1 files changed, 66 insertions, 30 deletions

diff --git a/Experiment/MarkovModel/markov_tofig.ipynb b/Experiment/MarkovModel/markov_tofig.ipynb
index 84ab001..b2d8d27 100644
--- a/Experiment/MarkovModel/markov_tofig.ipynb
+++ b/Experiment/MarkovModel/markov_tofig.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [
     {
@@ -16,6 +16,7 @@
    "source": [
     "import pandas as pd\n",
     "import numpy as np\n",
+    "N = 20\n",
     "date = '2019-12-20_21'\n",
     "root_dir = '/Users/Leo/Documents/github/GradProj/'\n",
     "train_path = root_dir + 'Experiment/MarkovModel/CsvFile/' + date + '/train.csv'\n",
@@ -27,7 +28,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 36,
    "metadata": {
     "collapsed": true
    },
@@ -49,7 +50,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
@@ -57,18 +58,18 @@
      "output_type": "stream",
      "text": [
       "                    precision    recall        f1\n",
-      "LogisticRegression   0.712848  0.712848  0.712848\n",
-      "SVM                  0.733191  0.733191  0.733191\n",
-      "GaussianNB           0.685011  0.685011  0.685011\n",
-      "tree                 0.805996  0.805996  0.805996\n",
-      "RandomForest         0.800000  0.800000  0.800000\n"
+      "LogisticRegression   0.682227  0.682227  0.682227\n",
+      "SVM                  0.706852  0.706852  0.706852\n",
+      "GaussianNB           0.680086  0.680086  0.680086\n",
+      "tree                 0.736188  0.736188  0.736188\n",
+      "RandomForest         0.736831  0.736831  0.736831\n"
      ]
     },
     {
      "data": {
-      "image/png": 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PZW7bVJ07d6Z79+506NCh2b6HtX1Z/rZ2AxZUbNcCe6yl/dHA7z9KUVZstbW1dO3alV69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\n",
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icpB+itmb5H2oH8Kd016ehylbzx0ASV8nuUjyBsnMkHsAoyPi3lwLaz0fJpn9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\nAAAASUVORK5CYII=\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1a106506d8>"
+       "<matplotlib.figure.Figure at 0x1a10748940>"
       ]
      },
      "metadata": {},
@@ -131,7 +132,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -139,18 +140,18 @@
      "output_type": "stream",
      "text": [
       "                    precision    recall        f1\n",
-      "LogisticRegression   0.974732  0.974732  0.974732\n",
-      "SVM                  0.959529  0.959529  0.959529\n",
-      "GaussianNB           0.934904  0.934904  0.934904\n",
-      "tree                 0.994433  0.994433  0.994433\n",
-      "RandomForest         0.995717  0.995717  0.995717\n"
+      "LogisticRegression   0.833619  0.833619  0.833619\n",
+      "SVM                  0.817773  0.817773  0.817773\n",
+      "GaussianNB           0.738116  0.738116  0.738116\n",
+      "tree                 0.967452  0.967452  0.967452\n",
+      "RandomForest         0.967452  0.967452  0.967452\n"
      ]
     },
     {
      "data": {
-      "image/png": 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qpsVlYH2zQxZ1VkhbQ9IQyiYZbC3LDRY13A8ApkXEEkknkFxQHVWalN/MDEDS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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1a1ce90dd8>"
+       "<matplotlib.figure.Figure at 0x1a1deb3438>"
       ]
      },
      "metadata": {},
@@ -213,30 +214,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "                    precision    recall        f1\n",
-      "LogisticRegression   0.983298  0.983298  0.983298\n",
-      "SVM                  0.964668  0.964668  0.964668\n",
-      "GaussianNB           0.966595  0.966595  0.966595\n",
-      "tree                 0.997002  0.997002  0.997002\n",
-      "RandomForest         0.998929  0.998929  0.998929\n"
+      "[4670, 5]\n",
+      "0.32762312633832974\n",
+      "0.998692810458\n",
+      "0.998692810458\n",
