RandomForestClassifier.ipynb

{"cells":[{"cell_type":"markdown","metadata":{"id":"KRQwjBoS0XGy"},"source":["## Setup"]},{"cell_type":"code","execution_count":1,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"KpfqPcu8ZCtD","outputId":"1316646f-3a23-484d-cfa0-3999ca2c6e2e","executionInfo":{"status":"ok","timestamp":1652682749342,"user_tz":-330,"elapsed":60812,"user":{"displayName":"ABDUR RAHAMAN","userId":"18268289993785820909"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}],"source":["from google.colab import drive\n","drive.mount('/content/drive');"]},{"cell_type":"code","execution_count":2,"metadata":{"id":"mbQEdDbPdbrO","executionInfo":{"status":"ok","timestamp":1652682772918,"user_tz":-330,"elapsed":1963,"user":{"displayName":"ABDUR RAHAMAN","userId":"18268289993785820909"}}},"outputs":[],"source":["import pandas as pd\n","import numpy as np\n","import seaborn as sb\n","import matplotlib.pyplot as plt\n","from sklearn.model_selection import train_test_split\n","from sklearn.preprocessing import StandardScaler\n","from sklearn import model_selection\n","from sklearn.model_selection import cross_val_score\n","from sklearn.metrics import classification_report, confusion_matrix\n","from sklearn.ensemble import RandomForestClassifier\n","from imblearn.under_sampling import TomekLinks\n","from imblearn.under_sampling import NearMiss\n","from collections import Counter\n","from imblearn.over_sampling import SMOTE\n","from sklearn.model_selection import RandomizedSearchCV\n","from sklearn.model_selection import GridSearchCV\n","from imblearn.under_sampling import RandomUnderSampler\n","from random import randint as r"]},{"cell_type":"code","execution_count":3,"metadata":{"id":"ZyANpMQDbsHh","executionInfo":{"status":"ok","timestamp":1652682791473,"user_tz":-330,"elapsed":4466,"user":{"displayName":"ABDUR RAHAMAN","userId":"18268289993785820909"}}},"outputs":[],"source":["#data = pd.read_excel(io='/content/drive/MyDrive/Biology /Biology Research Project/sclc labelled data.xlsx',sheet_name=\"Sheet1\")\n","data = pd.read_excel(io='/content/drive/MyDrive/Biology Research Project/sclc labelled data.xlsx',sheet_name=\"Sheet1\")"]},{"cell_type":"markdown","metadata":{"id":"QfPTy63d0dgj"},"source":["## Data Preprocess"]},{"cell_type":"code","execution_count":4,"metadata":{"id":"GebgPDx1bxFf","executionInfo":{"status":"ok","timestamp":1652682814461,"user_tz":-330,"elapsed":517,"user":{"displayName":"ABDUR RAHAMAN","userId":"18268289993785820909"}}},"outputs":[],"source":["data['NSCLC'] = data['NSCLC'].fillna(0)\n","data['SCLC'] = data['SCLC'].fillna(0)\n","x = data.iloc[:,[3,4,6]]\n","y_sclc = data.iloc[:,8]\n","y_nsclc = data.iloc[:,9]"]},{"cell_type":"code","execution_count":5,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"lBMdO4abk9_9","outputId":"2357d088-ff05-4910-f1c8-c0137793a657","executionInfo":{"status":"ok","timestamp":1652682816505,"user_tz":-330,"elapsed":12,"user":{"displayName":"ABDUR RAHAMAN","userId":"18268289993785820909"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["SCLC Original dataset shape: Counter({0.0: 18857, 1.0: 921})\n","SCLC Resample dataset shape: Counter({0.0: 921, 1.0: 921})\n","NSCLC Original dataset shape: Counter({0.0: 19087, 1.0: 691})\n","NSCLC Resample dataset shape: Counter({0.0: 691, 1.0: 691})\n"]}],"source":["nm = NearMiss()\n","print('SCLC Original dataset shape:', Counter(y_sclc))\n","x_nm_sclc, y_nm_sclc = nm.fit_resample(x, y_sclc)\n","print('SCLC Resample dataset shape:', Counter(y_nm_sclc))\n","print('NSCLC Original dataset shape:', Counter(y_nsclc))\n","x_nm_nsclc, y_nm_nsclc = nm.fit_resample(x, y_nsclc)\n","print('NSCLC Resample dataset shape:', Counter(y_nm_nsclc))"]},{"cell_type":"code","execution_count":6,"metadata":{"id":"DUhZ7SjogbDg","executionInfo":{"status":"ok","timestamp":1652682818952,"user_tz":-330,"elapsed":735,"user":{"displayName":"ABDUR RAHAMAN","userId":"18268289993785820909"}}},"outputs":[],"source":["x_sclc = x_nm_sclc\n","x_nsclc = x_nm_nsclc\n","y_sclc = y_nm_sclc\n","y_nsclc = y_nm_nsclc"]},{"cell_type":"markdown","metadata":{"id":"_Oy3XurH0jPa"},"source":["### Dividing data into Train and Test"]},{"cell_type":"code","execution_count":7,"metadata":{"id":"t8Ks1RYfb0cG","executionInfo":{"status":"ok","timestamp":1652682822057,"user_tz":-330,"elapsed":428,"user":{"displayName":"ABDUR RAHAMAN","userId":"18268289993785820909"}}},"outputs":[],"source":["xtrain_sclc, xtest_sclc, ytrain_sclc, ytest_sclc = train_test_split(x_sclc, y_sclc, test_size = 0.20)\n","xtrain_nsclc, xtest_nsclc, ytrain_nsclc, ytest_nsclc = train_test_split(x_nsclc, y_nsclc, test_size = 0.20)"]},{"cell_type":"markdown","metadata":{"id":"1ESeCyX60pXC"},"source":["### Scaling Data"]},{"cell_type":"code","execution_count":8,"metadata":{"id":"7-rkZwVsb4zc","executionInfo":{"status":"ok","timestamp":1652682824414,"user_tz":-330,"elapsed":6,"user":{"displayName":"ABDUR