)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "It looks much better now! Again this should give you a feeling on why scaling is so important for Machine Learning."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### Customer segment prediction\n",
- "Now we want to predict the customer segment. We extend our pipeline with a **Logistic Regression** estimator."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 22,
- "metadata": {},
- "outputs": [],
- "source": [
- "split = train_test_split(X_wine, y_wine, test_size=0.2, random_state=3)\n",
- "(X_train_wine, X_test_wine, y_train_wine, y_test_wine) = split"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 23,
- "metadata": {
- "scrolled": true
- },
- "outputs": [],
- "source": [
- "model = Pipeline([\n",
- " (\"scaler\", StandardScaler()), \n",
- " (\"pca\", PCA(n_components=0.9)),\n",
- " (\"clf\", LogisticRegression())\n",
- "])"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "> Fit the model to our training data and calculate the accuracy on the test set."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 24,
- "metadata": {
- "solution2": "shown"
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "0.9722222222222222"
- ]
- },
- "execution_count": 24,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "model.fit(X_train_wine, y_train_wine)\n",
- "\n",
- "y_pred_wine = model.predict(X_test_wine)\n",
- "accuracy_score(y_pred_wine, y_test_wine)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## t-Distributed Stochastic Neighbor Embedding (t-SNE) \n",
- "t-SNE is a [manifold learning](https://scikit-learn.org/stable/modules/manifold.html) which reduces the dimensionality while trying to keep similar instances close and dissimilar apart. It is mostly used for visualization, in particular to visualize clusters of instances in high-dimensional space.\n",
- "t-SNE constructs a probability distribution $p$ over the dataset $X$ and then another probability distribution $q$ in a lower dimensional data space $Y$, making both distributions as \"close\" as possible."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "We now transform our data using the [TSNE](https://scikit-learn.org/stable/modules/generated/sklearn.manifold.TSNE.html#sklearn.manifold.TSNE) class from scikit-learn. As t-SNE is based on nearest neighbor search, it is crucial to normalize our data first."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 25,
- "metadata": {
- "scrolled": false
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<matplotlib.collections.PathCollection at 0x7f375f87ca90>"
- ]
- },
- "execution_count": 25,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 720x576 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "pipe = Pipeline([\n",
- " (\"scaler\", StandardScaler()), \n",
- " (\"tsne\", TSNE(n_components=2)),\n",
- "])\n",
- "\n",
- "X_tsne_wine = pipe.fit_transform(X_wine)\n",
- "\n",
- "plt.figure(figsize=(10, 8))\n",
- "plt.scatter(X_tsne_wine[:, 0], X_tsne_wine[:, 1], c=y_wine)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "The visualization looks really nice. We can see what t-SNE tries to do: Keep similar instances clos and dissimalar apart."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Please note: As t-SNE does not support a `transform` function (it needs to be fitted to the data first) it should not be used in combination with an estimator. "
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Assignment: Using PCA with the autoscout dataset\n",
- "Your assignment is now to apply the dimensionality techniques to the well known autoscout dataset."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Here is the the dataset from Autoscout24. We reuse the steps that we developed in the regression exercise to read and clean the data:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 26,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
- "\n",
- " .dataframe tbody tr th {\n",
- " vertical-align: top;\n",
- " }\n",
- "\n",
- " .dataframe thead th {\n",
- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>Price</th>\n",
- " <th>Mileage</th>\n",
- " <th>Doors</th>\n",
- " <th>Horsepower</th>\n",
- " <th>EngineSize</th>\n",
- " <th>Seats</th>\n",
- " <th>Cylinders</th>\n",
- " <th>Gears</th>\n",
- " <th>Year</th>\n",
- " <th>Age</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>44800</td>\n",
- " <td>27600</td>\n",
- " <td>2</td>\n",
- " <td>320</td>\n",
- " <td>4973</td>\n",
- " <td>2</td>\n",
- " <td>8</td>\n",
- " <td>5</td>\n",
- " <td>1996</td>\n",
