refactor(nb): Create and implement helper function train_and_evaluate_model
This commit is contained in:
nuluh
@@ -537,8 +537,8 @@
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"outputs": [],
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"source": [
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"source": [
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"# len(y_data[0])\n",
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"len(y_data[0])\n",
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"y_data"
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"# y_data"
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{
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@@ -621,137 +621,15 @@
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"metadata": {},
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"metadata": {},
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"outputs": [],
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"outputs": [],
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"source": [
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"source": [
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"accuracies1 = []\n",
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"def train_and_evaluate_model(model, model_name, sensor_label, x_train, y_train, x_test, y_test):\n",
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"accuracies2 = []\n",
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" model.fit(x_train, y_train)\n",
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"\n",
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" y_pred = model.predict(x_test)\n",
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"\n",
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" accuracy = accuracy_score(y_test, y_pred) * 100\n",
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"# 1. Random Forest\n",
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" return {\n",
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"rf_model1 = RandomForestClassifier()\n",
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" \"model\": model_name,\n",
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"rf_model1.fit(x_train1, y_train)\n",
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" \"sensor\": sensor_label,\n",
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"rf_pred1 = rf_model1.predict(x_test1)\n",
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" \"accuracy\": accuracy\n",
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"acc1 = accuracy_score(y_test, rf_pred1) * 100\n",
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" }"
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"accuracies1.append(acc1)\n",
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"# format with color coded if acc1 > 90\n",
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"acc1 = f\"\\033[92m{acc1:.2f}\\033[00m\" if acc1 > 90 else f\"{acc1:.2f}\"\n",
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"print(\"Random Forest Accuracy for sensor 1:\", acc1)\n",
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"rf_model2 = RandomForestClassifier()\n",
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"rf_model2.fit(x_train2, y_train)\n",
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"rf_pred2 = rf_model2.predict(x_test2)\n",
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"acc2 = accuracy_score(y_test, rf_pred2) * 100\n",
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"accuracies2.append(acc2)\n",
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"# format with color coded if acc2 > 90\n",
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"acc2 = f\"\\033[92m{acc2:.2f}\\033[00m\" if acc2 > 90 else f\"{acc2:.2f}\"\n",
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"print(\"Random Forest Accuracy for sensor 2:\", acc2)\n",
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"# print(rf_pred)\n",
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"# print(y_test)\n",
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"\n",
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"# 2. Bagged Trees\n",
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"bagged_model1 = BaggingClassifier(estimator=DecisionTreeClassifier(), n_estimators=10)\n",
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"bagged_model1.fit(x_train1, y_train)\n",
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"bagged_pred1 = bagged_model1.predict(x_test1)\n",
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"acc1 = accuracy_score(y_test, bagged_pred1) * 100\n",
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"accuracies1.append(acc1)\n",
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"# format with color coded if acc1 > 90\n",
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"acc1 = f\"\\033[92m{acc1:.2f}\\033[00m\" if acc1 > 90 else f\"{acc1:.2f}\"\n",
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"print(\"Bagged Trees Accuracy for sensor 1:\", acc1)\n",
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"bagged_model2 = BaggingClassifier(estimator=DecisionTreeClassifier(), n_estimators=10)\n",
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"bagged_model2.fit(x_train2, y_train)\n",
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"bagged_pred2 = bagged_model2.predict(x_test2)\n",
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"acc2 = accuracy_score(y_test, bagged_pred2) * 100\n",
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"accuracies2.append(acc2)\n",
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"# format with color coded if acc2 > 90\n",
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"acc2 = f\"\\033[92m{acc2:.2f}\\033[00m\" if acc2 > 90 else f\"{acc2:.2f}\"\n",
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"print(\"Bagged Trees Accuracy for sensor 2:\", acc2)\n",
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"\n",
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"# 3. Decision Tree\n",
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"dt_model = DecisionTreeClassifier()\n",
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"dt_model.fit(x_train1, y_train)\n",
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"dt_pred1 = dt_model.predict(x_test1)\n",
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"acc1 = accuracy_score(y_test, dt_pred1) * 100\n",
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"accuracies1.append(acc1)\n",
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"# format with color coded if acc1 > 90\n",
