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.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tmp_trainer/model_state.pdparams filter=lfs diff=lfs merge=lfs -text

.ipynb_checkpoints/P-tunning-checkpoint.ipynb

@@ -0,0 +1,143 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "pycharm": {
+     "is_executing": true
+    }
+   },
+   "outputs": [
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'utilities'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[5], line 14\u001b[0m\n\u001b[0;32m     11\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mtransformers\u001b[39;00m\n\u001b[0;32m     12\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mpd\u001b[39;00m\n\u001b[1;32m---> 14\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mutilities\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;241m*\u001b[39m\n\u001b[0;32m     15\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtransformers\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m AutoTokenizer\n\u001b[0;32m     16\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtransformers\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m AutoModelForCausalLM\n",
+      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'utilities'"
+     ]
+    }
+   ],
+   "source": [
+    "import datasets\n",
+    "import tempfile\n",
+    "import logging\n",
+    "import random\n",
+    "import config\n",
+    "import os\n",
+    "import yaml\n",
+    "import logging\n",
+    "import time\n",
+    "import torch\n",
+    "import transformers\n",
+    "import pandas as pd\n",
+    "\n",
+    "from utilities import *\n",
+    "from transformers import AutoTokenizer\n",
+    "from transformers import AutoModelForCausalLM\n",
+    "from transformers import TrainingArguments\n",
+    "from transformers import AutoModelForCausalLM\n",
+    "from llama import BasicModelRunner\n",
+    "from llama import BasicModelRunner\n",
+    "\n",
+    "logger = logging.getLogger(__name__)\n",
+    "global_config = None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dataset_path = \"lamini/lamini_docs\"\n",
+    "use_hf = True"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model_name = \"EleutherAI/pythia-70m\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "training_config = {\n",
+    "    \"model\": {\n",
+    "        \"pretrained_name\": model_name,\n",
+    "        \"max_length\" : 2048\n",
+    "    },\n",
+    "    \"datasets\": {\n",
+    "        \"use_hf\": use_hf,\n",
+    "        \"path\": dataset_path\n",
+    "    },\n",
+    "    \"verbose\": True\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'AutoTokenizer' 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[6], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m tokenizer \u001b[38;5;241m=\u001b[39m \u001b[43mAutoTokenizer\u001b[49m\u001b[38;5;241m.\u001b[39mfrom_pretrained(model_name)\n\u001b[0;32m      2\u001b[0m tokenizer\u001b[38;5;241m.\u001b[39mpad_token \u001b[38;5;241m=\u001b[39m tokenizer\u001b[38;5;241m.\u001b[39meos_token\n\u001b[0;32m      3\u001b[0m train_dataset, test_dataset \u001b[38;5;241m=\u001b[39m tokenize_and_split_data(training_config, tokenizer)\n",
+      "\u001b[1;31mNameError\u001b[0m: name 'AutoTokenizer' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
+    "tokenizer.pad_token = tokenizer.eos_token\n",
+    "train_dataset, test_dataset = tokenize_and_split_data(training_config, tokenizer)\n",
+    "\n",
+    "print(train_dataset)\n",
+    "print(test_dataset)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.9.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}