+      "0.998692810458\n"
      ]
     },
     {
      "data": {
-      "image/png": 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9yW0/kr+PByPihYxLyoSkiRExoE4X1eSI6Jt1bY0p1AlVSV+IiH9KOrTOpq0k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\nTkSuQmCC\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1a1de3cb00>"
+       "\"\\nrf_precision = precision_score(y_test, y_pred, average='micro')\\nrf_recall = recall_score(y_test, y_pred, average='micro')\\nrf_f1 = f1_score(y_test, y_pred, average='micro')\\nscore_df = pd.DataFrame(np.zeros((5,3)),index = ['LogisticRegression', 'SVM', 'GaussianNB', 'tree', 'RandomForest'],                         columns = ['precision', 'recall', 'f1'])\\nscore_df.loc['LogisticRegression'] = [lr_precision, lr_recall, lr_f1]\\nscore_df.loc['SVM'] = [svm_precision, svm_recall, svm_f1]\\nscore_df.loc['GaussianNB'] = [gn_precision, gn_recall, gn_f1]\\nscore_df.loc['tree'] = [tr_precision, tr_recall, tr_f1]\\nscore_df.loc['RandomForest'] = [rf_precision, rf_recall, rf_f1]\\nax = score_df.plot.bar(title='type+length+burst markov')\\nfig = ax.get_figure()\\nprint(score_df)\\n#fig.savefig('../figure/type_length_burst.svg')\\n\""
       ]
      },
+     "execution_count": 37,
      "metadata": {},
-     "output_type": "display_data"
+     "output_type": "execute_result"
     }
    ],
    "source": [
@@ -245,6 +245,27 @@
     "y_train = train_df['label'].values.copy()\n",
     "x_test = test_df.iloc[:,0:15].values.copy()\n",
     "y_test = test_df['label'].values.copy()\n",
+    "\n",
+    "def my_pred(y_pred, y_test, proba):\n",
+    "    y_pred1 = list()\n",
+    "    y_test1 = list()\n",
+    "    [rows, clos] = proba.shape\n",
+    "    print([rows, clos])\n",
+    "    for i in range(rows):\n",
+    "        temp = max(proba[i])\n",
+    "        if temp < 0.95:\n",
+    "            continue\n",
+    "        y_pred1.append(y_pred[i])\n",
+    "        y_test1.append(y_test[i])\n",
+    "    f1 = f1_score(y_test1, y_pred1, average='micro')\n",
+    "    recall = recall_score(y_test1, y_pred1, average='micro')\n",
+    "    precision = precision_score(y_test1, y_pred1, average='micro')\n",
+    "    print(len(y_test1) / len(y_test))\n",
+    "    print(precision)\n",
+    "    print(recall)\n",
+    "    print(f1)\n",
+    "\n",
+    "'''\n",
     "lr_classifer = LogisticRegression()\n",
     "lr_classifer.fit(x_train, y_train)\n",
     "y_pred = lr_classifer.predict(x_test)\n",
@@ -272,14 +293,19 @@
     "tr_precision = precision_score(y_test, y_pred, average='micro')\n",
     "tr_recall = recall_score(y_test, y_pred, average='micro')\n",
     "tr_f1 = f1_score(y_test, y_pred, average='micro')\n",
+    "'''\n",
     "\n",
     "rf_classifer = RandomForestClassifier()\n",
     "rf_classifer.fit(x_train, y_train)\n",
     "y_pred = rf_classifer.predict(x_test)\n",
+    "proba = rf_classifer.predict_proba(x_test)\n",
+    "my_pred(y_pred, y_test, proba)\n",
+    "\n",
+    "\n",
+    "'''\n",
     "rf_precision = precision_score(y_test, y_pred, average='micro')\n",
     "rf_recall = recall_score(y_test, y_pred, average='micro')\n",
     "rf_f1 = f1_score(y_test, y_pred, average='micro')\n",
-    "\n",
     "score_df = pd.DataFrame(np.zeros((5,3)),index = ['LogisticRegression', 'SVM', 'GaussianNB', 'tree', 'RandomForest'], \\\n",
     "                        columns = ['precision', 'recall', 'f1'])\n",
     "score_df.loc['LogisticRegression'] = [lr_precision, lr_recall, lr_f1]\n",
@@ -290,7 +316,8 @@
     "ax = score_df.plot.bar(title='type+length+burst markov')\n",
     "fig = ax.get_figure()\n",
     "print(score_df)\n",
-    "#fig.savefig('../figure/type_length_burst.svg')"
+    "#fig.savefig('../figure/type_length_burst.svg')\n",
+    "'''"
    ]
   },
   {
@@ -301,6 +328,15 @@
    },
    "outputs": [],
    "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {