example_usage.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6ff4d1a4-2aca-413e-9c68-5243cec63647",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/rly/mambaforge/envs/hdmf-ai/lib/python3.11/site-packages/hdmf/utils.py:668: UserWarning: Ignoring cached namespace 'hdmf-common' version 1.5.1 because version 1.8.0 is already loaded.\n",
      "  return func(args[0], **pargs)\n",
      "/Users/rly/mambaforge/envs/hdmf-ai/lib/python3.11/site-packages/hdmf/utils.py:668: UserWarning: Ignoring cached namespace 'core' version 2.5.0 because version 2.6.0-alpha is already loaded.\n",
      "  return func(args[0], **pargs)\n",
      "/Users/rly/mambaforge/envs/hdmf-ai/lib/python3.11/site-packages/hdmf/utils.py:668: UserWarning: Ignoring cached namespace 'hdmf-experimental' version 0.2.0 because version 0.5.0 is already loaded.\n",
      "  return func(args[0], **pargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                           spike_times unit_name  \\\n",
      "id                                                                 \n",
      "0    [0.019766666666666665, 0.028533333333333334, 0...        12   \n",
      "1    [0.019766666666666665, 0.07186666666666666, 0....       424   \n",
      "2    [0.020266666666666665, 0.04423333333333333, 0....        78   \n",
      "3    [0.020766666666666666, 0.023266666666666668, 0...       105   \n",
      "4    [0.022333333333333334, 0.1083, 0.1305, 0.13916...       110   \n",
      "..                                                 ...       ...   \n",
      "553  [1422.7791666666667, 2570.4848666666667, 2575....       543   \n",
      "554  [1448.522, 1477.2797666666668, 1522.4593666666...       291   \n",
      "555  [1481.3955333333333, 1573.5977333333333, 1575....       515   \n",
      "556  [1527.9567333333334, 1750.9875666666667, 2421....       246   \n",
      "557  [2161.375666666667, 2454.7655666666665, 2710.4...       422   \n",
      "\n",
      "     presence_ratio_standard_deviation  contamination  noise_cutoff  \\\n",
      "id                                                                    \n",
      "0                             5.258554       0.083082    136.186874   \n",
      "1                           287.292619       0.007035     38.420545   \n",
      "2                            42.179534       0.006504     15.857428   \n",
      "3                            75.238846       0.003428     56.012060   \n",
      "4                            52.149411       0.002114      0.515300   \n",
      "..                                 ...            ...           ...   \n",
      "553                           1.736014       0.000000     25.459525   \n",
      "554                           5.888234       0.781129     -0.719935   \n",
      "555                           1.159141       0.000000     10.750188   \n",
      "556                           1.320937       9.734769      6.934761   \n",
      "557                           1.328322       0.000000     -0.691095   \n",
      "\n",
      "     mean_relative_depth  sliding_refractory_period_violation cosmos_location  \\\n",
      "id                                                                              \n",
      "0                   80.0                                  0.0              MB   \n",
      "1                  100.0                                  1.0              MB   \n",
      "2                  100.0                                  1.0              MB   \n",
      "3                  120.0                                  1.0              MB   \n",
      "4                  160.0                                  1.0              MB   \n",
      "..                   ...                                  ...             ...   \n",
      "553               2600.0                                  0.0             HPF   \n",
      "554               2600.0                                  0.0             HPF   \n",
      "555               3160.0                                  0.0       Isocortex   \n",
      "556               3400.0                                  0.0       Isocortex   \n",
      "557               2520.0                                  0.0             HPF   \n",
      "\n",
      "     maximum_amplitude  maximum_amplitude_channel  ...  median_amplitude  \\\n",
      "id                                                 ...                     \n",
      "0             0.000175                        6.0  ...          0.000087   \n",
