Repository: mitre/menelaus (language: python)

Issue:
Update EDDM init docstring to use Google style

Add accuracy calculations to the example notebook plots for concept drift detectors
I think we had this set up at one point, and then removed it for whatever reason. MD3 already has it. Just need to calculate the running accuracy from the original model and compare it to the running accuracy after retraining. That gives us a contrast between "the model" and "the model-detector pair."

- [x] stepd
- [x] lfr
- [x] md3
- [x] adwin_outcome
- [x] eddm
- [x] ddm

This should probably also add the prequential error.


Discussion / hints:

Fix patch:
diff --git a/README.md b/README.md
index 943cde28..f3f93b25 100644
--- a/README.md
+++ b/README.md
@@ -118,13 +118,13 @@ follows:
 
 ```python
 import pandas as pd
-from menelaus.concept_drift import ADWIN
+from menelaus.concept_drift import ADWINOutcome
 from menelaus.data_drift import KdqTreeStreaming
 
 df = pd.read_csv('example.csv')
 
 # use a detector that searches for concept drift
-detector = ADWIN()
+detector = ADWINOutcome()
 for i, row in df.iterrows():
    detector.update(row['y_true'], row['y_predicted'], X=None)
    if detector.drift_state is not None:
diff --git a/docs/source/examples/change_detection/change_detection_examples.ipynb b/docs/source/examples/change_detection/change_detection_examples.ipynb
index a8f532b0..126e8761 100644
--- a/docs/source/examples/change_detection/change_detection_examples.ipynb
+++ b/docs/source/examples/change_detection/change_detection_examples.ipynb
@@ -19,13 +19,18 @@
     "conditional distributions P(y|var1) and P(y|var2). The drift occurs from index \n",
     "1000 to 1250, and affects 66% of the sample.\n",
     "\n",
+    "Rainfall is a real source of weather data. We use the first 1000 samples, where\n",
+    "no drift has been injected; but many features are cyclical, and haven't been\n",
+    "corrected, so change does occur.\n",
+    "\n",
     "These change detectors can be applied to any given single variable; below, \n",
-    "they are applied to var2."
+    "they are applied to `var2` and the `max_sustained_wind_speed` columns in the \n",
+    "respective datasets."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -35,13 +40,13 @@
     "import pandas as pd\n",
     "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
-    "from menelaus.change_detection import PageHinkley, CUSUM\n",
-    "from menelaus.datasets import fetch_circle_data"
+    "from menelaus.change_detection import ADWIN, CUSUM, PageHinkley\n",
+    "from menelaus.datasets import fetch_circle_data, fetch_rainfall_data"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -49,27 +54,48 @@
     "\n",
     "# read in Circle dataset\n",
     "df = fetch_circle_data()\n",
-    "drift_start, drift_end = 1000, 1250"
+    "drift_start, drift_end = 1000, 1250\n",
+    "\n",
+    "rainfall_df = fetch_rainfall_data()"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Page-Hinkley (PH) Test"
+    "## Cumulative Sum (CUSUM) Test"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- This monitors a moving average of var2, starting from an initial estimate of mean\n",
+    "and standard deviation.\n",
+    "\n",
+    "- It will only alarm if 50 or more samples have been observed since\n",
+    "initialization/drift.\n",
+    "\n",
+    "- This will alarm if var2 passes a critical value controlled by delta and\n",
+    "threshold in either direction, positive or negative.\n",
+    "\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
     "## Setup ##\n",
-    "\n",
-    "# Set up one-directional PH test: this will only alarm if the mean of the\n",
-    "# monitored variable decreases, and only after seeing 30 or more samples.\n",
-    "ph = PageHinkley(delta=0.01, threshold=15, direction=\"negative\", burn_in=30)\n",
+    "cusum = CUSUM(\n",
+    "    target=np.mean(df.loc[:drift_start, \"var2\"]),  # mean / std of 'Var 2' pre-drift\n",
+    "    sd_hat=np.std(df.loc[:drift_start, \"var2\"]),\n",
+    "    burn_in=50,\n",
+    "    delta=0.005,\n",
+    "    threshold=40,\n",
+    "    direction=None,\n",
+    ")\n",
     "\n",
     "# setup DF to record results\n",
     "status = pd.DataFrame(columns=[\"index\", \"actual value\", \"drift_detected\"])\n",
@@ -77,15 +103,38 @@
     "# iterate through data; feed each sample to the detector, in turn\n",
     "for i in range(len(df)):\n",
     "    obs = df[\"var2\"][i]\n",
-    "    ph.update(X=obs)\n",
-    "    status.loc[i] = [i, obs, ph.drift_state]"
+    "    cusum.update(obs)\n",
+    "    status.loc[i] = [i, obs, cusum.drift_state]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x1b50c3ee860>"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1440x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "## Plotting ##\n",
     "\n",
@@ -95,7 +144,7 @@
     "plt.grid(False, axis=\"x\")\n",
     "plt.xticks(fontsize=16)\n",
     "plt.yticks(fontsize=16)\n",
-    "plt.title(\"PH Test Results\", fontsize=22)\n",
+    "plt.title(\"CUSUM Test Results\", fontsize=22)\n",
     "plt.ylabel(\"Value\", fontsize=18)\n",
     "plt.xlabel(\"Index\", fontsize=18)\n",
     "ylims = [-0.05, 1.1]\n",
@@ -117,44 +166,29 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Page-Hinkley alarms shortly after the drift induction window closes, and then makes\n",
-    "several apparently erroneous alarms afterwards. The parameters may not be\n",
-    "well-chosen for the new regime.\n",
-    "Change detection algorithms come out of process control, so a priori\n",
-    "characterization of the bounds of the process, not performed here, would not\n",
-    "be unreasonable.\n"
+    "CUSUM alarms several times within the drift induction window, roughly halfway\n",
+    "through. After the alarm is reset, change is detected a few more times,\n",
+    "including an apparently erroneous detection after the drift induction window\n",
+    "is passed. The current threshold settings may then be too sensitive for the\n",
+    "new regime.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
+    "\n",
     "plt.show()\n",
-    "# plt.savefig(\"example_Page-Hinkley_detections.png\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Cumulative Sum (CUSUM) Test"
+    "# plt.savefig(\"example_CUSUM_detections.png\")"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "- This monitors a moving average of var2, starting from an initial estimate of mean\n",
-    "and standard deviation.\n",
-    "\n",
-    "- It will only alarm if 50 or more samples have been observed since\n",
-    "initialization/drift.\n",
-    "\n",
-    "- This will alarm if var2 passes a critical value controlled by delta and\n",
-    "threshold in either direction, positive or negative.\n",
-    "\n"
+    "## Page-Hinkley (PH) Test"
    ]
   },
   {
@@ -164,14 +198,10 @@
    "outputs": [],
    "source": [
     "## Setup ##\n",
-    "cusum = CUSUM(\n",
-    "    target=np.mean(df.loc[:drift_start, \"var2\"]),  # mean / std of 'Var 2' pre-drift\n",
-    "    sd_hat=np.std(df.loc[:drift_start, \"var2\"]),\n",
-    "    burn_in=50,\n",
-    "    delta=0.005,\n",
-    "    threshold=40,\n",
-    "    direction=None,\n",
-    ")\n",
+    "\n",
+    "# Set up one-directional PH test: this will only alarm if the mean of the\n",
+    "# monitored variable decreases, and only after seeing 30 or more samples.\n",
+    "ph = PageHinkley(delta=0.01, threshold=15, direction=\"negative\", burn_in=30)\n",
     "\n",
     "# setup DF to record results\n",
     "status = pd.DataFrame(columns=[\"index\", \"actual value\", \"drift_detected\"])\n",
@@ -179,8 +209,8 @@
     "# iterate through data; feed each sample to the detector, in turn\n",
     "for i in range(len(df)):\n",
     "    obs = df[\"var2\"][i]\n",
-    "    cusum.update(obs)\n",
-    "    status.loc[i] = [i, obs, cusum.drift_state]"
+    "    ph.update(X=obs)\n",
