coco
354 lines
12 KiB
Plaintext
354 lines
12 KiB
Plaintext
{
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"cells": [
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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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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import pandas as pd \n",
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"import seaborn as sns\n",
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"from IPython.display import display\n",
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"import matplotlib.pyplot as plt\n",
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"from mpl_toolkits.mplot3d import Axes3D\n",
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"import sklearn\n",
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"%matplotlib inline\n"
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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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"outputs": [],
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"source": [
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"try :\n",
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" #PH值 - done\n",
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" #data = pd.read_csv(\"data-ph.csv\")\n",
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" #微蛋白 - done\n",
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" #data = pd.read_csv(\"data-mau.csv\") \n",
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" #蛋白质 - done\n",
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" #data = pd.read_csv(\"data-pro.csv\") \n",
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" #亚硝酸盐 - done\n",
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" #data = pd.read_csv(\"data-nit.csv\") \n",
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" \n",
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" #肌酐\n",
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" #data = pd.read_csv(\"data-cre.csv\") \n",
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" #葡萄糖\n",
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" #data = pd.read_csv(\"data-glu.csv\") \n",
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" \n",
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"\n",
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" #通体 数据不正确\n",
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" data = pd.read_csv(\"mix-mau-data.csv\") \n",
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" #data = pd.read_excel(\"data-ket.xlsx\") \n",
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"\n",
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" #比重\n",
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" #data = pd.read_csv(\"data-sg.csv\") \n",
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" #抗坏血酸\n",
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" #data = pd.read_csv(\"data-vc.csv\") \n",
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" \n",
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" #白细胞 - done\n",
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" #data = pd.read_csv(\"data-wbc.csv\") \n",
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" #尿胆原 - done\n",
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" #data = pd.read_csv(\"data-uro.csv\") \n",
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" #尿钙 -- done\n",
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" #data1 = pd.read_csv(\"data-uca.csv\")\n",
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" #data = pd.read_csv(\"data-uca2.csv\")\n",
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" #data = data1.append(data2);\n",
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" #胆红素 - done\n",
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" #data = pd.read_csv(\"data-bil.csv\") \n",
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" #潜血 - done\n",
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" #data = pd.read_csv(\"data-bld.csv\") \n",
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"\n",
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" \n",
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" \n",
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" print (\"load data successful !!!!!\")\n",
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"except :\n",
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" print (\"load data error !!!!!!!!!!\")"
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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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"outputs": [],
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"source": [
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"train_labels = data[\"index\"]\n",
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"train_features = data.drop(\"index\",axis=1)\n",
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"\n",
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"train_features.describe()\n"
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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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"outputs": [],
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"source": [
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"#from sklearn.model_selection import KFold\n",
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"from sklearn.ensemble import RandomForestClassifier\n",
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"from sklearn.metrics import accuracy_score\n",
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"from sklearn.svm import SVC\n",
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"from sklearn.metrics import f1_score\n",
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"from sklearn.metrics import precision_score\n",
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"from sklearn.metrics import recall_score\n",
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"\n",
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"\n",
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"from sklearn.ensemble import ExtraTreesClassifier\n",
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"from sklearn.ensemble import AdaBoostClassifier\n",
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"\n",
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"from sklearn.cross_validation import train_test_split\n",
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"X_train ,X_test,y_train,y_test = train_test_split(train_features,train_labels,test_size = 0.4, random_state = 0)\n",
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"#X_train ,X_test,y_train,y_test = train_test_split(train_features,train_labels,test_size = 0.2, random_state = 20)\n"
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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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"outputs": [],
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"source": [
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"from sklearn.tree import DecisionTreeClassifier\n",
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"from sklearn.metrics import classification_report,confusion_matrix\n",
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"\n",
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"dtree = DecisionTreeClassifier(criterion='gini',max_depth=None)\n",
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"dtree.fit(X_train,y_train)\n",
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"predictions = dtree.predict(X_test)\n",
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"\n",
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"print(\"-----------classification_report----\\n\")\n",
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"print(classification_report(y_test,predictions))\n",
