partial_norm.ipynb

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 "cells": [
  {
   "source": [
    "## Testing MinMaxScaler() partial_fit for online normalization"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import the functions needed for validate and comparsion\n",
    "\n",
    "import numpy as np\n",
    "import cupy as cp\n",
    "import pandas as pd\n",
    "from timeit import default_timer as timer\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
    "from skmultiflow.data import FileStream\n",
    "from skmultiflow.neural_networks import PerceptronMask\n",
    "from skmultiflow.data.random_rbf_generator_drift import RandomRBFGeneratorDrift\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "# using plotly for plots\n",
    "#import plotly.graph_objects as go\n",
    "import plotly.express as px\n",
    "from plotly.subplots import make_subplots\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# stability measurment as proposed in \"Measurment the Stability of Feature Selection\"\n",
    "\n",
    "def pearson_stability_ij(arr1,arr2):\n",
    "    d = len(arr1)\n",
    "    k_i = np.sum(arr1)\n",
    "    k_j = np.sum(arr2)\n",
    "    x_hat_i = k_i / d\n",
    "    x_hat_j = k_j / d\n",
    "    arr1 = arr1 - x_hat_i\n",
    "    arr2 = arr2 - x_hat_j\n",
    "    dividend = 1/d * np.sum(arr1*arr2)\n",
    "    divisor = np.sqrt(1/d*np.sum(arr1**2))*np.sqrt(1/d*np.sum(arr2**2))\n",
    "    return dividend/divisor\n",
    "\n",
    "def stability_factor(selected_ftrs):\n",
    "   M = len(selected_ftrs)\n",
    "   sum_stabilities = 0\n",
    "   for i in range(M):\n",
    "       for j in range(i+1, M):\n",
    "           sum_stabilities += pearson_stability_ij(selected_ftrs[i], selected_ftrs[j])\n",
    "   return 1/(M*(M-1))*sum_stabilities * 2   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import algorithms\n",
    "from fires import FIRES\n",
    "from ofs import OFS, MC_OFS\n",
    "from ofssgr import OFSSGD, MC_OFSSGD\n",
    "from fsds import StreamFeatWeight"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Human Activity Recognition\n",
    "# labels changed from [1,...,6] to [0,...,5]\n",
    "# rows shuffled\n",
    "# split into train set with 7352 instances and test set with 2948\n",
    "stream = FileStream('datasets/Multiclass/har_train.csv', target_idx = 561)\n",
    "stream.prepare_for_use()\n",
    "dataset_name = \"har\"\n",
    "n_selected_ftr = 100\n",
    "n_window = 10\n",
    "batch_size = 20\n",
    "weights = None\n",
    "\n",
    "check_ftrs = False\n",
    "\n",
    "\n",
    "# load test data\n",
    "test_data = pd.read_csv('datasets/Multiclass/har_test.csv')\n",
    "test_y = test_data[\"Class\"].to_numpy()\n",
    "test_x = test_data.drop(columns=\"Class\").to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_true_ftrs_indices(label_names, start_char):\n",
    "    indices = []\n",
    "    for i in range(len(label_names)):\n",
    "        if label_names[i].startswith(start_char):\n",
    "            indices.append(i)\n",
    "\n",
    "    return indices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# synthetic dataset 1 (see data_generation.ipynb)\n",
    "stream = FileStream('datasets/Multiclass/dataset_4_test.csv', target_idx=210)\n",
    "stream.prepare_for_use()\n",
    "dataset_name = \"syn_ds_1\"\n",
    "n_selected_ftr = 100 # 15 are really informative\n",
    "n_window = 10\n",
    "batch_size = 100\n",
    "weights = None\n",
    "\n",
    "# load test data\n",
    "test_data = pd.read_csv(\"datasets/Multiclass/dataset_4_test.csv\")\n",
    "test_y = test_data[\"label\"].to_numpy()\n",
    "test_x = test_data.drop(columns=\"label\").to_numpy()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "PerceptronMask(alpha=0.0001, class_weight=None, early_stopping=False, eta0=1.0,\n",
       "               fit_intercept=True, max_iter=1000, n_iter_no_change=5,\n",
       "               n_jobs=None, penalty=None, random_state=0, shuffle=True,\n",
       "               tol=0.001, validation_fraction=0.1, verbose=0, warm_start=False)"
      ]
     },
     "metadata": {},
     "execution_count": 16
    }
   ],
   "source": [
    "stream.restart()\n",
    "predictor = PerceptronMask()\n",
    "scaler = MinMaxScaler()\n",
    "x,y = stream.next_sample(batch_size=batch_size)\n",
    "# online normalization\n",
    "scaler.partial_fit(x)\n",
    "x = scaler.transform(x)\n",
    "\n",