- " <td>12</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>22800</td>\n",
- " <td>18300</td>\n",
- " <td>4</td>\n",
- " <td>286</td>\n",
- " <td>4398</td>\n",
- " <td>5</td>\n",
- " <td>8</td>\n",
- " <td>5</td>\n",
- " <td>1999</td>\n",
- " <td>15</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>183710</td>\n",
- " <td>650</td>\n",
- " <td>5</td>\n",
- " <td>350</td>\n",
- " <td>4172</td>\n",
- " <td>5</td>\n",
- " <td>8</td>\n",
- " <td>6</td>\n",
- " <td>2008</td>\n",
- " <td>24</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>19900</td>\n",
- " <td>32000</td>\n",
- " <td>5</td>\n",
- " <td>150</td>\n",
- " <td>2198</td>\n",
- " <td>7</td>\n",
- " <td>4</td>\n",
- " <td>6</td>\n",
- " <td>2006</td>\n",
- " <td>22</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>18999</td>\n",
- " <td>118000</td>\n",
- " <td>5</td>\n",
- " <td>163</td>\n",
- " <td>2401</td>\n",
- " <td>5</td>\n",
- " <td>5</td>\n",
- " <td>5</td>\n",
- " <td>2003</td>\n",
- " <td>19</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " Price Mileage Doors Horsepower EngineSize Seats Cylinders Gears \\\n",
- "0 44800 27600 2 320 4973 2 8 5 \n",
- "1 22800 18300 4 286 4398 5 8 5 \n",
- "2 183710 650 5 350 4172 5 8 6 \n",
- "3 19900 32000 5 150 2198 7 4 6 \n",
- "4 18999 118000 5 163 2401 5 5 5 \n",
- "\n",
- " Year Age \n",
- "0 1996 12 \n",
- "1 1999 15 \n",
- "2 2008 24 \n",
- "3 2006 22 \n",
- "4 2003 19 "
- ]
- },
- "execution_count": 26,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "df = pd.read_csv(\"cars.csv\")\n",
- "df['Age'] = df.Year-1984\n",
- "df.drop(['Color', 'Name', 'Registration'], axis='columns', inplace=True)\n",
- "df.drop_duplicates(inplace=True)\n",
- "df.drop([17010, 7734, 47002, 44369, 24720, 50574, 36542, 42611,\n",
- " 22513, 12773, 21501, 2424, 52910, 29735, 43004, 47125], axis='rows', inplace=True)\n",
- "df.drop(df.index[df.EngineSize > 7500], axis='rows', inplace=True)\n",
- "df.head()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "> Now reduce the dimensionality of the data using **PCA** and answer the questions on ILIAS. Don't forget to scale the data first!"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 27,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<AxesSubplot:xlabel='# of Features'>"
- ]
- },
- "execution_count": 27,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 720x576 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "# TODO: Remove solution\n",
- "pipe = Pipeline([\n",
- " (\"scaler\", StandardScaler()), \n",
- " (\"pca\", PCA(random_state=0))\n",
- "])\n",
- "\n",
- "pipe.fit(df.values)\n",
- "\n",
- "v_ratio = pipe[\"pca\"].explained_variance_ratio_\n",
- "data = pd.DataFrame({'# of Features':range(1, len(v_ratio)+1), '% Variance explained':np.cumsum(v_ratio*100)})\n",
- "data.plot(x=0, y=1, xticks=range(1, len(v_ratio)+1), grid=True, figsize=(10,8))"
- ]
- }
- ],
- "metadata": {
- "jupytext": {
- "text_representation": {
- "extension": ".py",
- "format_name": "percent",
- "format_version": "1.2",
- "jupytext_version": "0.8.6"
- }
- },
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.8.6"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/notebooks/13A Recommender Systems/Recommender Systems.ipynb b/notebooks/13A Recommender Systems/Recommender Systems.ipynb
index 33f51cd..a617eed 100644
--- a/notebooks/13A Recommender Systems/Recommender Systems.ipynb
+++ b/notebooks/13A Recommender Systems/Recommender Systems.ipynb
@@ -9,7 +9,7 @@
},
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -25,6 +25,9 @@
"\n",
"from sklearn.decomposition import NMF\n",
"\n",
+ "import warnings\n",
+ "warnings.filterwarnings(\"ignore\", category=FutureWarning)\n",
+ "\n",
"%matplotlib inline"
]
},
@@ -56,84 +59,9 @@
},
{
"cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
- "\n",
- " .dataframe tbody tr th {\n",
- " vertical-align: top;\n",
- " }\n",
- "\n",
- " .dataframe thead th {\n",
- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>user_id</th>\n",
- " <th>category_id</th>\n",
- " <th>rating</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>1</td>\n",
- " <td>1</td>\n",
- " <td>4</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>1</td>\n",
- " <td>2</td>\n",
- " <td>3</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>1</td>\n",
- " <td>5</td>\n",
- " <td>5</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>2</td>\n",
- " <td>1</td>\n",
- " <td>5</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>2</td>\n",
- " <td>3</td>\n",
- " <td>4</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " user_id category_id rating\n",