      "1             0.000159                        8.0  ...          0.000077   \n",
      "2             0.000159                        9.0  ...          0.000090   \n",
      "3             0.000201                       10.0  ...          0.000083   \n",
      "4             0.000211                       14.0  ...          0.000115   \n",
      "..                 ...                        ...  ...               ...   \n",
      "553           0.000139                      258.0  ...          0.000080   \n",
      "554           0.000276                      259.0  ...          0.000118   \n",
      "555           0.000121                      315.0  ...          0.000085   \n",
      "556           0.000126                      338.0  ...          0.000074   \n",
      "557           0.000342                      251.0  ...          0.000236   \n",
      "\n",
      "            drift  minimum_amplitude spike_count firing_rate  \\\n",
      "id                                                             \n",
      "0    6.848070e+04           0.000082      4319.0    0.995299   \n",
      "1    2.249384e+06           0.000054    155672.0   35.874101   \n",
      "2    3.517302e+05           0.000066     37810.0    8.713190   \n",
      "3    2.860285e+06           0.000060    265558.0   61.196968   \n",
      "4    1.021966e+06           0.000065    157876.0   36.382005   \n",
      "..            ...                ...         ...         ...   \n",
      "553  9.665525e+03           0.000074       873.0    0.201180   \n",
      "554  9.882481e+03           0.000069      1992.0    0.459050   \n",
      "555  5.706065e+03           0.000076       534.0    0.123059   \n",
      "556  2.543755e+03           0.000059       399.0    0.091948   \n",
      "557  7.951559e+02           0.000143       318.0    0.073282   \n",
      "\n",
      "     missed_spikes_estimate  standard_deviation_amplitude  allen_location  \\\n",
      "id                                                                          \n",
      "0                  0.500000                      0.725555              RN   \n",
      "1                  0.148918                      1.481336              RN   \n",
      "2                  0.059499                      1.184573              RN   \n",
      "3                  0.183454                      1.535417              RN   \n",
      "4                  0.006965                      1.608456             MRN   \n",
      "..                      ...                           ...             ...   \n",
      "553                0.500000                      0.822730             CA1   \n",
      "554                0.009546                      1.394312             CA1   \n",
      "555                0.500000                      0.829071          VISpm5   \n",
      "556                     NaN                      1.284709        VISam2/3   \n",
      "557                     NaN                      1.050623             CA1   \n",
      "\n",
      "                                      spike_amplitudes  presence_ratio  \n",
      "id                                                                      \n",
      "0    [8.288184885804525e-05, 0.00011442759068298115...        0.997701  \n",
      "1    [4.6580973513486687e-05, 5.296578081321374e-05...        0.816092  \n",
      "2    [6.32776225415224e-05, 9.328839624452988e-05, ...        0.997701  \n",
      "3    [0.00016554799180735244, 0.0001820722989910208...        0.997701  \n",
      "4    [0.00015009149841748693, 0.0001974641043535484...        0.997701  \n",
      "..                                                 ...             ...  \n",
      "553  [6.087924810443521e-05, 6.153988903027088e-05,...        0.779310  \n",
      "554  [0.0001497319921455542, 0.00012844935458089068...        0.802299  \n",
      "555  [9.970061409456434e-05, 7.378086503872224e-05,...        0.701149  \n",
      "556  [9.996891360609634e-05, 9.484602794567695e-05,...        0.471264  \n",
      "557  [5.5044274830256504e-05, 5.894106134079688e-05...        0.326437  \n",
      "\n",
      "[558 rows x 25 columns]\n",
      "There are 558 units in this dataset.\n"
     ]
    }
   ],
   "source": [
    "from hdmf_ai import ResultsTable  # NOTE: because hdmf_ai modifies the hdmf common namespace, it is important that hdmf_ai is imported before pynwb\n",
    "from pynwb import NWBHDF5IO\n",
    "\n",
    "# read an NWB file from local disk\n",
