328 lines
54 KiB
Plaintext
328 lines
54 KiB
Plaintext
{
|
|
"cells": [
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 1,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"end2\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"import pandas as pd\n",
|
|
"import numpy as np\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",
|
|
"test_path = root_dir + 'Experiment/MarkovModel/CsvFile/' + date + '/test.csv'\n",
|
|
"train_df = pd.read_csv(train_path,index_col=0)\n",
|
|
"test_df = pd.read_csv(test_path,index_col=0)\n",
|
|
"print('end2')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 4,
|
|
"metadata": {
|
|
"collapsed": true
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"from sklearn.linear_model import LogisticRegression\n",
|
|
"from sklearn.svm import SVC\n",
|
|
"from sklearn.naive_bayes import GaussianNB\n",
|
|
"from sklearn import tree\n",
|
|
"from sklearn.ensemble import RandomForestClassifier\n",
|
|
"from sklearn.metrics import f1_score,recall_score,precision_score\n",
|
|
"\n",
|
|
"%matplotlib inline\n",
|
|
"import os\n",
|
|
"import numpy as np\n",
|
|
"import pandas as pd\n",
|
|
"import matplotlib.pyplot as plt"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 5,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"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"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAFcCAYAAAAzq/4LAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
|
|
"text/plain": [
|
|
"<matplotlib.figure.Figure at 0x1a106506d8>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"#type markov\n",
|
|
"x_train = train_df.iloc[:,0:5].values.copy()\n",
|
|
"y_train = train_df['label'].values.copy()\n",
|
|
"x_test = test_df.iloc[:,0:5].values.copy()\n",
|
|
"y_test = test_df['label'].values.copy()\n",
|
|
"lr_classifer = LogisticRegression()\n",
|
|
"lr_classifer.fit(x_train, y_train)\n",
|
|
"y_pred = lr_classifer.predict(x_test)\n",
|
|
"lr_precision = precision_score(y_test, y_pred, average='micro')\n",
|
|
"lr_recall = recall_score(y_test, y_pred, average='micro')\n",
|
|
"lr_f1 = f1_score(y_test, y_pred, average='micro')\n",
|
|
"\n",
|
|
"svm_classifer = SVC()\n",
|
|
"svm_classifer.fit(x_train, y_train)\n",
|
|
"y_pred = svm_classifer.predict(x_test)\n",
|
|
"svm_precision = precision_score(y_test, y_pred, average='micro')\n",
|
|
"svm_recall = recall_score(y_test, y_pred, average='micro')\n",
|
|
"svm_f1 = f1_score(y_test, y_pred, average='micro')\n",
|
|
"\n",
|
|
"gn_classifer = GaussianNB()\n",
|
|
"gn_classifer.fit(x_train, y_train)\n",
|
|
"y_pred = gn_classifer.predict(x_test)\n",
|
|
"gn_precision = precision_score(y_test, y_pred, average='micro')\n",
|
|
"gn_recall = recall_score(y_test, y_pred, average='micro')\n",
|
|
"gn_f1 = f1_score(y_test, y_pred, average='micro')\n",
|
|
"\n",
|
|
"tr_classifer = tree.DecisionTreeClassifier()\n",
|
|
"tr_classifer.fit(x_train, y_train)\n",
|
|
"y_pred = tr_classifer.predict(x_test)\n",
|
|
"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",
|
|
"rf_classifer = RandomForestClassifier()\n",
|
|
"rf_classifer.fit(x_train, y_train)\n",
|
|
"y_pred = rf_classifer.predict(x_test)\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",
|
|
"score_df.loc['SVM'] = [svm_precision, svm_recall, svm_f1]\n",
|
|
"score_df.loc['GaussianNB'] = [gn_precision, gn_recall, gn_f1]\n",
|
|
"score_df.loc['tree'] = [tr_precision, tr_recall, tr_f1]\n",
|
|
"score_df.loc['RandomForest'] = [rf_precision, rf_recall, rf_f1]\n",
|
|
"print(score_df)\n",
|
|
"ax = score_df.plot.bar(title='type markov')\n",
|
|
"fig = ax.get_figure()\n",
|
|
"#fig.savefig('../figure/type.svg')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 7,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"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"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAFcCAYAAAAzq/4LAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nhxzuZmY55HA3M8shh7uZWQ79fya0+LBZPbtPAAAAAElFTkSuQmCC\n",
|
|
"text/plain": [
|
|
"<matplotlib.figure.Figure at 0x1a1ce90dd8>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"#type+length markov\n",
|
|
"x_train = train_df.iloc[:,0:10].values.copy()\n",
|
|
"y_train = train_df['label'].values.copy()\n",
|
|
"x_test = test_df.iloc[:,0:10].values.copy()\n",
|
|
"y_test = test_df['label'].values.copy()\n",
|
|
"lr_classifer = LogisticRegression()\n",
