diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index be7b956..c3218ed 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -25,4 +25,4 @@ jobs: run: uv sync --extra dev - name: Run ruff - run: uv run ruff check src/ + run: uvx ruff check src/ diff --git a/.gitignore b/.gitignore index efa407c..8d251f2 100644 --- a/.gitignore +++ b/.gitignore @@ -1,3 +1,5 @@ +docs/generated/ + # Byte-compiled / optimized / DLL files __pycache__/ *.py[cod] diff --git a/CHANGELOG.md b/CHANGELOG.md index 3cb0e23..732b9f3 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -8,6 +8,18 @@ and this project adheres to [Semantic Versioning][]. [keep a changelog]: https://keepachangelog.com/en/1.0.0/ [semantic versioning]: https://semver.org/spec/v2.0.0.html +## Version 0.2.0 + +> [!WARNING] +> **Breaking changes** +> New changes to the API, please see the documentation + +### Changed + +- new api documentation +- new api changes +- use uvx for running tools + ## Version 0.1.0 **NOTE**: Use this version with initial release of the AI models `v1.0`. diff --git a/docs/api.md b/docs/api.md index e223148..cde1922 100644 --- a/docs/api.md +++ b/docs/api.md @@ -1,5 +1,35 @@ # API +## scanvi_explainer + +### Utilities: `utils` + +```{eval-rst} +.. currentmodule:: scanvi_explainer +``` + +```{eval-rst} +.. autosummary:: + :toctree: generated + :nosignatures: + + utils.train_test_group_split +``` + +### Plots: `plots` + +```{eval-rst} +.. currentmodule:: scanvi_explainer +``` + +```{eval-rst} +.. autosummary:: + :toctree: generated + :nosignatures: + + plots.feature_plot +``` + ## SCANVIDeep ```{eval-rst} @@ -12,4 +42,4 @@ :nosignatures: scanvi_deep -``` \ No newline at end of file +``` diff --git a/docs/conf.py b/docs/conf.py index 156920a..209e1d5 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -84,10 +84,14 @@ } intersphinx_mapping = { - "python": ("https://docs.python.org/3", None), "anndata": ("https://anndata.readthedocs.io/en/stable/", None), - "scanpy": ("https://scanpy.readthedocs.io/en/stable/", None), + "huggingface_hub": ("https://huggingface.co/docs/huggingface_hub/main/en", None), + "matplotlib": ("https://matplotlib.org/", None), "numpy": ("https://numpy.org/doc/stable/", None), + "pandas": ("https://pandas.pydata.org/docs/", None), + "python": ("https://docs.python.org/3", None), + "sklearn": ("https://scikit-learn.org/stable/", None), + "torch": ("https://pytorch.org/docs/master/", None), } # List of patterns, relative to source directory, that match files and @@ -119,5 +123,5 @@ nitpick_ignore = [ # If building the documentation fails because of a missing link that is outside your control, # you can add an exception to this list. - # ("py:class", "igraph.Graph"), -] \ No newline at end of file + ("optional"), +] diff --git a/docs/generated/scanvi_explainer.scanvi_deep.rst b/docs/generated/scanvi_explainer.scanvi_deep.rst deleted file mode 100644 index c2408d0..0000000 --- a/docs/generated/scanvi_explainer.scanvi_deep.rst +++ /dev/null @@ -1,25 +0,0 @@ -scanvi\_explainer.scanvi\_deep -============================== - -.. automodule:: scanvi_explainer.scanvi_deep - - - .. rubric:: Functions - - .. autosummary:: - - add_interim_values - deeplift_grad - deeplift_tensor_grad - get_target_input - linear_1d - maxpool - nonlinear_1d - passthrough - - .. rubric:: Classes - - .. autosummary:: - - SCANVIDeep - \ No newline at end of file diff --git a/docs/notebooks/Example.ipynb b/docs/notebooks/Example.ipynb index 1da1fe4..879ced6 100644 --- a/docs/notebooks/Example.ipynb +++ b/docs/notebooks/Example.ipynb @@ -6,131 +6,62 @@ "source": [ "# scANVI Explainer demo\n", "\n", - "date: 12-09-2024\n", + "date: 13-09-2024\n", "\n", "author: Martin Proks" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "~/projects/data/Brickman/conda/envs/scvi-1.1.5/bin/pip\n" + ] + } + ], "source": [ "!which pip" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import shap\n", - "import torch\n", - "import numpy as np\n", - "import pandas as pd\n", - "import seaborn as sns\n", - "import matplotlib.pyplot as plt\n", - "from anndata import AnnData\n", "from scvi.hub import HubModel\n", - "from scanvi_explainer import SCANVIDeep\n", - "from sklearn.model_selection import train_test_split\n", - "\n", - "import warnings\n", - "\n", "from numba.core.errors import NumbaDeprecationWarning\n", - "warnings.simplefilter('ignore', category=NumbaDeprecationWarning)\n", - "\n", - "\n", - "def train_test_group_split(adata: AnnData, groupby: str):\n", - " \"\"\"\n", - " Function to split anndata object 80/20 per group in format\n", - " required for SCANVIDeep explainer.