ich kann das alles nicht mehr
arman
parent
c9109e1430
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{
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"cells": [
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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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"import json\n",
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"import ml_helper\n",
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"import ml_plots\n",
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"import re\n",
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"import numpy as np\n",
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"import pandas as pd\n",
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"import matplotlib.pyplot as plt\n",
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"import pandas as pd\n",
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"from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score\n",
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"import matplotlib.gridspec as gridspec\n",
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"from sklearn.linear_model import LinearRegression"
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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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"COLORS = {\n",
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" \n",
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" 'yellow': '#FFBE0B',\n",
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" 'orange': '#FB5607',\n",
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" 'blue': '#3A86FF',\n",
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" 'pink': '#FF006E' ,\n",
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" 'lila': '#8338EC',\n",
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" 'rot': '#2a9d8f' \n",
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"}"
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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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"# load latest data if keyword is in the file name\n",
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"hist_name_cnn_norm = ml_helper.get_newest_file('histories/', name='CNN_history', extension=\".json\", ensemble=False)\n",
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"print(f\"Loading {hist_name_cnn_norm}\")\n",
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"\n",
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"hist_name_cnn = ml_helper.get_newest_file('histories/', name='CNN', extension=\".json\", ensemble=True)\n",
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"print(f\"Loading {hist_name_cnn}\")"
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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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"with open(\"histories/ensemble_preds_CNN_2025-02-16_18-06-10.json\", 'r') as file:\n",
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" ensemble_avg_prediction = json.load(file)\n",
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" print(len(ensemble_avg_prediction))\n",
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" "
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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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"with open(hist_name_cnn_norm, 'r') as file:\n",
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" hist_cnn_norm = json.load(file)\n",
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"\n",
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"labels_cnn_norm = hist_cnn_norm['test_labels']\n",
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"preds_cnn_norm = hist_cnn_norm['test_preds']"
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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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"with open(hist_name_cnn[0], 'r') as file:\n",
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" hist_cnn = json.load(file)\n",
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"\n",
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"labels_cnn = hist_cnn['test_labels']\n",
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"preds_cnn = hist_cnn['test_preds']"
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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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"with open(hist_name_cnn[1], 'r') as file:\n",
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" hist_cnn1 = json.load(file)\n",
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"\n",
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"labels_cnn1 = hist_cnn1['test_labels']\n",
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"preds_cnn1 = hist_cnn1['test_preds']"
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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": 2,
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"metadata": {},
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"outputs": [
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{
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"ename": "NameError",
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"evalue": "name 'hist_name_transformer' is not defined",
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"output_type": "error",
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"traceback": [
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"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
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"Input \u001b[1;32mIn [2]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mopen\u001b[39m(\u001b[43mhist_name_transformer\u001b[49m[\u001b[38;5;241m2\u001b[39m], \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124m'\u001b[39m) \u001b[38;5;28;01mas\u001b[39;00m file:\n\u001b[0;32m 2\u001b[0m hist_transformer2 \u001b[38;5;241m=\u001b[39m json\u001b[38;5;241m.\u001b[39mload(file)\n\u001b[0;32m 4\u001b[0m labels_t2 \u001b[38;5;241m=\u001b[39m hist_transformer2[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtest_labels\u001b[39m\u001b[38;5;124m'\u001b[39m]\n",
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"\u001b[1;31mNameError\u001b[0m: name 'hist_name_transformer' is not defined"
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]
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}
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],
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"source": [
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"with open(hist_name_cnn[2], 'r') as file:\n",
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" hist_cnn2 = json.load(file)\n",
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"\n",
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"labels_cnn2 = hist_cnn2['test_labels']\n",
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"preds_cnn2 = hist_cnn2['test_preds']"
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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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"with open(hist_name_cnn[4], 'r') as file:\n",
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" hist_cnn3 = json.load(file)\n",
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"\n",
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"labels_cnn3 = hist_cnn3['test_labels']\n",
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"preds_cnn3 = hist_cnn3['test_preds']"
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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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"with open(hist_name_cnn[5], 'r') as file:\n",
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" hist_cnn4 = json.load(file)\n",
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"\n",
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"labels_cnn4 = hist_cnn4['test_labels']\n",
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"preds_cnn4 = hist_cnn4['test_preds']"
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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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"def save_plot(plt, title):\n",
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" pass"
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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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"def plot_training_histories(hist_datas, names, colors, title='Training History', include_val=True, save=True):\n",
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" fig, axs = plt.subplots(1, 2, figsize=(12, 5))\n",
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" color_keys = list(colors.keys())\n",
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" color_counter = 0\n",
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"\n",
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" for hist_data, name in zip(hist_datas, names):\n",
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" epochs = range(1, len(hist_data['train_loss']) + 1)\n",
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"\n",
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" color = colors[color_keys[color_counter]]\n",
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" color_counter = (color_counter + 1) % len(color_keys)\n",
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"\n",
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" # Plot RMSE\n",
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" axs[1].plot(epochs, hist_data['train_rmse'], label=f'{name} Train RMSE', color=color)\n",
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" if include_val:\n",
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" axs[1].plot(epochs, hist_data['val_rmse'], label=f'{name} Validation RMSE', color=color, linestyle='dashed')\n",
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" axs[1].set_title('RMSE')\n",
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" axs[1].set_xlabel('Epochs')\n",
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" axs[1].set_ylabel('RMSE')\n",
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" axs[1].legend()\n",
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"\n",
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" # Plot Loss\n",
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" axs[0].plot(epochs, hist_data['train_loss'], label=f'{name} Train Loss', color=color)\n",
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" if include_val:\n",
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" axs[0].plot(epochs, hist_data['val_loss'], label=f'{name} Validation Loss', color=color, linestyle='dashed')\n",
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" axs[0].set_title('Loss')\n",
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" axs[0].set_xlabel('Epochs')\n",
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" axs[0].set_ylabel('Loss')\n",
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" axs[0].legend()\n",
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"\n",
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" plt.tight_layout()\n",
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" plt.suptitle(title)\n",
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"\n",
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" # Save plot\n",
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" if save:\n",
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" save_plot(plt, title)\n",
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" \n",
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" return plt\n",
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"\n",
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"plot_training_histories([hist_cnn, hist_cnn2, hist_cnn3], ['Average', 'Transformer1', 'Transformer2'], colors=COLORS, include_val=False).show()"
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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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"def plot_distributions(true_values, predicted_values_list, names, colors, title='Distribution of Predicted and True Values', save=True):\n",
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" plt.figure(figsize=(10, 6))\n",
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" color_keys = list(colors.keys())\n",
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" color_counter = 0\n",
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"\n",
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" # Plot true values\n",
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" plt.hist(true_values, bins=20, color=colors[\"blue\"], edgecolor='black', alpha=0.7, label='True Values')\n",
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"\n",
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" # Plot predicted values for each model\n",
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" for predicted_values, name in zip(predicted_values_list, names):\n",
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" color = colors[color_keys[color_counter]]\n",
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" color_counter = (color_counter + 1) % len(color_keys)\n",
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" plt.hist(predicted_values, bins=20, color=color, edgecolor='black', alpha=0.7, label=f'{name} Predicted Values')\n",
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"\n",
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" plt.title(title)\n",
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" plt.xlabel('Score')\n",
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" plt.ylabel('Frequency')\n",
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" plt.legend()\n",
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" plt.grid(axis='y', linestyle='--', alpha=0.7)\n",
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" # save plot\n",
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" if save:\n",
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" save_plot(plt, title)\n",
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" return plt\n",
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"\n",
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"plot_distributions(labels_cnn,[ preds_cnn2, preds_cnn3, ensemble_avg_prediction ], [ 'Transformer1', 'Transformer2', 'Average'], colors=COLORS).show()\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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"\n",
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"def plot_multiple_residuals(labels, preds_list, names, colors, title='Residuals Plot', save=True):\n",
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" fig = plt.figure(figsize=(14, 6))\n",
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" gs = gridspec.GridSpec(1, 2, width_ratios=[4, 1])\n",
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" color_keys = list(colors.keys())\n",
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" color_counter = 0\n",
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"\n",
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" # Main plot\n",
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" ax0 = plt.subplot(gs[0])\n",
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"\n",
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" for preds, name in zip(preds_list, names):\n",
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" residuals = np.array(preds) - np.array(labels)\n",
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" color = colors[color_keys[color_counter]]\n",
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" color_counter = (color_counter + 1) % len(color_keys)\n",
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"\n",
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" ax0.scatter(labels, residuals, label=f'{name} Residuals', color=color, alpha=0.5)\n",
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"\n",
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" # Fit linear regression model to residuals\n",
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" labels_reshaped = np.array(labels).reshape(-1, 1)\n",
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" model = LinearRegression()\n",
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" model.fit(labels_reshaped, residuals)\n",
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" trend_line = model.predict(labels_reshaped)\n",
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"\n",
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" # Plot trend line\n",
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" ax0.plot(labels, trend_line, color=color, label=f'{name} Trend Line', linewidth=2)\n",
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"\n",
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" ax0.set_xlabel('True Values')\n",
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" ax0.set_ylabel('Residuals')\n",
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" ax0.axhline(y=0, color='k', linestyle='--')\n",
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" ax0.set_title(title)\n",
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" ax0.legend()\n",
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"\n",
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" color_counter = 0\n",
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" # Side plot for distribution of residuals\n",
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" ax1 = plt.subplot(gs[1], sharey=ax0)\n",
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" for preds, name in zip(preds_list, names):\n",
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" residuals = np.array(preds) - np.array(labels)\n",
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" color = colors[color_keys[color_counter]]\n",
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" color_counter = (color_counter + 1) % len(color_keys)\n",
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" ax1.hist(residuals, bins=30, alpha=0.5, color=color, orientation='horizontal', label=f'{name} Residuals')\n",
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"\n",
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" ax1.set_xlabel('Frequency')\n",
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" ax1.set_title('Distribution of Residuals')\n",
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" ax1.yaxis.tick_right()\n",
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" ax1.yaxis.set_label_position(\"right\")\n",
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"\n",
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" plt.tight_layout()\n",
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" # Save plot\n",
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" if save:\n",
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" save_plot(plt, title)\n",
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" \n",
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" return plt\n",
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"plot_multiple_residuals(labels_cnn, [preds_cnn2, preds_cnn3, ensemble_avg_prediction], ['Transformer1', 'Transformer2', 'Average'], colors=COLORS).show()"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "base",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.12"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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@ -46,7 +46,7 @@
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"hist_name_trans_norm = ml_helper.get_newest_file('histories/', name='Transformer_history', extension=\".json\", ensemble=False)\n",
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"print(f\"Loading {hist_name_trans_norm}\")\n",
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"\n",
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"hist_name_transformer = get_newest_file('histories/', name='Transformer', extension=\".json\", ensemble=True)\n",
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"hist_name_transformer = ml_helper.get_newest_file('histories/', name='Transformer', extension=\".json\", ensemble=True)\n",
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"print(f\"Loading {hist_name_transformer}\")"
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]
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},
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Loading…
Reference in New Issue