RAHAMAN","userId":"18268289993785820909"}}},"outputs":[],"source":["sc_x = StandardScaler()\n","xtrain_sclc = sc_x.fit_transform(xtrain_sclc)\n","xtest_sclc = sc_x.transform(xtest_sclc)\n","sc_x = StandardScaler()\n","xtrain_nsclc = sc_x.fit_transform(xtrain_nsclc) \n","xtest_nsclc = sc_x.transform(xtest_nsclc)"]},{"cell_type":"markdown","metadata":{"id":"npfu8CAw04pd"},"source":["## Fine Tuning"]},{"cell_type":"code","execution_count":9,"metadata":{"id":"1D2aFINyqv6Q","executionInfo":{"status":"ok","timestamp":1652682842014,"user_tz":-330,"elapsed":9,"user":{"displayName":"ABDUR RAHAMAN","userId":"18268289993785820909"}}},"outputs":[],"source":["n_estimators = [int(x) for x in np.linspace(start = 200, stop = 2000, num = 10)]\n","max_features = ['auto', 'sqrt','log2']\n","max_depth = [int(x) for x in np.linspace(10, 110, num = 11)]\n","max_depth.append(None)\n","min_samples_split = [2, 5, 10]\n","min_samples_leaf = [1, 2, 4]\n","bootstrap = [True, False]\n","random_grid = {'n_estimators': n_estimators,\n","               'max_features': max_features,\n","               'max_depth': max_depth,\n","               'min_samples_split': min_samples_split,\n","               'min_samples_leaf': min_samples_leaf,\n","               'bootstrap': bootstrap}"]},{"cell_type":"code","execution_count":10,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"uiqI9UeEp9fi","outputId":"564becba-4abb-41ff-9259-84088a614728","executionInfo":{"status":"ok","timestamp":1652683384219,"user_tz":-330,"elapsed":537298,"user":{"displayName":"ABDUR RAHAMAN","userId":"18268289993785820909"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Fitting 3 folds for each of 100 candidates, totalling 300 fits\n"]},{"output_type":"execute_result","data":{"text/plain":["{'bootstrap': True,\n"," 'max_depth': 30,\n"," 'max_features': 'auto',\n"," 'min_samples_leaf': 4,\n"," 'min_samples_split': 10,\n"," 'n_estimators': 1000}"]},"metadata":{},"execution_count":10}],"source":["rfc = RandomForestClassifier()\n","rf_random_sclc = RandomizedSearchCV(estimator = rfc, param_distributions = random_grid, n_iter = 100, cv = 3, verbose=2, n_jobs = -1)\n","rf_random_sclc.fit(xtrain_sclc, ytrain_sclc)\n","params_sclc = rf_random_sclc.best_params_\n","params_sclc"]},{"cell_type":"code","execution_count":11,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"-CDohyMmdxtz","outputId":"89587dec-c34c-4512-c1a9-38f5b36abc8a","executionInfo":{"status":"ok","timestamp":1652683860999,"user_tz":-330,"elapsed":475340,"user":{"displayName":"ABDUR RAHAMAN","userId":"18268289993785820909"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Fitting 3 folds for each of 100 candidates, totalling 300 fits\n"]},{"output_type":"execute_result","data":{"text/plain":["{'bootstrap': True,\n"," 'max_depth': None,\n"," 'max_features': 'auto',\n"," 'min_samples_leaf': 4,\n"," 'min_samples_split': 10,\n"," 'n_estimators': 1800}"]},"metadata":{},"execution_count":11}],"source":["rfc = RandomForestClassifier()\n","rf_random_nsclc = RandomizedSearchCV(estimator = rfc, param_distributions = random_grid, n_iter = 100, cv = 3, verbose=2, n_jobs = -1)\n","rf_random_nsclc.fit(xtrain_nsclc, ytrain_nsclc)\n","params_nsclc = rf_random_nsclc.best_params_\n","params_nsclc"]},{"cell_type":"markdown","metadata":{"id":"d7a9OFbf768j"},"source":["# Training and Testing"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"H14b4DXGonmj","outputId":"a5bff5f0-70a1-4521-9345-a2cae96e141a","executionInfo":{"status":"ok","timestamp":1652543081718,"user_tz":-330,"elapsed":3132,"user":{"displayName":"ABHAY H KASHYAP","userId":"14045568865603292285"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["=== Confusion Matrix ===\n","[[150  25]\n"," [ 43 151]]\n","\n","\n","=== Classification Report ===\n","              precision    recall  f1-score   support\n","\n","         0.0       0.78      0.86      0.82       175\n","         1.0       0.86      0.78      0.82       194\n","\n","    accuracy                           0.82       369\n","   macro avg       0.82      0.82      0.82       369\n","weighted avg       0.82      0.82      0.82       369\n","\n"]}],"source":["rfc_sclc = RandomForestClassifier(**params_sclc)\n","rfc_sclc.fit(xtrain_sclc,ytrain_sclc)\n","rfc_predict_sclc = rfc_sclc.predict(xtest_sclc)\n","print(\"=== Confusion Matrix ===\")\n","print(confusion_matrix(ytest_sclc, rfc_predict_sclc))\n","print('\\n')\n","print(\"=== Classification Report ===\")\n","print(classification_report(ytest_sclc, rfc_predict_sclc))"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"YQM5QBpdKJm6","outputId":"8523d99c-902d-4f43-a0a7-4145ee986278","executionInfo":{"status":"ok","timestamp":1652543242262,"user_tz":-330,"elapsed":533,"user":{"displayName":"ABHAY