- "0 1 1 4\n",
- "1 1 2 3\n",
- "2 1 5 5\n",
- "3 2 1 5\n",
- "4 2 3 4"
- ]
- },
- "execution_count": 2,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
"source": [
"deals = pd.read_csv(\"deals_dummy.csv\")\n",
"deals.head()"
@@ -141,84 +69,9 @@
},
{
"cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
- "\n",
- " .dataframe tbody tr th {\n",
- " vertical-align: top;\n",
- " }\n",
- "\n",
- " .dataframe thead th {\n",
- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>category_id</th>\n",
- " <th>name</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>1</td>\n",
- " <td>A</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>2</td>\n",
- " <td>B</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>3</td>\n",
- " <td>C</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
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- " <td>D</td>\n",
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- " <td>6</td>\n",
- " <td>F</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
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- " category_id name\n",
- "0 1 A\n",
- "1 2 B\n",
- "2 3 C\n",
- "3 4 D\n",
- "4 5 E\n",
- "5 6 F"
- ]
- },
- "execution_count": 3,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
"source": [
"categories = pd.read_csv(\"categories_dummy.csv\")\n",
"categories"
@@ -234,7 +87,7 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -265,7 +118,7 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -291,98 +144,9 @@
},
{
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- " </tr>\n",
- " <tr>\n",
- " <th>5</th>\n",
- " <td>C</td>\n",
- " <td>A</td>\n",
- " <td>D</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>6</th>\n",
- " <td>D</td>\n",
- " <td>F</td>\n",
- " <td>E</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " 0 1 2\n",
- "user_id \n",
- "1 C B E\n",
- "2 C E D\n",
- "3 C B D\n",
- "4 E B C\n",
- "5 C A D\n",
- "6 D F E"
- ]
- },
- "execution_count": 6,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
"source": [
"n_recommendations = 3\n",
"\n",
@@ -408,7 +172,7 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -437,98 +201,9 @@
},
{
"cell_type": "code",
- "execution_count": 8,
- "metadata": {},
- "outputs": [
- {
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- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
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- " <tr>\n",
- " <th>5</th>\n",
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- " <td>C</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>6</th>\n",
- " <td>A</td>\n",
- " <td>B</td>\n",
- " <td>C</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " 0 1 2\n",
- "user_id \n",
- "1 A B C\n",
- "2 A B C\n",
- "3 A B C\n",
- "4 A B C\n",
- "5 A B C\n",
- "6 A B C"
- ]
- },
- "execution_count": 8,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
"source": [
"n_recommendations = 3\n",
"\n",
@@ -568,7 +243,7 @@
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": null,
"metadata": {
"solution2": "hidden",
"solution2_first": true
@@ -599,7 +274,7 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": null,
"metadata": {
"jupyter": {
"source_hidden": true
@@ -638,137 +313,9 @@
},
{
"cell_type": "code",
- "execution_count": 12,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "(6, 6)\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/tmp/ipykernel_4675/51284516.py:8: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
- " deals = deals.append(dummy_deals, ignore_index=True)\n"
- ]
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- " <td>2</td>\n",
- " <td>4</td>\n",
- " <td>0</td>\n",
- " <td>5</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- "category_name A B C D E F\n",
- "user_id \n",
- "1 4 3 0 0 5 0\n",
- "2 5 0 4 0 4 0\n",
- "3 4 0 5 3 4 0\n",
- "4 0 3 0 0 0 5\n",
- "5 0 4 0 0 0 4\n",
- "6 0 0 2 4 0 5"
- ]
- },
- "execution_count": 12,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
"source": [
"user_to_item_matrix = RecommenderSystem().get_user_to_item_matrix(deals, categories)\n",
"print(user_to_item_matrix.shape)\n",
@@ -799,7 +346,7 @@
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": null,
"metadata": {
"solution2": "hidden",
"solution2_first": true
@@ -820,7 +367,7 @@
},
{
"cell_type": "code",
- "execution_count": 14,
+ "execution_count": null,
"metadata": {
"jupyter": {
"source_hidden": true
@@ -845,7 +392,7 @@
},
{
"cell_type": "code",
- "execution_count": 15,
+ "execution_count": null,
"metadata": {
"code_folding": [],
"solution2": "hidden",
@@ -876,7 +423,7 @@
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{
"cell_type": "code",
- "execution_count": 16,
+ "execution_count": null,
"metadata": {
"jupyter": {
"source_hidden": true
@@ -909,130 +456,9 @@
},
{
"cell_type": "code",
- "execution_count": 17,
- "metadata": {},
- "outputs": [
- {
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- "(6, 6)\n"
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- " <td>0.685728</td>\n",
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- " </tr>\n",
- " <tr>\n",
- " <th>E</th>\n",
- " <td>0.982456</td>\n",
- " <td>0.340733</td>\n",
- " <td>0.710819</td>\n",
- " <td>0.317888</td>\n",
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- " A B C D E F\n",
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- "D 0.317888 0.000000 0.685728 1.000000 0.317888 0.492366\n",
- "E 0.982456 0.340733 0.710819 0.317888 1.000000 0.000000"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
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- "<AxesSubplot:>"
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- "execution_count": 17,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
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\n",
- "text/plain": [
- "<Figure size 432x288 with 2 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
"source": [
"item_to_item_matrix = ItemToItemRecommenderSystem().get_item_to_item_matrix(user_to_item_matrix)\n",
"\n",
@@ -1057,7 +483,7 @@
},
{
"cell_type": "code",
- "execution_count": 18,
+ "execution_count": null,
"metadata": {
"solution2": "hidden",
"solution2_first": true
@@ -1082,7 +508,7 @@
},
{
"cell_type": "code",
- "execution_count": 19,
+ "execution_count": null,
"metadata": {
"jupyter": {
"source_hidden": true
@@ -1128,7 +554,7 @@
},
{
"cell_type": "code",
- "execution_count": 20,
+ "execution_count": null,
"metadata": {
"solution2": "hidden",
"solution2_first": true
@@ -1181,7 +607,7 @@
},
{
"cell_type": "code",
- "execution_count": 21,
+ "execution_count": null,
"metadata": {
"jupyter": {
"source_hidden": true
@@ -1247,20 +673,9 @@
},
{
"cell_type": "code",
- "execution_count": 22,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "4.473333333333334"
- ]
- },
- "execution_count": 22,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
"source": [
"(0.71 * 5 + 0.79 * 4)/(0.71 + 0.79)"
]
@@ -1274,20 +689,9 @@
},
{
"cell_type": "code",
- "execution_count": 23,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "4.5"
- ]
- },
- "execution_count": 23,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
"source": [
"(0.32 * 5 + 0.32 * 4) / (0.32 + 0.32)"
]
@@ -1301,20 +705,9 @@
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+ "execution_count": null,
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"source": [
"(0.65 * 3)/0.65"
]
@@ -1328,29 +721,11 @@
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{
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- "execution_count": 25,
+ "execution_count": null,
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- "execution_count": 25,
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+ "outputs": [],
"source": [
"user_to_item_vector = user_to_item_matrix.iloc[0]\n",
"user_to_item_vector"
@@ -1358,28 +733,9 @@
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- " deals = deals.append(dummy_deals, ignore_index=True)\n"
- ]
- },
- {
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+ "execution_count": null,
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+ "outputs": [],
"source": [
"recommender = ItemToItemRecommenderSystem()\n",
"recommender.fit(deals, categories)\n",
@@ -1403,7 +759,7 @@
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{
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@@ -1425,139 +781,9 @@
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+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
"source": [
"recommender = ItemToItemRecommenderSystem()\n",
"recommender.fit(deals, categories)\n",
@@ -1595,7 +821,7 @@
},
{
"cell_type": "code",
- "execution_count": 29,
+ "execution_count": null,
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@@ -1615,7 +841,7 @@
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@@ -1649,73 +875,9 @@
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- "/tmp/ipykernel_4675/51284516.py:8: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
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- "output_type": "stream",
- "text": [
- "actual: ['A' 'B' 'E'] recommendations: [['D' 'C']] -> 0.0\n",
