{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "e15ff842", "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import warnings\n", "import plotly.offline as py\n", "# py.init_notebook_mode(connected=True)\n", "import plotly.graph_objs as go\n", "import plotly.tools as tls\n", "import plotly.figure_factory as ff\n", "import seaborn as sns\n", "# warnings.filterwarnings('ignore') #ignore warning messages" ] }, { "cell_type": "code", "execution_count": 2, "id": "cf08041d", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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395TRG002948054.50000058.5101032...0.4764930.4764932.4535830.0032292.327038e+0618.5623770.0137660.0180420.0002880.012257
396TRG002954049.25000034.3000133...0.4183820.4183822.9956030.0042431.005061e+06156.6271790.0022280.1360150.0221480.002098
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" ], "text/plain": [ " ID pCR (outcome) RelapseFreeSurvival (outcome) Age ER PgR \\\n", "395 TRG002948 0 54.500000 58.5 1 0 \n", "396 TRG002954 0 49.250000 34.3 0 0 \n", "397 TRG002958 0 48.500000 53.3 0 0 \n", "398 TRG002961 0 47.500000 68.8 1 0 \n", "399 TRG002962 0 46.916667 46.0 1 0 \n", "\n", " HER2 TrippleNegative ChemoGrade Proliferation ... \\\n", "395 1 0 3 2 ... \n", "396 0 1 3 3 ... \n", "397 0 1 2 1 ... \n", "398 0 0 3 3 ... \n", "399 0 0 2 1 ... \n", "\n", " original_glszm_SmallAreaHighGrayLevelEmphasis \\\n", "395 0.476493 \n", "396 0.418382 \n", "397 0.527779 \n", "398 0.313693 \n", "399 0.670229 \n", "\n", " original_glszm_SmallAreaLowGrayLevelEmphasis original_glszm_ZoneEntropy \\\n", "395 0.476493 2.453583 \n", "396 0.418382 2.995603 \n", "397 0.527778 1.500000 \n", "398 0.313693 3.573557 \n", "399 0.670229 1.857045 \n", "\n", " original_glszm_ZonePercentage original_glszm_ZoneVariance \\\n", "395 0.003229 2.327038e+06 \n", "396 0.004243 1.005061e+06 \n", "397 0.003728 2.132007e+05 \n", "398 0.001112 2.008034e+07 \n", "399 0.006706 5.609262e+05 \n", "\n", " original_ngtdm_Busyness original_ngtdm_Coarseness \\\n", "395 18.562377 0.013766 \n", "396 156.627179 0.002228 \n", "397 0.996746 0.252582 \n", "398 204.864200 0.001372 \n", "399 9.609163 0.026591 \n", "\n", " original_ngtdm_Complexity original_ngtdm_Contrast \\\n", "395 0.018042 0.000288 \n", "396 0.136015 0.022148 \n", "397 0.007380 0.000037 \n", "398 0.054063 0.003697 \n", "399 0.018682 0.000311 \n", "\n", " original_ngtdm_Strength \n", "395 0.012257 \n", "396 0.002098 \n", "397 0.231059 \n", "398 0.001368 \n", "399 0.022676 \n", "\n", "[5 rows x 120 columns]" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.read_excel(\"TrainDataset2023.xls\")\n", "df.tail()" ] }, { "cell_type": "code", "execution_count": 3, "id": "6c971a55", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(400, 120)" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.shape" ] }, { "cell_type": "code", "execution_count": 4, "id": "ba26513a", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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pCR (outcome)RelapseFreeSurvival (outcome)
count400.000000400.000000
mean12.69750056.000208
std111.10741727.137584
min0.0000000.000000
25%0.00000038.000000
50%0.00000055.000000
75%0.00000073.000000
max999.000000144.000000
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" ], "text/plain": [ " pCR (outcome) RelapseFreeSurvival (outcome)\n", "count 400.000000 400.000000\n", "mean 12.697500 56.000208\n", "std 111.107417 27.137584\n", "min 0.000000 0.000000\n", "25% 0.000000 38.000000\n", "50% 0.000000 55.000000\n", "75% 0.000000 73.000000\n", "max 999.000000 144.000000" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.iloc[:,0:3].describe()" ] }, { "cell_type": "code", "execution_count": 5, "id": "00865ac9", "metadata": {}, "outputs": [], "source": [ "# replace the 999 values with None, to make it easier for data imputation.\n", "df=df.replace(999, None)" ] }, { "cell_type": "code", "execution_count": 6, "id": "c6497858", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['ID', 'pCR (outcome)', 'RelapseFreeSurvival (outcome)', 'Age', 'ER',\n", " 'PgR', 'HER2', 'TrippleNegative', 'ChemoGrade', 'Proliferation',\n", " ...\n", " 'original_glszm_SmallAreaHighGrayLevelEmphasis',\n", " 'original_glszm_SmallAreaLowGrayLevelEmphasis',\n", " 'original_glszm_ZoneEntropy', 'original_glszm_ZonePercentage',\n", " 'original_glszm_ZoneVariance', 'original_ngtdm_Busyness',\n", " 'original_ngtdm_Coarseness', 'original_ngtdm_Complexity',\n", " 'original_ngtdm_Contrast', 'original_ngtdm_Strength'],\n", " dtype='object', length=120)" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.columns" ] }, { "cell_type": "code", "execution_count": 7, "id": "ed8600fe", "metadata": {}, "outputs": [], "source": [ "# the ID column is not needed.\n", "Df_ = df.iloc[:,1:13]" ] }, { "cell_type": "code", "execution_count": 8, "id": "093b41d1", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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pCR (outcome)RelapseFreeSurvival (outcome)AgeERPgRHER2TrippleNegativeChemoGradeProliferationHistologyTypeLNStatusTumourStage
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"title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 } } }, "title": { "text": "Missing Values in first 12 columns" } } }, "text/html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# the first 12 columns have missing values. Vizualizing them.\n", "\n", "null_feat = pd.DataFrame(len(Df_['pCR (outcome)']) - Df_.isnull().sum(), columns = ['Count'])\n", "print(null_feat)\n", "trace = go.Bar(x = null_feat.index, y = null_feat['Count'] ,opacity = 0.8, marker=dict(color = 'lightgrey',\n", " line=dict(color='#000000',width=1.5)))\n", "\n", "layout = dict(title = \"Missing Values in first 12 columns\")\n", " \n", "fig = dict(data = [trace], layout=layout)\n", "py.iplot(fig)" ] }, { "cell_type": "code", "execution_count": 10, "id": "2e3df9c1", "metadata": {}, "outputs": [], "source": [ "# Data imputation using mode\n", "\n", "columns = Df_.columns\n", "for col in Df_.columns:\n", " Df_[col].fillna(Df_[col].mode()[0], inplace=True)\n", " " ] }, { "cell_type": "code", "execution_count": 11, "id": "3d274755", "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "linkText": "Export to plot.ly", "plotlyServerURL": "https://plot.ly", "showLink": false }, "data": [ { "marker": { "color": 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pCR (outcome)RelapseFreeSurvival (outcome)AgeERPgRHER2TrippleNegativeChemoGradeProliferationHistologyType...original_glszm_SmallAreaHighGrayLevelEmphasisoriginal_glszm_SmallAreaLowGrayLevelEmphasisoriginal_glszm_ZoneEntropyoriginal_glszm_ZonePercentageoriginal_glszm_ZoneVarianceoriginal_ngtdm_Busynessoriginal_ngtdm_Coarsenessoriginal_ngtdm_Complexityoriginal_ngtdm_Contrastoriginal_ngtdm_Strength
count400.000000400.000000400.000000400.000000400.000000400.000000400.000000400.000000400.000000400.000000...4.000000e+024.000000e+024.000000e+02400.0000004.000000e+02400.000000400.000000400.000000400.000000400.000000
mean0.21000056.00020851.8046740.5475000.4050000.3000000.3325002.3975001.5725001.147500...3.957637e-013.911005e-012.722189e+000.0033475.679717e+07178.31124632500.0326200.0569350.0059650.029322
std0.40781827.13758410.9485220.4983620.4915070.4588310.4716990.5001190.7656430.355048...1.666319e-011.615922e-017.648849e-010.0024197.063846e+081045.453432177545.9215680.0471790.0083790.115915
min0.0000000.00000023.0000000.0000000.0000000.0000000.0000001.0000001.0000001.000000...7.050000e-117.050000e-11-3.200000e-160.0000080.000000e+000.0000000.0002480.0000000.0000000.000000
25%0.00000038.00000044.5167690.0000000.0000000.0000000.0000002.0000001.0000001.000000...3.199017e-013.184398e-012.340783e+000.0013891.030473e+0618.7605700.0018260.0186280.0003100.001464
50%0.00000055.00000051.0195071.0000000.0000000.0000000.0000002.0000001.0000001.000000...4.095627e-014.054695e-012.814884e+000.0029443.277334e+0667.9296590.0043830.0477400.0023300.003276
75%0.00000073.00000060.0000001.0000001.0000001.0000001.0000003.0000002.0000001.000000...5.000049e-014.956920e-013.304411e+000.0047989.079686e+06157.3702940.0137690.0853210.0079620.009479
max1.000000144.00000079.6030121.0000001.0000001.0000001.0000003.0000003.0000002.000000...8.773779e-018.571429e-014.947427e+000.0113011.390001e+1020764.6937901000000.0000000.2851000.0607421.145601
\n", "

8 rows × 119 columns

\n", "