H KASHYAP","userId":"14045568865603292285"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["=== Confusion Matrix ===\n","[[134  13]\n"," [ 25 105]]\n","\n","\n","=== Classification Report ===\n","              precision    recall  f1-score   support\n","\n","         0.0       0.84      0.91      0.88       147\n","         1.0       0.89      0.81      0.85       130\n","\n","    accuracy                           0.86       277\n","   macro avg       0.87      0.86      0.86       277\n","weighted avg       0.86      0.86      0.86       277\n","\n"]}],"source":["rfc_nsclc = RandomForestClassifier(**params_nsclc)\n","rfc_nsclc.fit(xtrain_nsclc,ytrain_nsclc)\n","rfc_predict_nsclc = rfc_nsclc.predict(xtest_nsclc)\n","print(\"=== Confusion Matrix ===\")\n","print(confusion_matrix(ytest_nsclc, rfc_predict_nsclc))\n","print('\\n')\n","print(\"=== Classification Report ===\")\n","print(classification_report(ytest_nsclc, rfc_predict_nsclc))"]},{"cell_type":"markdown","metadata":{"id":"yS2RsJms9DAL"},"source":["# Precision Graph Plotting"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"0nXPIL4xcaY_"},"outputs":[],"source":["rfc_sclc = RandomForestClassifier(**params_sclc)\n","rfc_nsclc = RandomForestClassifier(**params_nsclc)\n","n = []\n","sclc = []\n","nsclc = []\n","for i in range(5,45):\n","  n.append(i)\n","  rfc_cv_score_sclc = cross_val_score(rfc_sclc, x_sclc, y_sclc, cv=i, scoring='average_precision')\n","  sclc.append(np.mean(rfc_cv_score_sclc))\n","  rfc_cv_score_nsclc = cross_val_score(rfc_nsclc, x_nsclc, y_nsclc, cv=i, scoring='average_precision')\n","  nsclc.append(np.mean(rfc_cv_score_nsclc))\n","\n","n = np.array(n)\n","nsclc = np.array(nsclc)\n","sclc = np.array(sclc) "]},{"cell_type":"code","execution_count":null,"metadata":{"id":"ZGa7kNJZb9v-"},"outputs":[],"source":["# figure\n","fig, ax = plt.subplots(1, figsize=(20,10), facecolor='#FFFFFF')\n","ax.set_facecolor('#FFFFFF')\n","# data\n","price = nsclc\n","rate = sclc\n","# plots\n","plt.plot(n, price, marker='o', markersize=4.5, color='#7a7777', linewidth=1)\n","plt.plot(n, rate, marker='*', markersize=6, color='#7a7777', linewidth=1)\n","# ticks n title\n","# plt.title('Price and Exchange Rate\\n', loc='left', color='black', fontsize=16)\n","ax.tick_params(axis='both', colors='black')\n","plt.xticks(n[::3])\n","# # spines\n","ax.spines['right'].set_visible(False)\n","ax.spines['top'].set_visible(False)\n","ax.spines['left'].set_color('black')\n","ax.spines['bottom'].set_color('black')\n","# grid\n","ax.set_axisbelow(True)\n","ax.yaxis.grid(color='#7a7777', linestyle='dashed', alpha=0.5)\n","plt.ylim(0.86,)\n","plt.xlabel('Number of Folds',fontsize=18) \n","plt.ylabel('Average Precision',fontsize=18) \n","# legend\n","legend = plt.legend(['NSCLC', 'SCLC'], frameon=True, ncol=2, loc = \"lower right\",fontsize = '20')\n","plt.setp(legend.get_texts(), color='black')\n","plt.savefig(\"results.png\")"]},{"cell_type":"markdown","metadata":{"id":"9fgsHi850H8f"},"source":["# Functions to run above code\n","\n"]},{"cell_type":"markdown","metadata":{"id":"_B6C4-jkDpKM"},"source":["### Helper Functions"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"GY7OVqPB0Gua"},"outputs":[],"source":["def preprocess(data):\n","  data['NSCLC'] = data['NSCLC'].fillna(0)\n","  data['SCLC'] = data['SCLC'].fillna(0)\n","  x = data.iloc[:,[3,4,6]]\n","  y_sclc = data.iloc[:,8]\n","  y_nsclc = data.iloc[:,9]\n","  nm = NearMiss()\n","  print('SCLC Original dataset shape:', Counter(y_sclc))\n","  x_nm_sclc, y_nm_sclc = nm.fit_resample(x, y_sclc)\n","  print('SCLC Resample dataset shape:', Counter(y_nm_sclc))\n","  print('NSCLC Original dataset shape:', Counter(y_nsclc))\n","  x_nm_nsclc, y_nm_nsclc = nm.fit_resample(x, y_nsclc)\n","  print('NSCLC Resample dataset shape:', Counter(y_nm_nsclc))\n","  x_sclc = x_nm_sclc\n","  x_nsclc = x_nm_nsclc\n","  y_sclc = y_nm_sclc\n","  y_nsclc = y_nm_nsclc\n","  xtrain_sclc, xtest_sclc, ytrain_sclc, ytest_sclc = train_test_split(x_sclc, y_sclc, test_size = 0.20)\n","  xtrain_nsclc, xtest_nsclc, ytrain_nsclc, ytest_nsclc = train_test_split(x_nsclc, y_nsclc, test_size = 0.20)\n","  sc_x = StandardScaler()\n","  xtrain_sclc = sc_x.fit_transform(xtrain_sclc)\n","  xtest_sclc = sc_x.transform(xtest_sclc)\n","  sc_x = StandardScaler()\n","  xtrain_nsclc = sc_x.fit_transform(xtrain_nsclc) \n","  xtest_nsclc = sc_x.transform(xtest_nsclc)\n","  return (x_sclc, y_sclc, x_nsclc, y_nsclc, xtrain_sclc, xtest_sclc, ytrain_sclc, ytest_sclc, xtrain_nsclc, xtest_nsclc, ytrain_nsclc, ytest_nsclc)\n","\n","\n","def finetune(xtrain_sclc, ytrain_sclc, xtrain_nsclc, ytrain_nsclc):\n","  n_estimators = [int(x) for x in np.linspace(start = 200, stop = 2000, num = 10)]\n","  max_features = ['auto', 'sqrt','log2']\n","  max_depth = [int(x) for x in np.linspace(10, 110, num = 