- "actual: ['A' 'C' 'E'] recommendations: [['B' 'F']] -> 0.0\n",
- "actual: ['A' 'C' 'D' 'E'] recommendations: [['B' 'F']] -> 0.0\n",
- "actual: ['B' 'F'] recommendations: [['D' 'C']] -> 0.0\n",
- "actual: ['B' 'F'] recommendations: [['E' 'D']] -> 0.0\n",
- "actual: ['C' 'D' 'F'] recommendations: [['B' 'E']] -> 0.0\n"
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+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
"source": [
"recommender = ItemToItemRecommenderSystem()\n",
"recommender.fit(deals, categories)\n",
@@ -1766,91 +928,9 @@
},
{
"cell_type": "code",
- "execution_count": 32,
- "metadata": {},
- "outputs": [
- {
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+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
"source": [
"deals = pd.read_csv(\"deals.csv\")\n",
"print(deals.shape)\n",
@@ -1866,32 +946,9 @@
},
{
"cell_type": "code",
- "execution_count": 33,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
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\n",
- "text/plain": [
- "<Figure size 720x720 with 4 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
"source": [
"categories_per_user = deals.groupby(\"user_id\").agg({\"category_id\": \"count\"}).reset_index()\n",
"categories_per_user.columns = [\"user\", \"#categories\"]\n",
@@ -1929,84 +986,9 @@
},
{
"cell_type": "code",
- "execution_count": 34,
- "metadata": {},
- "outputs": [
- {
- "data": {
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- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>user_id</th>\n",
- " <th>category_id</th>\n",
- " <th>rating</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>11135</td>\n",
- " <td>6</td>\n",
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- " <tr>\n",
- " <th>1</th>\n",
- " <td>11135</td>\n",
- " <td>7</td>\n",
- " <td>3</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>11135</td>\n",
- " <td>8</td>\n",
- " <td>9</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>11135</td>\n",
- " <td>10</td>\n",
- " <td>7</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>11135</td>\n",
- " <td>11</td>\n",
- " <td>1</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " user_id category_id rating\n",
- "0 11135 6 1\n",
- "1 11135 7 3\n",
- "2 11135 8 9\n",
- "3 11135 10 7\n",
- "4 11135 11 1"
- ]
- },
- "execution_count": 34,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
"source": [
"deals = deals.rename(columns={\"views\": \"rating\"})\n",
"deals.head()"
@@ -2014,85 +996,9 @@
},
{
"cell_type": "code",
- "execution_count": 35,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "(69, 2)\n"
- ]
- },
- {
- "data": {
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- " .dataframe tbody tr th {\n",
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- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>category_id</th>\n",
- " <th>name</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>1</td>\n",
- " <td>Elektronik</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>2</td>\n",
- " <td>Computer</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>3</td>\n",
- " <td>Fotografie</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>6</td>\n",
- " <td>Fashion</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>7</td>\n",
- " <td>Food</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " category_id name\n",
- "0 1 Elektronik\n",
- "1 2 Computer\n",
- "2 3 Fotografie\n",
- "3 6 Fashion\n",
- "4 7 Food"
- ]
- },
- "execution_count": 35,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
"source": [
"categories = pd.read_csv(\"categories.csv\")\n",
"print(categories.shape)\n",
@@ -2108,92 +1014,9 @@
},
{
"cell_type": "code",
- "execution_count": 36,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
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- "text/plain": [
- " user_id category_id rating\n",
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- "execution_count": 36,
- "metadata": {},
- "output_type": "execute_result"
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- ],
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
"source": [
"train, test = train_test_split(deals, test_size=0.05, random_state=42)\n",
"print(\"train\", train.shape)\n",
@@ -2218,7 +1041,7 @@
},
{
"cell_type": "code",
- "execution_count": 37,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -2234,163 +1057,9 @@
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- " <td>Bäder</td>\n",
- " <td>Sprachaufenthalt</td>\n",
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- " <td>Outdoor</td>\n",
- " <td>Openair</td>\n",