    "predictor.partial_fit(x,y, stream.target_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "fires_model = FIRES(n_total_ftr=stream.n_features,\n",
    "                    target_values=stream.target_values,\n",
    "                    mu_init=0,\n",
    "                    sigma_init=1,\n",
    "                    model='softmax',\n",
    "                    class_probabilities=weights)\n",
    "\n",
    "       "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "The whole FIRES run took 2.137562492000143\n"
     ]
    }
   ],
   "source": [
    "fires_cuda_accuracy = []\n",
    "#fsds_f1 = []\n",
    "#fires_cuda_times = []\n",
    "\n",
    "fires_cuda_selected_ftrs = []\n",
    "fires_cuda_stability = []\n",
    "\n",
    "start_time_all = timer()\n",
    "while stream.has_more_samples():\n",
    "    # Load a new sample\n",
    "    x, y = stream.next_sample(batch_size=batch_size)\n",
    "    scaler.partial_fit(x)\n",
    "    x = scaler.transform(x)\n",
    "\n",
    "    # Select features\n",
    "    #start_time = timer()\n",
    "    ftr_weights = fires_model.weigh_features(x,y)\n",
    "    ftr_selection = np.argsort(ftr_weights)[::-1][:n_selected_ftr]\n",
    "    #fires_cuda_times.append(timer()-start_time)\n",
    "\n",
    "    # Truncate x (retain only selected features, 'remove' all others, e.g. by replacing them with 0)\n",
    "    x_reduced = np.zeros(x.shape)\n",
    "    x_reduced[:, ftr_selection] = x[:, ftr_selection]\n",
    "\n",
    "    # stability test\n",
    "    ftr_array = np.zeros(stream.n_features)\n",
    "    ftr_array[ftr_selection] = 1\n",
    "    fires_cuda_selected_ftrs.append(ftr_array)\n",
    "\n",
    "    if len(fires_cuda_selected_ftrs) >= 10:\n",
    "        stability = stability_factor(fires_cuda_selected_ftrs[-10:])\n",
    "        fires_cuda_stability.append(stability)\n",
    "\n",
    "\n",
    "    # Test\n",
    "    y_pred = predictor.predict(x_reduced)\n",
    "    \n",
    "    fires_cuda_accuracy.append(accuracy_score(y, y_pred))\n",
    "    #fsds_f1.append(f1_score(y, y_pred, average=None, labels=stream.target_values))\n",
    "\n",
    "\n",
    "    # Train\n",
    "    predictor.partial_fit(x_reduced, y)\n",
    "\n",
    "# Restart the FileStream\n",
    "end_time_all = timer()\n",
    "fires_cuda_run_time = timer() - start_time_all\n",
    "print(\"The whole FIRES run took {}\".format(fires_cuda_run_time))\n",
    "\n",
    "fires_moving_average = pd.Series(fires_cuda_accuracy).rolling(window=n_window).mean().iloc[n_window-1:].values\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "For the test dataset the previous trained predictor reached: 0.3046\n"
     ]
    }
   ],
   "source": [
    "test_x_selected = np.zeros(test_x.shape)\n",
    "test_x_selected[:,ftr_selection] = test_x[:,ftr_selection]\n",
    "y_pred = predictor.predict(test_x)\n",
    "accuracy_fires = accuracy_score(test_y, y_pred)\n",
    "print(\"For the test dataset the previous trained predictor reached: {}\".format(accuracy_fires))"
   ]
  },
  {
   "source": [
    "### without normalization\n"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "PerceptronMask(alpha=0.0001, class_weight=None, early_stopping=False, eta0=1.0,\n",
       "               fit_intercept=True, max_iter=1000, n_iter_no_change=5,\n",
       "               n_jobs=None, penalty=None, random_state=0, shuffle=True,\n",
       "               tol=0.001, validation_fraction=0.1, verbose=0, warm_start=False)"
      ]
     },
     "metadata": {},
     "execution_count": 20
    }
   ],
   "source": [
    "stream.restart()\n",
    "predictor = PerceptronMask()\n",
    "x,y = stream.next_sample(batch_size=batch_size)\n",
    "\n",
    "predictor.partial_fit(x,y, stream.target_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "fires_model = FIRES(n_total_ftr=stream.n_features,\n",
    "                         target_values=stream.target_values,\n",
    "                         mu_init=0,\n",
    "                         sigma_init=1,\n",
    "                         model='softmax',\n",
    "                         class_probabilities=weights)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "The whole FIRES run took 2.158005478000632\n"
     ]
    }
   ],
   "source": [
    "fires_cuda_accuracy = []\n",
    "#fsds_f1 = []\n",
    "#fires_cuda_times = []\n",
    "\n",
    "fires_cuda_selected_ftrs = []\n",
    "fires_stability = []\n",
    "\n",
    "start_time_all = timer()\n",
    "while stream.has_more_samples():\n",
    "    # Load a new sample\n",