    "filepath = \"/Users/rly/Documents/NWB_Data/dandisets/000409/sub-CSHL047/sub-CSHL047_ses-b52182e7-39f6-4914-9717-136db589706e_behavior+ecephys+image.nwb\"\n",
    "io = NWBHDF5IO(filepath, \"r\")\n",
    "nwbfile = io.read()\n",
    "\n",
    "# the NWB Units table stores information about the sorted single units (putative neurons) after preprocessing\n",
    "# and spike sorting. each row represents a single unit. this dataset includes many metadata fields (table columns) for\n",
    "# each unit.\n",
    "units = nwbfile.units\n",
    "\n",
    "# the Units table can be most readily viewed as a pandas DataFrame\n",
    "units_df = units.to_dataframe()\n",
    "print(units_df)\n",
    "print(f\"There are {len(units_df)} units in this dataset.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "68611331-8162-46bc-b408-0ae1aa482395",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 5 unique Cosmos locations in this dataset.\n",
      "The logistic regression achieved a score of 0.47904191616766467 on the test set!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/rly/mambaforge/envs/hdmf-ai/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "# run a simple classifier on the units data to predict the location (brain area) of the unit based on\n",
    "# the amplitude, firing rate, spike count, and presence ratio. there are several ways to label the brain\n",
    "# area of a unit. here, we use the label using the coarsest atlas, the Cosmos atlas, which has a total\n",
    "# of 12 annotation regions. in this dataset, there are 5 unique Cosmos locations.\n",
    "enc = LabelEncoder()\n",
    "y = np.uint(enc.fit_transform(units[\"cosmos_location\"].data[:]))\n",
    "unique_labels = enc.classes_\n",
    "print(f\"There are {len(unique_labels)} unique Cosmos locations in this dataset.\")\n",
    "\n",
    "# split the data into training and testing sets\n",
    "proportion_train = 0.7\n",
    "n_train_samples = int(np.round(proportion_train * len(units_df)))\n",
    "n_test_samples = len(units) - n_train_samples\n",
    "# train = 0, validate = 1, test = 2\n",
    "tvt = np.array([0] * n_train_samples + [2] * n_test_samples)\n",
    "np.random.shuffle(tvt)\n",
    "\n",
    "feature_names = [\n",
    "    \"median_amplitude\",\n",
    "    \"standard_deviation_amplitude\",\n",
    "    \"firing_rate\",\n",
    "    \"spike_count\",\n",
    "    \"presence_ratio\",\n",
    "]\n",
    "X = units_df[feature_names]\n",
    "X_train = X[tvt == 0]\n",
    "y_train = y[tvt == 0]\n",
    "X_test = X[tvt == 2]\n",
    "y_test = y[tvt == 2]\n",
    "\n",
    "# run logistic regression\n",
    "logreg = LogisticRegression()\n",
    "logreg.fit(X_train, y_train)\n",
    "predictions_all = logreg.predict(X)\n",
    "prediction_proba_all = logreg.predict_proba(X)\n",
    "prediction_log_proba_all = logreg.predict_log_proba(X)\n",
    "score = logreg.score(X_test, y_test)\n",
    "print(f\"The logistic regression achieved a score of {score} on the test set!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1f93fc0f-6098-4769-9ae1-d29b1d2eeb60",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    tvt_split  true_label                              predicted_probability  \\\n",
      "id                                                                             \n",
      "0        test           2  [0.3062620171344619, 0.4118762178000316, 0.209...   \n",
      "1       train           2  [0.022162796962580895, 0.029600696160931746, 0...   \n",
      "2       train           2  [0.24781230936966467, 0.33159672522808925, 0.3...   \n",
      "3       train           2  [0.0010764913933024565, 0.0014851515814415539,...   \n",
      "4       train           2  [0.02275027657533403, 0.030764134673612387, 0....   \n",
      "..        ...         ...                                                ...   \n",
      "553      test           0  [0.30415485770991196, 0.4005894872411785, 0.19...   \n",
      "554     train           0  [0.3340095255181371, 0.4349756760531751, 0.172...   \n",
      "555     train           1  [0.30222976392447676, 0.3954133144575274, 0.18...   \n",
      "556      test           1  [0.32269639174950426, 0.4098561444755049, 0.17...   \n",
      "557      test           0  [0.3079885124076165, 0.38864473641058367, 0.19...   \n",
      "\n",
      "     predicted_class                                            samples  \\\n",
      "id                                                                        \n",
      "0                  1                                            spik...   \n",