\n", - " \"\"\"\n", - " groups = adata.obs.groupby(groupby)\n", - " train, test = [], []\n", - " for _, cells in groups.groups.items():\n", - " train_test = train_test_split(cells.values, test_size=0.2)\n", - " \n", - " train.append(train_test[0])\n", - " test.append(train_test[1])\n", - "\n", - " train, test = np.concatenate(train), np.concatenate(test)\n", - "\n", - " X_train = {\n", - " 'X': torch.from_numpy(adata[train].layers['counts'].A).type(torch.DoubleTensor),\n", - " 'batch': torch.from_numpy(adata[train].obs.batch.cat.codes.values[:, np.newaxis]),\n", - " 'labels': torch.from_numpy(adata[train].obs.ct.cat.codes.values[:, np.newaxis])\n", - " }\n", - "\n", - " X_test = {\n", - " 'X': torch.from_numpy(adata[test].layers['counts'].A).type(torch.DoubleTensor),\n", - " 'batch': torch.from_numpy(adata[test].obs.batch.cat.codes.values[:, np.newaxis]),\n", - " 'labels': torch.from_numpy(adata[test].obs.ct.cat.codes.values[:, np.newaxis])\n", - " }\n", - " \n", - " return X_train, X_test\n", - "\n", - "\n", - "def feature_plot(X_test, shap_values: np.ndarray, classes: pd.Index, features: np.ndarray, subset: bool = False):\n", - " \"\"\"\n", - " Prints feature contribution (absolute mean SHAP value) for each cell type (top 10).\n", - "\n", - " X_test: \n", - " test dataset\n", - " shap_values: \n", - " SHAP values\n", - " classes: \n", - " list of classifiers (cell types in this case)\n", - " features: \n", - " list of genes (HVGs)\n", - " subset: \n", - " If True calculate contribution by subsetting for test cells which belong to that particual classifier\n", - " Else Be generic and return contributing features even when testing set has different cell types\n", - " \"\"\"\n", - " fig, ax = plt.subplots(8, 2, sharex=False, figsize=[20, 40])\n", - " \n", - " for idx, ct in enumerate(classes):\n", - " \n", - " shaps = pd.DataFrame(shap_values[idx], columns=features)\n", - "\n", - " if subset:\n", - " shaps['ct'] = X_test['labels']\n", - " shaps = shaps.query('ct == @idx').iloc[:, :-1]\n", "\n", - " tmp_avg = shaps\\\n", - " .mean(axis=0)\\\n", - " .sort_values(ascending=False)\\\n", - " .reset_index()\\\n", - " .rename(columns={'index':'feature',0:'weight'})\\\n", - " \n", - " positive = tmp_avg.query('weight > 0').head(5)\n", - " negative = tmp_avg.query('weight < 0').tail(5)\n", - "\n", - " avg = pd.concat([positive, negative])\n", - " title = f'Mean(SHAP value average importance for: {ct}'\n", - " \n", - " else:\n", - " avg = shaps\\\n", - " .abs()\\\n", - " .mean(axis=0)\\\n", - " .sort_values(ascending=False)\\\n", - " .reset_index()\\\n", - " .rename(columns={'index':'feature',0:'weight'})\\\n", - " .query('weight > 0')\\\n", - " .head(10)\n", - " title = f'Mean(|SHAP value|) average importance for: {ct}'\n", - " \n", - " sns.barplot(x='weight', y='feature', data=avg, ax=ax[idx // 2, idx % 2])\n", - " ax[idx // 2, idx % 2].set_title(title)" + "import warnings\n", + "warnings.simplefilter('ignore', category=NumbaDeprecationWarning)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "ec1c8cb52c134c028313cf16bd80d68b", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Fetching 3 files: 0%| | 0/3 [00:00ScanVI Model with the following params: \n", + "unlabeled_category: Unknown, n_hidden: 128, n_latent: 10, n_layers: 2, dropout_rate: 0.1, dispersion: gene, \n", + "gene_likelihood: nb\n", + "Training status: Trained\n", + "Model's adata is minified?: False\n", + "\n" + ], + "text/plain": [ + "ScanVI Model with the following params: \n", + "unlabeled_category: Unknown, n_hidden: \u001b[1;36m128\u001b[0m, n_latent: \u001b[1;36m10\u001b[0m, n_layers: \u001b[1;36m2\u001b[0m, dropout_rate: \u001b[1;36m0.1\u001b[0m, dispersion: gene, \n", + "gene_likelihood: nb\n", + "Training status: Trained\n", + "Model's adata is minified?: \u001b[3;91mFalse\u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "lvae = hmo.model\n", "lvae" @@ -151,40 +144,111 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ - "background, test = train_test_group_split(lvae.adata, groupby='ct')" + "from scanvi_explainer import SCANVIDeep\n", + "from scanvi_explainer.plots import feature_plot" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/maps/projects/dan1/data/Brickman/github/scanvi-explainer/src/scanvi_explainer/utils.py:104: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at ../torch/csrc/utils/tensor_numpy.cpp:206.)\n", + " REGISTRY_KEYS.BATCH_KEY: torch.from_numpy(\n" + ] + }, + { + "data": { + "text/html": [ + "
SCANVIDeep with the following parameters:\n",
