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Anpassung des Pfades

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klara 2024-06-05 16:47:14 +02:00
parent c815233ed5
commit 6a98b0a955
1 changed files with 104 additions and 41 deletions
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@ -11,7 +11,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 4, "execution_count": 29,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -19,67 +19,130 @@
"import seaborn as sns\n", "import seaborn as sns\n",
"import matplotlib.pyplot as plt\n", "import matplotlib.pyplot as plt\n",
"import pickle\n", "import pickle\n",
"import sys\n",
"\n",
"\n",
"from scipy.stats import chi2_contingency\n", "from scipy.stats import chi2_contingency\n",
"from data_helper import *\n" "sys.path.append('../scripts')\n",
"import data_helper\n"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 6, "execution_count": 28,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
"name": "stdout", "name": "stdout",
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"Reading SB\n",
"Length of SB: 50\n",
"Reading AFIB\n",
"Length of AFIB: 27\n",
"Reading GSVT\n", "Reading GSVT\n",
"Length of GSVT: 0\n", "Reading AFIB\n",
"Reading SR\n", "Reading SR\n",
"Length of SR: 13\n", "Reading SB\n",
"Chi-Square Statistic: 38.266574797751275\n", "Number of patients per category:\n",
"P-value: 0.0004730210823940083\n", "GSVT: 0\n",
"Chi-Square Statistic for SB in 60-70 vs others: 1.4858035714285718\n", "AFIB: 27\n",
"P-value for SB in 60-70 vs others: 0.22286870264719977\n" "SR: 13\n",
"SB: 50\n"
] ]
} }
], ],
"source": [ "source": [
"#path = \"C:/Studium/dsa/data\"\n", "data = data_helper.load_data(only_demographic=False)\n",
"#path = \"C:/Users/Nils/Documents/HS-Mannheim/0000_MASTER/DSA/EKG_Prog/data\"\n",
"path = \"C:/Users/klara/projects/DSA/data\"\n",
"\n", "\n",
"categories_dict = {\n", "print(\"Number of patients per category:\")\n",
"'SB': [426177001],\n", "for cat_name in data.keys():\n",
"'AFIB': [164889003, 164890007],\n", " print(f\"{cat_name}: {len(data[cat_name])}\")"
"'GSVT': [426761007, 713422000, 233896004, 233897008, 713422000],\n", ]
"'SR': [426783006, 427393009]\n", },
"}\n", {
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Reading GSVT\n",
"Reading AFIB\n",
"Reading SR\n",
"Reading SB\n"
]
},
{
"ename": "ValueError",
"evalue": "All arrays must be of the same length",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[1;32mIn[27], line 3\u001b[0m\n\u001b[0;32m 1\u001b[0m data_org \u001b[38;5;241m=\u001b[39m data_helper\u001b[38;5;241m.\u001b[39mload_data(only_demographic\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[1;32m----> 3\u001b[0m df_dgc \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mDataFrame\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata_org\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python311\\site-packages\\pandas\\core\\frame.py:767\u001b[0m, in \u001b[0;36mDataFrame.__init__\u001b[1;34m(self, data, index, columns, dtype, copy)\u001b[0m\n\u001b[0;32m 761\u001b[0m mgr \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_init_mgr(\n\u001b[0;32m 762\u001b[0m data, axes\u001b[38;5;241m=\u001b[39m{\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mindex\u001b[39m\u001b[38;5;124m\"\u001b[39m: index, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcolumns\u001b[39m\u001b[38;5;124m\"\u001b[39m: columns}, dtype\u001b[38;5;241m=\u001b[39mdtype, copy\u001b[38;5;241m=\u001b[39mcopy\n\u001b[0;32m 763\u001b[0m )\n\u001b[0;32m 765\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(data, \u001b[38;5;28mdict\u001b[39m):\n\u001b[0;32m 766\u001b[0m \u001b[38;5;66;03m# GH#38939 de facto copy defaults to False only in non-dict cases\u001b[39;00m\n\u001b[1;32m--> 767\u001b[0m mgr \u001b[38;5;241m=\u001b[39m \u001b[43mdict_to_mgr\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mindex\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcolumns\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtyp\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmanager\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 768\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(data, ma\u001b[38;5;241m.\u001b[39mMaskedArray):\n\u001b[0;32m 769\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mma\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m mrecords\n",