408-h2.csv

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+202.0,1.0,1.0,87.0,4.0,37.1,96.0,21.0,114.0,54.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,2.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,2.0,2.0,2.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0
+395.0,1.0,1.0,91.0,20.0,36.5,93.0,28.0,133.0,72.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,2.0,2.0,2.0,1.0,2.0,2.0,2.0,1.0,0.0,0.0,0.0,1.0,2.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0
+234.0,0.0,0.0,87.0,3.0,37.2,92.0,22.0,173.0,84.0,0.0,0.0,2.0,2.0,2.0,0.0,0.0,0.0,0.0,0.0,2.0,2.0,2.0,2.0,2.0,2.0,2.0,1.0,1.0,0.0,0.0,0.0,1.0,2.0,0.0,0.0,0.0,0.0,2.0,1.0,2.0,1.0
+116.0,1.0,1.0,77.0,2.0,36.8,90.0,22.0,156.0,83.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,0.0,2.0,2.0,2.0,2.0,1.0,1.0,1.0,0.0,0.0,0.0,2.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0
+27.0,0.0,1.0,56.0,3.0,36.5,93.0,18.0,133.0,87.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,1.0,1.0,1.0,2.0,1.0,1.0,2.0,1.0,2.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,2.0,1.0,0.0
+391.0,1.0,1.0,91.0,16.0,36.1,98.0,25.0,132.0,84.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,2.0,2.0,1.0,1.0,2.0,2.0,1.0,0.0,0.0,0.0,2.0,2.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0
+26.0,0.0,0.0,99.0,1.0,37.0,84.0,24.0,142.0,72.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,2.0,0.0,2.0,1.0,2.0,2.0,1.0,0.0,2.0,2.0,1.0,2.0,0.0,1.0,2.0,1.0,2.0,2.0,1.0,2.0,0.0
+343.0,1.0,1.0,81.0,16.0,36.7,92.0,26.0,111.0,54.0,3.0,0.0,2.0,2.0,0.0,0.0,1.0,0.0,0.0,0.0,2.0,2.0,0.0,2.0,1.0,2.0,1.0,2.0,0.0,0.0,0.0,0.0,2.0,1.0,1.0,2.0,1.0,2.0,2.0,2.0,2.0,1.0
+58.0,1.0,1.0,59.0,5.0,36.7,98.0,20.0,130.0,80.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,2.0,1.0,0.0,2.0,2.0,2.0,2.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,2.0,1.0,1.0,0.0
+349.0,1.0,1.0,83.0,9.0,36.6,75.0,25.0,120.0,65.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,2.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,2.0,1.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0
+109.0,1.0,1.0,76.0,2.0,36.4,52.0,20.0,108.0,63.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,2.0,2.0,2.0,1.0,2.0,2.0,1.0,2.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0
+3.0,0.0,0.0,69.0,2.0,38.2,106.0,23.0,131.0,80.0,0.0,0.0,2.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,2.0,1.0,1.0,2.0,0.0,0.0,0.0,0.0,2.0,1.0,0.0,0.0,0.0,0.0,2.0,1.0,2.0,0.0
+12.0,0.0,0.0,81.0,4.0,36.8,101.0,22.0,151.0,97.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,1.0,0.0,1.0,1.0,1.0,2.0,1.0,0.0,2.0,0.0,2.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0

