Repository: mitre/menelaus (language: python)

Issue:
set_reference should copy the passed dataframe in most cases
One thing we hadn't considered so far is that the user can pass `X` as the reference data set and then modify it. This behavior seems undesirable. The detectors ought to store a copy. It'll increase the memory footprint, but this doesn't seem avoidable.

I don't believe this is a problem for any(?) of the streaming detectors, because they shouldn't actually be storing the data directly, but that should also be checked and corrected.

- CUSUM is appending values internally, but as these are one-dimensional, it doesn't seem like a problem. It looks like `list.append` is creating a copy of the value and sticking it on the tail.
- MD3 needs updating.
- The other detectors aren't storing the values directly.
Follow multi-inheritance pattern for all applicable detectors
### Task

After #46, all remaining detectors outside of `KdqTree` will need to be updated, to now use the `StreamingDetector` and `BatchDetector` ABCs if they are meant to service both options. Such detectors will (likely) also need to implement their own parent algorithm class, e.g. `KdqTreeDetector` as part of the multi-inheritance pattern.

### Impact

This will significantly progress the broad refactor of the drift detectors' object design. See also #15
MD3 API modifications
MD3's API differs from the other detectors, because it is intended for a semi-supervised context. It may be desirable to address some or all of these:
- Currently, MD3 has a set_reference to pass in a batch of data as the reference batch, which is inconsistent with the other streaming detectors. We could (1) leave this as is, (2) leave this as is and add set_reference to the other streaming detectors to make them consistent, (3) make MD3's API compatible with both an initial batch or stream-based data for setting the reference, or (4) change MD3 to be compatible only with stream-based data for setting the reference.
- The `waiting_for_oracle` state and `give_oracle_label` method are unusual. You could imagine gathering the labeled samples as we go, rather than using an oracle function, and maintaining that as a buffer, with some scheme to "forget" "old-enough" labeled samples. This is not as-written in the paper, though.
- The number of requested oracle samples and number of retraining samples are decoupled in our implementation, potentially. It may be worth making note of this in the docstring.


Discussion / hints:

Fix patch:
diff --git a/docs/source/examples/data_drift/data_drift_examples.ipynb b/docs/source/examples/data_drift/data_drift_examples.ipynb
index 18a9d600..ccdbd970 100644
--- a/docs/source/examples/data_drift/data_drift_examples.ipynb
+++ b/docs/source/examples/data_drift/data_drift_examples.ipynb
@@ -24,7 +24,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -45,7 +45,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -496,7 +496,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -520,22 +520,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "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"
-    }
-   ],
+   "outputs": [],
    "source": [
     "## Plotting ##\n",
     "\n",
@@ -602,1062 +589,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.plotly.v1+json": {
-       "config": {
-        "plotlyServerURL": "https://plot.ly"
-       },
-       "data": [
-        {
-         "domain": {
-          "x": [
-           0,
-           1
-          ],
-          "y": [
-           0,
-           1
-          ]
-         },
-         "hovertemplate": "name=%{label}<br>parent_idx=%{parent}<br>idx=%{id}<br>kss=%{color}<extra></extra>",
-         "ids": [
-          2002186130560,
-          2002186128688,
-          2002186088320,
-          2002185824640,
-          2001634003120,
-          2002185830256,
-          2001634007200,
-          2002185816144,
-          2001634003552,
-          2002186128832,
-          2002186092304,
-          2002186125712,
-          2002186125520,
-          2002186092496,
-          2002186125472,
-          2002186088032,
-          2002186088080,
-          2002186128592,
-          2002186130464,
-          2002186129648,
-          2002186129504,
-          2002186129072,
-          2002186129360,
-          2002186129168,
-          2002186129024,
-          2002186128928,
-          2002186129312,
-          2002186129600,
-          2002186130320,
-          2002186130128,
-          2002186129792,
-          2002186129936,
-          2002186129888,
-          2002186130080,
-          2002186130272
-         ],
-         "labels": [
-          "kdqTree",
-          "var1 <= 0.501",
-          "var2 <= 0.501",
-          "var1 <= 0.251",
-          "var1 > 0.251",
-          "var2 <= 0.257",
-          "var2 > 0.257",
-          "var1 <= 0.389",
-          "var1 > 0.389",
-          "var2 > 0.501",
-          "var1 <= 0.257",
-          "var1 > 0.257",
-          "var2 <= 0.75",
-          "var1 <= 0.377",
-          "var1 > 0.377",
-          "var2 <= 0.611",
-          "var2 > 0.611",
-          "var2 > 0.75",
-          "var1 > 0.501",
-          "var2 <= 0.495",
-          "var1 <= 0.751",
-          "var2 <= 0.247",
-          "var2 > 0.247",
-          "var1 <= 0.622",
-          "var2 <= 0.385",
-          "var2 > 0.385",
-          "var1 > 0.622",
-          "var1 > 0.751",
-          "var2 > 0.495",
-          "var1 <= 0.752",
-          "var2 <= 0.741",
-          "var1 <= 0.621",
-          "var1 > 0.621",
-          "var2 > 0.741",
-          "var1 > 0.752"
-         ],
-         "marker": {
-          "coloraxis": "coloraxis",
-          "colors": [
-           0.00019587572945470354,
-           0.00036325727399100227,
-           0.00021530950089404186,
-           0.00030988519170381274,
-           0.0009930340157818657,
-           0.004245022003903583,
-           0.005797506507254954,
-           0.006509640869187448,
-           0.0011345366943429445,
-           0.00006601689249610167,
-           0.00358447396279675,
-           0.0033613804682664124,
-           0.006565326642334661,
-           0.000832293386983562,
-           0.014219566649360256,
-           0.0064537319063342445,
-           0.007563684497333821,
-           0.0007236066800360092,
-           0.00036325727399100227,
-           0.00009520705662976244,
-           0.00997851404213388,
-           0.0022544217810038184,
-           0.007130435753376324,
-           0.005072465358480487,
-           0.0016574109446171551,
-           0.0033888602033288646,
-           0.0018257644010860625,
-           0.015552453724763098,
-           0.0009708012404880152,
-           0.00004402227775473881,
-           3.5073626782251583e-7,
-           0.0002294235106565061,
-           0.0005156721284902098,
-           0.00021565560434624442,
-           0.003149411845131695
-          ]
-         },
-         "name": "",
-         "parents": [
-          null,
-          2002186130560,
-          2002186128688,
-          2002186088320,
-          2002186088320,
-          2001634003120,
-          2001634003120,
-          2001634007200,
-          2001634007200,
-          2002186128688,
-          2002186128832,
-          2002186128832,
-          2002186125712,
-          2002186125520,
-          2002186125520,
-          2002186125472,
-          2002186125472,
-          2002186125712,
-          2002186130560,
-          2002186130464,
-          2002186129648,
-          2002186129504,
-          2002186129504,
-          2002186129360,
-          2002186129168,
-          2002186129168,
-          2002186129360,
-          2002186129648,
-          2002186130464,
-          2002186130320,
-          2002186130128,
-          2002186129792,
-          2002186129792,
-          2002186130128,
-          2002186130320
-         ],
-         "root": {
-          "color": "lightgrey"
-         },
-         "type": "treemap"
-        }
-       ],
-       "layout": {
-        "coloraxis": {
-         "colorbar": {
-          "title": {
-           "text": "kss"
-          }
-         },
-         "colorscale": [
-          [
-           0,
-           "rgb(247,251,255)"
-          ],
-          [
-           0.125,
-           "rgb(222,235,247)"
-          ],
-          [
-           0.25,
-           "rgb(198,219,239)"
-          ],
-          [
-           0.375,
-           "rgb(158,202,225)"
-          ],
-          [
-           0.5,
-           "rgb(107,174,214)"
-          ],
-          [
-           0.625,
-           "rgb(66,146,198)"
-          ],
-          [
-           0.75,
-           "rgb(33,113,181)"
-          ],
-          [
-           0.875,
-           "rgb(8,81,156)"
-          ],
-          [
-           1,
-           "rgb(8,48,107)"
-          ]
-         ]
-        },
-        "legend": {
-         "tracegroupgap": 0
-        },
-        "template": {
-         "data": {
-          "bar": [
-           {
-            "error_x": {
-             "color": "#2a3f5f"
-            },
-            "error_y": {
-             "color": "#2a3f5f"
-            },
-            "marker": {
-             "line": {
-              "color": "#E5ECF6",
-              "width": 0.5
-             },
-             "pattern": {
-              "fillmode": "overlay",
-              "size": 10,
-              "solidity": 0.2
-             }
-            },
-            "type": "bar"
-           }
-          ],
-          "barpolar": [
-           {
-            "marker": {
-             "line": {
-              "color": "#E5ECF6",
-              "width": 0.5
-             },
-             "pattern": {
-              "fillmode": "overlay",
-              "size": 10,
-              "solidity": 0.2
-             }
-            },
-            "type": "barpolar"
-           }
-          ],
-          "carpet": [
-           {
-            "aaxis": {
-             "endlinecolor": "#2a3f5f",
-             "gridcolor": "white",
-             "linecolor": "white",
-             "minorgridcolor": "white",
-             "startlinecolor": "#2a3f5f"
-            },
-            "baxis": {
-             "endlinecolor": "#2a3f5f",
-             "gridcolor": "white",
-             "linecolor": "white",
-             "minorgridcolor": "white",
-             "startlinecolor": "#2a3f5f"
-            },
-            "type": "carpet"
-           }
-          ],
-          "choropleth": [
-           {
-            "colorbar": {
-             "outlinewidth": 0,
-             "ticks": ""
-            },
-            "type": "choropleth"
-           }
-          ],
-          "contour": [
-           {
-            "colorbar": {
-             "outlinewidth": 0,
-             "ticks": ""
-            },
-            "colorscale": [
-             [
-              0,
-              "#0d0887"
-             ],
-             [
-              0.1111111111111111,
-              "#46039f"
-             ],
-             [
-              0.2222222222222222,
-              "#7201a8"
-             ],
-             [
-              0.3333333333333333,
-              "#9c179e"
-             ],
-             [
-              0.4444444444444444,
-              "#bd3786"
-             ],
-             [
-              0.5555555555555556,
-              "#d8576b"
-             ],
-             [
-              0.6666666666666666,
-              "#ed7953"
-             ],
-             [
-              0.7777777777777778,
-              "#fb9f3a"
-             ],
-             [
-              0.8888888888888888,
-              "#fdca26"
-             ],
-             [
-              1,
-              "#f0f921"
-             ]
-            ],
-            "type": "contour"
-           }
-          ],
-          "contourcarpet": [
-           {
-            "colorbar": {
-             "outlinewidth": 0,
-             "ticks": ""
-            },
-            "type": "contourcarpet"
-           }
-          ],
-          "heatmap": [
-           {
-            "colorbar": {
-             "outlinewidth": 0,
-             "ticks": ""
-            },
-            "colorscale": [
-             [
-              0,
-              "#0d0887"
-             ],
-             [
-              0.1111111111111111,
-              "#46039f"
-             ],
-             [
-              0.2222222222222222,
-              "#7201a8"
-             ],
-             [
-              0.3333333333333333,
-              "#9c179e"
-             ],
-             [
-              0.4444444444444444,
-              "#bd3786"
-             ],
-             [
-              0.5555555555555556,
-              "#d8576b"
-             ],
-             [
-              0.6666666666666666,
-              "#ed7953"
-             ],
-             [
-              0.7777777777777778,
-              "#fb9f3a"
-             ],
-             [
-              0.8888888888888888,
-              "#fdca26"
-             ],
-             [
-              1,
-              "#f0f921"
-             ]
-            ],
-            "type": "heatmap"
-           }
-          ],
-          "heatmapgl": [
-           {
-            "colorbar": {
-             "outlinewidth": 0,
-             "ticks": ""
-            },
-            "colorscale": [
-             [
-              0,
-              "#0d0887"
-             ],
-             [
-              0.1111111111111111,
-              "#46039f"
-             ],
-             [
-              0.2222222222222222,
-              "#7201a8"
-             ],
-             [
-              0.3333333333333333,
-              "#9c179e"
-             ],
-             [
-              0.4444444444444444,
-              "#bd3786"
-             ],
-             [
-              0.5555555555555556,
-              "#d8576b"
-             ],
-             [
-              0.6666666666666666,
-              "#ed7953"
-             ],
-             [
-              0.7777777777777778,
-              "#fb9f3a"
-             ],
-             [
-              0.8888888888888888,
-              "#fdca26"
-             ],
-             [
-              1,
-              "#f0f921"
-             ]
-            ],
-            "type": "heatmapgl"
-           }
-          ],
-          "histogram": [
-           {
-            "marker": {
-             "pattern": {
-              "fillmode": "overlay",
-              "size": 10,
-              "solidity": 0.2
-             }
-            },
-            "type": "histogram"
-           }
-          ],
-          "histogram2d": [
-           {
-            "colorbar": {
-             "outlinewidth": 0,
-             "ticks": ""
-            },
-            "colorscale": [
-             [
-              0,
-              "#0d0887"
-             ],
-             [
-              0.1111111111111111,
-              "#46039f"
-             ],
-             [
-              0.2222222222222222,
-              "#7201a8"
-             ],
-             [
-              0.3333333333333333,
-              "#9c179e"
-             ],
-             [
-              0.4444444444444444,
-              "#bd3786"
-             ],
-             [
-              0.5555555555555556,
-              "#d8576b"
-             ],
-             [
-              0.6666666666666666,
-              "#ed7953"
-             ],
-             [
-              0.7777777777777778,
-              "#fb9f3a"
-             ],
-             [
-              0.8888888888888888,