+    "    status.loc[i] = [i, obs, ph.drift_state]"
    ]
   },
   {
@@ -197,7 +227,7 @@
     "plt.grid(False, axis=\"x\")\n",
     "plt.xticks(fontsize=16)\n",
     "plt.yticks(fontsize=16)\n",
-    "plt.title(\"CUSUM Test Results\", fontsize=22)\n",
+    "plt.title(\"PH Test Results\", fontsize=22)\n",
     "plt.ylabel(\"Value\", fontsize=18)\n",
     "plt.xlabel(\"Index\", fontsize=18)\n",
     "ylims = [-0.05, 1.1]\n",
@@ -212,29 +242,130 @@
     "    label=\"Drift Detected\",\n",
     "    color=\"red\",\n",
     ")\n",
-    "plt.legend()"
+    "plt.legend()\n",
+    "plt.show()\n",
+    "# plt.savefig(\"example_Page-Hinkley_detections.png\")"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "CUSUM alarms several times within the drift induction window, roughly halfway\n",
-    "through. After the alarm is reset, change is detected a few more times,\n",
-    "including an apparently erroneous detection after the drift induction window\n",
-    "is passed. The current threshold settings may then be too sensitive for the\n",
-    "new regime.\n"
+    "Page-Hinkley alarms shortly after the drift induction window closes, and then makes\n",
+    "several apparently erroneous alarms afterwards. The parameters may not be\n",
+    "well-chosen for the new regime.\n",
+    "Change detection algorithms come out of process control, so a priori\n",
+    "characterization of the bounds of the process, not performed here, would not\n",
+    "be unreasonable.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## ADaptive WINdowing (ADWIN)\n",
+    "\n",
+    "ADWIN is a change detection algorithm that can be used to monitor a real-valued number. ADWIN maintains a window of the data stream, which grows to the right as new elements are received. When the mean of the feature in one of the subwindows is different enough, ADWIN drops older elements in its window until this ceases to be the case."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
+    "## Setup ##\n",
+    "\n",
+    "adwin = ADWIN()\n",
+    "\n",
+    "# setup DF to record results\n",
+    "status = pd.DataFrame(columns=[\"index\", \"actual value\", \"drift_detected\", \"ADWIN mean\"])\n",
+    "df2 = rainfall_df.loc[:1000, 'max_sustained_wind_speed']\n",
+    "rec_list = []\n",
+    "\n",
+    "# iterate through data; feed each sample to the detector, in turn\n",
+    "for i in range(len(df2)):\n",
+    "    obs = df2[i]\n",
+    "    adwin.update(X=obs)\n",
+    "    status.loc[i] = [i, obs, adwin.drift_state, adwin.mean()]\n",
+    "\n",
+    "    #monitor the size of ADWIN's window as it changes\n",
+    "    if adwin.drift_state == \"drift\":\n",
+    "        retrain_start = adwin.retraining_recs[0]\n",
+    "        retrain_end = adwin.retraining_recs[1]\n",
+    "        rec_list.append([retrain_start, retrain_end])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1440x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "## Plotting ##\n",
+    "\n",
+    "# plot the monitored variable and the status of the detector\n",
+    "plt.figure(figsize=(20, 6))\n",
+    "plt.scatter(\"index\", \"actual value\", data=status, label=\"max_sustained_wind_speed\", alpha=.5)\n",
+    "plt.plot(\"index\", \"ADWIN mean\", data=status, color='blue', linewidth=3)\n",
+    "plt.grid(False, axis=\"x\")\n",
+    "plt.xticks(fontsize=16)\n",
+    "plt.yticks(fontsize=16)\n",
+    "plt.title(\"ADWIN Results\", fontsize=22)\n",
+    "plt.ylabel(\"Value\", fontsize=18)\n",
+    "plt.xlabel(\"Index\", fontsize=18)\n",
+    "ylims = [-2, 6]\n",
+    "plt.ylim(ylims)\n",
     "\n",
+    "plt.vlines(\n",
+    "    x=status.loc[status[\"drift_detected\"] == \"drift\"][\"index\"],\n",
+    "    ymin=ylims[0],\n",
+    "    ymax=ylims[1],\n",
+    "    label=\"Drift Detected\",\n",
+    "    color=\"red\",\n",
+    ")\n",
+    "\n",
+    "# Create a list of lines that indicate the retraining windows.\n",
+    "# Space them evenly, vertically.\n",
+    "rec_list = pd.DataFrame(rec_list)\n",
+    "rec_list[\"y_val\"] = np.linspace(\n",
+    "    start=0.6 * (ylims[1] - ylims[0]) + ylims[0],\n",
+    "    stop=0.8 * ylims[1],\n",
+    "    num=len(rec_list),\n",
+    ")\n",
+    "\n",
+    "# Draw green lines that indicate where retraining occurred\n",
+    "plt.hlines(\n",
+    "    y=rec_list[\"y_val\"][::-1],\n",
+    "    xmin=rec_list[0],\n",
+    "    xmax=rec_list[1],\n",
+    "    color=\"black\",\n",
+    "    label=\"New Observation Windows\",\n",
+    ")\n",
+    "\n",
+    "plt.legend()\n",
     "plt.show()\n",
-    "# plt.savefig(\"example_CUSUM_detections.png\")"
+    "# plt.savefig(\"example_ADWIN.png\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "ADWIN monitors the running average of the `max_sustained_wind_speed` column and, once that mean begins to change enough around index 600, shrinks its observation window (in black) to only include more recent samples. This process repeats as further changes are detected. We can see that the size of the observation window shrinks and grows as the incoming data changes."
    ]
   }
  ],
diff --git a/docs/source/examples/concept_drift/concept_drift_examples.ipynb b/docs/source/examples/concept_drift/concept_drift_examples.ipynb
index 379848f8..4acd3979 100644
--- a/docs/source/examples/concept_drift/concept_drift_examples.ipynb
+++ b/docs/source/examples/concept_drift/concept_drift_examples.ipynb
@@ -19,7 +19,7 @@
                 "conditional distributions P(y|var1) and P(y|var2). The drift occurs from index \n",
                 "1000 to 1250, and affects 66% of the sample.\n",
                 "\n",
-                "Rainfall is a real data source that concept drift has been injected into. This\n",
+                "Rainfall is a real data source into which concept drift has been injected. This\n",
                 "set contains approximately 18,000 samples, and the data has been standardized.\n",
                 "Drift starts from index 12,000 and continues through the rest of the dataset.\n",
                 "In this example, we take the first 10,000 samples of the dataset for training\n",
@@ -51,9 +51,8 @@
                 "from sklearn.linear_model import SGDClassifier\n",
                 "from sklearn import svm\n",
                 "from sklearn.base import clone\n",
-                "from menelaus.concept_drift import LinearFourRates, ADWIN, DDM, EDDM, STEPD, MD3\n",
-                "from menelaus.datasets import fetch_circle_data\n",
-                "from menelaus.datasets import fetch_rainfall_data"
+                "from menelaus.concept_drift import LinearFourRates, ADWINOutcome, DDM, EDDM, STEPD, MD3\n",
+                "from menelaus.datasets import fetch_circle_data, fetch_rainfall_data"
             ]
         },
         {
@@ -265,12 +264,14 @@
             "cell_type": "markdown",
             "metadata": {},
             "source": [
-                "ADWIN can be used to monitor the accuracy of a classifier. ADWIN maintains a window of the data stream, which grows to the right as new elements are received. When the mean of the feature in one of the subwindows is different enough, ADWIN drops older elements in its window until this ceases to be the case."
+                "ADWIN is a change detection algorithm that can be used to monitor a real-valued number. ADWIN maintains a window of the data stream, which grows to the right as new elements are received. When the mean of the feature in one of the subwindows is different enough, ADWIN drops older elements in its window until this ceases to be the case.\n",
+                "\n",
+                "It can be used to monitor the accuracy of a classifier by checking `y_true == y_pred` at each time step. So, for convenience, `concept_drift.ADWINOutcome`, takes `y_true` and `y_pred` as arugments, as shown below. `change_detection.ADWIN` can be used more generally, as shown in the change detection examples."