    "    x, y = stream.next_sample(batch_size=batch_size)\n",
    "    \n",
    "    # Select features\n",
    "    #start_time = timer()\n",
    "    ftr_weights = fires_model.weigh_features(x,y)\n",
    "    ftr_selection = np.argsort(ftr_weights)[::-1][:n_selected_ftr]\n",
    "    #fires_cuda_times.append(timer()-start_time)\n",
    "\n",
    "    # Truncate x (retain only selected features, 'remove' all others, e.g. by replacing them with 0)\n",
    "    x_reduced = np.zeros(x.shape)\n",
    "    x_reduced[:, ftr_selection] = x[:, ftr_selection]\n",
    "\n",
    "    # stability test\n",
    "    ftr_array = np.zeros(stream.n_features)\n",
    "    ftr_array[ftr_selection] = 1\n",
    "    fires_cuda_selected_ftrs.append(ftr_array)\n",
    "\n",
    "    if len(fires_cuda_selected_ftrs) >= 10:\n",
    "        stability = stability_factor(fires_cuda_selected_ftrs[-10:])\n",
    "        fires_stability.append(stability)\n",
    "\n",
    "\n",
    "    # Test\n",
    "    y_pred = predictor.predict(x_reduced)\n",
    "    \n",
    "    fires_cuda_accuracy.append(accuracy_score(y, y_pred))\n",
    "    #fsds_f1.append(f1_score(y, y_pred, average=None, labels=stream.target_values))\n",
    "\n",
    "\n",
    "    # Train\n",
    "    predictor.partial_fit(x_reduced, y)\n",
    "\n",
    "# Restart the FileStream\n",
    "end_time_all = timer()\n",
    "fires_cuda_run_time = timer() - start_time_all\n",
    "print(\"The whole FIRES run took {}\".format(fires_cuda_run_time))\n",
    "\n",
    "fires_moving_average_org = pd.Series(fires_cuda_accuracy).rolling(window=n_window).mean().iloc[n_window-1:].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "For the test dataset the previous trained predictor reached: 0.1584\n"
     ]
    }
   ],
   "source": [
    "test_x_selected = np.zeros(test_x.shape)\n",
    "test_x_selected[:,ftr_selection] = test_x[:,ftr_selection]\n",
    "y_pred = predictor.predict(test_x)\n",
    "accuracy_fires = accuracy_score(test_y, y_pred)\n",
    "print(\"For the test dataset the previous trained predictor reached: {}\".format(accuracy_fires))"
   ]
  },
  {
   "source": [
    "### data completely normalized\n"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Human Activity Recognition\n",
    "# labels changed from [1,...,6] to [0,...,5]\n",
    "# rows shuffled\n",
    "# split into train set with 7352 instances and test set with 2948\n",
    "stream = FileStream('datasets/Multiclass/har_train_norm.csv', target_idx = 561)\n",
    "stream.prepare_for_use()\n",
    "dataset_name = \"har\"\n",
    "n_selected_ftr = 100\n",
    "n_window = 10\n",
    "batch_size = 20\n",
    "weights = None\n",
    "\n",
    "check_ftrs = False\n",
    "\n",
    "\n",
    "# load test data\n",
    "test_data = pd.read_csv('datasets/Multiclass/har_test_norm.csv')\n",
    "test_y = test_data[\"Class\"].to_numpy()\n",
    "test_x = test_data.drop(columns=\"Class\").to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "PerceptronMask(alpha=0.0001, class_weight=None, early_stopping=False, eta0=1.0,\n",
       "               fit_intercept=True, max_iter=1000, n_iter_no_change=5,\n",
       "               n_jobs=None, penalty=None, random_state=0, shuffle=True,\n",
       "               tol=0.001, validation_fraction=0.1, verbose=0, warm_start=False)"
      ]
     },
     "metadata": {},
     "execution_count": 14
    }
   ],
   "source": [
    "stream.restart()\n",
    "predictor = PerceptronMask()\n",
    "x,y = stream.next_sample(batch_size=batch_size)\n",
    "\n",
    "predictor.partial_fit(x,y, stream.target_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "fires_model = FIRES(n_total_ftr=stream.n_features,\n",
    "                         target_values=stream.target_values,\n",
    "                         mu_init=0,\n",
    "                         sigma_init=1,\n",
    "                         model='softmax',\n",
    "                         class_probabilities=weights)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "The whole FIRES run took 7.336717555999712\n"
     ]
    }
   ],
   "source": [
    "fires_cuda_accuracy = []\n",
    "#fsds_f1 = []\n",
    "#fires_cuda_times = []\n",
    "\n",
    "fires_cuda_selected_ftrs = []\n",
    "fires_pre_stability = []\n",
    "\n",
    "start_time_all = timer()\n",
    "while stream.has_more_samples():\n",
    "    # Load a new sample\n",
    "    x, y = stream.next_sample(batch_size=batch_size)\n",
    "    \n",
    "    # Select features\n",
    "    #start_time = timer()\n",