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"acc1 = f\"\\033[92m{acc1:.2f}\\033[00m\" if acc1 > 90 else f\"{acc1:.2f}\"\n",
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"print(\"Decision Tree Accuracy for sensor 1:\", acc1)\n",
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"dt_model2 = DecisionTreeClassifier()\n",
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"dt_model2.fit(x_train2, y_train)\n",
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"dt_pred2 = dt_model2.predict(x_test2)\n",
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"acc2 = accuracy_score(y_test, dt_pred2) * 100\n",
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"accuracies2.append(acc2)\n",
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"# format with color coded if acc2 > 90\n",
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"acc2 = f\"\\033[92m{acc2:.2f}\\033[00m\" if acc2 > 90 else f\"{acc2:.2f}\"\n",
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"print(\"Decision Tree Accuracy for sensor 2:\", acc2)\n",
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"\n",
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"# 4. KNeighbors\n",
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"knn_model = KNeighborsClassifier()\n",
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"knn_model.fit(x_train1, y_train)\n",
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"knn_pred1 = knn_model.predict(x_test1)\n",
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"acc1 = accuracy_score(y_test, knn_pred1) * 100\n",
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"accuracies1.append(acc1)\n",
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"# format with color coded if acc1 > 90\n",
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"acc1 = f\"\\033[92m{acc1:.2f}\\033[00m\" if acc1 > 90 else f\"{acc1:.2f}\"\n",
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"print(\"KNeighbors Accuracy for sensor 1:\", acc1)\n",
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"knn_model2 = KNeighborsClassifier()\n",
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"knn_model2.fit(x_train2, y_train)\n",
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"knn_pred2 = knn_model2.predict(x_test2)\n",
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"acc2 = accuracy_score(y_test, knn_pred2) * 100\n",
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"accuracies2.append(acc2)\n",
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"# format with color coded if acc2 > 90\n",
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"acc2 = f\"\\033[92m{acc2:.2f}\\033[00m\" if acc2 > 90 else f\"{acc2:.2f}\"\n",
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"print(\"KNeighbors Accuracy for sensor 2:\", acc2)\n",
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"\n",
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"# 5. Linear Discriminant Analysis\n",
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"lda_model = LinearDiscriminantAnalysis()\n",
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"lda_model.fit(x_train1, y_train)\n",
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"lda_pred1 = lda_model.predict(x_test1)\n",
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"acc1 = accuracy_score(y_test, lda_pred1) * 100\n",
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"accuracies1.append(acc1)\n",
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"# format with color coded if acc1 > 90\n",
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"acc1 = f\"\\033[92m{acc1:.2f}\\033[00m\" if acc1 > 90 else f\"{acc1:.2f}\"\n",
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"print(\"Linear Discriminant Analysis Accuracy for sensor 1:\", acc1)\n",
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"lda_model2 = LinearDiscriminantAnalysis()\n",
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"lda_model2.fit(x_train2, y_train)\n",
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"lda_pred2 = lda_model2.predict(x_test2)\n",
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"acc2 = accuracy_score(y_test, lda_pred2) * 100\n",
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"accuracies2.append(acc2)\n",
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"# format with color coded if acc2 > 90\n",
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"acc2 = f\"\\033[92m{acc2:.2f}\\033[00m\" if acc2 > 90 else f\"{acc2:.2f}\"\n",
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"print(\"Linear Discriminant Analysis Accuracy for sensor 2:\", acc2)\n",
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"\n",
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"# 6. Support Vector Machine\n",
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"svm_model = SVC()\n",
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"svm_model.fit(x_train1, y_train)\n",
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"svm_pred1 = svm_model.predict(x_test1)\n",
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"acc1 = accuracy_score(y_test, svm_pred1) * 100\n",
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"accuracies1.append(acc1)\n",
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"# format with color coded if acc1 > 90\n",
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"acc1 = f\"\\033[92m{acc1:.2f}\\033[00m\" if acc1 > 90 else f\"{acc1:.2f}\"\n",
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"print(\"Support Vector Machine Accuracy for sensor 1:\", acc1)\n",
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"svm_model2 = SVC()\n",
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"svm_model2.fit(x_train2, y_train)\n",
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"svm_pred2 = svm_model2.predict(x_test2)\n",
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"acc2 = accuracy_score(y_test, svm_pred2) * 100\n",
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"accuracies2.append(acc2)\n",
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"# format with color coded if acc2 > 90\n",
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"acc2 = f\"\\033[92m{acc2:.2f}\\033[00m\" if acc2 > 90 else f\"{acc2:.2f}\"\n",
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"print(\"Support Vector Machine Accuracy for sensor 2:\", acc2)\n",
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"\n",
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"# 7. XGBoost\n",
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"xgboost_model = XGBClassifier()\n",
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"xgboost_model.fit(x_train1, y_train)\n",
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"xgboost_pred1 = xgboost_model.predict(x_test1)\n",