             ]
         },
         {
             "cell_type": "code",
-            "execution_count": null,
+            "execution_count": 36,
             "metadata": {},
             "outputs": [],
             "source": [
@@ -279,12 +280,18 @@
                 "# Set up classifier: train on first training_size rows\n",
                 "X_train = df.loc[0:training_size, [\"var1\", \"var2\"]]\n",
                 "y_train = df.loc[0:training_size, \"y\"]\n",
+                "X_test = df.loc[training_size:len(df), [\"var1\", \"var2\"]]\n",
+                "y_pred = df.loc[training_size:len(df), \"y\"]\n",
                 "\n",
                 "np.random.seed(123)\n",
                 "clf = GaussianNB()\n",
                 "clf.fit(X_train, y_train)\n",
                 "\n",
-                "adwin = ADWIN()\n",
+                "# get running accuracy from the original classifier to compare performance\n",
+                "acc_orig = np.cumsum(clf.predict(X_test) == y_pred)\n",
+                "acc_orig = acc_orig / np.cumsum(np.repeat(1, len(acc_orig)))\n",
+                "\n",
+                "adwin = ADWINOutcome()\n",
                 "\n",
                 "# Set up DF to record results.\n",
                 "status = pd.DataFrame(\n",
@@ -296,7 +303,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": null,
+            "execution_count": 37,
             "metadata": {},
             "outputs": [],
             "source": [
@@ -336,19 +343,35 @@
                 "        clf = GaussianNB()\n",
                 "        clf.fit(X_train, y_train)\n",
                 "\n",
-                "    n += 1"
+                "    n += 1\n",
+                "\n",
+                "status['original_accuracy'] = acc_orig"
             ]
         },
         {
             "cell_type": "code",
-            "execution_count": null,
+            "execution_count": 42,
             "metadata": {},
-            "outputs": [],
+            "outputs": [
+                {
+                    "data": {
+                        "image/png": 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",
+                        "text/plain": [
+                            "<Figure size 1440x432 with 1 Axes>"
+                        ]
+                    },
+                    "metadata": {
+                        "needs_background": "light"
+                    },
+                    "output_type": "display_data"
+                }
+            ],
             "source": [
                 "## Plotting ##\n",
                 "\n",
                 "plt.figure(figsize=(20, 6))\n",
-                "plt.scatter(\"index\", \"accuracy\", data=status, label=\"Accuracy\")\n",
+                "plt.scatter(\"index\", \"original_accuracy\", data=status, label=\"Original Accuracy\", color='red')\n",
+                "plt.scatter(\"index\", \"accuracy\", data=status, label=\"Retrain Accuracy\", color='green')\n",
                 "plt.grid(False, axis=\"x\")\n",
                 "plt.xticks(fontsize=16)\n",
                 "plt.yticks(fontsize=16)\n",
@@ -387,7 +410,9 @@
                 "    label=\"Retraining Windows\",\n",
                 ")\n",
                 "\n",
-                "plt.legend(loc='lower right')"
+                "plt.legend(loc='lower right')\n",
+                "plt.show()\n",
+                "# plt.savefig(\"example_ADWINOutcome.png\")"
             ]
         },
         {
@@ -396,19 +421,10 @@
             "source": [
                 "\n",
                 "After drift is induced, the accuracy decreases enough for ADWIN to shrink its\n",
-                "window and alarm;  subsequent windows also include data from the old regime,\n",
-                "so drift continues to be detected until the window shrinks enough to be\n",
-                "comprised mostly by the new regime.\n"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "plt.show()\n",
-                "# plt.savefig(\"example_ADWIN.png\")"
+                "window and alarm;  subsequent windows also include data from the old regime, so\n",
+                "drift continues to be detected until the window shrinks enough to be comprised\n",
+                "mostly by the new regime. We can see that, by retraining on the new window of\n",
+                "data reported by ADWIN, the accuracy of the classifier is improved over time."
             ]
         },
         {
@@ -1155,4 +1171,4 @@
     },
     "nbformat": 4,
     "nbformat_minor": 2
-}
+}
\ No newline at end of file
diff --git a/examples/change_detection_examples.py b/examples/change_detection_examples.py
index 90aa8af3..ef727b8a 100644
--- a/examples/change_detection_examples.py
+++ b/examples/change_detection_examples.py
@@ -11,10 +11,15 @@
 # conditional distributions P(y|var1) and P(y|var2). The drift occurs from index 
 # 1000 to 1250, and affects 66% of the sample.
 # 
+# Rainfall is a real source of weather data. We use the first 1000 samples, where
+# no drift has been injected; but many features are cyclical, and haven't been
+# corrected, so change does occur.
+# 
 # These change detectors can be applied to any given single variable; below, 
-# they are applied to var2.
+# they are applied to `var2` and the `max_sustained_wind_speed` columns in the 
+# respective datasets.
 
-# In[ ]:
+# In[1]:
 
 
 ## Imports ##
@@ -23,11 +28,11 @@
 import pandas as pd
 import numpy as np
 import matplotlib.pyplot as plt
-from menelaus.change_detection import PageHinkley, CUSUM
-from menelaus.datasets import fetch_circle_data
+from menelaus.change_detection import ADWIN, CUSUM, PageHinkley
+from menelaus.datasets import fetch_circle_data, fetch_rainfall_data
 
 
-# In[ ]:
+# In[2]:
 
 
 ## Import Data ##
@@ -36,17 +41,34 @@
 df = fetch_circle_data()
 drift_start, drift_end = 1000, 1250
 
+rainfall_df = fetch_rainfall_data()
 
-# ## Page-Hinkley (PH) Test
 
-# In[ ]:
+# ## Cumulative Sum (CUSUM) Test
 
+# - This monitors a moving average of var2, starting from an initial estimate of mean
+# and standard deviation.
+# 
+# - It will only alarm if 50 or more samples have been observed since
+# initialization/drift.
+# 
+# - This will alarm if var2 passes a critical value controlled by delta and
+# threshold in either direction, positive or negative.
+# 
+# 
 
-## Setup ##
+# In[5]:
 
-# Set up one-directional PH test: this will only alarm if the mean of the
-# monitored variable decreases, and only after seeing 30 or more samples.
-ph = PageHinkley(delta=0.01, threshold=15, direction="negative", burn_in=30)
+
+## Setup ##
+cusum = CUSUM(
+    target=np.mean(df.loc[:drift_start, "var2"]),  # mean / std of 'Var 2' pre-drift
+    sd_hat=np.std(df.loc[:drift_start, "var2"]),
+    burn_in=50,
+    delta=0.005,
+    threshold=40,
+    direction=None,
+)
 
 # setup DF to record results
 status = pd.DataFrame(columns=["index", "actual value", "drift_detected"])
@@ -54,11 +76,11 @@
 # iterate through data; feed each sample to the detector, in turn
 for i in range(len(df)):
     obs = df["var2"][i]
-    ph.update(X=obs)
-    status.loc[i] = [i, obs, ph.drift_state]
+    cusum.update(obs)
+    status.loc[i] = [i, obs, cusum.drift_state]
 
 
-# In[ ]:
+# In[6]:
 
 
 ## Plotting ##
@@ -69,7 +91,7 @@
 plt.grid(False, axis="x")
 plt.xticks(fontsize=16)
 plt.yticks(fontsize=16)
-plt.title("PH Test Results", fontsize=22)
+plt.title("CUSUM Test Results", fontsize=22)
 plt.ylabel("Value", fontsize=18)
 plt.xlabel("Index", fontsize=18)
 ylims = [-0.05, 1.1]
@@ -87,46 +109,31 @@
 plt.legend()
 
 
-# Page-Hinkley alarms shortly after the drift induction window closes, and then makes
-# several apparently erroneous alarms afterwards. The parameters may not be
-# well-chosen for the new regime.
-# Change detection algorithms come out of process control, so a priori
-# characterization of the bounds of the process, not performed here, would not
-# be unreasonable.
+# CUSUM alarms several times within the drift induction window, roughly halfway
+# through. After the alarm is reset, change is detected a few more times,
+# including an apparently erroneous detection after the drift induction window
+# is passed. The current threshold settings may then be too sensitive for the
+# new regime.