11)]\n","  max_depth.append(None)\n","  min_samples_split = [2, 5, 10]\n","  min_samples_leaf = [1, 2, 4]\n","  bootstrap = [True, False]\n","  random_grid = {'n_estimators': n_estimators,\n","                'max_features': max_features,\n","                'max_depth': max_depth,\n","                'min_samples_split': min_samples_split,\n","                'min_samples_leaf': min_samples_leaf,\n","                'bootstrap': bootstrap}\n","  rfc = RandomForestClassifier()\n","  rf_random_sclc = RandomizedSearchCV(estimator = rfc, param_distributions = random_grid, n_iter = 100, cv = 3, verbose=2, n_jobs = -1)\n","  rf_random_sclc.fit(xtrain_sclc, ytrain_sclc)\n","  params_sclc = rf_random_sclc.best_params_\n","  rfc = RandomForestClassifier()\n","  rf_random_nsclc = RandomizedSearchCV(estimator = rfc, param_distributions = random_grid, n_iter = 100, cv = 3, verbose=2, n_jobs = -1)\n","  rf_random_nsclc.fit(xtrain_nsclc, ytrain_nsclc)\n","  params_nsclc = rf_random_nsclc.best_params_\n","  return(params_sclc, params_nsclc)\n","\n","def train_test(params_sclc, params_nsclc, xtrain_sclc, ytrain_sclc, xtest_sclc, ytest_sclc, xtrain_nsclc, ytrain_nsclc, xtest_nsclc, ytest_nsclc):\n","  rfc_sclc = RandomForestClassifier(**params_sclc)\n","  rfc_sclc.fit(xtrain_sclc, ytrain_sclc)\n","  rfc_predict_sclc = rfc_sclc.predict(xtest_sclc)\n","  print(\"=== Confusion Matrix ===\")\n","  print(confusion_matrix(ytest_sclc, rfc_predict_sclc))\n","  print('\\n')\n","  print(\"=== Classification Report ===\")\n","  print(classification_report(ytest_sclc, rfc_predict_sclc))\n","  rfc_nsclc = RandomForestClassifier(**params_nsclc)\n","  rfc_nsclc.fit(xtrain_nsclc, ytrain_nsclc)\n","  rfc_predict_nsclc = rfc_nsclc.predict(xtest_nsclc)\n","  print(\"=== Confusion Matrix ===\")\n","  print(confusion_matrix(ytest_nsclc, rfc_predict_nsclc))\n","  print('\\n')\n","  print(\"=== Classification Report ===\")\n","  print(classification_report(ytest_nsclc, rfc_predict_nsclc))\n","\n","def plot(params_sclc, params_nsclc, x_sclc, y_sclc, x_nsclc, y_nsclc):\n","  rfc_sclc = RandomForestClassifier(**params_sclc)\n","  rfc_nsclc = RandomForestClassifier(**params_nsclc)\n","  n = []\n","  sclc = []\n","  nsclc = []\n","  for i in range(5,45):\n","    n.append(i)\n","    rfc_cv_score_sclc = cross_val_score(rfc_sclc, x_sclc, y_sclc, cv=i, scoring='average_precision')\n","    sclc.append(np.mean(rfc_cv_score_sclc))\n","    rfc_cv_score_nsclc = cross_val_score(rfc_nsclc, x_nsclc, y_nsclc, cv=i, scoring='average_precision')\n","    nsclc.append(np.mean(rfc_cv_score_nsclc))\n","\n","  n = np.array(n)\n","  nsclc = np.array(nsclc)\n","  sclc = np.array(sclc)\n","  # figure\n","  fig, ax = plt.subplots(1, figsize=(20,10), facecolor='#FFFFFF')\n","  ax.set_facecolor('#FFFFFF')\n","  # data\n","  price = nsclc\n","  rate = sclc\n","  # plots\n","  plt.plot(n, price, marker='o', markersize=4.5, color='#7a7777', linewidth=1)\n","  plt.plot(n, rate, marker='*', markersize=6, color='#7a7777', linewidth=1)\n","  # ticks n title\n","  # plt.title('Price and Exchange Rate\\n', loc='left', color='black', fontsize=16)\n","  ax.tick_params(axis='both', colors='black')\n","  plt.xticks(n[::3])\n","  # # spines\n","  ax.spines['right'].set_visible(False)\n","  ax.spines['top'].set_visible(False)\n","  ax.spines['left'].set_color('black')\n","  ax.spines['bottom'].set_color('black')\n","  # grid\n","  ax.set_axisbelow(True)\n","  ax.yaxis.grid(color='#7a7777', linestyle='dashed', alpha=0.5)\n","  plt.ylim(0.86,)\n","  plt.xlabel('Number of Folds',fontsize=18) \n","  plt.ylabel('Average Precision',fontsize=18) \n","  # legend\n","  legend = plt.legend(['NSCLC', 'SCLC'], frameon=True, ncol=2, loc = \"lower right\",fontsize = '20')\n","  plt.setp(legend.get_texts(), color='black')\n","  plt.savefig(\"results.png\")"]},{"cell_type":"markdown","metadata":{"id":"97xAI5UnCDzd"},"source":["### Main Function to run the code and plot the precision for n folds"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"B6PoAKCzBSUP"},"outputs":[],"source":["def main():\n","  data = pd.read_excel(io='/content/drive/MyDrive/Biology /Biology Research Project/sclc labelled data.xlsx',sheet_name=\"Sheet1\")\n","  (x_sclc, y_sclc, x_nsclc, y_nsclc, xtrain_sclc, xtest_sclc, ytrain_sclc, ytest_sclc, xtrain_nsclc, xtest_nsclc, ytrain_nsclc, ytest_nsclc) = preprocess(data)\n","  (params_sclc, params_nsclc) = finetune(xtrain_sclc, ytrain_sclc, xtrain_nsclc, ytrain_nsclc)\n","  train_test(params_sclc, params_nsclc, xtrain_sclc, ytrain_sclc, xtest_sclc, ytest_sclc, xtrain_nsclc, ytrain_nsclc, xtest_nsclc, ytest_nsclc)\n","  plot(params_sclc, params_nsclc, x_sclc, y_sclc, x_nsclc, y_nsclc)\n","\n","main()"]},{"cell_type":"markdown","metadata":{"id":"q7cP4-91CPzz"},"source":["### Test function to run the code and test it on the given input."