+       "train_size=0.8, test_size=0.2, batch_size=128, labels_key=ct, layers_key=counts\n",
+       "training_on=cuda:0\n",
+       "
\n" + ], + "text/plain": [ + "SCANVIDeep with the following parameters:\n", + "\u001b[33mtrain_size\u001b[0m=\u001b[1;36m0\u001b[0m\u001b[1;36m.8\u001b[0m, \u001b[33mtest_size\u001b[0m=\u001b[1;36m0\u001b[0m\u001b[1;36m.2\u001b[0m, \u001b[33mbatch_size\u001b[0m=\u001b[1;36m128\u001b[0m, \u001b[33mlabels_key\u001b[0m=\u001b[35mct\u001b[0m, \u001b[33mlayers_key\u001b[0m=\u001b[35mcounts\u001b[0m\n", + "\u001b[33mtraining_on\u001b[0m=\u001b[35mcu\u001b[0m\u001b[1;92mda\u001b[0m\u001b[1;92m:0\u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "e = SCANVIDeep(lvae.module, background)" + "e = SCANVIDeep(lvae, train_size=0.8, batch_size=128)\n", + "e" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Allocated: 0.02 GB\n", + "Cached : 0.04 GB\n", + "\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "7eafe5d2d84a4412b5680e5971d2ee73", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/15 [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "shap.summary_plot(\n", " shap_values, \n", - " test['X'], \n", + " e.test['X'], \n", " feature_names=lvae.adata.var_names, \n", " class_names=lvae.adata.obs.ct.cat.categories\n", ")" @@ -192,11 +256,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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iYmK2l5/V+WIY977dAAAAuRF5Nnn27cizybMNgzw7v+XZ4eHhxvTp042VK1cas2bNMh5//HFDkvHBBx9ka/6IiAijfPny5u+DBg3K9PdiVu737xCAnMfw6QBgJe3bt9c///yj1atXKyUlRatXr87yDsavvvpK3t7eaty4sf7++2/zp1q1avLw8LC4y9fV1dX87/Q7xuvWrasrV67owIEDFsv18PCweN+Ok5OTatasqaNHj5rbzp49K0ny9fXNENf8+fP1/vvvKyQkRMuXL9eQIUNUtmxZNWzYUCdOnMjQf/To0Vq7dm2Gz1NPPZWh782bN7V48WJ16NDBfPdvgwYNVLhwYUVHR2e6nxo1aiQ/Pz8FBgaqY8eO8vDw0PLly1WsWLFM+6cvs1ChQlq8eLG57fz581q7dq06dOhgbrO3tzff4Z6WlqZz587p5s2bql69unbv3p3l8u/FsmXLlJaWpvbt21scZ39/f4WGht51CLnbj/2NGzd09uxZlS5dWj4+PhYxdujQQTdu3NCyZcvMbT/88IMuXLhg3mbDMLR06VK1atVKhmFYxNOkSRMlJydn2O4uXbpYxCDdGho6/X1nqampOnv2rDw8PFSmTBmL+b/77jsVK1ZMTz/9tLnNxcVFPXr0sFheXFycDh06pOeff15nz541x3T58mU1bNhQmzZtUlpa2h33U1Zeeukl8799fHxUpkwZubu7q3379ub2MmXKyMfHx+JnJN3LL79scQd679695eDgoG+//VaStG7dOl2/fl0DBw60eAdcjx495OXlpW+++cZiec7OzpkOOZiV7O7rdN26dbN4aqNu3bqSZN62PXv2KD4+XgMHDszw5ET6z+S5c+f0448/qn379ubfN3///bfOnj2rJk2a6NChQ5n+Lribb7/9Vv7+/hbvKHR0dFT//v116dIlbdy40aL/s88+Kz8/vwzLMQwj23evS9LMmTMz/H5as2ZNhn7lypXT2rVrtWLFCr322mtyd3fXpUuXsr+B/9+VK1fUrl07ubq6atKkSRbT2rdvr/nz56tz585q06aN3njjDX3//fc6e/asJkyYcM/rupPsbjcAAEBuR55Nnv1v5Nnk2bcjz7bNPHvr1q0aMGCAnn76afXq1Uu7du3S448/rtdff13//PNPtmNNd/HiRUmSp6fnPc97L3+HAOQ8hk8HACvx8/NTo0aNtHDhQl25ckWpqal67rnnMu176NAhJScnZ/n+sNOnT5v//fvvv2vkyJH68ccfzf+JS5ecnGzxvXjx4hneteXr66tff/01wzoMw8jQZmdnpz59+qhPnz46e/astm7dqg8//FBr1qxRx44dtXnzZov+FSpUUKNGjTIs5/PPP8/Q9sMPP+jMmTOqWbOmxfuX6tevry+++EJvv/22RcIj3fqPdlhYmBwcHFSkSBGVKVMmQ59/c3Bw0LPPPquFCxfq2rVrcnZ21rJly3Tjxg2LZF2SPvnkE02dOlUHDhzQjRs3zO3/Ht7rfh06dEiGYWQ5NPLtiWBm/vnnH02cOFHz58/XiRMnLI7Z7ce+UqVKeuyxx7R48WJ1795d0q0h3QoVKmR+P9yZM2d04cIFzZkzR3PmzMl0fbefd1Lm+yEtLU0zZszQBx98oPj4eIt37t0+VNaxY8dUqlSpDOdj6dKlLb4fOnRIkjIMSXe75OTkTC8u3YmLi0uGZM/b2zvTnxFvb+9M32H27+Pm4eGhgIAA87u7jh07JulWwn87JycnlSxZ0jw9XbFixSyS6bvJ7r5OV6JECYvv6fssfdvSh0V8/PHHs1zn4cOHZRiGRo0apVGjRmXa5/Tp03e8YJaZY8eOKTQ0NMPPb9myZc3Tb/ewfgZr1qyp6tWr37Wfl5eX+XdZ69attXDhQrVu3Vq7d+9WpUqVsrWu1NRUdezYUfv27dOaNWsyDLuXmTp16uiJJ57QunXrzG1nzpyxONYeHh7y8PDIVgzpsrvdAAAAuR159v8hz76FPJs8+3bk2badZ6dzcnJS3759zQXyOnXq6NKlSxZFdnt7+0yL/umxSLdugrpX9/J3CEDOoygOAFb0/PPPq0ePHjp58qSaNWuW5Xus0tLS7njndvp/4i5cuKCIiAh5eXlp/PjxKlWqlFxcXLR7924NHTo0w5299vb2mS7v9iQv/T/5mSUntytYsKCefvppPf3004qMjNTGjRt17Ngx8zvR7lX6tt5+9/DtNm7cqPr161u03W9hp2PHjpo9e7bWrFmjNm3a6Msvv9Rjjz1m8Z/uzz//XF27dlWbNm306quvqnDhwrK3t9fEiRPNSU1W/p3spbs9mZJuHWeTyaQ1a9ZkemzuVujq16+f5s+fr4EDB6pWrVry9vaWyWRSx44dMxz7Dh06aMKECfr777/l6empVatWqVOnTnJwcDDHIkkvvPBClolxxYoVLb7/++51SXrrrbc0atQo/ec//9Ebb7yhAgUKyM7OTgMHDryvO83T55k8eXKW75e714KglPXPQnZ+Rh6VzPbnndzrvn4Y25a+3CFDhpjfkfdv/77g8ijc67562J555hm9+OKLWrRoUbaT9R49emj16tWKjo42XyTLjsDAQB08eND8vUaNGhYXL8aMGaOxY8dme3kAAAC2hjw7a+TZ5NmZIc/OGnm29dxPnn27wMBASbeevJekKVOmaNy4cebpQUFB5psr/u2xxx6TJO3duzfLn4k7ye7fIQA5j6I4AFhR27Zt1bNnT/38888Ww4r9W6lSpbRu3TrVrl37jv8pjYmJ0dmzZ7Vs2TLVq1fP3B4fH3/fMab/R/BellG9enVt3LhRSUlJ95WsX758WStXrlSHDh0yvZuyf//+io6OzpCs36969eopICBAixcvVp06dfTjjz9qxIgRFn2WLFmikiVLatmyZRbJ95gxY+66fF9fX124cCFD+7/vwi1VqpQMw1BISIjCwsLueTuWLFmiLl26aOrUqea2q1evZrruDh06aNy4cVq6dKmKFCmiixcvqmPHjubpfn5+8vT0VGpqaqZPHdxLTPXr19fHH39s0X7hwgUVKlTI/D0oKEj79u2TYRgW+/f2pxekW/tIsryDOLc4dOiQxTl56dIlJSUlqXnz5pJk/lk4ePCgSpYsae53/fp1xcfHZ3t7srr4k