diff --git a/docs/source/tutorial_notebooks/.ipynb_checkpoints/deepof_preprocessing_tutorial-checkpoint.ipynb b/docs/source/tutorial_notebooks/.ipynb_checkpoints/deepof_preprocessing_tutorial-checkpoint.ipynb
index f84c0df70e5e01e8249f6cce5dcac54f81ce82e9..dc910808867c2d5f68730343aa94678d436d512b 100644
--- a/docs/source/tutorial_notebooks/.ipynb_checkpoints/deepof_preprocessing_tutorial-checkpoint.ipynb
+++ b/docs/source/tutorial_notebooks/.ipynb_checkpoints/deepof_preprocessing_tutorial-checkpoint.ipynb
@@ -42,7 +42,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 2,
"id": "4d85f5bf",
"metadata": {
"scrolled": false
@@ -64,7 +64,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 3,
"id": "e438d39f",
"metadata": {},
"outputs": [],
@@ -107,25 +107,25 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 4,
"id": "88c90e17",
"metadata": {},
"outputs": [],
"source": [
- "# my_deepof_project = deepof.data.Project(\n",
- "# project_path=os.path.join(\"tutorial_files\"),\n",
- "# video_path=os.path.join(\"tutorial_files/Videos/\"),\n",
- "# table_path=os.path.join(\"tutorial_files/Tables/\"),\n",
- "# project_name=\"tutorial_project\",\n",
- "# arena=\"circular-manual\",\n",
- "# animal_ids=[\"B\", \"W\"],\n",
- "# video_format=\".mp4\",\n",
- "# exclude_bodyparts=[\"Tail_1\", \"Tail_2\", \"Tail_tip\"],\n",
- "# video_scale=380,\n",
- "# enable_iterative_imputation=10,\n",
- "# smooth_alpha=1,\n",
- "# exp_conditions=None,\n",
- "# )"
+ "my_deepof_project = deepof.data.Project(\n",
+ " project_path=os.path.join(\"tutorial_files\"),\n",
+ " video_path=os.path.join(\"tutorial_files/Videos/\"),\n",
+ " table_path=os.path.join(\"tutorial_files/Tables/\"),\n",
+ " project_name=\"tutorial_project\",\n",
+ " arena=\"circular-manual\",\n",
+ " animal_ids=[\"B\", \"W\"],\n",
+ " video_format=\".mp4\",\n",
+ " exclude_bodyparts=[\"Tail_1\", \"Tail_2\", \"Tail_tip\"],\n",
+ " video_scale=380,\n",
+ " enable_iterative_imputation=10,\n",
+ " smooth_alpha=1,\n",
+ " exp_conditions=None,\n",
+ ")"
]
},
{
@@ -156,12 +156,12 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 5,
"id": "d2f00bc8",
"metadata": {},
"outputs": [],
"source": [
- "# my_deepof_project = my_deepof_project.create()"
+ "my_deepof_project = my_deepof_project.create()"
]
},
{
@@ -214,7 +214,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 6,
"id": "899cbab7",
"metadata": {},
"outputs": [],
@@ -249,10 +249,19 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 7,
"id": "ee51de4a",
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "deepof analysis of 6 videos\n",
+ "<class 'deepof.data.Coordinates'>\n"
+ ]
+ }
+ ],
"source": [
"print(my_deepof_project)\n",
"print(type(my_deepof_project))"
@@ -284,10 +293,417 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 8,
"id": "e94d1215",
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
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+ "00:00:00.039935995 0.671672 35.836244 14.183559 14.703795 ... \n",
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+ "... ... ... ... ... ... \n",
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+ "00:09:58.960064004 -12.239039 10.390575 -6.179682 -15.503151 -18.331690 \n",
+ "\n",
+ " \n",
+ " y \n",
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+ "\n",
+ "[14999 rows x 44 columns]"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"my_deepof_project.get_coords(polar=False, center=\"Center\", align=\"Spine_1\")['20191204_Day2_SI_JB08_Test_54']"
]
@@ -310,32 +726,1261 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 9,
"id": "3c0688fb",
"metadata": {
"scrolled": false
},
- "outputs": [],
+ "outputs": [
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+ "... ... ... \n",
+ "00:09:58.800320021 14.777048 18.947365 \n",
+ "00:09:58.840256017 14.639053 18.991163 \n",
+ "00:09:58.880192012 15.440113 19.010041 \n",
+ "00:09:58.920128008 15.440113 19.010041 \n",
+ "00:09:58.960064004 15.440113 19.010041 \n",
+ "\n",
+ "[14999 rows x 26 columns]"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"my_deepof_project.get_distances()['20191204_Day2_SI_JB08_Test_54']"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 10,
"id": "674f6247",
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
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+ "</div>"
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+ "\n",
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+ "00:00:00 1.011682 ... \n",
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+ "00:00:00.079871991 1.366577 ... \n",
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+ "00:09:58.920128008 0.859763 ... \n",
+ "00:09:58.960064004 1.002433 ... \n",
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"source": [
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]
},
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"source": [
"my_deepof_project.get_areas()['20191204_Day2_SI_JB08_Test_54']"
]
@@ -350,10 +1995,411 @@
},
{
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+ " <td>706.975280</td>\n",
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+ " </tr>\n",
+ " <tr>\n",
+ " <th>...</th>\n",
+ " <td>...</td>\n",
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+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
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+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>00:09:58.800320021</th>\n",
+ " <td>108.366647</td>\n",
+ " <td>302.971861</td>\n",
+ " <td>127.398353</td>\n",
+ " <td>297.067658</td>\n",
+ " <td>106.577644</td>\n",
+ " <td>339.902678</td>\n",
+ " <td>118.557281</td>\n",
+ " <td>318.548034</td>\n",
+ " <td>91.342528</td>\n",
+ " <td>360.831938</td>\n",
+ " <td>...</td>\n",
+ " <td>267.896393</td>\n",
+ " <td>517.758606</td>\n",
+ " <td>364.598702</td>\n",
+ " <td>512.092068</td>\n",
+ " <td>562.957165</td>\n",
+ " <td>1882.932433</td>\n",
+ " <td>516.368794</td>\n",
+ " <td>466.566421</td>\n",
+ " <td>607.161201</td>\n",
+ " <td>1974.352930</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>00:09:58.840256017</th>\n",
+ " <td>108.605596</td>\n",
+ " <td>310.532362</td>\n",
+ " <td>127.530082</td>\n",
+ " <td>302.013032</td>\n",
+ " <td>104.571034</td>\n",
+ " <td>350.229231</td>\n",
+ " <td>120.285217</td>\n",
+ " <td>323.409210</td>\n",
+ " <td>93.433890</td>\n",
+ " <td>367.135740</td>\n",
+ " <td>...</td>\n",
+ " <td>267.855316</td>\n",
+ " <td>517.770020</td>\n",
+ " <td>342.771541</td>\n",
+ " <td>514.231920</td>\n",
+ " <td>543.299019</td>\n",
+ " <td>1832.083470</td>\n",
+ " <td>517.334943</td>\n",
+ " <td>468.582411</td>\n",
+ " <td>611.765902</td>\n",
+ " <td>1979.455000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>00:09:58.880192012</th>\n",
+ " <td>108.844546</td>\n",
+ " <td>318.092862</td>\n",
+ " <td>124.715041</td>\n",
+ " <td>311.241029</td>\n",
+ " <td>102.564423</td>\n",
+ " <td>360.555784</td>\n",
+ " <td>119.021164</td>\n",
+ " <td>334.388275</td>\n",
+ " <td>86.603620</td>\n",
+ " <td>377.042506</td>\n",
+ " <td>...</td>\n",
+ " <td>267.961975</td>\n",
+ " <td>517.801758</td>\n",
+ " <td>388.332158</td>\n",
+ " <td>529.127829</td>\n",
+ " <td>558.340318</td>\n",
+ " <td>1889.399535</td>\n",
+ " <td>516.875687</td>\n",
+ " <td>468.401897</td>\n",
+ " <td>613.444346</td>\n",
+ " <td>1980.834529</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>00:09:58.920128008</th>\n",
+ " <td>108.844546</td>\n",
+ " <td>318.092862</td>\n",
+ " <td>124.715041</td>\n",
+ " <td>311.241029</td>\n",
+ " <td>102.564423</td>\n",
+ " <td>360.555784</td>\n",
+ " <td>119.021164</td>\n",
+ " <td>334.388275</td>\n",
+ " <td>86.603620</td>\n",
+ " <td>377.042506</td>\n",
+ " <td>...</td>\n",
+ " <td>267.961975</td>\n",
+ " <td>517.801758</td>\n",
+ " <td>388.332158</td>\n",
+ " <td>529.127829</td>\n",
+ " <td>558.340318</td>\n",
+ " <td>1889.399535</td>\n",
+ " <td>516.875687</td>\n",
+ " <td>468.401897</td>\n",
+ " <td>613.444346</td>\n",
+ " <td>1980.834529</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>00:09:58.960064004</th>\n",
+ " <td>108.844546</td>\n",
+ " <td>318.092862</td>\n",
+ " <td>124.715041</td>\n",
+ " <td>311.241029</td>\n",
+ " <td>102.564423</td>\n",
