fix chapter 6

Author: sharanharsoor

chapter6/Change-Points_Anomalies.ipynb

@@ -11,7 +11,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Change point detection"
+    "# Anomaly detection"
    ]
   },
   {
@@ -20,7 +20,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "!pip install ruptures"
+    "!pip install alibi_detect"
    ]
   },
   {
@@ -29,11 +29,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import matplotlib.pyplot as plt\n",
+    "from alibi_detect.datasets import fetch_kdd\n",
     "\n",
-    "plt.style.use(\"seaborn-whitegrid\")\n",
-    "plt.rcParams[\"font.family\"] = \"Times New Roman\"\n",
-    "plt.rcParams[\"font.size\"] = \"17\""
+    "intrusions = fetch_kdd()"
    ]
   },
   {
@@ -42,12 +40,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import numpy as np\n",
-    "import ruptures as rpt\n",
-    "\n",
-    "signal, bkps = rpt.pw_constant(\n",
-    "    n_samples=500, n_features=3, n_bkps=2, noise_std=5.0, delta=(1, 20)\n",
-    ")"
+    "intrusions[\"target\"].sum() / len(intrusions[\"target\"])"
    ]
   },
   {
@@ -56,7 +49,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "rpt.display(signal, bkps)"
+    "intrusions[\"feature_names\"]"
    ]
   },
   {
@@ -65,7 +58,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "signal.shape"
+    "scores = od.score(intrusions[\"data\"][:, 0])"
    ]
   },
   {
@@ -74,7 +67,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "bkps"
+    "import pandas as pd\n",
+    "\n",
+    "pd.Series(intrusions[\"data\"][:, 0]).plot();"
    ]
   },
   {
@@ -83,19 +78,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# \"l1\", \"rbf\", \"linear\", \"normal\", \"ar\"\n",
-    "algo = rpt.Binseg(model=\"l1\").fit(signal)\n",
-    "my_bkps = algo.predict(n_bkps=3)\n",
+    "from alibi_detect.od import SpectralResidual\n",
     "\n",
-    "# show results\n",
-    "rpt.show.display(signal, bkps, my_bkps, figsize=(10, 6))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Anomaly detection"
+    "od = SpectralResidual(\n",
+    "    threshold=1.0, window_amp=20, window_local=20, n_est_points=10, n_grad_points=5\n",
+    ")\n",
+    "intrusion_outliers = od.predict(intrusions[\"data\"][:,0])"
    ]
   },
   {
@@ -104,18 +92,23 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "!pip install alibi_detect"
+    "import matplotlib\n",
+    "\n",
+    "ax = pd.Series(intrusions[\"data\"][:, 0], name=\"data\").plot(\n",
+    "    legend=False, figsize=(12, 6)\n",
+    ")\n",
+    "ax2 = ax.twinx()\n",
+    "ax = pd.Series(scores, name=\"scores\").plot(\n",
+    "    ax=ax2, legend=False, color=\"r\", marker=matplotlib.markers.CARETDOWNBASE\n",
+    ")\n",
+    "ax.figure.legend(bbox_to_anchor=(1, 1), loc=\"upper left\");"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "from alibi_detect.datasets import fetch_kdd\n",
-    "\n",
-    "intrusions = fetch_kdd()"
+    "# Change point detection"
    ]
   },
   {
@@ -124,7 +117,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "intrusions[\"target\"].sum() / len(intrusions[\"target\"])"
+    "!pip install ruptures"
    ]
   },
   {
@@ -133,7 +126,13 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "intrusions[\"feature_names\"]"
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import ruptures as rpt\n",
+    "\n",
+    "plt.style.use(\"seaborn-whitegrid\")\n",
+    "plt.rcParams[\"font.family\"] = \"Times New Roman\"\n",
+    "plt.rcParams[\"font.size\"] = \"17\""
    ]
   },
   {
@@ -142,12 +141,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from alibi_detect.od import SpectralResidual\n",
-    "\n",
-    "od = SpectralResidual(\n",
-    "    threshold=1.0, window_amp=20, window_local=20, n_est_points=10, n_grad_points=5\n",
-    ")\n",
-    "intrusion_outliers = od.predict(intrusions[\"data\"])"
+    "signal, bkps = rpt.pw_constant(\n",
+    "    n_samples=500, n_features=3, n_bkps=2, noise_std=5.0, delta=(1, 20)\n",
+    ")"
    ]
   },
   {
@@ -156,7 +152,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "scores = od.score(intrusions[\"data\"][:, 0])"
+    "rpt.display(signal, bkps)"
    ]
   },
   {
@@ -165,9 +161,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import pandas as pd\n",
-    "\n",
-    "pd.Series(intrusions[\"data\"][:, 0]).plot();"
+    "signal.shape"
    ]
   },
   {
@@ -176,24 +170,22 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import matplotlib\n",
-    "\n",
-    "ax = pd.Series(intrusions[\"data\"][:, 0], name=\"data\").plot(\n",
-    "    legend=False, figsize=(12, 6)\n",
-    ")\n",
-    "ax2 = ax.twinx()\n",
-    "ax = pd.Series(scores, name=\"scores\").plot(\n",
-    "    ax=ax2, legend=False, color=\"r\", marker=matplotlib.markers.CARETDOWNBASE\n",
-    ")\n",
-    "ax.figure.legend(bbox_to_anchor=(1, 1), loc=\"upper left\");"
+    "bkps"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "# \"l1\", \"rbf\", \"linear\", \"normal\", \"ar\"\n",
+    "algo = rpt.Binseg(model=\"l1\").fit(signal)\n",
+    "my_bkps = algo.predict(n_bkps=3)\n",
+    "\n",
+    "# show results\n",
+    "rpt.show.display(signal, bkps, my_bkps, figsize=(10, 6))"
+   ]
   }
  ],
  "metadata": {