      "1                  2                                            spik...   \n",
      "2                  2                                            spik...   \n",
      "3                  2                                            spik...   \n",
      "4                  2                                            spik...   \n",
      "..               ...                                                ...   \n",
      "553                1                                             spi...   \n",
      "554                1                                             spi...   \n",
      "555                1                                             spi...   \n",
      "556                1                                             spi...   \n",
      "557                1                                             spi...   \n",
      "\n",
      "                                       log_probability  \n",
      "id                                                      \n",
      "0    [-1.1833142782703214, -0.8870324170073532, -1....  \n",
      "1    [-3.8093402083447496, -3.5199573989782915, -0....  \n",
      "2    [-1.3950836364055648, -1.103835731438974, -0.9...  \n",
      "3    [-6.834048236266172, -6.51223843689196, -0.015...  \n",
      "4    [-3.7831779765015354, -3.4814057261603106, -0....  \n",
      "..                                                 ...  \n",
      "553  [-1.1902183069146826, -0.9148180986280143, -1....  \n",
      "554  [-1.0965857668967296, -0.832465166576011, -1.7...  \n",
      "555  [-1.1965677431777917, -0.9278236954247783, -1....  \n",
      "556  [-1.131043361426138, -0.8919490479849729, -1.7...  \n",
      "557  [-1.1776927940819957, -0.9450896266612661, -1....  \n",
      "\n",
      "[558 rows x 6 columns]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/rly/mambaforge/envs/hdmf-ai/lib/python3.11/site-packages/hdmf/container.py:213: UserWarning: EnumData is experimental -- it may be removed in the future and is not guaranteed to maintain backward compatibility\n",
      "  warn(_exp_warn_msg(cls))\n",
      "/Users/rly/mambaforge/envs/hdmf-ai/lib/python3.11/site-packages/hdmf/utils.py:668: UserWarning: Column 'samples' is predefined in ResultsTable with table=True which does not match the entered table argument. The predefined table spec will be ignored. Please ensure the new column complies with the spec. This will raise an error in a future version of HDMF.\n",
      "  return func(args[0], **pargs)\n",
      "/Users/rly/mambaforge/envs/hdmf-ai/lib/python3.11/site-packages/hdmf/utils.py:668: UserWarning: Column 'samples' is predefined in ResultsTable with class=<class 'hdmf.common.table.VectorData'> which does not match the entered col_cls argument. The predefined class spec will be ignored. Please ensure the new column complies with the spec. This will raise an error in a future version of HDMF.\n",
      "  return func(args[0], **pargs)\n"
     ]
    }
   ],
   "source": [
    "results_table = ResultsTable(\n",
    "    name=\"logistic_regression_results\",\n",
    "    description=\"Results of a simple logisitic regression on the units table\",\n",
    "    n_samples=len(units),\n",
    "    # not the actual model, just a placeholder for demonstration purposes\n",
    "    pre_trained_model=[\"Bloom v1.3\"],\n",
    ")\n",
    "results_table.add_tvt_split(tvt)\n",
    "# use the text labels which will become an EnumData with uint encoding\n",
    "results_table.add_true_label(y)\n",
    "results_table.add_predicted_probability(prediction_proba_all)\n",
    "results_table.add_predicted_class(predictions_all)\n",
    "results_table.add_samples(data=np.arange(len(X)), description=\"all the samples\", table=units)\n",
    "\n",
    "# add a custom column to the results table to store the log probabilities\n",
    "results_table.add_column(\n",
    "    name=\"log_probability\",\n",
    "    data=prediction_log_proba_all,\n",
    "    description=\"The log probability of each class prediction.\",\n",
    ")\n",
    "\n",
    "print(results_table.to_dataframe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6282e998-39ec-4458-9b78-fecda2544ede",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>spike_times</th>\n",
       "      <th>unit_name</th>\n",
       "      <th>presence_ratio_standard_deviation</th>\n",
       "      <th>contamination</th>\n",
       "      <th>noise_cutoff</th>\n",
       "      <th>mean_relative_depth</th>\n",