+ " <td>360.555784</td>\n",
+ " <td>119.021164</td>\n",
+ " <td>334.388275</td>\n",
+ " <td>86.603620</td>\n",
+ " <td>377.042506</td>\n",
+ " <td>...</td>\n",
+ " <td>267.961975</td>\n",
+ " <td>517.801758</td>\n",
+ " <td>388.332158</td>\n",
+ " <td>529.127829</td>\n",
+ " <td>558.340318</td>\n",
+ " <td>1889.399535</td>\n",
+ " <td>516.875687</td>\n",
+ " <td>468.401897</td>\n",
+ " <td>613.444346</td>\n",
+ " <td>1980.834529</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>14999 rows × 52 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " (B_Center, x) (B_Center, y) (B_Left_bhip, x) \\\n",
+ "00:00:00 223.193283 184.124115 222.160935 \n",
+ "00:00:00.039935995 222.081848 185.302109 221.340759 \n",
+ "00:00:00.079871991 220.547333 186.246719 219.854218 \n",
+ "00:00:00.119807987 218.467209 184.700256 216.118439 \n",
+ "00:00:00.159743982 219.402542 182.458069 214.480026 \n",
+ "... ... ... ... \n",
+ "00:09:58.800320021 108.366647 302.971861 127.398353 \n",
+ "00:09:58.840256017 108.605596 310.532362 127.530082 \n",
+ "00:09:58.880192012 108.844546 318.092862 124.715041 \n",
+ "00:09:58.920128008 108.844546 318.092862 124.715041 \n",
+ "00:09:58.960064004 108.844546 318.092862 124.715041 \n",
+ "\n",
+ " (B_Left_bhip, y) (B_Left_ear, x) (B_Left_ear, y) \\\n",
+ "00:00:00 202.799423 191.852982 166.609666 \n",
+ "00:00:00.039935995 203.517593 192.753159 164.698335 \n",
+ "00:00:00.079871991 203.126160 197.765198 159.669617 \n",
+ "00:00:00.119807987 201.998322 202.173355 153.366486 \n",
+ "00:00:00.159743982 200.481461 206.756088 150.536057 \n",
+ "... ... ... ... \n",
+ "00:09:58.800320021 297.067658 106.577644 339.902678 \n",
+ "00:09:58.840256017 302.013032 104.571034 350.229231 \n",
+ "00:09:58.880192012 311.241029 102.564423 360.555784 \n",
+ "00:09:58.920128008 311.241029 102.564423 360.555784 \n",
+ "00:09:58.960064004 311.241029 102.564423 360.555784 \n",
+ "\n",
+ " (B_Left_fhip, x) (B_Left_fhip, y) (B_Nose, x) \\\n",
+ "00:00:00 201.709718 187.509186 188.298004 \n",
+ "00:00:00.039935995 201.841401 188.076859 194.544312 \n",
+ "00:00:00.079871991 201.092896 184.567551 205.235306 \n",
+ "00:00:00.119807987 199.648239 181.772736 214.989639 \n",
+ "00:00:00.159743982 201.725708 177.796661 221.262161 \n",
+ "... ... ... ... \n",
+ "00:09:58.800320021 118.557281 318.548034 91.342528 \n",
+ "00:09:58.840256017 120.285217 323.409210 93.433890 \n",
+ "00:09:58.880192012 119.021164 334.388275 86.603620 \n",
+ "00:09:58.920128008 119.021164 334.388275 86.603620 \n",
+ "00:09:58.960064004 119.021164 334.388275 86.603620 \n",
+ "\n",
+ " (B_Nose, y) ... (W_Tail_base, x) (W_Tail_base, y) \\\n",
+ "00:00:00 147.322081 ... 321.353759 97.605996 \n",
+ "00:00:00.039935995 145.935454 ... 332.379546 98.838112 \n",
+ "00:00:00.079871991 142.500976 ... 350.856689 101.147659 \n",
+ "00:00:00.119807987 138.281066 ... 361.710663 101.918587 \n",
+ "00:00:00.159743982 133.663879 ... 369.073914 106.446114 \n",
+ "... ... ... ... ... \n",
+ "00:09:58.800320021 360.831938 ... 267.896393 517.758606 \n",
+ "00:09:58.840256017 367.135740 ... 267.855316 517.770020 \n",
+ "00:09:58.880192012 377.042506 ... 267.961975 517.801758 \n",
+ "00:09:58.920128008 377.042506 ... 267.961975 517.801758 \n",
+ "00:09:58.960064004 377.042506 ... 267.961975 517.801758 \n",
+ "\n",
+ " B_head_area B_torso_area B_back_area B_full_area \\\n",
+ "00:00:00 287.018462 467.472460 513.431000 1697.086099 \n",
+ "00:00:00.039935995 298.006089 461.593907 511.611552 1664.187297 \n",
+ "00:00:00.079871991 283.907493 436.994147 491.022208 1571.659017 \n",
+ "00:00:00.119807987 336.017364 459.434553 495.609970 1674.157290 \n",
+ "00:00:00.159743982 338.565821 456.785954 510.514167 1706.762126 \n",
+ "... ... ... ... ... \n",
+ "00:09:58.800320021 364.598702 512.092068 562.957165 1882.932433 \n",
+ "00:09:58.840256017 342.771541 514.231920 543.299019 1832.083470 \n",
+ "00:09:58.880192012 388.332158 529.127829 558.340318 1889.399535 \n",
+ "00:09:58.920128008 388.332158 529.127829 558.340318 1889.399535 \n",
+ "00:09:58.960064004 388.332158 529.127829 558.340318 1889.399535 \n",
+ "\n",
+ " W_head_area W_torso_area W_back_area W_full_area \n",
+ "00:00:00 588.316685 575.287843 557.837678 2402.446220 \n",
+ "00:00:00.039935995 532.941463 711.610814 747.171324 2612.577515 \n",
+ "00:00:00.079871991 615.224965 659.274363 696.773878 2680.819039 \n",
+ "00:00:00.119807987 664.955274 698.278394 709.587295 2725.074795 \n",
+ "00:00:00.159743982 589.501549 713.393985 706.975280 2732.936140 \n",
+ "... ... ... ... ... \n",
+ "00:09:58.800320021 516.368794 466.566421 607.161201 1974.352930 \n",
+ "00:09:58.840256017 517.334943 468.582411 611.765902 1979.455000 \n",
+ "00:09:58.880192012 516.875687 468.401897 613.444346 1980.834529 \n",
+ "00:09:58.920128008 516.875687 468.401897 613.444346 1980.834529 \n",
+ "00:09:58.960064004 516.875687 468.401897 613.444346 1980.834529 \n",
+ "\n",
+ "[14999 rows x 52 columns]"
+ ]
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"my_deepof_project.get_coords().merge(my_deepof_project.get_areas())['20191204_Day2_SI_JB08_Test_54']"
]
@@ -378,7 +2424,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 13,
"id": "51040e42",
"metadata": {},
"outputs": [],
@@ -399,10 +2445,24 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 14,
"id": "a0da64d3",
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "{'20191204_Day2_SI_JB08_Test_54': CSDS\n",
+ "0 Nonstressed, '20191204_Day2_SI_JB08_Test_56': CSDS\n",
+ "0 Stressed, '20191204_Day2_SI_JB08_Test_61': CSDS\n",
+ "0 Stressed, '20191204_Day2_SI_JB08_Test_62': CSDS\n",
+ "0 Stressed, '20191204_Day2_SI_JB08_Test_63': CSDS\n",
+ "0 Nonstressed, '20191204_Day2_SI_JB08_Test_64': CSDS\n",
+ "0 Nonstressed}\n"
+ ]
+ }
+ ],
"source": [
"print(my_deepof_project.get_exp_conditions)"
]
@@ -417,10 +2477,53 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 15,
"id": "79da9944",
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>CSDS</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>Nonstressed</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " CSDS\n",
+ "0 Nonstressed"
+ ]
+ },
+ "execution_count": 15,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"my_deepof_project.get_exp_conditions['20191204_Day2_SI_JB08_Test_54']"
]
@@ -459,12 +2562,23 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 16,
"id": "69d82405",
"metadata": {
"scrolled": false
},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 1200x600 with 2 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
"source": [
"sns.set_context(\"notebook\")\n",
"\n",
@@ -516,12 +2630,4867 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 17,
"id": "b0f13977",
"metadata": {
"scrolled": false
},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
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+ "bHIAAAAAAAAAAG1kaXJhcHBsAAAAAAAAAAAAAAAALWlsc3QAAAAlqXRvbwAAAB1kYXRhAAAAAQAA\n",
+ "AABMYXZmNTkuMjcuMTAw\n",
+ "\">\n",
+ " Your browser does not support the video tag.\n",
+ "</video>"
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
"source": [
"from IPython import display\n",
"\n",
diff --git a/docs/source/tutorial_notebooks/.ipynb_checkpoints/deepof_unsupervised_tutorial-checkpoint.ipynb b/docs/source/tutorial_notebooks/.ipynb_checkpoints/deepof_unsupervised_tutorial-checkpoint.ipynb
index 3067cc365bfdaf52722821355a314c357a0fb6fc..5a016b1c17ada9d6826eecb30e1c8c12b33094f2 100644
--- a/docs/source/tutorial_notebooks/.ipynb_checkpoints/deepof_unsupervised_tutorial-checkpoint.ipynb
+++ b/docs/source/tutorial_notebooks/.ipynb_checkpoints/deepof_unsupervised_tutorial-checkpoint.ipynb
@@ -362,7 +362,7 @@
},
{
"cell_type": "markdown",
- "id": "69596a01",
+ "id": "cc7567ed",
"metadata": {},
"source": [
"We can also plot it:"
@@ -371,7 +371,7 @@
{
"cell_type": "code",
"execution_count": 10,
- "id": "9d55035c",
+ "id": "369e390a",
"metadata": {},
"outputs": [
{
@@ -393,7 +393,7 @@
},
{
"cell_type": "markdown",
- "id": "fa781f8a",
+ "id": "441b03a3",
"metadata": {},
"source": [
"Finally, the two last objects correspond to the TableDict object with the features (useful later on, as we'll see shortly) and the already explained global scaler.\n",
@@ -411,7 +411,7 @@
},
{
"cell_type": "markdown",
- "id": "be50a162",
+ "id": "32bc99c4",
"metadata": {},
"source": [
"The core idea of deep clustering is to embed our input (the motion features over time) with a neural network, and retrieve a set of embeddings per time point (a reduced representation in the form of a vector) each of which is assigned to a cluster. In this context, clusters correspond to systematic behaviors the model observes in the provided cohort.\n",
@@ -431,7 +431,7 @@
},
{
"cell_type": "markdown",
- "id": "ad2a70a0",
+ "id": "15052f81",
"metadata": {},
"source": [
"The third class of models (not included for now in this tutorial) use a different architecture altogether, which is based in [contrastive learning](https://lilianweng.github.io/posts/2021-05-31-contrastive/). This produces embeddings without the need for a decoder, but clustering must be conducted post-hoc, which has some disadvantages.\n",
@@ -452,7 +452,7 @@
},
{
"cell_type": "markdown",
- "id": "24e5eed2",
+ "id": "629b10d6",
"metadata": {},
"source": [
"**NOTE**: Trained weights are saved under __Trained_models/trained_weights__ in your project directory. If you'd like to manually select the weights to load, browse to the corresponding folder and input the name of the file of choice, instead of a boolean.\n",
@@ -463,28 +463,28 @@
{
"cell_type": "code",
"execution_count": 11,
- "id": "e34023a0",
+ "id": "191aed1d",
"metadata": {},
"outputs": [],
"source": [
- "# trained_model = my_deepof_project.deep_unsupervised_embedding(\n",
- "# preprocessed_object=graph_preprocessed_coords, # Change to preprocessed_coords to use non-graph embeddings\n",
- "# adjacency_matrix=adj_matrix,\n",
- "# embedding_model=\"VaDE\", # Can also be set to 'VQVAE' and 'Contrastive'\n",
- "# epochs=10,\n",
- "# encoder_type=\"recurrent\", # Can also be set to 'TCN' and 'transformer'\n",
- "# n_components=10,\n",
- "# latent_dim=4,\n",
- "# batch_size=1024,\n",
- "# verbose=False, # Set to True to follow the training loop\n",
- "# interaction_regularization=0.0,\n",
- "# pretrained=True, # Set to False to train a new model!\n",
- "# )"
+ "trained_model = my_deepof_project.deep_unsupervised_embedding(\n",
+ " preprocessed_object=graph_preprocessed_coords, # Change to preprocessed_coords to use non-graph embeddings\n",
+ " adjacency_matrix=adj_matrix,\n",
+ " embedding_model=\"VaDE\", # Can also be set to 'VQVAE' and 'Contrastive'\n",
+ " epochs=10,\n",
+ " encoder_type=\"recurrent\", # Can also be set to 'TCN' and 'transformer'\n",
+ " n_components=10,\n",
+ " latent_dim=4,\n",
+ " batch_size=1024,\n",
+ " verbose=False, # Set to True to follow the training loop\n",
+ " interaction_regularization=0.0,\n",
+ " pretrained=True, # Set to False to train a new model!\n",
+ ")"
]
},
{
"cell_type": "markdown",
- "id": "07d673b4",
+ "id": "a9061c9d",
"metadata": {},
"source": [
"While we won't explore the models themselves in detail in this tutorial, some hints you may want to try on your own are:\n",
@@ -496,7 +496,7 @@
},
{
"cell_type": "markdown",
- "id": "f38213a7",
+ "id": "6d862ef4",
"metadata": {},
"source": [
"Once we have a trained model, it's time to finally get our embeddings! This can be done using the deepof.model_utils.embedding_per_video() function. This will return TableDict objects containing the embeddings, soft_counts, and breaks per experiment. These are:\n",
@@ -513,19 +513,19 @@
"metadata": {},
"outputs": [],
"source": [
- "# # Get embeddings, soft_counts, and breaks per video\n",
- "# embeddings, soft_counts, breaks = deepof.model_utils.embedding_per_video(\n",
- "# coordinates=my_deepof_project,\n",
- "# to_preprocess=to_preprocess, \n",
- "# model=trained_model,\n",
- "# animal_id=\"B\",\n",
- "# global_scaler=global_scaler,\n",
- "# )"
+ "# Get embeddings, soft_counts, and breaks per video\n",
+ "embeddings, soft_counts, breaks = deepof.model_utils.embedding_per_video(\n",
+ " coordinates=my_deepof_project,\n",
+ " to_preprocess=to_preprocess, \n",
+ " model=trained_model,\n",
+ " animal_id=\"B\",\n",
+ " global_scaler=global_scaler,\n",
+ ")"
]
},
{
"cell_type": "markdown",
- "id": "09b3c873",
+ "id": "d84d94af",
"metadata": {},
"source": [
"As the training set in this tutorial is quite small, we'll load embeddings from a model trained on the full dataset of 53 videos for the rest of the presented analyses. Feel free to try the remaining cells with your trained model by skipping the cell below, though!"
@@ -534,7 +534,7 @@
{
"cell_type": "code",
"execution_count": 13,
- "id": "e0d71ded",
+ "id": "88b26895",
"metadata": {},
"outputs": [],
"source": [
@@ -564,7 +564,7 @@
},
{
"cell_type": "markdown",
- "id": "e5ef2406",
+ "id": "802f38d6",
"metadata": {},
"source": [
"So we finally have our trained embeddings, and our data has been clustered. Let's see what sort of tools DeepOF offers to analyze the results, then!"
@@ -580,7 +580,7 @@
},
{
"cell_type": "markdown",
- "id": "edef8ae2",
+ "id": "c1b8a8a0",
"metadata": {},
"source": [
"The first thing we can do is to visualize the cluster space. If you went through the tutorial on supervised analysis, you're already familiar with the deepof.visuals.plot_embeddings() function. Here we'll run it again, with slightly different parameters (passing embeddings, soft_counts, and breaks instead of the supervised annotations). As in the previous tutorial, all figures shown in the documentation version of this tutorial were produced using the full 53 animal dataset."
@@ -640,7 +640,7 @@
},
{
"cell_type": "markdown",
- "id": "3e44b83d",
+ "id": "c352aafe",
"metadata": {},
"source": [
"The figure on the left now shows time points in a UMAP projection of the latent space, where colors indicate different clusters. The figure on the right aggregates all time points in a given animal as a vector of counts per behavior (indicating how much time each animal spends on each cluster). We can already see a clear separation between conditions, in a fully unsupervised way!"
@@ -648,7 +648,7 @@
},
{
"cell_type": "markdown",
- "id": "828660e6",
+ "id": "49989e8b",
"metadata": {},
"source": [
"### Generating Gantt charts with all clusters"
@@ -656,7 +656,7 @@
},
{
"cell_type": "markdown",
- "id": "fab59107",
+ "id": "c6b08e7c",
"metadata": {},
"source": [
"Like we did for the supervised annotations, we can also visualize assigned clusters over time using Gantt charts:"
@@ -702,7 +702,7 @@
},
{
"cell_type": "markdown",
- "id": "ae6e088a",
+ "id": "a8b23167",
"metadata": {},
"source": [
"Next, let's quantify the distance between our experimental distributions. To measure how far the behavior of stressed animals is from that of controls, we'll use the [Wasserstein distance](http://alexhwilliams.info/itsneuronalblog/2020/10/09/optimal-transport/) between the distributions shown on the right panel of the figure above. Moreover, we'll see how that distance evolves over time. To this end, we can use the deepof.visuals.plot_distance_between_conditions() function. "
@@ -761,7 +761,7 @@
},
{
"cell_type": "markdown",
- "id": "4d03ca6b",
+ "id": "c89c8988",
"metadata": {},
"source": [
"In the figure above, you can see two curves. The grey one in the background measures how the distance between conditions evolves over a growing window over time. We start with 10 seconds of data, and add one at the time until all 600 are included (the underlying videos are 10-minutes long). Peaks in this curve can then point towards points in the time series that maximize the difference between conditions. Here, we see a maximum at 125 seconds, which is compatible with the habituation of the mice to the novel environment in which they were introduced (see the main paper for details).\n",
@@ -772,7 +772,7 @@
{
"cell_type": "code",
"execution_count": 20,
- "id": "153ac63e",
+ "id": "cf839e2c",
"metadata": {},
"outputs": [
{
@@ -824,7 +824,7 @@
},
{
"cell_type": "markdown",
- "id": "9a303266",
+ "id": "33a99a92",
"metadata": {},
"source": [
"Where we clearly see that the overlap between the distributions is greater in the figure on the right (and therefore their Wasserstein distance lower)."
