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input to influence models
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‎input_performance_influence_models.ipynb

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"cell_type": "code",
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"execution_count": 82,
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"metadata": {},
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"outputs": [],
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"source": [
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"import tuxml\n",
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"import pandas as pd \n",
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"\n",
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"df = tuxml.load_dataset()"
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]
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},
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" X86_LOCAL_APIC OPENVSWITCH TEXTSEARCH_FSM LOCKDEP_SUPPORT \\\n",
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},
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"execution_count": 83,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"df.sort_values(\"vmlinux\")[:5]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 84,
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"metadata": {},
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"outputs": [],
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"source": [
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"size_methods = [\"vmlinux\", \"GZIP-bzImage\", \"GZIP-vmlinux\", \"GZIP\", \"BZIP2-bzImage\", \n",
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" \"BZIP2-vmlinux\", \"BZIP2\", \"LZMA-bzImage\", \"LZMA-vmlinux\", \"LZMA\", \"XZ-bzImage\", \"XZ-vmlinux\", \"XZ\", \n",
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" \"LZO-bzImage\", \"LZO-vmlinux\", \"LZO\", \"LZ4-bzImage\", \"LZ4-vmlinux\", \"LZ4\"]\n",
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"additional_ft = [\"nbyes\", \"nbno\", \"nbmodule\", \"nbyesmodule\"]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 86,
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"metadata": {},
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"outputs": [],
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"source": [
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"import xml.etree.cElementTree as ET\n",
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"\n",
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"df = df.drop(columns=[\"cid\"]).drop(columns=size_methods)[:500]\n",
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"#columns = list(df.drop(columns=[\"cid\"]).drop(columns=size_methods).columns.values)\n",
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"columns = list(df.columns.values)\n",
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"root = ET.Element(\"results\")\n",
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"\n",
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"for row in df.iterrows():\n",
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" config=\"\"\n",
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" \n",
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" for i in columns:\n",
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" if (df[i][row[0]]==1):\n",
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" config=config+i+\",\"\n",
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" \n",
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" doc = ET.SubElement(root, \"row\")\n",
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"\n",
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" ET.SubElement(doc, \"data\", columname=\"Configuration\").text = config\n",
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" ET.SubElement(doc, \"data\", columname=\"Performance\").text = str(df[\"vmlinux\"][row[0]])\n",
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"\n",
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"tree = ET.ElementTree(root)\n",
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"tree.write(\"measurements.xml\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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}
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],
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.0"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}

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