app.py

@@ -0,0 +1,238 @@
+import torch
+
+from load import load_model
+import gradio as gr
+
+
+
+class Neuro_net(torch.nn.Module):
+    def __init__(self):
+        super(Neuro_net, self).__init__()
+        self.layer1 = torch.nn.Linear(40, 20)
+        self.layer2 = torch.nn.Linear(20, 10)
+        self.layer3 = torch.nn.Linear(10, 5)
+        self.layer4 = torch.nn.Linear(5, 2)
+        self.layer5 = torch.nn.Softmax(dim=0)
+
+    def forward(self, input):
+        tensor = torch.relu(self.layer1(input))
+        tensor = torch.relu(self.layer2(tensor))
+        tensor = torch.relu(self.layer3(tensor))
+        tensor = self.layer4(tensor)
+        tensor = self.layer5(tensor)
+        return tensor
+
+
+
+def test(a):
+    return "heigh"
+
+def load_beforeFL(a0,a1,a2,a3,a4,a5,a6,a7,a8,a9,
+                a10,a11,a12,a13,a14,a15,a16,a17,a18,a19,
+                a20,a21,a22,a23,a24,a25,a26,a27,a28,a29,
+                a30,a31,a32,a33,a34,a35,a36,a37,a38,a39):
+    input = []
+    input.append(a0)
+    input.append(a1)
+    input.append(a2)
+    input.append(a3)
+    input.append(a4)
+    input.append(a5)
+    input.append(a6)
+    input.append(a7)
+    input.append(a8)
+    input.append(a9)
+    input.append(a10)
+    input.append(a11)
+    input.append(a12)
+    input.append(a13)
+    input.append(a14)
+    input.append(a15)
+    input.append(a16)
+    input.append(a17)
+    input.append(a18)
+    input.append(a19)
+    input.append(a20)
+    input.append(a21)
+    input.append(a22)
+    input.append(a23)
+    input.append(a24)
+    input.append(a25)
+    input.append(a26)
+    input.append(a27)
+    input.append(a28)
+    input.append(a29)
+    input.append(a30)
+    input.append(a31)
+    input.append(a32)
+    input.append(a33)
+    input.append(a34)
+    input.append(a35)
+    input.append(a36)
+    input.append(a37)
+    input.append(a38)
+    input.append(a39)
+    print(input)
+    output = load_model("Yes",input)
+    return output
+
+
+def load_beforeTIDM(a0,a1,a2,a3,a4,a5,a6,a7,a8,a9,
+                a10,a11,a12,a13,a14,a15,a16,a17,a18,a19,
+                a20,a21,a22,a23,a24,a25,a26,a27,a28,a29,
+                a30,a31,a32,a33,a34,a35,a36,a37,a38,a39):
+    input = []
+    input.append(a0)
+    input.append(a1)
+    input.append(a2)
+    input.append(a3)
+    input.append(a4)
+    input.append(a5)
+    input.append(a6)
+    input.append(a7)
+    input.append(a8)
+    input.append(a9)
+    input.append(a10)
+    input.append(a11)
+    input.append(a12)
+    input.append(a13)
+    input.append(a14)
+    input.append(a15)
+    input.append(a16)
+    input.append(a17)
+    input.append(a18)
+    input.append(a19)
+    input.append(a20)
+    input.append(a21)
+    input.append(a22)
+    input.append(a23)
+    input.append(a24)
+    input.append(a25)
+    input.append(a26)
+    input.append(a27)
+    input.append(a28)
+    input.append(a29)
+    input.append(a30)
+    input.append(a31)
+    input.append(a32)
+    input.append(a33)
+    input.append(a34)
+    input.append(a35)
+    input.append(a36)
+    input.append(a37)
+    input.append(a38)
+    input.append(a39)
+    print(input)
+    output = load_model("No",input)
+    return output
+em = [
+                1.0,1.0,78.0,7.0,37.3,110.0,21.0,130.0,81.0,3.0,0.0,2.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,2.0,0.0,2.0,1.0,1.0,2.0,2.0,1.0,0.0,0.0,0.0,2.0,2.0,1.0,2.0,1.0,1.0,1.0,2.0,1.0
+    ]
+with gr.Blocks(css="style.css") as demo:
+
+    gr.Markdown("# Practice of Distributed Machine Learning in Clinical Modeling for Chronic Obstructive Pulmonary Disease Demo 🖍️")
+    title = "Practice of Distributed Machine Learning in Clinical Modeling for Chronic Obstructive Pulmonary Disease Demo",