|
|
"lr_classifer.fit(x_train, y_train)\n",
|
|
"y_pred = lr_classifer.predict(x_test)\n",
|
|
"lr_precision = precision_score(y_test, y_pred, average='micro')\n",
|
|
"lr_recall = recall_score(y_test, y_pred, average='micro')\n",
|
|
"lr_f1 = f1_score(y_test, y_pred, average='micro')\n",
|
|
"\n",
|
|
"svm_classifer = SVC()\n",
|
|
"svm_classifer.fit(x_train, y_train)\n",
|
|
"y_pred = svm_classifer.predict(x_test)\n",
|
|
"svm_precision = precision_score(y_test, y_pred, average='micro')\n",
|
|
"svm_recall = recall_score(y_test, y_pred, average='micro')\n",
|
|
"svm_f1 = f1_score(y_test, y_pred, average='micro')\n",
|
|
"\n",
|
|
"gn_classifer = GaussianNB()\n",
|
|
"gn_classifer.fit(x_train, y_train)\n",
|
|
"y_pred = gn_classifer.predict(x_test)\n",
|
|
"gn_precision = precision_score(y_test, y_pred, average='micro')\n",
|
|
"gn_recall = recall_score(y_test, y_pred, average='micro')\n",
|
|
"gn_f1 = f1_score(y_test, y_pred, average='micro')\n",
|
|
"\n",
|
|
"tr_classifer = tree.DecisionTreeClassifier()\n",
|
|
"tr_classifer.fit(x_train, y_train)\n",
|
|
"y_pred = tr_classifer.predict(x_test)\n",
|
|
"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",
|
|
"rf_classifer = RandomForestClassifier()\n",
|
|
"rf_classifer.fit(x_train, y_train)\n",
|
|
"y_pred = rf_classifer.predict(x_test)\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",
|
|
"score_df.loc['SVM'] = [svm_precision, svm_recall, svm_f1]\n",
|
|
"score_df.loc['GaussianNB'] = [gn_precision, gn_recall, gn_f1]\n",
|
|
"score_df.loc['tree'] = [tr_precision, tr_recall, tr_f1]\n",
|
|
"score_df.loc['RandomForest'] = [rf_precision, rf_recall, rf_f1]\n",
|
|
"print(score_df)\n",
|
|
"ax = score_df.plot.bar(title='type+length markov')\n",
|
|
"fig = ax.get_figure()\n",
|
|
"#fig.savefig('../figure/type_length.svg')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 9,
|
|
"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"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAFcCAYAAAAzq/4LAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nTkSuQmCC\n",
|
|
"text/plain": [
|
|
"<matplotlib.figure.Figure at 0x1a1de3cb00>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"#type+length+burst markov\n",
|
|
"x_train = train_df.iloc[:,0:15].values.copy()\n",
|
|
"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",
|
|
"lr_classifer = LogisticRegression()\n",
|
|
"lr_classifer.fit(x_train, y_train)\n",
|
|
"y_pred = lr_classifer.predict(x_test)\n",
|
|
"lr_precision = precision_score(y_test, y_pred, average='micro')\n",
|
|
"lr_recall = recall_score(y_test, y_pred, average='micro')\n",
|
|
"lr_f1 = f1_score(y_test, y_pred, average='micro')\n",
|
|
"\n",
|
|
"svm_classifer = SVC()\n",
|
|
"svm_classifer.fit(x_train, y_train)\n",
|
|
"y_pred = svm_classifer.predict(x_test)\n",
|
|
"svm_precision = precision_score(y_test, y_pred, average='micro')\n",
|
|
"svm_recall = recall_score(y_test, y_pred, average='micro')\n",
|
|
"svm_f1 = f1_score(y_test, y_pred, average='micro')\n",
|
|
"\n",
|
|
"gn_classifer = GaussianNB()\n",
|
|
"gn_classifer.fit(x_train, y_train)\n",
|
|
"y_pred = gn_classifer.predict(x_test)\n",
|
|
"gn_precision = precision_score(y_test, y_pred, average='micro')\n",
|
|
"gn_recall = recall_score(y_test, y_pred, average='micro')\n",
|
|
"gn_f1 = f1_score(y_test, y_pred, average='micro')\n",
|
|
"\n",
|
|
"tr_classifer = tree.DecisionTreeClassifier()\n",
|
|
"tr_classifer.fit(x_train, y_train)\n",
|
|
"y_pred = tr_classifer.predict(x_test)\n",
|
|
"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",
|
|
"rf_classifer = RandomForestClassifier()\n",
|
|
"rf_classifer.fit(x_train, y_train)\n",
|
|
"y_pred = rf_classifer.predict(x_test)\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",
|
|
"score_df.loc['SVM'] = [svm_precision, svm_recall, svm_f1]\n",
|
|
"score_df.loc['GaussianNB'] = [gn_precision, gn_recall, gn_f1]\n",
|
|
"score_df.loc['tree'] = [tr_precision, tr_recall, tr_f1]\n",
|
|
"score_df.loc['RandomForest'] = [rf_precision, rf_recall, rf_f1]\n",
|
|
"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')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"collapsed": true
|
|
},
|
|
"outputs": [],
|
|
"source": []
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 3",
|
|
"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": "3.6.2"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 2
|
|
}
|