{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Ensemble models to submission" ] }, { "cell_type": "code", "execution_count": 242, "metadata": {}, "outputs": [], "source": [ "import os\n", "import numpy as np\n", "import pandas as pd\n", "\n", "import matplotlib.pyplot as plt\n", "from tqdm import tqdm\n", "\n", "import sys\n", "import warnings\n", "if not sys.warnoptions:\n", " warnings.simplefilter(\"ignore\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Load models prediction" ] }, { "cell_type": "code", "execution_count": 243, "metadata": {}, "outputs": [], "source": [ "submission_status = pd.read_csv('/media/daitran/Data_SSD/code/SmartInsideAI/github/2021AIChamp_Submission/src/status_models/weights/resnext50_32x4d_768_CosineAnnealingWarmRestarts_TaylorCrossEntropyLoss/submission_status.csv')\n", "submission_type = pd.read_csv('/media/daitran/Data_SSD/code/SmartInsideAI/github/2021AIChamp_Submission/src/type_model/weights/tf_efficientnetv2_s_in21k_512_CosineAnnealingWarmRestarts_TaylorCrossEntropyLoss/submission_type.csv')\n", "submission_fault = pd.read_csv('/media/daitran/Data_SSD/code/SmartInsideAI/github/2021AIChamp_Submission/src/fault_model/weights/resnext50_32x4d_768_CosineAnnealingWarmRestarts_TaylorCrossEntropyLoss/submission_fault.csv')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## STATUS model" ] }, { "cell_type": "code", "execution_count": 244, "metadata": {}, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "1 178\n", "0 44\n", "Name: status, dtype: int64\n" ] } ], "source": [ "# Create df \n", "status_nums = submission_status[\"status\"].value_counts()\n", "status_nums.plot.bar()\n", "plt.show()\n", "print(status_nums)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## TYPE model" ] }, { "cell_type": "code", "execution_count": 245, "metadata": {}, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "0 97\n", "6 38\n", "7 26\n", "3 23\n", "1 18\n", "2 12\n", "4 6\n", "5 2\n", "Name: type, dtype: int64\n" ] } ], "source": [ "# Create df \n", "type_nums = submission_type[\"type\"].value_counts()\n", "type_nums.plot.bar()\n", "plt.show()\n", "print(type_nums)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## FAULT model" ] }, { "cell_type": "code", "execution_count": 246, "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "1 75\n", "10 57\n", "0 53\n", "3 12\n", "2 11\n", "7 10\n", "12 2\n", "16 1\n", "14 1\n", "Name: fault, dtype: int64\n" ] } ], "source": [ "fault_nums = submission_fault[\"fault\"].value_counts()\n", "fault_nums.plot.bar()\n", "plt.show()\n", "print(fault_nums)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Convert to submission formart" ] }, { "cell_type": "code", "execution_count": 247, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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01679R341_20200409_OH01_DG06_2_30.JPG정상COS정상
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21678H971_20200514_OH01_DG06_2_33.JPG정상COS정상
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41679R341_20200409_OH01_DG06_2_12.JPG정상COS정상