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"acc1 = accuracy_score(y_test, xgboost_pred1) * 100\n",
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"accuracies1.append(acc1)\n",
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"# format with color coded if acc1 > 90\n",
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"acc1 = f\"\\033[92m{acc1:.2f}\\033[00m\" if acc1 > 90 else f\"{acc1:.2f}\"\n",
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"print(\"XGBoost Accuracy:\", acc1)\n",
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"xgboost_model2 = XGBClassifier()\n",
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"xgboost_model2.fit(x_train2, y_train)\n",
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"xgboost_pred2 = xgboost_model2.predict(x_test2)\n",
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"acc2 = accuracy_score(y_test, xgboost_pred2) * 100\n",
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"accuracies2.append(acc2)\n",
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"# format with color coded if acc2 > 90\n",
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"acc2 = f\"\\033[92m{acc2:.2f}\\033[00m\" if acc2 > 90 else f\"{acc2:.2f}\"\n",
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"print(\"XGBoost Accuracy:\", acc2)"
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]
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]
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},
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},
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{
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{
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@@ -760,8 +638,59 @@
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"metadata": {},
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"metadata": {},
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"outputs": [],
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"outputs": [],
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"source": [
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"source": [
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"print(accuracies1)\n",
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"# Define models for sensor1\n",
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"print(accuracies2)"
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"models_sensor1 = {\n",
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" # \"Random Forest\": RandomForestClassifier(),\n",
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" # \"Bagged Trees\": BaggingClassifier(estimator=DecisionTreeClassifier(), n_estimators=10),\n",
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" # \"Decision Tree\": DecisionTreeClassifier(),\n",
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" # \"KNN\": KNeighborsClassifier(),\n",
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" # \"LDA\": LinearDiscriminantAnalysis(),\n",
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" \"SVM\": SVC(),\n",
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" \"XGBoost\": XGBClassifier()\n",
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"}\n",
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"\n",
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"results_sensor1 = []\n",
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"for name, model in models_sensor1.items():\n",
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" res = train_and_evaluate_model(model, name, \"sensor1\", x_train1, y_train, x_test1, y_test)\n",
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" results_sensor1.append(res)\n",
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" print(f\"{name} on sensor1: Accuracy = {res['accuracy']:.2f}%\")\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"models_sensor2 = {\n",
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" # \"Random Forest\": RandomForestClassifier(),\n",
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" # \"Bagged Trees\": BaggingClassifier(estimator=DecisionTreeClassifier(), n_estimators=10),\n",
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" # \"Decision Tree\": DecisionTreeClassifier(),\n",
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" # \"KNN\": KNeighborsClassifier(),\n",
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" # \"LDA\": LinearDiscriminantAnalysis(),\n",
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" \"SVM\": SVC(),\n",
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" \"XGBoost\": XGBClassifier()\n",
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"}\n",
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"\n",
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"results_sensor2 = []\n",
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"for name, model in models_sensor2.items():\n",
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" res = train_and_evaluate_model(model, name, \"sensor2\", x_train2, y_train, x_test2, y_test)\n",
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" results_sensor2.append(res)\n",
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" print(f\"{name} on sensor2: Accuracy = {res['accuracy']:.2f}%\")\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"all_results = {\n",
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" \"sensor1\": results_sensor1,\n",
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" \"sensor2\": results_sensor2\n",
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"}\n",
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"\n",
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"print(all_results)"
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]
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]
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},
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},
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{
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{
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@@ -773,36 +702,48 @@
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"import numpy as np\n",
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"import matplotlib.pyplot as plt\n",
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"\n",
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"\n",
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"models = [rf_model, bagged_model, dt_model, knn_model, lda_model, svm_model, xgboost_model]\n",
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"def prepare_plot_data(results_dict):\n",