- " <td>Computer</td>\n",
- " </tr>\n",
- " <tr>\n",
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- " <td>Tattoo & Piercing</td>\n",
- " <td>Computer</td>\n",
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- " <td>Fitness</td>\n",
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- " <tr>\n",
- " <th>8634</th>\n",
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- " <td>Coiffure</td>\n",
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- " <td>Haushaltsgeräte</td>\n",
- " <td>Club</td>\n",
- " </tr>\n",
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- " <th>10660</th>\n",
- " <td>Freizeitparks</td>\n",
- " <td>Bar</td>\n",
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- " <td>Skaten</td>\n",
- " <td>Kaffee & Kuchen</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>15717</th>\n",
- " <td>Openair</td>\n",
- " <td>Musik</td>\n",
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- " </tr>\n",
- " <tr>\n",
- " <th>15822</th>\n",
- " <td>Fussball</td>\n",
- " <td>Reisen</td>\n",
- " <td>Kleider</td>\n",
- " <td>Smartphone</td>\n",
- " <td>Club</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>17229</th>\n",
- " <td>Computer</td>\n",
- " <td>Imbiss</td>\n",
- " <td>Health & Beauty</td>\n",
- " <td>Club</td>\n",
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- " <th>18584</th>\n",
- " <td>Openair</td>\n",
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- " <td>Sport</td>\n",
- " <td>Fahrdienst</td>\n",
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- "</div>"
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- "\n",
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- ]
- },
- "execution_count": 38,
- "metadata": {},
- "output_type": "execute_result"
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- ],
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
"source": [
"recommender = RandomRecommender()\n",
"recommender.fit(train, categories)\n",
@@ -2400,20 +1069,9 @@
},
{
"cell_type": "code",
- "execution_count": 39,
- "metadata": {},
- "outputs": [
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- "execution_count": 39,
- "metadata": {},
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+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
"source": [
"precision = evaluate(recommender, train, test, categories, n_recommendations)\n",
"precision"
@@ -2428,150 +1086,9 @@
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- " <tr>\n",
- " <th>7102</th>\n",
- " <td>Elektronik</td>\n",
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- " <tr>\n",
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- "18584 Elektronik Computer Fotografie Fashion Food"
- ]
- },
- "execution_count": 40,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
"source": [
"recommender = TopNRecommender()\n",
"recommender.fit(train, categories)\n",
@@ -2588,193 +1105,9 @@
},
{
"cell_type": "code",
- "execution_count": 41,
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/tmp/ipykernel_4675/51284516.py:8: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
- " deals = deals.append(dummy_deals, ignore_index=True)\n",
- "/tmp/ipykernel_4675/4045523426.py:3: RuntimeWarning: invalid value encountered in double_scalars\n",
- " sim = np.dot(v1, v2) / (np.linalg.norm(v1) * np.linalg.norm(v2))\n",
- "/tmp/ipykernel_4675/51284516.py:8: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
- " deals = deals.append(dummy_deals, ignore_index=True)\n"
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- "text/plain": [
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- "17229 Zahnarzt Fahrzeuge Imbiss Hotels Home & Living\n",
- "18584 Zahnarzt Fahrzeuge Imbiss Hotels Home & Living"
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- },
- "execution_count": 41,
- "metadata": {},
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- ],
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
"source": [
"recommender = ItemToItemRecommenderSystem()\n",
"recommender.fit(train, categories)\n",
@@ -2798,59 +1131,9 @@
},
{
"cell_type": "code",
- "execution_count": 42,
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/tmp/ipykernel_4675/51284516.py:8: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
- " deals = deals.append(dummy_deals, ignore_index=True)\n"
- ]
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"precision = evaluate(recommender, train, test, categories, n_recommendations=5, n_neighbors=12)\n",
"precision"
@@ -2873,70 +1156,9 @@
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--
GitLab