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" ], "text/plain": [ " file status type fault\n", "0 1679R341_20200409_OH01_DG06_2_30.JPG 정상 COS 정상\n", "1 1778D551_20200514_OH01_DG06_2_5.JPG 정상 COS 정상\n", "2 1678H971_20200514_OH01_DG06_2_33.JPG 정상 COS 정상\n", "3 1778D521_20200514_OH01_DG06_2_15.JPG 정상 COS 정상\n", "4 1679R341_20200409_OH01_DG06_2_12.JPG 정상 COS 정상" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "TRAIN_IMAGE_DIR = \"/media/daitran/Data_SSD/data/SmartinsideAI/2021AIChamp/11.한국전력공사_데이터/train\"\n", "DIR = \"/media/daitran/Data_SSD/data/SmartinsideAI/2021AIChamp/11.한국전력공사_데이터\"\n", "\n", "train_df_path = os.path.join(DIR, \"train_output.csv\")\n", "train_df = pd.read_csv(train_df_path)\n", "display(train_df.head())\n", "\n", "# print(\"Train sample: {}\".format(len(train_df)))" ] }, { "cell_type": "code", "execution_count": 248, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['정상' '불량']\n", "['COS' 'GS' 'LA' 'LP자기재' 'LP폴리머' '전주' '현수애자자기' '현수애자폴리머']\n", "['정상' '부식' '석면노출' '크랙' '아크흔적' '파손' '휴즈링크 석면 노출' '박리' '휴즈링크 석면노출' '홀더불량'\n", " '설치불량' '균열' '침식' '탄화' '정상차측탈락' '설치불량 (정상)' '불순물' '탈락정상' '탈락' '커버없음'\n", " '박리 (정상)' '커버탈락' '스프리트핀유실']\n" ] } ], "source": [ "status_label_kr = train_df[\"status\"].unique()\n", "print(status_label_kr)\n", "type_label_kr = train_df[\"type\"].unique()\n", "print(type_label_kr)\n", "fault_label_kr = train_df[\"fault\"].unique()\n", "print(fault_label_kr)\n" ] }, { "cell_type": "code", "execution_count": 249, "metadata": {}, "outputs": [], "source": [ "def change_label_names(class_ids, label_kr):\n", " convert_dict = {}\n", " for num in range(len(class_ids)):\n", " convert_dict[class_ids[num]] = label_kr[num]\n", " return convert_dict" ] }, { "cell_type": "code", "execution_count": 250, "metadata": {}, "outputs": [], "source": [ "class_ids_status = [0,1]\n", "status_convert_dict = change_label_names(class_ids_status, status_label_kr)\n", "submission_status[\"status\"].replace(status_convert_dict, inplace=True)\n", "# submission_status" ] }, { "cell_type": "code", "execution_count": 251, "metadata": {}, "outputs": [], "source": [ "class_ids_type = []\n", "for i in range(len(type_label_kr)):\n", " class_ids_type.append(i)\n", " \n", "type_convert_dict = change_label_names(class_ids_type, type_label_kr)\n", "submission_type[\"type\"].replace(type_convert_dict, inplace=True)\n", "# submission_type" ] }, { "cell_type": "code", "execution_count": 252, "metadata": {}, "outputs": [], "source": [ "class_ids_fault = []\n", "for i in range(len(fault_label_kr)):\n", " class_ids_fault.append(i)" ] }, { "cell_type": "code", "execution_count": 253, "metadata": {}, "outputs": [], "source": [ "fault_convert_dict = change_label_names(class_ids_fault, fault_label_kr)\n", "submission_fault[\"fault\"].replace(fault_convert_dict, inplace=True)\n", "# submission_fault" ] }, { "cell_type": "code", "execution_count": 254, "metadata": {}, "outputs": [], "source": [ "submission_final = pd.read_csv('/media/daitran/Data_SSD/data/SmartinsideAI/2021AIChamp/11.한국전력공사_데이터/test_output_sample.csv')" ] }, { "cell_type": "code", "execution_count": 255, "metadata": {}, "outputs": [], "source": [ "submission_final['status'] = submission_status['status']\n", "submission_final['type'] = submission_type['type']\n", "submission_final['fault'] = submission_fault['fault']" ] }, { "cell_type": "code", "execution_count": 256, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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00.jpg정상현수애자폴리머정상
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222 rows × 4 columns

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" ], "text/plain": [ " file status type fault\n", "0 0.jpg 정상 현수애자폴리머 정상\n", "1 1.jpg 불량 현수애자폴리머 설치불량\n", "2 2.jpg 정상 COS 정상\n", "3 3.jpg 불량 LP자기재 정상\n", "4 4.jpg 불량 COS 석면노출\n", ".. ... ... ... ...