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"background_save":true,"base_uri":"https://localhost:8080/","height":449},"id":"RUO8sso3LXWT","outputId":"62e7b5b4-3ad7-40da-8c2e-4b10cd6287c4"},"outputs":[{"data":{"text/html":["\n","  <div id=\"df-6fe2ddd8-60ce-4a48-8a8a-31359f4d1a0f\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Unnamed: 0</th>\n","      <th>Tags</th>\n","      <th>Name</th>\n","      <th>FC</th>\n","      <th>logFC</th>\n","      <th>logCPM</th>\n","      <th>P-Value</th>\n","      <th>FDR</th>\n","      <th>SCLC</th>\n","      <th>NSCLC</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>12238</th>\n","      <td>12238.0</td>\n","      <td>[]</td>\n","      <td>FAR2</td>\n","      <td>-1.036364</td>\n","      <td>-0.051530</td>\n","      <td>6.047611</td>\n","      <td>0.768052</td>\n","      <td>0.889167</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>12239</th>\n","      <td>12239.0</td>\n","      <td>[]</td>\n","      <td>ATF6</td>\n","      <td>-1.074289</td>\n","      <td>-0.103382</td>\n","      <td>8.154670</td>\n","      <td>0.260387</td>\n","      <td>0.490329</td>\n","      <td>1.0</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>12240</th>\n","      <td>12240.0</td>\n","      <td>[]</td>\n","      <td>ATF3</td>\n","      <td>-1.197653</td>\n","      <td>-0.260209</td>\n","      <td>6.107613</td>\n","      <td>0.301848</td>\n","      <td>0.533602</td>\n","      <td>1.0</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>12241</th>\n","      <td>12241.0</td>\n","      <td>[]</td>\n","      <td>LOC441087</td>\n","      <td>1.365118</td>\n","      <td>0.449026</td>\n","      <td>-1.056675</td>\n","      <td>0.084938</td>\n","      <td>0.258926</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>12242</th>\n","      <td>12242.0</td>\n","      <td>[]</td>\n","      <td>GATA6</td>\n","      <td>-1.275932</td>\n","      <td>-0.351551</td>\n","      <td>4.885739</td>\n","      <td>0.114056</td>\n","      <td>0.305799</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>12243</th>\n","      <td>12243.0</td>\n","      <td>[]</td>\n","      <td>TPRG1L</td>\n","      <td>1.004909</td>\n","      <td>0.007065</td>\n","      <td>4.665519</td>\n","      <td>0.976440</td>\n","      <td>0.994635</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>12244</th>\n","      <td>12244.0</td>\n","      <td>[]</td>\n","      <td>PDGFA</td>\n","      <td>-1.243270</td>\n","      <td>-0.314139</td>\n","      <td>4.715532</td>\n","      <td>0.088348</td>\n","      <td>0.265474</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","    </tr>\n","    <tr>\n","      <th>12245</th>\n","      <td>12245.0</td>\n","      <td>[]</td>\n","      <td>GATA2</td>\n","      <td>1.032892</td>\n","      <td>0.046689</td>\n","      <td>2.803374</td>\n","      <td>0.842767</td>\n","      <td>0.931684</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>12246</th>\n","      <td>12246.0</td>\n","      <td>[]</td>\n","      <td>CIAO3</td>\n","      <td>-1.148757</td>\n","      <td>-0.200073</td>\n","      <td>3.933105</td>\n","      <td>0.075367</td>\n","      <td>0.242021</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>12247</th>\n","      <td>12247.0</td>\n","      <td>[]</td>\n","      <td>CIAO1</td>\n","      <td>1.123722</td>\n","      <td>0.168285</td>\n","      <td>5.935793</td>\n","      <td>0.131505</td>\n","      <td>0.332089</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>12248</th>\n","      <td>12248.0</td>\n","      <td>[]</td>\n","      <td>EPB41L5</td>\n","      <td>-1.504651</td>\n","      <td>-0.589428</td>\n","      <td>7.674006</td>\n","      <td>0.000487</td>\n","      <td>0.012852</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>12249</th>\n","      <td>12249.0</td>\n","      <td>[]</td>\n","      <td>PDGFD</td>\n","      <td>-1.260623</td>\n","      <td>-0.334137</td>\n","      <td>5.855667</td>\n","      <td>0.125071</td>\n","      <td>0.322722</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","    </tr>\n","    <tr>\n","      <th>12250</th>\n","      <td>12250.0</td>\n","      <td>[]</td>\n","      <td>EPB41L2</td>\n","      <td>1.179566</td>\n","      <td>0.238256</td>\n","      <td>7.043599</td>\n","      <td>0.144521</td>\n","      <td>0.350371</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>12251</th>\n","      <td>12251.0</td>\n","      <td>[]</td>\n","      <td>PDGFC</td>\n","      <td>-1.506741</td>\n","      <td>-0.591432</td>\n","      <td>7.891498</td>\n","      <td>0.000298</td>\n","      <td>0.009267</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","    </tr>\n","    <tr>\n","      <th>12252</th>\n","      <td>12252.0</td>\n","      <td>[]</td>\n","      <td>LOC100507053</td>\n","      <td>-1.215763</td>\n","      <td>-0.281862</td>\n","      <td>5.163591</td>\n","      <td>0.148860</td>\n","      <td>0.356350</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>12253</th>\n","      <td>12253.0</td>\n","      <td>[]</td>\n","      <td>EPB41L3</td>\n","      <td>1.221163</td>\n","      <td>0.288255</td>\n","      <td>6.511856</td>\n","      <td>0.165384</td>\n","      <td>0.378512</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>12254</th>\n","      <td>12254.0</td>\n","      <td>[]</td>\n","      <td>PIP5K1A</td>\n","      <td>1.011516</td>\n","      <td>0.016518</td>\n","      <td>7.225447</td>\n","      <td>0.857265</td>\n","      <td>0.939069</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>12255</th>\n","      <td>12255.0</td>\n","      <td>[]</td>\n","      <td>PIP5K1B</td>\n","      <td>-1.036260</td>\n","      <td>-0.051386</td>\n","      <td>6.607497</td>\n","      <td>0.810784</td>\n","      <td>0.913194</td>\n","      <td>1.0</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>12256</th>\n","      <td>12256.0</td>\n","      <td>[]</td>\n","      <td>PIP5K1C</td>\n","      <td>1.009869</td>\n","      <td>0.014168</td>\n","      <td>5.455605</td>\n","      <td>0.907544</td>\n","      <td>0.961805</td>\n","      <td>1.0</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>12257</th>\n","      <td>12257.0</td>\n","      <td>[]</td>\n","      <td>TRAJ20</td>\n","      <td>1.661555</td>\n","      <td>0.732534</td>\n","      <td>-1.092900</td>\n","      <td>0.031441</td>\n","      <td>0.144468</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6fe2ddd8-60ce-4a48-8a8a-31359f4d1a0f')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" 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P-Value       FDR  SCLC  NSCLC\n","12238     12238.0   []          FAR2 -1.036364  ...  0.768052  0.889167   NaN    NaN\n","12239     12239.0   []          ATF6 -1.074289  ...  0.260387  0.490329   1.0    NaN\n","12240     12240.0   []          ATF3 -1.197653  ...  0.301848  0.533602   1.0    NaN\n","12241     12241.0   []     LOC441087  1.365118  ...  0.084938  0.258926   NaN    NaN\n","12242     12242.0   []         GATA6 -1.275932  ...  0.114056  0.305799   NaN    NaN\n","12243     12243.0   []        TPRG1L  1.004909  ...  0.976440  0.994635   NaN    NaN\n","12244     12244.0   []         PDGFA -1.243270  ...  0.088348  0.265474   1.0    1.0\n","12245     12245.0   []         GATA2  1.032892  ...  0.842767  0.931684   NaN    NaN\n","12246     12246.0   []         CIAO3 -1.148757  ...  0.075367  0.242021   NaN    NaN\n","12247     12247.0   []         CIAO1  1.123722  ...  0.131505  0.332089   NaN    NaN\n","12248     12248.0   []       EPB41L5 -1.504651  ...  0.000487  0.012852   NaN    NaN\n","12249     12249.0   []         PDGFD -1.260623  ...  0.125071  0.322722   1.0    1.0\n","12250     12250.0   []       EPB41L2  1.179566  ...  0.144521  0.350371   NaN    NaN\n","12251     12251.0   []         PDGFC -1.506741  ...  0.000298  0.009267   1.0    1.0\n","12252     12252.0   []  LOC100507053 -1.215763  ...  0.148860  0.356350   NaN    NaN\n","12253     12253.0   []       EPB41L3  1.221163  ...  0.165384  0.378512   NaN    NaN\n","12254     12254.0   []       PIP5K1A  1.011516  ...  0.857265  0.939069   NaN    NaN\n","12255     12255.0   []       PIP5K1B -1.036260  ...  0.810784  0.913194   1.0    NaN\n","12256     12256.0   []       PIP5K1C  1.009869  ...  0.907544  0.961805   1.0    NaN\n","12257     12257.0   []        TRAJ20  1.661555  ...  0.031441  0.144468   NaN    NaN\n","\n","[20 rows x 10 columns]"]},"execution_count":null,"metadata":{},"output_type":"execute_result"}],"source":["test_input = data.iloc[30:40,:]\n","\n","# index = r(0,19900)\n","# test_input = data.iloc[index:index+20,:]"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"48u23U2rCQMp","outputId":"0662afb6-39aa-4258-ee77-5c7d59974c85"},"outputs":[{"name":"stdout","output_type":"stream","text":["SCLC Original dataset shape: Counter({0.0: 18857, 1.0: 921})\n","SCLC Resample dataset shape: Counter({0.0: 921, 1.0: 921})\n","NSCLC Original dataset shape: Counter({0.0: 19087, 1.0: 691})\n","NSCLC Resample dataset shape: Counter({0.0: 691, 1.0: 691})\n","Fitting 3 folds for each of 100 candidates, totalling 300 fits\n","Fitting 3 folds for each of 100 candidates, totalling 300 fits\n"]},{"name":"stderr","output_type":"stream","text":["/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  # Remove the CWD from sys.path while we load stuff.\n","/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:11: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  # This is added back by InteractiveShellApp.init_path()\n","/usr/local/lib/python3.7/dist-packages/sklearn/base.py:444: UserWarning: X has feature names, but RandomForestClassifier was fitted without feature names\n","  f\"X has feature names, but {self.