-              "#fdca26"
-             ],
-             [
-              1,
-              "#f0f921"
-             ]
-            ],
-            "type": "histogram2d"
-           }
-          ],
-          "histogram2dcontour": [
-           {
-            "colorbar": {
-             "outlinewidth": 0,
-             "ticks": ""
-            },
-            "colorscale": [
-             [
-              0,
-              "#0d0887"
-             ],
-             [
-              0.1111111111111111,
-              "#46039f"
-             ],
-             [
-              0.2222222222222222,
-              "#7201a8"
-             ],
-             [
-              0.3333333333333333,
-              "#9c179e"
-             ],
-             [
-              0.4444444444444444,
-              "#bd3786"
-             ],
-             [
-              0.5555555555555556,
-              "#d8576b"
-             ],
-             [
-              0.6666666666666666,
-              "#ed7953"
-             ],
-             [
-              0.7777777777777778,
-              "#fb9f3a"
-             ],
-             [
-              0.8888888888888888,
-              "#fdca26"
-             ],
-             [
-              1,
-              "#f0f921"
-             ]
-            ],
-            "type": "histogram2dcontour"
-           }
-          ],
-          "mesh3d": [
-           {
-            "colorbar": {
-             "outlinewidth": 0,
-             "ticks": ""
-            },
-            "type": "mesh3d"
-           }
-          ],
-          "parcoords": [
-           {
-            "line": {
-             "colorbar": {
-              "outlinewidth": 0,
-              "ticks": ""
-             }
-            },
-            "type": "parcoords"
-           }
-          ],
-          "pie": [
-           {
-            "automargin": true,
-            "type": "pie"
-           }
-          ],
-          "scatter": [
-           {
-            "marker": {
-             "colorbar": {
-              "outlinewidth": 0,
-              "ticks": ""
-             }
-            },
-            "type": "scatter"
-           }
-          ],
-          "scatter3d": [
-           {
-            "line": {
-             "colorbar": {
-              "outlinewidth": 0,
-              "ticks": ""
-             }
-            },
-            "marker": {
-             "colorbar": {
-              "outlinewidth": 0,
-              "ticks": ""
-             }
-            },
-            "type": "scatter3d"
-           }
-          ],
-          "scattercarpet": [
-           {
-            "marker": {
-             "colorbar": {
-              "outlinewidth": 0,
-              "ticks": ""
-             }
-            },
-            "type": "scattercarpet"
-           }
-          ],
-          "scattergeo": [
-           {
-            "marker": {
-             "colorbar": {
-              "outlinewidth": 0,
-              "ticks": ""
-             }
-            },
-            "type": "scattergeo"
-           }
-          ],
-          "scattergl": [
-           {
-            "marker": {
-             "colorbar": {
-              "outlinewidth": 0,
-              "ticks": ""
-             }
-            },
-            "type": "scattergl"
-           }
-          ],
-          "scattermapbox": [
-           {
-            "marker": {
-             "colorbar": {
-              "outlinewidth": 0,
-              "ticks": ""
-             }
-            },
-            "type": "scattermapbox"
-           }
-          ],
-          "scatterpolar": [
-           {
-            "marker": {
-             "colorbar": {
-              "outlinewidth": 0,
-              "ticks": ""
-             }
-            },
-            "type": "scatterpolar"
-           }
-          ],
-          "scatterpolargl": [
-           {
-            "marker": {
-             "colorbar": {
-              "outlinewidth": 0,
-              "ticks": ""
-             }
-            },
-            "type": "scatterpolargl"
-           }
-          ],
-          "scatterternary": [
-           {
-            "marker": {
-             "colorbar": {
-              "outlinewidth": 0,
-              "ticks": ""
-             }
-            },
-            "type": "scatterternary"
-           }
-          ],
-          "surface": [
-           {
-            "colorbar": {
-             "outlinewidth": 0,
-             "ticks": ""
-            },
-            "colorscale": [
-             [
-              0,
-              "#0d0887"
-             ],
-             [
-              0.1111111111111111,
-              "#46039f"
-             ],
-             [
-              0.2222222222222222,
-              "#7201a8"
-             ],
-             [
-              0.3333333333333333,
-              "#9c179e"
-             ],
-             [
-              0.4444444444444444,
-              "#bd3786"
-             ],
-             [
-              0.5555555555555556,
-              "#d8576b"
-             ],
-             [
-              0.6666666666666666,
-              "#ed7953"
-             ],
-             [
-              0.7777777777777778,
-              "#fb9f3a"
-             ],
-             [
-              0.8888888888888888,
-              "#fdca26"
-             ],
-             [
-              1,
-              "#f0f921"
-             ]
-            ],
-            "type": "surface"
-           }
-          ],
-          "table": [
-           {
-            "cells": {
-             "fill": {
-              "color": "#EBF0F8"
-             },
-             "line": {
-              "color": "white"
-             }
-            },
-            "header": {
-             "fill": {
-              "color": "#C8D4E3"
-             },
-             "line": {
-              "color": "white"
-             }
-            },
-            "type": "table"
-           }
-          ]
-         },
-         "layout": {
-          "annotationdefaults": {
-           "arrowcolor": "#2a3f5f",
-           "arrowhead": 0,
-           "arrowwidth": 1
-          },
-          "autotypenumbers": "strict",
-          "coloraxis": {
-           "colorbar": {
-            "outlinewidth": 0,
-            "ticks": ""
-           }
-          },
-          "colorscale": {
-           "diverging": [
-            [
-             0,
-             "#8e0152"
-            ],
-            [
-             0.1,
-             "#c51b7d"
-            ],
-            [
-             0.2,
-             "#de77ae"
-            ],
-            [
-             0.3,
-             "#f1b6da"
-            ],
-            [
-             0.4,
-             "#fde0ef"
-            ],
-            [
-             0.5,
-             "#f7f7f7"
-            ],
-            [
-             0.6,
-             "#e6f5d0"
-            ],
-            [
-             0.7,
-             "#b8e186"
-            ],
-            [
-             0.8,
-             "#7fbc41"
-            ],
-            [
-             0.9,
-             "#4d9221"
-            ],
-            [
-             1,
-             "#276419"
-            ]
-           ],
-           "sequential": [
-            [
-             0,
-             "#0d0887"
-            ],
-            [
-             0.1111111111111111,
-             "#46039f"
-            ],
-            [
-             0.2222222222222222,
-             "#7201a8"
-            ],
-            [
-             0.3333333333333333,
-             "#9c179e"
-            ],
-            [
-             0.4444444444444444,
-             "#bd3786"
-            ],
-            [
-             0.5555555555555556,
-             "#d8576b"
-            ],
-            [
-             0.6666666666666666,
-             "#ed7953"
-            ],
-            [
-             0.7777777777777778,
-             "#fb9f3a"
-            ],
-            [
-             0.8888888888888888,
-             "#fdca26"
-            ],
-            [
-             1,
-             "#f0f921"
-            ]
-           ],
-           "sequentialminus": [
-            [
-             0,
-             "#0d0887"
-            ],
-            [
-             0.1111111111111111,
-             "#46039f"
-            ],
-            [
-             0.2222222222222222,
-             "#7201a8"
-            ],
-            [
-             0.3333333333333333,
-             "#9c179e"
-            ],
-            [
-             0.4444444444444444,
-             "#bd3786"
-            ],
-            [
-             0.5555555555555556,
-             "#d8576b"
-            ],
-            [
-             0.6666666666666666,
-             "#ed7953"
-            ],
-            [
-             0.7777777777777778,
-             "#fb9f3a"
-            ],
-            [
-             0.8888888888888888,
-             "#fdca26"
-            ],
-            [
-             1,
-             "#f0f921"
-            ]
-           ]
-          },
-          "colorway": [
-           "#636efa",
-           "#EF553B",
-           "#00cc96",
-           "#ab63fa",
-           "#FFA15A",
-           "#19d3f3",
-           "#FF6692",
-           "#B6E880",
-           "#FF97FF",
-           "#FECB52"
-          ],
-          "font": {
-           "color": "#2a3f5f"
-          },
-          "geo": {
-           "bgcolor": "white",
-           "lakecolor": "white",
-           "landcolor": "#E5ECF6",
-           "showlakes": true,
-           "showland": true,
-           "subunitcolor": "white"
-          },
-          "hoverlabel": {
-           "align": "left"
-          },
-          "hovermode": "closest",
-          "mapbox": {
-           "style": "light"
-          },
-          "paper_bgcolor": "white",
-          "plot_bgcolor": "#E5ECF6",
-          "polar": {
-           "angularaxis": {
-            "gridcolor": "white",
-            "linecolor": "white",
-            "ticks": ""
-           },
-           "bgcolor": "#E5ECF6",
-           "radialaxis": {
-            "gridcolor": "white",
-            "linecolor": "white",
-            "ticks": ""
-           }
-          },
-          "scene": {
-           "xaxis": {
-            "backgroundcolor": "#E5ECF6",
-            "gridcolor": "white",
-            "gridwidth": 2,
-            "linecolor": "white",
-            "showbackground": true,
-            "ticks": "",
-            "zerolinecolor": "white"
-           },
-           "yaxis": {
-            "backgroundcolor": "#E5ECF6",
-            "gridcolor": "white",
-            "gridwidth": 2,
-            "linecolor": "white",
-            "showbackground": true,
-            "ticks": "",
-            "zerolinecolor": "white"
-           },
-           "zaxis": {
-            "backgroundcolor": "#E5ECF6",
-            "gridcolor": "white",
-            "gridwidth": 2,
-            "linecolor": "white",
-            "showbackground": true,
-            "ticks": "",
-            "zerolinecolor": "white"
-           }
-          },
-          "shapedefaults": {
-           "line": {
-            "color": "#2a3f5f"
-           }
-          },
-          "ternary": {
-           "aaxis": {
-            "gridcolor": "white",
-            "linecolor": "white",
-            "ticks": ""
-           },
-           "baxis": {
-            "gridcolor": "white",
-            "linecolor": "white",
-            "ticks": ""
-           },
-           "bgcolor": "#E5ECF6",
-           "caxis": {
-            "gridcolor": "white",
-            "linecolor": "white",
-            "ticks": ""
-           }
-          },
-          "title": {
-           "x": 0.05
-          },
-          "xaxis": {
-           "automargin": true,
-           "gridcolor": "white",
-           "linecolor": "white",
-           "ticks": "",
-           "title": {
-            "standoff": 15
-           },
-           "zerolinecolor": "white",
-           "zerolinewidth": 2
-          },
-          "yaxis": {
-           "automargin": true,
-           "gridcolor": "white",
-           "linecolor": "white",
-           "ticks": "",
-           "title": {
-            "standoff": 15
-           },
-           "zerolinecolor": "white",
-           "zerolinewidth": 2
-          }
-         }
-        },
-        "title": {
-         "text": "Index 1506"
-        }
-       }
-      }
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "## Kulldorff Spatial Scan Statistic (KSS) ##\n",
     "for title, df_plot in plot_data.items():\n",
diff --git a/src/menelaus/concept_drift/md3.py b/src/menelaus/concept_drift/md3.py
index f087fec5..c9505e41 100644
--- a/src/menelaus/concept_drift/md3.py
+++ b/src/menelaus/concept_drift/md3.py
@@ -1,3 +1,4 @@
+import copy
 import numpy as np
 import pandas as pd
 from sklearn.model_selection import KFold
@@ -116,8 +117,10 @@ def set_reference(self, X, y_true=None, y_pred=None, target_name=None):
                 dataframe which is the target variable
         """
 
