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Code/1. Feature Generation/Local Weighted Measures.ipynb

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+{"cells":[{"cell_type":"code","execution_count":null,"id":"b6465562-8ef4-4cce-b589-4bf396b72d88","metadata":{"ExecuteTime":{"end_time":"2022-09-01T14:16:20.681431Z","start_time":"2022-09-01T14:16:18.996297Z"},"id":"b6465562-8ef4-4cce-b589-4bf396b72d88","outputId":"f51bb94a-fd6a-4327-c450-cfb1dccd18ef"},"outputs":[{"name":"stdout","output_type":"stream","text":["Metal device set to: Apple M1 Pro\n"]},{"name":"stderr","output_type":"stream","text":["2022-09-29 16:40:43.892988: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:305] Could not identify NUMA node of platform GPU ID 0, defaulting to 0. Your kernel may not have been built with NUMA support.\n","2022-09-29 16:40:43.893315: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:271] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 0 MB memory) -> physical PluggableDevice (device: 0, name: METAL, pci bus id: <undefined>)\n"]}],"source":["#imports \n","\n","from stellargraph import StellarGraph\n","import stellargraph as sg\n","from stellargraph.mapper import PaddedGraphGenerator\n","from stellargraph.layer import DeepGraphCNN\n","import pandas as pd\n","import os\n","import numpy as np\n","import networkx as nx\n","import matplotlib.pyplot as plt\n","\n","from sklearn import model_selection\n","from IPython.display import display, HTML\n","\n","from tensorflow.keras import Model\n","from tensorflow.keras.optimizers import Adam\n","from tensorflow.keras.layers import Dense, Conv1D, MaxPool1D, Dropout, Flatten\n","from tensorflow.keras.losses import binary_crossentropy\n","import tensorflow as tf\n","from tensorflow import keras"]},{"cell_type":"code","execution_count":null,"id":"35043e60-8ee2-466a-b238-0155bd5ed901","metadata":{"ExecuteTime":{"end_time":"2022-09-01T14:16:20.691586Z","start_time":"2022-09-01T14:16:20.682684Z"},"id":"35043e60-8ee2-466a-b238-0155bd5ed901","outputId":"64523b08-d833-4810-f7a7-f41d4742d925"},"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>Subject</th>\n","      <th>Subject Type</th>\n","      <th>Label</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>40013</td>\n","      <td>Control</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>40014</td>\n","      <td>Control</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>40017</td>\n","      <td>Control</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>40018</td>\n","      <td>Control</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>40019</td>\n","      <td>Control</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>416</th>\n","      <td>2168</td>\n","      <td>Patient</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>417</th>\n","      <td>2169</td>\n","      <td>Patient</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>418</th>\n","      <td>2170</td>\n","      <td>Patient</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>419</th>\n","      <td>2171</td>\n","      <td>Patient</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>420</th>\n","      <td>2172</td>\n","      <td>Patient</td>\n","      <td>1</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>421 rows × 3 columns</p>\n","</div>"],"text/plain":["     Subject Subject Type  Label\n","0      40013      Control      0\n","1      40014      Control      0\n","2      40017      Control      0\n","3      40018      Control      0\n","4      40019      Control      0\n","..       ...          ...    ...