    "    ftr_weights = fires_model.weigh_features(x,y)\n",
    "    ftr_selection = np.argsort(ftr_weights)[::-1][:n_selected_ftr]\n",
    "    #fires_cuda_times.append(timer()-start_time)\n",
    "\n",
    "    # Truncate x (retain only selected features, 'remove' all others, e.g. by replacing them with 0)\n",
    "    x_reduced = np.zeros(x.shape)\n",
    "    x_reduced[:, ftr_selection] = x[:, ftr_selection]\n",
    "\n",
    "    # stability test\n",
    "    ftr_array = np.zeros(stream.n_features)\n",
    "    ftr_array[ftr_selection] = 1\n",
    "    fires_cuda_selected_ftrs.append(ftr_array)\n",
    "\n",
    "    if len(fires_cuda_selected_ftrs) >= 10:\n",
    "        stability = stability_factor(fires_cuda_selected_ftrs[-10:])\n",
    "        fires_pre_stability.append(stability)\n",
    "\n",
    "\n",
    "    # Test\n",
    "    y_pred = predictor.predict(x_reduced)\n",
    "    \n",
    "    fires_cuda_accuracy.append(accuracy_score(y, y_pred))\n",
    "    #fsds_f1.append(f1_score(y, y_pred, average=None, labels=stream.target_values))\n",
    "\n",
    "\n",
    "    # Train\n",
    "    predictor.partial_fit(x_reduced, y)\n",
    "\n",
    "# Restart the FileStream\n",
    "end_time_all = timer()\n",
    "fires_cuda_run_time = timer() - start_time_all\n",
    "print(\"The whole FIRES run took {}\".format(fires_cuda_run_time))\n",
    "\n",
    "fires_moving_average_pre = pd.Series(fires_cuda_accuracy).rolling(window=n_window).mean().iloc[n_window-1:].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "For the test dataset the previous trained predictor reached: 0.8679565512559403\n"
     ]
    }
   ],
   "source": [
    "test_x_selected = np.zeros(test_x.shape)\n",
    "test_x_selected[:,ftr_selection] = test_x[:,ftr_selection]\n",
    "y_pred = predictor.predict(test_x)\n",
    "accuracy_fires = accuracy_score(test_y, y_pred)\n",
    "print(\"For the test dataset the previous trained predictor reached: {}\".format(accuracy_fires))"
   ]
  },
  {
   "source": [
    "### Plots"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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        "plotlyServerURL": "https://plot.ly"
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   "source": [
    "col = [\"normalized\", \"original\"]#, \"previous_normalized\"]\n",
    "data = {\"normalized\":fires_moving_average, \"original\": fires_moving_average_org}#, \"previous_normalized\":fires_moving_average_pre}\n",
    "df = pd.DataFrame(data, columns=col)\n",
    "fig = px.line(df, title=\"Accuracy\", labels={\"index\":\"batches\", \"value\":\"accuracy\"})\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "output_type": "error",
     "ename": "NameError",
     "evalue": "name 'fires_pre_stability' is not defined",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-25-2102d5aeebf9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mcol\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m\"normalized\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"original\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"previous_normalized\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m\"normalized\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mfires_cuda_stability\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"original\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mfires_stability\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"previous_normalized\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mfires_pre_stability\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcol\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mfig\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtitle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"Stability\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m\"index\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\"batches\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"value\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\"stability\"\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'fires_pre_stability' is not defined"
     ]
    }
   ],
   "source": [
    "col = [\"normalized\", \"original\", \"previous_normalized\"]\n",
    "data = {\"normalized\":fires_cuda_stability, \"original\": fires_stability, \"previous_normalized\":fires_pre_stability}\n",
    "df = pd.DataFrame(data, columns=col)\n",
    "fig = px.line(df, title=\"Stability\", labels={\"index\":\"batches\", \"value\":\"stability\"})\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}