"File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python311\\site-packages\\pandas\\core\\internals\\construction.py:503\u001b[0m, in \u001b[0;36mdict_to_mgr\u001b[1;34m(data, index, columns, dtype, typ, copy)\u001b[0m\n\u001b[0;32m 499\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 500\u001b[0m \u001b[38;5;66;03m# dtype check to exclude e.g. range objects, scalars\u001b[39;00m\n\u001b[0;32m 501\u001b[0m arrays \u001b[38;5;241m=\u001b[39m [x\u001b[38;5;241m.\u001b[39mcopy() \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(x, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdtype\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;28;01melse\u001b[39;00m x \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m arrays]\n\u001b[1;32m--> 503\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43marrays_to_mgr\u001b[49m\u001b[43m(\u001b[49m\u001b[43marrays\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcolumns\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mindex\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtyp\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtyp\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconsolidate\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python311\\site-packages\\pandas\\core\\internals\\construction.py:114\u001b[0m, in \u001b[0;36marrays_to_mgr\u001b[1;34m(arrays, columns, index, dtype, verify_integrity, typ, consolidate)\u001b[0m\n\u001b[0;32m 111\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m verify_integrity:\n\u001b[0;32m 112\u001b[0m \u001b[38;5;66;03m# figure out the index, if necessary\u001b[39;00m\n\u001b[0;32m 113\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m index \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m--> 114\u001b[0m index \u001b[38;5;241m=\u001b[39m \u001b[43m_extract_index\u001b[49m\u001b[43m(\u001b[49m\u001b[43marrays\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 115\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 116\u001b[0m index \u001b[38;5;241m=\u001b[39m ensure_index(index)\n",
"File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python311\\site-packages\\pandas\\core\\internals\\construction.py:677\u001b[0m, in \u001b[0;36m_extract_index\u001b[1;34m(data)\u001b[0m\n\u001b[0;32m 675\u001b[0m lengths \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(\u001b[38;5;28mset\u001b[39m(raw_lengths))\n\u001b[0;32m 676\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(lengths) \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m--> 677\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAll arrays must be of the same length\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 679\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m have_dicts:\n\u001b[0;32m 680\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m 681\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMixing dicts with non-Series may lead to ambiguous ordering.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 682\u001b[0m )\n",
"\u001b[1;31mValueError\u001b[0m: All arrays must be of the same length"
]
}
],
"source": [
"data_org = data_helper.load_data(only_demographic=False)\n",
"\n", "\n",
"data = {}\n", "df_dgc = pd.DataFrame(data_org)"
"for cat_name in categories_dict.keys():\n", ]
" print(f\"Reading {cat_name}\")\n", },
" with open(f'{path}/{cat_name}.pkl', 'rb') as f:\n", {
" records = pickle.load(f)\n", "cell_type": "code",