\n","416     2168      Patient      1\n","417     2169      Patient      1\n","418     2170      Patient      1\n","419     2171      Patient      1\n","420     2172      Patient      1\n","\n","[421 rows x 3 columns]"]},"execution_count":2,"metadata":{},"output_type":"execute_result"}],"source":["combined_phenotype = '/content/drive/MyDrive/SUPPLEMENTARY CODE/Data/Metadata/phenotypic_data.csv'\n","labels_mappings= pd.read_csv(combined_phenotype)\n","labels_mappings"]},{"cell_type":"code","execution_count":null,"id":"41482575-e663-4a45-bb6a-89a1bb957e8c","metadata":{"ExecuteTime":{"end_time":"2022-09-01T14:16:20.694814Z","start_time":"2022-09-01T14:16:20.692476Z"},"id":"41482575-e663-4a45-bb6a-89a1bb957e8c"},"outputs":[],"source":["def row_transform(arr, threshold):\n","  for i in range(len(arr)):\n","      arr[i] = arr[i] if arr[i]> threshold else abs(arr[i])+0.0001\n","  return arr\n","\n","def binarize(df, threshold):\n","    df = df.transform(lambda x: row_transform(x, threshold))"]},{"cell_type":"code","execution_count":null,"id":"64ad9c6b-103c-4c08-a601-8c676fead8a9","metadata":{"ExecuteTime":{"end_time":"2022-09-01T14:16:20.698854Z","start_time":"2022-09-01T14:16:20.697197Z"},"id":"64ad9c6b-103c-4c08-a601-8c676fead8a9"},"outputs":[],"source":["uw = '/content/drive/MyDrive/SUPPLEMENTARY CODE/Data/Preprocessed Data/UCLA Weighted v2'"]},{"cell_type":"code","execution_count":null,"id":"05fff533","metadata":{"ExecuteTime":{"end_time":"2022-09-01T14:16:20.701227Z","start_time":"2022-09-01T14:16:20.699595Z"},"id":"05fff533"},"outputs":[],"source":["dirs=[uw]\n","count = 0 "]},{"cell_type":"code","execution_count":null,"id":"1a9bd4a7","metadata":{"ExecuteTime":{"end_time":"2022-09-01T14:16:20.703445Z","start_time":"2022-09-01T14:16:20.701965Z"},"id":"1a9bd4a7"},"outputs":[],"source":["graphs = list()\n","graph_labels = list()\n","sub_lst = list(range(0,164))"]},{"cell_type":"code","execution_count":null,"id":"8e19c8b2-515c-4199-b03c-78b846699ee1","metadata":{"ExecuteTime":{"end_time":"2022-09-04T12:28:03.198174Z","start_time":"2022-09-01T14:16:20.704592Z"},"id":"8e19c8b2-515c-4199-b03c-78b846699ee1","outputId":"6ada93f3-345b-4cfa-9e8f-6a66703244b1"},"outputs":[{"name":"stdout","output_type":"stream","text":["1\n","2\n","3\n","4\n","5\n","6\n","7\n","8\n","9\n","10\n","11\n","12\n","13\n","14\n","15\n","16\n","17\n","18\n","19\n","20\n","21\n"]}],"source":["for dir in dirs:\n","    for file in os.listdir(dir):\n","        if file.endswith(\".csv\"):\n","            subject = file[14:18]\n","            df = pd.read_csv(dir+'/'+file, header=None)\n","            binarize(df,0)\n","            df = df.replace(np.nan ,0)\n","            G = nx.from_pandas_adjacency(df)\n","\n","            #adding other attributes\n","            wd = dict(G.degree(weight='weight'))\n","            cv = nx.closeness_vitality(G, node=None, weight=\"weight\", wiener_index=None)\n","            andeg = nx.average_neighbor_degree(G, weight=\"weight\")\n","            cons = nx.constraint(G, nodes=None, weight=\"weight\")\n","            es = nx.effective_size(G, nodes=None, weight=\"weight\")\n","            \n","            node_features_data=list(zip(wd.values(),cv.values(),andeg.values(),cons.values(),es.values()))\n","            node_features_columns= ['weighted degree','closeness vitality','avg neighbor degree',\n","                                    'constraint','effective size']\n","            node_data= pd.DataFrame(node_features_data, index=sub_lst, columns=node_features_columns)\n","            \n","            #saving individual node features\n","            csv_path='weighted_node_features_'+str(subject)+'.csv'\n","            node_data.to_csv(csv_path)\n","            \n","            count+=1\n","            print(count)"]}],"metadata":{"kernelspec":{"display_name":"Python 3 (ipykernel)","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.10.6"},"toc":{"base_numbering":1,"nav_menu":{},"number_sections":true,"sideBar":true,"skip_h1_title":false,"title_cell":"Table of Contents","title_sidebar":"Contents","toc_cell":false,"toc_position":{},"toc_section_display":true,"toc_window_display":false},"colab":{"provenance":[]}},"nbformat":4,"nbformat_minor":5}