+    description = "First fill in the clinical data such as the patient's laboratory examination, and then click the prediction button. Wait for a moment, and in the output box, you will obtain a prediction of the risk of worsening in AECOPD patients🖍"
+    # gr.TextArea(description)
+    gr.Textbox(value=description,label="How to use this website🖊️🤗🤗🤗🖊️")
+    with gr.Column():
+        with gr.Row():
+            input_0 = gr.Number(value=em[0],label="smoking（Yes=1,No=0）")
+            input_1 = gr.Number(value=em[1],label="Sex(Female=1， Male=0)")
+            input_2 = gr.Number(value=em[2],label="Age")
+            input_3 = gr.Number(value=em[3],label="Number of hospitalizations")
+            input_4 = gr.Number(value=em[4],label="Temperature")
+            input_5 = gr.Number(value=em[5],label="Pulse")
+            input_6 = gr.Number(value=em[6],label="Respiratory rate")
+            input_7 = gr.Number(value=em[7],label=" Systolic pressure")
+    with gr.Column():
+        with gr.Row():
+            input_8 = gr.Number(value=em[8],label="Diastolic pressure")
+            input_9 = gr.Number(value=em[9],label="pulmonary heart disease(yes=3,No=0)")
+            input_10 = gr.Number(value=em[10],label="Bronchiectasia(yes=3,No=0)")
+            input_11 = gr.Number(value=em[11],label="hypertension(yes=2,No=0)")
+            input_12 = gr.Number(value=em[12],label="diabetes(yes=2,No=0)")
+            input_13 = gr.Number(value=em[13],label="coronary heart disease(yes=2,No=0)")
+            input_14 = gr.Number(value=em[14],label="chronic kidney disease(yes=1,No=0)")
+            input_15 = gr.Number(value=em[15],label="malignancy(yes=1,No=0)")
+    with gr.Column():
+        with gr.Row():
+            input_16 = gr.Number(value=em[16],label="Cerebrovascular disease(yes=1,No=0)")
+            input_17 = gr.Number(value=em[17],label="viral hepatitis(yes=1,No=0)")
+            input_18 = gr.Number(value=em[18],label="cirrhosis(yes=1,No=0)")
+            input_19 = gr.Number(value=em[19],label="wbc(0-2=0,2-4=1,>4=2)")
+            input_20 = gr.Number(value=em[20],label="C-reactive protein((RC[-1]),(RC[-1]>0.068,RC[-1]<8.2)1,2)))")
+            input_21 = gr.Number(value=em[21],label="high sensitivity c reactive protein(0-3=1)")
+            input_22 = gr.Number(value=em[22],label="erythrocyte sedimentation rate(0-20=1)")
+            input_23 = gr.Number(value=em[23],label="Absolute value of lymphocytes")
+    with gr.Column():
+        with gr.Row():
+            input_24 = gr.Number(value=em[24],label="anc")
+            input_25 = gr.Number(value=em[25],label="Absolute value of eosinophils")
+            input_26 = gr.Number(value=em[26],label="Absolute value of monocytes")
+            input_27 = gr.Number(value=em[27],label="Serum procalcitonin detection")
+            input_28 = gr.Number(value=em[28],label="Oxygen Saturation")
+            input_29 = gr.Number(value=em[29],label="partial pressure of carbon dioxide")
+            input_30 = gr.Number(value=em[30],label="Oxygen partial pressure inhalation oxygen concentration ratio greater than 300")
+            input_31 = gr.Number(value=em[31],label="albumin")
+    with gr.Column():
+        with gr.Row():
+            input_32 = gr.Number(value=em[32],label="globulin")
+            input_33 = gr.Number(value=em[33],label="Low density lipoprotein cholesterol")
+            input_34 = gr.Number(value=em[34],label="High-density lipoprotein cholesterol")
+            input_35 = gr.Number(value=em[35],label="total cholesterol")
+            input_36 = gr.Number(value=em[36],label="triglyceride")