919nV3p+/q3337LMrb07XB0dHyoxyMoKEi//vqr0tLSLO5iTx+a8n4vAj4q165dU1paWoanhLLy6quvav78+Zo+fbrF0HXZcfToUYu72aOjoy2Gg7v93AIAAMiPyLMzR55Nnp2OPDsj8uy8n2f/W/qQ9en5c+fOnVWnTh3z9Dv93m/WrJns7e31+eef68UXX7zndWf37xCAnMc7xQHAijw8PDRr1iyNHTtWrVq1yrJf+/btlZqaqjfeeCPDtJs3b5qTsfQ7Um+/A/X69ev64IMP7jvGYsWKKTAwUDt37rRoP3nypPbt25eh//Xr17V+/XrZ2dnd952ry5cv1+XLl9WnTx8999xzGT4tW7bU0qVLde3atfta/r/Z2dnpueee09dff63PPvtMN2/ezDCkW2b7dvv27frpp5/uuvxSpUopOTnZYri8pKQkLV++3KLfM888I3t7e40bNy7DXcSGYZjfO5cVe3v7DPO99957Ge6Ul24Nj1WhQgUtXrxYixcvVkBAgMUFHnt7ez377LNaunSpfvvttwzznzlz5o6x3Cmmr776KsP7r5o0aaITJ05o1apV5rarV69q7ty5Fv2qVaumUqVKacqUKZm+Wyq7cT0Kc+bMsRjub9asWbp586aaNWsm6da7+JycnPTuu+9a7JOPP/5YycnJatGiRbbW4+7unukFmOzu6+yqWrWqQkJCNH369AzrS19P4cKFFRkZqdmzZyspKSnDMu73eDRv3lwnT560SB5v3ryp9957Tx4eHoqIiMjWcg4cOKDExMT7iiEzFy5csDjG6T766CNJsniCJv39kn///bdF38mTJ2vKlCl6/fXXNWDAgCzXldm++/bbb7Vr1y41bdrU3Fa7dm01atTI/KEoDgAA8jvy7MyRZ5NnS+TZWSHPzrt5dmb7IyUlRdOnT1ehQoVUrVo1SbduNrg9d65du3aWMQUGBqpHjx764Ycf9N5772WYnpaWpqlTp+rPP//MdP7s/h0CkPN4UhwArOxO72xKFxERoZ49e2rixImKi4vTU089JUdHRx06dEhfffWVZsyYoeeee07h4eHy9fVVly5d1L9/f5lMJn322WcPPARV69attXz5cou7i//880/VrFlTDRo0UMOGDeXv76/Tp0/riy++0C+//KKBAwfe112z0q0nHwsWLKjw8PBMpz/99NOaO3euvvnmGz3zzDP3vV2369Chg9577z2NGTNGFSpUML9TKV3Lli21bNkytW3bVi1atFB8fLw+/PBDlStXLtOk8XYdO3bU0KFD1bZtW/Xv319XrlzRrFmzFBYWpt27d5v7lSpVSm+++aaGDx+uhIQEtWnTRp6enoqPj9fy5cv18ssva8iQIVmup2XLlvrss8/k7e2tcuXK6aefftK6desyfc9V+jaPHj1aLi4u6t69e4b3Sk2aNEkbNmzQE088oR49eqhcuXI6d+6cdu/erXXr1pmHoLqTli1bavz48erWrZvCw8O1d+9eRUdHZyjc9ezZU++//746deqkAQMGKCAgQNHR0XJxcZH0f3dt29nZ6aOPPlKzZs1Uvnx5devWTcWKFdOJEye0YcMGeXl56euvv75rXI/C9evX1bBhQ7Vv314HDx7UBx98oDp16ujpp5+WdOvu5OHDh2vcuHFq2rSpnn76aXO/GjVq6IUXXsjWeqpVq6ZZs2bpzTffVOnSpVW4cGE1aNAg2/s6u+zs7DRr1iy1atVKlStXVrdu3RQQEKADBw7o999/1/fffy/p1jsG69SpowoVKqhHjx4qWbKkTp06pZ9++kl//vmnfvnll3te98svv6zZs2era9eu2rVrl4KDg7VkyRJt3bpV06dPl6enZ7a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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "feature_plot(test, shap_values, classes=lvae.adata.obs.ct.cat.categories, features=lvae.adata.var_names, subset=True)" + "feature_plot(e, shap_values, subset=True, top_n=10)" ] }, { @@ -209,9 +284,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python [conda env:conda-scvi-1.0.0]", + "display_name": "Python [conda env:conda-scvi-1.1.5]", "language": "python", - "name": "conda-env-conda-scvi-1.0.0-py" + "name": "conda-env-conda-scvi-1.1.5-py" }, "language_info": { "codemirror_mode": { @@ -223,7 +298,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.11" + "version": "3.10.13" } }, "nbformat": 4, diff --git a/pyproject.toml b/pyproject.toml index acd53d9..8476c33 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -4,7 +4,7 @@ build-backend = "hatchling.build" [project] name = "scanvi-explainer" -version = "0.1.0" +version = "0.2.0" description = "Exapliner of scANVI using SHAP" authors = [ {name = "Martin Proks", email = "martin.proks@sund.ku.dk"}, @@ -15,7 +15,8 @@ license = {file = "LICENSE"} requires-python = ">=3.10" keywords = ["shap", "scanvi", "explainer", "interpretability"] dependencies = [ - "ruff>=0.6.4", + "anndata", + "rich", "shap>=0.41.0", ] classifiers = [ @@ -35,7 +36,7 @@ Issues = "https://github.com/brickmanlab/scanvi-explainer/issues" Changelog = "https://github.com/brickmanlab/scanvi-explainer/blob/master/CHANGELOG.md" [project.optional-dependencies] -dev = ["ruff", "huggingface_hub"] +dev = ["ruff", "scvi-tools", "huggingface_hub"] doc = [ "setuptools", "sphinx", diff --git a/src/scanvi_explainer/__init__.py b/src/scanvi_explainer/__init__.py index aff9d80..3b099e0 100644 --- a/src/scanvi_explainer/__init__.py +++ b/src/scanvi_explainer/__init__.py @@ -1,15 +1,11 @@ +import importlib.util + from .scanvi_deep import SCANVIDeep -try: - import torch -except ImportError: +if not importlib.util.find_spec("torch"): raise ImportError("Missing torch package! Run pip install torch") -try: - from scvi.model import SCANVI -except ImportError: - raise ImportError("Missing scvi-tools package! Run pip install scvi-tools") +if not importlib.util.find_spec("scvi"): + raise ImportError("Missing torch package! Run pip install torch") -__all__ = [ - "SCANVIDeep" -] \ No newline at end of file +__all__ = ["SCANVIDeep", "utils", "plots"] diff --git a/src/scanvi_explainer/plots.py b/src/scanvi_explainer/plots.py new file mode 100644 index 0000000..9dc1deb --- /dev/null +++ b/src/scanvi_explainer/plots.py @@ -0,0 +1,88 @@ +import matplotlib.pyplot as plt +import numpy.typing as npt +import pandas as pd +import seaborn as sns +from scvi import