" data[cat_name] = records\n", "execution_count": 21,
" print(f\"Length of {cat_name}: {len(records)}\")\n", "metadata": {},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'df_dgc' is not defined",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[1;32mIn[21], line 36\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[38;5;66;03m# #path = \"C:/Studium/dsa/data\"\u001b[39;00m\n\u001b[0;32m 2\u001b[0m \u001b[38;5;66;03m# #path = \"C:/Users/Nils/Documents/HS-Mannheim/0000_MASTER/DSA/EKG_Prog/data\"\u001b[39;00m\n\u001b[0;32m 3\u001b[0m \u001b[38;5;66;03m# path = \"C:/Users/klara/projects/DSA/data\"\u001b[39;00m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 33\u001b[0m \n\u001b[0;32m 34\u001b[0m \u001b[38;5;66;03m# Change from group to category\u001b[39;00m\n\u001b[0;32m 35\u001b[0m age_categories \u001b[38;5;241m=\u001b[39m [\u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m10\u001b[39m, \u001b[38;5;241m20\u001b[39m, \u001b[38;5;241m30\u001b[39m, \u001b[38;5;241m40\u001b[39m, \u001b[38;5;241m50\u001b[39m, \u001b[38;5;241m60\u001b[39m, \u001b[38;5;241m70\u001b[39m, \u001b[38;5;241m80\u001b[39m, \u001b[38;5;241m90\u001b[39m]\n\u001b[1;32m---> 36\u001b[0m df_dgc[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mage_group\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mcut(\u001b[43mdf_dgc\u001b[49m[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mage\u001b[39m\u001b[38;5;124m'\u001b[39m], bins\u001b[38;5;241m=\u001b[39mage_categories)\n\u001b[0;32m 37\u001b[0m corr_matrix_age_diag\u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mcrosstab(df_dgc[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mage_group\u001b[39m\u001b[38;5;124m'\u001b[39m], df_dgc[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdiag\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[0;32m 39\u001b[0m \u001b[38;5;66;03m# Chi-square test\u001b[39;00m\n",
"\u001b[1;31mNameError\u001b[0m: name 'df_dgc' is not defined"
]
}
],
"source": [
"# #path = \"C:/Studium/dsa/data\"\n",
"# #path = \"C:/Users/Nils/Documents/HS-Mannheim/0000_MASTER/DSA/EKG_Prog/data\"\n",
"# path = \"C:/Users/klara/projects/DSA/data\"\n",
"\n", "\n",
"data_demographic = {'age':[], 'diag':[], 'gender':[]}\n", "# categories_dict = {\n",
"for cat_name, records in data.items():\n", "# 'SB': [426177001],\n",
" for record in records:\n", "# 'AFIB': [164889003, 164890007],\n",
" age = record.comments[0].split(' ')[1]\n", "# 'GSVT': [426761007, 713422000, 233896004, 233897008, 713422000],\n",
" sex = record.comments[1].split(' ')[1]\n", "# 'SR': [426783006, 427393009]\n",
" if age == 'NaN' or sex == 'NaN':\n", "# }\n",
" continue\n",
" # cut Age: from alter string \n",
" data_demographic['age'].append(int(age))\n",
" data_demographic['diag'].append(cat_name)\n",
" data_demographic['gender'].append(sex)\n",
"\n", "\n",
"df_dgc = pd.DataFrame(data_demographic)\n", "# data = {}\n",
"# for cat_name in categories_dict.keys():\n",
"# print(f\"Reading {cat_name}\")\n",
"# with open(f'{path}/{cat_name}.pkl', 'rb') as f:\n",
"# records = pickle.load(f)\n",
"# data[cat_name] = records\n",
"# print(f\"Length of {cat_name}: {len(records)}\")\n",
"\n",
"# data_demographic = {'age':[], 'diag':[], 'gender':[]}\n",
"# for cat_name, records in data.items():\n",
"# for record in records:\n",
"# age = record.comments[0].split(' ')[1]\n",
"# sex = record.comments[1].split(' ')[1]\n",
"# if age == 'NaN' or sex == 'NaN':\n",
"# continue\n",
"# # cut Age: from alter string \n",
"# data_demographic['age'].append(int(age))\n",
"# data_demographic['diag'].append(cat_name)\n",
"# data_demographic['gender'].append(sex)\n",
"\n",
"# df_dgc = pd.DataFrame(data_demographic)\n",
"\n", "\n",
"# Change from group to category\n", "# Change from group to category\n",
"age_categories = [0, 10, 20, 30, 40, 50, 60, 70, 80, 90]\n", "age_categories = [0, 10, 20, 30, 40, 50, 60, 70, 80, 90]\n",