-        self.reference_batch_features = X.loc[:, X.columns != target_name]
-        self.reference_batch_target = X.loc[:, X.columns == target_name]
+        self.reference_batch_features = copy.deepcopy(
+            X.loc[:, X.columns != target_name]
+        )
+        self.reference_batch_target = copy.deepcopy(X.loc[:, X.columns == target_name])
 
         self.reference_distribution = self.calculate_distribution_statistics(X)
 
diff --git a/src/menelaus/data_drift/cdbd.py b/src/menelaus/data_drift/cdbd.py
index 118dcef5..7f01f40d 100644
--- a/src/menelaus/data_drift/cdbd.py
+++ b/src/menelaus/data_drift/cdbd.py
@@ -162,13 +162,12 @@ def set_reference(self, X, y_true=None, y_pred=None):
             y_true (numpy.array): true labels for dataset - not used by CDBD
             y_pred (numpy.array): predicted labels for dataset - not used by CDBD
         """
+        # Ensure only being used with 1 variable in reference
+        if X.shape[1] != 1:
+            raise ValueError("CDBD should only be used to monitor 1 variable.")
 
         super().set_reference(X, y_true, y_pred)
 
-        # Ensure only being used with 1 variable in reference
-        if self._num_features != 1:
-            raise ValueError("CDBD should only be used to monitor 1 variable")
-
     def update(self, X, y_true=None, y_pred=None):
         """
         Update the detector with a new test batch. If drift is detected, new
diff --git a/src/menelaus/data_drift/hdddm.py b/src/menelaus/data_drift/hdddm.py
index 5e526db8..a453acef 100644
--- a/src/menelaus/data_drift/hdddm.py
+++ b/src/menelaus/data_drift/hdddm.py
@@ -213,9 +213,4 @@ def update(self, X, y_true=None, y_pred=None):
           y_true (numpy.ndarray): true labels of next batch - not used in HDDDM
           y_pred (numpy.ndarray): predicted labels of next batch - not used in HDDDM
         """
-
-        # Ensure batch datasets have equivalent features
-        if not self._num_features == X.shape[1]:
-            raise ValueError("Batch features must match")
-
         super().update(X, y_true, y_pred)
diff --git a/src/menelaus/data_drift/histogram_density_method.py b/src/menelaus/data_drift/histogram_density_method.py
index b8248fd3..9f8df377 100644
--- a/src/menelaus/data_drift/histogram_density_method.py
+++ b/src/menelaus/data_drift/histogram_density_method.py
@@ -1,11 +1,12 @@
+import copy
 import numpy as np
 import pandas as pd
 import scipy.stats
-from menelaus.drift_detector import DriftDetector
+from menelaus.drift_detector import BatchDetector
 from scipy.spatial.distance import jensenshannon
 
 
-class HistogramDensityMethod(DriftDetector):
+class HistogramDensityMethod(BatchDetector):
     """
     The Histogram Density Method (HDM) is the base class for both HDDDM and
     CDBD. HDDDM differs from CDBD by relying upon the Hellinger distance measure
@@ -231,10 +232,9 @@ def set_reference(self, X, y_true=None, y_pred=None):
             y_true (numpy.array): true labels for dataset - not used by HDM
             y_pred (numpy.array): predicted labels for dataset - not used by HDM
         """
-
+        super().set_reference(X, y_true, y_pred)
         # Initialize attributes
-        self.reference = X
-        self._num_features = self.reference.shape[1]
+        self.reference = copy.deepcopy(X)
         self.reset()
 
     def update(self, X, y_true=None, y_pred=None):
@@ -259,7 +259,7 @@ def update(self, X, y_true=None, y_pred=None):
         # Estimate reference and test histograms
         mins = []
         maxes = []
-        for f in range(self._num_features):
+        for f in range(self._input_col_dim):
             reference_variable = self.reference.iloc[:, f]
             test_variable = X.iloc[:, f]
             mins.append(np.concatenate((reference_variable, test_variable)).min())
@@ -270,26 +270,26 @@ def update(self, X, y_true=None, y_pred=None):
         # Divergence metric
         total_distance = 0
         feature_distances = []
-        for f in range(self._num_features):
+        for f in range(self._input_col_dim):
             f_distance = self.distance_function(
                 self._reference_density[f], test_density[f]
             )
             total_distance += f_distance
             feature_distances.append(f_distance)
-        self.current_distance = (1 / self._num_features) * total_distance
-        self.distances[self.total_updates] = self.current_distance
+        self.current_distance = (1 / self._input_col_dim) * total_distance
+        self.distances[self.total_batches] = self.current_distance
 