" ], "text/plain": [ " pCR (outcome) RelapseFreeSurvival (outcome) Age ER \\\n", "count 400.000000 400.000000 400.000000 400.000000 \n", "mean 0.210000 56.000208 51.804674 0.547500 \n", "std 0.407818 27.137584 10.948522 0.498362 \n", "min 0.000000 0.000000 23.000000 0.000000 \n", "25% 0.000000 38.000000 44.516769 0.000000 \n", "50% 0.000000 55.000000 51.019507 1.000000 \n", "75% 0.000000 73.000000 60.000000 1.000000 \n", "max 1.000000 144.000000 79.603012 1.000000 \n", "\n", " PgR HER2 TrippleNegative ChemoGrade Proliferation \\\n", "count 400.000000 400.000000 400.000000 400.000000 400.000000 \n", "mean 0.405000 0.300000 0.332500 2.397500 1.572500 \n", "std 0.491507 0.458831 0.471699 0.500119 0.765643 \n", "min 0.000000 0.000000 0.000000 1.000000 1.000000 \n", "25% 0.000000 0.000000 0.000000 2.000000 1.000000 \n", "50% 0.000000 0.000000 0.000000 2.000000 1.000000 \n", "75% 1.000000 1.000000 1.000000 3.000000 2.000000 \n", "max 1.000000 1.000000 1.000000 3.000000 3.000000 \n", "\n", " HistologyType ... original_glszm_SmallAreaHighGrayLevelEmphasis \\\n", "count 400.000000 ... 4.000000e+02 \n", "mean 1.147500 ... 3.957637e-01 \n", "std 0.355048 ... 1.666319e-01 \n", "min 1.000000 ... 7.050000e-11 \n", "25% 1.000000 ... 3.199017e-01 \n", "50% 1.000000 ... 4.095627e-01 \n", "75% 1.000000 ... 5.000049e-01 \n", "max 2.000000 ... 8.773779e-01 \n", "\n", " original_glszm_SmallAreaLowGrayLevelEmphasis \\\n", "count 4.000000e+02 \n", "mean 3.911005e-01 \n", "std 1.615922e-01 \n", "min 7.050000e-11 \n", "25% 3.184398e-01 \n", "50% 4.054695e-01 \n", "75% 4.956920e-01 \n", "max 8.571429e-01 \n", "\n", " original_glszm_ZoneEntropy original_glszm_ZonePercentage \\\n", "count 4.000000e+02 400.000000 \n", "mean 2.722189e+00 0.003347 \n", "std 7.648849e-01 0.002419 \n", "min -3.200000e-16 0.000008 \n", "25% 2.340783e+00 0.001389 \n", "50% 2.814884e+00 0.002944 \n", "75% 3.304411e+00 0.004798 \n", "max 4.947427e+00 0.011301 \n", "\n", " original_glszm_ZoneVariance original_ngtdm_Busyness \\\n", "count 4.000000e+02 400.000000 \n", "mean 5.679717e+07 178.311246 \n", "std 7.063846e+08 1045.453432 \n", "min 0.000000e+00 0.000000 \n", "25% 1.030473e+06 18.760570 \n", "50% 3.277334e+06 67.929659 \n", "75% 9.079686e+06 157.370294 \n", "max 1.390001e+10 20764.693790 \n", "\n", " original_ngtdm_Coarseness original_ngtdm_Complexity \\\n", "count 400.000000 400.000000 \n", "mean 32500.032620 0.056935 \n", "std 177545.921568 0.047179 \n", "min 0.000248 0.000000 \n", "25% 0.001826 0.018628 \n", "50% 0.004383 0.047740 \n", "75% 0.013769 0.085321 \n", "max 1000000.000000 0.285100 \n", "\n", " original_ngtdm_Contrast original_ngtdm_Strength \n", "count 400.000000 400.000000 \n", "mean 0.005965 0.029322 \n", "std 0.008379 0.115915 \n", "min 0.000000 0.000000 \n", "25% 0.000310 0.001464 \n", "50% 0.002330 0.003276 \n", "75% 0.007962 0.009479 \n", "max 0.060742 1.145601 \n", "\n", "[8 rows x 119 columns]" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Df_imputed.describe()" ] }, { "cell_type": "code", "execution_count": 15, "id": "17f5a2c5", "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Age is the only continuous feature in the clinical features, drawing a boxplot reveals that it has no outliers\n", "\n", "plt.boxplot(Df_imputed['Age'])\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 16, "id": "4bb89753", "metadata": {}, "outputs": [], "source": [ "#drop RFS column which is not needed for classification\n", "\n", "Df_imputed = Df_imputed.drop(\"RelapseFreeSurvival (outcome)\", axis = 1)" ] }, { "cell_type": "markdown", "id": "d495fdb5", "metadata": {}, "source": [ "### PCA" ] }, { "cell_type": "markdown", "id": "2a8a6bb5", "metadata": {}, "source": [ "**Here are the steps followed for performing PCA:**\n", "\n", "1. Perform one-hot encoding to transform categorical data set to numerical data set\n", "2. Perform training / test split of the dataset\n", "3. Standardize the training and test data set\n", "4. Construct covariance matrix of the training data set\n", "5. Construct eigendecomposition of the covariance matrix\n", "6. Select the most important features using explained variance\n", "7. Construct project matrix; In the code below, the projection matrix is created using the five eigenvectors that correspond to the top five eigenvalues (largest), to capture about 75% of the variance in this dataset\n", "8. Transform the training data set into new feature subspace" ] }, { "cell_type": "code", "execution_count": 17, "id": "863e0896", "metadata": {}, "outputs": [], "source": [ "# only column 11 onwards taken for pca (MRI features)\n", "\n", "Df_forPCA = Df_imputed.iloc[:,11:]" ] }, { "cell_type": "code", "execution_count": 18, "id": "422c57e8", "metadata": {}, "outputs": [], "source": [ "X_forPCA = Df_forPCA\n", "Y_forPCA = Df_imputed[[\"pCR (outcome)\"]]" ] }, { "cell_type": "code", "execution_count": 19, "id": "be45d0c0", "metadata": {}, "outputs": [], "source": [ "from sklearn.decomposition import PCA\n", "from sklearn.preprocessing import StandardScaler\n", "from itertools import chain" ] }, { "cell_type": "code", "execution_count": 20, "id": "f1b05fcc", "metadata": {}, "outputs": [], "source": [ "# calcualting the explained variance gives us an idea of how many components to select after PCA\n", "#To make a PCA, normalize data is essential\n", "\n", "X_pca = X_forPCA.values\n", "X_std = StandardScaler().fit_transform(X_pca)\n", "\n", "pca = PCA(svd_solver='full')\n", "pca_std = pca.fit(X_std, Y_forPCA).transform(X_std)\n", "\n", "pca_std = pd.DataFrame(pca_std)\n", "pca_std = pca_std.merge(Y_forPCA, left_index = True, right_index = True, how = 'left')\n", "pca_std['pCR (outcome)'] = pca_std['pCR (outcome)'].replace({1:'cancer',0:'no cancer'})\n", "\n" ] }, { "cell_type": "code", "execution_count": 21, "id": "6ed2f471", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.8185716214347369" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Explained_variance\n", "\n", "var_pca = pd.DataFrame(pca.explained_variance_ratio_)\n", "var_pca[0:6].values.sum()" ] }, { "cell_type": "code", "execution_count": 22, "id": "bcab9637", "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "linkText": "Export to plot.ly", "plotlyServerURL": "https://plot.ly", "showLink": false }, "data": [ { 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Pie chart to vizualize contribution of each component\n", "\n", "labels = ['COMP1','COMP2','COMP3','COMP4','COMP5','COMP6','COMP7','COMP8','COMP9','COMP10','COMP11to180']\n", "colors = ['gold', 'lightgreen', 'lightcoral', 'lightskyblue', 'lightgrey', 'orange', 'pink', 'cyan', 'canary', 'copper', 'white']\n", "\n", "trace = go.Pie(labels = labels, values = var_pca[0].values, opacity = 0.8,\n", " textfont=dict(size=15),\n", " marker=dict(colors=colors, \n", " line=dict(color='#000000', width=1.5)))\n", "\n", "\n", "layout = dict(title = 'PCA : components and explained variance')\n", " \n", " \n", "fig = dict(data = [trace], layout=layout)\n", "py.iplot(fig)" ] }, { "cell_type": "code", "execution_count": null, "id": "fe9b3f37", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 23, "id": "e68976e6", "metadata": {}, "outputs": [], "source": [ "# we will take the first 6 components as they are responsible for 0.81 of the variance.\n", "# Standardize the features\n", "scaler = StandardScaler()\n", "X_std = scaler.fit_transform(X_forPCA)\n" ] }, { "cell_type": "code", "execution_count": 24, "id": "6efebf64", "metadata": {}, "outputs": [], "source": [ "# Perform PCA\n", "pca = PCA(n_components=6) # Reduce to 6 principal components\n", "\n", "Y_pca = pd.DataFrame(Y_forPCA)\n", "X_pca = pca.fit_transform(X_std)" ] }, { "cell_type": "code", "execution_count": 25, "id": "28a674f1", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.8185716214347369" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sum(pca.explained_variance_ratio_)" ] }, { "cell_type": "code", "execution_count": 26, "id": "a65f6496", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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pCR (outcome)AgeERPgRHER2TrippleNegativeChemoGradeProliferationHistologyTypeLNStatusTumourStage012345
0141.000013311212.5961871.4764900.8353223.5726523.6808441.588340
1039.0110033112-3.402929-1.950037-1.8252971.893426-1.3925850.782602
2131.0000121102-3.831085-2.983421-2.1576452.095281-0.6864642.161302
3035.0000133113-6.312342-1.559572-1.8190642.526369-0.7933460.518627
4061.0100021102-2.275703-3.647228-2.0089273.357714-0.8701792.050370
......................................................
