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Remove torchaudio, add test data and bump version to 0.2.2 (#15)
* Add synthetic data for integration test * Remove torchaudio * Bump version to 0.2.2 Co-authored-by: Sheng, Caibin <[email protected]>
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| Original file line number | Diff line number | Diff line change |
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| @@ -1,2 +1,2 @@ | ||
| # -*- coding: utf-8 -*- | ||
| __version__ = '0.2.1' | ||
| __version__ = '0.2.2' |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,208 @@ | ||
| { | ||
| "cells": [ | ||
| { | ||
| "cell_type": "markdown", | ||
| "id": "ac5906d1-7f3a-4661-8235-8bca20ec9c45", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## integration test" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 1, | ||
| "id": "aa6514e7-19b3-4b91-9f6f-e2ad125bf9d7", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "import warnings\n", | ||
| "warnings.filterwarnings(\"ignore\", category=DeprecationWarning)\n", | ||
| "\n", | ||
| "from scAR import model\n", | ||
| "import pandas as pd" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 2, | ||
| "id": "9f1b9ec0-8452-4f17-983a-324e2d2e1920", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "raw_count = pd.read_pickle('raw_counts.pickle')\n", | ||
| "empty_profile = pd.read_pickle('ambient_profile.pickle')\n", | ||
| "expected_output = pd.read_pickle('output_assignment.pickle')" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 3, | ||
| "id": "476f7dba-8bbb-4f7b-9344-b2f0abf8eab1", | ||
| "metadata": {}, | ||
| "outputs": [ | ||
| { | ||
| "name": "stdout", | ||
| "output_type": "stream", | ||
| "text": [ | ||
| "..Running VAE using the following param set:\n", | ||
| "......scAR mode: CROPseq\n", | ||
| "......count model: binomial\n", | ||
| "......num_input_feature: 5\n", | ||
| "......NN_layer1: 150\n", | ||
| "......NN_layer2: 100\n", | ||
| "......latent_space: 15\n", | ||
| "......dropout_prob: 0\n", | ||
| "......kld_weight: 1e-05\n", | ||
| "......lr: 0.001\n", | ||
| "......lr_step_size: 5\n", | ||
| "......lr_gamma: 0.97\n", | ||
| "===========================================\n", | ||
| " Training.....\n", | ||
| "100%|██████████| 40/40 [00:09<00:00, 4.21it/s]\n", | ||
| "===========================================\n", | ||
| " Inferring .....\n" | ||
| ] | ||
| } | ||
| ], | ||
| "source": [ | ||
| "scarObj = model(raw_count=raw_count.values,\n", | ||
| " empty_profile=empty_profile,\n", | ||
| " scRNAseq_tech='CROPseq')\n", | ||
| "\n", | ||
| "scarObj.train(epochs=40,\n", | ||
| " batch_size=64,)\n", | ||
| "\n", | ||
| "scarObj.inference()" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "id": "c1bc7f76-b22a-4fe4-a297-d4db3dfd80c3", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "**test whether the output is expected**" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 4, | ||
| "id": "604f97ab-62ae-4eed-8ea4-82bb4f2ea002", | ||
| "metadata": {}, | ||
| "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>sgRNAs</th>\n", | ||
| " <th>n_sgRNAs</th>\n", | ||
| " </tr>\n", | ||
| " </thead>\n", | ||
| " <tbody>\n", | ||
| " <tr>\n", | ||
| " <th>0</th>\n", | ||
| " <td>1</td>\n", | ||
| " <td>1</td>\n", | ||
| " </tr>\n", | ||
| " <tr>\n", | ||
| " <th>1</th>\n", | ||
| " <td>3</td>\n", | ||
| " <td>1</td>\n", | ||
| " </tr>\n", | ||
| " <tr>\n", | ||
| " <th>2</th>\n", | ||
| " <td>4</td>\n", | ||
| " <td>1</td>\n", | ||
| " </tr>\n", | ||
| " <tr>\n", | ||
| " <th>3</th>\n", | ||
| " <td>0</td>\n", | ||
| " <td>1</td>\n", | ||
| " </tr>\n", | ||
| " <tr>\n", | ||
| " <th>4</th>\n", | ||
| " <td>2</td>\n", | ||
| " <td>1</td>\n", | ||
| " </tr>\n", | ||
| " </tbody>\n", | ||
| "</table>\n", | ||
| "</div>" | ||
| ], | ||
| "text/plain": [ | ||
| " sgRNAs n_sgRNAs\n", | ||
| "0 1 1\n", | ||
| "1 3 1\n", | ||
| "2 4 1\n", | ||
| "3 0 1\n", | ||
| "4 2 1" | ||
| ] | ||
| }, | ||
| "execution_count": 4, | ||
| "metadata": {}, | ||
| "output_type": "execute_result" | ||
| } | ||
| ], | ||
| "source": [ | ||
| "scarObj.feature_assignment.head()" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 5, | ||
| "id": "9710ad5d-744e-448a-be7e-b49fb8ed22cc", | ||
| "metadata": {}, | ||
| "outputs": [ | ||
| { | ||
| "data": { | ||
| "text/plain": [ | ||
| "True" | ||
| ] | ||
| }, | ||
| "execution_count": 5, | ||
| "metadata": {}, | ||
| "output_type": "execute_result" | ||
| } | ||
| ], | ||
| "source": [ | ||
| "scarObj.feature_assignment.equals(expected_output)" | ||
| ] | ||
| } | ||
| ], | ||
| "metadata": { | ||
| "kernelspec": { | ||
| "display_name": "scAR-0.2", | ||
| "language": "python", | ||
| "name": "scar-0.2" | ||
| }, | ||
| "language_info": { | ||
| "codemirror_mode": { | ||
| "name": "ipython", | ||
| "version": 3 | ||
| }, | ||
| "file_extension": ".py", | ||
| "mimetype": "text/x-python", | ||
| "name": "python", | ||
| "nbconvert_exporter": "python", | ||
| "pygments_lexer": "ipython3", | ||
| "version": "3.10.4" | ||
| } | ||
| }, | ||
| "nbformat": 4, | ||
| "nbformat_minor": 5 | ||
| } |
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