         # For each feature, calculate Epsilon, difference in distances
-        if self.total_updates > 1:
+        if self.total_batches > 1:
             self.feature_epsilons = [
                 a_i - b_i
                 for a_i, b_i in zip(feature_distances, self._prev_feature_distances)
             ]
 
         # Compute Epsilon and Beta
-        if self.updates_since_reset >= 2:
+        if self.batches_since_reset >= 2:
 
-            if self.updates_since_reset == 2 and self.detect_batch != 3:
+            if self.batches_since_reset == 2 and self.detect_batch != 3:
                 initial_epsilon = self._estimate_initial_epsilon(
                     self.reference, self.subsets, mins, maxes
                 )
@@ -297,20 +297,20 @@ def update(self, X, y_true=None, y_pred=None):
 
             current_epsilon = abs(self.current_distance - self._prev_distance) * 1.0
             self.epsilon.append(current_epsilon)
-            self.epsilon_values[self.total_updates] = current_epsilon
+            self.epsilon_values[self.total_batches] = current_epsilon
 
-            condition1 = bool(self.updates_since_reset >= 2 and self.detect_batch != 3)
-            condition2 = bool(self.updates_since_reset >= 3 and self.detect_batch == 3)
+            condition1 = bool(self.batches_since_reset >= 2 and self.detect_batch != 3)
+            condition2 = bool(self.batches_since_reset >= 3 and self.detect_batch == 3)
             if condition1 or condition2:
 
                 self.beta = self._adaptive_threshold(self.statistic, test_n)
-                self.thresholds[self.total_updates] = self.beta
+                self.thresholds[self.total_batches] = self.beta
 
                 # Detect drift
                 if current_epsilon > self.beta:
 
                     # Feature information
-                    if self._num_features > 1:
+                    if self._input_col_dim > 1:
 
                         self.feature_info = {
                             "Epsilons": self.feature_epsilons,
@@ -322,7 +322,7 @@ def update(self, X, y_true=None, y_pred=None):
 
                     self._drift_state = "drift"
                     self.reference = X
-                    self._lambda = self.total_updates
+                    self._lambda = self.total_batches
 
         if self._drift_state != "drift":
             self._prev_distance = self.current_distance
@@ -335,7 +335,7 @@ def update(self, X, y_true=None, y_pred=None):
     def reset(self):
         """
         Initialize relevant attributes to original values, to ensure information
-        only stored from updates_since_reset (lambda) onwards. Intended for use
+        only stored from batches_since_reset (lambda) onwards. Intended for use
         after ``drift_state == 'drift'``.
         """
 
@@ -384,7 +384,7 @@ def _build_histograms(self, dataset, min_values, max_values):
                 bins=self._bins,
                 range=(min_values[f], max_values[f]),
             )[0]
-            for f in range(self._num_features)
+            for f in range(self._input_col_dim)
         ]
 
         return histograms
@@ -429,22 +429,22 @@ def _adaptive_threshold(self, stat, test_n):
             Adaptive threshold Beta.
         """
 
-        if self.updates_since_reset == 3 and self.detect_batch != 3:
+        if self.batches_since_reset == 3 and self.detect_batch != 3:
             self.total_epsilon -= self.epsilon[0]
             self.epsilon = self.epsilon[1:]
 
         # update scale for denominator (t - lambda - 1), accounting for our initial Epsilon estimate
-        if self.updates_since_reset == 2 and self.detect_batch != 3:
+        if self.batches_since_reset == 2 and self.detect_batch != 3:
             d_scale = 1
         else:
-            d_scale = self.total_updates - self._lambda - 1
+            d_scale = self.total_batches - self._lambda - 1
 
-        # Increment running mean of epsilon from updates_since_reset (lambda) -> t-1
+        # Increment running mean of epsilon from batches_since_reset (lambda) -> t-1
         self.total_epsilon += self.epsilon[-2]  # was -2 before total samples change...
 
         epsilon_hat = (1 / d_scale) * self.total_epsilon
 
-        # Compute standard deviation for updates_since_reset (lambda) -> t-1
+        # Compute standard deviation for batches_since_reset (lambda) -> t-1
         total_stdev = sum(
             (self.epsilon[i] - epsilon_hat) ** 2 for i in range(len(self.epsilon) - 1)
         )
@@ -506,7 +506,7 @@ def _estimate_initial_epsilon(
 
                 # Divergence metric
                 total_distance = 0
-                for f in range(self._num_features):
+                for f in range(self._input_col_dim):
                     f_distance = self.distance_function(subset1[f], subset2[f])
                     total_distance += f_distance
                 distances.append(total_distance)
diff --git a/src/menelaus/data_drift/kdq_tree.py b/src/menelaus/data_drift/kdq_tree.py
index ef867376..68ad259d 100644
--- a/src/menelaus/data_drift/kdq_tree.py
+++ b/src/menelaus/data_drift/kdq_tree.py
@@ -1,3 +1,4 @@
+import copy
 import numpy as np
 from numpy import unique
 import pandas as pd
@@ -98,41 +99,6 @@ def reset(self):
         self._test_data_size = 0
         self._kdqtree = None
         self._critical_dist = None
-        self.input_cols = None
-
-    def _prepare_data(self, data):
-        """
-        Validates and processes new data to be used in update sequence,
-        adjusting saved feature names where needed.
-
-        Args:
-            data (pandas.DataFrame or numpy.ndarray): new data for update
-
-        Returns
-            pandas.DataFrame or numpy.ndarray: validated data for update
-        """
-        if isinstance(data, pd.DataFrame):
-            if self.input_cols is None:
-                # The first update with a dataframe will constrain subsequent
-                # input. This will also fire if set_reference has been used with
-                # a dataframe.
-                self.input_cols = data.columns
-            elif self.input_cols is not None:
-                if not data.columns.equals(self.input_cols):
-                    raise ValueError(
-                        "Columns of new data must match with columns of reference data."
-                    )
-            ary = data.values
-        elif isinstance(data, np.ndarray):
-            # This allows starting with a dataframe, then later passing bare
-            # numpy arrays. For now, assume users are not miscreants.
-            ary = data
-        else:
-            raise ValueError(
-                """This method is only available for data inputs in the form of 
-                a Pandas DataFrame or a Numpy Array."""
-            )
-        return ary
 
     def _evaluate_kdqtree(self, ary, input_type):
         """
@@ -245,7 +211,7 @@ def to_plotly_dataframe(
             )
         else:
             return self._kdqtree.to_plotly_dataframe(
-                tree_id1, tree_id2, max_depth, self.input_cols
+                tree_id1, tree_id2, max_depth, self._input_cols
             )
 
     def _get_critical_kld(self, ref_counts, sample_size):
@@ -407,9 +373,9 @@ def update(self, X, y_true=None, y_pred=None):
 