395058.5101032114-4.421618-3.627292-0.8077340.4311160.4010400.350318
396034.300013310210.7657701.8860690.430573-0.233894-0.3286210.065289
397053.3000121102-2.991509-5.684329-0.548646-6.0531063.5337111.468567
398068.81000331130.3713240.7990430.4456635.7959880.859414-2.789871
399046.0100021112-2.744353-4.132731-1.735130-4.106370-0.7574452.916702
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400 rows × 17 columns

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" ], "text/plain": [ " pCR (outcome) Age ER PgR HER2 TrippleNegative ChemoGrade \n", "0 1 41.0 0 0 0 1 3 \\\n", "1 0 39.0 1 1 0 0 3 \n", "2 1 31.0 0 0 0 1 2 \n", "3 0 35.0 0 0 0 1 3 \n", "4 0 61.0 1 0 0 0 2 \n", ".. ... ... .. ... ... ... ... \n", "395 0 58.5 1 0 1 0 3 \n", "396 0 34.3 0 0 0 1 3 \n", "397 0 53.3 0 0 0 1 2 \n", "398 0 68.8 1 0 0 0 3 \n", "399 0 46.0 1 0 0 0 2 \n", "\n", " Proliferation HistologyType LNStatus TumourStage 0 1 \n", "0 3 1 1 2 12.596187 1.476490 \\\n", "1 3 1 1 2 -3.402929 -1.950037 \n", "2 1 1 0 2 -3.831085 -2.983421 \n", "3 3 1 1 3 -6.312342 -1.559572 \n", "4 1 1 0 2 -2.275703 -3.647228 \n", ".. ... ... ... ... ... ... \n", "395 2 1 1 4 -4.421618 -3.627292 \n", "396 3 1 0 2 10.765770 1.886069 \n", "397 1 1 0 2 -2.991509 -5.684329 \n", "398 3 1 1 3 0.371324 0.799043 \n", "399 1 1 1 2 -2.744353 -4.132731 \n", "\n", " 2 3 4 5 \n", "0 0.835322 3.572652 3.680844 1.588340 \n", "1 -1.825297 1.893426 -1.392585 0.782602 \n", "2 -2.157645 2.095281 -0.686464 2.161302 \n", "3 -1.819064 2.526369 -0.793346 0.518627 \n", "4 -2.008927 3.357714 -0.870179 2.050370 \n", ".. ... ... ... ... \n", "395 -0.807734 0.431116 0.401040 0.350318 \n", "396 0.430573 -0.233894 -0.328621 0.065289 \n", "397 -0.548646 -6.053106 3.533711 1.468567 \n", "398 0.445663 5.795988 0.859414 -2.789871 \n", "399 -1.735130 -4.106370 -0.757445 2.916702 \n", "\n", "[400 rows x 17 columns]" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X_pca = pd.DataFrame(X_pca)\n", "Df_afterPCA = Df_imputed.iloc[:,0:11].merge(X_pca, left_index = True, right_index = True, how = 'right')\n", "Df_afterPCA" ] }, { "cell_type": "code", "execution_count": 27, "id": "d7e18c8d", "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/html": [ "
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pCR (outcome)AgeERPgRHER2TrippleNegativeChemoGradeProliferationHistologyTypeLNStatusTumourStageCOMP0COMP1COMP2COMP3COMP4COMP5
0141.000013311212.5961871.4764900.8353223.5726523.6808441.588340
1039.0110033112-3.402929-1.950037-1.8252971.893426-1.3925850.782602
2131.0000121102-3.831085-2.983421-2.1576452.095281-0.6864642.161302
3035.0000133113-6.312342-1.559572-1.8190642.526369-0.7933460.518627
4061.0100021102-2.275703-3.647228-2.0089273.357714-0.8701792.050370
......................................................
395058.5101032114-4.421618-3.627292-0.8077340.4311160.4010400.350318
396034.300013310210.7657701.8860690.430573-0.233894-0.3286210.065289
397053.3000121102-2.991509-5.684329-0.548646-6.0531063.5337111.468567
398068.81000331130.3713240.7990430.4456635.7959880.859414-2.789871
399046.0100021112-2.744353-4.132731-1.735130-4.106370-0.7574452.916702
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400 rows × 17 columns

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" ], "text/plain": [ " pCR (outcome) Age ER PgR HER2 TrippleNegative ChemoGrade \n", "0 1 41.0 0 0 0 1 3 \\\n", "1 0 39.0 1 1 0 0 3 \n", "2 1 31.0 0 0 0 1 2 \n", "3 0 35.0 0 0 0 1 3 \n", "4 0 61.0 1 0 0 0 2 \n", ".. ... ... .. ... ... ... ... \n", "395 0 58.5 1 0 1 0 3 \n", "396 0 34.3 0 0 0 1 3 \n", "397 0 53.3 0 0 0 1 2 \n", "398 0 68.8 1 0 0 0 3 \n", "399 0 46.0 1 0 0 0 2 \n", "\n", " Proliferation HistologyType LNStatus TumourStage COMP0 COMP1 \n", "0 3 1 1 2 12.596187 1.476490 \\\n", "1 3 1 1 2 -3.402929 -1.950037 \n", "2 1 1 0 2 -3.831085 -2.983421 \n", "3 3 1 1 3 -6.312342 -1.559572 \n", "4 1 1 0 2 -2.275703 -3.647228 \n", ".. ... ... ... ... ... ... \n", "395 2 1 1 4 -4.421618 -3.627292 \n", "396 3 1 0 2 10.765770 1.886069 \n", "397 1 1 0 2 -2.991509 -5.684329 \n", "398 3 1 1 3 0.371324 0.799043 \n", "399 1 1 1 2 -2.744353 -4.132731 \n", "\n", " COMP2 COMP3 COMP4 COMP5 \n", "0 0.835322 3.572652 3.680844 1.588340 \n", "1 -1.825297 1.893426 -1.392585 0.782602 \n", "2 -2.157645 2.095281 -0.686464 2.161302 \n", "3 -1.819064 2.526369 -0.793346 0.518627 \n", "4 -2.008927 3.357714 -0.870179 2.050370 \n", ".. ... ... ... ... \n", "395 -0.807734 0.431116 0.401040 0.350318 \n", "396 0.430573 -0.233894 -0.328621 0.065289 \n", "397 -0.548646 -6.053106 3.533711 1.468567 \n", "398 0.445663 5.795988 0.859414 -2.789871 \n", "399 -1.735130 -4.106370 -0.757445 2.916702 \n", "\n", "[400 rows x 17 columns]" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "new_column_names = {0: 'COMP0', 1: 'COMP1', 2: 'COMP2',3: 'COMP3',4:'COMP4',5:'COMP5'}\n", "Df_afterPCA = Df_afterPCA.rename(columns=new_column_names)\n", "Df_afterPCA" ] }, { "cell_type": "code", "execution_count": 28, "id": "06da5229", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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pCR (outcome)AgeERPgRHER2TrippleNegativeChemoGradeProliferationHistologyTypeLNStatusTumourStageCOMP0COMP1COMP2COMP3COMP4COMP5
count400.000000400.000000400.000000400.000000400.000000400.000000400.000000400.000000400.000000400.000000400.0000004.000000e+024.000000e+024.000000e+024.000000e+024.000000e+024.000000e+02
mean0.21000051.8046740.5475000.4050000.3000000.3325002.3975001.5725001.1475000.5350002.6075001.421085e-16-3.552714e-17-8.437695e-17-1.065814e-161.776357e-17-3.552714e-17
std0.40781810.9485220.4983620.4915070.4588310.4716990.5001190.7656430.3550480.4993980.8974735.913835e+003.956354e+003.625338e+003.498424e+002.576519e+002.271563e+00
min0.00000023.0000000.0000000.0000000.0000000.0000001.0000001.0000001.0000000.0000001.000000-2.137126e+01-7.155386e+00-3.817510e+00-1.234668e+01-6.612577e+00-1.025440e+01
25%0.00000044.5167690.0000000.0000000.0000000.0000002.0000001.0000001.0000000.0000002.000000-4.380918e+00-2.300239e+00-1.501204e+00-2.016138e+00-1.603999e+00-1.087117e+00
50%0.00000051.0195071.0000000.0000000.0000000.0000002.0000001.0000001.0000001.0000002.000000-6.291532e-01-4.220578e-01-7.452754e-013.711719e-01-2.808058e-01-3.972333e-02
75%0.00000060.0000001.0000001.0000001.0000001.0000003.0000002.0000001.0000001.0000003.0000003.844593e+001.489531e+001.908735e-012.303221e+001.190874e+001.120237e+00
max1.00000079.6030121.0000001.0000001.0000001.0000003.0000003.0000002.0000001.0000004.0000002.246977e+014.495662e+012.031779e+011.283187e+011.878213e+012.207831e+01
\n", "