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"print(\"-----------confusion_matrix---------\\n\")\n",
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"cm=confusion_matrix(y_test,predictions)\n",
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"print(cm)\n",
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"print(\"------------------------------------\\n\")\n",
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"print (\"Accuracy of prediction:\",round((cm[0,0]+cm[1,1])/cm.sum(),3))\n",
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"print(\"-----------------------------------\\n\")\n",
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"print (\"DecisionTree accuracy score:\" , accuracy_score(y_test,predictions))\n",
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"print (\"f1 score:\" , f1_score(y_test,predictions,average='micro'))\n",
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"print (\"precision_score:\" , precision_score(y_test,predictions,average='micro'))\n",
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"print (\"recall_score:\" , recall_score(y_test,predictions,average='micro'))"
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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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"outputs": [],
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"source": [
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"from sklearn.ensemble import RandomForestClassifier\n",
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"from sklearn.metrics import classification_report\n",
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"\n",
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"rfc = RandomForestClassifier(n_estimators=600)\n",
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"rfc.fit(X_train, y_train)\n",
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"rfc_pred = rfc.predict(X_test)\n",
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"cr = classification_report(y_test,predictions)\n",
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"print(cr)\n",
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"cm = confusion_matrix(y_test,rfc_pred)\n",
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"print(cm)\n",
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"\n",
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"print(\"---------------------------------\\n\")\n",
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"print (\"Accuracy of prediction:\",round((cm[0,0]+cm[1,1])/cm.sum(),3))\n",
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"print (\"RandomForest accuracy score:\" , accuracy_score(y_test,rfc_pred))\n",
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"print(\"---------------------------------\\n\")\n",
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"print (\"f1 score:\" , f1_score(y_test,rfc_pred,average='micro'))\n",
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"print (\"precision_score:\" , precision_score(y_test,rfc_pred,average='micro'))\n",
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"print (\"recall_score:\" , recall_score(y_test,rfc_pred,average='micro'))"
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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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"outputs": [],
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"source": [
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"from sklearn.model_selection import KFold\n",
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"\n",
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"X = train_features.values\n",
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"y = train_labels.values\n",
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"\n",
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"kf = KFold(n_splits=5)\n",
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"kf.get_n_splits(X)\n",
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"\n",
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"print(kf) \n",
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"\n",
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"for train_index, test_index in kf.split(X):\n",
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" print(\"TRAIN:\", train_index, \"TEST:\", test_index)\n",
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" X_train, X_test = X[train_index], X[test_index]\n",
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" y_train, y_test = y[train_index], y[test_index]\n",
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" \n",
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" \n",
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" from datetime import datetime\n",
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" trarining_start_time = datetime.now()\n",
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"\n",
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" rfc = RandomForestClassifier(n_estimators=600)\n",
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" rfc.fit(X_train, y_train)\n",
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" rfc_pred = rfc.predict(X_test) \n",
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" print (\"svm linear accuracy score:\" , accuracy_score(y_test,rfc_pred))\n",
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" print (\"f1 score:\" , f1_score(y_test,rfc_pred,average='micro'))\n",
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" print (\"precision_score:\" , precision_score(y_test,rfc_pred,average='micro'))\n",
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" print (\"recall_score:\" , recall_score(y_test,rfc_pred,average='micro'))\n",
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"\n",
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" training_stop_time = datetime.now()\n",
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"\n",
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" print (\"runing time:\",(training_stop_time - trarining_start_time))\n",
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" print(\"\\n\\n\")"
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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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"outputs": [],
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"source": [
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"from sklearn.model_selection import KFold\n",
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"\n",
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"X = train_features.values\n",
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"y = train_labels.values\n",
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"\n",
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"kf = KFold(n_splits=5)\n",
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"kf.get_n_splits(X)\n",
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"\n",
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"print(kf) \n",
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"\n",
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"for train_index, test_index in kf.split(X):\n",
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" print(\"TRAIN:\", train_index, \"TEST:\", test_index)\n",
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" X_train, X_test = X[train_index], X[test_index]\n",
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" y_train, y_test = y[train_index], y[test_index]\n",
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" \n",
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" \n",
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" from datetime import datetime\n",
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" trarining_start_time = datetime.now()\n",
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"\n",