\n", "217 217.jpg 불량 현수애자폴리머 설치불량\n", "218 218.jpg 불량 LP자기재 박리\n", "219 219.jpg 불량 LA 정상차측탈락\n", "220 220.jpg 불량 COS 부식\n", "221 221.jpg 불량 현수애자자기 설치불량\n", "\n", "[222 rows x 4 columns]" ] }, "execution_count": 256, "metadata": {}, "output_type": "execute_result" } ], "source": [ "submission_final" ] }, { "cell_type": "code", "execution_count": 257, "metadata": {}, "outputs": [], "source": [ "submission_final.to_csv('./single_temp/test_output_sample.csv', index=False)" ] }, { "cell_type": "code", "execution_count": 258, "metadata": {}, "outputs": [], "source": [ "updated_submission = submission_final" ] }, { "cell_type": "code", "execution_count": 259, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "fault-침식 to 아크흔적\n", "91\n", "fault-침식 to 아크흔적\n", "118\n" ] } ], "source": [ "changeable_row_idxs = []\n", "for row_idx in range(len(updated_submission)):\n", " if updated_submission.iloc[row_idx]['fault'] == '정상' and updated_submission.iloc[row_idx]['status'] != '정상':\n", " # submission_final.iloc[row_idx]['status'] = '정상'\n", " # print(row_idx)\n", " changeable_row_idxs.append(row_idx)\n", " updated_submission.loc[row_idx, 'status'] = '정상'\n", " elif updated_submission.iloc[row_idx]['fault'] == '침식':\n", " print(\"fault-침식 to 아크흔적\")\n", " print(row_idx)\n", " changeable_row_idxs.append(row_idx)\n", " updated_submission.loc[row_idx]['fault'] = '아크흔적'" ] }, { "cell_type": "code", "execution_count": 260, "metadata": {}, "outputs": [], "source": [ "updated_submission.to_csv('./single_temp/updated_test_output_sample.csv', index=False)" ] }, { "cell_type": "code", "execution_count": 261, "metadata": {}, "outputs": [], "source": [ "# Compare updated with the best ensemble\n", "submission_final = updated_submission" ] }, { "cell_type": "code", "execution_count": 262, "metadata": {}, "outputs": [], "source": [ "best_LB = pd.read_csv('/media/daitran/Data_SSD/code/SmartInsideAI/github/2021AIChamp_Submission/src/ensemble/temp/test_output_sample.csv')" ] }, { "cell_type": "code", "execution_count": 263, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 222/222 [00:00<00:00, 9483.77it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Number of predicted different: 12\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "# Check status\n", "diff_idxs = []\n", "for row_idx in tqdm(range(len(best_LB))):\n", " if best_LB.iloc[row_idx]['status'] != submission_final.iloc[row_idx]['status']:\n", " diff_idxs.append(row_idx)\n", "print(\"Number of predicted different: {}\".format(len(diff_idxs)))" ] }, { "cell_type": "code", "execution_count": 264, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 222/222 [00:00<00:00, 7261.45it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Number of predicted different: 5\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "# Check status\n", "diff_idxs = []\n", "for row_idx in tqdm(range(len(best_LB))):\n", " if best_LB.iloc[row_idx]['type'] != submission_final.iloc[row_idx]['type']:\n", " diff_idxs.append(row_idx)\n", "print(\"Number of predicted different: {}\".format(len(diff_idxs)))" ] }, { "cell_type": "code", "execution_count": 265, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 222/222 [00:00<00:00, 6208.65it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Number of predicted different: 12\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "\n", "# Check status\n", "diff_idxs = []\n", "for row_idx in tqdm(range(len(best_LB))):\n", " if best_LB.iloc[row_idx]['fault'] != submission_final.iloc[row_idx]['fault']:\n", " diff_idxs.append(row_idx)\n", "print(\"Number of predicted different: {}\".format(len(diff_idxs)))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "interpreter": { "hash": "97c2b951ffe1b39582ff62c0714b50db3703a0a549b3a3e068cb513d4429895b" }, "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": "3.10.9" } }, "nbformat": 4, "nbformat_minor": 4 }