REGISTRY_KEYS + +from .scanvi_deep import SCANVIDeep + + +def feature_plot( + explainer: SCANVIDeep, + shap_values: npt.NDArray, + subset: bool = False, + top_n: int = 10, +) -> None: + """Prints feature contribution (absolute mean SHAP value) for each cell type (top 10). + + Parameters + ---------- + explainer : SCANVIDeep + SCANVIDeep explainer + shap_values : npt.NDArray + Expected SHAP values + subset : bool, optional + When set to true, calculate contribution by subsetting for test cells which belong to that + particual classifier. + When set to false, be generic and return contributing features even when testing set has + different cell types. + """ + + groupby = explainer.labels_key + classes = explainer.adata.obs[groupby].cat.categories + features = explainer.adata.var_names + + nrows = classes.size // 2 + classes.size % 2 + fig, ax = plt.subplots(nrows, 2, sharex=False, figsize=[20, 40]) + + for idx, ct in enumerate(classes): + + shaps = pd.DataFrame(shap_values[idx], columns=features) + + if subset: + shaps[groupby] = explainer.test[REGISTRY_KEYS.LABELS_KEY] + shaps = shaps[shaps[groupby] == idx].iloc[:, :-1] + + tmp_avg = ( + shaps.mean(axis=0) + .sort_values(ascending=False) + .reset_index() + .rename(columns={"index": "feature", 0: "weight"}) + ) + positive = ( + tmp_avg.query("weight > 0") + .head(top_n // 2) + .assign(contribution="positive") + ) + negative = ( + tmp_avg.query("weight < 0") + .tail(top_n // 2) + .assign(contribution="negative") + ) + + avg = pd.concat([positive, negative]) + title = f"Mean(SHAP value average importance for: {ct}" + + else: + avg = ( + shaps.abs() + .mean(axis=0) + .sort_values(ascending=False) + .reset_index() + .rename(columns={"index": "feature", 0: "weight"}) + .query("weight > 0") + .head(10) + ) + title = f"Mean(|SHAP value|) average importance for: {ct}" + + sns.barplot( + x="weight", + y="feature", + hue="contribution", + palette=["red", "blue"], + data=avg, + ax=ax[idx // 2, idx % 2], + ) + ax[idx // 2, idx % 2].set_title(title) + ax[idx // 2, idx % 2].legend(title="Contribution", loc="lower right") + fig.tight_layout() diff --git a/src/scanvi_explainer/scanvi_deep.py b/src/scanvi_explainer/scanvi_deep.py index 509d60a..d232db3 100644 --- a/src/scanvi_explainer/scanvi_deep.py +++ b/src/scanvi_explainer/scanvi_deep.py @@ -1,49 +1,119 @@ +import logging import warnings import numpy as np +import rich from packaging import version +from scvi import REGISTRY_KEYS +from scvi.model._scanvi import SCANVI from shap import Explainer -from tqdm import tqdm +from torch import Tensor +from tqdm.auto import tqdm + +from .utils import get_labels_key, get_layer_key, train_test_group_split torch = None class SCANVIDeep(Explainer): - def __init__(self, model, data): - # try and import pytorch - global torch - if torch is None: - import torch - - if version.parse(torch.__version__) < version.parse("0.4"): - warnings.warn( - "Your PyTorch version is older than 0.4 and not supported." - ) + """SCANVIDeep is extension of DeepExplainer + + Parameters + ---------- + Explainer : type[Explainer] + Main Explainer class from shap package + """ + + def __init__( + self, + model: SCANVI, + train_size: float = 0.8, + batch_size: int = 128, + ): + """Constructor setting up expected values. + + Currently categorical not continuous covariates are not supported. + + Parameters + ---------- + model : SCANVI + Trained scANVI model + train_size : float, optional + Training size (background), by default 0.8 + batch_size : int, optional + Number of cells used from each group, by default 128 + To ignore the batch_size subsetting, set batch_size=-1 + """ + import torch + + if version.parse(torch.__version__) < version.parse("0.4"): + warnings.warn("Your PyTorch version is older than 0.4 and not supported.") + + if not isinstance(model, SCANVI): + raise ValueError( + f"Provided model is not scANVI! Provided instead: {type(model)}" + ) + + self.labels_key = get_labels_key(model) + self.layer_key = get_layer_key(model) + self.data, self.test = train_test_group_split( + model.adata, + self.labels_key, + train_size, + batch_size, + self.layer_key, + ) - # check if we have multiple inputs - if type(data) != list: - data = [data] - self.data = data + self.adata = model.adata self.layer = None + self.train_size = train_size + self.batch_size = batch_size self.input_handle = None - self.interim = False self.expected_value = None # to keep the DeepExplainer base happy - self.model = model.eval() + self.model = model.module.eval() with torch.no_grad(): - outputs = model.classifier(model(*data)[0]["z"]) + outputs = self.model.classify( + self.data[REGISTRY_KEYS.X_KEY], + batch_index=self.data[REGISTRY_KEYS.BATCH_KEY], + cat_covs=None, + cont_covs=None, + use_posterior_mean=True, + ) self.device = model.device self.multi_output = True self.num_outputs = outputs.shape[1] self.expected_value = outputs.mean(0).cpu().numpy() + # cleanup + del outputs + torch.cuda.empty_cache() + + def __repr__(self): + text = f"{self.