       "      <th>sliding_refractory_period_violation</th>\n",
       "      <th>cosmos_location</th>\n",
       "      <th>maximum_amplitude</th>\n",
       "      <th>maximum_amplitude_channel</th>\n",
       "      <th>...</th>\n",
       "      <th>median_amplitude</th>\n",
       "      <th>drift</th>\n",
       "      <th>minimum_amplitude</th>\n",
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       "      <th>firing_rate</th>\n",
       "      <th>missed_spikes_estimate</th>\n",
       "      <th>standard_deviation_amplitude</th>\n",
       "      <th>allen_location</th>\n",
       "      <th>spike_amplitudes</th>\n",
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       "    </tr>\n",
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       "      <th>0</th>\n",
       "      <td>[0.019766666666666665, 0.028533333333333334, 0...</td>\n",
       "      <td>12</td>\n",
       "      <td>5.258554</td>\n",
       "      <td>0.083082</td>\n",
       "      <td>136.186874</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>MB</td>\n",
       "      <td>0.000175</td>\n",
       "      <td>6.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000087</td>\n",
       "      <td>68480.697449</td>\n",
       "      <td>0.000082</td>\n",
       "      <td>4319.0</td>\n",
       "      <td>0.995299</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.725555</td>\n",
       "      <td>RN</td>\n",
       "      <td>[8.288184885804525e-05, 0.00011442759068298115...</td>\n",
       "      <td>0.997701</td>\n",
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      "text/plain": [
       "                                          spike_times unit_name  \\\n",
       "id                                                                \n",
       "0   [0.019766666666666665, 0.028533333333333334, 0...        12   \n",
       "\n",
       "    presence_ratio_standard_deviation  contamination  noise_cutoff  \\\n",
       "id                                                                   \n",
       "0                            5.258554       0.083082    136.186874   \n",
       "\n",
       "    mean_relative_depth  sliding_refractory_period_violation cosmos_location  \\\n",
       "id                                                                             \n",
       "0                  80.0                                  0.0              MB   \n",
       "\n",
       "    maximum_amplitude  maximum_amplitude_channel  ...  median_amplitude  \\\n",
       "id                                                ...                     \n",
       "0            0.000175                        6.0  ...          0.000087   \n",
       "\n",
       "           drift  minimum_amplitude spike_count firing_rate  \\\n",
       "id                                                            \n",
       "0   68480.697449           0.000082      4319.0    0.995299   \n",
       "\n",
       "    missed_spikes_estimate  standard_deviation_amplitude  allen_location  \\\n",
       "id                                                                         \n",
       "0                      0.5                      0.725555              RN   \n",
       "\n",
       "                                     spike_amplitudes  presence_ratio  \n",
       "id                                                                     \n",
       "0   [8.288184885804525e-05, 0.00011442759068298115...        0.997701  \n",
       "\n",
       "[1 rows x 25 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# access information about the first sample using the ResultsTable link to the input data\n",
    "results_table[\"samples\"][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5653415e-fdd4-455d-a75a-7ce51f7b4aad",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        </script>\n",
       "        <div class='container-wrap'><div class='container-header'><div class='xr-obj-type'><h3>logistic_regression_results (ResultsTable)</h3></div></div><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\"\">description: </span><span class=\"field-value\">Results of a simple logisitic regression on the units table</span></div><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\"\"><b>pre_trained_model</b></summary><div style=\"margin-left: 20px;\" class=\"container-fields\"><span class=\"field-key\" title=\"[0]\">0: </span><span class=\"field-value\">Bloom v1.3</span></div></details><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\"\"><b>table</b></summary><table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tvt_split</th>\n",
