364 lines
41 KiB
Plaintext
364 lines
41 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 35,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"end2\n"
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]
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}
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],
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"source": [
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"import pandas as pd\n",
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"import numpy as np\n",
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"N = 20\n",
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"date = '2019-12-20_21'\n",
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"root_dir = '/Users/Leo/Documents/github/GradProj/'\n",
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"train_path = root_dir + 'Experiment/MarkovModel/CsvFile/' + date + '/train.csv'\n",
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"test_path = root_dir + 'Experiment/MarkovModel/CsvFile/' + date + '/test.csv'\n",
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"train_df = pd.read_csv(train_path,index_col=0)\n",
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"test_df = pd.read_csv(test_path,index_col=0)\n",
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"print('end2')"
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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": 36,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"from sklearn.linear_model import LogisticRegression\n",
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"from sklearn.svm import SVC\n",
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"from sklearn.naive_bayes import GaussianNB\n",
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"from sklearn import tree\n",
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"from sklearn.ensemble import RandomForestClassifier\n",
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"from sklearn.metrics import f1_score,recall_score,precision_score\n",
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"\n",
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"%matplotlib inline\n",
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"import os\n",
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"import numpy as np\n",
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"import pandas as pd\n",
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"import matplotlib.pyplot as plt"
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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": 25,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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" precision recall f1\n",
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"LogisticRegression 0.682227 0.682227 0.682227\n",
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"SVM 0.706852 0.706852 0.706852\n",
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"GaussianNB 0.680086 0.680086 0.680086\n",
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"tree 0.736188 0.736188 0.736188\n",
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"RandomForest 0.736831 0.736831 0.736831\n"
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]
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},
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{
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"data": {
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niS222KLFPvVYcTjcLRcO9g3n986ycLibmRVQ9itEZi2k9+i7q3q+SaftW9XzNccxhxzD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\nAAAASUVORK5CYII=\n",
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"text/plain": [
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"<matplotlib.figure.Figure at 0x1a10748940>"
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]
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},
|
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"#type markov\n",
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"x_train = train_df.iloc[:,0:5].values.copy()\n",
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"y_train = train_df['label'].values.copy()\n",
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"x_test = test_df.iloc[:,0:5].values.copy()\n",
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"y_test = test_df['label'].values.copy()\n",
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"lr_classifer = LogisticRegression()\n",
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"lr_classifer.fit(x_train, y_train)\n",
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"y_pred = lr_classifer.predict(x_test)\n",
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"lr_precision = precision_score(y_test, y_pred, average='micro')\n",
|
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"lr_recall = recall_score(y_test, y_pred, average='micro')\n",
|
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"lr_f1 = f1_score(y_test, y_pred, average='micro')\n",
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"\n",
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"svm_classifer = SVC()\n",
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"svm_classifer.fit(x_train, y_train)\n",
|
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"y_pred = svm_classifer.predict(x_test)\n",
|
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"svm_precision = precision_score(y_test, y_pred, average='micro')\n",
|
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"svm_recall = recall_score(y_test, y_pred, average='micro')\n",
|
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"svm_f1 = f1_score(y_test, y_pred, average='micro')\n",
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"\n",
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"gn_classifer = GaussianNB()\n",
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"gn_classifer.fit(x_train, y_train)\n",
|
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"y_pred = gn_classifer.predict(x_test)\n",
|
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"gn_precision = precision_score(y_test, y_pred, average='micro')\n",
|
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"gn_recall = recall_score(y_test, y_pred, average='micro')\n",
|
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"gn_f1 = f1_score(y_test, y_pred, average='micro')\n",
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"\n",
|
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"tr_classifer = tree.DecisionTreeClassifier()\n",
|
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"tr_classifer.fit(x_train, y_train)\n",
|
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"y_pred = tr_classifer.predict(x_test)\n",
|
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"tr_precision = precision_score(y_test, y_pred, average='micro')\n",
|
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"tr_recall = recall_score(y_test, y_pred, average='micro')\n",
|
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"tr_f1 = f1_score(y_test, y_pred, average='micro')\n",
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"\n",
|
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"rf_classifer = RandomForestClassifier()\n",
|
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"rf_classifer.fit(x_train, y_train)\n",
|
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"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",
|
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"\n",
|
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"score_df = pd.DataFrame(np.zeros((5,3)),index = ['LogisticRegression', 'SVM', 'GaussianNB', 'tree', 'RandomForest'], \\\n",
|
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" columns = ['precision', 'recall', 'f1'])\n",
|
|
"score_df.loc['LogisticRegression'] = [lr_precision, lr_recall, lr_f1]\n",
|
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"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": 13,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
" precision recall f1\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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iQuz8+fNXP66vr2/xn2+2qVXS5z4J2FVSd0ntSS6Yjm/Q5lXgEABJewIdgIXVLNTMzCrX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\n",
|
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"text/plain": [
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"<matplotlib.figure.Figure at 0x1a1deb3438>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
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}
|
|
],
|
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"source": [
|
|
"#type+length markov\n",
|
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"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": 37,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"[4670, 5]\n",
|
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"0.32762312633832974\n",
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"0.998692810458\n",
|
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"0.998692810458\n",
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"0.998692810458\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"\"\\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\""
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]
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},
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"execution_count": 37,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"#type+length+burst markov\n",
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"x_train = train_df.iloc[:,0:15].values.copy()\n",
|
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"y_train = train_df['label'].values.copy()\n",
|
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"x_test = test_df.iloc[:,0:15].values.copy()\n",
|
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"y_test = test_df['label'].values.copy()\n",
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"\n",
|
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"def my_pred(y_pred, y_test, proba):\n",
|
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" y_pred1 = list()\n",
|
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" y_test1 = list()\n",
|
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" [rows, clos] = proba.shape\n",
|
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" print([rows, clos])\n",
|
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" for i in range(rows):\n",
|
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" temp = max(proba[i])\n",
|
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" if temp < 0.95:\n",
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" continue\n",
|
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" y_pred1.append(y_pred[i])\n",
|
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" y_test1.append(y_test[i])\n",
|
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" f1 = f1_score(y_test1, y_pred1, average='micro')\n",
|
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" recall = recall_score(y_test1, y_pred1, average='micro')\n",
|
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" precision = precision_score(y_test1, y_pred1, average='micro')\n",
|
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" print(len(y_test1) / len(y_test))\n",
|
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" print(precision)\n",
|
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" print(recall)\n",
|
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" print(f1)\n",
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"\n",
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"'''\n",
|
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"lr_classifer = LogisticRegression()\n",
|
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"lr_classifer.fit(x_train, y_train)\n",
|
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"y_pred = lr_classifer.predict(x_test)\n",
|
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"lr_precision = precision_score(y_test, y_pred, average='micro')\n",
|
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"lr_recall = recall_score(y_test, y_pred, average='micro')\n",
|
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"lr_f1 = f1_score(y_test, y_pred, average='micro')\n",
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"\n",
|
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"svm_classifer = SVC()\n",
|
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"svm_classifer.fit(x_train, y_train)\n",
|
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"y_pred = svm_classifer.predict(x_test)\n",
|
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"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",
|
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"\n",
|
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"gn_classifer = GaussianNB()\n",
|
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"gn_classifer.fit(x_train, y_train)\n",
|
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"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",
|
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"\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",
|
|
"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')\n",
|
|
"'''"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"collapsed": true
|
|
},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"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"
|
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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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