+            input_37 = gr.Number(value=em[37],label="urea nitrogen")
+            input_38 = gr.Number(value=em[38],label="uric acid")
+            input_39 = gr.Number(value=em[39],label="creatinine")
+    # with gr.Column():
+    gr.Markdown("## Prediction")
+    #     gr.Textbox(value="[0.0,0.0,87.0,5.0,36.9,81.0,33.0,138.0,62.0,0.0,0.0,2.0,2.0,2.0,0.0,0.0,0.0,0.0,0.0,2.0,1.0,0.0,0.0,1.0,2.0,2.0,2.0,1.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,2.0,2.0,1.0]", label="For eample:")
+    btn = gr.Button(value="CML (Click here to predict)",elem_classes="slide")
+    TIDM = gr.Textbox(label="Deterioration  risk of AECOPD")
+    btn.click(fn=load_beforeTIDM,inputs=[input_0,input_1,input_2,input_3,input_4,input_5,input_6,input_7,input_8,input_9,
+                                 input_10,input_11,input_12,input_13,input_14,input_15,input_16,input_17,input_18,input_19,
+                                 input_20,input_21,input_22,input_23,input_24,input_25,input_26,input_27,input_28,input_29,
+                                 input_30,input_31,input_32,input_33,input_34,input_35,input_36,input_37,input_38,input_39],outputs=TIDM)
+
+    btn_FL = gr.Button(value="FL(Click here to predict)",elem_classes="slide")
+    FL = gr.Textbox(label="Deterioration  risk of AECOPD")
+    btn_FL.click(fn=load_beforeFL,
+                 inputs=[input_0, input_1, input_2, input_3, input_4, input_5, input_6, input_7, input_8, input_9,
+                         input_10, input_11, input_12, input_13, input_14, input_15, input_16, input_17, input_18,
+                         input_19,
+                         input_20, input_21, input_22, input_23, input_24, input_25, input_26, input_27, input_28,
+                         input_29,
+                         input_30, input_31, input_32, input_33, input_34, input_35, input_36, input_37, input_38,
+                         input_39], outputs=FL)
+    gr.Markdown("## Examples")
+    gr.Examples(
+        examples=[
+            [
+                1.0,1.0,78.0,7.0,37.3,110.0,21.0,130.0,81.0,3.0,0.0,2.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,2.0,0.0,2.0,1.0,1.0,2.0,2.0,1.0,0.0,0.0,0.0,2.0,2.0,1.0,2.0,1.0,1.0,1.0,2.0,1.0
+            ]
+
+        ],
+        inputs=[input_0, input_1, input_2, input_3, input_4, input_5, input_6, input_7, input_8, input_9,
+                input_10, input_11, input_12, input_13, input_14, input_15, input_16, input_17, input_18,
+                input_19,
+                input_20, input_21, input_22, input_23, input_24, input_25, input_26, input_27, input_28,
+                input_29,
+                input_30, input_31, input_32, input_33, input_34, input_35, input_36, input_37, input_38,
+                input_39], outputs=TIDM,
+        fn=load_beforeTIDM,
+        cache_examples=True)
+    # gr.Markdown("## FL Examples")
+    #
+    # gr.Examples(
+    #     examples=[[0.0,1.0,88.0,2.0,36.5,112.0,22.0,101.0,74.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,1.0,0.0,1.0,2.0,2.0,1.0,2.0,1.0,2.0,1.0,2.0,1.0,1.0,2.0,2.0,2.0,2.0,2.0,2.0,2.0]],
+    #     inputs=[input_0, input_1, input_2, input_3, input_4, input_5, input_6, input_7, input_8, input_9,
+    #             input_10, input_11, input_12, input_13, input_14, input_15, input_16, input_17, input_18,
+    #             input_19,
+    #             input_20, input_21, input_22, input_23, input_24, input_25, input_26, input_27, input_28,
+    #             input_29,
+    #             input_30, input_31, input_32, input_33, input_34, input_35, input_36, input_37, input_38,
+    #             input_39], outputs=FL,
+    #     fn=load_beforeFL,
+    #     cache_examples=True)
+demo.launch()