         StreamingDetector.update(self, X, y_true, y_pred)
         if isinstance(X, pd.DataFrame):
-            ary = X.values
+            ary = copy.deepcopy(X.values)
         else:
-            ary = X
+            ary = copy.deepcopy(X)
         KdqTreeDetector._evaluate_kdqtree(self, ary, "stream")
 
 
@@ -508,17 +474,16 @@ def set_reference(self, X, y_true=None, y_pred=None):
             y_true (numpy.array): actual labels of dataset - not used in KdqTree
             y_pred (numpy.array): predicted labels of dataset - not used in KdqTree
         """
-        if isinstance(X, pd.DataFrame):
+        super().set_reference(X, y_true, y_pred)
+        ary = copy.deepcopy(X)
+        if isinstance(ary, pd.DataFrame):
             # XXX - notice how inner_set calling KLD requires us to continue
             #       branching on input_type, which is not ideal - Anmol Srivastava
-            self._inner_set_reference(X.values, input_type="batch")
-            self.input_cols = X.columns
-        elif isinstance(X, np.ndarray):
-            self._inner_set_reference(X, input_type="batch")
-        else:
-            raise ValueError(
-                "This method is only available for data inputs in the form of a Pandas DataFrame or a Numpy Array."
-            )
+            self._inner_set_reference(ary.values, input_type="batch")
+        elif isinstance(ary, np.ndarray):
+            # This maybe ought to put some more effort into trying to format the
+            # user's input as a numpy array, as in _validate_X.
+            self._inner_set_reference(ary, input_type="batch")
 
     def update(self, X, y_true=None, y_pred=None):
         """
@@ -539,14 +504,16 @@ def update(self, X, y_true=None, y_pred=None):
             y_true (numpy.ndarray): true labels of input data - not used in KdqTree
             y_pred (numpy.ndarray): predicted labels of input data - not used in KdqTree
         """
-        ary = self._prepare_data(X)
 
         if self.drift_state == "drift":
+            # Note that set_reference resets the detector.
             self.set_reference(self.ref_data)
 
         BatchDetector.update(self, X, y_true, y_pred)
-        KdqTreeDetector._evaluate_kdqtree(self, ary, "batch")
 
-        # if _evaluate_kdqtree resulted in drift for batch data, do a redundant check
-        if self.drift_state == "drift" and isinstance(X, pd.DataFrame):
-            self.input_cols = X.columns
+        if isinstance(X, pd.DataFrame):
+            ary = copy.deepcopy(X.values)
+        elif isinstance(X, np.ndarray):
+            ary = copy.deepcopy(X)
+
+        KdqTreeDetector._evaluate_kdqtree(self, ary, "batch")
diff --git a/src/menelaus/drift_detector.py b/src/menelaus/drift_detector.py
index 3b56f08c..fef0567c 100644
--- a/src/menelaus/drift_detector.py
+++ b/src/menelaus/drift_detector.py
@@ -15,8 +15,8 @@ def __init__(self, *args, **kwargs):
         self._total_samples = 0
         self._samples_since_reset = 0
         self._drift_state = None
-        self.input_cols = None
-        self.input_col_dim = None
+        self._input_cols = None
+        self._input_col_dim = None
 
     @abstractmethod
     def update(self, X, y_true, y_pred):
@@ -59,11 +59,11 @@ def _validate_X(self, X):
         if isinstance(X, DataFrame):
             ary = X
             # The first update with a dataframe will constrain subsequent input.
-            if self.input_cols is None:
-                self.input_cols = ary.columns
-                self.input_col_dim = len(self.input_cols)
-            elif self.input_cols is not None:
-                if not ary.columns.equals(self.input_cols):
+            if self._input_cols is None:
+                self._input_cols = ary.columns
+                self._input_col_dim = len(self._input_cols)
+            elif self._input_cols is not None:
+                if not ary.columns.equals(self._input_cols):
                     raise ValueError(
                         "Columns of new data must match with columns of prior data."
                     )
@@ -74,12 +74,12 @@ def _validate_X(self, X):
                 ary = ary.reshape(-1, 1)
             elif len(ary.shape) == 1:
                 ary = ary.reshape(1, -1)
-            if self.input_col_dim is None:
+            if self._input_col_dim is None:
                 # This allows starting with a dataframe, then later passing bare
                 # numpy arrays. For now, assume users are not miscreants.
-                self.input_col_dim = ary.shape[1]
-            elif self.input_col_dim is not None:
-                if ary.shape[1] != self.input_col_dim:
+                self._input_col_dim = ary.shape[1]
+            elif self._input_col_dim is not None:
+                if ary.shape[1] != self._input_col_dim:
                     raise ValueError(
                         "Column-dimension of new data must match prior data."
                     )
@@ -180,6 +180,8 @@ def __init__(self, *args, **kwargs):
         self._total_batches = 0
         self._batches_since_reset = 0
         self._drift_state = None
+        self._input_cols = None
+        self._input_col_dim = None
 
     @abstractmethod
     def update(self, X, y_true, y_pred):
@@ -191,6 +193,7 @@ def update(self, X, y_true, y_pred):
             y_true (numpy.ndarray): if applicable, true labels of input data
             y_pred (numpy.ndarray): if applicable, predicted labels of input data
         """
+        self._validate_input(X, y_true, y_pred)
         self.total_batches += 1
         self.batches_since_reset += 1
 
@@ -204,7 +207,7 @@ def set_reference(self, X, y_true, y_pred):
             y_true (numpy.array): actual labels of dataset
             y_pred (numpy.array): predicted labels of dataset
         """
-        raise NotImplementedError
+        self._validate_input(X, y_true, y_pred)
 
     @abstractmethod
     def reset(self, *args, **kwargs):
@@ -215,6 +218,87 @@ def reset(self, *args, **kwargs):
         self.batches_since_reset = 0
         self.drift_state = None
 