 # 
 
-# In[ ]:
+# In[7]:
 
 
-plt.show()
-# plt.savefig("example_Page-Hinkley_detections.png")
 
+plt.show()
+# plt.savefig("example_CUSUM_detections.png")
 
-# ## Cumulative Sum (CUSUM) Test
 
-# - This monitors a moving average of var2, starting from an initial estimate of mean
-# and standard deviation.
-# 
-# - It will only alarm if 50 or more samples have been observed since
-# initialization/drift.
-# 
-# - This will alarm if var2 passes a critical value controlled by delta and
-# threshold in either direction, positive or negative.
-# 
-# 
+# ## Page-Hinkley (PH) Test
 
 # In[ ]:
 
 
 ## Setup ##
-cusum = CUSUM(
-    target=np.mean(df.loc[:drift_start, "var2"]),  # mean / std of 'Var 2' pre-drift
-    sd_hat=np.std(df.loc[:drift_start, "var2"]),
-    burn_in=50,
-    delta=0.005,
-    threshold=40,
-    direction=None,
-)
+
+# Set up one-directional PH test: this will only alarm if the mean of the
+# monitored variable decreases, and only after seeing 30 or more samples.
+ph = PageHinkley(delta=0.01, threshold=15, direction="negative", burn_in=30)
 
 # setup DF to record results
 status = pd.DataFrame(columns=["index", "actual value", "drift_detected"])
@@ -134,8 +141,8 @@
 # iterate through data; feed each sample to the detector, in turn
 for i in range(len(df)):
     obs = df["var2"][i]
-    cusum.update(obs)
-    status.loc[i] = [i, obs, cusum.drift_state]
+    ph.update(X=obs)
+    status.loc[i] = [i, obs, ph.drift_state]
 
 
 # In[ ]:
@@ -149,7 +156,7 @@
 plt.grid(False, axis="x")
 plt.xticks(fontsize=16)
 plt.yticks(fontsize=16)
-plt.title("CUSUM Test Results", fontsize=22)
+plt.title("PH Test Results", fontsize=22)
 plt.ylabel("Value", fontsize=18)
 plt.xlabel("Index", fontsize=18)
 ylims = [-0.05, 1.1]
@@ -165,19 +172,94 @@
     color="red",
 )
 plt.legend()
+plt.show()
+# plt.savefig("example_Page-Hinkley_detections.png")
 
 
-# CUSUM alarms several times within the drift induction window, roughly halfway
-# through. After the alarm is reset, change is detected a few more times,
-# including an apparently erroneous detection after the drift induction window
-# is passed. The current threshold settings may then be too sensitive for the
-# new regime.
+# Page-Hinkley alarms shortly after the drift induction window closes, and then makes
+# several apparently erroneous alarms afterwards. The parameters may not be
+# well-chosen for the new regime.
+# Change detection algorithms come out of process control, so a priori
+# characterization of the bounds of the process, not performed here, would not
+# be unreasonable.
 # 
 
-# In[ ]:
+# ## ADaptive WINdowing (ADWIN)
+# 
+# ADWIN is a change detection algorithm that can be used to monitor a real-valued number. ADWIN maintains a window of the data stream, which grows to the right as new elements are received. When the mean of the feature in one of the subwindows is different enough, ADWIN drops older elements in its window until this ceases to be the case.
 
+# In[3]:
 
 
+## Setup ##
+
+adwin = ADWIN()
+
+# setup DF to record results
+status = pd.DataFrame(columns=["index", "actual value", "drift_detected", "ADWIN mean"])
+df2 = rainfall_df.loc[:1000, 'max_sustained_wind_speed']
+rec_list = []
+
+# iterate through data; feed each sample to the detector, in turn
+for i in range(len(df2)):
+    obs = df2[i]
+    adwin.update(X=obs)
+    status.loc[i] = [i, obs, adwin.drift_state, adwin.mean()]
+
+    #monitor the size of ADWIN's window as it changes
+    if adwin.drift_state == "drift":
+        retrain_start = adwin.retraining_recs[0]
+        retrain_end = adwin.retraining_recs[1]
+        rec_list.append([retrain_start, retrain_end])
+
+
+# In[4]:
+
+
+## Plotting ##
+
+# plot the monitored variable and the status of the detector
+plt.figure(figsize=(20, 6))
+plt.scatter("index", "actual value", data=status, label="max_sustained_wind_speed", alpha=.5)
+plt.plot("index", "ADWIN mean", data=status, color='blue', linewidth=3)
+plt.grid(False, axis="x")
+plt.xticks(fontsize=16)
+plt.yticks(fontsize=16)
+plt.title("ADWIN Results", fontsize=22)
+plt.ylabel("Value", fontsize=18)
+plt.xlabel("Index", fontsize=18)
+ylims = [-2, 6]
+plt.ylim(ylims)
+
+plt.vlines(
+    x=status.loc[status["drift_detected"] == "drift"]["index"],
+    ymin=ylims[0],
+    ymax=ylims[1],
+    label="Drift Detected",
+    color="red",
+)
+
+# Create a list of lines that indicate the retraining windows.
+# Space them evenly, vertically.
+rec_list = pd.DataFrame(rec_list)
+rec_list["y_val"] = np.linspace(
+    start=0.6 * (ylims[1] - ylims[0]) + ylims[0],
+    stop=0.8 * ylims[1],
+    num=len(rec_list),
+)
+
+# Draw green lines that indicate where retraining occurred
+plt.hlines(
+    y=rec_list["y_val"][::-1],
+    xmin=rec_list[0],
+    xmax=rec_list[1],
+    color="black",
+    label="New Observation Windows",
+)
+
+plt.legend()
 plt.show()
-# plt.savefig("example_CUSUM_detections.png")
+# plt.savefig("example_ADWIN.png")
+
 
+# ADWIN monitors the running average of the `max_sustained_wind_speed` column and, once that mean begins to change enough around index 600, shrinks its observation window (in black) to only include more recent samples. This process repeats as further changes are detected. We can see that the size of the observation window shrinks and grows as the incoming data changes.
diff --git a/examples/concept_drift_examples.py b/examples/concept_drift_examples.py
index efcc927c..8fd20b7f 100644
--- a/examples/concept_drift_examples.py
+++ b/examples/concept_drift_examples.py
@@ -3,28 +3,28 @@
 
 # # Concept Drift Detector Examples
 
-# The examples in this notebook show how to set up, run, and produce output from detectors in the 
-# concept_drift module. The parameters aren't necessarily tuned for best 
+# The examples in this notebook show how to set up, run, and produce output from detectors in the
+# concept_drift module. The parameters aren't necessarily tuned for best
 # performance for the input data, just notional.
-# 
-# Circle is a synthetic data source, where drift occurs in both var1, var2, and the 
-# conditional distributions P(y|var1) and P(y|var2). The drift occurs from index 
+#
+# Circle is a synthetic data source, where drift occurs in both var1, var2, and the
+# conditional distributions P(y|var1) and P(y|var2). The drift occurs from index
 # 1000 to 1250, and affects 66% of the sample.
-# 
-# Rainfall is a real data source that concept drift has been injected into. This
+#
+# Rainfall is a real data source into which concept drift has been injected. This
 # set contains approximately 18,000 samples, and the data has been standardized.
 # Drift starts from index 12,000 and continues through the rest of the dataset.
 # In this example, we take the first 10,000 samples of the dataset for training
 # an initial classifier, and then use the remaining samples for testing.
-# 
-# These detectors are generally to be applied to the true class and predicted class 
+#
+# These detectors are generally to be applied to the true class and predicted class