@@ -840,7 +840,7 @@
},
{
"cell_type": "markdown",
- "id": "1a45da1e",
+ "id": "31b63f8d",
"metadata": {},
"source": [
"Next, and as we did in the last tutorial on supervised annotation, we can test for enrichment in cluster expression! This way we can detect and pinpoint specific behavioral differences between our cohorts. Let's compare how the enrichment plots look for the whole data and the first time bin, respectively:"
@@ -909,7 +909,7 @@
},
{
"cell_type": "markdown",
- "id": "6921418f",
+ "id": "92feeaff",
"metadata": {},
"source": [
"We can see how, as expected, there are many more differences in the first time bin than in the entire time series. Moreover, we see a few clusters (1 and 5, for example) that are highly enriched in stressed animals in the first time bin, but not different at all when looking at the whole time series. We'll visualize what these are at the end of this tutorial."
@@ -925,7 +925,7 @@
},
{
"cell_type": "markdown",
- "id": "e4ab6334",
+ "id": "70c6bbfa",
"metadata": {},
"source": [
"Aside from exploring cluster enrichment, DeepOF provides tools to gain insight into cluster dynamics. That is, how transitions between different clusters look like. For example, we can have a look at the transition matrices per condition with deepof.visuals.plot_transitions():"
@@ -973,7 +973,7 @@
},
{
"cell_type": "markdown",
- "id": "6cdb3e38",
+ "id": "f0e56fb5",
"metadata": {},
"source": [
"Here we can see heatmaps depicting how common transitions between components are. Rows and columns are clustered, to put clusters with common transitions between them together. As it may be hard to retrieve patterns from heatmaps visualized this way, the function can also represent them as graphs, where more common transitions are depicted with thicker edges:"
@@ -982,7 +982,7 @@
{
"cell_type": "code",
"execution_count": 50,
- "id": "3b9cfa6c",
+ "id": "faab121a",
"metadata": {},
"outputs": [
{
@@ -1018,7 +1018,7 @@
},
{
"cell_type": "markdown",
- "id": "525ff8d7",
+ "id": "d164cfee",
"metadata": {},
"source": [
"We can see here how clusters enrich in each condition: clusters 2, 3, 4, 6, 7, and 8 for a tight cluster for non-stressed animals, whereas 0, 1, 5, and 9 do so for stressed animals. This means that transitions between clusters enriched in a certain condition are indeed more common than transitions between clusters enriched across conditions."
@@ -1026,7 +1026,7 @@
},
{
"cell_type": "markdown",
- "id": "e7aa8f24",
+ "id": "770d341b",
"metadata": {},
"source": [
"Moreover, we can explore how the overall behavioral entropy (which measures how predictable behavior for a given animal is) compares across experimental conditions. To obtain these values, DeepOF runs a set of simulations on the transition matrices depicted above, until the population of clusters converges to a stationary distribution. Entropy is then computed on this distribution for each animal, and the obtained values per condition are compared. The function that allows users to run this analysis is deepof.visuals.plot_stationary_entropy(), and it's executed as in the cell below:"
@@ -1067,7 +1067,7 @@
},
{
"cell_type": "markdown",
- "id": "4a54e923",
+ "id": "0eadd4a0",
"metadata": {},
"source": [
"Here we see that behavioral entropy is significantly lower in stressed animals with respect to non-stressed ones! This could be explained by the richer behavioral repertoire that non-stressed animals explore. Upon visualizing the attached videos, one can see that much of the time stressed animals are showing behaviors closer to freezing, while non-stressed animals tend to transition between different exploratory behaviors."
@@ -1075,7 +1075,7 @@
},
{
"cell_type": "markdown",
- "id": "d187259e",
+ "id": "67b86b1e",
"metadata": {},
"source": [
"So far we've seen how to run the unsupervised pipeline to get both embeddings and cluster assignments, as well as how to compare the results across experimental conditions. In the last part of this tutorial, we'll explore one of the most important questions you may be asking: how do I know what the clusters mean?"
@@ -1091,7 +1091,7 @@
},
{
"cell_type": "markdown",
- "id": "25e4b624",
+ "id": "7a5ec20c",
"metadata": {},
"source": [
"To interpret cluster assignments, DeepOF relies on two complementary approaches. The first one is to train a set of supervised classifiers that can predict cluster assignments given a set of features describing the sliding window input, such as distances, speeds, and areas of different body parts and regions, as well as the supervised annotators we generated in the last tutorial (if provided). For a more detailed description of the included features, refer to either the main DeepOF paper or the full API reference."
@@ -1099,7 +1099,7 @@
},
{
"cell_type": "markdown",
- "id": "d69e8fc3",
+ "id": "8d70c34d",
"metadata": {},
"source": [
"There are three steps in the cluster interpretation pipeline: feature extraction, classifier training, and SHAP value computation.\n",
@@ -1112,7 +1112,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "a3bbdff0",
+ "id": "b85443e0",
"metadata": {},
"outputs": [],
"source": [
@@ -1149,7 +1149,7 @@
},
{
"cell_type": "markdown",
- "id": "72cc83f5",
+ "id": "fbdc242b",
"metadata": {},
"source": [
"The function returns three objects. The first one is a data frame with all extracted features over 10000 sampled sliding windows."
@@ -1158,7 +1158,7 @@
{
"cell_type": "code",
"execution_count": 84,
- "id": "fef93a5b",
+ "id": "90b58662",
"metadata": {
"scrolled": false
},
@@ -1583,7 +1583,7 @@
},
{
"cell_type": "markdown",
- "id": "9f32a878",
+ "id": "47e50918",
"metadata": {},
"source": [
"The second one includes all corresponding cluster assignments as hard count integers (notice that we only took assignments that were made with a probability of 0.9 or higher, to select the most representative samples of each cluster)."
@@ -1592,7 +1592,7 @@
{
"cell_type": "code",
"execution_count": 86,
- "id": "8618306b",
+ "id": "2aa254e0",
"metadata": {},
"outputs": [
{
@@ -1612,7 +1612,7 @@
},
{
"cell_type": "markdown",
- "id": "7685617b",
+ "id": "16ca4af7",
"metadata": {},
"source": [
"Finally, the third one refers to the breaks per sample, as described before.\n",
@@ -1661,7 +1661,7 @@
},
{
"cell_type": "markdown",
- "id": "b37b65e8",
+ "id": "ed92148d",
"metadata": {},
"source": [
"Great, we got some results! Before moving to interpretation, we can visualize performance using deepof.visuals.plot_cluster_detection_performance(). The 'visualization' parameter can take one of two values: 'confusion_matrix' and 'balanced_accuracy', corresponding to the two panels in the figure below."