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" clf_svm_linear = SVC(kernel='linear', gamma=0.02, C=1)\n",
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" clf_svm_linear = clf_svm_linear.fit(X_train, y_train)\n",
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" #print(clf_svm_linear.predict(X_test))\n",
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" pred = clf_svm_linear.predict(X_test)\n",
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" print (\"svm linear accuracy score:\" , accuracy_score(y_test,pred))\n",
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" print (\"f1 score:\" , f1_score(y_test,pred,average='micro'))\n",
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" print (\"precision_score:\" , precision_score(y_test,pred,average='micro'))\n",
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" print (\"recall_score:\" , recall_score(y_test,pred,average='micro'))\n",
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"\n",
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" training_stop_time = datetime.now()\n",
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"\n",
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" print (\"runing time:\",(training_stop_time - trarining_start_time))\n",
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" print(\"\\n\\n\")"
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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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"outputs": [],
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"source": [
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"from datetime import datetime\n",
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"trarining_start_time = datetime.now()\n",
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"\n",
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"X_train ,X_test,y_train,y_test = train_test_split(train_features,train_labels,test_size = 0.4, random_state = 0)\n",
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"\n",
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"#clf_svm_linear = SVC(kernel = 'linear',gamma=0.00001,C=0.01)\n",
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"clf_svm_linear = SVC(kernel = 'linear',gamma=0.02,C=1)\n",
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"clf_svm_linear = clf_svm_linear.fit(X_train, y_train)\n",
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"pred = clf_svm_linear.predict(X_test)\n",
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"#print (\"svm linear accuracy score:\" , accuracy_score(y_test,pred))\n",
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"#print (\"f1 score:\" , f1_score(y_test,pred,average='micro'))\n",
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"#print (\"precision_score:\" , precision_score(y_test,pred,average=None))\n",
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"#print (\"recall_score :\" , recall_score(y_test,pred,average=None))\n",
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"print (\"svm linear accuracy score:\" , accuracy_score(y_test,pred))\n",
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"print (\"f1 score:\" , f1_score(y_test,pred,average='micro'))\n",
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"print (\"precision_score:\" , precision_score(y_test,pred,average='micro'))\n",
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"print (\"recall_score:\" , recall_score(y_test,pred,average='micro'))\n",
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"\n",
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"training_stop_time = datetime.now()\n",
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"print (\"runing clf_svm_linear time:\",(training_stop_time - trarining_start_time))"
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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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"outputs": [],
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"source": [
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"from sklearn_porter import Porter\n",
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"\n",
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"porter_clf_svm_liner_14items = Porter(clf_svm_linear, language='c').export()\n",
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"\n",
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"#print(porter_clf_svm_liner_ph)\n",
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"f = open(\"new14modal/svm_bil.c\",'wb')\n",
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"f.write(porter_clf_svm_liner_14items.encode())\n",
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"f.close()"
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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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"outputs": [],
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"source": [
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"from sklearn_porter import Porter\n",
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"\n",
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"porter_clf_svm_liner_14items = Porter(clf_svm_linear, language='js').export()\n",
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"\n",
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"#print(porter_clf_svm_linear)\n",
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"f = open(\"new14modal/svm_bil.js\",'wb')\n",
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"#f = open(\"clf_svm_linear_125100_low_feature_data.txt\",'wb')\n",
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"f.write(porter_clf_svm_liner_14items.encode())\n",
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"f.close()"
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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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"outputs": [],
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"source": [
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"from sklearn_porter import Porter\n",
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"\n",
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"porter_clf_svm_liner_14items = Porter(rfc, language='c').export()\n",
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"\n",
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"#print(porter_clf_svm_liner_ph)\n",
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"f = open(\"new14modal/rfc_bil.c\",'wb')\n",
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"f.write(porter_clf_svm_liner_14items.encode())\n",
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"f.close()"
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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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"outputs": [],
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"source": [
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"from sklearn_porter import Porter\n",
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"\n",
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"porter_clf_svm_liner_14items = Porter(rfc, language='js').export()\n",
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"\n",
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"#print(porter_clf_svm_linear)\n",
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"f = open(\"new14modal/rfc_bil.js\",'wb')\n",
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"#f = open(\"clf_svm_linear_125100_low_feature_data.txt\",'wb')\n",
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"f.write(porter_clf_svm_liner_14items.encode())\n",
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"f.close()"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.5"
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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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