 # from a particular model. So, each of the summary plots displays the running
 # accuracy of the classifier alongside the drift detector's output.
-# 
-# They also track the indices of portions of the incoming data stream which are 
-# more similar to each other -- i.e., data that seems to be part of the same 
+#
+# They also track the indices of portions of the incoming data stream which are
+# more similar to each other -- i.e., data that seems to be part of the same
 # concept, which could be used to retrain a model.
-# 
+#
 # NOTE: The LinearFourRates example has a relatively long runtime, roughly 5 minutes.
 
 # In[ ]:
@@ -39,9 +39,8 @@
 from sklearn.linear_model import SGDClassifier
 from sklearn import svm
 from sklearn.base import clone
-from menelaus.concept_drift import LinearFourRates, ADWIN, DDM, EDDM, STEPD, MD3
-from menelaus.datasets import fetch_circle_data
-from menelaus.datasets import fetch_rainfall_data
+from menelaus.concept_drift import LinearFourRates, ADWINOutcome, DDM, EDDM, STEPD, MD3
+from menelaus.datasets import fetch_circle_data, fetch_rainfall_data
 
 
 # In[ ]:
@@ -58,8 +57,27 @@
 rainfall_df = fetch_rainfall_data()
 rainfall_drift_start, rainfall_drift_end = 12000, 18158
 rainfall_training_size = 10000
-rainfall_columns = ["temperature", "dew_point", "sea_level_pressure", "visibility", "average_wind_speed", "max_sustained_wind_speed", "minimum_temperature", "maximum_temperature", "rain"]
-rainfall_features = ["temperature", "dew_point", "sea_level_pressure", "visibility", "average_wind_speed", "max_sustained_wind_speed", "minimum_temperature", "maximum_temperature"]
+rainfall_columns = [
+    "temperature",
+    "dew_point",
+    "sea_level_pressure",
+    "visibility",
+    "average_wind_speed",
+    "max_sustained_wind_speed",
+    "minimum_temperature",
+    "maximum_temperature",
+    "rain",
+]
+rainfall_features = [
+    "temperature",
+    "dew_point",
+    "sea_level_pressure",
+    "visibility",
+    "average_wind_speed",
+    "max_sustained_wind_speed",
+    "minimum_temperature",
+    "maximum_temperature",
+]
 rainfall_df[rainfall_features] = rainfall_df[rainfall_features].astype(float)
 
 
@@ -196,16 +214,16 @@
 plt.legend()
 
 
-# 
+#
 # One of the four rates immediately passes outside its threshold when drift is
 # induced. The same occurs shortly after leaving the drift region. The
 # recommended retraining data includes most of the drift induction window and
 # the data after regime change.
-# 
+#
 # The classifier's accuracy decreases again later, which causes the detector to
 # enter a "warning" state. Note that the retraining recommendations *begin* with
 # the index corresponding to the warning state, and end where drift is detected.
-# 
+#
 
 # In[ ]:
 
@@ -215,9 +233,11 @@
 
 # ## ADaptive WINdowing (ADWIN)
 
-# ADWIN can be used to monitor the accuracy of a classifier. ADWIN maintains a window of the data stream, which grows to the right as new elements are received. When the mean of the feature in one of the subwindows is different enough, ADWIN drops older elements in its window until this ceases to be the case.
+# ADWIN is a change detection algorithm that can be used to monitor a real-valued number. ADWIN maintains a window of the data stream, which grows to the right as new elements are received. When the mean of the feature in one of the subwindows is different enough, ADWIN drops older elements in its window until this ceases to be the case.
+#
+# It can be used to monitor the accuracy of a classifier by checking `y_true == y_pred` at each time step. So, for convenience, `concept_drift.ADWINOutcome`, takes `y_true` and `y_pred` as arugments, as shown below. `change_detection.ADWIN` can be used more generally, as shown in the change detection examples.
 
-# In[ ]:
+# In[36]:
 
 
 ## Setup ##
@@ -225,12 +245,18 @@
 # Set up classifier: train on first training_size rows
 X_train = df.loc[0:training_size, ["var1", "var2"]]
 y_train = df.loc[0:training_size, "y"]
+X_test = df.loc[training_size : len(df), ["var1", "var2"]]
+y_pred = df.loc[training_size : len(df), "y"]
 
 np.random.seed(123)
 clf = GaussianNB()
 clf.fit(X_train, y_train)
 
-adwin = ADWIN()
+# get running accuracy from the original classifier to compare performance
+acc_orig = np.cumsum(clf.predict(X_test) == y_pred)
+acc_orig = acc_orig / np.cumsum(np.repeat(1, len(acc_orig)))
+
+adwin = ADWINOutcome()
 
 # Set up DF to record results.
 status = pd.DataFrame(
@@ -240,7 +266,7 @@
 rec_list = []
 
 
-# In[ ]:
+# In[37]:
 
 
 # run ADWIN
@@ -281,14 +307,19 @@
 
     n += 1
 
+status["original_accuracy"] = acc_orig
 
-# In[ ]:
+
+# In[42]:
 
 
 ## Plotting ##
 
 plt.figure(figsize=(20, 6))
-plt.scatter("index", "accuracy", data=status, label="Accuracy")
+plt.scatter(
+    "index", "original_accuracy", data=status, label="Original Accuracy", color="red"
+)
+plt.scatter("index", "accuracy", data=status, label="Retrain Accuracy", color="green")
 plt.grid(False, axis="x")
 plt.xticks(fontsize=16)
 plt.yticks(fontsize=16)
@@ -327,22 +358,17 @@
     label="Retraining Windows",
 )
 
-plt.legend(loc='lower right')
+plt.legend(loc="lower right")
+plt.show()
+# plt.savefig("example_ADWINOutcome.png")
 
 
-# 
+#
 # After drift is induced, the accuracy decreases enough for ADWIN to shrink its
-# window and alarm;  subsequent windows also include data from the old regime,
-# so drift continues to be detected until the window shrinks enough to be
-# comprised mostly by the new regime.
-# 
-
-# In[ ]:
-
-
-plt.show()
-# plt.savefig("example_ADWIN.png")
-
+# window and alarm;  subsequent windows also include data from the old regime, so
+# drift continues to be detected until the window shrinks enough to be comprised
+# mostly by the new regime. We can see that, by retraining on the new window of
+# data reported by ADWIN, the accuracy of the classifier is improved over time.
 
 # ## Drift Detection Method (DDM)
 
@@ -361,7 +387,7 @@
 clf.fit(X_train, y_train)
 
 
-# 
+#
 # These parameter values are chosen somewhat arbitrarily.
 # At least 100 samples must be seen before DDM tests for drift (the n_threshold
 # parameter); the other two define the warning and drift regions. The minimum
@@ -369,7 +395,7 @@
 # warning_scale and drift_scale roughly correspond to how many standard standard
 # deviations away the current estimate must be in order for the detector to
 # alarm.
-# 
+#
 
 # In[ ]:
 
@@ -479,12 +505,12 @@
 plt.legend()
 
 
-# 
+#
 # DDM initially alarms during the drift induction window, triggering retraining.
 # The subsequent dip in accuracy is large enough to put the detector in the
 # "warning" state, but not large enough for "drift" to be identified.
-# 
-# 
+#
+#
 
 # In[ ]:
 
@@ -512,13 +538,13 @@
 
 # - n_threshold specifies the number of new samples which must be observed before
 # tests for drift are run.
-# 
+#
 # - The warning_thresh and drift_thresh values roughly correspond to the ratio of
 # the 95th percentile for the current distance distribution vs. the 95th percentile
 # for the "best" distance distribution observed so far. So, lower values correspond to less conservative monitoring - the current
 # distance between errors is allowed to be a smaller fraction of the "best"
 # distance.
-# 
+#
 
 # In[ ]:
 
@@ -632,7 +658,7 @@
 # retraining. The later increase in the error rate causes the detector to enter
 # the warning state, but is not large enough to be identified as drift with this
 # threshold setting.
-# 
+#
 
 # In[ ]:
 
@@ -764,14 +790,14 @@
     label="Retraining Windows",
 )
 
-plt.legend(loc='upper left')
+plt.legend(loc="upper left")
 
 
 # STEPD identifies drift quite early in the drift induction window, triggering
 # retraining on a relatively small amount of data; after this, the online
 # classifier updates sufficiently that its accuracy is roughly flat over the
 # remaining data, albeit with a big enough change to trigger more warnings.
-# 
+#
 
 # In[ ]:
 
@@ -795,7 +821,7 @@
 y_train = rainfall_df.loc[0:rainfall_training_size, "rain"]
 
 np.random.seed(123)
-clf = svm.SVC(kernel='linear')
+clf = svm.SVC(kernel="linear")
 clf.fit(X_train, y_train.values.ravel())
 retrain_clf = clone(clf)
 retrain_clf.fit(X_train, y_train.values.ravel())
@@ -807,7 +833,14 @@
 
 # Set up DF to record results.
 status = pd.DataFrame(
-    columns=["index", "y", "margin_density", "original_accuracy", "retrain_accuracy", "drift_detected"]
+    columns=[
+        "index",
+        "y",
+        "margin_density",
+        "original_accuracy",
+        "retrain_accuracy",
+        "drift_detected",
+    ]
 )
 correct_orig, correct_retrain = 0, 0
 n = 1
@@ -825,7 +858,7 @@
     y_pred_orig = int(clf.predict(X_test))
     y_pred_retrain = int(retrain_clf.predict(X_test))
     y_true = int(rainfall_df.loc[[i], "rain"])
-    
+
     # increment accuracy
     if y_pred_orig == y_true:
         correct_orig += 1
@@ -839,7 +872,9 @@
         oracle_label = rainfall_df.loc[[i], rainfall_columns]
         md3.give_oracle_label(oracle_label)
         if md3.waiting_for_oracle == False:
-            retrain_clf.fit(md3.reference_batch_features, md3.reference_batch_target.values.ravel())
+            retrain_clf.fit(
+                md3.reference_batch_features, md3.reference_batch_target.values.ravel()
+            )
         status.loc[i] = [
             i,
             y_true,
@@ -860,13 +895,13 @@
             accuracy_retrain,
             md3.drift_state,
         ]
-    
+
     # If there was a drift warning, track the window of the labeled
     # oracle data used
     if md3.drift_state == "warning":
         oracle_start = i + 1
         oracle_end = i + md3.oracle_data_length_required
-        
+
         oracle_list.append([oracle_start, oracle_end])
 
     n += 1
@@ -879,8 +914,12 @@
 
 plt.figure(figsize=(20, 6))
 plt.scatter("index", "margin_density", data=status, label="Margin Density")
-plt.scatter("index", "original_accuracy", data=status, label="Original Accuracy", color="red")
-plt.scatter("index", "retrain_accuracy", data=status, label="Retrain Accuracy", color="green")
+plt.scatter(
+    "index", "original_accuracy", data=status, label="Original Accuracy", color="red"
+)
+plt.scatter(
+    "index", "retrain_accuracy", data=status, label="Retrain Accuracy", color="green"
+)
 plt.grid(False, axis="x")
 plt.xticks(fontsize=16)
 plt.yticks(fontsize=16)
@@ -890,7 +929,9 @@
 ylims = [-0.05, 1.1]
 plt.ylim(ylims)
 
-plt.axvspan(rainfall_drift_start, rainfall_drift_end, alpha=0.5, label="Drift Induction Window")
+plt.axvspan(
+    rainfall_drift_start, rainfall_drift_end, alpha=0.5, label="Drift Induction Window"
+)
 
 # Draw red lines that indicate where drift was detected
 plt.vlines(
@@ -939,4 +980,3 @@
 
 plt.show()
 # plt.savefig("example_MD3.png")
-
diff --git a/setup.cfg b/setup.cfg
index c6f1d114..c0f97825 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -3,7 +3,7 @@
 
 [metadata]
 name = menelaus
-version = 0.1.2
+version = 0.2.0
 license = Apache 2.0
 description = This library implements algorithms for detecting data drift and concept drift for ML and statistics applications.
 long_description = file: readme_short.rst
diff --git a/src/menelaus/change_detection/__init__.py b/src/menelaus/change_detection/__init__.py
index cdf054d2..7b3a133c 100644
--- a/src/menelaus/change_detection/__init__.py
+++ b/src/menelaus/change_detection/__init__.py
@@ -6,5 +6,6 @@
 either upward or downward changes in a sequence. 
 """
 
-from menelaus.change_detection.page_hinkley import PageHinkley
+from menelaus.change_detection.adwin import ADWIN
 from menelaus.change_detection.cusum import CUSUM
+from menelaus.change_detection.page_hinkley import PageHinkley
diff --git a/src/menelaus/concept_drift/adwin.py b/src/menelaus/change_detection/adwin.py
similarity index 95%
rename from src/menelaus/concept_drift/adwin.py
rename to src/menelaus/change_detection/adwin.py
index 5e2fe160..54bdbc3b 100644
--- a/src/menelaus/concept_drift/adwin.py
+++ b/src/menelaus/change_detection/adwin.py
@@ -1,3 +1,5 @@
+# most of these get called within loops (or an outer loop on the detector),
+# so this is more efficient
 from numpy import (
     power,
     log,
@@ -7,15 +9,13 @@
     zeros,
 )
 
-# most of these get called within loops (or an outer loop on the detector),
-# so this is more efficient
-from menelaus.drift_detector import DriftDetector
+from menelaus.drift_detector import StreamingDetector
 
 
-class ADWIN(DriftDetector):
+class ADWIN(StreamingDetector):
     """ADWIN (ADaptive WINdowing) is a drift detection algorithm which uses a
     sliding window to estimate the running mean and variance of a given
-    statistic.
+    real-valued number.
 
     As each sample is added, ADWIN stores a running estimate (mean and variance)
     for a given statistic, calculated over a sliding window which will grow to
@@ -37,17 +37,15 @@ class ADWIN(DriftDetector):
     Ref. :cite:t:`bifet2007learning`
 
     Attributes:
-        total_updates (int): number of samples the drift detector has ever
+        total_samples (int): number of samples the drift detector has ever
             been updated with
-        updates_since_reset (int): number of samples since the last time the
+        samples_since_reset (int): number of samples since the last time the
             drift detector was reset
         drift_state (str): detector's current drift state. Can take values
             ``"drift"`` or ``None``.
 
     """
 
-    input_type = "stream"
-
     def __init__(
         self,
         delta=0.002,
@@ -104,21 +102,15 @@ def __init__(
         self._window_size = 0
         self._retraining_recs = [None, None]
 
-    def update(self, y_true=None, y_pred=None, X=None):
+    def update(self, X, y_true=None, y_pred=None):
         """Update the detector with a new sample.
 
         Args:
-          y_true: actual class of next sample
-          y_pred: predicted class of next sample
-          X: next sample in the stream of data - not used in ADWIN
+          y_true: actual class of next sample - not used for change detectors
+          y_pred: predicted class of next sample - not used for change detectors
+          X: next sample in the stream of data
         """
 
-        # Technically, ADWIN could monitor the running mean of some feature
-        # aside from accuracy, but that leads to potentially indeterminate input
-        # (user passes the full y_true, y_pred, X triplet), so -- maybe if
-        # there's a big enough need.
-        new_value = int(y_true == y_pred)
-
         if self.drift_state is not None:
             # note that the other attributes should *not* be initialized after drift
             self.reset()
@@ -126,7 +118,7 @@ def update(self, y_true=None, y_pred=None, X=None):
         super().update(X, y_true, y_pred)
         # add new sample to the head of the window
         self._window_size += 1
-        self._add_sample(new_value)
+        self._add_sample(X)
         self._shrink_window()
 
     def reset(self):
@@ -228,7 +220,7 @@ def _shrink_window(self):
         size and set the ``drift_state`` to ``"drift"``.
         """
         if (
-            self.total_updates % self.new_sample_thresh == 0
+            self.total_samples % self.new_sample_thresh == 0
             and self._window_size > self.window_size_thresh
         ):
             # either we reduced the window and must restart to check the new
@@ -280,8 +272,8 @@ def _shrink_window(self):
                             if self._window_size > 0:
                                 n_elements0 -= self._remove_last()
                                 self._retraining_recs = (
-                                    self.total_updates - self._window_size,
-                                    self.total_updates - 1,
+                                    self.total_samples - self._window_size,
+                                    self.total_samples - 1,
                                 )
                                 exit_shrink = True
                                 break
diff --git a/src/menelaus/concept_drift/__init__.py b/src/menelaus/concept_drift/__init__.py
index 3e88fd48..9de6612b 100644
--- a/src/menelaus/concept_drift/__init__.py
+++ b/src/menelaus/concept_drift/__init__.py
@@ -12,7 +12,7 @@
 multiple metrics simultaneously, such as true positive, false positive, true
 negative, and false negative rates. 
 """
-from menelaus.concept_drift.adwin import ADWIN
+from menelaus.concept_drift.adwin_outcome import ADWINOutcome
 from menelaus.concept_drift.ddm import DDM
 from menelaus.concept_drift.eddm import EDDM
 from menelaus.concept_drift.lfr import LinearFourRates
diff --git a/src/menelaus/concept_drift/adwin_outcome.py b/src/menelaus/concept_drift/adwin_outcome.py
new file mode 100644
index 00000000..39e8203a
--- /dev/null
+++ b/src/menelaus/concept_drift/adwin_outcome.py
@@ -0,0 +1,127 @@
+from menelaus.change_detection.adwin import ADWIN
+
+
+class ADWINOutcome(ADWIN):
+    """ADWIN (ADaptive WINdowing) is a drift detection algorithm which uses a
+    sliding window to estimate the running mean and variance of a given
+    real-valued number. ADWINOutcome specifically expects ``y_true``, ``y_pred``, and
+    uses that input to monitor the running accuracy of a classifier.
+
+    As each sample is added, ADWIN stores a running estimate (mean and variance)
+    for a given statistic, calculated over a sliding window which will grow to
+    the right until drift is detected. The condition for drift is defined over
+    pairs of subwindows at certain cutpoints within the current window. If, for
+    any such pair, the difference between the running estimates of the statistic
+    is over a certain threshold (controlled by delta), we identify drift, and
+    remove the oldest elements of the window until all differences are again
+    below the threshold.
+
+    The running estimates in each subwindow are maintained by storing summaries
+    of the elements in "buckets," which, in this implementation, are themselves
+    stored in the ``bucket_row_list`` attribute, whose total size scales with the
+    ``max_buckets`` parameter.
+
+    When drift occurs, the index of the element at the beginning of ADWIN's new
+    window is stored in ``self.retraining_recs``.
+
+    Ref. :cite:t:`bifet2007learning`
+
+    Attributes:
+        total_samples (int): number of samples the drift detector has ever
+            been updated with
+        samples_since_reset (int): number of samples since the last time the
+            drift detector was reset
+        drift_state (str): detector's current drift state. Can take values
+            ``"drift"`` or ``None``.
+
+    """
+
+    def __init__(
+        self,
+        delta=0.002,
+        max_buckets=5,
+        new_sample_thresh=32,
+        window_size_thresh=10,
+        subwindow_size_thresh=5,
+        conservative_bound=False,
+    ):
+        """
+
+        Args:
+            delta (float, optional): confidence value on on 0 to 1. ADWIN will
+                incorrectly detect drift with at most probability ``delta``, and
+                correctly detect drift with at least probability ``1 - delta``.
+                Defaults to 0.002.
+            max_buckets (int, optional): the maximum number of buckets to
+                maintain in each BucketRow. Corresponds to the "M" parameter in
+                Bifet 2006. Defaults to 5.
+            new_sample_thresh (int, optional): the drift detection procedure
+                will run every ``new_sample_thresh samples``, not in between.
+                Defaults to 32.
+            window_size_thresh (int, optional): the minimum number of samples in
+                the window required to check for drift. Defaults to 10.
+            subwindow_size_thresh (int, optional): the minimum number of samples
+                in each subwindow reqired to check it for drift. Defaults to 5.
+            conservative_bound (bool, optional): whether to assume a 'large
+                enough' sample when constructing drift cutoff. Defaults to ``False``.
+
+        Raises:
+            ValueError: If ``ADWIN.delta`` is not on the range 0 to 1.
+        """
+        super().__init__(
+            delta=0.002,
+            max_buckets=5,
+            new_sample_thresh=32,
+            window_size_thresh=10,
+            subwindow_size_thresh=5,
+            conservative_bound=False,
+        )
+
+    def update(self, y_true, y_pred, X=None):
+        """Update the detector with a new sample.
+
+        Args:
+          y_true: actual class of next sample
+          y_pred: predicted class of next sample
+          X: next sample in the stream of data. Not used for this
+            accuracy-based ADWIN. See ``change_detection.ADWIN`` for that
+            application.
+        """
+
+        new_value = int(y_true == y_pred)
+
+        # This class is here to avoid asking the user to provide such a direct
+        # function of (y_true, y_pred) in the X argument, which is unintuitive.
+        super().update(new_value, y_true=None, y_pred=None)
+
+    def reset(self):
+        """Initialize the detector's drift state and other relevant attributes.
+        Intended for use after ``drift_state == 'drift'``.
+        """
+        super().reset()
+
+    @property
+    def retraining_recs(self):
+        """Recommended indices for retraining. If drift is detected,
+        set to ``[beginning of ADWIN's new window, end of ADWIN's new window]``.
+        If these are e.g. the 5th and 13th sample that ADWIN has been updated
+        with, the values with be ``[4, 12]``.
+
+        Returns:
+            list: the current retraining recommendations
+        """
+        return self._retraining_recs
+
+    def mean(self):
+        """
+        Returns:
+            float: the estimated average of the passed stream, using the current window
+        """
+        return super().mean()
+
+    def variance(self):
+        """
+        Returns:
+            float: the estimated variance of the passed stream, using the current window
+        """
+        return super().variance()
diff --git a/src/menelaus/concept_drift/eddm.py b/src/menelaus/concept_drift/eddm.py
index 187c43ec..7504e2dc 100644
--- a/src/menelaus/concept_drift/eddm.py
+++ b/src/menelaus/concept_drift/eddm.py
@@ -39,12 +39,13 @@ class EDDM(DriftDetector):
 
     def __init__(self, n_threshold=30, warning_thresh=0.95, drift_thresh=0.9):
         """
-        :param n_threshold: the minimum number of samples required to test
-            whether drift has occurred
-        :param warning_thresh: defines the threshold over which to enter the
-            warning state.
-        :param drift_thresh: defines the threshold over which to enter the
-            drift state.
+        Args:
+            n_threshold (int, optional): the minimum number of samples required
+                to test whether drift has occurred. Defaults to 30.
+            warning_thresh (float, optional): defines the threshold over which
+                to enter the warning state. Defaults to 0.95.
+            drift_thresh (float, optional): defines the threshold over which to
+                enter the drift state. Defaults to 0.9.
         """
         super().__init__()
         self.warning_thresh = warning_thresh
diff --git a/src/menelaus/concept_drift/lfr.py b/src/menelaus/concept_drift/lfr.py
index 68afbf63..6ccf2643 100644
--- a/src/menelaus/concept_drift/lfr.py
+++ b/src/menelaus/concept_drift/lfr.py
@@ -46,6 +46,7 @@ def __init__(
         subsample=1,
         rates_tracked=["tpr", "tnr", "ppv", "npv"],
         parallelize=False,
+        round_val=4,
     ):
         """
         Args:
@@ -76,6 +77,10 @@ def __init__(
                 algorithm will be parallelized or not. Advantageous for large
                 datasets, but will slow down runtime for fewer data due to
                 overhead of threading. Defaults to False.