@@ -1724,7 +1724,7 @@
},
{
"cell_type": "markdown",
- "id": "a5def188",
+ "id": "e0133396",
"metadata": {},
"source": [
"The confusion matrix on the left shows how the proportion of allocations the model we just trained made in the validation set (all folds are aggregated). The diagonal corresponds then to correct assignments, whereas common errors are shown as off-diagonal high values. This way we can see there are some miss assignments that are more common than others: although the diagonal elements are the highest for all rows, cluster 4 is often confused with cluster 8, for example. This can be interpreted as a measure of similarity behind the underlying behaviors.\n",
@@ -1734,7 +1734,7 @@
},
{
"cell_type": "markdown",
- "id": "bfd44f0f",
+ "id": "d7e370e3",
"metadata": {},
"source": [
"With all this out of the way, we're ready to delve into cluster interpretation. To this end, DeepOF relies in [Shapley additive explanations (SHAP)](https://shap.readthedocs.io/en/latest/index.html) a widely adopted toolkit for permutation-based computation of global and local feature importance. Let's run it and explore the results:\n",
@@ -1757,7 +1757,7 @@
{
"cell_type": "code",
"execution_count": 107,
- "id": "cf106c17",
+ "id": "f0d7d490",
"metadata": {},
"outputs": [],
"source": [
@@ -1767,7 +1767,7 @@
},
{
"cell_type": "markdown",
- "id": "f93fae81",
+ "id": "15309215",
"metadata": {},
"source": [
"The function returns the SHAP values (a detailed explanation of which is beyond the scope of this tutorial, although we will explain how to interpret them), the explainer object, and a formatted version of the same data frame with features we saw before.\n",
@@ -1776,29 +1776,15 @@
},
{
"cell_type": "code",
- "execution_count": 175,
- "id": "bb37236b",
- "metadata": {
- "tags": [
- "hide-input"
- ]
- },
+ "execution_count": 181,
+ "id": "8aa16988",
+ "metadata": {},
"outputs": [
{
"data": {
- "text/html": [
- "<style>\n",
- " .widget-radio-box {\n",
- " flex-direction: row !important; \n",
- " }\n",
- " .widget-radio-box label{\n",
- " margin:1px !important;\n",
- " width: 120px !important;\n",
- " }\n",
- "</style>\n"
- ],
+ "image/png": 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\n",
"text/plain": [
- "<IPython.core.display.HTML object>"
+ "<Figure size 800x550 with 1 Axes>"
]
},
"metadata": {},
@@ -1806,35 +1792,34 @@
}
],
"source": [
- "%%html\n",
- "<style>\n",
- " .widget-radio-box {\n",
- " flex-direction: row !important; \n",
- " }\n",
- " .widget-radio-box label{\n",
- " margin:1px !important;\n",
- " width: 120px !important;\n",
- " }\n",
- "</style>"
+ "deepof.visuals.plot_shap_swarm_per_cluster(\n",
+ " my_deepof_project, \n",
+ " data_to_explain, \n",
+ " shap_values, \n",
+ " \"all\", \n",
+ " show=True,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ec9722c6",
+ "metadata": {},
+ "source": [
+ "The _x_ axis in the figure above depicts the average absolute SHAP values, a measure of feature importance. The _y_ axis shows the names of the top 8 features the model uses to detect across all clusters. Here, we're looking at **global** feature importance for the model; even though this plot is not very informative regarding individual clusters, we can indeed interpret that the most important features overall are speed, spine stretch (distance between the center of the animal and spine 1 — see the scheme in the landing page of the documentation for details), and the huddle classifier."
]
},
{
"cell_type": "code",
- "execution_count": 173,
- "id": "e87279c0",
- "metadata": {
- "scrolled": false
- },
+ "execution_count": 184,
+ "id": "ed262976",
+ "metadata": {},
"outputs": [
{
"data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "6e838581dcbd46ddbb2ba2d3ed5da4c6",
- "version_major": 2,
- "version_minor": 0
- },
+ "image/png": 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\n",
"text/plain": [
- "interactive(children=(RadioButtons(description='cluster', options=('all', 0, 1, 2, 3, 4, 5, 6, 7, 8, 9), value…"
+ "<Figure size 800x550 with 2 Axes>"
]
},
"metadata": {},
@@ -1842,38 +1827,21 @@
}
],
"source": [
- "# Plot swarm plots per cluster\n",
- "@interact()\n",
- "def plot_shap_swarm_per_cluster(\n",
- " cluster=RadioButtons(options=[\"all\"] + list(range(10))),\n",
- "):\n",
- " \n",
- " deepof.visuals.plot_shap_swarm_per_cluster(\n",
- " my_deepof_project, \n",
- " data_to_explain, \n",
- " shap_values, \n",
- " cluster, \n",
- " show=False,\n",
- " )\n",
- " \n",
- " plt.tight_layout()\n",
- " plt.show()"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "d9222eb2",
- "metadata": {},
- "source": [
- "Running the cell above (or showing the default panel upon loading the documentation) will show a multicolor horizontal bar chart. The _x_ axis depicts the average absolute SHAP values, a measure of feature importance. The _y_ axis shows the names of the top 8 features the model uses to detect across all clusters. Here, we're looking at **global** feature importance for the model; even though this plot is not very informative regarding individual clusters, we can indeed interpret that the most important features overall are speed, spine stretch (distance between the center of the animal and spine 1 — see the scheme in the landing page of the documentation for details), and the huddle classifier."
+ "deepof.visuals.plot_shap_swarm_per_cluster(\n",
+ " my_deepof_project, \n",
+ " data_to_explain, \n",
+ " shap_values, \n",
+ " cluster=1, \n",
+ " show=True,\n",
+ ")"
]
},
{
"cell_type": "markdown",
- "id": "b1f461bf",
+ "id": "cc1d181e",
"metadata": {},
"source": [
- "Selecting any cluster from the drop-down menu will change the figure completely. What we're looking now is called a bee swarm plot, and it's one of the main visualizations the SHAP package has to offer. The y-axis is the same as before (but it's now based on feature importance to detect the cluster that was specifically selected). The _x_ axis, however, shows the raw SHAP values instead of the global absolute value. Positive values indicate an association with the model selecting this specific cluster, whereas negative values indicate an association with the model selecting against this specific cluster. Finally, the color indicates the underlying feature value.\n",
+ "Selecting any cluster in the 'cluster' parameter will render a different kind of plot. What we're looking now is called a bee swarm plot, and it's one of the main visualizations the SHAP package has to offer. The y-axis is the same as before (but it's now based on feature importance to detect the cluster that was specifically selected). The _x_ axis, however, shows the raw SHAP values instead of the global absolute value. Positive values indicate an association with the model selecting this specific cluster, whereas negative values indicate an association with the model selecting against this specific cluster. Finally, the color indicates the underlying feature value.\n",
"\n",
"Thus, interpretation lies in detecting associations between the sign of the SHAP values and their color. Red values to the right mean positive associations between a feature and a cluster, whereas blue values on the right mean a negative association. Let's see an example to make it more explicit. \n",
"\n",
@@ -1892,7 +1860,7 @@
},
{
"cell_type": "markdown",
- "id": "d67ce569",
+ "id": "630e91c2",
"metadata": {},
"source": [
"First, we can use the same animation function we saw in the first tutorial, with a few additions. If we pass the embeddings and soft_counts objects into the embedding and cluster_assignments parameters respectively, as well as a specific video to experiment_id, and the index of a selected_cluster, an animated figure with two panels will be created.\n",
@@ -1903,7 +1871,7 @@
{
"cell_type": "code",
"execution_count": 178,
- "id": "c38debac",
+ "id": "adf8fdd1",
"metadata": {},
"outputs": [
{
@@ -3557,7 +3525,7 @@
},
{
"cell_type": "markdown",
- "id": "0b12f6e7",
+ "id": "ff1eb07a",
"metadata": {},
"source": [
"Second, deepof.visuals.export_annotated_video() can output direct samples taken from the dataset into a real video. The function will concatenate samples from all available videos, and generate a video with samples from each cluster. The parameter frame_limit_per_video controls how many frames per video should be included (to prevent the video from ending up being too large, especially in big datasets). Let's indeed run it, and load some samples from cluster 1 and into the notebook!"
@@ -3580,15 +3548,15 @@
},
{
"cell_type": "markdown",
- "id": "4a64a816",
+ "id": "43e3d627",
"metadata": {},
"source": [
- "<img src=\"./tutorial_files/tutorial_project/Out_videos/deepof_unsupervised_annotation_cluster_1_sample_AdobeExpress.gif\" width=\"450\" align=\"center\">"
+ "<img src=\"./tutorial_files/tutorial_project/Out_videos/deepof_unsupervised_annotation_cluster_1_sample_AdobeExpress.gif\" width=\"350\">"
]
},
{
"cell_type": "markdown",
- "id": "8b8bff8f",
+ "id": "886cf927",
"metadata": {},
"source": [
"### Wrapping up"
@@ -3596,7 +3564,7 @@
},
{
"cell_type": "markdown",
- "id": "f1be68b3",
+ "id": "3b78d536",
"metadata": {},
"source": [
"Thank you for making it until the end. In this three-part tutorial series, we covered how to load data into DeepOF, and how to run and interpret results in both supervised and unsupervised pipelines. Stay tuned for more content, and make sure to raise an issue in our GitHub repository if you have any questions!"
diff --git a/docs/source/tutorial_notebooks/deepof_preprocessing_tutorial.ipynb b/docs/source/tutorial_notebooks/deepof_preprocessing_tutorial.ipynb
index b5266e94fa41652728b959b544f4da319f920f65..dc910808867c2d5f68730343aa94678d436d512b 100644
--- a/docs/source/tutorial_notebooks/deepof_preprocessing_tutorial.ipynb
+++ b/docs/source/tutorial_notebooks/deepof_preprocessing_tutorial.ipynb
@@ -112,20 +112,20 @@
"metadata": {},
"outputs": [],
"source": [
- "# my_deepof_project = deepof.data.Project(\n",
- "# project_path=os.path.join(\"tutorial_files\"),\n",
- "# video_path=os.path.join(\"tutorial_files/Videos/\"),\n",
- "# table_path=os.path.join(\"tutorial_files/Tables/\"),\n",
- "# project_name=\"tutorial_project\",\n",
- "# arena=\"circular-manual\",\n",
- "# animal_ids=[\"B\", \"W\"],\n",
- "# video_format=\".mp4\",\n",
- "# exclude_bodyparts=[\"Tail_1\", \"Tail_2\", \"Tail_tip\"],\n",
- "# video_scale=380,\n",
- "# enable_iterative_imputation=10,\n",
- "# smooth_alpha=1,\n",
- "# exp_conditions=None,\n",
- "# )"
+ "my_deepof_project = deepof.data.Project(\n",
+ " project_path=os.path.join(\"tutorial_files\"),\n",
+ " video_path=os.path.join(\"tutorial_files/Videos/\"),\n",
+ " table_path=os.path.join(\"tutorial_files/Tables/\"),\n",
+ " project_name=\"tutorial_project\",\n",
+ " arena=\"circular-manual\",\n",
+ " animal_ids=[\"B\", \"W\"],\n",
+ " video_format=\".mp4\",\n",
+ " exclude_bodyparts=[\"Tail_1\", \"Tail_2\", \"Tail_tip\"],\n",
+ " video_scale=380,\n",
+ " enable_iterative_imputation=10,\n",
+ " smooth_alpha=1,\n",
+ " exp_conditions=None,\n",
+ ")"
]
},
{
@@ -161,7 +161,7 @@
"metadata": {},
"outputs": [],
"source": [
- "# my_deepof_project = my_deepof_project.create()"
+ "my_deepof_project = my_deepof_project.create()"
]
},
{
diff --git a/docs/source/tutorial_notebooks/deepof_unsupervised_tutorial.ipynb b/docs/source/tutorial_notebooks/deepof_unsupervised_tutorial.ipynb
index 3067cc365bfdaf52722821355a314c357a0fb6fc..5a016b1c17ada9d6826eecb30e1c8c12b33094f2 100644
--- a/docs/source/tutorial_notebooks/deepof_unsupervised_tutorial.ipynb
+++ b/docs/source/tutorial_notebooks/deepof_unsupervised_tutorial.ipynb
@@ -362,7 +362,7 @@
},
{
"cell_type": "markdown",
- "id": "69596a01",
+ "id": "cc7567ed",
"metadata": {},
"source": [
"We can also plot it:"
@@ -371,7 +371,7 @@
{
"cell_type": "code",
"execution_count": 10,
- "id": "9d55035c",
+ "id": "369e390a",
"metadata": {},
"outputs": [
{
@@ -393,7 +393,7 @@
},
{
"cell_type": "markdown",
- "id": "fa781f8a",
+ "id": "441b03a3",
"metadata": {},
"source": [
"Finally, the two last objects correspond to the TableDict object with the features (useful later on, as we'll see shortly) and the already explained global scaler.\n",
@@ -411,7 +411,7 @@
},
{
"cell_type": "markdown",
- "id": "be50a162",
+ "id": "32bc99c4",
"metadata": {},
"source": [
"The core idea of deep clustering is to embed our input (the motion features over time) with a neural network, and retrieve a set of embeddings per time point (a reduced representation in the form of a vector) each of which is assigned to a cluster. In this context, clusters correspond to systematic behaviors the model observes in the provided cohort.\n",
@@ -431,7 +431,7 @@
},
{
"cell_type": "markdown",
- "id": "ad2a70a0",
+ "id": "15052f81",
"metadata": {},
"source": [
"The third class of models (not included for now in this tutorial) use a different architecture altogether, which is based in [contrastive learning](https://lilianweng.github.io/posts/2021-05-31-contrastive/). This produces embeddings without the need for a decoder, but clustering must be conducted post-hoc, which has some disadvantages.\n",
@@ -452,7 +452,7 @@
},
{
"cell_type": "markdown",
- "id": "24e5eed2",
+ "id": "629b10d6",
"metadata": {},
"source": [
"**NOTE**: Trained weights are saved under __Trained_models/trained_weights__ in your project directory. If you'd like to manually select the weights to load, browse to the corresponding folder and input the name of the file of choice, instead of a boolean.\n",
@@ -463,28 +463,28 @@
{
"cell_type": "code",
"execution_count": 11,
- "id": "e34023a0",
+ "id": "191aed1d",
"metadata": {},
"outputs": [],
"source": [
- "# trained_model = my_deepof_project.deep_unsupervised_embedding(\n",
- "# preprocessed_object=graph_preprocessed_coords, # Change to preprocessed_coords to use non-graph embeddings\n",
- "# adjacency_matrix=adj_matrix,\n",
- "# embedding_model=\"VaDE\", # Can also be set to 'VQVAE' and 'Contrastive'\n",
- "# epochs=10,\n",
- "# encoder_type=\"recurrent\", # Can also be set to 'TCN' and 'transformer'\n",
- "# n_components=10,\n",
- "# latent_dim=4,\n",
- "# batch_size=1024,\n",
- "# verbose=False, # Set to True to follow the training loop\n",
- "# interaction_regularization=0.0,\n",
- "# pretrained=True, # Set to False to train a new model!\n",
- "# )"
+ "trained_model = my_deepof_project.deep_unsupervised_embedding(\n",
+ " preprocessed_object=graph_preprocessed_coords, # Change to preprocessed_coords to use non-graph embeddings\n",
+ " adjacency_matrix=adj_matrix,\n",
+ " embedding_model=\"VaDE\", # Can also be set to 'VQVAE' and 'Contrastive'\n",
+ " epochs=10,\n",
+ " encoder_type=\"recurrent\", # Can also be set to 'TCN' and 'transformer'\n",
+ " n_components=10,\n",
+ " latent_dim=4,\n",
+ " batch_size=1024,\n",
+ " verbose=False, # Set to True to follow the training loop\n",
+ " interaction_regularization=0.0,\n",
+ " pretrained=True, # Set to False to train a new model!\n",
+ ")"
]
},
{
"cell_type": "markdown",
- "id": "07d673b4",
+ "id": "a9061c9d",
"metadata": {},
"source": [
"While we won't explore the models themselves in detail in this tutorial, some hints you may want to try on your own are:\n",
@@ -496,7 +496,7 @@
},
{
"cell_type": "markdown",
- "id": "f38213a7",
+ "id": "6d862ef4",
"metadata": {},
"source": [
"Once we have a trained model, it's time to finally get our embeddings! This can be done using the deepof.model_utils.embedding_per_video() function. This will return TableDict objects containing the embeddings, soft_counts, and breaks per experiment. These are:\n",
@@ -513,19 +513,19 @@
"metadata": {},
"outputs": [],
"source": [
- "# # Get embeddings, soft_counts, and breaks per video\n",
- "# embeddings, soft_counts, breaks = deepof.model_utils.embedding_per_video(\n",
- "# coordinates=my_deepof_project,\n",
- "# to_preprocess=to_preprocess, \n",
- "# model=trained_model,\n",
- "# animal_id=\"B\",\n",
- "# global_scaler=global_scaler,\n",
- "# )"
+ "# Get embeddings, soft_counts, and breaks per video\n",
+ "embeddings, soft_counts, breaks = deepof.model_utils.embedding_per_video(\n",
+ " coordinates=my_deepof_project,\n",
+ " to_preprocess=to_preprocess, \n",
+ " model=trained_model,\n",
+ " animal_id=\"B\",\n",
+ " global_scaler=global_scaler,\n",
+ ")"
]
},
{
"cell_type": "markdown",
- "id": "09b3c873",
+ "id": "d84d94af",
"metadata": {},
"source": [
"As the training set in this tutorial is quite small, we'll load embeddings from a model trained on the full dataset of 53 videos for the rest of the presented analyses. Feel free to try the remaining cells with your trained model by skipping the cell below, though!"
@@ -534,7 +534,7 @@
{
"cell_type": "code",
"execution_count": 13,
- "id": "e0d71ded",
+ "id": "88b26895",
"metadata": {},
"outputs": [],
"source": [
@@ -564,7 +564,7 @@
},
{
"cell_type": "markdown",
- "id": "e5ef2406",
+ "id": "802f38d6",
"metadata": {},
"source": [
"So we finally have our trained embeddings, and our data has been clustered. Let's see what sort of tools DeepOF offers to analyze the results, then!"
@@ -580,7 +580,7 @@
},
{
"cell_type": "markdown",
- "id": "edef8ae2",
+ "id": "c1b8a8a0",
"metadata": {},
"source": [
"The first thing we can do is to visualize the cluster space. If you went through the tutorial on supervised analysis, you're already familiar with the deepof.visuals.plot_embeddings() function. Here we'll run it again, with slightly different parameters (passing embeddings, soft_counts, and breaks instead of the supervised annotations). As in the previous tutorial, all figures shown in the documentation version of this tutorial were produced using the full 53 animal dataset."
@@ -640,7 +640,7 @@
},
{
"cell_type": "markdown",
- "id": "3e44b83d",
+ "id": "c352aafe",
"metadata": {},
"source": [
"The figure on the left now shows time points in a UMAP projection of the latent space, where colors indicate different clusters. The figure on the right aggregates all time points in a given animal as a vector of counts per behavior (indicating how much time each animal spends on each cluster). We can already see a clear separation between conditions, in a fully unsupervised way!"
@@ -648,7 +648,7 @@
},
{
"cell_type": "markdown",
- "id": "828660e6",
+ "id": "49989e8b",
"metadata": {},
"source": [
"### Generating Gantt charts with all clusters"
@@ -656,7 +656,7 @@
},
{
"cell_type": "markdown",
- "id": "fab59107",
+ "id": "c6b08e7c",
"metadata": {},
"source": [
"Like we did for the supervised annotations, we can also visualize assigned clusters over time using Gantt charts:"
@@ -702,7 +702,7 @@
},
{
"cell_type": "markdown",
- "id": "ae6e088a",
+ "id": "a8b23167",
"metadata": {},
"source": [
"Next, let's quantify the distance between our experimental distributions. To measure how far the behavior of stressed animals is from that of controls, we'll use the [Wasserstein distance](http://alexhwilliams.info/itsneuronalblog/2020/10/09/optimal-transport/) between the distributions shown on the right panel of the figure above. Moreover, we'll see how that distance evolves over time. To this end, we can use the deepof.visuals.plot_distance_between_conditions() function. "
@@ -761,7 +761,7 @@
},
{
"cell_type": "markdown",
- "id": "4d03ca6b",
+ "id": "c89c8988",
"metadata": {},
"source": [
"In the figure above, you can see two curves. The grey one in the background measures how the distance between conditions evolves over a growing window over time. We start with 10 seconds of data, and add one at the time until all 600 are included (the underlying videos are 10-minutes long). Peaks in this curve can then point towards points in the time series that maximize the difference between conditions. Here, we see a maximum at 125 seconds, which is compatible with the habituation of the mice to the novel environment in which they were introduced (see the main paper for details).\n",
@@ -772,7 +772,7 @@
{
"cell_type": "code",
"execution_count": 20,
- "id": "153ac63e",
+ "id": "cf839e2c",
"metadata": {},
"outputs": [
{
@@ -824,7 +824,7 @@
},
{
"cell_type": "markdown",
- "id": "9a303266",
+ "id": "33a99a92",
"metadata": {},
"source": [
"Where we clearly see that the overlap between the distributions is greater in the figure on the right (and therefore their Wasserstein distance lower)."
@@ -840,7 +840,7 @@
},
{
"cell_type": "markdown",
- "id": "1a45da1e",
+ "id": "31b63f8d",
"metadata": {},
"source": [
"Next, and as we did in the last tutorial on supervised annotation, we can test for enrichment in cluster expression! This way we can detect and pinpoint specific behavioral differences between our cohorts. Let's compare how the enrichment plots look for the whole data and the first time bin, respectively:"
@@ -909,7 +909,7 @@
},
{
"cell_type": "markdown",
- "id": "6921418f",
+ "id": "92feeaff",
"metadata": {},
"source": [
"We can see how, as expected, there are many more differences in the first time bin than in the entire time series. Moreover, we see a few clusters (1 and 5, for example) that are highly enriched in stressed animals in the first time bin, but not different at all when looking at the whole time series. We'll visualize what these are at the end of this tutorial."
@@ -925,7 +925,7 @@
},
{
"cell_type": "markdown",
- "id": "e4ab6334",
+ "id": "70c6bbfa",
"metadata": {},
"source": [
"Aside from exploring cluster enrichment, DeepOF provides tools to gain insight into cluster dynamics. That is, how transitions between different clusters look like. For example, we can have a look at the transition matrices per condition with deepof.visuals.plot_transitions():"
@@ -973,7 +973,7 @@
},
{
"cell_type": "markdown",
- "id": "6cdb3e38",
+ "id": "f0e56fb5",
"metadata": {},
"source": [
"Here we can see heatmaps depicting how common transitions between components are. Rows and columns are clustered, to put clusters with common transitions between them together. As it may be hard to retrieve patterns from heatmaps visualized this way, the function can also represent them as graphs, where more common transitions are depicted with thicker edges:"
@@ -982,7 +982,7 @@
{
"cell_type": "code",
"execution_count": 50,
- "id": "3b9cfa6c",
+ "id": "faab121a",
"metadata": {},
"outputs": [
{
@@ -1018,7 +1018,7 @@
},
{
"cell_type": "markdown",
- "id": "525ff8d7",
+ "id": "d164cfee",
"metadata": {},
"source": [
"We can see here how clusters enrich in each condition: clusters 2, 3, 4, 6, 7, and 8 for a tight cluster for non-stressed animals, whereas 0, 1, 5, and 9 do so for stressed animals. This means that transitions between clusters enriched in a certain condition are indeed more common than transitions between clusters enriched across conditions."
@@ -1026,7 +1026,7 @@
},
{
"cell_type": "markdown",
- "id": "e7aa8f24",
+ "id": "770d341b",
"metadata": {},
"source": [
"Moreover, we can explore how the overall behavioral entropy (which measures how predictable behavior for a given animal is) compares across experimental conditions. To obtain these values, DeepOF runs a set of simulations on the transition matrices depicted above, until the population of clusters converges to a stationary distribution. Entropy is then computed on this distribution for each animal, and the obtained values per condition are compared. The function that allows users to run this analysis is deepof.visuals.plot_stationary_entropy(), and it's executed as in the cell below:"
@@ -1067,7 +1067,7 @@
},
{
"cell_type": "markdown",
- "id": "4a54e923",
+ "id": "0eadd4a0",
"metadata": {},
"source": [
"Here we see that behavioral entropy is significantly lower in stressed animals with respect to non-stressed ones! This could be explained by the richer behavioral repertoire that non-stressed animals explore. Upon visualizing the attached videos, one can see that much of the time stressed animals are showing behaviors closer to freezing, while non-stressed animals tend to transition between different exploratory behaviors."
@@ -1075,7 +1075,7 @@
},
{
"cell_type": "markdown",
- "id": "d187259e",
+ "id": "67b86b1e",
"metadata": {},
"source": [
"So far we've seen how to run the unsupervised pipeline to get both embeddings and cluster assignments, as well as how to compare the results across experimental conditions. In the last part of this tutorial, we'll explore one of the most important questions you may be asking: how do I know what the clusters mean?"
@@ -1091,7 +1091,7 @@
},
{
"cell_type": "markdown",
- "id": "25e4b624",
+ "id": "7a5ec20c",
"metadata": {},
"source": [
"To interpret cluster assignments, DeepOF relies on two complementary approaches. The first one is to train a set of supervised classifiers that can predict cluster assignments given a set of features describing the sliding window input, such as distances, speeds, and areas of different body parts and regions, as well as the supervised annotators we generated in the last tutorial (if provided). For a more detailed description of the included features, refer to either the main DeepOF paper or the full API reference."
@@ -1099,7 +1099,7 @@
},
{
"cell_type": "markdown",
- "id": "d69e8fc3",
+ "id": "8d70c34d",
"metadata": {},
"source": [
"There are three steps in the cluster interpretation pipeline: feature extraction, classifier training, and SHAP value computation.\n",
@@ -1112,7 +1112,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "a3bbdff0",
+ "id": "b85443e0",
"metadata": {},
"outputs": [],
"source": [
@@ -1149,7 +1149,7 @@
},
{
"cell_type": "markdown",
- "id": "72cc83f5",
+ "id": "fbdc242b",
"metadata": {},
"source": [
"The function returns three objects. The first one is a data frame with all extracted features over 10000 sampled sliding windows."
@@ -1158,7 +1158,7 @@
{
"cell_type": "code",
"execution_count": 84,
- "id": "fef93a5b",
+ "id": "90b58662",
"metadata": {
"scrolled": false
},
@@ -1583,7 +1583,7 @@
},
{
"cell_type": "markdown",
- "id": "9f32a878",
+ "id": "47e50918",
"metadata": {},
"source": [
"The second one includes all corresponding cluster assignments as hard count integers (notice that we only took assignments that were made with a probability of 0.9 or higher, to select the most representative samples of each cluster)."
@@ -1592,7 +1592,7 @@
{
"cell_type": "code",
"execution_count": 86,
- "id": "8618306b",
+ "id": "2aa254e0",
"metadata": {},
"outputs": [
{
@@ -1612,7 +1612,7 @@
},
{
"cell_type": "markdown",
- "id": "7685617b",
+ "id": "16ca4af7",
"metadata": {},
"source": [
"Finally, the third one refers to the breaks per sample, as described before.\n",
@@ -1661,7 +1661,7 @@
},
{
"cell_type": "markdown",
- "id": "b37b65e8",
+ "id": "ed92148d",
"metadata": {},
"source": [
"Great, we got some results! Before moving to interpretation, we can visualize performance using deepof.visuals.plot_cluster_detection_performance(). The 'visualization' parameter can take one of two values: 'confusion_matrix' and 'balanced_accuracy', corresponding to the two panels in the figure below."