+    def _validate_X(self, X):
+        """Validate that the input only contains one observation, and that its
+        dimensions/column names match earlier input. If there is no
+        earlier input, store the dimension/column names.
+
+        Args:
+            X (array-like or numeric): Input features.
+
+        Raises:
+            ValueError: if a dataframe has ever been passed, raised if X's
+                column names don't match
+            ValueError: if an array has ever been passed, raised if X's number
+                of columns don't match
+        """
+        if isinstance(X, DataFrame):
+            ary = X
+            # The first update with a dataframe will constrain subsequent input.
+            if self._input_cols is None:
+                self._input_cols = ary.columns
+                self._input_col_dim = len(self._input_cols)
+            elif self._input_cols is not None:
+                if not ary.columns.equals(self._input_cols):
+                    raise ValueError(
+                        "Columns of new data must match with columns of prior data."
+                    )
+        else:
+            ary = copy.copy(X)
+            ary = np.array(ary)
+            # Batch size of 1 is technically allowed, but it'll probably break downstream
+            if len(ary.shape) == 0:
+                ary = ary.reshape(-1, 1)
+            elif len(ary.shape) == 1:
+                ary = ary.reshape(1, -1)
+            if self._input_col_dim is None:
+                # This allows starting with a dataframe, then later passing bare
+                # numpy arrays. For now, assume users are not miscreants.
+                self._input_col_dim = ary.shape[1]
+            elif self._input_col_dim is not None:
+                if ary.shape[1] != self._input_col_dim:
+                    raise ValueError(
+                        "Column-dimension of new data must match prior data."
+                    )
+
+    def _validate_y(self, y):
+        """Validate that input contains only one column.
+
+        Args:
+            y (numeric): the current value for `y_true` or `y_pred`, given to
+                `update`.
+
+        Raises:
+            ValueError: if an array has been passed that has more than one column
+        """
+        ary = np.array(y)
+        if len(ary.shape) == 0:
+            ary = ary.reshape(-1, 1)
+        elif len(ary.shape) == 1:
+            ary = ary.reshape(1, -1)
+        if ary.shape[1] != 1:
+            raise ValueError(
+                "Input for streaming detectors should contain only one column."
+            )
+
+    def _validate_input(self, X, y_true, y_pred):
+        """Helper method for `update` and `set_reference`. Validates whether the
+        input is appropriate for a batch detector. Errors will be raised if X's
+        dimensions don't match prior input, or a y input has more than one
+        column.
+
+        Args:
+            X (numpy.ndarray): input data
+            y_true (numpy.ndarray): if applicable, true labels of input data
+            y_pred (numpy.ndarray): if applicable, predicted labels of input data
+        """
+        if X is not None:
+            self._validate_X(X)
+        if y_true is not None:
+            self._validate_y(y_true)
+        if y_pred is not None:
+            self._validate_y(y_pred)
+
     @property
     def total_batches(self):
         """Total number of batches the drift detector has been updated with.


Test patch:
diff --git a/tests/menelaus/data_drift/test_histogram_density_method.py b/tests/menelaus/data_drift/test_histogram_density_method.py
index 68373965..befa44ba 100644
--- a/tests/menelaus/data_drift/test_histogram_density_method.py
+++ b/tests/menelaus/data_drift/test_histogram_density_method.py
@@ -82,8 +82,8 @@ def test_detect_batch_1_init():
     )
     hdm.set_reference(reference)
 
-    assert hdm.total_updates == 1
-    assert hdm.updates_since_reset == 1
+    assert hdm.total_batches == 1
+    assert hdm.batches_since_reset == 1
     assert hdm.drift_state == None
     assert hdm._prev_feature_distances != []
     assert hdm.reference_n == len(reference)
@@ -109,8 +109,8 @@ def test_detect_batch_1_no_drift():
     # update with identical test batch
     hdm.update(reference)
 
-    assert hdm.total_updates == 2
-    assert hdm.updates_since_reset == 2
+    assert hdm.total_batches == 2
+    assert hdm.batches_since_reset == 2
     assert hdm.drift_state == None
     assert hdm.reference_n == len(reference) * 2
     assert hdm.feature_epsilons != []
@@ -195,8 +195,8 @@ def test_detect_batch_1_reset():
     # test reset updating with identical batch to test
     hdm.update(test)
 
-    assert hdm.total_updates == 4
-    assert hdm.updates_since_reset == 2
+    assert hdm.total_batches == 4
+    assert hdm.batches_since_reset == 2
     assert len(hdm.epsilon) == 2  # epsilon is reset
     assert hdm.beta != None
     assert hdm.drift_state is None
@@ -217,8 +217,8 @@ def test_detect_batch_2_init():
     )
     hdm.set_reference(reference)
 
-    assert hdm.total_updates == 0
-    assert hdm.updates_since_reset == 0
+    assert hdm.total_batches == 0
+    assert hdm.batches_since_reset == 0
     assert hdm.drift_state == None
     assert hdm.reference_n == len(reference)
 
@@ -249,7 +249,7 @@ def test_detect_batch_2_no_drift():
     hdm.update(reference)
 
     assert hdm.drift_state == None
-    assert hdm.total_updates == 2
+    assert hdm.total_batches == 2
     assert hdm.feature_epsilons != []
     assert len(hdm.epsilon) == 2
     assert hdm.beta != None
@@ -314,8 +314,8 @@ def test_detect_batch_2_reset():
     # test reset updating with identical batch to test batch 2
     hdm.update(test)
 
-    assert hdm.total_updates == 3
-    assert hdm.updates_since_reset == 1
+    assert hdm.total_batches == 3
+    assert hdm.batches_since_reset == 1
     assert len(hdm.epsilon) == 0  # epsilon is reset
     assert hdm.beta != None
     assert hdm._drift_state == None
@@ -336,8 +336,8 @@ def test_detect_batch_3_init():
     )
     hdm.set_reference(reference)
 
-    assert hdm.total_updates == 0
-    assert hdm.updates_since_reset == 0
+    assert hdm.total_batches == 0
+    assert hdm.batches_since_reset == 0
     assert hdm.drift_state == None
     assert hdm.reference_n == len(reference)
 
@@ -371,7 +371,7 @@ def test_detect_batch_3_no_drift():
     hdm.update(reference)
 
     assert hdm.drift_state == None
-    assert hdm.total_updates == 3
+    assert hdm.total_batches == 3
     assert hdm.feature_epsilons != []
     assert len(hdm.epsilon) == 2
     assert hdm.beta != None
@@ -442,8 +442,8 @@ def test_detect_batch_3_reset():
     # test reset updating with identical batch to test batch 3
     hdm.update(test)
 
-    assert hdm.total_updates == 4
-    assert hdm.updates_since_reset == 1
+    assert hdm.total_batches == 4
+    assert hdm.batches_since_reset == 1
     assert len(hdm.epsilon) == 0  # epsilon is reset
     assert hdm.beta != None
     assert hdm._drift_state == None
diff --git a/tests/menelaus/data_drift/test_kdq_tree.py b/tests/menelaus/data_drift/test_kdq_tree.py
index 57ca135b..6c2a441c 100644
--- a/tests/menelaus/data_drift/test_kdq_tree.py
+++ b/tests/menelaus/data_drift/test_kdq_tree.py
@@ -70,7 +70,7 @@ def test_reset_stream(kdq_det_stream):
     assert det.drift_state is None
     det.reset()
     det.update(new_sample)
-    assert det.input_cols.equals(new_sample.columns)
+    assert det._input_cols.equals(new_sample.columns)
 
 
 def test_reset_batch(kdq_det_batch):
@@ -87,11 +87,11 @@ def test_set_reference(kdq_det_batch):
     det.update(new_sample)
     assert det.drift_state is None
 
-    # make sure that input_cols is set appropriately when initialized
+    # make sure that _input_cols is set appropriately when initialized
     det = det = KdqTreeBatch(count_ubound=1, bootstrap_samples=10)
     new_sample = pd.DataFrame(np.random.sample((1, NUM_FEATURES)))
     det.set_reference(new_sample)
-    assert det.input_cols.equals(new_sample.columns)
+    assert det._input_col_dim == new_sample.shape[1]
 
 
 def test_init_validation_stream(kdq_det_stream):
diff --git a/tests/menelaus/test_drift_detector.py b/tests/menelaus/test_drift_detector.py
index 0f066138..aa682420 100644
--- a/tests/menelaus/test_drift_detector.py
+++ b/tests/menelaus/test_drift_detector.py
@@ -11,13 +11,6 @@
 BatchDetector.__abstractmethods__ = set()
 