       "      <th>true_label</th>\n",
       "      <th>predicted_probability</th>\n",
       "      <th>predicted_class</th>\n",
       "      <th>samples</th>\n",
       "      <th>log_probability</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>[0.3062620171344619, 0.4118762178000316, 0.20988046099736338, 0.04890714752571586, 0.02307415654242729]</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>[-1.1833142782703214, -0.8870324170073532, -1.5612171436853666, -3.017831727012486, -3.7690420521355175]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>[0.022162796962580895, 0.029600696160931746, 0.9136374685313745, 0.03459903834511291, 5.037640062909857e-22]</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>[-3.8093402083447496, -3.5199573989782915, -0.09032142895619419, -3.3639293907985515, -49.039934314931145]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>[0.24781230936966467, 0.33159672522808925, 0.37279662191900254, 0.04779317482624241, 1.1686570012080219e-06]</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>[-1.3950836364055648, -1.103835731438974, -0.9867222575878216, -3.0408724357415013, -13.659655330668379]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>[0.0010764913933024565, 0.0014851515814415539, 0.9850122152347303, 0.012426141790525586, 1.1759901715724957e-36]</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>[-6.834048236266172, -6.51223843689196, -0.015101236633409822, -4.387952816611791, -82.73095285585052]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table><p>... and 554 more rows.</p></details></div>"
      ],
      "text/plain": [
       "logistic_regression_results hdmf_ai.results_table.ResultsTable at 0x6306101584\n",
       "Fields:\n",
       "  colnames: ['tvt_split' 'true_label' 'predicted_probability' 'predicted_class'\n",
       " 'samples' 'log_probability']\n",
       "  columns: (\n",
       "    tvt_split <class 'hdmf.common.table.EnumData'>,\n",
       "    tvt_split_elements <class 'hdmf.common.table.VectorData'>,\n",
       "    true_label <class 'hdmf.common.table.VectorData'>,\n",
       "    predicted_probability <class 'abc.ClassProbability'>,\n",
       "    predicted_class <class 'abc.ClassLabel'>,\n",
       "    samples <class 'hdmf.common.table.DynamicTableRegion'>,\n",
       "    log_probability <class 'hdmf.common.table.VectorData'>\n",
       "  )\n",
       "  description: Results of a simple logisitic regression on the units table\n",
       "  id: id <class 'hdmf.common.table.ElementIdentifiers'>\n",
       "  pre_trained_model: ['Bloom v1.3']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# add the results table to the in-memory NWB file\n",
    "nwbfile.add_analysis(results_table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f49c5210-b071-4259-b741-2ddb2d2353db",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/rly/mambaforge/envs/hdmf-ai/lib/python3.11/site-packages/hdmf/container.py:213: UserWarning: HERD is experimental -- it may be removed in the future and is not guaranteed to maintain backward compatibility\n",
      "  warn(_exp_warn_msg(cls))\n",
      "/Users/rly/mambaforge/envs/hdmf-ai/lib/python3.11/site-packages/hdmf/build/objectmapper.py:260: DtypeConversionWarning: Spec 'ResultsTable/VectorData': Value with data type int64 is being converted to data type uint64 (min specification: uint8).\n",
      "  warnings.warn(full_warning_msg, DtypeConversionWarning)\n"
     ]
    }
   ],
   "source": [
    "# add information about the model used to generate the results table\n",
    "from hdmf.common import HERD\n",
    "\n",
    "# annotate the model with a DOI using HDMF HERD\n",
    "herd = HERD()\n",
    "herd.add_ref(\n",
    "    file=nwbfile,\n",
    "    container=results_table,\n",
    "    attribute=\"pre_trained_model\",\n",
    "    key='Bloom v1.3',\n",
    "    entity_id='doi:10.57967/hf/0003',\n",
    "    entity_uri='https://doi.org/10.57967/hf/0003'\n",
    ")\n",
    "herd.to_zip(path='./HERD.zip')\n",
    "\n",
    "# remove the acquisition data from the NWB file to reduce file size\n",
    "for x in list(nwbfile.acquisition.keys()):\n",
    "    nwbfile.acquisition.pop(x)\n",
    "\n",
    "# export the results table to a new NWB file\n",
    "with NWBHDF5IO(\"results.nwb\", \"w\") as export_io:\n",
    "    export_io.export(src_io=io, nwbfile=nwbfile)"
   ]
  }
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