" ], "text/plain": [ " pCR (outcome) Age ER PgR HER2 \n", "count 400.000000 400.000000 400.000000 400.000000 400.000000 \\\n", "mean 0.210000 51.804674 0.547500 0.405000 0.300000 \n", "std 0.407818 10.948522 0.498362 0.491507 0.458831 \n", "min 0.000000 23.000000 0.000000 0.000000 0.000000 \n", "25% 0.000000 44.516769 0.000000 0.000000 0.000000 \n", "50% 0.000000 51.019507 1.000000 0.000000 0.000000 \n", "75% 0.000000 60.000000 1.000000 1.000000 1.000000 \n", "max 1.000000 79.603012 1.000000 1.000000 1.000000 \n", "\n", " TrippleNegative ChemoGrade Proliferation HistologyType LNStatus \n", "count 400.000000 400.000000 400.000000 400.000000 400.000000 \\\n", "mean 0.332500 2.397500 1.572500 1.147500 0.535000 \n", "std 0.471699 0.500119 0.765643 0.355048 0.499398 \n", "min 0.000000 1.000000 1.000000 1.000000 0.000000 \n", "25% 0.000000 2.000000 1.000000 1.000000 0.000000 \n", "50% 0.000000 2.000000 1.000000 1.000000 1.000000 \n", "75% 1.000000 3.000000 2.000000 1.000000 1.000000 \n", "max 1.000000 3.000000 3.000000 2.000000 1.000000 \n", "\n", " TumourStage COMP0 COMP1 COMP2 COMP3 \n", "count 400.000000 4.000000e+02 4.000000e+02 4.000000e+02 4.000000e+02 \\\n", "mean 2.607500 1.421085e-16 -3.552714e-17 -8.437695e-17 -1.065814e-16 \n", "std 0.897473 5.913835e+00 3.956354e+00 3.625338e+00 3.498424e+00 \n", "min 1.000000 -2.137126e+01 -7.155386e+00 -3.817510e+00 -1.234668e+01 \n", "25% 2.000000 -4.380918e+00 -2.300239e+00 -1.501204e+00 -2.016138e+00 \n", "50% 2.000000 -6.291532e-01 -4.220578e-01 -7.452754e-01 3.711719e-01 \n", "75% 3.000000 3.844593e+00 1.489531e+00 1.908735e-01 2.303221e+00 \n", "max 4.000000 2.246977e+01 4.495662e+01 2.031779e+01 1.283187e+01 \n", "\n", " COMP4 COMP5 \n", "count 4.000000e+02 4.000000e+02 \n", "mean 1.776357e-17 -3.552714e-17 \n", "std 2.576519e+00 2.271563e+00 \n", "min -6.612577e+00 -1.025440e+01 \n", "25% -1.603999e+00 -1.087117e+00 \n", "50% -2.808058e-01 -3.972333e-02 \n", "75% 1.190874e+00 1.120237e+00 \n", "max 1.878213e+01 2.207831e+01 " ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Df_afterPCA.describe()" ] }, { "cell_type": "code", "execution_count": 29, "id": "20d42ae7", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['pCR (outcome)', 'Age', 'ER', 'PgR', 'HER2', 'TrippleNegative',\n", " 'ChemoGrade', 'Proliferation', 'HistologyType', 'LNStatus',\n", " 'TumourStage', 'COMP0', 'COMP1', 'COMP2', 'COMP3', 'COMP4', 'COMP5'],\n", " dtype='object')" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Df_afterPCA.columns" ] }, { "cell_type": "code", "execution_count": 30, "id": "9a46fb8e", "metadata": {}, "outputs": [], "source": [ "# Age column needs to be standardized as the other clinical features are categiorical\n", "\n", "Df_afterPCA[['Age']] = StandardScaler().fit_transform(Df_afterPCA[['Age']])" ] }, { "cell_type": "code", "execution_count": 31, "id": "5827fee9", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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pCR (outcome)AgeERPgRHER2TrippleNegativeChemoGradeProliferationHistologyTypeLNStatusTumourStageCOMP0COMP1COMP2COMP3COMP4COMP5
count400.0000004.000000e+02400.000000400.000000400.000000400.000000400.000000400.000000400.000000400.000000400.0000004.000000e+024.000000e+024.000000e+024.000000e+024.000000e+024.000000e+02
mean0.210000-9.325873e-170.5475000.4050000.3000000.3325002.3975001.5725001.1475000.5350002.6075001.421085e-16-3.552714e-17-8.437695e-17-1.065814e-161.776357e-17-3.552714e-17
std0.4078181.001252e+000.4983620.4915070.4588310.4716990.5001190.7656430.3550480.4993980.8974735.913835e+003.956354e+003.625338e+003.498424e+002.576519e+002.271563e+00
min0.000000-2.634214e+000.0000000.0000000.0000000.0000001.0000001.0000001.0000000.0000001.000000-2.137126e+01-7.155386e+00-3.817510e+00-1.234668e+01-6.612577e+00-1.025440e+01
25%0.000000-6.664855e-010.0000000.0000000.0000000.0000002.0000001.0000001.0000000.0000002.000000-4.380918e+00-2.300239e+00-1.501204e+00-2.016138e+00-1.603999e+00-1.087117e+00
50%0.000000-7.180426e-021.0000000.0000000.0000000.0000002.0000001.0000001.0000001.0000002.000000-6.291532e-01-4.220578e-01-7.452754e-013.711719e-01-2.808058e-01-3.972333e-02
75%0.0000007.494700e-011.0000001.0000001.0000001.0000003.0000002.0000001.0000001.0000003.0000003.844593e+001.489531e+001.908735e-012.303221e+001.190874e+001.120237e+00
max1.0000002.542183e+001.0000001.0000001.0000001.0000003.0000003.0000002.0000001.0000004.0000002.246977e+014.495662e+012.031779e+011.283187e+011.878213e+012.207831e+01
\n", "
" ], "text/plain": [ " pCR (outcome) Age ER PgR HER2 \n", "count 400.000000 4.000000e+02 400.000000 400.000000 400.000000 \\\n", "mean 0.210000 -9.325873e-17 0.547500 0.405000 0.300000 \n", "std 0.407818 1.001252e+00 0.498362 0.491507 0.458831 \n", "min 0.000000 -2.634214e+00 0.000000 0.000000 0.000000 \n", "25% 0.000000 -6.664855e-01 0.000000 0.000000 0.000000 \n", "50% 0.000000 -7.180426e-02 1.000000 0.000000 0.000000 \n", "75% 0.000000 7.494700e-01 1.000000 1.000000 1.000000 \n", "max 1.000000 2.542183e+00 1.000000 1.000000 1.000000 \n", "\n", " TrippleNegative ChemoGrade Proliferation HistologyType LNStatus \n", "count 400.000000 400.000000 400.000000 400.000000 400.000000 \\\n", "mean 0.332500 2.397500 1.572500 1.147500 0.535000 \n", "std 0.471699 0.500119 0.765643 0.355048 0.499398 \n", "min 0.000000 1.000000 1.000000 1.000000 0.000000 \n", "25% 0.000000 2.000000 1.000000 1.000000 0.000000 \n", "50% 0.000000 2.000000 1.000000 1.000000 1.000000 \n", "75% 1.000000 3.000000 2.000000 1.000000 1.000000 \n", "max 1.000000 3.000000 3.000000 2.000000 1.000000 \n", "\n", " TumourStage COMP0 COMP1 COMP2 COMP3 \n", "count 400.000000 4.000000e+02 4.000000e+02 4.000000e+02 4.000000e+02 \\\n", "mean 2.607500 1.421085e-16 -3.552714e-17 -8.437695e-17 -1.065814e-16 \n", "std 0.897473 5.913835e+00 3.956354e+00 3.625338e+00 3.498424e+00 \n", "min 1.000000 -2.137126e+01 -7.155386e+00 -3.817510e+00 -1.234668e+01 \n", "25% 2.000000 -4.380918e+00 -2.300239e+00 -1.501204e+00 -2.016138e+00 \n", "50% 2.000000 -6.291532e-01 -4.220578e-01 -7.452754e-01 3.711719e-01 \n", "75% 3.000000 3.844593e+00 1.489531e+00 1.908735e-01 2.303221e+00 \n", "max 4.000000 2.246977e+01 4.495662e+01 2.031779e+01 1.283187e+01 \n", "\n", " COMP4 COMP5 \n", "count 4.000000e+02 4.000000e+02 \n", "mean 1.776357e-17 -3.552714e-17 \n", "std 2.576519e+00 2.271563e+00 \n", "min -6.612577e+00 -1.025440e+01 \n", "25% -1.603999e+00 -1.087117e+00 \n", "50% -2.808058e-01 -3.972333e-02 \n", "75% 1.190874e+00 1.120237e+00 \n", "max 1.878213e+01 2.207831e+01 " ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Df_afterPCA.describe()" ] }, { "cell_type": "code", "execution_count": null, "id": "4400d37f", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 32, "id": "a2cb8e4b", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['pCR (outcome)', 'Age', 'ER', 'PgR', 'HER2', 'TrippleNegative',\n", " 'ChemoGrade', 'Proliferation', 'HistologyType', 'LNStatus',\n", " 'TumourStage', 'COMP0', 'COMP1', 'COMP2', 'COMP3', 'COMP4', 'COMP5'],\n", " dtype='object')" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Df_afterPCA.columns" ] }, { "cell_type": "code", "execution_count": 33, "id": "c10aff68", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Correlation matrix between the features.\n", "\n", "cor=Df_afterPCA.corr()\n", "plt.figure(figsize=(20, 10))\n", "sns.heatmap(cor, annot=True ,cmap='Reds')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 34, "id": "adc7d1cb", "metadata": {}, "outputs": [], "source": [ "# Proliferation dropped due to high correlation with other feature\n", "\n", "Df_afterPCA = Df_afterPCA.drop(columns=['Proliferation'])" ] }, { "cell_type": "code", "execution_count": 35, "id": "3db422fc", "metadata": {}, "outputs": [], "source": [ "X = Df_afterPCA.drop(\"pCR (outcome)\", axis = 1)\n", "Y = Df_afterPCA[[\"pCR (outcome)\"]]" ] }, { "cell_type": "code", "execution_count": 36, "id": "bbbf7c99", "metadata": {}, "outputs": [], "source": [ "# Before looking into oversampling we need to split the data into train and test\n", "\n", "from sklearn.model_selection import train_test_split\n", "\n", "X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.3, random_state=1234, stratify = Y)\n", "#X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.2, random_state=0)" ] }, { "cell_type": "code", "execution_count": 37, "id": "7deda9f6", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "pCR (outcome)\n", "0 221\n", "1 59\n", "Name: count, dtype: int64\n", "pCR (outcome)\n", "0 95\n", "1 25\n", "Name: count, dtype: int64\n" ] } ], "source": [ "print(y_train.value_counts())\n", "print(y_test.value_counts())" ] }, { "cell_type": "markdown", "id": "47880ba0", "metadata": {}, "source": [ "### Data Oversampling" ] }, { "cell_type": "markdown", "id": "06ed1e9d", "metadata": {}, "source": [ "**Our data is not balanced. We have very less instances of pCR =1.