 
-def test_set_reference_batch():
-    """Ensure error on call to set_reference from batch ABC"""
-    det = BatchDetector()
-    with pytest.raises(NotImplementedError) as _:
-        det.set_reference(None, None, None)
-
-
 def test_drift_state_validation_streaming():
     """Ensure ValueError on invalid drift_state for StreamingDetector"""
     det = StreamingDetector()
@@ -86,6 +79,38 @@ def test_streaming_validation_X_dimensions():
         det.update(input4, y_true=None, y_pred=None)
 
 
+def test_batch_validation_y_one_column():
+    det = BatchDetector()
+    det.update(X=None, y_true=1, y_pred=[1])
+    with pytest.raises(ValueError) as _:
+        det.update(
+            X=None, y_true=np.array([[1, 2], [1, 2]]), y_pred=np.array([[0], [0]])
+        )
+
+
+def test_batch_validation_X_columns():
+    det = BatchDetector()
+    input1 = pd.DataFrame({"a": [1], "b": [2]})
+    input2 = pd.DataFrame({"c": [1], "d": [2]})
+    det.update(input1, y_true=None, y_pred=None)
+    with pytest.raises(ValueError) as _:
+        det.update(input2, y_true=None, y_pred=None)
+
+
+def test_batch_validation_X_dimensions():
+    det = BatchDetector()
+    input1 = np.array([1, 2, 3])
+    input2 = pd.DataFrame(input1.reshape(1, -1), columns=["a", "b", "c"])
+    input3 = input2.values
+    input4 = np.array([1, 2, 3, 4])
+
+    det.update(input1, y_true=None, y_pred=None)
+    det.update(input2, y_true=None, y_pred=None)
+    det.update(input3, y_true=None, y_pred=None)
+    with pytest.raises(ValueError) as _:
+        det.update(input4, y_true=None, y_pred=None)
+
+
 def test_input_type():
     det = DriftDetector()
     det._input_type = "stream"
diff --git a/tests/menelaus/test_ensemble.py b/tests/menelaus/test_ensemble.py
index 5559f63c..6dbd61f4 100644
--- a/tests/menelaus/test_ensemble.py
+++ b/tests/menelaus/test_ensemble.py
@@ -6,46 +6,50 @@
 
 
 def test_batch_ensemble_1():
-    '''Ensure batch ensemble executes with no drift'''
+    """Ensure batch ensemble executes with no drift"""
     kdq1 = KdqTreeBatch(bootstrap_samples=1)
     kdq2 = KdqTreeBatch(bootstrap_samples=1)
     kdq3 = KdqTreeBatch(bootstrap_samples=1)
-    be = BatchEnsemble(detectors={'k1': kdq1, 'k2': kdq2, 'k3': kdq3}, evaluator='simple-majority')
-    df = pd.DataFrame({'a': [0,0], 'b':[0,0], 'c':[0,0]})
+    be = BatchEnsemble(
+        detectors={"k1": kdq1, "k2": kdq2, "k3": kdq3}, evaluator="simple-majority"
+    )
+    df = pd.DataFrame({"a": [0, 0], "b": [0, 0], "c": [0, 0]})
     be.set_reference(df.loc[:0])
     be.update(df.loc[1:])
     assert be.drift_state == None
 
+
 def test_batch_ensemble_2():
-    '''Ensure batch ensemble executes when columns specified'''
+    """Ensure batch ensemble executes when columns specified"""
     kdq1 = KdqTreeBatch(bootstrap_samples=1)
     kdq2 = KdqTreeBatch(bootstrap_samples=1)
     be = BatchEnsemble(
-        detectors={'k1': kdq1, 'k2': kdq2},
-        evaluator='simple-majority',
-        columns={'k1': ['a'], 'k2': ['b', 'c']}
+        detectors={"k1": kdq1, "k2": kdq2},
+        evaluator="simple-majority",
+        columns={"k1": ["a"], "k2": ["b", "c"]},
     )
-    df = pd.DataFrame({'a': [0,10.0], 'b':[0,11.0], 'c':[0,12.0]})
+    df = pd.DataFrame({"a": [0, 10.0], "b": [0, 11.0], "c": [0, 12.0]})
     be.set_reference(df.loc[:0])
     be.update(df.loc[1:])
-    assert len(be.detectors['k1'].input_cols) == 1
-    assert len(be.detectors['k2'].input_cols) == 2
+    assert len(be.detectors["k1"]._input_cols) == 1
+    assert len(be.detectors["k2"]._input_cols) == 2
+
 
 def test_batch_ensemble_reset_1():
-    '''Ensure reset works in batch ensemble and its detectors'''
+    """Ensure reset works in batch ensemble and its detectors"""
     kdq1 = KdqTreeBatch(bootstrap_samples=1)
     kdq2 = KdqTreeBatch(bootstrap_samples=1)
     be = BatchEnsemble(
-        detectors={'k1': kdq1, 'k2': kdq2},
-        evaluator='simple-majority',
-        columns={'k1': ['a'], 'k2': ['b', 'c']}
+        detectors={"k1": kdq1, "k2": kdq2},
+        evaluator="simple-majority",
+        columns={"k1": ["a"], "k2": ["b", "c"]},
     )
-    df = pd.DataFrame({'a': [0,10.0], 'b':[0,11.0], 'c':[0,12.0]})
+    df = pd.DataFrame({"a": [0, 10.0], "b": [0, 11.0], "c": [0, 12.0]})
     be.set_reference(df.loc[:0])
     be.update(df.loc[1:])
-    be.drift_state = 'drift'
-    be.detectors['k1'].drift_state = 'drift'
-    be.detectors['k2'].drift_state = 'drift'
+    be.drift_state = "drift"
+    be.detectors["k1"].drift_state = "drift"
+    be.detectors["k2"].drift_state = "drift"
     be.reset()
     assert be.batches_since_reset == 0
     assert be.drift_state == None
@@ -55,18 +59,20 @@ def test_batch_ensemble_reset_1():
         assert be.detectors[det_key].drift_state == None
         assert be.detectors[det_key].total_batches == 1
 
+
 def test_eval_simple_majority_1():
-    '''Ensure simple majority scheme can identify drift'''
+    """Ensure simple majority scheme can identify drift"""
     det1 = det2 = KdqTreeBatch()
-    det1.drift_state = det2.drift_state = 'drift'
+    det1.drift_state = det2.drift_state = "drift"
     det3 = KdqTreeBatch()
     det3.drift_state = None
-    assert eval_simple_majority([det1, det2, det3]) == 'drift'
+    assert eval_simple_majority([det1, det2, det3]) == "drift"
+
 
 def test_eval_simple_majority_2():
-    '''Ensure simple majority scheme does not false alarm'''
+    """Ensure simple majority scheme does not false alarm"""
     det1 = det2 = KdqTreeBatch()
     det1.drift_state = det2.drift_state = None
     det3 = KdqTreeBatch()
-    det3.drift_state = 'drift'
-    assert eval_simple_majority([det1, det2, det3]) == None
\ No newline at end of file
+    det3.drift_state = "drift"
+    assert eval_simple_majority([det1, det2, det3]) == None