__class__.__name__} was fitted without\"\n","/usr/local/lib/python3.7/dist-packages/sklearn/base.py:444: UserWarning: X has feature names, but RandomForestClassifier was fitted without feature names\n","  f\"X has feature names, but {self.__class__.__name__} was fitted without\"\n"]}],"source":["def train_and_test(test_input):\n","  data = pd.read_excel(io='/content/drive/MyDrive/Biology /Biology Research Project/sclc labelled data.xlsx',sheet_name=\"Sheet1\")\n","  (x_sclc, y_sclc, x_nsclc, y_nsclc, xtrain_sclc, xtest_sclc, ytrain_sclc, ytest_sclc, xtrain_nsclc, xtest_nsclc, ytrain_nsclc, ytest_nsclc) = preprocess(data)\n","  (params_sclc, params_nsclc) = finetune(xtrain_sclc, ytrain_sclc, xtrain_nsclc, ytrain_nsclc)\n","  rfc_sclc = RandomForestClassifier(**params_sclc)\n","  rfc_sclc.fit(xtrain_sclc, ytrain_sclc)\n","  rfc_nsclc = RandomForestClassifier(**params_nsclc)\n","  rfc_nsclc.fit(xtrain_nsclc, ytrain_nsclc)\n","\n","  test_input['NSCLC'] = test_input['NSCLC'].fillna(0)\n","  test_input['SCLC'] = test_input['SCLC'].fillna(0)\n","  text_x = test_input.iloc[:,[3,4,6]]\n","  test_y_sclc = test_input.iloc[:,8]\n","  test_y_nsclc = test_input.iloc[:,9]\n","  sc_x = StandardScaler()\n","  test_x = sc_x.fit_transform(text_x)\n","\n","  rfc_predict_sclc = np.array(rfc_sclc.predict(text_x))\n","  rfc_predict_nsclc = np.array(rfc_nsclc.predict(text_x))\n","  common_biomarkers = (rfc_predict_sclc + rfc_predict_nsclc)//2\n","  return (rfc_predict_sclc, rfc_predict_nsclc, common_biomarkers)\n","\n","\n","\n","(rfc_predict_sclc, rfc_predict_nsclc, common_biomarkers) = train_and_test(test_input)"]},{"cell_type":"code","source":["test_input"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":676},"id":"TlopJeC6ZHkY","outputId":"5497935b-b077-4b32-fb34-e25771a04526"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["\n","  <div id=\"df-6499d75a-f3f8-4f9e-a2d6-6153ede59d9e\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        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<td>-0.103382</td>\n","      <td>8.154670</td>\n","      <td>0.260387</td>\n","      <td>0.490329</td>\n","      <td>1.0</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>12240</th>\n","      <td>12240.0</td>\n","      <td>[]</td>\n","      <td>ATF3</td>\n","      <td>-1.197653</td>\n","      <td>-0.260209</td>\n","      <td>6.107613</td>\n","      <td>0.301848</td>\n","      <td>0.533602</td>\n","      <td>1.0</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>12241</th>\n","      <td>12241.0</td>\n","      <td>[]</td>\n","      <td>LOC441087</td>\n","      <td>1.365118</td>\n","      <td>0.449026</td>\n","      <td>-1.056675</td>\n","      <td>0.084938</td>\n","      <td>0.258926</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>12242</th>\n","      <td>12242.0</td>\n","      <td>[]</td>\n","      <td>GATA6</td>\n","      <td>-1.275932</td>\n","      <td>-0.351551</td>\n","      <td>4.885739</td>\n","      <td>0.114056</td>\n","      <td>0.305799</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>12243</th>\n","      <td>12243.0</td>\n","      <td>[]</td>\n","      <td>TPRG1L</td>\n","      <td>1.004909</td>\n","      <td>0.007065</td>\n","      <td>4.665519</td>\n","      <td>0.976440</td>\n","      <td>0.994635</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>12244</th>\n","      <td>12244.0</td>\n","      <td>[]</td>\n","      <td>PDGFA</td>\n","      <td>-1.243270</td>\n","      <td>-0.314139</td>\n","      <td>4.715532</td>\n","      <td>0.088348</td>\n","      <td>0.265474</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","    </tr>\n","    <tr>\n","      <th>12245</th>\n","      <td>12245.0</td>\n","      <td>[]</td>\n","      <td>GATA2</td>\n","      <td>1.032892</td>\n","      <td>0.046689</td>\n","      <td>2.803374</td>\n","      <td>0.842767</td>\n","      <td>0.931684</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>12246</th>\n","      <td>12246.0</td>\n","      <td>[]</td>\n","      <td>CIAO3</td>\n","      <td>-1.148757</td>\n","      <td>-0.200073</td>\n","      <td>3.933105</td>\n","      <td>0.075367</td>\n","      <td>0.242021</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>12247</th>\n","      <td>12247.0</td>\n","      <td>[]</td>\n","      <td>CIAO1</td>\n","      <td>1.123722</td>\n","      <td>0.168285</td>\n","      <td>5.935793</td>\n","      <td>0.131505</td>\n","      <td>0.332089</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>12248</th>\n","      <td>12248.0</td>\n","      <td>[]</td>\n","      <td>EPB41L5</td>\n","      <td>-1.504651</td>\n","      <td>-0.589428</td>\n","      <td>7.674006</td>\n","      <td>0.000487</td>\n","      <td>0.012852</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>12249</th>\n","      <td>12249.0</td>\n","      <td>[]</td>\n","      <td>PDGFD</td>\n","      <td>-1.260623</td>\n","      <td>-0.334137</td>\n","      <td>5.855667</td>\n","      <td>0.125071</td>\n","      <td>0.322722</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","    </tr>\n","    <tr>\n","      <th>12250</th>\n","      <td>12250.0</td>\n","      <td>[]</td>\n","      <td>EPB41L2</td>\n","      <td>1.179566</td>\n","      <td>0.238256</td>\n","      <td>7.043599</td>\n","      <td>0.144521</td>\n","      <td>0.350371</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>12251</th>\n","      <td>12251.0</td>\n","      <td>[]</td>\n","      <td>PDGFC</td>\n","      <td>-1.506741</td>\n","      <td>-0.591432</td>\n","      <td>7.891498</td>\n","      <td>0.000298</td>\n","      <td>0.009267</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","    </tr>\n","    <tr>\n","      <th>12252</th>\n","      <td>12252.0</td>\n","      <td>[]</td>\n","      <td>LOC100507053</td>\n","      <td>-1.215763</td>\n","      <td>-0.281862</td>\n","      <td>5.163591</td>\n","      <td>0.148860</td>\n","      <td>0.356350</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>12253</th>\n","      <td>12253.0</td>\n","      <td>[]</td>\n","      <td>EPB41L3</td>\n","      <td>1.221163</td>\n","      <td>0.288255</td>\n","      <td>6.511856</td>\n","      <td>0.165384</td>\n","      <td>0.378512</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>12254</th>\n","      <td>12254.0</td>\n","      <td>[]</td>\n","      <td>PIP5K1A</td>\n","      <td>1.011516</td>\n","      <td>0.016518</td>\n","      <td>7.225447</td>\n","      <td>0.857265</td>\n","      <td>0.939069</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>12255</th>\n","      <td>12255.0</td>\n","      <td>[]</td>\n","      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P-Value       FDR  SCLC  NSCLC\n","12238     12238.0   []          FAR2 -1.036364  ...  0.768052  0.889167   NaN    NaN\n","12239     12239.0   []          ATF6 -1.074289  ...  0.260387  0.490329   1.0    NaN\n","12240     12240.0   []          ATF3 -1.197653  ...  0.301848  0.533602   1.0    NaN\n","12241     12241.0   []     LOC441087  1.365118  ...  0.084938  0.258926   NaN    NaN\n","12242     12242.0   []         GATA6 -1.275932  ...  0.114056  0.305799   NaN    NaN\n","12243     12243.0   []        TPRG1L  1.004909  ...  0.976440  0.994635   NaN    NaN\n","12244     12244.0   []         PDGFA -1.243270  ...  0.088348  0.265474   1.0    1.0\n","12245     12245.0   []         GATA2  1.032892  ...  0.842767  0.931684   NaN    NaN\n","12246     12246.0   []         CIAO3 -1.148757  ...  0.075367  0.242021   NaN    NaN\n","12247     12247.0   []         CIAO1  1.123722  ...  0.131505  0.332089   NaN    NaN\n","12248     12248.0   []       EPB41L5 -1.504651  ...  0.000487  0.012852   NaN    NaN\n","12249     12249.0   []         PDGFD -1.260623  ...  0.125071  0.322722   1.0    1.0\n","12250     12250.0   []       EPB41L2  1.179566  ...  0.144521  0.350371   NaN    NaN\n","12251     12251.0   []         PDGFC -1.506741  ...  0.000298  0.009267   1.0    1.0\n","12252     12252.0   []  LOC100507053 -1.215763  ...  0.148860  0.356350   NaN    NaN\n","12253     12253.0   []       EPB41L3  1.221163  ...  0.165384  0.378512   NaN    NaN\n","12254     12254.0   []       PIP5K1A  1.011516  ...  0.857265  0.939069   NaN    NaN\n","12255     12255.0   []       PIP5K1B -1.036260  ...  0.810784  0.913194   1.0    NaN\n","12256     12256.0   []       PIP5K1C  1.009869  ...  0.907544  0.961805   1.0    NaN\n","12257     12257.0   []        TRAJ20  1.661555  ...  0.031441  0.144468   NaN    NaN\n","\n","[20 rows x 10 columns]"]},"metadata":{},"execution_count":14}]},{"cell_type":"code","execution_count":null,"metadata":{"id":"C5zZRygjS6qu","colab":{"base_uri":"https://localhost:8080/"},"outputId":"70286bf7-901d-4f8e-8734-291049aa1714"},"outputs":[{"output_type":"stream","name":"stdout","text":["SCLC Biomarkers\n","ATF6\n","ATF3\n","LOC441087\n","PDGFA\n","PDGFD\n","PDGFC\n","PIP5K1C\n","\n","NSCLC Biomarkers\n","TPRG1L\n","PDGFA\n","PDGFD\n","PDGFC\n","PIP5K1A\n","\n","Common Biomarkers\n","PDGFA\n","PDGFD\n","PDGFC\n"]}],"source":["print(\"SCLC Biomarkers\")\n","for (i, val) in enumerate(rfc_predict_sclc):\n","  if val == 1:\n","    print(test_input.iloc[i,:].Name)\n","print(\"\\nNSCLC Biomarkers\")\n","for (i, val) in enumerate(rfc_predict_nsclc):\n","  if val == 1:\n","    print(test_input.iloc[i,:].Name)\n","print(\"\\nCommon Biomarkers\")\n","for (i, val) in enumerate(common_biomarkers):\n","  if val == 1:\n","    print(test_input.iloc[i,:].Name)"]}],"metadata":{"colab":{"collapsed_sections":[],"name":"Copy of RandomForestClassifier.ipynb","provenance":[]},"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"name":"python"}},"nbformat":4,"nbformat_minor":0}