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"model_names = [\"Random Forest\", \"Bagged Trees\", \"Decision Tree\", \"KNN\", \"LDA\", \"SVM\", \"XGBoost\"]\n",
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" # Gather unique model names\n",
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" models_set = {entry['model'] for sensor in results_dict.values() for entry in sensor}\n",
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" models = sorted(list(models_set))\n",
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" \n",
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" # Create dictionaries mapping sensor -> accuracy list ordered by model name\n",
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" sensor_accuracies = {}\n",
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" for sensor, entries in results_dict.items():\n",
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" # Build a mapping: model -> accuracy for the given sensor\n",
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" mapping = {entry['model']: entry['accuracy'] for entry in entries}\n",
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" # Order the accuracies consistent with the sorted model names\n",
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" sensor_accuracies[sensor] = [mapping.get(model, 0) for model in models]\n",
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" \n",
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" return models, sensor_accuracies\n",
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"\n",
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"\n",
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"bar_width = 0.35 # Width of each bar\n",
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"def plot_accuracies(models, sensor_accuracies):\n",
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"index = np.arange(len(model_names)) # Index for the bars\n",
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" bar_width = 0.35\n",
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" x = np.arange(len(models))\n",
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" sensors = list(sensor_accuracies.keys())\n",
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" \n",
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" plt.figure(figsize=(10, 6))\n",
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" # Assume two sensors for plotting grouped bars\n",
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" plt.bar(x - bar_width/2, sensor_accuracies[sensors[0]], width=bar_width, color='blue', label=sensors[0])\n",
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" plt.bar(x + bar_width/2, sensor_accuracies[sensors[1]], width=bar_width, color='orange', label=sensors[1])\n",
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" \n",
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" # Add text labels on top of bars\n",
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" for i, (a1, a2) in enumerate(zip(sensor_accuracies[sensors[0]], sensor_accuracies[sensors[1]])):\n",
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" plt.text(x[i] - bar_width/2, a1 + 0.1, f\"{a1:.2f}%\", ha='center', va='bottom', color='black')\n",
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" plt.text(x[i] + bar_width/2, a2 + 0.1, f\"{a2:.2f}%\", ha='center', va='bottom', color='black')\n",
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" \n",
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" plt.xlabel('Model Name')\n",
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" plt.ylabel('Accuracy (%)')\n",
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" plt.title('Accuracy of Classifiers for Each Sensor')\n",
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" plt.xticks(x, models)\n",
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" plt.legend()\n",
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" plt.ylim(0, 105)\n",
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" plt.tight_layout()\n",
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" plt.show()\n",
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"\n",
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"\n",
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"# Plotting the bar graph\n",
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"# Use the functions\n",
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"plt.figure(figsize=(14, 8))\n",
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"models, sensor_accuracies = prepare_plot_data(all_results)\n",
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"\n",
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"plot_accuracies(models, sensor_accuracies)\n"
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"# Bar plot for Sensor 1\n",
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"plt.bar(index, accuracies1, width=bar_width, color='blue', label='Sensor 1')\n",
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"\n",
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"# Bar plot for Sensor 2\n",
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"plt.bar(index + bar_width, accuracies2, width=bar_width, color='orange', label='Sensor 2')\n",
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"\n",
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"# Add values on top of each bar\n",
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"for i, acc1, acc2 in zip(index, accuracies1, accuracies2):\n",
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" plt.text(i, acc1 + .1, f'{acc1:.2f}%', ha='center', va='bottom', color='black')\n",
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" plt.text(i + bar_width, acc2 + 1, f'{acc2:.2f}%', ha='center', va='bottom', color='black')\n",
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"\n",
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"# Customize the plot\n",
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"plt.xlabel('Model Name →')\n",
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"plt.ylabel('Accuracy →')\n",
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"plt.title('Accuracy of classifiers for Sensors 1 and 2 with 513 features')\n",
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"plt.xticks(index + bar_width / 2, model_names) # Set x-tick positions\n",
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"plt.legend()\n",
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"plt.ylim(0, 100)\n",
|
|
||||||
"\n",
|
|
||||||
"# Show the plot\n",
|
|
||||||
"plt.show()\n"
|
|
||||||
]
|
]
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
|
|||||||
Reference in New Issue
Block a user