gradio_cached_examples/63/log.csv

@@ -0,0 +1,5 @@
+Future risk of illness,flag,username,timestamp
+"H1 predict: Height.
+H2 predict: Low.
+H3 predict: Height.
+",,,2023-10-02 20:56:28.086482

gradio_cached_examples/64/log.csv

@@ -0,0 +1,5 @@
+Future risk of illness,flag,username,timestamp
+"H1 predict: Height.
+H2 predict: Low.
+H3 predict: Height.
+",,,2023-10-02 20:56:28.086482

load.py

@@ -0,0 +1,138 @@
+import torch
+import pandas as pd
+from sklearn.model_selection import train_test_split
+class GetDataSet(object):
+    def __init__(self):
+        self.train_data = None
+        self.train_label = None
+        self.test_data = None
+        self.test_label = None
+        self.copdDataSetConstruct()
+
+    def copdDataSetConstruct(self):
+        data = pd.read_csv('408-h2.csv',encoding='gbk')
+
+        x = data.drop(['id','level'], axis=1)
+
+        y = data['level']
+
+        x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.4, random_state=5)  # random_state 随机种子
+        #print(x_train)
+        #print(y_train)
+        x_tr = x_train.loc[:].values
+        y_tr = y_train.loc[:].values
+        x_te = x_test.loc[:].values
+        y_te = y_test.loc[:].values
+
+        self.train_data = x_tr
+        self.train_label = y_tr
+        self.test_data = x_te
+        self.test_label = y_te
+class Neuro_net(torch.nn.Module):
+    def __init__(self):
+        super(Neuro_net, self).__init__()
+        self.layer1 = torch.nn.Linear(40, 20)
+        self.layer2 = torch.nn.Linear(20, 10)
+        self.layer3 = torch.nn.Linear(10, 5)
+        self.layer4 = torch.nn.Linear(5, 2)
+        self.layer5 = torch.nn.Softmax(dim=0)
+
+    def forward(self, input):
+        tensor = torch.relu(self.layer1(input))
+        tensor = torch.relu(self.layer2(tensor))
+        tensor = torch.relu(self.layer3(tensor))
+        tensor = self.layer4(tensor)
+        tensor = self.layer5(tensor)
+        return tensor
+
+def load_model(fl,input):
+    data_test_h1 = GetDataSet()
+    data_test_h1.test_data[0] = input
+    input = data_test_h1.test_data
+    if fl == "Yes":
+        model = torch.load("net_gb.pt")
+        print(fl)
+        print(input)
+        input = torch.from_numpy(input).to(torch.float32)
+        # model.eval()
+        with torch.no_grad():
+            # output = model(torch.tensor([0.0,0.0,87.0,5.0,36.9,81.0,33.0,138.0,62.0,0.0,0.0,2.0,2.0,2.0,0.0,0.0,0.0,0.0,0.0,2.0,1.0,0.0,0.0,1.0,2.0,2.0,2.0,1.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,2.0,2.0,1.0]))
+            output = model(torch.tensor(input))
+        print(output)
+        # print(len([0.0,0.0,87.0,5.0,36.9,81.0,33.0,138.0,62.0,0.0,0.0,2.0,2.0,2.0,0.0,0.0,0.0,0.0,0.0,2.0,1.0,0.0,0.0,1.0,2.0,2.0,2.0,1.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,2.0,2.0,1.0]))
+        pridect_h1_y = torch.max(output,dim=1)[1]
+        pridect_h1_label = pridect_h1_y.data.numpy()
+        print(pridect_h1_y)
+        if int(pridect_h1_label[0])==1:
+            return "FL predict: Height."
+        else:
+            return "FL predict: Low."
+    else:
+        model_h1 = torch.load("net_h1.pt")
+        model_h2 = torch.load("net_h2.pt")
+        model_h3 = torch.load("net_h3.pt")
+        print(fl)
+        print(input)
+        input = torch.from_numpy(input).to(torch.float32)
+        model_h1.eval()
+        model_h2.eval()
+        model_h3.eval()
+        with torch.no_grad():
+            # output = model(torch.tensor(1.0,1.0,64.0,2.0,37.1,98.0,20.0,120.0,70.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,1.0,0.0,1.0,2.0,2.0,1.0,2.0,0.0,2.0,0.0,2.0,2.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0))
+            output_h1 = model_h1(torch.tensor(input))
+            output_h2 = model_h2(torch.tensor(input))
+            output_h3 = model_h3(torch.tensor(input))
+        print(output_h1)
+        print(output_h2)
+        print(output_h3)
+        # print(len([0.0,0.0,87.0,5.0,36.9,81.0,33.0,138.0,62.0,0.0,0.0,2.0,2.0,2.0,0.0,0.0,0.0,0.0,0.0,2.0,1.0,0.0,0.0,1.0,2.0,2.0,2.0,1.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,2.0,2.0,1.0]))
+        # print(len(output_h1))
+        pridect_h1_y = torch.max(output_h1,dim = 1)[1]
+        pridect_h1_label = pridect_h1_y.data.numpy()
+        pridect_h2_y = torch.max(output_h2,dim = 1)[1]
+        pridect_h2_label = pridect_h2_y.data.numpy()
+        pridect_h3_y = torch.max(output_h3,dim = 1)[1]
+        pridect_h3_label = pridect_h3_y.data.numpy()
+
+        # print(pridect_h1_y)
+        # print(pridect_h2_y)
+        # print(pridect_h3_y)
+        print(pridect_h1_label)
+        print(pridect_h2_label)
+        print(pridect_h3_label)
+        output = ""
+        if int(pridect_h1_label[0]) == 1:
+            print("sick")
+            output +="H1 predict: Height.\n"
+        else:
+            print("no sick")
+            output += "H1 predict: Low.\n"
+        if int(pridect_h2_label[0]) == 1:
+            print("sick")
+            output += "H2 predict: Height.\n"
+
+        else:
+            print("no sick")
+            output += "H2 predict: Low.\n"
+        if int(pridect_h3_label[0]) == 1:
+            print("sick")
+            output += "H3 predict: Height.\n"
+
+        else:
+            print("no sick")
+            output += "H3 predict: Low.\n"
+        return output
+
+
+
+# print(data_test_h1.train_data[0])
+# print(len(data_test_h1.train_data))
+
+# test_h1_x = torch.from_numpy(data_test_h1.test_data).float()
+# test_h1_y = torch.tensor(data_test_h1.test_label)
+# a = [1.0,1.0,78.0,7.0,37.3,110.0,21.0,130.0,81.0,3.0,0.0,2.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,2.0,0.0,2.0,1.0,1.0,2.0,2.0,1.0,0.0,0.0,0.0,2.0,2.0,1.0,2.0,1.0,1.0,1.0,2.0,1.0]
+# data_test_h1.train_data[0] = a
+# print(data_test_h1.train_data[25])
+# print(load_model("No",data_test_h1.train_data))
+# print(load_model("Yes",data_test_h1.train_data))
+# print("=============================")