**" ] }, { "cell_type": "markdown", "id": "ecb3b38f", "metadata": {}, "source": [ "#### SMOTEEEN\n", "In order to balance the data we are first using a hybrid data augumentation technique called SMOTEEEN (SMOTE + ENN)\n", "SMOTE (Synthetic Minority Oversampling Technique), ENN (Ensemble Neural Networks)" ] }, { "cell_type": "code", "execution_count": 38, "id": "b5086f85", "metadata": {}, "outputs": [], "source": [ "# from imblearn.combine import SMOTEENN" ] }, { "cell_type": "code", "execution_count": 39, "id": "48e884d8", "metadata": {}, "outputs": [], "source": [ "# from collections import Counter" ] }, { "cell_type": "code", "execution_count": 40, "id": "98696447", "metadata": {}, "outputs": [], "source": [ "# counter = Y.value_counts()\n", "# print ('Before', counter)\n", "# # oversampling the train dataset using SMOTE + ENN\n", "# smenn = SMOTEENN()\n", "# X_smenn, Y_smenn = smenn.fit_resample(X_train, y_train)\n", "# counter = Y_smenn.value_counts()\n", "# print ('After',counter)" ] }, { "cell_type": "code", "execution_count": null, "id": "3e7d2480", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "id": "cfa576e4", "metadata": {}, "source": [ "**Adaptive Synthetic Sampling Approach**" ] }, { "cell_type": "code", "execution_count": 41, "id": "81b6f872", "metadata": {}, "outputs": [], "source": [ "from imblearn.over_sampling import ADASYN" ] }, { "cell_type": "code", "execution_count": 42, "id": "a9a5a798", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Before pCR (outcome)\n", "0 221\n", "1 59\n", "Name: count, dtype: int64\n", "After pCR (outcome)\n", "1 231\n", "0 221\n", "Name: count, dtype: int64\n" ] } ], "source": [ "counter = y_train.value_counts()\n", "print ('Before', counter)\n", "# oversampling the train dataset using ADASYN\n", "ada = ADASYN(random_state=130, sampling_strategy='auto')\n", "X_ada, Y_ada = ada.fit_resample(X_train, y_train)\n", "counter = Y_ada.value_counts()\n", "print ('After', counter)" ] }, { "cell_type": "code", "execution_count": 43, "id": "cea8efc0", "metadata": {}, "outputs": [], "source": [ "X_train = X_ada\n", "y_train = Y_ada" ] }, { "cell_type": "code", "execution_count": null, "id": "9e2c8275", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "id": "6c3f8218", "metadata": {}, "source": [ "#### Random OverSampling" ] }, { "cell_type": "code", "execution_count": 44, "id": "57eec054", "metadata": {}, "outputs": [], "source": [ "# from imblearn.over_sampling import RandomOverSampler" ] }, { "cell_type": "code", "execution_count": 45, "id": "3ec1b3d9", "metadata": {}, "outputs": [], "source": [ "# rndm_sampler = RandomOverSampler(sampling_strategy='minority')" ] }, { "cell_type": "code", "execution_count": 46, "id": "caf8bfe5", "metadata": {}, "outputs": [], "source": [ "# X_over, y_over = rndm_sampler.fit_resample(X_train, y_train)" ] }, { "cell_type": "code", "execution_count": 47, "id": "74509bbe", "metadata": {}, "outputs": [], "source": [ "# print(y_train.value_counts())\n", "# print(y_over.value_counts())" ] }, { "cell_type": "code", "execution_count": 48, "id": "6c0283ac", "metadata": {}, "outputs": [], "source": [ "# X_train = X_over\n", "# y_train = y_over" ] }, { "cell_type": "markdown", "id": "7262fa4e", "metadata": {}, "source": [ "The best results were achieved through Adaptive Synthetic Sampling Approach" ] }, { "cell_type": "code", "execution_count": null, "id": "1310b54e", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "id": "940e2187", "metadata": {}, "source": [ "## Modelling" ] }, { "cell_type": "markdown", "id": "7885a497", "metadata": {}, "source": [ "### Simple ANN" ] }, { "cell_type": "code", "execution_count": 102, "id": "91f1077c", "metadata": {}, "outputs": [], "source": [ "import tensorflow as tf\n", "from tensorflow.keras import Sequential\n", "from tensorflow.keras.layers import Dense, Dropout" ] }, { "cell_type": "code", "execution_count": 244, "id": "fac960b1", "metadata": {}, "outputs": [], "source": [ "# Define a neural network model\n", "act_func = tf.keras.layers.LeakyReLU(alpha=0.1)\n", "model = Sequential([\n", " Dense(18, activation=act_func, input_shape=(15,)),\n", " Dense(22, activation=act_func),\n", " Dense(12, activation=act_func),\n", " Dense(6, activation=act_func),\n", " Dense(2, activation='softmax')\n", "])\n" ] }, { "cell_type": "code", "execution_count": 245, "id": "c2855f5d", "metadata": {}, "outputs": [], "source": [ "# Compile the model\n", "model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.01), \n", " loss=tf.losses.sparse_categorical_crossentropy, metrics=['accuracy'])" ] }, { "cell_type": "code", "execution_count": 246, "id": "73eb4813", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Model: \"sequential_19\"\n", "_________________________________________________________________\n", " Layer (type) Output Shape Param # \n", "=================================================================\n", " dense_107 (Dense) (None, 18) 288 \n", " \n", " dense_108 (Dense) (None, 22) 418 \n", " \n", " dense_109 (Dense) (None, 12) 276 \n", " \n", " dense_110 (Dense) (None, 6) 78 \n", " \n", " dense_111 (Dense) (None, 2) 14 \n", " \n", "=================================================================\n", "Total params: 1,074\n", "Trainable params: 1,074\n", "Non-trainable params: 0\n", "_________________________________________________________________\n" ] } ], "source": [ "model.summary()" ] }, { "cell_type": "code", "execution_count": 247, "id": "e829c87f", "metadata": {}, "outputs": [], "source": [ "# Train the model using PCA-transformed data\n", "traning = model.fit(X_train, y_train, epochs=200, batch_size=32, verbose=0)" ] }, { "cell_type": "code", "execution_count": 248, "id": "b5f955b4", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "4/4 [==============================] - 0s 2ms/step\n" ] } ], "source": [ "# Making predictions on test data\n", "y_pred = model.predict(X_test)\n", "# y_pred = np.round(y_pred).flatten() # Round predictions for binary classification" ] }, { "cell_type": "code", "execution_count": 249, "id": "83c4dcec", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0]\n" ] } ], "source": [ "# since softmax outpust probabilities, we take \n", "y_pred = [int(np.argmax(x)) for x in y_pred]\n", "print(y_pred)" ] }, { "cell_type": "code", "execution_count": 250, "id": "1c9d39d7", "metadata": {}, "outputs": [], "source": [ "from sklearn.metrics import accuracy_score\n", "from sklearn.metrics import classification_report, confusion_matrix" ] }, { "cell_type": "code", "execution_count": 251, "id": "8c7085bd", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accuracy: 0.725\n" ] } ], "source": [ "# Calculating accuracy\n", "accuracy = accuracy_score(y_test, y_pred)\n", "print(f\"Accuracy: {accuracy}\")" ] }, { "cell_type": "code", "execution_count": 252, "id": "95c53d25", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " precision recall f1-score support\n", "\n", " 0 0.82 0.83 0.83 95\n", " 1 0.33 0.32 0.33 25\n", "\n", " accuracy 0.73 120\n", " macro avg 0.58 0.58 0.58 120\n", "weighted avg 0.72 0.72 0.72 120\n", "\n", "[[79 16]\n", " [17 8]]\n" ] } ], "source": [ "# Print classification report and confusion matrix\n", "print(classification_report(y_test, y_pred))\n", "print(confusion_matrix(y_test, y_pred))" ] }, { "cell_type": "code", "execution_count": 253, "id": "6f2b337a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.5757894736842105\n" ] } ], "source": [ "from sklearn.metrics import balanced_accuracy_score\n", "print(balanced_accuracy_score(y_test, y_pred))" ] }, { "cell_type": "code", "execution_count": 254, "id": "1f31037d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Keras weights file () saving:\n", "...layers\\dense\n", "......vars\n", ".........0\n", ".........1\n", "...layers\\dense\\activation\n", "......vars\n", "...layers\\dense_1\n", "......vars\n", ".........0\n", ".........1\n", "...layers\\dense_2\n", "......vars\n", ".........0\n", ".........1\n", "...layers\\dense_3\n", "......vars\n", ".........0\n", ".........1\n", "...layers\\dense_4\n", "......vars\n", ".........0\n", ".........1\n", "...metrics\\mean\n", "......vars\n", ".........0\n", ".........1\n", "...metrics\\mean_metric_wrapper\n", "......vars\n", ".........0\n", ".........1\n", "...optimizer\n", "......vars\n", ".........0\n", ".........1\n", ".........10\n", ".........11\n", ".........12\n", ".........13\n", ".........14\n", ".........15\n", ".........16\n", ".........17\n", ".........18\n", ".........19\n", ".........2\n", ".........20\n", ".........3\n", ".........4\n", ".........5\n", ".........6\n", ".........7\n", ".........8\n", ".........9\n", "...vars\n", "Keras model archive saving:\n", "File Name Modified Size\n", "config.json 2023-12-17 22:08:39 3747\n", "metadata.json 2023-12-17 22:08:39 64\n", "variables.h5 2023-12-17 22:08:39 46608\n" ] }, { "data": { "text/plain": [ "['DeepLrng_0.57Bal_0.72Acc_0Imbalance.pkl']" ] }, "execution_count": 254, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# import joblib\n", "# joblib.dump(model, 'DeepLrng_0.57Bal_0.72Acc_0Imbalance.pkl')" ] }, { "cell_type": "code", "execution_count": 255, "id": "1d669a5c", "metadata": {}, "outputs": [], "source": [ "# balAcc_deepL = balanced_accuracy_score(y_test, y_pred)" ] }, { "cell_type": "markdown", "id": "c5150641", "metadata": {}, "source": [ "### XGBoost" ] }, { "cell_type": "code", "execution_count": 256, "id": "6fd838a0", "metadata": {}, "outputs": [], "source": [ "from xgboost import XGBClassifier\n", "import matplotlib.pyplot as pyplot" ] }, { "cell_type": "code", "execution_count": 257, "id": "526ab347", "metadata": {}, "outputs": [], "source": [ "evalset = [(X_train, y_train), (X_test, y_test)]" ] }, { "cell_type": "code", "execution_count": 272, "id": "7a88db2e", "metadata": {}, "outputs": [], "source": [ "model = XGBClassifier(min_child_weight=1, max_depth=10, learning_rate=0.05, gamma=0.1, colsample_bytree=0.4, booster='gbtree')" ] }, { "cell_type": "code", "execution_count": null, "id": "1d24b4fb", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 273, "id": "dba3a6e3", "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[0]\tvalidation_0-logloss:0.67661\tvalidation_1-logloss:0.70328\n", "[1]\tvalidation_0-logloss:0.65521\tvalidation_1-logloss:0.69531\n", "[2]\tvalidation_0-logloss:0.63482\tvalidation_1-logloss:0.69321\n", "[3]\tvalidation_0-logloss:0.61969\tvalidation_1-logloss:0.68630\n", "[4]\tvalidation_0-logloss:0.59719\tvalidation_1-logloss:0.67694\n", "[5]\tvalidation_0-logloss:0.57770\tvalidation_1-logloss:0.66516\n", "[6]\tvalidation_0-logloss:0.55879\tvalidation_1-logloss:0.65911\n", "[7]\tvalidation_0-logloss:0.54115\tvalidation_1-logloss:0.65018\n", "[8]\tvalidation_0-logloss:0.52642\tvalidation_1-logloss:0.64157\n", "[9]\tvalidation_0-logloss:0.51256\tvalidation_1-logloss:0.63656\n", "[10]\tvalidation_0-logloss:0.49827\tvalidation_1-logloss:0.62891\n", "[11]\tvalidation_0-logloss:0.48187\tvalidation_1-logloss:0.62065\n", "[12]\tvalidation_0-logloss:0.47075\tvalidation_1-logloss:0.61621\n", 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XGBClassifier(base_score=None, booster='gbtree', callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=0.4, device=None, early_stopping_rounds=None,\n",
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       "              interaction_constraints=None, learning_rate=0.05, max_bin=None,\n",
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       "              multi_strategy=None, n_estimators=None, n_jobs=None,\n",
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In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
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" ], "text/plain": [ "XGBClassifier(base_score=None, booster='gbtree', callbacks=None,\n", " colsample_bylevel=None, colsample_bynode=None,\n", " colsample_bytree=0.4, device=None, early_stopping_rounds=None,\n", " enable_categorical=False, eval_metric=None, feature_types=None,\n", " gamma=0.1, grow_policy=None, importance_type=None,\n", " interaction_constraints=None, learning_rate=0.05, max_bin=None,\n", " max_cat_threshold=None, max_cat_to_onehot=None,\n", " max_delta_step=None, max_depth=10, max_leaves=None,\n", " min_child_weight=1, missing=nan, monotone_constraints=None,\n", " multi_strategy=None, n_estimators=None, n_jobs=None,\n", " num_parallel_tree=None, random_state=None, ...)" ] }, "execution_count": 273, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.fit(X_train, y_train, eval_metric='logloss', eval_set=evalset)" ] }, { "cell_type": "code", "execution_count": 274, "id": "c4259ffe", "metadata": {}, "outputs": [], "source": [ "results = model.evals_result()" ] }, { "cell_type": "code", "execution_count": 275, "id": "16e42c9c", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# plot learning curves\n", "\n", "pyplot.plot(results['validation_0']['logloss'], label='train')\n", "pyplot.plot(results['validation_1']['logloss'], label='test')\n", "# show the legend\n", "pyplot.legend()\n", "# show the plot\n", "pyplot.show()" ] }, { "cell_type": "code", "execution_count": 276, "id": "43313de6", "metadata": {}, "outputs": [], "source": [ "y_pred = model.predict(X_test)\n", "# since XGBoost outputs probabilities insted of binary values we have\n", "# to round them to the nearest\n", "y_pred = [round(value) for value in y_pred] \n" ] }, { "cell_type": "code", "execution_count": 277, "id": "8a841d6d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " precision recall f1-score support\n", "\n", " 0 0.82 0.84 0.83 95\n", " 1 0.35 0.32 0.33 25\n", "\n", " accuracy 0.73 120\n", " macro avg 0.59 0.58 0.58 120\n", "weighted avg 0.73 0.73 0.73 120\n", "\n", "[[80 15]\n", " [17 8]]\n" ] } ], "source": [ "from sklearn.metrics import classification_report, confusion_matrix\n", "\n", "# Print classification report and confusion matrix\n", "print(classification_report(y_test, y_pred))\n", "print(confusion_matrix(y_test, y_pred))" ] }, { "cell_type": "code", "execution_count": 