Created
January 14, 2019 13:43
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KNIME from Jupyter_demo.ipynb
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| { | |
| "cells": [ | |
| { | |
| "metadata": {}, | |
| "cell_type": "markdown", | |
| "source": "# Using KNIME Analytics Platform from Python\n\nThe new [knime module for Python](https://pypi.org/project/knime/), released at the same time as [v3.7 of KNIME Analytics Platform](https://www.knime.com/knime-analytics-platform-37-highlights), allows KNIME workflows to be called from Python and integrates nicely with the Jupyter notebook.\n\nHere we'll demonstrate using a KNIME workflow that performs a complicated database query (the query is against a PostgreSQL instance of [ChEMBL24](https://www.ebi.ac.uk/chembl/) that we have running in AWS) that is parameterized on user-supplied information. \n\nThe results of the query are summarized in a couple of different ways and made available in two separate output tables." | |
| }, | |
| { | |
| "metadata": {}, | |
| "cell_type": "markdown", | |
| "source": "Start by importing the knime(py) module, which can be installed from pip: `pip install knime` and then tell it where to find the KNIME executable. If you have KNIME installed in a standard location this second step isn't necessary, but I tend to have KNIME installed on my desktop." | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "import pandas as pd\nimport knime\nknime.executable_path = \"c:/Users/glandrum/Desktop/KNIME_3.7/knime.exe\"", | |
| "execution_count": 1, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": {}, | |
| "cell_type": "markdown", | |
| "source": "Define the location of the workspace:" | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "workspace = \"c:/Users/glandrum/KNIME workspaces/KNIME nightly\"", | |
| "execution_count": 2, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": {}, | |
| "cell_type": "markdown", | |
| "source": "Now pick a workflow and make sure it's the right one by displaying an SVG of the workflow:" | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "workflow = \"KNIME Stories/Jupyter KNIME Jupyter/Fetch ChEMBL Target Data\"\nknime.Workflow(workflow_path=workflow,workspace_path=workspace)", | |
| "execution_count": 3, | |
| "outputs": [ | |
| { | |
| "data": { | |
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| "text/plain": "<knime.LocalWorkflow at 0x1ecc011b6c8>" | |
| }, | |
| "execution_count": 3, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": {}, | |
| "cell_type": "markdown", | |
| "source": "We need to provide the workflow with a table containing the terms we want to query for. For this workflow we are providing ChEMBL target IDs.\n\nWe'll do this by providing a Pandas DataFrame:" | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "interesting_targets = {'target_chembl_id':[\"CHEMBL214\",\"CHEMBL219\",\"CHEMBL1983\"]}\ndf = pd.DataFrame(interesting_targets)", | |
| "execution_count": 4, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": {}, | |
| "cell_type": "markdown", | |
| "source": "Now execute the workflow with that DataFrame as one of the inputs:" | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "with knime.Workflow(workflow_path=workflow,workspace_path=workspace) as wf:\n wf.data_table_inputs[0] = df\n wf.execute()\n", | |
| "execution_count": 5, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": {}, | |
| "cell_type": "markdown", | |
| "source": "The two output tables are also present as Pandas DataFrames:" | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "len(wf.data_table_outputs)", | |
| "execution_count": 6, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": "2" | |
| }, | |
| "execution_count": 6, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": {}, | |
| "cell_type": "markdown", | |
| "source": "The first data table has one summary level of the data - the number of compounds for which there is data for each readout of each assay that was run against each target:" | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "len(wf.data_table_outputs[0])", | |
| "execution_count": 7, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": "2067" | |
| }, | |
| "execution_count": 7, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": {}, | |
| "cell_type": "markdown", | |
| "source": "That's too many rows to look at all at once, so let's just dispay the first few:" | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "wf.data_table_outputs[0].head()", | |
| "execution_count": 8, | |
| "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>standard_type</th>\n <th>description</th>\n <th>assay_type</th>\n <th>assay_chembl_id</th>\n <th>pref_name</th>\n <th>organism</th>\n <th>target_chembl_id</th>\n <th>COUNT(molregno)</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>Ki</td>\n <td>Antagonist activity at 5HT1A</td>\n <td>F</td>\n <td>CHEMBL1106423</td>\n <td>Serotonin 1a (5-HT1a) receptor</td>\n <td>Homo sapiens</td>\n <td>CHEMBL214</td>\n <td>88</td>\n </tr>\n <tr>\n <th>1</th>\n <td>Ki</td>\n <td>Binding affinity to human 5-hydroxytryptamine ...</td>\n <td>B</td>\n <td>CHEMBL615772</td>\n <td>Serotonin 1a (5-HT1a) receptor</td>\n <td>Homo sapiens</td>\n <td>CHEMBL214</td>\n <td>81</td>\n </tr>\n <tr>\n <th>2</th>\n <td>Emax</td>\n <td>Intrinsic activity at human 5-HT1A receptor ex...</td>\n <td>F</td>\n <td>CHEMBL1259257</td>\n <td>Serotonin 1a (5-HT1a) receptor</td>\n <td>Homo sapiens</td>\n <td>CHEMBL214</td>\n <td>75</td>\n </tr>\n <tr>\n <th>3</th>\n <td>EC50</td>\n <td>Intrinsic activity at human 5-HT1A receptor ex...</td>\n <td>F</td>\n <td>CHEMBL1259255</td>\n <td>Serotonin 1a (5-HT1a) receptor</td>\n <td>Homo sapiens</td>\n <td>CHEMBL214</td>\n <td>74</td>\n </tr>\n <tr>\n <th>4</th>\n <td>Ki</td>\n <td>Binding affinity to 5HT1A receptor</td>\n <td>B</td>\n <td>CHEMBL1785228</td>\n <td>Serotonin 1a (5-HT1a) receptor</td>\n <td>Homo sapiens</td>\n <td>CHEMBL214</td>\n <td>66</td>\n </tr>\n </tbody>\n</table>\n</div>", | |
| "text/plain": " standard_type description assay_type \\\n0 Ki Antagonist activity at 5HT1A F \n1 Ki Binding affinity to human 5-hydroxytryptamine ... B \n2 Emax Intrinsic activity at human 5-HT1A receptor ex... F \n3 EC50 Intrinsic activity at human 5-HT1A receptor ex... F \n4 Ki Binding affinity to 5HT1A receptor B \n\n assay_chembl_id pref_name organism \\\n0 CHEMBL1106423 Serotonin 1a (5-HT1a) receptor Homo sapiens \n1 CHEMBL615772 Serotonin 1a (5-HT1a) receptor Homo sapiens \n2 CHEMBL1259257 Serotonin 1a (5-HT1a) receptor Homo sapiens \n3 CHEMBL1259255 Serotonin 1a (5-HT1a) receptor Homo sapiens \n4 CHEMBL1785228 Serotonin 1a (5-HT1a) receptor Homo sapiens \n\n target_chembl_id COUNT(molregno) \n0 CHEMBL214 88 \n1 CHEMBL214 81 \n2 CHEMBL214 75 \n3 CHEMBL214 74 \n4 CHEMBL214 66 " | |
| }, | |
| "execution_count": 8, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": {}, | |
| "cell_type": "markdown", | |
| "source": "The second data table rolls the data up in even further, it shows the number of compounds that were tested in all assays that were run against each target:" | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "wf.data_table_outputs[1]", | |
| "execution_count": 9, | |
| "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>pref_name</th>\n <th>organism</th>\n <th>target_chembl_id</th>\n <th>COUNT(molregno)</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>Serotonin 1a (5-HT1a) receptor</td>\n <td>Homo sapiens</td>\n <td>CHEMBL214</td>\n <td>8089</td>\n </tr>\n <tr>\n <th>1</th>\n <td>Dopamine D4 receptor</td>\n <td>Homo sapiens</td>\n <td>CHEMBL219</td>\n <td>6090</td>\n </tr>\n <tr>\n <th>2</th>\n <td>Serotonin 1d (5-HT1d) receptor</td>\n <td>Homo sapiens</td>\n <td>CHEMBL1983</td>\n <td>2497</td>\n </tr>\n </tbody>\n</table>\n</div>", | |
| "text/plain": " pref_name organism target_chembl_id \\\n0 Serotonin 1a (5-HT1a) receptor Homo sapiens CHEMBL214 \n1 Dopamine D4 receptor Homo sapiens CHEMBL219 \n2 Serotonin 1d (5-HT1d) receptor Homo sapiens CHEMBL1983 \n\n COUNT(molregno) \n0 8089 \n1 6090 \n2 2497 " | |
| }, | |
| "execution_count": 9, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": {}, | |
| "cell_type": "markdown", | |
| "source": "That's it!" | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "", | |
| "execution_count": null, | |
| "outputs": [] | |
| } | |
| ], | |
| "metadata": { | |
| "_draft": { | |
| "nbviewer_url": "https://gist.github.com/9c563ac4eaafff99929c5a4c2e39ed9e" | |
| }, | |
| "gist": { | |
| "id": "9c563ac4eaafff99929c5a4c2e39ed9e", | |
| "data": { | |
| "description": "KNIME from Jupyter_demo.ipynb", | |
| "public": true | |
| } | |
| }, | |
| "kernelspec": { | |
| "name": "python3", | |
| "display_name": "Python 3", | |
| "language": "python" | |
| }, | |
| "language_info": { | |
| "name": "python", | |
| "version": "3.6.6", | |
| "mimetype": "text/x-python", | |
| "codemirror_mode": { | |
| "name": "ipython", | |
| "version": 3 | |
| }, | |
| "pygments_lexer": "ipython3", | |
| "nbconvert_exporter": "python", | |
| "file_extension": ".py" | |
| } | |
| }, | |
| "nbformat": 4, | |
| "nbformat_minor": 2 | |
| } |
This is very useful thank you!
In terms of execution time, would there be any optimization possible? It's quite long to execute even the smallest workflow (8 seconds to create and sort 4 cells). I would assume most of this time is used to load the workflow?
@maxime-beck : because this needs to launch a KNIME Analytics Platform instance each time you run a workflow, it's always going to take a while. I find it most useful when combined with workflows that already take a long time to execute.
If you're a KNIME Server customer: there will be an updated version of the package soon that can take advantage of a KNIME Server to execute the workflows. This will allow a much lower latency than we get when using things locally.
@greglandrum I see that the library is invoking the KNIME Batch Application but that CLI doesn't provide a way to just start an instance and keep it running. Is there any plan to add that feature to the Batch Application anytime soon? That would greatly improve the performances of the Jupyter integration.
@mbezy: I have the same question. Have you found the answer yet?
Hi, thanks for sharing the code!
This is very very useful.
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Hi, thanks for sharing the code!
One question: if there are several container input in the Knime workflow, how can one know in which order to provide the inputs in the Python code (using the "data_table_inputs" function)?
Thank you!