+            round_val: number of decimal points the estimate rate is rounded to
+                when stored in bounds dictionary. The greater the ``round_val``, the
+                more precise the bounds dictionary will be, and the longer the
+                runtime. (Default value = 4)
         """
         super().__init__()
         self.time_decay_factor = time_decay_factor
@@ -86,6 +91,7 @@ def __init__(
         self.subsample = subsample
         self.rates_tracked = rates_tracked
         self.parallelize = parallelize
+        self.round_val = round_val
         self.all_drift_states = []
         self._warning_states = {
             0: {"tpr": False, "tnr": False, "ppv": False, "npv": False}
@@ -126,7 +132,7 @@ def reset(self):
     #       LFR.update(1,1) without misinterpretation, but exposes them to a
     #       potential issue where LFR.update(X, y, y) would assign arguments
     #       incorrectly.
-    def update(self, y_true, y_pred, X=None, round_val=4):
+    def update(self, y_true, y_pred, X=None):
         """Update detector with a new observation:
 
         #. Updates confusion matrix (``self._confusion``) with new predictions
@@ -143,10 +149,6 @@ def update(self, y_true, y_pred, X=None, round_val=4):
             y_true: actual class
             y_pred: predicted class
             X: new sample - not used in LFR
-            round_val: number of decimal points the estimate rate is rounded to
-                when stored in bounds dictionary. The greater the ``round_val``, the
-                more precise the bounds dictionary will be, and the longer the
-                runtime. (Default value = 4)
         """
 
         if self.drift_state == "drift":
@@ -216,8 +218,8 @@ def _calculate_rate_bounds(rate):
                 est_rate = new_rates[rate]
                 curr_denom = self._denominators[rate + "_N"]
 
-                r_est_rate = round(est_rate, round_val)
-                r_curr_denom = round(curr_denom, round_val)
+                r_est_rate = round(est_rate, self.round_val)
+                r_curr_denom = round(curr_denom, self.round_val)
 
                 bound_dict = self._update_bounds_dict(
                     est_rate, curr_denom, r_est_rate, r_curr_denom
diff --git a/src/menelaus/datasets/make_example_data.py b/src/menelaus/datasets/make_example_data.py
index 748031a3..b150db1b 100644
--- a/src/menelaus/datasets/make_example_data.py
+++ b/src/menelaus/datasets/make_example_data.py
@@ -165,4 +165,5 @@ def fetch_rainfall_data():
         ],
     )
     df = df.iloc[1:, :].reset_index(drop=True)
+    df = df.apply(pd.to_numeric)
     return df


Test patch:
diff --git a/tests/menelaus/concept_drift/test_adwin.py b/tests/menelaus/change_detection/test_adwin.py
similarity index 87%
rename from tests/menelaus/concept_drift/test_adwin.py
rename to tests/menelaus/change_detection/test_adwin.py
index 02d104a5..3595f729 100644
--- a/tests/menelaus/concept_drift/test_adwin.py
+++ b/tests/menelaus/change_detection/test_adwin.py
@@ -1,6 +1,6 @@
 """Methods for checking simple behaviors of ADWIN."""
 import pytest
-from menelaus.concept_drift.adwin import ADWIN, _BucketRow, _BucketRowList
+from menelaus.change_detection.adwin import ADWIN, _BucketRow, _BucketRowList
 
 
 def test_compression():
@@ -10,9 +10,9 @@ def test_compression():
     max_buckets = 1
     det = ADWIN(max_buckets=max_buckets, delta=0.000001)
     n_samples = 10
-    for element in [(1, 0), (0, 0), (1, 1)]:
+    for element in [1, 2, 3]:
         for _ in range(n_samples):
-            det.update(y_true=element[0], y_pred=element[1])
+            det.update(element)
     curr = det._bucket_row_list.head
 
     overflow = False
@@ -35,7 +35,7 @@ def test_mean():
     assert det.mean() == 0
     n_samples = 5
     for _ in range(n_samples):
-        det.update(y_true=1, y_pred=1)
+        det.update(1)
     assert det.mean() == 1
 
 
@@ -45,8 +45,8 @@ def test_variance():
     assert det.variance() == 0
     n_samples = 10
     for _ in range(n_samples):
-        det.update(y_true=1, y_pred=1)
-        det.update(y_true=0, y_pred=1)
+        det.update(1)
+        det.update(0)
     assert det.variance() == 0.25
 
 
@@ -56,13 +56,13 @@ def test_drift():
     - ADWIN.retraining_recs takes on the proper values before and after drift
     """
     det = ADWIN(new_sample_thresh=2)
-    n_samples = 50
+    n_samples = 20
     for _ in range(n_samples):
-        det.update(y_true=1, y_pred=1)
+        det.update(0.1)
 
     drift_found = False
     for _ in range(n_samples):
-        det.update(y_true=1, y_pred=0)
+        det.update(100)
         drift_found = (det.drift_state == "drift") or drift_found
         if drift_found:
             break
@@ -73,7 +73,7 @@ def test_drift():
     assert det.retraining_recs is not None
 
     # do we reset properly?
-    det.update(y_true=1, y_pred=0)
+    det.update(100)
     assert det.drift_state is None
     assert det.retraining_recs == [None, None]
 
@@ -83,13 +83,13 @@ def test_conservative_bound():
     very drifting stream.
     """
     det = ADWIN(new_sample_thresh=2, conservative_bound=True)
-    n_samples = 50
+    n_samples = 20
     for _ in range(n_samples):
-        det.update(y_true=1, y_pred=1)
+        det.update(0.1)
 
     drift_found = False
     for _ in range(n_samples):
-        det.update(y_true=1, y_pred=0)
+        det.update(100)
         drift_found = (det.drift_state == "drift") or drift_found
         if drift_found:
             break
diff --git a/tests/menelaus/concept_drift/test_adwin_outcome.py b/tests/menelaus/concept_drift/test_adwin_outcome.py
new file mode 100644
index 00000000..9b46c26b
--- /dev/null
+++ b/tests/menelaus/concept_drift/test_adwin_outcome.py
@@ -0,0 +1,66 @@
+import pytest
+from menelaus.concept_drift.adwin_outcome import ADWINOutcome
+
+# All but the first test here are completely redundant with change_detection.test_adwin.
+# Because we want to get the docstrings to generate properly, and also be search-able,
+# they're aliased, which means these need to run to get coverage.
+
+
+def test_aliased_input():
+    det = ADWINOutcome()
+
+    det.update(y_true=1, y_pred=0)
+    assert det.total_samples == 1
+
+    # the alias shouldn't take any "feature" input
+    with pytest.raises(TypeError) as _:
+        det.update(X="hello!")
+
+
+def test_mean():
+    """Check that ADWIN.mean returns sane values"""
+    det = ADWINOutcome(new_sample_thresh=2)
+    assert det.mean() == 0
+    n_samples = 5
+    for _ in range(n_samples):
+        det.update(1, 1)
+    assert det.mean() == 1
+
+
+def test_variance():
+    """Check that ADWIN.variance returns sane values"""
+    det = ADWINOutcome(new_sample_thresh=2)
+    assert det.variance() == 0
+    n_samples = 10
+    for _ in range(n_samples):
+        det.update(1, 1)
+        det.update(0, 1)
+    assert det.variance() == 0.25
+
+
+def test_drift():
+    """Check that, for a very drifting data stream:
+    - drift is identified
+    - ADWIN.retraining_recs takes on the proper values before and after drift
+    """
+    det = ADWINOutcome(new_sample_thresh=2)
+    n_samples = 60
+    for _ in range(n_samples):
+        det.update(1, 1)
+
+    drift_found = False
+    for _ in range(n_samples):
+        det.update(0, 1)
+        drift_found = (det.drift_state == "drift") or drift_found
+        if drift_found:
+            break
+    # confirm drift is found with very different stream
+    assert drift_found is True
+    # better refactored as a separate test with determined, desired window
+    # but for now, just confirm that it's populated as part of the normal flow
+    assert det.retraining_recs is not None
+
+    # do we reset properly?
+    det.update(1, 1)
+    assert det.drift_state is None
+    assert det.retraining_recs == [None, None]
diff --git a/tests/menelaus/datasets/test_example_data.py b/tests/menelaus/datasets/test_example_data.py
index 8509854a..a5898c4c 100644
--- a/tests/menelaus/datasets/test_example_data.py
+++ b/tests/menelaus/datasets/test_example_data.py
@@ -2,7 +2,7 @@
 from menelaus.datasets.make_example_data import (
     make_example_batch_data,
     fetch_circle_data,
-    fetch_rainfall_data
+    fetch_rainfall_data,
 )
 
 
@@ -67,5 +67,5 @@ def test_circle_data():
 
 def test_rainfall_data():
     df = fetch_rainfall_data()
-    assert all(df.dtypes == object)
+    assert df.select_dtypes("number").shape == df.shape
     assert df.shape == (18159, 9)