278, "id": "d794033a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.5810526315789474\n" ] } ], "source": [ "from sklearn.metrics import balanced_accuracy_score\n", "print(balanced_accuracy_score(y_test, y_pred))" ] }, { "cell_type": "code", "execution_count": 279, "id": "312380b7", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['XGBoost_0.73_0.58Bal_0Imbalance_final.pkl']" ] }, "execution_count": 279, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# import joblib\n", "# joblib.dump(model, 'XGBoost_0.73_0.58Bal_0Imbalance_final.pkl')" ] }, { "cell_type": "code", "execution_count": 280, "id": "26725c9b", "metadata": {}, "outputs": [], "source": [ "# balAcc_XGB = balanced_accuracy_score(y_test, y_pred)" ] }, { "cell_type": "code", "execution_count": 268, "id": "ea0f5e05", "metadata": {}, "outputs": [], "source": [ "from sklearn.model_selection import GridSearchCV\n", "from sklearn.metrics import make_scorer, balanced_accuracy_score" ] }, { "cell_type": "code", "execution_count": null, "id": "31cc1391", "metadata": {}, "outputs": [], "source": [ "# bl_scorer = make_scorer(balanced_accuracy_score)\n", "\n", "# clf = GridSearchCV(XGBClassifier(), {\n", "# 'booster': ['gblinear', 'gbtree', 'dart'],\n", "# 'max_depth': [5, 6, 7, 8, 9, 10]\n", "# }, cv=3, return_train_score = False, scoring=bl_scorer)\n", "\n", "# clf.fit(X_train, y_train)\n", "# clf_results = pd.DataFrame(clf.cv_results_)\n", "# clf_results" ] }, { "cell_type": "code", "execution_count": 269, "id": "aab2e4d6", "metadata": {}, "outputs": [], "source": [ "params = {\n", " 'learning_rate' :[ 0.05, 0.10, 0.15, 0.20, 0.25, 0.30 ],\n", " 'max_depth' :[3,4,5,6,8,10,12,15],\n", " 'min_child_weight' :[1,3,5,7],\n", " 'gamma' : [0.0, 0.1, 0.2, 0.3, 0.4],\n", " 'colsample_bytree' :[0.3,0.4,0.5,0.7],\n", " 'booster': ['gbtree', 'dart']\n", "}" ] }, { "cell_type": "code", "execution_count": 270, "id": "2612239f", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Fitting 3 folds for each of 5 candidates, totalling 15 fits\n" ] }, { "data": { "text/html": [ "
XGBClassifier(base_score=None, booster='gbtree', callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=0.4, device=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=0.1, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=0.05, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=10, max_leaves=None,\n",
       "              min_child_weight=1, missing=nan, monotone_constraints=None,\n",
       "              multi_strategy=None, n_estimators=None, n_jobs=None,\n",
       "              num_parallel_tree=None, random_state=None, ...)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" ], "text/plain": [ "XGBClassifier(base_score=None, booster='gbtree', callbacks=None,\n", " colsample_bylevel=None, colsample_bynode=None,\n", " colsample_bytree=0.4, device=None, early_stopping_rounds=None,\n", " enable_categorical=False, eval_metric=None, feature_types=None,\n", " gamma=0.1, grow_policy=None, importance_type=None,\n", " interaction_constraints=None, learning_rate=0.05, max_bin=None,\n", " max_cat_threshold=None, max_cat_to_onehot=None,\n", " max_delta_step=None, max_depth=10, max_leaves=None,\n", " min_child_weight=1, missing=nan, monotone_constraints=None,\n", " multi_strategy=None, n_estimators=None, n_jobs=None,\n", " num_parallel_tree=None, random_state=None, ...)" ] }, "execution_count": 270, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.model_selection import RandomizedSearchCV\n", "rmCV = RandomizedSearchCV(XGBClassifier(), param_distributions=params, n_iter=5, scoring='roc_auc', n_jobs =-1, cv=3, verbose=3)\n", "rmCV.fit(X_train, y_train)\n", "rmCV.best_estimator_" ] }, { "cell_type": "code", "execution_count": 271, "id": "2829402b", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'min_child_weight': 1,\n", " 'max_depth': 10,\n", " 'learning_rate': 0.05,\n", " 'gamma': 0.1,\n", " 'colsample_bytree': 0.4,\n", " 'booster': 'gbtree'}" ] }, "execution_count": 271, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rmCV.best_params_" ] }, { "cell_type": "code", "execution_count": null, "id": "f4d39c94", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "id": "7de4de33", "metadata": {}, "source": [ "### LightGBM" ] }, { "cell_type": "code", "execution_count": 281, "id": "8e3b9cbd", "metadata": {}, "outputs": [], "source": [ "from lightgbm import LGBMClassifier" ] }, { "cell_type": "code", "execution_count": 282, "id": "ed6f29fd", "metadata": {}, "outputs": [], "source": [ "model = LGBMClassifier()" ] }, { "cell_type": "code", "execution_count": 283, "id": "36d16ee3", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\USER\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:99: DataConversionWarning:\n", "\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "\n", "C:\\Users\\USER\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:134: DataConversionWarning:\n", "\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[LightGBM] [Info] Number of positive: 231, number of negative: 221\n", "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000557 seconds.\n", "You can set `force_row_wise=true` to remove the overhead.\n", "And if memory is not enough, you can set `force_col_wise=true`.\n", "[LightGBM] [Info] Total Bins 1080\n", "[LightGBM] [Info] Number of data points in the train set: 452, number of used features: 15\n", "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.511062 -> initscore=0.044255\n", "[LightGBM] [Info] Start training from score 0.044255\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No 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LGBMClassifier()
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" ], "text/plain": [ "LGBMClassifier()" ] }, "execution_count": 283, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.fit(X_train, y_train)" ] }, { "cell_type": "code", "execution_count": 284, "id": "9a2674d7", "metadata": {}, "outputs": [], "source": [ "y_pred = model.predict(X_test)\n", "# since LightGBM outputs probabilities insted of binary values we have\n", "# to round them to the nearest\n", "y_pred = [round(value) for value in y_pred] " ] }, { "cell_type": "code", "execution_count": 285, "id": "b4a0b802", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " precision recall f1-score support\n", "\n", " 0 0.82 0.84 0.83 95\n", " 1 0.35 0.32 0.33 25\n", "\n", " accuracy 0.73 120\n", " macro avg 0.59 0.58 0.58 120\n", "weighted avg 0.73 0.73 0.73 120\n", "\n", "[[80 15]\n", " [17 8]]\n" ] } ], "source": [ "from sklearn.metrics import classification_report, confusion_matrix\n", "\n", "# Print classification report and confusion matrix\n", "print(classification_report(y_test, y_pred))\n", "print(confusion_matrix(y_test, y_pred))" ] }, { "cell_type": "code", "execution_count": 286, "id": "e3d411a4", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.5810526315789474\n" ] } ], "source": [ "from sklearn.metrics import balanced_accuracy_score\n", "print(balanced_accuracy_score(y_test, y_pred))" ] }, { "cell_type": "code", "execution_count": 287, "id": "25e15b80", "metadata": {}, "outputs": [], "source": [ "# balAcc_LGBM = balanced_accuracy_score(y_test, y_pred)" ] }, { "cell_type": "markdown", "id": "5dcb7f85", "metadata": {}, "source": [ "### Logistic Regression" ] }, { "cell_type": "code", "execution_count": 288, "id": "c669d69a", "metadata": {}, "outputs": [], "source": [ "from sklearn.model_selection import train_test_split\n", "from sklearn.linear_model import LogisticRegression\n", "from sklearn.model_selection import KFold" ] }, { "cell_type": "code", "execution_count": 289, "id": "42f9bc94", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
LogisticRegression(max_iter=500)
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" ], "text/plain": [ "LogisticRegression(max_iter=500)" ] }, "execution_count": 289, "metadata": {}, "output_type": "execute_result" } ], "source": [ "logReg = LogisticRegression(max_iter=500, tol=0.0001)\n", "logReg.fit(X_train, y_train.values.ravel())" ] }, { "cell_type": "code", "execution_count": 290, "id": "445ca097", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0,\n", " 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1,\n", " 0, 0, 0, 1, 0, 0, 0, 0, 0, 0], dtype=int64)" ] }, "execution_count": 290, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y_pred=logReg.predict(X_test)\n", "y_pred" ] }, { "cell_type": "code", "execution_count": 291, "id": "771ca9f1", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[77, 18],\n", " [18, 7]], dtype=int64)" ] }, "execution_count": 291, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn import metrics\n", "from sklearn.metrics import classification_report, confusion_matrix\n", "cnf_matrix = metrics.confusion_matrix(y_test, y_pred)\n", "cnf_matrix" ] }, { "cell_type": "code", "execution_count": 292, "id": "da36200e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " precision recall f1-score support\n", "\n", " 0 0.81 0.81 0.81 95\n", " 1 0.28 0.28 0.28 25\n", "\n", " accuracy 0.70 120\n", " macro avg 0.55 0.55 0.55 120\n", "weighted avg 0.70 0.70 0.70 120\n", "\n" ] } ], "source": [ "print(classification_report(y_test, y_pred))" ] }, { "cell_type": "code", "execution_count": 293, "id": "ecb51543", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.5452631578947369\n" ] } ], "source": [ "from sklearn.metrics import balanced_accuracy_score\n", "print(balanced_accuracy_score(y_test, y_pred))" ] }, { "cell_type": "code", "execution_count": 294, "id": "a662c876", "metadata": {}, "outputs": [], "source": [ "# balAcc_LogReg = balanced_accuracy_score(y_test, y_pred)" ] }, { "cell_type": "markdown", "id": "275688a7", "metadata": {}, "source": [ "### SVC" ] }, { "cell_type": "code", "execution_count": 295, "id": "b02b9f4c", "metadata": {}, "outputs": [], "source": [ "from sklearn.svm import SVC\n", "from sklearn.metrics import classification_report, confusion_matrix" ] }, { "cell_type": "code", "execution_count": 308, "id": "93c0eddb", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
SVC(gamma=0.1)
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" ], "text/plain": [ "SVC(gamma=0.1)" ] }, "execution_count": 308, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model = SVC(kernel = 'rbf', gamma=0.1, C=1.0)\n", "model.fit(X_train, y_train.values.ravel())" ] }, { "cell_type": "code", "execution_count": 309, "id": "f555f2f0", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " precision recall f1-score support\n", "\n", " 0 0.82 0.87 0.85 95\n", " 1 0.37 0.28 0.32 25\n", "\n", " accuracy 0.75 120\n", " macro avg 0.60 0.58 0.58 120\n", "weighted avg 0.73 0.75 0.74 120\n", "\n" ] } ], "source": [ "y_pred = model.predict(X_test)\n", "print(classification_report(y_test, y_pred))" ] }, { "cell_type": "code", "execution_count": 310, "id": "054e912a", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[83, 12],\n", " [18, 7]], dtype=int64)" ] }, "execution_count": 310, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cnf_matrix = confusion_matrix(y_test, y_pred)\n", "cnf_matrix" ] }, { "cell_type": "code", "execution_count": 311, "id": "aa6362cb", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.5768421052631579\n" ] } ], "source": [ "from sklearn.metrics import balanced_accuracy_score\n", "print(balanced_accuracy_score(y_test, y_pred))" ] }, { "cell_type": "code", "execution_count": 312, "id": "1f367280", "metadata": {}, "outputs": [], "source": [ "# clf = GridSearchCV(SVC(), {\n", "# 'kernel': ['linear', 'poly', 'rbf', 'sigmoid'],\n", "# 'C': [0.1, 1, 10, 50]\n", "# }, cv=3, return_train_score = False)\n", "\n", "# clf.fit(X_ada, Y_ada)\n", "# clf_results = pd.DataFrame(clf.cv_results_)\n", "# clf_results" ] }, { "cell_type": "code", "execution_count": 313, "id": "e973a1ce", "metadata": {}, "outputs": [], "source": [ "params = {\n", " 'kernel': ['linear', 'poly', 'rbf', 'sigmoid'],\n", " 'C': np.logspace(-3, 2, 6),\n", " 'gamma' : np.logspace(-3, 2, 6)\n", "}" ] }, { "cell_type": "code", "execution_count": 306, "id": "85bc8d41", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Fitting 3 folds for each of 5 candidates, totalling 15 fits\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\USER\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\utils\\validation.py:1143: DataConversionWarning:\n", "\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "\n" ] }, { "data": { "text/html": [ "
SVC(gamma=0.1)
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" ], "text/plain": [ "SVC(gamma=0.1)" ] }, "execution_count": 306, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.model_selection import RandomizedSearchCV\n", "rmCV = RandomizedSearchCV(SVC(), param_distributions=params, n_iter=5, scoring='roc_auc', n_jobs =-1, cv=3, verbose=3)\n", "rmCV.fit(X_train, y_train)\n", "rmCV.best_estimator_" ] }, { "cell_type": "code", "execution_count": 307, "id": "fb2d74c8", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'kernel': 'rbf', 'gamma': 0.1, 'C': 1.0}" ] }, "execution_count": 307, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rmCV.best_params_" ] }, { "cell_type": "code", "execution_count": 314, "id": "b8c2ae90", "metadata": {}, "outputs": [], "source": [ "# balAcc_SVC = balanced_accuracy_score(y_test, y_pred)" ] }, { "cell_type": "code", "execution_count": null, "id": "40fa50ce", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 327, "id": "8b513ea4", "metadata": {}, "outputs": [], "source": [ "import math\n", "acc_val = [balAcc_deepL, balAcc_LGBM, balAcc_LogReg, balAcc_SVC, balAcc_XGB]\n", "rounded_acc_val = []\n", "\n", "for var in acc_val:\n", " rounded_var = round(var, 3)\n", " rounded_acc_val.append(rounded_var)" ] }, { "cell_type": "code", "execution_count": 329, "id": "79520186", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "models = ['NeuralNet', 'LightGBM', 'LogReg', 'SVC', 'XGBoost']\n", "\n", "# Define colors for each bar\n", "colors = ['cyan', 'green', 'orange', 'red', 'purple']\n", "\n", "# Create a figure and axis for bar plot\n", "fig, ax = plt.subplots(figsize=(8, 6))\n", "\n", "# Plot the bar plot\n", "bars = ax.bar(models, rounded_acc_val, color=colors)\n", "\n", "# Add value labels to each bar\n", "for bar in bars:\n", " height = bar.get_height()\n", " ax.annotate(f'{height}', xy=(bar.get_x() + bar.get_width() / 2, height),\n", " xytext=(0, 3), textcoords=\"offset points\",\n", " ha='center', va='bottom')\n", "\n", "ax.set_ylabel('Balanced Accuracy')\n", "ax.set_title('Balanced Accuracy Score Comparison')\n", "\n", "# Show the plot\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "id": "1744cc36", "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": "3.11.5" } }, "nbformat": 4, "nbformat_minor": 5 }