@@ -1724,7 +1724,7 @@
},
{
"cell_type": "markdown",
- "id": "a5def188",
+ "id": "e0133396",
"metadata": {},
"source": [
"The confusion matrix on the left shows how the proportion of allocations the model we just trained made in the validation set (all folds are aggregated). The diagonal corresponds then to correct assignments, whereas common errors are shown as off-diagonal high values. This way we can see there are some miss assignments that are more common than others: although the diagonal elements are the highest for all rows, cluster 4 is often confused with cluster 8, for example. This can be interpreted as a measure of similarity behind the underlying behaviors.\n",
@@ -1734,7 +1734,7 @@
},
{
"cell_type": "markdown",
- "id": "bfd44f0f",
+ "id": "d7e370e3",
"metadata": {},
"source": [
"With all this out of the way, we're ready to delve into cluster interpretation. To this end, DeepOF relies in [Shapley additive explanations (SHAP)](https://shap.readthedocs.io/en/latest/index.html) a widely adopted toolkit for permutation-based computation of global and local feature importance. Let's run it and explore the results:\n",
@@ -1757,7 +1757,7 @@
{
"cell_type": "code",
"execution_count": 107,
- "id": "cf106c17",
+ "id": "f0d7d490",
"metadata": {},
"outputs": [],
"source": [
@@ -1767,7 +1767,7 @@
},
{
"cell_type": "markdown",
- "id": "f93fae81",
+ "id": "15309215",
"metadata": {},
"source": [
"The function returns the SHAP values (a detailed explanation of which is beyond the scope of this tutorial, although we will explain how to interpret them), the explainer object, and a formatted version of the same data frame with features we saw before.\n",
@@ -1776,29 +1776,15 @@
},
{
"cell_type": "code",
- "execution_count": 175,
- "id": "bb37236b",
- "metadata": {
- "tags": [
- "hide-input"
- ]
- },
+ "execution_count": 181,
+ "id": "8aa16988",
+ "metadata": {},
"outputs": [
{
"data": {
- "text/html": [
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- " .widget-radio-box {\n",
- " flex-direction: row !important; \n",
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- " .widget-radio-box label{\n",
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- " width: 120px !important;\n",
- " }\n",
- "</style>\n"
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+ "image/png": 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\n",
"text/plain": [
- "<IPython.core.display.HTML object>"
+ "<Figure size 800x550 with 1 Axes>"
]
},
"metadata": {},
@@ -1806,35 +1792,34 @@
}
],
"source": [
- "%%html\n",
- "<style>\n",
- " .widget-radio-box {\n",
- " flex-direction: row !important; \n",
- " }\n",
- " .widget-radio-box label{\n",
- " margin:1px !important;\n",
- " width: 120px !important;\n",
- " }\n",
- "</style>"
+ "deepof.visuals.plot_shap_swarm_per_cluster(\n",
+ " my_deepof_project, \n",
+ " data_to_explain, \n",
+ " shap_values, \n",
+ " \"all\", \n",
+ " show=True,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ec9722c6",
+ "metadata": {},
+ "source": [
+ "The _x_ axis in the figure above depicts the average absolute SHAP values, a measure of feature importance. The _y_ axis shows the names of the top 8 features the model uses to detect across all clusters. Here, we're looking at **global** feature importance for the model; even though this plot is not very informative regarding individual clusters, we can indeed interpret that the most important features overall are speed, spine stretch (distance between the center of the animal and spine 1 — see the scheme in the landing page of the documentation for details), and the huddle classifier."
]
},
{
"cell_type": "code",
- "execution_count": 173,
- "id": "e87279c0",
- "metadata": {
- "scrolled": false
- },
+ "execution_count": 184,
+ "id": "ed262976",
+ "metadata": {},
"outputs": [
{
"data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "6e838581dcbd46ddbb2ba2d3ed5da4c6",
- "version_major": 2,
- "version_minor": 0
- },
+ "image/png": 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\n",
"text/plain": [
- "interactive(children=(RadioButtons(description='cluster', options=('all', 0, 1, 2, 3, 4, 5, 6, 7, 8, 9), value…"
+ "<Figure size 800x550 with 2 Axes>"
]
},
"metadata": {},
@@ -1842,38 +1827,21 @@
}
],
"source": [
- "# Plot swarm plots per cluster\n",
- "@interact()\n",
- "def plot_shap_swarm_per_cluster(\n",
- " cluster=RadioButtons(options=[\"all\"] + list(range(10))),\n",
- "):\n",
- " \n",
- " deepof.visuals.plot_shap_swarm_per_cluster(\n",
- " my_deepof_project, \n",
- " data_to_explain, \n",
- " shap_values, \n",
- " cluster, \n",
- " show=False,\n",
- " )\n",
- " \n",
- " plt.tight_layout()\n",
- " plt.show()"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "d9222eb2",
- "metadata": {},
- "source": [
- "Running the cell above (or showing the default panel upon loading the documentation) will show a multicolor horizontal bar chart. The _x_ axis depicts the average absolute SHAP values, a measure of feature importance. The _y_ axis shows the names of the top 8 features the model uses to detect across all clusters. Here, we're looking at **global** feature importance for the model; even though this plot is not very informative regarding individual clusters, we can indeed interpret that the most important features overall are speed, spine stretch (distance between the center of the animal and spine 1 — see the scheme in the landing page of the documentation for details), and the huddle classifier."
+ "deepof.visuals.plot_shap_swarm_per_cluster(\n",
+ " my_deepof_project, \n",
+ " data_to_explain, \n",
+ " shap_values, \n",
+ " cluster=1, \n",
+ " show=True,\n",
+ ")"
]
},
{
"cell_type": "markdown",
- "id": "b1f461bf",
+ "id": "cc1d181e",
"metadata": {},
"source": [
- "Selecting any cluster from the drop-down menu will change the figure completely. What we're looking now is called a bee swarm plot, and it's one of the main visualizations the SHAP package has to offer. The y-axis is the same as before (but it's now based on feature importance to detect the cluster that was specifically selected). The _x_ axis, however, shows the raw SHAP values instead of the global absolute value. Positive values indicate an association with the model selecting this specific cluster, whereas negative values indicate an association with the model selecting against this specific cluster. Finally, the color indicates the underlying feature value.\n",
+ "Selecting any cluster in the 'cluster' parameter will render a different kind of plot. What we're looking now is called a bee swarm plot, and it's one of the main visualizations the SHAP package has to offer. The y-axis is the same as before (but it's now based on feature importance to detect the cluster that was specifically selected). The _x_ axis, however, shows the raw SHAP values instead of the global absolute value. Positive values indicate an association with the model selecting this specific cluster, whereas negative values indicate an association with the model selecting against this specific cluster. Finally, the color indicates the underlying feature value.\n",
"\n",
"Thus, interpretation lies in detecting associations between the sign of the SHAP values and their color. Red values to the right mean positive associations between a feature and a cluster, whereas blue values on the right mean a negative association. Let's see an example to make it more explicit. \n",
"\n",
@@ -1892,7 +1860,7 @@
},
{
"cell_type": "markdown",
- "id": "d67ce569",
+ "id": "630e91c2",
"metadata": {},
"source": [
"First, we can use the same animation function we saw in the first tutorial, with a few additions. If we pass the embeddings and soft_counts objects into the embedding and cluster_assignments parameters respectively, as well as a specific video to experiment_id, and the index of a selected_cluster, an animated figure with two panels will be created.\n",
@@ -1903,7 +1871,7 @@
{
"cell_type": "code",
"execution_count": 178,
- "id": "c38debac",
+ "id": "adf8fdd1",
"metadata": {},
"outputs": [
{
@@ -3557,7 +3525,7 @@
},
{
"cell_type": "markdown",
- "id": "0b12f6e7",
+ "id": "ff1eb07a",
"metadata": {},
"source": [
"Second, deepof.visuals.export_annotated_video() can output direct samples taken from the dataset into a real video. The function will concatenate samples from all available videos, and generate a video with samples from each cluster. The parameter frame_limit_per_video controls how many frames per video should be included (to prevent the video from ending up being too large, especially in big datasets). Let's indeed run it, and load some samples from cluster 1 and into the notebook!"
@@ -3580,15 +3548,15 @@
},
{
"cell_type": "markdown",
- "id": "4a64a816",
+ "id": "43e3d627",
"metadata": {},
"source": [
- "<img src=\"./tutorial_files/tutorial_project/Out_videos/deepof_unsupervised_annotation_cluster_1_sample_AdobeExpress.gif\" width=\"450\" align=\"center\">"
+ "<img src=\"./tutorial_files/tutorial_project/Out_videos/deepof_unsupervised_annotation_cluster_1_sample_AdobeExpress.gif\" width=\"350\">"
]
},
{
"cell_type": "markdown",
- "id": "8b8bff8f",
+ "id": "886cf927",
"metadata": {},
"source": [
"### Wrapping up"
@@ -3596,7 +3564,7 @@
},
{
"cell_type": "markdown",
- "id": "f1be68b3",
+ "id": "3b78d536",
"metadata": {},
"source": [
"Thank you for making it until the end. In this three-part tutorial series, we covered how to load data into DeepOF, and how to run and interpret results in both supervised and unsupervised pipelines. Stay tuned for more content, and make sure to raise an issue in our GitHub repository if you have any questions!"