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requirement.txt

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+torch

style.css

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+.slide {
+
+    border: 2px solid #2980b9;
+    color: #2980b9;
+    position: relative;
+    overflow: hidden;
+    z-index: 1;
+    transition: .5s;
+
+}
+
+.slide::before {
+    content: "";
+    position: absolute;
+    z-index: -1;
+    width: 0;
+    height: 100%;
+    left: 0;
+    background-color: #2980b9;
+    transition: ease-in-out .5s;
+}
+
+.slide:hover::before {
+    width: 100%;
+}
+#b_1{
+    background-color: #4CAF50;
+}

tmp_trainer/all_results.json

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+    "epoch": 3.0,
+    "train_loss": 2.680424372355143,
+    "train_runtime": 11.0862,
+    "train_samples_per_second": 0.8118215323828388,
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tmp_trainer/special_tokens_map.json

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tmp_trainer/template_config.json

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+[{"text": "sentence1"}, {"hard": "和"}, {"text": "sentence2"}, {"hard": "说的是"}, {"mask": null, "length": 1}, {"hard": "同的事情。"}]
+{"class": "ManualTemplate"}

tmp_trainer/tokenizer_config.json

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tmp_trainer/train_results.json

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tmp_trainer/verbalizer_config.json

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+{"0": ["不"], "1": ["相"]}