__class__.__name__} with the following parameters:\n" + text += f"train_size={self.train_size}, test_size={(1.0 - self.train_size):.1f}, batch_size={self.batch_size}, " + text += f"labels_key={self.labels_key}, layers_key={self.layer_key}\n" + text += f"training_on={self.device}" + rich.print(text) + return "" + + def get_train_test(self) -> tuple[dict[str, Tensor], dict[str, Tensor]]: + return self.data, self.test + + def memory_stats(self): + """Helper function to track CUDA memory usage.""" + import torch + + if self.device.type == "cuda": + print(f"Allocated: {(torch.cuda.memory_allocated(0) / 1024**3):.2f} GB") + print(f"Cached : {(torch.cuda.memory_reserved(0) / 1024**3):.2f} GB") + print() def add_target_handle(self, layer): input_handle = layer.register_forward_hook(get_target_input) self.target_handle = input_handle def add_handles(self, model, forward_handle, backward_handle): - """ - Add handles to all non-container layers in the model. + """Add handles to all non-container layers in the model. Recursively for non-container layers """ handles_list = [] @@ -55,12 +125,11 @@ def add_handles(self, model, forward_handle, backward_handle): ) else: # leaves handles_list.append(model.register_forward_hook(forward_handle)) - handles_list.append(model.register_backward_hook(backward_handle)) + handles_list.append(model.register_full_backward_hook(backward_handle)) return handles_list def remove_attributes(self, model): - """ - Removes the x and y attributes which were added by the forward handles + """Removes the x and y attributes which were added by the forward handles Recursively searches for non-container layers """ for child in model.children(): @@ -76,110 +145,85 @@ def remove_attributes(self, model): except AttributeError: pass - def gradient(self, idx, inputs): - self.model.zero_grad() - X = [inputs["X"].requires_grad_()] - outputs = self.model.classifier(self.model(inputs)[0]["z"]) + def gradient(self, idx, input_x, input_batch): + import torch + logging.debug("Starting gradient") + self.model.zero_grad() + X = input_x.requires_grad_() + + outputs = self.model.classify( + input_x, + batch_index=input_batch, + cat_covs=None, + cont_covs=None, + use_posterior_mean=True, + ) selected = [val for val in outputs[:, idx]] - grads = [] - for idx, x in enumerate(X): - grad = torch.autograd.grad( - selected, - x, - retain_graph=True if idx + 1 < len(X) else None, - allow_unused=True, - )[0] - if grad is not None: - grad = grad.cpu().numpy() - else: - grad = torch.zeros_like(X[idx]).cpu().numpy() - grads.append(grad) - return grads - def shap_values(self, X): + grad = torch.autograd.grad(selected, X, retain_graph=False, allow_unused=True)[ + 0 + ] + if grad is not None: + return grad.cpu().numpy() + + return torch.zeros_like(X).cpu().numpy() + + def shap_values(self): + import torch + # X ~ self.model_input # X_data ~ self.data - assert type(X) != list, "Expected a single tensor model input!" - X = [X] - - X_batch = [x["batch"].detach().to(self.device) for x in X] - X_labels = [x["labels"].detach().to(self.device) for x in X] - X = [x["X"].detach().to(self.device) for x in X] + X_batch = self.test[REGISTRY_KEYS.BATCH_KEY].detach() + X = self.test[REGISTRY_KEYS.X_KEY].detach() model_output_ranks = ( - torch.ones((X[0].shape[0], self.num_outputs)).int() + torch.ones((X.shape[0], self.num_outputs)).int() * torch.arange(0, self.num_outputs).int() ) # add the gradient handles handles = self.add_handles(self.model, add_interim_values, deeplift_grad) - if self.interim: - self.add_target_handle(self.layer) # compute the attributions output_phis = [] - for i in tqdm(range(model_output_ranks.shape[1])): - phis = [] + self.memory_stats() - for k in range(len(X)): - phis.append(np.zeros(X[k].shape)) + for i in tqdm(range(model_output_ranks.shape[1])): + phis = np.zeros(X.shape) - for j in range(X[0].shape[0]): + for j in range(X.shape[0]): # tile the inputs to line up with the background data samples - # correct, it will replicate each row to match the background, but I have no idea - # why, and at this point I'm to afraid to ask - # moving along - tiled_X = [ - X[l][j : j + 1].repeat( - (self.data[l]["X"].shape[0],) - + tuple([1 for k in range(len(X[l].shape) - 1)]) - ) - for l in range(len(X)) - ] - joint_x = [ - torch.cat((tiled_X[l], self.data[l]["X"].to(self.device)), dim=0) - for l in range(len(X)) - ] + # correct, it will replicate each row to match the background + tiled_X = X[j : j + 1].repeat( + (self.data[REGISTRY_KEYS.X_KEY].shape[0],) + + tuple([1 for _ in range(len(X.shape) - 1)]) + ) + + joint_x = torch.cat((tiled_X, self.data[REGISTRY_KEYS.X_KEY]), dim=0) + joint_batch = X_batch[j].repeat(joint_x.shape[0], 1) # run attribution computation graph feature_ind = model_output_ranks[j, i] - - # joint_x need to inject the batch and library size - joint_x_injected = { - "X": joint_x[0], - "batch": X_batch[0][j].repeat(joint_x[0].shape[0], 1), - "labels": X_labels[0][j].repeat(joint_x[0].shape[0], 1), - } - sample_phis = self.gradient(feature_ind, joint_x_injected) + sample_phis = self.gradient(feature_ind, joint_x, joint_batch) # assign the attributions to the right part of the output arrays - for l in range(len(X)): - phis[l][j] = ( - ( - torch.from_numpy( - sample_phis[l][self.data[l]["X"].shape[0] :] - ).to(self.device) - * ( - X[l][j : j + 1].to(self.device) - - self.data[l]["X"].to(self.device) - ) - ) - .cpu() - .detach() - .numpy() - .mean(0) + phis[j] = ( + torch.from_numpy( + sample_phis[self.data[REGISTRY_KEYS.X_KEY].shape[0] :] ) + * (X[j : j + 1] - self.data[REGISTRY_KEYS.X_KEY]) + ).mean(0) + # .cpu().detach().numpy().mean(0) - output_phis.append(phis[0]) + output_phis.append(phis) + torch.cuda.empty_cache() # cleanup; remove all gradient handles for handle in handles: handle.remove() self.remove_attributes(self.model) - if self.interim: - self.target_handle.remove() - + torch.cuda.empty_cache() return output_phis @@ -197,7 +241,7 @@ def deeplift_grad(module, grad_input, grad_output): if op_handler[module_type].__name__ not in ["passthrough", "linear_1d"]: return op_handler[module_type](module, grad_input, grad_output) else: - print("Warning: unrecognized nn.Module: {}".format(module_type)) + warnings.warn(f"unrecognized nn.Module: {module_type}") return grad_input @@ -205,6 +249,8 @@ def add_interim_values(module, input, output): """The forward hook used to save interim tensors, detached from the graph. Used to calculate the multipliers """ + import torch + try: del module.x except AttributeError: @@ -229,15 +275,13 @@ def add_interim_values(module, input, output): if func_name in ["maxpool", "nonlinear_1d"]: # only save tensors if necessary if type(input) is tuple: - setattr(module, "x", torch.nn.Parameter(input[0].detach())) + module.x = torch.nn.Parameter(input[0].detach()) else: - setattr(module, "x", torch.nn.Parameter(input.detach())) + module.x = torch.nn.Parameter(input.detach()) if type(output) is tuple: - setattr(module, "y", torch.nn.Parameter(output[0].detach())) + module.y = torch.nn.Parameter(output[0].detach()) else: - setattr(module, "y", torch.nn.Parameter(output.detach())) - if module_type in failure_case_modules: - input[0].register_hook(deeplift_tensor_grad) + module.y = torch.nn.Parameter(output.detach()) def get_target_input(module, input, output): @@ -248,27 +292,7 @@ def get_target_input(module, input, output): del module.target_input except AttributeError: pass - setattr(module, "target_input", input) - - -# From the documentation: "The current implementation will not have the presented behavior for -# complex Module that perform many operations. In some failure cases, grad_input and grad_output -# will only contain the gradients for a subset of the inputs and outputs. -# The tensor hook below handles such failure cases (currently, MaxPool1d). In such cases, the deeplift -# grad should still be computed, and then appended to the complex_model_gradients list. The tensor hook -# will then retrieve the proper gradient from this list. - - -failure_case_modules = ["MaxPool1d"] - - -def deeplift_tensor_grad(grad): - return_grad = complex_module_gradients[-1] - del complex_module_gradients[-1] - return return_grad - - -complex_module_gradients = [] + module.target_input = input def passthrough(module, grad_input, grad_output): @@ -277,6 +301,8 @@ def passthrough(module, grad_input, grad_output): def maxpool(module, grad_input, grad_output): + import torch + pool_to_unpool = { "MaxPool1d": torch.nn.functional.max_unpool1d, "MaxPool2d": torch.nn.functional.max_unpool2d, @@ -318,18 +344,14 @@ def maxpool(module, grad_input, grad_output): ), 2, ) - org_input_shape = grad_input[0].shape # for the maxpool 1d + grad_input = [None for _ in grad_input] grad_input[0] = torch.where( torch.abs(delta_in) < 1e-7, torch.zeros_like(delta_in), (xmax_pos + rmax_pos) / delta_in, ).repeat(dup0) - if module.__class__.__name__ == "MaxPool1d": - complex_module_gradients.append(grad_input[0]) - # the grad input that is returned doesn't matter, since it will immediately be - # be overridden by the grad in the complex_module_gradient - grad_input[0] = torch.ones(org_input_shape) + return tuple(grad_input) @@ -339,6 +361,8 @@ def linear_1d(module, grad_input, grad_output): def nonlinear_1d(module, grad_input, grad_output): + import torch + delta_out = ( module.y[: int(module.y.shape[0] / 2)] - module.y[int(module.y.shape[0] / 2) :] ) @@ -390,8 +414,8 @@ def nonlinear_1d(module, grad_input, grad_output): op_handler["Tanh"] = nonlinear_1d op_handler["Softplus"] = nonlinear_1d op_handler["Softmax"] = nonlinear_1d +op_handler["SELU"] = nonlinear_1d op_handler["MaxPool1d"] = maxpool op_handler["MaxPool2d"] = maxpool op_handler["MaxPool3d"] = maxpool - diff --git a/src/scanvi_explainer/utils.py b/src/scanvi_explainer/utils.py new file mode 100644 index 0000000..8553875 --- /dev/null +++ b/src/scanvi_explainer/utils.py @@ -0,0 +1,124 @@ +import logging + +import anndata +import numpy as np +import torch +from scvi import REGISTRY_KEYS +from scvi.model._scanvi import SCANVI +from sklearn.model_selection import train_test_split + + +def get_labels_key(model: SCANVI) -> str: + """Retrieve key from `.obs` with labels used to train the classifier + + Parameters + ---------- + model : SCANVI + SCANVI model + + Returns + ------- + str + labels key + """ + return model.registry_["setup_args"]["labels_key"] + + +def get_layer_key(model: SCANVI) -> str: + """Retrieve layers key of counts which the model was trained on + + Parameters + ---------- + model : SCANVI + SCANVI model + + Returns + ------- + str + layers key + """ + return model.registry_["setup_args"]["layer"] + + +def train_test_group_split( + adata: anndata.AnnData, + groupby: str, + train_size: float = 0.8, + batch_size: int = 128, + layer: str = "counts", +) -> tuple[dict[str, torch.Tensor]]: + """Function to split anndata object 80/20 per group in format required for SCANVIDeep explainer. + + Bigger datasets might not fit to the GPU memory. To overcome this issue we recommend setting + `batch_size` to 128, meaning each group will only use randomly sampled 128 cells. This speeds + up the explainer at the cost of correctness. We suggest bootstrapping this process multiple + times. + + Parameters + ---------- + adata : anndata.AnnData + Annotated dataset + groupby : str + Column in metadata by which the dataset should be split by + train_size : float, optional + Training size (background), by default 0.8 + batch_size : int, optional + Number of cells used from each group, by default 128 + + Returns + ------- + tuple[dict[str, torch.Tensor]] + Train and test splits + """ + + if groupby not in adata.obs.columns: + raise ValueError(f"Provided group {groupby} not found in `.obs` metadata!") + + if 0.0 < train_size > 1.0: + raise ValueError("Training size has to be between 0 to 1!") + + rng = np.random.default_rng() + test_size = 1.0 - train_size + groups = adata.obs.groupby(groupby) + + train, test = [], [] + for _, cells in groups.groups.items(): + + cells = ( + rng.choice(cells.values, batch_size, replace=False) + if len(cells.values) > batch_size and batch_size != -1 + else cells.values + ) + train_split, test_split = train_test_split(cells, test_size=test_size) + + train.append(train_split) + test.append(test_split) + logging.debug(f"{cells.shape} / {train_split.shape} / {test_split.shape}") + + train, test = np.concatenate(train), np.concatenate(test) + + X_train = { + REGISTRY_KEYS.X_KEY: torch.from_numpy(adata[train].layers[layer].A).type( + torch.float32 + ), + REGISTRY_KEYS.BATCH_KEY: torch.from_numpy( + adata[train].obs[REGISTRY_KEYS.BATCH_KEY].cat.codes.values[:, np.newaxis] + ), + REGISTRY_KEYS.LABELS_KEY: torch.from_numpy( + adata[train].obs[groupby].cat.codes.values[:, np.newaxis] + ), + } + + X_test = { + REGISTRY_KEYS.X_KEY: torch.from_numpy(adata[test].layers[layer].A).type( + torch.float32 + ), + REGISTRY_KEYS.BATCH_KEY: torch.from_numpy( + adata[test].obs[REGISTRY_KEYS.BATCH_KEY].cat.codes.values[:, np.newaxis] + ), + REGISTRY_KEYS.LABELS_KEY: torch.from_numpy( + adata[test].obs[groupby].cat.codes.values[:, np.newaxis] + ), + } + + return X_train, X_test diff --git 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